From 27b2821cf16948962c7a6f513621a1eba60b8cf3 Mon Sep 17 00:00:00 2001
From: Takuya UESHIN <ueshin@happy-camper.st>
Date: Thu, 24 Apr 2014 09:57:28 -0700
Subject: [PATCH 001/810] [SPARK-1610] [SQL] Fix Cast to use exact type value
 when cast from BooleanType to NumericTy...

...pe.

`Cast` from `BooleanType` to `NumericType` are all using `Int` value.
But it causes `ClassCastException` when the casted value is used by the following evaluation like the code below:

``` scala
scala> import org.apache.spark.sql.catalyst._
import org.apache.spark.sql.catalyst._

scala> import types._
import types._

scala> import expressions._
import expressions._

scala> Add(Cast(Literal(true), ShortType), Literal(1.toShort)).eval()
java.lang.ClassCastException: java.lang.Integer cannot be cast to java.lang.Short
	at scala.runtime.BoxesRunTime.unboxToShort(BoxesRunTime.java:102)
	at scala.math.Numeric$ShortIsIntegral$.plus(Numeric.scala:72)
	at org.apache.spark.sql.catalyst.expressions.Add$$anonfun$eval$2.apply(arithmetic.scala:58)
	at org.apache.spark.sql.catalyst.expressions.Add$$anonfun$eval$2.apply(arithmetic.scala:58)
	at org.apache.spark.sql.catalyst.expressions.Expression.n2(Expression.scala:114)
	at org.apache.spark.sql.catalyst.expressions.Add.eval(arithmetic.scala:58)
	at .<init>(<console>:17)
	at .<clinit>(<console>)
	at .<init>(<console>:7)
	at .<clinit>(<console>)
	at $print(<console>)
	at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
	at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
	at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
	at java.lang.reflect.Method.invoke(Method.java:483)
	at scala.tools.nsc.interpreter.IMain$ReadEvalPrint.call(IMain.scala:734)
	at scala.tools.nsc.interpreter.IMain$Request.loadAndRun(IMain.scala:983)
	at scala.tools.nsc.interpreter.IMain.loadAndRunReq$1(IMain.scala:573)
	at scala.tools.nsc.interpreter.IMain.interpret(IMain.scala:604)
	at scala.tools.nsc.interpreter.IMain.interpret(IMain.scala:568)
	at scala.tools.nsc.interpreter.ILoop.reallyInterpret$1(ILoop.scala:760)
	at scala.tools.nsc.interpreter.ILoop.interpretStartingWith(ILoop.scala:805)
	at scala.tools.nsc.interpreter.ILoop.command(ILoop.scala:717)
	at scala.tools.nsc.interpreter.ILoop.processLine$1(ILoop.scala:581)
	at scala.tools.nsc.interpreter.ILoop.innerLoop$1(ILoop.scala:588)
	at scala.tools.nsc.interpreter.ILoop.loop(ILoop.scala:591)
	at scala.tools.nsc.interpreter.ILoop$$anonfun$process$1.apply$mcZ$sp(ILoop.scala:882)
	at scala.tools.nsc.interpreter.ILoop$$anonfun$process$1.apply(ILoop.scala:837)
	at scala.tools.nsc.interpreter.ILoop$$anonfun$process$1.apply(ILoop.scala:837)
	at scala.tools.nsc.util.ScalaClassLoader$.savingContextLoader(ScalaClassLoader.scala:135)
	at scala.tools.nsc.interpreter.ILoop.process(ILoop.scala:837)
	at scala.tools.nsc.MainGenericRunner.runTarget$1(MainGenericRunner.scala:83)
	at scala.tools.nsc.MainGenericRunner.process(MainGenericRunner.scala:96)
	at scala.tools.nsc.MainGenericRunner$.main(MainGenericRunner.scala:105)
	at scala.tools.nsc.MainGenericRunner.main(MainGenericRunner.scala)
```

Author: Takuya UESHIN <ueshin@happy-camper.st>

Closes #533 from ueshin/issues/SPARK-1610 and squashes the following commits:

70f36e8 [Takuya UESHIN] Fix Cast to use exact type value when cast from BooleanType to NumericType.
---
 .../apache/spark/sql/catalyst/expressions/Cast.scala   | 10 +++++-----
 .../expressions/ExpressionEvaluationSuite.scala        |  7 +++++++
 2 files changed, 12 insertions(+), 5 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
index 1f3fab09e956..8b79b0cd65a8 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
@@ -111,7 +111,7 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression {
     case StringType => nullOrCast[String](_, s => try s.toLong catch {
       case _: NumberFormatException => null
     })
-    case BooleanType => nullOrCast[Boolean](_, b => if(b) 1 else 0)
+    case BooleanType => nullOrCast[Boolean](_, b => if(b) 1L else 0L)
     case TimestampType => nullOrCast[Timestamp](_, t => timestampToLong(t))
     case DecimalType => nullOrCast[BigDecimal](_, _.toLong)
     case x: NumericType => b => x.numeric.asInstanceOf[Numeric[Any]].toLong(b)
@@ -131,7 +131,7 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression {
     case StringType => nullOrCast[String](_, s => try s.toShort catch {
       case _: NumberFormatException => null
     })
-    case BooleanType => nullOrCast[Boolean](_, b => if(b) 1 else 0)
+    case BooleanType => nullOrCast[Boolean](_, b => if(b) 1.toShort else 0.toShort)
     case TimestampType => nullOrCast[Timestamp](_, t => timestampToLong(t).toShort)
     case DecimalType => nullOrCast[BigDecimal](_, _.toShort)
     case x: NumericType => b => x.numeric.asInstanceOf[Numeric[Any]].toInt(b).toShort
@@ -141,7 +141,7 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression {
     case StringType => nullOrCast[String](_, s => try s.toByte catch {
       case _: NumberFormatException => null
     })
-    case BooleanType => nullOrCast[Boolean](_, b => if(b) 1 else 0)
+    case BooleanType => nullOrCast[Boolean](_, b => if(b) 1.toByte else 0.toByte)
     case TimestampType => nullOrCast[Timestamp](_, t => timestampToLong(t).toByte)
     case DecimalType => nullOrCast[BigDecimal](_, _.toByte)
     case x: NumericType => b => x.numeric.asInstanceOf[Numeric[Any]].toInt(b).toByte
@@ -162,7 +162,7 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression {
     case StringType => nullOrCast[String](_, s => try s.toDouble catch {
       case _: NumberFormatException => null
     })
-    case BooleanType => nullOrCast[Boolean](_, b => if(b) 1 else 0)
+    case BooleanType => nullOrCast[Boolean](_, b => if(b) 1d else 0d)
     case TimestampType => nullOrCast[Timestamp](_, t => timestampToDouble(t))
     case DecimalType => nullOrCast[BigDecimal](_, _.toDouble)
     case x: NumericType => b => x.numeric.asInstanceOf[Numeric[Any]].toDouble(b)
@@ -172,7 +172,7 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression {
     case StringType => nullOrCast[String](_, s => try s.toFloat catch {
       case _: NumberFormatException => null
     })
-    case BooleanType => nullOrCast[Boolean](_, b => if(b) 1 else 0)
+    case BooleanType => nullOrCast[Boolean](_, b => if(b) 1f else 0f)
     case TimestampType => nullOrCast[Timestamp](_, t => timestampToDouble(t).toFloat)
     case DecimalType => nullOrCast[BigDecimal](_, _.toFloat)
     case x: NumericType => b => x.numeric.asInstanceOf[Numeric[Any]].toFloat(b)
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala
index 2cd0d2b0e138..4ce0dff9e158 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala
@@ -237,6 +237,13 @@ class ExpressionEvaluationSuite extends FunSuite {
     checkEvaluation("2012-12-11" cast DoubleType, null)
     checkEvaluation(Literal(123) cast IntegerType, 123)
 
+    checkEvaluation(Literal(23d) + Cast(true, DoubleType), 24)
+    checkEvaluation(Literal(23) + Cast(true, IntegerType), 24)
+    checkEvaluation(Literal(23f) + Cast(true, FloatType), 24)
+    checkEvaluation(Literal(BigDecimal(23)) + Cast(true, DecimalType), 24)
+    checkEvaluation(Literal(23.toByte) + Cast(true, ByteType), 24)
+    checkEvaluation(Literal(23.toShort) + Cast(true, ShortType), 24)
+
     intercept[Exception] {evaluate(Literal(1) cast BinaryType, null)}
   }
 

From faeb761cbe37521ec7ee6bbab7bcbc4bb1abb328 Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Thu, 24 Apr 2014 09:59:44 -0700
Subject: [PATCH 002/810] Small changes to release script

---
 dev/create-release/create-release.sh | 4 +---
 make-distribution.sh                 | 1 +
 2 files changed, 2 insertions(+), 3 deletions(-)

diff --git a/dev/create-release/create-release.sh b/dev/create-release/create-release.sh
index ad38c8d53eb2..f1aa0f8f99e1 100755
--- a/dev/create-release/create-release.sh
+++ b/dev/create-release/create-release.sh
@@ -92,7 +92,6 @@ make_binary_release() {
   cp spark-$RELEASE_VERSION-bin-$NAME/spark-$RELEASE_VERSION-bin-$NAME.tgz .
   rm -rf spark-$RELEASE_VERSION-bin-$NAME
 
-  tar cvzf spark-$RELEASE_VERSION-bin-$NAME.tgz spark-$RELEASE_VERSION-bin-$NAME
   echo $GPG_PASSPHRASE | gpg --passphrase-fd 0 --armour \
     --output spark-$RELEASE_VERSION-bin-$NAME.tgz.asc \
     --detach-sig spark-$RELEASE_VERSION-bin-$NAME.tgz
@@ -102,7 +101,6 @@ make_binary_release() {
   echo $GPG_PASSPHRASE | gpg --passphrase-fd 0 --print-md \
     SHA512 spark-$RELEASE_VERSION-bin-$NAME.tgz > \
     spark-$RELEASE_VERSION-bin-$NAME.tgz.sha
-  rm -rf spark-$RELEASE_VERSION-bin-$NAME
 }
 
 make_binary_release "hadoop1" "--hadoop 1.0.4"
@@ -114,7 +112,7 @@ echo "Copying release tarballs"
 ssh $USER_NAME@people.apache.org \
   mkdir /home/$USER_NAME/public_html/spark-$RELEASE_VERSION-$RC_NAME
 rc_folder=spark-$RELEASE_VERSION-$RC_NAME
-scp spark* \
+scp spark-* \
   $USER_NAME@people.apache.org:/home/$USER_NAME/public_html/$rc_folder/
 
 # Docs
diff --git a/make-distribution.sh b/make-distribution.sh
index 83dfc745857c..62a28217740c 100755
--- a/make-distribution.sh
+++ b/make-distribution.sh
@@ -141,6 +141,7 @@ cp $FWDIR/examples/target/scala*/spark-examples*.jar "$DISTDIR/lib/"
 # Copy other things
 mkdir "$DISTDIR"/conf
 cp "$FWDIR"/conf/*.template "$DISTDIR"/conf
+cp "$FWDIR"/conf/slaves "$DISTDIR"/conf
 cp -r "$FWDIR/bin" "$DISTDIR"
 cp -r "$FWDIR/python" "$DISTDIR"
 cp -r "$FWDIR/sbin" "$DISTDIR"

From 6338a93f10e8b5f4365f71afd1086bdde64afad5 Mon Sep 17 00:00:00 2001
From: Sean Owen <sowen@cloudera.com>
Date: Thu, 24 Apr 2014 10:06:18 -0700
Subject: [PATCH 003/810] SPARK-1488. Squash more language feature warnings in
 new commits by importing implicitConversion

A recent commit reintroduced some of the same warnings that SPARK-1488 resolved. These are just a few more of the same changes to remove these warnings.

Author: Sean Owen <sowen@cloudera.com>

Closes #528 from srowen/SPARK-1488.2 and squashes the following commits:

62d592c [Sean Owen] More feature warnings in tests
4e2e94b [Sean Owen] Squash more language feature warnings in new commits by importing implicitConversion
---
 .../org/apache/spark/streaming/api/java/JavaInputDStream.scala  | 1 +
 .../apache/spark/streaming/api/java/JavaPairInputDStream.scala  | 2 ++
 .../spark/streaming/api/java/JavaPairReceiverInputDStream.scala | 1 +
 .../spark/streaming/api/java/JavaReceiverInputDStream.scala     | 1 +
 .../scala/org/apache/spark/streaming/NetworkReceiverSuite.scala | 1 +
 .../org/apache/spark/streaming/StreamingListenerSuite.scala     | 1 +
 6 files changed, 7 insertions(+)

diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaInputDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaInputDStream.scala
index 91f8d342d2bf..ae5e693df93d 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaInputDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaInputDStream.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.streaming.api.java
 
+import scala.language.implicitConversions
 import scala.reflect.ClassTag
 
 import org.apache.spark.streaming.dstream.InputDStream
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaPairInputDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaPairInputDStream.scala
index add858530862..e6ff8a0cb545 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaPairInputDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaPairInputDStream.scala
@@ -18,6 +18,8 @@
 package org.apache.spark.streaming.api.java
 
 import org.apache.spark.streaming.dstream.InputDStream
+
+import scala.language.implicitConversions
 import scala.reflect.ClassTag
 
 /**
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaPairReceiverInputDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaPairReceiverInputDStream.scala
index 974b3e451642..294bc2be1898 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaPairReceiverInputDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaPairReceiverInputDStream.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.streaming.api.java
 
+import scala.language.implicitConversions
 import scala.reflect.ClassTag
 
 import org.apache.spark.streaming.dstream.ReceiverInputDStream
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaReceiverInputDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaReceiverInputDStream.scala
index 340ef979808b..8142dc59ea7c 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaReceiverInputDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaReceiverInputDStream.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.streaming.api.java
 
+import scala.language.implicitConversions
 import scala.reflect.ClassTag
 
 import org.apache.spark.streaming.dstream.ReceiverInputDStream
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/NetworkReceiverSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/NetworkReceiverSuite.scala
index 5c0415ad14eb..45304c76b092 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/NetworkReceiverSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/NetworkReceiverSuite.scala
@@ -20,6 +20,7 @@ package org.apache.spark.streaming
 import java.nio.ByteBuffer
 
 import scala.collection.mutable.ArrayBuffer
+import scala.language.postfixOps
 
 import org.apache.spark.SparkConf
 import org.apache.spark.storage.{StorageLevel, StreamBlockId}
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/StreamingListenerSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/StreamingListenerSuite.scala
index 542c697ae312..458dd3a2b13d 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/StreamingListenerSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/StreamingListenerSuite.scala
@@ -20,6 +20,7 @@ package org.apache.spark.streaming
 import scala.collection.mutable.ArrayBuffer
 import scala.concurrent.Future
 import scala.concurrent.ExecutionContext.Implicits.global
+import scala.language.postfixOps
 
 import org.apache.spark.storage.StorageLevel
 import org.apache.spark.streaming.dstream.DStream

From 78a49b2532d4751257654dfe55a564bcd10701b3 Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Thu, 24 Apr 2014 11:13:40 -0700
Subject: [PATCH 004/810] SPARK-1611: Fix incorrect initialization order in
 AppendOnlyMap

JIRA: https://issues.apache.org/jira/browse/SPARK-1611

Author: zsxwing <zsxwing@gmail.com>

Closes #534 from zsxwing/SPARK-1611 and squashes the following commits:

96af089 [zsxwing] SPARK-1611: Fix incorrect initialization order in AppendOnlyMap
---
 .../org/apache/spark/util/collection/AppendOnlyMap.scala      | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/util/collection/AppendOnlyMap.scala b/core/src/main/scala/org/apache/spark/util/collection/AppendOnlyMap.scala
index ad38250ad339..1a6f1c2b5579 100644
--- a/core/src/main/scala/org/apache/spark/util/collection/AppendOnlyMap.scala
+++ b/core/src/main/scala/org/apache/spark/util/collection/AppendOnlyMap.scala
@@ -40,6 +40,8 @@ class AppendOnlyMap[K, V](initialCapacity: Int = 64)
   require(initialCapacity <= (1 << 29), "Can't make capacity bigger than 2^29 elements")
   require(initialCapacity >= 1, "Invalid initial capacity")
 
+  private val LOAD_FACTOR = 0.7
+
   private var capacity = nextPowerOf2(initialCapacity)
   private var mask = capacity - 1
   private var curSize = 0
@@ -57,8 +59,6 @@ class AppendOnlyMap[K, V](initialCapacity: Int = 64)
   private var destroyed = false
   private val destructionMessage = "Map state is invalid from destructive sorting!"
 
-  private val LOAD_FACTOR = 0.7
-
   /** Get the value for a given key */
   def apply(key: K): V = {
     assert(!destroyed, destructionMessage)

From bd375094a1480b0ff9c16ab8ddd2dba8731506df Mon Sep 17 00:00:00 2001
From: Thomas Graves <tgraves@apache.org>
Date: Thu, 24 Apr 2014 11:15:12 -0700
Subject: [PATCH 005/810] Spark 1490 Add kerberos support to the HistoryServer

Here I've added the ability for the History server to login from a kerberos keytab file so that the history server can be run as a super user and stay up for along period of time while reading the history files from HDFS.

Author: Thomas Graves <tgraves@apache.org>

Closes #513 from tgravescs/SPARK-1490 and squashes the following commits:

e204a99 [Thomas Graves] remove extra logging
5418daa [Thomas Graves] fix typo in config
0076b99 [Thomas Graves] Update docs
4d76545 [Thomas Graves] SPARK-1490 Add kerberos support to the HistoryServer
---
 .../apache/spark/deploy/SparkHadoopUtil.scala |  4 ++++
 .../spark/deploy/history/HistoryServer.scala  | 16 +++++++++++++
 docs/monitoring.md                            | 24 +++++++++++++++++++
 3 files changed, 44 insertions(+)

diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala b/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala
index 9bdbfb33bf54..498fcc520ac5 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala
@@ -75,6 +75,10 @@ class SparkHadoopUtil {
 
   def getSecretKeyFromUserCredentials(key: String): Array[Byte] = { null }
 
+  def loginUserFromKeytab(principalName: String, keytabFilename: String) { 
+    UserGroupInformation.loginUserFromKeytab(principalName, keytabFilename)
+  }
+
 }
 
 object SparkHadoopUtil {
diff --git a/core/src/main/scala/org/apache/spark/deploy/history/HistoryServer.scala b/core/src/main/scala/org/apache/spark/deploy/history/HistoryServer.scala
index b8f56234d37e..d7a3246bcfb4 100644
--- a/core/src/main/scala/org/apache/spark/deploy/history/HistoryServer.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/history/HistoryServer.scala
@@ -22,6 +22,7 @@ import scala.collection.mutable
 import org.apache.hadoop.fs.{FileStatus, Path}
 
 import org.apache.spark.{Logging, SecurityManager, SparkConf}
+import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.scheduler._
 import org.apache.spark.ui.{WebUI, SparkUI}
 import org.apache.spark.ui.JettyUtils._
@@ -257,6 +258,7 @@ object HistoryServer {
   val STATIC_RESOURCE_DIR = SparkUI.STATIC_RESOURCE_DIR
 
   def main(argStrings: Array[String]) {
+    initSecurity()
     val args = new HistoryServerArguments(argStrings)
     val securityManager = new SecurityManager(conf)
     val server = new HistoryServer(args.logDir, securityManager, conf)
@@ -266,6 +268,20 @@ object HistoryServer {
     while(true) { Thread.sleep(Int.MaxValue) }
     server.stop()
   }
+
+  def initSecurity() {
+    // If we are accessing HDFS and it has security enabled (Kerberos), we have to login
+    // from a keytab file so that we can access HDFS beyond the kerberos ticket expiration.
+    // As long as it is using Hadoop rpc (hdfs://), a relogin will automatically
+    // occur from the keytab.
+    if (conf.getBoolean("spark.history.kerberos.enabled", false)) {
+      // if you have enabled kerberos the following 2 params must be set
+      val principalName = conf.get("spark.history.kerberos.principal")
+      val keytabFilename = conf.get("spark.history.kerberos.keytab")
+      SparkHadoopUtil.get.loginUserFromKeytab(principalName, keytabFilename)
+    }
+  }
+
 }
 
 
diff --git a/docs/monitoring.md b/docs/monitoring.md
index 144be3daf120..347a9b1f1a32 100644
--- a/docs/monitoring.md
+++ b/docs/monitoring.md
@@ -91,6 +91,30 @@ represents an application's event logs. This creates a web interface at
       The port to which the web interface of the history server binds.
     </td>
   </tr>
+  <tr>
+    <td>spark.history.kerberos.enabled</td>
+    <td>false</td>
+    <td>
+      Indicates whether the history server should use kerberos to login. This is useful
+      if the history server is accessing HDFS files on a secure Hadoop cluster. If this is 
+      true it looks uses the configs <code>spark.history.kerberos.principal</code> and
+      <code>spark.history.kerberos.keytab</code>. 
+    </td>
+  </tr>
+  <tr>
+    <td>spark.history.kerberos.principal</td>
+    <td>(none)</td>
+    <td>
+      Kerberos principal name for the History Server.
+    </td>
+  </tr>
+  <tr>
+    <td>spark.history.kerberos.keytab</td>
+    <td>(none)</td>
+    <td>
+      Location of the kerberos keytab file for the History Server.
+    </td>
+  </tr>
 </table>
 
 Note that in all of these UIs, the tables are sortable by clicking their headers,

From c5c1916dd1b77e22759d58b5b361c56672983e3e Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Thu, 24 Apr 2014 14:54:01 -0700
Subject: [PATCH 006/810] SPARK-1494 Don't initialize classes loaded by MIMA
 excludes, attempt 2

[WIP]

Looks like scala reflection was invoking the static initializer:
```
...
	at org.apache.spark.sql.test.TestSQLContext$.<init>(TestSQLContext.scala:25)
	at org.apache.spark.sql.test.TestSQLContext$.<clinit>(TestSQLContext.scala)
	at java.lang.Class.forName0(Native Method)
	at java.lang.Class.forName(Class.java:270)
	at scala.reflect.runtime.JavaMirrors$JavaMirror.javaClass(JavaMirrors.scala:500)
	at scala.reflect.runtime.JavaMirrors$JavaMirror.tryJavaClass(JavaMirrors.scala:505)
	at scala.reflect.runtime.SymbolLoaders$PackageScope.lookupEntry(SymbolLoaders.scala:109)
...
```

Need to make sure that this doesn't change the exclusion semantics before merging.

Author: Michael Armbrust <michael@databricks.com>

Closes #526 from marmbrus/mima and squashes the following commits:

8168dea [Michael Armbrust] Spurious change
afba262 [Michael Armbrust] Prevent Scala reflection from running static class initializer.
---
 .../main/scala/org/apache/spark/tools/GenerateMIMAIgnore.scala  | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tools/src/main/scala/org/apache/spark/tools/GenerateMIMAIgnore.scala b/tools/src/main/scala/org/apache/spark/tools/GenerateMIMAIgnore.scala
index d9978b02a9f3..a433e8e2e89f 100644
--- a/tools/src/main/scala/org/apache/spark/tools/GenerateMIMAIgnore.scala
+++ b/tools/src/main/scala/org/apache/spark/tools/GenerateMIMAIgnore.scala
@@ -50,7 +50,7 @@ object GenerateMIMAIgnore {
            is a module or class. */
 
         val privateAsClass = mirror
-          .staticClass(className)
+          .classSymbol(Class.forName(className, false, classLoader))
           .privateWithin
           .fullName
           .startsWith(packageName)

From a03ac222d84025a1036750e1179136a13f75dea7 Mon Sep 17 00:00:00 2001
From: Sandeep <sandeep@techaddict.me>
Date: Thu, 24 Apr 2014 15:07:23 -0700
Subject: [PATCH 007/810] Fix Scala Style

Any comments are welcome

Author: Sandeep <sandeep@techaddict.me>

Closes #531 from techaddict/stylefix-1 and squashes the following commits:

7492730 [Sandeep] Pass 4
98b2428 [Sandeep] fix rxin suggestions
b5e2e6f [Sandeep] Pass 3
05932d7 [Sandeep] fix if else styling 2
08690e5 [Sandeep] fix if else styling
---
 .../scala/org/apache/spark/Accumulators.scala |  7 +-
 .../spark/deploy/SparkSubmitArguments.scala   |  3 +-
 .../apache/spark/deploy/master/Master.scala   |  3 +-
 .../spark/deploy/worker/DriverRunner.scala    |  8 ++-
 .../spark/deploy/worker/ui/LogPage.scala      | 16 +++--
 .../apache/spark/storage/BlockManager.scala   |  8 ++-
 .../spark/util/BoundedPriorityQueue.scala     | 12 ++--
 .../org/apache/spark/util/FileLogger.scala    |  4 +-
 .../scala/org/apache/spark/util/Utils.scala   |  3 +-
 .../scala/org/apache/spark/rdd/RDDSuite.scala |  4 +-
 .../org/apache/spark/examples/LogQuery.scala  |  3 +-
 .../examples/clickstream/PageViewStream.scala |  7 +-
 .../apache/spark/graphx/GraphOpsSuite.scala   |  7 +-
 .../apache/spark/repl/SparkExprTyper.scala    | 13 ++--
 .../sql/parquet/ParquetTableOperations.scala  |  5 +-
 .../sql/parquet/ParquetTableSupport.scala     |  7 +-
 .../streaming/scheduler/ReceiverTracker.scala |  3 +-
 .../spark/deploy/yarn/ApplicationMaster.scala |  9 +--
 .../spark/deploy/yarn/ExecutorLauncher.scala  |  3 +-
 .../deploy/yarn/YarnAllocationHandler.scala   | 67 ++++++++++---------
 20 files changed, 109 insertions(+), 83 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/Accumulators.scala b/core/src/main/scala/org/apache/spark/Accumulators.scala
index d5f3e3f6ec49..6d652faae149 100644
--- a/core/src/main/scala/org/apache/spark/Accumulators.scala
+++ b/core/src/main/scala/org/apache/spark/Accumulators.scala
@@ -104,8 +104,11 @@ class Accumulable[R, T] (
    * Set the accumulator's value; only allowed on master.
    */
   def value_= (newValue: R) {
-    if (!deserialized) value_ = newValue
-    else throw new UnsupportedOperationException("Can't assign accumulator value in task")
+    if (!deserialized) {
+      value_ = newValue
+    } else {
+      throw new UnsupportedOperationException("Can't assign accumulator value in task")
+    }
   }
 
   /**
diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
index cc976565cc72..c3e8c6b8c65a 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
@@ -66,8 +66,7 @@ private[spark] class SparkSubmitArguments(args: Array[String]) {
         if (k.startsWith("spark")) {
           defaultProperties(k) = v
           if (verbose) SparkSubmit.printStream.println(s"Adding default property: $k=$v")
-        }
-        else {
+        } else {
           SparkSubmit.printWarning(s"Ignoring non-spark config property: $k=$v")
         }
       }
diff --git a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
index 81f990bfa651..fdb633bd3360 100644
--- a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
@@ -237,8 +237,7 @@ private[spark] class Master(
             if (waitingDrivers.contains(d)) {
               waitingDrivers -= d
               self ! DriverStateChanged(driverId, DriverState.KILLED, None)
-            }
-            else {
+            } else {
               // We just notify the worker to kill the driver here. The final bookkeeping occurs
               // on the return path when the worker submits a state change back to the master
               // to notify it that the driver was successfully killed.
diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/DriverRunner.scala b/core/src/main/scala/org/apache/spark/deploy/worker/DriverRunner.scala
index f918b42c83bc..662d37871e7a 100644
--- a/core/src/main/scala/org/apache/spark/deploy/worker/DriverRunner.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/worker/DriverRunner.scala
@@ -91,9 +91,11 @@ private[spark] class DriverRunner(
         }
 
         val state =
-          if (killed) { DriverState.KILLED }
-          else if (finalException.isDefined) { DriverState.ERROR }
-          else {
+          if (killed) {
+            DriverState.KILLED
+          } else if (finalException.isDefined) {
+            DriverState.ERROR
+          } else {
             finalExitCode match {
               case Some(0) => DriverState.FINISHED
               case _ => DriverState.FAILED
diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/ui/LogPage.scala b/core/src/main/scala/org/apache/spark/deploy/worker/ui/LogPage.scala
index fec120794862..8381f59672ea 100644
--- a/core/src/main/scala/org/apache/spark/deploy/worker/ui/LogPage.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/worker/ui/LogPage.scala
@@ -89,8 +89,7 @@ private[spark] class LogPage(parent: WorkerWebUI) extends WebUIPage("logPage") {
             Previous {Utils.bytesToString(math.min(byteLength, startByte))}
           </button>
         </a>
-      }
-      else {
+      } else {
         <button type="button" class="btn btn-default" disabled="disabled">
           Previous 0 B
         </button>
@@ -104,8 +103,7 @@ private[spark] class LogPage(parent: WorkerWebUI) extends WebUIPage("logPage") {
             Next {Utils.bytesToString(math.min(byteLength, logLength - endByte))}
           </button>
         </a>
-      }
-      else {
+      } else {
         <button type="button" class="btn btn-default" disabled="disabled">
           Next 0 B
         </button>
@@ -137,9 +135,13 @@ private[spark] class LogPage(parent: WorkerWebUI) extends WebUIPage("logPage") {
     val logLength = file.length()
     val getOffset = offset.getOrElse(logLength - defaultBytes)
     val startByte =
-      if (getOffset < 0) 0L
-      else if (getOffset > logLength) logLength
-      else getOffset
+      if (getOffset < 0) {
+        0L
+      } else if (getOffset > logLength) {
+        logLength
+      } else {
+        getOffset
+      }
     val logPageLength = math.min(byteLength, maxBytes)
     val endByte = math.min(startByte + logPageLength, logLength)
     (startByte, endByte)
diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala
index ccd5c5320abe..02ba5ecf5245 100644
--- a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala
+++ b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala
@@ -281,7 +281,9 @@ private[spark] class BlockManager(
       val onDiskSize = status.diskSize
       master.updateBlockInfo(
         blockManagerId, blockId, storageLevel, inMemSize, onDiskSize, inTachyonSize)
-    } else true
+    } else {
+      true
+    }
   }
 
   /**
@@ -676,7 +678,7 @@ private[spark] class BlockManager(
               tachyonStore.putValues(blockId, iterator, level, false)
             case ArrayBufferValues(array) =>
               tachyonStore.putValues(blockId, array, level, false)
-            case ByteBufferValues(bytes) => 
+            case ByteBufferValues(bytes) =>
               bytes.rewind()
               tachyonStore.putBytes(blockId, bytes, level)
           }
@@ -695,7 +697,7 @@ private[spark] class BlockManager(
               diskStore.putValues(blockId, iterator, level, askForBytes)
             case ArrayBufferValues(array) =>
               diskStore.putValues(blockId, array, level, askForBytes)
-            case ByteBufferValues(bytes) => 
+            case ByteBufferValues(bytes) =>
               bytes.rewind()
               diskStore.putBytes(blockId, bytes, level)
           }
diff --git a/core/src/main/scala/org/apache/spark/util/BoundedPriorityQueue.scala b/core/src/main/scala/org/apache/spark/util/BoundedPriorityQueue.scala
index b9f4a5d720b9..1b2b1932e0c3 100644
--- a/core/src/main/scala/org/apache/spark/util/BoundedPriorityQueue.scala
+++ b/core/src/main/scala/org/apache/spark/util/BoundedPriorityQueue.scala
@@ -43,8 +43,11 @@ private[spark] class BoundedPriorityQueue[A](maxSize: Int)(implicit ord: Orderin
   }
 
   override def +=(elem: A): this.type = {
-    if (size < maxSize) underlying.offer(elem)
-    else maybeReplaceLowest(elem)
+    if (size < maxSize) {
+      underlying.offer(elem)
+    } else {
+      maybeReplaceLowest(elem)
+    }
     this
   }
 
@@ -59,7 +62,8 @@ private[spark] class BoundedPriorityQueue[A](maxSize: Int)(implicit ord: Orderin
     if (head != null && ord.gt(a, head)) {
       underlying.poll()
       underlying.offer(a)
-    } else false
+    } else {
+      false
+    }
   }
 }
-
diff --git a/core/src/main/scala/org/apache/spark/util/FileLogger.scala b/core/src/main/scala/org/apache/spark/util/FileLogger.scala
index 7d47b2a72aff..1ed3b70bb24f 100644
--- a/core/src/main/scala/org/apache/spark/util/FileLogger.scala
+++ b/core/src/main/scala/org/apache/spark/util/FileLogger.scala
@@ -113,7 +113,9 @@ private[spark] class FileLogger(
    * @param withTime Whether to prepend message with a timestamp
    */
   def log(msg: String, withTime: Boolean = false) {
-    val writeInfo = if (!withTime) msg else {
+    val writeInfo = if (!withTime) {
+      msg
+    } else {
       val date = new Date(System.currentTimeMillis())
       dateFormat.get.format(date) + ": " + msg
     }
diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala
index a3af4e7b9169..d333e2a88c18 100644
--- a/core/src/main/scala/org/apache/spark/util/Utils.scala
+++ b/core/src/main/scala/org/apache/spark/util/Utils.scala
@@ -811,8 +811,7 @@ private[spark] object Utils extends Logging {
           } else {
             el.getMethodName
           }
-        }
-        else {
+        } else {
           firstUserLine = el.getLineNumber
           firstUserFile = el.getFileName
           firstUserClass = el.getClassName
diff --git a/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala b/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala
index d7c90346d88a..2676558bfc86 100644
--- a/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala
+++ b/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala
@@ -381,8 +381,8 @@ class RDDSuite extends FunSuite with SharedSparkContext {
       val prng42 = new Random(42)
       val prng43 = new Random(43)
       Array(1, 2, 3, 4, 5, 6).filter{i =>
-        if (i < 4) 0 == prng42.nextInt(3)
-        else 0 == prng43.nextInt(3)}
+        if (i < 4) 0 == prng42.nextInt(3) else 0 == prng43.nextInt(3)
+      }
     }
     assert(sample.size === checkSample.size)
     for (i <- 0 until sample.size) assert(sample(i) === checkSample(i))
diff --git a/examples/src/main/scala/org/apache/spark/examples/LogQuery.scala b/examples/src/main/scala/org/apache/spark/examples/LogQuery.scala
index 820e87d04f3f..f77a444ff7a9 100644
--- a/examples/src/main/scala/org/apache/spark/examples/LogQuery.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/LogQuery.scala
@@ -49,8 +49,7 @@ object LogQuery {
       System.getenv("SPARK_HOME"), SparkContext.jarOfClass(this.getClass).toSeq)
 
     val dataSet =
-      if (args.length == 2) sc.textFile(args(1))
-      else sc.parallelize(exampleApacheLogs)
+      if (args.length == 2) sc.textFile(args(1)) else sc.parallelize(exampleApacheLogs)
     // scalastyle:off
     val apacheLogRegex =
       """^([\d.]+) (\S+) (\S+) \[([\w\d:/]+\s[+\-]\d{4})\] "(.+?)" (\d{3}) ([\d\-]+) "([^"]+)" "([^"]+)".*""".r
diff --git a/examples/src/main/scala/org/apache/spark/streaming/examples/clickstream/PageViewStream.scala b/examples/src/main/scala/org/apache/spark/streaming/examples/clickstream/PageViewStream.scala
index edc769c59734..673013f7cf94 100644
--- a/examples/src/main/scala/org/apache/spark/streaming/examples/clickstream/PageViewStream.scala
+++ b/examples/src/main/scala/org/apache/spark/streaming/examples/clickstream/PageViewStream.scala
@@ -69,8 +69,11 @@ object PageViewStream {
         val normalCount = statuses.filter(_ == 200).size
         val errorCount = statuses.size - normalCount
         val errorRatio = errorCount.toFloat / statuses.size
-        if (errorRatio > 0.05) {"%s: **%s**".format(zip, errorRatio)}
-        else {"%s: %s".format(zip, errorRatio)}
+        if (errorRatio > 0.05) {
+          "%s: **%s**".format(zip, errorRatio)
+        } else {
+          "%s: %s".format(zip, errorRatio)
+        }
     }
 
     // Return the number unique users in last 15 seconds
diff --git a/graphx/src/test/scala/org/apache/spark/graphx/GraphOpsSuite.scala b/graphx/src/test/scala/org/apache/spark/graphx/GraphOpsSuite.scala
index a467ca1ae715..ea94d4accb63 100644
--- a/graphx/src/test/scala/org/apache/spark/graphx/GraphOpsSuite.scala
+++ b/graphx/src/test/scala/org/apache/spark/graphx/GraphOpsSuite.scala
@@ -165,8 +165,11 @@ class GraphOpsSuite extends FunSuite with LocalSparkContext {
       // not have any edges in the specified direction.
       assert(edges.count === 50)
       edges.collect.foreach {
-        case (vid, edges) => if (vid > 0 && vid < 49) assert(edges.size == 2)
-        else assert(edges.size == 1)
+        case (vid, edges) => if (vid > 0 && vid < 49) {
+          assert(edges.size == 2)
+        } else {
+          assert(edges.size == 1)
+        }
       }
       edges.collect.foreach {
         case (vid, edges) =>
diff --git a/repl/src/main/scala/org/apache/spark/repl/SparkExprTyper.scala b/repl/src/main/scala/org/apache/spark/repl/SparkExprTyper.scala
index dcc139544e2c..f8432c8af6ed 100644
--- a/repl/src/main/scala/org/apache/spark/repl/SparkExprTyper.scala
+++ b/repl/src/main/scala/org/apache/spark/repl/SparkExprTyper.scala
@@ -47,9 +47,13 @@ trait SparkExprTyper extends Logging {
     var isIncomplete = false
     reporter.withIncompleteHandler((_, _) => isIncomplete = true) {
       val trees = codeParser.stmts(line)
-      if (reporter.hasErrors) Some(Nil)
-      else if (isIncomplete) None
-      else Some(trees)
+      if (reporter.hasErrors) {
+        Some(Nil)
+      } else if (isIncomplete) {
+        None
+      } else {
+        Some(trees)
+      }
     }
   }
   // def parsesAsExpr(line: String) = {
@@ -70,8 +74,7 @@ trait SparkExprTyper extends Logging {
           val sym0 = symbolOfTerm(name)
           // drop NullaryMethodType
           val sym = sym0.cloneSymbol setInfo afterTyper(sym0.info.finalResultType)
-          if (sym.info.typeSymbol eq UnitClass) NoSymbol
-          else sym
+          if (sym.info.typeSymbol eq UnitClass) NoSymbol else sym
         case _          => NoSymbol
       }
     }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala
index d5846baa72ad..f825ca3c028e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala
@@ -203,8 +203,9 @@ case class InsertIntoParquetTable(
     val stageId = sc.newRddId()
 
     val taskIdOffset =
-      if (overwrite) 1
-      else {
+      if (overwrite) {
+        1
+      } else {
         FileSystemHelper
           .findMaxTaskId(NewFileOutputFormat.getOutputPath(job).toString, job.getConfiguration) + 1
       }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala
index 84b1b4609458..71ba0fecce47 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala
@@ -158,8 +158,11 @@ private[parquet] class CatalystGroupConverter(
     a => a.dataType match {
       case ctype: NativeType =>
         // note: for some reason matching for StringType fails so use this ugly if instead
-        if (ctype == StringType) new CatalystPrimitiveStringConverter(this, schema.indexOf(a))
-        else new CatalystPrimitiveConverter(this, schema.indexOf(a))
+        if (ctype == StringType) {
+          new CatalystPrimitiveStringConverter(this, schema.indexOf(a))
+        } else {
+          new CatalystPrimitiveConverter(this, schema.indexOf(a))
+        }
       case _ => throw new RuntimeException(
         s"unable to convert datatype ${a.dataType.toString} in CatalystGroupConverter")
     }
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala
index 3d2537f6f23d..557e0961d594 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala
@@ -240,8 +240,7 @@ class ReceiverTracker(ssc: StreamingContext) extends Logging {
         if (hasLocationPreferences) {
           val receiversWithPreferences = receivers.map(r => (r, Seq(r.preferredLocation.get)))
           ssc.sc.makeRDD[Receiver[_]](receiversWithPreferences)
-        }
-        else {
+        } else {
           ssc.sc.makeRDD(receivers, receivers.size)
         }
 
diff --git a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
index 2f74965900ba..fc13dbecb455 100644
--- a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
+++ b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
@@ -147,12 +147,12 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration,
     // LOCAL_DIRS => 2.X, YARN_LOCAL_DIRS => 0.23.X
     val localDirs = Option(System.getenv("YARN_LOCAL_DIRS"))
       .orElse(Option(System.getenv("LOCAL_DIRS")))
- 
+
     localDirs match {
       case None => throw new Exception("Yarn Local dirs can't be empty")
       case Some(l) => l
     }
-  } 
+  }
 
   private def getApplicationAttemptId(): ApplicationAttemptId = {
     val envs = System.getenv()
@@ -321,8 +321,9 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration,
             logInfo("Allocating %d containers to make up for (potentially) lost containers".
               format(missingExecutorCount))
             yarnAllocator.allocateContainers(missingExecutorCount)
+          } else {
+            sendProgress()
           }
-          else sendProgress()
           Thread.sleep(sleepTime)
         }
       }
@@ -361,7 +362,7 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration,
         return
       }
       isFinished = true
-      
+
       logInfo("finishApplicationMaster with " + status)
       if (registered) {
         val finishReq = Records.newRecord(classOf[FinishApplicationMasterRequest])
diff --git a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala
index ea356f33eb99..65b7215afbd4 100644
--- a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala
+++ b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala
@@ -243,8 +243,9 @@ class ExecutorLauncher(args: ApplicationMasterArguments, conf: Configuration, sp
             logInfo("Allocating " + missingExecutorCount +
               " containers to make up for (potentially ?) lost containers")
             yarnAllocator.allocateContainers(missingExecutorCount)
+          } else {
+            sendProgress()
           }
-          else sendProgress()
           Thread.sleep(sleepTime)
         }
       }
diff --git a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala
index 95f0f9d0ff2b..856391e52b2d 100644
--- a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala
+++ b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala
@@ -60,12 +60,12 @@ object AllocationType extends Enumeration {
  */
 private[yarn] class YarnAllocationHandler(
     val conf: Configuration,
-    val resourceManager: AMRMProtocol, 
+    val resourceManager: AMRMProtocol,
     val appAttemptId: ApplicationAttemptId,
     val maxExecutors: Int,
     val executorMemory: Int,
     val executorCores: Int,
-    val preferredHostToCount: Map[String, Int], 
+    val preferredHostToCount: Map[String, Int],
     val preferredRackToCount: Map[String, Int],
     val sparkConf: SparkConf)
   extends Logging {
@@ -136,9 +136,10 @@ private[yarn] class YarnAllocationHandler(
           val containers = hostToContainers.getOrElseUpdate(host, new ArrayBuffer[Container]())
 
           containers += container
+        } else {
+          // Add all ignored containers to released list
+          releasedContainerList.add(container.getId())
         }
-        // Add all ignored containers to released list
-        else releasedContainerList.add(container.getId())
       }
 
       // Find the appropriate containers to use. Slightly non trivial groupBy ...
@@ -159,8 +160,7 @@ private[yarn] class YarnAllocationHandler(
           dataLocalContainers.put(candidateHost, remainingContainers)
           // all consumed
           remainingContainers = null
-        }
-        else if (requiredHostCount > 0) {
+        } else if (requiredHostCount > 0) {
           // Container list has more containers than we need for data locality.
           // Split into two : data local container count of (remainingContainers.size -
           // requiredHostCount) and rest as remainingContainer
@@ -170,7 +170,7 @@ private[yarn] class YarnAllocationHandler(
           // remainingContainers = remaining
 
           // yarn has nasty habit of allocating a tonne of containers on a host - discourage this :
-          // add remaining to release list. If we have insufficient containers, next allocation 
+          // add remaining to release list. If we have insufficient containers, next allocation
           // cycle will reallocate (but wont treat it as data local)
           for (container <- remaining) releasedContainerList.add(container.getId())
           remainingContainers = null
@@ -182,7 +182,7 @@ private[yarn] class YarnAllocationHandler(
 
           if (rack != null){
             val maxExpectedRackCount = preferredRackToCount.getOrElse(rack, 0)
-            val requiredRackCount = maxExpectedRackCount - allocatedContainersOnRack(rack) - 
+            val requiredRackCount = maxExpectedRackCount - allocatedContainersOnRack(rack) -
               rackLocalContainers.get(rack).getOrElse(List()).size
 
 
@@ -191,8 +191,7 @@ private[yarn] class YarnAllocationHandler(
               dataLocalContainers.put(rack, remainingContainers)
               // All consumed
               remainingContainers = null
-            }
-            else if (requiredRackCount > 0) {
+            } else if (requiredRackCount > 0) {
               // container list has more containers than we need for data locality.
               // Split into two : data local container count of (remainingContainers.size -
               // requiredRackCount) and rest as remainingContainer
@@ -213,7 +212,7 @@ private[yarn] class YarnAllocationHandler(
         }
       }
 
-      // Now that we have split the containers into various groups, go through them in order : 
+      // Now that we have split the containers into various groups, go through them in order :
       // first host local, then rack local and then off rack (everything else).
       // Note that the list we create below tries to ensure that not all containers end up within a
       // host if there are sufficiently large number of hosts/containers.
@@ -238,8 +237,7 @@ private[yarn] class YarnAllocationHandler(
           releasedContainerList.add(containerId)
           // reset counter back to old value.
           numExecutorsRunning.decrementAndGet()
-        }
-        else {
+        } else {
           // Deallocate + allocate can result in reusing id's wrongly - so use a different counter
           // (executorIdCounter)
           val executorId = executorIdCounter.incrementAndGet().toString
@@ -293,8 +291,7 @@ private[yarn] class YarnAllocationHandler(
         // Was this released by us ? If yes, then simply remove from containerSet and move on.
         if (pendingReleaseContainers.containsKey(containerId)) {
           pendingReleaseContainers.remove(containerId)
-        }
-        else {
+        } else {
           // Simply decrement count - next iteration of ReporterThread will take care of allocating.
           numExecutorsRunning.decrementAndGet()
           logInfo("Completed container %s (state: %s, exit status: %s)".format(
@@ -319,8 +316,11 @@ private[yarn] class YarnAllocationHandler(
             assert (containerSet != null)
 
             containerSet -= containerId
-            if (containerSet.isEmpty) allocatedHostToContainersMap.remove(host)
-            else allocatedHostToContainersMap.update(host, containerSet)
+            if (containerSet.isEmpty) {
+              allocatedHostToContainersMap.remove(host)
+            } else {
+              allocatedHostToContainersMap.update(host, containerSet)
+            }
 
             allocatedContainerToHostMap -= containerId
 
@@ -328,8 +328,11 @@ private[yarn] class YarnAllocationHandler(
             val rack = YarnAllocationHandler.lookupRack(conf, host)
             if (rack != null) {
               val rackCount = allocatedRackCount.getOrElse(rack, 0) - 1
-              if (rackCount > 0) allocatedRackCount.put(rack, rackCount)
-              else allocatedRackCount.remove(rack)
+              if (rackCount > 0) {
+                allocatedRackCount.put(rack, rackCount)
+              } else {
+                allocatedRackCount.remove(rack)
+              }
             }
           }
         }
@@ -365,10 +368,10 @@ private[yarn] class YarnAllocationHandler(
       }
     }
 
-    val requestedContainers: ArrayBuffer[ResourceRequest] = 
+    val requestedContainers: ArrayBuffer[ResourceRequest] =
       new ArrayBuffer[ResourceRequest](rackToCounts.size)
     for ((rack, count) <- rackToCounts){
-      requestedContainers += 
+      requestedContainers +=
         createResourceRequest(AllocationType.RACK, rack, count, YarnAllocationHandler.PRIORITY)
     }
 
@@ -401,11 +404,10 @@ private[yarn] class YarnAllocationHandler(
         preferredHostToCount.isEmpty)
       resourceRequests = List(createResourceRequest(
         AllocationType.ANY, null, numExecutors, YarnAllocationHandler.PRIORITY))
-    }
-    else {
-      // request for all hosts in preferred nodes and for numExecutors - 
+    } else {
+      // request for all hosts in preferred nodes and for numExecutors -
       // candidates.size, request by default allocation policy.
-      val hostContainerRequests: ArrayBuffer[ResourceRequest] = 
+      val hostContainerRequests: ArrayBuffer[ResourceRequest] =
         new ArrayBuffer[ResourceRequest](preferredHostToCount.size)
       for ((candidateHost, candidateCount) <- preferredHostToCount) {
         val requiredCount = candidateCount - allocatedContainersOnHost(candidateHost)
@@ -449,8 +451,7 @@ private[yarn] class YarnAllocationHandler(
     if (numExecutors > 0) {
       logInfo("Allocating %d executor containers with %d of memory each.".format(numExecutors,
         executorMemory + YarnAllocationHandler.MEMORY_OVERHEAD))
-    }
-    else {
+    } else {
       logDebug("Empty allocation req ..  release : " + releasedContainerList)
     }
 
@@ -467,7 +468,7 @@ private[yarn] class YarnAllocationHandler(
 
 
   private def createResourceRequest(
-    requestType: AllocationType.AllocationType, 
+    requestType: AllocationType.AllocationType,
     resource:String,
     numExecutors: Int,
     priority: Int): ResourceRequest = {
@@ -528,7 +529,7 @@ private[yarn] class YarnAllocationHandler(
     if (! retval.isEmpty) {
       releasedContainerList.removeAll(retval)
       for (v <- retval) pendingReleaseContainers.put(v, true)
-      logInfo("Releasing " + retval.size + " containers. pendingReleaseContainers : " + 
+      logInfo("Releasing " + retval.size + " containers. pendingReleaseContainers : " +
         pendingReleaseContainers)
     }
 
@@ -539,7 +540,7 @@ private[yarn] class YarnAllocationHandler(
 object YarnAllocationHandler {
 
   val ANY_HOST = "*"
-  // All requests are issued with same priority : we do not (yet) have any distinction between 
+  // All requests are issued with same priority : we do not (yet) have any distinction between
   // request types (like map/reduce in hadoop for example)
   val PRIORITY = 1
 
@@ -548,7 +549,7 @@ object YarnAllocationHandler {
 
   // Host to rack map - saved from allocation requests
   // We are expecting this not to change.
-  // Note that it is possible for this to change : and RM will indicate that to us via update 
+  // Note that it is possible for this to change : and RM will indicate that to us via update
   // response to allocate. But we are punting on handling that for now.
   private val hostToRack = new ConcurrentHashMap[String, String]()
   private val rackToHostSet = new ConcurrentHashMap[String, JSet[String]]()
@@ -565,7 +566,7 @@ object YarnAllocationHandler {
       conf,
       resourceManager,
       appAttemptId,
-      args.numExecutors, 
+      args.numExecutors,
       args.executorMemory,
       args.executorCores,
       Map[String, Int](),
@@ -587,7 +588,7 @@ object YarnAllocationHandler {
       conf,
       resourceManager,
       appAttemptId,
-      args.numExecutors, 
+      args.numExecutors,
       args.executorMemory,
       args.executorCores,
       hostToCount,

From f99af8529b6969986f0c3e03f6ff9b7bb9d53ece Mon Sep 17 00:00:00 2001
From: CodingCat <zhunansjtu@gmail.com>
Date: Thu, 24 Apr 2014 15:55:18 -0700
Subject: [PATCH 008/810] SPARK-1104: kill Process in workerThread of
 ExecutorRunner

As reported in https://spark-project.atlassian.net/browse/SPARK-1104

By @pwendell: "Sometimes due to large shuffles executors will take a long time shutting down. In particular this can happen if large numbers of shuffle files are around (this will be alleviated by SPARK-1103, but nonetheless...).

The symptom is you have DEAD workers sitting around in the UI and the existing workers keep trying to re-register but can't because they've been assumed dead."

In this patch, I add lines in the handler of InterruptedException in workerThread of executorRunner, so that the process.destroy() and process.waitFor() can only block the workerThread instead of blocking the worker Actor...

---------

analysis: process.destroy() is a blocking method, i.e. it only returns when all shutdownHook threads return...so calling it in Worker thread will make Worker block for a long while....

about what will happen on the shutdown hooks when the JVM process is killed: http://www.tutorialspoint.com/java/lang/runtime_addshutdownhook.htm

Author: CodingCat <zhunansjtu@gmail.com>

Closes #35 from CodingCat/SPARK-1104 and squashes the following commits:

85767da [CodingCat] add null checking and remove unnecessary killProce
3107aeb [CodingCat] address Aaron's comments
eb615ba [CodingCat] kill the process when the error happens
0accf2f [CodingCat] set process to null after killed it
1d511c8 [CodingCat] kill Process in workerThread
---
 .../spark/deploy/worker/ExecutorRunner.scala  | 31 +++++++++----------
 1 file changed, 14 insertions(+), 17 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/ExecutorRunner.scala b/core/src/main/scala/org/apache/spark/deploy/worker/ExecutorRunner.scala
index f94cd685e8eb..205140368273 100644
--- a/core/src/main/scala/org/apache/spark/deploy/worker/ExecutorRunner.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/worker/ExecutorRunner.scala
@@ -58,30 +58,29 @@ private[spark] class ExecutorRunner(
       override def run() { fetchAndRunExecutor() }
     }
     workerThread.start()
-
     // Shutdown hook that kills actors on shutdown.
     shutdownHook = new Thread() {
       override def run() {
-        if (process != null) {
-          logInfo("Shutdown hook killing child process.")
-          process.destroy()
-          process.waitFor()
-        }
+        killProcess()
       }
     }
     Runtime.getRuntime.addShutdownHook(shutdownHook)
   }
 
+  private def killProcess() {
+    if (process != null) {
+      logInfo("Killing process!")
+      process.destroy()
+      process.waitFor()
+    }
+  }
+
   /** Stop this executor runner, including killing the process it launched */
   def kill() {
     if (workerThread != null) {
+      // the workerThread will kill the child process when interrupted
       workerThread.interrupt()
       workerThread = null
-      if (process != null) {
-        logInfo("Killing process!")
-        process.destroy()
-        process.waitFor()
-      }
       state = ExecutorState.KILLED
       worker ! ExecutorStateChanged(appId, execId, state, None, None)
       Runtime.getRuntime.removeShutdownHook(shutdownHook)
@@ -128,7 +127,6 @@ private[spark] class ExecutorRunner(
       // parent process for the executor command
       env.put("SPARK_LAUNCH_WITH_SCALA", "0")
       process = builder.start()
-
       val header = "Spark Executor Command: %s\n%s\n\n".format(
         command.mkString("\"", "\" \"", "\""), "=" * 40)
 
@@ -148,14 +146,13 @@ private[spark] class ExecutorRunner(
       val message = "Command exited with code " + exitCode
       worker ! ExecutorStateChanged(appId, execId, state, Some(message), Some(exitCode))
     } catch {
-      case interrupted: InterruptedException =>
+      case interrupted: InterruptedException => {
         logInfo("Runner thread for executor " + fullId + " interrupted")
-
+        killProcess()
+      }
       case e: Exception => {
         logError("Error running executor", e)
-        if (process != null) {
-          process.destroy()
-        }
+        killProcess()
         state = ExecutorState.FAILED
         val message = e.getClass + ": " + e.getMessage
         worker ! ExecutorStateChanged(appId, execId, state, Some(message), None)

From 35e3d199f04fba3230625002a458d43b9578b2e8 Mon Sep 17 00:00:00 2001
From: Arun Ramakrishnan <smartnut007@gmail.com>
Date: Thu, 24 Apr 2014 17:27:16 -0700
Subject: [PATCH 009/810] SPARK-1438 RDD.sample() make seed param optional

copying form previous pull request https://github.com/apache/spark/pull/462

Its probably better to let the underlying language implementation take care of the default . This was easier to do with python as the default value for seed in random and numpy random is None.

In Scala/Java side it might mean propagating an Option or null(oh no!) down the chain until where the Random is constructed. But, looks like the convention in some other methods was to use System.nanoTime. So, followed that convention.

Conflict with overloaded method in sql.SchemaRDD.sample which also defines default params.
sample(fraction, withReplacement=false, seed=math.random)
Scala does not allow more than one overloaded to have default params. I believe the author intended to override the RDD.sample method and not overload it. So, changed it.

If backward compatible is important, 3 new method can be introduced (without default params) like this
sample(fraction)
sample(fraction, withReplacement)
sample(fraction, withReplacement, seed)

Added some tests for the scala RDD takeSample method.

Author: Arun Ramakrishnan <smartnut007@gmail.com>

This patch had conflicts when merged, resolved by
Committer: Matei Zaharia <matei@databricks.com>

Closes #477 from smartnut007/master and squashes the following commits:

07bb06e [Arun Ramakrishnan] SPARK-1438 fixing more space formatting issues
b9ebfe2 [Arun Ramakrishnan] SPARK-1438 removing redundant import of random in python rddsampler
8d05b1a [Arun Ramakrishnan] SPARK-1438 RDD . Replace System.nanoTime with a Random generated number. python: use a separate instance of Random instead of seeding language api global Random instance.
69619c6 [Arun Ramakrishnan] SPARK-1438 fix spacing issue
0c247db [Arun Ramakrishnan] SPARK-1438 RDD language apis to support optional seed in RDD methods sample/takeSample
---
 .../apache/spark/api/java/JavaDoubleRDD.scala |  9 +++++-
 .../apache/spark/api/java/JavaPairRDD.scala   |  9 +++++-
 .../org/apache/spark/api/java/JavaRDD.scala   |  9 +++++-
 .../apache/spark/api/java/JavaRDDLike.scala   |  6 +++-
 .../spark/rdd/PartitionwiseSampledRDD.scala   |  5 +--
 .../main/scala/org/apache/spark/rdd/RDD.scala | 11 ++++---
 .../scala/org/apache/spark/util/Utils.scala   |  2 ++
 .../scala/org/apache/spark/rdd/RDDSuite.scala | 21 ++++++++++++-
 python/pyspark/rdd.py                         | 13 ++++----
 python/pyspark/rddsampler.py                  | 31 +++++++++----------
 .../plans/logical/basicOperators.scala        |  2 +-
 .../org/apache/spark/sql/SchemaRDD.scala      |  5 +--
 .../spark/sql/execution/basicOperators.scala  |  6 ++--
 13 files changed, 88 insertions(+), 41 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaDoubleRDD.scala b/core/src/main/scala/org/apache/spark/api/java/JavaDoubleRDD.scala
index 4330cef3965e..a6123bd108c1 100644
--- a/core/src/main/scala/org/apache/spark/api/java/JavaDoubleRDD.scala
+++ b/core/src/main/scala/org/apache/spark/api/java/JavaDoubleRDD.scala
@@ -30,6 +30,7 @@ import org.apache.spark.partial.{BoundedDouble, PartialResult}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.storage.StorageLevel
 import org.apache.spark.util.StatCounter
+import org.apache.spark.util.Utils
 
 class JavaDoubleRDD(val srdd: RDD[scala.Double]) extends JavaRDDLike[JDouble, JavaDoubleRDD] {
 
@@ -133,7 +134,13 @@ class JavaDoubleRDD(val srdd: RDD[scala.Double]) extends JavaRDDLike[JDouble, Ja
   /**
    * Return a sampled subset of this RDD.
    */
-  def sample(withReplacement: Boolean, fraction: JDouble, seed: Int): JavaDoubleRDD =
+  def sample(withReplacement: Boolean, fraction: JDouble): JavaDoubleRDD =
+    sample(withReplacement, fraction, Utils.random.nextLong)
+    
+  /**
+   * Return a sampled subset of this RDD.
+   */
+  def sample(withReplacement: Boolean, fraction: JDouble, seed: Long): JavaDoubleRDD =
     fromRDD(srdd.sample(withReplacement, fraction, seed))
 
   /**
diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala b/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala
index b3ec270281ae..554c06535864 100644
--- a/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala
+++ b/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala
@@ -39,6 +39,7 @@ import org.apache.spark.api.java.function.{Function => JFunction, Function2 => J
 import org.apache.spark.partial.{BoundedDouble, PartialResult}
 import org.apache.spark.rdd.{OrderedRDDFunctions, RDD}
 import org.apache.spark.storage.StorageLevel
+import org.apache.spark.util.Utils
 
 class JavaPairRDD[K, V](val rdd: RDD[(K, V)])
                        (implicit val kClassTag: ClassTag[K], implicit val vClassTag: ClassTag[V])
@@ -119,7 +120,13 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])
   /**
    * Return a sampled subset of this RDD.
    */
-  def sample(withReplacement: Boolean, fraction: Double, seed: Int): JavaPairRDD[K, V] =
+  def sample(withReplacement: Boolean, fraction: Double): JavaPairRDD[K, V] =
+    sample(withReplacement, fraction, Utils.random.nextLong)
+    
+  /**
+   * Return a sampled subset of this RDD.
+   */
+  def sample(withReplacement: Boolean, fraction: Double, seed: Long): JavaPairRDD[K, V] =
     new JavaPairRDD[K, V](rdd.sample(withReplacement, fraction, seed))
 
   /**
diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaRDD.scala b/core/src/main/scala/org/apache/spark/api/java/JavaRDD.scala
index 327c1552dc94..dc698dea75e4 100644
--- a/core/src/main/scala/org/apache/spark/api/java/JavaRDD.scala
+++ b/core/src/main/scala/org/apache/spark/api/java/JavaRDD.scala
@@ -24,6 +24,7 @@ import org.apache.spark._
 import org.apache.spark.api.java.function.{Function => JFunction}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.storage.StorageLevel
+import org.apache.spark.util.Utils
 
 class JavaRDD[T](val rdd: RDD[T])(implicit val classTag: ClassTag[T])
   extends JavaRDDLike[T, JavaRDD[T]] {
@@ -98,7 +99,13 @@ class JavaRDD[T](val rdd: RDD[T])(implicit val classTag: ClassTag[T])
   /**
    * Return a sampled subset of this RDD.
    */
-  def sample(withReplacement: Boolean, fraction: Double, seed: Int): JavaRDD[T] =
+  def sample(withReplacement: Boolean, fraction: Double): JavaRDD[T] =
+    sample(withReplacement, fraction, Utils.random.nextLong)
+    
+  /**
+   * Return a sampled subset of this RDD.
+   */
+  def sample(withReplacement: Boolean, fraction: Double, seed: Long): JavaRDD[T] =
     wrapRDD(rdd.sample(withReplacement, fraction, seed))
 
   /**
diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala b/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala
index 725c423a53e3..574a98636a61 100644
--- a/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala
+++ b/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala
@@ -34,6 +34,7 @@ import org.apache.spark.api.java.function.{Function => JFunction, Function2 => J
 import org.apache.spark.partial.{BoundedDouble, PartialResult}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.storage.StorageLevel
+import org.apache.spark.util.Utils
 
 trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable {
   def wrapRDD(rdd: RDD[T]): This
@@ -394,7 +395,10 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable {
     new java.util.ArrayList(arr)
   }
 
-  def takeSample(withReplacement: Boolean, num: Int, seed: Int): JList[T] = {
+  def takeSample(withReplacement: Boolean, num: Int): JList[T] = 
+    takeSample(withReplacement, num, Utils.random.nextLong)
+    
+  def takeSample(withReplacement: Boolean, num: Int, seed: Long): JList[T] = {
     import scala.collection.JavaConversions._
     val arr: java.util.Collection[T] = rdd.takeSample(withReplacement, num, seed).toSeq
     new java.util.ArrayList(arr)
diff --git a/core/src/main/scala/org/apache/spark/rdd/PartitionwiseSampledRDD.scala b/core/src/main/scala/org/apache/spark/rdd/PartitionwiseSampledRDD.scala
index b4e3bb5d75e1..b5b8a5706deb 100644
--- a/core/src/main/scala/org/apache/spark/rdd/PartitionwiseSampledRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/PartitionwiseSampledRDD.scala
@@ -23,6 +23,7 @@ import scala.reflect.ClassTag
 
 import org.apache.spark.{Partition, TaskContext}
 import org.apache.spark.util.random.RandomSampler
+import org.apache.spark.util.Utils
 
 private[spark]
 class PartitionwiseSampledRDDPartition(val prev: Partition, val seed: Long)
@@ -38,14 +39,14 @@ class PartitionwiseSampledRDDPartition(val prev: Partition, val seed: Long)
  *
  * @param prev RDD to be sampled
  * @param sampler a random sampler
- * @param seed random seed, default to System.nanoTime
+ * @param seed random seed
  * @tparam T input RDD item type
  * @tparam U sampled RDD item type
  */
 private[spark] class PartitionwiseSampledRDD[T: ClassTag, U: ClassTag](
     prev: RDD[T],
     sampler: RandomSampler[T, U],
-    @transient seed: Long = System.nanoTime)
+    @transient seed: Long = Utils.random.nextLong)
   extends RDD[U](prev) {
 
   override def getPartitions: Array[Partition] = {
diff --git a/core/src/main/scala/org/apache/spark/rdd/RDD.scala b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
index 6c897cc03b64..e8bbfbf01679 100644
--- a/core/src/main/scala/org/apache/spark/rdd/RDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
@@ -341,7 +341,9 @@ abstract class RDD[T: ClassTag](
   /**
    * Return a sampled subset of this RDD.
    */
-  def sample(withReplacement: Boolean, fraction: Double, seed: Int): RDD[T] = {
+  def sample(withReplacement: Boolean, 
+      fraction: Double, 
+      seed: Long = Utils.random.nextLong): RDD[T] = {
     require(fraction >= 0.0, "Invalid fraction value: " + fraction)
     if (withReplacement) {
       new PartitionwiseSampledRDD[T, T](this, new PoissonSampler[T](fraction), seed)
@@ -354,11 +356,11 @@ abstract class RDD[T: ClassTag](
    * Randomly splits this RDD with the provided weights.
    *
    * @param weights weights for splits, will be normalized if they don't sum to 1
-   * @param seed random seed, default to System.nanoTime
+   * @param seed random seed
    *
    * @return split RDDs in an array
    */
-  def randomSplit(weights: Array[Double], seed: Long = System.nanoTime): Array[RDD[T]] = {
+  def randomSplit(weights: Array[Double], seed: Long = Utils.random.nextLong): Array[RDD[T]] = {
     val sum = weights.sum
     val normalizedCumWeights = weights.map(_ / sum).scanLeft(0.0d)(_ + _)
     normalizedCumWeights.sliding(2).map { x =>
@@ -366,7 +368,8 @@ abstract class RDD[T: ClassTag](
     }.toArray
   }
 
-  def takeSample(withReplacement: Boolean, num: Int, seed: Int): Array[T] = {
+  def takeSample(withReplacement: Boolean, num: Int, seed: Long = Utils.random.nextLong): Array[T] =
+  {
     var fraction = 0.0
     var total = 0
     val multiplier = 3.0
diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala
index d333e2a88c18..084a71c4caeb 100644
--- a/core/src/main/scala/org/apache/spark/util/Utils.scala
+++ b/core/src/main/scala/org/apache/spark/util/Utils.scala
@@ -46,6 +46,8 @@ import org.apache.spark.serializer.{DeserializationStream, SerializationStream,
 private[spark] object Utils extends Logging {
 
   val osName = System.getProperty("os.name")
+  
+  val random = new Random()
 
   /** Serialize an object using Java serialization */
   def serialize[T](o: T): Array[Byte] = {
diff --git a/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala b/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala
index 2676558bfc86..8da9a0da700e 100644
--- a/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala
+++ b/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala
@@ -463,7 +463,13 @@ class RDDSuite extends FunSuite with SharedSparkContext {
 
   test("takeSample") {
     val data = sc.parallelize(1 to 100, 2)
-
+    
+    for (num <- List(5, 20, 100)) {
+      val sample = data.takeSample(withReplacement=false, num=num)
+      assert(sample.size === num)        // Got exactly num elements
+      assert(sample.toSet.size === num)  // Elements are distinct
+      assert(sample.forall(x => 1 <= x && x <= 100), "elements not in [1, 100]")
+    }
     for (seed <- 1 to 5) {
       val sample = data.takeSample(withReplacement=false, 20, seed)
       assert(sample.size === 20)        // Got exactly 20 elements
@@ -481,6 +487,19 @@ class RDDSuite extends FunSuite with SharedSparkContext {
       assert(sample.size === 20)        // Got exactly 20 elements
       assert(sample.forall(x => 1 <= x && x <= 100), "elements not in [1, 100]")
     }
+    {
+      val sample = data.takeSample(withReplacement=true, num=20)
+      assert(sample.size === 20)        // Got exactly 100 elements
+      assert(sample.toSet.size <= 20, "sampling with replacement returned all distinct elements")
+      assert(sample.forall(x => 1 <= x && x <= 100), "elements not in [1, 100]")
+    }
+    {
+      val sample = data.takeSample(withReplacement=true, num=100)
+      assert(sample.size === 100)        // Got exactly 100 elements
+      // Chance of getting all distinct elements is astronomically low, so test we got < 100
+      assert(sample.toSet.size < 100, "sampling with replacement returned all distinct elements")
+      assert(sample.forall(x => 1 <= x && x <= 100), "elements not in [1, 100]")
+    }
     for (seed <- 1 to 5) {
       val sample = data.takeSample(withReplacement=true, 100, seed)
       assert(sample.size === 100)        // Got exactly 100 elements
diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py
index 91fc7e637e2c..d73ab7006e9c 100644
--- a/python/pyspark/rdd.py
+++ b/python/pyspark/rdd.py
@@ -30,6 +30,7 @@
 from threading import Thread
 import warnings
 import heapq
+from random import Random
 
 from pyspark.serializers import NoOpSerializer, CartesianDeserializer, \
     BatchedSerializer, CloudPickleSerializer, PairDeserializer, pack_long
@@ -332,7 +333,7 @@ def distinct(self):
                    .reduceByKey(lambda x, _: x) \
                    .map(lambda (x, _): x)
 
-    def sample(self, withReplacement, fraction, seed):
+    def sample(self, withReplacement, fraction, seed=None):
         """
         Return a sampled subset of this RDD (relies on numpy and falls back
         on default random generator if numpy is unavailable).
@@ -344,7 +345,7 @@ def sample(self, withReplacement, fraction, seed):
         return self.mapPartitionsWithIndex(RDDSampler(withReplacement, fraction, seed).func, True)
 
     # this is ported from scala/spark/RDD.scala
-    def takeSample(self, withReplacement, num, seed):
+    def takeSample(self, withReplacement, num, seed=None):
         """
         Return a fixed-size sampled subset of this RDD (currently requires numpy).
 
@@ -381,13 +382,11 @@ def takeSample(self, withReplacement, num, seed):
         # If the first sample didn't turn out large enough, keep trying to take samples;
         # this shouldn't happen often because we use a big multiplier for their initial size.
         # See: scala/spark/RDD.scala
+        rand = Random(seed)
         while len(samples) < total:
-            if seed > sys.maxint - 2:
-                seed = -1
-            seed += 1
-            samples = self.sample(withReplacement, fraction, seed).collect()
+            samples = self.sample(withReplacement, fraction, rand.randint(0, sys.maxint)).collect()
 
-        sampler = RDDSampler(withReplacement, fraction, seed+1)
+        sampler = RDDSampler(withReplacement, fraction, rand.randint(0, sys.maxint))
         sampler.shuffle(samples)
         return samples[0:total]
 
diff --git a/python/pyspark/rddsampler.py b/python/pyspark/rddsampler.py
index aca2ef3b51e9..845a267e311c 100644
--- a/python/pyspark/rddsampler.py
+++ b/python/pyspark/rddsampler.py
@@ -19,7 +19,7 @@
 import random
 
 class RDDSampler(object):
-    def __init__(self, withReplacement, fraction, seed):
+    def __init__(self, withReplacement, fraction, seed=None):
         try:
             import numpy
             self._use_numpy = True
@@ -27,7 +27,7 @@ def __init__(self, withReplacement, fraction, seed):
             print >> sys.stderr, "NumPy does not appear to be installed. Falling back to default random generator for sampling."
             self._use_numpy = False
 
-        self._seed = seed
+        self._seed = seed if seed is not None else random.randint(0, sys.maxint)
         self._withReplacement = withReplacement
         self._fraction = fraction
         self._random = None
@@ -38,17 +38,14 @@ def initRandomGenerator(self, split):
         if self._use_numpy:
             import numpy
             self._random = numpy.random.RandomState(self._seed)
-            for _ in range(0, split):
-                # discard the next few values in the sequence to have a
-                # different seed for the different splits
-                self._random.randint(sys.maxint)
         else:
-            import random
-            random.seed(self._seed)
-            for _ in range(0, split):
-                # discard the next few values in the sequence to have a
-                # different seed for the different splits
-                random.randint(0, sys.maxint)
+            self._random = random.Random(self._seed)
+
+        for _ in range(0, split):
+            # discard the next few values in the sequence to have a
+            # different seed for the different splits
+            self._random.randint(0, sys.maxint)
+
         self._split = split
         self._rand_initialized = True
 
@@ -59,7 +56,7 @@ def getUniformSample(self, split):
         if self._use_numpy:
             return self._random.random_sample()
         else:
-            return random.uniform(0.0, 1.0)
+            return self._random.uniform(0.0, 1.0)
 
     def getPoissonSample(self, split, mean):
         if not self._rand_initialized or split != self._split:
@@ -73,26 +70,26 @@ def getPoissonSample(self, split, mean):
             num_arrivals = 1
             cur_time = 0.0
 
-            cur_time += random.expovariate(mean)
+            cur_time += self._random.expovariate(mean)
 
             if cur_time > 1.0:
                 return 0
 
             while(cur_time <= 1.0):
-                cur_time += random.expovariate(mean)
+                cur_time += self._random.expovariate(mean)
                 num_arrivals += 1
 
             return (num_arrivals - 1)
     
     def shuffle(self, vals):
-        if self._random == None or split != self._split:
+        if self._random == None:
             self.initRandomGenerator(0)  # this should only ever called on the master so
             # the split does not matter
         
         if self._use_numpy:
             self._random.shuffle(vals)
         else:
-            random.shuffle(vals, self._random)
+            self._random.shuffle(vals, self._random.random)
 
     def func(self, split, iterator):
         if self._withReplacement:            
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
index 397473e17886..732708e146b0 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
@@ -168,7 +168,7 @@ case class LowerCaseSchema(child: LogicalPlan) extends UnaryNode {
   def references = Set.empty
 }
 
-case class Sample(fraction: Double, withReplacement: Boolean, seed: Int, child: LogicalPlan)
+case class Sample(fraction: Double, withReplacement: Boolean, seed: Long, child: LogicalPlan)
     extends UnaryNode {
 
   def output = child.output
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala
index 6cb0e0f61f2d..ca6e0a696405 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala
@@ -256,10 +256,11 @@ class SchemaRDD(
    * @group Query
    */
   @Experimental
+  override
   def sample(
-      fraction: Double,
       withReplacement: Boolean = true,
-      seed: Int = (math.random * 1000).toInt) =
+      fraction: Double,
+      seed: Long) =
     new SchemaRDD(sqlContext, Sample(fraction, withReplacement, seed, logicalPlan))
 
   /**
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
index e4cf2020a1a9..d807187a5ffb 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
@@ -57,9 +57,9 @@ case class Filter(condition: Expression, child: SparkPlan) extends UnaryNode {
  * :: DeveloperApi ::
  */
 @DeveloperApi
-case class Sample(fraction: Double, withReplacement: Boolean, seed: Int, child: SparkPlan)
-    extends UnaryNode {
-
+case class Sample(fraction: Double, withReplacement: Boolean, seed: Long, child: SparkPlan)
+  extends UnaryNode
+{
   override def output = child.output
 
   // TODO: How to pick seed?

From 526a518bf32ad55b926a26f16086f445fd0ae29f Mon Sep 17 00:00:00 2001
From: Tathagata Das <tathagata.das1565@gmail.com>
Date: Thu, 24 Apr 2014 18:18:22 -0700
Subject: [PATCH 010/810] [SPARK-1592][streaming] Automatically remove
 streaming input blocks

The raw input data is stored as blocks in BlockManagers. Earlier they were cleared by cleaner ttl. Now since streaming does not require cleaner TTL to be set, the block would not get cleared. This increases up the Spark's memory usage, which is not even accounted and shown in the Spark storage UI. It may cause the data blocks to spill over to disk, which eventually slows down the receiving of data (persisting to memory become bottlenecked by writing to disk).

The solution in this PR is to automatically remove those blocks. The mechanism to keep track of which BlockRDDs (which has presents the raw data blocks as a RDD) can be safely cleared already exists. Just use it to explicitly remove blocks from BlockRDDs.

Author: Tathagata Das <tathagata.das1565@gmail.com>

Closes #512 from tdas/block-rdd-unpersist and squashes the following commits:

d25e610 [Tathagata Das] Merge remote-tracking branch 'apache/master' into block-rdd-unpersist
5f46d69 [Tathagata Das] Merge remote-tracking branch 'apache/master' into block-rdd-unpersist
2c320cd [Tathagata Das] Updated configuration with spark.streaming.unpersist setting.
2d4b2fd [Tathagata Das] Automatically removed input blocks
---
 .../scala/org/apache/spark/rdd/BlockRDD.scala | 45 +++++++++--
 docs/configuration.md                         |  7 +-
 .../org/apache/spark/streaming/Time.scala     |  2 +-
 .../spark/streaming/dstream/DStream.scala     | 16 +++-
 .../streaming/BasicOperationsSuite.scala      | 76 ++++++++++++++++++-
 .../spark/streaming/InputStreamsSuite.scala   | 13 ----
 .../streaming/NetworkReceiverSuite.scala      |  1 +
 7 files changed, 135 insertions(+), 25 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/rdd/BlockRDD.scala b/core/src/main/scala/org/apache/spark/rdd/BlockRDD.scala
index e6c4a6d3794a..c64da8804d16 100644
--- a/core/src/main/scala/org/apache/spark/rdd/BlockRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/BlockRDD.scala
@@ -19,24 +19,30 @@ package org.apache.spark.rdd
 
 import scala.reflect.ClassTag
 
-import org.apache.spark.{Partition, SparkContext, SparkEnv, TaskContext}
+import org.apache.spark._
 import org.apache.spark.storage.{BlockId, BlockManager}
+import scala.Some
 
 private[spark] class BlockRDDPartition(val blockId: BlockId, idx: Int) extends Partition {
   val index = idx
 }
 
 private[spark]
-class BlockRDD[T: ClassTag](sc: SparkContext, @transient blockIds: Array[BlockId])
+class BlockRDD[T: ClassTag](@transient sc: SparkContext, @transient val blockIds: Array[BlockId])
   extends RDD[T](sc, Nil) {
 
   @transient lazy val locations_ = BlockManager.blockIdsToHosts(blockIds, SparkEnv.get)
+  @volatile private var _isValid = true
 
-  override def getPartitions: Array[Partition] = (0 until blockIds.size).map(i => {
-    new BlockRDDPartition(blockIds(i), i).asInstanceOf[Partition]
-  }).toArray
+  override def getPartitions: Array[Partition] = {
+    assertValid()
+    (0 until blockIds.size).map(i => {
+      new BlockRDDPartition(blockIds(i), i).asInstanceOf[Partition]
+    }).toArray
+  }
 
   override def compute(split: Partition, context: TaskContext): Iterator[T] = {
+    assertValid()
     val blockManager = SparkEnv.get.blockManager
     val blockId = split.asInstanceOf[BlockRDDPartition].blockId
     blockManager.get(blockId) match {
@@ -47,7 +53,36 @@ class BlockRDD[T: ClassTag](sc: SparkContext, @transient blockIds: Array[BlockId
   }
 
   override def getPreferredLocations(split: Partition): Seq[String] = {
+    assertValid()
     locations_(split.asInstanceOf[BlockRDDPartition].blockId)
   }
+
+  /**
+   * Remove the data blocks that this BlockRDD is made from. NOTE: This is an
+   * irreversible operation, as the data in the blocks cannot be recovered back
+   * once removed. Use it with caution.
+   */
+  private[spark] def removeBlocks() {
+    blockIds.foreach { blockId =>
+      sc.env.blockManager.master.removeBlock(blockId)
+    }
+    _isValid = false
+  }
+
+  /**
+   * Whether this BlockRDD is actually usable. This will be false if the data blocks have been
+   * removed using `this.removeBlocks`.
+   */
+  private[spark] def isValid: Boolean = {
+    _isValid
+  }
+
+  /** Check if this BlockRDD is valid. If not valid, exception is thrown. */
+  private[spark] def assertValid() {
+    if (!_isValid) {
+      throw new SparkException(
+        "Attempted to use %s after its blocks have been removed!".format(toString))
+    }
+  }
 }
 
diff --git a/docs/configuration.md b/docs/configuration.md
index e7e1dd56cf12..8d3442625b47 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -469,10 +469,13 @@ Apart from these, the following properties are also available, and may be useful
 </tr>
 <tr>
   <td>spark.streaming.unpersist</td>
-  <td>false</td>
+  <td>true</td>
   <td>
     Force RDDs generated and persisted by Spark Streaming to be automatically unpersisted from
-    Spark's memory. Setting this to true is likely to reduce Spark's RDD memory usage.
+    Spark's memory. The raw input data received by Spark Streaming is also automatically cleared.
+    Setting this to false will allow the raw data and persisted RDDs to be accessible outside the
+    streaming application as they will not be cleared automatically. But it comes at the cost of
+    higher memory usage in Spark.
   </td>
 </tr>
 <tr>
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/Time.scala b/streaming/src/main/scala/org/apache/spark/streaming/Time.scala
index 6a6b00a778b4..37b3b28fa01c 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/Time.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/Time.scala
@@ -68,5 +68,5 @@ case class Time(private val millis: Long) {
 }
 
 object Time {
-  val ordering = Ordering.by((time: Time) => time.millis)
+  implicit val ordering = Ordering.by((time: Time) => time.millis)
 }
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala
index d393cc03cb33..f69f69e0c44a 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala
@@ -25,7 +25,7 @@ import scala.reflect.ClassTag
 import java.io.{IOException, ObjectInputStream, ObjectOutputStream}
 
 import org.apache.spark.Logging
-import org.apache.spark.rdd.RDD
+import org.apache.spark.rdd.{BlockRDD, RDD}
 import org.apache.spark.storage.StorageLevel
 import org.apache.spark.util.MetadataCleaner
 import org.apache.spark.streaming._
@@ -340,13 +340,23 @@ abstract class DStream[T: ClassTag] (
    * this to clear their own metadata along with the generated RDDs.
    */
   private[streaming] def clearMetadata(time: Time) {
+    val unpersistData = ssc.conf.getBoolean("spark.streaming.unpersist", true)
     val oldRDDs = generatedRDDs.filter(_._1 <= (time - rememberDuration))
     logDebug("Clearing references to old RDDs: [" +
       oldRDDs.map(x => s"${x._1} -> ${x._2.id}").mkString(", ") + "]")
     generatedRDDs --= oldRDDs.keys
-    if (ssc.conf.getBoolean("spark.streaming.unpersist", false)) {
+    if (unpersistData) {
       logDebug("Unpersisting old RDDs: " + oldRDDs.values.map(_.id).mkString(", "))
-      oldRDDs.values.foreach(_.unpersist(false))
+      oldRDDs.values.foreach { rdd =>
+        rdd.unpersist(false)
+        // Explicitly remove blocks of BlockRDD
+        rdd match {
+          case b: BlockRDD[_] =>
+            logInfo("Removing blocks of RDD " + b + " of time " + time)
+            b.removeBlocks()
+          case _ =>
+        }
+      }
     }
     logDebug("Cleared " + oldRDDs.size + " RDDs that were older than " +
       (time - rememberDuration) + ": " + oldRDDs.keys.mkString(", "))
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/BasicOperationsSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/BasicOperationsSuite.scala
index 8aec27e39478..4792ca1f8ae3 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/BasicOperationsSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/BasicOperationsSuite.scala
@@ -19,7 +19,7 @@ package org.apache.spark.streaming
 
 import org.apache.spark.streaming.StreamingContext._
 
-import org.apache.spark.rdd.RDD
+import org.apache.spark.rdd.{BlockRDD, RDD}
 import org.apache.spark.SparkContext._
 
 import util.ManualClock
@@ -27,6 +27,8 @@ import org.apache.spark.{SparkContext, SparkConf}
 import org.apache.spark.streaming.dstream.{WindowedDStream, DStream}
 import scala.collection.mutable.{SynchronizedBuffer, ArrayBuffer}
 import scala.reflect.ClassTag
+import org.apache.spark.storage.StorageLevel
+import scala.collection.mutable
 
 class BasicOperationsSuite extends TestSuiteBase {
   test("map") {
@@ -450,6 +452,78 @@ class BasicOperationsSuite extends TestSuiteBase {
     assert(!stateStream.generatedRDDs.contains(Time(4000)))
   }
 
+  test("rdd cleanup - input blocks and persisted RDDs") {
+    // Actually receive data over through receiver to create BlockRDDs
+
+    // Start the server
+    val testServer = new TestServer()
+    testServer.start()
+
+    // Set up the streaming context and input streams
+    val ssc = new StreamingContext(conf, batchDuration)
+    val networkStream = ssc.socketTextStream("localhost", testServer.port, StorageLevel.MEMORY_AND_DISK)
+    val mappedStream = networkStream.map(_ + ".").persist()
+    val outputBuffer = new ArrayBuffer[Seq[String]] with SynchronizedBuffer[Seq[String]]
+    val outputStream = new TestOutputStream(mappedStream, outputBuffer)
+
+    outputStream.register()
+    ssc.start()
+
+    // Feed data to the server to send to the network receiver
+    val clock = ssc.scheduler.clock.asInstanceOf[ManualClock]
+    val input = Seq(1, 2, 3, 4, 5, 6)
+
+    val blockRdds = new mutable.HashMap[Time, BlockRDD[_]]
+    val persistentRddIds = new mutable.HashMap[Time, Int]
+
+    def collectRddInfo() { // get all RDD info required for verification
+      networkStream.generatedRDDs.foreach { case (time, rdd) =>
+        blockRdds(time) = rdd.asInstanceOf[BlockRDD[_]]
+      }
+      mappedStream.generatedRDDs.foreach { case (time, rdd) =>
+        persistentRddIds(time) = rdd.id
+      }
+    }
+
+    Thread.sleep(200)
+    for (i <- 0 until input.size) {
+      testServer.send(input(i).toString + "\n")
+      Thread.sleep(200)
+      clock.addToTime(batchDuration.milliseconds)
+      collectRddInfo()
+    }
+
+    Thread.sleep(200)
+    collectRddInfo()
+    logInfo("Stopping server")
+    testServer.stop()
+    logInfo("Stopping context")
+
+    // verify data has been received
+    assert(outputBuffer.size > 0)
+    assert(blockRdds.size > 0)
+    assert(persistentRddIds.size > 0)
+
+    import Time._
+
+    val latestPersistedRddId = persistentRddIds(persistentRddIds.keySet.max)
+    val earliestPersistedRddId = persistentRddIds(persistentRddIds.keySet.min)
+    val latestBlockRdd = blockRdds(blockRdds.keySet.max)
+    val earliestBlockRdd = blockRdds(blockRdds.keySet.min)
+    // verify that the latest mapped RDD is persisted but the earliest one has been unpersisted
+    assert(ssc.sparkContext.persistentRdds.contains(latestPersistedRddId))
+    assert(!ssc.sparkContext.persistentRdds.contains(earliestPersistedRddId))
+
+    // verify that the latest input blocks are present but the earliest blocks have been removed
+    assert(latestBlockRdd.isValid)
+    assert(latestBlockRdd.collect != null)
+    assert(!earliestBlockRdd.isValid)
+    earliestBlockRdd.blockIds.foreach { blockId =>
+      assert(!ssc.sparkContext.env.blockManager.master.contains(blockId))
+    }
+    ssc.stop()
+  }
+
   /** Test cleanup of RDDs in DStream metadata */
   def runCleanupTest[T: ClassTag](
       conf2: SparkConf,
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala
index 3bad871b5c58..b55b7834c90c 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala
@@ -42,8 +42,6 @@ import org.apache.spark.streaming.receiver.{ActorHelper, Receiver}
 
 class InputStreamsSuite extends TestSuiteBase with BeforeAndAfter {
 
-  val testPort = 9999
-
   test("socket input stream") {
     // Start the server
     val testServer = new TestServer()
@@ -288,17 +286,6 @@ class TestServer(portToBind: Int = 0) extends Logging {
   def port = serverSocket.getLocalPort
 }
 
-object TestServer {
-  def main(args: Array[String]) {
-    val s = new TestServer()
-    s.start()
-    while(true) {
-      Thread.sleep(1000)
-      s.send("hello")
-    }
-  }
-}
-
 /** This is an actor for testing actor input stream */
 class TestActor(port: Int) extends Actor with ActorHelper {
 
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/NetworkReceiverSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/NetworkReceiverSuite.scala
index 45304c76b092..ff3619a59042 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/NetworkReceiverSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/NetworkReceiverSuite.scala
@@ -29,6 +29,7 @@ import org.scalatest.FunSuite
 import org.scalatest.concurrent.Timeouts
 import org.scalatest.concurrent.Eventually._
 import org.scalatest.time.SpanSugar._
+import scala.language.postfixOps
 
 /** Testsuite for testing the network receiver behavior */
 class NetworkReceiverSuite extends FunSuite with Timeouts {

From 4660991e679eda158a3ae8039b686eae197a71d1 Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Thu, 24 Apr 2014 18:21:00 -0700
Subject: [PATCH 011/810] [SQL] Add support for parsing indexing into arrays in
 SQL.

Author: Michael Armbrust <michael@databricks.com>

Closes #518 from marmbrus/parseArrayIndex and squashes the following commits:

afd2d6b [Michael Armbrust] 100 chars
c3d6026 [Michael Armbrust] Add support for parsing indexing into arrays in SQL.
---
 .../apache/spark/sql/catalyst/SqlParser.scala    | 10 +++++++---
 .../org/apache/spark/sql/SQLQuerySuite.scala     | 16 ++++++++++++++++
 .../scala/org/apache/spark/sql/TestData.scala    |  7 +++++++
 3 files changed, 30 insertions(+), 3 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
index 13a19d0adf5e..8c76a3aa9654 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
@@ -20,6 +20,7 @@ package org.apache.spark.sql.catalyst
 import scala.language.implicitConversions
 import scala.util.parsing.combinator.lexical.StdLexical
 import scala.util.parsing.combinator.syntactical.StandardTokenParsers
+import scala.util.parsing.combinator.PackratParsers
 import scala.util.parsing.input.CharArrayReader.EofCh
 
 import org.apache.spark.sql.catalyst.analysis._
@@ -39,7 +40,7 @@ import org.apache.spark.sql.catalyst.types._
  * This is currently included mostly for illustrative purposes.  Users wanting more complete support
  * for a SQL like language should checkout the HiveQL support in the sql/hive sub-project.
  */
-class SqlParser extends StandardTokenParsers {
+class SqlParser extends StandardTokenParsers with PackratParsers {
   def apply(input: String): LogicalPlan = {
     phrase(query)(new lexical.Scanner(input)) match {
       case Success(r, x) => r
@@ -152,7 +153,7 @@ class SqlParser extends StandardTokenParsers {
 
   lexical.delimiters += (
     "@", "*", "+", "-", "<", "=", "<>", "!=", "<=", ">=", ">", "/", "(", ")",
-    ",", ";", "%", "{", "}", ":"
+    ",", ";", "%", "{", "}", ":", "[", "]"
   )
 
   protected def assignAliases(exprs: Seq[Expression]): Seq[NamedExpression] = {
@@ -339,7 +340,10 @@ class SqlParser extends StandardTokenParsers {
   protected lazy val floatLit: Parser[String] =
     elem("decimal", _.isInstanceOf[lexical.FloatLit]) ^^ (_.chars)
 
-  protected lazy val baseExpression: Parser[Expression] =
+  protected lazy val baseExpression: PackratParser[Expression] =
+    expression ~ "[" ~  expression <~ "]" ^^ {
+      case base ~ _ ~ ordinal => GetItem(base, ordinal)
+    } |
     TRUE ^^^ Literal(true, BooleanType) |
     FALSE ^^^ Literal(false, BooleanType) |
     cast |
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index 4c4fd6dbbedb..dde957d715a2 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -28,6 +28,22 @@ class SQLQuerySuite extends QueryTest {
   // Make sure the tables are loaded.
   TestData
 
+  test("index into array") {
+    checkAnswer(
+      sql("SELECT data, data[0], data[0] + data[1], data[0 + 1] FROM arrayData"),
+      arrayData.map(d => (d.data, d.data(0), d.data(0) + d.data(1), d.data(1))).collect().toSeq)
+  }
+
+  test("index into array of arrays") {
+    checkAnswer(
+      sql(
+        "SELECT nestedData, nestedData[0][0], nestedData[0][0] + nestedData[0][1] FROM arrayData"),
+      arrayData.map(d =>
+        (d.nestedData,
+         d.nestedData(0)(0),
+         d.nestedData(0)(0) + d.nestedData(0)(1))).collect().toSeq)
+  }
+
   test("agg") {
     checkAnswer(
       sql("SELECT a, SUM(b) FROM testData2 GROUP BY a"),
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala b/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala
index 271b1d9fcacf..002b7f0adafa 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala
@@ -66,4 +66,11 @@ object TestData {
       LowerCaseData(3, "c") ::
       LowerCaseData(4, "d") :: Nil)
   lowerCaseData.registerAsTable("lowerCaseData")
+
+  case class ArrayData(data: Seq[Int], nestedData: Seq[Seq[Int]])
+  val arrayData =
+    TestSQLContext.sparkContext.parallelize(
+      ArrayData(Seq(1,2,3), Seq(Seq(1,2,3))) ::
+      ArrayData(Seq(2,3,4), Seq(Seq(2,3,4))) :: Nil)
+  arrayData.registerAsTable("arrayData")
 }

From 44da5ab2dea6dcf1e13d624784741141883870bb Mon Sep 17 00:00:00 2001
From: Thomas Graves <tgraves@apache.org>
Date: Thu, 24 Apr 2014 18:38:10 -0700
Subject: [PATCH 012/810] Spark 1489 Fix the HistoryServer view acls

This allows the view acls set by the user to be enforced by the history server.  It also fixes filters being applied properly.

Author: Thomas Graves <tgraves@apache.org>

Closes #509 from tgravescs/SPARK-1489 and squashes the following commits:

869c186 [Thomas Graves] change to either acls enabled or disabled
0d8333c [Thomas Graves] Add history ui policy to allow acls to either use application set, history server force acls on, or off
65148b5 [Thomas Graves] SPARK-1489 Fix the HistoryServer view acls
---
 .../org/apache/spark/SecurityManager.scala    | 26 ++++++--
 .../spark/deploy/history/HistoryServer.scala  | 14 ++++-
 .../scheduler/ApplicationEventListener.scala  | 11 ++++
 .../org/apache/spark/ui/JettyUtils.scala      |  4 +-
 .../scala/org/apache/spark/ui/SparkUI.scala   |  8 +++
 .../apache/spark/SecurityManagerSuite.scala   | 63 +++++++++++++++++++
 docs/monitoring.md                            | 13 ++++
 7 files changed, 128 insertions(+), 11 deletions(-)
 create mode 100644 core/src/test/scala/org/apache/spark/SecurityManagerSuite.scala

diff --git a/core/src/main/scala/org/apache/spark/SecurityManager.scala b/core/src/main/scala/org/apache/spark/SecurityManager.scala
index b52f2d4f416b..b4b006780125 100644
--- a/core/src/main/scala/org/apache/spark/SecurityManager.scala
+++ b/core/src/main/scala/org/apache/spark/SecurityManager.scala
@@ -19,8 +19,6 @@ package org.apache.spark
 
 import java.net.{Authenticator, PasswordAuthentication}
 
-import scala.collection.mutable.ArrayBuffer
-
 import org.apache.hadoop.io.Text
 
 import org.apache.spark.deploy.SparkHadoopUtil
@@ -139,13 +137,13 @@ private[spark] class SecurityManager(sparkConf: SparkConf) extends Logging {
   private val sparkSecretLookupKey = "sparkCookie"
 
   private val authOn = sparkConf.getBoolean("spark.authenticate", false)
-  private val uiAclsOn = sparkConf.getBoolean("spark.ui.acls.enable", false)
+  private var uiAclsOn = sparkConf.getBoolean("spark.ui.acls.enable", false)
 
+  private var viewAcls: Set[String] = _
   // always add the current user and SPARK_USER to the viewAcls
-  private val aclUsers = ArrayBuffer[String](System.getProperty("user.name", ""),
+  private val defaultAclUsers = Seq[String](System.getProperty("user.name", ""),
     Option(System.getenv("SPARK_USER")).getOrElse(""))
-  aclUsers ++= sparkConf.get("spark.ui.view.acls", "").split(',')
-  private val viewAcls = aclUsers.map(_.trim()).filter(!_.isEmpty).toSet
+  setViewAcls(defaultAclUsers, sparkConf.get("spark.ui.view.acls", ""))
 
   private val secretKey = generateSecretKey()
   logInfo("SecurityManager, is authentication enabled: " + authOn +
@@ -170,6 +168,20 @@ private[spark] class SecurityManager(sparkConf: SparkConf) extends Logging {
     )
   }
 
+  private[spark] def setViewAcls(defaultUsers: Seq[String], allowedUsers: String) {
+    viewAcls = (defaultUsers ++ allowedUsers.split(',')).map(_.trim()).filter(!_.isEmpty).toSet 
+    logInfo("Changing view acls to: " + viewAcls.mkString(","))
+  }
+
+  private[spark] def setViewAcls(defaultUser: String, allowedUsers: String) {
+    setViewAcls(Seq[String](defaultUser), allowedUsers)
+  }
+
+  private[spark] def setUIAcls(aclSetting: Boolean) { 
+    uiAclsOn = aclSetting 
+    logInfo("Changing acls enabled to: " + uiAclsOn)
+  }
+
   /**
    * Generates or looks up the secret key.
    *
@@ -222,6 +234,8 @@ private[spark] class SecurityManager(sparkConf: SparkConf) extends Logging {
    * @return true is the user has permission, otherwise false
    */
   def checkUIViewPermissions(user: String): Boolean = {
+    logDebug("user=" + user + " uiAclsEnabled=" + uiAclsEnabled() + " viewAcls=" + 
+      viewAcls.mkString(","))
     if (uiAclsEnabled() && (user != null) && (!viewAcls.contains(user))) false else true
   }
 
diff --git a/core/src/main/scala/org/apache/spark/deploy/history/HistoryServer.scala b/core/src/main/scala/org/apache/spark/deploy/history/HistoryServer.scala
index d7a3246bcfb4..1238bbf9da2f 100644
--- a/core/src/main/scala/org/apache/spark/deploy/history/HistoryServer.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/history/HistoryServer.scala
@@ -168,17 +168,21 @@ class HistoryServer(
    * directory. If this file exists, the associated application is regarded to be completed, in
    * which case the server proceeds to render the SparkUI. Otherwise, the server does nothing.
    */
-  private def renderSparkUI(logDir: FileStatus, logInfo: EventLoggingInfo) {
+  private def renderSparkUI(logDir: FileStatus, elogInfo: EventLoggingInfo) {
     val path = logDir.getPath
     val appId = path.getName
-    val replayBus = new ReplayListenerBus(logInfo.logPaths, fileSystem, logInfo.compressionCodec)
+    val replayBus = new ReplayListenerBus(elogInfo.logPaths, fileSystem, elogInfo.compressionCodec)
     val appListener = new ApplicationEventListener
     replayBus.addListener(appListener)
-    val ui = new SparkUI(conf, replayBus, appId, "/history/" + appId)
+    val appConf = conf.clone()
+    val appSecManager = new SecurityManager(appConf)
+    val ui = new SparkUI(conf, appSecManager, replayBus, appId, "/history/" + appId)
 
     // Do not call ui.bind() to avoid creating a new server for each application
     replayBus.replay()
     if (appListener.applicationStarted) {
+      appSecManager.setUIAcls(HISTORY_UI_ACLS_ENABLED)
+      appSecManager.setViewAcls(appListener.sparkUser, appListener.viewAcls)
       attachSparkUI(ui)
       val appName = appListener.appName
       val sparkUser = appListener.sparkUser
@@ -202,6 +206,7 @@ class HistoryServer(
   private def attachSparkUI(ui: SparkUI) {
     assert(serverInfo.isDefined, "HistoryServer must be bound before attaching SparkUIs")
     ui.getHandlers.foreach(attachHandler)
+    addFilters(ui.getHandlers, conf)
   }
 
   /** Detach a reconstructed UI from this server. Only valid after bind(). */
@@ -255,6 +260,9 @@ object HistoryServer {
   // The port to which the web UI is bound
   val WEB_UI_PORT = conf.getInt("spark.history.ui.port", 18080)
 
+  // set whether to enable or disable view acls for all applications
+  val HISTORY_UI_ACLS_ENABLED = conf.getBoolean("spark.history.ui.acls.enable", false)
+
   val STATIC_RESOURCE_DIR = SparkUI.STATIC_RESOURCE_DIR
 
   def main(argStrings: Array[String]) {
diff --git a/core/src/main/scala/org/apache/spark/scheduler/ApplicationEventListener.scala b/core/src/main/scala/org/apache/spark/scheduler/ApplicationEventListener.scala
index c1001227151a..cd5d44ad4a7e 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/ApplicationEventListener.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/ApplicationEventListener.scala
@@ -28,6 +28,8 @@ private[spark] class ApplicationEventListener extends SparkListener {
   var sparkUser = "<Not Started>"
   var startTime = -1L
   var endTime = -1L
+  var viewAcls = ""
+  var enableViewAcls = false
 
   def applicationStarted = startTime != -1
 
@@ -47,4 +49,13 @@ private[spark] class ApplicationEventListener extends SparkListener {
   override def onApplicationEnd(applicationEnd: SparkListenerApplicationEnd) {
     endTime = applicationEnd.time
   }
+
+  override def onEnvironmentUpdate(environmentUpdate: SparkListenerEnvironmentUpdate) {
+    synchronized {
+      val environmentDetails = environmentUpdate.environmentDetails
+      val allProperties = environmentDetails("Spark Properties").toMap
+      viewAcls = allProperties.getOrElse("spark.ui.view.acls", "")
+      enableViewAcls = allProperties.getOrElse("spark.ui.acls.enable", "false").toBoolean
+    }
+  }
 }
diff --git a/core/src/main/scala/org/apache/spark/ui/JettyUtils.scala b/core/src/main/scala/org/apache/spark/ui/JettyUtils.scala
index fdeb15b5d058..b3ac2320f343 100644
--- a/core/src/main/scala/org/apache/spark/ui/JettyUtils.scala
+++ b/core/src/main/scala/org/apache/spark/ui/JettyUtils.scala
@@ -137,8 +137,8 @@ private[spark] object JettyUtils extends Logging {
     contextHandler
   }
 
-  /** Add security filters, if any, do the given list of ServletContextHandlers */
-  private def addFilters(handlers: Seq[ServletContextHandler], conf: SparkConf) {
+  /** Add filters, if any, to the given list of ServletContextHandlers */
+  def addFilters(handlers: Seq[ServletContextHandler], conf: SparkConf) {
     val filters: Array[String] = conf.get("spark.ui.filters", "").split(',').map(_.trim())
     filters.foreach {
       case filter : String =>
diff --git a/core/src/main/scala/org/apache/spark/ui/SparkUI.scala b/core/src/main/scala/org/apache/spark/ui/SparkUI.scala
index 2fef1a635427..097a1b81e1dd 100644
--- a/core/src/main/scala/org/apache/spark/ui/SparkUI.scala
+++ b/core/src/main/scala/org/apache/spark/ui/SparkUI.scala
@@ -43,6 +43,14 @@ private[spark] class SparkUI(
   def this(conf: SparkConf, listenerBus: SparkListenerBus, appName: String, basePath: String) =
     this(null, conf, new SecurityManager(conf), listenerBus, appName, basePath)
 
+  def this(
+      conf: SparkConf,
+      securityManager: SecurityManager,
+      listenerBus: SparkListenerBus,
+      appName: String,
+      basePath: String) =
+    this(null, conf, securityManager, listenerBus, appName, basePath)
+
   // If SparkContext is not provided, assume the associated application is not live
   val live = sc != null
 
diff --git a/core/src/test/scala/org/apache/spark/SecurityManagerSuite.scala b/core/src/test/scala/org/apache/spark/SecurityManagerSuite.scala
new file mode 100644
index 000000000000..e39093e24d68
--- /dev/null
+++ b/core/src/test/scala/org/apache/spark/SecurityManagerSuite.scala
@@ -0,0 +1,63 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark
+
+import scala.collection.mutable.ArrayBuffer
+
+import org.scalatest.FunSuite
+
+class SecurityManagerSuite extends FunSuite {
+
+  test("set security with conf") {
+    val conf = new SparkConf
+    conf.set("spark.authenticate", "true")
+    conf.set("spark.authenticate.secret", "good")
+    conf.set("spark.ui.acls.enable", "true")
+    conf.set("spark.ui.view.acls", "user1,user2")
+    val securityManager = new SecurityManager(conf);
+    assert(securityManager.isAuthenticationEnabled() === true)
+    assert(securityManager.uiAclsEnabled() === true)
+    assert(securityManager.checkUIViewPermissions("user1") === true)
+    assert(securityManager.checkUIViewPermissions("user2") === true)
+    assert(securityManager.checkUIViewPermissions("user3") === false)
+  }
+
+  test("set security with api") {
+    val conf = new SparkConf
+    conf.set("spark.ui.view.acls", "user1,user2")
+    val securityManager = new SecurityManager(conf);
+    securityManager.setUIAcls(true)
+    assert(securityManager.uiAclsEnabled() === true)
+    securityManager.setUIAcls(false)
+    assert(securityManager.uiAclsEnabled() === false)
+
+    // acls are off so doesn't matter what view acls set to
+    assert(securityManager.checkUIViewPermissions("user4") === true)
+
+    securityManager.setUIAcls(true)
+    assert(securityManager.uiAclsEnabled() === true)
+    securityManager.setViewAcls(ArrayBuffer[String]("user5"), "user6,user7")
+    assert(securityManager.checkUIViewPermissions("user1") === false)
+    assert(securityManager.checkUIViewPermissions("user5") === true)
+    assert(securityManager.checkUIViewPermissions("user6") === true)
+    assert(securityManager.checkUIViewPermissions("user7") === true)
+    assert(securityManager.checkUIViewPermissions("user8") === false)
+    assert(securityManager.checkUIViewPermissions(null) === true)
+  }
+}
+
diff --git a/docs/monitoring.md b/docs/monitoring.md
index 347a9b1f1a32..6f35fc37c407 100644
--- a/docs/monitoring.md
+++ b/docs/monitoring.md
@@ -115,6 +115,19 @@ represents an application's event logs. This creates a web interface at
       Location of the kerberos keytab file for the History Server.
     </td>
   </tr>
+  <tr>
+    <td>spark.history.ui.acls.enable</td>
+    <td>false</td>
+    <td>
+      Specifies whether acls should be checked to authorize users viewing the applications.
+      If enabled, access control checks are made regardless of what the individual application had 
+      set for <code>spark.ui.acls.enable</code> when the application was run. The application owner
+      will always have authorization to view their own application and any users specified via 
+      <code>spark.ui.view.acls</code> when the application was run will also have authorization
+      to view that application. 
+      If disabled, no access control checks are made. 
+    </td>
+  </tr>
 </table>
 
 Note that in all of these UIs, the tables are sortable by clicking their headers,

From 80429f3e2ab786d103297652922c3d8da3cf5a01 Mon Sep 17 00:00:00 2001
From: jerryshao <saisai.shao@intel.com>
Date: Thu, 24 Apr 2014 18:56:57 -0700
Subject: [PATCH 013/810] [SPARK-1510] Spark Streaming metrics source for
 metrics system

This pulls in changes made by @jerryshao in https://github.com/apache/spark/pull/424 and merges with the master.

Author: jerryshao <saisai.shao@intel.com>
Author: Tathagata Das <tathagata.das1565@gmail.com>

Closes #545 from tdas/streaming-metrics and squashes the following commits:

034b443 [Tathagata Das] Merge remote-tracking branch 'apache-github/master' into streaming-metrics
fb3b0a5 [jerryshao] Modify according master update
21939f5 [jerryshao] Style changes according to style check error
976116b [jerryshao] Add StreamSource in StreamingContext for better monitoring through metrics system
---
 .../spark/streaming/StreamingContext.scala    |  4 +
 .../spark/streaming/StreamingSource.scala     | 73 +++++++++++++++++++
 .../ui/StreamingJobProgressListener.scala     |  3 +-
 3 files changed, 79 insertions(+), 1 deletion(-)
 create mode 100644 streaming/src/main/scala/org/apache/spark/streaming/StreamingSource.scala

diff --git a/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala b/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala
index 1c8954305821..e0677b795cb9 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala
@@ -154,6 +154,10 @@ class StreamingContext private[streaming] (
 
   private[streaming] val uiTab = new StreamingTab(this)
 
+  /** Register streaming source to metrics system */
+  private val streamingSource = new StreamingSource(this)
+  SparkEnv.get.metricsSystem.registerSource(streamingSource)
+
   /** Enumeration to identify current state of the StreamingContext */
   private[streaming] object StreamingContextState extends Enumeration {
     type CheckpointState = Value
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/StreamingSource.scala b/streaming/src/main/scala/org/apache/spark/streaming/StreamingSource.scala
new file mode 100644
index 000000000000..774adc3c23c2
--- /dev/null
+++ b/streaming/src/main/scala/org/apache/spark/streaming/StreamingSource.scala
@@ -0,0 +1,73 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.streaming
+
+import com.codahale.metrics.{Gauge, MetricRegistry}
+
+import org.apache.spark.metrics.source.Source
+import org.apache.spark.streaming.ui.StreamingJobProgressListener
+
+private[streaming] class StreamingSource(ssc: StreamingContext) extends Source {
+  val metricRegistry = new MetricRegistry
+  val sourceName = "%s.StreamingMetrics".format(ssc.sparkContext.appName)
+
+  val streamingListener = ssc.uiTab.listener
+
+  private def registerGauge[T](name: String, f: StreamingJobProgressListener => T,
+      defaultValue: T) {
+    metricRegistry.register(MetricRegistry.name("streaming", name), new Gauge[T] {
+      override def getValue: T = Option(f(streamingListener)).getOrElse(defaultValue)
+    })
+  }
+
+  // Gauge for number of network receivers
+  registerGauge("receivers", _.numReceivers, 0)
+
+  // Gauge for number of total completed batches
+  registerGauge("totalCompletedBatches", _.numTotalCompletedBatches, 0L)
+
+  // Gauge for number of unprocessed batches
+  registerGauge("unprocessedBatches", _.numUnprocessedBatches, 0L)
+
+  // Gauge for number of waiting batches
+  registerGauge("waitingBatches", _.waitingBatches.size, 0L)
+
+  // Gauge for number of running batches
+  registerGauge("runningBatches", _.runningBatches.size, 0L)
+
+  // Gauge for number of retained completed batches
+  registerGauge("retainedCompletedBatches", _.retainedCompletedBatches.size, 0L)
+
+  // Gauge for last completed batch, useful for monitoring the streaming job's running status,
+  // displayed data -1 for any abnormal condition.
+  registerGauge("lastCompletedBatch_submissionTime",
+    _.lastCompletedBatch.map(_.submissionTime).getOrElse(-1L), -1L)
+  registerGauge("lastCompletedBatch_processStartTime",
+    _.lastCompletedBatch.flatMap(_.processingStartTime).getOrElse(-1L), -1L)
+  registerGauge("lastCompletedBatch_processEndTime",
+    _.lastCompletedBatch.flatMap(_.processingEndTime).getOrElse(-1L), -1L)
+
+  // Gauge for last received batch, useful for monitoring the streaming job's running status,
+  // displayed data -1 for any abnormal condition.
+  registerGauge("lastReceivedBatch_submissionTime",
+    _.lastCompletedBatch.map(_.submissionTime).getOrElse(-1L), -1L)
+  registerGauge("lastReceivedBatch_processStartTime",
+    _.lastCompletedBatch.flatMap(_.processingStartTime).getOrElse(-1L), -1L)
+  registerGauge("lastReceivedBatch_processEndTime",
+    _.lastCompletedBatch.flatMap(_.processingEndTime).getOrElse(-1L), -1L)
+}
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingJobProgressListener.scala b/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingJobProgressListener.scala
index bf637c144631..14c33c728bfe 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingJobProgressListener.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingJobProgressListener.scala
@@ -28,7 +28,8 @@ import org.apache.spark.streaming.scheduler.StreamingListenerBatchSubmitted
 import org.apache.spark.util.Distribution
 
 
-private[ui] class StreamingJobProgressListener(ssc: StreamingContext) extends StreamingListener {
+private[streaming] class StreamingJobProgressListener(ssc: StreamingContext)
+  extends StreamingListener {
 
   private val waitingBatchInfos = new HashMap[Time, BatchInfo]
   private val runningBatchInfos = new HashMap[Time, BatchInfo]

From ee6f7e22a449837864072e3cd2b6696005f134f1 Mon Sep 17 00:00:00 2001
From: Andrew Or <andrewor14@gmail.com>
Date: Thu, 24 Apr 2014 20:18:15 -0700
Subject: [PATCH 014/810] [SPARK-1615] Synchronize accesses to the
 LiveListenerBus' event queue

Original poster is @zsxwing, who reported this bug in #516.

Much of SparkListenerSuite relies on LiveListenerBus's `waitUntilEmpty()` method. As the name suggests, this waits until the event queue is empty. However, the following race condition could happen:

(1) We dequeue an event
(2) The queue is empty, we return true (even though the event has not been processed)
(3) The test asserts something assuming that all listeners have finished executing (and fails)
(4) The listeners receive and process the event

This PR makes (1) and (4) atomic by synchronizing around it. To do that, however, we must avoid using `eventQueue.take`, which is blocking and will cause a deadlock if we synchronize around it. As a workaround, we use the non-blocking `eventQueue.poll` + a semaphore to provide the same semantics.

This has been a possible race condition for a long time, but for some reason we've never run into it.

Author: Andrew Or <andrewor14@gmail.com>

Closes #544 from andrewor14/stage-info-test-fix and squashes the following commits:

3cbe40c [Andrew Or] Merge github.com:apache/spark into stage-info-test-fix
56dbbcb [Andrew Or] Check if event is actually added before releasing semaphore
eb486ae [Andrew Or] Synchronize accesses to the LiveListenerBus' event queue
---
 .../spark/scheduler/LiveListenerBus.scala     | 38 ++++++++++++++-----
 .../spark/scheduler/SparkListenerSuite.scala  |  5 ++-
 2 files changed, 31 insertions(+), 12 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/scheduler/LiveListenerBus.scala b/core/src/main/scala/org/apache/spark/scheduler/LiveListenerBus.scala
index cbac4c13ca6f..dec3316bf774 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/LiveListenerBus.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/LiveListenerBus.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.scheduler
 
-import java.util.concurrent.LinkedBlockingQueue
+import java.util.concurrent.{LinkedBlockingQueue, Semaphore}
 
 import org.apache.spark.Logging
 
@@ -36,16 +36,24 @@ private[spark] class LiveListenerBus extends SparkListenerBus with Logging {
   private val eventQueue = new LinkedBlockingQueue[SparkListenerEvent](EVENT_QUEUE_CAPACITY)
   private var queueFullErrorMessageLogged = false
   private var started = false
+
+  // A counter that represents the number of events produced and consumed in the queue
+  private val eventLock = new Semaphore(0)
+
   private val listenerThread = new Thread("SparkListenerBus") {
     setDaemon(true)
     override def run() {
       while (true) {
-        val event = eventQueue.take
-        if (event == SparkListenerShutdown) {
-          // Get out of the while loop and shutdown the daemon thread
-          return
+        eventLock.acquire()
+        // Atomically remove and process this event
+        LiveListenerBus.this.synchronized {
+          val event = eventQueue.poll
+          if (event == SparkListenerShutdown) {
+            // Get out of the while loop and shutdown the daemon thread
+            return
+          }
+          Option(event).foreach(postToAll)
         }
-        postToAll(event)
       }
     }
   }
@@ -67,7 +75,9 @@ private[spark] class LiveListenerBus extends SparkListenerBus with Logging {
 
   def post(event: SparkListenerEvent) {
     val eventAdded = eventQueue.offer(event)
-    if (!eventAdded && !queueFullErrorMessageLogged) {
+    if (eventAdded) {
+      eventLock.release()
+    } else if (!queueFullErrorMessageLogged) {
       logError("Dropping SparkListenerEvent because no remaining room in event queue. " +
         "This likely means one of the SparkListeners is too slow and cannot keep up with the " +
         "rate at which tasks are being started by the scheduler.")
@@ -76,13 +86,13 @@ private[spark] class LiveListenerBus extends SparkListenerBus with Logging {
   }
 
   /**
-   * Waits until there are no more events in the queue, or until the specified time has elapsed.
-   * Used for testing only. Returns true if the queue has emptied and false is the specified time
+   * For testing only. Wait until there are no more events in the queue, or until the specified
+   * time has elapsed. Return true if the queue has emptied and false is the specified time
    * elapsed before the queue emptied.
    */
   def waitUntilEmpty(timeoutMillis: Int): Boolean = {
     val finishTime = System.currentTimeMillis + timeoutMillis
-    while (!eventQueue.isEmpty) {
+    while (!queueIsEmpty) {
       if (System.currentTimeMillis > finishTime) {
         return false
       }
@@ -93,6 +103,14 @@ private[spark] class LiveListenerBus extends SparkListenerBus with Logging {
     true
   }
 
+  /**
+   * Return whether the event queue is empty.
+   *
+   * The use of synchronized here guarantees that all events that once belonged to this queue
+   * have already been processed by all attached listeners, if this returns true.
+   */
+  def queueIsEmpty: Boolean = synchronized { eventQueue.isEmpty }
+
   def stop() {
     if (!started) {
       throw new IllegalStateException("Attempted to stop a listener bus that has not yet started!")
diff --git a/core/src/test/scala/org/apache/spark/scheduler/SparkListenerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/SparkListenerSuite.scala
index ba048ced32a9..4e9fd07e68a2 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/SparkListenerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/SparkListenerSuite.scala
@@ -29,7 +29,8 @@ import org.apache.spark.SparkContext._
 import org.apache.spark.executor.TaskMetrics
 
 class SparkListenerSuite extends FunSuite with LocalSparkContext with ShouldMatchers
-    with BeforeAndAfter with BeforeAndAfterAll {
+  with BeforeAndAfter with BeforeAndAfterAll {
+
   /** Length of time to wait while draining listener events. */
   val WAIT_TIMEOUT_MILLIS = 10000
 
@@ -37,7 +38,7 @@ class SparkListenerSuite extends FunSuite with LocalSparkContext with ShouldMatc
     sc = new SparkContext("local", "SparkListenerSuite")
   }
 
-  override def afterAll {
+  override def afterAll() {
     System.clearProperty("spark.akka.frameSize")
   }
 

From e53eb4f0159ebd4d72c4bbc3586fdfc66ccacab7 Mon Sep 17 00:00:00 2001
From: Ahir Reddy <ahirreddy@gmail.com>
Date: Thu, 24 Apr 2014 20:21:10 -0700
Subject: [PATCH 015/810] [SPARK-986]: Job cancelation for PySpark

* Additions to the PySpark API to cancel jobs
* Monitor Thread in PythonRDD to kill Python workers if a task is interrupted

Author: Ahir Reddy <ahirreddy@gmail.com>

Closes #541 from ahirreddy/python-cancel and squashes the following commits:

dfdf447 [Ahir Reddy] Changed success -> completed and made logging message clearer
6c860ab [Ahir Reddy] PR Comments
4b4100a [Ahir Reddy] Success flag
adba6ed [Ahir Reddy] Destroy python workers
27a2f8f [Ahir Reddy] Start the writer thread...
d422f7b [Ahir Reddy] Remove unnecesssary vals
adda337 [Ahir Reddy] Busy wait on the ocntext.interrupted flag, and then kill the python worker
d9e472f [Ahir Reddy] Revert "removed unnecessary vals"
5b9cae5 [Ahir Reddy] removed unnecessary vals
07b54d9 [Ahir Reddy] Fix canceling unit test
8ae9681 [Ahir Reddy] Don't interrupt worker
7722342 [Ahir Reddy] Monitor Thread for python workers
db04e16 [Ahir Reddy] Added canceling api to PySpark
---
 .../scala/org/apache/spark/SparkEnv.scala     |  8 +++
 .../apache/spark/api/python/PythonRDD.scala   | 30 ++++++++++-
 python/pyspark/context.py                     | 52 +++++++++++++++++--
 3 files changed, 86 insertions(+), 4 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/SparkEnv.scala b/core/src/main/scala/org/apache/spark/SparkEnv.scala
index 915315ed7443..bea435ec34ce 100644
--- a/core/src/main/scala/org/apache/spark/SparkEnv.scala
+++ b/core/src/main/scala/org/apache/spark/SparkEnv.scala
@@ -97,6 +97,14 @@ class SparkEnv (
       pythonWorkers.getOrElseUpdate(key, new PythonWorkerFactory(pythonExec, envVars)).create()
     }
   }
+
+  private[spark]
+  def destroyPythonWorker(pythonExec: String, envVars: Map[String, String]) {
+    synchronized {
+      val key = (pythonExec, envVars)
+      pythonWorkers(key).stop()
+    }
+  }
 }
 
 object SparkEnv extends Logging {
diff --git a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
index 0d71fdbb03ec..1498b017a793 100644
--- a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
+++ b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
@@ -110,13 +110,41 @@ private[spark] class PythonRDD[T: ClassTag](
       }
     }.start()
 
+    // Necessary to distinguish between a task that has failed and a task that is finished
+    @volatile var complete: Boolean = false
+
+    // It is necessary to have a monitor thread for python workers if the user cancels with
+    // interrupts disabled. In that case we will need to explicitly kill the worker, otherwise the
+    // threads can block indefinitely.
+    new Thread(s"Worker Monitor for $pythonExec") {
+      override def run() {
+        // Kill the worker if it is interrupted or completed
+        // When a python task completes, the context is always set to interupted
+        while (!context.interrupted) {
+          Thread.sleep(2000)
+        }
+        if (!complete) {
+          try {
+            logWarning("Incomplete task interrupted: Attempting to kill Python Worker")
+            env.destroyPythonWorker(pythonExec, envVars.toMap)
+          } catch {
+            case e: Exception =>
+              logError("Exception when trying to kill worker", e)
+          }
+        }
+      }
+    }.start()
+
     /*
      * Partial fix for SPARK-1019: Attempts to stop reading the input stream since
      * other completion callbacks might invalidate the input. Because interruption
      * is not synchronous this still leaves a potential race where the interruption is
      * processed only after the stream becomes invalid.
      */
-    context.addOnCompleteCallback(() => context.interrupted = true)
+    context.addOnCompleteCallback{ () =>
+      complete = true // Indicate that the task has completed successfully
+      context.interrupted = true
+    }
 
     // Return an iterator that read lines from the process's stdout
     val stream = new DataInputStream(new BufferedInputStream(worker.getInputStream, bufferSize))
diff --git a/python/pyspark/context.py b/python/pyspark/context.py
index f63cc4a55fb9..c74dc5fd4f85 100644
--- a/python/pyspark/context.py
+++ b/python/pyspark/context.py
@@ -429,7 +429,7 @@ def _getJavaStorageLevel(self, storageLevel):
                                storageLevel.deserialized,
                                storageLevel.replication)
 
-    def setJobGroup(self, groupId, description):
+    def setJobGroup(self, groupId, description, interruptOnCancel=False):
         """
         Assigns a group ID to all the jobs started by this thread until the group ID is set to a
         different value or cleared.
@@ -437,8 +437,41 @@ def setJobGroup(self, groupId, description):
         Often, a unit of execution in an application consists of multiple Spark actions or jobs.
         Application programmers can use this method to group all those jobs together and give a
         group description. Once set, the Spark web UI will associate such jobs with this group.
-        """
-        self._jsc.setJobGroup(groupId, description)
+
+        The application can use L{SparkContext.cancelJobGroup} to cancel all
+        running jobs in this group.
+
+        >>> import thread, threading
+        >>> from time import sleep
+        >>> result = "Not Set"
+        >>> lock = threading.Lock()
+        >>> def map_func(x):
+        ...     sleep(100)
+        ...     return x * x
+        >>> def start_job(x):
+        ...     global result
+        ...     try:
+        ...         sc.setJobGroup("job_to_cancel", "some description")
+        ...         result = sc.parallelize(range(x)).map(map_func).collect()
+        ...     except Exception as e:
+        ...         result = "Cancelled"
+        ...     lock.release()
+        >>> def stop_job():
+        ...     sleep(5)
+        ...     sc.cancelJobGroup("job_to_cancel")
+        >>> supress = lock.acquire()
+        >>> supress = thread.start_new_thread(start_job, (10,))
+        >>> supress = thread.start_new_thread(stop_job, tuple())
+        >>> supress = lock.acquire()
+        >>> print result
+        Cancelled
+
+        If interruptOnCancel is set to true for the job group, then job cancellation will result
+        in Thread.interrupt() being called on the job's executor threads. This is useful to help ensure
+        that the tasks are actually stopped in a timely manner, but is off by default due to HDFS-1208,
+        where HDFS may respond to Thread.interrupt() by marking nodes as dead.
+        """
+        self._jsc.setJobGroup(groupId, description, interruptOnCancel)
 
     def setLocalProperty(self, key, value):
         """
@@ -460,6 +493,19 @@ def sparkUser(self):
         """
         return self._jsc.sc().sparkUser()
 
+    def cancelJobGroup(self, groupId):
+        """
+        Cancel active jobs for the specified group. See L{SparkContext.setJobGroup}
+        for more information.
+        """
+        self._jsc.sc().cancelJobGroup(groupId)
+
+    def cancelAllJobs(self):
+        """
+        Cancel all jobs that have been scheduled or are running.
+        """
+        self._jsc.sc().cancelAllJobs()
+
 def _test():
     import atexit
     import doctest

From d5c6ae6cc3305b9aa3185486b5b6ba0a6e5aca90 Mon Sep 17 00:00:00 2001
From: tmalaska <ted.malaska@cloudera.com>
Date: Thu, 24 Apr 2014 20:31:17 -0700
Subject: [PATCH 016/810] SPARK-1584: Upgrade Flume dependency to 1.4.0

Updated the Flume dependency in the maven pom file and the scala build file.

Author: tmalaska <ted.malaska@cloudera.com>

Closes #507 from tmalaska/master and squashes the following commits:

79492c8 [tmalaska] excluded all thrift
159c3f1 [tmalaska] fixed the flume pom file issues
5bf56a7 [tmalaska] Upgrade flume version
---
 external/flume/pom.xml   | 6 +++++-
 project/SparkBuild.scala | 3 ++-
 2 files changed, 7 insertions(+), 2 deletions(-)

diff --git a/external/flume/pom.xml b/external/flume/pom.xml
index f21963531574..b84ca0c5e1dd 100644
--- a/external/flume/pom.xml
+++ b/external/flume/pom.xml
@@ -61,12 +61,16 @@
     <dependency>
       <groupId>org.apache.flume</groupId>
       <artifactId>flume-ng-sdk</artifactId>
-      <version>1.2.0</version>
+      <version>1.4.0</version>
       <exclusions>
         <exclusion>
           <groupId>org.jboss.netty</groupId>
           <artifactId>netty</artifactId>
         </exclusion>
+        <exclusion>
+          <groupId>org.apache.thrift</groupId>
+          <artifactId>libthrift</artifactId>
+        </exclusion>
       </exclusions>
     </dependency>
     <dependency>
diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index b8af2bbd2ef6..9cbc188c4d48 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -313,6 +313,7 @@ object SparkBuild extends Build {
   val excludeCurator = ExclusionRule(organization = "org.apache.curator")
   val excludePowermock = ExclusionRule(organization = "org.powermock")
   val excludeFastutil = ExclusionRule(organization = "it.unimi.dsi")
+  val excludeThrift = ExclusionRule(organization = "org.apache.thrift")
 
   def sparkPreviousArtifact(id: String, organization: String = "org.apache.spark",
       version: String = "0.9.0-incubating", crossVersion: String = "2.10"): Option[sbt.ModuleID] = {
@@ -607,7 +608,7 @@ object SparkBuild extends Build {
     name := "spark-streaming-flume",
     previousArtifact := sparkPreviousArtifact("spark-streaming-flume"),
     libraryDependencies ++= Seq(
-      "org.apache.flume" % "flume-ng-sdk" % "1.2.0" % "compile" excludeAll(excludeNetty)
+      "org.apache.flume" % "flume-ng-sdk" % "1.4.0" % "compile" excludeAll(excludeNetty, excludeThrift)
     )
   )
 

From 968c0187a12f5ae4a696c02c1ff088e998ed7edd Mon Sep 17 00:00:00 2001
From: Mridul Muralidharan <mridulm80@apache.org>
Date: Thu, 24 Apr 2014 20:48:33 -0700
Subject: [PATCH 017/810] SPARK-1586 Windows build fixes

Unfortunately, this is not exhaustive - particularly hive tests still fail due to path issues.

Author: Mridul Muralidharan <mridulm80@apache.org>

This patch had conflicts when merged, resolved by
Committer: Matei Zaharia <matei@databricks.com>

Closes #505 from mridulm/windows_fixes and squashes the following commits:

ef12283 [Mridul Muralidharan] Move to org.apache.commons.lang3 for StringEscapeUtils. Earlier version was buggy appparently
cdae406 [Mridul Muralidharan] Remove leaked changes from > 2G fix branch
3267f4b [Mridul Muralidharan] Fix build failures
35b277a [Mridul Muralidharan] Fix Scalastyle failures
bc69d14 [Mridul Muralidharan] Change from hardcoded path separator
10c4d78 [Mridul Muralidharan] Use explicit encoding while using getBytes
1337abd [Mridul Muralidharan] fix classpath while running in windows
---
 bin/compute-classpath.cmd                     | 157 ++++++++++--------
 .../org/apache/spark/SparkSaslClient.scala    |   6 +-
 .../org/apache/spark/SparkSaslServer.scala    |   8 +-
 .../apache/spark/api/python/PythonRDD.scala   |   2 +-
 .../apache/spark/network/ReceiverTest.scala   |   2 +-
 .../org/apache/spark/network/SenderTest.scala |   2 +-
 .../scala/org/apache/spark/rdd/PipedRDD.scala |   8 +-
 .../scala/org/apache/spark/util/Utils.scala   |  24 ++-
 .../java/org/apache/spark/JavaAPISuite.java   |   4 +-
 .../streaming/examples/MQTTWordCount.scala    |   2 +-
 .../streaming/flume/FlumeStreamSuite.scala    |   2 +-
 .../streaming/mqtt/MQTTInputDStream.scala     |   2 +-
 .../org/apache/spark/repl/ReplSuite.scala     |   6 +-
 .../spark/sql/columnar/ColumnType.scala       |   6 +-
 .../spark/sql/columnar/ColumnTypeSuite.scala  |  44 ++++-
 .../spark/sql/hive/ScriptTransformation.scala |   2 +-
 .../org/apache/spark/sql/hive/TestHive.scala  |   9 +-
 .../execution/BigDataBenchmarkSuite.scala     |   2 +-
 .../hive/execution/HiveComparisonTest.scala   |   5 +-
 .../execution/HiveCompatibilitySuite.scala    |   6 +-
 .../deploy/yarn/YarnSparkHadoopUtil.scala     |   2 +-
 21 files changed, 185 insertions(+), 116 deletions(-)

diff --git a/bin/compute-classpath.cmd b/bin/compute-classpath.cmd
index 4f60bff19cb9..065553eb3193 100644
--- a/bin/compute-classpath.cmd
+++ b/bin/compute-classpath.cmd
@@ -1,69 +1,88 @@
-@echo off
-
-rem
-rem Licensed to the Apache Software Foundation (ASF) under one or more
-rem contributor license agreements.  See the NOTICE file distributed with
-rem this work for additional information regarding copyright ownership.
-rem The ASF licenses this file to You under the Apache License, Version 2.0
-rem (the "License"); you may not use this file except in compliance with
-rem the License.  You may obtain a copy of the License at
-rem
-rem    http://www.apache.org/licenses/LICENSE-2.0
-rem
-rem Unless required by applicable law or agreed to in writing, software
-rem distributed under the License is distributed on an "AS IS" BASIS,
-rem WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-rem See the License for the specific language governing permissions and
-rem limitations under the License.
-rem
-
-rem This script computes Spark's classpath and prints it to stdout; it's used by both the "run"
-rem script and the ExecutorRunner in standalone cluster mode.
-
-set SCALA_VERSION=2.10
-
-rem Figure out where the Spark framework is installed
-set FWDIR=%~dp0..\
-
-rem Load environment variables from conf\spark-env.cmd, if it exists
-if exist "%FWDIR%conf\spark-env.cmd" call "%FWDIR%conf\spark-env.cmd"
-
-rem Build up classpath
-set CLASSPATH=%FWDIR%conf
-if exist "%FWDIR%RELEASE" (
-  for %%d in ("%FWDIR%jars\spark-assembly*.jar") do (
-    set ASSEMBLY_JAR=%%d
-  )
-) else (
-  for %%d in ("%FWDIR%assembly\target\scala-%SCALA_VERSION%\spark-assembly*hadoop*.jar") do (
-    set ASSEMBLY_JAR=%%d
-  )
-)
-set CLASSPATH=%CLASSPATH%;%ASSEMBLY_JAR%
-
-if "x%SPARK_TESTING%"=="x1" (
-  rem Add test clases to path
-  set CLASSPATH=%CLASSPATH%;%FWDIR%core\target\scala-%SCALA_VERSION%\test-classes
-  set CLASSPATH=%CLASSPATH%;%FWDIR%repl\target\scala-%SCALA_VERSION%\test-classes
-  set CLASSPATH=%CLASSPATH%;%FWDIR%mllib\target\scala-%SCALA_VERSION%\test-classes
-  set CLASSPATH=%CLASSPATH%;%FWDIR%bagel\target\scala-%SCALA_VERSION%\test-classes
-  set CLASSPATH=%CLASSPATH%;%FWDIR%streaming\target\scala-%SCALA_VERSION%\test-classes
-)
-
-rem Add hadoop conf dir - else FileSystem.*, etc fail
-rem Note, this assumes that there is either a HADOOP_CONF_DIR or YARN_CONF_DIR which hosts
-rem the configurtion files.
-if "x%HADOOP_CONF_DIR%"=="x" goto no_hadoop_conf_dir
-  set CLASSPATH=%CLASSPATH%;%HADOOP_CONF_DIR%
-:no_hadoop_conf_dir
-
-if "x%YARN_CONF_DIR%"=="x" goto no_yarn_conf_dir
-  set CLASSPATH=%CLASSPATH%;%YARN_CONF_DIR%
-:no_yarn_conf_dir
-
-rem A bit of a hack to allow calling this script within run2.cmd without seeing output
-if "%DONT_PRINT_CLASSPATH%"=="1" goto exit
-
-echo %CLASSPATH%
-
-:exit
+@echo off
+
+rem
+rem Licensed to the Apache Software Foundation (ASF) under one or more
+rem contributor license agreements.  See the NOTICE file distributed with
+rem this work for additional information regarding copyright ownership.
+rem The ASF licenses this file to You under the Apache License, Version 2.0
+rem (the "License"); you may not use this file except in compliance with
+rem the License.  You may obtain a copy of the License at
+rem
+rem    http://www.apache.org/licenses/LICENSE-2.0
+rem
+rem Unless required by applicable law or agreed to in writing, software
+rem distributed under the License is distributed on an "AS IS" BASIS,
+rem WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+rem See the License for the specific language governing permissions and
+rem limitations under the License.
+rem
+
+rem This script computes Spark's classpath and prints it to stdout; it's used by both the "run"
+rem script and the ExecutorRunner in standalone cluster mode.
+
+set SCALA_VERSION=2.10
+
+rem Figure out where the Spark framework is installed
+set FWDIR=%~dp0..\
+
+rem Load environment variables from conf\spark-env.cmd, if it exists
+if exist "%FWDIR%conf\spark-env.cmd" call "%FWDIR%conf\spark-env.cmd"
+
+rem Build up classpath
+set CLASSPATH=%FWDIR%conf
+if exist "%FWDIR%RELEASE" (
+  for %%d in ("%FWDIR%jars\spark-assembly*.jar") do (
+    set ASSEMBLY_JAR=%%d
+  )
+) else (
+  for %%d in ("%FWDIR%assembly\target\scala-%SCALA_VERSION%\spark-assembly*hadoop*.jar") do (
+    set ASSEMBLY_JAR=%%d
+  )
+)
+
+set CLASSPATH=%CLASSPATH%;%ASSEMBLY_JAR%
+
+set SPARK_CLASSES=%FWDIR%core\target\scala-%SCALA_VERSION%\classes
+set SPARK_CLASSES=%SPARK_CLASSES%;%FWDIR%repl\target\scala-%SCALA_VERSION%\classes
+set SPARK_CLASSES=%SPARK_CLASSES%;%FWDIR%mllib\target\scala-%SCALA_VERSION%\classes
+set SPARK_CLASSES=%SPARK_CLASSES%;%FWDIR%bagel\target\scala-%SCALA_VERSION%\classes
+set SPARK_CLASSES=%SPARK_CLASSES%;%FWDIR%graphx\target\scala-%SCALA_VERSION%\classes
+set SPARK_CLASSES=%SPARK_CLASSES%;%FWDIR%streaming\target\scala-%SCALA_VERSION%\classes
+set SPARK_CLASSES=%SPARK_CLASSES%;%FWDIR%tools\target\scala-%SCALA_VERSION%\classes
+set SPARK_CLASSES=%SPARK_CLASSES%;%FWDIR%sql\catalyst\target\scala-%SCALA_VERSION%\classes
+set SPARK_CLASSES=%SPARK_CLASSES%;%FWDIR%sql\core\target\scala-%SCALA_VERSION%\classes
+set SPARK_CLASSES=%SPARK_CLASSES%;%FWDIR%sql\hive\target\scala-%SCALA_VERSION%\classes
+
+set SPARK_TEST_CLASSES=%FWDIR%core\target\scala-%SCALA_VERSION%\test-classes
+set SPARK_TEST_CLASSES=%SPARK_TEST_CLASSES%;%FWDIR%repl\target\scala-%SCALA_VERSION%\test-classes
+set SPARK_TEST_CLASSES=%SPARK_TEST_CLASSES%;%FWDIR%mllib\target\scala-%SCALA_VERSION%\test-classes
+set SPARK_TEST_CLASSES=%SPARK_TEST_CLASSES%;%FWDIR%bagel\target\scala-%SCALA_VERSION%\test-classes
+set SPARK_TEST_CLASSES=%SPARK_TEST_CLASSES%;%FWDIR%graphx\target\scala-%SCALA_VERSION%\test-classes
+set SPARK_TEST_CLASSES=%SPARK_TEST_CLASSES%;%FWDIR%streaming\target\scala-%SCALA_VERSION%\test-classes
+set SPARK_TEST_CLASSES=%SPARK_TEST_CLASSES%;%FWDIR%sql\catalyst\target\scala-%SCALA_VERSION%\test-classes
+set SPARK_TEST_CLASSES=%SPARK_TEST_CLASSES%;%FWDIR%sql\core\target\scala-%SCALA_VERSION%\test-classes
+set SPARK_TEST_CLASSES=%SPARK_TEST_CLASSES%;%FWDIR%sql\hive\target\scala-%SCALA_VERSION%\test-classes
+
+if "x%SPARK_TESTING%"=="x1" (
+  rem Add test clases to path - note, add SPARK_CLASSES and SPARK_TEST_CLASSES before CLASSPATH
+  rem so that local compilation takes precedence over assembled jar
+  set CLASSPATH=%SPARK_CLASSES%;%SPARK_TEST_CLASSES%;%CLASSPATH%
+)
+
+rem Add hadoop conf dir - else FileSystem.*, etc fail
+rem Note, this assumes that there is either a HADOOP_CONF_DIR or YARN_CONF_DIR which hosts
+rem the configurtion files.
+if "x%HADOOP_CONF_DIR%"=="x" goto no_hadoop_conf_dir
+  set CLASSPATH=%CLASSPATH%;%HADOOP_CONF_DIR%
+:no_hadoop_conf_dir
+
+if "x%YARN_CONF_DIR%"=="x" goto no_yarn_conf_dir
+  set CLASSPATH=%CLASSPATH%;%YARN_CONF_DIR%
+:no_yarn_conf_dir
+
+rem A bit of a hack to allow calling this script within run2.cmd without seeing output
+if "%DONT_PRINT_CLASSPATH%"=="1" goto exit
+
+echo %CLASSPATH%
+
+:exit
diff --git a/core/src/main/scala/org/apache/spark/SparkSaslClient.scala b/core/src/main/scala/org/apache/spark/SparkSaslClient.scala
index 5b14c4291d91..65003b6ac6a0 100644
--- a/core/src/main/scala/org/apache/spark/SparkSaslClient.scala
+++ b/core/src/main/scala/org/apache/spark/SparkSaslClient.scala
@@ -111,10 +111,10 @@ private[spark] class SparkSaslClient(securityMgr: SecurityManager)  extends Logg
     CallbackHandler {
 
     private val userName: String =
-      SparkSaslServer.encodeIdentifier(securityMgr.getSaslUser().getBytes())
+      SparkSaslServer.encodeIdentifier(securityMgr.getSaslUser().getBytes("utf-8"))
     private val secretKey = securityMgr.getSecretKey()
-    private val userPassword: Array[Char] =
-      SparkSaslServer.encodePassword(if (secretKey != null) secretKey.getBytes() else "".getBytes())
+    private val userPassword: Array[Char] = SparkSaslServer.encodePassword(
+        if (secretKey != null) secretKey.getBytes("utf-8") else "".getBytes("utf-8"))
 
     /**
      * Implementation used to respond to SASL request from the server.
diff --git a/core/src/main/scala/org/apache/spark/SparkSaslServer.scala b/core/src/main/scala/org/apache/spark/SparkSaslServer.scala
index 6161a6fb7ae8..f6b0a9132aca 100644
--- a/core/src/main/scala/org/apache/spark/SparkSaslServer.scala
+++ b/core/src/main/scala/org/apache/spark/SparkSaslServer.scala
@@ -89,7 +89,7 @@ private[spark] class SparkSaslServer(securityMgr: SecurityManager) extends Loggi
     extends CallbackHandler {
 
     private val userName: String =
-      SparkSaslServer.encodeIdentifier(securityMgr.getSaslUser().getBytes())
+      SparkSaslServer.encodeIdentifier(securityMgr.getSaslUser().getBytes("utf-8"))
 
     override def handle(callbacks: Array[Callback]) {
       logDebug("In the sasl server callback handler")
@@ -101,7 +101,7 @@ private[spark] class SparkSaslServer(securityMgr: SecurityManager) extends Loggi
         case pc: PasswordCallback => {
           logDebug("handle: SASL server callback: setting userPassword")
           val password: Array[Char] =
-            SparkSaslServer.encodePassword(securityMgr.getSecretKey().getBytes())
+            SparkSaslServer.encodePassword(securityMgr.getSecretKey().getBytes("utf-8"))
           pc.setPassword(password)
         }
         case rc: RealmCallback => {
@@ -159,7 +159,7 @@ private[spark] object SparkSaslServer {
    * @return Base64-encoded string
    */
   def encodeIdentifier(identifier: Array[Byte]): String = {
-    new String(Base64.encodeBase64(identifier))
+    new String(Base64.encodeBase64(identifier), "utf-8")
   }
 
   /**
@@ -168,7 +168,7 @@ private[spark] object SparkSaslServer {
    * @return password as a char array.
    */
   def encodePassword(password: Array[Byte]): Array[Char] = {
-    new String(Base64.encodeBase64(password)).toCharArray()
+    new String(Base64.encodeBase64(password), "utf-8").toCharArray()
   }
 }
 
diff --git a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
index 1498b017a793..672c344a5659 100644
--- a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
+++ b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
@@ -187,7 +187,7 @@ private[spark] class PythonRDD[T: ClassTag](
               val exLength = stream.readInt()
               val obj = new Array[Byte](exLength)
               stream.readFully(obj)
-              throw new PythonException(new String(obj), readerException)
+              throw new PythonException(new String(obj, "utf-8"), readerException)
             case SpecialLengths.END_OF_DATA_SECTION =>
               // We've finished the data section of the output, but we can still
               // read some accumulator updates:
diff --git a/core/src/main/scala/org/apache/spark/network/ReceiverTest.scala b/core/src/main/scala/org/apache/spark/network/ReceiverTest.scala
index 9dc51e0d401f..53a6038a9b59 100644
--- a/core/src/main/scala/org/apache/spark/network/ReceiverTest.scala
+++ b/core/src/main/scala/org/apache/spark/network/ReceiverTest.scala
@@ -28,7 +28,7 @@ private[spark] object ReceiverTest {
 
     manager.onReceiveMessage((msg: Message, id: ConnectionManagerId) => {
       /* println("Received [" + msg + "] from [" + id + "] at " + System.currentTimeMillis) */
-      val buffer = ByteBuffer.wrap("response".getBytes)
+      val buffer = ByteBuffer.wrap("response".getBytes("utf-8"))
       Some(Message.createBufferMessage(buffer, msg.id))
     })
     Thread.currentThread.join()
diff --git a/core/src/main/scala/org/apache/spark/network/SenderTest.scala b/core/src/main/scala/org/apache/spark/network/SenderTest.scala
index 14c094c6177d..b8ea7c2cff9a 100644
--- a/core/src/main/scala/org/apache/spark/network/SenderTest.scala
+++ b/core/src/main/scala/org/apache/spark/network/SenderTest.scala
@@ -54,7 +54,7 @@ private[spark] object SenderTest {
       val responseStr = manager.sendMessageReliablySync(targetConnectionManagerId, dataMessage)
         .map { response =>
           val buffer = response.asInstanceOf[BufferMessage].buffers(0)
-          new String(buffer.array)
+          new String(buffer.array, "utf-8")
         }.getOrElse("none")
 
       val finishTime = System.currentTimeMillis
diff --git a/core/src/main/scala/org/apache/spark/rdd/PipedRDD.scala b/core/src/main/scala/org/apache/spark/rdd/PipedRDD.scala
index e441d4a40ccd..5d77d3737845 100644
--- a/core/src/main/scala/org/apache/spark/rdd/PipedRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/PipedRDD.scala
@@ -87,10 +87,10 @@ private[spark] class PipedRDD[T: ClassTag](
     // When spark.worker.separated.working.directory option is turned on, each
     // task will be run in separate directory. This should be resolve file
     // access conflict issue
-    val taskDirectory = "./tasks/" + java.util.UUID.randomUUID.toString
+    val taskDirectory = "tasks" + File.separator + java.util.UUID.randomUUID.toString
     var workInTaskDirectory = false
     logDebug("taskDirectory = " + taskDirectory)
-    if (separateWorkingDir == true) {
+    if (separateWorkingDir) {
       val currentDir = new File(".")
       logDebug("currentDir = " + currentDir.getAbsolutePath())
       val taskDirFile = new File(taskDirectory)
@@ -106,13 +106,13 @@ private[spark] class PipedRDD[T: ClassTag](
         for (file <- currentDir.list(tasksDirFilter)) {
           val fileWithDir = new File(currentDir, file)
           Utils.symlink(new File(fileWithDir.getAbsolutePath()),
-            new File(taskDirectory + "/" + fileWithDir.getName()))
+            new File(taskDirectory + File.separator + fileWithDir.getName()))
         }
         pb.directory(taskDirFile)
         workInTaskDirectory = true
       } catch {
         case e: Exception => logError("Unable to setup task working directory: " + e.getMessage +
-          " (" + taskDirectory + ")")
+          " (" + taskDirectory + ")", e)
       }
     }
 
diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala
index 084a71c4caeb..8351f7156a5e 100644
--- a/core/src/main/scala/org/apache/spark/util/Utils.scala
+++ b/core/src/main/scala/org/apache/spark/util/Utils.scala
@@ -30,6 +30,7 @@ import scala.io.Source
 import scala.reflect.ClassTag
 
 import com.google.common.io.Files
+import org.apache.commons.lang.SystemUtils
 import com.google.common.util.concurrent.ThreadFactoryBuilder
 import org.apache.hadoop.fs.{FileSystem, FileUtil, Path}
 import org.json4s._
@@ -45,10 +46,13 @@ import org.apache.spark.serializer.{DeserializationStream, SerializationStream,
  */
 private[spark] object Utils extends Logging {
 
-  val osName = System.getProperty("os.name")
-  
   val random = new Random()
 
+  def sparkBin(sparkHome: String, which: String): File = {
+    val suffix = if (SystemUtils.IS_OS_WINDOWS) ".cmd" else ""
+    new File(sparkHome + File.separator + "bin", which + suffix)
+  }
+
   /** Serialize an object using Java serialization */
   def serialize[T](o: T): Array[Byte] = {
     val bos = new ByteArrayOutputStream()
@@ -605,7 +609,7 @@ private[spark] object Utils extends Logging {
    */
   def isSymlink(file: File): Boolean = {
     if (file == null) throw new NullPointerException("File must not be null")
-    if (osName.startsWith("Windows")) return false
+    if (SystemUtils.IS_OS_WINDOWS) return false
     val fileInCanonicalDir = if (file.getParent() == null) {
       file
     } else {
@@ -1008,10 +1012,18 @@ private[spark] object Utils extends Logging {
     if (dst.isAbsolute()) {
       throw new IOException("Destination must be relative")
     }
-    val linkCmd = if (osName.startsWith("Windows")) "copy" else "ln -sf"
+    var cmdSuffix = ""
+    val linkCmd = if (SystemUtils.IS_OS_WINDOWS) {
+      // refer to http://technet.microsoft.com/en-us/library/cc771254.aspx
+      cmdSuffix = " /s /e /k /h /y /i"
+      "cmd /c xcopy "
+    } else {
+      cmdSuffix = ""
+      "ln -sf "
+    }
     import scala.sys.process._
-    (linkCmd + " " + src.getAbsolutePath() + " " + dst.getPath()) lines_! ProcessLogger(line =>
-       (logInfo(line)))
+    (linkCmd + src.getAbsolutePath() + " " + dst.getPath() + cmdSuffix) lines_!
+      ProcessLogger(line => (logInfo(line)))
   }
 
 
diff --git a/core/src/test/java/org/apache/spark/JavaAPISuite.java b/core/src/test/java/org/apache/spark/JavaAPISuite.java
index 8d2e9f184634..76c6f5af82a5 100644
--- a/core/src/test/java/org/apache/spark/JavaAPISuite.java
+++ b/core/src/test/java/org/apache/spark/JavaAPISuite.java
@@ -610,8 +610,8 @@ public void textFiles() throws IOException {
 
   @Test
   public void wholeTextFiles() throws IOException {
-    byte[] content1 = "spark is easy to use.\n".getBytes();
-    byte[] content2 = "spark is also easy to use.\n".getBytes();
+    byte[] content1 = "spark is easy to use.\n".getBytes("utf-8");
+    byte[] content2 = "spark is also easy to use.\n".getBytes("utf-8");
 
     File tempDir = Files.createTempDir();
     String tempDirName = tempDir.getAbsolutePath();
diff --git a/examples/src/main/scala/org/apache/spark/streaming/examples/MQTTWordCount.scala b/examples/src/main/scala/org/apache/spark/streaming/examples/MQTTWordCount.scala
index 3d3c27ed7820..62aef0fb4710 100644
--- a/examples/src/main/scala/org/apache/spark/streaming/examples/MQTTWordCount.scala
+++ b/examples/src/main/scala/org/apache/spark/streaming/examples/MQTTWordCount.scala
@@ -56,7 +56,7 @@ object MQTTPublisher {
     val msg: String = "hello mqtt demo for spark streaming"
 
     while (true) {
-      val message: MqttMessage = new MqttMessage(String.valueOf(msg).getBytes())
+      val message: MqttMessage = new MqttMessage(String.valueOf(msg).getBytes("utf-8"))
       msgtopic.publish(message)
       println("Published data. topic: " + msgtopic.getName() + " Message: " + message)
     }
diff --git a/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumeStreamSuite.scala b/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumeStreamSuite.scala
index 78603200d2d8..dd287d0ef90a 100644
--- a/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumeStreamSuite.scala
+++ b/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumeStreamSuite.scala
@@ -57,7 +57,7 @@ class FlumeStreamSuite extends TestSuiteBase {
 
     for (i <- 0 until input.size) {
       val event = new AvroFlumeEvent
-      event.setBody(ByteBuffer.wrap(input(i).toString.getBytes()))
+      event.setBody(ByteBuffer.wrap(input(i).toString.getBytes("utf-8")))
       event.setHeaders(Map[CharSequence, CharSequence]("test" -> "header"))
       client.append(event)
       Thread.sleep(500)
diff --git a/external/mqtt/src/main/scala/org/apache/spark/streaming/mqtt/MQTTInputDStream.scala b/external/mqtt/src/main/scala/org/apache/spark/streaming/mqtt/MQTTInputDStream.scala
index 0beee8b4157a..77661f71ada2 100644
--- a/external/mqtt/src/main/scala/org/apache/spark/streaming/mqtt/MQTTInputDStream.scala
+++ b/external/mqtt/src/main/scala/org/apache/spark/streaming/mqtt/MQTTInputDStream.scala
@@ -92,7 +92,7 @@ class MQTTReceiver(
 
       // Handles Mqtt message
       override def messageArrived(arg0: String, arg1: MqttMessage) {
-        store(new String(arg1.getPayload()))
+        store(new String(arg1.getPayload(),"utf-8"))
       }
 
       override def deliveryComplete(arg0: IMqttDeliveryToken) {
diff --git a/repl/src/test/scala/org/apache/spark/repl/ReplSuite.scala b/repl/src/test/scala/org/apache/spark/repl/ReplSuite.scala
index 4155007c6d33..e33f4f980305 100644
--- a/repl/src/test/scala/org/apache/spark/repl/ReplSuite.scala
+++ b/repl/src/test/scala/org/apache/spark/repl/ReplSuite.scala
@@ -25,6 +25,7 @@ import scala.collection.mutable.ArrayBuffer
 import com.google.common.io.Files
 import org.scalatest.FunSuite
 import org.apache.spark.SparkContext
+import org.apache.commons.lang3.StringEscapeUtils
 
 
 class ReplSuite extends FunSuite {
@@ -185,11 +186,12 @@ class ReplSuite extends FunSuite {
     out.close()
     val output = runInterpreter("local",
       """
-        |var file = sc.textFile("%s/input").cache()
+        |var file = sc.textFile("%s").cache()
         |file.count()
         |file.count()
         |file.count()
-      """.stripMargin.format(tempDir.getAbsolutePath))
+      """.stripMargin.format(StringEscapeUtils.escapeJava(
+        tempDir.getAbsolutePath + File.separator + "input")))
     assertDoesNotContain("error:", output)
     assertDoesNotContain("Exception", output)
     assertContains("res0: Long = 3", output)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala
index 5be76890afe3..4cd52d828813 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala
@@ -200,10 +200,10 @@ private[sql] object SHORT extends NativeColumnType(ShortType, 6, 2) {
 }
 
 private[sql] object STRING extends NativeColumnType(StringType, 7, 8) {
-  override def actualSize(v: String): Int = v.getBytes.length + 4
+  override def actualSize(v: String): Int = v.getBytes("utf-8").length + 4
 
   override def append(v: String, buffer: ByteBuffer) {
-    val stringBytes = v.getBytes()
+    val stringBytes = v.getBytes("utf-8")
     buffer.putInt(stringBytes.length).put(stringBytes, 0, stringBytes.length)
   }
 
@@ -211,7 +211,7 @@ private[sql] object STRING extends NativeColumnType(StringType, 7, 8) {
     val length = buffer.getInt()
     val stringBytes = new Array[Byte](length)
     buffer.get(stringBytes, 0, length)
-    new String(stringBytes)
+    new String(stringBytes, "utf-8")
   }
 
   override def setField(row: MutableRow, ordinal: Int, value: String) {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnTypeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnTypeSuite.scala
index 1d3608ed2d9a..325173cf95fd 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnTypeSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnTypeSuite.scala
@@ -58,7 +58,7 @@ class ColumnTypeSuite extends FunSuite {
     checkActualSize(DOUBLE,  Double.MaxValue, 8)
     checkActualSize(FLOAT,   Float.MaxValue,  4)
     checkActualSize(BOOLEAN, true,            1)
-    checkActualSize(STRING,  "hello",         4 + 5)
+    checkActualSize(STRING,  "hello",         4 + "hello".getBytes("utf-8").length)
 
     val binary = Array.fill[Byte](4)(0: Byte)
     checkActualSize(BINARY,  binary, 4 + 4)
@@ -91,14 +91,16 @@ class ColumnTypeSuite extends FunSuite {
   testNativeColumnType[StringType.type](
     STRING,
     (buffer: ByteBuffer, string: String) => {
-      val bytes = string.getBytes()
-      buffer.putInt(bytes.length).put(string.getBytes)
+
+      val bytes = string.getBytes("utf-8")
+      buffer.putInt(bytes.length)
+      buffer.put(bytes)
     },
     (buffer: ByteBuffer) => {
       val length = buffer.getInt()
       val bytes = new Array[Byte](length)
-      buffer.get(bytes, 0, length)
-      new String(bytes)
+      buffer.get(bytes)
+      new String(bytes, "utf-8")
     })
 
   testColumnType[BinaryType.type, Array[Byte]](
@@ -161,9 +163,13 @@ class ColumnTypeSuite extends FunSuite {
 
       buffer.rewind()
       seq.foreach { expected =>
+        println("buffer = " + buffer + ", expected = " + expected)
+        val extracted = columnType.extract(buffer)
         assert(
-          expected === columnType.extract(buffer),
-          "Extracted value didn't equal to the original one")
+          expected === extracted,
+          "Extracted value didn't equal to the original one. " +
+            hexDump(expected) + " != " + hexDump(extracted) +
+            ", buffer = " + dumpBuffer(buffer.duplicate().rewind().asInstanceOf[ByteBuffer]))
       }
     }
 
@@ -179,4 +185,28 @@ class ColumnTypeSuite extends FunSuite {
       }
     }
   }
+
+  private def hexDump(value: Any): String = {
+    if (value.isInstanceOf[String]) {
+      val sb = new StringBuilder()
+      for (ch <- value.asInstanceOf[String].toCharArray) {
+        sb.append(Integer.toHexString(ch & 0xffff)).append(' ')
+      }
+      if (! sb.isEmpty) sb.setLength(sb.length - 1)
+      sb.toString()
+    } else {
+      // for now ..
+      hexDump(value.toString)
+    }
+  }
+
+  private def dumpBuffer(buff: ByteBuffer): Any = {
+    val sb = new StringBuilder()
+    while (buff.hasRemaining) {
+      val b = buff.get()
+      sb.append(Integer.toHexString(b & 0xff)).append(' ')
+    }
+    if (! sb.isEmpty) sb.setLength(sb.length - 1)
+    sb.toString()
+  }
 }
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/ScriptTransformation.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/ScriptTransformation.scala
index 610fa9cb843c..8258ee5fef0e 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/ScriptTransformation.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/ScriptTransformation.scala
@@ -71,7 +71,7 @@ case class ScriptTransformation(
       iter
         .map(outputProjection)
         // TODO: Use SerDe
-        .map(_.mkString("", "\t", "\n").getBytes).foreach(outputStream.write)
+        .map(_.mkString("", "\t", "\n").getBytes("utf-8")).foreach(outputStream.write)
       outputStream.close()
       readerThread.join()
       outputLines.toIterator
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/TestHive.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/TestHive.scala
index 74110ee27b76..3ad66a3d7f45 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/TestHive.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/TestHive.scala
@@ -100,14 +100,15 @@ class TestHiveContext(sc: SparkContext) extends LocalHiveContext(sc) {
   hiveFilesTemp.delete()
   hiveFilesTemp.mkdir()
 
-  val inRepoTests = if (System.getProperty("user.dir").endsWith("sql/hive")) {
-    new File("src/test/resources/")
+  val inRepoTests = if (System.getProperty("user.dir").endsWith("sql" + File.separator + "hive")) {
+    new File("src" + File.separator + "test" + File.separator + "resources" + File.separator)
   } else {
-    new File("sql/hive/src/test/resources")
+    new File("sql" + File.separator + "hive" + File.separator + "src" + File.separator + "test" + 
+      File.separator + "resources")
   }
 
   def getHiveFile(path: String): File = {
-    val stripped = path.replaceAll("""\.\.\/""", "")
+    val stripped = path.replaceAll("""\.\.\/""", "").replace('/', File.separatorChar)
     hiveDevHome
       .map(new File(_, stripped))
       .filter(_.exists)
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/BigDataBenchmarkSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/BigDataBenchmarkSuite.scala
index 9b9a823b6e95..42a82c1fbf5c 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/BigDataBenchmarkSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/BigDataBenchmarkSuite.scala
@@ -26,7 +26,7 @@ import org.apache.spark.sql.hive.test.TestHive._
  * https://amplab.cs.berkeley.edu/benchmark/
  */
 class BigDataBenchmarkSuite extends HiveComparisonTest {
-  val testDataDirectory = new File("target/big-data-benchmark-testdata")
+  val testDataDirectory = new File("target" + File.separator + "big-data-benchmark-testdata")
 
   val testTables = Seq(
     TestTable(
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala
index ea17e6e93b41..edff38b90107 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala
@@ -78,7 +78,8 @@ abstract class HiveComparisonTest
       .map(name => new File(targetDir, s"$suiteName.$name"))
 
   /** The local directory with cached golden answer will be stored. */
-  protected val answerCache = new File("src/test/resources/golden")
+  protected val answerCache = new File("src" + File.separator + "test" +
+    File.separator + "resources" + File.separator + "golden")
   if (!answerCache.exists) {
     answerCache.mkdir()
   }
@@ -120,7 +121,7 @@ abstract class HiveComparisonTest
   protected val cacheDigest = java.security.MessageDigest.getInstance("MD5")
   protected def getMd5(str: String): String = {
     val digest = java.security.MessageDigest.getInstance("MD5")
-    digest.update(str.getBytes)
+    digest.update(str.getBytes("utf-8"))
     new java.math.BigInteger(1, digest.digest).toString(16)
   }
 
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
index dfe88b960b63..0bb76f31c373 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
@@ -17,6 +17,8 @@
 
 package org.apache.spark.sql.hive.execution
 
+import java.io.File
+
 import org.scalatest.BeforeAndAfter
 
 import org.apache.spark.sql.hive.test.TestHive
@@ -26,7 +28,9 @@ import org.apache.spark.sql.hive.test.TestHive
  */
 class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
   // TODO: bundle in jar files... get from classpath
-  lazy val hiveQueryDir = TestHive.getHiveFile("ql/src/test/queries/clientpositive")
+  lazy val hiveQueryDir = TestHive.getHiveFile("ql" + File.separator + "src" +
+    File.separator + "test" + File.separator + "queries" + File.separator + "clientpositive")
+
   def testCases = hiveQueryDir.listFiles.map(f => f.getName.stripSuffix(".q") -> f)
 
   override def beforeAll() {
diff --git a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala
index 832d45b3ad10..718cb19f5726 100644
--- a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala
+++ b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala
@@ -64,7 +64,7 @@ class YarnSparkHadoopUtil extends SparkHadoopUtil {
 
   override def addSecretKeyToUserCredentials(key: String, secret: String) {
     val creds = new Credentials()
-    creds.addSecretKey(new Text(key), secret.getBytes())
+    creds.addSecretKey(new Text(key), secret.getBytes("utf-8"))
     addCurrentUserCredentials(creds)
   }
 

From cd12dd9bde91303d0341180e5f70d2a03d6b65b6 Mon Sep 17 00:00:00 2001
From: Tathagata Das <tathagata.das1565@gmail.com>
Date: Thu, 24 Apr 2014 21:34:37 -0700
Subject: [PATCH 018/810] [SPARK-1617] and [SPARK-1618] Improvements to
 streaming ui and bug fix to socket receiver

1617: These changes expose the receiver state (active or inactive) and last error in the UI
1618: If the socket receiver cannot connect in the first attempt, it should try to restart after a delay. That was broken, as the thread that restarts (hence, stops) the receiver waited on Thread.join on itself!

Author: Tathagata Das <tathagata.das1565@gmail.com>

Closes #540 from tdas/streaming-ui-fix and squashes the following commits:

e469434 [Tathagata Das] Merge remote-tracking branch 'apache-github/master' into streaming-ui-fix
dbddf75 [Tathagata Das] Style fix.
66df1a5 [Tathagata Das] Merge remote-tracking branch 'apache/master' into streaming-ui-fix
ad98bc9 [Tathagata Das] Refactored streaming listener to use ReceiverInfo.
d7f849c [Tathagata Das] Revert "Moved BatchInfo from streaming.scheduler to streaming.ui"
5c80919 [Tathagata Das] Moved BatchInfo from streaming.scheduler to streaming.ui
da244f6 [Tathagata Das] Fixed socket receiver as well as made receiver state and error visible in the streamign UI.
---
 .../scala/org/apache/spark/ui/UIUtils.scala   |  2 +-
 .../dstream/SocketInputDStream.scala          | 49 +++++++---------
 .../streaming/receiver/ActorReceiver.scala    | 12 +++-
 .../spark/streaming/receiver/Receiver.scala   |  5 +-
 .../streaming/receiver/ReceiverMessage.scala  |  4 +-
 .../receiver/ReceiverSupervisor.scala         | 58 +++++++++++--------
 .../receiver/ReceiverSupervisorImpl.scala     | 24 +++++---
 .../spark/streaming/scheduler/BatchInfo.scala |  3 +
 .../streaming/scheduler/ReceiverInfo.scala    | 37 ++++++++++++
 .../streaming/scheduler/ReceiverTracker.scala | 40 ++++++++-----
 .../scheduler/StreamingListener.scala         | 25 +++++++-
 .../ui/StreamingJobProgressListener.scala     | 18 +++++-
 .../spark/streaming/ui/StreamingPage.scala    | 20 +++++--
 .../streaming/NetworkReceiverSuite.scala      |  8 ++-
 .../streaming/StreamingListenerSuite.scala    | 15 ++---
 15 files changed, 217 insertions(+), 103 deletions(-)
 create mode 100644 streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverInfo.scala

diff --git a/core/src/main/scala/org/apache/spark/ui/UIUtils.scala b/core/src/main/scala/org/apache/spark/ui/UIUtils.scala
index cf987a1ab02c..a3d6a1821245 100644
--- a/core/src/main/scala/org/apache/spark/ui/UIUtils.scala
+++ b/core/src/main/scala/org/apache/spark/ui/UIUtils.scala
@@ -122,7 +122,7 @@ private[spark] object UIUtils extends Logging {
       }
     }
     if (unit.isEmpty) {
-      "%d".formatLocal(Locale.US, value)
+      "%d".formatLocal(Locale.US, value.toInt)
     } else {
       "%.1f%s".formatLocal(Locale.US, value, unit)
     }
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/SocketInputDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/SocketInputDStream.scala
index 1e32727eacfa..8b72bcf20653 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/SocketInputDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/SocketInputDStream.scala
@@ -50,49 +50,42 @@ class SocketReceiver[T: ClassTag](
     storageLevel: StorageLevel
   ) extends Receiver[T](storageLevel) with Logging {
 
-  var socket: Socket = null
-  var receivingThread: Thread = null
-
   def onStart() {
-    receivingThread = new Thread("Socket Receiver") {
-      override def run() {
-        connect()
-        receive()
-      }
-    }
-    receivingThread.start()
+    // Start the thread that receives data over a connection
+    new Thread("Socket Receiver") {
+      setDaemon(true)
+      override def run() { receive() }
+    }.start()
   }
 
   def onStop() {
-    if (socket != null) {
-      socket.close()
-    }
-    socket = null
-    if (receivingThread != null) {
-      receivingThread.join()
-    }
+    // There is nothing much to do as the thread calling receive()
+    // is designed to stop by itself isStopped() returns false
   }
 
-  def connect() {
+  /** Create a socket connection and receive data until receiver is stopped */
+  def receive() {
+    var socket: Socket = null
     try {
       logInfo("Connecting to " + host + ":" + port)
       socket = new Socket(host, port)
-    } catch {
-      case e: Exception =>
-        restart("Could not connect to " + host + ":" + port, e)
-    }
-  }
-
-  def receive() {
-    try {
       logInfo("Connected to " + host + ":" + port)
       val iterator = bytesToObjects(socket.getInputStream())
       while(!isStopped && iterator.hasNext) {
         store(iterator.next)
       }
+      logInfo("Stopped receiving")
+      restart("Retrying connecting to " + host + ":" + port)
     } catch {
-      case e: Exception =>
-        restart("Error receiving data from socket", e)
+      case e: java.net.ConnectException =>
+        restart("Error connecting to " + host + ":" + port, e)
+      case t: Throwable =>
+        restart("Error receiving data", t)
+    } finally {
+      if (socket != null) {
+        socket.close()
+        logInfo("Closed socket to " + host + ":" + port)
+      }
     }
   }
 }
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ActorReceiver.scala b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ActorReceiver.scala
index 821cf19481d4..743be58950c0 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ActorReceiver.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ActorReceiver.scala
@@ -28,8 +28,13 @@ import akka.actor.SupervisorStrategy.{Escalate, Restart}
 import org.apache.spark.{Logging, SparkEnv}
 import org.apache.spark.storage.StorageLevel
 import java.nio.ByteBuffer
+import org.apache.spark.annotation.DeveloperApi
 
-/** A helper with set of defaults for supervisor strategy */
+/**
+ * :: DeveloperApi ::
+ * A helper with set of defaults for supervisor strategy
+ */
+@DeveloperApi
 object ActorSupervisorStrategy {
 
   val defaultStrategy = OneForOneStrategy(maxNrOfRetries = 10, withinTimeRange =
@@ -40,6 +45,7 @@ object ActorSupervisorStrategy {
 }
 
 /**
+ * :: DeveloperApi ::
  * A receiver trait to be mixed in with your Actor to gain access to
  * the API for pushing received data into Spark Streaming for being processed.
  *
@@ -61,6 +67,7 @@ object ActorSupervisorStrategy {
  *       to ensure the type safety, i.e parametrized type of push block and InputDStream
  *       should be same.
  */
+@DeveloperApi
 trait ActorHelper {
 
   self: Actor => // to ensure that this can be added to Actor classes only
@@ -92,10 +99,12 @@ trait ActorHelper {
 }
 
 /**
+ * :: DeveloperApi ::
  * Statistics for querying the supervisor about state of workers. Used in
  * conjunction with `StreamingContext.actorStream` and
  * [[org.apache.spark.streaming.receiver.ActorHelper]].
  */
+@DeveloperApi
 case class Statistics(numberOfMsgs: Int,
   numberOfWorkers: Int,
   numberOfHiccups: Int,
@@ -188,4 +197,3 @@ private[streaming] class ActorReceiver[T: ClassTag](
     supervisor ! PoisonPill
   }
 }
-
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/receiver/Receiver.scala b/streaming/src/main/scala/org/apache/spark/streaming/receiver/Receiver.scala
index 44eecf1dd256..524c1b8d8ce4 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/receiver/Receiver.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/receiver/Receiver.scala
@@ -23,8 +23,10 @@ import scala.collection.mutable.ArrayBuffer
 import scala.collection.JavaConversions._
 
 import org.apache.spark.storage.StorageLevel
+import org.apache.spark.annotation.DeveloperApi
 
 /**
+ * :: DeveloperApi ::
  * Abstract class of a receiver that can be run on worker nodes to receive external data. A
  * custom receiver can be defined by defining the functions onStart() and onStop(). onStart()
  * should define the setup steps necessary to start receiving data,
@@ -51,6 +53,7 @@ import org.apache.spark.storage.StorageLevel
  *  }
  * }}}
  */
+@DeveloperApi
 abstract class Receiver[T](val storageLevel: StorageLevel) extends Serializable {
 
   /**
@@ -198,7 +201,7 @@ abstract class Receiver[T](val storageLevel: StorageLevel) extends Serializable
 
   /** Check if receiver has been marked for stopping. */
   def isStopped(): Boolean = {
-    !executor.isReceiverStarted()
+    executor.isReceiverStopped()
   }
 
   /** Get unique identifier of this receiver. */
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverMessage.scala b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverMessage.scala
index 6ab3ca6ea5fa..bf39d1e891ca 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverMessage.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverMessage.scala
@@ -18,6 +18,6 @@
 package org.apache.spark.streaming.receiver
 
 /** Messages sent to the NetworkReceiver. */
-private[streaming] sealed trait NetworkReceiverMessage
-private[streaming] object StopReceiver extends NetworkReceiverMessage
+private[streaming] sealed trait ReceiverMessage
+private[streaming] object StopReceiver extends ReceiverMessage
 
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisor.scala b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisor.scala
index 256b3335e49a..09be3a50d2df 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisor.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisor.scala
@@ -88,15 +88,29 @@ private[streaming] abstract class ReceiverSupervisor(
   /** Report errors. */
   def reportError(message: String, throwable: Throwable)
 
-  /** Start the executor */
+  /** Called when supervisor is started */
+  protected def onStart() { }
+
+  /** Called when supervisor is stopped */
+  protected def onStop(message: String, error: Option[Throwable]) { }
+
+  /** Called when receiver is started */
+  protected def onReceiverStart() { }
+
+  /** Called when receiver is stopped */
+  protected def onReceiverStop(message: String, error: Option[Throwable]) { }
+
+  /** Start the supervisor */
   def start() {
+    onStart()
     startReceiver()
   }
 
-  /** Mark the executor and the receiver for stopping */
+  /** Mark the supervisor and the receiver for stopping */
   def stop(message: String, error: Option[Throwable]) {
     stoppingError = error.orNull
     stopReceiver(message, error)
+    onStop(message, error)
     stopLatch.countDown()
   }
 
@@ -104,6 +118,8 @@ private[streaming] abstract class ReceiverSupervisor(
   def startReceiver(): Unit = synchronized {
     try {
       logInfo("Starting receiver")
+      receiver.onStart()
+      logInfo("Called receiver onStart")
       onReceiverStart()
       receiverState = Started
     } catch {
@@ -115,7 +131,10 @@ private[streaming] abstract class ReceiverSupervisor(
   /** Stop receiver */
   def stopReceiver(message: String, error: Option[Throwable]): Unit = synchronized {
     try {
+      logInfo("Stopping receiver with message: " + message + ": " + error.getOrElse(""))
       receiverState = Stopped
+      receiver.onStop()
+      logInfo("Called receiver onStop")
       onReceiverStop(message, error)
     } catch {
       case t: Throwable =>
@@ -130,41 +149,32 @@ private[streaming] abstract class ReceiverSupervisor(
 
   /** Restart receiver with delay */
   def restartReceiver(message: String, error: Option[Throwable], delay: Int) {
-    logWarning("Restarting receiver with delay " + delay + " ms: " + message,
-      error.getOrElse(null))
-    stopReceiver("Restarting receiver with delay " + delay + "ms: " + message, error)
-    future {
+    Future {
+      logWarning("Restarting receiver with delay " + delay + " ms: " + message,
+        error.getOrElse(null))
+      stopReceiver("Restarting receiver with delay " + delay + "ms: " + message, error)
       logDebug("Sleeping for " + delay)
       Thread.sleep(delay)
-      logDebug("Starting receiver again")
+      logInfo("Starting receiver again")
       startReceiver()
       logInfo("Receiver started again")
     }
   }
 
-  /** Called when the receiver needs to be started */
-  protected def onReceiverStart(): Unit = synchronized {
-    // Call user-defined onStart()
-    logInfo("Calling receiver onStart")
-    receiver.onStart()
-    logInfo("Called receiver onStart")
-  }
-
-  /** Called when the receiver needs to be stopped */
-  protected def onReceiverStop(message: String, error: Option[Throwable]): Unit = synchronized {
-    // Call user-defined onStop()
-    logInfo("Calling receiver onStop")
-    receiver.onStop()
-    logInfo("Called receiver onStop")
-  }
-
   /** Check if receiver has been marked for stopping */
   def isReceiverStarted() = {
     logDebug("state = " + receiverState)
     receiverState == Started
   }
 
-  /** Wait the thread until the executor is stopped */
+  /** Check if receiver has been marked for stopping */
+  def isReceiverStopped() = {
+    logDebug("state = " + receiverState)
+    receiverState == Stopped
+  }
+
+
+  /** Wait the thread until the supervisor is stopped */
   def awaitTermination() {
     stopLatch.await()
     logInfo("Waiting for executor stop is over")
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisorImpl.scala b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisorImpl.scala
index 2a3521bd46ae..ce8316bb1489 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisorImpl.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisorImpl.scala
@@ -79,6 +79,8 @@ private[streaming] class ReceiverSupervisorImpl(
           logInfo("Received stop signal")
           stop("Stopped by driver", None)
       }
+
+      def ref = self
     }), "Receiver-" + streamId + "-" + System.currentTimeMillis())
 
   /** Unique block ids if one wants to add blocks directly */
@@ -154,14 +156,23 @@ private[streaming] class ReceiverSupervisorImpl(
     logWarning("Reported error " + message + " - " + error)
   }
 
-  override def onReceiverStart() {
+  override protected def onStart() {
     blockGenerator.start()
-    super.onReceiverStart()
   }
 
-  override def onReceiverStop(message: String, error: Option[Throwable]) {
-    super.onReceiverStop(message, error)
+  override protected def onStop(message: String, error: Option[Throwable]) {
     blockGenerator.stop()
+    env.actorSystem.stop(actor)
+  }
+
+  override protected def onReceiverStart() {
+    val msg = RegisterReceiver(
+      streamId, receiver.getClass.getSimpleName, Utils.localHostName(), actor)
+    val future = trackerActor.ask(msg)(askTimeout)
+    Await.result(future, askTimeout)
+  }
+
+  override protected def onReceiverStop(message: String, error: Option[Throwable]) {
     logInfo("Deregistering receiver " + streamId)
     val errorString = error.map(Throwables.getStackTraceAsString).getOrElse("")
     val future = trackerActor.ask(
@@ -170,11 +181,6 @@ private[streaming] class ReceiverSupervisorImpl(
     logInfo("Stopped receiver " + streamId)
   }
 
-  override def stop(message: String, error: Option[Throwable]) {
-    super.stop(message, error)
-    env.actorSystem.stop(actor)
-  }
-
   /** Generate new block ID */
   private def nextBlockId = StreamBlockId(streamId, newBlockId.getAndIncrement)
 }
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/BatchInfo.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/BatchInfo.scala
index 9c69a2a4e21f..a68aecb88111 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/BatchInfo.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/BatchInfo.scala
@@ -18,8 +18,10 @@
 package org.apache.spark.streaming.scheduler
 
 import org.apache.spark.streaming.Time
+import org.apache.spark.annotation.DeveloperApi
 
 /**
+ * :: DeveloperApi ::
  * Class having information on completed batches.
  * @param batchTime   Time of the batch
  * @param submissionTime  Clock time of when jobs of this batch was submitted to
@@ -27,6 +29,7 @@ import org.apache.spark.streaming.Time
  * @param processingStartTime Clock time of when the first job of this batch started processing
  * @param processingEndTime Clock time of when the last job of this batch finished processing
  */
+@DeveloperApi
 case class BatchInfo(
     batchTime: Time,
     receivedBlockInfo: Map[Int, Array[ReceivedBlockInfo]],
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverInfo.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverInfo.scala
new file mode 100644
index 000000000000..d7e39c528c51
--- /dev/null
+++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverInfo.scala
@@ -0,0 +1,37 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.streaming.scheduler
+
+import akka.actor.ActorRef
+import org.apache.spark.annotation.DeveloperApi
+
+/**
+ * :: DeveloperApi ::
+ * Class having information about a receiver
+ */
+@DeveloperApi
+case class ReceiverInfo(
+    streamId: Int,
+    name: String,
+    private[streaming] val actor: ActorRef,
+    active: Boolean,
+    location: String,
+    lastErrorMessage: String = "",
+    lastError: String = ""
+   ) {
+}
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala
index 557e0961d594..5307fe189d71 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala
@@ -28,13 +28,8 @@ import org.apache.spark.streaming.{StreamingContext, Time}
 import org.apache.spark.streaming.receiver.{Receiver, ReceiverSupervisorImpl, StopReceiver}
 import org.apache.spark.util.AkkaUtils
 
-/** Information about receiver */
-case class ReceiverInfo(streamId: Int, typ: String, location: String) {
-  override def toString = s"$typ-$streamId"
-}
-
 /** Information about blocks received by the receiver */
-case class ReceivedBlockInfo(
+private[streaming] case class ReceivedBlockInfo(
     streamId: Int,
     blockId: StreamBlockId,
     numRecords: Long,
@@ -69,7 +64,7 @@ class ReceiverTracker(ssc: StreamingContext) extends Logging {
   val receiverInputStreams = ssc.graph.getReceiverInputStreams()
   val receiverInputStreamMap = Map(receiverInputStreams.map(x => (x.id, x)): _*)
   val receiverExecutor = new ReceiverLauncher()
-  val receiverInfo = new HashMap[Int, ActorRef] with SynchronizedMap[Int, ActorRef]
+  val receiverInfo = new HashMap[Int, ReceiverInfo] with SynchronizedMap[Int, ReceiverInfo]
   val receivedBlockInfo = new HashMap[Int, SynchronizedQueue[ReceivedBlockInfo]]
     with SynchronizedMap[Int, SynchronizedQueue[ReceivedBlockInfo]]
   val timeout = AkkaUtils.askTimeout(ssc.conf)
@@ -129,17 +124,23 @@ class ReceiverTracker(ssc: StreamingContext) extends Logging {
     if (!receiverInputStreamMap.contains(streamId)) {
       throw new Exception("Register received for unexpected id " + streamId)
     }
-    receiverInfo += ((streamId, receiverActor))
-    ssc.scheduler.listenerBus.post(StreamingListenerReceiverStarted(
-      ReceiverInfo(streamId, typ, host)
-    ))
+    receiverInfo(streamId) = ReceiverInfo(
+      streamId, s"${typ}-${streamId}", receiverActor, true, host)
+    ssc.scheduler.listenerBus.post(StreamingListenerReceiverStarted(receiverInfo(streamId)))
     logInfo("Registered receiver for stream " + streamId + " from " + sender.path.address)
   }
 
   /** Deregister a receiver */
   def deregisterReceiver(streamId: Int, message: String, error: String) {
-    receiverInfo -= streamId
-    ssc.scheduler.listenerBus.post(StreamingListenerReceiverStopped(streamId, message, error))
+    val newReceiverInfo = receiverInfo.get(streamId) match {
+      case Some(oldInfo) =>
+        oldInfo.copy(actor = null, active = false, lastErrorMessage = message, lastError = error)
+      case None =>
+        logWarning("No prior receiver info")
+        ReceiverInfo(streamId, "", null, false, "", lastErrorMessage = message, lastError = error)
+    }
+    receiverInfo(streamId) = newReceiverInfo
+    ssc.scheduler.listenerBus.post(StreamingListenerReceiverStopped(receiverInfo(streamId)))
     val messageWithError = if (error != null && !error.isEmpty) {
       s"$message - $error"
     } else {
@@ -157,7 +158,15 @@ class ReceiverTracker(ssc: StreamingContext) extends Logging {
 
   /** Report error sent by a receiver */
   def reportError(streamId: Int, message: String, error: String) {
-    ssc.scheduler.listenerBus.post(StreamingListenerReceiverError(streamId, message, error))
+    val newReceiverInfo = receiverInfo.get(streamId) match {
+      case Some(oldInfo) =>
+        oldInfo.copy(lastErrorMessage = message, lastError = error)
+      case None =>
+        logWarning("No prior receiver info")
+        ReceiverInfo(streamId, "", null, false, "", lastErrorMessage = message, lastError = error)
+    }
+    receiverInfo(streamId) = newReceiverInfo
+    ssc.scheduler.listenerBus.post(StreamingListenerReceiverError(receiverInfo(streamId)))
     val messageWithError = if (error != null && !error.isEmpty) {
       s"$message - $error"
     } else {
@@ -270,7 +279,8 @@ class ReceiverTracker(ssc: StreamingContext) extends Logging {
     /** Stops the receivers. */
     private def stopReceivers() {
       // Signal the receivers to stop
-      receiverInfo.values.foreach(_ ! StopReceiver)
+      receiverInfo.values.flatMap { info => Option(info.actor)}
+                         .foreach { _ ! StopReceiver }
       logInfo("Sent stop signal to all " + receiverInfo.size + " receivers")
     }
   }
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/StreamingListener.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/StreamingListener.scala
index 9d6ec1fa3354..ed1aa114e19d 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/StreamingListener.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/StreamingListener.scala
@@ -20,28 +20,45 @@ package org.apache.spark.streaming.scheduler
 import scala.collection.mutable.Queue
 
 import org.apache.spark.util.Distribution
+import org.apache.spark.annotation.DeveloperApi
 
-/** Base trait for events related to StreamingListener */
+/**
+ * :: DeveloperApi ::
+ * Base trait for events related to StreamingListener
+ */
+@DeveloperApi
 sealed trait StreamingListenerEvent
 
+@DeveloperApi
 case class StreamingListenerBatchSubmitted(batchInfo: BatchInfo) extends StreamingListenerEvent
+
+@DeveloperApi
 case class StreamingListenerBatchCompleted(batchInfo: BatchInfo) extends StreamingListenerEvent
+
+@DeveloperApi
 case class StreamingListenerBatchStarted(batchInfo: BatchInfo) extends StreamingListenerEvent
 
+@DeveloperApi
 case class StreamingListenerReceiverStarted(receiverInfo: ReceiverInfo)
   extends StreamingListenerEvent
-case class StreamingListenerReceiverError(streamId: Int, message: String, error: String)
+
+@DeveloperApi
+case class StreamingListenerReceiverError(receiverInfo: ReceiverInfo)
   extends StreamingListenerEvent
-case class StreamingListenerReceiverStopped(streamId: Int, message: String, error: String)
+
+@DeveloperApi
+case class StreamingListenerReceiverStopped(receiverInfo: ReceiverInfo)
   extends StreamingListenerEvent
 
 /** An event used in the listener to shutdown the listener daemon thread. */
 private[scheduler] case object StreamingListenerShutdown extends StreamingListenerEvent
 
 /**
+ * :: DeveloperApi ::
  * A listener interface for receiving information about an ongoing streaming
  * computation.
  */
+@DeveloperApi
 trait StreamingListener {
 
   /** Called when a receiver has been started */
@@ -65,9 +82,11 @@ trait StreamingListener {
 
 
 /**
+ * :: DeveloperApi ::
  * A simple StreamingListener that logs summary statistics across Spark Streaming batches
  * @param numBatchInfos Number of last batches to consider for generating statistics (default: 10)
  */
+@DeveloperApi
 class StatsReportListener(numBatchInfos: Int = 10) extends StreamingListener {
   // Queue containing latest completed batches
   val batchInfos = new Queue[BatchInfo]()
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingJobProgressListener.scala b/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingJobProgressListener.scala
index 14c33c728bfe..f61069b56db5 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingJobProgressListener.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingJobProgressListener.scala
@@ -23,9 +23,9 @@ import scala.collection.mutable.{Queue, HashMap}
 import org.apache.spark.streaming.scheduler.StreamingListenerReceiverStarted
 import org.apache.spark.streaming.scheduler.StreamingListenerBatchStarted
 import org.apache.spark.streaming.scheduler.BatchInfo
-import org.apache.spark.streaming.scheduler.ReceiverInfo
 import org.apache.spark.streaming.scheduler.StreamingListenerBatchSubmitted
 import org.apache.spark.util.Distribution
+import org.apache.spark.Logging
 
 
 private[streaming] class StreamingJobProgressListener(ssc: StreamingContext)
@@ -40,9 +40,21 @@ private[streaming] class StreamingJobProgressListener(ssc: StreamingContext)
 
   val batchDuration = ssc.graph.batchDuration.milliseconds
 
-  override def onReceiverStarted(receiverStarted: StreamingListenerReceiverStarted) = {
+  override def onReceiverStarted(receiverStarted: StreamingListenerReceiverStarted) {
     synchronized {
-      receiverInfos.put(receiverStarted.receiverInfo.streamId, receiverStarted.receiverInfo)
+      receiverInfos(receiverStarted.receiverInfo.streamId) = receiverStarted.receiverInfo
+    }
+  }
+
+  override def onReceiverError(receiverError: StreamingListenerReceiverError) {
+    synchronized {
+      receiverInfos(receiverError.receiverInfo.streamId) = receiverError.receiverInfo
+    }
+  }
+
+  override def onReceiverStopped(receiverStopped: StreamingListenerReceiverStopped) {
+    synchronized {
+      receiverInfos(receiverStopped.receiverInfo.streamId) = receiverStopped.receiverInfo
     }
   }
 
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingPage.scala b/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingPage.scala
index 8fe1219356cd..451b23e01c99 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingPage.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingPage.scala
@@ -78,25 +78,33 @@ private[ui] class StreamingPage(parent: StreamingTab)
     val table = if (receivedRecordDistributions.size > 0) {
       val headerRow = Seq(
         "Receiver",
+        "Status",
         "Location",
         "Records in last batch\n[" + formatDate(Calendar.getInstance().getTime()) + "]",
         "Minimum rate\n[records/sec]",
-        "25th percentile rate\n[records/sec]",
         "Median rate\n[records/sec]",
-        "75th percentile rate\n[records/sec]",
-        "Maximum rate\n[records/sec]"
+        "Maximum rate\n[records/sec]",
+        "Last Error"
       )
       val dataRows = (0 until listener.numReceivers).map { receiverId =>
         val receiverInfo = listener.receiverInfo(receiverId)
-        val receiverName = receiverInfo.map(_.toString).getOrElse(s"Receiver-$receiverId")
+        val receiverName = receiverInfo.map(_.name).getOrElse(s"Receiver-$receiverId")
+        val receiverActive = receiverInfo.map { info =>
+          if (info.active) "ACTIVE" else "INACTIVE"
+        }.getOrElse(emptyCell)
         val receiverLocation = receiverInfo.map(_.location).getOrElse(emptyCell)
         val receiverLastBatchRecords = formatNumber(lastBatchReceivedRecord(receiverId))
         val receivedRecordStats = receivedRecordDistributions(receiverId).map { d =>
-          d.getQuantiles().map(r => formatNumber(r.toLong))
+          d.getQuantiles(Seq(0.0, 0.5, 1.0)).map(r => formatNumber(r.toLong))
         }.getOrElse {
           Seq(emptyCell, emptyCell, emptyCell, emptyCell, emptyCell)
         }
-        Seq(receiverName, receiverLocation, receiverLastBatchRecords) ++ receivedRecordStats
+        val receiverLastError = listener.receiverInfo(receiverId).map { info =>
+          val msg = s"${info.lastErrorMessage} - ${info.lastError}"
+          if (msg.size > 100) msg.take(97) + "..." else msg
+        }.getOrElse(emptyCell)
+        Seq(receiverName, receiverActive, receiverLocation, receiverLastBatchRecords) ++
+          receivedRecordStats ++ Seq(receiverLastError)
       }
       Some(listingTable(headerRow, dataRows))
     } else {
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/NetworkReceiverSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/NetworkReceiverSuite.scala
index ff3619a59042..303d149d285e 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/NetworkReceiverSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/NetworkReceiverSuite.scala
@@ -94,9 +94,13 @@ class NetworkReceiverSuite extends FunSuite with Timeouts {
 
     // Verify restarting actually stops and starts the receiver
     receiver.restart("restarting", null, 100)
-    assert(receiver.isStopped)
-    assert(receiver.onStopCalled)
+    eventually(timeout(50 millis), interval(10 millis)) {
+      // receiver will be stopped async
+      assert(receiver.isStopped)
+      assert(receiver.onStopCalled)
+    }
     eventually(timeout(1000 millis), interval(100 millis)) {
+      // receiver will be started async
       assert(receiver.onStartCalled)
       assert(executor.isReceiverStarted)
       assert(receiver.isStarted)
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/StreamingListenerSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/StreamingListenerSuite.scala
index 458dd3a2b13d..ef0efa552cea 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/StreamingListenerSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/StreamingListenerSuite.scala
@@ -66,7 +66,7 @@ class StreamingListenerSuite extends TestSuiteBase with ShouldMatchers {
 
   test("receiver info reporting") {
     val ssc = new StreamingContext("local[2]", "test", Milliseconds(1000))
-    val inputStream = ssc.networkStream(new StreamingListenerSuiteReceiver)
+    val inputStream = ssc.receiverStream(new StreamingListenerSuiteReceiver)
     inputStream.foreachRDD(_.count)
 
     val collector = new ReceiverInfoCollector
@@ -75,8 +75,8 @@ class StreamingListenerSuite extends TestSuiteBase with ShouldMatchers {
     ssc.start()
     try {
       eventually(timeout(1000 millis), interval(20 millis)) {
-        collector.startedReceiverInfo should have size 1
-        collector.startedReceiverInfo(0).streamId should equal (0)
+        collector.startedReceiverStreamIds.size should be >= 1
+        collector.startedReceiverStreamIds(0) should equal (0)
         collector.stoppedReceiverStreamIds should have size 1
         collector.stoppedReceiverStreamIds(0) should equal (0)
         collector.receiverErrors should have size 1
@@ -108,20 +108,21 @@ class BatchInfoCollector extends StreamingListener {
 
 /** Listener that collects information on processed batches */
 class ReceiverInfoCollector extends StreamingListener {
-  val startedReceiverInfo = new ArrayBuffer[ReceiverInfo]
+  val startedReceiverStreamIds = new ArrayBuffer[Int]
   val stoppedReceiverStreamIds = new ArrayBuffer[Int]()
   val receiverErrors = new ArrayBuffer[(Int, String, String)]()
 
   override def onReceiverStarted(receiverStarted: StreamingListenerReceiverStarted) {
-    startedReceiverInfo += receiverStarted.receiverInfo
+    startedReceiverStreamIds += receiverStarted.receiverInfo.streamId
   }
 
   override def onReceiverStopped(receiverStopped: StreamingListenerReceiverStopped) {
-    stoppedReceiverStreamIds += receiverStopped.streamId
+    stoppedReceiverStreamIds += receiverStopped.receiverInfo.streamId
   }
 
   override def onReceiverError(receiverError: StreamingListenerReceiverError) {
-    receiverErrors += ((receiverError.streamId, receiverError.message, receiverError.error))
+    receiverErrors += ((receiverError.receiverInfo.streamId,
+      receiverError.receiverInfo.lastErrorMessage, receiverError.receiverInfo.lastError))
   }
 }
 

From 86ff8b10270bbe2579cdb1dc2297a9f4e145973e Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Thu, 24 Apr 2014 21:42:33 -0700
Subject: [PATCH 019/810] Generalize pattern for planning hash joins.

This will be helpful for [SPARK-1495](https://issues.apache.org/jira/browse/SPARK-1495) and other cases where we want to have custom hash join implementations but don't want to repeat the logic for finding the join keys.

Author: Michael Armbrust <michael@databricks.com>

Closes #418 from marmbrus/hashFilter and squashes the following commits:

d5cc79b [Michael Armbrust] Address @rxin 's comments.
366b6d9 [Michael Armbrust] style fixes
14560eb [Michael Armbrust] Generalize pattern for planning hash joins.
f4809c1 [Michael Armbrust] Move common functions to PredicateHelper.
---
 .../sql/catalyst/expressions/predicates.scala | 29 ++++++++---
 .../sql/catalyst/planning/patterns.scala      | 52 +++++++++++++++++++
 .../spark/sql/execution/SparkStrategies.scala | 49 +++--------------
 3 files changed, 82 insertions(+), 48 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
index da5b2cf5b036..82c7af684459 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
@@ -17,10 +17,11 @@
 
 package org.apache.spark.sql.catalyst.expressions
 
-import org.apache.spark.sql.catalyst.trees
-import org.apache.spark.sql.catalyst.errors.TreeNodeException
 import org.apache.spark.sql.catalyst.analysis.UnresolvedException
-import org.apache.spark.sql.catalyst.types.{BooleanType, StringType, TimestampType}
+import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
+import org.apache.spark.sql.catalyst.trees
+import org.apache.spark.sql.catalyst.types.BooleanType
+
 
 object InterpretedPredicate {
   def apply(expression: Expression): (Row => Boolean) = {
@@ -37,10 +38,26 @@ trait Predicate extends Expression {
 }
 
 trait PredicateHelper {
-  def splitConjunctivePredicates(condition: Expression): Seq[Expression] = condition match {
-    case And(cond1, cond2) => splitConjunctivePredicates(cond1) ++ splitConjunctivePredicates(cond2)
-    case other => other :: Nil
+  protected def splitConjunctivePredicates(condition: Expression): Seq[Expression] = {
+    condition match {
+      case And(cond1, cond2) =>
+        splitConjunctivePredicates(cond1) ++ splitConjunctivePredicates(cond2)
+      case other => other :: Nil
+    }
   }
+
+  /**
+   * Returns true if `expr` can be evaluated using only the output of `plan`.  This method
+   * can be used to determine when is is acceptable to move expression evaluation within a query
+   * plan.
+   *
+   * For example consider a join between two relations R(a, b) and S(c, d).
+   *
+   * `canEvaluate(Equals(a,b), R)` returns `true` where as `canEvaluate(Equals(a,c), R)` returns
+   * `false`.
+   */
+  protected def canEvaluate(expr: Expression, plan: LogicalPlan): Boolean =
+    expr.references.subsetOf(plan.outputSet)
 }
 
 abstract class BinaryPredicate extends BinaryExpression with Predicate {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala
index 6dd816aa91dd..0e3a8a6bd30a 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala
@@ -19,7 +19,10 @@ package org.apache.spark.sql.catalyst.planning
 
 import scala.annotation.tailrec
 
+import org.apache.spark.sql.Logging
+
 import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.plans._
 import org.apache.spark.sql.catalyst.plans.logical._
 
 /**
@@ -101,6 +104,55 @@ object PhysicalOperation extends PredicateHelper {
   }
 }
 
+/**
+ * A pattern that finds joins with equality conditions that can be evaluated using hashing
+ * techniques.  For inner joins, any filters on top of the join operator are also matched.
+ */
+object HashFilteredJoin extends Logging with PredicateHelper {
+  /** (joinType, rightKeys, leftKeys, condition, leftChild, rightChild) */
+  type ReturnType =
+    (JoinType, Seq[Expression], Seq[Expression], Option[Expression], LogicalPlan, LogicalPlan)
+
+  def unapply(plan: LogicalPlan): Option[ReturnType] = plan match {
+    // All predicates can be evaluated for inner join (i.e., those that are in the ON
+    // clause and WHERE clause.)
+    case FilteredOperation(predicates, join @ Join(left, right, Inner, condition)) =>
+      logger.debug(s"Considering hash inner join on: ${predicates ++ condition}")
+      splitPredicates(predicates ++ condition, join)
+    case join @ Join(left, right, joinType, condition) =>
+      logger.debug(s"Considering hash join on: $condition")
+      splitPredicates(condition.toSeq, join)
+    case _ => None
+  }
+
+  // Find equi-join predicates that can be evaluated before the join, and thus can be used
+  // as join keys.
+  def splitPredicates(allPredicates: Seq[Expression], join: Join): Option[ReturnType] = {
+    val Join(left, right, joinType, _) = join
+    val (joinPredicates, otherPredicates) = allPredicates.partition {
+      case Equals(l, r) if (canEvaluate(l, left) && canEvaluate(r, right)) ||
+        (canEvaluate(l, right) && canEvaluate(r, left)) => true
+      case _ => false
+    }
+
+    val joinKeys = joinPredicates.map {
+      case Equals(l, r) if canEvaluate(l, left) && canEvaluate(r, right) => (l, r)
+      case Equals(l, r) if canEvaluate(l, right) && canEvaluate(r, left) => (r, l)
+    }
+
+    // Do not consider this strategy if there are no join keys.
+    if (joinKeys.nonEmpty) {
+      val leftKeys = joinKeys.map(_._1)
+      val rightKeys = joinKeys.map(_._2)
+
+      Some((joinType, leftKeys, rightKeys, otherPredicates.reduceOption(And), left, right))
+    } else {
+      logger.debug(s"Avoiding hash join with no join keys.")
+      None
+    }
+  }
+}
+
 /**
  * A pattern that collects all adjacent unions and returns their children as a Seq.
  */
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
index 500fde19719a..f763106da4e0 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
@@ -28,51 +28,16 @@ import org.apache.spark.sql.parquet._
 private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
   self: SQLContext#SparkPlanner =>
 
-  object HashJoin extends Strategy {
+  object HashJoin extends Strategy with PredicateHelper {
     def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
-      case FilteredOperation(predicates, logical.Join(left, right, Inner, condition)) =>
-        logger.debug(s"Considering join: ${predicates ++ condition}")
-        // Find equi-join predicates that can be evaluated before the join, and thus can be used
-        // as join keys. Note we can only mix in the conditions with other predicates because the
-        // match above ensures that this is and Inner join.
-        val (joinPredicates, otherPredicates) = (predicates ++ condition).partition {
-          case Equals(l, r) if (canEvaluate(l, left) && canEvaluate(r, right)) ||
-                               (canEvaluate(l, right) && canEvaluate(r, left)) => true
-          case _ => false
-        }
-
-        val joinKeys = joinPredicates.map {
-          case Equals(l,r) if canEvaluate(l, left) && canEvaluate(r, right) => (l, r)
-          case Equals(l,r) if canEvaluate(l, right) && canEvaluate(r, left) => (r, l)
-        }
-
-        // Do not consider this strategy if there are no join keys.
-        if (joinKeys.nonEmpty) {
-          val leftKeys = joinKeys.map(_._1)
-          val rightKeys = joinKeys.map(_._2)
-
-          val joinOp = execution.HashJoin(
-            leftKeys, rightKeys, BuildRight, planLater(left), planLater(right))
-
-          // Make sure other conditions are met if present.
-          if (otherPredicates.nonEmpty) {
-            execution.Filter(combineConjunctivePredicates(otherPredicates), joinOp) :: Nil
-          } else {
-            joinOp :: Nil
-          }
-        } else {
-          logger.debug(s"Avoiding spark join with no join keys.")
-          Nil
-        }
+      // Find inner joins where at least some predicates can be evaluated by matching hash keys
+      // using the HashFilteredJoin pattern.
+      case HashFilteredJoin(Inner, leftKeys, rightKeys, condition, left, right) =>
+        val hashJoin =
+          execution.HashJoin(leftKeys, rightKeys, BuildRight, planLater(left), planLater(right))
+        condition.map(Filter(_, hashJoin)).getOrElse(hashJoin) :: Nil
       case _ => Nil
     }
-
-    private def combineConjunctivePredicates(predicates: Seq[Expression]) =
-      predicates.reduceLeft(And)
-
-    /** Returns true if `expr` can be evaluated using only the output of `plan`. */
-    protected def canEvaluate(expr: Expression, plan: LogicalPlan): Boolean =
-      expr.references subsetOf plan.outputSet
   }
 
   object PartialAggregation extends Strategy {

From 06e82d94b6eba0b174b54c58280b3daca2f26dd8 Mon Sep 17 00:00:00 2001
From: Andrew Or <andrewor14@gmail.com>
Date: Thu, 24 Apr 2014 21:51:17 -0700
Subject: [PATCH 020/810] [Typo] In the maven docs: chd -> cdh

Author: Andrew Or <andrewor14@gmail.com>

Closes #548 from andrewor14/doc-typo and squashes the following commits:

3eaf4c4 [Andrew Or] chd -> cdh
---
 docs/building-with-maven.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/building-with-maven.md b/docs/building-with-maven.md
index 9cebaf12283f..771d1933a13e 100644
--- a/docs/building-with-maven.md
+++ b/docs/building-with-maven.md
@@ -45,7 +45,7 @@ For Apache Hadoop 2.x, 0.23.x, Cloudera CDH MRv2, and other Hadoop versions with
     $ mvn -Pyarn-alpha -Dhadoop.version=2.0.5-alpha -Dyarn.version=2.0.5-alpha -DskipTests clean package
 
     # Cloudera CDH 4.2.0 with MapReduce v2
-    $ mvn -Pyarn-alpha -Dhadoop.version=2.0.0-cdh4.2.0 -Dyarn.version=2.0.0-chd4.2.0 -DskipTests clean package
+    $ mvn -Pyarn-alpha -Dhadoop.version=2.0.0-cdh4.2.0 -Dyarn.version=2.0.0-cdh4.2.0 -DskipTests clean package
 
     # Apache Hadoop 2.2.X ( e.g. 2.2.0 as below ) and newer
     $ mvn -Pyarn -Dhadoop.version=2.2.0 -Dyarn.version=2.2.0 -DskipTests clean package

From 095b5182536a43e2ae738be93294ee5215d86581 Mon Sep 17 00:00:00 2001
From: Sandeep <sandeep@techaddict.me>
Date: Thu, 24 Apr 2014 21:51:52 -0700
Subject: [PATCH 021/810] Fix [SPARK-1078]: Remove the Unnecessary lift-json
 dependency

Remove the Unnecessary lift-json dependency from pom.xml

Author: Sandeep <sandeep@techaddict.me>

Closes #536 from techaddict/FIX-SPARK-1078 and squashes the following commits:

bd0fd1d [Sandeep] Fix [SPARK-1078]: Replace lift-json with json4s-jackson. Remove the Unnecessary lift-json dependency from pom.xml
---
 pom.xml | 18 ++----------------
 1 file changed, 2 insertions(+), 16 deletions(-)

diff --git a/pom.xml b/pom.xml
index 4ff18afa227b..e292775c6267 100644
--- a/pom.xml
+++ b/pom.xml
@@ -380,20 +380,6 @@
         <artifactId>derby</artifactId>
         <version>10.4.2.0</version>
       </dependency>
-      <dependency>
-        <groupId>net.liftweb</groupId>
-        <artifactId>lift-json_${scala.binary.version}</artifactId>
-        <version>2.5.1</version>
-        <!-- see also exclusion for json4s; this is necessary since it depends on
-             scala-library and scalap 2.10.0, but we use 2.10.4, and only override
-             scala-library -->
-        <exclusions>
-          <exclusion>
-            <groupId>org.scala-lang</groupId>
-            <artifactId>scalap</artifactId>
-          </exclusion>
-        </exclusions>
-      </dependency>
       <dependency>
         <groupId>com.codahale.metrics</groupId>
         <artifactId>metrics-core</artifactId>
@@ -851,7 +837,7 @@
       </modules>
 
     </profile>
-    
+
    <!-- Build without Hadoop dependencies that are included in some runtime environments. -->
     <profile>
       <id>hadoop-provided</id>
@@ -896,6 +882,6 @@
         </dependency>
       </dependencies>
     </profile>
-    
+
   </profiles>
 </project>

From e03bc379ee03fde0ee4fa578d3c39aae35c63f01 Mon Sep 17 00:00:00 2001
From: Holden Karau <holden@pigscanfly.ca>
Date: Thu, 24 Apr 2014 23:07:54 -0700
Subject: [PATCH 022/810] SPARK-1242 Add aggregate to python rdd

Author: Holden Karau <holden@pigscanfly.ca>

Closes #139 from holdenk/add_aggregate_to_python_api and squashes the following commits:

0f39ae3 [Holden Karau] Merge in master
4879c75 [Holden Karau] CR feedback, fix issue with empty RDDs in aggregate
70b4724 [Holden Karau] Style fixes from code review
96b047b [Holden Karau] Add aggregate to python rdd
---
 python/pyspark/rdd.py | 31 +++++++++++++++++++++++++++++--
 1 file changed, 29 insertions(+), 2 deletions(-)

diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py
index d73ab7006e9c..a59778c72130 100644
--- a/python/pyspark/rdd.py
+++ b/python/pyspark/rdd.py
@@ -599,7 +599,7 @@ def _collect_iterator_through_file(self, iterator):
     def reduce(self, f):
         """
         Reduces the elements of this RDD using the specified commutative and
-        associative binary operator.
+        associative binary operator. Currently reduces partitions locally.
 
         >>> from operator import add
         >>> sc.parallelize([1, 2, 3, 4, 5]).reduce(add)
@@ -641,7 +641,34 @@ def func(iterator):
         vals = self.mapPartitions(func).collect()
         return reduce(op, vals, zeroValue)
 
-    # TODO: aggregate
+    def aggregate(self, zeroValue, seqOp, combOp):
+        """
+        Aggregate the elements of each partition, and then the results for all
+        the partitions, using a given combine functions and a neutral "zero
+        value."
+
+        The functions C{op(t1, t2)} is allowed to modify C{t1} and return it
+        as its result value to avoid object allocation; however, it should not
+        modify C{t2}.
+
+        The first function (seqOp) can return a different result type, U, than
+        the type of this RDD. Thus, we need one operation for merging a T into an U
+        and one operation for merging two U
+
+        >>> seqOp = (lambda x, y: (x[0] + y, x[1] + 1))
+        >>> combOp = (lambda x, y: (x[0] + y[0], x[1] + y[1]))
+        >>> sc.parallelize([1, 2, 3, 4]).aggregate((0, 0), seqOp, combOp)
+        (10, 4)
+        >>> sc.parallelize([]).aggregate((0, 0), seqOp, combOp)
+        (0, 0)
+        """
+        def func(iterator):
+            acc = zeroValue
+            for obj in iterator:
+                acc = seqOp(acc, obj)
+            yield acc
+
+        return self.mapPartitions(func).fold(zeroValue, combOp)
         
 
     def max(self):

From 45ad7f0ca7be4a89c066b246b0fee0c0c2e7c759 Mon Sep 17 00:00:00 2001
From: Aaron Davidson <aaron@databricks.com>
Date: Thu, 24 Apr 2014 23:22:03 -0700
Subject: [PATCH 023/810] Call correct stop().

Oopsie in #504.

Author: Aaron Davidson <aaron@databricks.com>

Closes #527 from aarondav/stop and squashes the following commits:

8d1446a [Aaron Davidson] Call correct stop().
---
 .../main/scala/org/apache/spark/storage/DiskBlockManager.scala | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala b/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala
index 054f66a8b726..cf6ef0029a86 100644
--- a/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala
+++ b/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala
@@ -150,11 +150,12 @@ private[spark] class DiskBlockManager(shuffleManager: ShuffleBlockManager, rootD
     Runtime.getRuntime.addShutdownHook(new Thread("delete Spark local dirs") {
       override def run() {
         logDebug("Shutdown hook called")
-        stop()
+        DiskBlockManager.this.stop()
       }
     })
   }
 
+  /** Cleanup local dirs and stop shuffle sender. */
   private[spark] def stop() {
     localDirs.foreach { localDir =>
       if (localDir.isDirectory() && localDir.exists()) {

From 6e101f1183f92769779bc8ac14813c063bf1ff3f Mon Sep 17 00:00:00 2001
From: Sean Owen <sowen@cloudera.com>
Date: Thu, 24 Apr 2014 23:34:00 -0700
Subject: [PATCH 024/810] SPARK-1607. Replace octal literals, removed in Scala
 2.11, with hex literals

Octal literals like "0700" are deprecated in Scala 2.10, generating a warning. They have been removed entirely in 2.11. See https://issues.scala-lang.org/browse/SI-7618

This change simply replaces two uses of octals with hex literals, which seemed the next-best representation since they express a bit mask (file permission in particular)

Author: Sean Owen <sowen@cloudera.com>

Closes #529 from srowen/SPARK-1607 and squashes the following commits:

1ee0e67 [Sean Owen] Use Integer.parseInt(...,8) for octal literal instead of hex equivalent
0102f3d [Sean Owen] Replace octal literals, removed in Scala 2.11, with hex literals
---
 .../scala/org/apache/spark/deploy/yarn/ClientBase.scala     | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala
index b403292d9cb2..eb95d784318e 100644
--- a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala
+++ b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala
@@ -59,9 +59,11 @@ trait ClientBase extends Logging {
   private val distCacheMgr = new ClientDistributedCacheManager()
 
   // Staging directory is private! -> rwx--------
-  val STAGING_DIR_PERMISSION: FsPermission = FsPermission.createImmutable(0700: Short)
+  val STAGING_DIR_PERMISSION: FsPermission =
+    FsPermission.createImmutable(Integer.parseInt("700", 8): Short)
   // App files are world-wide readable and owner writable -> rw-r--r--
-  val APP_FILE_PERMISSION: FsPermission = FsPermission.createImmutable(0644: Short)
+  val APP_FILE_PERMISSION: FsPermission =
+    FsPermission.createImmutable(Integer.parseInt("644", 8): Short)
 
   // TODO(harvey): This could just go in ClientArguments.
   def validateArgs() = {

From dc3b640a0ab3501b678b591be3e99fbcf3badbec Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Thu, 24 Apr 2014 23:59:16 -0700
Subject: [PATCH 025/810] SPARK-1619 Launch spark-shell with spark-submit

This simplifies the shell a bunch and passes all arguments through to spark-submit.

There is a tiny incompatibility from 0.9.1 which is that you can't put `-c` _or_ `--cores`, only `--cores`. However, spark-submit will give a good error message in this case, I don't think many people used this, and it's a trivial change for users.

Author: Patrick Wendell <pwendell@gmail.com>

Closes #542 from pwendell/spark-shell and squashes the following commits:

9eb3e6f [Patrick Wendell] Updating Spark docs
b552459 [Patrick Wendell] Andrew's feedback
97720fa [Patrick Wendell] Review feedback
aa2900b [Patrick Wendell] SPARK-1619 Launch spark-shell with spark-submit
---
 .gitignore                                    |   1 +
 bin/spark-shell                               | 177 +-----------------
 bin/spark-submit                              |  10 +-
 .../org/apache/spark/deploy/SparkSubmit.scala |  10 +-
 .../spark/deploy/SparkSubmitArguments.scala   |   2 +-
 docs/scala-programming-guide.md               |   9 +-
 docs/spark-debugger.md                        |   2 +-
 docs/spark-standalone.md                      |   4 +-
 docs/streaming-programming-guide.md           |   6 +-
 make-distribution.sh                          |   2 +-
 .../org/apache/spark/repl/SparkILoop.scala    |   5 +-
 11 files changed, 39 insertions(+), 189 deletions(-)

diff --git a/.gitignore b/.gitignore
index cd9f90d55932..857e9feb953b 100644
--- a/.gitignore
+++ b/.gitignore
@@ -18,6 +18,7 @@ conf/java-opts
 conf/spark-env.sh
 conf/streaming-env.sh
 conf/log4j.properties
+conf/spark-defaults.conf
 docs/_site
 docs/api
 target/
diff --git a/bin/spark-shell b/bin/spark-shell
index ea12d256b23a..f1f3c18877ed 100755
--- a/bin/spark-shell
+++ b/bin/spark-shell
@@ -19,9 +19,8 @@
 
 #
 # Shell script for starting the Spark Shell REPL
-# Note that it will set MASTER to spark://${SPARK_MASTER_IP}:${SPARK_MASTER_PORT}
-# if those two env vars are set in spark-env.sh but MASTER is not.
 
+args="$@"
 cygwin=false
 case "`uname`" in
     CYGWIN*) cygwin=true;;
@@ -30,133 +29,16 @@ esac
 # Enter posix mode for bash
 set -o posix
 
+if [[ "$@" == *--help* ]]; then
+  echo "Usage: ./bin/spark-shell [options]"
+  ./bin/spark-submit --help 2>&1 | grep -v Usage 1>&2
+  exit 0
+fi
+
 ## Global script variables
 FWDIR="$(cd `dirname $0`/..; pwd)"
 
-SPARK_REPL_OPTS="${SPARK_REPL_OPTS:-""}"
-DEFAULT_MASTER="local[*]"
-MASTER=${MASTER:-""}
-
-info_log=0
-
-#CLI Color Templates
-txtund=$(tput sgr 0 1)          # Underline
-txtbld=$(tput bold)             # Bold
-bldred=${txtbld}$(tput setaf 1) # red
-bldyel=${txtbld}$(tput setaf 3) # yellow
-bldblu=${txtbld}$(tput setaf 4) # blue
-bldwht=${txtbld}$(tput setaf 7) # white
-txtrst=$(tput sgr0)             # Reset
-info=${bldwht}*${txtrst}        # Feedback
-pass=${bldblu}*${txtrst}
-warn=${bldred}*${txtrst}
-ques=${bldblu}?${txtrst}
-
-# Helper function to describe the script usage
-function usage() {
-    cat << EOF
-${txtbld}Usage${txtrst}: spark-shell [OPTIONS]
-
-${txtbld}OPTIONS${txtrst}:
-    -h  --help              : Print this help information.
-    -c  --cores             : The maximum number of cores to be used by the Spark Shell.
-    -em --executor-memory   : The memory used by each executor of the Spark Shell, the number 
-                              is followed by m for megabytes or g for gigabytes, e.g. "1g".
-    -dm --driver-memory     : The memory used by the Spark Shell, the number is followed 
-                              by m for megabytes or g for gigabytes, e.g. "1g".
-    -m  --master            : A full string that describes the Spark Master, defaults to "local[*]"
-                              e.g. "spark://localhost:7077".
-    --log-conf              : Enables logging of the supplied SparkConf as INFO at start of the
-                              Spark Context.
-
-e.g.
-    spark-shell -m spark://localhost:7077 -c 4 -dm 512m -em 2g
-
-EOF
-}
-
-function out_error(){
-    echo -e "${txtund}${bldred}ERROR${txtrst}: $1"
-    usage
-    exit 1
-}
-
-function log_info(){
-    [ $info_log -eq 1 ] && echo -e "${bldyel}INFO${txtrst}: $1"
-}
-
-function log_warn(){
-    echo -e "${txtund}${bldyel}WARN${txtrst}: $1"
-}
-
-# PATTERNS used to validate more than one optional arg.
-ARG_FLAG_PATTERN="^-"
-MEM_PATTERN="^[0-9]+[m|g|M|G]$"
-NUM_PATTERN="^[0-9]+$"
-PORT_PATTERN="^[0-9]+$"
-
-# Setters for optional args.
-function set_cores(){
-    CORE_PATTERN="^[0-9]+$"
-    if [[ "$1" =~ $CORE_PATTERN ]]; then
-        SPARK_REPL_OPTS="$SPARK_REPL_OPTS -Dspark.cores.max=$1"
-    else
-        out_error "wrong format for $2"
-    fi
-}
-
-function set_em(){
-    if [[ $1 =~ $MEM_PATTERN ]]; then
-      SPARK_REPL_OPTS="$SPARK_REPL_OPTS -Dspark.executor.memory=$1"
-    else
-      out_error "wrong format for $2"
-    fi
-}
-
-function set_dm(){
-    if [[ $1 =~ $MEM_PATTERN ]]; then
-      export SPARK_DRIVER_MEMORY=$1
-    else
-      out_error "wrong format for $2"
-    fi
-}
-
-function set_spark_log_conf(){
-    SPARK_REPL_OPTS="$SPARK_REPL_OPTS -Dspark.logConf=$1"
-}
-
-function set_spark_master(){
-    if ! [[ "$1" =~ $ARG_FLAG_PATTERN ]]; then
-        export MASTER="$1"
-    else
-        out_error "wrong format for $2"
-    fi
-}
-
-function resolve_spark_master(){
-    # Set MASTER from spark-env if possible
-    DEFAULT_SPARK_MASTER_PORT=7077
-    if [ -z "$MASTER" ]; then
-        . $FWDIR/bin/load-spark-env.sh
-        if [ -n "$SPARK_MASTER_IP" ]; then
-            SPARK_MASTER_PORT="${SPARK_MASTER_PORT:-"$DEFAULT_SPARK_MASTER_PORT"}"
-            export MASTER="spark://${SPARK_MASTER_IP}:${SPARK_MASTER_PORT}"
-        fi
-    fi
-
-    if [ -z "$MASTER" ]; then
-        export MASTER="$DEFAULT_MASTER"
-    fi
-
-}
-
 function main(){
-    log_info "Base Directory set to $FWDIR"
-    
-    resolve_spark_master
-    log_info "Spark Master is $MASTER"
-
-    log_info "Spark REPL options  $SPARK_REPL_OPTS"
     if $cygwin; then
         # Workaround for issue involving JLine and Cygwin
         # (see http://sourceforge.net/p/jline/bugs/40/).
@@ -165,55 +47,14 @@ function main(){
         # (see https://github.com/sbt/sbt/issues/562).
         stty -icanon min 1 -echo > /dev/null 2>&1
         export SPARK_REPL_OPTS="$SPARK_REPL_OPTS -Djline.terminal=unix"
-        $FWDIR/bin/spark-class org.apache.spark.repl.Main "$@"
+        $FWDIR/bin/spark-submit spark-internal "$args" --class org.apache.spark.repl.Main
         stty icanon echo > /dev/null 2>&1
     else
         export SPARK_REPL_OPTS
-        $FWDIR/bin/spark-class org.apache.spark.repl.Main "$@"
+        $FWDIR/bin/spark-submit spark-internal "$args" --class org.apache.spark.repl.Main
     fi
 }
 
-for option in "$@"
-do
-     case $option in
-         -h  | --help )
-             usage
-             exit 1
-             ;;
-         -c  | --cores)
-             shift
-             _1=$1
-             shift
-             set_cores $_1 "-c/--cores"
-             ;;
-         -em | --executor-memory)
-             shift
-             _1=$1
-             shift
-             set_em $_1 "-em/--executor-memory"
-             ;;
-         -dm | --driver-memory)
-             shift
-             _1=$1
-             shift
-             set_dm $_1 "-dm/--driver-memory"
-             ;;
-         -m | --master)
-             shift
-             _1=$1
-             shift
-             set_spark_master $_1 "-m/--master"
-             ;;
-         --log-conf)
-             shift
-             set_spark_log_conf "true"
-             info_log=1
-             ;;
-         ?)
-             ;;
-     esac
-done
-
 # Copy restore-TTY-on-exit functions from Scala script so spark-shell exits properly even in
 # binary distribution of Spark where Scala is not installed
 exit_status=127
diff --git a/bin/spark-submit b/bin/spark-submit
index 498d0b27bacd..b2a1dca721df 100755
--- a/bin/spark-submit
+++ b/bin/spark-submit
@@ -21,15 +21,15 @@ export SPARK_HOME="$(cd `dirname $0`/..; pwd)"
 ORIG_ARGS=$@
 
 while (($#)); do
-  if [ $1 = "--deploy-mode" ]; then
+  if [ "$1" = "--deploy-mode" ]; then
     DEPLOY_MODE=$2
-  elif [ $1 = "--driver-memory" ]; then
+  elif [ "$1" = "--driver-memory" ]; then
     DRIVER_MEMORY=$2
-  elif [ $1 = "--driver-library-path" ]; then
+  elif [ "$1" = "--driver-library-path" ]; then
     export _SPARK_LIBRARY_PATH=$2
-  elif [ $1 = "--driver-class-path" ]; then
+  elif [ "$1" = "--driver-class-path" ]; then
     export SPARK_CLASSPATH="$SPARK_CLASSPATH:$2"
-  elif [ $1 = "--driver-java-options" ]; then
+  elif [ "$1" = "--driver-java-options" ]; then
     export SPARK_JAVA_OPTS="$SPARK_JAVA_OPTS $2"
   fi
   shift
diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
index a2efddbfe3f6..58aa6d951a20 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
@@ -38,6 +38,12 @@ object SparkSubmit {
 
   private var clusterManager: Int = LOCAL
 
+  /**
+   * A special jar name that indicates the class being run is inside of Spark itself,
+   * and therefore no user jar is needed.
+   */
+  private val RESERVED_JAR_NAME = "spark-internal"
+
   def main(args: Array[String]) {
     val appArgs = new SparkSubmitArguments(args)
     if (appArgs.verbose) {
@@ -113,7 +119,9 @@ object SparkSubmit {
 
     if (!deployOnCluster) {
       childMainClass = appArgs.mainClass
-      childClasspath += appArgs.primaryResource
+      if (appArgs.primaryResource != RESERVED_JAR_NAME) {
+        childClasspath += appArgs.primaryResource
+      }
     } else if (clusterManager == YARN) {
       childMainClass = "org.apache.spark.deploy.yarn.Client"
       childArgs += ("--jar", appArgs.primaryResource)
diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
index c3e8c6b8c65a..c545b093ac82 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
@@ -107,7 +107,7 @@ private[spark] class SparkSubmitArguments(args: Array[String]) {
     deployMode = Option(deployMode).getOrElse(System.getenv("DEPLOY_MODE"))
 
     // Global defaults. These should be keep to minimum to avoid confusing behavior.
-    master = Option(master).getOrElse("local")
+    master = Option(master).getOrElse("local[*]")
   }
 
   /** Ensure that required fields exists. Call this only once all defaults are loaded. */
diff --git a/docs/scala-programming-guide.md b/docs/scala-programming-guide.md
index a3171709ff05..b8d89cf00ffb 100644
--- a/docs/scala-programming-guide.md
+++ b/docs/scala-programming-guide.md
@@ -60,17 +60,18 @@ which avoids hard-coding the master name in your application.
 
 In the Spark shell, a special interpreter-aware SparkContext is already created for you, in the
 variable called `sc`. Making your own SparkContext will not work. You can set which master the
-context connects to using the `MASTER` environment variable, and you can add JARs to the classpath
-with the `ADD_JARS` variable. For example, to run `bin/spark-shell` on exactly four cores, use
+context connects to using the `--master` argument, and you can add JARs to the classpath
+by passing a comma separated list to the `--jars` argument. For example, to run 
+`bin/spark-shell` on exactly four cores, use
 
 {% highlight bash %}
-$ MASTER=local[4] ./bin/spark-shell
+$ ./bin/spark-shell --master local[4]
 {% endhighlight %}
 
 Or, to also add `code.jar` to its classpath, use:
 
 {% highlight bash %}
-$ MASTER=local[4] ADD_JARS=code.jar ./bin/spark-shell
+$ ./bin/spark-shell --master local[4] --jars code.jar
 {% endhighlight %}
 
 ### Master URLs
diff --git a/docs/spark-debugger.md b/docs/spark-debugger.md
index 891c2bfa8943..35d06c51aaf0 100644
--- a/docs/spark-debugger.md
+++ b/docs/spark-debugger.md
@@ -39,7 +39,7 @@ where `path/to/event-log` is where you want the event log to go relative to `$SP
 
 ### Loading the event log into the debugger
 
-1. Run a Spark shell with `MASTER=<i>host</i> ./bin/spark-shell`.
+1. Run a Spark shell with `./bin/spark-shell --master <i>hist</i>`.
 2. Use `EventLogReader` to load the event log as follows:
     {% highlight scala %}
 spark> val r = new spark.EventLogReader(sc, Some("path/to/event-log"))
diff --git a/docs/spark-standalone.md b/docs/spark-standalone.md
index 7e4eea323aa6..dc7f206e0399 100644
--- a/docs/spark-standalone.md
+++ b/docs/spark-standalone.md
@@ -139,12 +139,12 @@ constructor](scala-programming-guide.html#initializing-spark).
 
 To run an interactive Spark shell against the cluster, run the following command:
 
-    MASTER=spark://IP:PORT ./bin/spark-shell
+    ./bin/spark-shell --master spark://IP:PORT
 
 Note that if you are running spark-shell from one of the spark cluster machines, the `bin/spark-shell` script will
 automatically set MASTER from the `SPARK_MASTER_IP` and `SPARK_MASTER_PORT` variables in `conf/spark-env.sh`.
 
-You can also pass an option `-c <numCores>` to control the number of cores that spark-shell uses on the cluster.
+You can also pass an option `--cores <numCores>` to control the number of cores that spark-shell uses on the cluster.
 
 # Launching Compiled Spark Applications
 
diff --git a/docs/streaming-programming-guide.md b/docs/streaming-programming-guide.md
index 946d6c4879cf..7ad06427cac5 100644
--- a/docs/streaming-programming-guide.md
+++ b/docs/streaming-programming-guide.md
@@ -272,12 +272,10 @@ Time: 1357008430000 ms
     </td>
 </table>
 
-If you plan to run the Scala code for Spark Streaming-based use cases in the Spark
-shell, you should start the shell with the SparkConfiguration pre-configured to
-discard old batches periodically:
+You can also use Spark Streaming directly from the Spark shell:
 
 {% highlight bash %}
-$ SPARK_JAVA_OPTS=-Dspark.cleaner.ttl=10000 bin/spark-shell
+$ bin/spark-shell
 {% endhighlight %}
 
 ... and create your StreamingContext by wrapping the existing interactive shell
diff --git a/make-distribution.sh b/make-distribution.sh
index 62a28217740c..55fe6c09d0d6 100755
--- a/make-distribution.sh
+++ b/make-distribution.sh
@@ -36,7 +36,7 @@
 # 2) cd to deploy dir; ./sbin/start-master.sh
 # 3) Verify master is up by visiting web page, ie http://master-ip:8080.  Note the spark:// URL.
 # 4) ./sbin/start-slave.sh 1 <<spark:// URL>>
-# 5) MASTER="spark://my-master-ip:7077" ./bin/spark-shell
+# 5) ./bin/spark-shell --master spark://my-master-ip:7077
 #
 
 # Figure out where the Spark framework is installed
diff --git a/repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala b/repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala
index beb40e87024b..296da740687e 100644
--- a/repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala
+++ b/repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala
@@ -963,8 +963,9 @@ class SparkILoop(in0: Option[BufferedReader], protected val out: JPrintWriter,
     val master = this.master match {
       case Some(m) => m
       case None => {
-        val prop = System.getenv("MASTER")
-        if (prop != null) prop else "local[*]"
+        val envMaster = sys.env.get("MASTER")
+        val propMaster = sys.props.get("spark.master")
+        envMaster.orElse(propMaster).getOrElse("local[*]")
       }
     }
     master

From a24d918c71f6ac4adbe3ae363ef69f4658118938 Mon Sep 17 00:00:00 2001
From: Matei Zaharia <matei@databricks.com>
Date: Fri, 25 Apr 2014 11:12:41 -0700
Subject: [PATCH 026/810] SPARK-1621 Upgrade Chill to 0.3.6

It registers more Scala classes, including things like Ranges that we had to register manually before. See https://github.com/twitter/chill/releases for Chill's change log.

Author: Matei Zaharia <matei@databricks.com>

Closes #543 from mateiz/chill-0.3.6 and squashes the following commits:

a1dc5e0 [Matei Zaharia] Upgrade Chill to 0.3.6 and remove our special registration of Ranges
---
 .../org/apache/spark/serializer/KryoSerializer.scala | 12 +++++-------
 pom.xml                                              |  2 +-
 project/SparkBuild.scala                             |  6 +++---
 3 files changed, 9 insertions(+), 11 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala b/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala
index d1e8c3ef6362..c4daec7875d2 100644
--- a/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala
+++ b/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala
@@ -58,7 +58,9 @@ class KryoSerializer(conf: SparkConf)
     // Do this before we invoke the user registrator so the user registrator can override this.
     kryo.setReferences(referenceTracking)
 
-    for (cls <- KryoSerializer.toRegister) kryo.register(cls)
+    for (cls <- KryoSerializer.toRegister) {
+      kryo.register(cls)
+    }
 
     // Allow sending SerializableWritable
     kryo.register(classOf[SerializableWritable[_]], new KryoJavaSerializer())
@@ -77,7 +79,7 @@ class KryoSerializer(conf: SparkConf)
     }
 
     // Register Chill's classes; we do this after our ranges and the user's own classes to let
-    // our code override the generic serialziers in Chill for things like Seq
+    // our code override the generic serializers in Chill for things like Seq
     new AllScalaRegistrar().apply(kryo)
 
     kryo.setClassLoader(classLoader)
@@ -176,10 +178,6 @@ private[serializer] object KryoSerializer {
     classOf[GetBlock],
     classOf[MapStatus],
     classOf[BlockManagerId],
-    classOf[Array[Byte]],
-    (1 to 10).getClass,
-    (1 until 10).getClass,
-    (1L to 10L).getClass,
-    (1L until 10L).getClass
+    classOf[Array[Byte]]
   )
 }
diff --git a/pom.xml b/pom.xml
index e292775c6267..e9117998252f 100644
--- a/pom.xml
+++ b/pom.xml
@@ -125,7 +125,7 @@
     <parquet.version>1.3.2</parquet.version>
     <jblas.version>1.2.3</jblas.version>
     <jetty.version>8.1.14.v20131031</jetty.version>
-    <chill.version>0.3.1</chill.version>
+    <chill.version>0.3.6</chill.version>
     <codahale.metrics.version>3.0.0</codahale.metrics.version>
 
     <PermGen>64m</PermGen>
diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index 9cbc188c4d48..48f234ce87a4 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -294,7 +294,7 @@ object SparkBuild extends Build {
   ) ++ net.virtualvoid.sbt.graph.Plugin.graphSettings ++ ScalaStyleSettings ++ genjavadocSettings
 
   val akkaVersion = "2.2.3-shaded-protobuf"
-  val chillVersion = "0.3.1"
+  val chillVersion = "0.3.6"
   val codahaleMetricsVersion = "3.0.0"
   val jblasVersion = "1.2.3"
   val jettyVersion = "8.1.14.v20131031"
@@ -412,8 +412,8 @@ object SparkBuild extends Build {
   )
 
   def examplesSettings = sharedSettings ++ Seq(
-    name := "spark-examples",  
-    jarName in assembly <<= version map { 
+    name := "spark-examples",
+    jarName in assembly <<= version map {
       v => "spark-examples-" + v + "-hadoop" + hadoopVersion + ".jar" },
     libraryDependencies ++= Seq(
       "com.twitter"          %% "algebird-core"   % "0.1.11",

From 25a276dd21457fa0db76b6be93765682a50b784a Mon Sep 17 00:00:00 2001
From: WangTao <barneystinson@aliyun.com>
Date: Fri, 25 Apr 2014 11:47:01 -0700
Subject: [PATCH 027/810] Delete the val that never used

It seems that the val "startTime" and "endTime" is never used, so delete them.

Author: WangTao <barneystinson@aliyun.com>

Closes #553 from WangTaoTheTonic/master and squashes the following commits:

4fcb639 [WangTao] Delete the val that never used
---
 .../main/scala/org/apache/spark/storage/BlockMessage.scala    | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/storage/BlockMessage.scala b/core/src/main/scala/org/apache/spark/storage/BlockMessage.scala
index 337b45b727de..a2bfce7b4a0f 100644
--- a/core/src/main/scala/org/apache/spark/storage/BlockMessage.scala
+++ b/core/src/main/scala/org/apache/spark/storage/BlockMessage.scala
@@ -57,7 +57,6 @@ private[spark] class BlockMessage() {
   }
 
   def set(buffer: ByteBuffer) {
-    val startTime = System.currentTimeMillis
     /*
     println()
     println("BlockMessage: ")
@@ -100,7 +99,6 @@ private[spark] class BlockMessage() {
       data.flip()
     }
 
-    val finishTime = System.currentTimeMillis
   }
 
   def set(bufferMsg: BufferMessage) {
@@ -115,7 +113,6 @@ private[spark] class BlockMessage() {
   def getLevel: StorageLevel =  level
 
   def toBufferMessage: BufferMessage = {
-    val startTime = System.currentTimeMillis
     val buffers = new ArrayBuffer[ByteBuffer]()
     var buffer = ByteBuffer.allocate(4 + 4 + id.name.length * 2)
     buffer.putInt(typ).putInt(id.name.length)
@@ -153,7 +150,6 @@ private[spark] class BlockMessage() {
     println()
     println()
     */
-    val finishTime = System.currentTimeMillis
     Message.createBufferMessage(buffers)
   }
 

From 8aaef5c75624b48806a05943454f7b72f3ca1bdc Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?baishuo=28=E7=99=BD=E7=A1=95=29?= <vc_java@hotmail.com>
Date: Fri, 25 Apr 2014 13:18:49 -0700
Subject: [PATCH 028/810] Update KafkaWordCount.scala
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

modify the required args number

Author: baishuo(白硕) <vc_java@hotmail.com>

Closes #523 from baishuo/master and squashes the following commits:

0368ba9 [baishuo(白硕)] Update KafkaWordCount.scala
---
 .../org/apache/spark/streaming/examples/KafkaWordCount.scala    | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/src/main/scala/org/apache/spark/streaming/examples/KafkaWordCount.scala b/examples/src/main/scala/org/apache/spark/streaming/examples/KafkaWordCount.scala
index 3a18bd02b89a..2aa4f1474a59 100644
--- a/examples/src/main/scala/org/apache/spark/streaming/examples/KafkaWordCount.scala
+++ b/examples/src/main/scala/org/apache/spark/streaming/examples/KafkaWordCount.scala
@@ -71,7 +71,7 @@ object KafkaWordCount {
 object KafkaWordCountProducer {
 
   def main(args: Array[String]) {
-    if (args.length < 2) {
+    if (args.length < 4) {
       System.err.println("Usage: KafkaWordCountProducer <metadataBrokerList> <topic> " +
         "<messagesPerSec> <wordsPerMessage>")
       System.exit(1)

From df6d81425bf3b8830988288069f6863de873aee2 Mon Sep 17 00:00:00 2001
From: Sean Owen <sowen@cloudera.com>
Date: Fri, 25 Apr 2014 14:17:38 -0700
Subject: [PATCH 029/810] SPARK-1607. HOTFIX: Fix syntax adapting Int result to
 Short

Sorry folks. This should make the change for SPARK-1607 compile again. Verified this time with the yarn build enabled.

Author: Sean Owen <sowen@cloudera.com>

Closes #556 from srowen/SPARK-1607.2 and squashes the following commits:

e3fe7a3 [Sean Owen] Fix syntax adapting Int result to Short
---
 .../main/scala/org/apache/spark/deploy/yarn/ClientBase.scala  | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala
index eb95d784318e..f2be8217a2f8 100644
--- a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala
+++ b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala
@@ -60,10 +60,10 @@ trait ClientBase extends Logging {
 
   // Staging directory is private! -> rwx--------
   val STAGING_DIR_PERMISSION: FsPermission =
-    FsPermission.createImmutable(Integer.parseInt("700", 8): Short)
+    FsPermission.createImmutable(Integer.parseInt("700", 8).toShort)
   // App files are world-wide readable and owner writable -> rw-r--r--
   val APP_FILE_PERMISSION: FsPermission =
-    FsPermission.createImmutable(Integer.parseInt("644", 8): Short)
+    FsPermission.createImmutable(Integer.parseInt("644", 8).toShort)
 
   // TODO(harvey): This could just go in ClientArguments.
   def validateArgs() = {

From 027f1b85f961ce16ee069afe3d90a36dce009994 Mon Sep 17 00:00:00 2001
From: CodingCat <zhunansjtu@gmail.com>
Date: Fri, 25 Apr 2014 16:04:48 -0700
Subject: [PATCH 030/810] SPARK-1235: manage the DAGScheduler EventProcessActor
 with supervisor and refactor the DAGScheduler with Akka

https://spark-project.atlassian.net/browse/SPARK-1235

In the current implementation, the running job will hang if the DAGScheduler crashes for some reason (eventProcessActor throws exception in receive() )

The reason is that the actor will automatically restart when the exception is thrown during the running but is not captured properly (Akka behaviour), and the JobWaiters are still waiting there for the completion of the tasks

In this patch, I refactored the DAGScheduler with Akka and manage the eventProcessActor with supervisor, so that upon the failure of a eventProcessActor, the supervisor will terminate the EventProcessActor and close the SparkContext

thanks for @kayousterhout and @markhamstra to give the hints in JIRA

Author: CodingCat <zhunansjtu@gmail.com>
Author: Xiangrui Meng <meng@databricks.com>
Author: Nan Zhu <CodingCat@users.noreply.github.com>

Closes #186 from CodingCat/SPARK-1235 and squashes the following commits:

a7fb0ee [CodingCat] throw Exception on failure of creating DAG
124d82d [CodingCat] blocking the constructor until event actor is ready
baf2d38 [CodingCat] fix the issue brought by non-blocking actorOf
35c886a [CodingCat] fix bug
82d08b3 [CodingCat] calling actorOf on system to ensure it is blocking
310a579 [CodingCat] style fix
cd02d9a [Nan Zhu] small fix
561cfbc [CodingCat] recover doCheckpoint
c048d0e [CodingCat] call submitWaitingStages for every event
a9eea039 [CodingCat] address Matei's comments
ac878ab [CodingCat] typo fix
5d1636a [CodingCat] re-trigger the test.....
9dfb033 [CodingCat] remove unnecessary changes
a7a2a97 [CodingCat] add StageCancelled message
fdf3b17 [CodingCat] just to retrigger the test......
089bc2f [CodingCat] address andrew's comments
228f4b0 [CodingCat] address comments from Mark
b68c1c7 [CodingCat] refactor DAGScheduler with Akka
810efd8 [Xiangrui Meng] akka solution
---
 .../scala/org/apache/spark/SparkContext.scala |  20 +-
 .../main/scala/org/apache/spark/rdd/RDD.scala |   6 +-
 .../apache/spark/scheduler/DAGScheduler.scala | 419 ++++++++++--------
 .../spark/scheduler/DAGSchedulerEvent.scala   |   4 +-
 .../spark/scheduler/TaskSetManager.scala      |   2 +-
 .../spark/scheduler/DAGSchedulerSuite.scala   |  58 ++-
 .../scheduler/TaskSchedulerImplSuite.scala    |   2 +-
 7 files changed, 290 insertions(+), 221 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index e9d2f5757963..eb14d87467af 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -300,10 +300,17 @@ class SparkContext(config: SparkConf) extends Logging {
 
   // Create and start the scheduler
   private[spark] var taskScheduler = SparkContext.createTaskScheduler(this, master)
-  taskScheduler.start()
+  @volatile private[spark] var dagScheduler: DAGScheduler = _
+  try {
+    dagScheduler = new DAGScheduler(this)
+  } catch {
+    case e: Exception => throw
+      new SparkException("DAGScheduler cannot be initialized due to %s".format(e.getMessage))
+  }
 
-  @volatile private[spark] var dagScheduler = new DAGScheduler(this)
-  dagScheduler.start()
+  // start TaskScheduler after taskScheduler sets DAGScheduler reference in DAGScheduler's
+  // constructor
+  taskScheduler.start()
 
   private[spark] val cleaner: Option[ContextCleaner] = {
     if (conf.getBoolean("spark.cleaner.referenceTracking", true)) {
@@ -1022,8 +1029,8 @@ class SparkContext(config: SparkConf) extends Logging {
       partitions: Seq[Int],
       allowLocal: Boolean,
       resultHandler: (Int, U) => Unit) {
-    partitions.foreach{ p =>
-      require(p >= 0 && p < rdd.partitions.size, s"Invalid partition requested: $p")
+    if (dagScheduler == null) {
+      throw new SparkException("SparkContext has been shutdown")
     }
     val callSite = getCallSite
     val cleanedFunc = clean(func)
@@ -1132,9 +1139,6 @@ class SparkContext(config: SparkConf) extends Logging {
       resultHandler: (Int, U) => Unit,
       resultFunc: => R): SimpleFutureAction[R] =
   {
-    partitions.foreach{ p =>
-      require(p >= 0 && p < rdd.partitions.size, s"Invalid partition requested: $p")
-    }
     val cleanF = clean(processPartition)
     val callSite = getCallSite
     val waiter = dagScheduler.submitJob(
diff --git a/core/src/main/scala/org/apache/spark/rdd/RDD.scala b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
index e8bbfbf01679..3b3524f33e81 100644
--- a/core/src/main/scala/org/apache/spark/rdd/RDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
@@ -1142,9 +1142,9 @@ abstract class RDD[T: ClassTag](
   @transient private var doCheckpointCalled = false
 
   /**
-   * Performs the checkpointing of this RDD by saving this. It is called by the DAGScheduler
-   * after a job using this RDD has completed (therefore the RDD has been materialized and
-   * potentially stored in memory). doCheckpoint() is called recursively on the parent RDDs.
+   * Performs the checkpointing of this RDD by saving this. It is called after a job using this RDD
+   * has completed (therefore the RDD has been materialized and potentially stored in memory).
+   * doCheckpoint() is called recursively on the parent RDDs.
    */
   private[spark] def doCheckpoint() {
     if (!doCheckpointCalled) {
diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
index dbde9b591dcc..ff411e24a3d8 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
@@ -22,10 +22,16 @@ import java.util.Properties
 import java.util.concurrent.atomic.AtomicInteger
 
 import scala.collection.mutable.{ArrayBuffer, HashMap, HashSet, Map}
+import scala.concurrent.Await
 import scala.concurrent.duration._
+import scala.language.postfixOps
 import scala.reflect.ClassTag
 
 import akka.actor._
+import akka.actor.OneForOneStrategy
+import akka.actor.SupervisorStrategy.Stop
+import akka.pattern.ask
+import akka.util.Timeout
 
 import org.apache.spark._
 import org.apache.spark.executor.TaskMetrics
@@ -47,14 +53,11 @@ import org.apache.spark.util.Utils
  * not caused by shuffle file loss are handled by the TaskScheduler, which will retry each task
  * a small number of times before cancelling the whole stage.
  *
- * THREADING: This class runs all its logic in a single thread executing the run() method, to which
- * events are submitted using a synchronized queue (eventQueue). The public API methods, such as
- * runJob, taskEnded and executorLost, post events asynchronously to this queue. All other methods
- * should be private.
  */
 private[spark]
 class DAGScheduler(
-    taskScheduler: TaskScheduler,
+    private[scheduler] val sc: SparkContext,
+    private[scheduler] val taskScheduler: TaskScheduler,
     listenerBus: LiveListenerBus,
     mapOutputTracker: MapOutputTrackerMaster,
     blockManagerMaster: BlockManagerMaster,
@@ -65,6 +68,7 @@ class DAGScheduler(
 
   def this(sc: SparkContext, taskScheduler: TaskScheduler) = {
     this(
+      sc,
       taskScheduler,
       sc.listenerBus,
       sc.env.mapOutputTracker.asInstanceOf[MapOutputTrackerMaster],
@@ -74,8 +78,6 @@ class DAGScheduler(
 
   def this(sc: SparkContext) = this(sc, sc.taskScheduler)
 
-  private var eventProcessActor: ActorRef = _
-
   private[scheduler] val nextJobId = new AtomicInteger(0)
   private[scheduler] def numTotalJobs: Int = nextJobId.get()
   private val nextStageId = new AtomicInteger(0)
@@ -113,50 +115,31 @@ class DAGScheduler(
   //       stray messages to detect.
   private val failedEpoch = new HashMap[String, Long]
 
-  taskScheduler.setDAGScheduler(this)
+  private val dagSchedulerActorSupervisor =
+    env.actorSystem.actorOf(Props(new DAGSchedulerActorSupervisor(this)))
 
-  /**
-   * Starts the event processing actor.  The actor has two responsibilities:
-   *
-   * 1. Waits for events like job submission, task finished, task failure etc., and calls
-   *    [[org.apache.spark.scheduler.DAGScheduler.processEvent()]] to process them.
-   * 2. Schedules a periodical task to resubmit failed stages.
-   *
-   * NOTE: the actor cannot be started in the constructor, because the periodical task references
-   * some internal states of the enclosing [[org.apache.spark.scheduler.DAGScheduler]] object, thus
-   * cannot be scheduled until the [[org.apache.spark.scheduler.DAGScheduler]] is fully constructed.
-   */
-  def start() {
-    eventProcessActor = env.actorSystem.actorOf(Props(new Actor {
-      /**
-       * The main event loop of the DAG scheduler.
-       */
-      def receive = {
-        case event: DAGSchedulerEvent =>
-          logTrace("Got event of type " + event.getClass.getName)
-
-          /**
-           * All events are forwarded to `processEvent()`, so that the event processing logic can
-           * easily tested without starting a dedicated actor.  Please refer to `DAGSchedulerSuite`
-           * for details.
-           */
-          if (!processEvent(event)) {
-            submitWaitingStages()
-          } else {
-            context.stop(self)
-          }
-      }
-    }))
+  private[scheduler] var eventProcessActor: ActorRef = _
+
+  private def initializeEventProcessActor() {
+    // blocking the thread until supervisor is started, which ensures eventProcessActor is
+    // not null before any job is submitted
+    implicit val timeout = Timeout(30 seconds)
+    val initEventActorReply =
+      dagSchedulerActorSupervisor ? Props(new DAGSchedulerEventProcessActor(this))
+    eventProcessActor = Await.result(initEventActorReply, timeout.duration).
+      asInstanceOf[ActorRef]
   }
 
+  initializeEventProcessActor()
+
   // Called by TaskScheduler to report task's starting.
   def taskStarted(task: Task[_], taskInfo: TaskInfo) {
     eventProcessActor ! BeginEvent(task, taskInfo)
   }
 
   // Called to report that a task has completed and results are being fetched remotely.
-  def taskGettingResult(task: Task[_], taskInfo: TaskInfo) {
-    eventProcessActor ! GettingResultEvent(task, taskInfo)
+  def taskGettingResult(taskInfo: TaskInfo) {
+    eventProcessActor ! GettingResultEvent(taskInfo)
   }
 
   // Called by TaskScheduler to report task completions or failures.
@@ -436,7 +419,7 @@ class DAGScheduler(
   {
     // Check to make sure we are not launching a task on a partition that does not exist.
     val maxPartitions = rdd.partitions.length
-    partitions.find(p => p >= maxPartitions).foreach { p =>
+    partitions.find(p => p >= maxPartitions || p < 0).foreach { p =>
       throw new IllegalArgumentException(
         "Attempting to access a non-existent partition: " + p + ". " +
           "Total number of partitions: " + maxPartitions)
@@ -511,6 +494,15 @@ class DAGScheduler(
     eventProcessActor ! AllJobsCancelled
   }
 
+  private[scheduler] def doCancelAllJobs() {
+    // Cancel all running jobs.
+    runningStages.map(_.jobId).foreach(handleJobCancellation(_,
+      reason = "as part of cancellation of all jobs"))
+    activeJobs.clear() // These should already be empty by this point,
+    jobIdToActiveJob.clear() // but just in case we lost track of some jobs...
+    submitWaitingStages()
+  }
+
   /**
    * Cancel all jobs associated with a running or scheduled stage.
    */
@@ -518,148 +510,30 @@ class DAGScheduler(
     eventProcessActor ! StageCancelled(stageId)
   }
 
-  /**
-   * Process one event retrieved from the event processing actor.
-   *
-   * @param event The event to be processed.
-   * @return `true` if we should stop the event loop.
-   */
-  private[scheduler] def processEvent(event: DAGSchedulerEvent): Boolean = {
-    event match {
-      case JobSubmitted(jobId, rdd, func, partitions, allowLocal, callSite, listener, properties) =>
-        var finalStage: Stage = null
-        try {
-          // New stage creation may throw an exception if, for example, jobs are run on a HadoopRDD
-          // whose underlying HDFS files have been deleted.
-          finalStage = newStage(rdd, partitions.size, None, jobId, Some(callSite))
-        } catch {
-          case e: Exception =>
-            logWarning("Creating new stage failed due to exception - job: " + jobId, e)
-            listener.jobFailed(e)
-            return false
-        }
-        val job = new ActiveJob(jobId, finalStage, func, partitions, callSite, listener, properties)
-        clearCacheLocs()
-        logInfo("Got job " + job.jobId + " (" + callSite + ") with " + partitions.length +
-                " output partitions (allowLocal=" + allowLocal + ")")
-        logInfo("Final stage: " + finalStage + " (" + finalStage.name + ")")
-        logInfo("Parents of final stage: " + finalStage.parents)
-        logInfo("Missing parents: " + getMissingParentStages(finalStage))
-        if (allowLocal && finalStage.parents.size == 0 && partitions.length == 1) {
-          // Compute very short actions like first() or take() with no parent stages locally.
-          listenerBus.post(SparkListenerJobStart(job.jobId, Array[Int](), properties))
-          runLocally(job)
-        } else {
-          jobIdToActiveJob(jobId) = job
-          activeJobs += job
-          resultStageToJob(finalStage) = job
-          listenerBus.post(
-            SparkListenerJobStart(job.jobId, jobIdToStageIds(jobId).toArray, properties))
-          submitStage(finalStage)
-        }
-
-      case StageCancelled(stageId) =>
-        handleStageCancellation(stageId)
-
-      case JobCancelled(jobId) =>
-        handleJobCancellation(jobId)
-
-      case JobGroupCancelled(groupId) =>
-        // Cancel all jobs belonging to this job group.
-        // First finds all active jobs with this group id, and then kill stages for them.
-        val activeInGroup = activeJobs.filter(activeJob =>
-          groupId == activeJob.properties.get(SparkContext.SPARK_JOB_GROUP_ID))
-        val jobIds = activeInGroup.map(_.jobId)
-        jobIds.foreach(jobId => handleJobCancellation(jobId,
-          "as part of cancelled job group %s".format(groupId)))
-
-      case AllJobsCancelled =>
-        // Cancel all running jobs.
-        runningStages.map(_.jobId).foreach(jobId => handleJobCancellation(jobId,
-          "as part of cancellation of all jobs"))
-        activeJobs.clear()      // These should already be empty by this point,
-        jobIdToActiveJob.clear()   // but just in case we lost track of some jobs...
-
-      case ExecutorAdded(execId, host) =>
-        handleExecutorAdded(execId, host)
-
-      case ExecutorLost(execId) =>
-        handleExecutorLost(execId)
-
-      case BeginEvent(task, taskInfo) =>
-        for (
-          stage <- stageIdToStage.get(task.stageId);
-          stageInfo <- stageToInfos.get(stage)
-        ) {
-          if (taskInfo.serializedSize > TASK_SIZE_TO_WARN * 1024 &&
-              !stageInfo.emittedTaskSizeWarning) {
-            stageInfo.emittedTaskSizeWarning = true
-            logWarning(("Stage %d (%s) contains a task of very large " +
-              "size (%d KB). The maximum recommended task size is %d KB.").format(
-              task.stageId, stageInfo.name, taskInfo.serializedSize / 1024, TASK_SIZE_TO_WARN))
-          }
-        }
-        listenerBus.post(SparkListenerTaskStart(task.stageId, taskInfo))
-
-      case GettingResultEvent(task, taskInfo) =>
-        listenerBus.post(SparkListenerTaskGettingResult(taskInfo))
-
-      case completion @ CompletionEvent(task, reason, _, _, taskInfo, taskMetrics) =>
-        val stageId = task.stageId
-        val taskType = Utils.getFormattedClassName(task)
-        listenerBus.post(SparkListenerTaskEnd(stageId, taskType, reason, taskInfo, taskMetrics))
-        handleTaskCompletion(completion)
-
-      case TaskSetFailed(taskSet, reason) =>
-        stageIdToStage.get(taskSet.stageId).foreach { abortStage(_, reason) }
-
-      case ResubmitFailedStages =>
-        if (failedStages.size > 0) {
-          // Failed stages may be removed by job cancellation, so failed might be empty even if
-          // the ResubmitFailedStages event has been scheduled.
-          resubmitFailedStages()
-        }
-
-      case StopDAGScheduler =>
-        // Cancel any active jobs
-        for (job <- activeJobs) {
-          val error = new SparkException("Job cancelled because SparkContext was shut down")
-          job.listener.jobFailed(error)
-          // Tell the listeners that all of the running stages have ended.  Don't bother
-          // cancelling the stages because if the DAG scheduler is stopped, the entire application
-          // is in the process of getting stopped.
-          val stageFailedMessage = "Stage cancelled because SparkContext was shut down"
-          runningStages.foreach { stage =>
-            val info = stageToInfos(stage)
-            info.stageFailed(stageFailedMessage)
-            listenerBus.post(SparkListenerStageCompleted(info))
-          }
-          listenerBus.post(SparkListenerJobEnd(job.jobId, JobFailed(error)))
-        }
-        return true
-    }
-    false
-  }
-
   /**
    * Resubmit any failed stages. Ordinarily called after a small amount of time has passed since
    * the last fetch failure.
    */
   private[scheduler] def resubmitFailedStages() {
-    logInfo("Resubmitting failed stages")
-    clearCacheLocs()
-    val failedStagesCopy = failedStages.toArray
-    failedStages.clear()
-    for (stage <- failedStagesCopy.sortBy(_.jobId)) {
-      submitStage(stage)
+    if (failedStages.size > 0) {
+      // Failed stages may be removed by job cancellation, so failed might be empty even if
+      // the ResubmitFailedStages event has been scheduled.
+      logInfo("Resubmitting failed stages")
+      clearCacheLocs()
+      val failedStagesCopy = failedStages.toArray
+      failedStages.clear()
+      for (stage <- failedStagesCopy.sortBy(_.jobId)) {
+        submitStage(stage)
+      }
     }
+    submitWaitingStages()
   }
 
   /**
    * Check for waiting or failed stages which are now eligible for resubmission.
    * Ordinarily run on every iteration of the event loop.
    */
-  private[scheduler] def submitWaitingStages() {
+  private def submitWaitingStages() {
     // TODO: We might want to run this less often, when we are sure that something has become
     // runnable that wasn't before.
     logTrace("Checking for newly runnable parent stages")
@@ -730,6 +604,102 @@ class DAGScheduler(
     }
   }
 
+  private[scheduler] def handleJobGroupCancelled(groupId: String) {
+    // Cancel all jobs belonging to this job group.
+    // First finds all active jobs with this group id, and then kill stages for them.
+    val activeInGroup = activeJobs.filter(activeJob =>
+      groupId == activeJob.properties.get(SparkContext.SPARK_JOB_GROUP_ID))
+    val jobIds = activeInGroup.map(_.jobId)
+    jobIds.foreach(handleJobCancellation(_, "part of cancelled job group %s".format(groupId)))
+    submitWaitingStages()
+  }
+
+  private[scheduler] def handleBeginEvent(task: Task[_], taskInfo: TaskInfo) {
+    for (stage <- stageIdToStage.get(task.stageId); stageInfo <- stageToInfos.get(stage)) {
+      if (taskInfo.serializedSize > DAGScheduler.TASK_SIZE_TO_WARN * 1024 &&
+        !stageInfo.emittedTaskSizeWarning) {
+        stageInfo.emittedTaskSizeWarning = true
+        logWarning(("Stage %d (%s) contains a task of very large " +
+          "size (%d KB). The maximum recommended task size is %d KB.").format(
+            task.stageId, stageInfo.name, taskInfo.serializedSize / 1024,
+            DAGScheduler.TASK_SIZE_TO_WARN))
+      }
+    }
+    listenerBus.post(SparkListenerTaskStart(task.stageId, taskInfo))
+    submitWaitingStages()
+  }
+
+  private[scheduler] def handleTaskSetFailed(taskSet: TaskSet, reason: String) {
+    stageIdToStage.get(taskSet.stageId).foreach {abortStage(_, reason) }
+    submitWaitingStages()
+  }
+
+  private[scheduler] def cleanUpAfterSchedulerStop() {
+    for (job <- activeJobs) {
+      val error = new SparkException("Job cancelled because SparkContext was shut down")
+      job.listener.jobFailed(error)
+      // Tell the listeners that all of the running stages have ended.  Don't bother
+      // cancelling the stages because if the DAG scheduler is stopped, the entire application
+      // is in the process of getting stopped.
+      val stageFailedMessage = "Stage cancelled because SparkContext was shut down"
+      runningStages.foreach { stage =>
+        val info = stageToInfos(stage)
+        info.stageFailed(stageFailedMessage)
+        listenerBus.post(SparkListenerStageCompleted(info))
+      }
+      listenerBus.post(SparkListenerJobEnd(job.jobId, JobFailed(error)))
+    }
+  }
+
+  private[scheduler] def handleGetTaskResult(taskInfo: TaskInfo) {
+    listenerBus.post(SparkListenerTaskGettingResult(taskInfo))
+    submitWaitingStages()
+  }
+
+  private[scheduler] def handleJobSubmitted(jobId: Int,
+      finalRDD: RDD[_],
+      func: (TaskContext, Iterator[_]) => _,
+      partitions: Array[Int],
+      allowLocal: Boolean,
+      callSite: String,
+      listener: JobListener,
+      properties: Properties = null)
+  {
+    var finalStage: Stage = null
+    try {
+      // New stage creation may throw an exception if, for example, jobs are run on a
+      // HadoopRDD whose underlying HDFS files have been deleted.
+      finalStage = newStage(finalRDD, partitions.size, None, jobId, Some(callSite))
+    } catch {
+      case e: Exception =>
+        logWarning("Creating new stage failed due to exception - job: " + jobId, e)
+        listener.jobFailed(e)
+        return
+    }
+    if (finalStage != null) {
+      val job = new ActiveJob(jobId, finalStage, func, partitions, callSite, listener, properties)
+      clearCacheLocs()
+      logInfo("Got job %s (%s) with %d output partitions (allowLocal=%s)".format(
+        job.jobId, callSite, partitions.length, allowLocal))
+      logInfo("Final stage: " + finalStage + "(" + finalStage.name + ")")
+      logInfo("Parents of final stage: " + finalStage.parents)
+      logInfo("Missing parents: " + getMissingParentStages(finalStage))
+      if (allowLocal && finalStage.parents.size == 0 && partitions.length == 1) {
+        // Compute very short actions like first() or take() with no parent stages locally.
+        listenerBus.post(SparkListenerJobStart(job.jobId, Array[Int](), properties))
+        runLocally(job)
+      } else {
+        jobIdToActiveJob(jobId) = job
+        activeJobs += job
+        resultStageToJob(finalStage) = job
+        listenerBus.post(SparkListenerJobStart(job.jobId, jobIdToStageIds(jobId).toArray,
+          properties))
+        submitStage(finalStage)
+      }
+    }
+    submitWaitingStages()
+  }
+
   /** Submits stage, but first recursively submits any missing parents. */
   private def submitStage(stage: Stage) {
     val jobId = activeJobForStage(stage)
@@ -819,9 +789,12 @@ class DAGScheduler(
    * Responds to a task finishing. This is called inside the event loop so it assumes that it can
    * modify the scheduler's internal state. Use taskEnded() to post a task end event from outside.
    */
-  private def handleTaskCompletion(event: CompletionEvent) {
+  private[scheduler] def handleTaskCompletion(event: CompletionEvent) {
     val task = event.task
-
+    val stageId = task.stageId
+    val taskType = Utils.getFormattedClassName(task)
+    listenerBus.post(SparkListenerTaskEnd(stageId, taskType, event.reason, event.taskInfo,
+      event.taskMetrics))
     if (!stageIdToStage.contains(task.stageId)) {
       // Skip all the actions if the stage has been cancelled.
       return
@@ -964,6 +937,7 @@ class DAGScheduler(
         // Unrecognized failure - also do nothing. If the task fails repeatedly, the TaskScheduler
         // will abort the job.
     }
+    submitWaitingStages()
   }
 
   /**
@@ -973,7 +947,7 @@ class DAGScheduler(
    * Optionally the epoch during which the failure was caught can be passed to avoid allowing
    * stray fetch failures from possibly retriggering the detection of a node as lost.
    */
-  private def handleExecutorLost(execId: String, maybeEpoch: Option[Long] = None) {
+  private[scheduler] def handleExecutorLost(execId: String, maybeEpoch: Option[Long] = None) {
     val currentEpoch = maybeEpoch.getOrElse(mapOutputTracker.getEpoch)
     if (!failedEpoch.contains(execId) || failedEpoch(execId) < currentEpoch) {
       failedEpoch(execId) = currentEpoch
@@ -993,17 +967,19 @@ class DAGScheduler(
       logDebug("Additional executor lost message for " + execId +
                "(epoch " + currentEpoch + ")")
     }
+    submitWaitingStages()
   }
 
-  private def handleExecutorAdded(execId: String, host: String) {
+  private[scheduler] def handleExecutorAdded(execId: String, host: String) {
     // remove from failedEpoch(execId) ?
     if (failedEpoch.contains(execId)) {
       logInfo("Host added was in lost list earlier: " + host)
       failedEpoch -= execId
     }
+    submitWaitingStages()
   }
 
-  private def handleStageCancellation(stageId: Int) {
+  private[scheduler] def handleStageCancellation(stageId: Int) {
     if (stageIdToJobIds.contains(stageId)) {
       val jobsThatUseStage: Array[Int] = stageIdToJobIds(stageId).toArray
       jobsThatUseStage.foreach(jobId => {
@@ -1012,22 +988,24 @@ class DAGScheduler(
     } else {
       logInfo("No active jobs to kill for Stage " + stageId)
     }
+    submitWaitingStages()
   }
 
-  private def handleJobCancellation(jobId: Int, reason: String = "") {
+  private[scheduler] def handleJobCancellation(jobId: Int, reason: String = "") {
     if (!jobIdToStageIds.contains(jobId)) {
       logDebug("Trying to cancel unregistered job " + jobId)
     } else {
       failJobAndIndependentStages(jobIdToActiveJob(jobId),
         "Job %d cancelled %s".format(jobId, reason), None)
     }
+    submitWaitingStages()
   }
 
   /**
    * Aborts all jobs depending on a particular Stage. This is called in response to a task set
    * being canceled by the TaskScheduler. Use taskSetFailed() to inject this event from outside.
    */
-  private def abortStage(failedStage: Stage, reason: String) {
+  private[scheduler] def abortStage(failedStage: Stage, reason: String) {
     if (!stageIdToStage.contains(failedStage.id)) {
       // Skip all the actions if the stage has been removed.
       return
@@ -1156,13 +1134,88 @@ class DAGScheduler(
   }
 
   def stop() {
-    if (eventProcessActor != null) {
-      eventProcessActor ! StopDAGScheduler
-    }
+    logInfo("Stopping DAGScheduler")
+    dagSchedulerActorSupervisor ! PoisonPill
     taskScheduler.stop()
   }
 }
 
+private[scheduler] class DAGSchedulerActorSupervisor(dagScheduler: DAGScheduler)
+  extends Actor with Logging {
+
+  override val supervisorStrategy =
+    OneForOneStrategy() {
+      case x: Exception =>
+        logError("eventProcesserActor failed due to the error %s; shutting down SparkContext"
+          .format(x.getMessage))
+        dagScheduler.doCancelAllJobs()
+        dagScheduler.sc.stop()
+        Stop
+    }
+
+  def receive = {
+    case p: Props => sender ! context.actorOf(p)
+    case _ => logWarning("received unknown message in DAGSchedulerActorSupervisor")
+  }
+}
+
+private[scheduler] class DAGSchedulerEventProcessActor(dagScheduler: DAGScheduler)
+  extends Actor with Logging {
+
+  override def preStart() {
+    // set DAGScheduler for taskScheduler to ensure eventProcessActor is always
+    // valid when the messages arrive
+    dagScheduler.taskScheduler.setDAGScheduler(dagScheduler)
+  }
+
+  /**
+   * The main event loop of the DAG scheduler.
+   */
+  def receive = {
+    case JobSubmitted(jobId, rdd, func, partitions, allowLocal, callSite, listener, properties) =>
+      dagScheduler.handleJobSubmitted(jobId, rdd, func, partitions, allowLocal, callSite,
+        listener, properties)
+
+    case StageCancelled(stageId) =>
+      dagScheduler.handleStageCancellation(stageId)
+
+    case JobCancelled(jobId) =>
+      dagScheduler.handleJobCancellation(jobId)
+
+    case JobGroupCancelled(groupId) =>
+      dagScheduler.handleJobGroupCancelled(groupId)
+
+    case AllJobsCancelled =>
+      dagScheduler.doCancelAllJobs()
+
+    case ExecutorAdded(execId, host) =>
+      dagScheduler.handleExecutorAdded(execId, host)
+
+    case ExecutorLost(execId) =>
+      dagScheduler.handleExecutorLost(execId)
+
+    case BeginEvent(task, taskInfo) =>
+      dagScheduler.handleBeginEvent(task, taskInfo)
+
+    case GettingResultEvent(taskInfo) =>
+      dagScheduler.handleGetTaskResult(taskInfo)
+
+    case completion @ CompletionEvent(task, reason, _, _, taskInfo, taskMetrics) =>
+      dagScheduler.handleTaskCompletion(completion)
+
+    case TaskSetFailed(taskSet, reason) =>
+      dagScheduler.handleTaskSetFailed(taskSet, reason)
+
+    case ResubmitFailedStages =>
+      dagScheduler.resubmitFailedStages()
+  }
+
+  override def postStop() {
+    // Cancel any active jobs in postStop hook
+    dagScheduler.cleanUpAfterSchedulerStop()
+  }
+}
+
 private[spark] object DAGScheduler {
   // The time, in millis, to wait for fetch failure events to stop coming in after one is detected;
   // this is a simplistic way to avoid resubmitting tasks in the non-fetchable map stage one by one
diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGSchedulerEvent.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGSchedulerEvent.scala
index 0800c5684c60..23f57441b4b1 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/DAGSchedulerEvent.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/DAGSchedulerEvent.scala
@@ -57,7 +57,7 @@ private[scheduler]
 case class BeginEvent(task: Task[_], taskInfo: TaskInfo) extends DAGSchedulerEvent
 
 private[scheduler]
-case class GettingResultEvent(task: Task[_], taskInfo: TaskInfo) extends DAGSchedulerEvent
+case class GettingResultEvent(taskInfo: TaskInfo) extends DAGSchedulerEvent
 
 private[scheduler] case class CompletionEvent(
     task: Task[_],
@@ -76,5 +76,3 @@ private[scheduler]
 case class TaskSetFailed(taskSet: TaskSet, reason: String) extends DAGSchedulerEvent
 
 private[scheduler] case object ResubmitFailedStages extends DAGSchedulerEvent
-
-private[scheduler] case object StopDAGScheduler extends DAGSchedulerEvent
diff --git a/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala b/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala
index a81b834267f0..f3bd0797aa03 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala
@@ -465,7 +465,7 @@ private[spark] class TaskSetManager(
   def handleTaskGettingResult(tid: Long) = {
     val info = taskInfos(tid)
     info.markGettingResult()
-    sched.dagScheduler.taskGettingResult(tasks(info.index), info)
+    sched.dagScheduler.taskGettingResult(info)
   }
 
   /**
diff --git a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
index ff69eb7e53f8..d172dd1ac8e1 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
@@ -21,6 +21,8 @@ import scala.Tuple2
 import scala.collection.mutable.{HashSet, HashMap, Map}
 import scala.language.reflectiveCalls
 
+import akka.actor._
+import akka.testkit.{ImplicitSender, TestKit, TestActorRef}
 import org.scalatest.{BeforeAndAfter, FunSuite}
 
 import org.apache.spark._
@@ -28,19 +30,16 @@ import org.apache.spark.rdd.RDD
 import org.apache.spark.scheduler.SchedulingMode.SchedulingMode
 import org.apache.spark.storage.{BlockId, BlockManagerId, BlockManagerMaster}
 
-/**
- * Tests for DAGScheduler. These tests directly call the event processing functions in DAGScheduler
- * rather than spawning an event loop thread as happens in the real code. They use EasyMock
- * to mock out two classes that DAGScheduler interacts with: TaskScheduler (to which TaskSets are
- * submitted) and BlockManagerMaster (from which cache locations are retrieved and to which dead
- * host notifications are sent). In addition, tests may check for side effects on a non-mocked
- * MapOutputTracker instance.
- *
- * Tests primarily consist of running DAGScheduler#processEvent and
- * DAGScheduler#submitWaitingStages (via test utility functions like runEvent or respondToTaskSet)
- * and capturing the resulting TaskSets from the mock TaskScheduler.
- */
-class DAGSchedulerSuite extends FunSuite with BeforeAndAfter with LocalSparkContext {
+class BuggyDAGEventProcessActor extends Actor {
+  val state = 0
+  def receive = {
+    case _ => throw new SparkException("error")
+  }
+}
+
+class DAGSchedulerSuite extends TestKit(ActorSystem("DAGSchedulerSuite")) with FunSuite
+  with ImplicitSender with BeforeAndAfter with LocalSparkContext {
+
   val conf = new SparkConf
   /** Set of TaskSets the DAGScheduler has requested executed. */
   val taskSets = scala.collection.mutable.Buffer[TaskSet]()
@@ -82,6 +81,7 @@ class DAGSchedulerSuite extends FunSuite with BeforeAndAfter with LocalSparkCont
 
   var mapOutputTracker: MapOutputTrackerMaster = null
   var scheduler: DAGScheduler = null
+  var dagEventProcessTestActor: TestActorRef[DAGSchedulerEventProcessActor] = null
 
   /**
    * Set of cache locations to return from our mock BlockManagerMaster.
@@ -121,6 +121,7 @@ class DAGSchedulerSuite extends FunSuite with BeforeAndAfter with LocalSparkCont
     results.clear()
     mapOutputTracker = new MapOutputTrackerMaster(conf)
     scheduler = new DAGScheduler(
+        sc,
         taskScheduler,
         sc.listenerBus,
         mapOutputTracker,
@@ -131,10 +132,13 @@ class DAGSchedulerSuite extends FunSuite with BeforeAndAfter with LocalSparkCont
         runLocallyWithinThread(job)
       }
     }
+    dagEventProcessTestActor = TestActorRef[DAGSchedulerEventProcessActor](
+      Props(classOf[DAGSchedulerEventProcessActor], scheduler))(system)
   }
 
-  after {
-    scheduler.stop()
+  override def afterAll() {
+    super.afterAll()
+    TestKit.shutdownActorSystem(system)
   }
 
   /**
@@ -178,8 +182,7 @@ class DAGSchedulerSuite extends FunSuite with BeforeAndAfter with LocalSparkCont
    * DAGScheduler event loop.
    */
   private def runEvent(event: DAGSchedulerEvent) {
-    assert(!scheduler.processEvent(event))
-    scheduler.submitWaitingStages()
+    dagEventProcessTestActor.receive(event)
   }
 
   /**
@@ -209,7 +212,7 @@ class DAGSchedulerSuite extends FunSuite with BeforeAndAfter with LocalSparkCont
       listener: JobListener = jobListener): Int = {
     val jobId = scheduler.nextJobId.getAndIncrement()
     runEvent(JobSubmitted(jobId, rdd, func, partitions, allowLocal, null, listener))
-    return jobId
+    jobId
   }
 
   /** Sends TaskSetFailed to the scheduler. */
@@ -223,19 +226,17 @@ class DAGSchedulerSuite extends FunSuite with BeforeAndAfter with LocalSparkCont
   }
 
   test("zero split job") {
-    val rdd = makeRdd(0, Nil)
     var numResults = 0
     val fakeListener = new JobListener() {
       override def taskSucceeded(partition: Int, value: Any) = numResults += 1
       override def jobFailed(exception: Exception) = throw exception
     }
-    submit(rdd, Array(), listener = fakeListener)
+    submit(makeRdd(0, Nil), Array(), listener = fakeListener)
     assert(numResults === 0)
   }
 
   test("run trivial job") {
-    val rdd = makeRdd(1, Nil)
-    submit(rdd, Array(0))
+    submit(makeRdd(1, Nil), Array(0))
     complete(taskSets(0), List((Success, 42)))
     assert(results === Map(0 -> 42))
     assertDataStructuresEmpty
@@ -529,6 +530,18 @@ class DAGSchedulerSuite extends FunSuite with BeforeAndAfter with LocalSparkCont
     assertDataStructuresEmpty
   }
 
+  test("DAGSchedulerActorSupervisor closes the SparkContext when EventProcessActor crashes") {
+    val actorSystem = ActorSystem("test")
+    val supervisor = actorSystem.actorOf(
+      Props(classOf[DAGSchedulerActorSupervisor], scheduler), "dagSupervisor")
+    supervisor ! Props[BuggyDAGEventProcessActor]
+    val child = expectMsgType[ActorRef]
+    watch(child)
+    child ! "hi"
+    expectMsgPF(){ case Terminated(child) => () }
+    assert(scheduler.sc.dagScheduler === null)
+  }
+
   /**
    * Assert that the supplied TaskSet has exactly the given hosts as its preferred locations.
    * Note that this checks only the host and not the executor ID.
@@ -561,3 +574,4 @@ class DAGSchedulerSuite extends FunSuite with BeforeAndAfter with LocalSparkCont
     assert(scheduler.waitingStages.isEmpty)
   }
 }
+
diff --git a/core/src/test/scala/org/apache/spark/scheduler/TaskSchedulerImplSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/TaskSchedulerImplSuite.scala
index 2fb750d9ee37..a8b605c5b212 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/TaskSchedulerImplSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/TaskSchedulerImplSuite.scala
@@ -305,7 +305,7 @@ class TaskSchedulerImplSuite extends FunSuite with LocalSparkContext with Loggin
       override def taskStarted(task: Task[_], taskInfo: TaskInfo) {}
       override def executorAdded(execId: String, host: String) {}
     }
-
+    taskScheduler.setDAGScheduler(dagScheduler)
     // Give zero core offers. Should not generate any tasks
     val zeroCoreWorkerOffers = Seq(new WorkerOffer("executor0", "host0", 0),
       new WorkerOffer("executor1", "host1", 0))

From 87cf35c2d6acc9649b3fb05648b79b9862b3959b Mon Sep 17 00:00:00 2001
From: Sandy Ryza <sandy@cloudera.com>
Date: Fri, 25 Apr 2014 17:55:04 -0700
Subject: [PATCH 031/810] SPARK-1632. Remove unnecessary boxing in compares in
 ExternalAppendOnlyM...

...ap

Author: Sandy Ryza <sandy@cloudera.com>

Closes #559 from sryza/sandy-spark-1632 and squashes the following commits:

a6cd352 [Sandy Ryza] Only compute hashes once
04e3884 [Sandy Ryza] SPARK-1632. Remove unnecessary boxing in compares in ExternalAppendOnlyMap
---
 .../spark/util/collection/ExternalAppendOnlyMap.scala     | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala b/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala
index d615767284c0..170f09be2153 100644
--- a/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala
+++ b/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala
@@ -337,8 +337,8 @@ class ExternalAppendOnlyMap[K, V, C](
       }
 
       override def compareTo(other: StreamBuffer): Int = {
-        // minus sign because mutable.PriorityQueue dequeues the max, not the min
-        -minKeyHash.compareTo(other.minKeyHash)
+        // descending order because mutable.PriorityQueue dequeues the max, not the min
+        if (other.minKeyHash < minKeyHash) -1 else if (other.minKeyHash == minKeyHash) 0 else 1
       }
     }
   }
@@ -422,7 +422,9 @@ class ExternalAppendOnlyMap[K, V, C](
 private[spark] object ExternalAppendOnlyMap {
   private class KCComparator[K, C] extends Comparator[(K, C)] {
     def compare(kc1: (K, C), kc2: (K, C)): Int = {
-      kc1._1.hashCode().compareTo(kc2._1.hashCode())
+      val hash1 = kc1._1.hashCode()
+      val hash2 = kc2._1.hashCode()
+      if (hash1 < hash2) -1 else if (hash1 == hash2) 0 else 1
     }
   }
 }

From 058797c1722c9251f6bc6ad2672cb0e79146b04f Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Fri, 25 Apr 2014 19:04:34 -0700
Subject: [PATCH 032/810] [Spark-1382] Fix NPE in DStream.slice (updated
 version of #365)

@zsxwing I cherry-picked your changes and merged the master. #365 had some conflicts once again!

Author: zsxwing <zsxwing@gmail.com>
Author: Tathagata Das <tathagata.das1565@gmail.com>

Closes #562 from tdas/SPARK-1382 and squashes the following commits:

e2962c1 [Tathagata Das] Merge remote-tracking branch 'apache-github/master' into SPARK-1382
20968d9 [zsxwing] Replace Exception with SparkException in DStream
e476651 [zsxwing] Merge remote-tracking branch 'origin/master' into SPARK-1382
35ba56a [zsxwing] SPARK-1382: Fix NPE in DStream.slice
---
 .../spark/streaming/dstream/DStream.scala     | 22 ++++++++++---------
 .../streaming/BasicOperationsSuite.scala      | 12 +++++++++-
 2 files changed, 23 insertions(+), 11 deletions(-)

diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala
index f69f69e0c44a..4709a6238164 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala
@@ -18,20 +18,19 @@
 package org.apache.spark.streaming.dstream
 
 
+import java.io.{IOException, ObjectInputStream, ObjectOutputStream}
+
 import scala.deprecated
 import scala.collection.mutable.HashMap
 import scala.reflect.ClassTag
 
-import java.io.{IOException, ObjectInputStream, ObjectOutputStream}
-
-import org.apache.spark.Logging
+import org.apache.spark.{Logging, SparkException}
 import org.apache.spark.rdd.{BlockRDD, RDD}
 import org.apache.spark.storage.StorageLevel
-import org.apache.spark.util.MetadataCleaner
 import org.apache.spark.streaming._
 import org.apache.spark.streaming.StreamingContext._
 import org.apache.spark.streaming.scheduler.Job
-import org.apache.spark.streaming.Duration
+import org.apache.spark.util.MetadataCleaner
 
 /**
  * A Discretized Stream (DStream), the basic abstraction in Spark Streaming, is a continuous
@@ -144,7 +143,7 @@ abstract class DStream[T: ClassTag] (
    */
   private[streaming] def initialize(time: Time) {
     if (zeroTime != null && zeroTime != time) {
-      throw new Exception("ZeroTime is already initialized to " + zeroTime
+      throw new SparkException("ZeroTime is already initialized to " + zeroTime
         + ", cannot initialize it again to " + time)
     }
     zeroTime = time
@@ -220,7 +219,7 @@ abstract class DStream[T: ClassTag] (
         "which requires " + this.getClass.getSimpleName + " to remember generated RDDs for more " +
         "than " + rememberDuration.milliseconds / 1000 + " seconds. But Spark's metadata cleanup" +
         "delay is set to " + metadataCleanerDelay + " seconds, which is not sufficient. Please " +
-        "set the Java property 'spark.cleaner.delay' to more than " +
+        "set the Java cleaner delay to more than " +
         math.ceil(rememberDuration.milliseconds / 1000.0).toInt + " seconds."
     )
 
@@ -235,7 +234,7 @@ abstract class DStream[T: ClassTag] (
 
   private[streaming] def setContext(s: StreamingContext) {
     if (ssc != null && ssc != s) {
-      throw new Exception("Context is already set in " + this + ", cannot set it again")
+      throw new SparkException("Context is already set in " + this + ", cannot set it again")
     }
     ssc = s
     logInfo("Set context for " + this)
@@ -244,7 +243,7 @@ abstract class DStream[T: ClassTag] (
 
   private[streaming] def setGraph(g: DStreamGraph) {
     if (graph != null && graph != g) {
-      throw new Exception("Graph is already set in " + this + ", cannot set it again")
+      throw new SparkException("Graph is already set in " + this + ", cannot set it again")
     }
     graph = g
     dependencies.foreach(_.setGraph(graph))
@@ -261,7 +260,7 @@ abstract class DStream[T: ClassTag] (
   /** Checks whether the 'time' is valid wrt slideDuration for generating RDD */
   private[streaming] def isTimeValid(time: Time): Boolean = {
     if (!isInitialized) {
-      throw new Exception (this + " has not been initialized")
+      throw new SparkException (this + " has not been initialized")
     } else if (time <= zeroTime || ! (time - zeroTime).isMultipleOf(slideDuration)) {
       logInfo("Time " + time + " is invalid as zeroTime is " + zeroTime +
         " and slideDuration is " + slideDuration + " and difference is " + (time - zeroTime))
@@ -728,6 +727,9 @@ abstract class DStream[T: ClassTag] (
    * Return all the RDDs between 'fromTime' to 'toTime' (both included)
    */
   def slice(fromTime: Time, toTime: Time): Seq[RDD[T]] = {
+    if (!isInitialized) {
+      throw new SparkException(this + " has not been initialized")
+    }
     if (!(fromTime - zeroTime).isMultipleOf(slideDuration)) {
       logWarning("fromTime (" + fromTime + ") is not a multiple of slideDuration ("
         + slideDuration + ")")
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/BasicOperationsSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/BasicOperationsSuite.scala
index 4792ca1f8ae3..04925886c39e 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/BasicOperationsSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/BasicOperationsSuite.scala
@@ -23,7 +23,7 @@ import org.apache.spark.rdd.{BlockRDD, RDD}
 import org.apache.spark.SparkContext._
 
 import util.ManualClock
-import org.apache.spark.{SparkContext, SparkConf}
+import org.apache.spark.{SparkException, SparkConf}
 import org.apache.spark.streaming.dstream.{WindowedDStream, DStream}
 import scala.collection.mutable.{SynchronizedBuffer, ArrayBuffer}
 import scala.reflect.ClassTag
@@ -398,6 +398,16 @@ class BasicOperationsSuite extends TestSuiteBase {
     Thread.sleep(1000)
   }
 
+  test("slice - has not been initialized") {
+    val ssc = new StreamingContext(conf, Seconds(1))
+    val input = Seq(Seq(1), Seq(2), Seq(3), Seq(4))
+    val stream = new TestInputStream[Int](ssc, input, 2)
+    val thrown = intercept[SparkException] {
+      stream.slice(new Time(0), new Time(1000))
+    }
+    assert(thrown.getMessage.contains("has not been initialized"))
+  }
+
   val cleanupTestInput = (0 until 10).map(x => Seq(x, x + 1)).toSeq
 
   test("rdd cleanup - map and window") {

From e6e44e46e345640fd89be9f10cdc448b44d30cc1 Mon Sep 17 00:00:00 2001
From: wangfei <wangfei1@huawei.com>
Date: Sat, 26 Apr 2014 14:38:42 -0700
Subject: [PATCH 033/810] add note of how to support table with more than 22
 fields

Author: wangfei <wangfei1@huawei.com>

Closes #564 from scwf/patch-6 and squashes the following commits:

a331876 [wangfei] Update sql-programming-guide.md
685135b [wangfei] Update sql-programming-guide.md
10b3dc0 [wangfei] Update sql-programming-guide.md
1c40480 [wangfei] add note of how to support table with 22 fields
---
 docs/sql-programming-guide.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md
index 3ca6cc95bd2d..0c743c9d6010 100644
--- a/docs/sql-programming-guide.md
+++ b/docs/sql-programming-guide.md
@@ -114,6 +114,8 @@ val sqlContext = new org.apache.spark.sql.SQLContext(sc)
 import sqlContext._
 
 // Define the schema using a case class.
+// Note: Case classes in Scala 2.10 can support only up to 22 fields. To work around this limit, 
+// you can use custom classes that implement the Product interface.
 case class Person(name: String, age: Int)
 
 // Create an RDD of Person objects and register it as a table.

From 8e37ed6eb81687140b6cdb00f4ec609ec7ba9be1 Mon Sep 17 00:00:00 2001
From: Takuya UESHIN <ueshin@happy-camper.st>
Date: Sat, 26 Apr 2014 14:39:54 -0700
Subject: [PATCH 034/810] [SPARK-1608] [SQL] Fix Cast.nullable when cast from
 StringType to NumericType/TimestampType.

`Cast.nullable` should be `true` when cast from `StringType` to `NumericType` or `TimestampType`.
Because if `StringType` expression has an illegal number string or illegal timestamp string, the casted value becomes `null`.

Author: Takuya UESHIN <ueshin@happy-camper.st>

Closes #532 from ueshin/issues/SPARK-1608 and squashes the following commits:

065d37c [Takuya UESHIN] Add tests to check nullabilities of cast expressions.
f278ed7 [Takuya UESHIN] Revert test to keep it readable and concise.
9fc9380 [Takuya UESHIN] Fix Cast.nullable when cast from StringType to NumericType/TimestampType.
---
 .../apache/spark/sql/catalyst/expressions/Cast.scala |  6 +++++-
 .../expressions/ExpressionEvaluationSuite.scala      | 12 ++++++++++++
 2 files changed, 17 insertions(+), 1 deletion(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
index 8b79b0cd65a8..40d2b42a0cda 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
@@ -24,7 +24,11 @@ import org.apache.spark.sql.catalyst.types._
 /** Cast the child expression to the target data type. */
 case class Cast(child: Expression, dataType: DataType) extends UnaryExpression {
   override def foldable = child.foldable
-  def nullable = child.nullable
+  def nullable = (child.dataType, dataType) match {
+    case (StringType, _: NumericType) => true
+    case (StringType, TimestampType)  => true
+    case _                            => child.nullable
+  }
   override def toString = s"CAST($child, $dataType)"
 
   type EvaluatedType = Any
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala
index 4ce0dff9e158..d287ad73b9e9 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala
@@ -245,6 +245,18 @@ class ExpressionEvaluationSuite extends FunSuite {
     checkEvaluation(Literal(23.toShort) + Cast(true, ShortType), 24)
 
     intercept[Exception] {evaluate(Literal(1) cast BinaryType, null)}
+
+    assert(("abcdef" cast StringType).nullable === false)
+    assert(("abcdef" cast BinaryType).nullable === false)
+    assert(("abcdef" cast BooleanType).nullable === false)
+    assert(("abcdef" cast TimestampType).nullable === true)
+    assert(("abcdef" cast LongType).nullable === true)
+    assert(("abcdef" cast IntegerType).nullable === true)
+    assert(("abcdef" cast ShortType).nullable === true)
+    assert(("abcdef" cast ByteType).nullable === true)
+    assert(("abcdef" cast DecimalType).nullable === true)
+    assert(("abcdef" cast DoubleType).nullable === true)
+    assert(("abcdef" cast FloatType).nullable === true)
   }
 
   test("timestamp") {

From 762af4e9c2837c8ca125838b2ca01e328ee6669f Mon Sep 17 00:00:00 2001
From: Sandeep <sandeep@techaddict.me>
Date: Sat, 26 Apr 2014 19:04:33 -0700
Subject: [PATCH 035/810] SPARK-1467: Make StorageLevel.apply() factory methods
 Developer APIs

We may want to evolve these in the future to add things like SSDs, so let's mark them as experimental for now. Long-term the right solution might be some kind of builder. The stable API should be the existing StorageLevel constants.

Author: Sandeep <sandeep@techaddict.me>

Closes #551 from techaddict/SPARK-1467 and squashes the following commits:

6bdda24 [Sandeep] SPARK-1467: Make StorageLevel.apply() factory methods as Developer Api's We may want to evolve these in the future to add things like SSDs, so let's mark them as experimental for now. Long-term the right solution might be some kind of builder. The stable API should be the existing StorageLevel constants.
---
 .../apache/spark/storage/StorageLevel.scala   | 26 ++++++++++++++++---
 1 file changed, 22 insertions(+), 4 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/storage/StorageLevel.scala b/core/src/main/scala/org/apache/spark/storage/StorageLevel.scala
index 95e71de2d3f1..c9a52e0366d9 100644
--- a/core/src/main/scala/org/apache/spark/storage/StorageLevel.scala
+++ b/core/src/main/scala/org/apache/spark/storage/StorageLevel.scala
@@ -19,6 +19,8 @@ package org.apache.spark.storage
 
 import java.io.{Externalizable, IOException, ObjectInput, ObjectOutput}
 
+import org.apache.spark.annotation.DeveloperApi
+
 /**
  * Flags for controlling the storage of an RDD. Each StorageLevel records whether to use memory,
  * or Tachyon, whether to drop the RDD to disk if it falls out of memory or Tachyon , whether to
@@ -142,21 +144,37 @@ object StorageLevel {
   val MEMORY_AND_DISK_SER_2 = new StorageLevel(true, true, false, false, 2)
   val OFF_HEAP = new StorageLevel(false, false, true, false)
 
-  /** Create a new StorageLevel object without setting useOffHeap */
+  /**
+   * :: DeveloperApi ::
+   * Create a new StorageLevel object without setting useOffHeap
+   */
+  @DeveloperApi
   def apply(useDisk: Boolean, useMemory: Boolean, useOffHeap: Boolean,
     deserialized: Boolean, replication: Int) = getCachedStorageLevel(
       new StorageLevel(useDisk, useMemory, useOffHeap, deserialized, replication))
 
-  /** Create a new StorageLevel object */
+  /**
+   * :: DeveloperApi ::
+   * Create a new StorageLevel object
+   */
+  @DeveloperApi
   def apply(useDisk: Boolean, useMemory: Boolean,
     deserialized: Boolean, replication: Int = 1) = getCachedStorageLevel(
       new StorageLevel(useDisk, useMemory, false, deserialized, replication))
 
-  /** Create a new StorageLevel object from its integer representation */
+  /**
+   * :: DeveloperApi ::
+   * Create a new StorageLevel object from its integer representation
+   */
+  @DeveloperApi
   def apply(flags: Int, replication: Int): StorageLevel =
     getCachedStorageLevel(new StorageLevel(flags, replication))
 
-  /** Read StorageLevel object from ObjectInput stream */
+  /**
+   * :: DeveloperApi ::
+   * Read StorageLevel object from ObjectInput stream
+   */
+  @DeveloperApi
   def apply(in: ObjectInput): StorageLevel = {
     val obj = new StorageLevel()
     obj.readExternal(in)

From aa9a7f5db7bffcdbcd7fd53694c606d3a8cdd21f Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Sat, 26 Apr 2014 19:24:29 -0700
Subject: [PATCH 036/810] SPARK-1606: Infer user application arguments instead
 of requiring --arg.

This modifies spark-submit to do something more like the Hadoop `jar`
command. Now we have the following syntax:

./bin/spark-submit [options] user.jar [user options]

Author: Patrick Wendell <pwendell@gmail.com>

Closes #563 from pwendell/spark-submit and squashes the following commits:

32241fc [Patrick Wendell] Review feedback
3adfb69 [Patrick Wendell] Small fix
bc48139 [Patrick Wendell] SPARK-1606: Infer user application arguments instead of requiring --arg.
---
 .../org/apache/spark/deploy/SparkSubmit.scala |   1 -
 .../spark/deploy/SparkSubmitArguments.scala   | 226 +++++++++---------
 .../scala/org/apache/spark/util/Utils.scala   |   7 +
 .../spark/deploy/SparkSubmitSuite.scala       |  77 +++---
 docs/cluster-overview.md                      |  22 +-
 docs/quick-start.md                           |  10 +-
 6 files changed, 181 insertions(+), 162 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
index 58aa6d951a20..24edc6068437 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
@@ -185,7 +185,6 @@ object SparkSubmit {
     if (clusterManager == STANDALONE) {
       val existingJars = sysProps.get("spark.jars").map(x => x.split(",").toSeq).getOrElse(Seq())
       sysProps.put("spark.jars", (existingJars ++ Seq(appArgs.primaryResource)).mkString(","))
-      println("SPARK JARS" + sysProps.get("spark.jars"))
     }
 
     if (deployOnCluster && clusterManager == STANDALONE) {
diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
index c545b093ac82..58d9e9add764 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
@@ -21,14 +21,15 @@ import java.io.{File, FileInputStream, IOException}
 import java.util.Properties
 
 import scala.collection.JavaConversions._
-import scala.collection.mutable.{HashMap, ArrayBuffer}
+import scala.collection.mutable.{ArrayBuffer, HashMap}
 
 import org.apache.spark.SparkException
+import org.apache.spark.util.Utils
 
 /**
  * Parses and encapsulates arguments from the spark-submit script.
  */
-private[spark] class SparkSubmitArguments(args: Array[String]) {
+private[spark] class SparkSubmitArguments(args: Seq[String]) {
   var master: String = null
   var deployMode: String = null
   var executorMemory: String = null
@@ -118,8 +119,7 @@ private[spark] class SparkSubmitArguments(args: Array[String]) {
 
     if (master.startsWith("yarn")) {
       val hasHadoopEnv = sys.env.contains("HADOOP_CONF_DIR") || sys.env.contains("YARN_CONF_DIR")
-      val testing = sys.env.contains("SPARK_TESTING")
-      if (!hasHadoopEnv && !testing) {
+      if (!hasHadoopEnv && !Utils.isTesting) {
         throw new Exception(s"When running with master '$master' " +
           "either HADOOP_CONF_DIR or YARN_CONF_DIR must be set in the environment.")
       }
@@ -156,119 +156,121 @@ private[spark] class SparkSubmitArguments(args: Array[String]) {
     """.stripMargin
   }
 
-  private def parseOpts(opts: List[String]): Unit = opts match {
-    case ("--name") :: value :: tail =>
-      name = value
-      parseOpts(tail)
+  /** Fill in values by parsing user options. */
+  private def parseOpts(opts: Seq[String]): Unit = {
+    // Delineates parsing of Spark options from parsing of user options.
+    var inSparkOpts = true
+    parse(opts)
 
-    case ("--master") :: value :: tail =>
-      master = value
-      parseOpts(tail)
+    def parse(opts: Seq[String]): Unit = opts match {
+      case ("--name") :: value :: tail =>
+        name = value
+        parse(tail)
 
-    case ("--class") :: value :: tail =>
-      mainClass = value
-      parseOpts(tail)
+      case ("--master") :: value :: tail =>
+        master = value
+        parse(tail)
 
-    case ("--deploy-mode") :: value :: tail =>
-      if (value != "client" && value != "cluster") {
-        SparkSubmit.printErrorAndExit("--deploy-mode must be either \"client\" or \"cluster\"")
-      }
-      deployMode = value
-      parseOpts(tail)
-
-    case ("--num-executors") :: value :: tail =>
-      numExecutors = value
-      parseOpts(tail)
-
-    case ("--total-executor-cores") :: value :: tail =>
-      totalExecutorCores = value
-      parseOpts(tail)
-
-    case ("--executor-cores") :: value :: tail =>
-      executorCores = value
-      parseOpts(tail)
-
-    case ("--executor-memory") :: value :: tail =>
-      executorMemory = value
-      parseOpts(tail)
-
-    case ("--driver-memory") :: value :: tail =>
-      driverMemory = value
-      parseOpts(tail)
-
-    case ("--driver-cores") :: value :: tail =>
-      driverCores = value
-      parseOpts(tail)
-
-    case ("--driver-class-path") :: value :: tail =>
-      driverExtraClassPath = value
-      parseOpts(tail)
-
-    case ("--driver-java-options") :: value :: tail =>
-      driverExtraJavaOptions = value
-      parseOpts(tail)
-
-    case ("--driver-library-path") :: value :: tail =>
-      driverExtraLibraryPath = value
-      parseOpts(tail)
-
-    case ("--properties-file") :: value :: tail =>
-      propertiesFile = value
-      parseOpts(tail)
-
-    case ("--supervise") :: tail =>
-      supervise = true
-      parseOpts(tail)
-
-    case ("--queue") :: value :: tail =>
-      queue = value
-      parseOpts(tail)
-
-    case ("--files") :: value :: tail =>
-      files = value
-      parseOpts(tail)
-
-    case ("--archives") :: value :: tail =>
-      archives = value
-      parseOpts(tail)
-
-    case ("--arg") :: value :: tail =>
-      childArgs += value
-      parseOpts(tail)
-
-    case ("--jars") :: value :: tail =>
-      jars = value
-      parseOpts(tail)
-
-    case ("--help" | "-h") :: tail =>
-      printUsageAndExit(0)
-
-    case ("--verbose" | "-v") :: tail =>
-      verbose = true
-      parseOpts(tail)
-
-    case value :: tail =>
-      if (value.startsWith("-")) {
-        val errMessage = s"Unrecognized option '$value'."
-        val suggestion: Option[String] = value match {
-          case v if v.startsWith("--") && v.contains("=") =>
-            val parts = v.split("=")
-            Some(s"Perhaps you want '${parts(0)} ${parts(1)}'?")
-          case _ =>
-            None
+      case ("--class") :: value :: tail =>
+        mainClass = value
+        parse(tail)
+
+      case ("--deploy-mode") :: value :: tail =>
+        if (value != "client" && value != "cluster") {
+          SparkSubmit.printErrorAndExit("--deploy-mode must be either \"client\" or \"cluster\"")
+        }
+        deployMode = value
+        parse(tail)
+
+      case ("--num-executors") :: value :: tail =>
+        numExecutors = value
+        parse(tail)
+
+      case ("--total-executor-cores") :: value :: tail =>
+        totalExecutorCores = value
+        parse(tail)
+
+      case ("--executor-cores") :: value :: tail =>
+        executorCores = value
+        parse(tail)
+
+      case ("--executor-memory") :: value :: tail =>
+        executorMemory = value
+        parse(tail)
+
+      case ("--driver-memory") :: value :: tail =>
+        driverMemory = value
+        parse(tail)
+
+      case ("--driver-cores") :: value :: tail =>
+        driverCores = value
+        parse(tail)
+
+      case ("--driver-class-path") :: value :: tail =>
+        driverExtraClassPath = value
+        parse(tail)
+
+      case ("--driver-java-options") :: value :: tail =>
+        driverExtraJavaOptions = value
+        parse(tail)
+
+      case ("--driver-library-path") :: value :: tail =>
+        driverExtraLibraryPath = value
+        parse(tail)
+
+      case ("--properties-file") :: value :: tail =>
+        propertiesFile = value
+        parse(tail)
+
+      case ("--supervise") :: tail =>
+        supervise = true
+        parse(tail)
+
+      case ("--queue") :: value :: tail =>
+        queue = value
+        parse(tail)
+
+      case ("--files") :: value :: tail =>
+        files = value
+        parse(tail)
+
+      case ("--archives") :: value :: tail =>
+        archives = value
+        parse(tail)
+
+      case ("--jars") :: value :: tail =>
+        jars = value
+        parse(tail)
+
+      case ("--help" | "-h") :: tail =>
+        printUsageAndExit(0)
+
+      case ("--verbose" | "-v") :: tail =>
+        verbose = true
+        parse(tail)
+
+      case value :: tail =>
+        if (inSparkOpts) {
+          value match {
+            // convert --foo=bar to --foo bar
+            case v if v.startsWith("--") && v.contains("=") && v.split("=").size == 2 =>
+              val parts = v.split("=")
+              parse(Seq(parts(0), parts(1)) ++ tail)
+            case v if v.startsWith("-") =>
+              val errMessage = s"Unrecognized option '$value'."
+              SparkSubmit.printErrorAndExit(errMessage)
+            case v =>
+             primaryResource = v
+             inSparkOpts = false
+             parse(tail)
+          }
+        } else {
+          childArgs += value
+          parse(tail)
         }
-        SparkSubmit.printErrorAndExit(errMessage + suggestion.map(" " + _).getOrElse(""))
-      }
 
-      if (primaryResource != null) {
-        val error = s"Found two conflicting resources, $value and $primaryResource." +
-          " Expecting only one resource."
-        SparkSubmit.printErrorAndExit(error)
+      case Nil =>
       }
-      primaryResource = value
-      parseOpts(tail)
-
-    case Nil =>
   }
 
   private def printUsageAndExit(exitCode: Int, unknownParam: Any = null) {
@@ -277,7 +279,7 @@ private[spark] class SparkSubmitArguments(args: Array[String]) {
       outStream.println("Unknown/unsupported param " + unknownParam)
     }
     outStream.println(
-      """Usage: spark-submit <app jar> [options]
+      """Usage: spark-submit [options] <app jar> [app options]
         |Options:
         |  --master MASTER_URL         spark://host:port, mesos://host:port, yarn, or local.
         |  --deploy-mode DEPLOY_MODE   Mode to deploy the app in, either 'client' or 'cluster'.
diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala
index 8351f7156a5e..5a55e7df3483 100644
--- a/core/src/main/scala/org/apache/spark/util/Utils.scala
+++ b/core/src/main/scala/org/apache/spark/util/Utils.scala
@@ -1056,4 +1056,11 @@ private[spark] object Utils extends Logging {
   def getHadoopFileSystem(path: String): FileSystem = {
     getHadoopFileSystem(new URI(path))
   }
+
+  /**
+   * Indicates whether Spark is currently running unit tests.
+   */
+  private[spark] def isTesting = {
+    sys.env.contains("SPARK_TESTING") || sys.props.contains("spark.testing")
+  }
 }
diff --git a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala
index 657b44668d38..10a65c75cc62 100644
--- a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala
@@ -28,6 +28,9 @@ import org.scalatest.FunSuite
 import org.scalatest.matchers.ShouldMatchers
 
 class SparkSubmitSuite extends FunSuite with ShouldMatchers {
+  def beforeAll() {
+    System.setProperty("spark.testing", "true")
+  }
 
   val noOpOutputStream = new OutputStream {
     def write(b: Int) = {}
@@ -74,33 +77,35 @@ class SparkSubmitSuite extends FunSuite with ShouldMatchers {
     testPrematureExit(Array("--help"), "Usage: spark-submit")
   }
 
-  test("prints error with unrecognized option") {
+  test("prints error with unrecognized options") {
     testPrematureExit(Array("--blarg"), "Unrecognized option '--blarg'")
     testPrematureExit(Array("-bleg"), "Unrecognized option '-bleg'")
-    testPrematureExit(Array("--master=abc"),
-      "Unrecognized option '--master=abc'. Perhaps you want '--master abc'?")
   }
 
-  test("handles multiple binary definitions") {
-    val adjacentJars = Array("foo.jar", "bar.jar")
-    testPrematureExit(adjacentJars, "error: Found two conflicting resources")
+  test("handle binary specified but not class") {
+    testPrematureExit(Array("foo.jar"), "Must specify a main class")
+  }
 
-    val nonAdjacentJars =
-      Array("foo.jar", "--master", "123", "--class", "abc", "bar.jar")
-    testPrematureExit(nonAdjacentJars, "error: Found two conflicting resources")
+  test("handles arguments with --key=val") {
+    val clArgs = Seq("--jars=one.jar,two.jar,three.jar", "--name=myApp")
+    val appArgs = new SparkSubmitArguments(clArgs)
+    appArgs.jars should be ("one.jar,two.jar,three.jar")
+    appArgs.name should be ("myApp")
   }
 
-  test("handle binary specified but not class") {
-    testPrematureExit(Array("foo.jar"), "Must specify a main class")
+  test("handles arguments to user program") {
+    val clArgs = Seq("--name", "myApp", "userjar.jar", "some", "--random", "args", "here")
+    val appArgs = new SparkSubmitArguments(clArgs)
+    appArgs.childArgs should be (Seq("some", "--random", "args", "here"))
   }
 
   test("handles YARN cluster mode") {
-    val clArgs = Array("thejar.jar", "--deploy-mode", "cluster",
+    val clArgs = Seq("--deploy-mode", "cluster",
       "--master", "yarn", "--executor-memory", "5g", "--executor-cores", "5",
       "--class", "org.SomeClass", "--jars", "one.jar,two.jar,three.jar",
-      "--arg", "arg1", "--arg", "arg2", "--driver-memory", "4g",
-      "--queue", "thequeue", "--files", "file1.txt,file2.txt",
-      "--archives", "archive1.txt,archive2.txt", "--num-executors", "6")
+      "--driver-memory", "4g", "--queue", "thequeue", "--files", "file1.txt,file2.txt",
+      "--archives", "archive1.txt,archive2.txt", "--num-executors", "6",
+      "thejar.jar", "arg1", "arg2")
     val appArgs = new SparkSubmitArguments(clArgs)
     val (childArgs, classpath, sysProps, mainClass) = createLaunchEnv(appArgs)
     val childArgsStr = childArgs.mkString(" ")
@@ -121,12 +126,12 @@ class SparkSubmitSuite extends FunSuite with ShouldMatchers {
   }
 
   test("handles YARN client mode") {
-    val clArgs = Array("thejar.jar", "--deploy-mode", "client",
+    val clArgs = Seq("--deploy-mode", "client",
       "--master", "yarn", "--executor-memory", "5g", "--executor-cores", "5",
       "--class", "org.SomeClass", "--jars", "one.jar,two.jar,three.jar",
-      "--arg", "arg1", "--arg", "arg2", "--driver-memory", "4g",
-      "--queue", "thequeue", "--files", "file1.txt,file2.txt",
-      "--archives", "archive1.txt,archive2.txt", "--num-executors", "6")
+      "--driver-memory", "4g", "--queue", "thequeue", "--files", "file1.txt,file2.txt",
+      "--archives", "archive1.txt,archive2.txt", "--num-executors", "6", "thejar.jar",
+      "arg1", "arg2")
     val appArgs = new SparkSubmitArguments(clArgs)
     val (childArgs, classpath, sysProps, mainClass) = createLaunchEnv(appArgs)
     childArgs.mkString(" ") should be ("arg1 arg2")
@@ -144,9 +149,9 @@ class SparkSubmitSuite extends FunSuite with ShouldMatchers {
   }
 
   test("handles standalone cluster mode") {
-    val clArgs = Array("thejar.jar", "--deploy-mode", "cluster",
-      "--master", "spark://h:p", "--class", "org.SomeClass", "--arg", "arg1", "--arg", "arg2",
-      "--supervise", "--driver-memory", "4g", "--driver-cores", "5")
+    val clArgs = Seq("--deploy-mode", "cluster",
+      "--master", "spark://h:p", "--class", "org.SomeClass",
+      "--supervise", "--driver-memory", "4g", "--driver-cores", "5", "thejar.jar", "arg1", "arg2")
     val appArgs = new SparkSubmitArguments(clArgs)
     val (childArgs, classpath, sysProps, mainClass) = createLaunchEnv(appArgs)
     val childArgsStr = childArgs.mkString(" ")
@@ -158,10 +163,9 @@ class SparkSubmitSuite extends FunSuite with ShouldMatchers {
   }
 
   test("handles standalone client mode") {
-    val clArgs = Array("thejar.jar", "--deploy-mode", "client",
+    val clArgs = Seq("--deploy-mode", "client",
       "--master", "spark://h:p", "--executor-memory", "5g", "--total-executor-cores", "5",
-      "--class", "org.SomeClass", "--arg", "arg1", "--arg", "arg2",
-      "--driver-memory", "4g")
+      "--class", "org.SomeClass", "--driver-memory", "4g", "thejar.jar", "arg1", "arg2")
     val appArgs = new SparkSubmitArguments(clArgs)
     val (childArgs, classpath, sysProps, mainClass) = createLaunchEnv(appArgs)
     childArgs.mkString(" ") should be ("arg1 arg2")
@@ -172,10 +176,9 @@ class SparkSubmitSuite extends FunSuite with ShouldMatchers {
   }
 
   test("handles mesos client mode") {
-    val clArgs = Array("thejar.jar", "--deploy-mode", "client",
+    val clArgs = Seq("--deploy-mode", "client",
       "--master", "mesos://h:p", "--executor-memory", "5g", "--total-executor-cores", "5",
-      "--class", "org.SomeClass", "--arg", "arg1", "--arg", "arg2",
-      "--driver-memory", "4g")
+      "--class", "org.SomeClass", "--driver-memory", "4g", "thejar.jar", "arg1", "arg2")
     val appArgs = new SparkSubmitArguments(clArgs)
     val (childArgs, classpath, sysProps, mainClass) = createLaunchEnv(appArgs)
     childArgs.mkString(" ") should be ("arg1 arg2")
@@ -187,22 +190,24 @@ class SparkSubmitSuite extends FunSuite with ShouldMatchers {
 
   test("launch simple application with spark-submit") {
     runSparkSubmit(
-      Seq("unUsed.jar",
+      Seq(
         "--class", SimpleApplicationTest.getClass.getName.stripSuffix("$"),
         "--name", "testApp",
-        "--master", "local"))
+        "--master", "local",
+        "unUsed.jar"))
   }
 
   test("spark submit includes jars passed in through --jar") {
     val jar1 = TestUtils.createJarWithClasses(Seq("SparkSubmitClassA"))
     val jar2 = TestUtils.createJarWithClasses(Seq("SparkSubmitClassB"))
     val jarsString = Seq(jar1, jar2).map(j => j.toString).mkString(",")
-    runSparkSubmit(
-      Seq("unUsed.jar",
-        "--class", JarCreationTest.getClass.getName.stripSuffix("$"),
-        "--name", "testApp",
-        "--master", "local-cluster[2,1,512]",
-        "--jars", jarsString))
+    val args = Seq(
+      "--class", JarCreationTest.getClass.getName.stripSuffix("$"),
+      "--name", "testApp",
+      "--master", "local-cluster[2,1,512]",
+      "--jars", jarsString,
+      "unused.jar")
+    runSparkSubmit(args)
   }
 
   // NOTE: This is an expensive operation in terms of time (10 seconds+). Use sparingly.
diff --git a/docs/cluster-overview.md b/docs/cluster-overview.md
index dcc063042628..b011679fede2 100644
--- a/docs/cluster-overview.md
+++ b/docs/cluster-overview.md
@@ -73,30 +73,34 @@ the bin directory. This script takes care of setting up the classpath with Spark
 dependencies, and can support different cluster managers and deploy modes that Spark supports.
 It's usage is
 
-    ./bin/spark-submit <app jar> --class path.to.your.Class [other options..]
+    ./bin/spark-submit --class path.to.your.Class [options] <app jar> [app options]
 
-To enumerate all options available to `spark-submit` run it with the `--help` flag.
-Here are a few examples of common options:
+When calling `spark-submit`, `[app options]` will be passed along to your application's
+main class. To enumerate all options available to `spark-submit` run it with 
+the `--help` flag. Here are a few examples of common options:
 
 {% highlight bash %}
 # Run application locally
-./bin/spark-submit my-app.jar \
+./bin/spark-submit \
   --class my.main.ClassName
-  --master local[8]
+  --master local[8] \
+  my-app.jar
 
 # Run on a Spark cluster
-./bin/spark-submit my-app.jar \
+./bin/spark-submit \
   --class my.main.ClassName
   --master spark://mycluster:7077 \
   --executor-memory 20G \
-  --total-executor-cores 100
+  --total-executor-cores 100 \
+  my-app.jar
 
 # Run on a YARN cluster
-HADOOP_CONF_DIR=XX /bin/spark-submit my-app.jar \
+HADOOP_CONF_DIR=XX /bin/spark-submit \
   --class my.main.ClassName
   --master yarn-cluster \  # can also be `yarn-client` for client mode
   --executor-memory 20G \
-  --num-executors 50
+  --num-executors 50 \
+  my-app.jar
 {% endhighlight %}
 
 ### Loading Configurations from a File
diff --git a/docs/quick-start.md b/docs/quick-start.md
index 68afa6e1bff9..64996b52e040 100644
--- a/docs/quick-start.md
+++ b/docs/quick-start.md
@@ -179,9 +179,10 @@ $ sbt package
 [info] Packaging {..}/{..}/target/scala-2.10/simple-project_2.10-1.0.jar
 
 # Use spark-submit to run your application
-$ YOUR_SPARK_HOME/bin/spark-submit target/scala-2.10/simple-project_2.10-1.0.jar \
+$ YOUR_SPARK_HOME/bin/spark-submit \
   --class "SimpleApp" \
-  --master local[4]
+  --master local[4] \
+  target/scala-2.10/simple-project_2.10-1.0.jar
 ...
 Lines with a: 46, Lines with b: 23
 {% endhighlight %}
@@ -272,9 +273,10 @@ $ mvn package
 [INFO] Building jar: {..}/{..}/target/simple-project-1.0.jar
 
 # Use spark-submit to run your application
-$ YOUR_SPARK_HOME/bin/spark-submit target/simple-project-1.0.jar \
+$ YOUR_SPARK_HOME/bin/spark-submit \
   --class "SimpleApp" \
-  --master local[4]
+  --master local[4] \
+  target/simple-project-1.0.jar
 ...
 Lines with a: 46, Lines with b: 23
 {% endhighlight %}

From 7b2527d74deac9512f8ee9ad6d4b060f05e1ab26 Mon Sep 17 00:00:00 2001
From: Rahul Singhal <rahul.singhal@guavus.com>
Date: Sun, 27 Apr 2014 15:17:06 -0700
Subject: [PATCH 037/810] SPARK-1650: Correctly identify maven project version

Better account for various side-effect outputs while executing
"mvn help:evaluate -Dexpression=project.version"

Author: Rahul Singhal <rahul.singhal@guavus.com>

Closes #572 from rahulsinghaliitd/SPARK-1650 and squashes the following commits:

fd6a611 [Rahul Singhal] SPARK-1650: Correctly identify maven project version
---
 make-distribution.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/make-distribution.sh b/make-distribution.sh
index 55fe6c09d0d6..661d1ff5e54c 100755
--- a/make-distribution.sh
+++ b/make-distribution.sh
@@ -43,7 +43,7 @@
 FWDIR="$(cd `dirname $0`; pwd)"
 DISTDIR="$FWDIR/dist"
 
-VERSION=$(mvn help:evaluate -Dexpression=project.version |grep -v "INFO")
+VERSION=$(mvn help:evaluate -Dexpression=project.version | grep -v "INFO" | tail -n 1)
 if [ $? == -1 ] ;then
     echo -e "You need Maven installed to build Spark."
     echo -e "Download Maven from https://maven.apache.org."

From fe65beeaab737a1c9922b84452ca661b39075cfa Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Sun, 27 Apr 2014 15:41:57 -0700
Subject: [PATCH 038/810] SPARK-1648 Support closing JIRA's as part of merge
 script.

Adds an automated hook in the merge script that can close the JIRA,
set the fix versions, and leave a comment on the JIRA indicating the
PR in which it was resolved. This ensures that (a) we always close JIRA's
when issues are merged and (b) there is a link to the pull request in every JIRA.

This requires a python library called `jira-client`. We could look at embedding this
library in our project, but it seemed simple enough to just gracefully disable this
feature if it is not installed. It can be installed with `pip install jira-client`.

Author: Patrick Wendell <pwendell@gmail.com>

Closes #570 from pwendell/jira-pr-merge and squashes the following commits:

3022b96 [Patrick Wendell] SPARK-1648 Support closing JIRA's as part of merge script.
---
 dev/merge_spark_pr.py | 114 ++++++++++++++++++++++++++++++++++++++----
 1 file changed, 105 insertions(+), 9 deletions(-)

diff --git a/dev/merge_spark_pr.py b/dev/merge_spark_pr.py
index 7a61943e9481..8d15c05d50ad 100755
--- a/dev/merge_spark_pr.py
+++ b/dev/merge_spark_pr.py
@@ -26,19 +26,33 @@
 
 import json
 import os
+import re
 import subprocess
 import sys
 import tempfile
 import urllib2
 
+try:
+  import jira.client
+  JIRA_IMPORTED=True
+except ImportError:
+  JIRA_IMPORTED=False
+
 # Location of your Spark git development area
 SPARK_HOME = os.environ.get("SPARK_HOME", "/home/patrick/Documents/spark")
 # Remote name which points to the Gihub site
 PR_REMOTE_NAME = os.environ.get("PR_REMOTE_NAME", "apache-github")
 # Remote name which points to Apache git
 PUSH_REMOTE_NAME = os.environ.get("PUSH_REMOTE_NAME", "apache")
-
-GIT_API_BASE = "https://api.github.com/repos/apache/spark"
+# ASF JIRA username
+JIRA_USERNAME = os.environ.get("JIRA_USERNAME", "pwendell")
+# ASF JIRA password
+JIRA_PASSWORD = os.environ.get("JIRA_PASSWORD", "1234")
+
+GITHUB_BASE = "https://github.com/apache/spark/pull"
+GITHUB_API_BASE = "https://api.github.com/repos/apache/spark"
+JIRA_BASE = "https://issues.apache.org/jira/browse"
+JIRA_API_BASE = "https://issues.apache.org/jira"
 # Prefix added to temporary branches
 BRANCH_PREFIX = "PR_TOOL"
 
@@ -145,8 +159,7 @@ def merge_pr(pr_num, target_ref):
   return merge_hash
 
 
-def maybe_cherry_pick(pr_num, merge_hash, default_branch):
-  continue_maybe("Would you like to pick %s into another branch?" % merge_hash)
+def cherry_pick(pr_num, merge_hash, default_branch):
   pick_ref = raw_input("Enter a branch name [%s]: " % default_branch)
   if pick_ref == "":
     pick_ref = default_branch
@@ -171,14 +184,86 @@ def maybe_cherry_pick(pr_num, merge_hash, default_branch):
 
   print("Pull request #%s picked into %s!" % (pr_num, pick_ref))
   print("Pick hash: %s" % pick_hash)
+  return pick_ref
+
+def fix_version_from_branch(branch, versions):
+  # Note: Assumes this is a sorted (newest->oldest) list of un-released versions
+  if branch == "master":
+    return versions[0]
+  else:
+    branch_ver = branch.replace("branch-", "")
+    return filter(lambda x: x.name.startswith(branch_ver), versions)[-1]
+
+def resolve_jira(title, merge_branches, comment):
+  asf_jira = jira.client.JIRA({'server': JIRA_API_BASE},
+    basic_auth=(JIRA_USERNAME, JIRA_PASSWORD))
+
+  default_jira_id = ""
+  search = re.findall("SPARK-[0-9]{4,5}", title)
+  if len(search) > 0:
+    default_jira_id = search[0]
+
+  jira_id = raw_input("Enter a JIRA id [%s]: " % default_jira_id)
+  if jira_id == "":
+    jira_id = default_jira_id
 
-branches = get_json("%s/branches" % GIT_API_BASE)
+  try:
+    issue = asf_jira.issue(jira_id)
+  except Exception as e:
+    fail("ASF JIRA could not find %s\n%s" % (jira_id, e))
+
+  cur_status = issue.fields.status.name
+  cur_summary = issue.fields.summary
+  cur_assignee = issue.fields.assignee
+  if cur_assignee == None:
+    cur_assignee = "NOT ASSIGNED!!!"
+  else:
+    cur_assignee = cur_assignee.displayName
+
+  if cur_status == "Resolved" or cur_status == "Closed":
+    fail("JIRA issue %s already has status '%s'" % (jira_id, cur_status))
+  print ("=== JIRA %s ===" % jira_id)
+  print ("summary\t\t%s\nassignee\t%s\nstatus\t\t%s\nurl\t\t%s/%s\n" % (
+    cur_summary, cur_assignee, cur_status, JIRA_BASE, jira_id))
+
+  versions = asf_jira.project_versions("SPARK")
+  versions = sorted(versions, key = lambda x: x.name, reverse=True)
+  versions = filter(lambda x: x.raw['released'] == False, versions)
+
+  default_fix_versions = map(lambda x: fix_version_from_branch(x, versions).name, merge_branches)
+  for v in default_fix_versions:
+    # Handles the case where we have forked a release branch but not yet made the release.
+    # In this case, if the PR is committed to the master branch and the release branch, we
+    # only consider the release branch to be the fix version. E.g. it is not valid to have
+    # both 1.1.0 and 1.0.0 as fix versions.
+    (major, minor, patch) = v.split(".")
+    if patch == 0:
+      previous = "%s.%s.%s" % (major, int(minor) - 1, 0)
+      if previous in default_fix_versions:
+        default_fix_versions = filter(lambda x: x != v, default_fix_versions)
+  default_fix_versions = ",".join(default_fix_versions)
+
+  fix_versions = raw_input("Enter comma-separated fix version(s) [%s]: " % default_fix_versions)
+  if fix_versions == "":
+    fix_versions = default_fix_versions
+  fix_versions = fix_versions.replace(" ", "").split(",")
+
+  def get_version_json(version_str):
+    return filter(lambda v: v.name == version_str, versions)[0].raw
+  jira_fix_versions = map(lambda v: get_version_json(v), fix_versions)
+
+  resolve = filter(lambda a: a['name'] == "Resolve Issue", asf_jira.transitions(jira_id))[0]
+  asf_jira.transition_issue(jira_id, resolve["id"], fixVersions=jira_fix_versions, comment=comment)
+
+  print "Succesfully resolved %s with fixVersions=%s!" % (jira_id, fix_versions)
+
+branches = get_json("%s/branches" % GITHUB_API_BASE)
 branch_names = filter(lambda x: x.startswith("branch-"), [x['name'] for x in branches])
 # Assumes branch names can be sorted lexicographically
 latest_branch = sorted(branch_names, reverse=True)[0]
 
 pr_num = raw_input("Which pull request would you like to merge? (e.g. 34): ")
-pr = get_json("%s/pulls/%s" % (GIT_API_BASE, pr_num))
+pr = get_json("%s/pulls/%s" % (GITHUB_API_BASE, pr_num))
 
 url = pr["url"]
 title = pr["title"]
@@ -208,11 +293,22 @@ def maybe_cherry_pick(pr_num, merge_hash, default_branch):
   continue_maybe(msg)
 
 print ("\n=== Pull Request #%s ===" % pr_num)
-print("title\t%s\nsource\t%s\ntarget\t%s\nurl\t%s" % (
+print ("title\t%s\nsource\t%s\ntarget\t%s\nurl\t%s" % (
   title, pr_repo_desc, target_ref, url))
 continue_maybe("Proceed with merging pull request #%s?" % pr_num)
 
+merged_refs = [target_ref]
+
 merge_hash = merge_pr(pr_num, target_ref)
 
-while True:
-  maybe_cherry_pick(pr_num, merge_hash, latest_branch)
+pick_prompt = "Would you like to pick %s into another branch?" % merge_hash
+while raw_input("\n%s (y/n): " % pick_prompt).lower() == "y":
+  merged_refs = merged_refs + [cherry_pick(pr_num, merge_hash, latest_branch)]
+
+if JIRA_IMPORTED:
+  continue_maybe("Would you like to update an associated JIRA?")
+  jira_comment = "Issue resolved by pull request %s\n[%s/%s]" % (pr_num, GITHUB_BASE, pr_num)
+  resolve_jira(title, merged_refs, jira_comment)
+else:
+  print "Could not find jira-python library. Run 'sudo pip install jira-python' to install."
+  print "Exiting without trying to close the associated JIRA."

From eefb90d382747c29d7537630ed5ad2c783bb8263 Mon Sep 17 00:00:00 2001
From: Rahul Singhal <rahul.singhal@guavus.com>
Date: Sun, 27 Apr 2014 15:50:48 -0700
Subject: [PATCH 039/810] SPARK-1651: Delete existing deployment directory

Small bug fix to make sure the "spark contents" are copied to the
deployment directory correctly.

Author: Rahul Singhal <rahul.singhal@guavus.com>

Closes #573 from rahulsinghaliitd/SPARK-1651 and squashes the following commits:

402c999 [Rahul Singhal] SPARK-1651: Delete existing deployment directory
---
 make-distribution.sh | 1 +
 1 file changed, 1 insertion(+)

diff --git a/make-distribution.sh b/make-distribution.sh
index 661d1ff5e54c..4ac80efae0ab 100755
--- a/make-distribution.sh
+++ b/make-distribution.sh
@@ -178,6 +178,7 @@ fi
 if [ "$MAKE_TGZ" == "true" ]; then
   TARDIR_NAME=spark-$VERSION-bin-$NAME
   TARDIR="$FWDIR/$TARDIR_NAME"
+  rm -rf "$TARDIR"
   cp -r "$DISTDIR" "$TARDIR"
   tar czf "spark-$VERSION-bin-$NAME.tgz" -C "$FWDIR" "$TARDIR_NAME"
   rm -rf "$TARDIR"

From 3d9fb09681308abd2066d0d02f2438f5a17c9dd9 Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Sun, 27 Apr 2014 15:45:17 -0700
Subject: [PATCH 040/810] HOTFIX: Minor patch to merge script.

---
 dev/merge_spark_pr.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/dev/merge_spark_pr.py b/dev/merge_spark_pr.py
index 8d15c05d50ad..83618c8068d3 100755
--- a/dev/merge_spark_pr.py
+++ b/dev/merge_spark_pr.py
@@ -237,7 +237,7 @@ def resolve_jira(title, merge_branches, comment):
     # only consider the release branch to be the fix version. E.g. it is not valid to have
     # both 1.1.0 and 1.0.0 as fix versions.
     (major, minor, patch) = v.split(".")
-    if patch == 0:
+    if patch == "0":
       previous = "%s.%s.%s" % (major, int(minor) - 1, 0)
       if previous in default_fix_versions:
         default_fix_versions = filter(lambda x: x != v, default_fix_versions)

From 6b3c6e5dd8e74435f71ecdb224db532550ef407b Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Sun, 27 Apr 2014 17:40:56 -0700
Subject: [PATCH 041/810] SPARK-1145: Memory mapping with many small blocks can
 cause JVM allocation failures

This includes some minor code clean-up as well. The main change is that small files are not memory mapped. There is a nicer way to write that code block using Scala's `Try` but to make it easy to back port and as simple as possible, I opted for the more explicit but less pretty format.

Author: Patrick Wendell <pwendell@gmail.com>

Closes #43 from pwendell/block-iter-logging and squashes the following commits:

1cff512 [Patrick Wendell] Small issue from merge.
49f6c269 [Patrick Wendell] Merge remote-tracking branch 'apache/master' into block-iter-logging
4943351 [Patrick Wendell] Added a test and feedback on mateis review
a637a18 [Patrick Wendell] Review feedback and adding rewind() when reading byte buffers.
b76b95f [Patrick Wendell] Review feedback
4e1514e [Patrick Wendell] Don't memory map for small files
d238b88 [Patrick Wendell] Some logging and clean-up
---
 .../spark/storage/BlockFetcherIterator.scala  | 22 +++----
 .../apache/spark/storage/BlockManager.scala   |  3 +-
 .../org/apache/spark/storage/DiskStore.scala  | 16 ++++-
 .../scala/org/apache/spark/util/Utils.scala   |  3 +-
 .../spark/storage/BlockManagerSuite.scala     | 58 +++++++++++++++++--
 docs/configuration.md                         |  9 +++
 6 files changed, 91 insertions(+), 20 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/storage/BlockFetcherIterator.scala b/core/src/main/scala/org/apache/spark/storage/BlockFetcherIterator.scala
index ace9cd51c96b..a02dd9441d67 100644
--- a/core/src/main/scala/org/apache/spark/storage/BlockFetcherIterator.scala
+++ b/core/src/main/scala/org/apache/spark/storage/BlockFetcherIterator.scala
@@ -148,6 +148,12 @@ object BlockFetcherIterator {
     }
 
     protected def splitLocalRemoteBlocks(): ArrayBuffer[FetchRequest] = {
+      // Make remote requests at most maxBytesInFlight / 5 in length; the reason to keep them
+      // smaller than maxBytesInFlight is to allow multiple, parallel fetches from up to 5
+      // nodes, rather than blocking on reading output from one node.
+      val targetRequestSize = math.max(maxBytesInFlight / 5, 1L)
+      logInfo("maxBytesInFlight: " + maxBytesInFlight + ", targetRequestSize: " + targetRequestSize)
+
       // Split local and remote blocks. Remote blocks are further split into FetchRequests of size
       // at most maxBytesInFlight in order to limit the amount of data in flight.
       val remoteRequests = new ArrayBuffer[FetchRequest]
@@ -159,11 +165,6 @@ object BlockFetcherIterator {
           _numBlocksToFetch += localBlocksToFetch.size
         } else {
           numRemote += blockInfos.size
-          // Make our requests at least maxBytesInFlight / 5 in length; the reason to keep them
-          // smaller than maxBytesInFlight is to allow multiple, parallel fetches from up to 5
-          // nodes, rather than blocking on reading output from one node.
-          val minRequestSize = math.max(maxBytesInFlight / 5, 1L)
-          logInfo("maxBytesInFlight: " + maxBytesInFlight + ", minRequest: " + minRequestSize)
           val iterator = blockInfos.iterator
           var curRequestSize = 0L
           var curBlocks = new ArrayBuffer[(BlockId, Long)]
@@ -178,11 +179,12 @@ object BlockFetcherIterator {
             } else if (size < 0) {
               throw new BlockException(blockId, "Negative block size " + size)
             }
-            if (curRequestSize >= minRequestSize) {
+            if (curRequestSize >= targetRequestSize) {
               // Add this FetchRequest
               remoteRequests += new FetchRequest(address, curBlocks)
               curRequestSize = 0
               curBlocks = new ArrayBuffer[(BlockId, Long)]
+              logDebug(s"Creating fetch request of $curRequestSize at $address")
             }
           }
           // Add in the final request
@@ -191,7 +193,7 @@ object BlockFetcherIterator {
           }
         }
       }
-      logInfo("Getting " + _numBlocksToFetch + " non-zero-bytes blocks out of " +
+      logInfo("Getting " + _numBlocksToFetch + " non-empty blocks out of " +
         totalBlocks + " blocks")
       remoteRequests
     }
@@ -226,8 +228,8 @@ object BlockFetcherIterator {
         sendRequest(fetchRequests.dequeue())
       }
 
-      val numGets = remoteRequests.size - fetchRequests.size
-      logInfo("Started " + numGets + " remote gets in " + Utils.getUsedTimeMs(startTime))
+      val numFetches = remoteRequests.size - fetchRequests.size
+      logInfo("Started " + numFetches + " remote fetches in" + Utils.getUsedTimeMs(startTime))
 
       // Get Local Blocks
       startTime = System.currentTimeMillis
@@ -327,7 +329,7 @@ object BlockFetcherIterator {
       }
 
       copiers = startCopiers(conf.getInt("spark.shuffle.copier.threads", 6))
-      logInfo("Started " + fetchRequestsSync.size + " remote gets in " +
+      logInfo("Started " + fetchRequestsSync.size + " remote fetches in " +
         Utils.getUsedTimeMs(startTime))
 
       // Get Local Blocks
diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala
index 02ba5ecf5245..6d7d4f922e1f 100644
--- a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala
+++ b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala
@@ -46,11 +46,12 @@ private[spark] class BlockManager(
     val master: BlockManagerMaster,
     val defaultSerializer: Serializer,
     maxMemory: Long,
-    val conf: SparkConf,
+    val _conf: SparkConf,
     securityManager: SecurityManager,
     mapOutputTracker: MapOutputTracker)
   extends Logging {
 
+  def conf = _conf
   val shuffleBlockManager = new ShuffleBlockManager(this)
   val diskBlockManager = new DiskBlockManager(shuffleBlockManager,
     conf.get("spark.local.dir",  System.getProperty("java.io.tmpdir")))
diff --git a/core/src/main/scala/org/apache/spark/storage/DiskStore.scala b/core/src/main/scala/org/apache/spark/storage/DiskStore.scala
index 36ee4bcc41c6..0ab9fad42271 100644
--- a/core/src/main/scala/org/apache/spark/storage/DiskStore.scala
+++ b/core/src/main/scala/org/apache/spark/storage/DiskStore.scala
@@ -33,6 +33,8 @@ import org.apache.spark.util.Utils
 private class DiskStore(blockManager: BlockManager, diskManager: DiskBlockManager)
   extends BlockStore(blockManager) with Logging {
 
+  val minMemoryMapBytes = blockManager.conf.getLong("spark.storage.memoryMapThreshold", 2 * 4096L)
+
   override def getSize(blockId: BlockId): Long = {
     diskManager.getBlockLocation(blockId).length
   }
@@ -94,12 +96,20 @@ private class DiskStore(blockManager: BlockManager, diskManager: DiskBlockManage
   override def getBytes(blockId: BlockId): Option[ByteBuffer] = {
     val segment = diskManager.getBlockLocation(blockId)
     val channel = new RandomAccessFile(segment.file, "r").getChannel()
-    val buffer = try {
-      channel.map(MapMode.READ_ONLY, segment.offset, segment.length)
+
+    try {
+      // For small files, directly read rather than memory map
+      if (segment.length < minMemoryMapBytes) {
+        val buf = ByteBuffer.allocate(segment.length.toInt)
+        channel.read(buf, segment.offset)
+        buf.flip()
+        Some(buf)
+      } else {
+        Some(channel.map(MapMode.READ_ONLY, segment.offset, segment.length))
+      }
     } finally {
       channel.close()
     }
-    Some(buffer)
   }
 
   override def getValues(blockId: BlockId): Option[Iterator[Any]] = {
diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala
index 5a55e7df3483..b678604ff81c 100644
--- a/core/src/main/scala/org/apache/spark/util/Utils.scala
+++ b/core/src/main/scala/org/apache/spark/util/Utils.scala
@@ -553,8 +553,7 @@ private[spark] object Utils extends Logging {
   }
 
   /**
-   * Return the string to tell how long has passed in seconds. The passing parameter should be in
-   * millisecond.
+   * Return the string to tell how long has passed in milliseconds.
    */
   def getUsedTimeMs(startTimeMs: Long): String = {
     " " + (System.currentTimeMillis - startTimeMs) + " ms"
diff --git a/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala b/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala
index 907428db80af..00deecc1c3ca 100644
--- a/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala
@@ -17,12 +17,15 @@
 
 package org.apache.spark.storage
 
-import java.nio.ByteBuffer
+import java.nio.{ByteBuffer, MappedByteBuffer}
+import java.util.Arrays
 
 import akka.actor._
-import org.scalatest.BeforeAndAfter
-import org.scalatest.FunSuite
-import org.scalatest.PrivateMethodTester
+import org.apache.spark.SparkConf
+import org.apache.spark.serializer.{JavaSerializer, KryoSerializer}
+import org.apache.spark.util.{AkkaUtils, ByteBufferInputStream, SizeEstimator, Utils}
+import org.mockito.Mockito.{mock, when}
+import org.scalatest.{BeforeAndAfter, FunSuite, PrivateMethodTester}
 import org.scalatest.concurrent.Eventually._
 import org.scalatest.concurrent.Timeouts._
 import org.scalatest.matchers.ShouldMatchers._
@@ -785,6 +788,53 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT
     }
   }
 
+  test("reads of memory-mapped and non memory-mapped files are equivalent") {
+    val confKey = "spark.storage.memoryMapThreshold"
+
+    // Create a non-trivial (not all zeros) byte array
+    var counter = 0.toByte
+    def incr = {counter = (counter + 1).toByte; counter;}
+    val bytes = Array.fill[Byte](1000)(incr)
+    val byteBuffer = ByteBuffer.wrap(bytes)
+
+    val blockId = BlockId("rdd_1_2")
+
+    // This sequence of mocks makes these tests fairly brittle. It would
+    // be nice to refactor classes involved in disk storage in a way that
+    // allows for easier testing.
+    val blockManager = mock(classOf[BlockManager])
+    val shuffleBlockManager = mock(classOf[ShuffleBlockManager])
+    when(shuffleBlockManager.conf).thenReturn(conf)
+    val diskBlockManager = new DiskBlockManager(shuffleBlockManager,
+      System.getProperty("java.io.tmpdir"))
+
+    when(blockManager.conf).thenReturn(conf.clone.set(confKey, 0.toString))
+    val diskStoreMapped = new DiskStore(blockManager, diskBlockManager)
+    diskStoreMapped.putBytes(blockId, byteBuffer, StorageLevel.DISK_ONLY)
+    val mapped = diskStoreMapped.getBytes(blockId).get
+
+    when(blockManager.conf).thenReturn(conf.clone.set(confKey, (1000 * 1000).toString))
+    val diskStoreNotMapped = new DiskStore(blockManager, diskBlockManager)
+    diskStoreNotMapped.putBytes(blockId, byteBuffer, StorageLevel.DISK_ONLY)
+    val notMapped = diskStoreNotMapped.getBytes(blockId).get
+
+    // Not possible to do isInstanceOf due to visibility of HeapByteBuffer
+    assert(notMapped.getClass.getName.endsWith("HeapByteBuffer"),
+      "Expected HeapByteBuffer for un-mapped read")
+    assert(mapped.isInstanceOf[MappedByteBuffer], "Expected MappedByteBuffer for mapped read")
+
+    def arrayFromByteBuffer(in: ByteBuffer): Array[Byte] = {
+      val array = new Array[Byte](in.remaining())
+      in.get(array)
+      array
+    }
+
+    val mappedAsArray = arrayFromByteBuffer(mapped)
+    val notMappedAsArray = arrayFromByteBuffer(notMapped)
+    assert(Arrays.equals(mappedAsArray, bytes))
+    assert(Arrays.equals(notMappedAsArray, bytes))
+  }
+  
   test("updated block statuses") {
     store = new BlockManager("<driver>", actorSystem, master, serializer, 1200, conf,
       securityMgr, mapOutputTracker)
diff --git a/docs/configuration.md b/docs/configuration.md
index 8d3442625b47..b078c7c1112c 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -131,6 +131,15 @@ Apart from these, the following properties are also available, and may be useful
     <code>spark.storage.memoryFraction</code>.
   </td>
 </tr>
+<tr>
+  <td>spark.storage.memoryMapThreshold</td>
+  <td>8192</td>
+  <td>
+    Size of a block, in bytes, above which Spark memory maps when reading a block from disk.
+    This prevents Spark from memory mapping very small blocks. In general, memory
+    mapping has high overhead for blocks close to or below the page size of the operating system.
+  </td>
+</tr>
 <tr>
   <td>spark.tachyonStore.baseDir</td>
   <td>System.getProperty("java.io.tmpdir")</td>

From 71f4d2612a1be2904ed1536280680abc2dd212e7 Mon Sep 17 00:00:00 2001
From: witgo <witgo@qq.com>
Date: Sun, 27 Apr 2014 19:41:02 -0700
Subject: [PATCH 042/810] Fix SPARK-1609:  Executor fails to start when
 Command.extraJavaOptions contains multiple Java options

Author: witgo <witgo@qq.com>

Closes #547 from witgo/SPARK-1609 and squashes the following commits:

deb6a4c [witgo] review commit
91da0bb [witgo] Merge branch 'master' of https://github.com/apache/spark into SPARK-1609
0640852 [witgo] review commit
8f90b22 [witgo] Merge branch 'master' of https://github.com/apache/spark into SPARK-1609
bcf36cb [witgo] Merge branch 'master' of https://github.com/apache/spark into SPARK-1609
1185605 [witgo] fix extraJavaOptions split
f7c0ab7 [witgo] bugfix
86fc4bb [witgo] bugfix
8a265b7 [witgo] Fix SPARK-1609: Executor fails to start when use spark-submit
---
 .../org/apache/spark/deploy/worker/CommandUtils.scala    | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/CommandUtils.scala b/core/src/main/scala/org/apache/spark/deploy/worker/CommandUtils.scala
index 9103c885fa96..3e615e753b34 100644
--- a/core/src/main/scala/org/apache/spark/deploy/worker/CommandUtils.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/worker/CommandUtils.scala
@@ -48,7 +48,8 @@ object CommandUtils extends Logging {
   def buildJavaOpts(command: Command, memory: Int, sparkHome: String): Seq[String] = {
     val memoryOpts = Seq(s"-Xms${memory}M", s"-Xmx${memory}M")
     // Note, this will coalesce multiple options into a single command component
-    val extraOpts = command.extraJavaOptions.toSeq
+    val extraOpts = command.extraJavaOptions.map(Utils.splitCommandString).getOrElse(Seq())
+
     val libraryOpts =
       if (command.libraryPathEntries.size > 0) {
         val joined = command.libraryPathEntries.mkString(File.pathSeparator)
@@ -62,10 +63,10 @@ object CommandUtils extends Logging {
     val classPath = Utils.executeAndGetOutput(
       Seq(sparkHome + "/bin/compute-classpath" + ext),
       extraEnvironment=command.environment)
-    val userClassPath = command.classPathEntries.mkString(File.pathSeparator)
-    val classPathWithUser = classPath + File.pathSeparator + userClassPath
+    val userClassPath = command.classPathEntries ++ Seq(classPath)
 
-    Seq("-cp", classPathWithUser) ++ libraryOpts ++ extraOpts ++ memoryOpts
+    Seq("-cp", userClassPath.filterNot(_.isEmpty).mkString(File.pathSeparator)) ++
+      libraryOpts ++ extraOpts ++ memoryOpts
   }
 
   /** Spawn a thread that will redirect a given stream to a file */

From ea01affc34a8b816f92a71e0f8f6901397c8a4a3 Mon Sep 17 00:00:00 2001
From: Cheng Hao <hao.cheng@intel.com>
Date: Sun, 27 Apr 2014 23:57:29 -0700
Subject: [PATCH 043/810] Update the import package name for TestHive in sbt
 shell

sbt/sbt hive/console will fail as TestHive changed its package from "org.apache.spark.sql.hive" to "org.apache.spark.sql.hive.test".

Author: Cheng Hao <hao.cheng@intel.com>

Closes #574 from chenghao-intel/hive_console and squashes the following commits:

de14035 [Cheng Hao] Update the import package name for TestHive in sbt shell
---
 project/SparkBuild.scala | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index 48f234ce87a4..5adfbe373cb0 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -506,7 +506,7 @@ object SparkBuild extends Build {
         |import org.apache.spark.sql.catalyst.util._
         |import org.apache.spark.sql.execution
         |import org.apache.spark.sql.hive._
-        |import org.apache.spark.sql.hive.TestHive._
+        |import org.apache.spark.sql.hive.test.TestHive._
         |import org.apache.spark.sql.parquet.ParquetTestData""".stripMargin
   )
 

From f735884414a15c0c07df60068ee11f9da47eff77 Mon Sep 17 00:00:00 2001
From: Cheng Hao <hao.cheng@intel.com>
Date: Sun, 27 Apr 2014 23:59:42 -0700
Subject: [PATCH 044/810] [SQL]Append some missing types for HiveUDF

Add the missing types

Author: Cheng Hao <hao.cheng@intel.com>

Closes #459 from chenghao-intel/missing_types and squashes the following commits:

21cba2e [Cheng Hao] Append some missing types for HiveUDF
---
 .../org/apache/spark/sql/hive/hiveUdfs.scala  | 58 +++++++++++++++----
 1 file changed, 48 insertions(+), 10 deletions(-)

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala
index a09270eb7b13..c7de4ab6d395 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala
@@ -70,24 +70,26 @@ private[hive] object HiveFunctionRegistry
   }
 
   def javaClassToDataType(clz: Class[_]): DataType = clz match {
+    // writable
     case c: Class[_] if c == classOf[hadoopIo.DoubleWritable] => DoubleType
     case c: Class[_] if c == classOf[hiveIo.DoubleWritable] => DoubleType
     case c: Class[_] if c == classOf[hiveIo.HiveDecimalWritable] => DecimalType
     case c: Class[_] if c == classOf[hiveIo.ByteWritable] => ByteType
     case c: Class[_] if c == classOf[hiveIo.ShortWritable] => ShortType
+    case c: Class[_] if c == classOf[hiveIo.TimestampWritable] => TimestampType
     case c: Class[_] if c == classOf[hadoopIo.Text] => StringType
     case c: Class[_] if c == classOf[hadoopIo.IntWritable] => IntegerType
     case c: Class[_] if c == classOf[hadoopIo.LongWritable] => LongType
     case c: Class[_] if c == classOf[hadoopIo.FloatWritable] => FloatType
     case c: Class[_] if c == classOf[hadoopIo.BooleanWritable] => BooleanType
+    case c: Class[_] if c == classOf[hadoopIo.BytesWritable] => BinaryType
+    
+    // java class
     case c: Class[_] if c == classOf[java.lang.String] => StringType
-    case c: Class[_] if c == java.lang.Short.TYPE => ShortType
-    case c: Class[_] if c == java.lang.Integer.TYPE => IntegerType
-    case c: Class[_] if c == java.lang.Long.TYPE => LongType
-    case c: Class[_] if c == java.lang.Double.TYPE => DoubleType
-    case c: Class[_] if c == java.lang.Byte.TYPE => ByteType
-    case c: Class[_] if c == java.lang.Float.TYPE => FloatType
-    case c: Class[_] if c == java.lang.Boolean.TYPE => BooleanType
+    case c: Class[_] if c == classOf[java.sql.Timestamp] => TimestampType
+    case c: Class[_] if c == classOf[HiveDecimal] => DecimalType
+    case c: Class[_] if c == classOf[java.math.BigDecimal] => DecimalType
+    case c: Class[_] if c == classOf[Array[Byte]] => BinaryType
     case c: Class[_] if c == classOf[java.lang.Short] => ShortType
     case c: Class[_] if c == classOf[java.lang.Integer] => IntegerType
     case c: Class[_] if c == classOf[java.lang.Long] => LongType
@@ -95,6 +97,16 @@ private[hive] object HiveFunctionRegistry
     case c: Class[_] if c == classOf[java.lang.Byte] => ByteType
     case c: Class[_] if c == classOf[java.lang.Float] => FloatType
     case c: Class[_] if c == classOf[java.lang.Boolean] => BooleanType
+    
+    // primitive type
+    case c: Class[_] if c == java.lang.Short.TYPE => ShortType
+    case c: Class[_] if c == java.lang.Integer.TYPE => IntegerType
+    case c: Class[_] if c == java.lang.Long.TYPE => LongType
+    case c: Class[_] if c == java.lang.Double.TYPE => DoubleType
+    case c: Class[_] if c == java.lang.Byte.TYPE => ByteType
+    case c: Class[_] if c == java.lang.Float.TYPE => FloatType
+    case c: Class[_] if c == java.lang.Boolean.TYPE => BooleanType
+    
     case c: Class[_] if c.isArray => ArrayType(javaClassToDataType(c.getComponentType))
   }
 }
@@ -111,11 +123,19 @@ private[hive] trait HiveFunctionFactory {
     case i: hadoopIo.IntWritable => i.get
     case t: hadoopIo.Text => t.toString
     case l: hadoopIo.LongWritable => l.get
-    case d: hadoopIo.DoubleWritable => d.get()
+    case d: hadoopIo.DoubleWritable => d.get
     case d: hiveIo.DoubleWritable => d.get
     case s: hiveIo.ShortWritable => s.get
-    case b: hadoopIo.BooleanWritable => b.get()
+    case b: hadoopIo.BooleanWritable => b.get
     case b: hiveIo.ByteWritable => b.get
+    case b: hadoopIo.FloatWritable => b.get
+    case b: hadoopIo.BytesWritable => {
+      val bytes = new Array[Byte](b.getLength)
+      System.arraycopy(b.getBytes(), 0, bytes, 0, b.getLength)
+      bytes
+    }
+    case t: hiveIo.TimestampWritable => t.getTimestamp
+    case b: hiveIo.HiveDecimalWritable => BigDecimal(b.getHiveDecimal().bigDecimalValue())
     case list: java.util.List[_] => list.map(unwrap)
     case map: java.util.Map[_,_] => map.map { case (k, v) => (unwrap(k), unwrap(v)) }.toMap
     case array: Array[_] => array.map(unwrap).toSeq
@@ -127,6 +147,9 @@ private[hive] trait HiveFunctionFactory {
     case p: java.lang.Byte => p
     case p: java.lang.Boolean => p
     case str: String => str
+    case p: BigDecimal => p
+    case p: Array[Byte] => p
+    case p: java.sql.Timestamp => p
   }
 }
 
@@ -252,13 +275,17 @@ private[hive] trait HiveInspectors {
 
   /** Converts native catalyst types to the types expected by Hive */
   def wrap(a: Any): AnyRef = a match {
-    case s: String => new hadoopIo.Text(s)
+    case s: String => new hadoopIo.Text(s) // TODO why should be Text?
     case i: Int => i: java.lang.Integer
     case b: Boolean => b: java.lang.Boolean
+    case f: Float => f: java.lang.Float
     case d: Double => d: java.lang.Double
     case l: Long => l: java.lang.Long
     case l: Short => l: java.lang.Short
     case l: Byte => l: java.lang.Byte
+    case b: BigDecimal => b.bigDecimal
+    case b: Array[Byte] => b
+    case t: java.sql.Timestamp => t
     case s: Seq[_] => seqAsJavaList(s.map(wrap))
     case m: Map[_,_] =>
       mapAsJavaMap(m.map { case (k, v) => wrap(k) -> wrap(v) })
@@ -280,6 +307,8 @@ private[hive] trait HiveInspectors {
     case ByteType => PrimitiveObjectInspectorFactory.javaByteObjectInspector
     case NullType => PrimitiveObjectInspectorFactory.javaVoidObjectInspector
     case BinaryType => PrimitiveObjectInspectorFactory.javaByteArrayObjectInspector
+    case TimestampType => PrimitiveObjectInspectorFactory.javaTimestampObjectInspector
+    case DecimalType => PrimitiveObjectInspectorFactory.javaHiveDecimalObjectInspector
   }
 
   def inspectorToDataType(inspector: ObjectInspector): DataType = inspector match {
@@ -307,6 +336,14 @@ private[hive] trait HiveInspectors {
     case _: JavaShortObjectInspector => ShortType
     case _: WritableByteObjectInspector => ByteType
     case _: JavaByteObjectInspector => ByteType
+    case _: WritableFloatObjectInspector => FloatType
+    case _: JavaFloatObjectInspector => FloatType
+    case _: WritableBinaryObjectInspector => BinaryType
+    case _: JavaBinaryObjectInspector => BinaryType
+    case _: WritableHiveDecimalObjectInspector => DecimalType
+    case _: JavaHiveDecimalObjectInspector => DecimalType
+    case _: WritableTimestampObjectInspector => TimestampType
+    case _: JavaTimestampObjectInspector => TimestampType
   }
 
   implicit class typeInfoConversions(dt: DataType) {
@@ -324,6 +361,7 @@ private[hive] trait HiveInspectors {
       case ShortType => shortTypeInfo
       case StringType => stringTypeInfo
       case DecimalType => decimalTypeInfo
+      case TimestampType => timestampTypeInfo
       case NullType => voidTypeInfo
     }
   }

From 1d84964bf80f4e69e54d62286c3861c2362342d0 Mon Sep 17 00:00:00 2001
From: Tathagata Das <tathagata.das1565@gmail.com>
Date: Mon, 28 Apr 2014 13:58:09 -0700
Subject: [PATCH 045/810] [SPARK-1633][Streaming] Java API unit test and
 example for custom streaming receiver in Java

Author: Tathagata Das <tathagata.das1565@gmail.com>

Closes #558 from tdas/more-fixes and squashes the following commits:

c0c84e6 [Tathagata Das] Removing extra println()
d8a8cf4 [Tathagata Das] More tweaks to make unit test work in Jenkins.
b7caa98 [Tathagata Das] More tweaks.
d337367 [Tathagata Das] More tweaks
22d6f2d [Tathagata Das] Merge remote-tracking branch 'apache/master' into more-fixes
40a961b [Tathagata Das] Modified java test to reduce flakiness.
9410ca6 [Tathagata Das] Merge remote-tracking branch 'apache/master' into more-fixes
86d9147 [Tathagata Das] scala style fix
2f3d7b1 [Tathagata Das] Added Scala custom receiver example.
d677611 [Tathagata Das] Merge remote-tracking branch 'apache/master' into more-fixes
bec3fc2 [Tathagata Das] Added license.
51d6514 [Tathagata Das] Fixed docs on receiver.
81aafa0 [Tathagata Das] Added Java test for Receiver API, and added JavaCustomReceiver example.
---
 .../examples/JavaCustomReceiver.java          | 152 ++++++++++++++++++
 .../examples/JavaNetworkWordCount.java        |   5 +-
 .../streaming/examples/CustomReceiver.scala   | 108 +++++++++++++
 .../api/java/JavaStreamingContext.scala       |   2 +-
 .../spark/streaming/receiver/Receiver.scala   |  90 ++++++++---
 .../spark/streaming/JavaReceiverAPISuite.java | 144 +++++++++++++++++
 .../spark/streaming/JavaTestUtils.scala       |   4 +-
 .../spark/streaming/InputStreamsSuite.scala   |   3 +-
 .../streaming/StreamingContextSuite.scala     |   2 +-
 .../spark/streaming/TestSuiteBase.scala       |   1 +
 10 files changed, 476 insertions(+), 35 deletions(-)
 create mode 100644 examples/src/main/java/org/apache/spark/streaming/examples/JavaCustomReceiver.java
 create mode 100644 examples/src/main/scala/org/apache/spark/streaming/examples/CustomReceiver.scala
 create mode 100644 streaming/src/test/java/org/apache/spark/streaming/JavaReceiverAPISuite.java

diff --git a/examples/src/main/java/org/apache/spark/streaming/examples/JavaCustomReceiver.java b/examples/src/main/java/org/apache/spark/streaming/examples/JavaCustomReceiver.java
new file mode 100644
index 000000000000..a94fa621dc32
--- /dev/null
+++ b/examples/src/main/java/org/apache/spark/streaming/examples/JavaCustomReceiver.java
@@ -0,0 +1,152 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.streaming.examples;
+
+import com.google.common.collect.Lists;
+
+import org.apache.spark.api.java.function.FlatMapFunction;
+import org.apache.spark.api.java.function.Function2;
+import org.apache.spark.api.java.function.PairFunction;
+import org.apache.spark.storage.StorageLevel;
+import org.apache.spark.streaming.Duration;
+import org.apache.spark.streaming.api.java.JavaDStream;
+import org.apache.spark.streaming.api.java.JavaPairDStream;
+import org.apache.spark.streaming.api.java.JavaStreamingContext;
+import org.apache.spark.streaming.receiver.Receiver;
+import scala.Tuple2;
+
+import java.io.BufferedReader;
+import java.io.InputStreamReader;
+import java.net.ConnectException;
+import java.net.Socket;
+import java.util.regex.Pattern;
+
+/**
+ * Custom Receiver that receives data over a socket. Received bytes is interpreted as
+ * text and \n delimited lines are considered as records. They are then counted and printed.
+ *
+ * Usage: JavaCustomReceiver <master> <hostname> <port>
+ *   <master> is the Spark master URL. In local mode, <master> should be 'local[n]' with n > 1.
+ *   <hostname> and <port> of the TCP server that Spark Streaming would connect to receive data.
+ *
+ * To run this on your local machine, you need to first run a Netcat server
+ *    `$ nc -lk 9999`
+ * and then run the example
+ *    `$ ./run org.apache.spark.streaming.examples.JavaCustomReceiver local[2] localhost 9999`
+ */
+
+public class JavaCustomReceiver extends Receiver<String> {
+  private static final Pattern SPACE = Pattern.compile(" ");
+
+  public static void main(String[] args) {
+    if (args.length < 3) {
+      System.err.println("Usage: JavaNetworkWordCount <master> <hostname> <port>\n" +
+          "In local mode, <master> should be 'local[n]' with n > 1");
+      System.exit(1);
+    }
+
+    StreamingExamples.setStreamingLogLevels();
+
+    // Create the context with a 1 second batch size
+    JavaStreamingContext ssc = new JavaStreamingContext(args[0], "JavaNetworkWordCount",
+            new Duration(1000), System.getenv("SPARK_HOME"),
+            JavaStreamingContext.jarOfClass(JavaNetworkWordCount.class));
+
+    // Create a input stream with the custom receiver on target ip:port and count the
+    // words in input stream of \n delimited text (eg. generated by 'nc')
+    JavaDStream<String> lines = ssc.receiverStream(
+      new JavaCustomReceiver(args[1], Integer.parseInt(args[2])));
+    JavaDStream<String> words = lines.flatMap(new FlatMapFunction<String, String>() {
+      @Override
+      public Iterable<String> call(String x) {
+        return Lists.newArrayList(SPACE.split(x));
+      }
+    });
+    JavaPairDStream<String, Integer> wordCounts = words.mapToPair(
+      new PairFunction<String, String, Integer>() {
+        @Override public Tuple2<String, Integer> call(String s) {
+          return new Tuple2<String, Integer>(s, 1);
+        }
+      }).reduceByKey(new Function2<Integer, Integer, Integer>() {
+        @Override
+        public Integer call(Integer i1, Integer i2) {
+          return i1 + i2;
+        }
+      });
+
+    wordCounts.print();
+    ssc.start();
+    ssc.awaitTermination();
+  }
+
+  // ============= Receiver code that receives data over a socket ==============
+
+  String host = null;
+  int port = -1;
+
+  public JavaCustomReceiver(String host_ , int port_) {
+    super(StorageLevel.MEMORY_AND_DISK_2());
+    host = host_;
+    port = port_;
+  }
+
+  public void onStart() {
+    // Start the thread that receives data over a connection
+    new Thread()  {
+      @Override public void run() {
+        receive();
+      }
+    }.start();
+  }
+
+  public void onStop() {
+    // There is nothing much to do as the thread calling receive()
+    // is designed to stop by itself isStopped() returns false
+  }
+
+  /** Create a socket connection and receive data until receiver is stopped */
+  private void receive() {
+    Socket socket = null;
+    String userInput = null;
+
+    try {
+      // connect to the server
+      socket = new Socket(host, port);
+
+      BufferedReader reader = new BufferedReader(new InputStreamReader(socket.getInputStream()));
+
+      // Until stopped or connection broken continue reading
+      while (!isStopped() && (userInput = reader.readLine()) != null) {
+        System.out.println("Received data '" + userInput + "'");
+        store(userInput);
+      }
+      reader.close();
+      socket.close();
+
+      // Restart in an attempt to connect again when server is active again
+      restart("Trying to connect again");
+    } catch(ConnectException ce) {
+      // restart if could not connect to server
+      restart("Could not connect", ce);
+    } catch(Throwable t) {
+      restart("Error receiving data", t);
+    }
+  }
+}
+
+
diff --git a/examples/src/main/java/org/apache/spark/streaming/examples/JavaNetworkWordCount.java b/examples/src/main/java/org/apache/spark/streaming/examples/JavaNetworkWordCount.java
index 7f68d451e9b3..0cc9d0ae1a08 100644
--- a/examples/src/main/java/org/apache/spark/streaming/examples/JavaNetworkWordCount.java
+++ b/examples/src/main/java/org/apache/spark/streaming/examples/JavaNetworkWordCount.java
@@ -31,7 +31,7 @@
 
 /**
  * Counts words in UTF8 encoded, '\n' delimited text received from the network every second.
- * Usage: NetworkWordCount <master> <hostname> <port>
+ * Usage: JavaNetworkWordCount <master> <hostname> <port>
  *   <master> is the Spark master URL. In local mode, <master> should be 'local[n]' with n > 1.
  *   <hostname> and <port> describe the TCP server that Spark Streaming would connect to receive data.
  *
@@ -43,9 +43,6 @@
 public final class JavaNetworkWordCount {
   private static final Pattern SPACE = Pattern.compile(" ");
 
-  private JavaNetworkWordCount() {
-  }
-
   public static void main(String[] args) {
     if (args.length < 3) {
       System.err.println("Usage: JavaNetworkWordCount <master> <hostname> <port>\n" +
diff --git a/examples/src/main/scala/org/apache/spark/streaming/examples/CustomReceiver.scala b/examples/src/main/scala/org/apache/spark/streaming/examples/CustomReceiver.scala
new file mode 100644
index 000000000000..eebffd824983
--- /dev/null
+++ b/examples/src/main/scala/org/apache/spark/streaming/examples/CustomReceiver.scala
@@ -0,0 +1,108 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.streaming.examples
+
+import java.io.{InputStreamReader, BufferedReader, InputStream}
+import java.net.Socket
+
+import org.apache.spark.Logging
+import org.apache.spark.storage.StorageLevel
+import org.apache.spark.streaming.{Seconds, StreamingContext}
+import org.apache.spark.streaming.StreamingContext._
+import org.apache.spark.streaming.receiver.Receiver
+
+/**
+ * Custom Receiver that receives data over a socket. Received bytes is interpreted as
+ * text and \n delimited lines are considered as records. They are then counted and printed.
+ *
+ * Usage: CustomReceiver <master> <hostname> <port>
+ *   <master> is the Spark master URL. In local mode, <master> should be 'local[n]' with n > 1.
+ *   <hostname> and <port> of the TCP server that Spark Streaming would connect to receive data.
+ *
+ * To run this on your local machine, you need to first run a Netcat server
+ *    `$ nc -lk 9999`
+ * and then run the example
+ *    `$ ./run org.apache.spark.streaming.examples.CustomReceiver local[2] localhost 9999`
+ */
+object CustomReceiver {
+  def main(args: Array[String]) {
+    if (args.length < 3) {
+      System.err.println("Usage: NetworkWordCount <master> <hostname> <port>\n" +
+        "In local mode, <master> should be 'local[n]' with n > 1")
+      System.exit(1)
+    }
+
+    StreamingExamples.setStreamingLogLevels()
+
+    // Create the context with a 1 second batch size
+    val ssc = new StreamingContext(args(0), "NetworkWordCount", Seconds(1),
+      System.getenv("SPARK_HOME"), StreamingContext.jarOfClass(this.getClass).toSeq)
+
+    // Create a input stream with the custom receiver on target ip:port and count the
+    // words in input stream of \n delimited text (eg. generated by 'nc')
+    val lines = ssc.receiverStream(new CustomReceiver(args(1), args(2).toInt))
+    val words = lines.flatMap(_.split(" "))
+    val wordCounts = words.map(x => (x, 1)).reduceByKey(_ + _)
+    wordCounts.print()
+    ssc.start()
+    ssc.awaitTermination()
+  }
+}
+
+
+class CustomReceiver(host: String, port: Int)
+  extends Receiver[String](StorageLevel.MEMORY_AND_DISK_2) with Logging {
+
+  def onStart() {
+    // Start the thread that receives data over a connection
+    new Thread("Socket Receiver") {
+      override def run() { receive() }
+    }.start()
+  }
+
+  def onStop() {
+   // There is nothing much to do as the thread calling receive()
+   // is designed to stop by itself isStopped() returns false
+  }
+
+  /** Create a socket connection and receive data until receiver is stopped */
+  private def receive() {
+   var socket: Socket = null
+   var userInput: String = null
+   try {
+     logInfo("Connecting to " + host + ":" + port)
+     socket = new Socket(host, port)
+     logInfo("Connected to " + host + ":" + port)
+     val reader = new BufferedReader(new InputStreamReader(socket.getInputStream(), "UTF-8"))
+     userInput = reader.readLine()
+     while(!isStopped && userInput != null) {
+       store(userInput)
+       userInput = reader.readLine()
+     }
+     reader.close()
+     socket.close()
+     logInfo("Stopped receiving")
+     restart("Trying to connect again")
+   } catch {
+     case e: java.net.ConnectException =>
+       restart("Error connecting to " + host + ":" + port, e)
+     case t: Throwable =>
+       restart("Error receiving data", t)
+   }
+  }
+}
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala
index fbb2e9f85dd1..75a3e9334e6d 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala
@@ -390,7 +390,7 @@ class JavaStreamingContext(val ssc: StreamingContext) {
      * Find more details at: http://spark.apache.org/docs/latest/streaming-custom-receivers.html
      * @param receiver Custom implementation of Receiver
      */
-  def receiverStream[T](receiver: Receiver[T]): ReceiverInputDStream[T] = {
+  def receiverStream[T](receiver: Receiver[T]): JavaReceiverInputDStream[T] = {
     implicit val cm: ClassTag[T] =
       implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[T]]
     ssc.receiverStream(receiver)
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/receiver/Receiver.scala b/streaming/src/main/scala/org/apache/spark/streaming/receiver/Receiver.scala
index 524c1b8d8ce4..b310c22b3ab7 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/receiver/Receiver.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/receiver/Receiver.scala
@@ -30,28 +30,55 @@ import org.apache.spark.annotation.DeveloperApi
  * Abstract class of a receiver that can be run on worker nodes to receive external data. A
  * custom receiver can be defined by defining the functions onStart() and onStop(). onStart()
  * should define the setup steps necessary to start receiving data,
- * and onStop() should define the cleanup steps necessary to stop receiving data. A custom
- * receiver would look something like this.
+ * and onStop() should define the cleanup steps necessary to stop receiving data.
  *
- * @example {{{
+ * A custom receiver in Scala would look like this.
+ *
+ * {{{
  *  class MyReceiver(storageLevel: StorageLevel) extends NetworkReceiver[String](storageLevel) {
- *    def onStart() {
- *      // Setup stuff (start threads, open sockets, etc.) to start receiving data.
- *      // Must start new thread to receive data, as onStart() must be non-blocking.
+ *      def onStart() {
+ *          // Setup stuff (start threads, open sockets, etc.) to start receiving data.
+ *          // Must start new thread to receive data, as onStart() must be non-blocking.
  *
- *      // Call store(...) in those threads to store received data into Spark's memory.
+ *          // Call store(...) in those threads to store received data into Spark's memory.
  *
- *      // Call stop(...), restart() or reportError(...) on any thread based on how
- *      // different errors should be handled.
+ *          // Call stop(...), restart(...) or reportError(...) on any thread based on how
+ *          // different errors needs to be handled.
  *
- *      // See corresponding method documentation for more details
- *    }
+ *          // See corresponding method documentation for more details
+ *      }
  *
- *    def onStop() {
- *      // Cleanup stuff (stop threads, close sockets, etc.) to stop receiving data.
- *    }
+ *      def onStop() {
+ *          // Cleanup stuff (stop threads, close sockets, etc.) to stop receiving data.
+ *      }
  *  }
  * }}}
+ *
+ * A custom receiver in Java would look like this.
+ *
+ * {{{
+ * class MyReceiver extends Receiver<String> {
+ *     public MyReceiver(StorageLevel storageLevel) {
+ *         super(storageLevel);
+ *     }
+ *
+ *     public void onStart() {
+ *          // Setup stuff (start threads, open sockets, etc.) to start receiving data.
+ *          // Must start new thread to receive data, as onStart() must be non-blocking.
+ *
+ *          // Call store(...) in those threads to store received data into Spark's memory.
+ *
+ *          // Call stop(...), restart(...) or reportError(...) on any thread based on how
+ *          // different errors needs to be handled.
+ *
+ *          // See corresponding method documentation for more details
+ *     }
+ *
+ *     public void onStop() {
+ *          // Cleanup stuff (stop threads, close sockets, etc.) to stop receiving data.
+ *     }
+ * }
+ * }}}
  */
 @DeveloperApi
 abstract class Receiver[T](val storageLevel: StorageLevel) extends Serializable {
@@ -156,30 +183,34 @@ abstract class Receiver[T](val storageLevel: StorageLevel) extends Serializable
   }
 
   /**
-   * Restart the receiver. This will call `onStop()` immediately and return.
-   * Asynchronously, after a delay, `onStart()` will be called.
+   * Restart the receiver. This method schedules the restart and returns
+   * immediately. The stopping and subsequent starting of the receiver
+   * (by calling `onStop()` and `onStart()`) is performed asynchronously
+   * in a background thread. The delay between the stopping and the starting
+   * is defined by the Spark configuration `spark.streaming.receiverRestartDelay`.
    * The `message` will be reported to the driver.
-   * The delay is defined by the Spark configuration
-   * `spark.streaming.receiverRestartDelay`.
    */
   def restart(message: String) {
     executor.restartReceiver(message)
   }
 
   /**
-   * Restart the receiver. This will call `onStop()` immediately and return.
-   * Asynchronously, after a delay, `onStart()` will be called.
+   * Restart the receiver. This method schedules the restart and returns
+   * immediately. The stopping and subsequent starting of the receiver
+   * (by calling `onStop()` and `onStart()`) is performed asynchronously
+   * in a background thread. The delay between the stopping and the starting
+   * is defined by the Spark configuration `spark.streaming.receiverRestartDelay`.
    * The `message` and `exception` will be reported to the driver.
-   * The delay is defined by the Spark configuration
-   * `spark.streaming.receiverRestartDelay`.
    */
   def restart(message: String, error: Throwable) {
     executor.restartReceiver(message, Some(error))
   }
 
   /**
-   * Restart the receiver. This will call `onStop()` immediately and return.
-   * Asynchronously, after the given delay, `onStart()` will be called.
+   * Restart the receiver. This method schedules the restart and returns
+   * immediately. The stopping and subsequent starting of the receiver
+   * (by calling `onStop()` and `onStart()`) is performed asynchronously
+   * in a background thread.
    */
   def restart(message: String, error: Throwable, millisecond: Int) {
     executor.restartReceiver(message, Some(error), millisecond)
@@ -195,16 +226,23 @@ abstract class Receiver[T](val storageLevel: StorageLevel) extends Serializable
     executor.stop(message, Some(error))
   }
 
+  /** Check if the receiver has started or not. */
   def isStarted(): Boolean = {
     executor.isReceiverStarted()
   }
 
-  /** Check if receiver has been marked for stopping. */
+  /**
+   * Check if receiver has been marked for stopping. Use this to identify when
+   * the receiving of data should be stopped.
+   */
   def isStopped(): Boolean = {
     executor.isReceiverStopped()
   }
 
-  /** Get unique identifier of this receiver. */
+  /**
+   * Get the unique identifier the receiver input stream that this
+   * receiver is associated with.
+   */
   def streamId = id
 
   /*
diff --git a/streaming/src/test/java/org/apache/spark/streaming/JavaReceiverAPISuite.java b/streaming/src/test/java/org/apache/spark/streaming/JavaReceiverAPISuite.java
new file mode 100644
index 000000000000..1b0787fe69de
--- /dev/null
+++ b/streaming/src/test/java/org/apache/spark/streaming/JavaReceiverAPISuite.java
@@ -0,0 +1,144 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.streaming;
+
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.streaming.api.java.JavaDStream;
+import org.apache.spark.streaming.api.java.JavaReceiverInputDStream;
+import org.apache.spark.streaming.api.java.JavaStreamingContext;
+import static org.junit.Assert.*;
+
+import org.junit.After;
+import org.junit.Before;
+import org.junit.Test;
+
+import org.apache.spark.storage.StorageLevel;
+import org.apache.spark.streaming.receiver.Receiver;
+import org.apache.spark.api.java.function.Function;
+
+import java.io.BufferedReader;
+import java.io.InputStreamReader;
+import java.io.Serializable;
+import java.net.ConnectException;
+import java.net.Socket;
+import java.util.concurrent.atomic.AtomicInteger;
+import java.util.concurrent.atomic.AtomicLong;
+
+public class JavaReceiverAPISuite implements Serializable {
+
+  @Before
+  public void setUp() {
+    System.clearProperty("spark.streaming.clock");
+  }
+
+  @After
+  public void tearDown() {
+    System.clearProperty("spark.streaming.clock");
+  }
+
+  @Test
+  public void testReceiver() throws InterruptedException {
+    TestServer server = new TestServer(0);
+    server.start();
+
+    final AtomicLong dataCounter = new AtomicLong(0);
+
+    try {
+      JavaStreamingContext ssc = new JavaStreamingContext("local[2]", "test", new Duration(200));
+      JavaReceiverInputDStream<String> input =
+        ssc.receiverStream(new JavaSocketReceiver("localhost", server.port()));
+      JavaDStream<String> mapped = input.map(new Function<String, String>() {
+        @Override
+        public String call(String v1) throws Exception {
+          return v1 + ".";
+        }
+      });
+      mapped.foreachRDD(new Function<JavaRDD<String>, Void>() {
+        @Override
+        public Void call(JavaRDD<String> rdd) throws Exception {
+        long count = rdd.count();
+        dataCounter.addAndGet(count);
+        return null;
+        }
+      });
+
+      ssc.start();
+      long startTime = System.currentTimeMillis();
+      long timeout = 10000;
+
+      Thread.sleep(200);
+      for (int i = 0; i < 6; i++) {
+        server.send("" + i + "\n"); // \n to make sure these are separate lines
+        Thread.sleep(100);
+      }
+      while (dataCounter.get() == 0 && System.currentTimeMillis() - startTime < timeout) {
+        Thread.sleep(100);
+      }
+      ssc.stop();
+      assertTrue(dataCounter.get() > 0);
+    } finally {
+      server.stop();
+    }
+  }
+}
+
+class JavaSocketReceiver extends Receiver<String> {
+
+  String host = null;
+  int port = -1;
+
+  public JavaSocketReceiver(String host_ , int port_) {
+    super(StorageLevel.MEMORY_AND_DISK());
+    host = host_;
+    port = port_;
+  }
+
+  @Override
+  public void onStart() {
+    new Thread()  {
+      @Override public void run() {
+        receive();
+      }
+    }.start();
+  }
+
+  @Override
+  public void onStop() {
+  }
+
+  private void receive() {
+    Socket socket = null;
+    try {
+      socket = new Socket(host, port);
+      BufferedReader in = new BufferedReader(new InputStreamReader(socket.getInputStream()));
+      String userInput;
+      while ((userInput = in.readLine()) != null) {
+        store(userInput);
+      }
+      in.close();
+      socket.close();
+    } catch(ConnectException ce) {
+      ce.printStackTrace();
+      restart("Could not connect", ce);
+    } catch(Throwable t) {
+      t.printStackTrace();
+      restart("Error receiving data", t);
+    }
+  }
+}
+
diff --git a/streaming/src/test/java/org/apache/spark/streaming/JavaTestUtils.scala b/streaming/src/test/java/org/apache/spark/streaming/JavaTestUtils.scala
index 33f6df8f8817..c0ea0491c313 100644
--- a/streaming/src/test/java/org/apache/spark/streaming/JavaTestUtils.scala
+++ b/streaming/src/test/java/org/apache/spark/streaming/JavaTestUtils.scala
@@ -26,6 +26,7 @@ import org.apache.spark.streaming._
 import java.util.ArrayList
 import collection.JavaConversions._
 import org.apache.spark.api.java.JavaRDDLike
+import org.apache.spark.streaming.dstream.DStream
 
 /** Exposes streaming test functionality in a Java-friendly way. */
 trait JavaTestBase extends TestSuiteBase {
@@ -51,8 +52,7 @@ trait JavaTestBase extends TestSuiteBase {
    * [[org.apache.spark.streaming.TestOutputStream]].
    **/
   def attachTestOutputStream[T, This <: JavaDStreamLike[T, This, R], R <: JavaRDDLike[T, R]](
-      dstream: JavaDStreamLike[T, This, R]) =
-  {
+      dstream: JavaDStreamLike[T, This, R]) = {
     implicit val cm: ClassTag[T] =
       implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[T]]
     val ostream = new TestOutputStreamWithPartitions(dstream.dstream)
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala
index b55b7834c90c..3fa254065cc4 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala
@@ -49,7 +49,8 @@ class InputStreamsSuite extends TestSuiteBase with BeforeAndAfter {
 
     // Set up the streaming context and input streams
     val ssc = new StreamingContext(conf, batchDuration)
-    val networkStream = ssc.socketTextStream("localhost", testServer.port, StorageLevel.MEMORY_AND_DISK)
+    val networkStream = ssc.socketTextStream(
+      "localhost", testServer.port, StorageLevel.MEMORY_AND_DISK)
     val outputBuffer = new ArrayBuffer[Seq[String]] with SynchronizedBuffer[Seq[String]]
     val outputStream = new TestOutputStream(networkStream, outputBuffer)
     def output = outputBuffer.flatMap(x => x)
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala
index 3e2b25af8409..ee0bc8b7d6a7 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala
@@ -165,7 +165,7 @@ class StreamingContextSuite extends FunSuite with BeforeAndAfter with Timeouts w
       ssc = new StreamingContext(sc, Milliseconds(100))
       var runningCount = 0
       TestReceiver.counter.set(1)
-      val input = ssc.networkStream(new TestReceiver)
+      val input = ssc.receiverStream(new TestReceiver)
       input.count.foreachRDD(rdd => {
         val count = rdd.first()
         runningCount += count.toInt
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/TestSuiteBase.scala b/streaming/src/test/scala/org/apache/spark/streaming/TestSuiteBase.scala
index 4f63fd37822c..8036f77c973a 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/TestSuiteBase.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/TestSuiteBase.scala
@@ -155,6 +155,7 @@ trait TestSuiteBase extends FunSuite with BeforeAndAfter with Logging {
   def afterFunction() {
     // To avoid Akka rebinding to the same port, since it doesn't unbind immediately on shutdown
     System.clearProperty("spark.driver.port")
+    System.clearProperty("spark.streaming.clock")
   }
 
   before(beforeFunction)

From 8421034e793c0960373a0a1d694ce334ad36e747 Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Mon, 28 Apr 2014 13:58:42 -0700
Subject: [PATCH 046/810] Changes to dev release script

---
 dev/create-release/create-release.sh | 59 +++++++++++++++-------------
 1 file changed, 32 insertions(+), 27 deletions(-)

diff --git a/dev/create-release/create-release.sh b/dev/create-release/create-release.sh
index f1aa0f8f99e1..ae1b41544443 100755
--- a/dev/create-release/create-release.sh
+++ b/dev/create-release/create-release.sh
@@ -21,47 +21,52 @@
 # Publishes releases to Maven and packages/copies binary release artifacts.
 # Expects to be run in a totally empty directory.
 #
+# Options:
+#  --package-only   only packages an existing release candidate
+#
 # Would be nice to add:
 #  - Send output to stderr and have useful logging in stdout
-#  - Have this use sbt rather than Maven release plug in
 
-GIT_USERNAME=pwendell
-GIT_PASSWORD=XXX
-GPG_PASSPHRASE=XXX
-GIT_BRANCH=branch-0.9
-RELEASE_VERSION=0.9.0-incubating
-RC_NAME=rc2
-USER_NAME=pwendell
+GIT_USERNAME=${GIT_USERNAME:-pwendell}
+GIT_PASSWORD=${GIT_PASSWORD:-XXX}
+GPG_PASSPHRASE=${GPG_PASSPHRASE:-XXX}
+GIT_BRANCH=${GIT_BRANCH:-branch-1.0}
+RELEASE_VERSION=${RELEASE_VERSION:-1.0.0}
+RC_NAME=${RC_NAME:-rc2}
+USER_NAME=${USER_NAME:-pwendell}
 
 set -e
 
 GIT_TAG=v$RELEASE_VERSION-$RC_NAME
 
-# Artifact publishing
-
-git clone https://git-wip-us.apache.org/repos/asf/spark.git -b $GIT_BRANCH
-cd spark
-export MAVEN_OPTS="-Xmx3g -XX:MaxPermSize=1g -XX:ReservedCodeCacheSize=1g"
+if [[ ! "$@" =~ --package-only ]]; then
+  echo "Creating and publishing release"
+  # Artifact publishing
+  git clone https://git-wip-us.apache.org/repos/asf/spark.git -b $GIT_BRANCH
+  cd spark
+  export MAVEN_OPTS="-Xmx3g -XX:MaxPermSize=1g -XX:ReservedCodeCacheSize=1g"
 
-mvn -Pyarn release:clean
+  mvn -Pyarn release:clean
 
-mvn -DskipTests \
-  -Darguments="-DskipTests=true -Dhadoop.version=2.2.0 -Dyarn.version=2.2.0 -Dgpg.passphrase=${GPG_PASSPHRASE}" \
-  -Dusername=$GIT_USERNAME -Dpassword=$GIT_PASSWORD \
-  -Dhadoop.version=2.2.0 -Dyarn.version=2.2.0 \
-  -Pyarn -Phive -Pspark-ganglia-lgpl\
-  -Dtag=$GIT_TAG -DautoVersionSubmodules=true \
-  --batch-mode release:prepare
+  mvn -DskipTests \
+    -Darguments="-DskipTests=true -Dhadoop.version=2.2.0 -Dyarn.version=2.2.0 -Dgpg.passphrase=${GPG_PASSPHRASE}" \
+    -Dusername=$GIT_USERNAME -Dpassword=$GIT_PASSWORD \
+    -Dhadoop.version=2.2.0 -Dyarn.version=2.2.0 \
+    -Pyarn -Phive -Pspark-ganglia-lgpl\
+    -Dtag=$GIT_TAG -DautoVersionSubmodules=true \
+    --batch-mode release:prepare
 
-mvn -DskipTests \
-  -Darguments="-DskipTests=true -Dhadoop.version=2.2.0 -Dyarn.version=2.2.0 -Dgpg.passphrase=${GPG_PASSPHRASE}" \
-  -Dhadoop.version=2.2.0 -Dyarn.version=2.2.0 \
-  -Pyarn -Phive -Pspark-ganglia-lgpl\
-  release:perform
+  mvn -DskipTests \
+    -Darguments="-DskipTests=true -Dhadoop.version=2.2.0 -Dyarn.version=2.2.0 -Dgpg.passphrase=${GPG_PASSPHRASE}" \
+    -Dhadoop.version=2.2.0 -Dyarn.version=2.2.0 \
+    -Pyarn -Phive -Pspark-ganglia-lgpl\
+    release:perform
 
-rm -rf spark
+  rm -rf spark
+fi
 
 # Source and binary tarballs
+echo "Packaging release tarballs"
 git clone https://git-wip-us.apache.org/repos/asf/spark.git
 cd spark
 git checkout --force $GIT_TAG

From cae054aaf41ca0ee585231896db67169b61af689 Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Mon, 28 Apr 2014 17:26:57 -0700
Subject: [PATCH 047/810] SPARK-1652: Spark submit should fail gracefully if
 YARN not enabled

Author: Patrick Wendell <pwendell@gmail.com>

Closes #579 from pwendell/spark-submit-yarn-2 and squashes the following commits:

05e1b11 [Patrick Wendell] Small fix
d2a40ad [Patrick Wendell] SPARK-1652: Spark submit should fail gracefully if YARN support not enabled
---
 .../scala/org/apache/spark/deploy/SparkSubmit.scala    | 10 ++++++++++
 core/src/main/scala/org/apache/spark/util/Utils.scala  |  6 ++++++
 2 files changed, 16 insertions(+)

diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
index 24edc6068437..c463ee09993a 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
@@ -23,6 +23,7 @@ import java.net.{URI, URL}
 import scala.collection.mutable.{ArrayBuffer, HashMap, Map}
 
 import org.apache.spark.executor.ExecutorURLClassLoader
+import org.apache.spark.util.Utils
 
 /**
  * Scala code behind the spark-submit script.  The script handles setting up the classpath with
@@ -128,6 +129,15 @@ object SparkSubmit {
       childArgs += ("--class", appArgs.mainClass)
     }
 
+    if (clusterManager == YARN) {
+      // The choice of class is arbitrary, could use any spark-yarn class
+      if (!Utils.classIsLoadable("org.apache.spark.deploy.yarn.Client") && !Utils.isTesting) {
+        val msg = "Could not load YARN classes. This copy of Spark may not have been compiled " +
+          "with YARN support."
+        throw new Exception(msg)
+      }
+    }
+
     val options = List[OptionAssigner](
       new OptionAssigner(appArgs.master, ALL_CLUSTER_MGRS, false, sysProp = "spark.master"),
       new OptionAssigner(appArgs.driverExtraClassPath, STANDALONE | YARN, true,
diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala
index b678604ff81c..79f314c8dd36 100644
--- a/core/src/main/scala/org/apache/spark/util/Utils.scala
+++ b/core/src/main/scala/org/apache/spark/util/Utils.scala
@@ -28,6 +28,7 @@ import scala.collection.Map
 import scala.collection.mutable.ArrayBuffer
 import scala.io.Source
 import scala.reflect.ClassTag
+import scala.util.Try
 
 import com.google.common.io.Files
 import org.apache.commons.lang.SystemUtils
@@ -137,6 +138,11 @@ private[spark] object Utils extends Logging {
   def getContextOrSparkClassLoader =
     Option(Thread.currentThread().getContextClassLoader).getOrElse(getSparkClassLoader)
 
+  /** Determines whether the provided class is loadable in the current thread. */
+  def classIsLoadable(clazz: String): Boolean = {
+    Try { Class.forName(clazz, false, getContextOrSparkClassLoader) }.isSuccess
+  }
+
   /**
    * Primitive often used when writing {@link java.nio.ByteBuffer} to {@link java.io.DataOutput}.
    */

From 949e393101e19cd00591a9930c4b364278e22609 Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Mon, 28 Apr 2014 17:29:22 -0700
Subject: [PATCH 048/810] SPARK-1654 and SPARK-1653: Fixes in spark-submit.

Deals with two issues:
1. Spark shell didn't correctly pass quoted arguments to spark-submit.
```./bin/spark-shell --driver-java-options "-Dfoo=f -Dbar=b"```
2. Spark submit used deprecated environment variables (SPARK_CLASSPATH)
   which triggered warnings. Now we use new, more narrowly scoped,
   variables.

Author: Patrick Wendell <pwendell@gmail.com>

Closes #576 from pwendell/spark-submit and squashes the following commits:

67004c9 [Patrick Wendell] SPARK-1654 and SPARK-1653: Fixes in spark-submit.
---
 bin/compute-classpath.sh                              |  2 +-
 bin/spark-class                                       |  9 +++++----
 bin/spark-shell                                       | 11 +++++------
 bin/spark-submit                                      |  6 +++---
 .../apache/spark/deploy/SparkSubmitArguments.scala    |  4 +++-
 5 files changed, 17 insertions(+), 15 deletions(-)

diff --git a/bin/compute-classpath.sh b/bin/compute-classpath.sh
index 3a59f599fd7d..b0218531e9eb 100755
--- a/bin/compute-classpath.sh
+++ b/bin/compute-classpath.sh
@@ -28,7 +28,7 @@ FWDIR="$(cd `dirname $0`/..; pwd)"
 . $FWDIR/bin/load-spark-env.sh
 
 # Build up classpath
-CLASSPATH="$SPARK_CLASSPATH:$FWDIR/conf"
+CLASSPATH="$SPARK_CLASSPATH:$SPARK_SUBMIT_CLASSPATH:$FWDIR/conf"
 
 ASSEMBLY_DIR="$FWDIR/assembly/target/scala-$SCALA_VERSION"
 
diff --git a/bin/spark-class b/bin/spark-class
index 6871e180c9fa..e8160c8af64c 100755
--- a/bin/spark-class
+++ b/bin/spark-class
@@ -73,11 +73,13 @@ case "$1" in
     OUR_JAVA_MEM=${SPARK_EXECUTOR_MEMORY:-$DEFAULT_MEM}
     ;;
 
-  # All drivers use SPARK_JAVA_OPTS + SPARK_DRIVER_MEMORY. The repl also uses SPARK_REPL_OPTS.
-  'org.apache.spark.repl.Main')
-    OUR_JAVA_OPTS="$SPARK_JAVA_OPTS $SPARK_REPL_OPTS"
+  # Spark submit uses SPARK_SUBMIT_OPTS and SPARK_JAVA_OPTS
+    'org.apache.spark.deploy.SparkSubmit')
+    OUR_JAVA_OPTS="$SPARK_JAVA_OPTS $SPARK_SUBMIT_OPTS \
+      -Djava.library.path=$SPARK_SUBMIT_LIBRARY_PATH"
     OUR_JAVA_MEM=${SPARK_DRIVER_MEMORY:-$DEFAULT_MEM}
     ;;
+
   *)
     OUR_JAVA_OPTS="$SPARK_JAVA_OPTS"
     OUR_JAVA_MEM=${SPARK_DRIVER_MEMORY:-$DEFAULT_MEM}
@@ -98,7 +100,6 @@ fi
 
 # Set JAVA_OPTS to be able to load native libraries and to set heap size
 JAVA_OPTS="$OUR_JAVA_OPTS"
-JAVA_OPTS="$JAVA_OPTS -Djava.library.path=$_SPARK_LIBRARY_PATH"
 JAVA_OPTS="$JAVA_OPTS -Xms$OUR_JAVA_MEM -Xmx$OUR_JAVA_MEM"
 # Load extra JAVA_OPTS from conf/java-opts, if it exists
 if [ -e "$FWDIR/conf/java-opts" ] ; then
diff --git a/bin/spark-shell b/bin/spark-shell
index f1f3c18877ed..7f03349c5e91 100755
--- a/bin/spark-shell
+++ b/bin/spark-shell
@@ -20,7 +20,6 @@
 #
 # Shell script for starting the Spark Shell REPL
 
-args="$@"
 cygwin=false
 case "`uname`" in
     CYGWIN*) cygwin=true;;
@@ -46,12 +45,12 @@ function main(){
         # "Backspace sends ^H" setting in "Keys" section of the Mintty options
         # (see https://github.com/sbt/sbt/issues/562).
         stty -icanon min 1 -echo > /dev/null 2>&1
-        export SPARK_REPL_OPTS="$SPARK_REPL_OPTS -Djline.terminal=unix"
-        $FWDIR/bin/spark-submit spark-internal "$args" --class org.apache.spark.repl.Main
+        export SPARK_SUBMIT_OPTS="$SPARK_SUBMIT_OPTS -Djline.terminal=unix"
+        $FWDIR/bin/spark-submit spark-internal "$@" --class org.apache.spark.repl.Main
         stty icanon echo > /dev/null 2>&1
     else
-        export SPARK_REPL_OPTS
-        $FWDIR/bin/spark-submit spark-internal "$args" --class org.apache.spark.repl.Main
+        export SPARK_SUBMIT_OPTS
+        $FWDIR/bin/spark-submit spark-internal "$@" --class org.apache.spark.repl.Main
     fi
 }
 
@@ -83,7 +82,7 @@ if [[ ! $? ]]; then
   saved_stty=""
 fi
 
-main
+main "$@"
 
 # record the exit status lest it be overwritten:
 # then reenable echo and propagate the code.
diff --git a/bin/spark-submit b/bin/spark-submit
index b2a1dca721df..dd0d95d9d400 100755
--- a/bin/spark-submit
+++ b/bin/spark-submit
@@ -26,11 +26,11 @@ while (($#)); do
   elif [ "$1" = "--driver-memory" ]; then
     DRIVER_MEMORY=$2
   elif [ "$1" = "--driver-library-path" ]; then
-    export _SPARK_LIBRARY_PATH=$2
+    export SPARK_SUBMIT_LIBRARY_PATH=$2
   elif [ "$1" = "--driver-class-path" ]; then
-    export SPARK_CLASSPATH="$SPARK_CLASSPATH:$2"
+    export SPARK_SUBMIT_CLASSPATH=$2
   elif [ "$1" = "--driver-java-options" ]; then
-    export SPARK_JAVA_OPTS="$SPARK_JAVA_OPTS $2"
+    export SPARK_SUBMIT_OPTS=$2
   fi
   shift
 done
diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
index 58d9e9add764..5834dc40f1f9 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
@@ -298,7 +298,9 @@ private[spark] class SparkSubmitArguments(args: Seq[String]) {
         |  --driver-memory MEM         Memory for driver (e.g. 1000M, 2G) (Default: 512M).
         |  --driver-java-options       Extra Java options to pass to the driver
         |  --driver-library-path       Extra library path entries to pass to the driver
-        |  --driver-class-path         Extra class path entries to pass to the driver
+        |  --driver-class-path         Extra class path entries to pass to the driver. Note that
+        |                              jars added with --jars are automatically included in the
+        |                              classpath.
         |
         |  --executor-memory MEM       Memory per executor (e.g. 1000M, 2G) (Default: 1G).
         |

From 9f7a095184d6c7a9b1bbac55efcc3d878f876768 Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Mon, 28 Apr 2014 18:14:59 -0700
Subject: [PATCH 049/810] SPARK-1652: Remove incorrect deprecation warning in
 spark-submit

This is a straightforward fix.

Author: Patrick Wendell <pwendell@gmail.com>

This patch had conflicts when merged, resolved by
Committer: Patrick Wendell <pwendell@gmail.com>

Closes #578 from pwendell/spark-submit-yarn and squashes the following commits:

96027c7 [Patrick Wendell] Test fixes
b5be173 [Patrick Wendell] Review feedback
4ac9cac [Patrick Wendell] SPARK-1652: spark-submit for yarn prints warnings even though calling as expected
---
 .../main/scala/org/apache/spark/deploy/SparkSubmit.scala    | 3 +++
 .../scala/org/apache/spark/deploy/SparkSubmitSuite.scala    | 5 +++--
 .../main/scala/org/apache/spark/deploy/yarn/Client.scala    | 6 ++++--
 .../main/scala/org/apache/spark/deploy/yarn/Client.scala    | 6 ++++--
 4 files changed, 14 insertions(+), 6 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
index c463ee09993a..d131f1809c99 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
@@ -137,6 +137,9 @@ object SparkSubmit {
         throw new Exception(msg)
       }
     }
+    
+    // Special flag to avoid deprecation warnings at the client
+    sysProps("SPARK_SUBMIT") = "true"
 
     val options = List[OptionAssigner](
       new OptionAssigner(appArgs.master, ALL_CLUSTER_MGRS, false, sysProp = "spark.master"),
diff --git a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala
index 10a65c75cc62..b3541b4a40b7 100644
--- a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala
@@ -122,7 +122,7 @@ class SparkSubmitSuite extends FunSuite with ShouldMatchers {
     childArgsStr should include ("--num-executors 6")
     mainClass should be ("org.apache.spark.deploy.yarn.Client")
     classpath should have length (0)
-    sysProps should have size (0)
+    sysProps should have size (1)
   }
 
   test("handles YARN client mode") {
@@ -146,6 +146,7 @@ class SparkSubmitSuite extends FunSuite with ShouldMatchers {
     sysProps("spark.yarn.dist.files") should be ("file1.txt,file2.txt")
     sysProps("spark.yarn.dist.archives") should be ("archive1.txt,archive2.txt")
     sysProps("spark.executor.instances") should be ("6")
+    sysProps("SPARK_SUBMIT") should be ("true")
   }
 
   test("handles standalone cluster mode") {
@@ -159,7 +160,7 @@ class SparkSubmitSuite extends FunSuite with ShouldMatchers {
     childArgsStr should include ("launch spark://h:p thejar.jar org.SomeClass arg1 arg2")
     mainClass should be ("org.apache.spark.deploy.Client")
     classpath should have length (0)
-    sysProps should have size (1) // contains --jar entry
+    sysProps should have size (2) // contains --jar entry and SPARK_SUBMIT
   }
 
   test("handles standalone client mode") {
diff --git a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/Client.scala b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
index 00c7649e68e1..8226207de42b 100644
--- a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
+++ b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
@@ -169,8 +169,10 @@ class Client(clientArgs: ClientArguments, hadoopConf: Configuration, spConf: Spa
 object Client {
 
   def main(argStrings: Array[String]) {
-    println("WARNING: This client is deprecated and will be removed in a future version of Spark.")
-    println("Use ./bin/spark-submit with \"--master yarn\"")
+    if (!sys.props.contains("SPARK_SUBMIT")) {
+      println("WARNING: This client is deprecated and will be removed in a " +
+        "future version of Spark. Use ./bin/spark-submit with \"--master yarn\"")
+    }
 
     // Set an env variable indicating we are running in YARN mode.
     // Note that anything with SPARK prefix gets propagated to all (remote) processes
diff --git a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/Client.scala b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
index 2e2fb5d4fa78..1b6bfb42a5c1 100644
--- a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
+++ b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
@@ -171,8 +171,10 @@ class Client(clientArgs: ClientArguments, hadoopConf: Configuration, spConf: Spa
 object Client {
 
   def main(argStrings: Array[String]) {
-    println("WARNING: This client is deprecated and will be removed in a future version of Spark.")
-    println("Use ./bin/spark-submit with \"--master yarn\"")
+    if (!sys.props.contains("SPARK_SUBMIT")) {
+      println("WARNING: This client is deprecated and will be removed in a " +
+        "future version of Spark. Use ./bin/spark-submit with \"--master yarn\"")
+    }
 
     // Set an env variable indicating we are running in YARN mode.
     // Note: anything env variable with SPARK_ prefix gets propagated to all (remote) processes -

From 030f2c2126d5075576cd6d83a1ee7462c48b953b Mon Sep 17 00:00:00 2001
From: witgo <witgo@qq.com>
Date: Mon, 28 Apr 2014 22:50:51 -0700
Subject: [PATCH 050/810] Improved build configuration

1, Fix SPARK-1441: compile spark core error with hadoop 0.23.x
2, Fix SPARK-1491: maven hadoop-provided profile fails to build
3, Fix org.scala-lang: * ,org.apache.avro:* inconsistent versions dependency
4, A modified on the sql/catalyst/pom.xml,sql/hive/pom.xml,sql/core/pom.xml (Four spaces formatted into two spaces)

Author: witgo <witgo@qq.com>

Closes #480 from witgo/format_pom and squashes the following commits:

03f652f [witgo] review commit
b452680 [witgo] Merge branch 'master' of https://github.com/apache/spark into format_pom
bee920d [witgo] revert fix SPARK-1629: Spark Core missing commons-lang dependence
7382a07 [witgo] Merge branch 'master' of https://github.com/apache/spark into format_pom
6902c91 [witgo] fix SPARK-1629: Spark Core missing commons-lang dependence
0da4bc3 [witgo] merge master
d1718ed [witgo] Merge branch 'master' of https://github.com/apache/spark into format_pom
e345919 [witgo] add avro dependency to yarn-alpha
77fad08 [witgo] Merge branch 'master' of https://github.com/apache/spark into format_pom
62d0862 [witgo] Fix org.scala-lang: * inconsistent versions dependency
1a162d7 [witgo] Merge branch 'master' of https://github.com/apache/spark into format_pom
934f24d [witgo] review commit
cf46edc [witgo] exclude jruby
06e7328 [witgo] Merge branch 'SparkBuild' into format_pom
99464d2 [witgo] fix maven hadoop-provided profile fails to build
0c6c1fc [witgo] Fix compile spark core error with hadoop 0.23.x
6851bec [witgo] Maintain consistent SparkBuild.scala, pom.xml
---
 bagel/pom.xml               |  14 ---
 core/pom.xml                |  22 -----
 docs/building-with-maven.md |  10 +-
 examples/pom.xml            |  18 +---
 external/flume/pom.xml      |  14 ---
 external/kafka/pom.xml      |  14 ---
 external/mqtt/pom.xml       |  14 ---
 external/twitter/pom.xml    |  14 ---
 external/zeromq/pom.xml     |  14 ---
 graphx/pom.xml              |  14 ---
 make-distribution.sh        |  15 ++-
 mllib/pom.xml               |  14 ---
 pom.xml                     |  79 +++++++++++++--
 project/SparkBuild.scala    |   9 +-
 repl/pom.xml                |  14 ---
 sql/catalyst/pom.xml        | 109 ++++++++++-----------
 sql/core/pom.xml            | 122 +++++++++++------------
 sql/hive/pom.xml            | 186 +++++++++++++++++-------------------
 streaming/pom.xml           |  14 ---
 tools/pom.xml               |  14 ---
 yarn/alpha/pom.xml          |  14 ---
 yarn/pom.xml                |   9 --
 yarn/stable/pom.xml         |  14 ---
 23 files changed, 295 insertions(+), 466 deletions(-)

diff --git a/bagel/pom.xml b/bagel/pom.xml
index 142f75c5d2c6..355f437c5b16 100644
--- a/bagel/pom.xml
+++ b/bagel/pom.xml
@@ -31,20 +31,6 @@
   <name>Spark Project Bagel</name>
   <url>http://spark.apache.org/</url>
 
-  <profiles>
-    <profile>
-      <!-- SPARK-1121: SPARK-1121: Adds an explicit dependency on Avro to work around
-           a Hadoop 0.23.X issue -->
-      <id>yarn-alpha</id>
-      <dependencies>
-        <dependency>
-          <groupId>org.apache.avro</groupId>
-          <artifactId>avro</artifactId>
-        </dependency>
-      </dependencies>
-    </profile>
-  </profiles>
-
   <dependencies>
     <dependency>
       <groupId>org.apache.spark</groupId>
diff --git a/core/pom.xml b/core/pom.xml
index 058b7acba73c..73f573a41405 100644
--- a/core/pom.xml
+++ b/core/pom.xml
@@ -30,19 +30,6 @@
   <packaging>jar</packaging>
   <name>Spark Project Core</name>
   <url>http://spark.apache.org/</url>
-  <!-- SPARK-1121: Adds an explicit dependency on Avro to work around a Hadoop 0.23.X issue -->
-  <profiles>
-    <profile>
-      <id>yarn-alpha</id>
-      <dependencies>
-        <dependency>
-          <groupId>org.apache.avro</groupId>
-          <artifactId>avro</artifactId>
-        </dependency>
-      </dependencies>
-    </profile>
-  </profiles>
-
   <dependencies>
     <dependency>
       <groupId>org.apache.hadoop</groupId>
@@ -147,15 +134,6 @@
       <groupId>org.json4s</groupId>
       <artifactId>json4s-jackson_${scala.binary.version}</artifactId>
       <version>3.2.6</version>
-      <!-- see also exclusion for lift-json; this is necessary since it depends on
-         scala-library and scalap 2.10.0, but we use 2.10.4, and only override
-         scala-library -->
-      <exclusions>
-        <exclusion>
-        <groupId>org.scala-lang</groupId>
-        <artifactId>scalap</artifactId>
-        </exclusion>
-      </exclusions>
     </dependency>
     <dependency>
       <groupId>colt</groupId>
diff --git a/docs/building-with-maven.md b/docs/building-with-maven.md
index 771d1933a13e..a5e530346740 100644
--- a/docs/building-with-maven.md
+++ b/docs/building-with-maven.md
@@ -39,7 +39,10 @@ For Apache Hadoop versions 1.x, Cloudera CDH MRv1, and other Hadoop versions wit
     # Cloudera CDH 4.2.0 with MapReduce v1
     $ mvn -Dhadoop.version=2.0.0-mr1-cdh4.2.0 -DskipTests clean package
 
-For Apache Hadoop 2.x, 0.23.x, Cloudera CDH MRv2, and other Hadoop versions with YARN, you should enable the "yarn-alpha" or "yarn" profile and set the "hadoop.version", "yarn.version" property:
+    # Apache Hadoop 0.23.x
+    $ mvn -Phadoop-0.23 -Dhadoop.version=0.23.7 -DskipTests clean package
+
+For Apache Hadoop 2.x, 0.23.x, Cloudera CDH MRv2, and other Hadoop versions with YARN, you can enable the "yarn-alpha" or "yarn" profile and set the "hadoop.version", "yarn.version" property. Note that Hadoop 0.23.X requires a special `-Phadoop-0.23` profile:
 
     # Apache Hadoop 2.0.5-alpha
     $ mvn -Pyarn-alpha -Dhadoop.version=2.0.5-alpha -Dyarn.version=2.0.5-alpha -DskipTests clean package
@@ -47,9 +50,12 @@ For Apache Hadoop 2.x, 0.23.x, Cloudera CDH MRv2, and other Hadoop versions with
     # Cloudera CDH 4.2.0 with MapReduce v2
     $ mvn -Pyarn-alpha -Dhadoop.version=2.0.0-cdh4.2.0 -Dyarn.version=2.0.0-cdh4.2.0 -DskipTests clean package
 
-    # Apache Hadoop 2.2.X ( e.g. 2.2.0 as below ) and newer
+    # Apache Hadoop 2.2.X (e.g. 2.2.0 as below) and newer
     $ mvn -Pyarn -Dhadoop.version=2.2.0 -Dyarn.version=2.2.0 -DskipTests clean package
 
+    # Apache Hadoop 0.23.x
+    $ mvn -Pyarn-alpha -Phadoop-0.23 -Dhadoop.version=0.23.7 -Dyarn.version=0.23.7 -DskipTests clean package
+
 ## Spark Tests in Maven ##
 
 Tests are run by default via the [ScalaTest Maven plugin](http://www.scalatest.org/user_guide/using_the_scalatest_maven_plugin). Some of the require Spark to be packaged first, so always run `mvn package` with `-DskipTests` the first time. You can then run the tests with `mvn -Dhadoop.version=... test`.
diff --git a/examples/pom.xml b/examples/pom.xml
index 704d6df7c57f..a2d1b1973661 100644
--- a/examples/pom.xml
+++ b/examples/pom.xml
@@ -31,20 +31,6 @@
   <name>Spark Project Examples</name>
   <url>http://spark.apache.org/</url>
 
-  <profiles>
-    <profile>
-      <!-- SPARK-1121: SPARK-1121: Adds an explicit dependency on Avro to work around
-           a Hadoop 0.23.X issue -->
-      <id>yarn-alpha</id>
-      <dependencies>
-        <dependency>
-          <groupId>org.apache.avro</groupId>
-          <artifactId>avro</artifactId>
-        </dependency>
-      </dependencies>
-    </profile>
-  </profiles>
-
   <dependencies>
     <dependency>
       <groupId>org.apache.spark</groupId>
@@ -124,6 +110,10 @@
           <groupId>commons-logging</groupId>
           <artifactId>commons-logging</artifactId>
         </exclusion>
+        <exclusion>
+          <groupId>org.jruby</groupId>
+          <artifactId>jruby-complete</artifactId>
+        </exclusion>
       </exclusions>
     </dependency>
     <dependency>
diff --git a/external/flume/pom.xml b/external/flume/pom.xml
index b84ca0c5e1dd..03d3b2394f51 100644
--- a/external/flume/pom.xml
+++ b/external/flume/pom.xml
@@ -31,20 +31,6 @@
   <name>Spark Project External Flume</name>
   <url>http://spark.apache.org/</url>
 
-  <profiles>
-    <profile>
-      <!-- SPARK-1121: SPARK-1121: Adds an explicit dependency on Avro to work around
-           a Hadoop 0.23.X issue -->
-      <id>yarn-alpha</id>
-      <dependencies>
-        <dependency>
-          <groupId>org.apache.avro</groupId>
-          <artifactId>avro</artifactId>
-        </dependency>
-      </dependencies>
-    </profile>
-  </profiles>
-
   <dependencies>
     <dependency>
       <groupId>org.apache.spark</groupId>
diff --git a/external/kafka/pom.xml b/external/kafka/pom.xml
index 343e1fabd823..979eb0ca624b 100644
--- a/external/kafka/pom.xml
+++ b/external/kafka/pom.xml
@@ -31,20 +31,6 @@
   <name>Spark Project External Kafka</name>
   <url>http://spark.apache.org/</url>
 
-  <profiles>
-    <profile>
-      <!-- SPARK-1121: SPARK-1121: Adds an explicit dependency on Avro to work around
-           a Hadoop 0.23.X issue -->
-      <id>yarn-alpha</id>
-      <dependencies>
-        <dependency>
-          <groupId>org.apache.avro</groupId>
-          <artifactId>avro</artifactId>
-        </dependency>
-      </dependencies>
-    </profile>
-  </profiles>
-
   <dependencies>
     <dependency>
       <groupId>org.apache.spark</groupId>
diff --git a/external/mqtt/pom.xml b/external/mqtt/pom.xml
index 3710a63541d7..9aa1c1a9f5b8 100644
--- a/external/mqtt/pom.xml
+++ b/external/mqtt/pom.xml
@@ -31,20 +31,6 @@
   <name>Spark Project External MQTT</name>
   <url>http://spark.apache.org/</url>
 
-  <profiles>
-    <profile>
-      <!-- SPARK-1121: SPARK-1121: Adds an explicit dependency on Avro to work around
-           a Hadoop 0.23.X issue -->
-      <id>yarn-alpha</id>
-      <dependencies>
-         <dependency>
-           <groupId>org.apache.avro</groupId>
-           <artifactId>avro</artifactId>
-         </dependency>
-      </dependencies>
-    </profile>
-  </profiles>
-
   <dependencies>
     <dependency>
       <groupId>org.apache.spark</groupId>
diff --git a/external/twitter/pom.xml b/external/twitter/pom.xml
index 398b9f4fbaa7..a44345959471 100644
--- a/external/twitter/pom.xml
+++ b/external/twitter/pom.xml
@@ -31,20 +31,6 @@
   <name>Spark Project External Twitter</name>
   <url>http://spark.apache.org/</url>
 
-  <profiles>
-    <profile>
-      <!-- SPARK-1121: SPARK-1121: Adds an explicit dependency on Avro to work around
-           a Hadoop 0.23.X issue -->
-      <id>yarn-alpha</id>
-      <dependencies>
-        <dependency>
-          <groupId>org.apache.avro</groupId>
-          <artifactId>avro</artifactId>
-        </dependency>
-      </dependencies>
-    </profile>
-  </profiles>
-
   <dependencies>
     <dependency>
       <groupId>org.apache.spark</groupId>
diff --git a/external/zeromq/pom.xml b/external/zeromq/pom.xml
index 77e957f40464..a40e55876e64 100644
--- a/external/zeromq/pom.xml
+++ b/external/zeromq/pom.xml
@@ -31,20 +31,6 @@
   <name>Spark Project External ZeroMQ</name>
   <url>http://spark.apache.org/</url>
 
-  <profiles>
-    <profile>
-      <!-- SPARK-1121: SPARK-1121: Adds an explicit dependency on Avro to work around
-           a Hadoop 0.23.X issue -->
-      <id>yarn-alpha</id>
-      <dependencies>
-        <dependency>
-          <groupId>org.apache.avro</groupId>
-          <artifactId>avro</artifactId>
-        </dependency>
-      </dependencies>
-    </profile>
-  </profiles>
-
   <dependencies>
     <dependency>
       <groupId>org.apache.spark</groupId>
diff --git a/graphx/pom.xml b/graphx/pom.xml
index b4c67ddcd8ca..dc108d2fe7fb 100644
--- a/graphx/pom.xml
+++ b/graphx/pom.xml
@@ -31,20 +31,6 @@
   <name>Spark Project GraphX</name>
   <url>http://spark.apache.org/</url>
 
-  <profiles>
-    <profile>
-      <!-- SPARK-1121: SPARK-1121: Adds an explicit dependency on Avro to work around
-           a Hadoop 0.23.X issue -->
-      <id>yarn-alpha</id>
-      <dependencies>
-        <dependency>
-          <groupId>org.apache.avro</groupId>
-          <artifactId>avro</artifactId>
-        </dependency>
-      </dependencies>
-    </profile>
-  </profiles>
-
   <dependencies>
     <dependency>
       <groupId>org.apache.spark</groupId>
diff --git a/make-distribution.sh b/make-distribution.sh
index 4ac80efae0ab..c05dcd89d90a 100755
--- a/make-distribution.sh
+++ b/make-distribution.sh
@@ -123,10 +123,19 @@ else
 fi
 
 if [ "$SPARK_YARN" == "true" ]; then
-  mvn clean package -DskipTests -Pyarn -Dhadoop.version=$SPARK_HADOOP_VERSION \
-    -Dyarn.version=$SPARK_HADOOP_VERSION $MAYBE_HIVE
+  if [[ "$SPARK_HADOOP_VERSION" =~ "0.23." ]]; then
+    mvn clean package -DskipTests -Pyarn-alpha -Dhadoop.version=$SPARK_HADOOP_VERSION \
+      -Dyarn.version=$SPARK_HADOOP_VERSION $MAYBE_HIVE -Phadoop-0.23
+  else
+    mvn clean package -DskipTests -Pyarn -Dhadoop.version=$SPARK_HADOOP_VERSION \
+      -Dyarn.version=$SPARK_HADOOP_VERSION $MAYBE_HIVE
+  fi
 else
-  mvn clean package -DskipTests -Dhadoop.version=$SPARK_HADOOP_VERSION $MAYBE_HIVE
+  if [[ "$SPARK_HADOOP_VERSION" =~ "0.23." ]]; then
+    mvn clean package -Phadoop-0.23 -DskipTests -Dhadoop.version=$SPARK_HADOOP_VERSION $MAYBE_HIVE
+  else
+    mvn clean package -DskipTests -Dhadoop.version=$SPARK_HADOOP_VERSION $MAYBE_HIVE
+  fi
 fi
 
 # Make directories
diff --git a/mllib/pom.xml b/mllib/pom.xml
index e7ce00efc4af..cdd33dbb7970 100644
--- a/mllib/pom.xml
+++ b/mllib/pom.xml
@@ -31,20 +31,6 @@
   <name>Spark Project ML Library</name>
   <url>http://spark.apache.org/</url>
 
-  <profiles>
-    <profile>
-      <!-- SPARK-1121: SPARK-1121: Adds an explicit dependency on Avro to work around
-           a Hadoop 0.23.X issue -->
-      <id>yarn-alpha</id>
-      <dependencies>
-        <dependency>
-          <groupId>org.apache.avro</groupId>
-          <artifactId>avro</artifactId>
-        </dependency>
-      </dependencies>
-    </profile>
-  </profiles>
-
   <dependencies>
     <dependency>
       <groupId>org.apache.spark</groupId>
diff --git a/pom.xml b/pom.xml
index e9117998252f..646753fe3030 100644
--- a/pom.xml
+++ b/pom.xml
@@ -127,6 +127,7 @@
     <jetty.version>8.1.14.v20131031</jetty.version>
     <chill.version>0.3.6</chill.version>
     <codahale.metrics.version>3.0.0</codahale.metrics.version>
+    <avro.version>1.7.4</avro.version>
 
     <PermGen>64m</PermGen>
     <MaxPermGen>512m</MaxPermGen>
@@ -293,7 +294,7 @@
           <exclusion>
             <groupId>org.ow2.asm</groupId>
             <artifactId>asm-commons</artifactId>
-         </exclusion>
+          </exclusion>
         </exclusions>
       </dependency>
       <dependency>
@@ -308,7 +309,7 @@
           <exclusion>
             <groupId>org.ow2.asm</groupId>
             <artifactId>asm-commons</artifactId>
-         </exclusion>
+          </exclusion>
         </exclusions>
       </dependency>
       <dependency>
@@ -425,6 +426,16 @@
         <artifactId>scala-library</artifactId>
         <version>${scala.version}</version>
       </dependency>
+      <dependency>
+        <groupId>org.scala-lang</groupId>
+        <artifactId>scala-actors</artifactId>
+        <version>${scala.version}</version>
+      </dependency>
+      <dependency>
+        <groupId>org.scala-lang</groupId>
+        <artifactId>scalap</artifactId>
+        <version>${scala.version}</version>
+      </dependency>
       <dependency>
         <groupId>org.scalatest</groupId>
         <artifactId>scalatest_${scala.binary.version}</artifactId>
@@ -492,7 +503,45 @@
       <dependency>
         <groupId>org.apache.avro</groupId>
         <artifactId>avro</artifactId>
-        <version>1.7.4</version>
+        <version>${avro.version}</version>
+        <exclusions>
+          <exclusion>
+            <groupId>org.jboss.netty</groupId>
+            <artifactId>netty</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>io.netty</groupId>
+            <artifactId>netty</artifactId>
+          </exclusion>
+        </exclusions>
+      </dependency>
+      <dependency>
+        <groupId>org.apache.avro</groupId>
+        <artifactId>avro-ipc</artifactId>
+        <version>${avro.version}</version>
+        <exclusions>
+          <exclusion>
+            <groupId>org.mortbay.jetty</groupId>
+            <artifactId>jetty</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.mortbay.jetty</groupId>
+            <artifactId>jetty-util</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.mortbay.jetty</groupId>
+            <artifactId>servlet-api</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.apache.velocity</groupId>
+            <artifactId>velocity</artifactId>
+          </exclusion>
+        </exclusions>
+      </dependency>
+      <dependency>
+        <groupId>org.apache.avro</groupId>
+        <artifactId>avro-mapred</artifactId>
+        <version>${avro.version}</version>
         <exclusions>
           <exclusion>
             <groupId>org.jboss.netty</groupId>
@@ -687,7 +736,7 @@
             <junitxml>.</junitxml>
             <filereports>${project.build.directory}/SparkTestSuite.txt</filereports>
             <argLine>-Xmx3g -XX:MaxPermSize=${MaxPermGen} -XX:ReservedCodeCacheSize=512m</argLine>
-            <stderr />
+            <stderr/>
           </configuration>
           <executions>
             <execution>
@@ -742,7 +791,7 @@
         <artifactId>build-helper-maven-plugin</artifactId>
         <executions>
           <execution>
-           <id>add-scala-sources</id>
+            <id>add-scala-sources</id>
             <phase>generate-sources</phase>
             <goals>
               <goal>add-source</goal>
@@ -779,6 +828,17 @@
   </build>
 
   <profiles>
+    <!-- SPARK-1121: Adds an explicit dependency on Avro to work around a Hadoop 0.23.X issue -->
+    <profile>
+      <id>hadoop-0.23</id>
+      <dependencies>
+        <dependency>
+          <groupId>org.apache.avro</groupId>
+          <artifactId>avro</artifactId>
+        </dependency>
+      </dependencies>
+    </profile>
+
     <profile>
       <id>yarn-alpha</id>
       <properties>
@@ -790,6 +850,12 @@
       <modules>
         <module>yarn</module>
       </modules>
+      <dependencies>
+        <dependency>
+          <groupId>org.apache.avro</groupId>
+          <artifactId>avro</artifactId>
+        </dependency>
+      </dependencies>
     </profile>
 
     <!-- Ganglia integration is not included by default due to LGPL-licensed code -->
@@ -838,7 +904,7 @@
 
     </profile>
 
-   <!-- Build without Hadoop dependencies that are included in some runtime environments. -->
+    <!-- Build without Hadoop dependencies that are included in some runtime environments. -->
     <profile>
       <id>hadoop-provided</id>
       <activation>
@@ -878,6 +944,7 @@
         <dependency>
           <groupId>org.apache.zookeeper</groupId>
           <artifactId>zookeeper</artifactId>
+          <version>3.4.5</version>
           <scope>provided</scope>
         </dependency>
       </dependencies>
diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index 5adfbe373cb0..a9504f392608 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -106,7 +106,7 @@ object SparkBuild extends Build {
     case Some(v) => v.toBoolean
   }
   lazy val hadoopClient = if (hadoopVersion.startsWith("0.20.") || hadoopVersion == "1.0.0") "hadoop-core" else "hadoop-client"
-  val maybeAvro = if (hadoopVersion.startsWith("0.23.") && isYarnEnabled) Seq("org.apache.avro" % "avro" % "1.7.4") else Seq()
+  val maybeAvro = if (hadoopVersion.startsWith("0.23.")) Seq("org.apache.avro" % "avro" % "1.7.4") else Seq()
 
   lazy val isHiveEnabled = Properties.envOrNone("SPARK_HIVE") match {
     case None => DEFAULT_HIVE
@@ -224,7 +224,7 @@ object SparkBuild extends Build {
       <parent>
         <groupId>org.apache</groupId>
         <artifactId>apache</artifactId>
-        <version>13</version>
+        <version>14</version>
       </parent>
       <url>http://spark.apache.org/</url>
       <licenses>
@@ -250,7 +250,7 @@ object SparkBuild extends Build {
       </developers>
       <issueManagement>
         <system>JIRA</system>
-        <url>https://spark-project.atlassian.net/browse/SPARK</url>
+        <url>https://issues.apache.org/jira/browse/SPARK</url>
       </issueManagement>
     ),
 
@@ -313,6 +313,7 @@ object SparkBuild extends Build {
   val excludeCurator = ExclusionRule(organization = "org.apache.curator")
   val excludePowermock = ExclusionRule(organization = "org.powermock")
   val excludeFastutil = ExclusionRule(organization = "it.unimi.dsi")
+  val excludeJruby = ExclusionRule(organization = "org.jruby")
   val excludeThrift = ExclusionRule(organization = "org.apache.thrift")
 
   def sparkPreviousArtifact(id: String, organization: String = "org.apache.spark",
@@ -417,7 +418,7 @@ object SparkBuild extends Build {
       v => "spark-examples-" + v + "-hadoop" + hadoopVersion + ".jar" },
     libraryDependencies ++= Seq(
       "com.twitter"          %% "algebird-core"   % "0.1.11",
-      "org.apache.hbase" % "hbase" % HBASE_VERSION excludeAll(excludeNetty, excludeAsm, excludeOldAsm, excludeCommonsLogging),
+      "org.apache.hbase" % "hbase" % HBASE_VERSION excludeAll(excludeNetty, excludeAsm, excludeOldAsm, excludeCommonsLogging, excludeJruby),
       "org.apache.cassandra" % "cassandra-all" % "1.2.6"
         exclude("com.google.guava", "guava")
         exclude("com.googlecode.concurrentlinkedhashmap", "concurrentlinkedhashmap-lru")
diff --git a/repl/pom.xml b/repl/pom.xml
index 78d2fe13c27e..b761a176ce25 100644
--- a/repl/pom.xml
+++ b/repl/pom.xml
@@ -31,20 +31,6 @@
   <name>Spark Project REPL</name>
   <url>http://spark.apache.org/</url>
 
-  <!-- SPARK-1121: SPARK-1121: Adds an explicit dependency on Avro to work around
-       a Hadoop 0.23.X issue -->
-  <profiles>
-    <profile>
-      <id>yarn-alpha</id>
-      <dependencies>
-        <dependency>
-          <groupId>org.apache.avro</groupId>
-          <artifactId>avro</artifactId>
-        </dependency>
-      </dependencies>
-    </profile>
-  </profiles>
-
   <properties>
     <deb.install.path>/usr/share/spark</deb.install.path>
     <deb.user>root</deb.user>
diff --git a/sql/catalyst/pom.xml b/sql/catalyst/pom.xml
index 9d5c6a857bb0..8d2e4baf69e3 100644
--- a/sql/catalyst/pom.xml
+++ b/sql/catalyst/pom.xml
@@ -16,67 +16,56 @@
   ~ limitations under the License.
   -->
 
-<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
-    <modelVersion>4.0.0</modelVersion>
-    <parent>
-        <groupId>org.apache.spark</groupId>
-        <artifactId>spark-parent</artifactId>
-        <version>1.0.0-SNAPSHOT</version>
-        <relativePath>../../pom.xml</relativePath>
-    </parent>
-
+<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
+  <modelVersion>4.0.0</modelVersion>
+  <parent>
     <groupId>org.apache.spark</groupId>
-    <artifactId>spark-catalyst_2.10</artifactId>
-    <packaging>jar</packaging>
-    <name>Spark Project Catalyst</name>
-    <url>http://spark.apache.org/</url>
+    <artifactId>spark-parent</artifactId>
+    <version>1.0.0-SNAPSHOT</version>
+    <relativePath>../../pom.xml</relativePath>
+  </parent>
 
-    <profiles>
-      <profile>
-        <id>yarn-alpha</id>
-        <dependencies>
-          <dependency>
-            <groupId>org.apache.avro</groupId>
-            <artifactId>avro</artifactId>
-          </dependency>
-        </dependencies>
-      </profile>
-    </profiles>
+  <groupId>org.apache.spark</groupId>
+  <artifactId>spark-catalyst_2.10</artifactId>
+  <packaging>jar</packaging>
+  <name>Spark Project Catalyst</name>
+  <url>http://spark.apache.org/</url>
 
-    <dependencies>
-        <dependency>
-            <groupId>org.scala-lang</groupId>
-            <artifactId>scala-reflect</artifactId>
-        </dependency>
-        <dependency>
-            <groupId>org.apache.spark</groupId>
-            <artifactId>spark-core_${scala.binary.version}</artifactId>
-            <version>${project.version}</version>
-        </dependency>
-        <dependency>
-            <groupId>com.typesafe</groupId>
-            <artifactId>scalalogging-slf4j_${scala.binary.version}</artifactId>
-            <version>1.0.1</version>
-        </dependency>
-        <dependency>
-            <groupId>org.scalatest</groupId>
-            <artifactId>scalatest_${scala.binary.version}</artifactId>
-            <scope>test</scope>
-        </dependency>
-        <dependency>
-            <groupId>org.scalacheck</groupId>
-            <artifactId>scalacheck_${scala.binary.version}</artifactId>
-            <scope>test</scope>
-        </dependency>
-    </dependencies>
-    <build>
-        <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory>
-        <testOutputDirectory>target/scala-${scala.binary.version}/test-classes</testOutputDirectory>
-        <plugins>
-            <plugin>
-                <groupId>org.scalatest</groupId>
-                <artifactId>scalatest-maven-plugin</artifactId>
-            </plugin>
-        </plugins>
-    </build>
+  <dependencies>
+    <dependency>
+      <groupId>org.scala-lang</groupId>
+      <artifactId>scala-reflect</artifactId>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.spark</groupId>
+      <artifactId>spark-core_${scala.binary.version}</artifactId>
+      <version>${project.version}</version>
+    </dependency>
+    <dependency>
+      <groupId>com.typesafe</groupId>
+      <artifactId>scalalogging-slf4j_${scala.binary.version}</artifactId>
+      <version>1.0.1</version>
+    </dependency>
+    <dependency>
+      <groupId>org.scalatest</groupId>
+      <artifactId>scalatest_${scala.binary.version}</artifactId>
+      <scope>test</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.scalacheck</groupId>
+      <artifactId>scalacheck_${scala.binary.version}</artifactId>
+      <scope>test</scope>
+    </dependency>
+  </dependencies>
+  <build>
+    <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory>
+    <testOutputDirectory>target/scala-${scala.binary.version}/test-classes</testOutputDirectory>
+    <plugins>
+      <plugin>
+        <groupId>org.scalatest</groupId>
+        <artifactId>scalatest-maven-plugin</artifactId>
+      </plugin>
+    </plugins>
+  </build>
 </project>
diff --git a/sql/core/pom.xml b/sql/core/pom.xml
index 85580ed6b822..fb3b190b4ec5 100644
--- a/sql/core/pom.xml
+++ b/sql/core/pom.xml
@@ -16,72 +16,62 @@
   ~ limitations under the License.
   -->
 
-<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
-    <modelVersion>4.0.0</modelVersion>
-    <parent>
-        <groupId>org.apache.spark</groupId>
-        <artifactId>spark-parent</artifactId>
-        <version>1.0.0-SNAPSHOT</version>
-        <relativePath>../../pom.xml</relativePath>
-    </parent>
-
+<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
+  <modelVersion>4.0.0</modelVersion>
+  <parent>
     <groupId>org.apache.spark</groupId>
-    <artifactId>spark-sql_2.10</artifactId>
-    <packaging>jar</packaging>
-    <name>Spark Project SQL</name>
-    <url>http://spark.apache.org/</url>
-    <profiles>
-      <profile>
-        <id>yarn-alpha</id>
-        <dependencies>
-          <dependency>
-            <groupId>org.apache.avro</groupId>
-            <artifactId>avro</artifactId>
-          </dependency>
-        </dependencies>
-      </profile>
-    </profiles>
+    <artifactId>spark-parent</artifactId>
+    <version>1.0.0-SNAPSHOT</version>
+    <relativePath>../../pom.xml</relativePath>
+  </parent>
+
+  <groupId>org.apache.spark</groupId>
+  <artifactId>spark-sql_2.10</artifactId>
+  <packaging>jar</packaging>
+  <name>Spark Project SQL</name>
+  <url>http://spark.apache.org/</url>
 
-    <dependencies>
-        <dependency>
-            <groupId>org.apache.spark</groupId>
-            <artifactId>spark-core_${scala.binary.version}</artifactId>
-            <version>${project.version}</version>
-        </dependency>
-        <dependency>
-            <groupId>org.apache.spark</groupId>
-            <artifactId>spark-catalyst_${scala.binary.version}</artifactId>
-            <version>${project.version}</version>
-        </dependency>
-        <dependency>
-            <groupId>com.twitter</groupId>
-            <artifactId>parquet-column</artifactId>
-            <version>${parquet.version}</version>
-        </dependency>
-        <dependency>
-            <groupId>com.twitter</groupId>
-            <artifactId>parquet-hadoop</artifactId>
-            <version>${parquet.version}</version>
-        </dependency>
-        <dependency>
-            <groupId>org.scalatest</groupId>
-            <artifactId>scalatest_${scala.binary.version}</artifactId>
-            <scope>test</scope>
-        </dependency>
-        <dependency>
-            <groupId>org.scalacheck</groupId>
-            <artifactId>scalacheck_${scala.binary.version}</artifactId>
-            <scope>test</scope>
-        </dependency>
-    </dependencies>
-    <build>
-        <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory>
-        <testOutputDirectory>target/scala-${scala.binary.version}/test-classes</testOutputDirectory>
-        <plugins>
-            <plugin>
-                <groupId>org.scalatest</groupId>
-                <artifactId>scalatest-maven-plugin</artifactId>
-            </plugin>
-        </plugins>
-    </build>
+  <dependencies>
+    <dependency>
+      <groupId>org.apache.spark</groupId>
+      <artifactId>spark-core_${scala.binary.version}</artifactId>
+      <version>${project.version}</version>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.spark</groupId>
+      <artifactId>spark-catalyst_${scala.binary.version}</artifactId>
+      <version>${project.version}</version>
+    </dependency>
+    <dependency>
+      <groupId>com.twitter</groupId>
+      <artifactId>parquet-column</artifactId>
+      <version>${parquet.version}</version>
+    </dependency>
+    <dependency>
+      <groupId>com.twitter</groupId>
+      <artifactId>parquet-hadoop</artifactId>
+      <version>${parquet.version}</version>
+    </dependency>
+    <dependency>
+      <groupId>org.scalatest</groupId>
+      <artifactId>scalatest_${scala.binary.version}</artifactId>
+      <scope>test</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.scalacheck</groupId>
+      <artifactId>scalacheck_${scala.binary.version}</artifactId>
+      <scope>test</scope>
+    </dependency>
+  </dependencies>
+  <build>
+    <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory>
+    <testOutputDirectory>target/scala-${scala.binary.version}/test-classes</testOutputDirectory>
+    <plugins>
+      <plugin>
+        <groupId>org.scalatest</groupId>
+        <artifactId>scalatest-maven-plugin</artifactId>
+      </plugin>
+    </plugins>
+  </build>
 </project>
diff --git a/sql/hive/pom.xml b/sql/hive/pom.xml
index a662da76ce25..889d249146b8 100644
--- a/sql/hive/pom.xml
+++ b/sql/hive/pom.xml
@@ -16,105 +16,95 @@
   ~ limitations under the License.
   -->
 
-<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
-    <modelVersion>4.0.0</modelVersion>
-    <parent>
-        <groupId>org.apache.spark</groupId>
-        <artifactId>spark-parent</artifactId>
-        <version>1.0.0-SNAPSHOT</version>
-        <relativePath>../../pom.xml</relativePath>
-    </parent>
-
+<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
+  <modelVersion>4.0.0</modelVersion>
+  <parent>
     <groupId>org.apache.spark</groupId>
-    <artifactId>spark-hive_2.10</artifactId>
-    <packaging>jar</packaging>
-    <name>Spark Project Hive</name>
-    <url>http://spark.apache.org/</url>
-    <profiles>
-      <profile>
-        <id>yarn-alpha</id>
-        <dependencies>
-          <dependency>
-            <groupId>org.apache.avro</groupId>
-            <artifactId>avro</artifactId>
-          </dependency>
-        </dependencies>
-      </profile>
-    </profiles>
+    <artifactId>spark-parent</artifactId>
+    <version>1.0.0-SNAPSHOT</version>
+    <relativePath>../../pom.xml</relativePath>
+  </parent>
+
+  <groupId>org.apache.spark</groupId>
+  <artifactId>spark-hive_2.10</artifactId>
+  <packaging>jar</packaging>
+  <name>Spark Project Hive</name>
+  <url>http://spark.apache.org/</url>
 
-    <dependencies>
-        <dependency>
-            <groupId>org.apache.spark</groupId>
-            <artifactId>spark-core_${scala.binary.version}</artifactId>
-            <version>${project.version}</version>
-        </dependency>
-        <dependency>
-            <groupId>org.apache.spark</groupId>
-            <artifactId>spark-sql_${scala.binary.version}</artifactId>
-            <version>${project.version}</version>
-        </dependency>
-        <dependency>
-            <groupId>org.apache.hive</groupId>
-            <artifactId>hive-metastore</artifactId>
-            <version>${hive.version}</version>
-        </dependency>
-        <dependency>
-            <groupId>org.apache.hive</groupId>
-            <artifactId>hive-exec</artifactId>
-            <version>${hive.version}</version>
-        </dependency>
-        <dependency>
-            <groupId>org.codehaus.jackson</groupId>
-            <artifactId>jackson-mapper-asl</artifactId>
-        </dependency>
-        <dependency>
-            <groupId>org.apache.hive</groupId>
-            <artifactId>hive-serde</artifactId>
-            <version>${hive.version}</version>
-        </dependency>
-        <dependency>
-            <groupId>org.scalatest</groupId>
-            <artifactId>scalatest_${scala.binary.version}</artifactId>
-            <scope>test</scope>
-        </dependency>
-        <dependency>
-            <groupId>org.scalacheck</groupId>
-            <artifactId>scalacheck_${scala.binary.version}</artifactId>
-            <scope>test</scope>
-        </dependency>
-    </dependencies>
-    <build>
-        <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory>
-        <testOutputDirectory>target/scala-${scala.binary.version}/test-classes</testOutputDirectory>
-        <plugins>
-            <plugin>
-                <groupId>org.scalatest</groupId>
-                <artifactId>scalatest-maven-plugin</artifactId>
-            </plugin>
+  <dependencies>
+    <dependency>
+      <groupId>org.apache.spark</groupId>
+      <artifactId>spark-core_${scala.binary.version}</artifactId>
+      <version>${project.version}</version>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.spark</groupId>
+      <artifactId>spark-sql_${scala.binary.version}</artifactId>
+      <version>${project.version}</version>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.hive</groupId>
+      <artifactId>hive-metastore</artifactId>
+      <version>${hive.version}</version>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.hive</groupId>
+      <artifactId>hive-exec</artifactId>
+      <version>${hive.version}</version>
+    </dependency>
+    <dependency>
+      <groupId>org.codehaus.jackson</groupId>
+      <artifactId>jackson-mapper-asl</artifactId>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.hive</groupId>
+      <artifactId>hive-serde</artifactId>
+      <version>${hive.version}</version>
+    </dependency>
+    <dependency>
+      <groupId>org.scalatest</groupId>
+      <artifactId>scalatest_${scala.binary.version}</artifactId>
+      <scope>test</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.scalacheck</groupId>
+      <artifactId>scalacheck_${scala.binary.version}</artifactId>
+      <scope>test</scope>
+    </dependency>
+  </dependencies>
+  <build>
+    <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory>
+    <testOutputDirectory>target/scala-${scala.binary.version}/test-classes</testOutputDirectory>
+    <plugins>
+      <plugin>
+        <groupId>org.scalatest</groupId>
+        <artifactId>scalatest-maven-plugin</artifactId>
+      </plugin>
 
-            <!-- Deploy datanucleus jars to the spark/lib_managed/jars directory -->
-            <plugin>
-                <groupId>org.apache.maven.plugins</groupId>
-                <artifactId>maven-dependency-plugin</artifactId>
-                <version>2.4</version>
-                <executions>
-                    <execution>
-                        <id>copy-dependencies</id>
-                        <phase>package</phase>
-                        <goals>
-                            <goal>copy-dependencies</goal>
-                        </goals>
-                        <configuration>
-                            <!-- basedir is spark/sql/hive/ -->
-                            <outputDirectory>${basedir}/../../lib_managed/jars</outputDirectory>
-                            <overWriteReleases>false</overWriteReleases>
-                            <overWriteSnapshots>false</overWriteSnapshots>
-                            <overWriteIfNewer>true</overWriteIfNewer>
-                            <includeGroupIds>org.datanucleus</includeGroupIds>
-                        </configuration>
-                    </execution>
-                </executions>
-            </plugin>
-        </plugins>
-    </build>
+      <!-- Deploy datanucleus jars to the spark/lib_managed/jars directory -->
+      <plugin>
+        <groupId>org.apache.maven.plugins</groupId>
+        <artifactId>maven-dependency-plugin</artifactId>
+        <version>2.4</version>
+        <executions>
+          <execution>
+            <id>copy-dependencies</id>
+            <phase>package</phase>
+            <goals>
+              <goal>copy-dependencies</goal>
+            </goals>
+            <configuration>
+              <!-- basedir is spark/sql/hive/ -->
+              <outputDirectory>${basedir}/../../lib_managed/jars</outputDirectory>
+              <overWriteReleases>false</overWriteReleases>
+              <overWriteSnapshots>false</overWriteSnapshots>
+              <overWriteIfNewer>true</overWriteIfNewer>
+              <includeGroupIds>org.datanucleus</includeGroupIds>
+            </configuration>
+          </execution>
+        </executions>
+      </plugin>
+    </plugins>
+  </build>
 </project>
diff --git a/streaming/pom.xml b/streaming/pom.xml
index 93b1c5a37aff..6435224a1467 100644
--- a/streaming/pom.xml
+++ b/streaming/pom.xml
@@ -31,20 +31,6 @@
   <name>Spark Project Streaming</name>
   <url>http://spark.apache.org/</url>
 
-  <profiles>
-    <profile>
-      <!-- SPARK-1121: SPARK-1121: Adds an explicit dependency on Avro to work around
-           a Hadoop 0.23.X issue -->
-      <id>yarn-alpha</id>
-      <dependencies>
-        <dependency>
-          <groupId>org.apache.avro</groupId>
-          <artifactId>avro</artifactId>
-        </dependency>
-      </dependencies>
-    </profile>
-  </profiles>
-
   <dependencies>
     <dependency>
       <groupId>org.apache.spark</groupId>
diff --git a/tools/pom.xml b/tools/pom.xml
index ae2ba64e07c2..1875c497bc61 100644
--- a/tools/pom.xml
+++ b/tools/pom.xml
@@ -30,20 +30,6 @@
   <name>Spark Project Tools</name>
   <url>http://spark.apache.org/</url>
 
-  <!-- SPARK-1121: SPARK-1121: Adds an explicit dependency on Avro to work around
-       a Hadoop 0.23.X issue -->
-  <profiles>
-    <profile>
-      <id>yarn-alpha</id>
-      <dependencies>
-        <dependency>
-          <groupId>org.apache.avro</groupId>
-          <artifactId>avro</artifactId>
-        </dependency>
-      </dependencies>
-    </profile>
-  </profiles>
-
   <dependencies>
     <dependency>
       <groupId>org.apache.spark</groupId>
diff --git a/yarn/alpha/pom.xml b/yarn/alpha/pom.xml
index d0aeaceb0d23..e076ca1d44b9 100644
--- a/yarn/alpha/pom.xml
+++ b/yarn/alpha/pom.xml
@@ -24,20 +24,6 @@
     <relativePath>../pom.xml</relativePath>
   </parent>
 
-  <profiles>
-    <profile>
-      <!-- SPARK-1121: SPARK-1121: Adds an explicit dependency on Avro to work around
-           a Hadoop 0.23.X issue -->
-      <id>yarn-alpha</id>
-      <dependencies>
-        <dependency>
-          <groupId>org.apache.avro</groupId>
-          <artifactId>avro</artifactId>
-        </dependency>
-      </dependencies>
-    </profile>
-  </profiles>
-
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-yarn-alpha_2.10</artifactId>
   <packaging>jar</packaging>
diff --git a/yarn/pom.xml b/yarn/pom.xml
index 3342cb65edcd..02f36627431b 100644
--- a/yarn/pom.xml
+++ b/yarn/pom.xml
@@ -70,15 +70,6 @@
       <modules>
         <module>alpha</module>
       </modules>
-
-      <!-- SPARK-1121: SPARK-1121: Adds an explicit dependency on Avro to work around
-           a Hadoop 0.23.X issue -->
-      <dependencies>
-         <dependency>
-           <groupId>org.apache.avro</groupId>
-           <artifactId>avro</artifactId>
-         </dependency>
-      </dependencies>
     </profile>
 
     <profile>
diff --git a/yarn/stable/pom.xml b/yarn/stable/pom.xml
index e7915d12aef6..0780f251b595 100644
--- a/yarn/stable/pom.xml
+++ b/yarn/stable/pom.xml
@@ -24,20 +24,6 @@
     <relativePath>../pom.xml</relativePath>
   </parent>
 
-  <profiles>
-    <profile>
-      <!-- SPARK-1121: SPARK-1121: Adds an explicit dependency on Avro to work around
-           a Hadoop 0.23.X issue -->
-      <id>yarn-alpha</id>
-      <dependencies>
-        <dependency>
-          <groupId>org.apache.avro</groupId>
-          <artifactId>avro</artifactId>
-        </dependency>
-      </dependencies>
-    </profile>
-  </profiles>
-
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-yarn_2.10</artifactId>
   <packaging>jar</packaging>

From 719c8bc65e8a4096d09a6a9f15c90da3ddd7d9d4 Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Tue, 29 Apr 2014 00:10:17 -0700
Subject: [PATCH 051/810] HOTFIX: Bug in release script

---
 dev/create-release/create-release.sh | 1 +
 1 file changed, 1 insertion(+)

diff --git a/dev/create-release/create-release.sh b/dev/create-release/create-release.sh
index ae1b41544443..44da8f7cbf53 100755
--- a/dev/create-release/create-release.sh
+++ b/dev/create-release/create-release.sh
@@ -62,6 +62,7 @@ if [[ ! "$@" =~ --package-only ]]; then
     -Pyarn -Phive -Pspark-ganglia-lgpl\
     release:perform
 
+  cd ..
   rm -rf spark
 fi
 

From 497be3ca2d8f0600e927f8f036177fcd3bb6e229 Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Tue, 29 Apr 2014 00:36:15 -0700
Subject: [PATCH 052/810] Minor fix to python table caching API.

Author: Michael Armbrust <michael@databricks.com>

Closes #585 from marmbrus/pythonCacheTable and squashes the following commits:

7ec1f91 [Michael Armbrust] Minor fix to python table caching API.
---
 python/pyspark/sql.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/python/pyspark/sql.py b/python/pyspark/sql.py
index 114fa138d0de..1a62031db5c4 100644
--- a/python/pyspark/sql.py
+++ b/python/pyspark/sql.py
@@ -143,13 +143,13 @@ def table(self, tableName):
         """
         return SchemaRDD(self._ssql_ctx.table(tableName), self)
 
-    def cacheTable(tableName):
+    def cacheTable(self, tableName):
         """
         Caches the specified table in-memory.
         """
         self._ssql_ctx.cacheTable(tableName)
 
-    def uncacheTable(tableName):
+    def uncacheTable(self, tableName):
         """
         Removes the specified table from the in-memory cache.
         """

From 3f38334f441940ed0a5bbf5588ca7f22d3940359 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Tue, 29 Apr 2014 00:41:03 -0700
Subject: [PATCH 053/810] [SPARK-1636][MLLIB] Move main methods to examples

* `NaiveBayes` -> `SparseNaiveBayes`
* `KMeans` -> `DenseKMeans`
* `SVMWithSGD` and `LogisticRegerssionWithSGD` -> `BinaryClassification`
* `ALS` -> `MovieLensALS`
* `LinearRegressionWithSGD`, `LassoWithSGD`, and `RidgeRegressionWithSGD` -> `LinearRegression`
* `DecisionTree` -> `DecisionTreeRunner`

`scopt` is used for parsing command-line parameters. `scopt` has MIT license and it only depends on `scala-library`.

Example help message:

~~~
BinaryClassification: an example app for binary classification.
Usage: BinaryClassification [options] <input>

  --numIterations <value>
        number of iterations
  --stepSize <value>
        initial step size, default: 1.0
  --algorithm <value>
        algorithm (SVM,LR), default: LR
  --regType <value>
        regularization type (L1,L2), default: L2
  --regParam <value>
        regularization parameter, default: 0.1
  <input>
        input paths to labeled examples in LIBSVM format
~~~

Author: Xiangrui Meng <meng@databricks.com>

Closes #584 from mengxr/mllib-main and squashes the following commits:

7b58c60 [Xiangrui Meng] minor
6e35d7e [Xiangrui Meng] make imports explicit and fix code style
c6178c9 [Xiangrui Meng] update TS PCA/SVD to use new spark-submit
6acff75 [Xiangrui Meng] use scopt for DecisionTreeRunner
be86069 [Xiangrui Meng] use main instead of extending App
b3edf68 [Xiangrui Meng] move DecisionTree's main method to examples
8bfaa5a [Xiangrui Meng] change NaiveBayesParams to Params
fe23dcb [Xiangrui Meng] remove main from KMeans and add DenseKMeans as an example
67f4448 [Xiangrui Meng] remove main methods from linear regression algorithms and add LinearRegression example
b066bbc [Xiangrui Meng] remove main from ALS and add MovieLensALS example
b040f3b [Xiangrui Meng] change BinaryClassificationParams to Params
577945b [Xiangrui Meng] remove unused imports from NB
3d299bc [Xiangrui Meng] remove main from LR/SVM and add an example app for binary classification
f70878e [Xiangrui Meng] remove main from NaiveBayes and add an example NaiveBayes app
01ec2cd [Xiangrui Meng] Merge branch 'master' into mllib-main
9420692 [Xiangrui Meng] add scopt to examples dependencies
---
 examples/pom.xml                              |   5 +
 .../examples/mllib/BinaryClassification.scala | 145 ++++++++++++++++
 .../examples/mllib/DecisionTreeRunner.scala   | 161 ++++++++++++++++++
 .../spark/examples/mllib/DenseKMeans.scala    | 109 ++++++++++++
 .../examples/mllib/LinearRegression.scala     | 125 ++++++++++++++
 .../spark/examples/mllib/MovieLensALS.scala   | 131 ++++++++++++++
 .../examples/mllib/SparseNaiveBayes.scala     | 102 +++++++++++
 .../spark/examples/mllib/TallSkinnyPCA.scala  |  12 +-
 .../spark/examples/mllib/TallSkinnySVD.scala  |  12 +-
 .../classification/LogisticRegression.scala   |  18 +-
 .../mllib/classification/NaiveBayes.scala     |  22 +--
 .../spark/mllib/classification/SVM.scala      |  18 +-
 .../spark/mllib/clustering/KMeans.scala       |  25 +--
 .../spark/mllib/recommendation/ALS.scala      |  45 +----
 .../apache/spark/mllib/regression/Lasso.scala |  17 --
 .../mllib/regression/LinearRegression.scala   |  16 --
 .../mllib/regression/RidgeRegression.scala    |  19 ---
 .../spark/mllib/tree/DecisionTree.scala       | 131 +-------------
 project/SparkBuild.scala                      |   3 +-
 19 files changed, 795 insertions(+), 321 deletions(-)
 create mode 100644 examples/src/main/scala/org/apache/spark/examples/mllib/BinaryClassification.scala
 create mode 100644 examples/src/main/scala/org/apache/spark/examples/mllib/DecisionTreeRunner.scala
 create mode 100644 examples/src/main/scala/org/apache/spark/examples/mllib/DenseKMeans.scala
 create mode 100644 examples/src/main/scala/org/apache/spark/examples/mllib/LinearRegression.scala
 create mode 100644 examples/src/main/scala/org/apache/spark/examples/mllib/MovieLensALS.scala
 create mode 100644 examples/src/main/scala/org/apache/spark/examples/mllib/SparseNaiveBayes.scala

diff --git a/examples/pom.xml b/examples/pom.xml
index a2d1b1973661..e1fc149d87f1 100644
--- a/examples/pom.xml
+++ b/examples/pom.xml
@@ -166,6 +166,11 @@
         </exclusion>
       </exclusions>
     </dependency>
+    <dependency>
+      <groupId>com.github.scopt</groupId>
+      <artifactId>scopt_${scala.binary.version}</artifactId>
+      <version>3.2.0</version>
+    </dependency>
   </dependencies>
 
   <build>
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/BinaryClassification.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/BinaryClassification.scala
new file mode 100644
index 000000000000..ec9de022c1d4
--- /dev/null
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/BinaryClassification.scala
@@ -0,0 +1,145 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.examples.mllib
+
+import org.apache.log4j.{Level, Logger}
+import scopt.OptionParser
+
+import org.apache.spark.{SparkConf, SparkContext}
+import org.apache.spark.mllib.classification.{LogisticRegressionWithSGD, SVMWithSGD}
+import org.apache.spark.mllib.evaluation.binary.BinaryClassificationMetrics
+import org.apache.spark.mllib.util.MLUtils
+import org.apache.spark.mllib.optimization.{SquaredL2Updater, L1Updater}
+
+/**
+ * An example app for binary classification. Run with
+ * {{{
+ * ./bin/run-example org.apache.spark.examples.mllib.BinaryClassification
+ * }}}
+ * If you use it as a template to create your own app, please use `spark-submit` to submit your app.
+ */
+object BinaryClassification {
+
+  object Algorithm extends Enumeration {
+    type Algorithm = Value
+    val SVM, LR = Value
+  }
+
+  object RegType extends Enumeration {
+    type RegType = Value
+    val L1, L2 = Value
+  }
+
+  import Algorithm._
+  import RegType._
+
+  case class Params(
+      input: String = null,
+      numIterations: Int = 100,
+      stepSize: Double = 1.0,
+      algorithm: Algorithm = LR,
+      regType: RegType = L2,
+      regParam: Double = 0.1)
+
+  def main(args: Array[String]) {
+    val defaultParams = Params()
+
+    val parser = new OptionParser[Params]("BinaryClassification") {
+      head("BinaryClassification: an example app for binary classification.")
+      opt[Int]("numIterations")
+        .text("number of iterations")
+        .action((x, c) => c.copy(numIterations = x))
+      opt[Double]("stepSize")
+        .text(s"initial step size, default: ${defaultParams.stepSize}")
+        .action((x, c) => c.copy(stepSize = x))
+      opt[String]("algorithm")
+        .text(s"algorithm (${Algorithm.values.mkString(",")}), " +
+        s"default: ${defaultParams.algorithm}")
+        .action((x, c) => c.copy(algorithm = Algorithm.withName(x)))
+      opt[String]("regType")
+        .text(s"regularization type (${RegType.values.mkString(",")}), " +
+        s"default: ${defaultParams.regType}")
+        .action((x, c) => c.copy(regType = RegType.withName(x)))
+      opt[Double]("regParam")
+        .text(s"regularization parameter, default: ${defaultParams.regParam}")
+      arg[String]("<input>")
+        .required()
+        .text("input paths to labeled examples in LIBSVM format")
+        .action((x, c) => c.copy(input = x))
+    }
+
+    parser.parse(args, defaultParams).map { params =>
+      run(params)
+    } getOrElse {
+      sys.exit(1)
+    }
+  }
+
+  def run(params: Params) {
+    val conf = new SparkConf().setAppName(s"BinaryClassification with $params")
+    val sc = new SparkContext(conf)
+
+    Logger.getRootLogger.setLevel(Level.WARN)
+
+    val examples = MLUtils.loadLibSVMData(sc, params.input).cache()
+
+    val splits = examples.randomSplit(Array(0.8, 0.2))
+    val training = splits(0).cache()
+    val test = splits(1).cache()
+
+    val numTraining = training.count()
+    val numTest = test.count()
+    println(s"Training: $numTraining, test: $numTest.")
+
+    examples.unpersist(blocking = false)
+
+    val updater = params.regType match {
+      case L1 => new L1Updater()
+      case L2 => new SquaredL2Updater()
+    }
+
+    val model = params.algorithm match {
+      case LR =>
+        val algorithm = new LogisticRegressionWithSGD()
+        algorithm.optimizer
+          .setNumIterations(params.numIterations)
+          .setStepSize(params.stepSize)
+          .setUpdater(updater)
+          .setRegParam(params.regParam)
+        algorithm.run(training).clearThreshold()
+      case SVM =>
+        val algorithm = new SVMWithSGD()
+        algorithm.optimizer
+          .setNumIterations(params.numIterations)
+          .setStepSize(params.stepSize)
+          .setUpdater(updater)
+          .setRegParam(params.regParam)
+        algorithm.run(training).clearThreshold()
+    }
+
+    val prediction = model.predict(test.map(_.features))
+    val predictionAndLabel = prediction.zip(test.map(_.label))
+
+    val metrics = new BinaryClassificationMetrics(predictionAndLabel)
+
+    println(s"Test areaUnderPR = ${metrics.areaUnderPR()}.")
+    println(s"Test areaUnderROC = ${metrics.areaUnderROC()}.")
+
+    sc.stop()
+  }
+}
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/DecisionTreeRunner.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/DecisionTreeRunner.scala
new file mode 100644
index 000000000000..0bd847d7bab3
--- /dev/null
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/DecisionTreeRunner.scala
@@ -0,0 +1,161 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.examples.mllib
+
+import scopt.OptionParser
+
+import org.apache.spark.{SparkConf, SparkContext}
+import org.apache.spark.SparkContext._
+import org.apache.spark.mllib.linalg.Vector
+import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.mllib.tree.{DecisionTree, impurity}
+import org.apache.spark.mllib.tree.configuration.{Algo, Strategy}
+import org.apache.spark.mllib.tree.configuration.Algo._
+import org.apache.spark.mllib.tree.model.DecisionTreeModel
+import org.apache.spark.mllib.util.MLUtils
+import org.apache.spark.rdd.RDD
+
+/**
+ * An example runner for decision tree. Run with
+ * {{{
+ * ./bin/spark-example org.apache.spark.examples.mllib.DecisionTreeRunner [options]
+ * }}}
+ * If you use it as a template to create your own app, please use `spark-submit` to submit your app.
+ */
+object DecisionTreeRunner {
+
+  object ImpurityType extends Enumeration {
+    type ImpurityType = Value
+    val Gini, Entropy, Variance = Value
+  }
+
+  import ImpurityType._
+
+  case class Params(
+      input: String = null,
+      algo: Algo = Classification,
+      maxDepth: Int = 5,
+      impurity: ImpurityType = Gini,
+      maxBins: Int = 20)
+
+  def main(args: Array[String]) {
+    val defaultParams = Params()
+
+    val parser = new OptionParser[Params]("DecisionTreeRunner") {
+      head("DecisionTreeRunner: an example decision tree app.")
+      opt[String]("algo")
+        .text(s"algorithm (${Algo.values.mkString(",")}), default: ${defaultParams.algo}")
+        .action((x, c) => c.copy(algo = Algo.withName(x)))
+      opt[String]("impurity")
+        .text(s"impurity type (${ImpurityType.values.mkString(",")}), " +
+          s"default: ${defaultParams.impurity}")
+        .action((x, c) => c.copy(impurity = ImpurityType.withName(x)))
+      opt[Int]("maxDepth")
+        .text(s"max depth of the tree, default: ${defaultParams.maxDepth}")
+        .action((x, c) => c.copy(maxDepth = x))
+      opt[Int]("maxBins")
+        .text(s"max number of bins, default: ${defaultParams.maxBins}")
+        .action((x, c) => c.copy(maxBins = x))
+      arg[String]("<input>")
+        .text("input paths to labeled examples in dense format (label,f0 f1 f2 ...)")
+        .required()
+        .action((x, c) => c.copy(input = x))
+      checkConfig { params =>
+        if (params.algo == Classification &&
+            (params.impurity == Gini || params.impurity == Entropy)) {
+          success
+        } else if (params.algo == Regression && params.impurity == Variance) {
+          success
+        } else {
+          failure(s"Algo ${params.algo} is not compatible with impurity ${params.impurity}.")
+        }
+      }
+    }
+
+    parser.parse(args, defaultParams).map { params =>
+      run(params)
+    }.getOrElse {
+      sys.exit(1)
+    }
+  }
+
+  def run(params: Params) {
+    val conf = new SparkConf().setAppName("DecisionTreeRunner")
+    val sc = new SparkContext(conf)
+
+    // Load training data and cache it.
+    val examples = MLUtils.loadLabeledData(sc, params.input).cache()
+
+    val splits = examples.randomSplit(Array(0.8, 0.2))
+    val training = splits(0).cache()
+    val test = splits(1).cache()
+
+    val numTraining = training.count()
+    val numTest = test.count()
+
+    println(s"numTraining = $numTraining, numTest = $numTest.")
+
+    examples.unpersist(blocking = false)
+
+    val impurityCalculator = params.impurity match {
+      case Gini => impurity.Gini
+      case Entropy => impurity.Entropy
+      case Variance => impurity.Variance
+    }
+
+    val strategy = new Strategy(params.algo, impurityCalculator, params.maxDepth, params.maxBins)
+    val model = DecisionTree.train(training, strategy)
+
+    if (params.algo == Classification) {
+      val accuracy = accuracyScore(model, test)
+      println(s"Test accuracy = $accuracy.")
+    }
+
+    if (params.algo == Regression) {
+      val mse = meanSquaredError(model, test)
+      println(s"Test mean squared error = $mse.")
+    }
+
+    sc.stop()
+  }
+
+  /**
+   * Calculates the classifier accuracy.
+   */
+  private def accuracyScore(
+      model: DecisionTreeModel,
+      data: RDD[LabeledPoint],
+      threshold: Double = 0.5): Double = {
+    def predictedValue(features: Vector): Double = {
+      if (model.predict(features) < threshold) 0.0 else 1.0
+    }
+    val correctCount = data.filter(y => predictedValue(y.features) == y.label).count()
+    val count = data.count()
+    correctCount.toDouble / count
+  }
+
+  /**
+   * Calculates the mean squared error for regression.
+   */
+  private def meanSquaredError(tree: DecisionTreeModel, data: RDD[LabeledPoint]): Double = {
+    data.map { y =>
+      val err = tree.predict(y.features) - y.label
+      err * err
+    }.mean()
+  }
+}
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/DenseKMeans.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/DenseKMeans.scala
new file mode 100644
index 000000000000..f96bc1bf00b9
--- /dev/null
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/DenseKMeans.scala
@@ -0,0 +1,109 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.examples.mllib
+
+import org.apache.log4j.{Level, Logger}
+import scopt.OptionParser
+
+import org.apache.spark.{SparkConf, SparkContext}
+import org.apache.spark.mllib.clustering.KMeans
+import org.apache.spark.mllib.linalg.Vectors
+
+/**
+ * An example k-means app. Run with
+ * {{{
+ * ./bin/spark-example org.apache.spark.examples.mllib.DenseKMeans [options] <input>
+ * }}}
+ * If you use it as a template to create your own app, please use `spark-submit` to submit your app.
+ */
+object DenseKMeans {
+
+  object InitializationMode extends Enumeration {
+    type InitializationMode = Value
+    val Random, Parallel = Value
+  }
+
+  import InitializationMode._
+
+  case class Params(
+      input: String = null,
+      k: Int = -1,
+      numIterations: Int = 10,
+      initializationMode: InitializationMode = Parallel)
+
+  def main(args: Array[String]) {
+    val defaultParams = Params()
+
+    val parser = new OptionParser[Params]("DenseKMeans") {
+      head("DenseKMeans: an example k-means app for dense data.")
+      opt[Int]('k', "k")
+        .required()
+        .text(s"number of clusters, required")
+        .action((x, c) => c.copy(k = x))
+      opt[Int]("numIterations")
+        .text(s"number of iterations, default; ${defaultParams.numIterations}")
+        .action((x, c) => c.copy(numIterations = x))
+      opt[String]("initMode")
+        .text(s"initialization mode (${InitializationMode.values.mkString(",")}), " +
+        s"default: ${defaultParams.initializationMode}")
+        .action((x, c) => c.copy(initializationMode = InitializationMode.withName(x)))
+      arg[String]("<input>")
+        .text("input paths to examples")
+        .required()
+        .action((x, c) => c.copy(input = x))
+    }
+
+    parser.parse(args, defaultParams).map { params =>
+      run(params)
+    }.getOrElse {
+      sys.exit(1)
+    }
+  }
+
+  def run(params: Params) {
+    val conf = new SparkConf().setAppName(s"DenseKMeans with $params")
+    val sc = new SparkContext(conf)
+
+    Logger.getRootLogger.setLevel(Level.WARN)
+
+    val examples = sc.textFile(params.input).map { line =>
+      Vectors.dense(line.split(' ').map(_.toDouble))
+    }.cache()
+
+    val numExamples = examples.count()
+
+    println(s"numExamples = $numExamples.")
+
+    val initMode = params.initializationMode match {
+      case Random => KMeans.RANDOM
+      case Parallel => KMeans.K_MEANS_PARALLEL
+    }
+
+    val model = new KMeans()
+      .setInitializationMode(initMode)
+      .setK(params.k)
+      .setMaxIterations(params.numIterations)
+      .run(examples)
+
+    val cost = model.computeCost(examples)
+
+    println(s"Total cost = $cost.")
+
+    sc.stop()
+  }
+}
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/LinearRegression.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/LinearRegression.scala
new file mode 100644
index 000000000000..1723ca693102
--- /dev/null
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/LinearRegression.scala
@@ -0,0 +1,125 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.examples.mllib
+
+import org.apache.log4j.{Level, Logger}
+import scopt.OptionParser
+
+import org.apache.spark.{SparkConf, SparkContext}
+import org.apache.spark.mllib.regression.LinearRegressionWithSGD
+import org.apache.spark.mllib.util.{MulticlassLabelParser, MLUtils}
+import org.apache.spark.mllib.optimization.{SimpleUpdater, SquaredL2Updater, L1Updater}
+
+/**
+ * An example app for linear regression. Run with
+ * {{{
+ * ./bin/run-example org.apache.spark.examples.mllib.LinearRegression
+ * }}}
+ * If you use it as a template to create your own app, please use `spark-submit` to submit your app.
+ */
+object LinearRegression extends App {
+
+  object RegType extends Enumeration {
+    type RegType = Value
+    val NONE, L1, L2 = Value
+  }
+
+  import RegType._
+
+  case class Params(
+      input: String = null,
+      numIterations: Int = 100,
+      stepSize: Double = 1.0,
+      regType: RegType = L2,
+      regParam: Double = 0.1)
+
+  val defaultParams = Params()
+
+  val parser = new OptionParser[Params]("LinearRegression") {
+    head("LinearRegression: an example app for linear regression.")
+    opt[Int]("numIterations")
+      .text("number of iterations")
+      .action((x, c) => c.copy(numIterations = x))
+    opt[Double]("stepSize")
+      .text(s"initial step size, default: ${defaultParams.stepSize}")
+      .action((x, c) => c.copy(stepSize = x))
+    opt[String]("regType")
+      .text(s"regularization type (${RegType.values.mkString(",")}), " +
+      s"default: ${defaultParams.regType}")
+      .action((x, c) => c.copy(regType = RegType.withName(x)))
+    opt[Double]("regParam")
+      .text(s"regularization parameter, default: ${defaultParams.regParam}")
+    arg[String]("<input>")
+      .required()
+      .text("input paths to labeled examples in LIBSVM format")
+      .action((x, c) => c.copy(input = x))
+  }
+
+  parser.parse(args, defaultParams).map { params =>
+    run(params)
+  } getOrElse {
+    sys.exit(1)
+  }
+
+  def run(params: Params) {
+    val conf = new SparkConf().setAppName(s"LinearRegression with $params")
+    val sc = new SparkContext(conf)
+
+    Logger.getRootLogger.setLevel(Level.WARN)
+
+    val examples = MLUtils.loadLibSVMData(sc, params.input, MulticlassLabelParser).cache()
+
+    val splits = examples.randomSplit(Array(0.8, 0.2))
+    val training = splits(0).cache()
+    val test = splits(1).cache()
+
+    val numTraining = training.count()
+    val numTest = test.count()
+    println(s"Training: $numTraining, test: $numTest.")
+
+    examples.unpersist(blocking = false)
+
+    val updater = params.regType match {
+      case NONE => new SimpleUpdater()
+      case L1 => new L1Updater()
+      case L2 => new SquaredL2Updater()
+    }
+
+    val algorithm = new LinearRegressionWithSGD()
+    algorithm.optimizer
+      .setNumIterations(params.numIterations)
+      .setStepSize(params.stepSize)
+      .setUpdater(updater)
+      .setRegParam(params.regParam)
+
+    val model = algorithm.run(training)
+
+    val prediction = model.predict(test.map(_.features))
+    val predictionAndLabel = prediction.zip(test.map(_.label))
+
+    val loss = predictionAndLabel.map { case (p, l) =>
+      val err = p - l
+      err * err
+    }.reduce(_ + _)
+    val rmse = math.sqrt(loss / numTest)
+
+    println(s"Test RMSE = $rmse.")
+
+    sc.stop()
+  }
+}
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/MovieLensALS.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/MovieLensALS.scala
new file mode 100644
index 000000000000..703f02255b94
--- /dev/null
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/MovieLensALS.scala
@@ -0,0 +1,131 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.examples.mllib
+
+import com.esotericsoftware.kryo.Kryo
+import org.apache.log4j.{Level, Logger}
+import scopt.OptionParser
+
+import org.apache.spark.{SparkConf, SparkContext}
+import org.apache.spark.SparkContext._
+import org.apache.spark.mllib.recommendation.{ALS, MatrixFactorizationModel, Rating}
+import org.apache.spark.rdd.RDD
+import org.apache.spark.serializer.{KryoSerializer, KryoRegistrator}
+
+/**
+ * An example app for ALS on MovieLens data (http://grouplens.org/datasets/movielens/).
+ */
+object MovieLensALS {
+
+  class ALSRegistrator extends KryoRegistrator {
+    override def registerClasses(kryo: Kryo) {
+      kryo.register(classOf[Rating])
+    }
+  }
+
+  case class Params(
+      input: String = null,
+      kryo: Boolean = false,
+      numIterations: Int = 20,
+      lambda: Double = 1.0,
+      rank: Int = 10)
+
+  def main(args: Array[String]) {
+    val defaultParams = Params()
+
+    val parser = new OptionParser[Params]("MovieLensALS") {
+      head("MovieLensALS: an example app for ALS on MovieLens data.")
+      opt[Int]("rank")
+        .text(s"rank, default: ${defaultParams.rank}}")
+        .action((x, c) => c.copy(rank = x))
+      opt[Int]("numIterations")
+        .text(s"number of iterations, default: ${defaultParams.numIterations}")
+        .action((x, c) => c.copy(numIterations = x))
+      opt[Double]("lambda")
+        .text(s"lambda (smoothing constant), default: ${defaultParams.lambda}")
+        .action((x, c) => c.copy(lambda = x))
+      opt[Unit]("kryo")
+        .text(s"use Kryo serialization")
+        .action((_, c) => c.copy(kryo = true))
+      arg[String]("<input>")
+        .required()
+        .text("input paths to a MovieLens dataset of ratings")
+        .action((x, c) => c.copy(input = x))
+    }
+
+    parser.parse(args, defaultParams).map { params =>
+      run(params)
+    } getOrElse {
+      System.exit(1)
+    }
+  }
+
+  def run(params: Params) {
+    val conf = new SparkConf().setAppName(s"MovieLensALS with $params")
+    if (params.kryo) {
+      conf.set("spark.serializer", classOf[KryoSerializer].getName)
+        .set("spark.kryo.registrator", classOf[ALSRegistrator].getName)
+        .set("spark.kryoserializer.buffer.mb", "8")
+    }
+    val sc = new SparkContext(conf)
+
+    Logger.getRootLogger.setLevel(Level.WARN)
+
+    val ratings = sc.textFile(params.input).map { line =>
+      val fields = line.split("::")
+      Rating(fields(0).toInt, fields(1).toInt, fields(2).toDouble)
+    }.cache()
+
+    val numRatings = ratings.count()
+    val numUsers = ratings.map(_.user).distinct().count()
+    val numMovies = ratings.map(_.product).distinct().count()
+
+    println(s"Got $numRatings ratings from $numUsers users on $numMovies movies.")
+
+    val splits = ratings.randomSplit(Array(0.8, 0.2))
+    val training = splits(0).cache()
+    val test = splits(1).cache()
+
+    val numTraining = training.count()
+    val numTest = test.count()
+    println(s"Training: $numTraining, test: $numTest.")
+
+    ratings.unpersist(blocking = false)
+
+    val model = new ALS()
+      .setRank(params.rank)
+      .setIterations(params.numIterations)
+      .setLambda(params.lambda)
+      .run(training)
+
+    val rmse = computeRmse(model, test, numTest)
+
+    println(s"Test RMSE = $rmse.")
+
+    sc.stop()
+  }
+
+  /** Compute RMSE (Root Mean Squared Error). */
+  def computeRmse(model: MatrixFactorizationModel, data: RDD[Rating], n: Long) = {
+    val predictions: RDD[Rating] = model.predict(data.map(x => (x.user, x.product)))
+    val predictionsAndRatings = predictions.map(x => ((x.user, x.product), x.rating))
+      .join(data.map(x => ((x.user, x.product), x.rating)))
+      .values
+    math.sqrt(predictionsAndRatings.map(x => (x._1 - x._2) * (x._1 - x._2)).reduce(_ + _) / n)
+  }
+}
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/SparseNaiveBayes.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/SparseNaiveBayes.scala
new file mode 100644
index 000000000000..25b6768b8d72
--- /dev/null
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/SparseNaiveBayes.scala
@@ -0,0 +1,102 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.examples.mllib
+
+import org.apache.log4j.{Level, Logger}
+import scopt.OptionParser
+
+import org.apache.spark.{SparkConf, SparkContext}
+import org.apache.spark.mllib.classification.NaiveBayes
+import org.apache.spark.mllib.util.{MLUtils, MulticlassLabelParser}
+
+/**
+ * An example naive Bayes app. Run with
+ * {{{
+ * ./bin/spark-example org.apache.spark.examples.mllib.SparseNaiveBayes [options] <input>
+ * }}}
+ * If you use it as a template to create your own app, please use `spark-submit` to submit your app.
+ */
+object SparseNaiveBayes {
+
+  case class Params(
+      input: String = null,
+      minPartitions: Int = 0,
+      numFeatures: Int = -1,
+      lambda: Double = 1.0)
+
+  def main(args: Array[String]) {
+    val defaultParams = Params()
+
+    val parser = new OptionParser[Params]("SparseNaiveBayes") {
+      head("SparseNaiveBayes: an example naive Bayes app for LIBSVM data.")
+      opt[Int]("numPartitions")
+        .text("min number of partitions")
+        .action((x, c) => c.copy(minPartitions = x))
+      opt[Int]("numFeatures")
+        .text("number of features")
+        .action((x, c) => c.copy(numFeatures = x))
+      opt[Double]("lambda")
+        .text(s"lambda (smoothing constant), default: ${defaultParams.lambda}")
+        .action((x, c) => c.copy(lambda = x))
+      arg[String]("<input>")
+        .text("input paths to labeled examples in LIBSVM format")
+        .required()
+        .action((x, c) => c.copy(input = x))
+    }
+
+    parser.parse(args, defaultParams).map { params =>
+      run(params)
+    }.getOrElse {
+      sys.exit(1)
+    }
+  }
+
+  def run(params: Params) {
+    val conf = new SparkConf().setAppName(s"SparseNaiveBayes with $params")
+    val sc = new SparkContext(conf)
+
+    Logger.getRootLogger.setLevel(Level.WARN)
+
+    val minPartitions =
+      if (params.minPartitions > 0) params.minPartitions else sc.defaultMinPartitions
+
+    val examples = MLUtils.loadLibSVMData(sc, params.input, MulticlassLabelParser,
+      params.numFeatures, minPartitions)
+    // Cache examples because it will be used in both training and evaluation.
+    examples.cache()
+
+    val splits = examples.randomSplit(Array(0.8, 0.2))
+    val training = splits(0)
+    val test = splits(1)
+
+    val numTraining = training.count()
+    val numTest = test.count()
+
+    println(s"numTraining = $numTraining, numTest = $numTest.")
+
+    val model = new NaiveBayes().setLambda(params.lambda).run(training)
+
+    val prediction = model.predict(test.map(_.features))
+    val predictionAndLabel = prediction.zip(test.map(_.label))
+    val accuracy = predictionAndLabel.filter(x => x._1 == x._2).count().toDouble / numTest
+
+    println(s"Test accuracy = $accuracy.")
+
+    sc.stop()
+  }
+}
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/TallSkinnyPCA.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/TallSkinnyPCA.scala
index 39e71cdab432..3cd9cb743e30 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/TallSkinnyPCA.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/TallSkinnyPCA.scala
@@ -35,20 +35,16 @@ import org.apache.spark.mllib.linalg.Vectors
  */
 object TallSkinnyPCA {
   def main(args: Array[String]) {
-    if (args.length != 2) {
-      System.err.println("Usage: TallSkinnyPCA <master> <file>")
+    if (args.length != 1) {
+      System.err.println("Usage: TallSkinnyPCA <input>")
       System.exit(1)
     }
 
-    val conf = new SparkConf()
-      .setMaster(args(0))
-      .setAppName("TallSkinnyPCA")
-      .setSparkHome(System.getenv("SPARK_HOME"))
-      .setJars(SparkContext.jarOfClass(this.getClass).toSeq)
+    val conf = new SparkConf().setAppName("TallSkinnyPCA")
     val sc = new SparkContext(conf)
 
     // Load and parse the data file.
-    val rows = sc.textFile(args(1)).map { line =>
+    val rows = sc.textFile(args(0)).map { line =>
       val values = line.split(' ').map(_.toDouble)
       Vectors.dense(values)
     }
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/TallSkinnySVD.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/TallSkinnySVD.scala
index 2b7de2acc65d..4d6690318615 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/TallSkinnySVD.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/TallSkinnySVD.scala
@@ -35,20 +35,16 @@ import org.apache.spark.mllib.linalg.Vectors
  */
 object TallSkinnySVD {
   def main(args: Array[String]) {
-    if (args.length != 2) {
-      System.err.println("Usage: TallSkinnySVD <master> <file>")
+    if (args.length != 1) {
+      System.err.println("Usage: TallSkinnySVD <input>")
       System.exit(1)
     }
 
-    val conf = new SparkConf()
-      .setMaster(args(0))
-      .setAppName("TallSkinnySVD")
-      .setSparkHome(System.getenv("SPARK_HOME"))
-      .setJars(SparkContext.jarOfClass(this.getClass).toSeq)
+    val conf = new SparkConf().setAppName("TallSkinnySVD")
     val sc = new SparkContext(conf)
 
     // Load and parse the data file.
-    val rows = sc.textFile(args(1)).map { line =>
+    val rows = sc.textFile(args(0)).map { line =>
       val values = line.split(' ').map(_.toDouble)
       Vectors.dense(values)
     }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala
index 4f9eaacf67fe..780e8bae42b8 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala
@@ -17,11 +17,10 @@
 
 package org.apache.spark.mllib.classification
 
-import org.apache.spark.SparkContext
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.mllib.optimization._
 import org.apache.spark.mllib.regression._
-import org.apache.spark.mllib.util.{DataValidators, MLUtils}
+import org.apache.spark.mllib.util.DataValidators
 import org.apache.spark.rdd.RDD
 
 /**
@@ -183,19 +182,4 @@ object LogisticRegressionWithSGD {
       numIterations: Int): LogisticRegressionModel = {
     train(input, numIterations, 1.0, 1.0)
   }
-
-  def main(args: Array[String]) {
-    if (args.length != 4) {
-      println("Usage: LogisticRegression <master> <input_dir> <step_size> " +
-        "<niters>")
-      System.exit(1)
-    }
-    val sc = new SparkContext(args(0), "LogisticRegression")
-    val data = MLUtils.loadLabeledData(sc, args(1))
-    val model = LogisticRegressionWithSGD.train(data, args(3).toInt, args(2).toDouble)
-    println("Weights: " + model.weights)
-    println("Intercept: " + model.intercept)
-
-    sc.stop()
-  }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala
index 18658850a2f6..f6f62ce2de04 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala
@@ -20,11 +20,10 @@ package org.apache.spark.mllib.classification
 import breeze.linalg.{DenseMatrix => BDM, DenseVector => BDV, argmax => brzArgmax, sum => brzSum}
 
 import org.apache.spark.annotation.Experimental
-import org.apache.spark.{Logging, SparkContext}
+import org.apache.spark.Logging
 import org.apache.spark.SparkContext._
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.mllib.regression.LabeledPoint
-import org.apache.spark.mllib.util.MLUtils
 import org.apache.spark.rdd.RDD
 
 /**
@@ -158,23 +157,4 @@ object NaiveBayes {
   def train(input: RDD[LabeledPoint], lambda: Double): NaiveBayesModel = {
     new NaiveBayes(lambda).run(input)
   }
-
-  def main(args: Array[String]) {
-    if (args.length != 2 && args.length != 3) {
-      println("Usage: NaiveBayes <master> <input_dir> [<lambda>]")
-      System.exit(1)
-    }
-    val sc = new SparkContext(args(0), "NaiveBayes")
-    val data = MLUtils.loadLabeledData(sc, args(1))
-    val model = if (args.length == 2) {
-      NaiveBayes.train(data)
-    } else {
-      NaiveBayes.train(data, args(2).toDouble)
-    }
-
-    println("Pi\n: " + model.pi)
-    println("Theta:\n" + model.theta)
-
-    sc.stop()
-  }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala
index 956654b1fe90..81b126717e9a 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala
@@ -17,11 +17,10 @@
 
 package org.apache.spark.mllib.classification
 
-import org.apache.spark.SparkContext
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.mllib.optimization._
 import org.apache.spark.mllib.regression._
-import org.apache.spark.mllib.util.{DataValidators, MLUtils}
+import org.apache.spark.mllib.util.DataValidators
 import org.apache.spark.rdd.RDD
 
 /**
@@ -183,19 +182,4 @@ object SVMWithSGD {
   def train(input: RDD[LabeledPoint], numIterations: Int): SVMModel = {
     train(input, numIterations, 1.0, 1.0, 1.0)
   }
-
-  def main(args: Array[String]) {
-    if (args.length != 5) {
-      println("Usage: SVM <master> <input_dir> <step_size> <regularization_parameter> <niters>")
-      System.exit(1)
-    }
-    val sc = new SparkContext(args(0), "SVM")
-    val data = MLUtils.loadLabeledData(sc, args(1))
-    val model = SVMWithSGD.train(data, args(4).toInt, args(2).toDouble, args(3).toDouble)
-
-    println("Weights: " + model.weights)
-    println("Intercept: " + model.intercept)
-
-    sc.stop()
-  }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala
index dee9ef07e41e..a64c5d44be40 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala
@@ -21,8 +21,7 @@ import scala.collection.mutable.ArrayBuffer
 
 import breeze.linalg.{DenseVector => BDV, Vector => BV, norm => breezeNorm}
 
-import org.apache.spark.annotation.Experimental
-import org.apache.spark.{Logging, SparkContext}
+import org.apache.spark.Logging
 import org.apache.spark.SparkContext._
 import org.apache.spark.mllib.linalg.{Vector, Vectors}
 import org.apache.spark.mllib.util.MLUtils
@@ -396,28 +395,6 @@ object KMeans {
       v2: BreezeVectorWithNorm): Double = {
     MLUtils.fastSquaredDistance(v1.vector, v1.norm, v2.vector, v2.norm)
   }
-
-  @Experimental
-  def main(args: Array[String]) {
-    if (args.length < 4) {
-      println("Usage: KMeans <master> <input_file> <k> <max_iterations> [<runs>]")
-      System.exit(1)
-    }
-    val (master, inputFile, k, iters) = (args(0), args(1), args(2).toInt, args(3).toInt)
-    val runs = if (args.length >= 5) args(4).toInt else 1
-    val sc = new SparkContext(master, "KMeans")
-    val data = sc.textFile(inputFile)
-      .map(line => Vectors.dense(line.split(' ').map(_.toDouble)))
-      .cache()
-    val model = KMeans.train(data, k, iters, runs)
-    val cost = model.computeCost(data)
-    println("Cluster centers:")
-    for (c <- model.clusterCenters) {
-      println("  " + c)
-    }
-    println("Cost: " + cost)
-    System.exit(0)
-  }
 }
 
 /**
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala
index 60fb73f2b5be..2a77e1a9efb2 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala
@@ -23,15 +23,13 @@ import scala.util.Random
 import scala.util.Sorting
 import scala.util.hashing.byteswap32
 
-import com.esotericsoftware.kryo.Kryo
 import org.jblas.{DoubleMatrix, SimpleBlas, Solve}
 
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.broadcast.Broadcast
-import org.apache.spark.{Logging, HashPartitioner, Partitioner, SparkContext, SparkConf}
+import org.apache.spark.{Logging, HashPartitioner, Partitioner}
 import org.apache.spark.storage.StorageLevel
 import org.apache.spark.rdd.RDD
-import org.apache.spark.serializer.KryoRegistrator
 import org.apache.spark.SparkContext._
 import org.apache.spark.util.Utils
 
@@ -707,45 +705,4 @@ object ALS {
     : MatrixFactorizationModel = {
     trainImplicit(ratings, rank, iterations, 0.01, -1, 1.0)
   }
-
-  private class ALSRegistrator extends KryoRegistrator {
-    override def registerClasses(kryo: Kryo) {
-      kryo.register(classOf[Rating])
-    }
-  }
-
-  def main(args: Array[String]) {
-    if (args.length < 5 || args.length > 9) {
-      println("Usage: ALS <master> <ratings_file> <rank> <iterations> <output_dir> " +
-        "[<lambda>] [<implicitPrefs>] [<alpha>] [<blocks>]")
-      System.exit(1)
-    }
-    val (master, ratingsFile, rank, iters, outputDir) =
-      (args(0), args(1), args(2).toInt, args(3).toInt, args(4))
-    val lambda = if (args.length >= 6) args(5).toDouble else 0.01
-    val implicitPrefs = if (args.length >= 7) args(6).toBoolean else false
-    val alpha = if (args.length >= 8) args(7).toDouble else 1
-    val blocks = if (args.length == 9) args(8).toInt else -1
-    val conf = new SparkConf()
-      .set("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
-      .set("spark.kryo.registrator",  classOf[ALSRegistrator].getName)
-      .set("spark.kryo.referenceTracking", "false")
-      .set("spark.kryoserializer.buffer.mb", "8")
-      .set("spark.locality.wait", "10000")
-    val sc = new SparkContext(master, "ALS", conf)
-
-    val ratings = sc.textFile(ratingsFile).map { line =>
-      val fields = line.split(',')
-      Rating(fields(0).toInt, fields(1).toInt, fields(2).toDouble)
-    }
-    val model = new ALS(rank = rank, iterations = iters, lambda = lambda,
-      numBlocks = blocks, implicitPrefs = implicitPrefs, alpha = alpha).run(ratings)
-
-    model.userFeatures.map{ case (id, vec) => id + "," + vec.mkString(" ") }
-                      .saveAsTextFile(outputDir + "/userFeatures")
-    model.productFeatures.map{ case (id, vec) => id + "," + vec.mkString(" ") }
-                         .saveAsTextFile(outputDir + "/productFeatures")
-    println("Final user/product features written to " + outputDir)
-    sc.stop()
-  }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala
index 5f0812fd2e0e..0e6fb1b1caa4 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala
@@ -17,10 +17,8 @@
 
 package org.apache.spark.mllib.regression
 
-import org.apache.spark.SparkContext
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.mllib.optimization._
-import org.apache.spark.mllib.util.MLUtils
 import org.apache.spark.rdd.RDD
 
 /**
@@ -173,19 +171,4 @@ object LassoWithSGD {
       numIterations: Int): LassoModel = {
     train(input, numIterations, 1.0, 1.0, 1.0)
   }
-
-  def main(args: Array[String]) {
-    if (args.length != 5) {
-      println("Usage: Lasso <master> <input_dir> <step_size> <regularization_parameter> <niters>")
-      System.exit(1)
-    }
-    val sc = new SparkContext(args(0), "Lasso")
-    val data = MLUtils.loadLabeledData(sc, args(1))
-    val model = LassoWithSGD.train(data, args(4).toInt, args(2).toDouble, args(3).toDouble)
-
-    println("Weights: " + model.weights)
-    println("Intercept: " + model.intercept)
-
-    sc.stop()
-  }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala
index 228fa8db3e72..1532ff90d846 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala
@@ -17,11 +17,9 @@
 
 package org.apache.spark.mllib.regression
 
-import org.apache.spark.SparkContext
 import org.apache.spark.rdd.RDD
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.mllib.optimization._
-import org.apache.spark.mllib.util.MLUtils
 
 /**
  * Regression model trained using LinearRegression.
@@ -156,18 +154,4 @@ object LinearRegressionWithSGD {
       numIterations: Int): LinearRegressionModel = {
     train(input, numIterations, 1.0, 1.0)
   }
-
-  def main(args: Array[String]) {
-    if (args.length != 5) {
-      println("Usage: LinearRegression <master> <input_dir> <step_size> <niters>")
-      System.exit(1)
-    }
-    val sc = new SparkContext(args(0), "LinearRegression")
-    val data = MLUtils.loadLabeledData(sc, args(1))
-    val model = LinearRegressionWithSGD.train(data, args(3).toInt, args(2).toDouble)
-    println("Weights: " + model.weights)
-    println("Intercept: " + model.intercept)
-
-    sc.stop()
-  }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala
index e702027c7c17..5f7e25a9b8be 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala
@@ -17,10 +17,8 @@
 
 package org.apache.spark.mllib.regression
 
-import org.apache.spark.SparkContext
 import org.apache.spark.rdd.RDD
 import org.apache.spark.mllib.optimization._
-import org.apache.spark.mllib.util.MLUtils
 import org.apache.spark.mllib.linalg.Vector
 
 /**
@@ -170,21 +168,4 @@ object RidgeRegressionWithSGD {
       numIterations: Int): RidgeRegressionModel = {
     train(input, numIterations, 1.0, 1.0, 1.0)
   }
-
-  def main(args: Array[String]) {
-    if (args.length != 5) {
-      println("Usage: RidgeRegression <master> <input_dir> <step_size> " +
-        "<regularization_parameter> <niters>")
-      System.exit(1)
-    }
-    val sc = new SparkContext(args(0), "RidgeRegression")
-    val data = MLUtils.loadLabeledData(sc, args(1))
-    val model = RidgeRegressionWithSGD.train(data, args(4).toInt, args(2).toDouble,
-        args(3).toDouble)
-
-    println("Weights: " + model.weights)
-    println("Intercept: " + model.intercept)
-
-    sc.stop()
-  }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
index f68076f42625..59ed01debf15 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
@@ -18,18 +18,16 @@
 package org.apache.spark.mllib.tree
 
 import org.apache.spark.annotation.Experimental
-import org.apache.spark.{Logging, SparkContext}
-import org.apache.spark.SparkContext._
+import org.apache.spark.Logging
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.tree.configuration.Strategy
 import org.apache.spark.mllib.tree.configuration.Algo._
 import org.apache.spark.mllib.tree.configuration.FeatureType._
 import org.apache.spark.mllib.tree.configuration.QuantileStrategy._
-import org.apache.spark.mllib.tree.impurity.{Entropy, Gini, Impurity, Variance}
+import org.apache.spark.mllib.tree.impurity.Impurity
 import org.apache.spark.mllib.tree.model._
 import org.apache.spark.rdd.RDD
 import org.apache.spark.util.random.XORShiftRandom
-import org.apache.spark.mllib.linalg.{Vector, Vectors}
 
 /**
  * :: Experimental ::
@@ -1028,129 +1026,4 @@ object DecisionTree extends Serializable with Logging {
         throw new UnsupportedOperationException("approximate histogram not supported yet.")
     }
   }
-
-  private val usage = """
-    Usage: DecisionTreeRunner <master>[slices] --algo <Classification,
-    Regression> --trainDataDir path --testDataDir path --maxDepth num [--impurity <Gini,Entropy,
-    Variance>] [--maxBins num]
-              """
-
-  def main(args: Array[String]) {
-
-    if (args.length < 2) {
-      System.err.println(usage)
-      System.exit(1)
-    }
-
-    val sc = new SparkContext(args(0), "DecisionTree")
-
-    val argList = args.toList.drop(1)
-    type OptionMap = Map[Symbol, Any]
-
-    def nextOption(map : OptionMap, list: List[String]): OptionMap = {
-      list match {
-        case Nil => map
-        case "--algo" :: string :: tail => nextOption(map ++ Map('algo -> string), tail)
-        case "--impurity" :: string :: tail => nextOption(map ++ Map('impurity -> string), tail)
-        case "--maxDepth" :: string :: tail => nextOption(map ++ Map('maxDepth -> string), tail)
-        case "--maxBins" :: string :: tail => nextOption(map ++ Map('maxBins -> string), tail)
-        case "--trainDataDir" :: string :: tail => nextOption(map ++ Map('trainDataDir -> string)
-          , tail)
-        case "--testDataDir" :: string :: tail => nextOption(map ++ Map('testDataDir -> string),
-          tail)
-        case string :: Nil =>  nextOption(map ++ Map('infile -> string), list.tail)
-        case option :: tail => logError("Unknown option " + option)
-          sys.exit(1)
-      }
-    }
-    val options = nextOption(Map(), argList)
-    logDebug(options.toString())
-
-    // Load training data.
-    val trainData = loadLabeledData(sc, options.get('trainDataDir).get.toString)
-
-    // Identify the type of algorithm.
-    val algoStr =  options.get('algo).get.toString
-    val algo = algoStr match {
-      case "Classification" => Classification
-      case "Regression" => Regression
-    }
-
-    // Identify the type of impurity.
-    val impurityStr = options.getOrElse('impurity,
-      if (algo == Classification) "Gini" else "Variance").toString
-    val impurity = impurityStr match {
-      case "Gini" => Gini
-      case "Entropy" => Entropy
-      case "Variance" => Variance
-    }
-
-    val maxDepth = options.getOrElse('maxDepth, "1").toString.toInt
-    val maxBins = options.getOrElse('maxBins, "100").toString.toInt
-
-    val strategy = new Strategy(algo, impurity, maxDepth, maxBins)
-    val model = DecisionTree.train(trainData, strategy)
-
-    // Load test data.
-    val testData = loadLabeledData(sc, options.get('testDataDir).get.toString)
-
-    // Measure algorithm accuracy
-    if (algo == Classification) {
-      val accuracy = accuracyScore(model, testData)
-      logDebug("accuracy = " + accuracy)
-    }
-
-    if (algo == Regression) {
-      val mse = meanSquaredError(model, testData)
-      logDebug("mean square error = " + mse)
-    }
-
-    sc.stop()
-  }
-
-  /**
-   * Load labeled data from a file. The data format used here is
-   * <L>, <f1> <f2> ...,
-   * where <f1>, <f2> are feature values in Double and <L> is the corresponding label as Double.
-   *
-   * @param sc SparkContext
-   * @param dir Directory to the input data files.
-   * @return An RDD of LabeledPoint. Each labeled point has two elements: the first element is
-   *         the label, and the second element represents the feature values (an array of Double).
-   */
-  private def loadLabeledData(sc: SparkContext, dir: String): RDD[LabeledPoint] = {
-    sc.textFile(dir).map { line =>
-      val parts = line.trim().split(",")
-      val label = parts(0).toDouble
-      val features = Vectors.dense(parts.slice(1,parts.length).map(_.toDouble))
-      LabeledPoint(label, features)
-    }
-  }
-
-  // TODO: Port this method to a generic metrics package.
-  /**
-   * Calculates the classifier accuracy.
-   */
-  private def accuracyScore(model: DecisionTreeModel, data: RDD[LabeledPoint],
-      threshold: Double = 0.5): Double = {
-    def predictedValue(features: Vector) = {
-      if (model.predict(features) < threshold) 0.0 else 1.0
-    }
-    val correctCount = data.filter(y => predictedValue(y.features) == y.label).count()
-    val count = data.count()
-    logDebug("correct prediction count = " +  correctCount)
-    logDebug("data count = " + count)
-    correctCount.toDouble / count
-  }
-
-  // TODO: Port this method to a generic metrics package
-  /**
-   * Calculates the mean squared error for regression.
-   */
-  private def meanSquaredError(tree: DecisionTreeModel, data: RDD[LabeledPoint]): Double = {
-    data.map { y =>
-      val err = tree.predict(y.features) - y.label
-      err * err
-    }.mean()
-  }
 }
diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index a9504f392608..51f733511116 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -426,7 +426,8 @@ object SparkBuild extends Build {
         exclude("io.netty", "netty")
         exclude("jline","jline")
         exclude("org.apache.cassandra.deps", "avro")
-        excludeAll(excludeSLF4J)
+        excludeAll(excludeSLF4J),
+      "com.github.scopt" %% "scopt" % "3.2.0"
     )
   ) ++ assemblySettings ++ extraAssemblySettings
 

From f04bcaf6fc3379f54f722caf70b221a82b4fcc12 Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Tue, 29 Apr 2014 00:53:32 -0700
Subject: [PATCH 054/810] HOTFIX: minor change to release script

---
 dev/create-release/create-release.sh | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/dev/create-release/create-release.sh b/dev/create-release/create-release.sh
index 44da8f7cbf53..01064cdc4de2 100755
--- a/dev/create-release/create-release.sh
+++ b/dev/create-release/create-release.sh
@@ -115,9 +115,9 @@ make_binary_release "hadoop2" "--with-yarn --hadoop 2.2.0"
 
 # Copy data
 echo "Copying release tarballs"
-ssh $USER_NAME@people.apache.org \
-  mkdir /home/$USER_NAME/public_html/spark-$RELEASE_VERSION-$RC_NAME
 rc_folder=spark-$RELEASE_VERSION-$RC_NAME
+ssh $USER_NAME@people.apache.org \
+  mkdir /home/$USER_NAME/public_html/$rc_folder
 scp spark-* \
   $USER_NAME@people.apache.org:/home/$USER_NAME/public_html/$rc_folder/
 
@@ -127,6 +127,8 @@ cd docs
 PRODUCTION=1 jekyll build
 echo "Copying release documentation"
 rc_docs_folder=${rc_folder}-docs
+ssh $USER_NAME@people.apache.org \
+  mkdir /home/$USER_NAME/public_html/$rc_docs_folder
 rsync -r _site/* $USER_NAME@people.apache.org /home/$USER_NAME/public_html/$rc_docs_folder
 
 echo "Release $RELEASE_VERSION completed:"

From 9a1184a8a9fa679b5ba8cf376b2c71ed1fb6e961 Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Tue, 29 Apr 2014 00:59:38 -0700
Subject: [PATCH 055/810] HOTFIX: minor change to release script

---
 dev/create-release/create-release.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/dev/create-release/create-release.sh b/dev/create-release/create-release.sh
index 01064cdc4de2..33552a74920a 100755
--- a/dev/create-release/create-release.sh
+++ b/dev/create-release/create-release.sh
@@ -129,7 +129,7 @@ echo "Copying release documentation"
 rc_docs_folder=${rc_folder}-docs
 ssh $USER_NAME@people.apache.org \
   mkdir /home/$USER_NAME/public_html/$rc_docs_folder
-rsync -r _site/* $USER_NAME@people.apache.org /home/$USER_NAME/public_html/$rc_docs_folder
+rsync -r _site/* $USER_NAME@people.apache.org:/home/$USER_NAME/public_html/$rc_docs_folder
 
 echo "Release $RELEASE_VERSION completed:"
 echo "Git tag:\t $GIT_TAG"

From 8db0f7e28f5f0330a3344705ff48d8e7b97c383f Mon Sep 17 00:00:00 2001
From: Thomas Graves <tgraves@apache.org>
Date: Tue, 29 Apr 2014 09:19:48 -0500
Subject: [PATCH 056/810] SPARK-1557 Set permissions on event log
 files/directories

This adds minimal setting of event log directory/files permissions.  To have a secure environment the user must manually create the top level event log directory and set permissions up.   We can add logic to do that automatically later if we want.

Author: Thomas Graves <tgraves@apache.org>

Closes #538 from tgravescs/SPARK-1557 and squashes the following commits:

e471d8e [Thomas Graves] rework
d8b6620 [Thomas Graves] update use of octal
3ca9b79 [Thomas Graves] Updated based on comments
5a09709 [Thomas Graves] add in missing import
3150ed6 [Thomas Graves] SPARK-1557 Set permissions on event log files/directories
---
 .../scheduler/EventLoggingListener.scala      |  6 ++++-
 .../org/apache/spark/util/FileLogger.scala    | 22 ++++++++++++++-----
 docs/security.md                              |  2 ++
 3 files changed, 24 insertions(+), 6 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/scheduler/EventLoggingListener.scala b/core/src/main/scala/org/apache/spark/scheduler/EventLoggingListener.scala
index 2fe65cd944b6..d822a8e55111 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/EventLoggingListener.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/EventLoggingListener.scala
@@ -21,6 +21,7 @@ import scala.collection.mutable
 
 import org.apache.hadoop.conf.Configuration
 import org.apache.hadoop.fs.{FileSystem, Path}
+import org.apache.hadoop.fs.permission.FsPermission
 import org.json4s.jackson.JsonMethods._
 
 import org.apache.spark.{Logging, SparkConf, SparkContext}
@@ -54,7 +55,7 @@ private[spark] class EventLoggingListener(
 
   private val logger =
     new FileLogger(logDir, conf, hadoopConfiguration, outputBufferSize, shouldCompress,
-      shouldOverwrite)
+      shouldOverwrite, Some(LOG_FILE_PERMISSIONS))
 
   /**
    * Begin logging events.
@@ -124,6 +125,9 @@ private[spark] object EventLoggingListener extends Logging {
   val SPARK_VERSION_PREFIX = "SPARK_VERSION_"
   val COMPRESSION_CODEC_PREFIX = "COMPRESSION_CODEC_"
   val APPLICATION_COMPLETE = "APPLICATION_COMPLETE"
+  val LOG_FILE_PERMISSIONS: FsPermission =
+    FsPermission.createImmutable(Integer.parseInt("770", 8).toShort)
+
 
   // A cache for compression codecs to avoid creating the same codec many times
   private val codecMap = new mutable.HashMap[String, CompressionCodec]
diff --git a/core/src/main/scala/org/apache/spark/util/FileLogger.scala b/core/src/main/scala/org/apache/spark/util/FileLogger.scala
index 1ed3b70bb24f..0965e0f0f782 100644
--- a/core/src/main/scala/org/apache/spark/util/FileLogger.scala
+++ b/core/src/main/scala/org/apache/spark/util/FileLogger.scala
@@ -24,6 +24,7 @@ import java.util.Date
 
 import org.apache.hadoop.conf.Configuration
 import org.apache.hadoop.fs.{FileSystem, FSDataOutputStream, Path}
+import org.apache.hadoop.fs.permission.FsPermission
 
 import org.apache.spark.{Logging, SparkConf}
 import org.apache.spark.io.CompressionCodec
@@ -42,7 +43,8 @@ private[spark] class FileLogger(
     hadoopConfiguration: Configuration,
     outputBufferSize: Int = 8 * 1024, // 8 KB
     compress: Boolean = false,
-    overwrite: Boolean = true)
+    overwrite: Boolean = true,
+    dirPermissions: Option[FsPermission] = None)
   extends Logging {
 
   private val dateFormat = new ThreadLocal[SimpleDateFormat]() {
@@ -79,16 +81,25 @@ private[spark] class FileLogger(
     if (!fileSystem.mkdirs(path)) {
       throw new IOException("Error in creating log directory: %s".format(logDir))
     }
+    if (dirPermissions.isDefined) {
+      val fsStatus = fileSystem.getFileStatus(path)
+      if (fsStatus.getPermission().toShort() != dirPermissions.get.toShort) {
+        fileSystem.setPermission(path, dirPermissions.get)
+      }
+    }
   }
 
   /**
    * Create a new writer for the file identified by the given path.
+   * If the permissions are not passed in, it will default to use the permissions
+   * (dirpermissions) used when class was instantiated.
    */
-  private def createWriter(fileName: String): PrintWriter = {
+  private def createWriter(fileName: String, perms: Option[FsPermission] = None): PrintWriter = {
     val logPath = logDir + "/" + fileName
     val uri = new URI(logPath)
     val defaultFs = FileSystem.getDefaultUri(hadoopConfiguration).getScheme
     val isDefaultLocal = (defaultFs == null || defaultFs == "file")
+    val path = new Path(logPath)
 
     /* The Hadoop LocalFileSystem (r1.0.4) has known issues with syncing (HADOOP-7844).
      * Therefore, for local files, use FileOutputStream instead. */
@@ -97,11 +108,11 @@ private[spark] class FileLogger(
         // Second parameter is whether to append
         new FileOutputStream(uri.getPath, !overwrite)
       } else {
-        val path = new Path(logPath)
         hadoopDataStream = Some(fileSystem.create(path, overwrite))
         hadoopDataStream.get
       }
 
+    perms.orElse(dirPermissions).foreach {p => fileSystem.setPermission(path, p)}
     val bstream = new BufferedOutputStream(dstream, outputBufferSize)
     val cstream = if (compress) compressionCodec.compressedOutputStream(bstream) else bstream
     new PrintWriter(cstream)
@@ -152,15 +163,16 @@ private[spark] class FileLogger(
   /**
    * Start a writer for a new file, closing the existing one if it exists.
    * @param fileName Name of the new file, defaulting to the file index if not provided.
+   * @param perms Permissions to put on the new file.
    */
-  def newFile(fileName: String = "") {
+  def newFile(fileName: String = "", perms: Option[FsPermission] = None) {
     fileIndex += 1
     writer.foreach(_.close())
     val name = fileName match {
       case "" => fileIndex.toString
       case _ => fileName
     }
-    writer = Some(createWriter(name))
+    writer = Some(createWriter(name, perms))
   }
 
   /**
diff --git a/docs/security.md b/docs/security.md
index 9e4218fbcfe7..90c69915f517 100644
--- a/docs/security.md
+++ b/docs/security.md
@@ -7,6 +7,8 @@ Spark currently supports authentication via a shared secret. Authentication can
 
 The Spark UI can also be secured by using javax servlet filters. A user may want to secure the UI if it has data that other users should not be allowed to see. The javax servlet filter specified by the user can authenticate the user and then once the user is logged in, Spark can compare that user versus the view acls to make sure they are authorized to view the UI. The configs 'spark.ui.acls.enable' and 'spark.ui.view.acls' control the behavior of the acls. Note that the person who started the application always has view access to the UI.
 
+If your applications are using event logging, the directory where the event logs go (`spark.eventLog.dir`) should be manually created and have the proper permissions set on it. If you want those log files secure, the permissions should be set to drwxrwxrwxt for that directory. The owner of the directory should be the super user who is running the history server and the group permissions should be restricted to super user group. This will allow all users to write to the directory but will prevent unprivileged users from removing or renaming a file unless they own the file or directory. The event log files will be created by Spark with permissions such that only the user and group have read and write access.
+
 For Spark on Yarn deployments, configuring `spark.authenticate` to true will automatically handle generating and distributing the shared secret. Each application will use a unique shared secret. The Spark UI uses the standard YARN web application proxy mechanism and will authenticate via any installed Hadoop filters. If an authentication filter is enabled, the acls controls can be used by control which users can via the Spark UI. 
 
 For other types of Spark deployments, the spark config `spark.authenticate.secret` should be configured on each of the nodes. This secret will be used by all the Master/Workers and applications. The UI can be secured using a javax servlet filter installed via `spark.ui.filters`. If an authentication filter is enabled, the acls controls can be used by control which users can via the Spark UI.

From 7d1505841069c6ecc3fa7e4896db535f18e4ce84 Mon Sep 17 00:00:00 2001
From: witgo <witgo@qq.com>
Date: Tue, 29 Apr 2014 11:30:47 -0700
Subject: [PATCH 057/810] SPARK-1509: add zipWithIndex zipWithUniqueId methods
 to java api

Author: witgo <witgo@qq.com>

Closes #423 from witgo/zipWithIndex and squashes the following commits:

039ec04 [witgo] Merge branch 'master' of https://github.com/apache/spark into zipWithIndex
24d74c9 [witgo] review commit
763a5e4 [witgo] Merge branch 'master' of https://github.com/apache/spark into zipWithIndex
59747d1 [witgo] review commit
7bf4d06 [witgo] Merge branch 'master' of https://github.com/apache/spark into zipWithIndex
daa8f84 [witgo] review commit
4070613 [witgo] Merge branch 'master' of https://github.com/apache/spark into zipWithIndex
18e6c97 [witgo] java api zipWithIndex test
11e2e7f [witgo] add zipWithIndex zipWithUniqueId methods to java api
---
 .../apache/spark/api/java/JavaRDDLike.scala   | 22 ++++++++++++-
 .../java/org/apache/spark/JavaAPISuite.java   | 31 ++++++++++++++-----
 2 files changed, 45 insertions(+), 8 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala b/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala
index 574a98636a61..af06d1dca94f 100644
--- a/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala
+++ b/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.api.java
 
 import java.util.{Comparator, List => JList, Iterator => JIterator}
-import java.lang.{Iterable => JIterable}
+import java.lang.{Iterable => JIterable, Long => JLong}
 
 import scala.collection.JavaConversions._
 import scala.reflect.ClassTag
@@ -264,6 +264,26 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable {
       rdd.zipPartitions(other.rdd)(fn)(other.classTag, fakeClassTag[V]))(fakeClassTag[V])
   }
 
+  /**
+   * Zips this RDD with generated unique Long ids. Items in the kth partition will get ids k, n+k,
+   * 2*n+k, ..., where n is the number of partitions. So there may exist gaps, but this method
+   * won't trigger a spark job, which is different from [[org.apache.spark.rdd.RDD#zipWithIndex]].
+   */
+  def zipWithUniqueId(): JavaPairRDD[T, JLong] = {
+    JavaPairRDD.fromRDD(rdd.zipWithUniqueId()).asInstanceOf[JavaPairRDD[T, JLong]]
+  }
+
+  /**
+   * Zips this RDD with its element indices. The ordering is first based on the partition index
+   * and then the ordering of items within each partition. So the first item in the first
+   * partition gets index 0, and the last item in the last partition receives the largest index.
+   * This is similar to Scala's zipWithIndex but it uses Long instead of Int as the index type.
+   * This method needs to trigger a spark job when this RDD contains more than one partitions.
+   */
+  def zipWithIndex(): JavaPairRDD[T, JLong] = {
+    JavaPairRDD.fromRDD(rdd.zipWithIndex()).asInstanceOf[JavaPairRDD[T, JLong]]
+  }
+
   // Actions (launch a job to return a value to the user program)
 
   /**
diff --git a/core/src/test/java/org/apache/spark/JavaAPISuite.java b/core/src/test/java/org/apache/spark/JavaAPISuite.java
index 76c6f5af82a5..c3e03cea917b 100644
--- a/core/src/test/java/org/apache/spark/JavaAPISuite.java
+++ b/core/src/test/java/org/apache/spark/JavaAPISuite.java
@@ -182,13 +182,30 @@ public void call(String s) {
     Assert.assertEquals(2, foreachCalls);
   }
 
-    @Test
-    public void toLocalIterator() {
-        List<Integer> correct = Arrays.asList(1, 2, 3, 4);
-        JavaRDD<Integer> rdd = sc.parallelize(correct);
-        List<Integer> result = Lists.newArrayList(rdd.toLocalIterator());
-        Assert.assertTrue(correct.equals(result));
-    }
+  @Test
+  public void toLocalIterator() {
+    List<Integer> correct = Arrays.asList(1, 2, 3, 4);
+    JavaRDD<Integer> rdd = sc.parallelize(correct);
+    List<Integer> result = Lists.newArrayList(rdd.toLocalIterator());
+    Assert.assertTrue(correct.equals(result));
+  }
+
+  @Test
+  public void zipWithUniqueId() {
+    List<Integer> dataArray = Arrays.asList(1, 2, 3, 4);
+    JavaPairRDD<Integer, Long> zip = sc.parallelize(dataArray).zipWithUniqueId();
+    JavaRDD<Long> indexes = zip.values();
+    Assert.assertTrue(new HashSet<Long>(indexes.collect()).size() == 4);
+  }
+
+  @Test
+  public void zipWithIndex() {
+    List<Integer> dataArray = Arrays.asList(1, 2, 3, 4);
+    JavaPairRDD<Integer, Long> zip = sc.parallelize(dataArray).zipWithIndex();
+    JavaRDD<Long> indexes = zip.values();
+    List<Long> correctIndexes = Arrays.asList(0L, 1L, 2L, 3L);
+    Assert.assertTrue(indexes.collect().equals(correctIndexes));
+  }
 
   @SuppressWarnings("unchecked")
   @Test

From bf8d0aa27844b1e58f131d49a3f668d1614ca6e7 Mon Sep 17 00:00:00 2001
From: Sandy Ryza <sandy@cloudera.com>
Date: Tue, 29 Apr 2014 12:54:02 -0700
Subject: [PATCH 058/810] SPARK-1588.  Restore SPARK_YARN_USER_ENV and
 SPARK_JAVA_OPTS for YARN.

Author: Sandy Ryza <sandy@cloudera.com>

Closes #586 from sryza/sandy-spark-1588 and squashes the following commits:

35eb38e [Sandy Ryza] Scalify
b361684 [Sandy Ryza] SPARK-1588.  Restore SPARK_YARN_USER_ENV and SPARK_JAVA_OPTS for YARN.
---
 .../apache/spark/deploy/yarn/ClientBase.scala    | 16 +++++++++++++---
 .../spark/deploy/yarn/ExecutorRunnableUtil.scala |  4 ++--
 2 files changed, 15 insertions(+), 5 deletions(-)

diff --git a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala
index f2be8217a2f8..27a518ccda45 100644
--- a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala
+++ b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala
@@ -263,9 +263,13 @@ trait ClientBase extends Logging {
     distCacheMgr.setDistFilesEnv(env)
     distCacheMgr.setDistArchivesEnv(env)
 
-    // Allow users to specify some environment variables.
-    YarnSparkHadoopUtil.setEnvFromInputString(env, System.getenv("SPARK_YARN_USER_ENV"),
-      File.pathSeparator)
+    sys.env.get("SPARK_YARN_USER_ENV").foreach { userEnvs =>
+      // Allow users to specify some environment variables.
+      YarnSparkHadoopUtil.setEnvFromInputString(env, userEnvs, File.pathSeparator)
+
+      // Pass SPARK_YARN_USER_ENV itself to the AM so it can use it to set up executor environments.
+      env("SPARK_YARN_USER_ENV") = userEnvs
+    }
 
     env
   }
@@ -322,6 +326,12 @@ trait ClientBase extends Logging {
       JAVA_OPTS += "-XX:CMSIncrementalDutyCycle=10"
     }
 
+    // SPARK_JAVA_OPTS is deprecated, but for backwards compatibility:
+    sys.env.get("SPARK_JAVA_OPTS").foreach { opts =>
+      sparkConf.set("spark.executor.extraJavaOptions", opts)
+      sparkConf.set("spark.driver.extraJavaOptions", opts)
+    }
+
     // TODO: it might be nicer to pass these as an internal environment variable rather than
     // as Java options, due to complications with string parsing of nested quotes.
     if (args.amClass == classOf[ExecutorLauncher].getName) {
diff --git a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnableUtil.scala b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnableUtil.scala
index 7d07f6f68046..96f8aa93394f 100644
--- a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnableUtil.scala
+++ b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnableUtil.scala
@@ -71,8 +71,8 @@ trait ExecutorRunnableUtil extends Logging {
     /*
         else {
           // If no java_opts specified, default to using -XX:+CMSIncrementalMode
-          // It might be possible that other modes/config is being done in SPARK_JAVA_OPTS, so we dont
-          // want to mess with it.
+          // It might be possible that other modes/config is being done in spark.executor.extraJavaOptions,
+          // so we dont want to mess with it.
           // In our expts, using (default) throughput collector has severe perf ramnifications in
           // multi-tennent machines
           // The options are based on

From d33df1c151f8e982edd7324edc06d8cd3024dd34 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Tue, 29 Apr 2014 18:06:45 -0700
Subject: [PATCH 059/810] [SPARK-1674] fix interrupted system call error in
 pyspark's RDD.pipe

`RDD.pipe`'s doctest throws interrupted system call exception on Mac. It can be fixed by wrapping `pipe.stdout.readline` in an iterator.

Author: Xiangrui Meng <meng@databricks.com>

Closes #594 from mengxr/pyspark-pipe and squashes the following commits:

cc32ac9 [Xiangrui Meng] fix interrupted system call error in pyspark's RDD.pipe
---
 python/pyspark/rdd.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py
index a59778c72130..3a1c56af5b22 100644
--- a/python/pyspark/rdd.py
+++ b/python/pyspark/rdd.py
@@ -537,8 +537,8 @@ def pipe(self, command, env={}):
         """
         Return an RDD created by piping elements to a forked external process.
 
-        >>> sc.parallelize([1, 2, 3]).pipe('cat').collect()
-        ['1', '2', '3']
+        >>> sc.parallelize(['1', '2', '', '3']).pipe('cat').collect()
+        ['1', '2', '', '3']
         """
         def func(iterator):
             pipe = Popen(shlex.split(command), env=env, stdin=PIPE, stdout=PIPE)
@@ -547,7 +547,7 @@ def pipe_objs(out):
                     out.write(str(obj).rstrip('\n') + '\n')
                 out.close()
             Thread(target=pipe_objs, args=[pipe.stdin]).start()
-            return (x.rstrip('\n') for x in pipe.stdout)
+            return (x.rstrip('\n') for x in iter(pipe.stdout.readline, ''))
         return self.mapPartitions(func)
 
     def foreach(self, f):

From 5c0cd5c1a594c181a3f7536639122ab7d97b271b Mon Sep 17 00:00:00 2001
From: Tor Myklebust <tmyklebu@gmail.com>
Date: Tue, 29 Apr 2014 22:04:34 -0700
Subject: [PATCH 060/810] [SPARK-1646] Micro-optimisation of ALS

This change replaces some Scala `for` and `foreach` constructs with `while` constructs.  There may be a slight performance gain on the order of 1-2% when training an ALS model.

I trained an ALS model on the Movielens 10M-rating dataset repeatedly both with and without these changes.  All 7 runs in both columns were done in a Scala `for` loop like this:

    for (iter <- 0 to 10) {
      val before = System.currentTimeMillis()
      val model = ALS.train(rats, 20, 10)
      val after = System.currentTimeMillis()
      println("%d ms".format(after-before))
      println("rmse %g".format(computeRmse(model, rats, numRatings)))
    }

The timings were done on a multiuser machine, and I stopped one set of timings after 7 had been completed.  It would be nice if somebody with dedicated hardware could confirm my timings.

    After           Before
    121980 ms       122041 ms
    117069 ms       117127 ms
    115332 ms       117523 ms
    115381 ms       117402 ms
    114635 ms       116550 ms
    114140 ms       114076 ms
    112993 ms       117200 ms

Ratios are about 1.0005, 1.0005, 1.019, 1.0175, 1.01671, 0.99944, and 1.03723.  I therefore suspect these changes make for a slight performance gain on the order of 1-2%.

Author: Tor Myklebust <tmyklebu@gmail.com>

Closes #568 from tmyklebu/alsopt and squashes the following commits:

5ded80f [Tor Myklebust] Fix style.
79595ff [Tor Myklebust] Fix style error.
4ef0313 [Tor Myklebust] Merge branch 'master' of github.com:apache/spark into alsopt
114fb74 [Tor Myklebust] Turn some 'for' loops into 'while' loops.
dcf583a [Tor Myklebust] Remove the partitioner member variable; instead, thread that needle everywhere it needs to go.
23d6f91 [Tor Myklebust] Stop making the partitioner configurable.
495784f [Tor Myklebust] Merge branch 'master' of https://github.com/apache/spark
674933a [Tor Myklebust] Fix style.
40edc23 [Tor Myklebust] Fix missing space.
f841345 [Tor Myklebust] Fix daft bug creating 'pairs', also for -> foreach.
5ec9e6c [Tor Myklebust] Clean a couple of things up using 'map'.
36a0f43 [Tor Myklebust] Make the partitioner private.
d872b09 [Tor Myklebust] Add negative id ALS test.
df27697 [Tor Myklebust] Support custom partitioners.  Currently we use the same partitioner for users and products.
c90b6d8 [Tor Myklebust] Scramble user and product ids before bucketing.
c774d7d [Tor Myklebust] Make the partitioner a member variable and use it instead of modding directly.
---
 .../spark/mllib/recommendation/ALS.scala      | 22 ++++++++++++++-----
 1 file changed, 17 insertions(+), 5 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala
index 2a77e1a9efb2..0cf9a7f90908 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala
@@ -472,13 +472,15 @@ class ALS private (
     // Compute the XtX and Xy values for each user by adding products it rated in each product
     // block
     for (productBlock <- 0 until numBlocks) {
-      for (p <- 0 until blockFactors(productBlock).length) {
+      var p = 0
+      while (p < blockFactors(productBlock).length) {
         val x = wrapDoubleArray(blockFactors(productBlock)(p))
         tempXtX.fill(0.0)
         dspr(1.0, x, tempXtX)
         val (us, rs) = inLinkBlock.ratingsForBlock(productBlock)(p)
-        for (i <- 0 until us.length) {
-          if (implicitPrefs) {
+        if (implicitPrefs) {
+          var i = 0
+          while (i < us.length) {
             // Extension to the original paper to handle rs(i) < 0. confidence is a function
             // of |rs(i)| instead so that it is never negative:
             val confidence = 1 + alpha * abs(rs(i))
@@ -489,11 +491,17 @@ class ALS private (
             if (rs(i) > 0) {
               SimpleBlas.axpy(confidence, x, userXy(us(i)))
             }
-          } else {
+            i += 1
+          }
+        } else {
+          var i = 0
+          while (i < us.length) {
             userXtX(us(i)).addi(tempXtX)
             SimpleBlas.axpy(rs(i), x, userXy(us(i)))
+            i += 1
           }
         }
+        p += 1
       }
     }
 
@@ -502,7 +510,11 @@ class ALS private (
       // Compute the full XtX matrix from the lower-triangular part we got above
       fillFullMatrix(userXtX(index), fullXtX)
       // Add regularization
-      (0 until rank).foreach(i => fullXtX.data(i*rank + i) += lambda)
+      var i = 0
+      while (i < rank) {
+        fullXtX.data(i * rank + i) += lambda
+        i += 1
+      }
       // Solve the resulting matrix, which is symmetric and positive-definite
       if (implicitPrefs) {
         Solve.solvePositive(fullXtX.addi(YtY.get.value), userXy(index)).data

From b3d2ab6b35b31b79ded2a1361f0d4eb17ab78b08 Mon Sep 17 00:00:00 2001
From: Chen Chao <crazyjvm@gmail.com>
Date: Tue, 29 Apr 2014 22:05:40 -0700
Subject: [PATCH 061/810] Args for worker rather than master

Args for worker rather than master

Author: Chen Chao <crazyjvm@gmail.com>

Closes #587 from CrazyJvm/patch-6 and squashes the following commits:

b54b89f [Chen Chao] Args for worker rather than master
---
 .../scala/org/apache/spark/deploy/worker/WorkerArguments.scala  | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/WorkerArguments.scala b/core/src/main/scala/org/apache/spark/deploy/worker/WorkerArguments.scala
index 3836bf219ed3..dc5158102054 100644
--- a/core/src/main/scala/org/apache/spark/deploy/worker/WorkerArguments.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/worker/WorkerArguments.scala
@@ -22,7 +22,7 @@ import java.lang.management.ManagementFactory
 import org.apache.spark.util.{IntParam, MemoryParam, Utils}
 
 /**
- * Command-line parser for the master.
+ * Command-line parser for the worker.
  */
 private[spark] class WorkerArguments(args: Array[String]) {
   var host = Utils.localHostName()

From 7025dda8fa84b57d6f12bc770df2fa10eef21d88 Mon Sep 17 00:00:00 2001
From: WangTao <barneystinson@aliyun.com>
Date: Tue, 29 Apr 2014 22:07:20 -0700
Subject: [PATCH 062/810] Handle the vals that never used

In XORShiftRandom.scala, use val "million" instead of constant "1e6.toInt".
Delete vals that never used in other files.

Author: WangTao <barneystinson@aliyun.com>

Closes #565 from WangTaoTheTonic/master and squashes the following commits:

17cacfc [WangTao] Handle the unused assignment, method parameters and symbol inspected by Intellij IDEA
37b4090 [WangTao] Handle the vals that never used
---
 .../main/scala/org/apache/spark/network/SecurityMessage.scala   | 1 -
 .../scala/org/apache/spark/partial/GroupedMeanEvaluator.scala   | 1 -
 .../scala/org/apache/spark/storage/BlockFetcherIterator.scala   | 2 --
 .../scala/org/apache/spark/util/random/XORShiftRandom.scala     | 2 +-
 .../src/main/java/org/apache/spark/examples/JavaLogQuery.java   | 1 -
 examples/src/main/java/org/apache/spark/examples/JavaTC.java    | 2 +-
 .../main/java/org/apache/spark/examples/sql/JavaSparkSQL.java   | 1 -
 7 files changed, 2 insertions(+), 8 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/network/SecurityMessage.scala b/core/src/main/scala/org/apache/spark/network/SecurityMessage.scala
index a1dfc4094cca..9af9e2e8e9e5 100644
--- a/core/src/main/scala/org/apache/spark/network/SecurityMessage.scala
+++ b/core/src/main/scala/org/apache/spark/network/SecurityMessage.scala
@@ -106,7 +106,6 @@ private[spark] class SecurityMessage() extends Logging {
    * @return BufferMessage
    */
   def toBufferMessage: BufferMessage = {
-    val startTime = System.currentTimeMillis
     val buffers = new ArrayBuffer[ByteBuffer]()
 
     // 4 bytes for the length of the connectionId
diff --git a/core/src/main/scala/org/apache/spark/partial/GroupedMeanEvaluator.scala b/core/src/main/scala/org/apache/spark/partial/GroupedMeanEvaluator.scala
index b5111891ed35..af26c3d59ac0 100644
--- a/core/src/main/scala/org/apache/spark/partial/GroupedMeanEvaluator.scala
+++ b/core/src/main/scala/org/apache/spark/partial/GroupedMeanEvaluator.scala
@@ -61,7 +61,6 @@ private[spark] class GroupedMeanEvaluator[T](totalOutputs: Int, confidence: Doub
     } else if (outputsMerged == 0) {
       new HashMap[T, BoundedDouble]
     } else {
-      val p = outputsMerged.toDouble / totalOutputs
       val studentTCacher = new StudentTCacher(confidence)
       val result = new JHashMap[T, BoundedDouble](sums.size)
       val iter = sums.entrySet.iterator()
diff --git a/core/src/main/scala/org/apache/spark/storage/BlockFetcherIterator.scala b/core/src/main/scala/org/apache/spark/storage/BlockFetcherIterator.scala
index a02dd9441d67..408a79708805 100644
--- a/core/src/main/scala/org/apache/spark/storage/BlockFetcherIterator.scala
+++ b/core/src/main/scala/org/apache/spark/storage/BlockFetcherIterator.scala
@@ -118,11 +118,9 @@ object BlockFetcherIterator {
       })
       bytesInFlight += req.size
       val sizeMap = req.blocks.toMap  // so we can look up the size of each blockID
-      val fetchStart = System.currentTimeMillis()
       val future = connectionManager.sendMessageReliably(cmId, blockMessageArray.toBufferMessage)
       future.onSuccess {
         case Some(message) => {
-          val fetchDone = System.currentTimeMillis()
           val bufferMessage = message.asInstanceOf[BufferMessage]
           val blockMessageArray = BlockMessageArray.fromBufferMessage(bufferMessage)
           for (blockMessage <- blockMessageArray) {
diff --git a/core/src/main/scala/org/apache/spark/util/random/XORShiftRandom.scala b/core/src/main/scala/org/apache/spark/util/random/XORShiftRandom.scala
index 7f220383f9f8..55b571370617 100644
--- a/core/src/main/scala/org/apache/spark/util/random/XORShiftRandom.scala
+++ b/core/src/main/scala/org/apache/spark/util/random/XORShiftRandom.scala
@@ -91,7 +91,7 @@ private[spark] object XORShiftRandom {
     val xorRand = new XORShiftRandom(seed)
 
     // this is just to warm up the JIT - we're not timing anything
-    timeIt(1e6.toInt) {
+    timeIt(million) {
       javaRand.nextInt()
       xorRand.nextInt()
     }
diff --git a/examples/src/main/java/org/apache/spark/examples/JavaLogQuery.java b/examples/src/main/java/org/apache/spark/examples/JavaLogQuery.java
index 2a4278d3c30e..3f7a87953801 100644
--- a/examples/src/main/java/org/apache/spark/examples/JavaLogQuery.java
+++ b/examples/src/main/java/org/apache/spark/examples/JavaLogQuery.java
@@ -75,7 +75,6 @@ public String toString() {
 
   public static Tuple3<String, String, String> extractKey(String line) {
     Matcher m = apacheLogRegex.matcher(line);
-    List<String> key = Collections.emptyList();
     if (m.find()) {
       String ip = m.group(1);
       String user = m.group(3);
diff --git a/examples/src/main/java/org/apache/spark/examples/JavaTC.java b/examples/src/main/java/org/apache/spark/examples/JavaTC.java
index 1d776940f06c..d66b9ba265fe 100644
--- a/examples/src/main/java/org/apache/spark/examples/JavaTC.java
+++ b/examples/src/main/java/org/apache/spark/examples/JavaTC.java
@@ -85,7 +85,7 @@ public Tuple2<Integer, Integer> call(Tuple2<Integer, Integer> e) {
         }
     });
 
-    long oldCount = 0;
+    long oldCount;
     long nextCount = tc.count();
     do {
       oldCount = nextCount;
diff --git a/examples/src/main/java/org/apache/spark/examples/sql/JavaSparkSQL.java b/examples/src/main/java/org/apache/spark/examples/sql/JavaSparkSQL.java
index b5b438e9753a..d62a72f53443 100644
--- a/examples/src/main/java/org/apache/spark/examples/sql/JavaSparkSQL.java
+++ b/examples/src/main/java/org/apache/spark/examples/sql/JavaSparkSQL.java
@@ -23,7 +23,6 @@
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.JavaSparkContext;
 import org.apache.spark.api.java.function.Function;
-import org.apache.spark.api.java.function.VoidFunction;
 
 import org.apache.spark.sql.api.java.JavaSQLContext;
 import org.apache.spark.sql.api.java.JavaSchemaRDD;

From ff5be9a41e52454e0f9cae83dd1fd50fbeaa684a Mon Sep 17 00:00:00 2001
From: Sandy Ryza <sandy@cloudera.com>
Date: Tue, 29 Apr 2014 23:24:34 -0700
Subject: [PATCH 063/810] SPARK-1004.  PySpark on YARN

This reopens https://github.com/apache/incubator-spark/pull/640 against the new repo

Author: Sandy Ryza <sandy@cloudera.com>

Closes #30 from sryza/sandy-spark-1004 and squashes the following commits:

89889d4 [Sandy Ryza] Move unzipping py4j to the generate-resources phase so that it gets included in the jar the first time
5165a02 [Sandy Ryza] Fix docs
fd0df79 [Sandy Ryza] PySpark on YARN
---
 bin/pyspark                                   |  1 +
 bin/pyspark2.cmd                              |  1 +
 core/pom.xml                                  | 42 +++++++++++++++++++
 .../api/python/PythonWorkerFactory.scala      | 10 +----
 docs/python-programming-guide.md              |  3 ++
 python/.gitignore                             |  3 ++
 python/lib/PY4J_VERSION.txt                   |  1 -
 python/pyspark/__init__.py                    |  7 ----
 python/pyspark/java_gateway.py                | 29 ++++++++++++-
 python/pyspark/tests.py                       |  4 +-
 sbin/spark-config.sh                          |  3 ++
 11 files changed, 85 insertions(+), 19 deletions(-)
 delete mode 100644 python/lib/PY4J_VERSION.txt

diff --git a/bin/pyspark b/bin/pyspark
index cad982bc3347..f5558853e8a4 100755
--- a/bin/pyspark
+++ b/bin/pyspark
@@ -46,6 +46,7 @@ export PYSPARK_PYTHON
 
 # Add the PySpark classes to the Python path:
 export PYTHONPATH=$SPARK_HOME/python/:$PYTHONPATH
+export PYTHONPATH=$SPARK_HOME/python/lib/py4j-0.8.1-src.zip:$PYTHONPATH
 
 # Load the PySpark shell.py script when ./pyspark is used interactively:
 export OLD_PYTHONSTARTUP=$PYTHONSTARTUP
diff --git a/bin/pyspark2.cmd b/bin/pyspark2.cmd
index 95791095ec93..d7cfd5eec501 100644
--- a/bin/pyspark2.cmd
+++ b/bin/pyspark2.cmd
@@ -45,6 +45,7 @@ rem Figure out which Python to use.
 if "x%PYSPARK_PYTHON%"=="x" set PYSPARK_PYTHON=python
 
 set PYTHONPATH=%FWDIR%python;%PYTHONPATH%
+set PYTHONPATH=%FWDIR%python\lib\py4j-0.8.1-src.zip;%PYTHONPATH%
 
 set OLD_PYTHONSTARTUP=%PYTHONSTARTUP%
 set PYTHONSTARTUP=%FWDIR%python\pyspark\shell.py
diff --git a/core/pom.xml b/core/pom.xml
index 73f573a41405..822b5b1dd7cc 100644
--- a/core/pom.xml
+++ b/core/pom.xml
@@ -294,6 +294,48 @@
           </environmentVariables>
         </configuration>
       </plugin>
+      <!-- Unzip py4j so we can include its files in the jar -->
+      <plugin>
+        <groupId>org.codehaus.mojo</groupId>
+        <artifactId>exec-maven-plugin</artifactId>
+        <version>1.2.1</version>
+        <executions>
+          <execution>
+            <phase>generate-resources</phase>
+            <goals>
+              <goal>exec</goal>
+            </goals>
+          </execution>
+        </executions>
+        <configuration>
+          <executable>unzip</executable>
+          <workingDirectory>../python</workingDirectory>
+          <arguments>
+            <argument>-o</argument>
+            <argument>lib/py4j*.zip</argument>
+            <argument>-d</argument>
+            <argument>build</argument>
+          </arguments>
+        </configuration>
+      </plugin>
     </plugins>
+    
+    <resources>
+      <resource>
+        <directory>src/main/resources</directory>
+      </resource>
+      <resource>
+        <directory>../python</directory>
+        <includes>
+          <include>pyspark/*.py</include>
+        </includes>
+      </resource>
+      <resource>
+        <directory>../python/build</directory>
+        <includes>
+          <include>py4j/*.py</include>
+        </includes>
+      </resource>
+    </resources>
   </build>
 </project>
diff --git a/core/src/main/scala/org/apache/spark/api/python/PythonWorkerFactory.scala b/core/src/main/scala/org/apache/spark/api/python/PythonWorkerFactory.scala
index a5f0f3d5e7ea..02799ce0091b 100644
--- a/core/src/main/scala/org/apache/spark/api/python/PythonWorkerFactory.scala
+++ b/core/src/main/scala/org/apache/spark/api/python/PythonWorkerFactory.scala
@@ -78,12 +78,9 @@ private[spark] class PythonWorkerFactory(pythonExec: String, envVars: Map[String
       serverSocket = new ServerSocket(0, 1, InetAddress.getByAddress(Array(127, 0, 0, 1)))
 
       // Create and start the worker
-      val sparkHome = new ProcessBuilder().environment().get("SPARK_HOME")
-      val pb = new ProcessBuilder(Seq(pythonExec, sparkHome + "/python/pyspark/worker.py"))
+      val pb = new ProcessBuilder(Seq(pythonExec, "-m", "pyspark.worker"))
       val workerEnv = pb.environment()
       workerEnv.putAll(envVars)
-      val pythonPath = sparkHome + "/python/" + File.pathSeparator + workerEnv.get("PYTHONPATH")
-      workerEnv.put("PYTHONPATH", pythonPath)
       val worker = pb.start()
 
       // Redirect the worker's stderr to ours
@@ -154,12 +151,9 @@ private[spark] class PythonWorkerFactory(pythonExec: String, envVars: Map[String
 
       try {
         // Create and start the daemon
-        val sparkHome = new ProcessBuilder().environment().get("SPARK_HOME")
-        val pb = new ProcessBuilder(Seq(pythonExec, sparkHome + "/python/pyspark/daemon.py"))
+        val pb = new ProcessBuilder(Seq(pythonExec, "-m", "pyspark.daemon"))
         val workerEnv = pb.environment()
         workerEnv.putAll(envVars)
-        val pythonPath = sparkHome + "/python/" + File.pathSeparator + workerEnv.get("PYTHONPATH")
-        workerEnv.put("PYTHONPATH", pythonPath)
         daemon = pb.start()
 
         // Redirect the stderr to ours
diff --git a/docs/python-programming-guide.md b/docs/python-programming-guide.md
index 98233bf556b7..98c456228af9 100644
--- a/docs/python-programming-guide.md
+++ b/docs/python-programming-guide.md
@@ -63,6 +63,9 @@ All of PySpark's library dependencies, including [Py4J](http://py4j.sourceforge.
 Standalone PySpark applications should be run using the `bin/pyspark` script, which automatically configures the Java and Python environment using the settings in `conf/spark-env.sh` or `.cmd`.
 The script automatically adds the `bin/pyspark` package to the `PYTHONPATH`.
 
+# Running PySpark on YARN
+
+To run PySpark against a YARN cluster, simply set the MASTER environment variable to "yarn-client".
 
 # Interactive Use
 
diff --git a/python/.gitignore b/python/.gitignore
index 5c56e638f923..80b361ffbd51 100644
--- a/python/.gitignore
+++ b/python/.gitignore
@@ -1,2 +1,5 @@
 *.pyc
 docs/
+pyspark.egg-info
+build/
+dist/
diff --git a/python/lib/PY4J_VERSION.txt b/python/lib/PY4J_VERSION.txt
deleted file mode 100644
index 04a0cd52a8d9..000000000000
--- a/python/lib/PY4J_VERSION.txt
+++ /dev/null
@@ -1 +0,0 @@
-b7924aabe9c5e63f0a4d8bbd17019534c7ec014e
diff --git a/python/pyspark/__init__.py b/python/pyspark/__init__.py
index 73fe7378ffa6..07df8697bd1a 100644
--- a/python/pyspark/__init__.py
+++ b/python/pyspark/__init__.py
@@ -49,13 +49,6 @@
       Main entry point for accessing data stored in Apache Hive..
 """
 
-
-
-import sys
-import os
-sys.path.insert(0, os.path.join(os.environ["SPARK_HOME"], "python/lib/py4j-0.8.1-src.zip"))
-
-
 from pyspark.conf import SparkConf
 from pyspark.context import SparkContext
 from pyspark.sql import SQLContext
diff --git a/python/pyspark/java_gateway.py b/python/pyspark/java_gateway.py
index 6bb6c877c942..032d960e4099 100644
--- a/python/pyspark/java_gateway.py
+++ b/python/pyspark/java_gateway.py
@@ -24,10 +24,11 @@
 from py4j.java_gateway import java_import, JavaGateway, GatewayClient
 
 
-SPARK_HOME = os.environ["SPARK_HOME"]
+def launch_gateway():
+    SPARK_HOME = os.environ["SPARK_HOME"]
 
+    set_env_vars_for_yarn()
 
-def launch_gateway():
     # Launch the Py4j gateway using Spark's run command so that we pick up the
     # proper classpath and settings from spark-env.sh
     on_windows = platform.system() == "Windows"
@@ -70,3 +71,27 @@ def run(self):
     java_import(gateway.jvm, "org.apache.spark.sql.hive.TestHiveContext")
     java_import(gateway.jvm, "scala.Tuple2")
     return gateway
+
+def set_env_vars_for_yarn():
+    # Add the spark jar, which includes the pyspark files, to the python path
+    env_map = parse_env(os.environ.get("SPARK_YARN_USER_ENV", ""))
+    if "PYTHONPATH" in env_map:
+        env_map["PYTHONPATH"] += ":spark.jar"
+    else:
+        env_map["PYTHONPATH"] = "spark.jar"
+
+    os.environ["SPARK_YARN_USER_ENV"] = ",".join(k + '=' + v for (k, v) in env_map.items())
+
+def parse_env(env_str):
+    # Turns a comma-separated of env settings into a dict that maps env vars to
+    # their values.
+    env = {}
+    for var_str in env_str.split(","):
+        parts = var_str.split("=")
+        if len(parts) == 2:
+            env[parts[0]] = parts[1]
+        elif len(var_str) > 0:
+            print "Invalid entry in SPARK_YARN_USER_ENV: " + var_str
+            sys.exit(1)
+    
+    return env
diff --git a/python/pyspark/tests.py b/python/pyspark/tests.py
index 527104587fd3..8cf9d9cf1bd6 100644
--- a/python/pyspark/tests.py
+++ b/python/pyspark/tests.py
@@ -30,10 +30,12 @@
 
 from pyspark.context import SparkContext
 from pyspark.files import SparkFiles
-from pyspark.java_gateway import SPARK_HOME
 from pyspark.serializers import read_int
 
 
+SPARK_HOME = os.environ["SPARK_HOME"]
+
+
 class PySparkTestCase(unittest.TestCase):
 
     def setUp(self):
diff --git a/sbin/spark-config.sh b/sbin/spark-config.sh
index cd2c7b7b0d49..147b506dd5ca 100755
--- a/sbin/spark-config.sh
+++ b/sbin/spark-config.sh
@@ -34,3 +34,6 @@ this="$config_bin/$script"
 export SPARK_PREFIX=`dirname "$this"`/..
 export SPARK_HOME=${SPARK_PREFIX}
 export SPARK_CONF_DIR="$SPARK_HOME/conf"
+# Add the PySpark classes to the PYTHONPATH:
+export PYTHONPATH=$SPARK_HOME/python:$PYTHONPATH
+export PYTHONPATH=$SPARK_HOME/python/lib/py4j-0.8.1-src.zip:$PYTHONPATH

From 55100daa6509bed851f6932845deffa861fef245 Mon Sep 17 00:00:00 2001
From: witgo <witgo@qq.com>
Date: Wed, 30 Apr 2014 09:49:45 -0700
Subject: [PATCH 064/810] Fix SPARK-1629: Spark should inline use of
 commons-lang `SystemUtils.IS_...

...OS_WINDOWS`

Author: witgo <witgo@qq.com>

Closes #569 from witgo/SPARK-1629 and squashes the following commits:

31520eb [witgo] Merge branch 'master' of https://github.com/apache/spark into SPARK-1629
fcaafd7 [witgo] merge mastet
49e248e [witgo] Fix SPARK-1629: Spark should inline use of commons-lang `SystemUtils.IS_OS_WINDOWS`
---
 .../main/scala/org/apache/spark/util/Utils.scala    | 13 +++++++++----
 1 file changed, 9 insertions(+), 4 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala
index 79f314c8dd36..2c934a4bac5d 100644
--- a/core/src/main/scala/org/apache/spark/util/Utils.scala
+++ b/core/src/main/scala/org/apache/spark/util/Utils.scala
@@ -31,7 +31,6 @@ import scala.reflect.ClassTag
 import scala.util.Try
 
 import com.google.common.io.Files
-import org.apache.commons.lang.SystemUtils
 import com.google.common.util.concurrent.ThreadFactoryBuilder
 import org.apache.hadoop.fs.{FileSystem, FileUtil, Path}
 import org.json4s._
@@ -50,7 +49,7 @@ private[spark] object Utils extends Logging {
   val random = new Random()
 
   def sparkBin(sparkHome: String, which: String): File = {
-    val suffix = if (SystemUtils.IS_OS_WINDOWS) ".cmd" else ""
+    val suffix = if (isWindows) ".cmd" else ""
     new File(sparkHome + File.separator + "bin", which + suffix)
   }
 
@@ -614,7 +613,7 @@ private[spark] object Utils extends Logging {
    */
   def isSymlink(file: File): Boolean = {
     if (file == null) throw new NullPointerException("File must not be null")
-    if (SystemUtils.IS_OS_WINDOWS) return false
+    if (isWindows) return false
     val fileInCanonicalDir = if (file.getParent() == null) {
       file
     } else {
@@ -1018,7 +1017,7 @@ private[spark] object Utils extends Logging {
       throw new IOException("Destination must be relative")
     }
     var cmdSuffix = ""
-    val linkCmd = if (SystemUtils.IS_OS_WINDOWS) {
+    val linkCmd = if (isWindows) {
       // refer to http://technet.microsoft.com/en-us/library/cc771254.aspx
       cmdSuffix = " /s /e /k /h /y /i"
       "cmd /c xcopy "
@@ -1062,6 +1061,12 @@ private[spark] object Utils extends Logging {
     getHadoopFileSystem(new URI(path))
   }
 
+  /**
+   * return true if this is Windows.
+   */
+  def isWindows = Option(System.getProperty("os.name")).
+    map(_.startsWith("Windows")).getOrElse(false)
+
   /**
    * Indicates whether Spark is currently running unit tests.
    */

From 98b65593bdcfea54010f8c0fdf2a2b840f18c283 Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Thu, 1 May 2014 01:15:51 -0700
Subject: [PATCH 065/810] SPARK-1691: Support quoted arguments inside of
 spark-submit.

This is a fairly straightforward fix. The bug was reported by @vanzin and the fix was proposed by @deanwampler and myself. Please take a look!

Author: Patrick Wendell <pwendell@gmail.com>

Closes #609 from pwendell/quotes and squashes the following commits:

8bed767 [Patrick Wendell] SPARK-1691: Support quoted arguments inside of spark-submit.
---
 bin/spark-submit | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/bin/spark-submit b/bin/spark-submit
index dd0d95d9d400..49bc26252cad 100755
--- a/bin/spark-submit
+++ b/bin/spark-submit
@@ -18,7 +18,7 @@
 #
 
 export SPARK_HOME="$(cd `dirname $0`/..; pwd)"
-ORIG_ARGS=$@
+ORIG_ARGS=("$@")
 
 while (($#)); do
   if [ "$1" = "--deploy-mode" ]; then
@@ -39,5 +39,5 @@ if [ ! -z $DRIVER_MEMORY ] && [ ! -z $DEPLOY_MODE ] && [ $DEPLOY_MODE = "client"
   export SPARK_MEM=$DRIVER_MEMORY
 fi
 
-$SPARK_HOME/bin/spark-class org.apache.spark.deploy.SparkSubmit $ORIG_ARGS
+$SPARK_HOME/bin/spark-class org.apache.spark.deploy.SparkSubmit "${ORIG_ARGS[@]}"
 

From a43d9c14f2083d6632f410d74db98476e0e4d986 Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Thu, 1 May 2014 21:32:43 -0700
Subject: [PATCH 066/810] [SQL] SPARK-1661 - Fix regex_serde test

The JIRA in question is actually reporting a bug with Shark, but I wanted to make sure Spark SQL did not have similar problems.  This fixes a bug in our parsing code that was preventing the test from executing, but it looks like the RegexSerDe is working in Spark SQL.

Author: Michael Armbrust <michael@databricks.com>

Closes #595 from marmbrus/fixRegexSerdeTest and squashes the following commits:

a4dc612 [Michael Armbrust] Add files created by hive to gitignore.
efa6402 [Michael Armbrust] Fix Hive serde_regex test.
---
 .gitignore                                    |  5 +++
 .../org/apache/spark/sql/hive/HiveQl.scala    |  6 ++-
 ...e_regex-0-60462d14b99bb445b02800e9fb22760e | 22 +++++++++++
 ...e_regex-1-dea03bd88cbaabcf438b398e23c139f6 |  0
 ..._regex-10-c5b3ec90419a40660e5f83736241c429 | 38 +++++++++++++++++++
 ..._regex-11-b00710a6f7a3e5b92c2e17da54cd9ad1 |  0
 ...e_regex-2-9d00484beaee46cf72b154a1351aeee9 |  0
 ...e_regex-3-817190d8871b70611483cd2abe2e55dc |  0
 ...e_regex-4-c3e345183543f40a14d2dd742ebd5346 |  2 +
 ...e_regex-5-e2d14a76a87a6014c6d2d3501a0c9129 |  2 +
 ...e_regex-6-f818821654f219d1f4e2482951fae4f1 |  0
 ...e_regex-7-4db287576a17c0963219ca63ee0b20e0 | 22 +++++++++++
 ...e_regex-8-c429ee76b751e674992f61a29c95af77 |  0
 ...e_regex-9-f0e8d394ad18dcbd381792fe9bd8894b |  0
 .../execution/HiveCompatibilitySuite.scala    |  1 +
 15 files changed, 97 insertions(+), 1 deletion(-)
 create mode 100644 sql/hive/src/test/resources/golden/serde_regex-0-60462d14b99bb445b02800e9fb22760e
 create mode 100644 sql/hive/src/test/resources/golden/serde_regex-1-dea03bd88cbaabcf438b398e23c139f6
 create mode 100644 sql/hive/src/test/resources/golden/serde_regex-10-c5b3ec90419a40660e5f83736241c429
 create mode 100644 sql/hive/src/test/resources/golden/serde_regex-11-b00710a6f7a3e5b92c2e17da54cd9ad1
 create mode 100644 sql/hive/src/test/resources/golden/serde_regex-2-9d00484beaee46cf72b154a1351aeee9
 create mode 100644 sql/hive/src/test/resources/golden/serde_regex-3-817190d8871b70611483cd2abe2e55dc
 create mode 100644 sql/hive/src/test/resources/golden/serde_regex-4-c3e345183543f40a14d2dd742ebd5346
 create mode 100644 sql/hive/src/test/resources/golden/serde_regex-5-e2d14a76a87a6014c6d2d3501a0c9129
 create mode 100644 sql/hive/src/test/resources/golden/serde_regex-6-f818821654f219d1f4e2482951fae4f1
 create mode 100644 sql/hive/src/test/resources/golden/serde_regex-7-4db287576a17c0963219ca63ee0b20e0
 create mode 100644 sql/hive/src/test/resources/golden/serde_regex-8-c429ee76b751e674992f61a29c95af77
 create mode 100644 sql/hive/src/test/resources/golden/serde_regex-9-f0e8d394ad18dcbd381792fe9bd8894b

diff --git a/.gitignore b/.gitignore
index 857e9feb953b..a204456d4208 100644
--- a/.gitignore
+++ b/.gitignore
@@ -49,3 +49,8 @@ unit-tests.log
 /lib/
 rat-results.txt
 scalastyle.txt
+
+# For Hive
+metastore_db/
+metastore/
+warehouse/
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
index 1777e96b6713..1f688fe1117f 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
@@ -347,7 +347,11 @@ private[hive] object HiveQl {
   protected def nodeToPlan(node: Node): LogicalPlan = node match {
     // Just fake explain for any of the native commands.
     case Token("TOK_EXPLAIN", explainArgs) if nativeCommands contains explainArgs.head.getText =>
-      NoRelation
+      ExplainCommand(NoRelation)
+    // Create tables aren't native commands due to CTAS queries, but we still don't need to
+    // explain them.
+    case Token("TOK_EXPLAIN", explainArgs) if explainArgs.head.getText == "TOK_CREATETABLE" =>
+      ExplainCommand(NoRelation)
     case Token("TOK_EXPLAIN", explainArgs) =>
       // Ignore FORMATTED if present.
       val Some(query) :: _ :: _ :: Nil =
diff --git a/sql/hive/src/test/resources/golden/serde_regex-0-60462d14b99bb445b02800e9fb22760e b/sql/hive/src/test/resources/golden/serde_regex-0-60462d14b99bb445b02800e9fb22760e
new file mode 100644
index 000000000000..d00ee7786a57
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/serde_regex-0-60462d14b99bb445b02800e9fb22760e
@@ -0,0 +1,22 @@
+ABSTRACT SYNTAX TREE:
+  (TOK_CREATETABLE (TOK_TABNAME serde_regex) TOK_LIKETABLE (TOK_TABCOLLIST (TOK_TABCOL host TOK_STRING) (TOK_TABCOL identity TOK_STRING) (TOK_TABCOL user TOK_STRING) (TOK_TABCOL time TOK_STRING) (TOK_TABCOL request TOK_STRING) (TOK_TABCOL status TOK_STRING) (TOK_TABCOL size TOK_INT) (TOK_TABCOL referer TOK_STRING) (TOK_TABCOL agent TOK_STRING)) (TOK_TABLESERIALIZER (TOK_SERDENAME 'org.apache.hadoop.hive.serde2.RegexSerDe' (TOK_TABLEPROPERTIES (TOK_TABLEPROPLIST (TOK_TABLEPROPERTY "input.regex" "([^ ]*) ([^ ]*) ([^ ]*) (-|\\[[^\\]]*\\]) ([^ \"]*|\"[^\"]*\") (-|[0-9]*) (-|[0-9]*)(?: ([^ \"]*|\"[^\"]*\") ([^ \"]*|\"[^\"]*\"))?"))))) TOK_TBLTEXTFILE)
+
+STAGE DEPENDENCIES:
+  Stage-0 is a root stage
+
+STAGE PLANS:
+  Stage: Stage-0
+      Create Table Operator:
+        Create Table
+          columns: host string, identity string, user string, time string, request string, status string, size int, referer string, agent string
+          if not exists: false
+          input format: org.apache.hadoop.mapred.TextInputFormat
+          # buckets: -1
+          output format: org.apache.hadoop.hive.ql.io.IgnoreKeyTextOutputFormat
+          serde name: org.apache.hadoop.hive.serde2.RegexSerDe
+          serde properties:
+            input.regex ([^ ]*) ([^ ]*) ([^ ]*) (-|\[[^\]]*\]) ([^ "]*|"[^"]*") (-|[0-9]*) (-|[0-9]*)(?: ([^ "]*|"[^"]*") ([^ "]*|"[^"]*"))?
+          name: serde_regex
+          isExternal: false
+
+
diff --git a/sql/hive/src/test/resources/golden/serde_regex-1-dea03bd88cbaabcf438b398e23c139f6 b/sql/hive/src/test/resources/golden/serde_regex-1-dea03bd88cbaabcf438b398e23c139f6
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/serde_regex-10-c5b3ec90419a40660e5f83736241c429 b/sql/hive/src/test/resources/golden/serde_regex-10-c5b3ec90419a40660e5f83736241c429
new file mode 100644
index 000000000000..3e290231c27e
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/serde_regex-10-c5b3ec90419a40660e5f83736241c429
@@ -0,0 +1,38 @@
+NULL	0
+NULL	0
+-1234567890.123456789	-1234567890
+-4400	4400
+-1255.49	-1255
+-1.122	-11
+-1.12	-1
+-1.12	-1
+-0.333	0
+-0.33	0
+-0.3	0
+0	0
+0	0
+0.01	0
+0.02	0
+0.1	0
+0.2	0
+0.3	0
+0.33	0
+0.333	0
+0.9999999999999999999999999	1
+1	1
+1	1
+1.12	1
+1.122	1
+2	2
+2	2
+3.14	3
+3.14	3
+3.14	3
+3.14	4
+10	10
+20	20
+100	100
+124	124
+125.2	125
+200	200
+1234567890.12345678	1234567890
diff --git a/sql/hive/src/test/resources/golden/serde_regex-11-b00710a6f7a3e5b92c2e17da54cd9ad1 b/sql/hive/src/test/resources/golden/serde_regex-11-b00710a6f7a3e5b92c2e17da54cd9ad1
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/serde_regex-2-9d00484beaee46cf72b154a1351aeee9 b/sql/hive/src/test/resources/golden/serde_regex-2-9d00484beaee46cf72b154a1351aeee9
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/serde_regex-3-817190d8871b70611483cd2abe2e55dc b/sql/hive/src/test/resources/golden/serde_regex-3-817190d8871b70611483cd2abe2e55dc
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/serde_regex-4-c3e345183543f40a14d2dd742ebd5346 b/sql/hive/src/test/resources/golden/serde_regex-4-c3e345183543f40a14d2dd742ebd5346
new file mode 100644
index 000000000000..c55f3dd47557
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/serde_regex-4-c3e345183543f40a14d2dd742ebd5346
@@ -0,0 +1,2 @@
+127.0.0.1	-	frank	[10/Oct/2000:13:55:36 -0700]	"GET /apache_pb.gif HTTP/1.0"	200	2326	NULL	NULL
+127.0.0.1	-	-	[26/May/2009:00:00:00 +0000]	"GET /someurl/?track=Blabla(Main) HTTP/1.1"	200	5864	-	"Mozilla/5.0 (Windows; U; Windows NT 6.0; en-US) AppleWebKit/525.19 (KHTML, like Gecko) Chrome/1.0.154.65 Safari/525.19"
diff --git a/sql/hive/src/test/resources/golden/serde_regex-5-e2d14a76a87a6014c6d2d3501a0c9129 b/sql/hive/src/test/resources/golden/serde_regex-5-e2d14a76a87a6014c6d2d3501a0c9129
new file mode 100644
index 000000000000..8bd185bc66eb
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/serde_regex-5-e2d14a76a87a6014c6d2d3501a0c9129
@@ -0,0 +1,2 @@
+127.0.0.1	2326	200	[10/Oct/2000:13:55:36 -0700]
+127.0.0.1	5864	200	[26/May/2009:00:00:00 +0000]
diff --git a/sql/hive/src/test/resources/golden/serde_regex-6-f818821654f219d1f4e2482951fae4f1 b/sql/hive/src/test/resources/golden/serde_regex-6-f818821654f219d1f4e2482951fae4f1
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/serde_regex-7-4db287576a17c0963219ca63ee0b20e0 b/sql/hive/src/test/resources/golden/serde_regex-7-4db287576a17c0963219ca63ee0b20e0
new file mode 100644
index 000000000000..da61769c6599
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/serde_regex-7-4db287576a17c0963219ca63ee0b20e0
@@ -0,0 +1,22 @@
+ABSTRACT SYNTAX TREE:
+  (TOK_CREATETABLE (TOK_TABNAME serde_regex1) TOK_LIKETABLE (TOK_TABCOLLIST (TOK_TABCOL key TOK_DECIMAL) (TOK_TABCOL value TOK_INT)) (TOK_TABLESERIALIZER (TOK_SERDENAME 'org.apache.hadoop.hive.serde2.RegexSerDe' (TOK_TABLEPROPERTIES (TOK_TABLEPROPLIST (TOK_TABLEPROPERTY "input.regex" "([^ ]*) ([^ ]*)"))))) TOK_TBLTEXTFILE)
+
+STAGE DEPENDENCIES:
+  Stage-0 is a root stage
+
+STAGE PLANS:
+  Stage: Stage-0
+      Create Table Operator:
+        Create Table
+          columns: key decimal, value int
+          if not exists: false
+          input format: org.apache.hadoop.mapred.TextInputFormat
+          # buckets: -1
+          output format: org.apache.hadoop.hive.ql.io.IgnoreKeyTextOutputFormat
+          serde name: org.apache.hadoop.hive.serde2.RegexSerDe
+          serde properties:
+            input.regex ([^ ]*) ([^ ]*)
+          name: serde_regex1
+          isExternal: false
+
+
diff --git a/sql/hive/src/test/resources/golden/serde_regex-8-c429ee76b751e674992f61a29c95af77 b/sql/hive/src/test/resources/golden/serde_regex-8-c429ee76b751e674992f61a29c95af77
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/serde_regex-9-f0e8d394ad18dcbd381792fe9bd8894b b/sql/hive/src/test/resources/golden/serde_regex-9-f0e8d394ad18dcbd381792fe9bd8894b
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
index 0bb76f31c373..6c08e63a3949 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
@@ -568,6 +568,7 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     "select_unquote_and",
     "select_unquote_not",
     "select_unquote_or",
+    "serde_regex",
     "serde_reported_schema",
     "set_variable_sub",
     "show_describe_func_quotes",

From 55c760ff9bc8079e3df3b63a6ba4e6778a33da47 Mon Sep 17 00:00:00 2001
From: wangfei <wangfei_hello@126.com>
Date: Thu, 1 May 2014 21:37:22 -0700
Subject: [PATCH 067/810] fix the spelling mistake

Author: wangfei <wangfei_hello@126.com>

Closes #614 from scwf/pxcw and squashes the following commits:

d1016ba [wangfei] fix spelling mistake
---
 core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala b/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala
index dd0a1360abe1..195da8146005 100755
--- a/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala
@@ -244,7 +244,7 @@ private[spark] class Worker(
           }
         } catch {
           case e: Exception => {
-            logError("Failed to launch exector %s/%d for %s".format(appId, execId, appDesc.name))
+            logError("Failed to launch executor %s/%d for %s".format(appId, execId, appDesc.name))
             if (executors.contains(appId + "/" + execId)) {
               executors(appId + "/" + execId).kill()
               executors -= appId + "/" + execId

From 40cf6d31019c5402e5eb08158856242d20697ba4 Mon Sep 17 00:00:00 2001
From: witgo <witgo@qq.com>
Date: Thu, 1 May 2014 21:39:40 -0700
Subject: [PATCH 068/810]  SPARK-1659: improvements spark-submit usage

Author: witgo <witgo@qq.com>

Closes #581 from witgo/SPARK-1659 and squashes the following commits:

0b2cf98 [witgo] Delete spark-submit obsolete usage: "--arg ARG"
---
 .../scala/org/apache/spark/deploy/SparkSubmitArguments.scala    | 2 --
 1 file changed, 2 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
index 5834dc40f1f9..45defb9a3f92 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
@@ -284,8 +284,6 @@ private[spark] class SparkSubmitArguments(args: Seq[String]) {
         |  --master MASTER_URL         spark://host:port, mesos://host:port, yarn, or local.
         |  --deploy-mode DEPLOY_MODE   Mode to deploy the app in, either 'client' or 'cluster'.
         |  --class CLASS_NAME          Name of your app's main class (required for Java apps).
-        |  --arg ARG                   Argument to be passed to your application's main class. This
-        |                              option can be specified multiple times for multiple args.
         |  --name NAME                 The name of your application (Default: 'Spark').
         |  --jars JARS                 A comma-separated list of local jars to include on the
         |                              driver classpath and that SparkContext.addJar will work

From 394d8cb1c4dfd1e496562009e716b8fc06be22cd Mon Sep 17 00:00:00 2001
From: Andrew Or <andrewor14@gmail.com>
Date: Thu, 1 May 2014 21:42:06 -0700
Subject: [PATCH 069/810] Add tests for FileLogger, EventLoggingListener, and
 ReplayListenerBus

Modifications to Spark core are limited to exposing functionality to test files + minor style fixes.
(728 / 769 lines are from tests)

Author: Andrew Or <andrewor14@gmail.com>

Closes #591 from andrewor14/event-log-tests and squashes the following commits:

2883837 [Andrew Or] Merge branch 'master' of github.com:apache/spark into event-log-tests
c3afcea [Andrew Or] Compromise
2d5daf8 [Andrew Or] Use temp directory provided by the OS rather than /tmp
2b52151 [Andrew Or] Remove unnecessary file delete + add a comment
62010fd [Andrew Or] More cleanup (renaming variables, updating comments etc)
ad2beff [Andrew Or] Clean up EventLoggingListenerSuite + modify a few comments
862e752 [Andrew Or] Merge branch 'master' of github.com:apache/spark into event-log-tests
e0ba2f8 [Andrew Or] Fix test failures caused by race condition in processing/mutating events
b990453 [Andrew Or] ReplayListenerBus suite - tests do not all pass yet
ab66a84 [Andrew Or] Tests for FileLogger + delete file after tests
187bb25 [Andrew Or] Formatting and renaming variables
769336f [Andrew Or] Merge branch 'master' of github.com:apache/spark into event-log-tests
5d38ffe [Andrew Or] Clean up EventLoggingListenerSuite + add comments
e12f4b1 [Andrew Or] Preliminary tests for EventLoggingListener (need major cleanup)
---
 .../scheduler/EventLoggingListener.scala      |  40 +-
 .../spark/scheduler/SparkListenerBus.scala    |   2 +-
 .../org/apache/spark/util/FileLogger.scala    |  28 +-
 .../org/apache/spark/util/JsonProtocol.scala  |  10 +-
 .../scala/org/apache/spark/util/Utils.scala   |  18 +-
 .../scheduler/EventLoggingListenerSuite.scala | 400 ++++++++++++++++++
 .../spark/scheduler/ReplayListenerSuite.scala | 166 ++++++++
 .../apache/spark/util/FileLoggerSuite.scala   | 163 +++++++
 8 files changed, 791 insertions(+), 36 deletions(-)
 create mode 100644 core/src/test/scala/org/apache/spark/scheduler/EventLoggingListenerSuite.scala
 create mode 100644 core/src/test/scala/org/apache/spark/scheduler/ReplayListenerSuite.scala
 create mode 100644 core/src/test/scala/org/apache/spark/util/FileLoggerSuite.scala

diff --git a/core/src/main/scala/org/apache/spark/scheduler/EventLoggingListener.scala b/core/src/main/scala/org/apache/spark/scheduler/EventLoggingListener.scala
index d822a8e55111..7968a0691db1 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/EventLoggingListener.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/EventLoggingListener.scala
@@ -18,13 +18,16 @@
 package org.apache.spark.scheduler
 
 import scala.collection.mutable
+import scala.collection.mutable.ArrayBuffer
 
 import org.apache.hadoop.conf.Configuration
 import org.apache.hadoop.fs.{FileSystem, Path}
 import org.apache.hadoop.fs.permission.FsPermission
+import org.json4s.JsonAST.JValue
 import org.json4s.jackson.JsonMethods._
 
 import org.apache.spark.{Logging, SparkConf, SparkContext}
+import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.io.CompressionCodec
 import org.apache.spark.util.{FileLogger, JsonProtocol}
 
@@ -40,31 +43,36 @@ import org.apache.spark.util.{FileLogger, JsonProtocol}
  */
 private[spark] class EventLoggingListener(
     appName: String,
-    conf: SparkConf,
-    hadoopConfiguration: Configuration)
+    sparkConf: SparkConf,
+    hadoopConf: Configuration = SparkHadoopUtil.get.newConfiguration())
   extends SparkListener with Logging {
 
   import EventLoggingListener._
 
-  private val shouldCompress = conf.getBoolean("spark.eventLog.compress", false)
-  private val shouldOverwrite = conf.getBoolean("spark.eventLog.overwrite", false)
-  private val outputBufferSize = conf.getInt("spark.eventLog.buffer.kb", 100) * 1024
-  private val logBaseDir = conf.get("spark.eventLog.dir", "/tmp/spark-events").stripSuffix("/")
+  private val shouldCompress = sparkConf.getBoolean("spark.eventLog.compress", false)
+  private val shouldOverwrite = sparkConf.getBoolean("spark.eventLog.overwrite", false)
+  private val testing = sparkConf.getBoolean("spark.eventLog.testing", false)
+  private val outputBufferSize = sparkConf.getInt("spark.eventLog.buffer.kb", 100) * 1024
+  private val logBaseDir = sparkConf.get("spark.eventLog.dir", DEFAULT_LOG_DIR).stripSuffix("/")
   private val name = appName.replaceAll("[ :/]", "-").toLowerCase + "-" + System.currentTimeMillis
   val logDir = logBaseDir + "/" + name
 
-  private val logger =
-    new FileLogger(logDir, conf, hadoopConfiguration, outputBufferSize, shouldCompress,
-      shouldOverwrite, Some(LOG_FILE_PERMISSIONS))
+  protected val logger = new FileLogger(logDir, sparkConf, hadoopConf, outputBufferSize,
+    shouldCompress, shouldOverwrite, Some(LOG_FILE_PERMISSIONS))
+
+  // For testing. Keep track of all JSON serialized events that have been logged.
+  private[scheduler] val loggedEvents = new ArrayBuffer[JValue]
 
   /**
    * Begin logging events.
    * If compression is used, log a file that indicates which compression library is used.
    */
   def start() {
+    logger.start()
     logInfo("Logging events to %s".format(logDir))
     if (shouldCompress) {
-      val codec = conf.get("spark.io.compression.codec", CompressionCodec.DEFAULT_COMPRESSION_CODEC)
+      val codec =
+        sparkConf.get("spark.io.compression.codec", CompressionCodec.DEFAULT_COMPRESSION_CODEC)
       logger.newFile(COMPRESSION_CODEC_PREFIX + codec)
     }
     logger.newFile(SPARK_VERSION_PREFIX + SparkContext.SPARK_VERSION)
@@ -73,11 +81,14 @@ private[spark] class EventLoggingListener(
 
   /** Log the event as JSON. */
   private def logEvent(event: SparkListenerEvent, flushLogger: Boolean = false) {
-    val eventJson = compact(render(JsonProtocol.sparkEventToJson(event)))
-    logger.logLine(eventJson)
+    val eventJson = JsonProtocol.sparkEventToJson(event)
+    logger.logLine(compact(render(eventJson)))
     if (flushLogger) {
       logger.flush()
     }
+    if (testing) {
+      loggedEvents += eventJson
+    }
   }
 
   // Events that do not trigger a flush
@@ -121,13 +132,12 @@ private[spark] class EventLoggingListener(
 }
 
 private[spark] object EventLoggingListener extends Logging {
+  val DEFAULT_LOG_DIR = "/tmp/spark-events"
   val LOG_PREFIX = "EVENT_LOG_"
   val SPARK_VERSION_PREFIX = "SPARK_VERSION_"
   val COMPRESSION_CODEC_PREFIX = "COMPRESSION_CODEC_"
   val APPLICATION_COMPLETE = "APPLICATION_COMPLETE"
-  val LOG_FILE_PERMISSIONS: FsPermission =
-    FsPermission.createImmutable(Integer.parseInt("770", 8).toShort)
-
+  val LOG_FILE_PERMISSIONS = FsPermission.createImmutable(Integer.parseInt("770", 8).toShort)
 
   // A cache for compression codecs to avoid creating the same codec many times
   private val codecMap = new mutable.HashMap[String, CompressionCodec]
diff --git a/core/src/main/scala/org/apache/spark/scheduler/SparkListenerBus.scala b/core/src/main/scala/org/apache/spark/scheduler/SparkListenerBus.scala
index d6df193d9bcf..0286aac8769b 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/SparkListenerBus.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/SparkListenerBus.scala
@@ -37,7 +37,7 @@ private[spark] trait SparkListenerBus {
    * Post an event to all attached listeners. This does nothing if the event is
    * SparkListenerShutdown.
    */
-  protected def postToAll(event: SparkListenerEvent) {
+  def postToAll(event: SparkListenerEvent) {
     event match {
       case stageSubmitted: SparkListenerStageSubmitted =>
         sparkListeners.foreach(_.onStageSubmitted(stageSubmitted))
diff --git a/core/src/main/scala/org/apache/spark/util/FileLogger.scala b/core/src/main/scala/org/apache/spark/util/FileLogger.scala
index 0965e0f0f782..0e6d21b22023 100644
--- a/core/src/main/scala/org/apache/spark/util/FileLogger.scala
+++ b/core/src/main/scala/org/apache/spark/util/FileLogger.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.util
 
-import java.io.{FileOutputStream, BufferedOutputStream, PrintWriter, IOException}
+import java.io.{BufferedOutputStream, FileOutputStream, IOException, PrintWriter}
 import java.net.URI
 import java.text.SimpleDateFormat
 import java.util.Date
@@ -27,6 +27,7 @@ import org.apache.hadoop.fs.{FileSystem, FSDataOutputStream, Path}
 import org.apache.hadoop.fs.permission.FsPermission
 
 import org.apache.spark.{Logging, SparkConf}
+import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.io.CompressionCodec
 
 /**
@@ -39,8 +40,8 @@ import org.apache.spark.io.CompressionCodec
  */
 private[spark] class FileLogger(
     logDir: String,
-    conf: SparkConf,
-    hadoopConfiguration: Configuration,
+    sparkConf: SparkConf,
+    hadoopConf: Configuration = SparkHadoopUtil.get.newConfiguration(),
     outputBufferSize: Int = 8 * 1024, // 8 KB
     compress: Boolean = false,
     overwrite: Boolean = true,
@@ -55,14 +56,19 @@ private[spark] class FileLogger(
   var fileIndex = 0
 
   // Only used if compression is enabled
-  private lazy val compressionCodec = CompressionCodec.createCodec(conf)
+  private lazy val compressionCodec = CompressionCodec.createCodec(sparkConf)
 
   // Only defined if the file system scheme is not local
   private var hadoopDataStream: Option[FSDataOutputStream] = None
 
   private var writer: Option[PrintWriter] = None
 
-  createLogDir()
+  /**
+   * Start this logger by creating the logging directory.
+   */
+  def start() {
+    createLogDir()
+  }
 
   /**
    * Create a logging directory with the given path.
@@ -83,7 +89,7 @@ private[spark] class FileLogger(
     }
     if (dirPermissions.isDefined) {
       val fsStatus = fileSystem.getFileStatus(path)
-      if (fsStatus.getPermission().toShort() != dirPermissions.get.toShort) {
+      if (fsStatus.getPermission.toShort != dirPermissions.get.toShort) {
         fileSystem.setPermission(path, dirPermissions.get)
       }
     }
@@ -92,14 +98,14 @@ private[spark] class FileLogger(
   /**
    * Create a new writer for the file identified by the given path.
    * If the permissions are not passed in, it will default to use the permissions
-   * (dirpermissions) used when class was instantiated.
+   * (dirPermissions) used when class was instantiated.
    */
   private def createWriter(fileName: String, perms: Option[FsPermission] = None): PrintWriter = {
     val logPath = logDir + "/" + fileName
     val uri = new URI(logPath)
-    val defaultFs = FileSystem.getDefaultUri(hadoopConfiguration).getScheme
-    val isDefaultLocal = (defaultFs == null || defaultFs == "file")
     val path = new Path(logPath)
+    val defaultFs = FileSystem.getDefaultUri(hadoopConf).getScheme
+    val isDefaultLocal = defaultFs == null || defaultFs == "file"
 
     /* The Hadoop LocalFileSystem (r1.0.4) has known issues with syncing (HADOOP-7844).
      * Therefore, for local files, use FileOutputStream instead. */
@@ -112,7 +118,7 @@ private[spark] class FileLogger(
         hadoopDataStream.get
       }
 
-    perms.orElse(dirPermissions).foreach {p => fileSystem.setPermission(path, p)}
+    perms.orElse(dirPermissions).foreach { p => fileSystem.setPermission(path, p) }
     val bstream = new BufferedOutputStream(dstream, outputBufferSize)
     val cstream = if (compress) compressionCodec.compressedOutputStream(bstream) else bstream
     new PrintWriter(cstream)
@@ -127,7 +133,7 @@ private[spark] class FileLogger(
     val writeInfo = if (!withTime) {
       msg
     } else {
-      val date = new Date(System.currentTimeMillis())
+      val date = new Date(System.currentTimeMillis)
       dateFormat.get.format(date) + ": " + msg
     }
     writer.foreach(_.print(writeInfo))
diff --git a/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala b/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala
index 9aed3e098565..09825087bb04 100644
--- a/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala
+++ b/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala
@@ -646,11 +646,11 @@ private[spark] object JsonProtocol {
   }
 
   def propertiesFromJson(json: JValue): Properties = {
-    val properties = new Properties()
-    if (json != JNothing) {
-      mapFromJson(json).map { case (k, v) => properties.setProperty(k, v) }
-    }
-    properties
+    Utils.jsonOption(json).map { value =>
+      val properties = new Properties
+      mapFromJson(json).foreach { case (k, v) => properties.setProperty(k, v) }
+      properties
+    }.getOrElse(null)
   }
 
   def UUIDFromJson(json: JValue): UUID = {
diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala
index 2c934a4bac5d..536a740140c0 100644
--- a/core/src/main/scala/org/apache/spark/util/Utils.scala
+++ b/core/src/main/scala/org/apache/spark/util/Utils.scala
@@ -1062,15 +1062,25 @@ private[spark] object Utils extends Logging {
   }
 
   /**
-   * return true if this is Windows.
+   * Return the absolute path of a file in the given directory.
    */
-  def isWindows = Option(System.getProperty("os.name")).
-    map(_.startsWith("Windows")).getOrElse(false)
+  def getFilePath(dir: File, fileName: String): Path = {
+    assert(dir.isDirectory)
+    val path = new File(dir, fileName).getAbsolutePath
+    new Path(path)
+  }
+
+  /**
+   * Return true if this is Windows.
+   */
+  def isWindows = {
+    Option(System.getProperty("os.name")).exists(_.startsWith("Windows"))
+  }
 
   /**
    * Indicates whether Spark is currently running unit tests.
    */
-  private[spark] def isTesting = {
+  def isTesting = {
     sys.env.contains("SPARK_TESTING") || sys.props.contains("spark.testing")
   }
 }
diff --git a/core/src/test/scala/org/apache/spark/scheduler/EventLoggingListenerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/EventLoggingListenerSuite.scala
new file mode 100644
index 000000000000..95f5bcd85566
--- /dev/null
+++ b/core/src/test/scala/org/apache/spark/scheduler/EventLoggingListenerSuite.scala
@@ -0,0 +1,400 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.scheduler
+
+import scala.collection.mutable
+import scala.io.Source
+import scala.util.Try
+
+import com.google.common.io.Files
+import org.apache.hadoop.fs.{FileStatus, Path}
+import org.json4s.jackson.JsonMethods._
+import org.scalatest.{BeforeAndAfter, FunSuite}
+
+import org.apache.spark.{SparkConf, SparkContext}
+import org.apache.spark.io.CompressionCodec
+import org.apache.spark.util.{JsonProtocol, Utils}
+
+/**
+ * Test whether EventLoggingListener logs events properly.
+ *
+ * This tests whether EventLoggingListener actually creates special files while logging events,
+ * whether the parsing of these special files is correct, and whether the logged events can be
+ * read and deserialized into actual SparkListenerEvents.
+ */
+class EventLoggingListenerSuite extends FunSuite with BeforeAndAfter {
+  private val fileSystem = Utils.getHadoopFileSystem("/")
+  private val allCompressionCodecs = Seq[String](
+    "org.apache.spark.io.LZFCompressionCodec",
+    "org.apache.spark.io.SnappyCompressionCodec"
+  )
+  private val testDir = Files.createTempDir()
+  private val logDirPath = Utils.getFilePath(testDir, "spark-events")
+
+  after {
+    Try { fileSystem.delete(logDirPath, true) }
+  }
+
+  test("Parse names of special files") {
+    testParsingFileName()
+  }
+
+  test("Verify special files exist") {
+    testSpecialFilesExist()
+  }
+
+  test("Verify special files exist with compression") {
+    allCompressionCodecs.foreach { codec =>
+      testSpecialFilesExist(compressionCodec = Some(codec))
+    }
+  }
+
+  test("Parse event logging info") {
+    testParsingLogInfo()
+  }
+
+  test("Parse event logging info with compression") {
+    allCompressionCodecs.foreach { codec =>
+      testParsingLogInfo(compressionCodec = Some(codec))
+    }
+  }
+
+  test("Basic event logging") {
+    testEventLogging()
+  }
+
+  test("Basic event logging with compression") {
+    allCompressionCodecs.foreach { codec =>
+      testEventLogging(compressionCodec = Some(codec))
+    }
+  }
+
+  test("End-to-end event logging") {
+    testApplicationEventLogging()
+  }
+
+  test("End-to-end event logging with compression") {
+    allCompressionCodecs.foreach { codec =>
+      testApplicationEventLogging(compressionCodec = Some(codec))
+    }
+  }
+
+
+  /* ----------------- *
+   * Actual test logic *
+   * ----------------- */
+
+  import EventLoggingListenerSuite._
+
+  /**
+   * Test whether names of special files are correctly identified and parsed.
+   */
+  private def testParsingFileName() {
+    val logPrefix = EventLoggingListener.LOG_PREFIX
+    val sparkVersionPrefix = EventLoggingListener.SPARK_VERSION_PREFIX
+    val compressionCodecPrefix = EventLoggingListener.COMPRESSION_CODEC_PREFIX
+    val applicationComplete = EventLoggingListener.APPLICATION_COMPLETE
+    assert(EventLoggingListener.isEventLogFile(logPrefix + "0"))
+    assert(EventLoggingListener.isEventLogFile(logPrefix + "100"))
+    assert(EventLoggingListener.isEventLogFile(logPrefix + "ANYTHING"))
+    assert(EventLoggingListener.isSparkVersionFile(sparkVersionPrefix + "0.9.1"))
+    assert(EventLoggingListener.isSparkVersionFile(sparkVersionPrefix + "1.0.0"))
+    assert(EventLoggingListener.isSparkVersionFile(sparkVersionPrefix + "ANYTHING"))
+    assert(EventLoggingListener.isApplicationCompleteFile(applicationComplete))
+    allCompressionCodecs.foreach { codec =>
+      assert(EventLoggingListener.isCompressionCodecFile(compressionCodecPrefix + codec))
+    }
+
+    // Negatives
+    assert(!EventLoggingListener.isEventLogFile("The greatest man of all mankind"))
+    assert(!EventLoggingListener.isSparkVersionFile("Will never falter in the face of death!"))
+    assert(!EventLoggingListener.isCompressionCodecFile("Unless he chooses to leave behind"))
+    assert(!EventLoggingListener.isApplicationCompleteFile("The very treasure he calls Macbeth"))
+
+    // Verify that parsing is correct
+    assert(EventLoggingListener.parseSparkVersion(sparkVersionPrefix + "1.0.0") === "1.0.0")
+    allCompressionCodecs.foreach { codec =>
+      assert(EventLoggingListener.parseCompressionCodec(compressionCodecPrefix + codec) === codec)
+    }
+  }
+
+  /**
+   * Test whether the special files produced by EventLoggingListener exist.
+   *
+   * There should be exactly one event log and one spark version file throughout the entire
+   * execution. If a compression codec is specified, then the compression codec file should
+   * also exist. Only after the application has completed does the test expect the application
+   * completed file to be present.
+   */
+  private def testSpecialFilesExist(compressionCodec: Option[String] = None) {
+
+    def assertFilesExist(logFiles: Array[FileStatus], loggerStopped: Boolean) {
+      val numCompressionCodecFiles = if (compressionCodec.isDefined) 1 else 0
+      val numApplicationCompleteFiles = if (loggerStopped) 1 else 0
+      assert(logFiles.size === 2 + numCompressionCodecFiles + numApplicationCompleteFiles)
+      assert(eventLogsExist(logFiles))
+      assert(sparkVersionExists(logFiles))
+      assert(compressionCodecExists(logFiles) === compressionCodec.isDefined)
+      assert(applicationCompleteExists(logFiles) === loggerStopped)
+      assertSparkVersionIsValid(logFiles)
+      compressionCodec.foreach { codec =>
+        assertCompressionCodecIsValid(logFiles, codec)
+      }
+    }
+
+    // Verify logging directory exists
+    val conf = getLoggingConf(logDirPath, compressionCodec)
+    val eventLogger = new EventLoggingListener("test", conf)
+    eventLogger.start()
+    val logPath = new Path(eventLogger.logDir)
+    assert(fileSystem.exists(logPath))
+    val logDir = fileSystem.getFileStatus(logPath)
+    assert(logDir.isDir)
+
+    // Verify special files are as expected before stop()
+    var logFiles = fileSystem.listStatus(logPath)
+    assert(logFiles != null)
+    assertFilesExist(logFiles, loggerStopped = false)
+
+    // Verify special files are as expected after stop()
+    eventLogger.stop()
+    logFiles = fileSystem.listStatus(logPath)
+    assertFilesExist(logFiles, loggerStopped = true)
+  }
+
+  /**
+   * Test whether EventLoggingListener correctly parses the correct information from the logs.
+   *
+   * This includes whether it returns the correct Spark version, compression codec (if any),
+   * and the application's completion status.
+   */
+  private def testParsingLogInfo(compressionCodec: Option[String] = None) {
+
+    def assertInfoCorrect(info: EventLoggingInfo, loggerStopped: Boolean) {
+      assert(info.logPaths.size > 0)
+      assert(info.sparkVersion === SparkContext.SPARK_VERSION)
+      assert(info.compressionCodec.isDefined === compressionCodec.isDefined)
+      info.compressionCodec.foreach { codec =>
+        assert(compressionCodec.isDefined)
+        val expectedCodec = compressionCodec.get.split('.').last
+        assert(codec.getClass.getSimpleName === expectedCodec)
+      }
+      assert(info.applicationComplete === loggerStopped)
+    }
+
+    // Verify that all information is correctly parsed before stop()
+    val conf = getLoggingConf(logDirPath, compressionCodec)
+    val eventLogger = new EventLoggingListener("test", conf)
+    eventLogger.start()
+    var eventLoggingInfo = EventLoggingListener.parseLoggingInfo(eventLogger.logDir, fileSystem)
+    assertInfoCorrect(eventLoggingInfo, loggerStopped = false)
+
+    // Verify that all information is correctly parsed after stop()
+    eventLogger.stop()
+    eventLoggingInfo = EventLoggingListener.parseLoggingInfo(eventLogger.logDir, fileSystem)
+    assertInfoCorrect(eventLoggingInfo, loggerStopped = true)
+  }
+
+  /**
+   * Test basic event logging functionality.
+   *
+   * This creates two simple events, posts them to the EventLoggingListener, and verifies that
+   * exactly these two events are logged in the expected file.
+   */
+  private def testEventLogging(compressionCodec: Option[String] = None) {
+    val conf = getLoggingConf(logDirPath, compressionCodec)
+    val eventLogger = new EventLoggingListener("test", conf)
+    val listenerBus = new LiveListenerBus
+    val applicationStart = SparkListenerApplicationStart("Greatest App (N)ever", 125L, "Mickey")
+    val applicationEnd = SparkListenerApplicationEnd(1000L)
+
+    // A comprehensive test on JSON de/serialization of all events is in JsonProtocolSuite
+    eventLogger.start()
+    listenerBus.start()
+    listenerBus.addListener(eventLogger)
+    listenerBus.postToAll(applicationStart)
+    listenerBus.postToAll(applicationEnd)
+
+    // Verify file contains exactly the two events logged
+    val eventLoggingInfo = EventLoggingListener.parseLoggingInfo(eventLogger.logDir, fileSystem)
+    assert(eventLoggingInfo.logPaths.size > 0)
+    val lines = readFileLines(eventLoggingInfo.logPaths.head, eventLoggingInfo.compressionCodec)
+    assert(lines.size === 2)
+    assert(lines(0).contains("SparkListenerApplicationStart"))
+    assert(lines(1).contains("SparkListenerApplicationEnd"))
+    assert(JsonProtocol.sparkEventFromJson(parse(lines(0))) === applicationStart)
+    assert(JsonProtocol.sparkEventFromJson(parse(lines(1))) === applicationEnd)
+    eventLogger.stop()
+  }
+
+  /**
+   * Test end-to-end event logging functionality in an application.
+   * This runs a simple Spark job and asserts that the expected events are logged when expected.
+   */
+  private def testApplicationEventLogging(compressionCodec: Option[String] = None) {
+    val conf = getLoggingConf(logDirPath, compressionCodec)
+    val sc = new SparkContext("local", "test", conf)
+    assert(sc.eventLogger.isDefined)
+    val eventLogger = sc.eventLogger.get
+    val expectedLogDir = logDirPath.toString
+    assert(eventLogger.logDir.startsWith(expectedLogDir))
+
+    // Begin listening for events that trigger asserts
+    val eventExistenceListener = new EventExistenceListener(eventLogger)
+    sc.addSparkListener(eventExistenceListener)
+
+    // Trigger asserts for whether the expected events are actually logged
+    sc.parallelize(1 to 10000).count()
+    sc.stop()
+
+    // Ensure all asserts have actually been triggered
+    eventExistenceListener.assertAllCallbacksInvoked()
+  }
+
+  /**
+   * Assert that all of the specified events are logged by the given EventLoggingListener.
+   */
+  private def assertEventsExist(eventLogger: EventLoggingListener, events: Seq[String]) {
+    val eventLoggingInfo = EventLoggingListener.parseLoggingInfo(eventLogger.logDir, fileSystem)
+    assert(eventLoggingInfo.logPaths.size > 0)
+    val lines = readFileLines(eventLoggingInfo.logPaths.head, eventLoggingInfo.compressionCodec)
+    val eventSet = mutable.Set(events: _*)
+    lines.foreach { line =>
+      eventSet.foreach { event =>
+        if (line.contains(event)) {
+          val parsedEvent = JsonProtocol.sparkEventFromJson(parse(line))
+          val eventType = Utils.getFormattedClassName(parsedEvent)
+          if (eventType == event) {
+            eventSet.remove(event)
+          }
+        }
+      }
+    }
+    assert(eventSet.isEmpty, "The following events are missing: " + eventSet.toSeq)
+  }
+
+  /**
+   * Read all lines from the file specified by the given path.
+   * If a compression codec is specified, use it to read the file.
+   */
+  private def readFileLines(
+      filePath: Path,
+      compressionCodec: Option[CompressionCodec]): Seq[String] = {
+    val fstream = fileSystem.open(filePath)
+    val cstream =
+      compressionCodec.map { codec =>
+        codec.compressedInputStream(fstream)
+      }.getOrElse(fstream)
+    Source.fromInputStream(cstream).getLines().toSeq
+  }
+
+  /**
+   * A listener that asserts certain events are logged by the given EventLoggingListener.
+   * This is necessary because events are posted asynchronously in a different thread.
+   */
+  private class EventExistenceListener(eventLogger: EventLoggingListener) extends SparkListener {
+    var jobStarted = false
+    var jobEnded = false
+    var appEnded = false
+
+    override def onJobStart(jobStart: SparkListenerJobStart) {
+      assertEventsExist(eventLogger, Seq[String](
+        Utils.getFormattedClassName(SparkListenerApplicationStart),
+        Utils.getFormattedClassName(SparkListenerBlockManagerAdded),
+        Utils.getFormattedClassName(SparkListenerEnvironmentUpdate)
+      ))
+      jobStarted = true
+    }
+
+    override def onJobEnd(jobEnd: SparkListenerJobEnd) {
+      assertEventsExist(eventLogger, Seq[String](
+        Utils.getFormattedClassName(SparkListenerJobStart),
+        Utils.getFormattedClassName(SparkListenerJobEnd),
+        Utils.getFormattedClassName(SparkListenerStageSubmitted),
+        Utils.getFormattedClassName(SparkListenerStageCompleted),
+        Utils.getFormattedClassName(SparkListenerTaskStart),
+        Utils.getFormattedClassName(SparkListenerTaskEnd)
+      ))
+      jobEnded = true
+    }
+
+    override def onApplicationEnd(applicationEnd: SparkListenerApplicationEnd) {
+      assertEventsExist(eventLogger, Seq[String](
+        Utils.getFormattedClassName(SparkListenerApplicationEnd)
+      ))
+      appEnded = true
+    }
+
+    def assertAllCallbacksInvoked() {
+      assert(jobStarted, "JobStart callback not invoked!")
+      assert(jobEnded, "JobEnd callback not invoked!")
+      assert(appEnded, "ApplicationEnd callback not invoked!")
+    }
+  }
+
+
+  /* -------------------------------------------------------- *
+   * Helper methods for validating state of the special files *
+   * -------------------------------------------------------- */
+
+  private def eventLogsExist(logFiles: Array[FileStatus]): Boolean = {
+    logFiles.map(_.getPath.getName).exists(EventLoggingListener.isEventLogFile)
+  }
+
+  private def sparkVersionExists(logFiles: Array[FileStatus]): Boolean = {
+    logFiles.map(_.getPath.getName).exists(EventLoggingListener.isSparkVersionFile)
+  }
+
+  private def compressionCodecExists(logFiles: Array[FileStatus]): Boolean = {
+    logFiles.map(_.getPath.getName).exists(EventLoggingListener.isCompressionCodecFile)
+  }
+
+  private def applicationCompleteExists(logFiles: Array[FileStatus]): Boolean = {
+    logFiles.map(_.getPath.getName).exists(EventLoggingListener.isApplicationCompleteFile)
+  }
+
+  private def assertSparkVersionIsValid(logFiles: Array[FileStatus]) {
+    val file = logFiles.map(_.getPath.getName).find(EventLoggingListener.isSparkVersionFile)
+    assert(file.isDefined)
+    assert(EventLoggingListener.parseSparkVersion(file.get) === SparkContext.SPARK_VERSION)
+  }
+
+  private def assertCompressionCodecIsValid(logFiles: Array[FileStatus], compressionCodec: String) {
+    val file = logFiles.map(_.getPath.getName).find(EventLoggingListener.isCompressionCodecFile)
+    assert(file.isDefined)
+    assert(EventLoggingListener.parseCompressionCodec(file.get) === compressionCodec)
+  }
+
+}
+
+
+object EventLoggingListenerSuite {
+
+  /** Get a SparkConf with event logging enabled. */
+  def getLoggingConf(logDir: Path, compressionCodec: Option[String] = None) = {
+    val conf = new SparkConf
+    conf.set("spark.eventLog.enabled", "true")
+    conf.set("spark.eventLog.testing", "true")
+    conf.set("spark.eventLog.dir", logDir.toString)
+    compressionCodec.foreach { codec =>
+      conf.set("spark.eventLog.compress", "true")
+      conf.set("spark.io.compression.codec", codec)
+    }
+    conf
+  }
+}
diff --git a/core/src/test/scala/org/apache/spark/scheduler/ReplayListenerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/ReplayListenerSuite.scala
new file mode 100644
index 000000000000..d1fe1fc34896
--- /dev/null
+++ b/core/src/test/scala/org/apache/spark/scheduler/ReplayListenerSuite.scala
@@ -0,0 +1,166 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.scheduler
+
+import java.io.PrintWriter
+
+import scala.util.Try
+
+import com.google.common.io.Files
+import org.json4s.jackson.JsonMethods._
+import org.scalatest.{BeforeAndAfter, FunSuite}
+
+import org.apache.spark.SparkContext._
+import org.apache.spark.{SparkConf, SparkContext}
+import org.apache.spark.io.CompressionCodec
+import org.apache.spark.util.{JsonProtocol, Utils}
+
+/**
+ * Test whether ReplayListenerBus replays events from logs correctly.
+ */
+class ReplayListenerSuite extends FunSuite with BeforeAndAfter {
+  private val fileSystem = Utils.getHadoopFileSystem("/")
+  private val allCompressionCodecs = Seq[String](
+    "org.apache.spark.io.LZFCompressionCodec",
+    "org.apache.spark.io.SnappyCompressionCodec"
+  )
+  private val testDir = Files.createTempDir()
+
+  after {
+    Try { fileSystem.delete(Utils.getFilePath(testDir, "events.txt"), true) }
+    Try { fileSystem.delete(Utils.getFilePath(testDir, "test-replay"), true) }
+  }
+
+  test("Simple replay") {
+    testSimpleReplay()
+  }
+
+  test("Simple replay with compression") {
+    allCompressionCodecs.foreach { codec =>
+      testSimpleReplay(Some(codec))
+    }
+  }
+
+  // This assumes the correctness of EventLoggingListener
+  test("End-to-end replay") {
+    testApplicationReplay()
+  }
+
+  // This assumes the correctness of EventLoggingListener
+  test("End-to-end replay with compression") {
+    allCompressionCodecs.foreach { codec =>
+      testApplicationReplay(Some(codec))
+    }
+  }
+
+
+  /* ----------------- *
+   * Actual test logic *
+   * ----------------- */
+
+  /**
+   * Test simple replaying of events.
+   */
+  private def testSimpleReplay(codecName: Option[String] = None) {
+    val logFilePath = Utils.getFilePath(testDir, "events.txt")
+    val codec = codecName.map(getCompressionCodec)
+    val fstream = fileSystem.create(logFilePath)
+    val cstream = codec.map(_.compressedOutputStream(fstream)).getOrElse(fstream)
+    val writer = new PrintWriter(cstream)
+    val applicationStart = SparkListenerApplicationStart("Greatest App (N)ever", 125L, "Mickey")
+    val applicationEnd = SparkListenerApplicationEnd(1000L)
+    writer.println(compact(render(JsonProtocol.sparkEventToJson(applicationStart))))
+    writer.println(compact(render(JsonProtocol.sparkEventToJson(applicationEnd))))
+    writer.close()
+    val replayer = new ReplayListenerBus(Seq(logFilePath), fileSystem, codec)
+    val conf = EventLoggingListenerSuite.getLoggingConf(logFilePath, codecName)
+    val eventMonster = new EventMonster(conf)
+    replayer.addListener(eventMonster)
+    replayer.replay()
+    assert(eventMonster.loggedEvents.size === 2)
+    assert(eventMonster.loggedEvents(0) === JsonProtocol.sparkEventToJson(applicationStart))
+    assert(eventMonster.loggedEvents(1) === JsonProtocol.sparkEventToJson(applicationEnd))
+  }
+
+  /**
+   * Test end-to-end replaying of events.
+   *
+   * This test runs a few simple jobs with event logging enabled, and compares each emitted
+   * event to the corresponding event replayed from the event logs. This test makes the
+   * assumption that the event logging behavior is correct (tested in a separate suite).
+   */
+  private def testApplicationReplay(codecName: Option[String] = None) {
+    val logDirPath = Utils.getFilePath(testDir, "test-replay")
+    val conf = EventLoggingListenerSuite.getLoggingConf(logDirPath, codecName)
+    val sc = new SparkContext("local-cluster[2,1,512]", "Test replay", conf)
+
+    // Run a few jobs
+    sc.parallelize(1 to 100, 1).count()
+    sc.parallelize(1 to 100, 2).map(i => (i, i)).count()
+    sc.parallelize(1 to 100, 3).map(i => (i, i)).groupByKey().count()
+    sc.parallelize(1 to 100, 4).map(i => (i, i)).groupByKey().persist().count()
+    sc.stop()
+
+    // Prepare information needed for replay
+    val codec = codecName.map(getCompressionCodec)
+    val applications = fileSystem.listStatus(logDirPath)
+    assert(applications != null && applications.size > 0)
+    val eventLogDir = applications.sortBy(_.getAccessTime).last
+    assert(eventLogDir.isDir)
+    val logFiles = fileSystem.listStatus(eventLogDir.getPath)
+    assert(logFiles != null && logFiles.size > 0)
+    val logFile = logFiles.find(_.getPath.getName.startsWith("EVENT_LOG_"))
+    assert(logFile.isDefined)
+    val logFilePath = logFile.get.getPath
+
+    // Replay events
+    val replayer = new ReplayListenerBus(Seq(logFilePath), fileSystem, codec)
+    val eventMonster = new EventMonster(conf)
+    replayer.addListener(eventMonster)
+    replayer.replay()
+
+    // Verify the same events are replayed in the same order
+    assert(sc.eventLogger.isDefined)
+    val originalEvents = sc.eventLogger.get.loggedEvents
+    val replayedEvents = eventMonster.loggedEvents
+    originalEvents.zip(replayedEvents).foreach { case (e1, e2) => assert(e1 === e2) }
+  }
+
+  /**
+   * A simple listener that buffers all the events it receives.
+   *
+   * The event buffering functionality must be implemented within EventLoggingListener itself.
+   * This is because of the following race condition: the event may be mutated between being
+   * processed by one listener and being processed by another. Thus, in order to establish
+   * a fair comparison between the original events and the replayed events, both functionalities
+   * must be implemented within one listener (i.e. the EventLoggingListener).
+   *
+   * This child listener inherits only the event buffering functionality, but does not actually
+   * log the events.
+   */
+  private class EventMonster(conf: SparkConf) extends EventLoggingListener("test", conf) {
+    logger.close()
+  }
+
+  private def getCompressionCodec(codecName: String) = {
+    val conf = new SparkConf
+    conf.set("spark.io.compression.codec", codecName)
+    CompressionCodec.createCodec(conf)
+  }
+
+}
diff --git a/core/src/test/scala/org/apache/spark/util/FileLoggerSuite.scala b/core/src/test/scala/org/apache/spark/util/FileLoggerSuite.scala
new file mode 100644
index 000000000000..f675e1e5b498
--- /dev/null
+++ b/core/src/test/scala/org/apache/spark/util/FileLoggerSuite.scala
@@ -0,0 +1,163 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.util
+
+import java.io.IOException
+
+import scala.io.Source
+import scala.util.Try
+
+import com.google.common.io.Files
+import org.apache.hadoop.fs.Path
+import org.scalatest.{BeforeAndAfter, FunSuite}
+
+import org.apache.spark.SparkConf
+import org.apache.spark.io.CompressionCodec
+
+/**
+ * Test writing files through the FileLogger.
+ */
+class FileLoggerSuite extends FunSuite with BeforeAndAfter {
+  private val fileSystem = Utils.getHadoopFileSystem("/")
+  private val allCompressionCodecs = Seq[String](
+    "org.apache.spark.io.LZFCompressionCodec",
+    "org.apache.spark.io.SnappyCompressionCodec"
+  )
+  private val testDir = Files.createTempDir()
+  private val logDirPath = Utils.getFilePath(testDir, "test-file-logger")
+  private val logDirPathString = logDirPath.toString
+
+  after {
+    Try { fileSystem.delete(logDirPath, true) }
+  }
+
+  test("Simple logging") {
+    testSingleFile()
+  }
+
+  test ("Simple logging with compression") {
+    allCompressionCodecs.foreach { codec =>
+      testSingleFile(Some(codec))
+    }
+  }
+
+  test("Logging multiple files") {
+    testMultipleFiles()
+  }
+
+  test("Logging multiple files with compression") {
+    allCompressionCodecs.foreach { codec =>
+      testMultipleFiles(Some(codec))
+    }
+  }
+
+  test("Logging when directory already exists") {
+    // Create the logging directory multiple times
+    new FileLogger(logDirPathString, new SparkConf, overwrite = true).start()
+    new FileLogger(logDirPathString, new SparkConf, overwrite = true).start()
+    new FileLogger(logDirPathString, new SparkConf, overwrite = true).start()
+
+    // If overwrite is not enabled, an exception should be thrown
+    intercept[IOException] {
+      new FileLogger(logDirPathString, new SparkConf, overwrite = false).start()
+    }
+  }
+
+
+  /* ----------------- *
+   * Actual test logic *
+   * ----------------- */
+
+  /**
+   * Test logging to a single file.
+   */
+  private def testSingleFile(codecName: Option[String] = None) {
+    val conf = getLoggingConf(codecName)
+    val codec = codecName.map { c => CompressionCodec.createCodec(conf) }
+    val logger =
+      if (codecName.isDefined) {
+        new FileLogger(logDirPathString, conf, compress = true)
+      } else {
+        new FileLogger(logDirPathString, conf)
+      }
+    logger.start()
+    assert(fileSystem.exists(logDirPath))
+    assert(fileSystem.getFileStatus(logDirPath).isDir)
+    assert(fileSystem.listStatus(logDirPath).size === 0)
+
+    logger.newFile()
+    val files = fileSystem.listStatus(logDirPath)
+    assert(files.size === 1)
+    val firstFile = files.head
+    val firstFilePath = firstFile.getPath
+
+    logger.log("hello")
+    logger.flush()
+    assert(readFileContent(firstFilePath, codec) === "hello")
+
+    logger.log(" world")
+    logger.close()
+    assert(readFileContent(firstFilePath, codec) === "hello world")
+  }
+
+  /**
+   * Test logging to multiple files.
+   */
+  private def testMultipleFiles(codecName: Option[String] = None) {
+    val conf = getLoggingConf(codecName)
+    val codec = codecName.map { c => CompressionCodec.createCodec(conf) }
+    val logger =
+      if (codecName.isDefined) {
+        new FileLogger(logDirPathString, conf, compress = true)
+      } else {
+        new FileLogger(logDirPathString, conf)
+      }
+    logger.start()
+    logger.newFile("Jean_Valjean")
+    logger.logLine("Who am I?")
+    logger.logLine("Destiny?")
+    logger.newFile("John_Valjohn")
+    logger.logLine("One")
+    logger.logLine("Two three...")
+    logger.newFile("Wolverine")
+    logger.logLine("There was a time")
+    logger.logLine("A time when our enemies knew honor.")
+    logger.close()
+    assert(readFileContent(new Path(logDirPath, "Jean_Valjean"), codec) === "Who am I?\nDestiny?")
+    assert(readFileContent(new Path(logDirPath, "John_Valjohn"), codec) === "One\nTwo three...")
+    assert(readFileContent(new Path(logDirPath, "Wolverine"), codec) ===
+      "There was a time\nA time when our enemies knew honor.")
+  }
+
+  /**
+   * Read the content of the file specified by the given path.
+   * If a compression codec is specified, use it to read the file.
+   */
+  private def readFileContent(logPath: Path, codec: Option[CompressionCodec] = None): String = {
+    val fstream = fileSystem.open(logPath)
+    val cstream = codec.map(_.compressedInputStream(fstream)).getOrElse(fstream)
+    Source.fromInputStream(cstream).getLines().mkString("\n")
+  }
+
+  private def getLoggingConf(codecName: Option[String]) = {
+    val conf = new SparkConf
+    codecName.foreach { c => conf.set("spark.io.compression.codec", c) }
+    conf
+  }
+
+}

From f25ebed9f4552bc2c88a96aef06729d9fc2ee5b3 Mon Sep 17 00:00:00 2001
From: witgo <witgo@qq.com>
Date: Fri, 2 May 2014 12:40:27 -0700
Subject: [PATCH 070/810] SPARK-1695: java8-tests compiler error: package
 com.google.common.co...

...llections does not exist

Author: witgo <witgo@qq.com>

Closes #611 from witgo/SPARK-1695 and squashes the following commits:

d77a887 [witgo] Fix SPARK-1695: java8-tests compiler error: package com.google.common.collections does not exist
---
 .../src/test/java/org/apache/spark/Java8APISuite.java           | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/extras/java8-tests/src/test/java/org/apache/spark/Java8APISuite.java b/extras/java8-tests/src/test/java/org/apache/spark/Java8APISuite.java
index 7eb8b45fc3cf..feabca673348 100644
--- a/extras/java8-tests/src/test/java/org/apache/spark/Java8APISuite.java
+++ b/extras/java8-tests/src/test/java/org/apache/spark/Java8APISuite.java
@@ -23,7 +23,7 @@
 
 import scala.Tuple2;
 
-import com.google.common.collections.Iterables;
+import com.google.common.collect.Iterables;
 import com.google.common.base.Optional;
 import com.google.common.io.Files;
 import org.apache.hadoop.io.IntWritable;

From 4bf24f7897e1c67ca5f96dec05480e571f05ee1d Mon Sep 17 00:00:00 2001
From: wangfei <wangfei_hello@126.com>
Date: Fri, 2 May 2014 21:34:54 -0700
Subject: [PATCH 071/810] delete no use var

Author: wangfei <wangfei_hello@126.com>

Closes #613 from scwf/masterIndex and squashes the following commits:

1463056 [wangfei] delete no use var: masterIndex
---
 core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala | 2 --
 1 file changed, 2 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala b/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala
index 195da8146005..cd6bd2cd29fc 100755
--- a/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala
@@ -71,8 +71,6 @@ private[spark] class Worker(
   // TTL for app folders/data;  after TTL expires it will be cleaned up
   val APP_DATA_RETENTION_SECS = conf.getLong("spark.worker.cleanup.appDataTtl", 7 * 24 * 3600)
 
-  // Index into masterUrls that we're currently trying to register with.
-  var masterIndex = 0
 
   val masterLock: Object = new Object()
   var master: ActorSelection = null

From 2b961d88079d7a3f9da63d5175d7b61f6dec762b Mon Sep 17 00:00:00 2001
From: Sandy Ryza <sandy@cloudera.com>
Date: Fri, 2 May 2014 21:42:31 -0700
Subject: [PATCH 072/810] SPARK-1492. Update Spark YARN docs to use
 spark-submit

Author: Sandy Ryza <sandy@cloudera.com>

Closes #601 from sryza/sandy-spark-1492 and squashes the following commits:

5df1634 [Sandy Ryza] Address additional comments from Patrick.
be46d1f [Sandy Ryza] Address feedback from Marcelo and Patrick
867a3ea [Sandy Ryza] SPARK-1492. Update Spark YARN docs to use spark-submit
---
 docs/cluster-overview.md |  15 ++---
 docs/running-on-yarn.md  | 117 ++++++++++-----------------------------
 2 files changed, 38 insertions(+), 94 deletions(-)

diff --git a/docs/cluster-overview.md b/docs/cluster-overview.md
index b011679fede2..79b0061e2c5a 100644
--- a/docs/cluster-overview.md
+++ b/docs/cluster-overview.md
@@ -86,7 +86,7 @@ the `--help` flag. Here are a few examples of common options:
   --master local[8] \
   my-app.jar
 
-# Run on a Spark cluster
+# Run on a Spark standalone cluster
 ./bin/spark-submit \
   --class my.main.ClassName
   --master spark://mycluster:7077 \
@@ -118,21 +118,22 @@ If you are ever unclear where configuration options are coming from. fine-graine
 information can be printed by adding the `--verbose` option to `./spark-submit`.
 
 ### Advanced Dependency Management
-When using `./bin/spark-submit` jars will be automatically transferred to the cluster. For many
-users this is sufficient. However, advanced users can add jars by calling `addFile` or `addJar`
-on an existing SparkContext. This can be used to distribute JAR files (Java/Scala) or .egg and
-.zip libraries (Python) to executors. Spark uses the following URL scheme to allow different
+When using `./bin/spark-submit` the app jar along with any jars included with the `--jars` option
+will be automatically transferred to the cluster. `--jars` can also be used to distribute .egg and .zip
+libraries for Python to executors. Spark uses the following URL scheme to allow different
 strategies for disseminating jars:
 
 - **file:** - Absolute paths and `file:/` URIs are served by the driver's HTTP file server, and
-  every executor pulls the file from the driver HTTP server
+  every executor pulls the file from the driver HTTP server.
 - **hdfs:**, **http:**, **https:**, **ftp:** - these pull down files and JARs from the URI as expected
 - **local:** - a URI starting with local:/ is expected to exist as a local file on each worker node.  This
   means that no network IO will be incurred, and works well for large files/JARs that are pushed to each worker,
   or shared via NFS, GlusterFS, etc.
 
 Note that JARs and files are copied to the working directory for each SparkContext on the executor nodes.
-Over time this can use up a significant amount of space and will need to be cleaned up.
+This can use up a significant amount of space over time and will need to be cleaned up. With YARN, cleanup
+is handled automatically, and with Spark standalone, automatic cleanup can be configured with the
+`spark.worker.cleanup.appDataTtl` property.
 
 # Monitoring
 
diff --git a/docs/running-on-yarn.md b/docs/running-on-yarn.md
index 9765062ec689..68183ee8b461 100644
--- a/docs/running-on-yarn.md
+++ b/docs/running-on-yarn.md
@@ -5,27 +5,13 @@ title: Launching Spark on YARN
 
 Support for running on [YARN (Hadoop
 NextGen)](http://hadoop.apache.org/docs/r2.0.2-alpha/hadoop-yarn/hadoop-yarn-site/YARN.html)
-was added to Spark in version 0.6.0, and improved in 0.7.0 and 0.8.0.
-
-# Building a YARN-Enabled Assembly JAR
-
-We need a consolidated Spark JAR (which bundles all the required dependencies) to run Spark jobs on a YARN cluster.
-This can be built by setting the Hadoop version and `SPARK_YARN` environment variable, as follows:
-
-    SPARK_HADOOP_VERSION=2.0.5-alpha SPARK_YARN=true sbt/sbt assembly
-
-The assembled JAR will be something like this:
-`./assembly/target/scala-{{site.SCALA_BINARY_VERSION}}/spark-assembly_{{site.SPARK_VERSION}}-hadoop2.0.5.jar`.
-
-The build process now also supports new YARN versions (2.2.x). See below.
+was added to Spark in version 0.6.0, and improved in subsequent releases.
 
 # Preparations
 
-- Building a YARN-enabled assembly (see above).
-- The assembled jar can be installed into HDFS or used locally.
-- Your application code must be packaged into a separate JAR file.
-
-If you want to test out the YARN deployment mode, you can use the current Spark examples. A `spark-examples_{{site.SCALA_BINARY_VERSION}}-{{site.SPARK_VERSION}}` file can be generated by running `sbt/sbt assembly`. NOTE: since the documentation you're reading is for Spark version {{site.SPARK_VERSION}}, we are assuming here that you have downloaded Spark {{site.SPARK_VERSION}} or checked it out of source control. If you are using a different version of Spark, the version numbers in the jar generated by the sbt package command will obviously be different.
+Running Spark-on-YARN requires a binary distribution of Spark which is built with YARN support.
+Binary distributions can be downloaded from the Spark project website. 
+To build Spark yourself, refer to the [building with maven guide](building-with-maven.html).
 
 # Configuration
 
@@ -44,86 +30,47 @@ System Properties:
 * `spark.yarn.max.executor.failures`, the maximum number of executor failures before failing the application. Default is the number of executors requested times 2 with minimum of 3.
 * `spark.yarn.historyServer.address`, the address of the Spark history server (i.e. host.com:18080). The address should not contain a scheme (http://). Defaults to not being set since the history server is an optional service. This address is given to the Yarn ResourceManager when the Spark application finishes to link the application from the ResourceManager UI to the Spark history server UI. 
 
+By default, Spark on YARN will use a Spark jar installed locally, but the Spark jar can also be in a world-readable location on HDFS. This allows YARN to cache it on nodes so that it doesn't need to be distributed each time an application runs. To point to a jar on HDFS, export SPARK_JAR=hdfs:///some/path.
+
 # Launching Spark on YARN
 
 Ensure that HADOOP_CONF_DIR or YARN_CONF_DIR points to the directory which contains the (client side) configuration files for the Hadoop cluster.
-These configs are used to connect to the cluster, write to the dfs, and connect to the YARN ResourceManager.
+These configs are used to write to the dfs and connect to the YARN ResourceManager.
 
 There are two deploy modes that can be used to launch Spark applications on YARN. In yarn-cluster mode, the Spark driver runs inside an application master process which is managed by YARN on the cluster, and the client can go away after initiating the application. In yarn-client mode, the driver runs in the client process, and the application master is only used for requesting resources from YARN.
 
 Unlike in Spark standalone and Mesos mode, in which the master's address is specified in the "master" parameter, in YARN mode the ResourceManager's address is picked up from the Hadoop configuration.  Thus, the master parameter is simply "yarn-client" or "yarn-cluster".
 
-The spark-submit script described in the [cluster mode overview](cluster-overview.html) provides the most straightforward way to submit a compiled Spark application to YARN in either deploy mode. For info on the lower-level invocations it uses, read ahead. For running spark-shell against YARN, skip down to the yarn-client section. 
-
-## Launching a Spark application with yarn-cluster mode.
-
-The command to launch the Spark application on the cluster is as follows:
-
-    SPARK_JAR=<SPARK_ASSEMBLY_JAR_FILE> ./bin/spark-class org.apache.spark.deploy.yarn.Client \
-      --jar <YOUR_APP_JAR_FILE> \
-      --class <APP_MAIN_CLASS> \
-      --arg <APP_MAIN_ARGUMENT> \
-      --num-executors <NUMBER_OF_EXECUTOR_PROCESSES> \
-      --driver-memory <MEMORY_FOR_ApplicationMaster> \
-      --executor-memory <MEMORY_PER_EXECUTOR> \
-      --executor-cores <CORES_PER_EXECUTOR> \
-      --name <application_name> \
-      --queue <queue_name> \
-      --addJars <any_local_files_used_in_SparkContext.addJar> \
-      --files <files_for_distributed_cache> \
-      --archives <archives_for_distributed_cache>
-
-To pass multiple arguments the "arg" option can be specified multiple times. For example:
-
-    # Build the Spark assembly JAR and the Spark examples JAR
-    $ SPARK_HADOOP_VERSION=2.0.5-alpha SPARK_YARN=true sbt/sbt assembly
-
-    # Configure logging
-    $ cp conf/log4j.properties.template conf/log4j.properties
-
-    # Submit Spark's ApplicationMaster to YARN's ResourceManager, and instruct Spark to run the SparkPi example
-    $ SPARK_JAR=./assembly/target/scala-{{site.SCALA_BINARY_VERSION}}/spark-assembly-{{site.SPARK_VERSION}}-hadoop2.0.5-alpha.jar \
-        ./bin/spark-class org.apache.spark.deploy.yarn.Client \
-          --jar examples/target/scala-{{site.SCALA_BINARY_VERSION}}/spark-examples-assembly-{{site.SPARK_VERSION}}.jar \
-          --class org.apache.spark.examples.SparkPi \
-          --arg yarn-cluster \
-          --arg 5 \
-          --num-executors 3 \
-          --driver-memory 4g \
-          --executor-memory 2g \
-          --executor-cores 1
-
-The above starts a YARN client program which starts the default Application Master. Then SparkPi will be run as a child thread of Application Master. The client will periodically poll the Application Master for status updates and display them in the console. The client will exit once your application has finished running.  Refer to the "Viewing Logs" section below for how to see driver and executor logs.
-
-Because the application is run on a remote machine where the Application Master is running, applications that involve local interaction, such as spark-shell, will not work.
-
-## Launching a Spark application with yarn-client mode.
-
-With yarn-client mode, the application will be launched locally, just like running an application or spark-shell on Local / Mesos / Standalone client mode. The launch method is also the same, just make sure to specify the master URL as "yarn-client". You also need to export the env value for SPARK_JAR.
+To launch a Spark application in yarn-cluster mode:
 
-Configuration in yarn-client mode:
+    ./bin/spark-submit --class path.to.your.Class --master yarn-cluster [options] <app jar> [app options]
+    
+For example:
 
-In order to tune executor cores/number/memory etc., you need to export environment variables or add them to the spark configuration file (./conf/spark_env.sh). The following are the list of options.
+    $ ./bin/spark-submit --class org.apache.spark.examples.SparkPi \
+        --master yarn-cluster \
+        --num-executors 3 \
+        --driver-memory 4g \
+        --executor-memory 2g \
+        --executor-cores 1
+        examples/target/scala-{{site.SCALA_BINARY_VERSION}}/spark-examples-assembly-{{site.SPARK_VERSION}}.jar \
+        yarn-cluster 5
 
-* `SPARK_EXECUTOR_INSTANCES`, Number of executors to start (Default: 2)
-* `SPARK_EXECUTOR_CORES`, Number of cores per executor (Default: 1).
-* `SPARK_EXECUTOR_MEMORY`, Memory per executor (e.g. 1000M, 2G) (Default: 1G)
-* `SPARK_DRIVER_MEMORY`, Memory for driver (e.g. 1000M, 2G) (Default: 512 Mb)
-* `SPARK_YARN_APP_NAME`, The name of your application (Default: Spark)
-* `SPARK_YARN_QUEUE`, The YARN queue to use for allocation requests (Default: 'default')
-* `SPARK_YARN_DIST_FILES`, Comma separated list of files to be distributed with the job.
-* `SPARK_YARN_DIST_ARCHIVES`, Comma separated list of archives to be distributed with the job.
+The above starts a YARN client program which starts the default Application Master. Then SparkPi will be run as a child thread of Application Master. The client will periodically poll the Application Master for status updates and display them in the console. The client will exit once your application has finished running.  Refer to the "Viewing Logs" section below for how to see driver and executor logs.
 
-For example:
+To launch a Spark application in yarn-client mode, do the same, but replace "yarn-cluster" with "yarn-client".  To run spark-shell:
 
-    SPARK_JAR=./assembly/target/scala-{{site.SCALA_BINARY_VERSION}}/spark-assembly-{{site.SPARK_VERSION}}-hadoop2.0.5-alpha.jar \
-    ./bin/run-example org.apache.spark.examples.SparkPi yarn-client
+    $ MASTER=yarn-client ./bin/spark-shell
 
-or
+## Adding additional jars
 
-    SPARK_JAR=./assembly/target/scala-{{site.SCALA_BINARY_VERSION}}/spark-assembly-{{site.SPARK_VERSION}}-hadoop2.0.5-alpha.jar \
-    MASTER=yarn-client ./bin/spark-shell
+In yarn-cluster mode, the driver runs on a different machine than the client, so SparkContext.addJar won't work out of the box with files that are local to the client. To make files on the client available to SparkContext.addJar, include them with the `--jars` option in the launch command. 
 
+    $ ./bin/spark-submit --class my.main.Class \
+        --master yarn-cluster \
+        --jars my-other-jar.jar,my-other-other-jar.jar
+        my-main-jar.jar
+        yarn-cluster 5
 
 # Viewing logs
 
@@ -135,13 +82,9 @@ will print out the contents of all log files from all containers from the given
 
 When log aggregation isn't turned on, logs are retained locally on each machine under YARN_APP_LOGS_DIR, which is usually configured to /tmp/logs or $HADOOP_HOME/logs/userlogs depending on the Hadoop version and installation. Viewing logs for a container requires going to the host that contains them and looking in this directory.  Subdirectories organize log files by application ID and container ID.
 
-# Building Spark for Hadoop/YARN 2.2.x
-
-See [Building Spark with Maven](building-with-maven.html) for instructions on how to build Spark using Maven.
-
 # Important notes
 
 - Before Hadoop 2.2, YARN does not support cores in container resource requests. Thus, when running against an earlier version, the numbers of cores given via command line arguments cannot be passed to YARN.  Whether core requests are honored in scheduling decisions depends on which scheduler is in use and how it is configured.
 - The local directories used by Spark executors will be the local directories configured for YARN (Hadoop YARN config yarn.nodemanager.local-dirs). If the user specifies spark.local.dir, it will be ignored.
 - The --files and --archives options support specifying file names with the # similar to Hadoop. For example you can specify: --files localtest.txt#appSees.txt and this will upload the file you have locally named localtest.txt into HDFS but this will be linked to by the name appSees.txt, and your application should use the name as appSees.txt to reference it when running on YARN.
-- The --addJars option allows the SparkContext.addJar function to work if you are using it with local files. It does not need to be used if you are using it with HDFS, HTTP, HTTPS, or FTP files.
+- The --jars option allows the SparkContext.addJar function to work if you are using it with local files and running in yarn-cluster mode. It does not need to be used if you are using it with HDFS, HTTP, HTTPS, or FTP files.

From 0a14421765b672305e8f32ded4a9a1f6f7241d8d Mon Sep 17 00:00:00 2001
From: Aaron Davidson <aaron@databricks.com>
Date: Fri, 2 May 2014 23:55:13 -0700
Subject: [PATCH 073/810] SPARK-1700: Close socket file descriptors on task
 completion

This will ensure that sockets do not build up over the course of a job, and that cancellation successfully cleans up sockets.

Tested in standalone mode. More file descriptors spawn than expected (around 1000ish rather than the expected 8ish) but they do not pile up between runs, or as high as before (where they went up to around 5k).

Author: Aaron Davidson <aaron@databricks.com>

Closes #623 from aarondav/pyspark2 and squashes the following commits:

0ca13bb [Aaron Davidson] SPARK-1700: Close socket file descriptors on task completion
---
 .../scala/org/apache/spark/api/python/PythonRDD.scala | 11 ++++++++++-
 1 file changed, 10 insertions(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
index 672c344a5659..61407007087c 100644
--- a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
+++ b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
@@ -54,7 +54,16 @@ private[spark] class PythonRDD[T: ClassTag](
   override def compute(split: Partition, context: TaskContext): Iterator[Array[Byte]] = {
     val startTime = System.currentTimeMillis
     val env = SparkEnv.get
-    val worker = env.createPythonWorker(pythonExec, envVars.toMap)
+    val worker: Socket = env.createPythonWorker(pythonExec, envVars.toMap)
+
+    // Ensure worker socket is closed on task completion. Closing sockets is idempotent.
+    context.addOnCompleteCallback(() =>
+      try {
+        worker.close()
+      } catch {
+        case e: Exception => logWarning("Failed to close worker socket", e)
+      }
+    )
 
     @volatile var readerException: Exception = null
 

From 9347565f4188cf1574c6dc49fcde91eb286be955 Mon Sep 17 00:00:00 2001
From: ArcherShao <ArcherShao@users.noreply.github.com>
Date: Sat, 3 May 2014 00:17:36 -0700
Subject: [PATCH 074/810] Update SchemaRDD.scala

Modify spelling errors

Author: ArcherShao <ArcherShao@users.noreply.github.com>

Closes #619 from ArcherShao/patch-1 and squashes the following commits:

2957195 [ArcherShao] Update SchemaRDD.scala
---
 .../src/main/scala/org/apache/spark/sql/SchemaRDD.scala   | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala
index ca6e0a696405..d7782d6b3281 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala
@@ -133,7 +133,7 @@ class SchemaRDD(
     new SchemaRDD(sqlContext, Project(exprs, logicalPlan))
 
   /**
-   * Filters the ouput, only returning those rows where `condition` evaluates to true.
+   * Filters the output, only returning those rows where `condition` evaluates to true.
    *
    * {{{
    *   schemaRDD.where('a === 'b)
@@ -151,9 +151,9 @@ class SchemaRDD(
    *
    * @param otherPlan the [[SchemaRDD]] that should be joined with this one.
    * @param joinType One of `Inner`, `LeftOuter`, `RightOuter`, or `FullOuter`. Defaults to `Inner.`
-   * @param on       An optional condition for the join operation.  This is equivilent to the `ON`
+   * @param on       An optional condition for the join operation.  This is equivalent to the `ON`
    *                 clause in standard SQL.  In the case of `Inner` joins, specifying a
-   *                 `condition` is equivilent to adding `where` clauses after the `join`.
+   *                 `condition` is equivalent to adding `where` clauses after the `join`.
    *
    * @group Query
    */
@@ -195,7 +195,7 @@ class SchemaRDD(
 
   /**
    * Applies a qualifier to the attributes of this relation.  Can be used to disambiguate attributes
-   * with the same name, for example, when peforming self-joins.
+   * with the same name, for example, when performing self-joins.
    *
    * {{{
    *   val x = schemaRDD.where('a === 1).as('x)

From 3d0a02dff3011e8894d98d903cd086bc95e56807 Mon Sep 17 00:00:00 2001
From: Thomas Graves <tgraves@apache.org>
Date: Sat, 3 May 2014 10:59:05 -0700
Subject: [PATCH 075/810] [WIP] SPARK-1676: Cache Hadoop UGIs by default to
 prevent FileSystem leak

Move the doAs in Executor higher up so that we only have 1 ugi and aren't leaking filesystems.
Fix spark on yarn to work when the cluster is running as user "yarn" but the clients are launched as the user and want to read/write to hdfs as the user.

Note this hasn't been fully tested yet.  Need to test in standalone mode.

Putting this up for people to look at and possibly test.  I don't have access to a mesos cluster.

This is alternative to https://github.com/apache/spark/pull/607

Author: Thomas Graves <tgraves@apache.org>

Closes #621 from tgravescs/SPARK-1676 and squashes the following commits:

244d55a [Thomas Graves] fix line length
44163d4 [Thomas Graves] Rework
9398853 [Thomas Graves] change to have doAs in executor higher up.
---
 .../apache/spark/deploy/SparkHadoopUtil.scala | 17 +++++--
 .../CoarseGrainedExecutorBackend.scala        | 44 +++++++++++--------
 .../org/apache/spark/executor/Executor.scala  |  4 +-
 .../spark/executor/MesosExecutorBackend.scala | 14 +++---
 .../spark/deploy/yarn/ApplicationMaster.scala | 10 ++---
 .../spark/deploy/yarn/ExecutorLauncher.scala  |  7 ++-
 .../spark/deploy/yarn/ApplicationMaster.scala | 12 ++---
 .../spark/deploy/yarn/ExecutorLauncher.scala  |  7 ++-
 8 files changed, 69 insertions(+), 46 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala b/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala
index 498fcc520ac5..e2df1b895412 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala
@@ -24,25 +24,36 @@ import org.apache.hadoop.mapred.JobConf
 import org.apache.hadoop.security.Credentials
 import org.apache.hadoop.security.UserGroupInformation
 
-import org.apache.spark.{SparkContext, SparkException}
+import org.apache.spark.{Logging, SparkContext, SparkException}
 
 import scala.collection.JavaConversions._
 
 /**
  * Contains util methods to interact with Hadoop from Spark.
  */
-class SparkHadoopUtil {
+class SparkHadoopUtil extends Logging {
   val conf: Configuration = newConfiguration()
   UserGroupInformation.setConfiguration(conf)
 
-  def runAsUser(user: String)(func: () => Unit) {
+  /**
+   * Runs the given function with a Hadoop UserGroupInformation as a thread local variable
+   * (distributed to child threads), used for authenticating HDFS and YARN calls.
+   *
+   * IMPORTANT NOTE: If this function is going to be called repeated in the same process
+   * you need to look https://issues.apache.org/jira/browse/HDFS-3545 and possibly
+   * do a FileSystem.closeAllForUGI in order to avoid leaking Filesystems
+   */
+  def runAsSparkUser(func: () => Unit) {
+    val user = Option(System.getenv("SPARK_USER")).getOrElse(SparkContext.SPARK_UNKNOWN_USER)
     if (user != SparkContext.SPARK_UNKNOWN_USER) {
+      logDebug("running as user: " + user)
       val ugi = UserGroupInformation.createRemoteUser(user)
       transferCredentials(UserGroupInformation.getCurrentUser(), ugi)
       ugi.doAs(new PrivilegedExceptionAction[Unit] {
         def run: Unit = func()
       })
     } else {
+      logDebug("running as SPARK_UNKNOWN_USER")
       func()
     }
   }
diff --git a/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala b/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala
index 9ac7365f47f9..e912ae8a5d3c 100644
--- a/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala
+++ b/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala
@@ -22,8 +22,9 @@ import java.nio.ByteBuffer
 import akka.actor._
 import akka.remote._
 
-import org.apache.spark.{SecurityManager, SparkConf, Logging}
+import org.apache.spark.{Logging, SecurityManager, SparkConf}
 import org.apache.spark.TaskState.TaskState
+import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.deploy.worker.WorkerWatcher
 import org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages._
 import org.apache.spark.util.{AkkaUtils, Utils}
@@ -94,25 +95,30 @@ private[spark] class CoarseGrainedExecutorBackend(
 
 private[spark] object CoarseGrainedExecutorBackend {
   def run(driverUrl: String, executorId: String, hostname: String, cores: Int,
-          workerUrl: Option[String]) {
-    // Debug code
-    Utils.checkHost(hostname)
-
-    val conf = new SparkConf
-    // Create a new ActorSystem to run the backend, because we can't create a SparkEnv / Executor
-    // before getting started with all our system properties, etc
-    val (actorSystem, boundPort) = AkkaUtils.createActorSystem("sparkExecutor", hostname, 0,
-      indestructible = true, conf = conf, new SecurityManager(conf))
-    // set it
-    val sparkHostPort = hostname + ":" + boundPort
-    actorSystem.actorOf(
-      Props(classOf[CoarseGrainedExecutorBackend], driverUrl, executorId,
-        sparkHostPort, cores),
-      name = "Executor")
-    workerUrl.foreach{ url =>
-      actorSystem.actorOf(Props(classOf[WorkerWatcher], url), name = "WorkerWatcher")
+    workerUrl: Option[String]) {
+
+    SparkHadoopUtil.get.runAsSparkUser { () =>
+        // Debug code
+        Utils.checkHost(hostname)
+
+        val conf = new SparkConf
+        // Create a new ActorSystem to run the backend, because we can't create a
+        // SparkEnv / Executor before getting started with all our system properties, etc
+        val (actorSystem, boundPort) = AkkaUtils.createActorSystem("sparkExecutor", hostname, 0,
+          indestructible = true, conf = conf, new SecurityManager(conf))
+        // set it
+        val sparkHostPort = hostname + ":" + boundPort
+        actorSystem.actorOf(
+          Props(classOf[CoarseGrainedExecutorBackend], driverUrl, executorId,
+            sparkHostPort, cores),
+          name = "Executor")
+        workerUrl.foreach {
+          url =>
+            actorSystem.actorOf(Props(classOf[WorkerWatcher], url), name = "WorkerWatcher")
+        }
+        actorSystem.awaitTermination()
+
     }
-    actorSystem.awaitTermination()
   }
 
   def main(args: Array[String]) {
diff --git a/core/src/main/scala/org/apache/spark/executor/Executor.scala b/core/src/main/scala/org/apache/spark/executor/Executor.scala
index 272bcda5f8f2..98e7e0be813b 100644
--- a/core/src/main/scala/org/apache/spark/executor/Executor.scala
+++ b/core/src/main/scala/org/apache/spark/executor/Executor.scala
@@ -128,8 +128,6 @@ private[spark] class Executor(
   // Maintains the list of running tasks.
   private val runningTasks = new ConcurrentHashMap[Long, TaskRunner]
 
-  val sparkUser = Option(System.getenv("SPARK_USER")).getOrElse(SparkContext.SPARK_UNKNOWN_USER)
-
   def launchTask(context: ExecutorBackend, taskId: Long, serializedTask: ByteBuffer) {
     val tr = new TaskRunner(context, taskId, serializedTask)
     runningTasks.put(taskId, tr)
@@ -172,7 +170,7 @@ private[spark] class Executor(
       }
     }
 
-    override def run(): Unit = SparkHadoopUtil.get.runAsUser(sparkUser) { () =>
+    override def run() {
       val startTime = System.currentTimeMillis()
       SparkEnv.set(env)
       Thread.currentThread.setContextClassLoader(replClassLoader)
diff --git a/core/src/main/scala/org/apache/spark/executor/MesosExecutorBackend.scala b/core/src/main/scala/org/apache/spark/executor/MesosExecutorBackend.scala
index 64e24506e803..9b56f711e0e0 100644
--- a/core/src/main/scala/org/apache/spark/executor/MesosExecutorBackend.scala
+++ b/core/src/main/scala/org/apache/spark/executor/MesosExecutorBackend.scala
@@ -23,10 +23,10 @@ import com.google.protobuf.ByteString
 import org.apache.mesos.{Executor => MesosExecutor, ExecutorDriver, MesosExecutorDriver, MesosNativeLibrary}
 import org.apache.mesos.Protos.{TaskStatus => MesosTaskStatus, _}
 
-import org.apache.spark.Logging
-import org.apache.spark.TaskState
+import org.apache.spark.{Logging, TaskState}
 import org.apache.spark.TaskState.TaskState
 import org.apache.spark.util.Utils
+import org.apache.spark.deploy.SparkHadoopUtil
 
 private[spark] class MesosExecutorBackend
   extends MesosExecutor
@@ -95,9 +95,11 @@ private[spark] class MesosExecutorBackend
  */
 private[spark] object MesosExecutorBackend {
   def main(args: Array[String]) {
-    MesosNativeLibrary.load()
-    // Create a new Executor and start it running
-    val runner = new MesosExecutorBackend()
-    new MesosExecutorDriver(runner).run()
+    SparkHadoopUtil.get.runAsSparkUser { () =>
+        MesosNativeLibrary.load()
+        // Create a new Executor and start it running
+        val runner = new MesosExecutorBackend()
+        new MesosExecutorDriver(runner).run()
+    }
   }
 }
diff --git a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
index fc13dbecb455..8f0ecb855718 100644
--- a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
+++ b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
@@ -70,9 +70,6 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration,
 
   private var registered = false
 
-  private val sparkUser = Option(System.getenv("SPARK_USER")).getOrElse(
-    SparkContext.SPARK_UNKNOWN_USER)
-
   def run() {
     // Setup the directories so things go to yarn approved directories rather
     // then user specified and /tmp.
@@ -192,7 +189,8 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration,
       false /* initialize */ ,
       Thread.currentThread.getContextClassLoader).getMethod("main", classOf[Array[String]])
     val t = new Thread {
-      override def run(): Unit = SparkHadoopUtil.get.runAsUser(sparkUser) { () =>
+      override def run() {
+
         var successed = false
         try {
           // Copy
@@ -480,6 +478,8 @@ object ApplicationMaster {
 
   def main(argStrings: Array[String]) {
     val args = new ApplicationMasterArguments(argStrings)
-    new ApplicationMaster(args).run()
+    SparkHadoopUtil.get.runAsSparkUser { () =>
+      new ApplicationMaster(args).run()
+    }
   }
 }
diff --git a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala
index 65b7215afbd4..a3bd91590fc2 100644
--- a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala
+++ b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala
@@ -29,10 +29,11 @@ import org.apache.hadoop.yarn.util.{ConverterUtils, Records}
 import akka.actor._
 import akka.remote._
 import akka.actor.Terminated
-import org.apache.spark.{Logging, SecurityManager, SparkConf, SparkContext}
+import org.apache.spark.{Logging, SecurityManager, SparkConf}
 import org.apache.spark.util.{Utils, AkkaUtils}
 import org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend
 import org.apache.spark.scheduler.SplitInfo
+import org.apache.spark.deploy.SparkHadoopUtil
 
 /**
  * An application master that allocates executors on behalf of a driver that is running outside
@@ -279,6 +280,8 @@ class ExecutorLauncher(args: ApplicationMasterArguments, conf: Configuration, sp
 object ExecutorLauncher {
   def main(argStrings: Array[String]) {
     val args = new ApplicationMasterArguments(argStrings)
-    new ExecutorLauncher(args).run()
+    SparkHadoopUtil.get.runAsSparkUser { () =>
+      new ExecutorLauncher(args).run()
+    }
   }
 }
diff --git a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
index 90e807160d4b..c1dfe3f53b40 100644
--- a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
+++ b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
@@ -71,9 +71,6 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration,
     sparkConf.getInt("spark.yarn.max.worker.failures", math.max(args.numExecutors * 2, 3)))
 
   private var registered = false
-  
-  private val sparkUser = Option(System.getenv("SPARK_USER")).getOrElse(
-    SparkContext.SPARK_UNKNOWN_USER)
 
   def run() {
     // Setup the directories so things go to YARN approved directories rather
@@ -179,8 +176,9 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration,
       false /* initialize */ ,
       Thread.currentThread.getContextClassLoader).getMethod("main", classOf[Array[String]])
     val t = new Thread {
-      override def run(): Unit = SparkHadoopUtil.get.runAsUser(sparkUser) { () =>
-        var successed = false
+      override def run() {
+
+      var successed = false
         try {
           // Copy
           var mainArgs: Array[String] = new Array[String](args.userArgs.size)
@@ -462,6 +460,8 @@ object ApplicationMaster {
 
   def main(argStrings: Array[String]) {
     val args = new ApplicationMasterArguments(argStrings)
-    new ApplicationMaster(args).run()
+    SparkHadoopUtil.get.runAsSparkUser { () =>
+      new ApplicationMaster(args).run()
+    }
   }
 }
diff --git a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala
index a14bb377aa13..a4ce8766d347 100644
--- a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala
+++ b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala
@@ -28,12 +28,13 @@ import org.apache.hadoop.yarn.util.{ConverterUtils, Records}
 import akka.actor._
 import akka.remote._
 import akka.actor.Terminated
-import org.apache.spark.{Logging, SecurityManager, SparkConf, SparkContext}
+import org.apache.spark.{Logging, SecurityManager, SparkConf}
 import org.apache.spark.util.{Utils, AkkaUtils}
 import org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend
 import org.apache.spark.scheduler.SplitInfo
 import org.apache.hadoop.yarn.client.api.AMRMClient
 import org.apache.hadoop.yarn.client.api.AMRMClient.ContainerRequest
+import org.apache.spark.deploy.SparkHadoopUtil
 
 /**
  * An application master that allocates executors on behalf of a driver that is running outside
@@ -255,6 +256,8 @@ class ExecutorLauncher(args: ApplicationMasterArguments, conf: Configuration, sp
 object ExecutorLauncher {
   def main(argStrings: Array[String]) {
     val args = new ApplicationMasterArguments(argStrings)
-    new ExecutorLauncher(args).run()
+    SparkHadoopUtil.get.runAsSparkUser { () =>
+      new ExecutorLauncher(args).run()
+    }
   }
 }

From 11d54941760f86706e28f7ace8ece664c9164ba6 Mon Sep 17 00:00:00 2001
From: Sean Owen <sowen@cloudera.com>
Date: Sat, 3 May 2014 12:31:31 -0700
Subject: [PATCH 076/810] SPARK-1663. Corrections for several compile errors in
 streaming code examples, and updates to follow API changes

I gave the Streaming code examples, both Scala and Java, a test run today. I turned up a number of small errors, mostly compile errors in the Java examples. There were a few typos in the Scala too.

I also took the liberty of adding things like imports, since in several cases they are not obvious. Feel free to push back on some changes.

There's one thing I haven't quite addressed in the changes. `JavaPairDStream` uses the Java API version of `Function2` in almost all cases, as `JFunction2`. However it uses `scala.Function2` in:

```
  def reduceByKeyAndWindow(reduceFunc: Function2[V, V, V], windowDuration: Duration)
  :JavaPairDStream[K, V] = {
    dstream.reduceByKeyAndWindow(reduceFunc, windowDuration)
  }
```

Is that a typo?

Also, in Scala, I could not get this to compile:
```
val windowedWordCounts = pairs.reduceByKeyAndWindow(_ + _, Seconds(30), Seconds(10))
error: missing parameter type for expanded function ((x$1, x$2) => x$1.$plus(x$2))
```

You can see my fix below but am I missing something?

Otherwise I can say these all worked for me!

Author: Sean Owen <sowen@cloudera.com>

Closes #589 from srowen/SPARK-1663 and squashes the following commits:

65a906b [Sean Owen] Corrections for several compile errors in streaming code examples, and updates to follow API changes
---
 docs/streaming-programming-guide.md | 62 +++++++++++++++++------------
 1 file changed, 36 insertions(+), 26 deletions(-)

diff --git a/docs/streaming-programming-guide.md b/docs/streaming-programming-guide.md
index 7ad06427cac5..b22bb45828b2 100644
--- a/docs/streaming-programming-guide.md
+++ b/docs/streaming-programming-guide.md
@@ -76,16 +76,19 @@ Besides Spark's configuration, we specify that any DStream will be processed
 in 1 second batches.
 
 {% highlight scala %}
-// Create a StreamingContext with a SparkConf configuration
-val ssc = new StreamingContext(sparkConf, Seconds(1))
+import org.apache.spark.api.java.function._
+import org.apache.spark.streaming._
+import org.apache.spark.streaming.api._
+// Create a StreamingContext with a local master
+val ssc = new StreamingContext("local", "NetworkWordCount", Seconds(1))
 {% endhighlight %}
 
 Using this context, we then create a new DStream
 by specifying the IP address and port of the data server.
 
 {% highlight scala %}
-// Create a DStream that will connect to serverIP:serverPort
-val lines = ssc.socketTextStream(serverIP, serverPort)
+// Create a DStream that will connect to serverIP:serverPort, like localhost:9999
+val lines = ssc.socketTextStream("localhost", 9999)
 {% endhighlight %}
 
 This `lines` DStream represents the stream of data that will be received from the data
@@ -103,6 +106,7 @@ each line will be split into multiple words and the stream of words is represent
 `words` DStream.  Next, we want to count these words.
 
 {% highlight scala %}
+import org.apache.spark.streaming.StreamingContext._
 // Count each word in each batch
 val pairs = words.map(word => (word, 1))
 val wordCounts = pairs.reduceByKey(_ + _)
@@ -138,16 +142,20 @@ functionality. Besides Spark's configuration, we specify that any DStream would
 in 1 second batches.
 
 {% highlight java %}
-// Create a StreamingContext with a SparkConf configuration
-JavaStreamingContext jssc = StreamingContext(sparkConf, new Duration(1000))
+import org.apache.spark.api.java.function.*;
+import org.apache.spark.streaming.*;
+import org.apache.spark.streaming.api.java.*;
+import scala.Tuple2;
+// Create a StreamingContext with a local master
+JavaStreamingContext jssc = new JavaStreamingContext("local", "JavaNetworkWordCount", new Duration(1000))
 {% endhighlight %}
 
 Using this context, we then create a new DStream
 by specifying the IP address and port of the data server.
 
 {% highlight java %}
-// Create a DStream that will connect to serverIP:serverPort
-JavaDStream<String> lines = jssc.socketTextStream(serverIP, serverPort);
+// Create a DStream that will connect to serverIP:serverPort, like localhost:9999
+JavaDStream<String> lines = jssc.socketTextStream("localhost", 9999);
 {% endhighlight %}
 
 This `lines` DStream represents the stream of data that will be received from the data
@@ -159,7 +167,7 @@ space into words.
 JavaDStream<String> words = lines.flatMap(
   new FlatMapFunction<String, String>() {
     @Override public Iterable<String> call(String x) {
-      return Lists.newArrayList(x.split(" "));
+      return Arrays.asList(x.split(" "));
     }
   });
 {% endhighlight %}
@@ -359,7 +367,7 @@ as explained earlier. Finally, the last two parameters are needed to deploy your
  if running in distributed mode, as described in the
  [Spark programming guide](scala-programming-guide.html#deploying-code-on-a-cluster).
  Additionally, the underlying SparkContext can be accessed as
-`streamingContext.sparkContext`.
+`ssc.sparkContext`.
 
 The batch interval must be set based on the latency requirements of your application
 and available cluster resources. See the [Performance Tuning](#setting-the-right-batch-size)
@@ -399,7 +407,7 @@ These operations are discussed in detail in later sections.
 
 ## Input Sources
 
-We have already taken a look at the `streamingContext.socketTextStream(...)` in the [quick
+We have already taken a look at the `ssc.socketTextStream(...)` in the [quick
 example](#a-quick-example) which creates a DStream from text
 data received over a TCP socket connection. Besides sockets, the core Spark Streaming API provides
 methods for creating DStreams from files and Akka actors as input sources.
@@ -409,12 +417,12 @@ Specifically, for files, the DStream can be created as
 <div class="codetabs">
 <div data-lang="scala">
 {% highlight scala %}
-streamingContext.fileStream(dataDirectory)
+ssc.fileStream(dataDirectory)
 {% endhighlight %}
 </div>
 <div data-lang="java">
 {% highlight java %}
-javaStreamingContext.fileStream(dataDirectory);
+jssc.fileStream(dataDirectory);
 {% endhighlight %}
 </div>
 </div>
@@ -443,13 +451,13 @@ project dependencies, you can create a DStream from Kafka as
 <div data-lang="scala">
 {% highlight scala %}
 import org.apache.spark.streaming.kafka._
-KafkaUtils.createStream(streamingContext, kafkaParams, ...)
+KafkaUtils.createStream(ssc, kafkaParams, ...)
 {% endhighlight %}
 </div>
 <div data-lang="java">
 {% highlight java %}
-import org.apache.spark.streaming.kafka.*
-KafkaUtils.createStream(javaStreamingContext, kafkaParams, ...);
+import org.apache.spark.streaming.kafka.*;
+KafkaUtils.createStream(jssc, kafkaParams, ...);
 {% endhighlight %}
 </div>
 </div>
@@ -578,13 +586,14 @@ val runningCounts = pairs.updateStateByKey[Int](updateFunction _)
 <div data-lang="java" markdown="1">
 
 {% highlight java %}
+import com.google.common.base.Optional;
 Function2<List<Integer>, Optional<Integer>, Optional<Integer>> updateFunction =
   new Function2<List<Integer>, Optional<Integer>, Optional<Integer>>() {
     @Override public Optional<Integer> call(List<Integer> values, Optional<Integer> state) {
       Integer newSum = ...  // add the new values with the previous running count to get the new count
-      return Optional.of(newSum)
+      return Optional.of(newSum);
     }
-  }
+  };
 {% endhighlight %}
 
 This is applied on a DStream containing words (say, the `pairs` DStream containing `(word,
@@ -617,9 +626,9 @@ spam information (maybe generated with Spark as well) and then filtering based o
 <div data-lang="scala" markdown="1">
 
 {% highlight scala %}
-val spamInfoRDD = sparkContext.hadoopFile(...) // RDD containing spam information
+val spamInfoRDD = ssc.sparkContext.newAPIHadoopRDD(...) // RDD containing spam information
 
-val cleanedDStream = inputDStream.transform(rdd => {
+val cleanedDStream = wordCounts.transform(rdd => {
   rdd.join(spamInfoRDD).filter(...) // join data stream with spam information to do data cleaning
   ...
 })
@@ -629,13 +638,14 @@ val cleanedDStream = inputDStream.transform(rdd => {
 <div data-lang="java" markdown="1">
 
 {% highlight java %}
+import org.apache.spark.streaming.api.java.*;
 // RDD containing spam information
-JavaPairRDD<String, Double> spamInfoRDD = javaSparkContext.hadoopFile(...);
+final JavaPairRDD<String, Double> spamInfoRDD = jssc.sparkContext().newAPIHadoopRDD(...);
 
-JavaPairDStream<String, Integer> cleanedDStream = inputDStream.transform(
+JavaPairDStream<String, Integer> cleanedDStream = wordCounts.transform(
   new Function<JavaPairRDD<String, Integer>, JavaPairRDD<String, Integer>>() {
     @Override public JavaPairRDD<String, Integer> call(JavaPairRDD<String, Integer> rdd) throws Exception {
-      rdd.join(spamInfoRDD).filter(...) // join data stream with spam information to do data cleaning
+      rdd.join(spamInfoRDD).filter(...); // join data stream with spam information to do data cleaning
       ...
     }
   });
@@ -684,7 +694,7 @@ operation `reduceByKeyAndWindow`.
 
 {% highlight scala %}
 // Reduce last 30 seconds of data, every 10 seconds
-val windowedWordCounts = pairs.reduceByKeyAndWindow(_ + _, Seconds(30), Seconds(10))
+val windowedWordCounts = pairs.reduceByKeyAndWindow((a:Int,b:Int) => (a + b), Seconds(30), Seconds(10))
 {% endhighlight %}
 
 </div>
@@ -699,7 +709,7 @@ Function2<Integer, Integer, Integer> reduceFunc = new Function2<Integer, Integer
 };
 
 // Reduce last 30 seconds of data, every 10 seconds
-JavaPairDStream<String, Integer> windowedWordCounts = pair.reduceByKeyAndWindow(reduceFunc, new Duration(30000), new Duration(10000));
+JavaPairDStream<String, Integer> windowedWordCounts = pairs.reduceByKeyAndWindow(reduceFunc, new Duration(30000), new Duration(10000));
 {% endhighlight %}
 
 </div>
@@ -1087,7 +1097,7 @@ This behavior is made simple by using `JavaStreamingContext.getOrCreate`. This i
 {% highlight java %}
 // Create a factory object that can create a and setup a new JavaStreamingContext
 JavaStreamingContextFactory contextFactory = new JavaStreamingContextFactory() {
-  JavaStreamingContextFactory create() {
+  @Override public JavaStreamingContext create() {
     JavaStreamingContext jssc = new JavaStreamingContext(...);  // new context
     JavaDStream<String> lines = jssc.socketTextStream(...);     // create DStreams
     ...

From ce72c72aecdcf50e031f0dd1a5ef0e6dbf8f40b5 Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian.cs.zju@gmail.com>
Date: Sat, 3 May 2014 13:23:52 -0700
Subject: [PATCH 077/810] [Bugfix] Tachyon file cleanup logical error

Should lookup `shutdownDeleteTachyonPaths` instead of `shutdownDeletePaths`. Together with a minor style clean up: `find {...}.isDefined` to `exists {...}`.

Author: Cheng Lian <lian.cs.zju@gmail.com>

Closes #575 from liancheng/tachyonFix and squashes the following commits:

deb8f31 [Cheng Lian] Fixed logical error in when cleanup Tachyon files and minor style cleanup
---
 core/src/main/scala/org/apache/spark/util/Utils.scala | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala
index 536a740140c0..acd7eef6d25e 100644
--- a/core/src/main/scala/org/apache/spark/util/Utils.scala
+++ b/core/src/main/scala/org/apache/spark/util/Utils.scala
@@ -220,9 +220,9 @@ private[spark] object Utils extends Logging {
   def hasRootAsShutdownDeleteDir(file: File): Boolean = {
     val absolutePath = file.getAbsolutePath()
     val retval = shutdownDeletePaths.synchronized {
-      shutdownDeletePaths.find { path =>
+      shutdownDeletePaths.exists { path =>
         !absolutePath.equals(path) && absolutePath.startsWith(path)
-      }.isDefined
+      }
     }
     if (retval) {
       logInfo("path = " + file + ", already present as root for deletion.")
@@ -235,10 +235,10 @@ private[spark] object Utils extends Logging {
   // paths - resulting in Exception and incomplete cleanup.
   def hasRootAsShutdownDeleteDir(file: TachyonFile): Boolean = {
     val absolutePath = file.getPath()
-    val retval = shutdownDeletePaths.synchronized {
-      shutdownDeletePaths.find { path =>
+    val retval = shutdownDeleteTachyonPaths.synchronized {
+      shutdownDeleteTachyonPaths.exists { path =>
         !absolutePath.equals(path) && absolutePath.startsWith(path)
-      }.isDefined
+      }
     }
     if (retval) {
       logInfo("path = " + file + ", already present as root for deletion.")

From 34719ba32ed421701eaa08bd47ce953cd9267ad7 Mon Sep 17 00:00:00 2001
From: Aaron Davidson <aaron@databricks.com>
Date: Sat, 3 May 2014 13:27:10 -0700
Subject: [PATCH 078/810] SPARK-1689 AppClient should indicate app is dead()
 when removed

Previously, we indicated disconnected(), which keeps the application in a limbo state where it has no executors but thinks it will get them soon.

This is a bug fix that hopefully can be included in 1.0.

Author: Aaron Davidson <aaron@databricks.com>

Closes #605 from aarondav/appremoved and squashes the following commits:

bea02a2 [Aaron Davidson] SPARK-1689 AppClient should indicate app is dead() when removed
---
 .../org/apache/spark/deploy/client/AppClient.scala   | 12 +++++-------
 .../spark/deploy/client/AppClientListener.scala      |  4 ++--
 .../org/apache/spark/deploy/client/TestClient.scala  |  4 ++--
 .../cluster/SparkDeploySchedulerBackend.scala        |  6 +++---
 4 files changed, 12 insertions(+), 14 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/client/AppClient.scala b/core/src/main/scala/org/apache/spark/deploy/client/AppClient.scala
index 8901806de926..57085fc33714 100644
--- a/core/src/main/scala/org/apache/spark/deploy/client/AppClient.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/client/AppClient.scala
@@ -92,8 +92,7 @@ private[spark] class AppClient(
           if (registered) {
             retryTimer.cancel()
           } else if (retries >= REGISTRATION_RETRIES) {
-            logError("All masters are unresponsive! Giving up.")
-            markDead()
+            markDead("All masters are unresponsive! Giving up.")
           } else {
             tryRegisterAllMasters()
           }
@@ -126,8 +125,7 @@ private[spark] class AppClient(
         listener.connected(appId)
 
       case ApplicationRemoved(message) =>
-        logError("Master removed our application: %s; stopping client".format(message))
-        markDisconnected()
+        markDead("Master removed our application: %s".format(message))
         context.stop(self)
 
       case ExecutorAdded(id: Int, workerId: String, hostPort: String, cores: Int, memory: Int) =>
@@ -158,7 +156,7 @@ private[spark] class AppClient(
         logWarning(s"Could not connect to $address: $cause")
 
       case StopAppClient =>
-        markDead()
+        markDead("Application has been stopped.")
         sender ! true
         context.stop(self)
     }
@@ -173,9 +171,9 @@ private[spark] class AppClient(
       }
     }
 
-    def markDead() {
+    def markDead(reason: String) {
       if (!alreadyDead) {
-        listener.dead()
+        listener.dead(reason)
         alreadyDead = true
       }
     }
diff --git a/core/src/main/scala/org/apache/spark/deploy/client/AppClientListener.scala b/core/src/main/scala/org/apache/spark/deploy/client/AppClientListener.scala
index 1f20aa3dfa39..e584952a9ad8 100644
--- a/core/src/main/scala/org/apache/spark/deploy/client/AppClientListener.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/client/AppClientListener.scala
@@ -30,8 +30,8 @@ private[spark] trait AppClientListener {
   /** Disconnection may be a temporary state, as we fail over to a new Master. */
   def disconnected(): Unit
 
-  /** Dead means that we couldn't find any Masters to connect to, and have given up. */
-  def dead(): Unit
+  /** An application death is an unrecoverable failure condition. */
+  def dead(reason: String): Unit
 
   def executorAdded(fullId: String, workerId: String, hostPort: String, cores: Int, memory: Int)
 
diff --git a/core/src/main/scala/org/apache/spark/deploy/client/TestClient.scala b/core/src/main/scala/org/apache/spark/deploy/client/TestClient.scala
index 888dd45e93c6..e15a87bd38fd 100644
--- a/core/src/main/scala/org/apache/spark/deploy/client/TestClient.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/client/TestClient.scala
@@ -33,8 +33,8 @@ private[spark] object TestClient {
       System.exit(0)
     }
 
-    def dead() {
-      logInfo("Could not connect to master")
+    def dead(reason: String) {
+      logInfo("Application died with error: " + reason)
       System.exit(0)
     }
 
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala
index 9544ca05dca7..cefa41729964 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala
@@ -83,10 +83,10 @@ private[spark] class SparkDeploySchedulerBackend(
     }
   }
 
-  override def dead() {
+  override def dead(reason: String) {
     if (!stopping) {
-      logError("Spark cluster looks dead, giving up.")
-      scheduler.error("Spark cluster looks down")
+      logError("Application has been killed. Reason: " + reason)
+      scheduler.error(reason)
     }
   }
 

From 4669a84ab10296e01a5fbbae1de9574b793b7ed5 Mon Sep 17 00:00:00 2001
From: "Allan Douglas R. de Oliveira" <allan@chaordicsystems.com>
Date: Sat, 3 May 2014 16:52:19 -0700
Subject: [PATCH 079/810] EC2 configurable workers

Added option to configure number of worker instances and to set SPARK_MASTER_OPTS

Depends on: https://github.com/mesos/spark-ec2/pull/46

Author: Allan Douglas R. de Oliveira <allan@chaordicsystems.com>

Closes #612 from douglaz/ec2_configurable_workers and squashes the following commits:

d6c5d65 [Allan Douglas R. de Oliveira] Added master opts parameter
6c34671 [Allan Douglas R. de Oliveira] Use number of worker instances as string on template
ba528b9 [Allan Douglas R. de Oliveira] Added SPARK_WORKER_INSTANCES parameter
---
 ec2/deploy.generic/root/spark-ec2/ec2-variables.sh |  2 ++
 ec2/spark_ec2.py                                   | 12 ++++++++++--
 2 files changed, 12 insertions(+), 2 deletions(-)

diff --git a/ec2/deploy.generic/root/spark-ec2/ec2-variables.sh b/ec2/deploy.generic/root/spark-ec2/ec2-variables.sh
index 42e8faa26ed0..3570891be804 100644
--- a/ec2/deploy.generic/root/spark-ec2/ec2-variables.sh
+++ b/ec2/deploy.generic/root/spark-ec2/ec2-variables.sh
@@ -28,3 +28,5 @@ export SPARK_VERSION="{{spark_version}}"
 export SHARK_VERSION="{{shark_version}}"
 export HADOOP_MAJOR_VERSION="{{hadoop_major_version}}"
 export SWAP_MB="{{swap}}"
+export SPARK_WORKER_INSTANCES="{{spark_worker_instances}}"
+export SPARK_MASTER_OPTS="{{spark_master_opts}}"
diff --git a/ec2/spark_ec2.py b/ec2/spark_ec2.py
index 31209a662bbe..db393748a33b 100755
--- a/ec2/spark_ec2.py
+++ b/ec2/spark_ec2.py
@@ -103,6 +103,12 @@ def parse_args():
       help="When destroying a cluster, delete the security groups that were created")
   parser.add_option("--use-existing-master", action="store_true", default=False,
       help="Launch fresh slaves, but use an existing stopped master if possible")
+  parser.add_option("--worker-instances", type="int", default=1,
+      help="Number of instances per worker: variable SPARK_WORKER_INSTANCES (default: 1)")
+  parser.add_option("--master-opts", type="string", default="",
+      help="Extra options to give to master through SPARK_MASTER_OPTS variable (e.g -Dspark.worker.timeout=180)")
+
+
 
   (opts, args) = parser.parse_args()
   if len(args) != 2:
@@ -223,7 +229,7 @@ def launch_cluster(conn, opts, cluster_name):
     sys.exit(1)
   if opts.key_pair is None:
     print >> stderr, "ERROR: Must provide a key pair name (-k) to use on instances."
-    sys.exit(1)    
+    sys.exit(1)
   print "Setting up security groups..."
   master_group = get_or_make_group(conn, cluster_name + "-master")
   slave_group = get_or_make_group(conn, cluster_name + "-slaves")
@@ -551,7 +557,9 @@ def deploy_files(conn, root_dir, opts, master_nodes, slave_nodes, modules):
     "modules": '\n'.join(modules),
     "spark_version": spark_v,
     "shark_version": shark_v,
-    "hadoop_major_version": opts.hadoop_major_version
+    "hadoop_major_version": opts.hadoop_major_version,
+    "spark_worker_instances": "%d" % opts.worker_instances,
+    "spark_master_opts": opts.master_opts
   }
 
   # Create a temp directory in which we will place all the files to be

From b295714708476b2904e205ac6dc58867e205546e Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Sat, 3 May 2014 18:38:44 -0700
Subject: [PATCH 080/810] [SQL] Better logging when applying rules.

Author: Michael Armbrust <michael@databricks.com>

Closes #616 from marmbrus/ruleLogging and squashes the following commits:

39c09fe [Michael Armbrust] Fix off by one error.
5af3537 [Michael Armbrust] Better logging when applying rules.
---
 .../sql/catalyst/rules/RuleExecutor.scala     | 28 ++++++++++++++++---
 1 file changed, 24 insertions(+), 4 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/rules/RuleExecutor.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/rules/RuleExecutor.scala
index 9db96f89dd03..e32adb76fe14 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/rules/RuleExecutor.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/rules/RuleExecutor.scala
@@ -50,18 +50,18 @@ abstract class RuleExecutor[TreeType <: TreeNode[_]] extends Logging {
     var curPlan = plan
 
     batches.foreach { batch =>
+      val batchStartPlan = curPlan
       var iteration = 1
       var lastPlan = curPlan
-      curPlan = batch.rules.foldLeft(curPlan) { case (plan, rule) => rule(plan) }
+      var continue = true
 
       // Run until fix point (or the max number of iterations as specified in the strategy.
-      while (iteration < batch.strategy.maxIterations && !curPlan.fastEquals(lastPlan)) {
-        lastPlan = curPlan
+      while (continue) {
         curPlan = batch.rules.foldLeft(curPlan) {
           case (plan, rule) =>
             val result = rule(plan)
             if (!result.fastEquals(plan)) {
-              logger.debug(
+              logger.trace(
                 s"""
                   |=== Applying Rule ${rule.ruleName} ===
                   |${sideBySide(plan.treeString, result.treeString).mkString("\n")}
@@ -71,6 +71,26 @@ abstract class RuleExecutor[TreeType <: TreeNode[_]] extends Logging {
             result
         }
         iteration += 1
+        if (iteration > batch.strategy.maxIterations) {
+          logger.info(s"Max iterations ($iteration) reached for batch ${batch.name}")
+          continue = false
+        }
+
+        if (curPlan.fastEquals(lastPlan)) {
+          logger.trace(s"Fixed point reached for batch ${batch.name} after $iteration iterations.")
+          continue = false
+        }
+        lastPlan = curPlan
+      }
+
+      if (!batchStartPlan.fastEquals(curPlan)) {
+        logger.debug(
+          s"""
+          |=== Result of Batch ${batch.name} ===
+          |${sideBySide(plan.treeString, curPlan.treeString).mkString("\n")}
+        """.stripMargin)
+      } else {
+        logger.trace(s"Batch ${batch.name} has no effect.")
       }
     }
 

From 92b2902ca06eddd250bb0f50f365ed69f127a842 Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Sat, 3 May 2014 23:13:51 -0700
Subject: [PATCH 081/810] Whitelist Hive Tests

This is ready when Jenkins is.

Author: Michael Armbrust <michael@databricks.com>

Closes #596 from marmbrus/moreTests and squashes the following commits:

85be703 [Michael Armbrust] Blacklist MR required tests.
35bc311 [Michael Armbrust] Add hive golden answers.
ede98fd [Michael Armbrust] More hive gitignore
da096ea [Michael Armbrust] update whitelist
---
 .gitignore                                    |   1 +
 ...on_table-0-8e765b54f15b948fc88392da69da283 |   0
 ...n_table-1-aee4ce62fc2631423af0f569f4448353 |   0
 ...n_table-2-b1feb4a197caf28d5223e72e10a91e78 |   0
 ...ocation-0-1c3ace37d0bbb5c8033c48cde7680d21 |   1 +
 ...ncefile-0-7df8fcbfff1c967de72295b90c530776 |   0
 ...ncefile-1-3114fdebb1e9b0830de9e9c3fff8a67e |   0
 ...ncefile-2-80ec34a069bc561aa6dc87314391b131 |   4 ++
 ...ncefile-3-699522b1ff3f38cf6a75b06d83820b87 |   0
 ...ncefile-4-adc1ec67836b26b60d8547c4996bfd8f |  10 ++++
 ...at_text-0-c84258297070d93009fabc59c1f40a1e |   0
 ...at_text-1-6375f4ba7af19f94c6afc366dc75429e |   0
 ...at_text-2-80ec34a069bc561aa6dc87314391b131 |   4 ++
 ...at_text-3-699522b1ff3f38cf6a75b06d83820b87 |   0
 ...at_text-4-adc1ec67836b26b60d8547c4996bfd8f |  10 ++++
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 ...input15-1-86edc12357bf278d5e601a654358c32f |   0
 ...input15-2-ae5e0fbdf88ecca2c7d67df1ad141919 |   2 +
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 ...putddl2-2-7c1c0606b5ea8a1c90d46fe221993b57 |  10 ++++
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 ...putddl3-2-73f945a673d2e388847c317f683f160c |   2 +
 ...ry_data-4-3ebc340f7f63740f8534706d42dd37ca | Bin 0 -> 114 bytes
 ...terjoin-1-8c68ce10095a8924c68c8ee4b3c0071a |  22 ++++++++
 ...outerjoin-2-8e5d5472f2e214c091c879f6830a0c |   0
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 ...rderby-12-7943485bcc72b2040c45c62e45ac8853 |   0
 ...rderby-13-988591bf9ab008fdc4d71560aa57a736 |  32 ++++++++++++
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 ...orderby-2-be7423a5e77b7289153f74bc3fd97f44 |   0
 ...orderby-3-8ae9591fe39cd390619181e9664a92c1 |   1 +
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 ...orderby-5-a58344acc57aaa38e2cb5f11a0576681 |   1 +
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 ...orderby-7-7943485bcc72b2040c45c62e45ac8853 |   0
 ...orderby-8-988591bf9ab008fdc4d71560aa57a736 |  32 ++++++++++++
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 ...apjoin9-0-31cd5ae86d250c1c873260c4b73d2336 |   0
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 ..._format-1-d498fb503b8f47db4741fdad3d266b4a |   0
 ..._format-2-42119039bf8023f90b7f474f235c5dd5 |   1 +
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 ..._format-5-ef3052815ec41b5957627698ba06707b |   0
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 ..._format-8-fb2e7127e07ad9f7e97ad3df3eba3a35 |   0
 ..._format-9-433d5dbbcf75ff68d6897f31baa46841 |   1 +
 ...udf_hour-4-73bfac513b993dedbe143306865a44a |   1 +
 ..._substr-8-ba6ca6bac87ca99aabd60b8e76537ade |   1 +
 ..._substr-9-a9aa82080133620d017160f6a644455d |   1 +
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 ...union25-1-8f4a32f3c1551b4e4f22c3b776a92043 |   0
 ...union25-2-f61103bb2045761ba95828898e63b92b |   0
 .../execution/HiveCompatibilitySuite.scala    |  43 ++++++++++++++--
 87 files changed, 328 insertions(+), 4 deletions(-)
 create mode 100644 sql/hive/src/test/resources/golden/create_union_table-0-8e765b54f15b948fc88392da69da283
 create mode 100644 sql/hive/src/test/resources/golden/create_union_table-1-aee4ce62fc2631423af0f569f4448353
 create mode 100644 sql/hive/src/test/resources/golden/create_union_table-2-b1feb4a197caf28d5223e72e10a91e78
 create mode 100644 sql/hive/src/test/resources/golden/ctas_uses_database_location-0-1c3ace37d0bbb5c8033c48cde7680d21
 create mode 100644 sql/hive/src/test/resources/golden/fileformat_sequencefile-0-7df8fcbfff1c967de72295b90c530776
 create mode 100644 sql/hive/src/test/resources/golden/fileformat_sequencefile-1-3114fdebb1e9b0830de9e9c3fff8a67e
 create mode 100644 sql/hive/src/test/resources/golden/fileformat_sequencefile-2-80ec34a069bc561aa6dc87314391b131
 create mode 100644 sql/hive/src/test/resources/golden/fileformat_sequencefile-3-699522b1ff3f38cf6a75b06d83820b87
 create mode 100644 sql/hive/src/test/resources/golden/fileformat_sequencefile-4-adc1ec67836b26b60d8547c4996bfd8f
 create mode 100644 sql/hive/src/test/resources/golden/fileformat_text-0-c84258297070d93009fabc59c1f40a1e
 create mode 100644 sql/hive/src/test/resources/golden/fileformat_text-1-6375f4ba7af19f94c6afc366dc75429e
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 create mode 100644 sql/hive/src/test/resources/golden/input15-0-1570712216ce86428ee39974242ae961
 create mode 100644 sql/hive/src/test/resources/golden/input15-1-86edc12357bf278d5e601a654358c32f
 create mode 100644 sql/hive/src/test/resources/golden/input15-2-ae5e0fbdf88ecca2c7d67df1ad141919
 create mode 100644 sql/hive/src/test/resources/golden/inputddl1-0-52f161c97a2a02494d26ee0737b28364
 create mode 100644 sql/hive/src/test/resources/golden/inputddl1-1-dfe05b7247284b326f39481b3d66a0c3
 create mode 100644 sql/hive/src/test/resources/golden/inputddl1-2-b516db5218781d89aebd2e4813ea3660
 create mode 100644 sql/hive/src/test/resources/golden/inputddl2-0-4519aea06db13ec6780f60b4d101d262
 create mode 100644 sql/hive/src/test/resources/golden/inputddl2-1-f526507cae3cad09c69d20c7f0e36abe
 create mode 100644 sql/hive/src/test/resources/golden/inputddl2-2-7c1c0606b5ea8a1c90d46fe221993b57
 create mode 100644 sql/hive/src/test/resources/golden/inputddl3-0-a14253f6c752c9f5e9f56875152f8353
 create mode 100644 sql/hive/src/test/resources/golden/inputddl3-1-f066827dd440213f306ab7d680bc8526
 create mode 100644 sql/hive/src/test/resources/golden/inputddl3-2-73f945a673d2e388847c317f683f160c
 create mode 100644 sql/hive/src/test/resources/golden/load_binary_data-4-3ebc340f7f63740f8534706d42dd37ca
 create mode 100644 sql/hive/src/test/resources/golden/mapjoin_filter_on_outerjoin-1-8c68ce10095a8924c68c8ee4b3c0071a
 create mode 100644 sql/hive/src/test/resources/golden/mapjoin_filter_on_outerjoin-2-8e5d5472f2e214c091c879f6830a0c
 create mode 100644 sql/hive/src/test/resources/golden/mapjoin_filter_on_outerjoin-3-381cc29f131d8caba744dd9fe6c121e7
 create mode 100644 sql/hive/src/test/resources/golden/mapjoin_filter_on_outerjoin-4-ce1ef910fff98f174931cc641f7cef3a
 create mode 100644 sql/hive/src/test/resources/golden/mapjoin_filter_on_outerjoin-5-82cbc3186de23f3a2411e9ab87c0008c
 create mode 100644 sql/hive/src/test/resources/golden/mapjoin_filter_on_outerjoin-6-c6844de37be0926316f5fbf36a905306
 create mode 100644 sql/hive/src/test/resources/golden/parallel_orderby-0-3ca1c197c5209d2fc9423fe84ad66e7d
 create mode 100644 sql/hive/src/test/resources/golden/parallel_orderby-1-be7423a5e77b7289153f74bc3fd97f44
 create mode 100644 sql/hive/src/test/resources/golden/parallel_orderby-10-767848077fc2929ccedfd28e50564b19
 create mode 100644 sql/hive/src/test/resources/golden/parallel_orderby-11-39767502cbda75590c0c4b8fd089b793
 create mode 100644 sql/hive/src/test/resources/golden/parallel_orderby-12-7943485bcc72b2040c45c62e45ac8853
 create mode 100644 sql/hive/src/test/resources/golden/parallel_orderby-13-988591bf9ab008fdc4d71560aa57a736
 create mode 100644 sql/hive/src/test/resources/golden/parallel_orderby-14-d93951df2ffc18dc09ab0ba2d46f1823
 create mode 100644 sql/hive/src/test/resources/golden/parallel_orderby-2-be7423a5e77b7289153f74bc3fd97f44
 create mode 100644 sql/hive/src/test/resources/golden/parallel_orderby-3-8ae9591fe39cd390619181e9664a92c1
 create mode 100644 sql/hive/src/test/resources/golden/parallel_orderby-4-a928b93138e9c5547c40ff2024b2b4b6
 create mode 100644 sql/hive/src/test/resources/golden/parallel_orderby-5-a58344acc57aaa38e2cb5f11a0576681
 create mode 100644 sql/hive/src/test/resources/golden/parallel_orderby-6-670ec1b1f28d92d72a924c29d622aa8f
 create mode 100644 sql/hive/src/test/resources/golden/parallel_orderby-7-7943485bcc72b2040c45c62e45ac8853
 create mode 100644 sql/hive/src/test/resources/golden/parallel_orderby-8-988591bf9ab008fdc4d71560aa57a736
 create mode 100644 sql/hive/src/test/resources/golden/parallel_orderby-9-d93951df2ffc18dc09ab0ba2d46f1823
 create mode 100644 sql/hive/src/test/resources/golden/smb_mapjoin9-0-31cd5ae86d250c1c873260c4b73d2336
 create mode 100644 sql/hive/src/test/resources/golden/smb_mapjoin9-1-c5c86975c400b3a1ec0f522b75635338
 create mode 100644 sql/hive/src/test/resources/golden/smb_mapjoin9-10-ffe97dc8c1df3195982e38263fbe8717
 create mode 100644 sql/hive/src/test/resources/golden/smb_mapjoin9-11-43d53504df013e6b35f81811138a167a
 create mode 100644 sql/hive/src/test/resources/golden/smb_mapjoin9-12-7cccbdffc32975f8935eeba14a28147
 create mode 100644 sql/hive/src/test/resources/golden/smb_mapjoin9-13-d0eac0de35b5a7595e4567edec8e555d
 create mode 100644 sql/hive/src/test/resources/golden/smb_mapjoin9-14-f0de4fd75ca13f379fbea16010dfd3f9
 create mode 100644 sql/hive/src/test/resources/golden/smb_mapjoin9-15-b89ea2173180c8ae423d856f943e061f
 create mode 100644 sql/hive/src/test/resources/golden/smb_mapjoin9-16-5645429104736d72293601247e874df7
 create mode 100644 sql/hive/src/test/resources/golden/smb_mapjoin9-17-1018a99eaede08dc2b427d3fb4b91d6b
 create mode 100644 sql/hive/src/test/resources/golden/smb_mapjoin9-18-bda1fd3b5d0326dc3a41a8fb480b6c1c
 create mode 100644 sql/hive/src/test/resources/golden/smb_mapjoin9-19-823e8f68baaa45d6b761b9b9890bb902
 create mode 100644 sql/hive/src/test/resources/golden/smb_mapjoin9-2-3b0f76816be2c1b18a2058027a19bc9f
 create mode 100644 sql/hive/src/test/resources/golden/smb_mapjoin9-20-78ea4515eba2d8a79bb6895133a82051
 create mode 100644 sql/hive/src/test/resources/golden/smb_mapjoin9-3-86473a0498e4361e4db0b4a22f2e8571
 create mode 100644 sql/hive/src/test/resources/golden/smb_mapjoin9-4-b89ea2173180c8ae423d856f943e061f
 create mode 100644 sql/hive/src/test/resources/golden/smb_mapjoin9-5-94bc4acde9b9c9944e2eb9a9c57d3450
 create mode 100644 sql/hive/src/test/resources/golden/smb_mapjoin9-6-6c0963a1bdf2def39b79bea21a9cdda9
 create mode 100644 sql/hive/src/test/resources/golden/smb_mapjoin9-7-ba9bc547b5d2a4912f4f8567fea4009c
 create mode 100644 sql/hive/src/test/resources/golden/smb_mapjoin9-8-d528ff47621b8c86b370f72512a7cd3c
 create mode 100644 sql/hive/src/test/resources/golden/smb_mapjoin9-9-e39f59c35ebbe686a18d45d9d8bf3ab0
 create mode 100644 sql/hive/src/test/resources/golden/symlink_text_input_format-0-3b2fa9592648fc07c0d43e0d7d7f9411
 create mode 100644 sql/hive/src/test/resources/golden/symlink_text_input_format-1-d498fb503b8f47db4741fdad3d266b4a
 create mode 100644 sql/hive/src/test/resources/golden/symlink_text_input_format-2-42119039bf8023f90b7f474f235c5dd5
 create mode 100644 sql/hive/src/test/resources/golden/symlink_text_input_format-3-77b57147024eb6b28cc9f525fdaab615
 create mode 100644 sql/hive/src/test/resources/golden/symlink_text_input_format-4-676cb274a770a6b9ca86df5dc7f912d4
 create mode 100644 sql/hive/src/test/resources/golden/symlink_text_input_format-5-ef3052815ec41b5957627698ba06707b
 create mode 100644 sql/hive/src/test/resources/golden/symlink_text_input_format-6-891be0baec05e358a647dcca77724446
 create mode 100644 sql/hive/src/test/resources/golden/symlink_text_input_format-7-208bcc9c918cbeb52907c8871be19cd5
 create mode 100644 sql/hive/src/test/resources/golden/symlink_text_input_format-8-fb2e7127e07ad9f7e97ad3df3eba3a35
 create mode 100644 sql/hive/src/test/resources/golden/symlink_text_input_format-9-433d5dbbcf75ff68d6897f31baa46841
 create mode 100644 sql/hive/src/test/resources/golden/udf_hour-4-73bfac513b993dedbe143306865a44a
 create mode 100644 sql/hive/src/test/resources/golden/udf_substr-8-ba6ca6bac87ca99aabd60b8e76537ade
 create mode 100644 sql/hive/src/test/resources/golden/udf_substr-9-a9aa82080133620d017160f6a644455d
 create mode 100644 sql/hive/src/test/resources/golden/union25-0-f4e883ee4edf8fbb95efed8ae41cff1c
 create mode 100644 sql/hive/src/test/resources/golden/union25-1-8f4a32f3c1551b4e4f22c3b776a92043
 create mode 100644 sql/hive/src/test/resources/golden/union25-2-f61103bb2045761ba95828898e63b92b

diff --git a/.gitignore b/.gitignore
index a204456d4208..32b603f1bc84 100644
--- a/.gitignore
+++ b/.gitignore
@@ -54,3 +54,4 @@ scalastyle.txt
 metastore_db/
 metastore/
 warehouse/
+TempStatsStore/
diff --git a/sql/hive/src/test/resources/golden/create_union_table-0-8e765b54f15b948fc88392da69da283 b/sql/hive/src/test/resources/golden/create_union_table-0-8e765b54f15b948fc88392da69da283
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/create_union_table-1-aee4ce62fc2631423af0f569f4448353 b/sql/hive/src/test/resources/golden/create_union_table-1-aee4ce62fc2631423af0f569f4448353
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/create_union_table-2-b1feb4a197caf28d5223e72e10a91e78 b/sql/hive/src/test/resources/golden/create_union_table-2-b1feb4a197caf28d5223e72e10a91e78
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/ctas_uses_database_location-0-1c3ace37d0bbb5c8033c48cde7680d21 b/sql/hive/src/test/resources/golden/ctas_uses_database_location-0-1c3ace37d0bbb5c8033c48cde7680d21
new file mode 100644
index 000000000000..573541ac9702
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/ctas_uses_database_location-0-1c3ace37d0bbb5c8033c48cde7680d21
@@ -0,0 +1 @@
+0
diff --git a/sql/hive/src/test/resources/golden/fileformat_sequencefile-0-7df8fcbfff1c967de72295b90c530776 b/sql/hive/src/test/resources/golden/fileformat_sequencefile-0-7df8fcbfff1c967de72295b90c530776
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/fileformat_sequencefile-1-3114fdebb1e9b0830de9e9c3fff8a67e b/sql/hive/src/test/resources/golden/fileformat_sequencefile-1-3114fdebb1e9b0830de9e9c3fff8a67e
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/fileformat_sequencefile-2-80ec34a069bc561aa6dc87314391b131 b/sql/hive/src/test/resources/golden/fileformat_sequencefile-2-80ec34a069bc561aa6dc87314391b131
new file mode 100644
index 000000000000..6280b32facd6
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/fileformat_sequencefile-2-80ec34a069bc561aa6dc87314391b131
@@ -0,0 +1,4 @@
+key                 	int                 	None                
+value               	string              	None                
+	 	 
+Detailed Table Information	Table(tableName:dest1, dbName:default, owner:marmbrus, createTime:1398823397, lastAccessTime:0, retention:0, sd:StorageDescriptor(cols:[FieldSchema(name:key, type:int, comment:null), FieldSchema(name:value, type:string, comment:null)], location:file:/private/var/folders/36/cjkbrr953xg2p_krwrmn8h_r0000gn/T/sparkHiveWarehouse6323689881248298063/dest1, inputFormat:org.apache.hadoop.mapred.SequenceFileInputFormat, outputFormat:org.apache.hadoop.hive.ql.io.HiveSequenceFileOutputFormat, compressed:false, numBuckets:-1, serdeInfo:SerDeInfo(name:null, serializationLib:org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe, parameters:{serialization.format=1}), bucketCols:[], sortCols:[], parameters:{}, skewedInfo:SkewedInfo(skewedColNames:[], skewedColValues:[], skewedColValueLocationMaps:{}), storedAsSubDirectories:false), partitionKeys:[], parameters:{transient_lastDdlTime=1398823397}, viewOriginalText:null, viewExpandedText:null, tableType:MANAGED_TABLE)	
diff --git a/sql/hive/src/test/resources/golden/fileformat_sequencefile-3-699522b1ff3f38cf6a75b06d83820b87 b/sql/hive/src/test/resources/golden/fileformat_sequencefile-3-699522b1ff3f38cf6a75b06d83820b87
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/fileformat_sequencefile-4-adc1ec67836b26b60d8547c4996bfd8f b/sql/hive/src/test/resources/golden/fileformat_sequencefile-4-adc1ec67836b26b60d8547c4996bfd8f
new file mode 100644
index 000000000000..e614f37ecc8b
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/fileformat_sequencefile-4-adc1ec67836b26b60d8547c4996bfd8f
@@ -0,0 +1,10 @@
+0	val_0
+4	val_4
+8	val_8
+0	val_0
+0	val_0
+5	val_5
+5	val_5
+2	val_2
+5	val_5
+9	val_9
diff --git a/sql/hive/src/test/resources/golden/fileformat_text-0-c84258297070d93009fabc59c1f40a1e b/sql/hive/src/test/resources/golden/fileformat_text-0-c84258297070d93009fabc59c1f40a1e
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/fileformat_text-1-6375f4ba7af19f94c6afc366dc75429e b/sql/hive/src/test/resources/golden/fileformat_text-1-6375f4ba7af19f94c6afc366dc75429e
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/fileformat_text-2-80ec34a069bc561aa6dc87314391b131 b/sql/hive/src/test/resources/golden/fileformat_text-2-80ec34a069bc561aa6dc87314391b131
new file mode 100644
index 000000000000..e793ec2f946e
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/fileformat_text-2-80ec34a069bc561aa6dc87314391b131
@@ -0,0 +1,4 @@
+key                 	int                 	None                
+value               	string              	None                
+	 	 
+Detailed Table Information	Table(tableName:dest1, dbName:default, owner:marmbrus, createTime:1398823407, lastAccessTime:0, retention:0, sd:StorageDescriptor(cols:[FieldSchema(name:key, type:int, comment:null), FieldSchema(name:value, type:string, comment:null)], location:file:/private/var/folders/36/cjkbrr953xg2p_krwrmn8h_r0000gn/T/sparkHiveWarehouse6323689881248298063/dest1, inputFormat:org.apache.hadoop.mapred.TextInputFormat, outputFormat:org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat, compressed:false, numBuckets:-1, serdeInfo:SerDeInfo(name:null, serializationLib:org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe, parameters:{serialization.format=1}), bucketCols:[], sortCols:[], parameters:{}, skewedInfo:SkewedInfo(skewedColNames:[], skewedColValues:[], skewedColValueLocationMaps:{}), storedAsSubDirectories:false), partitionKeys:[], parameters:{transient_lastDdlTime=1398823407}, viewOriginalText:null, viewExpandedText:null, tableType:MANAGED_TABLE)	
diff --git a/sql/hive/src/test/resources/golden/fileformat_text-3-699522b1ff3f38cf6a75b06d83820b87 b/sql/hive/src/test/resources/golden/fileformat_text-3-699522b1ff3f38cf6a75b06d83820b87
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/fileformat_text-4-adc1ec67836b26b60d8547c4996bfd8f b/sql/hive/src/test/resources/golden/fileformat_text-4-adc1ec67836b26b60d8547c4996bfd8f
new file mode 100644
index 000000000000..e614f37ecc8b
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/fileformat_text-4-adc1ec67836b26b60d8547c4996bfd8f
@@ -0,0 +1,10 @@
+0	val_0
+4	val_4
+8	val_8
+0	val_0
+0	val_0
+5	val_5
+5	val_5
+2	val_2
+5	val_5
+9	val_9
diff --git a/sql/hive/src/test/resources/golden/input15-0-1570712216ce86428ee39974242ae961 b/sql/hive/src/test/resources/golden/input15-0-1570712216ce86428ee39974242ae961
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/input15-1-86edc12357bf278d5e601a654358c32f b/sql/hive/src/test/resources/golden/input15-1-86edc12357bf278d5e601a654358c32f
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/input15-2-ae5e0fbdf88ecca2c7d67df1ad141919 b/sql/hive/src/test/resources/golden/input15-2-ae5e0fbdf88ecca2c7d67df1ad141919
new file mode 100644
index 000000000000..ded361eb294f
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/input15-2-ae5e0fbdf88ecca2c7d67df1ad141919
@@ -0,0 +1,2 @@
+key                 	int                 	None                
+value               	string              	None                
diff --git a/sql/hive/src/test/resources/golden/inputddl1-0-52f161c97a2a02494d26ee0737b28364 b/sql/hive/src/test/resources/golden/inputddl1-0-52f161c97a2a02494d26ee0737b28364
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/inputddl1-1-dfe05b7247284b326f39481b3d66a0c3 b/sql/hive/src/test/resources/golden/inputddl1-1-dfe05b7247284b326f39481b3d66a0c3
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/inputddl1-2-b516db5218781d89aebd2e4813ea3660 b/sql/hive/src/test/resources/golden/inputddl1-2-b516db5218781d89aebd2e4813ea3660
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/inputddl2-0-4519aea06db13ec6780f60b4d101d262 b/sql/hive/src/test/resources/golden/inputddl2-0-4519aea06db13ec6780f60b4d101d262
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/inputddl2-1-f526507cae3cad09c69d20c7f0e36abe b/sql/hive/src/test/resources/golden/inputddl2-1-f526507cae3cad09c69d20c7f0e36abe
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/inputddl2-2-7c1c0606b5ea8a1c90d46fe221993b57 b/sql/hive/src/test/resources/golden/inputddl2-2-7c1c0606b5ea8a1c90d46fe221993b57
new file mode 100644
index 000000000000..679d54cb5cb5
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/inputddl2-2-7c1c0606b5ea8a1c90d46fe221993b57
@@ -0,0 +1,10 @@
+key                 	int                 	None                
+value               	string              	None                
+ds                  	string              	None                
+country             	string              	None                
+	 	 
+# Partition Information	 	 
+# col_name            	data_type           	comment             
+	 	 
+ds                  	string              	None                
+country             	string              	None                
diff --git a/sql/hive/src/test/resources/golden/inputddl3-0-a14253f6c752c9f5e9f56875152f8353 b/sql/hive/src/test/resources/golden/inputddl3-0-a14253f6c752c9f5e9f56875152f8353
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/inputddl3-1-f066827dd440213f306ab7d680bc8526 b/sql/hive/src/test/resources/golden/inputddl3-1-f066827dd440213f306ab7d680bc8526
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/inputddl3-2-73f945a673d2e388847c317f683f160c b/sql/hive/src/test/resources/golden/inputddl3-2-73f945a673d2e388847c317f683f160c
new file mode 100644
index 000000000000..ded361eb294f
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/inputddl3-2-73f945a673d2e388847c317f683f160c
@@ -0,0 +1,2 @@
+key                 	int                 	None                
+value               	string              	None                
diff --git a/sql/hive/src/test/resources/golden/load_binary_data-4-3ebc340f7f63740f8534706d42dd37ca b/sql/hive/src/test/resources/golden/load_binary_data-4-3ebc340f7f63740f8534706d42dd37ca
new file mode 100644
index 0000000000000000000000000000000000000000..de5212a3c320f772b6a3a2b246202cd99d07c012
GIT binary patch
literal 114
zcmXYpK@xx<3<DEy>1zZO;W_B|0{Q<~&`YzM%(UUWxDW~|hJHUCips`;RC1i<INDUF
c%~AVr0KXzDL<68h$w;br!hhG{pu+0#0#j=g(EtDd

literal 0
HcmV?d00001

diff --git a/sql/hive/src/test/resources/golden/mapjoin_filter_on_outerjoin-1-8c68ce10095a8924c68c8ee4b3c0071a b/sql/hive/src/test/resources/golden/mapjoin_filter_on_outerjoin-1-8c68ce10095a8924c68c8ee4b3c0071a
new file mode 100644
index 000000000000..f81251e1360f
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/mapjoin_filter_on_outerjoin-1-8c68ce10095a8924c68c8ee4b3c0071a
@@ -0,0 +1,22 @@
+NULL	NULL	66	val_66	66	val_66
+NULL	NULL	98	val_98	98	val_98
+NULL	NULL	98	val_98	98	val_98
+NULL	NULL	128		128	val_128
+NULL	NULL	128		128	val_128
+NULL	NULL	128		128	val_128
+NULL	NULL	146	val_146	146	val_146
+NULL	NULL	146	val_146	146	val_146
+NULL	NULL	150	val_150	150	val_150
+NULL	NULL	213	val_213	213	val_213
+NULL	NULL	213	val_213	213	val_213
+NULL	NULL	224		224	val_224
+NULL	NULL	224		224	val_224
+NULL	NULL	238	val_238	238	val_238
+NULL	NULL	238	val_238	238	val_238
+NULL	NULL	255	val_255	255	val_255
+NULL	NULL	255	val_255	255	val_255
+NULL	NULL	273	val_273	273	val_273
+NULL	NULL	273	val_273	273	val_273
+NULL	NULL	273	val_273	273	val_273
+NULL	NULL	278	val_278	278	val_278
+NULL	NULL	278	val_278	278	val_278
diff --git a/sql/hive/src/test/resources/golden/mapjoin_filter_on_outerjoin-2-8e5d5472f2e214c091c879f6830a0c b/sql/hive/src/test/resources/golden/mapjoin_filter_on_outerjoin-2-8e5d5472f2e214c091c879f6830a0c
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/mapjoin_filter_on_outerjoin-3-381cc29f131d8caba744dd9fe6c121e7 b/sql/hive/src/test/resources/golden/mapjoin_filter_on_outerjoin-3-381cc29f131d8caba744dd9fe6c121e7
new file mode 100644
index 000000000000..f81251e1360f
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/mapjoin_filter_on_outerjoin-3-381cc29f131d8caba744dd9fe6c121e7
@@ -0,0 +1,22 @@
+NULL	NULL	66	val_66	66	val_66
+NULL	NULL	98	val_98	98	val_98
+NULL	NULL	98	val_98	98	val_98
+NULL	NULL	128		128	val_128
+NULL	NULL	128		128	val_128
+NULL	NULL	128		128	val_128
+NULL	NULL	146	val_146	146	val_146
+NULL	NULL	146	val_146	146	val_146
+NULL	NULL	150	val_150	150	val_150
+NULL	NULL	213	val_213	213	val_213
+NULL	NULL	213	val_213	213	val_213
+NULL	NULL	224		224	val_224
+NULL	NULL	224		224	val_224
+NULL	NULL	238	val_238	238	val_238
+NULL	NULL	238	val_238	238	val_238
+NULL	NULL	255	val_255	255	val_255
+NULL	NULL	255	val_255	255	val_255
+NULL	NULL	273	val_273	273	val_273
+NULL	NULL	273	val_273	273	val_273
+NULL	NULL	273	val_273	273	val_273
+NULL	NULL	278	val_278	278	val_278
+NULL	NULL	278	val_278	278	val_278
diff --git a/sql/hive/src/test/resources/golden/mapjoin_filter_on_outerjoin-4-ce1ef910fff98f174931cc641f7cef3a b/sql/hive/src/test/resources/golden/mapjoin_filter_on_outerjoin-4-ce1ef910fff98f174931cc641f7cef3a
new file mode 100644
index 000000000000..573541ac9702
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/mapjoin_filter_on_outerjoin-4-ce1ef910fff98f174931cc641f7cef3a
@@ -0,0 +1 @@
+0
diff --git a/sql/hive/src/test/resources/golden/mapjoin_filter_on_outerjoin-5-82cbc3186de23f3a2411e9ab87c0008c b/sql/hive/src/test/resources/golden/mapjoin_filter_on_outerjoin-5-82cbc3186de23f3a2411e9ab87c0008c
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/mapjoin_filter_on_outerjoin-6-c6844de37be0926316f5fbf36a905306 b/sql/hive/src/test/resources/golden/mapjoin_filter_on_outerjoin-6-c6844de37be0926316f5fbf36a905306
new file mode 100644
index 000000000000..f81251e1360f
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/mapjoin_filter_on_outerjoin-6-c6844de37be0926316f5fbf36a905306
@@ -0,0 +1,22 @@
+NULL	NULL	66	val_66	66	val_66
+NULL	NULL	98	val_98	98	val_98
+NULL	NULL	98	val_98	98	val_98
+NULL	NULL	128		128	val_128
+NULL	NULL	128		128	val_128
+NULL	NULL	128		128	val_128
+NULL	NULL	146	val_146	146	val_146
+NULL	NULL	146	val_146	146	val_146
+NULL	NULL	150	val_150	150	val_150
+NULL	NULL	213	val_213	213	val_213
+NULL	NULL	213	val_213	213	val_213
+NULL	NULL	224		224	val_224
+NULL	NULL	224		224	val_224
+NULL	NULL	238	val_238	238	val_238
+NULL	NULL	238	val_238	238	val_238
+NULL	NULL	255	val_255	255	val_255
+NULL	NULL	255	val_255	255	val_255
+NULL	NULL	273	val_273	273	val_273
+NULL	NULL	273	val_273	273	val_273
+NULL	NULL	273	val_273	273	val_273
+NULL	NULL	278	val_278	278	val_278
+NULL	NULL	278	val_278	278	val_278
diff --git a/sql/hive/src/test/resources/golden/parallel_orderby-0-3ca1c197c5209d2fc9423fe84ad66e7d b/sql/hive/src/test/resources/golden/parallel_orderby-0-3ca1c197c5209d2fc9423fe84ad66e7d
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/parallel_orderby-1-be7423a5e77b7289153f74bc3fd97f44 b/sql/hive/src/test/resources/golden/parallel_orderby-1-be7423a5e77b7289153f74bc3fd97f44
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/parallel_orderby-10-767848077fc2929ccedfd28e50564b19 b/sql/hive/src/test/resources/golden/parallel_orderby-10-767848077fc2929ccedfd28e50564b19
new file mode 100644
index 000000000000..573541ac9702
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/parallel_orderby-10-767848077fc2929ccedfd28e50564b19
@@ -0,0 +1 @@
+0
diff --git a/sql/hive/src/test/resources/golden/parallel_orderby-11-39767502cbda75590c0c4b8fd089b793 b/sql/hive/src/test/resources/golden/parallel_orderby-11-39767502cbda75590c0c4b8fd089b793
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/parallel_orderby-12-7943485bcc72b2040c45c62e45ac8853 b/sql/hive/src/test/resources/golden/parallel_orderby-12-7943485bcc72b2040c45c62e45ac8853
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/parallel_orderby-13-988591bf9ab008fdc4d71560aa57a736 b/sql/hive/src/test/resources/golden/parallel_orderby-13-988591bf9ab008fdc4d71560aa57a736
new file mode 100644
index 000000000000..b431d3fc6dcf
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/parallel_orderby-13-988591bf9ab008fdc4d71560aa57a736
@@ -0,0 +1,32 @@
+# col_name            	data_type           	comment             
+	 	 
+key                 	string              	None                
+value               	string              	None                
+	 	 
+# Detailed Table Information	 	 
+Database:           	default             	 
+Owner:              	marmbrus            	 
+CreateTime:         	Tue Apr 29 20:55:07 PDT 2014	 
+LastAccessTime:     	UNKNOWN             	 
+Protect Mode:       	None                	 
+Retention:          	0                   	 
+Location:           	file:/private/var/folders/36/cjkbrr953xg2p_krwrmn8h_r0000gn/T/sparkHiveWarehouse6323689881248298063/total_ordered	 
+Table Type:         	MANAGED_TABLE       	 
+Table Parameters:	 	 
+	numFiles            	1                   
+	numPartitions       	0                   
+	numRows             	48                  
+	rawDataSize         	512                 
+	totalSize           	560                 
+	transient_lastDdlTime	1398830107          
+	 	 
+# Storage Information	 	 
+SerDe Library:      	org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe	 
+InputFormat:        	org.apache.hadoop.mapred.TextInputFormat	 
+OutputFormat:       	org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat	 
+Compressed:         	No                  	 
+Num Buckets:        	-1                  	 
+Bucket Columns:     	[]                  	 
+Sort Columns:       	[]                  	 
+Storage Desc Params:	 	 
+	serialization.format	1                   
diff --git a/sql/hive/src/test/resources/golden/parallel_orderby-14-d93951df2ffc18dc09ab0ba2d46f1823 b/sql/hive/src/test/resources/golden/parallel_orderby-14-d93951df2ffc18dc09ab0ba2d46f1823
new file mode 100644
index 000000000000..3b733e2d6c45
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/parallel_orderby-14-d93951df2ffc18dc09ab0ba2d46f1823
@@ -0,0 +1,48 @@
+128	val_128
+128	val_128
+150	val_150
+150	val_150
+165	val_165
+165	val_165
+193	val_193
+193	val_193
+213	val_213
+213	val_213
+213	val_213
+213	val_213
+213	val_214
+213	val_214
+224	val_224
+224	val_224
+238	val_238
+238	val_238
+238	val_239
+238	val_239
+238	val_240
+238	val_240
+255	val_255
+255	val_255
+265	val_265
+265	val_265
+27	val_27
+27	val_27
+273	val_273
+273	val_273
+278	val_278
+278	val_278
+311	val_311
+311	val_311
+369	val_369
+369	val_369
+401	val_401
+401	val_401
+409	val_409
+409	val_409
+484	val_484
+484	val_484
+66	val_66
+66	val_66
+86	val_86
+86	val_86
+98	val_98
+98	val_98
diff --git a/sql/hive/src/test/resources/golden/parallel_orderby-2-be7423a5e77b7289153f74bc3fd97f44 b/sql/hive/src/test/resources/golden/parallel_orderby-2-be7423a5e77b7289153f74bc3fd97f44
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/parallel_orderby-3-8ae9591fe39cd390619181e9664a92c1 b/sql/hive/src/test/resources/golden/parallel_orderby-3-8ae9591fe39cd390619181e9664a92c1
new file mode 100644
index 000000000000..573541ac9702
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/parallel_orderby-3-8ae9591fe39cd390619181e9664a92c1
@@ -0,0 +1 @@
+0
diff --git a/sql/hive/src/test/resources/golden/parallel_orderby-4-a928b93138e9c5547c40ff2024b2b4b6 b/sql/hive/src/test/resources/golden/parallel_orderby-4-a928b93138e9c5547c40ff2024b2b4b6
new file mode 100644
index 000000000000..573541ac9702
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/parallel_orderby-4-a928b93138e9c5547c40ff2024b2b4b6
@@ -0,0 +1 @@
+0
diff --git a/sql/hive/src/test/resources/golden/parallel_orderby-5-a58344acc57aaa38e2cb5f11a0576681 b/sql/hive/src/test/resources/golden/parallel_orderby-5-a58344acc57aaa38e2cb5f11a0576681
new file mode 100644
index 000000000000..573541ac9702
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/parallel_orderby-5-a58344acc57aaa38e2cb5f11a0576681
@@ -0,0 +1 @@
+0
diff --git a/sql/hive/src/test/resources/golden/parallel_orderby-6-670ec1b1f28d92d72a924c29d622aa8f b/sql/hive/src/test/resources/golden/parallel_orderby-6-670ec1b1f28d92d72a924c29d622aa8f
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/parallel_orderby-7-7943485bcc72b2040c45c62e45ac8853 b/sql/hive/src/test/resources/golden/parallel_orderby-7-7943485bcc72b2040c45c62e45ac8853
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/parallel_orderby-8-988591bf9ab008fdc4d71560aa57a736 b/sql/hive/src/test/resources/golden/parallel_orderby-8-988591bf9ab008fdc4d71560aa57a736
new file mode 100644
index 000000000000..7f67251e6178
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/parallel_orderby-8-988591bf9ab008fdc4d71560aa57a736
@@ -0,0 +1,32 @@
+# col_name            	data_type           	comment             
+	 	 
+key                 	string              	None                
+value               	string              	None                
+	 	 
+# Detailed Table Information	 	 
+Database:           	default             	 
+Owner:              	marmbrus            	 
+CreateTime:         	Tue Apr 29 20:54:55 PDT 2014	 
+LastAccessTime:     	UNKNOWN             	 
+Protect Mode:       	None                	 
+Retention:          	0                   	 
+Location:           	file:/private/var/folders/36/cjkbrr953xg2p_krwrmn8h_r0000gn/T/sparkHiveWarehouse6323689881248298063/total_ordered	 
+Table Type:         	MANAGED_TABLE       	 
+Table Parameters:	 	 
+	numFiles            	1                   
+	numPartitions       	0                   
+	numRows             	48                  
+	rawDataSize         	512                 
+	totalSize           	560                 
+	transient_lastDdlTime	1398830095          
+	 	 
+# Storage Information	 	 
+SerDe Library:      	org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe	 
+InputFormat:        	org.apache.hadoop.mapred.TextInputFormat	 
+OutputFormat:       	org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat	 
+Compressed:         	No                  	 
+Num Buckets:        	-1                  	 
+Bucket Columns:     	[]                  	 
+Sort Columns:       	[]                  	 
+Storage Desc Params:	 	 
+	serialization.format	1                   
diff --git a/sql/hive/src/test/resources/golden/parallel_orderby-9-d93951df2ffc18dc09ab0ba2d46f1823 b/sql/hive/src/test/resources/golden/parallel_orderby-9-d93951df2ffc18dc09ab0ba2d46f1823
new file mode 100644
index 000000000000..3b733e2d6c45
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/parallel_orderby-9-d93951df2ffc18dc09ab0ba2d46f1823
@@ -0,0 +1,48 @@
+128	val_128
+128	val_128
+150	val_150
+150	val_150
+165	val_165
+165	val_165
+193	val_193
+193	val_193
+213	val_213
+213	val_213
+213	val_213
+213	val_213
+213	val_214
+213	val_214
+224	val_224
+224	val_224
+238	val_238
+238	val_238
+238	val_239
+238	val_239
+238	val_240
+238	val_240
+255	val_255
+255	val_255
+265	val_265
+265	val_265
+27	val_27
+27	val_27
+273	val_273
+273	val_273
+278	val_278
+278	val_278
+311	val_311
+311	val_311
+369	val_369
+369	val_369
+401	val_401
+401	val_401
+409	val_409
+409	val_409
+484	val_484
+484	val_484
+66	val_66
+66	val_66
+86	val_86
+86	val_86
+98	val_98
+98	val_98
diff --git a/sql/hive/src/test/resources/golden/smb_mapjoin9-0-31cd5ae86d250c1c873260c4b73d2336 b/sql/hive/src/test/resources/golden/smb_mapjoin9-0-31cd5ae86d250c1c873260c4b73d2336
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/smb_mapjoin9-1-c5c86975c400b3a1ec0f522b75635338 b/sql/hive/src/test/resources/golden/smb_mapjoin9-1-c5c86975c400b3a1ec0f522b75635338
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/smb_mapjoin9-10-ffe97dc8c1df3195982e38263fbe8717 b/sql/hive/src/test/resources/golden/smb_mapjoin9-10-ffe97dc8c1df3195982e38263fbe8717
new file mode 100644
index 000000000000..573541ac9702
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/smb_mapjoin9-10-ffe97dc8c1df3195982e38263fbe8717
@@ -0,0 +1 @@
+0
diff --git a/sql/hive/src/test/resources/golden/smb_mapjoin9-11-43d53504df013e6b35f81811138a167a b/sql/hive/src/test/resources/golden/smb_mapjoin9-11-43d53504df013e6b35f81811138a167a
new file mode 100644
index 000000000000..573541ac9702
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/smb_mapjoin9-11-43d53504df013e6b35f81811138a167a
@@ -0,0 +1 @@
+0
diff --git a/sql/hive/src/test/resources/golden/smb_mapjoin9-12-7cccbdffc32975f8935eeba14a28147 b/sql/hive/src/test/resources/golden/smb_mapjoin9-12-7cccbdffc32975f8935eeba14a28147
new file mode 100644
index 000000000000..573541ac9702
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/smb_mapjoin9-12-7cccbdffc32975f8935eeba14a28147
@@ -0,0 +1 @@
+0
diff --git a/sql/hive/src/test/resources/golden/smb_mapjoin9-13-d0eac0de35b5a7595e4567edec8e555d b/sql/hive/src/test/resources/golden/smb_mapjoin9-13-d0eac0de35b5a7595e4567edec8e555d
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/smb_mapjoin9-14-f0de4fd75ca13f379fbea16010dfd3f9 b/sql/hive/src/test/resources/golden/smb_mapjoin9-14-f0de4fd75ca13f379fbea16010dfd3f9
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/smb_mapjoin9-15-b89ea2173180c8ae423d856f943e061f b/sql/hive/src/test/resources/golden/smb_mapjoin9-15-b89ea2173180c8ae423d856f943e061f
new file mode 100644
index 000000000000..573541ac9702
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/smb_mapjoin9-15-b89ea2173180c8ae423d856f943e061f
@@ -0,0 +1 @@
+0
diff --git a/sql/hive/src/test/resources/golden/smb_mapjoin9-16-5645429104736d72293601247e874df7 b/sql/hive/src/test/resources/golden/smb_mapjoin9-16-5645429104736d72293601247e874df7
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/smb_mapjoin9-17-1018a99eaede08dc2b427d3fb4b91d6b b/sql/hive/src/test/resources/golden/smb_mapjoin9-17-1018a99eaede08dc2b427d3fb4b91d6b
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/smb_mapjoin9-18-bda1fd3b5d0326dc3a41a8fb480b6c1c b/sql/hive/src/test/resources/golden/smb_mapjoin9-18-bda1fd3b5d0326dc3a41a8fb480b6c1c
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/smb_mapjoin9-19-823e8f68baaa45d6b761b9b9890bb902 b/sql/hive/src/test/resources/golden/smb_mapjoin9-19-823e8f68baaa45d6b761b9b9890bb902
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/smb_mapjoin9-2-3b0f76816be2c1b18a2058027a19bc9f b/sql/hive/src/test/resources/golden/smb_mapjoin9-2-3b0f76816be2c1b18a2058027a19bc9f
new file mode 100644
index 000000000000..573541ac9702
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/smb_mapjoin9-2-3b0f76816be2c1b18a2058027a19bc9f
@@ -0,0 +1 @@
+0
diff --git a/sql/hive/src/test/resources/golden/smb_mapjoin9-20-78ea4515eba2d8a79bb6895133a82051 b/sql/hive/src/test/resources/golden/smb_mapjoin9-20-78ea4515eba2d8a79bb6895133a82051
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/smb_mapjoin9-3-86473a0498e4361e4db0b4a22f2e8571 b/sql/hive/src/test/resources/golden/smb_mapjoin9-3-86473a0498e4361e4db0b4a22f2e8571
new file mode 100644
index 000000000000..573541ac9702
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/smb_mapjoin9-3-86473a0498e4361e4db0b4a22f2e8571
@@ -0,0 +1 @@
+0
diff --git a/sql/hive/src/test/resources/golden/smb_mapjoin9-4-b89ea2173180c8ae423d856f943e061f b/sql/hive/src/test/resources/golden/smb_mapjoin9-4-b89ea2173180c8ae423d856f943e061f
new file mode 100644
index 000000000000..573541ac9702
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/smb_mapjoin9-4-b89ea2173180c8ae423d856f943e061f
@@ -0,0 +1 @@
+0
diff --git a/sql/hive/src/test/resources/golden/smb_mapjoin9-5-94bc4acde9b9c9944e2eb9a9c57d3450 b/sql/hive/src/test/resources/golden/smb_mapjoin9-5-94bc4acde9b9c9944e2eb9a9c57d3450
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/smb_mapjoin9-6-6c0963a1bdf2def39b79bea21a9cdda9 b/sql/hive/src/test/resources/golden/smb_mapjoin9-6-6c0963a1bdf2def39b79bea21a9cdda9
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/smb_mapjoin9-7-ba9bc547b5d2a4912f4f8567fea4009c b/sql/hive/src/test/resources/golden/smb_mapjoin9-7-ba9bc547b5d2a4912f4f8567fea4009c
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/smb_mapjoin9-8-d528ff47621b8c86b370f72512a7cd3c b/sql/hive/src/test/resources/golden/smb_mapjoin9-8-d528ff47621b8c86b370f72512a7cd3c
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/smb_mapjoin9-9-e39f59c35ebbe686a18d45d9d8bf3ab0 b/sql/hive/src/test/resources/golden/smb_mapjoin9-9-e39f59c35ebbe686a18d45d9d8bf3ab0
new file mode 100644
index 000000000000..573541ac9702
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/smb_mapjoin9-9-e39f59c35ebbe686a18d45d9d8bf3ab0
@@ -0,0 +1 @@
+0
diff --git a/sql/hive/src/test/resources/golden/symlink_text_input_format-0-3b2fa9592648fc07c0d43e0d7d7f9411 b/sql/hive/src/test/resources/golden/symlink_text_input_format-0-3b2fa9592648fc07c0d43e0d7d7f9411
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/symlink_text_input_format-1-d498fb503b8f47db4741fdad3d266b4a b/sql/hive/src/test/resources/golden/symlink_text_input_format-1-d498fb503b8f47db4741fdad3d266b4a
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/symlink_text_input_format-2-42119039bf8023f90b7f474f235c5dd5 b/sql/hive/src/test/resources/golden/symlink_text_input_format-2-42119039bf8023f90b7f474f235c5dd5
new file mode 100644
index 000000000000..3a2e3f4984a0
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/symlink_text_input_format-2-42119039bf8023f90b7f474f235c5dd5
@@ -0,0 +1 @@
+-1
diff --git a/sql/hive/src/test/resources/golden/symlink_text_input_format-3-77b57147024eb6b28cc9f525fdaab615 b/sql/hive/src/test/resources/golden/symlink_text_input_format-3-77b57147024eb6b28cc9f525fdaab615
new file mode 100644
index 000000000000..3a2e3f4984a0
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/symlink_text_input_format-3-77b57147024eb6b28cc9f525fdaab615
@@ -0,0 +1 @@
+-1
diff --git a/sql/hive/src/test/resources/golden/symlink_text_input_format-4-676cb274a770a6b9ca86df5dc7f912d4 b/sql/hive/src/test/resources/golden/symlink_text_input_format-4-676cb274a770a6b9ca86df5dc7f912d4
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/symlink_text_input_format-5-ef3052815ec41b5957627698ba06707b b/sql/hive/src/test/resources/golden/symlink_text_input_format-5-ef3052815ec41b5957627698ba06707b
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/symlink_text_input_format-6-891be0baec05e358a647dcca77724446 b/sql/hive/src/test/resources/golden/symlink_text_input_format-6-891be0baec05e358a647dcca77724446
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/symlink_text_input_format-7-208bcc9c918cbeb52907c8871be19cd5 b/sql/hive/src/test/resources/golden/symlink_text_input_format-7-208bcc9c918cbeb52907c8871be19cd5
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/symlink_text_input_format-8-fb2e7127e07ad9f7e97ad3df3eba3a35 b/sql/hive/src/test/resources/golden/symlink_text_input_format-8-fb2e7127e07ad9f7e97ad3df3eba3a35
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/symlink_text_input_format-9-433d5dbbcf75ff68d6897f31baa46841 b/sql/hive/src/test/resources/golden/symlink_text_input_format-9-433d5dbbcf75ff68d6897f31baa46841
new file mode 100644
index 000000000000..573541ac9702
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/symlink_text_input_format-9-433d5dbbcf75ff68d6897f31baa46841
@@ -0,0 +1 @@
+0
diff --git a/sql/hive/src/test/resources/golden/udf_hour-4-73bfac513b993dedbe143306865a44a b/sql/hive/src/test/resources/golden/udf_hour-4-73bfac513b993dedbe143306865a44a
new file mode 100644
index 000000000000..b1bd38b62a08
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/udf_hour-4-73bfac513b993dedbe143306865a44a
@@ -0,0 +1 @@
+13
diff --git a/sql/hive/src/test/resources/golden/udf_substr-8-ba6ca6bac87ca99aabd60b8e76537ade b/sql/hive/src/test/resources/golden/udf_substr-8-ba6ca6bac87ca99aabd60b8e76537ade
new file mode 100644
index 000000000000..c14d8d4279c5
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/udf_substr-8-ba6ca6bac87ca99aabd60b8e76537ade
@@ -0,0 +1 @@
+NULL	NULL	NULL	NULL	NULL	A	AB	ABC	ABC	A	AB	ABC	ABC	B	BC	BC	BC	C	C	C	C		C	C	C	C	B	BC	BC	BC	A	AB	ABC	ABC	
diff --git a/sql/hive/src/test/resources/golden/udf_substr-9-a9aa82080133620d017160f6a644455d b/sql/hive/src/test/resources/golden/udf_substr-9-a9aa82080133620d017160f6a644455d
new file mode 100644
index 000000000000..94a57d86c88f
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/udf_substr-9-a9aa82080133620d017160f6a644455d
@@ -0,0 +1 @@
+玩	玩	玩玩玩 abc	玩玩玩
diff --git a/sql/hive/src/test/resources/golden/union25-0-f4e883ee4edf8fbb95efed8ae41cff1c b/sql/hive/src/test/resources/golden/union25-0-f4e883ee4edf8fbb95efed8ae41cff1c
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/union25-1-8f4a32f3c1551b4e4f22c3b776a92043 b/sql/hive/src/test/resources/golden/union25-1-8f4a32f3c1551b4e4f22c3b776a92043
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/union25-2-f61103bb2045761ba95828898e63b92b b/sql/hive/src/test/resources/golden/union25-2-f61103bb2045761ba95828898e63b92b
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
index 6c08e63a3949..d83732b51e9c 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
@@ -112,6 +112,8 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     "stats1.*",
     "stats20",
     "alter_merge_stats",
+    "columnstats.*",
+
 
     // Hive seems to think 1.0 > NaN = true && 1.0 < NaN = false... which is wrong.
     // http://stackoverflow.com/a/1573715
@@ -176,6 +178,7 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
    */
   override def whiteList = Seq(
     "add_part_exist",
+    "add_part_multiple",
     "add_partition_no_whitelist",
     "add_partition_with_whitelist",
     "alias_casted_column",
@@ -290,6 +293,7 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     "disable_file_format_check",
     "drop_function",
     "drop_index",
+    "drop_multi_partitions",
     "drop_partitions_filter",
     "drop_partitions_filter2",
     "drop_partitions_filter3",
@@ -302,6 +306,8 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     "escape_orderby1",
     "escape_sortby1",
     "fetch_aggregation",
+    "fileformat_sequencefile",
+    "fileformat_text",
     "filter_join_breaktask",
     "filter_join_breaktask2",
     "groupby1",
@@ -310,6 +316,10 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     "groupby1_map_nomap",
     "groupby1_map_skew",
     "groupby1_noskew",
+    "groupby2",
+    "groupby2_map",
+    "groupby2_map_skew",
+    "groupby2_noskew",
     "groupby4",
     "groupby4_map",
     "groupby4_map_skew",
@@ -333,10 +343,12 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     "groupby8_noskew",
     "groupby9",
     "groupby_distinct_samekey",
+    "groupby_map_ppr",
     "groupby_multi_insert_common_distinct",
     "groupby_multi_single_reducer2",
     "groupby_mutli_insert_common_distinct",
     "groupby_neg_float",
+    "groupby_ppr",
     "groupby_sort_10",
     "groupby_sort_2",
     "groupby_sort_3",
@@ -352,13 +364,17 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     "inoutdriver",
     "input",
     "input0",
+    "input1",
+    "input10",
     "input11",
     "input11_limit",
     "input12",
     "input12_hadoop20",
     "input14",
+    "input15",
     "input19",
     "input1_limit",
+    "input2",
     "input21",
     "input22",
     "input23",
@@ -367,6 +383,8 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     "input26",
     "input28",
     "input2_limit",
+    "input3",
+    "input4",
     "input40",
     "input41",
     "input4_cb_delim",
@@ -374,9 +392,6 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     "input7",
     "input8",
     "input9",
-    "inputddl4",
-    "inputddl7",
-    "inputddl8",
     "input_limit",
     "input_part0",
     "input_part1",
@@ -391,6 +406,13 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     "input_part8",
     "input_part9",
     "input_testsequencefile",
+    "inputddl1",
+    "inputddl2",
+    "inputddl3",
+    "inputddl4",
+    "inputddl6",
+    "inputddl7",
+    "inputddl8",
     "insert1",
     "insert2_overwrite_partitions",
     "insert_compressed",
@@ -452,7 +474,9 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     "join_reorder4",
     "join_star",
     "join_view",
+    "lateral_view",
     "lateral_view_cp",
+    "lateral_view_outer",
     "lateral_view_ppd",
     "lineage1",
     "literal_double",
@@ -463,6 +487,7 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     "loadpart1",
     "louter_join_ppr",
     "mapjoin_distinct",
+    "mapjoin_filter_on_outerjoin",
     "mapjoin_mapjoin",
     "mapjoin_subquery",
     "mapjoin_subquery2",
@@ -577,6 +602,7 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     "skewjoinopt13",
     "skewjoinopt18",
     "skewjoinopt9",
+    "smb_mapjoin9",
     "smb_mapjoin_1",
     "smb_mapjoin_10",
     "smb_mapjoin_13",
@@ -621,8 +647,11 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     "udf_10_trims",
     "udf2",
     "udf6",
+    "udf7",
     "udf8",
     "udf9",
+    "udf_E",
+    "udf_PI",
     "udf_abs",
     "udf_acos",
     "udf_add",
@@ -646,6 +675,7 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     "udf_ceil",
     "udf_ceiling",
     "udf_concat",
+    "udf_concat_insert1",
     "udf_concat_insert2",
     "udf_concat_ws",
     "udf_conv",
@@ -660,6 +690,7 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     "udf_div",
     "udf_double",
     "udf_E",
+    "udf_elt",
     "udf_exp",
     "udf_field",
     "udf_find_in_set",
@@ -669,9 +700,11 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     "udf_from_unixtime",
     "udf_greaterthan",
     "udf_greaterthanorequal",
+    "udf_hash",
     "udf_hex",
     "udf_if",
     "udf_index",
+    "udf_instr",
     "udf_int",
     "udf_isnotnull",
     "udf_isnull",
@@ -682,6 +715,7 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     "udf_lessthanorequal",
     "udf_like",
     "udf_ln",
+    "udf_locate",
     "udf_log",
     "udf_log10",
     "udf_log2",
@@ -740,9 +774,9 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     "udf_trim",
     "udf_ucase",
     "udf_upper",
-    "udf_variance",
     "udf_var_pop",
     "udf_var_samp",
+    "udf_variance",
     "udf_weekofyear",
     "udf_when",
     "udf_xpath",
@@ -768,6 +802,7 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     "union22",
     "union23",
     "union24",
+    "union25",
     "union26",
     "union27",
     "union28",

From fb0543224bcedb8ae3aab4a7ddcc6111a03378fe Mon Sep 17 00:00:00 2001
From: witgo <witgo@qq.com>
Date: Sat, 3 May 2014 23:32:12 -0700
Subject: [PATCH 082/810] The default version of yarn is equal to the hadoop
 version

This is a part of [PR 590](https://github.com/apache/spark/pull/590)

Author: witgo <witgo@qq.com>

Closes #626 from witgo/yarn_version and squashes the following commits:

c390631 [witgo] restore  the yarn dependency declarations
f8a4ad8 [witgo] revert remove the dependency of avro in yarn-alpha
2df6cf5 [witgo] review commit
a1d876a [witgo] review commit
20e7e3e [witgo] review commit
c76763b [witgo] The default value of yarn.version is equal to hadoop.version
---
 bin/compute-classpath.sh    |  1 +
 docs/building-with-maven.md |  9 ++++++---
 pom.xml                     | 15 +++++++++------
 yarn/pom.xml                |  5 ++---
 4 files changed, 18 insertions(+), 12 deletions(-)

diff --git a/bin/compute-classpath.sh b/bin/compute-classpath.sh
index b0218531e9eb..70ac71e45db4 100755
--- a/bin/compute-classpath.sh
+++ b/bin/compute-classpath.sh
@@ -44,6 +44,7 @@ if [ -f "$ASSEMBLY_DIR"/spark-assembly*hadoop*-deps.jar ]; then
   CLASSPATH="$CLASSPATH:$FWDIR/sql/catalyst/target/scala-$SCALA_VERSION/classes"
   CLASSPATH="$CLASSPATH:$FWDIR/sql/core/target/scala-$SCALA_VERSION/classes"
   CLASSPATH="$CLASSPATH:$FWDIR/sql/hive/target/scala-$SCALA_VERSION/classes"
+  CLASSPATH="$CLASSPATH:$FWDIR/yarn/stable/target/scala-$SCALA_VERSION/classes"
 
   DEPS_ASSEMBLY_JAR=`ls "$ASSEMBLY_DIR"/spark-assembly*hadoop*-deps.jar`
   CLASSPATH="$CLASSPATH:$DEPS_ASSEMBLY_JAR"
diff --git a/docs/building-with-maven.md b/docs/building-with-maven.md
index a5e530346740..e447dfea3bac 100644
--- a/docs/building-with-maven.md
+++ b/docs/building-with-maven.md
@@ -45,17 +45,20 @@ For Apache Hadoop versions 1.x, Cloudera CDH MRv1, and other Hadoop versions wit
 For Apache Hadoop 2.x, 0.23.x, Cloudera CDH MRv2, and other Hadoop versions with YARN, you can enable the "yarn-alpha" or "yarn" profile and set the "hadoop.version", "yarn.version" property. Note that Hadoop 0.23.X requires a special `-Phadoop-0.23` profile:
 
     # Apache Hadoop 2.0.5-alpha
-    $ mvn -Pyarn-alpha -Dhadoop.version=2.0.5-alpha -Dyarn.version=2.0.5-alpha -DskipTests clean package
+    $ mvn -Pyarn-alpha -Dhadoop.version=2.0.5-alpha -DskipTests clean package
 
     # Cloudera CDH 4.2.0 with MapReduce v2
-    $ mvn -Pyarn-alpha -Dhadoop.version=2.0.0-cdh4.2.0 -Dyarn.version=2.0.0-cdh4.2.0 -DskipTests clean package
+    $ mvn -Pyarn-alpha -Dhadoop.version=2.0.0-cdh4.2.0 -DskipTests clean package
 
     # Apache Hadoop 2.2.X (e.g. 2.2.0 as below) and newer
-    $ mvn -Pyarn -Dhadoop.version=2.2.0 -Dyarn.version=2.2.0 -DskipTests clean package
+    $ mvn -Pyarn -Dhadoop.version=2.2.0 -DskipTests clean package
 
     # Apache Hadoop 0.23.x
     $ mvn -Pyarn-alpha -Phadoop-0.23 -Dhadoop.version=0.23.7 -Dyarn.version=0.23.7 -DskipTests clean package
 
+    # Different versions of HDFS and YARN.
+    $ mvn -Pyarn-alpha -Dhadoop.version=2.3.0 -Dyarn.version=0.23.7 -DskipTests clean package
+
 ## Spark Tests in Maven ##
 
 Tests are run by default via the [ScalaTest Maven plugin](http://www.scalatest.org/user_guide/using_the_scalatest_maven_plugin). Some of the require Spark to be packaged first, so always run `mvn package` with `-DskipTests` the first time. You can then run the tests with `mvn -Dhadoop.version=... test`.
diff --git a/pom.xml b/pom.xml
index 646753fe3030..ebd359a9de17 100644
--- a/pom.xml
+++ b/pom.xml
@@ -16,7 +16,8 @@
   ~ limitations under the License.
   -->
 
-<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
+<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
   <modelVersion>4.0.0</modelVersion>
   <parent>
     <groupId>org.apache</groupId>
@@ -119,7 +120,7 @@
     <log4j.version>1.2.17</log4j.version>
     <hadoop.version>1.0.4</hadoop.version>
     <protobuf.version>2.4.1</protobuf.version>
-    <yarn.version>0.23.7</yarn.version>
+    <yarn.version>${hadoop.version}</yarn.version>
     <hbase.version>0.94.6</hbase.version>
     <hive.version>0.12.0</hive.version>
     <parquet.version>1.3.2</parquet.version>
@@ -135,7 +136,8 @@
 
   <repositories>
     <repository>
-      <id>maven-repo</id> <!-- This should be at top, it makes maven try the central repo first and then others and hence faster dep resolution -->
+      <id>maven-repo</id>
+      <!-- This should be at top, it makes maven try the central repo first and then others and hence faster dep resolution -->
       <name>Maven Repository</name>
       <!-- HTTPS is unavailable for Maven Central -->
       <url>http://repo.maven.apache.org/maven2</url>
@@ -847,15 +849,16 @@
         <hadoop.version>0.23.7</hadoop.version>
         <!--<hadoop.version>2.0.5-alpha</hadoop.version> -->
       </properties>
-      <modules>
-        <module>yarn</module>
-      </modules>
       <dependencies>
         <dependency>
           <groupId>org.apache.avro</groupId>
           <artifactId>avro</artifactId>
         </dependency>
       </dependencies>
+      <modules>
+        <module>yarn</module>
+      </modules>
+
     </profile>
 
     <!-- Ganglia integration is not included by default due to LGPL-licensed code -->
diff --git a/yarn/pom.xml b/yarn/pom.xml
index 02f36627431b..e0094738266b 100644
--- a/yarn/pom.xml
+++ b/yarn/pom.xml
@@ -28,7 +28,7 @@
   <artifactId>yarn-parent_2.10</artifactId>
   <packaging>pom</packaging>
   <name>Spark Project YARN Parent POM</name>
-  
+
   <dependencies>
     <dependency>
       <groupId>org.apache.spark</groupId>
@@ -50,7 +50,6 @@
     <dependency>
       <groupId>org.apache.hadoop</groupId>
       <artifactId>hadoop-client</artifactId>
-      <version>${yarn.version}</version>
     </dependency>
     <dependency>
       <groupId>org.scalatest</groupId>
@@ -128,7 +127,7 @@
               <target>
                 <property name="spark.classpath" refid="maven.test.classpath" />
                 <property environment="env" />
-                <fail message="Please set the SCALA_HOME (or SCALA_LIBRARY_PATH if scala is on the path) environment variables and retry.">  
+                <fail message="Please set the SCALA_HOME (or SCALA_LIBRARY_PATH if scala is on the path) environment variables and retry.">
                   <condition>
                     <not>
                       <or>

From e97a2e6717e75c70c5474f2d211682565909e557 Mon Sep 17 00:00:00 2001
From: Rahul Singhal <rahul.singhal@guavus.com>
Date: Sun, 4 May 2014 11:08:39 -0700
Subject: [PATCH 083/810] SPARK-1658: Correctly identify if maven is installed
 and working

The current test is checking the exit code of "tail" rather than "mvn".
This new check will make sure that mvn is installed and was able to
execute the "version command".

Author: Rahul Singhal <rahul.singhal@guavus.com>

Closes #580 from rahulsinghaliitd/SPARK-1658 and squashes the following commits:

83c0313 [Rahul Singhal] SPARK-1658: Correctly identify if maven is installed and working
bf821b9 [Rahul Singhal] SPARK-1658: Correctly identify if maven is installed and working
---
 make-distribution.sh | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/make-distribution.sh b/make-distribution.sh
index c05dcd89d90a..dc8aa56a2e97 100755
--- a/make-distribution.sh
+++ b/make-distribution.sh
@@ -43,8 +43,9 @@
 FWDIR="$(cd `dirname $0`; pwd)"
 DISTDIR="$FWDIR/dist"
 
-VERSION=$(mvn help:evaluate -Dexpression=project.version | grep -v "INFO" | tail -n 1)
-if [ $? == -1 ] ;then
+set -o pipefail
+VERSION=$(mvn help:evaluate -Dexpression=project.version 2>/dev/null | grep -v "INFO" | tail -n 1)
+if [ $? != 0 ]; then
     echo -e "You need Maven installed to build Spark."
     echo -e "Download Maven from https://maven.apache.org."
     exit -1;

From 0088cede592540f35f9aec0f24dc1d9bd690d878 Mon Sep 17 00:00:00 2001
From: Sean Owen <sowen@cloudera.com>
Date: Sun, 4 May 2014 11:55:29 -0700
Subject: [PATCH 084/810] SPARK-1663. (Addendum) Fix signature of one version
 of JavaPairRDDStream.reduceByKeyAndWindow()

It appears that one of these methods doesn't use `org.apache.spark.api.java.function.Function2` like all the others, but uses Scala's `Function2`.

Author: Sean Owen <sowen@cloudera.com>

Closes #633 from srowen/SPARK-1663.2 and squashes the following commits:

1e0232d [Sean Owen] Fix signature of one version of reduceByKeyAndWindow to use Java API Function2, as apparently intended
---
 .../org/apache/spark/streaming/api/java/JavaPairDStream.scala   | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaPairDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaPairDStream.scala
index cb8e8f00a7b8..c4bdf01fa374 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaPairDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaPairDStream.scala
@@ -262,7 +262,7 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])(
    * @param windowDuration width of the window; must be a multiple of this DStream's
    *                       batching interval
    */
-  def reduceByKeyAndWindow(reduceFunc: Function2[V, V, V], windowDuration: Duration)
+  def reduceByKeyAndWindow(reduceFunc: JFunction2[V, V, V], windowDuration: Duration)
   :JavaPairDStream[K, V] = {
     dstream.reduceByKeyAndWindow(reduceFunc, windowDuration)
   }

From 0c98a8f6a761b941a9e0cf6fde6df8d0b8d24057 Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Sun, 4 May 2014 12:22:23 -0700
Subject: [PATCH 085/810] SPARK-1703 Warn users if Spark is run on JRE6 but
 compiled with JDK7.

This add some guards and good warning messages if users hit this issue. /cc @aarondav with whom I discussed parts of the design.

Author: Patrick Wendell <pwendell@gmail.com>

Closes #627 from pwendell/jdk6 and squashes the following commits:

a38a958 [Patrick Wendell] Code review feedback
94e9f84 [Patrick Wendell] SPARK-1703 Warn users if Spark is run on JRE6 but compiled with JDK7.
---
 bin/compute-classpath.sh | 14 ++++++++++++++
 bin/spark-class          |  9 ++++++++-
 make-distribution.sh     | 14 ++++++++++++++
 3 files changed, 36 insertions(+), 1 deletion(-)

diff --git a/bin/compute-classpath.sh b/bin/compute-classpath.sh
index 70ac71e45db4..8dc547b379f5 100755
--- a/bin/compute-classpath.sh
+++ b/bin/compute-classpath.sh
@@ -32,6 +32,12 @@ CLASSPATH="$SPARK_CLASSPATH:$SPARK_SUBMIT_CLASSPATH:$FWDIR/conf"
 
 ASSEMBLY_DIR="$FWDIR/assembly/target/scala-$SCALA_VERSION"
 
+if [ -n "${JAVA_HOME}" ]; then
+  JAR_CMD="${JAVA_HOME}/bin/jar"
+else
+  JAR_CMD="jar"
+fi
+
 # First check if we have a dependencies jar. If so, include binary classes with the deps jar
 if [ -f "$ASSEMBLY_DIR"/spark-assembly*hadoop*-deps.jar ]; then
   CLASSPATH="$CLASSPATH:$FWDIR/core/target/scala-$SCALA_VERSION/classes"
@@ -55,6 +61,14 @@ else
   else
     ASSEMBLY_JAR=`ls "$ASSEMBLY_DIR"/spark-assembly*hadoop*.jar`
   fi
+  jar_error_check=$($JAR_CMD -tf $ASSEMBLY_JAR org/apache/spark/SparkContext 2>&1)
+  if [[ "$jar_error_check" =~ "invalid CEN header" ]]; then
+    echo "Loading Spark jar with '$JAR_CMD' failed. "
+    echo "This is likely because Spark was compiled with Java 7 and run "
+    echo "with Java 6. (see SPARK-1703). Please use Java 7 to run Spark "
+    echo "or build Spark with Java 6."
+    exit 1
+  fi
   CLASSPATH="$CLASSPATH:$ASSEMBLY_JAR"
 fi
 
diff --git a/bin/spark-class b/bin/spark-class
index e8160c8af64c..72f8b9bf9a49 100755
--- a/bin/spark-class
+++ b/bin/spark-class
@@ -138,7 +138,14 @@ if [ -e "$TOOLS_DIR"/target/spark-tools*[0-9Tg].jar ]; then
 fi
 
 # Compute classpath using external script
-CLASSPATH=`$FWDIR/bin/compute-classpath.sh`
+classpath_output=$($FWDIR/bin/compute-classpath.sh)
+if [[ "$?" != "0" ]]; then
+  echo "$classpath_output"
+  exit 1
+else
+  CLASSPATH=$classpath_output
+fi
+
 if [[ "$1" =~ org.apache.spark.tools.* ]]; then
   CLASSPATH="$CLASSPATH:$SPARK_TOOLS_JAR"
 fi
diff --git a/make-distribution.sh b/make-distribution.sh
index dc8aa56a2e97..2ab64ff4598a 100755
--- a/make-distribution.sh
+++ b/make-distribution.sh
@@ -51,6 +51,20 @@ if [ $? != 0 ]; then
     exit -1;
 fi
 
+if [ -z "${JAVA_HOME}" ]; then
+  echo "Error: JAVA_HOME is not set, cannot proceed."
+  exit -1
+fi
+
+JAVA_CMD=$JAVA_HOME/bin/java
+JAVA_VERSION=$($JAVA_CMD -version 2>&1)
+if ! [[ "$JAVA_VERSION" =~ "1.6" ]]; then
+  echo "Error: JAVA_HOME must point to a JDK 6 installation (see SPARK-1703)."
+  echo "Output from 'java -version' was:"
+  echo "$JAVA_VERSION"
+  exit -1
+fi
+
 # Initialize defaults
 SPARK_HADOOP_VERSION=1.0.4
 SPARK_YARN=false

From f5041579ff573f988b673c2506fa4edc32f5ad84 Mon Sep 17 00:00:00 2001
From: Sean Owen <sowen@cloudera.com>
Date: Sun, 4 May 2014 17:43:28 -0700
Subject: [PATCH 086/810] SPARK-1629. Addendum: Depend on commons lang3
 (already used by tachyon) as it's used in ReplSuite, and return to use lang3
 utility in Utils.scala

For consideration. This was proposed in related discussion: https://github.com/apache/spark/pull/569

Author: Sean Owen <sowen@cloudera.com>

Closes #635 from srowen/SPARK-1629.2 and squashes the following commits:

a442b98 [Sean Owen] Depend on commons lang3 (already used by tachyon) as it's used in ReplSuite, and return to use lang3 utility in Utils.scala
---
 core/pom.xml                                          | 4 ++++
 core/src/main/scala/org/apache/spark/util/Utils.scala | 5 ++---
 pom.xml                                               | 5 +++++
 project/SparkBuild.scala                              | 1 +
 4 files changed, 12 insertions(+), 3 deletions(-)

diff --git a/core/pom.xml b/core/pom.xml
index 822b5b1dd7cc..36c71e67b56a 100644
--- a/core/pom.xml
+++ b/core/pom.xml
@@ -69,6 +69,10 @@
       <groupId>com.google.guava</groupId>
       <artifactId>guava</artifactId>
     </dependency>
+    <dependency>
+      <groupId>org.apache.commons</groupId>
+      <artifactId>commons-lang3</artifactId>
+    </dependency>
     <dependency>
       <groupId>com.google.code.findbugs</groupId>
       <artifactId>jsr305</artifactId>
diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala
index acd7eef6d25e..bef4dab3d7cc 100644
--- a/core/src/main/scala/org/apache/spark/util/Utils.scala
+++ b/core/src/main/scala/org/apache/spark/util/Utils.scala
@@ -32,6 +32,7 @@ import scala.util.Try
 
 import com.google.common.io.Files
 import com.google.common.util.concurrent.ThreadFactoryBuilder
+import org.apache.commons.lang3.SystemUtils
 import org.apache.hadoop.fs.{FileSystem, FileUtil, Path}
 import org.json4s._
 import tachyon.client.{TachyonFile,TachyonFS}
@@ -1073,9 +1074,7 @@ private[spark] object Utils extends Logging {
   /**
    * Return true if this is Windows.
    */
-  def isWindows = {
-    Option(System.getProperty("os.name")).exists(_.startsWith("Windows"))
-  }
+  def isWindows = SystemUtils.IS_OS_WINDOWS
 
   /**
    * Indicates whether Spark is currently running unit tests.
diff --git a/pom.xml b/pom.xml
index ebd359a9de17..7678043484a4 100644
--- a/pom.xml
+++ b/pom.xml
@@ -221,6 +221,11 @@
         <artifactId>guava</artifactId>
         <version>14.0.1</version>
       </dependency>
+      <dependency>
+        <groupId>org.apache.commons</groupId>
+        <artifactId>commons-lang3</artifactId>
+        <version>3.3.2</version>
+      </dependency>
       <dependency>
         <groupId>com.google.code.findbugs</groupId>
         <artifactId>jsr305</artifactId>
diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index 51f733511116..19aa3c0607a2 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -326,6 +326,7 @@ object SparkBuild extends Build {
     name := "spark-core",
     libraryDependencies ++= Seq(
         "com.google.guava"           % "guava"            % "14.0.1",
+        "org.apache.commons"         % "commons-lang3"    % "3.3.2",
         "com.google.code.findbugs"   % "jsr305"           % "1.3.9",
         "log4j"                      % "log4j"            % "1.2.17",
         "org.slf4j"                  % "slf4j-api"        % slf4jVersion,

From d940e4c16aaa7b60daf1229a99bc4d3455c0240d Mon Sep 17 00:00:00 2001
From: witgo <witgo@qq.com>
Date: Sun, 4 May 2014 17:48:52 -0700
Subject: [PATCH 087/810] SPARK-1693: Most of the tests throw a
 java.lang.SecurityException when s...

...park built for hadoop 2.3.0 , 2.4.0

Author: witgo <witgo@qq.com>

Closes #628 from witgo/SPARK-1693_new and squashes the following commits:

e3af968 [witgo] Merge branch 'master' of https://github.com/apache/spark into SPARK-1693_new
dc63905 [witgo] SPARK-1693: Most of the tests throw a java.lang.SecurityException when spark built for hadoop 2.3.0 , 2.4.0
---
 pom.xml | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/pom.xml b/pom.xml
index 7678043484a4..08c3ac64436b 100644
--- a/pom.xml
+++ b/pom.xml
@@ -601,6 +601,10 @@
             <groupId>org.jboss.netty</groupId>
             <artifactId>netty</artifactId>
           </exclusion>
+          <exclusion>
+            <groupId>javax.servlet</groupId>
+            <artifactId>servlet-api</artifactId>
+          </exclusion>
         </exclusions>
       </dependency>
 
@@ -621,6 +625,10 @@
             <groupId>org.jboss.netty</groupId>
             <artifactId>netty</artifactId>
           </exclusion>
+          <exclusion>
+            <groupId>javax.servlet</groupId>
+            <artifactId>servlet-api</artifactId>
+          </exclusion>
         </exclusions>
       </dependency>
       <dependency>

From bcb9b7fd4a656f9a6741220a6623441567ded0a4 Mon Sep 17 00:00:00 2001
From: "Allan Douglas R. de Oliveira" <allan@chaordicsystems.com>
Date: Sun, 4 May 2014 20:36:51 -0700
Subject: [PATCH 088/810] EC2 script should exit with non-zero code on
 UsageError

This is specially import because some ssh errors are raised as UsageError, preventing an automated usage of the script from detecting the failure.

Author: Allan Douglas R. de Oliveira <allan@chaordicsystems.com>

Closes #638 from douglaz/ec2_exit_code_fix and squashes the following commits:

5915e6d [Allan Douglas R. de Oliveira] EC2 script should exit with non-zero code on UsageError
---
 ec2/spark_ec2.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/ec2/spark_ec2.py b/ec2/spark_ec2.py
index db393748a33b..0f6d5a1fa81e 100755
--- a/ec2/spark_ec2.py
+++ b/ec2/spark_ec2.py
@@ -814,6 +814,7 @@ def main():
     real_main()
   except UsageError, e:
     print >> stderr, "\nError:\n", e
+    sys.exit(1)
 
 
 if __name__ == "__main__":

From b48a55ae9ff2976c5fe6f5776a6d4659e828ee24 Mon Sep 17 00:00:00 2001
From: Sandeep <sandeep@techaddict.me>
Date: Sun, 4 May 2014 20:51:53 -0700
Subject: [PATCH 089/810] SPARK-1710: spark-submit should print better errors
 than "InvocationTargetException"

Catching the InvocationTargetException, printing getTargetException.

Author: Sandeep <sandeep@techaddict.me>

Closes #630 from techaddict/SPARK-1710 and squashes the following commits:

834d79b [Sandeep] changes from srowen  suggestions
109d604 [Sandeep] SPARK-1710: spark-submit should print better errors than "InvocationTargetException"
---
 .../scala/org/apache/spark/deploy/SparkSubmit.scala  | 12 ++++++++++--
 1 file changed, 10 insertions(+), 2 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
index d131f1809c99..fb30e8a70f68 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
@@ -18,6 +18,7 @@
 package org.apache.spark.deploy
 
 import java.io.{File, PrintStream}
+import java.lang.reflect.InvocationTargetException
 import java.net.{URI, URL}
 
 import scala.collection.mutable.{ArrayBuffer, HashMap, Map}
@@ -137,7 +138,7 @@ object SparkSubmit {
         throw new Exception(msg)
       }
     }
-    
+
     // Special flag to avoid deprecation warnings at the client
     sysProps("SPARK_SUBMIT") = "true"
 
@@ -253,7 +254,14 @@ object SparkSubmit {
 
     val mainClass = Class.forName(childMainClass, true, loader)
     val mainMethod = mainClass.getMethod("main", new Array[String](0).getClass)
-    mainMethod.invoke(null, childArgs.toArray)
+    try {
+      mainMethod.invoke(null, childArgs.toArray)
+    } catch {
+      case e: InvocationTargetException => e.getCause match {
+        case cause: Throwable => throw cause
+        case null => throw e
+      }
+    }
   }
 
   private def addJarToClasspath(localJar: String, loader: ExecutorURLClassLoader) {

From bb2bb0cf6efcb1d18846e6c4904f3cd2da452f9f Mon Sep 17 00:00:00 2001
From: msiddalingaiah <madhu@madhu.com>
Date: Sun, 4 May 2014 21:59:10 -0700
Subject: [PATCH 090/810] Address SPARK-1717

I tested the change locally with Spark 0.9.1, but I can't test with 1.0.0 because there was no AMI for it at the time. It's a trivial fix, so it shouldn't cause any problems.

Author: msiddalingaiah <madhu@madhu.com>

Closes #641 from msiddalingaiah/master and squashes the following commits:

a4f7404 [msiddalingaiah] Address SPARK-1717
---
 ec2/spark_ec2.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/ec2/spark_ec2.py b/ec2/spark_ec2.py
index 0f6d5a1fa81e..0a8d6ca5d751 100755
--- a/ec2/spark_ec2.py
+++ b/ec2/spark_ec2.py
@@ -612,7 +612,7 @@ def ssh_command(opts):
   return ['ssh'] + ssh_args(opts)
 
 
-# Run a command on a host through ssh, retrying up to two times
+# Run a command on a host through ssh, retrying up to five times
 # and then throwing an exception if ssh continues to fail.
 def ssh(host, opts, command):
   tries = 0
@@ -621,7 +621,7 @@ def ssh(host, opts, command):
       return subprocess.check_call(
         ssh_command(opts) + ['-t', '-t', '%s@%s' % (opts.user, host), stringify_command(command)])
     except subprocess.CalledProcessError as e:
-      if (tries > 2):
+      if (tries > 5):
         # If this was an ssh failure, provide the user with hints.
         if e.returncode == 255:
           raise UsageError("Failed to SSH to remote host {0}.\nPlease check that you have provided the correct --identity-file and --key-pair parameters and try again.".format(host))
@@ -648,7 +648,7 @@ def ssh_write(host, opts, command, input):
     status = proc.wait()
     if status == 0:
       break
-    elif (tries > 2):
+    elif (tries > 5):
       raise RuntimeError("ssh_write failed with error %s" % proc.returncode)
     else:
       print >> stderr, "Error {0} while executing remote command, retrying after 30 seconds".format(status)

From f2eb070acc81e60096ee8d4ddf8da2b24a11da72 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Mon, 5 May 2014 00:52:06 -0700
Subject: [PATCH 091/810] Updated doc for spark.closure.serializer to indicate
 only Java serializer work.

See discussion from http://apache-spark-developers-list.1001551.n3.nabble.com/bug-using-kryo-as-closure-serializer-td6473.html

Author: Reynold Xin <rxin@apache.org>

Closes #642 from rxin/docs-ser and squashes the following commits:

a507db5 [Reynold Xin] Use "Java" instead of default.
5eb8cdd [Reynold Xin] Updated doc for spark.closure.serializer to indicate only the default serializer work.
---
 docs/configuration.md | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/docs/configuration.md b/docs/configuration.md
index b078c7c1112c..81ad895d90e6 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -289,8 +289,7 @@ Apart from these, the following properties are also available, and may be useful
   <td>spark.closure.serializer</td>
   <td>org.apache.spark.serializer.<br />JavaSerializer</td>
   <td>
-    Serializer class to use for closures. Generally Java is fine unless your distributed functions
-    (e.g. map functions) reference large objects in the driver program.
+    Serializer class to use for closures. Currently only the Java serializer is supported.
   </td>
 </tr>
 <tr>

From 73b0cbcc241cca3d318ff74340e80b02f884acbd Mon Sep 17 00:00:00 2001
From: Sean Owen <sowen@cloudera.com>
Date: Mon, 5 May 2014 10:33:49 -0700
Subject: [PATCH 092/810] SPARK-1556. jets3t dep doesn't update properly with
 newer Hadoop versions

See related discussion at https://github.com/apache/spark/pull/468

This PR may still overstep what you have in mind, but let me put it on the table to start. Besides fixing the issue, it has one substantive change, and that is to manage Hadoop-specific things only in Hadoop-related profiles. This does _not_ remove `yarn.version`.

- Moves the YARN and Hadoop profiles together in pom.xml. Sorry that this makes the diff a little hard to grok but the changes are only as follows.
- Removes `hadoop.major.version`
- Introduce `hadoop-2.2` and `hadoop-2.3` profiles to control Hadoop-specific changes:
  - like the protobuf version issue - this was only 'solved' now by enabling YARN for 2.2+, which is really an orthogonal issue
  - like the jets3t version issue now
- Hadoop profiles set an appropriate default `hadoop.version`, that can be overridden
- _(YARN profiles in the parent now only exist to add the sub-module)_
- Fixes the jets3t dependency issue
 - and makes it a runtime dependency
 - and centralizes config of this guy in the parent pom
- Updates build docs
- Updates SBT build too
  - and fixes a regex problem along the way

Author: Sean Owen <sowen@cloudera.com>

Closes #629 from srowen/SPARK-1556 and squashes the following commits:

c3fa967 [Sean Owen] Fix hadoop-2.4 profile typo in doc
a2105fd [Sean Owen] Add hadoop-2.4 profile and don't set hadoop.version in profiles
274f4f9 [Sean Owen] Make jets3t a runtime dependency, and bring its exclusion up into parent config
bbed826 [Sean Owen] Use jets3t 0.9.0 for Hadoop 2.3+ (and correct similar regex issue in SBT build)
f21f356 [Sean Owen] Build changes to set up for jets3t fix
---
 core/pom.xml                |  6 ---
 docs/building-with-maven.md | 51 +++++++++++++++++-----
 pom.xml                     | 84 ++++++++++++++++++++++---------------
 project/SparkBuild.scala    |  5 ++-
 4 files changed, 94 insertions(+), 52 deletions(-)

diff --git a/core/pom.xml b/core/pom.xml
index 36c71e67b56a..c24c7be20408 100644
--- a/core/pom.xml
+++ b/core/pom.xml
@@ -38,12 +38,6 @@
     <dependency>
       <groupId>net.java.dev.jets3t</groupId>
       <artifactId>jets3t</artifactId>
-      <exclusions>
-        <exclusion>
-          <groupId>commons-logging</groupId>
-          <artifactId>commons-logging</artifactId>
-        </exclusion>
-      </exclusions>
     </dependency>
     <dependency>
       <groupId>org.apache.curator</groupId>
diff --git a/docs/building-with-maven.md b/docs/building-with-maven.md
index e447dfea3bac..cac01ded60d9 100644
--- a/docs/building-with-maven.md
+++ b/docs/building-with-maven.md
@@ -29,9 +29,22 @@ You can fix this by setting the `MAVEN_OPTS` variable as discussed before.
 
 ## Specifying the Hadoop version ##
 
-Because HDFS is not protocol-compatible across versions, if you want to read from HDFS, you'll need to build Spark against the specific HDFS version in your environment. You can do this through the "hadoop.version" property. If unset, Spark will build against Hadoop 1.0.4 by default.
-
-For Apache Hadoop versions 1.x, Cloudera CDH MRv1, and other Hadoop versions without YARN, use:
+Because HDFS is not protocol-compatible across versions, if you want to read from HDFS, you'll need to build Spark against the specific HDFS version in your environment. You can do this through the "hadoop.version" property. If unset, Spark will build against Hadoop 1.0.4 by default. Note that certain build profiles are required for particular Hadoop versions:
+
+<table class="table">
+  <thead>
+    <tr><th>Hadoop version</th><th>Profile required</th></tr>
+  </thead>
+  <tbody>
+    <tr><td>0.23.x</td><td>hadoop-0.23</td></tr>
+    <tr><td>1.x to 2.1.x</td><td>(none)</td></tr>
+    <tr><td>2.2.x</td><td>hadoop-2.2</td></tr>
+    <tr><td>2.3.x</td><td>hadoop-2.3</td></tr>
+    <tr><td>2.4.x</td><td>hadoop-2.4</td></tr>
+  </tbody>
+</table>
+
+For Apache Hadoop versions 1.x, Cloudera CDH "mr1" distributions, and other Hadoop versions without YARN, use:
 
     # Apache Hadoop 1.2.1
     $ mvn -Dhadoop.version=1.2.1 -DskipTests clean package
@@ -42,22 +55,40 @@ For Apache Hadoop versions 1.x, Cloudera CDH MRv1, and other Hadoop versions wit
     # Apache Hadoop 0.23.x
     $ mvn -Phadoop-0.23 -Dhadoop.version=0.23.7 -DskipTests clean package
 
-For Apache Hadoop 2.x, 0.23.x, Cloudera CDH MRv2, and other Hadoop versions with YARN, you can enable the "yarn-alpha" or "yarn" profile and set the "hadoop.version", "yarn.version" property. Note that Hadoop 0.23.X requires a special `-Phadoop-0.23` profile:
+For Apache Hadoop 2.x, 0.23.x, Cloudera CDH, and other Hadoop versions with YARN, you can enable the "yarn-alpha" or "yarn" profile and optionally set the "yarn.version" property if it is different from "hadoop.version". The additional build profile required depends on the YARN version:
+
+<table class="table">
+  <thead>
+    <tr><th>YARN version</th><th>Profile required</th></tr>
+  </thead>
+  <tbody>
+    <tr><td>0.23.x to 2.1.x</td><td>yarn-alpha</td></tr>
+    <tr><td>2.2.x and later</td><td>yarn</td></tr>
+  </tbody>
+</table>
+
+Examples:
 
     # Apache Hadoop 2.0.5-alpha
     $ mvn -Pyarn-alpha -Dhadoop.version=2.0.5-alpha -DskipTests clean package
 
-    # Cloudera CDH 4.2.0 with MapReduce v2
+    # Cloudera CDH 4.2.0
     $ mvn -Pyarn-alpha -Dhadoop.version=2.0.0-cdh4.2.0 -DskipTests clean package
 
-    # Apache Hadoop 2.2.X (e.g. 2.2.0 as below) and newer
-    $ mvn -Pyarn -Dhadoop.version=2.2.0 -DskipTests clean package
-
     # Apache Hadoop 0.23.x
-    $ mvn -Pyarn-alpha -Phadoop-0.23 -Dhadoop.version=0.23.7 -Dyarn.version=0.23.7 -DskipTests clean package
+    $ mvn -Pyarn-alpha -Phadoop-0.23 -Dhadoop.version=0.23.7 -DskipTests clean package
+
+    # Apache Hadoop 2.2.X
+    $ mvn -Pyarn -Phadoop-2.2 -Dhadoop.version=2.2.0 -DskipTests clean package
+
+    # Apache Hadoop 2.3.X
+    $ mvn -Pyarn -Phadoop-2.3 -Dhadoop.version=2.3.0 -DskipTests clean package
+
+    # Apache Hadoop 2.4.X
+    $ mvn -Pyarn -Phadoop-2.4 -Dhadoop.version=2.4.0 -DskipTests clean package
 
     # Different versions of HDFS and YARN.
-    $ mvn -Pyarn-alpha -Dhadoop.version=2.3.0 -Dyarn.version=0.23.7 -DskipTests clean package
+    $ mvn -Pyarn-alpha -Phadoop-2.3 -Dhadoop.version=2.3.0 -Dyarn.version=0.23.7 -DskipTests clean package
 
 ## Spark Tests in Maven ##
 
diff --git a/pom.xml b/pom.xml
index 08c3ac64436b..e4b5c36d6917 100644
--- a/pom.xml
+++ b/pom.xml
@@ -129,6 +129,7 @@
     <chill.version>0.3.6</chill.version>
     <codahale.metrics.version>3.0.0</codahale.metrics.version>
     <avro.version>1.7.4</avro.version>
+    <jets3t.version>0.7.1</jets3t.version>
 
     <PermGen>64m</PermGen>
     <MaxPermGen>512m</MaxPermGen>
@@ -560,10 +561,18 @@
           </exclusion>
         </exclusions>
       </dependency>
+      <!-- See SPARK-1556 for info on this dependency: -->
       <dependency>
         <groupId>net.java.dev.jets3t</groupId>
         <artifactId>jets3t</artifactId>
-        <version>0.7.1</version>
+        <version>${jets3t.version}</version>
+        <scope>runtime</scope>
+        <exclusions>
+          <exclusion>
+            <groupId>commons-logging</groupId>
+            <artifactId>commons-logging</artifactId>
+          </exclusion>
+        </exclusions>
       </dependency>
       <dependency>
         <groupId>org.apache.hadoop</groupId>
@@ -843,36 +852,6 @@
   </build>
 
   <profiles>
-    <!-- SPARK-1121: Adds an explicit dependency on Avro to work around a Hadoop 0.23.X issue -->
-    <profile>
-      <id>hadoop-0.23</id>
-      <dependencies>
-        <dependency>
-          <groupId>org.apache.avro</groupId>
-          <artifactId>avro</artifactId>
-        </dependency>
-      </dependencies>
-    </profile>
-
-    <profile>
-      <id>yarn-alpha</id>
-      <properties>
-        <hadoop.major.version>2</hadoop.major.version>
-        <!-- 0.23.* is same as 2.0.* - except hardened to run production jobs -->
-        <hadoop.version>0.23.7</hadoop.version>
-        <!--<hadoop.version>2.0.5-alpha</hadoop.version> -->
-      </properties>
-      <dependencies>
-        <dependency>
-          <groupId>org.apache.avro</groupId>
-          <artifactId>avro</artifactId>
-        </dependency>
-      </dependencies>
-      <modules>
-        <module>yarn</module>
-      </modules>
-
-    </profile>
 
     <!-- Ganglia integration is not included by default due to LGPL-licensed code -->
     <profile>
@@ -907,17 +886,54 @@
 
     </profile>
 
+    <!-- A series of build profiles where customizations for particular Hadoop releases can be made -->
+
     <profile>
-      <id>yarn</id>
+      <id>hadoop-0.23</id>
+      <!-- SPARK-1121: Adds an explicit dependency on Avro to work around a Hadoop 0.23.X issue -->
+      <dependencies>
+        <dependency>
+          <groupId>org.apache.avro</groupId>
+          <artifactId>avro</artifactId>
+        </dependency>
+      </dependencies>
+    </profile>
+
+    <profile>
+      <id>hadoop-2.2</id>
+      <properties>
+        <protobuf.version>2.5.0</protobuf.version>
+      </properties>
+    </profile>
+
+    <profile>
+      <id>hadoop-2.3</id>
       <properties>
-        <hadoop.major.version>2</hadoop.major.version>
-        <hadoop.version>2.2.0</hadoop.version>
         <protobuf.version>2.5.0</protobuf.version>
+        <jets3t.version>0.9.0</jets3t.version>
       </properties>
+    </profile>
+
+    <profile>
+      <id>hadoop-2.4</id>
+      <properties>
+        <protobuf.version>2.5.0</protobuf.version>
+        <jets3t.version>0.9.0</jets3t.version>
+      </properties>
+    </profile>
+
+    <profile>
+      <id>yarn-alpha</id>
       <modules>
         <module>yarn</module>
       </modules>
+    </profile>
 
+    <profile>
+      <id>yarn</id>
+      <modules>
+        <module>yarn</module>
+      </modules>
     </profile>
 
     <!-- Build without Hadoop dependencies that are included in some runtime environments. -->
diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index 19aa3c0607a2..a2597e3e6ddd 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -95,7 +95,7 @@ object SparkBuild extends Build {
   lazy val hadoopVersion = Properties.envOrElse("SPARK_HADOOP_VERSION", DEFAULT_HADOOP_VERSION)
   lazy val isNewHadoop = Properties.envOrNone("SPARK_IS_NEW_HADOOP") match {
     case None => {
-      val isNewHadoopVersion = "2.[2-9]+".r.findFirstIn(hadoopVersion).isDefined
+      val isNewHadoopVersion = "^2\\.[2-9]+".r.findFirstIn(hadoopVersion).isDefined
       (isNewHadoopVersion|| DEFAULT_IS_NEW_HADOOP)
     }
     case Some(v) => v.toBoolean
@@ -297,6 +297,7 @@ object SparkBuild extends Build {
   val chillVersion = "0.3.6"
   val codahaleMetricsVersion = "3.0.0"
   val jblasVersion = "1.2.3"
+  val jets3tVersion = if ("^2\\.[3-9]+".r.findFirstIn(hadoopVersion).isDefined) "0.9.0" else "0.7.1"
   val jettyVersion = "8.1.14.v20131031"
   val hiveVersion = "0.12.0"
   val parquetVersion = "1.3.2"
@@ -343,7 +344,7 @@ object SparkBuild extends Build {
         "colt"                       % "colt"             % "1.2.0",
         "org.apache.mesos"           % "mesos"            % "0.13.0",
         "commons-net"                % "commons-net"      % "2.2",
-        "net.java.dev.jets3t"        % "jets3t"           % "0.7.1" excludeAll(excludeCommonsLogging),
+        "net.java.dev.jets3t"        % "jets3t"           % jets3tVersion excludeAll(excludeCommonsLogging),
         "org.apache.derby"           % "derby"            % "10.4.2.0"                     % "test",
         "org.apache.hadoop"          % hadoopClient       % hadoopVersion excludeAll(excludeNetty, excludeAsm, excludeCommonsLogging, excludeSLF4J, excludeOldAsm),
         "org.apache.curator"         % "curator-recipes"  % "2.4.0" excludeAll(excludeNetty),

From 3292e2a71bfb5df5ba156cf7557747d164d12291 Mon Sep 17 00:00:00 2001
From: Bouke van der Bijl <boukevanderbijl@gmail.com>
Date: Mon, 5 May 2014 11:19:35 -0700
Subject: [PATCH 093/810] SPARK-1721: Reset the thread classLoader in the Mesos
 Executor

This is because Mesos calls it with a different environment or something, the result is that the Spark jar is missing and it can't load classes.

This fixes http://apache-spark-user-list.1001560.n3.nabble.com/java-lang-ClassNotFoundException-spark-on-mesos-td3510.html

I have no idea whether this is the right fix, I can only confirm that it fixes the issue for us.

The `registered` method is called from mesos (https://github.com/apache/mesos/blob/765ff9bc2ac5a12d4362f8235b572a37d646390a/src/java/jni/org_apache_mesos_MesosExecutorDriver.cpp)

I am unsure which commit caused this regression

Author: Bouke van der Bijl <boukevanderbijl@gmail.com>

Closes #620 from bouk/mesos-classloader-fix and squashes the following commits:

c13eae0 [Bouke van der Bijl] Use getContextOrSparkClassLoader in SparkEnv and CompressionCodec
---
 core/src/main/scala/org/apache/spark/SparkEnv.scala           | 4 +---
 .../src/main/scala/org/apache/spark/io/CompressionCodec.scala | 3 ++-
 2 files changed, 3 insertions(+), 4 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/SparkEnv.scala b/core/src/main/scala/org/apache/spark/SparkEnv.scala
index bea435ec34ce..d40ed27da539 100644
--- a/core/src/main/scala/org/apache/spark/SparkEnv.scala
+++ b/core/src/main/scala/org/apache/spark/SparkEnv.scala
@@ -156,13 +156,11 @@ object SparkEnv extends Logging {
       conf.set("spark.driver.port",  boundPort.toString)
     }
 
-    val classLoader = Thread.currentThread.getContextClassLoader
-
     // Create an instance of the class named by the given Java system property, or by
     // defaultClassName if the property is not set, and return it as a T
     def instantiateClass[T](propertyName: String, defaultClassName: String): T = {
       val name = conf.get(propertyName,  defaultClassName)
-      val cls = Class.forName(name, true, classLoader)
+      val cls = Class.forName(name, true, Utils.getContextOrSparkClassLoader)
       // First try with the constructor that takes SparkConf. If we can't find one,
       // use a no-arg constructor instead.
       try {
diff --git a/core/src/main/scala/org/apache/spark/io/CompressionCodec.scala b/core/src/main/scala/org/apache/spark/io/CompressionCodec.scala
index e1a5ee316bb6..4b0fe1ab8299 100644
--- a/core/src/main/scala/org/apache/spark/io/CompressionCodec.scala
+++ b/core/src/main/scala/org/apache/spark/io/CompressionCodec.scala
@@ -24,6 +24,7 @@ import org.xerial.snappy.{SnappyInputStream, SnappyOutputStream}
 
 import org.apache.spark.SparkConf
 import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.util.Utils
 
 /**
  * :: DeveloperApi ::
@@ -49,7 +50,7 @@ private[spark] object CompressionCodec {
   }
 
   def createCodec(conf: SparkConf, codecName: String): CompressionCodec = {
-    val ctor = Class.forName(codecName, true, Thread.currentThread.getContextClassLoader)
+    val ctor = Class.forName(codecName, true, Utils.getContextOrSparkClassLoader)
       .getConstructor(classOf[SparkConf])
     ctor.newInstance(conf).asInstanceOf[CompressionCodec]
   }

From a975a19f21e71f448b3fdb2ed4461e28ef439900 Mon Sep 17 00:00:00 2001
From: Tathagata Das <tathagata.das1565@gmail.com>
Date: Mon, 5 May 2014 15:28:19 -0700
Subject: [PATCH 094/810] [SPARK-1504], [SPARK-1505], [SPARK-1558] Updated
 Spark Streaming guide

- SPARK-1558: Updated custom receiver guide to match it with the new API
- SPARK-1504: Added deployment and monitoring subsection to streaming
- SPARK-1505: Added migration guide for migrating from 0.9.x and below to Spark 1.0
- Updated various Java streaming examples to use JavaReceiverInputDStream to highlight the API change.
- Removed the requirement for cleaner ttl from streaming guide

Author: Tathagata Das <tathagata.das1565@gmail.com>

Closes #652 from tdas/doc-fix and squashes the following commits:

cb4f4b7 [Tathagata Das] Possible fix for flaky graceful shutdown test.
ab71f7f [Tathagata Das] Merge remote-tracking branch 'apache-github/master' into doc-fix
8d6ff9b [Tathagata Das] Addded migration guide to Spark Streaming.
7d171df [Tathagata Das] Added reference to JavaReceiverInputStream in examples and streaming guide.
49edd7c [Tathagata Das] Change java doc links to use Java docs.
11528d7 [Tathagata Das] Updated links on index page.
ff80970 [Tathagata Das] More updates to streaming guide.
4dc42e9 [Tathagata Das] Added monitoring and other documentation in the streaming guide.
14c6564 [Tathagata Das] Updated custom receiver guide.
---
 docs/configuration.md                         |   6 +-
 docs/index.md                                 |   6 +-
 docs/streaming-custom-receivers.md            | 273 ++++++++++++------
 docs/streaming-programming-guide.md           | 200 ++++++++++---
 .../examples/JavaCustomReceiver.java          |   3 +-
 .../examples/JavaFlumeEventCount.java         |   2 +-
 .../examples/JavaKafkaWordCount.java          |   4 +-
 .../examples/JavaNetworkWordCount.java        |   5 +-
 .../spark/streaming/receiver/Receiver.scala   |   6 +-
 .../streaming/StreamingContextSuite.scala     |   1 +
 10 files changed, 360 insertions(+), 146 deletions(-)

diff --git a/docs/configuration.md b/docs/configuration.md
index 81ad895d90e6..d6f316ba5f77 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -462,7 +462,7 @@ Apart from these, the following properties are also available, and may be useful
   <td>(infinite)</td>
   <td>
     Duration (seconds) of how long Spark will remember any metadata (stages generated, tasks generated, etc.).
-    Periodic cleanups will ensure that metadata older than this duration will be forgetten. This is
+    Periodic cleanups will ensure that metadata older than this duration will be forgotten. This is
     useful for running Spark for many hours / days (for example, running 24/7 in case of Spark Streaming
     applications). Note that any RDD that persists in memory for more than this duration will be cleared as well.
   </td>
@@ -471,8 +471,8 @@ Apart from these, the following properties are also available, and may be useful
   <td>spark.streaming.blockInterval</td>
   <td>200</td>
   <td>
-    Duration (milliseconds) of how long to batch new objects coming from network receivers used
-    in Spark Streaming.
+    Interval (milliseconds) at which data received by Spark Streaming receivers is coalesced
+    into blocks of data before storing them in Spark.
   </td>
 </tr>
 <tr>
diff --git a/docs/index.md b/docs/index.md
index 6fc9a4f03bb1..2daa208b3b90 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -112,10 +112,10 @@ Note that on Windows, you need to set the environment variables on separate line
 * [Shark](http://shark.cs.berkeley.edu): Apache Hive over Spark
 * [Mailing Lists](http://spark.apache.org/mailing-lists.html): ask questions about Spark here
 * [AMP Camps](http://ampcamp.berkeley.edu/): a series of training camps at UC Berkeley that featured talks and
-  exercises about Spark, Shark, Mesos, and more. [Videos](http://ampcamp.berkeley.edu/agenda-2012),
-  [slides](http://ampcamp.berkeley.edu/agenda-2012) and [exercises](http://ampcamp.berkeley.edu/exercises-2012) are
+  exercises about Spark, Shark, Spark Streaming, Mesos, and more. [Videos](http://ampcamp.berkeley.edu/3/),
+  [slides](http://ampcamp.berkeley.edu/3/) and [exercises](http://ampcamp.berkeley.edu/3/exercises/) are
   available online for free.
-* [Code Examples](http://spark.apache.org/examples.html): more are also available in the [examples subfolder](https://github.com/apache/spark/tree/master/examples/src/main/scala/) of Spark
+* [Code Examples](http://spark.apache.org/examples.html): more are also available in the [examples subfolder](https://github.com/apache/spark/tree/master/examples/src/main/scala/org/apache/spark/) of Spark
 * [Paper Describing Spark](http://www.cs.berkeley.edu/~matei/papers/2012/nsdi_spark.pdf)
 * [Paper Describing Spark Streaming](http://www.eecs.berkeley.edu/Pubs/TechRpts/2012/EECS-2012-259.pdf)
 
diff --git a/docs/streaming-custom-receivers.md b/docs/streaming-custom-receivers.md
index 3cfa4516cc12..a2dc3a8961df 100644
--- a/docs/streaming-custom-receivers.md
+++ b/docs/streaming-custom-receivers.md
@@ -3,126 +3,219 @@ layout: global
 title: Spark Streaming Custom Receivers
 ---
 
-A "Spark Streaming" receiver can be a simple network stream, streams of messages from a message queue, files etc. A receiver can also assume roles more than just receiving data like filtering, preprocessing, to name a few of the possibilities. The api to plug-in any user defined custom receiver is thus provided to encourage development of receivers which may be well suited to ones specific need.
+Spark Streaming can receive streaming data from any arbitrary data source beyond
+the one's for which it has in-built support (that is, beyond Flume, Kafka, files, sockets, etc.).
+This requires the developer to implement a *receiver* that is customized for receiving data from
+the concerned data source. This guide walks through the process of implementing a custom receiver
+and using it in a Spark Streaming application.
+
+### Implementing a Custom Receiver
+
+This starts with implementing a [Receiver](api/scala/index.html#org.apache.spark.streaming.receiver.Receiver).
+A custom receiver must extend this abstract class by implementing two methods
+- `onStart()`: Things to do to start receiving data.
+- `onStop()`: Things to do to stop receiving data.
+
+Note that `onStart()` and `onStop()` must not block indefinitely. Typically, onStart() would start the threads
+that responsible for receiving the data and `onStop()` would ensure that the receiving by those threads
+are stopped. The receiving threads can also use `isStopped()`, a `Receiver` method, to check whether they
+should stop receiving data.
+
+Once the data is received, that data can be stored inside Spark
+by calling `store(data)`, which is a method provided by the
+[Receiver](api/scala/index.html#org.apache.spark.streaming.receiver.Receiver) class.
+There are number of flavours of `store()` which allow you store the received data
+record-at-a-time or as whole collection of objects / serialized bytes.
+
+Any exception in the receiving threads should be caught and handled properly to avoid silent
+failures of the receiver. `restart(<exception>)` will restart the receiver by
+asynchronously calling `onStop()` and then calling `onStart()` after a delay.
+`stop(<exception>)` will call `onStop()` and terminate the receiver. Also, `reportError(<error>)`
+reports a error message to the driver (visible in the logs and UI) without stopping / restarting
+the receiver.
+
+The following is a custom receiver that receives a stream of text over a socket. It treats
+'\n' delimited lines in the text stream as records and stores them with Spark. If the receiving thread
+has any error connecting or receiving, the receiver is restarted to make another attempt to connect.
+
+<div class="codetabs">
+<div data-lang="scala"  markdown="1" >
 
-This guide shows the programming model and features by walking through a simple sample receiver and corresponding Spark Streaming application.
+{% highlight scala %}
 
-### Writing a Simple Receiver
+class CustomReceiver(host: String, port: Int)
+  extends Receiver[String](StorageLevel.MEMORY_AND_DISK_2) with Logging {
+
+  def onStart() {
+    // Start the thread that receives data over a connection
+    new Thread("Socket Receiver") {
+      override def run() { receive() }
+    }.start()
+  }
+
+  def onStop() {
+   // There is nothing much to do as the thread calling receive()
+   // is designed to stop by itself isStopped() returns false
+  }
+
+  /** Create a socket connection and receive data until receiver is stopped */
+  private def receive() {
+    var socket: Socket = null
+    var userInput: String = null
+    try {
+     // Connect to host:port
+     socket = new Socket(host, port)
+
+     // Until stopped or connection broken continue reading
+     val reader = new BufferedReader(new InputStreamReader(socket.getInputStream(), "UTF-8"))
+     userInput = reader.readLine()
+     while(!isStopped && userInput != null) {
+       store(userInput)
+       userInput = reader.readLine()
+     }
+     reader.close()
+     socket.close()
+
+     // Restart in an attempt to connect again when server is active again
+     restart("Trying to connect again")
+    } catch {
+     case e: java.net.ConnectException =>
+       // restart if could not connect to server
+       restart("Error connecting to " + host + ":" + port, e)
+     case t: Throwable =>
+       // restart if there is any other error
+       restart("Error receiving data", t)
+    }
+  }
+}
 
-This starts with implementing [NetworkReceiver](api/scala/index.html#org.apache.spark.streaming.dstream.NetworkReceiver).
+{% endhighlight %}
 
-The following is a simple socket text-stream receiver.
+</div>
+<div data-lang="java" markdown="1">
+
+{% highlight java %}
+
+public class JavaCustomReceiver extends Receiver<String> {
+
+  String host = null;
+  int port = -1;
+
+  public JavaCustomReceiver(String host_ , int port_) {
+    super(StorageLevel.MEMORY_AND_DISK_2());
+    host = host_;
+    port = port_;
+  }
+
+  public void onStart() {
+    // Start the thread that receives data over a connection
+    new Thread()  {
+      @Override public void run() {
+        receive();
+      }
+    }.start();
+  }
+
+  public void onStop() {
+    // There is nothing much to do as the thread calling receive()
+    // is designed to stop by itself isStopped() returns false
+  }
+
+  /** Create a socket connection and receive data until receiver is stopped */
+  private void receive() {
+    Socket socket = null;
+    String userInput = null;
+
+    try {
+      // connect to the server
+      socket = new Socket(host, port);
+
+      BufferedReader reader = new BufferedReader(new InputStreamReader(socket.getInputStream()));
+
+      // Until stopped or connection broken continue reading
+      while (!isStopped() && (userInput = reader.readLine()) != null) {
+        System.out.println("Received data '" + userInput + "'");
+        store(userInput);
+      }
+      reader.close();
+      socket.close();
+
+      // Restart in an attempt to connect again when server is active again
+      restart("Trying to connect again");
+    } catch(ConnectException ce) {
+      // restart if could not connect to server
+      restart("Could not connect", ce);
+    } catch(Throwable t) {
+      // restart if there is any other error
+      restart("Error receiving data", t);
+    }
+  }
+}
 
-{% highlight scala %}
-       class SocketTextStreamReceiver(host: String, port: Int)
-         extends NetworkReceiver[String]
-       {
-         protected lazy val blocksGenerator: BlockGenerator =
-           new BlockGenerator(StorageLevel.MEMORY_ONLY_SER_2)
-
-         protected def onStart() = {
-           blocksGenerator.start()
-           val socket = new Socket(host, port)
-           val dataInputStream = new BufferedReader(new InputStreamReader(socket.getInputStream(), "UTF-8"))
-           var data: String = dataInputStream.readLine()
-           while (data != null) {
-             blocksGenerator += data
-             data = dataInputStream.readLine()
-           }
-         }
-
-         protected def onStop() {
-           blocksGenerator.stop()
-         }
-       }
 {% endhighlight %}
 
+</div>
+</div>
 
-All we did here is extended NetworkReceiver and called blockGenerator's API method (i.e. +=) to push our blocks of data. Please refer to scala-docs of NetworkReceiver for more details.
 
+### Using the custom receiver in a Spark Streaming application
 
-### An Actor as Receiver
+The custom receiver can be used in a Spark Streaming application by using
+`streamingContext.receiverStream(<instance of custom receiver>)`. This will create
+input DStream using data received by the instance of custom receiver, as shown below
 
-This starts with implementing [Actor](#References)
-
-Following is a simple socket text-stream receiver, which is appearently overly simplified using Akka's socket.io api.
+<div class="codetabs">
+<div data-lang="scala"  markdown="1" >
 
 {% highlight scala %}
-       class SocketTextStreamReceiver (host:String,
-         port:Int,
-         bytesToString: ByteString => String) extends Actor with Receiver {
-
-          override def preStart = IOManager(context.system).connect(host, port)
-
-          def receive = {
-           case IO.Read(socket, bytes) => pushBlock(bytesToString(bytes))
-         }
-
-       }
+// Assuming ssc is the StreamingContext
+val customReceiverStream = ssc.receiverStream(new CustomReceiver(host, port))
+val words = lines.flatMap(_.split(" "))
+...
 {% endhighlight %}
 
-All we did here is mixed in trait Receiver and called pushBlock api method to push our blocks of data. Please refer to scala-docs of Receiver for more details.
-
-### A Sample Spark Application
+The full source code is in the example [CustomReceiver.scala](https://github.com/apache/spark/blob/master/examples/src/main/scala/org/apache/spark/streaming/examples/CustomReceiver.scala).
 
-* First create a Spark streaming context with master url and batchduration.
+</div>
+<div data-lang="java" markdown="1">
 
-{% highlight scala %}
-    val ssc = new StreamingContext(master, "WordCountCustomStreamSource",
-      Seconds(batchDuration))
+{% highlight java %}
+// Assuming ssc is the JavaStreamingContext
+JavaDStream<String> customReceiverStream = ssc.receiverStream(new JavaCustomReceiver(host, port));
+JavaDStream<String> words = lines.flatMap(new FlatMapFunction<String, String>() { ... });
+...
 {% endhighlight %}
 
-* Plug-in the custom receiver into the spark streaming context and create a DStream.
+The full source code is in the example [JavaCustomReceiver.java](https://github.com/apache/spark/blob/master/examples/src/main/java/org/apache/spark/streaming/examples/JavaCustomReceiver.java).
 
-{% highlight scala %}
-    val lines = ssc.networkStream[String](new SocketTextStreamReceiver(
-      "localhost", 8445))
-{% endhighlight %}
+</div>
+</div>
 
-* OR Plug-in the actor as receiver into the spark streaming context and create a DStream.
 
-{% highlight scala %}
-    val lines = ssc.actorStream[String](Props(new SocketTextStreamReceiver(
-      "localhost",8445, z => z.utf8String)),"SocketReceiver")
-{% endhighlight %}
 
-* Process it.
+### Implementing and Using a Custom Actor-based Receiver
 
-{% highlight scala %}
-    val words = lines.flatMap(_.split(" "))
-    val wordCounts = words.map(x => (x, 1)).reduceByKey(_ + _)
+Custom [Akka Actors](http://doc.akka.io/docs/akka/2.2.4/scala/actors.html) can also be used to
+receive data. The [`ActorHelper`](api/scala/index.html#org.apache.spark.streaming.receiver.ActorHelper)
+trait can be applied on any Akka actor, which allows received data to be stored in Spark using
+ `store(...)` methods. The supervisor strategy of this actor can be configured to handle failures, etc.
 
-    wordCounts.print()
-    ssc.start()
+{% highlight scala %}
+class CustomActor extends Actor with ActorHelper {
+  def receive = {
+   case data: String => store(data)
+  }
+}
 {% endhighlight %}
 
-* After processing it, stream can be tested using the netcat utility.
-
-     $ nc -l localhost 8445
-     hello world
-     hello hello
-
-
-## Multiple Homogeneous/Heterogeneous Receivers.
-
-A DStream union operation is provided for taking union on multiple input streams.
+And a new input stream can be created with this custom actor as
 
 {% highlight scala %}
-    val lines = ssc.actorStream[String](Props(new SocketTextStreamReceiver(
-      "localhost",8445, z => z.utf8String)),"SocketReceiver")
-
-    // Another socket stream receiver
-    val lines2 = ssc.actorStream[String](Props(new SocketTextStreamReceiver(
-      "localhost",8446, z => z.utf8String)),"SocketReceiver")
-
-    val union = lines.union(lines2)
+// Assuming ssc is the StreamingContext
+val lines = ssc.actorStream[String](Props(new CustomActor()), "CustomReceiver")
 {% endhighlight %}
 
-Above stream can be easily process as described earlier.
-
-_A more comprehensive example is provided in the spark streaming examples_
+See [ActorWordCount.scala](https://github.com/apache/spark/blob/master/examples/src/main/scala/org/apache/spark/streaming/examples/ActorWordCount.scala)
+for an end-to-end example.
 
-## References
 
-1.[Akka Actor documentation](http://doc.akka.io/docs/akka/2.0.5/scala/actors.html)
-2.[NetworkReceiver](api/scala/index.html#org.apache.spark.streaming.dstream.NetworkReceiver)
diff --git a/docs/streaming-programming-guide.md b/docs/streaming-programming-guide.md
index b22bb45828b2..e8b718b30356 100644
--- a/docs/streaming-programming-guide.md
+++ b/docs/streaming-programming-guide.md
@@ -136,7 +136,7 @@ The complete code can be found in the Spark Streaming example
 <div data-lang="java" markdown="1">
 
 First, we create a
-[JavaStreamingContext](api/scala/index.html#org.apache.spark.streaming.api.java.JavaStreamingContext) object,
+[JavaStreamingContext](api/java/org/apache/spark/streaming/api/java/JavaStreamingContext.html) object,
 which is the main entry point for all streaming
 functionality. Besides Spark's configuration, we specify that any DStream would be processed
 in 1 second batches.
@@ -155,7 +155,7 @@ by specifying the IP address and port of the data server.
 
 {% highlight java %}
 // Create a DStream that will connect to serverIP:serverPort, like localhost:9999
-JavaDStream<String> lines = jssc.socketTextStream("localhost", 9999);
+JavaReceiverInputDStream<String> lines = jssc.socketTextStream("localhost", 9999);
 {% endhighlight %}
 
 This `lines` DStream represents the stream of data that will be received from the data
@@ -863,6 +863,51 @@ For DStreams that must be checkpointed (that is, DStreams created by `updateStat
 `reduceByKeyAndWindow` with inverse function), the checkpoint interval of the DStream is by
 default set to a multiple of the DStream's sliding interval such that its at least 10 seconds.
 
+## Deployment
+A Spark Streaming application is deployed on a cluster in the same way as any other Spark application.
+Please refer to the [deployment guide](cluster-overview.html) for more details.
+
+If a running Spark Streaming application needs to be upgraded (with new application code), then
+there are two possible mechanism.
+
+- The upgraded Spark Streaming application is started and run in parallel to the existing application.
+Once the new one (receiving the same data as the old one) has been warmed up and ready
+for prime time, the old one be can be brought down. Note that this can be done for data sources that support
+sending the data to two destinations (i.e., the earlier and upgraded applications).
+
+- The existing application is shutdown gracefully (see
+[`StreamingContext.stop(...)`](api/scala/index.html#org.apache.spark.streaming.StreamingContext)
+or [`JavaStreamingContext.stop(...)`](api/java/org/apache/spark/streaming/api/java/JavaStreamingContext.html)
+for graceful shutdown options) which ensure data that have been received is completely
+processed before shutdown. Then the
+upgraded application can be started, which will start processing from the same point where the earlier
+application left off. Note that this can be done only with input sources that support source-side buffering
+(like Kafka, and Flume) as data needs to be buffered while the previous application down and
+the upgraded application is not yet up.
+
+## Monitoring
+Beyond Spark's [monitoring capabilities](monitoring.html), there are additional capabilities
+specific to Spark Streaming. When a StreamingContext is used, the
+[Spark web UI](monitoring.html#web-interfaces) shows
+an additional `Streaming` tab which shows statistics about running receivers (whether
+receivers are active, number of records received, receiver error, etc.)
+and completed batches (batch processing times, queueing delays, etc.). This can be used to
+monitor the progress of the streaming application.
+
+The following two metrics in web UI is particularly important -
+*Processing Time* and *Scheduling Delay* (under *Batch Processing Statistics*). The first is the
+time to process each batch of data, and the second is the time a batch waits in a queue
+for the processing of previous batches to finish. If the batch processing time is consistently more
+than the batch interval and/or the queueing delay keeps increasing, then it indicates the system is
+not able to process the batches as fast they are being generated and falling behind.
+In that case, consider
+[reducing](#reducing-the-processing-time-of-each-batch) the batch processing time.
+
+The progress of a Spark Streaming program can also be monitored using the
+[StreamingListener](api/scala/index.html#org.apache.spark.scheduler.StreamingListener) interface,
+which allows you to get receiver status and processing times. Note that this is a developer API
+and it is likely to be improved upon (i.e., more information reported) in the future.
+
 ***************************************************************************************************  
 
 # Performance Tuning
@@ -875,7 +920,8 @@ improve the performance of you application. At a high level, you need to conside
   Reducing the processing time of each batch of data by efficiently using cluster resources.
 </li>
 <li>
-  Setting the right batch size such that the data processing can keep up with the data ingestion.
+  Setting the right batch size such that the batches of data can be processed as fast as they
+  are received (that is, data processing keeps up with the data ingestion).
 </li>
 </ol>
 
@@ -884,7 +930,30 @@ There are a number of optimizations that can be done in Spark to minimize the pr
 each batch. These have been discussed in detail in [Tuning Guide](tuning.html). This section
 highlights some of the most important ones.
 
-### Level of Parallelism
+### Level of Parallelism in Data Receiving
+Receiving data over the network (like Kafka, Flume, socket, etc.) requires the data to deserialized
+and stored in Spark. If the data receiving becomes a bottleneck in the system, then consider
+parallelizing the data receiving. Note that each input DStream
+creates a single receiver (running on a worker machine) that receives a single stream of data.
+Receiving multiple data streams can therefore be achieved by creating multiple input DStreams
+and configuring them to receive different partitions of the data stream from the source(s).
+For example, a single Kafka input stream receiving two topics of data can be split into two
+Kafka input streams, each receiving only one topic. This would run two receivers on two workers,
+thus allowing data to received in parallel, and increasing overall throughput.
+
+Another parameter that should be considered is the receiver's blocking interval. For most receivers,
+the received data is coalesced together into large blocks of data before storing inside Spark's memory.
+The number of blocks in each batch determines the number of tasks that will be used to process those
+the received data in a map-like transformation. This blocking interval is determined by the
+[configuration parameter](configuration.html) `spark.streaming.blockInterval` and the default value
+is 200 milliseconds.
+
+An alternative to receiving data with multiple input streams / receivers is to explicitly repartition
+the input data stream (using `inputStream.repartition(<number of partitions>)`).
+This distributes the received batches of data across all the machines in the cluster
+before further processing.
+
+### Level of Parallelism in Data Processing
 Cluster resources maybe under-utilized if the number of parallel tasks used in any stage of the
 computation is not high enough. For example, for distributed reduce operations like `reduceByKey`
 and `reduceByKeyAndWindow`, the default number of parallel tasks is 8. You can pass the level of
@@ -921,16 +990,22 @@ These changes may reduce batch processing time by 100s of milliseconds,
 thus allowing sub-second batch size to be viable.
 
 ## Setting the Right Batch Size
-For a Spark Streaming application running on a cluster to be stable, the processing of the data
-streams must keep up with the rate of ingestion of the data streams. Depending on the type of
-computation, the batch size used may have significant impact on the rate of ingestion that can be
-sustained by the Spark Streaming application on a fixed cluster resources. For example, let us
+For a Spark Streaming application running on a cluster to be stable, the system should be able to
+process data as fast as it is being received. In other words, batches of data should be processed
+as fast as they are being generated. Whether this is true for an application can be found by
+[monitoring](#monitoring) the processing times in the streaming web UI, where the batch
+processing time should be less than the batch interval.
+
+Depending on the nature of the streaming
+computation, the batch interval used may have significant impact on the data rates that can be
+sustained by the application on a fixed set of cluster resources. For example, let us
 consider the earlier WordCountNetwork example. For a particular data rate, the system may be able
-to keep up with reporting word counts every 2 seconds (i.e., batch size of 2 seconds), but not
-every 500 milliseconds.
+to keep up with reporting word counts every 2 seconds (i.e., batch interval of 2 seconds), but not
+every 500 milliseconds. So the batch interval needs to be set such that the expected data rate in
+production can be sustained.
 
 A good approach to figure out the right batch size for your application is to test it with a
-conservative batch size (say, 5-10 seconds) and a low data rate. To verify whether the system
+conservative batch interval (say, 5-10 seconds) and a low data rate. To verify whether the system
 is able to keep up with data rate, you can check the value of the end-to-end delay experienced
 by each processed batch (either look for "Total delay" in Spark driver log4j logs, or use the
 [StreamingListener](api/scala/index.html#org.apache.spark.streaming.scheduler.StreamingListener)
@@ -942,29 +1017,6 @@ data rate and/or reducing the batch size. Note that momentary increase in the de
 temporary data rate increases maybe fine as long as the delay reduces back to a low value
 (i.e., less than batch size).
 
-## 24/7 Operation
-By default, Spark does not forget any of the metadata (RDDs generated, stages processed, etc.).
-But for a Spark Streaming application to operate 24/7, it is necessary for Spark to do periodic
-cleanup of it metadata. This can be enabled by setting the
-[configuration property](configuration.html#spark-properties) `spark.cleaner.ttl` to the number of
-seconds you want any metadata to persist. For example, setting `spark.cleaner.ttl` to 600 would
-cause Spark periodically cleanup all metadata and persisted RDDs that are older than 10 minutes.
-Note, that this property needs to be set before the SparkContext is created.
-
-This value is closely tied with any window operation that is being used. Any window operation
-would require the input data to be persisted in memory for at least the duration of the window.
-Hence it is necessary to set the delay to at least the value of the largest window operation used
-in the Spark Streaming application. If this delay is set too low, the application will throw an
-exception saying so.
-
-## Monitoring
-Besides Spark's in-built [monitoring capabilities](monitoring.html),
-the progress of a Spark Streaming program can also be monitored using the [StreamingListener]
-(api/scala/index.html#org.apache.spark.scheduler.StreamingListener) interface,
-which allows you to get statistics of batch processing times, queueing delays,
-and total end-to-end delays. Note that this is still an experimental API and it is likely to be
-improved upon (i.e., more information reported) in the future.
-
 ## Memory Tuning
 Tuning the memory usage and GC behavior of Spark applications have been discussed in great detail
 in the [Tuning Guide](tuning.html). It is recommended that you read that. In this section,
@@ -1249,18 +1301,80 @@ in the file. This is what the sequence of outputs would be with and without a dr
 If the driver had crashed in the middle of the processing of time 3, then it will process time 3
 and output 30 after recovery.
 
+***************************************************************************************************  
+
+# Migration Guide from 0.9.1 or below to 1.x
+Between Spark 0.9.1 and Spark 1.0, there were a few API changes made to ensure future API stability.
+This section elaborates the steps required to migrate your existing code to 1.0.
+
+**Input DStreams**: All operations that create an input stream (e.g., `StreamingContext.socketStream`,
+`FlumeUtils.createStream`, etc.) now returns
+[InputDStream](api/scala/index.html#org.apache.spark.streaming.dstream.InputDStream) /
+[ReceiverInputDStream](api/scala/index.html#org.apache.spark.streaming.dstream.ReceiverInputDStream)
+(instead of DStream) for Scala, and [JavaInputDStream](api/java/org/apache/spark/streaming/api/java/JavaInputDStream.html) /
+[JavaPairInputDStream](api/java/org/apache/spark/streaming/api/java/JavaPairInputDStream.html) /
+[JavaReceiverInputDStream](api/java/org/apache/spark/streaming/api/java/JavaReceiverInputDStream.html) /
+[JavaPairReceiverInputDStream](api/java/org/apache/spark/streaming/api/java/JavaPairReceiverInputDStream.html)
+(instead of JavaDStream) for Java. This ensures that functionality specific to input streams can
+be added to these classes in the future without breaking binary compatibility.
+Note that your existing Spark Streaming applications should not require any change
+(as these new classes are subclasses of DStream/JavaDStream) but may require recompilation with Spark 1.0.
+
+**Custom Network Receivers**: Since the release to Spark Streaming, custom network receivers could be defined
+in Scala using the class NetworkReceiver. However, the API was limited in terms of error handling
+and reporting, and could not be used from Java. Starting Spark 1.0, this class has been
+replaced by [Receiver](api/scala/index.html#org.apache.spark.streaming.receiver.Receiver) which has
+the following advantages.
+
+* Methods like `stop` and `restart` have been added to for better control of the lifecycle of a receiver. See
+the [custom receiver guide](streaming-custom-receiver.html) for more details.
+* Custom receivers can be implemented using both Scala and Java.
+
+To migrate your existing custom receivers from the earlier NetworkReceiver to the new Receiver, you have
+to do the following.
+
+* Make your custom receiver class extend
+[`org.apache.spark.streaming.receiver.Receiver`](api/scala/index.html#org.apache.spark.streaming.receiver.Receiver)
+instead of `org.apache.spark.streaming.dstream.NetworkReceiver`.
+* Earlier, a BlockGenerator object had to be created by the custom receiver, to which received data was
+added for being stored in Spark. It had to be explicitly started and stopped from `onStart()` and `onStop()`
+methods. The new Receiver class makes this unnecessary as it adds a set of methods named `store(<data>)`
+that can be called to store the data in Spark. So, to migrate your custom network receiver, remove any
+BlockGenerator object (does not exist any more in Spark 1.0 anyway), and use `store(...)` methods on
+received data.
+
+**Actor-based Receivers**: Data could have been received using any Akka Actors by extending the actor class with
+`org.apache.spark.streaming.receivers.Receiver` trait. This has been renamed to
+[`org.apache.spark.streaming.receiver.ActorHelper`](api/scala/index.html#org.apache.spark.streaming.receiver.ActorHelper)
+and the `pushBlock(...)` methods to store received data has been renamed to `store(...)`. Other helper classes in
+the `org.apache.spark.streaming.receivers` package were also moved
+to [`org.apache.spark.streaming.receiver`](api/scala/index.html#org.apache.spark.streaming.receiver.package)
+package and renamed for better clarity.
+
+***************************************************************************************************
+
 # Where to Go from Here
 
 * API documentation
-  - Main docs of StreamingContext and DStreams in [Scala](api/scala/index.html#org.apache.spark.streaming.package)
-    and [Java](api/scala/index.html#org.apache.spark.streaming.api.java.package)
-  - Additional docs for
-    [Kafka](api/scala/index.html#org.apache.spark.streaming.kafka.KafkaUtils$),
-    [Flume](api/scala/index.html#org.apache.spark.streaming.flume.FlumeUtils$),
-    [Twitter](api/scala/index.html#org.apache.spark.streaming.twitter.TwitterUtils$),
-    [ZeroMQ](api/scala/index.html#org.apache.spark.streaming.zeromq.ZeroMQUtils$), and
-    [MQTT](api/scala/index.html#org.apache.spark.streaming.mqtt.MQTTUtils$)
+  - Scala docs
+    * [StreamingContext](api/scala/index.html#org.apache.spark.streaming.StreamingContext) and
+  [DStream](api/scala/index.html#org.apache.spark.streaming.dstream.DStream)
+    * [KafkaUtils](api/scala/index.html#org.apache.spark.streaming.kafka.KafkaUtils$),
+    [FlumeUtils](api/scala/index.html#org.apache.spark.streaming.flume.FlumeUtils$),
+    [TwitterUtils](api/scala/index.html#org.apache.spark.streaming.twitter.TwitterUtils$),
+    [ZeroMQUtils](api/scala/index.html#org.apache.spark.streaming.zeromq.ZeroMQUtils$), and
+    [MQTTUtils](api/scala/index.html#org.apache.spark.streaming.mqtt.MQTTUtils$)
+  - Java docs
+    * [JavaStreamingContext](api/java/org/apache/spark/streaming/api/java/JavaStreamingContext.html),
+    [JavaDStream](api/java/org/apache/spark/streaming/api/java/JavaDStream.html) and
+    [PairJavaDStream](api/java/org/apache/spark/streaming/api/java/PairJavaDStream.html)
+    * [KafkaUtils](api/java/org/apache/spark/streaming/kafka/KafkaUtils.html),
+    [FlumeUtils](api/java/org/apache/spark/streaming/flume/FlumeUtils.html),
+    [TwitterUtils](api/java/org/apache/spark/streaming/twitter/TwitterUtils.html),
+    [ZeroMQUtils](api/java/org/apache/spark/streaming/zeromq/ZeroMQUtils.html), and
+    [MQTTUtils](api/java/org/apache/spark/streaming/mqtt/MQTTUtils.html)
 
 * More examples in [Scala]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/scala/org/apache/spark/streaming/examples)
   and [Java]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/java/org/apache/spark/streaming/examples)
-* [Paper](http://www.eecs.berkeley.edu/Pubs/TechRpts/2012/EECS-2012-259.pdf) describing Spark Streaming.
+* [Paper](http://www.eecs.berkeley.edu/Pubs/TechRpts/2012/EECS-2012-259.pdf) and
+[video](http://youtu.be/g171ndOHgJ0) describing Spark Streaming.
diff --git a/examples/src/main/java/org/apache/spark/streaming/examples/JavaCustomReceiver.java b/examples/src/main/java/org/apache/spark/streaming/examples/JavaCustomReceiver.java
index a94fa621dc32..e36c7800be23 100644
--- a/examples/src/main/java/org/apache/spark/streaming/examples/JavaCustomReceiver.java
+++ b/examples/src/main/java/org/apache/spark/streaming/examples/JavaCustomReceiver.java
@@ -26,6 +26,7 @@
 import org.apache.spark.streaming.Duration;
 import org.apache.spark.streaming.api.java.JavaDStream;
 import org.apache.spark.streaming.api.java.JavaPairDStream;
+import org.apache.spark.streaming.api.java.JavaReceiverInputDStream;
 import org.apache.spark.streaming.api.java.JavaStreamingContext;
 import org.apache.spark.streaming.receiver.Receiver;
 import scala.Tuple2;
@@ -69,7 +70,7 @@ public static void main(String[] args) {
 
     // Create a input stream with the custom receiver on target ip:port and count the
     // words in input stream of \n delimited text (eg. generated by 'nc')
-    JavaDStream<String> lines = ssc.receiverStream(
+    JavaReceiverInputDStream<String> lines = ssc.receiverStream(
       new JavaCustomReceiver(args[1], Integer.parseInt(args[2])));
     JavaDStream<String> words = lines.flatMap(new FlatMapFunction<String, String>() {
       @Override
diff --git a/examples/src/main/java/org/apache/spark/streaming/examples/JavaFlumeEventCount.java b/examples/src/main/java/org/apache/spark/streaming/examples/JavaFlumeEventCount.java
index f061001dd264..c59f7538f8a8 100644
--- a/examples/src/main/java/org/apache/spark/streaming/examples/JavaFlumeEventCount.java
+++ b/examples/src/main/java/org/apache/spark/streaming/examples/JavaFlumeEventCount.java
@@ -58,7 +58,7 @@ public static void main(String[] args) {
     JavaStreamingContext ssc = new JavaStreamingContext(master, "FlumeEventCount", batchInterval,
             System.getenv("SPARK_HOME"),
             JavaStreamingContext.jarOfClass(JavaFlumeEventCount.class));
-    JavaDStream<SparkFlumeEvent> flumeStream = FlumeUtils.createStream(ssc, "localhost", port);
+    JavaReceiverInputDStream<SparkFlumeEvent> flumeStream = FlumeUtils.createStream(ssc, "localhost", port);
 
     flumeStream.count();
 
diff --git a/examples/src/main/java/org/apache/spark/streaming/examples/JavaKafkaWordCount.java b/examples/src/main/java/org/apache/spark/streaming/examples/JavaKafkaWordCount.java
index d704be08d694..8da9bcd05ac3 100644
--- a/examples/src/main/java/org/apache/spark/streaming/examples/JavaKafkaWordCount.java
+++ b/examples/src/main/java/org/apache/spark/streaming/examples/JavaKafkaWordCount.java
@@ -29,6 +29,7 @@
 import org.apache.spark.streaming.Duration;
 import org.apache.spark.streaming.api.java.JavaDStream;
 import org.apache.spark.streaming.api.java.JavaPairDStream;
+import org.apache.spark.streaming.api.java.JavaPairReceiverInputDStream;
 import org.apache.spark.streaming.api.java.JavaStreamingContext;
 import org.apache.spark.streaming.kafka.KafkaUtils;
 import scala.Tuple2;
@@ -73,7 +74,8 @@ public static void main(String[] args) {
       topicMap.put(topic, numThreads);
     }
 
-    JavaPairDStream<String, String> messages = KafkaUtils.createStream(jssc, args[1], args[2], topicMap);
+    JavaPairReceiverInputDStream<String, String> messages =
+            KafkaUtils.createStream(jssc, args[1], args[2], topicMap);
 
     JavaDStream<String> lines = messages.map(new Function<Tuple2<String, String>, String>() {
       @Override
diff --git a/examples/src/main/java/org/apache/spark/streaming/examples/JavaNetworkWordCount.java b/examples/src/main/java/org/apache/spark/streaming/examples/JavaNetworkWordCount.java
index 0cc9d0ae1a08..098c329ff680 100644
--- a/examples/src/main/java/org/apache/spark/streaming/examples/JavaNetworkWordCount.java
+++ b/examples/src/main/java/org/apache/spark/streaming/examples/JavaNetworkWordCount.java
@@ -18,6 +18,7 @@
 package org.apache.spark.streaming.examples;
 
 import com.google.common.collect.Lists;
+import org.apache.spark.streaming.api.java.JavaReceiverInputDStream;
 import scala.Tuple2;
 import org.apache.spark.api.java.function.FlatMapFunction;
 import org.apache.spark.api.java.function.Function2;
@@ -57,9 +58,9 @@ public static void main(String[] args) {
             new Duration(1000), System.getenv("SPARK_HOME"),
             JavaStreamingContext.jarOfClass(JavaNetworkWordCount.class));
 
-    // Create a NetworkInputDStream on target ip:port and count the
+    // Create a JavaReceiverInputDStream on target ip:port and count the
     // words in input stream of \n delimited text (eg. generated by 'nc')
-    JavaDStream<String> lines = ssc.socketTextStream(args[1], Integer.parseInt(args[2]));
+    JavaReceiverInputDStream<String> lines = ssc.socketTextStream(args[1], Integer.parseInt(args[2]));
     JavaDStream<String> words = lines.flatMap(new FlatMapFunction<String, String>() {
       @Override
       public Iterable<String> call(String x) {
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/receiver/Receiver.scala b/streaming/src/main/scala/org/apache/spark/streaming/receiver/Receiver.scala
index b310c22b3ab7..5acf8a9a811e 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/receiver/Receiver.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/receiver/Receiver.scala
@@ -28,9 +28,11 @@ import org.apache.spark.annotation.DeveloperApi
 /**
  * :: DeveloperApi ::
  * Abstract class of a receiver that can be run on worker nodes to receive external data. A
- * custom receiver can be defined by defining the functions onStart() and onStop(). onStart()
+ * custom receiver can be defined by defining the functions `onStart()` and `onStop()`. `onStart()`
  * should define the setup steps necessary to start receiving data,
- * and onStop() should define the cleanup steps necessary to stop receiving data.
+ * and `onStop()` should define the cleanup steps necessary to stop receiving data.
+ * Exceptions while receiving can be handled either by restarting the receiver with `restart(...)`
+ * or stopped completely by `stop(...)` or
  *
  * A custom receiver in Scala would look like this.
  *
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala
index ee0bc8b7d6a7..cd86019f63e7 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala
@@ -183,6 +183,7 @@ class StreamingContextSuite extends FunSuite with BeforeAndAfter with Timeouts w
         "Received records = " + TestReceiver.counter.get() + ", " +
           "processed records = " + runningCount
       )
+      Thread.sleep(100)
     }
   }
 

From cf0a8f0204bb8acdaf441b03c924c278fef08e28 Mon Sep 17 00:00:00 2001
From: Andrew Or <andrewor14@gmail.com>
Date: Mon, 5 May 2014 16:28:07 -0700
Subject: [PATCH 095/810] [SPARK-1681] Include datanucleus jars in Spark Hive
 distribution

This copies the datanucleus jars over from `lib_managed` into `dist/lib`, if any. The `CLASSPATH` must also be updated to reflect this change.

Author: Andrew Or <andrewor14@gmail.com>

Closes #610 from andrewor14/hive-distribution and squashes the following commits:

a4bc96f [Andrew Or] Rename search path in jar error check
fa205e1 [Andrew Or] Merge branch 'master' of github.com:apache/spark into hive-distribution
7855f58 [Andrew Or] Have jar command respect JAVA_HOME + check for jar errors both cases
c16bbfd [Andrew Or] Merge branch 'master' of github.com:apache/spark into hive-distribution
32f6826 [Andrew Or] Leave the double colons
940a1bb [Andrew Or] Add back 2>/dev/null
58357cc [Andrew Or] Include datanucleus jars in Spark distribution built with Hive support
---
 bin/compute-classpath.sh | 58 +++++++++++++++++++++++-----------------
 make-distribution.sh     | 11 +++++---
 2 files changed, 40 insertions(+), 29 deletions(-)

diff --git a/bin/compute-classpath.sh b/bin/compute-classpath.sh
index 8dc547b379f5..7df43a555d56 100755
--- a/bin/compute-classpath.sh
+++ b/bin/compute-classpath.sh
@@ -32,8 +32,8 @@ CLASSPATH="$SPARK_CLASSPATH:$SPARK_SUBMIT_CLASSPATH:$FWDIR/conf"
 
 ASSEMBLY_DIR="$FWDIR/assembly/target/scala-$SCALA_VERSION"
 
-if [ -n "${JAVA_HOME}" ]; then
-  JAR_CMD="${JAVA_HOME}/bin/jar"
+if [ -n "$JAVA_HOME" ]; then
+  JAR_CMD="$JAVA_HOME/bin/jar"
 else
   JAR_CMD="jar"
 fi
@@ -52,40 +52,48 @@ if [ -f "$ASSEMBLY_DIR"/spark-assembly*hadoop*-deps.jar ]; then
   CLASSPATH="$CLASSPATH:$FWDIR/sql/hive/target/scala-$SCALA_VERSION/classes"
   CLASSPATH="$CLASSPATH:$FWDIR/yarn/stable/target/scala-$SCALA_VERSION/classes"
 
-  DEPS_ASSEMBLY_JAR=`ls "$ASSEMBLY_DIR"/spark-assembly*hadoop*-deps.jar`
-  CLASSPATH="$CLASSPATH:$DEPS_ASSEMBLY_JAR"
+  ASSEMBLY_JAR=$(ls "$ASSEMBLY_DIR"/spark-assembly*hadoop*-deps.jar 2>/dev/null)
 else
   # Else use spark-assembly jar from either RELEASE or assembly directory
   if [ -f "$FWDIR/RELEASE" ]; then
-    ASSEMBLY_JAR=`ls "$FWDIR"/lib/spark-assembly*hadoop*.jar`
+    ASSEMBLY_JAR=$(ls "$FWDIR"/lib/spark-assembly*hadoop*.jar 2>/dev/null)
   else
-    ASSEMBLY_JAR=`ls "$ASSEMBLY_DIR"/spark-assembly*hadoop*.jar`
+    ASSEMBLY_JAR=$(ls "$ASSEMBLY_DIR"/spark-assembly*hadoop*.jar 2>/dev/null)
   fi
-  jar_error_check=$($JAR_CMD -tf $ASSEMBLY_JAR org/apache/spark/SparkContext 2>&1)
-  if [[ "$jar_error_check" =~ "invalid CEN header" ]]; then
-    echo "Loading Spark jar with '$JAR_CMD' failed. "
-    echo "This is likely because Spark was compiled with Java 7 and run "
-    echo "with Java 6. (see SPARK-1703). Please use Java 7 to run Spark "
-    echo "or build Spark with Java 6."
-    exit 1
-  fi
-  CLASSPATH="$CLASSPATH:$ASSEMBLY_JAR"
 fi
 
+# Verify that versions of java used to build the jars and run Spark are compatible
+jar_error_check=$("$JAR_CMD" -tf "$ASSEMBLY_JAR" nonexistent/class/path 2>&1)
+if [[ "$jar_error_check" =~ "invalid CEN header" ]]; then
+  echo "Loading Spark jar with '$JAR_CMD' failed. "
+  echo "This is likely because Spark was compiled with Java 7 and run "
+  echo "with Java 6. (see SPARK-1703). Please use Java 7 to run Spark "
+  echo "or build Spark with Java 6."
+  exit 1
+fi
+
+CLASSPATH="$CLASSPATH:$ASSEMBLY_JAR"
+
 # When Hive support is needed, Datanucleus jars must be included on the classpath.
-# Datanucleus jars do not work if only included in the  uber jar as plugin.xml metadata is lost.
+# Datanucleus jars do not work if only included in the uber jar as plugin.xml metadata is lost.
 # Both sbt and maven will populate "lib_managed/jars/" with the datanucleus jars when Spark is
 # built with Hive, so first check if the datanucleus jars exist, and then ensure the current Spark
 # assembly is built for Hive, before actually populating the CLASSPATH with the jars.
 # Note that this check order is faster (by up to half a second) in the case where Hive is not used.
-num_datanucleus_jars=$(ls "$FWDIR"/lib_managed/jars/ 2>/dev/null | grep "datanucleus-.*\\.jar" | wc -l)
-if [ $num_datanucleus_jars -gt 0 ]; then
-  AN_ASSEMBLY_JAR=${ASSEMBLY_JAR:-$DEPS_ASSEMBLY_JAR}
-  num_hive_files=$(jar tvf "$AN_ASSEMBLY_JAR" org/apache/hadoop/hive/ql/exec 2>/dev/null | wc -l)
-  if [ $num_hive_files -gt 0 ]; then
+if [ -f "$FWDIR/RELEASE" ]; then
+  datanucleus_dir="$FWDIR"/lib
+else
+  datanucleus_dir="$FWDIR"/lib_managed/jars
+fi
+
+datanucleus_jars=$(find "$datanucleus_dir" 2>/dev/null | grep "datanucleus-.*\\.jar")
+datanucleus_jars=$(echo "$datanucleus_jars" | tr "\n" : | sed s/:$//g)
+
+if [ -n "$datanucleus_jars" ]; then
+  hive_files=$("$JAR_CMD" -tf "$ASSEMBLY_JAR" org/apache/hadoop/hive/ql/exec 2>/dev/null)
+  if [ -n "$hive_files" ]; then
     echo "Spark assembly has been built with Hive, including Datanucleus jars on classpath" 1>&2
-    DATANUCLEUSJARS=$(echo "$FWDIR/lib_managed/jars"/datanucleus-*.jar | tr " " :)
-    CLASSPATH=$CLASSPATH:$DATANUCLEUSJARS
+    CLASSPATH="$CLASSPATH:$datanucleus_jars"
   fi
 fi
 
@@ -105,10 +113,10 @@ fi
 # Add hadoop conf dir if given -- otherwise FileSystem.*, etc fail !
 # Note, this assumes that there is either a HADOOP_CONF_DIR or YARN_CONF_DIR which hosts
 # the configurtion files.
-if [ "x" != "x$HADOOP_CONF_DIR" ]; then
+if [ -n "$HADOOP_CONF_DIR" ]; then
   CLASSPATH="$CLASSPATH:$HADOOP_CONF_DIR"
 fi
-if [ "x" != "x$YARN_CONF_DIR" ]; then
+if [ -n "$YARN_CONF_DIR" ]; then
   CLASSPATH="$CLASSPATH:$YARN_CONF_DIR"
 fi
 
diff --git a/make-distribution.sh b/make-distribution.sh
index 2ab64ff4598a..ff18d01e7a61 100755
--- a/make-distribution.sh
+++ b/make-distribution.sh
@@ -51,13 +51,13 @@ if [ $? != 0 ]; then
     exit -1;
 fi
 
-if [ -z "${JAVA_HOME}" ]; then
+if [ -z "$JAVA_HOME" ]; then
   echo "Error: JAVA_HOME is not set, cannot proceed."
   exit -1
 fi
 
-JAVA_CMD=$JAVA_HOME/bin/java
-JAVA_VERSION=$($JAVA_CMD -version 2>&1)
+JAVA_CMD="$JAVA_HOME"/bin/java
+JAVA_VERSION=$("$JAVA_CMD" -version 2>&1)
 if ! [[ "$JAVA_VERSION" =~ "1.6" ]]; then
   echo "Error: JAVA_HOME must point to a JDK 6 installation (see SPARK-1703)."
   echo "Output from 'java -version' was:"
@@ -162,6 +162,10 @@ echo "Spark $VERSION built for Hadoop $SPARK_HADOOP_VERSION" > "$DISTDIR/RELEASE
 cp $FWDIR/assembly/target/scala*/*assembly*hadoop*.jar "$DISTDIR/lib/"
 cp $FWDIR/examples/target/scala*/spark-examples*.jar "$DISTDIR/lib/"
 
+if [ "$SPARK_HIVE" == "true" ]; then
+  cp $FWDIR/lib_managed/jars/datanucleus*.jar "$DISTDIR/lib/"
+fi
+
 # Copy other things
 mkdir "$DISTDIR"/conf
 cp "$FWDIR"/conf/*.template "$DISTDIR"/conf
@@ -170,7 +174,6 @@ cp -r "$FWDIR/bin" "$DISTDIR"
 cp -r "$FWDIR/python" "$DISTDIR"
 cp -r "$FWDIR/sbin" "$DISTDIR"
 
-
 # Download and copy in tachyon, if requested
 if [ "$SPARK_TACHYON" == "true" ]; then
   TACHYON_VERSION="0.4.1"

From 8e724dcbad411c533e4f0cd053aa878e8a60694d Mon Sep 17 00:00:00 2001
From: Sandy Ryza <sandy@cloudera.com>
Date: Mon, 5 May 2014 18:26:34 -0700
Subject: [PATCH 096/810] SPARK-1728. JavaRDDLike.mapPartitionsWithIndex
 requires ClassTag

Author: Sandy Ryza <sandy@cloudera.com>

Closes #657 from sryza/sandy-spark-1728 and squashes the following commits:

4751443 [Sandy Ryza] SPARK-1728. JavaRDDLike.mapPartitionsWithIndex requires ClassTag
---
 .../main/scala/org/apache/spark/api/java/JavaRDDLike.scala    | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala b/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala
index af06d1dca94f..619bfd75be8e 100644
--- a/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala
+++ b/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala
@@ -75,11 +75,11 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable {
    * Return a new RDD by applying a function to each partition of this RDD, while tracking the index
    * of the original partition.
    */
-  def mapPartitionsWithIndex[R: ClassTag](
+  def mapPartitionsWithIndex[R](
       f: JFunction2[java.lang.Integer, java.util.Iterator[T], java.util.Iterator[R]],
       preservesPartitioning: Boolean = false): JavaRDD[R] =
     new JavaRDD(rdd.mapPartitionsWithIndex(((a,b) => f(a,asJavaIterator(b))),
-        preservesPartitioning))
+        preservesPartitioning)(fakeClassTag))(fakeClassTag)
 
   /**
    * Return a new RDD by applying a function to all elements of this RDD.

From ea10b3126167af3f50f7c2a70e1d942e839fcb66 Mon Sep 17 00:00:00 2001
From: Andrew Or <andrewor14@gmail.com>
Date: Mon, 5 May 2014 18:32:14 -0700
Subject: [PATCH 097/810] Expose SparkListeners and relevant classes as
 DeveloperApi

Hopefully this can go into 1.0, as a few people on the user list have asked for this.

Author: Andrew Or <andrewor14@gmail.com>

Closes #648 from andrewor14/expose-listeners and squashes the following commits:

e45e1ef [Andrew Or] Add missing colons (minor)
350d643 [Andrew Or] Expose SparkListeners and relevant classes as DeveloperApi
---
 .../org/apache/spark/storage/BlockId.scala    | 24 +++++++++++++------
 .../apache/spark/storage/BlockManagerId.scala | 15 +++++++-----
 .../storage/BlockManagerMasterActor.scala     |  4 +++-
 .../apache/spark/storage/StorageLevel.scala   | 17 +++++++------
 .../spark/storage/StorageStatusListener.scala |  7 ++++--
 .../apache/spark/storage/StorageUtils.scala   |  9 +++++--
 .../apache/spark/ui/env/EnvironmentTab.scala  |  5 +++-
 .../apache/spark/ui/exec/ExecutorsTab.scala   |  5 +++-
 .../spark/ui/jobs/ExecutorSummary.scala       | 10 ++++++--
 .../spark/ui/jobs/JobProgressListener.scala   |  8 +++++--
 .../apache/spark/ui/storage/StorageTab.scala  |  7 ++++--
 11 files changed, 78 insertions(+), 33 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/storage/BlockId.scala b/core/src/main/scala/org/apache/spark/storage/BlockId.scala
index cffea28fbf79..42ec181b00bb 100644
--- a/core/src/main/scala/org/apache/spark/storage/BlockId.scala
+++ b/core/src/main/scala/org/apache/spark/storage/BlockId.scala
@@ -19,14 +19,18 @@ package org.apache.spark.storage
 
 import java.util.UUID
 
+import org.apache.spark.annotation.DeveloperApi
+
 /**
+ * :: DeveloperApi ::
  * Identifies a particular Block of data, usually associated with a single file.
  * A Block can be uniquely identified by its filename, but each type of Block has a different
  * set of keys which produce its unique name.
  *
  * If your BlockId should be serializable, be sure to add it to the BlockId.apply() method.
  */
-private[spark] sealed abstract class BlockId {
+@DeveloperApi
+sealed abstract class BlockId {
   /** A globally unique identifier for this Block. Can be used for ser/de. */
   def name: String
 
@@ -44,24 +48,29 @@ private[spark] sealed abstract class BlockId {
   }
 }
 
-private[spark] case class RDDBlockId(rddId: Int, splitIndex: Int) extends BlockId {
+@DeveloperApi
+case class RDDBlockId(rddId: Int, splitIndex: Int) extends BlockId {
   def name = "rdd_" + rddId + "_" + splitIndex
 }
 
-private[spark] case class ShuffleBlockId(shuffleId: Int, mapId: Int, reduceId: Int)
+@DeveloperApi
+case class ShuffleBlockId(shuffleId: Int, mapId: Int, reduceId: Int)
   extends BlockId {
   def name = "shuffle_" + shuffleId + "_" + mapId + "_" + reduceId
 }
 
-private[spark] case class BroadcastBlockId(broadcastId: Long, field: String = "") extends BlockId {
+@DeveloperApi
+case class BroadcastBlockId(broadcastId: Long, field: String = "") extends BlockId {
   def name = "broadcast_" + broadcastId + (if (field == "") "" else "_" + field)
 }
 
-private[spark] case class TaskResultBlockId(taskId: Long) extends BlockId {
+@DeveloperApi
+case class TaskResultBlockId(taskId: Long) extends BlockId {
   def name = "taskresult_" + taskId
 }
 
-private[spark] case class StreamBlockId(streamId: Int, uniqueId: Long) extends BlockId {
+@DeveloperApi
+case class StreamBlockId(streamId: Int, uniqueId: Long) extends BlockId {
   def name = "input-" + streamId + "-" + uniqueId
 }
 
@@ -75,7 +84,8 @@ private[spark] case class TestBlockId(id: String) extends BlockId {
   def name = "test_" + id
 }
 
-private[spark] object BlockId {
+@DeveloperApi
+object BlockId {
   val RDD = "rdd_([0-9]+)_([0-9]+)".r
   val SHUFFLE = "shuffle_([0-9]+)_([0-9]+)_([0-9]+)".r
   val BROADCAST = "broadcast_([0-9]+)([_A-Za-z0-9]*)".r
diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManagerId.scala b/core/src/main/scala/org/apache/spark/storage/BlockManagerId.scala
index be537d77309b..b1585bd8199d 100644
--- a/core/src/main/scala/org/apache/spark/storage/BlockManagerId.scala
+++ b/core/src/main/scala/org/apache/spark/storage/BlockManagerId.scala
@@ -20,17 +20,20 @@ package org.apache.spark.storage
 import java.io.{Externalizable, IOException, ObjectInput, ObjectOutput}
 import java.util.concurrent.ConcurrentHashMap
 
+import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.util.Utils
 
 /**
+ * :: DeveloperApi ::
  * This class represent an unique identifier for a BlockManager.
- * The first 2 constructors of this class is made private to ensure that
- * BlockManagerId objects can be created only using the apply method in
- * the companion object. This allows de-duplication of ID objects.
- * Also, constructor parameters are private to ensure that parameters cannot
- * be modified from outside this class.
+ *
+ * The first 2 constructors of this class is made private to ensure that BlockManagerId objects
+ * can be created only using the apply method in the companion object. This allows de-duplication
+ * of ID objects. Also, constructor parameters are private to ensure that parameters cannot be
+ * modified from outside this class.
  */
-private[spark] class BlockManagerId private (
+@DeveloperApi
+class BlockManagerId private (
     private var executorId_ : String,
     private var host_ : String,
     private var port_ : Int,
diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterActor.scala b/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterActor.scala
index 63fa5d3eb654..98fa0df6ec28 100644
--- a/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterActor.scala
+++ b/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterActor.scala
@@ -28,6 +28,7 @@ import akka.actor.{Actor, ActorRef, Cancellable}
 import akka.pattern.ask
 
 import org.apache.spark.{Logging, SparkConf, SparkException}
+import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.scheduler._
 import org.apache.spark.storage.BlockManagerMessages._
 import org.apache.spark.util.{AkkaUtils, Utils}
@@ -411,7 +412,8 @@ class BlockManagerMasterActor(val isLocal: Boolean, conf: SparkConf, listenerBus
   }
 }
 
-private[spark] case class BlockStatus(
+@DeveloperApi
+case class BlockStatus(
     storageLevel: StorageLevel,
     memSize: Long,
     diskSize: Long,
diff --git a/core/src/main/scala/org/apache/spark/storage/StorageLevel.scala b/core/src/main/scala/org/apache/spark/storage/StorageLevel.scala
index c9a52e0366d9..363de93e067b 100644
--- a/core/src/main/scala/org/apache/spark/storage/StorageLevel.scala
+++ b/core/src/main/scala/org/apache/spark/storage/StorageLevel.scala
@@ -22,14 +22,17 @@ import java.io.{Externalizable, IOException, ObjectInput, ObjectOutput}
 import org.apache.spark.annotation.DeveloperApi
 
 /**
+ * :: DeveloperApi ::
  * Flags for controlling the storage of an RDD. Each StorageLevel records whether to use memory,
  * or Tachyon, whether to drop the RDD to disk if it falls out of memory or Tachyon , whether to
  * keep the data in memory in a serialized format, and whether to replicate the RDD partitions on
  * multiple nodes.
+ *
  * The [[org.apache.spark.storage.StorageLevel$]] singleton object contains some static constants
  * for commonly useful storage levels. To create your own storage level object, use the
  * factory method of the singleton object (`StorageLevel(...)`).
  */
+@DeveloperApi
 class StorageLevel private(
     private var useDisk_ : Boolean,
     private var useMemory_ : Boolean,
@@ -54,9 +57,9 @@ class StorageLevel private(
   assert(replication < 40, "Replication restricted to be less than 40 for calculating hashcodes")
 
   if (useOffHeap) {
-    require(useDisk == false, "Off-heap storage level does not support using disk")
-    require(useMemory == false, "Off-heap storage level does not support using heap memory")
-    require(deserialized == false, "Off-heap storage level does not support deserialized storage")
+    require(!useDisk, "Off-heap storage level does not support using disk")
+    require(!useMemory, "Off-heap storage level does not support using heap memory")
+    require(!deserialized, "Off-heap storage level does not support deserialized storage")
     require(replication == 1, "Off-heap storage level does not support multiple replication")
   }
 
@@ -146,7 +149,7 @@ object StorageLevel {
 
   /**
    * :: DeveloperApi ::
-   * Create a new StorageLevel object without setting useOffHeap
+   * Create a new StorageLevel object without setting useOffHeap.
    */
   @DeveloperApi
   def apply(useDisk: Boolean, useMemory: Boolean, useOffHeap: Boolean,
@@ -155,7 +158,7 @@ object StorageLevel {
 
   /**
    * :: DeveloperApi ::
-   * Create a new StorageLevel object
+   * Create a new StorageLevel object.
    */
   @DeveloperApi
   def apply(useDisk: Boolean, useMemory: Boolean,
@@ -164,7 +167,7 @@ object StorageLevel {
 
   /**
    * :: DeveloperApi ::
-   * Create a new StorageLevel object from its integer representation
+   * Create a new StorageLevel object from its integer representation.
    */
   @DeveloperApi
   def apply(flags: Int, replication: Int): StorageLevel =
@@ -172,7 +175,7 @@ object StorageLevel {
 
   /**
    * :: DeveloperApi ::
-   * Read StorageLevel object from ObjectInput stream
+   * Read StorageLevel object from ObjectInput stream.
    */
   @DeveloperApi
   def apply(in: ObjectInput): StorageLevel = {
diff --git a/core/src/main/scala/org/apache/spark/storage/StorageStatusListener.scala b/core/src/main/scala/org/apache/spark/storage/StorageStatusListener.scala
index 7a174959037b..a6e6627d54e0 100644
--- a/core/src/main/scala/org/apache/spark/storage/StorageStatusListener.scala
+++ b/core/src/main/scala/org/apache/spark/storage/StorageStatusListener.scala
@@ -19,12 +19,15 @@ package org.apache.spark.storage
 
 import scala.collection.mutable
 
+import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.scheduler._
 
 /**
- * A SparkListener that maintains executor storage status
+ * :: DeveloperApi ::
+ * A SparkListener that maintains executor storage status.
  */
-private[spark] class StorageStatusListener extends SparkListener {
+@DeveloperApi
+class StorageStatusListener extends SparkListener {
   private val executorIdToStorageStatus = mutable.Map[String, StorageStatus]()
 
   def storageStatusList = executorIdToStorageStatus.values.toSeq
diff --git a/core/src/main/scala/org/apache/spark/storage/StorageUtils.scala b/core/src/main/scala/org/apache/spark/storage/StorageUtils.scala
index 1eddd1cdc483..6f3252a2f6d3 100644
--- a/core/src/main/scala/org/apache/spark/storage/StorageUtils.scala
+++ b/core/src/main/scala/org/apache/spark/storage/StorageUtils.scala
@@ -21,9 +21,14 @@ import scala.collection.Map
 import scala.collection.mutable
 
 import org.apache.spark.SparkContext
+import org.apache.spark.annotation.DeveloperApi
 
-/** Storage information for each BlockManager. */
-private[spark] class StorageStatus(
+/**
+ * :: DeveloperApi ::
+ * Storage information for each BlockManager.
+ */
+@DeveloperApi
+class StorageStatus(
     val blockManagerId: BlockManagerId,
     val maxMem: Long,
     val blocks: mutable.Map[BlockId, BlockStatus] = mutable.Map.empty) {
diff --git a/core/src/main/scala/org/apache/spark/ui/env/EnvironmentTab.scala b/core/src/main/scala/org/apache/spark/ui/env/EnvironmentTab.scala
index 03b46e1bd59a..bbbe55ecf44a 100644
--- a/core/src/main/scala/org/apache/spark/ui/env/EnvironmentTab.scala
+++ b/core/src/main/scala/org/apache/spark/ui/env/EnvironmentTab.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.ui.env
 
+import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.scheduler._
 import org.apache.spark.ui._
 
@@ -30,9 +31,11 @@ private[ui] class EnvironmentTab(parent: SparkUI) extends WebUITab(parent, "envi
 }
 
 /**
+ * :: DeveloperApi ::
  * A SparkListener that prepares information to be displayed on the EnvironmentTab
  */
-private[ui] class EnvironmentListener extends SparkListener {
+@DeveloperApi
+class EnvironmentListener extends SparkListener {
   var jvmInformation = Seq[(String, String)]()
   var sparkProperties = Seq[(String, String)]()
   var systemProperties = Seq[(String, String)]()
diff --git a/core/src/main/scala/org/apache/spark/ui/exec/ExecutorsTab.scala b/core/src/main/scala/org/apache/spark/ui/exec/ExecutorsTab.scala
index 5678bf34ac73..91d37b835b19 100644
--- a/core/src/main/scala/org/apache/spark/ui/exec/ExecutorsTab.scala
+++ b/core/src/main/scala/org/apache/spark/ui/exec/ExecutorsTab.scala
@@ -20,6 +20,7 @@ package org.apache.spark.ui.exec
 import scala.collection.mutable.HashMap
 
 import org.apache.spark.ExceptionFailure
+import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.scheduler._
 import org.apache.spark.storage.StorageStatusListener
 import org.apache.spark.ui.{SparkUI, WebUITab}
@@ -34,9 +35,11 @@ private[ui] class ExecutorsTab(parent: SparkUI) extends WebUITab(parent, "execut
 }
 
 /**
+ * :: DeveloperApi ::
  * A SparkListener that prepares information to be displayed on the ExecutorsTab
  */
-private[ui] class ExecutorsListener(storageStatusListener: StorageStatusListener)
+@DeveloperApi
+class ExecutorsListener(storageStatusListener: StorageStatusListener)
   extends SparkListener {
 
   val executorToTasksActive = HashMap[String, Int]()
diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/ExecutorSummary.scala b/core/src/main/scala/org/apache/spark/ui/jobs/ExecutorSummary.scala
index 1dfe1d4f1fa1..2aaf6329b792 100644
--- a/core/src/main/scala/org/apache/spark/ui/jobs/ExecutorSummary.scala
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/ExecutorSummary.scala
@@ -17,8 +17,14 @@
 
 package org.apache.spark.ui.jobs
 
-/** class for reporting aggregated metrics for each executors in stageUI */
-private[ui] class ExecutorSummary {
+import org.apache.spark.annotation.DeveloperApi
+
+/**
+ * :: DeveloperApi ::
+ * Class for reporting aggregated metrics for each executor in stage UI.
+ */
+@DeveloperApi
+class ExecutorSummary {
   var taskTime : Long = 0
   var failedTasks : Int = 0
   var succeededTasks : Int = 0
diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/JobProgressListener.scala b/core/src/main/scala/org/apache/spark/ui/jobs/JobProgressListener.scala
index 0db4afa701b4..396cbcbc8d26 100644
--- a/core/src/main/scala/org/apache/spark/ui/jobs/JobProgressListener.scala
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/JobProgressListener.scala
@@ -20,19 +20,22 @@ package org.apache.spark.ui.jobs
 import scala.collection.mutable.{HashMap, ListBuffer}
 
 import org.apache.spark.{ExceptionFailure, SparkConf, SparkContext, Success}
+import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.executor.TaskMetrics
 import org.apache.spark.scheduler._
 import org.apache.spark.scheduler.SchedulingMode.SchedulingMode
 import org.apache.spark.storage.BlockManagerId
 
 /**
+ * :: DeveloperApi ::
  * Tracks task-level information to be displayed in the UI.
  *
  * All access to the data structures in this class must be synchronized on the
  * class, since the UI thread and the EventBus loop may otherwise be reading and
  * updating the internal data structures concurrently.
  */
-private[ui] class JobProgressListener(conf: SparkConf) extends SparkListener {
+@DeveloperApi
+class JobProgressListener(conf: SparkConf) extends SparkListener {
 
   import JobProgressListener._
 
@@ -246,7 +249,8 @@ private[ui] class JobProgressListener(conf: SparkConf) extends SparkListener {
 
 }
 
-private[ui] case class TaskUIData(
+@DeveloperApi
+case class TaskUIData(
     taskInfo: TaskInfo,
     taskMetrics: Option[TaskMetrics] = None,
     exception: Option[ExceptionFailure] = None)
diff --git a/core/src/main/scala/org/apache/spark/ui/storage/StorageTab.scala b/core/src/main/scala/org/apache/spark/ui/storage/StorageTab.scala
index 07ec297841f2..c4bb7aab5039 100644
--- a/core/src/main/scala/org/apache/spark/ui/storage/StorageTab.scala
+++ b/core/src/main/scala/org/apache/spark/ui/storage/StorageTab.scala
@@ -19,6 +19,7 @@ package org.apache.spark.ui.storage
 
 import scala.collection.mutable
 
+import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.ui._
 import org.apache.spark.scheduler._
 import org.apache.spark.storage.{RDDInfo, StorageStatusListener, StorageUtils}
@@ -35,9 +36,11 @@ private[ui] class StorageTab(parent: SparkUI) extends WebUITab(parent, "storage"
 }
 
 /**
- * A SparkListener that prepares information to be displayed on the BlockManagerUI
+ * :: DeveloperApi ::
+ * A SparkListener that prepares information to be displayed on the BlockManagerUI.
  */
-private[ui] class StorageListener(storageStatusListener: StorageStatusListener)
+@DeveloperApi
+class StorageListener(storageStatusListener: StorageStatusListener)
   extends SparkListener {
 
   private val _rddInfoMap = mutable.Map[Int, RDDInfo]()

From 98750a74daf7e2b873da85d2d5067f47e3bbdc4e Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Mon, 5 May 2014 18:32:54 -0700
Subject: [PATCH 098/810] [SPARK-1594][MLLIB] Cleaning up MLlib APIs and guide

Final pass before the v1.0 release.

* Remove `VectorRDDs`
* Move `BinaryClassificationMetrics` from `evaluation.binary` to `evaluation`
* Change default value of `addIntercept` to false and allow to add intercept in Ridge and Lasso.
* Clean `DecisionTree` package doc and test suite.
* Mark model constructors `private[spark]`
* Rename `loadLibSVMData` to `loadLibSVMFile` and hide `LabelParser` from users.
* Add `saveAsLibSVMFile`.
* Add `appendBias` to `MLUtils`.

Author: Xiangrui Meng <meng@databricks.com>

Closes #524 from mengxr/mllib-cleaning and squashes the following commits:

295dc8b [Xiangrui Meng] update loadLibSVMFile doc
1977ac1 [Xiangrui Meng] fix doc of appendBias
649fcf0 [Xiangrui Meng] rename loadLibSVMData to loadLibSVMFile; hide LabelParser from user APIs
54b812c [Xiangrui Meng] add appendBias
a71e7d0 [Xiangrui Meng] add saveAsLibSVMFile
d976295 [Xiangrui Meng] Merge branch 'master' into mllib-cleaning
b7e5cec [Xiangrui Meng] remove some experimental annotations and make model constructors private[mllib]
9b02b93 [Xiangrui Meng] minor code style update
a593ddc [Xiangrui Meng] fix python tests
fc28c18 [Xiangrui Meng] mark more classes experimental
f6cbbff [Xiangrui Meng] fix Java tests
0af70b0 [Xiangrui Meng] minor
6e139ef [Xiangrui Meng] Merge branch 'master' into mllib-cleaning
94e6dce [Xiangrui Meng] move BinaryLabelCounter and BinaryConfusionMatrixImpl to evaluation.binary
df34907 [Xiangrui Meng] clean DecisionTreeSuite to use LocalSparkContext
c81807f [Xiangrui Meng] set the default value of AddIntercept to false
03389c0 [Xiangrui Meng] allow to add intercept in Ridge and Lasso
c66c56f [Xiangrui Meng] move tree md to package object doc
a2695df [Xiangrui Meng] update guide for BinaryClassificationMetrics
9194f4c [Xiangrui Meng] move BinaryClassificationMetrics one level up
1c1a0e3 [Xiangrui Meng] remove VectorRDDs because it only contains one function that is not necessary for us to maintain
---
 docs/mllib-linear-methods.md                  |   2 +-
 .../examples/mllib/BinaryClassification.scala |   4 +-
 .../examples/mllib/LinearRegression.scala     |   4 +-
 .../examples/mllib/SparseNaiveBayes.scala     |   4 +-
 .../classification/LogisticRegression.scala   |   7 +-
 .../mllib/classification/NaiveBayes.scala     |   8 +-
 .../spark/mllib/classification/SVM.scala      |   7 +-
 .../spark/mllib/clustering/KMeans.scala       |   2 +
 .../spark/mllib/clustering/KMeansModel.scala  |   2 +-
 .../BinaryClassificationMetrics.scala         | 144 +++++-----------
 .../binary/BinaryConfusionMatrix.scala        |  29 ++++
 .../binary/BinaryLabelCounter.scala           |  50 ++++++
 .../linalg/SingularValueDecomposition.scala   |   8 +-
 .../linalg/distributed/CoordinateMatrix.scala |   2 +
 .../mllib/linalg/distributed/RowMatrix.scala  |   1 +
 .../mllib/optimization/GradientDescent.scala  |  16 +-
 .../MatrixFactorizationModel.scala            |  10 +-
 .../GeneralizedLinearAlgorithm.scala          |  15 +-
 .../apache/spark/mllib/regression/Lasso.scala |  12 +-
 .../mllib/regression/LinearRegression.scala   |   2 +-
 .../mllib/regression/RidgeRegression.scala    |  12 +-
 .../org/apache/spark/mllib/tree/README.md     |  17 --
 .../VectorRDDs.scala => tree/package.scala}   |  19 +--
 .../spark/mllib/util/LabelParsers.scala       |  13 +-
 .../org/apache/spark/mllib/util/MLUtils.scala | 155 +++++++++++++-----
 .../JavaLogisticRegressionSuite.java          |   6 +-
 .../mllib/classification/JavaSVMSuite.java    |   3 +-
 .../regression/JavaLinearRegressionSuite.java |   3 +-
 .../LogisticRegressionSuite.scala             |  20 +--
 .../spark/mllib/classification/SVMSuite.scala |   5 +-
 .../BinaryClassificationMetricsSuite.scala    |   3 +-
 .../spark/mllib/rdd/VectorRDDsSuite.scala     |  33 ----
 .../spark/mllib/recommendation/ALSSuite.scala |   1 -
 .../spark/mllib/regression/LassoSuite.scala   |   6 -
 .../regression/LinearRegressionSuite.scala    |   2 +-
 .../regression/RidgeRegressionSuite.scala     |   6 -
 .../spark/mllib/tree/DecisionTreeSuite.scala  |  16 +-
 .../spark/mllib/util/MLUtilsSuite.scala       |  66 ++++++--
 python/pyspark/mllib/classification.py        |   4 +-
 39 files changed, 390 insertions(+), 329 deletions(-)
 rename mllib/src/main/scala/org/apache/spark/mllib/evaluation/{binary => }/BinaryClassificationMetrics.scala (64%)
 create mode 100644 mllib/src/main/scala/org/apache/spark/mllib/evaluation/binary/BinaryLabelCounter.scala
 delete mode 100644 mllib/src/main/scala/org/apache/spark/mllib/tree/README.md
 rename mllib/src/main/scala/org/apache/spark/mllib/{rdd/VectorRDDs.scala => tree/package.scala} (69%)
 rename mllib/src/test/scala/org/apache/spark/mllib/evaluation/{binary => }/BinaryClassificationMetricsSuite.scala (96%)
 delete mode 100644 mllib/src/test/scala/org/apache/spark/mllib/rdd/VectorRDDsSuite.scala

diff --git a/docs/mllib-linear-methods.md b/docs/mllib-linear-methods.md
index 810844069849..ebb555f974bf 100644
--- a/docs/mllib-linear-methods.md
+++ b/docs/mllib-linear-methods.md
@@ -180,7 +180,7 @@ error.
 {% highlight scala %}
 import org.apache.spark.SparkContext
 import org.apache.spark.mllib.classification.SVMWithSGD
-import org.apache.spark.mllib.evaluation.binary.BinaryClassificationMetrics
+import org.apache.spark.mllib.evaluation.BinaryClassificationMetrics
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.linalg.Vectors
 import org.apache.spark.mllib.util.MLUtils
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/BinaryClassification.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/BinaryClassification.scala
index ec9de022c1d4..4001908c9801 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/BinaryClassification.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/BinaryClassification.scala
@@ -22,7 +22,7 @@ import scopt.OptionParser
 
 import org.apache.spark.{SparkConf, SparkContext}
 import org.apache.spark.mllib.classification.{LogisticRegressionWithSGD, SVMWithSGD}
-import org.apache.spark.mllib.evaluation.binary.BinaryClassificationMetrics
+import org.apache.spark.mllib.evaluation.BinaryClassificationMetrics
 import org.apache.spark.mllib.util.MLUtils
 import org.apache.spark.mllib.optimization.{SquaredL2Updater, L1Updater}
 
@@ -96,7 +96,7 @@ object BinaryClassification {
 
     Logger.getRootLogger.setLevel(Level.WARN)
 
-    val examples = MLUtils.loadLibSVMData(sc, params.input).cache()
+    val examples = MLUtils.loadLibSVMFile(sc, params.input).cache()
 
     val splits = examples.randomSplit(Array(0.8, 0.2))
     val training = splits(0).cache()
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/LinearRegression.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/LinearRegression.scala
index 1723ca693102..658d370f8656 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/LinearRegression.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/LinearRegression.scala
@@ -22,7 +22,7 @@ import scopt.OptionParser
 
 import org.apache.spark.{SparkConf, SparkContext}
 import org.apache.spark.mllib.regression.LinearRegressionWithSGD
-import org.apache.spark.mllib.util.{MulticlassLabelParser, MLUtils}
+import org.apache.spark.mllib.util.MLUtils
 import org.apache.spark.mllib.optimization.{SimpleUpdater, SquaredL2Updater, L1Updater}
 
 /**
@@ -82,7 +82,7 @@ object LinearRegression extends App {
 
     Logger.getRootLogger.setLevel(Level.WARN)
 
-    val examples = MLUtils.loadLibSVMData(sc, params.input, MulticlassLabelParser).cache()
+    val examples = MLUtils.loadLibSVMFile(sc, params.input, multiclass = true).cache()
 
     val splits = examples.randomSplit(Array(0.8, 0.2))
     val training = splits(0).cache()
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/SparseNaiveBayes.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/SparseNaiveBayes.scala
index 25b6768b8d72..537e68a0991a 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/SparseNaiveBayes.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/SparseNaiveBayes.scala
@@ -75,8 +75,8 @@ object SparseNaiveBayes {
     val minPartitions =
       if (params.minPartitions > 0) params.minPartitions else sc.defaultMinPartitions
 
-    val examples = MLUtils.loadLibSVMData(sc, params.input, MulticlassLabelParser,
-      params.numFeatures, minPartitions)
+    val examples =
+      MLUtils.loadLibSVMFile(sc, params.input, multiclass = true, params.numFeatures, minPartitions)
     // Cache examples because it will be used in both training and evaluation.
     examples.cache()
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala
index 780e8bae42b8..90aa8ac998ba 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.mllib.classification
 
+import org.apache.spark.annotation.Experimental
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.mllib.optimization._
 import org.apache.spark.mllib.regression._
@@ -29,7 +30,7 @@ import org.apache.spark.rdd.RDD
  * @param weights Weights computed for every feature.
  * @param intercept Intercept computed for this model.
  */
-class LogisticRegressionModel(
+class LogisticRegressionModel private[mllib] (
     override val weights: Vector,
     override val intercept: Double)
   extends GeneralizedLinearModel(weights, intercept) with ClassificationModel with Serializable {
@@ -37,18 +38,22 @@ class LogisticRegressionModel(
   private var threshold: Option[Double] = Some(0.5)
 
   /**
+   * :: Experimental ::
    * Sets the threshold that separates positive predictions from negative predictions. An example
    * with prediction score greater than or equal to this threshold is identified as an positive,
    * and negative otherwise. The default value is 0.5.
    */
+  @Experimental
   def setThreshold(threshold: Double): this.type = {
     this.threshold = Some(threshold)
     this
   }
 
   /**
+   * :: Experimental ::
    * Clears the threshold so that `predict` will output raw prediction scores.
    */
+  @Experimental
   def clearThreshold(): this.type = {
     threshold = None
     this
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala
index f6f62ce2de04..b6e0c4a80e27 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala
@@ -19,7 +19,6 @@ package org.apache.spark.mllib.classification
 
 import breeze.linalg.{DenseMatrix => BDM, DenseVector => BDV, argmax => brzArgmax, sum => brzSum}
 
-import org.apache.spark.annotation.Experimental
 import org.apache.spark.Logging
 import org.apache.spark.SparkContext._
 import org.apache.spark.mllib.linalg.Vector
@@ -27,7 +26,6 @@ import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.rdd.RDD
 
 /**
- * :: Experimental ::
  * Model for Naive Bayes Classifiers.
  *
  * @param labels list of labels
@@ -35,8 +33,7 @@ import org.apache.spark.rdd.RDD
  * @param theta log of class conditional probabilities, whose dimension is C-by-D,
  *              where D is number of features
  */
-@Experimental
-class NaiveBayesModel(
+class NaiveBayesModel private[mllib] (
     val labels: Array[Double],
     val pi: Array[Double],
     val theta: Array[Array[Double]]) extends ClassificationModel with Serializable {
@@ -124,6 +121,9 @@ class NaiveBayes private (private var lambda: Double) extends Serializable with
   }
 }
 
+/**
+ * Top-level methods for calling naive Bayes.
+ */
 object NaiveBayes {
   /**
    * Trains a Naive Bayes model given an RDD of `(label, features)` pairs.
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala
index 81b126717e9a..e05213536e64 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.mllib.classification
 
+import org.apache.spark.annotation.Experimental
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.mllib.optimization._
 import org.apache.spark.mllib.regression._
@@ -29,7 +30,7 @@ import org.apache.spark.rdd.RDD
  * @param weights Weights computed for every feature.
  * @param intercept Intercept computed for this model.
  */
-class SVMModel(
+class SVMModel private[mllib] (
     override val weights: Vector,
     override val intercept: Double)
   extends GeneralizedLinearModel(weights, intercept) with ClassificationModel with Serializable {
@@ -37,18 +38,22 @@ class SVMModel(
   private var threshold: Option[Double] = Some(0.0)
 
   /**
+   * :: Experimental ::
    * Sets the threshold that separates positive predictions from negative predictions. An example
    * with prediction score greater than or equal to this threshold is identified as an positive,
    * and negative otherwise. The default value is 0.0.
    */
+  @Experimental
   def setThreshold(threshold: Double): this.type = {
     this.threshold = Some(threshold)
     this
   }
 
   /**
+   * :: Experimental ::
    * Clears the threshold so that `predict` will output raw prediction scores.
    */
+  @Experimental
   def clearThreshold(): this.type = {
     threshold = None
     this
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala
index a64c5d44be40..de22fbb6ffc1 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala
@@ -21,6 +21,7 @@ import scala.collection.mutable.ArrayBuffer
 
 import breeze.linalg.{DenseVector => BDV, Vector => BV, norm => breezeNorm}
 
+import org.apache.spark.annotation.Experimental
 import org.apache.spark.Logging
 import org.apache.spark.SparkContext._
 import org.apache.spark.mllib.linalg.{Vector, Vectors}
@@ -81,6 +82,7 @@ class KMeans private (
    * this many times with random starting conditions (configured by the initialization mode), then
    * return the best clustering found over any run. Default: 1.
    */
+  @Experimental
   def setRuns(runs: Int): KMeans = {
     if (runs <= 0) {
       throw new IllegalArgumentException("Number of runs must be positive")
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala
index 18abbf2758b8..ce14b0624193 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala
@@ -24,7 +24,7 @@ import org.apache.spark.mllib.linalg.Vector
 /**
  * A clustering model for K-means. Each point belongs to the cluster with the closest center.
  */
-class KMeansModel(val clusterCenters: Array[Vector]) extends Serializable {
+class KMeansModel private[mllib] (val clusterCenters: Array[Vector]) extends Serializable {
 
   /** Total number of clusters. */
   def k: Int = clusterCenters.length
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/binary/BinaryClassificationMetrics.scala b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetrics.scala
similarity index 64%
rename from mllib/src/main/scala/org/apache/spark/mllib/evaluation/binary/BinaryClassificationMetrics.scala
rename to mllib/src/main/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetrics.scala
index ed7b0fc94336..079743742d86 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/binary/BinaryClassificationMetrics.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetrics.scala
@@ -15,83 +15,22 @@
  * limitations under the License.
  */
 
-package org.apache.spark.mllib.evaluation.binary
+package org.apache.spark.mllib.evaluation
 
-import org.apache.spark.rdd.{UnionRDD, RDD}
-import org.apache.spark.SparkContext._
-import org.apache.spark.mllib.evaluation.AreaUnderCurve
+import org.apache.spark.annotation.Experimental
 import org.apache.spark.Logging
+import org.apache.spark.SparkContext._
+import org.apache.spark.mllib.evaluation.binary._
+import org.apache.spark.rdd.{RDD, UnionRDD}
 
 /**
- * Implementation of [[org.apache.spark.mllib.evaluation.binary.BinaryConfusionMatrix]].
- *
- * @param count label counter for labels with scores greater than or equal to the current score
- * @param totalCount label counter for all labels
- */
-private case class BinaryConfusionMatrixImpl(
-    count: LabelCounter,
-    totalCount: LabelCounter) extends BinaryConfusionMatrix with Serializable {
-
-  /** number of true positives */
-  override def numTruePositives: Long = count.numPositives
-
-  /** number of false positives */
-  override def numFalsePositives: Long = count.numNegatives
-
-  /** number of false negatives */
-  override def numFalseNegatives: Long = totalCount.numPositives - count.numPositives
-
-  /** number of true negatives */
-  override def numTrueNegatives: Long = totalCount.numNegatives - count.numNegatives
-
-  /** number of positives */
-  override def numPositives: Long = totalCount.numPositives
-
-  /** number of negatives */
-  override def numNegatives: Long = totalCount.numNegatives
-}
-
-/**
+ * :: Experimental ::
  * Evaluator for binary classification.
  *
  * @param scoreAndLabels an RDD of (score, label) pairs.
  */
-class BinaryClassificationMetrics(scoreAndLabels: RDD[(Double, Double)])
-  extends Serializable with Logging {
-
-  private lazy val (
-      cumulativeCounts: RDD[(Double, LabelCounter)],
-      confusions: RDD[(Double, BinaryConfusionMatrix)]) = {
-    // Create a bin for each distinct score value, count positives and negatives within each bin,
-    // and then sort by score values in descending order.
-    val counts = scoreAndLabels.combineByKey(
-      createCombiner = (label: Double) => new LabelCounter(0L, 0L) += label,
-      mergeValue = (c: LabelCounter, label: Double) => c += label,
-      mergeCombiners = (c1: LabelCounter, c2: LabelCounter) => c1 += c2
-    ).sortByKey(ascending = false)
-    val agg = counts.values.mapPartitions({ iter =>
-      val agg = new LabelCounter()
-      iter.foreach(agg += _)
-      Iterator(agg)
-    }, preservesPartitioning = true).collect()
-    val partitionwiseCumulativeCounts =
-      agg.scanLeft(new LabelCounter())((agg: LabelCounter, c: LabelCounter) => agg.clone() += c)
-    val totalCount = partitionwiseCumulativeCounts.last
-    logInfo(s"Total counts: $totalCount")
-    val cumulativeCounts = counts.mapPartitionsWithIndex(
-      (index: Int, iter: Iterator[(Double, LabelCounter)]) => {
-        val cumCount = partitionwiseCumulativeCounts(index)
-        iter.map { case (score, c) =>
-          cumCount += c
-          (score, cumCount.clone())
-        }
-      }, preservesPartitioning = true)
-    cumulativeCounts.persist()
-    val confusions = cumulativeCounts.map { case (score, cumCount) =>
-      (score, BinaryConfusionMatrixImpl(cumCount, totalCount).asInstanceOf[BinaryConfusionMatrix])
-    }
-    (cumulativeCounts, confusions)
-  }
+@Experimental
+class BinaryClassificationMetrics(scoreAndLabels: RDD[(Double, Double)]) extends Logging {
 
   /** Unpersist intermediate RDDs used in the computation. */
   def unpersist() {
@@ -154,6 +93,41 @@ class BinaryClassificationMetrics(scoreAndLabels: RDD[(Double, Double)])
   /** Returns the (threshold, recall) curve. */
   def recallByThreshold(): RDD[(Double, Double)] = createCurve(Recall)
 
+  private lazy val (
+    cumulativeCounts: RDD[(Double, BinaryLabelCounter)],
+    confusions: RDD[(Double, BinaryConfusionMatrix)]) = {
+    // Create a bin for each distinct score value, count positives and negatives within each bin,
+    // and then sort by score values in descending order.
+    val counts = scoreAndLabels.combineByKey(
+      createCombiner = (label: Double) => new BinaryLabelCounter(0L, 0L) += label,
+      mergeValue = (c: BinaryLabelCounter, label: Double) => c += label,
+      mergeCombiners = (c1: BinaryLabelCounter, c2: BinaryLabelCounter) => c1 += c2
+    ).sortByKey(ascending = false)
+    val agg = counts.values.mapPartitions({ iter =>
+      val agg = new BinaryLabelCounter()
+      iter.foreach(agg += _)
+      Iterator(agg)
+    }, preservesPartitioning = true).collect()
+    val partitionwiseCumulativeCounts =
+      agg.scanLeft(new BinaryLabelCounter())(
+        (agg: BinaryLabelCounter, c: BinaryLabelCounter) => agg.clone() += c)
+    val totalCount = partitionwiseCumulativeCounts.last
+    logInfo(s"Total counts: $totalCount")
+    val cumulativeCounts = counts.mapPartitionsWithIndex(
+      (index: Int, iter: Iterator[(Double, BinaryLabelCounter)]) => {
+        val cumCount = partitionwiseCumulativeCounts(index)
+        iter.map { case (score, c) =>
+          cumCount += c
+          (score, cumCount.clone())
+        }
+      }, preservesPartitioning = true)
+    cumulativeCounts.persist()
+    val confusions = cumulativeCounts.map { case (score, cumCount) =>
+      (score, BinaryConfusionMatrixImpl(cumCount, totalCount).asInstanceOf[BinaryConfusionMatrix])
+    }
+    (cumulativeCounts, confusions)
+  }
+
   /** Creates a curve of (threshold, metric). */
   private def createCurve(y: BinaryClassificationMetricComputer): RDD[(Double, Double)] = {
     confusions.map { case (s, c) =>
@@ -170,35 +144,3 @@ class BinaryClassificationMetrics(scoreAndLabels: RDD[(Double, Double)])
     }
   }
 }
-
-/**
- * A counter for positives and negatives.
- *
- * @param numPositives number of positive labels
- * @param numNegatives number of negative labels
- */
-private class LabelCounter(
-    var numPositives: Long = 0L,
-    var numNegatives: Long = 0L) extends Serializable {
-
-  /** Processes a label. */
-  def +=(label: Double): LabelCounter = {
-    // Though we assume 1.0 for positive and 0.0 for negative, the following check will handle
-    // -1.0 for negative as well.
-    if (label > 0.5) numPositives += 1L else numNegatives += 1L
-    this
-  }
-
-  /** Merges another counter. */
-  def +=(other: LabelCounter): LabelCounter = {
-    numPositives += other.numPositives
-    numNegatives += other.numNegatives
-    this
-  }
-
-  override def clone: LabelCounter = {
-    new LabelCounter(numPositives, numNegatives)
-  }
-
-  override def toString: String = s"{numPos: $numPositives, numNeg: $numNegatives}"
-}
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/binary/BinaryConfusionMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/binary/BinaryConfusionMatrix.scala
index 75a75b216002..559c6ef7e725 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/binary/BinaryConfusionMatrix.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/binary/BinaryConfusionMatrix.scala
@@ -39,3 +39,32 @@ private[evaluation] trait BinaryConfusionMatrix {
   /** number of negatives */
   def numNegatives: Long = numFalsePositives + numTrueNegatives
 }
+
+/**
+ * Implementation of [[org.apache.spark.mllib.evaluation.binary.BinaryConfusionMatrix]].
+ *
+ * @param count label counter for labels with scores greater than or equal to the current score
+ * @param totalCount label counter for all labels
+ */
+private[evaluation] case class BinaryConfusionMatrixImpl(
+    count: BinaryLabelCounter,
+    totalCount: BinaryLabelCounter) extends BinaryConfusionMatrix {
+
+  /** number of true positives */
+  override def numTruePositives: Long = count.numPositives
+
+  /** number of false positives */
+  override def numFalsePositives: Long = count.numNegatives
+
+  /** number of false negatives */
+  override def numFalseNegatives: Long = totalCount.numPositives - count.numPositives
+
+  /** number of true negatives */
+  override def numTrueNegatives: Long = totalCount.numNegatives - count.numNegatives
+
+  /** number of positives */
+  override def numPositives: Long = totalCount.numPositives
+
+  /** number of negatives */
+  override def numNegatives: Long = totalCount.numNegatives
+}
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/binary/BinaryLabelCounter.scala b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/binary/BinaryLabelCounter.scala
new file mode 100644
index 000000000000..1e610c20092a
--- /dev/null
+++ b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/binary/BinaryLabelCounter.scala
@@ -0,0 +1,50 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.mllib.evaluation.binary
+
+/**
+ * A counter for positives and negatives.
+ *
+ * @param numPositives number of positive labels
+ * @param numNegatives number of negative labels
+ */
+private[evaluation] class BinaryLabelCounter(
+    var numPositives: Long = 0L,
+    var numNegatives: Long = 0L) extends Serializable {
+
+  /** Processes a label. */
+  def +=(label: Double): BinaryLabelCounter = {
+    // Though we assume 1.0 for positive and 0.0 for negative, the following check will handle
+    // -1.0 for negative as well.
+    if (label > 0.5) numPositives += 1L else numNegatives += 1L
+    this
+  }
+
+  /** Merges another counter. */
+  def +=(other: BinaryLabelCounter): BinaryLabelCounter = {
+    numPositives += other.numPositives
+    numNegatives += other.numNegatives
+    this
+  }
+
+  override def clone: BinaryLabelCounter = {
+    new BinaryLabelCounter(numPositives, numNegatives)
+  }
+
+  override def toString: String = s"{numPos: $numPositives, numNeg: $numNegatives}"
+}
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/SingularValueDecomposition.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/SingularValueDecomposition.scala
index 46b105457430..9669c364bad8 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/SingularValueDecomposition.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/SingularValueDecomposition.scala
@@ -17,5 +17,11 @@
 
 package org.apache.spark.mllib.linalg
 
-/** Represents singular value decomposition (SVD) factors. */
+import org.apache.spark.annotation.Experimental
+
+/**
+ * :: Experimental ::
+ * Represents singular value decomposition (SVD) factors.
+ */
+@Experimental
 case class SingularValueDecomposition[UType, VType](U: UType, s: Vector, V: VType)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/CoordinateMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/CoordinateMatrix.scala
index 56b8fdcda66e..06d8915f3bfa 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/CoordinateMatrix.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/CoordinateMatrix.scala
@@ -25,11 +25,13 @@ import org.apache.spark.SparkContext._
 import org.apache.spark.mllib.linalg.Vectors
 
 /**
+ * :: Experimental ::
  * Represents an entry in an distributed matrix.
  * @param i row index
  * @param j column index
  * @param value value of the entry
  */
+@Experimental
 case class MatrixEntry(i: Long, j: Long, value: Double)
 
 /**
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
index 0c0afcd9ec0d..b10857fe7c8a 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
@@ -427,6 +427,7 @@ class RowMatrix(
   }
 }
 
+@Experimental
 object RowMatrix {
 
   /**
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/optimization/GradientDescent.scala b/mllib/src/main/scala/org/apache/spark/mllib/optimization/GradientDescent.scala
index c75909bac924..7030eeabe400 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/optimization/GradientDescent.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/optimization/GradientDescent.scala
@@ -21,19 +21,17 @@ import scala.collection.mutable.ArrayBuffer
 
 import breeze.linalg.{DenseVector => BDV}
 
-import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.annotation.{Experimental, DeveloperApi}
 import org.apache.spark.Logging
 import org.apache.spark.rdd.RDD
 import org.apache.spark.mllib.linalg.{Vectors, Vector}
 
 /**
- * :: DeveloperApi ::
  * Class used to solve an optimization problem using Gradient Descent.
  * @param gradient Gradient function to be used.
  * @param updater Updater to be used to update weights after every iteration.
  */
-@DeveloperApi
-class GradientDescent(private var gradient: Gradient, private var updater: Updater)
+class GradientDescent private[mllib] (private var gradient: Gradient, private var updater: Updater)
   extends Optimizer with Logging {
 
   private var stepSize: Double = 1.0
@@ -51,9 +49,11 @@ class GradientDescent(private var gradient: Gradient, private var updater: Updat
   }
 
   /**
+   * :: Experimental ::
    * Set fraction of data to be used for each SGD iteration.
    * Default 1.0 (corresponding to deterministic/classical gradient descent)
    */
+  @Experimental
   def setMiniBatchFraction(fraction: Double): this.type = {
     this.miniBatchFraction = fraction
     this
@@ -95,6 +95,14 @@ class GradientDescent(private var gradient: Gradient, private var updater: Updat
     this
   }
 
+  /**
+   * :: DeveloperApi ::
+   * Runs gradient descent on the given training data.
+   * @param data training data
+   * @param initialWeights initial weights
+   * @return solution vector
+   */
+  @DeveloperApi
   def optimize(data: RDD[(Double, Vector)], initialWeights: Vector): Vector = {
     val (weights, _) = GradientDescent.runMiniBatchSGD(
       data,
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
index 471546cd82c7..899286d235a9 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.mllib.recommendation
 
-import org.jblas._
+import org.jblas.DoubleMatrix
 
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.api.java.JavaRDD
@@ -25,7 +25,6 @@ import org.apache.spark.rdd.RDD
 import org.apache.spark.SparkContext._
 import org.apache.spark.mllib.api.python.PythonMLLibAPI
 
-
 /**
  * Model representing the result of matrix factorization.
  *
@@ -35,12 +34,10 @@ import org.apache.spark.mllib.api.python.PythonMLLibAPI
  * @param productFeatures RDD of tuples where each tuple represents the productId
  *                        and the features computed for this product.
  */
-class MatrixFactorizationModel(
+class MatrixFactorizationModel private[mllib] (
     val rank: Int,
     val userFeatures: RDD[(Int, Array[Double])],
-    val productFeatures: RDD[(Int, Array[Double])])
-  extends Serializable
-{
+    val productFeatures: RDD[(Int, Array[Double])]) extends Serializable {
   /** Predict the rating of one user for one product. */
   def predict(user: Int, product: Int): Double = {
     val userVector = new DoubleMatrix(userFeatures.lookup(user).head)
@@ -76,6 +73,7 @@ class MatrixFactorizationModel(
    * @param usersProductsJRDD A JavaRDD with serialized tuples (user, product)
    * @return JavaRDD of serialized Rating objects.
    */
+  @DeveloperApi
   def predict(usersProductsJRDD: JavaRDD[Array[Byte]]): JavaRDD[Array[Byte]] = {
     val pythonAPI = new PythonMLLibAPI()
     val usersProducts = usersProductsJRDD.rdd.map(xBytes => pythonAPI.unpackTuple(xBytes))
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala
index d969e7aa6006..8cca926f1c92 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala
@@ -19,13 +19,14 @@ package org.apache.spark.mllib.regression
 
 import breeze.linalg.{DenseVector => BDV, SparseVector => BSV}
 
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.{Logging, SparkException}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.mllib.optimization._
 import org.apache.spark.mllib.linalg.{Vectors, Vector}
 
 /**
+ * :: DeveloperApi ::
  * GeneralizedLinearModel (GLM) represents a model trained using
  * GeneralizedLinearAlgorithm. GLMs consist of a weight vector and
  * an intercept.
@@ -33,6 +34,7 @@ import org.apache.spark.mllib.linalg.{Vectors, Vector}
  * @param weights Weights computed for every feature.
  * @param intercept Intercept computed for this model.
  */
+@DeveloperApi
 abstract class GeneralizedLinearModel(val weights: Vector, val intercept: Double)
   extends Serializable {
 
@@ -72,9 +74,11 @@ abstract class GeneralizedLinearModel(val weights: Vector, val intercept: Double
 }
 
 /**
+ * :: DeveloperApi ::
  * GeneralizedLinearAlgorithm implements methods to train a Generalized Linear Model (GLM).
  * This class should be extended with an Optimizer to create a new GLM.
  */
+@DeveloperApi
 abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel]
   extends Logging with Serializable {
 
@@ -83,8 +87,8 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel]
   /** The optimizer to solve the problem. */
   def optimizer: Optimizer
 
-  /** Whether to add intercept (default: true). */
-  protected var addIntercept: Boolean = true
+  /** Whether to add intercept (default: false). */
+  protected var addIntercept: Boolean = false
 
   protected var validateData: Boolean = true
 
@@ -94,7 +98,8 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel]
   protected def createModel(weights: Vector, intercept: Double): M
 
   /**
-   * Set if the algorithm should add an intercept. Default true.
+   * Set if the algorithm should add an intercept. Default false.
+   * We set the default to false because adding the intercept will cause memory allocation.
    */
   def setIntercept(addIntercept: Boolean): this.type = {
     this.addIntercept = addIntercept
@@ -102,10 +107,8 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel]
   }
 
   /**
-   * :: Experimental ::
    * Set if the algorithm should validate data before training. Default true.
    */
-  @Experimental
   def setValidateData(validateData: Boolean): this.type = {
     this.validateData = validateData
     this
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala
index 0e6fb1b1caa4..a05dfc045fb8 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.mllib.regression
 
+import org.apache.spark.annotation.Experimental
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.mllib.optimization._
 import org.apache.spark.rdd.RDD
@@ -27,7 +28,7 @@ import org.apache.spark.rdd.RDD
  * @param weights Weights computed for every feature.
  * @param intercept Intercept computed for this model.
  */
-class LassoModel(
+class LassoModel private[mllib] (
     override val weights: Vector,
     override val intercept: Double)
   extends GeneralizedLinearModel(weights, intercept)
@@ -64,21 +65,12 @@ class LassoWithSGD private (
     .setRegParam(regParam)
     .setMiniBatchFraction(miniBatchFraction)
 
-  // We don't want to penalize the intercept, so set this to false.
-  super.setIntercept(false)
-
   /**
    * Construct a Lasso object with default parameters: {stepSize: 1.0, numIterations: 100,
    * regParam: 1.0, miniBatchFraction: 1.0}.
    */
   def this() = this(1.0, 100, 1.0, 1.0)
 
-  override def setIntercept(addIntercept: Boolean): this.type = {
-    // TODO: Support adding intercept.
-    if (addIntercept) throw new UnsupportedOperationException("Adding intercept is not supported.")
-    this
-  }
-
   override protected def createModel(weights: Vector, intercept: Double) = {
     new LassoModel(weights, intercept)
   }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala
index 1532ff90d846..0ebad4eb58d8 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala
@@ -27,7 +27,7 @@ import org.apache.spark.mllib.optimization._
  * @param weights Weights computed for every feature.
  * @param intercept Intercept computed for this model.
  */
-class LinearRegressionModel(
+class LinearRegressionModel private[mllib] (
     override val weights: Vector,
     override val intercept: Double)
   extends GeneralizedLinearModel(weights, intercept) with RegressionModel with Serializable {
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala
index 5f7e25a9b8be..bd983bac001a 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.mllib.regression
 
+import org.apache.spark.annotation.Experimental
 import org.apache.spark.rdd.RDD
 import org.apache.spark.mllib.optimization._
 import org.apache.spark.mllib.linalg.Vector
@@ -27,7 +28,7 @@ import org.apache.spark.mllib.linalg.Vector
  * @param weights Weights computed for every feature.
  * @param intercept Intercept computed for this model.
  */
-class RidgeRegressionModel(
+class RidgeRegressionModel private[mllib] (
     override val weights: Vector,
     override val intercept: Double)
   extends GeneralizedLinearModel(weights, intercept)
@@ -65,21 +66,12 @@ class RidgeRegressionWithSGD private (
     .setRegParam(regParam)
     .setMiniBatchFraction(miniBatchFraction)
 
-  // We don't want to penalize the intercept in RidgeRegression, so set this to false.
-  super.setIntercept(false)
-
   /**
    * Construct a RidgeRegression object with default parameters: {stepSize: 1.0, numIterations: 100,
    * regParam: 1.0, miniBatchFraction: 1.0}.
    */
   def this() = this(1.0, 100, 1.0, 1.0)
 
-  override def setIntercept(addIntercept: Boolean): this.type = {
-    // TODO: Support adding intercept.
-    if (addIntercept) throw new UnsupportedOperationException("Adding intercept is not supported.")
-    this
-  }
-
   override protected def createModel(weights: Vector, intercept: Double) = {
     new RidgeRegressionModel(weights, intercept)
   }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/README.md b/mllib/src/main/scala/org/apache/spark/mllib/tree/README.md
deleted file mode 100644
index 0fd71aa9735b..000000000000
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/README.md
+++ /dev/null
@@ -1,17 +0,0 @@
-This package contains the default implementation of the decision tree algorithm.
-
-The decision tree algorithm supports:
-+ Binary classification
-+ Regression
-+ Information loss calculation with entropy and gini for classification and variance for regression
-+ Both continuous and categorical features
-
-# Tree improvements
-+ Node model pruning
-+ Printing to dot files
-
-# Future Ensemble Extensions
-
-+ Random forests
-+ Boosting
-+ Extremely randomized trees
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/rdd/VectorRDDs.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/package.scala
similarity index 69%
rename from mllib/src/main/scala/org/apache/spark/mllib/rdd/VectorRDDs.scala
rename to mllib/src/main/scala/org/apache/spark/mllib/tree/package.scala
index 9096d6a1a16d..bcaacc1b1f19 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/rdd/VectorRDDs.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/package.scala
@@ -15,18 +15,15 @@
  * limitations under the License.
  */
 
-package org.apache.spark.mllib.rdd
-
-import org.apache.spark.rdd.RDD
-import org.apache.spark.mllib.linalg.{Vectors, Vector}
+package org.apache.spark.mllib
 
 /**
- * Factory methods for `RDD[Vector]`.
+ * This package contains the default implementation of the decision tree algorithm, which supports:
+ *  - binary classification,
+ *  - regression,
+ *  - information loss calculation with entropy and Gini for classification and
+ *    variance for regression,
+ *  - both continuous and categorical features.
  */
-object VectorRDDs {
-
-  /**
-   * Converts an `RDD[Array[Double]]` to `RDD[Vector]`.
-   */
-  def fromArrayRDD(rdd: RDD[Array[Double]]): RDD[Vector] = rdd.map(v => Vectors.dense(v))
+package object tree {
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/LabelParsers.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/LabelParsers.scala
index f7966d3ebb61..e25bf18b780b 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/util/LabelParsers.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/util/LabelParsers.scala
@@ -18,16 +18,23 @@
 package org.apache.spark.mllib.util
 
 /** Trait for label parsers. */
-trait LabelParser extends Serializable {
+private trait LabelParser extends Serializable {
   /** Parses a string label into a double label. */
   def parse(labelString: String): Double
 }
 
+/** Factory methods for label parsers. */
+private object LabelParser {
+  def getInstance(multiclass: Boolean): LabelParser = {
+    if (multiclass) MulticlassLabelParser else BinaryLabelParser
+  }
+}
+
 /**
  * Label parser for binary labels, which outputs 1.0 (positive) if the value is greater than 0.5,
  * or 0.0 (negative) otherwise. So it works with +1/-1 labeling and +1/0 labeling.
  */
-object BinaryLabelParser extends LabelParser {
+private object BinaryLabelParser extends LabelParser {
   /** Gets the default instance of BinaryLabelParser. */
   def getInstance(): LabelParser = this
 
@@ -41,7 +48,7 @@ object BinaryLabelParser extends LabelParser {
 /**
  * Label parser for multiclass labels, which converts the input label to double.
  */
-object MulticlassLabelParser extends LabelParser {
+private object MulticlassLabelParser extends LabelParser {
   /** Gets the default instance of MulticlassLabelParser. */
   def getInstance(): LabelParser = this
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala
index 3d6e7e0d5c95..e598b6cb171a 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala
@@ -19,16 +19,17 @@ package org.apache.spark.mllib.util
 
 import scala.reflect.ClassTag
 
-import breeze.linalg.{Vector => BV, SparseVector => BSV, squaredDistance => breezeSquaredDistance}
+import breeze.linalg.{Vector => BV, DenseVector => BDV, SparseVector => BSV,
+  squaredDistance => breezeSquaredDistance}
 
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.SparkContext
 import org.apache.spark.rdd.RDD
 import org.apache.spark.rdd.PartitionwiseSampledRDD
-import org.apache.spark.SparkContext._
 import org.apache.spark.util.random.BernoulliSampler
 import org.apache.spark.mllib.regression.LabeledPoint
-import org.apache.spark.mllib.linalg.Vectors
+import org.apache.spark.mllib.linalg.{Vector, Vectors}
+import org.apache.spark.storage.StorageLevel
 
 /**
  * Helper methods to load, save and pre-process data used in ML Lib.
@@ -54,13 +55,16 @@ object MLUtils {
    *
    * @param sc Spark context
    * @param path file or directory path in any Hadoop-supported file system URI
-   * @param labelParser parser for labels, default: 1.0 if label > 0.5 or 0.0 otherwise
+   * @param labelParser parser for labels
    * @param numFeatures number of features, which will be determined from the input data if a
-   *                    negative value is given. The default value is -1.
-   * @param minPartitions min number of partitions, default: sc.defaultMinPartitions
+   *                    nonpositive value is given. This is useful when the dataset is already split
+   *                    into multiple files and you want to load them separately, because some
+   *                    features may not present in certain files, which leads to inconsistent
+   *                    feature dimensions.
+   * @param minPartitions min number of partitions
    * @return labeled data stored as an RDD[LabeledPoint]
    */
-  def loadLibSVMData(
+  private def loadLibSVMFile(
       sc: SparkContext,
       path: String,
       labelParser: LabelParser,
@@ -68,63 +72,112 @@ object MLUtils {
       minPartitions: Int): RDD[LabeledPoint] = {
     val parsed = sc.textFile(path, minPartitions)
       .map(_.trim)
-      .filter(!_.isEmpty)
-      .map(_.split(' '))
+      .filter(line => !(line.isEmpty || line.startsWith("#")))
+      .map { line =>
+        val items = line.split(' ')
+        val label = labelParser.parse(items.head)
+        val (indices, values) = items.tail.map { item =>
+          val indexAndValue = item.split(':')
+          val index = indexAndValue(0).toInt - 1 // Convert 1-based indices to 0-based.
+          val value = indexAndValue(1).toDouble
+          (index, value)
+        }.unzip
+        (label, indices.toArray, values.toArray)
+      }
+
     // Determine number of features.
-    val d = if (numFeatures >= 0) {
+    val d = if (numFeatures > 0) {
       numFeatures
     } else {
-      parsed.map { items =>
-        if (items.length > 1) {
-          items.last.split(':')(0).toInt
-        } else {
-          0
-        }
-      }.reduce(math.max)
+      parsed.persist(StorageLevel.MEMORY_ONLY)
+      parsed.map { case (label, indices, values) =>
+        indices.lastOption.getOrElse(0)
+      }.reduce(math.max) + 1
     }
-    parsed.map { items =>
-      val label = labelParser.parse(items.head)
-      val (indices, values) = items.tail.map { item =>
-        val indexAndValue = item.split(':')
-        val index = indexAndValue(0).toInt - 1
-        val value = indexAndValue(1).toDouble
-        (index, value)
-      }.unzip
-      LabeledPoint(label, Vectors.sparse(d, indices.toArray, values.toArray))
+
+    parsed.map { case (label, indices, values) =>
+      LabeledPoint(label, Vectors.sparse(d, indices, values))
     }
   }
 
-  // Convenient methods for calling from Java.
+  // Convenient methods for `loadLibSVMFile`.
 
   /**
-   * Loads binary labeled data in the LIBSVM format into an RDD[LabeledPoint],
-   * with number of features determined automatically and the default number of partitions.
+   * Loads labeled data in the LIBSVM format into an RDD[LabeledPoint].
+   * The LIBSVM format is a text-based format used by LIBSVM and LIBLINEAR.
+   * Each line represents a labeled sparse feature vector using the following format:
+   * {{{label index1:value1 index2:value2 ...}}}
+   * where the indices are one-based and in ascending order.
+   * This method parses each line into a [[org.apache.spark.mllib.regression.LabeledPoint]],
+   * where the feature indices are converted to zero-based.
+   *
+   * @param sc Spark context
+   * @param path file or directory path in any Hadoop-supported file system URI
+   * @param multiclass whether the input labels contain more than two classes. If false, any label
+   *                   with value greater than 0.5 will be mapped to 1.0, or 0.0 otherwise. So it
+   *                   works for both +1/-1 and 1/0 cases. If true, the double value parsed directly
+   *                   from the label string will be used as the label value.
+   * @param numFeatures number of features, which will be determined from the input data if a
+   *                    nonpositive value is given. This is useful when the dataset is already split
+   *                    into multiple files and you want to load them separately, because some
+   *                    features may not present in certain files, which leads to inconsistent
+   *                    feature dimensions.
+   * @param minPartitions min number of partitions
+   * @return labeled data stored as an RDD[LabeledPoint]
    */
-  def loadLibSVMData(sc: SparkContext, path: String): RDD[LabeledPoint] =
-    loadLibSVMData(sc, path, BinaryLabelParser, -1, sc.defaultMinPartitions)
+   def loadLibSVMFile(
+      sc: SparkContext,
+      path: String,
+      multiclass: Boolean,
+      numFeatures: Int,
+      minPartitions: Int): RDD[LabeledPoint] =
+    loadLibSVMFile(sc, path, LabelParser.getInstance(multiclass), numFeatures, minPartitions)
 
   /**
-   * Loads labeled data in the LIBSVM format into an RDD[LabeledPoint],
-   * with the given label parser, number of features determined automatically,
-   * and the default number of partitions.
+   * Loads labeled data in the LIBSVM format into an RDD[LabeledPoint], with the default number of
+   * partitions.
    */
-  def loadLibSVMData(
+  def loadLibSVMFile(
       sc: SparkContext,
       path: String,
-      labelParser: LabelParser): RDD[LabeledPoint] =
-    loadLibSVMData(sc, path, labelParser, -1, sc.defaultMinPartitions)
+      multiclass: Boolean,
+      numFeatures: Int): RDD[LabeledPoint] =
+    loadLibSVMFile(sc, path, multiclass, numFeatures, sc.defaultMinPartitions)
 
   /**
-   * Loads labeled data in the LIBSVM format into an RDD[LabeledPoint],
-   * with the given label parser, number of features specified explicitly,
-   * and the default number of partitions.
+   * Loads labeled data in the LIBSVM format into an RDD[LabeledPoint], with the number of features
+   * determined automatically and the default number of partitions.
    */
-  def loadLibSVMData(
+  def loadLibSVMFile(
       sc: SparkContext,
       path: String,
-      labelParser: LabelParser,
-      numFeatures: Int): RDD[LabeledPoint] =
-    loadLibSVMData(sc, path, labelParser, numFeatures, sc.defaultMinPartitions)
+      multiclass: Boolean): RDD[LabeledPoint] =
+    loadLibSVMFile(sc, path, multiclass, -1, sc.defaultMinPartitions)
+
+  /**
+   * Loads binary labeled data in the LIBSVM format into an RDD[LabeledPoint], with number of
+   * features determined automatically and the default number of partitions.
+   */
+  def loadLibSVMFile(sc: SparkContext, path: String): RDD[LabeledPoint] =
+    loadLibSVMFile(sc, path, multiclass = false, -1, sc.defaultMinPartitions)
+
+  /**
+   * Save labeled data in LIBSVM format.
+   * @param data an RDD of LabeledPoint to be saved
+   * @param dir directory to save the data
+   *
+   * @see [[org.apache.spark.mllib.util.MLUtils#loadLibSVMFile]]
+   */
+  def saveAsLibSVMFile(data: RDD[LabeledPoint], dir: String) {
+    // TODO: allow to specify label precision and feature precision.
+    val dataStr = data.map { case LabeledPoint(label, features) =>
+      val featureStrings = features.toBreeze.activeIterator.map { case (i, v) =>
+        s"${i + 1}:$v"
+      }
+      (Iterator(label) ++ featureStrings).mkString(" ")
+    }
+    dataStr.saveAsTextFile(dir)
+  }
 
   /**
    * :: Experimental ::
@@ -163,10 +216,12 @@ object MLUtils {
   }
 
   /**
+   * :: Experimental ::
    * Return a k element array of pairs of RDDs with the first element of each pair
    * containing the training data, a complement of the validation data and the second
    * element, the validation data, containing a unique 1/kth of the data. Where k=numFolds.
    */
+  @Experimental
   def kFold[T: ClassTag](rdd: RDD[T], numFolds: Int, seed: Int): Array[(RDD[T], RDD[T])] = {
     val numFoldsF = numFolds.toFloat
     (1 to numFolds).map { fold =>
@@ -178,6 +233,18 @@ object MLUtils {
     }.toArray
   }
 
+  /**
+   * Returns a new vector with `1.0` (bias) appended to the input vector.
+   */
+  def appendBias(vector: Vector): Vector = {
+    val vector1 = vector.toBreeze match {
+      case dv: BDV[Double] => BDV.vertcat(dv, new BDV[Double](Array(1.0)))
+      case sv: BSV[Double] => BSV.vertcat(sv, new BSV[Double](Array(0), Array(1.0), 1))
+      case v: Any => throw new IllegalArgumentException("Do not support vector type " + v.getClass)
+    }
+    Vectors.fromBreeze(vector1)
+  }
+
   /**
    * Returns the squared Euclidean distance between two vectors. The following formula will be used
    * if it does not introduce too much numerical error:
diff --git a/mllib/src/test/java/org/apache/spark/mllib/classification/JavaLogisticRegressionSuite.java b/mllib/src/test/java/org/apache/spark/mllib/classification/JavaLogisticRegressionSuite.java
index e18e3bc6a86b..d75d3a6b2673 100644
--- a/mllib/src/test/java/org/apache/spark/mllib/classification/JavaLogisticRegressionSuite.java
+++ b/mllib/src/test/java/org/apache/spark/mllib/classification/JavaLogisticRegressionSuite.java
@@ -68,6 +68,7 @@ public void runLRUsingConstructor() {
         LogisticRegressionSuite.generateLogisticInputAsList(A, B, nPoints, 17);
 
     LogisticRegressionWithSGD lrImpl = new LogisticRegressionWithSGD();
+    lrImpl.setIntercept(true);
     lrImpl.optimizer().setStepSize(1.0)
                       .setRegParam(1.0)
                       .setNumIterations(100);
@@ -80,8 +81,8 @@ public void runLRUsingConstructor() {
   @Test
   public void runLRUsingStaticMethods() {
     int nPoints = 10000;
-    double A = 2.0;
-    double B = -1.5;
+    double A = 0.0;
+    double B = -2.5;
 
     JavaRDD<LabeledPoint> testRDD = sc.parallelize(
         LogisticRegressionSuite.generateLogisticInputAsList(A, B, nPoints, 42), 2).cache();
@@ -92,6 +93,7 @@ public void runLRUsingStaticMethods() {
         testRDD.rdd(), 100, 1.0, 1.0);
 
     int numAccurate = validatePrediction(validationData, model);
+      System.out.println(numAccurate);
     Assert.assertTrue(numAccurate > nPoints * 4.0 / 5.0);
   }
 
diff --git a/mllib/src/test/java/org/apache/spark/mllib/classification/JavaSVMSuite.java b/mllib/src/test/java/org/apache/spark/mllib/classification/JavaSVMSuite.java
index 4701a5e54502..667f76a1bd55 100644
--- a/mllib/src/test/java/org/apache/spark/mllib/classification/JavaSVMSuite.java
+++ b/mllib/src/test/java/org/apache/spark/mllib/classification/JavaSVMSuite.java
@@ -67,6 +67,7 @@ public void runSVMUsingConstructor() {
         SVMSuite.generateSVMInputAsList(A, weights, nPoints, 17);
 
     SVMWithSGD svmSGDImpl = new SVMWithSGD();
+    svmSGDImpl.setIntercept(true);
     svmSGDImpl.optimizer().setStepSize(1.0)
                           .setRegParam(1.0)
                           .setNumIterations(100);
@@ -79,7 +80,7 @@ public void runSVMUsingConstructor() {
   @Test
   public void runSVMUsingStaticMethods() {
     int nPoints = 10000;
-    double A = 2.0;
+    double A = 0.0;
     double[] weights = {-1.5, 1.0};
 
     JavaRDD<LabeledPoint> testRDD = sc.parallelize(SVMSuite.generateSVMInputAsList(A,
diff --git a/mllib/src/test/java/org/apache/spark/mllib/regression/JavaLinearRegressionSuite.java b/mllib/src/test/java/org/apache/spark/mllib/regression/JavaLinearRegressionSuite.java
index 5a4410a63264..7151e553512b 100644
--- a/mllib/src/test/java/org/apache/spark/mllib/regression/JavaLinearRegressionSuite.java
+++ b/mllib/src/test/java/org/apache/spark/mllib/regression/JavaLinearRegressionSuite.java
@@ -68,6 +68,7 @@ public void runLinearRegressionUsingConstructor() {
             LinearDataGenerator.generateLinearInputAsList(A, weights, nPoints, 17, 0.1);
 
     LinearRegressionWithSGD linSGDImpl = new LinearRegressionWithSGD();
+    linSGDImpl.setIntercept(true);
     LinearRegressionModel model = linSGDImpl.run(testRDD.rdd());
 
     int numAccurate = validatePrediction(validationData, model);
@@ -77,7 +78,7 @@ public void runLinearRegressionUsingConstructor() {
   @Test
   public void runLinearRegressionUsingStaticMethods() {
     int nPoints = 100;
-    double A = 3.0;
+    double A = 0.0;
     double[] weights = {10, 10};
 
     JavaRDD<LabeledPoint> testRDD = sc.parallelize(
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala
index 1e03c9df820b..4d7b984e3ec2 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala
@@ -46,24 +46,14 @@ object LogisticRegressionSuite {
     val rnd = new Random(seed)
     val x1 = Array.fill[Double](nPoints)(rnd.nextGaussian())
 
-    // NOTE: if U is uniform[0, 1] then ln(u) - ln(1-u) is Logistic(0,1)
-    val unifRand = new scala.util.Random(45)
-    val rLogis = (0 until nPoints).map { i =>
-      val u = unifRand.nextDouble()
-      math.log(u) - math.log(1.0-u)
-    }
-
-    // y <- A + B*x + rLogis()
-    // y <- as.numeric(y > 0)
-    val y: Seq[Int] = (0 until nPoints).map { i =>
-      val yVal = offset + scale * x1(i) + rLogis(i)
-      if (yVal > 0) 1 else 0
+    val y = (0 until nPoints).map { i =>
+      val p = 1.0 / (1.0 + math.exp(-(offset + scale * x1(i))))
+      if (rnd.nextDouble() < p) 1.0 else 0.0
     }
 
     val testData = (0 until nPoints).map(i => LabeledPoint(y(i), Vectors.dense(Array(x1(i)))))
     testData
   }
-
 }
 
 class LogisticRegressionSuite extends FunSuite with LocalSparkContext with ShouldMatchers {
@@ -85,7 +75,7 @@ class LogisticRegressionSuite extends FunSuite with LocalSparkContext with Shoul
 
     val testRDD = sc.parallelize(testData, 2)
     testRDD.cache()
-    val lr = new LogisticRegressionWithSGD()
+    val lr = new LogisticRegressionWithSGD().setIntercept(true)
     lr.optimizer.setStepSize(10.0).setNumIterations(20)
 
     val model = lr.run(testRDD)
@@ -118,7 +108,7 @@ class LogisticRegressionSuite extends FunSuite with LocalSparkContext with Shoul
     testRDD.cache()
 
     // Use half as many iterations as the previous test.
-    val lr = new LogisticRegressionWithSGD()
+    val lr = new LogisticRegressionWithSGD().setIntercept(true)
     lr.optimizer.setStepSize(10.0).setNumIterations(10)
 
     val model = lr.run(testRDD, initialWeights)
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/classification/SVMSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/classification/SVMSuite.scala
index dfacbfeee6fb..77d6f04b3232 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/classification/SVMSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/classification/SVMSuite.scala
@@ -69,7 +69,6 @@ class SVMSuite extends FunSuite with LocalSparkContext {
     assert(numOffPredictions < input.length / 5)
   }
 
-
   test("SVM using local random SGD") {
     val nPoints = 10000
 
@@ -83,7 +82,7 @@ class SVMSuite extends FunSuite with LocalSparkContext {
     val testRDD = sc.parallelize(testData, 2)
     testRDD.cache()
 
-    val svm = new SVMWithSGD()
+    val svm = new SVMWithSGD().setIntercept(true)
     svm.optimizer.setStepSize(1.0).setRegParam(1.0).setNumIterations(100)
 
     val model = svm.run(testRDD)
@@ -115,7 +114,7 @@ class SVMSuite extends FunSuite with LocalSparkContext {
     val testRDD = sc.parallelize(testData, 2)
     testRDD.cache()
 
-    val svm = new SVMWithSGD()
+    val svm = new SVMWithSGD().setIntercept(true)
     svm.optimizer.setStepSize(1.0).setRegParam(1.0).setNumIterations(100)
 
     val model = svm.run(testRDD, initialWeights)
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/evaluation/binary/BinaryClassificationMetricsSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetricsSuite.scala
similarity index 96%
rename from mllib/src/test/scala/org/apache/spark/mllib/evaluation/binary/BinaryClassificationMetricsSuite.scala
rename to mllib/src/test/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetricsSuite.scala
index 173fdaefab3d..9d16182f9d8c 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/evaluation/binary/BinaryClassificationMetricsSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetricsSuite.scala
@@ -15,12 +15,11 @@
  * limitations under the License.
  */
 
-package org.apache.spark.mllib.evaluation.binary
+package org.apache.spark.mllib.evaluation
 
 import org.scalatest.FunSuite
 
 import org.apache.spark.mllib.util.LocalSparkContext
-import org.apache.spark.mllib.evaluation.AreaUnderCurve
 
 class BinaryClassificationMetricsSuite extends FunSuite with LocalSparkContext {
   test("binary evaluation metrics") {
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/rdd/VectorRDDsSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/rdd/VectorRDDsSuite.scala
deleted file mode 100644
index 692f025e959a..000000000000
--- a/mllib/src/test/scala/org/apache/spark/mllib/rdd/VectorRDDsSuite.scala
+++ /dev/null
@@ -1,33 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.mllib.rdd
-
-import org.scalatest.FunSuite
-
-import org.apache.spark.mllib.linalg.Vectors
-import org.apache.spark.mllib.util.LocalSparkContext
-
-class VectorRDDsSuite extends FunSuite with LocalSparkContext {
-
-  test("from array rdd") {
-    val data = Seq(Array(1.0, 2.0), Array(3.0, 4.0))
-    val arrayRdd = sc.parallelize(data, 2)
-    val vectorRdd = VectorRDDs.fromArrayRDD(arrayRdd)
-    assert(arrayRdd.collect().map(v => Vectors.dense(v)) === vectorRdd.collect())
-  }
-}
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/recommendation/ALSSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/recommendation/ALSSuite.scala
index 4dfcd4b52ec6..2d944f3eb7ff 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/recommendation/ALSSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/recommendation/ALSSuite.scala
@@ -27,7 +27,6 @@ import org.jblas.DoubleMatrix
 
 import org.apache.spark.mllib.util.LocalSparkContext
 import org.apache.spark.SparkContext._
-import org.apache.spark.Partitioner
 
 object ALSSuite {
 
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/regression/LassoSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/regression/LassoSuite.scala
index 6aad9eb84e13..bfa42959c8ea 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/regression/LassoSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/regression/LassoSuite.scala
@@ -112,10 +112,4 @@ class LassoSuite extends FunSuite with LocalSparkContext {
     // Test prediction on Array.
     validatePrediction(validationData.map(row => model.predict(row.features)), validationData)
   }
-
-  test("do not support intercept") {
-    intercept[UnsupportedOperationException] {
-      new LassoWithSGD().setIntercept(true)
-    }
-  }
 }
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/regression/LinearRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/regression/LinearRegressionSuite.scala
index 2f7d30708ce1..7aaad7d7a3e3 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/regression/LinearRegressionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/regression/LinearRegressionSuite.scala
@@ -37,7 +37,7 @@ class LinearRegressionSuite extends FunSuite with LocalSparkContext {
   test("linear regression") {
     val testRDD = sc.parallelize(LinearDataGenerator.generateLinearInput(
       3.0, Array(10.0, 10.0), 100, 42), 2).cache()
-    val linReg = new LinearRegressionWithSGD()
+    val linReg = new LinearRegressionWithSGD().setIntercept(true)
     linReg.optimizer.setNumIterations(1000).setStepSize(1.0)
 
     val model = linReg.run(testRDD)
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/regression/RidgeRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/regression/RidgeRegressionSuite.scala
index f66fc6ea6c1e..67768e17fbe6 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/regression/RidgeRegressionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/regression/RidgeRegressionSuite.scala
@@ -72,10 +72,4 @@ class RidgeRegressionSuite extends FunSuite with LocalSparkContext {
     assert(ridgeErr < linearErr,
       "ridgeError (" + ridgeErr + ") was not less than linearError(" + linearErr + ")")
   }
-
-  test("do not support intercept") {
-    intercept[UnsupportedOperationException] {
-      new RidgeRegressionWithSGD().setIntercept(true)
-    }
-  }
 }
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala
index 350130c914f2..be383aab714d 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala
@@ -17,10 +17,8 @@
 
 package org.apache.spark.mllib.tree
 
-import org.scalatest.BeforeAndAfterAll
 import org.scalatest.FunSuite
 
-import org.apache.spark.SparkContext
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.tree.impurity.{Entropy, Gini, Variance}
 import org.apache.spark.mllib.tree.model.Filter
@@ -28,19 +26,9 @@ import org.apache.spark.mllib.tree.configuration.Strategy
 import org.apache.spark.mllib.tree.configuration.Algo._
 import org.apache.spark.mllib.tree.configuration.FeatureType._
 import org.apache.spark.mllib.linalg.Vectors
+import org.apache.spark.mllib.util.LocalSparkContext
 
-class DecisionTreeSuite extends FunSuite with BeforeAndAfterAll {
-
-  @transient private var sc: SparkContext = _
-
-  override def beforeAll() {
-    sc = new SparkContext("local", "test")
-  }
-
-  override def afterAll() {
-    sc.stop()
-    System.clearProperty("spark.driver.port")
-  }
+class DecisionTreeSuite extends FunSuite with LocalSparkContext {
 
   test("split and bin calculation") {
     val arr = DecisionTreeSuite.generateOrderedLabeledPointsWithLabel1()
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/util/MLUtilsSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/util/MLUtilsSuite.scala
index 674378a34ce3..3f64baf6fe41 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/util/MLUtilsSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/util/MLUtilsSuite.scala
@@ -19,8 +19,8 @@ package org.apache.spark.mllib.util
 
 import java.io.File
 
+import scala.io.Source
 import scala.math
-import scala.util.Random
 
 import org.scalatest.FunSuite
 
@@ -29,7 +29,8 @@ import breeze.linalg.{DenseVector => BDV, SparseVector => BSV, norm => breezeNor
 import com.google.common.base.Charsets
 import com.google.common.io.Files
 
-import org.apache.spark.mllib.linalg.Vectors
+import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vectors}
+import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.util.MLUtils._
 
 class MLUtilsSuite extends FunSuite with LocalSparkContext {
@@ -58,7 +59,7 @@ class MLUtilsSuite extends FunSuite with LocalSparkContext {
     }
   }
 
-  test("loadLibSVMData") {
+  test("loadLibSVMFile") {
     val lines =
       """
         |+1 1:1.0 3:2.0 5:3.0
@@ -70,8 +71,8 @@ class MLUtilsSuite extends FunSuite with LocalSparkContext {
     Files.write(lines, file, Charsets.US_ASCII)
     val path = tempDir.toURI.toString
 
-    val pointsWithNumFeatures = MLUtils.loadLibSVMData(sc, path, BinaryLabelParser, 6).collect()
-    val pointsWithoutNumFeatures = MLUtils.loadLibSVMData(sc, path).collect()
+    val pointsWithNumFeatures = loadLibSVMFile(sc, path, multiclass = false, 6).collect()
+    val pointsWithoutNumFeatures = loadLibSVMFile(sc, path).collect()
 
     for (points <- Seq(pointsWithNumFeatures, pointsWithoutNumFeatures)) {
       assert(points.length === 3)
@@ -83,29 +84,54 @@ class MLUtilsSuite extends FunSuite with LocalSparkContext {
       assert(points(2).features === Vectors.sparse(6, Seq((1, 4.0), (3, 5.0), (5, 6.0))))
     }
 
-    val multiclassPoints = MLUtils.loadLibSVMData(sc, path, MulticlassLabelParser).collect()
+    val multiclassPoints = loadLibSVMFile(sc, path, multiclass = true).collect()
     assert(multiclassPoints.length === 3)
     assert(multiclassPoints(0).label === 1.0)
     assert(multiclassPoints(1).label === -1.0)
     assert(multiclassPoints(2).label === -1.0)
 
-    try {
-      file.delete()
-      tempDir.delete()
-    } catch {
-      case t: Throwable =>
-    }
+    deleteQuietly(tempDir)
+  }
+
+  test("saveAsLibSVMFile") {
+    val examples = sc.parallelize(Seq(
+      LabeledPoint(1.1, Vectors.sparse(3, Seq((0, 1.23), (2, 4.56)))),
+      LabeledPoint(0.0, Vectors.dense(1.01, 2.02, 3.03))
+    ), 2)
+    val tempDir = Files.createTempDir()
+    val outputDir = new File(tempDir, "output")
+    MLUtils.saveAsLibSVMFile(examples, outputDir.toURI.toString)
+    val lines = outputDir.listFiles()
+      .filter(_.getName.startsWith("part-"))
+      .flatMap(Source.fromFile(_).getLines())
+      .toSet
+    val expected = Set("1.1 1:1.23 3:4.56", "0.0 1:1.01 2:2.02 3:3.03")
+    assert(lines === expected)
+    deleteQuietly(tempDir)
+  }
+
+  test("appendBias") {
+    val sv = Vectors.sparse(3, Seq((0, 1.0), (2, 3.0)))
+    val sv1 = appendBias(sv).asInstanceOf[SparseVector]
+    assert(sv1.size === 4)
+    assert(sv1.indices === Array(0, 2, 3))
+    assert(sv1.values === Array(1.0, 3.0, 1.0))
+
+    val dv = Vectors.dense(1.0, 0.0, 3.0)
+    val dv1 = appendBias(dv).asInstanceOf[DenseVector]
+    assert(dv1.size === 4)
+    assert(dv1.values === Array(1.0, 0.0, 3.0, 1.0))
   }
 
   test("kFold") {
     val data = sc.parallelize(1 to 100, 2)
     val collectedData = data.collect().sorted
-    val twoFoldedRdd = MLUtils.kFold(data, 2, 1)
+    val twoFoldedRdd = kFold(data, 2, 1)
     assert(twoFoldedRdd(0)._1.collect().sorted === twoFoldedRdd(1)._2.collect().sorted)
     assert(twoFoldedRdd(0)._2.collect().sorted === twoFoldedRdd(1)._1.collect().sorted)
     for (folds <- 2 to 10) {
       for (seed <- 1 to 5) {
-        val foldedRdds = MLUtils.kFold(data, folds, seed)
+        val foldedRdds = kFold(data, folds, seed)
         assert(foldedRdds.size === folds)
         foldedRdds.map { case (training, validation) =>
           val result = validation.union(training).collect().sorted
@@ -132,4 +158,16 @@ class MLUtilsSuite extends FunSuite with LocalSparkContext {
     }
   }
 
+  /** Delete a file/directory quietly. */
+  def deleteQuietly(f: File) {
+    if (f.isDirectory) {
+      f.listFiles().foreach(deleteQuietly)
+    }
+    try {
+      f.delete()
+    } catch {
+      case _: Throwable =>
+    }
+  }
 }
+
diff --git a/python/pyspark/mllib/classification.py b/python/pyspark/mllib/classification.py
index c5844597c95f..6772e4337ef3 100644
--- a/python/pyspark/mllib/classification.py
+++ b/python/pyspark/mllib/classification.py
@@ -90,7 +90,7 @@ class SVMModel(LinearModel):
     >>> svm.predict(array([1.0])) > 0
     True
     >>> sparse_data = [
-    ...     LabeledPoint(0.0, SparseVector(2, {0: 0.0})),
+    ...     LabeledPoint(0.0, SparseVector(2, {0: -1.0})),
     ...     LabeledPoint(1.0, SparseVector(2, {1: 1.0})),
     ...     LabeledPoint(0.0, SparseVector(2, {0: 0.0})),
     ...     LabeledPoint(1.0, SparseVector(2, {1: 2.0}))
@@ -98,7 +98,7 @@ class SVMModel(LinearModel):
     >>> svm = SVMWithSGD.train(sc.parallelize(sparse_data))
     >>> svm.predict(SparseVector(2, {1: 1.0})) > 0
     True
-    >>> svm.predict(SparseVector(2, {1: 0.0})) <= 0
+    >>> svm.predict(SparseVector(2, {0: -1.0})) <= 0
     True
     """
     def predict(self, x):

From 6d721c5f7131f7c9fe56c524133d70cb37f1222d Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian.cs.zju@gmail.com>
Date: Mon, 5 May 2014 19:38:59 -0700
Subject: [PATCH 099/810] [SPARK-1678][SPARK-1679] In-memory compression bug
 fix and made compression configurable, disabled by default

In-memory compression is now configurable in `SparkConf` by the `spark.sql.inMemoryCompression.enabled` property, and is disabled by default.

To help code review, the bug fix is in [the first commit](https://github.com/liancheng/spark/commit/d537a367edf0bf24d0b925cc58b21d805ccbc11f), compression configuration is in [the second one](https://github.com/liancheng/spark/commit/4ce09aa8aa820bbbbbaa0f3f084a6cff1d4e6195).

Author: Cheng Lian <lian.cs.zju@gmail.com>

Closes #608 from liancheng/spark-1678 and squashes the following commits:

66c3a8d [Cheng Lian] Renamed in-memory compression configuration key
f8fb3a0 [Cheng Lian] Added assertion for testing .hasNext of various decoder
4ce09aa [Cheng Lian] Made in-memory compression configurable via SparkConf
d537a36 [Cheng Lian] Fixed SPARK-1678
---
 .../org/apache/spark/sql/SQLContext.scala     |  7 +++--
 .../spark/sql/columnar/ColumnBuilder.scala    | 17 ++++++++---
 .../columnar/InMemoryColumnarTableScan.scala  |  8 +++--
 .../sql/columnar/NullableColumnBuilder.scala  |  4 +--
 .../CompressibleColumnAccessor.scala          |  4 ++-
 .../CompressibleColumnBuilder.scala           | 12 +++++++-
 .../compression/compressionSchemes.scala      |  2 +-
 .../spark/sql/execution/SparkPlan.scala       |  2 +-
 .../scala/org/apache/spark/sql/TestData.scala | 11 +++++++
 .../columnar/InMemoryColumnarQuerySuite.scala | 30 +++++++++++++++++--
 .../compression/BooleanBitSetSuite.scala      |  7 ++++-
 .../compression/DictionaryEncodingSuite.scala |  7 +++--
 .../compression/IntegralDeltaSuite.scala      |  7 ++++-
 .../compression/RunLengthEncodingSuite.scala  |  7 +++--
 .../TestCompressibleColumnBuilder.scala       |  2 +-
 .../spark/sql/hive/HiveMetastoreCatalog.scala |  2 +-
 .../spark/sql/hive/HiveStrategies.scala       |  2 +-
 17 files changed, 105 insertions(+), 26 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
index e25201a6c177..bfebfa0c28c5 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
@@ -162,8 +162,11 @@ class SQLContext(@transient val sparkContext: SparkContext)
   /** Caches the specified table in-memory. */
   def cacheTable(tableName: String): Unit = {
     val currentTable = catalog.lookupRelation(None, tableName)
+    val useCompression =
+      sparkContext.conf.getBoolean("spark.sql.inMemoryColumnarStorage.compressed", false)
     val asInMemoryRelation =
-      InMemoryColumnarTableScan(currentTable.output, executePlan(currentTable).executedPlan)
+      InMemoryColumnarTableScan(
+        currentTable.output, executePlan(currentTable).executedPlan, useCompression)
 
     catalog.registerTable(None, tableName, SparkLogicalPlan(asInMemoryRelation))
   }
@@ -173,7 +176,7 @@ class SQLContext(@transient val sparkContext: SparkContext)
     EliminateAnalysisOperators(catalog.lookupRelation(None, tableName)) match {
       // This is kind of a hack to make sure that if this was just an RDD registered as a table,
       // we reregister the RDD as a table.
-      case SparkLogicalPlan(inMem @ InMemoryColumnarTableScan(_, e: ExistingRdd)) =>
+      case SparkLogicalPlan(inMem @ InMemoryColumnarTableScan(_, e: ExistingRdd, _)) =>
         inMem.cachedColumnBuffers.unpersist()
         catalog.unregisterTable(None, tableName)
         catalog.registerTable(None, tableName, SparkLogicalPlan(e))
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnBuilder.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnBuilder.scala
index 048ee66bff44..4be048cd742d 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnBuilder.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnBuilder.scala
@@ -28,7 +28,7 @@ private[sql] trait ColumnBuilder {
   /**
    * Initializes with an approximate lower bound on the expected number of elements in this column.
    */
-  def initialize(initialSize: Int, columnName: String = "")
+  def initialize(initialSize: Int, columnName: String = "", useCompression: Boolean = false)
 
   /**
    * Appends `row(ordinal)` to the column builder.
@@ -55,7 +55,11 @@ private[sql] class BasicColumnBuilder[T <: DataType, JvmType](
 
   protected var buffer: ByteBuffer = _
 
-  override def initialize(initialSize: Int, columnName: String = "") = {
+  override def initialize(
+      initialSize: Int,
+      columnName: String = "",
+      useCompression: Boolean = false) = {
+
     val size = if (initialSize == 0) DEFAULT_INITIAL_BUFFER_SIZE else initialSize
     this.columnName = columnName
 
@@ -130,7 +134,12 @@ private[sql] object ColumnBuilder {
     }
   }
 
-  def apply(typeId: Int, initialSize: Int = 0, columnName: String = ""): ColumnBuilder = {
+  def apply(
+      typeId: Int,
+      initialSize: Int = 0,
+      columnName: String = "",
+      useCompression: Boolean = false): ColumnBuilder = {
+
     val builder = (typeId match {
       case INT.typeId     => new IntColumnBuilder
       case LONG.typeId    => new LongColumnBuilder
@@ -144,7 +153,7 @@ private[sql] object ColumnBuilder {
       case GENERIC.typeId => new GenericColumnBuilder
     }).asInstanceOf[ColumnBuilder]
 
-    builder.initialize(initialSize, columnName)
+    builder.initialize(initialSize, columnName, useCompression)
     builder
   }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala
index 8a2473304742..fdf28e1bb126 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala
@@ -20,8 +20,12 @@ package org.apache.spark.sql.columnar
 import org.apache.spark.sql.catalyst.expressions.{GenericMutableRow, Attribute}
 import org.apache.spark.sql.execution.{SparkPlan, LeafNode}
 import org.apache.spark.sql.Row
+import org.apache.spark.SparkConf
 
-private[sql] case class InMemoryColumnarTableScan(attributes: Seq[Attribute], child: SparkPlan)
+private[sql] case class InMemoryColumnarTableScan(
+    attributes: Seq[Attribute],
+    child: SparkPlan,
+    useCompression: Boolean)
   extends LeafNode {
 
   override def output: Seq[Attribute] = attributes
@@ -30,7 +34,7 @@ private[sql] case class InMemoryColumnarTableScan(attributes: Seq[Attribute], ch
     val output = child.output
     val cached = child.execute().mapPartitions { iterator =>
       val columnBuilders = output.map { attribute =>
-        ColumnBuilder(ColumnType(attribute.dataType).typeId, 0, attribute.name)
+        ColumnBuilder(ColumnType(attribute.dataType).typeId, 0, attribute.name, useCompression)
       }.toArray
 
       var row: Row = null
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/NullableColumnBuilder.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/NullableColumnBuilder.scala
index 2a3b6fc1e46d..d008806eedbe 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/NullableColumnBuilder.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/NullableColumnBuilder.scala
@@ -40,12 +40,12 @@ private[sql] trait NullableColumnBuilder extends ColumnBuilder {
   private var pos: Int = _
   private var nullCount: Int = _
 
-  abstract override def initialize(initialSize: Int, columnName: String) {
+  abstract override def initialize(initialSize: Int, columnName: String, useCompression: Boolean) {
     nulls = ByteBuffer.allocate(1024)
     nulls.order(ByteOrder.nativeOrder())
     pos = 0
     nullCount = 0
-    super.initialize(initialSize, columnName)
+    super.initialize(initialSize, columnName, useCompression)
   }
 
   abstract override def appendFrom(row: Row, ordinal: Int) {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/CompressibleColumnAccessor.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/CompressibleColumnAccessor.scala
index 878cb84de106..b4120a3d4368 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/CompressibleColumnAccessor.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/CompressibleColumnAccessor.scala
@@ -32,5 +32,7 @@ private[sql] trait CompressibleColumnAccessor[T <: NativeType] extends ColumnAcc
     decoder = CompressionScheme(underlyingBuffer.getInt()).decoder(buffer, columnType)
   }
 
-  abstract override def extractSingle(buffer: ByteBuffer): T#JvmType = decoder.next()
+  abstract override def hasNext = super.hasNext || decoder.hasNext
+
+  override def extractSingle(buffer: ByteBuffer): T#JvmType = decoder.next()
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/CompressibleColumnBuilder.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/CompressibleColumnBuilder.scala
index 0f808f68f2ee..4c6675c3c87b 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/CompressibleColumnBuilder.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/CompressibleColumnBuilder.scala
@@ -47,7 +47,17 @@ private[sql] trait CompressibleColumnBuilder[T <: NativeType]
 
   import CompressionScheme._
 
-  val compressionEncoders = schemes.filter(_.supports(columnType)).map(_.encoder[T])
+  var compressionEncoders: Seq[Encoder[T]] = _
+
+  abstract override def initialize(initialSize: Int, columnName: String, useCompression: Boolean) {
+    compressionEncoders =
+      if (useCompression) {
+        schemes.filter(_.supports(columnType)).map(_.encoder[T])
+      } else {
+        Seq(PassThrough.encoder)
+      }
+    super.initialize(initialSize, columnName, useCompression)
+  }
 
   protected def isWorthCompressing(encoder: Encoder[T]) = {
     encoder.compressionRatio < 0.8
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/compressionSchemes.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/compressionSchemes.scala
index 800009d3195e..8cf9ec74ca2d 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/compressionSchemes.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/compressionSchemes.scala
@@ -157,7 +157,7 @@ private[sql] case object RunLengthEncoding extends CompressionScheme {
       currentValue
     }
 
-    override def hasNext = buffer.hasRemaining
+    override def hasNext = valueCount < run || buffer.hasRemaining
   }
 }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
index 50124dd40744..235a9b169246 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
@@ -77,7 +77,7 @@ case class SparkLogicalPlan(alreadyPlanned: SparkPlan)
     SparkLogicalPlan(
       alreadyPlanned match {
         case ExistingRdd(output, rdd) => ExistingRdd(output.map(_.newInstance), rdd)
-        case scan @ InMemoryColumnarTableScan(output, child) =>
+        case scan @ InMemoryColumnarTableScan(output, _, _) =>
           scan.copy(attributes = output.map(_.newInstance))
         case _ => sys.error("Multiple instance of the same relation detected.")
       }).asInstanceOf[this.type]
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala b/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala
index 002b7f0adafa..b5973c0f51be 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala
@@ -73,4 +73,15 @@ object TestData {
       ArrayData(Seq(1,2,3), Seq(Seq(1,2,3))) ::
       ArrayData(Seq(2,3,4), Seq(Seq(2,3,4))) :: Nil)
   arrayData.registerAsTable("arrayData")
+
+  case class StringData(s: String)
+  val repeatedData =
+    TestSQLContext.sparkContext.parallelize(List.fill(2)(StringData("test")))
+  repeatedData.registerAsTable("repeatedData")
+
+  val nullableRepeatedData =
+    TestSQLContext.sparkContext.parallelize(
+      List.fill(2)(StringData(null)) ++
+      List.fill(2)(StringData("test")))
+  nullableRepeatedData.registerAsTable("nullableRepeatedData")
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala
index 16a13b8a7496..31c5dfba9295 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala
@@ -28,14 +28,14 @@ class InMemoryColumnarQuerySuite extends QueryTest {
 
   test("simple columnar query") {
     val plan = TestSQLContext.executePlan(testData.logicalPlan).executedPlan
-    val scan = SparkLogicalPlan(InMemoryColumnarTableScan(plan.output, plan))
+    val scan = SparkLogicalPlan(InMemoryColumnarTableScan(plan.output, plan, true))
 
     checkAnswer(scan, testData.collect().toSeq)
   }
 
   test("projection") {
     val plan = TestSQLContext.executePlan(testData.select('value, 'key).logicalPlan).executedPlan
-    val scan = SparkLogicalPlan(InMemoryColumnarTableScan(plan.output, plan))
+    val scan = SparkLogicalPlan(InMemoryColumnarTableScan(plan.output, plan, true))
 
     checkAnswer(scan, testData.collect().map {
       case Row(key: Int, value: String) => value -> key
@@ -44,9 +44,33 @@ class InMemoryColumnarQuerySuite extends QueryTest {
 
   test("SPARK-1436 regression: in-memory columns must be able to be accessed multiple times") {
     val plan = TestSQLContext.executePlan(testData.logicalPlan).executedPlan
-    val scan = SparkLogicalPlan(InMemoryColumnarTableScan(plan.output, plan))
+    val scan = SparkLogicalPlan(InMemoryColumnarTableScan(plan.output, plan, true))
 
     checkAnswer(scan, testData.collect().toSeq)
     checkAnswer(scan, testData.collect().toSeq)
   }
+
+  test("SPARK-1678 regression: compression must not lose repeated values") {
+    checkAnswer(
+      sql("SELECT * FROM repeatedData"),
+      repeatedData.collect().toSeq)
+
+    TestSQLContext.cacheTable("repeatedData")
+
+    checkAnswer(
+      sql("SELECT * FROM repeatedData"),
+      repeatedData.collect().toSeq)
+  }
+
+  test("with null values") {
+    checkAnswer(
+      sql("SELECT * FROM nullableRepeatedData"),
+      nullableRepeatedData.collect().toSeq)
+
+    TestSQLContext.cacheTable("nullableRepeatedData")
+
+    checkAnswer(
+      sql("SELECT * FROM nullableRepeatedData"),
+      nullableRepeatedData.collect().toSeq)
+  }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/BooleanBitSetSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/BooleanBitSetSuite.scala
index a754f98f7fbf..93259a19b9fe 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/BooleanBitSetSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/BooleanBitSetSuite.scala
@@ -72,7 +72,12 @@ class BooleanBitSetSuite extends FunSuite {
     buffer.rewind().position(headerSize + 4)
 
     val decoder = BooleanBitSet.decoder(buffer, BOOLEAN)
-    values.foreach(expectResult(_, "Wrong decoded value")(decoder.next()))
+    if (values.nonEmpty) {
+      values.foreach {
+        assert(decoder.hasNext)
+        expectResult(_, "Wrong decoded value")(decoder.next())
+      }
+    }
     assert(!decoder.hasNext)
   }
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/DictionaryEncodingSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/DictionaryEncodingSuite.scala
index eab27987e08e..198dcd881934 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/DictionaryEncodingSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/DictionaryEncodingSuite.scala
@@ -98,8 +98,11 @@ class DictionaryEncodingSuite extends FunSuite {
 
         val decoder = DictionaryEncoding.decoder(buffer, columnType)
 
-        inputSeq.foreach { i =>
-          expectResult(values(i), "Wrong decoded value")(decoder.next())
+        if (inputSeq.nonEmpty) {
+          inputSeq.foreach { i =>
+            assert(decoder.hasNext)
+            expectResult(values(i), "Wrong decoded value")(decoder.next())
+          }
         }
 
         assert(!decoder.hasNext)
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/IntegralDeltaSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/IntegralDeltaSuite.scala
index ce419ca7269b..46af6e001c63 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/IntegralDeltaSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/IntegralDeltaSuite.scala
@@ -96,7 +96,12 @@ class IntegralDeltaSuite extends FunSuite {
       buffer.rewind().position(headerSize + 4)
 
       val decoder = scheme.decoder(buffer, columnType)
-      input.foreach(expectResult(_, "Wrong decoded value")(decoder.next()))
+      if (input.nonEmpty) {
+        input.foreach{
+          assert(decoder.hasNext)
+          expectResult(_, "Wrong decoded value")(decoder.next())
+        }
+      }
       assert(!decoder.hasNext)
     }
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/RunLengthEncodingSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/RunLengthEncodingSuite.scala
index 89f9b60a4397..d3b73ba19d47 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/RunLengthEncodingSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/RunLengthEncodingSuite.scala
@@ -81,8 +81,11 @@ class RunLengthEncodingSuite extends FunSuite {
 
       val decoder = RunLengthEncoding.decoder(buffer, columnType)
 
-      inputSeq.foreach { i =>
-        expectResult(values(i), "Wrong decoded value")(decoder.next())
+      if (inputSeq.nonEmpty) {
+        inputSeq.foreach { i =>
+          assert(decoder.hasNext)
+          expectResult(values(i), "Wrong decoded value")(decoder.next())
+        }
       }
 
       assert(!decoder.hasNext)
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/TestCompressibleColumnBuilder.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/TestCompressibleColumnBuilder.scala
index 81bf5e99d19b..6d688ea95cfc 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/TestCompressibleColumnBuilder.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/TestCompressibleColumnBuilder.scala
@@ -38,7 +38,7 @@ object TestCompressibleColumnBuilder {
       scheme: CompressionScheme) = {
 
     val builder = new TestCompressibleColumnBuilder(columnStats, columnType, Seq(scheme))
-    builder.initialize(0)
+    builder.initialize(0, "", useCompression = true)
     builder
   }
 }
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
index 6c907887db79..ba837a274c51 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
@@ -130,7 +130,7 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with
         castChildOutput(p, table, child)
 
       case p @ logical.InsertIntoTable(SparkLogicalPlan(InMemoryColumnarTableScan(
-        _, HiveTableScan(_, table, _))), _, child, _) =>
+        _, HiveTableScan(_, table, _), _)), _, child, _) =>
         castChildOutput(p, table, child)
     }
 
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala
index d9a6e0e88932..b2157074a41b 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala
@@ -45,7 +45,7 @@ private[hive] trait HiveStrategies {
       case logical.InsertIntoTable(table: MetastoreRelation, partition, child, overwrite) =>
         InsertIntoHiveTable(table, partition, planLater(child), overwrite)(hiveContext) :: Nil
       case logical.InsertIntoTable(SparkLogicalPlan(InMemoryColumnarTableScan(
-        _, HiveTableScan(_, table, _))), partition, child, overwrite) =>
+        _, HiveTableScan(_, table, _), _)), partition, child, overwrite) =>
         InsertIntoHiveTable(table, partition, planLater(child), overwrite)(hiveContext) :: Nil
       case _ => Nil
     }

From a2262cdb7aa30e9f45043f1440d4b02bc3340f9f Mon Sep 17 00:00:00 2001
From: Andrew Or <andrewor14@gmail.com>
Date: Mon, 5 May 2014 22:14:47 -0700
Subject: [PATCH 100/810] [SPARK-1735] Add the missing special profiles to
 make-distribution.sh

73b0cbcc241cca3d318ff74340e80b02f884acbd introduced a few special profiles that are not covered in the `make-distribution.sh`. This affects hadoop versions 2.2.x, 2.3.x, and 2.4.x. Without these special profiles, a java version error for protobufs is thrown at run time.

I took the opportunity to rewrite the way we construct the maven command. Previously, the only hadoop version that triggered the `yarn-alpha` profile was 0.23.x, which was inconsistent with the [docs](https://github.com/apache/spark/blob/master/docs/building-with-maven.md). This is now generalized to hadoop versions from 0.23.x to 2.1.x.

Author: Andrew Or <andrewor14@gmail.com>

Closes #660 from andrewor14/hadoop-distribution and squashes the following commits:

6740126 [Andrew Or] Generalize the yarn profile to hadoop versions 2.2+
88f192d [Andrew Or] Add the required special profiles to make-distribution.sh
---
 make-distribution.sh | 47 +++++++++++++++++++++++++-------------------
 1 file changed, 27 insertions(+), 20 deletions(-)

diff --git a/make-distribution.sh b/make-distribution.sh
index ff18d01e7a61..8a63133bc45d 100755
--- a/make-distribution.sh
+++ b/make-distribution.sh
@@ -47,7 +47,7 @@ set -o pipefail
 VERSION=$(mvn help:evaluate -Dexpression=project.version 2>/dev/null | grep -v "INFO" | tail -n 1)
 if [ $? != 0 ]; then
     echo -e "You need Maven installed to build Spark."
-    echo -e "Download Maven from https://maven.apache.org."
+    echo -e "Download Maven from https://maven.apache.org/"
     exit -1;
 fi
 
@@ -131,27 +131,34 @@ cd $FWDIR
 
 export MAVEN_OPTS="-Xmx2g -XX:MaxPermSize=512M -XX:ReservedCodeCacheSize=512m"
 
-if [ "$SPARK_HIVE" == "true" ]; then
-  MAYBE_HIVE="-Phive"
-else
-  MAYBE_HIVE=""
-fi
-
-if [ "$SPARK_YARN" == "true" ]; then
-  if [[ "$SPARK_HADOOP_VERSION" =~ "0.23." ]]; then
-    mvn clean package -DskipTests -Pyarn-alpha -Dhadoop.version=$SPARK_HADOOP_VERSION \
-      -Dyarn.version=$SPARK_HADOOP_VERSION $MAYBE_HIVE -Phadoop-0.23
-  else
-    mvn clean package -DskipTests -Pyarn -Dhadoop.version=$SPARK_HADOOP_VERSION \
-      -Dyarn.version=$SPARK_HADOOP_VERSION $MAYBE_HIVE
-  fi
-else
-  if [[ "$SPARK_HADOOP_VERSION" =~ "0.23." ]]; then
-    mvn clean package -Phadoop-0.23 -DskipTests -Dhadoop.version=$SPARK_HADOOP_VERSION $MAYBE_HIVE
-  else
-    mvn clean package -DskipTests -Dhadoop.version=$SPARK_HADOOP_VERSION $MAYBE_HIVE
+BUILD_COMMAND="mvn clean package"
+
+# Use special profiles for hadoop versions 0.23.x, 2.2.x, 2.3.x, 2.4.x
+if [[ "$SPARK_HADOOP_VERSION" =~ ^0\.23\. ]]; then BUILD_COMMAND="$BUILD_COMMAND -Phadoop-0.23"; fi
+if [[ "$SPARK_HADOOP_VERSION" =~ ^2\.2\. ]]; then BUILD_COMMAND="$BUILD_COMMAND -Phadoop-2.2"; fi
+if [[ "$SPARK_HADOOP_VERSION" =~ ^2\.3\. ]]; then BUILD_COMMAND="$BUILD_COMMAND -Phadoop-2.3"; fi
+if [[ "$SPARK_HADOOP_VERSION" =~ ^2\.4\. ]]; then BUILD_COMMAND="$BUILD_COMMAND -Phadoop-2.4"; fi
+if [[ "$SPARK_HIVE" == "true" ]]; then BUILD_COMMAND="$BUILD_COMMAND -Phive"; fi
+if [[ "$SPARK_YARN" == "true" ]]; then
+  # For hadoop versions 0.23.x to 2.1.x, use the yarn-alpha profile
+  if [[ "$SPARK_HADOOP_VERSION" =~ ^0\.2[3-9]\. ]] ||
+     [[ "$SPARK_HADOOP_VERSION" =~ ^0\.[3-9][0-9]\. ]] ||
+     [[ "$SPARK_HADOOP_VERSION" =~ ^1\.[0-9]\. ]] ||
+     [[ "$SPARK_HADOOP_VERSION" =~ ^2\.[0-1]\. ]]; then
+    BUILD_COMMAND="$BUILD_COMMAND -Pyarn-alpha"
+  # For hadoop versions 2.2+, use the yarn profile
+  elif [[ "$SPARK_HADOOP_VERSION" =~ ^2.[2-9]. ]]; then
+    BUILD_COMMAND="$BUILD_COMMAND -Pyarn"
   fi
+  BUILD_COMMAND="$BUILD_COMMAND -Dyarn.version=$SPARK_HADOOP_VERSION"
 fi
+BUILD_COMMAND="$BUILD_COMMAND -Dhadoop.version=$SPARK_HADOOP_VERSION"
+BUILD_COMMAND="$BUILD_COMMAND -DskipTests"
+
+# Actually build the jar
+echo -e "\nBuilding with..."
+echo -e "\$ $BUILD_COMMAND\n"
+${BUILD_COMMAND}
 
 # Make directories
 rm -rf "$DISTDIR"

From 3c64750bdd4c2d0a5562f90aead37be81627cc9d Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Mon, 5 May 2014 22:59:42 -0700
Subject: [PATCH 101/810] [SQL] SPARK-1732 - Support for null primitive values.

I also removed a println that I bumped into.

Author: Michael Armbrust <michael@databricks.com>

Closes #658 from marmbrus/nullPrimitives and squashes the following commits:

a3ec4f3 [Michael Armbrust] Remove println.
695606b [Michael Armbrust] Support for null primatives from using scala and java reflection.
---
 .../spark/sql/catalyst/ScalaReflection.scala  | 14 ++++-
 .../spark/sql/api/java/JavaSQLContext.scala   |  8 +++
 .../org/apache/spark/sql/api/java/Row.scala   |  2 +-
 .../spark/sql/execution/basicOperators.scala  |  3 +-
 .../sql/ScalaReflectionRelationSuite.scala    | 34 +++++++++++
 .../spark/sql/api/java/JavaSQLSuite.scala     | 61 +++++++++++++++++++
 .../spark/sql/columnar/ColumnTypeSuite.scala  |  5 +-
 7 files changed, 122 insertions(+), 5 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala
index 446d0e0bd7f5..792ef6cee6f5 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala
@@ -44,7 +44,8 @@ object ScalaReflection {
     case t if t <:< typeOf[Product] =>
       val params = t.member("<init>": TermName).asMethod.paramss
       StructType(
-        params.head.map(p => StructField(p.name.toString, schemaFor(p.typeSignature), true)))
+        params.head.map(p =>
+          StructField(p.name.toString, schemaFor(p.typeSignature), nullable = true)))
     // Need to decide if we actually need a special type here.
     case t if t <:< typeOf[Array[Byte]] => BinaryType
     case t if t <:< typeOf[Array[_]] =>
@@ -58,6 +59,17 @@ object ScalaReflection {
     case t if t <:< typeOf[String] => StringType
     case t if t <:< typeOf[Timestamp] => TimestampType
     case t if t <:< typeOf[BigDecimal] => DecimalType
+    case t if t <:< typeOf[Option[_]] =>
+      val TypeRef(_, _, Seq(optType)) = t
+      schemaFor(optType)
+    case t if t <:< typeOf[java.lang.Integer] => IntegerType
+    case t if t <:< typeOf[java.lang.Long] => LongType
+    case t if t <:< typeOf[java.lang.Double] => DoubleType
+    case t if t <:< typeOf[java.lang.Float] => FloatType
+    case t if t <:< typeOf[java.lang.Short] => ShortType
+    case t if t <:< typeOf[java.lang.Byte] => ByteType
+    case t if t <:< typeOf[java.lang.Boolean] => BooleanType
+    // TODO: The following datatypes could be marked as non-nullable.
     case t if t <:< definitions.IntTpe => IntegerType
     case t if t <:< definitions.LongTpe => LongType
     case t if t <:< definitions.DoubleTpe => DoubleType
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSQLContext.scala
index a7347088794a..57facbe10fc9 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSQLContext.scala
@@ -132,6 +132,14 @@ class JavaSQLContext(sparkContext: JavaSparkContext) {
         case c: Class[_] if c == java.lang.Byte.TYPE => ByteType
         case c: Class[_] if c == java.lang.Float.TYPE => FloatType
         case c: Class[_] if c == java.lang.Boolean.TYPE => BooleanType
+
+        case c: Class[_] if c == classOf[java.lang.Short] => ShortType
+        case c: Class[_] if c == classOf[java.lang.Integer] => IntegerType
+        case c: Class[_] if c == classOf[java.lang.Long] => LongType
+        case c: Class[_] if c == classOf[java.lang.Double] => DoubleType
+        case c: Class[_] if c == classOf[java.lang.Byte] => ByteType
+        case c: Class[_] if c == classOf[java.lang.Float] => FloatType
+        case c: Class[_] if c == classOf[java.lang.Boolean] => BooleanType
       }
       // TODO: Nullability could be stricter.
       AttributeReference(property.getName, dataType, nullable = true)()
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/api/java/Row.scala b/sql/core/src/main/scala/org/apache/spark/sql/api/java/Row.scala
index 362fe769581d..9b0dd2176149 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/api/java/Row.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/api/java/Row.scala
@@ -22,7 +22,7 @@ import org.apache.spark.sql.catalyst.expressions.{Row => ScalaRow}
 /**
  * A result row from a SparkSQL query.
  */
-class Row(row: ScalaRow) extends Serializable {
+class Row(private[spark] val row: ScalaRow) extends Serializable {
 
   /** Returns the number of columns present in this Row. */
   def length: Int = row.length
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
index d807187a5ffb..8969794c6993 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
@@ -164,6 +164,7 @@ case class Sort(
 @DeveloperApi
 object ExistingRdd {
   def convertToCatalyst(a: Any): Any = a match {
+    case o: Option[_] => o.orNull
     case s: Seq[Any] => s.map(convertToCatalyst)
     case p: Product => new GenericRow(p.productIterator.map(convertToCatalyst).toArray)
     case other => other
@@ -180,7 +181,7 @@ object ExistingRdd {
         bufferedIterator.map { r =>
           var i = 0
           while (i < mutableRow.length) {
-            mutableRow(i) = r.productElement(i)
+            mutableRow(i) = convertToCatalyst(r.productElement(i))
             i += 1
           }
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/ScalaReflectionRelationSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/ScalaReflectionRelationSuite.scala
index 1cbf973c3491..f2934da9a031 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/ScalaReflectionRelationSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/ScalaReflectionRelationSuite.scala
@@ -36,6 +36,24 @@ case class ReflectData(
     timestampField: Timestamp,
     seqInt: Seq[Int])
 
+case class NullReflectData(
+    intField: java.lang.Integer,
+    longField: java.lang.Long,
+    floatField: java.lang.Float,
+    doubleField: java.lang.Double,
+    shortField: java.lang.Short,
+    byteField: java.lang.Byte,
+    booleanField: java.lang.Boolean)
+
+case class OptionalReflectData(
+    intField: Option[Int],
+    longField: Option[Long],
+    floatField: Option[Float],
+    doubleField: Option[Double],
+    shortField: Option[Short],
+    byteField: Option[Byte],
+    booleanField: Option[Boolean])
+
 case class ReflectBinary(data: Array[Byte])
 
 class ScalaReflectionRelationSuite extends FunSuite {
@@ -48,6 +66,22 @@ class ScalaReflectionRelationSuite extends FunSuite {
     assert(sql("SELECT * FROM reflectData").collect().head === data.productIterator.toSeq)
   }
 
+  test("query case class RDD with nulls") {
+    val data = NullReflectData(null, null, null, null, null, null, null)
+    val rdd = sparkContext.parallelize(data :: Nil)
+    rdd.registerAsTable("reflectNullData")
+
+    assert(sql("SELECT * FROM reflectNullData").collect().head === Seq.fill(7)(null))
+  }
+
+  test("query case class RDD with Nones") {
+    val data = OptionalReflectData(None, None, None, None, None, None, None)
+    val rdd = sparkContext.parallelize(data :: Nil)
+    rdd.registerAsTable("reflectOptionalData")
+
+    assert(sql("SELECT * FROM reflectOptionalData").collect().head === Seq.fill(7)(null))
+  }
+
   // Equality is broken for Arrays, so we test that separately.
   test("query binary data") {
     val rdd = sparkContext.parallelize(ReflectBinary(Array[Byte](1)) :: Nil)
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/api/java/JavaSQLSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/api/java/JavaSQLSuite.scala
index def0e046a383..9fff7222fe84 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/api/java/JavaSQLSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/api/java/JavaSQLSuite.scala
@@ -35,6 +35,17 @@ class PersonBean extends Serializable {
   var age: Int = _
 }
 
+class AllTypesBean extends Serializable {
+  @BeanProperty var stringField: String = _
+  @BeanProperty var intField: java.lang.Integer = _
+  @BeanProperty var longField: java.lang.Long = _
+  @BeanProperty var floatField: java.lang.Float = _
+  @BeanProperty var doubleField: java.lang.Double = _
+  @BeanProperty var shortField: java.lang.Short = _
+  @BeanProperty var byteField: java.lang.Byte = _
+  @BeanProperty var booleanField: java.lang.Boolean = _
+}
+
 class JavaSQLSuite extends FunSuite {
   val javaCtx = new JavaSparkContext(TestSQLContext.sparkContext)
   val javaSqlCtx = new JavaSQLContext(javaCtx)
@@ -50,4 +61,54 @@ class JavaSQLSuite extends FunSuite {
     schemaRDD.registerAsTable("people")
     javaSqlCtx.sql("SELECT * FROM people").collect()
   }
+
+  test("all types in JavaBeans") {
+    val bean = new AllTypesBean
+    bean.setStringField("")
+    bean.setIntField(0)
+    bean.setLongField(0)
+    bean.setFloatField(0.0F)
+    bean.setDoubleField(0.0)
+    bean.setShortField(0.toShort)
+    bean.setByteField(0.toByte)
+    bean.setBooleanField(false)
+
+    val rdd = javaCtx.parallelize(bean :: Nil)
+    val schemaRDD = javaSqlCtx.applySchema(rdd, classOf[AllTypesBean])
+    schemaRDD.registerAsTable("allTypes")
+
+    assert(
+      javaSqlCtx.sql(
+        """
+          |SELECT stringField, intField, longField, floatField, doubleField, shortField, byteField,
+          |       booleanField
+          |FROM allTypes
+        """.stripMargin).collect.head.row ===
+      Seq("", 0, 0L, 0F, 0.0, 0.toShort, 0.toByte, false))
+  }
+
+  test("all types null in JavaBeans") {
+    val bean = new AllTypesBean
+    bean.setStringField(null)
+    bean.setIntField(null)
+    bean.setLongField(null)
+    bean.setFloatField(null)
+    bean.setDoubleField(null)
+    bean.setShortField(null)
+    bean.setByteField(null)
+    bean.setBooleanField(null)
+
+    val rdd = javaCtx.parallelize(bean :: Nil)
+    val schemaRDD = javaSqlCtx.applySchema(rdd, classOf[AllTypesBean])
+    schemaRDD.registerAsTable("allTypes")
+
+    assert(
+      javaSqlCtx.sql(
+        """
+          |SELECT stringField, intField, longField, floatField, doubleField, shortField, byteField,
+          |       booleanField
+          |FROM allTypes
+        """.stripMargin).collect.head.row ===
+        Seq.fill(8)(null))
+  }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnTypeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnTypeSuite.scala
index 325173cf95fd..71be41056768 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnTypeSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnTypeSuite.scala
@@ -21,11 +21,12 @@ import java.nio.ByteBuffer
 
 import org.scalatest.FunSuite
 
+import org.apache.spark.sql.Logging
 import org.apache.spark.sql.catalyst.types._
 import org.apache.spark.sql.columnar.ColumnarTestUtils._
 import org.apache.spark.sql.execution.SparkSqlSerializer
 
-class ColumnTypeSuite extends FunSuite {
+class ColumnTypeSuite extends FunSuite with Logging {
   val DEFAULT_BUFFER_SIZE = 512
 
   test("defaultSize") {
@@ -163,7 +164,7 @@ class ColumnTypeSuite extends FunSuite {
 
       buffer.rewind()
       seq.foreach { expected =>
-        println("buffer = " + buffer + ", expected = " + expected)
+        logger.info("buffer = " + buffer + ", expected = " + expected)
         val extracted = columnType.extract(buffer)
         assert(
           expected === extracted,

From 0a5a46811475a7accac1ed8dfefeeba9554456db Mon Sep 17 00:00:00 2001
From: ArcherShao <ArcherShao@users.noreply.github.com>
Date: Tue, 6 May 2014 10:12:59 -0700
Subject: [PATCH 102/810] Update OpenHashSet.scala

Modify wrong comment of function addWithoutResize.

Author: ArcherShao <ArcherShao@users.noreply.github.com>

Closes #667 from ArcherShao/patch-3 and squashes the following commits:

a607358 [ArcherShao] Update OpenHashSet.scala
---
 .../scala/org/apache/spark/util/collection/OpenHashSet.scala  | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/util/collection/OpenHashSet.scala b/core/src/main/scala/org/apache/spark/util/collection/OpenHashSet.scala
index 19af4f8cbe42..4e363b74f4be 100644
--- a/core/src/main/scala/org/apache/spark/util/collection/OpenHashSet.scala
+++ b/core/src/main/scala/org/apache/spark/util/collection/OpenHashSet.scala
@@ -115,10 +115,10 @@ class OpenHashSet[@specialized(Long, Int) T: ClassTag](
    * The caller is responsible for calling rehashIfNeeded.
    *
    * Use (retval & POSITION_MASK) to get the actual position, and
-   * (retval & EXISTENCE_MASK) != 0 for prior existence.
+   * (retval & NONEXISTENCE_MASK) == 0 for prior existence.
    *
    * @return The position where the key is placed, plus the highest order bit is set if the key
-   *         exists previously.
+   *         does not exists previously.
    */
   def addWithoutResize(k: T): Int = {
     var pos = hashcode(hasher.hash(k)) & _mask

From 1e829905c791fbf1dfd8e0c1caa62ead7354605e Mon Sep 17 00:00:00 2001
From: Thomas Graves <tgraves@apache.org>
Date: Tue, 6 May 2014 12:00:09 -0700
Subject: [PATCH 103/810] SPARK-1474: Spark on yarn assembly doesn't include
 AmIpFilter

We use org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter in spark on yarn but are not included it in the assembly jar.

I tested this on yarn cluster by removing the yarn jars from the classpath and spark runs fine now.

Author: Thomas Graves <tgraves@apache.org>

Closes #406 from tgravescs/SPARK-1474 and squashes the following commits:

1548bf9 [Thomas Graves] SPARK-1474: Spark on yarn assembly doesn't include org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter
---
 pom.xml                  | 25 ++++++++++++++++++++++++-
 project/SparkBuild.scala |  3 ++-
 yarn/pom.xml             |  4 ++++
 3 files changed, 30 insertions(+), 2 deletions(-)

diff --git a/pom.xml b/pom.xml
index e4b5c36d6917..fb904e868cfa 100644
--- a/pom.xml
+++ b/pom.xml
@@ -616,7 +616,25 @@
           </exclusion>
         </exclusions>
       </dependency>
-
+      <dependency>
+        <groupId>org.apache.hadoop</groupId>
+        <artifactId>hadoop-yarn-server-web-proxy</artifactId>
+        <version>${yarn.version}</version>
+        <exclusions>
+          <exclusion>
+            <groupId>asm</groupId>
+            <artifactId>asm</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.ow2.asm</groupId>
+            <artifactId>asm</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.jboss.netty</groupId>
+            <artifactId>netty</artifactId>
+          </exclusion>
+        </exclusions>
+      </dependency>
       <dependency>
         <groupId>org.apache.hadoop</groupId>
         <artifactId>hadoop-yarn-client</artifactId>
@@ -958,6 +976,11 @@
           <artifactId>hadoop-yarn-common</artifactId>
           <scope>provided</scope>
         </dependency>
+        <dependency>
+          <groupId>org.apache.hadoop</groupId>
+          <artifactId>hadoop-yarn-server-web-proxy</artifactId>
+          <scope>provided</scope>
+        </dependency>
         <dependency>
           <groupId>org.apache.hadoop</groupId>
           <artifactId>hadoop-yarn-client</artifactId>
diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index a2597e3e6ddd..1ad05d9e46dd 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -563,7 +563,8 @@ object SparkBuild extends Build {
       "org.apache.hadoop" % hadoopClient         % hadoopVersion excludeAll(excludeNetty, excludeAsm, excludeOldAsm),
       "org.apache.hadoop" % "hadoop-yarn-api"    % hadoopVersion excludeAll(excludeNetty, excludeAsm, excludeOldAsm),
       "org.apache.hadoop" % "hadoop-yarn-common" % hadoopVersion excludeAll(excludeNetty, excludeAsm, excludeOldAsm),
-      "org.apache.hadoop" % "hadoop-yarn-client" % hadoopVersion excludeAll(excludeNetty, excludeAsm, excludeOldAsm)
+      "org.apache.hadoop" % "hadoop-yarn-client" % hadoopVersion excludeAll(excludeNetty, excludeAsm, excludeOldAsm),
+      "org.apache.hadoop" % "hadoop-yarn-server-web-proxy" % hadoopVersion excludeAll(excludeNetty, excludeAsm, excludeOldAsm)
     )
   )
 
diff --git a/yarn/pom.xml b/yarn/pom.xml
index e0094738266b..6993c89525d8 100644
--- a/yarn/pom.xml
+++ b/yarn/pom.xml
@@ -43,6 +43,10 @@
       <groupId>org.apache.hadoop</groupId>
       <artifactId>hadoop-yarn-common</artifactId>
     </dependency>
+    <dependency>
+      <groupId>org.apache.hadoop</groupId>
+      <artifactId>hadoop-yarn-server-web-proxy</artifactId>
+    </dependency>
     <dependency>
       <groupId>org.apache.hadoop</groupId>
       <artifactId>hadoop-yarn-client</artifactId>

From 7b978c1ac59718b85e512c46105b6af641afc3dc Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Tue, 6 May 2014 12:07:46 -0700
Subject: [PATCH 104/810] Fix two download suggestions in the docs:

1) On the quick start page provide a direct link to the downloads (suggested by @pbailis).
2) On the index page, don't suggest users always have to build Spark, since many won't.

Author: Patrick Wendell <pwendell@gmail.com>

Closes #662 from pwendell/quick-start and squashes the following commits:

0622f27 [Patrick Wendell] Fix two download suggestions in the docs:
---
 docs/index.md       | 36 ++++++++++--------------------------
 docs/quick-start.md |  8 +++-----
 2 files changed, 13 insertions(+), 31 deletions(-)

diff --git a/docs/index.md b/docs/index.md
index 2daa208b3b90..e3647717a1f1 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -9,17 +9,18 @@ It also supports a rich set of higher-level tools including [Shark](http://shark
 
 # Downloading
 
-Get Spark by visiting the [downloads page](http://spark.apache.org/downloads.html) of the Apache Spark site. This documentation is for Spark version {{site.SPARK_VERSION}}.
+Get Spark by visiting the [downloads page](http://spark.apache.org/downloads.html) of the Apache Spark site. This documentation is for Spark version {{site.SPARK_VERSION}}. The downloads page 
+contains Spark packages for many popular HDFS versions. If you'd like to build Spark from 
+scratch, visit the [building with Maven](building-with-maven.html) page.
 
-Spark runs on both Windows and UNIX-like systems (e.g. Linux, Mac OS). All you need to run it is to have `java` to installed on your system `PATH`, or the `JAVA_HOME` environment variable pointing to a Java installation.
+Spark runs on both Windows and UNIX-like systems (e.g. Linux, Mac OS). All you need to run it is 
+to have `java` to installed on your system `PATH`, or the `JAVA_HOME` environment variable 
+pointing to a Java installation.
 
-# Building
-
-Spark uses [Simple Build Tool](http://www.scala-sbt.org), which is bundled with it. To compile the code, go into the top-level Spark directory and run
-
-    sbt/sbt assembly
-
-For its Scala API, Spark {{site.SPARK_VERSION}} depends on Scala {{site.SCALA_BINARY_VERSION}}. If you write applications in Scala, you will need to use a compatible Scala version (e.g. {{site.SCALA_BINARY_VERSION}}.X) -- newer major versions may not work. You can get the right version of Scala from [scala-lang.org](http://www.scala-lang.org/download/).
+For its Scala API, Spark {{site.SPARK_VERSION}} depends on Scala {{site.SCALA_BINARY_VERSION}}. 
+If you write applications in Scala, you will need to use a compatible Scala version 
+(e.g. {{site.SCALA_BINARY_VERSION}}.X) -- newer major versions may not work. You can get the 
+right version of Scala from [scala-lang.org](http://www.scala-lang.org/download/).
 
 # Running the Examples and Shell
 
@@ -50,23 +51,6 @@ options for deployment:
 * [Apache Mesos](running-on-mesos.html)
 * [Hadoop YARN](running-on-yarn.html)
 
-# A Note About Hadoop Versions
-
-Spark uses the Hadoop-client library to talk to HDFS and other Hadoop-supported
-storage systems. Because the HDFS protocol has changed in different versions of
-Hadoop, you must build Spark against the same version that your cluster uses.
-By default, Spark links to Hadoop 1.0.4. You can change this by setting the
-`SPARK_HADOOP_VERSION` variable when compiling:
-
-    SPARK_HADOOP_VERSION=2.2.0 sbt/sbt assembly
-
-In addition, if you wish to run Spark on [YARN](running-on-yarn.html), set
-`SPARK_YARN` to `true`:
-
-    SPARK_HADOOP_VERSION=2.0.5-alpha SPARK_YARN=true sbt/sbt assembly
-
-Note that on Windows, you need to set the environment variables on separate lines, e.g., `set SPARK_HADOOP_VERSION=1.2.1`.
-
 # Where to Go from Here
 
 **Programming guides:**
diff --git a/docs/quick-start.md b/docs/quick-start.md
index 64996b52e040..478b790f92e1 100644
--- a/docs/quick-start.md
+++ b/docs/quick-start.md
@@ -9,11 +9,9 @@ title: Quick Start
 This tutorial provides a quick introduction to using Spark. We will first introduce the API through Spark's interactive Scala shell (don't worry if you don't know Scala -- you will not need much for this), then show how to write standalone applications in Scala, Java, and Python.
 See the [programming guide](scala-programming-guide.html) for a more complete reference.
 
-To follow along with this guide, you only need to have successfully built Spark on one machine. Simply go into your Spark directory and run:
-
-{% highlight bash %}
-$ sbt/sbt assembly
-{% endhighlight %}
+To follow along with this guide, first download a packaged release of Spark from the
+[Spark website](http://spark.apache.org/downloads.html). Since we won't be using HDFS,
+you can download a package for any version of Hadoop.
 
 # Interactive Analysis with the Spark Shell
 

From fbfe69de69aa6767d95167711622ec34c59a1f6d Mon Sep 17 00:00:00 2001
From: Mark Hamstra <markhamstra@gmail.com>
Date: Tue, 6 May 2014 12:53:39 -0700
Subject: [PATCH 105/810] [SPARK-1685] Cancel retryTimer on restart of Worker
 or AppClient

See https://issues.apache.org/jira/browse/SPARK-1685 for a more complete description, but in essence: If the Worker or AppClient actor restarts before successfully registering with Master, multiple retryTimers will be running, which will lead to less than the full number of registration retries being attempted before the new actor is forced to give up.

Author: Mark Hamstra <markhamstra@gmail.com>

Closes #602 from markhamstra/SPARK-1685 and squashes the following commits:

11cc088 [Mark Hamstra] retryTimer -> registrationRetryTimer
69c348c [Mark Hamstra] Cancel retryTimer on restart of Worker or AppClient
---
 .../org/apache/spark/deploy/client/AppClient.scala  | 13 +++++++++----
 .../org/apache/spark/deploy/worker/Worker.scala     | 10 ++++++----
 2 files changed, 15 insertions(+), 8 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/client/AppClient.scala b/core/src/main/scala/org/apache/spark/deploy/client/AppClient.scala
index 57085fc33714..896913d79660 100644
--- a/core/src/main/scala/org/apache/spark/deploy/client/AppClient.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/client/AppClient.scala
@@ -60,6 +60,7 @@ private[spark] class AppClient(
     var master: ActorSelection = null
     var alreadyDisconnected = false  // To avoid calling listener.disconnected() multiple times
     var alreadyDead = false  // To avoid calling listener.dead() multiple times
+    var registrationRetryTimer: Option[Cancellable] = None
 
     override def preStart() {
       context.system.eventStream.subscribe(self, classOf[RemotingLifecycleEvent])
@@ -83,21 +84,20 @@ private[spark] class AppClient(
 
     def registerWithMaster() {
       tryRegisterAllMasters()
-
       import context.dispatcher
       var retries = 0
-      lazy val retryTimer: Cancellable =
+      registrationRetryTimer = Some {
         context.system.scheduler.schedule(REGISTRATION_TIMEOUT, REGISTRATION_TIMEOUT) {
           retries += 1
           if (registered) {
-            retryTimer.cancel()
+            registrationRetryTimer.foreach(_.cancel())
           } else if (retries >= REGISTRATION_RETRIES) {
             markDead("All masters are unresponsive! Giving up.")
           } else {
             tryRegisterAllMasters()
           }
         }
-      retryTimer // start timer
+      }
     }
 
     def changeMaster(url: String) {
@@ -177,6 +177,11 @@ private[spark] class AppClient(
         alreadyDead = true
       }
     }
+
+    override def postStop() {
+      registrationRetryTimer.foreach(_.cancel())
+    }
+
   }
 
   def start() {
diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala b/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala
index cd6bd2cd29fc..85d25dc7dbfa 100755
--- a/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala
@@ -100,6 +100,8 @@ private[spark] class Worker(
   val metricsSystem = MetricsSystem.createMetricsSystem("worker", conf, securityMgr)
   val workerSource = new WorkerSource(this)
 
+  var registrationRetryTimer: Option[Cancellable] = None
+
   def coresFree: Int = cores - coresUsed
   def memoryFree: Int = memory - memoryUsed
 
@@ -161,13 +163,12 @@ private[spark] class Worker(
 
   def registerWithMaster() {
     tryRegisterAllMasters()
-
     var retries = 0
-    lazy val retryTimer: Cancellable =
+    registrationRetryTimer = Some {
       context.system.scheduler.schedule(REGISTRATION_TIMEOUT, REGISTRATION_TIMEOUT) {
         retries += 1
         if (registered) {
-          retryTimer.cancel()
+          registrationRetryTimer.foreach(_.cancel())
         } else if (retries >= REGISTRATION_RETRIES) {
           logError("All masters are unresponsive! Giving up.")
           System.exit(1)
@@ -175,7 +176,7 @@ private[spark] class Worker(
           tryRegisterAllMasters()
         }
       }
-    retryTimer // start timer
+    }
   }
 
   override def receive = {
@@ -344,6 +345,7 @@ private[spark] class Worker(
   }
 
   override def postStop() {
+    registrationRetryTimer.foreach(_.cancel())
     executors.values.foreach(_.kill())
     drivers.values.foreach(_.kill())
     webUi.stop()

From ec09acdd4a72333e1c9c2e9d8e12e9c4c07770c8 Mon Sep 17 00:00:00 2001
From: witgo <witgo@qq.com>
Date: Tue, 6 May 2014 14:17:39 -0700
Subject: [PATCH 106/810] SPARK-1734: spark-submit throws an exception:
 Exception in thread "main"...

... java.lang.ClassNotFoundException: org.apache.spark.broadcast.TorrentBroadcastFactory

Author: witgo <witgo@qq.com>

Closes #665 from witgo/SPARK-1734 and squashes the following commits:

cacf238 [witgo] SPARK-1734: spark-submit throws an exception: Exception in thread "main" java.lang.ClassNotFoundException: org.apache.spark.broadcast.TorrentBroadcastFactory
---
 .../scala/org/apache/spark/deploy/SparkSubmitArguments.scala    | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
index 45defb9a3f92..7031cdd9b4ae 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
@@ -333,6 +333,6 @@ object SparkSubmitArguments {
         val message = s"Failed when loading Spark properties file ${file.getName}"
         throw new SparkException(message, e)
     }
-    properties.stringPropertyNames().toSeq.map(k => (k, properties(k)))
+    properties.stringPropertyNames().toSeq.map(k => (k, properties(k).trim))
   }
 }

From 951a5d939863b42da83ac2569d5e9d7ed680e119 Mon Sep 17 00:00:00 2001
From: Matei Zaharia <matei@databricks.com>
Date: Tue, 6 May 2014 15:12:35 -0700
Subject: [PATCH 107/810] [SPARK-1549] Add Python support to spark-submit

This PR updates spark-submit to allow submitting Python scripts (currently only with deploy-mode=client, but that's all that was supported before) and updates the PySpark code to properly find various paths, etc. One significant change is that we assume we can always find the Python files either from the Spark assembly JAR (which will happen with the Maven assembly build in make-distribution.sh) or from SPARK_HOME (which will exist in local mode even if you use sbt assembly, and should be enough for testing). This means we no longer need a weird hack to modify the environment for YARN.

This patch also updates the Python worker manager to run python with -u, which means unbuffered output (send it to our logs right away instead of waiting a while after stuff was written); this should simplify debugging.

In addition, it fixes https://issues.apache.org/jira/browse/SPARK-1709, setting the main class from a JAR's Main-Class attribute if not specified by the user, and fixes a few help strings and style issues in spark-submit.

In the future we may want to make the `pyspark` shell use spark-submit as well, but it seems unnecessary for 1.0.

Author: Matei Zaharia <matei@databricks.com>

Closes #664 from mateiz/py-submit and squashes the following commits:

15e9669 [Matei Zaharia] Fix some uses of path.separator property
051278c [Matei Zaharia] Small style fixes
0afe886 [Matei Zaharia] Add license headers
4650412 [Matei Zaharia] Add pyFiles to PYTHONPATH in executors, remove old YARN stuff, add tests
15f8e1e [Matei Zaharia] Set PYTHONPATH in PythonWorkerFactory in case it wasn't set from outside
47c0655 [Matei Zaharia] More work to make spark-submit work with Python:
d4375bd [Matei Zaharia] Clean up description of spark-submit args a bit and add Python ones
---
 assembly/pom.xml                              |  13 --
 core/pom.xml                                  |   5 +
 .../scala/org/apache/spark/SparkEnv.scala     |   4 +-
 .../apache/spark/api/python/PythonUtils.scala |  42 ++++
 .../api/python/PythonWorkerFactory.scala      |   9 +-
 .../apache/spark/deploy/PythonRunner.scala    |  84 ++++++++
 .../org/apache/spark/deploy/SparkSubmit.scala | 183 ++++++++++--------
 .../spark/deploy/SparkSubmitArguments.scala   |  84 +++++---
 .../scala/org/apache/spark/util/Utils.scala   |   8 +-
 .../spark/deploy/SparkSubmitSuite.scala       |   6 +-
 docs/python-programming-guide.md              |  28 +--
 project/SparkBuild.scala                      |   4 +-
 python/pyspark/context.py                     |   6 +
 python/pyspark/java_gateway.py                |  89 ++++-----
 python/pyspark/tests.py                       | 131 ++++++++++++-
 .../org/apache/spark/repl/ReplSuite.scala     |   3 +-
 16 files changed, 505 insertions(+), 194 deletions(-)
 create mode 100644 core/src/main/scala/org/apache/spark/api/python/PythonUtils.scala
 create mode 100644 core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala

diff --git a/assembly/pom.xml b/assembly/pom.xml
index bdb38806492a..7d123fb1d7f0 100644
--- a/assembly/pom.xml
+++ b/assembly/pom.xml
@@ -40,14 +40,6 @@
     <deb.user>root</deb.user>
   </properties>
 
-  <repositories>
-    <!-- A repository in the local filesystem for the Py4J JAR, which is not in Maven central -->
-    <repository>
-      <id>lib</id>
-      <url>file://${project.basedir}/lib</url>
-    </repository>
-  </repositories>
-
   <dependencies>
     <dependency>
       <groupId>org.apache.spark</groupId>
@@ -84,11 +76,6 @@
       <artifactId>spark-sql_${scala.binary.version}</artifactId>
       <version>${project.version}</version>
     </dependency>
-    <dependency>
-      <groupId>net.sf.py4j</groupId>
-      <artifactId>py4j</artifactId>
-      <version>0.8.1</version>
-    </dependency>
   </dependencies>
 
   <build>
diff --git a/core/pom.xml b/core/pom.xml
index c24c7be20408..8fe215ab2428 100644
--- a/core/pom.xml
+++ b/core/pom.xml
@@ -247,6 +247,11 @@
       <artifactId>pyrolite</artifactId>
       <version>2.0.1</version>
     </dependency>
+    <dependency>
+      <groupId>net.sf.py4j</groupId>
+      <artifactId>py4j</artifactId>
+      <version>0.8.1</version>
+    </dependency>
   </dependencies>
   <build>
     <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory>
diff --git a/core/src/main/scala/org/apache/spark/SparkEnv.scala b/core/src/main/scala/org/apache/spark/SparkEnv.scala
index d40ed27da539..806e77d98fc5 100644
--- a/core/src/main/scala/org/apache/spark/SparkEnv.scala
+++ b/core/src/main/scala/org/apache/spark/SparkEnv.scala
@@ -17,6 +17,8 @@
 
 package org.apache.spark
 
+import java.io.File
+
 import scala.collection.JavaConversions._
 import scala.collection.mutable
 import scala.concurrent.Await
@@ -304,7 +306,7 @@ object SparkEnv extends Logging {
       k == "java.class.path"
     }.getOrElse(("", ""))
     val classPathEntries = classPathProperty._2
-      .split(conf.get("path.separator", ":"))
+      .split(File.pathSeparator)
       .filterNot(e => e.isEmpty)
       .map(e => (e, "System Classpath"))
     val addedJarsAndFiles = (addedJars ++ addedFiles).map((_, "Added By User"))
diff --git a/core/src/main/scala/org/apache/spark/api/python/PythonUtils.scala b/core/src/main/scala/org/apache/spark/api/python/PythonUtils.scala
new file mode 100644
index 000000000000..cf69fa1d53fd
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/api/python/PythonUtils.scala
@@ -0,0 +1,42 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.api.python
+
+import java.io.File
+
+import scala.collection.mutable.ArrayBuffer
+
+import org.apache.spark.SparkContext
+
+private[spark] object PythonUtils {
+  /** Get the PYTHONPATH for PySpark, either from SPARK_HOME, if it is set, or from our JAR */
+  def sparkPythonPath: String = {
+    val pythonPath = new ArrayBuffer[String]
+    for (sparkHome <- sys.env.get("SPARK_HOME")) {
+      pythonPath += Seq(sparkHome, "python").mkString(File.separator)
+      pythonPath += Seq(sparkHome, "python", "lib", "py4j-0.8.1-src.zip").mkString(File.separator)
+    }
+    pythonPath ++= SparkContext.jarOfObject(this)
+    pythonPath.mkString(File.pathSeparator)
+  }
+
+  /** Merge PYTHONPATHS with the appropriate separator. Ignores blank strings. */
+  def mergePythonPaths(paths: String*): String = {
+    paths.filter(_ != "").mkString(File.pathSeparator)
+  }
+}
diff --git a/core/src/main/scala/org/apache/spark/api/python/PythonWorkerFactory.scala b/core/src/main/scala/org/apache/spark/api/python/PythonWorkerFactory.scala
index 02799ce0091b..b0bf4e052b3e 100644
--- a/core/src/main/scala/org/apache/spark/api/python/PythonWorkerFactory.scala
+++ b/core/src/main/scala/org/apache/spark/api/python/PythonWorkerFactory.scala
@@ -37,6 +37,9 @@ private[spark] class PythonWorkerFactory(pythonExec: String, envVars: Map[String
   val daemonHost = InetAddress.getByAddress(Array(127, 0, 0, 1))
   var daemonPort: Int = 0
 
+  val pythonPath = PythonUtils.mergePythonPaths(
+    PythonUtils.sparkPythonPath, envVars.getOrElse("PYTHONPATH", ""))
+
   def create(): Socket = {
     if (useDaemon) {
       createThroughDaemon()
@@ -78,9 +81,10 @@ private[spark] class PythonWorkerFactory(pythonExec: String, envVars: Map[String
       serverSocket = new ServerSocket(0, 1, InetAddress.getByAddress(Array(127, 0, 0, 1)))
 
       // Create and start the worker
-      val pb = new ProcessBuilder(Seq(pythonExec, "-m", "pyspark.worker"))
+      val pb = new ProcessBuilder(Seq(pythonExec, "-u", "-m", "pyspark.worker"))
       val workerEnv = pb.environment()
       workerEnv.putAll(envVars)
+      workerEnv.put("PYTHONPATH", pythonPath)
       val worker = pb.start()
 
       // Redirect the worker's stderr to ours
@@ -151,9 +155,10 @@ private[spark] class PythonWorkerFactory(pythonExec: String, envVars: Map[String
 
       try {
         // Create and start the daemon
-        val pb = new ProcessBuilder(Seq(pythonExec, "-m", "pyspark.daemon"))
+        val pb = new ProcessBuilder(Seq(pythonExec, "-u", "-m", "pyspark.daemon"))
         val workerEnv = pb.environment()
         workerEnv.putAll(envVars)
+        workerEnv.put("PYTHONPATH", pythonPath)
         daemon = pb.start()
 
         // Redirect the stderr to ours
diff --git a/core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala b/core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala
new file mode 100644
index 000000000000..f2e7c7a508b3
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala
@@ -0,0 +1,84 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.deploy
+
+import java.io.{IOException, File, InputStream, OutputStream}
+
+import scala.collection.mutable.ArrayBuffer
+import scala.collection.JavaConversions._
+
+import org.apache.spark.SparkContext
+import org.apache.spark.api.python.PythonUtils
+
+/**
+ * A main class used by spark-submit to launch Python applications. It executes python as a
+ * subprocess and then has it connect back to the JVM to access system properties, etc.
+ */
+object PythonRunner {
+  def main(args: Array[String]) {
+    val primaryResource = args(0)
+    val pyFiles = args(1)
+    val otherArgs = args.slice(2, args.length)
+
+    val pythonExec = sys.env.get("PYSPARK_PYTHON").getOrElse("python") // TODO: get this from conf
+
+    // Launch a Py4J gateway server for the process to connect to; this will let it see our
+    // Java system properties and such
+    val gatewayServer = new py4j.GatewayServer(null, 0)
+    gatewayServer.start()
+
+    // Build up a PYTHONPATH that includes the Spark assembly JAR (where this class is), the
+    // python directories in SPARK_HOME (if set), and any files in the pyFiles argument
+    val pathElements = new ArrayBuffer[String]
+    pathElements ++= pyFiles.split(",")
+    pathElements += PythonUtils.sparkPythonPath
+    pathElements += sys.env.getOrElse("PYTHONPATH", "")
+    val pythonPath = PythonUtils.mergePythonPaths(pathElements: _*)
+
+    // Launch Python process
+    val builder = new ProcessBuilder(Seq(pythonExec, "-u", primaryResource) ++ otherArgs)
+    val env = builder.environment()
+    env.put("PYTHONPATH", pythonPath)
+    env.put("PYSPARK_GATEWAY_PORT", "" + gatewayServer.getListeningPort)
+    builder.redirectErrorStream(true) // Ugly but needed for stdout and stderr to synchronize
+    val process = builder.start()
+
+    new RedirectThread(process.getInputStream, System.out, "redirect output").start()
+
+    System.exit(process.waitFor())
+  }
+
+  /**
+   * A utility class to redirect the child process's stdout or stderr
+   */
+  class RedirectThread(in: InputStream, out: OutputStream, name: String) extends Thread(name) {
+    setDaemon(true)
+    override def run() {
+      scala.util.control.Exception.ignoring(classOf[IOException]) {
+        // FIXME: We copy the stream on the level of bytes to avoid encoding problems.
+        val buf = new Array[Byte](1024)
+        var len = in.read(buf)
+        while (len != -1) {
+          out.write(buf, 0, len)
+          out.flush()
+          len = in.read(buf)
+        }
+      }
+    }
+  }
+}
diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
index fb30e8a70f68..e39723f38347 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
@@ -60,11 +60,11 @@ object SparkSubmit {
   private[spark] var exitFn: () => Unit = () => System.exit(-1)
 
   private[spark] def printErrorAndExit(str: String) = {
-    printStream.println("error: " + str)
-    printStream.println("run with --help for more information or --verbose for debugging output")
+    printStream.println("Error: " + str)
+    printStream.println("Run with --help for usage help or --verbose for debug output")
     exitFn()
   }
-  private[spark] def printWarning(str: String) = printStream.println("warning: " + str)
+  private[spark] def printWarning(str: String) = printStream.println("Warning: " + str)
 
   /**
    * @return
@@ -72,43 +72,43 @@ object SparkSubmit {
    *         entries for the child, a list of system propertes, a list of env vars
    *         and the main class for the child
    */
-  private[spark] def createLaunchEnv(appArgs: SparkSubmitArguments): (ArrayBuffer[String],
+  private[spark] def createLaunchEnv(args: SparkSubmitArguments): (ArrayBuffer[String],
       ArrayBuffer[String], Map[String, String], String) = {
-    if (appArgs.master.startsWith("local")) {
+    if (args.master.startsWith("local")) {
       clusterManager = LOCAL
-    } else if (appArgs.master.startsWith("yarn")) {
+    } else if (args.master.startsWith("yarn")) {
       clusterManager = YARN
-    } else if (appArgs.master.startsWith("spark")) {
+    } else if (args.master.startsWith("spark")) {
       clusterManager = STANDALONE
-    } else if (appArgs.master.startsWith("mesos")) {
+    } else if (args.master.startsWith("mesos")) {
       clusterManager = MESOS
     } else {
-      printErrorAndExit("master must start with yarn, mesos, spark, or local")
+      printErrorAndExit("Master must start with yarn, mesos, spark, or local")
     }
 
     // Because "yarn-cluster" and "yarn-client" encapsulate both the master
     // and deploy mode, we have some logic to infer the master and deploy mode
     // from each other if only one is specified, or exit early if they are at odds.
-    if (appArgs.deployMode == null &&
-        (appArgs.master == "yarn-standalone" || appArgs.master == "yarn-cluster")) {
-      appArgs.deployMode = "cluster"
+    if (args.deployMode == null &&
+        (args.master == "yarn-standalone" || args.master == "yarn-cluster")) {
+      args.deployMode = "cluster"
     }
-    if (appArgs.deployMode == "cluster" && appArgs.master == "yarn-client") {
+    if (args.deployMode == "cluster" && args.master == "yarn-client") {
       printErrorAndExit("Deploy mode \"cluster\" and master \"yarn-client\" are not compatible")
     }
-    if (appArgs.deployMode == "client" &&
-        (appArgs.master == "yarn-standalone" || appArgs.master == "yarn-cluster")) {
-      printErrorAndExit("Deploy mode \"client\" and master \"" + appArgs.master
+    if (args.deployMode == "client" &&
+        (args.master == "yarn-standalone" || args.master == "yarn-cluster")) {
+      printErrorAndExit("Deploy mode \"client\" and master \"" + args.master
         + "\" are not compatible")
     }
-    if (appArgs.deployMode == "cluster" && appArgs.master.startsWith("yarn")) {
-      appArgs.master = "yarn-cluster"
+    if (args.deployMode == "cluster" && args.master.startsWith("yarn")) {
+      args.master = "yarn-cluster"
     }
-    if (appArgs.deployMode != "cluster" && appArgs.master.startsWith("yarn")) {
-      appArgs.master = "yarn-client"
+    if (args.deployMode != "cluster" && args.master.startsWith("yarn")) {
+      args.master = "yarn-client"
     }
 
-    val deployOnCluster = Option(appArgs.deployMode).getOrElse("client") == "cluster"
+    val deployOnCluster = Option(args.deployMode).getOrElse("client") == "cluster"
 
     val childClasspath = new ArrayBuffer[String]()
     val childArgs = new ArrayBuffer[String]()
@@ -116,76 +116,93 @@ object SparkSubmit {
     var childMainClass = ""
 
     if (clusterManager == MESOS && deployOnCluster) {
-      printErrorAndExit("Mesos does not support running the driver on the cluster")
+      printErrorAndExit("Cannot currently run driver on the cluster in Mesos")
     }
 
+    // If we're running a Python app, set the Java class to run to be our PythonRunner, add
+    // Python files to deployment list, and pass the main file and Python path to PythonRunner
+    if (args.isPython) {
+      if (deployOnCluster) {
+        printErrorAndExit("Cannot currently run Python driver programs on cluster")
+      }
+      args.mainClass = "org.apache.spark.deploy.PythonRunner"
+      args.files = mergeFileLists(args.files, args.pyFiles, args.primaryResource)
+      val pyFiles = Option(args.pyFiles).getOrElse("")
+      args.childArgs = ArrayBuffer(args.primaryResource, pyFiles) ++ args.childArgs
+      args.primaryResource = RESERVED_JAR_NAME
+      sysProps("spark.submit.pyFiles") = pyFiles
+    }
+
+    // If we're deploying into YARN, use yarn.Client as a wrapper around the user class
     if (!deployOnCluster) {
-      childMainClass = appArgs.mainClass
-      if (appArgs.primaryResource != RESERVED_JAR_NAME) {
-        childClasspath += appArgs.primaryResource
+      childMainClass = args.mainClass
+      if (args.primaryResource != RESERVED_JAR_NAME) {
+        childClasspath += args.primaryResource
       }
     } else if (clusterManager == YARN) {
       childMainClass = "org.apache.spark.deploy.yarn.Client"
-      childArgs += ("--jar", appArgs.primaryResource)
-      childArgs += ("--class", appArgs.mainClass)
+      childArgs += ("--jar", args.primaryResource)
+      childArgs += ("--class", args.mainClass)
     }
 
+    // Make sure YARN is included in our build if we're trying to use it
     if (clusterManager == YARN) {
-      // The choice of class is arbitrary, could use any spark-yarn class
       if (!Utils.classIsLoadable("org.apache.spark.deploy.yarn.Client") && !Utils.isTesting) {
-        val msg = "Could not load YARN classes. This copy of Spark may not have been compiled " +
-          "with YARN support."
-        throw new Exception(msg)
+        printErrorAndExit("Could not load YARN classes. " +
+          "This copy of Spark may not have been compiled with YARN support.")
       }
     }
 
     // Special flag to avoid deprecation warnings at the client
     sysProps("SPARK_SUBMIT") = "true"
 
+    // A list of rules to map each argument to system properties or command-line options in
+    // each deploy mode; we iterate through these below
     val options = List[OptionAssigner](
-      new OptionAssigner(appArgs.master, ALL_CLUSTER_MGRS, false, sysProp = "spark.master"),
-      new OptionAssigner(appArgs.driverExtraClassPath, STANDALONE | YARN, true,
+      OptionAssigner(args.master, ALL_CLUSTER_MGRS, false, sysProp = "spark.master"),
+      OptionAssigner(args.driverExtraClassPath, STANDALONE | YARN, true,
         sysProp = "spark.driver.extraClassPath"),
-      new OptionAssigner(appArgs.driverExtraJavaOptions, STANDALONE | YARN, true,
+      OptionAssigner(args.driverExtraJavaOptions, STANDALONE | YARN, true,
         sysProp = "spark.driver.extraJavaOptions"),
-      new OptionAssigner(appArgs.driverExtraLibraryPath, STANDALONE | YARN, true,
+      OptionAssigner(args.driverExtraLibraryPath, STANDALONE | YARN, true,
         sysProp = "spark.driver.extraLibraryPath"),
-      new OptionAssigner(appArgs.driverMemory, YARN, true, clOption = "--driver-memory"),
-      new OptionAssigner(appArgs.name, YARN, true, clOption = "--name"),
-      new OptionAssigner(appArgs.queue, YARN, true, clOption = "--queue"),
-      new OptionAssigner(appArgs.queue, YARN, false, sysProp = "spark.yarn.queue"),
-      new OptionAssigner(appArgs.numExecutors, YARN, true, clOption = "--num-executors"),
-      new OptionAssigner(appArgs.numExecutors, YARN, false, sysProp = "spark.executor.instances"),
-      new OptionAssigner(appArgs.executorMemory, YARN, true, clOption = "--executor-memory"),
-      new OptionAssigner(appArgs.executorMemory, STANDALONE | MESOS | YARN, false,
+      OptionAssigner(args.driverMemory, YARN, true, clOption = "--driver-memory"),
+      OptionAssigner(args.name, YARN, true, clOption = "--name"),
+      OptionAssigner(args.queue, YARN, true, clOption = "--queue"),
+      OptionAssigner(args.queue, YARN, false, sysProp = "spark.yarn.queue"),
+      OptionAssigner(args.numExecutors, YARN, true, clOption = "--num-executors"),
+      OptionAssigner(args.numExecutors, YARN, false, sysProp = "spark.executor.instances"),
+      OptionAssigner(args.executorMemory, YARN, true, clOption = "--executor-memory"),
+      OptionAssigner(args.executorMemory, STANDALONE | MESOS | YARN, false,
         sysProp = "spark.executor.memory"),
-      new OptionAssigner(appArgs.driverMemory, STANDALONE, true, clOption = "--memory"),
-      new OptionAssigner(appArgs.driverCores, STANDALONE, true, clOption = "--cores"),
-      new OptionAssigner(appArgs.executorCores, YARN, true, clOption = "--executor-cores"),
-      new OptionAssigner(appArgs.executorCores, YARN, false, sysProp = "spark.executor.cores"),
-      new OptionAssigner(appArgs.totalExecutorCores, STANDALONE | MESOS, false,
+      OptionAssigner(args.driverMemory, STANDALONE, true, clOption = "--memory"),
+      OptionAssigner(args.driverCores, STANDALONE, true, clOption = "--cores"),
+      OptionAssigner(args.executorCores, YARN, true, clOption = "--executor-cores"),
+      OptionAssigner(args.executorCores, YARN, false, sysProp = "spark.executor.cores"),
+      OptionAssigner(args.totalExecutorCores, STANDALONE | MESOS, false,
         sysProp = "spark.cores.max"),
-      new OptionAssigner(appArgs.files, YARN, false, sysProp = "spark.yarn.dist.files"),
-      new OptionAssigner(appArgs.files, YARN, true, clOption = "--files"),
-      new OptionAssigner(appArgs.archives, YARN, false, sysProp = "spark.yarn.dist.archives"),
-      new OptionAssigner(appArgs.archives, YARN, true, clOption = "--archives"),
-      new OptionAssigner(appArgs.jars, YARN, true, clOption = "--addJars"),
-      new OptionAssigner(appArgs.files, LOCAL | STANDALONE | MESOS, true, sysProp = "spark.files"),
-      new OptionAssigner(appArgs.jars, LOCAL | STANDALONE | MESOS, false, sysProp = "spark.jars"),
-      new OptionAssigner(appArgs.name, LOCAL | STANDALONE | MESOS, false,
-        sysProp = "spark.app.name")
+      OptionAssigner(args.files, YARN, false, sysProp = "spark.yarn.dist.files"),
+      OptionAssigner(args.files, YARN, true, clOption = "--files"),
+      OptionAssigner(args.archives, YARN, false, sysProp = "spark.yarn.dist.archives"),
+      OptionAssigner(args.archives, YARN, true, clOption = "--archives"),
+      OptionAssigner(args.jars, YARN, true, clOption = "--addJars"),
+      OptionAssigner(args.files, LOCAL | STANDALONE | MESOS, false, sysProp = "spark.files"),
+      OptionAssigner(args.files, LOCAL | STANDALONE | MESOS, true, sysProp = "spark.files"),
+      OptionAssigner(args.jars, LOCAL | STANDALONE | MESOS, false, sysProp = "spark.jars"),
+      OptionAssigner(args.name, LOCAL | STANDALONE | MESOS, false, sysProp = "spark.app.name")
     )
 
     // For client mode make any added jars immediately visible on the classpath
-    if (appArgs.jars != null && !deployOnCluster) {
-      for (jar <- appArgs.jars.split(",")) {
+    if (args.jars != null && !deployOnCluster) {
+      for (jar <- args.jars.split(",")) {
         childClasspath += jar
       }
     }
 
+    // Map all arguments to command-line options or system properties for our chosen mode
     for (opt <- options) {
       if (opt.value != null && deployOnCluster == opt.deployOnCluster &&
-        (clusterManager & opt.clusterManager) != 0) {
+          (clusterManager & opt.clusterManager) != 0) {
         if (opt.clOption != null) {
           childArgs += (opt.clOption, opt.value)
         } else if (opt.sysProp != null) {
@@ -197,32 +214,35 @@ object SparkSubmit {
     // For standalone mode, add the application jar automatically so the user doesn't have to
     // call sc.addJar. TODO: Standalone mode in the cluster
     if (clusterManager == STANDALONE) {
-      val existingJars = sysProps.get("spark.jars").map(x => x.split(",").toSeq).getOrElse(Seq())
-      sysProps.put("spark.jars", (existingJars ++ Seq(appArgs.primaryResource)).mkString(","))
+      var jars = sysProps.get("spark.jars").map(x => x.split(",").toSeq).getOrElse(Seq())
+      if (args.primaryResource != RESERVED_JAR_NAME) {
+        jars = jars ++ Seq(args.primaryResource)
+      }
+      sysProps.put("spark.jars", jars.mkString(","))
     }
 
     if (deployOnCluster && clusterManager == STANDALONE) {
-      if (appArgs.supervise) {
+      if (args.supervise) {
         childArgs += "--supervise"
       }
 
       childMainClass = "org.apache.spark.deploy.Client"
       childArgs += "launch"
-      childArgs += (appArgs.master, appArgs.primaryResource, appArgs.mainClass)
+      childArgs += (args.master, args.primaryResource, args.mainClass)
     }
 
     // Arguments to be passed to user program
-    if (appArgs.childArgs != null) {
+    if (args.childArgs != null) {
       if (!deployOnCluster || clusterManager == STANDALONE) {
-        childArgs ++= appArgs.childArgs
+        childArgs ++= args.childArgs
       } else if (clusterManager == YARN) {
-        for (arg <- appArgs.childArgs) {
+        for (arg <- args.childArgs) {
           childArgs += ("--arg", arg)
         }
       }
     }
 
-    for ((k, v) <- appArgs.getDefaultSparkProperties) {
+    for ((k, v) <- args.getDefaultSparkProperties) {
       if (!sysProps.contains(k)) sysProps(k) = v
     }
 
@@ -230,8 +250,8 @@ object SparkSubmit {
   }
 
   private def launch(childArgs: ArrayBuffer[String], childClasspath: ArrayBuffer[String],
-      sysProps: Map[String, String], childMainClass: String, verbose: Boolean = false) {
-
+      sysProps: Map[String, String], childMainClass: String, verbose: Boolean = false)
+  {
     if (verbose) {
       printStream.println(s"Main class:\n$childMainClass")
       printStream.println(s"Arguments:\n${childArgs.mkString("\n")}")
@@ -273,15 +293,26 @@ object SparkSubmit {
     val url = localJarFile.getAbsoluteFile.toURI.toURL
     loader.addURL(url)
   }
+
+  /**
+   * Merge a sequence of comma-separated file lists, some of which may be null to indicate
+   * no files, into a single comma-separated string.
+   */
+  private[spark] def mergeFileLists(lists: String*): String = {
+    val merged = lists.filter(_ != null)
+                      .flatMap(_.split(","))
+                      .mkString(",")
+    if (merged == "") null else merged
+  }
 }
 
 /**
  * Provides an indirection layer for passing arguments as system properties or flags to
  * the user's driver program or to downstream launcher tools.
  */
-private[spark] class OptionAssigner(val value: String,
-  val clusterManager: Int,
-  val deployOnCluster: Boolean,
-  val clOption: String = null,
-  val sysProp: String = null
-) { }
+private[spark] case class OptionAssigner(
+    value: String,
+    clusterManager: Int,
+    deployOnCluster: Boolean,
+    clOption: String = null,
+    sysProp: String = null)
diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
index 7031cdd9b4ae..2d327aa3fb27 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
@@ -19,6 +19,7 @@ package org.apache.spark.deploy
 
 import java.io.{File, FileInputStream, IOException}
 import java.util.Properties
+import java.util.jar.JarFile
 
 import scala.collection.JavaConversions._
 import scala.collection.mutable.{ArrayBuffer, HashMap}
@@ -52,6 +53,8 @@ private[spark] class SparkSubmitArguments(args: Seq[String]) {
   var childArgs: ArrayBuffer[String] = new ArrayBuffer[String]()
   var jars: String = null
   var verbose: Boolean = false
+  var isPython: Boolean = false
+  var pyFiles: String = null
 
   parseOpts(args.toList)
   loadDefaults()
@@ -76,7 +79,7 @@ private[spark] class SparkSubmitArguments(args: Seq[String]) {
   }
 
   /** Fill in any undefined values based on the current properties file or built-in defaults. */
-  private def loadDefaults() = {
+  private def loadDefaults(): Unit = {
 
     // Use common defaults file, if not specified by user
     if (propertiesFile == null) {
@@ -107,15 +110,43 @@ private[spark] class SparkSubmitArguments(args: Seq[String]) {
     master = Option(master).getOrElse(System.getenv("MASTER"))
     deployMode = Option(deployMode).getOrElse(System.getenv("DEPLOY_MODE"))
 
+    // Try to set main class from JAR if no --class argument is given
+    if (mainClass == null && !isPython && primaryResource != null) {
+      try {
+        val jar = new JarFile(primaryResource)
+        // Note that this might still return null if no main-class is set; we catch that later
+        mainClass = jar.getManifest.getMainAttributes.getValue("Main-Class")
+      } catch {
+        case e: Exception =>
+          SparkSubmit.printErrorAndExit("Failed to read JAR: " + primaryResource)
+          return
+      }
+    }
+
     // Global defaults. These should be keep to minimum to avoid confusing behavior.
     master = Option(master).getOrElse("local[*]")
+
+    // Set name from main class if not given
+    name = Option(name).orElse(Option(mainClass)).orNull
+    if (name == null && primaryResource != null) {
+      name = Utils.stripDirectory(primaryResource)
+    }
   }
 
   /** Ensure that required fields exists. Call this only once all defaults are loaded. */
   private def checkRequiredArguments() = {
-    if (args.length == 0) printUsageAndExit(-1)
-    if (primaryResource == null) SparkSubmit.printErrorAndExit("Must specify a primary resource")
-    if (mainClass == null) SparkSubmit.printErrorAndExit("Must specify a main class with --class")
+    if (args.length == 0) {
+      printUsageAndExit(-1)
+    }
+    if (primaryResource == null) {
+      SparkSubmit.printErrorAndExit("Must specify a primary resource (JAR or Python file)")
+    }
+    if (mainClass == null && !isPython) {
+      SparkSubmit.printErrorAndExit("No main class set in JAR; please specify one with --class")
+    }
+    if (pyFiles != null && !isPython) {
+      SparkSubmit.printErrorAndExit("--py-files given but primary resource is not a Python script")
+    }
 
     if (master.startsWith("yarn")) {
       val hasHadoopEnv = sys.env.contains("HADOOP_CONF_DIR") || sys.env.contains("YARN_CONF_DIR")
@@ -143,6 +174,7 @@ private[spark] class SparkSubmitArguments(args: Seq[String]) {
     |  queue                   $queue
     |  numExecutors            $numExecutors
     |  files                   $files
+    |  pyFiles                 $pyFiles
     |  archives                $archives
     |  mainClass               $mainClass
     |  primaryResource         $primaryResource
@@ -234,6 +266,10 @@ private[spark] class SparkSubmitArguments(args: Seq[String]) {
         files = value
         parse(tail)
 
+      case ("--py-files") :: value :: tail =>
+        pyFiles = value
+        parse(tail)
+
       case ("--archives") :: value :: tail =>
         archives = value
         parse(tail)
@@ -260,9 +296,10 @@ private[spark] class SparkSubmitArguments(args: Seq[String]) {
               val errMessage = s"Unrecognized option '$value'."
               SparkSubmit.printErrorAndExit(errMessage)
             case v =>
-             primaryResource = v
-             inSparkOpts = false
-             parse(tail)
+              primaryResource = v
+              inSparkOpts = false
+              isPython = v.endsWith(".py")
+              parse(tail)
           }
         } else {
           childArgs += value
@@ -270,7 +307,7 @@ private[spark] class SparkSubmitArguments(args: Seq[String]) {
         }
 
       case Nil =>
-      }
+    }
   }
 
   private def printUsageAndExit(exitCode: Int, unknownParam: Any = null) {
@@ -279,23 +316,26 @@ private[spark] class SparkSubmitArguments(args: Seq[String]) {
       outStream.println("Unknown/unsupported param " + unknownParam)
     }
     outStream.println(
-      """Usage: spark-submit [options] <app jar> [app options]
+      """Usage: spark-submit [options] <app jar | python file> [app options]
         |Options:
         |  --master MASTER_URL         spark://host:port, mesos://host:port, yarn, or local.
-        |  --deploy-mode DEPLOY_MODE   Mode to deploy the app in, either 'client' or 'cluster'.
-        |  --class CLASS_NAME          Name of your app's main class (required for Java apps).
-        |  --name NAME                 The name of your application (Default: 'Spark').
-        |  --jars JARS                 A comma-separated list of local jars to include on the
-        |                              driver classpath and that SparkContext.addJar will work
-        |                              with. Doesn't work on standalone with 'cluster' deploy mode.
-        |  --files FILES               Comma separated list of files to be placed in the working dir
-        |                              of each executor.
+        |  --deploy-mode DEPLOY_MODE   Where to run the driver program: either "client" to run
+        |                              on the local machine, or "cluster" to run inside cluster.
+        |  --class CLASS_NAME          Your application's main class (for Java / Scala apps).
+        |  --name NAME                 A name of your application.
+        |  --jars JARS                 Comma-separated list of local jars to include on the driver
+        |                              and executor classpaths. Doesn't work for drivers in
+        |                              standalone mode with "cluster" deploy mode.
+        |  --py-files PY_FILES         Comma-separated list of .zip or .egg files to place on the
+        |                              PYTHONPATH for Python apps.
+        |  --files FILES               Comma-separated list of files to be placed in the working
+        |                              directory of each executor.
         |  --properties-file FILE      Path to a file from which to load extra properties. If not
         |                              specified, this will look for conf/spark-defaults.conf.
         |
         |  --driver-memory MEM         Memory for driver (e.g. 1000M, 2G) (Default: 512M).
-        |  --driver-java-options       Extra Java options to pass to the driver
-        |  --driver-library-path       Extra library path entries to pass to the driver
+        |  --driver-java-options       Extra Java options to pass to the driver.
+        |  --driver-library-path       Extra library path entries to pass to the driver.
         |  --driver-class-path         Extra class path entries to pass to the driver. Note that
         |                              jars added with --jars are automatically included in the
         |                              classpath.
@@ -311,10 +351,10 @@ private[spark] class SparkSubmitArguments(args: Seq[String]) {
         |
         | YARN-only:
         |  --executor-cores NUM        Number of cores per executor (Default: 1).
-        |  --queue QUEUE_NAME          The YARN queue to submit to (Default: 'default').
-        |  --num-executors NUM         Number of executors to (Default: 2).
+        |  --queue QUEUE_NAME          The YARN queue to submit to (Default: "default").
+        |  --num-executors NUM         Number of executors to launch (Default: 2).
         |  --archives ARCHIVES         Comma separated list of archives to be extracted into the
-        |                              working dir of each executor.""".stripMargin
+        |                              working directory of each executor.""".stripMargin
     )
     SparkSubmit.exitFn()
   }
diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala
index bef4dab3d7cc..202bd46956f8 100644
--- a/core/src/main/scala/org/apache/spark/util/Utils.scala
+++ b/core/src/main/scala/org/apache/spark/util/Utils.scala
@@ -46,7 +46,6 @@ import org.apache.spark.serializer.{DeserializationStream, SerializationStream,
  * Various utility methods used by Spark.
  */
 private[spark] object Utils extends Logging {
-
   val random = new Random()
 
   def sparkBin(sparkHome: String, which: String): File = {
@@ -1082,4 +1081,11 @@ private[spark] object Utils extends Logging {
   def isTesting = {
     sys.env.contains("SPARK_TESTING") || sys.props.contains("spark.testing")
   }
+
+  /**
+   * Strip the directory from a path name
+   */
+  def stripDirectory(path: String): String = {
+    path.split(File.separator).last
+  }
 }
diff --git a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala
index b3541b4a40b7..d7e3b22ed476 100644
--- a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala
@@ -83,7 +83,7 @@ class SparkSubmitSuite extends FunSuite with ShouldMatchers {
   }
 
   test("handle binary specified but not class") {
-    testPrematureExit(Array("foo.jar"), "Must specify a main class")
+    testPrematureExit(Array("foo.jar"), "No main class")
   }
 
   test("handles arguments with --key=val") {
@@ -94,9 +94,9 @@ class SparkSubmitSuite extends FunSuite with ShouldMatchers {
   }
 
   test("handles arguments to user program") {
-    val clArgs = Seq("--name", "myApp", "userjar.jar", "some", "--random", "args", "here")
+    val clArgs = Seq("--name", "myApp", "--class", "Foo", "userjar.jar", "some", "--weird", "args")
     val appArgs = new SparkSubmitArguments(clArgs)
-    appArgs.childArgs should be (Seq("some", "--random", "args", "here"))
+    appArgs.childArgs should be (Seq("some", "--weird", "args"))
   }
 
   test("handles YARN cluster mode") {
diff --git a/docs/python-programming-guide.md b/docs/python-programming-guide.md
index 98c456228af9..8ea22e15a4b6 100644
--- a/docs/python-programming-guide.md
+++ b/docs/python-programming-guide.md
@@ -60,12 +60,9 @@ By default, PySpark requires `python` to be available on the system `PATH` and u
 
 All of PySpark's library dependencies, including [Py4J](http://py4j.sourceforge.net/), are bundled with PySpark and automatically imported.
 
-Standalone PySpark applications should be run using the `bin/pyspark` script, which automatically configures the Java and Python environment using the settings in `conf/spark-env.sh` or `.cmd`.
-The script automatically adds the `bin/pyspark` package to the `PYTHONPATH`.
+Standalone PySpark applications should be run using the `bin/spark-submit` script, which automatically
+configures the Java and Python environment for running Spark.
 
-# Running PySpark on YARN
-
-To run PySpark against a YARN cluster, simply set the MASTER environment variable to "yarn-client".
 
 # Interactive Use
 
@@ -103,7 +100,7 @@ $ MASTER=local[4] ./bin/pyspark
 
 ## IPython
 
-It is also possible to launch PySpark in [IPython](http://ipython.org), the
+It is also possible to launch the PySpark shell in [IPython](http://ipython.org), the
 enhanced Python interpreter. PySpark works with IPython 1.0.0 and later. To
 use IPython, set the `IPYTHON` variable to `1` when running `bin/pyspark`:
 
@@ -123,18 +120,17 @@ IPython also works on a cluster or on multiple cores if you set the `MASTER` env
 
 # Standalone Programs
 
-PySpark can also be used from standalone Python scripts by creating a SparkContext in your script and running the script using `bin/pyspark`.
+PySpark can also be used from standalone Python scripts by creating a SparkContext in your script and running the script using `bin/spark-submit`.
 The Quick Start guide includes a [complete example](quick-start.html#a-standalone-app-in-python) of a standalone Python application.
 
-Code dependencies can be deployed by listing them in the `pyFiles` option in the SparkContext constructor:
+Code dependencies can be deployed by passing .zip or .egg files in the `--py-files` option of `spark-submit`:
 
-{% highlight python %}
-from pyspark import SparkContext
-sc = SparkContext("local", "App Name", pyFiles=['MyFile.py', 'lib.zip', 'app.egg'])
+{% highlight bash %}
+./bin/spark-submit --py-files lib1.zip,lib2.zip my_script.py
 {% endhighlight %}
 
 Files listed here will be added to the `PYTHONPATH` and shipped to remote worker machines.
-Code dependencies can be added to an existing SparkContext using its `addPyFile()` method.
+Code dependencies can also be added to an existing SparkContext at runtime using its `addPyFile()` method.
 
 You can set [configuration properties](configuration.html#spark-properties) by passing a
 [SparkConf](api/python/pyspark.conf.SparkConf-class.html) object to SparkContext:
@@ -142,12 +138,16 @@ You can set [configuration properties](configuration.html#spark-properties) by p
 {% highlight python %}
 from pyspark import SparkConf, SparkContext
 conf = (SparkConf()
-         .setMaster("local")
          .setAppName("My app")
          .set("spark.executor.memory", "1g"))
 sc = SparkContext(conf = conf)
 {% endhighlight %}
 
+`spark-submit` supports launching Python applications on standalone, Mesos or YARN clusters, through
+its `--master` argument. However, it currently requires the Python driver program to run on the local
+machine, not the cluster (i.e. the `--deploy-mode` parameter cannot be `cluster`).
+
+
 # API Docs
 
 [API documentation](api/python/index.html) for PySpark is available as Epydoc.
@@ -164,6 +164,6 @@ some example applications.
 PySpark also includes several sample programs in the [`python/examples` folder](https://github.com/apache/spark/tree/master/python/examples).
 You can run them by passing the files to `pyspark`; e.g.:
 
-    ./bin/pyspark python/examples/wordcount.py
+    ./bin/spark-submit python/examples/wordcount.py
 
 Each program prints usage help when run without arguments.
diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index 1ad05d9e46dd..7f9746ec4acc 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -356,7 +356,8 @@ object SparkBuild extends Build {
         "com.twitter"                % "chill-java"       % chillVersion excludeAll(excludeAsm),
         "org.tachyonproject"         % "tachyon"          % "0.4.1-thrift" excludeAll(excludeHadoop, excludeCurator, excludeEclipseJetty, excludePowermock),
         "com.clearspring.analytics"  % "stream"           % "2.5.1" excludeAll(excludeFastutil),
-        "org.spark-project"          % "pyrolite"         % "2.0.1"
+        "org.spark-project"          % "pyrolite"         % "2.0.1",
+        "net.sf.py4j"                % "py4j"             % "0.8.1"
       ),
     libraryDependencies ++= maybeAvro
   )
@@ -569,7 +570,6 @@ object SparkBuild extends Build {
   )
 
   def assemblyProjSettings = sharedSettings ++ Seq(
-    libraryDependencies += "net.sf.py4j" % "py4j" % "0.8.1",
     name := "spark-assembly",
     assembleDeps in Compile <<= (packageProjects.map(packageBin in Compile in _) ++ Seq(packageDependency in Compile)).dependOn,
     jarName in assembly <<= version map { v => "spark-assembly-" + v + "-hadoop" + hadoopVersion + ".jar" },
diff --git a/python/pyspark/context.py b/python/pyspark/context.py
index c74dc5fd4f85..c7dc85ea0354 100644
--- a/python/pyspark/context.py
+++ b/python/pyspark/context.py
@@ -158,6 +158,12 @@ def __init__(self, master=None, appName=None, sparkHome=None, pyFiles=None,
         for path in (pyFiles or []):
             self.addPyFile(path)
 
+        # Deploy code dependencies set by spark-submit; these will already have been added
+        # with SparkContext.addFile, so we just need to add them
+        for path in self._conf.get("spark.submit.pyFiles", "").split(","):
+            if path != "":
+                self._python_includes.append(os.path.basename(path))
+
         # Create a temporary directory inside spark.local.dir:
         local_dir = self._jvm.org.apache.spark.util.Utils.getLocalDir(self._jsc.sc().conf())
         self._temp_dir = \
diff --git a/python/pyspark/java_gateway.py b/python/pyspark/java_gateway.py
index 032d960e4099..3d0936fdca91 100644
--- a/python/pyspark/java_gateway.py
+++ b/python/pyspark/java_gateway.py
@@ -27,39 +27,43 @@
 def launch_gateway():
     SPARK_HOME = os.environ["SPARK_HOME"]
 
-    set_env_vars_for_yarn()
-
-    # Launch the Py4j gateway using Spark's run command so that we pick up the
-    # proper classpath and settings from spark-env.sh
-    on_windows = platform.system() == "Windows"
-    script = "./bin/spark-class.cmd" if on_windows else "./bin/spark-class"
-    command = [os.path.join(SPARK_HOME, script), "py4j.GatewayServer",
-               "--die-on-broken-pipe", "0"]
-    if not on_windows:
-        # Don't send ctrl-c / SIGINT to the Java gateway:
-        def preexec_func():
-            signal.signal(signal.SIGINT, signal.SIG_IGN)
-        proc = Popen(command, stdout=PIPE, stdin=PIPE, preexec_fn=preexec_func)
+    gateway_port = -1
+    if "PYSPARK_GATEWAY_PORT" in os.environ:
+        gateway_port = int(os.environ["PYSPARK_GATEWAY_PORT"])
     else:
-        # preexec_fn not supported on Windows
-        proc = Popen(command, stdout=PIPE, stdin=PIPE)
-    # Determine which ephemeral port the server started on:
-    port = int(proc.stdout.readline())
-    # Create a thread to echo output from the GatewayServer, which is required
-    # for Java log output to show up:
-    class EchoOutputThread(Thread):
-        def __init__(self, stream):
-            Thread.__init__(self)
-            self.daemon = True
-            self.stream = stream
+        # Launch the Py4j gateway using Spark's run command so that we pick up the
+        # proper classpath and settings from spark-env.sh
+        on_windows = platform.system() == "Windows"
+        script = "./bin/spark-class.cmd" if on_windows else "./bin/spark-class"
+        command = [os.path.join(SPARK_HOME, script), "py4j.GatewayServer",
+                   "--die-on-broken-pipe", "0"]
+        if not on_windows:
+            # Don't send ctrl-c / SIGINT to the Java gateway:
+            def preexec_func():
+                signal.signal(signal.SIGINT, signal.SIG_IGN)
+            proc = Popen(command, stdout=PIPE, stdin=PIPE, preexec_fn=preexec_func)
+        else:
+            # preexec_fn not supported on Windows
+            proc = Popen(command, stdout=PIPE, stdin=PIPE)
+        # Determine which ephemeral port the server started on:
+        gateway_port = int(proc.stdout.readline())
+        # Create a thread to echo output from the GatewayServer, which is required
+        # for Java log output to show up:
+        class EchoOutputThread(Thread):
+            def __init__(self, stream):
+                Thread.__init__(self)
+                self.daemon = True
+                self.stream = stream
+
+            def run(self):
+                while True:
+                    line = self.stream.readline()
+                    sys.stderr.write(line)
+        EchoOutputThread(proc.stdout).start()
 
-        def run(self):
-            while True:
-                line = self.stream.readline()
-                sys.stderr.write(line)
-    EchoOutputThread(proc.stdout).start()
     # Connect to the gateway
-    gateway = JavaGateway(GatewayClient(port=port), auto_convert=False)
+    gateway = JavaGateway(GatewayClient(port=gateway_port), auto_convert=False)
+
     # Import the classes used by PySpark
     java_import(gateway.jvm, "org.apache.spark.SparkConf")
     java_import(gateway.jvm, "org.apache.spark.api.java.*")
@@ -70,28 +74,5 @@ def run(self):
     java_import(gateway.jvm, "org.apache.spark.sql.hive.LocalHiveContext")
     java_import(gateway.jvm, "org.apache.spark.sql.hive.TestHiveContext")
     java_import(gateway.jvm, "scala.Tuple2")
-    return gateway
 
-def set_env_vars_for_yarn():
-    # Add the spark jar, which includes the pyspark files, to the python path
-    env_map = parse_env(os.environ.get("SPARK_YARN_USER_ENV", ""))
-    if "PYTHONPATH" in env_map:
-        env_map["PYTHONPATH"] += ":spark.jar"
-    else:
-        env_map["PYTHONPATH"] = "spark.jar"
-
-    os.environ["SPARK_YARN_USER_ENV"] = ",".join(k + '=' + v for (k, v) in env_map.items())
-
-def parse_env(env_str):
-    # Turns a comma-separated of env settings into a dict that maps env vars to
-    # their values.
-    env = {}
-    for var_str in env_str.split(","):
-        parts = var_str.split("=")
-        if len(parts) == 2:
-            env[parts[0]] = parts[1]
-        elif len(var_str) > 0:
-            print "Invalid entry in SPARK_YARN_USER_ENV: " + var_str
-            sys.exit(1)
-    
-    return env
+    return gateway
diff --git a/python/pyspark/tests.py b/python/pyspark/tests.py
index 8cf9d9cf1bd6..64f2eeb12b4f 100644
--- a/python/pyspark/tests.py
+++ b/python/pyspark/tests.py
@@ -22,11 +22,14 @@
 from fileinput import input
 from glob import glob
 import os
+import re
 import shutil
+import subprocess
 import sys
-from tempfile import NamedTemporaryFile
+import tempfile
 import time
 import unittest
+import zipfile
 
 from pyspark.context import SparkContext
 from pyspark.files import SparkFiles
@@ -55,7 +58,7 @@ class TestCheckpoint(PySparkTestCase):
 
     def setUp(self):
         PySparkTestCase.setUp(self)
-        self.checkpointDir = NamedTemporaryFile(delete=False)
+        self.checkpointDir = tempfile.NamedTemporaryFile(delete=False)
         os.unlink(self.checkpointDir.name)
         self.sc.setCheckpointDir(self.checkpointDir.name)
 
@@ -148,7 +151,7 @@ def test_save_as_textfile_with_unicode(self):
         # Regression test for SPARK-970
         x = u"\u00A1Hola, mundo!"
         data = self.sc.parallelize([x])
-        tempFile = NamedTemporaryFile(delete=True)
+        tempFile = tempfile.NamedTemporaryFile(delete=True)
         tempFile.close()
         data.saveAsTextFile(tempFile.name)
         raw_contents = ''.join(input(glob(tempFile.name + "/part-0000*")))
@@ -172,7 +175,7 @@ def test_cartesian_on_textfile(self):
 
     def test_deleting_input_files(self):
         # Regression test for SPARK-1025
-        tempFile = NamedTemporaryFile(delete=False)
+        tempFile = tempfile.NamedTemporaryFile(delete=False)
         tempFile.write("Hello World!")
         tempFile.close()
         data = self.sc.textFile(tempFile.name)
@@ -236,5 +239,125 @@ def test_termination_sigterm(self):
         from signal import SIGTERM
         self.do_termination_test(lambda daemon: os.kill(daemon.pid, SIGTERM))
 
+
+class TestSparkSubmit(unittest.TestCase):
+    def setUp(self):
+        self.programDir = tempfile.mkdtemp()
+        self.sparkSubmit = os.path.join(os.environ.get("SPARK_HOME"), "bin", "spark-submit")
+
+    def tearDown(self):
+        shutil.rmtree(self.programDir)
+
+    def createTempFile(self, name, content):
+        """
+        Create a temp file with the given name and content and return its path.
+        Strips leading spaces from content up to the first '|' in each line.
+        """
+        pattern = re.compile(r'^ *\|', re.MULTILINE)
+        content = re.sub(pattern, '', content.strip())
+        path = os.path.join(self.programDir, name)
+        with open(path, "w") as f:
+            f.write(content)
+        return path
+
+    def createFileInZip(self, name, content):
+        """
+        Create a zip archive containing a file with the given content and return its path.
+        Strips leading spaces from content up to the first '|' in each line.
+        """
+        pattern = re.compile(r'^ *\|', re.MULTILINE)
+        content = re.sub(pattern, '', content.strip())
+        path = os.path.join(self.programDir, name + ".zip")
+        with zipfile.ZipFile(path, 'w') as zip:
+            zip.writestr(name, content)
+        return path
+
+    def test_single_script(self):
+        """Submit and test a single script file"""
+        script = self.createTempFile("test.py", """
+            |from pyspark import SparkContext
+            |
+            |sc = SparkContext()
+            |print sc.parallelize([1, 2, 3]).map(lambda x: x * 2).collect()
+            """)
+        proc = subprocess.Popen([self.sparkSubmit, script], stdout=subprocess.PIPE)
+        out, err = proc.communicate()
+        self.assertEqual(0, proc.returncode)
+        self.assertIn("[2, 4, 6]", out)
+
+    def test_script_with_local_functions(self):
+        """Submit and test a single script file calling a global function"""
+        script = self.createTempFile("test.py", """
+            |from pyspark import SparkContext
+            |
+            |def foo(x):
+            |    return x * 3
+            |
+            |sc = SparkContext()
+            |print sc.parallelize([1, 2, 3]).map(foo).collect()
+            """)
+        proc = subprocess.Popen([self.sparkSubmit, script], stdout=subprocess.PIPE)
+        out, err = proc.communicate()
+        self.assertEqual(0, proc.returncode)
+        self.assertIn("[3, 6, 9]", out)
+
+    def test_module_dependency(self):
+        """Submit and test a script with a dependency on another module"""
+        script = self.createTempFile("test.py", """
+            |from pyspark import SparkContext
+            |from mylib import myfunc
+            |
+            |sc = SparkContext()
+            |print sc.parallelize([1, 2, 3]).map(myfunc).collect()
+            """)
+        zip = self.createFileInZip("mylib.py", """
+            |def myfunc(x):
+            |    return x + 1
+            """)
+        proc = subprocess.Popen([self.sparkSubmit, "--py-files", zip, script],
+            stdout=subprocess.PIPE)
+        out, err = proc.communicate()
+        self.assertEqual(0, proc.returncode)
+        self.assertIn("[2, 3, 4]", out)
+
+    def test_module_dependency_on_cluster(self):
+        """Submit and test a script with a dependency on another module on a cluster"""
+        script = self.createTempFile("test.py", """
+            |from pyspark import SparkContext
+            |from mylib import myfunc
+            |
+            |sc = SparkContext()
+            |print sc.parallelize([1, 2, 3]).map(myfunc).collect()
+            """)
+        zip = self.createFileInZip("mylib.py", """
+            |def myfunc(x):
+            |    return x + 1
+            """)
+        proc = subprocess.Popen(
+            [self.sparkSubmit, "--py-files", zip, "--master", "local-cluster[1,1,512]", script],
+            stdout=subprocess.PIPE)
+        out, err = proc.communicate()
+        self.assertEqual(0, proc.returncode)
+        self.assertIn("[2, 3, 4]", out)
+
+    def test_single_script_on_cluster(self):
+        """Submit and test a single script on a cluster"""
+        script = self.createTempFile("test.py", """
+            |from pyspark import SparkContext
+            |
+            |def foo(x):
+            |    return x * 2
+            |
+            |sc = SparkContext()
+            |print sc.parallelize([1, 2, 3]).map(foo).collect()
+            """)
+        proc = subprocess.Popen(
+            [self.sparkSubmit, "--master", "local-cluster[1,1,512]", script],
+            stdout=subprocess.PIPE)
+        out, err = proc.communicate()
+        self.assertEqual(0, proc.returncode)
+        self.assertIn("[2, 4, 6]", out)
+
+
 if __name__ == "__main__":
     unittest.main()
diff --git a/repl/src/test/scala/org/apache/spark/repl/ReplSuite.scala b/repl/src/test/scala/org/apache/spark/repl/ReplSuite.scala
index e33f4f980305..566d96e16ed8 100644
--- a/repl/src/test/scala/org/apache/spark/repl/ReplSuite.scala
+++ b/repl/src/test/scala/org/apache/spark/repl/ReplSuite.scala
@@ -45,8 +45,7 @@ class ReplSuite extends FunSuite {
     }
     val interp = new SparkILoop(in, new PrintWriter(out), master)
     org.apache.spark.repl.Main.interp = interp
-    val separator = System.getProperty("path.separator")
-    interp.process(Array("-classpath", paths.mkString(separator)))
+    interp.process(Array("-classpath", paths.mkString(File.pathSeparator)))
     org.apache.spark.repl.Main.interp = null
     if (interp.sparkContext != null) {
       interp.sparkContext.stop()

From 39b8b1489ff92697e4aeec997cdc436c7079d6f8 Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Tue, 6 May 2014 15:41:46 -0700
Subject: [PATCH 108/810] SPARK-1737: Warn rather than fail when Java 7+ is
 used to create distributions

Also moves a few lines of code around in make-distribution.sh.

Author: Patrick Wendell <pwendell@gmail.com>

Closes #669 from pwendell/make-distribution and squashes the following commits:

8bfac49 [Patrick Wendell] Small fix
46918ec [Patrick Wendell] SPARK-1737: Warn rather than fail when Java 7+ is used to create distributions.
---
 make-distribution.sh | 20 ++++++++++----------
 1 file changed, 10 insertions(+), 10 deletions(-)

diff --git a/make-distribution.sh b/make-distribution.sh
index 8a63133bc45d..ebcd8c74fc5a 100755
--- a/make-distribution.sh
+++ b/make-distribution.sh
@@ -39,18 +39,11 @@
 # 5) ./bin/spark-shell --master spark://my-master-ip:7077
 #
 
+set -o pipefail
 # Figure out where the Spark framework is installed
 FWDIR="$(cd `dirname $0`; pwd)"
 DISTDIR="$FWDIR/dist"
 
-set -o pipefail
-VERSION=$(mvn help:evaluate -Dexpression=project.version 2>/dev/null | grep -v "INFO" | tail -n 1)
-if [ $? != 0 ]; then
-    echo -e "You need Maven installed to build Spark."
-    echo -e "Download Maven from https://maven.apache.org/"
-    exit -1;
-fi
-
 if [ -z "$JAVA_HOME" ]; then
   echo "Error: JAVA_HOME is not set, cannot proceed."
   exit -1
@@ -59,10 +52,17 @@ fi
 JAVA_CMD="$JAVA_HOME"/bin/java
 JAVA_VERSION=$("$JAVA_CMD" -version 2>&1)
 if ! [[ "$JAVA_VERSION" =~ "1.6" ]]; then
-  echo "Error: JAVA_HOME must point to a JDK 6 installation (see SPARK-1703)."
+  echo "***NOTE***: JAVA_HOME is not set to a JDK 6 installation. The resulting"
+  echo "            distribution will not support Java 6. See SPARK-1703."
   echo "Output from 'java -version' was:"
   echo "$JAVA_VERSION"
-  exit -1
+fi
+
+VERSION=$(mvn help:evaluate -Dexpression=project.version 2>/dev/null | grep -v "INFO" | tail -n 1)
+if [ $? != 0 ]; then
+    echo -e "You need Maven installed to build Spark."
+    echo -e "Download Maven from https://maven.apache.org/"
+    exit -1;
 fi
 
 # Initialize defaults

From a000b5c3b0438c17e9973df4832c320210c29c27 Mon Sep 17 00:00:00 2001
From: Sandeep <sandeep@techaddict.me>
Date: Tue, 6 May 2014 17:27:52 -0700
Subject: [PATCH 109/810] SPARK-1637: Clean up examples for 1.0

- [x] Move all of them into subpackages of org.apache.spark.examples (right now some are in org.apache.spark.streaming.examples, for instance, and others are in org.apache.spark.examples.mllib)
- [x] Move Python examples into examples/src/main/python
- [x] Update docs to reflect these changes

Author: Sandeep <sandeep@techaddict.me>

This patch had conflicts when merged, resolved by
Committer: Matei Zaharia <matei@databricks.com>

Closes #571 from techaddict/SPARK-1637 and squashes the following commits:

47ef86c [Sandeep] Changes based on Discussions on PR, removing use of RawTextHelper from examples
8ed2d3f [Sandeep] Docs Updated for changes, Change for java examples
5f96121 [Sandeep] Move Python examples into examples/src/main/python
0a8dd77 [Sandeep] Move all Scala Examples to org.apache.spark.examples (some are in org.apache.spark.streaming.examples, for instance, and others are in org.apache.spark.examples.mllib)
---
 docs/index.md                                 |  4 ++--
 docs/python-programming-guide.md              |  4 ++--
 docs/streaming-programming-guide.md           | 23 +++++++++----------
 .../examples => examples/mllib}/JavaALS.java  |  2 +-
 .../mllib}/JavaKMeans.java                    |  2 +-
 .../examples => examples/mllib}/JavaLR.java   |  2 +-
 .../streaming}/JavaFlumeEventCount.java       |  3 ++-
 .../streaming}/JavaKafkaWordCount.java        |  5 ++--
 .../streaming}/JavaNetworkWordCount.java      |  5 ++--
 .../streaming}/JavaQueueStream.java           |  3 ++-
 .../src/main/python}/als.py                   |  0
 .../src/main/python}/kmeans.py                |  2 +-
 .../src/main/python}/logistic_regression.py   |  2 +-
 .../src/main/python}/mllib/kmeans.py          |  0
 .../main/python}/mllib/logistic_regression.py |  0
 .../src/main/python}/pagerank.py              |  0
 .../src/main/python}/pi.py                    |  0
 .../src/main/python}/sort.py                  |  0
 .../src/main/python}/transitive_closure.py    |  0
 .../src/main/python}/wordcount.py             |  0
 .../sql}/RDDRelation.scala                    |  2 +-
 .../sql/hive}/HiveFromSpark.scala             |  2 +-
 .../streaming}/ActorWordCount.scala           |  8 +++----
 .../streaming}/FlumeEventCount.scala          |  2 +-
 .../streaming}/HdfsWordCount.scala            |  4 ++--
 .../streaming}/KafkaWordCount.scala           |  7 +++---
 .../streaming}/MQTTWordCount.scala            |  6 ++---
 .../streaming}/NetworkWordCount.scala         |  4 ++--
 .../streaming}/QueueStream.scala              |  2 +-
 .../streaming}/RawNetworkGrep.scala           |  6 +----
 .../RecoverableNetworkWordCount.scala         |  6 ++---
 .../streaming}/StatefulNetworkWordCount.scala |  4 ++--
 .../streaming}/StreamingExamples.scala        |  2 +-
 .../streaming}/TwitterAlgebirdCMS.scala       |  2 +-
 .../streaming}/TwitterAlgebirdHLL.scala       |  2 +-
 .../streaming}/TwitterPopularTags.scala       |  2 +-
 .../streaming}/ZeroMQWordCount.scala          |  6 ++---
 .../clickstream/PageViewGenerator.scala       |  6 ++---
 .../clickstream/PageViewStream.scala          |  8 +++----
 .../spark/streaming/util/RawTextHelper.scala  |  3 +--
 40 files changed, 69 insertions(+), 72 deletions(-)
 rename examples/src/main/java/org/apache/spark/{mllib/examples => examples/mllib}/JavaALS.java (98%)
 rename examples/src/main/java/org/apache/spark/{mllib/examples => examples/mllib}/JavaKMeans.java (98%)
 rename examples/src/main/java/org/apache/spark/{mllib/examples => examples/mllib}/JavaLR.java (98%)
 rename examples/src/main/java/org/apache/spark/{streaming/examples => examples/streaming}/JavaFlumeEventCount.java (96%)
 rename examples/src/main/java/org/apache/spark/{streaming/examples => examples/streaming}/JavaKafkaWordCount.java (96%)
 rename examples/src/main/java/org/apache/spark/{streaming/examples => examples/streaming}/JavaNetworkWordCount.java (95%)
 rename examples/src/main/java/org/apache/spark/{streaming/examples => examples/streaming}/JavaQueueStream.java (96%)
 rename {python/examples => examples/src/main/python}/als.py (100%)
 rename {python/examples => examples/src/main/python}/kmeans.py (98%)
 rename {python/examples => examples/src/main/python}/logistic_regression.py (97%)
 rename {python/examples => examples/src/main/python}/mllib/kmeans.py (100%)
 rename {python/examples => examples/src/main/python}/mllib/logistic_regression.py (100%)
 rename {python/examples => examples/src/main/python}/pagerank.py (100%)
 rename {python/examples => examples/src/main/python}/pi.py (100%)
 rename {python/examples => examples/src/main/python}/sort.py (100%)
 rename {python/examples => examples/src/main/python}/transitive_closure.py (100%)
 rename {python/examples => examples/src/main/python}/wordcount.py (100%)
 rename examples/src/main/scala/org/apache/spark/{sql/examples => examples/sql}/RDDRelation.scala (98%)
 rename examples/src/main/scala/org/apache/spark/{sql/examples => examples/sql/hive}/HiveFromSpark.scala (98%)
 rename examples/src/main/scala/org/apache/spark/{streaming/examples => examples/streaming}/ActorWordCount.scala (95%)
 rename examples/src/main/scala/org/apache/spark/{streaming/examples => examples/streaming}/FlumeEventCount.scala (98%)
 rename examples/src/main/scala/org/apache/spark/{streaming/examples => examples/streaming}/HdfsWordCount.scala (95%)
 rename examples/src/main/scala/org/apache/spark/{streaming/examples => examples/streaming}/KafkaWordCount.scala (93%)
 rename examples/src/main/scala/org/apache/spark/{streaming/examples => examples/streaming}/MQTTWordCount.scala (95%)
 rename examples/src/main/scala/org/apache/spark/{streaming/examples => examples/streaming}/NetworkWordCount.scala (95%)
 rename examples/src/main/scala/org/apache/spark/{streaming/examples => examples/streaming}/QueueStream.scala (97%)
 rename examples/src/main/scala/org/apache/spark/{streaming/examples => examples/streaming}/RawNetworkGrep.scala (92%)
 rename examples/src/main/scala/org/apache/spark/{streaming/examples => examples/streaming}/RecoverableNetworkWordCount.scala (96%)
 rename examples/src/main/scala/org/apache/spark/{streaming/examples => examples/streaming}/StatefulNetworkWordCount.scala (96%)
 rename examples/src/main/scala/org/apache/spark/{streaming/examples => examples/streaming}/StreamingExamples.scala (97%)
 rename examples/src/main/scala/org/apache/spark/{streaming/examples => examples/streaming}/TwitterAlgebirdCMS.scala (99%)
 rename examples/src/main/scala/org/apache/spark/{streaming/examples => examples/streaming}/TwitterAlgebirdHLL.scala (98%)
 rename examples/src/main/scala/org/apache/spark/{streaming/examples => examples/streaming}/TwitterPopularTags.scala (98%)
 rename examples/src/main/scala/org/apache/spark/{streaming/examples => examples/streaming}/ZeroMQWordCount.scala (95%)
 rename examples/src/main/scala/org/apache/spark/{streaming/examples => examples/streaming}/clickstream/PageViewGenerator.scala (95%)
 rename examples/src/main/scala/org/apache/spark/{streaming/examples => examples/streaming}/clickstream/PageViewStream.scala (94%)

diff --git a/docs/index.md b/docs/index.md
index e3647717a1f1..a2f1a84371ff 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -24,11 +24,11 @@ right version of Scala from [scala-lang.org](http://www.scala-lang.org/download/
 
 # Running the Examples and Shell
 
-Spark comes with several sample programs.  Scala and Java examples are in the `examples` directory, and Python examples are in `python/examples`.
+Spark comes with several sample programs.  Scala, Java and Python examples are in the `examples/src/main` directory.
 To run one of the Java or Scala sample programs, use `./bin/run-example <class> <params>` in the top-level Spark directory
 (the `bin/run-example` script sets up the appropriate paths and launches that program).
 For example, try `./bin/run-example org.apache.spark.examples.SparkPi local`.
-To run a Python sample program, use `./bin/pyspark <sample-program> <params>`.  For example, try `./bin/pyspark ./python/examples/pi.py local`.
+To run a Python sample program, use `./bin/pyspark <sample-program> <params>`.  For example, try `./bin/pyspark ./examples/src/main/python/pi.py local`.
 
 Each example prints usage help when run with no parameters.
 
diff --git a/docs/python-programming-guide.md b/docs/python-programming-guide.md
index 8ea22e15a4b6..6813963bb080 100644
--- a/docs/python-programming-guide.md
+++ b/docs/python-programming-guide.md
@@ -161,9 +161,9 @@ some example applications.
 
 # Where to Go from Here
 
-PySpark also includes several sample programs in the [`python/examples` folder](https://github.com/apache/spark/tree/master/python/examples).
+PySpark also includes several sample programs in the [`examples/src/main/python` folder](https://github.com/apache/spark/tree/master/examples/src/main/python).
 You can run them by passing the files to `pyspark`; e.g.:
 
-    ./bin/spark-submit python/examples/wordcount.py
+    ./bin/spark-submit examples/src/main/python/wordcount.py
 
 Each program prints usage help when run without arguments.
diff --git a/docs/streaming-programming-guide.md b/docs/streaming-programming-guide.md
index e8b718b30356..939599aa6855 100644
--- a/docs/streaming-programming-guide.md
+++ b/docs/streaming-programming-guide.md
@@ -129,7 +129,7 @@ ssc.awaitTermination()  // Wait for the computation to terminate
 {% endhighlight %}
 
 The complete code can be found in the Spark Streaming example
-[NetworkWordCount]({{site.SPARK_GITHUB_URL}}/blob/master/examples/src/main/scala/org/apache/spark/streaming/examples/NetworkWordCount.scala).
+[NetworkWordCount]({{site.SPARK_GITHUB_URL}}/blob/master/examples/src/main/scala/org/apache/spark/examples/streaming/NetworkWordCount.scala).
 <br>
 
 </div>
@@ -215,7 +215,7 @@ jssc.awaitTermination();   // Wait for the computation to terminate
 {% endhighlight %}
 
 The complete code can be found in the Spark Streaming example
-[JavaNetworkWordCount]({{site.SPARK_GITHUB_URL}}/blob/master/examples/src/main/java/org/apache/spark/streaming/examples/JavaNetworkWordCount.java).
+[JavaNetworkWordCount]({{site.SPARK_GITHUB_URL}}/blob/master/examples/src/main/java/org/apache/spark/examples/streaming/JavaNetworkWordCount.java).
 <br>
 
 </div>
@@ -234,12 +234,12 @@ Then, in a different terminal, you can start the example by using
 <div class="codetabs">
 <div data-lang="scala" markdown="1">
 {% highlight bash %}
-$ ./bin/run-example org.apache.spark.streaming.examples.NetworkWordCount local[2] localhost 9999
+$ ./bin/run-example org.apache.spark.examples.streaming.NetworkWordCount local[2] localhost 9999
 {% endhighlight %}
 </div>
 <div data-lang="java" markdown="1">
 {% highlight bash %}
-$ ./bin/run-example org.apache.spark.streaming.examples.JavaNetworkWordCount local[2] localhost 9999
+$ ./bin/run-example org.apache.spark.examples.streaming.JavaNetworkWordCount local[2] localhost 9999
 {% endhighlight %}
 </div>
 </div>
@@ -268,7 +268,7 @@ hello world
 {% highlight bash %}
 # TERMINAL 2: RUNNING NetworkWordCount or JavaNetworkWordCount
 
-$ ./bin/run-example org.apache.spark.streaming.examples.NetworkWordCount local[2] localhost 9999
+$ ./bin/run-example org.apache.spark.examples.streaming.NetworkWordCount local[2] localhost 9999
 ...
 -------------------------------------------
 Time: 1357008430000 ms
@@ -609,7 +609,7 @@ JavaPairDStream<String, Integer> runningCounts = pairs.updateStateByKey(updateFu
 The update function will be called for each word, with `newValues` having a sequence of 1's (from
 the `(word, 1)` pairs) and the `runningCount` having the previous count. For the complete
 Scala code, take a look at the example
-[StatefulNetworkWordCount]({{site.SPARK_GITHUB_URL}}/blob/master/examples/src/main/scala/org/apache/spark/streaming/examples/StatefulNetworkWordCount.scala).
+[StatefulNetworkWordCount]({{site.SPARK_GITHUB_URL}}/blob/master/examples/src/main/scala/org/apache/spark/examples/streaming/StatefulNetworkWordCount.scala).
 
 <h4>Transform Operation</h4>
 
@@ -1135,7 +1135,7 @@ If the `checkpointDirectory` exists, then the context will be recreated from the
 If the directory does not exist (i.e., running for the first time),
 then the function `functionToCreateContext` will be called to create a new
 context and set up the DStreams. See the Scala example
-[RecoverableNetworkWordCount]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/scala/org/apache/spark/streaming/examples/RecoverableNetworkWordCount.scala).
+[RecoverableNetworkWordCount]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/scala/org/apache/spark/examples/streaming/RecoverableNetworkWordCount.scala).
 This example appends the word counts of network data into a file.
 
 You can also explicitly create a `StreamingContext` from the checkpoint data and start the
@@ -1174,7 +1174,7 @@ If the `checkpointDirectory` exists, then the context will be recreated from the
 If the directory does not exist (i.e., running for the first time),
 then the function `contextFactory` will be called to create a new
 context and set up the DStreams. See the Scala example
-[JavaRecoverableWordCount]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/scala/org/apache/spark/streaming/examples/JavaRecoverableWordCount.scala)
+[JavaRecoverableWordCount]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/scala/org/apache/spark/examples/streaming/JavaRecoverableWordCount.scala)
 (note that this example is missing in the 0.9 release, so you can test it using the master branch).
 This example appends the word counts of network data into a file.
 
@@ -1374,7 +1374,6 @@ package and renamed for better clarity.
     [ZeroMQUtils](api/java/org/apache/spark/streaming/zeromq/ZeroMQUtils.html), and
     [MQTTUtils](api/java/org/apache/spark/streaming/mqtt/MQTTUtils.html)
 
-* More examples in [Scala]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/scala/org/apache/spark/streaming/examples)
-  and [Java]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/java/org/apache/spark/streaming/examples)
-* [Paper](http://www.eecs.berkeley.edu/Pubs/TechRpts/2012/EECS-2012-259.pdf) and
-[video](http://youtu.be/g171ndOHgJ0) describing Spark Streaming.
+* More examples in [Scala]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/scala/org/apache/spark/examples/streaming)
+  and [Java]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/java/org/apache/spark/examples/streaming)
+* [Paper](http://www.eecs.berkeley.edu/Pubs/TechRpts/2012/EECS-2012-259.pdf) and [video](http://youtu.be/g171ndOHgJ0) describing Spark Streaming.
diff --git a/examples/src/main/java/org/apache/spark/mllib/examples/JavaALS.java b/examples/src/main/java/org/apache/spark/examples/mllib/JavaALS.java
similarity index 98%
rename from examples/src/main/java/org/apache/spark/mllib/examples/JavaALS.java
rename to examples/src/main/java/org/apache/spark/examples/mllib/JavaALS.java
index c516199d61c7..4533c4c5f241 100644
--- a/examples/src/main/java/org/apache/spark/mllib/examples/JavaALS.java
+++ b/examples/src/main/java/org/apache/spark/examples/mllib/JavaALS.java
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.mllib.examples;
+package org.apache.spark.examples.mllib;
 
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.JavaSparkContext;
diff --git a/examples/src/main/java/org/apache/spark/mllib/examples/JavaKMeans.java b/examples/src/main/java/org/apache/spark/examples/mllib/JavaKMeans.java
similarity index 98%
rename from examples/src/main/java/org/apache/spark/mllib/examples/JavaKMeans.java
rename to examples/src/main/java/org/apache/spark/examples/mllib/JavaKMeans.java
index 7461609ab9e8..0cfb8e69ed28 100644
--- a/examples/src/main/java/org/apache/spark/mllib/examples/JavaKMeans.java
+++ b/examples/src/main/java/org/apache/spark/examples/mllib/JavaKMeans.java
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.mllib.examples;
+package org.apache.spark.examples.mllib;
 
 import java.util.regex.Pattern;
 
diff --git a/examples/src/main/java/org/apache/spark/mllib/examples/JavaLR.java b/examples/src/main/java/org/apache/spark/examples/mllib/JavaLR.java
similarity index 98%
rename from examples/src/main/java/org/apache/spark/mllib/examples/JavaLR.java
rename to examples/src/main/java/org/apache/spark/examples/mllib/JavaLR.java
index e3ab87cc722f..f6e48b498727 100644
--- a/examples/src/main/java/org/apache/spark/mllib/examples/JavaLR.java
+++ b/examples/src/main/java/org/apache/spark/examples/mllib/JavaLR.java
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.mllib.examples;
+package org.apache.spark.examples.mllib;
 
 import java.util.regex.Pattern;
 
diff --git a/examples/src/main/java/org/apache/spark/streaming/examples/JavaFlumeEventCount.java b/examples/src/main/java/org/apache/spark/examples/streaming/JavaFlumeEventCount.java
similarity index 96%
rename from examples/src/main/java/org/apache/spark/streaming/examples/JavaFlumeEventCount.java
rename to examples/src/main/java/org/apache/spark/examples/streaming/JavaFlumeEventCount.java
index c59f7538f8a8..a5ece68cef87 100644
--- a/examples/src/main/java/org/apache/spark/streaming/examples/JavaFlumeEventCount.java
+++ b/examples/src/main/java/org/apache/spark/examples/streaming/JavaFlumeEventCount.java
@@ -15,9 +15,10 @@
  * limitations under the License.
  */
 
-package org.apache.spark.streaming.examples;
+package org.apache.spark.examples.streaming;
 
 import org.apache.spark.api.java.function.Function;
+import org.apache.spark.examples.streaming.StreamingExamples;
 import org.apache.spark.streaming.*;
 import org.apache.spark.streaming.api.java.*;
 import org.apache.spark.streaming.flume.FlumeUtils;
diff --git a/examples/src/main/java/org/apache/spark/streaming/examples/JavaKafkaWordCount.java b/examples/src/main/java/org/apache/spark/examples/streaming/JavaKafkaWordCount.java
similarity index 96%
rename from examples/src/main/java/org/apache/spark/streaming/examples/JavaKafkaWordCount.java
rename to examples/src/main/java/org/apache/spark/examples/streaming/JavaKafkaWordCount.java
index 8da9bcd05ac3..da51eb189a64 100644
--- a/examples/src/main/java/org/apache/spark/streaming/examples/JavaKafkaWordCount.java
+++ b/examples/src/main/java/org/apache/spark/examples/streaming/JavaKafkaWordCount.java
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.streaming.examples;
+package org.apache.spark.examples.streaming;
 
 import java.util.Map;
 import java.util.HashMap;
@@ -26,6 +26,7 @@
 import org.apache.spark.api.java.function.Function;
 import org.apache.spark.api.java.function.Function2;
 import org.apache.spark.api.java.function.PairFunction;
+import org.apache.spark.examples.streaming.StreamingExamples;
 import org.apache.spark.streaming.Duration;
 import org.apache.spark.streaming.api.java.JavaDStream;
 import org.apache.spark.streaming.api.java.JavaPairDStream;
@@ -44,7 +45,7 @@
  *   <numThreads> is the number of threads the kafka consumer should use
  *
  * Example:
- *    `./bin/run-example org.apache.spark.streaming.examples.JavaKafkaWordCount local[2] zoo01,zoo02,
+ *    `./bin/run-example org.apache.spark.examples.streaming.JavaKafkaWordCount local[2] zoo01,zoo02,
  *    zoo03 my-consumer-group topic1,topic2 1`
  */
 
diff --git a/examples/src/main/java/org/apache/spark/streaming/examples/JavaNetworkWordCount.java b/examples/src/main/java/org/apache/spark/examples/streaming/JavaNetworkWordCount.java
similarity index 95%
rename from examples/src/main/java/org/apache/spark/streaming/examples/JavaNetworkWordCount.java
rename to examples/src/main/java/org/apache/spark/examples/streaming/JavaNetworkWordCount.java
index 098c329ff680..ac84991d87b8 100644
--- a/examples/src/main/java/org/apache/spark/streaming/examples/JavaNetworkWordCount.java
+++ b/examples/src/main/java/org/apache/spark/examples/streaming/JavaNetworkWordCount.java
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.streaming.examples;
+package org.apache.spark.examples.streaming;
 
 import com.google.common.collect.Lists;
 import org.apache.spark.streaming.api.java.JavaReceiverInputDStream;
@@ -23,6 +23,7 @@
 import org.apache.spark.api.java.function.FlatMapFunction;
 import org.apache.spark.api.java.function.Function2;
 import org.apache.spark.api.java.function.PairFunction;
+import org.apache.spark.examples.streaming.StreamingExamples;
 import org.apache.spark.streaming.Duration;
 import org.apache.spark.streaming.api.java.JavaDStream;
 import org.apache.spark.streaming.api.java.JavaPairDStream;
@@ -39,7 +40,7 @@
  * To run this on your local machine, you need to first run a Netcat server
  *    `$ nc -lk 9999`
  * and then run the example
- *    `$ ./run org.apache.spark.streaming.examples.JavaNetworkWordCount local[2] localhost 9999`
+ *    `$ ./run org.apache.spark.examples.streaming.JavaNetworkWordCount local[2] localhost 9999`
  */
 public final class JavaNetworkWordCount {
   private static final Pattern SPACE = Pattern.compile(" ");
diff --git a/examples/src/main/java/org/apache/spark/streaming/examples/JavaQueueStream.java b/examples/src/main/java/org/apache/spark/examples/streaming/JavaQueueStream.java
similarity index 96%
rename from examples/src/main/java/org/apache/spark/streaming/examples/JavaQueueStream.java
rename to examples/src/main/java/org/apache/spark/examples/streaming/JavaQueueStream.java
index 88ad341641e0..819311968fac 100644
--- a/examples/src/main/java/org/apache/spark/streaming/examples/JavaQueueStream.java
+++ b/examples/src/main/java/org/apache/spark/examples/streaming/JavaQueueStream.java
@@ -15,13 +15,14 @@
  * limitations under the License.
  */
 
-package org.apache.spark.streaming.examples;
+package org.apache.spark.examples.streaming;
 
 import com.google.common.collect.Lists;
 import scala.Tuple2;
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.function.Function2;
 import org.apache.spark.api.java.function.PairFunction;
+import org.apache.spark.examples.streaming.StreamingExamples;
 import org.apache.spark.streaming.Duration;
 import org.apache.spark.streaming.api.java.JavaDStream;
 import org.apache.spark.streaming.api.java.JavaPairDStream;
diff --git a/python/examples/als.py b/examples/src/main/python/als.py
similarity index 100%
rename from python/examples/als.py
rename to examples/src/main/python/als.py
diff --git a/python/examples/kmeans.py b/examples/src/main/python/kmeans.py
similarity index 98%
rename from python/examples/kmeans.py
rename to examples/src/main/python/kmeans.py
index d8387b0b183e..e3596488faf9 100755
--- a/python/examples/kmeans.py
+++ b/examples/src/main/python/kmeans.py
@@ -18,7 +18,7 @@
 """
 The K-means algorithm written from scratch against PySpark. In practice,
 one may prefer to use the KMeans algorithm in MLlib, as shown in
-python/examples/mllib/kmeans.py.
+examples/src/main/python/mllib/kmeans.py.
 
 This example requires NumPy (http://www.numpy.org/).
 """
diff --git a/python/examples/logistic_regression.py b/examples/src/main/python/logistic_regression.py
similarity index 97%
rename from python/examples/logistic_regression.py
rename to examples/src/main/python/logistic_regression.py
index 28d52e6a40b4..fe5373cf799b 100755
--- a/python/examples/logistic_regression.py
+++ b/examples/src/main/python/logistic_regression.py
@@ -20,7 +20,7 @@
 to act on batches of input data using efficient matrix operations.
 
 In practice, one may prefer to use the LogisticRegression algorithm in
-MLlib, as shown in python/examples/mllib/logistic_regression.py.
+MLlib, as shown in examples/src/main/python/mllib/logistic_regression.py.
 """
 
 from collections import namedtuple
diff --git a/python/examples/mllib/kmeans.py b/examples/src/main/python/mllib/kmeans.py
similarity index 100%
rename from python/examples/mllib/kmeans.py
rename to examples/src/main/python/mllib/kmeans.py
diff --git a/python/examples/mllib/logistic_regression.py b/examples/src/main/python/mllib/logistic_regression.py
similarity index 100%
rename from python/examples/mllib/logistic_regression.py
rename to examples/src/main/python/mllib/logistic_regression.py
diff --git a/python/examples/pagerank.py b/examples/src/main/python/pagerank.py
similarity index 100%
rename from python/examples/pagerank.py
rename to examples/src/main/python/pagerank.py
diff --git a/python/examples/pi.py b/examples/src/main/python/pi.py
similarity index 100%
rename from python/examples/pi.py
rename to examples/src/main/python/pi.py
diff --git a/python/examples/sort.py b/examples/src/main/python/sort.py
similarity index 100%
rename from python/examples/sort.py
rename to examples/src/main/python/sort.py
diff --git a/python/examples/transitive_closure.py b/examples/src/main/python/transitive_closure.py
similarity index 100%
rename from python/examples/transitive_closure.py
rename to examples/src/main/python/transitive_closure.py
diff --git a/python/examples/wordcount.py b/examples/src/main/python/wordcount.py
similarity index 100%
rename from python/examples/wordcount.py
rename to examples/src/main/python/wordcount.py
diff --git a/examples/src/main/scala/org/apache/spark/sql/examples/RDDRelation.scala b/examples/src/main/scala/org/apache/spark/examples/sql/RDDRelation.scala
similarity index 98%
rename from examples/src/main/scala/org/apache/spark/sql/examples/RDDRelation.scala
rename to examples/src/main/scala/org/apache/spark/examples/sql/RDDRelation.scala
index 8210ad977f06..ff9254b044c2 100644
--- a/examples/src/main/scala/org/apache/spark/sql/examples/RDDRelation.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/sql/RDDRelation.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.examples
+package org.apache.spark.examples.sql
 
 import org.apache.spark.SparkContext
 import org.apache.spark.sql.SQLContext
diff --git a/examples/src/main/scala/org/apache/spark/sql/examples/HiveFromSpark.scala b/examples/src/main/scala/org/apache/spark/examples/sql/hive/HiveFromSpark.scala
similarity index 98%
rename from examples/src/main/scala/org/apache/spark/sql/examples/HiveFromSpark.scala
rename to examples/src/main/scala/org/apache/spark/examples/sql/hive/HiveFromSpark.scala
index 62329bde8448..66ce93a26ef4 100644
--- a/examples/src/main/scala/org/apache/spark/sql/examples/HiveFromSpark.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/sql/hive/HiveFromSpark.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.hive.examples
+package org.apache.spark.examples.sql.hive
 
 import org.apache.spark.SparkContext
 import org.apache.spark.sql._
diff --git a/examples/src/main/scala/org/apache/spark/streaming/examples/ActorWordCount.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/ActorWordCount.scala
similarity index 95%
rename from examples/src/main/scala/org/apache/spark/streaming/examples/ActorWordCount.scala
rename to examples/src/main/scala/org/apache/spark/examples/streaming/ActorWordCount.scala
index c845dd8904c9..84cf43df0f96 100644
--- a/examples/src/main/scala/org/apache/spark/streaming/examples/ActorWordCount.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/ActorWordCount.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.streaming.examples
+package org.apache.spark.examples.streaming
 
 import scala.collection.mutable.LinkedList
 import scala.reflect.ClassTag
@@ -78,7 +78,7 @@ class FeederActor extends Actor {
  * goes and subscribe to a typical publisher/feeder actor and receives
  * data.
  *
- * @see [[org.apache.spark.streaming.examples.FeederActor]]
+ * @see [[org.apache.spark.examples.streaming.FeederActor]]
  */
 class SampleActorReceiver[T: ClassTag](urlOfPublisher: String)
 extends Actor with ActorHelper {
@@ -131,9 +131,9 @@ object FeederActor {
  *   <hostname> and <port> describe the AkkaSystem that Spark Sample feeder is running on.
  *
  * To run this example locally, you may run Feeder Actor as
- *    `$ ./bin/run-example org.apache.spark.streaming.examples.FeederActor 127.0.1.1 9999`
+ *    `$ ./bin/run-example org.apache.spark.examples.streaming.FeederActor 127.0.1.1 9999`
  * and then run the example
- *    `./bin/run-example org.apache.spark.streaming.examples.ActorWordCount local[2] 127.0.1.1 9999`
+ *    `./bin/run-example org.apache.spark.examples.streaming.ActorWordCount local[2] 127.0.1.1 9999`
  */
 object ActorWordCount {
   def main(args: Array[String]) {
diff --git a/examples/src/main/scala/org/apache/spark/streaming/examples/FlumeEventCount.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/FlumeEventCount.scala
similarity index 98%
rename from examples/src/main/scala/org/apache/spark/streaming/examples/FlumeEventCount.scala
rename to examples/src/main/scala/org/apache/spark/examples/streaming/FlumeEventCount.scala
index 26b602453412..5b2a1035fc77 100644
--- a/examples/src/main/scala/org/apache/spark/streaming/examples/FlumeEventCount.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/FlumeEventCount.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.streaming.examples
+package org.apache.spark.examples.streaming
 
 import org.apache.spark.storage.StorageLevel
 import org.apache.spark.streaming._
diff --git a/examples/src/main/scala/org/apache/spark/streaming/examples/HdfsWordCount.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/HdfsWordCount.scala
similarity index 95%
rename from examples/src/main/scala/org/apache/spark/streaming/examples/HdfsWordCount.scala
rename to examples/src/main/scala/org/apache/spark/examples/streaming/HdfsWordCount.scala
index 7f86fc792aac..b440956ba313 100644
--- a/examples/src/main/scala/org/apache/spark/streaming/examples/HdfsWordCount.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/HdfsWordCount.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.streaming.examples
+package org.apache.spark.examples.streaming
 
 import org.apache.spark.streaming.{Seconds, StreamingContext}
 import org.apache.spark.streaming.StreamingContext._
@@ -27,7 +27,7 @@ import org.apache.spark.streaming.StreamingContext._
  *   <directory> is the directory that Spark Streaming will use to find and read new text files.
  *
  * To run this on your local machine on directory `localdir`, run this example
- *    `$ ./bin/run-example org.apache.spark.streaming.examples.HdfsWordCount local[2] localdir`
+ *    `$ ./bin/run-example org.apache.spark.examples.streaming.HdfsWordCount local[2] localdir`
  * Then create a text file in `localdir` and the words in the file will get counted.
  */
 object HdfsWordCount {
diff --git a/examples/src/main/scala/org/apache/spark/streaming/examples/KafkaWordCount.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/KafkaWordCount.scala
similarity index 93%
rename from examples/src/main/scala/org/apache/spark/streaming/examples/KafkaWordCount.scala
rename to examples/src/main/scala/org/apache/spark/examples/streaming/KafkaWordCount.scala
index 2aa4f1474a59..c3aae5af05b1 100644
--- a/examples/src/main/scala/org/apache/spark/streaming/examples/KafkaWordCount.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/KafkaWordCount.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.streaming.examples
+package org.apache.spark.examples.streaming
 
 import java.util.Properties
 
@@ -24,7 +24,6 @@ import kafka.producer._
 import org.apache.spark.streaming._
 import org.apache.spark.streaming.StreamingContext._
 import org.apache.spark.streaming.kafka._
-import org.apache.spark.streaming.util.RawTextHelper._
 
 // scalastyle:off
 /**
@@ -37,7 +36,7 @@ import org.apache.spark.streaming.util.RawTextHelper._
  *   <numThreads> is the number of threads the kafka consumer should use
  *
  * Example:
- *    `./bin/run-example org.apache.spark.streaming.examples.KafkaWordCount local[2] zoo01,zoo02,zoo03 my-consumer-group topic1,topic2 1`
+ *    `./bin/run-example org.apache.spark.examples.streaming.KafkaWordCount local[2] zoo01,zoo02,zoo03 my-consumer-group topic1,topic2 1`
  */
 // scalastyle:on
 object KafkaWordCount {
@@ -59,7 +58,7 @@ object KafkaWordCount {
     val lines = KafkaUtils.createStream(ssc, zkQuorum, group, topicpMap).map(_._2)
     val words = lines.flatMap(_.split(" "))
     val wordCounts = words.map(x => (x, 1L))
-      .reduceByKeyAndWindow(add _, subtract _, Minutes(10), Seconds(2), 2)
+      .reduceByKeyAndWindow(_ + _, _ - _, Minutes(10), Seconds(2), 2)
     wordCounts.print()
 
     ssc.start()
diff --git a/examples/src/main/scala/org/apache/spark/streaming/examples/MQTTWordCount.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/MQTTWordCount.scala
similarity index 95%
rename from examples/src/main/scala/org/apache/spark/streaming/examples/MQTTWordCount.scala
rename to examples/src/main/scala/org/apache/spark/examples/streaming/MQTTWordCount.scala
index 62aef0fb4710..47bf1e5a0643 100644
--- a/examples/src/main/scala/org/apache/spark/streaming/examples/MQTTWordCount.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/MQTTWordCount.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.streaming.examples
+package org.apache.spark.examples.streaming
 
 import org.eclipse.paho.client.mqttv3.{MqttClient, MqttClientPersistence, MqttException, MqttMessage, MqttTopic}
 import org.eclipse.paho.client.mqttv3.persist.MqttDefaultFilePersistence
@@ -79,9 +79,9 @@ object MQTTPublisher {
  *   <MqttbrokerUrl> and <topic> describe where Mqtt publisher is running.
  *
  * To run this example locally, you may run publisher as
- *    `$ ./bin/run-example org.apache.spark.streaming.examples.MQTTPublisher tcp://localhost:1883 foo`
+ *    `$ ./bin/run-example org.apache.spark.examples.streaming.MQTTPublisher tcp://localhost:1883 foo`
  * and run the example as
- *    `$ ./bin/run-example org.apache.spark.streaming.examples.MQTTWordCount local[2] tcp://localhost:1883 foo`
+ *    `$ ./bin/run-example org.apache.spark.examples.streaming.MQTTWordCount local[2] tcp://localhost:1883 foo`
  */
 // scalastyle:on
 object MQTTWordCount {
diff --git a/examples/src/main/scala/org/apache/spark/streaming/examples/NetworkWordCount.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/NetworkWordCount.scala
similarity index 95%
rename from examples/src/main/scala/org/apache/spark/streaming/examples/NetworkWordCount.scala
rename to examples/src/main/scala/org/apache/spark/examples/streaming/NetworkWordCount.scala
index 272ab1121245..acfe9a4da359 100644
--- a/examples/src/main/scala/org/apache/spark/streaming/examples/NetworkWordCount.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/NetworkWordCount.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.streaming.examples
+package org.apache.spark.examples.streaming
 
 import org.apache.spark.streaming.{Seconds, StreamingContext}
 import org.apache.spark.streaming.StreamingContext._
@@ -32,7 +32,7 @@ import org.apache.spark.storage.StorageLevel
  * To run this on your local machine, you need to first run a Netcat server
  *    `$ nc -lk 9999`
  * and then run the example
- *    `$ ./bin/run-example org.apache.spark.streaming.examples.NetworkWordCount local[2] localhost 9999`
+ *    `$ ./bin/run-example org.apache.spark.examples.streaming.NetworkWordCount local[2] localhost 9999`
  */
 // scalastyle:on
 object NetworkWordCount {
diff --git a/examples/src/main/scala/org/apache/spark/streaming/examples/QueueStream.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/QueueStream.scala
similarity index 97%
rename from examples/src/main/scala/org/apache/spark/streaming/examples/QueueStream.scala
rename to examples/src/main/scala/org/apache/spark/examples/streaming/QueueStream.scala
index ff2a205ec1c1..f92f72f2de87 100644
--- a/examples/src/main/scala/org/apache/spark/streaming/examples/QueueStream.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/QueueStream.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.streaming.examples
+package org.apache.spark.examples.streaming
 
 import scala.collection.mutable.SynchronizedQueue
 
diff --git a/examples/src/main/scala/org/apache/spark/streaming/examples/RawNetworkGrep.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/RawNetworkGrep.scala
similarity index 92%
rename from examples/src/main/scala/org/apache/spark/streaming/examples/RawNetworkGrep.scala
rename to examples/src/main/scala/org/apache/spark/examples/streaming/RawNetworkGrep.scala
index d915c0c39b33..1b0319a04643 100644
--- a/examples/src/main/scala/org/apache/spark/streaming/examples/RawNetworkGrep.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/RawNetworkGrep.scala
@@ -15,11 +15,10 @@
  * limitations under the License.
  */
 
-package org.apache.spark.streaming.examples
+package org.apache.spark.examples.streaming
 
 import org.apache.spark.storage.StorageLevel
 import org.apache.spark.streaming._
-import org.apache.spark.streaming.util.RawTextHelper
 import org.apache.spark.util.IntParam
 
 /**
@@ -52,9 +51,6 @@ object RawNetworkGrep {
     val ssc = new StreamingContext(master, "RawNetworkGrep", Milliseconds(batchMillis),
       System.getenv("SPARK_HOME"), StreamingContext.jarOfClass(this.getClass).toSeq)
 
-    // Warm up the JVMs on master and slave for JIT compilation to kick in
-    RawTextHelper.warmUp(ssc.sparkContext)
-
     val rawStreams = (1 to numStreams).map(_ =>
       ssc.rawSocketStream[String](host, port, StorageLevel.MEMORY_ONLY_SER_2)).toArray
     val union = ssc.union(rawStreams)
diff --git a/examples/src/main/scala/org/apache/spark/streaming/examples/RecoverableNetworkWordCount.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/RecoverableNetworkWordCount.scala
similarity index 96%
rename from examples/src/main/scala/org/apache/spark/streaming/examples/RecoverableNetworkWordCount.scala
rename to examples/src/main/scala/org/apache/spark/examples/streaming/RecoverableNetworkWordCount.scala
index 4aacbb199141..b0bc31cc66ab 100644
--- a/examples/src/main/scala/org/apache/spark/streaming/examples/RecoverableNetworkWordCount.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/RecoverableNetworkWordCount.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.streaming.examples
+package org.apache.spark.examples.streaming
 
 import org.apache.spark.streaming.{Time, Seconds, StreamingContext}
 import org.apache.spark.streaming.StreamingContext._
@@ -44,7 +44,7 @@ import java.nio.charset.Charset
  *
  * and run the example as
  *
- *      `$ ./run-example org.apache.spark.streaming.examples.RecoverableNetworkWordCount \
+ *      `$ ./run-example org.apache.spark.examples.streaming.RecoverableNetworkWordCount \
  *              local[2] localhost 9999 ~/checkpoint/ ~/out`
  *
  * If the directory ~/checkpoint/ does not exist (e.g. running for the first time), it will create
@@ -56,7 +56,7 @@ import java.nio.charset.Charset
  *
  *      `$ ./spark-class org.apache.spark.deploy.Client -s launch <cluster-url> \
  *              <path-to-examples-jar> \
- *              org.apache.spark.streaming.examples.RecoverableNetworkWordCount <cluster-url> \
+ *              org.apache.spark.examples.streaming.RecoverableNetworkWordCount <cluster-url> \
  *              localhost 9999 ~/checkpoint ~/out`
  *
  * <path-to-examples-jar> would typically be
diff --git a/examples/src/main/scala/org/apache/spark/streaming/examples/StatefulNetworkWordCount.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/StatefulNetworkWordCount.scala
similarity index 96%
rename from examples/src/main/scala/org/apache/spark/streaming/examples/StatefulNetworkWordCount.scala
rename to examples/src/main/scala/org/apache/spark/examples/streaming/StatefulNetworkWordCount.scala
index ef94c9298dd9..8001d56c98d8 100644
--- a/examples/src/main/scala/org/apache/spark/streaming/examples/StatefulNetworkWordCount.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/StatefulNetworkWordCount.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.streaming.examples
+package org.apache.spark.examples.streaming
 
 import org.apache.spark.streaming._
 import org.apache.spark.streaming.StreamingContext._
@@ -31,7 +31,7 @@ import org.apache.spark.streaming.StreamingContext._
  * To run this on your local machine, you need to first run a Netcat server
  *    `$ nc -lk 9999`
  * and then run the example
- *    `$ ./bin/run-example org.apache.spark.streaming.examples.StatefulNetworkWordCount local[2] localhost 9999`
+ *    `$ ./bin/run-example org.apache.spark.examples.streaming.StatefulNetworkWordCount local[2] localhost 9999`
  */
 // scalastyle:on
 object StatefulNetworkWordCount {
diff --git a/examples/src/main/scala/org/apache/spark/streaming/examples/StreamingExamples.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/StreamingExamples.scala
similarity index 97%
rename from examples/src/main/scala/org/apache/spark/streaming/examples/StreamingExamples.scala
rename to examples/src/main/scala/org/apache/spark/examples/streaming/StreamingExamples.scala
index 99f1502046f5..8396e65d0d58 100644
--- a/examples/src/main/scala/org/apache/spark/streaming/examples/StreamingExamples.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/StreamingExamples.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.streaming.examples
+package org.apache.spark.examples.streaming
 
 import org.apache.spark.Logging
 
diff --git a/examples/src/main/scala/org/apache/spark/streaming/examples/TwitterAlgebirdCMS.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterAlgebirdCMS.scala
similarity index 99%
rename from examples/src/main/scala/org/apache/spark/streaming/examples/TwitterAlgebirdCMS.scala
rename to examples/src/main/scala/org/apache/spark/examples/streaming/TwitterAlgebirdCMS.scala
index c38905e8f366..b12617d88178 100644
--- a/examples/src/main/scala/org/apache/spark/streaming/examples/TwitterAlgebirdCMS.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterAlgebirdCMS.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.streaming.examples
+package org.apache.spark.examples.streaming
 
 import com.twitter.algebird._
 
diff --git a/examples/src/main/scala/org/apache/spark/streaming/examples/TwitterAlgebirdHLL.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterAlgebirdHLL.scala
similarity index 98%
rename from examples/src/main/scala/org/apache/spark/streaming/examples/TwitterAlgebirdHLL.scala
rename to examples/src/main/scala/org/apache/spark/examples/streaming/TwitterAlgebirdHLL.scala
index c067046f9020..22f232c72545 100644
--- a/examples/src/main/scala/org/apache/spark/streaming/examples/TwitterAlgebirdHLL.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterAlgebirdHLL.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.streaming.examples
+package org.apache.spark.examples.streaming
 
 import com.twitter.algebird.HyperLogLogMonoid
 import com.twitter.algebird.HyperLogLog._
diff --git a/examples/src/main/scala/org/apache/spark/streaming/examples/TwitterPopularTags.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterPopularTags.scala
similarity index 98%
rename from examples/src/main/scala/org/apache/spark/streaming/examples/TwitterPopularTags.scala
rename to examples/src/main/scala/org/apache/spark/examples/streaming/TwitterPopularTags.scala
index 2597c8178862..5b58e94600a1 100644
--- a/examples/src/main/scala/org/apache/spark/streaming/examples/TwitterPopularTags.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterPopularTags.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.streaming.examples
+package org.apache.spark.examples.streaming
 
 import org.apache.spark.streaming.{Seconds, StreamingContext}
 import StreamingContext._
diff --git a/examples/src/main/scala/org/apache/spark/streaming/examples/ZeroMQWordCount.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/ZeroMQWordCount.scala
similarity index 95%
rename from examples/src/main/scala/org/apache/spark/streaming/examples/ZeroMQWordCount.scala
rename to examples/src/main/scala/org/apache/spark/examples/streaming/ZeroMQWordCount.scala
index 109ff855b522..de46e5f5b10b 100644
--- a/examples/src/main/scala/org/apache/spark/streaming/examples/ZeroMQWordCount.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/ZeroMQWordCount.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.streaming.examples
+package org.apache.spark.examples.streaming
 
 import akka.actor.ActorSystem
 import akka.actor.actorRef2Scala
@@ -68,9 +68,9 @@ object SimpleZeroMQPublisher {
  *   <zeroMQurl> and <topic> describe where zeroMq publisher is running.
  *
  * To run this example locally, you may run publisher as
- *    `$ ./bin/run-example org.apache.spark.streaming.examples.SimpleZeroMQPublisher tcp://127.0.1.1:1234 foo.bar`
+ *    `$ ./bin/run-example org.apache.spark.examples.streaming.SimpleZeroMQPublisher tcp://127.0.1.1:1234 foo.bar`
  * and run the example as
- *    `$ ./bin/run-example org.apache.spark.streaming.examples.ZeroMQWordCount local[2] tcp://127.0.1.1:1234 foo`
+ *    `$ ./bin/run-example org.apache.spark.examples.streaming.ZeroMQWordCount local[2] tcp://127.0.1.1:1234 foo`
  */
 // scalastyle:on
 object ZeroMQWordCount {
diff --git a/examples/src/main/scala/org/apache/spark/streaming/examples/clickstream/PageViewGenerator.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/clickstream/PageViewGenerator.scala
similarity index 95%
rename from examples/src/main/scala/org/apache/spark/streaming/examples/clickstream/PageViewGenerator.scala
rename to examples/src/main/scala/org/apache/spark/examples/streaming/clickstream/PageViewGenerator.scala
index 251f65fe4df9..97e0cb92078d 100644
--- a/examples/src/main/scala/org/apache/spark/streaming/examples/clickstream/PageViewGenerator.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/clickstream/PageViewGenerator.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.streaming.examples.clickstream
+package org.apache.spark.examples.streaming.clickstream
 
 import java.net.ServerSocket
 import java.io.PrintWriter
@@ -40,8 +40,8 @@ object PageView extends Serializable {
 /** Generates streaming events to simulate page views on a website.
   *
   * This should be used in tandem with PageViewStream.scala. Example:
-  * $ ./bin/run-example org.apache.spark.streaming.examples.clickstream.PageViewGenerator 44444 10
-  * $ ./bin/run-example org.apache.spark.streaming.examples.clickstream.PageViewStream errorRatePerZipCode localhost 44444
+  * $ ./bin/run-example org.apache.spark.examples.streaming.clickstream.PageViewGenerator 44444 10
+  * $ ./bin/run-example org.apache.spark.examples.streaming.clickstream.PageViewStream errorRatePerZipCode localhost 44444
   *
   * When running this, you may want to set the root logging level to ERROR in
   * conf/log4j.properties to reduce the verbosity of the output.
diff --git a/examples/src/main/scala/org/apache/spark/streaming/examples/clickstream/PageViewStream.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/clickstream/PageViewStream.scala
similarity index 94%
rename from examples/src/main/scala/org/apache/spark/streaming/examples/clickstream/PageViewStream.scala
rename to examples/src/main/scala/org/apache/spark/examples/streaming/clickstream/PageViewStream.scala
index 673013f7cf94..d30ceffbe29c 100644
--- a/examples/src/main/scala/org/apache/spark/streaming/examples/clickstream/PageViewStream.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/clickstream/PageViewStream.scala
@@ -15,19 +15,19 @@
  * limitations under the License.
  */
 
-package org.apache.spark.streaming.examples.clickstream
+package org.apache.spark.examples.streaming.clickstream
 
 import org.apache.spark.SparkContext._
 import org.apache.spark.streaming.{Seconds, StreamingContext}
 import org.apache.spark.streaming.StreamingContext._
-import org.apache.spark.streaming.examples.StreamingExamples
+import org.apache.spark.examples.streaming.StreamingExamples
 // scalastyle:off
 /** Analyses a streaming dataset of web page views. This class demonstrates several types of
   * operators available in Spark streaming.
   *
   * This should be used in tandem with PageViewStream.scala. Example:
-  * $ ./bin/run-example org.apache.spark.streaming.examples.clickstream.PageViewGenerator 44444 10
-  * $ ./bin/run-example org.apache.spark.streaming.examples.clickstream.PageViewStream errorRatePerZipCode localhost 44444
+  * $ ./bin/run-example org.apache.spark.examples.streaming.clickstream.PageViewGenerator 44444 10
+  * $ ./bin/run-example org.apache.spark.examples.streaming.clickstream.PageViewStream errorRatePerZipCode localhost 44444
   */
 // scalastyle:on
 object PageViewStream {
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/util/RawTextHelper.scala b/streaming/src/main/scala/org/apache/spark/streaming/util/RawTextHelper.scala
index bbf57ef9275c..a73d6f3bf066 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/util/RawTextHelper.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/util/RawTextHelper.scala
@@ -25,7 +25,7 @@ import scala.collection.JavaConversions.mapAsScalaMap
 private[streaming]
 object RawTextHelper {
 
-  /** 
+  /**
    * Splits lines and counts the words.
    */
   def splitAndCountPartitions(iter: Iterator[String]): Iterator[(String, Long)] = {
@@ -114,4 +114,3 @@ object RawTextHelper {
 
   def max(v1: Long, v2: Long) = math.max(v1, v2)
 }
-

From 25ad8f93012730115a8a1fac649fe3e842c045b3 Mon Sep 17 00:00:00 2001
From: Sean Owen <sowen@cloudera.com>
Date: Tue, 6 May 2014 20:07:22 -0700
Subject: [PATCH 110/810] SPARK-1727. Correct small compile errors, typos, and
 markdown issues in (primarly) MLlib docs

While play-testing the Scala and Java code examples in the MLlib docs, I noticed a number of small compile errors, and some typos. This led to finding and fixing a few similar items in other docs.

Then in the course of building the site docs to check the result, I found a few small suggestions for the build instructions. I also found a few more formatting and markdown issues uncovered when I accidentally used maruku instead of kramdown.

Author: Sean Owen <sowen@cloudera.com>

Closes #653 from srowen/SPARK-1727 and squashes the following commits:

6e7c38a [Sean Owen] Final doc updates - one more compile error, and use of mean instead of sum and count
8f5e847 [Sean Owen] Fix markdown syntax issues that maruku flags, even though we use kramdown (but only those that do not affect kramdown's output)
99966a9 [Sean Owen] Update issue tracker URL in docs
23c9ac3 [Sean Owen] Add Scala Naive Bayes example, to use existing example data file (whose format needed a tweak)
8c81982 [Sean Owen] Fix small compile errors and typos across MLlib docs
---
 docs/README.md                         |  9 ++---
 docs/_config.yml                       |  2 +-
 docs/bagel-programming-guide.md        |  2 +-
 docs/cluster-overview.md               |  2 +-
 docs/configuration.md                  | 10 +++---
 docs/java-programming-guide.md         | 20 +++++------
 docs/mllib-basics.md                   | 14 +++++---
 docs/mllib-clustering.md               |  4 +--
 docs/mllib-collaborative-filtering.md  |  2 +-
 docs/mllib-decision-tree.md            |  8 ++---
 docs/mllib-dimensionality-reduction.md |  7 ++++
 docs/mllib-guide.md                    |  2 +-
 docs/mllib-linear-methods.md           | 13 +++----
 docs/mllib-naive-bayes.md              | 48 +++++++++++++++++---------
 docs/scala-programming-guide.md        |  9 +++--
 docs/sql-programming-guide.md          |  1 +
 mllib/data/sample_naive_bayes_data.txt | 12 +++----
 17 files changed, 97 insertions(+), 68 deletions(-)

diff --git a/docs/README.md b/docs/README.md
index 75b1811ba99a..f1eb644f9340 100644
--- a/docs/README.md
+++ b/docs/README.md
@@ -14,9 +14,10 @@ The markdown code can be compiled to HTML using the
 [Jekyll tool](http://jekyllrb.com).
 To use the `jekyll` command, you will need to have Jekyll installed. 
 The easiest way to do this is via a Ruby Gem, see the 
-[jekyll installation instructions](http://jekyllrb.com/docs/installation). 
-Compiling the site with Jekyll will create a directory called 
-_site containing index.html as well as the rest of the compiled files.
+[jekyll installation instructions](http://jekyllrb.com/docs/installation).
+If not already installed, you need to install `kramdown` with `sudo gem install kramdown`.
+Execute `jekyll` from the `docs/` directory. Compiling the site with Jekyll will create a directory called
+`_site` containing index.html as well as the rest of the compiled files.
 
 You can modify the default Jekyll build as follows:
 
@@ -44,6 +45,6 @@ You can build just the Spark scaladoc by running `sbt/sbt doc` from the SPARK_PR
 
 Similarly, you can build just the PySpark epydoc by running `epydoc --config epydoc.conf` from the SPARK_PROJECT_ROOT/pyspark directory. Documentation is only generated for classes that are listed as public in `__init__.py`.
 
-When you run `jekyll` in the docs directory, it will also copy over the scaladoc for the various Spark subprojects into the docs directory (and then also into the _site directory). We use a jekyll plugin to run `sbt/sbt doc` before building the site so if you haven't run it (recently) it may take some time as it generates all of the scaladoc.  The jekyll plugin also generates the PySpark docs using [epydoc](http://epydoc.sourceforge.net/).
+When you run `jekyll` in the `docs` directory, it will also copy over the scaladoc for the various Spark subprojects into the `docs` directory (and then also into the `_site` directory). We use a jekyll plugin to run `sbt/sbt doc` before building the site so if you haven't run it (recently) it may take some time as it generates all of the scaladoc.  The jekyll plugin also generates the PySpark docs using [epydoc](http://epydoc.sourceforge.net/).
 
 NOTE: To skip the step of building and copying over the Scala and Python API docs, run `SKIP_API=1 jekyll`.
diff --git a/docs/_config.yml b/docs/_config.yml
index d585b8c5ea76..d177e38f884c 100644
--- a/docs/_config.yml
+++ b/docs/_config.yml
@@ -8,5 +8,5 @@ SPARK_VERSION_SHORT: 1.0.0
 SCALA_BINARY_VERSION: "2.10"
 SCALA_VERSION: "2.10.4"
 MESOS_VERSION: 0.13.0
-SPARK_ISSUE_TRACKER_URL: https://spark-project.atlassian.net
+SPARK_ISSUE_TRACKER_URL: https://issues.apache.org/jira/browse/SPARK
 SPARK_GITHUB_URL: https://github.com/apache/spark
diff --git a/docs/bagel-programming-guide.md b/docs/bagel-programming-guide.md
index da6d0c9dcd97..14f43cb6d394 100644
--- a/docs/bagel-programming-guide.md
+++ b/docs/bagel-programming-guide.md
@@ -46,7 +46,7 @@ import org.apache.spark.bagel.Bagel._
 Next, we load a sample graph from a text file as a distributed dataset and package it into `PRVertex` objects. We also cache the distributed dataset because Bagel will use it multiple times and we'd like to avoid recomputing it.
 
 {% highlight scala %}
-val input = sc.textFile("pagerank_data.txt")
+val input = sc.textFile("data/pagerank_data.txt")
 
 val numVerts = input.count()
 
diff --git a/docs/cluster-overview.md b/docs/cluster-overview.md
index 79b0061e2c5a..162c415b5883 100644
--- a/docs/cluster-overview.md
+++ b/docs/cluster-overview.md
@@ -181,7 +181,7 @@ The following table summarizes terms you'll see used to refer to cluster concept
       <td>Distinguishes where the driver process runs. In "cluster" mode, the framework launches
         the driver inside of the cluster. In "client" mode, the submitter launches the driver
         outside of the cluster.</td>
-    <tr>
+    </tr>
     <tr>
       <td>Worker node</td>
       <td>Any node that can run application code in the cluster</td>
diff --git a/docs/configuration.md b/docs/configuration.md
index d6f316ba5f77..5b034e3cb3d4 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -26,10 +26,10 @@ application name), as well as arbitrary key-value pairs through the `set()` meth
 initialize an application as follows:
 
 {% highlight scala %}
-val conf = new SparkConf()
-             .setMaster("local")
-             .setAppName("My application")
-             .set("spark.executor.memory", "1g")
+val conf = new SparkConf().
+             setMaster("local").
+             setAppName("My application").
+             set("spark.executor.memory", "1g")
 val sc = new SparkContext(conf)
 {% endhighlight %}
 
@@ -318,7 +318,7 @@ Apart from these, the following properties are also available, and may be useful
     When serializing using org.apache.spark.serializer.JavaSerializer, the serializer caches
     objects to prevent writing redundant data, however that stops garbage collection of those
     objects. By calling 'reset' you flush that info from the serializer, and allow old
-    objects to be collected. To turn off this periodic reset set it to a value of <= 0.
+    objects to be collected. To turn off this periodic reset set it to a value &lt;= 0.
     By default it will reset the serializer every 10,000 objects.
   </td>
 </tr>
diff --git a/docs/java-programming-guide.md b/docs/java-programming-guide.md
index 07c8512bf929..c34eb28fc06a 100644
--- a/docs/java-programming-guide.md
+++ b/docs/java-programming-guide.md
@@ -55,7 +55,7 @@ classes.  RDD methods like `map` are overloaded by specialized `PairFunction`
 and `DoubleFunction` classes, allowing them to return RDDs of the appropriate
 types.  Common methods like `filter` and `sample` are implemented by
 each specialized RDD class, so filtering a `PairRDD` returns a new `PairRDD`,
-etc (this acheives the "same-result-type" principle used by the [Scala collections
+etc (this achieves the "same-result-type" principle used by the [Scala collections
 framework](http://docs.scala-lang.org/overviews/core/architecture-of-scala-collections.html)).
 
 ## Function Interfaces
@@ -102,7 +102,7 @@ the following changes:
   `Function` classes will need to use `implements` rather than `extends`.
 * Certain transformation functions now have multiple versions depending
   on the return type. In Spark core, the map functions (`map`, `flatMap`, and
-  `mapPartitons`) have type-specific versions, e.g. 
+  `mapPartitions`) have type-specific versions, e.g.
   [`mapToPair`](api/java/org/apache/spark/api/java/JavaRDDLike.html#mapToPair(org.apache.spark.api.java.function.PairFunction))
   and [`mapToDouble`](api/java/org/apache/spark/api/java/JavaRDDLike.html#mapToDouble(org.apache.spark.api.java.function.DoubleFunction)).
   Spark Streaming also uses the same approach, e.g. [`transformToPair`](api/java/org/apache/spark/streaming/api/java/JavaDStreamLike.html#transformToPair(org.apache.spark.api.java.function.Function)).
@@ -115,11 +115,11 @@ As an example, we will implement word count using the Java API.
 import org.apache.spark.api.java.*;
 import org.apache.spark.api.java.function.*;
 
-JavaSparkContext sc = new JavaSparkContext(...);
-JavaRDD<String> lines = ctx.textFile("hdfs://...");
+JavaSparkContext jsc = new JavaSparkContext(...);
+JavaRDD<String> lines = jsc.textFile("hdfs://...");
 JavaRDD<String> words = lines.flatMap(
   new FlatMapFunction<String, String>() {
-    public Iterable<String> call(String s) {
+    @Override public Iterable<String> call(String s) {
       return Arrays.asList(s.split(" "));
     }
   }
@@ -140,10 +140,10 @@ Here, the `FlatMapFunction` was created inline; another option is to subclass
 
 {% highlight java %}
 class Split extends FlatMapFunction<String, String> {
-  public Iterable<String> call(String s) {
+  @Override public Iterable<String> call(String s) {
     return Arrays.asList(s.split(" "));
   }
-);
+}
 JavaRDD<String> words = lines.flatMap(new Split());
 {% endhighlight %}
 
@@ -162,8 +162,8 @@ Continuing with the word count example, we map each word to a `(word, 1)` pair:
 import scala.Tuple2;
 JavaPairRDD<String, Integer> ones = words.mapToPair(
   new PairFunction<String, String, Integer>() {
-    public Tuple2<String, Integer> call(String s) {
-      return new Tuple2(s, 1);
+    @Override public Tuple2<String, Integer> call(String s) {
+      return new Tuple2<String, Integer>(s, 1);
     }
   }
 );
@@ -178,7 +178,7 @@ occurrences of each word:
 {% highlight java %}
 JavaPairRDD<String, Integer> counts = ones.reduceByKey(
   new Function2<Integer, Integer, Integer>() {
-    public Integer call(Integer i1, Integer i2) {
+    @Override public Integer call(Integer i1, Integer i2) {
       return i1 + i2;
     }
   }
diff --git a/docs/mllib-basics.md b/docs/mllib-basics.md
index 710ce1721fe2..704308802d65 100644
--- a/docs/mllib-basics.md
+++ b/docs/mllib-basics.md
@@ -9,7 +9,7 @@ title: <a href="mllib-guide.html">MLlib</a> - Basics
 MLlib supports local vectors and matrices stored on a single machine, 
 as well as distributed matrices backed by one or more RDDs.
 In the current implementation, local vectors and matrices are simple data models 
-to serve public interfaces. The underly linear algebra operations are provided by
+to serve public interfaces. The underlying linear algebra operations are provided by
 [Breeze](http://www.scalanlp.org/) and [jblas](http://jblas.org/).
 A training example used in supervised learning is called "labeled point" in MLlib.
 
@@ -205,7 +205,7 @@ import org.apache.spark.mllib.regression.LabeledPoint;
 import org.apache.spark.mllib.util.MLUtils;
 import org.apache.spark.rdd.RDDimport;
 
-RDD[LabeledPoint] training = MLUtils.loadLibSVMData(sc, "mllib/data/sample_libsvm_data.txt")
+RDD<LabeledPoint> training = MLUtils.loadLibSVMData(jsc, "mllib/data/sample_libsvm_data.txt");
 {% endhighlight %}
 </div>
 </div>
@@ -307,6 +307,7 @@ A [`RowMatrix`](api/mllib/index.html#org.apache.spark.mllib.linalg.distributed.R
 created from a `JavaRDD<Vector>` instance.  Then we can compute its column summary statistics.
 
 {% highlight java %}
+import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.mllib.linalg.Vector;
 import org.apache.spark.mllib.linalg.distributed.RowMatrix;
 
@@ -348,10 +349,10 @@ val mat: RowMatrix = ... // a RowMatrix
 val summary: MultivariateStatisticalSummary = mat.computeColumnSummaryStatistics()
 println(summary.mean) // a dense vector containing the mean value for each column
 println(summary.variance) // column-wise variance
-println(summary.numNonzers) // number of nonzeros in each column
+println(summary.numNonzeros) // number of nonzeros in each column
 
 // Compute the covariance matrix.
-val Cov: Matrix = mat.computeCovariance()
+val cov: Matrix = mat.computeCovariance()
 {% endhighlight %}
 </div>
 </div>
@@ -397,11 +398,12 @@ wrapper over `(long, Vector)`.  An `IndexedRowMatrix` can be converted to a `Row
 its row indices.
 
 {% highlight java %}
+import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.mllib.linalg.distributed.IndexedRow;
 import org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix;
 import org.apache.spark.mllib.linalg.distributed.RowMatrix;
 
-JavaRDD[IndexedRow] rows = ... // a JavaRDD of indexed rows
+JavaRDD<IndexedRow> rows = ... // a JavaRDD of indexed rows
 // Create an IndexedRowMatrix from a JavaRDD<IndexedRow>.
 IndexedRowMatrix mat = new IndexedRowMatrix(rows.rdd());
 
@@ -458,7 +460,9 @@ wrapper over `(long, long, double)`.  A `CoordinateMatrix` can be converted to a
 with sparse rows by calling `toIndexedRowMatrix`.
 
 {% highlight scala %}
+import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.mllib.linalg.distributed.CoordinateMatrix;
+import org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix;
 import org.apache.spark.mllib.linalg.distributed.MatrixEntry;
 
 JavaRDD<MatrixEntry> entries = ... // a JavaRDD of matrix entries
diff --git a/docs/mllib-clustering.md b/docs/mllib-clustering.md
index b3293afe408d..276868fa8490 100644
--- a/docs/mllib-clustering.md
+++ b/docs/mllib-clustering.md
@@ -18,7 +18,7 @@ models are trained for each cluster).
 MLlib supports
 [k-means](http://en.wikipedia.org/wiki/K-means_clustering) clustering, one of
 the most commonly used clustering algorithms that clusters the data points into
-predfined number of clusters. The MLlib implementation includes a parallelized
+predefined number of clusters. The MLlib implementation includes a parallelized
 variant of the [k-means++](http://en.wikipedia.org/wiki/K-means%2B%2B) method
 called [kmeans||](http://theory.stanford.edu/~sergei/papers/vldb12-kmpar.pdf).
 The implementation in MLlib has the following parameters:  
@@ -30,7 +30,7 @@ initialization via k-means\|\|.
 * *runs* is the number of times to run the k-means algorithm (k-means is not
 guaranteed to find a globally optimal solution, and when run multiple times on
 a given dataset, the algorithm returns the best clustering result).
-* *initializiationSteps* determines the number of steps in the k-means\|\| algorithm.
+* *initializationSteps* determines the number of steps in the k-means\|\| algorithm.
 * *epsilon* determines the distance threshold within which we consider k-means to have converged. 
 
 ## Examples
diff --git a/docs/mllib-collaborative-filtering.md b/docs/mllib-collaborative-filtering.md
index 79f5e3a7ca4f..f486c56e5590 100644
--- a/docs/mllib-collaborative-filtering.md
+++ b/docs/mllib-collaborative-filtering.md
@@ -77,7 +77,7 @@ val ratesAndPreds = ratings.map{
 }.join(predictions)
 val MSE = ratesAndPreds.map{
     case ((user, product), (r1, r2)) =>  math.pow((r1- r2), 2)
-}.reduce(_ + _)/ratesAndPreds.count
+}.mean()
 println("Mean Squared Error = " + MSE)
 {% endhighlight %}
 
diff --git a/docs/mllib-decision-tree.md b/docs/mllib-decision-tree.md
index 069376699073..296277e58b34 100644
--- a/docs/mllib-decision-tree.md
+++ b/docs/mllib-decision-tree.md
@@ -83,19 +83,19 @@ Section 9.2.4 in
 [Elements of Statistical Machine Learning](http://statweb.stanford.edu/~tibs/ElemStatLearn/) for
 details). For example, for a binary classification problem with one categorical feature with three
 categories A, B and C with corresponding proportion of label 1 as 0.2, 0.6 and 0.4, the categorical
-features are orded as A followed by C followed B or A, B, C. The two split candidates are A \| C, B
+features are ordered as A followed by C followed B or A, B, C. The two split candidates are A \| C, B
 and A , B \| C where \| denotes the split.
 
 ### Stopping rule
 
 The recursive tree construction is stopped at a node when one of the two conditions is met:
 
-1. The node depth is equal to the `maxDepth` training parammeter
+1. The node depth is equal to the `maxDepth` training parameter
 2. No split candidate leads to an information gain at the node.
 
 ### Practical limitations
 
-1. The tree implementation stores an Array[Double] of size *O(#features \* #splits \* 2^maxDepth)*
+1. The tree implementation stores an `Array[Double]` of size *O(#features \* #splits \* 2^maxDepth)*
    in memory for aggregating histograms over partitions. The current implementation might not scale
    to very deep trees since the memory requirement grows exponentially with tree depth.
 2. The implemented algorithm reads both sparse and dense data. However, it is not optimized for
@@ -178,7 +178,7 @@ val valuesAndPreds = parsedData.map { point =>
   val prediction = model.predict(point.features)
   (point.label, prediction)
 }
-val MSE = valuesAndPreds.map{ case(v, p) => math.pow((v - p), 2)}.reduce(_ + _)/valuesAndPreds.count
+val MSE = valuesAndPreds.map{ case(v, p) => math.pow((v - p), 2)}.mean()
 println("training Mean Squared Error = " + MSE)
 {% endhighlight %}
 </div>
diff --git a/docs/mllib-dimensionality-reduction.md b/docs/mllib-dimensionality-reduction.md
index 4e9ecf7c006f..ab24663cfe25 100644
--- a/docs/mllib-dimensionality-reduction.md
+++ b/docs/mllib-dimensionality-reduction.md
@@ -44,6 +44,10 @@ say, less than $1000$, but many rows, which we call *tall-and-skinny*.
 <div class="codetabs">
 <div data-lang="scala" markdown="1">
 {% highlight scala %}
+import org.apache.spark.mllib.linalg.Matrix
+import org.apache.spark.mllib.linalg.distributed.RowMatrix
+import org.apache.spark.mllib.linalg.SingularValueDecomposition
+
 val mat: RowMatrix = ...
 
 // Compute the top 20 singular values and corresponding singular vectors.
@@ -74,6 +78,9 @@ and use them to project the vectors into a low-dimensional space.
 The number of columns should be small, e.g, less than 1000.
 
 {% highlight scala %}
+import org.apache.spark.mllib.linalg.Matrix
+import org.apache.spark.mllib.linalg.distributed.RowMatrix
+
 val mat: RowMatrix = ...
 
 // Compute the top 10 principal components.
diff --git a/docs/mllib-guide.md b/docs/mllib-guide.md
index c49f857d0755..842ca5c8c6d8 100644
--- a/docs/mllib-guide.md
+++ b/docs/mllib-guide.md
@@ -94,7 +94,7 @@ import org.apache.spark.mllib.linalg.Vector;
 import org.apache.spark.mllib.linalg.Vectors;
 
 double[] array = ... // a double array
-Vector vector = Vectors.dense(array) // a dense vector
+Vector vector = Vectors.dense(array); // a dense vector
 {% endhighlight %}
 
 [`Vectors`](api/mllib/index.html#org.apache.spark.mllib.linalg.Vectors$) provides factory methods to
diff --git a/docs/mllib-linear-methods.md b/docs/mllib-linear-methods.md
index ebb555f974bf..40b7a7f80708 100644
--- a/docs/mllib-linear-methods.md
+++ b/docs/mllib-linear-methods.md
@@ -63,7 +63,7 @@ methods MLlib supports:
   <tbody>
     <tr>
       <td>hinge loss</td><td>$\max \{0, 1-y \wv^T \x \}, \quad y \in \{-1, +1\}$</td>
-      <td>$\begin{cases}-y \cdot \x & \text{if $y \wv^T \x <1$}, \\ 0 &
+      <td>$\begin{cases}-y \cdot \x &amp; \text{if $y \wv^T \x &lt;1$}, \\ 0 &amp;
 \text{otherwise}.\end{cases}$</td>
     </tr>
     <tr>
@@ -225,10 +225,11 @@ algorithm for 200 iterations.
 import org.apache.spark.mllib.optimization.L1Updater
 
 val svmAlg = new SVMWithSGD()
-svmAlg.optimizer.setNumIterations(200)
-  .setRegParam(0.1)
-  .setUpdater(new L1Updater)
-val modelL1 = svmAlg.run(parsedData)
+svmAlg.optimizer.
+  setNumIterations(200).
+  setRegParam(0.1).
+  setUpdater(new L1Updater)
+val modelL1 = svmAlg.run(training)
 {% endhighlight %}
 
 Similarly, you can use replace `SVMWithSGD` by
@@ -322,7 +323,7 @@ val valuesAndPreds = parsedData.map { point =>
   val prediction = model.predict(point.features)
   (point.label, prediction)
 }
-val MSE = valuesAndPreds.map{case(v, p) => math.pow((v - p), 2)}.reduce(_ + _) / valuesAndPreds.count
+val MSE = valuesAndPreds.map{case(v, p) => math.pow((v - p), 2)}.mean()
 println("training Mean Squared Error = " + MSE)
 {% endhighlight %}
 
diff --git a/docs/mllib-naive-bayes.md b/docs/mllib-naive-bayes.md
index 6160fe5b2fe8..c47508b7daa2 100644
--- a/docs/mllib-naive-bayes.md
+++ b/docs/mllib-naive-bayes.md
@@ -7,13 +7,13 @@ Naive Bayes is a simple multiclass classification algorithm with the assumption
 between every pair of features. Naive Bayes can be trained very efficiently. Within a single pass to
 the training data, it computes the conditional probability distribution of each feature given label,
 and then it applies Bayes' theorem to compute the conditional probability distribution of label
-given an observation and use it for prediction. For more details, please visit the wikipedia page
+given an observation and use it for prediction. For more details, please visit the Wikipedia page
 [Naive Bayes classifier](http://en.wikipedia.org/wiki/Naive_Bayes_classifier).
 
 In MLlib, we implemented multinomial naive Bayes, which is typically used for document
 classification. Within that context, each observation is a document, each feature represents a term,
-whose value is the frequency of the term. For its formulation, please visit the wikipedia page
-[Multinomial naive Bayes](http://en.wikipedia.org/wiki/Naive_Bayes_classifier#Multinomial_naive_Bayes)
+whose value is the frequency of the term. For its formulation, please visit the Wikipedia page
+[Multinomial Naive Bayes](http://en.wikipedia.org/wiki/Naive_Bayes_classifier#Multinomial_naive_Bayes)
 or the section
 [Naive Bayes text classification](http://nlp.stanford.edu/IR-book/html/htmledition/naive-bayes-text-classification-1.html)
 from the book Introduction to Information
@@ -36,9 +36,18 @@ can be used for evaluation and prediction.
 
 {% highlight scala %}
 import org.apache.spark.mllib.classification.NaiveBayes
-
-val training: RDD[LabeledPoint] = ... // training set
-val test: RDD[LabeledPoint] = ... // test set
+import org.apache.spark.mllib.linalg.Vectors
+import org.apache.spark.mllib.regression.LabeledPoint
+
+val data = sc.textFile("mllib/data/sample_naive_bayes_data.txt")
+val parsedData = data.map { line =>
+  val parts = line.split(',')
+  LabeledPoint(parts(0).toDouble, Vectors.dense(parts(1).split(' ').map(_.toDouble)))
+}
+// Split data into training (60%) and test (40%).
+val splits = parsedData.randomSplit(Array(0.6, 0.4), seed = 11L)
+val training = splits(0)
+val test = splits(1)
 
 val model = NaiveBayes.train(training, lambda = 1.0)
 val prediction = model.predict(test.map(_.features))
@@ -58,29 +67,36 @@ optionally smoothing parameter `lambda` as input, and output a
 can be used for evaluation and prediction.
 
 {% highlight java %}
+import org.apache.spark.api.java.JavaPairRDD;
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.api.java.function.Function;
 import org.apache.spark.mllib.classification.NaiveBayes;
+import org.apache.spark.mllib.classification.NaiveBayesModel;
+import org.apache.spark.mllib.regression.LabeledPoint;
+import scala.Tuple2;
 
 JavaRDD<LabeledPoint> training = ... // training set
 JavaRDD<LabeledPoint> test = ... // test set
 
-NaiveBayesModel model = NaiveBayes.train(training.rdd(), 1.0);
+final NaiveBayesModel model = NaiveBayes.train(training.rdd(), 1.0);
 
-JavaRDD<Double> prediction = model.predict(test.map(new Function<LabeledPoint, Vector>() {
-    public Vector call(LabeledPoint p) {
-      return p.features();
+JavaRDD<Double> prediction =
+  test.map(new Function<LabeledPoint, Double>() {
+    @Override public Double call(LabeledPoint p) {
+      return model.predict(p.features());
     }
-  })
+  });
 JavaPairRDD<Double, Double> predictionAndLabel = 
   prediction.zip(test.map(new Function<LabeledPoint, Double>() {
-    public Double call(LabeledPoint p) {
+    @Override public Double call(LabeledPoint p) {
       return p.label();
     }
-  })
+  }));
 double accuracy = 1.0 * predictionAndLabel.filter(new Function<Tuple2<Double, Double>, Boolean>() {
-    public Boolean call(Tuple2<Double, Double> pl) {
+    @Override public Boolean call(Tuple2<Double, Double> pl) {
       return pl._1() == pl._2();
     }
-  }).count() / test.count()
+  }).count() / test.count();
 {% endhighlight %}
 </div>
 
@@ -93,7 +109,7 @@ smoothing parameter `lambda` as input, and output a
 [NaiveBayesModel](api/pyspark/pyspark.mllib.classification.NaiveBayesModel-class.html), which can be
 used for evaluation and prediction.
 
-<!--- TODO: Make Python's example consistent with Scala's and Java's. --->
+<!-- TODO: Make Python's example consistent with Scala's and Java's. -->
 {% highlight python %}
 from pyspark.mllib.regression import LabeledPoint
 from pyspark.mllib.classification import NaiveBayes
diff --git a/docs/scala-programming-guide.md b/docs/scala-programming-guide.md
index b8d89cf00ffb..e7ceaa22c330 100644
--- a/docs/scala-programming-guide.md
+++ b/docs/scala-programming-guide.md
@@ -48,12 +48,12 @@ how to access a cluster. To create a `SparkContext` you first need to build a `S
 that contains information about your application.
 
 {% highlight scala %}
-val conf = new SparkConf().setAppName(<app name>).setMaster(<master>)
+val conf = new SparkConf().setAppName(appName).setMaster(master)
 new SparkContext(conf)
 {% endhighlight %}
 
-The `<master>` parameter is a string specifying a [Spark, Mesos or YARN cluster URL](#master-urls)
-to connect to, or a special "local" string to run in local mode, as described below. `<app name>` is
+The `master` parameter is a string specifying a [Spark, Mesos or YARN cluster URL](#master-urls)
+to connect to, or a special "local" string to run in local mode, as described below. `appName` is
 a name for your application, which will be shown in the cluster web UI. It's also possible to set
 these variables [using a configuration file](cluster-overview.html#loading-configurations-from-a-file)
 which avoids hard-coding the master name in your application.
@@ -81,9 +81,8 @@ The master URL passed to Spark can be in one of the following formats:
 <table class="table">
 <tr><th>Master URL</th><th>Meaning</th></tr>
 <tr><td> local </td><td> Run Spark locally with one worker thread (i.e. no parallelism at all). </td></tr>
-<tr><td> local[K] </td><td> Run Spark locally with K worker threads (ideally, set this to the number of cores on your machine).
+<tr><td> local[K] </td><td> Run Spark locally with K worker threads (ideally, set this to the number of cores on your machine). </td></tr>
 <tr><td> local[*] </td><td> Run Spark locally with as many worker threads as logical cores on your machine.</td></tr>
-</td></tr>
 <tr><td> spark://HOST:PORT </td><td> Connect to the given <a href="spark-standalone.html">Spark standalone
         cluster</a> master. The port must be whichever one your master is configured to use, which is 7077 by default.
 </td></tr>
diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md
index 0c743c9d6010..8a785450adfd 100644
--- a/docs/sql-programming-guide.md
+++ b/docs/sql-programming-guide.md
@@ -416,3 +416,4 @@ results = hiveCtx.hql("FROM src SELECT key, value").collect()
 {% endhighlight %}
 
 </div>
+</div>
diff --git a/mllib/data/sample_naive_bayes_data.txt b/mllib/data/sample_naive_bayes_data.txt
index f874adbaf466..981da382d6ac 100644
--- a/mllib/data/sample_naive_bayes_data.txt
+++ b/mllib/data/sample_naive_bayes_data.txt
@@ -1,6 +1,6 @@
-0, 1 0 0
-0, 2 0 0
-1, 0 1 0
-1, 0 2 0
-2, 0 0 1
-2, 0 0 2
+0,1 0 0
+0,2 0 0
+1,0 1 0
+1,0 2 0
+2,0 0 1
+2,0 0 2

From 48ba3b8cdc3bdc7c67bc465d1f047fa3f44d7085 Mon Sep 17 00:00:00 2001
From: Ethan Jewett <esjewett@gmail.com>
Date: Tue, 6 May 2014 20:50:08 -0700
Subject: [PATCH 111/810] Proposal: clarify Scala programming guide on caching
 ...

... with regards to saved map output. Wording taken partially from Matei Zaharia's email to the Spark user list. http://apache-spark-user-list.1001560.n3.nabble.com/performance-improvement-on-second-operation-without-caching-td5227.html

Author: Ethan Jewett <esjewett@gmail.com>

Closes #668 from esjewett/Doc-update and squashes the following commits:

11793ce [Ethan Jewett] Update based on feedback
171e670 [Ethan Jewett] Clarify Scala programming guide on caching ...
---
 docs/scala-programming-guide.md | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/docs/scala-programming-guide.md b/docs/scala-programming-guide.md
index e7ceaa22c330..f25e9cca8852 100644
--- a/docs/scala-programming-guide.md
+++ b/docs/scala-programming-guide.md
@@ -145,7 +145,7 @@ RDDs support two types of operations: *transformations*, which create a new data
 
 All transformations in Spark are <i>lazy</i>, in that they do not compute their results right away. Instead, they just remember the transformations applied to some base dataset (e.g. a file). The transformations are only computed when an action requires a result to be returned to the driver program. This design enables Spark to run more efficiently -- for example, we can realize that a dataset created through `map` will be used in a `reduce` and return only the result of the `reduce` to the driver, rather than the larger mapped dataset.
 
-By default, each transformed RDD is recomputed each time you run an action on it. However, you may also *persist* an RDD in memory using the `persist` (or `cache`) method, in which case Spark will keep the elements around on the cluster for much faster access the next time you query it. There is also support for persisting datasets on disk, or replicated across the cluster. The next section in this document describes these options.
+By default, each transformed RDD may be recomputed each time you run an action on it. However, you may also *persist* an RDD in memory using the `persist` (or `cache`) method, in which case Spark will keep the elements around on the cluster for much faster access the next time you query it. There is also support for persisting datasets on disk, or replicated across the cluster. The next section in this document describes these options.
 
 The following tables list the transformations and actions currently supported (see also the [RDD API doc](api/scala/index.html#org.apache.spark.rdd.RDD) for details):
 
@@ -279,8 +279,8 @@ it is computed in an action, it will be kept in memory on the nodes. The cache i
 if any partition of an RDD is lost, it will automatically be recomputed using the transformations
 that originally created it.
 
-In addition, each RDD can be stored using a different *storage level*, allowing you, for example, to
-persist the dataset on disk, or persist it in memory but as serialized Java objects (to save space),
+In addition, each persisted RDD can be stored using a different *storage level*, allowing you, for example,
+to persist the dataset on disk, or persist it in memory but as serialized Java objects (to save space),
 or replicate it across nodes, or store the data in off-heap memory in [Tachyon](http://tachyon-project.org/).
 These levels are chosen by passing a
 [`org.apache.spark.storage.StorageLevel`](api/scala/index.html#org.apache.spark.storage.StorageLevel)
@@ -330,6 +330,8 @@ available storage levels is:
 </tr>
 </table>
 
+Spark sometimes automatically persists intermediate state from RDD operations, even without users calling persist() or cache(). In particular, if a shuffle happens when computing an RDD, Spark will keep the outputs from the map side of the shuffle on disk to avoid re-computing the entire dependency graph if an RDD is re-used. We still recommend users call persist() if they plan to re-use an RDD iteratively.
+
 ### Which Storage Level to Choose?
 
 Spark's storage levels are meant to provide different trade-offs between memory usage and CPU

From fdae095de2daa1fc3b343c05e515235756d856a4 Mon Sep 17 00:00:00 2001
From: Sandeep <sandeep@techaddict.me>
Date: Tue, 6 May 2014 21:55:05 -0700
Subject: [PATCH 112/810] [HOTFIX] SPARK-1637: There are some Streaming
 examples added after the PR #571 was last updated.

This resulted in Compilation Errors.
cc @mateiz project not compiling currently.

Author: Sandeep <sandeep@techaddict.me>

Closes #673 from techaddict/SPARK-1637-HOTFIX and squashes the following commits:

b512f4f [Sandeep] [SPARK-1637][HOTFIX] There are some Streaming examples added after the PR #571 was last updated. This resulted in Compilation Errors.
---
 .../examples => examples/streaming}/JavaCustomReceiver.java | 6 ++----
 .../examples => examples/streaming}/CustomReceiver.scala    | 4 ++--
 2 files changed, 4 insertions(+), 6 deletions(-)
 rename examples/src/main/java/org/apache/spark/{streaming/examples => examples/streaming}/JavaCustomReceiver.java (98%)
 rename examples/src/main/scala/org/apache/spark/{streaming/examples => examples/streaming}/CustomReceiver.scala (97%)

diff --git a/examples/src/main/java/org/apache/spark/streaming/examples/JavaCustomReceiver.java b/examples/src/main/java/org/apache/spark/examples/streaming/JavaCustomReceiver.java
similarity index 98%
rename from examples/src/main/java/org/apache/spark/streaming/examples/JavaCustomReceiver.java
rename to examples/src/main/java/org/apache/spark/examples/streaming/JavaCustomReceiver.java
index e36c7800be23..7f558f3ee713 100644
--- a/examples/src/main/java/org/apache/spark/streaming/examples/JavaCustomReceiver.java
+++ b/examples/src/main/java/org/apache/spark/examples/streaming/JavaCustomReceiver.java
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.streaming.examples;
+package org.apache.spark.examples.streaming;
 
 import com.google.common.collect.Lists;
 
@@ -48,7 +48,7 @@
  * To run this on your local machine, you need to first run a Netcat server
  *    `$ nc -lk 9999`
  * and then run the example
- *    `$ ./run org.apache.spark.streaming.examples.JavaCustomReceiver local[2] localhost 9999`
+ *    `$ ./run org.apache.spark.examples.streaming.JavaCustomReceiver local[2] localhost 9999`
  */
 
 public class JavaCustomReceiver extends Receiver<String> {
@@ -149,5 +149,3 @@ private void receive() {
     }
   }
 }
-
-
diff --git a/examples/src/main/scala/org/apache/spark/streaming/examples/CustomReceiver.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/CustomReceiver.scala
similarity index 97%
rename from examples/src/main/scala/org/apache/spark/streaming/examples/CustomReceiver.scala
rename to examples/src/main/scala/org/apache/spark/examples/streaming/CustomReceiver.scala
index eebffd824983..e317e2d36ae4 100644
--- a/examples/src/main/scala/org/apache/spark/streaming/examples/CustomReceiver.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/CustomReceiver.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.streaming.examples
+package org.apache.spark.examples.streaming
 
 import java.io.{InputStreamReader, BufferedReader, InputStream}
 import java.net.Socket
@@ -37,7 +37,7 @@ import org.apache.spark.streaming.receiver.Receiver
  * To run this on your local machine, you need to first run a Netcat server
  *    `$ nc -lk 9999`
  * and then run the example
- *    `$ ./run org.apache.spark.streaming.examples.CustomReceiver local[2] localhost 9999`
+ *    `$ ./run org.apache.spark.examples.streaming.CustomReceiver local[2] localhost 9999`
  */
 object CustomReceiver {
   def main(args: Array[String]) {

From 913a0a9c0a87e164723ebf9616b883b6329bac71 Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Wed, 7 May 2014 00:11:05 -0700
Subject: [PATCH 113/810] SPARK-1746: Support setting SPARK_JAVA_OPTS on
 executors for backwards compatibility

Author: Patrick Wendell <pwendell@gmail.com>

Closes #676 from pwendell/worker-opts and squashes the following commits:

54456c4 [Patrick Wendell] SPARK-1746: Support setting SPARK_JAVA_OPTS on executors for backwards compatibility
---
 .../org/apache/spark/deploy/worker/CommandUtils.scala | 11 +++++++++--
 1 file changed, 9 insertions(+), 2 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/CommandUtils.scala b/core/src/main/scala/org/apache/spark/deploy/worker/CommandUtils.scala
index 3e615e753b34..c7f0f244ea2f 100644
--- a/core/src/main/scala/org/apache/spark/deploy/worker/CommandUtils.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/worker/CommandUtils.scala
@@ -47,9 +47,16 @@ object CommandUtils extends Logging {
    */
   def buildJavaOpts(command: Command, memory: Int, sparkHome: String): Seq[String] = {
     val memoryOpts = Seq(s"-Xms${memory}M", s"-Xmx${memory}M")
-    // Note, this will coalesce multiple options into a single command component
     val extraOpts = command.extraJavaOptions.map(Utils.splitCommandString).getOrElse(Seq())
 
+    // Exists for backwards compatibility with older Spark versions
+    val workerLocalOpts = Option(getenv("SPARK_JAVA_OPTS")).map(Utils.splitCommandString)
+      .getOrElse(Nil)
+    if (workerLocalOpts.length > 0) {
+      logWarning("SPARK_JAVA_OPTS was set on the worker. It is deprecated in Spark 1.0.")
+      logWarning("Set SPARK_LOCAL_DIRS for node-specific storage locations.")
+    }
+
     val libraryOpts =
       if (command.libraryPathEntries.size > 0) {
         val joined = command.libraryPathEntries.mkString(File.pathSeparator)
@@ -66,7 +73,7 @@ object CommandUtils extends Logging {
     val userClassPath = command.classPathEntries ++ Seq(classPath)
 
     Seq("-cp", userClassPath.filterNot(_.isEmpty).mkString(File.pathSeparator)) ++
-      libraryOpts ++ extraOpts ++ memoryOpts
+      libraryOpts ++ extraOpts ++ workerLocalOpts ++ memoryOpts
   }
 
   /** Spawn a thread that will redirect a given stream to a file */

From 3eb53bd59e828275471d41730e6de601a887416d Mon Sep 17 00:00:00 2001
From: Cheng Hao <hao.cheng@intel.com>
Date: Wed, 7 May 2014 03:37:12 -0400
Subject: [PATCH 114/810] [WIP][Spark-SQL] Optimize the Constant Folding for
 Expression

Currently, expression does not support the "constant null" well in constant folding.
e.g. Sum(a, 0) actually always produces Literal(0, NumericType) in runtime.

For example:
```
explain select isnull(key+null)  from src;
== Logical Plan ==
Project [HiveGenericUdf#isnull((key#30 + CAST(null, IntegerType))) AS c_0#28]
 MetastoreRelation default, src, None

== Optimized Logical Plan ==
Project [true AS c_0#28]
 MetastoreRelation default, src, None

== Physical Plan ==
Project [true AS c_0#28]
 HiveTableScan [], (MetastoreRelation default, src, None), None
```

I've create a new Optimization rule called NullPropagation for such kind of constant folding.

Author: Cheng Hao <hao.cheng@intel.com>
Author: Michael Armbrust <michael@databricks.com>

Closes #482 from chenghao-intel/optimize_constant_folding and squashes the following commits:

2f14b50 [Cheng Hao] Fix code style issues
68b9fad [Cheng Hao] Remove the Literal pattern matching for NullPropagation
29c8166 [Cheng Hao] Update the code for feedback of code review
50444cc [Cheng Hao] Remove the unnecessary null checking
80f9f18 [Cheng Hao] Update the UnitTest for aggregation constant folding
27ea3d7 [Cheng Hao] Fix Constant Folding Bugs & Add More Unittests
b28e03a [Cheng Hao] Merge pull request #1 from marmbrus/pr/482
9ccefdb [Michael Armbrust] Add tests for optimized expression evaluation.
543ef9d [Cheng Hao] fix code style issues
9cf0396 [Cheng Hao] update code according to the code review comment
536c005 [Cheng Hao] Add Exceptional case for constant folding
3c045c7 [Cheng Hao] Optimize the Constant Folding by adding more rules
2645d4f [Cheng Hao] Constant Folding(null propagation)
---
 .../spark/sql/catalyst/dsl/package.scala      |  22 +-
 .../sql/catalyst/expressions/Expression.scala |   1 -
 .../sql/catalyst/expressions/SortOrder.scala  |   6 +-
 .../catalyst/expressions/complexTypes.scala   |  34 +-
 .../sql/catalyst/expressions/predicates.scala |   3 +-
 .../sql/catalyst/optimizer/Optimizer.scala    |  67 ++++
 .../ExpressionEvaluationSuite.scala           | 115 ++++++-
 .../ExpressionOptimizationSuite.scala         |  36 ++
 .../org/apache/spark/sql/hive/hiveUdfs.scala  |  11 +
 ...M_COUNT-0-45f5619d9e4510195fe67f7c8d14a5c0 | 309 ++++++++++++++++++
 ...M_COUNT-0-a393cfc24ad74f930f3284743254c10c | 309 ++++++++++++++++++
 ...M_COUNT-0-ae497f1556f548c1e2da9244397a985d | 309 ++++++++++++++++++
 ...M_COUNT-0-c60b6075da793b826db5eb4b08d7bab9 | 309 ++++++++++++++++++
 .../sql/hive/execution/HiveQuerySuite.scala   |   3 +
 14 files changed, 1502 insertions(+), 32 deletions(-)
 create mode 100644 sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ExpressionOptimizationSuite.scala
 create mode 100644 sql/hive/src/test/resources/golden/Constant Folding Optimization for AVG_SUM_COUNT-0-45f5619d9e4510195fe67f7c8d14a5c0
 create mode 100644 sql/hive/src/test/resources/golden/Constant Folding Optimization for AVG_SUM_COUNT-0-a393cfc24ad74f930f3284743254c10c
 create mode 100644 sql/hive/src/test/resources/golden/Constant Folding Optimization for AVG_SUM_COUNT-0-ae497f1556f548c1e2da9244397a985d
 create mode 100644 sql/hive/src/test/resources/golden/Constant Folding Optimization for AVG_SUM_COUNT-0-c60b6075da793b826db5eb4b08d7bab9

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala
index 987befe8e22e..dc83485df195 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala
@@ -114,37 +114,37 @@ package object dsl {
       def attr = analysis.UnresolvedAttribute(s)
 
       /** Creates a new AttributeReference of type boolean */
-      def boolean = AttributeReference(s, BooleanType, nullable = false)()
+      def boolean = AttributeReference(s, BooleanType, nullable = true)()
 
       /** Creates a new AttributeReference of type byte */
-      def byte = AttributeReference(s, ByteType, nullable = false)()
+      def byte = AttributeReference(s, ByteType, nullable = true)()
 
       /** Creates a new AttributeReference of type short */
-      def short = AttributeReference(s, ShortType, nullable = false)()
+      def short = AttributeReference(s, ShortType, nullable = true)()
 
       /** Creates a new AttributeReference of type int */
-      def int = AttributeReference(s, IntegerType, nullable = false)()
+      def int = AttributeReference(s, IntegerType, nullable = true)()
 
       /** Creates a new AttributeReference of type long */
-      def long = AttributeReference(s, LongType, nullable = false)()
+      def long = AttributeReference(s, LongType, nullable = true)()
 
       /** Creates a new AttributeReference of type float */
-      def float = AttributeReference(s, FloatType, nullable = false)()
+      def float = AttributeReference(s, FloatType, nullable = true)()
 
       /** Creates a new AttributeReference of type double */
-      def double = AttributeReference(s, DoubleType, nullable = false)()
+      def double = AttributeReference(s, DoubleType, nullable = true)()
 
       /** Creates a new AttributeReference of type string */
-      def string = AttributeReference(s, StringType, nullable = false)()
+      def string = AttributeReference(s, StringType, nullable = true)()
 
       /** Creates a new AttributeReference of type decimal */
-      def decimal = AttributeReference(s, DecimalType, nullable = false)()
+      def decimal = AttributeReference(s, DecimalType, nullable = true)()
 
       /** Creates a new AttributeReference of type timestamp */
-      def timestamp = AttributeReference(s, TimestampType, nullable = false)()
+      def timestamp = AttributeReference(s, TimestampType, nullable = true)()
 
       /** Creates a new AttributeReference of type binary */
-      def binary = AttributeReference(s, BinaryType, nullable = false)()
+      def binary = AttributeReference(s, BinaryType, nullable = true)()
     }
 
     implicit class DslAttribute(a: AttributeReference) {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
index dd9332ada80d..41398ff956ed 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
@@ -44,7 +44,6 @@ abstract class Expression extends TreeNode[Expression] {
    *  - A [[expressions.Cast Cast]] or [[expressions.UnaryMinus UnaryMinus]] is foldable if its
    *    child is foldable.
    */
-  // TODO: Supporting more foldable expressions. For example, deterministic Hive UDFs.
   def foldable: Boolean = false
   def nullable: Boolean
   def references: Set[Attribute]
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SortOrder.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SortOrder.scala
index 08b2f11d20f5..d2b7685e7306 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SortOrder.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SortOrder.scala
@@ -18,6 +18,7 @@
 package org.apache.spark.sql.catalyst.expressions
 
 import org.apache.spark.sql.catalyst.errors.TreeNodeException
+import org.apache.spark.sql.catalyst.trees
 
 abstract sealed class SortDirection
 case object Ascending extends SortDirection
@@ -27,7 +28,10 @@ case object Descending extends SortDirection
  * An expression that can be used to sort a tuple.  This class extends expression primarily so that
  * transformations over expression will descend into its child.
  */
-case class SortOrder(child: Expression, direction: SortDirection) extends UnaryExpression {
+case class SortOrder(child: Expression, direction: SortDirection) extends Expression 
+    with trees.UnaryNode[Expression] {
+
+  override def references = child.references
   override def dataType = child.dataType
   override def nullable = child.nullable
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypes.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypes.scala
index c947155cb701..195ca2eb3d58 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypes.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypes.scala
@@ -28,6 +28,7 @@ case class GetItem(child: Expression, ordinal: Expression) extends Expression {
   val children = child :: ordinal :: Nil
   /** `Null` is returned for invalid ordinals. */
   override def nullable = true
+  override def foldable = child.foldable && ordinal.foldable
   override def references = children.flatMap(_.references).toSet
   def dataType = child.dataType match {
     case ArrayType(dt) => dt
@@ -40,23 +41,27 @@ case class GetItem(child: Expression, ordinal: Expression) extends Expression {
   override def toString = s"$child[$ordinal]"
 
   override def eval(input: Row): Any = {
-    if (child.dataType.isInstanceOf[ArrayType]) {
-      val baseValue = child.eval(input).asInstanceOf[Seq[_]]
-      val o = ordinal.eval(input).asInstanceOf[Int]
-      if (baseValue == null) {
-        null
-      } else if (o >= baseValue.size || o < 0) {
-        null
-      } else {
-        baseValue(o)
-      }
+    val value = child.eval(input)
+    if (value == null) {
+      null
     } else {
-      val baseValue = child.eval(input).asInstanceOf[Map[Any, _]]
       val key = ordinal.eval(input)
-      if (baseValue == null) {
+      if (key == null) {
         null
       } else {
-        baseValue.get(key).orNull
+        if (child.dataType.isInstanceOf[ArrayType]) {
+          val baseValue = value.asInstanceOf[Seq[_]]
+          val o = key.asInstanceOf[Int]
+          if (o >= baseValue.size || o < 0) {
+            null
+          } else {
+            baseValue(o)
+          }
+        } else {
+          val baseValue = value.asInstanceOf[Map[Any, _]]
+          val key = ordinal.eval(input)
+          baseValue.get(key).orNull
+        }
       }
     }
   }
@@ -69,7 +74,8 @@ case class GetField(child: Expression, fieldName: String) extends UnaryExpressio
   type EvaluatedType = Any
 
   def dataType = field.dataType
-  def nullable = field.nullable
+  override def nullable = field.nullable
+  override def foldable = child.foldable
 
   protected def structType = child.dataType match {
     case s: StructType => s
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
index 82c7af684459..6ee479939d25 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
@@ -65,8 +65,7 @@ abstract class BinaryPredicate extends BinaryExpression with Predicate {
   def nullable = left.nullable || right.nullable
 }
 
-case class Not(child: Expression) extends Predicate with trees.UnaryNode[Expression] {
-  def references = child.references
+case class Not(child: Expression) extends UnaryExpression with Predicate {
   override def foldable = child.foldable
   def nullable = child.nullable
   override def toString = s"NOT $child"
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
index c0a09a16ac98..3037d45cc6e3 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
@@ -26,6 +26,7 @@ import org.apache.spark.sql.catalyst.types._
 object Optimizer extends RuleExecutor[LogicalPlan] {
   val batches =
     Batch("ConstantFolding", Once,
+      NullPropagation,
       ConstantFolding,
       BooleanSimplification,
       SimplifyFilters,
@@ -85,6 +86,72 @@ object ColumnPruning extends Rule[LogicalPlan] {
   }
 }
 
+/**
+ * Replaces [[catalyst.expressions.Expression Expressions]] that can be statically evaluated with
+ * equivalent [[catalyst.expressions.Literal Literal]] values. This rule is more specific with 
+ * Null value propagation from bottom to top of the expression tree.
+ */
+object NullPropagation extends Rule[LogicalPlan] {
+  def apply(plan: LogicalPlan): LogicalPlan = plan transform {
+    case q: LogicalPlan => q transformExpressionsUp {
+      case e @ Count(Literal(null, _)) => Literal(0, e.dataType)
+      case e @ Sum(Literal(c, _)) if c == 0 => Literal(0, e.dataType)
+      case e @ Average(Literal(c, _)) if c == 0 => Literal(0.0, e.dataType)
+      case e @ IsNull(c) if c.nullable == false => Literal(false, BooleanType)
+      case e @ IsNotNull(c) if c.nullable == false => Literal(true, BooleanType)
+      case e @ GetItem(Literal(null, _), _) => Literal(null, e.dataType)
+      case e @ GetItem(_, Literal(null, _)) => Literal(null, e.dataType)
+      case e @ GetField(Literal(null, _), _) => Literal(null, e.dataType)
+      case e @ Coalesce(children) => {
+        val newChildren = children.filter(c => c match {
+          case Literal(null, _) => false
+          case _ => true
+        })
+        if (newChildren.length == 0) {
+          Literal(null, e.dataType)
+        } else if (newChildren.length == 1) {
+          newChildren(0)
+        } else {
+          Coalesce(newChildren)
+        }
+      }
+      case e @ If(Literal(v, _), trueValue, falseValue) => if (v == true) trueValue else falseValue
+      case e @ In(Literal(v, _), list) if (list.exists(c => c match {
+          case Literal(candidate, _) if candidate == v => true
+          case _ => false
+        })) => Literal(true, BooleanType)
+      case e: UnaryMinus => e.child match {
+        case Literal(null, _) => Literal(null, e.dataType)
+        case _ => e
+      }
+      case e: Cast => e.child match {
+        case Literal(null, _) => Literal(null, e.dataType)
+        case _ => e
+      }
+      case e: Not => e.child match {
+        case Literal(null, _) => Literal(null, e.dataType)
+        case _ => e
+      }
+      // Put exceptional cases above if any
+      case e: BinaryArithmetic => e.children match {
+        case Literal(null, _) :: right :: Nil => Literal(null, e.dataType)
+        case left :: Literal(null, _) :: Nil => Literal(null, e.dataType)
+        case _ => e
+      }
+      case e: BinaryComparison => e.children match {
+        case Literal(null, _) :: right :: Nil => Literal(null, e.dataType)
+        case left :: Literal(null, _) :: Nil => Literal(null, e.dataType)
+        case _ => e
+      }
+      case e: StringRegexExpression => e.children match {
+        case Literal(null, _) :: right :: Nil => Literal(null, e.dataType)
+        case left :: Literal(null, _) :: Nil => Literal(null, e.dataType)
+        case _ => e
+      }
+    }
+  }
+}
+
 /**
  * Replaces [[catalyst.expressions.Expression Expressions]] that can be statically evaluated with
  * equivalent [[catalyst.expressions.Literal Literal]] values.
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala
index d287ad73b9e9..91605d0a260e 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala
@@ -108,9 +108,7 @@ class ExpressionEvaluationSuite extends FunSuite {
       truthTable.foreach {
         case (l,r,answer) =>
           val expr = op(Literal(l, BooleanType), Literal(r, BooleanType))
-          val result = expr.eval(null)
-          if (result != answer)
-            fail(s"$expr should not evaluate to $result, expected: $answer")
+          checkEvaluation(expr, answer)
       }
     }
   }
@@ -131,6 +129,7 @@ class ExpressionEvaluationSuite extends FunSuite {
 
   test("LIKE literal Regular Expression") {
     checkEvaluation(Literal(null, StringType).like("a"), null)
+    checkEvaluation(Literal("a", StringType).like(Literal(null, StringType)), null)
     checkEvaluation(Literal(null, StringType).like(Literal(null, StringType)), null)
     checkEvaluation("abdef" like "abdef", true)
     checkEvaluation("a_%b" like "a\\__b", true)
@@ -159,9 +158,14 @@ class ExpressionEvaluationSuite extends FunSuite {
     checkEvaluation("abc" like regEx, true, new GenericRow(Array[Any]("a%")))
     checkEvaluation("abc" like regEx, false, new GenericRow(Array[Any]("b%")))
     checkEvaluation("abc" like regEx, false, new GenericRow(Array[Any]("bc%")))
+    
+    checkEvaluation(Literal(null, StringType) like regEx, null, new GenericRow(Array[Any]("bc%")))
   }
 
   test("RLIKE literal Regular Expression") {
+    checkEvaluation(Literal(null, StringType) rlike "abdef", null)
+    checkEvaluation("abdef" rlike Literal(null, StringType), null)
+    checkEvaluation(Literal(null, StringType) rlike Literal(null, StringType), null)
     checkEvaluation("abdef" rlike "abdef", true)
     checkEvaluation("abbbbc" rlike "a.*c", true)
 
@@ -257,6 +261,8 @@ class ExpressionEvaluationSuite extends FunSuite {
     assert(("abcdef" cast DecimalType).nullable === true)
     assert(("abcdef" cast DoubleType).nullable === true)
     assert(("abcdef" cast FloatType).nullable === true)
+
+    checkEvaluation(Cast(Literal(null, IntegerType), ShortType), null)
   }
 
   test("timestamp") {
@@ -287,5 +293,108 @@ class ExpressionEvaluationSuite extends FunSuite {
     // A test for higher precision than millis
     checkEvaluation(Cast(Cast(0.00000001, TimestampType), DoubleType), 0.00000001)
   }
+  
+  test("null checking") {
+    val row = new GenericRow(Array[Any]("^Ba*n", null, true, null))
+    val c1 = 'a.string.at(0)
+    val c2 = 'a.string.at(1)
+    val c3 = 'a.boolean.at(2)
+    val c4 = 'a.boolean.at(3)
+
+    checkEvaluation(IsNull(c1), false, row)
+    checkEvaluation(IsNotNull(c1), true, row)
+
+    checkEvaluation(IsNull(c2), true, row)
+    checkEvaluation(IsNotNull(c2), false, row)
+
+    checkEvaluation(IsNull(Literal(1, ShortType)), false)
+    checkEvaluation(IsNotNull(Literal(1, ShortType)), true)
+
+    checkEvaluation(IsNull(Literal(null, ShortType)), true)
+    checkEvaluation(IsNotNull(Literal(null, ShortType)), false)
+    
+    checkEvaluation(Coalesce(c1 :: c2 :: Nil), "^Ba*n", row)
+    checkEvaluation(Coalesce(Literal(null, StringType) :: Nil), null, row)
+    checkEvaluation(Coalesce(Literal(null, StringType) :: c1 :: c2 :: Nil), "^Ba*n", row)
+
+    checkEvaluation(If(c3, Literal("a", StringType), Literal("b", StringType)), "a", row)
+    checkEvaluation(If(c3, c1, c2), "^Ba*n", row)
+    checkEvaluation(If(c4, c2, c1), "^Ba*n", row)
+    checkEvaluation(If(Literal(null, BooleanType), c2, c1), "^Ba*n", row)
+    checkEvaluation(If(Literal(true, BooleanType), c1, c2), "^Ba*n", row)
+    checkEvaluation(If(Literal(false, BooleanType), c2, c1), "^Ba*n", row)
+    checkEvaluation(If(Literal(false, BooleanType), 
+      Literal("a", StringType), Literal("b", StringType)), "b", row)
+
+    checkEvaluation(In(c1, c1 :: c2 :: Nil), true, row)
+    checkEvaluation(In(Literal("^Ba*n", StringType), 
+      Literal("^Ba*n", StringType) :: Nil), true, row)
+    checkEvaluation(In(Literal("^Ba*n", StringType),
+      Literal("^Ba*n", StringType) :: c2 :: Nil), true, row)
+  }
+
+  test("complex type") {
+    val row = new GenericRow(Array[Any](
+      "^Ba*n",                                  // 0 
+      null.asInstanceOf[String],                // 1
+      new GenericRow(Array[Any]("aa", "bb")),   // 2
+      Map("aa"->"bb"),                          // 3
+      Seq("aa", "bb")                           // 4
+    ))
+
+    val typeS = StructType(
+      StructField("a", StringType, true) :: StructField("b", StringType, true) :: Nil
+    )
+    val typeMap = MapType(StringType, StringType)
+    val typeArray = ArrayType(StringType)
+
+    checkEvaluation(GetItem(BoundReference(3, AttributeReference("c", typeMap)()), 
+      Literal("aa")), "bb", row)
+    checkEvaluation(GetItem(Literal(null, typeMap), Literal("aa")), null, row)
+    checkEvaluation(GetItem(Literal(null, typeMap), Literal(null, StringType)), null, row)
+    checkEvaluation(GetItem(BoundReference(3, AttributeReference("c", typeMap)()), 
+      Literal(null, StringType)), null, row)
+
+    checkEvaluation(GetItem(BoundReference(4, AttributeReference("c", typeArray)()), 
+      Literal(1)), "bb", row)
+    checkEvaluation(GetItem(Literal(null, typeArray), Literal(1)), null, row)
+    checkEvaluation(GetItem(Literal(null, typeArray), Literal(null, IntegerType)), null, row)
+    checkEvaluation(GetItem(BoundReference(4, AttributeReference("c", typeArray)()), 
+      Literal(null, IntegerType)), null, row)
+
+    checkEvaluation(GetField(BoundReference(2, AttributeReference("c", typeS)()), "a"), "aa", row)
+    checkEvaluation(GetField(Literal(null, typeS), "a"), null, row)
+  }
+
+  test("arithmetic") {
+    val row = new GenericRow(Array[Any](1, 2, 3, null))
+    val c1 = 'a.int.at(0)
+    val c2 = 'a.int.at(1)
+    val c3 = 'a.int.at(2)
+    val c4 = 'a.int.at(3)
+
+    checkEvaluation(UnaryMinus(c1), -1, row)
+    checkEvaluation(UnaryMinus(Literal(100, IntegerType)), -100)
+
+    checkEvaluation(Add(c1, c4), null, row)
+    checkEvaluation(Add(c1, c2), 3, row)
+    checkEvaluation(Add(c1, Literal(null, IntegerType)), null, row)
+    checkEvaluation(Add(Literal(null, IntegerType), c2), null, row)
+    checkEvaluation(Add(Literal(null, IntegerType), Literal(null, IntegerType)), null, row)
+  }
+
+  test("BinaryComparison") {
+    val row = new GenericRow(Array[Any](1, 2, 3, null))
+    val c1 = 'a.int.at(0)
+    val c2 = 'a.int.at(1)
+    val c3 = 'a.int.at(2)
+    val c4 = 'a.int.at(3)
+
+    checkEvaluation(LessThan(c1, c4), null, row)
+    checkEvaluation(LessThan(c1, c2), true, row)
+    checkEvaluation(LessThan(c1, Literal(null, IntegerType)), null, row)
+    checkEvaluation(LessThan(Literal(null, IntegerType), c2), null, row)
+    checkEvaluation(LessThan(Literal(null, IntegerType), Literal(null, IntegerType)), null, row)
+  }
 }
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ExpressionOptimizationSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ExpressionOptimizationSuite.scala
new file mode 100644
index 000000000000..890d6289b9df
--- /dev/null
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ExpressionOptimizationSuite.scala
@@ -0,0 +1,36 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.optimizer
+
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.plans.logical._
+
+/**
+ * Overrides our expression evaluation tests and reruns them after optimization has occured.  This
+ * is to ensure that constant folding and other optimizations do not break anything.
+ */
+class ExpressionOptimizationSuite extends ExpressionEvaluationSuite {
+  override def checkEvaluation(
+      expression: Expression,
+      expected: Any,
+      inputRow: Row = EmptyRow): Unit = {
+    val plan = Project(Alias(expression, s"Optimized($expression)")() :: Nil, NoRelation)
+    val optimizedPlan = Optimizer(plan)
+    super.checkEvaluation(optimizedPlan.expressions.head, expected, inputRow)
+  }
+}
\ No newline at end of file
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala
index c7de4ab6d395..d50e2c65b7b3 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala
@@ -22,6 +22,7 @@ import scala.collection.mutable.ArrayBuffer
 import org.apache.hadoop.hive.common.`type`.HiveDecimal
 import org.apache.hadoop.hive.ql.exec.UDF
 import org.apache.hadoop.hive.ql.exec.{FunctionInfo, FunctionRegistry}
+import org.apache.hadoop.hive.ql.udf.{UDFType => HiveUDFType}
 import org.apache.hadoop.hive.ql.udf.generic._
 import org.apache.hadoop.hive.serde2.objectinspector._
 import org.apache.hadoop.hive.serde2.objectinspector.primitive._
@@ -237,6 +238,16 @@ private[hive] case class HiveGenericUdf(name: String, children: Seq[Expression])
   @transient
   protected lazy val returnInspector = function.initialize(argumentInspectors.toArray)
 
+  @transient
+  protected lazy val isUDFDeterministic = {
+    val udfType = function.getClass().getAnnotation(classOf[HiveUDFType])
+    (udfType != null && udfType.deterministic())
+  }
+
+  override def foldable = {
+    isUDFDeterministic && children.foldLeft(true)((prev, n) => prev && n.foldable)
+  }
+
   val dataType: DataType = inspectorToDataType(returnInspector)
 
   override def eval(input: Row): Any = {
diff --git a/sql/hive/src/test/resources/golden/Constant Folding Optimization for AVG_SUM_COUNT-0-45f5619d9e4510195fe67f7c8d14a5c0 b/sql/hive/src/test/resources/golden/Constant Folding Optimization for AVG_SUM_COUNT-0-45f5619d9e4510195fe67f7c8d14a5c0
new file mode 100644
index 000000000000..7643569a2c23
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/Constant Folding Optimization for AVG_SUM_COUNT-0-45f5619d9e4510195fe67f7c8d14a5c0	
@@ -0,0 +1,309 @@
+0.0	0	0	0	3
+2.0	0	2	0	1
+4.0	0	4	0	1
+5.0	0	15	0	3
+8.0	0	8	0	1
+9.0	0	9	0	1
+10.0	0	10	0	1
+11.0	0	11	0	1
+12.0	0	24	0	2
+15.0	0	30	0	2
+17.0	0	17	0	1
+18.0	0	36	0	2
+19.0	0	19	0	1
+20.0	0	20	0	1
+24.0	0	48	0	2
+26.0	0	52	0	2
+27.0	0	27	0	1
+28.0	0	28	0	1
+30.0	0	30	0	1
+33.0	0	33	0	1
+34.0	0	34	0	1
+35.0	0	105	0	3
+37.0	0	74	0	2
+41.0	0	41	0	1
+42.0	0	84	0	2
+43.0	0	43	0	1
+44.0	0	44	0	1
+47.0	0	47	0	1
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+53.0	0	53	0	1
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+57.0	0	57	0	1
+58.0	0	116	0	2
+64.0	0	64	0	1
+65.0	0	65	0	1
+66.0	0	66	0	1
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+72.0	0	144	0	2
+74.0	0	74	0	1
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+84.0	0	168	0	2
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+86.0	0	86	0	1
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+90.0	0	270	0	3
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diff --git a/sql/hive/src/test/resources/golden/Constant Folding Optimization for AVG_SUM_COUNT-0-a393cfc24ad74f930f3284743254c10c b/sql/hive/src/test/resources/golden/Constant Folding Optimization for AVG_SUM_COUNT-0-a393cfc24ad74f930f3284743254c10c
new file mode 100644
index 000000000000..f23b45c32ecc
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/Constant Folding Optimization for AVG_SUM_COUNT-0-a393cfc24ad74f930f3284743254c10c	
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diff --git a/sql/hive/src/test/resources/golden/Constant Folding Optimization for AVG_SUM_COUNT-0-ae497f1556f548c1e2da9244397a985d b/sql/hive/src/test/resources/golden/Constant Folding Optimization for AVG_SUM_COUNT-0-ae497f1556f548c1e2da9244397a985d
new file mode 100644
index 000000000000..7839d714c25d
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/Constant Folding Optimization for AVG_SUM_COUNT-0-ae497f1556f548c1e2da9244397a985d	
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diff --git a/sql/hive/src/test/resources/golden/Constant Folding Optimization for AVG_SUM_COUNT-0-c60b6075da793b826db5eb4b08d7bab9 b/sql/hive/src/test/resources/golden/Constant Folding Optimization for AVG_SUM_COUNT-0-c60b6075da793b826db5eb4b08d7bab9
new file mode 100644
index 000000000000..cd6b14ac2a42
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/Constant Folding Optimization for AVG_SUM_COUNT-0-c60b6075da793b826db5eb4b08d7bab9	
@@ -0,0 +1,309 @@
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diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
index d224d2ee600e..87a92d83383a 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
@@ -33,6 +33,9 @@ class HiveQuerySuite extends HiveComparisonTest {
     hiveql("FROM src SELECT key").collect()
   }
 
+  createQueryTest("Constant Folding Optimization for AVG_SUM_COUNT",
+    "SELECT AVG(0), SUM(0), COUNT(null), COUNT(value) FROM src GROUP BY key")
+
   createQueryTest("Simple Average",
     "SELECT AVG(key) FROM src")
 

From 967635a2425a769b932eea0984fe697d6721cab0 Mon Sep 17 00:00:00 2001
From: Kan Zhang <kzhang@apache.org>
Date: Wed, 7 May 2014 09:41:31 -0700
Subject: [PATCH 115/810] [SPARK-1460] Returning SchemaRDD instead of normal
 RDD on Set operations...

... that do not change schema

Author: Kan Zhang <kzhang@apache.org>

Closes #448 from kanzhang/SPARK-1460 and squashes the following commits:

111e388 [Kan Zhang] silence MiMa errors in EdgeRDD and VertexRDD
91dc787 [Kan Zhang] Taking into account newly added Ordering param
79ed52a [Kan Zhang] [SPARK-1460] Returning SchemaRDD on Set operations that do not change schema
---
 .../main/scala/org/apache/spark/rdd/RDD.scala |  10 +-
 .../org/apache/spark/graphx/EdgeRDD.scala     |  10 +-
 .../org/apache/spark/graphx/VertexRDD.scala   |  10 +-
 project/MimaBuild.scala                       |   2 +
 python/pyspark/sql.py                         |  29 ++++
 .../org/apache/spark/sql/SchemaRDD.scala      |  67 ++++++++-
 .../spark/sql/api/java/JavaSchemaRDD.scala    | 140 ++++++++++++++++++
 7 files changed, 246 insertions(+), 22 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/rdd/RDD.scala b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
index 3b3524f33e81..a1ca612cc9a0 100644
--- a/core/src/main/scala/org/apache/spark/rdd/RDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
@@ -128,7 +128,7 @@ abstract class RDD[T: ClassTag](
   @transient var name: String = null
 
   /** Assign a name to this RDD */
-  def setName(_name: String): RDD[T] = {
+  def setName(_name: String): this.type = {
     name = _name
     this
   }
@@ -138,7 +138,7 @@ abstract class RDD[T: ClassTag](
    * it is computed. This can only be used to assign a new storage level if the RDD does not
    * have a storage level set yet..
    */
-  def persist(newLevel: StorageLevel): RDD[T] = {
+  def persist(newLevel: StorageLevel): this.type = {
     // TODO: Handle changes of StorageLevel
     if (storageLevel != StorageLevel.NONE && newLevel != storageLevel) {
       throw new UnsupportedOperationException(
@@ -152,10 +152,10 @@ abstract class RDD[T: ClassTag](
   }
 
   /** Persist this RDD with the default storage level (`MEMORY_ONLY`). */
-  def persist(): RDD[T] = persist(StorageLevel.MEMORY_ONLY)
+  def persist(): this.type = persist(StorageLevel.MEMORY_ONLY)
 
   /** Persist this RDD with the default storage level (`MEMORY_ONLY`). */
-  def cache(): RDD[T] = persist()
+  def cache(): this.type = persist()
 
   /**
    * Mark the RDD as non-persistent, and remove all blocks for it from memory and disk.
@@ -163,7 +163,7 @@ abstract class RDD[T: ClassTag](
    * @param blocking Whether to block until all blocks are deleted.
    * @return This RDD.
    */
-  def unpersist(blocking: Boolean = true): RDD[T] = {
+  def unpersist(blocking: Boolean = true): this.type = {
     logInfo("Removing RDD " + id + " from persistence list")
     sc.unpersistRDD(id, blocking)
     storageLevel = StorageLevel.NONE
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/EdgeRDD.scala b/graphx/src/main/scala/org/apache/spark/graphx/EdgeRDD.scala
index 6d04bf790e3a..fa78ca99b889 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/EdgeRDD.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/EdgeRDD.scala
@@ -51,18 +51,12 @@ class EdgeRDD[@specialized ED: ClassTag](
 
   override def collect(): Array[Edge[ED]] = this.map(_.copy()).collect()
 
-  override def persist(newLevel: StorageLevel): EdgeRDD[ED] = {
+  override def persist(newLevel: StorageLevel): this.type = {
     partitionsRDD.persist(newLevel)
     this
   }
 
-  /** Persist this RDD with the default storage level (`MEMORY_ONLY`). */
-  override def persist(): EdgeRDD[ED] = persist(StorageLevel.MEMORY_ONLY)
-
-  /** Persist this RDD with the default storage level (`MEMORY_ONLY`). */
-  override def cache(): EdgeRDD[ED] = persist()
-
-  override def unpersist(blocking: Boolean = true): EdgeRDD[ED] = {
+  override def unpersist(blocking: Boolean = true): this.type = {
     partitionsRDD.unpersist(blocking)
     this
   }
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/VertexRDD.scala b/graphx/src/main/scala/org/apache/spark/graphx/VertexRDD.scala
index d6788d4d4b9f..f0fc605c8857 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/VertexRDD.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/VertexRDD.scala
@@ -71,18 +71,12 @@ class VertexRDD[@specialized VD: ClassTag](
   override protected def getPreferredLocations(s: Partition): Seq[String] =
     partitionsRDD.preferredLocations(s)
 
-  override def persist(newLevel: StorageLevel): VertexRDD[VD] = {
+  override def persist(newLevel: StorageLevel): this.type = {
     partitionsRDD.persist(newLevel)
     this
   }
 
-  /** Persist this RDD with the default storage level (`MEMORY_ONLY`). */
-  override def persist(): VertexRDD[VD] = persist(StorageLevel.MEMORY_ONLY)
-
-  /** Persist this RDD with the default storage level (`MEMORY_ONLY`). */
-  override def cache(): VertexRDD[VD] = persist()
-
-  override def unpersist(blocking: Boolean = true): VertexRDD[VD] = {
+  override def unpersist(blocking: Boolean = true): this.type = {
     partitionsRDD.unpersist(blocking)
     this
   }
diff --git a/project/MimaBuild.scala b/project/MimaBuild.scala
index d540dc0a986e..efdb38e907d1 100644
--- a/project/MimaBuild.scala
+++ b/project/MimaBuild.scala
@@ -74,6 +74,8 @@ object MimaBuild {
           ) ++
           excludeSparkClass("rdd.ClassTags") ++
           excludeSparkClass("util.XORShiftRandom") ++
+          excludeSparkClass("graphx.EdgeRDD") ++
+          excludeSparkClass("graphx.VertexRDD") ++
           excludeSparkClass("mllib.recommendation.MFDataGenerator") ++
           excludeSparkClass("mllib.optimization.SquaredGradient") ++
           excludeSparkClass("mllib.regression.RidgeRegressionWithSGD") ++
diff --git a/python/pyspark/sql.py b/python/pyspark/sql.py
index 1a62031db5c4..6789d7002b3b 100644
--- a/python/pyspark/sql.py
+++ b/python/pyspark/sql.py
@@ -360,6 +360,35 @@ def getCheckpointFile(self):
         else:
             return None
 
+    def coalesce(self, numPartitions, shuffle=False):
+        rdd = self._jschema_rdd.coalesce(numPartitions, shuffle)
+        return SchemaRDD(rdd, self.sql_ctx)
+
+    def distinct(self):
+        rdd = self._jschema_rdd.distinct()
+        return SchemaRDD(rdd, self.sql_ctx)
+
+    def intersection(self, other):
+        if (other.__class__ is SchemaRDD):
+            rdd = self._jschema_rdd.intersection(other._jschema_rdd)
+            return SchemaRDD(rdd, self.sql_ctx)
+        else:
+            raise ValueError("Can only intersect with another SchemaRDD")
+
+    def repartition(self, numPartitions):
+        rdd = self._jschema_rdd.repartition(numPartitions)
+        return SchemaRDD(rdd, self.sql_ctx)
+
+    def subtract(self, other, numPartitions=None):
+        if (other.__class__ is SchemaRDD):
+            if numPartitions is None:
+                rdd = self._jschema_rdd.subtract(other._jschema_rdd)
+            else:
+                rdd = self._jschema_rdd.subtract(other._jschema_rdd, numPartitions)
+            return SchemaRDD(rdd, self.sql_ctx)
+        else:
+            raise ValueError("Can only subtract another SchemaRDD")
+
 def _test():
     import doctest
     from pyspark.context import SparkContext
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala
index d7782d6b3281..34200be3ac95 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala
@@ -19,14 +19,16 @@ package org.apache.spark.sql
 
 import net.razorvine.pickle.Pickler
 
-import org.apache.spark.{Dependency, OneToOneDependency, Partition, TaskContext}
+import org.apache.spark.{Dependency, OneToOneDependency, Partition, Partitioner, TaskContext}
 import org.apache.spark.annotation.{AlphaComponent, Experimental}
 import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.api.java.JavaSchemaRDD
 import org.apache.spark.sql.catalyst.analysis._
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.logical._
 import org.apache.spark.sql.catalyst.plans.{Inner, JoinType}
 import org.apache.spark.sql.catalyst.types.BooleanType
+import org.apache.spark.sql.execution.{ExistingRdd, SparkLogicalPlan}
 import org.apache.spark.api.java.JavaRDD
 import java.util.{Map => JMap}
 
@@ -296,6 +298,13 @@ class SchemaRDD(
    */
   def toSchemaRDD = this
 
+  /**
+   * Returns this RDD as a JavaSchemaRDD.
+   *
+   * @group schema
+   */
+  def toJavaSchemaRDD: JavaSchemaRDD = new JavaSchemaRDD(sqlContext, logicalPlan)
+
   private[sql] def javaToPython: JavaRDD[Array[Byte]] = {
     val fieldNames: Seq[String] = this.queryExecution.analyzed.output.map(_.name)
     this.mapPartitions { iter =>
@@ -314,4 +323,60 @@ class SchemaRDD(
       }
     }
   }
+
+  /**
+   * Creates SchemaRDD by applying own schema to derived RDD. Typically used to wrap return value
+   * of base RDD functions that do not change schema.
+   *
+   * @param rdd RDD derived from this one and has same schema
+   *
+   * @group schema
+   */
+  private def applySchema(rdd: RDD[Row]): SchemaRDD = {
+    new SchemaRDD(sqlContext, SparkLogicalPlan(ExistingRdd(logicalPlan.output, rdd)))
+  }
+
+  // =======================================================================
+  // Base RDD functions that do NOT change schema
+  // =======================================================================
+
+  // Transformations (return a new RDD)
+
+  override def coalesce(numPartitions: Int, shuffle: Boolean = false)
+                       (implicit ord: Ordering[Row] = null): SchemaRDD =
+    applySchema(super.coalesce(numPartitions, shuffle)(ord))
+
+  override def distinct(): SchemaRDD =
+    applySchema(super.distinct())
+
+  override def distinct(numPartitions: Int)
+                       (implicit ord: Ordering[Row] = null): SchemaRDD =
+    applySchema(super.distinct(numPartitions)(ord))
+
+  override def filter(f: Row => Boolean): SchemaRDD =
+    applySchema(super.filter(f))
+
+  override def intersection(other: RDD[Row]): SchemaRDD =
+    applySchema(super.intersection(other))
+
+  override def intersection(other: RDD[Row], partitioner: Partitioner)
+                           (implicit ord: Ordering[Row] = null): SchemaRDD =
+    applySchema(super.intersection(other, partitioner)(ord))
+
+  override def intersection(other: RDD[Row], numPartitions: Int): SchemaRDD =
+    applySchema(super.intersection(other, numPartitions))
+
+  override def repartition(numPartitions: Int)
+                          (implicit ord: Ordering[Row] = null): SchemaRDD =
+    applySchema(super.repartition(numPartitions)(ord))
+
+  override def subtract(other: RDD[Row]): SchemaRDD =
+    applySchema(super.subtract(other))
+
+  override def subtract(other: RDD[Row], numPartitions: Int): SchemaRDD =
+    applySchema(super.subtract(other, numPartitions))
+
+  override def subtract(other: RDD[Row], p: Partitioner)
+                       (implicit ord: Ordering[Row] = null): SchemaRDD =
+    applySchema(super.subtract(other, p)(ord))
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSchemaRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSchemaRDD.scala
index d43d672938f5..22f57b758dd0 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSchemaRDD.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSchemaRDD.scala
@@ -17,10 +17,13 @@
 
 package org.apache.spark.sql.api.java
 
+import org.apache.spark.Partitioner
 import org.apache.spark.api.java.{JavaRDDLike, JavaRDD}
+import org.apache.spark.api.java.function.{Function => JFunction}
 import org.apache.spark.sql.{SQLContext, SchemaRDD, SchemaRDDLike}
 import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
 import org.apache.spark.rdd.RDD
+import org.apache.spark.storage.StorageLevel
 
 /**
  * An RDD of [[Row]] objects that is returned as the result of a Spark SQL query.  In addition to
@@ -45,4 +48,141 @@ class JavaSchemaRDD(
   override def wrapRDD(rdd: RDD[Row]): JavaRDD[Row] = JavaRDD.fromRDD(rdd)
 
   val rdd = baseSchemaRDD.map(new Row(_))
+
+  override def toString: String = baseSchemaRDD.toString
+
+  // =======================================================================
+  // Base RDD functions that do NOT change schema
+  // =======================================================================
+
+  // Common RDD functions
+
+  /** Persist this RDD with the default storage level (`MEMORY_ONLY`). */
+  def cache(): JavaSchemaRDD = {
+    baseSchemaRDD.cache()
+    this
+  }
+
+  /** Persist this RDD with the default storage level (`MEMORY_ONLY`). */
+  def persist(): JavaSchemaRDD = {
+    baseSchemaRDD.persist()
+    this
+  }
+
+  /**
+   * Set this RDD's storage level to persist its values across operations after the first time
+   * it is computed. This can only be used to assign a new storage level if the RDD does not
+   * have a storage level set yet..
+   */
+  def persist(newLevel: StorageLevel): JavaSchemaRDD = {
+    baseSchemaRDD.persist(newLevel)
+    this
+  }
+
+  /**
+   * Mark the RDD as non-persistent, and remove all blocks for it from memory and disk.
+   *
+   * @param blocking Whether to block until all blocks are deleted.
+   * @return This RDD.
+   */
+  def unpersist(blocking: Boolean = true): JavaSchemaRDD = {
+    baseSchemaRDD.unpersist(blocking)
+    this
+  }
+
+  /** Assign a name to this RDD */
+  def setName(name: String): JavaSchemaRDD = {
+    baseSchemaRDD.setName(name)
+    this
+  }
+
+  // Transformations (return a new RDD)
+
+  /**
+   * Return a new RDD that is reduced into `numPartitions` partitions.
+   */
+  def coalesce(numPartitions: Int, shuffle: Boolean = false): JavaSchemaRDD =
+    baseSchemaRDD.coalesce(numPartitions, shuffle).toJavaSchemaRDD
+
+  /**
+   * Return a new RDD containing the distinct elements in this RDD.
+   */
+  def distinct(): JavaSchemaRDD =
+    baseSchemaRDD.distinct().toJavaSchemaRDD
+
+  /**
+   * Return a new RDD containing the distinct elements in this RDD.
+   */
+  def distinct(numPartitions: Int): JavaSchemaRDD =
+    baseSchemaRDD.distinct(numPartitions).toJavaSchemaRDD
+
+  /**
+   * Return a new RDD containing only the elements that satisfy a predicate.
+   */
+  def filter(f: JFunction[Row, java.lang.Boolean]): JavaSchemaRDD =
+    baseSchemaRDD.filter(x => f.call(new Row(x)).booleanValue()).toJavaSchemaRDD
+
+  /**
+   * Return the intersection of this RDD and another one. The output will not contain any
+   * duplicate elements, even if the input RDDs did.
+   *
+   * Note that this method performs a shuffle internally.
+   */
+  def intersection(other: JavaSchemaRDD): JavaSchemaRDD =
+    this.baseSchemaRDD.intersection(other.baseSchemaRDD).toJavaSchemaRDD
+
+  /**
+   * Return the intersection of this RDD and another one. The output will not contain any
+   * duplicate elements, even if the input RDDs did.
+   *
+   * Note that this method performs a shuffle internally.
+   *
+   * @param partitioner Partitioner to use for the resulting RDD
+   */
+  def intersection(other: JavaSchemaRDD, partitioner: Partitioner): JavaSchemaRDD =
+    this.baseSchemaRDD.intersection(other.baseSchemaRDD, partitioner).toJavaSchemaRDD
+
+  /**
+   * Return the intersection of this RDD and another one. The output will not contain any
+   * duplicate elements, even if the input RDDs did.  Performs a hash partition across the cluster
+   *
+   * Note that this method performs a shuffle internally.
+   *
+   * @param numPartitions How many partitions to use in the resulting RDD
+   */
+  def intersection(other: JavaSchemaRDD, numPartitions: Int): JavaSchemaRDD =
+    this.baseSchemaRDD.intersection(other.baseSchemaRDD, numPartitions).toJavaSchemaRDD
+
+  /**
+   * Return a new RDD that has exactly `numPartitions` partitions.
+   *
+   * Can increase or decrease the level of parallelism in this RDD. Internally, this uses
+   * a shuffle to redistribute data.
+   *
+   * If you are decreasing the number of partitions in this RDD, consider using `coalesce`,
+   * which can avoid performing a shuffle.
+   */
+  def repartition(numPartitions: Int): JavaSchemaRDD =
+    baseSchemaRDD.repartition(numPartitions).toJavaSchemaRDD
+
+  /**
+   * Return an RDD with the elements from `this` that are not in `other`.
+   *
+   * Uses `this` partitioner/partition size, because even if `other` is huge, the resulting
+   * RDD will be <= us.
+   */
+  def subtract(other: JavaSchemaRDD): JavaSchemaRDD =
+    this.baseSchemaRDD.subtract(other.baseSchemaRDD).toJavaSchemaRDD
+
+  /**
+   * Return an RDD with the elements from `this` that are not in `other`.
+   */
+  def subtract(other: JavaSchemaRDD, numPartitions: Int): JavaSchemaRDD =
+    this.baseSchemaRDD.subtract(other.baseSchemaRDD, numPartitions).toJavaSchemaRDD
+
+  /**
+   * Return an RDD with the elements from `this` that are not in `other`.
+   */
+  def subtract(other: JavaSchemaRDD, p: Partitioner): JavaSchemaRDD =
+    this.baseSchemaRDD.subtract(other.baseSchemaRDD, p).toJavaSchemaRDD
 }

From 3308722ca03f2bfa792e9a2cff9c894b967983d9 Mon Sep 17 00:00:00 2001
From: Aaron Davidson <aaron@databricks.com>
Date: Wed, 7 May 2014 09:48:31 -0700
Subject: [PATCH 116/810] SPARK-1579: Clean up PythonRDD and avoid swallowing
 IOExceptions

This patch includes several cleanups to PythonRDD, focused around fixing [SPARK-1579](https://issues.apache.org/jira/browse/SPARK-1579) cleanly. Listed in order of approximate importance:

- The Python daemon waits for Spark to close the socket before exiting,
  in order to avoid causing spurious IOExceptions in Spark's
  `PythonRDD::WriterThread`.
- Removes the Python Monitor Thread, which polled for task cancellations
  in order to kill the Python worker. Instead, we do this in the
  onCompleteCallback, since this is guaranteed to be called during
  cancellation.
- Adds a "completed" variable to TaskContext to avoid the issue noted in
  [SPARK-1019](https://issues.apache.org/jira/browse/SPARK-1019), where onCompleteCallbacks may be execution-order dependent.
  Along with this, I removed the "context.interrupted = true" flag in
  the onCompleteCallback.
- Extracts PythonRDD::WriterThread to its own class.

Since this patch provides an alternative solution to [SPARK-1019](https://issues.apache.org/jira/browse/SPARK-1019), I did test it with

```
sc.textFile("latlon.tsv").take(5)
```

many times without error.

Additionally, in order to test the unswallowed exceptions, I performed

```
sc.textFile("s3n://<big file>").count()
```

and cut my internet during execution. Prior to this patch, we got the "stdin writer exited early" message, which was unhelpful. Now, we get the SocketExceptions propagated through Spark to the user and get proper (though unsuccessful) task retries.

Author: Aaron Davidson <aaron@databricks.com>

Closes #640 from aarondav/pyspark-io and squashes the following commits:

b391ff8 [Aaron Davidson] Detect "clean socket shutdowns" and stop waiting on the socket
c0c49da [Aaron Davidson] SPARK-1579: Clean up PythonRDD and avoid swallowing IOExceptions
---
 .../scala/org/apache/spark/TaskContext.scala  |   5 +
 .../apache/spark/api/python/PythonRDD.scala   | 217 +++++++++---------
 .../spark/scheduler/ShuffleMapTask.scala      |  10 +-
 python/pyspark/context.py                     |   2 +-
 python/pyspark/daemon.py                      |  14 +-
 5 files changed, 141 insertions(+), 107 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/TaskContext.scala b/core/src/main/scala/org/apache/spark/TaskContext.scala
index dc012cc38134..fc4812753d00 100644
--- a/core/src/main/scala/org/apache/spark/TaskContext.scala
+++ b/core/src/main/scala/org/apache/spark/TaskContext.scala
@@ -42,9 +42,13 @@ class TaskContext(
   // List of callback functions to execute when the task completes.
   @transient private val onCompleteCallbacks = new ArrayBuffer[() => Unit]
 
+  // Set to true when the task is completed, before the onCompleteCallbacks are executed.
+  @volatile var completed: Boolean = false
+
   /**
    * Add a callback function to be executed on task completion. An example use
    * is for HadoopRDD to register a callback to close the input stream.
+   * Will be called in any situation - success, failure, or cancellation.
    * @param f Callback function.
    */
   def addOnCompleteCallback(f: () => Unit) {
@@ -52,6 +56,7 @@ class TaskContext(
   }
 
   def executeOnCompleteCallbacks() {
+    completed = true
     // Process complete callbacks in the reverse order of registration
     onCompleteCallbacks.reverse.foreach{_()}
   }
diff --git a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
index 61407007087c..fecd9762f3f6 100644
--- a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
+++ b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
@@ -56,122 +56,37 @@ private[spark] class PythonRDD[T: ClassTag](
     val env = SparkEnv.get
     val worker: Socket = env.createPythonWorker(pythonExec, envVars.toMap)
 
-    // Ensure worker socket is closed on task completion. Closing sockets is idempotent.
-    context.addOnCompleteCallback(() =>
+    // Start a thread to feed the process input from our parent's iterator
+    val writerThread = new WriterThread(env, worker, split, context)
+
+    context.addOnCompleteCallback { () =>
+      writerThread.shutdownOnTaskCompletion()
+
+      // Cleanup the worker socket. This will also cause the Python worker to exit.
       try {
         worker.close()
       } catch {
         case e: Exception => logWarning("Failed to close worker socket", e)
       }
-    )
-
-    @volatile var readerException: Exception = null
-
-    // Start a thread to feed the process input from our parent's iterator
-    new Thread("stdin writer for " + pythonExec) {
-      override def run() {
-        try {
-          SparkEnv.set(env)
-          val stream = new BufferedOutputStream(worker.getOutputStream, bufferSize)
-          val dataOut = new DataOutputStream(stream)
-          // Partition index
-          dataOut.writeInt(split.index)
-          // sparkFilesDir
-          PythonRDD.writeUTF(SparkFiles.getRootDirectory, dataOut)
-          // Broadcast variables
-          dataOut.writeInt(broadcastVars.length)
-          for (broadcast <- broadcastVars) {
-            dataOut.writeLong(broadcast.id)
-            dataOut.writeInt(broadcast.value.length)
-            dataOut.write(broadcast.value)
-          }
-          // Python includes (*.zip and *.egg files)
-          dataOut.writeInt(pythonIncludes.length)
-          for (include <- pythonIncludes) {
-            PythonRDD.writeUTF(include, dataOut)
-          }
-          dataOut.flush()
-          // Serialized command:
-          dataOut.writeInt(command.length)
-          dataOut.write(command)
-          // Data values
-          PythonRDD.writeIteratorToStream(parent.iterator(split, context), dataOut)
-          dataOut.flush()
-          worker.shutdownOutput()
-        } catch {
-
-          case e: java.io.FileNotFoundException =>
-            readerException = e
-            Try(worker.shutdownOutput()) // kill Python worker process
-
-          case e: IOException =>
-            // This can happen for legitimate reasons if the Python code stops returning data
-            // before we are done passing elements through, e.g., for take(). Just log a message to
-            // say it happened (as it could also be hiding a real IOException from a data source).
-            logInfo("stdin writer to Python finished early (may not be an error)", e)
-
-          case e: Exception =>
-            // We must avoid throwing exceptions here, because the thread uncaught exception handler
-            // will kill the whole executor (see Executor).
-            readerException = e
-            Try(worker.shutdownOutput()) // kill Python worker process
-        }
-      }
-    }.start()
-
-    // Necessary to distinguish between a task that has failed and a task that is finished
-    @volatile var complete: Boolean = false
-
-    // It is necessary to have a monitor thread for python workers if the user cancels with
-    // interrupts disabled. In that case we will need to explicitly kill the worker, otherwise the
-    // threads can block indefinitely.
-    new Thread(s"Worker Monitor for $pythonExec") {
-      override def run() {
-        // Kill the worker if it is interrupted or completed
-        // When a python task completes, the context is always set to interupted
-        while (!context.interrupted) {
-          Thread.sleep(2000)
-        }
-        if (!complete) {
-          try {
-            logWarning("Incomplete task interrupted: Attempting to kill Python Worker")
-            env.destroyPythonWorker(pythonExec, envVars.toMap)
-          } catch {
-            case e: Exception =>
-              logError("Exception when trying to kill worker", e)
-          }
-        }
-      }
-    }.start()
-
-    /*
-     * Partial fix for SPARK-1019: Attempts to stop reading the input stream since
-     * other completion callbacks might invalidate the input. Because interruption
-     * is not synchronous this still leaves a potential race where the interruption is
-     * processed only after the stream becomes invalid.
-     */
-    context.addOnCompleteCallback{ () =>
-      complete = true // Indicate that the task has completed successfully
-      context.interrupted = true
     }
 
+    writerThread.start()
+    new MonitorThread(env, worker, context).start()
+
     // Return an iterator that read lines from the process's stdout
     val stream = new DataInputStream(new BufferedInputStream(worker.getInputStream, bufferSize))
     val stdoutIterator = new Iterator[Array[Byte]] {
       def next(): Array[Byte] = {
         val obj = _nextObj
         if (hasNext) {
-          // FIXME: can deadlock if worker is waiting for us to
-          // respond to current message (currently irrelevant because
-          // output is shutdown before we read any input)
           _nextObj = read()
         }
         obj
       }
 
       private def read(): Array[Byte] = {
-        if (readerException != null) {
-          throw readerException
+        if (writerThread.exception.isDefined) {
+          throw writerThread.exception.get
         }
         try {
           stream.readInt() match {
@@ -190,13 +105,14 @@ private[spark] class PythonRDD[T: ClassTag](
               val total = finishTime - startTime
               logInfo("Times: total = %s, boot = %s, init = %s, finish = %s".format(total, boot,
                 init, finish))
-              read
+              read()
             case SpecialLengths.PYTHON_EXCEPTION_THROWN =>
               // Signals that an exception has been thrown in python
               val exLength = stream.readInt()
               val obj = new Array[Byte](exLength)
               stream.readFully(obj)
-              throw new PythonException(new String(obj, "utf-8"), readerException)
+              throw new PythonException(new String(obj, "utf-8"),
+                writerThread.exception.getOrElse(null))
             case SpecialLengths.END_OF_DATA_SECTION =>
               // We've finished the data section of the output, but we can still
               // read some accumulator updates:
@@ -210,10 +126,15 @@ private[spark] class PythonRDD[T: ClassTag](
               Array.empty[Byte]
           }
         } catch {
-          case e: Exception if readerException != null =>
+
+          case e: Exception if context.interrupted =>
+            logDebug("Exception thrown after task interruption", e)
+            throw new TaskKilledException
+
+          case e: Exception if writerThread.exception.isDefined =>
             logError("Python worker exited unexpectedly (crashed)", e)
-            logError("Python crash may have been caused by prior exception:", readerException)
-            throw readerException
+            logError("This may have been caused by a prior exception:", writerThread.exception.get)
+            throw writerThread.exception.get
 
           case eof: EOFException =>
             throw new SparkException("Python worker exited unexpectedly (crashed)", eof)
@@ -224,10 +145,100 @@ private[spark] class PythonRDD[T: ClassTag](
 
       def hasNext = _nextObj.length != 0
     }
-    stdoutIterator
+    new InterruptibleIterator(context, stdoutIterator)
   }
 
   val asJavaRDD : JavaRDD[Array[Byte]] = JavaRDD.fromRDD(this)
+
+  /**
+   * The thread responsible for writing the data from the PythonRDD's parent iterator to the
+   * Python process.
+   */
+  class WriterThread(env: SparkEnv, worker: Socket, split: Partition, context: TaskContext)
+    extends Thread(s"stdout writer for $pythonExec") {
+
+    @volatile private var _exception: Exception = null
+
+    setDaemon(true)
+
+    /** Contains the exception thrown while writing the parent iterator to the Python process. */
+    def exception: Option[Exception] = Option(_exception)
+
+    /** Terminates the writer thread, ignoring any exceptions that may occur due to cleanup. */
+    def shutdownOnTaskCompletion() {
+      assert(context.completed)
+      this.interrupt()
+    }
+
+    override def run() {
+      try {
+        SparkEnv.set(env)
+        val stream = new BufferedOutputStream(worker.getOutputStream, bufferSize)
+        val dataOut = new DataOutputStream(stream)
+        // Partition index
+        dataOut.writeInt(split.index)
+        // sparkFilesDir
+        PythonRDD.writeUTF(SparkFiles.getRootDirectory, dataOut)
+        // Broadcast variables
+        dataOut.writeInt(broadcastVars.length)
+        for (broadcast <- broadcastVars) {
+          dataOut.writeLong(broadcast.id)
+          dataOut.writeInt(broadcast.value.length)
+          dataOut.write(broadcast.value)
+        }
+        // Python includes (*.zip and *.egg files)
+        dataOut.writeInt(pythonIncludes.length)
+        for (include <- pythonIncludes) {
+          PythonRDD.writeUTF(include, dataOut)
+        }
+        dataOut.flush()
+        // Serialized command:
+        dataOut.writeInt(command.length)
+        dataOut.write(command)
+        // Data values
+        PythonRDD.writeIteratorToStream(parent.iterator(split, context), dataOut)
+        dataOut.flush()
+      } catch {
+        case e: Exception if context.completed || context.interrupted =>
+          logDebug("Exception thrown after task completion (likely due to cleanup)", e)
+
+        case e: Exception =>
+          // We must avoid throwing exceptions here, because the thread uncaught exception handler
+          // will kill the whole executor (see org.apache.spark.executor.Executor).
+          _exception = e
+      } finally {
+        Try(worker.shutdownOutput()) // kill Python worker process
+      }
+    }
+  }
+
+  /**
+   * It is necessary to have a monitor thread for python workers if the user cancels with
+   * interrupts disabled. In that case we will need to explicitly kill the worker, otherwise the
+   * threads can block indefinitely.
+   */
+  class MonitorThread(env: SparkEnv, worker: Socket, context: TaskContext)
+    extends Thread(s"Worker Monitor for $pythonExec") {
+
+    setDaemon(true)
+
+    override def run() {
+      // Kill the worker if it is interrupted, checking until task completion.
+      // TODO: This has a race condition if interruption occurs, as completed may still become true.
+      while (!context.interrupted && !context.completed) {
+        Thread.sleep(2000)
+      }
+      if (!context.completed) {
+        try {
+          logWarning("Incomplete task interrupted: Attempting to kill Python Worker")
+          env.destroyPythonWorker(pythonExec, envVars.toMap)
+        } catch {
+          case e: Exception =>
+            logError("Exception when trying to kill worker", e)
+        }
+      }
+    }
+  }
 }
 
 /** Thrown for exceptions in user Python code. */
diff --git a/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala b/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala
index 02b62de7e36b..2259df0b56ba 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala
@@ -17,11 +17,13 @@
 
 package org.apache.spark.scheduler
 
+import scala.language.existentials
+
 import java.io._
 import java.util.zip.{GZIPInputStream, GZIPOutputStream}
 
 import scala.collection.mutable.HashMap
-import scala.language.existentials
+import scala.util.Try
 
 import org.apache.spark._
 import org.apache.spark.executor.ShuffleWriteMetrics
@@ -196,7 +198,11 @@ private[spark] class ShuffleMapTask(
     } finally {
       // Release the writers back to the shuffle block manager.
       if (shuffle != null && shuffle.writers != null) {
-        shuffle.releaseWriters(success)
+        try {
+          shuffle.releaseWriters(success)
+        } catch {
+          case e: Exception => logError("Failed to release shuffle writers", e)
+        }
       }
       // Execute the callbacks on task completion.
       context.executeOnCompleteCallbacks()
diff --git a/python/pyspark/context.py b/python/pyspark/context.py
index c7dc85ea0354..cac133d0fcf6 100644
--- a/python/pyspark/context.py
+++ b/python/pyspark/context.py
@@ -453,7 +453,7 @@ def setJobGroup(self, groupId, description, interruptOnCancel=False):
         >>> lock = threading.Lock()
         >>> def map_func(x):
         ...     sleep(100)
-        ...     return x * x
+        ...     raise Exception("Task should have been cancelled")
         >>> def start_job(x):
         ...     global result
         ...     try:
diff --git a/python/pyspark/daemon.py b/python/pyspark/daemon.py
index eb18ec08c913..b2f226a55ec1 100644
--- a/python/pyspark/daemon.py
+++ b/python/pyspark/daemon.py
@@ -74,6 +74,17 @@ def handle_sigchld(*args):
                 raise
     signal.signal(SIGCHLD, handle_sigchld)
 
+    # Blocks until the socket is closed by draining the input stream
+    # until it raises an exception or returns EOF.
+    def waitSocketClose(sock):
+        try:
+            while True:
+                # Empty string is returned upon EOF (and only then).
+                if sock.recv(4096) == '':
+                    return
+        except:
+            pass
+
     # Handle clients
     while not should_exit():
         # Wait until a client arrives or we have to exit
@@ -105,7 +116,8 @@ def handle_sigchld(*args):
                     exit_code = exc.code
                 finally:
                     outfile.flush()
-                    sock.close()
+                    # The Scala side will close the socket upon task completion.
+                    waitSocketClose(sock)
                     os._exit(compute_real_exit_code(exit_code))
             else:
                 sock.close()

From ca43186867f0120c29d1b27cfee0c7ff4a107d84 Mon Sep 17 00:00:00 2001
From: Cheng Hao <hao.cheng@intel.com>
Date: Wed, 7 May 2014 16:54:58 -0400
Subject: [PATCH 117/810] [SQL] Fix Performance Issue in data type casting

Using lazy val object instead of function in the class Cast, which improved the performance nearly by 2X in my local micro-benchmark.

Author: Cheng Hao <hao.cheng@intel.com>

Closes #679 from chenghao-intel/fix_type_casting and squashes the following commits:

71b0902 [Cheng Hao] using lazy val object instead of function for data type casting
---
 .../scala/org/apache/spark/sql/catalyst/expressions/Cast.scala  | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
index 40d2b42a0cda..0b3a4e728ec5 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
@@ -182,7 +182,7 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression {
     case x: NumericType => b => x.numeric.asInstanceOf[Numeric[Any]].toFloat(b)
   }
 
-  def cast: Any => Any = dataType match {
+  private lazy val cast: Any => Any = dataType match {
     case StringType => castToString
     case BinaryType => castToBinary
     case DecimalType => castToDecimal

From 7f6f4a1035ae0c9fa2029fe991f621ca263d53e0 Mon Sep 17 00:00:00 2001
From: Andrew Ash <andrew@andrewash.com>
Date: Wed, 7 May 2014 17:24:12 -0400
Subject: [PATCH 118/810] Nicer logging for SecurityManager startup

Happy to open a jira ticket if you'd like to track one there.

Author: Andrew Ash <andrew@andrewash.com>

Closes #678 from ash211/SecurityManagerLogging and squashes the following commits:

2aa0b7a [Andrew Ash] Nicer logging for SecurityManager startup
---
 core/src/main/scala/org/apache/spark/SecurityManager.scala | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/SecurityManager.scala b/core/src/main/scala/org/apache/spark/SecurityManager.scala
index b4b006780125..74aa441619bd 100644
--- a/core/src/main/scala/org/apache/spark/SecurityManager.scala
+++ b/core/src/main/scala/org/apache/spark/SecurityManager.scala
@@ -146,8 +146,9 @@ private[spark] class SecurityManager(sparkConf: SparkConf) extends Logging {
   setViewAcls(defaultAclUsers, sparkConf.get("spark.ui.view.acls", ""))
 
   private val secretKey = generateSecretKey()
-  logInfo("SecurityManager, is authentication enabled: " + authOn +
-    " are ui acls enabled: " + uiAclsOn + " users with view permissions: " + viewAcls.toString())
+  logInfo("SecurityManager: authentication " + (if (authOn) "enabled" else "disabled") +
+    "; ui acls " + (if (uiAclsOn) "enabled" else "disabled") +
+    "; users with view permissions: " + viewAcls.toString())
 
   // Set our own authenticator to properly negotiate user/password for HTTP connections.
   // This is needed by the HTTP client fetching from the HttpServer. Put here so its

From d00981a95185229fd1594d5c030a00f219fb1a14 Mon Sep 17 00:00:00 2001
From: Andrew Ash <andrew@andrewash.com>
Date: Wed, 7 May 2014 17:24:49 -0400
Subject: [PATCH 119/810] Typo fix: fetchting -> fetching

Author: Andrew Ash <andrew@andrewash.com>

Closes #680 from ash211/patch-3 and squashes the following commits:

9ce3746 [Andrew Ash] Typo fix: fetchting -> fetching
---
 make-distribution.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/make-distribution.sh b/make-distribution.sh
index ebcd8c74fc5a..759e555b4b69 100755
--- a/make-distribution.sh
+++ b/make-distribution.sh
@@ -189,7 +189,7 @@ if [ "$SPARK_TACHYON" == "true" ]; then
   TMPD=`mktemp -d 2>/dev/null || mktemp -d -t 'disttmp'`
 
   pushd $TMPD > /dev/null
-  echo "Fetchting tachyon tgz"
+  echo "Fetching tachyon tgz"
   wget "$TACHYON_URL"
 
   tar xf "tachyon-${TACHYON_VERSION}-bin.tar.gz"

From 5200872243aa5906dc8a06772e61d75f19557aac Mon Sep 17 00:00:00 2001
From: Andrew Or <andrewor14@gmail.com>
Date: Wed, 7 May 2014 14:35:22 -0700
Subject: [PATCH 120/810] [SPARK-1688] Propagate PySpark worker stderr to
 driver

When at least one of the following conditions is true, PySpark cannot be loaded:

1. PYTHONPATH is not set
2. PYTHONPATH does not contain the python directory (or jar, in the case of YARN)
3. The jar does not contain pyspark files (YARN)
4. The jar does not contain py4j files (YARN)

However, we currently throw the same random `java.io.EOFException` for all of the above cases, when trying to read from the python daemon's output. This message is super unhelpful.

This PR includes the python stderr and the PYTHONPATH in the exception propagated to the driver. Now, the exception message looks something like:

```
Error from python worker:
  : No module named pyspark
PYTHONPATH was:
  /path/to/spark/python:/path/to/some/jar
java.io.EOFException
  <stack trace>
```

whereas before it was just

```
java.io.EOFException
  <stack trace>
```

Author: Andrew Or <andrewor14@gmail.com>

Closes #603 from andrewor14/pyspark-exception and squashes the following commits:

10d65d3 [Andrew Or] Throwable -> Exception, worker -> daemon
862d1d7 [Andrew Or] Merge branch 'master' of github.com:apache/spark into pyspark-exception
a5ed798 [Andrew Or] Use block string and interpolation instead of var (minor)
cc09c45 [Andrew Or] Account for the fact that the python daemon may not have terminated yet
444f019 [Andrew Or] Use the new RedirectThread + include system PYTHONPATH
aab00ae [Andrew Or] Merge branch 'master' of github.com:apache/spark into pyspark-exception
0cc2402 [Andrew Or] Merge branch 'master' of github.com:apache/spark into pyspark-exception
783efe2 [Andrew Or] Make python daemon stderr indentation consistent
9524172 [Andrew Or] Avoid potential NPE / error stream contention + Move things around
29f9688 [Andrew Or] Add back original exception type
e92d36b [Andrew Or] Include python worker stderr in the exception propagated to the driver
7c69360 [Andrew Or] Merge branch 'master' of github.com:apache/spark into pyspark-exception
cdbc185 [Andrew Or] Fix python attribute not found exception when PYTHONPATH is not set
dcc0353 [Andrew Or] Check both python and system environment variables for PYTHONPATH
6c09c21 [Andrew Or] Validate PYTHONPATH and PySpark modules before starting python workers
---
 .../apache/spark/api/python/PythonUtils.scala |  27 +++-
 .../api/python/PythonWorkerFactory.scala      | 136 ++++++++----------
 .../apache/spark/deploy/PythonRunner.scala    |  24 +---
 .../scala/org/apache/spark/util/Utils.scala   |  37 +++++
 4 files changed, 123 insertions(+), 101 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/api/python/PythonUtils.scala b/core/src/main/scala/org/apache/spark/api/python/PythonUtils.scala
index cf69fa1d53fd..6d3e257c4d5d 100644
--- a/core/src/main/scala/org/apache/spark/api/python/PythonUtils.scala
+++ b/core/src/main/scala/org/apache/spark/api/python/PythonUtils.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.api.python
 
-import java.io.File
+import java.io.{File, InputStream, IOException, OutputStream}
 
 import scala.collection.mutable.ArrayBuffer
 
@@ -40,3 +40,28 @@ private[spark] object PythonUtils {
     paths.filter(_ != "").mkString(File.pathSeparator)
   }
 }
+
+
+/**
+ * A utility class to redirect the child process's stdout or stderr.
+ */
+private[spark] class RedirectThread(
+    in: InputStream,
+    out: OutputStream,
+    name: String)
+  extends Thread(name) {
+
+  setDaemon(true)
+  override def run() {
+    scala.util.control.Exception.ignoring(classOf[IOException]) {
+      // FIXME: We copy the stream on the level of bytes to avoid encoding problems.
+      val buf = new Array[Byte](1024)
+      var len = in.read(buf)
+      while (len != -1) {
+        out.write(buf, 0, len)
+        out.flush()
+        len = in.read(buf)
+      }
+    }
+  }
+}
diff --git a/core/src/main/scala/org/apache/spark/api/python/PythonWorkerFactory.scala b/core/src/main/scala/org/apache/spark/api/python/PythonWorkerFactory.scala
index b0bf4e052b3e..002f2acd94de 100644
--- a/core/src/main/scala/org/apache/spark/api/python/PythonWorkerFactory.scala
+++ b/core/src/main/scala/org/apache/spark/api/python/PythonWorkerFactory.scala
@@ -17,15 +17,18 @@
 
 package org.apache.spark.api.python
 
-import java.io.{DataInputStream, File, IOException, OutputStreamWriter}
+import java.io.{DataInputStream, InputStream, OutputStreamWriter}
 import java.net.{InetAddress, ServerSocket, Socket, SocketException}
 
 import scala.collection.JavaConversions._
 
 import org.apache.spark._
+import org.apache.spark.util.Utils
 
 private[spark] class PythonWorkerFactory(pythonExec: String, envVars: Map[String, String])
-    extends Logging {
+  extends Logging {
+
+  import PythonWorkerFactory._
 
   // Because forking processes from Java is expensive, we prefer to launch a single Python daemon
   // (pyspark/daemon.py) and tell it to fork new workers for our tasks. This daemon currently
@@ -38,7 +41,9 @@ private[spark] class PythonWorkerFactory(pythonExec: String, envVars: Map[String
   var daemonPort: Int = 0
 
   val pythonPath = PythonUtils.mergePythonPaths(
-    PythonUtils.sparkPythonPath, envVars.getOrElse("PYTHONPATH", ""))
+    PythonUtils.sparkPythonPath,
+    envVars.getOrElse("PYTHONPATH", ""),
+    sys.env.getOrElse("PYTHONPATH", ""))
 
   def create(): Socket = {
     if (useDaemon) {
@@ -61,12 +66,11 @@ private[spark] class PythonWorkerFactory(pythonExec: String, envVars: Map[String
       try {
         new Socket(daemonHost, daemonPort)
       } catch {
-        case exc: SocketException => {
+        case exc: SocketException =>
           logWarning("Python daemon unexpectedly quit, attempting to restart")
           stopDaemon()
           startDaemon()
           new Socket(daemonHost, daemonPort)
-        }
         case e: Throwable => throw e
       }
     }
@@ -87,39 +91,8 @@ private[spark] class PythonWorkerFactory(pythonExec: String, envVars: Map[String
       workerEnv.put("PYTHONPATH", pythonPath)
       val worker = pb.start()
 
-      // Redirect the worker's stderr to ours
-      new Thread("stderr reader for " + pythonExec) {
-        setDaemon(true)
-        override def run() {
-          scala.util.control.Exception.ignoring(classOf[IOException]) {
-            // FIXME: We copy the stream on the level of bytes to avoid encoding problems.
-            val in = worker.getErrorStream
-            val buf = new Array[Byte](1024)
-            var len = in.read(buf)
-            while (len != -1) {
-              System.err.write(buf, 0, len)
-              len = in.read(buf)
-            }
-          }
-        }
-      }.start()
-
-      // Redirect worker's stdout to our stderr
-      new Thread("stdout reader for " + pythonExec) {
-        setDaemon(true)
-        override def run() {
-          scala.util.control.Exception.ignoring(classOf[IOException]) {
-            // FIXME: We copy the stream on the level of bytes to avoid encoding problems.
-            val in = worker.getInputStream
-            val buf = new Array[Byte](1024)
-            var len = in.read(buf)
-            while (len != -1) {
-              System.err.write(buf, 0, len)
-              len = in.read(buf)
-            }
-          }
-        }
-      }.start()
+      // Redirect worker stdout and stderr
+      redirectStreamsToStderr(worker.getInputStream, worker.getErrorStream)
 
       // Tell the worker our port
       val out = new OutputStreamWriter(worker.getOutputStream)
@@ -142,10 +115,6 @@ private[spark] class PythonWorkerFactory(pythonExec: String, envVars: Map[String
     null
   }
 
-  def stop() {
-    stopDaemon()
-  }
-
   private def startDaemon() {
     synchronized {
       // Is it already running?
@@ -161,46 +130,38 @@ private[spark] class PythonWorkerFactory(pythonExec: String, envVars: Map[String
         workerEnv.put("PYTHONPATH", pythonPath)
         daemon = pb.start()
 
-        // Redirect the stderr to ours
-        new Thread("stderr reader for " + pythonExec) {
-          setDaemon(true)
-          override def run() {
-            scala.util.control.Exception.ignoring(classOf[IOException]) {
-              // FIXME: We copy the stream on the level of bytes to avoid encoding problems.
-              val in = daemon.getErrorStream
-              val buf = new Array[Byte](1024)
-              var len = in.read(buf)
-              while (len != -1) {
-                System.err.write(buf, 0, len)
-                len = in.read(buf)
-              }
-            }
-          }
-        }.start()
-
         val in = new DataInputStream(daemon.getInputStream)
         daemonPort = in.readInt()
 
-        // Redirect further stdout output to our stderr
-        new Thread("stdout reader for " + pythonExec) {
-          setDaemon(true)
-          override def run() {
-            scala.util.control.Exception.ignoring(classOf[IOException]) {
-              // FIXME: We copy the stream on the level of bytes to avoid encoding problems.
-              val buf = new Array[Byte](1024)
-              var len = in.read(buf)
-              while (len != -1) {
-                System.err.write(buf, 0, len)
-                len = in.read(buf)
-              }
-            }
-          }
-        }.start()
+        // Redirect daemon stdout and stderr
+        redirectStreamsToStderr(in, daemon.getErrorStream)
+
       } catch {
-        case e: Throwable => {
+        case e: Exception =>
+
+          // If the daemon exists, wait for it to finish and get its stderr
+          val stderr = Option(daemon)
+            .flatMap { d => Utils.getStderr(d, PROCESS_WAIT_TIMEOUT_MS) }
+            .getOrElse("")
+
           stopDaemon()
-          throw e
-        }
+
+          if (stderr != "") {
+            val formattedStderr = stderr.replace("\n", "\n  ")
+            val errorMessage = s"""
+              |Error from python worker:
+              |  $formattedStderr
+              |PYTHONPATH was:
+              |  $pythonPath
+              |$e"""
+
+            // Append error message from python daemon, but keep original stack trace
+            val wrappedException = new SparkException(errorMessage.stripMargin)
+            wrappedException.setStackTrace(e.getStackTrace)
+            throw wrappedException
+          } else {
+            throw e
+          }
       }
 
       // Important: don't close daemon's stdin (daemon.getOutputStream) so it can correctly
@@ -208,6 +169,19 @@ private[spark] class PythonWorkerFactory(pythonExec: String, envVars: Map[String
     }
   }
 
+  /**
+   * Redirect the given streams to our stderr in separate threads.
+   */
+  private def redirectStreamsToStderr(stdout: InputStream, stderr: InputStream) {
+    try {
+      new RedirectThread(stdout, System.err, "stdout reader for " + pythonExec).start()
+      new RedirectThread(stderr, System.err, "stderr reader for " + pythonExec).start()
+    } catch {
+      case e: Exception =>
+        logError("Exception in redirecting streams", e)
+    }
+  }
+
   private def stopDaemon() {
     synchronized {
       // Request shutdown of existing daemon by sending SIGTERM
@@ -219,4 +193,12 @@ private[spark] class PythonWorkerFactory(pythonExec: String, envVars: Map[String
       daemonPort = 0
     }
   }
+
+  def stop() {
+    stopDaemon()
+  }
+}
+
+private object PythonWorkerFactory {
+  val PROCESS_WAIT_TIMEOUT_MS = 10000
 }
diff --git a/core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala b/core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala
index f2e7c7a508b3..e20d4486c8f0 100644
--- a/core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala
@@ -17,13 +17,10 @@
 
 package org.apache.spark.deploy
 
-import java.io.{IOException, File, InputStream, OutputStream}
-
 import scala.collection.mutable.ArrayBuffer
 import scala.collection.JavaConversions._
 
-import org.apache.spark.SparkContext
-import org.apache.spark.api.python.PythonUtils
+import org.apache.spark.api.python.{PythonUtils, RedirectThread}
 
 /**
  * A main class used by spark-submit to launch Python applications. It executes python as a
@@ -62,23 +59,4 @@ object PythonRunner {
 
     System.exit(process.waitFor())
   }
-
-  /**
-   * A utility class to redirect the child process's stdout or stderr
-   */
-  class RedirectThread(in: InputStream, out: OutputStream, name: String) extends Thread(name) {
-    setDaemon(true)
-    override def run() {
-      scala.util.control.Exception.ignoring(classOf[IOException]) {
-        // FIXME: We copy the stream on the level of bytes to avoid encoding problems.
-        val buf = new Array[Byte](1024)
-        var len = in.read(buf)
-        while (len != -1) {
-          out.write(buf, 0, len)
-          out.flush()
-          len = in.read(buf)
-        }
-      }
-    }
-  }
 }
diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala
index 202bd46956f8..3f0ed61c5bbf 100644
--- a/core/src/main/scala/org/apache/spark/util/Utils.scala
+++ b/core/src/main/scala/org/apache/spark/util/Utils.scala
@@ -1088,4 +1088,41 @@ private[spark] object Utils extends Logging {
   def stripDirectory(path: String): String = {
     path.split(File.separator).last
   }
+
+  /**
+   * Wait for a process to terminate for at most the specified duration.
+   * Return whether the process actually terminated after the given timeout.
+   */
+  def waitForProcess(process: Process, timeoutMs: Long): Boolean = {
+    var terminated = false
+    val startTime = System.currentTimeMillis
+    while (!terminated) {
+      try {
+        process.exitValue
+        terminated = true
+      } catch {
+        case e: IllegalThreadStateException =>
+          // Process not terminated yet
+          if (System.currentTimeMillis - startTime > timeoutMs) {
+            return false
+          }
+          Thread.sleep(100)
+      }
+    }
+    true
+  }
+
+  /**
+   * Return the stderr of a process after waiting for the process to terminate.
+   * If the process does not terminate within the specified timeout, return None.
+   */
+  def getStderr(process: Process, timeoutMs: Long): Option[String] = {
+    val terminated = Utils.waitForProcess(process, timeoutMs)
+    if (terminated) {
+      Some(Source.fromInputStream(process.getErrorStream).getLines().mkString("\n"))
+    } else {
+      None
+    }
+  }
+
 }

From 4bec84b6a23e1e642708a70a6c7ef7b3d1df9b3e Mon Sep 17 00:00:00 2001
From: Thomas Graves <tgraves@apache.org>
Date: Wed, 7 May 2014 15:51:53 -0700
Subject: [PATCH 121/810] SPARK-1569 Spark on Yarn, authentication broken by
 pr299

Pass the configs as java options since the executor needs to know before it registers whether to create the connection using authentication or not.    We could see about passing only the authentication configs but for now I just had it pass them all.

I also updating it to use a list to construct the command to make it the same as ClientBase and avoid any issues with spaces.

Author: Thomas Graves <tgraves@apache.org>

Closes #649 from tgravescs/SPARK-1569 and squashes the following commits:

0178ab8 [Thomas Graves] add akka settings
22a8735 [Thomas Graves] Change to only path spark.auth* configs
8ccc1d4 [Thomas Graves] SPARK-1569 Spark on Yarn, authentication broken
---
 .../deploy/yarn/ExecutorRunnableUtil.scala    | 49 ++++++++++++-------
 1 file changed, 30 insertions(+), 19 deletions(-)

diff --git a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnableUtil.scala b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnableUtil.scala
index 96f8aa93394f..32f8861dc950 100644
--- a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnableUtil.scala
+++ b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnableUtil.scala
@@ -21,7 +21,7 @@ import java.io.File
 import java.net.URI
 
 import scala.collection.JavaConversions._
-import scala.collection.mutable.HashMap
+import scala.collection.mutable.{HashMap, ListBuffer}
 
 import org.apache.hadoop.fs.Path
 import org.apache.hadoop.yarn.api._
@@ -44,9 +44,9 @@ trait ExecutorRunnableUtil extends Logging {
       hostname: String,
       executorMemory: Int,
       executorCores: Int,
-      localResources: HashMap[String, LocalResource]) = {
+      localResources: HashMap[String, LocalResource]): List[String] = {
     // Extra options for the JVM
-    var JAVA_OPTS = ""
+    val JAVA_OPTS = ListBuffer[String]()
     // Set the JVM memory
     val executorMemoryString = executorMemory + "m"
     JAVA_OPTS += "-Xms" + executorMemoryString + " -Xmx" + executorMemoryString + " "
@@ -56,10 +56,21 @@ trait ExecutorRunnableUtil extends Logging {
       JAVA_OPTS += opts
     }
 
-    JAVA_OPTS += " -Djava.io.tmpdir=" +
-      new Path(Environment.PWD.$(), YarnConfiguration.DEFAULT_CONTAINER_TEMP_DIR) + " "
+    JAVA_OPTS += "-Djava.io.tmpdir=" +
+      new Path(Environment.PWD.$(), YarnConfiguration.DEFAULT_CONTAINER_TEMP_DIR)
     JAVA_OPTS += ClientBase.getLog4jConfiguration(localResources)
 
+    // Certain configs need to be passed here because they are needed before the Executor
+    // registers with the Scheduler and transfers the spark configs. Since the Executor backend
+    // uses Akka to connect to the scheduler, the akka settings are needed as well as the
+    // authentication settings.
+    sparkConf.getAll.
+      filter { case (k, v) => k.startsWith("spark.auth") || k.startsWith("spark.akka") }.
+      foreach { case (k, v) => JAVA_OPTS += "-D" + k + "=" + "\\\"" + v + "\\\"" }
+
+    sparkConf.getAkkaConf.
+      foreach { case (k, v) => JAVA_OPTS += "-D" + k + "=" + "\\\"" + v + "\\\"" }
+
     // Commenting it out for now - so that people can refer to the properties if required. Remove
     // it once cpuset version is pushed out.
     // The context is, default gc for server class machines end up using all cores to do gc - hence
@@ -85,25 +96,25 @@ trait ExecutorRunnableUtil extends Logging {
         }
     */
 
-    val commands = List[String](
-      Environment.JAVA_HOME.$() + "/bin/java" +
-      " -server " +
+    val commands = Seq(Environment.JAVA_HOME.$() + "/bin/java",
+      "-server",
       // Kill if OOM is raised - leverage yarn's failure handling to cause rescheduling.
       // Not killing the task leaves various aspects of the executor and (to some extent) the jvm in
       // an inconsistent state.
       // TODO: If the OOM is not recoverable by rescheduling it on different node, then do
       // 'something' to fail job ... akin to blacklisting trackers in mapred ?
-      " -XX:OnOutOfMemoryError='kill %p' " +
-      JAVA_OPTS +
-      " org.apache.spark.executor.CoarseGrainedExecutorBackend " +
-      masterAddress + " " +
-      slaveId + " " +
-      hostname + " " +
-      executorCores +
-      " 1> " + ApplicationConstants.LOG_DIR_EXPANSION_VAR + "/stdout" +
-      " 2> " + ApplicationConstants.LOG_DIR_EXPANSION_VAR + "/stderr")
-
-    commands
+      "-XX:OnOutOfMemoryError='kill %p'") ++
+      JAVA_OPTS ++
+      Seq("org.apache.spark.executor.CoarseGrainedExecutorBackend",
+      masterAddress.toString,
+      slaveId.toString,
+      hostname.toString,
+      executorCores.toString,
+      "1>", ApplicationConstants.LOG_DIR_EXPANSION_VAR + "/stdout",
+      "2>", ApplicationConstants.LOG_DIR_EXPANSION_VAR + "/stderr")
+
+    // TODO: it would be nicer to just make sure there are no null commands here
+    commands.map(s => if (s == null) "null" else s).toList
   }
 
   private def setupDistributedCache(

From 3188553f73970270717a7fee4a116e29ad4becc9 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Wed, 7 May 2014 16:01:11 -0700
Subject: [PATCH 122/810] [SPARK-1743][MLLIB] add loadLibSVMFile and
 saveAsLibSVMFile to pyspark

Make loading/saving labeled data easier for pyspark users.

Also changed type check in `SparseVector` to allow numpy integers.

Author: Xiangrui Meng <meng@databricks.com>

Closes #672 from mengxr/pyspark-mllib-util and squashes the following commits:

2943fa7 [Xiangrui Meng] format docs
d61668d [Xiangrui Meng] add loadLibSVMFile and saveAsLibSVMFile to pyspark
---
 python/pyspark/mllib/linalg.py |   3 +-
 python/pyspark/mllib/util.py   | 177 +++++++++++++++++++++++++++++++++
 2 files changed, 178 insertions(+), 2 deletions(-)
 create mode 100644 python/pyspark/mllib/util.py

diff --git a/python/pyspark/mllib/linalg.py b/python/pyspark/mllib/linalg.py
index 0aa3a51de706..7511ca7573dd 100644
--- a/python/pyspark/mllib/linalg.py
+++ b/python/pyspark/mllib/linalg.py
@@ -49,8 +49,7 @@ def __init__(self, size, *args):
         >>> print SparseVector(4, [1, 3], [1.0, 5.5])
         [1: 1.0, 3: 5.5]
         """
-        assert type(size) == int, "first argument must be an int"
-        self.size = size
+        self.size = int(size)
         assert 1 <= len(args) <= 2, "must pass either 2 or 3 arguments"
         if len(args) == 1:
             pairs = args[0]
diff --git a/python/pyspark/mllib/util.py b/python/pyspark/mllib/util.py
new file mode 100644
index 000000000000..50d0cdd08762
--- /dev/null
+++ b/python/pyspark/mllib/util.py
@@ -0,0 +1,177 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import numpy as np
+
+from pyspark.mllib.linalg import Vectors, SparseVector
+from pyspark.mllib.regression import LabeledPoint
+from pyspark.mllib._common import _convert_vector
+
+class MLUtils:
+    """
+    Helper methods to load, save and pre-process data used in MLlib.
+    """
+
+    @staticmethod
+    def _parse_libsvm_line(line, multiclass):
+        """
+        Parses a line in LIBSVM format into (label, indices, values).
+        """
+        items = line.split(None)
+        label = float(items[0])
+        if not multiclass:
+            label = 1.0 if label > 0.5 else 0.0
+        nnz = len(items) - 1
+        indices = np.zeros(nnz, dtype=np.int32)
+        values = np.zeros(nnz)
+        for i in xrange(nnz):
+            index, value = items[1 + i].split(":")
+            indices[i] = int(index) - 1
+            values[i] = float(value)
+        return label, indices, values
+
+
+    @staticmethod
+    def _convert_labeled_point_to_libsvm(p):
+        """Converts a LabeledPoint to a string in LIBSVM format."""
+        items = [str(p.label)]
+        v = _convert_vector(p.features)
+        if type(v) == np.ndarray:
+            for i in xrange(len(v)):
+                items.append(str(i + 1) + ":" + str(v[i]))
+        elif type(v) == SparseVector:
+            nnz = len(v.indices)
+            for i in xrange(nnz):
+                items.append(str(v.indices[i] + 1) + ":" + str(v.values[i]))
+        else:
+            raise TypeError("_convert_labeled_point_to_libsvm needs either ndarray or SparseVector"
+                            " but got " % type(v))
+        return " ".join(items)
+
+
+    @staticmethod
+    def loadLibSVMFile(sc, path, multiclass=False, numFeatures=-1, minPartitions=None):
+        """
+        Loads labeled data in the LIBSVM format into an RDD of
+        LabeledPoint. The LIBSVM format is a text-based format used by
+        LIBSVM and LIBLINEAR. Each line represents a labeled sparse
+        feature vector using the following format:
+
+        label index1:value1 index2:value2 ...
+
+        where the indices are one-based and in ascending order. This
+        method parses each line into a LabeledPoint, where the feature
+        indices are converted to zero-based.
+
+        @param sc: Spark context
+        @param path: file or directory path in any Hadoop-supported file
+                     system URI
+        @param multiclass: whether the input labels contain more than
+                           two classes. If false, any label with value
+                           greater than 0.5 will be mapped to 1.0, or
+                           0.0 otherwise. So it works for both +1/-1 and
+                           1/0 cases. If true, the double value parsed
+                           directly from the label string will be used
+                           as the label value.
+        @param numFeatures: number of features, which will be determined
+                            from the input data if a nonpositive value
+                            is given. This is useful when the dataset is
+                            already split into multiple files and you
+                            want to load them separately, because some
+                            features may not present in certain files,
+                            which leads to inconsistent feature
+                            dimensions.
+        @param minPartitions: min number of partitions
+        @return: labeled data stored as an RDD of LabeledPoint
+
+        >>> from tempfile import NamedTemporaryFile
+        >>> from pyspark.mllib.util import MLUtils
+        >>> tempFile = NamedTemporaryFile(delete=True)
+        >>> tempFile.write("+1 1:1.0 3:2.0 5:3.0\\n-1\\n-1 2:4.0 4:5.0 6:6.0")
+        >>> tempFile.flush()
+        >>> examples = MLUtils.loadLibSVMFile(sc, tempFile.name).collect()
+        >>> multiclass_examples = MLUtils.loadLibSVMFile(sc, tempFile.name, True).collect()
+        >>> tempFile.close()
+        >>> examples[0].label
+        1.0
+        >>> examples[0].features.size
+        6
+        >>> print examples[0].features
+        [0: 1.0, 2: 2.0, 4: 3.0]
+        >>> examples[1].label
+        0.0
+        >>> examples[1].features.size
+        6
+        >>> print examples[1].features
+        []
+        >>> examples[2].label
+        0.0
+        >>> examples[2].features.size
+        6
+        >>> print examples[2].features
+        [1: 4.0, 3: 5.0, 5: 6.0]
+        >>> multiclass_examples[1].label
+        -1.0
+        """
+
+        lines = sc.textFile(path, minPartitions)
+        parsed = lines.map(lambda l: MLUtils._parse_libsvm_line(l, multiclass))
+        if numFeatures <= 0:
+            parsed.cache()
+            numFeatures = parsed.map(lambda x: 0 if x[1].size == 0 else x[1][-1]).reduce(max) + 1
+        return parsed.map(lambda x: LabeledPoint(x[0], Vectors.sparse(numFeatures, x[1], x[2])))
+
+
+    @staticmethod
+    def saveAsLibSVMFile(data, dir):
+        """
+        Save labeled data in LIBSVM format.
+
+        @param data: an RDD of LabeledPoint to be saved
+        @param dir: directory to save the data
+
+        >>> from tempfile import NamedTemporaryFile
+        >>> from fileinput import input
+        >>> from glob import glob
+        >>> from pyspark.mllib.util import MLUtils
+        >>> examples = [LabeledPoint(1.1, Vectors.sparse(3, [(0, 1.23), (2, 4.56)])), \
+                        LabeledPoint(0.0, Vectors.dense([1.01, 2.02, 3.03]))]
+        >>> tempFile = NamedTemporaryFile(delete=True)
+        >>> tempFile.close()
+        >>> MLUtils.saveAsLibSVMFile(sc.parallelize(examples), tempFile.name)
+        >>> ''.join(sorted(input(glob(tempFile.name + "/part-0000*"))))
+        '0.0 1:1.01 2:2.02 3:3.03\\n1.1 1:1.23 3:4.56\\n'
+        """
+        lines = data.map(lambda p: MLUtils._convert_labeled_point_to_libsvm(p))
+        lines.saveAsTextFile(dir)
+
+
+def _test():
+    import doctest
+    from pyspark.context import SparkContext
+    globs = globals().copy()
+    # The small batch size here ensures that we see multiple batches,
+    # even in these small test examples:
+    globs['sc'] = SparkContext('local[2]', 'PythonTest', batchSize=2)
+    (failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
+    globs['sc'].stop()
+    if failure_count:
+        exit(-1)
+
+
+if __name__ == "__main__":
+    _test()

From 0c19bb161b9b2b96c0c55d3ea09e81fd798cbec0 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?baishuo=28=E7=99=BD=E7=A1=95=29?= <vc_java@hotmail.com>
Date: Wed, 7 May 2014 16:02:55 -0700
Subject: [PATCH 123/810] Update GradientDescentSuite.scala
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

use more faster way to construct an array

Author: baishuo(白硕) <vc_java@hotmail.com>

Closes #588 from baishuo/master and squashes the following commits:

45b95fb [baishuo(白硕)] Update GradientDescentSuite.scala
c03b61c [baishuo(白硕)] Update GradientDescentSuite.scala
b666d27 [baishuo(白硕)] Update GradientDescentSuite.scala
---
 .../spark/mllib/optimization/GradientDescentSuite.scala     | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/mllib/src/test/scala/org/apache/spark/mllib/optimization/GradientDescentSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/optimization/GradientDescentSuite.scala
index c4b433499a09..8a16284118cf 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/optimization/GradientDescentSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/optimization/GradientDescentSuite.scala
@@ -81,11 +81,11 @@ class GradientDescentSuite extends FunSuite with LocalSparkContext with ShouldMa
     // Add a extra variable consisting of all 1.0's for the intercept.
     val testData = GradientDescentSuite.generateGDInput(A, B, nPoints, 42)
     val data = testData.map { case LabeledPoint(label, features) =>
-      label -> Vectors.dense(1.0, features.toArray: _*)
+      label -> Vectors.dense(1.0 +: features.toArray)
     }
 
     val dataRDD = sc.parallelize(data, 2).cache()
-    val initialWeightsWithIntercept = Vectors.dense(1.0, initialWeights: _*)
+    val initialWeightsWithIntercept = Vectors.dense(1.0 +: initialWeights.toArray)
 
     val (_, loss) = GradientDescent.runMiniBatchSGD(
       dataRDD,
@@ -111,7 +111,7 @@ class GradientDescentSuite extends FunSuite with LocalSparkContext with ShouldMa
     // Add a extra variable consisting of all 1.0's for the intercept.
     val testData = GradientDescentSuite.generateGDInput(2.0, -1.5, 10000, 42)
     val data = testData.map { case LabeledPoint(label, features) =>
-      label -> Vectors.dense(1.0, features.toArray: _*)
+      label -> Vectors.dense(1.0 +: features.toArray)
     }
 
     val dataRDD = sc.parallelize(data, 2).cache()

From f269b016acb17b24d106dc2b32a1be389489bb01 Mon Sep 17 00:00:00 2001
From: Manish Amde <manish9ue@gmail.com>
Date: Wed, 7 May 2014 17:08:38 -0700
Subject: [PATCH 124/810] SPARK-1544 Add support for deep decision trees.

@etrain and I came with a PR for arbitrarily deep decision trees at the cost of multiple passes over the data at deep tree levels.

To summarize:
1) We take a parameter that indicates the amount of memory users want to reserve for computation on each worker (and 2x that at the driver).
2) Using that information, we calculate two things - the maximum depth to which we train as usual (which is, implicitly, the maximum number of nodes we want to train in parallel), and the size of the groups we should use in the case where we exceed this depth.

cc: @atalwalkar, @hirakendu, @mengxr

Author: Manish Amde <manish9ue@gmail.com>
Author: manishamde <manish9ue@gmail.com>
Author: Evan Sparks <sparks@cs.berkeley.edu>

Closes #475 from manishamde/deep_tree and squashes the following commits:

968ca9d [Manish Amde] merged master
7fc9545 [Manish Amde] added docs
ce004a1 [Manish Amde] minor formatting
b27ad2c [Manish Amde] formatting
426bb28 [Manish Amde] programming guide blurb
8053fed [Manish Amde] more formatting
5eca9e4 [Manish Amde] grammar
4731cda [Manish Amde] formatting
5e82202 [Manish Amde] added documentation, fixed off by 1 error in max level calculation
cbd9f14 [Manish Amde] modified scala.math to math
dad9652 [Manish Amde] removed unused imports
e0426ee [Manish Amde] renamed parameter
718506b [Manish Amde] added unit test
1517155 [Manish Amde] updated documentation
9dbdabe [Manish Amde] merge from master
719d009 [Manish Amde] updating user documentation
fecf89a [manishamde] Merge pull request #6 from etrain/deep_tree
0287772 [Evan Sparks] Fixing scalastyle issue.
2f1e093 [Manish Amde] minor: added doc for maxMemory parameter
2f6072c [manishamde] Merge pull request #5 from etrain/deep_tree
abc5a23 [Evan Sparks] Parameterizing max memory.
50b143a [Manish Amde] adding support for very deep trees
---
 docs/mllib-decision-tree.md                   |  15 +--
 .../examples/mllib/DecisionTreeRunner.scala   |   2 +-
 .../spark/mllib/tree/DecisionTree.scala       | 103 ++++++++++++++++--
 .../mllib/tree/configuration/Strategy.scala   |   6 +-
 .../spark/mllib/tree/DecisionTreeSuite.scala  |  84 ++++++++++++--
 5 files changed, 177 insertions(+), 33 deletions(-)

diff --git a/docs/mllib-decision-tree.md b/docs/mllib-decision-tree.md
index 296277e58b34..acf0feff42a8 100644
--- a/docs/mllib-decision-tree.md
+++ b/docs/mllib-decision-tree.md
@@ -93,17 +93,14 @@ The recursive tree construction is stopped at a node when one of the two conditi
 1. The node depth is equal to the `maxDepth` training parameter
 2. No split candidate leads to an information gain at the node.
 
+### Max memory requirements
+
+For faster processing, the decision tree algorithm performs simultaneous histogram computations for all nodes at each level of the tree. This could lead to high memory requirements at deeper levels of the tree leading to memory overflow errors. To alleviate this problem, a 'maxMemoryInMB' training parameter is provided which specifies the maximum amount of memory at the workers (twice as much at the master) to be allocated to the histogram computation. The default value is conservatively chosen to be 128 MB to allow the decision algorithm to work in most scenarios. Once the memory requirements for a level-wise computation crosses the `maxMemoryInMB` threshold, the node training tasks at each subsequent level is split into smaller tasks.
+
 ### Practical limitations
 
-1. The tree implementation stores an `Array[Double]` of size *O(#features \* #splits \* 2^maxDepth)*
-   in memory for aggregating histograms over partitions. The current implementation might not scale
-   to very deep trees since the memory requirement grows exponentially with tree depth.
-2. The implemented algorithm reads both sparse and dense data. However, it is not optimized for
-   sparse input.
-3. Python is not supported in this release.
- 
-We are planning to solve these problems in the near future. Please drop us a line if you encounter
-any issues.
+1. The implemented algorithm reads both sparse and dense data. However, it is not optimized for sparse input.
+2. Python is not supported in this release.
 
 ## Examples
 
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/DecisionTreeRunner.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/DecisionTreeRunner.scala
index 0bd847d7bab3..9832bec90d7e 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/DecisionTreeRunner.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/DecisionTreeRunner.scala
@@ -51,7 +51,7 @@ object DecisionTreeRunner {
       algo: Algo = Classification,
       maxDepth: Int = 5,
       impurity: ImpurityType = Gini,
-      maxBins: Int = 20)
+      maxBins: Int = 100)
 
   def main(args: Array[String]) {
     val defaultParams = Params()
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
index 59ed01debf15..0fe30a3e7040 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
@@ -54,12 +54,13 @@ class DecisionTree (private val strategy: Strategy) extends Serializable with Lo
     // Find the splits and the corresponding bins (interval between the splits) using a sample
     // of the input data.
     val (splits, bins) = DecisionTree.findSplitsBins(input, strategy)
-    logDebug("numSplits = " + bins(0).length)
+    val numBins = bins(0).length
+    logDebug("numBins = " + numBins)
 
     // depth of the decision tree
     val maxDepth = strategy.maxDepth
     // the max number of nodes possible given the depth of the tree
-    val maxNumNodes = scala.math.pow(2, maxDepth).toInt - 1
+    val maxNumNodes = math.pow(2, maxDepth).toInt - 1
     // Initialize an array to hold filters applied to points for each node.
     val filters = new Array[List[Filter]](maxNumNodes)
     // The filter at the top node is an empty list.
@@ -68,7 +69,28 @@ class DecisionTree (private val strategy: Strategy) extends Serializable with Lo
     val parentImpurities = new Array[Double](maxNumNodes)
     // dummy value for top node (updated during first split calculation)
     val nodes = new Array[Node](maxNumNodes)
+    // num features
+    val numFeatures = input.take(1)(0).features.size
+
+    // Calculate level for single group construction
 
+    // Max memory usage for aggregates
+    val maxMemoryUsage = strategy.maxMemoryInMB * 1024 * 1024
+    logDebug("max memory usage for aggregates = " + maxMemoryUsage + " bytes.")
+    val numElementsPerNode =
+      strategy.algo match {
+        case Classification => 2 * numBins * numFeatures
+        case Regression => 3 * numBins * numFeatures
+      }
+
+    logDebug("numElementsPerNode = " + numElementsPerNode)
+    val arraySizePerNode = 8 * numElementsPerNode // approx. memory usage for bin aggregate array
+    val maxNumberOfNodesPerGroup = math.max(maxMemoryUsage / arraySizePerNode, 1)
+    logDebug("maxNumberOfNodesPerGroup = " + maxNumberOfNodesPerGroup)
+    // nodes at a level is 2^level. level is zero indexed.
+    val maxLevelForSingleGroup = math.max(
+      (math.log(maxNumberOfNodesPerGroup) / math.log(2)).floor.toInt, 0)
+    logDebug("max level for single group = " + maxLevelForSingleGroup)
 
     /*
      * The main idea here is to perform level-wise training of the decision tree nodes thus
@@ -88,7 +110,7 @@ class DecisionTree (private val strategy: Strategy) extends Serializable with Lo
 
       // Find best split for all nodes at a level.
       val splitsStatsForLevel = DecisionTree.findBestSplits(input, parentImpurities, strategy,
-        level, filters, splits, bins)
+        level, filters, splits, bins, maxLevelForSingleGroup)
 
       for ((nodeSplitStats, index) <- splitsStatsForLevel.view.zipWithIndex) {
         // Extract info for nodes at the current level.
@@ -98,7 +120,7 @@ class DecisionTree (private val strategy: Strategy) extends Serializable with Lo
           filters)
         logDebug("final best split = " + nodeSplitStats._1)
       }
-      require(scala.math.pow(2, level) == splitsStatsForLevel.length)
+      require(math.pow(2, level) == splitsStatsForLevel.length)
       // Check whether all the nodes at the current level at leaves.
       val allLeaf = splitsStatsForLevel.forall(_._2.gain <= 0)
       logDebug("all leaf = " + allLeaf)
@@ -109,6 +131,10 @@ class DecisionTree (private val strategy: Strategy) extends Serializable with Lo
       }
     }
 
+    logDebug("#####################################")
+    logDebug("Extracting tree model")
+    logDebug("#####################################")
+
     // Initialize the top or root node of the tree.
     val topNode = nodes(0)
     // Build the full tree using the node info calculated in the level-wise best split calculations.
@@ -127,7 +153,7 @@ class DecisionTree (private val strategy: Strategy) extends Serializable with Lo
       nodes: Array[Node]): Unit = {
     val split = nodeSplitStats._1
     val stats = nodeSplitStats._2
-    val nodeIndex = scala.math.pow(2, level).toInt - 1 + index
+    val nodeIndex = math.pow(2, level).toInt - 1 + index
     val isLeaf = (stats.gain <= 0) || (level == strategy.maxDepth - 1)
     val node = new Node(nodeIndex, stats.predict, isLeaf, Some(split), None, None, Some(stats))
     logDebug("Node = " + node)
@@ -148,7 +174,7 @@ class DecisionTree (private val strategy: Strategy) extends Serializable with Lo
     var i = 0
     while (i <= 1) {
      // Calculate the index of the node from the node level and the index at the current level.
-      val nodeIndex = scala.math.pow(2, level + 1).toInt - 1 + 2 * index + i
+      val nodeIndex = math.pow(2, level + 1).toInt - 1 + 2 * index + i
       if (level < maxDepth - 1) {
         val impurity = if (i == 0) {
           nodeSplitStats._2.leftImpurity
@@ -249,7 +275,8 @@ object DecisionTree extends Serializable with Logging {
   private val InvalidBinIndex = -1
 
   /**
-   * Returns an array of optimal splits for all nodes at a given level
+   * Returns an array of optimal splits for all nodes at a given level. Splits the task into
+   * multiple groups if the level-wise training task could lead to memory overflow.
    *
    * @param input RDD of [[org.apache.spark.mllib.regression.LabeledPoint]] used as training data
    *              for DecisionTree
@@ -260,6 +287,7 @@ object DecisionTree extends Serializable with Logging {
    * @param filters Filters for all nodes at a given level
    * @param splits possible splits for all features
    * @param bins possible bins for all features
+   * @param maxLevelForSingleGroup the deepest level for single-group level-wise computation.
    * @return array of splits with best splits for all nodes at a given level.
    */
   protected[tree] def findBestSplits(
@@ -269,7 +297,57 @@ object DecisionTree extends Serializable with Logging {
       level: Int,
       filters: Array[List[Filter]],
       splits: Array[Array[Split]],
-      bins: Array[Array[Bin]]): Array[(Split, InformationGainStats)] = {
+      bins: Array[Array[Bin]],
+      maxLevelForSingleGroup: Int): Array[(Split, InformationGainStats)] = {
+    // split into groups to avoid memory overflow during aggregation
+    if (level > maxLevelForSingleGroup) {
+      // When information for all nodes at a given level cannot be stored in memory,
+      // the nodes are divided into multiple groups at each level with the number of groups
+      // increasing exponentially per level. For example, if maxLevelForSingleGroup is 10,
+      // numGroups is equal to 2 at level 11 and 4 at level 12, respectively.
+      val numGroups = math.pow(2, (level - maxLevelForSingleGroup)).toInt
+      logDebug("numGroups = " + numGroups)
+      var bestSplits = new Array[(Split, InformationGainStats)](0)
+      // Iterate over each group of nodes at a level.
+      var groupIndex = 0
+      while (groupIndex < numGroups) {
+        val bestSplitsForGroup = findBestSplitsPerGroup(input, parentImpurities, strategy, level,
+          filters, splits, bins, numGroups, groupIndex)
+        bestSplits = Array.concat(bestSplits, bestSplitsForGroup)
+        groupIndex += 1
+      }
+      bestSplits
+    } else {
+      findBestSplitsPerGroup(input, parentImpurities, strategy, level, filters, splits, bins)
+    }
+  }
+
+    /**
+   * Returns an array of optimal splits for a group of nodes at a given level
+   *
+   * @param input RDD of [[org.apache.spark.mllib.regression.LabeledPoint]] used as training data
+   *              for DecisionTree
+   * @param parentImpurities Impurities for all parent nodes for the current level
+   * @param strategy [[org.apache.spark.mllib.tree.configuration.Strategy]] instance containing
+   *                parameters for construction the DecisionTree
+   * @param level Level of the tree
+   * @param filters Filters for all nodes at a given level
+   * @param splits possible splits for all features
+   * @param bins possible bins for all features
+   * @param numGroups total number of node groups at the current level. Default value is set to 1.
+   * @param groupIndex index of the node group being processed. Default value is set to 0.
+   * @return array of splits with best splits for all nodes at a given level.
+   */
+  private def findBestSplitsPerGroup(
+      input: RDD[LabeledPoint],
+      parentImpurities: Array[Double],
+      strategy: Strategy,
+      level: Int,
+      filters: Array[List[Filter]],
+      splits: Array[Array[Split]],
+      bins: Array[Array[Bin]],
+      numGroups: Int = 1,
+      groupIndex: Int = 0): Array[(Split, InformationGainStats)] = {
 
     /*
      * The high-level description for the best split optimizations are noted here.
@@ -296,7 +374,7 @@ object DecisionTree extends Serializable with Logging {
      */
 
     // common calculations for multiple nested methods
-    val numNodes = scala.math.pow(2, level).toInt
+    val numNodes = math.pow(2, level).toInt / numGroups
     logDebug("numNodes = " + numNodes)
     // Find the number of features by looking at the first sample.
     val numFeatures = input.first().features.size
@@ -304,12 +382,15 @@ object DecisionTree extends Serializable with Logging {
     val numBins = bins(0).length
     logDebug("numBins = " + numBins)
 
+    // shift when more than one group is used at deep tree level
+    val groupShift = numNodes * groupIndex
+
     /** Find the filters used before reaching the current code. */
     def findParentFilters(nodeIndex: Int): List[Filter] = {
       if (level == 0) {
         List[Filter]()
       } else {
-        val nodeFilterIndex = scala.math.pow(2, level).toInt - 1 + nodeIndex
+        val nodeFilterIndex = math.pow(2, level).toInt - 1 + nodeIndex + groupShift
         filters(nodeFilterIndex)
       }
     }
@@ -878,7 +959,7 @@ object DecisionTree extends Serializable with Logging {
     // Iterating over all nodes at this level
     var node = 0
     while (node < numNodes) {
-      val nodeImpurityIndex = scala.math.pow(2, level).toInt - 1 + node
+      val nodeImpurityIndex = math.pow(2, level).toInt - 1 + node + groupShift
       val binsForNode: Array[Double] = getBinDataForNode(node)
       logDebug("nodeImpurityIndex = " + nodeImpurityIndex)
       val parentNodeImpurity = parentImpurities(nodeImpurityIndex)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala
index 8767aca47cd5..1b505fd76eb7 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala
@@ -35,6 +35,9 @@ import org.apache.spark.mllib.tree.configuration.QuantileStrategy._
  *                                k) implies the feature n is categorical with k categories 0,
  *                                1, 2, ... , k-1. It's important to note that features are
  *                                zero-indexed.
+ * @param maxMemoryInMB maximum memory in MB allocated to histogram aggregation. Default value is
+ *                      128 MB.
+ *
  */
 @Experimental
 class Strategy (
@@ -43,4 +46,5 @@ class Strategy (
     val maxDepth: Int,
     val maxBins: Int = 100,
     val quantileCalculationStrategy: QuantileStrategy = Sort,
-    val categoricalFeaturesInfo: Map[Int, Int] = Map[Int, Int]()) extends Serializable
+    val categoricalFeaturesInfo: Map[Int, Int] = Map[Int, Int](),
+    val maxMemoryInMB: Int = 128) extends Serializable
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala
index be383aab714d..35e92d71dc63 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala
@@ -22,7 +22,8 @@ import org.scalatest.FunSuite
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.tree.impurity.{Entropy, Gini, Variance}
 import org.apache.spark.mllib.tree.model.Filter
-import org.apache.spark.mllib.tree.configuration.Strategy
+import org.apache.spark.mllib.tree.model.Split
+import org.apache.spark.mllib.tree.configuration.{FeatureType, Strategy}
 import org.apache.spark.mllib.tree.configuration.Algo._
 import org.apache.spark.mllib.tree.configuration.FeatureType._
 import org.apache.spark.mllib.linalg.Vectors
@@ -242,7 +243,7 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
       categoricalFeaturesInfo = Map(0 -> 3, 1-> 3))
     val (splits, bins) = DecisionTree.findSplitsBins(rdd, strategy)
     val bestSplits = DecisionTree.findBestSplits(rdd, new Array(7), strategy, 0,
-      Array[List[Filter]](), splits, bins)
+      Array[List[Filter]](), splits, bins, 10)
 
     val split = bestSplits(0)._1
     assert(split.categories.length === 1)
@@ -269,7 +270,7 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
       categoricalFeaturesInfo = Map(0 -> 3, 1-> 3))
     val (splits, bins) = DecisionTree.findSplitsBins(rdd,strategy)
     val bestSplits = DecisionTree.findBestSplits(rdd, new Array(7), strategy, 0,
-      Array[List[Filter]](), splits, bins)
+      Array[List[Filter]](), splits, bins, 10)
 
     val split = bestSplits(0)._1
     assert(split.categories.length === 1)
@@ -298,7 +299,7 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
     assert(bins(0).length === 100)
 
     val bestSplits = DecisionTree.findBestSplits(rdd, new Array(7), strategy, 0,
-      Array[List[Filter]](), splits, bins)
+      Array[List[Filter]](), splits, bins, 10)
     assert(bestSplits.length === 1)
     assert(bestSplits(0)._1.feature === 0)
     assert(bestSplits(0)._1.threshold === 10)
@@ -321,7 +322,7 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
     assert(bins(0).length === 100)
 
     val bestSplits = DecisionTree.findBestSplits(rdd, Array(0.0), strategy, 0,
-      Array[List[Filter]](), splits, bins)
+      Array[List[Filter]](), splits, bins, 10)
     assert(bestSplits.length === 1)
     assert(bestSplits(0)._1.feature === 0)
     assert(bestSplits(0)._1.threshold === 10)
@@ -345,7 +346,7 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
     assert(bins(0).length === 100)
 
     val bestSplits = DecisionTree.findBestSplits(rdd, Array(0.0), strategy, 0,
-      Array[List[Filter]](), splits, bins)
+      Array[List[Filter]](), splits, bins, 10)
     assert(bestSplits.length === 1)
     assert(bestSplits(0)._1.feature === 0)
     assert(bestSplits(0)._1.threshold === 10)
@@ -369,7 +370,7 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
     assert(bins(0).length === 100)
 
     val bestSplits = DecisionTree.findBestSplits(rdd, Array(0.0), strategy, 0,
-      Array[List[Filter]](), splits, bins)
+      Array[List[Filter]](), splits, bins, 10)
     assert(bestSplits.length === 1)
     assert(bestSplits(0)._1.feature === 0)
     assert(bestSplits(0)._1.threshold === 10)
@@ -378,13 +379,60 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
     assert(bestSplits(0)._2.rightImpurity === 0)
     assert(bestSplits(0)._2.predict === 1)
   }
+
+  test("test second level node building with/without groups") {
+    val arr = DecisionTreeSuite.generateOrderedLabeledPoints()
+    assert(arr.length === 1000)
+    val rdd = sc.parallelize(arr)
+    val strategy = new Strategy(Classification, Entropy, 3, 100)
+    val (splits, bins) = DecisionTree.findSplitsBins(rdd, strategy)
+    assert(splits.length === 2)
+    assert(splits(0).length === 99)
+    assert(bins.length === 2)
+    assert(bins(0).length === 100)
+    assert(splits(0).length === 99)
+    assert(bins(0).length === 100)
+
+    val leftFilter = Filter(new Split(0, 400, FeatureType.Continuous, List()), -1)
+    val rightFilter = Filter(new Split(0, 400, FeatureType.Continuous, List()) ,1)
+    val filters = Array[List[Filter]](List(), List(leftFilter), List(rightFilter))
+    val parentImpurities = Array(0.5, 0.5, 0.5)
+
+    // Single group second level tree construction.
+    val bestSplits = DecisionTree.findBestSplits(rdd, parentImpurities, strategy, 1, filters,
+      splits, bins, 10)
+    assert(bestSplits.length === 2)
+    assert(bestSplits(0)._2.gain > 0)
+    assert(bestSplits(1)._2.gain > 0)
+
+    // maxLevelForSingleGroup parameter is set to 0 to force splitting into groups for second
+    // level tree construction.
+    val bestSplitsWithGroups = DecisionTree.findBestSplits(rdd, parentImpurities, strategy, 1,
+      filters, splits, bins, 0)
+    assert(bestSplitsWithGroups.length === 2)
+    assert(bestSplitsWithGroups(0)._2.gain > 0)
+    assert(bestSplitsWithGroups(1)._2.gain > 0)
+
+    // Verify whether the splits obtained using single group and multiple group level
+    // construction strategies are the same.
+    for (i <- 0 until bestSplits.length) {
+      assert(bestSplits(i)._1 === bestSplitsWithGroups(i)._1)
+      assert(bestSplits(i)._2.gain === bestSplitsWithGroups(i)._2.gain)
+      assert(bestSplits(i)._2.impurity === bestSplitsWithGroups(i)._2.impurity)
+      assert(bestSplits(i)._2.leftImpurity === bestSplitsWithGroups(i)._2.leftImpurity)
+      assert(bestSplits(i)._2.rightImpurity === bestSplitsWithGroups(i)._2.rightImpurity)
+      assert(bestSplits(i)._2.predict === bestSplitsWithGroups(i)._2.predict)
+    }
+
+  }
+
 }
 
 object DecisionTreeSuite {
 
   def generateOrderedLabeledPointsWithLabel0(): Array[LabeledPoint] = {
     val arr = new Array[LabeledPoint](1000)
-    for (i <- 0 until 1000){
+    for (i <- 0 until 1000) {
       val lp = new LabeledPoint(0.0, Vectors.dense(i.toDouble, 1000.0 - i))
       arr(i) = lp
     }
@@ -393,17 +441,31 @@ object DecisionTreeSuite {
 
   def generateOrderedLabeledPointsWithLabel1(): Array[LabeledPoint] = {
     val arr = new Array[LabeledPoint](1000)
-    for (i <- 0 until 1000){
+    for (i <- 0 until 1000) {
       val lp = new LabeledPoint(1.0, Vectors.dense(i.toDouble, 999.0 - i))
       arr(i) = lp
     }
     arr
   }
 
+  def generateOrderedLabeledPoints(): Array[LabeledPoint] = {
+    val arr = new Array[LabeledPoint](1000)
+    for (i <- 0 until 1000) {
+      if (i < 600) {
+        val lp = new LabeledPoint(0.0, Vectors.dense(i.toDouble, 1000.0 - i))
+        arr(i) = lp
+      } else {
+        val lp = new LabeledPoint(1.0, Vectors.dense(i.toDouble, 1000.0 - i))
+        arr(i) = lp
+      }
+    }
+    arr
+  }
+
   def generateCategoricalDataPoints(): Array[LabeledPoint] = {
     val arr = new Array[LabeledPoint](1000)
-    for (i <- 0 until 1000){
-      if (i < 600){
+    for (i <- 0 until 1000) {
+      if (i < 600) {
         arr(i) = new LabeledPoint(1.0, Vectors.dense(0.0, 1.0))
       } else {
         arr(i) = new LabeledPoint(0.0, Vectors.dense(1.0, 0.0))

From 108c4c16cc82af2e161d569d2c23849bdbf4aadb Mon Sep 17 00:00:00 2001
From: Sandeep <sandeep@techaddict.me>
Date: Thu, 8 May 2014 00:15:05 -0400
Subject: [PATCH 125/810] SPARK-1668: Add implicit preference as an option to
 examples/MovieLensALS

Add --implicitPrefs as an command-line option to the example app MovieLensALS under examples/

Author: Sandeep <sandeep@techaddict.me>

Closes #597 from techaddict/SPARK-1668 and squashes the following commits:

8b371dc [Sandeep] Second Pass on reviews by mengxr
eca9d37 [Sandeep] based on mengxr's suggestions
937e54c [Sandeep] Changes
5149d40 [Sandeep] Changes based on review
1dd7657 [Sandeep] use mean()
42444d7 [Sandeep] Based on Suggestions by mengxr
e3082fa [Sandeep] SPARK-1668: Add implicit preference as an option to examples/MovieLensALS Add --implicitPrefs as an command-line option to the example app MovieLensALS under examples/
---
 .../spark/examples/mllib/MovieLensALS.scala   | 55 ++++++++++++++++---
 1 file changed, 46 insertions(+), 9 deletions(-)

diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/MovieLensALS.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/MovieLensALS.scala
index 703f02255b94..0e4447e0de24 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/MovieLensALS.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/MovieLensALS.scala
@@ -43,7 +43,8 @@ object MovieLensALS {
       kryo: Boolean = false,
       numIterations: Int = 20,
       lambda: Double = 1.0,
-      rank: Int = 10)
+      rank: Int = 10,
+      implicitPrefs: Boolean = false)
 
   def main(args: Array[String]) {
     val defaultParams = Params()
@@ -62,6 +63,9 @@ object MovieLensALS {
       opt[Unit]("kryo")
         .text(s"use Kryo serialization")
         .action((_, c) => c.copy(kryo = true))
+      opt[Unit]("implicitPrefs")
+        .text("use implicit preference")
+        .action((_, c) => c.copy(implicitPrefs = true))
       arg[String]("<input>")
         .required()
         .text("input paths to a MovieLens dataset of ratings")
@@ -88,7 +92,25 @@ object MovieLensALS {
 
     val ratings = sc.textFile(params.input).map { line =>
       val fields = line.split("::")
-      Rating(fields(0).toInt, fields(1).toInt, fields(2).toDouble)
+      if (params.implicitPrefs) {
+        /*
+         * MovieLens ratings are on a scale of 1-5:
+         * 5: Must see
+         * 4: Will enjoy
+         * 3: It's okay
+         * 2: Fairly bad
+         * 1: Awful
+         * So we should not recommend a movie if the predicted rating is less than 3.
+         * To map ratings to confidence scores, we use
+         * 5 -> 2.5, 4 -> 1.5, 3 -> 0.5, 2 -> -0.5, 1 -> -1.5. This mappings means unobserved
+         * entries are generally between It's okay and Fairly bad.
+         * The semantics of 0 in this expanded world of non-positive weights
+         * are "the same as never having interacted at all".
+         */
+        Rating(fields(0).toInt, fields(1).toInt, fields(2).toDouble - 2.5)
+      } else {
+        Rating(fields(0).toInt, fields(1).toInt, fields(2).toDouble)
+      }
     }.cache()
 
     val numRatings = ratings.count()
@@ -99,7 +121,18 @@ object MovieLensALS {
 
     val splits = ratings.randomSplit(Array(0.8, 0.2))
     val training = splits(0).cache()
-    val test = splits(1).cache()
+    val test = if (params.implicitPrefs) {
+      /*
+       * 0 means "don't know" and positive values mean "confident that the prediction should be 1".
+       * Negative values means "confident that the prediction should be 0".
+       * We have in this case used some kind of weighted RMSE. The weight is the absolute value of
+       * the confidence. The error is the difference between prediction and either 1 or 0,
+       * depending on whether r is positive or negative.
+       */
+      splits(1).map(x => Rating(x.user, x.product, if (x.rating > 0) 1.0 else 0.0))
+    } else {
+      splits(1)
+    }.cache()
 
     val numTraining = training.count()
     val numTest = test.count()
@@ -111,9 +144,10 @@ object MovieLensALS {
       .setRank(params.rank)
       .setIterations(params.numIterations)
       .setLambda(params.lambda)
+      .setImplicitPrefs(params.implicitPrefs)
       .run(training)
 
-    val rmse = computeRmse(model, test, numTest)
+    val rmse = computeRmse(model, test, params.implicitPrefs)
 
     println(s"Test RMSE = $rmse.")
 
@@ -121,11 +155,14 @@ object MovieLensALS {
   }
 
   /** Compute RMSE (Root Mean Squared Error). */
-  def computeRmse(model: MatrixFactorizationModel, data: RDD[Rating], n: Long) = {
+  def computeRmse(model: MatrixFactorizationModel, data: RDD[Rating], implicitPrefs: Boolean) = {
+
+    def mapPredictedRating(r: Double) = if (implicitPrefs) math.max(math.min(r, 1.0), 0.0) else r
+
     val predictions: RDD[Rating] = model.predict(data.map(x => (x.user, x.product)))
-    val predictionsAndRatings = predictions.map(x => ((x.user, x.product), x.rating))
-      .join(data.map(x => ((x.user, x.product), x.rating)))
-      .values
-    math.sqrt(predictionsAndRatings.map(x => (x._1 - x._2) * (x._1 - x._2)).reduce(_ + _) / n)
+    val predictionsAndRatings = predictions.map{ x =>
+      ((x.user, x.product), mapPredictedRating(x.rating))
+    }.join(data.map(x => ((x.user, x.product), x.rating))).values
+    math.sqrt(predictionsAndRatings.map(x => (x._1 - x._2) * (x._1 - x._2)).mean())
   }
 }

From 6ed7e2cd01955adfbb3960e2986b6d19eaee8717 Mon Sep 17 00:00:00 2001
From: Evan Sparks <evan.sparks@gmail.com>
Date: Thu, 8 May 2014 00:24:36 -0400
Subject: [PATCH 126/810] Use numpy directly for matrix multiply.

Using matrix multiply to compute XtX and XtY yields a 5-20x speedup depending on problem size.

For example - the following takes 19s locally after this change vs. 5m21s before the change. (16x speedup).
bin/pyspark examples/src/main/python/als.py local[8] 1000 1000 50 10 10

Author: Evan Sparks <evan.sparks@gmail.com>

Closes #687 from etrain/patch-1 and squashes the following commits:

e094dbc [Evan Sparks] Touching only diaganols on update.
d1ab9b6 [Evan Sparks] Use numpy directly for matrix multiply.
---
 examples/src/main/python/als.py | 15 +++++++--------
 1 file changed, 7 insertions(+), 8 deletions(-)

diff --git a/examples/src/main/python/als.py b/examples/src/main/python/als.py
index a77dfb257783..33700ab4f8c5 100755
--- a/examples/src/main/python/als.py
+++ b/examples/src/main/python/als.py
@@ -36,14 +36,13 @@ def rmse(R, ms, us):
 def update(i, vec, mat, ratings):
     uu = mat.shape[0]
     ff = mat.shape[1]
-    XtX = matrix(np.zeros((ff, ff)))
-    Xty = np.zeros((ff, 1))
-
-    for j in range(uu):
-        v = mat[j, :]
-        XtX += v.T * v
-        Xty += v.T * ratings[i, j]
-    XtX += np.eye(ff, ff) * LAMBDA * uu
+    
+    XtX = mat.T * mat
+    XtY = mat.T * ratings[i, :].T
+    
+    for j in range(ff):
+        XtX[j,j] += LAMBDA * uu
+    
     return np.linalg.solve(XtX, Xty)
 
 if __name__ == "__main__":

From 19c8fb02bc2c2f76c3c45bfff4b8d093be9d7c66 Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Thu, 8 May 2014 01:08:43 -0400
Subject: [PATCH 127/810] [SQL] Improve SparkSQL Aggregates

* Add native min/max (was using hive before).
* Handle nulls correctly in Avg and Sum.

Author: Michael Armbrust <michael@databricks.com>

Closes #683 from marmbrus/aggFixes and squashes the following commits:

64fe30b [Michael Armbrust] Improve SparkSQL Aggregates * Add native min/max (was using hive before). * Handle nulls correctly in Avg and Sum.
---
 .../apache/spark/sql/catalyst/SqlParser.scala |  4 +
 .../sql/catalyst/expressions/aggregates.scala | 85 ++++++++++++++++---
 .../org/apache/spark/sql/SQLQuerySuite.scala  |  7 ++
 .../scala/org/apache/spark/sql/TestData.scala | 10 +++
 4 files changed, 96 insertions(+), 10 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
index 8c76a3aa9654..b3a3a1ef1b5e 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
@@ -114,6 +114,8 @@ class SqlParser extends StandardTokenParsers with PackratParsers {
   protected val JOIN = Keyword("JOIN")
   protected val LEFT = Keyword("LEFT")
   protected val LIMIT = Keyword("LIMIT")
+  protected val MAX = Keyword("MAX")
+  protected val MIN = Keyword("MIN")
   protected val NOT = Keyword("NOT")
   protected val NULL = Keyword("NULL")
   protected val ON = Keyword("ON")
@@ -318,6 +320,8 @@ class SqlParser extends StandardTokenParsers with PackratParsers {
     COUNT ~> "(" ~> DISTINCT ~> expression <~ ")" ^^ { case exp => CountDistinct(exp :: Nil) } |
     FIRST ~> "(" ~> expression <~ ")" ^^ { case exp => First(exp) } |
     AVG ~> "(" ~> expression <~ ")" ^^ { case exp => Average(exp) } |
+    MIN ~> "(" ~> expression <~ ")" ^^ { case exp => Min(exp) } |
+    MAX ~> "(" ~> expression <~ ")" ^^ { case exp => Max(exp) } |
     IF ~> "(" ~> expression ~ "," ~ expression ~ "," ~ expression <~ ")" ^^ {
       case c ~ "," ~ t ~ "," ~ f => If(c,t,f)
     } |
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
index b152f95f96c7..7777d372903e 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
@@ -86,6 +86,67 @@ abstract class AggregateFunction
   override def newInstance() = makeCopy(productIterator.map { case a: AnyRef => a }.toArray)
 }
 
+case class Min(child: Expression) extends PartialAggregate with trees.UnaryNode[Expression] {
+  override def references = child.references
+  override def nullable = child.nullable
+  override def dataType = child.dataType
+  override def toString = s"MIN($child)"
+
+  override def asPartial: SplitEvaluation = {
+    val partialMin = Alias(Min(child), "PartialMin")()
+    SplitEvaluation(Min(partialMin.toAttribute), partialMin :: Nil)
+  }
+
+  override def newInstance() = new MinFunction(child, this)
+}
+
+case class MinFunction(expr: Expression, base: AggregateExpression) extends AggregateFunction {
+  def this() = this(null, null) // Required for serialization.
+
+  var currentMin: Any = _
+
+  override def update(input: Row): Unit = {
+    if (currentMin == null) {
+      currentMin = expr.eval(input)
+    } else if(GreaterThan(Literal(currentMin, expr.dataType), expr).eval(input) == true) {
+      currentMin = expr.eval(input)
+    }
+  }
+
+  override def eval(input: Row): Any = currentMin
+}
+
+case class Max(child: Expression) extends PartialAggregate with trees.UnaryNode[Expression] {
+  override def references = child.references
+  override def nullable = child.nullable
+  override def dataType = child.dataType
+  override def toString = s"MAX($child)"
+
+  override def asPartial: SplitEvaluation = {
+    val partialMax = Alias(Max(child), "PartialMax")()
+    SplitEvaluation(Max(partialMax.toAttribute), partialMax :: Nil)
+  }
+
+  override def newInstance() = new MaxFunction(child, this)
+}
+
+case class MaxFunction(expr: Expression, base: AggregateExpression) extends AggregateFunction {
+  def this() = this(null, null) // Required for serialization.
+
+  var currentMax: Any = _
+
+  override def update(input: Row): Unit = {
+    if (currentMax == null) {
+      currentMax = expr.eval(input)
+    } else if(LessThan(Literal(currentMax, expr.dataType), expr).eval(input) == true) {
+      currentMax = expr.eval(input)
+    }
+  }
+
+  override def eval(input: Row): Any = currentMax
+}
+
+
 case class Count(child: Expression) extends PartialAggregate with trees.UnaryNode[Expression] {
   override def references = child.references
   override def nullable = false
@@ -97,7 +158,7 @@ case class Count(child: Expression) extends PartialAggregate with trees.UnaryNod
     SplitEvaluation(Sum(partialCount.toAttribute), partialCount :: Nil)
   }
 
-  override def newInstance()= new CountFunction(child, this)
+  override def newInstance() = new CountFunction(child, this)
 }
 
 case class CountDistinct(expressions: Seq[Expression]) extends AggregateExpression {
@@ -106,7 +167,7 @@ case class CountDistinct(expressions: Seq[Expression]) extends AggregateExpressi
   override def nullable = false
   override def dataType = IntegerType
   override def toString = s"COUNT(DISTINCT ${expressions.mkString(",")}})"
-  override def newInstance()= new CountDistinctFunction(expressions, this)
+  override def newInstance() = new CountDistinctFunction(expressions, this)
 }
 
 case class Average(child: Expression) extends PartialAggregate with trees.UnaryNode[Expression] {
@@ -126,7 +187,7 @@ case class Average(child: Expression) extends PartialAggregate with trees.UnaryN
       partialCount :: partialSum :: Nil)
   }
 
-  override def newInstance()= new AverageFunction(child, this)
+  override def newInstance() = new AverageFunction(child, this)
 }
 
 case class Sum(child: Expression) extends PartialAggregate with trees.UnaryNode[Expression] {
@@ -142,7 +203,7 @@ case class Sum(child: Expression) extends PartialAggregate with trees.UnaryNode[
       partialSum :: Nil)
   }
 
-  override def newInstance()= new SumFunction(child, this)
+  override def newInstance() = new SumFunction(child, this)
 }
 
 case class SumDistinct(child: Expression)
@@ -153,7 +214,7 @@ case class SumDistinct(child: Expression)
   override def dataType = child.dataType
   override def toString = s"SUM(DISTINCT $child)"
 
-  override def newInstance()= new SumDistinctFunction(child, this)
+  override def newInstance() = new SumDistinctFunction(child, this)
 }
 
 case class First(child: Expression) extends PartialAggregate with trees.UnaryNode[Expression] {
@@ -168,7 +229,7 @@ case class First(child: Expression) extends PartialAggregate with trees.UnaryNod
       First(partialFirst.toAttribute),
       partialFirst :: Nil)
   }
-  override def newInstance()= new FirstFunction(child, this)
+  override def newInstance() = new FirstFunction(child, this)
 }
 
 case class AverageFunction(expr: Expression, base: AggregateExpression)
@@ -176,11 +237,13 @@ case class AverageFunction(expr: Expression, base: AggregateExpression)
 
   def this() = this(null, null) // Required for serialization.
 
+  private val zero = Cast(Literal(0), expr.dataType)
+
   private var count: Long = _
-  private val sum = MutableLiteral(Cast(Literal(0), expr.dataType).eval(EmptyRow))
+  private val sum = MutableLiteral(zero.eval(EmptyRow))
   private val sumAsDouble = Cast(sum, DoubleType)
 
-  private val addFunction = Add(sum, expr)
+  private val addFunction = Add(sum, Coalesce(Seq(expr, zero)))
 
   override def eval(input: Row): Any =
     sumAsDouble.eval(EmptyRow).asInstanceOf[Double] / count.toDouble
@@ -209,9 +272,11 @@ case class CountFunction(expr: Expression, base: AggregateExpression) extends Ag
 case class SumFunction(expr: Expression, base: AggregateExpression) extends AggregateFunction {
   def this() = this(null, null) // Required for serialization.
 
-  private val sum = MutableLiteral(Cast(Literal(0), expr.dataType).eval(null))
+  private val zero = Cast(Literal(0), expr.dataType)
+
+  private val sum = MutableLiteral(zero.eval(null))
 
-  private val addFunction = Add(sum, expr)
+  private val addFunction = Add(sum, Coalesce(Seq(expr, zero)))
 
   override def update(input: Row): Unit = {
     sum.update(addFunction, input)
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index dde957d715a2..e966d89c30cf 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -50,6 +50,13 @@ class SQLQuerySuite extends QueryTest {
       Seq((1,3),(2,3),(3,3)))
   }
 
+  test("aggregates with nulls") {
+    checkAnswer(
+      sql("SELECT MIN(a), MAX(a), AVG(a), SUM(a), COUNT(a) FROM nullInts"),
+      (1, 3, 2, 6, 3) :: Nil
+    )
+  }
+
   test("select *") {
     checkAnswer(
       sql("SELECT * FROM testData"),
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala b/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala
index b5973c0f51be..aa71e274f7f4 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala
@@ -84,4 +84,14 @@ object TestData {
       List.fill(2)(StringData(null)) ++
       List.fill(2)(StringData("test")))
   nullableRepeatedData.registerAsTable("nullableRepeatedData")
+
+  case class NullInts(a: Integer)
+  val nullInts =
+    TestSQLContext.sparkContext.parallelize(
+      NullInts(1) ::
+      NullInts(2) ::
+      NullInts(3) ::
+      NullInts(null) :: Nil
+    )
+  nullInts.registerAsTable("nullInts")
 }

From 44dd57fb66bb676d753ad8d9757f9f4c03364113 Mon Sep 17 00:00:00 2001
From: Prashant Sharma <prashant.s@imaginea.com>
Date: Thu, 8 May 2014 10:23:05 -0700
Subject: [PATCH 128/810] SPARK-1565, update examples to be used with
 spark-submit script.

Commit for initial feedback, basically I am curious if we should prompt user for providing args esp. when its mandatory. And can we skip if they are not ?

Also few other things that did not work like
`bin/spark-submit examples/target/scala-2.10/spark-examples-1.0.0-SNAPSHOT-hadoop1.0.4.jar --class org.apache.spark.examples.SparkALS --arg 100 500 10 5 2`

Not all the args get passed properly, may be I have messed up something will try to sort it out hopefully.

Author: Prashant Sharma <prashant.s@imaginea.com>

Closes #552 from ScrapCodes/SPARK-1565/update-examples and squashes the following commits:

669dd23 [Prashant Sharma] Review comments
2727e70 [Prashant Sharma] SPARK-1565, update examples to be used with spark-submit script.
---
 .gitignore                                    |  1 +
 .../scala/org/apache/spark/SparkContext.scala |  8 ++--
 .../org/apache/spark/examples/JavaHdfsLR.java | 13 ++++---
 .../apache/spark/examples/JavaLogQuery.java   | 13 +++----
 .../apache/spark/examples/JavaPageRank.java   | 15 +++++---
 .../apache/spark/examples/JavaSparkPi.java    | 18 ++++-----
 .../org/apache/spark/examples/JavaTC.java     | 24 ++++++------
 .../apache/spark/examples/JavaWordCount.java  | 12 +++---
 .../apache/spark/examples/mllib/JavaALS.java  | 22 +++++------
 .../spark/examples/mllib/JavaKMeans.java      | 22 +++++------
 .../apache/spark/examples/mllib/JavaLR.java   | 18 ++++-----
 .../spark/examples/sql/JavaSparkSQL.java      |  5 ++-
 .../streaming/JavaFlumeEventCount.java        | 19 ++++------
 .../streaming/JavaKafkaWordCount.java         | 27 +++++++-------
 .../streaming/JavaNetworkWordCount.java       | 25 ++++++-------
 .../examples/streaming/JavaQueueStream.java   | 22 +++++------
 .../apache/spark/examples/BroadcastTest.scala | 22 +++++------
 .../spark/examples/CassandraCQLTest.scala     | 19 +++++-----
 .../apache/spark/examples/CassandraTest.scala | 10 ++---
 .../examples/ExceptionHandlingTest.scala      | 11 ++----
 .../apache/spark/examples/GroupByTest.scala   | 25 ++++++-------
 .../org/apache/spark/examples/HBaseTest.scala |  6 +--
 .../org/apache/spark/examples/HdfsTest.scala  |  4 +-
 .../org/apache/spark/examples/LogQuery.scala  | 14 +++----
 .../spark/examples/MultiBroadcastTest.scala   | 17 ++++-----
 .../examples/SimpleSkewedGroupByTest.scala    | 24 ++++++------
 .../spark/examples/SkewedGroupByTest.scala    | 25 ++++++-------
 .../org/apache/spark/examples/SparkALS.scala  | 18 +++------
 .../apache/spark/examples/SparkHdfsLR.scala   | 13 ++++---
 .../apache/spark/examples/SparkKMeans.scala   | 18 ++++-----
 .../org/apache/spark/examples/SparkLR.scala   | 11 ++----
 .../apache/spark/examples/SparkPageRank.scala | 14 +++----
 .../org/apache/spark/examples/SparkPi.scala   | 10 ++---
 .../org/apache/spark/examples/SparkTC.scala   | 12 ++----
 .../spark/examples/SparkTachyonHdfsLR.scala   | 12 ++----
 .../spark/examples/SparkTachyonPi.scala       | 10 ++---
 .../examples/bagel/WikipediaPageRank.scala    | 10 ++---
 .../bagel/WikipediaPageRankStandalone.scala   | 10 ++---
 .../examples/graphx/LiveJournalPageRank.scala |  6 +--
 .../spark/examples/sql/RDDRelation.scala      |  5 ++-
 .../examples/sql/hive/HiveFromSpark.scala     |  5 ++-
 .../examples/streaming/ActorWordCount.scala   | 21 +++++------
 .../examples/streaming/FlumeEventCount.scala  | 14 +++----
 .../examples/streaming/HdfsWordCount.scala    | 18 ++++-----
 .../examples/streaming/KafkaWordCount.scala   | 21 +++++------
 .../examples/streaming/MQTTWordCount.scala    | 26 ++++++-------
 .../examples/streaming/NetworkWordCount.scala | 23 +++++-------
 .../examples/streaming/QueueStream.scala      | 10 ++---
 .../examples/streaming/RawNetworkGrep.scala   | 16 ++++----
 .../RecoverableNetworkWordCount.scala         | 37 ++++++++++---------
 .../streaming/StatefulNetworkWordCount.scala  | 21 +++++------
 .../streaming/TwitterAlgebirdCMS.scala        | 15 +++-----
 .../streaming/TwitterAlgebirdHLL.scala        | 14 +++----
 .../streaming/TwitterPopularTags.scala        | 13 ++-----
 .../examples/streaming/ZeroMQWordCount.scala  | 23 ++++++------
 .../apache/spark/graphx/lib/Analytics.scala   | 18 +++++----
 56 files changed, 405 insertions(+), 480 deletions(-)

diff --git a/.gitignore b/.gitignore
index 32b603f1bc84..ad72588b472d 100644
--- a/.gitignore
+++ b/.gitignore
@@ -49,6 +49,7 @@ unit-tests.log
 /lib/
 rat-results.txt
 scalastyle.txt
+conf/*.conf
 
 # For Hive
 metastore_db/
diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index eb14d87467af..9d7c2c8d3d63 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -74,10 +74,10 @@ class SparkContext(config: SparkConf) extends Logging {
    * be generated using [[org.apache.spark.scheduler.InputFormatInfo.computePreferredLocations]]
    * from a list of input files or InputFormats for the application.
    */
-    @DeveloperApi
-    def this(config: SparkConf, preferredNodeLocationData: Map[String, Set[SplitInfo]]) = {
-      this(config)
-      this.preferredNodeLocationData = preferredNodeLocationData
+  @DeveloperApi
+  def this(config: SparkConf, preferredNodeLocationData: Map[String, Set[SplitInfo]]) = {
+    this(config)
+    this.preferredNodeLocationData = preferredNodeLocationData
   }
 
   /**
diff --git a/examples/src/main/java/org/apache/spark/examples/JavaHdfsLR.java b/examples/src/main/java/org/apache/spark/examples/JavaHdfsLR.java
index bd9627402175..6c177de359b6 100644
--- a/examples/src/main/java/org/apache/spark/examples/JavaHdfsLR.java
+++ b/examples/src/main/java/org/apache/spark/examples/JavaHdfsLR.java
@@ -17,6 +17,7 @@
 
 package org.apache.spark.examples;
 
+import org.apache.spark.SparkConf;
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.JavaSparkContext;
 import org.apache.spark.api.java.function.Function;
@@ -103,16 +104,16 @@ public static void printWeights(double[] a) {
 
   public static void main(String[] args) {
 
-    if (args.length < 3) {
-      System.err.println("Usage: JavaHdfsLR <master> <file> <iters>");
+    if (args.length < 2) {
+      System.err.println("Usage: JavaHdfsLR <file> <iters>");
       System.exit(1);
     }
 
-    JavaSparkContext sc = new JavaSparkContext(args[0], "JavaHdfsLR",
-        System.getenv("SPARK_HOME"), JavaSparkContext.jarOfClass(JavaHdfsLR.class));
-    JavaRDD<String> lines = sc.textFile(args[1]);
+    SparkConf sparkConf = new SparkConf().setAppName("JavaHdfsLR");
+    JavaSparkContext sc = new JavaSparkContext(sparkConf);
+    JavaRDD<String> lines = sc.textFile(args[0]);
     JavaRDD<DataPoint> points = lines.map(new ParsePoint()).cache();
-    int ITERATIONS = Integer.parseInt(args[2]);
+    int ITERATIONS = Integer.parseInt(args[1]);
 
     // Initialize w to a random value
     double[] w = new double[D];
diff --git a/examples/src/main/java/org/apache/spark/examples/JavaLogQuery.java b/examples/src/main/java/org/apache/spark/examples/JavaLogQuery.java
index 3f7a87953801..812e9d5580cb 100644
--- a/examples/src/main/java/org/apache/spark/examples/JavaLogQuery.java
+++ b/examples/src/main/java/org/apache/spark/examples/JavaLogQuery.java
@@ -20,6 +20,7 @@
 import com.google.common.collect.Lists;
 import scala.Tuple2;
 import scala.Tuple3;
+import org.apache.spark.SparkConf;
 import org.apache.spark.api.java.JavaPairRDD;
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.JavaSparkContext;
@@ -34,6 +35,8 @@
 
 /**
  * Executes a roll up-style query against Apache logs.
+ *  
+ * Usage: JavaLogQuery [logFile]
  */
 public final class JavaLogQuery {
 
@@ -97,15 +100,11 @@ public static Stats extractStats(String line) {
   }
 
   public static void main(String[] args) {
-    if (args.length == 0) {
-      System.err.println("Usage: JavaLogQuery <master> [logFile]");
-      System.exit(1);
-    }
 
-    JavaSparkContext jsc = new JavaSparkContext(args[0], "JavaLogQuery",
-      System.getenv("SPARK_HOME"), JavaSparkContext.jarOfClass(JavaLogQuery.class));
+    SparkConf sparkConf = new SparkConf().setAppName("JavaLogQuery");
+    JavaSparkContext jsc = new JavaSparkContext(sparkConf);
 
-    JavaRDD<String> dataSet = (args.length == 2) ? jsc.textFile(args[1]) : jsc.parallelize(exampleApacheLogs);
+    JavaRDD<String> dataSet = (args.length == 1) ? jsc.textFile(args[0]) : jsc.parallelize(exampleApacheLogs);
 
     JavaPairRDD<Tuple3<String, String, String>, Stats> extracted = dataSet.mapToPair(new PairFunction<String, Tuple3<String, String, String>, Stats>() {
       @Override
diff --git a/examples/src/main/java/org/apache/spark/examples/JavaPageRank.java b/examples/src/main/java/org/apache/spark/examples/JavaPageRank.java
index e31f676f5fd4..7ea6df9c1724 100644
--- a/examples/src/main/java/org/apache/spark/examples/JavaPageRank.java
+++ b/examples/src/main/java/org/apache/spark/examples/JavaPageRank.java
@@ -18,9 +18,12 @@
 package org.apache.spark.examples;
 
 
+
 import scala.Tuple2;
 
 import com.google.common.collect.Iterables;
+
+import org.apache.spark.SparkConf;
 import org.apache.spark.api.java.JavaPairRDD;
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.JavaSparkContext;
@@ -54,20 +57,20 @@ public Double call(Double a, Double b) {
   }
 
   public static void main(String[] args) throws Exception {
-    if (args.length < 3) {
-      System.err.println("Usage: JavaPageRank <master> <file> <number_of_iterations>");
+    if (args.length < 2) {
+      System.err.println("Usage: JavaPageRank <file> <number_of_iterations>");
       System.exit(1);
     }
 
-    JavaSparkContext ctx = new JavaSparkContext(args[0], "JavaPageRank",
-      System.getenv("SPARK_HOME"), JavaSparkContext.jarOfClass(JavaPageRank.class));
+    SparkConf sparkConf = new SparkConf().setAppName("JavaPageRank");
+    JavaSparkContext ctx = new JavaSparkContext(sparkConf);
 
     // Loads in input file. It should be in format of:
     //     URL         neighbor URL
     //     URL         neighbor URL
     //     URL         neighbor URL
     //     ...
-    JavaRDD<String> lines = ctx.textFile(args[1], 1);
+    JavaRDD<String> lines = ctx.textFile(args[0], 1);
 
     // Loads all URLs from input file and initialize their neighbors.
     JavaPairRDD<String, Iterable<String>> links = lines.mapToPair(new PairFunction<String, String, String>() {
@@ -87,7 +90,7 @@ public Double call(Iterable<String> rs) {
     });
 
     // Calculates and updates URL ranks continuously using PageRank algorithm.
-    for (int current = 0; current < Integer.parseInt(args[2]); current++) {
+    for (int current = 0; current < Integer.parseInt(args[1]); current++) {
       // Calculates URL contributions to the rank of other URLs.
       JavaPairRDD<String, Double> contribs = links.join(ranks).values()
         .flatMapToPair(new PairFlatMapFunction<Tuple2<Iterable<String>, Double>, String, Double>() {
diff --git a/examples/src/main/java/org/apache/spark/examples/JavaSparkPi.java b/examples/src/main/java/org/apache/spark/examples/JavaSparkPi.java
index ac8df02c4630..11157d7573fa 100644
--- a/examples/src/main/java/org/apache/spark/examples/JavaSparkPi.java
+++ b/examples/src/main/java/org/apache/spark/examples/JavaSparkPi.java
@@ -17,6 +17,7 @@
 
 package org.apache.spark.examples;
 
+import org.apache.spark.SparkConf;
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.JavaSparkContext;
 import org.apache.spark.api.java.function.Function;
@@ -25,19 +26,18 @@
 import java.util.ArrayList;
 import java.util.List;
 
-/** Computes an approximation to pi */
+/** 
+ * Computes an approximation to pi
+ * Usage: JavaSparkPi [slices]
+ */
 public final class JavaSparkPi {
+  
 
   public static void main(String[] args) throws Exception {
-    if (args.length == 0) {
-      System.err.println("Usage: JavaSparkPi <master> [slices]");
-      System.exit(1);
-    }
-
-    JavaSparkContext jsc = new JavaSparkContext(args[0], "JavaSparkPi",
-      System.getenv("SPARK_HOME"), JavaSparkContext.jarOfClass(JavaSparkPi.class));
+    SparkConf sparkConf = new SparkConf().setAppName("JavaSparkPi");
+    JavaSparkContext jsc = new JavaSparkContext(sparkConf);
 
-    int slices = (args.length == 2) ? Integer.parseInt(args[1]) : 2;
+    int slices = (args.length == 1) ? Integer.parseInt(args[0]) : 2;
     int n = 100000 * slices;
     List<Integer> l = new ArrayList<Integer>(n);
     for (int i = 0; i < n; i++) {
diff --git a/examples/src/main/java/org/apache/spark/examples/JavaTC.java b/examples/src/main/java/org/apache/spark/examples/JavaTC.java
index d66b9ba265fe..2563fcdd234b 100644
--- a/examples/src/main/java/org/apache/spark/examples/JavaTC.java
+++ b/examples/src/main/java/org/apache/spark/examples/JavaTC.java
@@ -17,19 +17,22 @@
 
 package org.apache.spark.examples;
 
-import scala.Tuple2;
-import org.apache.spark.api.java.JavaPairRDD;
-import org.apache.spark.api.java.JavaSparkContext;
-import org.apache.spark.api.java.function.PairFunction;
-
 import java.util.ArrayList;
 import java.util.HashSet;
 import java.util.List;
 import java.util.Random;
 import java.util.Set;
 
+import scala.Tuple2;
+
+import org.apache.spark.SparkConf;
+import org.apache.spark.api.java.JavaPairRDD;
+import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.api.java.function.PairFunction;
+
 /**
  * Transitive closure on a graph, implemented in Java.
+ * Usage: JavaTC [slices]
  */
 public final class JavaTC {
 
@@ -61,14 +64,9 @@ public Tuple2<Integer, Integer> call(Tuple2<Integer, Tuple2<Integer, Integer>> t
   }
 
   public static void main(String[] args) {
-    if (args.length == 0) {
-      System.err.println("Usage: JavaTC <host> [<slices>]");
-      System.exit(1);
-    }
-
-    JavaSparkContext sc = new JavaSparkContext(args[0], "JavaTC",
-        System.getenv("SPARK_HOME"), JavaSparkContext.jarOfClass(JavaTC.class));
-    Integer slices = (args.length > 1) ? Integer.parseInt(args[1]): 2;
+    SparkConf sparkConf = new SparkConf().setAppName("JavaHdfsLR");
+    JavaSparkContext sc = new JavaSparkContext(sparkConf);
+    Integer slices = (args.length > 0) ? Integer.parseInt(args[0]): 2;
     JavaPairRDD<Integer, Integer> tc = sc.parallelizePairs(generateGraph(), slices).cache();
 
     // Linear transitive closure: each round grows paths by one edge,
diff --git a/examples/src/main/java/org/apache/spark/examples/JavaWordCount.java b/examples/src/main/java/org/apache/spark/examples/JavaWordCount.java
index 87c1b8098196..9a6a944f7ede 100644
--- a/examples/src/main/java/org/apache/spark/examples/JavaWordCount.java
+++ b/examples/src/main/java/org/apache/spark/examples/JavaWordCount.java
@@ -18,6 +18,7 @@
 package org.apache.spark.examples;
 
 import scala.Tuple2;
+import org.apache.spark.SparkConf;
 import org.apache.spark.api.java.JavaPairRDD;
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.JavaSparkContext;
@@ -33,14 +34,15 @@ public final class JavaWordCount {
   private static final Pattern SPACE = Pattern.compile(" ");
 
   public static void main(String[] args) throws Exception {
-    if (args.length < 2) {
-      System.err.println("Usage: JavaWordCount <master> <file>");
+
+    if (args.length < 1) {
+      System.err.println("Usage: JavaWordCount <file>");
       System.exit(1);
     }
 
-    JavaSparkContext ctx = new JavaSparkContext(args[0], "JavaWordCount",
-        System.getenv("SPARK_HOME"), JavaSparkContext.jarOfClass(JavaWordCount.class));
-    JavaRDD<String> lines = ctx.textFile(args[1], 1);
+    SparkConf sparkConf = new SparkConf().setAppName("JavaWordCount");
+    JavaSparkContext ctx = new JavaSparkContext(sparkConf);
+    JavaRDD<String> lines = ctx.textFile(args[0], 1);
 
     JavaRDD<String> words = lines.flatMap(new FlatMapFunction<String, String>() {
       @Override
diff --git a/examples/src/main/java/org/apache/spark/examples/mllib/JavaALS.java b/examples/src/main/java/org/apache/spark/examples/mllib/JavaALS.java
index 4533c4c5f241..8d381d4e0a94 100644
--- a/examples/src/main/java/org/apache/spark/examples/mllib/JavaALS.java
+++ b/examples/src/main/java/org/apache/spark/examples/mllib/JavaALS.java
@@ -17,6 +17,7 @@
 
 package org.apache.spark.examples.mllib;
 
+import org.apache.spark.SparkConf;
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.JavaSparkContext;
 import org.apache.spark.api.java.function.Function;
@@ -57,23 +58,22 @@ public String call(Tuple2<Object, double[]> element) {
 
   public static void main(String[] args) {
 
-    if (args.length != 5 && args.length != 6) {
+    if (args.length < 4) {
       System.err.println(
-          "Usage: JavaALS <master> <ratings_file> <rank> <iterations> <output_dir> [<blocks>]");
+        "Usage: JavaALS <ratings_file> <rank> <iterations> <output_dir> [<blocks>]");
       System.exit(1);
     }
-
-    int rank = Integer.parseInt(args[2]);
-    int iterations = Integer.parseInt(args[3]);
-    String outputDir = args[4];
+    SparkConf sparkConf = new SparkConf().setAppName("JavaALS");
+    int rank = Integer.parseInt(args[1]);
+    int iterations = Integer.parseInt(args[2]);
+    String outputDir = args[3];
     int blocks = -1;
-    if (args.length == 6) {
-      blocks = Integer.parseInt(args[5]);
+    if (args.length == 5) {
+      blocks = Integer.parseInt(args[4]);
     }
 
-    JavaSparkContext sc = new JavaSparkContext(args[0], "JavaALS",
-        System.getenv("SPARK_HOME"), JavaSparkContext.jarOfClass(JavaALS.class));
-    JavaRDD<String> lines = sc.textFile(args[1]);
+    JavaSparkContext sc = new JavaSparkContext(sparkConf);
+    JavaRDD<String> lines = sc.textFile(args[0]);
 
     JavaRDD<Rating> ratings = lines.map(new ParseRating());
 
diff --git a/examples/src/main/java/org/apache/spark/examples/mllib/JavaKMeans.java b/examples/src/main/java/org/apache/spark/examples/mllib/JavaKMeans.java
index 0cfb8e69ed28..f796123a2572 100644
--- a/examples/src/main/java/org/apache/spark/examples/mllib/JavaKMeans.java
+++ b/examples/src/main/java/org/apache/spark/examples/mllib/JavaKMeans.java
@@ -19,6 +19,7 @@
 
 import java.util.regex.Pattern;
 
+import org.apache.spark.SparkConf;
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.JavaSparkContext;
 import org.apache.spark.api.java.function.Function;
@@ -48,24 +49,21 @@ public Vector call(String line) {
   }
 
   public static void main(String[] args) {
-
-    if (args.length < 4) {
+    if (args.length < 3) {
       System.err.println(
-          "Usage: JavaKMeans <master> <input_file> <k> <max_iterations> [<runs>]");
+        "Usage: JavaKMeans <input_file> <k> <max_iterations> [<runs>]");
       System.exit(1);
     }
-
-    String inputFile = args[1];
-    int k = Integer.parseInt(args[2]);
-    int iterations = Integer.parseInt(args[3]);
+    String inputFile = args[0];
+    int k = Integer.parseInt(args[1]);
+    int iterations = Integer.parseInt(args[2]);
     int runs = 1;
 
-    if (args.length >= 5) {
-      runs = Integer.parseInt(args[4]);
+    if (args.length >= 4) {
+      runs = Integer.parseInt(args[3]);
     }
-
-    JavaSparkContext sc = new JavaSparkContext(args[0], "JavaKMeans",
-        System.getenv("SPARK_HOME"), JavaSparkContext.jarOfClass(JavaKMeans.class));
+    SparkConf sparkConf = new SparkConf().setAppName("JavaKMeans");
+    JavaSparkContext sc = new JavaSparkContext(sparkConf);
     JavaRDD<String> lines = sc.textFile(inputFile);
 
     JavaRDD<Vector> points = lines.map(new ParsePoint());
diff --git a/examples/src/main/java/org/apache/spark/examples/mllib/JavaLR.java b/examples/src/main/java/org/apache/spark/examples/mllib/JavaLR.java
index f6e48b498727..eceb6927d555 100644
--- a/examples/src/main/java/org/apache/spark/examples/mllib/JavaLR.java
+++ b/examples/src/main/java/org/apache/spark/examples/mllib/JavaLR.java
@@ -19,6 +19,7 @@
 
 import java.util.regex.Pattern;
 
+import org.apache.spark.SparkConf;
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.JavaSparkContext;
 import org.apache.spark.api.java.function.Function;
@@ -51,17 +52,16 @@ public LabeledPoint call(String line) {
   }
 
   public static void main(String[] args) {
-    if (args.length != 4) {
-      System.err.println("Usage: JavaLR <master> <input_dir> <step_size> <niters>");
+    if (args.length != 3) {
+      System.err.println("Usage: JavaLR <input_dir> <step_size> <niters>");
       System.exit(1);
     }
-
-    JavaSparkContext sc = new JavaSparkContext(args[0], "JavaLR",
-        System.getenv("SPARK_HOME"), JavaSparkContext.jarOfClass(JavaLR.class));
-    JavaRDD<String> lines = sc.textFile(args[1]);
+    SparkConf sparkConf = new SparkConf().setAppName("JavaLR");
+    JavaSparkContext sc = new JavaSparkContext(sparkConf);
+    JavaRDD<String> lines = sc.textFile(args[0]);
     JavaRDD<LabeledPoint> points = lines.map(new ParsePoint()).cache();
-    double stepSize = Double.parseDouble(args[2]);
-    int iterations = Integer.parseInt(args[3]);
+    double stepSize = Double.parseDouble(args[1]);
+    int iterations = Integer.parseInt(args[2]);
 
     // Another way to configure LogisticRegression
     //
@@ -73,7 +73,7 @@ public static void main(String[] args) {
     // LogisticRegressionModel model = lr.train(points.rdd());
 
     LogisticRegressionModel model = LogisticRegressionWithSGD.train(points.rdd(),
-        iterations, stepSize);
+      iterations, stepSize);
 
     System.out.print("Final w: " + model.weights());
 
diff --git a/examples/src/main/java/org/apache/spark/examples/sql/JavaSparkSQL.java b/examples/src/main/java/org/apache/spark/examples/sql/JavaSparkSQL.java
index d62a72f53443..ad5ec84b71e6 100644
--- a/examples/src/main/java/org/apache/spark/examples/sql/JavaSparkSQL.java
+++ b/examples/src/main/java/org/apache/spark/examples/sql/JavaSparkSQL.java
@@ -20,6 +20,7 @@
 import java.io.Serializable;
 import java.util.List;
 
+import org.apache.spark.SparkConf;
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.JavaSparkContext;
 import org.apache.spark.api.java.function.Function;
@@ -51,8 +52,8 @@ public void setAge(int age) {
   }
 
   public static void main(String[] args) throws Exception {
-    JavaSparkContext ctx = new JavaSparkContext("local", "JavaSparkSQL",
-        System.getenv("SPARK_HOME"), JavaSparkContext.jarOfClass(JavaSparkSQL.class));
+    SparkConf sparkConf = new SparkConf().setAppName("JavaSparkSQL");
+    JavaSparkContext ctx = new JavaSparkContext(sparkConf);
     JavaSQLContext sqlCtx = new JavaSQLContext(ctx);
 
     // Load a text file and convert each line to a Java Bean.
diff --git a/examples/src/main/java/org/apache/spark/examples/streaming/JavaFlumeEventCount.java b/examples/src/main/java/org/apache/spark/examples/streaming/JavaFlumeEventCount.java
index a5ece68cef87..400b68c2215b 100644
--- a/examples/src/main/java/org/apache/spark/examples/streaming/JavaFlumeEventCount.java
+++ b/examples/src/main/java/org/apache/spark/examples/streaming/JavaFlumeEventCount.java
@@ -17,6 +17,7 @@
 
 package org.apache.spark.examples.streaming;
 
+import org.apache.spark.SparkConf;
 import org.apache.spark.api.java.function.Function;
 import org.apache.spark.examples.streaming.StreamingExamples;
 import org.apache.spark.streaming.*;
@@ -31,9 +32,8 @@
  *  an Avro server on at the request host:port address and listen for requests.
  *  Your Flume AvroSink should be pointed to this address.
  *
- *  Usage: JavaFlumeEventCount <master> <host> <port>
+ *  Usage: JavaFlumeEventCount <host> <port>
  *
- *    <master> is a Spark master URL
  *    <host> is the host the Flume receiver will be started on - a receiver
  *           creates a server and listens for flume events.
  *    <port> is the port the Flume receiver will listen on.
@@ -43,22 +43,19 @@ private JavaFlumeEventCount() {
   }
 
   public static void main(String[] args) {
-    if (args.length != 3) {
-      System.err.println("Usage: JavaFlumeEventCount <master> <host> <port>");
+    if (args.length != 2) {
+      System.err.println("Usage: JavaFlumeEventCount <host> <port>");
       System.exit(1);
     }
 
     StreamingExamples.setStreamingLogLevels();
 
-    String master = args[0];
-    String host = args[1];
-    int port = Integer.parseInt(args[2]);
+    String host = args[0];
+    int port = Integer.parseInt(args[1]);
 
     Duration batchInterval = new Duration(2000);
-
-    JavaStreamingContext ssc = new JavaStreamingContext(master, "FlumeEventCount", batchInterval,
-            System.getenv("SPARK_HOME"),
-            JavaStreamingContext.jarOfClass(JavaFlumeEventCount.class));
+    SparkConf sparkConf = new SparkConf().setAppName("JavaFlumeEventCount");
+    JavaStreamingContext ssc = new JavaStreamingContext(sparkConf, batchInterval);
     JavaReceiverInputDStream<SparkFlumeEvent> flumeStream = FlumeUtils.createStream(ssc, "localhost", port);
 
     flumeStream.count();
diff --git a/examples/src/main/java/org/apache/spark/examples/streaming/JavaKafkaWordCount.java b/examples/src/main/java/org/apache/spark/examples/streaming/JavaKafkaWordCount.java
index da51eb189a64..6a74cc50d19e 100644
--- a/examples/src/main/java/org/apache/spark/examples/streaming/JavaKafkaWordCount.java
+++ b/examples/src/main/java/org/apache/spark/examples/streaming/JavaKafkaWordCount.java
@@ -21,7 +21,11 @@
 import java.util.HashMap;
 import java.util.regex.Pattern;
 
+
+import scala.Tuple2;
+
 import com.google.common.collect.Lists;
+import org.apache.spark.SparkConf;
 import org.apache.spark.api.java.function.FlatMapFunction;
 import org.apache.spark.api.java.function.Function;
 import org.apache.spark.api.java.function.Function2;
@@ -33,19 +37,18 @@
 import org.apache.spark.streaming.api.java.JavaPairReceiverInputDStream;
 import org.apache.spark.streaming.api.java.JavaStreamingContext;
 import org.apache.spark.streaming.kafka.KafkaUtils;
-import scala.Tuple2;
 
 /**
  * Consumes messages from one or more topics in Kafka and does wordcount.
- * Usage: JavaKafkaWordCount <master> <zkQuorum> <group> <topics> <numThreads>
- *   <master> is the Spark master URL. In local mode, <master> should be 'local[n]' with n > 1.
+ * Usage: JavaKafkaWordCount <zkQuorum> <group> <topics> <numThreads>
  *   <zkQuorum> is a list of one or more zookeeper servers that make quorum
  *   <group> is the name of kafka consumer group
  *   <topics> is a list of one or more kafka topics to consume from
  *   <numThreads> is the number of threads the kafka consumer should use
  *
  * Example:
- *    `./bin/run-example org.apache.spark.examples.streaming.JavaKafkaWordCount local[2] zoo01,zoo02,
+ *    `./bin/spark-submit examples.jar \
+ *    --class org.apache.spark.examples.streaming.JavaKafkaWordCount zoo01,zoo02, \
  *    zoo03 my-consumer-group topic1,topic2 1`
  */
 
@@ -56,27 +59,25 @@ private JavaKafkaWordCount() {
   }
 
   public static void main(String[] args) {
-    if (args.length < 5) {
-      System.err.println("Usage: KafkaWordCount <master> <zkQuorum> <group> <topics> <numThreads>");
+    if (args.length < 4) {
+      System.err.println("Usage: JavaKafkaWordCount <zkQuorum> <group> <topics> <numThreads>");
       System.exit(1);
     }
 
     StreamingExamples.setStreamingLogLevels();
-
+    SparkConf sparkConf = new SparkConf().setAppName("JavaKafkaWordCount");
     // Create the context with a 1 second batch size
-    JavaStreamingContext jssc = new JavaStreamingContext(args[0], "KafkaWordCount",
-            new Duration(2000), System.getenv("SPARK_HOME"),
-            JavaStreamingContext.jarOfClass(JavaKafkaWordCount.class));
+    JavaStreamingContext jssc = new JavaStreamingContext(sparkConf, new Duration(2000));
 
-    int numThreads = Integer.parseInt(args[4]);
+    int numThreads = Integer.parseInt(args[3]);
     Map<String, Integer> topicMap = new HashMap<String, Integer>();
-    String[] topics = args[3].split(",");
+    String[] topics = args[2].split(",");
     for (String topic: topics) {
       topicMap.put(topic, numThreads);
     }
 
     JavaPairReceiverInputDStream<String, String> messages =
-            KafkaUtils.createStream(jssc, args[1], args[2], topicMap);
+            KafkaUtils.createStream(jssc, args[0], args[1], topicMap);
 
     JavaDStream<String> lines = messages.map(new Function<Tuple2<String, String>, String>() {
       @Override
diff --git a/examples/src/main/java/org/apache/spark/examples/streaming/JavaNetworkWordCount.java b/examples/src/main/java/org/apache/spark/examples/streaming/JavaNetworkWordCount.java
index ac84991d87b8..e5cbd39f437c 100644
--- a/examples/src/main/java/org/apache/spark/examples/streaming/JavaNetworkWordCount.java
+++ b/examples/src/main/java/org/apache/spark/examples/streaming/JavaNetworkWordCount.java
@@ -17,9 +17,10 @@
 
 package org.apache.spark.examples.streaming;
 
-import com.google.common.collect.Lists;
-import org.apache.spark.streaming.api.java.JavaReceiverInputDStream;
 import scala.Tuple2;
+import com.google.common.collect.Lists;
+
+import org.apache.spark.SparkConf;
 import org.apache.spark.api.java.function.FlatMapFunction;
 import org.apache.spark.api.java.function.Function2;
 import org.apache.spark.api.java.function.PairFunction;
@@ -27,41 +28,39 @@
 import org.apache.spark.streaming.Duration;
 import org.apache.spark.streaming.api.java.JavaDStream;
 import org.apache.spark.streaming.api.java.JavaPairDStream;
+import org.apache.spark.streaming.api.java.JavaReceiverInputDStream;
 import org.apache.spark.streaming.api.java.JavaStreamingContext;
 
 import java.util.regex.Pattern;
 
 /**
  * Counts words in UTF8 encoded, '\n' delimited text received from the network every second.
- * Usage: JavaNetworkWordCount <master> <hostname> <port>
- *   <master> is the Spark master URL. In local mode, <master> should be 'local[n]' with n > 1.
+ * Usage: JavaNetworkWordCount <hostname> <port>
  *   <hostname> and <port> describe the TCP server that Spark Streaming would connect to receive data.
  *
  * To run this on your local machine, you need to first run a Netcat server
  *    `$ nc -lk 9999`
  * and then run the example
- *    `$ ./run org.apache.spark.examples.streaming.JavaNetworkWordCount local[2] localhost 9999`
+ *    `$ ./bin/spark-submit examples.jar \
+ *    --class org.apache.spark.examples.streaming.JavaNetworkWordCount localhost 9999`
  */
 public final class JavaNetworkWordCount {
   private static final Pattern SPACE = Pattern.compile(" ");
 
   public static void main(String[] args) {
-    if (args.length < 3) {
-      System.err.println("Usage: JavaNetworkWordCount <master> <hostname> <port>\n" +
-          "In local mode, <master> should be 'local[n]' with n > 1");
+    if (args.length < 2) {
+      System.err.println("Usage: JavaNetworkWordCount <hostname> <port>");
       System.exit(1);
     }
 
     StreamingExamples.setStreamingLogLevels();
-
+    SparkConf sparkConf = new SparkConf().setAppName("JavaNetworkWordCount");
     // Create the context with a 1 second batch size
-    JavaStreamingContext ssc = new JavaStreamingContext(args[0], "JavaNetworkWordCount",
-            new Duration(1000), System.getenv("SPARK_HOME"),
-            JavaStreamingContext.jarOfClass(JavaNetworkWordCount.class));
+    JavaStreamingContext ssc = new JavaStreamingContext(sparkConf,  new Duration(1000));
 
     // Create a JavaReceiverInputDStream on target ip:port and count the
     // words in input stream of \n delimited text (eg. generated by 'nc')
-    JavaReceiverInputDStream<String> lines = ssc.socketTextStream(args[1], Integer.parseInt(args[2]));
+    JavaReceiverInputDStream<String> lines = ssc.socketTextStream(args[0], Integer.parseInt(args[1]));
     JavaDStream<String> words = lines.flatMap(new FlatMapFunction<String, String>() {
       @Override
       public Iterable<String> call(String x) {
diff --git a/examples/src/main/java/org/apache/spark/examples/streaming/JavaQueueStream.java b/examples/src/main/java/org/apache/spark/examples/streaming/JavaQueueStream.java
index 819311968fac..4ce8437f8270 100644
--- a/examples/src/main/java/org/apache/spark/examples/streaming/JavaQueueStream.java
+++ b/examples/src/main/java/org/apache/spark/examples/streaming/JavaQueueStream.java
@@ -17,8 +17,16 @@
 
 package org.apache.spark.examples.streaming;
 
-import com.google.common.collect.Lists;
+
+import java.util.LinkedList;
+import java.util.List;
+import java.util.Queue;
+
 import scala.Tuple2;
+
+import com.google.common.collect.Lists;
+
+import org.apache.spark.SparkConf;
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.function.Function2;
 import org.apache.spark.api.java.function.PairFunction;
@@ -28,25 +36,17 @@
 import org.apache.spark.streaming.api.java.JavaPairDStream;
 import org.apache.spark.streaming.api.java.JavaStreamingContext;
 
-import java.util.LinkedList;
-import java.util.List;
-import java.util.Queue;
-
 public final class JavaQueueStream {
   private JavaQueueStream() {
   }
 
   public static void main(String[] args) throws Exception {
-    if (args.length < 1) {
-      System.err.println("Usage: JavaQueueStream <master>");
-      System.exit(1);
-    }
 
     StreamingExamples.setStreamingLogLevels();
+    SparkConf sparkConf = new SparkConf().setAppName("JavaQueueStream");
 
     // Create the context
-    JavaStreamingContext ssc = new JavaStreamingContext(args[0], "QueueStream", new Duration(1000),
-            System.getenv("SPARK_HOME"), JavaStreamingContext.jarOfClass(JavaQueueStream.class));
+    JavaStreamingContext ssc = new JavaStreamingContext(sparkConf, new Duration(1000));
 
     // Create the queue through which RDDs can be pushed to
     // a QueueInputDStream
diff --git a/examples/src/main/scala/org/apache/spark/examples/BroadcastTest.scala b/examples/src/main/scala/org/apache/spark/examples/BroadcastTest.scala
index f6dfd2c4c621..973049b95a7b 100644
--- a/examples/src/main/scala/org/apache/spark/examples/BroadcastTest.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/BroadcastTest.scala
@@ -17,28 +17,26 @@
 
 package org.apache.spark.examples
 
-import org.apache.spark.SparkContext
+import org.apache.spark.{SparkConf, SparkContext}
 
+/**
+  * Usage: BroadcastTest [slices] [numElem] [broadcastAlgo] [blockSize]
+  */
 object BroadcastTest {
   def main(args: Array[String]) {
-    if (args.length == 0) {
-      System.err.println("Usage: BroadcastTest <master> [slices] [numElem] [broadcastAlgo]" +
-        " [blockSize]")
-      System.exit(1)
-    }
 
-    val bcName = if (args.length > 3) args(3) else "Http"
-    val blockSize = if (args.length > 4) args(4) else "4096"
+    val bcName = if (args.length > 2) args(2) else "Http"
+    val blockSize = if (args.length > 3) args(3) else "4096"
 
     System.setProperty("spark.broadcast.factory", "org.apache.spark.broadcast." + bcName +
       "BroadcastFactory")
     System.setProperty("spark.broadcast.blockSize", blockSize)
+    val sparkConf = new SparkConf().setAppName("Broadcast Test")
 
-    val sc = new SparkContext(args(0), "Broadcast Test",
-      System.getenv("SPARK_HOME"), SparkContext.jarOfClass(this.getClass).toSeq)
+    val sc = new SparkContext(sparkConf)
 
-    val slices = if (args.length > 1) args(1).toInt else 2
-    val num = if (args.length > 2) args(2).toInt else 1000000
+    val slices = if (args.length > 0) args(0).toInt else 2
+    val num = if (args.length > 1) args(1).toInt else 1000000
 
     val arr1 = new Array[Int](num)
     for (i <- 0 until arr1.length) {
diff --git a/examples/src/main/scala/org/apache/spark/examples/CassandraCQLTest.scala b/examples/src/main/scala/org/apache/spark/examples/CassandraCQLTest.scala
index 3798329fc2f4..9a00701f985f 100644
--- a/examples/src/main/scala/org/apache/spark/examples/CassandraCQLTest.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/CassandraCQLTest.scala
@@ -30,7 +30,7 @@ import org.apache.cassandra.hadoop.cql3.CqlOutputFormat
 import org.apache.cassandra.utils.ByteBufferUtil
 import org.apache.hadoop.mapreduce.Job
 
-import org.apache.spark.SparkContext
+import org.apache.spark.{SparkConf, SparkContext}
 import org.apache.spark.SparkContext._
 
 /*
@@ -65,19 +65,18 @@ import org.apache.spark.SparkContext._
 /**
  * This example demonstrates how to read and write to cassandra column family created using CQL3
  * using Spark.
- * Parameters : <spark_master> <cassandra_node> <cassandra_port>
- * Usage: ./bin/run-example org.apache.spark.examples.CassandraCQLTest local[2] localhost 9160
- *
+ * Parameters : <cassandra_node> <cassandra_port>
+ * Usage: ./bin/spark-submit examples.jar \
+ *  --class org.apache.spark.examples.CassandraCQLTest localhost 9160
  */
 object CassandraCQLTest {
 
   def main(args: Array[String]) {
-    val sc = new SparkContext(args(0),
-               "CQLTestApp",
-               System.getenv("SPARK_HOME"),
-               SparkContext.jarOfClass(this.getClass).toSeq)
-    val cHost: String = args(1)
-    val cPort: String = args(2)
+    val sparkConf = new SparkConf().setAppName("CQLTestApp")
+
+    val sc = new SparkContext(sparkConf)
+    val cHost: String = args(0)
+    val cPort: String = args(1)
     val KeySpace = "retail"
     val InputColumnFamily = "ordercf"
     val OutputColumnFamily = "salecount"
diff --git a/examples/src/main/scala/org/apache/spark/examples/CassandraTest.scala b/examples/src/main/scala/org/apache/spark/examples/CassandraTest.scala
index ed5d2f9e46f2..91ba364a346a 100644
--- a/examples/src/main/scala/org/apache/spark/examples/CassandraTest.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/CassandraTest.scala
@@ -30,7 +30,7 @@ import org.apache.cassandra.thrift._
 import org.apache.cassandra.utils.ByteBufferUtil
 import org.apache.hadoop.mapreduce.Job
 
-import org.apache.spark.SparkContext
+import org.apache.spark.{SparkConf, SparkContext}
 import org.apache.spark.SparkContext._
 
 /*
@@ -38,10 +38,10 @@ import org.apache.spark.SparkContext._
  * support for Hadoop.
  *
  * To run this example, run this file with the following command params -
- * <spark_master> <cassandra_node> <cassandra_port>
+ * <cassandra_node> <cassandra_port>
  *
  * So if you want to run this on localhost this will be,
- * local[3] localhost 9160
+ * localhost 9160
  *
  * The example makes some assumptions:
  * 1. You have already created a keyspace called casDemo and it has a column family named Words
@@ -54,9 +54,9 @@ import org.apache.spark.SparkContext._
 object CassandraTest {
 
   def main(args: Array[String]) {
-
+    val sparkConf = new SparkConf().setAppName("casDemo")
     // Get a SparkContext
-    val sc = new SparkContext(args(0), "casDemo")
+    val sc = new SparkContext(sparkConf)
 
     // Build the job configuration with ConfigHelper provided by Cassandra
     val job = new Job()
diff --git a/examples/src/main/scala/org/apache/spark/examples/ExceptionHandlingTest.scala b/examples/src/main/scala/org/apache/spark/examples/ExceptionHandlingTest.scala
index f0dcef431b2e..d42f63e87052 100644
--- a/examples/src/main/scala/org/apache/spark/examples/ExceptionHandlingTest.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/ExceptionHandlingTest.scala
@@ -17,17 +17,12 @@
 
 package org.apache.spark.examples
 
-import org.apache.spark.SparkContext
+import org.apache.spark.{SparkConf, SparkContext}
 
 object ExceptionHandlingTest {
   def main(args: Array[String]) {
-    if (args.length == 0) {
-      System.err.println("Usage: ExceptionHandlingTest <master>")
-      System.exit(1)
-    }
-
-    val sc = new SparkContext(args(0), "ExceptionHandlingTest",
-      System.getenv("SPARK_HOME"), SparkContext.jarOfClass(this.getClass).toSeq)
+    val sparkConf = new SparkConf().setAppName("ExceptionHandlingTest")
+    val sc = new SparkContext(sparkConf)
     sc.parallelize(0 until sc.defaultParallelism).foreach { i =>
       if (math.random > 0.75) {
         throw new Exception("Testing exception handling")
diff --git a/examples/src/main/scala/org/apache/spark/examples/GroupByTest.scala b/examples/src/main/scala/org/apache/spark/examples/GroupByTest.scala
index e67bb29a4940..efd91bb05498 100644
--- a/examples/src/main/scala/org/apache/spark/examples/GroupByTest.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/GroupByTest.scala
@@ -19,24 +19,21 @@ package org.apache.spark.examples
 
 import java.util.Random
 
-import org.apache.spark.SparkContext
+import org.apache.spark.{SparkConf, SparkContext}
 import org.apache.spark.SparkContext._
 
+/**
+  * Usage: GroupByTest [numMappers] [numKVPairs] [KeySize] [numReducers]
+  */
 object GroupByTest {
   def main(args: Array[String]) {
-    if (args.length == 0) {
-      System.err.println(
-        "Usage: GroupByTest <master> [numMappers] [numKVPairs] [KeySize] [numReducers]")
-      System.exit(1)
-    }
-
-    var numMappers = if (args.length > 1) args(1).toInt else 2
-    var numKVPairs = if (args.length > 2) args(2).toInt else 1000
-    var valSize = if (args.length > 3) args(3).toInt else 1000
-    var numReducers = if (args.length > 4) args(4).toInt else numMappers
-
-    val sc = new SparkContext(args(0), "GroupBy Test",
-      System.getenv("SPARK_HOME"), SparkContext.jarOfClass(this.getClass).toSeq)
+    val sparkConf = new SparkConf().setAppName("GroupBy Test")
+    var numMappers = if (args.length > 0) args(0).toInt else 2
+    var numKVPairs = if (args.length > 1) args(1).toInt else 1000
+    var valSize = if (args.length > 2) args(2).toInt else 1000
+    var numReducers = if (args.length > 3) args(3).toInt else numMappers
+
+    val sc = new SparkContext(sparkConf)
 
     val pairs1 = sc.parallelize(0 until numMappers, numMappers).flatMap { p =>
       val ranGen = new Random
diff --git a/examples/src/main/scala/org/apache/spark/examples/HBaseTest.scala b/examples/src/main/scala/org/apache/spark/examples/HBaseTest.scala
index adbd1c02fa2e..a8c338480e6e 100644
--- a/examples/src/main/scala/org/apache/spark/examples/HBaseTest.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/HBaseTest.scala
@@ -26,11 +26,9 @@ import org.apache.spark.rdd.NewHadoopRDD
 
 object HBaseTest {
   def main(args: Array[String]) {
-    val sc = new SparkContext(args(0), "HBaseTest",
-      System.getenv("SPARK_HOME"), SparkContext.jarOfClass(this.getClass).toSeq)
-
+    val sparkConf = new SparkConf().setAppName("HBaseTest")
+    val sc = new SparkContext(sparkConf)
     val conf = HBaseConfiguration.create()
-
     // Other options for configuring scan behavior are available. More information available at
     // http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/mapreduce/TableInputFormat.html
     conf.set(TableInputFormat.INPUT_TABLE, args(1))
diff --git a/examples/src/main/scala/org/apache/spark/examples/HdfsTest.scala b/examples/src/main/scala/org/apache/spark/examples/HdfsTest.scala
index c7a4884af10b..331de3ad1ef5 100644
--- a/examples/src/main/scala/org/apache/spark/examples/HdfsTest.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/HdfsTest.scala
@@ -21,8 +21,8 @@ import org.apache.spark._
 
 object HdfsTest {
   def main(args: Array[String]) {
-    val sc = new SparkContext(args(0), "HdfsTest",
-      System.getenv("SPARK_HOME"), SparkContext.jarOfClass(this.getClass).toSeq)
+    val sparkConf = new SparkConf().setAppName("HdfsTest")
+    val sc = new SparkContext(sparkConf)
     val file = sc.textFile(args(1))
     val mapped = file.map(s => s.length).cache()
     for (iter <- 1 to 10) {
diff --git a/examples/src/main/scala/org/apache/spark/examples/LogQuery.scala b/examples/src/main/scala/org/apache/spark/examples/LogQuery.scala
index f77a444ff7a9..4c655b84fde2 100644
--- a/examples/src/main/scala/org/apache/spark/examples/LogQuery.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/LogQuery.scala
@@ -17,11 +17,13 @@
 
 package org.apache.spark.examples
 
-import org.apache.spark.SparkContext
+import org.apache.spark.{SparkConf, SparkContext}
 import org.apache.spark.SparkContext._
 
 /**
  * Executes a roll up-style query against Apache logs.
+ *  
+ * Usage: LogQuery [logFile]
  */
 object LogQuery {
   val exampleApacheLogs = List(
@@ -40,16 +42,12 @@ object LogQuery {
   )
 
   def main(args: Array[String]) {
-    if (args.length == 0) {
-      System.err.println("Usage: LogQuery <master> [logFile]")
-      System.exit(1)
-    }
 
-    val sc = new SparkContext(args(0), "Log Query",
-      System.getenv("SPARK_HOME"), SparkContext.jarOfClass(this.getClass).toSeq)
+    val sparkConf = new SparkConf().setAppName("Log Query")
+    val sc = new SparkContext(sparkConf)
 
     val dataSet =
-      if (args.length == 2) sc.textFile(args(1)) else sc.parallelize(exampleApacheLogs)
+      if (args.length == 1) sc.textFile(args(0)) else sc.parallelize(exampleApacheLogs)
     // scalastyle:off
     val apacheLogRegex =
       """^([\d.]+) (\S+) (\S+) \[([\w\d:/]+\s[+\-]\d{4})\] "(.+?)" (\d{3}) ([\d\-]+) "([^"]+)" "([^"]+)".*""".r
diff --git a/examples/src/main/scala/org/apache/spark/examples/MultiBroadcastTest.scala b/examples/src/main/scala/org/apache/spark/examples/MultiBroadcastTest.scala
index c8985eae33de..2a5c0c0defe1 100644
--- a/examples/src/main/scala/org/apache/spark/examples/MultiBroadcastTest.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/MultiBroadcastTest.scala
@@ -18,20 +18,19 @@
 package org.apache.spark.examples
 
 import org.apache.spark.rdd.RDD
-import org.apache.spark.SparkContext
+import org.apache.spark.{SparkConf, SparkContext}
 
+/**
+  * Usage: MultiBroadcastTest [slices] [numElem]
+  */
 object MultiBroadcastTest {
   def main(args: Array[String]) {
-    if (args.length == 0) {
-      System.err.println("Usage: MultiBroadcastTest <master> [<slices>] [numElem]")
-      System.exit(1)
-    }
 
-    val sc = new SparkContext(args(0), "Multi-Broadcast Test",
-      System.getenv("SPARK_HOME"), SparkContext.jarOfClass(this.getClass).toSeq)
+    val sparkConf = new SparkConf().setAppName("Multi-Broadcast Test")
+    val sc = new SparkContext(sparkConf)
 
-    val slices = if (args.length > 1) args(1).toInt else 2
-    val num = if (args.length > 2) args(2).toInt else 1000000
+    val slices = if (args.length > 0) args(0).toInt else 2
+    val num = if (args.length > 1) args(1).toInt else 1000000
 
     val arr1 = new Array[Int](num)
     for (i <- 0 until arr1.length) {
diff --git a/examples/src/main/scala/org/apache/spark/examples/SimpleSkewedGroupByTest.scala b/examples/src/main/scala/org/apache/spark/examples/SimpleSkewedGroupByTest.scala
index 54e8503711e3..5291ab81f459 100644
--- a/examples/src/main/scala/org/apache/spark/examples/SimpleSkewedGroupByTest.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/SimpleSkewedGroupByTest.scala
@@ -19,25 +19,23 @@ package org.apache.spark.examples
 
 import java.util.Random
 
-import org.apache.spark.SparkContext
+import org.apache.spark.{SparkConf, SparkContext}
 import org.apache.spark.SparkContext._
 
+/**
+  * Usage: SimpleSkewedGroupByTest [numMappers] [numKVPairs] [valSize] [numReducers] [ratio]
+  */
 object SimpleSkewedGroupByTest {
   def main(args: Array[String]) {
-    if (args.length == 0) {
-      System.err.println("Usage: SimpleSkewedGroupByTest <master> " +
-        "[numMappers] [numKVPairs] [valSize] [numReducers] [ratio]")
-      System.exit(1)
-    }
 
-    var numMappers = if (args.length > 1) args(1).toInt else 2
-    var numKVPairs = if (args.length > 2) args(2).toInt else 1000
-    var valSize = if (args.length > 3) args(3).toInt else 1000
-    var numReducers = if (args.length > 4) args(4).toInt else numMappers
-    var ratio = if (args.length > 5) args(5).toInt else 5.0
+    val sparkConf = new SparkConf().setAppName("SimpleSkewedGroupByTest")
+    var numMappers = if (args.length > 0) args(0).toInt else 2
+    var numKVPairs = if (args.length > 1) args(1).toInt else 1000
+    var valSize = if (args.length > 2) args(2).toInt else 1000
+    var numReducers = if (args.length > 3) args(3).toInt else numMappers
+    var ratio = if (args.length > 4) args(4).toInt else 5.0
 
-    val sc = new SparkContext(args(0), "GroupBy Test",
-      System.getenv("SPARK_HOME"), SparkContext.jarOfClass(this.getClass).toSeq)
+    val sc = new SparkContext(sparkConf)
 
     val pairs1 = sc.parallelize(0 until numMappers, numMappers).flatMap { p =>
       val ranGen = new Random
diff --git a/examples/src/main/scala/org/apache/spark/examples/SkewedGroupByTest.scala b/examples/src/main/scala/org/apache/spark/examples/SkewedGroupByTest.scala
index 1c5f22e1c00b..017d4e1e5ce1 100644
--- a/examples/src/main/scala/org/apache/spark/examples/SkewedGroupByTest.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/SkewedGroupByTest.scala
@@ -19,24 +19,21 @@ package org.apache.spark.examples
 
 import java.util.Random
 
-import org.apache.spark.SparkContext
+import org.apache.spark.{SparkConf, SparkContext}
 import org.apache.spark.SparkContext._
 
+/**
+  * Usage: GroupByTest [numMappers] [numKVPairs] [KeySize] [numReducers]
+  */
 object SkewedGroupByTest {
   def main(args: Array[String]) {
-    if (args.length == 0) {
-      System.err.println(
-        "Usage: GroupByTest <master> [numMappers] [numKVPairs] [KeySize] [numReducers]")
-      System.exit(1)
-    }
-
-    var numMappers = if (args.length > 1) args(1).toInt else 2
-    var numKVPairs = if (args.length > 2) args(2).toInt else 1000
-    var valSize = if (args.length > 3) args(3).toInt else 1000
-    var numReducers = if (args.length > 4) args(4).toInt else numMappers
-
-    val sc = new SparkContext(args(0), "GroupBy Test",
-      System.getenv("SPARK_HOME"), SparkContext.jarOfClass(this.getClass).toSeq)
+    val sparkConf = new SparkConf().setAppName("GroupBy Test")
+    var numMappers = if (args.length > 0) args(0).toInt else 2
+    var numKVPairs = if (args.length > 1) args(1).toInt else 1000
+    var valSize = if (args.length > 2) args(2).toInt else 1000
+    var numReducers = if (args.length > 3) args(3).toInt else numMappers
+
+    val sc = new SparkContext(sparkConf)
 
     val pairs1 = sc.parallelize(0 until numMappers, numMappers).flatMap { p =>
       val ranGen = new Random
diff --git a/examples/src/main/scala/org/apache/spark/examples/SparkALS.scala b/examples/src/main/scala/org/apache/spark/examples/SparkALS.scala
index 0dc726aecdd2..5cbc966bf06c 100644
--- a/examples/src/main/scala/org/apache/spark/examples/SparkALS.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/SparkALS.scala
@@ -88,32 +88,24 @@ object SparkALS {
   }
 
   def main(args: Array[String]) {
-    if (args.length == 0) {
-      System.err.println("Usage: SparkALS <master> [<M> <U> <F> <iters> <slices>]")
-      System.exit(1)
-    }
-
-    var host = ""
     var slices = 0
 
-    val options = (0 to 5).map(i => if (i < args.length) Some(args(i)) else None)
+    val options = (0 to 4).map(i => if (i < args.length) Some(args(i)) else None)
 
     options.toArray match {
-      case Array(host_, m, u, f, iters, slices_) =>
-        host = host_.get
+      case Array(m, u, f, iters, slices_) =>
         M = m.getOrElse("100").toInt
         U = u.getOrElse("500").toInt
         F = f.getOrElse("10").toInt
         ITERATIONS = iters.getOrElse("5").toInt
         slices = slices_.getOrElse("2").toInt
       case _ =>
-        System.err.println("Usage: SparkALS <master> [<M> <U> <F> <iters> <slices>]")
+        System.err.println("Usage: SparkALS [M] [U] [F] [iters] [slices]")
         System.exit(1)
     }
     printf("Running with M=%d, U=%d, F=%d, iters=%d\n", M, U, F, ITERATIONS)
-
-    val sc = new SparkContext(host, "SparkALS",
-      System.getenv("SPARK_HOME"), SparkContext.jarOfClass(this.getClass).toSeq)
+    val sparkConf = new SparkConf().setAppName("SparkALS")
+    val sc = new SparkContext(sparkConf)
 
     val R = generateR()
 
diff --git a/examples/src/main/scala/org/apache/spark/examples/SparkHdfsLR.scala b/examples/src/main/scala/org/apache/spark/examples/SparkHdfsLR.scala
index 3a6f18c33ea4..4906a696e90a 100644
--- a/examples/src/main/scala/org/apache/spark/examples/SparkHdfsLR.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/SparkHdfsLR.scala
@@ -49,20 +49,21 @@ object SparkHdfsLR {
   }
 
   def main(args: Array[String]) {
-    if (args.length < 3) {
-      System.err.println("Usage: SparkHdfsLR <master> <file> <iters>")
+    if (args.length < 2) {
+      System.err.println("Usage: SparkHdfsLR <file> <iters>")
       System.exit(1)
     }
-    val inputPath = args(1)
+
+    val sparkConf = new SparkConf().setAppName("SparkHdfsLR")
+    val inputPath = args(0)
     val conf = SparkHadoopUtil.get.newConfiguration()
-    val sc = new SparkContext(args(0), "SparkHdfsLR",
-      System.getenv("SPARK_HOME"), SparkContext.jarOfClass(this.getClass).toSeq, Map(),
+    val sc = new SparkContext(sparkConf,
       InputFormatInfo.computePreferredLocations(
         Seq(new InputFormatInfo(conf, classOf[org.apache.hadoop.mapred.TextInputFormat], inputPath))
       ))
     val lines = sc.textFile(inputPath)
     val points = lines.map(parsePoint _).cache()
-    val ITERATIONS = args(2).toInt
+    val ITERATIONS = args(1).toInt
 
     // Initialize w to a random value
     var w = DenseVector.fill(D){2 * rand.nextDouble - 1}
diff --git a/examples/src/main/scala/org/apache/spark/examples/SparkKMeans.scala b/examples/src/main/scala/org/apache/spark/examples/SparkKMeans.scala
index dcae9591b040..4d28e0aad659 100644
--- a/examples/src/main/scala/org/apache/spark/examples/SparkKMeans.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/SparkKMeans.scala
@@ -21,7 +21,7 @@ import java.util.Random
 
 import breeze.linalg.{Vector, DenseVector, squaredDistance}
 
-import org.apache.spark.SparkContext
+import org.apache.spark.{SparkConf, SparkContext}
 import org.apache.spark.SparkContext._
 
 /**
@@ -52,16 +52,16 @@ object SparkKMeans {
   }
 
   def main(args: Array[String]) {
-    if (args.length < 4) {
-        System.err.println("Usage: SparkLocalKMeans <master> <file> <k> <convergeDist>")
-        System.exit(1)
+    if (args.length < 3) {
+      System.err.println("Usage: SparkKMeans <file> <k> <convergeDist>")
+      System.exit(1)
     }
-    val sc = new SparkContext(args(0), "SparkLocalKMeans",
-      System.getenv("SPARK_HOME"), SparkContext.jarOfClass(this.getClass).toSeq)
-    val lines = sc.textFile(args(1))
+    val sparkConf = new SparkConf().setAppName("SparkKMeans")
+    val sc = new SparkContext(sparkConf)
+    val lines = sc.textFile(args(0))
     val data = lines.map(parseVector _).cache()
-    val K = args(2).toInt
-    val convergeDist = args(3).toDouble
+    val K = args(1).toInt
+    val convergeDist = args(2).toDouble
 
     val kPoints = data.takeSample(withReplacement = false, K, 42).toArray
     var tempDist = 1.0
diff --git a/examples/src/main/scala/org/apache/spark/examples/SparkLR.scala b/examples/src/main/scala/org/apache/spark/examples/SparkLR.scala
index 4f74882ccbea..99ceb3089e9f 100644
--- a/examples/src/main/scala/org/apache/spark/examples/SparkLR.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/SparkLR.scala
@@ -27,6 +27,7 @@ import org.apache.spark._
 
 /**
  * Logistic regression based classification.
+ * Usage: SparkLR [slices]
  */
 object SparkLR {
   val N = 10000  // Number of data points
@@ -47,13 +48,9 @@ object SparkLR {
   }
 
   def main(args: Array[String]) {
-    if (args.length == 0) {
-      System.err.println("Usage: SparkLR <master> [<slices>]")
-      System.exit(1)
-    }
-    val sc = new SparkContext(args(0), "SparkLR",
-      System.getenv("SPARK_HOME"), SparkContext.jarOfClass(this.getClass).toSeq)
-    val numSlices = if (args.length > 1) args(1).toInt else 2
+    val sparkConf = new SparkConf().setAppName("SparkLR")
+    val sc = new SparkContext(sparkConf)
+    val numSlices = if (args.length > 0) args(0).toInt else 2
     val points = sc.parallelize(generateData, numSlices).cache()
 
     // Initialize w to a random value
diff --git a/examples/src/main/scala/org/apache/spark/examples/SparkPageRank.scala b/examples/src/main/scala/org/apache/spark/examples/SparkPageRank.scala
index fa41c5c56094..40b36c779afd 100644
--- a/examples/src/main/scala/org/apache/spark/examples/SparkPageRank.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/SparkPageRank.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.examples
 
 import org.apache.spark.SparkContext._
-import org.apache.spark.SparkContext
+import org.apache.spark.{SparkConf, SparkContext}
 
 /**
  * Computes the PageRank of URLs from an input file. Input file should
@@ -31,14 +31,10 @@ import org.apache.spark.SparkContext
  */
 object SparkPageRank {
   def main(args: Array[String]) {
-    if (args.length < 3) {
-      System.err.println("Usage: PageRank <master> <file> <number_of_iterations>")
-      System.exit(1)
-    }
-    var iters = args(2).toInt
-    val ctx = new SparkContext(args(0), "PageRank",
-      System.getenv("SPARK_HOME"), SparkContext.jarOfClass(this.getClass).toSeq)
-    val lines = ctx.textFile(args(1), 1)
+    val sparkConf = new SparkConf().setAppName("PageRank")
+    var iters = args(1).toInt
+    val ctx = new SparkContext(sparkConf)
+    val lines = ctx.textFile(args(0), 1)
     val links = lines.map{ s =>
       val parts = s.split("\\s+")
       (parts(0), parts(1))
diff --git a/examples/src/main/scala/org/apache/spark/examples/SparkPi.scala b/examples/src/main/scala/org/apache/spark/examples/SparkPi.scala
index d8f572050422..9fbb0a800d73 100644
--- a/examples/src/main/scala/org/apache/spark/examples/SparkPi.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/SparkPi.scala
@@ -24,13 +24,9 @@ import org.apache.spark._
 /** Computes an approximation to pi */
 object SparkPi {
   def main(args: Array[String]) {
-    if (args.length == 0) {
-      System.err.println("Usage: SparkPi <master> [<slices>]")
-      System.exit(1)
-    }
-    val spark = new SparkContext(args(0), "SparkPi",
-      System.getenv("SPARK_HOME"), SparkContext.jarOfClass(this.getClass).toSeq)
-    val slices = if (args.length > 1) args(1).toInt else 2
+    val conf = new SparkConf().setAppName("Spark Pi")
+    val spark = new SparkContext(conf)
+    val slices = if (args.length > 0) args(0).toInt else 2
     val n = 100000 * slices
     val count = spark.parallelize(1 to n, slices).map { i =>
       val x = random * 2 - 1
diff --git a/examples/src/main/scala/org/apache/spark/examples/SparkTC.scala b/examples/src/main/scala/org/apache/spark/examples/SparkTC.scala
index 17d983cd875d..f7f83086df3d 100644
--- a/examples/src/main/scala/org/apache/spark/examples/SparkTC.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/SparkTC.scala
@@ -20,7 +20,7 @@ package org.apache.spark.examples
 import scala.util.Random
 import scala.collection.mutable
 
-import org.apache.spark.SparkContext
+import org.apache.spark.{SparkConf, SparkContext}
 import org.apache.spark.SparkContext._
 
 /**
@@ -42,13 +42,9 @@ object SparkTC {
   }
 
   def main(args: Array[String]) {
-    if (args.length == 0) {
-      System.err.println("Usage: SparkTC <master> [<slices>]")
-      System.exit(1)
-    }
-    val spark = new SparkContext(args(0), "SparkTC",
-      System.getenv("SPARK_HOME"), SparkContext.jarOfClass(this.getClass).toSeq)
-    val slices = if (args.length > 1) args(1).toInt else 2
+    val sparkConf = new SparkConf().setAppName("SparkTC")
+    val spark = new SparkContext(sparkConf)
+    val slices = if (args.length > 0) args(0).toInt else 2
     var tc = spark.parallelize(generateGraph, slices).cache()
 
     // Linear transitive closure: each round grows paths by one edge,
diff --git a/examples/src/main/scala/org/apache/spark/examples/SparkTachyonHdfsLR.scala b/examples/src/main/scala/org/apache/spark/examples/SparkTachyonHdfsLR.scala
index 7e43c384bdb9..22127621867e 100644
--- a/examples/src/main/scala/org/apache/spark/examples/SparkTachyonHdfsLR.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/SparkTachyonHdfsLR.scala
@@ -51,20 +51,16 @@ object SparkTachyonHdfsLR {
   }
 
   def main(args: Array[String]) {
-    if (args.length < 3) {
-      System.err.println("Usage: SparkTachyonHdfsLR <master> <file> <iters>")
-      System.exit(1)
-    }
-    val inputPath = args(1)
+    val inputPath = args(0)
     val conf = SparkHadoopUtil.get.newConfiguration()
-    val sc = new SparkContext(args(0), "SparkTachyonHdfsLR",
-      System.getenv("SPARK_HOME"), SparkContext.jarOfClass(this.getClass).toSeq, Map(),
+    val sparkConf = new SparkConf().setAppName("SparkTachyonHdfsLR")
+    val sc = new SparkContext(sparkConf,
       InputFormatInfo.computePreferredLocations(
         Seq(new InputFormatInfo(conf, classOf[org.apache.hadoop.mapred.TextInputFormat], inputPath))
       ))
     val lines = sc.textFile(inputPath)
     val points = lines.map(parsePoint _).persist(StorageLevel.OFF_HEAP)
-    val ITERATIONS = args(2).toInt
+    val ITERATIONS = args(1).toInt
 
     // Initialize w to a random value
     var w = DenseVector.fill(D){2 * rand.nextDouble - 1}
diff --git a/examples/src/main/scala/org/apache/spark/examples/SparkTachyonPi.scala b/examples/src/main/scala/org/apache/spark/examples/SparkTachyonPi.scala
index 93459110e4e0..7743f7968b10 100644
--- a/examples/src/main/scala/org/apache/spark/examples/SparkTachyonPi.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/SparkTachyonPi.scala
@@ -28,14 +28,10 @@ import org.apache.spark.storage.StorageLevel
  */
 object SparkTachyonPi {
   def main(args: Array[String]) {
-    if (args.length == 0) {
-      System.err.println("Usage: SparkTachyonPi <master> [<slices>]")
-      System.exit(1)
-    }
-    val spark = new SparkContext(args(0), "SparkTachyonPi",
-      System.getenv("SPARK_HOME"), SparkContext.jarOfClass(this.getClass).toSeq)
+    val sparkConf = new SparkConf().setAppName("SparkTachyonPi")
+    val spark = new SparkContext(sparkConf)
 
-    val slices = if (args.length > 1) args(1).toInt else 2
+    val slices = if (args.length > 0) args(0).toInt else 2
     val n = 100000 * slices
 
     val rdd = spark.parallelize(1 to n, slices)
diff --git a/examples/src/main/scala/org/apache/spark/examples/bagel/WikipediaPageRank.scala b/examples/src/main/scala/org/apache/spark/examples/bagel/WikipediaPageRank.scala
index 25bd55ca88b9..235c3bf82024 100644
--- a/examples/src/main/scala/org/apache/spark/examples/bagel/WikipediaPageRank.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/bagel/WikipediaPageRank.scala
@@ -32,22 +32,22 @@ import scala.xml.{XML,NodeSeq}
  */
 object WikipediaPageRank {
   def main(args: Array[String]) {
-    if (args.length < 5) {
+    if (args.length < 4) {
       System.err.println(
-        "Usage: WikipediaPageRank <inputFile> <threshold> <numPartitions> <host> <usePartitioner>")
+        "Usage: WikipediaPageRank <inputFile> <threshold> <numPartitions> <usePartitioner>")
       System.exit(-1)
     }
     val sparkConf = new SparkConf()
+    sparkConf.setAppName("WikipediaPageRank")
     sparkConf.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
     sparkConf.set("spark.kryo.registrator",  classOf[PRKryoRegistrator].getName)
 
     val inputFile = args(0)
     val threshold = args(1).toDouble
     val numPartitions = args(2).toInt
-    val host = args(3)
-    val usePartitioner = args(4).toBoolean
+    val usePartitioner = args(3).toBoolean
 
-    sparkConf.setMaster(host).setAppName("WikipediaPageRank")
+    sparkConf.setAppName("WikipediaPageRank")
     val sc = new SparkContext(sparkConf)
 
     // Parse the Wikipedia page data into a graph
diff --git a/examples/src/main/scala/org/apache/spark/examples/bagel/WikipediaPageRankStandalone.scala b/examples/src/main/scala/org/apache/spark/examples/bagel/WikipediaPageRankStandalone.scala
index dee3cb6c0aba..a197dac87d6d 100644
--- a/examples/src/main/scala/org/apache/spark/examples/bagel/WikipediaPageRankStandalone.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/bagel/WikipediaPageRankStandalone.scala
@@ -30,22 +30,20 @@ import org.apache.spark.rdd.RDD
 
 object WikipediaPageRankStandalone {
   def main(args: Array[String]) {
-    if (args.length < 5) {
+    if (args.length < 4) {
       System.err.println("Usage: WikipediaPageRankStandalone <inputFile> <threshold> " +
-        "<numIterations> <host> <usePartitioner>")
+        "<numIterations> <usePartitioner>")
       System.exit(-1)
     }
     val sparkConf = new SparkConf()
     sparkConf.set("spark.serializer", "spark.bagel.examples.WPRSerializer")
 
-
     val inputFile = args(0)
     val threshold = args(1).toDouble
     val numIterations = args(2).toInt
-    val host = args(3)
-    val usePartitioner = args(4).toBoolean
+    val usePartitioner = args(3).toBoolean
 
-    sparkConf.setMaster(host).setAppName("WikipediaPageRankStandalone")
+    sparkConf.setAppName("WikipediaPageRankStandalone")
 
     val sc = new SparkContext(sparkConf)
 
diff --git a/examples/src/main/scala/org/apache/spark/examples/graphx/LiveJournalPageRank.scala b/examples/src/main/scala/org/apache/spark/examples/graphx/LiveJournalPageRank.scala
index d58fddff2b5e..6ef3b62dcbed 100644
--- a/examples/src/main/scala/org/apache/spark/examples/graphx/LiveJournalPageRank.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/graphx/LiveJournalPageRank.scala
@@ -28,9 +28,9 @@ import org.apache.spark.graphx.lib.Analytics
  */
 object LiveJournalPageRank {
   def main(args: Array[String]) {
-    if (args.length < 2) {
+    if (args.length < 1) {
       System.err.println(
-        "Usage: LiveJournalPageRank <master> <edge_list_file>\n" +
+        "Usage: LiveJournalPageRank <edge_list_file>\n" +
           "    [--tol=<tolerance>]\n" +
           "        The tolerance allowed at convergence (smaller => more accurate). Default is " +
           "0.001.\n" +
@@ -44,6 +44,6 @@ object LiveJournalPageRank {
       System.exit(-1)
     }
 
-    Analytics.main(args.patch(1, List("pagerank"), 0))
+    Analytics.main(args.patch(0, List("pagerank"), 0))
   }
 }
diff --git a/examples/src/main/scala/org/apache/spark/examples/sql/RDDRelation.scala b/examples/src/main/scala/org/apache/spark/examples/sql/RDDRelation.scala
index ff9254b044c2..61c460c6b1de 100644
--- a/examples/src/main/scala/org/apache/spark/examples/sql/RDDRelation.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/sql/RDDRelation.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.examples.sql
 
-import org.apache.spark.SparkContext
+import org.apache.spark.{SparkConf, SparkContext}
 import org.apache.spark.sql.SQLContext
 
 // One method for defining the schema of an RDD is to make a case class with the desired column
@@ -26,7 +26,8 @@ case class Record(key: Int, value: String)
 
 object RDDRelation {
   def main(args: Array[String]) {
-    val sc = new SparkContext("local", "RDDRelation")
+    val sparkConf = new SparkConf().setAppName("RDDRelation")
+    val sc = new SparkContext(sparkConf)
     val sqlContext = new SQLContext(sc)
 
     // Importing the SQL context gives access to all the SQL functions and implicit conversions.
diff --git a/examples/src/main/scala/org/apache/spark/examples/sql/hive/HiveFromSpark.scala b/examples/src/main/scala/org/apache/spark/examples/sql/hive/HiveFromSpark.scala
index 66ce93a26ef4..b262fabbe0e0 100644
--- a/examples/src/main/scala/org/apache/spark/examples/sql/hive/HiveFromSpark.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/sql/hive/HiveFromSpark.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.examples.sql.hive
 
-import org.apache.spark.SparkContext
+import org.apache.spark.{SparkConf, SparkContext}
 import org.apache.spark.sql._
 import org.apache.spark.sql.hive.LocalHiveContext
 
@@ -25,7 +25,8 @@ object HiveFromSpark {
   case class Record(key: Int, value: String)
 
   def main(args: Array[String]) {
-    val sc = new SparkContext("local", "HiveFromSpark")
+    val sparkConf = new SparkConf().setAppName("HiveFromSpark")
+    val sc = new SparkContext(sparkConf)
 
     // A local hive context creates an instance of the Hive Metastore in process, storing the
     // the warehouse data in the current directory.  This location can be overridden by
diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/ActorWordCount.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/ActorWordCount.scala
index 84cf43df0f96..e29e16a9c1b1 100644
--- a/examples/src/main/scala/org/apache/spark/examples/streaming/ActorWordCount.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/ActorWordCount.scala
@@ -126,31 +126,30 @@ object FeederActor {
 /**
  * A sample word count program demonstrating the use of plugging in
  * Actor as Receiver
- * Usage: ActorWordCount <master> <hostname> <port>
- *   <master> is the Spark master URL. In local mode, <master> should be 'local[n]' with n > 1.
+ * Usage: ActorWordCount <hostname> <port>
  *   <hostname> and <port> describe the AkkaSystem that Spark Sample feeder is running on.
  *
  * To run this example locally, you may run Feeder Actor as
- *    `$ ./bin/run-example org.apache.spark.examples.streaming.FeederActor 127.0.1.1 9999`
+ *    `./bin/spark-submit examples.jar \
+ *    --class org.apache.spark.examples.streaming.FeederActor 127.0.1.1 9999`
  * and then run the example
- *    `./bin/run-example org.apache.spark.examples.streaming.ActorWordCount local[2] 127.0.1.1 9999`
+ *    `./bin/spark-submit examples.jar --class org.apache.spark.examples.streaming.ActorWordCount \
+ *     127.0.1.1 9999`
  */
 object ActorWordCount {
   def main(args: Array[String]) {
-    if (args.length < 3) {
+    if (args.length < 2) {
       System.err.println(
-        "Usage: ActorWordCount <master> <hostname> <port>" +
-        "In local mode, <master> should be 'local[n]' with n > 1")
+        "Usage: ActorWordCount <hostname> <port>")
       System.exit(1)
     }
 
     StreamingExamples.setStreamingLogLevels()
 
-    val Seq(master, host, port) = args.toSeq
-
+    val Seq(host, port) = args.toSeq
+    val sparkConf = new SparkConf().setAppName("ActorWordCount")
     // Create the context and set the batch size
-    val ssc = new StreamingContext(master, "ActorWordCount", Seconds(2),
-      System.getenv("SPARK_HOME"), StreamingContext.jarOfClass(this.getClass).toSeq)
+    val ssc = new StreamingContext(sparkConf, Seconds(2))
 
     /*
      * Following is the use of actorStream to plug in custom actor as receiver
diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/FlumeEventCount.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/FlumeEventCount.scala
index 5b2a1035fc77..38362edac27f 100644
--- a/examples/src/main/scala/org/apache/spark/examples/streaming/FlumeEventCount.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/FlumeEventCount.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.examples.streaming
 
+import org.apache.spark.SparkConf
 import org.apache.spark.storage.StorageLevel
 import org.apache.spark.streaming._
 import org.apache.spark.streaming.flume._
@@ -29,9 +30,8 @@ import org.apache.spark.util.IntParam
  *  an Avro server on at the request host:port address and listen for requests.
  *  Your Flume AvroSink should be pointed to this address.
  *
- *  Usage: FlumeEventCount <master> <host> <port>
+ *  Usage: FlumeEventCount <host> <port>
  *
- *    <master> is a Spark master URL
  *    <host> is the host the Flume receiver will be started on - a receiver
  *           creates a server and listens for flume events.
  *    <port> is the port the Flume receiver will listen on.
@@ -40,21 +40,21 @@ object FlumeEventCount {
   def main(args: Array[String]) {
     if (args.length != 3) {
       System.err.println(
-        "Usage: FlumeEventCount <master> <host> <port>")
+        "Usage: FlumeEventCount <host> <port>")
       System.exit(1)
     }
 
     StreamingExamples.setStreamingLogLevels()
 
-    val Array(master, host, IntParam(port)) = args
+    val Array(host, IntParam(port)) = args
 
     val batchInterval = Milliseconds(2000)
+    val sparkConf = new SparkConf().setAppName("FlumeEventCount")
     // Create the context and set the batch size
-    val ssc = new StreamingContext(master, "FlumeEventCount", batchInterval,
-      System.getenv("SPARK_HOME"), StreamingContext.jarOfClass(this.getClass).toSeq)
+    val ssc = new StreamingContext(sparkConf, batchInterval)
 
     // Create a flume stream
-    val stream = FlumeUtils.createStream(ssc, host,port,StorageLevel.MEMORY_ONLY_SER_2)
+    val stream = FlumeUtils.createStream(ssc, host, port, StorageLevel.MEMORY_ONLY_SER_2)
 
     // Print out the count of events received from this server in each batch
     stream.count().map(cnt => "Received " + cnt + " flume events." ).print()
diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/HdfsWordCount.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/HdfsWordCount.scala
index b440956ba313..55ac48cfb6d1 100644
--- a/examples/src/main/scala/org/apache/spark/examples/streaming/HdfsWordCount.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/HdfsWordCount.scala
@@ -17,35 +17,35 @@
 
 package org.apache.spark.examples.streaming
 
+import org.apache.spark.SparkConf
 import org.apache.spark.streaming.{Seconds, StreamingContext}
 import org.apache.spark.streaming.StreamingContext._
 
 /**
  * Counts words in new text files created in the given directory
- * Usage: HdfsWordCount <master> <directory>
- *   <master> is the Spark master URL.
+ * Usage: HdfsWordCount <directory>
  *   <directory> is the directory that Spark Streaming will use to find and read new text files.
  *
  * To run this on your local machine on directory `localdir`, run this example
- *    `$ ./bin/run-example org.apache.spark.examples.streaming.HdfsWordCount local[2] localdir`
+ *    `$ ./bin/spark-submit examples.jar \
+ *    --class org.apache.spark.examples.streaming.HdfsWordCount localdir`
  * Then create a text file in `localdir` and the words in the file will get counted.
  */
 object HdfsWordCount {
   def main(args: Array[String]) {
-    if (args.length < 2) {
-      System.err.println("Usage: HdfsWordCount <master> <directory>")
+    if (args.length < 1) {
+      System.err.println("Usage: HdfsWordCount <directory>")
       System.exit(1)
     }
 
     StreamingExamples.setStreamingLogLevels()
-
+    val sparkConf = new SparkConf().setAppName("HdfsWordCount")
     // Create the context
-    val ssc = new StreamingContext(args(0), "HdfsWordCount", Seconds(2),
-      System.getenv("SPARK_HOME"), StreamingContext.jarOfClass(this.getClass).toSeq)
+    val ssc = new StreamingContext(sparkConf, Seconds(2))
 
     // Create the FileInputDStream on the directory and use the
     // stream to count words in new files created
-    val lines = ssc.textFileStream(args(1))
+    val lines = ssc.textFileStream(args(0))
     val words = lines.flatMap(_.split(" "))
     val wordCounts = words.map(x => (x, 1)).reduceByKey(_ + _)
     wordCounts.print()
diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/KafkaWordCount.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/KafkaWordCount.scala
index c3aae5af05b1..3af806981f37 100644
--- a/examples/src/main/scala/org/apache/spark/examples/streaming/KafkaWordCount.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/KafkaWordCount.scala
@@ -24,34 +24,33 @@ import kafka.producer._
 import org.apache.spark.streaming._
 import org.apache.spark.streaming.StreamingContext._
 import org.apache.spark.streaming.kafka._
+import org.apache.spark.SparkConf
 
-// scalastyle:off
 /**
  * Consumes messages from one or more topics in Kafka and does wordcount.
- * Usage: KafkaWordCount <master> <zkQuorum> <group> <topics> <numThreads>
- *   <master> is the Spark master URL. In local mode, <master> should be 'local[n]' with n > 1.
+ * Usage: KafkaWordCount <zkQuorum> <group> <topics> <numThreads>
  *   <zkQuorum> is a list of one or more zookeeper servers that make quorum
  *   <group> is the name of kafka consumer group
  *   <topics> is a list of one or more kafka topics to consume from
  *   <numThreads> is the number of threads the kafka consumer should use
  *
  * Example:
- *    `./bin/run-example org.apache.spark.examples.streaming.KafkaWordCount local[2] zoo01,zoo02,zoo03 my-consumer-group topic1,topic2 1`
+ *    `./bin/spark-submit examples.jar \
+ *    --class org.apache.spark.examples.streaming.KafkaWordCount local[2] zoo01,zoo02,zoo03 \
+ *    my-consumer-group topic1,topic2 1`
  */
-// scalastyle:on
 object KafkaWordCount {
   def main(args: Array[String]) {
-    if (args.length < 5) {
-      System.err.println("Usage: KafkaWordCount <master> <zkQuorum> <group> <topics> <numThreads>")
+    if (args.length < 4) {
+      System.err.println("Usage: KafkaWordCount <zkQuorum> <group> <topics> <numThreads>")
       System.exit(1)
     }
 
     StreamingExamples.setStreamingLogLevels()
 
-    val Array(master, zkQuorum, group, topics, numThreads) = args
-
-    val ssc =  new StreamingContext(master, "KafkaWordCount", Seconds(2),
-      System.getenv("SPARK_HOME"), StreamingContext.jarOfClass(this.getClass).toSeq)
+    val Array(zkQuorum, group, topics, numThreads) = args
+    val sparkConf = new SparkConf().setAppName("KafkaWordCount")
+    val ssc =  new StreamingContext(sparkConf, Seconds(2))
     ssc.checkpoint("checkpoint")
 
     val topicpMap = topics.split(",").map((_,numThreads.toInt)).toMap
diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/MQTTWordCount.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/MQTTWordCount.scala
index 47bf1e5a0643..3a10daa9ab84 100644
--- a/examples/src/main/scala/org/apache/spark/examples/streaming/MQTTWordCount.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/MQTTWordCount.scala
@@ -24,6 +24,7 @@ import org.apache.spark.storage.StorageLevel
 import org.apache.spark.streaming.{Seconds, StreamingContext}
 import org.apache.spark.streaming.StreamingContext._
 import org.apache.spark.streaming.mqtt._
+import org.apache.spark.SparkConf
 
 /**
  * A simple Mqtt publisher for demonstration purposes, repeatedly publishes
@@ -64,7 +65,6 @@ object MQTTPublisher {
   }
 }
 
-// scalastyle:off
 /**
  * A sample wordcount with MqttStream stream
  *
@@ -74,30 +74,28 @@ object MQTTPublisher {
  * Eclipse paho project provides Java library for Mqtt Client http://www.eclipse.org/paho/
  * Example Java code for Mqtt Publisher and Subscriber can be found here
  * https://bitbucket.org/mkjinesh/mqttclient
- * Usage: MQTTWordCount <master> <MqttbrokerUrl> <topic>
- * In local mode, <master> should be 'local[n]' with n > 1
- *   <MqttbrokerUrl> and <topic> describe where Mqtt publisher is running.
+ * Usage: MQTTWordCount <MqttbrokerUrl> <topic>
+\ *   <MqttbrokerUrl> and <topic> describe where Mqtt publisher is running.
  *
  * To run this example locally, you may run publisher as
- *    `$ ./bin/run-example org.apache.spark.examples.streaming.MQTTPublisher tcp://localhost:1883 foo`
+ *    `$ ./bin/spark-submit examples.jar \
+ *    --class org.apache.spark.examples.streaming.MQTTPublisher tcp://localhost:1883 foo`
  * and run the example as
- *    `$ ./bin/run-example org.apache.spark.examples.streaming.MQTTWordCount local[2] tcp://localhost:1883 foo`
+ *    `$ ./bin/spark-submit examples.jar \
+ *    --class org.apache.spark.examples.streaming.MQTTWordCount tcp://localhost:1883 foo`
  */
-// scalastyle:on
 object MQTTWordCount {
 
   def main(args: Array[String]) {
-    if (args.length < 3) {
+    if (args.length < 2) {
       System.err.println(
-        "Usage: MQTTWordCount <master> <MqttbrokerUrl> <topic>" +
-          " In local mode, <master> should be 'local[n]' with n > 1")
+        "Usage: MQTTWordCount <MqttbrokerUrl> <topic>")
       System.exit(1)
     }
 
-    val Seq(master, brokerUrl, topic) = args.toSeq
-
-    val ssc = new StreamingContext(master, "MqttWordCount", Seconds(2), System.getenv("SPARK_HOME"),
-    StreamingContext.jarOfClass(this.getClass).toSeq)
+    val Seq(brokerUrl, topic) = args.toSeq
+    val sparkConf = new SparkConf().setAppName("MQTTWordCount")
+    val ssc = new StreamingContext(sparkConf, Seconds(2))
     val lines = MQTTUtils.createStream(ssc, brokerUrl, topic, StorageLevel.MEMORY_ONLY_SER_2)
 
     val words = lines.flatMap(x => x.toString.split(" "))
diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/NetworkWordCount.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/NetworkWordCount.scala
index acfe9a4da359..ad7a199b2c0a 100644
--- a/examples/src/main/scala/org/apache/spark/examples/streaming/NetworkWordCount.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/NetworkWordCount.scala
@@ -17,41 +17,38 @@
 
 package org.apache.spark.examples.streaming
 
+import org.apache.spark.SparkConf
 import org.apache.spark.streaming.{Seconds, StreamingContext}
 import org.apache.spark.streaming.StreamingContext._
 import org.apache.spark.storage.StorageLevel
 
-// scalastyle:off
 /**
  * Counts words in text encoded with UTF8 received from the network every second.
  *
- * Usage: NetworkWordCount <master> <hostname> <port>
- *   <master> is the Spark master URL. In local mode, <master> should be 'local[n]' with n > 1.
- *   <hostname> and <port> describe the TCP server that Spark Streaming would connect to receive data.
+ * Usage: NetworkWordCount <hostname> <port>
+ * <hostname> and <port> describe the TCP server that Spark Streaming would connect to receive data.
  *
  * To run this on your local machine, you need to first run a Netcat server
  *    `$ nc -lk 9999`
  * and then run the example
- *    `$ ./bin/run-example org.apache.spark.examples.streaming.NetworkWordCount local[2] localhost 9999`
+ *    `$ ./bin/spark-submit examples.jar \
+ *    --class org.apache.spark.examples.streaming.NetworkWordCount localhost 9999`
  */
-// scalastyle:on
 object NetworkWordCount {
   def main(args: Array[String]) {
-    if (args.length < 3) {
-      System.err.println("Usage: NetworkWordCount <master> <hostname> <port>\n" +
-        "In local mode, <master> should be 'local[n]' with n > 1")
+    if (args.length < 2) {
+      System.err.println("Usage: NetworkWordCount <hostname> <port>")
       System.exit(1)
     }
 
     StreamingExamples.setStreamingLogLevels()
-
+    val sparkConf = new SparkConf().setAppName("NetworkWordCount");
     // Create the context with a 1 second batch size
-    val ssc = new StreamingContext(args(0), "NetworkWordCount", Seconds(1),
-      System.getenv("SPARK_HOME"), StreamingContext.jarOfClass(this.getClass).toSeq)
+    val ssc = new StreamingContext(sparkConf, Seconds(1))
 
     // Create a NetworkInputDStream on target ip:port and count the
     // words in input stream of \n delimited text (eg. generated by 'nc')
-    val lines = ssc.socketTextStream(args(1), args(2).toInt, StorageLevel.MEMORY_ONLY_SER)
+    val lines = ssc.socketTextStream(args(0), args(1).toInt, StorageLevel.MEMORY_ONLY_SER)
     val words = lines.flatMap(_.split(" "))
     val wordCounts = words.map(x => (x, 1)).reduceByKey(_ + _)
     wordCounts.print()
diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/QueueStream.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/QueueStream.scala
index f92f72f2de87..4caa90659111 100644
--- a/examples/src/main/scala/org/apache/spark/examples/streaming/QueueStream.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/QueueStream.scala
@@ -19,6 +19,7 @@ package org.apache.spark.examples.streaming
 
 import scala.collection.mutable.SynchronizedQueue
 
+import org.apache.spark.SparkConf
 import org.apache.spark.rdd.RDD
 import org.apache.spark.streaming.{Seconds, StreamingContext}
 import org.apache.spark.streaming.StreamingContext._
@@ -26,16 +27,11 @@ import org.apache.spark.streaming.StreamingContext._
 object QueueStream {
 
   def main(args: Array[String]) {
-    if (args.length < 1) {
-      System.err.println("Usage: QueueStream <master>")
-      System.exit(1)
-    }
 
     StreamingExamples.setStreamingLogLevels()
-
+    val sparkConf = new SparkConf().setAppName("QueueStream")
     // Create the context
-    val ssc = new StreamingContext(args(0), "QueueStream", Seconds(1),
-      System.getenv("SPARK_HOME"), StreamingContext.jarOfClass(this.getClass).toSeq)
+    val ssc = new StreamingContext(sparkConf, Seconds(1))
 
     // Create the queue through which RDDs can be pushed to
     // a QueueInputDStream
diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/RawNetworkGrep.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/RawNetworkGrep.scala
index 1b0319a04643..a9aaa445bccb 100644
--- a/examples/src/main/scala/org/apache/spark/examples/streaming/RawNetworkGrep.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/RawNetworkGrep.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.examples.streaming
 
+import org.apache.spark.SparkConf
 import org.apache.spark.storage.StorageLevel
 import org.apache.spark.streaming._
 import org.apache.spark.util.IntParam
@@ -27,29 +28,26 @@ import org.apache.spark.util.IntParam
  * will only work with spark.streaming.util.RawTextSender running on all worker nodes
  * and with Spark using Kryo serialization (set Java property "spark.serializer" to
  * "org.apache.spark.serializer.KryoSerializer").
- * Usage: RawNetworkGrep <master> <numStreams> <host> <port> <batchMillis>
- *   <master> is the Spark master URL
+ * Usage: RawNetworkGrep <numStreams> <host> <port> <batchMillis>
  *   <numStream> is the number rawNetworkStreams, which should be same as number
  *               of work nodes in the cluster
  *   <host> is "localhost".
  *   <port> is the port on which RawTextSender is running in the worker nodes.
  *   <batchMillise> is the Spark Streaming batch duration in milliseconds.
  */
-
 object RawNetworkGrep {
   def main(args: Array[String]) {
-    if (args.length != 5) {
-      System.err.println("Usage: RawNetworkGrep <master> <numStreams> <host> <port> <batchMillis>")
+    if (args.length != 4) {
+      System.err.println("Usage: RawNetworkGrep <numStreams> <host> <port> <batchMillis>")
       System.exit(1)
     }
 
     StreamingExamples.setStreamingLogLevels()
 
-    val Array(master, IntParam(numStreams), host, IntParam(port), IntParam(batchMillis)) = args
-
+    val Array(IntParam(numStreams), host, IntParam(port), IntParam(batchMillis)) = args
+    val sparkConf = new SparkConf().setAppName("RawNetworkGrep")
     // Create the context
-    val ssc = new StreamingContext(master, "RawNetworkGrep", Milliseconds(batchMillis),
-      System.getenv("SPARK_HOME"), StreamingContext.jarOfClass(this.getClass).toSeq)
+    val ssc = new StreamingContext(sparkConf, Duration(batchMillis))
 
     val rawStreams = (1 to numStreams).map(_ =>
       ssc.rawSocketStream[String](host, port, StorageLevel.MEMORY_ONLY_SER_2)).toArray
diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/RecoverableNetworkWordCount.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/RecoverableNetworkWordCount.scala
index b0bc31cc66ab..ace785d9fe4c 100644
--- a/examples/src/main/scala/org/apache/spark/examples/streaming/RecoverableNetworkWordCount.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/RecoverableNetworkWordCount.scala
@@ -17,19 +17,21 @@
 
 package org.apache.spark.examples.streaming
 
+import java.io.File
+import java.nio.charset.Charset
+
+import com.google.common.io.Files
+
+import org.apache.spark.SparkConf
+import org.apache.spark.rdd.RDD
 import org.apache.spark.streaming.{Time, Seconds, StreamingContext}
 import org.apache.spark.streaming.StreamingContext._
 import org.apache.spark.util.IntParam
-import java.io.File
-import org.apache.spark.rdd.RDD
-import com.google.common.io.Files
-import java.nio.charset.Charset
 
 /**
  * Counts words in text encoded with UTF8 received from the network every second.
  *
- * Usage: NetworkWordCount <master> <hostname> <port> <checkpoint-directory> <output-file>
- *   <master> is the Spark master URL. In local mode, <master> should be 'local[n]' with n > 1.
+ * Usage: NetworkWordCount <hostname> <port> <checkpoint-directory> <output-file>
  *   <hostname> and <port> describe the TCP server that Spark Streaming would connect to receive
  *   data. <checkpoint-directory> directory to HDFS-compatible file system which checkpoint data
  *   <output-file> file to which the word counts will be appended
@@ -44,8 +46,9 @@ import java.nio.charset.Charset
  *
  * and run the example as
  *
- *      `$ ./run-example org.apache.spark.examples.streaming.RecoverableNetworkWordCount \
- *              local[2] localhost 9999 ~/checkpoint/ ~/out`
+ *      `$ ./bin/spark-submit examples.jar \
+ *      --class org.apache.spark.examples.streaming.RecoverableNetworkWordCount \
+ *              localhost 9999 ~/checkpoint/ ~/out`
  *
  * If the directory ~/checkpoint/ does not exist (e.g. running for the first time), it will create
  * a new StreamingContext (will print "Creating new context" to the console). Otherwise, if
@@ -67,17 +70,16 @@ import java.nio.charset.Charset
 
 object RecoverableNetworkWordCount {
 
-  def createContext(master: String, ip: String, port: Int, outputPath: String) = {
+  def createContext(ip: String, port: Int, outputPath: String) = {
 
     // If you do not see this printed, that means the StreamingContext has been loaded
     // from the new checkpoint
     println("Creating new context")
     val outputFile = new File(outputPath)
     if (outputFile.exists()) outputFile.delete()
-
+    val sparkConf = new SparkConf().setAppName("RecoverableNetworkWordCount")
     // Create the context with a 1 second batch size
-    val ssc = new StreamingContext(master, "RecoverableNetworkWordCount", Seconds(1),
-      System.getenv("SPARK_HOME"), StreamingContext.jarOfClass(this.getClass).toSeq)
+    val ssc = new StreamingContext(sparkConf, Seconds(1))
 
     // Create a NetworkInputDStream on target ip:port and count the
     // words in input stream of \n delimited text (eg. generated by 'nc')
@@ -94,13 +96,12 @@ object RecoverableNetworkWordCount {
   }
 
   def main(args: Array[String]) {
-    if (args.length != 5) {
+    if (args.length != 4) {
       System.err.println("You arguments were " + args.mkString("[", ", ", "]"))
       System.err.println(
         """
-          |Usage: RecoverableNetworkWordCount <master> <hostname> <port> <checkpoint-directory>
-          |     <output-file> <master> is the Spark master URL. In local mode, <master> should be
-          |     'local[n]' with n > 1. <hostname> and <port> describe the TCP server that Spark
+          |Usage: RecoverableNetworkWordCount <hostname> <port> <checkpoint-directory>
+          |     <output-file>. <hostname> and <port> describe the TCP server that Spark
           |     Streaming would connect to receive data. <checkpoint-directory> directory to
           |     HDFS-compatible file system which checkpoint data <output-file> file to which the
           |     word counts will be appended
@@ -111,10 +112,10 @@ object RecoverableNetworkWordCount {
       )
       System.exit(1)
     }
-    val Array(master, ip, IntParam(port), checkpointDirectory, outputPath) = args
+    val Array(ip, IntParam(port), checkpointDirectory, outputPath) = args
     val ssc = StreamingContext.getOrCreate(checkpointDirectory,
       () => {
-        createContext(master, ip, port, outputPath)
+        createContext(ip, port, outputPath)
       })
     ssc.start()
     ssc.awaitTermination()
diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/StatefulNetworkWordCount.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/StatefulNetworkWordCount.scala
index 8001d56c98d8..5e1415f3cc53 100644
--- a/examples/src/main/scala/org/apache/spark/examples/streaming/StatefulNetworkWordCount.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/StatefulNetworkWordCount.scala
@@ -17,28 +17,27 @@
 
 package org.apache.spark.examples.streaming
 
+import org.apache.spark.SparkConf
 import org.apache.spark.streaming._
 import org.apache.spark.streaming.StreamingContext._
-// scalastyle:off
+
 /**
  * Counts words cumulatively in UTF8 encoded, '\n' delimited text received from the network every
  * second.
- * Usage: StatefulNetworkWordCount <master> <hostname> <port>
- *   <master> is the Spark master URL. In local mode, <master> should be 'local[n]' with n > 1.
+ * Usage: StatefulNetworkWordCount <hostname> <port>
  *   <hostname> and <port> describe the TCP server that Spark Streaming would connect to receive
  *   data.
  *
  * To run this on your local machine, you need to first run a Netcat server
  *    `$ nc -lk 9999`
  * and then run the example
- *    `$ ./bin/run-example org.apache.spark.examples.streaming.StatefulNetworkWordCount local[2] localhost 9999`
+ *    `$ ./bin/spark-submit examples.jar
+ *    --class org.apache.spark.examples.streaming.StatefulNetworkWordCount localhost 9999`
  */
-// scalastyle:on
 object StatefulNetworkWordCount {
   def main(args: Array[String]) {
-    if (args.length < 3) {
-      System.err.println("Usage: StatefulNetworkWordCount <master> <hostname> <port>\n" +
-        "In local mode, <master> should be 'local[n]' with n > 1")
+    if (args.length < 2) {
+      System.err.println("Usage: StatefulNetworkWordCount <hostname> <port>")
       System.exit(1)
     }
 
@@ -52,14 +51,14 @@ object StatefulNetworkWordCount {
       Some(currentCount + previousCount)
     }
 
+    val sparkConf = new SparkConf().setAppName("NetworkWordCumulativeCountUpdateStateByKey")
     // Create the context with a 1 second batch size
-    val ssc = new StreamingContext(args(0), "NetworkWordCumulativeCountUpdateStateByKey",
-      Seconds(1), System.getenv("SPARK_HOME"), StreamingContext.jarOfClass(this.getClass).toSeq)
+    val ssc = new StreamingContext(sparkConf, Seconds(1))
     ssc.checkpoint(".")
 
     // Create a NetworkInputDStream on target ip:port and count the
     // words in input stream of \n delimited test (eg. generated by 'nc')
-    val lines = ssc.socketTextStream(args(1), args(2).toInt)
+    val lines = ssc.socketTextStream(args(0), args(1).toInt)
     val words = lines.flatMap(_.split(" "))
     val wordDstream = words.map(x => (x, 1))
 
diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterAlgebirdCMS.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterAlgebirdCMS.scala
index b12617d88178..683752ac9624 100644
--- a/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterAlgebirdCMS.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterAlgebirdCMS.scala
@@ -19,11 +19,13 @@ package org.apache.spark.examples.streaming
 
 import com.twitter.algebird._
 
+import org.apache.spark.SparkConf
 import org.apache.spark.SparkContext._
 import org.apache.spark.storage.StorageLevel
 import org.apache.spark.streaming.{Seconds, StreamingContext}
 import org.apache.spark.streaming.StreamingContext._
 import org.apache.spark.streaming.twitter._
+
 // scalastyle:off
 /**
  * Illustrates the use of the Count-Min Sketch, from Twitter's Algebird library, to compute
@@ -49,12 +51,6 @@ import org.apache.spark.streaming.twitter._
 // scalastyle:on
 object TwitterAlgebirdCMS {
   def main(args: Array[String]) {
-    if (args.length < 1) {
-      System.err.println("Usage: TwitterAlgebirdCMS <master>" +
-        " [filter1] [filter2] ... [filter n]")
-      System.exit(1)
-    }
-
     StreamingExamples.setStreamingLogLevels()
 
     // CMS parameters
@@ -65,10 +61,9 @@ object TwitterAlgebirdCMS {
     // K highest frequency elements to take
     val TOPK = 10
 
-    val (master, filters) = (args.head, args.tail)
-
-    val ssc = new StreamingContext(master, "TwitterAlgebirdCMS", Seconds(10),
-      System.getenv("SPARK_HOME"), StreamingContext.jarOfClass(this.getClass).toSeq)
+    val filters = args
+    val sparkConf = new SparkConf().setAppName("TwitterAlgebirdCMS")
+    val ssc = new StreamingContext(sparkConf, Seconds(10))
     val stream = TwitterUtils.createStream(ssc, None, filters, StorageLevel.MEMORY_ONLY_SER_2)
 
     val users = stream.map(status => status.getUser.getId)
diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterAlgebirdHLL.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterAlgebirdHLL.scala
index 22f232c72545..62db5e663b8a 100644
--- a/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterAlgebirdHLL.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterAlgebirdHLL.scala
@@ -23,6 +23,8 @@ import com.twitter.algebird.HyperLogLog._
 import org.apache.spark.storage.StorageLevel
 import org.apache.spark.streaming.{Seconds, StreamingContext}
 import org.apache.spark.streaming.twitter._
+import org.apache.spark.SparkConf
+
 // scalastyle:off
 /**
  * Illustrates the use of the HyperLogLog algorithm, from Twitter's Algebird library, to compute
@@ -42,20 +44,14 @@ import org.apache.spark.streaming.twitter._
 // scalastyle:on
 object TwitterAlgebirdHLL {
   def main(args: Array[String]) {
-    if (args.length < 1) {
-      System.err.println("Usage: TwitterAlgebirdHLL <master>" +
-        " [filter1] [filter2] ... [filter n]")
-      System.exit(1)
-    }
 
     StreamingExamples.setStreamingLogLevels()
 
     /** Bit size parameter for HyperLogLog, trades off accuracy vs size */
     val BIT_SIZE = 12
-    val (master, filters) = (args.head, args.tail)
-
-    val ssc = new StreamingContext(master, "TwitterAlgebirdHLL", Seconds(5),
-      System.getenv("SPARK_HOME"), StreamingContext.jarOfClass(this.getClass).toSeq)
+    val filters = args
+    val sparkConf = new SparkConf().setAppName("TwitterAlgebirdHLL")
+    val ssc = new StreamingContext(sparkConf, Seconds(5))
     val stream = TwitterUtils.createStream(ssc, None, filters, StorageLevel.MEMORY_ONLY_SER)
 
     val users = stream.map(status => status.getUser.getId)
diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterPopularTags.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterPopularTags.scala
index 5b58e94600a1..1ddff22cb8a4 100644
--- a/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterPopularTags.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterPopularTags.scala
@@ -21,6 +21,7 @@ import org.apache.spark.streaming.{Seconds, StreamingContext}
 import StreamingContext._
 import org.apache.spark.SparkContext._
 import org.apache.spark.streaming.twitter._
+import org.apache.spark.SparkConf
 
 /**
  * Calculates popular hashtags (topics) over sliding 10 and 60 second windows from a Twitter
@@ -30,18 +31,12 @@ import org.apache.spark.streaming.twitter._
  */
 object TwitterPopularTags {
   def main(args: Array[String]) {
-    if (args.length < 1) {
-      System.err.println("Usage: TwitterPopularTags <master>" +
-        " [filter1] [filter2] ... [filter n]")
-      System.exit(1)
-    }
 
     StreamingExamples.setStreamingLogLevels()
 
-    val (master, filters) = (args.head, args.tail)
-
-    val ssc = new StreamingContext(master, "TwitterPopularTags", Seconds(2),
-      System.getenv("SPARK_HOME"), StreamingContext.jarOfClass(this.getClass).toSeq)
+    val filters = args
+    val sparkConf = new SparkConf().setAppName("TwitterPopularTags")
+    val ssc = new StreamingContext(sparkConf, Seconds(2))
     val stream = TwitterUtils.createStream(ssc, None, filters)
 
     val hashTags = stream.flatMap(status => status.getText.split(" ").filter(_.startsWith("#")))
diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/ZeroMQWordCount.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/ZeroMQWordCount.scala
index de46e5f5b10b..7ade3f1018ee 100644
--- a/examples/src/main/scala/org/apache/spark/examples/streaming/ZeroMQWordCount.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/ZeroMQWordCount.scala
@@ -28,6 +28,7 @@ import org.apache.spark.streaming.StreamingContext._
 import org.apache.spark.streaming.zeromq._
 
 import scala.language.implicitConversions
+import org.apache.spark.SparkConf
 
 /**
  * A simple publisher for demonstration purposes, repeatedly publishes random Messages
@@ -63,30 +64,28 @@ object SimpleZeroMQPublisher {
  * Install zeroMQ (release 2.1) core libraries. [ZeroMQ Install guide]
  * (http://www.zeromq.org/intro:get-the-software)
  *
- * Usage: ZeroMQWordCount <master> <zeroMQurl> <topic>
- * In local mode, <master> should be 'local[n]' with n > 1
+ * Usage: ZeroMQWordCount <zeroMQurl> <topic>
  *   <zeroMQurl> and <topic> describe where zeroMq publisher is running.
  *
  * To run this example locally, you may run publisher as
- *    `$ ./bin/run-example org.apache.spark.examples.streaming.SimpleZeroMQPublisher tcp://127.0.1.1:1234 foo.bar`
+ *    `$ ./bin/spark-submit examples.jar \
+ *    --class org.apache.spark.examples.streaming.SimpleZeroMQPublisher tcp://127.0.1.1:1234 foo.bar`
  * and run the example as
- *    `$ ./bin/run-example org.apache.spark.examples.streaming.ZeroMQWordCount local[2] tcp://127.0.1.1:1234 foo`
+ *    `$ ./bin/spark-submit examples.jar \
+ *    --class org.apache.spark.examples.streaming.ZeroMQWordCount tcp://127.0.1.1:1234 foo`
  */
 // scalastyle:on
 object ZeroMQWordCount {
   def main(args: Array[String]) {
-    if (args.length < 3) {
-      System.err.println(
-        "Usage: ZeroMQWordCount <master> <zeroMQurl> <topic>" +
-          "In local mode, <master> should be 'local[n]' with n > 1")
+    if (args.length < 2) {
+      System.err.println("Usage: ZeroMQWordCount <zeroMQurl> <topic>")
       System.exit(1)
     }
     StreamingExamples.setStreamingLogLevels()
-    val Seq(master, url, topic) = args.toSeq
-
+    val Seq(url, topic) = args.toSeq
+    val sparkConf = new SparkConf().setAppName("ZeroMQWordCount")
     // Create the context and set the batch size
-    val ssc = new StreamingContext(master, "ZeroMQWordCount", Seconds(2),
-      System.getenv("SPARK_HOME"), StreamingContext.jarOfClass(this.getClass).toSeq)
+    val ssc = new StreamingContext(sparkConf, Seconds(2))
 
     def bytesToStringIterator(x: Seq[ByteString]) = (x.map(_.utf8String)).iterator
 
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/lib/Analytics.scala b/graphx/src/main/scala/org/apache/spark/graphx/lib/Analytics.scala
index fa533a512d53..d901d4fe225f 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/lib/Analytics.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/lib/Analytics.scala
@@ -27,10 +27,14 @@ import org.apache.spark.graphx.PartitionStrategy._
 object Analytics extends Logging {
 
   def main(args: Array[String]): Unit = {
-    val host = args(0)
-    val taskType = args(1)
-    val fname = args(2)
-    val options =  args.drop(3).map { arg =>
+    if (args.length < 2) {
+      System.err.println("Usage: Analytics <taskType> <file> [other options]")
+      System.exit(1)
+    }
+
+    val taskType = args(0)
+    val fname = args(1)
+    val options =  args.drop(2).map { arg =>
       arg.dropWhile(_ == '-').split('=') match {
         case Array(opt, v) => (opt -> v)
         case _ => throw new IllegalArgumentException("Invalid argument: " + arg)
@@ -71,7 +75,7 @@ object Analytics extends Logging {
         println("|             PageRank               |")
         println("======================================")
 
-        val sc = new SparkContext(host, "PageRank(" + fname + ")", conf)
+        val sc = new SparkContext(conf.setAppName("PageRank(" + fname + ")"))
 
         val unpartitionedGraph = GraphLoader.edgeListFile(sc, fname,
           minEdgePartitions = numEPart).cache()
@@ -115,7 +119,7 @@ object Analytics extends Logging {
         println("|      Connected Components          |")
         println("======================================")
 
-        val sc = new SparkContext(host, "ConnectedComponents(" + fname + ")", conf)
+        val sc = new SparkContext(conf.setAppName("ConnectedComponents(" + fname + ")"))
         val unpartitionedGraph = GraphLoader.edgeListFile(sc, fname,
           minEdgePartitions = numEPart).cache()
         val graph = partitionStrategy.foldLeft(unpartitionedGraph)(_.partitionBy(_))
@@ -137,7 +141,7 @@ object Analytics extends Logging {
         println("======================================")
         println("|      Triangle Count                |")
         println("======================================")
-        val sc = new SparkContext(host, "TriangleCount(" + fname + ")", conf)
+        val sc = new SparkContext(conf.setAppName("TriangleCount(" + fname + ")"))
         val graph = GraphLoader.edgeListFile(sc, fname, canonicalOrientation = true,
           minEdgePartitions = numEPart).partitionBy(partitionStrategy).cache()
         val triangles = TriangleCount.run(graph)

From c3f8b78c211df6c5adae74f37e39fb55baeff723 Mon Sep 17 00:00:00 2001
From: Andrew Or <andrewor14@gmail.com>
Date: Thu, 8 May 2014 12:13:07 -0700
Subject: [PATCH 129/810] [SPARK-1745] Move interrupted flag from TaskContext
 constructor (minor)

It makes little sense to start a TaskContext that is interrupted. Indeed, I searched for all use cases of it and didn't find a single instance in which `interrupted` is true on construction.

This was inspired by reviewing #640, which adds an additional `@volatile var completed` that is similar. These are not the most urgent changes, but I wanted to push them out before I forget.

Author: Andrew Or <andrewor14@gmail.com>

Closes #675 from andrewor14/task-context and squashes the following commits:

9575e02 [Andrew Or] Add space
69455d1 [Andrew Or] Merge branch 'master' of github.com:apache/spark into task-context
c471490 [Andrew Or] Oops, removed one flag too many. Adding it back.
85311f8 [Andrew Or] Move interrupted flag from TaskContext constructor
---
 .../scala/org/apache/spark/TaskContext.scala  | 20 ++++++++++---------
 .../spark/scheduler/ShuffleMapTask.scala      |  3 +--
 .../java/org/apache/spark/JavaAPISuite.java   |  2 +-
 .../org/apache/spark/CacheManagerSuite.scala  | 10 +++-------
 .../org/apache/spark/PipedRDDSuite.scala      |  4 +---
 5 files changed, 17 insertions(+), 22 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/TaskContext.scala b/core/src/main/scala/org/apache/spark/TaskContext.scala
index fc4812753d00..51f40c339d13 100644
--- a/core/src/main/scala/org/apache/spark/TaskContext.scala
+++ b/core/src/main/scala/org/apache/spark/TaskContext.scala
@@ -28,13 +28,12 @@ import org.apache.spark.executor.TaskMetrics
  */
 @DeveloperApi
 class TaskContext(
-  val stageId: Int,
-  val partitionId: Int,
-  val attemptId: Long,
-  val runningLocally: Boolean = false,
-  @volatile var interrupted: Boolean = false,
-  private[spark] val taskMetrics: TaskMetrics = TaskMetrics.empty
-) extends Serializable {
+    val stageId: Int,
+    val partitionId: Int,
+    val attemptId: Long,
+    val runningLocally: Boolean = false,
+    private[spark] val taskMetrics: TaskMetrics = TaskMetrics.empty)
+  extends Serializable {
 
   @deprecated("use partitionId", "0.8.1")
   def splitId = partitionId
@@ -42,7 +41,10 @@ class TaskContext(
   // List of callback functions to execute when the task completes.
   @transient private val onCompleteCallbacks = new ArrayBuffer[() => Unit]
 
-  // Set to true when the task is completed, before the onCompleteCallbacks are executed.
+  // Whether the corresponding task has been killed.
+  @volatile var interrupted: Boolean = false
+
+  // Whether the task has completed, before the onCompleteCallbacks are executed.
   @volatile var completed: Boolean = false
 
   /**
@@ -58,6 +60,6 @@ class TaskContext(
   def executeOnCompleteCallbacks() {
     completed = true
     // Process complete callbacks in the reverse order of registration
-    onCompleteCallbacks.reverse.foreach{_()}
+    onCompleteCallbacks.reverse.foreach { _() }
   }
 }
diff --git a/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala b/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala
index 2259df0b56ba..4b0324f2b544 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala
@@ -23,7 +23,6 @@ import java.io._
 import java.util.zip.{GZIPInputStream, GZIPOutputStream}
 
 import scala.collection.mutable.HashMap
-import scala.util.Try
 
 import org.apache.spark._
 import org.apache.spark.executor.ShuffleWriteMetrics
@@ -70,7 +69,7 @@ private[spark] object ShuffleMapTask {
   }
 
   // Since both the JarSet and FileSet have the same format this is used for both.
-  def deserializeFileSet(bytes: Array[Byte]) : HashMap[String, Long] = {
+  def deserializeFileSet(bytes: Array[Byte]): HashMap[String, Long] = {
     val in = new GZIPInputStream(new ByteArrayInputStream(bytes))
     val objIn = new ObjectInputStream(in)
     val set = objIn.readObject().asInstanceOf[Array[(String, Long)]].toMap
diff --git a/core/src/test/java/org/apache/spark/JavaAPISuite.java b/core/src/test/java/org/apache/spark/JavaAPISuite.java
index c3e03cea917b..191201582792 100644
--- a/core/src/test/java/org/apache/spark/JavaAPISuite.java
+++ b/core/src/test/java/org/apache/spark/JavaAPISuite.java
@@ -597,7 +597,7 @@ public void persist() {
   @Test
   public void iterator() {
     JavaRDD<Integer> rdd = sc.parallelize(Arrays.asList(1, 2, 3, 4, 5), 2);
-    TaskContext context = new TaskContext(0, 0, 0, false, false, new TaskMetrics());
+    TaskContext context = new TaskContext(0, 0, 0, false, new TaskMetrics());
     Assert.assertEquals(1, rdd.iterator(rdd.splits().get(0), context).next().intValue());
   }
 
diff --git a/core/src/test/scala/org/apache/spark/CacheManagerSuite.scala b/core/src/test/scala/org/apache/spark/CacheManagerSuite.scala
index fd5b0906e676..4f178db40f63 100644
--- a/core/src/test/scala/org/apache/spark/CacheManagerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/CacheManagerSuite.scala
@@ -23,7 +23,6 @@ import org.scalatest.{BeforeAndAfter, FunSuite}
 import org.scalatest.mock.EasyMockSugar
 
 import org.apache.spark.rdd.RDD
-import org.apache.spark.executor.TaskMetrics
 import org.apache.spark.storage._
 
 // TODO: Test the CacheManager's thread-safety aspects
@@ -59,8 +58,7 @@ class CacheManagerSuite extends FunSuite with BeforeAndAfter with EasyMockSugar
     }
 
     whenExecuting(blockManager) {
-      val context = new TaskContext(0, 0, 0, interrupted = false, runningLocally = false,
-        taskMetrics = TaskMetrics.empty)
+      val context = new TaskContext(0, 0, 0)
       val value = cacheManager.getOrCompute(rdd, split, context, StorageLevel.MEMORY_ONLY)
       assert(value.toList === List(1, 2, 3, 4))
     }
@@ -72,8 +70,7 @@ class CacheManagerSuite extends FunSuite with BeforeAndAfter with EasyMockSugar
     }
 
     whenExecuting(blockManager) {
-      val context = new TaskContext(0, 0, 0, interrupted = false, runningLocally = false,
-        taskMetrics = TaskMetrics.empty)
+      val context = new TaskContext(0, 0, 0)
       val value = cacheManager.getOrCompute(rdd, split, context, StorageLevel.MEMORY_ONLY)
       assert(value.toList === List(5, 6, 7))
     }
@@ -86,8 +83,7 @@ class CacheManagerSuite extends FunSuite with BeforeAndAfter with EasyMockSugar
     }
 
     whenExecuting(blockManager) {
-      val context = new TaskContext(0, 0, 0, runningLocally = true, interrupted = false,
-        taskMetrics = TaskMetrics.empty)
+      val context = new TaskContext(0, 0, 0, runningLocally = true)
       val value = cacheManager.getOrCompute(rdd, split, context, StorageLevel.MEMORY_ONLY)
       assert(value.toList === List(1, 2, 3, 4))
     }
diff --git a/core/src/test/scala/org/apache/spark/PipedRDDSuite.scala b/core/src/test/scala/org/apache/spark/PipedRDDSuite.scala
index 0bb6a6b09c5b..db56a4acdd6f 100644
--- a/core/src/test/scala/org/apache/spark/PipedRDDSuite.scala
+++ b/core/src/test/scala/org/apache/spark/PipedRDDSuite.scala
@@ -178,14 +178,12 @@ class PipedRDDSuite extends FunSuite with SharedSparkContext {
       }
       val hadoopPart1 = generateFakeHadoopPartition()
       val pipedRdd = new PipedRDD(nums, "printenv " + varName)
-      val tContext = new TaskContext(0, 0, 0, interrupted = false, runningLocally = false,
-        taskMetrics = TaskMetrics.empty)
+      val tContext = new TaskContext(0, 0, 0)
       val rddIter = pipedRdd.compute(hadoopPart1, tContext)
       val arr = rddIter.toArray
       assert(arr(0) == "/some/path")
     } else {
       // printenv isn't available so just pass the test
-      assert(true)
     }
   }
 

From 5c5e7d5809d337ce41a7a90eb9201e12803aba48 Mon Sep 17 00:00:00 2001
From: Evan Sparks <evan.sparks@gmail.com>
Date: Thu, 8 May 2014 13:07:30 -0700
Subject: [PATCH 130/810] Fixing typo in als.py

XtY should be Xty.

Author: Evan Sparks <evan.sparks@gmail.com>

Closes #696 from etrain/patch-2 and squashes the following commits:

634cb8d [Evan Sparks] Fixing typo in als.py
---
 examples/src/main/python/als.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/src/main/python/als.py b/examples/src/main/python/als.py
index 33700ab4f8c5..01552dc1d449 100755
--- a/examples/src/main/python/als.py
+++ b/examples/src/main/python/als.py
@@ -38,7 +38,7 @@ def update(i, vec, mat, ratings):
     ff = mat.shape[1]
     
     XtX = mat.T * mat
-    XtY = mat.T * ratings[i, :].T
+    Xty = mat.T * ratings[i, :].T
     
     for j in range(ff):
         XtX[j,j] += LAMBDA * uu

From 322b1808d21143dc323493203929488d69e8878a Mon Sep 17 00:00:00 2001
From: Takuya UESHIN <ueshin@happy-camper.st>
Date: Thu, 8 May 2014 15:31:47 -0700
Subject: [PATCH 131/810] [SPARK-1754] [SQL] Add missing arithmetic DSL
 operations.

Add missing arithmetic DSL operations: `unary_-`, `%`.

Author: Takuya UESHIN <ueshin@happy-camper.st>

Closes #689 from ueshin/issues/SPARK-1754 and squashes the following commits:

a09ef69 [Takuya UESHIN] Add also missing ! (not) operation.
f73ae2c [Takuya UESHIN] Remove redundant tests.
5b3f087 [Takuya UESHIN] Add tests relating DSL operations.
e09c5b8 [Takuya UESHIN] Add missing arithmetic DSL operations.
---
 .../apache/spark/sql/catalyst/dsl/package.scala  |  4 ++++
 .../expressions/ExpressionEvaluationSuite.scala  | 16 +++++++++++++++-
 2 files changed, 19 insertions(+), 1 deletion(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala
index dc83485df195..78d3a1d8096a 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala
@@ -57,10 +57,14 @@ package object dsl {
   trait ImplicitOperators {
     def expr: Expression
 
+    def unary_- = UnaryMinus(expr)
+    def unary_! = Not(expr)
+
     def + (other: Expression) = Add(expr, other)
     def - (other: Expression) = Subtract(expr, other)
     def * (other: Expression) = Multiply(expr, other)
     def / (other: Expression) = Divide(expr, other)
+    def % (other: Expression) = Remainder(expr, other)
 
     def && (other: Expression) = And(expr, other)
     def || (other: Expression) = Or(expr, other)
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala
index 91605d0a260e..344d8a304fc1 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala
@@ -61,7 +61,7 @@ class ExpressionEvaluationSuite extends FunSuite {
   test("3VL Not") {
     notTrueTable.foreach {
       case (v, answer) =>
-        val expr = Not(Literal(v, BooleanType))
+        val expr = ! Literal(v, BooleanType)
         val result = expr.eval(null)
         if (result != answer)
           fail(s"$expr should not evaluate to $result, expected: $answer")    }
@@ -381,6 +381,13 @@ class ExpressionEvaluationSuite extends FunSuite {
     checkEvaluation(Add(c1, Literal(null, IntegerType)), null, row)
     checkEvaluation(Add(Literal(null, IntegerType), c2), null, row)
     checkEvaluation(Add(Literal(null, IntegerType), Literal(null, IntegerType)), null, row)
+
+    checkEvaluation(-c1, -1, row)
+    checkEvaluation(c1 + c2, 3, row)
+    checkEvaluation(c1 - c2, -1, row)
+    checkEvaluation(c1 * c2, 2, row)
+    checkEvaluation(c1 / c2, 0, row)
+    checkEvaluation(c1 % c2, 1, row)
   }
 
   test("BinaryComparison") {
@@ -395,6 +402,13 @@ class ExpressionEvaluationSuite extends FunSuite {
     checkEvaluation(LessThan(c1, Literal(null, IntegerType)), null, row)
     checkEvaluation(LessThan(Literal(null, IntegerType), c2), null, row)
     checkEvaluation(LessThan(Literal(null, IntegerType), Literal(null, IntegerType)), null, row)
+
+    checkEvaluation(c1 < c2, true, row)
+    checkEvaluation(c1 <= c2, true, row)
+    checkEvaluation(c1 > c2, false, row)
+    checkEvaluation(c1 >= c2, false, row)
+    checkEvaluation(c1 === c2, false, row)
+    checkEvaluation(c1 !== c2, true, row)
   }
 }
 

From d38febee46ed156b0c8ec64757db6c290e488421 Mon Sep 17 00:00:00 2001
From: DB Tsai <dbtsai@alpinenow.com>
Date: Thu, 8 May 2014 17:52:32 -0700
Subject: [PATCH 132/810] MLlib documentation fix

Fixed the documentation for that `loadLibSVMData` is changed to `loadLibSVMFile`.

Author: DB Tsai <dbtsai@alpinenow.com>

Closes #703 from dbtsai/dbtsai-docfix and squashes the following commits:

71dd508 [DB Tsai] loadLibSVMData is changed to loadLibSVMFile
---
 docs/mllib-basics.md         | 8 ++++----
 docs/mllib-linear-methods.md | 2 +-
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/docs/mllib-basics.md b/docs/mllib-basics.md
index 704308802d65..aa9321a54709 100644
--- a/docs/mllib-basics.md
+++ b/docs/mllib-basics.md
@@ -184,7 +184,7 @@ After loading, the feature indices are converted to zero-based.
 <div class="codetabs">
 <div data-lang="scala" markdown="1">
 
-[`MLUtils.loadLibSVMData`](api/mllib/index.html#org.apache.spark.mllib.util.MLUtils$) reads training
+[`MLUtils.loadLibSVMFile`](api/mllib/index.html#org.apache.spark.mllib.util.MLUtils$) reads training
 examples stored in LIBSVM format.
 
 {% highlight scala %}
@@ -192,12 +192,12 @@ import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.util.MLUtils
 import org.apache.spark.rdd.RDD
 
-val training: RDD[LabeledPoint] = MLUtils.loadLibSVMData(sc, "mllib/data/sample_libsvm_data.txt")
+val training: RDD[LabeledPoint] = MLUtils.loadLibSVMFile(sc, "mllib/data/sample_libsvm_data.txt")
 {% endhighlight %}
 </div>
 
 <div data-lang="java" markdown="1">
-[`MLUtils.loadLibSVMData`](api/mllib/index.html#org.apache.spark.mllib.util.MLUtils$) reads training
+[`MLUtils.loadLibSVMFile`](api/mllib/index.html#org.apache.spark.mllib.util.MLUtils$) reads training
 examples stored in LIBSVM format.
 
 {% highlight java %}
@@ -205,7 +205,7 @@ import org.apache.spark.mllib.regression.LabeledPoint;
 import org.apache.spark.mllib.util.MLUtils;
 import org.apache.spark.rdd.RDDimport;
 
-RDD<LabeledPoint> training = MLUtils.loadLibSVMData(jsc, "mllib/data/sample_libsvm_data.txt");
+RDD<LabeledPoint> training = MLUtils.loadLibSVMFile(jsc, "mllib/data/sample_libsvm_data.txt");
 {% endhighlight %}
 </div>
 </div>
diff --git a/docs/mllib-linear-methods.md b/docs/mllib-linear-methods.md
index 40b7a7f80708..eff617d8641e 100644
--- a/docs/mllib-linear-methods.md
+++ b/docs/mllib-linear-methods.md
@@ -186,7 +186,7 @@ import org.apache.spark.mllib.linalg.Vectors
 import org.apache.spark.mllib.util.MLUtils
 
 // Load training data in LIBSVM format.
-val data = MLUtils.loadLibSVMData(sc, "mllib/data/sample_libsvm_data.txt")
+val data = MLUtils.loadLibSVMFile(sc, "mllib/data/sample_libsvm_data.txt")
 
 // Split data into training (60%) and test (40%).
 val splits = data.randomSplit(Array(0.6, 0.4), seed = 11L)

From 910a13b3c52a6309068b4997da6df6b7d6058a1b Mon Sep 17 00:00:00 2001
From: DB Tsai <dbtsai@alpinenow.com>
Date: Thu, 8 May 2014 17:53:22 -0700
Subject: [PATCH 133/810] [SPARK-1157][MLlib] Bug fix: lossHistory should
 exclude rejection steps, and remove miniBatch

Getting the lossHistory from Breeze's API which already excludes the rejection steps in line search. Also, remove the miniBatch in LBFGS since those quasi-Newton methods approximate the inverse of Hessian. It doesn't make sense if the gradients are computed from a varying objective.

Author: DB Tsai <dbtsai@alpinenow.com>

Closes #582 from dbtsai/dbtsai-lbfgs-bug and squashes the following commits:

9cc6cf9 [DB Tsai] Removed the miniBatch in LBFGS.
1ba6a33 [DB Tsai] Formatting the code.
d72c679 [DB Tsai] Using Breeze's states to get the loss.
---
 .../spark/mllib/optimization/LBFGS.scala      | 63 ++++++++-----------
 .../spark/mllib/optimization/LBFGSSuite.scala | 15 ++---
 2 files changed, 30 insertions(+), 48 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/optimization/LBFGS.scala b/mllib/src/main/scala/org/apache/spark/mllib/optimization/LBFGS.scala
index 969a0c5f7c95..8f187c9df510 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/optimization/LBFGS.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/optimization/LBFGS.scala
@@ -42,7 +42,6 @@ class LBFGS(private var gradient: Gradient, private var updater: Updater)
   private var convergenceTol = 1E-4
   private var maxNumIterations = 100
   private var regParam = 0.0
-  private var miniBatchFraction = 1.0
 
   /**
    * Set the number of corrections used in the LBFGS update. Default 10.
@@ -57,14 +56,6 @@ class LBFGS(private var gradient: Gradient, private var updater: Updater)
     this
   }
 
-  /**
-   * Set fraction of data to be used for each L-BFGS iteration. Default 1.0.
-   */
-  def setMiniBatchFraction(fraction: Double): this.type = {
-    this.miniBatchFraction = fraction
-    this
-  }
-
   /**
    * Set the convergence tolerance of iterations for L-BFGS. Default 1E-4.
    * Smaller value will lead to higher accuracy with the cost of more iterations.
@@ -110,7 +101,7 @@ class LBFGS(private var gradient: Gradient, private var updater: Updater)
   }
 
   override def optimize(data: RDD[(Double, Vector)], initialWeights: Vector): Vector = {
-    val (weights, _) = LBFGS.runMiniBatchLBFGS(
+    val (weights, _) = LBFGS.runLBFGS(
       data,
       gradient,
       updater,
@@ -118,7 +109,6 @@ class LBFGS(private var gradient: Gradient, private var updater: Updater)
       convergenceTol,
       maxNumIterations,
       regParam,
-      miniBatchFraction,
       initialWeights)
     weights
   }
@@ -132,10 +122,8 @@ class LBFGS(private var gradient: Gradient, private var updater: Updater)
 @DeveloperApi
 object LBFGS extends Logging {
   /**
-   * Run Limited-memory BFGS (L-BFGS) in parallel using mini batches.
-   * In each iteration, we sample a subset (fraction miniBatchFraction) of the total data
-   * in order to compute a gradient estimate.
-   * Sampling, and averaging the subgradients over this subset is performed using one standard
+   * Run Limited-memory BFGS (L-BFGS) in parallel.
+   * Averaging the subgradients over different partitions is performed using one standard
    * spark map-reduce in each iteration.
    *
    * @param data - Input data for L-BFGS. RDD of the set of data examples, each of
@@ -147,14 +135,12 @@ object LBFGS extends Logging {
    * @param convergenceTol - The convergence tolerance of iterations for L-BFGS
    * @param maxNumIterations - Maximal number of iterations that L-BFGS can be run.
    * @param regParam - Regularization parameter
-   * @param miniBatchFraction - Fraction of the input data set that should be used for
-   *                          one iteration of L-BFGS. Default value 1.0.
    *
    * @return A tuple containing two elements. The first element is a column matrix containing
    *         weights for every feature, and the second element is an array containing the loss
    *         computed for every iteration.
    */
-  def runMiniBatchLBFGS(
+  def runLBFGS(
       data: RDD[(Double, Vector)],
       gradient: Gradient,
       updater: Updater,
@@ -162,23 +148,33 @@ object LBFGS extends Logging {
       convergenceTol: Double,
       maxNumIterations: Int,
       regParam: Double,
-      miniBatchFraction: Double,
       initialWeights: Vector): (Vector, Array[Double]) = {
 
     val lossHistory = new ArrayBuffer[Double](maxNumIterations)
 
     val numExamples = data.count()
-    val miniBatchSize = numExamples * miniBatchFraction
 
     val costFun =
-      new CostFun(data, gradient, updater, regParam, miniBatchFraction, lossHistory, miniBatchSize)
+      new CostFun(data, gradient, updater, regParam, numExamples)
 
     val lbfgs = new BreezeLBFGS[BDV[Double]](maxNumIterations, numCorrections, convergenceTol)
 
-    val weights = Vectors.fromBreeze(
-      lbfgs.minimize(new CachedDiffFunction(costFun), initialWeights.toBreeze.toDenseVector))
+    val states =
+      lbfgs.iterations(new CachedDiffFunction(costFun), initialWeights.toBreeze.toDenseVector)
+
+    /**
+     * NOTE: lossSum and loss is computed using the weights from the previous iteration
+     * and regVal is the regularization value computed in the previous iteration as well.
+     */
+    var state = states.next()
+    while(states.hasNext) {
+      lossHistory.append(state.value)
+      state = states.next()
+    }
+    lossHistory.append(state.value)
+    val weights = Vectors.fromBreeze(state.x)
 
-    logInfo("LBFGS.runMiniBatchSGD finished. Last 10 losses %s".format(
+    logInfo("LBFGS.runLBFGS finished. Last 10 losses %s".format(
       lossHistory.takeRight(10).mkString(", ")))
 
     (weights, lossHistory.toArray)
@@ -193,9 +189,7 @@ object LBFGS extends Logging {
     gradient: Gradient,
     updater: Updater,
     regParam: Double,
-    miniBatchFraction: Double,
-    lossHistory: ArrayBuffer[Double],
-    miniBatchSize: Double) extends DiffFunction[BDV[Double]] {
+    numExamples: Long) extends DiffFunction[BDV[Double]] {
 
     private var i = 0
 
@@ -204,8 +198,7 @@ object LBFGS extends Logging {
       val localData = data
       val localGradient = gradient
 
-      val (gradientSum, lossSum) = localData.sample(false, miniBatchFraction, 42 + i)
-        .aggregate((BDV.zeros[Double](weights.size), 0.0))(
+      val (gradientSum, lossSum) = localData.aggregate((BDV.zeros[Double](weights.size), 0.0))(
           seqOp = (c, v) => (c, v) match { case ((grad, loss), (label, features)) =>
             val l = localGradient.compute(
               features, label, Vectors.fromBreeze(weights), Vectors.fromBreeze(grad))
@@ -223,7 +216,7 @@ object LBFGS extends Logging {
         Vectors.fromBreeze(weights),
         Vectors.dense(new Array[Double](weights.size)), 0, 1, regParam)._2
 
-      val loss = lossSum / miniBatchSize + regVal
+      val loss = lossSum / numExamples + regVal
       /**
        * It will return the gradient part of regularization using updater.
        *
@@ -245,14 +238,8 @@ object LBFGS extends Logging {
         Vectors.fromBreeze(weights),
         Vectors.dense(new Array[Double](weights.size)), 1, 1, regParam)._1.toBreeze
 
-      // gradientTotal = gradientSum / miniBatchSize + gradientTotal
-      axpy(1.0 / miniBatchSize, gradientSum, gradientTotal)
-
-      /**
-       * NOTE: lossSum and loss is computed using the weights from the previous iteration
-       * and regVal is the regularization value computed in the previous iteration as well.
-       */
-      lossHistory.append(loss)
+      // gradientTotal = gradientSum / numExamples + gradientTotal
+      axpy(1.0 / numExamples, gradientSum, gradientTotal)
 
       i += 1
 
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/optimization/LBFGSSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/optimization/LBFGSSuite.scala
index f33770aed30b..6af1b502eb4d 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/optimization/LBFGSSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/optimization/LBFGSSuite.scala
@@ -59,7 +59,7 @@ class LBFGSSuite extends FunSuite with LocalSparkContext with ShouldMatchers {
     val convergenceTol = 1e-12
     val maxNumIterations = 10
 
-    val (_, loss) = LBFGS.runMiniBatchLBFGS(
+    val (_, loss) = LBFGS.runLBFGS(
       dataRDD,
       gradient,
       simpleUpdater,
@@ -67,7 +67,6 @@ class LBFGSSuite extends FunSuite with LocalSparkContext with ShouldMatchers {
       convergenceTol,
       maxNumIterations,
       regParam,
-      miniBatchFrac,
       initialWeightsWithIntercept)
 
     // Since the cost function is convex, the loss is guaranteed to be monotonically decreasing
@@ -104,7 +103,7 @@ class LBFGSSuite extends FunSuite with LocalSparkContext with ShouldMatchers {
     val convergenceTol = 1e-12
     val maxNumIterations = 10
 
-    val (weightLBFGS, lossLBFGS) = LBFGS.runMiniBatchLBFGS(
+    val (weightLBFGS, lossLBFGS) = LBFGS.runLBFGS(
       dataRDD,
       gradient,
       squaredL2Updater,
@@ -112,7 +111,6 @@ class LBFGSSuite extends FunSuite with LocalSparkContext with ShouldMatchers {
       convergenceTol,
       maxNumIterations,
       regParam,
-      miniBatchFrac,
       initialWeightsWithIntercept)
 
     val numGDIterations = 50
@@ -150,7 +148,7 @@ class LBFGSSuite extends FunSuite with LocalSparkContext with ShouldMatchers {
     val maxNumIterations = 8
     var convergenceTol = 0.0
 
-    val (_, lossLBFGS1) = LBFGS.runMiniBatchLBFGS(
+    val (_, lossLBFGS1) = LBFGS.runLBFGS(
       dataRDD,
       gradient,
       squaredL2Updater,
@@ -158,7 +156,6 @@ class LBFGSSuite extends FunSuite with LocalSparkContext with ShouldMatchers {
       convergenceTol,
       maxNumIterations,
       regParam,
-      miniBatchFrac,
       initialWeightsWithIntercept)
 
     // Note that the first loss is computed with initial weights,
@@ -166,7 +163,7 @@ class LBFGSSuite extends FunSuite with LocalSparkContext with ShouldMatchers {
     assert(lossLBFGS1.length == 9)
 
     convergenceTol = 0.1
-    val (_, lossLBFGS2) = LBFGS.runMiniBatchLBFGS(
+    val (_, lossLBFGS2) = LBFGS.runLBFGS(
       dataRDD,
       gradient,
       squaredL2Updater,
@@ -174,7 +171,6 @@ class LBFGSSuite extends FunSuite with LocalSparkContext with ShouldMatchers {
       convergenceTol,
       maxNumIterations,
       regParam,
-      miniBatchFrac,
       initialWeightsWithIntercept)
 
     // Based on observation, lossLBFGS2 runs 3 iterations, no theoretically guaranteed.
@@ -182,7 +178,7 @@ class LBFGSSuite extends FunSuite with LocalSparkContext with ShouldMatchers {
     assert((lossLBFGS2(2) - lossLBFGS2(3)) / lossLBFGS2(2) < convergenceTol)
 
     convergenceTol = 0.01
-    val (_, lossLBFGS3) = LBFGS.runMiniBatchLBFGS(
+    val (_, lossLBFGS3) = LBFGS.runLBFGS(
       dataRDD,
       gradient,
       squaredL2Updater,
@@ -190,7 +186,6 @@ class LBFGSSuite extends FunSuite with LocalSparkContext with ShouldMatchers {
       convergenceTol,
       maxNumIterations,
       regParam,
-      miniBatchFrac,
       initialWeightsWithIntercept)
 
     // With smaller convergenceTol, it takes more steps.

From 191279ce4edb940821d11a6b25cd33c8ad0af054 Mon Sep 17 00:00:00 2001
From: Funes <tianshaocun@gmail.com>
Date: Thu, 8 May 2014 17:54:10 -0700
Subject: [PATCH 134/810] Bug fix of sparse vector conversion

Fixed a small bug caused by the inconsistency of index/data array size and vector length.

Author: Funes <tianshaocun@gmail.com>
Author: funes <tianshaocun@gmail.com>

Closes #661 from funes/bugfix and squashes the following commits:

edb2b9d [funes] remove unused import
75dced3 [Funes] update test case
d129a66 [Funes] Add test for sparse breeze by vector builder
64e7198 [Funes] Copy data only when necessary
b85806c [Funes] Bug fix of sparse vector conversion
---
 .../scala/org/apache/spark/mllib/linalg/Vectors.scala    | 6 +++++-
 .../spark/mllib/linalg/BreezeVectorConversionSuite.scala | 9 +++++++++
 2 files changed, 14 insertions(+), 1 deletion(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
index 7cdf6bd56acd..84d223908c1f 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
@@ -136,7 +136,11 @@ object Vectors {
           new DenseVector(v.toArray)  // Can't use underlying array directly, so make a new one
         }
       case v: BSV[Double] =>
-        new SparseVector(v.length, v.index, v.data)
+        if (v.index.length == v.used) {
+          new SparseVector(v.length, v.index, v.data)
+        } else {
+          new SparseVector(v.length, v.index.slice(0, v.used), v.data.slice(0, v.used))
+        }
       case v: BV[_] =>
         sys.error("Unsupported Breeze vector type: " + v.getClass.getName)
     }
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/linalg/BreezeVectorConversionSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/linalg/BreezeVectorConversionSuite.scala
index aacaa300849a..8abdac72902c 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/linalg/BreezeVectorConversionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/linalg/BreezeVectorConversionSuite.scala
@@ -55,4 +55,13 @@ class BreezeVectorConversionSuite extends FunSuite {
     assert(vec.indices.eq(indices), "should not copy data")
     assert(vec.values.eq(values), "should not copy data")
   }
+
+  test("sparse breeze with partially-used arrays to vector") {
+    val activeSize = 3
+    val breeze = new BSV[Double](indices, values, activeSize, n)
+    val vec = Vectors.fromBreeze(breeze).asInstanceOf[SparseVector]
+    assert(vec.size === n)
+    assert(vec.indices === indices.slice(0, activeSize))
+    assert(vec.values === values.slice(0, activeSize))
+  }
 }

From 2fd2752e572921a9010614eb1c1238c493d34a7c Mon Sep 17 00:00:00 2001
From: Bouke van der Bijl <boukevanderbijl@gmail.com>
Date: Thu, 8 May 2014 20:43:37 -0700
Subject: [PATCH 135/810] Include the sbin/spark-config.sh in spark-executor

This is needed because broadcast values are broken on pyspark on Mesos, it tries to import pyspark but can't, as the PYTHONPATH is not set due to changes in ff5be9a4

https://issues.apache.org/jira/browse/SPARK-1725

Author: Bouke van der Bijl <boukevanderbijl@gmail.com>

Closes #651 from bouk/include-spark-config-in-mesos-executor and squashes the following commits:

b2f1295 [Bouke van der Bijl] Inline PYTHONPATH in spark-executor
eedbbcc [Bouke van der Bijl] Include the sbin/spark-config.sh in spark-executor
---
 sbin/spark-executor | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/sbin/spark-executor b/sbin/spark-executor
index de5bfab56312..336549f29c9c 100755
--- a/sbin/spark-executor
+++ b/sbin/spark-executor
@@ -19,5 +19,8 @@
 
 FWDIR="$(cd `dirname $0`/..; pwd)"
 
+export PYTHONPATH=$FWDIR/python:$PYTHONPATH
+export PYTHONPATH=$FWDIR/python/lib/py4j-0.8.1-src.zip:$PYTHONPATH
+
 echo "Running spark-executor with framework dir = $FWDIR"
 exec $FWDIR/bin/spark-class org.apache.spark.executor.MesosExecutorBackend

From 8b7841299439b7dc590b2f7e2339f24e8f3e19f6 Mon Sep 17 00:00:00 2001
From: Andrew Or <andrewor14@gmail.com>
Date: Thu, 8 May 2014 20:45:29 -0700
Subject: [PATCH 136/810] [SPARK-1755] Respect SparkSubmit --name on YARN

Right now, SparkSubmit ignores the `--name` flag for both yarn-client and yarn-cluster. This is a bug.

In client mode, SparkSubmit treats `--name` as a [cluster config](https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala#L170) and does not propagate this to SparkContext.

In cluster mode, SparkSubmit passes this flag to `org.apache.spark.deploy.yarn.Client`, which only uses it for the [YARN ResourceManager](https://github.com/apache/spark/blob/master/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/Client.scala#L80), but does not propagate this to SparkContext.

This PR ensures that `spark.app.name` is always set if SparkSubmit receives the `--name` flag, which is what the usage promises. This makes it possible for applications to start a SparkContext with an empty conf `val sc = new SparkContext(new SparkConf)`, and inherit the app name from SparkSubmit.

Tested both modes on a YARN cluster.

Author: Andrew Or <andrewor14@gmail.com>

Closes #699 from andrewor14/yarn-app-name and squashes the following commits:

98f6a79 [Andrew Or] Fix tests
dea932f [Andrew Or] Merge branch 'master' of github.com:apache/spark into yarn-app-name
c86d9ca [Andrew Or] Respect SparkSubmit --name on YARN
---
 .../scala/org/apache/spark/deploy/SparkSubmit.scala    |  9 +++++----
 .../org/apache/spark/deploy/SparkSubmitSuite.scala     | 10 ++++++----
 2 files changed, 11 insertions(+), 8 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
index e39723f38347..16de6f7cdb10 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
@@ -160,6 +160,7 @@ object SparkSubmit {
     // each deploy mode; we iterate through these below
     val options = List[OptionAssigner](
       OptionAssigner(args.master, ALL_CLUSTER_MGRS, false, sysProp = "spark.master"),
+      OptionAssigner(args.name, ALL_CLUSTER_MGRS, false, sysProp = "spark.app.name"),
       OptionAssigner(args.driverExtraClassPath, STANDALONE | YARN, true,
         sysProp = "spark.driver.extraClassPath"),
       OptionAssigner(args.driverExtraJavaOptions, STANDALONE | YARN, true,
@@ -167,7 +168,7 @@ object SparkSubmit {
       OptionAssigner(args.driverExtraLibraryPath, STANDALONE | YARN, true,
         sysProp = "spark.driver.extraLibraryPath"),
       OptionAssigner(args.driverMemory, YARN, true, clOption = "--driver-memory"),
-      OptionAssigner(args.name, YARN, true, clOption = "--name"),
+      OptionAssigner(args.name, YARN, true, clOption = "--name", sysProp = "spark.app.name"),
       OptionAssigner(args.queue, YARN, true, clOption = "--queue"),
       OptionAssigner(args.queue, YARN, false, sysProp = "spark.yarn.queue"),
       OptionAssigner(args.numExecutors, YARN, true, clOption = "--num-executors"),
@@ -188,8 +189,7 @@ object SparkSubmit {
       OptionAssigner(args.jars, YARN, true, clOption = "--addJars"),
       OptionAssigner(args.files, LOCAL | STANDALONE | MESOS, false, sysProp = "spark.files"),
       OptionAssigner(args.files, LOCAL | STANDALONE | MESOS, true, sysProp = "spark.files"),
-      OptionAssigner(args.jars, LOCAL | STANDALONE | MESOS, false, sysProp = "spark.jars"),
-      OptionAssigner(args.name, LOCAL | STANDALONE | MESOS, false, sysProp = "spark.app.name")
+      OptionAssigner(args.jars, LOCAL | STANDALONE | MESOS, false, sysProp = "spark.jars")
     )
 
     // For client mode make any added jars immediately visible on the classpath
@@ -205,7 +205,8 @@ object SparkSubmit {
           (clusterManager & opt.clusterManager) != 0) {
         if (opt.clOption != null) {
           childArgs += (opt.clOption, opt.value)
-        } else if (opt.sysProp != null) {
+        }
+        if (opt.sysProp != null) {
           sysProps.put(opt.sysProp, opt.value)
         }
       }
diff --git a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala
index d7e3b22ed476..c9edb03cdeb0 100644
--- a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala
@@ -104,7 +104,7 @@ class SparkSubmitSuite extends FunSuite with ShouldMatchers {
       "--master", "yarn", "--executor-memory", "5g", "--executor-cores", "5",
       "--class", "org.SomeClass", "--jars", "one.jar,two.jar,three.jar",
       "--driver-memory", "4g", "--queue", "thequeue", "--files", "file1.txt,file2.txt",
-      "--archives", "archive1.txt,archive2.txt", "--num-executors", "6",
+      "--archives", "archive1.txt,archive2.txt", "--num-executors", "6", "--name", "beauty",
       "thejar.jar", "arg1", "arg2")
     val appArgs = new SparkSubmitArguments(clArgs)
     val (childArgs, classpath, sysProps, mainClass) = createLaunchEnv(appArgs)
@@ -122,7 +122,8 @@ class SparkSubmitSuite extends FunSuite with ShouldMatchers {
     childArgsStr should include ("--num-executors 6")
     mainClass should be ("org.apache.spark.deploy.yarn.Client")
     classpath should have length (0)
-    sysProps should have size (1)
+    sysProps("spark.app.name") should be ("beauty")
+    sysProps("SPARK_SUBMIT") should be ("true")
   }
 
   test("handles YARN client mode") {
@@ -130,8 +131,8 @@ class SparkSubmitSuite extends FunSuite with ShouldMatchers {
       "--master", "yarn", "--executor-memory", "5g", "--executor-cores", "5",
       "--class", "org.SomeClass", "--jars", "one.jar,two.jar,three.jar",
       "--driver-memory", "4g", "--queue", "thequeue", "--files", "file1.txt,file2.txt",
-      "--archives", "archive1.txt,archive2.txt", "--num-executors", "6", "thejar.jar",
-      "arg1", "arg2")
+      "--archives", "archive1.txt,archive2.txt", "--num-executors", "6", "--name", "trill",
+      "thejar.jar", "arg1", "arg2")
     val appArgs = new SparkSubmitArguments(clArgs)
     val (childArgs, classpath, sysProps, mainClass) = createLaunchEnv(appArgs)
     childArgs.mkString(" ") should be ("arg1 arg2")
@@ -140,6 +141,7 @@ class SparkSubmitSuite extends FunSuite with ShouldMatchers {
     classpath should contain ("one.jar")
     classpath should contain ("two.jar")
     classpath should contain ("three.jar")
+    sysProps("spark.app.name") should be ("trill")
     sysProps("spark.executor.memory") should be ("5g")
     sysProps("spark.executor.cores") should be ("5")
     sysProps("spark.yarn.queue") should be ("thequeue")

From 3f779d872d8459b262b3db9e4d12b011910b6ce9 Mon Sep 17 00:00:00 2001
From: Marcelo Vanzin <vanzin@cloudera.com>
Date: Thu, 8 May 2014 20:46:11 -0700
Subject: [PATCH 137/810] [SPARK-1631] Correctly set the Yarn app name when
 launching the AM.

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #539 from vanzin/yarn-app-name and squashes the following commits:

7d1ca4f [Marcelo Vanzin] [SPARK-1631] Correctly set the Yarn app name when launching the AM.
---
 .../scheduler/cluster/YarnClientSchedulerBackend.scala      | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala b/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala
index ce2dde0631ed..2924189077b7 100644
--- a/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala
+++ b/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala
@@ -35,10 +35,10 @@ private[spark] class YarnClientSchedulerBackend(
 
   private[spark] def addArg(optionName: String, envVar: String, sysProp: String,
       arrayBuf: ArrayBuffer[String]) {
-    if (System.getProperty(sysProp) != null) {
-      arrayBuf += (optionName, System.getProperty(sysProp))
-    } else if (System.getenv(envVar) != null) {
+    if (System.getenv(envVar) != null) {
       arrayBuf += (optionName, System.getenv(envVar))
+    } else if (sc.getConf.contains(sysProp)) {
+      arrayBuf += (optionName, sc.getConf.get(sysProp))
     }
   }
 

From 06b15baab25951d124bbe6b64906f4139e037deb Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Thu, 8 May 2014 22:26:17 -0700
Subject: [PATCH 138/810] SPARK-1565 (Addendum): Replace `run-example` with
 `spark-submit`.

Gives a nicely formatted message to the user when `run-example` is run to
tell them to use `spark-submit`.

Author: Patrick Wendell <pwendell@gmail.com>

Closes #704 from pwendell/examples and squashes the following commits:

1996ee8 [Patrick Wendell] Feedback form Andrew
3eb7803 [Patrick Wendell] Suggestions from TD
2474668 [Patrick Wendell] SPARK-1565 (Addendum): Replace `run-example` with `spark-submit`.
---
 README.md                                     | 19 +++--
 bin/pyspark                                   |  2 +-
 bin/run-example                               | 71 +++++--------------
 bin/spark-class                               |  2 +-
 .../main/scala/org/apache/spark/rdd/RDD.scala |  4 +-
 docs/running-on-yarn.md                       |  2 +-
 make-distribution.sh                          |  2 +
 7 files changed, 37 insertions(+), 65 deletions(-)

diff --git a/README.md b/README.md
index e2d1dcb5672f..9c2e32b90f16 100644
--- a/README.md
+++ b/README.md
@@ -39,17 +39,22 @@ And run the following command, which should also return 1000:
 ## Example Programs
 
 Spark also comes with several sample programs in the `examples` directory.
-To run one of them, use `./bin/run-example <class> <params>`. For example:
+To run one of them, use `./bin/run-example <class> [params]`. For example:
 
-    ./bin/run-example org.apache.spark.examples.SparkLR local[2]
+    ./bin/run-example org.apache.spark.examples.SparkLR
 
-will run the Logistic Regression example locally on 2 CPUs.
+will run the Logistic Regression example locally.
 
-Each of the example programs prints usage help if no params are given.
+You can set the MASTER environment variable when running examples to submit
+examples to a cluster. This can be a mesos:// or spark:// URL, 
+"yarn-cluster" or "yarn-client" to run on YARN, and "local" to run 
+locally with one thread, or "local[N]" to run locally with N threads. You 
+can also use an abbreviated class name if the class is in the `examples`
+package. For instance:
 
-All of the Spark samples take a `<master>` parameter that is the cluster URL
-to connect to. This can be a mesos:// or spark:// URL, or "local" to run
-locally with one thread, or "local[N]" to run locally with N threads.
+    MASTER=spark://host:7077 ./bin/run-example SparkPi
+
+Many of the example programs print usage help if no params are given.
 
 ## Running Tests
 
diff --git a/bin/pyspark b/bin/pyspark
index f5558853e8a4..10e35e0f1734 100755
--- a/bin/pyspark
+++ b/bin/pyspark
@@ -31,7 +31,7 @@ if [ ! -f "$FWDIR/RELEASE" ]; then
   ls "$FWDIR"/assembly/target/scala-$SCALA_VERSION/spark-assembly*hadoop*.jar >& /dev/null
   if [[ $? != 0 ]]; then
     echo "Failed to find Spark assembly in $FWDIR/assembly/target" >&2
-    echo "You need to build Spark with sbt/sbt assembly before running this program" >&2
+    echo "You need to build Spark before running this program" >&2
     exit 1
   fi
 fi
diff --git a/bin/run-example b/bin/run-example
index d8a94f2e31e0..146951ac0ee5 100755
--- a/bin/run-example
+++ b/bin/run-example
@@ -17,28 +17,10 @@
 # limitations under the License.
 #
 
-cygwin=false
-case "`uname`" in
-    CYGWIN*) cygwin=true;;
-esac
-
 SCALA_VERSION=2.10
 
-# Figure out where the Scala framework is installed
 FWDIR="$(cd `dirname $0`/..; pwd)"
-
-# Export this as SPARK_HOME
 export SPARK_HOME="$FWDIR"
-
-. $FWDIR/bin/load-spark-env.sh
-
-if [ -z "$1" ]; then
-  echo "Usage: run-example <example-class> [<args>]" >&2
-  exit 1
-fi
-
-# Figure out the JAR file that our examples were packaged into. This includes a bit of a hack
-# to avoid the -sources and -doc packages that are built by publish-local.
 EXAMPLES_DIR="$FWDIR"/examples
 
 if [ -f "$FWDIR/RELEASE" ]; then
@@ -49,46 +31,29 @@ fi
 
 if [[ -z $SPARK_EXAMPLES_JAR ]]; then
   echo "Failed to find Spark examples assembly in $FWDIR/lib or $FWDIR/examples/target" >&2
-  echo "You need to build Spark with sbt/sbt assembly before running this program" >&2
+  echo "You need to build Spark before running this program" >&2
   exit 1
 fi
 
+EXAMPLE_MASTER=${MASTER:-"local[*]"}
 
-# Since the examples JAR ideally shouldn't include spark-core (that dependency should be
-# "provided"), also add our standard Spark classpath, built using compute-classpath.sh.
-CLASSPATH=`$FWDIR/bin/compute-classpath.sh`
-CLASSPATH="$SPARK_EXAMPLES_JAR:$CLASSPATH"
-
-if $cygwin; then
-    CLASSPATH=`cygpath -wp $CLASSPATH`
-    export SPARK_EXAMPLES_JAR=`cygpath -w $SPARK_EXAMPLES_JAR`
-fi
-
-# Find java binary
-if [ -n "${JAVA_HOME}" ]; then
-  RUNNER="${JAVA_HOME}/bin/java"
-else
-  if [ `command -v java` ]; then
-    RUNNER="java"
-  else
-    echo "JAVA_HOME is not set" >&2
-    exit 1
-  fi
-fi
-
-# Set JAVA_OPTS to be able to load native libraries and to set heap size
-JAVA_OPTS="$SPARK_JAVA_OPTS"
-# Load extra JAVA_OPTS from conf/java-opts, if it exists
-if [ -e "$FWDIR/conf/java-opts" ] ; then
-  JAVA_OPTS="$JAVA_OPTS `cat $FWDIR/conf/java-opts`"
+if [ -n "$1" ]; then
+  EXAMPLE_CLASS="$1"
+  shift
+else 
+  echo "usage: ./bin/run-example <example-class> [example-args]" 
+  echo "  - set MASTER=XX to use a specific master"
+  echo "  - can use abbreviated example class name (e.g. SparkPi, mllib.MovieLensALS)"
+  echo
+  exit -1
 fi
-export JAVA_OPTS
 
-if [ "$SPARK_PRINT_LAUNCH_COMMAND" == "1" ]; then
-  echo -n "Spark Command: "
-  echo "$RUNNER" -cp "$CLASSPATH" $JAVA_OPTS "$@"
-  echo "========================================"
-  echo
+if [[ ! $EXAMPLE_CLASS == org.apache.spark.examples* ]]; then
+  EXAMPLE_CLASS="org.apache.spark.examples.$EXAMPLE_CLASS"
 fi
 
-exec "$RUNNER" -cp "$CLASSPATH" $JAVA_OPTS "$@"
+./bin/spark-submit \
+  --master $EXAMPLE_MASTER \
+  --class $EXAMPLE_CLASS \
+  $SPARK_EXAMPLES_JAR \
+  "$@"
diff --git a/bin/spark-class b/bin/spark-class
index 72f8b9bf9a49..6480ccb58d6a 100755
--- a/bin/spark-class
+++ b/bin/spark-class
@@ -114,7 +114,7 @@ if [ ! -f "$FWDIR/RELEASE" ]; then
   jars_list=$(ls "$FWDIR"/assembly/target/scala-$SCALA_VERSION/ | grep "spark-assembly.*hadoop.*.jar")
   if [ "$num_jars" -eq "0" ]; then
     echo "Failed to find Spark assembly in $FWDIR/assembly/target/scala-$SCALA_VERSION/" >&2
-    echo "You need to build Spark with 'sbt/sbt assembly' before running this program." >&2
+    echo "You need to build Spark before running this program." >&2
     exit 1
   fi
   if [ "$num_jars" -gt "1" ]; then
diff --git a/core/src/main/scala/org/apache/spark/rdd/RDD.scala b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
index a1ca612cc9a0..9d8d8044f07e 100644
--- a/core/src/main/scala/org/apache/spark/rdd/RDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
@@ -330,9 +330,9 @@ abstract class RDD[T: ClassTag](
     if (shuffle) {
       // include a shuffle step so that our upstream tasks are still distributed
       new CoalescedRDD(
-        new ShuffledRDD[T, Null, (T, Null)](map(x => (x, null)),
+        new ShuffledRDD[Int, T, (Int, T)](map(x => (Utils.random.nextInt(), x)),
         new HashPartitioner(numPartitions)),
-        numPartitions).keys
+        numPartitions).values
     } else {
       new CoalescedRDD(this, numPartitions)
     }
diff --git a/docs/running-on-yarn.md b/docs/running-on-yarn.md
index 68183ee8b461..c56359429680 100644
--- a/docs/running-on-yarn.md
+++ b/docs/running-on-yarn.md
@@ -53,7 +53,7 @@ For example:
         --driver-memory 4g \
         --executor-memory 2g \
         --executor-cores 1
-        examples/target/scala-{{site.SCALA_BINARY_VERSION}}/spark-examples-assembly-{{site.SPARK_VERSION}}.jar \
+        lib/spark-examples*.jar \
         yarn-cluster 5
 
 The above starts a YARN client program which starts the default Application Master. Then SparkPi will be run as a child thread of Application Master. The client will periodically poll the Application Master for status updates and display them in the console. The client will exit once your application has finished running.  Refer to the "Viewing Logs" section below for how to see driver and executor logs.
diff --git a/make-distribution.sh b/make-distribution.sh
index 759e555b4b69..1cc2844703fb 100755
--- a/make-distribution.sh
+++ b/make-distribution.sh
@@ -40,6 +40,8 @@
 #
 
 set -o pipefail
+set -e
+
 # Figure out where the Spark framework is installed
 FWDIR="$(cd `dirname $0`; pwd)"
 DISTDIR="$FWDIR/dist"

From 7db47c463fefc244e9c100d4aab90451c3828261 Mon Sep 17 00:00:00 2001
From: Sandeep <sandeep@techaddict.me>
Date: Thu, 8 May 2014 22:30:17 -0700
Subject: [PATCH 139/810] SPARK-1775: Unneeded lock in
 ShuffleMapTask.deserializeInfo

This was used in the past to have a cache of deserialized ShuffleMapTasks, but that's been removed, so there's no need for a lock. It slows down Spark when task descriptions are large, e.g. due to large lineage graphs or local variables.

Author: Sandeep <sandeep@techaddict.me>

Closes #707 from techaddict/SPARK-1775 and squashes the following commits:

18d8ebf [Sandeep] SPARK-1775: Unneeded lock in ShuffleMapTask.deserializeInfo This was used in the past to have a cache of deserialized ShuffleMapTasks, but that's been removed, so there's no need for a lock. It slows down Spark when task descriptions are large, e.g. due to large lineage graphs or local variables.
---
 .../apache/spark/scheduler/ShuffleMapTask.scala  | 16 +++++++---------
 1 file changed, 7 insertions(+), 9 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala b/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala
index 4b0324f2b544..9ba586f7581c 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala
@@ -57,15 +57,13 @@ private[spark] object ShuffleMapTask {
   }
 
   def deserializeInfo(stageId: Int, bytes: Array[Byte]): (RDD[_], ShuffleDependency[_,_]) = {
-    synchronized {
-      val loader = Thread.currentThread.getContextClassLoader
-      val in = new GZIPInputStream(new ByteArrayInputStream(bytes))
-      val ser = SparkEnv.get.closureSerializer.newInstance()
-      val objIn = ser.deserializeStream(in)
-      val rdd = objIn.readObject().asInstanceOf[RDD[_]]
-      val dep = objIn.readObject().asInstanceOf[ShuffleDependency[_,_]]
-      (rdd, dep)
-    }
+    val loader = Thread.currentThread.getContextClassLoader
+    val in = new GZIPInputStream(new ByteArrayInputStream(bytes))
+    val ser = SparkEnv.get.closureSerializer.newInstance()
+    val objIn = ser.deserializeStream(in)
+    val rdd = objIn.readObject().asInstanceOf[RDD[_]]
+    val dep = objIn.readObject().asInstanceOf[ShuffleDependency[_,_]]
+    (rdd, dep)
   }
 
   // Since both the JarSet and FileSet have the same format this is used for both.

From 4c60fd1e8c526278b7e5544d6164050d1aee0338 Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Thu, 8 May 2014 22:33:06 -0700
Subject: [PATCH 140/810] MINOR: Removing dead code.

Meant to do this when patching up the last merge.
---
 .../main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala   | 1 -
 1 file changed, 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala b/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala
index 9ba586f7581c..ed0f56f1abdf 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala
@@ -57,7 +57,6 @@ private[spark] object ShuffleMapTask {
   }
 
   def deserializeInfo(stageId: Int, bytes: Array[Byte]): (RDD[_], ShuffleDependency[_,_]) = {
-    val loader = Thread.currentThread.getContextClassLoader
     val in = new GZIPInputStream(new ByteArrayInputStream(bytes))
     val ser = SparkEnv.get.closureSerializer.newInstance()
     val objIn = ser.deserializeStream(in)

From 32868f31f88aebd580ab9329dc51a30c26af7a74 Mon Sep 17 00:00:00 2001
From: Tathagata Das <tathagata.das1565@gmail.com>
Date: Thu, 8 May 2014 22:34:08 -0700
Subject: [PATCH 141/810] Converted bang to ask to avoid scary warning when a
 block is removed

Removing a block through the blockmanager gave a scary warning messages in the driver.
```
2014-05-08 20:16:19,172 WARN BlockManagerMasterActor: Got unknown message: true
2014-05-08 20:16:19,172 WARN BlockManagerMasterActor: Got unknown message: true
2014-05-08 20:16:19,172 WARN BlockManagerMasterActor: Got unknown message: true
```

This is because the [BlockManagerSlaveActor](https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/storage/BlockManagerSlaveActor.scala#L44) would send back an acknowledgement ("true"). But the BlockManagerMasterActor would have sent the RemoveBlock message as a send, not as ask(), so would reject the receiver "true" as a unknown message.
@pwendell

Author: Tathagata Das <tathagata.das1565@gmail.com>

Closes #708 from tdas/bm-fix and squashes the following commits:

ed4ef15 [Tathagata Das] Converted bang to ask to avoid scary warning when a block is removed.
---
 .../org/apache/spark/storage/BlockManagerMasterActor.scala      | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterActor.scala b/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterActor.scala
index 98fa0df6ec28..6aed322eeb18 100644
--- a/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterActor.scala
+++ b/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterActor.scala
@@ -250,7 +250,7 @@ class BlockManagerMasterActor(val isLocal: Boolean, conf: SparkConf, listenerBus
           // Remove the block from the slave's BlockManager.
           // Doesn't actually wait for a confirmation and the message might get lost.
           // If message loss becomes frequent, we should add retry logic here.
-          blockManager.get.slaveActor ! RemoveBlock(blockId)
+          blockManager.get.slaveActor.ask(RemoveBlock(blockId))(akkaTimeout)
         }
       }
     }

From bd67551ee724fd7cce4f2e2977a862216c992ef5 Mon Sep 17 00:00:00 2001
From: witgo <witgo@qq.com>
Date: Fri, 9 May 2014 01:51:26 -0700
Subject: [PATCH 142/810] [SPARK-1760]: fix building spark with maven
 documentation

Author: witgo <witgo@qq.com>

Closes #712 from witgo/building-with-maven and squashes the following commits:

215523b [witgo] fix building spark with maven documentation
---
 docs/building-with-maven.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/building-with-maven.md b/docs/building-with-maven.md
index cac01ded60d9..b6dd553bbe06 100644
--- a/docs/building-with-maven.md
+++ b/docs/building-with-maven.md
@@ -96,7 +96,7 @@ Tests are run by default via the [ScalaTest Maven plugin](http://www.scalatest.o
 
 The ScalaTest plugin also supports running only a specific test suite as follows:
 
-    $ mvn -Dhadoop.version=... -Dsuites=org.apache.spark.repl.ReplSuite test
+    $ mvn -Dhadoop.version=... -DwildcardSuites=org.apache.spark.repl.ReplSuite test
 
 
 ## Continuous Compilation ##

From 59577df14c06417676a9ffdd599f5713c448e299 Mon Sep 17 00:00:00 2001
From: Aaron Davidson <aaron@databricks.com>
Date: Fri, 9 May 2014 14:51:34 -0700
Subject: [PATCH 143/810] SPARK-1770: Revert accidental(?) fix

Looks like this change was accidentally committed here: https://github.com/apache/spark/commit/06b15baab25951d124bbe6b64906f4139e037deb
but the change does not show up in the PR itself (#704).

Other than not intending to go in with that PR, this also broke the test JavaAPISuite.repartition.

Author: Aaron Davidson <aaron@databricks.com>

Closes #716 from aarondav/shufflerand and squashes the following commits:

b1cf70b [Aaron Davidson] SPARK-1770: Revert accidental(?) fix
---
 core/src/main/scala/org/apache/spark/rdd/RDD.scala | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/rdd/RDD.scala b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
index 9d8d8044f07e..a1ca612cc9a0 100644
--- a/core/src/main/scala/org/apache/spark/rdd/RDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
@@ -330,9 +330,9 @@ abstract class RDD[T: ClassTag](
     if (shuffle) {
       // include a shuffle step so that our upstream tasks are still distributed
       new CoalescedRDD(
-        new ShuffledRDD[Int, T, (Int, T)](map(x => (Utils.random.nextInt(), x)),
+        new ShuffledRDD[T, Null, (T, Null)](map(x => (x, null)),
         new HashPartitioner(numPartitions)),
-        numPartitions).values
+        numPartitions).keys
     } else {
       new CoalescedRDD(this, numPartitions)
     }

From 2f452cbaf35dbc609ab48ec0ee5e3dd7b6b9b790 Mon Sep 17 00:00:00 2001
From: CodingCat <zhunansjtu@gmail.com>
Date: Fri, 9 May 2014 21:50:23 -0700
Subject: [PATCH 144/810] SPARK-1686: keep schedule() calling in the main
 thread

https://issues.apache.org/jira/browse/SPARK-1686

moved from original JIRA (by @markhamstra):

In deploy.master.Master, the completeRecovery method is the last thing to be called when a standalone Master is recovering from failure. It is responsible for resetting some state, relaunching drivers, and eventually resuming its scheduling duties.

There are currently four places in Master.scala where completeRecovery is called. Three of them are from within the actor's receive method, and aren't problems. The last starts from within receive when the ElectedLeader message is received, but the actual completeRecovery() call is made from the Akka scheduler. That means that it will execute on a different scheduler thread, and Master itself will end up running (i.e., schedule() ) from that Akka scheduler thread.

In this PR, I added a new master message TriggerSchedule to trigger the "local" call of schedule() in the scheduler thread

Author: CodingCat <zhunansjtu@gmail.com>

Closes #639 from CodingCat/SPARK-1686 and squashes the following commits:

81bb4ca [CodingCat] rename variable
69e0a2a [CodingCat] style fix
36a2ac0 [CodingCat] address Aaron's comments
ec9b7bb [CodingCat] address the comments
02b37ca [CodingCat] keep schedule() calling in the main thread
---
 .../org/apache/spark/deploy/master/Master.scala   | 15 ++++++++++++---
 1 file changed, 12 insertions(+), 3 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
index fdb633bd3360..f254f5585ba2 100644
--- a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
@@ -104,6 +104,8 @@ private[spark] class Master(
 
   var leaderElectionAgent: ActorRef = _
 
+  private var recoveryCompletionTask: Cancellable = _
+
   // As a temporary workaround before better ways of configuring memory, we allow users to set
   // a flag that will perform round-robin scheduling across the nodes (spreading out each app
   // among all the nodes) instead of trying to consolidate each app onto a small # of nodes.
@@ -152,6 +154,10 @@ private[spark] class Master(
   }
 
   override def postStop() {
+    // prevent the CompleteRecovery message sending to restarted master
+    if (recoveryCompletionTask != null) {
+      recoveryCompletionTask.cancel()
+    }
     webUi.stop()
     fileSystemsUsed.foreach(_.close())
     masterMetricsSystem.stop()
@@ -171,10 +177,13 @@ private[spark] class Master(
       logInfo("I have been elected leader! New state: " + state)
       if (state == RecoveryState.RECOVERING) {
         beginRecovery(storedApps, storedDrivers, storedWorkers)
-        context.system.scheduler.scheduleOnce(WORKER_TIMEOUT millis) { completeRecovery() }
+        recoveryCompletionTask = context.system.scheduler.scheduleOnce(WORKER_TIMEOUT millis, self,
+          CompleteRecovery)
       }
     }
 
+    case CompleteRecovery => completeRecovery()
+
     case RevokedLeadership => {
       logError("Leadership has been revoked -- master shutting down.")
       System.exit(0)
@@ -465,7 +474,7 @@ private[spark] class Master(
    * Schedule the currently available resources among waiting apps. This method will be called
    * every time a new app joins or resource availability changes.
    */
-  def schedule() {
+  private def schedule() {
     if (state != RecoveryState.ALIVE) { return }
 
     // First schedule drivers, they take strict precedence over applications
@@ -485,7 +494,7 @@ private[spark] class Master(
       // Try to spread out each app among all the nodes, until it has all its cores
       for (app <- waitingApps if app.coresLeft > 0) {
         val usableWorkers = workers.toArray.filter(_.state == WorkerState.ALIVE)
-                                   .filter(canUse(app, _)).sortBy(_.coresFree).reverse
+          .filter(canUse(app, _)).sortBy(_.coresFree).reverse
         val numUsable = usableWorkers.length
         val assigned = new Array[Int](numUsable) // Number of cores to give on each node
         var toAssign = math.min(app.coresLeft, usableWorkers.map(_.coresFree).sum)

From 561510867a1b79beef57acf9df65c9f88481435d Mon Sep 17 00:00:00 2001
From: witgo <witgo@qq.com>
Date: Sat, 10 May 2014 10:15:04 -0700
Subject: [PATCH 145/810] [SPARK-1644] The org.datanucleus:* should not be
 packaged into spark-assembly-*.jar

Author: witgo <witgo@qq.com>

Closes #688 from witgo/SPARK-1644 and squashes the following commits:

56ad6ac [witgo] review commit
87c03e4 [witgo] Merge branch 'master' of https://github.com/apache/spark into SPARK-1644
6ffa7e4 [witgo] review commit
a597414 [witgo] The org.datanucleus:* should not be packaged into spark-assembly-*.jar
---
 assembly/pom.xml         |  1 +
 project/SparkBuild.scala | 11 ++++++-----
 2 files changed, 7 insertions(+), 5 deletions(-)

diff --git a/assembly/pom.xml b/assembly/pom.xml
index 7d123fb1d7f0..6c4d46aeb67b 100644
--- a/assembly/pom.xml
+++ b/assembly/pom.xml
@@ -96,6 +96,7 @@
             <filter>
               <artifact>*:*</artifact>
               <excludes>
+                <exclude>org.datanucleus:*</exclude>
                 <exclude>META-INF/*.SF</exclude>
                 <exclude>META-INF/*.DSA</exclude>
                 <exclude>META-INF/*.RSA</exclude>
diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index 7f9746ec4acc..27e9505ec983 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -579,12 +579,13 @@ object SparkBuild extends Build {
   def extraAssemblySettings() = Seq(
     test in assembly := {},
     mergeStrategy in assembly := {
-      case m if m.toLowerCase.endsWith("manifest.mf") => MergeStrategy.discard
-      case m if m.toLowerCase.matches("meta-inf.*\\.sf$") => MergeStrategy.discard
-      case "log4j.properties" => MergeStrategy.discard
+      case PathList("org", "datanucleus", xs @ _*)             => MergeStrategy.discard
+      case m if m.toLowerCase.endsWith("manifest.mf")          => MergeStrategy.discard
+      case m if m.toLowerCase.matches("meta-inf.*\\.sf$")      => MergeStrategy.discard
+      case "log4j.properties"                                  => MergeStrategy.discard
       case m if m.toLowerCase.startsWith("meta-inf/services/") => MergeStrategy.filterDistinctLines
-      case "reference.conf" => MergeStrategy.concat
-      case _ => MergeStrategy.first
+      case "reference.conf"                                    => MergeStrategy.concat
+      case _                                                   => MergeStrategy.first
     }
   )
 

From 4d6055329846f5e09472e5f844127a5ab5880e15 Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Sat, 10 May 2014 11:48:01 -0700
Subject: [PATCH 146/810] [SQL] Upgrade parquet library.

I think we are hitting this issue in some perf tests: https://github.com/Parquet/parquet-mr/commit/6aed5288fd4a1398063a5a219b2ae4a9f71b02cf

Credit to @aarondav !

Author: Michael Armbrust <michael@databricks.com>

Closes #684 from marmbrus/upgradeParquet and squashes the following commits:

e10a619 [Michael Armbrust] Upgrade parquet library.
---
 pom.xml                  | 2 +-
 project/SparkBuild.scala | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/pom.xml b/pom.xml
index fb904e868cfa..e0bff60a54cd 100644
--- a/pom.xml
+++ b/pom.xml
@@ -123,7 +123,7 @@
     <yarn.version>${hadoop.version}</yarn.version>
     <hbase.version>0.94.6</hbase.version>
     <hive.version>0.12.0</hive.version>
-    <parquet.version>1.3.2</parquet.version>
+    <parquet.version>1.4.3</parquet.version>
     <jblas.version>1.2.3</jblas.version>
     <jetty.version>8.1.14.v20131031</jetty.version>
     <chill.version>0.3.6</chill.version>
diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index 27e9505ec983..af882b3ea7be 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -300,7 +300,7 @@ object SparkBuild extends Build {
   val jets3tVersion = if ("^2\\.[3-9]+".r.findFirstIn(hadoopVersion).isDefined) "0.9.0" else "0.7.1"
   val jettyVersion = "8.1.14.v20131031"
   val hiveVersion = "0.12.0"
-  val parquetVersion = "1.3.2"
+  val parquetVersion = "1.4.3"
   val slf4jVersion = "1.7.5"
 
   val excludeNetty = ExclusionRule(organization = "org.jboss.netty")

From 8e94d2721a9d3d36697e13f8cc6567ae8aeee78b Mon Sep 17 00:00:00 2001
From: Takuya UESHIN <ueshin@happy-camper.st>
Date: Sat, 10 May 2014 12:03:27 -0700
Subject: [PATCH 147/810] [SPARK-1778] [SQL] Add 'limit' transformation to
 SchemaRDD.

Add `limit` transformation to `SchemaRDD`.

Author: Takuya UESHIN <ueshin@happy-camper.st>

Closes #711 from ueshin/issues/SPARK-1778 and squashes the following commits:

33169df [Takuya UESHIN] Add 'limit' transformation to SchemaRDD.
---
 .../src/main/scala/org/apache/spark/sql/SchemaRDD.scala  | 9 +++++++++
 .../test/scala/org/apache/spark/sql/DslQuerySuite.scala  | 6 ++++++
 2 files changed, 15 insertions(+)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala
index 34200be3ac95..2569815ebb20 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala
@@ -178,6 +178,15 @@ class SchemaRDD(
   def orderBy(sortExprs: SortOrder*): SchemaRDD =
     new SchemaRDD(sqlContext, Sort(sortExprs, logicalPlan))
 
+  /**
+   * Limits the results by the given expressions.
+   * {{{
+   *   schemaRDD.limit(10)
+   * }}}
+   */
+  def limit(limitExpr: Expression): SchemaRDD =
+    new SchemaRDD(sqlContext, Limit(limitExpr, logicalPlan))
+
   /**
    * Performs a grouping followed by an aggregation.
    *
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala
index be0f4a4c73b3..92a707ea5750 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala
@@ -71,6 +71,12 @@ class DslQuerySuite extends QueryTest {
       Seq((3,1), (3,2), (2,1), (2,2), (1,1), (1,2)))
   }
 
+  test("limit") {
+    checkAnswer(
+      testData.limit(10),
+      testData.take(10).toSeq)
+  }
+
   test("average") {
     checkAnswer(
       testData2.groupBy()(Average('a)),

From 7eefc9d2b3f6ebc0ecb5562da7323f1e06afbb35 Mon Sep 17 00:00:00 2001
From: Matei Zaharia <matei@databricks.com>
Date: Sat, 10 May 2014 12:10:24 -0700
Subject: [PATCH 148/810] SPARK-1708. Add a ClassTag on Serializer and things
 that depend on it

This pull request contains a rebased patch from @heathermiller (https://github.com/heathermiller/spark/pull/1) to add ClassTags on Serializer and types that depend on it (Broadcast and AccumulableCollection). Putting these in the public API signatures now will allow us to use Scala Pickling for serialization down the line without breaking binary compatibility.

One question remaining is whether we also want them on Accumulator -- Accumulator is passed as part of a bigger Task or TaskResult object via the closure serializer so it doesn't seem super useful to add the ClassTag there. Broadcast and AccumulableCollection in contrast were being serialized directly.

CC @rxin, @pwendell, @heathermiller

Author: Matei Zaharia <matei@databricks.com>

Closes #700 from mateiz/spark-1708 and squashes the following commits:

1a3d8b0 [Matei Zaharia] Use fake ClassTag in Java
3b449ed [Matei Zaharia] test fix
2209a27 [Matei Zaharia] Code style fixes
9d48830 [Matei Zaharia] Add a ClassTag on Serializer and things that depend on it
---
 .../scala/org/apache/spark/Accumulators.scala |  7 +--
 .../scala/org/apache/spark/SparkContext.scala |  4 +-
 .../spark/api/java/JavaSparkContext.scala     |  2 +-
 .../apache/spark/broadcast/Broadcast.scala    |  4 +-
 .../spark/broadcast/BroadcastFactory.scala    |  4 +-
 .../spark/broadcast/BroadcastManager.scala    |  4 +-
 .../spark/broadcast/HttpBroadcast.scala       |  7 ++-
 .../broadcast/HttpBroadcastFactory.scala      |  4 +-
 .../spark/broadcast/TorrentBroadcast.scala    |  4 +-
 .../broadcast/TorrentBroadcastFactory.scala   |  4 +-
 .../org/apache/spark/rdd/CheckpointRDD.scala  |  4 +-
 .../spark/rdd/ParallelCollectionRDD.scala     |  2 +-
 .../apache/spark/rdd/RDDCheckpointData.scala  |  2 +-
 .../spark/serializer/JavaSerializer.scala     | 13 +++---
 .../spark/serializer/KryoSerializer.scala     | 12 ++---
 .../apache/spark/serializer/Serializer.scala  | 17 +++----
 .../scala/org/apache/spark/util/Utils.scala   |  2 +-
 .../serializer/KryoSerializerSuite.scala      | 11 ++---
 .../bagel/WikipediaPageRankStandalone.scala   | 12 ++---
 .../spark/graphx/impl/Serializers.scala       | 45 ++++++++++---------
 .../apache/spark/graphx/SerializerSuite.scala |  5 ++-
 .../sql/execution/SparkSqlSerializer.scala    |  6 ++-
 22 files changed, 103 insertions(+), 72 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/Accumulators.scala b/core/src/main/scala/org/apache/spark/Accumulators.scala
index 6d652faae149..cdfd338081fa 100644
--- a/core/src/main/scala/org/apache/spark/Accumulators.scala
+++ b/core/src/main/scala/org/apache/spark/Accumulators.scala
@@ -21,6 +21,7 @@ import java.io.{ObjectInputStream, Serializable}
 
 import scala.collection.generic.Growable
 import scala.collection.mutable.Map
+import scala.reflect.ClassTag
 
 import org.apache.spark.serializer.JavaSerializer
 
@@ -164,9 +165,9 @@ trait AccumulableParam[R, T] extends Serializable {
   def zero(initialValue: R): R
 }
 
-private[spark]
-class GrowableAccumulableParam[R <% Growable[T] with TraversableOnce[T] with Serializable, T]
-  extends AccumulableParam[R,T] {
+private[spark] class
+GrowableAccumulableParam[R <% Growable[T] with TraversableOnce[T] with Serializable: ClassTag, T]
+  extends AccumulableParam[R, T] {
 
   def addAccumulator(growable: R, elem: T): R = {
     growable += elem
diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index 9d7c2c8d3d63..c639b3e15ded 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -756,7 +756,7 @@ class SparkContext(config: SparkConf) extends Logging {
    * Growable and TraversableOnce are the standard APIs that guarantee += and ++=, implemented by
    * standard mutable collections. So you can use this with mutable Map, Set, etc.
    */
-  def accumulableCollection[R <% Growable[T] with TraversableOnce[T] with Serializable, T]
+  def accumulableCollection[R <% Growable[T] with TraversableOnce[T] with Serializable: ClassTag, T]
       (initialValue: R): Accumulable[R, T] = {
     val param = new GrowableAccumulableParam[R,T]
     new Accumulable(initialValue, param)
@@ -767,7 +767,7 @@ class SparkContext(config: SparkConf) extends Logging {
    * [[org.apache.spark.broadcast.Broadcast]] object for reading it in distributed functions.
    * The variable will be sent to each cluster only once.
    */
-  def broadcast[T](value: T): Broadcast[T] = {
+  def broadcast[T: ClassTag](value: T): Broadcast[T] = {
     val bc = env.broadcastManager.newBroadcast[T](value, isLocal)
     cleaner.foreach(_.registerBroadcastForCleanup(bc))
     bc
diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala b/core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala
index 8b95cda51164..a7cfee6d0171 100644
--- a/core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala
@@ -447,7 +447,7 @@ class JavaSparkContext(val sc: SparkContext) extends JavaSparkContextVarargsWork
    * [[org.apache.spark.broadcast.Broadcast]] object for reading it in distributed functions.
    * The variable will be sent to each cluster only once.
    */
-  def broadcast[T](value: T): Broadcast[T] = sc.broadcast(value)
+  def broadcast[T](value: T): Broadcast[T] = sc.broadcast(value)(fakeClassTag)
 
   /** Shut down the SparkContext. */
   def stop() {
diff --git a/core/src/main/scala/org/apache/spark/broadcast/Broadcast.scala b/core/src/main/scala/org/apache/spark/broadcast/Broadcast.scala
index 738a3b1bed7f..76956f6a345d 100644
--- a/core/src/main/scala/org/apache/spark/broadcast/Broadcast.scala
+++ b/core/src/main/scala/org/apache/spark/broadcast/Broadcast.scala
@@ -21,6 +21,8 @@ import java.io.Serializable
 
 import org.apache.spark.SparkException
 
+import scala.reflect.ClassTag
+
 /**
  * A broadcast variable. Broadcast variables allow the programmer to keep a read-only variable
  * cached on each machine rather than shipping a copy of it with tasks. They can be used, for
@@ -50,7 +52,7 @@ import org.apache.spark.SparkException
  * @param id A unique identifier for the broadcast variable.
  * @tparam T Type of the data contained in the broadcast variable.
  */
-abstract class Broadcast[T](val id: Long) extends Serializable {
+abstract class Broadcast[T: ClassTag](val id: Long) extends Serializable {
 
   /**
    * Flag signifying whether the broadcast variable is valid
diff --git a/core/src/main/scala/org/apache/spark/broadcast/BroadcastFactory.scala b/core/src/main/scala/org/apache/spark/broadcast/BroadcastFactory.scala
index 8c8ce9b1691a..a8c827030a1e 100644
--- a/core/src/main/scala/org/apache/spark/broadcast/BroadcastFactory.scala
+++ b/core/src/main/scala/org/apache/spark/broadcast/BroadcastFactory.scala
@@ -17,6 +17,8 @@
 
 package org.apache.spark.broadcast
 
+import scala.reflect.ClassTag
+
 import org.apache.spark.SecurityManager
 import org.apache.spark.SparkConf
 import org.apache.spark.annotation.DeveloperApi
@@ -31,7 +33,7 @@ import org.apache.spark.annotation.DeveloperApi
 @DeveloperApi
 trait BroadcastFactory {
   def initialize(isDriver: Boolean, conf: SparkConf, securityMgr: SecurityManager): Unit
-  def newBroadcast[T](value: T, isLocal: Boolean, id: Long): Broadcast[T]
+  def newBroadcast[T: ClassTag](value: T, isLocal: Boolean, id: Long): Broadcast[T]
   def unbroadcast(id: Long, removeFromDriver: Boolean, blocking: Boolean): Unit
   def stop(): Unit
 }
diff --git a/core/src/main/scala/org/apache/spark/broadcast/BroadcastManager.scala b/core/src/main/scala/org/apache/spark/broadcast/BroadcastManager.scala
index cf62aca4d45e..c88be6aba690 100644
--- a/core/src/main/scala/org/apache/spark/broadcast/BroadcastManager.scala
+++ b/core/src/main/scala/org/apache/spark/broadcast/BroadcastManager.scala
@@ -19,6 +19,8 @@ package org.apache.spark.broadcast
 
 import java.util.concurrent.atomic.AtomicLong
 
+import scala.reflect.ClassTag
+
 import org.apache.spark._
 
 private[spark] class BroadcastManager(
@@ -56,7 +58,7 @@ private[spark] class BroadcastManager(
 
   private val nextBroadcastId = new AtomicLong(0)
 
-  def newBroadcast[T](value_ : T, isLocal: Boolean) = {
+  def newBroadcast[T: ClassTag](value_ : T, isLocal: Boolean) = {
     broadcastFactory.newBroadcast[T](value_, isLocal, nextBroadcastId.getAndIncrement())
   }
 
diff --git a/core/src/main/scala/org/apache/spark/broadcast/HttpBroadcast.scala b/core/src/main/scala/org/apache/spark/broadcast/HttpBroadcast.scala
index 29372f16f2ca..78fc286e5192 100644
--- a/core/src/main/scala/org/apache/spark/broadcast/HttpBroadcast.scala
+++ b/core/src/main/scala/org/apache/spark/broadcast/HttpBroadcast.scala
@@ -22,6 +22,8 @@ import java.io.{BufferedInputStream, BufferedOutputStream}
 import java.net.{URL, URLConnection, URI}
 import java.util.concurrent.TimeUnit
 
+import scala.reflect.ClassTag
+
 import org.apache.spark.{HttpServer, Logging, SecurityManager, SparkConf, SparkEnv}
 import org.apache.spark.io.CompressionCodec
 import org.apache.spark.storage.{BroadcastBlockId, StorageLevel}
@@ -34,7 +36,8 @@ import org.apache.spark.util.{MetadataCleaner, MetadataCleanerType, TimeStampedH
  * (through a HTTP server running at the driver) and stored in the BlockManager of the
  * executor to speed up future accesses.
  */
-private[spark] class HttpBroadcast[T](@transient var value_ : T, isLocal: Boolean, id: Long)
+private[spark] class HttpBroadcast[T: ClassTag](
+    @transient var value_ : T, isLocal: Boolean, id: Long)
   extends Broadcast[T](id) with Logging with Serializable {
 
   def getValue = value_
@@ -173,7 +176,7 @@ private[spark] object HttpBroadcast extends Logging {
     files += file.getAbsolutePath
   }
 
-  def read[T](id: Long): T = {
+  def read[T: ClassTag](id: Long): T = {
     logDebug("broadcast read server: " +  serverUri + " id: broadcast-" + id)
     val url = serverUri + "/" + BroadcastBlockId(id).name
 
diff --git a/core/src/main/scala/org/apache/spark/broadcast/HttpBroadcastFactory.scala b/core/src/main/scala/org/apache/spark/broadcast/HttpBroadcastFactory.scala
index e3f6cdc6154d..d5a031e2bbb5 100644
--- a/core/src/main/scala/org/apache/spark/broadcast/HttpBroadcastFactory.scala
+++ b/core/src/main/scala/org/apache/spark/broadcast/HttpBroadcastFactory.scala
@@ -17,6 +17,8 @@
 
 package org.apache.spark.broadcast
 
+import scala.reflect.ClassTag
+
 import org.apache.spark.{SecurityManager, SparkConf}
 
 /**
@@ -29,7 +31,7 @@ class HttpBroadcastFactory extends BroadcastFactory {
     HttpBroadcast.initialize(isDriver, conf, securityMgr)
   }
 
-  def newBroadcast[T](value_ : T, isLocal: Boolean, id: Long) =
+  def newBroadcast[T: ClassTag](value_ : T, isLocal: Boolean, id: Long) =
     new HttpBroadcast[T](value_, isLocal, id)
 
   def stop() { HttpBroadcast.stop() }
diff --git a/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcast.scala b/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcast.scala
index 2659274c5e98..734de37ba115 100644
--- a/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcast.scala
+++ b/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcast.scala
@@ -19,6 +19,7 @@ package org.apache.spark.broadcast
 
 import java.io.{ByteArrayInputStream, ObjectInputStream, ObjectOutputStream}
 
+import scala.reflect.ClassTag
 import scala.math
 import scala.util.Random
 
@@ -44,7 +45,8 @@ import org.apache.spark.util.Utils
  *  copies of the broadcast data (one per executor) as done by the
  *  [[org.apache.spark.broadcast.HttpBroadcast]].
  */
-private[spark] class TorrentBroadcast[T](@transient var value_ : T, isLocal: Boolean, id: Long)
+private[spark] class TorrentBroadcast[T: ClassTag](
+    @transient var value_ : T, isLocal: Boolean, id: Long)
   extends Broadcast[T](id) with Logging with Serializable {
 
   def getValue = value_
diff --git a/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcastFactory.scala b/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcastFactory.scala
index d216b5871814..1de8396a0e17 100644
--- a/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcastFactory.scala
+++ b/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcastFactory.scala
@@ -17,6 +17,8 @@
 
 package org.apache.spark.broadcast
 
+import scala.reflect.ClassTag
+
 import org.apache.spark.{SecurityManager, SparkConf}
 
 /**
@@ -30,7 +32,7 @@ class TorrentBroadcastFactory extends BroadcastFactory {
     TorrentBroadcast.initialize(isDriver, conf)
   }
 
-  def newBroadcast[T](value_ : T, isLocal: Boolean, id: Long) =
+  def newBroadcast[T: ClassTag](value_ : T, isLocal: Boolean, id: Long) =
     new TorrentBroadcast[T](value_, isLocal, id)
 
   def stop() { TorrentBroadcast.stop() }
diff --git a/core/src/main/scala/org/apache/spark/rdd/CheckpointRDD.scala b/core/src/main/scala/org/apache/spark/rdd/CheckpointRDD.scala
index 888af541cf97..34c51b833025 100644
--- a/core/src/main/scala/org/apache/spark/rdd/CheckpointRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/CheckpointRDD.scala
@@ -84,7 +84,7 @@ private[spark] object CheckpointRDD extends Logging {
     "part-%05d".format(splitId)
   }
 
-  def writeToFile[T](
+  def writeToFile[T: ClassTag](
       path: String,
       broadcastedConf: Broadcast[SerializableWritable[Configuration]],
       blockSize: Int = -1
@@ -160,7 +160,7 @@ private[spark] object CheckpointRDD extends Logging {
     val conf = SparkHadoopUtil.get.newConfiguration()
     val fs = path.getFileSystem(conf)
     val broadcastedConf = sc.broadcast(new SerializableWritable(conf))
-    sc.runJob(rdd, CheckpointRDD.writeToFile(path.toString, broadcastedConf, 1024) _)
+    sc.runJob(rdd, CheckpointRDD.writeToFile[Int](path.toString, broadcastedConf, 1024) _)
     val cpRDD = new CheckpointRDD[Int](sc, path.toString)
     assert(cpRDD.partitions.length == rdd.partitions.length, "Number of partitions is not the same")
     assert(cpRDD.collect.toList == rdd.collect.toList, "Data of partitions not the same")
diff --git a/core/src/main/scala/org/apache/spark/rdd/ParallelCollectionRDD.scala b/core/src/main/scala/org/apache/spark/rdd/ParallelCollectionRDD.scala
index 5f03d7d650a3..2425929fc73c 100644
--- a/core/src/main/scala/org/apache/spark/rdd/ParallelCollectionRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/ParallelCollectionRDD.scala
@@ -77,7 +77,7 @@ private[spark] class ParallelCollectionPartition[T: ClassTag](
         slice = in.readInt()
 
         val ser = sfactory.newInstance()
-        Utils.deserializeViaNestedStream(in, ser)(ds => values = ds.readObject())
+        Utils.deserializeViaNestedStream(in, ser)(ds => values = ds.readObject[Seq[T]]())
     }
   }
 }
diff --git a/core/src/main/scala/org/apache/spark/rdd/RDDCheckpointData.scala b/core/src/main/scala/org/apache/spark/rdd/RDDCheckpointData.scala
index 953f0555e57c..c3b2a33fb54d 100644
--- a/core/src/main/scala/org/apache/spark/rdd/RDDCheckpointData.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/RDDCheckpointData.scala
@@ -92,7 +92,7 @@ private[spark] class RDDCheckpointData[T: ClassTag](@transient rdd: RDD[T])
     // Save to file, and reload it as an RDD
     val broadcastedConf = rdd.context.broadcast(
       new SerializableWritable(rdd.context.hadoopConfiguration))
-    rdd.context.runJob(rdd, CheckpointRDD.writeToFile(path.toString, broadcastedConf) _)
+    rdd.context.runJob(rdd, CheckpointRDD.writeToFile[T](path.toString, broadcastedConf) _)
     val newRDD = new CheckpointRDD[T](rdd.context, path.toString)
     if (newRDD.partitions.size != rdd.partitions.size) {
       throw new SparkException(
diff --git a/core/src/main/scala/org/apache/spark/serializer/JavaSerializer.scala b/core/src/main/scala/org/apache/spark/serializer/JavaSerializer.scala
index e9163deaf203..0a7e1ec53967 100644
--- a/core/src/main/scala/org/apache/spark/serializer/JavaSerializer.scala
+++ b/core/src/main/scala/org/apache/spark/serializer/JavaSerializer.scala
@@ -20,6 +20,8 @@ package org.apache.spark.serializer
 import java.io._
 import java.nio.ByteBuffer
 
+import scala.reflect.ClassTag
+
 import org.apache.spark.SparkConf
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.util.ByteBufferInputStream
@@ -36,7 +38,7 @@ private[spark] class JavaSerializationStream(out: OutputStream, counterReset: In
    * But only call it every 10,000th time to avoid bloated serialization streams (when
    * the stream 'resets' object class descriptions have to be re-written)
    */
-  def writeObject[T](t: T): SerializationStream = {
+  def writeObject[T: ClassTag](t: T): SerializationStream = {
     objOut.writeObject(t)
     if (counterReset > 0 && counter >= counterReset) {
       objOut.reset()
@@ -46,6 +48,7 @@ private[spark] class JavaSerializationStream(out: OutputStream, counterReset: In
     }
     this
   }
+
   def flush() { objOut.flush() }
   def close() { objOut.close() }
 }
@@ -57,12 +60,12 @@ extends DeserializationStream {
       Class.forName(desc.getName, false, loader)
   }
 
-  def readObject[T](): T = objIn.readObject().asInstanceOf[T]
+  def readObject[T: ClassTag](): T = objIn.readObject().asInstanceOf[T]
   def close() { objIn.close() }
 }
 
 private[spark] class JavaSerializerInstance(counterReset: Int) extends SerializerInstance {
-  def serialize[T](t: T): ByteBuffer = {
+  def serialize[T: ClassTag](t: T): ByteBuffer = {
     val bos = new ByteArrayOutputStream()
     val out = serializeStream(bos)
     out.writeObject(t)
@@ -70,13 +73,13 @@ private[spark] class JavaSerializerInstance(counterReset: Int) extends Serialize
     ByteBuffer.wrap(bos.toByteArray)
   }
 
-  def deserialize[T](bytes: ByteBuffer): T = {
+  def deserialize[T: ClassTag](bytes: ByteBuffer): T = {
     val bis = new ByteBufferInputStream(bytes)
     val in = deserializeStream(bis)
     in.readObject().asInstanceOf[T]
   }
 
-  def deserialize[T](bytes: ByteBuffer, loader: ClassLoader): T = {
+  def deserialize[T: ClassTag](bytes: ByteBuffer, loader: ClassLoader): T = {
     val bis = new ByteBufferInputStream(bytes)
     val in = deserializeStream(bis, loader)
     in.readObject().asInstanceOf[T]
diff --git a/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala b/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala
index c4daec7875d2..5286f7b4c211 100644
--- a/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala
+++ b/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala
@@ -31,6 +31,8 @@ import org.apache.spark.scheduler.MapStatus
 import org.apache.spark.storage._
 import org.apache.spark.storage.{GetBlock, GotBlock, PutBlock}
 
+import scala.reflect.ClassTag
+
 /**
  * A Spark serializer that uses the [[https://code.google.com/p/kryo/ Kryo serialization library]].
  *
@@ -95,7 +97,7 @@ private[spark]
 class KryoSerializationStream(kryo: Kryo, outStream: OutputStream) extends SerializationStream {
   val output = new KryoOutput(outStream)
 
-  def writeObject[T](t: T): SerializationStream = {
+  def writeObject[T: ClassTag](t: T): SerializationStream = {
     kryo.writeClassAndObject(output, t)
     this
   }
@@ -108,7 +110,7 @@ private[spark]
 class KryoDeserializationStream(kryo: Kryo, inStream: InputStream) extends DeserializationStream {
   val input = new KryoInput(inStream)
 
-  def readObject[T](): T = {
+  def readObject[T: ClassTag](): T = {
     try {
       kryo.readClassAndObject(input).asInstanceOf[T]
     } catch {
@@ -131,18 +133,18 @@ private[spark] class KryoSerializerInstance(ks: KryoSerializer) extends Serializ
   lazy val output = ks.newKryoOutput()
   lazy val input = new KryoInput()
 
-  def serialize[T](t: T): ByteBuffer = {
+  def serialize[T: ClassTag](t: T): ByteBuffer = {
     output.clear()
     kryo.writeClassAndObject(output, t)
     ByteBuffer.wrap(output.toBytes)
   }
 
-  def deserialize[T](bytes: ByteBuffer): T = {
+  def deserialize[T: ClassTag](bytes: ByteBuffer): T = {
     input.setBuffer(bytes.array)
     kryo.readClassAndObject(input).asInstanceOf[T]
   }
 
-  def deserialize[T](bytes: ByteBuffer, loader: ClassLoader): T = {
+  def deserialize[T: ClassTag](bytes: ByteBuffer, loader: ClassLoader): T = {
     val oldClassLoader = kryo.getClassLoader
     kryo.setClassLoader(loader)
     input.setBuffer(bytes.array)
diff --git a/core/src/main/scala/org/apache/spark/serializer/Serializer.scala b/core/src/main/scala/org/apache/spark/serializer/Serializer.scala
index f2c8f9b6218d..ee26970a3d87 100644
--- a/core/src/main/scala/org/apache/spark/serializer/Serializer.scala
+++ b/core/src/main/scala/org/apache/spark/serializer/Serializer.scala
@@ -20,6 +20,8 @@ package org.apache.spark.serializer
 import java.io.{ByteArrayOutputStream, EOFException, InputStream, OutputStream}
 import java.nio.ByteBuffer
 
+import scala.reflect.ClassTag
+
 import org.apache.spark.SparkEnv
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.util.{ByteBufferInputStream, NextIterator}
@@ -59,17 +61,17 @@ object Serializer {
  */
 @DeveloperApi
 trait SerializerInstance {
-  def serialize[T](t: T): ByteBuffer
+  def serialize[T: ClassTag](t: T): ByteBuffer
 
-  def deserialize[T](bytes: ByteBuffer): T
+  def deserialize[T: ClassTag](bytes: ByteBuffer): T
 
-  def deserialize[T](bytes: ByteBuffer, loader: ClassLoader): T
+  def deserialize[T: ClassTag](bytes: ByteBuffer, loader: ClassLoader): T
 
   def serializeStream(s: OutputStream): SerializationStream
 
   def deserializeStream(s: InputStream): DeserializationStream
 
-  def serializeMany[T](iterator: Iterator[T]): ByteBuffer = {
+  def serializeMany[T: ClassTag](iterator: Iterator[T]): ByteBuffer = {
     // Default implementation uses serializeStream
     val stream = new ByteArrayOutputStream()
     serializeStream(stream).writeAll(iterator)
@@ -85,18 +87,17 @@ trait SerializerInstance {
   }
 }
 
-
 /**
  * :: DeveloperApi ::
  * A stream for writing serialized objects.
  */
 @DeveloperApi
 trait SerializationStream {
-  def writeObject[T](t: T): SerializationStream
+  def writeObject[T: ClassTag](t: T): SerializationStream
   def flush(): Unit
   def close(): Unit
 
-  def writeAll[T](iter: Iterator[T]): SerializationStream = {
+  def writeAll[T: ClassTag](iter: Iterator[T]): SerializationStream = {
     while (iter.hasNext) {
       writeObject(iter.next())
     }
@@ -111,7 +112,7 @@ trait SerializationStream {
  */
 @DeveloperApi
 trait DeserializationStream {
-  def readObject[T](): T
+  def readObject[T: ClassTag](): T
   def close(): Unit
 
   /**
diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala
index 3f0ed61c5bbf..95777fbf57d8 100644
--- a/core/src/main/scala/org/apache/spark/util/Utils.scala
+++ b/core/src/main/scala/org/apache/spark/util/Utils.scala
@@ -850,7 +850,7 @@ private[spark] object Utils extends Logging {
   /**
    * Clone an object using a Spark serializer.
    */
-  def clone[T](value: T, serializer: SerializerInstance): T = {
+  def clone[T: ClassTag](value: T, serializer: SerializerInstance): T = {
     serializer.deserialize[T](serializer.serialize(value))
   }
 
diff --git a/core/src/test/scala/org/apache/spark/serializer/KryoSerializerSuite.scala b/core/src/test/scala/org/apache/spark/serializer/KryoSerializerSuite.scala
index 5d4673aebe9e..cdd6b3d8feed 100644
--- a/core/src/test/scala/org/apache/spark/serializer/KryoSerializerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/serializer/KryoSerializerSuite.scala
@@ -18,6 +18,7 @@
 package org.apache.spark.serializer
 
 import scala.collection.mutable
+import scala.reflect.ClassTag
 
 import com.esotericsoftware.kryo.Kryo
 import org.scalatest.FunSuite
@@ -31,7 +32,7 @@ class KryoSerializerSuite extends FunSuite with SharedSparkContext {
 
   test("basic types") {
     val ser = new KryoSerializer(conf).newInstance()
-    def check[T](t: T) {
+    def check[T: ClassTag](t: T) {
       assert(ser.deserialize[T](ser.serialize(t)) === t)
     }
     check(1)
@@ -61,7 +62,7 @@ class KryoSerializerSuite extends FunSuite with SharedSparkContext {
 
   test("pairs") {
     val ser = new KryoSerializer(conf).newInstance()
-    def check[T](t: T) {
+    def check[T: ClassTag](t: T) {
       assert(ser.deserialize[T](ser.serialize(t)) === t)
     }
     check((1, 1))
@@ -85,7 +86,7 @@ class KryoSerializerSuite extends FunSuite with SharedSparkContext {
 
   test("Scala data structures") {
     val ser = new KryoSerializer(conf).newInstance()
-    def check[T](t: T) {
+    def check[T: ClassTag](t: T) {
       assert(ser.deserialize[T](ser.serialize(t)) === t)
     }
     check(List[Int]())
@@ -108,7 +109,7 @@ class KryoSerializerSuite extends FunSuite with SharedSparkContext {
 
   test("ranges") {
     val ser = new KryoSerializer(conf).newInstance()
-    def check[T](t: T) {
+    def check[T: ClassTag](t: T) {
       assert(ser.deserialize[T](ser.serialize(t)) === t)
       // Check that very long ranges don't get written one element at a time
       assert(ser.serialize(t).limit < 100)
@@ -129,7 +130,7 @@ class KryoSerializerSuite extends FunSuite with SharedSparkContext {
 
   test("custom registrator") {
     val ser = new KryoSerializer(conf).newInstance()
-    def check[T](t: T) {
+    def check[T: ClassTag](t: T) {
       assert(ser.deserialize[T](ser.serialize(t)) === t)
     }
 
diff --git a/examples/src/main/scala/org/apache/spark/examples/bagel/WikipediaPageRankStandalone.scala b/examples/src/main/scala/org/apache/spark/examples/bagel/WikipediaPageRankStandalone.scala
index a197dac87d6d..576a3e371b99 100644
--- a/examples/src/main/scala/org/apache/spark/examples/bagel/WikipediaPageRankStandalone.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/bagel/WikipediaPageRankStandalone.scala
@@ -28,6 +28,8 @@ import org.apache.spark.serializer.{DeserializationStream, SerializationStream,
 import org.apache.spark.SparkContext._
 import org.apache.spark.rdd.RDD
 
+import scala.reflect.ClassTag
+
 object WikipediaPageRankStandalone {
   def main(args: Array[String]) {
     if (args.length < 4) {
@@ -143,15 +145,15 @@ class WPRSerializer extends org.apache.spark.serializer.Serializer {
 }
 
 class WPRSerializerInstance extends SerializerInstance {
-  def serialize[T](t: T): ByteBuffer = {
+  def serialize[T: ClassTag](t: T): ByteBuffer = {
     throw new UnsupportedOperationException()
   }
 
-  def deserialize[T](bytes: ByteBuffer): T = {
+  def deserialize[T: ClassTag](bytes: ByteBuffer): T = {
     throw new UnsupportedOperationException()
   }
 
-  def deserialize[T](bytes: ByteBuffer, loader: ClassLoader): T = {
+  def deserialize[T: ClassTag](bytes: ByteBuffer, loader: ClassLoader): T = {
     throw new UnsupportedOperationException()
   }
 
@@ -167,7 +169,7 @@ class WPRSerializerInstance extends SerializerInstance {
 class WPRSerializationStream(os: OutputStream) extends SerializationStream {
   val dos = new DataOutputStream(os)
 
-  def writeObject[T](t: T): SerializationStream = t match {
+  def writeObject[T: ClassTag](t: T): SerializationStream = t match {
     case (id: String, wrapper: ArrayBuffer[_]) => wrapper(0) match {
       case links: Array[String] => {
         dos.writeInt(0) // links
@@ -200,7 +202,7 @@ class WPRSerializationStream(os: OutputStream) extends SerializationStream {
 class WPRDeserializationStream(is: InputStream) extends DeserializationStream {
   val dis = new DataInputStream(is)
 
-  def readObject[T](): T = {
+  def readObject[T: ClassTag](): T = {
     val typeId = dis.readInt()
     typeId match {
       case 0 => {
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/Serializers.scala b/graphx/src/main/scala/org/apache/spark/graphx/impl/Serializers.scala
index 2f0531ee5f37..1de42eeca1f0 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/impl/Serializers.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/impl/Serializers.scala
@@ -17,20 +17,22 @@
 
 package org.apache.spark.graphx.impl
 
+import scala.language.existentials
+
 import java.io.{EOFException, InputStream, OutputStream}
 import java.nio.ByteBuffer
 
+import scala.reflect.ClassTag
+
 import org.apache.spark.graphx._
 import org.apache.spark.serializer._
 
-import scala.language.existentials
-
 private[graphx]
 class VertexIdMsgSerializer extends Serializer with Serializable {
   override def newInstance(): SerializerInstance = new ShuffleSerializerInstance {
 
     override def serializeStream(s: OutputStream) = new ShuffleSerializationStream(s) {
-      def writeObject[T](t: T) = {
+      def writeObject[T: ClassTag](t: T) = {
         val msg = t.asInstanceOf[(VertexId, _)]
         writeVarLong(msg._1, optimizePositive = false)
         this
@@ -38,7 +40,7 @@ class VertexIdMsgSerializer extends Serializer with Serializable {
     }
 
     override def deserializeStream(s: InputStream) = new ShuffleDeserializationStream(s) {
-      override def readObject[T](): T = {
+      override def readObject[T: ClassTag](): T = {
         (readVarLong(optimizePositive = false), null).asInstanceOf[T]
       }
     }
@@ -51,7 +53,7 @@ class IntVertexBroadcastMsgSerializer extends Serializer with Serializable {
   override def newInstance(): SerializerInstance = new ShuffleSerializerInstance {
 
     override def serializeStream(s: OutputStream) = new ShuffleSerializationStream(s) {
-      def writeObject[T](t: T) = {
+      def writeObject[T: ClassTag](t: T) = {
         val msg = t.asInstanceOf[VertexBroadcastMsg[Int]]
         writeVarLong(msg.vid, optimizePositive = false)
         writeInt(msg.data)
@@ -60,7 +62,7 @@ class IntVertexBroadcastMsgSerializer extends Serializer with Serializable {
     }
 
     override def deserializeStream(s: InputStream) = new ShuffleDeserializationStream(s) {
-      override def readObject[T](): T = {
+      override def readObject[T: ClassTag](): T = {
         val a = readVarLong(optimizePositive = false)
         val b = readInt()
         new VertexBroadcastMsg[Int](0, a, b).asInstanceOf[T]
@@ -75,7 +77,7 @@ class LongVertexBroadcastMsgSerializer extends Serializer with Serializable {
   override def newInstance(): SerializerInstance = new ShuffleSerializerInstance {
 
     override def serializeStream(s: OutputStream) = new ShuffleSerializationStream(s) {
-      def writeObject[T](t: T) = {
+      def writeObject[T: ClassTag](t: T) = {
         val msg = t.asInstanceOf[VertexBroadcastMsg[Long]]
         writeVarLong(msg.vid, optimizePositive = false)
         writeLong(msg.data)
@@ -84,7 +86,7 @@ class LongVertexBroadcastMsgSerializer extends Serializer with Serializable {
     }
 
     override def deserializeStream(s: InputStream) = new ShuffleDeserializationStream(s) {
-      override def readObject[T](): T = {
+      override def readObject[T: ClassTag](): T = {
         val a = readVarLong(optimizePositive = false)
         val b = readLong()
         new VertexBroadcastMsg[Long](0, a, b).asInstanceOf[T]
@@ -99,7 +101,7 @@ class DoubleVertexBroadcastMsgSerializer extends Serializer with Serializable {
   override def newInstance(): SerializerInstance = new ShuffleSerializerInstance {
 
     override def serializeStream(s: OutputStream) = new ShuffleSerializationStream(s) {
-      def writeObject[T](t: T) = {
+      def writeObject[T: ClassTag](t: T) = {
         val msg = t.asInstanceOf[VertexBroadcastMsg[Double]]
         writeVarLong(msg.vid, optimizePositive = false)
         writeDouble(msg.data)
@@ -108,7 +110,7 @@ class DoubleVertexBroadcastMsgSerializer extends Serializer with Serializable {
     }
 
     override def deserializeStream(s: InputStream) = new ShuffleDeserializationStream(s) {
-      def readObject[T](): T = {
+      def readObject[T: ClassTag](): T = {
         val a = readVarLong(optimizePositive = false)
         val b = readDouble()
         new VertexBroadcastMsg[Double](0, a, b).asInstanceOf[T]
@@ -123,7 +125,7 @@ class IntAggMsgSerializer extends Serializer with Serializable {
   override def newInstance(): SerializerInstance = new ShuffleSerializerInstance {
 
     override def serializeStream(s: OutputStream) = new ShuffleSerializationStream(s) {
-      def writeObject[T](t: T) = {
+      def writeObject[T: ClassTag](t: T) = {
         val msg = t.asInstanceOf[(VertexId, Int)]
         writeVarLong(msg._1, optimizePositive = false)
         writeUnsignedVarInt(msg._2)
@@ -132,7 +134,7 @@ class IntAggMsgSerializer extends Serializer with Serializable {
     }
 
     override def deserializeStream(s: InputStream) = new ShuffleDeserializationStream(s) {
-      override def readObject[T](): T = {
+      override def readObject[T: ClassTag](): T = {
         val a = readVarLong(optimizePositive = false)
         val b = readUnsignedVarInt()
         (a, b).asInstanceOf[T]
@@ -147,7 +149,7 @@ class LongAggMsgSerializer extends Serializer with Serializable {
   override def newInstance(): SerializerInstance = new ShuffleSerializerInstance {
 
     override def serializeStream(s: OutputStream) = new ShuffleSerializationStream(s) {
-      def writeObject[T](t: T) = {
+      def writeObject[T: ClassTag](t: T) = {
         val msg = t.asInstanceOf[(VertexId, Long)]
         writeVarLong(msg._1, optimizePositive = false)
         writeVarLong(msg._2, optimizePositive = true)
@@ -156,7 +158,7 @@ class LongAggMsgSerializer extends Serializer with Serializable {
     }
 
     override def deserializeStream(s: InputStream) = new ShuffleDeserializationStream(s) {
-      override def readObject[T](): T = {
+      override def readObject[T: ClassTag](): T = {
         val a = readVarLong(optimizePositive = false)
         val b = readVarLong(optimizePositive = true)
         (a, b).asInstanceOf[T]
@@ -171,7 +173,7 @@ class DoubleAggMsgSerializer extends Serializer with Serializable {
   override def newInstance(): SerializerInstance = new ShuffleSerializerInstance {
 
     override def serializeStream(s: OutputStream) = new ShuffleSerializationStream(s) {
-      def writeObject[T](t: T) = {
+      def writeObject[T: ClassTag](t: T) = {
         val msg = t.asInstanceOf[(VertexId, Double)]
         writeVarLong(msg._1, optimizePositive = false)
         writeDouble(msg._2)
@@ -180,7 +182,7 @@ class DoubleAggMsgSerializer extends Serializer with Serializable {
     }
 
     override def deserializeStream(s: InputStream) = new ShuffleDeserializationStream(s) {
-      def readObject[T](): T = {
+      def readObject[T: ClassTag](): T = {
         val a = readVarLong(optimizePositive = false)
         val b = readDouble()
         (a, b).asInstanceOf[T]
@@ -196,7 +198,7 @@ class DoubleAggMsgSerializer extends Serializer with Serializable {
 private[graphx]
 abstract class ShuffleSerializationStream(s: OutputStream) extends SerializationStream {
   // The implementation should override this one.
-  def writeObject[T](t: T): SerializationStream
+  def writeObject[T: ClassTag](t: T): SerializationStream
 
   def writeInt(v: Int) {
     s.write(v >> 24)
@@ -309,7 +311,7 @@ abstract class ShuffleSerializationStream(s: OutputStream) extends Serialization
 private[graphx]
 abstract class ShuffleDeserializationStream(s: InputStream) extends DeserializationStream {
   // The implementation should override this one.
-  def readObject[T](): T
+  def readObject[T: ClassTag](): T
 
   def readInt(): Int = {
     val first = s.read()
@@ -398,11 +400,12 @@ abstract class ShuffleDeserializationStream(s: InputStream) extends Deserializat
 
 private[graphx] sealed trait ShuffleSerializerInstance extends SerializerInstance {
 
-  override def serialize[T](t: T): ByteBuffer = throw new UnsupportedOperationException
+  override def serialize[T: ClassTag](t: T): ByteBuffer = throw new UnsupportedOperationException
 
-  override def deserialize[T](bytes: ByteBuffer): T = throw new UnsupportedOperationException
+  override def deserialize[T: ClassTag](bytes: ByteBuffer): T =
+    throw new UnsupportedOperationException
 
-  override def deserialize[T](bytes: ByteBuffer, loader: ClassLoader): T =
+  override def deserialize[T: ClassTag](bytes: ByteBuffer, loader: ClassLoader): T =
     throw new UnsupportedOperationException
 
   // The implementation should override the following two.
diff --git a/graphx/src/test/scala/org/apache/spark/graphx/SerializerSuite.scala b/graphx/src/test/scala/org/apache/spark/graphx/SerializerSuite.scala
index 73438d953596..91caa6b605a1 100644
--- a/graphx/src/test/scala/org/apache/spark/graphx/SerializerSuite.scala
+++ b/graphx/src/test/scala/org/apache/spark/graphx/SerializerSuite.scala
@@ -20,6 +20,7 @@ package org.apache.spark.graphx
 import java.io.{EOFException, ByteArrayInputStream, ByteArrayOutputStream}
 
 import scala.util.Random
+import scala.reflect.ClassTag
 
 import org.scalatest.FunSuite
 
@@ -164,7 +165,7 @@ class SerializerSuite extends FunSuite with LocalSparkContext {
     def testVarLongEncoding(v: Long, optimizePositive: Boolean) {
       val bout = new ByteArrayOutputStream
       val stream = new ShuffleSerializationStream(bout) {
-        def writeObject[T](t: T): SerializationStream = {
+        def writeObject[T: ClassTag](t: T): SerializationStream = {
           writeVarLong(t.asInstanceOf[Long], optimizePositive = optimizePositive)
           this
         }
@@ -173,7 +174,7 @@ class SerializerSuite extends FunSuite with LocalSparkContext {
 
       val bin = new ByteArrayInputStream(bout.toByteArray)
       val dstream = new ShuffleDeserializationStream(bin) {
-        def readObject[T](): T = {
+        def readObject[T: ClassTag](): T = {
           readVarLong(optimizePositive).asInstanceOf[T]
         }
       }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer.scala
index 5067c14ddffe..1c6e29b3cdee 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer.scala
@@ -19,6 +19,8 @@ package org.apache.spark.sql.execution
 
 import java.nio.ByteBuffer
 
+import scala.reflect.ClassTag
+
 import com.esotericsoftware.kryo.io.{Input, Output}
 import com.esotericsoftware.kryo.{Serializer, Kryo}
 
@@ -59,11 +61,11 @@ private[sql] object SparkSqlSerializer {
     new KryoSerializer(sparkConf)
   }
 
-  def serialize[T](o: T): Array[Byte] = {
+  def serialize[T: ClassTag](o: T): Array[Byte] = {
     ser.newInstance().serialize(o).array()
   }
 
-  def deserialize[T](bytes: Array[Byte]): T  = {
+  def deserialize[T: ClassTag](bytes: Array[Byte]): T  = {
     ser.newInstance().deserialize[T](ByteBuffer.wrap(bytes))
   }
 }

From c05d11bb307eaba40c5669da2d374c28debaa55a Mon Sep 17 00:00:00 2001
From: Andy Konwinski <andykonwinski@gmail.com>
Date: Sat, 10 May 2014 12:46:51 -0700
Subject: [PATCH 149/810] fix broken in link in python docs

Author: Andy Konwinski <andykonwinski@gmail.com>

Closes #650 from andyk/python-docs-link-fix and squashes the following commits:

a1f9d51 [Andy Konwinski] fix broken in link in python docs
---
 docs/python-programming-guide.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/python-programming-guide.md b/docs/python-programming-guide.md
index 6813963bb080..39fb5f0c99ca 100644
--- a/docs/python-programming-guide.md
+++ b/docs/python-programming-guide.md
@@ -45,7 +45,7 @@ errors = logData.filter(is_error)
 
 PySpark will automatically ship these functions to executors, along with any objects that they reference.
 Instances of classes will be serialized and shipped to executors by PySpark, but classes themselves cannot be automatically distributed to executors.
-The [Standalone Use](#standalone-use) section describes how to ship code dependencies to executors.
+The [Standalone Use](#standalone-programs) section describes how to ship code dependencies to executors.
 
 In addition, PySpark fully supports interactive use---simply run `./bin/pyspark` to launch an interactive shell.
 

From 3776f2f283842543ff766398292532c6e94221cc Mon Sep 17 00:00:00 2001
From: Bouke van der Bijl <boukevanderbijl@gmail.com>
Date: Sat, 10 May 2014 13:02:13 -0700
Subject: [PATCH 150/810] Add Python includes to path before depickling
 broadcast values

This fixes https://issues.apache.org/jira/browse/SPARK-1731 by adding the Python includes to the PYTHONPATH before depickling the broadcast values

@airhorns

Author: Bouke van der Bijl <boukevanderbijl@gmail.com>

Closes #656 from bouk/python-includes-before-broadcast and squashes the following commits:

7b0dfe4 [Bouke van der Bijl] Add Python includes to path before depickling broadcast values
---
 .../org/apache/spark/api/python/PythonRDD.scala    | 10 +++++-----
 python/pyspark/worker.py                           | 14 +++++++-------
 2 files changed, 12 insertions(+), 12 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
index fecd9762f3f6..388b838d78bb 100644
--- a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
+++ b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
@@ -179,6 +179,11 @@ private[spark] class PythonRDD[T: ClassTag](
         dataOut.writeInt(split.index)
         // sparkFilesDir
         PythonRDD.writeUTF(SparkFiles.getRootDirectory, dataOut)
+        // Python includes (*.zip and *.egg files)
+        dataOut.writeInt(pythonIncludes.length)
+        for (include <- pythonIncludes) {
+          PythonRDD.writeUTF(include, dataOut)
+        }
         // Broadcast variables
         dataOut.writeInt(broadcastVars.length)
         for (broadcast <- broadcastVars) {
@@ -186,11 +191,6 @@ private[spark] class PythonRDD[T: ClassTag](
           dataOut.writeInt(broadcast.value.length)
           dataOut.write(broadcast.value)
         }
-        // Python includes (*.zip and *.egg files)
-        dataOut.writeInt(pythonIncludes.length)
-        for (include <- pythonIncludes) {
-          PythonRDD.writeUTF(include, dataOut)
-        }
         dataOut.flush()
         // Serialized command:
         dataOut.writeInt(command.length)
diff --git a/python/pyspark/worker.py b/python/pyspark/worker.py
index 4c214ef35968..f43210c6c030 100644
--- a/python/pyspark/worker.py
+++ b/python/pyspark/worker.py
@@ -56,13 +56,6 @@ def main(infile, outfile):
         SparkFiles._root_directory = spark_files_dir
         SparkFiles._is_running_on_worker = True
 
-        # fetch names and values of broadcast variables
-        num_broadcast_variables = read_int(infile)
-        for _ in range(num_broadcast_variables):
-            bid = read_long(infile)
-            value = pickleSer._read_with_length(infile)
-            _broadcastRegistry[bid] = Broadcast(bid, value)
-
         # fetch names of includes (*.zip and *.egg files) and construct PYTHONPATH
         sys.path.append(spark_files_dir) # *.py files that were added will be copied here
         num_python_includes =  read_int(infile)
@@ -70,6 +63,13 @@ def main(infile, outfile):
             filename = utf8_deserializer.loads(infile)
             sys.path.append(os.path.join(spark_files_dir, filename))
 
+        # fetch names and values of broadcast variables
+        num_broadcast_variables = read_int(infile)
+        for _ in range(num_broadcast_variables):
+            bid = read_long(infile)
+            value = pickleSer._read_with_length(infile)
+            _broadcastRegistry[bid] = Broadcast(bid, value)
+
         command = pickleSer._read_with_length(infile)
         (func, deserializer, serializer) = command
         init_time = time.time()

From 6c2691d0a0ed46a8b8093e05a4708706cf187168 Mon Sep 17 00:00:00 2001
From: Kan Zhang <kzhang@apache.org>
Date: Sat, 10 May 2014 14:01:08 -0700
Subject: [PATCH 151/810] [SPARK-1690] Tolerating empty elements when saving
 Python RDD to text files

Tolerate empty strings in PythonRDD

Author: Kan Zhang <kzhang@apache.org>

Closes #644 from kanzhang/SPARK-1690 and squashes the following commits:

c62ad33 [Kan Zhang] Adding Python doctest
473ec4b [Kan Zhang] [SPARK-1690] Tolerating empty elements when saving Python RDD to text files
---
 .../scala/org/apache/spark/api/python/PythonRDD.scala     | 5 +++--
 python/pyspark/rdd.py                                     | 8 ++++++++
 2 files changed, 11 insertions(+), 2 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
index 388b838d78bb..2971c277aa86 100644
--- a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
+++ b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
@@ -94,6 +94,7 @@ private[spark] class PythonRDD[T: ClassTag](
               val obj = new Array[Byte](length)
               stream.readFully(obj)
               obj
+            case 0 => Array.empty[Byte]
             case SpecialLengths.TIMING_DATA =>
               // Timing data from worker
               val bootTime = stream.readLong()
@@ -123,7 +124,7 @@ private[spark] class PythonRDD[T: ClassTag](
                 stream.readFully(update)
                 accumulator += Collections.singletonList(update)
               }
-              Array.empty[Byte]
+              null
           }
         } catch {
 
@@ -143,7 +144,7 @@ private[spark] class PythonRDD[T: ClassTag](
 
       var _nextObj = read()
 
-      def hasNext = _nextObj.length != 0
+      def hasNext = _nextObj != null
     }
     new InterruptibleIterator(context, stdoutIterator)
   }
diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py
index 3a1c56af5b22..4f74824ba4cf 100644
--- a/python/pyspark/rdd.py
+++ b/python/pyspark/rdd.py
@@ -891,6 +891,14 @@ def saveAsTextFile(self, path):
         >>> from glob import glob
         >>> ''.join(sorted(input(glob(tempFile.name + "/part-0000*"))))
         '0\\n1\\n2\\n3\\n4\\n5\\n6\\n7\\n8\\n9\\n'
+
+        Empty lines are tolerated when saving to text files.
+
+        >>> tempFile2 = NamedTemporaryFile(delete=True)
+        >>> tempFile2.close()
+        >>> sc.parallelize(['', 'foo', '', 'bar', '']).saveAsTextFile(tempFile2.name)
+        >>> ''.join(sorted(input(glob(tempFile2.name + "/part-0000*"))))
+        '\\n\\n\\nbar\\nfoo\\n'
         """
         def func(split, iterator):
             for x in iterator:

From 905173df57b90f90ebafb22e43f55164445330e6 Mon Sep 17 00:00:00 2001
From: Ankur Dave <ankurdave@gmail.com>
Date: Sat, 10 May 2014 14:48:07 -0700
Subject: [PATCH 152/810] Unify GraphImpl RDDs + other graph load optimizations

This PR makes the following changes, primarily in e4fbd329aef85fe2c38b0167255d2a712893d683:

1. *Unify RDDs to avoid zipPartitions.* A graph used to be four RDDs: vertices, edges, routing table, and triplet view. This commit merges them down to two: vertices (with routing table), and edges (with replicated vertices).

2. *Avoid duplicate shuffle in graph building.* We used to do two shuffles when building a graph: one to extract routing information from the edges and move it to the vertices, and another to find nonexistent vertices referred to by edges. With this commit, the latter is done as a side effect of the former.

3. *Avoid no-op shuffle when joins are fully eliminated.* This is a side effect of unifying the edges and the triplet view.

4. *Join elimination for mapTriplets.*

5. *Ship only the needed vertex attributes when upgrading the triplet view.* If the triplet view already contains source attributes, and we now need both attributes, only ship destination attributes rather than re-shipping both. This is done in `ReplicatedVertexView#upgrade`.

Author: Ankur Dave <ankurdave@gmail.com>

Closes #497 from ankurdave/unify-rdds and squashes the following commits:

332ab43 [Ankur Dave] Merge remote-tracking branch 'apache-spark/master' into unify-rdds
4933e2e [Ankur Dave] Exclude RoutingTable from binary compatibility check
5ba8789 [Ankur Dave] Add GraphX upgrade guide from Spark 0.9.1
13ac845 [Ankur Dave] Merge remote-tracking branch 'apache-spark/master' into unify-rdds
a04765c [Ankur Dave] Remove unnecessary toOps call
57202e8 [Ankur Dave] Replace case with pair parameter
75af062 [Ankur Dave] Add explicit return types
04d3ae5 [Ankur Dave] Convert implicit parameter to context bound
c88b269 [Ankur Dave] Revert upgradeIterator to if-in-a-loop
0d3584c [Ankur Dave] EdgePartition.size should be val
2a928b2 [Ankur Dave] Set locality wait
10b3596 [Ankur Dave] Clean up public API
ae36110 [Ankur Dave] Fix style errors
e4fbd32 [Ankur Dave] Unify GraphImpl RDDs + other graph load optimizations
d6d60e2 [Ankur Dave] In GraphLoader, coalesce to minEdgePartitions
62c7b78 [Ankur Dave] In Analytics, take PageRank numIter
d64e8d4 [Ankur Dave] Log current Pregel iteration
---
 docs/graphx-programming-guide.md              |  22 +-
 .../org/apache/spark/graphx/EdgeRDD.scala     |  56 +--
 .../org/apache/spark/graphx/EdgeTriplet.scala |   2 +
 .../scala/org/apache/spark/graphx/Graph.scala |   2 +-
 .../spark/graphx/GraphKryoRegistrator.scala   |   8 +-
 .../org/apache/spark/graphx/GraphLoader.scala |  10 +-
 .../org/apache/spark/graphx/GraphOps.scala    |  17 +-
 .../org/apache/spark/graphx/Pregel.scala      |   6 +-
 .../org/apache/spark/graphx/VertexRDD.scala   | 166 ++++++---
 .../spark/graphx/impl/EdgePartition.scala     | 132 +++++--
 .../graphx/impl/EdgePartitionBuilder.scala    |  18 +-
 .../graphx/impl/EdgeTripletIterator.scala     |  50 ++-
 .../apache/spark/graphx/impl/GraphImpl.scala  | 344 +++++++-----------
 .../graphx/impl/MessageToPartition.scala      |  21 +-
 .../graphx/impl/ReplicatedVertexView.scala    | 238 ++++--------
 .../spark/graphx/impl/RoutingTable.scala      |  82 -----
 .../graphx/impl/RoutingTablePartition.scala   | 158 ++++++++
 .../spark/graphx/impl/Serializers.scala       |  29 ++
 .../impl/ShippableVertexPartition.scala       | 149 ++++++++
 .../spark/graphx/impl/VertexPartition.scala   | 269 ++------------
 .../graphx/impl/VertexPartitionBase.scala     |  91 +++++
 .../graphx/impl/VertexPartitionBaseOps.scala  | 245 +++++++++++++
 .../apache/spark/graphx/lib/Analytics.scala   |   8 +-
 .../org/apache/spark/graphx/GraphSuite.scala  |  10 +-
 .../graphx/impl/EdgePartitionSuite.scala      |  48 ++-
 .../impl/EdgeTripletIteratorSuite.scala       |  10 +-
 .../graphx/impl/VertexPartitionSuite.scala    |  11 -
 project/MimaBuild.scala                       |   2 +
 28 files changed, 1353 insertions(+), 851 deletions(-)
 delete mode 100644 graphx/src/main/scala/org/apache/spark/graphx/impl/RoutingTable.scala
 create mode 100644 graphx/src/main/scala/org/apache/spark/graphx/impl/RoutingTablePartition.scala
 create mode 100644 graphx/src/main/scala/org/apache/spark/graphx/impl/ShippableVertexPartition.scala
 create mode 100644 graphx/src/main/scala/org/apache/spark/graphx/impl/VertexPartitionBase.scala
 create mode 100644 graphx/src/main/scala/org/apache/spark/graphx/impl/VertexPartitionBaseOps.scala

diff --git a/docs/graphx-programming-guide.md b/docs/graphx-programming-guide.md
index 07be8ba58efa..42ab27bf55cc 100644
--- a/docs/graphx-programming-guide.md
+++ b/docs/graphx-programming-guide.md
@@ -86,6 +86,12 @@ support the [Bagel API](api/scala/index.html#org.apache.spark.bagel.package) and
 [Bagel programming guide](bagel-programming-guide.html). However, we encourage Bagel users to
 explore the new GraphX API and comment on issues that may complicate the transition from Bagel.
 
+## Upgrade Guide from Spark 0.9.1
+
+GraphX in Spark {{site.SPARK_VERSION}} contains one user-facing interface change from Spark 0.9.1. [`EdgeRDD`][EdgeRDD] may now store adjacent vertex attributes to construct the triplets, so it has gained a type parameter. The edges of a graph of type `Graph[VD, ED]` are of type `EdgeRDD[ED, VD]` rather than `EdgeRDD[ED]`.
+
+[EdgeRDD]: api/scala/index.html#org.apache.spark.graphx.EdgeRDD
+
 # Getting Started
 
 To get started you first need to import Spark and GraphX into your project, as follows:
@@ -145,12 +151,12 @@ the vertices and edges of the graph:
 {% highlight scala %}
 class Graph[VD, ED] {
   val vertices: VertexRDD[VD]
-  val edges: EdgeRDD[ED]
+  val edges: EdgeRDD[ED, VD]
 }
 {% endhighlight %}
 
-The classes `VertexRDD[VD]` and `EdgeRDD[ED]` extend and are optimized versions of `RDD[(VertexID,
-VD)]` and `RDD[Edge[ED]]` respectively.  Both `VertexRDD[VD]` and `EdgeRDD[ED]` provide  additional
+The classes `VertexRDD[VD]` and `EdgeRDD[ED, VD]` extend and are optimized versions of `RDD[(VertexID,
+VD)]` and `RDD[Edge[ED]]` respectively.  Both `VertexRDD[VD]` and `EdgeRDD[ED, VD]` provide  additional
 functionality built around graph computation and leverage internal optimizations.  We discuss the
 `VertexRDD` and `EdgeRDD` API in greater detail in the section on [vertex and edge
 RDDs](#vertex_and_edge_rdds) but for now they can be thought of as simply RDDs of the form:
@@ -302,7 +308,7 @@ class Graph[VD, ED] {
   val degrees: VertexRDD[Int]
   // Views of the graph as collections =============================================================
   val vertices: VertexRDD[VD]
-  val edges: EdgeRDD[ED]
+  val edges: EdgeRDD[ED, VD]
   val triplets: RDD[EdgeTriplet[VD, ED]]
   // Functions for caching graphs ==================================================================
   def persist(newLevel: StorageLevel = StorageLevel.MEMORY_ONLY): Graph[VD, ED]
@@ -908,7 +914,7 @@ val setC: VertexRDD[Double] = setA.innerJoin(setB)((id, a, b) => a + b)
 
 ## EdgeRDDs
 
-The `EdgeRDD[ED]`, which extends `RDD[Edge[ED]]` organizes the edges in blocks partitioned using one
+The `EdgeRDD[ED, VD]`, which extends `RDD[Edge[ED]]` organizes the edges in blocks partitioned using one
 of the various partitioning strategies defined in [`PartitionStrategy`][PartitionStrategy].  Within
 each partition, edge attributes and adjacency structure, are stored separately enabling maximum
 reuse when changing attribute values.
@@ -918,11 +924,11 @@ reuse when changing attribute values.
 The three additional functions exposed by the `EdgeRDD` are:
 {% highlight scala %}
 // Transform the edge attributes while preserving the structure
-def mapValues[ED2](f: Edge[ED] => ED2): EdgeRDD[ED2]
+def mapValues[ED2](f: Edge[ED] => ED2): EdgeRDD[ED2, VD]
 // Revere the edges reusing both attributes and structure
-def reverse: EdgeRDD[ED]
+def reverse: EdgeRDD[ED, VD]
 // Join two `EdgeRDD`s partitioned using the same partitioning strategy.
-def innerJoin[ED2, ED3](other: EdgeRDD[ED2])(f: (VertexId, VertexId, ED, ED2) => ED3): EdgeRDD[ED3]
+def innerJoin[ED2, ED3](other: EdgeRDD[ED2, VD])(f: (VertexId, VertexId, ED, ED2) => ED3): EdgeRDD[ED3, VD]
 {% endhighlight %}
 
 In most applications we have found that operations on the `EdgeRDD` are accomplished through the
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/EdgeRDD.scala b/graphx/src/main/scala/org/apache/spark/graphx/EdgeRDD.scala
index fa78ca99b889..a8fc09507251 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/EdgeRDD.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/EdgeRDD.scala
@@ -20,16 +20,19 @@ package org.apache.spark.graphx
 import scala.reflect.{classTag, ClassTag}
 
 import org.apache.spark.{OneToOneDependency, Partition, Partitioner, TaskContext}
-import org.apache.spark.graphx.impl.EdgePartition
 import org.apache.spark.rdd.RDD
 import org.apache.spark.storage.StorageLevel
 
+import org.apache.spark.graphx.impl.EdgePartition
+
 /**
- * `EdgeRDD[ED]` extends `RDD[Edge[ED]]` by storing the edges in columnar format on each partition
- * for performance.
+ * `EdgeRDD[ED, VD]` extends `RDD[Edge[ED]]` by storing the edges in columnar format on each
+ * partition for performance. It may additionally store the vertex attributes associated with each
+ * edge to provide the triplet view. Shipping of the vertex attributes is managed by
+ * `impl.ReplicatedVertexView`.
  */
-class EdgeRDD[@specialized ED: ClassTag](
-    val partitionsRDD: RDD[(PartitionID, EdgePartition[ED])])
+class EdgeRDD[@specialized ED: ClassTag, VD: ClassTag](
+    val partitionsRDD: RDD[(PartitionID, EdgePartition[ED, VD])])
   extends RDD[Edge[ED]](partitionsRDD.context, List(new OneToOneDependency(partitionsRDD))) {
 
   partitionsRDD.setName("EdgeRDD")
@@ -45,8 +48,12 @@ class EdgeRDD[@specialized ED: ClassTag](
     partitionsRDD.partitioner.orElse(Some(Partitioner.defaultPartitioner(partitionsRDD)))
 
   override def compute(part: Partition, context: TaskContext): Iterator[Edge[ED]] = {
-    val p = firstParent[(PartitionID, EdgePartition[ED])].iterator(part, context)
-    p.next._2.iterator.map(_.copy())
+    val p = firstParent[(PartitionID, EdgePartition[ED, VD])].iterator(part, context)
+    if (p.hasNext) {
+      p.next._2.iterator.map(_.copy())
+    } else {
+      Iterator.empty
+    }
   }
 
   override def collect(): Array[Edge[ED]] = this.map(_.copy()).collect()
@@ -61,11 +68,15 @@ class EdgeRDD[@specialized ED: ClassTag](
     this
   }
 
-  private[graphx] def mapEdgePartitions[ED2: ClassTag](
-      f: (PartitionID, EdgePartition[ED]) => EdgePartition[ED2]): EdgeRDD[ED2] = {
-    new EdgeRDD[ED2](partitionsRDD.mapPartitions({ iter =>
-      val (pid, ep) = iter.next()
-      Iterator(Tuple2(pid, f(pid, ep)))
+  private[graphx] def mapEdgePartitions[ED2: ClassTag, VD2: ClassTag](
+      f: (PartitionID, EdgePartition[ED, VD]) => EdgePartition[ED2, VD2]): EdgeRDD[ED2, VD2] = {
+    new EdgeRDD[ED2, VD2](partitionsRDD.mapPartitions({ iter =>
+      if (iter.hasNext) {
+        val (pid, ep) = iter.next()
+        Iterator(Tuple2(pid, f(pid, ep)))
+      } else {
+        Iterator.empty
+      }
     }, preservesPartitioning = true))
   }
 
@@ -76,7 +87,7 @@ class EdgeRDD[@specialized ED: ClassTag](
    * @param f the function from an edge to a new edge value
    * @return a new EdgeRDD containing the new edge values
    */
-  def mapValues[ED2: ClassTag](f: Edge[ED] => ED2): EdgeRDD[ED2] =
+  def mapValues[ED2: ClassTag](f: Edge[ED] => ED2): EdgeRDD[ED2, VD] =
     mapEdgePartitions((pid, part) => part.map(f))
 
   /**
@@ -84,7 +95,14 @@ class EdgeRDD[@specialized ED: ClassTag](
    *
    * @return a new EdgeRDD containing all the edges reversed
    */
-  def reverse: EdgeRDD[ED] = mapEdgePartitions((pid, part) => part.reverse)
+  def reverse: EdgeRDD[ED, VD] = mapEdgePartitions((pid, part) => part.reverse)
+
+  /** Removes all edges but those matching `epred` and where both vertices match `vpred`. */
+  def filter(
+      epred: EdgeTriplet[VD, ED] => Boolean,
+      vpred: (VertexId, VD) => Boolean): EdgeRDD[ED, VD] = {
+    mapEdgePartitions((pid, part) => part.filter(epred, vpred))
+  }
 
   /**
    * Inner joins this EdgeRDD with another EdgeRDD, assuming both are partitioned using the same
@@ -96,19 +114,15 @@ class EdgeRDD[@specialized ED: ClassTag](
    *         with values supplied by `f`
    */
   def innerJoin[ED2: ClassTag, ED3: ClassTag]
-      (other: EdgeRDD[ED2])
-      (f: (VertexId, VertexId, ED, ED2) => ED3): EdgeRDD[ED3] = {
+      (other: EdgeRDD[ED2, _])
+      (f: (VertexId, VertexId, ED, ED2) => ED3): EdgeRDD[ED3, VD] = {
     val ed2Tag = classTag[ED2]
     val ed3Tag = classTag[ED3]
-    new EdgeRDD[ED3](partitionsRDD.zipPartitions(other.partitionsRDD, true) {
+    new EdgeRDD[ED3, VD](partitionsRDD.zipPartitions(other.partitionsRDD, true) {
       (thisIter, otherIter) =>
         val (pid, thisEPart) = thisIter.next()
         val (_, otherEPart) = otherIter.next()
         Iterator(Tuple2(pid, thisEPart.innerJoin(otherEPart)(f)(ed2Tag, ed3Tag)))
     })
   }
-
-  private[graphx] def collectVertexIds(): RDD[VertexId] = {
-    partitionsRDD.flatMap { case (_, p) => Array.concat(p.srcIds, p.dstIds) }
-  }
 }
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/EdgeTriplet.scala b/graphx/src/main/scala/org/apache/spark/graphx/EdgeTriplet.scala
index dfc6a801587d..9d473d5ebda4 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/EdgeTriplet.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/EdgeTriplet.scala
@@ -63,4 +63,6 @@ class EdgeTriplet[VD, ED] extends Edge[ED] {
     if (srcId == vid) srcAttr else { assert(dstId == vid); dstAttr }
 
   override def toString = ((srcId, srcAttr), (dstId, dstAttr), attr).toString()
+
+  def toTuple: ((VertexId, VD), (VertexId, VD), ED) = ((srcId, srcAttr), (dstId, dstAttr), attr)
 }
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/Graph.scala b/graphx/src/main/scala/org/apache/spark/graphx/Graph.scala
index 50395868902d..dc5dac4fdad5 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/Graph.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/Graph.scala
@@ -59,7 +59,7 @@ abstract class Graph[VD: ClassTag, ED: ClassTag] protected () extends Serializab
    * along with their vertex data.
    *
    */
-  @transient val edges: EdgeRDD[ED]
+  @transient val edges: EdgeRDD[ED, VD]
 
   /**
    * An RDD containing the edge triplets, which are edges along with the vertex data associated with
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/GraphKryoRegistrator.scala b/graphx/src/main/scala/org/apache/spark/graphx/GraphKryoRegistrator.scala
index dd380d8c182c..d295d0127ac7 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/GraphKryoRegistrator.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/GraphKryoRegistrator.scala
@@ -19,10 +19,11 @@ package org.apache.spark.graphx
 
 import com.esotericsoftware.kryo.Kryo
 
-import org.apache.spark.graphx.impl._
 import org.apache.spark.serializer.KryoRegistrator
-import org.apache.spark.util.collection.BitSet
 import org.apache.spark.util.BoundedPriorityQueue
+import org.apache.spark.util.collection.BitSet
+
+import org.apache.spark.graphx.impl._
 
 /**
  * Registers GraphX classes with Kryo for improved performance.
@@ -33,8 +34,9 @@ class GraphKryoRegistrator extends KryoRegistrator {
     kryo.register(classOf[Edge[Object]])
     kryo.register(classOf[MessageToPartition[Object]])
     kryo.register(classOf[VertexBroadcastMsg[Object]])
+    kryo.register(classOf[RoutingTableMessage])
     kryo.register(classOf[(VertexId, Object)])
-    kryo.register(classOf[EdgePartition[Object]])
+    kryo.register(classOf[EdgePartition[Object, Object]])
     kryo.register(classOf[BitSet])
     kryo.register(classOf[VertexIdToIndexMap])
     kryo.register(classOf[VertexAttributeBlock[Object]])
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/GraphLoader.scala b/graphx/src/main/scala/org/apache/spark/graphx/GraphLoader.scala
index 18858466db27..389490c13984 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/GraphLoader.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/GraphLoader.scala
@@ -47,8 +47,7 @@ object GraphLoader extends Logging {
    * @param path the path to the file (e.g., /home/data/file or hdfs://file)
    * @param canonicalOrientation whether to orient edges in the positive
    *        direction
-   * @param minEdgePartitions the number of partitions for the
-   *        the edge RDD
+   * @param minEdgePartitions the number of partitions for the edge RDD
    */
   def edgeListFile(
       sc: SparkContext,
@@ -60,8 +59,9 @@ object GraphLoader extends Logging {
     val startTime = System.currentTimeMillis
 
     // Parse the edge data table directly into edge partitions
-    val edges = sc.textFile(path, minEdgePartitions).mapPartitionsWithIndex { (pid, iter) =>
-      val builder = new EdgePartitionBuilder[Int]
+    val lines = sc.textFile(path, minEdgePartitions).coalesce(minEdgePartitions)
+    val edges = lines.mapPartitionsWithIndex { (pid, iter) =>
+      val builder = new EdgePartitionBuilder[Int, Int]
       iter.foreach { line =>
         if (!line.isEmpty && line(0) != '#') {
           val lineArray = line.split("\\s+")
@@ -78,7 +78,7 @@ object GraphLoader extends Logging {
         }
       }
       Iterator((pid, builder.toEdgePartition))
-    }.cache()
+    }.cache().setName("GraphLoader.edgeListFile - edges (%s)".format(path))
     edges.count()
 
     logInfo("It took %d ms to load the edges".format(System.currentTimeMillis - startTime))
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/GraphOps.scala b/graphx/src/main/scala/org/apache/spark/graphx/GraphOps.scala
index 4997fbc3cbcd..edd5b79da152 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/GraphOps.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/GraphOps.scala
@@ -18,11 +18,13 @@
 package org.apache.spark.graphx
 
 import scala.reflect.ClassTag
-import org.apache.spark.SparkContext._
+import scala.util.Random
+
 import org.apache.spark.SparkException
-import org.apache.spark.graphx.lib._
+import org.apache.spark.SparkContext._
 import org.apache.spark.rdd.RDD
-import scala.util.Random
+
+import org.apache.spark.graphx.lib._
 
 /**
  * Contains additional functionality for [[Graph]]. All operations are expressed in terms of the
@@ -43,19 +45,22 @@ class GraphOps[VD: ClassTag, ED: ClassTag](graph: Graph[VD, ED]) extends Seriali
    * The in-degree of each vertex in the graph.
    * @note Vertices with no in-edges are not returned in the resulting RDD.
    */
-  @transient lazy val inDegrees: VertexRDD[Int] = degreesRDD(EdgeDirection.In)
+  @transient lazy val inDegrees: VertexRDD[Int] =
+    degreesRDD(EdgeDirection.In).setName("GraphOps.inDegrees")
 
   /**
    * The out-degree of each vertex in the graph.
    * @note Vertices with no out-edges are not returned in the resulting RDD.
    */
-  @transient lazy val outDegrees: VertexRDD[Int] = degreesRDD(EdgeDirection.Out)
+  @transient lazy val outDegrees: VertexRDD[Int] =
+    degreesRDD(EdgeDirection.Out).setName("GraphOps.outDegrees")
 
   /**
    * The degree of each vertex in the graph.
    * @note Vertices with no edges are not returned in the resulting RDD.
    */
-  @transient lazy val degrees: VertexRDD[Int] = degreesRDD(EdgeDirection.Either)
+  @transient lazy val degrees: VertexRDD[Int] =
+    degreesRDD(EdgeDirection.Either).setName("GraphOps.degrees")
 
   /**
    * Computes the neighboring vertex degrees.
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/Pregel.scala b/graphx/src/main/scala/org/apache/spark/graphx/Pregel.scala
index ac07a594a12e..4572eab2875b 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/Pregel.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/Pregel.scala
@@ -18,6 +18,7 @@
 package org.apache.spark.graphx
 
 import scala.reflect.ClassTag
+import org.apache.spark.Logging
 
 
 /**
@@ -52,7 +53,7 @@ import scala.reflect.ClassTag
  * }}}
  *
  */
-object Pregel {
+object Pregel extends Logging {
 
   /**
    * Execute a Pregel-like iterative vertex-parallel abstraction.  The
@@ -142,6 +143,9 @@ object Pregel {
       // hides oldMessages (depended on by newVerts), newVerts (depended on by messages), and the
       // vertices of prevG (depended on by newVerts, oldMessages, and the vertices of g).
       activeMessages = messages.count()
+
+      logInfo("Pregel finished iteration " + i)
+
       // Unpersist the RDDs hidden by newly-materialized RDDs
       oldMessages.unpersist(blocking=false)
       newVerts.unpersist(blocking=false)
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/VertexRDD.scala b/graphx/src/main/scala/org/apache/spark/graphx/VertexRDD.scala
index f0fc605c8857..8c62897037b6 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/VertexRDD.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/VertexRDD.scala
@@ -24,8 +24,11 @@ import org.apache.spark.SparkContext._
 import org.apache.spark.rdd._
 import org.apache.spark.storage.StorageLevel
 
-import org.apache.spark.graphx.impl.MsgRDDFunctions
-import org.apache.spark.graphx.impl.VertexPartition
+import org.apache.spark.graphx.impl.RoutingTablePartition
+import org.apache.spark.graphx.impl.ShippableVertexPartition
+import org.apache.spark.graphx.impl.VertexAttributeBlock
+import org.apache.spark.graphx.impl.RoutingTableMessageRDDFunctions._
+import org.apache.spark.graphx.impl.VertexRDDFunctions._
 
 /**
  * Extends `RDD[(VertexId, VD)]` by ensuring that there is only one entry for each vertex and by
@@ -33,6 +36,9 @@ import org.apache.spark.graphx.impl.VertexPartition
  * joined efficiently. All operations except [[reindex]] preserve the index. To construct a
  * `VertexRDD`, use the [[org.apache.spark.graphx.VertexRDD$ VertexRDD object]].
  *
+ * Additionally, stores routing information to enable joining the vertex attributes with an
+ * [[EdgeRDD]].
+ *
  * @example Construct a `VertexRDD` from a plain RDD:
  * {{{
  * // Construct an initial vertex set
@@ -50,13 +56,11 @@ import org.apache.spark.graphx.impl.VertexPartition
  * @tparam VD the vertex attribute associated with each vertex in the set.
  */
 class VertexRDD[@specialized VD: ClassTag](
-    val partitionsRDD: RDD[VertexPartition[VD]])
+    val partitionsRDD: RDD[ShippableVertexPartition[VD]])
   extends RDD[(VertexId, VD)](partitionsRDD.context, List(new OneToOneDependency(partitionsRDD))) {
 
   require(partitionsRDD.partitioner.isDefined)
 
-  partitionsRDD.setName("VertexRDD")
-
   /**
    * Construct a new VertexRDD that is indexed by only the visible vertices. The resulting
    * VertexRDD will be based on a different index and can no longer be quickly joined with this
@@ -71,6 +75,16 @@ class VertexRDD[@specialized VD: ClassTag](
   override protected def getPreferredLocations(s: Partition): Seq[String] =
     partitionsRDD.preferredLocations(s)
 
+  override def setName(_name: String): this.type = {
+    if (partitionsRDD.name != null) {
+      partitionsRDD.setName(partitionsRDD.name + ", " + _name)
+    } else {
+      partitionsRDD.setName(_name)
+    }
+    this
+  }
+  setName("VertexRDD")
+
   override def persist(newLevel: StorageLevel): this.type = {
     partitionsRDD.persist(newLevel)
     this
@@ -90,14 +104,14 @@ class VertexRDD[@specialized VD: ClassTag](
    * Provides the `RDD[(VertexId, VD)]` equivalent output.
    */
   override def compute(part: Partition, context: TaskContext): Iterator[(VertexId, VD)] = {
-    firstParent[VertexPartition[VD]].iterator(part, context).next.iterator
+    firstParent[ShippableVertexPartition[VD]].iterator(part, context).next.iterator
   }
 
   /**
    * Applies a function to each `VertexPartition` of this RDD and returns a new VertexRDD.
    */
   private[graphx] def mapVertexPartitions[VD2: ClassTag](
-    f: VertexPartition[VD] => VertexPartition[VD2])
+      f: ShippableVertexPartition[VD] => ShippableVertexPartition[VD2])
     : VertexRDD[VD2] = {
     val newPartitionsRDD = partitionsRDD.mapPartitions(_.map(f), preservesPartitioning = true)
     new VertexRDD(newPartitionsRDD)
@@ -208,10 +222,8 @@ class VertexRDD[@specialized VD: ClassTag](
       case _ =>
         new VertexRDD[VD3](
           partitionsRDD.zipPartitions(
-            other.partitionBy(this.partitioner.get), preservesPartitioning = true)
-          { (part, msgs) =>
-            val vertexPartition: VertexPartition[VD] = part.next()
-            Iterator(vertexPartition.leftJoin(msgs)(f))
+            other.copartitionWithVertices(this.partitioner.get), preservesPartitioning = true) {
+            (partIter, msgs) => partIter.map(_.leftJoin(msgs)(f))
           }
         )
     }
@@ -254,10 +266,8 @@ class VertexRDD[@specialized VD: ClassTag](
       case _ =>
         new VertexRDD(
           partitionsRDD.zipPartitions(
-            other.partitionBy(this.partitioner.get), preservesPartitioning = true)
-          { (part, msgs) =>
-            val vertexPartition: VertexPartition[VD] = part.next()
-            Iterator(vertexPartition.innerJoin(msgs)(f))
+            other.copartitionWithVertices(this.partitioner.get), preservesPartitioning = true) {
+            (partIter, msgs) => partIter.map(_.innerJoin(msgs)(f))
           }
         )
     }
@@ -276,14 +286,31 @@ class VertexRDD[@specialized VD: ClassTag](
    */
   def aggregateUsingIndex[VD2: ClassTag](
       messages: RDD[(VertexId, VD2)], reduceFunc: (VD2, VD2) => VD2): VertexRDD[VD2] = {
-    val shuffled = MsgRDDFunctions.partitionForAggregation(messages, this.partitioner.get)
+    val shuffled = messages.copartitionWithVertices(this.partitioner.get)
     val parts = partitionsRDD.zipPartitions(shuffled, true) { (thisIter, msgIter) =>
-      val vertexPartition: VertexPartition[VD] = thisIter.next()
-      Iterator(vertexPartition.aggregateUsingIndex(msgIter, reduceFunc))
+      thisIter.map(_.aggregateUsingIndex(msgIter, reduceFunc))
     }
     new VertexRDD[VD2](parts)
   }
 
+  /**
+   * Returns a new `VertexRDD` reflecting a reversal of all edge directions in the corresponding
+   * [[EdgeRDD]].
+   */
+  def reverseRoutingTables(): VertexRDD[VD] =
+    this.mapVertexPartitions(vPart => vPart.withRoutingTable(vPart.routingTable.reverse))
+
+  /** Generates an RDD of vertex attributes suitable for shipping to the edge partitions. */
+  private[graphx] def shipVertexAttributes(
+      shipSrc: Boolean, shipDst: Boolean): RDD[(PartitionID, VertexAttributeBlock[VD])] = {
+    partitionsRDD.mapPartitions(_.flatMap(_.shipVertexAttributes(shipSrc, shipDst)))
+  }
+
+  /** Generates an RDD of vertex IDs suitable for shipping to the edge partitions. */
+  private[graphx] def shipVertexIds(): RDD[(PartitionID, Array[VertexId])] = {
+    partitionsRDD.mapPartitions(_.flatMap(_.shipVertexIds()))
+  }
+
 } // end of VertexRDD
 
 
@@ -293,52 +320,101 @@ class VertexRDD[@specialized VD: ClassTag](
 object VertexRDD {
 
   /**
-   * Construct a `VertexRDD` from an RDD of vertex-attribute pairs.
-   * Duplicate entries are removed arbitrarily.
+   * Constructs a standalone `VertexRDD` (one that is not set up for efficient joins with an
+   * [[EdgeRDD]]) from an RDD of vertex-attribute pairs. Duplicate entries are removed arbitrarily.
    *
    * @tparam VD the vertex attribute type
    *
-   * @param rdd the collection of vertex-attribute pairs
+   * @param vertices the collection of vertex-attribute pairs
    */
-  def apply[VD: ClassTag](rdd: RDD[(VertexId, VD)]): VertexRDD[VD] = {
-    val partitioned: RDD[(VertexId, VD)] = rdd.partitioner match {
-      case Some(p) => rdd
-      case None => rdd.partitionBy(new HashPartitioner(rdd.partitions.size))
+  def apply[VD: ClassTag](vertices: RDD[(VertexId, VD)]): VertexRDD[VD] = {
+    val vPartitioned: RDD[(VertexId, VD)] = vertices.partitioner match {
+      case Some(p) => vertices
+      case None => vertices.copartitionWithVertices(new HashPartitioner(vertices.partitions.size))
     }
-    val vertexPartitions = partitioned.mapPartitions(
-      iter => Iterator(VertexPartition(iter)),
+    val vertexPartitions = vPartitioned.mapPartitions(
+      iter => Iterator(ShippableVertexPartition(iter)),
       preservesPartitioning = true)
     new VertexRDD(vertexPartitions)
   }
 
   /**
-   * Constructs a `VertexRDD` from an RDD of vertex-attribute pairs, merging duplicates using
-   * `mergeFunc`.
+   * Constructs a `VertexRDD` from an RDD of vertex-attribute pairs. Duplicate vertex entries are
+   * removed arbitrarily. The resulting `VertexRDD` will be joinable with `edges`, and any missing
+   * vertices referred to by `edges` will be created with the attribute `defaultVal`.
    *
    * @tparam VD the vertex attribute type
    *
-   * @param rdd the collection of vertex-attribute pairs
-   * @param mergeFunc the associative, commutative merge function.
+   * @param vertices the collection of vertex-attribute pairs
+   * @param edges the [[EdgeRDD]] that these vertices may be joined with
+   * @param defaultVal the vertex attribute to use when creating missing vertices
    */
-  def apply[VD: ClassTag](rdd: RDD[(VertexId, VD)], mergeFunc: (VD, VD) => VD): VertexRDD[VD] = {
-    val partitioned: RDD[(VertexId, VD)] = rdd.partitioner match {
-      case Some(p) => rdd
-      case None => rdd.partitionBy(new HashPartitioner(rdd.partitions.size))
+  def apply[VD: ClassTag](
+      vertices: RDD[(VertexId, VD)], edges: EdgeRDD[_, _], defaultVal: VD): VertexRDD[VD] = {
+    VertexRDD(vertices, edges, defaultVal, (a, b) => b)
+  }
+
+  /**
+   * Constructs a `VertexRDD` from an RDD of vertex-attribute pairs. Duplicate vertex entries are
+   * merged using `mergeFunc`. The resulting `VertexRDD` will be joinable with `edges`, and any
+   * missing vertices referred to by `edges` will be created with the attribute `defaultVal`.
+   *
+   * @tparam VD the vertex attribute type
+   *
+   * @param vertices the collection of vertex-attribute pairs
+   * @param edges the [[EdgeRDD]] that these vertices may be joined with
+   * @param defaultVal the vertex attribute to use when creating missing vertices
+   * @param mergeFunc the commutative, associative duplicate vertex attribute merge function
+   */
+  def apply[VD: ClassTag](
+      vertices: RDD[(VertexId, VD)], edges: EdgeRDD[_, _], defaultVal: VD, mergeFunc: (VD, VD) => VD
+    ): VertexRDD[VD] = {
+    val vPartitioned: RDD[(VertexId, VD)] = vertices.partitioner match {
+      case Some(p) => vertices
+      case None => vertices.copartitionWithVertices(new HashPartitioner(vertices.partitions.size))
+    }
+    val routingTables = createRoutingTables(edges, vPartitioned.partitioner.get)
+    val vertexPartitions = vPartitioned.zipPartitions(routingTables, preservesPartitioning = true) {
+      (vertexIter, routingTableIter) =>
+        val routingTable =
+          if (routingTableIter.hasNext) routingTableIter.next() else RoutingTablePartition.empty
+        Iterator(ShippableVertexPartition(vertexIter, routingTable, defaultVal))
     }
-    val vertexPartitions = partitioned.mapPartitions(
-      iter => Iterator(VertexPartition(iter)),
-      preservesPartitioning = true)
     new VertexRDD(vertexPartitions)
   }
 
   /**
-   * Constructs a VertexRDD from the vertex IDs in `vids`, taking attributes from `rdd` and using
-   * `defaultVal` otherwise.
+   * Constructs a `VertexRDD` containing all vertices referred to in `edges`. The vertices will be
+   * created with the attribute `defaultVal`. The resulting `VertexRDD` will be joinable with
+   * `edges`.
+   *
+   * @tparam VD the vertex attribute type
+   *
+   * @param edges the [[EdgeRDD]] referring to the vertices to create
+   * @param numPartitions the desired number of partitions for the resulting `VertexRDD`
+   * @param defaultVal the vertex attribute to use when creating missing vertices
    */
-  def apply[VD: ClassTag](vids: RDD[VertexId], rdd: RDD[(VertexId, VD)], defaultVal: VD)
-    : VertexRDD[VD] = {
-    VertexRDD(vids.map(vid => (vid, defaultVal))).leftJoin(rdd) { (vid, default, value) =>
-      value.getOrElse(default)
-    }
+  def fromEdges[VD: ClassTag](
+      edges: EdgeRDD[_, _], numPartitions: Int, defaultVal: VD): VertexRDD[VD] = {
+    val routingTables = createRoutingTables(edges, new HashPartitioner(numPartitions))
+    val vertexPartitions = routingTables.mapPartitions({ routingTableIter =>
+      val routingTable =
+        if (routingTableIter.hasNext) routingTableIter.next() else RoutingTablePartition.empty
+      Iterator(ShippableVertexPartition(Iterator.empty, routingTable, defaultVal))
+    }, preservesPartitioning = true)
+    new VertexRDD(vertexPartitions)
+  }
+
+  private def createRoutingTables(
+      edges: EdgeRDD[_, _], vertexPartitioner: Partitioner): RDD[RoutingTablePartition] = {
+    // Determine which vertices each edge partition needs by creating a mapping from vid to pid.
+    val vid2pid = edges.partitionsRDD.mapPartitions(_.flatMap(
+      Function.tupled(RoutingTablePartition.edgePartitionToMsgs)))
+      .setName("VertexRDD.createRoutingTables - vid2pid (aggregation)")
+
+    val numEdgePartitions = edges.partitions.size
+    vid2pid.copartitionWithVertices(vertexPartitioner).mapPartitions(
+      iter => Iterator(RoutingTablePartition.fromMsgs(numEdgePartitions, iter)),
+      preservesPartitioning = true)
   }
 }
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgePartition.scala b/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgePartition.scala
index b7c472e905a9..871e81f8d245 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgePartition.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgePartition.scala
@@ -17,39 +17,86 @@
 
 package org.apache.spark.graphx.impl
 
-import scala.reflect.ClassTag
+import scala.reflect.{classTag, ClassTag}
 
 import org.apache.spark.graphx._
 import org.apache.spark.graphx.util.collection.PrimitiveKeyOpenHashMap
 
 /**
- * A collection of edges stored in 3 large columnar arrays (src, dst, attribute). The arrays are
- * clustered by src.
+ * A collection of edges stored in columnar format, along with any vertex attributes referenced. The
+ * edges are stored in 3 large columnar arrays (src, dst, attribute). The arrays are clustered by
+ * src. There is an optional active vertex set for filtering computation on the edges.
+ *
+ * @tparam ED the edge attribute type
+ * @tparam VD the vertex attribute type
  *
  * @param srcIds the source vertex id of each edge
  * @param dstIds the destination vertex id of each edge
  * @param data the attribute associated with each edge
  * @param index a clustered index on source vertex id
- * @tparam ED the edge attribute type.
+ * @param vertices a map from referenced vertex ids to their corresponding attributes. Must
+ *   contain all vertex ids from `srcIds` and `dstIds`, though not necessarily valid attributes for
+ *   those vertex ids. The mask is not used.
+ * @param activeSet an optional active vertex set for filtering computation on the edges
  */
 private[graphx]
-class EdgePartition[@specialized(Char, Int, Boolean, Byte, Long, Float, Double) ED: ClassTag](
+class EdgePartition[
+    @specialized(Char, Int, Boolean, Byte, Long, Float, Double) ED: ClassTag, VD: ClassTag](
     @transient val srcIds: Array[VertexId],
     @transient val dstIds: Array[VertexId],
     @transient val data: Array[ED],
-    @transient val index: PrimitiveKeyOpenHashMap[VertexId, Int]) extends Serializable {
+    @transient val index: PrimitiveKeyOpenHashMap[VertexId, Int],
+    @transient val vertices: VertexPartition[VD],
+    @transient val activeSet: Option[VertexSet] = None
+  ) extends Serializable {
+
+  /** Return a new `EdgePartition` with the specified edge data. */
+  def withData[ED2: ClassTag](data_ : Array[ED2]): EdgePartition[ED2, VD] = {
+    new EdgePartition(srcIds, dstIds, data_, index, vertices, activeSet)
+  }
+
+  /** Return a new `EdgePartition` with the specified vertex partition. */
+  def withVertices[VD2: ClassTag](
+      vertices_ : VertexPartition[VD2]): EdgePartition[ED, VD2] = {
+    new EdgePartition(srcIds, dstIds, data, index, vertices_, activeSet)
+  }
+
+  /** Return a new `EdgePartition` with the specified active set, provided as an iterator. */
+  def withActiveSet(iter: Iterator[VertexId]): EdgePartition[ED, VD] = {
+    val newActiveSet = new VertexSet
+    iter.foreach(newActiveSet.add(_))
+    new EdgePartition(srcIds, dstIds, data, index, vertices, Some(newActiveSet))
+  }
+
+  /** Return a new `EdgePartition` with the specified active set. */
+  def withActiveSet(activeSet_ : Option[VertexSet]): EdgePartition[ED, VD] = {
+    new EdgePartition(srcIds, dstIds, data, index, vertices, activeSet_)
+  }
+
+  /** Return a new `EdgePartition` with updates to vertex attributes specified in `iter`. */
+  def updateVertices(iter: Iterator[(VertexId, VD)]): EdgePartition[ED, VD] = {
+    this.withVertices(vertices.innerJoinKeepLeft(iter))
+  }
+
+  /** Look up vid in activeSet, throwing an exception if it is None. */
+  def isActive(vid: VertexId): Boolean = {
+    activeSet.get.contains(vid)
+  }
+
+  /** The number of active vertices, if any exist. */
+  def numActives: Option[Int] = activeSet.map(_.size)
 
   /**
    * Reverse all the edges in this partition.
    *
    * @return a new edge partition with all edges reversed.
    */
-  def reverse: EdgePartition[ED] = {
-    val builder = new EdgePartitionBuilder(size)
+  def reverse: EdgePartition[ED, VD] = {
+    val builder = new EdgePartitionBuilder(size)(classTag[ED], classTag[VD])
     for (e <- iterator) {
       builder.add(e.dstId, e.srcId, e.attr)
     }
-    builder.toEdgePartition
+    builder.toEdgePartition.withVertices(vertices).withActiveSet(activeSet)
   }
 
   /**
@@ -64,7 +111,7 @@ class EdgePartition[@specialized(Char, Int, Boolean, Byte, Long, Float, Double)
    * @return a new edge partition with the result of the function `f`
    *         applied to each edge
    */
-  def map[ED2: ClassTag](f: Edge[ED] => ED2): EdgePartition[ED2] = {
+  def map[ED2: ClassTag](f: Edge[ED] => ED2): EdgePartition[ED2, VD] = {
     val newData = new Array[ED2](data.size)
     val edge = new Edge[ED]()
     val size = data.size
@@ -76,7 +123,7 @@ class EdgePartition[@specialized(Char, Int, Boolean, Byte, Long, Float, Double)
       newData(i) = f(edge)
       i += 1
     }
-    new EdgePartition(srcIds, dstIds, newData, index)
+    this.withData(newData)
   }
 
   /**
@@ -91,7 +138,7 @@ class EdgePartition[@specialized(Char, Int, Boolean, Byte, Long, Float, Double)
    * @tparam ED2 the type of the new attribute
    * @return a new edge partition with the attribute values replaced
    */
-  def map[ED2: ClassTag](iter: Iterator[ED2]): EdgePartition[ED2] = {
+  def map[ED2: ClassTag](iter: Iterator[ED2]): EdgePartition[ED2, VD] = {
     // Faster than iter.toArray, because the expected size is known.
     val newData = new Array[ED2](data.size)
     var i = 0
@@ -100,7 +147,23 @@ class EdgePartition[@specialized(Char, Int, Boolean, Byte, Long, Float, Double)
       i += 1
     }
     assert(newData.size == i)
-    new EdgePartition(srcIds, dstIds, newData, index)
+    this.withData(newData)
+  }
+
+  /**
+   * Construct a new edge partition containing only the edges matching `epred` and where both
+   * vertices match `vpred`.
+   */
+  def filter(
+      epred: EdgeTriplet[VD, ED] => Boolean,
+      vpred: (VertexId, VD) => Boolean): EdgePartition[ED, VD] = {
+    val filtered = tripletIterator().filter(et =>
+      vpred(et.srcId, et.srcAttr) && vpred(et.dstId, et.dstAttr) && epred(et))
+    val builder = new EdgePartitionBuilder[ED, VD]
+    for (e <- filtered) {
+      builder.add(e.srcId, e.dstId, e.attr)
+    }
+    builder.toEdgePartition.withVertices(vertices).withActiveSet(activeSet)
   }
 
   /**
@@ -119,8 +182,8 @@ class EdgePartition[@specialized(Char, Int, Boolean, Byte, Long, Float, Double)
    * @param merge a commutative associative merge operation
    * @return a new edge partition without duplicate edges
    */
-  def groupEdges(merge: (ED, ED) => ED): EdgePartition[ED] = {
-    val builder = new EdgePartitionBuilder[ED]
+  def groupEdges(merge: (ED, ED) => ED): EdgePartition[ED, VD] = {
+    val builder = new EdgePartitionBuilder[ED, VD]
     var currSrcId: VertexId = null.asInstanceOf[VertexId]
     var currDstId: VertexId = null.asInstanceOf[VertexId]
     var currAttr: ED = null.asInstanceOf[ED]
@@ -141,11 +204,11 @@ class EdgePartition[@specialized(Char, Int, Boolean, Byte, Long, Float, Double)
     if (size > 0) {
       builder.add(currSrcId, currDstId, currAttr)
     }
-    builder.toEdgePartition
+    builder.toEdgePartition.withVertices(vertices).withActiveSet(activeSet)
   }
 
   /**
-   * Apply `f` to all edges present in both `this` and `other` and return a new EdgePartition
+   * Apply `f` to all edges present in both `this` and `other` and return a new `EdgePartition`
    * containing the resulting edges.
    *
    * If there are multiple edges with the same src and dst in `this`, `f` will be invoked once for
@@ -155,9 +218,9 @@ class EdgePartition[@specialized(Char, Int, Boolean, Byte, Long, Float, Double)
    * once.
    */
   def innerJoin[ED2: ClassTag, ED3: ClassTag]
-      (other: EdgePartition[ED2])
-      (f: (VertexId, VertexId, ED, ED2) => ED3): EdgePartition[ED3] = {
-    val builder = new EdgePartitionBuilder[ED3]
+      (other: EdgePartition[ED2, _])
+      (f: (VertexId, VertexId, ED, ED2) => ED3): EdgePartition[ED3, VD] = {
+    val builder = new EdgePartitionBuilder[ED3, VD]
     var i = 0
     var j = 0
     // For i = index of each edge in `this`...
@@ -175,7 +238,7 @@ class EdgePartition[@specialized(Char, Int, Boolean, Byte, Long, Float, Double)
       }
       i += 1
     }
-    builder.toEdgePartition
+    builder.toEdgePartition.withVertices(vertices).withActiveSet(activeSet)
   }
 
   /**
@@ -183,7 +246,7 @@ class EdgePartition[@specialized(Char, Int, Boolean, Byte, Long, Float, Double)
    *
    * @return size of the partition
    */
-  def size: Int = srcIds.size
+  val size: Int = srcIds.size
 
   /** The number of unique source vertices in the partition. */
   def indexSize: Int = index.size
@@ -211,10 +274,35 @@ class EdgePartition[@specialized(Char, Int, Boolean, Byte, Long, Float, Double)
     }
   }
 
+  /**
+   * Get an iterator over the edge triplets in this partition.
+   *
+   * It is safe to keep references to the objects from this iterator.
+   */
+  def tripletIterator(
+      includeSrc: Boolean = true, includeDst: Boolean = true): Iterator[EdgeTriplet[VD, ED]] = {
+    new EdgeTripletIterator(this, includeSrc, includeDst)
+  }
+
+  /**
+   * Upgrade the given edge iterator into a triplet iterator.
+   *
+   * Be careful not to keep references to the objects from this iterator. To improve GC performance
+   * the same object is re-used in `next()`.
+   */
+  def upgradeIterator(
+      edgeIter: Iterator[Edge[ED]], includeSrc: Boolean = true, includeDst: Boolean = true)
+    : Iterator[EdgeTriplet[VD, ED]] = {
+    new ReusingEdgeTripletIterator(edgeIter, this, includeSrc, includeDst)
+  }
+
   /**
    * Get an iterator over the edges in this partition whose source vertex ids match srcIdPred. The
    * iterator is generated using an index scan, so it is efficient at skipping edges that don't
    * match srcIdPred.
+   *
+   * Be careful not to keep references to the objects from this iterator. To improve GC performance
+   * the same object is re-used in `next()`.
    */
   def indexIterator(srcIdPred: VertexId => Boolean): Iterator[Edge[ED]] =
     index.iterator.filter(kv => srcIdPred(kv._1)).flatMap(Function.tupled(clusterIterator))
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgePartitionBuilder.scala b/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgePartitionBuilder.scala
index 63ccccb056b4..ecb49bef42e4 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgePartitionBuilder.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgePartitionBuilder.scala
@@ -20,12 +20,14 @@ package org.apache.spark.graphx.impl
 import scala.reflect.ClassTag
 import scala.util.Sorting
 
+import org.apache.spark.util.collection.{BitSet, OpenHashSet, PrimitiveVector}
+
 import org.apache.spark.graphx._
 import org.apache.spark.graphx.util.collection.PrimitiveKeyOpenHashMap
-import org.apache.spark.util.collection.PrimitiveVector
 
 private[graphx]
-class EdgePartitionBuilder[@specialized(Long, Int, Double) ED: ClassTag](size: Int = 64) {
+class EdgePartitionBuilder[@specialized(Long, Int, Double) ED: ClassTag, VD: ClassTag](
+    size: Int = 64) {
   var edges = new PrimitiveVector[Edge[ED]](size)
 
   /** Add a new edge to the partition. */
@@ -33,7 +35,7 @@ class EdgePartitionBuilder[@specialized(Long, Int, Double) ED: ClassTag](size: I
     edges += Edge(src, dst, d)
   }
 
-  def toEdgePartition: EdgePartition[ED] = {
+  def toEdgePartition: EdgePartition[ED, VD] = {
     val edgeArray = edges.trim().array
     Sorting.quickSort(edgeArray)(Edge.lexicographicOrdering)
     val srcIds = new Array[VertexId](edgeArray.size)
@@ -57,6 +59,14 @@ class EdgePartitionBuilder[@specialized(Long, Int, Double) ED: ClassTag](size: I
         i += 1
       }
     }
-    new EdgePartition(srcIds, dstIds, data, index)
+
+    // Create and populate a VertexPartition with vids from the edges, but no attributes
+    val vidsIter = srcIds.iterator ++ dstIds.iterator
+    val vertexIds = new OpenHashSet[VertexId]
+    vidsIter.foreach(vid => vertexIds.add(vid))
+    val vertices = new VertexPartition(
+      vertexIds, new Array[VD](vertexIds.capacity), vertexIds.getBitSet)
+
+    new EdgePartition(srcIds, dstIds, data, index, vertices)
   }
 }
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgeTripletIterator.scala b/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgeTripletIterator.scala
index 220a89d73d71..ebb0b9418d65 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgeTripletIterator.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgeTripletIterator.scala
@@ -23,32 +23,62 @@ import org.apache.spark.graphx._
 import org.apache.spark.graphx.util.collection.PrimitiveKeyOpenHashMap
 
 /**
- * The Iterator type returned when constructing edge triplets. This class technically could be
- * an anonymous class in GraphImpl.triplets, but we name it here explicitly so it is easier to
- * debug / profile.
+ * The Iterator type returned when constructing edge triplets. This could be an anonymous class in
+ * EdgePartition.tripletIterator, but we name it here explicitly so it is easier to debug / profile.
  */
 private[impl]
 class EdgeTripletIterator[VD: ClassTag, ED: ClassTag](
-    val vidToIndex: VertexIdToIndexMap,
-    val vertexArray: Array[VD],
-    val edgePartition: EdgePartition[ED])
+    val edgePartition: EdgePartition[ED, VD],
+    val includeSrc: Boolean,
+    val includeDst: Boolean)
   extends Iterator[EdgeTriplet[VD, ED]] {
 
   // Current position in the array.
   private var pos = 0
 
-  private val vmap = new PrimitiveKeyOpenHashMap[VertexId, VD](vidToIndex, vertexArray)
-
   override def hasNext: Boolean = pos < edgePartition.size
 
   override def next() = {
     val triplet = new EdgeTriplet[VD, ED]
     triplet.srcId = edgePartition.srcIds(pos)
-    triplet.srcAttr = vmap(triplet.srcId)
+    if (includeSrc) {
+      triplet.srcAttr = edgePartition.vertices(triplet.srcId)
+    }
     triplet.dstId = edgePartition.dstIds(pos)
-    triplet.dstAttr = vmap(triplet.dstId)
+    if (includeDst) {
+      triplet.dstAttr = edgePartition.vertices(triplet.dstId)
+    }
     triplet.attr = edgePartition.data(pos)
     pos += 1
     triplet
   }
 }
+
+/**
+ * An Iterator type for internal use that reuses EdgeTriplet objects. This could be an anonymous
+ * class in EdgePartition.upgradeIterator, but we name it here explicitly so it is easier to debug /
+ * profile.
+ */
+private[impl]
+class ReusingEdgeTripletIterator[VD: ClassTag, ED: ClassTag](
+    val edgeIter: Iterator[Edge[ED]],
+    val edgePartition: EdgePartition[ED, VD],
+    val includeSrc: Boolean,
+    val includeDst: Boolean)
+  extends Iterator[EdgeTriplet[VD, ED]] {
+
+  private val triplet = new EdgeTriplet[VD, ED]
+
+  override def hasNext = edgeIter.hasNext
+
+  override def next() = {
+    triplet.set(edgeIter.next())
+    if (includeSrc) {
+      triplet.srcAttr = edgePartition.vertices(triplet.srcId)
+    }
+    if (includeDst) {
+      triplet.dstAttr = edgePartition.vertices(triplet.dstId)
+    }
+    triplet
+  }
+}
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/GraphImpl.scala b/graphx/src/main/scala/org/apache/spark/graphx/impl/GraphImpl.scala
index 9eabccdee48d..2f2d0e03fd7b 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/impl/GraphImpl.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/impl/GraphImpl.scala
@@ -19,54 +19,45 @@ package org.apache.spark.graphx.impl
 
 import scala.reflect.{classTag, ClassTag}
 
-import org.apache.spark.util.collection.PrimitiveVector
-import org.apache.spark.{HashPartitioner, Partitioner}
+import org.apache.spark.HashPartitioner
 import org.apache.spark.SparkContext._
+import org.apache.spark.rdd.{RDD, ShuffledRDD}
+import org.apache.spark.storage.StorageLevel
+
 import org.apache.spark.graphx._
 import org.apache.spark.graphx.impl.GraphImpl._
 import org.apache.spark.graphx.impl.MsgRDDFunctions._
 import org.apache.spark.graphx.util.BytecodeUtils
-import org.apache.spark.rdd.{ShuffledRDD, RDD}
-import org.apache.spark.storage.StorageLevel
-import org.apache.spark.util.ClosureCleaner
 
 
 /**
- * A graph that supports computation on graphs.
+ * An implementation of [[org.apache.spark.graphx.Graph]] to support computation on graphs.
  *
- * Graphs are represented using two classes of data: vertex-partitioned and
- * edge-partitioned. `vertices` contains vertex attributes, which are vertex-partitioned. `edges`
- * contains edge attributes, which are edge-partitioned. For operations on vertex neighborhoods,
- * vertex attributes are replicated to the edge partitions where they appear as sources or
- * destinations. `routingTable` stores the routing information for shipping vertex attributes to
- * edge partitions. `replicatedVertexView` stores a view of the replicated vertex attributes created
- * using the routing table.
+ * Graphs are represented using two RDDs: `vertices`, which contains vertex attributes and the
+ * routing information for shipping vertex attributes to edge partitions, and
+ * `replicatedVertexView`, which contains edges and the vertex attributes mentioned by each edge.
  */
 class GraphImpl[VD: ClassTag, ED: ClassTag] protected (
     @transient val vertices: VertexRDD[VD],
-    @transient val edges: EdgeRDD[ED],
-    @transient val routingTable: RoutingTable,
-    @transient val replicatedVertexView: ReplicatedVertexView[VD])
+    @transient val replicatedVertexView: ReplicatedVertexView[VD, ED])
   extends Graph[VD, ED] with Serializable {
 
   /** Default constructor is provided to support serialization */
-  protected def this() = this(null, null, null, null)
+  protected def this() = this(null, null)
+
+  @transient override val edges: EdgeRDD[ED, VD] = replicatedVertexView.edges
 
   /** Return a RDD that brings edges together with their source and destination vertices. */
-  @transient override val triplets: RDD[EdgeTriplet[VD, ED]] = {
-    val vdTag = classTag[VD]
-    val edTag = classTag[ED]
-    edges.partitionsRDD.zipPartitions(
-      replicatedVertexView.get(true, true), true) { (ePartIter, vPartIter) =>
-      val (pid, ePart) = ePartIter.next()
-      val (_, vPart) = vPartIter.next()
-      new EdgeTripletIterator(vPart.index, vPart.values, ePart)(vdTag, edTag)
-    }
+  @transient override lazy val triplets: RDD[EdgeTriplet[VD, ED]] = {
+    replicatedVertexView.upgrade(vertices, true, true)
+    replicatedVertexView.edges.partitionsRDD.mapPartitions(_.flatMap {
+      case (pid, part) => part.tripletIterator()
+    })
   }
 
   override def persist(newLevel: StorageLevel): Graph[VD, ED] = {
     vertices.persist(newLevel)
-    edges.persist(newLevel)
+    replicatedVertexView.edges.persist(newLevel)
     this
   }
 
@@ -74,14 +65,15 @@ class GraphImpl[VD: ClassTag, ED: ClassTag] protected (
 
   override def unpersistVertices(blocking: Boolean = true): Graph[VD, ED] = {
     vertices.unpersist(blocking)
-    replicatedVertexView.unpersist(blocking)
+    // TODO: unpersist the replicated vertices in `replicatedVertexView` but leave the edges alone
     this
   }
 
   override def partitionBy(partitionStrategy: PartitionStrategy): Graph[VD, ED] = {
-    val numPartitions = edges.partitions.size
+    val numPartitions = replicatedVertexView.edges.partitions.size
     val edTag = classTag[ED]
-    val newEdges = new EdgeRDD(edges.map { e =>
+    val vdTag = classTag[VD]
+    val newEdges = new EdgeRDD(replicatedVertexView.edges.map { e =>
       val part: PartitionID = partitionStrategy.getPartition(e.srcId, e.dstId, numPartitions)
 
       // Should we be using 3-tuple or an optimized class
@@ -89,105 +81,79 @@ class GraphImpl[VD: ClassTag, ED: ClassTag] protected (
     }
       .partitionBy(new HashPartitioner(numPartitions))
       .mapPartitionsWithIndex( { (pid, iter) =>
-        val builder = new EdgePartitionBuilder[ED]()(edTag)
+        val builder = new EdgePartitionBuilder[ED, VD]()(edTag, vdTag)
         iter.foreach { message =>
           val data = message.data
           builder.add(data._1, data._2, data._3)
         }
         val edgePartition = builder.toEdgePartition
         Iterator((pid, edgePartition))
-      }, preservesPartitioning = true).cache())
-    GraphImpl(vertices, newEdges)
+      }, preservesPartitioning = true))
+    GraphImpl.fromExistingRDDs(vertices, newEdges)
   }
 
   override def reverse: Graph[VD, ED] = {
-    val newETable = edges.mapEdgePartitions((pid, part) => part.reverse)
-    GraphImpl(vertices, newETable)
+    new GraphImpl(vertices.reverseRoutingTables(), replicatedVertexView.reverse())
   }
 
   override def mapVertices[VD2: ClassTag](f: (VertexId, VD) => VD2): Graph[VD2, ED] = {
     if (classTag[VD] equals classTag[VD2]) {
+      vertices.cache()
       // The map preserves type, so we can use incremental replication
       val newVerts = vertices.mapVertexPartitions(_.map(f)).cache()
       val changedVerts = vertices.asInstanceOf[VertexRDD[VD2]].diff(newVerts)
-      val newReplicatedVertexView = new ReplicatedVertexView[VD2](
-        changedVerts, edges, routingTable,
-        Some(replicatedVertexView.asInstanceOf[ReplicatedVertexView[VD2]]))
-      new GraphImpl(newVerts, edges, routingTable, newReplicatedVertexView)
+      val newReplicatedVertexView = replicatedVertexView.asInstanceOf[ReplicatedVertexView[VD2, ED]]
+        .updateVertices(changedVerts)
+      new GraphImpl(newVerts, newReplicatedVertexView)
     } else {
       // The map does not preserve type, so we must re-replicate all vertices
-      GraphImpl(vertices.mapVertexPartitions(_.map(f)), edges, routingTable)
+      GraphImpl(vertices.mapVertexPartitions(_.map(f)), replicatedVertexView.edges)
     }
   }
 
   override def mapEdges[ED2: ClassTag](
       f: (PartitionID, Iterator[Edge[ED]]) => Iterator[ED2]): Graph[VD, ED2] = {
-    val newETable = edges.mapEdgePartitions((pid, part) => part.map(f(pid, part.iterator)))
-    new GraphImpl(vertices, newETable , routingTable, replicatedVertexView)
+    val newEdges = replicatedVertexView.edges
+      .mapEdgePartitions((pid, part) => part.map(f(pid, part.iterator)))
+    new GraphImpl(vertices, replicatedVertexView.withEdges(newEdges))
   }
 
   override def mapTriplets[ED2: ClassTag](
       f: (PartitionID, Iterator[EdgeTriplet[VD, ED]]) => Iterator[ED2]): Graph[VD, ED2] = {
-    val newEdgePartitions =
-      edges.partitionsRDD.zipPartitions(replicatedVertexView.get(true, true), true) {
-        (ePartIter, vTableReplicatedIter) =>
-        val (ePid, edgePartition) = ePartIter.next()
-        val (vPid, vPart) = vTableReplicatedIter.next()
-        assert(!vTableReplicatedIter.hasNext)
-        assert(ePid == vPid)
-        val et = new EdgeTriplet[VD, ED]
-        val inputIterator = edgePartition.iterator.map { e =>
-          et.set(e)
-          et.srcAttr = vPart(e.srcId)
-          et.dstAttr = vPart(e.dstId)
-          et
-        }
-        // Apply the user function to the vertex partition
-        val outputIter = f(ePid, inputIterator)
-        // Consume the iterator to update the edge attributes
-        val newEdgePartition = edgePartition.map(outputIter)
-        Iterator((ePid, newEdgePartition))
-      }
-    new GraphImpl(vertices, new EdgeRDD(newEdgePartitions), routingTable, replicatedVertexView)
+    vertices.cache()
+    val mapUsesSrcAttr = accessesVertexAttr(f, "srcAttr")
+    val mapUsesDstAttr = accessesVertexAttr(f, "dstAttr")
+    replicatedVertexView.upgrade(vertices, mapUsesSrcAttr, mapUsesDstAttr)
+    val newEdges = replicatedVertexView.edges.mapEdgePartitions { (pid, part) =>
+      part.map(f(pid, part.tripletIterator(mapUsesSrcAttr, mapUsesDstAttr)))
+    }
+    new GraphImpl(vertices, replicatedVertexView.withEdges(newEdges))
   }
 
   override def subgraph(
       epred: EdgeTriplet[VD, ED] => Boolean = x => true,
       vpred: (VertexId, VD) => Boolean = (a, b) => true): Graph[VD, ED] = {
+    vertices.cache()
     // Filter the vertices, reusing the partitioner and the index from this graph
     val newVerts = vertices.mapVertexPartitions(_.filter(vpred))
-
-    // Filter the edges
-    val edTag = classTag[ED]
-    val newEdges = new EdgeRDD[ED](triplets.filter { et =>
-      vpred(et.srcId, et.srcAttr) && vpred(et.dstId, et.dstAttr) && epred(et)
-    }.mapPartitionsWithIndex( { (pid, iter) =>
-      val builder = new EdgePartitionBuilder[ED]()(edTag)
-      iter.foreach { et => builder.add(et.srcId, et.dstId, et.attr) }
-      val edgePartition = builder.toEdgePartition
-      Iterator((pid, edgePartition))
-    }, preservesPartitioning = true)).cache()
-
-    // Reuse the previous ReplicatedVertexView unmodified. The replicated vertices that have been
-    // removed will be ignored, since we only refer to replicated vertices when they are adjacent to
-    // an edge.
-    new GraphImpl(newVerts, newEdges, new RoutingTable(newEdges, newVerts), replicatedVertexView)
-  } // end of subgraph
+    // Filter the triplets. We must always upgrade the triplet view fully because vpred always runs
+    // on both src and dst vertices
+    replicatedVertexView.upgrade(vertices, true, true)
+    val newEdges = replicatedVertexView.edges.filter(epred, vpred)
+    new GraphImpl(newVerts, replicatedVertexView.withEdges(newEdges))
+  }
 
   override def mask[VD2: ClassTag, ED2: ClassTag] (
       other: Graph[VD2, ED2]): Graph[VD, ED] = {
     val newVerts = vertices.innerJoin(other.vertices) { (vid, v, w) => v }
-    val newEdges = edges.innerJoin(other.edges) { (src, dst, v, w) => v }
-    // Reuse the previous ReplicatedVertexView unmodified. The replicated vertices that have been
-    // removed will be ignored, since we only refer to replicated vertices when they are adjacent to
-    // an edge.
-    new GraphImpl(newVerts, newEdges, routingTable, replicatedVertexView)
+    val newEdges = replicatedVertexView.edges.innerJoin(other.edges) { (src, dst, v, w) => v }
+    new GraphImpl(newVerts, replicatedVertexView.withEdges(newEdges))
   }
 
   override def groupEdges(merge: (ED, ED) => ED): Graph[VD, ED] = {
-    ClosureCleaner.clean(merge)
-    val newETable = edges.mapEdgePartitions((pid, part) => part.groupEdges(merge))
-    new GraphImpl(vertices, newETable, routingTable, replicatedVertexView)
+    val newEdges = replicatedVertexView.edges.mapEdgePartitions(
+      (pid, part) => part.groupEdges(merge))
+    new GraphImpl(vertices, replicatedVertexView.withEdges(newEdges))
   }
 
   // ///////////////////////////////////////////////////////////////////////////////////////////////
@@ -199,68 +165,58 @@ class GraphImpl[VD: ClassTag, ED: ClassTag] protected (
       reduceFunc: (A, A) => A,
       activeSetOpt: Option[(VertexRDD[_], EdgeDirection)] = None): VertexRDD[A] = {
 
-    ClosureCleaner.clean(mapFunc)
-    ClosureCleaner.clean(reduceFunc)
+    vertices.cache()
 
     // For each vertex, replicate its attribute only to partitions where it is
     // in the relevant position in an edge.
     val mapUsesSrcAttr = accessesVertexAttr(mapFunc, "srcAttr")
     val mapUsesDstAttr = accessesVertexAttr(mapFunc, "dstAttr")
-    val vs = activeSetOpt match {
+    replicatedVertexView.upgrade(vertices, mapUsesSrcAttr, mapUsesDstAttr)
+    val view = activeSetOpt match {
       case Some((activeSet, _)) =>
-        replicatedVertexView.get(mapUsesSrcAttr, mapUsesDstAttr, activeSet)
+        replicatedVertexView.withActiveSet(activeSet)
       case None =>
-        replicatedVertexView.get(mapUsesSrcAttr, mapUsesDstAttr)
+        replicatedVertexView
     }
     val activeDirectionOpt = activeSetOpt.map(_._2)
 
     // Map and combine.
-    val preAgg = edges.partitionsRDD.zipPartitions(vs, true) { (ePartIter, vPartIter) =>
-      val (ePid, edgePartition) = ePartIter.next()
-      val (vPid, vPart) = vPartIter.next()
-      assert(!vPartIter.hasNext)
-      assert(ePid == vPid)
-      // Choose scan method
-      val activeFraction = vPart.numActives.getOrElse(0) / edgePartition.indexSize.toFloat
-      val edgeIter = activeDirectionOpt match {
-        case Some(EdgeDirection.Both) =>
-          if (activeFraction < 0.8) {
-            edgePartition.indexIterator(srcVertexId => vPart.isActive(srcVertexId))
-              .filter(e => vPart.isActive(e.dstId))
-          } else {
-            edgePartition.iterator.filter(e => vPart.isActive(e.srcId) && vPart.isActive(e.dstId))
-          }
-        case Some(EdgeDirection.Either) =>
-          // TODO: Because we only have a clustered index on the source vertex ID, we can't filter
-          // the index here. Instead we have to scan all edges and then do the filter.
-          edgePartition.iterator.filter(e => vPart.isActive(e.srcId) || vPart.isActive(e.dstId))
-        case Some(EdgeDirection.Out) =>
-          if (activeFraction < 0.8) {
-            edgePartition.indexIterator(srcVertexId => vPart.isActive(srcVertexId))
-          } else {
-            edgePartition.iterator.filter(e => vPart.isActive(e.srcId))
-          }
-        case Some(EdgeDirection.In) =>
-          edgePartition.iterator.filter(e => vPart.isActive(e.dstId))
-        case _ => // None
-          edgePartition.iterator
-      }
-
-      // Scan edges and run the map function
-      val et = new EdgeTriplet[VD, ED]
-      val mapOutputs = edgeIter.flatMap { e =>
-        et.set(e)
-        if (mapUsesSrcAttr) {
-          et.srcAttr = vPart(e.srcId)
-        }
-        if (mapUsesDstAttr) {
-          et.dstAttr = vPart(e.dstId)
+    val preAgg = view.edges.partitionsRDD.mapPartitions(_.flatMap {
+      case (pid, edgePartition) =>
+        // Choose scan method
+        val activeFraction = edgePartition.numActives.getOrElse(0) / edgePartition.indexSize.toFloat
+        val edgeIter = activeDirectionOpt match {
+          case Some(EdgeDirection.Both) =>
+            if (activeFraction < 0.8) {
+              edgePartition.indexIterator(srcVertexId => edgePartition.isActive(srcVertexId))
+                .filter(e => edgePartition.isActive(e.dstId))
+            } else {
+              edgePartition.iterator.filter(e =>
+                edgePartition.isActive(e.srcId) && edgePartition.isActive(e.dstId))
+            }
+          case Some(EdgeDirection.Either) =>
+            // TODO: Because we only have a clustered index on the source vertex ID, we can't filter
+            // the index here. Instead we have to scan all edges and then do the filter.
+            edgePartition.iterator.filter(e =>
+              edgePartition.isActive(e.srcId) || edgePartition.isActive(e.dstId))
+          case Some(EdgeDirection.Out) =>
+            if (activeFraction < 0.8) {
+              edgePartition.indexIterator(srcVertexId => edgePartition.isActive(srcVertexId))
+            } else {
+              edgePartition.iterator.filter(e => edgePartition.isActive(e.srcId))
+            }
+          case Some(EdgeDirection.In) =>
+            edgePartition.iterator.filter(e => edgePartition.isActive(e.dstId))
+          case _ => // None
+            edgePartition.iterator
         }
-        mapFunc(et)
-      }
-      // Note: This doesn't allow users to send messages to arbitrary vertices.
-      vPart.aggregateUsingIndex(mapOutputs, reduceFunc).iterator
-    }
+
+        // Scan edges and run the map function
+        val mapOutputs = edgePartition.upgradeIterator(edgeIter, mapUsesSrcAttr, mapUsesDstAttr)
+          .flatMap(mapFunc(_))
+        // Note: This doesn't allow users to send messages to arbitrary vertices.
+        edgePartition.vertices.aggregateUsingIndex(mapOutputs, reduceFunc).iterator
+    }).setName("GraphImpl.mapReduceTriplets - preAgg")
 
     // do the final reduction reusing the index map
     vertices.aggregateUsingIndex(preAgg, reduceFunc)
@@ -268,20 +224,19 @@ class GraphImpl[VD: ClassTag, ED: ClassTag] protected (
 
   override def outerJoinVertices[U: ClassTag, VD2: ClassTag]
       (other: RDD[(VertexId, U)])
-      (updateF: (VertexId, VD, Option[U]) => VD2): Graph[VD2, ED] =
-  {
+      (updateF: (VertexId, VD, Option[U]) => VD2): Graph[VD2, ED] = {
     if (classTag[VD] equals classTag[VD2]) {
+      vertices.cache()
       // updateF preserves type, so we can use incremental replication
-      val newVerts = vertices.leftJoin(other)(updateF)
+      val newVerts = vertices.leftJoin(other)(updateF).cache()
       val changedVerts = vertices.asInstanceOf[VertexRDD[VD2]].diff(newVerts)
-      val newReplicatedVertexView = new ReplicatedVertexView[VD2](
-        changedVerts, edges, routingTable,
-        Some(replicatedVertexView.asInstanceOf[ReplicatedVertexView[VD2]]))
-      new GraphImpl(newVerts, edges, routingTable, newReplicatedVertexView)
+      val newReplicatedVertexView = replicatedVertexView.asInstanceOf[ReplicatedVertexView[VD2, ED]]
+        .updateVertices(changedVerts)
+      new GraphImpl(newVerts, newReplicatedVertexView)
     } else {
       // updateF does not preserve type, so we must re-replicate all vertices
       val newVerts = vertices.leftJoin(other)(updateF)
-      GraphImpl(newVerts, edges, routingTable)
+      GraphImpl(newVerts, replicatedVertexView.edges)
     }
   }
 
@@ -298,73 +253,68 @@ class GraphImpl[VD: ClassTag, ED: ClassTag] protected (
 
 object GraphImpl {
 
+  /** Create a graph from edges, setting referenced vertices to `defaultVertexAttr`. */
   def apply[VD: ClassTag, ED: ClassTag](
       edges: RDD[Edge[ED]],
-      defaultVertexAttr: VD): GraphImpl[VD, ED] =
-  {
+      defaultVertexAttr: VD): GraphImpl[VD, ED] = {
     fromEdgeRDD(createEdgeRDD(edges), defaultVertexAttr)
   }
 
+  /** Create a graph from EdgePartitions, setting referenced vertices to `defaultVertexAttr`. */
   def fromEdgePartitions[VD: ClassTag, ED: ClassTag](
-      edgePartitions: RDD[(PartitionID, EdgePartition[ED])],
+      edgePartitions: RDD[(PartitionID, EdgePartition[ED, VD])],
       defaultVertexAttr: VD): GraphImpl[VD, ED] = {
     fromEdgeRDD(new EdgeRDD(edgePartitions), defaultVertexAttr)
   }
 
+  /** Create a graph from vertices and edges, setting missing vertices to `defaultVertexAttr`. */
   def apply[VD: ClassTag, ED: ClassTag](
       vertices: RDD[(VertexId, VD)],
       edges: RDD[Edge[ED]],
-      defaultVertexAttr: VD): GraphImpl[VD, ED] =
-  {
-    val edgeRDD = createEdgeRDD(edges).cache()
-
-    // Get the set of all vids
-    val partitioner = Partitioner.defaultPartitioner(vertices)
-    val vPartitioned = vertices.partitionBy(partitioner)
-    val vidsFromEdges = collectVertexIdsFromEdges(edgeRDD, partitioner)
-    val vids = vPartitioned.zipPartitions(vidsFromEdges) { (vertexIter, vidsFromEdgesIter) =>
-      vertexIter.map(_._1) ++ vidsFromEdgesIter.map(_._1)
-    }
-
-    val vertexRDD = VertexRDD(vids, vPartitioned, defaultVertexAttr)
-
+      defaultVertexAttr: VD): GraphImpl[VD, ED] = {
+    val edgeRDD = createEdgeRDD(edges)(classTag[ED], classTag[VD]).cache()
+    val vertexRDD = VertexRDD(vertices, edgeRDD, defaultVertexAttr)
     GraphImpl(vertexRDD, edgeRDD)
   }
 
+  /** Create a graph from a VertexRDD and an EdgeRDD with arbitrary replicated vertices. */
   def apply[VD: ClassTag, ED: ClassTag](
       vertices: VertexRDD[VD],
-      edges: EdgeRDD[ED]): GraphImpl[VD, ED] = {
-    // Cache RDDs that are referenced multiple times
-    edges.cache()
-
-    GraphImpl(vertices, edges, new RoutingTable(edges, vertices))
+      edges: EdgeRDD[ED, _]): GraphImpl[VD, ED] = {
+    // Convert the vertex partitions in edges to the correct type
+    val newEdges = edges.mapEdgePartitions(
+      (pid, part) => part.withVertices(part.vertices.map(
+        (vid, attr) => null.asInstanceOf[VD])))
+    GraphImpl.fromExistingRDDs(vertices, newEdges)
   }
 
-  def apply[VD: ClassTag, ED: ClassTag](
+  /**
+   * Create a graph from a VertexRDD and an EdgeRDD with the same replicated vertex type as the
+   * vertices.
+   */
+  def fromExistingRDDs[VD: ClassTag, ED: ClassTag](
       vertices: VertexRDD[VD],
-      edges: EdgeRDD[ED],
-      routingTable: RoutingTable): GraphImpl[VD, ED] = {
-    // Cache RDDs that are referenced multiple times. `routingTable` is cached by default, so we
-    // don't cache it explicitly.
-    vertices.cache()
-    edges.cache()
-
-    new GraphImpl(
-      vertices, edges, routingTable, new ReplicatedVertexView(vertices, edges, routingTable))
+      edges: EdgeRDD[ED, VD]): GraphImpl[VD, ED] = {
+    new GraphImpl(vertices, new ReplicatedVertexView(edges))
   }
 
   /**
-   * Create the edge RDD, which is much more efficient for Java heap storage than the normal edges
-   * data structure (RDD[(VertexId, VertexId, ED)]).
-   *
-   * The edge RDD contains multiple partitions, and each partition contains only one RDD key-value
-   * pair: the key is the partition id, and the value is an EdgePartition object containing all the
-   * edges in a partition.
+   * Create a graph from an EdgeRDD with the correct vertex type, setting missing vertices to
+   * `defaultVertexAttr`. The vertices will have the same number of partitions as the EdgeRDD.
    */
-  private def createEdgeRDD[ED: ClassTag](
-      edges: RDD[Edge[ED]]): EdgeRDD[ED] = {
+  private def fromEdgeRDD[VD: ClassTag, ED: ClassTag](
+      edges: EdgeRDD[ED, VD],
+      defaultVertexAttr: VD): GraphImpl[VD, ED] = {
+    edges.cache()
+    val vertices = VertexRDD.fromEdges(edges, edges.partitions.size, defaultVertexAttr)
+    fromExistingRDDs(vertices, edges)
+  }
+
+  /** Create an EdgeRDD from a set of edges. */
+  private def createEdgeRDD[ED: ClassTag, VD: ClassTag](
+      edges: RDD[Edge[ED]]): EdgeRDD[ED, VD] = {
     val edgePartitions = edges.mapPartitionsWithIndex { (pid, iter) =>
-      val builder = new EdgePartitionBuilder[ED]
+      val builder = new EdgePartitionBuilder[ED, VD]
       iter.foreach { e =>
         builder.add(e.srcId, e.dstId, e.attr)
       }
@@ -373,24 +323,4 @@ object GraphImpl {
     new EdgeRDD(edgePartitions)
   }
 
-  private def fromEdgeRDD[VD: ClassTag, ED: ClassTag](
-      edges: EdgeRDD[ED],
-      defaultVertexAttr: VD): GraphImpl[VD, ED] = {
-    edges.cache()
-    // Get the set of all vids
-    val vids = collectVertexIdsFromEdges(edges, new HashPartitioner(edges.partitions.size))
-    // Create the VertexRDD.
-    val vertices = VertexRDD(vids.mapValues(x => defaultVertexAttr))
-    GraphImpl(vertices, edges)
-  }
-
-  /** Collects all vids mentioned in edges and partitions them by partitioner. */
-  private def collectVertexIdsFromEdges(
-      edges: EdgeRDD[_],
-      partitioner: Partitioner): RDD[(VertexId, Int)] = {
-    // TODO: Consider doing map side distinct before shuffle.
-    new ShuffledRDD[VertexId, Int, (VertexId, Int)](
-      edges.collectVertexIds.map(vid => (vid, 0)), partitioner)
-      .setSerializer(new VertexIdMsgSerializer)
-  }
 } // end of object GraphImpl
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/MessageToPartition.scala b/graphx/src/main/scala/org/apache/spark/graphx/impl/MessageToPartition.scala
index c45ba3d2f8c2..1c6d7e59e9a2 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/impl/MessageToPartition.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/impl/MessageToPartition.scala
@@ -89,7 +89,6 @@ class MsgRDDFunctions[T: ClassTag](self: RDD[MessageToPartition[T]]) {
 
 }
 
-
 private[graphx]
 object MsgRDDFunctions {
   implicit def rdd2PartitionRDDFunctions[T: ClassTag](rdd: RDD[MessageToPartition[T]]) = {
@@ -99,18 +98,28 @@ object MsgRDDFunctions {
   implicit def rdd2vertexMessageRDDFunctions[T: ClassTag](rdd: RDD[VertexBroadcastMsg[T]]) = {
     new VertexBroadcastMsgRDDFunctions(rdd)
   }
+}
 
-  def partitionForAggregation[T: ClassTag](msgs: RDD[(VertexId, T)], partitioner: Partitioner) = {
-    val rdd = new ShuffledRDD[VertexId, T, (VertexId, T)](msgs, partitioner)
+private[graphx]
+class VertexRDDFunctions[VD: ClassTag](self: RDD[(VertexId, VD)]) {
+  def copartitionWithVertices(partitioner: Partitioner): RDD[(VertexId, VD)] = {
+    val rdd = new ShuffledRDD[VertexId, VD, (VertexId, VD)](self, partitioner)
 
     // Set a custom serializer if the data is of int or double type.
-    if (classTag[T] == ClassTag.Int) {
+    if (classTag[VD] == ClassTag.Int) {
       rdd.setSerializer(new IntAggMsgSerializer)
-    } else if (classTag[T] == ClassTag.Long) {
+    } else if (classTag[VD] == ClassTag.Long) {
       rdd.setSerializer(new LongAggMsgSerializer)
-    } else if (classTag[T] == ClassTag.Double) {
+    } else if (classTag[VD] == ClassTag.Double) {
       rdd.setSerializer(new DoubleAggMsgSerializer)
     }
     rdd
   }
 }
+
+private[graphx]
+object VertexRDDFunctions {
+  implicit def rdd2VertexRDDFunctions[VD: ClassTag](rdd: RDD[(VertexId, VD)]) = {
+    new VertexRDDFunctions(rdd)
+  }
+}
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/ReplicatedVertexView.scala b/graphx/src/main/scala/org/apache/spark/graphx/impl/ReplicatedVertexView.scala
index a8154b63ce5f..3a0bba1b93b4 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/impl/ReplicatedVertexView.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/impl/ReplicatedVertexView.scala
@@ -21,192 +21,102 @@ import scala.reflect.{classTag, ClassTag}
 
 import org.apache.spark.SparkContext._
 import org.apache.spark.rdd.RDD
-import org.apache.spark.util.collection.{PrimitiveVector, OpenHashSet}
 
 import org.apache.spark.graphx._
 
 /**
- * A view of the vertices after they are shipped to the join sites specified in
- * `vertexPlacement`. The resulting view is co-partitioned with `edges`. If `prevViewOpt` is
- * specified, `updatedVerts` are treated as incremental updates to the previous view. Otherwise, a
- * fresh view is created.
- *
- * The view is always cached (i.e., once it is evaluated, it remains materialized). This avoids
- * constructing it twice if the user calls graph.triplets followed by graph.mapReduceTriplets, for
- * example. However, it means iterative algorithms must manually call `Graph.unpersist` on previous
- * iterations' graphs for best GC performance. See the implementation of
- * [[org.apache.spark.graphx.Pregel]] for an example.
+ * Manages shipping vertex attributes to the edge partitions of an
+ * [[org.apache.spark.graphx.EdgeRDD]]. Vertex attributes may be partially shipped to construct a
+ * triplet view with vertex attributes on only one side, and they may be updated. An active vertex
+ * set may additionally be shipped to the edge partitions. Be careful not to store a reference to
+ * `edges`, since it may be modified when the attribute shipping level is upgraded.
  */
 private[impl]
-class ReplicatedVertexView[VD: ClassTag](
-    updatedVerts: VertexRDD[VD],
-    edges: EdgeRDD[_],
-    routingTable: RoutingTable,
-    prevViewOpt: Option[ReplicatedVertexView[VD]] = None) {
+class ReplicatedVertexView[VD: ClassTag, ED: ClassTag](
+    var edges: EdgeRDD[ED, VD],
+    var hasSrcId: Boolean = false,
+    var hasDstId: Boolean = false) {
 
   /**
-   * Within each edge partition, create a local map from vid to an index into the attribute
-   * array. Each map contains a superset of the vertices that it will receive, because it stores
-   * vids from both the source and destination of edges. It must always include both source and
-   * destination vids because some operations, such as GraphImpl.mapReduceTriplets, rely on this.
+   * Return a new `ReplicatedVertexView` with the specified `EdgeRDD`, which must have the same
+   * shipping level.
    */
-  private val localVertexIdMap: RDD[(Int, VertexIdToIndexMap)] = prevViewOpt match {
-    case Some(prevView) =>
-      prevView.localVertexIdMap
-    case None =>
-      edges.partitionsRDD.mapPartitions(_.map {
-        case (pid, epart) =>
-          val vidToIndex = new VertexIdToIndexMap
-          epart.foreach { e =>
-            vidToIndex.add(e.srcId)
-            vidToIndex.add(e.dstId)
-          }
-          (pid, vidToIndex)
-      }, preservesPartitioning = true).cache().setName("ReplicatedVertexView localVertexIdMap")
-  }
-
-  private lazy val bothAttrs: RDD[(PartitionID, VertexPartition[VD])] = create(true, true)
-  private lazy val srcAttrOnly: RDD[(PartitionID, VertexPartition[VD])] = create(true, false)
-  private lazy val dstAttrOnly: RDD[(PartitionID, VertexPartition[VD])] = create(false, true)
-  private lazy val noAttrs: RDD[(PartitionID, VertexPartition[VD])] = create(false, false)
-
-  def unpersist(blocking: Boolean = true): ReplicatedVertexView[VD] = {
-    bothAttrs.unpersist(blocking)
-    srcAttrOnly.unpersist(blocking)
-    dstAttrOnly.unpersist(blocking)
-    noAttrs.unpersist(blocking)
-    // Don't unpersist localVertexIdMap because a future ReplicatedVertexView may be using it
-    // without modification
-    this
+  def withEdges[VD2: ClassTag, ED2: ClassTag](
+      edges_ : EdgeRDD[ED2, VD2]): ReplicatedVertexView[VD2, ED2] = {
+    new ReplicatedVertexView(edges_, hasSrcId, hasDstId)
   }
 
-  def get(includeSrc: Boolean, includeDst: Boolean): RDD[(PartitionID, VertexPartition[VD])] = {
-    (includeSrc, includeDst) match {
-      case (true, true) => bothAttrs
-      case (true, false) => srcAttrOnly
-      case (false, true) => dstAttrOnly
-      case (false, false) => noAttrs
-    }
+  /**
+   * Return a new `ReplicatedVertexView` where edges are reversed and shipping levels are swapped to
+   * match.
+   */
+  def reverse() = {
+    val newEdges = edges.mapEdgePartitions((pid, part) => part.reverse)
+    new ReplicatedVertexView(newEdges, hasDstId, hasSrcId)
   }
 
-  def get(
-      includeSrc: Boolean,
-      includeDst: Boolean,
-      actives: VertexRDD[_]): RDD[(PartitionID, VertexPartition[VD])] = {
-    // Ship active sets to edge partitions using vertexPlacement, but ignoring includeSrc and
-    // includeDst. These flags govern attribute shipping, but the activeness of a vertex must be
-    // shipped to all edges mentioning that vertex, regardless of whether the vertex attribute is
-    // also shipped there.
-    val shippedActives = routingTable.get(true, true)
-      .zipPartitions(actives.partitionsRDD)(ReplicatedVertexView.buildActiveBuffer(_, _))
-      .partitionBy(edges.partitioner.get)
-    // Update the view with shippedActives, setting activeness flags in the resulting
-    // VertexPartitions
-    get(includeSrc, includeDst).zipPartitions(shippedActives) { (viewIter, shippedActivesIter) =>
-      val (pid, vPart) = viewIter.next()
-      val newPart = vPart.replaceActives(shippedActivesIter.flatMap(_._2.iterator))
-      Iterator((pid, newPart))
+  /**
+   * Upgrade the shipping level in-place to the specified levels by shipping vertex attributes from
+   * `vertices`. This operation modifies the `ReplicatedVertexView`, and callers can access `edges`
+   * afterwards to obtain the upgraded view.
+   */
+  def upgrade(vertices: VertexRDD[VD], includeSrc: Boolean, includeDst: Boolean) {
+    val shipSrc = includeSrc && !hasSrcId
+    val shipDst = includeDst && !hasDstId
+    if (shipSrc || shipDst) {
+      val shippedVerts: RDD[(Int, VertexAttributeBlock[VD])] =
+        vertices.shipVertexAttributes(shipSrc, shipDst)
+          .setName("ReplicatedVertexView.upgrade(%s, %s) - shippedVerts %s %s (broadcast)".format(
+            includeSrc, includeDst, shipSrc, shipDst))
+          .partitionBy(edges.partitioner.get)
+      val newEdges = new EdgeRDD(edges.partitionsRDD.zipPartitions(shippedVerts) {
+        (ePartIter, shippedVertsIter) => ePartIter.map {
+          case (pid, edgePartition) =>
+            (pid, edgePartition.updateVertices(shippedVertsIter.flatMap(_._2.iterator)))
+        }
+      })
+      edges = newEdges
+      hasSrcId = includeSrc
+      hasDstId = includeDst
     }
   }
 
-  private def create(includeSrc: Boolean, includeDst: Boolean)
-    : RDD[(PartitionID, VertexPartition[VD])] = {
-    val vdTag = classTag[VD]
-
-    // Ship vertex attributes to edge partitions according to vertexPlacement
-    val verts = updatedVerts.partitionsRDD
-    val shippedVerts = routingTable.get(includeSrc, includeDst)
-      .zipPartitions(verts)(ReplicatedVertexView.buildBuffer(_, _)(vdTag))
+  /**
+   * Return a new `ReplicatedVertexView` where the `activeSet` in each edge partition contains only
+   * vertex ids present in `actives`. This ships a vertex id to all edge partitions where it is
+   * referenced, ignoring the attribute shipping level.
+   */
+  def withActiveSet(actives: VertexRDD[_]): ReplicatedVertexView[VD, ED] = {
+    val shippedActives = actives.shipVertexIds()
+      .setName("ReplicatedVertexView.withActiveSet - shippedActives (broadcast)")
       .partitionBy(edges.partitioner.get)
-    // TODO: Consider using a specialized shuffler.
-
-    prevViewOpt match {
-      case Some(prevView) =>
-        // Update prevView with shippedVerts, setting staleness flags in the resulting
-        // VertexPartitions
-        prevView.get(includeSrc, includeDst).zipPartitions(shippedVerts) {
-          (prevViewIter, shippedVertsIter) =>
-            val (pid, prevVPart) = prevViewIter.next()
-            val newVPart = prevVPart.innerJoinKeepLeft(shippedVertsIter.flatMap(_._2.iterator))
-            Iterator((pid, newVPart))
-        }.cache().setName("ReplicatedVertexView delta %s %s".format(includeSrc, includeDst))
 
-      case None =>
-        // Within each edge partition, place the shipped vertex attributes into the correct
-        // locations specified in localVertexIdMap
-        localVertexIdMap.zipPartitions(shippedVerts) { (mapIter, shippedVertsIter) =>
-          val (pid, vidToIndex) = mapIter.next()
-          assert(!mapIter.hasNext)
-          // Populate the vertex array using the vidToIndex map
-          val vertexArray = vdTag.newArray(vidToIndex.capacity)
-          for ((_, block) <- shippedVertsIter) {
-            for (i <- 0 until block.vids.size) {
-              val vid = block.vids(i)
-              val attr = block.attrs(i)
-              val ind = vidToIndex.getPos(vid)
-              vertexArray(ind) = attr
-            }
-          }
-          val newVPart = new VertexPartition(
-            vidToIndex, vertexArray, vidToIndex.getBitSet)(vdTag)
-          Iterator((pid, newVPart))
-        }.cache().setName("ReplicatedVertexView %s %s".format(includeSrc, includeDst))
-    }
-  }
-}
-
-private object ReplicatedVertexView {
-  protected def buildBuffer[VD: ClassTag](
-      pid2vidIter: Iterator[Array[Array[VertexId]]],
-      vertexPartIter: Iterator[VertexPartition[VD]]) = {
-    val pid2vid: Array[Array[VertexId]] = pid2vidIter.next()
-    val vertexPart: VertexPartition[VD] = vertexPartIter.next()
-
-    Iterator.tabulate(pid2vid.size) { pid =>
-      val vidsCandidate = pid2vid(pid)
-      val size = vidsCandidate.length
-      val vids = new PrimitiveVector[VertexId](pid2vid(pid).size)
-      val attrs = new PrimitiveVector[VD](pid2vid(pid).size)
-      var i = 0
-      while (i < size) {
-        val vid = vidsCandidate(i)
-        if (vertexPart.isDefined(vid)) {
-          vids += vid
-          attrs += vertexPart(vid)
-        }
-        i += 1
+    val newEdges = new EdgeRDD(edges.partitionsRDD.zipPartitions(shippedActives) {
+      (ePartIter, shippedActivesIter) => ePartIter.map {
+        case (pid, edgePartition) =>
+          (pid, edgePartition.withActiveSet(shippedActivesIter.flatMap(_._2.iterator)))
       }
-      (pid, new VertexAttributeBlock(vids.trim().array, attrs.trim().array))
-    }
+    })
+    new ReplicatedVertexView(newEdges, hasSrcId, hasDstId)
   }
 
-  protected def buildActiveBuffer(
-      pid2vidIter: Iterator[Array[Array[VertexId]]],
-      activePartIter: Iterator[VertexPartition[_]])
-    : Iterator[(Int, Array[VertexId])] = {
-    val pid2vid: Array[Array[VertexId]] = pid2vidIter.next()
-    val activePart: VertexPartition[_] = activePartIter.next()
+  /**
+   * Return a new `ReplicatedVertexView` where vertex attributes in edge partition are updated using
+   * `updates`. This ships a vertex attribute only to the edge partitions where it is in the
+   * position(s) specified by the attribute shipping level.
+   */
+  def updateVertices(updates: VertexRDD[VD]): ReplicatedVertexView[VD, ED] = {
+    val shippedVerts = updates.shipVertexAttributes(hasSrcId, hasDstId)
+      .setName("ReplicatedVertexView.updateVertices - shippedVerts %s %s (broadcast)".format(
+        hasSrcId, hasDstId))
+      .partitionBy(edges.partitioner.get)
 
-    Iterator.tabulate(pid2vid.size) { pid =>
-      val vidsCandidate = pid2vid(pid)
-      val size = vidsCandidate.length
-      val actives = new PrimitiveVector[VertexId](vidsCandidate.size)
-      var i = 0
-      while (i < size) {
-        val vid = vidsCandidate(i)
-        if (activePart.isDefined(vid)) {
-          actives += vid
-        }
-        i += 1
+    val newEdges = new EdgeRDD(edges.partitionsRDD.zipPartitions(shippedVerts) {
+      (ePartIter, shippedVertsIter) => ePartIter.map {
+        case (pid, edgePartition) =>
+          (pid, edgePartition.updateVertices(shippedVertsIter.flatMap(_._2.iterator)))
       }
-      (pid, actives.trim().array)
-    }
+    })
+    new ReplicatedVertexView(newEdges, hasSrcId, hasDstId)
   }
 }
-
-private[graphx]
-class VertexAttributeBlock[VD: ClassTag](val vids: Array[VertexId], val attrs: Array[VD])
-  extends Serializable {
-  def iterator: Iterator[(VertexId, VD)] =
-    (0 until vids.size).iterator.map { i => (vids(i), attrs(i)) }
-}
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/RoutingTable.scala b/graphx/src/main/scala/org/apache/spark/graphx/impl/RoutingTable.scala
deleted file mode 100644
index 022d5668e294..000000000000
--- a/graphx/src/main/scala/org/apache/spark/graphx/impl/RoutingTable.scala
+++ /dev/null
@@ -1,82 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.graphx.impl
-
-import org.apache.spark.SparkContext._
-import org.apache.spark.graphx._
-import org.apache.spark.rdd.RDD
-import org.apache.spark.storage.StorageLevel
-import org.apache.spark.util.collection.PrimitiveVector
-
-/**
- * Stores the locations of edge-partition join sites for each vertex attribute; that is, the routing
- * information for shipping vertex attributes to edge partitions. This is always cached because it
- * may be used multiple times in ReplicatedVertexView -- once to ship the vertex attributes and
- * (possibly) once to ship the active-set information.
- */
-private[impl]
-class RoutingTable(edges: EdgeRDD[_], vertices: VertexRDD[_]) {
-
-  val bothAttrs: RDD[Array[Array[VertexId]]] = createPid2Vid(true, true)
-  val srcAttrOnly: RDD[Array[Array[VertexId]]] = createPid2Vid(true, false)
-  val dstAttrOnly: RDD[Array[Array[VertexId]]] = createPid2Vid(false, true)
-  val noAttrs: RDD[Array[Array[VertexId]]] = createPid2Vid(false, false)
-
-  def get(includeSrcAttr: Boolean, includeDstAttr: Boolean): RDD[Array[Array[VertexId]]] =
-    (includeSrcAttr, includeDstAttr) match {
-      case (true, true) => bothAttrs
-      case (true, false) => srcAttrOnly
-      case (false, true) => dstAttrOnly
-      case (false, false) => noAttrs
-    }
-
-  private def createPid2Vid(
-      includeSrcAttr: Boolean, includeDstAttr: Boolean): RDD[Array[Array[VertexId]]] = {
-    // Determine which vertices each edge partition needs by creating a mapping from vid to pid.
-    val vid2pid: RDD[(VertexId, PartitionID)] = edges.partitionsRDD.mapPartitions { iter =>
-      val (pid: PartitionID, edgePartition: EdgePartition[_]) = iter.next()
-      val numEdges = edgePartition.size
-      val vSet = new VertexSet
-      if (includeSrcAttr) {  // Add src vertices to the set.
-        var i = 0
-        while (i < numEdges) {
-          vSet.add(edgePartition.srcIds(i))
-          i += 1
-        }
-      }
-      if (includeDstAttr) {  // Add dst vertices to the set.
-      var i = 0
-        while (i < numEdges) {
-          vSet.add(edgePartition.dstIds(i))
-          i += 1
-        }
-      }
-      vSet.iterator.map { vid => (vid, pid) }
-    }
-
-    val numEdgePartitions = edges.partitions.size
-    vid2pid.partitionBy(vertices.partitioner.get).mapPartitions { iter =>
-      val pid2vid = Array.fill(numEdgePartitions)(new PrimitiveVector[VertexId])
-      for ((vid, pid) <- iter) {
-        pid2vid(pid) += vid
-      }
-
-      Iterator(pid2vid.map(_.trim().array))
-    }.cache().setName("RoutingTable %s %s".format(includeSrcAttr, includeDstAttr))
-  }
-}
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/RoutingTablePartition.scala b/graphx/src/main/scala/org/apache/spark/graphx/impl/RoutingTablePartition.scala
new file mode 100644
index 000000000000..927e32ad0f44
--- /dev/null
+++ b/graphx/src/main/scala/org/apache/spark/graphx/impl/RoutingTablePartition.scala
@@ -0,0 +1,158 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.graphx.impl
+
+import scala.reflect.ClassTag
+
+import org.apache.spark.Partitioner
+import org.apache.spark.rdd.RDD
+import org.apache.spark.rdd.ShuffledRDD
+import org.apache.spark.util.collection.{BitSet, PrimitiveVector}
+
+import org.apache.spark.graphx._
+import org.apache.spark.graphx.util.collection.PrimitiveKeyOpenHashMap
+
+/**
+ * A message from the edge partition `pid` to the vertex partition containing `vid` specifying that
+ * the edge partition references `vid` in the specified `position` (src, dst, or both).
+*/
+private[graphx]
+class RoutingTableMessage(
+    var vid: VertexId,
+    var pid: PartitionID,
+    var position: Byte)
+  extends Product2[VertexId, (PartitionID, Byte)] with Serializable {
+  override def _1 = vid
+  override def _2 = (pid, position)
+  override def canEqual(that: Any): Boolean = that.isInstanceOf[RoutingTableMessage]
+}
+
+private[graphx]
+class RoutingTableMessageRDDFunctions(self: RDD[RoutingTableMessage]) {
+  /** Copartition an `RDD[RoutingTableMessage]` with the vertex RDD with the given `partitioner`. */
+  def copartitionWithVertices(partitioner: Partitioner): RDD[RoutingTableMessage] = {
+    new ShuffledRDD[VertexId, (PartitionID, Byte), RoutingTableMessage](self, partitioner)
+      .setSerializer(new RoutingTableMessageSerializer)
+  }
+}
+
+private[graphx]
+object RoutingTableMessageRDDFunctions {
+  import scala.language.implicitConversions
+
+  implicit def rdd2RoutingTableMessageRDDFunctions(rdd: RDD[RoutingTableMessage]) = {
+    new RoutingTableMessageRDDFunctions(rdd)
+  }
+}
+
+private[graphx]
+object RoutingTablePartition {
+  val empty: RoutingTablePartition = new RoutingTablePartition(Array.empty)
+
+  /** Generate a `RoutingTableMessage` for each vertex referenced in `edgePartition`. */
+  def edgePartitionToMsgs(pid: PartitionID, edgePartition: EdgePartition[_, _])
+    : Iterator[RoutingTableMessage] = {
+    // Determine which positions each vertex id appears in using a map where the low 2 bits
+    // represent src and dst
+    val map = new PrimitiveKeyOpenHashMap[VertexId, Byte]
+    edgePartition.srcIds.iterator.foreach { srcId =>
+      map.changeValue(srcId, 0x1, (b: Byte) => (b | 0x1).toByte)
+    }
+    edgePartition.dstIds.iterator.foreach { dstId =>
+      map.changeValue(dstId, 0x2, (b: Byte) => (b | 0x2).toByte)
+    }
+    map.iterator.map { vidAndPosition =>
+      new RoutingTableMessage(vidAndPosition._1, pid, vidAndPosition._2)
+    }
+  }
+
+  /** Build a `RoutingTablePartition` from `RoutingTableMessage`s. */
+  def fromMsgs(numEdgePartitions: Int, iter: Iterator[RoutingTableMessage])
+    : RoutingTablePartition = {
+    val pid2vid = Array.fill(numEdgePartitions)(new PrimitiveVector[VertexId])
+    val srcFlags = Array.fill(numEdgePartitions)(new PrimitiveVector[Boolean])
+    val dstFlags = Array.fill(numEdgePartitions)(new PrimitiveVector[Boolean])
+    for (msg <- iter) {
+      pid2vid(msg.pid) += msg.vid
+      srcFlags(msg.pid) += (msg.position & 0x1) != 0
+      dstFlags(msg.pid) += (msg.position & 0x2) != 0
+    }
+
+    new RoutingTablePartition(pid2vid.zipWithIndex.map {
+      case (vids, pid) => (vids.trim().array, toBitSet(srcFlags(pid)), toBitSet(dstFlags(pid)))
+    })
+  }
+
+  /** Compact the given vector of Booleans into a BitSet. */
+  private def toBitSet(flags: PrimitiveVector[Boolean]): BitSet = {
+    val bitset = new BitSet(flags.size)
+    var i = 0
+    while (i < flags.size) {
+      if (flags(i)) {
+        bitset.set(i)
+      }
+      i += 1
+    }
+    bitset
+  }
+}
+
+/**
+ * Stores the locations of edge-partition join sites for each vertex attribute in a particular
+ * vertex partition. This provides routing information for shipping vertex attributes to edge
+ * partitions.
+ */
+private[graphx]
+class RoutingTablePartition(
+    private val routingTable: Array[(Array[VertexId], BitSet, BitSet)]) {
+  /** The maximum number of edge partitions this `RoutingTablePartition` is built to join with. */
+  val numEdgePartitions: Int = routingTable.size
+
+  /** Returns the number of vertices that will be sent to the specified edge partition. */
+  def partitionSize(pid: PartitionID): Int = routingTable(pid)._1.size
+
+  /** Returns an iterator over all vertex ids stored in this `RoutingTablePartition`. */
+  def iterator: Iterator[VertexId] = routingTable.iterator.flatMap(_._1.iterator)
+
+  /** Returns a new RoutingTablePartition reflecting a reversal of all edge directions. */
+  def reverse: RoutingTablePartition = {
+    new RoutingTablePartition(routingTable.map {
+      case (vids, srcVids, dstVids) => (vids, dstVids, srcVids)
+    })
+  }
+
+  /**
+   * Runs `f` on each vertex id to be sent to the specified edge partition. Vertex ids can be
+   * filtered by the position they have in the edge partition.
+   */
+  def foreachWithinEdgePartition
+      (pid: PartitionID, includeSrc: Boolean, includeDst: Boolean)
+      (f: VertexId => Unit) {
+    val (vidsCandidate, srcVids, dstVids) = routingTable(pid)
+    val size = vidsCandidate.length
+    if (includeSrc && includeDst) {
+      // Avoid checks for performance
+      vidsCandidate.iterator.foreach(f)
+    } else if (!includeSrc && !includeDst) {
+      // Do nothing
+    } else {
+      val relevantVids = if (includeSrc) srcVids else dstVids
+      relevantVids.iterator.foreach { i => f(vidsCandidate(i)) }
+    }
+  }
+}
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/Serializers.scala b/graphx/src/main/scala/org/apache/spark/graphx/impl/Serializers.scala
index 1de42eeca1f0..033237f59721 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/impl/Serializers.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/impl/Serializers.scala
@@ -27,6 +27,35 @@ import scala.reflect.ClassTag
 import org.apache.spark.graphx._
 import org.apache.spark.serializer._
 
+private[graphx]
+class RoutingTableMessageSerializer extends Serializer with Serializable {
+  override def newInstance(): SerializerInstance = new ShuffleSerializerInstance {
+
+    override def serializeStream(s: OutputStream): SerializationStream =
+      new ShuffleSerializationStream(s) {
+        def writeObject[T: ClassTag](t: T): SerializationStream = {
+          val msg = t.asInstanceOf[RoutingTableMessage]
+          writeVarLong(msg.vid, optimizePositive = false)
+          writeUnsignedVarInt(msg.pid)
+          // TODO: Write only the bottom two bits of msg.position
+          s.write(msg.position)
+          this
+        }
+      }
+
+    override def deserializeStream(s: InputStream): DeserializationStream =
+      new ShuffleDeserializationStream(s) {
+        override def readObject[T: ClassTag](): T = {
+          val a = readVarLong(optimizePositive = false)
+          val b = readUnsignedVarInt()
+          val c = s.read()
+          if (c == -1) throw new EOFException
+          new RoutingTableMessage(a, b, c.toByte).asInstanceOf[T]
+        }
+      }
+  }
+}
+
 private[graphx]
 class VertexIdMsgSerializer extends Serializer with Serializable {
   override def newInstance(): SerializerInstance = new ShuffleSerializerInstance {
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/ShippableVertexPartition.scala b/graphx/src/main/scala/org/apache/spark/graphx/impl/ShippableVertexPartition.scala
new file mode 100644
index 000000000000..f4e221d4e05a
--- /dev/null
+++ b/graphx/src/main/scala/org/apache/spark/graphx/impl/ShippableVertexPartition.scala
@@ -0,0 +1,149 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.graphx.impl
+
+import scala.reflect.ClassTag
+
+import org.apache.spark.util.collection.{BitSet, PrimitiveVector}
+
+import org.apache.spark.graphx._
+import org.apache.spark.graphx.util.collection.PrimitiveKeyOpenHashMap
+
+/** Stores vertex attributes to ship to an edge partition. */
+private[graphx]
+class VertexAttributeBlock[VD: ClassTag](val vids: Array[VertexId], val attrs: Array[VD])
+  extends Serializable {
+  def iterator: Iterator[(VertexId, VD)] =
+    (0 until vids.size).iterator.map { i => (vids(i), attrs(i)) }
+}
+
+private[graphx]
+object ShippableVertexPartition {
+  /** Construct a `ShippableVertexPartition` from the given vertices without any routing table. */
+  def apply[VD: ClassTag](iter: Iterator[(VertexId, VD)]): ShippableVertexPartition[VD] =
+    apply(iter, RoutingTablePartition.empty, null.asInstanceOf[VD])
+
+  /**
+   * Construct a `ShippableVertexPartition` from the given vertices with the specified routing
+   * table, filling in missing vertices mentioned in the routing table using `defaultVal`.
+   */
+  def apply[VD: ClassTag](
+      iter: Iterator[(VertexId, VD)], routingTable: RoutingTablePartition, defaultVal: VD)
+    : ShippableVertexPartition[VD] = {
+    val fullIter = iter ++ routingTable.iterator.map(vid => (vid, defaultVal))
+    val (index, values, mask) = VertexPartitionBase.initFrom(fullIter, (a: VD, b: VD) => a)
+    new ShippableVertexPartition(index, values, mask, routingTable)
+  }
+
+  import scala.language.implicitConversions
+
+  /**
+   * Implicit conversion to allow invoking `VertexPartitionBase` operations directly on a
+   * `ShippableVertexPartition`.
+   */
+  implicit def shippablePartitionToOps[VD: ClassTag](partition: ShippableVertexPartition[VD]) =
+    new ShippableVertexPartitionOps(partition)
+
+  /**
+   * Implicit evidence that `ShippableVertexPartition` is a member of the
+   * `VertexPartitionBaseOpsConstructor` typeclass. This enables invoking `VertexPartitionBase`
+   * operations on a `ShippableVertexPartition` via an evidence parameter, as in
+   * [[VertexPartitionBaseOps]].
+   */
+  implicit object ShippableVertexPartitionOpsConstructor
+    extends VertexPartitionBaseOpsConstructor[ShippableVertexPartition] {
+    def toOps[VD: ClassTag](partition: ShippableVertexPartition[VD])
+      : VertexPartitionBaseOps[VD, ShippableVertexPartition] = shippablePartitionToOps(partition)
+  }
+}
+
+/**
+ * A map from vertex id to vertex attribute that additionally stores edge partition join sites for
+ * each vertex attribute, enabling joining with an [[org.apache.spark.graphx.EdgeRDD]].
+ */
+private[graphx]
+class ShippableVertexPartition[VD: ClassTag](
+    val index: VertexIdToIndexMap,
+    val values: Array[VD],
+    val mask: BitSet,
+    val routingTable: RoutingTablePartition)
+  extends VertexPartitionBase[VD] {
+
+  /** Return a new ShippableVertexPartition with the specified routing table. */
+  def withRoutingTable(routingTable_ : RoutingTablePartition): ShippableVertexPartition[VD] = {
+    new ShippableVertexPartition(index, values, mask, routingTable_)
+  }
+
+  /**
+   * Generate a `VertexAttributeBlock` for each edge partition keyed on the edge partition ID. The
+   * `VertexAttributeBlock` contains the vertex attributes from the current partition that are
+   * referenced in the specified positions in the edge partition.
+   */
+  def shipVertexAttributes(
+      shipSrc: Boolean, shipDst: Boolean): Iterator[(PartitionID, VertexAttributeBlock[VD])] = {
+    Iterator.tabulate(routingTable.numEdgePartitions) { pid =>
+      val initialSize = if (shipSrc && shipDst) routingTable.partitionSize(pid) else 64
+      val vids = new PrimitiveVector[VertexId](initialSize)
+      val attrs = new PrimitiveVector[VD](initialSize)
+      var i = 0
+      routingTable.foreachWithinEdgePartition(pid, shipSrc, shipDst) { vid =>
+        if (isDefined(vid)) {
+          vids += vid
+          attrs += this(vid)
+        }
+        i += 1
+      }
+      (pid, new VertexAttributeBlock(vids.trim().array, attrs.trim().array))
+    }
+  }
+
+  /**
+   * Generate a `VertexId` array for each edge partition keyed on the edge partition ID. The array
+   * contains the visible vertex ids from the current partition that are referenced in the edge
+   * partition.
+   */
+  def shipVertexIds(): Iterator[(PartitionID, Array[VertexId])] = {
+    Iterator.tabulate(routingTable.numEdgePartitions) { pid =>
+      val vids = new PrimitiveVector[VertexId](routingTable.partitionSize(pid))
+      var i = 0
+      routingTable.foreachWithinEdgePartition(pid, true, true) { vid =>
+        if (isDefined(vid)) {
+          vids += vid
+        }
+        i += 1
+      }
+      (pid, vids.trim().array)
+    }
+  }
+}
+
+private[graphx] class ShippableVertexPartitionOps[VD: ClassTag](self: ShippableVertexPartition[VD])
+  extends VertexPartitionBaseOps[VD, ShippableVertexPartition](self) {
+
+  def withIndex(index: VertexIdToIndexMap): ShippableVertexPartition[VD] = {
+    new ShippableVertexPartition(index, self.values, self.mask, self.routingTable)
+  }
+
+  def withValues[VD2: ClassTag](values: Array[VD2]): ShippableVertexPartition[VD2] = {
+    new ShippableVertexPartition(self.index, values, self.mask, self.routingTable)
+  }
+
+  def withMask(mask: BitSet): ShippableVertexPartition[VD] = {
+    new ShippableVertexPartition(self.index, self.values, mask, self.routingTable)
+  }
+}
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexPartition.scala b/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexPartition.scala
index 7a54b413dc8c..f1d174720a1b 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexPartition.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexPartition.scala
@@ -19,260 +19,59 @@ package org.apache.spark.graphx.impl
 
 import scala.reflect.ClassTag
 
-import org.apache.spark.Logging
+import org.apache.spark.util.collection.BitSet
+
 import org.apache.spark.graphx._
 import org.apache.spark.graphx.util.collection.PrimitiveKeyOpenHashMap
-import org.apache.spark.util.collection.BitSet
 
 private[graphx] object VertexPartition {
-
-  def apply[VD: ClassTag](iter: Iterator[(VertexId, VD)]): VertexPartition[VD] = {
-    val map = new PrimitiveKeyOpenHashMap[VertexId, VD]
-    iter.foreach { case (k, v) =>
-      map(k) = v
-    }
-    new VertexPartition(map.keySet, map._values, map.keySet.getBitSet)
-  }
-
-  def apply[VD: ClassTag](iter: Iterator[(VertexId, VD)], mergeFunc: (VD, VD) => VD)
-    : VertexPartition[VD] =
-  {
-    val map = new PrimitiveKeyOpenHashMap[VertexId, VD]
-    iter.foreach { case (k, v) =>
-      map.setMerge(k, v, mergeFunc)
-    }
-    new VertexPartition(map.keySet, map._values, map.keySet.getBitSet)
-  }
-}
-
-
-private[graphx]
-class VertexPartition[@specialized(Long, Int, Double) VD: ClassTag](
-    val index: VertexIdToIndexMap,
-    val values: Array[VD],
-    val mask: BitSet,
-    /** A set of vids of active vertices. May contain vids not in index due to join rewrite. */
-    private val activeSet: Option[VertexSet] = None)
-  extends Logging {
-
-  val capacity: Int = index.capacity
-
-  def size: Int = mask.cardinality()
-
-  /** Return the vertex attribute for the given vertex ID. */
-  def apply(vid: VertexId): VD = values(index.getPos(vid))
-
-  def isDefined(vid: VertexId): Boolean = {
-    val pos = index.getPos(vid)
-    pos >= 0 && mask.get(pos)
-  }
-
-  /** Look up vid in activeSet, throwing an exception if it is None. */
-  def isActive(vid: VertexId): Boolean = {
-    activeSet.get.contains(vid)
+  /** Construct a `VertexPartition` from the given vertices. */
+  def apply[VD: ClassTag](iter: Iterator[(VertexId, VD)])
+    : VertexPartition[VD] = {
+    val (index, values, mask) = VertexPartitionBase.initFrom(iter)
+    new VertexPartition(index, values, mask)
   }
 
-  /** The number of active vertices, if any exist. */
-  def numActives: Option[Int] = activeSet.map(_.size)
+  import scala.language.implicitConversions
 
   /**
-   * Pass each vertex attribute along with the vertex id through a map
-   * function and retain the original RDD's partitioning and index.
-   *
-   * @tparam VD2 the type returned by the map function
-   *
-   * @param f the function applied to each vertex id and vertex
-   * attribute in the RDD
-   *
-   * @return a new VertexPartition with values obtained by applying `f` to
-   * each of the entries in the original VertexRDD.  The resulting
-   * VertexPartition retains the same index.
+   * Implicit conversion to allow invoking `VertexPartitionBase` operations directly on a
+   * `VertexPartition`.
    */
-  def map[VD2: ClassTag](f: (VertexId, VD) => VD2): VertexPartition[VD2] = {
-    // Construct a view of the map transformation
-    val newValues = new Array[VD2](capacity)
-    var i = mask.nextSetBit(0)
-    while (i >= 0) {
-      newValues(i) = f(index.getValue(i), values(i))
-      i = mask.nextSetBit(i + 1)
-    }
-    new VertexPartition[VD2](index, newValues, mask)
-  }
-
-  /**
-   * Restrict the vertex set to the set of vertices satisfying the given predicate.
-   *
-   * @param pred the user defined predicate
-   *
-   * @note The vertex set preserves the original index structure which means that the returned
-   *       RDD can be easily joined with the original vertex-set. Furthermore, the filter only
-   *       modifies the bitmap index and so no new values are allocated.
-   */
-  def filter(pred: (VertexId, VD) => Boolean): VertexPartition[VD] = {
-    // Allocate the array to store the results into
-    val newMask = new BitSet(capacity)
-    // Iterate over the active bits in the old mask and evaluate the predicate
-    var i = mask.nextSetBit(0)
-    while (i >= 0) {
-      if (pred(index.getValue(i), values(i))) {
-        newMask.set(i)
-      }
-      i = mask.nextSetBit(i + 1)
-    }
-    new VertexPartition(index, values, newMask)
-  }
+  implicit def partitionToOps[VD: ClassTag](partition: VertexPartition[VD]) =
+    new VertexPartitionOps(partition)
 
   /**
-   * Hides vertices that are the same between this and other. For vertices that are different, keeps
-   * the values from `other`. The indices of `this` and `other` must be the same.
+   * Implicit evidence that `VertexPartition` is a member of the `VertexPartitionBaseOpsConstructor`
+   * typeclass. This enables invoking `VertexPartitionBase` operations on a `VertexPartition` via an
+   * evidence parameter, as in [[VertexPartitionBaseOps]].
    */
-  def diff(other: VertexPartition[VD]): VertexPartition[VD] = {
-    if (index != other.index) {
-      logWarning("Diffing two VertexPartitions with different indexes is slow.")
-      diff(createUsingIndex(other.iterator))
-    } else {
-      val newMask = mask & other.mask
-      var i = newMask.nextSetBit(0)
-      while (i >= 0) {
-        if (values(i) == other.values(i)) {
-          newMask.unset(i)
-        }
-        i = newMask.nextSetBit(i + 1)
-      }
-      new VertexPartition(index, other.values, newMask)
-    }
-  }
-
-  /** Left outer join another VertexPartition. */
-  def leftJoin[VD2: ClassTag, VD3: ClassTag]
-      (other: VertexPartition[VD2])
-      (f: (VertexId, VD, Option[VD2]) => VD3): VertexPartition[VD3] = {
-    if (index != other.index) {
-      logWarning("Joining two VertexPartitions with different indexes is slow.")
-      leftJoin(createUsingIndex(other.iterator))(f)
-    } else {
-      val newValues = new Array[VD3](capacity)
-
-      var i = mask.nextSetBit(0)
-      while (i >= 0) {
-        val otherV: Option[VD2] = if (other.mask.get(i)) Some(other.values(i)) else None
-        newValues(i) = f(index.getValue(i), values(i), otherV)
-        i = mask.nextSetBit(i + 1)
-      }
-      new VertexPartition(index, newValues, mask)
-    }
-  }
-
-  /** Left outer join another iterator of messages. */
-  def leftJoin[VD2: ClassTag, VD3: ClassTag]
-      (other: Iterator[(VertexId, VD2)])
-      (f: (VertexId, VD, Option[VD2]) => VD3): VertexPartition[VD3] = {
-    leftJoin(createUsingIndex(other))(f)
-  }
-
-  /** Inner join another VertexPartition. */
-  def innerJoin[U: ClassTag, VD2: ClassTag](other: VertexPartition[U])
-      (f: (VertexId, VD, U) => VD2): VertexPartition[VD2] = {
-    if (index != other.index) {
-      logWarning("Joining two VertexPartitions with different indexes is slow.")
-      innerJoin(createUsingIndex(other.iterator))(f)
-    } else {
-      val newMask = mask & other.mask
-      val newValues = new Array[VD2](capacity)
-      var i = newMask.nextSetBit(0)
-      while (i >= 0) {
-        newValues(i) = f(index.getValue(i), values(i), other.values(i))
-        i = newMask.nextSetBit(i + 1)
-      }
-      new VertexPartition(index, newValues, newMask)
-    }
-  }
-
-  /**
-   * Inner join an iterator of messages.
-   */
-  def innerJoin[U: ClassTag, VD2: ClassTag]
-      (iter: Iterator[Product2[VertexId, U]])
-      (f: (VertexId, VD, U) => VD2): VertexPartition[VD2] = {
-    innerJoin(createUsingIndex(iter))(f)
+  implicit object VertexPartitionOpsConstructor
+    extends VertexPartitionBaseOpsConstructor[VertexPartition] {
+    def toOps[VD: ClassTag](partition: VertexPartition[VD])
+      : VertexPartitionBaseOps[VD, VertexPartition] = partitionToOps(partition)
   }
+}
 
-  /**
-   * Similar effect as aggregateUsingIndex((a, b) => a)
-   */
-  def createUsingIndex[VD2: ClassTag](iter: Iterator[Product2[VertexId, VD2]])
-    : VertexPartition[VD2] = {
-    val newMask = new BitSet(capacity)
-    val newValues = new Array[VD2](capacity)
-    iter.foreach { case (vid, vdata) =>
-      val pos = index.getPos(vid)
-      if (pos >= 0) {
-        newMask.set(pos)
-        newValues(pos) = vdata
-      }
-    }
-    new VertexPartition[VD2](index, newValues, newMask)
-  }
+/** A map from vertex id to vertex attribute. */
+private[graphx] class VertexPartition[VD: ClassTag](
+    val index: VertexIdToIndexMap,
+    val values: Array[VD],
+    val mask: BitSet)
+  extends VertexPartitionBase[VD]
 
-  /**
-   * Similar to innerJoin, but vertices from the left side that don't appear in iter will remain in
-   * the partition, hidden by the bitmask.
-   */
-  def innerJoinKeepLeft(iter: Iterator[Product2[VertexId, VD]]): VertexPartition[VD] = {
-    val newMask = new BitSet(capacity)
-    val newValues = new Array[VD](capacity)
-    System.arraycopy(values, 0, newValues, 0, newValues.length)
-    iter.foreach { case (vid, vdata) =>
-      val pos = index.getPos(vid)
-      if (pos >= 0) {
-        newMask.set(pos)
-        newValues(pos) = vdata
-      }
-    }
-    new VertexPartition(index, newValues, newMask)
-  }
+private[graphx] class VertexPartitionOps[VD: ClassTag](self: VertexPartition[VD])
+  extends VertexPartitionBaseOps[VD, VertexPartition](self) {
 
-  def aggregateUsingIndex[VD2: ClassTag](
-      iter: Iterator[Product2[VertexId, VD2]],
-      reduceFunc: (VD2, VD2) => VD2): VertexPartition[VD2] = {
-    val newMask = new BitSet(capacity)
-    val newValues = new Array[VD2](capacity)
-    iter.foreach { product =>
-      val vid = product._1
-      val vdata = product._2
-      val pos = index.getPos(vid)
-      if (pos >= 0) {
-        if (newMask.get(pos)) {
-          newValues(pos) = reduceFunc(newValues(pos), vdata)
-        } else { // otherwise just store the new value
-          newMask.set(pos)
-          newValues(pos) = vdata
-        }
-      }
-    }
-    new VertexPartition[VD2](index, newValues, newMask)
+  def withIndex(index: VertexIdToIndexMap): VertexPartition[VD] = {
+    new VertexPartition(index, self.values, self.mask)
   }
 
-  def replaceActives(iter: Iterator[VertexId]): VertexPartition[VD] = {
-    val newActiveSet = new VertexSet
-    iter.foreach(newActiveSet.add(_))
-    new VertexPartition(index, values, mask, Some(newActiveSet))
+  def withValues[VD2: ClassTag](values: Array[VD2]): VertexPartition[VD2] = {
+    new VertexPartition(self.index, values, self.mask)
   }
 
-  /**
-   * Construct a new VertexPartition whose index contains only the vertices in the mask.
-   */
-  def reindex(): VertexPartition[VD] = {
-    val hashMap = new PrimitiveKeyOpenHashMap[VertexId, VD]
-    val arbitraryMerge = (a: VD, b: VD) => a
-    for ((k, v) <- this.iterator) {
-      hashMap.setMerge(k, v, arbitraryMerge)
-    }
-    new VertexPartition(hashMap.keySet, hashMap._values, hashMap.keySet.getBitSet)
+  def withMask(mask: BitSet): VertexPartition[VD] = {
+    new VertexPartition(self.index, self.values, mask)
   }
-
-  def iterator: Iterator[(VertexId, VD)] =
-    mask.iterator.map(ind => (index.getValue(ind), values(ind)))
-
-  def vidIterator: Iterator[VertexId] = mask.iterator.map(ind => index.getValue(ind))
 }
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexPartitionBase.scala b/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexPartitionBase.scala
new file mode 100644
index 000000000000..8d9e0204d27f
--- /dev/null
+++ b/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexPartitionBase.scala
@@ -0,0 +1,91 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.graphx.impl
+
+import scala.language.higherKinds
+import scala.reflect.ClassTag
+
+import org.apache.spark.util.collection.BitSet
+
+import org.apache.spark.graphx._
+import org.apache.spark.graphx.util.collection.PrimitiveKeyOpenHashMap
+
+private[graphx] object VertexPartitionBase {
+  /**
+   * Construct the constituents of a VertexPartitionBase from the given vertices, merging duplicate
+   * entries arbitrarily.
+   */
+  def initFrom[VD: ClassTag](iter: Iterator[(VertexId, VD)])
+    : (VertexIdToIndexMap, Array[VD], BitSet) = {
+    val map = new PrimitiveKeyOpenHashMap[VertexId, VD]
+    iter.foreach { pair =>
+      map(pair._1) = pair._2
+    }
+    (map.keySet, map._values, map.keySet.getBitSet)
+  }
+
+  /**
+   * Construct the constituents of a VertexPartitionBase from the given vertices, merging duplicate
+   * entries using `mergeFunc`.
+   */
+  def initFrom[VD: ClassTag](iter: Iterator[(VertexId, VD)], mergeFunc: (VD, VD) => VD)
+    : (VertexIdToIndexMap, Array[VD], BitSet) = {
+    val map = new PrimitiveKeyOpenHashMap[VertexId, VD]
+    iter.foreach { pair =>
+      map.setMerge(pair._1, pair._2, mergeFunc)
+    }
+    (map.keySet, map._values, map.keySet.getBitSet)
+  }
+}
+
+/**
+ * An abstract map from vertex id to vertex attribute. [[VertexPartition]] is the corresponding
+ * concrete implementation. [[VertexPartitionBaseOps]] provides a variety of operations for
+ * VertexPartitionBase and subclasses that provide implicit evidence of membership in the
+ * `VertexPartitionBaseOpsConstructor` typeclass (for example,
+ * [[VertexPartition.VertexPartitionOpsConstructor]]).
+ */
+private[graphx] abstract class VertexPartitionBase[@specialized(Long, Int, Double) VD: ClassTag] {
+
+  def index: VertexIdToIndexMap
+  def values: Array[VD]
+  def mask: BitSet
+
+  val capacity: Int = index.capacity
+
+  def size: Int = mask.cardinality()
+
+  /** Return the vertex attribute for the given vertex ID. */
+  def apply(vid: VertexId): VD = values(index.getPos(vid))
+
+  def isDefined(vid: VertexId): Boolean = {
+    val pos = index.getPos(vid)
+    pos >= 0 && mask.get(pos)
+  }
+
+  def iterator: Iterator[(VertexId, VD)] =
+    mask.iterator.map(ind => (index.getValue(ind), values(ind)))
+}
+
+/**
+ * A typeclass for subclasses of `VertexPartitionBase` representing the ability to wrap them in a
+ * `VertexPartitionBaseOps`.
+ */
+private[graphx] trait VertexPartitionBaseOpsConstructor[T[X] <: VertexPartitionBase[X]] {
+  def toOps[VD: ClassTag](partition: T[VD]): VertexPartitionBaseOps[VD, T]
+}
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexPartitionBaseOps.scala b/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexPartitionBaseOps.scala
new file mode 100644
index 000000000000..21ff615feca6
--- /dev/null
+++ b/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexPartitionBaseOps.scala
@@ -0,0 +1,245 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.graphx.impl
+
+import scala.language.higherKinds
+import scala.language.implicitConversions
+import scala.reflect.ClassTag
+
+import org.apache.spark.Logging
+import org.apache.spark.util.collection.BitSet
+
+import org.apache.spark.graphx._
+import org.apache.spark.graphx.util.collection.PrimitiveKeyOpenHashMap
+
+/**
+ * An class containing additional operations for subclasses of VertexPartitionBase that provide
+ * implicit evidence of membership in the `VertexPartitionBaseOpsConstructor` typeclass (for
+ * example, [[VertexPartition.VertexPartitionOpsConstructor]]).
+ */
+private[graphx] abstract class VertexPartitionBaseOps
+    [VD: ClassTag, Self[X] <: VertexPartitionBase[X] : VertexPartitionBaseOpsConstructor]
+    (self: Self[VD])
+    extends Logging {
+
+  def withIndex(index: VertexIdToIndexMap): Self[VD]
+  def withValues[VD2: ClassTag](values: Array[VD2]): Self[VD2]
+  def withMask(mask: BitSet): Self[VD]
+
+  /**
+   * Pass each vertex attribute along with the vertex id through a map
+   * function and retain the original RDD's partitioning and index.
+   *
+   * @tparam VD2 the type returned by the map function
+   *
+   * @param f the function applied to each vertex id and vertex
+   * attribute in the RDD
+   *
+   * @return a new VertexPartition with values obtained by applying `f` to
+   * each of the entries in the original VertexRDD.  The resulting
+   * VertexPartition retains the same index.
+   */
+  def map[VD2: ClassTag](f: (VertexId, VD) => VD2): Self[VD2] = {
+    // Construct a view of the map transformation
+    val newValues = new Array[VD2](self.capacity)
+    var i = self.mask.nextSetBit(0)
+    while (i >= 0) {
+      newValues(i) = f(self.index.getValue(i), self.values(i))
+      i = self.mask.nextSetBit(i + 1)
+    }
+    this.withValues(newValues)
+  }
+
+  /**
+   * Restrict the vertex set to the set of vertices satisfying the given predicate.
+   *
+   * @param pred the user defined predicate
+   *
+   * @note The vertex set preserves the original index structure which means that the returned
+   *       RDD can be easily joined with the original vertex-set. Furthermore, the filter only
+   *       modifies the bitmap index and so no new values are allocated.
+   */
+  def filter(pred: (VertexId, VD) => Boolean): Self[VD] = {
+    // Allocate the array to store the results into
+    val newMask = new BitSet(self.capacity)
+    // Iterate over the active bits in the old mask and evaluate the predicate
+    var i = self.mask.nextSetBit(0)
+    while (i >= 0) {
+      if (pred(self.index.getValue(i), self.values(i))) {
+        newMask.set(i)
+      }
+      i = self.mask.nextSetBit(i + 1)
+    }
+    this.withMask(newMask)
+  }
+
+  /**
+   * Hides vertices that are the same between this and other. For vertices that are different, keeps
+   * the values from `other`. The indices of `this` and `other` must be the same.
+   */
+  def diff(other: Self[VD]): Self[VD] = {
+    if (self.index != other.index) {
+      logWarning("Diffing two VertexPartitions with different indexes is slow.")
+      diff(createUsingIndex(other.iterator))
+    } else {
+      val newMask = self.mask & other.mask
+      var i = newMask.nextSetBit(0)
+      while (i >= 0) {
+        if (self.values(i) == other.values(i)) {
+          newMask.unset(i)
+        }
+        i = newMask.nextSetBit(i + 1)
+      }
+      this.withValues(other.values).withMask(newMask)
+    }
+  }
+
+  /** Left outer join another VertexPartition. */
+  def leftJoin[VD2: ClassTag, VD3: ClassTag]
+      (other: Self[VD2])
+      (f: (VertexId, VD, Option[VD2]) => VD3): Self[VD3] = {
+    if (self.index != other.index) {
+      logWarning("Joining two VertexPartitions with different indexes is slow.")
+      leftJoin(createUsingIndex(other.iterator))(f)
+    } else {
+      val newValues = new Array[VD3](self.capacity)
+
+      var i = self.mask.nextSetBit(0)
+      while (i >= 0) {
+        val otherV: Option[VD2] = if (other.mask.get(i)) Some(other.values(i)) else None
+        newValues(i) = f(self.index.getValue(i), self.values(i), otherV)
+        i = self.mask.nextSetBit(i + 1)
+      }
+      this.withValues(newValues)
+    }
+  }
+
+  /** Left outer join another iterator of messages. */
+  def leftJoin[VD2: ClassTag, VD3: ClassTag]
+      (other: Iterator[(VertexId, VD2)])
+      (f: (VertexId, VD, Option[VD2]) => VD3): Self[VD3] = {
+    leftJoin(createUsingIndex(other))(f)
+  }
+
+  /** Inner join another VertexPartition. */
+  def innerJoin[U: ClassTag, VD2: ClassTag]
+      (other: Self[U])
+      (f: (VertexId, VD, U) => VD2): Self[VD2] = {
+    if (self.index != other.index) {
+      logWarning("Joining two VertexPartitions with different indexes is slow.")
+      innerJoin(createUsingIndex(other.iterator))(f)
+    } else {
+      val newMask = self.mask & other.mask
+      val newValues = new Array[VD2](self.capacity)
+      var i = newMask.nextSetBit(0)
+      while (i >= 0) {
+        newValues(i) = f(self.index.getValue(i), self.values(i), other.values(i))
+        i = newMask.nextSetBit(i + 1)
+      }
+      this.withValues(newValues).withMask(newMask)
+    }
+  }
+
+  /**
+   * Inner join an iterator of messages.
+   */
+  def innerJoin[U: ClassTag, VD2: ClassTag]
+      (iter: Iterator[Product2[VertexId, U]])
+      (f: (VertexId, VD, U) => VD2): Self[VD2] = {
+    innerJoin(createUsingIndex(iter))(f)
+  }
+
+  /**
+   * Similar effect as aggregateUsingIndex((a, b) => a)
+   */
+  def createUsingIndex[VD2: ClassTag](iter: Iterator[Product2[VertexId, VD2]])
+    : Self[VD2] = {
+    val newMask = new BitSet(self.capacity)
+    val newValues = new Array[VD2](self.capacity)
+    iter.foreach { pair =>
+      val pos = self.index.getPos(pair._1)
+      if (pos >= 0) {
+        newMask.set(pos)
+        newValues(pos) = pair._2
+      }
+    }
+    this.withValues(newValues).withMask(newMask)
+  }
+
+  /**
+   * Similar to innerJoin, but vertices from the left side that don't appear in iter will remain in
+   * the partition, hidden by the bitmask.
+   */
+  def innerJoinKeepLeft(iter: Iterator[Product2[VertexId, VD]]): Self[VD] = {
+    val newMask = new BitSet(self.capacity)
+    val newValues = new Array[VD](self.capacity)
+    System.arraycopy(self.values, 0, newValues, 0, newValues.length)
+    iter.foreach { pair =>
+      val pos = self.index.getPos(pair._1)
+      if (pos >= 0) {
+        newMask.set(pos)
+        newValues(pos) = pair._2
+      }
+    }
+    this.withValues(newValues).withMask(newMask)
+  }
+
+  def aggregateUsingIndex[VD2: ClassTag](
+      iter: Iterator[Product2[VertexId, VD2]],
+      reduceFunc: (VD2, VD2) => VD2): Self[VD2] = {
+    val newMask = new BitSet(self.capacity)
+    val newValues = new Array[VD2](self.capacity)
+    iter.foreach { product =>
+      val vid = product._1
+      val vdata = product._2
+      val pos = self.index.getPos(vid)
+      if (pos >= 0) {
+        if (newMask.get(pos)) {
+          newValues(pos) = reduceFunc(newValues(pos), vdata)
+        } else { // otherwise just store the new value
+          newMask.set(pos)
+          newValues(pos) = vdata
+        }
+      }
+    }
+    this.withValues(newValues).withMask(newMask)
+  }
+
+  /**
+   * Construct a new VertexPartition whose index contains only the vertices in the mask.
+   */
+  def reindex(): Self[VD] = {
+    val hashMap = new PrimitiveKeyOpenHashMap[VertexId, VD]
+    val arbitraryMerge = (a: VD, b: VD) => a
+    for ((k, v) <- self.iterator) {
+      hashMap.setMerge(k, v, arbitraryMerge)
+    }
+    this.withIndex(hashMap.keySet).withValues(hashMap._values).withMask(hashMap.keySet.getBitSet)
+  }
+
+  /**
+   * Converts a vertex partition (in particular, one of type `Self`) into a
+   * `VertexPartitionBaseOps`. Within this class, this allows chaining the methods defined above,
+   * because these methods return a `Self` and this implicit conversion re-wraps that in a
+   * `VertexPartitionBaseOps`. This relies on the context bound on `Self`.
+   */
+  private implicit def toOps[VD2: ClassTag](
+      partition: Self[VD2]): VertexPartitionBaseOps[VD2, Self] = {
+    implicitly[VertexPartitionBaseOpsConstructor[Self]].toOps(partition)
+  }
+}
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/lib/Analytics.scala b/graphx/src/main/scala/org/apache/spark/graphx/lib/Analytics.scala
index d901d4fe225f..069e042ed94a 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/lib/Analytics.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/lib/Analytics.scala
@@ -55,6 +55,7 @@ object Analytics extends Logging {
     val conf = new SparkConf()
       .set("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
       .set("spark.kryo.registrator", "org.apache.spark.graphx.GraphKryoRegistrator")
+      .set("spark.locality.wait", "100000")
 
     taskType match {
       case "pagerank" =>
@@ -62,12 +63,14 @@ object Analytics extends Logging {
         var outFname = ""
         var numEPart = 4
         var partitionStrategy: Option[PartitionStrategy] = None
+        var numIterOpt: Option[Int] = None
 
         options.foreach{
           case ("tol", v) => tol = v.toFloat
           case ("output", v) => outFname = v
           case ("numEPart", v) => numEPart = v.toInt
           case ("partStrategy", v) => partitionStrategy = Some(pickPartitioner(v))
+          case ("numIter", v) => numIterOpt = Some(v.toInt)
           case (opt, _) => throw new IllegalArgumentException("Invalid option: " + opt)
         }
 
@@ -84,7 +87,10 @@ object Analytics extends Logging {
         println("GRAPHX: Number of vertices " + graph.vertices.count)
         println("GRAPHX: Number of edges " + graph.edges.count)
 
-        val pr = graph.pageRank(tol).vertices.cache()
+        val pr = (numIterOpt match {
+          case Some(numIter) => PageRank.run(graph, numIter)
+          case None => PageRank.runUntilConvergence(graph, tol)
+        }).vertices.cache()
 
         println("GRAPHX: Total rank: " + pr.map(_._2).reduce(_ + _))
 
diff --git a/graphx/src/test/scala/org/apache/spark/graphx/GraphSuite.scala b/graphx/src/test/scala/org/apache/spark/graphx/GraphSuite.scala
index 32b5fe481359..7b9bac5d9c8e 100644
--- a/graphx/src/test/scala/org/apache/spark/graphx/GraphSuite.scala
+++ b/graphx/src/test/scala/org/apache/spark/graphx/GraphSuite.scala
@@ -110,7 +110,7 @@ class GraphSuite extends FunSuite with LocalSparkContext {
       val p = 100
       val verts = 1 to n
       val graph = Graph.fromEdgeTuples(sc.parallelize(verts.flatMap(x =>
-        verts.filter(y => y % x == 0).map(y => (x: VertexId, y: VertexId))), p), 0)
+        verts.withFilter(y => y % x == 0).map(y => (x: VertexId, y: VertexId))), p), 0)
       assert(graph.edges.partitions.length === p)
       val partitionedGraph = graph.partitionBy(EdgePartition2D)
       assert(graph.edges.partitions.length === p)
@@ -120,7 +120,13 @@ class GraphSuite extends FunSuite with LocalSparkContext {
         val part = iter.next()._2
         Iterator((part.srcIds ++ part.dstIds).toSet)
       }.collect
-      assert(verts.forall(id => partitionSets.count(_.contains(id)) <= bound))
+      if (!verts.forall(id => partitionSets.count(_.contains(id)) <= bound)) {
+        val numFailures = verts.count(id => partitionSets.count(_.contains(id)) > bound)
+        val failure = verts.maxBy(id => partitionSets.count(_.contains(id)))
+        fail(("Replication bound test failed for %d/%d vertices. " +
+          "Example: vertex %d replicated to %d (> %f) partitions.").format(
+          numFailures, n, failure, partitionSets.count(_.contains(failure)), bound))
+      }
       // This should not be true for the default hash partitioning
       val partitionSetsUnpartitioned = graph.edges.partitionsRDD.mapPartitions { iter =>
         val part = iter.next()._2
diff --git a/graphx/src/test/scala/org/apache/spark/graphx/impl/EdgePartitionSuite.scala b/graphx/src/test/scala/org/apache/spark/graphx/impl/EdgePartitionSuite.scala
index e135d1d7ad6a..d2e0c01bc35e 100644
--- a/graphx/src/test/scala/org/apache/spark/graphx/impl/EdgePartitionSuite.scala
+++ b/graphx/src/test/scala/org/apache/spark/graphx/impl/EdgePartitionSuite.scala
@@ -26,10 +26,16 @@ import org.apache.spark.graphx._
 
 class EdgePartitionSuite extends FunSuite {
 
+  def makeEdgePartition[A: ClassTag](xs: Iterable[(Int, Int, A)]): EdgePartition[A, Int] = {
+    val builder = new EdgePartitionBuilder[A, Int]
+    for ((src, dst, attr) <- xs) { builder.add(src: VertexId, dst: VertexId, attr) }
+    builder.toEdgePartition
+  }
+
   test("reverse") {
     val edges = List(Edge(0, 1, 0), Edge(1, 2, 0), Edge(2, 0, 0))
     val reversedEdges = List(Edge(0, 2, 0), Edge(1, 0, 0), Edge(2, 1, 0))
-    val builder = new EdgePartitionBuilder[Int]
+    val builder = new EdgePartitionBuilder[Int, Nothing]
     for (e <- edges) {
       builder.add(e.srcId, e.dstId, e.attr)
     }
@@ -40,7 +46,7 @@ class EdgePartitionSuite extends FunSuite {
 
   test("map") {
     val edges = List(Edge(0, 1, 0), Edge(1, 2, 0), Edge(2, 0, 0))
-    val builder = new EdgePartitionBuilder[Int]
+    val builder = new EdgePartitionBuilder[Int, Nothing]
     for (e <- edges) {
       builder.add(e.srcId, e.dstId, e.attr)
     }
@@ -49,11 +55,22 @@ class EdgePartitionSuite extends FunSuite {
       edges.map(e => e.copy(attr = e.srcId + e.dstId)))
   }
 
+  test("filter") {
+    val edges = List(Edge(0, 1, 0), Edge(0, 2, 0), Edge(2, 0, 0))
+    val builder = new EdgePartitionBuilder[Int, Int]
+    for (e <- edges) {
+      builder.add(e.srcId, e.dstId, e.attr)
+    }
+    val edgePartition = builder.toEdgePartition
+    val filtered = edgePartition.filter(et => et.srcId == 0, (vid, attr) => vid == 0 || vid == 1)
+    assert(filtered.tripletIterator().toList.map(et => (et.srcId, et.dstId)) === List((0L, 1L)))
+  }
+
   test("groupEdges") {
     val edges = List(
       Edge(0, 1, 1), Edge(1, 2, 2), Edge(2, 0, 4), Edge(0, 1, 8), Edge(1, 2, 16), Edge(2, 0, 32))
     val groupedEdges = List(Edge(0, 1, 9), Edge(1, 2, 18), Edge(2, 0, 36))
-    val builder = new EdgePartitionBuilder[Int]
+    val builder = new EdgePartitionBuilder[Int, Nothing]
     for (e <- edges) {
       builder.add(e.srcId, e.dstId, e.attr)
     }
@@ -61,11 +78,19 @@ class EdgePartitionSuite extends FunSuite {
     assert(edgePartition.groupEdges(_ + _).iterator.map(_.copy()).toList === groupedEdges)
   }
 
+  test("upgradeIterator") {
+    val edges = List((0, 1, 0), (1, 0, 0))
+    val verts = List((0L, 1), (1L, 2))
+    val part = makeEdgePartition(edges).updateVertices(verts.iterator)
+    assert(part.upgradeIterator(part.iterator).map(_.toTuple).toList ===
+      part.tripletIterator().toList.map(_.toTuple))
+  }
+
   test("indexIterator") {
     val edgesFrom0 = List(Edge(0, 1, 0))
     val edgesFrom1 = List(Edge(1, 0, 0), Edge(1, 2, 0))
     val sortedEdges = edgesFrom0 ++ edgesFrom1
-    val builder = new EdgePartitionBuilder[Int]
+    val builder = new EdgePartitionBuilder[Int, Nothing]
     for (e <- Random.shuffle(sortedEdges)) {
       builder.add(e.srcId, e.dstId, e.attr)
     }
@@ -77,11 +102,6 @@ class EdgePartitionSuite extends FunSuite {
   }
 
   test("innerJoin") {
-    def makeEdgePartition[A: ClassTag](xs: Iterable[(Int, Int, A)]): EdgePartition[A] = {
-      val builder = new EdgePartitionBuilder[A]
-      for ((src, dst, attr) <- xs) { builder.add(src: VertexId, dst: VertexId, attr) }
-      builder.toEdgePartition
-    }
     val aList = List((0, 1, 0), (1, 0, 0), (1, 2, 0), (5, 4, 0), (5, 5, 0))
     val bList = List((0, 1, 0), (1, 0, 0), (1, 1, 0), (3, 4, 0), (5, 5, 0))
     val a = makeEdgePartition(aList)
@@ -90,4 +110,14 @@ class EdgePartitionSuite extends FunSuite {
     assert(a.innerJoin(b) { (src, dst, a, b) => a }.iterator.map(_.copy()).toList ===
       List(Edge(0, 1, 0), Edge(1, 0, 0), Edge(5, 5, 0)))
   }
+
+  test("isActive, numActives, replaceActives") {
+    val ep = new EdgePartitionBuilder[Nothing, Nothing].toEdgePartition
+      .withActiveSet(Iterator(0L, 2L, 0L))
+    assert(ep.isActive(0))
+    assert(!ep.isActive(1))
+    assert(ep.isActive(2))
+    assert(!ep.isActive(-1))
+    assert(ep.numActives == Some(2))
+  }
 }
diff --git a/graphx/src/test/scala/org/apache/spark/graphx/impl/EdgeTripletIteratorSuite.scala b/graphx/src/test/scala/org/apache/spark/graphx/impl/EdgeTripletIteratorSuite.scala
index 9cbb2d2acdc2..49b2704390fe 100644
--- a/graphx/src/test/scala/org/apache/spark/graphx/impl/EdgeTripletIteratorSuite.scala
+++ b/graphx/src/test/scala/org/apache/spark/graphx/impl/EdgeTripletIteratorSuite.scala
@@ -26,17 +26,11 @@ import org.apache.spark.graphx._
 
 class EdgeTripletIteratorSuite extends FunSuite {
   test("iterator.toList") {
-    val builder = new EdgePartitionBuilder[Int]
+    val builder = new EdgePartitionBuilder[Int, Int]
     builder.add(1, 2, 0)
     builder.add(1, 3, 0)
     builder.add(1, 4, 0)
-    val vidmap = new VertexIdToIndexMap
-    vidmap.add(1)
-    vidmap.add(2)
-    vidmap.add(3)
-    vidmap.add(4)
-    val vs = Array.fill(vidmap.capacity)(0)
-    val iter = new EdgeTripletIterator[Int, Int](vidmap, vs, builder.toEdgePartition)
+    val iter = new EdgeTripletIterator[Int, Int](builder.toEdgePartition, true, true)
     val result = iter.toList.map(et => (et.srcId, et.dstId))
     assert(result === Seq((1, 2), (1, 3), (1, 4)))
   }
diff --git a/graphx/src/test/scala/org/apache/spark/graphx/impl/VertexPartitionSuite.scala b/graphx/src/test/scala/org/apache/spark/graphx/impl/VertexPartitionSuite.scala
index a048d13fd12b..8bf1384d514c 100644
--- a/graphx/src/test/scala/org/apache/spark/graphx/impl/VertexPartitionSuite.scala
+++ b/graphx/src/test/scala/org/apache/spark/graphx/impl/VertexPartitionSuite.scala
@@ -30,17 +30,6 @@ class VertexPartitionSuite extends FunSuite {
     assert(!vp.isDefined(-1))
   }
 
-  test("isActive, numActives, replaceActives") {
-    val vp = VertexPartition(Iterator((0L, 1), (1L, 1)))
-      .filter { (vid, attr) => vid == 0 }
-      .replaceActives(Iterator(0, 2, 0))
-    assert(vp.isActive(0))
-    assert(!vp.isActive(1))
-    assert(vp.isActive(2))
-    assert(!vp.isActive(-1))
-    assert(vp.numActives == Some(2))
-  }
-
   test("map") {
     val vp = VertexPartition(Iterator((0L, 1), (1L, 1))).map { (vid, attr) => 2 }
     assert(vp(0) === 2)
diff --git a/project/MimaBuild.scala b/project/MimaBuild.scala
index efdb38e907d1..fafc9b36a77d 100644
--- a/project/MimaBuild.scala
+++ b/project/MimaBuild.scala
@@ -76,6 +76,8 @@ object MimaBuild {
           excludeSparkClass("util.XORShiftRandom") ++
           excludeSparkClass("graphx.EdgeRDD") ++
           excludeSparkClass("graphx.VertexRDD") ++
+          excludeSparkClass("graphx.impl.GraphImpl") ++
+          excludeSparkClass("graphx.impl.RoutingTable") ++
           excludeSparkClass("mllib.recommendation.MFDataGenerator") ++
           excludeSparkClass("mllib.optimization.SquaredGradient") ++
           excludeSparkClass("mllib.regression.RidgeRegressionWithSGD") ++

From 2b7bd29eb6ee5baf739eec143044ecfc296b9b1f Mon Sep 17 00:00:00 2001
From: Sean Owen <sowen@cloudera.com>
Date: Sat, 10 May 2014 20:50:40 -0700
Subject: [PATCH 153/810] SPARK-1789. Multiple versions of Netty dependencies
 cause FlumeStreamSuite failure

TL;DR is there is a bit of JAR hell trouble with Netty, that can be mostly resolved and will resolve a test failure.

I hit the error described at http://apache-spark-user-list.1001560.n3.nabble.com/SparkContext-startup-time-out-td1753.html while running FlumeStreamingSuite, and have for a short while (is it just me?)

velvia notes:
"I have found a workaround.  If you add akka 2.2.4 to your dependencies, then everything works, probably because akka 2.2.4 brings in newer version of Jetty."

There are at least 3 versions of Netty in play in the build:

- the new Flume 1.4.0 dependency brings in io.netty:netty:3.4.0.Final, and that is the immediate problem
- the custom version of akka 2.2.3 depends on io.netty:netty:3.6.6.
- but, Spark Core directly uses io.netty:netty-all:4.0.17.Final

The POMs try to exclude other versions of netty, but are excluding org.jboss.netty:netty, when in fact older versions of io.netty:netty (not netty-all) are also an issue.

The org.jboss.netty:netty excludes are largely unnecessary. I replaced many of them with io.netty:netty exclusions until everything agreed on io.netty:netty-all:4.0.17.Final.

But this didn't work, since Akka 2.2.3 doesn't work with Netty 4.x. Down-grading to 3.6.6.Final across the board made some Spark code not compile.

If the build *keeps* io.netty:netty:3.6.6.Final as well, everything seems to work. Part of the reason seems to be that Netty 3.x used the old `org.jboss.netty` packages. This is less than ideal, but is no worse than the current situation.

So this PR resolves the issue and improves the JAR hell, even if it leaves the existing theoretical Netty 3-vs-4 conflict:

- Remove org.jboss.netty excludes where possible, for clarity; they're not needed except with Hadoop artifacts
- Add io.netty:netty excludes where needed -- except, let akka keep its io.netty:netty
- Change a bit of test code that actually depended on Netty 3.x, to use 4.x equivalent
- Update SBT build accordingly

A better change would be to update Akka far enough such that it agrees on Netty 4.x, but I don't know if that's feasible.

Author: Sean Owen <sowen@cloudera.com>

Closes #723 from srowen/SPARK-1789 and squashes the following commits:

43661b7 [Sean Owen] Update and add Netty excludes to prevent some JAR conflicts that cause test issues
---
 .../org/apache/spark/LocalSparkContext.scala  |  3 +-
 examples/pom.xml                              |  4 +++
 external/flume/pom.xml                        |  2 +-
 external/mqtt/pom.xml                         |  6 ----
 external/twitter/pom.xml                      |  6 ----
 external/zeromq/pom.xml                       |  6 ----
 pom.xml                                       | 32 -----------------
 project/SparkBuild.scala                      | 35 ++++++++++---------
 8 files changed, 24 insertions(+), 70 deletions(-)

diff --git a/core/src/test/scala/org/apache/spark/LocalSparkContext.scala b/core/src/test/scala/org/apache/spark/LocalSparkContext.scala
index 4b972f88a954..53e367a61715 100644
--- a/core/src/test/scala/org/apache/spark/LocalSparkContext.scala
+++ b/core/src/test/scala/org/apache/spark/LocalSparkContext.scala
@@ -17,8 +17,7 @@
 
 package org.apache.spark
 
-import org.jboss.netty.logging.InternalLoggerFactory
-import org.jboss.netty.logging.Slf4JLoggerFactory
+import _root_.io.netty.util.internal.logging.{Slf4JLoggerFactory, InternalLoggerFactory}
 import org.scalatest.BeforeAndAfterAll
 import org.scalatest.BeforeAndAfterEach
 import org.scalatest.Suite
diff --git a/examples/pom.xml b/examples/pom.xml
index e1fc149d87f1..874bcd7916f3 100644
--- a/examples/pom.xml
+++ b/examples/pom.xml
@@ -106,6 +106,10 @@
           <groupId>org.jboss.netty</groupId>
           <artifactId>netty</artifactId>
         </exclusion>
+        <exclusion>
+          <groupId>io.netty</groupId>
+          <artifactId>netty</artifactId>
+        </exclusion>
         <exclusion>
           <groupId>commons-logging</groupId>
           <artifactId>commons-logging</artifactId>
diff --git a/external/flume/pom.xml b/external/flume/pom.xml
index 03d3b2394f51..6aec215687fe 100644
--- a/external/flume/pom.xml
+++ b/external/flume/pom.xml
@@ -50,7 +50,7 @@
       <version>1.4.0</version>
       <exclusions>
         <exclusion>
-          <groupId>org.jboss.netty</groupId>
+          <groupId>io.netty</groupId>
           <artifactId>netty</artifactId>
         </exclusion>
         <exclusion>
diff --git a/external/mqtt/pom.xml b/external/mqtt/pom.xml
index 9aa1c1a9f5b8..7b2dc5ba1d7f 100644
--- a/external/mqtt/pom.xml
+++ b/external/mqtt/pom.xml
@@ -53,12 +53,6 @@
       <groupId>${akka.group}</groupId>
       <artifactId>akka-zeromq_${scala.binary.version}</artifactId>
       <version>${akka.version}</version>
-      <exclusions>
-        <exclusion>
-          <groupId>org.jboss.netty</groupId>
-          <artifactId>netty</artifactId>
-        </exclusion>
-      </exclusions>
     </dependency>
     <dependency>
       <groupId>org.scalatest</groupId>
diff --git a/external/twitter/pom.xml b/external/twitter/pom.xml
index a44345959471..5766d3a0d44e 100644
--- a/external/twitter/pom.xml
+++ b/external/twitter/pom.xml
@@ -48,12 +48,6 @@
       <groupId>org.twitter4j</groupId>
       <artifactId>twitter4j-stream</artifactId>
       <version>3.0.3</version>
-      <exclusions>
-        <exclusion>
-          <groupId>org.jboss.netty</groupId>
-          <artifactId>netty</artifactId>
-        </exclusion>
-      </exclusions>
     </dependency>
     <dependency>
       <groupId>org.scalatest</groupId>
diff --git a/external/zeromq/pom.xml b/external/zeromq/pom.xml
index a40e55876e64..4ed4196bd866 100644
--- a/external/zeromq/pom.xml
+++ b/external/zeromq/pom.xml
@@ -48,12 +48,6 @@
       <groupId>${akka.group}</groupId>
       <artifactId>akka-zeromq_${scala.binary.version}</artifactId>
       <version>${akka.version}</version>
-      <exclusions>
-        <exclusion>
-          <groupId>org.jboss.netty</groupId>
-          <artifactId>netty</artifactId>
-        </exclusion>
-      </exclusions>
     </dependency>
     <dependency>
       <groupId>org.scalatest</groupId>
diff --git a/pom.xml b/pom.xml
index e0bff60a54cd..c4e1c6be52a1 100644
--- a/pom.xml
+++ b/pom.xml
@@ -324,45 +324,21 @@
         <groupId>${akka.group}</groupId>
         <artifactId>akka-actor_${scala.binary.version}</artifactId>
         <version>${akka.version}</version>
-        <exclusions>
-          <exclusion>
-            <groupId>org.jboss.netty</groupId>
-            <artifactId>netty</artifactId>
-          </exclusion>
-        </exclusions>
       </dependency>
       <dependency>
         <groupId>${akka.group}</groupId>
         <artifactId>akka-remote_${scala.binary.version}</artifactId>
         <version>${akka.version}</version>
-        <exclusions>
-          <exclusion>
-            <groupId>org.jboss.netty</groupId>
-            <artifactId>netty</artifactId>
-          </exclusion>
-        </exclusions>
       </dependency>
       <dependency>
         <groupId>${akka.group}</groupId>
         <artifactId>akka-slf4j_${scala.binary.version}</artifactId>
         <version>${akka.version}</version>
-        <exclusions>
-          <exclusion>
-            <groupId>org.jboss.netty</groupId>
-            <artifactId>netty</artifactId>
-          </exclusion>
-        </exclusions>
       </dependency>
       <dependency>
         <groupId>${akka.group}</groupId>
         <artifactId>akka-testkit_${scala.binary.version}</artifactId>
         <version>${akka.version}</version>
-        <exclusions>
-          <exclusion>
-            <groupId>org.jboss.netty</groupId>
-            <artifactId>netty</artifactId>
-          </exclusion>
-        </exclusions>
       </dependency>
       <dependency>
         <groupId>colt</groupId>
@@ -513,10 +489,6 @@
         <artifactId>avro</artifactId>
         <version>${avro.version}</version>
         <exclusions>
-          <exclusion>
-            <groupId>org.jboss.netty</groupId>
-            <artifactId>netty</artifactId>
-          </exclusion>
           <exclusion>
             <groupId>io.netty</groupId>
             <artifactId>netty</artifactId>
@@ -551,10 +523,6 @@
         <artifactId>avro-mapred</artifactId>
         <version>${avro.version}</version>
         <exclusions>
-          <exclusion>
-            <groupId>org.jboss.netty</groupId>
-            <artifactId>netty</artifactId>
-          </exclusion>
           <exclusion>
             <groupId>io.netty</groupId>
             <artifactId>netty</artifactId>
diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index af882b3ea7be..a12c61853e41 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -303,7 +303,8 @@ object SparkBuild extends Build {
   val parquetVersion = "1.4.3"
   val slf4jVersion = "1.7.5"
 
-  val excludeNetty = ExclusionRule(organization = "org.jboss.netty")
+  val excludeJBossNetty = ExclusionRule(organization = "org.jboss.netty")
+  val excludeIONetty = ExclusionRule(organization = "io.netty")
   val excludeEclipseJetty = ExclusionRule(organization = "org.eclipse.jetty")
   val excludeAsm = ExclusionRule(organization = "org.ow2.asm")
   val excludeOldAsm = ExclusionRule(organization = "asm")
@@ -337,8 +338,8 @@ object SparkBuild extends Build {
         "commons-daemon"             % "commons-daemon"   % "1.0.10", // workaround for bug HADOOP-9407
         "com.ning"                   % "compress-lzf"     % "1.0.0",
         "org.xerial.snappy"          % "snappy-java"      % "1.0.5",
-        "org.spark-project.akka"    %% "akka-remote"      % akkaVersion excludeAll(excludeNetty),
-        "org.spark-project.akka"    %% "akka-slf4j"       % akkaVersion excludeAll(excludeNetty),
+        "org.spark-project.akka"    %% "akka-remote"      % akkaVersion,
+        "org.spark-project.akka"    %% "akka-slf4j"       % akkaVersion,
         "org.spark-project.akka"    %% "akka-testkit"     % akkaVersion % "test",
         "org.json4s"                %% "json4s-jackson"   % "3.2.6" excludeAll(excludeScalap),
         "colt"                       % "colt"             % "1.2.0",
@@ -346,8 +347,8 @@ object SparkBuild extends Build {
         "commons-net"                % "commons-net"      % "2.2",
         "net.java.dev.jets3t"        % "jets3t"           % jets3tVersion excludeAll(excludeCommonsLogging),
         "org.apache.derby"           % "derby"            % "10.4.2.0"                     % "test",
-        "org.apache.hadoop"          % hadoopClient       % hadoopVersion excludeAll(excludeNetty, excludeAsm, excludeCommonsLogging, excludeSLF4J, excludeOldAsm),
-        "org.apache.curator"         % "curator-recipes"  % "2.4.0" excludeAll(excludeNetty),
+        "org.apache.hadoop"          % hadoopClient       % hadoopVersion excludeAll(excludeJBossNetty, excludeAsm, excludeCommonsLogging, excludeSLF4J, excludeOldAsm),
+        "org.apache.curator"         % "curator-recipes"  % "2.4.0" excludeAll(excludeJBossNetty),
         "com.codahale.metrics"       % "metrics-core"     % codahaleMetricsVersion,
         "com.codahale.metrics"       % "metrics-jvm"      % codahaleMetricsVersion,
         "com.codahale.metrics"       % "metrics-json"     % codahaleMetricsVersion,
@@ -421,7 +422,7 @@ object SparkBuild extends Build {
       v => "spark-examples-" + v + "-hadoop" + hadoopVersion + ".jar" },
     libraryDependencies ++= Seq(
       "com.twitter"          %% "algebird-core"   % "0.1.11",
-      "org.apache.hbase" % "hbase" % HBASE_VERSION excludeAll(excludeNetty, excludeAsm, excludeOldAsm, excludeCommonsLogging, excludeJruby),
+      "org.apache.hbase" % "hbase" % HBASE_VERSION excludeAll(excludeIONetty, excludeJBossNetty, excludeAsm, excludeOldAsm, excludeCommonsLogging, excludeJruby),
       "org.apache.cassandra" % "cassandra-all" % "1.2.6"
         exclude("com.google.guava", "guava")
         exclude("com.googlecode.concurrentlinkedhashmap", "concurrentlinkedhashmap-lru")
@@ -429,7 +430,7 @@ object SparkBuild extends Build {
         exclude("io.netty", "netty")
         exclude("jline","jline")
         exclude("org.apache.cassandra.deps", "avro")
-        excludeAll(excludeSLF4J),
+        excludeAll(excludeSLF4J, excludeIONetty),
       "com.github.scopt" %% "scopt" % "3.2.0"
     )
   ) ++ assemblySettings ++ extraAssemblySettings
@@ -561,11 +562,11 @@ object SparkBuild extends Build {
   def yarnEnabledSettings = Seq(
     libraryDependencies ++= Seq(
       // Exclude rule required for all ?
-      "org.apache.hadoop" % hadoopClient         % hadoopVersion excludeAll(excludeNetty, excludeAsm, excludeOldAsm),
-      "org.apache.hadoop" % "hadoop-yarn-api"    % hadoopVersion excludeAll(excludeNetty, excludeAsm, excludeOldAsm),
-      "org.apache.hadoop" % "hadoop-yarn-common" % hadoopVersion excludeAll(excludeNetty, excludeAsm, excludeOldAsm),
-      "org.apache.hadoop" % "hadoop-yarn-client" % hadoopVersion excludeAll(excludeNetty, excludeAsm, excludeOldAsm),
-      "org.apache.hadoop" % "hadoop-yarn-server-web-proxy" % hadoopVersion excludeAll(excludeNetty, excludeAsm, excludeOldAsm)
+      "org.apache.hadoop" % hadoopClient         % hadoopVersion excludeAll(excludeJBossNetty, excludeAsm, excludeOldAsm),
+      "org.apache.hadoop" % "hadoop-yarn-api"    % hadoopVersion excludeAll(excludeJBossNetty, excludeAsm, excludeOldAsm),
+      "org.apache.hadoop" % "hadoop-yarn-common" % hadoopVersion excludeAll(excludeJBossNetty, excludeAsm, excludeOldAsm),
+      "org.apache.hadoop" % "hadoop-yarn-client" % hadoopVersion excludeAll(excludeJBossNetty, excludeAsm, excludeOldAsm),
+      "org.apache.hadoop" % "hadoop-yarn-server-web-proxy" % hadoopVersion excludeAll(excludeJBossNetty, excludeAsm, excludeOldAsm)
     )
   )
 
@@ -593,7 +594,7 @@ object SparkBuild extends Build {
     name := "spark-streaming-twitter",
     previousArtifact := sparkPreviousArtifact("spark-streaming-twitter"),
     libraryDependencies ++= Seq(
-      "org.twitter4j" % "twitter4j-stream" % "3.0.3" excludeAll(excludeNetty)
+      "org.twitter4j" % "twitter4j-stream" % "3.0.3"
     )
   )
 
@@ -601,12 +602,12 @@ object SparkBuild extends Build {
     name := "spark-streaming-kafka",
     previousArtifact := sparkPreviousArtifact("spark-streaming-kafka"),
     libraryDependencies ++= Seq(
-      "com.github.sgroschupf"    % "zkclient"   % "0.1"          excludeAll(excludeNetty),
+      "com.github.sgroschupf"    % "zkclient"   % "0.1",
       "org.apache.kafka"        %% "kafka"      % "0.8.0"
         exclude("com.sun.jdmk", "jmxtools")
         exclude("com.sun.jmx", "jmxri")
         exclude("net.sf.jopt-simple", "jopt-simple")
-        excludeAll(excludeNetty, excludeSLF4J)
+        excludeAll(excludeSLF4J)
     )
   )
 
@@ -614,7 +615,7 @@ object SparkBuild extends Build {
     name := "spark-streaming-flume",
     previousArtifact := sparkPreviousArtifact("spark-streaming-flume"),
     libraryDependencies ++= Seq(
-      "org.apache.flume" % "flume-ng-sdk" % "1.4.0" % "compile" excludeAll(excludeNetty, excludeThrift)
+      "org.apache.flume" % "flume-ng-sdk" % "1.4.0" % "compile" excludeAll(excludeIONetty, excludeThrift)
     )
   )
 
@@ -622,7 +623,7 @@ object SparkBuild extends Build {
     name := "spark-streaming-zeromq",
     previousArtifact := sparkPreviousArtifact("spark-streaming-zeromq"),
     libraryDependencies ++= Seq(
-      "org.spark-project.akka" %% "akka-zeromq" % akkaVersion excludeAll(excludeNetty)
+      "org.spark-project.akka" %% "akka-zeromq" % akkaVersion
     )
   )
 

From 83e0424d87022e7a967088365931a08aa06ffd9f Mon Sep 17 00:00:00 2001
From: Andrew Or <andrewor14@gmail.com>
Date: Sat, 10 May 2014 20:58:02 -0700
Subject: [PATCH 154/810] [SPARK-1774] Respect SparkSubmit --jars on YARN
 (client)

SparkSubmit ignores `--jars` for YARN client. This is a bug.

This PR also automatically adds the application jar to `spark.jar`. Previously, when running as yarn-client, you must specify the jar additionally through `--files` (because `--jars` didn't work). Now you don't have to explicitly specify it through either.

Tested on a YARN cluster.

Author: Andrew Or <andrewor14@gmail.com>

Closes #710 from andrewor14/yarn-jars and squashes the following commits:

35d1928 [Andrew Or] Merge branch 'master' of github.com:apache/spark into yarn-jars
c27bf6c [Andrew Or] For yarn-cluster and python, do not add primaryResource to spark.jar
c92c5bf [Andrew Or] Minor cleanups
269f9f3 [Andrew Or] Fix format
013d840 [Andrew Or] Fix tests
1407474 [Andrew Or] Merge branch 'master' of github.com:apache/spark into yarn-jars
3bb75e8 [Andrew Or] Allow SparkSubmit --jars to take effect in yarn-client mode
---
 .../scala/org/apache/spark/SparkContext.scala |   2 +-
 .../org/apache/spark/deploy/SparkSubmit.scala |  39 ++++---
 .../spark/deploy/SparkSubmitSuite.scala       | 110 ++++++++++++------
 .../spark/deploy/yarn/ClientArguments.scala   |   4 +-
 4 files changed, 102 insertions(+), 53 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index c639b3e15ded..71bab295442f 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -917,7 +917,7 @@ class SparkContext(config: SparkConf) extends Logging {
             if (SparkHadoopUtil.get.isYarnMode() &&
                 (master == "yarn-standalone" || master == "yarn-cluster")) {
               // In order for this to work in yarn-cluster mode the user must specify the
-              // --addjars option to the client to upload the file into the distributed cache
+              // --addJars option to the client to upload the file into the distributed cache
               // of the AM to make it show up in the current working directory.
               val fileName = new Path(uri.getPath).getName()
               try {
diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
index 16de6f7cdb10..c6d3cbd2e728 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
@@ -67,8 +67,7 @@ object SparkSubmit {
   private[spark] def printWarning(str: String) = printStream.println("Warning: " + str)
 
   /**
-   * @return
-   *         a tuple containing the arguments for the child, a list of classpath
+   * @return a tuple containing the arguments for the child, a list of classpath
    *         entries for the child, a list of system propertes, a list of env vars
    *         and the main class for the child
    */
@@ -115,13 +114,16 @@ object SparkSubmit {
     val sysProps = new HashMap[String, String]()
     var childMainClass = ""
 
+    val isPython = args.isPython
+    val isYarnCluster = clusterManager == YARN && deployOnCluster
+
     if (clusterManager == MESOS && deployOnCluster) {
       printErrorAndExit("Cannot currently run driver on the cluster in Mesos")
     }
 
     // If we're running a Python app, set the Java class to run to be our PythonRunner, add
     // Python files to deployment list, and pass the main file and Python path to PythonRunner
-    if (args.isPython) {
+    if (isPython) {
       if (deployOnCluster) {
         printErrorAndExit("Cannot currently run Python driver programs on cluster")
       }
@@ -161,6 +163,7 @@ object SparkSubmit {
     val options = List[OptionAssigner](
       OptionAssigner(args.master, ALL_CLUSTER_MGRS, false, sysProp = "spark.master"),
       OptionAssigner(args.name, ALL_CLUSTER_MGRS, false, sysProp = "spark.app.name"),
+      OptionAssigner(args.name, YARN, true, clOption = "--name", sysProp = "spark.app.name"),
       OptionAssigner(args.driverExtraClassPath, STANDALONE | YARN, true,
         sysProp = "spark.driver.extraClassPath"),
       OptionAssigner(args.driverExtraJavaOptions, STANDALONE | YARN, true,
@@ -168,7 +171,8 @@ object SparkSubmit {
       OptionAssigner(args.driverExtraLibraryPath, STANDALONE | YARN, true,
         sysProp = "spark.driver.extraLibraryPath"),
       OptionAssigner(args.driverMemory, YARN, true, clOption = "--driver-memory"),
-      OptionAssigner(args.name, YARN, true, clOption = "--name", sysProp = "spark.app.name"),
+      OptionAssigner(args.driverMemory, STANDALONE, true, clOption = "--memory"),
+      OptionAssigner(args.driverCores, STANDALONE, true, clOption = "--cores"),
       OptionAssigner(args.queue, YARN, true, clOption = "--queue"),
       OptionAssigner(args.queue, YARN, false, sysProp = "spark.yarn.queue"),
       OptionAssigner(args.numExecutors, YARN, true, clOption = "--num-executors"),
@@ -176,20 +180,18 @@ object SparkSubmit {
       OptionAssigner(args.executorMemory, YARN, true, clOption = "--executor-memory"),
       OptionAssigner(args.executorMemory, STANDALONE | MESOS | YARN, false,
         sysProp = "spark.executor.memory"),
-      OptionAssigner(args.driverMemory, STANDALONE, true, clOption = "--memory"),
-      OptionAssigner(args.driverCores, STANDALONE, true, clOption = "--cores"),
       OptionAssigner(args.executorCores, YARN, true, clOption = "--executor-cores"),
       OptionAssigner(args.executorCores, YARN, false, sysProp = "spark.executor.cores"),
       OptionAssigner(args.totalExecutorCores, STANDALONE | MESOS, false,
         sysProp = "spark.cores.max"),
       OptionAssigner(args.files, YARN, false, sysProp = "spark.yarn.dist.files"),
       OptionAssigner(args.files, YARN, true, clOption = "--files"),
+      OptionAssigner(args.files, LOCAL | STANDALONE | MESOS, false, sysProp = "spark.files"),
+      OptionAssigner(args.files, LOCAL | STANDALONE | MESOS, true, sysProp = "spark.files"),
       OptionAssigner(args.archives, YARN, false, sysProp = "spark.yarn.dist.archives"),
       OptionAssigner(args.archives, YARN, true, clOption = "--archives"),
       OptionAssigner(args.jars, YARN, true, clOption = "--addJars"),
-      OptionAssigner(args.files, LOCAL | STANDALONE | MESOS, false, sysProp = "spark.files"),
-      OptionAssigner(args.files, LOCAL | STANDALONE | MESOS, true, sysProp = "spark.files"),
-      OptionAssigner(args.jars, LOCAL | STANDALONE | MESOS, false, sysProp = "spark.jars")
+      OptionAssigner(args.jars, ALL_CLUSTER_MGRS, false, sysProp = "spark.jars")
     )
 
     // For client mode make any added jars immediately visible on the classpath
@@ -212,9 +214,10 @@ object SparkSubmit {
       }
     }
 
-    // For standalone mode, add the application jar automatically so the user doesn't have to
-    // call sc.addJar. TODO: Standalone mode in the cluster
-    if (clusterManager == STANDALONE) {
+    // Add the application jar automatically so the user doesn't have to call sc.addJar
+    // For YARN cluster mode, the jar is already distributed on each node as "app.jar"
+    // For python files, the primary resource is already distributed as a regular file
+    if (!isYarnCluster && !isPython) {
       var jars = sysProps.get("spark.jars").map(x => x.split(",").toSeq).getOrElse(Seq())
       if (args.primaryResource != RESERVED_JAR_NAME) {
         jars = jars ++ Seq(args.primaryResource)
@@ -222,11 +225,11 @@ object SparkSubmit {
       sysProps.put("spark.jars", jars.mkString(","))
     }
 
+    // Standalone cluster specific configurations
     if (deployOnCluster && clusterManager == STANDALONE) {
       if (args.supervise) {
         childArgs += "--supervise"
       }
-
       childMainClass = "org.apache.spark.deploy.Client"
       childArgs += "launch"
       childArgs += (args.master, args.primaryResource, args.mainClass)
@@ -243,6 +246,7 @@ object SparkSubmit {
       }
     }
 
+    // Read from default spark properties, if any
     for ((k, v) <- args.getDefaultSparkProperties) {
       if (!sysProps.contains(k)) sysProps(k) = v
     }
@@ -250,9 +254,12 @@ object SparkSubmit {
     (childArgs, childClasspath, sysProps, childMainClass)
   }
 
-  private def launch(childArgs: ArrayBuffer[String], childClasspath: ArrayBuffer[String],
-      sysProps: Map[String, String], childMainClass: String, verbose: Boolean = false)
-  {
+  private def launch(
+      childArgs: ArrayBuffer[String],
+      childClasspath: ArrayBuffer[String],
+      sysProps: Map[String, String],
+      childMainClass: String,
+      verbose: Boolean = false) {
     if (verbose) {
       printStream.println(s"Main class:\n$childMainClass")
       printStream.println(s"Arguments:\n${childArgs.mkString("\n")}")
diff --git a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala
index c9edb03cdeb0..6c0deede5378 100644
--- a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala
@@ -87,25 +87,41 @@ class SparkSubmitSuite extends FunSuite with ShouldMatchers {
   }
 
   test("handles arguments with --key=val") {
-    val clArgs = Seq("--jars=one.jar,two.jar,three.jar", "--name=myApp")
+    val clArgs = Seq(
+      "--jars=one.jar,two.jar,three.jar",
+      "--name=myApp")
     val appArgs = new SparkSubmitArguments(clArgs)
     appArgs.jars should be ("one.jar,two.jar,three.jar")
     appArgs.name should be ("myApp")
   }
 
   test("handles arguments to user program") {
-    val clArgs = Seq("--name", "myApp", "--class", "Foo", "userjar.jar", "some", "--weird", "args")
+    val clArgs = Seq(
+      "--name", "myApp",
+      "--class", "Foo",
+      "userjar.jar",
+      "some",
+      "--weird", "args")
     val appArgs = new SparkSubmitArguments(clArgs)
     appArgs.childArgs should be (Seq("some", "--weird", "args"))
   }
 
   test("handles YARN cluster mode") {
-    val clArgs = Seq("--deploy-mode", "cluster",
-      "--master", "yarn", "--executor-memory", "5g", "--executor-cores", "5",
-      "--class", "org.SomeClass", "--jars", "one.jar,two.jar,three.jar",
-      "--driver-memory", "4g", "--queue", "thequeue", "--files", "file1.txt,file2.txt",
-      "--archives", "archive1.txt,archive2.txt", "--num-executors", "6", "--name", "beauty",
-      "thejar.jar", "arg1", "arg2")
+    val clArgs = Seq(
+      "--deploy-mode", "cluster",
+      "--master", "yarn",
+      "--executor-memory", "5g",
+      "--executor-cores", "5",
+      "--class", "org.SomeClass",
+      "--jars", "one.jar,two.jar,three.jar",
+      "--driver-memory", "4g",
+      "--queue", "thequeue",
+      "--files", "file1.txt,file2.txt",
+      "--archives", "archive1.txt,archive2.txt",
+      "--num-executors", "6",
+      "--name", "beauty",
+      "thejar.jar",
+      "arg1", "arg2")
     val appArgs = new SparkSubmitArguments(clArgs)
     val (childArgs, classpath, sysProps, mainClass) = createLaunchEnv(appArgs)
     val childArgsStr = childArgs.mkString(" ")
@@ -127,12 +143,21 @@ class SparkSubmitSuite extends FunSuite with ShouldMatchers {
   }
 
   test("handles YARN client mode") {
-    val clArgs = Seq("--deploy-mode", "client",
-      "--master", "yarn", "--executor-memory", "5g", "--executor-cores", "5",
-      "--class", "org.SomeClass", "--jars", "one.jar,two.jar,three.jar",
-      "--driver-memory", "4g", "--queue", "thequeue", "--files", "file1.txt,file2.txt",
-      "--archives", "archive1.txt,archive2.txt", "--num-executors", "6", "--name", "trill",
-      "thejar.jar", "arg1", "arg2")
+    val clArgs = Seq(
+      "--deploy-mode", "client",
+      "--master", "yarn",
+      "--executor-memory", "5g",
+      "--executor-cores", "5",
+      "--class", "org.SomeClass",
+      "--jars", "one.jar,two.jar,three.jar",
+      "--driver-memory", "4g",
+      "--queue", "thequeue",
+      "--files", "file1.txt,file2.txt",
+      "--archives", "archive1.txt,archive2.txt",
+      "--num-executors", "6",
+      "--name", "trill",
+      "thejar.jar",
+      "arg1", "arg2")
     val appArgs = new SparkSubmitArguments(clArgs)
     val (childArgs, classpath, sysProps, mainClass) = createLaunchEnv(appArgs)
     childArgs.mkString(" ") should be ("arg1 arg2")
@@ -142,6 +167,7 @@ class SparkSubmitSuite extends FunSuite with ShouldMatchers {
     classpath should contain ("two.jar")
     classpath should contain ("three.jar")
     sysProps("spark.app.name") should be ("trill")
+    sysProps("spark.jars") should be ("one.jar,two.jar,three.jar,thejar.jar")
     sysProps("spark.executor.memory") should be ("5g")
     sysProps("spark.executor.cores") should be ("5")
     sysProps("spark.yarn.queue") should be ("thequeue")
@@ -152,9 +178,15 @@ class SparkSubmitSuite extends FunSuite with ShouldMatchers {
   }
 
   test("handles standalone cluster mode") {
-    val clArgs = Seq("--deploy-mode", "cluster",
-      "--master", "spark://h:p", "--class", "org.SomeClass",
-      "--supervise", "--driver-memory", "4g", "--driver-cores", "5", "thejar.jar", "arg1", "arg2")
+    val clArgs = Seq(
+      "--deploy-mode", "cluster",
+      "--master", "spark://h:p",
+      "--class", "org.SomeClass",
+      "--supervise",
+      "--driver-memory", "4g",
+      "--driver-cores", "5",
+      "thejar.jar",
+      "arg1", "arg2")
     val appArgs = new SparkSubmitArguments(clArgs)
     val (childArgs, classpath, sysProps, mainClass) = createLaunchEnv(appArgs)
     val childArgsStr = childArgs.mkString(" ")
@@ -166,9 +198,15 @@ class SparkSubmitSuite extends FunSuite with ShouldMatchers {
   }
 
   test("handles standalone client mode") {
-    val clArgs = Seq("--deploy-mode", "client",
-      "--master", "spark://h:p", "--executor-memory", "5g", "--total-executor-cores", "5",
-      "--class", "org.SomeClass", "--driver-memory", "4g", "thejar.jar", "arg1", "arg2")
+    val clArgs = Seq(
+      "--deploy-mode", "client",
+      "--master", "spark://h:p",
+      "--executor-memory", "5g",
+      "--total-executor-cores", "5",
+      "--class", "org.SomeClass",
+      "--driver-memory", "4g",
+      "thejar.jar",
+      "arg1", "arg2")
     val appArgs = new SparkSubmitArguments(clArgs)
     val (childArgs, classpath, sysProps, mainClass) = createLaunchEnv(appArgs)
     childArgs.mkString(" ") should be ("arg1 arg2")
@@ -179,9 +217,15 @@ class SparkSubmitSuite extends FunSuite with ShouldMatchers {
   }
 
   test("handles mesos client mode") {
-    val clArgs = Seq("--deploy-mode", "client",
-      "--master", "mesos://h:p", "--executor-memory", "5g", "--total-executor-cores", "5",
-      "--class", "org.SomeClass", "--driver-memory", "4g", "thejar.jar", "arg1", "arg2")
+    val clArgs = Seq(
+      "--deploy-mode", "client",
+      "--master", "mesos://h:p",
+      "--executor-memory", "5g",
+      "--total-executor-cores", "5",
+      "--class", "org.SomeClass",
+      "--driver-memory", "4g",
+      "thejar.jar",
+      "arg1", "arg2")
     val appArgs = new SparkSubmitArguments(clArgs)
     val (childArgs, classpath, sysProps, mainClass) = createLaunchEnv(appArgs)
     childArgs.mkString(" ") should be ("arg1 arg2")
@@ -192,15 +236,17 @@ class SparkSubmitSuite extends FunSuite with ShouldMatchers {
   }
 
   test("launch simple application with spark-submit") {
-    runSparkSubmit(
-      Seq(
-        "--class", SimpleApplicationTest.getClass.getName.stripSuffix("$"),
-        "--name", "testApp",
-        "--master", "local",
-        "unUsed.jar"))
+    val unusedJar = TestUtils.createJarWithClasses(Seq.empty)
+    val args = Seq(
+      "--class", SimpleApplicationTest.getClass.getName.stripSuffix("$"),
+      "--name", "testApp",
+      "--master", "local",
+      unusedJar.toString)
+    runSparkSubmit(args)
   }
 
   test("spark submit includes jars passed in through --jar") {
+    val unusedJar = TestUtils.createJarWithClasses(Seq.empty)
     val jar1 = TestUtils.createJarWithClasses(Seq("SparkSubmitClassA"))
     val jar2 = TestUtils.createJarWithClasses(Seq("SparkSubmitClassB"))
     val jarsString = Seq(jar1, jar2).map(j => j.toString).mkString(",")
@@ -209,7 +255,7 @@ class SparkSubmitSuite extends FunSuite with ShouldMatchers {
       "--name", "testApp",
       "--master", "local-cluster[2,1,512]",
       "--jars", jarsString,
-      "unused.jar")
+      unusedJar.toString)
     runSparkSubmit(args)
   }
 
@@ -227,7 +273,7 @@ object JarCreationTest {
   def main(args: Array[String]) {
     val conf = new SparkConf()
     val sc = new SparkContext(conf)
-    val result = sc.makeRDD(1 to 100, 10).mapPartitions{ x =>
+    val result = sc.makeRDD(1 to 100, 10).mapPartitions { x =>
       var foundClasses = false
       try {
         Class.forName("SparkSubmitClassA", true, Thread.currentThread().getContextClassLoader)
@@ -248,7 +294,6 @@ object SimpleApplicationTest {
   def main(args: Array[String]) {
     val conf = new SparkConf()
     val sc = new SparkContext(conf)
-
     val configs = Seq("spark.master", "spark.app.name")
     for (config <- configs) {
       val masterValue = conf.get(config)
@@ -266,6 +311,5 @@ object SimpleApplicationTest {
           s"Master had $config=$masterValue but executor had $config=$executorValue")
       }
     }
-
   }
 }
diff --git a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala
index 3e4c739e34fe..b2c413b6d267 100644
--- a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala
+++ b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala
@@ -20,7 +20,7 @@ package org.apache.spark.deploy.yarn
 import scala.collection.mutable.{ArrayBuffer, HashMap}
 
 import org.apache.spark.SparkConf
-import org.apache.spark.scheduler.{InputFormatInfo, SplitInfo}
+import org.apache.spark.scheduler.InputFormatInfo
 import org.apache.spark.util.IntParam
 import org.apache.spark.util.MemoryParam
 
@@ -40,9 +40,7 @@ class ClientArguments(val args: Array[String], val sparkConf: SparkConf) {
   var amMemory: Int = 512 // MB
   var amClass: String = "org.apache.spark.deploy.yarn.ApplicationMaster"
   var appName: String = "Spark"
-  // TODO
   var inputFormatInfo: List[InputFormatInfo] = null
-  // TODO(harvey)
   var priority = 0
 
   parseArgs(args.toList)

From 70bcdef48a051028598d380d41dfce1c9bfb2b9b Mon Sep 17 00:00:00 2001
From: Prashant Sharma <prashant.s@imaginea.com>
Date: Sat, 10 May 2014 21:08:04 -0700
Subject: [PATCH 155/810] Enabled incremental build that comes with sbt 0.13.2

More info at. https://github.com/sbt/sbt/issues/1010

Author: Prashant Sharma <prashant.s@imaginea.com>

Closes #525 from ScrapCodes/sbt-inc-opt and squashes the following commits:

ba8fa42 [Prashant Sharma] Enabled incremental build that comes with sbt 0.13.2
---
 project/SparkBuild.scala | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index a12c61853e41..12791e490ae2 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -176,7 +176,7 @@ object SparkBuild extends Build {
     retrievePattern := "[type]s/[artifact](-[revision])(-[classifier]).[ext]",
     transitiveClassifiers in Scope.GlobalScope := Seq("sources"),
     testListeners <<= target.map(t => Seq(new eu.henkelmann.sbt.JUnitXmlTestsListener(t.getAbsolutePath))),
-
+    incOptions := incOptions.value.withNameHashing(true),
     // Fork new JVMs for tests and set Java options for those
     fork := true,
     javaOptions in Test += "-Dspark.home=" + sparkHome,

From 6bee01dd04ef73c6b829110ebcdd622d521ea8ff Mon Sep 17 00:00:00 2001
From: witgo <witgo@qq.com>
Date: Sun, 11 May 2014 14:34:27 -0700
Subject: [PATCH 156/810] remove outdated runtime Information scala home

Author: witgo <witgo@qq.com>

Closes #728 from witgo/scala_home and squashes the following commits:

cdfd8be [witgo] Merge branch 'master' of https://github.com/apache/spark into scala_home
fac094a [witgo] remove outdated runtime Information scala home
---
 core/src/main/scala/org/apache/spark/SparkEnv.scala | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/SparkEnv.scala b/core/src/main/scala/org/apache/spark/SparkEnv.scala
index 806e77d98fc5..19d507c0cf86 100644
--- a/core/src/main/scala/org/apache/spark/SparkEnv.scala
+++ b/core/src/main/scala/org/apache/spark/SparkEnv.scala
@@ -281,8 +281,7 @@ object SparkEnv extends Logging {
     val jvmInformation = Seq(
       ("Java Version", "%s (%s)".format(Properties.javaVersion, Properties.javaVendor)),
       ("Java Home", Properties.javaHome),
-      ("Scala Version", Properties.versionString),
-      ("Scala Home", Properties.scalaHome)
+      ("Scala Version", Properties.versionString)
     ).sorted
 
     // Spark properties

From 7d9cc9214bd06495f6838e355331dd2b5f1f7407 Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Sun, 11 May 2014 17:11:55 -0700
Subject: [PATCH 157/810] SPARK-1770: Load balance elements when
 repartitioning.

This patch adds better balancing when performing a repartition of an
RDD. Previously the elements in the RDD were hash partitioned, meaning
if the RDD was skewed certain partitions would end up being very large.

This commit adds load balancing of elements across the repartitioned
RDD splits. The load balancing is not perfect: a given output partition
can have up to N more elements than the average if there are N input
partitions. However, some randomization is used to minimize the
probabiliy that this happens.

Author: Patrick Wendell <pwendell@gmail.com>

Closes #727 from pwendell/load-balance and squashes the following commits:

f9da752 [Patrick Wendell] Response to Matei's feedback
acfa46a [Patrick Wendell] SPARK-1770: Load balance elements when repartitioning.
---
 .../main/scala/org/apache/spark/rdd/RDD.scala | 15 +++++++--
 .../scala/org/apache/spark/rdd/RDDSuite.scala | 33 +++++++++++++++++++
 2 files changed, 46 insertions(+), 2 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/rdd/RDD.scala b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
index a1ca612cc9a0..aa03e9276fb3 100644
--- a/core/src/main/scala/org/apache/spark/rdd/RDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
@@ -328,11 +328,22 @@ abstract class RDD[T: ClassTag](
   def coalesce(numPartitions: Int, shuffle: Boolean = false)(implicit ord: Ordering[T] = null)
       : RDD[T] = {
     if (shuffle) {
+      /** Distributes elements evenly across output partitions, starting from a random partition. */
+      def distributePartition(index: Int, items: Iterator[T]): Iterator[(Int, T)] = {
+        var position = (new Random(index)).nextInt(numPartitions)
+        items.map { t =>
+          // Note that the hash code of the key will just be the key itself. The HashPartitioner 
+          // will mod it with the number of total partitions.
+          position = position + 1
+          (position, t)
+        }
+      }
+
       // include a shuffle step so that our upstream tasks are still distributed
       new CoalescedRDD(
-        new ShuffledRDD[T, Null, (T, Null)](map(x => (x, null)),
+        new ShuffledRDD[Int, T, (Int, T)](mapPartitionsWithIndex(distributePartition),
         new HashPartitioner(numPartitions)),
-        numPartitions).keys
+        numPartitions).values
     } else {
       new CoalescedRDD(this, numPartitions)
     }
diff --git a/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala b/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala
index 8da9a0da700e..e686068f7a99 100644
--- a/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala
+++ b/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala
@@ -202,6 +202,39 @@ class RDDSuite extends FunSuite with SharedSparkContext {
     assert(repartitioned2.collect().toSet === (1 to 1000).toSet)
   }
 
+  test("repartitioned RDDs perform load balancing") {
+    // Coalesce partitions
+    val input = Array.fill(1000)(1)
+    val initialPartitions = 10
+    val data = sc.parallelize(input, initialPartitions)
+
+    val repartitioned1 = data.repartition(2)
+    assert(repartitioned1.partitions.size == 2)
+    val partitions1 = repartitioned1.glom().collect()
+    // some noise in balancing is allowed due to randomization
+    assert(math.abs(partitions1(0).length - 500) < initialPartitions)
+    assert(math.abs(partitions1(1).length - 500) < initialPartitions)
+    assert(repartitioned1.collect() === input)
+
+    def testSplitPartitions(input: Seq[Int], initialPartitions: Int, finalPartitions: Int) {
+      val data = sc.parallelize(input, initialPartitions)
+      val repartitioned = data.repartition(finalPartitions)
+      assert(repartitioned.partitions.size === finalPartitions)
+      val partitions = repartitioned.glom().collect()
+      // assert all elements are present
+      assert(repartitioned.collect().sortWith(_ > _).toSeq === input.toSeq.sortWith(_ > _).toSeq)
+      // assert no bucket is overloaded
+      for (partition <- partitions) {
+        val avg = input.size / finalPartitions
+        val maxPossible = avg + initialPartitions
+        assert(partition.length <=  maxPossible)
+      }
+    }
+
+    testSplitPartitions(Array.fill(100)(1), 10, 20)
+    testSplitPartitions(Array.fill(10000)(1) ++ Array.fill(10000)(2), 20, 100)
+  }
+
   test("coalesced RDDs") {
     val data = sc.parallelize(1 to 10, 10)
 

From 05c9aa9eb1b7f13cd40bbca23e6bc7e1d20e91cd Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Sun, 11 May 2014 18:17:34 -0700
Subject: [PATCH 158/810] SPARK-1652: Set driver memory correctly in
 spark-submit.

The previous check didn't account for the fact that the default
deploy mode is "client" unless otherwise specified. Also, this
sets the more narrowly defined SPARK_DRIVER_MEMORY instead of setting
SPARK_MEM.

Author: Patrick Wendell <pwendell@gmail.com>

Closes #730 from pwendell/spark-submit and squashes the following commits:

430b98f [Patrick Wendell] Feedback from Aaron
e788edf [Patrick Wendell] Changes based on Aaron's feedback
f508146 [Patrick Wendell] SPARK-1652: Set driver memory correctly in spark-submit.
---
 bin/spark-submit | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/bin/spark-submit b/bin/spark-submit
index 49bc26252cad..63903b17a290 100755
--- a/bin/spark-submit
+++ b/bin/spark-submit
@@ -35,8 +35,10 @@ while (($#)); do
   shift
 done
 
-if [ ! -z $DRIVER_MEMORY ] && [ ! -z $DEPLOY_MODE ] && [ $DEPLOY_MODE = "client" ]; then
-  export SPARK_MEM=$DRIVER_MEMORY
+DEPLOY_MODE=${DEPLOY_MODE:-"client"}
+
+if [ -n "$DRIVER_MEMORY" ] && [ $DEPLOY_MODE == "client" ]; then
+  export SPARK_DRIVER_MEMORY=$DRIVER_MEMORY
 fi
 
 $SPARK_HOME/bin/spark-class org.apache.spark.deploy.SparkSubmit "${ORIG_ARGS[@]}"

From f938a155b2a9c126b292d5403aca31de83d5105a Mon Sep 17 00:00:00 2001
From: "Joseph E. Gonzalez" <joseph.e.gonzalez@gmail.com>
Date: Sun, 11 May 2014 18:33:46 -0700
Subject: [PATCH 159/810] Fix error in 2d Graph Partitioner

Their was a minor bug in which negative partition ids could be generated when constructing a 2D partitioning of a graph.  This could lead to an inefficient 2D partition for large vertex id values.

Author: Joseph E. Gonzalez <joseph.e.gonzalez@gmail.com>

Closes #709 from jegonzal/fix_2d_partitioning and squashes the following commits:

937c562 [Joseph E. Gonzalez] fixing bug in 2d partitioning algorithm where negative partition ids could be generated.
---
 .../scala/org/apache/spark/graphx/PartitionStrategy.scala     | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/graphx/src/main/scala/org/apache/spark/graphx/PartitionStrategy.scala b/graphx/src/main/scala/org/apache/spark/graphx/PartitionStrategy.scala
index 0470d74cf9ef..1526ccef06fd 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/PartitionStrategy.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/PartitionStrategy.scala
@@ -78,8 +78,8 @@ object PartitionStrategy {
     override def getPartition(src: VertexId, dst: VertexId, numParts: PartitionID): PartitionID = {
       val ceilSqrtNumParts: PartitionID = math.ceil(math.sqrt(numParts)).toInt
       val mixingPrime: VertexId = 1125899906842597L
-      val col: PartitionID = ((math.abs(src) * mixingPrime) % ceilSqrtNumParts).toInt
-      val row: PartitionID = ((math.abs(dst) * mixingPrime) % ceilSqrtNumParts).toInt
+      val col: PartitionID = (math.abs(src * mixingPrime) % ceilSqrtNumParts).toInt
+      val row: PartitionID = (math.abs(dst * mixingPrime) % ceilSqrtNumParts).toInt
       (col * ceilSqrtNumParts + row) % numParts
     }
   }

From a6b02fb7486356493474c7f42bb714c9cce215ca Mon Sep 17 00:00:00 2001
From: Ankur Dave <ankurdave@gmail.com>
Date: Sun, 11 May 2014 19:20:42 -0700
Subject: [PATCH 160/810] SPARK-1786: Edge Partition Serialization

This appears to address the issue with edge partition serialization.  The solution appears to be just registering the `PrimitiveKeyOpenHashMap`.  However I noticed that we appear to have forked that code in GraphX but retained the same name (which is confusing).  I also renamed our local copy to `GraphXPrimitiveKeyOpenHashMap`.  We should consider dropping that and using the one in Spark if possible.

Author: Ankur Dave <ankurdave@gmail.com>
Author: Joseph E. Gonzalez <joseph.e.gonzalez@gmail.com>

Closes #724 from jegonzal/edge_partition_serialization and squashes the following commits:

b0a525a [Ankur Dave] Disable reference tracking to fix serialization test
bb7f548 [Ankur Dave] Add failing test for EdgePartition Kryo serialization
67dac22 [Joseph E. Gonzalez] Making EdgePartition serializable.
---
 .../spark/graphx/GraphKryoRegistrator.scala    |  9 ++++++---
 .../spark/graphx/impl/EdgePartition.scala      | 14 +++++++-------
 .../graphx/impl/EdgePartitionBuilder.scala     |  4 ++--
 .../graphx/impl/EdgeTripletIterator.scala      |  2 +-
 .../graphx/impl/RoutingTablePartition.scala    |  4 ++--
 .../graphx/impl/ShippableVertexPartition.scala |  2 +-
 .../spark/graphx/impl/VertexPartition.scala    |  2 +-
 .../graphx/impl/VertexPartitionBase.scala      |  6 +++---
 .../graphx/impl/VertexPartitionBaseOps.scala   |  4 ++--
 ...ala => GraphXPrimitiveKeyOpenHashMap.scala} |  2 +-
 .../spark/graphx/impl/EdgePartitionSuite.scala | 18 ++++++++++++++++++
 11 files changed, 44 insertions(+), 23 deletions(-)
 rename graphx/src/main/scala/org/apache/spark/graphx/util/collection/{PrimitiveKeyOpenHashMap.scala => GraphXPrimitiveKeyOpenHashMap.scala} (98%)

diff --git a/graphx/src/main/scala/org/apache/spark/graphx/GraphKryoRegistrator.scala b/graphx/src/main/scala/org/apache/spark/graphx/GraphKryoRegistrator.scala
index d295d0127ac7..f97f329c0e83 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/GraphKryoRegistrator.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/GraphKryoRegistrator.scala
@@ -24,6 +24,9 @@ import org.apache.spark.util.BoundedPriorityQueue
 import org.apache.spark.util.collection.BitSet
 
 import org.apache.spark.graphx.impl._
+import org.apache.spark.graphx.util.collection.GraphXPrimitiveKeyOpenHashMap
+import org.apache.spark.util.collection.OpenHashSet
+
 
 /**
  * Registers GraphX classes with Kryo for improved performance.
@@ -43,8 +46,8 @@ class GraphKryoRegistrator extends KryoRegistrator {
     kryo.register(classOf[PartitionStrategy])
     kryo.register(classOf[BoundedPriorityQueue[Object]])
     kryo.register(classOf[EdgeDirection])
-
-    // This avoids a large number of hash table lookups.
-    kryo.setReferences(false)
+    kryo.register(classOf[GraphXPrimitiveKeyOpenHashMap[VertexId, Int]])
+    kryo.register(classOf[OpenHashSet[Int]])
+    kryo.register(classOf[OpenHashSet[Long]])
   }
 }
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgePartition.scala b/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgePartition.scala
index 871e81f8d245..a5c9cd1f8b4e 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgePartition.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgePartition.scala
@@ -20,7 +20,7 @@ package org.apache.spark.graphx.impl
 import scala.reflect.{classTag, ClassTag}
 
 import org.apache.spark.graphx._
-import org.apache.spark.graphx.util.collection.PrimitiveKeyOpenHashMap
+import org.apache.spark.graphx.util.collection.GraphXPrimitiveKeyOpenHashMap
 
 /**
  * A collection of edges stored in columnar format, along with any vertex attributes referenced. The
@@ -42,12 +42,12 @@ import org.apache.spark.graphx.util.collection.PrimitiveKeyOpenHashMap
 private[graphx]
 class EdgePartition[
     @specialized(Char, Int, Boolean, Byte, Long, Float, Double) ED: ClassTag, VD: ClassTag](
-    @transient val srcIds: Array[VertexId],
-    @transient val dstIds: Array[VertexId],
-    @transient val data: Array[ED],
-    @transient val index: PrimitiveKeyOpenHashMap[VertexId, Int],
-    @transient val vertices: VertexPartition[VD],
-    @transient val activeSet: Option[VertexSet] = None
+    val srcIds: Array[VertexId] = null,
+    val dstIds: Array[VertexId] = null,
+    val data: Array[ED] = null,
+    val index: GraphXPrimitiveKeyOpenHashMap[VertexId, Int] = null,
+    val vertices: VertexPartition[VD] = null,
+    val activeSet: Option[VertexSet] = None
   ) extends Serializable {
 
   /** Return a new `EdgePartition` with the specified edge data. */
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgePartitionBuilder.scala b/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgePartitionBuilder.scala
index ecb49bef42e4..4520beb99151 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgePartitionBuilder.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgePartitionBuilder.scala
@@ -23,7 +23,7 @@ import scala.util.Sorting
 import org.apache.spark.util.collection.{BitSet, OpenHashSet, PrimitiveVector}
 
 import org.apache.spark.graphx._
-import org.apache.spark.graphx.util.collection.PrimitiveKeyOpenHashMap
+import org.apache.spark.graphx.util.collection.GraphXPrimitiveKeyOpenHashMap
 
 private[graphx]
 class EdgePartitionBuilder[@specialized(Long, Int, Double) ED: ClassTag, VD: ClassTag](
@@ -41,7 +41,7 @@ class EdgePartitionBuilder[@specialized(Long, Int, Double) ED: ClassTag, VD: Cla
     val srcIds = new Array[VertexId](edgeArray.size)
     val dstIds = new Array[VertexId](edgeArray.size)
     val data = new Array[ED](edgeArray.size)
-    val index = new PrimitiveKeyOpenHashMap[VertexId, Int]
+    val index = new GraphXPrimitiveKeyOpenHashMap[VertexId, Int]
     // Copy edges into columnar structures, tracking the beginnings of source vertex id clusters and
     // adding them to the index
     if (edgeArray.length > 0) {
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgeTripletIterator.scala b/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgeTripletIterator.scala
index ebb0b9418d65..56f79a7097fc 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgeTripletIterator.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgeTripletIterator.scala
@@ -20,7 +20,7 @@ package org.apache.spark.graphx.impl
 import scala.reflect.ClassTag
 
 import org.apache.spark.graphx._
-import org.apache.spark.graphx.util.collection.PrimitiveKeyOpenHashMap
+import org.apache.spark.graphx.util.collection.GraphXPrimitiveKeyOpenHashMap
 
 /**
  * The Iterator type returned when constructing edge triplets. This could be an anonymous class in
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/RoutingTablePartition.scala b/graphx/src/main/scala/org/apache/spark/graphx/impl/RoutingTablePartition.scala
index 927e32ad0f44..d02e9238adba 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/impl/RoutingTablePartition.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/impl/RoutingTablePartition.scala
@@ -25,7 +25,7 @@ import org.apache.spark.rdd.ShuffledRDD
 import org.apache.spark.util.collection.{BitSet, PrimitiveVector}
 
 import org.apache.spark.graphx._
-import org.apache.spark.graphx.util.collection.PrimitiveKeyOpenHashMap
+import org.apache.spark.graphx.util.collection.GraphXPrimitiveKeyOpenHashMap
 
 /**
  * A message from the edge partition `pid` to the vertex partition containing `vid` specifying that
@@ -69,7 +69,7 @@ object RoutingTablePartition {
     : Iterator[RoutingTableMessage] = {
     // Determine which positions each vertex id appears in using a map where the low 2 bits
     // represent src and dst
-    val map = new PrimitiveKeyOpenHashMap[VertexId, Byte]
+    val map = new GraphXPrimitiveKeyOpenHashMap[VertexId, Byte]
     edgePartition.srcIds.iterator.foreach { srcId =>
       map.changeValue(srcId, 0x1, (b: Byte) => (b | 0x1).toByte)
     }
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/ShippableVertexPartition.scala b/graphx/src/main/scala/org/apache/spark/graphx/impl/ShippableVertexPartition.scala
index f4e221d4e05a..dca54b8a7da8 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/impl/ShippableVertexPartition.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/impl/ShippableVertexPartition.scala
@@ -22,7 +22,7 @@ import scala.reflect.ClassTag
 import org.apache.spark.util.collection.{BitSet, PrimitiveVector}
 
 import org.apache.spark.graphx._
-import org.apache.spark.graphx.util.collection.PrimitiveKeyOpenHashMap
+import org.apache.spark.graphx.util.collection.GraphXPrimitiveKeyOpenHashMap
 
 /** Stores vertex attributes to ship to an edge partition. */
 private[graphx]
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexPartition.scala b/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexPartition.scala
index f1d174720a1b..55c7a19d1bda 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexPartition.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexPartition.scala
@@ -22,7 +22,7 @@ import scala.reflect.ClassTag
 import org.apache.spark.util.collection.BitSet
 
 import org.apache.spark.graphx._
-import org.apache.spark.graphx.util.collection.PrimitiveKeyOpenHashMap
+import org.apache.spark.graphx.util.collection.GraphXPrimitiveKeyOpenHashMap
 
 private[graphx] object VertexPartition {
   /** Construct a `VertexPartition` from the given vertices. */
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexPartitionBase.scala b/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexPartitionBase.scala
index 8d9e0204d27f..34939b24440a 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexPartitionBase.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexPartitionBase.scala
@@ -23,7 +23,7 @@ import scala.reflect.ClassTag
 import org.apache.spark.util.collection.BitSet
 
 import org.apache.spark.graphx._
-import org.apache.spark.graphx.util.collection.PrimitiveKeyOpenHashMap
+import org.apache.spark.graphx.util.collection.GraphXPrimitiveKeyOpenHashMap
 
 private[graphx] object VertexPartitionBase {
   /**
@@ -32,7 +32,7 @@ private[graphx] object VertexPartitionBase {
    */
   def initFrom[VD: ClassTag](iter: Iterator[(VertexId, VD)])
     : (VertexIdToIndexMap, Array[VD], BitSet) = {
-    val map = new PrimitiveKeyOpenHashMap[VertexId, VD]
+    val map = new GraphXPrimitiveKeyOpenHashMap[VertexId, VD]
     iter.foreach { pair =>
       map(pair._1) = pair._2
     }
@@ -45,7 +45,7 @@ private[graphx] object VertexPartitionBase {
    */
   def initFrom[VD: ClassTag](iter: Iterator[(VertexId, VD)], mergeFunc: (VD, VD) => VD)
     : (VertexIdToIndexMap, Array[VD], BitSet) = {
-    val map = new PrimitiveKeyOpenHashMap[VertexId, VD]
+    val map = new GraphXPrimitiveKeyOpenHashMap[VertexId, VD]
     iter.foreach { pair =>
       map.setMerge(pair._1, pair._2, mergeFunc)
     }
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexPartitionBaseOps.scala b/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexPartitionBaseOps.scala
index 21ff615feca6..a4f769b29401 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexPartitionBaseOps.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexPartitionBaseOps.scala
@@ -25,7 +25,7 @@ import org.apache.spark.Logging
 import org.apache.spark.util.collection.BitSet
 
 import org.apache.spark.graphx._
-import org.apache.spark.graphx.util.collection.PrimitiveKeyOpenHashMap
+import org.apache.spark.graphx.util.collection.GraphXPrimitiveKeyOpenHashMap
 
 /**
  * An class containing additional operations for subclasses of VertexPartitionBase that provide
@@ -224,7 +224,7 @@ private[graphx] abstract class VertexPartitionBaseOps
    * Construct a new VertexPartition whose index contains only the vertices in the mask.
    */
   def reindex(): Self[VD] = {
-    val hashMap = new PrimitiveKeyOpenHashMap[VertexId, VD]
+    val hashMap = new GraphXPrimitiveKeyOpenHashMap[VertexId, VD]
     val arbitraryMerge = (a: VD, b: VD) => a
     for ((k, v) <- self.iterator) {
       hashMap.setMerge(k, v, arbitraryMerge)
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/util/collection/PrimitiveKeyOpenHashMap.scala b/graphx/src/main/scala/org/apache/spark/graphx/util/collection/GraphXPrimitiveKeyOpenHashMap.scala
similarity index 98%
rename from graphx/src/main/scala/org/apache/spark/graphx/util/collection/PrimitiveKeyOpenHashMap.scala
rename to graphx/src/main/scala/org/apache/spark/graphx/util/collection/GraphXPrimitiveKeyOpenHashMap.scala
index 7b02e2ed1a9c..57b01b6f2e1f 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/util/collection/PrimitiveKeyOpenHashMap.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/util/collection/GraphXPrimitiveKeyOpenHashMap.scala
@@ -29,7 +29,7 @@ import scala.reflect._
  * Under the hood, it uses our OpenHashSet implementation.
  */
 private[graphx]
-class PrimitiveKeyOpenHashMap[@specialized(Long, Int) K: ClassTag,
+class GraphXPrimitiveKeyOpenHashMap[@specialized(Long, Int) K: ClassTag,
                               @specialized(Long, Int, Double) V: ClassTag](
     val keySet: OpenHashSet[K], var _values: Array[V])
   extends Iterable[(K, V)]
diff --git a/graphx/src/test/scala/org/apache/spark/graphx/impl/EdgePartitionSuite.scala b/graphx/src/test/scala/org/apache/spark/graphx/impl/EdgePartitionSuite.scala
index d2e0c01bc35e..28fd112f2b12 100644
--- a/graphx/src/test/scala/org/apache/spark/graphx/impl/EdgePartitionSuite.scala
+++ b/graphx/src/test/scala/org/apache/spark/graphx/impl/EdgePartitionSuite.scala
@@ -22,6 +22,9 @@ import scala.util.Random
 
 import org.scalatest.FunSuite
 
+import org.apache.spark.SparkConf
+import org.apache.spark.serializer.KryoSerializer
+
 import org.apache.spark.graphx._
 
 class EdgePartitionSuite extends FunSuite {
@@ -120,4 +123,19 @@ class EdgePartitionSuite extends FunSuite {
     assert(!ep.isActive(-1))
     assert(ep.numActives == Some(2))
   }
+
+  test("Kryo serialization") {
+    val aList = List((0, 1, 0), (1, 0, 0), (1, 2, 0), (5, 4, 0), (5, 5, 0))
+    val a: EdgePartition[Int, Int] = makeEdgePartition(aList)
+    val conf = new SparkConf()
+      .set("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
+      .set("spark.kryo.registrator", "org.apache.spark.graphx.GraphKryoRegistrator")
+    val s = new KryoSerializer(conf).newInstance()
+    val aSer: EdgePartition[Int, Int] = s.deserialize(s.serialize(a))
+    assert(aSer.srcIds.toList === a.srcIds.toList)
+    assert(aSer.dstIds.toList === a.dstIds.toList)
+    assert(aSer.data.toList === a.data.toList)
+    assert(aSer.index != null)
+    assert(aSer.vertices.iterator.toSet === a.vertices.iterator.toSet)
+  }
 }

From af15c82bfe2c3f73142b8f310784a0e85841539d Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Mon, 12 May 2014 10:49:03 -0700
Subject: [PATCH 161/810] Revert "SPARK-1786: Edge Partition Serialization"

This reverts commit a6b02fb7486356493474c7f42bb714c9cce215ca.
---
 .../spark/graphx/GraphKryoRegistrator.scala    |  9 +++------
 .../spark/graphx/impl/EdgePartition.scala      | 14 +++++++-------
 .../graphx/impl/EdgePartitionBuilder.scala     |  4 ++--
 .../graphx/impl/EdgeTripletIterator.scala      |  2 +-
 .../graphx/impl/RoutingTablePartition.scala    |  4 ++--
 .../graphx/impl/ShippableVertexPartition.scala |  2 +-
 .../spark/graphx/impl/VertexPartition.scala    |  2 +-
 .../graphx/impl/VertexPartitionBase.scala      |  6 +++---
 .../graphx/impl/VertexPartitionBaseOps.scala   |  4 ++--
 ...Map.scala => PrimitiveKeyOpenHashMap.scala} |  2 +-
 .../spark/graphx/impl/EdgePartitionSuite.scala | 18 ------------------
 11 files changed, 23 insertions(+), 44 deletions(-)
 rename graphx/src/main/scala/org/apache/spark/graphx/util/collection/{GraphXPrimitiveKeyOpenHashMap.scala => PrimitiveKeyOpenHashMap.scala} (98%)

diff --git a/graphx/src/main/scala/org/apache/spark/graphx/GraphKryoRegistrator.scala b/graphx/src/main/scala/org/apache/spark/graphx/GraphKryoRegistrator.scala
index f97f329c0e83..d295d0127ac7 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/GraphKryoRegistrator.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/GraphKryoRegistrator.scala
@@ -24,9 +24,6 @@ import org.apache.spark.util.BoundedPriorityQueue
 import org.apache.spark.util.collection.BitSet
 
 import org.apache.spark.graphx.impl._
-import org.apache.spark.graphx.util.collection.GraphXPrimitiveKeyOpenHashMap
-import org.apache.spark.util.collection.OpenHashSet
-
 
 /**
  * Registers GraphX classes with Kryo for improved performance.
@@ -46,8 +43,8 @@ class GraphKryoRegistrator extends KryoRegistrator {
     kryo.register(classOf[PartitionStrategy])
     kryo.register(classOf[BoundedPriorityQueue[Object]])
     kryo.register(classOf[EdgeDirection])
-    kryo.register(classOf[GraphXPrimitiveKeyOpenHashMap[VertexId, Int]])
-    kryo.register(classOf[OpenHashSet[Int]])
-    kryo.register(classOf[OpenHashSet[Long]])
+
+    // This avoids a large number of hash table lookups.
+    kryo.setReferences(false)
   }
 }
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgePartition.scala b/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgePartition.scala
index a5c9cd1f8b4e..871e81f8d245 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgePartition.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgePartition.scala
@@ -20,7 +20,7 @@ package org.apache.spark.graphx.impl
 import scala.reflect.{classTag, ClassTag}
 
 import org.apache.spark.graphx._
-import org.apache.spark.graphx.util.collection.GraphXPrimitiveKeyOpenHashMap
+import org.apache.spark.graphx.util.collection.PrimitiveKeyOpenHashMap
 
 /**
  * A collection of edges stored in columnar format, along with any vertex attributes referenced. The
@@ -42,12 +42,12 @@ import org.apache.spark.graphx.util.collection.GraphXPrimitiveKeyOpenHashMap
 private[graphx]
 class EdgePartition[
     @specialized(Char, Int, Boolean, Byte, Long, Float, Double) ED: ClassTag, VD: ClassTag](
-    val srcIds: Array[VertexId] = null,
-    val dstIds: Array[VertexId] = null,
-    val data: Array[ED] = null,
-    val index: GraphXPrimitiveKeyOpenHashMap[VertexId, Int] = null,
-    val vertices: VertexPartition[VD] = null,
-    val activeSet: Option[VertexSet] = None
+    @transient val srcIds: Array[VertexId],
+    @transient val dstIds: Array[VertexId],
+    @transient val data: Array[ED],
+    @transient val index: PrimitiveKeyOpenHashMap[VertexId, Int],
+    @transient val vertices: VertexPartition[VD],
+    @transient val activeSet: Option[VertexSet] = None
   ) extends Serializable {
 
   /** Return a new `EdgePartition` with the specified edge data. */
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgePartitionBuilder.scala b/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgePartitionBuilder.scala
index 4520beb99151..ecb49bef42e4 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgePartitionBuilder.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgePartitionBuilder.scala
@@ -23,7 +23,7 @@ import scala.util.Sorting
 import org.apache.spark.util.collection.{BitSet, OpenHashSet, PrimitiveVector}
 
 import org.apache.spark.graphx._
-import org.apache.spark.graphx.util.collection.GraphXPrimitiveKeyOpenHashMap
+import org.apache.spark.graphx.util.collection.PrimitiveKeyOpenHashMap
 
 private[graphx]
 class EdgePartitionBuilder[@specialized(Long, Int, Double) ED: ClassTag, VD: ClassTag](
@@ -41,7 +41,7 @@ class EdgePartitionBuilder[@specialized(Long, Int, Double) ED: ClassTag, VD: Cla
     val srcIds = new Array[VertexId](edgeArray.size)
     val dstIds = new Array[VertexId](edgeArray.size)
     val data = new Array[ED](edgeArray.size)
-    val index = new GraphXPrimitiveKeyOpenHashMap[VertexId, Int]
+    val index = new PrimitiveKeyOpenHashMap[VertexId, Int]
     // Copy edges into columnar structures, tracking the beginnings of source vertex id clusters and
     // adding them to the index
     if (edgeArray.length > 0) {
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgeTripletIterator.scala b/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgeTripletIterator.scala
index 56f79a7097fc..ebb0b9418d65 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgeTripletIterator.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgeTripletIterator.scala
@@ -20,7 +20,7 @@ package org.apache.spark.graphx.impl
 import scala.reflect.ClassTag
 
 import org.apache.spark.graphx._
-import org.apache.spark.graphx.util.collection.GraphXPrimitiveKeyOpenHashMap
+import org.apache.spark.graphx.util.collection.PrimitiveKeyOpenHashMap
 
 /**
  * The Iterator type returned when constructing edge triplets. This could be an anonymous class in
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/RoutingTablePartition.scala b/graphx/src/main/scala/org/apache/spark/graphx/impl/RoutingTablePartition.scala
index d02e9238adba..927e32ad0f44 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/impl/RoutingTablePartition.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/impl/RoutingTablePartition.scala
@@ -25,7 +25,7 @@ import org.apache.spark.rdd.ShuffledRDD
 import org.apache.spark.util.collection.{BitSet, PrimitiveVector}
 
 import org.apache.spark.graphx._
-import org.apache.spark.graphx.util.collection.GraphXPrimitiveKeyOpenHashMap
+import org.apache.spark.graphx.util.collection.PrimitiveKeyOpenHashMap
 
 /**
  * A message from the edge partition `pid` to the vertex partition containing `vid` specifying that
@@ -69,7 +69,7 @@ object RoutingTablePartition {
     : Iterator[RoutingTableMessage] = {
     // Determine which positions each vertex id appears in using a map where the low 2 bits
     // represent src and dst
-    val map = new GraphXPrimitiveKeyOpenHashMap[VertexId, Byte]
+    val map = new PrimitiveKeyOpenHashMap[VertexId, Byte]
     edgePartition.srcIds.iterator.foreach { srcId =>
       map.changeValue(srcId, 0x1, (b: Byte) => (b | 0x1).toByte)
     }
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/ShippableVertexPartition.scala b/graphx/src/main/scala/org/apache/spark/graphx/impl/ShippableVertexPartition.scala
index dca54b8a7da8..f4e221d4e05a 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/impl/ShippableVertexPartition.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/impl/ShippableVertexPartition.scala
@@ -22,7 +22,7 @@ import scala.reflect.ClassTag
 import org.apache.spark.util.collection.{BitSet, PrimitiveVector}
 
 import org.apache.spark.graphx._
-import org.apache.spark.graphx.util.collection.GraphXPrimitiveKeyOpenHashMap
+import org.apache.spark.graphx.util.collection.PrimitiveKeyOpenHashMap
 
 /** Stores vertex attributes to ship to an edge partition. */
 private[graphx]
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexPartition.scala b/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexPartition.scala
index 55c7a19d1bda..f1d174720a1b 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexPartition.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexPartition.scala
@@ -22,7 +22,7 @@ import scala.reflect.ClassTag
 import org.apache.spark.util.collection.BitSet
 
 import org.apache.spark.graphx._
-import org.apache.spark.graphx.util.collection.GraphXPrimitiveKeyOpenHashMap
+import org.apache.spark.graphx.util.collection.PrimitiveKeyOpenHashMap
 
 private[graphx] object VertexPartition {
   /** Construct a `VertexPartition` from the given vertices. */
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexPartitionBase.scala b/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexPartitionBase.scala
index 34939b24440a..8d9e0204d27f 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexPartitionBase.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexPartitionBase.scala
@@ -23,7 +23,7 @@ import scala.reflect.ClassTag
 import org.apache.spark.util.collection.BitSet
 
 import org.apache.spark.graphx._
-import org.apache.spark.graphx.util.collection.GraphXPrimitiveKeyOpenHashMap
+import org.apache.spark.graphx.util.collection.PrimitiveKeyOpenHashMap
 
 private[graphx] object VertexPartitionBase {
   /**
@@ -32,7 +32,7 @@ private[graphx] object VertexPartitionBase {
    */
   def initFrom[VD: ClassTag](iter: Iterator[(VertexId, VD)])
     : (VertexIdToIndexMap, Array[VD], BitSet) = {
-    val map = new GraphXPrimitiveKeyOpenHashMap[VertexId, VD]
+    val map = new PrimitiveKeyOpenHashMap[VertexId, VD]
     iter.foreach { pair =>
       map(pair._1) = pair._2
     }
@@ -45,7 +45,7 @@ private[graphx] object VertexPartitionBase {
    */
   def initFrom[VD: ClassTag](iter: Iterator[(VertexId, VD)], mergeFunc: (VD, VD) => VD)
     : (VertexIdToIndexMap, Array[VD], BitSet) = {
-    val map = new GraphXPrimitiveKeyOpenHashMap[VertexId, VD]
+    val map = new PrimitiveKeyOpenHashMap[VertexId, VD]
     iter.foreach { pair =>
       map.setMerge(pair._1, pair._2, mergeFunc)
     }
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexPartitionBaseOps.scala b/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexPartitionBaseOps.scala
index a4f769b29401..21ff615feca6 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexPartitionBaseOps.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexPartitionBaseOps.scala
@@ -25,7 +25,7 @@ import org.apache.spark.Logging
 import org.apache.spark.util.collection.BitSet
 
 import org.apache.spark.graphx._
-import org.apache.spark.graphx.util.collection.GraphXPrimitiveKeyOpenHashMap
+import org.apache.spark.graphx.util.collection.PrimitiveKeyOpenHashMap
 
 /**
  * An class containing additional operations for subclasses of VertexPartitionBase that provide
@@ -224,7 +224,7 @@ private[graphx] abstract class VertexPartitionBaseOps
    * Construct a new VertexPartition whose index contains only the vertices in the mask.
    */
   def reindex(): Self[VD] = {
-    val hashMap = new GraphXPrimitiveKeyOpenHashMap[VertexId, VD]
+    val hashMap = new PrimitiveKeyOpenHashMap[VertexId, VD]
     val arbitraryMerge = (a: VD, b: VD) => a
     for ((k, v) <- self.iterator) {
       hashMap.setMerge(k, v, arbitraryMerge)
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/util/collection/GraphXPrimitiveKeyOpenHashMap.scala b/graphx/src/main/scala/org/apache/spark/graphx/util/collection/PrimitiveKeyOpenHashMap.scala
similarity index 98%
rename from graphx/src/main/scala/org/apache/spark/graphx/util/collection/GraphXPrimitiveKeyOpenHashMap.scala
rename to graphx/src/main/scala/org/apache/spark/graphx/util/collection/PrimitiveKeyOpenHashMap.scala
index 57b01b6f2e1f..7b02e2ed1a9c 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/util/collection/GraphXPrimitiveKeyOpenHashMap.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/util/collection/PrimitiveKeyOpenHashMap.scala
@@ -29,7 +29,7 @@ import scala.reflect._
  * Under the hood, it uses our OpenHashSet implementation.
  */
 private[graphx]
-class GraphXPrimitiveKeyOpenHashMap[@specialized(Long, Int) K: ClassTag,
+class PrimitiveKeyOpenHashMap[@specialized(Long, Int) K: ClassTag,
                               @specialized(Long, Int, Double) V: ClassTag](
     val keySet: OpenHashSet[K], var _values: Array[V])
   extends Iterable[(K, V)]
diff --git a/graphx/src/test/scala/org/apache/spark/graphx/impl/EdgePartitionSuite.scala b/graphx/src/test/scala/org/apache/spark/graphx/impl/EdgePartitionSuite.scala
index 28fd112f2b12..d2e0c01bc35e 100644
--- a/graphx/src/test/scala/org/apache/spark/graphx/impl/EdgePartitionSuite.scala
+++ b/graphx/src/test/scala/org/apache/spark/graphx/impl/EdgePartitionSuite.scala
@@ -22,9 +22,6 @@ import scala.util.Random
 
 import org.scalatest.FunSuite
 
-import org.apache.spark.SparkConf
-import org.apache.spark.serializer.KryoSerializer
-
 import org.apache.spark.graphx._
 
 class EdgePartitionSuite extends FunSuite {
@@ -123,19 +120,4 @@ class EdgePartitionSuite extends FunSuite {
     assert(!ep.isActive(-1))
     assert(ep.numActives == Some(2))
   }
-
-  test("Kryo serialization") {
-    val aList = List((0, 1, 0), (1, 0, 0), (1, 2, 0), (5, 4, 0), (5, 5, 0))
-    val a: EdgePartition[Int, Int] = makeEdgePartition(aList)
-    val conf = new SparkConf()
-      .set("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
-      .set("spark.kryo.registrator", "org.apache.spark.graphx.GraphKryoRegistrator")
-    val s = new KryoSerializer(conf).newInstance()
-    val aSer: EdgePartition[Int, Int] = s.deserialize(s.serialize(a))
-    assert(aSer.srcIds.toList === a.srcIds.toList)
-    assert(aSer.dstIds.toList === a.dstIds.toList)
-    assert(aSer.data.toList === a.data.toList)
-    assert(aSer.index != null)
-    assert(aSer.vertices.iterator.toSet === a.vertices.iterator.toSet)
-  }
 }

From 3af1f386439cdddd42e545ad63d089f4dfdf9f8a Mon Sep 17 00:00:00 2001
From: Aaron Davidson <aaron@databricks.com>
Date: Mon, 12 May 2014 11:08:52 -0700
Subject: [PATCH 162/810] SPARK-1772 Stop catching Throwable, let Executors die

The main issue this patch fixes is [SPARK-1772](https://issues.apache.org/jira/browse/SPARK-1772), in which Executors may not die when fatal exceptions (e.g., OOM) are thrown. This patch causes Executors to delegate to the ExecutorUncaughtExceptionHandler when a fatal exception is thrown.

This patch also continues the fight in the neverending war against `case t: Throwable =>`, by only catching Exceptions in many places, and adding a wrapper for Threads and Runnables to make sure any uncaught exceptions are at least printed to the logs.

It also turns out that it is unlikely that the IndestructibleActorSystem actually works, given testing ([here](https://gist.github.com/aarondav/ca1f0cdcd50727f89c0d)). The uncaughtExceptionHandler is not called from the places that we expected it would be.
[SPARK-1620](https://issues.apache.org/jira/browse/SPARK-1620) deals with part of this issue, but refactoring our Actor Systems to ensure that exceptions are dealt with properly is a much bigger change, outside the scope of this PR.

Author: Aaron Davidson <aaron@databricks.com>

Closes #715 from aarondav/throwable and squashes the following commits:

f9b9bfe [Aaron Davidson] Remove other redundant 'throw e'
e937a0a [Aaron Davidson] Address Prashant and Matei's comments
1867867 [Aaron Davidson] [RFC] SPARK-1772 Stop catching Throwable, let Executors die
---
 .../org/apache/spark/ContextCleaner.scala     | 11 +--
 .../scala/org/apache/spark/SparkContext.scala | 12 ++--
 .../apache/spark/api/python/PythonRDD.scala   |  3 +-
 .../api/python/PythonWorkerFactory.scala      |  1 -
 .../org/apache/spark/deploy/Client.scala      |  2 +-
 .../apache/spark/deploy/SparkHadoopUtil.scala |  2 +-
 .../spark/deploy/history/HistoryServer.scala  |  8 +--
 .../apache/spark/deploy/master/Master.scala   |  4 +-
 .../spark/deploy/worker/DriverWrapper.scala   |  2 +-
 .../CoarseGrainedExecutorBackend.scala        |  2 +-
 .../org/apache/spark/executor/Executor.scala  | 37 +++-------
 .../ExecutorUncaughtExceptionHandler.scala    | 53 +++++++++++++++
 .../scheduler/EventLoggingListener.scala      |  4 +-
 .../spark/scheduler/TaskResultGetter.scala    |  8 +--
 .../spark/storage/DiskBlockManager.scala      |  6 +-
 .../spark/storage/TachyonBlockManager.scala   |  7 +-
 .../org/apache/spark/util/AkkaUtils.scala     | 11 +--
 .../util/IndestructibleActorSystem.scala      | 68 -------------------
 .../scala/org/apache/spark/util/Utils.scala   | 26 ++++++-
 19 files changed, 127 insertions(+), 140 deletions(-)
 create mode 100644 core/src/main/scala/org/apache/spark/executor/ExecutorUncaughtExceptionHandler.scala
 delete mode 100644 core/src/main/scala/org/apache/spark/util/IndestructibleActorSystem.scala

diff --git a/core/src/main/scala/org/apache/spark/ContextCleaner.scala b/core/src/main/scala/org/apache/spark/ContextCleaner.scala
index 54e08d7866f7..e2d2250982da 100644
--- a/core/src/main/scala/org/apache/spark/ContextCleaner.scala
+++ b/core/src/main/scala/org/apache/spark/ContextCleaner.scala
@@ -23,6 +23,7 @@ import scala.collection.mutable.{ArrayBuffer, SynchronizedBuffer}
 
 import org.apache.spark.broadcast.Broadcast
 import org.apache.spark.rdd.RDD
+import org.apache.spark.util.Utils
 
 /**
  * Classes that represent cleaning tasks.
@@ -110,7 +111,7 @@ private[spark] class ContextCleaner(sc: SparkContext) extends Logging {
   }
 
   /** Keep cleaning RDD, shuffle, and broadcast state. */
-  private def keepCleaning() {
+  private def keepCleaning(): Unit = Utils.logUncaughtExceptions {
     while (!stopped) {
       try {
         val reference = Option(referenceQueue.remove(ContextCleaner.REF_QUEUE_POLL_TIMEOUT))
@@ -128,7 +129,7 @@ private[spark] class ContextCleaner(sc: SparkContext) extends Logging {
           }
         }
       } catch {
-        case t: Throwable => logError("Error in cleaning thread", t)
+        case e: Exception => logError("Error in cleaning thread", e)
       }
     }
   }
@@ -141,7 +142,7 @@ private[spark] class ContextCleaner(sc: SparkContext) extends Logging {
       listeners.foreach(_.rddCleaned(rddId))
       logInfo("Cleaned RDD " + rddId)
     } catch {
-      case t: Throwable => logError("Error cleaning RDD " + rddId, t)
+      case e: Exception => logError("Error cleaning RDD " + rddId, e)
     }
   }
 
@@ -154,7 +155,7 @@ private[spark] class ContextCleaner(sc: SparkContext) extends Logging {
       listeners.foreach(_.shuffleCleaned(shuffleId))
       logInfo("Cleaned shuffle " + shuffleId)
     } catch {
-      case t: Throwable => logError("Error cleaning shuffle " + shuffleId, t)
+      case e: Exception => logError("Error cleaning shuffle " + shuffleId, e)
     }
   }
 
@@ -166,7 +167,7 @@ private[spark] class ContextCleaner(sc: SparkContext) extends Logging {
       listeners.foreach(_.broadcastCleaned(broadcastId))
       logInfo("Cleaned broadcast " + broadcastId)
     } catch {
-      case t: Throwable => logError("Error cleaning broadcast " + broadcastId, t)
+      case e: Exception => logError("Error cleaning broadcast " + broadcastId, e)
     }
   }
 
diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index 71bab295442f..e6121a705497 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -1494,8 +1494,8 @@ object SparkContext extends Logging {
         } catch {
           // TODO: Enumerate the exact reasons why it can fail
           // But irrespective of it, it means we cannot proceed !
-          case th: Throwable => {
-            throw new SparkException("YARN mode not available ?", th)
+          case e: Exception => {
+            throw new SparkException("YARN mode not available ?", e)
           }
         }
         val backend = new CoarseGrainedSchedulerBackend(scheduler, sc.env.actorSystem)
@@ -1510,8 +1510,8 @@ object SparkContext extends Logging {
           cons.newInstance(sc).asInstanceOf[TaskSchedulerImpl]
 
         } catch {
-          case th: Throwable => {
-            throw new SparkException("YARN mode not available ?", th)
+          case e: Exception => {
+            throw new SparkException("YARN mode not available ?", e)
           }
         }
 
@@ -1521,8 +1521,8 @@ object SparkContext extends Logging {
           val cons = clazz.getConstructor(classOf[TaskSchedulerImpl], classOf[SparkContext])
           cons.newInstance(scheduler, sc).asInstanceOf[CoarseGrainedSchedulerBackend]
         } catch {
-          case th: Throwable => {
-            throw new SparkException("YARN mode not available ?", th)
+          case e: Exception => {
+            throw new SparkException("YARN mode not available ?", e)
           }
         }
 
diff --git a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
index 2971c277aa86..57b28b997236 100644
--- a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
+++ b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
@@ -171,7 +171,7 @@ private[spark] class PythonRDD[T: ClassTag](
       this.interrupt()
     }
 
-    override def run() {
+    override def run(): Unit = Utils.logUncaughtExceptions {
       try {
         SparkEnv.set(env)
         val stream = new BufferedOutputStream(worker.getOutputStream, bufferSize)
@@ -282,7 +282,6 @@ private[spark] object PythonRDD {
       }
     } catch {
       case eof: EOFException => {}
-      case e: Throwable => throw e
     }
     JavaRDD.fromRDD(sc.sc.parallelize(objs, parallelism))
   }
diff --git a/core/src/main/scala/org/apache/spark/api/python/PythonWorkerFactory.scala b/core/src/main/scala/org/apache/spark/api/python/PythonWorkerFactory.scala
index 002f2acd94de..759cbe2c46c5 100644
--- a/core/src/main/scala/org/apache/spark/api/python/PythonWorkerFactory.scala
+++ b/core/src/main/scala/org/apache/spark/api/python/PythonWorkerFactory.scala
@@ -71,7 +71,6 @@ private[spark] class PythonWorkerFactory(pythonExec: String, envVars: Map[String
           stopDaemon()
           startDaemon()
           new Socket(daemonHost, daemonPort)
-        case e: Throwable => throw e
       }
     }
   }
diff --git a/core/src/main/scala/org/apache/spark/deploy/Client.scala b/core/src/main/scala/org/apache/spark/deploy/Client.scala
index 7ead1171525d..aeb159adc31d 100644
--- a/core/src/main/scala/org/apache/spark/deploy/Client.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/Client.scala
@@ -157,7 +157,7 @@ object Client {
     // TODO: See if we can initialize akka so return messages are sent back using the same TCP
     //       flow. Else, this (sadly) requires the DriverClient be routable from the Master.
     val (actorSystem, _) = AkkaUtils.createActorSystem(
-      "driverClient", Utils.localHostName(), 0, false, conf, new SecurityManager(conf))
+      "driverClient", Utils.localHostName(), 0, conf, new SecurityManager(conf))
 
     actorSystem.actorOf(Props(classOf[ClientActor], driverArgs, conf))
 
diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala b/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala
index e2df1b895412..148115d3ed35 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala
@@ -103,7 +103,7 @@ object SparkHadoopUtil {
           .newInstance()
           .asInstanceOf[SparkHadoopUtil]
       } catch {
-       case th: Throwable => throw new SparkException("Unable to load YARN support", th)
+       case e: Exception => throw new SparkException("Unable to load YARN support", e)
       }
     } else {
       new SparkHadoopUtil
diff --git a/core/src/main/scala/org/apache/spark/deploy/history/HistoryServer.scala b/core/src/main/scala/org/apache/spark/deploy/history/HistoryServer.scala
index 1238bbf9da2f..a9c11dca5678 100644
--- a/core/src/main/scala/org/apache/spark/deploy/history/HistoryServer.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/history/HistoryServer.scala
@@ -70,7 +70,7 @@ class HistoryServer(
    * TODO: Add a mechanism to update manually.
    */
   private val logCheckingThread = new Thread {
-    override def run() {
+    override def run(): Unit = Utils.logUncaughtExceptions {
       while (!stopped) {
         val now = System.currentTimeMillis
         if (now - lastLogCheckTime > UPDATE_INTERVAL_MS) {
@@ -154,7 +154,7 @@ class HistoryServer(
         numCompletedApplications = logInfos.size
 
       } catch {
-        case t: Throwable => logError("Exception in checking for event log updates", t)
+        case e: Exception => logError("Exception in checking for event log updates", e)
       }
     } else {
       logWarning("Attempted to check for event log updates before binding the server.")
@@ -231,8 +231,8 @@ class HistoryServer(
         dir.getModificationTime
       }
     } catch {
-      case t: Throwable =>
-        logError("Exception in accessing modification time of %s".format(dir.getPath), t)
+      case e: Exception =>
+        logError("Exception in accessing modification time of %s".format(dir.getPath), e)
         -1L
     }
   }
diff --git a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
index f254f5585ba2..c6dec305bffc 100644
--- a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
@@ -684,8 +684,8 @@ private[spark] class Master(
         webUi.attachSparkUI(ui)
         return true
       } catch {
-        case t: Throwable =>
-          logError("Exception in replaying log for application %s (%s)".format(appName, app.id), t)
+        case e: Exception =>
+          logError("Exception in replaying log for application %s (%s)".format(appName, app.id), e)
       }
     } else {
       logWarning("Application %s (%s) has no valid logs: %s".format(appName, app.id, eventLogDir))
diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/DriverWrapper.scala b/core/src/main/scala/org/apache/spark/deploy/worker/DriverWrapper.scala
index be15138f6240..05e242e6df70 100644
--- a/core/src/main/scala/org/apache/spark/deploy/worker/DriverWrapper.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/worker/DriverWrapper.scala
@@ -31,7 +31,7 @@ object DriverWrapper {
       case workerUrl :: mainClass :: extraArgs =>
         val conf = new SparkConf()
         val (actorSystem, _) = AkkaUtils.createActorSystem("Driver",
-          Utils.localHostName(), 0, false, conf, new SecurityManager(conf))
+          Utils.localHostName(), 0, conf, new SecurityManager(conf))
         actorSystem.actorOf(Props(classOf[WorkerWatcher], workerUrl), name = "workerWatcher")
 
         // Delegate to supplied main class
diff --git a/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala b/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala
index e912ae8a5d3c..84aec65b7765 100644
--- a/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala
+++ b/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala
@@ -105,7 +105,7 @@ private[spark] object CoarseGrainedExecutorBackend {
         // Create a new ActorSystem to run the backend, because we can't create a
         // SparkEnv / Executor before getting started with all our system properties, etc
         val (actorSystem, boundPort) = AkkaUtils.createActorSystem("sparkExecutor", hostname, 0,
-          indestructible = true, conf = conf, new SecurityManager(conf))
+          conf, new SecurityManager(conf))
         // set it
         val sparkHostPort = hostname + ":" + boundPort
         actorSystem.actorOf(
diff --git a/core/src/main/scala/org/apache/spark/executor/Executor.scala b/core/src/main/scala/org/apache/spark/executor/Executor.scala
index 98e7e0be813b..baee7a216a7c 100644
--- a/core/src/main/scala/org/apache/spark/executor/Executor.scala
+++ b/core/src/main/scala/org/apache/spark/executor/Executor.scala
@@ -74,28 +74,7 @@ private[spark] class Executor(
     // Setup an uncaught exception handler for non-local mode.
     // Make any thread terminations due to uncaught exceptions kill the entire
     // executor process to avoid surprising stalls.
-    Thread.setDefaultUncaughtExceptionHandler(
-      new Thread.UncaughtExceptionHandler {
-        override def uncaughtException(thread: Thread, exception: Throwable) {
-          try {
-            logError("Uncaught exception in thread " + thread, exception)
-
-            // We may have been called from a shutdown hook. If so, we must not call System.exit().
-            // (If we do, we will deadlock.)
-            if (!Utils.inShutdown()) {
-              if (exception.isInstanceOf[OutOfMemoryError]) {
-                System.exit(ExecutorExitCode.OOM)
-              } else {
-                System.exit(ExecutorExitCode.UNCAUGHT_EXCEPTION)
-              }
-            }
-          } catch {
-            case oom: OutOfMemoryError => Runtime.getRuntime.halt(ExecutorExitCode.OOM)
-            case t: Throwable => Runtime.getRuntime.halt(ExecutorExitCode.UNCAUGHT_EXCEPTION_TWICE)
-          }
-        }
-      }
-    )
+    Thread.setDefaultUncaughtExceptionHandler(ExecutorUncaughtExceptionHandler)
   }
 
   val executorSource = new ExecutorSource(this, executorId)
@@ -259,6 +238,11 @@ private[spark] class Executor(
         }
 
         case t: Throwable => {
+          // Attempt to exit cleanly by informing the driver of our failure.
+          // If anything goes wrong (or this was a fatal exception), we will delegate to
+          // the default uncaught exception handler, which will terminate the Executor.
+          logError("Exception in task ID " + taskId, t)
+
           val serviceTime = System.currentTimeMillis() - taskStart
           val metrics = attemptedTask.flatMap(t => t.metrics)
           for (m <- metrics) {
@@ -268,10 +252,11 @@ private[spark] class Executor(
           val reason = ExceptionFailure(t.getClass.getName, t.toString, t.getStackTrace, metrics)
           execBackend.statusUpdate(taskId, TaskState.FAILED, ser.serialize(reason))
 
-          // TODO: Should we exit the whole executor here? On the one hand, the failed task may
-          // have left some weird state around depending on when the exception was thrown, but on
-          // the other hand, maybe we could detect that when future tasks fail and exit then.
-          logError("Exception in task ID " + taskId, t)
+          // Don't forcibly exit unless the exception was inherently fatal, to avoid
+          // stopping other tasks unnecessarily.
+          if (Utils.isFatalError(t)) {
+            ExecutorUncaughtExceptionHandler.uncaughtException(t)
+          }
         }
       } finally {
         // TODO: Unregister shuffle memory only for ResultTask
diff --git a/core/src/main/scala/org/apache/spark/executor/ExecutorUncaughtExceptionHandler.scala b/core/src/main/scala/org/apache/spark/executor/ExecutorUncaughtExceptionHandler.scala
new file mode 100644
index 000000000000..b0e984c03964
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/executor/ExecutorUncaughtExceptionHandler.scala
@@ -0,0 +1,53 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.executor
+
+import org.apache.spark.Logging
+import org.apache.spark.util.Utils
+
+/**
+ * The default uncaught exception handler for Executors terminates the whole process, to avoid
+ * getting into a bad state indefinitely. Since Executors are relatively lightweight, it's better
+ * to fail fast when things go wrong.
+ */
+private[spark] object ExecutorUncaughtExceptionHandler
+  extends Thread.UncaughtExceptionHandler with Logging {
+
+  override def uncaughtException(thread: Thread, exception: Throwable) {
+    try {
+      logError("Uncaught exception in thread " + thread, exception)
+
+      // We may have been called from a shutdown hook. If so, we must not call System.exit().
+      // (If we do, we will deadlock.)
+      if (!Utils.inShutdown()) {
+        if (exception.isInstanceOf[OutOfMemoryError]) {
+          System.exit(ExecutorExitCode.OOM)
+        } else {
+          System.exit(ExecutorExitCode.UNCAUGHT_EXCEPTION)
+        }
+      }
+    } catch {
+      case oom: OutOfMemoryError => Runtime.getRuntime.halt(ExecutorExitCode.OOM)
+      case t: Throwable => Runtime.getRuntime.halt(ExecutorExitCode.UNCAUGHT_EXCEPTION_TWICE)
+    }
+  }
+
+  def uncaughtException(exception: Throwable) {
+    uncaughtException(Thread.currentThread(), exception)
+  }
+}
diff --git a/core/src/main/scala/org/apache/spark/scheduler/EventLoggingListener.scala b/core/src/main/scala/org/apache/spark/scheduler/EventLoggingListener.scala
index 7968a0691db1..a90b0d475c04 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/EventLoggingListener.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/EventLoggingListener.scala
@@ -206,8 +206,8 @@ private[spark] object EventLoggingListener extends Logging {
         applicationComplete = filePaths.exists { path => isApplicationCompleteFile(path.getName) }
       )
     } catch {
-      case t: Throwable =>
-        logError("Exception in parsing logging info from directory %s".format(logDir), t)
+      case e: Exception =>
+        logError("Exception in parsing logging info from directory %s".format(logDir), e)
       EventLoggingInfo.empty
     }
   }
diff --git a/core/src/main/scala/org/apache/spark/scheduler/TaskResultGetter.scala b/core/src/main/scala/org/apache/spark/scheduler/TaskResultGetter.scala
index c9ad2b151daf..99d305b36a95 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/TaskResultGetter.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/TaskResultGetter.scala
@@ -43,7 +43,7 @@ private[spark] class TaskResultGetter(sparkEnv: SparkEnv, scheduler: TaskSchedul
   def enqueueSuccessfulTask(
     taskSetManager: TaskSetManager, tid: Long, serializedData: ByteBuffer) {
     getTaskResultExecutor.execute(new Runnable {
-      override def run() {
+      override def run(): Unit = Utils.logUncaughtExceptions {
         try {
           val result = serializer.get().deserialize[TaskResult[_]](serializedData) match {
             case directResult: DirectTaskResult[_] => directResult
@@ -70,7 +70,7 @@ private[spark] class TaskResultGetter(sparkEnv: SparkEnv, scheduler: TaskSchedul
           case cnf: ClassNotFoundException =>
             val loader = Thread.currentThread.getContextClassLoader
             taskSetManager.abort("ClassNotFound with classloader: " + loader)
-          case ex: Throwable =>
+          case ex: Exception =>
             taskSetManager.abort("Exception while deserializing and fetching task: %s".format(ex))
         }
       }
@@ -81,7 +81,7 @@ private[spark] class TaskResultGetter(sparkEnv: SparkEnv, scheduler: TaskSchedul
     serializedData: ByteBuffer) {
     var reason : TaskEndReason = UnknownReason
     getTaskResultExecutor.execute(new Runnable {
-      override def run() {
+      override def run(): Unit = Utils.logUncaughtExceptions {
         try {
           if (serializedData != null && serializedData.limit() > 0) {
             reason = serializer.get().deserialize[TaskEndReason](
@@ -94,7 +94,7 @@ private[spark] class TaskResultGetter(sparkEnv: SparkEnv, scheduler: TaskSchedul
             val loader = Utils.getContextOrSparkClassLoader
             logError(
               "Could not deserialize TaskEndReason: ClassNotFound with classloader " + loader)
-          case ex: Throwable => {}
+          case ex: Exception => {}
         }
         scheduler.handleFailedTask(taskSetManager, tid, taskState, reason)
       }
diff --git a/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala b/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala
index cf6ef0029a86..3a7243a1ba19 100644
--- a/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala
+++ b/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala
@@ -148,7 +148,7 @@ private[spark] class DiskBlockManager(shuffleManager: ShuffleBlockManager, rootD
   private def addShutdownHook() {
     localDirs.foreach(localDir => Utils.registerShutdownDeleteDir(localDir))
     Runtime.getRuntime.addShutdownHook(new Thread("delete Spark local dirs") {
-      override def run() {
+      override def run(): Unit = Utils.logUncaughtExceptions {
         logDebug("Shutdown hook called")
         DiskBlockManager.this.stop()
       }
@@ -162,8 +162,8 @@ private[spark] class DiskBlockManager(shuffleManager: ShuffleBlockManager, rootD
         try {
           if (!Utils.hasRootAsShutdownDeleteDir(localDir)) Utils.deleteRecursively(localDir)
         } catch {
-          case t: Throwable =>
-            logError("Exception while deleting local spark dir: " + localDir, t)
+          case e: Exception =>
+            logError("Exception while deleting local spark dir: " + localDir, e)
         }
       }
     }
diff --git a/core/src/main/scala/org/apache/spark/storage/TachyonBlockManager.scala b/core/src/main/scala/org/apache/spark/storage/TachyonBlockManager.scala
index b0b967485656..a6cbe3aa440f 100644
--- a/core/src/main/scala/org/apache/spark/storage/TachyonBlockManager.scala
+++ b/core/src/main/scala/org/apache/spark/storage/TachyonBlockManager.scala
@@ -25,7 +25,6 @@ import tachyon.client.TachyonFile
 
 import org.apache.spark.Logging
 import org.apache.spark.executor.ExecutorExitCode
-import org.apache.spark.network.netty.ShuffleSender
 import org.apache.spark.util.Utils
 
 
@@ -137,7 +136,7 @@ private[spark] class TachyonBlockManager(
   private def addShutdownHook() {
     tachyonDirs.foreach(tachyonDir => Utils.registerShutdownDeleteDir(tachyonDir))
     Runtime.getRuntime.addShutdownHook(new Thread("delete Spark tachyon dirs") {
-      override def run() {
+      override def run(): Unit = Utils.logUncaughtExceptions {
         logDebug("Shutdown hook called")
         tachyonDirs.foreach { tachyonDir =>
           try {
@@ -145,8 +144,8 @@ private[spark] class TachyonBlockManager(
               Utils.deleteRecursively(tachyonDir, client)
             }
           } catch {
-            case t: Throwable =>
-              logError("Exception while deleting tachyon spark dir: " + tachyonDir, t)
+            case e: Exception =>
+              logError("Exception while deleting tachyon spark dir: " + tachyonDir, e)
           }
         }
       }
diff --git a/core/src/main/scala/org/apache/spark/util/AkkaUtils.scala b/core/src/main/scala/org/apache/spark/util/AkkaUtils.scala
index 8afe09a117eb..a8d12bb2a016 100644
--- a/core/src/main/scala/org/apache/spark/util/AkkaUtils.scala
+++ b/core/src/main/scala/org/apache/spark/util/AkkaUtils.scala
@@ -20,7 +20,7 @@ package org.apache.spark.util
 import scala.collection.JavaConversions.mapAsJavaMap
 import scala.concurrent.duration.{Duration, FiniteDuration}
 
-import akka.actor.{ActorSystem, ExtendedActorSystem, IndestructibleActorSystem}
+import akka.actor.{ActorSystem, ExtendedActorSystem}
 import com.typesafe.config.ConfigFactory
 import org.apache.log4j.{Level, Logger}
 
@@ -41,7 +41,7 @@ private[spark] object AkkaUtils extends Logging {
    * If indestructible is set to true, the Actor System will continue running in the event
    * of a fatal exception. This is used by [[org.apache.spark.executor.Executor]].
    */
-  def createActorSystem(name: String, host: String, port: Int, indestructible: Boolean = false,
+  def createActorSystem(name: String, host: String, port: Int,
     conf: SparkConf, securityManager: SecurityManager): (ActorSystem, Int) = {
 
     val akkaThreads   = conf.getInt("spark.akka.threads", 4)
@@ -101,12 +101,7 @@ private[spark] object AkkaUtils extends Logging {
       |akka.log-dead-letters-during-shutdown = $lifecycleEvents
       """.stripMargin))
 
-    val actorSystem = if (indestructible) {
-      IndestructibleActorSystem(name, akkaConf)
-    } else {
-      ActorSystem(name, akkaConf)
-    }
-
+    val actorSystem = ActorSystem(name, akkaConf)
     val provider = actorSystem.asInstanceOf[ExtendedActorSystem].provider
     val boundPort = provider.getDefaultAddress.port.get
     (actorSystem, boundPort)
diff --git a/core/src/main/scala/org/apache/spark/util/IndestructibleActorSystem.scala b/core/src/main/scala/org/apache/spark/util/IndestructibleActorSystem.scala
deleted file mode 100644
index 4188a869c13d..000000000000
--- a/core/src/main/scala/org/apache/spark/util/IndestructibleActorSystem.scala
+++ /dev/null
@@ -1,68 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-// Must be in akka.actor package as ActorSystemImpl is protected[akka].
-package akka.actor
-
-import scala.util.control.{ControlThrowable, NonFatal}
-
-import com.typesafe.config.Config
-
-/**
- * An akka.actor.ActorSystem which refuses to shut down in the event of a fatal exception
- * This is necessary as Spark Executors are allowed to recover from fatal exceptions
- * (see org.apache.spark.executor.Executor)
- */
-object IndestructibleActorSystem {
-  def apply(name: String, config: Config): ActorSystem =
-    apply(name, config, ActorSystem.findClassLoader())
-
-  def apply(name: String, config: Config, classLoader: ClassLoader): ActorSystem =
-    new IndestructibleActorSystemImpl(name, config, classLoader).start()
-}
-
-private[akka] class IndestructibleActorSystemImpl(
-    override val name: String,
-    applicationConfig: Config,
-    classLoader: ClassLoader)
-  extends ActorSystemImpl(name, applicationConfig, classLoader) {
-
-  protected override def uncaughtExceptionHandler: Thread.UncaughtExceptionHandler = {
-    val fallbackHandler = super.uncaughtExceptionHandler
-
-    new Thread.UncaughtExceptionHandler() {
-      def uncaughtException(thread: Thread, cause: Throwable): Unit = {
-        if (isFatalError(cause) && !settings.JvmExitOnFatalError) {
-          log.error(cause, "Uncaught fatal error from thread [{}] not shutting down " +
-            "ActorSystem [{}] tolerating and continuing.... ", thread.getName, name)
-          // shutdown()                 //TODO make it configurable
-        } else {
-          fallbackHandler.uncaughtException(thread, cause)
-        }
-      }
-    }
-  }
-
-  def isFatalError(e: Throwable): Boolean = {
-    e match {
-      case NonFatal(_) | _: InterruptedException | _: NotImplementedError | _: ControlThrowable =>
-        false
-      case _ =>
-        true
-    }
-  }
-}
diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala
index 95777fbf57d8..8f7594ada2ba 100644
--- a/core/src/main/scala/org/apache/spark/util/Utils.scala
+++ b/core/src/main/scala/org/apache/spark/util/Utils.scala
@@ -29,6 +29,7 @@ import scala.collection.mutable.ArrayBuffer
 import scala.io.Source
 import scala.reflect.ClassTag
 import scala.util.Try
+import scala.util.control.{ControlThrowable, NonFatal}
 
 import com.google.common.io.Files
 import com.google.common.util.concurrent.ThreadFactoryBuilder
@@ -41,7 +42,6 @@ import org.apache.spark.{Logging, SecurityManager, SparkConf, SparkException}
 import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.serializer.{DeserializationStream, SerializationStream, SerializerInstance}
 
-
 /**
  * Various utility methods used by Spark.
  */
@@ -1125,4 +1125,28 @@ private[spark] object Utils extends Logging {
     }
   }
 
+  /** 
+   * Executes the given block, printing and re-throwing any uncaught exceptions.
+   * This is particularly useful for wrapping code that runs in a thread, to ensure
+   * that exceptions are printed, and to avoid having to catch Throwable.
+   */
+  def logUncaughtExceptions[T](f: => T): T = {
+    try {
+      f
+    } catch {
+      case t: Throwable =>
+        logError(s"Uncaught exception in thread ${Thread.currentThread().getName}", t)
+        throw t
+    }
+  }
+
+  /** Returns true if the given exception was fatal. See docs for scala.util.control.NonFatal. */
+  def isFatalError(e: Throwable): Boolean = {
+    e match {
+      case NonFatal(_) | _: InterruptedException | _: NotImplementedError | _: ControlThrowable =>
+        false
+      case _ =>
+        true
+    }
+  }
 }

From d9c97ba39723d36d3dcb53a75a3a2565357c0955 Mon Sep 17 00:00:00 2001
From: Bernardo Gomez Palacio <bernardo.gomezpalacio@gmail.com>
Date: Mon, 12 May 2014 11:10:28 -0700
Subject: [PATCH 163/810] SPARK-1806: Upgrade Mesos dependency to 0.18.1

Enabled Mesos (0.18.1) dependency with shaded protobuf

Why is this needed?
Avoids any protobuf version collision between Mesos and any other
dependency in Spark e.g. Hadoop HDFS 2.2+ or 1.0.4.

Ticket: https://issues.apache.org/jira/browse/SPARK-1806

* Should close https://issues.apache.org/jira/browse/SPARK-1433

Author berngp

Author: Bernardo Gomez Palacio <bernardo.gomezpalacio@gmail.com>

Closes #741 from berngp/feature/SPARK-1806 and squashes the following commits:

5d70646 [Bernardo Gomez Palacio] SPARK-1806: Upgrade Mesos dependency to 0.18.1
---
 core/pom.xml                                           |  3 ++-
 .../apache/spark/executor/MesosExecutorBackend.scala   |  2 +-
 .../cluster/mesos/MesosSchedulerBackend.scala          |  2 +-
 pom.xml                                                | 10 +++++++++-
 project/SparkBuild.scala                               |  2 +-
 5 files changed, 14 insertions(+), 5 deletions(-)

diff --git a/core/pom.xml b/core/pom.xml
index 8fe215ab2428..bab50f5ce288 100644
--- a/core/pom.xml
+++ b/core/pom.xml
@@ -140,6 +140,7 @@
     <dependency>
       <groupId>org.apache.mesos</groupId>
       <artifactId>mesos</artifactId>
+      <classifier>${mesos.classifier}</classifier>
     </dependency>
     <dependency>
       <groupId>io.netty</groupId>
@@ -322,7 +323,7 @@
         </configuration>
       </plugin>
     </plugins>
-    
+
     <resources>
       <resource>
         <directory>src/main/resources</directory>
diff --git a/core/src/main/scala/org/apache/spark/executor/MesosExecutorBackend.scala b/core/src/main/scala/org/apache/spark/executor/MesosExecutorBackend.scala
index 9b56f711e0e0..74100498bb2b 100644
--- a/core/src/main/scala/org/apache/spark/executor/MesosExecutorBackend.scala
+++ b/core/src/main/scala/org/apache/spark/executor/MesosExecutorBackend.scala
@@ -19,7 +19,7 @@ package org.apache.spark.executor
 
 import java.nio.ByteBuffer
 
-import com.google.protobuf.ByteString
+import org.apache.mesos.protobuf.ByteString
 import org.apache.mesos.{Executor => MesosExecutor, ExecutorDriver, MesosExecutorDriver, MesosNativeLibrary}
 import org.apache.mesos.Protos.{TaskStatus => MesosTaskStatus, _}
 
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala
index c975f312324e..2cea1c8cf154 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala
@@ -24,7 +24,7 @@ import java.util.Collections
 import scala.collection.JavaConversions._
 import scala.collection.mutable.{ArrayBuffer, HashMap, HashSet}
 
-import com.google.protobuf.ByteString
+import org.apache.mesos.protobuf.ByteString
 import org.apache.mesos.{Scheduler => MScheduler}
 import org.apache.mesos._
 import org.apache.mesos.Protos.{TaskInfo => MesosTaskInfo, TaskState => MesosTaskState, _}
diff --git a/pom.xml b/pom.xml
index c4e1c6be52a1..dd1d262881b9 100644
--- a/pom.xml
+++ b/pom.xml
@@ -113,7 +113,8 @@
 
     <scala.version>2.10.4</scala.version>
     <scala.binary.version>2.10</scala.binary.version>
-    <mesos.version>0.13.0</mesos.version>
+    <mesos.version>0.18.1</mesos.version>
+    <mesos.classifier>shaded-protobuf</mesos.classifier>
     <akka.group>org.spark-project.akka</akka.group>
     <akka.version>2.2.3-shaded-protobuf</akka.version>
     <slf4j.version>1.7.5</slf4j.version>
@@ -349,6 +350,13 @@
         <groupId>org.apache.mesos</groupId>
         <artifactId>mesos</artifactId>
         <version>${mesos.version}</version>
+        <classifier>${mesos.classifier}</classifier>
+        <exclusions>
+          <exclusion>
+            <groupId>com.google.protobuf</groupId>
+            <artifactId>protobuf-java</artifactId>
+          </exclusion>
+        </exclusions>
       </dependency>
       <dependency>
         <groupId>commons-net</groupId>
diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index 12791e490ae2..57b3e22f8171 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -343,7 +343,7 @@ object SparkBuild extends Build {
         "org.spark-project.akka"    %% "akka-testkit"     % akkaVersion % "test",
         "org.json4s"                %% "json4s-jackson"   % "3.2.6" excludeAll(excludeScalap),
         "colt"                       % "colt"             % "1.2.0",
-        "org.apache.mesos"           % "mesos"            % "0.13.0",
+        "org.apache.mesos"           % "mesos"            % "0.18.1" classifier("shaded-protobuf") exclude("com.google.protobuf", "protobuf-java"),
         "commons-net"                % "commons-net"      % "2.2",
         "net.java.dev.jets3t"        % "jets3t"           % jets3tVersion excludeAll(excludeCommonsLogging),
         "org.apache.derby"           % "derby"            % "10.4.2.0"                     % "test",

From 0e2bde2030f8e455c5a269fc38d4ff05b395ca32 Mon Sep 17 00:00:00 2001
From: Ankur Dave <ankurdave@gmail.com>
Date: Mon, 12 May 2014 13:05:24 -0700
Subject: [PATCH 164/810] SPARK-1786: Reopening PR 724

Addressing issue in MimaBuild.scala.

Author: Ankur Dave <ankurdave@gmail.com>
Author: Joseph E. Gonzalez <joseph.e.gonzalez@gmail.com>

Closes #742 from jegonzal/edge_partition_serialization and squashes the following commits:

8ba6e0d [Ankur Dave] Add concatenation operators to MimaBuild.scala
cb2ed3a [Joseph E. Gonzalez] addressing missing exclusion in MimaBuild.scala
5d27824 [Ankur Dave] Disable reference tracking to fix serialization test
c0a9ae5 [Ankur Dave] Add failing test for EdgePartition Kryo serialization
a4a3faa [Joseph E. Gonzalez] Making EdgePartition serializable.
---
 .../spark/graphx/GraphKryoRegistrator.scala    |  9 ++++++---
 .../spark/graphx/impl/EdgePartition.scala      | 14 +++++++-------
 .../graphx/impl/EdgePartitionBuilder.scala     |  4 ++--
 .../graphx/impl/EdgeTripletIterator.scala      |  2 +-
 .../graphx/impl/RoutingTablePartition.scala    |  4 ++--
 .../graphx/impl/ShippableVertexPartition.scala |  2 +-
 .../spark/graphx/impl/VertexPartition.scala    |  2 +-
 .../graphx/impl/VertexPartitionBase.scala      |  6 +++---
 .../graphx/impl/VertexPartitionBaseOps.scala   |  4 ++--
 ...ala => GraphXPrimitiveKeyOpenHashMap.scala} |  2 +-
 .../spark/graphx/impl/EdgePartitionSuite.scala | 18 ++++++++++++++++++
 project/MimaBuild.scala                        |  8 +++++---
 12 files changed, 49 insertions(+), 26 deletions(-)
 rename graphx/src/main/scala/org/apache/spark/graphx/util/collection/{PrimitiveKeyOpenHashMap.scala => GraphXPrimitiveKeyOpenHashMap.scala} (98%)

diff --git a/graphx/src/main/scala/org/apache/spark/graphx/GraphKryoRegistrator.scala b/graphx/src/main/scala/org/apache/spark/graphx/GraphKryoRegistrator.scala
index d295d0127ac7..f97f329c0e83 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/GraphKryoRegistrator.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/GraphKryoRegistrator.scala
@@ -24,6 +24,9 @@ import org.apache.spark.util.BoundedPriorityQueue
 import org.apache.spark.util.collection.BitSet
 
 import org.apache.spark.graphx.impl._
+import org.apache.spark.graphx.util.collection.GraphXPrimitiveKeyOpenHashMap
+import org.apache.spark.util.collection.OpenHashSet
+
 
 /**
  * Registers GraphX classes with Kryo for improved performance.
@@ -43,8 +46,8 @@ class GraphKryoRegistrator extends KryoRegistrator {
     kryo.register(classOf[PartitionStrategy])
     kryo.register(classOf[BoundedPriorityQueue[Object]])
     kryo.register(classOf[EdgeDirection])
-
-    // This avoids a large number of hash table lookups.
-    kryo.setReferences(false)
+    kryo.register(classOf[GraphXPrimitiveKeyOpenHashMap[VertexId, Int]])
+    kryo.register(classOf[OpenHashSet[Int]])
+    kryo.register(classOf[OpenHashSet[Long]])
   }
 }
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgePartition.scala b/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgePartition.scala
index 871e81f8d245..a5c9cd1f8b4e 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgePartition.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgePartition.scala
@@ -20,7 +20,7 @@ package org.apache.spark.graphx.impl
 import scala.reflect.{classTag, ClassTag}
 
 import org.apache.spark.graphx._
-import org.apache.spark.graphx.util.collection.PrimitiveKeyOpenHashMap
+import org.apache.spark.graphx.util.collection.GraphXPrimitiveKeyOpenHashMap
 
 /**
  * A collection of edges stored in columnar format, along with any vertex attributes referenced. The
@@ -42,12 +42,12 @@ import org.apache.spark.graphx.util.collection.PrimitiveKeyOpenHashMap
 private[graphx]
 class EdgePartition[
     @specialized(Char, Int, Boolean, Byte, Long, Float, Double) ED: ClassTag, VD: ClassTag](
-    @transient val srcIds: Array[VertexId],
-    @transient val dstIds: Array[VertexId],
-    @transient val data: Array[ED],
-    @transient val index: PrimitiveKeyOpenHashMap[VertexId, Int],
-    @transient val vertices: VertexPartition[VD],
-    @transient val activeSet: Option[VertexSet] = None
+    val srcIds: Array[VertexId] = null,
+    val dstIds: Array[VertexId] = null,
+    val data: Array[ED] = null,
+    val index: GraphXPrimitiveKeyOpenHashMap[VertexId, Int] = null,
+    val vertices: VertexPartition[VD] = null,
+    val activeSet: Option[VertexSet] = None
   ) extends Serializable {
 
   /** Return a new `EdgePartition` with the specified edge data. */
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgePartitionBuilder.scala b/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgePartitionBuilder.scala
index ecb49bef42e4..4520beb99151 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgePartitionBuilder.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgePartitionBuilder.scala
@@ -23,7 +23,7 @@ import scala.util.Sorting
 import org.apache.spark.util.collection.{BitSet, OpenHashSet, PrimitiveVector}
 
 import org.apache.spark.graphx._
-import org.apache.spark.graphx.util.collection.PrimitiveKeyOpenHashMap
+import org.apache.spark.graphx.util.collection.GraphXPrimitiveKeyOpenHashMap
 
 private[graphx]
 class EdgePartitionBuilder[@specialized(Long, Int, Double) ED: ClassTag, VD: ClassTag](
@@ -41,7 +41,7 @@ class EdgePartitionBuilder[@specialized(Long, Int, Double) ED: ClassTag, VD: Cla
     val srcIds = new Array[VertexId](edgeArray.size)
     val dstIds = new Array[VertexId](edgeArray.size)
     val data = new Array[ED](edgeArray.size)
-    val index = new PrimitiveKeyOpenHashMap[VertexId, Int]
+    val index = new GraphXPrimitiveKeyOpenHashMap[VertexId, Int]
     // Copy edges into columnar structures, tracking the beginnings of source vertex id clusters and
     // adding them to the index
     if (edgeArray.length > 0) {
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgeTripletIterator.scala b/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgeTripletIterator.scala
index ebb0b9418d65..56f79a7097fc 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgeTripletIterator.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgeTripletIterator.scala
@@ -20,7 +20,7 @@ package org.apache.spark.graphx.impl
 import scala.reflect.ClassTag
 
 import org.apache.spark.graphx._
-import org.apache.spark.graphx.util.collection.PrimitiveKeyOpenHashMap
+import org.apache.spark.graphx.util.collection.GraphXPrimitiveKeyOpenHashMap
 
 /**
  * The Iterator type returned when constructing edge triplets. This could be an anonymous class in
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/RoutingTablePartition.scala b/graphx/src/main/scala/org/apache/spark/graphx/impl/RoutingTablePartition.scala
index 927e32ad0f44..d02e9238adba 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/impl/RoutingTablePartition.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/impl/RoutingTablePartition.scala
@@ -25,7 +25,7 @@ import org.apache.spark.rdd.ShuffledRDD
 import org.apache.spark.util.collection.{BitSet, PrimitiveVector}
 
 import org.apache.spark.graphx._
-import org.apache.spark.graphx.util.collection.PrimitiveKeyOpenHashMap
+import org.apache.spark.graphx.util.collection.GraphXPrimitiveKeyOpenHashMap
 
 /**
  * A message from the edge partition `pid` to the vertex partition containing `vid` specifying that
@@ -69,7 +69,7 @@ object RoutingTablePartition {
     : Iterator[RoutingTableMessage] = {
     // Determine which positions each vertex id appears in using a map where the low 2 bits
     // represent src and dst
-    val map = new PrimitiveKeyOpenHashMap[VertexId, Byte]
+    val map = new GraphXPrimitiveKeyOpenHashMap[VertexId, Byte]
     edgePartition.srcIds.iterator.foreach { srcId =>
       map.changeValue(srcId, 0x1, (b: Byte) => (b | 0x1).toByte)
     }
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/ShippableVertexPartition.scala b/graphx/src/main/scala/org/apache/spark/graphx/impl/ShippableVertexPartition.scala
index f4e221d4e05a..dca54b8a7da8 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/impl/ShippableVertexPartition.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/impl/ShippableVertexPartition.scala
@@ -22,7 +22,7 @@ import scala.reflect.ClassTag
 import org.apache.spark.util.collection.{BitSet, PrimitiveVector}
 
 import org.apache.spark.graphx._
-import org.apache.spark.graphx.util.collection.PrimitiveKeyOpenHashMap
+import org.apache.spark.graphx.util.collection.GraphXPrimitiveKeyOpenHashMap
 
 /** Stores vertex attributes to ship to an edge partition. */
 private[graphx]
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexPartition.scala b/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexPartition.scala
index f1d174720a1b..55c7a19d1bda 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexPartition.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexPartition.scala
@@ -22,7 +22,7 @@ import scala.reflect.ClassTag
 import org.apache.spark.util.collection.BitSet
 
 import org.apache.spark.graphx._
-import org.apache.spark.graphx.util.collection.PrimitiveKeyOpenHashMap
+import org.apache.spark.graphx.util.collection.GraphXPrimitiveKeyOpenHashMap
 
 private[graphx] object VertexPartition {
   /** Construct a `VertexPartition` from the given vertices. */
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexPartitionBase.scala b/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexPartitionBase.scala
index 8d9e0204d27f..34939b24440a 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexPartitionBase.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexPartitionBase.scala
@@ -23,7 +23,7 @@ import scala.reflect.ClassTag
 import org.apache.spark.util.collection.BitSet
 
 import org.apache.spark.graphx._
-import org.apache.spark.graphx.util.collection.PrimitiveKeyOpenHashMap
+import org.apache.spark.graphx.util.collection.GraphXPrimitiveKeyOpenHashMap
 
 private[graphx] object VertexPartitionBase {
   /**
@@ -32,7 +32,7 @@ private[graphx] object VertexPartitionBase {
    */
   def initFrom[VD: ClassTag](iter: Iterator[(VertexId, VD)])
     : (VertexIdToIndexMap, Array[VD], BitSet) = {
-    val map = new PrimitiveKeyOpenHashMap[VertexId, VD]
+    val map = new GraphXPrimitiveKeyOpenHashMap[VertexId, VD]
     iter.foreach { pair =>
       map(pair._1) = pair._2
     }
@@ -45,7 +45,7 @@ private[graphx] object VertexPartitionBase {
    */
   def initFrom[VD: ClassTag](iter: Iterator[(VertexId, VD)], mergeFunc: (VD, VD) => VD)
     : (VertexIdToIndexMap, Array[VD], BitSet) = {
-    val map = new PrimitiveKeyOpenHashMap[VertexId, VD]
+    val map = new GraphXPrimitiveKeyOpenHashMap[VertexId, VD]
     iter.foreach { pair =>
       map.setMerge(pair._1, pair._2, mergeFunc)
     }
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexPartitionBaseOps.scala b/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexPartitionBaseOps.scala
index 21ff615feca6..a4f769b29401 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexPartitionBaseOps.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexPartitionBaseOps.scala
@@ -25,7 +25,7 @@ import org.apache.spark.Logging
 import org.apache.spark.util.collection.BitSet
 
 import org.apache.spark.graphx._
-import org.apache.spark.graphx.util.collection.PrimitiveKeyOpenHashMap
+import org.apache.spark.graphx.util.collection.GraphXPrimitiveKeyOpenHashMap
 
 /**
  * An class containing additional operations for subclasses of VertexPartitionBase that provide
@@ -224,7 +224,7 @@ private[graphx] abstract class VertexPartitionBaseOps
    * Construct a new VertexPartition whose index contains only the vertices in the mask.
    */
   def reindex(): Self[VD] = {
-    val hashMap = new PrimitiveKeyOpenHashMap[VertexId, VD]
+    val hashMap = new GraphXPrimitiveKeyOpenHashMap[VertexId, VD]
     val arbitraryMerge = (a: VD, b: VD) => a
     for ((k, v) <- self.iterator) {
       hashMap.setMerge(k, v, arbitraryMerge)
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/util/collection/PrimitiveKeyOpenHashMap.scala b/graphx/src/main/scala/org/apache/spark/graphx/util/collection/GraphXPrimitiveKeyOpenHashMap.scala
similarity index 98%
rename from graphx/src/main/scala/org/apache/spark/graphx/util/collection/PrimitiveKeyOpenHashMap.scala
rename to graphx/src/main/scala/org/apache/spark/graphx/util/collection/GraphXPrimitiveKeyOpenHashMap.scala
index 7b02e2ed1a9c..57b01b6f2e1f 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/util/collection/PrimitiveKeyOpenHashMap.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/util/collection/GraphXPrimitiveKeyOpenHashMap.scala
@@ -29,7 +29,7 @@ import scala.reflect._
  * Under the hood, it uses our OpenHashSet implementation.
  */
 private[graphx]
-class PrimitiveKeyOpenHashMap[@specialized(Long, Int) K: ClassTag,
+class GraphXPrimitiveKeyOpenHashMap[@specialized(Long, Int) K: ClassTag,
                               @specialized(Long, Int, Double) V: ClassTag](
     val keySet: OpenHashSet[K], var _values: Array[V])
   extends Iterable[(K, V)]
diff --git a/graphx/src/test/scala/org/apache/spark/graphx/impl/EdgePartitionSuite.scala b/graphx/src/test/scala/org/apache/spark/graphx/impl/EdgePartitionSuite.scala
index d2e0c01bc35e..28fd112f2b12 100644
--- a/graphx/src/test/scala/org/apache/spark/graphx/impl/EdgePartitionSuite.scala
+++ b/graphx/src/test/scala/org/apache/spark/graphx/impl/EdgePartitionSuite.scala
@@ -22,6 +22,9 @@ import scala.util.Random
 
 import org.scalatest.FunSuite
 
+import org.apache.spark.SparkConf
+import org.apache.spark.serializer.KryoSerializer
+
 import org.apache.spark.graphx._
 
 class EdgePartitionSuite extends FunSuite {
@@ -120,4 +123,19 @@ class EdgePartitionSuite extends FunSuite {
     assert(!ep.isActive(-1))
     assert(ep.numActives == Some(2))
   }
+
+  test("Kryo serialization") {
+    val aList = List((0, 1, 0), (1, 0, 0), (1, 2, 0), (5, 4, 0), (5, 5, 0))
+    val a: EdgePartition[Int, Int] = makeEdgePartition(aList)
+    val conf = new SparkConf()
+      .set("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
+      .set("spark.kryo.registrator", "org.apache.spark.graphx.GraphKryoRegistrator")
+    val s = new KryoSerializer(conf).newInstance()
+    val aSer: EdgePartition[Int, Int] = s.deserialize(s.serialize(a))
+    assert(aSer.srcIds.toList === a.srcIds.toList)
+    assert(aSer.dstIds.toList === a.dstIds.toList)
+    assert(aSer.data.toList === a.data.toList)
+    assert(aSer.index != null)
+    assert(aSer.vertices.iterator.toSet === a.vertices.iterator.toSet)
+  }
 }
diff --git a/project/MimaBuild.scala b/project/MimaBuild.scala
index fafc9b36a77d..e147be7ddaa6 100644
--- a/project/MimaBuild.scala
+++ b/project/MimaBuild.scala
@@ -30,7 +30,7 @@ object MimaBuild {
 
     // Read package-private excludes from file
     val excludeFilePath = (base.getAbsolutePath + "/.mima-excludes")
-    val excludeFile = file(excludeFilePath) 
+    val excludeFile = file(excludeFilePath)
     val packagePrivateList: Seq[String] =
       if (!excludeFile.exists()) {
         Seq()
@@ -41,10 +41,10 @@ object MimaBuild {
     // Exclude a single class and its corresponding object
     def excludeClass(className: String) = {
       Seq(
-        excludePackage(className), 
+        excludePackage(className),
         ProblemFilters.exclude[MissingClassProblem](className),
         ProblemFilters.exclude[MissingTypesProblem](className),
-        excludePackage(className + "$"), 
+        excludePackage(className + "$"),
         ProblemFilters.exclude[MissingClassProblem](className + "$"),
         ProblemFilters.exclude[MissingTypesProblem](className + "$")
       )
@@ -78,6 +78,8 @@ object MimaBuild {
           excludeSparkClass("graphx.VertexRDD") ++
           excludeSparkClass("graphx.impl.GraphImpl") ++
           excludeSparkClass("graphx.impl.RoutingTable") ++
+          excludeSparkClass("graphx.util.collection.PrimitiveKeyOpenHashMap") ++
+          excludeSparkClass("graphx.util.collection.GraphXPrimitiveKeyOpenHashMap") ++
           excludeSparkClass("mllib.recommendation.MFDataGenerator") ++
           excludeSparkClass("mllib.optimization.SquaredGradient") ++
           excludeSparkClass("mllib.regression.RidgeRegressionWithSGD") ++

From 1e4a65e69489ff877e6da6f78b1c1306335e373c Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Mon, 12 May 2014 13:20:23 -0700
Subject: [PATCH 165/810] BUILD: Include Hive with default packages when
 creating a release

---
 dev/create-release/create-release.sh | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/dev/create-release/create-release.sh b/dev/create-release/create-release.sh
index 33552a74920a..b7a0a01c3bad 100755
--- a/dev/create-release/create-release.sh
+++ b/dev/create-release/create-release.sh
@@ -109,9 +109,9 @@ make_binary_release() {
     spark-$RELEASE_VERSION-bin-$NAME.tgz.sha
 }
 
-make_binary_release "hadoop1" "--hadoop 1.0.4"
-make_binary_release "cdh4" "--hadoop 2.0.0-mr1-cdh4.2.0"
-make_binary_release "hadoop2" "--with-yarn --hadoop 2.2.0"
+make_binary_release "hadoop1" "--with-hive --hadoop 1.0.4"
+make_binary_release "cdh4" "--with-hive --hadoop 2.0.0-mr1-cdh4.2.0"
+make_binary_release "hadoop2" "--with-hive --with-yarn --hadoop 2.2.0"
 
 # Copy data
 echo "Copying release tarballs"

From 7120a2979d0a9f0f54a88b2416be7ca10e74f409 Mon Sep 17 00:00:00 2001
From: Sean Owen <sowen@cloudera.com>
Date: Mon, 12 May 2014 14:16:19 -0700
Subject: [PATCH 166/810] SPARK-1798. Tests should clean up temp files
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Three issues related to temp files that tests generate – these should be touched up for hygiene but are not urgent.

Modules have a log4j.properties which directs the unit-test.log output file to a directory like `[module]/target/unit-test.log`. But this ends up creating `[module]/[module]/target/unit-test.log` instead of former.

The `work/` directory is not deleted by "mvn clean", in the parent and in modules. Neither is the `checkpoint/` directory created under the various external modules.

Many tests create a temp directory, which is not usually deleted. This can be largely resolved by calling `deleteOnExit()` at creation and trying to call `Utils.deleteRecursively` consistently to clean up, sometimes in an `@After` method.

_If anyone seconds the motion, I can create a more significant change that introduces a new test trait along the lines of `LocalSparkContext`, which provides management of temp directories for subclasses to take advantage of._

Author: Sean Owen <sowen@cloudera.com>

Closes #732 from srowen/SPARK-1798 and squashes the following commits:

5af578e [Sean Owen] Try to consistently delete test temp dirs and files, and set deleteOnExit() for each
b21b356 [Sean Owen] Remove work/ and checkpoint/ dirs with mvn clean
bdd0f41 [Sean Owen] Remove duplicate module dir in log4j.properties output path for tests
---
 bagel/src/test/resources/log4j.properties     |  2 +-
 .../scala/org/apache/spark/TestUtils.scala    |  1 +
 .../scala/org/apache/spark/util/Utils.scala   | 18 +++---
 .../java/org/apache/spark/JavaAPISuite.java   | 18 ++----
 core/src/test/resources/log4j.properties      |  2 +-
 .../org/apache/spark/CheckpointSuite.scala    |  5 +-
 .../org/apache/spark/FileServerSuite.scala    | 18 ++++--
 .../scala/org/apache/spark/FileSuite.scala    | 56 +++++++++----------
 .../WholeTextFileRecordReaderSuite.scala      |  4 +-
 .../spark/rdd/PairRDDFunctionsSuite.scala     |  2 +
 .../scheduler/EventLoggingListenerSuite.scala | 15 +++--
 .../spark/scheduler/ReplayListenerSuite.scala | 14 +++--
 .../spark/storage/DiskBlockManagerSuite.scala | 30 +++++++---
 .../apache/spark/util/FileLoggerSuite.scala   | 17 ++++--
 .../org/apache/spark/util/UtilsSuite.scala    |  1 +
 .../flume/src/test/resources/log4j.properties |  2 +-
 .../kafka/src/test/resources/log4j.properties |  2 +-
 .../mqtt/src/test/resources/log4j.properties  |  2 +-
 .../src/test/resources/log4j.properties       |  2 +-
 .../src/test/resources/log4j.properties       |  2 +-
 .../java/org/apache/spark/Java8APISuite.java  |  3 +
 graphx/src/test/resources/log4j.properties    |  2 +-
 mllib/src/test/resources/log4j.properties     |  2 +-
 .../spark/mllib/util/MLUtilsSuite.scala       | 17 ++----
 pom.xml                                       | 15 +++++
 repl/src/test/resources/log4j.properties      |  2 +-
 .../spark/repl/ExecutorClassLoaderSuite.scala | 24 ++++++--
 .../org/apache/spark/repl/ReplSuite.scala     |  3 +
 .../apache/spark/sql/InsertIntoSuite.scala    | 12 ++++
 .../org/apache/spark/sql/hive/TestHive.scala  |  1 +
 .../streaming/util/MasterFailureTest.scala    |  3 +
 .../apache/spark/streaming/JavaAPISuite.java  |  5 +-
 streaming/src/test/resources/log4j.properties |  2 +-
 .../spark/streaming/CheckpointSuite.scala     |  2 +
 .../spark/streaming/InputStreamsSuite.scala   |  1 +
 35 files changed, 193 insertions(+), 114 deletions(-)

diff --git a/bagel/src/test/resources/log4j.properties b/bagel/src/test/resources/log4j.properties
index 5cdcf35b23a6..30b4baa4d714 100644
--- a/bagel/src/test/resources/log4j.properties
+++ b/bagel/src/test/resources/log4j.properties
@@ -19,7 +19,7 @@
 log4j.rootCategory=INFO, file
 log4j.appender.file=org.apache.log4j.FileAppender
 log4j.appender.file.append=false
-log4j.appender.file.file=bagel/target/unit-tests.log
+log4j.appender.file.file=target/unit-tests.log
 log4j.appender.file.layout=org.apache.log4j.PatternLayout
 log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %p %c{1}: %m%n
 
diff --git a/core/src/main/scala/org/apache/spark/TestUtils.scala b/core/src/main/scala/org/apache/spark/TestUtils.scala
index 8ae02154823e..885c6829a2d7 100644
--- a/core/src/main/scala/org/apache/spark/TestUtils.scala
+++ b/core/src/main/scala/org/apache/spark/TestUtils.scala
@@ -43,6 +43,7 @@ private[spark] object TestUtils {
    */
   def createJarWithClasses(classNames: Seq[String], value: String = ""): URL = {
     val tempDir = Files.createTempDir()
+    tempDir.deleteOnExit()
     val files = for (name <- classNames) yield createCompiledClass(name, tempDir, value)
     val jarFile = new File(tempDir, "testJar-%s.jar".format(System.currentTimeMillis()))
     createJar(files, jarFile)
diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala
index 8f7594ada2ba..0631e5423792 100644
--- a/core/src/main/scala/org/apache/spark/util/Utils.scala
+++ b/core/src/main/scala/org/apache/spark/util/Utils.scala
@@ -586,15 +586,17 @@ private[spark] object Utils extends Logging {
    * Don't follow directories if they are symlinks.
    */
   def deleteRecursively(file: File) {
-    if ((file.isDirectory) && !isSymlink(file)) {
-      for (child <- listFilesSafely(file)) {
-        deleteRecursively(child)
+    if (file != null) {
+      if ((file.isDirectory) && !isSymlink(file)) {
+        for (child <- listFilesSafely(file)) {
+          deleteRecursively(child)
+        }
       }
-    }
-    if (!file.delete()) {
-      // Delete can also fail if the file simply did not exist
-      if (file.exists()) {
-        throw new IOException("Failed to delete: " + file.getAbsolutePath)
+      if (!file.delete()) {
+        // Delete can also fail if the file simply did not exist
+        if (file.exists()) {
+          throw new IOException("Failed to delete: " + file.getAbsolutePath)
+        }
       }
     }
   }
diff --git a/core/src/test/java/org/apache/spark/JavaAPISuite.java b/core/src/test/java/org/apache/spark/JavaAPISuite.java
index 191201582792..3dd79243ab5b 100644
--- a/core/src/test/java/org/apache/spark/JavaAPISuite.java
+++ b/core/src/test/java/org/apache/spark/JavaAPISuite.java
@@ -18,7 +18,6 @@
 package org.apache.spark;
 
 import java.io.*;
-import java.lang.StringBuilder;
 import java.util.*;
 
 import scala.Tuple2;
@@ -49,16 +48,20 @@
 import org.apache.spark.partial.PartialResult;
 import org.apache.spark.storage.StorageLevel;
 import org.apache.spark.util.StatCounter;
+import org.apache.spark.util.Utils;
 
 // The test suite itself is Serializable so that anonymous Function implementations can be
 // serialized, as an alternative to converting these anonymous classes to static inner classes;
 // see http://stackoverflow.com/questions/758570/.
 public class JavaAPISuite implements Serializable {
   private transient JavaSparkContext sc;
+  private transient File tempDir;
 
   @Before
   public void setUp() {
     sc = new JavaSparkContext("local", "JavaAPISuite");
+    tempDir = Files.createTempDir();
+    tempDir.deleteOnExit();
   }
 
   @After
@@ -67,6 +70,7 @@ public void tearDown() {
     sc = null;
     // To avoid Akka rebinding to the same port, since it doesn't unbind immediately on shutdown
     System.clearProperty("spark.driver.port");
+    Utils.deleteRecursively(tempDir);
   }
 
   static class ReverseIntComparator implements Comparator<Integer>, Serializable {
@@ -611,7 +615,6 @@ public void glom() {
 
   @Test
   public void textFiles() throws IOException {
-    File tempDir = Files.createTempDir();
     String outputDir = new File(tempDir, "output").getAbsolutePath();
     JavaRDD<Integer> rdd = sc.parallelize(Arrays.asList(1, 2, 3, 4));
     rdd.saveAsTextFile(outputDir);
@@ -630,7 +633,6 @@ public void wholeTextFiles() throws IOException {
     byte[] content1 = "spark is easy to use.\n".getBytes("utf-8");
     byte[] content2 = "spark is also easy to use.\n".getBytes("utf-8");
 
-    File tempDir = Files.createTempDir();
     String tempDirName = tempDir.getAbsolutePath();
     DataOutputStream ds = new DataOutputStream(new FileOutputStream(tempDirName + "/part-00000"));
     ds.write(content1);
@@ -653,7 +655,6 @@ public void wholeTextFiles() throws IOException {
 
   @Test
   public void textFilesCompressed() throws IOException {
-    File tempDir = Files.createTempDir();
     String outputDir = new File(tempDir, "output").getAbsolutePath();
     JavaRDD<Integer> rdd = sc.parallelize(Arrays.asList(1, 2, 3, 4));
     rdd.saveAsTextFile(outputDir, DefaultCodec.class);
@@ -667,7 +668,6 @@ public void textFilesCompressed() throws IOException {
   @SuppressWarnings("unchecked")
   @Test
   public void sequenceFile() {
-    File tempDir = Files.createTempDir();
     String outputDir = new File(tempDir, "output").getAbsolutePath();
     List<Tuple2<Integer, String>> pairs = Arrays.asList(
       new Tuple2<Integer, String>(1, "a"),
@@ -697,7 +697,6 @@ public Tuple2<Integer, String> call(Tuple2<IntWritable, Text> pair) {
   @SuppressWarnings("unchecked")
   @Test
   public void writeWithNewAPIHadoopFile() {
-    File tempDir = Files.createTempDir();
     String outputDir = new File(tempDir, "output").getAbsolutePath();
     List<Tuple2<Integer, String>> pairs = Arrays.asList(
       new Tuple2<Integer, String>(1, "a"),
@@ -728,7 +727,6 @@ public String call(Tuple2<IntWritable, Text> x) {
   @SuppressWarnings("unchecked")
   @Test
   public void readWithNewAPIHadoopFile() throws IOException {
-    File tempDir = Files.createTempDir();
     String outputDir = new File(tempDir, "output").getAbsolutePath();
     List<Tuple2<Integer, String>> pairs = Arrays.asList(
       new Tuple2<Integer, String>(1, "a"),
@@ -758,7 +756,6 @@ public String call(Tuple2<IntWritable, Text> x) {
 
   @Test
   public void objectFilesOfInts() {
-    File tempDir = Files.createTempDir();
     String outputDir = new File(tempDir, "output").getAbsolutePath();
     JavaRDD<Integer> rdd = sc.parallelize(Arrays.asList(1, 2, 3, 4));
     rdd.saveAsObjectFile(outputDir);
@@ -771,7 +768,6 @@ public void objectFilesOfInts() {
   @SuppressWarnings("unchecked")
   @Test
   public void objectFilesOfComplexTypes() {
-    File tempDir = Files.createTempDir();
     String outputDir = new File(tempDir, "output").getAbsolutePath();
     List<Tuple2<Integer, String>> pairs = Arrays.asList(
       new Tuple2<Integer, String>(1, "a"),
@@ -788,7 +784,6 @@ public void objectFilesOfComplexTypes() {
   @SuppressWarnings("unchecked")
   @Test
   public void hadoopFile() {
-    File tempDir = Files.createTempDir();
     String outputDir = new File(tempDir, "output").getAbsolutePath();
     List<Tuple2<Integer, String>> pairs = Arrays.asList(
       new Tuple2<Integer, String>(1, "a"),
@@ -818,7 +813,6 @@ public String call(Tuple2<IntWritable, Text> x) {
   @SuppressWarnings("unchecked")
   @Test
   public void hadoopFileCompressed() {
-    File tempDir = Files.createTempDir();
     String outputDir = new File(tempDir, "output_compressed").getAbsolutePath();
     List<Tuple2<Integer, String>> pairs = Arrays.asList(
         new Tuple2<Integer, String>(1, "a"),
@@ -948,7 +942,6 @@ public String call(Integer t) throws Exception {
 
   @Test
   public void checkpointAndComputation() {
-    File tempDir = Files.createTempDir();
     JavaRDD<Integer> rdd = sc.parallelize(Arrays.asList(1, 2, 3, 4, 5));
     sc.setCheckpointDir(tempDir.getAbsolutePath());
     Assert.assertEquals(false, rdd.isCheckpointed());
@@ -960,7 +953,6 @@ public void checkpointAndComputation() {
 
   @Test
   public void checkpointAndRestore() {
-    File tempDir = Files.createTempDir();
     JavaRDD<Integer> rdd = sc.parallelize(Arrays.asList(1, 2, 3, 4, 5));
     sc.setCheckpointDir(tempDir.getAbsolutePath());
     Assert.assertEquals(false, rdd.isCheckpointed());
diff --git a/core/src/test/resources/log4j.properties b/core/src/test/resources/log4j.properties
index f6fef03689a7..26b73a1b3974 100644
--- a/core/src/test/resources/log4j.properties
+++ b/core/src/test/resources/log4j.properties
@@ -19,7 +19,7 @@
 log4j.rootCategory=INFO, file
 log4j.appender.file=org.apache.log4j.FileAppender
 log4j.appender.file.append=false
-log4j.appender.file.file=core/target/unit-tests.log
+log4j.appender.file.file=target/unit-tests.log
 log4j.appender.file.layout=org.apache.log4j.PatternLayout
 log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %p %c{1}: %m%n
 
diff --git a/core/src/test/scala/org/apache/spark/CheckpointSuite.scala b/core/src/test/scala/org/apache/spark/CheckpointSuite.scala
index d2555b7c052c..64933f4b1046 100644
--- a/core/src/test/scala/org/apache/spark/CheckpointSuite.scala
+++ b/core/src/test/scala/org/apache/spark/CheckpointSuite.scala
@@ -35,6 +35,7 @@ class CheckpointSuite extends FunSuite with LocalSparkContext with Logging {
   override def beforeEach() {
     super.beforeEach()
     checkpointDir = File.createTempFile("temp", "")
+    checkpointDir.deleteOnExit()
     checkpointDir.delete()
     sc = new SparkContext("local", "test")
     sc.setCheckpointDir(checkpointDir.toString)
@@ -42,9 +43,7 @@ class CheckpointSuite extends FunSuite with LocalSparkContext with Logging {
 
   override def afterEach() {
     super.afterEach()
-    if (checkpointDir != null) {
-      checkpointDir.delete()
-    }
+    Utils.deleteRecursively(checkpointDir)
   }
 
   test("basic checkpointing") {
diff --git a/core/src/test/scala/org/apache/spark/FileServerSuite.scala b/core/src/test/scala/org/apache/spark/FileServerSuite.scala
index d651fbbac4e9..7e18f45de7b5 100644
--- a/core/src/test/scala/org/apache/spark/FileServerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/FileServerSuite.scala
@@ -24,9 +24,11 @@ import com.google.common.io.Files
 import org.scalatest.FunSuite
 
 import org.apache.spark.SparkContext._
+import org.apache.spark.util.Utils
 
 class FileServerSuite extends FunSuite with LocalSparkContext {
 
+  @transient var tmpDir: File = _
   @transient var tmpFile: File = _
   @transient var tmpJarUrl: String = _
 
@@ -38,15 +40,18 @@ class FileServerSuite extends FunSuite with LocalSparkContext {
 
   override def beforeAll() {
     super.beforeAll()
-    val tmpDir = new File(Files.createTempDir(), "test")
-    tmpDir.mkdir()
 
-    val textFile = new File(tmpDir, "FileServerSuite.txt")
+    tmpDir = Files.createTempDir()
+    tmpDir.deleteOnExit()
+    val testTempDir = new File(tmpDir, "test")
+    testTempDir.mkdir()
+
+    val textFile = new File(testTempDir, "FileServerSuite.txt")
     val pw = new PrintWriter(textFile)
     pw.println("100")
     pw.close()
 
-    val jarFile = new File(tmpDir, "test.jar")
+    val jarFile = new File(testTempDir, "test.jar")
     val jarStream = new FileOutputStream(jarFile)
     val jar = new JarOutputStream(jarStream, new java.util.jar.Manifest())
     System.setProperty("spark.authenticate", "false")
@@ -70,6 +75,11 @@ class FileServerSuite extends FunSuite with LocalSparkContext {
     tmpJarUrl = jarFile.toURI.toURL.toString
   }
 
+  override def afterAll() {
+    super.afterAll()
+    Utils.deleteRecursively(tmpDir)
+  }
+
   test("Distributing files locally") {
     sc = new SparkContext("local[4]", "test")
     sc.addFile(tmpFile.toString)
diff --git a/core/src/test/scala/org/apache/spark/FileSuite.scala b/core/src/test/scala/org/apache/spark/FileSuite.scala
index b9b668d3cc62..1f2206b1f037 100644
--- a/core/src/test/scala/org/apache/spark/FileSuite.scala
+++ b/core/src/test/scala/org/apache/spark/FileSuite.scala
@@ -30,12 +30,24 @@ import org.apache.hadoop.mapreduce.Job
 import org.scalatest.FunSuite
 
 import org.apache.spark.SparkContext._
+import org.apache.spark.util.Utils
 
 class FileSuite extends FunSuite with LocalSparkContext {
+  var tempDir: File = _
+
+  override def beforeEach() {
+    super.beforeEach()
+    tempDir = Files.createTempDir()
+    tempDir.deleteOnExit()
+  }
+
+  override def afterEach() {
+    super.afterEach()
+    Utils.deleteRecursively(tempDir)
+  }
 
   test("text files") {
     sc = new SparkContext("local", "test")
-    val tempDir = Files.createTempDir()
     val outputDir = new File(tempDir, "output").getAbsolutePath
     val nums = sc.makeRDD(1 to 4)
     nums.saveAsTextFile(outputDir)
@@ -49,7 +61,6 @@ class FileSuite extends FunSuite with LocalSparkContext {
 
   test("text files (compressed)") {
     sc = new SparkContext("local", "test")
-    val tempDir = Files.createTempDir()
     val normalDir = new File(tempDir, "output_normal").getAbsolutePath
     val compressedOutputDir = new File(tempDir, "output_compressed").getAbsolutePath
     val codec = new DefaultCodec()
@@ -71,7 +82,6 @@ class FileSuite extends FunSuite with LocalSparkContext {
 
   test("SequenceFiles") {
     sc = new SparkContext("local", "test")
-    val tempDir = Files.createTempDir()
     val outputDir = new File(tempDir, "output").getAbsolutePath
     val nums = sc.makeRDD(1 to 3).map(x => (x, "a" * x)) // (1,a), (2,aa), (3,aaa)
     nums.saveAsSequenceFile(outputDir)
@@ -82,7 +92,6 @@ class FileSuite extends FunSuite with LocalSparkContext {
 
   test("SequenceFile (compressed)") {
     sc = new SparkContext("local", "test")
-    val tempDir = Files.createTempDir()
     val normalDir = new File(tempDir, "output_normal").getAbsolutePath
     val compressedOutputDir = new File(tempDir, "output_compressed").getAbsolutePath
     val codec = new DefaultCodec()
@@ -104,7 +113,6 @@ class FileSuite extends FunSuite with LocalSparkContext {
 
   test("SequenceFile with writable key") {
     sc = new SparkContext("local", "test")
-    val tempDir = Files.createTempDir()
     val outputDir = new File(tempDir, "output").getAbsolutePath
     val nums = sc.makeRDD(1 to 3).map(x => (new IntWritable(x), "a" * x))
     nums.saveAsSequenceFile(outputDir)
@@ -115,7 +123,6 @@ class FileSuite extends FunSuite with LocalSparkContext {
 
   test("SequenceFile with writable value") {
     sc = new SparkContext("local", "test")
-    val tempDir = Files.createTempDir()
     val outputDir = new File(tempDir, "output").getAbsolutePath
     val nums = sc.makeRDD(1 to 3).map(x => (x, new Text("a" * x)))
     nums.saveAsSequenceFile(outputDir)
@@ -126,7 +133,6 @@ class FileSuite extends FunSuite with LocalSparkContext {
 
   test("SequenceFile with writable key and value") {
     sc = new SparkContext("local", "test")
-    val tempDir = Files.createTempDir()
     val outputDir = new File(tempDir, "output").getAbsolutePath
     val nums = sc.makeRDD(1 to 3).map(x => (new IntWritable(x), new Text("a" * x)))
     nums.saveAsSequenceFile(outputDir)
@@ -137,7 +143,6 @@ class FileSuite extends FunSuite with LocalSparkContext {
 
   test("implicit conversions in reading SequenceFiles") {
     sc = new SparkContext("local", "test")
-    val tempDir = Files.createTempDir()
     val outputDir = new File(tempDir, "output").getAbsolutePath
     val nums = sc.makeRDD(1 to 3).map(x => (x, "a" * x)) // (1,a), (2,aa), (3,aaa)
     nums.saveAsSequenceFile(outputDir)
@@ -154,7 +159,6 @@ class FileSuite extends FunSuite with LocalSparkContext {
 
   test("object files of ints") {
     sc = new SparkContext("local", "test")
-    val tempDir = Files.createTempDir()
     val outputDir = new File(tempDir, "output").getAbsolutePath
     val nums = sc.makeRDD(1 to 4)
     nums.saveAsObjectFile(outputDir)
@@ -165,7 +169,6 @@ class FileSuite extends FunSuite with LocalSparkContext {
 
   test("object files of complex types") {
     sc = new SparkContext("local", "test")
-    val tempDir = Files.createTempDir()
     val outputDir = new File(tempDir, "output").getAbsolutePath
     val nums = sc.makeRDD(1 to 3).map(x => (x, "a" * x))
     nums.saveAsObjectFile(outputDir)
@@ -177,7 +180,6 @@ class FileSuite extends FunSuite with LocalSparkContext {
   test("write SequenceFile using new Hadoop API") {
     import org.apache.hadoop.mapreduce.lib.output.SequenceFileOutputFormat
     sc = new SparkContext("local", "test")
-    val tempDir = Files.createTempDir()
     val outputDir = new File(tempDir, "output").getAbsolutePath
     val nums = sc.makeRDD(1 to 3).map(x => (new IntWritable(x), new Text("a" * x)))
     nums.saveAsNewAPIHadoopFile[SequenceFileOutputFormat[IntWritable, Text]](
@@ -189,7 +191,6 @@ class FileSuite extends FunSuite with LocalSparkContext {
   test("read SequenceFile using new Hadoop API") {
     import org.apache.hadoop.mapreduce.lib.input.SequenceFileInputFormat
     sc = new SparkContext("local", "test")
-    val tempDir = Files.createTempDir()
     val outputDir = new File(tempDir, "output").getAbsolutePath
     val nums = sc.makeRDD(1 to 3).map(x => (new IntWritable(x), new Text("a" * x)))
     nums.saveAsSequenceFile(outputDir)
@@ -200,7 +201,6 @@ class FileSuite extends FunSuite with LocalSparkContext {
 
   test("file caching") {
     sc = new SparkContext("local", "test")
-    val tempDir = Files.createTempDir()
     val out = new FileWriter(tempDir + "/input")
     out.write("Hello world!\n")
     out.write("What's up?\n")
@@ -214,67 +214,61 @@ class FileSuite extends FunSuite with LocalSparkContext {
 
   test ("prevent user from overwriting the empty directory (old Hadoop API)") {
     sc = new SparkContext("local", "test")
-    val tempdir = Files.createTempDir()
     val randomRDD = sc.parallelize(Array((1, "a"), (1, "a"), (2, "b"), (3, "c")), 1)
     intercept[FileAlreadyExistsException] {
-      randomRDD.saveAsTextFile(tempdir.getPath)
+      randomRDD.saveAsTextFile(tempDir.getPath)
     }
   }
 
   test ("prevent user from overwriting the non-empty directory (old Hadoop API)") {
     sc = new SparkContext("local", "test")
-    val tempdir = Files.createTempDir()
     val randomRDD = sc.parallelize(Array((1, "a"), (1, "a"), (2, "b"), (3, "c")), 1)
-    randomRDD.saveAsTextFile(tempdir.getPath + "/output")
-    assert(new File(tempdir.getPath + "/output/part-00000").exists() === true)
+    randomRDD.saveAsTextFile(tempDir.getPath + "/output")
+    assert(new File(tempDir.getPath + "/output/part-00000").exists() === true)
     intercept[FileAlreadyExistsException] {
-      randomRDD.saveAsTextFile(tempdir.getPath + "/output")
+      randomRDD.saveAsTextFile(tempDir.getPath + "/output")
     }
   }
 
   test ("prevent user from overwriting the empty directory (new Hadoop API)") {
     sc = new SparkContext("local", "test")
-    val tempdir = Files.createTempDir()
     val randomRDD = sc.parallelize(Array(("key1", "a"), ("key2", "a"), ("key3", "b"), ("key4", "c")), 1)
     intercept[FileAlreadyExistsException] {
-      randomRDD.saveAsNewAPIHadoopFile[NewTextOutputFormat[String, String]](tempdir.getPath)
+      randomRDD.saveAsNewAPIHadoopFile[NewTextOutputFormat[String, String]](tempDir.getPath)
     }
   }
 
   test ("prevent user from overwriting the non-empty directory (new Hadoop API)") {
     sc = new SparkContext("local", "test")
-    val tempdir = Files.createTempDir()
     val randomRDD = sc.parallelize(Array(("key1", "a"), ("key2", "a"), ("key3", "b"), ("key4", "c")), 1)
-    randomRDD.saveAsNewAPIHadoopFile[NewTextOutputFormat[String, String]](tempdir.getPath + "/output")
-    assert(new File(tempdir.getPath + "/output/part-r-00000").exists() === true)
+    randomRDD.saveAsNewAPIHadoopFile[NewTextOutputFormat[String, String]](tempDir.getPath + "/output")
+    assert(new File(tempDir.getPath + "/output/part-r-00000").exists() === true)
     intercept[FileAlreadyExistsException] {
-      randomRDD.saveAsNewAPIHadoopFile[NewTextOutputFormat[String, String]](tempdir.getPath)
+      randomRDD.saveAsNewAPIHadoopFile[NewTextOutputFormat[String, String]](tempDir.getPath)
     }
   }
 
   test ("save Hadoop Dataset through old Hadoop API") {
     sc = new SparkContext("local", "test")
-    val tempdir = Files.createTempDir()
     val randomRDD = sc.parallelize(Array(("key1", "a"), ("key2", "a"), ("key3", "b"), ("key4", "c")), 1)
     val job = new JobConf()
     job.setOutputKeyClass(classOf[String])
     job.setOutputValueClass(classOf[String])
     job.set("mapred.output.format.class", classOf[TextOutputFormat[String, String]].getName)
-    job.set("mapred.output.dir", tempdir.getPath + "/outputDataset_old")
+    job.set("mapred.output.dir", tempDir.getPath + "/outputDataset_old")
     randomRDD.saveAsHadoopDataset(job)
-    assert(new File(tempdir.getPath + "/outputDataset_old/part-00000").exists() === true)
+    assert(new File(tempDir.getPath + "/outputDataset_old/part-00000").exists() === true)
   }
 
   test ("save Hadoop Dataset through new Hadoop API") {
     sc = new SparkContext("local", "test")
-    val tempdir = Files.createTempDir()
     val randomRDD = sc.parallelize(Array(("key1", "a"), ("key2", "a"), ("key3", "b"), ("key4", "c")), 1)
     val job = new Job(sc.hadoopConfiguration)
     job.setOutputKeyClass(classOf[String])
     job.setOutputValueClass(classOf[String])
     job.setOutputFormatClass(classOf[NewTextOutputFormat[String, String]])
-    job.getConfiguration.set("mapred.output.dir", tempdir.getPath + "/outputDataset_new")
+    job.getConfiguration.set("mapred.output.dir", tempDir.getPath + "/outputDataset_new")
     randomRDD.saveAsNewAPIHadoopDataset(job.getConfiguration)
-    assert(new File(tempdir.getPath + "/outputDataset_new/part-r-00000").exists() === true)
+    assert(new File(tempDir.getPath + "/outputDataset_new/part-r-00000").exists() === true)
   }
 }
diff --git a/core/src/test/scala/org/apache/spark/input/WholeTextFileRecordReaderSuite.scala b/core/src/test/scala/org/apache/spark/input/WholeTextFileRecordReaderSuite.scala
index 33d6de9a7640..d5ebfb3f3fae 100644
--- a/core/src/test/scala/org/apache/spark/input/WholeTextFileRecordReaderSuite.scala
+++ b/core/src/test/scala/org/apache/spark/input/WholeTextFileRecordReaderSuite.scala
@@ -31,6 +31,7 @@ import org.scalatest.FunSuite
 import org.apache.hadoop.io.Text
 
 import org.apache.spark.SparkContext
+import org.apache.spark.util.Utils
 
 /**
  * Tests the correctness of
@@ -67,6 +68,7 @@ class WholeTextFileRecordReaderSuite extends FunSuite with BeforeAndAfterAll {
   test("Correctness of WholeTextFileRecordReader.") {
 
     val dir = Files.createTempDir()
+    dir.deleteOnExit()
     println(s"Local disk address is ${dir.toString}.")
 
     WholeTextFileRecordReaderSuite.files.foreach { case (filename, contents) =>
@@ -86,7 +88,7 @@ class WholeTextFileRecordReaderSuite extends FunSuite with BeforeAndAfterAll {
         s"file $filename contents can not match.")
     }
 
-    dir.delete()
+    Utils.deleteRecursively(dir)
   }
 }
 
diff --git a/core/src/test/scala/org/apache/spark/rdd/PairRDDFunctionsSuite.scala b/core/src/test/scala/org/apache/spark/rdd/PairRDDFunctionsSuite.scala
index 8f3e6bd21b75..1230565ea5b7 100644
--- a/core/src/test/scala/org/apache/spark/rdd/PairRDDFunctionsSuite.scala
+++ b/core/src/test/scala/org/apache/spark/rdd/PairRDDFunctionsSuite.scala
@@ -236,11 +236,13 @@ class PairRDDFunctionsSuite extends FunSuite with SharedSparkContext {
 
   test("zero-partition RDD") {
     val emptyDir = Files.createTempDir()
+    emptyDir.deleteOnExit()
     val file = sc.textFile(emptyDir.getAbsolutePath)
     assert(file.partitions.size == 0)
     assert(file.collect().toList === Nil)
     // Test that a shuffle on the file works, because this used to be a bug
     assert(file.map(line => (line, 1)).reduceByKey(_ + _).collect().toList === Nil)
+    emptyDir.delete()
   }
 
   test("keys and values") {
diff --git a/core/src/test/scala/org/apache/spark/scheduler/EventLoggingListenerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/EventLoggingListenerSuite.scala
index 95f5bcd85566..21e3db34b8b7 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/EventLoggingListenerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/EventLoggingListenerSuite.scala
@@ -19,7 +19,6 @@ package org.apache.spark.scheduler
 
 import scala.collection.mutable
 import scala.io.Source
-import scala.util.Try
 
 import com.google.common.io.Files
 import org.apache.hadoop.fs.{FileStatus, Path}
@@ -30,6 +29,8 @@ import org.apache.spark.{SparkConf, SparkContext}
 import org.apache.spark.io.CompressionCodec
 import org.apache.spark.util.{JsonProtocol, Utils}
 
+import java.io.File
+
 /**
  * Test whether EventLoggingListener logs events properly.
  *
@@ -43,11 +44,17 @@ class EventLoggingListenerSuite extends FunSuite with BeforeAndAfter {
     "org.apache.spark.io.LZFCompressionCodec",
     "org.apache.spark.io.SnappyCompressionCodec"
   )
-  private val testDir = Files.createTempDir()
-  private val logDirPath = Utils.getFilePath(testDir, "spark-events")
+  private var testDir: File = _
+  private var logDirPath: Path = _
+
+  before {
+    testDir = Files.createTempDir()
+    testDir.deleteOnExit()
+    logDirPath = Utils.getFilePath(testDir, "spark-events")
+  }
 
   after {
-    Try { fileSystem.delete(logDirPath, true) }
+    Utils.deleteRecursively(testDir)
   }
 
   test("Parse names of special files") {
diff --git a/core/src/test/scala/org/apache/spark/scheduler/ReplayListenerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/ReplayListenerSuite.scala
index d1fe1fc34896..d81499ac6abe 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/ReplayListenerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/ReplayListenerSuite.scala
@@ -17,9 +17,7 @@
 
 package org.apache.spark.scheduler
 
-import java.io.PrintWriter
-
-import scala.util.Try
+import java.io.{File, PrintWriter}
 
 import com.google.common.io.Files
 import org.json4s.jackson.JsonMethods._
@@ -39,11 +37,15 @@ class ReplayListenerSuite extends FunSuite with BeforeAndAfter {
     "org.apache.spark.io.LZFCompressionCodec",
     "org.apache.spark.io.SnappyCompressionCodec"
   )
-  private val testDir = Files.createTempDir()
+  private var testDir: File = _
+
+  before {
+    testDir = Files.createTempDir()
+    testDir.deleteOnExit()
+  }
 
   after {
-    Try { fileSystem.delete(Utils.getFilePath(testDir, "events.txt"), true) }
-    Try { fileSystem.delete(Utils.getFilePath(testDir, "test-replay"), true) }
+    Utils.deleteRecursively(testDir)
   }
 
   test("Simple replay") {
diff --git a/core/src/test/scala/org/apache/spark/storage/DiskBlockManagerSuite.scala b/core/src/test/scala/org/apache/spark/storage/DiskBlockManagerSuite.scala
index 42bfbf1bdfc7..2167718fd242 100644
--- a/core/src/test/scala/org/apache/spark/storage/DiskBlockManagerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/storage/DiskBlockManagerSuite.scala
@@ -23,18 +23,16 @@ import scala.collection.mutable
 import scala.language.reflectiveCalls
 
 import com.google.common.io.Files
-import org.scalatest.{BeforeAndAfterEach, FunSuite}
+import org.scalatest.{BeforeAndAfterAll, BeforeAndAfterEach, FunSuite}
 
 import org.apache.spark.SparkConf
+import org.apache.spark.util.Utils
 
-class DiskBlockManagerSuite extends FunSuite with BeforeAndAfterEach {
+class DiskBlockManagerSuite extends FunSuite with BeforeAndAfterEach with BeforeAndAfterAll {
   private val testConf = new SparkConf(false)
-  val rootDir0 = Files.createTempDir()
-  rootDir0.deleteOnExit()
-  val rootDir1 = Files.createTempDir()
-  rootDir1.deleteOnExit()
-  val rootDirs = rootDir0.getName + "," + rootDir1.getName
-  println("Created root dirs: " + rootDirs)
+  private var rootDir0: File = _
+  private var rootDir1: File = _
+  private var rootDirs: String = _
 
   // This suite focuses primarily on consolidation features,
   // so we coerce consolidation if not already enabled.
@@ -48,6 +46,22 @@ class DiskBlockManagerSuite extends FunSuite with BeforeAndAfterEach {
 
   var diskBlockManager: DiskBlockManager = _
 
+  override def beforeAll() {
+    super.beforeAll()
+    rootDir0 = Files.createTempDir()
+    rootDir0.deleteOnExit()
+    rootDir1 = Files.createTempDir()
+    rootDir1.deleteOnExit()
+    rootDirs = rootDir0.getName + "," + rootDir1.getName
+    println("Created root dirs: " + rootDirs)
+  }
+
+  override def afterAll() {
+    super.afterAll()
+    Utils.deleteRecursively(rootDir0)
+    Utils.deleteRecursively(rootDir1)
+  }
+
   override def beforeEach() {
     diskBlockManager = new DiskBlockManager(shuffleBlockManager, rootDirs)
     shuffleBlockManager.idToSegmentMap.clear()
diff --git a/core/src/test/scala/org/apache/spark/util/FileLoggerSuite.scala b/core/src/test/scala/org/apache/spark/util/FileLoggerSuite.scala
index f675e1e5b498..44332fc8dbc2 100644
--- a/core/src/test/scala/org/apache/spark/util/FileLoggerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/util/FileLoggerSuite.scala
@@ -17,10 +17,9 @@
 
 package org.apache.spark.util
 
-import java.io.IOException
+import java.io.{File, IOException}
 
 import scala.io.Source
-import scala.util.Try
 
 import com.google.common.io.Files
 import org.apache.hadoop.fs.Path
@@ -38,12 +37,18 @@ class FileLoggerSuite extends FunSuite with BeforeAndAfter {
     "org.apache.spark.io.LZFCompressionCodec",
     "org.apache.spark.io.SnappyCompressionCodec"
   )
-  private val testDir = Files.createTempDir()
-  private val logDirPath = Utils.getFilePath(testDir, "test-file-logger")
-  private val logDirPathString = logDirPath.toString
+  private var testDir: File = _
+  private var logDirPath: Path = _
+  private var logDirPathString: String = _
+
+  before {
+    testDir = Files.createTempDir()
+    logDirPath = Utils.getFilePath(testDir, "test-file-logger")
+    logDirPathString = logDirPath.toString
+  }
 
   after {
-    Try { fileSystem.delete(logDirPath, true) }
+    Utils.deleteRecursively(testDir)
   }
 
   test("Simple logging") {
diff --git a/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala b/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala
index eb7fb6318262..cf9e20d347dd 100644
--- a/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala
+++ b/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala
@@ -112,6 +112,7 @@ class UtilsSuite extends FunSuite {
 
   test("reading offset bytes of a file") {
     val tmpDir2 = Files.createTempDir()
+    tmpDir2.deleteOnExit()
     val f1Path = tmpDir2 + "/f1"
     val f1 = new FileOutputStream(f1Path)
     f1.write("1\n2\n3\n4\n5\n6\n7\n8\n9\n".getBytes(Charsets.UTF_8))
diff --git a/external/flume/src/test/resources/log4j.properties b/external/flume/src/test/resources/log4j.properties
index d1bd73a8430e..45d2ec676df6 100644
--- a/external/flume/src/test/resources/log4j.properties
+++ b/external/flume/src/test/resources/log4j.properties
@@ -20,7 +20,7 @@ log4j.rootCategory=INFO, file
 # log4j.appender.file=org.apache.log4j.FileAppender
 log4j.appender.file=org.apache.log4j.FileAppender
 log4j.appender.file.append=false
-log4j.appender.file.file=external/flume/target/unit-tests.log
+log4j.appender.file.file=target/unit-tests.log
 log4j.appender.file.layout=org.apache.log4j.PatternLayout
 log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %p %c{1}: %m%n
 
diff --git a/external/kafka/src/test/resources/log4j.properties b/external/kafka/src/test/resources/log4j.properties
index 38910d113050..45d2ec676df6 100644
--- a/external/kafka/src/test/resources/log4j.properties
+++ b/external/kafka/src/test/resources/log4j.properties
@@ -20,7 +20,7 @@ log4j.rootCategory=INFO, file
 # log4j.appender.file=org.apache.log4j.FileAppender
 log4j.appender.file=org.apache.log4j.FileAppender
 log4j.appender.file.append=false
-log4j.appender.file.file=external/kafka/target/unit-tests.log
+log4j.appender.file.file=target/unit-tests.log
 log4j.appender.file.layout=org.apache.log4j.PatternLayout
 log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %p %c{1}: %m%n
 
diff --git a/external/mqtt/src/test/resources/log4j.properties b/external/mqtt/src/test/resources/log4j.properties
index d0462c7336df..45d2ec676df6 100644
--- a/external/mqtt/src/test/resources/log4j.properties
+++ b/external/mqtt/src/test/resources/log4j.properties
@@ -20,7 +20,7 @@ log4j.rootCategory=INFO, file
 # log4j.appender.file=org.apache.log4j.FileAppender
 log4j.appender.file=org.apache.log4j.FileAppender
 log4j.appender.file.append=false
-log4j.appender.file.file=external/mqtt/target/unit-tests.log
+log4j.appender.file.file=target/unit-tests.log
 log4j.appender.file.layout=org.apache.log4j.PatternLayout
 log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %p %c{1}: %m%n
 
diff --git a/external/twitter/src/test/resources/log4j.properties b/external/twitter/src/test/resources/log4j.properties
index c918335fcdc7..45d2ec676df6 100644
--- a/external/twitter/src/test/resources/log4j.properties
+++ b/external/twitter/src/test/resources/log4j.properties
@@ -20,7 +20,7 @@ log4j.rootCategory=INFO, file
 # log4j.appender.file=org.apache.log4j.FileAppender
 log4j.appender.file=org.apache.log4j.FileAppender
 log4j.appender.file.append=false
-log4j.appender.file.file=external/twitter/target/unit-tests.log
+log4j.appender.file.file=target/unit-tests.log
 log4j.appender.file.layout=org.apache.log4j.PatternLayout
 log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %p %c{1}: %m%n
 
diff --git a/external/zeromq/src/test/resources/log4j.properties b/external/zeromq/src/test/resources/log4j.properties
index 304683dd0bac..45d2ec676df6 100644
--- a/external/zeromq/src/test/resources/log4j.properties
+++ b/external/zeromq/src/test/resources/log4j.properties
@@ -20,7 +20,7 @@ log4j.rootCategory=INFO, file
 # log4j.appender.file=org.apache.log4j.FileAppender
 log4j.appender.file=org.apache.log4j.FileAppender
 log4j.appender.file.append=false
-log4j.appender.file.file=external/zeromq/target/unit-tests.log
+log4j.appender.file.file=target/unit-tests.log
 log4j.appender.file.layout=org.apache.log4j.PatternLayout
 log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %p %c{1}: %m%n
 
diff --git a/extras/java8-tests/src/test/java/org/apache/spark/Java8APISuite.java b/extras/java8-tests/src/test/java/org/apache/spark/Java8APISuite.java
index feabca673348..84d3b6f243c7 100644
--- a/extras/java8-tests/src/test/java/org/apache/spark/Java8APISuite.java
+++ b/extras/java8-tests/src/test/java/org/apache/spark/Java8APISuite.java
@@ -39,6 +39,7 @@
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.JavaSparkContext;
 import org.apache.spark.api.java.function.*;
+import org.apache.spark.util.Utils;
 
 /**
  * Most of these tests replicate org.apache.spark.JavaAPISuite using java 8
@@ -249,6 +250,7 @@ public void mapPartitions() {
   @Test
   public void sequenceFile() {
     File tempDir = Files.createTempDir();
+    tempDir.deleteOnExit();
     String outputDir = new File(tempDir, "output").getAbsolutePath();
     List<Tuple2<Integer, String>> pairs = Arrays.asList(
       new Tuple2<Integer, String>(1, "a"),
@@ -265,6 +267,7 @@ public void sequenceFile() {
     JavaPairRDD<Integer, String> readRDD = sc.sequenceFile(outputDir, IntWritable.class, Text.class)
       .mapToPair(pair -> new Tuple2<Integer, String>(pair._1().get(), pair._2().toString()));
     Assert.assertEquals(pairs, readRDD.collect());
+    Utils.deleteRecursively(tempDir);
   }
 
   @Test
diff --git a/graphx/src/test/resources/log4j.properties b/graphx/src/test/resources/log4j.properties
index 85e57f0c4b50..26b73a1b3974 100644
--- a/graphx/src/test/resources/log4j.properties
+++ b/graphx/src/test/resources/log4j.properties
@@ -19,7 +19,7 @@
 log4j.rootCategory=INFO, file
 log4j.appender.file=org.apache.log4j.FileAppender
 log4j.appender.file.append=false
-log4j.appender.file.file=graphx/target/unit-tests.log
+log4j.appender.file.file=target/unit-tests.log
 log4j.appender.file.layout=org.apache.log4j.PatternLayout
 log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %p %c{1}: %m%n
 
diff --git a/mllib/src/test/resources/log4j.properties b/mllib/src/test/resources/log4j.properties
index 4265ba6e5de3..ddfc4ac6b23e 100644
--- a/mllib/src/test/resources/log4j.properties
+++ b/mllib/src/test/resources/log4j.properties
@@ -19,7 +19,7 @@
 log4j.rootCategory=INFO, file
 log4j.appender.file=org.apache.log4j.FileAppender
 log4j.appender.file.append=false
-log4j.appender.file.file=mllib/target/unit-tests.log
+log4j.appender.file.file=target/unit-tests.log
 log4j.appender.file.layout=org.apache.log4j.PatternLayout
 log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %p %c{1}: %m%n
 
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/util/MLUtilsSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/util/MLUtilsSuite.scala
index 3f64baf6fe41..3d05fb68988c 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/util/MLUtilsSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/util/MLUtilsSuite.scala
@@ -32,6 +32,7 @@ import com.google.common.io.Files
 import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vectors}
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.util.MLUtils._
+import org.apache.spark.util.Utils
 
 class MLUtilsSuite extends FunSuite with LocalSparkContext {
 
@@ -67,6 +68,7 @@ class MLUtilsSuite extends FunSuite with LocalSparkContext {
         |-1 2:4.0 4:5.0 6:6.0
       """.stripMargin
     val tempDir = Files.createTempDir()
+    tempDir.deleteOnExit()
     val file = new File(tempDir.getPath, "part-00000")
     Files.write(lines, file, Charsets.US_ASCII)
     val path = tempDir.toURI.toString
@@ -90,7 +92,7 @@ class MLUtilsSuite extends FunSuite with LocalSparkContext {
     assert(multiclassPoints(1).label === -1.0)
     assert(multiclassPoints(2).label === -1.0)
 
-    deleteQuietly(tempDir)
+    Utils.deleteRecursively(tempDir)
   }
 
   test("saveAsLibSVMFile") {
@@ -107,7 +109,7 @@ class MLUtilsSuite extends FunSuite with LocalSparkContext {
       .toSet
     val expected = Set("1.1 1:1.23 3:4.56", "0.0 1:1.01 2:2.02 3:3.03")
     assert(lines === expected)
-    deleteQuietly(tempDir)
+    Utils.deleteRecursively(tempDir)
   }
 
   test("appendBias") {
@@ -158,16 +160,5 @@ class MLUtilsSuite extends FunSuite with LocalSparkContext {
     }
   }
 
-  /** Delete a file/directory quietly. */
-  def deleteQuietly(f: File) {
-    if (f.isDirectory) {
-      f.listFiles().foreach(deleteQuietly)
-    }
-    try {
-      f.delete()
-    } catch {
-      case _: Throwable =>
-    }
-  }
 }
 
diff --git a/pom.xml b/pom.xml
index dd1d262881b9..5542a32a9136 100644
--- a/pom.xml
+++ b/pom.xml
@@ -796,6 +796,21 @@
             </execution>
           </executions>
         </plugin>
+        <plugin>
+          <groupId>org.apache.maven.plugins</groupId>
+          <artifactId>maven-clean-plugin</artifactId>
+          <version>2.5</version>
+          <configuration>
+            <filesets>
+              <fileset>
+                <directory>work</directory>
+              </fileset>
+              <fileset>
+                <directory>checkpoint</directory>
+              </fileset>
+            </filesets>
+          </configuration>
+        </plugin>
       </plugins>
     </pluginManagement>
 
diff --git a/repl/src/test/resources/log4j.properties b/repl/src/test/resources/log4j.properties
index a6d33e69d21f..9c4896e49698 100644
--- a/repl/src/test/resources/log4j.properties
+++ b/repl/src/test/resources/log4j.properties
@@ -19,7 +19,7 @@
 log4j.rootCategory=INFO, file
 log4j.appender.file=org.apache.log4j.FileAppender
 log4j.appender.file.append=false
-log4j.appender.file.file=repl/target/unit-tests.log
+log4j.appender.file.file=target/unit-tests.log
 log4j.appender.file.layout=org.apache.log4j.PatternLayout
 log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %p %c{1}: %m%n
 
diff --git a/repl/src/test/scala/org/apache/spark/repl/ExecutorClassLoaderSuite.scala b/repl/src/test/scala/org/apache/spark/repl/ExecutorClassLoaderSuite.scala
index 336df988a1b7..c0af7ceb6d3e 100644
--- a/repl/src/test/scala/org/apache/spark/repl/ExecutorClassLoaderSuite.scala
+++ b/repl/src/test/scala/org/apache/spark/repl/ExecutorClassLoaderSuite.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.repl
 
 import java.io.File
-import java.net.URLClassLoader
+import java.net.{URL, URLClassLoader}
 
 import org.scalatest.BeforeAndAfterAll
 import org.scalatest.FunSuite
@@ -26,21 +26,35 @@ import org.scalatest.FunSuite
 import com.google.common.io.Files
 
 import org.apache.spark.TestUtils
+import org.apache.spark.util.Utils
 
 class ExecutorClassLoaderSuite extends FunSuite with BeforeAndAfterAll {
 
   val childClassNames = List("ReplFakeClass1", "ReplFakeClass2")
   val parentClassNames = List("ReplFakeClass1", "ReplFakeClass2", "ReplFakeClass3")
-  val tempDir1 = Files.createTempDir()
-  val tempDir2 = Files.createTempDir()
-  val url1 = "file://" + tempDir1
-  val urls2 = List(tempDir2.toURI.toURL).toArray
+  var tempDir1: File = _
+  var tempDir2: File = _
+  var url1: String = _
+  var urls2: Array[URL] = _
 
   override def beforeAll() {
+    super.beforeAll()
+    tempDir1 = Files.createTempDir()
+    tempDir1.deleteOnExit()
+    tempDir2 = Files.createTempDir()
+    tempDir2.deleteOnExit()
+    url1 = "file://" + tempDir1
+    urls2 = List(tempDir2.toURI.toURL).toArray
     childClassNames.foreach(TestUtils.createCompiledClass(_, tempDir1, "1"))
     parentClassNames.foreach(TestUtils.createCompiledClass(_, tempDir2, "2"))
   }
 
+  override def afterAll() {
+    super.afterAll()
+    Utils.deleteRecursively(tempDir1)
+    Utils.deleteRecursively(tempDir2)
+  }
+
   test("child first") {
     val parentLoader = new URLClassLoader(urls2, null)
     val classLoader = new ExecutorClassLoader(url1, parentLoader, true)
diff --git a/repl/src/test/scala/org/apache/spark/repl/ReplSuite.scala b/repl/src/test/scala/org/apache/spark/repl/ReplSuite.scala
index 566d96e16ed8..95460aa20533 100644
--- a/repl/src/test/scala/org/apache/spark/repl/ReplSuite.scala
+++ b/repl/src/test/scala/org/apache/spark/repl/ReplSuite.scala
@@ -26,6 +26,7 @@ import com.google.common.io.Files
 import org.scalatest.FunSuite
 import org.apache.spark.SparkContext
 import org.apache.commons.lang3.StringEscapeUtils
+import org.apache.spark.util.Utils
 
 
 class ReplSuite extends FunSuite {
@@ -178,6 +179,7 @@ class ReplSuite extends FunSuite {
 
   test("interacting with files") {
     val tempDir = Files.createTempDir()
+    tempDir.deleteOnExit()
     val out = new FileWriter(tempDir + "/input")
     out.write("Hello world!\n")
     out.write("What's up?\n")
@@ -196,6 +198,7 @@ class ReplSuite extends FunSuite {
     assertContains("res0: Long = 3", output)
     assertContains("res1: Long = 3", output)
     assertContains("res2: Long = 3", output)
+    Utils.deleteRecursively(tempDir)
   }
 
   test("local-cluster mode") {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/InsertIntoSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/InsertIntoSuite.scala
index 73d87963b3a0..4f0b85f26254 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/InsertIntoSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/InsertIntoSuite.scala
@@ -29,6 +29,7 @@ class InsertIntoSuite extends QueryTest {
   test("insertInto() created parquet file") {
     val testFilePath = File.createTempFile("sparkSql", "pqt")
     testFilePath.delete()
+    testFilePath.deleteOnExit()
     val testFile = createParquetFile[TestData](testFilePath.getCanonicalPath)
     testFile.registerAsTable("createAndInsertTest")
 
@@ -76,11 +77,14 @@ class InsertIntoSuite extends QueryTest {
       sql("SELECT * FROM createAndInsertTest"),
       testData.collect().toSeq
     )
+
+    testFilePath.delete()
   }
 
   test("INSERT INTO parquet table") {
     val testFilePath = File.createTempFile("sparkSql", "pqt")
     testFilePath.delete()
+    testFilePath.deleteOnExit()
     val testFile = createParquetFile[TestData](testFilePath.getCanonicalPath)
     testFile.registerAsTable("createAndInsertSQLTest")
 
@@ -126,23 +130,31 @@ class InsertIntoSuite extends QueryTest {
       sql("SELECT * FROM createAndInsertSQLTest"),
       testData.collect().toSeq
     )
+
+    testFilePath.delete()
   }
 
   test("Double create fails when allowExisting = false") {
     val testFilePath = File.createTempFile("sparkSql", "pqt")
     testFilePath.delete()
+    testFilePath.deleteOnExit()
     val testFile = createParquetFile[TestData](testFilePath.getCanonicalPath)
 
     intercept[RuntimeException] {
       createParquetFile[TestData](testFilePath.getCanonicalPath, allowExisting = false)
     }
+
+    testFilePath.delete()
   }
 
   test("Double create does not fail when allowExisting = true") {
     val testFilePath = File.createTempFile("sparkSql", "pqt")
     testFilePath.delete()
+    testFilePath.deleteOnExit()
     val testFile = createParquetFile[TestData](testFilePath.getCanonicalPath)
 
     createParquetFile[TestData](testFilePath.getCanonicalPath, allowExisting = true)
+
+    testFilePath.delete()
   }
 }
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/TestHive.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/TestHive.scala
index 3ad66a3d7f45..fa7d010459c6 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/TestHive.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/TestHive.scala
@@ -99,6 +99,7 @@ class TestHiveContext(sc: SparkContext) extends LocalHiveContext(sc) {
   val hiveFilesTemp = File.createTempFile("catalystHiveFiles", "")
   hiveFilesTemp.delete()
   hiveFilesTemp.mkdir()
+  hiveFilesTemp.deleteOnExit()
 
   val inRepoTests = if (System.getProperty("user.dir").endsWith("sql" + File.separator + "hive")) {
     new File("src" + File.separator + "test" + File.separator + "resources" + File.separator)
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/util/MasterFailureTest.scala b/streaming/src/main/scala/org/apache/spark/streaming/util/MasterFailureTest.scala
index c48a38590e06..b3ed302db6a3 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/util/MasterFailureTest.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/util/MasterFailureTest.scala
@@ -21,6 +21,7 @@ import org.apache.spark.Logging
 import org.apache.spark.rdd.RDD
 import org.apache.spark.streaming._
 import org.apache.spark.streaming.dstream.{DStream, ForEachDStream}
+import org.apache.spark.util.Utils
 import StreamingContext._
 
 import scala.util.Random
@@ -380,6 +381,7 @@ class FileGeneratingThread(input: Seq[String], testDir: Path, interval: Long)
 
   override def run() {
     val localTestDir = Files.createTempDir()
+    localTestDir.deleteOnExit()
     var fs = testDir.getFileSystem(new Configuration())
     val maxTries = 3
     try {
@@ -421,6 +423,7 @@ class FileGeneratingThread(input: Seq[String], testDir: Path, interval: Long)
       case e: Exception => logWarning("File generating in killing thread", e)
     } finally {
       fs.close()
+      Utils.deleteRecursively(localTestDir)
     }
   }
 }
diff --git a/streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java b/streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java
index f9bfb9b7444c..ce58cb12a456 100644
--- a/streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java
+++ b/streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java
@@ -17,7 +17,6 @@
 
 package org.apache.spark.streaming;
 
-import org.apache.spark.streaming.api.java.*;
 import scala.Tuple2;
 
 import org.junit.Assert;
@@ -37,6 +36,8 @@
 import org.apache.spark.api.java.JavaSparkContext;
 import org.apache.spark.api.java.function.*;
 import org.apache.spark.storage.StorageLevel;
+import org.apache.spark.streaming.api.java.*;
+import org.apache.spark.util.Utils;
 
 // The test suite itself is Serializable so that anonymous Function implementations can be
 // serialized, as an alternative to converting these anonymous classes to static inner classes;
@@ -1606,6 +1607,7 @@ public void testCheckpointMasterRecovery() throws InterruptedException {
         Arrays.asList(8,7));
 
     File tempDir = Files.createTempDir();
+    tempDir.deleteOnExit();
     ssc.checkpoint(tempDir.getAbsolutePath());
 
     JavaDStream<String> stream = JavaCheckpointTestUtils.attachTestInputStream(ssc, inputData, 1);
@@ -1627,6 +1629,7 @@ public Integer call(String s) throws Exception {
     // will be re-processed after recovery
     List<List<Integer>> finalResult = JavaCheckpointTestUtils.runStreams(ssc, 2, 3);
     assertOrderInvariantEquals(expectedFinal, finalResult.subList(1, 3));
+    Utils.deleteRecursively(tempDir);
   }
 
 
diff --git a/streaming/src/test/resources/log4j.properties b/streaming/src/test/resources/log4j.properties
index 063529a9cbc6..45d2ec676df6 100644
--- a/streaming/src/test/resources/log4j.properties
+++ b/streaming/src/test/resources/log4j.properties
@@ -20,7 +20,7 @@ log4j.rootCategory=INFO, file
 # log4j.appender.file=org.apache.log4j.FileAppender
 log4j.appender.file=org.apache.log4j.FileAppender
 log4j.appender.file.append=false
-log4j.appender.file.file=streaming/target/unit-tests.log
+log4j.appender.file.file=target/unit-tests.log
 log4j.appender.file.layout=org.apache.log4j.PatternLayout
 log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %p %c{1}: %m%n
 
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala
index 25739956cb88..d20a7b728c74 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala
@@ -232,6 +232,7 @@ class CheckpointSuite extends TestSuiteBase {
   test("recovery with file input stream") {
     // Set up the streaming context and input streams
     val testDir = Files.createTempDir()
+    testDir.deleteOnExit()
     var ssc = new StreamingContext(master, framework, Seconds(1))
     ssc.checkpoint(checkpointDir)
     val fileStream = ssc.textFileStream(testDir.toString)
@@ -326,6 +327,7 @@ class CheckpointSuite extends TestSuiteBase {
     )
     // To ensure that all the inputs were received correctly
     assert(expectedOutput.last === output.last)
+    Utils.deleteRecursively(testDir)
   }
 
 
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala
index 3fa254065cc4..cd0aa4d0dce7 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala
@@ -98,6 +98,7 @@ class InputStreamsSuite extends TestSuiteBase with BeforeAndAfter {
 
     // Set up the streaming context and input streams
     val testDir = Files.createTempDir()
+    testDir.deleteOnExit()
     val ssc = new StreamingContext(conf, batchDuration)
     val fileStream = ssc.textFileStream(testDir.toString)
     val outputBuffer = new ArrayBuffer[Seq[String]] with SynchronizedBuffer[Seq[String]]

From 8586bf564fe010dfc19ef26874472a6f85e355fb Mon Sep 17 00:00:00 2001
From: Sean Owen <sowen@cloudera.com>
Date: Mon, 12 May 2014 14:17:25 -0700
Subject: [PATCH 167/810] SPARK-1802. Audit dependency graph when Spark is
 built with -Phive

This initial commit resolves the conflicts in the Hive profiles as noted in https://issues.apache.org/jira/browse/SPARK-1802 .

Most of the fix was to note that Hive drags in Avro, and so if the hive module depends on Spark's version of the `avro-*` dependencies, it will pull in our exclusions as needed too. But I found we need to copy some exclusions between the two Avro dependencies to get this right. And then had to squash some commons-logging intrusions.

This turned up another annoying find, that `hive-exec` is basically an "assembly" artifact that _also_ packages all of its transitive dependencies. This means the final assembly shows lots of collisions between itself and its dependencies, and even other project dependencies. I have a TODO to examine whether that is going to be a deal-breaker or not.

In the meantime I'm going to tack on a second commit to this PR that will also fix some similar, last collisions in the YARN profile.

Author: Sean Owen <sowen@cloudera.com>

Closes #744 from srowen/SPARK-1802 and squashes the following commits:

a856604 [Sean Owen] Resolve JAR version conflicts specific to Hive profile
---
 pom.xml          | 16 ++++++++++++++++
 sql/hive/pom.xml | 21 +++++++++++++++++++++
 2 files changed, 37 insertions(+)

diff --git a/pom.xml b/pom.xml
index 5542a32a9136..3554efafb976 100644
--- a/pom.xml
+++ b/pom.xml
@@ -535,6 +535,22 @@
             <groupId>io.netty</groupId>
             <artifactId>netty</artifactId>
           </exclusion>
+          <exclusion>
+            <groupId>org.mortbay.jetty</groupId>
+            <artifactId>jetty</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.mortbay.jetty</groupId>
+            <artifactId>jetty-util</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.mortbay.jetty</groupId>
+            <artifactId>servlet-api</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.apache.velocity</groupId>
+            <artifactId>velocity</artifactId>
+          </exclusion>
         </exclusions>
       </dependency>
       <!-- See SPARK-1556 for info on this dependency: -->
diff --git a/sql/hive/pom.xml b/sql/hive/pom.xml
index 889d249146b8..8b32451d7604 100644
--- a/sql/hive/pom.xml
+++ b/sql/hive/pom.xml
@@ -52,6 +52,12 @@
       <groupId>org.apache.hive</groupId>
       <artifactId>hive-exec</artifactId>
       <version>${hive.version}</version>
+      <exclusions>
+        <exclusion>
+          <groupId>commons-logging</groupId>
+          <artifactId>commons-logging</artifactId>
+        </exclusion>
+      </exclusions>
     </dependency>
     <dependency>
       <groupId>org.codehaus.jackson</groupId>
@@ -61,6 +67,21 @@
       <groupId>org.apache.hive</groupId>
       <artifactId>hive-serde</artifactId>
       <version>${hive.version}</version>
+      <exclusions>
+        <exclusion>
+          <groupId>commons-logging</groupId>
+          <artifactId>commons-logging</artifactId>
+        </exclusion>
+        <exclusion>
+          <groupId>commons-logging</groupId>
+          <artifactId>commons-logging-api</artifactId>
+        </exclusion>
+      </exclusions>
+    </dependency>
+    <!-- hive-serde already depends on avro, but this brings in customized config of avro deps from parent -->
+    <dependency>
+      <groupId>org.apache.avro</groupId>
+      <artifactId>avro</artifactId>
     </dependency>
     <dependency>
       <groupId>org.scalatest</groupId>

From 3ce526b168050c572a1feee8e0121e1426f7d9ee Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Mon, 12 May 2014 17:09:13 -0700
Subject: [PATCH 168/810] Rename testExecutorEnvs --> executorEnvs.

This was changed, but in fact, it's used for things other than tests.
So I've changed it back.

Author: Patrick Wendell <pwendell@gmail.com>

Closes #747 from pwendell/executor-env and squashes the following commits:

36a60a5 [Patrick Wendell] Rename testExecutorEnvs --> executorEnvs.
---
 .../main/scala/org/apache/spark/SparkContext.scala    | 11 +++++------
 .../cluster/SparkDeploySchedulerBackend.scala         |  2 +-
 .../cluster/mesos/CoarseMesosSchedulerBackend.scala   |  2 +-
 .../cluster/mesos/MesosSchedulerBackend.scala         |  2 +-
 4 files changed, 8 insertions(+), 9 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index e6121a705497..35beff0f8ef3 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -276,19 +276,18 @@ class SparkContext(config: SparkConf) extends Logging {
     .getOrElse(512)
 
   // Environment variables to pass to our executors.
-  // NOTE: This should only be used for test related settings.
-  private[spark] val testExecutorEnvs = HashMap[String, String]()
+  private[spark] val executorEnvs = HashMap[String, String]()
 
   // Convert java options to env vars as a work around
   // since we can't set env vars directly in sbt.
   for { (envKey, propKey) <- Seq(("SPARK_TESTING", "spark.testing"))
     value <- Option(System.getenv(envKey)).orElse(Option(System.getProperty(propKey)))} {
-    testExecutorEnvs(envKey) = value
+    executorEnvs(envKey) = value
   }
   // The Mesos scheduler backend relies on this environment variable to set executor memory.
   // TODO: Set this only in the Mesos scheduler.
-  testExecutorEnvs("SPARK_EXECUTOR_MEMORY") = executorMemory + "m"
-  testExecutorEnvs ++= conf.getExecutorEnv
+  executorEnvs("SPARK_EXECUTOR_MEMORY") = executorMemory + "m"
+  executorEnvs ++= conf.getExecutorEnv
 
   // Set SPARK_USER for user who is running SparkContext.
   val sparkUser = Option {
@@ -296,7 +295,7 @@ class SparkContext(config: SparkConf) extends Logging {
   }.getOrElse {
     SparkContext.SPARK_UNKNOWN_USER
   }
-  testExecutorEnvs("SPARK_USER") = sparkUser
+  executorEnvs("SPARK_USER") = sparkUser
 
   // Create and start the scheduler
   private[spark] var taskScheduler = SparkContext.createTaskScheduler(this, master)
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala
index cefa41729964..933f6e057151 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala
@@ -54,7 +54,7 @@ private[spark] class SparkDeploySchedulerBackend(
     }
 
     val command = Command(
-      "org.apache.spark.executor.CoarseGrainedExecutorBackend", args, sc.testExecutorEnvs,
+      "org.apache.spark.executor.CoarseGrainedExecutorBackend", args, sc.executorEnvs,
       classPathEntries, libraryPathEntries, extraJavaOpts)
     val sparkHome = sc.getSparkHome()
     val appDesc = new ApplicationDescription(sc.appName, maxCores, sc.executorMemory, command,
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala
index 2cd9d6c12eaf..cbe9bb093d1c 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala
@@ -122,7 +122,7 @@ private[spark] class CoarseMesosSchedulerBackend(
     val extraLibraryPath = conf.getOption(libraryPathOption).map(p => s"-Djava.library.path=$p")
     val extraOpts = Seq(extraJavaOpts, extraLibraryPath).flatten.mkString(" ")
 
-    sc.testExecutorEnvs.foreach { case (key, value) =>
+    sc.executorEnvs.foreach { case (key, value) =>
       environment.addVariables(Environment.Variable.newBuilder()
         .setName(key)
         .setValue(value)
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala
index 2cea1c8cf154..ff8356f67681 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala
@@ -90,7 +90,7 @@ private[spark] class MesosSchedulerBackend(
       "Spark home is not set; set it through the spark.home system " +
       "property, the SPARK_HOME environment variable or the SparkContext constructor"))
     val environment = Environment.newBuilder()
-    sc.testExecutorEnvs.foreach { case (key, value) =>
+    sc.executorEnvs.foreach { case (key, value) =>
       environment.addVariables(Environment.Variable.newBuilder()
         .setName(key)
         .setValue(value)

From 925d8b249b84d2706c52f0d1e29fb8dcd6de452e Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Mon, 12 May 2014 17:27:28 -0700
Subject: [PATCH 169/810] SPARK-1623: Use File objects instead of String's in
 HTTPBroadcast

This seems strictly better, and I think it's justified only the grounds of
clean-up. It might also fix issues with path conversions, but I haven't
yet isolated any instance of that happening.

/cc @srowen @tdas

Author: Patrick Wendell <pwendell@gmail.com>

Closes #749 from pwendell/broadcast-cleanup and squashes the following commits:

d6d54f2 [Patrick Wendell] SPARK-1623: Use File objects instead of string's in HTTPBroadcast
---
 .../scala/org/apache/spark/broadcast/HttpBroadcast.scala  | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/broadcast/HttpBroadcast.scala b/core/src/main/scala/org/apache/spark/broadcast/HttpBroadcast.scala
index 78fc286e5192..4f6cabaff2b9 100644
--- a/core/src/main/scala/org/apache/spark/broadcast/HttpBroadcast.scala
+++ b/core/src/main/scala/org/apache/spark/broadcast/HttpBroadcast.scala
@@ -112,7 +112,7 @@ private[spark] object HttpBroadcast extends Logging {
   private var securityManager: SecurityManager = null
 
   // TODO: This shouldn't be a global variable so that multiple SparkContexts can coexist
-  private val files = new TimeStampedHashSet[String]
+  private val files = new TimeStampedHashSet[File]
   private val httpReadTimeout = TimeUnit.MILLISECONDS.convert(5, TimeUnit.MINUTES).toInt
   private var compressionCodec: CompressionCodec = null
   private var cleaner: MetadataCleaner = null
@@ -173,7 +173,7 @@ private[spark] object HttpBroadcast extends Logging {
     val serOut = ser.serializeStream(out)
     serOut.writeObject(value)
     serOut.close()
-    files += file.getAbsolutePath
+    files += file
   }
 
   def read[T: ClassTag](id: Long): T = {
@@ -216,7 +216,7 @@ private[spark] object HttpBroadcast extends Logging {
     SparkEnv.get.blockManager.master.removeBroadcast(id, removeFromDriver, blocking)
     if (removeFromDriver) {
       val file = getFile(id)
-      files.remove(file.toString)
+      files.remove(file)
       deleteBroadcastFile(file)
     }
   }
@@ -232,7 +232,7 @@ private[spark] object HttpBroadcast extends Logging {
       val (file, time) = (entry.getKey, entry.getValue)
       if (time < cleanupTime) {
         iterator.remove()
-        deleteBroadcastFile(new File(file.toString))
+        deleteBroadcastFile(file)
       }
     }
   }

From 4b31f4ec7efab8eabf956284a99bfd96a58b79f7 Mon Sep 17 00:00:00 2001
From: Sean Owen <sowen@cloudera.com>
Date: Mon, 12 May 2014 17:35:29 -0700
Subject: [PATCH 170/810] SPARK-1802. (Addendium) Audit dependency graph when
 Spark is built with -Pyarn

Following on a few more items from SPARK-1802 --

The first commit touches up a few similar problems remaining with the YARN profile. I think this is worth cherry-picking.

The second commit is more of the same for hadoop-client, although the fix is a little more complex. It may or may not be worth bothering with.

Author: Sean Owen <sowen@cloudera.com>

Closes #746 from srowen/SPARK-1802.2 and squashes the following commits:

52aeb41 [Sean Owen] Add more commons-logging, servlet excludes to avoid conflicts in assembly when building for YARN
---
 pom.xml | 20 ++++++++++++++++++++
 1 file changed, 20 insertions(+)

diff --git a/pom.xml b/pom.xml
index 3554efafb976..4d4c5f63e44a 100644
--- a/pom.xml
+++ b/pom.xml
@@ -583,6 +583,10 @@
             <groupId>org.jboss.netty</groupId>
             <artifactId>netty</artifactId>
           </exclusion>
+          <exclusion>
+            <groupId>commons-logging</groupId>
+            <artifactId>commons-logging</artifactId>
+          </exclusion>
         </exclusions>
       </dependency>
       <dependency>
@@ -606,6 +610,10 @@
             <groupId>javax.servlet</groupId>
             <artifactId>servlet-api</artifactId>
           </exclusion>
+          <exclusion>
+            <groupId>commons-logging</groupId>
+            <artifactId>commons-logging</artifactId>
+          </exclusion>
         </exclusions>
       </dependency>
       <dependency>
@@ -625,6 +633,14 @@
             <groupId>org.jboss.netty</groupId>
             <artifactId>netty</artifactId>
           </exclusion>
+          <exclusion>
+            <groupId>javax.servlet</groupId>
+            <artifactId>servlet-api</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>commons-logging</groupId>
+            <artifactId>commons-logging</artifactId>
+          </exclusion>
         </exclusions>
       </dependency>
       <dependency>
@@ -648,6 +664,10 @@
             <groupId>javax.servlet</groupId>
             <artifactId>servlet-api</artifactId>
           </exclusion>
+          <exclusion>
+            <groupId>commons-logging</groupId>
+            <artifactId>commons-logging</artifactId>
+          </exclusion>
         </exclusions>
       </dependency>
       <dependency>

From beb9cbaca6dbbcaba77a34df692dc2e56b3e0638 Mon Sep 17 00:00:00 2001
From: Andrew Or <andrewor14@gmail.com>
Date: Mon, 12 May 2014 17:39:40 -0700
Subject: [PATCH 171/810] [SPARK-1736] Spark submit for Windows

Tested on Windows 7.

Author: Andrew Or <andrewor14@gmail.com>

Closes #745 from andrewor14/windows-submit and squashes the following commits:

c0b58fb [Andrew Or] Allow spaces in parameters
162e54d [Andrew Or] Merge branch 'master' of github.com:apache/spark into windows-submit
91597ce [Andrew Or] Make spark-shell.cmd use spark-submit.cmd
af6fd29 [Andrew Or] Add spark submit for Windows
---
 bin/spark-shell.cmd  |  5 ++--
 bin/spark-submit.cmd | 56 ++++++++++++++++++++++++++++++++++++++++++++
 2 files changed, 58 insertions(+), 3 deletions(-)
 create mode 100644 bin/spark-submit.cmd

diff --git a/bin/spark-shell.cmd b/bin/spark-shell.cmd
index 99799128eb73..ca0c722c926f 100755
--- a/bin/spark-shell.cmd
+++ b/bin/spark-shell.cmd
@@ -17,7 +17,6 @@ rem See the License for the specific language governing permissions and
 rem limitations under the License.
 rem
 
-rem Find the path of sbin
-set BIN=%~dp0..\bin\
+set SPARK_HOME=%~dp0..
 
-cmd /V /E /C %BIN%spark-class2.cmd org.apache.spark.repl.Main %*
+cmd /V /E /C %SPARK_HOME%\bin\spark-submit.cmd spark-internal %* --class org.apache.spark.repl.Main
diff --git a/bin/spark-submit.cmd b/bin/spark-submit.cmd
new file mode 100644
index 000000000000..6eb702ed8c56
--- /dev/null
+++ b/bin/spark-submit.cmd
@@ -0,0 +1,56 @@
+@echo off
+
+rem
+rem Licensed to the Apache Software Foundation (ASF) under one or more
+rem contributor license agreements.  See the NOTICE file distributed with
+rem this work for additional information regarding copyright ownership.
+rem The ASF licenses this file to You under the Apache License, Version 2.0
+rem (the "License"); you may not use this file except in compliance with
+rem the License.  You may obtain a copy of the License at
+rem
+rem    http://www.apache.org/licenses/LICENSE-2.0
+rem
+rem Unless required by applicable law or agreed to in writing, software
+rem distributed under the License is distributed on an "AS IS" BASIS,
+rem WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+rem See the License for the specific language governing permissions and
+rem limitations under the License.
+rem
+
+set SPARK_HOME=%~dp0..
+set ORIG_ARGS=%*
+
+rem Clear the values of all variables used
+set DEPLOY_MODE=
+set DRIVER_MEMORY=
+set SPARK_SUBMIT_LIBRARY_PATH=
+set SPARK_SUBMIT_CLASSPATH=
+set SPARK_SUBMIT_OPTS=
+set SPARK_DRIVER_MEMORY=
+
+:loop
+if [%1] == [] goto continue
+  if [%1] == [--deploy-mode] (
+    set DEPLOY_MODE=%2
+  ) else if [%1] == [--driver-memory] (
+    set DRIVER_MEMORY=%2
+  ) else if [%1] == [--driver-library-path] (
+    set SPARK_SUBMIT_LIBRARY_PATH=%2
+  ) else if [%1] == [--driver-class-path] (
+    set SPARK_SUBMIT_CLASSPATH=%2
+  ) else if [%1] == [--driver-java-options] (
+    set SPARK_SUBMIT_OPTS=%2
+  )
+  shift
+goto loop
+:continue
+
+if [%DEPLOY_MODE%] == [] (
+  set DEPLOY_MODE=client
+)
+
+if not [%DRIVER_MEMORY%] == [] if [%DEPLOY_MODE%] == [client] (
+  set SPARK_DRIVER_MEMORY=%DRIVER_MEMORY%
+)
+
+cmd /V /E /C %SPARK_HOME%\bin\spark-class.cmd org.apache.spark.deploy.SparkSubmit %ORIG_ARGS%

From 3e13b8c0bd46894392bd0fbd3e86dd55b8103858 Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Mon, 12 May 2014 15:40:48 -0700
Subject: [PATCH 172/810] Adding hadoop-2.2 profile to the build

---
 dev/create-release/create-release.sh | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/dev/create-release/create-release.sh b/dev/create-release/create-release.sh
index b7a0a01c3bad..c4e74990860b 100755
--- a/dev/create-release/create-release.sh
+++ b/dev/create-release/create-release.sh
@@ -52,14 +52,14 @@ if [[ ! "$@" =~ --package-only ]]; then
     -Darguments="-DskipTests=true -Dhadoop.version=2.2.0 -Dyarn.version=2.2.0 -Dgpg.passphrase=${GPG_PASSPHRASE}" \
     -Dusername=$GIT_USERNAME -Dpassword=$GIT_PASSWORD \
     -Dhadoop.version=2.2.0 -Dyarn.version=2.2.0 \
-    -Pyarn -Phive -Pspark-ganglia-lgpl\
+    -Pyarn -Phive -Phadoop-2.2 -Pspark-ganglia-lgpl\
     -Dtag=$GIT_TAG -DautoVersionSubmodules=true \
     --batch-mode release:prepare
 
   mvn -DskipTests \
     -Darguments="-DskipTests=true -Dhadoop.version=2.2.0 -Dyarn.version=2.2.0 -Dgpg.passphrase=${GPG_PASSPHRASE}" \
     -Dhadoop.version=2.2.0 -Dyarn.version=2.2.0 \
-    -Pyarn -Phive -Pspark-ganglia-lgpl\
+    -Pyarn -Phive -Phadoop-2.2 -Pspark-ganglia-lgpl\
     release:perform
 
   cd ..

From 2f1a3373583f9b34a121236c25f5142ba8729546 Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Mon, 12 May 2014 18:40:30 -0700
Subject: [PATCH 173/810] [SQL] Make Hive Metastore conversion functions
 publicly visible.

I need this to be public for the implementation of SharkServer2.  However, I think this functionality is generally useful and should be pretty stable.

Author: Michael Armbrust <michael@databricks.com>

Closes #750 from marmbrus/metastoreTypes and squashes the following commits:

f51b62e [Michael Armbrust] Make Hive Metastore conversion functions publicly visible.
---
 .../org/apache/spark/sql/hive/HiveMetastoreCatalog.scala  | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
index ba837a274c51..fa30f596f8c6 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
@@ -26,6 +26,7 @@ import org.apache.hadoop.hive.ql.plan.TableDesc
 import org.apache.hadoop.hive.ql.session.SessionState
 import org.apache.hadoop.hive.serde2.Deserializer
 
+import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.sql.Logging
 import org.apache.spark.sql.catalyst.analysis.{EliminateAnalysisOperators, Catalog}
 import org.apache.spark.sql.catalyst.expressions._
@@ -172,7 +173,12 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with
   override def unregisterAllTables() = {}
 }
 
-private[hive] object HiveMetastoreTypes extends RegexParsers {
+/**
+ * :: DeveloperApi ::
+ * Provides conversions between Spark SQL data types and Hive Metastore types.
+ */
+@DeveloperApi
+object HiveMetastoreTypes extends RegexParsers {
   protected lazy val primitiveType: Parser[DataType] =
     "string" ^^^ StringType |
     "float" ^^^ FloatType |

From a5150d199ca97ab2992bc2bb221a3ebf3d3450ba Mon Sep 17 00:00:00 2001
From: Andrew Ash <andrew@andrewash.com>
Date: Mon, 12 May 2014 18:46:28 -0700
Subject: [PATCH 174/810] Typo: resond -> respond

Author: Andrew Ash <andrew@andrewash.com>

Closes #743 from ash211/patch-4 and squashes the following commits:

c959f3b [Andrew Ash] Typo: resond -> respond
---
 .../spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala   | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala
index ff8356f67681..f08b19e6782e 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala
@@ -175,7 +175,7 @@ private[spark] class MesosSchedulerBackend(
   override def reregistered(d: SchedulerDriver, masterInfo: MasterInfo) {}
 
   /**
-   * Method called by Mesos to offer resources on slaves. We resond by asking our active task sets
+   * Method called by Mesos to offer resources on slaves. We respond by asking our active task sets
    * for tasks in order of priority. We fill each node with tasks in a round-robin manner so that
    * tasks are balanced across the cluster.
    */

From 5c2275d6e4639946fd11ff6403338c8a9ade3d1e Mon Sep 17 00:00:00 2001
From: DB Tsai <dbtsai@alpinenow.com>
Date: Mon, 12 May 2014 19:20:24 -0700
Subject: [PATCH 175/810] L-BFGS Documentation

Documentation for L-BFGS, and an example of training binary L2 logistic regression using L-BFGS.

Author: DB Tsai <dbtsai@alpinenow.com>

Closes #702 from dbtsai/dbtsai-lbfgs-doc and squashes the following commits:

0712215 [DB Tsai] Update
38fdfa1 [DB Tsai] Removed extra empty line
5745b64 [DB Tsai] Update again
e9e418e [DB Tsai] Update
7381521 [DB Tsai] L-BFGS Documentation
---
 docs/mllib-optimization.md | 120 +++++++++++++++++++++++++++++++++++--
 1 file changed, 116 insertions(+), 4 deletions(-)

diff --git a/docs/mllib-optimization.md b/docs/mllib-optimization.md
index bec3912b55dc..aa0dec213059 100644
--- a/docs/mllib-optimization.md
+++ b/docs/mllib-optimization.md
@@ -28,7 +28,6 @@ title: <a href="mllib-guide.html">MLlib</a> - Optimization
 ## Mathematical description
 
 ### Gradient descent
-
 The simplest method to solve optimization problems of the form `$\min_{\wv \in\R^d} \; f(\wv)$`
 is [gradient descent](http://en.wikipedia.org/wiki/Gradient_descent).
 Such first-order optimization methods (including gradient descent and stochastic variants
@@ -128,10 +127,19 @@ is sampled, i.e. `$|S|=$ miniBatchFraction $\cdot n = 1$`, then the algorithm is
 standard SGD. In that case, the step direction depends from the uniformly random sampling of the
 point.
 
-
+### Limited-memory BFGS (L-BFGS)
+[L-BFGS](http://en.wikipedia.org/wiki/Limited-memory_BFGS) is an optimization 
+algorithm in the family of quasi-Newton methods to solve the optimization problems of the form 
+`$\min_{\wv \in\R^d} \; f(\wv)$`. The L-BFGS method approximates the objective function locally as a 
+quadratic without evaluating the second partial derivatives of the objective function to construct the 
+Hessian matrix. The Hessian matrix is approximated by previous gradient evaluations, so there is no 
+vertical scalability issue (the number of training features) when computing the Hessian matrix 
+explicitly in Newton's method. As a result, L-BFGS often achieves rapider convergence compared with 
+other first-order optimization. 
 
 ## Implementation in MLlib
 
+### Gradient descent and stochastic gradient descent
 Gradient descent methods including stochastic subgradient descent (SGD) as
 included as a low-level primitive in `MLlib`, upon which various ML algorithms 
 are developed, see the 
@@ -142,12 +150,12 @@ The SGD method
 [GradientDescent.runMiniBatchSGD](api/scala/index.html#org.apache.spark.mllib.optimization.GradientDescent)
 has the following parameters:
 
-* `gradient` is a class that computes the stochastic gradient of the function
+* `Gradient` is a class that computes the stochastic gradient of the function
 being optimized, i.e., with respect to a single training example, at the
 current parameter value. MLlib includes gradient classes for common loss
 functions, e.g., hinge, logistic, least-squares.  The gradient class takes as
 input a training example, its label, and the current parameter value. 
-* `updater` is a class that performs the actual gradient descent step, i.e. 
+* `Updater` is a class that performs the actual gradient descent step, i.e. 
 updating the weights in each iteration, for a given gradient of the loss part.
 The updater is also responsible to perform the update from the regularization 
 part. MLlib includes updaters for cases without regularization, as well as
@@ -163,3 +171,107 @@ each iteration, to compute the gradient direction.
 Available algorithms for gradient descent:
 
 * [GradientDescent.runMiniBatchSGD](api/mllib/index.html#org.apache.spark.mllib.optimization.GradientDescent)
+
+### L-BFGS
+L-BFGS is currently only a low-level optimization primitive in `MLlib`. If you want to use L-BFGS in various 
+ML algorithms such as Linear Regression, and Logistic Regression, you have to pass the gradient of objective
+function, and updater into optimizer yourself instead of using the training APIs like 
+[LogisticRegressionWithSGD](api/mllib/index.html#org.apache.spark.mllib.classification.LogisticRegressionWithSGD).
+See the example below. It will be addressed in the next release. 
+
+The L1 regularization by using 
+[L1Updater](api/mllib/index.html#org.apache.spark.mllib.optimization.L1Updater) will not work since the 
+soft-thresholding logic in L1Updater is designed for gradient descent. See the developer's note.
+
+The L-BFGS method
+[LBFGS.runLBFGS](api/scala/index.html#org.apache.spark.mllib.optimization.LBFGS)
+has the following parameters:
+
+* `Gradient` is a class that computes the gradient of the objective function
+being optimized, i.e., with respect to a single training example, at the
+current parameter value. MLlib includes gradient classes for common loss
+functions, e.g., hinge, logistic, least-squares.  The gradient class takes as
+input a training example, its label, and the current parameter value. 
+* `Updater` is a class that computes the gradient and loss of objective function 
+of the regularization part for L-BFGS. MLlib includes updaters for cases without 
+regularization, as well as L2 regularizer. 
+* `numCorrections` is the number of corrections used in the L-BFGS update. 10 is 
+recommended.
+* `maxNumIterations` is the maximal number of iterations that L-BFGS can be run.
+* `regParam` is the regularization parameter when using regularization.
+
+The `return` is a tuple containing two elements. The first element is a column matrix
+containing weights for every feature, and the second element is an array containing 
+the loss computed for every iteration.
+
+Here is an example to train binary logistic regression with L2 regularization using
+L-BFGS optimizer. 
+{% highlight scala %}
+import org.apache.spark.SparkContext
+import org.apache.spark.mllib.evaluation.BinaryClassificationMetrics
+import org.apache.spark.mllib.linalg.Vectors
+import org.apache.spark.mllib.util.MLUtils
+import org.apache.spark.mllib.classification.LogisticRegressionModel
+
+val data = MLUtils.loadLibSVMFile(sc, "mllib/data/sample_libsvm_data.txt")
+val numFeatures = data.take(1)(0).features.size
+
+// Split data into training (60%) and test (40%).
+val splits = data.randomSplit(Array(0.6, 0.4), seed = 11L)
+
+// Append 1 into the training data as intercept.
+val training = splits(0).map(x => (x.label, MLUtils.appendBias(x.features))).cache()
+
+val test = splits(1)
+
+// Run training algorithm to build the model
+val numCorrections = 10
+val convergenceTol = 1e-4
+val maxNumIterations = 20
+val regParam = 0.1
+val initialWeightsWithIntercept = Vectors.dense(new Array[Double](numFeatures + 1))
+
+val (weightsWithIntercept, loss) = LBFGS.runLBFGS(
+  training,
+  new LogisticGradient(),
+  new SquaredL2Updater(),
+  numCorrections,
+  convergenceTol,
+  maxNumIterations,
+  regParam,
+  initialWeightsWithIntercept)
+
+val model = new LogisticRegressionModel(
+  Vectors.dense(weightsWithIntercept.toArray.slice(0, weightsWithIntercept.size - 1)),
+  weightsWithIntercept(weightsWithIntercept.size - 1))
+
+// Clear the default threshold.
+model.clearThreshold()
+
+// Compute raw scores on the test set.
+val scoreAndLabels = test.map { point =>
+  val score = model.predict(point.features)
+  (score, point.label)
+}
+
+// Get evaluation metrics.
+val metrics = new BinaryClassificationMetrics(scoreAndLabels)
+val auROC = metrics.areaUnderROC()
+
+println("Loss of each step in training process")
+loss.foreach(println)
+println("Area under ROC = " + auROC)
+{% endhighlight %}
+
+#### Developer's note
+Since the Hessian is constructed approximately from previous gradient evaluations, 
+the objective function can not be changed during the optimization process. 
+As a result, Stochastic L-BFGS will not work naively by just using miniBatch; 
+therefore, we don't provide this until we have better understanding.
+
+* `Updater` is a class originally designed for gradient decent which computes 
+the actual gradient descent step. However, we're able to take the gradient and 
+loss of objective function of regularization for L-BFGS by ignoring the part of logic
+only for gradient decent such as adaptive step size stuff. We will refactorize
+this into regularizer to replace updater to separate the logic between 
+regularization and step update later. 
\ No newline at end of file

From 9cf9f18973840f7287f7cfa5ce90efed3225bb30 Mon Sep 17 00:00:00 2001
From: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Date: Mon, 12 May 2014 19:21:06 -0700
Subject: [PATCH 176/810] Modify a typo in monitoring.md

As I mentioned in SPARK-1765, there is a word 'JXM' in monitoring.md.
I think it's typo for 'JMX'.

Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>

Closes #698 from sarutak/SPARK-1765 and squashes the following commits:

bae9843 [Kousuke Saruta] modified a typoe in monitoring.md
---
 docs/monitoring.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/monitoring.md b/docs/monitoring.md
index 6f35fc37c407..fffc58ac3923 100644
--- a/docs/monitoring.md
+++ b/docs/monitoring.md
@@ -156,7 +156,7 @@ Each instance can report to zero or more _sinks_. Sinks are contained in the
 
 * `ConsoleSink`: Logs metrics information to the console.
 * `CSVSink`: Exports metrics data to CSV files at regular intervals.
-* `JmxSink`: Registers metrics for viewing in a JXM console.
+* `JmxSink`: Registers metrics for viewing in a JMX console.
 * `MetricsServlet`: Adds a servlet within the existing Spark UI to serve metrics data as JSON data.
 * `GraphiteSink`: Sends metrics to a Graphite node.
 

From 156df87e7ca0e6cda2cc970ecd1466ce06f7576f Mon Sep 17 00:00:00 2001
From: Andrew Ash <andrew@andrewash.com>
Date: Mon, 12 May 2014 19:23:39 -0700
Subject: [PATCH 177/810] SPARK-1757 Failing test for saving null primitives
 with .saveAsParquetFile()

https://issues.apache.org/jira/browse/SPARK-1757

The first test succeeds, but the second test fails with exception:

```
[info] - save and load case class RDD with Nones as parquet *** FAILED *** (14 milliseconds)
[info]   java.lang.RuntimeException: Unsupported datatype StructType(List())
[info]   at scala.sys.package$.error(package.scala:27)
[info]   at org.apache.spark.sql.parquet.ParquetTypesConverter$.fromDataType(ParquetRelation.scala:201)
[info]   at org.apache.spark.sql.parquet.ParquetTypesConverter$$anonfun$1.apply(ParquetRelation.scala:235)
[info]   at org.apache.spark.sql.parquet.ParquetTypesConverter$$anonfun$1.apply(ParquetRelation.scala:235)
[info]   at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
[info]   at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
[info]   at scala.collection.immutable.List.foreach(List.scala:318)
[info]   at scala.collection.TraversableLike$class.map(TraversableLike.scala:244)
[info]   at scala.collection.AbstractTraversable.map(Traversable.scala:105)
[info]   at org.apache.spark.sql.parquet.ParquetTypesConverter$.convertFromAttributes(ParquetRelation.scala:234)
[info]   at org.apache.spark.sql.parquet.ParquetTypesConverter$.writeMetaData(ParquetRelation.scala:267)
[info]   at org.apache.spark.sql.parquet.ParquetRelation$.createEmpty(ParquetRelation.scala:143)
[info]   at org.apache.spark.sql.parquet.ParquetRelation$.create(ParquetRelation.scala:122)
[info]   at org.apache.spark.sql.execution.SparkStrategies$ParquetOperations$.apply(SparkStrategies.scala:139)
[info]   at org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$1.apply(QueryPlanner.scala:58)
[info]   at org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$1.apply(QueryPlanner.scala:58)
[info]   at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
[info]   at org.apache.spark.sql.catalyst.planning.QueryPlanner.apply(QueryPlanner.scala:59)
[info]   at org.apache.spark.sql.SQLContext$QueryExecution.sparkPlan$lzycompute(SQLContext.scala:264)
[info]   at org.apache.spark.sql.SQLContext$QueryExecution.sparkPlan(SQLContext.scala:264)
[info]   at org.apache.spark.sql.SQLContext$QueryExecution.executedPlan$lzycompute(SQLContext.scala:265)
[info]   at org.apache.spark.sql.SQLContext$QueryExecution.executedPlan(SQLContext.scala:265)
[info]   at org.apache.spark.sql.SQLContext$QueryExecution.toRdd$lzycompute(SQLContext.scala:268)
[info]   at org.apache.spark.sql.SQLContext$QueryExecution.toRdd(SQLContext.scala:268)
[info]   at org.apache.spark.sql.SchemaRDDLike$class.saveAsParquetFile(SchemaRDDLike.scala:66)
[info]   at org.apache.spark.sql.SchemaRDD.saveAsParquetFile(SchemaRDD.scala:98)
```

Author: Andrew Ash <andrew@andrewash.com>
Author: Michael Armbrust <michael@databricks.com>

Closes #690 from ash211/rdd-parquet-save and squashes the following commits:

747a0b9 [Andrew Ash] Merge pull request #1 from marmbrus/pr/690
54bd00e [Michael Armbrust] Need to put Option first since Option <: Seq.
8f3f281 [Andrew Ash] SPARK-1757 Add failing test for saving SparkSQL Schemas with Option[?] fields as parquet
---
 .../spark/sql/catalyst/ScalaReflection.scala  |  6 +--
 .../spark/sql/parquet/ParquetQuerySuite.scala | 44 +++++++++++++++++++
 2 files changed, 47 insertions(+), 3 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala
index 792ef6cee6f5..196695a0a188 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala
@@ -41,6 +41,9 @@ object ScalaReflection {
 
   /** Returns a catalyst DataType for the given Scala Type using reflection. */
   def schemaFor(tpe: `Type`): DataType = tpe match {
+    case t if t <:< typeOf[Option[_]] =>
+      val TypeRef(_, _, Seq(optType)) = t
+      schemaFor(optType)
     case t if t <:< typeOf[Product] =>
       val params = t.member("<init>": TermName).asMethod.paramss
       StructType(
@@ -59,9 +62,6 @@ object ScalaReflection {
     case t if t <:< typeOf[String] => StringType
     case t if t <:< typeOf[Timestamp] => TimestampType
     case t if t <:< typeOf[BigDecimal] => DecimalType
-    case t if t <:< typeOf[Option[_]] =>
-      val TypeRef(_, _, Seq(optType)) = t
-      schemaFor(optType)
     case t if t <:< typeOf[java.lang.Integer] => IntegerType
     case t if t <:< typeOf[java.lang.Long] => LongType
     case t if t <:< typeOf[java.lang.Double] => DoubleType
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala
index d9c9b9a076ab..ff1677eb8a48 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala
@@ -42,6 +42,20 @@ import org.apache.spark.sql.test.TestSQLContext._
 
 case class TestRDDEntry(key: Int, value: String)
 
+case class NullReflectData(
+    intField: java.lang.Integer,
+    longField: java.lang.Long,
+    floatField: java.lang.Float,
+    doubleField: java.lang.Double,
+    booleanField: java.lang.Boolean)
+
+case class OptionalReflectData(
+    intField: Option[Int],
+    longField: Option[Long],
+    floatField: Option[Float],
+    doubleField: Option[Double],
+    booleanField: Option[Boolean])
+
 class ParquetQuerySuite extends QueryTest with FunSuite with BeforeAndAfterAll {
   import TestData._
   TestData // Load test data tables.
@@ -195,5 +209,35 @@ class ParquetQuerySuite extends QueryTest with FunSuite with BeforeAndAfterAll {
     Utils.deleteRecursively(ParquetTestData.testDir)
     ParquetTestData.writeFile()
   }
+
+  test("save and load case class RDD with nulls as parquet") {
+    val data = NullReflectData(null, null, null, null, null)
+    val rdd = sparkContext.parallelize(data :: Nil)
+
+    val file = getTempFilePath("parquet")
+    val path = file.toString
+    rdd.saveAsParquetFile(path)
+    val readFile = parquetFile(path)
+
+    val rdd_saved = readFile.collect()
+    assert(rdd_saved(0) === Seq.fill(5)(null))
+    Utils.deleteRecursively(file)
+    assert(true)
+  }
+
+  test("save and load case class RDD with Nones as parquet") {
+    val data = OptionalReflectData(null, null, null, null, null)
+    val rdd = sparkContext.parallelize(data :: Nil)
+
+    val file = getTempFilePath("parquet")
+    val path = file.toString
+    rdd.saveAsParquetFile(path)
+    val readFile = parquetFile(path)
+
+    val rdd_saved = readFile.collect()
+    assert(rdd_saved(0) === Seq.fill(5)(null))
+    Utils.deleteRecursively(file)
+    assert(true)
+  }
 }
 

From ba96bb3d591130075763706526f86fb2aaffa3ae Mon Sep 17 00:00:00 2001
From: Andrew Or <andrewor14@gmail.com>
Date: Mon, 12 May 2014 19:42:35 -0700
Subject: [PATCH 178/810] [SPARK-1780] Non-existent SPARK_DAEMON_OPTS is
 lurking around

What they really mean is SPARK_DAEMON_***JAVA***_OPTS

Author: Andrew Or <andrewor14@gmail.com>

Closes #751 from andrewor14/spark-daemon-opts and squashes the following commits:

70c41f9 [Andrew Or] SPARK_DAEMON_OPTS -> SPARK_DAEMON_JAVA_OPTS
---
 conf/spark-env.sh.template                           | 2 +-
 core/src/main/scala/org/apache/spark/SparkConf.scala | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/conf/spark-env.sh.template b/conf/spark-env.sh.template
index f906be611a93..4479e1e34cd4 100755
--- a/conf/spark-env.sh.template
+++ b/conf/spark-env.sh.template
@@ -39,5 +39,5 @@
 # - SPARK_WORKER_DIR, to set the working directory of worker processes
 # - SPARK_WORKER_OPTS, to set config properties only for the worker (e.g. "-Dx=y")
 # - SPARK_HISTORY_OPTS, to set config properties only for the history server (e.g. "-Dx=y")
-# - SPARK_DAEMON_OPTS, to set config properties for all daemons (e.g. "-Dx=y")
+# - SPARK_DAEMON_JAVA_OPTS, to set config properties for all daemons (e.g. "-Dx=y")
 # - SPARK_PUBLIC_DNS, to set the public dns name of the master or workers
diff --git a/core/src/main/scala/org/apache/spark/SparkConf.scala b/core/src/main/scala/org/apache/spark/SparkConf.scala
index bd21fdc5a18e..800616622d7b 100644
--- a/core/src/main/scala/org/apache/spark/SparkConf.scala
+++ b/core/src/main/scala/org/apache/spark/SparkConf.scala
@@ -247,7 +247,7 @@ class SparkConf(loadDefaults: Boolean) extends Cloneable with Logging {
           | - ./spark-submit with conf/spark-defaults.conf to set defaults for an application
           | - ./spark-submit with --driver-java-options to set -X options for a driver
           | - spark.executor.extraJavaOptions to set -X options for executors
-          | - SPARK_DAEMON_OPTS to set java options for standalone daemons (i.e. master, worker)
+          | - SPARK_DAEMON_JAVA_OPTS to set java options for standalone daemons (master or worker)
         """.stripMargin
       logError(error)
 

From 2ffd1eafd28635dcecc0ac738d4a62c05d740925 Mon Sep 17 00:00:00 2001
From: Andrew Or <andrewor14@gmail.com>
Date: Mon, 12 May 2014 19:44:14 -0700
Subject: [PATCH 179/810] [SPARK-1753 / 1773 / 1814] Update outdated docs for
 spark-submit, YARN, standalone etc.

YARN
- SparkPi was updated to not take in master as an argument; we should update the docs to reflect that.
- The default YARN build guide should be in maven, not sbt.
- This PR also adds a paragraph on steps to debug a YARN application.

Standalone
- Emphasize spark-submit more. Right now it's one small paragraph preceding the legacy way of launching through `org.apache.spark.deploy.Client`.
- The way we set configurations / environment variables according to the old docs is outdated. This needs to reflect changes introduced by the Spark configuration changes we made.

In general, this PR also adds a little more documentation on the new spark-shell, spark-submit, spark-defaults.conf etc here and there.

Author: Andrew Or <andrewor14@gmail.com>

Closes #701 from andrewor14/yarn-docs and squashes the following commits:

e2c2312 [Andrew Or] Merge in changes in #752 (SPARK-1814)
25cfe7b [Andrew Or] Merge in the warning from SPARK-1753
a8c39c5 [Andrew Or] Minor changes
336bbd9 [Andrew Or] Tabs -> spaces
4d9d8f7 [Andrew Or] Merge branch 'master' of github.com:apache/spark into yarn-docs
041017a [Andrew Or] Abstract Spark submit documentation to cluster-overview.html
3cc0649 [Andrew Or] Detail how to set configurations + remove legacy instructions
5b7140a [Andrew Or] Merge branch 'master' of github.com:apache/spark into yarn-docs
85a51fc [Andrew Or] Update run-example, spark-shell, configuration etc.
c10e8c7 [Andrew Or] Merge branch 'master' of github.com:apache/spark into yarn-docs
381fe32 [Andrew Or] Update docs for standalone mode
757c184 [Andrew Or] Add a note about the requirements for the debugging trick
f8ca990 [Andrew Or] Merge branch 'master' of github.com:apache/spark into yarn-docs
924f04c [Andrew Or] Revert addition of --deploy-mode
d5fe17b [Andrew Or] Update the YARN docs
---
 conf/spark-defaults.conf.template        |  3 +-
 conf/spark-env.sh.template               |  4 +-
 docs/building-with-maven.md              |  7 +++
 docs/cluster-overview.md                 | 73 +++++++++++++++---------
 docs/configuration.md                    | 64 +++++++++++++--------
 docs/hadoop-third-party-distributions.md | 14 +++--
 docs/index.md                            | 34 +++++++----
 docs/java-programming-guide.md           |  5 +-
 docs/python-programming-guide.md         |  2 +-
 docs/quick-start.md                      |  4 +-
 docs/running-on-yarn.md                  | 15 +++--
 docs/scala-programming-guide.md          | 13 +++--
 docs/spark-standalone.md                 | 71 ++++++++++-------------
 13 files changed, 184 insertions(+), 125 deletions(-)

diff --git a/conf/spark-defaults.conf.template b/conf/spark-defaults.conf.template
index f840ff681d01..2779342769c1 100644
--- a/conf/spark-defaults.conf.template
+++ b/conf/spark-defaults.conf.template
@@ -2,6 +2,7 @@
 # This is useful for setting default environmental settings.
 
 # Example:
-# spark.master 	          spark://master:7077
+# spark.master            spark://master:7077
 # spark.eventLog.enabled  true
 # spark.eventLog.dir      hdfs://namenode:8021/directory
+# spark.serializer        org.apache.spark.serializer.KryoSerializer
diff --git a/conf/spark-env.sh.template b/conf/spark-env.sh.template
index 4479e1e34cd4..f8ffbf64278f 100755
--- a/conf/spark-env.sh.template
+++ b/conf/spark-env.sh.template
@@ -30,11 +30,11 @@
 
 # Options for the daemons used in the standalone deploy mode:
 # - SPARK_MASTER_IP, to bind the master to a different IP address or hostname
-# - SPARK_MASTER_PORT / SPARK_MASTER_WEBUI_PORT, to use non-default ports
+# - SPARK_MASTER_PORT / SPARK_MASTER_WEBUI_PORT, to use non-default ports for the master
 # - SPARK_MASTER_OPTS, to set config properties only for the master (e.g. "-Dx=y")
 # - SPARK_WORKER_CORES, to set the number of cores to use on this machine
 # - SPARK_WORKER_MEMORY, to set how much total memory workers have to give executors (e.g. 1000m, 2g)
-# - SPARK_WORKER_PORT / SPARK_WORKER_WEBUI_PORT
+# - SPARK_WORKER_PORT / SPARK_WORKER_WEBUI_PORT, to use non-default ports for the worker
 # - SPARK_WORKER_INSTANCES, to set the number of worker processes per node
 # - SPARK_WORKER_DIR, to set the working directory of worker processes
 # - SPARK_WORKER_OPTS, to set config properties only for the worker (e.g. "-Dx=y")
diff --git a/docs/building-with-maven.md b/docs/building-with-maven.md
index b6dd553bbe06..8b44535d8240 100644
--- a/docs/building-with-maven.md
+++ b/docs/building-with-maven.md
@@ -129,6 +129,13 @@ Java 8 tests are run when -Pjava8-tests profile is enabled, they will run in spi
 For these tests to run your system must have a JDK 8 installation. 
 If you have JDK 8 installed but it is not the system default, you can set JAVA_HOME to point to JDK 8 before running the tests.
 
+## Building for PySpark on YARN ##
+
+PySpark on YARN is only supported if the jar is built with maven. Further, there is a known problem
+with building this assembly jar on Red Hat based operating systems (see SPARK-1753). If you wish to
+run PySpark on a YARN cluster with Red Hat installed, we recommend that you build the jar elsewhere,
+then ship it over to the cluster. We are investigating the exact cause for this.
+
 ## Packaging without Hadoop dependencies for deployment on YARN ##
 
 The assembly jar produced by "mvn package" will, by default, include all of Spark's dependencies, including Hadoop and some of its ecosystem projects. On YARN deployments, this causes multiple versions of these to appear on executor classpaths: the version packaged in the Spark assembly and the version on each node, included with yarn.application.classpath.  The "hadoop-provided" profile builds the assembly without including Hadoop-ecosystem projects, like ZooKeeper and Hadoop itself. 
diff --git a/docs/cluster-overview.md b/docs/cluster-overview.md
index 162c415b5883..f05a755de7fe 100644
--- a/docs/cluster-overview.md
+++ b/docs/cluster-overview.md
@@ -66,62 +66,76 @@ script as shown here while passing your jar.
 For Python, you can use the `pyFiles` argument of SparkContext
 or its `addPyFile` method to add `.py`, `.zip` or `.egg` files to be distributed.
 
-### Launching Applications with ./bin/spark-submit
+### Launching Applications with Spark submit
 
 Once a user application is bundled, it can be launched using the `spark-submit` script located in
 the bin directory. This script takes care of setting up the classpath with Spark and its
-dependencies, and can support different cluster managers and deploy modes that Spark supports.
-It's usage is
+dependencies, and can support different cluster managers and deploy modes that Spark supports:
 
-    ./bin/spark-submit --class path.to.your.Class [options] <app jar> [app options]
+    ./bin/spark-submit \
+      --class <main-class>
+      --master <master-url> \
+      --deploy-mode <deploy-mode> \
+      ... // other options
+      <application-jar>
+      [application-arguments]
 
-When calling `spark-submit`, `[app options]` will be passed along to your application's
-main class. To enumerate all options available to `spark-submit` run it with 
-the `--help` flag. Here are a few examples of common options:
+    main-class: The entry point for your application (e.g. org.apache.spark.examples.SparkPi)
+    master-url: The URL of the master node (e.g. spark://23.195.26.187:7077)
+    deploy-mode: Whether to deploy this application within the cluster or from an external client (e.g. client)
+    application-jar: Path to a bundled jar including your application and all dependencies. The URL must be globally visible inside of your cluster, for instance, an `hdfs://` path or a `file://` path that is present on all nodes.
+    application-arguments: Space delimited arguments passed to the main method of <main-class>, if any
+
+To enumerate all options available to `spark-submit` run it with the `--help` flag. Here are a few
+examples of common options:
 
 {% highlight bash %}
 # Run application locally
 ./bin/spark-submit \
-  --class my.main.ClassName
+  --class org.apache.spark.examples.SparkPi
   --master local[8] \
-  my-app.jar
+  /path/to/examples.jar \
+  100
 
 # Run on a Spark standalone cluster
 ./bin/spark-submit \
-  --class my.main.ClassName
-  --master spark://mycluster:7077 \
+  --class org.apache.spark.examples.SparkPi
+  --master spark://207.184.161.138:7077 \
   --executor-memory 20G \
   --total-executor-cores 100 \
-  my-app.jar
+  /path/to/examples.jar \
+  1000
 
 # Run on a YARN cluster
-HADOOP_CONF_DIR=XX /bin/spark-submit \
-  --class my.main.ClassName
+HADOOP_CONF_DIR=XX ./bin/spark-submit \
+  --class org.apache.spark.examples.SparkPi
   --master yarn-cluster \  # can also be `yarn-client` for client mode
   --executor-memory 20G \
   --num-executors 50 \
-  my-app.jar
+  /path/to/examples.jar \
+  1000
 {% endhighlight %}
 
 ### Loading Configurations from a File
 
-The `spark-submit` script can load default `SparkConf` values from a properties file and pass them
-onto your application. By default it will read configuration options from
-`conf/spark-defaults.conf`. Any values specified in the file will be passed on to the
-application when run. They can obviate the need for certain flags to `spark-submit`: for
-instance, if `spark.master` property is set, you can safely omit the
+The `spark-submit` script can load default [Spark configuration values](configuration.html) from a
+properties file and pass them on to your application. By default it will read configuration options
+from `conf/spark-defaults.conf`. For more detail, see the section on
+[loading default configurations](configuration.html#loading-default-configurations).
+
+Loading default Spark configurations this way can obviate the need for certain flags to
+`spark-submit`. For instance, if the `spark.master` property is set, you can safely omit the
 `--master` flag from `spark-submit`. In general, configuration values explicitly set on a
-`SparkConf` take the highest precedence, then flags passed to `spark-submit`, then values
-in the defaults file.
+`SparkConf` take the highest precedence, then flags passed to `spark-submit`, then values in the
+defaults file.
 
-If you are ever unclear where configuration options are coming from. fine-grained debugging
-information can be printed by adding the `--verbose` option to `./spark-submit`.
+If you are ever unclear where configuration options are coming from, you can print out fine-grained
+debugging information by running `spark-submit` with the `--verbose` option.
 
 ### Advanced Dependency Management
-When using `./bin/spark-submit` the app jar along with any jars included with the `--jars` option
-will be automatically transferred to the cluster. `--jars` can also be used to distribute .egg and .zip
-libraries for Python to executors. Spark uses the following URL scheme to allow different
-strategies for disseminating jars:
+When using `spark-submit`, the application jar along with any jars included with the `--jars` option
+will be automatically transferred to the cluster. Spark uses the following URL scheme to allow
+different strategies for disseminating jars:
 
 - **file:** - Absolute paths and `file:/` URIs are served by the driver's HTTP file server, and
   every executor pulls the file from the driver HTTP server.
@@ -135,6 +149,9 @@ This can use up a significant amount of space over time and will need to be clea
 is handled automatically, and with Spark standalone, automatic cleanup can be configured with the
 `spark.worker.cleanup.appDataTtl` property.
 
+For python, the equivalent `--py-files` option can be used to distribute .egg and .zip libraries
+to executors.
+
 # Monitoring
 
 Each driver program has a web UI, typically on port 4040, that displays information about running
diff --git a/docs/configuration.md b/docs/configuration.md
index 5b034e3cb3d4..2eed96f704a4 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -5,9 +5,9 @@ title: Spark Configuration
 
 Spark provides three locations to configure the system:
 
-* [Spark properties](#spark-properties) control most application parameters and can be set by passing
-  a [SparkConf](api/scala/index.html#org.apache.spark.SparkConf) object to SparkContext, or through Java
-  system properties.
+* [Spark properties](#spark-properties) control most application parameters and can be set by
+  passing a [SparkConf](api/scala/index.html#org.apache.spark.SparkConf) object to SparkContext,
+  or through the `conf/spark-defaults.conf` properties file.
 * [Environment variables](#environment-variables) can be used to set per-machine settings, such as
   the IP address, through the `conf/spark-env.sh` script on each node.
 * [Logging](#configuring-logging) can be configured through `log4j.properties`.
@@ -15,25 +15,41 @@ Spark provides three locations to configure the system:
 
 # Spark Properties
 
-Spark properties control most application settings and are configured separately for each application.
-The preferred way to set them is by passing a [SparkConf](api/scala/index.html#org.apache.spark.SparkConf)
-class to your SparkContext constructor.
-Alternatively, Spark will also load them from Java system properties, for compatibility with old versions
-of Spark.
-
-SparkConf lets you configure most of the common properties to initialize a cluster (e.g., master URL and
-application name), as well as arbitrary key-value pairs through the `set()` method. For example, we could
-initialize an application as follows:
+Spark properties control most application settings and are configured separately for each
+application. The preferred way is to set them through
+[SparkConf](api/scala/index.html#org.apache.spark.SparkConf) and passing it as an argument to your
+SparkContext. SparkConf allows you to configure most of the common properties to initialize a
+cluster (e.g. master URL and application name), as well as arbitrary key-value pairs through the
+`set()` method. For example, we could initialize an application as follows:
 
 {% highlight scala %}
-val conf = new SparkConf().
-             setMaster("local").
-             setAppName("My application").
-             set("spark.executor.memory", "1g")
+val conf = new SparkConf
+             .setMaster("local")
+             .setAppName("CountingSheep")
+             .set("spark.executor.memory", "1g")
 val sc = new SparkContext(conf)
 {% endhighlight %}
 
-Most of the properties control internal settings that have reasonable default values. However,
+## Loading Default Configurations
+
+In the case of `spark-shell`, a SparkContext has already been created for you, so you cannot control
+the configuration properties through SparkConf. However, you can still set configuration properties
+through a default configuration file. By default, `spark-shell` (and more generally `spark-submit`)
+will read configuration options from `conf/spark-defaults.conf`, in which each line consists of a
+key and a value separated by whitespace. For example,
+
+    spark.master            spark://5.6.7.8:7077
+    spark.executor.memory   512m
+    spark.eventLog.enabled  true
+    spark.serializer        org.apache.spark.serializer.KryoSerializer
+
+Any values specified in the file will be passed on to the application, and merged with those
+specified through SparkConf. If the same configuration property exists in both `spark-defaults.conf`
+and SparkConf, then the latter will take precedence as it is the most application-specific.
+
+## All Configuration Properties
+
+Most of the properties that control internal settings have reasonable default values. However,
 there are at least five properties that you will commonly want to control:
 
 <table class="table">
@@ -101,9 +117,9 @@ Apart from these, the following properties are also available, and may be useful
   <td>spark.default.parallelism</td>
   <td>
     <ul>
+      <li>Local mode: number of cores on the local machine</li>
       <li>Mesos fine grained mode: 8</li>
-      <li>Local mode: core number of the local machine</li>
-      <li>Others: total core number of all executor nodes or 2, whichever is larger</li>
+      <li>Others: total number of cores on all executor nodes or 2, whichever is larger</li>
     </ul>
   </td>
   <td>
@@ -187,7 +203,7 @@ Apart from these, the following properties are also available, and may be useful
     Comma separated list of filter class names to apply to the Spark web ui. The filter should be a
     standard javax servlet Filter. Parameters to each filter can also be specified by setting a
     java system property of spark.&lt;class name of filter&gt;.params='param1=value1,param2=value2'
-    (e.g.-Dspark.ui.filters=com.test.filter1 -Dspark.com.test.filter1.params='param1=foo,param2=testing')
+    (e.g. -Dspark.ui.filters=com.test.filter1 -Dspark.com.test.filter1.params='param1=foo,param2=testing')
   </td>
 </tr>
 <tr>
@@ -696,7 +712,9 @@ Apart from these, the following properties are also available, and may be useful
 ## Viewing Spark Properties
 
 The application web UI at `http://<driver>:4040` lists Spark properties in the "Environment" tab.
-This is a useful place to check to make sure that your properties have been set correctly.
+This is a useful place to check to make sure that your properties have been set correctly. Note
+that only values explicitly specified through either `spark-defaults.conf` or SparkConf will
+appear. For all other configuration properties, you can assume the default value is used.
 
 # Environment Variables
 
@@ -714,8 +732,8 @@ The following variables can be set in `spark-env.sh`:
 * `PYSPARK_PYTHON`, the Python binary to use for PySpark
 * `SPARK_LOCAL_IP`, to configure which IP address of the machine to bind to.
 * `SPARK_PUBLIC_DNS`, the hostname your Spark program will advertise to other machines.
-* Options for the Spark [standalone cluster scripts](spark-standalone.html#cluster-launch-scripts), such as number of cores
-  to use on each machine and maximum memory.
+* Options for the Spark [standalone cluster scripts](spark-standalone.html#cluster-launch-scripts),
+  such as number of cores to use on each machine and maximum memory.
 
 Since `spark-env.sh` is a shell script, some of these can be set programmatically -- for example, you might
 compute `SPARK_LOCAL_IP` by looking up the IP of a specific network interface.
diff --git a/docs/hadoop-third-party-distributions.md b/docs/hadoop-third-party-distributions.md
index 454877a7fa8a..a0aeab5727bd 100644
--- a/docs/hadoop-third-party-distributions.md
+++ b/docs/hadoop-third-party-distributions.md
@@ -9,12 +9,14 @@ with these distributions:
 
 # Compile-time Hadoop Version
 
-When compiling Spark, you'll need to 
-[set the SPARK_HADOOP_VERSION flag](index.html#a-note-about-hadoop-versions):
+When compiling Spark, you'll need to specify the Hadoop version by defining the `hadoop.version`
+property. For certain versions, you will need to specify additional profiles. For more detail,
+see the guide on [building with maven](building-with-maven.html#specifying-the-hadoop-version):
 
-    SPARK_HADOOP_VERSION=1.0.4 sbt/sbt assembly
+    mvn -Dhadoop.version=1.0.4 -DskipTests clean package
+    mvn -Phadoop-2.2 -Dhadoop.version=2.2.0 -DskipTests clean package
 
-The table below lists the corresponding `SPARK_HADOOP_VERSION` code for each CDH/HDP release. Note that
+The table below lists the corresponding `hadoop.version` code for each CDH/HDP release. Note that
 some Hadoop releases are binary compatible across client versions. This means the pre-built Spark
 distribution may "just work" without you needing to compile. That said, we recommend compiling with 
 the _exact_ Hadoop version you are running to avoid any compatibility errors.
@@ -46,6 +48,10 @@ the _exact_ Hadoop version you are running to avoid any compatibility errors.
   </tr>
 </table>
 
+In SBT, the equivalent can be achieved by setting the SPARK_HADOOP_VERSION flag:
+
+    SPARK_HADOOP_VERSION=1.0.4 sbt/sbt assembly
+
 # Linking Applications to the Hadoop Version
 
 In addition to compiling Spark itself against the right version, you need to add a Maven dependency on that
diff --git a/docs/index.md b/docs/index.md
index a2f1a84371ff..48182a27d28a 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -24,21 +24,31 @@ right version of Scala from [scala-lang.org](http://www.scala-lang.org/download/
 
 # Running the Examples and Shell
 
-Spark comes with several sample programs.  Scala, Java and Python examples are in the `examples/src/main` directory.
-To run one of the Java or Scala sample programs, use `./bin/run-example <class> <params>` in the top-level Spark directory
-(the `bin/run-example` script sets up the appropriate paths and launches that program).
-For example, try `./bin/run-example org.apache.spark.examples.SparkPi local`.
-To run a Python sample program, use `./bin/pyspark <sample-program> <params>`.  For example, try `./bin/pyspark ./examples/src/main/python/pi.py local`.
+Spark comes with several sample programs.  Scala, Java and Python examples are in the
+`examples/src/main` directory. To run one of the Java or Scala sample programs, use
+`bin/run-example <class> [params]` in the top-level Spark directory. (Behind the scenes, this
+invokes the more general
+[Spark submit script](cluster-overview.html#launching-applications-with-spark-submit) for
+launching applications). For example,
 
-Each example prints usage help when run with no parameters.
+    ./bin/run-example SparkPi 10
 
-Note that all of the sample programs take a `<master>` parameter specifying the cluster URL
-to connect to. This can be a [URL for a distributed cluster](scala-programming-guide.html#master-urls),
-or `local` to run locally with one thread, or `local[N]` to run locally with N threads. You should start by using
-`local` for testing.
+You can also run Spark interactively through modified versions of the Scala shell. This is a
+great way to learn the framework.
 
-Finally, you can run Spark interactively through modified versions of the Scala shell (`./bin/spark-shell`) or
-Python interpreter (`./bin/pyspark`). These are a great way to learn the framework.
+    ./bin/spark-shell --master local[2]
+
+The `--master` option specifies the
+[master URL for a distributed cluster](scala-programming-guide.html#master-urls), or `local` to run
+locally with one thread, or `local[N]` to run locally with N threads. You should start by using
+`local` for testing. For a full list of options, run Spark shell with the `--help` option.
+
+Spark also provides a Python interface. To run an example Spark application written in Python, use
+`bin/pyspark <program> [params]`. For example,
+
+    ./bin/pyspark examples/src/main/python/pi.py local[2] 10
+
+or simply `bin/pyspark` without any arguments to run Spark interactively in a python interpreter.
 
 # Launching on a Cluster
 
diff --git a/docs/java-programming-guide.md b/docs/java-programming-guide.md
index c34eb28fc06a..943fdd9d019f 100644
--- a/docs/java-programming-guide.md
+++ b/docs/java-programming-guide.md
@@ -215,7 +215,4 @@ Spark includes several sample programs using the Java API in
 [`examples/src/main/java`](https://github.com/apache/spark/tree/master/examples/src/main/java/org/apache/spark/examples).  You can run them by passing the class name to the
 `bin/run-example` script included in Spark; for example:
 
-    ./bin/run-example org.apache.spark.examples.JavaWordCount
-
-Each example program prints usage help when run
-without any arguments.
+    ./bin/run-example JavaWordCount README.md
diff --git a/docs/python-programming-guide.md b/docs/python-programming-guide.md
index 39fb5f0c99ca..2ce2c346d7b0 100644
--- a/docs/python-programming-guide.md
+++ b/docs/python-programming-guide.md
@@ -164,6 +164,6 @@ some example applications.
 PySpark also includes several sample programs in the [`examples/src/main/python` folder](https://github.com/apache/spark/tree/master/examples/src/main/python).
 You can run them by passing the files to `pyspark`; e.g.:
 
-    ./bin/spark-submit examples/src/main/python/wordcount.py
+    ./bin/spark-submit examples/src/main/python/wordcount.py local[2] README.md
 
 Each program prints usage help when run without arguments.
diff --git a/docs/quick-start.md b/docs/quick-start.md
index 478b790f92e1..a4d01487bb49 100644
--- a/docs/quick-start.md
+++ b/docs/quick-start.md
@@ -18,7 +18,9 @@ you can download a package for any version of Hadoop.
 ## Basics
 
 Spark's interactive shell provides a simple way to learn the API, as well as a powerful tool to analyze datasets interactively.
-Start the shell by running `./bin/spark-shell` in the Spark directory.
+Start the shell by running the following in the Spark directory.
+
+    ./bin/spark-shell
 
 Spark's primary abstraction is a distributed collection of items called a Resilient Distributed Dataset (RDD). RDDs can be created from Hadoop InputFormats (such as HDFS files) or by transforming other RDDs. Let's make a new RDD from the text of the README file in the Spark source directory:
 
diff --git a/docs/running-on-yarn.md b/docs/running-on-yarn.md
index c56359429680..66c330fdee73 100644
--- a/docs/running-on-yarn.md
+++ b/docs/running-on-yarn.md
@@ -54,13 +54,13 @@ For example:
         --executor-memory 2g \
         --executor-cores 1
         lib/spark-examples*.jar \
-        yarn-cluster 5
+        10
 
 The above starts a YARN client program which starts the default Application Master. Then SparkPi will be run as a child thread of Application Master. The client will periodically poll the Application Master for status updates and display them in the console. The client will exit once your application has finished running.  Refer to the "Viewing Logs" section below for how to see driver and executor logs.
 
 To launch a Spark application in yarn-client mode, do the same, but replace "yarn-cluster" with "yarn-client".  To run spark-shell:
 
-    $ MASTER=yarn-client ./bin/spark-shell
+    $ ./bin/spark-shell --master yarn-client
 
 ## Adding additional jars
 
@@ -70,9 +70,9 @@ In yarn-cluster mode, the driver runs on a different machine than the client, so
         --master yarn-cluster \
         --jars my-other-jar.jar,my-other-other-jar.jar
         my-main-jar.jar
-        yarn-cluster 5
+        app_arg1 app_arg2
 
-# Viewing logs
+# Debugging your Application
 
 In YARN terminology, executors and application masters run inside "containers". YARN has two modes for handling container logs after an application has completed. If log aggregation is turned on (with the yarn.log-aggregation-enable config), container logs are copied to HDFS and deleted on the local machine. These logs can be viewed from anywhere on the cluster with the "yarn logs" command.
 
@@ -82,6 +82,13 @@ will print out the contents of all log files from all containers from the given
 
 When log aggregation isn't turned on, logs are retained locally on each machine under YARN_APP_LOGS_DIR, which is usually configured to /tmp/logs or $HADOOP_HOME/logs/userlogs depending on the Hadoop version and installation. Viewing logs for a container requires going to the host that contains them and looking in this directory.  Subdirectories organize log files by application ID and container ID.
 
+To review per-container launch environment, increase yarn.nodemanager.delete.debug-delay-sec to a
+large value (e.g. 36000), and then access the application cache through yarn.nodemanager.local-dirs
+on the nodes on which containers are launched. This directory contains the launch script, jars, and
+all environment variables used for launching each container. This process is useful for debugging
+classpath problems in particular. (Note that enabling this requires admin privileges on cluster
+settings and a restart of all node managers. Thus, this is not applicable to hosted clusters).
+
 # Important notes
 
 - Before Hadoop 2.2, YARN does not support cores in container resource requests. Thus, when running against an earlier version, the numbers of cores given via command line arguments cannot be passed to YARN.  Whether core requests are honored in scheduling decisions depends on which scheduler is in use and how it is configured.
diff --git a/docs/scala-programming-guide.md b/docs/scala-programming-guide.md
index f25e9cca8852..3ed86e460c01 100644
--- a/docs/scala-programming-guide.md
+++ b/docs/scala-programming-guide.md
@@ -56,7 +56,7 @@ The `master` parameter is a string specifying a [Spark, Mesos or YARN cluster UR
 to connect to, or a special "local" string to run in local mode, as described below. `appName` is
 a name for your application, which will be shown in the cluster web UI. It's also possible to set
 these variables [using a configuration file](cluster-overview.html#loading-configurations-from-a-file)
-which avoids hard-coding the master name in your application.
+which avoids hard-coding the master url in your application.
 
 In the Spark shell, a special interpreter-aware SparkContext is already created for you, in the
 variable called `sc`. Making your own SparkContext will not work. You can set which master the
@@ -74,6 +74,11 @@ Or, to also add `code.jar` to its classpath, use:
 $ ./bin/spark-shell --master local[4] --jars code.jar
 {% endhighlight %}
 
+For a complete list of options, run Spark shell with the `--help` option. Behind the scenes,
+Spark shell invokes the more general [Spark submit script](cluster-overview.html#launching-applications-with-spark-submit)
+used for launching applications, and passes on all of its parameters. As a result, these two scripts
+share the same parameters.
+
 ### Master URLs
 
 The master URL passed to Spark can be in one of the following formats:
@@ -98,7 +103,7 @@ cluster mode. The cluster location will be inferred based on the local Hadoop co
 </td></tr>
 </table>
 
-If no master URL is specified, the spark shell defaults to "local[*]".
+If no master URL is specified, the spark shell defaults to `local[*]`.
 
 # Resilient Distributed Datasets (RDDs)
 
@@ -432,9 +437,7 @@ res2: Int = 10
 You can see some [example Spark programs](http://spark.apache.org/examples.html) on the Spark website.
 In addition, Spark includes several samples in `examples/src/main/scala`. Some of them have both Spark versions and local (non-parallel) versions, allowing you to see what had to be changed to make the program run on a cluster. You can run them using by passing the class name to the `bin/run-example` script included in Spark; for example:
 
-    ./bin/run-example org.apache.spark.examples.SparkPi
-
-Each example program prints usage help when run without any arguments.
+    ./bin/run-example SparkPi
 
 For help on optimizing your program, the [configuration](configuration.html) and
 [tuning](tuning.html) guides provide information on best practices. They are especially important for
diff --git a/docs/spark-standalone.md b/docs/spark-standalone.md
index dc7f206e0399..eb3211b6b0e4 100644
--- a/docs/spark-standalone.md
+++ b/docs/spark-standalone.md
@@ -70,7 +70,7 @@ Once you've set up this file, you can launch or stop your cluster with the follo
 - `sbin/start-slaves.sh` - Starts a slave instance on each machine specified in the `conf/slaves` file.
 - `sbin/start-all.sh` - Starts both a master and a number of slaves as described above.
 - `sbin/stop-master.sh` - Stops the master that was started via the `bin/start-master.sh` script.
-- `sbin/stop-slaves.sh` - Stops the slave instances that were started via `bin/start-slaves.sh`.
+- `sbin/stop-slaves.sh` - Stops all slave instances on the machines specified in the `conf/slaves` file.
 - `sbin/stop-all.sh` - Stops both the master and the slaves as described above.
 
 Note that these scripts must be executed on the machine you want to run the Spark master on, not your local machine.
@@ -92,12 +92,8 @@ You can optionally configure the cluster further by setting environment variable
     <td>Port for the master web UI (default: 8080).</td>
   </tr>
   <tr>
-    <td><code>SPARK_WORKER_PORT</code></td>
-    <td>Start the Spark worker on a specific port (default: random).</td>
-  </tr>
-  <tr>
-    <td><code>SPARK_WORKER_DIR</code></td>
-    <td>Directory to run applications in, which will include both logs and scratch space (default: SPARK_HOME/work).</td>
+    <td><code>SPARK_MASTER_OPTS</code></td>
+    <td>Configuration properties that apply only to the master in the form "-Dx=y" (default: none).</td>
   </tr>
   <tr>
     <td><code>SPARK_WORKER_CORES</code></td>
@@ -107,6 +103,10 @@ You can optionally configure the cluster further by setting environment variable
     <td><code>SPARK_WORKER_MEMORY</code></td>
     <td>Total amount of memory to allow Spark applications to use on the machine, e.g. <code>1000m</code>, <code>2g</code> (default: total memory minus 1 GB); note that each application's <i>individual</i> memory is configured using its <code>spark.executor.memory</code> property.</td>
   </tr>
+  <tr>
+    <td><code>SPARK_WORKER_PORT</code></td>
+    <td>Start the Spark worker on a specific port (default: random).</td>
+  </tr>
   <tr>
     <td><code>SPARK_WORKER_WEBUI_PORT</code></td>
     <td>Port for the worker web UI (default: 8081).</td>
@@ -120,13 +120,25 @@ You can optionally configure the cluster further by setting environment variable
       or else each worker will try to use all the cores.
     </td>
   </tr>
+  <tr>
+    <td><code>SPARK_WORKER_DIR</code></td>
+    <td>Directory to run applications in, which will include both logs and scratch space (default: SPARK_HOME/work).</td>
+  </tr>
+  <tr>
+    <td><code>SPARK_WORKER_OPTS</code></td>
+    <td>Configuration properties that apply only to the worker in the form "-Dx=y" (default: none).</td>
+  </tr>
   <tr>
     <td><code>SPARK_DAEMON_MEMORY</code></td>
     <td>Memory to allocate to the Spark master and worker daemons themselves (default: 512m).</td>
   </tr>
   <tr>
     <td><code>SPARK_DAEMON_JAVA_OPTS</code></td>
-    <td>JVM options for the Spark master and worker daemons themselves (default: none).</td>
+    <td>JVM options for the Spark master and worker daemons themselves in the form "-Dx=y" (default: none).</td>
+  </tr>
+  <tr>
+    <td><code>SPARK_PUBLIC_DNS</code></td>
+    <td>The public DNS name of the Spark master and workers (default: none).</td>
   </tr>
 </table>
 
@@ -148,38 +160,17 @@ You can also pass an option `--cores <numCores>` to control the number of cores
 
 # Launching Compiled Spark Applications
 
-Spark supports two deploy modes. Spark applications may run with the driver inside the client process or entirely inside the cluster.
-
-The spark-submit script described in the [cluster mode overview](cluster-overview.html) provides the most straightforward way to submit a compiled Spark application to the cluster in either deploy mode. For info on the lower-level invocations used to launch an app inside the cluster, read ahead.
-
-## Launching Applications Inside the Cluster
-
-    ./bin/spark-class org.apache.spark.deploy.Client launch
-       [client-options] \
-       <cluster-url> <application-jar-url> <main-class> \
-       [application-options]
-
-    cluster-url: The URL of the master node.
-    application-jar-url: Path to a bundled jar including your application and all dependencies. Currently, the URL must be globally visible inside of your cluster, for instance, an `hdfs://` path or a `file://` path that is present on all nodes. 
-    main-class: The entry point for your application.
-
-    Client Options:
-      --memory <count> (amount of memory, in MB, allocated for your driver program)
-      --cores <count> (number of cores allocated for your driver program)
-      --supervise (whether to automatically restart your driver on application or node failure)
-      --verbose (prints increased logging output)
-
-Keep in mind that your driver program will be executed on a remote worker machine. You can control the execution environment in the following ways:
-
- * _Environment variables_: These will be captured from the environment in which you launch the client and applied when launching the driver program.
- * _Java options_: You can add java options by setting `SPARK_JAVA_OPTS` in the environment in which you launch the submission client.
- * _Dependencies_: You'll still need to call `sc.addJar` inside of your program to make your bundled application jar visible on all worker nodes.
-
-Once you submit a driver program, it will appear in the cluster management UI at port 8080 and
-be assigned an identifier. If you'd like to prematurely terminate the program, you can do so using
-the same client:
+Spark supports two deploy modes: applications may run with the driver inside the client process or
+entirely inside the cluster. The
+[Spark submit script](cluster-overview.html#launching-applications-with-spark-submit) provides the
+most straightforward way to submit a compiled Spark application to the cluster in either deploy
+mode.
 
-    ./bin/spark-class org.apache.spark.deploy.Client kill <driverId>
+If your application is launched through Spark submit, then the application jar is automatically
+distributed to all worker nodes. For any additional jars that your application depends on, you
+should specify them through the `--jars` flag using comma as a delimiter (e.g. `--jars jar1,jar2`).
+To control the application's configuration or execution environment, see
+[Spark Configuration](configuration.html).
 
 # Resource Scheduling
 
@@ -203,7 +194,7 @@ default for applications that don't set `spark.cores.max` to something less than
 Do this by adding the following to `conf/spark-env.sh`:
 
 {% highlight bash %}
-export SPARK_JAVA_OPTS="-Dspark.deploy.defaultCores=<value>"
+export SPARK_MASTER_OPTS="-Dspark.deploy.defaultCores=<value>"
 {% endhighlight %}
 
 This is useful on shared clusters where users might not have configured a maximum number of cores

From 2792bd016af2a67848e6f403c4e1e05e9f3e3c2a Mon Sep 17 00:00:00 2001
From: Sandy Ryza <sandy@cloudera.com>
Date: Mon, 12 May 2014 20:08:30 -0700
Subject: [PATCH 180/810] SPARK-1815. SparkContext should not be marked
 DeveloperApi

Author: Sandy Ryza <sandy@cloudera.com>

Closes #753 from sryza/sandy-spark-1815 and squashes the following commits:

957a8ac [Sandy Ryza] SPARK-1815. SparkContext should not be marked DeveloperApi
---
 core/src/main/scala/org/apache/spark/SparkContext.scala | 2 --
 1 file changed, 2 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index 35beff0f8ef3..c43b4fd6d926 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -50,7 +50,6 @@ import org.apache.spark.ui.SparkUI
 import org.apache.spark.util.{ClosureCleaner, MetadataCleaner, MetadataCleanerType, TimeStampedWeakValueHashMap, Utils}
 
 /**
- * :: DeveloperApi ::
  * Main entry point for Spark functionality. A SparkContext represents the connection to a Spark
  * cluster, and can be used to create RDDs, accumulators and broadcast variables on that cluster.
  *
@@ -58,7 +57,6 @@ import org.apache.spark.util.{ClosureCleaner, MetadataCleaner, MetadataCleanerTy
  *   this config overrides the default configs as well as system properties.
  */
 
-@DeveloperApi
 class SparkContext(config: SparkConf) extends Logging {
 
   // This is used only by YARN for now, but should be relevant to other cluster types (Mesos,

From 52d905296fd75b3bff34a16cb69e604b68b3c246 Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Mon, 12 May 2014 23:02:54 -0700
Subject: [PATCH 181/810] BUILD: Add more content to make-distribution.sh.

---
 make-distribution.sh | 13 +++++++++++++
 1 file changed, 13 insertions(+)

diff --git a/make-distribution.sh b/make-distribution.sh
index 1cc2844703fb..7a08d6b9151c 100755
--- a/make-distribution.sh
+++ b/make-distribution.sh
@@ -171,10 +171,22 @@ echo "Spark $VERSION built for Hadoop $SPARK_HADOOP_VERSION" > "$DISTDIR/RELEASE
 cp $FWDIR/assembly/target/scala*/*assembly*hadoop*.jar "$DISTDIR/lib/"
 cp $FWDIR/examples/target/scala*/spark-examples*.jar "$DISTDIR/lib/"
 
+# Copy example sources (needed for python and SQL)
+mkdir -p "$DISTDIR/examples/src/main"
+cp -r $FWDIR/examples/src/main "$DISTDIR/examples/src/" 
+
 if [ "$SPARK_HIVE" == "true" ]; then
   cp $FWDIR/lib_managed/jars/datanucleus*.jar "$DISTDIR/lib/"
 fi
 
+# Copy license and ASF files
+cp "$FWDIR/LICENSE" "$DISTDIR"
+cp "$FWDIR/NOTICE" "$DISTDIR"
+
+if [ -e $FWDIR/CHANGES.txt ]; then
+  cp "$FWDIR/CHANGES.txt" "$DISTDIR"
+fi
+
 # Copy other things
 mkdir "$DISTDIR"/conf
 cp "$FWDIR"/conf/*.template "$DISTDIR"/conf
@@ -182,6 +194,7 @@ cp "$FWDIR"/conf/slaves "$DISTDIR"/conf
 cp -r "$FWDIR/bin" "$DISTDIR"
 cp -r "$FWDIR/python" "$DISTDIR"
 cp -r "$FWDIR/sbin" "$DISTDIR"
+cp -r "$FWDIR/ec2" "$DISTDIR"
 
 # Download and copy in tachyon, if requested
 if [ "$SPARK_TACHYON" == "true" ]; then

From 16ffadcc4af21430b5079dc555bcd9d8cf1fa1fa Mon Sep 17 00:00:00 2001
From: William Benton <willb@redhat.com>
Date: Tue, 13 May 2014 13:45:23 -0700
Subject: [PATCH 182/810] SPARK-571: forbid return statements in cleaned
 closures

This patch checks top-level closure arguments to `ClosureCleaner.clean` for `return` statements and raises an exception if it finds any.  This is mainly a user-friendliness addition, since programs with return statements in closure arguments will currently fail upon RDD actions with a less-than-intuitive error message.

Author: William Benton <willb@redhat.com>

Closes #717 from willb/spark-571 and squashes the following commits:

c41eb7d [William Benton] Another test case for SPARK-571
30c42f4 [William Benton] Stylistic cleanups
559b16b [William Benton] Stylistic cleanups from review
de13b79 [William Benton] Style fixes
295b6a5 [William Benton] Forbid return statements in closure arguments.
b017c47 [William Benton] Added a test for SPARK-571
---
 .../apache/spark/util/ClosureCleaner.scala    | 23 ++++++++++-
 .../spark/util/ClosureCleanerSuite.scala      | 39 ++++++++++++++++++-
 2 files changed, 60 insertions(+), 2 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/util/ClosureCleaner.scala b/core/src/main/scala/org/apache/spark/util/ClosureCleaner.scala
index 2d05e09b1094..4916d9b86cca 100644
--- a/core/src/main/scala/org/apache/spark/util/ClosureCleaner.scala
+++ b/core/src/main/scala/org/apache/spark/util/ClosureCleaner.scala
@@ -25,7 +25,7 @@ import scala.collection.mutable.Set
 import com.esotericsoftware.reflectasm.shaded.org.objectweb.asm.{ClassReader, ClassVisitor, MethodVisitor, Type}
 import com.esotericsoftware.reflectasm.shaded.org.objectweb.asm.Opcodes._
 
-import org.apache.spark.Logging
+import org.apache.spark.{Logging, SparkException}
 
 private[spark] object ClosureCleaner extends Logging {
   // Get an ASM class reader for a given class from the JAR that loaded it
@@ -108,6 +108,9 @@ private[spark] object ClosureCleaner extends Logging {
     val outerObjects = getOuterObjects(func)
 
     val accessedFields = Map[Class[_], Set[String]]()
+    
+    getClassReader(func.getClass).accept(new ReturnStatementFinder(), 0)
+    
     for (cls <- outerClasses)
       accessedFields(cls) = Set[String]()
     for (cls <- func.getClass :: innerClasses)
@@ -180,6 +183,24 @@ private[spark] object ClosureCleaner extends Logging {
   }
 }
 
+private[spark]
+class ReturnStatementFinder extends ClassVisitor(ASM4) {
+  override def visitMethod(access: Int, name: String, desc: String,
+      sig: String, exceptions: Array[String]): MethodVisitor = {
+    if (name.contains("apply")) {
+      new MethodVisitor(ASM4) {
+        override def visitTypeInsn(op: Int, tp: String) {
+          if (op == NEW && tp.contains("scala/runtime/NonLocalReturnControl")) {
+            throw new SparkException("Return statements aren't allowed in Spark closures")
+          }
+        }
+      }
+    } else {
+      new MethodVisitor(ASM4) {}
+    }
+  }
+}
+
 private[spark]
 class FieldAccessFinder(output: Map[Class[_], Set[String]]) extends ClassVisitor(ASM4) {
   override def visitMethod(access: Int, name: String, desc: String,
diff --git a/core/src/test/scala/org/apache/spark/util/ClosureCleanerSuite.scala b/core/src/test/scala/org/apache/spark/util/ClosureCleanerSuite.scala
index d7e48e633e0e..054ef54e746a 100644
--- a/core/src/test/scala/org/apache/spark/util/ClosureCleanerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/util/ClosureCleanerSuite.scala
@@ -20,7 +20,7 @@ package org.apache.spark.util
 import org.scalatest.FunSuite
 
 import org.apache.spark.LocalSparkContext._
-import org.apache.spark.SparkContext
+import org.apache.spark.{SparkContext, SparkException}
 
 class ClosureCleanerSuite extends FunSuite {
   test("closures inside an object") {
@@ -50,6 +50,19 @@ class ClosureCleanerSuite extends FunSuite {
     val obj = new TestClassWithNesting(1)
     assert(obj.run() === 96) // 4 * (1+2+3+4) + 4 * (1+2+3+4) + 16 * 1
   }
+  
+  test("toplevel return statements in closures are identified at cleaning time") {
+    val ex = intercept[SparkException] {
+      TestObjectWithBogusReturns.run()
+    }
+    
+    assert(ex.getMessage.contains("Return statements aren't allowed in Spark closures"))
+  }
+
+  test("return statements from named functions nested in closures don't raise exceptions") {
+    val result = TestObjectWithNestedReturns.run()
+    assert(result == 1)
+  }
 }
 
 // A non-serializable class we create in closures to make sure that we aren't
@@ -108,6 +121,30 @@ class TestClassWithoutFieldAccess {
   }
 }
 
+object TestObjectWithBogusReturns {
+  def run(): Int = {
+    withSpark(new SparkContext("local", "test")) { sc =>
+      val nums = sc.parallelize(Array(1, 2, 3, 4))
+      // this return is invalid since it will transfer control outside the closure
+      nums.map {x => return 1 ; x * 2}
+      1
+    }
+  }
+}
+
+object TestObjectWithNestedReturns {
+  def run(): Int = {
+    withSpark(new SparkContext("local", "test")) { sc =>
+      val nums = sc.parallelize(Array(1, 2, 3, 4))
+      nums.map {x => 
+        // this return is fine since it will not transfer control outside the closure
+        def foo(): Int = { return 5; 1 }
+        foo()
+      }
+      1
+    }
+  }
+}
 
 object TestObjectWithNesting {
   def run(): Int = {

From d1e487473fd509f28daf28dcda856f3c2f1194ec Mon Sep 17 00:00:00 2001
From: Andrew Tulloch <andrew@tullo.ch>
Date: Tue, 13 May 2014 17:31:27 -0700
Subject: [PATCH 183/810] SPARK-1791 - SVM implementation does not use
 threshold parameter

Summary:
https://issues.apache.org/jira/browse/SPARK-1791

Simple fix, and backward compatible, since

- anyone who set the threshold was getting completely wrong answers.
- anyone who did not set the threshold had the default 0.0 value for the threshold anyway.

Test Plan:
Unit test added that is verified to fail under the old implementation,
and pass under the new implementation.

Reviewers:

CC:

Author: Andrew Tulloch <andrew@tullo.ch>

Closes #725 from ajtulloch/SPARK-1791-SVM and squashes the following commits:

770f55d [Andrew Tulloch] SPARK-1791 - SVM implementation does not use threshold parameter
---
 .../spark/mllib/classification/SVM.scala      |  2 +-
 .../spark/mllib/classification/SVMSuite.scala | 37 +++++++++++++++++++
 2 files changed, 38 insertions(+), 1 deletion(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala
index e05213536e64..316ecd713b71 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala
@@ -65,7 +65,7 @@ class SVMModel private[mllib] (
       intercept: Double) = {
     val margin = weightMatrix.toBreeze.dot(dataMatrix.toBreeze) + intercept
     threshold match {
-      case Some(t) => if (margin < 0) 0.0 else 1.0
+      case Some(t) => if (margin < t) 0.0 else 1.0
       case None => margin
     }
   }
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/classification/SVMSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/classification/SVMSuite.scala
index 77d6f04b3232..886c71dde3af 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/classification/SVMSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/classification/SVMSuite.scala
@@ -69,6 +69,43 @@ class SVMSuite extends FunSuite with LocalSparkContext {
     assert(numOffPredictions < input.length / 5)
   }
 
+  test("SVM with threshold") {
+    val nPoints = 10000
+
+    // NOTE: Intercept should be small for generating equal 0s and 1s
+    val A = 0.01
+    val B = -1.5
+    val C = 1.0
+
+    val testData = SVMSuite.generateSVMInput(A, Array[Double](B, C), nPoints, 42)
+
+    val testRDD = sc.parallelize(testData, 2)
+    testRDD.cache()
+
+    val svm = new SVMWithSGD().setIntercept(true)
+    svm.optimizer.setStepSize(1.0).setRegParam(1.0).setNumIterations(100)
+
+    val model = svm.run(testRDD)
+
+    val validationData = SVMSuite.generateSVMInput(A, Array[Double](B, C), nPoints, 17)
+    val validationRDD  = sc.parallelize(validationData, 2)
+
+    // Test prediction on RDD.
+
+    var predictions = model.predict(validationRDD.map(_.features)).collect()
+    assert(predictions.count(_ == 0.0) != predictions.length)
+
+    // High threshold makes all the predictions 0.0
+    model.setThreshold(10000.0)
+    predictions = model.predict(validationRDD.map(_.features)).collect()
+    assert(predictions.count(_ == 0.0) == predictions.length)
+
+    // Low threshold makes all the predictions 1.0
+    model.setThreshold(-10000.0)
+    predictions = model.predict(validationRDD.map(_.features)).collect()
+    assert(predictions.count(_ == 1.0) == predictions.length)
+  }
+
   test("SVM using local random SGD") {
     val nPoints = 10000
 

From 5c0dafc2c8734a421206a808b73be67b66264dd7 Mon Sep 17 00:00:00 2001
From: Andrew Or <andrewor14@gmail.com>
Date: Tue, 13 May 2014 18:32:32 -0700
Subject: [PATCH 184/810] [SPARK-1816] LiveListenerBus dies if a listener
 throws an exception

The solution is to wrap a try / catch / log around the posting of each event to each listener.

Author: Andrew Or <andrewor14@gmail.com>

Closes #759 from andrewor14/listener-die and squashes the following commits:

aee5107 [Andrew Or] Merge branch 'master' of github.com:apache/spark into listener-die
370939f [Andrew Or] Remove two layers of indirection
422d278 [Andrew Or] Explicitly throw an exception instead of 1 / 0
0df0e2a [Andrew Or] Try/catch and log exceptions when posting events
---
 .../spark/scheduler/LiveListenerBus.scala     | 36 ++++++++++---
 .../spark/scheduler/SparkListenerBus.scala    | 50 +++++++++++++------
 .../scala/org/apache/spark/util/Utils.scala   |  2 +-
 .../spark/scheduler/SparkListenerSuite.scala  | 50 +++++++++++++++++--
 4 files changed, 109 insertions(+), 29 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/scheduler/LiveListenerBus.scala b/core/src/main/scala/org/apache/spark/scheduler/LiveListenerBus.scala
index dec3316bf774..36a6e6338faa 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/LiveListenerBus.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/LiveListenerBus.scala
@@ -20,6 +20,7 @@ package org.apache.spark.scheduler
 import java.util.concurrent.{LinkedBlockingQueue, Semaphore}
 
 import org.apache.spark.Logging
+import org.apache.spark.util.Utils
 
 /**
  * Asynchronously passes SparkListenerEvents to registered SparkListeners.
@@ -42,7 +43,7 @@ private[spark] class LiveListenerBus extends SparkListenerBus with Logging {
 
   private val listenerThread = new Thread("SparkListenerBus") {
     setDaemon(true)
-    override def run() {
+    override def run(): Unit = Utils.logUncaughtExceptions {
       while (true) {
         eventLock.acquire()
         // Atomically remove and process this event
@@ -77,11 +78,8 @@ private[spark] class LiveListenerBus extends SparkListenerBus with Logging {
     val eventAdded = eventQueue.offer(event)
     if (eventAdded) {
       eventLock.release()
-    } else if (!queueFullErrorMessageLogged) {
-      logError("Dropping SparkListenerEvent because no remaining room in event queue. " +
-        "This likely means one of the SparkListeners is too slow and cannot keep up with the " +
-        "rate at which tasks are being started by the scheduler.")
-      queueFullErrorMessageLogged = true
+    } else {
+      logQueueFullErrorMessage()
     }
   }
 
@@ -96,13 +94,18 @@ private[spark] class LiveListenerBus extends SparkListenerBus with Logging {
       if (System.currentTimeMillis > finishTime) {
         return false
       }
-      /* Sleep rather than using wait/notify, because this is used only for testing and wait/notify
-       * add overhead in the general case. */
+      /* Sleep rather than using wait/notify, because this is used only for testing and
+       * wait/notify add overhead in the general case. */
       Thread.sleep(10)
     }
     true
   }
 
+  /**
+   * For testing only. Return whether the listener daemon thread is still alive.
+   */
+  def listenerThreadIsAlive: Boolean = synchronized { listenerThread.isAlive }
+
   /**
    * Return whether the event queue is empty.
    *
@@ -111,6 +114,23 @@ private[spark] class LiveListenerBus extends SparkListenerBus with Logging {
    */
   def queueIsEmpty: Boolean = synchronized { eventQueue.isEmpty }
 
+  /**
+   * Log an error message to indicate that the event queue is full. Do this only once.
+   */
+  private def logQueueFullErrorMessage(): Unit = {
+    if (!queueFullErrorMessageLogged) {
+      if (listenerThread.isAlive) {
+        logError("Dropping SparkListenerEvent because no remaining room in event queue. " +
+          "This likely means one of the SparkListeners is too slow and cannot keep up with" +
+          "the rate at which tasks are being started by the scheduler.")
+      } else {
+        logError("SparkListenerBus thread is dead! This means SparkListenerEvents have not" +
+          "been (and will no longer be) propagated to listeners for some time.")
+      }
+      queueFullErrorMessageLogged = true
+    }
+  }
+
   def stop() {
     if (!started) {
       throw new IllegalStateException("Attempted to stop a listener bus that has not yet started!")
diff --git a/core/src/main/scala/org/apache/spark/scheduler/SparkListenerBus.scala b/core/src/main/scala/org/apache/spark/scheduler/SparkListenerBus.scala
index 0286aac8769b..ed9fb24bc8ce 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/SparkListenerBus.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/SparkListenerBus.scala
@@ -20,10 +20,13 @@ package org.apache.spark.scheduler
 import scala.collection.mutable
 import scala.collection.mutable.ArrayBuffer
 
+import org.apache.spark.Logging
+import org.apache.spark.util.Utils
+
 /**
  * A SparkListenerEvent bus that relays events to its listeners
  */
-private[spark] trait SparkListenerBus {
+private[spark] trait SparkListenerBus extends Logging {
 
   // SparkListeners attached to this event bus
   protected val sparkListeners = new ArrayBuffer[SparkListener]
@@ -34,38 +37,53 @@ private[spark] trait SparkListenerBus {
   }
 
   /**
-   * Post an event to all attached listeners. This does nothing if the event is
-   * SparkListenerShutdown.
+   * Post an event to all attached listeners.
+   * This does nothing if the event is SparkListenerShutdown.
    */
   def postToAll(event: SparkListenerEvent) {
     event match {
       case stageSubmitted: SparkListenerStageSubmitted =>
-        sparkListeners.foreach(_.onStageSubmitted(stageSubmitted))
+        foreachListener(_.onStageSubmitted(stageSubmitted))
       case stageCompleted: SparkListenerStageCompleted =>
-        sparkListeners.foreach(_.onStageCompleted(stageCompleted))
+        foreachListener(_.onStageCompleted(stageCompleted))
       case jobStart: SparkListenerJobStart =>
-        sparkListeners.foreach(_.onJobStart(jobStart))
+        foreachListener(_.onJobStart(jobStart))
       case jobEnd: SparkListenerJobEnd =>
-        sparkListeners.foreach(_.onJobEnd(jobEnd))
+        foreachListener(_.onJobEnd(jobEnd))
       case taskStart: SparkListenerTaskStart =>
-        sparkListeners.foreach(_.onTaskStart(taskStart))
+        foreachListener(_.onTaskStart(taskStart))
       case taskGettingResult: SparkListenerTaskGettingResult =>
-        sparkListeners.foreach(_.onTaskGettingResult(taskGettingResult))
+        foreachListener(_.onTaskGettingResult(taskGettingResult))
       case taskEnd: SparkListenerTaskEnd =>
-        sparkListeners.foreach(_.onTaskEnd(taskEnd))
+        foreachListener(_.onTaskEnd(taskEnd))
       case environmentUpdate: SparkListenerEnvironmentUpdate =>
-        sparkListeners.foreach(_.onEnvironmentUpdate(environmentUpdate))
+        foreachListener(_.onEnvironmentUpdate(environmentUpdate))
       case blockManagerAdded: SparkListenerBlockManagerAdded =>
-        sparkListeners.foreach(_.onBlockManagerAdded(blockManagerAdded))
+        foreachListener(_.onBlockManagerAdded(blockManagerAdded))
       case blockManagerRemoved: SparkListenerBlockManagerRemoved =>
-        sparkListeners.foreach(_.onBlockManagerRemoved(blockManagerRemoved))
+        foreachListener(_.onBlockManagerRemoved(blockManagerRemoved))
       case unpersistRDD: SparkListenerUnpersistRDD =>
-        sparkListeners.foreach(_.onUnpersistRDD(unpersistRDD))
+        foreachListener(_.onUnpersistRDD(unpersistRDD))
       case applicationStart: SparkListenerApplicationStart =>
-        sparkListeners.foreach(_.onApplicationStart(applicationStart))
+        foreachListener(_.onApplicationStart(applicationStart))
       case applicationEnd: SparkListenerApplicationEnd =>
-        sparkListeners.foreach(_.onApplicationEnd(applicationEnd))
+        foreachListener(_.onApplicationEnd(applicationEnd))
       case SparkListenerShutdown =>
     }
   }
+
+  /**
+   * Apply the given function to all attached listeners, catching and logging any exception.
+   */
+  private def foreachListener(f: SparkListener => Unit): Unit = {
+    sparkListeners.foreach { listener =>
+      try {
+        f(listener)
+      } catch {
+        case e: Exception =>
+          logError(s"Listener ${Utils.getFormattedClassName(listener)} threw an exception", e)
+      }
+    }
+  }
+
 }
diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala
index 0631e5423792..99ef6dd1fa6e 100644
--- a/core/src/main/scala/org/apache/spark/util/Utils.scala
+++ b/core/src/main/scala/org/apache/spark/util/Utils.scala
@@ -1128,7 +1128,7 @@ private[spark] object Utils extends Logging {
   }
 
   /** 
-   * Executes the given block, printing and re-throwing any uncaught exceptions.
+   * Execute the given block, logging and re-throwing any uncaught exception.
    * This is particularly useful for wrapping code that runs in a thread, to ensure
    * that exceptions are printed, and to avoid having to catch Throwable.
    */
diff --git a/core/src/test/scala/org/apache/spark/scheduler/SparkListenerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/SparkListenerSuite.scala
index 4e9fd07e68a2..5426e578a9dd 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/SparkListenerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/SparkListenerSuite.scala
@@ -331,16 +331,47 @@ class SparkListenerSuite extends FunSuite with LocalSparkContext with ShouldMatc
     }
   }
 
-  def checkNonZeroAvg(m: Traversable[Long], msg: String) {
+  test("SparkListener moves on if a listener throws an exception") {
+    val badListener = new BadListener
+    val jobCounter1 = new BasicJobCounter
+    val jobCounter2 = new BasicJobCounter
+    val bus = new LiveListenerBus
+
+    // Propagate events to bad listener first
+    bus.addListener(badListener)
+    bus.addListener(jobCounter1)
+    bus.addListener(jobCounter2)
+    bus.start()
+
+    // Post events to all listeners, and wait until the queue is drained
+    (1 to 5).foreach { _ => bus.post(SparkListenerJobEnd(0, JobSucceeded)) }
+    assert(bus.waitUntilEmpty(WAIT_TIMEOUT_MILLIS))
+
+    // The exception should be caught, and the event should be propagated to other listeners
+    assert(bus.listenerThreadIsAlive)
+    assert(jobCounter1.count === 5)
+    assert(jobCounter2.count === 5)
+  }
+
+  /**
+   * Assert that the given list of numbers has an average that is greater than zero.
+   */
+  private def checkNonZeroAvg(m: Traversable[Long], msg: String) {
     assert(m.sum / m.size.toDouble > 0.0, msg)
   }
 
-  class BasicJobCounter extends SparkListener {
+  /**
+   * A simple listener that counts the number of jobs observed.
+   */
+  private class BasicJobCounter extends SparkListener {
     var count = 0
     override def onJobEnd(job: SparkListenerJobEnd) = count += 1
   }
 
-  class SaveStageAndTaskInfo extends SparkListener {
+  /**
+   * A simple listener that saves all task infos and task metrics.
+   */
+  private class SaveStageAndTaskInfo extends SparkListener {
     val stageInfos = mutable.Map[StageInfo, Seq[(TaskInfo, TaskMetrics)]]()
     var taskInfoMetrics = mutable.Buffer[(TaskInfo, TaskMetrics)]()
 
@@ -358,7 +389,10 @@ class SparkListenerSuite extends FunSuite with LocalSparkContext with ShouldMatc
     }
   }
 
-  class SaveTaskEvents extends SparkListener {
+  /**
+   * A simple listener that saves the task indices for all task events.
+   */
+  private class SaveTaskEvents extends SparkListener {
     val startedTasks = new mutable.HashSet[Int]()
     val startedGettingResultTasks = new mutable.HashSet[Int]()
     val endedTasks = new mutable.HashSet[Int]()
@@ -377,4 +411,12 @@ class SparkListenerSuite extends FunSuite with LocalSparkContext with ShouldMatc
       startedGettingResultTasks += taskGettingResult.taskInfo.index
     }
   }
+
+  /**
+   * A simple listener that throws an exception on job end.
+   */
+  private class BadListener extends SparkListener {
+    override def onJobEnd(jobEnd: SparkListenerJobEnd) = { throw new Exception }
+  }
+
 }

From 753b04dea4b04ba9d0dd0011f00e9d70367e76fc Mon Sep 17 00:00:00 2001
From: Ye Xianjin <advancedxy@gmail.com>
Date: Tue, 13 May 2014 19:03:51 -0700
Subject: [PATCH 185/810] [SPARK-1527] change rootDir*.getName to
 rootDir*.getAbsolutePath

JIRA issue: [SPARK-1527](https://issues.apache.org/jira/browse/SPARK-1527)

getName() only gets the last component of the file path. When deleting test-generated directories,
we should pass the generated directory's absolute path to DiskBlockManager.

Author: Ye Xianjin <advancedxy@gmail.com>

This patch had conflicts when merged, resolved by
Committer: Patrick Wendell <pwendell@gmail.com>

Closes #436 from advancedxy/SPARK-1527 and squashes the following commits:

4678bab [Ye Xianjin] change rootDir*.getname to rootDir*.getAbsolutePath so the temporary directories are deleted when the test is finished.
---
 .../scala/org/apache/spark/storage/DiskBlockManagerSuite.scala  | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/core/src/test/scala/org/apache/spark/storage/DiskBlockManagerSuite.scala b/core/src/test/scala/org/apache/spark/storage/DiskBlockManagerSuite.scala
index 2167718fd242..aaa771404973 100644
--- a/core/src/test/scala/org/apache/spark/storage/DiskBlockManagerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/storage/DiskBlockManagerSuite.scala
@@ -52,7 +52,7 @@ class DiskBlockManagerSuite extends FunSuite with BeforeAndAfterEach with Before
     rootDir0.deleteOnExit()
     rootDir1 = Files.createTempDir()
     rootDir1.deleteOnExit()
-    rootDirs = rootDir0.getName + "," + rootDir1.getName
+    rootDirs = rootDir0.getAbsolutePath + "," + rootDir1.getAbsolutePath
     println("Created root dirs: " + rootDirs)
   }
 

From 44233865cf8020741d862d33cc660c88e9315dea Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Tue, 13 May 2014 21:23:51 -0700
Subject: [PATCH 186/810] [SQL] Make it possible to create Java/Python
 SQLContexts from an existing Scala SQLContext.

Author: Michael Armbrust <michael@databricks.com>

Closes #761 from marmbrus/existingContext and squashes the following commits:

4651051 [Michael Armbrust] Make it possible to create Java/Python SQLContexts from an existing Scala SQLContext.
---
 python/pyspark/sql.py                                      | 7 +++++--
 .../org/apache/spark/sql/api/java/JavaSQLContext.scala     | 4 ++--
 2 files changed, 7 insertions(+), 4 deletions(-)

diff --git a/python/pyspark/sql.py b/python/pyspark/sql.py
index 6789d7002b3b..bbe69e7d8f89 100644
--- a/python/pyspark/sql.py
+++ b/python/pyspark/sql.py
@@ -28,7 +28,7 @@ class SQLContext:
     register L{SchemaRDD}s as tables, execute sql over tables, cache tables, and read parquet files.
     """
 
-    def __init__(self, sparkContext):
+    def __init__(self, sparkContext, sqlContext = None):
         """
         Create a new SQLContext.
 
@@ -58,10 +58,13 @@ def __init__(self, sparkContext):
         self._jvm = self._sc._jvm
         self._pythonToJavaMap = self._jvm.PythonRDD.pythonToJavaMap
 
+        if sqlContext:
+            self._scala_SQLContext = sqlContext
+
     @property
     def _ssql_ctx(self):
         """
-        Accessor for the JVM SparkSQL context.  Subclasses can overrite this property to provide
+        Accessor for the JVM SparkSQL context.  Subclasses can override this property to provide
         their own JVM Contexts.
         """
         if not hasattr(self, '_scala_SQLContext'):
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSQLContext.scala
index 57facbe10fc9..6f7d431b9a81 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSQLContext.scala
@@ -33,9 +33,9 @@ import org.apache.spark.util.Utils
 /**
  * The entry point for executing Spark SQL queries from a Java program.
  */
-class JavaSQLContext(sparkContext: JavaSparkContext) {
+class JavaSQLContext(val sqlContext: SQLContext) {
 
-  val sqlContext = new SQLContext(sparkContext.sc)
+  def this(sparkContext: JavaSparkContext) = this(new SQLContext(sparkContext.sc))
 
   /**
    * Executes a query expressed in SQL, returning the result as a JavaSchemaRDD

From 92cebada09a7e5a00ab48bcb350a9462949c33eb Mon Sep 17 00:00:00 2001
From: Syed Hashmi <shashmi@cloudera.com>
Date: Tue, 13 May 2014 21:24:23 -0700
Subject: [PATCH 187/810] [SPARK-1784] Add a new partitioner to allow
 specifying # of keys per partition

This change adds a new partitioner which allows users
to specify # of keys per partition.

Author: Syed Hashmi <shashmi@cloudera.com>

Closes #721 from syedhashmi/master and squashes the following commits:

4ca94cc [Syed Hashmi] [SPARK-1784] Add a new partitioner
---
 .../scala/org/apache/spark/Partitioner.scala  | 61 +++++++++++++++++++
 .../org/apache/spark/PartitioningSuite.scala  | 34 +++++++++++
 2 files changed, 95 insertions(+)

diff --git a/core/src/main/scala/org/apache/spark/Partitioner.scala b/core/src/main/scala/org/apache/spark/Partitioner.scala
index 9155159cf6ae..62747960618a 100644
--- a/core/src/main/scala/org/apache/spark/Partitioner.scala
+++ b/core/src/main/scala/org/apache/spark/Partitioner.scala
@@ -156,3 +156,64 @@ class RangePartitioner[K : Ordering : ClassTag, V](
       false
   }
 }
+
+/**
+ * A [[org.apache.spark.Partitioner]] that partitions records into specified bounds
+ * Default value is 1000. Once all partitions have bounds elements, the partitioner
+ * allocates 1 element per partition so eventually the smaller partitions are at most
+ * off by 1 key compared to the larger partitions.
+ */
+class BoundaryPartitioner[K : Ordering : ClassTag, V](
+                                                    partitions: Int,
+                                                    @transient rdd: RDD[_ <: Product2[K,V]],
+                                                    private val boundary: Int = 1000)
+  extends Partitioner {
+
+  // this array keeps track of keys assigned to a partition
+  // counts[0] refers to # of keys in partition 0 and so on
+  private val counts: Array[Int] = {
+    new Array[Int](numPartitions)
+  }
+
+  def numPartitions = math.abs(partitions)
+
+  /*
+  * Ideally, this should've been calculated based on # partitions and total keys
+  * But we are not calling count on RDD here to avoid calling an action.
+   * User has the flexibility of calling count and passing in any appropriate boundary
+   */
+  def keysPerPartition = boundary
+
+  var currPartition = 0
+
+  /*
+  * Pick current partition for the key until we hit the bound for keys / partition,
+  * start allocating to next partition at that time.
+  *
+  * NOTE: In case where we have lets say 2000 keys and user says 3 partitions with 500
+  * passed in as boundary, the first 500 will goto P1, 501-1000 go to P2, 1001-1500 go to P3,
+  * after that, next keys go to one partition at a time. So 1501 goes to P1, 1502 goes to P2,
+  * 1503 goes to P3 and so on.
+   */
+  def getPartition(key: Any): Int = {
+    val partition = currPartition
+    counts(partition) = counts(partition) + 1
+    /*
+    * Since we are filling up a partition before moving to next one (this helps in maintaining
+    * order of keys, in certain cases, it is possible to end up with empty partitions, like
+    * 3 partitions, 500 keys / partition and if rdd has 700 keys, 1 partition will be entirely
+    * empty.
+     */
+    if(counts(currPartition) >= keysPerPartition)
+      currPartition = (currPartition + 1) % numPartitions
+    partition
+  }
+
+  override def equals(other: Any): Boolean = other match {
+    case r: BoundaryPartitioner[_,_] =>
+      (r.counts.sameElements(counts) && r.boundary == boundary
+        && r.currPartition == currPartition)
+    case _ =>
+      false
+  }
+}
diff --git a/core/src/test/scala/org/apache/spark/PartitioningSuite.scala b/core/src/test/scala/org/apache/spark/PartitioningSuite.scala
index 7c30626a0c42..7d40395803f0 100644
--- a/core/src/test/scala/org/apache/spark/PartitioningSuite.scala
+++ b/core/src/test/scala/org/apache/spark/PartitioningSuite.scala
@@ -66,6 +66,40 @@ class PartitioningSuite extends FunSuite with SharedSparkContext with PrivateMet
     assert(descendingP4 != p4)
   }
 
+  test("BoundaryPartitioner equality") {
+    // Make an RDD where all the elements are the same so that the partition range bounds
+    // are deterministically all the same.
+    val rdd = sc.parallelize(1.to(4000)).map(x => (x, x))
+
+    val p2 = new BoundaryPartitioner(2, rdd, 1000)
+    val p4 = new BoundaryPartitioner(4, rdd, 1000)
+    val anotherP4 = new BoundaryPartitioner(4, rdd)
+
+    assert(p2 === p2)
+    assert(p4 === p4)
+    assert(p2 != p4)
+    assert(p4 != p2)
+    assert(p4 === anotherP4)
+    assert(anotherP4 === p4)
+  }
+
+  test("BoundaryPartitioner getPartition") {
+    val rdd = sc.parallelize(1.to(2000)).map(x => (x, x))
+    val partitioner = new BoundaryPartitioner(4, rdd,  500)
+    1.to(2000).map { element => {
+      val partition = partitioner.getPartition(element)
+      if (element <= 500) {
+        assert(partition === 0)
+      } else if (element > 501 && element <= 1000) {
+        assert(partition === 1)
+      } else if (element > 1001 && element <= 1500) {
+        assert(partition === 2)
+      } else if (element > 1501 && element <= 2000) {
+        assert(partition === 3)
+      }
+    }}
+  }
+
   test("RangePartitioner getPartition") {
     val rdd = sc.parallelize(1.to(2000)).map(x => (x, x))
     // We have different behaviour of getPartition for partitions with less than 1000 and more than

From c33b8dcbf65a3a0c5ee5e65cd1dcdbc7da36aa5f Mon Sep 17 00:00:00 2001
From: larvaboy <larvaboy@gmail.com>
Date: Tue, 13 May 2014 21:26:08 -0700
Subject: [PATCH 188/810] Implement ApproximateCountDistinct for SparkSql

Add the implementation for ApproximateCountDistinct to SparkSql. We use the HyperLogLog algorithm implemented in stream-lib, and do the count in two phases: 1) counting the number of distinct elements in each partitions, and 2) merge the HyperLogLog results from different partitions.

A simple serializer and test cases are added as well.

Author: larvaboy <larvaboy@gmail.com>

Closes #737 from larvaboy/master and squashes the following commits:

bd8ef3f [larvaboy] Add support of user-provided standard deviation to ApproxCountDistinct.
9ba8360 [larvaboy] Fix alignment and null handling issues.
95b4067 [larvaboy] Add a test case for count distinct and approximate count distinct.
f57917d [larvaboy] Add the parser for the approximate count.
a2d5d10 [larvaboy] Add ApproximateCountDistinct aggregates and functions.
7ad273a [larvaboy] Add SparkSql serializer for HyperLogLog.
1d9aacf [larvaboy] Fix a minor typo in the toString method of the Count case class.
653542b [larvaboy] Fix a couple of minor typos.
---
 .../apache/spark/rdd/PairRDDFunctions.scala   |  6 +-
 .../apache/spark/sql/catalyst/SqlParser.scala |  7 ++
 .../sql/catalyst/expressions/aggregates.scala | 78 ++++++++++++++++++-
 .../sql/execution/SparkSqlSerializer.scala    | 17 ++++
 .../org/apache/spark/sql/SQLQuerySuite.scala  | 21 ++++-
 5 files changed, 122 insertions(+), 7 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
index 5efb4388f6c7..bc6d204434ad 100644
--- a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
@@ -217,7 +217,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * Return approximate number of distinct values for each key in this RDD.
    * The accuracy of approximation can be controlled through the relative standard deviation
    * (relativeSD) parameter, which also controls the amount of memory used. Lower values result in
-   * more accurate counts but increase the memory footprint and vise versa. Uses the provided
+   * more accurate counts but increase the memory footprint and vice versa. Uses the provided
    * Partitioner to partition the output RDD.
    */
   def countApproxDistinctByKey(relativeSD: Double, partitioner: Partitioner): RDD[(K, Long)] = {
@@ -232,7 +232,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * Return approximate number of distinct values for each key in this RDD.
    * The accuracy of approximation can be controlled through the relative standard deviation
    * (relativeSD) parameter, which also controls the amount of memory used. Lower values result in
-   * more accurate counts but increase the memory footprint and vise versa. HashPartitions the
+   * more accurate counts but increase the memory footprint and vice versa. HashPartitions the
    * output RDD into numPartitions.
    *
    */
@@ -244,7 +244,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * Return approximate number of distinct values for each key this RDD.
    * The accuracy of approximation can be controlled through the relative standard deviation
    * (relativeSD) parameter, which also controls the amount of memory used. Lower values result in
-   * more accurate counts but increase the memory footprint and vise versa. The default value of
+   * more accurate counts but increase the memory footprint and vice versa. The default value of
    * relativeSD is 0.05. Hash-partitions the output RDD using the existing partitioner/parallelism
    * level.
    */
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
index b3a3a1ef1b5e..f2b9b2c1a3ad 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
@@ -93,6 +93,7 @@ class SqlParser extends StandardTokenParsers with PackratParsers {
   protected val AND = Keyword("AND")
   protected val AS = Keyword("AS")
   protected val ASC = Keyword("ASC")
+  protected val APPROXIMATE = Keyword("APPROXIMATE")
   protected val AVG = Keyword("AVG")
   protected val BY = Keyword("BY")
   protected val CAST = Keyword("CAST")
@@ -318,6 +319,12 @@ class SqlParser extends StandardTokenParsers with PackratParsers {
     COUNT ~> "(" ~ "*" <~ ")" ^^ { case _ => Count(Literal(1)) } |
     COUNT ~> "(" ~ expression <~ ")" ^^ { case dist ~ exp => Count(exp) } |
     COUNT ~> "(" ~> DISTINCT ~> expression <~ ")" ^^ { case exp => CountDistinct(exp :: Nil) } |
+    APPROXIMATE ~> COUNT ~> "(" ~> DISTINCT ~> expression <~ ")" ^^ {
+      case exp => ApproxCountDistinct(exp)
+    } |
+    APPROXIMATE ~> "(" ~> floatLit ~ ")" ~ COUNT ~ "(" ~ DISTINCT ~ expression <~ ")" ^^ {
+      case s ~ _ ~ _ ~ _ ~ _ ~ e => ApproxCountDistinct(e, s.toDouble)
+    } |
     FIRST ~> "(" ~> expression <~ ")" ^^ { case exp => First(exp) } |
     AVG ~> "(" ~> expression <~ ")" ^^ { case exp => Average(exp) } |
     MIN ~> "(" ~> expression <~ ")" ^^ { case exp => Min(exp) } |
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
index 7777d372903e..5dbaaa3b0ce3 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
@@ -17,6 +17,8 @@
 
 package org.apache.spark.sql.catalyst.expressions
 
+import com.clearspring.analytics.stream.cardinality.HyperLogLog
+
 import org.apache.spark.sql.catalyst.types._
 import org.apache.spark.sql.catalyst.trees
 import org.apache.spark.sql.catalyst.errors.TreeNodeException
@@ -146,7 +148,6 @@ case class MaxFunction(expr: Expression, base: AggregateExpression) extends Aggr
   override def eval(input: Row): Any = currentMax
 }
 
-
 case class Count(child: Expression) extends PartialAggregate with trees.UnaryNode[Expression] {
   override def references = child.references
   override def nullable = false
@@ -166,10 +167,47 @@ case class CountDistinct(expressions: Seq[Expression]) extends AggregateExpressi
   override def references = expressions.flatMap(_.references).toSet
   override def nullable = false
   override def dataType = IntegerType
-  override def toString = s"COUNT(DISTINCT ${expressions.mkString(",")}})"
+  override def toString = s"COUNT(DISTINCT ${expressions.mkString(",")})"
   override def newInstance() = new CountDistinctFunction(expressions, this)
 }
 
+case class ApproxCountDistinctPartition(child: Expression, relativeSD: Double)
+  extends AggregateExpression with trees.UnaryNode[Expression] {
+  override def references = child.references
+  override def nullable = false
+  override def dataType = child.dataType
+  override def toString = s"APPROXIMATE COUNT(DISTINCT $child)"
+  override def newInstance() = new ApproxCountDistinctPartitionFunction(child, this, relativeSD)
+}
+
+case class ApproxCountDistinctMerge(child: Expression, relativeSD: Double)
+  extends AggregateExpression with trees.UnaryNode[Expression] {
+  override def references = child.references
+  override def nullable = false
+  override def dataType = IntegerType
+  override def toString = s"APPROXIMATE COUNT(DISTINCT $child)"
+  override def newInstance() = new ApproxCountDistinctMergeFunction(child, this, relativeSD)
+}
+
+case class ApproxCountDistinct(child: Expression, relativeSD: Double = 0.05)
+  extends PartialAggregate with trees.UnaryNode[Expression] {
+  override def references = child.references
+  override def nullable = false
+  override def dataType = IntegerType
+  override def toString = s"APPROXIMATE COUNT(DISTINCT $child)"
+
+  override def asPartial: SplitEvaluation = {
+    val partialCount =
+      Alias(ApproxCountDistinctPartition(child, relativeSD), "PartialApproxCountDistinct")()
+
+    SplitEvaluation(
+      ApproxCountDistinctMerge(partialCount.toAttribute, relativeSD),
+      partialCount :: Nil)
+  }
+
+  override def newInstance() = new CountDistinctFunction(child :: Nil, this)
+}
+
 case class Average(child: Expression) extends PartialAggregate with trees.UnaryNode[Expression] {
   override def references = child.references
   override def nullable = false
@@ -269,6 +307,42 @@ case class CountFunction(expr: Expression, base: AggregateExpression) extends Ag
   override def eval(input: Row): Any = count
 }
 
+case class ApproxCountDistinctPartitionFunction(
+    expr: Expression,
+    base: AggregateExpression,
+    relativeSD: Double)
+  extends AggregateFunction {
+  def this() = this(null, null, 0) // Required for serialization.
+
+  private val hyperLogLog = new HyperLogLog(relativeSD)
+
+  override def update(input: Row): Unit = {
+    val evaluatedExpr = expr.eval(input)
+    if (evaluatedExpr != null) {
+      hyperLogLog.offer(evaluatedExpr)
+    }
+  }
+
+  override def eval(input: Row): Any = hyperLogLog
+}
+
+case class ApproxCountDistinctMergeFunction(
+    expr: Expression,
+    base: AggregateExpression,
+    relativeSD: Double)
+  extends AggregateFunction {
+  def this() = this(null, null, 0) // Required for serialization.
+
+  private val hyperLogLog = new HyperLogLog(relativeSD)
+
+  override def update(input: Row): Unit = {
+    val evaluatedExpr = expr.eval(input)
+    hyperLogLog.addAll(evaluatedExpr.asInstanceOf[HyperLogLog])
+  }
+
+  override def eval(input: Row): Any = hyperLogLog.cardinality()
+}
+
 case class SumFunction(expr: Expression, base: AggregateExpression) extends AggregateFunction {
   def this() = this(null, null) // Required for serialization.
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer.scala
index 1c6e29b3cdee..94c2a249ef8f 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer.scala
@@ -21,6 +21,7 @@ import java.nio.ByteBuffer
 
 import scala.reflect.ClassTag
 
+import com.clearspring.analytics.stream.cardinality.HyperLogLog
 import com.esotericsoftware.kryo.io.{Input, Output}
 import com.esotericsoftware.kryo.{Serializer, Kryo}
 
@@ -44,6 +45,8 @@ private[sql] class SparkSqlSerializer(conf: SparkConf) extends KryoSerializer(co
     kryo.register(classOf[scala.collection.Map[_,_]], new MapSerializer)
     kryo.register(classOf[org.apache.spark.sql.catalyst.expressions.GenericRow])
     kryo.register(classOf[org.apache.spark.sql.catalyst.expressions.GenericMutableRow])
+    kryo.register(classOf[com.clearspring.analytics.stream.cardinality.HyperLogLog],
+                  new HyperLogLogSerializer)
     kryo.register(classOf[scala.collection.mutable.ArrayBuffer[_]])
     kryo.register(classOf[scala.math.BigDecimal], new BigDecimalSerializer)
     kryo.setReferences(false)
@@ -81,6 +84,20 @@ private[sql] class BigDecimalSerializer extends Serializer[BigDecimal] {
   }
 }
 
+private[sql] class HyperLogLogSerializer extends Serializer[HyperLogLog] {
+  def write(kryo: Kryo, output: Output, hyperLogLog: HyperLogLog) {
+    val bytes = hyperLogLog.getBytes()
+    output.writeInt(bytes.length)
+    output.writeBytes(bytes)
+  }
+
+  def read(kryo: Kryo, input: Input, tpe: Class[HyperLogLog]): HyperLogLog = {
+    val length = input.readInt()
+    val bytes = input.readBytes(length)
+    HyperLogLog.Builder.build(bytes)
+  }
+}
+
 /**
  * Maps do not have a no arg constructor and so cannot be serialized by default. So, we serialize
  * them as `Array[(k,v)]`.
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index e966d89c30cf..524549eb544f 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -96,8 +96,25 @@ class SQLQuerySuite extends QueryTest {
   test("count") {
     checkAnswer(
       sql("SELECT COUNT(*) FROM testData2"),
-      testData2.count()
-    )
+      testData2.count())
+  }
+
+  test("count distinct") {
+    checkAnswer(
+      sql("SELECT COUNT(DISTINCT b) FROM testData2"),
+      2)
+  }
+
+  test("approximate count distinct") {
+    checkAnswer(
+      sql("SELECT APPROXIMATE COUNT(DISTINCT a) FROM testData2"),
+      3)
+  }
+
+  test("approximate count distinct with user provided standard deviation") {
+    checkAnswer(
+      sql("SELECT APPROXIMATE(0.04) COUNT(DISTINCT a) FROM testData2"),
+      3)
   }
 
   // No support for primitive nulls yet.

From 7bb9a521f35eb19576c6cc2da3fd385910270e46 Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Tue, 13 May 2014 23:24:51 -0700
Subject: [PATCH 189/810] Revert "[SPARK-1784] Add a new partitioner to allow
 specifying # of keys per partition"

This reverts commit 92cebada09a7e5a00ab48bcb350a9462949c33eb.
---
 .../scala/org/apache/spark/Partitioner.scala  | 61 -------------------
 .../org/apache/spark/PartitioningSuite.scala  | 34 -----------
 2 files changed, 95 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/Partitioner.scala b/core/src/main/scala/org/apache/spark/Partitioner.scala
index 62747960618a..9155159cf6ae 100644
--- a/core/src/main/scala/org/apache/spark/Partitioner.scala
+++ b/core/src/main/scala/org/apache/spark/Partitioner.scala
@@ -156,64 +156,3 @@ class RangePartitioner[K : Ordering : ClassTag, V](
       false
   }
 }
-
-/**
- * A [[org.apache.spark.Partitioner]] that partitions records into specified bounds
- * Default value is 1000. Once all partitions have bounds elements, the partitioner
- * allocates 1 element per partition so eventually the smaller partitions are at most
- * off by 1 key compared to the larger partitions.
- */
-class BoundaryPartitioner[K : Ordering : ClassTag, V](
-                                                    partitions: Int,
-                                                    @transient rdd: RDD[_ <: Product2[K,V]],
-                                                    private val boundary: Int = 1000)
-  extends Partitioner {
-
-  // this array keeps track of keys assigned to a partition
-  // counts[0] refers to # of keys in partition 0 and so on
-  private val counts: Array[Int] = {
-    new Array[Int](numPartitions)
-  }
-
-  def numPartitions = math.abs(partitions)
-
-  /*
-  * Ideally, this should've been calculated based on # partitions and total keys
-  * But we are not calling count on RDD here to avoid calling an action.
-   * User has the flexibility of calling count and passing in any appropriate boundary
-   */
-  def keysPerPartition = boundary
-
-  var currPartition = 0
-
-  /*
-  * Pick current partition for the key until we hit the bound for keys / partition,
-  * start allocating to next partition at that time.
-  *
-  * NOTE: In case where we have lets say 2000 keys and user says 3 partitions with 500
-  * passed in as boundary, the first 500 will goto P1, 501-1000 go to P2, 1001-1500 go to P3,
-  * after that, next keys go to one partition at a time. So 1501 goes to P1, 1502 goes to P2,
-  * 1503 goes to P3 and so on.
-   */
-  def getPartition(key: Any): Int = {
-    val partition = currPartition
-    counts(partition) = counts(partition) + 1
-    /*
-    * Since we are filling up a partition before moving to next one (this helps in maintaining
-    * order of keys, in certain cases, it is possible to end up with empty partitions, like
-    * 3 partitions, 500 keys / partition and if rdd has 700 keys, 1 partition will be entirely
-    * empty.
-     */
-    if(counts(currPartition) >= keysPerPartition)
-      currPartition = (currPartition + 1) % numPartitions
-    partition
-  }
-
-  override def equals(other: Any): Boolean = other match {
-    case r: BoundaryPartitioner[_,_] =>
-      (r.counts.sameElements(counts) && r.boundary == boundary
-        && r.currPartition == currPartition)
-    case _ =>
-      false
-  }
-}
diff --git a/core/src/test/scala/org/apache/spark/PartitioningSuite.scala b/core/src/test/scala/org/apache/spark/PartitioningSuite.scala
index 7d40395803f0..7c30626a0c42 100644
--- a/core/src/test/scala/org/apache/spark/PartitioningSuite.scala
+++ b/core/src/test/scala/org/apache/spark/PartitioningSuite.scala
@@ -66,40 +66,6 @@ class PartitioningSuite extends FunSuite with SharedSparkContext with PrivateMet
     assert(descendingP4 != p4)
   }
 
-  test("BoundaryPartitioner equality") {
-    // Make an RDD where all the elements are the same so that the partition range bounds
-    // are deterministically all the same.
-    val rdd = sc.parallelize(1.to(4000)).map(x => (x, x))
-
-    val p2 = new BoundaryPartitioner(2, rdd, 1000)
-    val p4 = new BoundaryPartitioner(4, rdd, 1000)
-    val anotherP4 = new BoundaryPartitioner(4, rdd)
-
-    assert(p2 === p2)
-    assert(p4 === p4)
-    assert(p2 != p4)
-    assert(p4 != p2)
-    assert(p4 === anotherP4)
-    assert(anotherP4 === p4)
-  }
-
-  test("BoundaryPartitioner getPartition") {
-    val rdd = sc.parallelize(1.to(2000)).map(x => (x, x))
-    val partitioner = new BoundaryPartitioner(4, rdd,  500)
-    1.to(2000).map { element => {
-      val partition = partitioner.getPartition(element)
-      if (element <= 500) {
-        assert(partition === 0)
-      } else if (element > 501 && element <= 1000) {
-        assert(partition === 1)
-      } else if (element > 1001 && element <= 1500) {
-        assert(partition === 2)
-      } else if (element > 1501 && element <= 2000) {
-        assert(partition === 3)
-      }
-    }}
-  }
-
   test("RangePartitioner getPartition") {
     val rdd = sc.parallelize(1.to(2000)).map(x => (x, x))
     // We have different behaviour of getPartition for partitions with less than 1000 and more than

From 6ce0884446d3571fd6e9d967a080a59c657543b1 Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Tue, 13 May 2014 23:27:22 -0700
Subject: [PATCH 190/810] [SQL] Improve column pruning.

Fixed a bug that was preventing us from ever pruning beneath Joins.

## TPC-DS Q3
### Before:
```
Aggregate false, [d_year#12,i_brand#65,i_brand_id#64], [d_year#12,i_brand_id#64 AS brand_id#0,i_brand#65 AS brand#1,SUM(PartialSum#79) AS sum_agg#2]
 Exchange (HashPartitioning [d_year#12:0,i_brand#65:1,i_brand_id#64:2], 150)
  Aggregate true, [d_year#12,i_brand#65,i_brand_id#64], [d_year#12,i_brand#65,i_brand_id#64,SUM(CAST(ss_ext_sales_price#49, DoubleType)) AS PartialSum#79]
   Project [d_year#12:6,i_brand#65:59,i_brand_id#64:58,ss_ext_sales_price#49:43]
    HashJoin [ss_item_sk#36], [i_item_sk#57], BuildRight
     Exchange (HashPartitioning [ss_item_sk#36:30], 150)
      HashJoin [d_date_sk#6], [ss_sold_date_sk#34], BuildRight
       Exchange (HashPartitioning [d_date_sk#6:0], 150)
        Filter (d_moy#14:8 = 12)
         HiveTableScan [d_date_sk#6,d_date_id#7,d_date#8,d_month_seq#9,d_week_seq#10,d_quarter_seq#11,d_year#12,d_dow#13,d_moy#14,d_dom#15,d_qoy#16,d_fy_year#17,d_fy_quarter_seq#18,d_fy_week_seq#19,d_day_name#20,d_quarter_name#21,d_holiday#22,d_weekend#23,d_following_holiday#24,d_first_dom#25,d_last_dom#26,d_same_day_ly#27,d_same_day_lq#28,d_current_day#29,d_current_week#30,d_current_month#31,d_current_quarter#32,d_current_year#33], (MetastoreRelation default, date_dim, Some(dt)), None
       Exchange (HashPartitioning [ss_sold_date_sk#34:0], 150)
        HiveTableScan [ss_sold_date_sk#34,ss_sold_time_sk#35,ss_item_sk#36,ss_customer_sk#37,ss_cdemo_sk#38,ss_hdemo_sk#39,ss_addr_sk#40,ss_store_sk#41,ss_promo_sk#42,ss_ticket_number#43,ss_quantity#44,ss_wholesale_cost#45,ss_list_price#46,ss_sales_price#47,ss_ext_discount_amt#48,ss_ext_sales_price#49,ss_ext_wholesale_cost#50,ss_ext_list_price#51,ss_ext_tax#52,ss_coupon_amt#53,ss_net_paid#54,ss_net_paid_inc_tax#55,ss_net_profit#56], (MetastoreRelation default, store_sales, None), None
     Exchange (HashPartitioning [i_item_sk#57:0], 150)
      Filter (i_manufact_id#70:13 = 436)
       HiveTableScan [i_item_sk#57,i_item_id#58,i_rec_start_date#59,i_rec_end_date#60,i_item_desc#61,i_current_price#62,i_wholesale_cost#63,i_brand_id#64,i_brand#65,i_class_id#66,i_class#67,i_category_id#68,i_category#69,i_manufact_id#70,i_manufact#71,i_size#72,i_formulation#73,i_color#74,i_units#75,i_container#76,i_manager_id#77,i_product_name#78], (MetastoreRelation default, item, None), None
```
### After
```
Aggregate false, [d_year#172,i_brand#225,i_brand_id#224], [d_year#172,i_brand_id#224 AS brand_id#160,i_brand#225 AS brand#161,SUM(PartialSum#239) AS sum_agg#162]
 Exchange (HashPartitioning [d_year#172:0,i_brand#225:1,i_brand_id#224:2], 150)
  Aggregate true, [d_year#172,i_brand#225,i_brand_id#224], [d_year#172,i_brand#225,i_brand_id#224,SUM(CAST(ss_ext_sales_price#209, DoubleType)) AS PartialSum#239]
   Project [d_year#172:1,i_brand#225:5,i_brand_id#224:3,ss_ext_sales_price#209:0]
    HashJoin [ss_item_sk#196], [i_item_sk#217], BuildRight
     Exchange (HashPartitioning [ss_item_sk#196:2], 150)
      Project [ss_ext_sales_price#209:2,d_year#172:1,ss_item_sk#196:3]
       HashJoin [d_date_sk#166], [ss_sold_date_sk#194], BuildRight
        Exchange (HashPartitioning [d_date_sk#166:0], 150)
         Project [d_date_sk#166:0,d_year#172:1]
          Filter (d_moy#174:2 = 12)
           HiveTableScan [d_date_sk#166,d_year#172,d_moy#174], (MetastoreRelation default, date_dim, Some(dt)), None
        Exchange (HashPartitioning [ss_sold_date_sk#194:2], 150)
         HiveTableScan [ss_ext_sales_price#209,ss_item_sk#196,ss_sold_date_sk#194], (MetastoreRelation default, store_sales, None), None
     Exchange (HashPartitioning [i_item_sk#217:1], 150)
      Project [i_brand_id#224:0,i_item_sk#217:1,i_brand#225:2]
       Filter (i_manufact_id#230:3 = 436)
        HiveTableScan [i_brand_id#224,i_item_sk#217,i_brand#225,i_manufact_id#230], (MetastoreRelation default, item, None), None
```

Author: Michael Armbrust <michael@databricks.com>

Closes #729 from marmbrus/fixPruning and squashes the following commits:

5feeff0 [Michael Armbrust] Improve column pruning.
---
 .../spark/sql/catalyst/optimizer/Optimizer.scala | 16 +++++++++++-----
 1 file changed, 11 insertions(+), 5 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
index 3037d45cc6e3..406ffd6801e9 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
@@ -25,13 +25,13 @@ import org.apache.spark.sql.catalyst.types._
 
 object Optimizer extends RuleExecutor[LogicalPlan] {
   val batches =
-    Batch("ConstantFolding", Once,
+    Batch("ConstantFolding", FixedPoint(100),
       NullPropagation,
       ConstantFolding,
       BooleanSimplification,
       SimplifyFilters,
       SimplifyCasts) ::
-    Batch("Filter Pushdown", Once,
+    Batch("Filter Pushdown", FixedPoint(100),
       CombineFilters,
       PushPredicateThroughProject,
       PushPredicateThroughInnerJoin,
@@ -49,17 +49,19 @@ object Optimizer extends RuleExecutor[LogicalPlan] {
  */
 object ColumnPruning extends Rule[LogicalPlan] {
   def apply(plan: LogicalPlan): LogicalPlan = plan transform {
+    // Eliminate attributes that are not needed to calculate the specified aggregates.
     case a @ Aggregate(_, _, child) if (child.outputSet -- a.references).nonEmpty =>
-      // Project away references that are not needed to calculate the required aggregates.
       a.copy(child = Project(a.references.toSeq, child))
 
+    // Eliminate unneeded attributes from either side of a Join.
     case Project(projectList, Join(left, right, joinType, condition)) =>
       // Collect the list of off references required either above or to evaluate the condition.
       val allReferences: Set[Attribute] =
         projectList.flatMap(_.references).toSet ++ condition.map(_.references).getOrElse(Set.empty)
-      /** Applies a projection when the child is producing unnecessary attributes */
+
+      /** Applies a projection only when the child is producing unnecessary attributes */
       def prunedChild(c: LogicalPlan) =
-        if ((allReferences.filter(c.outputSet.contains) -- c.outputSet).nonEmpty) {
+        if ((c.outputSet -- allReferences.filter(c.outputSet.contains)).nonEmpty) {
           Project(allReferences.filter(c.outputSet.contains).toSeq, c)
         } else {
           c
@@ -67,6 +69,7 @@ object ColumnPruning extends Rule[LogicalPlan] {
 
       Project(projectList, Join(prunedChild(left), prunedChild(right), joinType, condition))
 
+    // Combine adjacent Projects.
     case Project(projectList1, Project(projectList2, child)) =>
       // Create a map of Aliases to their values from the child projection.
       // e.g., 'SELECT ... FROM (SELECT a + b AS c, d ...)' produces Map(c -> Alias(a + b, c)).
@@ -83,6 +86,9 @@ object ColumnPruning extends Rule[LogicalPlan] {
       }).asInstanceOf[Seq[NamedExpression]]
 
       Project(substitutedProjection, child)
+
+    // Eliminate no-op Projects
+    case Project(projectList, child) if(child.output == projectList) => child
   }
 }
 

From b22952fa1f21c0b93208846b5e1941a9d2578c6f Mon Sep 17 00:00:00 2001
From: Koert Kuipers <koert@tresata.com>
Date: Wed, 14 May 2014 00:10:12 -0700
Subject: [PATCH 191/810] SPARK-1801. expose InterruptibleIterator and
 TaskKilledException in deve...

...loper api

Author: Koert Kuipers <koert@tresata.com>

Closes #764 from koertkuipers/feat-rdd-developerapi and squashes the following commits:

8516dd2 [Koert Kuipers] SPARK-1801. expose InterruptibleIterator and TaskKilledException in developer api
---
 .../scala/org/apache/spark/InterruptibleIterator.scala    | 6 +++++-
 .../main/scala/org/apache/spark/TaskKilledException.scala | 8 ++++++--
 2 files changed, 11 insertions(+), 3 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/InterruptibleIterator.scala b/core/src/main/scala/org/apache/spark/InterruptibleIterator.scala
index ec11dbbffaaf..f40baa8e4359 100644
--- a/core/src/main/scala/org/apache/spark/InterruptibleIterator.scala
+++ b/core/src/main/scala/org/apache/spark/InterruptibleIterator.scala
@@ -17,11 +17,15 @@
 
 package org.apache.spark
 
+import org.apache.spark.annotation.DeveloperApi
+
 /**
+ * :: DeveloperApi ::
  * An iterator that wraps around an existing iterator to provide task killing functionality.
  * It works by checking the interrupted flag in [[TaskContext]].
  */
-private[spark] class InterruptibleIterator[+T](val context: TaskContext, val delegate: Iterator[T])
+@DeveloperApi
+class InterruptibleIterator[+T](val context: TaskContext, val delegate: Iterator[T])
   extends Iterator[T] {
 
   def hasNext: Boolean = {
diff --git a/core/src/main/scala/org/apache/spark/TaskKilledException.scala b/core/src/main/scala/org/apache/spark/TaskKilledException.scala
index cbd6b2866e4f..ad487c4efb87 100644
--- a/core/src/main/scala/org/apache/spark/TaskKilledException.scala
+++ b/core/src/main/scala/org/apache/spark/TaskKilledException.scala
@@ -17,7 +17,11 @@
 
 package org.apache.spark
 
+import org.apache.spark.annotation.DeveloperApi
+
 /**
- * Exception for a task getting killed.
+ * :: DeveloperApi ::
+ * Exception thrown when a task is explicitly killed (i.e., task failure is expected).
  */
-private[spark] class TaskKilledException extends RuntimeException
+@DeveloperApi
+class TaskKilledException extends RuntimeException

From 54ae8328bd7d052ba347768cfb02cb5dfdd8045e Mon Sep 17 00:00:00 2001
From: Marcelo Vanzin <vanzin@cloudera.com>
Date: Wed, 14 May 2014 00:37:57 -0700
Subject: [PATCH 192/810] Fix dep exclusion: avro-ipc, not avro, depends on
 netty.

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #763 from vanzin/netty-dep-hell and squashes the following commits:

dfb6ce2 [Marcelo Vanzin] Fix dep exclusion: avro-ipc, not avro, depends on netty.
---
 pom.xml | 10 ++++------
 1 file changed, 4 insertions(+), 6 deletions(-)

diff --git a/pom.xml b/pom.xml
index 4d4c5f63e44a..786b6d4984d8 100644
--- a/pom.xml
+++ b/pom.xml
@@ -496,18 +496,16 @@
         <groupId>org.apache.avro</groupId>
         <artifactId>avro</artifactId>
         <version>${avro.version}</version>
-        <exclusions>
-          <exclusion>
-            <groupId>io.netty</groupId>
-            <artifactId>netty</artifactId>
-          </exclusion>
-        </exclusions>
       </dependency>
       <dependency>
         <groupId>org.apache.avro</groupId>
         <artifactId>avro-ipc</artifactId>
         <version>${avro.version}</version>
         <exclusions>
+          <exclusion>
+            <groupId>io.netty</groupId>
+            <artifactId>netty</artifactId>
+          </exclusion>
           <exclusion>
             <groupId>org.mortbay.jetty</groupId>
             <artifactId>jetty</artifactId>

From 69f750228f3ec8537a93da08e712596fa8004143 Mon Sep 17 00:00:00 2001
From: Andrew Or <andrewor14@gmail.com>
Date: Wed, 14 May 2014 00:54:33 -0700
Subject: [PATCH 193/810] [SPARK-1769] Executor loss causes NPE race condition

This PR replaces the Schedulable data structures in Pool.scala with thread-safe ones from java. Note that Scala's `with SynchronizedBuffer` trait is soon to be deprecated in 2.11 because it is ["inherently unreliable"](http://www.scala-lang.org/api/2.11.0/index.html#scala.collection.mutable.SynchronizedBuffer). We should slowly drift away from `SynchronizedBuffer` in other places too.

Note that this PR introduces an API-breaking change; `sc.getAllPools` now returns an Array rather than an ArrayBuffer. This is because we want this method to return an immutable copy rather than one may potentially confuse the user if they try to modify the copy, which takes no effect on the original data structure.

Author: Andrew Or <andrewor14@gmail.com>

Closes #762 from andrewor14/pool-npe and squashes the following commits:

383e739 [Andrew Or] JavaConverters -> JavaConversions
3f32981 [Andrew Or] Merge branch 'master' of github.com:apache/spark into pool-npe
769be19 [Andrew Or] Assorted minor changes
2189247 [Andrew Or] Merge branch 'master' of github.com:apache/spark into pool-npe
05ad9e9 [Andrew Or] Fix test - contains is not the same as containsKey
0921ea0 [Andrew Or] var -> val
07d720c [Andrew Or] Synchronize Schedulable data structures
---
 .../scala/org/apache/spark/SparkContext.scala | 20 +++++++-----
 .../org/apache/spark/scheduler/Pool.scala     | 31 ++++++++++---------
 .../apache/spark/scheduler/Schedulable.scala  |  6 ++--
 .../spark/scheduler/TaskSchedulerImpl.scala   |  2 +-
 .../scheduler/TaskSchedulerImplSuite.scala    |  2 +-
 5 files changed, 35 insertions(+), 26 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index c43b4fd6d926..032b3d744c61 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -17,15 +17,17 @@
 
 package org.apache.spark
 
+import scala.language.implicitConversions
+
 import java.io._
 import java.net.URI
 import java.util.concurrent.atomic.AtomicInteger
 import java.util.{Properties, UUID}
 import java.util.UUID.randomUUID
 import scala.collection.{Map, Set}
+import scala.collection.JavaConversions._
 import scala.collection.generic.Growable
-import scala.collection.mutable.{ArrayBuffer, HashMap}
-import scala.language.implicitConversions
+import scala.collection.mutable.HashMap
 import scala.reflect.{ClassTag, classTag}
 import org.apache.hadoop.conf.Configuration
 import org.apache.hadoop.fs.Path
@@ -836,18 +838,22 @@ class SparkContext(config: SparkConf) extends Logging {
   }
 
   /**
-   *  Return pools for fair scheduler
-   *  TODO(xiajunluan): We should take nested pools into account
+   * :: DeveloperApi ::
+   * Return pools for fair scheduler
    */
-  def getAllPools: ArrayBuffer[Schedulable] = {
-    taskScheduler.rootPool.schedulableQueue
+  @DeveloperApi
+  def getAllPools: Seq[Schedulable] = {
+    // TODO(xiajunluan): We should take nested pools into account
+    taskScheduler.rootPool.schedulableQueue.toSeq
   }
 
   /**
+   * :: DeveloperApi ::
    * Return the pool associated with the given name, if one exists
    */
+  @DeveloperApi
   def getPoolForName(pool: String): Option[Schedulable] = {
-    taskScheduler.rootPool.schedulableNameToSchedulable.get(pool)
+    Option(taskScheduler.rootPool.schedulableNameToSchedulable.get(pool))
   }
 
   /**
diff --git a/core/src/main/scala/org/apache/spark/scheduler/Pool.scala b/core/src/main/scala/org/apache/spark/scheduler/Pool.scala
index 187672c4e19e..174b73221afc 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/Pool.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/Pool.scala
@@ -17,8 +17,10 @@
 
 package org.apache.spark.scheduler
 
+import java.util.concurrent.{ConcurrentHashMap, ConcurrentLinkedQueue}
+
+import scala.collection.JavaConversions._
 import scala.collection.mutable.ArrayBuffer
-import scala.collection.mutable.HashMap
 
 import org.apache.spark.Logging
 import org.apache.spark.scheduler.SchedulingMode.SchedulingMode
@@ -35,18 +37,15 @@ private[spark] class Pool(
   extends Schedulable
   with Logging {
 
-  var schedulableQueue = new ArrayBuffer[Schedulable]
-  var schedulableNameToSchedulable = new HashMap[String, Schedulable]
-
+  val schedulableQueue = new ConcurrentLinkedQueue[Schedulable]
+  val schedulableNameToSchedulable = new ConcurrentHashMap[String, Schedulable]
   var weight = initWeight
   var minShare = initMinShare
   var runningTasks = 0
-
   var priority = 0
 
   // A pool's stage id is used to break the tie in scheduling.
   var stageId = -1
-
   var name = poolName
   var parent: Pool = null
 
@@ -60,19 +59,20 @@ private[spark] class Pool(
   }
 
   override def addSchedulable(schedulable: Schedulable) {
-    schedulableQueue += schedulable
-    schedulableNameToSchedulable(schedulable.name) = schedulable
+    require(schedulable != null)
+    schedulableQueue.add(schedulable)
+    schedulableNameToSchedulable.put(schedulable.name, schedulable)
     schedulable.parent = this
   }
 
   override def removeSchedulable(schedulable: Schedulable) {
-    schedulableQueue -= schedulable
-    schedulableNameToSchedulable -= schedulable.name
+    schedulableQueue.remove(schedulable)
+    schedulableNameToSchedulable.remove(schedulable.name)
   }
 
   override def getSchedulableByName(schedulableName: String): Schedulable = {
-    if (schedulableNameToSchedulable.contains(schedulableName)) {
-      return schedulableNameToSchedulable(schedulableName)
+    if (schedulableNameToSchedulable.containsKey(schedulableName)) {
+      return schedulableNameToSchedulable.get(schedulableName)
     }
     for (schedulable <- schedulableQueue) {
       val sched = schedulable.getSchedulableByName(schedulableName)
@@ -95,11 +95,12 @@ private[spark] class Pool(
     shouldRevive
   }
 
-  override def getSortedTaskSetQueue(): ArrayBuffer[TaskSetManager] = {
+  override def getSortedTaskSetQueue: ArrayBuffer[TaskSetManager] = {
     var sortedTaskSetQueue = new ArrayBuffer[TaskSetManager]
-    val sortedSchedulableQueue = schedulableQueue.sortWith(taskSetSchedulingAlgorithm.comparator)
+    val sortedSchedulableQueue =
+      schedulableQueue.toSeq.sortWith(taskSetSchedulingAlgorithm.comparator)
     for (schedulable <- sortedSchedulableQueue) {
-      sortedTaskSetQueue ++= schedulable.getSortedTaskSetQueue()
+      sortedTaskSetQueue ++= schedulable.getSortedTaskSetQueue
     }
     sortedTaskSetQueue
   }
diff --git a/core/src/main/scala/org/apache/spark/scheduler/Schedulable.scala b/core/src/main/scala/org/apache/spark/scheduler/Schedulable.scala
index ed24eb6a549d..a87ef030e69c 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/Schedulable.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/Schedulable.scala
@@ -17,6 +17,8 @@
 
 package org.apache.spark.scheduler
 
+import java.util.concurrent.ConcurrentLinkedQueue
+
 import scala.collection.mutable.ArrayBuffer
 
 import org.apache.spark.scheduler.SchedulingMode.SchedulingMode
@@ -28,7 +30,7 @@ import org.apache.spark.scheduler.SchedulingMode.SchedulingMode
 private[spark] trait Schedulable {
   var parent: Pool
   // child queues
-  def schedulableQueue: ArrayBuffer[Schedulable]
+  def schedulableQueue: ConcurrentLinkedQueue[Schedulable]
   def schedulingMode: SchedulingMode
   def weight: Int
   def minShare: Int
@@ -42,5 +44,5 @@ private[spark] trait Schedulable {
   def getSchedulableByName(name: String): Schedulable
   def executorLost(executorId: String, host: String): Unit
   def checkSpeculatableTasks(): Boolean
-  def getSortedTaskSetQueue(): ArrayBuffer[TaskSetManager]
+  def getSortedTaskSetQueue: ArrayBuffer[TaskSetManager]
 }
diff --git a/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala b/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala
index 5a68f38bc584..ffd1d9432682 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala
@@ -222,7 +222,7 @@ private[spark] class TaskSchedulerImpl(
     // Build a list of tasks to assign to each worker.
     val tasks = shuffledOffers.map(o => new ArrayBuffer[TaskDescription](o.cores))
     val availableCpus = shuffledOffers.map(o => o.cores).toArray
-    val sortedTaskSets = rootPool.getSortedTaskSetQueue()
+    val sortedTaskSets = rootPool.getSortedTaskSetQueue
     for (taskSet <- sortedTaskSets) {
       logDebug("parentName: %s, name: %s, runningTasks: %s".format(
         taskSet.parent.name, taskSet.name, taskSet.runningTasks))
diff --git a/core/src/test/scala/org/apache/spark/scheduler/TaskSchedulerImplSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/TaskSchedulerImplSuite.scala
index a8b605c5b212..7532da88c606 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/TaskSchedulerImplSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/TaskSchedulerImplSuite.scala
@@ -117,7 +117,7 @@ class TaskSchedulerImplSuite extends FunSuite with LocalSparkContext with Loggin
   }
 
   def resourceOffer(rootPool: Pool): Int = {
-    val taskSetQueue = rootPool.getSortedTaskSetQueue()
+    val taskSetQueue = rootPool.getSortedTaskSetQueue
     /* Just for Test*/
     for (manager <- taskSetQueue) {
        logInfo("parentName:%s, parent running tasks:%d, name:%s,runningTasks:%d".format(

From 68f28dabe9c7679be82e684385be216319beb610 Mon Sep 17 00:00:00 2001
From: Tathagata Das <tathagata.das1565@gmail.com>
Date: Wed, 14 May 2014 04:17:32 -0700
Subject: [PATCH 194/810] Fixed streaming examples docs to use run-example
 instead of spark-submit

Pretty self-explanatory

Author: Tathagata Das <tathagata.das1565@gmail.com>

Closes #722 from tdas/example-fix and squashes the following commits:

7839979 [Tathagata Das] Minor changes.
0673441 [Tathagata Das] Fixed java docs of java streaming example
e687123 [Tathagata Das] Fixed scala style errors.
9b8d112 [Tathagata Das] Fixed streaming examples docs to use run-example instead of spark-submit.
---
 .../streaming/JavaCustomReceiver.java         | 13 ++---
 .../streaming/JavaFlumeEventCount.java        |  6 +-
 .../streaming/JavaKafkaWordCount.java         |  6 +-
 .../streaming/JavaNetworkWordCount.java       | 13 +++--
 .../examples/streaming/ActorWordCount.scala   |  6 +-
 .../examples/streaming/CustomReceiver.scala   | 19 +++---
 .../examples/streaming/FlumeEventCount.scala  |  9 ++-
 .../examples/streaming/HdfsWordCount.scala    |  5 +-
 .../examples/streaming/KafkaWordCount.scala   |  6 +-
 .../examples/streaming/MQTTWordCount.scala    | 10 ++--
 .../examples/streaming/NetworkWordCount.scala | 14 +++--
 .../RecoverableNetworkWordCount.scala         |  7 +--
 .../streaming/StatefulNetworkWordCount.scala  |  6 +-
 .../streaming/TwitterPopularTags.scala        | 22 ++++++-
 .../examples/streaming/ZeroMQWordCount.scala  |  8 +--
 .../clickstream/PageViewGenerator.scala       | 10 ++--
 .../clickstream/PageViewStream.scala          |  7 ++-
 .../twitter/TwitterInputDStream.scala         | 58 +++++++++++--------
 18 files changed, 130 insertions(+), 95 deletions(-)

diff --git a/examples/src/main/java/org/apache/spark/examples/streaming/JavaCustomReceiver.java b/examples/src/main/java/org/apache/spark/examples/streaming/JavaCustomReceiver.java
index 7f558f3ee713..5622df5ce03f 100644
--- a/examples/src/main/java/org/apache/spark/examples/streaming/JavaCustomReceiver.java
+++ b/examples/src/main/java/org/apache/spark/examples/streaming/JavaCustomReceiver.java
@@ -19,6 +19,7 @@
 
 import com.google.common.collect.Lists;
 
+import org.apache.spark.SparkConf;
 import org.apache.spark.api.java.function.FlatMapFunction;
 import org.apache.spark.api.java.function.Function2;
 import org.apache.spark.api.java.function.PairFunction;
@@ -48,25 +49,23 @@
  * To run this on your local machine, you need to first run a Netcat server
  *    `$ nc -lk 9999`
  * and then run the example
- *    `$ ./run org.apache.spark.examples.streaming.JavaCustomReceiver local[2] localhost 9999`
+ *    `$ bin/run-example org.apache.spark.examples.streaming.JavaCustomReceiver localhost 9999`
  */
 
 public class JavaCustomReceiver extends Receiver<String> {
   private static final Pattern SPACE = Pattern.compile(" ");
 
   public static void main(String[] args) {
-    if (args.length < 3) {
-      System.err.println("Usage: JavaNetworkWordCount <master> <hostname> <port>\n" +
-          "In local mode, <master> should be 'local[n]' with n > 1");
+    if (args.length < 2) {
+      System.err.println("Usage: JavaNetworkWordCount <hostname> <port>");
       System.exit(1);
     }
 
     StreamingExamples.setStreamingLogLevels();
 
     // Create the context with a 1 second batch size
-    JavaStreamingContext ssc = new JavaStreamingContext(args[0], "JavaNetworkWordCount",
-            new Duration(1000), System.getenv("SPARK_HOME"),
-            JavaStreamingContext.jarOfClass(JavaNetworkWordCount.class));
+    SparkConf sparkConf = new SparkConf().setAppName("JavaCustomReceiver");
+    JavaStreamingContext ssc = new JavaStreamingContext(sparkConf, new Duration(1000));
 
     // Create a input stream with the custom receiver on target ip:port and count the
     // words in input stream of \n delimited text (eg. generated by 'nc')
diff --git a/examples/src/main/java/org/apache/spark/examples/streaming/JavaFlumeEventCount.java b/examples/src/main/java/org/apache/spark/examples/streaming/JavaFlumeEventCount.java
index 400b68c2215b..da56637fe891 100644
--- a/examples/src/main/java/org/apache/spark/examples/streaming/JavaFlumeEventCount.java
+++ b/examples/src/main/java/org/apache/spark/examples/streaming/JavaFlumeEventCount.java
@@ -33,10 +33,12 @@
  *  Your Flume AvroSink should be pointed to this address.
  *
  *  Usage: JavaFlumeEventCount <host> <port>
- *
  *    <host> is the host the Flume receiver will be started on - a receiver
  *           creates a server and listens for flume events.
  *    <port> is the port the Flume receiver will listen on.
+ *
+ *  To run this example:
+ *     `$ bin/run-example org.apache.spark.examples.streaming.JavaFlumeEventCount <host> <port>`
  */
 public final class JavaFlumeEventCount {
   private JavaFlumeEventCount() {
@@ -56,7 +58,7 @@ public static void main(String[] args) {
     Duration batchInterval = new Duration(2000);
     SparkConf sparkConf = new SparkConf().setAppName("JavaFlumeEventCount");
     JavaStreamingContext ssc = new JavaStreamingContext(sparkConf, batchInterval);
-    JavaReceiverInputDStream<SparkFlumeEvent> flumeStream = FlumeUtils.createStream(ssc, "localhost", port);
+    JavaReceiverInputDStream<SparkFlumeEvent> flumeStream = FlumeUtils.createStream(ssc, host, port);
 
     flumeStream.count();
 
diff --git a/examples/src/main/java/org/apache/spark/examples/streaming/JavaKafkaWordCount.java b/examples/src/main/java/org/apache/spark/examples/streaming/JavaKafkaWordCount.java
index 6a74cc50d19e..16ae9a3319ee 100644
--- a/examples/src/main/java/org/apache/spark/examples/streaming/JavaKafkaWordCount.java
+++ b/examples/src/main/java/org/apache/spark/examples/streaming/JavaKafkaWordCount.java
@@ -40,15 +40,15 @@
 
 /**
  * Consumes messages from one or more topics in Kafka and does wordcount.
+ *
  * Usage: JavaKafkaWordCount <zkQuorum> <group> <topics> <numThreads>
  *   <zkQuorum> is a list of one or more zookeeper servers that make quorum
  *   <group> is the name of kafka consumer group
  *   <topics> is a list of one or more kafka topics to consume from
  *   <numThreads> is the number of threads the kafka consumer should use
  *
- * Example:
- *    `./bin/spark-submit examples.jar \
- *    --class org.apache.spark.examples.streaming.JavaKafkaWordCount zoo01,zoo02, \
+ * To run this example:
+ *   `$ bin/run-example org.apache.spark.examples.streaming.JavaKafkaWordCount zoo01,zoo02, \
  *    zoo03 my-consumer-group topic1,topic2 1`
  */
 
diff --git a/examples/src/main/java/org/apache/spark/examples/streaming/JavaNetworkWordCount.java b/examples/src/main/java/org/apache/spark/examples/streaming/JavaNetworkWordCount.java
index e5cbd39f437c..45bcedebb411 100644
--- a/examples/src/main/java/org/apache/spark/examples/streaming/JavaNetworkWordCount.java
+++ b/examples/src/main/java/org/apache/spark/examples/streaming/JavaNetworkWordCount.java
@@ -24,7 +24,7 @@
 import org.apache.spark.api.java.function.FlatMapFunction;
 import org.apache.spark.api.java.function.Function2;
 import org.apache.spark.api.java.function.PairFunction;
-import org.apache.spark.examples.streaming.StreamingExamples;
+import org.apache.spark.api.java.StorageLevels;
 import org.apache.spark.streaming.Duration;
 import org.apache.spark.streaming.api.java.JavaDStream;
 import org.apache.spark.streaming.api.java.JavaPairDStream;
@@ -41,8 +41,7 @@
  * To run this on your local machine, you need to first run a Netcat server
  *    `$ nc -lk 9999`
  * and then run the example
- *    `$ ./bin/spark-submit examples.jar \
- *    --class org.apache.spark.examples.streaming.JavaNetworkWordCount localhost 9999`
+ *    `$ bin/run-example org.apache.spark.examples.streaming.JavaNetworkWordCount localhost 9999`
  */
 public final class JavaNetworkWordCount {
   private static final Pattern SPACE = Pattern.compile(" ");
@@ -54,13 +53,17 @@ public static void main(String[] args) {
     }
 
     StreamingExamples.setStreamingLogLevels();
-    SparkConf sparkConf = new SparkConf().setAppName("JavaNetworkWordCount");
+
     // Create the context with a 1 second batch size
+    SparkConf sparkConf = new SparkConf().setAppName("JavaNetworkWordCount");
     JavaStreamingContext ssc = new JavaStreamingContext(sparkConf,  new Duration(1000));
 
     // Create a JavaReceiverInputDStream on target ip:port and count the
     // words in input stream of \n delimited text (eg. generated by 'nc')
-    JavaReceiverInputDStream<String> lines = ssc.socketTextStream(args[0], Integer.parseInt(args[1]));
+    // Note that no duplication in storage level only for running locally.
+    // Replication necessary in distributed scenario for fault tolerance.
+    JavaReceiverInputDStream<String> lines = ssc.socketTextStream(
+            args[0], Integer.parseInt(args[1]), StorageLevels.MEMORY_AND_DISK_SER);
     JavaDStream<String> words = lines.flatMap(new FlatMapFunction<String, String>() {
       @Override
       public Iterable<String> call(String x) {
diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/ActorWordCount.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/ActorWordCount.scala
index e29e16a9c1b1..b433082dce1a 100644
--- a/examples/src/main/scala/org/apache/spark/examples/streaming/ActorWordCount.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/ActorWordCount.scala
@@ -130,11 +130,9 @@ object FeederActor {
  *   <hostname> and <port> describe the AkkaSystem that Spark Sample feeder is running on.
  *
  * To run this example locally, you may run Feeder Actor as
- *    `./bin/spark-submit examples.jar \
- *    --class org.apache.spark.examples.streaming.FeederActor 127.0.1.1 9999`
+ *    `$ bin/run-example org.apache.spark.examples.streaming.FeederActor 127.0.1.1 9999`
  * and then run the example
- *    `./bin/spark-submit examples.jar --class org.apache.spark.examples.streaming.ActorWordCount \
- *     127.0.1.1 9999`
+ *    `$ bin/run-example org.apache.spark.examples.streaming.ActorWordCount 127.0.1.1 9999`
  */
 object ActorWordCount {
   def main(args: Array[String]) {
diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/CustomReceiver.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/CustomReceiver.scala
index e317e2d36ae4..6bb659fbd8be 100644
--- a/examples/src/main/scala/org/apache/spark/examples/streaming/CustomReceiver.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/CustomReceiver.scala
@@ -20,7 +20,7 @@ package org.apache.spark.examples.streaming
 import java.io.{InputStreamReader, BufferedReader, InputStream}
 import java.net.Socket
 
-import org.apache.spark.Logging
+import org.apache.spark.{SparkConf, Logging}
 import org.apache.spark.storage.StorageLevel
 import org.apache.spark.streaming.{Seconds, StreamingContext}
 import org.apache.spark.streaming.StreamingContext._
@@ -30,32 +30,27 @@ import org.apache.spark.streaming.receiver.Receiver
  * Custom Receiver that receives data over a socket. Received bytes is interpreted as
  * text and \n delimited lines are considered as records. They are then counted and printed.
  *
- * Usage: CustomReceiver <master> <hostname> <port>
- *   <master> is the Spark master URL. In local mode, <master> should be 'local[n]' with n > 1.
- *   <hostname> and <port> of the TCP server that Spark Streaming would connect to receive data.
- *
  * To run this on your local machine, you need to first run a Netcat server
  *    `$ nc -lk 9999`
  * and then run the example
- *    `$ ./run org.apache.spark.examples.streaming.CustomReceiver local[2] localhost 9999`
+ *    `$ bin/run-example org.apache.spark.examples.streaming.CustomReceiver localhost 9999`
  */
 object CustomReceiver {
   def main(args: Array[String]) {
-    if (args.length < 3) {
-      System.err.println("Usage: NetworkWordCount <master> <hostname> <port>\n" +
-        "In local mode, <master> should be 'local[n]' with n > 1")
+    if (args.length < 2) {
+      System.err.println("Usage: CustomReceiver <hostname> <port>")
       System.exit(1)
     }
 
     StreamingExamples.setStreamingLogLevels()
 
     // Create the context with a 1 second batch size
-    val ssc = new StreamingContext(args(0), "NetworkWordCount", Seconds(1),
-      System.getenv("SPARK_HOME"), StreamingContext.jarOfClass(this.getClass).toSeq)
+    val sparkConf = new SparkConf().setAppName("CustomReceiver")
+    val ssc = new StreamingContext(sparkConf, Seconds(1))
 
     // Create a input stream with the custom receiver on target ip:port and count the
     // words in input stream of \n delimited text (eg. generated by 'nc')
-    val lines = ssc.receiverStream(new CustomReceiver(args(1), args(2).toInt))
+    val lines = ssc.receiverStream(new CustomReceiver(args(0), args(1).toInt))
     val words = lines.flatMap(_.split(" "))
     val wordCounts = words.map(x => (x, 1)).reduceByKey(_ + _)
     wordCounts.print()
diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/FlumeEventCount.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/FlumeEventCount.scala
index 38362edac27f..20e7df7c45b1 100644
--- a/examples/src/main/scala/org/apache/spark/examples/streaming/FlumeEventCount.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/FlumeEventCount.scala
@@ -31,14 +31,16 @@ import org.apache.spark.util.IntParam
  *  Your Flume AvroSink should be pointed to this address.
  *
  *  Usage: FlumeEventCount <host> <port>
- *
  *    <host> is the host the Flume receiver will be started on - a receiver
  *           creates a server and listens for flume events.
  *    <port> is the port the Flume receiver will listen on.
+ *
+ *  To run this example:
+ *    `$ bin/run-example org.apache.spark.examples.streaming.FlumeEventCount <host> <port> `
  */
 object FlumeEventCount {
   def main(args: Array[String]) {
-    if (args.length != 3) {
+    if (args.length < 2) {
       System.err.println(
         "Usage: FlumeEventCount <host> <port>")
       System.exit(1)
@@ -49,8 +51,9 @@ object FlumeEventCount {
     val Array(host, IntParam(port)) = args
 
     val batchInterval = Milliseconds(2000)
-    val sparkConf = new SparkConf().setAppName("FlumeEventCount")
+
     // Create the context and set the batch size
+    val sparkConf = new SparkConf().setAppName("FlumeEventCount")
     val ssc = new StreamingContext(sparkConf, batchInterval)
 
     // Create a flume stream
diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/HdfsWordCount.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/HdfsWordCount.scala
index 55ac48cfb6d1..6c24bc3ad09e 100644
--- a/examples/src/main/scala/org/apache/spark/examples/streaming/HdfsWordCount.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/HdfsWordCount.scala
@@ -27,8 +27,9 @@ import org.apache.spark.streaming.StreamingContext._
  *   <directory> is the directory that Spark Streaming will use to find and read new text files.
  *
  * To run this on your local machine on directory `localdir`, run this example
- *    `$ ./bin/spark-submit examples.jar \
- *    --class org.apache.spark.examples.streaming.HdfsWordCount localdir`
+ *    $ bin/run-example \
+ *       org.apache.spark.examples.streaming.HdfsWordCount localdir
+ *
  * Then create a text file in `localdir` and the words in the file will get counted.
  */
 object HdfsWordCount {
diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/KafkaWordCount.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/KafkaWordCount.scala
index 3af806981f37..566ba6f911e0 100644
--- a/examples/src/main/scala/org/apache/spark/examples/streaming/KafkaWordCount.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/KafkaWordCount.scala
@@ -35,9 +35,9 @@ import org.apache.spark.SparkConf
  *   <numThreads> is the number of threads the kafka consumer should use
  *
  * Example:
- *    `./bin/spark-submit examples.jar \
- *    --class org.apache.spark.examples.streaming.KafkaWordCount local[2] zoo01,zoo02,zoo03 \
- *    my-consumer-group topic1,topic2 1`
+ *    `$ bin/run-example \
+ *      org.apache.spark.examples.streaming.KafkaWordCount zoo01,zoo02,zoo03 \
+ *      my-consumer-group topic1,topic2 1`
  */
 object KafkaWordCount {
   def main(args: Array[String]) {
diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/MQTTWordCount.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/MQTTWordCount.scala
index 3a10daa9ab84..e4283e04a1b1 100644
--- a/examples/src/main/scala/org/apache/spark/examples/streaming/MQTTWordCount.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/MQTTWordCount.scala
@@ -75,14 +75,14 @@ object MQTTPublisher {
  * Example Java code for Mqtt Publisher and Subscriber can be found here
  * https://bitbucket.org/mkjinesh/mqttclient
  * Usage: MQTTWordCount <MqttbrokerUrl> <topic>
-\ *   <MqttbrokerUrl> and <topic> describe where Mqtt publisher is running.
+ *   <MqttbrokerUrl> and <topic> describe where Mqtt publisher is running.
  *
  * To run this example locally, you may run publisher as
- *    `$ ./bin/spark-submit examples.jar \
- *    --class org.apache.spark.examples.streaming.MQTTPublisher tcp://localhost:1883 foo`
+ *    `$ bin/run-example \
+ *      org.apache.spark.examples.streaming.MQTTPublisher tcp://localhost:1883 foo`
  * and run the example as
- *    `$ ./bin/spark-submit examples.jar \
- *    --class org.apache.spark.examples.streaming.MQTTWordCount tcp://localhost:1883 foo`
+ *    `$ bin/run-example \
+ *      org.apache.spark.examples.streaming.MQTTWordCount tcp://localhost:1883 foo`
  */
 object MQTTWordCount {
 
diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/NetworkWordCount.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/NetworkWordCount.scala
index ad7a199b2c0a..ae0a08c6cdb1 100644
--- a/examples/src/main/scala/org/apache/spark/examples/streaming/NetworkWordCount.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/NetworkWordCount.scala
@@ -23,7 +23,7 @@ import org.apache.spark.streaming.StreamingContext._
 import org.apache.spark.storage.StorageLevel
 
 /**
- * Counts words in text encoded with UTF8 received from the network every second.
+ * Counts words in UTF8 encoded, '\n' delimited text received from the network every second.
  *
  * Usage: NetworkWordCount <hostname> <port>
  * <hostname> and <port> describe the TCP server that Spark Streaming would connect to receive data.
@@ -31,8 +31,7 @@ import org.apache.spark.storage.StorageLevel
  * To run this on your local machine, you need to first run a Netcat server
  *    `$ nc -lk 9999`
  * and then run the example
- *    `$ ./bin/spark-submit examples.jar \
- *    --class org.apache.spark.examples.streaming.NetworkWordCount localhost 9999`
+ *    `$ bin/run-example org.apache.spark.examples.streaming.NetworkWordCount localhost 9999`
  */
 object NetworkWordCount {
   def main(args: Array[String]) {
@@ -42,13 +41,16 @@ object NetworkWordCount {
     }
 
     StreamingExamples.setStreamingLogLevels()
-    val sparkConf = new SparkConf().setAppName("NetworkWordCount");
+
     // Create the context with a 1 second batch size
+    val sparkConf = new SparkConf().setAppName("NetworkWordCount")
     val ssc = new StreamingContext(sparkConf, Seconds(1))
 
-    // Create a NetworkInputDStream on target ip:port and count the
+    // Create a socket stream on target ip:port and count the
     // words in input stream of \n delimited text (eg. generated by 'nc')
-    val lines = ssc.socketTextStream(args(0), args(1).toInt, StorageLevel.MEMORY_ONLY_SER)
+    // Note that no duplication in storage level only for running locally.
+    // Replication necessary in distributed scenario for fault tolerance.
+    val lines = ssc.socketTextStream(args(0), args(1).toInt, StorageLevel.MEMORY_AND_DISK_SER)
     val words = lines.flatMap(_.split(" "))
     val wordCounts = words.map(x => (x, 1)).reduceByKey(_ + _)
     wordCounts.print()
diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/RecoverableNetworkWordCount.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/RecoverableNetworkWordCount.scala
index ace785d9fe4c..6af3a0f33efc 100644
--- a/examples/src/main/scala/org/apache/spark/examples/streaming/RecoverableNetworkWordCount.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/RecoverableNetworkWordCount.scala
@@ -46,8 +46,7 @@ import org.apache.spark.util.IntParam
  *
  * and run the example as
  *
- *      `$ ./bin/spark-submit examples.jar \
- *      --class org.apache.spark.examples.streaming.RecoverableNetworkWordCount \
+ *      `$ ./bin/run-example org.apache.spark.examples.streaming.RecoverableNetworkWordCount \
  *              localhost 9999 ~/checkpoint/ ~/out`
  *
  * If the directory ~/checkpoint/ does not exist (e.g. running for the first time), it will create
@@ -57,7 +56,7 @@ import org.apache.spark.util.IntParam
  *
  * To run this example in a local standalone cluster with automatic driver recovery,
  *
- *      `$ ./spark-class org.apache.spark.deploy.Client -s launch <cluster-url> \
+ *      `$ bin/spark-class org.apache.spark.deploy.Client -s launch <cluster-url> \
  *              <path-to-examples-jar> \
  *              org.apache.spark.examples.streaming.RecoverableNetworkWordCount <cluster-url> \
  *              localhost 9999 ~/checkpoint ~/out`
@@ -81,7 +80,7 @@ object RecoverableNetworkWordCount {
     // Create the context with a 1 second batch size
     val ssc = new StreamingContext(sparkConf, Seconds(1))
 
-    // Create a NetworkInputDStream on target ip:port and count the
+    // Create a socket stream on target ip:port and count the
     // words in input stream of \n delimited text (eg. generated by 'nc')
     val lines = ssc.socketTextStream(ip, port)
     val words = lines.flatMap(_.split(" "))
diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/StatefulNetworkWordCount.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/StatefulNetworkWordCount.scala
index 5e1415f3cc53..daa1ced63c70 100644
--- a/examples/src/main/scala/org/apache/spark/examples/streaming/StatefulNetworkWordCount.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/StatefulNetworkWordCount.scala
@@ -31,8 +31,8 @@ import org.apache.spark.streaming.StreamingContext._
  * To run this on your local machine, you need to first run a Netcat server
  *    `$ nc -lk 9999`
  * and then run the example
- *    `$ ./bin/spark-submit examples.jar
- *    --class org.apache.spark.examples.streaming.StatefulNetworkWordCount localhost 9999`
+ *    `$ bin/run-example
+ *      org.apache.spark.examples.streaming.StatefulNetworkWordCount localhost 9999`
  */
 object StatefulNetworkWordCount {
   def main(args: Array[String]) {
@@ -51,7 +51,7 @@ object StatefulNetworkWordCount {
       Some(currentCount + previousCount)
     }
 
-    val sparkConf = new SparkConf().setAppName("NetworkWordCumulativeCountUpdateStateByKey")
+    val sparkConf = new SparkConf().setAppName("StatefulNetworkWordCount")
     // Create the context with a 1 second batch size
     val ssc = new StreamingContext(sparkConf, Seconds(1))
     ssc.checkpoint(".")
diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterPopularTags.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterPopularTags.scala
index 1ddff22cb8a4..f55d23ab3924 100644
--- a/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterPopularTags.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterPopularTags.scala
@@ -28,13 +28,29 @@ import org.apache.spark.SparkConf
  * stream. The stream is instantiated with credentials and optionally filters supplied by the
  * command line arguments.
  *
+ * Run this on your local machine as
+ *
  */
 object TwitterPopularTags {
   def main(args: Array[String]) {
+    if (args.length < 4) {
+      System.err.println("Usage: TwitterPopularTags <consumer key> <consumer secret> " +
+        "<access token> <access token secret> [<filters>]")
+      System.exit(1)
+    }
 
     StreamingExamples.setStreamingLogLevels()
 
-    val filters = args
+    val Array(consumerKey, consumerSecret, accessToken, accessTokenSecret) = args.take(4)
+    val filters = args.takeRight(args.length - 4)
+
+    // Set the system properties so that Twitter4j library used by twitter stream
+    // can use them to generat OAuth credentials
+    System.setProperty("twitter4j.oauth.consumerKey", consumerKey)
+    System.setProperty("twitter4j.oauth.consumerSecret", consumerSecret)
+    System.setProperty("twitter4j.oauth.accessToken", accessToken)
+    System.setProperty("twitter4j.oauth.accessTokenSecret", accessTokenSecret)
+
     val sparkConf = new SparkConf().setAppName("TwitterPopularTags")
     val ssc = new StreamingContext(sparkConf, Seconds(2))
     val stream = TwitterUtils.createStream(ssc, None, filters)
@@ -52,13 +68,13 @@ object TwitterPopularTags {
 
     // Print popular hashtags
     topCounts60.foreachRDD(rdd => {
-      val topList = rdd.take(5)
+      val topList = rdd.take(10)
       println("\nPopular topics in last 60 seconds (%s total):".format(rdd.count()))
       topList.foreach{case (count, tag) => println("%s (%s tweets)".format(tag, count))}
     })
 
     topCounts10.foreachRDD(rdd => {
-      val topList = rdd.take(5)
+      val topList = rdd.take(10)
       println("\nPopular topics in last 10 seconds (%s total):".format(rdd.count()))
       topList.foreach{case (count, tag) => println("%s (%s tweets)".format(tag, count))}
     })
diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/ZeroMQWordCount.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/ZeroMQWordCount.scala
index 7ade3f1018ee..79905af381a1 100644
--- a/examples/src/main/scala/org/apache/spark/examples/streaming/ZeroMQWordCount.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/ZeroMQWordCount.scala
@@ -68,11 +68,11 @@ object SimpleZeroMQPublisher {
  *   <zeroMQurl> and <topic> describe where zeroMq publisher is running.
  *
  * To run this example locally, you may run publisher as
- *    `$ ./bin/spark-submit examples.jar \
- *    --class org.apache.spark.examples.streaming.SimpleZeroMQPublisher tcp://127.0.1.1:1234 foo.bar`
+ *    `$ bin/run-example \
+ *      org.apache.spark.examples.streaming.SimpleZeroMQPublisher tcp://127.0.1.1:1234 foo.bar`
  * and run the example as
- *    `$ ./bin/spark-submit examples.jar \
- *    --class org.apache.spark.examples.streaming.ZeroMQWordCount tcp://127.0.1.1:1234 foo`
+ *    `$ bin/run-example \
+ *      org.apache.spark.examples.streaming.ZeroMQWordCount tcp://127.0.1.1:1234 foo`
  */
 // scalastyle:on
 object ZeroMQWordCount {
diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/clickstream/PageViewGenerator.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/clickstream/PageViewGenerator.scala
index 97e0cb92078d..8402491b6267 100644
--- a/examples/src/main/scala/org/apache/spark/examples/streaming/clickstream/PageViewGenerator.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/clickstream/PageViewGenerator.scala
@@ -40,11 +40,13 @@ object PageView extends Serializable {
 /** Generates streaming events to simulate page views on a website.
   *
   * This should be used in tandem with PageViewStream.scala. Example:
-  * $ ./bin/run-example org.apache.spark.examples.streaming.clickstream.PageViewGenerator 44444 10
-  * $ ./bin/run-example org.apache.spark.examples.streaming.clickstream.PageViewStream errorRatePerZipCode localhost 44444
   *
-  * When running this, you may want to set the root logging level to ERROR in
-  * conf/log4j.properties to reduce the verbosity of the output.
+  * To run the generator
+  * `$ bin/run-example org.apache.spark.examples.streaming.clickstream.PageViewGenerator 44444 10`
+  * To process the generated stream
+  * `$ bin/run-example \
+  *    org.apache.spark.examples.streaming.clickstream.PageViewStream errorRatePerZipCode localhost 44444`
+  *
   */
 // scalastyle:on
 object PageViewGenerator {
diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/clickstream/PageViewStream.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/clickstream/PageViewStream.scala
index d30ceffbe29c..d9b886eff77c 100644
--- a/examples/src/main/scala/org/apache/spark/examples/streaming/clickstream/PageViewStream.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/clickstream/PageViewStream.scala
@@ -26,8 +26,11 @@ import org.apache.spark.examples.streaming.StreamingExamples
   * operators available in Spark streaming.
   *
   * This should be used in tandem with PageViewStream.scala. Example:
-  * $ ./bin/run-example org.apache.spark.examples.streaming.clickstream.PageViewGenerator 44444 10
-  * $ ./bin/run-example org.apache.spark.examples.streaming.clickstream.PageViewStream errorRatePerZipCode localhost 44444
+  * To run the generator
+  * `$ bin/run-example org.apache.spark.examples.streaming.clickstream.PageViewGenerator 44444 10`
+  * To process the generated stream
+  * `$ bin/run-example \
+  *    org.apache.spark.examples.streaming.clickstream.PageViewStream errorRatePerZipCode localhost 44444`
   */
 // scalastyle:on
 object PageViewStream {
diff --git a/external/twitter/src/main/scala/org/apache/spark/streaming/twitter/TwitterInputDStream.scala b/external/twitter/src/main/scala/org/apache/spark/streaming/twitter/TwitterInputDStream.scala
index 7bca1407116f..5ea2e5549d7d 100644
--- a/external/twitter/src/main/scala/org/apache/spark/streaming/twitter/TwitterInputDStream.scala
+++ b/external/twitter/src/main/scala/org/apache/spark/streaming/twitter/TwitterInputDStream.scala
@@ -63,36 +63,48 @@ class TwitterReceiver(
     storageLevel: StorageLevel
   ) extends Receiver[Status](storageLevel) with Logging {
 
-  var twitterStream: TwitterStream = _
+  private var twitterStream: TwitterStream = _
 
   def onStart() {
-    twitterStream = new TwitterStreamFactory().getInstance(twitterAuth)
-    twitterStream.addListener(new StatusListener {
-      def onStatus(status: Status) = {
-        store(status)
-      }
-      // Unimplemented
-      def onDeletionNotice(statusDeletionNotice: StatusDeletionNotice) {}
-      def onTrackLimitationNotice(i: Int) {}
-      def onScrubGeo(l: Long, l1: Long) {}
-      def onStallWarning(stallWarning: StallWarning) {}
-      def onException(e: Exception) {
-        restart("Error receiving tweets", e)
-      }
-    })
+    try {
+      val newTwitterStream = new TwitterStreamFactory().getInstance(twitterAuth)
+      newTwitterStream.addListener(new StatusListener {
+        def onStatus(status: Status) = {
+          store(status)
+        }
+        // Unimplemented
+        def onDeletionNotice(statusDeletionNotice: StatusDeletionNotice) {}
+        def onTrackLimitationNotice(i: Int) {}
+        def onScrubGeo(l: Long, l1: Long) {}
+        def onStallWarning(stallWarning: StallWarning) {}
+        def onException(e: Exception) {
+          restart("Error receiving tweets", e)
+        }
+      })
 
-    val query = new FilterQuery
-    if (filters.size > 0) {
-      query.track(filters.toArray)
-      twitterStream.filter(query)
-    } else {
-      twitterStream.sample()
+      val query = new FilterQuery
+      if (filters.size > 0) {
+        query.track(filters.toArray)
+        newTwitterStream.filter(query)
+      } else {
+        newTwitterStream.sample()
+      }
+      setTwitterStream(newTwitterStream)
+      logInfo("Twitter receiver started")
+    } catch {
+      case e: Exception => restart("Error starting Twitter stream", e)
     }
-    logInfo("Twitter receiver started")
   }
 
   def onStop() {
-    twitterStream.shutdown()
+    setTwitterStream(null)
     logInfo("Twitter receiver stopped")
   }
+
+  private def setTwitterStream(newTwitterStream: TwitterStream) = synchronized {
+    if (twitterStream != null) {
+      twitterStream.shutdown()
+    }
+    twitterStream = newTwitterStream
+  }
 }

From 2e5a7cde223c8bf6d34e46b27ac94a965441584d Mon Sep 17 00:00:00 2001
From: Sean Owen <sowen@cloudera.com>
Date: Wed, 14 May 2014 09:38:33 -0700
Subject: [PATCH 195/810] SPARK-1827. LICENSE and NOTICE files need a refresh
 to contain transitive dependency info

LICENSE and NOTICE policy is explained here:

http://www.apache.org/dev/licensing-howto.html
http://www.apache.org/legal/3party.html

This leads to the following changes.

First, this change enables two extensions to maven-shade-plugin in assembly/ that will try to include and merge all NOTICE and LICENSE files. This can't hurt.

This generates a consolidated NOTICE file that I manually added to NOTICE.

Next, a list of all dependencies and their licenses was generated:
`mvn ... license:aggregate-add-third-party`
to create: `target/generated-sources/license/THIRD-PARTY.txt`

Each dependency is listed with one or more licenses. Determine the most-compatible license for each if there is more than one.

For "unknown" license dependencies, I manually evaluateD their license. Many are actually Apache projects or components of projects covered already. The only non-trivial one was Colt, which has its own (compatible) license.

I ignored Apache-licensed and public domain dependencies as these require no further action (beyond NOTICE above).

BSD and MIT licenses (permissive Category A licenses) are evidently supposed to be mentioned in LICENSE, so I added a section without output from the THIRD-PARTY.txt file appropriately.

Everything else, Category B licenses, are evidently mentioned in NOTICE (?) Same there.

LICENSE contained some license statements for source code that is redistributed. I left this as I think that is the right place to put it.

Author: Sean Owen <sowen@cloudera.com>

Closes #770 from srowen/SPARK-1827 and squashes the following commits:

a764504 [Sean Owen] Add LICENSE and NOTICE info for all transitive dependencies as of 1.0
---
 LICENSE          | 103 +++++++++
 NOTICE           | 572 ++++++++++++++++++++++++++++++++++++++++++++++-
 assembly/pom.xml |   2 +
 3 files changed, 671 insertions(+), 6 deletions(-)

diff --git a/LICENSE b/LICENSE
index 1c1c2c0255fa..383f079df8c8 100644
--- a/LICENSE
+++ b/LICENSE
@@ -428,3 +428,106 @@ LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON A
 THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
 NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE,
 EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+========================================================================
+For colt:
+========================================================================
+
+Copyright (c) 1999 CERN - European Organization for Nuclear Research.
+Permission to use, copy, modify, distribute and sell this software and its documentation for any purpose is hereby granted without fee, provided that the above copyright notice appear in all copies and that both that copyright notice and this permission notice appear in supporting documentation. CERN makes no representations about the suitability of this software for any purpose. It is provided "as is" without expressed or implied warranty.
+
+Packages hep.aida.*
+
+Written by Pavel Binko, Dino Ferrero Merlino, Wolfgang Hoschek, Tony Johnson, Andreas Pfeiffer, and others. Check the FreeHEP home page for more info. Permission to use and/or redistribute this work is granted under the terms of the LGPL License, with the exception that any usage related to military applications is expressly forbidden. The software and documentation made available under the terms of this license are provided with no warranty.
+
+
+========================================================================
+Fo SnapTree:
+========================================================================
+
+SNAPTREE LICENSE
+
+Copyright (c) 2009-2012 Stanford University, unless otherwise specified.
+All rights reserved.
+
+This software was developed by the Pervasive Parallelism Laboratory of
+Stanford University, California, USA.
+
+Permission to use, copy, modify, and distribute this software in source
+or binary form for any purpose with or without fee is hereby granted,
+provided that the following conditions are met:
+
+   1. Redistributions of source code must retain the above copyright
+      notice, this list of conditions and the following disclaimer.
+
+   2. Redistributions in binary form must reproduce the above copyright
+      notice, this list of conditions and the following disclaimer in the
+      documentation and/or other materials provided with the distribution.
+
+   3. Neither the name of Stanford University nor the names of its
+      contributors may be used to endorse or promote products derived
+      from this software without specific prior written permission.
+
+
+THIS SOFTWARE IS PROVIDED BY THE REGENTS AND CONTRIBUTORS ``AS IS'' AND
+ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+ARE DISCLAIMED. IN NO EVENT SHALL THE REGENTS OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
+LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY
+OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGE.
+
+
+========================================================================
+BSD-style licenses
+========================================================================
+
+The following components are provided under a BSD-style license. See project link for details.
+
+     (BSD 3 Clause) core (com.github.fommil.netlib:core:1.1.2 - https://github.com/fommil/netlib-java/core)
+     (BSD 3-clause style license) jblas (org.jblas:jblas:1.2.3 - http://jblas.org/)
+     (BSD License) AntLR Parser Generator (antlr:antlr:2.7.7 - http://www.antlr.org/)
+     (BSD License) Javolution (javolution:javolution:5.5.1 - http://javolution.org)
+     (BSD licence) ANTLR ST4 4.0.4 (org.antlr:ST4:4.0.4 - http://www.stringtemplate.org)
+     (BSD licence) ANTLR StringTemplate (org.antlr:stringtemplate:3.2.1 - http://www.stringtemplate.org)
+     (BSD style) Hamcrest Core (org.hamcrest:hamcrest-core:1.1 - no url defined)
+     (BSD) JLine (jline:jline:0.9.94 - http://jline.sourceforge.net)
+     (BSD) ParaNamer Core (com.thoughtworks.paranamer:paranamer:2.3 - http://paranamer.codehaus.org/paranamer)
+     (BSD) ParaNamer Core (com.thoughtworks.paranamer:paranamer:2.6 - http://paranamer.codehaus.org/paranamer)
+     (BSD-like) (The BSD License) jline (org.scala-lang:jline:2.10.4 - http://www.scala-lang.org/)
+     (BSD-like) Scala Actors library (org.scala-lang:scala-actors:2.10.4 - http://www.scala-lang.org/)
+     (BSD-like) Scala Compiler (org.scala-lang:scala-compiler:2.10.4 - http://www.scala-lang.org/)
+     (BSD-like) Scala Compiler (org.scala-lang:scala-reflect:2.10.4 - http://www.scala-lang.org/)
+     (BSD-like) Scala Library (org.scala-lang:scala-library:2.10.4 - http://www.scala-lang.org/)
+     (BSD-like) Scalap (org.scala-lang:scalap:2.10.4 - http://www.scala-lang.org/)
+     (BSD-style) scalacheck (org.scalacheck:scalacheck_2.10:1.10.0 - http://www.scalacheck.org)
+     (BSD-style) spire (org.spire-math:spire_2.10:0.7.1 - http://spire-math.org)
+     (BSD-style) spire-macros (org.spire-math:spire-macros_2.10:0.7.1 - http://spire-math.org)
+     (New BSD License) Kryo (com.esotericsoftware.kryo:kryo:2.21 - http://code.google.com/p/kryo/)
+     (New BSD License) MinLog (com.esotericsoftware.minlog:minlog:1.2 - http://code.google.com/p/minlog/)
+     (New BSD License) ReflectASM (com.esotericsoftware.reflectasm:reflectasm:1.07 - http://code.google.com/p/reflectasm/)
+     (New BSD license) Protocol Buffer Java API (com.google.protobuf:protobuf-java:2.5.0 - http://code.google.com/p/protobuf)
+     (New BSD license) Protocol Buffer Java API (org.spark-project.protobuf:protobuf-java:2.4.1-shaded - http://code.google.com/p/protobuf)
+     (The BSD License) Fortran to Java ARPACK (net.sourceforge.f2j:arpack_combined_all:0.1 - http://f2j.sourceforge.net)
+     (The BSD License) xmlenc Library (xmlenc:xmlenc:0.52 - http://xmlenc.sourceforge.net)
+     (The New BSD License) Py4J (net.sf.py4j:py4j:0.8.1 - http://py4j.sourceforge.net/)
+     (Two-clause BSD-style license) JUnit-Interface (com.novocode:junit-interface:0.10 - http://github.com/szeiger/junit-interface/)
+     (ISC/BSD License) jbcrypt (org.mindrot:jbcrypt:0.3m - http://www.mindrot.org/)
+
+========================================================================
+MIT licenses
+========================================================================
+
+The following components are provided under the MIT License. See project link for details.
+
+     (MIT License) JCL 1.1.1 implemented over SLF4J (org.slf4j:jcl-over-slf4j:1.7.5 - http://www.slf4j.org)
+     (MIT License) JUL to SLF4J bridge (org.slf4j:jul-to-slf4j:1.7.5 - http://www.slf4j.org)
+     (MIT License) SLF4J API Module (org.slf4j:slf4j-api:1.7.5 - http://www.slf4j.org)
+     (MIT License) SLF4J LOG4J-12 Binding (org.slf4j:slf4j-log4j12:1.7.5 - http://www.slf4j.org)
+     (MIT License) pyrolite (org.spark-project:pyrolite:2.0.1 - http://pythonhosted.org/Pyro4/)
+     (MIT License) scopt (com.github.scopt:scopt_2.10:3.2.0 - https://github.com/scopt/scopt)
+     (The MIT License) Mockito (org.mockito:mockito-all:1.8.5 - http://www.mockito.org)
diff --git a/NOTICE b/NOTICE
index 42f6c3a83572..452aef287165 100644
--- a/NOTICE
+++ b/NOTICE
@@ -4,11 +4,571 @@ Copyright 2014 The Apache Software Foundation.
 This product includes software developed at
 The Apache Software Foundation (http://www.apache.org/).
 
-In addition, this product includes:
 
-- JUnit (http://www.junit.org) is a testing framework for Java. We included it
-  under the terms of the Eclipse Public License v1.0.
+========================================================================
+Common Development and Distribution License 1.0
+========================================================================
 
-- JTransforms (https://sites.google.com/site/piotrwendykier/software/jtransforms)
-  provides fast transforms in Java. It is tri-licensed, and we included it under 
-  the terms of the Mozilla Public License v1.1.
+The following components are provided under the Common Development and Distribution License 1.0. See project link for details.
+
+     (CDDL 1.0) Glassfish Jasper (org.mortbay.jetty:jsp-2.1:6.1.14 - http://jetty.mortbay.org/project/modules/jsp-2.1)
+     (CDDL 1.0) Servlet Specification 2.5 API (org.mortbay.jetty:servlet-api-2.5:6.1.14 - http://jetty.mortbay.org/project/modules/servlet-api-2.5)
+     (COMMON DEVELOPMENT AND DISTRIBUTION LICENSE (CDDL) Version 1.0) (GNU General Public Library) Streaming API for XML (javax.xml.stream:stax-api:1.0-2 - no url defined)
+     (Common Development and Distribution License (CDDL) v1.0) JavaBeans Activation Framework (JAF) (javax.activation:activation:1.1 - http://java.sun.com/products/javabeans/jaf/index.jsp)
+
+========================================================================
+Common Development and Distribution License 1.1
+========================================================================
+
+The following components are provided under the Common Development and Distribution License 1.1. See project link for details.
+
+     (CDDL 1.1) (GPL2 w/ CPE) JAXB API bundle for GlassFish V3 (javax.xml.bind:jaxb-api:2.2.2 - https://jaxb.dev.java.net/)
+     (CDDL 1.1) (GPL2 w/ CPE) JAXB RI (com.sun.xml.bind:jaxb-impl:2.2.3-1 - http://jaxb.java.net/)
+     (CDDL 1.1) (GPL2 w/ CPE) jersey-core (com.sun.jersey:jersey-core:1.8 - https://jersey.dev.java.net/jersey-core/)
+     (CDDL 1.1) (GPL2 w/ CPE) jersey-core (com.sun.jersey:jersey-core:1.9 - https://jersey.java.net/jersey-core/)
+     (CDDL 1.1) (GPL2 w/ CPE) jersey-guice (com.sun.jersey.contribs:jersey-guice:1.9 - https://jersey.java.net/jersey-contribs/jersey-guice/)
+     (CDDL 1.1) (GPL2 w/ CPE) jersey-json (com.sun.jersey:jersey-json:1.8 - https://jersey.dev.java.net/jersey-json/)
+     (CDDL 1.1) (GPL2 w/ CPE) jersey-json (com.sun.jersey:jersey-json:1.9 - https://jersey.java.net/jersey-json/)
+     (CDDL 1.1) (GPL2 w/ CPE) jersey-server (com.sun.jersey:jersey-server:1.8 - https://jersey.dev.java.net/jersey-server/)
+     (CDDL 1.1) (GPL2 w/ CPE) jersey-server (com.sun.jersey:jersey-server:1.9 - https://jersey.java.net/jersey-server/)
+
+========================================================================
+Common Public License 1.0
+========================================================================
+
+The following components are provided under the Common Public 1.0 License. See project link for details.
+
+     (Common Public License Version 1.0) JUnit (junit:junit-dep:4.10 - http://junit.org)
+     (Common Public License Version 1.0) JUnit (junit:junit:3.8.1 - http://junit.org)
+     (Common Public License Version 1.0) JUnit (junit:junit:4.8.2 - http://junit.org)
+
+========================================================================
+Eclipse Public License 1.0
+========================================================================
+
+The following components are provided under the Eclipse Public License 1.0. See project link for details.
+
+     (Eclipse Public License - Version 1.0) mqtt-client (org.eclipse.paho:mqtt-client:0.4.0 - http://www.eclipse.org/paho/mqtt-client)
+     (Eclipse Public License v1.0) Eclipse JDT Core (org.eclipse.jdt:core:3.1.1 - http://www.eclipse.org/jdt/)
+
+========================================================================
+Mozilla Public License 1.0
+========================================================================
+
+The following components are provided under the Mozilla Public License 1.0. See project link for details.
+
+     (GPL) (LGPL) (MPL) JTransforms (com.github.rwl:jtransforms:2.4.0 - http://sourceforge.net/projects/jtransforms/)
+     (Mozilla Public License Version 1.1) jamon-runtime (org.jamon:jamon-runtime:2.3.1 - http://www.jamon.org/jamon-runtime/)
+
+
+
+========================================================================
+NOTICE files
+========================================================================
+
+The following NOTICEs are pertain to software distributed with this project.
+
+
+// ------------------------------------------------------------------
+// NOTICE file corresponding to the section 4d of The Apache License,
+// Version 2.0, in this case for
+// ------------------------------------------------------------------
+
+Apache Avro
+Copyright 2009-2013 The Apache Software Foundation
+
+This product includes software developed at
+The Apache Software Foundation (http://www.apache.org/).
+
+Apache Commons Codec
+Copyright 2002-2009 The Apache Software Foundation
+
+This product includes software developed by
+The Apache Software Foundation (http://www.apache.org/).
+
+--------------------------------------------------------------------------------
+src/test/org/apache/commons/codec/language/DoubleMetaphoneTest.java contains
+test data from http://aspell.sourceforge.net/test/batch0.tab.
+
+Copyright (C) 2002 Kevin Atkinson (kevina@gnu.org). Verbatim copying
+and distribution of this entire article is permitted in any medium,
+provided this notice is preserved.
+--------------------------------------------------------------------------------
+
+Apache HttpComponents HttpClient
+Copyright 1999-2011 The Apache Software Foundation
+
+This project contains annotations derived from JCIP-ANNOTATIONS
+Copyright (c) 2005 Brian Goetz and Tim Peierls. See http://www.jcip.net
+
+Apache HttpComponents HttpCore
+Copyright 2005-2011 The Apache Software Foundation
+
+Curator Recipes
+Copyright 2011-2014 The Apache Software Foundation
+
+Curator Framework
+Copyright 2011-2014 The Apache Software Foundation
+
+Curator Client
+Copyright 2011-2014 The Apache Software Foundation
+
+Apache Geronimo
+Copyright 2003-2008 The Apache Software Foundation
+
+Activation 1.1
+Copyright 2003-2007 The Apache Software Foundation
+
+Apache Commons Lang
+Copyright 2001-2014 The Apache Software Foundation
+
+This product includes software from the Spring Framework,
+under the Apache License 2.0 (see: StringUtils.containsWhitespace())
+
+Apache log4j
+Copyright 2007 The Apache Software Foundation
+
+# Compress LZF
+
+This library contains efficient implementation of LZF compression format,
+as well as additional helper classes that build on JDK-provided gzip (deflat)
+codec.
+
+## Licensing
+
+Library is licensed under Apache License 2.0, as per accompanying LICENSE file.
+
+## Credit
+
+Library has been written by Tatu Saloranta (tatu.saloranta@iki.fi).
+It was started at Ning, inc., as an official Open Source process used by
+platform backend, but after initial versions has been developed outside of
+Ning by supporting community.
+
+Other contributors include:
+
+* Jon Hartlaub (first versions of streaming reader/writer; unit tests)
+* Cedrik Lime: parallel LZF implementation
+
+Various community members have contributed bug reports, and suggested minor
+fixes; these can be found from file "VERSION.txt" in SCM.
+
+Objenesis
+Copyright 2006-2009 Joe Walnes, Henri Tremblay, Leonardo Mesquita
+
+Apache Commons Net
+Copyright 2001-2010 The Apache Software Foundation
+
+                            The Netty Project
+                            =================
+
+Please visit the Netty web site for more information:
+
+  * http://netty.io/
+
+Copyright 2011 The Netty Project
+
+The Netty Project licenses this file to you under the Apache License,
+version 2.0 (the "License"); you may not use this file except in compliance
+with the License. You may obtain a copy of the License at:
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+License for the specific language governing permissions and limitations
+under the License.
+
+Also, please refer to each LICENSE.<component>.txt file, which is located in
+the 'license' directory of the distribution file, for the license terms of the
+components that this product depends on.
+
+-------------------------------------------------------------------------------
+This product contains the extensions to Java Collections Framework which has
+been derived from the works by JSR-166 EG, Doug Lea, and Jason T. Greene:
+
+  * LICENSE:
+    * license/LICENSE.jsr166y.txt (Public Domain)
+  * HOMEPAGE:
+    * http://gee.cs.oswego.edu/cgi-bin/viewcvs.cgi/jsr166/
+    * http://viewvc.jboss.org/cgi-bin/viewvc.cgi/jbosscache/experimental/jsr166/
+
+This product contains a modified version of Robert Harder's Public Domain
+Base64 Encoder and Decoder, which can be obtained at:
+
+  * LICENSE:
+    * license/LICENSE.base64.txt (Public Domain)
+  * HOMEPAGE:
+    * http://iharder.sourceforge.net/current/java/base64/
+
+This product contains a modified version of 'JZlib', a re-implementation of
+zlib in pure Java, which can be obtained at:
+
+  * LICENSE:
+    * license/LICENSE.jzlib.txt (BSD Style License)
+  * HOMEPAGE:
+    * http://www.jcraft.com/jzlib/
+
+This product optionally depends on 'Protocol Buffers', Google's data
+interchange format, which can be obtained at:
+
+  * LICENSE:
+    * license/LICENSE.protobuf.txt (New BSD License)
+  * HOMEPAGE:
+    * http://code.google.com/p/protobuf/
+
+This product optionally depends on 'SLF4J', a simple logging facade for Java,
+which can be obtained at:
+
+  * LICENSE:
+    * license/LICENSE.slf4j.txt (MIT License)
+  * HOMEPAGE:
+    * http://www.slf4j.org/
+
+This product optionally depends on 'Apache Commons Logging', a logging
+framework, which can be obtained at:
+
+  * LICENSE:
+    * license/LICENSE.commons-logging.txt (Apache License 2.0)
+  * HOMEPAGE:
+    * http://commons.apache.org/logging/
+
+This product optionally depends on 'Apache Log4J', a logging framework,
+which can be obtained at:
+
+  * LICENSE:
+    * license/LICENSE.log4j.txt (Apache License 2.0)
+  * HOMEPAGE:
+    * http://logging.apache.org/log4j/
+
+This product optionally depends on 'JBoss Logging', a logging framework,
+which can be obtained at:
+
+  * LICENSE:
+    * license/LICENSE.jboss-logging.txt (GNU LGPL 2.1)
+  * HOMEPAGE:
+    * http://anonsvn.jboss.org/repos/common/common-logging-spi/
+
+This product optionally depends on 'Apache Felix', an open source OSGi
+framework implementation, which can be obtained at:
+
+  * LICENSE:
+    * license/LICENSE.felix.txt (Apache License 2.0)
+  * HOMEPAGE:
+    * http://felix.apache.org/
+
+This product optionally depends on 'Webbit', a Java event based
+WebSocket and HTTP server:
+
+  * LICENSE:
+    * license/LICENSE.webbit.txt (BSD License)
+  * HOMEPAGE:
+    * https://github.com/joewalnes/webbit
+
+# Jackson JSON processor
+
+Jackson is a high-performance, Free/Open Source JSON processing library.
+It was originally written by Tatu Saloranta (tatu.saloranta@iki.fi), and has
+been in development since 2007.
+It is currently developed by a community of developers, as well as supported
+commercially by FasterXML.com.
+
+Jackson core and extension components may be licensed under different licenses.
+To find the details that apply to this artifact see the accompanying LICENSE file.
+For more information, including possible other licensing options, contact
+FasterXML.com (http://fasterxml.com).
+
+## Credits
+
+A list of contributors may be found from CREDITS file, which is included
+in some artifacts (usually source distributions); but is always available
+from the source code management (SCM) system project uses.
+
+Jackson core and extension components may licensed under different licenses.
+To find the details that apply to this artifact see the accompanying LICENSE file.
+For more information, including possible other licensing options, contact
+FasterXML.com (http://fasterxml.com).
+
+mesos
+Copyright 2014 The Apache Software Foundation
+
+Apache Thrift
+Copyright 2006-2010 The Apache Software Foundation.
+
+   Apache Ant
+   Copyright 1999-2013 The Apache Software Foundation
+
+   The <sync> task is based on code Copyright (c) 2002, Landmark
+   Graphics Corp that has been kindly donated to the Apache Software
+   Foundation.
+
+Apache Commons IO
+Copyright 2002-2012 The Apache Software Foundation
+
+Apache Commons Math
+Copyright 2001-2013 The Apache Software Foundation
+
+===============================================================================
+
+The inverse error function implementation in the Erf class is based on CUDA
+code developed by Mike Giles, Oxford-Man Institute of Quantitative Finance,
+and published in GPU Computing Gems, volume 2, 2010.
+===============================================================================
+
+The BracketFinder (package org.apache.commons.math3.optimization.univariate)
+and PowellOptimizer (package org.apache.commons.math3.optimization.general)
+classes are based on the Python code in module "optimize.py" (version 0.5)
+developed by Travis E. Oliphant for the SciPy library (http://www.scipy.org/)
+Copyright © 2003-2009 SciPy Developers.
+===============================================================================
+
+The LinearConstraint, LinearObjectiveFunction, LinearOptimizer,
+RelationShip, SimplexSolver and SimplexTableau classes in package
+org.apache.commons.math3.optimization.linear include software developed by
+Benjamin McCann (http://www.benmccann.com) and distributed with
+the following copyright: Copyright 2009 Google Inc.
+===============================================================================
+
+This product includes software developed by the
+University of Chicago, as Operator of Argonne National
+Laboratory.
+The LevenbergMarquardtOptimizer class in package
+org.apache.commons.math3.optimization.general includes software
+translated from the lmder, lmpar and qrsolv Fortran routines
+from the Minpack package
+Minpack Copyright Notice (1999) University of Chicago.  All rights reserved
+===============================================================================
+
+The GraggBulirschStoerIntegrator class in package
+org.apache.commons.math3.ode.nonstiff includes software translated
+from the odex Fortran routine developed by E. Hairer and G. Wanner.
+Original source copyright:
+Copyright (c) 2004, Ernst Hairer
+===============================================================================
+
+The EigenDecompositionImpl class in package
+org.apache.commons.math3.linear includes software translated
+from some LAPACK Fortran routines.  Original source copyright:
+Copyright (c) 1992-2008 The University of Tennessee.  All rights reserved.
+===============================================================================
+
+The MersenneTwister class in package org.apache.commons.math3.random
+includes software translated from the 2002-01-26 version of
+the Mersenne-Twister generator written in C by Makoto Matsumoto and Takuji
+Nishimura. Original source copyright:
+Copyright (C) 1997 - 2002, Makoto Matsumoto and Takuji Nishimura,
+All rights reserved
+===============================================================================
+
+The LocalizedFormatsTest class in the unit tests is an adapted version of
+the OrekitMessagesTest class from the orekit library distributed under the
+terms of the Apache 2 licence. Original source copyright:
+Copyright 2010 CS Systèmes d'Information
+===============================================================================
+
+The HermiteInterpolator class and its corresponding test have been imported from
+the orekit library distributed under the terms of the Apache 2 licence. Original
+source copyright:
+Copyright 2010-2012 CS Systèmes d'Information
+===============================================================================
+
+The creation of the package "o.a.c.m.analysis.integration.gauss" was inspired
+by an original code donated by Sébastien Brisard.
+===============================================================================
+
+The complete text of licenses and disclaimers associated with the the original
+sources enumerated above at the time of code translation are in the LICENSE.txt
+file.
+
+This product currently only contains code developed by authors
+of specific components, as identified by the source code files;
+if such notes are missing files have been created by
+Tatu Saloranta.
+
+For additional credits (generally to people who reported problems)
+see CREDITS file.
+
+Apache Commons Lang
+Copyright 2001-2011 The Apache Software Foundation
+
+Apache Commons Compress
+Copyright 2002-2012 The Apache Software Foundation
+
+Apache Commons CLI
+Copyright 2001-2009 The Apache Software Foundation
+
+Google Guice - Extensions - Servlet
+Copyright 2006-2011 Google, Inc.
+
+Google Guice - Core Library
+Copyright 2006-2011 Google, Inc.
+
+Apache Jakarta HttpClient
+Copyright 1999-2007 The Apache Software Foundation
+
+Apache Hive
+Copyright 2008-2013 The Apache Software Foundation
+
+This product includes software developed by The Apache Software
+Foundation (http://www.apache.org/).
+
+This product includes software developed by The JDBM Project
+(http://jdbm.sourceforge.net/).
+
+This product includes/uses ANTLR (http://www.antlr.org/),
+Copyright (c) 2003-2011, Terrence Parr.
+
+This product includes/uses StringTemplate (http://www.stringtemplate.org/),
+Copyright (c) 2011, Terrence Parr.
+
+This product includes/uses ASM (http://asm.ow2.org/),
+Copyright (c) 2000-2007 INRIA, France Telecom.
+
+This product includes/uses org.json (http://www.json.org/java/index.html),
+Copyright (c) 2002 JSON.org
+
+This product includes/uses JLine (http://jline.sourceforge.net/),
+Copyright (c) 2002-2006, Marc Prud'hommeaux <mwp1@cornell.edu>.
+
+This product includes/uses SQLLine (http://sqlline.sourceforge.net),
+Copyright (c) 2002, 2003, 2004, 2005 Marc Prud'hommeaux <mwp1@cornell.edu>.
+
+This product includes/uses SLF4J (http://www.slf4j.org/),
+Copyright (c) 2004-2010 QOS.ch
+
+This product includes/uses Bootstrap (http://twitter.github.com/bootstrap/),
+Copyright (c) 2012 Twitter, Inc.
+
+This product includes/uses Glyphicons (http://glyphicons.com/),
+Copyright (c) 2010 - 2012 Jan Kovarík
+
+This product includes DataNucleus (http://www.datanucleus.org/)
+Copyright 2008-2008 DataNucleus
+
+This product includes Guava (http://code.google.com/p/guava-libraries/)
+Copyright (C) 2006 Google Inc.
+
+This product includes JavaEWAH (http://code.google.com/p/javaewah/)
+Copyright (C) 2011 Google Inc.
+
+Apache Commons Pool
+Copyright 1999-2009 The Apache Software Foundation
+
+=========================================================================
+==  NOTICE file corresponding to section 4(d) of the Apache License,   ==
+==  Version 2.0, in this case for the DataNucleus distribution.        ==
+=========================================================================
+
+===================================================================
+This product includes software developed by many individuals,
+including the following:
+===================================================================
+Erik Bengtson
+Andy Jefferson
+
+===================================================================
+This product has included contributions from some individuals,
+including the following:
+===================================================================
+
+===================================================================
+This product has included contributions from some individuals,
+including the following:
+===================================================================
+Joerg von Frantzius
+Thomas Marti
+Barry Haddow
+Marco Schulze
+Ralph Ullrich
+David Ezzio
+Brendan de Beer
+David Eaves
+Martin Taal
+Tony Lai
+Roland Szabo
+Marcus Mennemeier
+Xuan Baldauf
+Eric Sultan
+
+===================================================================
+This product also includes software developed by the TJDO project
+(http://tjdo.sourceforge.net/).
+===================================================================
+
+===================================================================
+This product includes software developed by many individuals,
+including the following:
+===================================================================
+Andy Jefferson
+Erik Bengtson
+Joerg von Frantzius
+Marco Schulze
+
+===================================================================
+This product has included contributions from some individuals,
+including the following:
+===================================================================
+Barry Haddow
+Ralph Ullrich
+David Ezzio
+Brendan de Beer
+David Eaves
+Martin Taal
+Tony Lai
+Roland Szabo
+Anton Troshin (Timesten)
+
+===================================================================
+This product also includes software developed by the Apache Commons project
+(http://commons.apache.org/).
+===================================================================
+
+Apache Java Data Objects (JDO)
+Copyright 2005-2006 The Apache Software Foundation
+
+=========================================================================
+==  NOTICE file corresponding to section 4(d) of the Apache License,   ==
+==  Version 2.0, in this case for the Apache Derby distribution.       ==
+=========================================================================
+
+Apache Derby
+Copyright 2004-2008 The Apache Software Foundation
+
+Portions of Derby were originally developed by
+International Business Machines Corporation and are
+licensed to the Apache Software Foundation under the
+"Software Grant and Corporate Contribution License Agreement",
+informally known as the "Derby CLA".
+The following copyright notice(s) were affixed to portions of the code
+with which this file is now or was at one time distributed
+and are placed here unaltered.
+
+(C) Copyright 1997,2004 International Business Machines Corporation.  All rights reserved.
+
+(C) Copyright IBM Corp. 2003.
+
+The portion of the functionTests under 'nist' was originally
+developed by the National Institute of Standards and Technology (NIST),
+an agency of the United States Department of Commerce, and adapted by
+International Business Machines Corporation in accordance with the NIST
+Software Acknowledgment and Redistribution document at
+http://www.itl.nist.gov/div897/ctg/sql_form.htm
+
+Apache Commons Collections
+Copyright 2001-2008 The Apache Software Foundation
+
+Apache Commons Configuration
+Copyright 2001-2008 The Apache Software Foundation
+
+Apache Jakarta Commons Digester
+Copyright 2001-2006 The Apache Software Foundation
+
+Apache Commons BeanUtils
+Copyright 2000-2008 The Apache Software Foundation
+
+Apache Avro Mapred API
+Copyright 2009-2013 The Apache Software Foundation
+
+Apache Avro IPC
+Copyright 2009-2013 The Apache Software Foundation
diff --git a/assembly/pom.xml b/assembly/pom.xml
index 6c4d46aeb67b..abd893533999 100644
--- a/assembly/pom.xml
+++ b/assembly/pom.xml
@@ -122,6 +122,8 @@
                 <transformer implementation="org.apache.maven.plugins.shade.resource.DontIncludeResourceTransformer">
                   <resource>log4j.properties</resource>
                 </transformer>
+                <transformer implementation="org.apache.maven.plugins.shade.resource.ApacheLicenseResourceTransformer"/>
+                <transformer implementation="org.apache.maven.plugins.shade.resource.ApacheNoticeResourceTransformer"/>
               </transformers>
             </configuration>
           </execution>

From d1d41ccee49a5c093cb61c791c01f64f2076b83e Mon Sep 17 00:00:00 2001
From: Andrew Ash <andrew@andrewash.com>
Date: Wed, 14 May 2014 09:45:33 -0700
Subject: [PATCH 196/810] SPARK-1818 Freshen Mesos documentation

Place more emphasis on using precompiled binary versions of Spark and Mesos
instead of encouraging the reader to compile from source.

Author: Andrew Ash <andrew@andrewash.com>

Closes #756 from ash211/spark-1818 and squashes the following commits:

7ef3b33 [Andrew Ash] Brief explanation of the interactions between Spark and Mesos
e7dea8e [Andrew Ash] Add troubleshooting and debugging section
956362d [Andrew Ash] Don't need to pass spark.executor.uri into the spark shell
de3353b [Andrew Ash] Wrap to 100char
7ebf6ef [Andrew Ash] Polish on the section on Mesos Master URLs
3dcc2c1 [Andrew Ash] Use --tgz parameter of make-distribution
41b68ed [Andrew Ash] Period at end of sentence; formatting on :5050
8bf2c53 [Andrew Ash] Update site.MESOS_VERSIOn to match /pom.xml
74f2040 [Andrew Ash] SPARK-1818 Freshen Mesos documentation
---
 docs/_config.yml         |   2 +-
 docs/running-on-mesos.md | 200 +++++++++++++++++++++++++++++++++------
 2 files changed, 174 insertions(+), 28 deletions(-)

diff --git a/docs/_config.yml b/docs/_config.yml
index d177e38f884c..45b78fe724a5 100644
--- a/docs/_config.yml
+++ b/docs/_config.yml
@@ -7,6 +7,6 @@ SPARK_VERSION: 1.0.0-SNAPSHOT
 SPARK_VERSION_SHORT: 1.0.0
 SCALA_BINARY_VERSION: "2.10"
 SCALA_VERSION: "2.10.4"
-MESOS_VERSION: 0.13.0
+MESOS_VERSION: 0.18.1
 SPARK_ISSUE_TRACKER_URL: https://issues.apache.org/jira/browse/SPARK
 SPARK_GITHUB_URL: https://github.com/apache/spark
diff --git a/docs/running-on-mesos.md b/docs/running-on-mesos.md
index 68259f0cb8ad..ef762aa7b8fc 100644
--- a/docs/running-on-mesos.md
+++ b/docs/running-on-mesos.md
@@ -3,19 +3,123 @@ layout: global
 title: Running Spark on Mesos
 ---
 
-Spark can run on clusters managed by [Apache Mesos](http://mesos.apache.org/). Follow the steps below to install Mesos and Spark:
-
-1. Download and build Spark using the instructions [here](index.html). **Note:** Don't forget to consider what version of HDFS you might want to use!
-2. Download, build, install, and start Mesos {{site.MESOS_VERSION}} on your cluster. You can download the Mesos distribution from a [mirror](http://www.apache.org/dyn/closer.cgi/mesos/{{site.MESOS_VERSION}}/). See the Mesos [Getting Started](http://mesos.apache.org/gettingstarted) page for more information. **Note:** If you want to run Mesos without installing it into the default paths on your system (e.g., if you don't have administrative privileges to install it), you should also pass the `--prefix` option to `configure` to tell it where to install. For example, pass `--prefix=/home/user/mesos`. By default the prefix is `/usr/local`.
-3. Create a Spark "distribution" using `make-distribution.sh`.
-4. Rename the `dist` directory created from `make-distribution.sh` to `spark-{{site.SPARK_VERSION}}`.
-5. Create a `tar` archive: `tar czf spark-{{site.SPARK_VERSION}}.tar.gz spark-{{site.SPARK_VERSION}}`
-6. Upload this archive to HDFS or another place accessible from Mesos via `http://`, e.g., [Amazon Simple Storage Service](http://aws.amazon.com/s3): `hadoop fs -put spark-{{site.SPARK_VERSION}}.tar.gz /path/to/spark-{{site.SPARK_VERSION}}.tar.gz`
-7. Create a file called `spark-env.sh` in Spark's `conf` directory, by copying `conf/spark-env.sh.template`, and add the following lines to it:
-   * `export MESOS_NATIVE_LIBRARY=<path to libmesos.so>`. This path is usually `<prefix>/lib/libmesos.so` (where the prefix is `/usr/local` by default, see above). Also, on Mac OS X, the library is called `libmesos.dylib` instead of `libmesos.so`.
-   * `export SPARK_EXECUTOR_URI=<path to spark-{{site.SPARK_VERSION}}.tar.gz uploaded above>`.
-   * `export MASTER=mesos://HOST:PORT` where HOST:PORT is the host and port (default: 5050) of your Mesos master (or `zk://...` if using Mesos with ZooKeeper).
-8. To run a Spark application against the cluster, when you create your `SparkContext`, pass the string `mesos://HOST:PORT` as the master URL. In addition, you'll need to set the `spark.executor.uri` property. For example:
+# Why Mesos
+
+Spark can run on hardware clusters managed by [Apache Mesos](http://mesos.apache.org/).
+
+The advantages of deploying Spark with Mesos include:
+- dynamic partitioning between Spark and other
+  [frameworks](https://mesos.apache.org/documentation/latest/mesos-frameworks/)
+- scalable partitioning between multiple instances of Spark
+
+# How it works
+
+In a standalone cluster deployment, the cluster manager in the below diagram is a Spark master
+instance.  When using Mesos, the Mesos master replaces the Spark master as the cluster manager.
+
+<p style="text-align: center;">
+  <img src="img/cluster-overview.png" title="Spark cluster components" alt="Spark cluster components" />
+</p>
+
+Now when a driver creates a job and starts issuing tasks for scheduling, Mesos determines what
+machines handle what tasks.  Because it takes into account other frameworks when scheduling these
+many short-lived tasks, multiple frameworks can coexist on the same cluster without resorting to a
+static partitioning of resources.
+
+To get started, follow the steps below to install Mesos and deploy Spark jobs via Mesos.
+
+
+# Installing Mesos
+
+Spark {{site.SPARK_VERSION}} is designed for use with Mesos {{site.MESOS_VERSION}} and does not
+require any special patches of Mesos.
+
+If you already have a Mesos cluster running, you can skip this Mesos installation step.
+
+Otherwise, installing Mesos for Spark is no different than installing Mesos for use by other
+frameworks.  You can install Mesos using either prebuilt packages or by compiling from source.
+
+## Prebuilt packages
+
+The Apache Mesos project only publishes source package releases, no binary releases.  But other
+third party projects publish binary releases that may be helpful in setting Mesos up.
+
+One of those is Mesosphere.  To install Mesos using the binary releases provided by Mesosphere:
+
+1. Download Mesos installation package from [downloads page](http://mesosphere.io/downloads/)
+2. Follow their instructions for installation and configuration
+
+The Mesosphere installation documents suggest setting up ZooKeeper to handle Mesos master failover,
+but Mesos can be run without ZooKeeper using a single master as well.
+
+## From source
+
+To install Mesos directly from the upstream project rather than a third party, install from source.
+
+1. Download the Mesos distribution from a
+   [mirror](http://www.apache.org/dyn/closer.cgi/mesos/{{site.MESOS_VERSION}}/)
+2. Follow the Mesos [Getting Started](http://mesos.apache.org/gettingstarted) page for compiling and
+   installing Mesos
+
+**Note:** If you want to run Mesos without installing it into the default paths on your system
+(e.g., if you lack administrative privileges to install it), you should also pass the
+`--prefix` option to `configure` to tell it where to install. For example, pass
+`--prefix=/home/user/mesos`. By default the prefix is `/usr/local`.
+
+## Verification
+
+To verify that the Mesos cluster is ready for Spark, navigate to the Mesos master webui at port
+`:5050`  Confirm that all expected machines are present in the slaves tab.
+
+
+# Connecting Spark to Mesos
+
+To use Mesos from Spark, you need a Spark distribution available in a place accessible by Mesos, and
+a Spark driver program configured to connect to Mesos.
+
+## Uploading Spark Distribution
+
+When Mesos runs a task on a Mesos slave for the first time, that slave must have a distribution of
+Spark available for running the Spark Mesos executor backend.  A distribution of Spark is just a
+compiled binary version of Spark.
+
+The Spark distribution can be hosted at any Hadoop URI, including HTTP via `http://`, [Amazon Simple
+Storage Service](http://aws.amazon.com/s3) via `s3://`, or HDFS via `hdfs:///`.
+
+To use a precompiled distribution:
+
+1. Download a Spark distribution from the Spark [download page](https://spark.apache.org/downloads.html)  
+2. Upload to hdfs/http/s3
+
+To host on HDFS, use the Hadoop fs put command: `hadoop fs -put spark-{{site.SPARK_VERSION}}.tar.gz
+/path/to/spark-{{site.SPARK_VERSION}}.tar.gz`
+
+
+Or if you are using a custom-compiled version of Spark, you will need to create a distribution using
+the `make-distribution.sh` script included in a Spark source tarball/checkout.
+
+1. Download and build Spark using the instructions [here](index.html)
+2. Create a Spark distribution using `make-distribution.sh --tgz`.
+3. Upload archive to http/s3/hdfs
+
+
+## Using a Mesos Master URL
+
+The Master URLs for Mesos are in the form `mesos://host:5050` for a single-master Mesos
+cluster, or `zk://host:2181` for a multi-master Mesos cluster using ZooKeeper.
+
+The driver also needs some configuration in `spark-env.sh` to interact properly with Mesos:
+
+1. In `spark.env.sh` set some environment variables:
+ * `export MESOS_NATIVE_LIBRARY=<path to libmesos.so>`. This path is typically
+   `<prefix>/lib/libmesos.so` where the prefix is `/usr/local` by default. See Mesos installation
+   instructions above. On Mac OS X, the library is called `libmesos.dylib` instead of
+   `libmesos.so`.
+ * `export SPARK_EXECUTOR_URI=<path to spark-{{site.SPARK_VERSION}}.tar.gz uploaded above>`.
+2. Also set `spark.executor.uri` to <path to spark-{{site.SPARK_VERSION}}.tar.gz>
+
+Now when starting a Spark application against the cluster, pass a `mesos://`
+or `zk://` URL as the master when creating a `SparkContext`. For example:
 
 {% highlight scala %}
 val conf = new SparkConf()
@@ -25,31 +129,73 @@ val conf = new SparkConf()
 val sc = new SparkContext(conf)
 {% endhighlight %}
 
+When running a shell the `spark.executor.uri` parameter is inherited from `SPARK_EXECUTOR_URI`, so
+it does not need to be redundantly passed in as a system property.
+
+{% highlight bash %}
+./bin/spark-shell --master mesos://host:5050
+{% endhighlight %}
+
+
 # Mesos Run Modes
 
-Spark can run over Mesos in two modes: "fine-grained" and "coarse-grained". In fine-grained mode, which is the default,
-each Spark task runs as a separate Mesos task. This allows multiple instances of Spark (and other frameworks) to share
-machines at a very fine granularity, where each application gets more or fewer machines as it ramps up, but it comes with an
-additional overhead in launching each task, which may be inappropriate for low-latency applications (e.g. interactive queries or serving web requests). The coarse-grained mode will instead
-launch only *one* long-running Spark task on each Mesos machine, and dynamically schedule its own "mini-tasks" within
-it. The benefit is much lower startup overhead, but at the cost of reserving the Mesos resources for the complete duration
-of the application.
+Spark can run over Mesos in two modes: "fine-grained" (default) and "coarse-grained".
+
+In "fine-grained" mode (default), each Spark task runs as a separate Mesos task. This allows
+multiple instances of Spark (and other frameworks) to share machines at a very fine granularity,
+where each application gets more or fewer machines as it ramps up and down, but it comes with an
+additional overhead in launching each task. This mode may be inappropriate for low-latency
+requirements like interactive queries or serving web requests.
+
+The "coarse-grained" mode will instead launch only *one* long-running Spark task on each Mesos
+machine, and dynamically schedule its own "mini-tasks" within it. The benefit is much lower startup
+overhead, but at the cost of reserving the Mesos resources for the complete duration of the
+application.
 
-To run in coarse-grained mode, set the `spark.mesos.coarse` property in your [SparkConf](configuration.html#spark-properties):
+To run in coarse-grained mode, set the `spark.mesos.coarse` property in your
+[SparkConf](configuration.html#spark-properties):
 
 {% highlight scala %}
 conf.set("spark.mesos.coarse", "true")
 {% endhighlight %}
 
-In addition, for coarse-grained mode, you can control the maximum number of resources Spark will acquire. By default,
-it will acquire *all* cores in the cluster (that get offered by Mesos), which only makes sense if you run just one
-application at a time. You can cap the maximum number of cores using `conf.set("spark.cores.max", "10")` (for example).
+In addition, for coarse-grained mode, you can control the maximum number of resources Spark will
+acquire. By default, it will acquire *all* cores in the cluster (that get offered by Mesos), which
+only makes sense if you run just one application at a time. You can cap the maximum number of cores
+using `conf.set("spark.cores.max", "10")` (for example).
 
 
 # Running Alongside Hadoop
 
-You can run Spark and Mesos alongside your existing Hadoop cluster by just launching them as a separate service on the machines. To access Hadoop data from Spark, just use a hdfs:// URL (typically `hdfs://<namenode>:9000/path`, but you can find the right URL on your Hadoop Namenode's web UI).
+You can run Spark and Mesos alongside your existing Hadoop cluster by just launching them as a
+separate service on the machines. To access Hadoop data from Spark, a full hdfs:// URL is required
+(typically `hdfs://<namenode>:9000/path`, but you can find the right URL on your Hadoop Namenode web
+UI).
+
+In addition, it is possible to also run Hadoop MapReduce on Mesos for better resource isolation and
+sharing between the two. In this case, Mesos will act as a unified scheduler that assigns cores to
+either Hadoop or Spark, as opposed to having them share resources via the Linux scheduler on each
+node. Please refer to [Hadoop on Mesos](https://github.com/mesos/hadoop).
+
+In either case, HDFS runs separately from Hadoop MapReduce, without being scheduled through Mesos.
+
+
+# Troubleshooting and Debugging
+
+A few places to look during debugging:
+
+- Mesos master on port `:5050`
+  - Slaves should appear in the slaves tab
+  - Spark applications should appear in the frameworks tab
+  - Tasks should appear in the details of a framework
+  - Check the stdout and stderr of the sandbox of failed tasks
+- Mesos logs
+  - Master and slave logs are both in `/var/log/mesos` by default
 
-In addition, it is possible to also run Hadoop MapReduce on Mesos, to get better resource isolation and sharing between the two. In this case, Mesos will act as a unified scheduler that assigns cores to either Hadoop or Spark, as opposed to having them share resources via the Linux scheduler on each node. Please refer to [Hadoop on Mesos](https://github.com/mesos/hadoop).
+And common pitfalls:
 
-In either case, HDFS runs separately from Hadoop MapReduce, without going through Mesos.
+- Spark assembly not reachable/accessible
+  - Slaves need to be able to download the distribution
+- Firewall blocking communications
+  - Check for messages about failed connections
+  - Temporarily disable firewalls for debugging and then poke appropriate holes

From d58cb33ffa9e98a64cecea7b40ce7bfbed145079 Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Wed, 14 May 2014 09:51:01 -0700
Subject: [PATCH 197/810] SPARK-1828: Created forked version of hive-exec that
 doesn't bundle other dependencies

See https://issues.apache.org/jira/browse/SPARK-1828 for more information.

This is being submitted to Jenkin's for testing. The dependency won't fully
propagate in Maven central for a few more hours.

Author: Patrick Wendell <pwendell@gmail.com>

Closes #767 from pwendell/hive-shaded and squashes the following commits:

ea10ac5 [Patrick Wendell] SPARK-1828: Created forked version of hive-exec that doesn't bundle other dependencies
---
 project/SparkBuild.scala | 6 +++---
 sql/hive/pom.xml         | 6 +++---
 2 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index 57b3e22f8171..8d56b4043138 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -489,9 +489,9 @@ object SparkBuild extends Build {
     name := "spark-hive",
     javaOptions += "-XX:MaxPermSize=1g",
     libraryDependencies ++= Seq(
-      "org.apache.hive" % "hive-metastore" % hiveVersion,
-      "org.apache.hive" % "hive-exec"      % hiveVersion,
-      "org.apache.hive" % "hive-serde"     % hiveVersion
+      "org.spark-project.hive" % "hive-metastore" % hiveVersion,
+      "org.spark-project.hive" % "hive-exec"      % hiveVersion,
+      "org.spark-project.hive" % "hive-serde"     % hiveVersion
     ),
     // Multiple queries rely on the TestHive singleton.  See comments there for more details.
     parallelExecution in Test := false,
diff --git a/sql/hive/pom.xml b/sql/hive/pom.xml
index 8b32451d7604..9254b70e64a0 100644
--- a/sql/hive/pom.xml
+++ b/sql/hive/pom.xml
@@ -44,12 +44,12 @@
       <version>${project.version}</version>
     </dependency>
     <dependency>
-      <groupId>org.apache.hive</groupId>
+      <groupId>org.spark-project.hive</groupId>
       <artifactId>hive-metastore</artifactId>
       <version>${hive.version}</version>
     </dependency>
     <dependency>
-      <groupId>org.apache.hive</groupId>
+      <groupId>org.spark-project.hive</groupId>
       <artifactId>hive-exec</artifactId>
       <version>${hive.version}</version>
       <exclusions>
@@ -64,7 +64,7 @@
       <artifactId>jackson-mapper-asl</artifactId>
     </dependency>
     <dependency>
-      <groupId>org.apache.hive</groupId>
+      <groupId>org.spark-project.hive</groupId>
       <artifactId>hive-serde</artifactId>
       <version>${hive.version}</version>
       <exclusions>

From 17f3075bc4aa8cbed165f7b367f70e84b1bc8db9 Mon Sep 17 00:00:00 2001
From: Mark Hamstra <markhamstra@gmail.com>
Date: Wed, 14 May 2014 10:07:25 -0700
Subject: [PATCH 198/810] [SPARK-1620] Handle uncaught exceptions in function
 run by Akka scheduler

If the intended behavior was that uncaught exceptions thrown in functions being run by the Akka scheduler would end up being handled by the default uncaught exception handler set in Executor, and if that behavior is, in fact, correct, then this is a way to accomplish that.  I'm not certain, though, that we shouldn't be doing something different to handle uncaught exceptions from some of these scheduled functions.

In any event, this PR covers all of the cases I comment on in [SPARK-1620](https://issues.apache.org/jira/browse/SPARK-1620).

Author: Mark Hamstra <markhamstra@gmail.com>

Closes #622 from markhamstra/SPARK-1620 and squashes the following commits:

071d193 [Mark Hamstra] refactored post-SPARK-1772
1a6a35e [Mark Hamstra] another style fix
d30eb94 [Mark Hamstra] scalastyle
3573ecd [Mark Hamstra] Use wrapped try/catch in Utils.tryOrExit
8fc0439 [Mark Hamstra] Make functions run by the Akka scheduler use Executor's UncaughtExceptionHandler
---
 .../apache/spark/deploy/client/AppClient.scala | 18 ++++++++++--------
 .../apache/spark/deploy/worker/Worker.scala    | 18 ++++++++++--------
 .../spark/scheduler/TaskSchedulerImpl.scala    |  3 ++-
 .../apache/spark/storage/BlockManager.scala    |  2 +-
 .../scala/org/apache/spark/util/Utils.scala    | 13 +++++++++++++
 5 files changed, 36 insertions(+), 18 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/client/AppClient.scala b/core/src/main/scala/org/apache/spark/deploy/client/AppClient.scala
index 896913d79660..d38e9e79204c 100644
--- a/core/src/main/scala/org/apache/spark/deploy/client/AppClient.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/client/AppClient.scala
@@ -30,7 +30,7 @@ import org.apache.spark.{Logging, SparkConf, SparkException}
 import org.apache.spark.deploy.{ApplicationDescription, ExecutorState}
 import org.apache.spark.deploy.DeployMessages._
 import org.apache.spark.deploy.master.Master
-import org.apache.spark.util.AkkaUtils
+import org.apache.spark.util.{Utils, AkkaUtils}
 
 /**
  * Interface allowing applications to speak with a Spark deploy cluster. Takes a master URL,
@@ -88,13 +88,15 @@ private[spark] class AppClient(
       var retries = 0
       registrationRetryTimer = Some {
         context.system.scheduler.schedule(REGISTRATION_TIMEOUT, REGISTRATION_TIMEOUT) {
-          retries += 1
-          if (registered) {
-            registrationRetryTimer.foreach(_.cancel())
-          } else if (retries >= REGISTRATION_RETRIES) {
-            markDead("All masters are unresponsive! Giving up.")
-          } else {
-            tryRegisterAllMasters()
+          Utils.tryOrExit {
+            retries += 1
+            if (registered) {
+              registrationRetryTimer.foreach(_.cancel())
+            } else if (retries >= REGISTRATION_RETRIES) {
+              markDead("All masters are unresponsive! Giving up.")
+            } else {
+              tryRegisterAllMasters()
+            }
           }
         }
       }
diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala b/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala
index 85d25dc7dbfa..134624c35a57 100755
--- a/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala
@@ -166,14 +166,16 @@ private[spark] class Worker(
     var retries = 0
     registrationRetryTimer = Some {
       context.system.scheduler.schedule(REGISTRATION_TIMEOUT, REGISTRATION_TIMEOUT) {
-        retries += 1
-        if (registered) {
-          registrationRetryTimer.foreach(_.cancel())
-        } else if (retries >= REGISTRATION_RETRIES) {
-          logError("All masters are unresponsive! Giving up.")
-          System.exit(1)
-        } else {
-          tryRegisterAllMasters()
+        Utils.tryOrExit {
+          retries += 1
+          if (registered) {
+            registrationRetryTimer.foreach(_.cancel())
+          } else if (retries >= REGISTRATION_RETRIES) {
+            logError("All masters are unresponsive! Giving up.")
+            System.exit(1)
+          } else {
+            tryRegisterAllMasters()
+          }
         }
       }
     }
diff --git a/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala b/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala
index ffd1d9432682..649eed213e09 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala
@@ -31,6 +31,7 @@ import scala.util.Random
 import org.apache.spark._
 import org.apache.spark.TaskState.TaskState
 import org.apache.spark.scheduler.SchedulingMode.SchedulingMode
+import org.apache.spark.util.Utils
 
 /**
  * Schedules tasks for multiple types of clusters by acting through a SchedulerBackend.
@@ -139,7 +140,7 @@ private[spark] class TaskSchedulerImpl(
       import sc.env.actorSystem.dispatcher
       sc.env.actorSystem.scheduler.schedule(SPECULATION_INTERVAL milliseconds,
             SPECULATION_INTERVAL milliseconds) {
-        checkSpeculatableTasks()
+        Utils.tryOrExit { checkSpeculatableTasks() }
       }
     }
   }
diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala
index 6d7d4f922e1f..653409581190 100644
--- a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala
+++ b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala
@@ -155,7 +155,7 @@ private[spark] class BlockManager(
     BlockManagerWorker.startBlockManagerWorker(this)
     if (!BlockManager.getDisableHeartBeatsForTesting(conf)) {
       heartBeatTask = actorSystem.scheduler.schedule(0.seconds, heartBeatFrequency.milliseconds) {
-        heartBeat()
+        Utils.tryOrExit { heartBeat() }
       }
     }
   }
diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala
index 99ef6dd1fa6e..d041bfa66a1c 100644
--- a/core/src/main/scala/org/apache/spark/util/Utils.scala
+++ b/core/src/main/scala/org/apache/spark/util/Utils.scala
@@ -40,6 +40,7 @@ import tachyon.client.{TachyonFile,TachyonFS}
 
 import org.apache.spark.{Logging, SecurityManager, SparkConf, SparkException}
 import org.apache.spark.deploy.SparkHadoopUtil
+import org.apache.spark.executor.ExecutorUncaughtExceptionHandler
 import org.apache.spark.serializer.{DeserializationStream, SerializationStream, SerializerInstance}
 
 /**
@@ -780,6 +781,18 @@ private[spark] object Utils extends Logging {
     output.toString
   }
 
+  /**
+   * Execute a block of code that evaluates to Unit, forwarding any uncaught exceptions to the
+   * default UncaughtExceptionHandler
+   */
+  def tryOrExit(block: => Unit) {
+    try {
+      block
+    } catch {
+      case t: Throwable => ExecutorUncaughtExceptionHandler.uncaughtException(t)
+    }
+  }
+
   /**
    * A regular expression to match classes of the "core" Spark API that we want to skip when
    * finding the call site of a method.

From fde82c1549c78f1eebbb21ec34e60befbbff65f5 Mon Sep 17 00:00:00 2001
From: witgo <witgo@qq.com>
Date: Wed, 14 May 2014 11:19:26 -0700
Subject: [PATCH 199/810] Fix: sbt test throw an java.lang.OutOfMemoryError:
 PermGen space

Author: witgo <witgo@qq.com>

Closes #773 from witgo/sbt_javaOptions and squashes the following commits:

26c7d38 [witgo] Improve sbt configuration
---
 .rat-excludes            | 5 +++++
 project/SparkBuild.scala | 1 +
 2 files changed, 6 insertions(+)

diff --git a/.rat-excludes b/.rat-excludes
index 50766954ef07..689467847d80 100644
--- a/.rat-excludes
+++ b/.rat-excludes
@@ -43,3 +43,8 @@ test.out/*
 .*iml
 service.properties
 db.lck
+build/*
+dist/*
+.*out
+.*ipr
+.*iws
diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index 8d56b4043138..6adec556322b 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -183,6 +183,7 @@ object SparkBuild extends Build {
     javaOptions in Test += "-Dspark.testing=1",
     javaOptions in Test += "-Dsun.io.serialization.extendedDebugInfo=true",
     javaOptions in Test ++= System.getProperties.filter(_._1 startsWith "spark").map { case (k,v) => s"-D$k=$v" }.toSeq,
+    javaOptions in Test ++= "-Xmx3g -XX:PermSize=128M -XX:MaxNewSize=256m -XX:MaxPermSize=1g".split(" ").toSeq,
     javaOptions += "-Xmx3g",
     // Show full stack trace and duration in test cases.
     testOptions in Test += Tests.Argument("-oDF"),

From a3315d7f4c7584dae2ee0aa33c6ec9e97b229b48 Mon Sep 17 00:00:00 2001
From: Andrew Ash <andrew@andrewash.com>
Date: Wed, 14 May 2014 12:01:14 -0700
Subject: [PATCH 200/810] SPARK-1829 Sub-second durations shouldn't round to "0
 s"

As "99 ms" up to 99 ms
As "0.1 s" from 0.1 s up to 0.9 s

https://issues.apache.org/jira/browse/SPARK-1829

Compare the first image to the second here: http://imgur.com/RaLEsSZ,7VTlgfo#0

Author: Andrew Ash <andrew@andrewash.com>

Closes #768 from ash211/spark-1829 and squashes the following commits:

1c15b8e [Andrew Ash] SPARK-1829 Format sub-second durations more appropriately
---
 core/src/main/scala/org/apache/spark/ui/UIUtils.scala | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/core/src/main/scala/org/apache/spark/ui/UIUtils.scala b/core/src/main/scala/org/apache/spark/ui/UIUtils.scala
index a3d6a1821245..a43314f48112 100644
--- a/core/src/main/scala/org/apache/spark/ui/UIUtils.scala
+++ b/core/src/main/scala/org/apache/spark/ui/UIUtils.scala
@@ -36,7 +36,13 @@ private[spark] object UIUtils extends Logging {
   def formatDate(timestamp: Long): String = dateFormat.get.format(new Date(timestamp))
 
   def formatDuration(milliseconds: Long): String = {
+    if (milliseconds < 100) {
+      return "%d ms".format(milliseconds)
+    }
     val seconds = milliseconds.toDouble / 1000
+    if (seconds < 1) {
+      return "%.1f s".format(seconds)
+    }
     if (seconds < 60) {
       return "%.0f s".format(seconds)
     }

From 65533c7ec03e7eedf5cd9756822863ab6f034ec9 Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Wed, 14 May 2014 12:53:30 -0700
Subject: [PATCH 201/810] SPARK-1833 - Have an empty SparkContext constructor.

This is nicer than relying on new SparkContext(new SparkConf())

Author: Patrick Wendell <pwendell@gmail.com>

Closes #774 from pwendell/spark-context and squashes the following commits:

ef9f12f [Patrick Wendell] SPARK-1833 - Have an empty SparkContext constructor.
---
 core/src/main/scala/org/apache/spark/SparkContext.scala | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index 032b3d744c61..634c10c33fc8 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -66,6 +66,12 @@ class SparkContext(config: SparkConf) extends Logging {
   // contains a map from hostname to a list of input format splits on the host.
   private[spark] var preferredNodeLocationData: Map[String, Set[SplitInfo]] = Map()
 
+  /**
+   * Create a SparkContext that loads settings from system properties (for instance, when
+   * launching with ./bin/spark-submit).
+   */
+  def this() = this(new SparkConf())
+
   /**
    * :: DeveloperApi ::
    * Alternative constructor for setting preferred locations where Spark will create executors.

From 94c6c06ea13032b80610b3f54401d2ef2aa4874a Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Wed, 14 May 2014 14:57:17 -0700
Subject: [PATCH 202/810] [FIX] do not load defaults when testing SparkConf in
 pyspark

The default constructor loads default properties, which can fail the test.

Author: Xiangrui Meng <meng@databricks.com>

Closes #775 from mengxr/pyspark-conf-fix and squashes the following commits:

83ef6c4 [Xiangrui Meng] do not load defaults when testing SparkConf in pyspark
---
 python/pyspark/conf.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/python/pyspark/conf.py b/python/pyspark/conf.py
index 49b68d57ab02..8eff4a242a52 100644
--- a/python/pyspark/conf.py
+++ b/python/pyspark/conf.py
@@ -33,7 +33,7 @@
 >>> sc.sparkHome == None
 True
 
->>> conf = SparkConf()
+>>> conf = SparkConf(loadDefaults=False)
 >>> conf.setSparkHome("/path")
 <pyspark.conf.SparkConf object at ...>
 >>> conf.get("spark.home")

From 601e37198b97ba52e72ac13213c391c932e97b67 Mon Sep 17 00:00:00 2001
From: Jacek Laskowski <jacek@japila.pl>
Date: Wed, 14 May 2014 15:45:52 -0700
Subject: [PATCH 203/810] String interpolation + some other small changes

After having been invited to make the change in https://github.com/apache/spark/commit/6bee01dd04ef73c6b829110ebcdd622d521ea8ff#commitcomment-6284165 by @witgo.

Author: Jacek Laskowski <jacek@japila.pl>

Closes #748 from jaceklaskowski/sparkenv-string-interpolation and squashes the following commits:

be6ebac [Jacek Laskowski] String interpolation + some other small changes
---
 .../main/scala/org/apache/spark/SparkEnv.scala | 18 ++++++++----------
 1 file changed, 8 insertions(+), 10 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/SparkEnv.scala b/core/src/main/scala/org/apache/spark/SparkEnv.scala
index 19d507c0cf86..720151a6b0f8 100644
--- a/core/src/main/scala/org/apache/spark/SparkEnv.scala
+++ b/core/src/main/scala/org/apache/spark/SparkEnv.scala
@@ -278,10 +278,11 @@ object SparkEnv extends Logging {
       addedJars: Seq[String],
       addedFiles: Seq[String]): Map[String, Seq[(String, String)]] = {
 
+    import Properties._
     val jvmInformation = Seq(
-      ("Java Version", "%s (%s)".format(Properties.javaVersion, Properties.javaVendor)),
-      ("Java Home", Properties.javaHome),
-      ("Scala Version", Properties.versionString)
+      ("Java Version", s"$javaVersion ($javaVendor)"),
+      ("Java Home", javaHome),
+      ("Scala Version", versionString)
     ).sorted
 
     // Spark properties
@@ -296,18 +297,15 @@ object SparkEnv extends Logging {
 
     // System properties that are not java classpaths
     val systemProperties = System.getProperties.iterator.toSeq
-    val otherProperties = systemProperties.filter { case (k, v) =>
+    val otherProperties = systemProperties.filter { case (k, _) =>
       k != "java.class.path" && !k.startsWith("spark.")
     }.sorted
 
     // Class paths including all added jars and files
-    val classPathProperty = systemProperties.find { case (k, v) =>
-      k == "java.class.path"
-    }.getOrElse(("", ""))
-    val classPathEntries = classPathProperty._2
+    val classPathEntries = javaClassPath
       .split(File.pathSeparator)
-      .filterNot(e => e.isEmpty)
-      .map(e => (e, "System Classpath"))
+      .filterNot(_.isEmpty)
+      .map((_, "System Classpath"))
     val addedJarsAndFiles = (addedJars ++ addedFiles).map((_, "Added By User"))
     val classPaths = (addedJarsAndFiles ++ classPathEntries).sorted
 

From e3d72a74ad007c2bf279d6a74cdaca948bdf0ddd Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Wed, 14 May 2014 17:18:30 -0700
Subject: [PATCH 204/810] [SPARK-1696][MLLIB] use alpha in dense dspr

It doesn't affect existing code because only `alpha = 1.0` is used in the code.

Author: Xiangrui Meng <meng@databricks.com>

Closes #778 from mengxr/mllib-dspr-fix and squashes the following commits:

a37402e [Xiangrui Meng] use alpha in dense dspr
---
 .../org/apache/spark/mllib/linalg/distributed/RowMatrix.scala   | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
index b10857fe7c8a..07dfadf2f786 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
@@ -440,7 +440,7 @@ object RowMatrix {
     val n = v.size
     v match {
       case dv: DenseVector =>
-        blas.dspr("U", n, 1.0, dv.values, 1, U)
+        blas.dspr("U", n, alpha, dv.values, 1, U)
       case sv: SparseVector =>
         val indices = sv.indices
         val values = sv.values

From 9ad096d55a3d8410f04056ebc87dbd8cba391870 Mon Sep 17 00:00:00 2001
From: andrewor14 <andrewor14@gmail.com>
Date: Wed, 14 May 2014 17:54:53 -0700
Subject: [PATCH 205/810] [Typo] propertes -> properties

Author: andrewor14 <andrewor14@gmail.com>

Closes #780 from andrewor14/submit-typo and squashes the following commits:

e70e057 [andrewor14] propertes -> properties
---
 core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
index c6d3cbd2e728..e86182e4c56c 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
@@ -68,7 +68,7 @@ object SparkSubmit {
 
   /**
    * @return a tuple containing the arguments for the child, a list of classpath
-   *         entries for the child, a list of system propertes, a list of env vars
+   *         entries for the child, a list of system properties, a list of env vars
    *         and the main class for the child
    */
   private[spark] def createLaunchEnv(args: SparkSubmitArguments): (ArrayBuffer[String],

From 44165fc91a31e6293a79031c89571e139d2c5356 Mon Sep 17 00:00:00 2001
From: wangfei <scnbwf@yeah.net>
Date: Wed, 14 May 2014 17:59:11 -0700
Subject: [PATCH 206/810] [SPARK-1826] fix the head notation of package object
 dsl

Author: wangfei <scnbwf@yeah.net>

Closes #765 from scwf/dslfix and squashes the following commits:

d2d1a9d [wangfei] Update package.scala
66ff53b [wangfei] fix the head notation of package object dsl
---
 .../spark/sql/catalyst/dsl/package.scala      | 21 +++++++++++--------
 1 file changed, 12 insertions(+), 9 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala
index 78d3a1d8096a..3cf163f9a9a7 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala
@@ -31,26 +31,29 @@ import org.apache.spark.sql.catalyst.types._
  * A collection of implicit conversions that create a DSL for constructing catalyst data structures.
  *
  * {{{
- *  scala> import catalyst.dsl._
+ *  scala> import org.apache.spark.sql.catalyst.dsl.expressions._
  *
  *  // Standard operators are added to expressions.
+ *  scala> import org.apache.spark.sql.catalyst.expressions.Literal
  *  scala> Literal(1) + Literal(1)
- *  res1: catalyst.expressions.Add = (1 + 1)
+ *  res0: org.apache.spark.sql.catalyst.expressions.Add = (1 + 1)
  *
  *  // There is a conversion from 'symbols to unresolved attributes.
  *  scala> 'a.attr
- *  res2: catalyst.analysis.UnresolvedAttribute = 'a
+ *  res1: org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute = 'a
  *
  *  // These unresolved attributes can be used to create more complicated expressions.
  *  scala> 'a === 'b
- *  res3: catalyst.expressions.Equals = ('a = 'b)
+ *  res2: org.apache.spark.sql.catalyst.expressions.Equals = ('a = 'b)
  *
  *  // SQL verbs can be used to construct logical query plans.
- *  scala> TestRelation('key.int, 'value.string).where('key === 1).select('value).analyze
- *  res4: catalyst.plans.logical.LogicalPlan =
- *  Project {value#1}
- *   Filter (key#0 = 1)
- *    TestRelation {key#0,value#1}
+ *  scala> import org.apache.spark.sql.catalyst.plans.logical._
+ *  scala> import org.apache.spark.sql.catalyst.dsl.plans._
+ *  scala> LocalRelation('key.int, 'value.string).where('key === 1).select('value).analyze
+ *  res3: org.apache.spark.sql.catalyst.plans.logical.LogicalPlan =
+ *  Project [value#3]
+ *   Filter (key#2 = 1)
+ *    LocalRelation [key#2,value#3], []
  * }}}
  */
 package object dsl {

From 2f639957f0bf70dddf1e698aa9e26007fb58bc67 Mon Sep 17 00:00:00 2001
From: Chen Chao <crazyjvm@gmail.com>
Date: Wed, 14 May 2014 18:20:20 -0700
Subject: [PATCH 207/810] default task number misleading in several places

  private[streaming] def defaultPartitioner(numPartitions: Int = self.ssc.sc.defaultParallelism){
    new HashPartitioner(numPartitions)
  }

it represents that the default task number in Spark Streaming relies on the variable defaultParallelism in SparkContext, which is decided by the config property spark.default.parallelism

the property "spark.default.parallelism" refers to https://github.com/apache/spark/pull/389

Author: Chen Chao <crazyjvm@gmail.com>

Closes #766 from CrazyJvm/patch-7 and squashes the following commits:

0b7efba [Chen Chao] Update streaming-programming-guide.md
cc5b66c [Chen Chao] default task number misleading in several places
---
 docs/streaming-programming-guide.md | 18 ++++++++++--------
 1 file changed, 10 insertions(+), 8 deletions(-)

diff --git a/docs/streaming-programming-guide.md b/docs/streaming-programming-guide.md
index 939599aa6855..0c125eb693a8 100644
--- a/docs/streaming-programming-guide.md
+++ b/docs/streaming-programming-guide.md
@@ -522,9 +522,9 @@ common ones are as follows.
   <td> <b>reduceByKey</b>(<i>func</i>, [<i>numTasks</i>]) </td>
   <td> When called on a DStream of (K, V) pairs, return a new DStream of (K, V) pairs where the
   values for each key are aggregated using the given reduce function. <b>Note:</b> By default,
-  this uses Spark's default number of parallel tasks (2 for local machine, 8 for a cluster) to
-  do the grouping. You can pass an optional <code>numTasks</code> argument to set a different
-  number of tasks.</td>
+  this uses Spark's default number of parallel tasks (2 for local mode, and in cluster mode the number
+  is determined by the config property <code>spark.default.parallelism</code>) to do the grouping.
+  You can pass an optional <code>numTasks</code> argument to set a different number of tasks.</td>
 </tr>
 <tr>
   <td> <b>join</b>(<i>otherStream</i>, [<i>numTasks</i>]) </td>
@@ -743,8 +743,9 @@ said two parameters - <i>windowLength</i> and <i>slideInterval</i>.
   <td> When called on a DStream of (K, V) pairs, returns a new DStream of (K, V)
   pairs where the values for each key are aggregated using the given reduce function <i>func</i>
   over batches in a sliding window. <b>Note:</b> By default, this uses Spark's default number of
-  parallel tasks (2 for local machine, 8 for a cluster) to do the grouping. You can pass an optional
-   <code>numTasks</code> argument to set a different number of tasks.
+  parallel tasks (2 for local mode, and in cluster mode the number is determined by the config
+  property <code>spark.default.parallelism</code>) to do the grouping. You can pass an optional
+  <code>numTasks</code> argument to set a different number of tasks.
   </td>
 </tr>
 <tr>
@@ -956,9 +957,10 @@ before further processing.
 ### Level of Parallelism in Data Processing
 Cluster resources maybe under-utilized if the number of parallel tasks used in any stage of the
 computation is not high enough. For example, for distributed reduce operations like `reduceByKey`
-and `reduceByKeyAndWindow`, the default number of parallel tasks is 8. You can pass the level of
-parallelism as an argument (see the
-[`PairDStreamFunctions`](api/scala/index.html#org.apache.spark.streaming.dstream.PairDStreamFunctions)
+and `reduceByKeyAndWindow`, the default number of parallel tasks is decided by the [config property]
+(configuration.html#spark-properties) `spark.default.parallelism`. You can pass the level of
+parallelism as an argument (see [`PairDStreamFunctions`]
+(api/scala/index.html#org.apache.spark.streaming.dstream.PairDStreamFunctions)
 documentation), or set the [config property](configuration.html#spark-properties)
 `spark.default.parallelism` to change the default.
 

From ad4e60ee7e2c49c24a9972312915f7f7253c7679 Mon Sep 17 00:00:00 2001
From: Tathagata Das <tathagata.das1565@gmail.com>
Date: Wed, 14 May 2014 21:13:41 -0700
Subject: [PATCH 208/810] [SPARK-1840] SparkListenerBus prints out scary error
 message when terminated normally

Running SparkPi example gave this error.
```
Pi is roughly 3.14374
14/05/14 18:16:19 ERROR Utils: Uncaught exception in thread SparkListenerBus
scala.runtime.NonLocalReturnControl$mcV$sp
```
This is due to the catch-all in the SparkListenerBus, which logged control throwable used by scala system

Author: Tathagata Das <tathagata.das1565@gmail.com>

Closes #783 from tdas/controlexception-fix and squashes the following commits:

a466c8d [Tathagata Das] Ignored control exceptions when logging all exceptions.
---
 core/src/main/scala/org/apache/spark/util/Utils.scala | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala
index d041bfa66a1c..388f7222428d 100644
--- a/core/src/main/scala/org/apache/spark/util/Utils.scala
+++ b/core/src/main/scala/org/apache/spark/util/Utils.scala
@@ -1149,6 +1149,8 @@ private[spark] object Utils extends Logging {
     try {
       f
     } catch {
+      case ct: ControlThrowable =>
+        throw ct
       case t: Throwable =>
         logError(s"Uncaught exception in thread ${Thread.currentThread().getName}", t)
         throw t

From f10de042b8e86adf51b70bae2d8589a5cbf02935 Mon Sep 17 00:00:00 2001
From: Matei Zaharia <matei@databricks.com>
Date: Wed, 14 May 2014 21:45:20 -0700
Subject: [PATCH 209/810] Add language tabs and Python version to interactive
 part of quick-start

This is an addition of some stuff that was missed in https://issues.apache.org/jira/browse/SPARK-1567. I've also updated the doc to show submitting the Python application with spark-submit.

Author: Matei Zaharia <matei@databricks.com>

Closes #782 from mateiz/spark-1567-extra and squashes the following commits:

6f8f2aa [Matei Zaharia] tweaks
9ed9874 [Matei Zaharia] tweaks
ae67c3e [Matei Zaharia] tweak
b303ba3 [Matei Zaharia] tweak
1433a4d [Matei Zaharia] Add language tabs and Python version to interactive part of quick-start guide
---
 docs/python-programming-guide.md |   2 +-
 docs/quick-start.md              | 151 +++++++++++++++++++++++++++----
 2 files changed, 133 insertions(+), 20 deletions(-)

diff --git a/docs/python-programming-guide.md b/docs/python-programming-guide.md
index 2ce2c346d7b0..17675acba6bb 100644
--- a/docs/python-programming-guide.md
+++ b/docs/python-programming-guide.md
@@ -121,7 +121,7 @@ IPython also works on a cluster or on multiple cores if you set the `MASTER` env
 # Standalone Programs
 
 PySpark can also be used from standalone Python scripts by creating a SparkContext in your script and running the script using `bin/spark-submit`.
-The Quick Start guide includes a [complete example](quick-start.html#a-standalone-app-in-python) of a standalone Python application.
+The Quick Start guide includes a [complete example](quick-start.html#standalone-applications) of a standalone Python application.
 
 Code dependencies can be deployed by passing .zip or .egg files in the `--py-files` option of `spark-submit`:
 
diff --git a/docs/quick-start.md b/docs/quick-start.md
index a4d01487bb49..33a0df103642 100644
--- a/docs/quick-start.md
+++ b/docs/quick-start.md
@@ -6,7 +6,9 @@ title: Quick Start
 * This will become a table of contents (this text will be scraped).
 {:toc}
 
-This tutorial provides a quick introduction to using Spark. We will first introduce the API through Spark's interactive Scala shell (don't worry if you don't know Scala -- you will not need much for this), then show how to write standalone applications in Scala, Java, and Python.
+This tutorial provides a quick introduction to using Spark. We will first introduce the API through Spark's
+interactive shell (in Python or Scala),
+then show how to write standalone applications in Java, Scala, and Python.
 See the [programming guide](scala-programming-guide.html) for a more complete reference.
 
 To follow along with this guide, first download a packaged release of Spark from the
@@ -17,8 +19,12 @@ you can download a package for any version of Hadoop.
 
 ## Basics
 
-Spark's interactive shell provides a simple way to learn the API, as well as a powerful tool to analyze datasets interactively.
-Start the shell by running the following in the Spark directory.
+Spark's shell provides a simple way to learn the API, as well as a powerful tool to analyze data interactively.
+It is available in either Scala (which runs on the Java VM and is thus a good way to use existing Java libraries)
+or Python. Start it by running the following in the Spark directory:
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
 
     ./bin/spark-shell
 
@@ -33,7 +39,7 @@ RDDs have _[actions](scala-programming-guide.html#actions)_, which return values
 
 {% highlight scala %}
 scala> textFile.count() // Number of items in this RDD
-res0: Long = 74
+res0: Long = 126
 
 scala> textFile.first() // First item in this RDD
 res1: String = # Apache Spark
@@ -53,12 +59,53 @@ scala> textFile.filter(line => line.contains("Spark")).count() // How many lines
 res3: Long = 15
 {% endhighlight %}
 
+</div>
+<div data-lang="python" markdown="1">
+
+    ./bin/pyspark
+
+Spark's primary abstraction is a distributed collection of items called a Resilient Distributed Dataset (RDD). RDDs can be created from Hadoop InputFormats (such as HDFS files) or by transforming other RDDs. Let's make a new RDD from the text of the README file in the Spark source directory:
+
+{% highlight python %}
+>>> textFile = sc.textFile("README.md")
+{% endhighlight %}
+
+RDDs have _[actions](scala-programming-guide.html#actions)_, which return values, and _[transformations](scala-programming-guide.html#transformations)_, which return pointers to new RDDs. Let's start with a few actions:
+
+{% highlight python %}
+>>> textFile.count() # Number of items in this RDD
+126
+
+>>> textFile.first() # First item in this RDD
+u'# Apache Spark'
+{% endhighlight %}
+
+Now let's use a transformation. We will use the [`filter`](scala-programming-guide.html#transformations) transformation to return a new RDD with a subset of the items in the file.
+
+{% highlight python %}
+>>> linesWithSpark = textFile.filter(lambda line: "Spark" in line)
+{% endhighlight %}
+
+We can chain together transformations and actions:
+
+{% highlight python %}
+>>> textFile.filter(lambda line: "Spark" in line).count() # How many lines contain "Spark"?
+15
+{% endhighlight %}
+
+</div>
+</div>
+
+
 ## More on RDD Operations
 RDD actions and transformations can be used for more complex computations. Let's say we want to find the line with the most words:
 
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+
 {% highlight scala %}
 scala> textFile.map(line => line.split(" ").size).reduce((a, b) => if (a > b) a else b)
-res4: Long = 16
+res4: Long = 15
 {% endhighlight %}
 
 This first maps a line to an integer value, creating a new RDD. `reduce` is called on that RDD to find the largest line count. The arguments to `map` and `reduce` are Scala function literals (closures), and can use any language feature or Scala/Java library. For example, we can easily call functions declared elsewhere. We'll use `Math.max()` function to make this code easier to understand:
@@ -68,26 +115,69 @@ scala> import java.lang.Math
 import java.lang.Math
 
 scala> textFile.map(line => line.split(" ").size).reduce((a, b) => Math.max(a, b))
-res5: Int = 16
+res5: Int = 15
 {% endhighlight %}
 
 One common data flow pattern is MapReduce, as popularized by Hadoop. Spark can implement MapReduce flows easily:
 
 {% highlight scala %}
 scala> val wordCounts = textFile.flatMap(line => line.split(" ")).map(word => (word, 1)).reduceByKey((a, b) => a + b)
-wordCounts: spark.RDD[(java.lang.String, Int)] = spark.ShuffledAggregatedRDD@71f027b8
+wordCounts: spark.RDD[(String, Int)] = spark.ShuffledAggregatedRDD@71f027b8
 {% endhighlight %}
 
 Here, we combined the [`flatMap`](scala-programming-guide.html#transformations), [`map`](scala-programming-guide.html#transformations) and [`reduceByKey`](scala-programming-guide.html#transformations) transformations to compute the per-word counts in the file as an RDD of (String, Int) pairs. To collect the word counts in our shell, we can use the [`collect`](scala-programming-guide.html#actions) action:
 
 {% highlight scala %}
 scala> wordCounts.collect()
-res6: Array[(java.lang.String, Int)] = Array((need,2), ("",43), (Extra,3), (using,1), (passed,1), (etc.,1), (its,1), (`/usr/local/lib/libmesos.so`,1), (`SCALA_HOME`,1), (option,1), (these,1), (#,1), (`PATH`,,2), (200,1), (To,3),...
+res6: Array[(String, Int)] = Array((means,1), (under,2), (this,3), (Because,1), (Python,2), (agree,1), (cluster.,1), ...)
 {% endhighlight %}
 
+</div>
+<div data-lang="python" markdown="1">
+
+{% highlight python %}
+>>> textFile.map(lambda line: len(line.split())).reduce(lambda a, b: a if (a > b) else b)
+15
+{% endhighlight %}
+
+This first maps a line to an integer value, creating a new RDD. `reduce` is called on that RDD to find the largest line count. The arguments to `map` and `reduce` are Python [anonymous functions (lambdas)](https://docs.python.org/2/reference/expressions.html#lambda),
+but we can also pass any top-level Python function we want.
+For example, we'll define a `max` function to make this code easier to understand:
+
+{% highlight python %}
+>>> def max(a, b):
+...     if a > b:
+...         return a
+...     else:
+...         return b
+...
+
+>>> textFile.map(lambda line: len(line.split())).reduce(max)
+15
+{% endhighlight %}
+
+One common data flow pattern is MapReduce, as popularized by Hadoop. Spark can implement MapReduce flows easily:
+
+{% highlight python %}
+>>> wordCounts = textFile.flatMap(lambda line: line.split()).map(lambda word: (word, 1)).reduceByKey(lambda a, b: a+b)
+{% endhighlight %}
+
+Here, we combined the [`flatMap`](scala-programming-guide.html#transformations), [`map`](scala-programming-guide.html#transformations) and [`reduceByKey`](scala-programming-guide.html#transformations) transformations to compute the per-word counts in the file as an RDD of (string, int) pairs. To collect the word counts in our shell, we can use the [`collect`](scala-programming-guide.html#actions) action:
+
+{% highlight python %}
+>>> wordCounts.collect()
+[(u'and', 9), (u'A', 1), (u'webpage', 1), (u'README', 1), (u'Note', 1), (u'"local"', 1), (u'variable', 1), ...]
+{% endhighlight %}
+
+</div>
+</div>
+
 ## Caching
 Spark also supports pulling data sets into a cluster-wide in-memory cache. This is very useful when data is accessed repeatedly, such as when querying a small "hot" dataset or when running an iterative algorithm like PageRank. As a simple example, let's mark our `linesWithSpark` dataset to be cached:
 
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+
 {% highlight scala %}
 scala> linesWithSpark.cache()
 res7: spark.RDD[String] = spark.FilteredRDD@17e51082
@@ -99,12 +189,33 @@ scala> linesWithSpark.count()
 res9: Long = 15
 {% endhighlight %}
 
-It may seem silly to use Spark to explore and cache a 30-line text file. The interesting part is
+It may seem silly to use Spark to explore and cache a 100-line text file. The interesting part is
 that these same functions can be used on very large data sets, even when they are striped across
 tens or hundreds of nodes. You can also do this interactively by connecting `bin/spark-shell` to
 a cluster, as described in the [programming guide](scala-programming-guide.html#initializing-spark).
 
-# A Standalone Application
+</div>
+<div data-lang="python" markdown="1">
+
+{% highlight python %}
+>>> linesWithSpark.cache()
+
+>>> linesWithSpark.count()
+15
+
+>>> linesWithSpark.count()
+15
+{% endhighlight %}
+
+It may seem silly to use Spark to explore and cache a 100-line text file. The interesting part is
+that these same functions can be used on very large data sets, even when they are striped across
+tens or hundreds of nodes. You can also do this interactively by connecting `bin/pyspark` to
+a cluster, as described in the [programming guide](scala-programming-guide.html#initializing-spark).
+
+</div>
+</div>
+
+# Standalone Applications
 Now say we wanted to write a standalone application using the Spark API. We will walk through a
 simple application in both Scala (with SBT), Java (with Maven), and Python.
 
@@ -115,7 +226,7 @@ We'll create a very simple Spark application in Scala. So simple, in fact, that
 named `SimpleApp.scala`:
 
 {% highlight scala %}
-/*** SimpleApp.scala ***/
+/* SimpleApp.scala */
 import org.apache.spark.SparkContext
 import org.apache.spark.SparkContext._
 import org.apache.spark.SparkConf
@@ -194,7 +305,7 @@ This example will use Maven to compile an application jar, but any similar build
 We'll create a very simple Spark application, `SimpleApp.java`:
 
 {% highlight java %}
-/*** SimpleApp.java ***/
+/* SimpleApp.java */
 import org.apache.spark.api.java.*;
 import org.apache.spark.SparkConf;
 import org.apache.spark.api.java.function.Function;
@@ -309,16 +420,18 @@ Note that you'll need to replace YOUR_SPARK_HOME with the location where Spark i
 As with the Scala and Java examples, we use a SparkContext to create RDDs.
 We can pass Python functions to Spark, which are automatically serialized along with any variables
 that they reference.
-For applications that use custom classes or third-party libraries, we can add those code
-dependencies to SparkContext to ensure that they will be available on remote machines; this is
-described in more detail in the [Python programming guide](python-programming-guide.html).
+For applications that use custom classes or third-party libraries, we can also add code
+dependencies to `spark-submit` through its `--py-files` argument by packaging them into a
+.zip file (see `spark-submit --help` for details).
 `SimpleApp` is simple enough that we do not need to specify any code dependencies.
 
-We can run this application using the `bin/pyspark` script:
+We can run this application using the `bin/spark-submit` script:
 
 {% highlight python %}
-$ cd $SPARK_HOME
-$ ./bin/pyspark SimpleApp.py
+# Use spark-submit to run your application
+$ YOUR_SPARK_HOME/bin/spark-submit \
+  --master local[4] \
+  SimpleApp.py
 ...
 Lines with a: 46, Lines with b: 23
 {% endhighlight python %}
@@ -326,7 +439,7 @@ Lines with a: 46, Lines with b: 23
 </div>
 </div>
 
-# Where to go from here
+# Where to Go from Here
 Congratulations on running your first Spark application!
 
 * For an in-depth overview of the API see "Programming Guides" menu section.

From 21570b463388194877003318317aafd842800cac Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Wed, 14 May 2014 22:24:04 -0700
Subject: [PATCH 210/810] Documentation: Encourage use of reduceByKey instead
 of groupByKey.

Author: Patrick Wendell <pwendell@gmail.com>

Closes #784 from pwendell/group-by-key and squashes the following commits:

9b4505f [Patrick Wendell] Small fix
6347924 [Patrick Wendell] Documentation: Encourage use of reduceByKey instead of groupByKey.
---
 .../org/apache/spark/api/java/JavaPairRDD.scala      | 12 ++++++++++++
 .../org/apache/spark/rdd/PairRDDFunctions.scala      | 12 ++++++++++++
 docs/scala-programming-guide.md                      |  4 ++++
 python/pyspark/rdd.py                                |  4 ++++
 4 files changed, 32 insertions(+)

diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala b/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala
index 554c06535864..4c8f9ed6fbc0 100644
--- a/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala
+++ b/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala
@@ -263,6 +263,10 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])
   /**
    * Group the values for each key in the RDD into a single sequence. Allows controlling the
    * partitioning of the resulting key-value pair RDD by passing a Partitioner.
+   *
+   * Note: If you are grouping in order to perform an aggregation (such as a sum or average) over
+   * each key, using [[JavaPairRDD.reduceByKey]] or [[JavaPairRDD.combineByKey]]
+   * will provide much better performance.
    */
   def groupByKey(partitioner: Partitioner): JavaPairRDD[K, JIterable[V]] =
     fromRDD(groupByResultToJava(rdd.groupByKey(partitioner)))
@@ -270,6 +274,10 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])
   /**
    * Group the values for each key in the RDD into a single sequence. Hash-partitions the
    * resulting RDD with into `numPartitions` partitions.
+   *
+   * Note: If you are grouping in order to perform an aggregation (such as a sum or average) over
+   * each key, using [[JavaPairRDD.reduceByKey]] or [[JavaPairRDD.combineByKey]]
+   * will provide much better performance.
    */
   def groupByKey(numPartitions: Int): JavaPairRDD[K, JIterable[V]] =
     fromRDD(groupByResultToJava(rdd.groupByKey(numPartitions)))
@@ -380,6 +388,10 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])
   /**
    * Group the values for each key in the RDD into a single sequence. Hash-partitions the
    * resulting RDD with the existing partitioner/parallelism level.
+   *
+   * Note: If you are grouping in order to perform an aggregation (such as a sum or average) over
+   * each key, using [[JavaPairRDD.reduceByKey]] or [[JavaPairRDD.combineByKey]]
+   * will provide much better performance.
    */
   def groupByKey(): JavaPairRDD[K, JIterable[V]] =
     fromRDD(groupByResultToJava(rdd.groupByKey()))
diff --git a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
index bc6d204434ad..223fef79261d 100644
--- a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
@@ -264,6 +264,10 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
   /**
    * Group the values for each key in the RDD into a single sequence. Allows controlling the
    * partitioning of the resulting key-value pair RDD by passing a Partitioner.
+   *
+   * Note: If you are grouping in order to perform an aggregation (such as a sum or average) over
+   * each key, using [[PairRDDFunctions.reduceByKey]] or [[PairRDDFunctions.combineByKey]]
+   * will provide much better performance.
    */
   def groupByKey(partitioner: Partitioner): RDD[(K, Iterable[V])] = {
     // groupByKey shouldn't use map side combine because map side combine does not
@@ -280,6 +284,10 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
   /**
    * Group the values for each key in the RDD into a single sequence. Hash-partitions the
    * resulting RDD with into `numPartitions` partitions.
+   *
+   * Note: If you are grouping in order to perform an aggregation (such as a sum or average) over
+   * each key, using [[PairRDDFunctions.reduceByKey]] or [[PairRDDFunctions.combineByKey]]
+   * will provide much better performance.
    */
   def groupByKey(numPartitions: Int): RDD[(K, Iterable[V])] = {
     groupByKey(new HashPartitioner(numPartitions))
@@ -365,6 +373,10 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
   /**
    * Group the values for each key in the RDD into a single sequence. Hash-partitions the
    * resulting RDD with the existing partitioner/parallelism level.
+   *
+   * Note: If you are grouping in order to perform an aggregation (such as a sum or average) over
+   * each key, using [[PairRDDFunctions.reduceByKey]] or [[PairRDDFunctions.combineByKey]]
+   * will provide much better performance,
    */
   def groupByKey(): RDD[(K, Iterable[V])] = {
     groupByKey(defaultPartitioner(self))
diff --git a/docs/scala-programming-guide.md b/docs/scala-programming-guide.md
index 3ed86e460c01..edaa7d0639a1 100644
--- a/docs/scala-programming-guide.md
+++ b/docs/scala-programming-guide.md
@@ -196,6 +196,10 @@ The following tables list the transformations and actions currently supported (s
 <tr>
   <td> <b>groupByKey</b>([<i>numTasks</i>]) </td>
   <td> When called on a dataset of (K, V) pairs, returns a dataset of (K, Seq[V]) pairs. <br />
+<b>Note:</b> If you are grouping in order to perform an aggregation (such as a sum or 
+  average) over each key, using `reduceByKey` or `combineByKey` will yield much better 
+  performance.
+<br />
 <b>Note:</b> By default, if the RDD already has a partitioner, the task number is decided by the partition number of the partitioner, or else relies on the value of <code>spark.default.parallelism</code> if the property is set , otherwise depends on the partition number of the RDD. You can pass an optional <code>numTasks</code> argument to set a different number of tasks.
   </td>
 </tr>
diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py
index 4f74824ba4cf..07578b8d937f 100644
--- a/python/pyspark/rdd.py
+++ b/python/pyspark/rdd.py
@@ -1152,6 +1152,10 @@ def groupByKey(self, numPartitions=None):
         Group the values for each key in the RDD into a single sequence.
         Hash-partitions the resulting RDD with into numPartitions partitions.
 
+        Note: If you are grouping in order to perform an aggregation (such as a
+        sum or average) over each key, using reduceByKey will provide much better
+        performance.
+
         >>> x = sc.parallelize([("a", 1), ("b", 1), ("a", 1)])
         >>> map((lambda (x,y): (x, list(y))), sorted(x.groupByKey().collect()))
         [('a', [1, 1]), ('b', [1])]

From 46324279dae2fa803267d788f7c56b0ed643b4c8 Mon Sep 17 00:00:00 2001
From: Prashant Sharma <prashant.s@imaginea.com>
Date: Wed, 14 May 2014 22:24:41 -0700
Subject: [PATCH 211/810] Package docs

This is a few changes based on the original patch by @scrapcodes.

Author: Prashant Sharma <prashant.s@imaginea.com>
Author: Patrick Wendell <pwendell@gmail.com>

Closes #785 from pwendell/package-docs and squashes the following commits:

c32b731 [Patrick Wendell] Changes based on Prashant's patch
c0463d3 [Prashant Sharma] added eof new line
ce8bf73 [Prashant Sharma] Added eof new line to all files.
4c35f2e [Prashant Sharma] SPARK-1563 Add package-info.java and package.scala files for all packages that appear in docs
---
 .../org/apache/spark/bagel/package-info.java  | 21 ++++++++++++++
 .../org/apache/spark/bagel/package.scala      | 23 +++++++++++++++
 .../spark/api/java/function/package-info.java | 23 +++++++++++++++
 .../spark/api/java/function/package.scala     | 25 +++++++++++++++++
 .../apache/spark/annotation/package-info.java | 23 +++++++++++++++
 .../org/apache/spark/annotation/package.scala | 25 +++++++++++++++++
 .../apache/spark/api/java/package-info.java   | 21 ++++++++++++++
 .../apache/spark/broadcast/package-info.java  | 21 ++++++++++++++
 .../org/apache/spark/broadcast/package.scala  |  2 +-
 .../apache/spark/executor/package-info.java   | 21 ++++++++++++++
 .../org/apache/spark/executor/package.scala   | 24 ++++++++++++++++
 .../org/apache/spark/io/package-info.java     | 21 ++++++++++++++
 .../scala/org/apache/spark/io/package.scala   | 23 +++++++++++++++
 .../apache/spark/metrics/sink/package.scala   | 23 +++++++++++++++
 .../apache/spark/metrics/source/package.scala | 23 +++++++++++++++
 .../org/apache/spark/partial/package.scala    | 28 +++++++++++++++++++
 .../org/apache/spark/rdd/package-info.java    | 21 ++++++++++++++
 .../scala/org/apache/spark/rdd/package.scala  | 23 +++++++++++++++
 .../apache/spark/scheduler/package-info.java  | 21 ++++++++++++++
 .../org/apache/spark/scheduler/package.scala  | 24 ++++++++++++++++
 .../apache/spark/serializer/package-info.java | 21 ++++++++++++++
 .../org/apache/spark/serializer/package.scala | 25 +++++++++++++++++
 .../org/apache/spark/util/package-info.java   | 21 ++++++++++++++
 .../scala/org/apache/spark/util/package.scala | 23 +++++++++++++++
 .../spark/util/random/package-info.java       | 21 ++++++++++++++
 .../apache/spark/util/random/package.scala    | 23 +++++++++++++++
 .../spark/streaming/flume/package-info.java   | 21 ++++++++++++++
 .../spark/streaming/flume/package.scala       | 23 +++++++++++++++
 .../spark/streaming/kafka/package-info.java   | 21 ++++++++++++++
 .../spark/streaming/kafka/package.scala       | 23 +++++++++++++++
 .../spark/streaming/mqtt/package-info.java    | 21 ++++++++++++++
 .../apache/spark/streaming/mqtt/package.scala | 23 +++++++++++++++
 .../spark/streaming/twitter/package-info.java | 21 ++++++++++++++
 .../spark/streaming/twitter/package.scala     | 23 +++++++++++++++
 .../spark/streaming/zeromq/package-info.java  | 21 ++++++++++++++
 .../spark/streaming/zeromq/package.scala      | 23 +++++++++++++++
 .../apache/spark/graphx/lib/package-info.java | 21 ++++++++++++++
 .../org/apache/spark/graphx/lib/package.scala | 23 +++++++++++++++
 .../org/apache/spark/graphx/package-info.java | 22 +++++++++++++++
 .../spark/graphx/util/package-info.java       | 21 ++++++++++++++
 .../apache/spark/graphx/util/package.scala    | 23 +++++++++++++++
 .../spark/mllib/api/python/package.scala      | 25 +++++++++++++++++
 .../org/apache/spark/mllib/package-info.java  | 21 ++++++++++++++
 .../org/apache/spark/mllib/package.scala      | 23 +++++++++++++++
 .../org/apache/spark/sql/package-info.java    | 21 ++++++++++++++
 .../apache/spark/sql/hive/package-info.java   | 18 ++++++++++++
 .../org/apache/spark/sql/hive/package.scala   | 20 +++++++++++++
 .../streaming/api/java/package-info.java      | 21 ++++++++++++++
 .../spark/streaming/api/java/package.scala    | 23 +++++++++++++++
 .../spark/streaming/dstream/package-info.java | 21 ++++++++++++++
 .../spark/streaming/dstream/package.scala     | 24 ++++++++++++++++
 51 files changed, 1116 insertions(+), 1 deletion(-)
 create mode 100644 bagel/src/main/scala/org/apache/spark/bagel/package-info.java
 create mode 100644 bagel/src/main/scala/org/apache/spark/bagel/package.scala
 create mode 100644 core/src/main/java/org/apache/spark/api/java/function/package-info.java
 create mode 100644 core/src/main/java/org/apache/spark/api/java/function/package.scala
 create mode 100644 core/src/main/scala/org/apache/spark/annotation/package-info.java
 create mode 100644 core/src/main/scala/org/apache/spark/annotation/package.scala
 create mode 100644 core/src/main/scala/org/apache/spark/api/java/package-info.java
 create mode 100644 core/src/main/scala/org/apache/spark/broadcast/package-info.java
 create mode 100644 core/src/main/scala/org/apache/spark/executor/package-info.java
 create mode 100644 core/src/main/scala/org/apache/spark/executor/package.scala
 create mode 100644 core/src/main/scala/org/apache/spark/io/package-info.java
 create mode 100644 core/src/main/scala/org/apache/spark/io/package.scala
 create mode 100644 core/src/main/scala/org/apache/spark/metrics/sink/package.scala
 create mode 100644 core/src/main/scala/org/apache/spark/metrics/source/package.scala
 create mode 100644 core/src/main/scala/org/apache/spark/partial/package.scala
 create mode 100644 core/src/main/scala/org/apache/spark/rdd/package-info.java
 create mode 100644 core/src/main/scala/org/apache/spark/rdd/package.scala
 create mode 100644 core/src/main/scala/org/apache/spark/scheduler/package-info.java
 create mode 100644 core/src/main/scala/org/apache/spark/scheduler/package.scala
 create mode 100644 core/src/main/scala/org/apache/spark/serializer/package-info.java
 create mode 100644 core/src/main/scala/org/apache/spark/serializer/package.scala
 create mode 100644 core/src/main/scala/org/apache/spark/util/package-info.java
 create mode 100644 core/src/main/scala/org/apache/spark/util/package.scala
 create mode 100644 core/src/main/scala/org/apache/spark/util/random/package-info.java
 create mode 100644 core/src/main/scala/org/apache/spark/util/random/package.scala
 create mode 100644 external/flume/src/main/scala/org/apache/spark/streaming/flume/package-info.java
 create mode 100644 external/flume/src/main/scala/org/apache/spark/streaming/flume/package.scala
 create mode 100644 external/kafka/src/main/scala/org/apache/spark/streaming/kafka/package-info.java
 create mode 100644 external/kafka/src/main/scala/org/apache/spark/streaming/kafka/package.scala
 create mode 100644 external/mqtt/src/main/scala/org/apache/spark/streaming/mqtt/package-info.java
 create mode 100644 external/mqtt/src/main/scala/org/apache/spark/streaming/mqtt/package.scala
 create mode 100644 external/twitter/src/main/scala/org/apache/spark/streaming/twitter/package-info.java
 create mode 100644 external/twitter/src/main/scala/org/apache/spark/streaming/twitter/package.scala
 create mode 100644 external/zeromq/src/main/scala/org/apache/spark/streaming/zeromq/package-info.java
 create mode 100644 external/zeromq/src/main/scala/org/apache/spark/streaming/zeromq/package.scala
 create mode 100644 graphx/src/main/scala/org/apache/spark/graphx/lib/package-info.java
 create mode 100644 graphx/src/main/scala/org/apache/spark/graphx/lib/package.scala
 create mode 100644 graphx/src/main/scala/org/apache/spark/graphx/package-info.java
 create mode 100644 graphx/src/main/scala/org/apache/spark/graphx/util/package-info.java
 create mode 100644 graphx/src/main/scala/org/apache/spark/graphx/util/package.scala
 create mode 100644 mllib/src/main/scala/org/apache/spark/mllib/api/python/package.scala
 create mode 100644 mllib/src/main/scala/org/apache/spark/mllib/package-info.java
 create mode 100644 mllib/src/main/scala/org/apache/spark/mllib/package.scala
 create mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/package-info.java
 create mode 100644 sql/hive/src/main/scala/org/apache/spark/sql/hive/package-info.java
 create mode 100644 sql/hive/src/main/scala/org/apache/spark/sql/hive/package.scala
 create mode 100644 streaming/src/main/scala/org/apache/spark/streaming/api/java/package-info.java
 create mode 100644 streaming/src/main/scala/org/apache/spark/streaming/api/java/package.scala
 create mode 100644 streaming/src/main/scala/org/apache/spark/streaming/dstream/package-info.java
 create mode 100644 streaming/src/main/scala/org/apache/spark/streaming/dstream/package.scala

diff --git a/bagel/src/main/scala/org/apache/spark/bagel/package-info.java b/bagel/src/main/scala/org/apache/spark/bagel/package-info.java
new file mode 100644
index 000000000000..81f26f276549
--- /dev/null
+++ b/bagel/src/main/scala/org/apache/spark/bagel/package-info.java
@@ -0,0 +1,21 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/**
+ * Bagel: An implementation of Pregel in Spark. THIS IS DEPRECATED - use Spark's GraphX library.
+ */
+package org.apache.spark.bagel;
\ No newline at end of file
diff --git a/bagel/src/main/scala/org/apache/spark/bagel/package.scala b/bagel/src/main/scala/org/apache/spark/bagel/package.scala
new file mode 100644
index 000000000000..2fb193457978
--- /dev/null
+++ b/bagel/src/main/scala/org/apache/spark/bagel/package.scala
@@ -0,0 +1,23 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark
+
+/**
+ * Bagel: An implementation of Pregel in Spark. THIS IS DEPRECATED - use Spark's GraphX library.
+ */
+package object bagel
diff --git a/core/src/main/java/org/apache/spark/api/java/function/package-info.java b/core/src/main/java/org/apache/spark/api/java/function/package-info.java
new file mode 100644
index 000000000000..463a42f23342
--- /dev/null
+++ b/core/src/main/java/org/apache/spark/api/java/function/package-info.java
@@ -0,0 +1,23 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/**
+ * Set of interfaces to represent functions in Spark's Java API. Users create implementations of
+ * these interfaces to pass functions to various Java API methods for Spark. Please visit Spark's
+ * Java programming guide for more details.
+ */
+package org.apache.spark.api.java.function;
\ No newline at end of file
diff --git a/core/src/main/java/org/apache/spark/api/java/function/package.scala b/core/src/main/java/org/apache/spark/api/java/function/package.scala
new file mode 100644
index 000000000000..7f91de653a64
--- /dev/null
+++ b/core/src/main/java/org/apache/spark/api/java/function/package.scala
@@ -0,0 +1,25 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.api.java
+
+/**
+ * Set of interfaces to represent functions in Spark's Java API. Users create implementations of
+ * these interfaces to pass functions to various Java API methods for Spark. Please visit Spark's
+ * Java programming guide for more details.
+ */
+package object function 
\ No newline at end of file
diff --git a/core/src/main/scala/org/apache/spark/annotation/package-info.java b/core/src/main/scala/org/apache/spark/annotation/package-info.java
new file mode 100644
index 000000000000..12c7afe6f108
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/annotation/package-info.java
@@ -0,0 +1,23 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/**
+ * Spark annotations to mark an API experimental or intended only for advanced usages by developers.
+ * This package consist of these annotations, which are used project wide and are reflected in
+ * Scala and Java docs.
+ */
+package org.apache.spark.annotation;
\ No newline at end of file
diff --git a/core/src/main/scala/org/apache/spark/annotation/package.scala b/core/src/main/scala/org/apache/spark/annotation/package.scala
new file mode 100644
index 000000000000..c3f4026a29d4
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/annotation/package.scala
@@ -0,0 +1,25 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark
+
+/**
+ * Spark annotations to mark an API experimental or intended only for advanced usages by developers.
+ * This package consist of these annotations, which are used project wide and are reflected in
+ * Scala and Java docs.
+ */
+package object annotation
diff --git a/core/src/main/scala/org/apache/spark/api/java/package-info.java b/core/src/main/scala/org/apache/spark/api/java/package-info.java
new file mode 100644
index 000000000000..10a480fc78e4
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/api/java/package-info.java
@@ -0,0 +1,21 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/**
+ * Spark Java programming APIs.
+ */
+package org.apache.spark.api.java;
\ No newline at end of file
diff --git a/core/src/main/scala/org/apache/spark/broadcast/package-info.java b/core/src/main/scala/org/apache/spark/broadcast/package-info.java
new file mode 100644
index 000000000000..1510e6e84c7a
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/broadcast/package-info.java
@@ -0,0 +1,21 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/**
+ * Spark's broadcast variables, used to broadcast immutable datasets to all nodes.
+ */
+package org.apache.spark.broadcast;
\ No newline at end of file
diff --git a/core/src/main/scala/org/apache/spark/broadcast/package.scala b/core/src/main/scala/org/apache/spark/broadcast/package.scala
index 01bf88629a7d..4f2c3c07161a 100644
--- a/core/src/main/scala/org/apache/spark/broadcast/package.scala
+++ b/core/src/main/scala/org/apache/spark/broadcast/package.scala
@@ -18,7 +18,7 @@
 package org.apache.spark
 
 /**
- * Package for broadcast variables. See [[broadcast.Broadcast]] for details.
+ * Spark's broadcast variables, used to broadcast immutable datasets to all nodes.
  */
 package object broadcast {
   // For package docs only
diff --git a/core/src/main/scala/org/apache/spark/executor/package-info.java b/core/src/main/scala/org/apache/spark/executor/package-info.java
new file mode 100644
index 000000000000..dd3b6815fb45
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/executor/package-info.java
@@ -0,0 +1,21 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/**
+ * Package for executor components used with various cluster managers.
+ */
+package org.apache.spark.executor;
\ No newline at end of file
diff --git a/core/src/main/scala/org/apache/spark/executor/package.scala b/core/src/main/scala/org/apache/spark/executor/package.scala
new file mode 100644
index 000000000000..ef471d761d4b
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/executor/package.scala
@@ -0,0 +1,24 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark
+
+/**
+ * Executor components used with various cluster managers.
+ * See [[org.apache.spark.executor.Executor]].
+ */
+package object executor
diff --git a/core/src/main/scala/org/apache/spark/io/package-info.java b/core/src/main/scala/org/apache/spark/io/package-info.java
new file mode 100644
index 000000000000..bea1bfdb6375
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/io/package-info.java
@@ -0,0 +1,21 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/**
+ * IO codecs used for compression.
+ */
+package org.apache.spark.io;
\ No newline at end of file
diff --git a/core/src/main/scala/org/apache/spark/io/package.scala b/core/src/main/scala/org/apache/spark/io/package.scala
new file mode 100644
index 000000000000..f987e66a5dc4
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/io/package.scala
@@ -0,0 +1,23 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark
+
+/**
+ * IO codecs used for compression. See [[org.apache.spark.io.CompressionCodec]].
+ */
+package object io
diff --git a/core/src/main/scala/org/apache/spark/metrics/sink/package.scala b/core/src/main/scala/org/apache/spark/metrics/sink/package.scala
new file mode 100644
index 000000000000..90e3aa70b99e
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/metrics/sink/package.scala
@@ -0,0 +1,23 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.metrics
+
+/**
+ * Sinks used in Spark's metrics system.
+ */
+package object sink 
diff --git a/core/src/main/scala/org/apache/spark/metrics/source/package.scala b/core/src/main/scala/org/apache/spark/metrics/source/package.scala
new file mode 100644
index 000000000000..1e59fed6f7d7
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/metrics/source/package.scala
@@ -0,0 +1,23 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.metrics
+
+/**
+ * Sources used in Spark's metrics system.
+ */
+package object source
diff --git a/core/src/main/scala/org/apache/spark/partial/package.scala b/core/src/main/scala/org/apache/spark/partial/package.scala
new file mode 100644
index 000000000000..62dc5cd25a16
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/partial/package.scala
@@ -0,0 +1,28 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark
+
+/**
+ * :: Experimental ::
+ *
+ * Support for approximate results. This provides convenient api and also implementation for
+ * approximate calculation.
+ *
+ * @see [[org.apache.spark.rdd.RDD.countApprox]]
+ */
+package object partial
diff --git a/core/src/main/scala/org/apache/spark/rdd/package-info.java b/core/src/main/scala/org/apache/spark/rdd/package-info.java
new file mode 100644
index 000000000000..176cc58179fb
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/rdd/package-info.java
@@ -0,0 +1,21 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/**
+ * Provides implementation's of various RDDs.
+ */
+package org.apache.spark.rdd;
\ No newline at end of file
diff --git a/core/src/main/scala/org/apache/spark/rdd/package.scala b/core/src/main/scala/org/apache/spark/rdd/package.scala
new file mode 100644
index 000000000000..55fc6e4d2b4d
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/rdd/package.scala
@@ -0,0 +1,23 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark
+
+/**
+ * Provides several RDD implementations. See [[org.apache.spark.rdd.RDD]].
+ */
+package object rdd
diff --git a/core/src/main/scala/org/apache/spark/scheduler/package-info.java b/core/src/main/scala/org/apache/spark/scheduler/package-info.java
new file mode 100644
index 000000000000..5b4a628d3cee
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/scheduler/package-info.java
@@ -0,0 +1,21 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/**
+ * Spark's DAG scheduler.
+ */
+package org.apache.spark.scheduler;
\ No newline at end of file
diff --git a/core/src/main/scala/org/apache/spark/scheduler/package.scala b/core/src/main/scala/org/apache/spark/scheduler/package.scala
new file mode 100644
index 000000000000..f0dbfc2ac5f4
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/scheduler/package.scala
@@ -0,0 +1,24 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark
+
+/**
+ * Spark's scheduling components. This includes the [[org.apache.spark.scheduler.DAGScheduler]] and
+ * lower level [[org.apache.spark.scheduler.TaskScheduler]].
+ */
+package object scheduler
diff --git a/core/src/main/scala/org/apache/spark/serializer/package-info.java b/core/src/main/scala/org/apache/spark/serializer/package-info.java
new file mode 100644
index 000000000000..4c0b73ab36a0
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/serializer/package-info.java
@@ -0,0 +1,21 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/**
+ * Pluggable serializers for RDD and shuffle data.
+ */
+package org.apache.spark.serializer;
\ No newline at end of file
diff --git a/core/src/main/scala/org/apache/spark/serializer/package.scala b/core/src/main/scala/org/apache/spark/serializer/package.scala
new file mode 100644
index 000000000000..8f09ef6f5b34
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/serializer/package.scala
@@ -0,0 +1,25 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark
+
+/**
+ * Pluggable serializers for RDD and shuffle data.
+ *
+ * @see [[org.apache.spark.serializer.Serializer]]
+ */
+package object serializer
diff --git a/core/src/main/scala/org/apache/spark/util/package-info.java b/core/src/main/scala/org/apache/spark/util/package-info.java
new file mode 100644
index 000000000000..819f54ee41a7
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/util/package-info.java
@@ -0,0 +1,21 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/**
+ * Spark utilities.
+ */
+package org.apache.spark.util;
\ No newline at end of file
diff --git a/core/src/main/scala/org/apache/spark/util/package.scala b/core/src/main/scala/org/apache/spark/util/package.scala
new file mode 100644
index 000000000000..c7099b85d225
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/util/package.scala
@@ -0,0 +1,23 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark
+
+/**
+ * Spark utilities.
+ */
+package object util
diff --git a/core/src/main/scala/org/apache/spark/util/random/package-info.java b/core/src/main/scala/org/apache/spark/util/random/package-info.java
new file mode 100644
index 000000000000..62c3762dd11b
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/util/random/package-info.java
@@ -0,0 +1,21 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/**
+ * Utilities for random number generation.
+ */
+package org.apache.spark.util.random;
\ No newline at end of file
diff --git a/core/src/main/scala/org/apache/spark/util/random/package.scala b/core/src/main/scala/org/apache/spark/util/random/package.scala
new file mode 100644
index 000000000000..f361125fe2e8
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/util/random/package.scala
@@ -0,0 +1,23 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.util
+
+/**
+ * Utilities for random number generation.
+ */
+package object random
diff --git a/external/flume/src/main/scala/org/apache/spark/streaming/flume/package-info.java b/external/flume/src/main/scala/org/apache/spark/streaming/flume/package-info.java
new file mode 100644
index 000000000000..d31aa5f5c096
--- /dev/null
+++ b/external/flume/src/main/scala/org/apache/spark/streaming/flume/package-info.java
@@ -0,0 +1,21 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/**
+ * Spark streaming receiver for Flume.
+ */
+package org.apache.spark.streaming.flume;
\ No newline at end of file
diff --git a/external/flume/src/main/scala/org/apache/spark/streaming/flume/package.scala b/external/flume/src/main/scala/org/apache/spark/streaming/flume/package.scala
new file mode 100644
index 000000000000..9bfab68c4b8b
--- /dev/null
+++ b/external/flume/src/main/scala/org/apache/spark/streaming/flume/package.scala
@@ -0,0 +1,23 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.streaming
+
+/**
+ * Spark streaming receiver for Flume.
+ */
+package object flume
diff --git a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/package-info.java b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/package-info.java
new file mode 100644
index 000000000000..947bae115a62
--- /dev/null
+++ b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/package-info.java
@@ -0,0 +1,21 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/**
+ * Kafka receiver for spark streaming.
+ */
+package org.apache.spark.streaming.kafka;
\ No newline at end of file
diff --git a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/package.scala b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/package.scala
new file mode 100644
index 000000000000..47c5187f8751
--- /dev/null
+++ b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/package.scala
@@ -0,0 +1,23 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.streaming
+
+/**
+ * Kafka receiver for spark streaming,
+ */
+package object kafka
diff --git a/external/mqtt/src/main/scala/org/apache/spark/streaming/mqtt/package-info.java b/external/mqtt/src/main/scala/org/apache/spark/streaming/mqtt/package-info.java
new file mode 100644
index 000000000000..728e0d8663d0
--- /dev/null
+++ b/external/mqtt/src/main/scala/org/apache/spark/streaming/mqtt/package-info.java
@@ -0,0 +1,21 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/**
+ * MQTT receiver for Spark Streaming.
+ */
+package org.apache.spark.streaming.mqtt;
\ No newline at end of file
diff --git a/external/mqtt/src/main/scala/org/apache/spark/streaming/mqtt/package.scala b/external/mqtt/src/main/scala/org/apache/spark/streaming/mqtt/package.scala
new file mode 100644
index 000000000000..63d0d138183a
--- /dev/null
+++ b/external/mqtt/src/main/scala/org/apache/spark/streaming/mqtt/package.scala
@@ -0,0 +1,23 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.streaming
+
+/**
+ * MQTT receiver for Spark Streaming.
+ */
+package object mqtt
diff --git a/external/twitter/src/main/scala/org/apache/spark/streaming/twitter/package-info.java b/external/twitter/src/main/scala/org/apache/spark/streaming/twitter/package-info.java
new file mode 100644
index 000000000000..258c0950a0aa
--- /dev/null
+++ b/external/twitter/src/main/scala/org/apache/spark/streaming/twitter/package-info.java
@@ -0,0 +1,21 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/**
+ * Twitter feed receiver for spark streaming.
+ */
+package org.apache.spark.streaming.twitter;
\ No newline at end of file
diff --git a/external/twitter/src/main/scala/org/apache/spark/streaming/twitter/package.scala b/external/twitter/src/main/scala/org/apache/spark/streaming/twitter/package.scala
new file mode 100644
index 000000000000..580e37fa8f81
--- /dev/null
+++ b/external/twitter/src/main/scala/org/apache/spark/streaming/twitter/package.scala
@@ -0,0 +1,23 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.streaming
+
+/**
+ * Twitter feed receiver for spark streaming.
+ */
+package object twitter
diff --git a/external/zeromq/src/main/scala/org/apache/spark/streaming/zeromq/package-info.java b/external/zeromq/src/main/scala/org/apache/spark/streaming/zeromq/package-info.java
new file mode 100644
index 000000000000..587c524e2120
--- /dev/null
+++ b/external/zeromq/src/main/scala/org/apache/spark/streaming/zeromq/package-info.java
@@ -0,0 +1,21 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/**
+ * Zeromq receiver for spark streaming.
+ */
+package org.apache.spark.streaming.zeromq;
\ No newline at end of file
diff --git a/external/zeromq/src/main/scala/org/apache/spark/streaming/zeromq/package.scala b/external/zeromq/src/main/scala/org/apache/spark/streaming/zeromq/package.scala
new file mode 100644
index 000000000000..65e6e57f2c05
--- /dev/null
+++ b/external/zeromq/src/main/scala/org/apache/spark/streaming/zeromq/package.scala
@@ -0,0 +1,23 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.streaming
+
+/**
+ * Zeromq receiver for spark streaming.
+ */
+package object zeromq
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/lib/package-info.java b/graphx/src/main/scala/org/apache/spark/graphx/lib/package-info.java
new file mode 100644
index 000000000000..49ad7263141c
--- /dev/null
+++ b/graphx/src/main/scala/org/apache/spark/graphx/lib/package-info.java
@@ -0,0 +1,21 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/**
+ * Internal support for MLLib Python API.
+ */
+package org.apache.spark.graphx.lib;
\ No newline at end of file
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/lib/package.scala b/graphx/src/main/scala/org/apache/spark/graphx/lib/package.scala
new file mode 100644
index 000000000000..7cfd617a32a3
--- /dev/null
+++ b/graphx/src/main/scala/org/apache/spark/graphx/lib/package.scala
@@ -0,0 +1,23 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.graphx
+
+/**
+ * Various analytics functions for graphs.
+ */
+package object lib
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/package-info.java b/graphx/src/main/scala/org/apache/spark/graphx/package-info.java
new file mode 100644
index 000000000000..f659cc518ebd
--- /dev/null
+++ b/graphx/src/main/scala/org/apache/spark/graphx/package-info.java
@@ -0,0 +1,22 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/**
+ * ALPHA COMPONENT
+ * GraphX is a graph processing framework built on top of Spark.
+ */
+package org.apache.spark.graphx;
\ No newline at end of file
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/util/package-info.java b/graphx/src/main/scala/org/apache/spark/graphx/util/package-info.java
new file mode 100644
index 000000000000..90cd1d46db17
--- /dev/null
+++ b/graphx/src/main/scala/org/apache/spark/graphx/util/package-info.java
@@ -0,0 +1,21 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/**
+ * Collections of utilities used by graphx.
+ */
+package org.apache.spark.graphx.util;
\ No newline at end of file
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/util/package.scala b/graphx/src/main/scala/org/apache/spark/graphx/util/package.scala
new file mode 100644
index 000000000000..2303541d2e42
--- /dev/null
+++ b/graphx/src/main/scala/org/apache/spark/graphx/util/package.scala
@@ -0,0 +1,23 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.graphx
+
+/**
+ * Collections of utilities used by graphx.
+ */
+package object util
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/api/python/package.scala b/mllib/src/main/scala/org/apache/spark/mllib/api/python/package.scala
new file mode 100644
index 000000000000..87bdc8558aaf
--- /dev/null
+++ b/mllib/src/main/scala/org/apache/spark/mllib/api/python/package.scala
@@ -0,0 +1,25 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.mllib.api
+
+/**
+ * Internal support for MLLib Python API.
+ *
+ * @see [[org.apache.spark.mllib.api.python.PythonMLLibAPI]]
+ */
+package object python
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/package-info.java b/mllib/src/main/scala/org/apache/spark/mllib/package-info.java
new file mode 100644
index 000000000000..4991bc9e972c
--- /dev/null
+++ b/mllib/src/main/scala/org/apache/spark/mllib/package-info.java
@@ -0,0 +1,21 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/**
+ * Spark's machine learning library.
+ */
+package org.apache.spark.mllib;
\ No newline at end of file
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/package.scala b/mllib/src/main/scala/org/apache/spark/mllib/package.scala
new file mode 100644
index 000000000000..5c2b2160c030
--- /dev/null
+++ b/mllib/src/main/scala/org/apache/spark/mllib/package.scala
@@ -0,0 +1,23 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark
+
+/**
+ * Spark's machine learning library.
+ */
+package object mllib
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/package-info.java b/sql/catalyst/src/main/scala/org/apache/spark/sql/package-info.java
new file mode 100644
index 000000000000..53603614518f
--- /dev/null
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/package-info.java
@@ -0,0 +1,21 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/**
+ * Allows the execution of relational queries, including those expressed in SQL using Spark.
+ */
+package org.apache.spark.sql;
\ No newline at end of file
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/package-info.java b/sql/hive/src/main/scala/org/apache/spark/sql/hive/package-info.java
new file mode 100644
index 000000000000..8b29fa7d1a8f
--- /dev/null
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/package-info.java
@@ -0,0 +1,18 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive;
\ No newline at end of file
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/package.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/package.scala
new file mode 100644
index 000000000000..a6c8ed4f7e86
--- /dev/null
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/package.scala
@@ -0,0 +1,20 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql
+
+package object hive
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/java/package-info.java b/streaming/src/main/scala/org/apache/spark/streaming/api/java/package-info.java
new file mode 100644
index 000000000000..d43d949d76bb
--- /dev/null
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/java/package-info.java
@@ -0,0 +1,21 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/**
+ * Java APIs for spark streaming.
+ */
+package org.apache.spark.streaming.api.java;
\ No newline at end of file
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/java/package.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/java/package.scala
new file mode 100644
index 000000000000..2479670f2a5b
--- /dev/null
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/java/package.scala
@@ -0,0 +1,23 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.streaming.api
+
+/**
+ * Spark streaming's Java API.
+ */
+package object java
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/package-info.java b/streaming/src/main/scala/org/apache/spark/streaming/dstream/package-info.java
new file mode 100644
index 000000000000..05ca2ddffd3c
--- /dev/null
+++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/package-info.java
@@ -0,0 +1,21 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/**
+ * Various implementations of DStreams.
+ */
+package org.apache.spark.streaming.dstream;
\ No newline at end of file
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/package.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/package.scala
new file mode 100644
index 000000000000..0179b92503e5
--- /dev/null
+++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/package.scala
@@ -0,0 +1,24 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.streaming
+
+/**
+ * Various implementations of DStream's.
+ * @see [[org.apache.spark.streaming.dstream.DStream]]
+ */
+package object dstream

From bae07e36a6e0fb7982405316646b452b4ff06acc Mon Sep 17 00:00:00 2001
From: witgo <witgo@qq.com>
Date: Wed, 14 May 2014 22:26:26 -0700
Subject: [PATCH 212/810] fix different versions of commons-lang dependency and
 apache/spark#746 addendum

Author: witgo <witgo@qq.com>

Closes #754 from witgo/commons-lang and squashes the following commits:

3ebab31 [witgo] merge master
f3b8fa2 [witgo] merge master
2083fae [witgo] repeat definition
5599cdb [witgo] multiple version of sbt  dependency
c1b66a1 [witgo] fix different versions of commons-lang dependency
---
 project/SparkBuild.scala | 12 +++++++-----
 sql/hive/pom.xml         |  8 ++++++++
 2 files changed, 15 insertions(+), 5 deletions(-)

diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index 6adec556322b..29dcd8678b47 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -318,6 +318,8 @@ object SparkBuild extends Build {
   val excludeFastutil = ExclusionRule(organization = "it.unimi.dsi")
   val excludeJruby = ExclusionRule(organization = "org.jruby")
   val excludeThrift = ExclusionRule(organization = "org.apache.thrift")
+  val excludeCommonsLang = ExclusionRule(organization = "commons-lang")
+  val excludeServletApi = ExclusionRule(organization = "javax.servlet", artifact = "servlet-api")
 
   def sparkPreviousArtifact(id: String, organization: String = "org.apache.spark",
       version: String = "0.9.0-incubating", crossVersion: String = "2.10"): Option[sbt.ModuleID] = {
@@ -491,7 +493,7 @@ object SparkBuild extends Build {
     javaOptions += "-XX:MaxPermSize=1g",
     libraryDependencies ++= Seq(
       "org.spark-project.hive" % "hive-metastore" % hiveVersion,
-      "org.spark-project.hive" % "hive-exec"      % hiveVersion,
+      "org.spark-project.hive" % "hive-exec"      % hiveVersion excludeAll(excludeCommonsLang, excludeCommonsLogging),
       "org.spark-project.hive" % "hive-serde"     % hiveVersion
     ),
     // Multiple queries rely on the TestHive singleton.  See comments there for more details.
@@ -564,10 +566,10 @@ object SparkBuild extends Build {
     libraryDependencies ++= Seq(
       // Exclude rule required for all ?
       "org.apache.hadoop" % hadoopClient         % hadoopVersion excludeAll(excludeJBossNetty, excludeAsm, excludeOldAsm),
-      "org.apache.hadoop" % "hadoop-yarn-api"    % hadoopVersion excludeAll(excludeJBossNetty, excludeAsm, excludeOldAsm),
-      "org.apache.hadoop" % "hadoop-yarn-common" % hadoopVersion excludeAll(excludeJBossNetty, excludeAsm, excludeOldAsm),
-      "org.apache.hadoop" % "hadoop-yarn-client" % hadoopVersion excludeAll(excludeJBossNetty, excludeAsm, excludeOldAsm),
-      "org.apache.hadoop" % "hadoop-yarn-server-web-proxy" % hadoopVersion excludeAll(excludeJBossNetty, excludeAsm, excludeOldAsm)
+      "org.apache.hadoop" % "hadoop-yarn-api"    % hadoopVersion excludeAll(excludeJBossNetty, excludeAsm, excludeOldAsm, excludeCommonsLogging),
+      "org.apache.hadoop" % "hadoop-yarn-common" % hadoopVersion excludeAll(excludeJBossNetty, excludeAsm, excludeOldAsm, excludeCommonsLogging),
+      "org.apache.hadoop" % "hadoop-yarn-client" % hadoopVersion excludeAll(excludeJBossNetty, excludeAsm, excludeOldAsm, excludeCommonsLogging),
+      "org.apache.hadoop" % "hadoop-yarn-server-web-proxy" % hadoopVersion excludeAll(excludeJBossNetty, excludeAsm, excludeOldAsm, excludeCommonsLogging, excludeServletApi)
     )
   )
 
diff --git a/sql/hive/pom.xml b/sql/hive/pom.xml
index 9254b70e64a0..4d0b2fa1452a 100644
--- a/sql/hive/pom.xml
+++ b/sql/hive/pom.xml
@@ -57,6 +57,10 @@
           <groupId>commons-logging</groupId>
           <artifactId>commons-logging</artifactId>
         </exclusion>
+        <exclusion>
+          <groupId>commons-lang</groupId>
+          <artifactId>commons-lang</artifactId>
+        </exclusion>
       </exclusions>
     </dependency>
     <dependency>
@@ -76,6 +80,10 @@
           <groupId>commons-logging</groupId>
           <artifactId>commons-logging-api</artifactId>
         </exclusion>
+        <exclusion>
+          <groupId>commons-lang</groupId>
+          <artifactId>commons-lang</artifactId>
+        </exclusion>
       </exclusions>
     </dependency>
     <!-- hive-serde already depends on avro, but this brings in customized config of avro deps from parent -->

From 514157f2e4c341378f4a606b66562f39fe3eba5e Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Wed, 14 May 2014 23:48:03 -0700
Subject: [PATCH 213/810] HOTFIX: Don't build Javadoc in Maven when creating
 releases.

Because we've added java package descriptions in some packages that don't
have any Java files, running the Javadoc target hits this issue:

http://bugs.sun.com/bugdatabase/view_bug.do?bug_id=4492654

To fix this I've simply removed the javadoc target when publishing
releases.
---
 dev/create-release/create-release.sh | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/dev/create-release/create-release.sh b/dev/create-release/create-release.sh
index c4e74990860b..c96afe822c6a 100755
--- a/dev/create-release/create-release.sh
+++ b/dev/create-release/create-release.sh
@@ -49,16 +49,18 @@ if [[ ! "$@" =~ --package-only ]]; then
   mvn -Pyarn release:clean
 
   mvn -DskipTests \
-    -Darguments="-DskipTests=true -Dhadoop.version=2.2.0 -Dyarn.version=2.2.0 -Dgpg.passphrase=${GPG_PASSPHRASE}" \
+    -Darguments="-DskipTests=true -Dmaven.javadoc.skip=true -Dhadoop.version=2.2.0 -Dyarn.version=2.2.0 -Dgpg.passphrase=${GPG_PASSPHRASE}" \
     -Dusername=$GIT_USERNAME -Dpassword=$GIT_PASSWORD \
+    -Dmaven.javadoc.skip=true \
     -Dhadoop.version=2.2.0 -Dyarn.version=2.2.0 \
     -Pyarn -Phive -Phadoop-2.2 -Pspark-ganglia-lgpl\
     -Dtag=$GIT_TAG -DautoVersionSubmodules=true \
     --batch-mode release:prepare
 
   mvn -DskipTests \
-    -Darguments="-DskipTests=true -Dhadoop.version=2.2.0 -Dyarn.version=2.2.0 -Dgpg.passphrase=${GPG_PASSPHRASE}" \
+    -Darguments="-DskipTests=true -Dmaven.javadoc.skip=true -Dhadoop.version=2.2.0 -Dyarn.version=2.2.0 -Dgpg.passphrase=${GPG_PASSPHRASE}" \
     -Dhadoop.version=2.2.0 -Dyarn.version=2.2.0 \
+    -Dmaven.javadoc.skip=true \
     -Pyarn -Phive -Phadoop-2.2 -Pspark-ganglia-lgpl\
     release:perform
 

From 3abe2b734a5578966f671c34f1de34b4446b90f1 Mon Sep 17 00:00:00 2001
From: Andrew Ash <andrew@andrewash.com>
Date: Thu, 15 May 2014 11:05:39 -0700
Subject: [PATCH 214/810] SPARK-1846 Ignore logs directory in RAT checks

https://issues.apache.org/jira/browse/SPARK-1846

Author: Andrew Ash <andrew@andrewash.com>

Closes #793 from ash211/SPARK-1846 and squashes the following commits:

3f50db5 [Andrew Ash] SPARK-1846 Ignore logs directory in RAT checks
---
 .rat-excludes | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.rat-excludes b/.rat-excludes
index 689467847d80..15589702c559 100644
--- a/.rat-excludes
+++ b/.rat-excludes
@@ -48,3 +48,4 @@ dist/*
 .*out
 .*ipr
 .*iws
+logs

From db8cc6f28abe4326cea6f53feb604920e4867a27 Mon Sep 17 00:00:00 2001
From: Takuya UESHIN <ueshin@happy-camper.st>
Date: Thu, 15 May 2014 11:20:21 -0700
Subject: [PATCH 215/810] [SPARK-1845] [SQL] Use AllScalaRegistrar for
 SparkSqlSerializer to register serializers of ...

...Scala collections.

When I execute `orderBy` or `limit` for `SchemaRDD` including `ArrayType` or `MapType`, `SparkSqlSerializer` throws the following exception:

```
com.esotericsoftware.kryo.KryoException: Class cannot be created (missing no-arg constructor): scala.collection.immutable.$colon$colon
```

or

```
com.esotericsoftware.kryo.KryoException: Class cannot be created (missing no-arg constructor): scala.collection.immutable.Vector
```

or

```
com.esotericsoftware.kryo.KryoException: Class cannot be created (missing no-arg constructor): scala.collection.immutable.HashMap$HashTrieMap
```

and so on.

This is because registrations of serializers for each concrete collections are missing in `SparkSqlSerializer`.
I believe it should use `AllScalaRegistrar`.
`AllScalaRegistrar` covers a lot of serializers for concrete classes of `Seq`, `Map` for `ArrayType`, `MapType`.

Author: Takuya UESHIN <ueshin@happy-camper.st>

Closes #790 from ueshin/issues/SPARK-1845 and squashes the following commits:

d1ed992 [Takuya UESHIN] Use AllScalaRegistrar for SparkSqlSerializer to register serializers of Scala collections.
---
 .../sql/execution/SparkSqlSerializer.scala    | 28 ++---------------
 .../org/apache/spark/sql/DslQuerySuite.scala  | 24 +++++++++++++++
 .../org/apache/spark/sql/SQLQuerySuite.scala  | 30 +++++++++++++++++++
 .../scala/org/apache/spark/sql/TestData.scala | 10 +++++++
 4 files changed, 66 insertions(+), 26 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer.scala
index 94c2a249ef8f..34b355e90669 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer.scala
@@ -24,6 +24,7 @@ import scala.reflect.ClassTag
 import com.clearspring.analytics.stream.cardinality.HyperLogLog
 import com.esotericsoftware.kryo.io.{Input, Output}
 import com.esotericsoftware.kryo.{Serializer, Kryo}
+import com.twitter.chill.AllScalaRegistrar
 
 import org.apache.spark.{SparkEnv, SparkConf}
 import org.apache.spark.serializer.KryoSerializer
@@ -35,22 +36,14 @@ private[sql] class SparkSqlSerializer(conf: SparkConf) extends KryoSerializer(co
     val kryo = new Kryo()
     kryo.setRegistrationRequired(false)
     kryo.register(classOf[MutablePair[_, _]])
-    kryo.register(classOf[Array[Any]])
-    // This is kinda hacky...
-    kryo.register(classOf[scala.collection.immutable.Map$Map1], new MapSerializer)
-    kryo.register(classOf[scala.collection.immutable.Map$Map2], new MapSerializer)
-    kryo.register(classOf[scala.collection.immutable.Map$Map3], new MapSerializer)
-    kryo.register(classOf[scala.collection.immutable.Map$Map4], new MapSerializer)
-    kryo.register(classOf[scala.collection.immutable.Map[_,_]], new MapSerializer)
-    kryo.register(classOf[scala.collection.Map[_,_]], new MapSerializer)
     kryo.register(classOf[org.apache.spark.sql.catalyst.expressions.GenericRow])
     kryo.register(classOf[org.apache.spark.sql.catalyst.expressions.GenericMutableRow])
     kryo.register(classOf[com.clearspring.analytics.stream.cardinality.HyperLogLog],
                   new HyperLogLogSerializer)
-    kryo.register(classOf[scala.collection.mutable.ArrayBuffer[_]])
     kryo.register(classOf[scala.math.BigDecimal], new BigDecimalSerializer)
     kryo.setReferences(false)
     kryo.setClassLoader(Utils.getSparkClassLoader)
+    new AllScalaRegistrar().apply(kryo)
     kryo
   }
 }
@@ -97,20 +90,3 @@ private[sql] class HyperLogLogSerializer extends Serializer[HyperLogLog] {
     HyperLogLog.Builder.build(bytes)
   }
 }
-
-/**
- * Maps do not have a no arg constructor and so cannot be serialized by default. So, we serialize
- * them as `Array[(k,v)]`.
- */
-private[sql] class MapSerializer extends Serializer[Map[_,_]] {
-  def write(kryo: Kryo, output: Output, map: Map[_,_]) {
-    kryo.writeObject(output, map.flatMap(e => Seq(e._1, e._2)).toArray)
-  }
-
-  def read(kryo: Kryo, input: Input, tpe: Class[Map[_,_]]): Map[_,_] = {
-    kryo.readObject(input, classOf[Array[Any]])
-      .sliding(2,2)
-      .map { case Array(k,v) => (k,v) }
-      .toMap
-  }
-}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala
index 92a707ea5750..f43e98d61409 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala
@@ -69,12 +69,36 @@ class DslQuerySuite extends QueryTest {
     checkAnswer(
       testData2.orderBy('a.desc, 'b.asc),
       Seq((3,1), (3,2), (2,1), (2,2), (1,1), (1,2)))
+
+    checkAnswer(
+      arrayData.orderBy(GetItem('data, 0).asc),
+      arrayData.collect().sortBy(_.data(0)).toSeq)
+
+    checkAnswer(
+      arrayData.orderBy(GetItem('data, 0).desc),
+      arrayData.collect().sortBy(_.data(0)).reverse.toSeq)
+
+    checkAnswer(
+      mapData.orderBy(GetItem('data, 1).asc),
+      mapData.collect().sortBy(_.data(1)).toSeq)
+
+    checkAnswer(
+      mapData.orderBy(GetItem('data, 1).desc),
+      mapData.collect().sortBy(_.data(1)).reverse.toSeq)
   }
 
   test("limit") {
     checkAnswer(
       testData.limit(10),
       testData.take(10).toSeq)
+
+    checkAnswer(
+      arrayData.limit(1),
+      arrayData.take(1).toSeq)
+
+    checkAnswer(
+      mapData.limit(1),
+      mapData.take(1).toSeq)
   }
 
   test("average") {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index 524549eb544f..189dccd5253e 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -85,6 +85,36 @@ class SQLQuerySuite extends QueryTest {
     checkAnswer(
       sql("SELECT * FROM testData2 ORDER BY a DESC, b ASC"),
       Seq((3,1), (3,2), (2,1), (2,2), (1,1), (1,2)))
+
+    checkAnswer(
+      sql("SELECT * FROM arrayData ORDER BY data[0] ASC"),
+      arrayData.collect().sortBy(_.data(0)).toSeq)
+
+    checkAnswer(
+      sql("SELECT * FROM arrayData ORDER BY data[0] DESC"),
+      arrayData.collect().sortBy(_.data(0)).reverse.toSeq)
+
+    checkAnswer(
+      sql("SELECT * FROM mapData ORDER BY data[1] ASC"),
+      mapData.collect().sortBy(_.data(1)).toSeq)
+
+    checkAnswer(
+      sql("SELECT * FROM mapData ORDER BY data[1] DESC"),
+      mapData.collect().sortBy(_.data(1)).reverse.toSeq)
+  }
+
+  test("limit") {
+    checkAnswer(
+      sql("SELECT * FROM testData LIMIT 10"),
+      testData.take(10).toSeq)
+
+    checkAnswer(
+      sql("SELECT * FROM arrayData LIMIT 1"),
+      arrayData.collect().take(1).toSeq)
+
+    checkAnswer(
+      sql("SELECT * FROM mapData LIMIT 1"),
+      mapData.collect().take(1).toSeq)
   }
 
   test("average") {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala b/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala
index aa71e274f7f4..1aca3872524d 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala
@@ -74,6 +74,16 @@ object TestData {
       ArrayData(Seq(2,3,4), Seq(Seq(2,3,4))) :: Nil)
   arrayData.registerAsTable("arrayData")
 
+  case class MapData(data: Map[Int, String])
+  val mapData =
+    TestSQLContext.sparkContext.parallelize(
+      MapData(Map(1 -> "a1", 2 -> "b1", 3 -> "c1", 4 -> "d1", 5 -> "e1")) ::
+      MapData(Map(1 -> "a2", 2 -> "b2", 3 -> "c2", 4 -> "d2")) ::
+      MapData(Map(1 -> "a3", 2 -> "b3", 3 -> "c3")) ::
+      MapData(Map(1 -> "a4", 2 -> "b4")) ::
+      MapData(Map(1 -> "a5")) :: Nil)
+  mapData.registerAsTable("mapData")
+
   case class StringData(s: String)
   val repeatedData =
     TestSQLContext.sparkContext.parallelize(List.fill(2)(StringData("test")))

From 94c9d6f59859ebc77fae112c2c42c64b7a4d7f83 Mon Sep 17 00:00:00 2001
From: Takuya UESHIN <ueshin@happy-camper.st>
Date: Thu, 15 May 2014 11:21:33 -0700
Subject: [PATCH 216/810] [SPARK-1819] [SQL] Fix GetField.nullable.

`GetField.nullable` should be `true` not only when `field.nullable` is `true` but also when `child.nullable` is `true`.

Author: Takuya UESHIN <ueshin@happy-camper.st>

Closes #757 from ueshin/issues/SPARK-1819 and squashes the following commits:

8781a11 [Takuya UESHIN] Modify a test to use named parameters.
5bfc77d [Takuya UESHIN] Fix GetField.nullable.
---
 .../sql/catalyst/expressions/complexTypes.scala     |  2 +-
 .../expressions/ExpressionEvaluationSuite.scala     | 13 +++++++++++++
 2 files changed, 14 insertions(+), 1 deletion(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypes.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypes.scala
index 195ca2eb3d58..b6aeae92f8be 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypes.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypes.scala
@@ -74,7 +74,7 @@ case class GetField(child: Expression, fieldName: String) extends UnaryExpressio
   type EvaluatedType = Any
 
   def dataType = field.dataType
-  override def nullable = field.nullable
+  override def nullable = child.nullable || field.nullable
   override def foldable = child.foldable
 
   protected def structType = child.dataType match {
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala
index 344d8a304fc1..1132a30b4276 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala
@@ -364,6 +364,19 @@ class ExpressionEvaluationSuite extends FunSuite {
 
     checkEvaluation(GetField(BoundReference(2, AttributeReference("c", typeS)()), "a"), "aa", row)
     checkEvaluation(GetField(Literal(null, typeS), "a"), null, row)
+
+    val typeS_notNullable = StructType(
+      StructField("a", StringType, nullable = false)
+        :: StructField("b", StringType, nullable = false) :: Nil
+    )
+
+    assert(GetField(BoundReference(2,
+      AttributeReference("c", typeS)()), "a").nullable === true)
+    assert(GetField(BoundReference(2,
+      AttributeReference("c", typeS_notNullable, nullable = false)()), "a").nullable === false)
+
+    assert(GetField(Literal(null, typeS), "a").nullable === true)
+    assert(GetField(Literal(null, typeS_notNullable), "a").nullable === true)
   }
 
   test("arithmetic") {

From d52761d67f42ad4d2ff02d96f0675fb3ab709f38 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Thu, 15 May 2014 11:59:59 -0700
Subject: [PATCH 217/810] [SPARK-1741][MLLIB] add predict(JavaRDD) to
 RegressionModel, ClassificationModel, and KMeans

`model.predict` returns a RDD of Scala primitive type (Int/Double), which is recognized as Object in Java. Adding predict(JavaRDD) could make life easier for Java users.

Added tests for KMeans, LinearRegression, and NaiveBayes.

Will update examples after https://github.com/apache/spark/pull/653 gets merged.

cc: @srowen

Author: Xiangrui Meng <meng@databricks.com>

Closes #670 from mengxr/predict-javardd and squashes the following commits:

b77ccd8 [Xiangrui Meng] Merge branch 'master' into predict-javardd
43caac9 [Xiangrui Meng] add predict(JavaRDD) to RegressionModel, ClassificationModel, and KMeans
---
 .../classification/ClassificationModel.scala  | 11 +++++++++-
 .../spark/mllib/clustering/KMeansModel.scala  |  5 +++++
 .../mllib/regression/RegressionModel.scala    | 11 +++++++++-
 .../classification/JavaNaiveBayesSuite.java   | 16 ++++++++++++++
 .../mllib/clustering/JavaKMeansSuite.java     | 14 +++++++++++++
 .../regression/JavaLinearRegressionSuite.java | 21 +++++++++++++++++++
 6 files changed, 76 insertions(+), 2 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/ClassificationModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/ClassificationModel.scala
index 6332301e30cb..b7a1d90d24d7 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/classification/ClassificationModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/ClassificationModel.scala
@@ -17,9 +17,10 @@
 
 package org.apache.spark.mllib.classification
 
+import org.apache.spark.annotation.Experimental
+import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.rdd.RDD
-import org.apache.spark.annotation.Experimental
 
 /**
  * :: Experimental ::
@@ -43,4 +44,12 @@ trait ClassificationModel extends Serializable {
    * @return predicted category from the trained model
    */
   def predict(testData: Vector): Double
+
+  /**
+   * Predict values for examples stored in a JavaRDD.
+   * @param testData JavaRDD representing data points to be predicted
+   * @return a JavaRDD[java.lang.Double] where each entry contains the corresponding prediction
+   */
+  def predict(testData: JavaRDD[Vector]): JavaRDD[java.lang.Double] =
+    predict(testData.rdd).toJavaRDD().asInstanceOf[JavaRDD[java.lang.Double]]
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala
index ce14b0624193..fba21aefaaac 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.mllib.clustering
 
+import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.rdd.RDD
 import org.apache.spark.SparkContext._
 import org.apache.spark.mllib.linalg.Vector
@@ -40,6 +41,10 @@ class KMeansModel private[mllib] (val clusterCenters: Array[Vector]) extends Ser
     points.map(p => KMeans.findClosest(centersWithNorm, new BreezeVectorWithNorm(p))._1)
   }
 
+  /** Maps given points to their cluster indices. */
+  def predict(points: JavaRDD[Vector]): JavaRDD[java.lang.Integer] =
+    predict(points.rdd).toJavaRDD().asInstanceOf[JavaRDD[java.lang.Integer]]
+
   /**
    * Return the K-means cost (sum of squared distances of points to their nearest center) for this
    * model on the given data.
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/RegressionModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/RegressionModel.scala
index b27e158b43f9..64b02f7a6e7a 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/RegressionModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/RegressionModel.scala
@@ -17,9 +17,10 @@
 
 package org.apache.spark.mllib.regression
 
+import org.apache.spark.annotation.Experimental
+import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.rdd.RDD
 import org.apache.spark.mllib.linalg.Vector
-import org.apache.spark.annotation.Experimental
 
 @Experimental
 trait RegressionModel extends Serializable {
@@ -38,4 +39,12 @@ trait RegressionModel extends Serializable {
    * @return Double prediction from the trained model
    */
   def predict(testData: Vector): Double
+
+  /**
+   * Predict values for examples stored in a JavaRDD.
+   * @param testData JavaRDD representing data points to be predicted
+   * @return a JavaRDD[java.lang.Double] where each entry contains the corresponding prediction
+   */
+  def predict(testData: JavaRDD[Vector]): JavaRDD[java.lang.Double] =
+    predict(testData.rdd).toJavaRDD().asInstanceOf[JavaRDD[java.lang.Double]]
 }
diff --git a/mllib/src/test/java/org/apache/spark/mllib/classification/JavaNaiveBayesSuite.java b/mllib/src/test/java/org/apache/spark/mllib/classification/JavaNaiveBayesSuite.java
index c80b1134ed1b..743a43a139c0 100644
--- a/mllib/src/test/java/org/apache/spark/mllib/classification/JavaNaiveBayesSuite.java
+++ b/mllib/src/test/java/org/apache/spark/mllib/classification/JavaNaiveBayesSuite.java
@@ -19,6 +19,8 @@
 
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.api.java.function.Function;
+import org.apache.spark.mllib.linalg.Vector;
 import org.apache.spark.mllib.linalg.Vectors;
 import org.apache.spark.mllib.regression.LabeledPoint;
 import org.junit.After;
@@ -87,4 +89,18 @@ public void runUsingStaticMethods() {
     int numAccurate2 = validatePrediction(POINTS, model2);
     Assert.assertEquals(POINTS.size(), numAccurate2);
   }
+
+  @Test
+  public void testPredictJavaRDD() {
+    JavaRDD<LabeledPoint> examples = sc.parallelize(POINTS, 2).cache();
+    NaiveBayesModel model = NaiveBayes.train(examples.rdd());
+    JavaRDD<Vector> vectors = examples.map(new Function<LabeledPoint, Vector>() {
+      @Override
+      public Vector call(LabeledPoint v) throws Exception {
+        return v.features();
+      }});
+    JavaRDD<Double> predictions = model.predict(vectors);
+    // Should be able to get the first prediction.
+    predictions.first();
+  }
 }
diff --git a/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaKMeansSuite.java b/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaKMeansSuite.java
index 49a614bd90ca..0c916ca37803 100644
--- a/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaKMeansSuite.java
+++ b/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaKMeansSuite.java
@@ -88,4 +88,18 @@ public void runKMeansUsingConstructor() {
       .run(data.rdd());
     assertEquals(expectedCenter, model.clusterCenters()[0]);
   }
+
+  @Test
+  public void testPredictJavaRDD() {
+    List<Vector> points = Lists.newArrayList(
+      Vectors.dense(1.0, 2.0, 6.0),
+      Vectors.dense(1.0, 3.0, 0.0),
+      Vectors.dense(1.0, 4.0, 6.0)
+    );
+    JavaRDD<Vector> data = sc.parallelize(points, 2);
+    KMeansModel model = new KMeans().setK(1).setMaxIterations(5).run(data.rdd());
+    JavaRDD<Integer> predictions = model.predict(data);
+    // Should be able to get the first prediction.
+    predictions.first();
+  }
 }
diff --git a/mllib/src/test/java/org/apache/spark/mllib/regression/JavaLinearRegressionSuite.java b/mllib/src/test/java/org/apache/spark/mllib/regression/JavaLinearRegressionSuite.java
index 7151e553512b..6dc687769103 100644
--- a/mllib/src/test/java/org/apache/spark/mllib/regression/JavaLinearRegressionSuite.java
+++ b/mllib/src/test/java/org/apache/spark/mllib/regression/JavaLinearRegressionSuite.java
@@ -25,8 +25,10 @@
 import org.junit.Before;
 import org.junit.Test;
 
+import org.apache.spark.api.java.function.Function;
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.mllib.linalg.Vector;
 import org.apache.spark.mllib.util.LinearDataGenerator;
 
 public class JavaLinearRegressionSuite implements Serializable {
@@ -92,4 +94,23 @@ public void runLinearRegressionUsingStaticMethods() {
     Assert.assertTrue(numAccurate > nPoints * 4.0 / 5.0);
   }
 
+  @Test
+  public void testPredictJavaRDD() {
+    int nPoints = 100;
+    double A = 0.0;
+    double[] weights = {10, 10};
+    JavaRDD<LabeledPoint> testRDD = sc.parallelize(
+      LinearDataGenerator.generateLinearInputAsList(A, weights, nPoints, 42, 0.1), 2).cache();
+    LinearRegressionWithSGD linSGDImpl = new LinearRegressionWithSGD();
+    LinearRegressionModel model = linSGDImpl.run(testRDD.rdd());
+    JavaRDD<Vector> vectors = testRDD.map(new Function<LabeledPoint, Vector>() {
+      @Override
+      public Vector call(LabeledPoint v) throws Exception {
+        return v.features();
+      }
+    });
+    JavaRDD<Double> predictions = model.predict(vectors);
+    // Should be able to get the first prediction.
+    predictions.first();
+  }
 }

From 08e7606a964e3d1ac1d565f33651ff0035c75044 Mon Sep 17 00:00:00 2001
From: Sandy Ryza <sandy@cloudera.com>
Date: Thu, 15 May 2014 16:35:39 -0700
Subject: [PATCH 218/810] SPARK-1851. Upgrade Avro dependency to 1.7.6 so Spark
 can read Avro file...

...s

Author: Sandy Ryza <sandy@cloudera.com>

Closes #795 from sryza/sandy-spark-1851 and squashes the following commits:

79c8227 [Sandy Ryza] SPARK-1851. Upgrade Avro dependency to 1.7.6 so Spark can read Avro files
---
 pom.xml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pom.xml b/pom.xml
index 786b6d4984d8..5df7036585b7 100644
--- a/pom.xml
+++ b/pom.xml
@@ -129,7 +129,7 @@
     <jetty.version>8.1.14.v20131031</jetty.version>
     <chill.version>0.3.6</chill.version>
     <codahale.metrics.version>3.0.0</codahale.metrics.version>
-    <avro.version>1.7.4</avro.version>
+    <avro.version>1.7.6</avro.version>
     <jets3t.version>0.7.1</jets3t.version>
 
     <PermGen>64m</PermGen>

From e66e31be51f396c8f6b7a45119b8b31c4d8cdf79 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Stevo=20Slavi=C4=87?= <sslavic@gmail.com>
Date: Thu, 15 May 2014 16:44:14 -0700
Subject: [PATCH 219/810] SPARK-1803 Replaced colon in filenames with a dash
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

This patch replaces colon in several filenames with dash to make these filenames Windows compatible.

Author: Stevo Slavić <sslavic@gmail.com>
Author: Stevo Slavic <sslavic@gmail.com>

Closes #739 from sslavic/SPARK-1803 and squashes the following commits:

3ec66eb [Stevo Slavic] Removed extra empty line which was causing test to fail
b967cc3 [Stevo Slavić] Aligned tests and names of test resources
2b12776 [Stevo Slavić] Fixed a typo in file name
1c5dfff [Stevo Slavić] Replaced colon in file name with dash
8f5bf7f [Stevo Slavić] Replaced colon in file name with dash
c5b5083 [Stevo Slavić] Replaced colon in file name with dash
a49801f [Stevo Slavić] Replaced colon in file name with dash
401d99e [Stevo Slavić] Replaced colon in file name with dash
40a9621 [Stevo Slavić] Replaced colon in file name with dash
4774580 [Stevo Slavić] Replaced colon in file name with dash
004f8bb [Stevo Slavić] Replaced colon in file name with dash
d6a3e2c [Stevo Slavić] Replaced colon in file name with dash
b585126 [Stevo Slavić] Replaced colon in file name with dash
028e48a [Stevo Slavić] Replaced colon in file name with dash
ece0507 [Stevo Slavić] Replaced colon in file name with dash
84f5d2f [Stevo Slavić] Replaced colon in file name with dash
2fc7854 [Stevo Slavić] Replaced colon in file name with dash
9e1467d [Stevo Slavić] Replaced colon in file name with dash
---
 ...y test-0-cb0737d268260da971487a8ac5e3d8be} |  0
 ...y test-0-c89dcddb8539bae211ea28cd1e695e35} |  0
 ...y test-0-9589ac54d6de575083765ee64051ebaa} |  0
 ...y test-0-d6d2726928df920ab99078015b8ff494} |  0
 ...ry test-0-cfe78c68302bd143d989c383c3b0477} |  0
 ...y test-0-358525951e893ed9686c46bf1f7447ee} |  0
 ...y test-0-d9d50146537344742300ce7b52c18c91} |  0
 ...y test-0-c25cdafe7d85c42a9d9cd559d88a6049} |  0
 ...y test-0-63ab78355dbc6fa0d7619c8a35b0f7a7} |  0
 ...y test-0-3adc3a7f76b2abd059904ba81a595db3} |  0
 ...y test-0-681332efaff7b12e3ca12d9f021cd344} |  0
 ...y test-0-3893ec7a86062215d9f817f1495a69bb} |  0
 ...y test-0-75148567b91227053f728d72f7dc7c10} |  0
 ...y test-0-4e4d2651e1e6e3c224242078d0201190} |  0
 ...y test-0-b6e59464383591f02408f8765ac6a5d5} |  0
 .../sql/hive/execution/PruningSuite.scala     | 30 +++++++++----------
 16 files changed, 15 insertions(+), 15 deletions(-)
 rename sql/hive/src/test/resources/golden/{Column pruning: filter alias in-lining - query test-0-cb0737d268260da971487a8ac5e3d8be => Column pruning - filter alias in-lining - query test-0-cb0737d268260da971487a8ac5e3d8be} (100%)
 rename sql/hive/src/test/resources/golden/{Column pruning: non-trivial top project with aliases - query test-0-c89dcddb8539bae211ea28cd1e695e35 => Column pruning - non-trivial top project with aliases - query test-0-c89dcddb8539bae211ea28cd1e695e35} (100%)
 rename sql/hive/src/test/resources/golden/{Column pruning: projects alias substituting - query test-0-9589ac54d6de575083765ee64051ebaa => Column pruning - projects alias substituting - query test-0-9589ac54d6de575083765ee64051ebaa} (100%)
 rename sql/hive/src/test/resources/golden/{Column pruning: simple top project without aliases - query test-0-d6d2726928df920ab99078015b8ff494 => Column pruning - simple top project without aliases - query test-0-d6d2726928df920ab99078015b8ff494} (100%)
 rename sql/hive/src/test/resources/golden/{Column pruning: with multiple projects - query test-0-cfe78c68302bd143d989c383c3b0477 => Column pruning - with multiple projects - query test-0-cfe78c68302bd143d989c383c3b0477} (100%)
 rename sql/hive/src/test/resources/golden/{Column pruning: with non-partitioned table - query test-0-358525951e893ed9686c46bf1f7447ee => Column pruning - with non-partitioned table - query test-0-358525951e893ed9686c46bf1f7447ee} (100%)
 rename sql/hive/src/test/resources/golden/{Column pruning: with partitioned table - query test-0-d9d50146537344742300ce7b52c18c91 => Column pruning - with partitioned table - query test-0-d9d50146537344742300ce7b52c18c91} (100%)
 rename sql/hive/src/test/resources/golden/{Column pruning: without filters - query test-0-c25cdafe7d85c42a9d9cd559d88a6049 => Column pruning - without filters - query test-0-c25cdafe7d85c42a9d9cd559d88a6049} (100%)
 rename sql/hive/src/test/resources/golden/{Partition pruning: all partitions pruned - query test-0-63ab78355dbc6fa0d7619c8a35b0f7a7 => Partition pruning - all partitions pruned - query test-0-63ab78355dbc6fa0d7619c8a35b0f7a7} (100%)
 rename sql/hive/src/test/resources/golden/{Partition pruning: left only 1 partition - query test-0-3adc3a7f76b2abd059904ba81a595db3 => Partition pruning - left only 1 partition - query test-0-3adc3a7f76b2abd059904ba81a595db3} (100%)
 rename sql/hive/src/test/resources/golden/{Partiton pruning: non-partitioned table - query test-0-681332efaff7b12e3ca12d9f021cd344 => Partition pruning - non-partitioned table - query test-0-681332efaff7b12e3ca12d9f021cd344} (100%)
 rename sql/hive/src/test/resources/golden/{Partition pruning: non-partitioned, non-trivial project - query test-0-3893ec7a86062215d9f817f1495a69bb => Partition pruning - non-partitioned, non-trivial project - query test-0-3893ec7a86062215d9f817f1495a69bb} (100%)
 rename sql/hive/src/test/resources/golden/{Partition pruning: pruning with both column key and partition key - query test-0-75148567b91227053f728d72f7dc7c10 => Partition pruning - pruning with both column key and partition key - query test-0-75148567b91227053f728d72f7dc7c10} (100%)
 rename sql/hive/src/test/resources/golden/{Partition pruning: with filter on int partition key - query test-0-4e4d2651e1e6e3c224242078d0201190 => Partition pruning - with filter on int partition key - query test-0-4e4d2651e1e6e3c224242078d0201190} (100%)
 rename sql/hive/src/test/resources/golden/{Partition pruning: with filter on string partition key - query test-0-b6e59464383591f02408f8765ac6a5d5 => Partition pruning - with filter on string partition key - query test-0-b6e59464383591f02408f8765ac6a5d5} (100%)

diff --git a/sql/hive/src/test/resources/golden/Column pruning: filter alias in-lining - query test-0-cb0737d268260da971487a8ac5e3d8be b/sql/hive/src/test/resources/golden/Column pruning - filter alias in-lining - query test-0-cb0737d268260da971487a8ac5e3d8be
similarity index 100%
rename from sql/hive/src/test/resources/golden/Column pruning: filter alias in-lining - query test-0-cb0737d268260da971487a8ac5e3d8be
rename to sql/hive/src/test/resources/golden/Column pruning - filter alias in-lining - query test-0-cb0737d268260da971487a8ac5e3d8be
diff --git a/sql/hive/src/test/resources/golden/Column pruning: non-trivial top project with aliases - query test-0-c89dcddb8539bae211ea28cd1e695e35 b/sql/hive/src/test/resources/golden/Column pruning - non-trivial top project with aliases - query test-0-c89dcddb8539bae211ea28cd1e695e35
similarity index 100%
rename from sql/hive/src/test/resources/golden/Column pruning: non-trivial top project with aliases - query test-0-c89dcddb8539bae211ea28cd1e695e35
rename to sql/hive/src/test/resources/golden/Column pruning - non-trivial top project with aliases - query test-0-c89dcddb8539bae211ea28cd1e695e35
diff --git a/sql/hive/src/test/resources/golden/Column pruning: projects alias substituting - query test-0-9589ac54d6de575083765ee64051ebaa b/sql/hive/src/test/resources/golden/Column pruning - projects alias substituting - query test-0-9589ac54d6de575083765ee64051ebaa
similarity index 100%
rename from sql/hive/src/test/resources/golden/Column pruning: projects alias substituting - query test-0-9589ac54d6de575083765ee64051ebaa
rename to sql/hive/src/test/resources/golden/Column pruning - projects alias substituting - query test-0-9589ac54d6de575083765ee64051ebaa
diff --git a/sql/hive/src/test/resources/golden/Column pruning: simple top project without aliases - query test-0-d6d2726928df920ab99078015b8ff494 b/sql/hive/src/test/resources/golden/Column pruning - simple top project without aliases - query test-0-d6d2726928df920ab99078015b8ff494
similarity index 100%
rename from sql/hive/src/test/resources/golden/Column pruning: simple top project without aliases - query test-0-d6d2726928df920ab99078015b8ff494
rename to sql/hive/src/test/resources/golden/Column pruning - simple top project without aliases - query test-0-d6d2726928df920ab99078015b8ff494
diff --git a/sql/hive/src/test/resources/golden/Column pruning: with multiple projects - query test-0-cfe78c68302bd143d989c383c3b0477 b/sql/hive/src/test/resources/golden/Column pruning - with multiple projects - query test-0-cfe78c68302bd143d989c383c3b0477
similarity index 100%
rename from sql/hive/src/test/resources/golden/Column pruning: with multiple projects - query test-0-cfe78c68302bd143d989c383c3b0477
rename to sql/hive/src/test/resources/golden/Column pruning - with multiple projects - query test-0-cfe78c68302bd143d989c383c3b0477
diff --git a/sql/hive/src/test/resources/golden/Column pruning: with non-partitioned table - query test-0-358525951e893ed9686c46bf1f7447ee b/sql/hive/src/test/resources/golden/Column pruning - with non-partitioned table - query test-0-358525951e893ed9686c46bf1f7447ee
similarity index 100%
rename from sql/hive/src/test/resources/golden/Column pruning: with non-partitioned table - query test-0-358525951e893ed9686c46bf1f7447ee
rename to sql/hive/src/test/resources/golden/Column pruning - with non-partitioned table - query test-0-358525951e893ed9686c46bf1f7447ee
diff --git a/sql/hive/src/test/resources/golden/Column pruning: with partitioned table - query test-0-d9d50146537344742300ce7b52c18c91 b/sql/hive/src/test/resources/golden/Column pruning - with partitioned table - query test-0-d9d50146537344742300ce7b52c18c91
similarity index 100%
rename from sql/hive/src/test/resources/golden/Column pruning: with partitioned table - query test-0-d9d50146537344742300ce7b52c18c91
rename to sql/hive/src/test/resources/golden/Column pruning - with partitioned table - query test-0-d9d50146537344742300ce7b52c18c91
diff --git a/sql/hive/src/test/resources/golden/Column pruning: without filters - query test-0-c25cdafe7d85c42a9d9cd559d88a6049 b/sql/hive/src/test/resources/golden/Column pruning - without filters - query test-0-c25cdafe7d85c42a9d9cd559d88a6049
similarity index 100%
rename from sql/hive/src/test/resources/golden/Column pruning: without filters - query test-0-c25cdafe7d85c42a9d9cd559d88a6049
rename to sql/hive/src/test/resources/golden/Column pruning - without filters - query test-0-c25cdafe7d85c42a9d9cd559d88a6049
diff --git a/sql/hive/src/test/resources/golden/Partition pruning: all partitions pruned - query test-0-63ab78355dbc6fa0d7619c8a35b0f7a7 b/sql/hive/src/test/resources/golden/Partition pruning - all partitions pruned - query test-0-63ab78355dbc6fa0d7619c8a35b0f7a7
similarity index 100%
rename from sql/hive/src/test/resources/golden/Partition pruning: all partitions pruned - query test-0-63ab78355dbc6fa0d7619c8a35b0f7a7
rename to sql/hive/src/test/resources/golden/Partition pruning - all partitions pruned - query test-0-63ab78355dbc6fa0d7619c8a35b0f7a7
diff --git a/sql/hive/src/test/resources/golden/Partition pruning: left only 1 partition - query test-0-3adc3a7f76b2abd059904ba81a595db3 b/sql/hive/src/test/resources/golden/Partition pruning - left only 1 partition - query test-0-3adc3a7f76b2abd059904ba81a595db3
similarity index 100%
rename from sql/hive/src/test/resources/golden/Partition pruning: left only 1 partition - query test-0-3adc3a7f76b2abd059904ba81a595db3
rename to sql/hive/src/test/resources/golden/Partition pruning - left only 1 partition - query test-0-3adc3a7f76b2abd059904ba81a595db3
diff --git a/sql/hive/src/test/resources/golden/Partiton pruning: non-partitioned table - query test-0-681332efaff7b12e3ca12d9f021cd344 b/sql/hive/src/test/resources/golden/Partition pruning - non-partitioned table - query test-0-681332efaff7b12e3ca12d9f021cd344
similarity index 100%
rename from sql/hive/src/test/resources/golden/Partiton pruning: non-partitioned table - query test-0-681332efaff7b12e3ca12d9f021cd344
rename to sql/hive/src/test/resources/golden/Partition pruning - non-partitioned table - query test-0-681332efaff7b12e3ca12d9f021cd344
diff --git a/sql/hive/src/test/resources/golden/Partition pruning: non-partitioned, non-trivial project - query test-0-3893ec7a86062215d9f817f1495a69bb b/sql/hive/src/test/resources/golden/Partition pruning - non-partitioned, non-trivial project - query test-0-3893ec7a86062215d9f817f1495a69bb
similarity index 100%
rename from sql/hive/src/test/resources/golden/Partition pruning: non-partitioned, non-trivial project - query test-0-3893ec7a86062215d9f817f1495a69bb
rename to sql/hive/src/test/resources/golden/Partition pruning - non-partitioned, non-trivial project - query test-0-3893ec7a86062215d9f817f1495a69bb
diff --git a/sql/hive/src/test/resources/golden/Partition pruning: pruning with both column key and partition key - query test-0-75148567b91227053f728d72f7dc7c10 b/sql/hive/src/test/resources/golden/Partition pruning - pruning with both column key and partition key - query test-0-75148567b91227053f728d72f7dc7c10
similarity index 100%
rename from sql/hive/src/test/resources/golden/Partition pruning: pruning with both column key and partition key - query test-0-75148567b91227053f728d72f7dc7c10
rename to sql/hive/src/test/resources/golden/Partition pruning - pruning with both column key and partition key - query test-0-75148567b91227053f728d72f7dc7c10
diff --git a/sql/hive/src/test/resources/golden/Partition pruning: with filter on int partition key - query test-0-4e4d2651e1e6e3c224242078d0201190 b/sql/hive/src/test/resources/golden/Partition pruning - with filter on int partition key - query test-0-4e4d2651e1e6e3c224242078d0201190
similarity index 100%
rename from sql/hive/src/test/resources/golden/Partition pruning: with filter on int partition key - query test-0-4e4d2651e1e6e3c224242078d0201190
rename to sql/hive/src/test/resources/golden/Partition pruning - with filter on int partition key - query test-0-4e4d2651e1e6e3c224242078d0201190
diff --git a/sql/hive/src/test/resources/golden/Partition pruning: with filter on string partition key - query test-0-b6e59464383591f02408f8765ac6a5d5 b/sql/hive/src/test/resources/golden/Partition pruning - with filter on string partition key - query test-0-b6e59464383591f02408f8765ac6a5d5
similarity index 100%
rename from sql/hive/src/test/resources/golden/Partition pruning: with filter on string partition key - query test-0-b6e59464383591f02408f8765ac6a5d5
rename to sql/hive/src/test/resources/golden/Partition pruning - with filter on string partition key - query test-0-b6e59464383591f02408f8765ac6a5d5
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/PruningSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/PruningSuite.scala
index 25eca397462f..34434449a0d7 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/PruningSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/PruningSuite.scala
@@ -28,7 +28,7 @@ import scala.collection.JavaConversions._
 class PruningSuite extends HiveComparisonTest {
   // Column pruning tests
 
-  createPruningTest("Column pruning: with partitioned table",
+  createPruningTest("Column pruning - with partitioned table",
     "SELECT key FROM srcpart WHERE ds = '2008-04-08' LIMIT 3",
     Seq("key"),
     Seq("key"),
@@ -36,43 +36,43 @@ class PruningSuite extends HiveComparisonTest {
       Seq("2008-04-08", "11"),
       Seq("2008-04-08", "12")))
 
-  createPruningTest("Column pruning: with non-partitioned table",
+  createPruningTest("Column pruning - with non-partitioned table",
     "SELECT key FROM src WHERE key > 10 LIMIT 3",
     Seq("key"),
     Seq("key"),
     Seq.empty)
 
-  createPruningTest("Column pruning: with multiple projects",
+  createPruningTest("Column pruning - with multiple projects",
     "SELECT c1 FROM (SELECT key AS c1 FROM src WHERE key > 10) t1 LIMIT 3",
     Seq("c1"),
     Seq("key"),
     Seq.empty)
 
-  createPruningTest("Column pruning: projects alias substituting",
+  createPruningTest("Column pruning - projects alias substituting",
     "SELECT c1 AS c2 FROM (SELECT key AS c1 FROM src WHERE key > 10) t1 LIMIT 3",
     Seq("c2"),
     Seq("key"),
     Seq.empty)
 
-  createPruningTest("Column pruning: filter alias in-lining",
+  createPruningTest("Column pruning - filter alias in-lining",
     "SELECT c1 FROM (SELECT key AS c1 FROM src WHERE key > 10) t1 WHERE c1 < 100 LIMIT 3",
     Seq("c1"),
     Seq("key"),
     Seq.empty)
 
-  createPruningTest("Column pruning: without filters",
+  createPruningTest("Column pruning - without filters",
     "SELECT c1 FROM (SELECT key AS c1 FROM src) t1 LIMIT 3",
     Seq("c1"),
     Seq("key"),
     Seq.empty)
 
-  createPruningTest("Column pruning: simple top project without aliases",
+  createPruningTest("Column pruning - simple top project without aliases",
     "SELECT key FROM (SELECT key FROM src WHERE key > 10) t1 WHERE key < 100 LIMIT 3",
     Seq("key"),
     Seq("key"),
     Seq.empty)
 
-  createPruningTest("Column pruning: non-trivial top project with aliases",
+  createPruningTest("Column pruning - non-trivial top project with aliases",
     "SELECT c1 * 2 AS double FROM (SELECT key AS c1 FROM src WHERE key > 10) t1 LIMIT 3",
     Seq("double"),
     Seq("key"),
@@ -80,19 +80,19 @@ class PruningSuite extends HiveComparisonTest {
 
   // Partition pruning tests
 
-  createPruningTest("Partition pruning: non-partitioned, non-trivial project",
+  createPruningTest("Partition pruning - non-partitioned, non-trivial project",
     "SELECT key * 2 AS double FROM src WHERE value IS NOT NULL",
     Seq("double"),
     Seq("key", "value"),
     Seq.empty)
 
-  createPruningTest("Partiton pruning: non-partitioned table",
+  createPruningTest("Partition pruning - non-partitioned table",
     "SELECT value FROM src WHERE key IS NOT NULL",
     Seq("value"),
     Seq("value", "key"),
     Seq.empty)
 
-  createPruningTest("Partition pruning: with filter on string partition key",
+  createPruningTest("Partition pruning - with filter on string partition key",
     "SELECT value, hr FROM srcpart1 WHERE ds = '2008-04-08'",
     Seq("value", "hr"),
     Seq("value", "hr"),
@@ -100,7 +100,7 @@ class PruningSuite extends HiveComparisonTest {
       Seq("2008-04-08", "11"),
       Seq("2008-04-08", "12")))
 
-  createPruningTest("Partition pruning: with filter on int partition key",
+  createPruningTest("Partition pruning - with filter on int partition key",
     "SELECT value, hr FROM srcpart1 WHERE hr < 12",
     Seq("value", "hr"),
     Seq("value", "hr"),
@@ -108,20 +108,20 @@ class PruningSuite extends HiveComparisonTest {
       Seq("2008-04-08", "11"),
       Seq("2008-04-09", "11")))
 
-  createPruningTest("Partition pruning: left only 1 partition",
+  createPruningTest("Partition pruning - left only 1 partition",
     "SELECT value, hr FROM srcpart1 WHERE ds = '2008-04-08' AND hr < 12",
     Seq("value", "hr"),
     Seq("value", "hr"),
     Seq(
       Seq("2008-04-08", "11")))
 
-  createPruningTest("Partition pruning: all partitions pruned",
+  createPruningTest("Partition pruning - all partitions pruned",
     "SELECT value, hr FROM srcpart1 WHERE ds = '2014-01-27' AND hr = 11",
     Seq("value", "hr"),
     Seq("value", "hr"),
     Seq.empty)
 
-  createPruningTest("Partition pruning: pruning with both column key and partition key",
+  createPruningTest("Partition pruning - pruning with both column key and partition key",
     "SELECT value, hr FROM srcpart1 WHERE value IS NOT NULL AND hr < 12",
     Seq("value", "hr"),
     Seq("value", "hr"),

From a4aafe5f9fb191533400caeafddf04986492c95f Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Thu, 15 May 2014 16:50:42 -0700
Subject: [PATCH 220/810] [SQL] Fix tiny/small ints from HiveMetastore.

Author: Michael Armbrust <michael@databricks.com>

Closes #797 from marmbrus/smallInt and squashes the following commits:

2db9dae [Michael Armbrust] Fix tiny/small ints from HiveMetastore.
---
 .../org/apache/spark/sql/hive/HiveMetastoreCatalog.scala    | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
index fa30f596f8c6..636c4f7b9319 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
@@ -183,7 +183,8 @@ object HiveMetastoreTypes extends RegexParsers {
     "string" ^^^ StringType |
     "float" ^^^ FloatType |
     "int" ^^^ IntegerType |
-    "tinyint" ^^^ ShortType |
+    "tinyint" ^^^ ByteType |
+    "smallint" ^^^ ShortType |
     "double" ^^^ DoubleType |
     "bigint" ^^^ LongType |
     "binary" ^^^ BinaryType |
@@ -227,7 +228,8 @@ object HiveMetastoreTypes extends RegexParsers {
     case StringType => "string"
     case FloatType => "float"
     case IntegerType => "int"
-    case ShortType =>"tinyint"
+    case ByteType => "tinyint"
+    case ShortType => "smallint"
     case DoubleType => "double"
     case LongType => "bigint"
     case BinaryType => "binary"

From e1e3416c4e5f6f32983597d74866dbb809cf6a5e Mon Sep 17 00:00:00 2001
From: Prashant Sharma <prashant.s@imaginea.com>
Date: Thu, 15 May 2014 16:58:37 -0700
Subject: [PATCH 221/810] Fixes a misplaced comment.

Fixes a misplaced comment from #785.

@pwendell

Author: Prashant Sharma <prashant.s@imaginea.com>

Closes #788 from ScrapCodes/patch-1 and squashes the following commits:

3ef6a69 [Prashant Sharma] Update package-info.java
67d9461 [Prashant Sharma] Update package-info.java
---
 .../main/scala/org/apache/spark/graphx/lib/package-info.java  | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/graphx/src/main/scala/org/apache/spark/graphx/lib/package-info.java b/graphx/src/main/scala/org/apache/spark/graphx/lib/package-info.java
index 49ad7263141c..e987ee8560ff 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/lib/package-info.java
+++ b/graphx/src/main/scala/org/apache/spark/graphx/lib/package-info.java
@@ -16,6 +16,6 @@
  */
 
 /**
- * Internal support for MLLib Python API.
+ * Various analytics functions for graphs.
  */
-package org.apache.spark.graphx.lib;
\ No newline at end of file
+package org.apache.spark.graphx.lib;

From 94c5139607ec876782e594012a108ebf55fa97db Mon Sep 17 00:00:00 2001
From: Huajian Mao <huajianmao@gmail.com>
Date: Thu, 15 May 2014 18:20:16 -0700
Subject: [PATCH 222/810] Typos in Spark

Author: Huajian Mao <huajianmao@gmail.com>

Closes #798 from huajianmao/patch-1 and squashes the following commits:

208a454 [Huajian Mao] A typo in Task
1b515af [Huajian Mao] A typo in the message
---
 core/src/main/scala/org/apache/spark/scheduler/Task.scala       | 2 +-
 .../scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala    | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/scheduler/Task.scala b/core/src/main/scala/org/apache/spark/scheduler/Task.scala
index 2ca3479c80ef..5871edeb856a 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/Task.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/Task.scala
@@ -33,7 +33,7 @@ import org.apache.spark.util.ByteBufferInputStream
  * - [[org.apache.spark.scheduler.ResultTask]]
  *
  * A Spark job consists of one or more stages. The very last stage in a job consists of multiple
- * ResultTask's, while earlier stages consist of ShuffleMapTasks. A ResultTask executes the task
+ * ResultTasks, while earlier stages consist of ShuffleMapTasks. A ResultTask executes the task
  * and sends the task output back to the driver application. A ShuffleMapTask executes the task
  * and divides the task output to multiple buckets (based on the task's partitioner).
  *
diff --git a/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala b/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala
index 649eed213e09..17292b4c15b8 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala
@@ -105,7 +105,7 @@ private[spark] class TaskSchedulerImpl(
     SchedulingMode.withName(schedulingModeConf.toUpperCase)
   } catch {
     case e: java.util.NoSuchElementException =>
-      throw new SparkException(s"Urecognized spark.scheduler.mode: $schedulingModeConf")
+      throw new SparkException(s"Unrecognized spark.scheduler.mode: $schedulingModeConf")
   }
 
   // This is a var so that we can reset it for testing purposes.

From bb98ecafce196ecc5bc3a1e4cc9264df7b752c6a Mon Sep 17 00:00:00 2001
From: Aaron Davidson <aaron@databricks.com>
Date: Thu, 15 May 2014 21:37:58 -0700
Subject: [PATCH 223/810] SPARK-1860: Do not cleanup application work/
 directories by default

This causes an unrecoverable error for applications that are running for longer
than 7 days that have jars added to the SparkContext, as the jars are cleaned up
even though the application is still running.

Author: Aaron Davidson <aaron@databricks.com>

Closes #800 from aarondav/shitty-defaults and squashes the following commits:

a573fbb [Aaron Davidson] SPARK-1860: Do not cleanup application work/ directories by default
---
 .../main/scala/org/apache/spark/deploy/worker/Worker.scala   | 2 +-
 docs/configuration.md                                        | 5 +++--
 2 files changed, 4 insertions(+), 3 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala b/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala
index 134624c35a57..fb9cc116cd08 100755
--- a/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala
@@ -65,7 +65,7 @@ private[spark] class Worker(
   val REGISTRATION_TIMEOUT = 20.seconds
   val REGISTRATION_RETRIES = 3
 
-  val CLEANUP_ENABLED = conf.getBoolean("spark.worker.cleanup.enabled", true)
+  val CLEANUP_ENABLED = conf.getBoolean("spark.worker.cleanup.enabled", false)
   // How often worker will clean up old app folders
   val CLEANUP_INTERVAL_MILLIS = conf.getLong("spark.worker.cleanup.interval", 60 * 30) * 1000
   // TTL for app folders/data;  after TTL expires it will be cleaned up
diff --git a/docs/configuration.md b/docs/configuration.md
index 2eed96f704a4..f89040d71477 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -390,10 +390,11 @@ Apart from these, the following properties are also available, and may be useful
 </tr>
 <tr>
   <td>spark.worker.cleanup.enabled</td>
-  <td>true</td>
+  <td>false</td>
   <td>
     Enable periodic cleanup of worker / application directories.  Note that this only affects standalone
-    mode, as YARN works differently.
+    mode, as YARN works differently. Applications directories are cleaned up regardless of whether
+    the application is still running.
   </td>
 </tr>
 <tr>

From a20fea98811d98958567780815fcf0d4fb4e28d4 Mon Sep 17 00:00:00 2001
From: Cheng Hao <hao.cheng@intel.com>
Date: Thu, 15 May 2014 22:12:34 -0700
Subject: [PATCH 224/810] [Spark-1461] Deferred Expression Evaluation
 (short-circuit evaluation)

This patch unify the foldable & nullable interface for Expression.
1) Deterministic-less UDF (like Rand()) can not be folded.
2) Short-circut will significantly improves the performance in Expression Evaluation, however, the stateful UDF should not be ignored in a short-circuit evaluation(e.g. in expression: col1 > 0 and row_sequence() < 1000, row_sequence() can not be ignored even if col1 > 0 is false)

I brought an concept of DeferredObject from Hive, which has 2 kinds of children classes (EagerResult / DeferredResult), the former requires triggering the evaluation before it's created, while the later trigger the evaluation when first called its get() method.

Author: Cheng Hao <hao.cheng@intel.com>

Closes #446 from chenghao-intel/expression_deferred_evaluation and squashes the following commits:

d2729de [Cheng Hao] Fix the codestyle issues
a08f09c [Cheng Hao] fix bug in or/and short-circuit evaluation
af2236b [Cheng Hao] revert the short-circuit expression evaluation for IF
b7861d2 [Cheng Hao] Add Support for Deferred Expression Evaluation
---
 .../sql/catalyst/expressions/predicates.scala | 47 +++++++++++++------
 .../org/apache/spark/sql/hive/hiveUdfs.scala  | 28 ++++++++---
 2 files changed, 53 insertions(+), 22 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
index 6ee479939d25..d11157853050 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
@@ -98,13 +98,19 @@ case class And(left: Expression, right: Expression) extends BinaryPredicate {
 
   override def eval(input: Row): Any = {
     val l = left.eval(input)
-    val r = right.eval(input)
-    if (l == false || r == false) {
-      false
-    } else if (l == null || r == null ) {
-      null
+    if (l == false) {
+       false
     } else {
-      true
+      val r = right.eval(input)
+      if (r == false) {
+        false
+      } else {
+        if (l != null && r != null) {
+          true
+        } else {
+          null
+        }
+      }
     }
   }
 }
@@ -114,13 +120,19 @@ case class Or(left: Expression, right: Expression) extends BinaryPredicate {
 
   override def eval(input: Row): Any = {
     val l = left.eval(input)
-    val r = right.eval(input)
-    if (l == true || r == true) {
+    if (l == true) {
       true
-    } else if (l == null || r == null) {
-      null
     } else {
-      false
+      val r = right.eval(input)
+      if (r == true) {
+        true
+      } else {
+        if (l != null && r != null) {
+          false
+        } else {
+          null
+        }
+      }
     }
   }
 }
@@ -133,8 +145,12 @@ case class Equals(left: Expression, right: Expression) extends BinaryComparison
   def symbol = "="
   override def eval(input: Row): Any = {
     val l = left.eval(input)
-    val r = right.eval(input)
-    if (l == null || r == null) null else l == r
+    if (l == null) {
+      null
+    } else {
+      val r = right.eval(input)
+      if (r == null) null else l == r
+    }
   }
 }
 
@@ -162,7 +178,7 @@ case class If(predicate: Expression, trueValue: Expression, falseValue: Expressi
     extends Expression {
 
   def children = predicate :: trueValue :: falseValue :: Nil
-  def nullable = trueValue.nullable || falseValue.nullable
+  override def nullable = trueValue.nullable || falseValue.nullable
   def references = children.flatMap(_.references).toSet
   override lazy val resolved = childrenResolved && trueValue.dataType == falseValue.dataType
   def dataType = {
@@ -175,8 +191,9 @@ case class If(predicate: Expression, trueValue: Expression, falseValue: Expressi
   }
 
   type EvaluatedType = Any
+
   override def eval(input: Row): Any = {
-    if (predicate.eval(input).asInstanceOf[Boolean]) {
+    if (true == predicate.eval(input)) {
       trueValue.eval(input)
     } else {
       falseValue.eval(input)
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala
index d50e2c65b7b3..572902042337 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala
@@ -248,17 +248,31 @@ private[hive] case class HiveGenericUdf(name: String, children: Seq[Expression])
     isUDFDeterministic && children.foldLeft(true)((prev, n) => prev && n.foldable)
   }
 
+  protected lazy val deferedObjects = Array.fill[DeferredObject](children.length)({
+    new DeferredObjectAdapter
+  })
+
+  // Adapter from Catalyst ExpressionResult to Hive DeferredObject
+  class DeferredObjectAdapter extends DeferredObject {
+    private var func: () => Any = _
+    def set(func: () => Any) {
+      this.func = func
+    }
+    override def prepare(i: Int) = {}
+    override def get(): AnyRef = wrap(func())
+  }
+
   val dataType: DataType = inspectorToDataType(returnInspector)
 
   override def eval(input: Row): Any = {
     returnInspector // Make sure initialized.
-    val args = children.map { v =>
-      new DeferredObject {
-        override def prepare(i: Int) = {}
-        override def get(): AnyRef = wrap(v.eval(input))
-      }
-    }.toArray
-    unwrap(function.evaluate(args))
+    var i = 0
+    while (i < children.length) {
+      val idx = i
+      deferedObjects(i).asInstanceOf[DeferredObjectAdapter].set(() => {children(idx).eval(input)})
+      i += 1
+    }
+    unwrap(function.evaluate(deferedObjects))
   }
 }
 

From 17702e280c4b0b030870962fcb3d50c3085ae862 Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Thu, 15 May 2014 23:31:43 -0700
Subject: [PATCH 225/810] SPARK-1862: Support for MapR in the Maven build.

Author: Patrick Wendell <pwendell@gmail.com>

Closes #803 from pwendell/mapr-support and squashes the following commits:

8df60e4 [Patrick Wendell] SPARK-1862: Support for MapR in the Maven build.
---
 pom.xml | 36 +++++++++++++++++++++++++++++++++++-
 1 file changed, 35 insertions(+), 1 deletion(-)

diff --git a/pom.xml b/pom.xml
index 5df7036585b7..088d4cacb8bb 100644
--- a/pom.xml
+++ b/pom.xml
@@ -123,6 +123,7 @@
     <protobuf.version>2.4.1</protobuf.version>
     <yarn.version>${hadoop.version}</yarn.version>
     <hbase.version>0.94.6</hbase.version>
+    <zookeeper.version>3.4.5</zookeeper.version>
     <hive.version>0.12.0</hive.version>
     <parquet.version>1.4.3</parquet.version>
     <jblas.version>1.2.3</jblas.version>
@@ -194,6 +195,17 @@
         <enabled>false</enabled>
       </snapshots>
     </repository>
+    <repository>
+      <id>mapr-repo</id>
+      <name>MapR Repository</name>
+      <url>http://repository.mapr.com/maven</url>
+      <releases>
+        <enabled>true</enabled>
+      </releases>
+      <snapshots>
+        <enabled>false</enabled>
+      </snapshots>
+    </repository>
   </repositories>
 
   <dependencyManagement>
@@ -490,6 +502,14 @@
             <groupId>commons-logging</groupId>
             <artifactId>commons-logging</artifactId>
           </exclusion>
+          <exclusion>
+            <groupId>org.mortbay.jetty</groupId>
+            <artifactId>servlet-api-2.5</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>junit</groupId>
+            <artifactId>junit</artifactId>
+          </exclusion>
         </exclusions>
       </dependency>
       <dependency>
@@ -979,6 +999,20 @@
       </modules>
     </profile>
 
+    <profile>
+      <id>mapr</id>
+      <activation>
+        <activeByDefault>false</activeByDefault>
+      </activation>
+      <properties>
+        <hadoop.version>1.0.3-mapr-3.0.3</hadoop.version>
+        <yarn.version>2.3.0-mapr-4.0.0-beta</yarn.version>
+        <hbase.version>0.94.17-mapr-1403</hbase.version>
+        <hbase.version>0.94.17-mapr-1403</hbase.version>
+        <zookeeper.version>3.4.5-mapr-1401</zookeeper.version>
+      </properties>
+    </profile>
+
     <!-- Build without Hadoop dependencies that are included in some runtime environments. -->
     <profile>
       <id>hadoop-provided</id>
@@ -1024,7 +1058,7 @@
         <dependency>
           <groupId>org.apache.zookeeper</groupId>
           <artifactId>zookeeper</artifactId>
-          <version>3.4.5</version>
+          <version>${zookeeper.version}</version>
           <scope>provided</scope>
         </dependency>
       </dependencies>

From e304eb998e3b2a44dc1f773cd75211679f217661 Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Thu, 15 May 2014 23:33:27 -0700
Subject: [PATCH 226/810] HOTFIX: Duplication of hbase version

---
 pom.xml | 1 -
 1 file changed, 1 deletion(-)

diff --git a/pom.xml b/pom.xml
index 088d4cacb8bb..86264d1132ec 100644
--- a/pom.xml
+++ b/pom.xml
@@ -1008,7 +1008,6 @@
         <hadoop.version>1.0.3-mapr-3.0.3</hadoop.version>
         <yarn.version>2.3.0-mapr-4.0.0-beta</yarn.version>
         <hbase.version>0.94.17-mapr-1403</hbase.version>
-        <hbase.version>0.94.17-mapr-1403</hbase.version>
         <zookeeper.version>3.4.5-mapr-1401</zookeeper.version>
       </properties>
     </profile>

From fa6de408a131a3e84350a60af74a92c323dfc5eb Mon Sep 17 00:00:00 2001
From: Zhen Peng <zhenpeng01@baidu.com>
Date: Fri, 16 May 2014 11:37:18 -0700
Subject: [PATCH 227/810] bugfix: overflow of graphx Edge compare function

Author: Zhen Peng <zhenpeng01@baidu.com>

Closes #769 from zhpengg/bugfix-graphx-edge-compare and squashes the following commits:

8a978ff [Zhen Peng] add ut for graphx Edge.lexicographicOrdering.compare
413c258 [Zhen Peng] there maybe a overflow for two Long's substraction
---
 .../scala/org/apache/spark/graphx/Edge.scala  | 10 ++++-
 .../org/apache/spark/graphx/EdgeSuite.scala   | 39 +++++++++++++++++++
 2 files changed, 47 insertions(+), 2 deletions(-)
 create mode 100644 graphx/src/test/scala/org/apache/spark/graphx/EdgeSuite.scala

diff --git a/graphx/src/main/scala/org/apache/spark/graphx/Edge.scala b/graphx/src/main/scala/org/apache/spark/graphx/Edge.scala
index 580faa086678..7e842ec4cc82 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/Edge.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/Edge.scala
@@ -56,7 +56,13 @@ case class Edge[@specialized(Char, Int, Boolean, Byte, Long, Float, Double) ED]
 
 object Edge {
   private[graphx] def lexicographicOrdering[ED] = new Ordering[Edge[ED]] {
-    override def compare(a: Edge[ED], b: Edge[ED]): Int =
-      (if (a.srcId != b.srcId) a.srcId - b.srcId else a.dstId - b.dstId).toInt
+    override def compare(a: Edge[ED], b: Edge[ED]): Int = {
+      if (a.srcId == b.srcId) {
+        if (a.dstId == b.dstId) 0
+        else if (a.dstId < b.dstId) -1
+        else 1
+      } else if (a.srcId < b.srcId) -1
+      else 1
+    }
   }
 }
diff --git a/graphx/src/test/scala/org/apache/spark/graphx/EdgeSuite.scala b/graphx/src/test/scala/org/apache/spark/graphx/EdgeSuite.scala
new file mode 100644
index 000000000000..5a2c73b41427
--- /dev/null
+++ b/graphx/src/test/scala/org/apache/spark/graphx/EdgeSuite.scala
@@ -0,0 +1,39 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.graphx
+
+import org.scalatest.FunSuite
+
+class EdgeSuite extends FunSuite {
+  test ("compare") {
+    // decending order
+    val testEdges: Array[Edge[Int]] = Array(
+      Edge(0x7FEDCBA987654321L, -0x7FEDCBA987654321L, 1), 
+      Edge(0x2345L, 0x1234L, 1), 
+      Edge(0x1234L, 0x5678L, 1), 
+      Edge(0x1234L, 0x2345L, 1), 
+      Edge(-0x7FEDCBA987654321L, 0x7FEDCBA987654321L, 1)
+    )
+    // to ascending order
+    val sortedEdges = testEdges.sorted(Edge.lexicographicOrdering[Int])
+    
+    for (i <- 0 until testEdges.length) {
+      assert(sortedEdges(i) == testEdges(testEdges.length - i - 1))
+    }
+  }
+}

From 032d6632ad4ab88c97c9e568b63169a114220a02 Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Fri, 16 May 2014 11:47:00 -0700
Subject: [PATCH 228/810] [SQL] Implement between in hql

Author: Michael Armbrust <michael@databricks.com>

Closes #804 from marmbrus/between and squashes the following commits:

ae24672 [Michael Armbrust] add golden answer.
d9997ef [Michael Armbrust] Implement between in hql.
9bd4433 [Michael Armbrust] Better error on parse failures.
---
 .../scala/org/apache/spark/sql/hive/HiveQl.scala | 16 ++++++++++++++++
 .../between-0-df3cf89fcf2ef64199a582fae14a3321   |  1 +
 .../sql/hive/execution/HiveQuerySuite.scala      |  4 ++++
 3 files changed, 21 insertions(+)
 create mode 100644 sql/hive/src/test/resources/golden/between-0-df3cf89fcf2ef64199a582fae14a3321

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
index 1f688fe1117f..93b9057a2381 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
@@ -233,6 +233,11 @@ private[hive] object HiveQl {
       }
     } catch {
       case e: Exception => throw new ParseException(sql, e)
+      case e: NotImplementedError => sys.error(
+        s"""
+          |Unsupported language features in query: $sql
+          |${dumpTree(getAst(sql))}
+        """.stripMargin)
     }
   }
 
@@ -865,6 +870,17 @@ private[hive] object HiveQl {
       IsNull(nodeToExpr(child))
     case Token("TOK_FUNCTION", Token("IN", Nil) :: value :: list) =>
       In(nodeToExpr(value), list.map(nodeToExpr))
+    case Token("TOK_FUNCTION",
+           Token("between", Nil) ::
+           Token("KW_FALSE", Nil) ::
+           target ::
+           minValue ::
+           maxValue :: Nil) =>
+
+      val targetExpression = nodeToExpr(target)
+      And(
+        GreaterThanOrEqual(targetExpression, nodeToExpr(minValue)),
+        LessThanOrEqual(targetExpression, nodeToExpr(maxValue)))
 
     /* Boolean Logic */
     case Token(AND(), left :: right:: Nil) => And(nodeToExpr(left), nodeToExpr(right))
diff --git a/sql/hive/src/test/resources/golden/between-0-df3cf89fcf2ef64199a582fae14a3321 b/sql/hive/src/test/resources/golden/between-0-df3cf89fcf2ef64199a582fae14a3321
new file mode 100644
index 000000000000..dcd1d8643e3c
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/between-0-df3cf89fcf2ef64199a582fae14a3321
@@ -0,0 +1 @@
+2	val_2
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
index 87a92d83383a..1a2b2f89182a 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
@@ -24,6 +24,10 @@ import org.apache.spark.sql.hive.test.TestHive._
  */
 class HiveQuerySuite extends HiveComparisonTest {
 
+  createQueryTest("between",
+    "SELECT * FROM src WHERE key between 1 and 2"
+  )
+
   test("Query expressed in SQL") {
     assert(sql("SELECT 1").collect() === Array(Seq(1)))
   }

From 40d6acd6ba2feccc600301f5c47d4f90157138b1 Mon Sep 17 00:00:00 2001
From: Andre Schumacher <andre.schumacher@iki.fi>
Date: Fri, 16 May 2014 13:41:41 -0700
Subject: [PATCH 229/810] SPARK-1487 [SQL] Support record filtering via
 predicate pushdown in Parquet

Simple filter predicates such as LessThan, GreaterThan, etc., where one side is a literal and the other one a NamedExpression are now pushed down to the underlying ParquetTableScan. Here are some results for a microbenchmark with a simple schema of six fields of different types where most records failed the test:

             | Uncompressed  | Compressed
-------------| ------------- | -------------
File size  |     10 GB  | 2 GB
Speedup |      2         | 1.8

Since mileage may vary I added a new option to SparkConf:

`org.apache.spark.sql.parquet.filter.pushdown`

Default value would be `true` and setting it to `false` disables the pushdown. When most rows are expected to pass the filter or when there are few fields performance can be better when pushdown is disabled. The default should fit situations with a reasonable number of (possibly nested) fields where not too many records on average pass the filter.

Because of an issue with Parquet ([see here](https://github.com/Parquet/parquet-mr/issues/371])) currently only predicates on non-nullable attributes are pushed down. If one would know that for a given table no optional fields have missing values one could also allow overriding this.

Author: Andre Schumacher <andre.schumacher@iki.fi>

Closes #511 from AndreSchumacher/parquet_filter and squashes the following commits:

16bfe83 [Andre Schumacher] Removing leftovers from merge during rebase
7b304ca [Andre Schumacher] Fixing formatting
c36d5cb [Andre Schumacher] Scalastyle
3da98db [Andre Schumacher] Second round of review feedback
7a78265 [Andre Schumacher] Fixing broken formatting in ParquetFilter
a86553b [Andre Schumacher] First round of code review feedback
b0f7806 [Andre Schumacher] Optimizing imports in ParquetTestData
85fea2d [Andre Schumacher] Adding SparkConf setting to disable filter predicate pushdown
f0ad3cf [Andre Schumacher] Undoing changes not needed for this PR
210e9cb [Andre Schumacher] Adding disjunctive filter predicates
a93a588 [Andre Schumacher] Adding unit test for filtering
6d22666 [Andre Schumacher] Extending ParquetFilters
93e8192 [Andre Schumacher] First commit Parquet record filtering
---
 .../spark/sql/execution/SparkStrategies.scala |  31 +-
 .../spark/sql/parquet/ParquetFilters.scala    | 436 ++++++++++++++++++
 .../sql/parquet/ParquetTableOperations.scala  |  90 +++-
 .../spark/sql/parquet/ParquetTestData.scala   |  90 +++-
 .../spark/sql/parquet/ParquetQuerySuite.scala | 135 +++++-
 5 files changed, 731 insertions(+), 51 deletions(-)
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetFilters.scala

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
index f763106da4e0..394a59700dba 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
@@ -140,12 +140,35 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
         InsertIntoParquetTable(relation, planLater(child), overwrite=true)(sparkContext) :: Nil
       case logical.InsertIntoTable(table: ParquetRelation, partition, child, overwrite) =>
         InsertIntoParquetTable(table, planLater(child), overwrite)(sparkContext) :: Nil
-      case PhysicalOperation(projectList, filters, relation: ParquetRelation) =>
-        // TODO: Should be pushing down filters as well.
+      case PhysicalOperation(projectList, filters: Seq[Expression], relation: ParquetRelation) => {
+        val remainingFilters =
+          if (sparkContext.conf.getBoolean(ParquetFilters.PARQUET_FILTER_PUSHDOWN_ENABLED, true)) {
+            filters.filter {
+              // Note: filters cannot be pushed down to Parquet if they contain more complex
+              // expressions than simple "Attribute cmp Literal" comparisons. Here we remove
+              // all filters that have been pushed down. Note that a predicate such as
+              // "(A AND B) OR C" can result in "A OR C" being pushed down.
+              filter =>
+                val recordFilter = ParquetFilters.createFilter(filter)
+                if (!recordFilter.isDefined) {
+                  // First case: the pushdown did not result in any record filter.
+                  true
+                } else {
+                  // Second case: a record filter was created; here we are conservative in
+                  // the sense that even if "A" was pushed and we check for "A AND B" we
+                  // still want to keep "A AND B" in the higher-level filter, not just "B".
+                  !ParquetFilters.findExpression(recordFilter.get, filter).isDefined
+                }
+            }
+          } else {
+            filters
+          }
         pruneFilterProject(
           projectList,
-          filters,
-          ParquetTableScan(_, relation, None)(sparkContext)) :: Nil
+          remainingFilters,
+          ParquetTableScan(_, relation, filters)(sparkContext)) :: Nil
+      }
+
       case _ => Nil
     }
   }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetFilters.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetFilters.scala
new file mode 100644
index 000000000000..052b0a919671
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetFilters.scala
@@ -0,0 +1,436 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.parquet
+
+import org.apache.hadoop.conf.Configuration
+
+import parquet.filter._
+import parquet.filter.ColumnPredicates._
+import parquet.column.ColumnReader
+
+import com.google.common.io.BaseEncoding
+
+import org.apache.spark.sql.catalyst.types._
+import org.apache.spark.sql.catalyst.expressions.{Predicate => CatalystPredicate}
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.execution.SparkSqlSerializer
+
+object ParquetFilters {
+  val PARQUET_FILTER_DATA = "org.apache.spark.sql.parquet.row.filter"
+  // set this to false if pushdown should be disabled
+  val PARQUET_FILTER_PUSHDOWN_ENABLED = "spark.sql.hints.parquetFilterPushdown"
+
+  def createRecordFilter(filterExpressions: Seq[Expression]): UnboundRecordFilter = {
+    val filters: Seq[CatalystFilter] = filterExpressions.collect {
+      case (expression: Expression) if createFilter(expression).isDefined =>
+        createFilter(expression).get
+    }
+    if (filters.length > 0) filters.reduce(AndRecordFilter.and) else null
+  }
+
+  def createFilter(expression: Expression): Option[CatalystFilter] = {
+    def createEqualityFilter(
+        name: String,
+        literal: Literal,
+        predicate: CatalystPredicate) = literal.dataType match {
+      case BooleanType =>
+        ComparisonFilter.createBooleanFilter(name, literal.value.asInstanceOf[Boolean], predicate)
+      case IntegerType =>
+        ComparisonFilter.createIntFilter(
+          name,
+          (x: Int) => x == literal.value.asInstanceOf[Int],
+          predicate)
+      case LongType =>
+        ComparisonFilter.createLongFilter(
+          name,
+          (x: Long) => x == literal.value.asInstanceOf[Long],
+          predicate)
+      case DoubleType =>
+        ComparisonFilter.createDoubleFilter(
+          name,
+          (x: Double) => x == literal.value.asInstanceOf[Double],
+          predicate)
+      case FloatType =>
+        ComparisonFilter.createFloatFilter(
+          name,
+          (x: Float) => x == literal.value.asInstanceOf[Float],
+          predicate)
+      case StringType =>
+        ComparisonFilter.createStringFilter(name, literal.value.asInstanceOf[String], predicate)
+    }
+    def createLessThanFilter(
+        name: String,
+        literal: Literal,
+        predicate: CatalystPredicate) = literal.dataType match {
+      case IntegerType =>
+        ComparisonFilter.createIntFilter(
+          name,
+          (x: Int) => x < literal.value.asInstanceOf[Int],
+          predicate)
+      case LongType =>
+        ComparisonFilter.createLongFilter(
+          name,
+          (x: Long) => x < literal.value.asInstanceOf[Long],
+          predicate)
+      case DoubleType =>
+        ComparisonFilter.createDoubleFilter(
+          name,
+          (x: Double) => x < literal.value.asInstanceOf[Double],
+          predicate)
+      case FloatType =>
+        ComparisonFilter.createFloatFilter(
+          name,
+          (x: Float) => x < literal.value.asInstanceOf[Float],
+          predicate)
+    }
+    def createLessThanOrEqualFilter(
+        name: String,
+        literal: Literal,
+        predicate: CatalystPredicate) = literal.dataType match {
+      case IntegerType =>
+        ComparisonFilter.createIntFilter(
+          name,
+          (x: Int) => x <= literal.value.asInstanceOf[Int],
+          predicate)
+      case LongType =>
+        ComparisonFilter.createLongFilter(
+          name,
+          (x: Long) => x <= literal.value.asInstanceOf[Long],
+          predicate)
+      case DoubleType =>
+        ComparisonFilter.createDoubleFilter(
+          name,
+          (x: Double) => x <= literal.value.asInstanceOf[Double],
+          predicate)
+      case FloatType =>
+        ComparisonFilter.createFloatFilter(
+          name,
+          (x: Float) => x <= literal.value.asInstanceOf[Float],
+          predicate)
+    }
+    // TODO: combine these two types somehow?
+    def createGreaterThanFilter(
+        name: String,
+        literal: Literal,
+        predicate: CatalystPredicate) = literal.dataType match {
+      case IntegerType =>
+        ComparisonFilter.createIntFilter(
+          name,
+          (x: Int) => x > literal.value.asInstanceOf[Int],
+          predicate)
+      case LongType =>
+        ComparisonFilter.createLongFilter(
+          name,
+          (x: Long) => x > literal.value.asInstanceOf[Long],
+          predicate)
+      case DoubleType =>
+        ComparisonFilter.createDoubleFilter(
+          name,
+          (x: Double) => x > literal.value.asInstanceOf[Double],
+          predicate)
+      case FloatType =>
+        ComparisonFilter.createFloatFilter(
+          name,
+          (x: Float) => x > literal.value.asInstanceOf[Float],
+          predicate)
+    }
+    def createGreaterThanOrEqualFilter(
+        name: String,
+        literal: Literal,
+        predicate: CatalystPredicate) = literal.dataType match {
+      case IntegerType =>
+        ComparisonFilter.createIntFilter(
+          name, (x: Int) => x >= literal.value.asInstanceOf[Int],
+          predicate)
+      case LongType =>
+        ComparisonFilter.createLongFilter(
+          name,
+          (x: Long) => x >= literal.value.asInstanceOf[Long],
+          predicate)
+      case DoubleType =>
+        ComparisonFilter.createDoubleFilter(
+          name,
+          (x: Double) => x >= literal.value.asInstanceOf[Double],
+          predicate)
+      case FloatType =>
+        ComparisonFilter.createFloatFilter(
+          name,
+          (x: Float) => x >= literal.value.asInstanceOf[Float],
+          predicate)
+    }
+
+    /**
+     * TODO: we currently only filter on non-nullable (Parquet REQUIRED) attributes until
+     * https://github.com/Parquet/parquet-mr/issues/371
+     * has been resolved.
+     */
+    expression match {
+      case p @ Or(left: Expression, right: Expression)
+          if createFilter(left).isDefined && createFilter(right).isDefined => {
+        // If either side of this Or-predicate is empty then this means
+        // it contains a more complex comparison than between attribute and literal
+        // (e.g., it contained a CAST). The only safe thing to do is then to disregard
+        // this disjunction, which could be contained in a conjunction. If it stands
+        // alone then it is also safe to drop it, since a Null return value of this
+        // function is interpreted as having no filters at all.
+        val leftFilter = createFilter(left).get
+        val rightFilter = createFilter(right).get
+        Some(new OrFilter(leftFilter, rightFilter))
+      }
+      case p @ And(left: Expression, right: Expression) => {
+        // This treats nested conjunctions; since either side of the conjunction
+        // may contain more complex filter expressions we may actually generate
+        // strictly weaker filter predicates in the process.
+        val leftFilter = createFilter(left)
+        val rightFilter = createFilter(right)
+        (leftFilter, rightFilter) match {
+          case (None, Some(filter)) => Some(filter)
+          case (Some(filter), None) => Some(filter)
+          case (_, _) =>
+            Some(new AndFilter(leftFilter.get, rightFilter.get))
+        }
+      }
+      case p @ Equals(left: Literal, right: NamedExpression) if !right.nullable =>
+        Some(createEqualityFilter(right.name, left, p))
+      case p @ Equals(left: NamedExpression, right: Literal) if !left.nullable =>
+        Some(createEqualityFilter(left.name, right, p))
+      case p @ LessThan(left: Literal, right: NamedExpression) if !right.nullable =>
+        Some(createLessThanFilter(right.name, left, p))
+      case p @ LessThan(left: NamedExpression, right: Literal) if !left.nullable =>
+        Some(createLessThanFilter(left.name, right, p))
+      case p @ LessThanOrEqual(left: Literal, right: NamedExpression) if !right.nullable =>
+        Some(createLessThanOrEqualFilter(right.name, left, p))
+      case p @ LessThanOrEqual(left: NamedExpression, right: Literal) if !left.nullable =>
+        Some(createLessThanOrEqualFilter(left.name, right, p))
+      case p @ GreaterThan(left: Literal, right: NamedExpression) if !right.nullable =>
+        Some(createGreaterThanFilter(right.name, left, p))
+      case p @ GreaterThan(left: NamedExpression, right: Literal) if !left.nullable =>
+        Some(createGreaterThanFilter(left.name, right, p))
+      case p @ GreaterThanOrEqual(left: Literal, right: NamedExpression) if !right.nullable =>
+        Some(createGreaterThanOrEqualFilter(right.name, left, p))
+      case p @ GreaterThanOrEqual(left: NamedExpression, right: Literal) if !left.nullable =>
+        Some(createGreaterThanOrEqualFilter(left.name, right, p))
+      case _ => None
+    }
+  }
+
+  /**
+   * Note: Inside the Hadoop API we only have access to `Configuration`, not to
+   * [[org.apache.spark.SparkContext]], so we cannot use broadcasts to convey
+   * the actual filter predicate.
+   */
+  def serializeFilterExpressions(filters: Seq[Expression], conf: Configuration): Unit = {
+    if (filters.length > 0) {
+      val serialized: Array[Byte] = SparkSqlSerializer.serialize(filters)
+      val encoded: String = BaseEncoding.base64().encode(serialized)
+      conf.set(PARQUET_FILTER_DATA, encoded)
+    }
+  }
+
+  /**
+   * Note: Inside the Hadoop API we only have access to `Configuration`, not to
+   * [[org.apache.spark.SparkContext]], so we cannot use broadcasts to convey
+   * the actual filter predicate.
+   */
+  def deserializeFilterExpressions(conf: Configuration): Seq[Expression] = {
+    val data = conf.get(PARQUET_FILTER_DATA)
+    if (data != null) {
+      val decoded: Array[Byte] = BaseEncoding.base64().decode(data)
+      SparkSqlSerializer.deserialize(decoded)
+    } else {
+      Seq()
+    }
+  }
+
+  /**
+   * Try to find the given expression in the tree of filters in order to
+   * determine whether it is safe to remove it from the higher level filters. Note
+   * that strictly speaking we could stop the search whenever an expression is found
+   * that contains this expression as subexpression (e.g., when searching for "a"
+   * and "(a or c)" is found) but we don't care about optimizations here since the
+   * filter tree is assumed to be small.
+   *
+   * @param filter The [[org.apache.spark.sql.parquet.CatalystFilter]] to expand
+   *               and search
+   * @param expression The expression to look for
+   * @return An optional [[org.apache.spark.sql.parquet.CatalystFilter]] that
+   *         contains the expression.
+   */
+  def findExpression(
+      filter: CatalystFilter,
+      expression: Expression): Option[CatalystFilter] = filter match {
+    case f @ OrFilter(_, leftFilter, rightFilter, _) =>
+      if (f.predicate == expression) {
+        Some(f)
+      } else {
+        val left = findExpression(leftFilter, expression)
+        if (left.isDefined) left else findExpression(rightFilter, expression)
+      }
+    case f @ AndFilter(_, leftFilter, rightFilter, _) =>
+      if (f.predicate == expression) {
+        Some(f)
+      } else {
+        val left = findExpression(leftFilter, expression)
+        if (left.isDefined) left else findExpression(rightFilter, expression)
+      }
+    case f @ ComparisonFilter(_, _, predicate) =>
+      if (predicate == expression) Some(f) else None
+    case _ => None
+  }
+}
+
+abstract private[parquet] class CatalystFilter(
+    @transient val predicate: CatalystPredicate) extends UnboundRecordFilter
+
+private[parquet] case class ComparisonFilter(
+    val columnName: String,
+    private var filter: UnboundRecordFilter,
+    @transient override val predicate: CatalystPredicate)
+  extends CatalystFilter(predicate) {
+  override def bind(readers: java.lang.Iterable[ColumnReader]): RecordFilter = {
+    filter.bind(readers)
+  }
+}
+
+private[parquet] case class OrFilter(
+    private var filter: UnboundRecordFilter,
+    @transient val left: CatalystFilter,
+    @transient val right: CatalystFilter,
+    @transient override val predicate: Or)
+  extends CatalystFilter(predicate) {
+  def this(l: CatalystFilter, r: CatalystFilter) =
+    this(
+      OrRecordFilter.or(l, r),
+      l,
+      r,
+      Or(l.predicate, r.predicate))
+
+  override def bind(readers: java.lang.Iterable[ColumnReader]): RecordFilter = {
+    filter.bind(readers)
+  }
+}
+
+private[parquet] case class AndFilter(
+    private var filter: UnboundRecordFilter,
+    @transient val left: CatalystFilter,
+    @transient val right: CatalystFilter,
+    @transient override val predicate: And)
+  extends CatalystFilter(predicate) {
+  def this(l: CatalystFilter, r: CatalystFilter) =
+    this(
+      AndRecordFilter.and(l, r),
+      l,
+      r,
+      And(l.predicate, r.predicate))
+
+  override def bind(readers: java.lang.Iterable[ColumnReader]): RecordFilter = {
+    filter.bind(readers)
+  }
+}
+
+private[parquet] object ComparisonFilter {
+  def createBooleanFilter(
+      columnName: String,
+      value: Boolean,
+      predicate: CatalystPredicate): CatalystFilter =
+    new ComparisonFilter(
+      columnName,
+      ColumnRecordFilter.column(
+        columnName,
+        ColumnPredicates.applyFunctionToBoolean(
+          new BooleanPredicateFunction {
+            def functionToApply(input: Boolean): Boolean = input == value
+          }
+      )),
+      predicate)
+
+  def createStringFilter(
+      columnName: String,
+      value: String,
+      predicate: CatalystPredicate): CatalystFilter =
+    new ComparisonFilter(
+      columnName,
+      ColumnRecordFilter.column(
+        columnName,
+        ColumnPredicates.applyFunctionToString (
+          new ColumnPredicates.PredicateFunction[String]  {
+            def functionToApply(input: String): Boolean = input == value
+          }
+      )),
+      predicate)
+
+  def createIntFilter(
+      columnName: String,
+      func: Int => Boolean,
+      predicate: CatalystPredicate): CatalystFilter =
+    new ComparisonFilter(
+      columnName,
+      ColumnRecordFilter.column(
+        columnName,
+        ColumnPredicates.applyFunctionToInteger(
+          new IntegerPredicateFunction {
+            def functionToApply(input: Int) = func(input)
+          }
+      )),
+      predicate)
+
+  def createLongFilter(
+      columnName: String,
+      func: Long => Boolean,
+      predicate: CatalystPredicate): CatalystFilter =
+    new ComparisonFilter(
+      columnName,
+      ColumnRecordFilter.column(
+        columnName,
+        ColumnPredicates.applyFunctionToLong(
+          new LongPredicateFunction {
+            def functionToApply(input: Long) = func(input)
+          }
+      )),
+      predicate)
+
+  def createDoubleFilter(
+      columnName: String,
+      func: Double => Boolean,
+      predicate: CatalystPredicate): CatalystFilter =
+    new ComparisonFilter(
+      columnName,
+      ColumnRecordFilter.column(
+        columnName,
+        ColumnPredicates.applyFunctionToDouble(
+          new DoublePredicateFunction {
+            def functionToApply(input: Double) = func(input)
+          }
+      )),
+      predicate)
+
+  def createFloatFilter(
+      columnName: String,
+      func: Float => Boolean,
+      predicate: CatalystPredicate): CatalystFilter =
+    new ComparisonFilter(
+      columnName,
+      ColumnRecordFilter.column(
+        columnName,
+        ColumnPredicates.applyFunctionToFloat(
+          new FloatPredicateFunction {
+            def functionToApply(input: Float) = func(input)
+          }
+      )),
+      predicate)
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala
index f825ca3c028e..65ba1246fbf9 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala
@@ -27,26 +27,27 @@ import org.apache.hadoop.mapreduce._
 import org.apache.hadoop.mapreduce.lib.input.{FileInputFormat => NewFileInputFormat}
 import org.apache.hadoop.mapreduce.lib.output.{FileOutputFormat => NewFileOutputFormat, FileOutputCommitter}
 
-import parquet.hadoop.{ParquetInputFormat, ParquetOutputFormat}
+import parquet.hadoop.{ParquetRecordReader, ParquetInputFormat, ParquetOutputFormat}
+import parquet.hadoop.api.ReadSupport
 import parquet.hadoop.util.ContextUtil
 import parquet.io.InvalidRecordException
 import parquet.schema.MessageType
 
-import org.apache.spark.{SerializableWritable, SparkContext, TaskContext}
+import org.apache.spark.{Logging, SerializableWritable, SparkContext, TaskContext}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.expressions.{Attribute, Expression, Row}
 import org.apache.spark.sql.execution.{LeafNode, SparkPlan, UnaryNode}
 
 /**
  * Parquet table scan operator. Imports the file that backs the given
- * [[ParquetRelation]] as a RDD[Row].
+ * [[org.apache.spark.sql.parquet.ParquetRelation]] as a ``RDD[Row]``.
  */
 case class ParquetTableScan(
     // note: output cannot be transient, see
     // https://issues.apache.org/jira/browse/SPARK-1367
     output: Seq[Attribute],
     relation: ParquetRelation,
-    columnPruningPred: Option[Expression])(
+    columnPruningPred: Seq[Expression])(
     @transient val sc: SparkContext)
   extends LeafNode {
 
@@ -62,18 +63,30 @@ case class ParquetTableScan(
     for (path <- fileList if !path.getName.startsWith("_")) {
       NewFileInputFormat.addInputPath(job, path)
     }
+
+    // Store Parquet schema in `Configuration`
     conf.set(
         RowReadSupport.PARQUET_ROW_REQUESTED_SCHEMA,
         ParquetTypesConverter.convertFromAttributes(output).toString)
-    // TODO: think about adding record filters
-    /* Comments regarding record filters: it would be nice to push down as much filtering
-      to Parquet as possible. However, currently it seems we cannot pass enough information
-      to materialize an (arbitrary) Catalyst [[Predicate]] inside Parquet's
-      ``FilteredRecordReader`` (via Configuration, for example). Simple
-      filter-rows-by-column-values however should be supported.
-    */
-    sc.newAPIHadoopRDD(conf, classOf[ParquetInputFormat[Row]], classOf[Void], classOf[Row])
-    .map(_._2)
+
+    // Store record filtering predicate in `Configuration`
+    // Note 1: the input format ignores all predicates that cannot be expressed
+    // as simple column predicate filters in Parquet. Here we just record
+    // the whole pruning predicate.
+    // Note 2: you can disable filter predicate pushdown by setting
+    // "spark.sql.hints.parquetFilterPushdown" to false inside SparkConf.
+    if (columnPruningPred.length > 0 &&
+      sc.conf.getBoolean(ParquetFilters.PARQUET_FILTER_PUSHDOWN_ENABLED, true)) {
+      ParquetFilters.serializeFilterExpressions(columnPruningPred, conf)
+    }
+
+    sc.newAPIHadoopRDD(
+      conf,
+      classOf[org.apache.spark.sql.parquet.FilteringParquetRowInputFormat],
+      classOf[Void],
+      classOf[Row])
+      .map(_._2)
+      .filter(_ != null) // Parquet's record filters may produce null values
   }
 
   override def otherCopyArgs = sc :: Nil
@@ -184,10 +197,19 @@ case class InsertIntoParquetTable(
 
   override def otherCopyArgs = sc :: Nil
 
-  // based on ``saveAsNewAPIHadoopFile`` in [[PairRDDFunctions]]
-  // TODO: Maybe PairRDDFunctions should use Product2 instead of Tuple2?
-  // .. then we could use the default one and could use [[MutablePair]]
-  // instead of ``Tuple2``
+  /**
+   * Stores the given Row RDD as a Hadoop file.
+   *
+   * Note: We cannot use ``saveAsNewAPIHadoopFile`` from [[org.apache.spark.rdd.PairRDDFunctions]]
+   * together with [[org.apache.spark.util.MutablePair]] because ``PairRDDFunctions`` uses
+   * ``Tuple2`` and not ``Product2``. Also, we want to allow appending files to an existing
+   * directory and need to determine which was the largest written file index before starting to
+   * write.
+   *
+   * @param rdd The [[org.apache.spark.rdd.RDD]] to writer
+   * @param path The directory to write to.
+   * @param conf A [[org.apache.hadoop.conf.Configuration]].
+   */
   private def saveAsHadoopFile(
       rdd: RDD[Row],
       path: String,
@@ -244,8 +266,10 @@ case class InsertIntoParquetTable(
   }
 }
 
-// TODO: this will be able to append to directories it created itself, not necessarily
-// to imported ones
+/**
+ * TODO: this will be able to append to directories it created itself, not necessarily
+ * to imported ones.
+ */
 private[parquet] class AppendingParquetOutputFormat(offset: Int)
   extends parquet.hadoop.ParquetOutputFormat[Row] {
   // override to accept existing directories as valid output directory
@@ -262,6 +286,30 @@ private[parquet] class AppendingParquetOutputFormat(offset: Int)
   }
 }
 
+/**
+ * We extend ParquetInputFormat in order to have more control over which
+ * RecordFilter we want to use.
+ */
+private[parquet] class FilteringParquetRowInputFormat
+  extends parquet.hadoop.ParquetInputFormat[Row] with Logging {
+  override def createRecordReader(
+      inputSplit: InputSplit,
+      taskAttemptContext: TaskAttemptContext): RecordReader[Void, Row] = {
+    val readSupport: ReadSupport[Row] = new RowReadSupport()
+
+    val filterExpressions =
+      ParquetFilters.deserializeFilterExpressions(ContextUtil.getConfiguration(taskAttemptContext))
+    if (filterExpressions.length > 0) {
+      logInfo(s"Pushing down predicates for RecordFilter: ${filterExpressions.mkString(", ")}")
+      new ParquetRecordReader[Row](
+        readSupport,
+        ParquetFilters.createRecordFilter(filterExpressions))
+    } else {
+      new ParquetRecordReader[Row](readSupport)
+    }
+  }
+}
+
 private[parquet] object FileSystemHelper {
   def listFiles(pathStr: String, conf: Configuration): Seq[Path] = {
     val origPath = new Path(pathStr)
@@ -278,7 +326,9 @@ private[parquet] object FileSystemHelper {
     fs.listStatus(path).map(_.getPath)
   }
 
-  // finds the maximum taskid in the output file names at the given path
+    /**
+     * Finds the maximum taskid in the output file names at the given path.
+     */
   def findMaxTaskId(pathStr: String, conf: Configuration): Int = {
     val files = FileSystemHelper.listFiles(pathStr, conf)
     // filename pattern is part-r-<int>.parquet
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTestData.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTestData.scala
index f37976f7313c..46c717298564 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTestData.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTestData.scala
@@ -19,15 +19,34 @@ package org.apache.spark.sql.parquet
 
 import org.apache.hadoop.conf.Configuration
 import org.apache.hadoop.fs.Path
-import org.apache.hadoop.mapreduce.Job
 
+import parquet.example.data.{GroupWriter, Group}
+import parquet.example.data.simple.SimpleGroup
 import parquet.hadoop.ParquetWriter
-import parquet.hadoop.util.ContextUtil
+import parquet.hadoop.api.WriteSupport
+import parquet.hadoop.api.WriteSupport.WriteContext
+import parquet.io.api.RecordConsumer
 import parquet.schema.{MessageType, MessageTypeParser}
 
-import org.apache.spark.sql.catalyst.expressions.GenericRow
 import org.apache.spark.util.Utils
 
+// Write support class for nested groups: ParquetWriter initializes GroupWriteSupport
+// with an empty configuration (it is after all not intended to be used in this way?)
+// and members are private so we need to make our own in order to pass the schema
+// to the writer.
+private class TestGroupWriteSupport(schema: MessageType) extends WriteSupport[Group] {
+  var groupWriter: GroupWriter = null
+  override def prepareForWrite(recordConsumer: RecordConsumer): Unit = {
+    groupWriter = new GroupWriter(recordConsumer, schema)
+  }
+  override def init(configuration: Configuration): WriteContext = {
+    new WriteContext(schema, new java.util.HashMap[String, String]())
+  }
+  override def write(record: Group) {
+    groupWriter.write(record)
+  }
+}
+
 private[sql] object ParquetTestData {
 
   val testSchema =
@@ -43,7 +62,7 @@ private[sql] object ParquetTestData {
   // field names for test assertion error messages
   val testSchemaFieldNames = Seq(
     "myboolean:Boolean",
-    "mtint:Int",
+    "myint:Int",
     "mystring:String",
     "mylong:Long",
     "myfloat:Float",
@@ -58,6 +77,18 @@ private[sql] object ParquetTestData {
       |}
     """.stripMargin
 
+  val testFilterSchema =
+    """
+      |message myrecord {
+      |required boolean myboolean;
+      |required int32 myint;
+      |required binary mystring;
+      |required int64 mylong;
+      |required float myfloat;
+      |required double mydouble;
+      |}
+    """.stripMargin
+
   // field names for test assertion error messages
   val subTestSchemaFieldNames = Seq(
     "myboolean:Boolean",
@@ -65,36 +96,57 @@ private[sql] object ParquetTestData {
   )
 
   val testDir = Utils.createTempDir()
+  val testFilterDir = Utils.createTempDir()
 
   lazy val testData = new ParquetRelation(testDir.toURI.toString)
 
   def writeFile() = {
     testDir.delete
     val path: Path = new Path(new Path(testDir.toURI), new Path("part-r-0.parquet"))
-    val job = new Job()
-    val configuration: Configuration = ContextUtil.getConfiguration(job)
     val schema: MessageType = MessageTypeParser.parseMessageType(testSchema)
+    val writeSupport = new TestGroupWriteSupport(schema)
+    val writer = new ParquetWriter[Group](path, writeSupport)
 
-    val writeSupport = new RowWriteSupport()
-    writeSupport.setSchema(schema, configuration)
-    val writer = new ParquetWriter(path, writeSupport)
     for(i <- 0 until 15) {
-      val data = new Array[Any](6)
+      val record = new SimpleGroup(schema)
       if (i % 3 == 0) {
-        data.update(0, true)
+        record.add(0, true)
       } else {
-        data.update(0, false)
+        record.add(0, false)
       }
       if (i % 5 == 0) {
-        data.update(1, 5)
+        record.add(1, 5)
+      }
+      record.add(2, "abc")
+      record.add(3, i.toLong << 33)
+      record.add(4, 2.5F)
+      record.add(5, 4.5D)
+      writer.write(record)
+    }
+    writer.close()
+  }
+
+  def writeFilterFile(records: Int = 200) = {
+    // for microbenchmark use: records = 300000000
+    testFilterDir.delete
+    val path: Path = new Path(new Path(testFilterDir.toURI), new Path("part-r-0.parquet"))
+    val schema: MessageType = MessageTypeParser.parseMessageType(testFilterSchema)
+    val writeSupport = new TestGroupWriteSupport(schema)
+    val writer = new ParquetWriter[Group](path, writeSupport)
+
+    for(i <- 0 to records) {
+      val record = new SimpleGroup(schema)
+      if (i % 4 == 0) {
+        record.add(0, true)
       } else {
-        data.update(1, null) // optional
+        record.add(0, false)
       }
-      data.update(2, "abc")
-      data.update(3, i.toLong << 33)
-      data.update(4, 2.5F)
-      data.update(5, 4.5D)
-      writer.write(new GenericRow(data.toArray))
+      record.add(1, i)
+      record.add(2, i.toString)
+      record.add(3, i.toLong)
+      record.add(4, i.toFloat + 0.5f)
+      record.add(5, i.toDouble + 0.5d)
+      writer.write(record)
     }
     writer.close()
   }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala
index ff1677eb8a48..65f4c17aeee3 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala
@@ -17,25 +17,25 @@
 
 package org.apache.spark.sql.parquet
 
-import java.io.File
-
 import org.scalatest.{BeforeAndAfterAll, FunSuite}
 
 import org.apache.hadoop.fs.{Path, FileSystem}
 import org.apache.hadoop.mapreduce.Job
 
 import parquet.hadoop.ParquetFileWriter
-import parquet.schema.MessageTypeParser
 import parquet.hadoop.util.ContextUtil
+import parquet.schema.MessageTypeParser
 
 import org.apache.spark.sql._
 import org.apache.spark.sql.catalyst.util.getTempFilePath
-import org.apache.spark.sql.catalyst.expressions.{AttributeReference, Row}
+import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.test.TestSQLContext
 import org.apache.spark.sql.TestData
+import org.apache.spark.sql.SchemaRDD
+import org.apache.spark.sql.catalyst.expressions.Row
+import org.apache.spark.sql.catalyst.expressions.Equals
+import org.apache.spark.sql.catalyst.types.IntegerType
 import org.apache.spark.util.Utils
-import org.apache.spark.sql.catalyst.types.{StringType, IntegerType, DataType}
-import org.apache.spark.sql.{parquet, SchemaRDD}
 
 // Implicits
 import org.apache.spark.sql.test.TestSQLContext._
@@ -64,12 +64,16 @@ class ParquetQuerySuite extends QueryTest with FunSuite with BeforeAndAfterAll {
 
   override def beforeAll() {
     ParquetTestData.writeFile()
+    ParquetTestData.writeFilterFile()
     testRDD = parquetFile(ParquetTestData.testDir.toString)
     testRDD.registerAsTable("testsource")
+    parquetFile(ParquetTestData.testFilterDir.toString)
+      .registerAsTable("testfiltersource")
   }
 
   override def afterAll() {
     Utils.deleteRecursively(ParquetTestData.testDir)
+    Utils.deleteRecursively(ParquetTestData.testFilterDir)
     // here we should also unregister the table??
   }
 
@@ -120,7 +124,7 @@ class ParquetQuerySuite extends QueryTest with FunSuite with BeforeAndAfterAll {
     val scanner = new ParquetTableScan(
       ParquetTestData.testData.output,
       ParquetTestData.testData,
-      None)(TestSQLContext.sparkContext)
+      Seq())(TestSQLContext.sparkContext)
     val projected = scanner.pruneColumns(ParquetTypesConverter
       .convertToAttributes(MessageTypeParser
       .parseMessageType(ParquetTestData.subTestSchema)))
@@ -196,7 +200,6 @@ class ParquetQuerySuite extends QueryTest with FunSuite with BeforeAndAfterAll {
     assert(true)
   }
 
-
   test("insert (appending) to same table via Scala API") {
     sql("INSERT INTO testsource SELECT * FROM testsource").collect()
     val double_rdd = sql("SELECT * FROM testsource").collect()
@@ -239,5 +242,121 @@ class ParquetQuerySuite extends QueryTest with FunSuite with BeforeAndAfterAll {
     Utils.deleteRecursively(file)
     assert(true)
   }
+
+  test("create RecordFilter for simple predicates") {
+    val attribute1 = new AttributeReference("first", IntegerType, false)()
+    val predicate1 = new Equals(attribute1, new Literal(1, IntegerType))
+    val filter1 = ParquetFilters.createFilter(predicate1)
+    assert(filter1.isDefined)
+    assert(filter1.get.predicate == predicate1, "predicates do not match")
+    assert(filter1.get.isInstanceOf[ComparisonFilter])
+    val cmpFilter1 = filter1.get.asInstanceOf[ComparisonFilter]
+    assert(cmpFilter1.columnName == "first", "column name incorrect")
+
+    val predicate2 = new LessThan(attribute1, new Literal(4, IntegerType))
+    val filter2 = ParquetFilters.createFilter(predicate2)
+    assert(filter2.isDefined)
+    assert(filter2.get.predicate == predicate2, "predicates do not match")
+    assert(filter2.get.isInstanceOf[ComparisonFilter])
+    val cmpFilter2 = filter2.get.asInstanceOf[ComparisonFilter]
+    assert(cmpFilter2.columnName == "first", "column name incorrect")
+
+    val predicate3 = new And(predicate1, predicate2)
+    val filter3 = ParquetFilters.createFilter(predicate3)
+    assert(filter3.isDefined)
+    assert(filter3.get.predicate == predicate3, "predicates do not match")
+    assert(filter3.get.isInstanceOf[AndFilter])
+
+    val predicate4 = new Or(predicate1, predicate2)
+    val filter4 = ParquetFilters.createFilter(predicate4)
+    assert(filter4.isDefined)
+    assert(filter4.get.predicate == predicate4, "predicates do not match")
+    assert(filter4.get.isInstanceOf[OrFilter])
+
+    val attribute2 = new AttributeReference("second", IntegerType, false)()
+    val predicate5 = new GreaterThan(attribute1, attribute2)
+    val badfilter = ParquetFilters.createFilter(predicate5)
+    assert(badfilter.isDefined === false)
+  }
+
+  test("test filter by predicate pushdown") {
+    for(myval <- Seq("myint", "mylong", "mydouble", "myfloat")) {
+      println(s"testing field $myval")
+      val query1 = sql(s"SELECT * FROM testfiltersource WHERE $myval < 150 AND $myval >= 100")
+      assert(
+        query1.queryExecution.executedPlan(0)(0).isInstanceOf[ParquetTableScan],
+        "Top operator should be ParquetTableScan after pushdown")
+      val result1 = query1.collect()
+      assert(result1.size === 50)
+      assert(result1(0)(1) === 100)
+      assert(result1(49)(1) === 149)
+      val query2 = sql(s"SELECT * FROM testfiltersource WHERE $myval > 150 AND $myval <= 200")
+      assert(
+        query2.queryExecution.executedPlan(0)(0).isInstanceOf[ParquetTableScan],
+        "Top operator should be ParquetTableScan after pushdown")
+      val result2 = query2.collect()
+      assert(result2.size === 50)
+      if (myval == "myint" || myval == "mylong") {
+        assert(result2(0)(1) === 151)
+        assert(result2(49)(1) === 200)
+      } else {
+        assert(result2(0)(1) === 150)
+        assert(result2(49)(1) === 199)
+      }
+    }
+    for(myval <- Seq("myint", "mylong")) {
+      val query3 = sql(s"SELECT * FROM testfiltersource WHERE $myval > 190 OR $myval < 10")
+      assert(
+        query3.queryExecution.executedPlan(0)(0).isInstanceOf[ParquetTableScan],
+        "Top operator should be ParquetTableScan after pushdown")
+      val result3 = query3.collect()
+      assert(result3.size === 20)
+      assert(result3(0)(1) === 0)
+      assert(result3(9)(1) === 9)
+      assert(result3(10)(1) === 191)
+      assert(result3(19)(1) === 200)
+    }
+    for(myval <- Seq("mydouble", "myfloat")) {
+      val result4 =
+        if (myval == "mydouble") {
+          val query4 = sql(s"SELECT * FROM testfiltersource WHERE $myval > 190.5 OR $myval < 10.0")
+          assert(
+            query4.queryExecution.executedPlan(0)(0).isInstanceOf[ParquetTableScan],
+            "Top operator should be ParquetTableScan after pushdown")
+          query4.collect()
+        } else {
+          // CASTs are problematic. Here myfloat will be casted to a double and it seems there is
+          // currently no way to specify float constants in SqlParser?
+          sql(s"SELECT * FROM testfiltersource WHERE $myval > 190.5 OR $myval < 10").collect()
+        }
+      assert(result4.size === 20)
+      assert(result4(0)(1) === 0)
+      assert(result4(9)(1) === 9)
+      assert(result4(10)(1) === 191)
+      assert(result4(19)(1) === 200)
+    }
+    val query5 = sql(s"SELECT * FROM testfiltersource WHERE myboolean = true AND myint < 40")
+    assert(
+      query5.queryExecution.executedPlan(0)(0).isInstanceOf[ParquetTableScan],
+      "Top operator should be ParquetTableScan after pushdown")
+    val booleanResult = query5.collect()
+    assert(booleanResult.size === 10)
+    for(i <- 0 until 10) {
+      if (!booleanResult(i).getBoolean(0)) {
+        fail(s"Boolean value in result row $i not true")
+      }
+      if (booleanResult(i).getInt(1) != i * 4) {
+        fail(s"Int value in result row $i should be ${4*i}")
+      }
+    }
+    val query6 = sql("SELECT * FROM testfiltersource WHERE mystring = \"100\"")
+    assert(
+      query6.queryExecution.executedPlan(0)(0).isInstanceOf[ParquetTableScan],
+      "Top operator should be ParquetTableScan after pushdown")
+    val stringResult = query6.collect()
+    assert(stringResult.size === 1)
+    assert(stringResult(0).getString(2) == "100", "stringvalue incorrect")
+    assert(stringResult(0).getInt(1) === 100)
+  }
 }
 

From fed6303f29250bd5e656dbdd731b38938c933a61 Mon Sep 17 00:00:00 2001
From: Matei Zaharia <matei@databricks.com>
Date: Fri, 16 May 2014 17:35:05 -0700
Subject: [PATCH 230/810] Tweaks to Mesos docs

- Mention Apache downloads first
- Shorten some wording

Author: Matei Zaharia <matei@databricks.com>

Closes #806 from mateiz/doc-update and squashes the following commits:

d9345cd [Matei Zaharia] typo
a179f8d [Matei Zaharia] Tweaks to Mesos docs
---
 docs/running-on-mesos.md | 71 +++++++++++++++++++---------------------
 1 file changed, 34 insertions(+), 37 deletions(-)

diff --git a/docs/running-on-mesos.md b/docs/running-on-mesos.md
index ef762aa7b8fc..df8687f81f3e 100644
--- a/docs/running-on-mesos.md
+++ b/docs/running-on-mesos.md
@@ -3,16 +3,15 @@ layout: global
 title: Running Spark on Mesos
 ---
 
-# Why Mesos
-
 Spark can run on hardware clusters managed by [Apache Mesos](http://mesos.apache.org/).
 
 The advantages of deploying Spark with Mesos include:
+
 - dynamic partitioning between Spark and other
   [frameworks](https://mesos.apache.org/documentation/latest/mesos-frameworks/)
 - scalable partitioning between multiple instances of Spark
 
-# How it works
+# How it Works
 
 In a standalone cluster deployment, the cluster manager in the below diagram is a Spark master
 instance.  When using Mesos, the Mesos master replaces the Spark master as the cluster manager.
@@ -37,11 +36,25 @@ require any special patches of Mesos.
 If you already have a Mesos cluster running, you can skip this Mesos installation step.
 
 Otherwise, installing Mesos for Spark is no different than installing Mesos for use by other
-frameworks.  You can install Mesos using either prebuilt packages or by compiling from source.
+frameworks.  You can install Mesos either from source or using prebuilt packages.
+
+## From Source
+
+To install Apache Mesos from source, follow these steps:
+
+1. Download a Mesos release from a
+   [mirror](http://www.apache.org/dyn/closer.cgi/mesos/{{site.MESOS_VERSION}}/)
+2. Follow the Mesos [Getting Started](http://mesos.apache.org/gettingstarted) page for compiling and
+   installing Mesos
+
+**Note:** If you want to run Mesos without installing it into the default paths on your system
+(e.g., if you lack administrative privileges to install it), pass the
+`--prefix` option to `configure` to tell it where to install. For example, pass
+`--prefix=/home/me/mesos`. By default the prefix is `/usr/local`.
 
-## Prebuilt packages
+## Third-Party Packages
 
-The Apache Mesos project only publishes source package releases, no binary releases.  But other
+The Apache Mesos project only publishes source releases, not binary packages.  But other
 third party projects publish binary releases that may be helpful in setting Mesos up.
 
 One of those is Mesosphere.  To install Mesos using the binary releases provided by Mesosphere:
@@ -52,20 +65,6 @@ One of those is Mesosphere.  To install Mesos using the binary releases provided
 The Mesosphere installation documents suggest setting up ZooKeeper to handle Mesos master failover,
 but Mesos can be run without ZooKeeper using a single master as well.
 
-## From source
-
-To install Mesos directly from the upstream project rather than a third party, install from source.
-
-1. Download the Mesos distribution from a
-   [mirror](http://www.apache.org/dyn/closer.cgi/mesos/{{site.MESOS_VERSION}}/)
-2. Follow the Mesos [Getting Started](http://mesos.apache.org/gettingstarted) page for compiling and
-   installing Mesos
-
-**Note:** If you want to run Mesos without installing it into the default paths on your system
-(e.g., if you lack administrative privileges to install it), you should also pass the
-`--prefix` option to `configure` to tell it where to install. For example, pass
-`--prefix=/home/user/mesos`. By default the prefix is `/usr/local`.
-
 ## Verification
 
 To verify that the Mesos cluster is ready for Spark, navigate to the Mesos master webui at port
@@ -74,32 +73,30 @@ To verify that the Mesos cluster is ready for Spark, navigate to the Mesos maste
 
 # Connecting Spark to Mesos
 
-To use Mesos from Spark, you need a Spark distribution available in a place accessible by Mesos, and
+To use Mesos from Spark, you need a Spark binary package available in a place accessible by Mesos, and
 a Spark driver program configured to connect to Mesos.
 
-## Uploading Spark Distribution
-
-When Mesos runs a task on a Mesos slave for the first time, that slave must have a distribution of
-Spark available for running the Spark Mesos executor backend.  A distribution of Spark is just a
-compiled binary version of Spark.
+## Uploading Spark Package
 
-The Spark distribution can be hosted at any Hadoop URI, including HTTP via `http://`, [Amazon Simple
-Storage Service](http://aws.amazon.com/s3) via `s3://`, or HDFS via `hdfs:///`.
+When Mesos runs a task on a Mesos slave for the first time, that slave must have a Spark binary
+package for running the Spark Mesos executor backend.
+The Spark package can be hosted at any Hadoop-accessible URI, including HTTP via `http://`,
+[Amazon Simple Storage Service](http://aws.amazon.com/s3) via `s3n://`, or HDFS via `hdfs://`.
 
-To use a precompiled distribution:
+To use a precompiled package:
 
-1. Download a Spark distribution from the Spark [download page](https://spark.apache.org/downloads.html)  
+1. Download a Spark binary package from the Spark [download page](https://spark.apache.org/downloads.html)
 2. Upload to hdfs/http/s3
 
 To host on HDFS, use the Hadoop fs put command: `hadoop fs -put spark-{{site.SPARK_VERSION}}.tar.gz
 /path/to/spark-{{site.SPARK_VERSION}}.tar.gz`
 
 
-Or if you are using a custom-compiled version of Spark, you will need to create a distribution using
+Or if you are using a custom-compiled version of Spark, you will need to create a package using
 the `make-distribution.sh` script included in a Spark source tarball/checkout.
 
 1. Download and build Spark using the instructions [here](index.html)
-2. Create a Spark distribution using `make-distribution.sh --tgz`.
+2. Create a binary package using `make-distribution.sh --tgz`.
 3. Upload archive to http/s3/hdfs
 
 
@@ -115,8 +112,8 @@ The driver also needs some configuration in `spark-env.sh` to interact properly
    `<prefix>/lib/libmesos.so` where the prefix is `/usr/local` by default. See Mesos installation
    instructions above. On Mac OS X, the library is called `libmesos.dylib` instead of
    `libmesos.so`.
- * `export SPARK_EXECUTOR_URI=<path to spark-{{site.SPARK_VERSION}}.tar.gz uploaded above>`.
-2. Also set `spark.executor.uri` to <path to spark-{{site.SPARK_VERSION}}.tar.gz>
+ * `export SPARK_EXECUTOR_URI=<URL of spark-{{site.SPARK_VERSION}}.tar.gz uploaded above>`.
+2. Also set `spark.executor.uri` to `<URL of spark-{{site.SPARK_VERSION}}.tar.gz>`.
 
 Now when starting a Spark application against the cluster, pass a `mesos://`
 or `zk://` URL as the master when creating a `SparkContext`. For example:
@@ -129,7 +126,7 @@ val conf = new SparkConf()
 val sc = new SparkContext(conf)
 {% endhighlight %}
 
-When running a shell the `spark.executor.uri` parameter is inherited from `SPARK_EXECUTOR_URI`, so
+When running a shell, the `spark.executor.uri` parameter is inherited from `SPARK_EXECUTOR_URI`, so
 it does not need to be redundantly passed in as a system property.
 
 {% highlight bash %}
@@ -168,7 +165,7 @@ using `conf.set("spark.cores.max", "10")` (for example).
 # Running Alongside Hadoop
 
 You can run Spark and Mesos alongside your existing Hadoop cluster by just launching them as a
-separate service on the machines. To access Hadoop data from Spark, a full hdfs:// URL is required
+separate service on the machines. To access Hadoop data from Spark, a full `hdfs://` URL is required
 (typically `hdfs://<namenode>:9000/path`, but you can find the right URL on your Hadoop Namenode web
 UI).
 
@@ -195,7 +192,7 @@ A few places to look during debugging:
 And common pitfalls:
 
 - Spark assembly not reachable/accessible
-  - Slaves need to be able to download the distribution
+  - Slaves must be able to download the Spark binary package from the `http://`, `hdfs://` or `s3n://` URL you gave
 - Firewall blocking communications
   - Check for messages about failed connections
   - Temporarily disable firewalls for debugging and then poke appropriate holes

From a80a6a139e729ee3f81ec4f0028e084d2d9f7e82 Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Fri, 16 May 2014 20:25:10 -0700
Subject: [PATCH 231/810] SPARK-1864 Look in spark conf instead of system
 properties when propagating configuration to executors.

Author: Michael Armbrust <michael@databricks.com>

Closes #808 from marmbrus/confClasspath and squashes the following commits:

4c31d57 [Michael Armbrust] Look in spark conf instead of system properties when propagating configuration to executors.
---
 .../scheduler/cluster/SparkDeploySchedulerBackend.scala  | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala
index 933f6e057151..9768670855f8 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala
@@ -46,12 +46,13 @@ private[spark] class SparkDeploySchedulerBackend(
       CoarseGrainedSchedulerBackend.ACTOR_NAME)
     val args = Seq(driverUrl, "{{EXECUTOR_ID}}", "{{HOSTNAME}}", "{{CORES}}", "{{WORKER_URL}}")
     val extraJavaOpts = sc.conf.getOption("spark.executor.extraJavaOptions")
-    val classPathEntries = sys.props.get("spark.executor.extraClassPath").toSeq.flatMap { cp =>
-      cp.split(java.io.File.pathSeparator)
-    }
-    val libraryPathEntries = sys.props.get("spark.executor.extraLibraryPath").toSeq.flatMap { cp =>
+    val classPathEntries = sc.conf.getOption("spark.executor.extraClassPath").toSeq.flatMap { cp =>
       cp.split(java.io.File.pathSeparator)
     }
+    val libraryPathEntries =
+      sc.conf.getOption("spark.executor.extraLibraryPath").toSeq.flatMap { cp =>
+        cp.split(java.io.File.pathSeparator)
+      }
 
     val command = Command(
       "org.apache.spark.executor.CoarseGrainedExecutorBackend", args, sc.executorEnvs,

From c0ab85d7320cea90e6331fb03a70349bc804c1b1 Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Fri, 16 May 2014 21:42:14 -0700
Subject: [PATCH 232/810] Version bump of spark-ec2 scripts

This will allow us to change things in spark-ec2 related to the 1.0 release.

Author: Patrick Wendell <pwendell@gmail.com>

Closes #809 from pwendell/spark-ec2 and squashes the following commits:

59117fb [Patrick Wendell] Version bump of spark-ec2 scripts
---
 ec2/spark_ec2.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ec2/spark_ec2.py b/ec2/spark_ec2.py
index 0a8d6ca5d751..4c5a99f5f6b2 100755
--- a/ec2/spark_ec2.py
+++ b/ec2/spark_ec2.py
@@ -443,7 +443,7 @@ def setup_cluster(conn, master_nodes, slave_nodes, opts, deploy_ssh_key):
 
   # NOTE: We should clone the repository before running deploy_files to
   # prevent ec2-variables.sh from being overwritten
-  ssh(master, opts, "rm -rf spark-ec2 && git clone https://github.com/mesos/spark-ec2.git -b v2")
+  ssh(master, opts, "rm -rf spark-ec2 && git clone https://github.com/mesos/spark-ec2.git -b v3")
 
   print "Deploying files to master..."
   deploy_files(conn, "deploy.generic", opts, master_nodes, slave_nodes, modules)

From 4b8ec6fcfd7a7ef0857d5b21917183c181301c95 Mon Sep 17 00:00:00 2001
From: Andrew Or <andrewor14@gmail.com>
Date: Fri, 16 May 2014 22:34:38 -0700
Subject: [PATCH 233/810] [SPARK-1808] Route bin/pyspark through Spark submit

**Problem.** For `bin/pyspark`, there is currently no other way to specify Spark configuration properties other than through `SPARK_JAVA_OPTS` in `conf/spark-env.sh`. However, this mechanism is supposedly deprecated. Instead, it needs to pick up configurations explicitly specified in `conf/spark-defaults.conf`.

**Solution.** Have `bin/pyspark` invoke `bin/spark-submit`, like all of its counterparts in Scala land (i.e. `bin/spark-shell`, `bin/run-example`). This has the additional benefit of making the invocation of all the user facing Spark scripts consistent.

**Details.** `bin/pyspark` inherently handles two cases: (1) running python applications and (2) running the python shell. For (1), Spark submit already handles running python applications. For cases in which `bin/pyspark` is given a python file, we can simply call pass the file directly to Spark submit and let it handle the rest.

For case (2), `bin/pyspark` starts a python process as before, which launches the JVM as a sub-process. The existing code already provides a code path to do this. All we needed to change is to use `bin/spark-submit` instead of `spark-class` to launch the JVM. This requires modifications to Spark submit to handle the pyspark shell as a special case.

This has been tested locally (OSX and Windows 7), on a standalone cluster, and on a YARN cluster. Running IPython also works as before, except now it takes in Spark submit arguments too.

Author: Andrew Or <andrewor14@gmail.com>

Closes #799 from andrewor14/pyspark-submit and squashes the following commits:

bf37e36 [Andrew Or] Minor changes
01066fa [Andrew Or] bin/pyspark for Windows
c8cb3bf [Andrew Or] Handle perverse app names (with escaped quotes)
1866f85 [Andrew Or] Windows is not cooperating
456d844 [Andrew Or] Guard against shlex hanging if PYSPARK_SUBMIT_ARGS is not set
7eebda8 [Andrew Or] Merge branch 'master' of github.com:apache/spark into pyspark-submit
b7ba0d8 [Andrew Or] Address a few comments (minor)
06eb138 [Andrew Or] Use shlex instead of writing our own parser
05879fa [Andrew Or] Merge branch 'master' of github.com:apache/spark into pyspark-submit
a823661 [Andrew Or] Fix --die-on-broken-pipe not propagated properly
6fba412 [Andrew Or] Deal with quotes + address various comments
fe4c8a7 [Andrew Or] Update --help for bin/pyspark
afe47bf [Andrew Or] Fix spark shell
f04aaa4 [Andrew Or] Merge branch 'master' of github.com:apache/spark into pyspark-submit
a371d26 [Andrew Or] Route bin/pyspark through Spark submit
---
 bin/pyspark                                   | 35 ++++++++++--
 bin/pyspark2.cmd                              | 21 ++++++-
 bin/spark-shell                               |  6 +-
 bin/spark-shell.cmd                           |  2 +-
 .../apache/spark/deploy/PythonRunner.scala    |  2 +-
 .../org/apache/spark/deploy/SparkSubmit.scala | 55 ++++++++++++++-----
 .../spark/deploy/SparkSubmitArguments.scala   |  6 +-
 .../scala/org/apache/spark/util/Utils.scala   |  2 +-
 python/pyspark/java_gateway.py                | 10 ++--
 python/pyspark/shell.py                       |  2 +-
 10 files changed, 107 insertions(+), 34 deletions(-)

diff --git a/bin/pyspark b/bin/pyspark
index 10e35e0f1734..9e1364e44c8c 100755
--- a/bin/pyspark
+++ b/bin/pyspark
@@ -25,6 +25,12 @@ export SPARK_HOME="$FWDIR"
 
 SCALA_VERSION=2.10
 
+if [[ "$@" = *--help ]] || [[ "$@" = *-h ]]; then
+  echo "Usage: ./bin/pyspark [options]"
+  ./bin/spark-submit --help 2>&1 | grep -v Usage 1>&2
+  exit 0
+fi
+
 # Exit if the user hasn't compiled Spark
 if [ ! -f "$FWDIR/RELEASE" ]; then
   # Exit if the user hasn't compiled Spark
@@ -52,13 +58,34 @@ export PYTHONPATH=$SPARK_HOME/python/lib/py4j-0.8.1-src.zip:$PYTHONPATH
 export OLD_PYTHONSTARTUP=$PYTHONSTARTUP
 export PYTHONSTARTUP=$FWDIR/python/pyspark/shell.py
 
+# If IPython options are specified, assume user wants to run IPython
 if [ -n "$IPYTHON_OPTS" ]; then
   IPYTHON=1
 fi
 
-# Only use ipython if no command line arguments were provided [SPARK-1134]
-if [[ "$IPYTHON" = "1" && $# = 0 ]] ; then
-  exec ipython $IPYTHON_OPTS
+# Build up arguments list manually to preserve quotes and backslashes.
+# We export Spark submit arguments as an environment variable because shell.py must run as a
+# PYTHONSTARTUP script, which does not take in arguments. This is required for IPython notebooks.
+
+PYSPARK_SUBMIT_ARGS=""
+whitespace="[[:space:]]"
+for i in "$@"; do
+  if [[ $i =~ \" ]]; then i=$(echo $i | sed 's/\"/\\\"/g'); fi
+  if [[ $i =~ $whitespace ]]; then i=\"$i\"; fi
+  PYSPARK_SUBMIT_ARGS="$PYSPARK_SUBMIT_ARGS $i"
+done
+export PYSPARK_SUBMIT_ARGS
+
+# If a python file is provided, directly run spark-submit.
+if [[ "$1" =~ \.py$ ]]; then
+  echo -e "\nWARNING: Running python applications through ./bin/pyspark is deprecated as of Spark 1.0." 1>&2
+  echo -e "Use ./bin/spark-submit <python file>\n" 1>&2
+  exec $FWDIR/bin/spark-submit "$@"
 else
-  exec "$PYSPARK_PYTHON" "$@"
+  # Only use ipython if no command line arguments were provided [SPARK-1134]
+  if [[ "$IPYTHON" = "1" ]]; then
+    exec ipython $IPYTHON_OPTS
+  else
+    exec "$PYSPARK_PYTHON"
+  fi
 fi
diff --git a/bin/pyspark2.cmd b/bin/pyspark2.cmd
index d7cfd5eec501..0ef9eea95342 100644
--- a/bin/pyspark2.cmd
+++ b/bin/pyspark2.cmd
@@ -31,7 +31,7 @@ set FOUND_JAR=0
 for %%d in ("%FWDIR%assembly\target\scala-%SCALA_VERSION%\spark-assembly*hadoop*.jar") do (
   set FOUND_JAR=1
 )
-if "%FOUND_JAR%"=="0" (
+if [%FOUND_JAR%] == [0] (
   echo Failed to find Spark assembly JAR.
   echo You need to build Spark with sbt\sbt assembly before running this program.
   goto exit
@@ -42,15 +42,30 @@ rem Load environment variables from conf\spark-env.cmd, if it exists
 if exist "%FWDIR%conf\spark-env.cmd" call "%FWDIR%conf\spark-env.cmd"
 
 rem Figure out which Python to use.
-if "x%PYSPARK_PYTHON%"=="x" set PYSPARK_PYTHON=python
+if [%PYSPARK_PYTHON%] == [] set PYSPARK_PYTHON=python
 
 set PYTHONPATH=%FWDIR%python;%PYTHONPATH%
 set PYTHONPATH=%FWDIR%python\lib\py4j-0.8.1-src.zip;%PYTHONPATH%
 
 set OLD_PYTHONSTARTUP=%PYTHONSTARTUP%
 set PYTHONSTARTUP=%FWDIR%python\pyspark\shell.py
+set PYSPARK_SUBMIT_ARGS=%*
 
 echo Running %PYSPARK_PYTHON% with PYTHONPATH=%PYTHONPATH%
 
-"%PYSPARK_PYTHON%" %*
+rem Check whether the argument is a file
+for /f %%i in ('echo %1^| findstr /R "\.py"') do (
+  set PYTHON_FILE=%%i
+)
+
+if [%PYTHON_FILE%] == [] (
+  %PYSPARK_PYTHON%
+) else (
+  echo.
+  echo WARNING: Running python applications through ./bin/pyspark.cmd is deprecated as of Spark 1.0.
+  echo Use ./bin/spark-submit ^<python file^>
+  echo.
+  "%FWDIR%\bin\spark-submit.cmd" %PYSPARK_SUBMIT_ARGS%
+)
+
 :exit
diff --git a/bin/spark-shell b/bin/spark-shell
index 7f03349c5e91..c158683ab3f9 100755
--- a/bin/spark-shell
+++ b/bin/spark-shell
@@ -28,7 +28,7 @@ esac
 # Enter posix mode for bash
 set -o posix
 
-if [[ "$@" == *--help* ]]; then
+if [[ "$@" = *--help ]] || [[ "$@" = *-h ]]; then
   echo "Usage: ./bin/spark-shell [options]"
   ./bin/spark-submit --help 2>&1 | grep -v Usage 1>&2
   exit 0
@@ -46,11 +46,11 @@ function main(){
         # (see https://github.com/sbt/sbt/issues/562).
         stty -icanon min 1 -echo > /dev/null 2>&1
         export SPARK_SUBMIT_OPTS="$SPARK_SUBMIT_OPTS -Djline.terminal=unix"
-        $FWDIR/bin/spark-submit spark-internal "$@" --class org.apache.spark.repl.Main
+        $FWDIR/bin/spark-submit spark-shell "$@" --class org.apache.spark.repl.Main
         stty icanon echo > /dev/null 2>&1
     else
         export SPARK_SUBMIT_OPTS
-        $FWDIR/bin/spark-submit spark-internal "$@" --class org.apache.spark.repl.Main
+        $FWDIR/bin/spark-submit spark-shell "$@" --class org.apache.spark.repl.Main
     fi
 }
 
diff --git a/bin/spark-shell.cmd b/bin/spark-shell.cmd
index ca0c722c926f..4b9708a8c03f 100755
--- a/bin/spark-shell.cmd
+++ b/bin/spark-shell.cmd
@@ -19,4 +19,4 @@ rem
 
 set SPARK_HOME=%~dp0..
 
-cmd /V /E /C %SPARK_HOME%\bin\spark-submit.cmd spark-internal %* --class org.apache.spark.repl.Main
+cmd /V /E /C %SPARK_HOME%\bin\spark-submit.cmd spark-shell %* --class org.apache.spark.repl.Main
diff --git a/core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala b/core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala
index e20d4486c8f0..2dfa02bd26f1 100644
--- a/core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala
@@ -42,7 +42,7 @@ object PythonRunner {
     // Build up a PYTHONPATH that includes the Spark assembly JAR (where this class is), the
     // python directories in SPARK_HOME (if set), and any files in the pyFiles argument
     val pathElements = new ArrayBuffer[String]
-    pathElements ++= pyFiles.split(",")
+    pathElements ++= Option(pyFiles).getOrElse("").split(",")
     pathElements += PythonUtils.sparkPythonPath
     pathElements += sys.env.getOrElse("PYTHONPATH", "")
     val pythonPath = PythonUtils.mergePythonPaths(pathElements: _*)
diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
index e86182e4c56c..a99b2176e2b5 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
@@ -41,10 +41,10 @@ object SparkSubmit {
   private var clusterManager: Int = LOCAL
 
   /**
-   * A special jar name that indicates the class being run is inside of Spark itself,
-   * and therefore no user jar is needed.
+   * Special primary resource names that represent shells rather than application jars.
    */
-  private val RESERVED_JAR_NAME = "spark-internal"
+  private val SPARK_SHELL = "spark-shell"
+  private val PYSPARK_SHELL = "pyspark-shell"
 
   def main(args: Array[String]) {
     val appArgs = new SparkSubmitArguments(args)
@@ -71,8 +71,8 @@ object SparkSubmit {
    *         entries for the child, a list of system properties, a list of env vars
    *         and the main class for the child
    */
-  private[spark] def createLaunchEnv(args: SparkSubmitArguments): (ArrayBuffer[String],
-      ArrayBuffer[String], Map[String, String], String) = {
+  private[spark] def createLaunchEnv(args: SparkSubmitArguments)
+      : (ArrayBuffer[String], ArrayBuffer[String], Map[String, String], String) = {
     if (args.master.startsWith("local")) {
       clusterManager = LOCAL
     } else if (args.master.startsWith("yarn")) {
@@ -121,24 +121,30 @@ object SparkSubmit {
       printErrorAndExit("Cannot currently run driver on the cluster in Mesos")
     }
 
-    // If we're running a Python app, set the Java class to run to be our PythonRunner, add
-    // Python files to deployment list, and pass the main file and Python path to PythonRunner
+    // If we're running a python app, set the main class to our specific python runner
     if (isPython) {
       if (deployOnCluster) {
         printErrorAndExit("Cannot currently run Python driver programs on cluster")
       }
-      args.mainClass = "org.apache.spark.deploy.PythonRunner"
-      args.files = mergeFileLists(args.files, args.pyFiles, args.primaryResource)
+      if (args.primaryResource == PYSPARK_SHELL) {
+        args.mainClass = "py4j.GatewayServer"
+        args.childArgs = ArrayBuffer("--die-on-broken-pipe", "0")
+      } else {
+        // If a python file is provided, add it to the child arguments and list of files to deploy.
+        // Usage: PythonAppRunner <main python file> <extra python files> [app arguments]
+        args.mainClass = "org.apache.spark.deploy.PythonRunner"
+        args.childArgs = ArrayBuffer(args.primaryResource, args.pyFiles) ++ args.childArgs
+        args.files = mergeFileLists(args.files, args.primaryResource)
+      }
       val pyFiles = Option(args.pyFiles).getOrElse("")
-      args.childArgs = ArrayBuffer(args.primaryResource, pyFiles) ++ args.childArgs
-      args.primaryResource = RESERVED_JAR_NAME
+      args.files = mergeFileLists(args.files, pyFiles)
       sysProps("spark.submit.pyFiles") = pyFiles
     }
 
     // If we're deploying into YARN, use yarn.Client as a wrapper around the user class
     if (!deployOnCluster) {
       childMainClass = args.mainClass
-      if (args.primaryResource != RESERVED_JAR_NAME) {
+      if (isUserJar(args.primaryResource)) {
         childClasspath += args.primaryResource
       }
     } else if (clusterManager == YARN) {
@@ -219,7 +225,7 @@ object SparkSubmit {
     // For python files, the primary resource is already distributed as a regular file
     if (!isYarnCluster && !isPython) {
       var jars = sysProps.get("spark.jars").map(x => x.split(",").toSeq).getOrElse(Seq())
-      if (args.primaryResource != RESERVED_JAR_NAME) {
+      if (isUserJar(args.primaryResource)) {
         jars = jars ++ Seq(args.primaryResource)
       }
       sysProps.put("spark.jars", jars.mkString(","))
@@ -293,7 +299,7 @@ object SparkSubmit {
   }
 
   private def addJarToClasspath(localJar: String, loader: ExecutorURLClassLoader) {
-    val localJarFile = new File(new URI(localJar).getPath())
+    val localJarFile = new File(new URI(localJar).getPath)
     if (!localJarFile.exists()) {
       printWarning(s"Jar $localJar does not exist, skipping.")
     }
@@ -302,6 +308,27 @@ object SparkSubmit {
     loader.addURL(url)
   }
 
+  /**
+   * Return whether the given primary resource represents a user jar.
+   */
+  private def isUserJar(primaryResource: String): Boolean = {
+    !isShell(primaryResource) && !isPython(primaryResource)
+  }
+
+  /**
+   * Return whether the given primary resource represents a shell.
+   */
+  private def isShell(primaryResource: String): Boolean = {
+    primaryResource == SPARK_SHELL || primaryResource == PYSPARK_SHELL
+  }
+
+  /**
+   * Return whether the given primary resource requires running python.
+   */
+  private[spark] def isPython(primaryResource: String): Boolean = {
+    primaryResource.endsWith(".py") || primaryResource == PYSPARK_SHELL
+  }
+
   /**
    * Merge a sequence of comma-separated file lists, some of which may be null to indicate
    * no files, into a single comma-separated string.
diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
index 2d327aa3fb27..264d4544cd31 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
@@ -298,11 +298,13 @@ private[spark] class SparkSubmitArguments(args: Seq[String]) {
             case v =>
               primaryResource = v
               inSparkOpts = false
-              isPython = v.endsWith(".py")
+              isPython = SparkSubmit.isPython(v)
               parse(tail)
           }
         } else {
-          childArgs += value
+          if (!value.isEmpty) {
+            childArgs += value
+          }
           parse(tail)
         }
 
diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala
index 388f7222428d..0c7cff019fce 100644
--- a/core/src/main/scala/org/apache/spark/util/Utils.scala
+++ b/core/src/main/scala/org/apache/spark/util/Utils.scala
@@ -1101,7 +1101,7 @@ private[spark] object Utils extends Logging {
    * Strip the directory from a path name
    */
   def stripDirectory(path: String): String = {
-    path.split(File.separator).last
+    new File(path).getName
   }
 
   /**
diff --git a/python/pyspark/java_gateway.py b/python/pyspark/java_gateway.py
index 3d0936fdca91..91ae8263f66b 100644
--- a/python/pyspark/java_gateway.py
+++ b/python/pyspark/java_gateway.py
@@ -18,12 +18,12 @@
 import os
 import sys
 import signal
+import shlex
 import platform
 from subprocess import Popen, PIPE
 from threading import Thread
 from py4j.java_gateway import java_import, JavaGateway, GatewayClient
 
-
 def launch_gateway():
     SPARK_HOME = os.environ["SPARK_HOME"]
 
@@ -34,9 +34,11 @@ def launch_gateway():
         # Launch the Py4j gateway using Spark's run command so that we pick up the
         # proper classpath and settings from spark-env.sh
         on_windows = platform.system() == "Windows"
-        script = "./bin/spark-class.cmd" if on_windows else "./bin/spark-class"
-        command = [os.path.join(SPARK_HOME, script), "py4j.GatewayServer",
-                   "--die-on-broken-pipe", "0"]
+        script = "./bin/spark-submit.cmd" if on_windows else "./bin/spark-submit"
+        submit_args = os.environ.get("PYSPARK_SUBMIT_ARGS")
+        submit_args = submit_args if submit_args is not None else ""
+        submit_args = shlex.split(submit_args)
+        command = [os.path.join(SPARK_HOME, script), "pyspark-shell"] + submit_args
         if not on_windows:
             # Don't send ctrl-c / SIGINT to the Java gateway:
             def preexec_func():
diff --git a/python/pyspark/shell.py b/python/pyspark/shell.py
index d172d588bfbd..ebd714db7a91 100644
--- a/python/pyspark/shell.py
+++ b/python/pyspark/shell.py
@@ -40,7 +40,7 @@
 if os.environ.get("SPARK_EXECUTOR_URI"):
     SparkContext.setSystemProperty("spark.executor.uri", os.environ["SPARK_EXECUTOR_URI"])
 
-sc = SparkContext(os.environ.get("MASTER", "local[*]"), "PySparkShell", pyFiles=add_files)
+sc = SparkContext(appName="PySparkShell", pyFiles=add_files)
 
 print("""Welcome to
       ____              __

From cf6cbe9f76c3b322a968c836d039fc5b70d4ce43 Mon Sep 17 00:00:00 2001
From: Andrew Or <andrewor14@gmail.com>
Date: Fri, 16 May 2014 22:36:23 -0700
Subject: [PATCH 234/810] [SPARK-1824] Remove <master> from Python examples

A recent PR (#552) fixed this for all Scala / Java examples. We need to do it for python too.

Note that this blocks on #799, which makes `bin/pyspark` go through Spark submit. With only the changes in this PR, the only way to run these examples is through Spark submit. Once #799 goes in, you can use `bin/pyspark` to run them too. For example,

```
bin/pyspark examples/src/main/python/pi.py 100 --master local-cluster[4,1,512]
```

Author: Andrew Or <andrewor14@gmail.com>

Closes #802 from andrewor14/python-examples and squashes the following commits:

cf50b9f [Andrew Or] De-indent python comments (minor)
50f80b1 [Andrew Or] Remove pyFiles from SparkContext construction
c362f69 [Andrew Or] Update docs to use spark-submit for python applications
7072c6a [Andrew Or] Merge branch 'master' of github.com:apache/spark into python-examples
427a5f0 [Andrew Or] Update docs
d32072c [Andrew Or] Remove <master> from examples + update usages
---
 docs/index.md                                 | 11 ++++---
 docs/python-programming-guide.md              | 32 ++++++++++---------
 examples/src/main/python/als.py               | 18 +++++------
 examples/src/main/python/kmeans.py            | 12 +++----
 .../src/main/python/logistic_regression.py    | 10 +++---
 examples/src/main/python/mllib/kmeans.py      | 10 +++---
 .../main/python/mllib/logistic_regression.py  | 10 +++---
 examples/src/main/python/pagerank.py          | 10 +++---
 examples/src/main/python/pi.py                | 10 +++---
 examples/src/main/python/sort.py              |  8 ++---
 .../src/main/python/transitive_closure.py     | 10 +++---
 examples/src/main/python/wordcount.py         |  8 ++---
 12 files changed, 77 insertions(+), 72 deletions(-)

diff --git a/docs/index.md b/docs/index.md
index 48182a27d28a..c9b10376cc80 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -43,12 +43,15 @@ The `--master` option specifies the
 locally with one thread, or `local[N]` to run locally with N threads. You should start by using
 `local` for testing. For a full list of options, run Spark shell with the `--help` option.
 
-Spark also provides a Python interface. To run an example Spark application written in Python, use
-`bin/pyspark <program> [params]`. For example,
+Spark also provides a Python interface. To run Spark interactively in a Python interpreter, use
+`bin/pyspark`. As in Spark shell, you can also pass in the `--master` option to configure your
+master URL.
 
-    ./bin/pyspark examples/src/main/python/pi.py local[2] 10
+    ./bin/pyspark --master local[2]
 
-or simply `bin/pyspark` without any arguments to run Spark interactively in a python interpreter.
+Example applications are also provided in Python. For example,
+
+    ./bin/spark-submit examples/src/main/python/pi.py 10
 
 # Launching on a Cluster
 
diff --git a/docs/python-programming-guide.md b/docs/python-programming-guide.md
index 17675acba6bb..b686bee1aebe 100644
--- a/docs/python-programming-guide.md
+++ b/docs/python-programming-guide.md
@@ -60,13 +60,9 @@ By default, PySpark requires `python` to be available on the system `PATH` and u
 
 All of PySpark's library dependencies, including [Py4J](http://py4j.sourceforge.net/), are bundled with PySpark and automatically imported.
 
-Standalone PySpark applications should be run using the `bin/spark-submit` script, which automatically
-configures the Java and Python environment for running Spark.
-
-
 # Interactive Use
 
-The `bin/pyspark` script launches a Python interpreter that is configured to run PySpark applications. To use `pyspark` interactively, first build Spark, then launch it directly from the command line without any options:
+The `bin/pyspark` script launches a Python interpreter that is configured to run PySpark applications. To use `pyspark` interactively, first build Spark, then launch it directly from the command line:
 
 {% highlight bash %}
 $ sbt/sbt assembly
@@ -83,20 +79,24 @@ The Python shell can be used explore data interactively and is a simple way to l
 {% endhighlight %}
 
 By default, the `bin/pyspark` shell creates SparkContext that runs applications locally on all of
-your machine's logical cores.
-To connect to a non-local cluster, or to specify a number of cores, set the `MASTER` environment variable.
-For example, to use the `bin/pyspark` shell with a [standalone Spark cluster](spark-standalone.html):
+your machine's logical cores. To connect to a non-local cluster, or to specify a number of cores,
+set the `--master` flag. For example, to use the `bin/pyspark` shell with a
+[standalone Spark cluster](spark-standalone.html):
 
 {% highlight bash %}
-$ MASTER=spark://IP:PORT ./bin/pyspark
+$ ./bin/pyspark --master spark://1.2.3.4:7077
 {% endhighlight %}
 
 Or, to use exactly four cores on the local machine:
 
 {% highlight bash %}
-$ MASTER=local[4] ./bin/pyspark
+$ ./bin/pyspark --master local[4]
 {% endhighlight %}
 
+Under the hood `bin/pyspark` is a wrapper around the
+[Spark submit script](cluster-overview.html#launching-applications-with-spark-submit), so these
+two scripts share the same list of options. For a complete list of options, run `bin/pyspark` with
+the `--help` option.
 
 ## IPython
 
@@ -115,13 +115,14 @@ the [IPython Notebook](http://ipython.org/notebook.html) with PyLab graphing sup
 $ IPYTHON_OPTS="notebook --pylab inline" ./bin/pyspark
 {% endhighlight %}
 
-IPython also works on a cluster or on multiple cores if you set the `MASTER` environment variable.
+IPython also works on a cluster or on multiple cores if you set the `--master` flag.
 
 
 # Standalone Programs
 
-PySpark can also be used from standalone Python scripts by creating a SparkContext in your script and running the script using `bin/spark-submit`.
-The Quick Start guide includes a [complete example](quick-start.html#standalone-applications) of a standalone Python application.
+PySpark can also be used from standalone Python scripts by creating a SparkContext in your script
+and running the script using `bin/spark-submit`. The Quick Start guide includes a
+[complete example](quick-start.html#standalone-applications) of a standalone Python application.
 
 Code dependencies can be deployed by passing .zip or .egg files in the `--py-files` option of `spark-submit`:
 
@@ -138,6 +139,7 @@ You can set [configuration properties](configuration.html#spark-properties) by p
 {% highlight python %}
 from pyspark import SparkConf, SparkContext
 conf = (SparkConf()
+         .setMaster("local")
          .setAppName("My app")
          .set("spark.executor.memory", "1g"))
 sc = SparkContext(conf = conf)
@@ -164,6 +166,6 @@ some example applications.
 PySpark also includes several sample programs in the [`examples/src/main/python` folder](https://github.com/apache/spark/tree/master/examples/src/main/python).
 You can run them by passing the files to `pyspark`; e.g.:
 
-    ./bin/spark-submit examples/src/main/python/wordcount.py local[2] README.md
+    ./bin/spark-submit examples/src/main/python/wordcount.py README.md
 
-Each program prints usage help when run without arguments.
+Each program prints usage help when run without the sufficient arguments.
diff --git a/examples/src/main/python/als.py b/examples/src/main/python/als.py
index 01552dc1d449..f0b46cd28b7a 100755
--- a/examples/src/main/python/als.py
+++ b/examples/src/main/python/als.py
@@ -46,15 +46,15 @@ def update(i, vec, mat, ratings):
     return np.linalg.solve(XtX, Xty)
 
 if __name__ == "__main__":
-    if len(sys.argv) < 2:
-        print >> sys.stderr, "Usage: als <master> <M> <U> <F> <iters> <slices>"
-        exit(-1)
-    sc = SparkContext(sys.argv[1], "PythonALS", pyFiles=[realpath(__file__)])
-    M = int(sys.argv[2]) if len(sys.argv) > 2 else 100
-    U = int(sys.argv[3]) if len(sys.argv) > 3 else 500
-    F = int(sys.argv[4]) if len(sys.argv) > 4 else 10
-    ITERATIONS = int(sys.argv[5]) if len(sys.argv) > 5 else 5
-    slices = int(sys.argv[6]) if len(sys.argv) > 6 else 2
+    """
+    Usage: als [M] [U] [F] [iterations] [slices]"
+    """
+    sc = SparkContext(appName="PythonALS")
+    M = int(sys.argv[1]) if len(sys.argv) > 1 else 100
+    U = int(sys.argv[2]) if len(sys.argv) > 2 else 500
+    F = int(sys.argv[3]) if len(sys.argv) > 3 else 10
+    ITERATIONS = int(sys.argv[4]) if len(sys.argv) > 4 else 5
+    slices = int(sys.argv[5]) if len(sys.argv) > 5 else 2
 
     print "Running ALS with M=%d, U=%d, F=%d, iters=%d, slices=%d\n" % \
             (M, U, F, ITERATIONS, slices)
diff --git a/examples/src/main/python/kmeans.py b/examples/src/main/python/kmeans.py
index e3596488faf9..fc16586c28a4 100755
--- a/examples/src/main/python/kmeans.py
+++ b/examples/src/main/python/kmeans.py
@@ -45,14 +45,14 @@ def closestPoint(p, centers):
 
 
 if __name__ == "__main__":
-    if len(sys.argv) < 5:
-        print >> sys.stderr, "Usage: kmeans <master> <file> <k> <convergeDist>"
+    if len(sys.argv) != 4:
+        print >> sys.stderr, "Usage: kmeans <file> <k> <convergeDist>"
         exit(-1)
-    sc = SparkContext(sys.argv[1], "PythonKMeans")
-    lines = sc.textFile(sys.argv[2])
+    sc = SparkContext(appName="PythonKMeans")
+    lines = sc.textFile(sys.argv[1])
     data = lines.map(parseVector).cache()
-    K = int(sys.argv[3])
-    convergeDist = float(sys.argv[4])
+    K = int(sys.argv[2])
+    convergeDist = float(sys.argv[3])
 
     kPoints = data.takeSample(False, K, 1)
     tempDist = 1.0
diff --git a/examples/src/main/python/logistic_regression.py b/examples/src/main/python/logistic_regression.py
index fe5373cf799b..0f22d0b32319 100755
--- a/examples/src/main/python/logistic_regression.py
+++ b/examples/src/main/python/logistic_regression.py
@@ -47,12 +47,12 @@ def readPointBatch(iterator):
     return [matrix]
 
 if __name__ == "__main__":
-    if len(sys.argv) != 4:
-        print >> sys.stderr, "Usage: logistic_regression <master> <file> <iters>"
+    if len(sys.argv) != 3:
+        print >> sys.stderr, "Usage: logistic_regression <file> <iterations>"
         exit(-1)
-    sc = SparkContext(sys.argv[1], "PythonLR", pyFiles=[realpath(__file__)])
-    points = sc.textFile(sys.argv[2]).mapPartitions(readPointBatch).cache()
-    iterations = int(sys.argv[3])
+    sc = SparkContext(appName="PythonLR")
+    points = sc.textFile(sys.argv[1]).mapPartitions(readPointBatch).cache()
+    iterations = int(sys.argv[2])
 
     # Initialize w to a random value
     w = 2 * np.random.ranf(size=D) - 1
diff --git a/examples/src/main/python/mllib/kmeans.py b/examples/src/main/python/mllib/kmeans.py
index dec82ff34fba..b308132c9aee 100755
--- a/examples/src/main/python/mllib/kmeans.py
+++ b/examples/src/main/python/mllib/kmeans.py
@@ -33,12 +33,12 @@ def parseVector(line):
 
 
 if __name__ == "__main__":
-    if len(sys.argv) < 4:
-        print >> sys.stderr, "Usage: kmeans <master> <file> <k>"
+    if len(sys.argv) != 3:
+        print >> sys.stderr, "Usage: kmeans <file> <k>"
         exit(-1)
-    sc = SparkContext(sys.argv[1], "KMeans")
-    lines = sc.textFile(sys.argv[2])
+    sc = SparkContext(appName="KMeans")
+    lines = sc.textFile(sys.argv[1])
     data = lines.map(parseVector)
-    k = int(sys.argv[3])
+    k = int(sys.argv[2])
     model = KMeans.train(data, k)
     print "Final centers: " + str(model.clusterCenters)
diff --git a/examples/src/main/python/mllib/logistic_regression.py b/examples/src/main/python/mllib/logistic_regression.py
index 8631051d00ff..6e0f7a4ee5a8 100755
--- a/examples/src/main/python/mllib/logistic_regression.py
+++ b/examples/src/main/python/mllib/logistic_regression.py
@@ -39,12 +39,12 @@ def parsePoint(line):
 
 
 if __name__ == "__main__":
-    if len(sys.argv) != 4:
-        print >> sys.stderr, "Usage: logistic_regression <master> <file> <iters>"
+    if len(sys.argv) != 3:
+        print >> sys.stderr, "Usage: logistic_regression <file> <iterations>"
         exit(-1)
-    sc = SparkContext(sys.argv[1], "PythonLR")
-    points = sc.textFile(sys.argv[2]).map(parsePoint)
-    iterations = int(sys.argv[3])
+    sc = SparkContext(appName="PythonLR")
+    points = sc.textFile(sys.argv[1]).map(parsePoint)
+    iterations = int(sys.argv[2])
     model = LogisticRegressionWithSGD.train(points, iterations)
     print "Final weights: " + str(model.weights)
     print "Final intercept: " + str(model.intercept)
diff --git a/examples/src/main/python/pagerank.py b/examples/src/main/python/pagerank.py
index cd774cf3a319..d350fa46fa49 100755
--- a/examples/src/main/python/pagerank.py
+++ b/examples/src/main/python/pagerank.py
@@ -36,19 +36,19 @@ def parseNeighbors(urls):
 
 
 if __name__ == "__main__":
-    if len(sys.argv) < 3:
-        print >> sys.stderr, "Usage: pagerank <master> <file> <number_of_iterations>"
+    if len(sys.argv) != 3:
+        print >> sys.stderr, "Usage: pagerank <file> <iterations>"
         exit(-1)
 
     # Initialize the spark context.
-    sc = SparkContext(sys.argv[1], "PythonPageRank")
+    sc = SparkContext(appName="PythonPageRank")
 
     # Loads in input file. It should be in format of:
     #     URL         neighbor URL
     #     URL         neighbor URL
     #     URL         neighbor URL
     #     ...
-    lines = sc.textFile(sys.argv[2], 1)
+    lines = sc.textFile(sys.argv[1], 1)
 
     # Loads all URLs from input file and initialize their neighbors.
     links = lines.map(lambda urls: parseNeighbors(urls)).distinct().groupByKey().cache()
@@ -57,7 +57,7 @@ def parseNeighbors(urls):
     ranks = links.map(lambda (url, neighbors): (url, 1.0))
 
     # Calculates and updates URL ranks continuously using PageRank algorithm.
-    for iteration in xrange(int(sys.argv[3])):
+    for iteration in xrange(int(sys.argv[2])):
         # Calculates URL contributions to the rank of other URLs.
         contribs = links.join(ranks).flatMap(lambda (url, (urls, rank)):
             computeContribs(urls, rank))
diff --git a/examples/src/main/python/pi.py b/examples/src/main/python/pi.py
index ab0645fc2f32..234720b55fa4 100755
--- a/examples/src/main/python/pi.py
+++ b/examples/src/main/python/pi.py
@@ -23,11 +23,11 @@
 
 
 if __name__ == "__main__":
-    if len(sys.argv) == 1:
-        print >> sys.stderr, "Usage: pi <master> [<slices>]"
-        exit(-1)
-    sc = SparkContext(sys.argv[1], "PythonPi")
-    slices = int(sys.argv[2]) if len(sys.argv) > 2 else 2
+    """
+        Usage: pi [slices]
+    """
+    sc = SparkContext(appName="PythonPi")
+    slices = int(sys.argv[1]) if len(sys.argv) > 1 else 2
     n = 100000 * slices
     def f(_):
         x = random() * 2 - 1
diff --git a/examples/src/main/python/sort.py b/examples/src/main/python/sort.py
index 5de20a6d98f4..4913ee926aa0 100755
--- a/examples/src/main/python/sort.py
+++ b/examples/src/main/python/sort.py
@@ -21,11 +21,11 @@
 
 
 if __name__ == "__main__":
-    if len(sys.argv) < 3:
-        print >> sys.stderr, "Usage: sort <master> <file>"
+    if len(sys.argv) != 2:
+        print >> sys.stderr, "Usage: sort <file>"
         exit(-1)
-    sc = SparkContext(sys.argv[1], "PythonSort")
-    lines = sc.textFile(sys.argv[2], 1)
+    sc = SparkContext(appName="PythonSort")
+    lines = sc.textFile(sys.argv[1], 1)
     sortedCount = lines.flatMap(lambda x: x.split(' ')) \
                   .map(lambda x: (int(x), 1)) \
                   .sortByKey(lambda x: x)
diff --git a/examples/src/main/python/transitive_closure.py b/examples/src/main/python/transitive_closure.py
index 744cce665160..8698369b13d8 100755
--- a/examples/src/main/python/transitive_closure.py
+++ b/examples/src/main/python/transitive_closure.py
@@ -36,11 +36,11 @@ def generateGraph():
 
 
 if __name__ == "__main__":
-    if len(sys.argv) == 1:
-        print >> sys.stderr, "Usage: transitive_closure <master> [<slices>]"
-        exit(-1)
-    sc = SparkContext(sys.argv[1], "PythonTransitiveClosure")
-    slices = int(sys.argv[2]) if len(sys.argv) > 2 else 2
+    """
+    Usage: transitive_closure [slices]
+    """
+    sc = SparkContext(appName="PythonTransitiveClosure")
+    slices = int(sys.argv[1]) if len(sys.argv) > 1 else 2
     tc = sc.parallelize(generateGraph(), slices).cache()
 
     # Linear transitive closure: each round grows paths by one edge,
diff --git a/examples/src/main/python/wordcount.py b/examples/src/main/python/wordcount.py
index b9139b9d7652..dcc095fdd0ed 100755
--- a/examples/src/main/python/wordcount.py
+++ b/examples/src/main/python/wordcount.py
@@ -22,11 +22,11 @@
 
 
 if __name__ == "__main__":
-    if len(sys.argv) < 3:
-        print >> sys.stderr, "Usage: wordcount <master> <file>"
+    if len(sys.argv) != 2:
+        print >> sys.stderr, "Usage: wordcount <file>"
         exit(-1)
-    sc = SparkContext(sys.argv[1], "PythonWordCount")
-    lines = sc.textFile(sys.argv[2], 1)
+    sc = SparkContext(appName="PythonWordCount")
+    lines = sc.textFile(sys.argv[1], 1)
     counts = lines.flatMap(lambda x: x.split(' ')) \
                   .map(lambda x: (x, 1)) \
                   .reduceByKey(add)

From 442808a7482b81c8de887c901b424683da62022e Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Fri, 16 May 2014 22:58:47 -0700
Subject: [PATCH 235/810] Make deprecation warning less severe

Just a small change. I think it's good not to scare people who are using the old options.

Author: Patrick Wendell <pwendell@gmail.com>

Closes #810 from pwendell/warnings and squashes the following commits:

cb8a311 [Patrick Wendell] Make deprecation warning less severe
---
 core/src/main/scala/org/apache/spark/SparkConf.scala | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/SparkConf.scala b/core/src/main/scala/org/apache/spark/SparkConf.scala
index 800616622d7b..8ce4b91cae8a 100644
--- a/core/src/main/scala/org/apache/spark/SparkConf.scala
+++ b/core/src/main/scala/org/apache/spark/SparkConf.scala
@@ -238,10 +238,10 @@ class SparkConf(loadDefaults: Boolean) extends Cloneable with Logging {
 
     // Check for legacy configs
     sys.env.get("SPARK_JAVA_OPTS").foreach { value =>
-      val error =
+      val warning =
         s"""
           |SPARK_JAVA_OPTS was detected (set to '$value').
-          |This has undefined behavior when running on a cluster and is deprecated in Spark 1.0+.
+          |This is deprecated in Spark 1.0+.
           |
           |Please instead use:
           | - ./spark-submit with conf/spark-defaults.conf to set defaults for an application
@@ -249,7 +249,7 @@ class SparkConf(loadDefaults: Boolean) extends Cloneable with Logging {
           | - spark.executor.extraJavaOptions to set -X options for executors
           | - SPARK_DAEMON_JAVA_OPTS to set java options for standalone daemons (master or worker)
         """.stripMargin
-      logError(error)
+      logWarning(warning)
 
       for (key <- Seq(executorOptsKey, driverOptsKey)) {
         if (getOption(key).isDefined) {
@@ -262,16 +262,16 @@ class SparkConf(loadDefaults: Boolean) extends Cloneable with Logging {
     }
 
     sys.env.get("SPARK_CLASSPATH").foreach { value =>
-      val error =
+      val warning =
         s"""
           |SPARK_CLASSPATH was detected (set to '$value').
-          | This has undefined behavior when running on a cluster and is deprecated in Spark 1.0+.
+          |This is deprecated in Spark 1.0+.
           |
           |Please instead use:
           | - ./spark-submit with --driver-class-path to augment the driver classpath
           | - spark.executor.extraClassPath to augment the executor classpath
         """.stripMargin
-      logError(error)
+      logWarning(warning)
 
       for (key <- Seq(executorClasspathKey, driverClassPathKey)) {
         if (getOption(key).isDefined) {

From ebcd2d68897f423614d0629758240da97dca4a12 Mon Sep 17 00:00:00 2001
From: Neville Li <neville@spotify.com>
Date: Sun, 18 May 2014 13:31:23 -0700
Subject: [PATCH 236/810] Fix spark-submit path in spark-shell & pyspark

Author: Neville Li <neville@spotify.com>

Closes #812 from nevillelyh/neville/v1.0 and squashes the following commits:

0dc33ed [Neville Li] Fix spark-submit path in pyspark
becec64 [Neville Li] Fix spark-submit path in spark-shell
---
 bin/pyspark     | 2 +-
 bin/spark-shell | 8 ++++----
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/bin/pyspark b/bin/pyspark
index 9e1364e44c8c..3908ffe79939 100755
--- a/bin/pyspark
+++ b/bin/pyspark
@@ -27,7 +27,7 @@ SCALA_VERSION=2.10
 
 if [[ "$@" = *--help ]] || [[ "$@" = *-h ]]; then
   echo "Usage: ./bin/pyspark [options]"
-  ./bin/spark-submit --help 2>&1 | grep -v Usage 1>&2
+  $FWDIR/bin/spark-submit --help 2>&1 | grep -v Usage 1>&2
   exit 0
 fi
 
diff --git a/bin/spark-shell b/bin/spark-shell
index c158683ab3f9..850e9507ec38 100755
--- a/bin/spark-shell
+++ b/bin/spark-shell
@@ -28,15 +28,15 @@ esac
 # Enter posix mode for bash
 set -o posix
 
+## Global script variables
+FWDIR="$(cd `dirname $0`/..; pwd)"
+
 if [[ "$@" = *--help ]] || [[ "$@" = *-h ]]; then
   echo "Usage: ./bin/spark-shell [options]"
-  ./bin/spark-submit --help 2>&1 | grep -v Usage 1>&2
+  $FWDIR/bin/spark-submit --help 2>&1 | grep -v Usage 1>&2
   exit 0
 fi
 
-## Global script variables
-FWDIR="$(cd `dirname $0`/..; pwd)"
-
 function main(){
     if $cygwin; then
         # Workaround for issue involving JLine and Cygwin

From 4ce479324bdcf603806fc90b5b0f4968c6de690e Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Sun, 18 May 2014 16:51:53 -0700
Subject: [PATCH 237/810] SPARK-1873: Add README.md file when making
 distributions

Author: Patrick Wendell <pwendell@gmail.com>

Closes #818 from pwendell/reamde and squashes the following commits:

4020b11 [Patrick Wendell] SPARK-1873: Add README.md file when making distributions
---
 make-distribution.sh | 1 +
 1 file changed, 1 insertion(+)

diff --git a/make-distribution.sh b/make-distribution.sh
index 7a08d6b9151c..1c89027d68be 100755
--- a/make-distribution.sh
+++ b/make-distribution.sh
@@ -191,6 +191,7 @@ fi
 mkdir "$DISTDIR"/conf
 cp "$FWDIR"/conf/*.template "$DISTDIR"/conf
 cp "$FWDIR"/conf/slaves "$DISTDIR"/conf
+cp "$FWDIR/README.md" "$DISTDIR"
 cp -r "$FWDIR/bin" "$DISTDIR"
 cp -r "$FWDIR/python" "$DISTDIR"
 cp -r "$FWDIR/sbin" "$DISTDIR"

From df0aa8353ab6d3b19d838c6fa95a93a64948309f Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Sun, 18 May 2014 17:00:57 -0700
Subject: [PATCH 238/810] [WIP][SPARK-1871][MLLIB] Improve MLlib guide for v1.0

Some improvements to MLlib guide:

1. [SPARK-1872] Update API links for unidoc.
2. [SPARK-1783] Added `page.displayTitle` to the global layout. If it is defined, use it instead of `page.title` for title display.
3. Add more Java/Python examples.

Author: Xiangrui Meng <meng@databricks.com>

Closes #816 from mengxr/mllib-doc and squashes the following commits:

ec2e407 [Xiangrui Meng] format scala example for ALS
cd9f40b [Xiangrui Meng] add a paragraph to summarize distributed matrix types
4617f04 [Xiangrui Meng] add python example to loadLibSVMFile and fix Java example
d6509c2 [Xiangrui Meng] [SPARK-1783] update mllib titles
561fdc0 [Xiangrui Meng] add a displayTitle option to global layout
195d06f [Xiangrui Meng] add Java example for summary stats and minor fix
9f1ff89 [Xiangrui Meng] update java api links in mllib-basics
7dad18e [Xiangrui Meng] update java api links in NB
3a0f4a6 [Xiangrui Meng] api/pyspark -> api/python
35bdeb9 [Xiangrui Meng] api/mllib -> api/scala
e4afaa8 [Xiangrui Meng] explicity state what might change
---
 docs/_layouts/global.html              |   6 +-
 docs/mllib-basics.md                   | 125 +++++++++++++++++--------
 docs/mllib-clustering.md               |   5 +-
 docs/mllib-collaborative-filtering.md  |  29 +++---
 docs/mllib-decision-tree.md            |   3 +-
 docs/mllib-dimensionality-reduction.md |   3 +-
 docs/mllib-guide.md                    |  19 ++--
 docs/mllib-linear-methods.md           |  21 +++--
 docs/mllib-naive-bayes.md              |  21 +++--
 docs/mllib-optimization.md             |  11 ++-
 10 files changed, 153 insertions(+), 90 deletions(-)

diff --git a/docs/_layouts/global.html b/docs/_layouts/global.html
index 8b543de57462..fb808129bb65 100755
--- a/docs/_layouts/global.html
+++ b/docs/_layouts/global.html
@@ -114,7 +114,11 @@
         </div>
 
         <div class="container" id="content">
-          <h1 class="title">{{ page.title }}</h1>
+          {% if page.displayTitle %}
+            <h1 class="title">{{ page.displayTitle }}</h1>
+          {% else %}
+            <h1 class="title">{{ page.title }}</h1>
+          {% endif %}
 
           {{ content }}
 
diff --git a/docs/mllib-basics.md b/docs/mllib-basics.md
index aa9321a54709..5796e16e8f99 100644
--- a/docs/mllib-basics.md
+++ b/docs/mllib-basics.md
@@ -1,6 +1,7 @@
 ---
 layout: global
-title: <a href="mllib-guide.html">MLlib</a> - Basics
+title: Basics - MLlib
+displayTitle: <a href="mllib-guide.html">MLlib</a> - Basics
 ---
 
 * Table of contents
@@ -26,11 +27,11 @@ of the vector.
 <div data-lang="scala" markdown="1">
 
 The base class of local vectors is
-[`Vector`](api/mllib/index.html#org.apache.spark.mllib.linalg.Vector), and we provide two
-implementations: [`DenseVector`](api/mllib/index.html#org.apache.spark.mllib.linalg.DenseVector) and
-[`SparseVector`](api/mllib/index.html#org.apache.spark.mllib.linalg.SparseVector).  We recommend
+[`Vector`](api/scala/index.html#org.apache.spark.mllib.linalg.Vector), and we provide two
+implementations: [`DenseVector`](api/scala/index.html#org.apache.spark.mllib.linalg.DenseVector) and
+[`SparseVector`](api/scala/index.html#org.apache.spark.mllib.linalg.SparseVector).  We recommend
 using the factory methods implemented in
-[`Vectors`](api/mllib/index.html#org.apache.spark.mllib.linalg.Vector) to create local vectors.
+[`Vectors`](api/scala/index.html#org.apache.spark.mllib.linalg.Vector) to create local vectors.
 
 {% highlight scala %}
 import org.apache.spark.mllib.linalg.{Vector, Vectors}
@@ -53,11 +54,11 @@ Scala imports `scala.collection.immutable.Vector` by default, so you have to imp
 <div data-lang="java" markdown="1">
 
 The base class of local vectors is
-[`Vector`](api/mllib/index.html#org.apache.spark.mllib.linalg.Vector), and we provide two
-implementations: [`DenseVector`](api/mllib/index.html#org.apache.spark.mllib.linalg.DenseVector) and
-[`SparseVector`](api/mllib/index.html#org.apache.spark.mllib.linalg.SparseVector).  We recommend
+[`Vector`](api/java/org/apache/spark/mllib/linalg/Vector.html), and we provide two
+implementations: [`DenseVector`](api/java/org/apache/spark/mllib/linalg/DenseVector.html) and
+[`SparseVector`](api/java/org/apache/spark/mllib/linalg/SparseVector.html).  We recommend
 using the factory methods implemented in
-[`Vectors`](api/mllib/index.html#org.apache.spark.mllib.linalg.Vector) to create local vectors.
+[`Vectors`](api/java/org/apache/spark/mllib/linalg/Vector.html) to create local vectors.
 
 {% highlight java %}
 import org.apache.spark.mllib.linalg.Vector;
@@ -78,13 +79,13 @@ MLlib recognizes the following types as dense vectors:
 
 and the following as sparse vectors:
 
-* MLlib's [`SparseVector`](api/pyspark/pyspark.mllib.linalg.SparseVector-class.html).
+* MLlib's [`SparseVector`](api/python/pyspark.mllib.linalg.SparseVector-class.html).
 * SciPy's
   [`csc_matrix`](http://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.csc_matrix.html#scipy.sparse.csc_matrix)
   with a single column
 
 We recommend using NumPy arrays over lists for efficiency, and using the factory methods implemented
-in [`Vectors`](api/pyspark/pyspark.mllib.linalg.Vectors-class.html) to create sparse vectors.
+in [`Vectors`](api/python/pyspark.mllib.linalg.Vectors-class.html) to create sparse vectors.
 
 {% highlight python %}
 import numpy as np
@@ -117,7 +118,7 @@ For multiclass classification, labels should be class indices staring from zero:
 <div data-lang="scala" markdown="1">
 
 A labeled point is represented by the case class
-[`LabeledPoint`](api/mllib/index.html#org.apache.spark.mllib.regression.LabeledPoint).
+[`LabeledPoint`](api/scala/index.html#org.apache.spark.mllib.regression.LabeledPoint).
 
 {% highlight scala %}
 import org.apache.spark.mllib.linalg.Vectors
@@ -134,7 +135,7 @@ val neg = LabeledPoint(0.0, Vectors.sparse(3, Array(0, 2), Array(1.0, 3.0)))
 <div data-lang="java" markdown="1">
 
 A labeled point is represented by
-[`LabeledPoint`](api/mllib/index.html#org.apache.spark.mllib.regression.LabeledPoint).
+[`LabeledPoint`](api/java/org/apache/spark/mllib/regression/LabeledPoint.html).
 
 {% highlight java %}
 import org.apache.spark.mllib.linalg.Vectors;
@@ -151,7 +152,7 @@ LabeledPoint neg = new LabeledPoint(1.0, Vectors.sparse(3, new int[] {0, 2}, new
 <div data-lang="python" markdown="1">
 
 A labeled point is represented by
-[`LabeledPoint`](api/pyspark/pyspark.mllib.regression.LabeledPoint-class.html).
+[`LabeledPoint`](api/python/pyspark.mllib.regression.LabeledPoint-class.html).
 
 {% highlight python %}
 from pyspark.mllib.linalg import SparseVector
@@ -184,7 +185,7 @@ After loading, the feature indices are converted to zero-based.
 <div class="codetabs">
 <div data-lang="scala" markdown="1">
 
-[`MLUtils.loadLibSVMFile`](api/mllib/index.html#org.apache.spark.mllib.util.MLUtils$) reads training
+[`MLUtils.loadLibSVMFile`](api/scala/index.html#org.apache.spark.mllib.util.MLUtils$) reads training
 examples stored in LIBSVM format.
 
 {% highlight scala %}
@@ -192,20 +193,32 @@ import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.util.MLUtils
 import org.apache.spark.rdd.RDD
 
-val training: RDD[LabeledPoint] = MLUtils.loadLibSVMFile(sc, "mllib/data/sample_libsvm_data.txt")
+val examples: RDD[LabeledPoint] = MLUtils.loadLibSVMFile(sc, "mllib/data/sample_libsvm_data.txt")
 {% endhighlight %}
 </div>
 
 <div data-lang="java" markdown="1">
-[`MLUtils.loadLibSVMFile`](api/mllib/index.html#org.apache.spark.mllib.util.MLUtils$) reads training
+[`MLUtils.loadLibSVMFile`](api/java/org/apache/spark/mllib/util/MLUtils.html) reads training
 examples stored in LIBSVM format.
 
 {% highlight java %}
 import org.apache.spark.mllib.regression.LabeledPoint;
 import org.apache.spark.mllib.util.MLUtils;
-import org.apache.spark.rdd.RDDimport;
+import org.apache.spark.api.java.JavaRDD;
+
+JavaRDD<LabeledPoint> examples = 
+  MLUtils.loadLibSVMFile(jsc.sc(), "mllib/data/sample_libsvm_data.txt").toJavaRDD();
+{% endhighlight %}
+</div>
+
+<div data-lang="python" markdown="1">
+[`MLUtils.loadLibSVMFile`](api/python/pyspark.mllib.util.MLUtils-class.html) reads training
+examples stored in LIBSVM format.
 
-RDD<LabeledPoint> training = MLUtils.loadLibSVMFile(jsc, "mllib/data/sample_libsvm_data.txt");
+{% highlight python %}
+from pyspark.mllib.util import MLUtils
+
+examples = MLUtils.loadLibSVMFile(sc, "mllib/data/sample_libsvm_data.txt")
 {% endhighlight %}
 </div>
 </div>
@@ -227,10 +240,10 @@ We are going to add sparse matrix in the next release.
 <div data-lang="scala" markdown="1">
 
 The base class of local matrices is
-[`Matrix`](api/mllib/index.html#org.apache.spark.mllib.linalg.Matrix), and we provide one
-implementation: [`DenseMatrix`](api/mllib/index.html#org.apache.spark.mllib.linalg.DenseMatrix).
+[`Matrix`](api/scala/index.html#org.apache.spark.mllib.linalg.Matrix), and we provide one
+implementation: [`DenseMatrix`](api/scala/index.html#org.apache.spark.mllib.linalg.DenseMatrix).
 Sparse matrix will be added in the next release.  We recommend using the factory methods implemented
-in [`Matrices`](api/mllib/index.html#org.apache.spark.mllib.linalg.Matrices) to create local
+in [`Matrices`](api/scala/index.html#org.apache.spark.mllib.linalg.Matrices) to create local
 matrices.
 
 {% highlight scala %}
@@ -244,10 +257,10 @@ val dm: Matrix = Matrices.dense(3, 2, Array(1.0, 3.0, 5.0, 2.0, 4.0, 6.0))
 <div data-lang="java" markdown="1">
 
 The base class of local matrices is
-[`Matrix`](api/mllib/index.html#org.apache.spark.mllib.linalg.Matrix), and we provide one
-implementation: [`DenseMatrix`](api/mllib/index.html#org.apache.spark.mllib.linalg.DenseMatrix).
+[`Matrix`](api/java/org/apache/spark/mllib/linalg/Matrix.html), and we provide one
+implementation: [`DenseMatrix`](api/java/org/apache/spark/mllib/linalg/DenseMatrix.html).
 Sparse matrix will be added in the next release.  We recommend using the factory methods implemented
-in [`Matrices`](api/mllib/index.html#org.apache.spark.mllib.linalg.Matrices) to create local
+in [`Matrices`](api/java/org/apache/spark/mllib/linalg/Matrices.html) to create local
 matrices.
 
 {% highlight java %}
@@ -269,6 +282,15 @@ and distributed matrices.  Converting a distributed matrix to a different format
 global shuffle, which is quite expensive.  We implemented three types of distributed matrices in
 this release and will add more types in the future.
 
+The basic type is called `RowMatrix`. A `RowMatrix` is a row-oriented distributed
+matrix without meaningful row indices, e.g., a collection of feature vectors.
+It is backed by an RDD of its rows, where each row is a local vector.
+We assume that the number of columns is not huge for a `RowMatrix`.
+An `IndexedRowMatrix` is similar to a `RowMatrix` but with row indices,
+which can be used for identifying rows and joins.
+A `CoordinateMatrix` is a distributed matrix stored in [coordinate list (COO)](https://en.wikipedia.org/wiki/Sparse_matrix) format,
+backed by an RDD of its entries.
+
 ***Note***
 
 The underlying RDDs of a distributed matrix must be deterministic, because we cache the matrix size.
@@ -284,7 +306,7 @@ limited by the integer range but it should be much smaller in practice.
 <div class="codetabs">
 <div data-lang="scala" markdown="1">
 
-A [`RowMatrix`](api/mllib/index.html#org.apache.spark.mllib.linalg.distributed.RowMatrix) can be
+A [`RowMatrix`](api/scala/index.html#org.apache.spark.mllib.linalg.distributed.RowMatrix) can be
 created from an `RDD[Vector]` instance.  Then we can compute its column summary statistics.
 
 {% highlight scala %}
@@ -303,7 +325,7 @@ val n = mat.numCols()
 
 <div data-lang="java" markdown="1">
 
-A [`RowMatrix`](api/mllib/index.html#org.apache.spark.mllib.linalg.distributed.RowMatrix) can be
+A [`RowMatrix`](api/java/org/apache/spark/mllib/linalg/distributed/RowMatrix.html) can be
 created from a `JavaRDD<Vector>` instance.  Then we can compute its column summary statistics.
 
 {% highlight java %}
@@ -333,8 +355,8 @@ which could be faster if the rows are sparse.
 <div class="codetabs">
 <div data-lang="scala" markdown="1">
 
-`RowMatrix#computeColumnSummaryStatistics` returns an instance of
-[`MultivariateStatisticalSummary`](api/mllib/index.html#org.apache.spark.mllib.stat.MultivariateStatisticalSummary),
+[`RowMatrix#computeColumnSummaryStatistics`](api/scala/index.html#org.apache.spark.mllib.linalg.distributed.RowMatrix) returns an instance of
+[`MultivariateStatisticalSummary`](api/scala/index.html#org.apache.spark.mllib.stat.MultivariateStatisticalSummary),
 which contains the column-wise max, min, mean, variance, and number of nonzeros, as well as the
 total count.
 
@@ -355,6 +377,31 @@ println(summary.numNonzeros) // number of nonzeros in each column
 val cov: Matrix = mat.computeCovariance()
 {% endhighlight %}
 </div>
+
+<div data-lang="java" markdown="1">
+
+[`RowMatrix#computeColumnSummaryStatistics`](api/java/org/apache/spark/mllib/linalg/distributed/RowMatrix.html#computeColumnSummaryStatistics()) returns an instance of
+[`MultivariateStatisticalSummary`](api/java/org/apache/spark/mllib/stat/MultivariateStatisticalSummary.html),
+which contains the column-wise max, min, mean, variance, and number of nonzeros, as well as the
+total count.
+
+{% highlight java %}
+import org.apache.spark.mllib.linalg.Matrix;
+import org.apache.spark.mllib.linalg.distributed.RowMatrix;
+import org.apache.spark.mllib.stat.MultivariateStatisticalSummary;
+
+RowMatrix mat = ... // a RowMatrix
+
+// Compute column summary statistics.
+MultivariateStatisticalSummary summary = mat.computeColumnSummaryStatistics();
+System.out.println(summary.mean()); // a dense vector containing the mean value for each column
+System.out.println(summary.variance()); // column-wise variance
+System.out.println(summary.numNonzeros()); // number of nonzeros in each column
+
+// Compute the covariance matrix.
+Matrix cov = mat.computeCovariance();
+{% endhighlight %}
+</div>
 </div>
 
 ### IndexedRowMatrix
@@ -366,9 +413,9 @@ an RDD of indexed rows, which each row is represented by its index (long-typed)
 <div data-lang="scala" markdown="1">
 
 An
-[`IndexedRowMatrix`](api/mllib/index.html#org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix)
+[`IndexedRowMatrix`](api/scala/index.html#org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix)
 can be created from an `RDD[IndexedRow]` instance, where
-[`IndexedRow`](api/mllib/index.html#org.apache.spark.mllib.linalg.distributed.IndexedRow) is a
+[`IndexedRow`](api/scala/index.html#org.apache.spark.mllib.linalg.distributed.IndexedRow) is a
 wrapper over `(Long, Vector)`.  An `IndexedRowMatrix` can be converted to a `RowMatrix` by dropping
 its row indices.
 
@@ -391,9 +438,9 @@ val rowMat: RowMatrix = mat.toRowMatrix()
 <div data-lang="java" markdown="1">
 
 An
-[`IndexedRowMatrix`](api/mllib/index.html#org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix)
+[`IndexedRowMatrix`](api/java/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrix.html)
 can be created from an `JavaRDD<IndexedRow>` instance, where
-[`IndexedRow`](api/mllib/index.html#org.apache.spark.mllib.linalg.distributed.IndexedRow) is a
+[`IndexedRow`](api/java/org/apache/spark/mllib/linalg/distributed/IndexedRow.html) is a
 wrapper over `(long, Vector)`.  An `IndexedRowMatrix` can be converted to a `RowMatrix` by dropping
 its row indices.
 
@@ -427,9 +474,9 @@ dimensions of the matrix are huge and the matrix is very sparse.
 <div data-lang="scala" markdown="1">
 
 A
-[`CoordinateMatrix`](api/mllib/index.html#org.apache.spark.mllib.linalg.distributed.CoordinateMatrix)
+[`CoordinateMatrix`](api/scala/index.html#org.apache.spark.mllib.linalg.distributed.CoordinateMatrix)
 can be created from an `RDD[MatrixEntry]` instance, where
-[`MatrixEntry`](api/mllib/index.html#org.apache.spark.mllib.linalg.distributed.MatrixEntry) is a
+[`MatrixEntry`](api/scala/index.html#org.apache.spark.mllib.linalg.distributed.MatrixEntry) is a
 wrapper over `(Long, Long, Double)`.  A `CoordinateMatrix` can be converted to a `IndexedRowMatrix`
 with sparse rows by calling `toIndexedRowMatrix`.  In this release, we do not provide other
 computation for `CoordinateMatrix`.
@@ -453,13 +500,13 @@ val indexedRowMatrix = mat.toIndexedRowMatrix()
 <div data-lang="java" markdown="1">
 
 A
-[`CoordinateMatrix`](api/mllib/index.html#org.apache.spark.mllib.linalg.distributed.CoordinateMatrix)
+[`CoordinateMatrix`](api/java/org/apache/spark/mllib/linalg/distributed/CoordinateMatrix.html)
 can be created from a `JavaRDD<MatrixEntry>` instance, where
-[`MatrixEntry`](api/mllib/index.html#org.apache.spark.mllib.linalg.distributed.MatrixEntry) is a
+[`MatrixEntry`](api/java/org/apache/spark/mllib/linalg/distributed/MatrixEntry.html) is a
 wrapper over `(long, long, double)`.  A `CoordinateMatrix` can be converted to a `IndexedRowMatrix`
 with sparse rows by calling `toIndexedRowMatrix`.
 
-{% highlight scala %}
+{% highlight java %}
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.mllib.linalg.distributed.CoordinateMatrix;
 import org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix;
@@ -467,7 +514,7 @@ import org.apache.spark.mllib.linalg.distributed.MatrixEntry;
 
 JavaRDD<MatrixEntry> entries = ... // a JavaRDD of matrix entries
 // Create a CoordinateMatrix from a JavaRDD<MatrixEntry>.
-CoordinateMatrix mat = new CoordinateMatrix(entries);
+CoordinateMatrix mat = new CoordinateMatrix(entries.rdd());
 
 // Get its size.
 long m = mat.numRows();
diff --git a/docs/mllib-clustering.md b/docs/mllib-clustering.md
index 276868fa8490..429cdf8d40ce 100644
--- a/docs/mllib-clustering.md
+++ b/docs/mllib-clustering.md
@@ -1,6 +1,7 @@
 ---
 layout: global
-title: <a href="mllib-guide.html">MLlib</a> - Clustering
+title: Clustering - MLlib
+displayTitle: <a href="mllib-guide.html">MLlib</a> - Clustering
 ---
 
 * Table of contents
@@ -40,7 +41,7 @@ a given dataset, the algorithm returns the best clustering result).
 Following code snippets can be executed in `spark-shell`.
 
 In the following example after loading and parsing data, we use the
-[`KMeans`](api/mllib/index.html#org.apache.spark.mllib.clustering.KMeans) object to cluster the data
+[`KMeans`](api/scala/index.html#org.apache.spark.mllib.clustering.KMeans) object to cluster the data
 into two clusters. The number of desired clusters is passed to the algorithm. We then compute Within
 Set Sum of Squared Error (WSSSE). You can reduce this error measure by increasing *k*. In fact the
 optimal *k* is usually one where there is an "elbow" in the WSSSE graph.
diff --git a/docs/mllib-collaborative-filtering.md b/docs/mllib-collaborative-filtering.md
index f486c56e5590..d51002f01567 100644
--- a/docs/mllib-collaborative-filtering.md
+++ b/docs/mllib-collaborative-filtering.md
@@ -1,6 +1,7 @@
 ---
 layout: global
-title: <a href="mllib-guide.html">MLlib</a> - Collaborative Filtering 
+title: Collaborative Filtering - MLlib
+displayTitle: <a href="mllib-guide.html">MLlib</a> - Collaborative Filtering 
 ---
 
 * Table of contents
@@ -48,7 +49,7 @@ user for an item.
 
 <div data-lang="scala" markdown="1">
 In the following example we load rating data. Each row consists of a user, a product and a rating.
-We use the default [ALS.train()](api/mllib/index.html#org.apache.spark.mllib.recommendation.ALS$) 
+We use the default [ALS.train()](api/scala/index.html#org.apache.spark.mllib.recommendation.ALS$) 
 method which assumes ratings are explicit. We evaluate the
 recommendation model by measuring the Mean Squared Error of rating prediction.
 
@@ -58,9 +59,9 @@ import org.apache.spark.mllib.recommendation.Rating
 
 // Load and parse the data
 val data = sc.textFile("mllib/data/als/test.data")
-val ratings = data.map(_.split(',') match {
-    case Array(user, item, rate) =>  Rating(user.toInt, item.toInt, rate.toDouble)
-})
+val ratings = data.map(_.split(',') match { case Array(user, item, rate) =>
+    Rating(user.toInt, item.toInt, rate.toDouble)
+  })
 
 // Build the recommendation model using ALS
 val rank = 10
@@ -68,15 +69,19 @@ val numIterations = 20
 val model = ALS.train(ratings, rank, numIterations, 0.01)
 
 // Evaluate the model on rating data
-val usersProducts = ratings.map{ case Rating(user, product, rate)  => (user, product)}
-val predictions = model.predict(usersProducts).map{
-    case Rating(user, product, rate) => ((user, product), rate)
+val usersProducts = ratings.map { case Rating(user, product, rate) =>
+  (user, product)
 }
-val ratesAndPreds = ratings.map{
-    case Rating(user, product, rate) => ((user, product), rate)
+val predictions = 
+  model.predict(usersProducts).map { case Rating(user, product, rate) => 
+    ((user, product), rate)
+  }
+val ratesAndPreds = ratings.map { case Rating(user, product, rate) => 
+  ((user, product), rate)
 }.join(predictions)
-val MSE = ratesAndPreds.map{
-    case ((user, product), (r1, r2)) =>  math.pow((r1- r2), 2)
+val MSE = ratesAndPreds.map { case ((user, product), (r1, r2)) => 
+  val err = (r1 - r2)
+  err * err
 }.mean()
 println("Mean Squared Error = " + MSE)
 {% endhighlight %}
diff --git a/docs/mllib-decision-tree.md b/docs/mllib-decision-tree.md
index acf0feff42a8..3002a66a4fdb 100644
--- a/docs/mllib-decision-tree.md
+++ b/docs/mllib-decision-tree.md
@@ -1,6 +1,7 @@
 ---
 layout: global
-title: <a href="mllib-guide.html">MLlib</a> - Decision Tree
+title: Decision Tree - MLlib
+displayTitle: <a href="mllib-guide.html">MLlib</a> - Decision Tree
 ---
 
 * Table of contents
diff --git a/docs/mllib-dimensionality-reduction.md b/docs/mllib-dimensionality-reduction.md
index ab24663cfe25..e3608075fbb1 100644
--- a/docs/mllib-dimensionality-reduction.md
+++ b/docs/mllib-dimensionality-reduction.md
@@ -1,6 +1,7 @@
 ---
 layout: global
-title: <a href="mllib-guide.html">MLlib</a> - Dimensionality Reduction
+title: Dimensionality Reduction - MLlib
+displayTitle: <a href="mllib-guide.html">MLlib</a> - Dimensionality Reduction
 ---
 
 * Table of contents
diff --git a/docs/mllib-guide.md b/docs/mllib-guide.md
index 842ca5c8c6d8..640ca8308538 100644
--- a/docs/mllib-guide.md
+++ b/docs/mllib-guide.md
@@ -27,8 +27,9 @@ filtering, dimensionality reduction, as well as underlying optimization primitiv
   * stochastic gradient descent
   * limited-memory BFGS (L-BFGS)
 
-MLlib is currently a *beta* component under active development.
-The APIs may change in the future releases, and we will provide migration guide between releases.
+MLlib is a new component under active development.
+The APIs marked `Experimental`/`DeveloperApi` may change in future releases, 
+and we will provide migration guide between releases.
 
 ## Dependencies
 
@@ -61,9 +62,9 @@ take advantage of sparsity in both storage and computation.
 <div data-lang="scala" markdown="1">
 
 We used to represent a feature vector by `Array[Double]`, which is replaced by
-[`Vector`](api/mllib/index.html#org.apache.spark.mllib.linalg.Vector) in v1.0. Algorithms that used
+[`Vector`](api/scala/index.html#org.apache.spark.mllib.linalg.Vector) in v1.0. Algorithms that used
 to accept `RDD[Array[Double]]` now take
-`RDD[Vector]`. [`LabeledPoint`](api/mllib/index.html#org.apache.spark.mllib.regression.LabeledPoint)
+`RDD[Vector]`. [`LabeledPoint`](api/scala/index.html#org.apache.spark.mllib.regression.LabeledPoint)
 is now a wrapper of `(Double, Vector)` instead of `(Double, Array[Double])`. Converting
 `Array[Double]` to `Vector` is straightforward:
 
@@ -74,7 +75,7 @@ val array: Array[Double] = ... // a double array
 val vector: Vector = Vectors.dense(array) // a dense vector
 {% endhighlight %}
 
-[`Vectors`](api/mllib/index.html#org.apache.spark.mllib.linalg.Vectors$) provides factory methods to create sparse vectors.
+[`Vectors`](api/scala/index.html#org.apache.spark.mllib.linalg.Vectors$) provides factory methods to create sparse vectors.
 
 *Note*. Scala imports `scala.collection.immutable.Vector` by default, so you have to import `org.apache.spark.mllib.linalg.Vector` explicitly to use MLlib's `Vector`.
 
@@ -83,9 +84,9 @@ val vector: Vector = Vectors.dense(array) // a dense vector
 <div data-lang="java" markdown="1">
 
 We used to represent a feature vector by `double[]`, which is replaced by
-[`Vector`](api/mllib/index.html#org.apache.spark.mllib.linalg.Vector) in v1.0. Algorithms that used
+[`Vector`](api/scala/index.html#org.apache.spark.mllib.linalg.Vector) in v1.0. Algorithms that used
 to accept `RDD<double[]>` now take
-`RDD<Vector>`. [`LabeledPoint`](api/mllib/index.html#org.apache.spark.mllib.regression.LabeledPoint)
+`RDD<Vector>`. [`LabeledPoint`](api/scala/index.html#org.apache.spark.mllib.regression.LabeledPoint)
 is now a wrapper of `(double, Vector)` instead of `(double, double[])`. Converting `double[]` to
 `Vector` is straightforward:
 
@@ -97,7 +98,7 @@ double[] array = ... // a double array
 Vector vector = Vectors.dense(array); // a dense vector
 {% endhighlight %}
 
-[`Vectors`](api/mllib/index.html#org.apache.spark.mllib.linalg.Vectors$) provides factory methods to
+[`Vectors`](api/scala/index.html#org.apache.spark.mllib.linalg.Vectors$) provides factory methods to
 create sparse vectors.
 
 </div>
@@ -106,7 +107,7 @@ create sparse vectors.
 
 We used to represent a labeled feature vector in a NumPy array, where the first entry corresponds to
 the label and the rest are features.  This representation is replaced by class
-[`LabeledPoint`](api/pyspark/pyspark.mllib.regression.LabeledPoint-class.html), which takes both
+[`LabeledPoint`](api/python/pyspark.mllib.regression.LabeledPoint-class.html), which takes both
 dense and sparse feature vectors.
 
 {% highlight python %}
diff --git a/docs/mllib-linear-methods.md b/docs/mllib-linear-methods.md
index eff617d8641e..4dfbebbcd04b 100644
--- a/docs/mllib-linear-methods.md
+++ b/docs/mllib-linear-methods.md
@@ -1,6 +1,7 @@
 ---
 layout: global
-title: <a href="mllib-guide.html">MLlib</a> - Linear Methods
+title: Linear Methods - MLlib
+displayTitle: <a href="mllib-guide.html">MLlib</a> - Linear Methods
 ---
 
 * Table of contents
@@ -233,7 +234,7 @@ val modelL1 = svmAlg.run(training)
 {% endhighlight %}
 
 Similarly, you can use replace `SVMWithSGD` by
-[`LogisticRegressionWithSGD`](api/mllib/index.html#org.apache.spark.mllib.classification.LogisticRegressionWithSGD).
+[`LogisticRegressionWithSGD`](api/scala/index.html#org.apache.spark.mllib.classification.LogisticRegressionWithSGD).
 
 </div>
 
@@ -328,8 +329,8 @@ println("training Mean Squared Error = " + MSE)
 {% endhighlight %}
 
 Similarly you can use
-[`RidgeRegressionWithSGD`](api/mllib/index.html#org.apache.spark.mllib.regression.RidgeRegressionWithSGD)
-and [`LassoWithSGD`](api/mllib/index.html#org.apache.spark.mllib.regression.LassoWithSGD).
+[`RidgeRegressionWithSGD`](api/scala/index.html#org.apache.spark.mllib.regression.RidgeRegressionWithSGD)
+and [`LassoWithSGD`](api/scala/index.html#org.apache.spark.mllib.regression.LassoWithSGD).
 
 </div>
 
@@ -380,11 +381,11 @@ all three possible regularizations (none, L1 or L2).
 
 Algorithms are all implemented in Scala:
 
-* [SVMWithSGD](api/mllib/index.html#org.apache.spark.mllib.classification.SVMWithSGD)
-* [LogisticRegressionWithSGD](api/mllib/index.html#org.apache.spark.mllib.classification.LogisticRegressionWithSGD)
-* [LinearRegressionWithSGD](api/mllib/index.html#org.apache.spark.mllib.regression.LinearRegressionWithSGD)
-* [RidgeRegressionWithSGD](api/mllib/index.html#org.apache.spark.mllib.regression.RidgeRegressionWithSGD)
-* [LassoWithSGD](api/mllib/index.html#org.apache.spark.mllib.regression.LassoWithSGD)
+* [SVMWithSGD](api/scala/index.html#org.apache.spark.mllib.classification.SVMWithSGD)
+* [LogisticRegressionWithSGD](api/scala/index.html#org.apache.spark.mllib.classification.LogisticRegressionWithSGD)
+* [LinearRegressionWithSGD](api/scala/index.html#org.apache.spark.mllib.regression.LinearRegressionWithSGD)
+* [RidgeRegressionWithSGD](api/scala/index.html#org.apache.spark.mllib.regression.RidgeRegressionWithSGD)
+* [LassoWithSGD](api/scala/index.html#org.apache.spark.mllib.regression.LassoWithSGD)
 
 Python calls the Scala implementation via
-[PythonMLLibAPI](api/mllib/index.html#org.apache.spark.mllib.api.python.PythonMLLibAPI).
+[PythonMLLibAPI](api/scala/index.html#org.apache.spark.mllib.api.python.PythonMLLibAPI).
diff --git a/docs/mllib-naive-bayes.md b/docs/mllib-naive-bayes.md
index c47508b7daa2..4b3a7cab3211 100644
--- a/docs/mllib-naive-bayes.md
+++ b/docs/mllib-naive-bayes.md
@@ -1,6 +1,7 @@
 ---
 layout: global
-title: <a href="mllib-guide.html">MLlib</a> - Naive Bayes
+title: Naive Bayes - MLlib
+displayTitle: <a href="mllib-guide.html">MLlib</a> - Naive Bayes
 ---
 
 Naive Bayes is a simple multiclass classification algorithm with the assumption of independence
@@ -27,11 +28,11 @@ sparsity. Since the training data is only used once, it is not necessary to cach
 <div class="codetabs">
 <div data-lang="scala" markdown="1">
 
-[NaiveBayes](api/mllib/index.html#org.apache.spark.mllib.classification.NaiveBayes$) implements
+[NaiveBayes](api/scala/index.html#org.apache.spark.mllib.classification.NaiveBayes$) implements
 multinomial naive Bayes. It takes an RDD of
-[LabeledPoint](api/mllib/index.html#org.apache.spark.mllib.regression.LabeledPoint) and an optional
+[LabeledPoint](api/scala/index.html#org.apache.spark.mllib.regression.LabeledPoint) and an optional
 smoothing parameter `lambda` as input, and output a
-[NaiveBayesModel](api/mllib/index.html#org.apache.spark.mllib.classification.NaiveBayesModel), which
+[NaiveBayesModel](api/scala/index.html#org.apache.spark.mllib.classification.NaiveBayesModel), which
 can be used for evaluation and prediction.
 
 {% highlight scala %}
@@ -59,11 +60,11 @@ val accuracy = 1.0 * predictionAndLabel.filter(x => x._1 == x._2).count() / test
 
 <div data-lang="java" markdown="1">
 
-[NaiveBayes](api/mllib/index.html#org.apache.spark.mllib.classification.NaiveBayes$) implements
+[NaiveBayes](api/java/org/apache/spark/mllib/classification/NaiveBayes.html) implements
 multinomial naive Bayes. It takes a Scala RDD of
-[LabeledPoint](api/mllib/index.html#org.apache.spark.mllib.regression.LabeledPoint) and an
+[LabeledPoint](api/java/org/apache/spark/mllib/regression/LabeledPoint.html) and an
 optionally smoothing parameter `lambda` as input, and output a
-[NaiveBayesModel](api/mllib/index.html#org.apache.spark.mllib.classification.NaiveBayesModel), which
+[NaiveBayesModel](api/java/org/apache/spark/mllib/classification/NaiveBayesModel.html), which
 can be used for evaluation and prediction.
 
 {% highlight java %}
@@ -102,11 +103,11 @@ double accuracy = 1.0 * predictionAndLabel.filter(new Function<Tuple2<Double, Do
 
 <div data-lang="python" markdown="1">
 
-[NaiveBayes](api/pyspark/pyspark.mllib.classification.NaiveBayes-class.html) implements multinomial
+[NaiveBayes](api/python/pyspark.mllib.classification.NaiveBayes-class.html) implements multinomial
 naive Bayes. It takes an RDD of
-[LabeledPoint](api/pyspark/pyspark.mllib.regression.LabeledPoint-class.html) and an optionally
+[LabeledPoint](api/python/pyspark.mllib.regression.LabeledPoint-class.html) and an optionally
 smoothing parameter `lambda` as input, and output a
-[NaiveBayesModel](api/pyspark/pyspark.mllib.classification.NaiveBayesModel-class.html), which can be
+[NaiveBayesModel](api/python/pyspark.mllib.classification.NaiveBayesModel-class.html), which can be
 used for evaluation and prediction.
 
 <!-- TODO: Make Python's example consistent with Scala's and Java's. -->
diff --git a/docs/mllib-optimization.md b/docs/mllib-optimization.md
index aa0dec213059..a22980d03a2f 100644
--- a/docs/mllib-optimization.md
+++ b/docs/mllib-optimization.md
@@ -1,6 +1,7 @@
 ---
 layout: global
-title: <a href="mllib-guide.html">MLlib</a> - Optimization
+title: Optimization - MLlib
+displayTitle: <a href="mllib-guide.html">MLlib</a> - Optimization
 ---
 
 * Table of contents
@@ -170,17 +171,17 @@ each iteration, to compute the gradient direction.
 
 Available algorithms for gradient descent:
 
-* [GradientDescent.runMiniBatchSGD](api/mllib/index.html#org.apache.spark.mllib.optimization.GradientDescent)
+* [GradientDescent.runMiniBatchSGD](api/scala/index.html#org.apache.spark.mllib.optimization.GradientDescent)
 
 ### L-BFGS
 L-BFGS is currently only a low-level optimization primitive in `MLlib`. If you want to use L-BFGS in various 
 ML algorithms such as Linear Regression, and Logistic Regression, you have to pass the gradient of objective
 function, and updater into optimizer yourself instead of using the training APIs like 
-[LogisticRegressionWithSGD](api/mllib/index.html#org.apache.spark.mllib.classification.LogisticRegressionWithSGD).
+[LogisticRegressionWithSGD](api/scala/index.html#org.apache.spark.mllib.classification.LogisticRegressionWithSGD).
 See the example below. It will be addressed in the next release. 
 
 The L1 regularization by using 
-[L1Updater](api/mllib/index.html#org.apache.spark.mllib.optimization.L1Updater) will not work since the 
+[L1Updater](api/scala/index.html#org.apache.spark.mllib.optimization.L1Updater) will not work since the 
 soft-thresholding logic in L1Updater is designed for gradient descent. See the developer's note.
 
 The L-BFGS method
@@ -274,4 +275,4 @@ the actual gradient descent step. However, we're able to take the gradient and
 loss of objective function of regularization for L-BFGS by ignoring the part of logic
 only for gradient decent such as adaptive step size stuff. We will refactorize
 this into regularizer to replace updater to separate the logic between 
-regularization and step update later. 
\ No newline at end of file
+regularization and step update later. 

From 7b70a7071894dd90ea1d0091542b3e13e7ef8d3a Mon Sep 17 00:00:00 2001
From: Matei Zaharia <matei@databricks.com>
Date: Mon, 19 May 2014 15:02:35 -0700
Subject: [PATCH 239/810] [SPARK-1876] Windows fixes to deal with latest
 distribution layout changes

- Look for JARs in the right place
- Launch examples the same way as on Unix
- Load datanucleus JARs if they exist
- Don't attempt to parse local paths as URIs in SparkSubmit, since paths with C:\ are not valid URIs
- Also fixed POM exclusion rules for datanucleus (it wasn't properly excluding it, whereas SBT was)

Author: Matei Zaharia <matei@databricks.com>

Closes #819 from mateiz/win-fixes and squashes the following commits:

d558f96 [Matei Zaharia] Fix comment
228577b [Matei Zaharia] Review comments
d3b71c7 [Matei Zaharia] Properly exclude datanucleus files in Maven assembly
144af84 [Matei Zaharia] Update Windows scripts to match latest binary package layout
---
 README.md                                     |  7 +--
 assembly/pom.xml                              |  2 +-
 bin/compute-classpath.cmd                     | 24 ++++++++-
 bin/run-example                               | 23 ++++-----
 bin/run-example2.cmd                          | 51 ++++++++++++++-----
 bin/spark-class2.cmd                          |  2 +
 .../org/apache/spark/deploy/SparkSubmit.scala |  2 +-
 7 files changed, 81 insertions(+), 30 deletions(-)

diff --git a/README.md b/README.md
index 9c2e32b90f16..6211a5889a3f 100644
--- a/README.md
+++ b/README.md
@@ -9,13 +9,14 @@ You can find the latest Spark documentation, including a programming
 guide, on the project webpage at <http://spark.apache.org/documentation.html>.
 This README file only contains basic setup instructions.
 
-
 ## Building Spark
 
 Spark is built on Scala 2.10. To build Spark and its example programs, run:
 
     ./sbt/sbt assembly
 
+(You do not need to do this if you downloaded a pre-built package.)
+
 ## Interactive Scala Shell
 
 The easiest way to start using Spark is through the Scala shell:
@@ -41,9 +42,9 @@ And run the following command, which should also return 1000:
 Spark also comes with several sample programs in the `examples` directory.
 To run one of them, use `./bin/run-example <class> [params]`. For example:
 
-    ./bin/run-example org.apache.spark.examples.SparkLR
+    ./bin/run-example SparkPi
 
-will run the Logistic Regression example locally.
+will run the Pi example locally.
 
 You can set the MASTER environment variable when running examples to submit
 examples to a cluster. This can be a mesos:// or spark:// URL, 
diff --git a/assembly/pom.xml b/assembly/pom.xml
index abd893533999..963357b9ab16 100644
--- a/assembly/pom.xml
+++ b/assembly/pom.xml
@@ -96,7 +96,7 @@
             <filter>
               <artifact>*:*</artifact>
               <excludes>
-                <exclude>org.datanucleus:*</exclude>
+                <exclude>org/datanucleus/**</exclude>
                 <exclude>META-INF/*.SF</exclude>
                 <exclude>META-INF/*.DSA</exclude>
                 <exclude>META-INF/*.RSA</exclude>
diff --git a/bin/compute-classpath.cmd b/bin/compute-classpath.cmd
index 065553eb3193..58710cd1bd54 100644
--- a/bin/compute-classpath.cmd
+++ b/bin/compute-classpath.cmd
@@ -20,6 +20,13 @@ rem
 rem This script computes Spark's classpath and prints it to stdout; it's used by both the "run"
 rem script and the ExecutorRunner in standalone cluster mode.
 
+rem If we're called from spark-class2.cmd, it already set enabledelayedexpansion and setting
+rem it here would stop us from affecting its copy of the CLASSPATH variable; otherwise we
+rem need to set it here because we use !datanucleus_jars! below.
+if "%DONT_PRINT_CLASSPATH%"=="1" goto skip_delayed_expansion
+setlocal enabledelayedexpansion
+:skip_delayed_expansion
+
 set SCALA_VERSION=2.10
 
 rem Figure out where the Spark framework is installed
@@ -31,7 +38,7 @@ if exist "%FWDIR%conf\spark-env.cmd" call "%FWDIR%conf\spark-env.cmd"
 rem Build up classpath
 set CLASSPATH=%FWDIR%conf
 if exist "%FWDIR%RELEASE" (
-  for %%d in ("%FWDIR%jars\spark-assembly*.jar") do (
+  for %%d in ("%FWDIR%lib\spark-assembly*.jar") do (
     set ASSEMBLY_JAR=%%d
   )
 ) else (
@@ -42,6 +49,21 @@ if exist "%FWDIR%RELEASE" (
 
 set CLASSPATH=%CLASSPATH%;%ASSEMBLY_JAR%
 
+rem When Hive support is needed, Datanucleus jars must be included on the classpath.
+rem Datanucleus jars do not work if only included in the uber jar as plugin.xml metadata is lost.
+rem Both sbt and maven will populate "lib_managed/jars/" with the datanucleus jars when Spark is
+rem built with Hive, so look for them there.
+if exist "%FWDIR%RELEASE" (
+  set datanucleus_dir=%FWDIR%lib
+) else (
+  set datanucleus_dir=%FWDIR%lib_managed\jars
+)
+set "datanucleus_jars="
+for %%d in ("%datanucleus_dir%\datanucleus-*.jar") do (
+  set datanucleus_jars=!datanucleus_jars!;%%d
+)
+set CLASSPATH=%CLASSPATH%;%datanucleus_jars%
+
 set SPARK_CLASSES=%FWDIR%core\target\scala-%SCALA_VERSION%\classes
 set SPARK_CLASSES=%SPARK_CLASSES%;%FWDIR%repl\target\scala-%SCALA_VERSION%\classes
 set SPARK_CLASSES=%SPARK_CLASSES%;%FWDIR%mllib\target\scala-%SCALA_VERSION%\classes
diff --git a/bin/run-example b/bin/run-example
index 146951ac0ee5..7caab31daef3 100755
--- a/bin/run-example
+++ b/bin/run-example
@@ -23,6 +23,16 @@ FWDIR="$(cd `dirname $0`/..; pwd)"
 export SPARK_HOME="$FWDIR"
 EXAMPLES_DIR="$FWDIR"/examples
 
+if [ -n "$1" ]; then
+  EXAMPLE_CLASS="$1"
+  shift
+else
+  echo "Usage: ./bin/run-example <example-class> [example-args]"
+  echo "  - set MASTER=XX to use a specific master"
+  echo "  - can use abbreviated example class name (e.g. SparkPi, mllib.LinearRegression)"
+  exit 1
+fi
+
 if [ -f "$FWDIR/RELEASE" ]; then
   export SPARK_EXAMPLES_JAR=`ls "$FWDIR"/lib/spark-examples-*hadoop*.jar`
 elif [ -e "$EXAMPLES_DIR"/target/scala-$SCALA_VERSION/spark-examples-*hadoop*.jar ]; then
@@ -37,17 +47,6 @@ fi
 
 EXAMPLE_MASTER=${MASTER:-"local[*]"}
 
-if [ -n "$1" ]; then
-  EXAMPLE_CLASS="$1"
-  shift
-else 
-  echo "usage: ./bin/run-example <example-class> [example-args]" 
-  echo "  - set MASTER=XX to use a specific master"
-  echo "  - can use abbreviated example class name (e.g. SparkPi, mllib.MovieLensALS)"
-  echo
-  exit -1
-fi
-
 if [[ ! $EXAMPLE_CLASS == org.apache.spark.examples* ]]; then
   EXAMPLE_CLASS="org.apache.spark.examples.$EXAMPLE_CLASS"
 fi
@@ -55,5 +54,5 @@ fi
 ./bin/spark-submit \
   --master $EXAMPLE_MASTER \
   --class $EXAMPLE_CLASS \
-  $SPARK_EXAMPLES_JAR \
+  "$SPARK_EXAMPLES_JAR" \
   "$@"
diff --git a/bin/run-example2.cmd b/bin/run-example2.cmd
index 40abb9af7424..eadedd7fa61f 100644
--- a/bin/run-example2.cmd
+++ b/bin/run-example2.cmd
@@ -30,7 +30,9 @@ if exist "%FWDIR%conf\spark-env.cmd" call "%FWDIR%conf\spark-env.cmd"
 
 rem Test that an argument was given
 if not "x%1"=="x" goto arg_given
-  echo Usage: run-example ^<example-class^> [^<args^>]
+  echo Usage: run-example ^<example-class^> [example-args]
+  echo   - set MASTER=XX to use a specific master
+  echo   - can use abbreviated example class name (e.g. SparkPi, mllib.LinearRegression)
   goto exit
 :arg_given
 
@@ -38,8 +40,14 @@ set EXAMPLES_DIR=%FWDIR%examples
 
 rem Figure out the JAR file that our examples were packaged into.
 set SPARK_EXAMPLES_JAR=
-for %%d in ("%EXAMPLES_DIR%\target\scala-%SCALA_VERSION%\spark-examples*assembly*.jar") do (
-  set SPARK_EXAMPLES_JAR=%%d
+if exist "%FWDIR%RELEASE" (
+  for %%d in ("%FWDIR%lib\spark-examples*.jar") do (
+    set SPARK_EXAMPLES_JAR=%%d
+  )
+) else (
+  for %%d in ("%EXAMPLES_DIR%\target\scala-%SCALA_VERSION%\spark-examples*.jar") do (
+    set SPARK_EXAMPLES_JAR=%%d
+  )
 )
 if "x%SPARK_EXAMPLES_JAR%"=="x" (
   echo Failed to find Spark examples assembly JAR.
@@ -47,15 +55,34 @@ if "x%SPARK_EXAMPLES_JAR%"=="x" (
   goto exit
 )
 
-rem Compute Spark classpath using external script
-set DONT_PRINT_CLASSPATH=1
-call "%FWDIR%bin\compute-classpath.cmd"
-set DONT_PRINT_CLASSPATH=0
-set CLASSPATH=%SPARK_EXAMPLES_JAR%;%CLASSPATH%
+rem Set master from MASTER environment variable if given
+if "x%MASTER%"=="x" (
+  set EXAMPLE_MASTER=local[*]
+) else (
+  set EXAMPLE_MASTER=%MASTER%
+)
+
+rem If the EXAMPLE_CLASS does not start with org.apache.spark.examples, add that
+set EXAMPLE_CLASS=%1
+set PREFIX=%EXAMPLE_CLASS:~0,25%
+if not %PREFIX%==org.apache.spark.examples (
+  set EXAMPLE_CLASS=org.apache.spark.examples.%EXAMPLE_CLASS%
+)
+
+rem Get the tail of the argument list, to skip the first one. This is surprisingly
+rem complicated on Windows.
+set "ARGS="
+:top
+shift
+if "%~1" neq "" (
+  set ARGS=%ARGS% "%~1"
+  goto :top
+)
+if defined ARGS set ARGS=%ARGS:~1%
 
-rem Figure out where java is.
-set RUNNER=java
-if not "x%JAVA_HOME%"=="x" set RUNNER=%JAVA_HOME%\bin\java
+call "%FWDIR%bin\spark-submit.cmd" ^
+  --master %EXAMPLE_MASTER% ^
+  --class %EXAMPLE_CLASS% ^
+  "%SPARK_EXAMPLES_JAR%" %ARGS%
 
-"%RUNNER%" -cp "%CLASSPATH%" %JAVA_OPTS% %*
 :exit
diff --git a/bin/spark-class2.cmd b/bin/spark-class2.cmd
index 4302c1b6b7ff..266edd9fa983 100755
--- a/bin/spark-class2.cmd
+++ b/bin/spark-class2.cmd
@@ -17,6 +17,8 @@ rem See the License for the specific language governing permissions and
 rem limitations under the License.
 rem
 
+setlocal enabledelayedexpansion
+
 set SCALA_VERSION=2.10
 
 rem Figure out where the Spark framework is installed
diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
index a99b2176e2b5..c54331c00fab 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
@@ -299,7 +299,7 @@ object SparkSubmit {
   }
 
   private def addJarToClasspath(localJar: String, loader: ExecutorURLClassLoader) {
-    val localJarFile = new File(new URI(localJar).getPath)
+    val localJarFile = new File(localJar)
     if (!localJarFile.exists()) {
       printWarning(s"Jar $localJar does not exist, skipping.")
     }

From 1811ba8ccb580979aa2e12019e6a82805f09ab53 Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Mon, 19 May 2014 16:41:31 -0700
Subject: [PATCH 240/810] SPARK-1878: Fix the incorrect initialization order

JIRA: https://issues.apache.org/jira/browse/SPARK-1878

Author: zsxwing <zsxwing@gmail.com>

Closes #822 from zsxwing/SPARK-1878 and squashes the following commits:

4a47e27 [zsxwing] SPARK-1878: Fix the incorrect initialization order
---
 .../spark/streaming/api/java/JavaStreamingContext.scala     | 6 +++---
 .../test/java/org/apache/spark/streaming/JavaAPISuite.java  | 4 ++++
 2 files changed, 7 insertions(+), 3 deletions(-)

diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala
index 75a3e9334e6d..18605cac7006 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala
@@ -142,12 +142,12 @@ class JavaStreamingContext(val ssc: StreamingContext) {
    */
   def this(path: String, hadoopConf: Configuration) = this(new StreamingContext(path, hadoopConf))
 
-  @deprecated("use sparkContext", "0.9.0")
-  val sc: JavaSparkContext = sparkContext
-
   /** The underlying SparkContext */
   val sparkContext = new JavaSparkContext(ssc.sc)
 
+  @deprecated("use sparkContext", "0.9.0")
+  val sc: JavaSparkContext = sparkContext
+
   /**
    * Create an input stream from network source hostname:port. Data is received using
    * a TCP socket and the receive bytes is interpreted as UTF8 encoded \n delimited
diff --git a/streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java b/streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java
index ce58cb12a456..4efeb8dfbe1a 100644
--- a/streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java
+++ b/streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java
@@ -55,6 +55,10 @@ public void equalIterable(Iterable<?> a, Iterable<?> b) {
       equalIterator(a.iterator(), b.iterator());
   }
 
+  @Test
+  public void testInitialization() {
+    Assert.assertNotNull(ssc.sc());
+  }
 
   @SuppressWarnings("unchecked")
   @Test

From 5af99d7617ba3b9fbfdb345ef9571b7dd41f45a1 Mon Sep 17 00:00:00 2001
From: Matei Zaharia <matei@databricks.com>
Date: Mon, 19 May 2014 18:42:28 -0700
Subject: [PATCH 241/810] SPARK-1879. Increase MaxPermSize since some of our
 builds have many classes

See https://issues.apache.org/jira/browse/SPARK-1879 -- builds with Hadoop2 and Hive ran out of PermGen space in spark-shell, when those things added up with the Scala compiler.

Note that users can still override it by setting their own Java options with this change. Their options will come later in the command string than the -XX:MaxPermSize=128m.

Author: Matei Zaharia <matei@databricks.com>

Closes #823 from mateiz/spark-1879 and squashes the following commits:

6bc0ee8 [Matei Zaharia] Increase MaxPermSize to 128m since some of our builds have lots of classes
---
 bin/spark-class                                               | 4 ++--
 bin/spark-class2.cmd                                          | 4 ++--
 .../scala/org/apache/spark/deploy/worker/CommandUtils.scala   | 4 +++-
 3 files changed, 7 insertions(+), 5 deletions(-)

diff --git a/bin/spark-class b/bin/spark-class
index 6480ccb58d6a..2e57295fd023 100755
--- a/bin/spark-class
+++ b/bin/spark-class
@@ -99,14 +99,14 @@ else
 fi
 
 # Set JAVA_OPTS to be able to load native libraries and to set heap size
-JAVA_OPTS="$OUR_JAVA_OPTS"
+JAVA_OPTS="-XX:MaxPermSize=128m $OUR_JAVA_OPTS"
 JAVA_OPTS="$JAVA_OPTS -Xms$OUR_JAVA_MEM -Xmx$OUR_JAVA_MEM"
 # Load extra JAVA_OPTS from conf/java-opts, if it exists
 if [ -e "$FWDIR/conf/java-opts" ] ; then
   JAVA_OPTS="$JAVA_OPTS `cat $FWDIR/conf/java-opts`"
 fi
 export JAVA_OPTS
-# Attention: when changing the way the JAVA_OPTS are assembled, the change must be reflected in ExecutorRunner.scala!
+# Attention: when changing the way the JAVA_OPTS are assembled, the change must be reflected in CommandUtils.scala!
 
 if [ ! -f "$FWDIR/RELEASE" ]; then
   # Exit if the user hasn't compiled Spark
diff --git a/bin/spark-class2.cmd b/bin/spark-class2.cmd
index 266edd9fa983..e420eb409e52 100755
--- a/bin/spark-class2.cmd
+++ b/bin/spark-class2.cmd
@@ -77,8 +77,8 @@ rem All drivers use SPARK_JAVA_OPTS + SPARK_DRIVER_MEMORY. The repl also uses SP
 )
 
 rem Set JAVA_OPTS to be able to load native libraries and to set heap size
-set JAVA_OPTS=%OUR_JAVA_OPTS% -Djava.library.path=%SPARK_LIBRARY_PATH% -Xms%OUR_JAVA_MEM% -Xmx%OUR_JAVA_MEM%
-rem Attention: when changing the way the JAVA_OPTS are assembled, the change must be reflected in ExecutorRunner.scala!
+set JAVA_OPTS=-XX:MaxPermSize=128m %OUR_JAVA_OPTS% -Djava.library.path=%SPARK_LIBRARY_PATH% -Xms%OUR_JAVA_MEM% -Xmx%OUR_JAVA_MEM%
+rem Attention: when changing the way the JAVA_OPTS are assembled, the change must be reflected in CommandUtils.scala!
 
 rem Test whether the user has built Spark
 if exist "%FWDIR%RELEASE" goto skip_build_test
diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/CommandUtils.scala b/core/src/main/scala/org/apache/spark/deploy/worker/CommandUtils.scala
index c7f0f244ea2f..4af5bc3afad6 100644
--- a/core/src/main/scala/org/apache/spark/deploy/worker/CommandUtils.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/worker/CommandUtils.scala
@@ -65,6 +65,8 @@ object CommandUtils extends Logging {
          Seq()
       }
 
+    val permGenOpt = Seq("-XX:MaxPermSize=128m")
+
     // Figure out our classpath with the external compute-classpath script
     val ext = if (System.getProperty("os.name").startsWith("Windows")) ".cmd" else ".sh"
     val classPath = Utils.executeAndGetOutput(
@@ -73,7 +75,7 @@ object CommandUtils extends Logging {
     val userClassPath = command.classPathEntries ++ Seq(classPath)
 
     Seq("-cp", userClassPath.filterNot(_.isEmpty).mkString(File.pathSeparator)) ++
-      libraryOpts ++ extraOpts ++ workerLocalOpts ++ memoryOpts
+      permGenOpt ++ libraryOpts ++ extraOpts ++ workerLocalOpts ++ memoryOpts
   }
 
   /** Spawn a thread that will redirect a given stream to a file */

From 6a2c5c610c259f62cb12d8cfc18bf59cdb334bb2 Mon Sep 17 00:00:00 2001
From: witgo <witgo@qq.com>
Date: Mon, 19 May 2014 19:40:29 -0700
Subject: [PATCH 242/810] [SPARK-1875]NoClassDefFoundError: StringUtils when
 building with hadoop 1.x and hive

Author: witgo <witgo@qq.com>

Closes #824 from witgo/SPARK-1875_commons-lang-2.6 and squashes the following commits:

ef7231d [witgo] review commit
ead3c3b [witgo] SPARK-1875:NoClassDefFoundError: StringUtils when building against Hadoop 1
---
 project/SparkBuild.scala | 3 +--
 sql/hive/pom.xml         | 8 --------
 2 files changed, 1 insertion(+), 10 deletions(-)

diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index 29dcd8678b47..b9d92340ff75 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -318,7 +318,6 @@ object SparkBuild extends Build {
   val excludeFastutil = ExclusionRule(organization = "it.unimi.dsi")
   val excludeJruby = ExclusionRule(organization = "org.jruby")
   val excludeThrift = ExclusionRule(organization = "org.apache.thrift")
-  val excludeCommonsLang = ExclusionRule(organization = "commons-lang")
   val excludeServletApi = ExclusionRule(organization = "javax.servlet", artifact = "servlet-api")
 
   def sparkPreviousArtifact(id: String, organization: String = "org.apache.spark",
@@ -493,7 +492,7 @@ object SparkBuild extends Build {
     javaOptions += "-XX:MaxPermSize=1g",
     libraryDependencies ++= Seq(
       "org.spark-project.hive" % "hive-metastore" % hiveVersion,
-      "org.spark-project.hive" % "hive-exec"      % hiveVersion excludeAll(excludeCommonsLang, excludeCommonsLogging),
+      "org.spark-project.hive" % "hive-exec"      % hiveVersion excludeAll(excludeCommonsLogging),
       "org.spark-project.hive" % "hive-serde"     % hiveVersion
     ),
     // Multiple queries rely on the TestHive singleton.  See comments there for more details.
diff --git a/sql/hive/pom.xml b/sql/hive/pom.xml
index 4d0b2fa1452a..9254b70e64a0 100644
--- a/sql/hive/pom.xml
+++ b/sql/hive/pom.xml
@@ -57,10 +57,6 @@
           <groupId>commons-logging</groupId>
           <artifactId>commons-logging</artifactId>
         </exclusion>
-        <exclusion>
-          <groupId>commons-lang</groupId>
-          <artifactId>commons-lang</artifactId>
-        </exclusion>
       </exclusions>
     </dependency>
     <dependency>
@@ -80,10 +76,6 @@
           <groupId>commons-logging</groupId>
           <artifactId>commons-logging-api</artifactId>
         </exclusion>
-        <exclusion>
-          <groupId>commons-lang</groupId>
-          <artifactId>commons-lang</artifactId>
-        </exclusion>
       </exclusions>
     </dependency>
     <!-- hive-serde already depends on avro, but this brings in customized config of avro deps from parent -->

From b0ce22e071da4cc62ec5e29abf7b1299b8e4a6b0 Mon Sep 17 00:00:00 2001
From: Aaron Davidson <aaron@databricks.com>
Date: Mon, 19 May 2014 20:55:26 -0700
Subject: [PATCH 243/810] SPARK-1689: Spark application should die when removed
 by Master

scheduler.error() will mask the error if there are active tasks. Being removed is a cataclysmic event for Spark applications, and should probably be treated as such.

Author: Aaron Davidson <aaron@databricks.com>

Closes #832 from aarondav/i-love-u and squashes the following commits:

9f1200f [Aaron Davidson] SPARK-1689: Spark application should die when removed by Master
---
 .../spark/scheduler/cluster/SparkDeploySchedulerBackend.scala   | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala
index 9768670855f8..9c07b3f7b695 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala
@@ -88,6 +88,8 @@ private[spark] class SparkDeploySchedulerBackend(
     if (!stopping) {
       logError("Application has been killed. Reason: " + reason)
       scheduler.error(reason)
+      // Ensure the application terminates, as we can no longer run jobs.
+      sc.stop()
     }
   }
 

From bcb9dce6f444a977c714117811bce0c54b417650 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Mon, 19 May 2014 21:29:33 -0700
Subject: [PATCH 244/810] [SPARK-1874][MLLIB] Clean up MLlib sample data

1. Added synthetic datasets for `MovieLensALS`, `LinearRegression`, `BinaryClassification`.
2. Embedded instructions in the help message of those example apps.

Per discussion with Matei on the JIRA page, new example data is under `data/mllib`.

Author: Xiangrui Meng <meng@databricks.com>

Closes #833 from mengxr/mllib-sample-data and squashes the following commits:

59f0a18 [Xiangrui Meng] add sample binary classification data
3c2f92f [Xiangrui Meng] add linear regression data
050f1ca [Xiangrui Meng] add a sample dataset for MovieLensALS example
---
 .../sample_binary_classification_data.txt     |  100 ++
 data/mllib/sample_linear_regression_data.txt  |  501 ++++++
 data/mllib/sample_movielens_data.txt          | 1501 +++++++++++++++++
 .../examples/mllib/BinaryClassification.scala |   12 +-
 .../examples/mllib/LinearRegression.scala     |   11 +-
 .../spark/examples/mllib/MovieLensALS.scala   |   15 +
 6 files changed, 2138 insertions(+), 2 deletions(-)
 create mode 100644 data/mllib/sample_binary_classification_data.txt
 create mode 100755 data/mllib/sample_linear_regression_data.txt
 create mode 100644 data/mllib/sample_movielens_data.txt

diff --git a/data/mllib/sample_binary_classification_data.txt b/data/mllib/sample_binary_classification_data.txt
new file mode 100644
index 000000000000..861c70cde7fd
--- /dev/null
+++ b/data/mllib/sample_binary_classification_data.txt
@@ -0,0 +1,100 @@
+0 128:51 129:159 130:253 131:159 132:50 155:48 156:238 157:252 158:252 159:252 160:237 182:54 183:227 184:253 185:252 186:239 187:233 188:252 189:57 190:6 208:10 209:60 210:224 211:252 212:253 213:252 214:202 215:84 216:252 217:253 218:122 236:163 237:252 238:252 239:252 240:253 241:252 242:252 243:96 244:189 245:253 246:167 263:51 264:238 265:253 266:253 267:190 268:114 269:253 270:228 271:47 272:79 273:255 274:168 290:48 291:238 292:252 293:252 294:179 295:12 296:75 297:121 298:21 301:253 302:243 303:50 317:38 318:165 319:253 320:233 321:208 322:84 329:253 330:252 331:165 344:7 345:178 346:252 347:240 348:71 349:19 350:28 357:253 358:252 359:195 372:57 373:252 374:252 375:63 385:253 386:252 387:195 400:198 401:253 402:190 413:255 414:253 415:196 427:76 428:246 429:252 430:112 441:253 442:252 443:148 455:85 456:252 457:230 458:25 467:7 468:135 469:253 470:186 471:12 483:85 484:252 485:223 494:7 495:131 496:252 497:225 498:71 511:85 512:252 513:145 521:48 522:165 523:252 524:173 539:86 540:253 541:225 548:114 549:238 550:253 551:162 567:85 568:252 569:249 570:146 571:48 572:29 573:85 574:178 575:225 576:253 577:223 578:167 579:56 595:85 596:252 597:252 598:252 599:229 600:215 601:252 602:252 603:252 604:196 605:130 623:28 624:199 625:252 626:252 627:253 628:252 629:252 630:233 631:145 652:25 653:128 654:252 655:253 656:252 657:141 658:37
+1 159:124 160:253 161:255 162:63 186:96 187:244 188:251 189:253 190:62 214:127 215:251 216:251 217:253 218:62 241:68 242:236 243:251 244:211 245:31 246:8 268:60 269:228 270:251 271:251 272:94 296:155 297:253 298:253 299:189 323:20 324:253 325:251 326:235 327:66 350:32 351:205 352:253 353:251 354:126 378:104 379:251 380:253 381:184 382:15 405:80 406:240 407:251 408:193 409:23 432:32 433:253 434:253 435:253 436:159 460:151 461:251 462:251 463:251 464:39 487:48 488:221 489:251 490:251 491:172 515:234 516:251 517:251 518:196 519:12 543:253 544:251 545:251 546:89 570:159 571:255 572:253 573:253 574:31 597:48 598:228 599:253 600:247 601:140 602:8 625:64 626:251 627:253 628:220 653:64 654:251 655:253 656:220 681:24 682:193 683:253 684:220
+1 125:145 126:255 127:211 128:31 152:32 153:237 154:253 155:252 156:71 180:11 181:175 182:253 183:252 184:71 209:144 210:253 211:252 212:71 236:16 237:191 238:253 239:252 240:71 264:26 265:221 266:253 267:252 268:124 269:31 293:125 294:253 295:252 296:252 297:108 322:253 323:252 324:252 325:108 350:255 351:253 352:253 353:108 378:253 379:252 380:252 381:108 406:253 407:252 408:252 409:108 434:253 435:252 436:252 437:108 462:255 463:253 464:253 465:170 490:253 491:252 492:252 493:252 494:42 518:149 519:252 520:252 521:252 522:144 546:109 547:252 548:252 549:252 550:144 575:218 576:253 577:253 578:255 579:35 603:175 604:252 605:252 606:253 607:35 631:73 632:252 633:252 634:253 635:35 659:31 660:211 661:252 662:253 663:35
+1 153:5 154:63 155:197 181:20 182:254 183:230 184:24 209:20 210:254 211:254 212:48 237:20 238:254 239:255 240:48 265:20 266:254 267:254 268:57 293:20 294:254 295:254 296:108 321:16 322:239 323:254 324:143 350:178 351:254 352:143 378:178 379:254 380:143 406:178 407:254 408:162 434:178 435:254 436:240 462:113 463:254 464:240 490:83 491:254 492:245 493:31 518:79 519:254 520:246 521:38 547:214 548:254 549:150 575:144 576:241 577:8 603:144 604:240 605:2 631:144 632:254 633:82 659:230 660:247 661:40 687:168 688:209 689:31
+1 152:1 153:168 154:242 155:28 180:10 181:228 182:254 183:100 209:190 210:254 211:122 237:83 238:254 239:162 265:29 266:254 267:248 268:25 293:29 294:255 295:254 296:103 321:29 322:254 323:254 324:109 349:29 350:254 351:254 352:109 377:29 378:254 379:254 380:109 405:29 406:255 407:254 408:109 433:29 434:254 435:254 436:109 461:29 462:254 463:254 464:63 489:29 490:254 491:254 492:28 517:29 518:254 519:254 520:28 545:29 546:254 547:254 548:35 573:29 574:254 575:254 576:109 601:6 602:212 603:254 604:109 630:203 631:254 632:178 658:155 659:254 660:190 686:32 687:199 688:104
+0 130:64 131:253 132:255 133:63 157:96 158:205 159:251 160:253 161:205 162:111 163:4 184:96 185:189 186:251 187:251 188:253 189:251 190:251 191:31 209:16 210:64 211:223 212:244 213:251 214:251 215:211 216:213 217:251 218:251 219:31 236:80 237:181 238:251 239:253 240:251 241:251 242:251 243:94 244:96 245:251 246:251 247:31 263:92 264:253 265:253 266:253 267:255 268:253 269:253 270:253 271:95 272:96 273:253 274:253 275:31 290:92 291:236 292:251 293:243 294:220 295:233 296:251 297:251 298:243 299:82 300:96 301:251 302:251 303:31 317:80 318:253 319:251 320:251 321:188 323:96 324:251 325:251 326:109 328:96 329:251 330:251 331:31 344:96 345:240 346:253 347:243 348:188 349:42 351:96 352:204 353:109 354:4 356:12 357:197 358:251 359:31 372:221 373:251 374:253 375:121 379:36 380:23 385:190 386:251 387:31 399:48 400:234 401:253 413:191 414:253 415:31 426:44 427:221 428:251 429:251 440:12 441:197 442:251 443:31 454:190 455:251 456:251 457:251 468:96 469:251 470:251 471:31 482:190 483:251 484:251 485:113 495:40 496:234 497:251 498:219 499:23 510:190 511:251 512:251 513:94 522:40 523:217 524:253 525:231 526:47 538:191 539:253 540:253 541:253 548:12 549:174 550:253 551:253 552:219 553:39 566:67 567:236 568:251 569:251 570:191 571:190 572:111 573:72 574:190 575:191 576:197 577:251 578:243 579:121 580:39 595:63 596:236 597:251 598:253 599:251 600:251 601:251 602:251 603:253 604:251 605:188 606:94 624:27 625:129 626:253 627:251 628:251 629:251 630:251 631:229 632:168 633:15 654:95 655:212 656:251 657:211 658:94 659:59
+1 159:121 160:254 161:136 186:13 187:230 188:253 189:248 190:99 213:4 214:118 215:253 216:253 217:225 218:42 241:61 242:253 243:253 244:253 245:74 268:32 269:206 270:253 271:253 272:186 273:9 296:211 297:253 298:253 299:239 300:69 324:254 325:253 326:253 327:133 351:142 352:255 353:253 354:186 355:8 378:149 379:229 380:254 381:207 382:21 405:54 406:229 407:253 408:254 409:105 433:152 434:254 435:254 436:213 437:26 460:112 461:251 462:253 463:253 464:26 487:29 488:212 489:253 490:250 491:149 514:36 515:214 516:253 517:253 518:137 542:75 543:253 544:253 545:253 546:59 570:93 571:253 572:253 573:189 574:17 598:224 599:253 600:253 601:84 625:43 626:235 627:253 628:126 629:1 653:99 654:248 655:253 656:119 682:225 683:235 684:49
+1 100:166 101:222 102:55 128:197 129:254 130:218 131:5 155:29 156:249 157:254 158:254 159:9 183:45 184:254 185:254 186:174 187:2 210:4 211:164 212:254 213:254 214:85 238:146 239:254 240:254 241:254 242:85 265:101 266:245 267:254 268:254 269:254 270:85 292:97 293:248 294:254 295:204 296:254 297:254 298:85 315:12 316:59 317:98 318:151 319:237 320:254 321:254 322:109 323:35 324:254 325:254 326:85 343:41 344:216 345:254 346:254 347:239 348:153 349:37 350:4 351:32 352:254 353:254 354:85 372:7 373:44 374:44 375:30 379:32 380:254 381:254 382:96 407:19 408:230 409:254 410:174 436:197 437:254 438:110 464:197 465:254 466:85 492:197 493:253 494:63 515:37 516:54 517:54 518:45 519:26 520:84 521:221 522:84 523:21 524:31 525:162 526:78 540:6 541:41 542:141 543:244 544:254 545:254 546:248 547:236 548:254 549:254 550:254 551:233 552:239 553:254 554:138 567:23 568:167 569:254 570:254 571:254 572:254 573:229 574:228 575:185 576:138 577:138 578:138 579:138 580:138 581:138 582:44 595:113 596:254 597:254 598:254 599:179 600:64 601:5 623:32 624:209 625:183 626:97
+0 155:53 156:255 157:253 158:253 159:253 160:124 183:180 184:253 185:251 186:251 187:251 188:251 189:145 190:62 209:32 210:217 211:241 212:253 213:251 214:251 215:251 216:251 217:253 218:107 237:37 238:251 239:251 240:253 241:251 242:251 243:251 244:251 245:253 246:107 265:166 266:251 267:251 268:253 269:251 270:96 271:148 272:251 273:253 274:107 291:73 292:253 293:253 294:253 295:253 296:130 299:110 300:253 301:255 302:108 319:73 320:251 321:251 322:251 323:251 327:109 328:251 329:253 330:107 347:202 348:251 349:251 350:251 351:225 354:6 355:129 356:251 357:253 358:107 375:150 376:251 377:251 378:251 379:71 382:115 383:251 384:251 385:253 386:107 403:253 404:251 405:251 406:173 407:20 410:217 411:251 412:251 413:253 414:107 430:182 431:255 432:253 433:216 438:218 439:253 440:253 441:182 457:63 458:221 459:253 460:251 461:215 465:84 466:236 467:251 468:251 469:77 485:109 486:251 487:253 488:251 489:215 492:11 493:160 494:251 495:251 496:96 513:109 514:251 515:253 516:251 517:137 520:150 521:251 522:251 523:251 524:71 541:109 542:251 543:253 544:251 545:35 547:130 548:253 549:251 550:251 551:173 552:20 569:110 570:253 571:255 572:253 573:98 574:150 575:253 576:255 577:253 578:164 597:109 598:251 599:253 600:251 601:251 602:251 603:251 604:253 605:251 606:35 625:93 626:241 627:253 628:251 629:251 630:251 631:251 632:216 633:112 634:5 654:103 655:253 656:251 657:251 658:251 659:251 683:124 684:251 685:225 686:71 687:71
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diff --git a/data/mllib/sample_linear_regression_data.txt b/data/mllib/sample_linear_regression_data.txt
new file mode 100755
index 000000000000..9aaaa4297548
--- /dev/null
+++ b/data/mllib/sample_linear_regression_data.txt
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diff --git a/data/mllib/sample_movielens_data.txt b/data/mllib/sample_movielens_data.txt
new file mode 100644
index 000000000000..f0eee19875f7
--- /dev/null
+++ b/data/mllib/sample_movielens_data.txt
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diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/BinaryClassification.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/BinaryClassification.scala
index 4001908c9801..56b02b65d872 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/BinaryClassification.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/BinaryClassification.scala
@@ -29,8 +29,9 @@ import org.apache.spark.mllib.optimization.{SquaredL2Updater, L1Updater}
 /**
  * An example app for binary classification. Run with
  * {{{
- * ./bin/run-example org.apache.spark.examples.mllib.BinaryClassification
+ * bin/run-example org.apache.spark.examples.mllib.BinaryClassification
  * }}}
+ * A synthetic dataset is located at `data/mllib/sample_binary_classification_data.txt`.
  * If you use it as a template to create your own app, please use `spark-submit` to submit your app.
  */
 object BinaryClassification {
@@ -81,6 +82,15 @@ object BinaryClassification {
         .required()
         .text("input paths to labeled examples in LIBSVM format")
         .action((x, c) => c.copy(input = x))
+      note(
+        """
+          |For example, the following command runs this app on a synthetic dataset:
+          |
+          | bin/spark-submit --class org.apache.spark.examples.mllib.BinaryClassification \
+          |  examples/target/scala-*/spark-examples-*.jar \
+          |  --algorithm LR --regType L2 --regParam 1.0 \
+          |  data/mllib/sample_binary_classification_data.txt
+        """.stripMargin)
     }
 
     parser.parse(args, defaultParams).map { params =>
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/LinearRegression.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/LinearRegression.scala
index 658d370f8656..4811bb70e4b2 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/LinearRegression.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/LinearRegression.scala
@@ -28,8 +28,9 @@ import org.apache.spark.mllib.optimization.{SimpleUpdater, SquaredL2Updater, L1U
 /**
  * An example app for linear regression. Run with
  * {{{
- * ./bin/run-example org.apache.spark.examples.mllib.LinearRegression
+ * bin/run-example org.apache.spark.examples.mllib.LinearRegression
  * }}}
+ * A synthetic dataset can be found at `data/mllib/sample_linear_regression_data.txt`.
  * If you use it as a template to create your own app, please use `spark-submit` to submit your app.
  */
 object LinearRegression extends App {
@@ -68,6 +69,14 @@ object LinearRegression extends App {
       .required()
       .text("input paths to labeled examples in LIBSVM format")
       .action((x, c) => c.copy(input = x))
+    note(
+      """
+        |For example, the following command runs this app on a synthetic dataset:
+        |
+        | bin/spark-submit --class org.apache.spark.examples.mllib.LinearRegression \
+        |  examples/target/scala-*/spark-examples-*.jar \
+        |  data/mllib/sample_linear_regression_data.txt
+      """.stripMargin)
   }
 
   parser.parse(args, defaultParams).map { params =>
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/MovieLensALS.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/MovieLensALS.scala
index 0e4447e0de24..6eb41e7ba36f 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/MovieLensALS.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/MovieLensALS.scala
@@ -29,6 +29,12 @@ import org.apache.spark.serializer.{KryoSerializer, KryoRegistrator}
 
 /**
  * An example app for ALS on MovieLens data (http://grouplens.org/datasets/movielens/).
+ * Run with
+ * {{{
+ * bin/run-example org.apache.spark.examples.mllib.MovieLensALS
+ * }}}
+ * A synthetic dataset in MovieLens format can be found at `data/mllib/sample_movielens_data.txt`.
+ * If you use it as a template to create your own app, please use `spark-submit` to submit your app.
  */
 object MovieLensALS {
 
@@ -70,6 +76,15 @@ object MovieLensALS {
         .required()
         .text("input paths to a MovieLens dataset of ratings")
         .action((x, c) => c.copy(input = x))
+      note(
+        """
+          |For example, the following command runs this app on a synthetic dataset:
+          |
+          | bin/spark-submit --class org.apache.spark.examples.mllib.MovieLensALS \
+          |  examples/target/scala-*/spark-examples-*.jar \
+          |  --rank 5 --numIterations 20 --lambda 1.0 --kryo \
+          |  data/mllib/sample_movielens_data.txt
+        """.stripMargin)
     }
 
     parser.parse(args, defaultParams).map { params =>

From 52eb54d02403a3c37d84b9da7cc1cdb261048cf8 Mon Sep 17 00:00:00 2001
From: Tathagata Das <tathagata.das1565@gmail.com>
Date: Mon, 19 May 2014 22:36:24 -0700
Subject: [PATCH 245/810] [Spark 1877] ClassNotFoundException when loading RDD
 with serialized objects

Updated version of #821

Author: Tathagata Das <tathagata.das1565@gmail.com>
Author: Ghidireac <bogdang@u448a5b0a73d45358d94a.ant.amazon.com>

Closes #835 from tdas/SPARK-1877 and squashes the following commits:

f346f71 [Tathagata Das] Addressed Patrick's comments.
fee0c5d [Ghidireac] SPARK-1877: ClassNotFoundException when loading RDD with serialized objects
---
 core/src/main/scala/org/apache/spark/SparkContext.scala | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index 634c10c33fc8..49737fa4be56 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -718,7 +718,7 @@ class SparkContext(config: SparkConf) extends Logging {
       minPartitions: Int = defaultMinPartitions
       ): RDD[T] = {
     sequenceFile(path, classOf[NullWritable], classOf[BytesWritable], minPartitions)
-      .flatMap(x => Utils.deserialize[Array[T]](x._2.getBytes))
+      .flatMap(x => Utils.deserialize[Array[T]](x._2.getBytes, Utils.getContextOrSparkClassLoader))
   }
 
   protected[spark] def checkpointFile[T: ClassTag](

From 7f0cfe47f4709843d70ceccc25dee7551206ce0d Mon Sep 17 00:00:00 2001
From: Tathagata Das <tathagata.das1565@gmail.com>
Date: Tue, 20 May 2014 10:27:12 -0700
Subject: [PATCH 246/810] [Hotfix] Blacklisted flaky HiveCompatibility test

`lateral_view_outer` query sometimes returns a different set of 10 rows.

Author: Tathagata Das <tathagata.das1565@gmail.com>

Closes #838 from tdas/hive-test-fix2 and squashes the following commits:

9128a0d [Tathagata Das] Blacklisted flaky HiveCompatibility test.
---
 .../spark/sql/hive/execution/HiveCompatibilitySuite.scala   | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
index d83732b51e9c..9031abf733cd 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
@@ -169,7 +169,10 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     "archive_corrupt",
 
     // No support for case sensitivity is resolution using hive properties atm.
-    "case_sensitivity"
+    "case_sensitivity",
+
+    // Flaky test, Hive sometimes returns different set of 10 rows.
+    "lateral_view_outer"
   )
 
   /**
@@ -476,7 +479,6 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     "join_view",
     "lateral_view",
     "lateral_view_cp",
-    "lateral_view_outer",
     "lateral_view_ppd",
     "lineage1",
     "literal_double",

From 6e337380fc47071fc7fb28d744e8209c729fe1e9 Mon Sep 17 00:00:00 2001
From: Sumedh Mungee <smungee@gmail.com>
Date: Wed, 21 May 2014 01:22:25 -0700
Subject: [PATCH 247/810] [SPARK-1250] Fixed misleading comments in
 bin/pyspark, bin/spark-class

Fixed a couple of misleading comments in bin/pyspark and bin/spark-class. The comments make it seem like the script is looking for the Scala installation when in fact it is looking for Spark.

Author: Sumedh Mungee <smungee@gmail.com>

Closes #843 from smungee/spark-1250-fix-comments and squashes the following commits:

26870f3 [Sumedh Mungee] [SPARK-1250] Fixed misleading comments in bin/pyspark and bin/spark-class
---
 bin/pyspark     | 2 +-
 bin/spark-class | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/bin/pyspark b/bin/pyspark
index 3908ffe79939..d0fa56f31913 100755
--- a/bin/pyspark
+++ b/bin/pyspark
@@ -17,7 +17,7 @@
 # limitations under the License.
 #
 
-# Figure out where the Scala framework is installed
+# Figure out where Spark is installed
 FWDIR="$(cd `dirname $0`/..; pwd)"
 
 # Export this as SPARK_HOME
diff --git a/bin/spark-class b/bin/spark-class
index 2e57295fd023..e884511010c6 100755
--- a/bin/spark-class
+++ b/bin/spark-class
@@ -24,7 +24,7 @@ esac
 
 SCALA_VERSION=2.10
 
-# Figure out where the Scala framework is installed
+# Figure out where Spark is installed
 FWDIR="$(cd `dirname $0`/..; pwd)"
 
 # Export this as SPARK_HOME

From 1014668f2727863fe46f9c75201ee459d093bf0c Mon Sep 17 00:00:00 2001
From: Andrew Or <andrewor14@gmail.com>
Date: Wed, 21 May 2014 01:23:34 -0700
Subject: [PATCH 248/810] [Docs] Correct example of creating a new SparkConf

The example code on the configuration page currently does not compile.

Author: Andrew Or <andrewor14@gmail.com>

Closes #842 from andrewor14/conf-docs and squashes the following commits:

aabff57 [Andrew Or] Correct example of creating a new SparkConf
---
 docs/configuration.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/configuration.md b/docs/configuration.md
index f89040d71477..462a9d9013d9 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -23,7 +23,7 @@ cluster (e.g. master URL and application name), as well as arbitrary key-value p
 `set()` method. For example, we could initialize an application as follows:
 
 {% highlight scala %}
-val conf = new SparkConf
+val conf = new SparkConf()
              .setMaster("local")
              .setAppName("CountingSheep")
              .set("spark.executor.memory", "1g")

From 7c79ef7d43de258ad9a5de15c590132bd78ce8dd Mon Sep 17 00:00:00 2001
From: Andrew Or <andrewor14@gmail.com>
Date: Wed, 21 May 2014 01:25:10 -0700
Subject: [PATCH 249/810] [Minor] Move JdbcRDDSuite to the correct package

It was in the wrong package

Author: Andrew Or <andrewor14@gmail.com>

Closes #839 from andrewor14/jdbc-suite and squashes the following commits:

f948c5a [Andrew Or] cache -> cache()
b215279 [Andrew Or] Move JdbcRDDSuite to the correct package
---
 .../scala/org/apache/spark/rdd/JdbcRDDSuite.scala    | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/core/src/test/scala/org/apache/spark/rdd/JdbcRDDSuite.scala b/core/src/test/scala/org/apache/spark/rdd/JdbcRDDSuite.scala
index 7c7f69b261a0..76e317d754ba 100644
--- a/core/src/test/scala/org/apache/spark/rdd/JdbcRDDSuite.scala
+++ b/core/src/test/scala/org/apache/spark/rdd/JdbcRDDSuite.scala
@@ -15,13 +15,13 @@
  * limitations under the License.
  */
 
-package org.apache.spark
+package org.apache.spark.rdd
 
 import java.sql._
 
 import org.scalatest.{BeforeAndAfter, FunSuite}
 
-import org.apache.spark.rdd.JdbcRDD
+import org.apache.spark.{LocalSparkContext, SparkContext}
 
 class JdbcRDDSuite extends FunSuite with BeforeAndAfter with LocalSparkContext {
 
@@ -35,18 +35,18 @@ class JdbcRDDSuite extends FunSuite with BeforeAndAfter with LocalSparkContext {
           ID INTEGER NOT NULL GENERATED ALWAYS AS IDENTITY (START WITH 1, INCREMENT BY 1),
           DATA INTEGER
         )""")
-      create.close
+      create.close()
       val insert = conn.prepareStatement("INSERT INTO FOO(DATA) VALUES(?)")
       (1 to 100).foreach { i =>
         insert.setInt(1, i * 2)
         insert.executeUpdate
       }
-      insert.close
+      insert.close()
     } catch {
       case e: SQLException if e.getSQLState == "X0Y32" =>
         // table exists
     } finally {
-      conn.close
+      conn.close()
     }
   }
 
@@ -57,7 +57,7 @@ class JdbcRDDSuite extends FunSuite with BeforeAndAfter with LocalSparkContext {
       () => { DriverManager.getConnection("jdbc:derby:target/JdbcRDDSuiteDb") },
       "SELECT DATA FROM FOO WHERE ? <= ID AND ID <= ?",
       1, 100, 3,
-      (r: ResultSet) => { r.getInt(1) } ).cache
+      (r: ResultSet) => { r.getInt(1) } ).cache()
 
     assert(rdd.count === 100)
     assert(rdd.reduce(_+_) === 10100)

From ba5d4a99425a2083fea2a9759050c5e770197e23 Mon Sep 17 00:00:00 2001
From: Andrew Or <andrewor14@gmail.com>
Date: Wed, 21 May 2014 11:59:05 -0700
Subject: [PATCH 250/810] [Typo] Stoped -> Stopped

Author: Andrew Or <andrewor14@gmail.com>

Closes #847 from andrewor14/yarn-typo and squashes the following commits:

c1906af [Andrew Or] Stoped -> Stopped
---
 .../spark/scheduler/cluster/YarnClientSchedulerBackend.scala    | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala b/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala
index 2924189077b7..0ac162538fc4 100644
--- a/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala
+++ b/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala
@@ -113,7 +113,7 @@ private[spark] class YarnClientSchedulerBackend(
   override def stop() {
     super.stop()
     client.stop()
-    logInfo("Stoped")
+    logInfo("Stopped")
   }
 
 }

From f18fd05b513b136363c94adb3e5b841f8bf48134 Mon Sep 17 00:00:00 2001
From: Kan Zhang <kzhang@apache.org>
Date: Wed, 21 May 2014 13:26:53 -0700
Subject: [PATCH 251/810] [SPARK-1519] Support minPartitions param of
 wholeTextFiles() in PySpark

Author: Kan Zhang <kzhang@apache.org>

Closes #697 from kanzhang/SPARK-1519 and squashes the following commits:

4f8d1ed [Kan Zhang] [SPARK-1519] Support minPartitions param of wholeTextFiles() in PySpark
---
 python/pyspark/context.py | 12 ++++++++++--
 1 file changed, 10 insertions(+), 2 deletions(-)

diff --git a/python/pyspark/context.py b/python/pyspark/context.py
index cac133d0fcf6..c9ff82d23b3c 100644
--- a/python/pyspark/context.py
+++ b/python/pyspark/context.py
@@ -211,6 +211,13 @@ def defaultParallelism(self):
         """
         return self._jsc.sc().defaultParallelism()
 
+    @property
+    def defaultMinPartitions(self):
+        """
+        Default min number of partitions for Hadoop RDDs when not given by user
+        """
+        return self._jsc.sc().defaultMinPartitions()
+
     def __del__(self):
         self.stop()
 
@@ -264,7 +271,7 @@ def textFile(self, name, minPartitions=None):
         return RDD(self._jsc.textFile(name, minPartitions), self,
                    UTF8Deserializer())
 
-    def wholeTextFiles(self, path):
+    def wholeTextFiles(self, path, minPartitions=None):
         """
         Read a directory of text files from HDFS, a local file system
         (available on all nodes), or any  Hadoop-supported file system
@@ -300,7 +307,8 @@ def wholeTextFiles(self, path):
         >>> sorted(textFiles.collect())
         [(u'.../1.txt', u'1'), (u'.../2.txt', u'2')]
         """
-        return RDD(self._jsc.wholeTextFiles(path), self,
+        minPartitions = minPartitions or self.defaultMinPartitions
+        return RDD(self._jsc.wholeTextFiles(path, minPartitions), self,
                    PairDeserializer(UTF8Deserializer(), UTF8Deserializer()))
 
     def _checkpointFile(self, name, input_deserializer):

From bb88875ad52e8209c25e8350af1fe4b7159086ae Mon Sep 17 00:00:00 2001
From: Takuya UESHIN <ueshin@happy-camper.st>
Date: Wed, 21 May 2014 15:37:47 -0700
Subject: [PATCH 252/810] [SPARK-1889] [SQL] Apply splitConjunctivePredicates
 to join condition while finding join ke...

...ys.

When tables are equi-joined by multiple-keys `HashJoin` should be used, but `CartesianProduct` and then `Filter` are used.
The join keys are paired by `And` expression so we need to apply `splitConjunctivePredicates` to join condition while finding join keys.

Author: Takuya UESHIN <ueshin@happy-camper.st>

Closes #836 from ueshin/issues/SPARK-1889 and squashes the following commits:

fe1c387 [Takuya UESHIN] Apply splitConjunctivePredicates to join condition while finding join keys.
---
 .../sql/catalyst/planning/patterns.scala      | 11 ++++++-----
 .../spark/sql/execution/PlannerSuite.scala    | 19 ++++++++++++++++++-
 2 files changed, 24 insertions(+), 6 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala
index 0e3a8a6bd30a..4544b32958c7 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala
@@ -129,11 +129,12 @@ object HashFilteredJoin extends Logging with PredicateHelper {
   // as join keys.
   def splitPredicates(allPredicates: Seq[Expression], join: Join): Option[ReturnType] = {
     val Join(left, right, joinType, _) = join
-    val (joinPredicates, otherPredicates) = allPredicates.partition {
-      case Equals(l, r) if (canEvaluate(l, left) && canEvaluate(r, right)) ||
-        (canEvaluate(l, right) && canEvaluate(r, left)) => true
-      case _ => false
-    }
+    val (joinPredicates, otherPredicates) =
+      allPredicates.flatMap(splitConjunctivePredicates).partition {
+        case Equals(l, r) if (canEvaluate(l, left) && canEvaluate(r, right)) ||
+          (canEvaluate(l, right) && canEvaluate(r, left)) => true
+        case _ => false
+      }
 
     val joinKeys = joinPredicates.map {
       case Equals(l, r) if canEvaluate(l, left) && canEvaluate(r, right) => (l, r)
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala
index e24c74a7a557..c563d6362754 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala
@@ -21,7 +21,7 @@ import org.scalatest.FunSuite
 
 import org.apache.spark.sql.TestData._
 import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.catalyst.plans.logical
+import org.apache.spark.sql.catalyst.plans._
 import org.apache.spark.sql.execution
 import org.apache.spark.sql.test.TestSQLContext._
 import org.apache.spark.sql.test.TestSQLContext.planner._
@@ -57,4 +57,21 @@ class PlannerSuite extends FunSuite {
     val planned = PartialAggregation(query)
     assert(planned.isEmpty)
   }
+
+  test("equi-join is hash-join") {
+    val x = testData2.as('x)
+    val y = testData2.as('y)
+    val join = x.join(y, Inner, Some("x.a".attr === "y.a".attr)).queryExecution.analyzed
+    val planned = planner.HashJoin(join)
+    assert(planned.size === 1)
+  }
+
+  test("multiple-key equi-join is hash-join") {
+    val x = testData2.as('x)
+    val y = testData2.as('y)
+    val join = x.join(y, Inner,
+      Some("x.a".attr === "y.a".attr && "x.b".attr === "y.b".attr)).queryExecution.analyzed
+    val planned = planner.HashJoin(join)
+    assert(planned.size === 1)
+  }
 }

From 2a948e7e1a345ae4e3d89ea24123410819d799d1 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Wed, 21 May 2014 18:49:12 -0700
Subject: [PATCH 253/810] Configuration documentation updates

1. Add < code > to configuration options
2. List env variables in tabular format to be consistent with other pages.
3. Moved Viewing Spark Properties section up.

This is against branch-1.0, but should be cherry picked into master as well.

Author: Reynold Xin <rxin@apache.org>

Closes #851 from rxin/doc-config and squashes the following commits:

28ac0d3 [Reynold Xin] Add <code> to configuration options, and list env variables in a table.

(cherry picked from commit 75af8bd3336d09e8c691e54ae9d2358fe1bf3723)
Signed-off-by: Reynold Xin <rxin@apache.org>
---
 docs/configuration.md | 194 +++++++++++++++++++++++-------------------
 1 file changed, 105 insertions(+), 89 deletions(-)

diff --git a/docs/configuration.md b/docs/configuration.md
index 462a9d9013d9..4d41c36e38e2 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -47,6 +47,13 @@ Any values specified in the file will be passed on to the application, and merge
 specified through SparkConf. If the same configuration property exists in both `spark-defaults.conf`
 and SparkConf, then the latter will take precedence as it is the most application-specific.
 
+## Viewing Spark Properties
+
+The application web UI at `http://<driver>:4040` lists Spark properties in the "Environment" tab.
+This is a useful place to check to make sure that your properties have been set correctly. Note
+that only values explicitly specified through either `spark-defaults.conf` or SparkConf will
+appear. For all other configuration properties, you can assume the default value is used.
+
 ## All Configuration Properties
 
 Most of the properties that control internal settings have reasonable default values. However,
@@ -55,14 +62,14 @@ there are at least five properties that you will commonly want to control:
 <table class="table">
 <tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr>
 <tr>
-  <td>spark.executor.memory</td>
+  <td><code>spark.executor.memory</code></td>
   <td>512m</td>
   <td>
     Amount of memory to use per executor process, in the same format as JVM memory strings (e.g. <code>512m</code>, <code>2g</code>).
   </td>
 </tr>
 <tr>
-  <td>spark.serializer</td>
+  <td><code>spark.serializer</code></td>
   <td>org.apache.spark.serializer.<br />JavaSerializer</td>
   <td>
     Class to use for serializing objects that will be sent over the network or need to be cached
@@ -73,7 +80,7 @@ there are at least five properties that you will commonly want to control:
   </td>
 </tr>
 <tr>
-  <td>spark.kryo.registrator</td>
+  <td><code>spark.kryo.registrator</code></td>
   <td>(none)</td>
   <td>
     If you use Kryo serialization, set this class to register your custom classes with Kryo.
@@ -83,7 +90,7 @@ there are at least five properties that you will commonly want to control:
   </td>
 </tr>
 <tr>
-  <td>spark.local.dir</td>
+  <td><code>spark.local.dir</code></td>
   <td>/tmp</td>
   <td>
     Directory to use for "scratch" space in Spark, including map output files and RDDs that get stored
@@ -95,7 +102,7 @@ there are at least five properties that you will commonly want to control:
   </td>
 </tr>
 <tr>
-  <td>spark.cores.max</td>
+  <td><code>spark.cores.max</code></td>
   <td>(not set)</td>
   <td>
     When running on a <a href="spark-standalone.html">standalone deploy cluster</a> or a
@@ -114,7 +121,7 @@ Apart from these, the following properties are also available, and may be useful
 <table class="table">
 <tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr>
 <tr>
-  <td>spark.default.parallelism</td>
+  <td><code>spark.default.parallelism</code></td>
   <td>
     <ul>
       <li>Local mode: number of cores on the local machine</li>
@@ -128,7 +135,7 @@ Apart from these, the following properties are also available, and may be useful
   </td>
 </tr>
 <tr>
-  <td>spark.storage.memoryFraction</td>
+  <td><code>spark.storage.memoryFraction</code></td>
   <td>0.6</td>
   <td>
     Fraction of Java heap to use for Spark's memory cache. This should not be larger than the "old"
@@ -137,7 +144,7 @@ Apart from these, the following properties are also available, and may be useful
   </td>
 </tr>
 <tr>
-  <td>spark.shuffle.memoryFraction</td>
+  <td><code>spark.shuffle.memoryFraction</code></td>
   <td>0.3</td>
   <td>
     Fraction of Java heap to use for aggregation and cogroups during shuffles, if
@@ -148,7 +155,7 @@ Apart from these, the following properties are also available, and may be useful
   </td>
 </tr>
 <tr>
-  <td>spark.storage.memoryMapThreshold</td>
+  <td><code>spark.storage.memoryMapThreshold</code></td>
   <td>8192</td>
   <td>
     Size of a block, in bytes, above which Spark memory maps when reading a block from disk.
@@ -157,7 +164,7 @@ Apart from these, the following properties are also available, and may be useful
   </td>
 </tr>
 <tr>
-  <td>spark.tachyonStore.baseDir</td>
+  <td><code>spark.tachyonStore.baseDir</code></td>
   <td>System.getProperty("java.io.tmpdir")</td>
   <td>
     Directories of the Tachyon File System that store RDDs. The Tachyon file system's URL is set by <code>spark.tachyonStore.url</code>.
@@ -165,14 +172,14 @@ Apart from these, the following properties are also available, and may be useful
   </td>
 </tr>
 <tr>
-  <td>spark.tachyonStore.url</td>
+  <td><code>spark.tachyonStore.url</code></td>
   <td>tachyon://localhost:19998</td>
   <td>
     The URL of the underlying Tachyon file system in the TachyonStore.
   </td>
 </tr>
 <tr>
-  <td>spark.mesos.coarse</td>
+  <td><code>spark.mesos.coarse</code></td>
   <td>false</td>
   <td>
     If set to "true", runs over Mesos clusters in
@@ -183,21 +190,21 @@ Apart from these, the following properties are also available, and may be useful
   </td>
 </tr>
 <tr>
-  <td>spark.ui.port</td>
+  <td><code>spark.ui.port</code></td>
   <td>4040</td>
   <td>
     Port for your application's dashboard, which shows memory and workload data
   </td>
 </tr>
 <tr>
-  <td>spark.ui.retainedStages</td>
+  <td><code>spark.ui.retainedStages</code></td>
   <td>1000</td>
   <td>
     How many stages the Spark UI remembers before garbage collecting.
   </td>
 </tr>
 <tr>
-  <td>spark.ui.filters</td>
+  <td><code>spark.ui.filters</code></td>
   <td>None</td>
   <td>
     Comma separated list of filter class names to apply to the Spark web ui. The filter should be a
@@ -207,7 +214,7 @@ Apart from these, the following properties are also available, and may be useful
   </td>
 </tr>
 <tr>
-  <td>spark.ui.acls.enable</td>
+  <td><code>spark.ui.acls.enable</code></td>
   <td>false</td>
   <td>
     Whether spark web ui acls should are enabled. If enabled, this checks to see if the user has
@@ -217,7 +224,7 @@ Apart from these, the following properties are also available, and may be useful
   </td>
 </tr>
 <tr>
-  <td>spark.ui.view.acls</td>
+  <td><code>spark.ui.view.acls</code></td>
   <td>Empty</td>
   <td>
     Comma separated list of users that have view access to the spark web ui. By default only the
@@ -225,35 +232,35 @@ Apart from these, the following properties are also available, and may be useful
   </td>
 </tr>
 <tr>
-  <td>spark.ui.killEnabled</td>
+  <td><code>spark.ui.killEnabled</code></td>
   <td>true</td>
   <td>
     Allows stages and corresponding jobs to be killed from the web ui.
   </td>
 </tr>
 <tr>
-  <td>spark.shuffle.compress</td>
+  <td><code>spark.shuffle.compress</code></td>
   <td>true</td>
   <td>
     Whether to compress map output files. Generally a good idea.
   </td>
 </tr>
 <tr>
-  <td>spark.shuffle.spill.compress</td>
+  <td><code>spark.shuffle.spill.compress</code></td>
   <td>true</td>
   <td>
     Whether to compress data spilled during shuffles.
   </td>
 </tr>
 <tr>
-  <td>spark.broadcast.compress</td>
+  <td><code>spark.broadcast.compress</code></td>
   <td>true</td>
   <td>
     Whether to compress broadcast variables before sending them. Generally a good idea.
   </td>
 </tr>
 <tr>
-  <td>spark.rdd.compress</td>
+  <td><code>spark.rdd.compress</code></td>
   <td>false</td>
   <td>
     Whether to compress serialized RDD partitions (e.g. for <code>StorageLevel.MEMORY_ONLY_SER</code>).
@@ -261,7 +268,7 @@ Apart from these, the following properties are also available, and may be useful
   </td>
 </tr>
 <tr>
-  <td>spark.io.compression.codec</td>
+  <td><code>spark.io.compression.codec</code></td>
   <td>org.apache.spark.io.<br />LZFCompressionCodec</td>
   <td>
     The codec used to compress internal data such as RDD partitions and shuffle outputs. By default, Spark provides two
@@ -269,14 +276,14 @@ Apart from these, the following properties are also available, and may be useful
   </td>
 </tr>
 <tr>
-  <td>spark.io.compression.snappy.block.size</td>
+  <td><code>spark.io.compression.snappy.block.size</code></td>
   <td>32768</td>
   <td>
     Block size (in bytes) used in Snappy compression, in the case when Snappy compression codec is used.
   </td>
 </tr>
 <tr>
-  <td>spark.scheduler.mode</td>
+  <td><code>spark.scheduler.mode</code></td>
   <td>FIFO</td>
   <td>
     The <a href="job-scheduling.html#scheduling-within-an-application">scheduling mode</a> between
@@ -286,14 +293,14 @@ Apart from these, the following properties are also available, and may be useful
   </td>
 </tr>
 <tr>
-  <td>spark.scheduler.revive.interval</td>
+  <td><code>spark.scheduler.revive.interval</code></td>
   <td>1000</td>
   <td>
     The interval length for the scheduler to revive the worker resource offers to run tasks. (in milliseconds)
   </td>
 </tr>
 <tr>
-  <td>spark.reducer.maxMbInFlight</td>
+  <td><code>spark.reducer.maxMbInFlight</code></td>
   <td>48</td>
   <td>
     Maximum size (in megabytes) of map outputs to fetch simultaneously from each reduce task. Since
@@ -302,14 +309,14 @@ Apart from these, the following properties are also available, and may be useful
   </td>
 </tr>
 <tr>
-  <td>spark.closure.serializer</td>
+  <td><code>spark.closure.serializer</code></td>
   <td>org.apache.spark.serializer.<br />JavaSerializer</td>
   <td>
     Serializer class to use for closures. Currently only the Java serializer is supported.
   </td>
 </tr>
 <tr>
-  <td>spark.kryo.referenceTracking</td>
+  <td><code>spark.kryo.referenceTracking</code></td>
   <td>true</td>
   <td>
     Whether to track references to the same object when serializing data with Kryo, which is
@@ -319,7 +326,7 @@ Apart from these, the following properties are also available, and may be useful
   </td>
 </tr>
 <tr>
-  <td>spark.kryoserializer.buffer.mb</td>
+  <td><code>spark.kryoserializer.buffer.mb</code></td>
   <td>2</td>
   <td>
     Maximum object size to allow within Kryo (the library needs to create a buffer at least as
@@ -328,7 +335,7 @@ Apart from these, the following properties are also available, and may be useful
   </td>
 </tr>
 <tr>
-  <td>spark.serializer.objectStreamReset</td>
+  <td><code>spark.serializer.objectStreamReset</code></td>
   <td>10000</td>
   <td>
     When serializing using org.apache.spark.serializer.JavaSerializer, the serializer caches
@@ -339,14 +346,14 @@ Apart from these, the following properties are also available, and may be useful
   </td>
 </tr>
 <tr>
-  <td>spark.broadcast.factory</td>
+  <td><code>spark.broadcast.factory</code></td>
   <td>org.apache.spark.broadcast.<br />HttpBroadcastFactory</td>
   <td>
     Which broadcast implementation to use.
   </td>
 </tr>
 <tr>
-  <td>spark.locality.wait</td>
+  <td><code>spark.locality.wait</code></td>
   <td>3000</td>
   <td>
     Number of milliseconds to wait to launch a data-local task before giving up and launching it
@@ -358,7 +365,7 @@ Apart from these, the following properties are also available, and may be useful
   </td>
 </tr>
 <tr>
-  <td>spark.locality.wait.process</td>
+  <td><code>spark.locality.wait.process</code></td>
   <td>spark.locality.wait</td>
   <td>
     Customize the locality wait for process locality. This affects tasks that attempt to access
@@ -366,7 +373,7 @@ Apart from these, the following properties are also available, and may be useful
   </td>
 </tr>
 <tr>
-  <td>spark.locality.wait.node</td>
+  <td><code>spark.locality.wait.node</code></td>
   <td>spark.locality.wait</td>
   <td>
     Customize the locality wait for node locality. For example, you can set this to 0 to skip
@@ -374,14 +381,14 @@ Apart from these, the following properties are also available, and may be useful
   </td>
 </tr>
 <tr>
-  <td>spark.locality.wait.rack</td>
+  <td><code>spark.locality.wait.rack</code></td>
   <td>spark.locality.wait</td>
   <td>
     Customize the locality wait for rack locality.
   </td>
 </tr>
 <tr>
-  <td>spark.worker.timeout</td>
+  <td><code>spark.worker.timeout</code></td>
   <td>60</td>
   <td>
     Number of seconds after which the standalone deploy master considers a worker lost if it
@@ -389,7 +396,7 @@ Apart from these, the following properties are also available, and may be useful
   </td>
 </tr>
 <tr>
-  <td>spark.worker.cleanup.enabled</td>
+  <td><code>spark.worker.cleanup.enabled</code></td>
   <td>false</td>
   <td>
     Enable periodic cleanup of worker / application directories.  Note that this only affects standalone
@@ -398,7 +405,7 @@ Apart from these, the following properties are also available, and may be useful
   </td>
 </tr>
 <tr>
-  <td>spark.worker.cleanup.interval</td>
+  <td><code>spark.worker.cleanup.interval</code></td>
   <td>1800 (30 minutes)</td>
   <td>
     Controls the interval, in seconds, at which the worker cleans up old application work dirs
@@ -406,7 +413,7 @@ Apart from these, the following properties are also available, and may be useful
   </td>
 </tr>
 <tr>
-  <td>spark.worker.cleanup.appDataTtl</td>
+  <td><code>spark.worker.cleanup.appDataTtl</code></td>
   <td>7 * 24 * 3600 (7 days)</td>
   <td>
     The number of seconds to retain application work directories on each worker.  This is a Time To Live
@@ -416,7 +423,7 @@ Apart from these, the following properties are also available, and may be useful
   </td>
 </tr>
 <tr>
-  <td>spark.akka.frameSize</td>
+  <td><code>spark.akka.frameSize</code></td>
   <td>10</td>
   <td>
     Maximum message size to allow in "control plane" communication (for serialized tasks and task
@@ -425,7 +432,7 @@ Apart from these, the following properties are also available, and may be useful
   </td>
 </tr>
 <tr>
-  <td>spark.akka.threads</td>
+  <td><code>spark.akka.threads</code></td>
   <td>4</td>
   <td>
     Number of actor threads to use for communication. Can be useful to increase on large clusters
@@ -433,49 +440,49 @@ Apart from these, the following properties are also available, and may be useful
   </td>
 </tr>
 <tr>
-  <td>spark.akka.timeout</td>
+  <td><code>spark.akka.timeout</code></td>
   <td>100</td>
   <td>
     Communication timeout between Spark nodes, in seconds.
   </td>
 </tr>
 <tr>
-  <td>spark.akka.heartbeat.pauses</td>
+  <td><code>spark.akka.heartbeat.pauses</code></td>
   <td>600</td>
   <td>
      This is set to a larger value to disable failure detector that comes inbuilt akka. It can be enabled again, if you plan to use this feature (Not recommended). Acceptable heart beat pause in seconds for akka. This can be used to control sensitivity to gc pauses. Tune this in combination of `spark.akka.heartbeat.interval` and `spark.akka.failure-detector.threshold` if you need to.
   </td>
 </tr>
 <tr>
-  <td>spark.akka.failure-detector.threshold</td>
+  <td><code>spark.akka.failure-detector.threshold</code></td>
   <td>300.0</td>
   <td>
      This is set to a larger value to disable failure detector that comes inbuilt akka. It can be enabled again, if you plan to use this feature (Not recommended). This maps to akka's `akka.remote.transport-failure-detector.threshold`. Tune this in combination of `spark.akka.heartbeat.pauses` and `spark.akka.heartbeat.interval` if you need to.
   </td>
 </tr>
 <tr>
-  <td>spark.akka.heartbeat.interval</td>
+  <td><code>spark.akka.heartbeat.interval</code></td>
   <td>1000</td>
   <td>
     This is set to a larger value to disable failure detector that comes inbuilt akka. It can be enabled again, if you plan to use this feature (Not recommended). A larger interval value in seconds reduces network overhead and a smaller value ( ~ 1 s) might be more informative for akka's failure detector. Tune this in combination of `spark.akka.heartbeat.pauses` and `spark.akka.failure-detector.threshold` if you need to. Only positive use case for using failure detector can be, a sensistive failure detector can help evict rogue executors really quick. However this is usually not the case as gc pauses and network lags are expected in a real spark cluster. Apart from that enabling this leads to a lot of exchanges of heart beats between nodes leading to flooding the network with those.
   </td>
 </tr>
 <tr>
-  <td>spark.driver.host</td>
+  <td><code>spark.driver.host</code></td>
   <td>(local hostname)</td>
   <td>
     Hostname or IP address for the driver to listen on.
   </td>
 </tr>
 <tr>
-  <td>spark.driver.port</td>
+  <td><code>spark.driver.port</code></td>
   <td>(random)</td>
   <td>
     Port for the driver to listen on.
   </td>
 </tr>
 <tr>
-  <td>spark.cleaner.ttl</td>
+  <td><code>spark.cleaner.ttl</code></td>
   <td>(infinite)</td>
   <td>
     Duration (seconds) of how long Spark will remember any metadata (stages generated, tasks generated, etc.).
@@ -485,7 +492,7 @@ Apart from these, the following properties are also available, and may be useful
   </td>
 </tr>
 <tr>
-  <td>spark.streaming.blockInterval</td>
+  <td><code>spark.streaming.blockInterval</code></td>
   <td>200</td>
   <td>
     Interval (milliseconds) at which data received by Spark Streaming receivers is coalesced
@@ -493,7 +500,7 @@ Apart from these, the following properties are also available, and may be useful
   </td>
 </tr>
 <tr>
-  <td>spark.streaming.unpersist</td>
+  <td><code>spark.streaming.unpersist</code></td>
   <td>true</td>
   <td>
     Force RDDs generated and persisted by Spark Streaming to be automatically unpersisted from
@@ -504,7 +511,7 @@ Apart from these, the following properties are also available, and may be useful
   </td>
 </tr>
 <tr>
-  <td>spark.task.maxFailures</td>
+  <td><code>spark.task.maxFailures</code></td>
   <td>4</td>
   <td>
     Number of individual task failures before giving up on the job.
@@ -512,7 +519,7 @@ Apart from these, the following properties are also available, and may be useful
   </td>
 </tr>
 <tr>
-  <td>spark.broadcast.blockSize</td>
+  <td><code>spark.broadcast.blockSize</code></td>
   <td>4096</td>
   <td>
     Size of each piece of a block in kilobytes for <code>TorrentBroadcastFactory</code>.
@@ -521,14 +528,14 @@ Apart from these, the following properties are also available, and may be useful
 </tr>
 
 <tr>
-  <td>spark.shuffle.consolidateFiles</td>
+  <td><code>spark.shuffle.consolidateFiles</code></td>
   <td>false</td>
   <td>
     If set to "true", consolidates intermediate files created during a shuffle. Creating fewer files can improve filesystem performance for shuffles with large numbers of reduce tasks. It is recommended to set this to "true" when using ext4 or xfs filesystems. On ext3, this option might degrade performance on machines with many (>8) cores due to filesystem limitations.
   </td>
 </tr>
 <tr>
-  <td>spark.shuffle.file.buffer.kb</td>
+  <td><code>spark.shuffle.file.buffer.kb</code></td>
   <td>100</td>
   <td>
     Size of the in-memory buffer for each shuffle file output stream, in kilobytes. These buffers
@@ -536,7 +543,7 @@ Apart from these, the following properties are also available, and may be useful
   </td>
 </tr>
 <tr>
-  <td>spark.shuffle.spill</td>
+  <td><code>spark.shuffle.spill</code></td>
   <td>true</td>
   <td>
     If set to "true", limits the amount of memory used during reduces by spilling data out to disk. This spilling
@@ -544,56 +551,56 @@ Apart from these, the following properties are also available, and may be useful
   </td>
 </tr>
 <tr>
-  <td>spark.speculation</td>
+  <td><code>spark.speculation</code></td>
   <td>false</td>
   <td>
     If set to "true", performs speculative execution of tasks. This means if one or more tasks are running slowly in a stage, they will be re-launched.
   </td>
 </tr>
 <tr>
-  <td>spark.speculation.interval</td>
+  <td><code>spark.speculation.interval</code></td>
   <td>100</td>
   <td>
     How often Spark will check for tasks to speculate, in milliseconds.
   </td>
 </tr>
 <tr>
-  <td>spark.speculation.quantile</td>
+  <td><code>spark.speculation.quantile</code></td>
   <td>0.75</td>
   <td>
     Percentage of tasks which must be complete before speculation is enabled for a particular stage.
   </td>
 </tr>
 <tr>
-  <td>spark.speculation.multiplier</td>
+  <td><code>spark.speculation.multiplier</code></td>
   <td>1.5</td>
   <td>
     How many times slower a task is than the median to be considered for speculation.
   </td>
 </tr>
 <tr>
-  <td>spark.logConf</td>
+  <td><code>spark.logConf</code></td>
   <td>false</td>
   <td>
     Whether to log the supplied SparkConf as INFO at start of spark context.
   </td>
 </tr>
 <tr>
-  <td>spark.eventLog.enabled</td>
+  <td><code>spark.eventLog.enabled</code></td>
   <td>false</td>
   <td>
     Whether to log spark events, useful for reconstructing the Web UI after the application has finished.
   </td>
 </tr>
 <tr>
-  <td>spark.eventLog.compress</td>
+  <td><code>spark.eventLog.compress</code></td>
   <td>false</td>
   <td>
     Whether to compress logged events, if <code>spark.eventLog.enabled</code> is true.
   </td>
 </tr>
 <tr>
-  <td>spark.eventLog.dir</td>
+  <td><code>spark.eventLog.dir</code></td>
   <td>file:///tmp/spark-events</td>
   <td>
     Base directory in which spark events are logged, if <code>spark.eventLog.enabled</code> is true.
@@ -602,7 +609,7 @@ Apart from these, the following properties are also available, and may be useful
   </td>
 </tr>
 <tr>
-  <td>spark.deploy.spreadOut</td>
+  <td><code>spark.deploy.spreadOut</code></td>
   <td>true</td>
   <td>
     Whether the standalone cluster manager should spread applications out across nodes or try
@@ -613,7 +620,7 @@ Apart from these, the following properties are also available, and may be useful
   </td>
 </tr>
 <tr>
-  <td>spark.deploy.defaultCores</td>
+  <td><code>spark.deploy.defaultCores</code></td>
   <td>(infinite)</td>
   <td>
     Default number of cores to give to applications in Spark's standalone mode if they don't
@@ -626,14 +633,14 @@ Apart from these, the following properties are also available, and may be useful
   </td>
 </tr>
 <tr>
-  <td>spark.files.overwrite</td>
+  <td><code>spark.files.overwrite</code></td>
   <td>false</td>
   <td>
     Whether to overwrite files added through SparkContext.addFile() when the target file exists and its contents do not match those of the source.
   </td>
 </tr>
 <tr>
-  <td>spark.files.fetchTimeout</td>
+  <td><code>spark.files.fetchTimeout</code></td>
   <td>false</td>
   <td>
     Communication timeout to use when fetching files added through SparkContext.addFile() from
@@ -641,7 +648,7 @@ Apart from these, the following properties are also available, and may be useful
   </td>
 </tr>
 <tr>
-  <td>spark.files.userClassPathFirst</td>
+  <td><code>spark.files.userClassPathFirst</code></td>
   <td>false</td>
   <td>
     (Experimental) Whether to give user-added jars precedence over Spark's own jars when
@@ -650,7 +657,7 @@ Apart from these, the following properties are also available, and may be useful
   </td>
 </tr>
 <tr>
-  <td>spark.authenticate</td>
+  <td><code>spark.authenticate</code></td>
   <td>false</td>
   <td>
     Whether spark authenticates its internal connections. See <code>spark.authenticate.secret</code> if not
@@ -658,7 +665,7 @@ Apart from these, the following properties are also available, and may be useful
   </td>
 </tr>
 <tr>
-  <td>spark.authenticate.secret</td>
+  <td><code>spark.authenticate.secret</code></td>
   <td>None</td>
   <td>
     Set the secret key used for Spark to authenticate between components. This needs to be set if
@@ -666,7 +673,7 @@ Apart from these, the following properties are also available, and may be useful
   </td>
 </tr>
 <tr>
-  <td>spark.core.connection.auth.wait.timeout</td>
+  <td><code>spark.core.connection.auth.wait.timeout</code></td>
   <td>30</td>
   <td>
     Number of seconds for the connection to wait for authentication to occur before timing
@@ -674,14 +681,14 @@ Apart from these, the following properties are also available, and may be useful
   </td>
 </tr>
 <tr>
-  <td>spark.task.cpus</td>
+  <td><code>spark.task.cpus</code></td>
   <td>1</td>
   <td>
     Number of cores to allocate for each task.
   </td>
 </tr>
 <tr>
-  <td>spark.executor.extraJavaOptions</td>
+  <td><code>spark.executor.extraJavaOptions</code></td>
   <td>(none)</td>
   <td>
     A string of extra JVM options to pass to executors. For instance, GC settings or other
@@ -692,7 +699,7 @@ Apart from these, the following properties are also available, and may be useful
   </td>
 </tr>
 <tr>
-  <td>spark.executor.extraClassPath</td>
+  <td><code>spark.executor.extraClassPath</code></td>
   <td>(none)</td>
   <td>
     Extra classpath entries to append to the classpath of executors. This exists primarily
@@ -701,7 +708,7 @@ Apart from these, the following properties are also available, and may be useful
   </td>
 </tr>
 <tr>
-  <td>spark.executor.extraLibraryPath</td>
+  <td><code>spark.executor.extraLibraryPath</code></td>
   <td>(none)</td>
   <td>
     Set a special library path to use when launching executor JVM's.
@@ -710,13 +717,6 @@ Apart from these, the following properties are also available, and may be useful
 
 </table>
 
-## Viewing Spark Properties
-
-The application web UI at `http://<driver>:4040` lists Spark properties in the "Environment" tab.
-This is a useful place to check to make sure that your properties have been set correctly. Note
-that only values explicitly specified through either `spark-defaults.conf` or SparkConf will
-appear. For all other configuration properties, you can assume the default value is used.
-
 # Environment Variables
 
 Certain Spark settings can be configured through environment variables, which are read from the `conf/spark-env.sh`
@@ -729,12 +729,28 @@ Note that `conf/spark-env.sh` does not exist by default when Spark is installed.
 
 The following variables can be set in `spark-env.sh`:
 
-* `JAVA_HOME`, the location where Java is installed (if it's not on your default `PATH`)
-* `PYSPARK_PYTHON`, the Python binary to use for PySpark
-* `SPARK_LOCAL_IP`, to configure which IP address of the machine to bind to.
-* `SPARK_PUBLIC_DNS`, the hostname your Spark program will advertise to other machines.
-* Options for the Spark [standalone cluster scripts](spark-standalone.html#cluster-launch-scripts),
-  such as number of cores to use on each machine and maximum memory.
+
+<table class="table">
+  <tr><th style="width:21%">Environment Variable</th><th>Meaning</th></tr>
+  <tr>
+    <td><code>JAVA_HOME</code></td>
+    <td>Location where Java is installed (if it's not on your default `PATH`).</td>
+  </tr>
+  <tr>
+    <td><code>PYSPARK_PYTHON</code></td>
+    <td>Python binary executable to use for PySpark.</td>
+  </tr>
+  <tr>
+    <td><code>SPARK_LOCAL_IP</code></td>
+    <td>IP address of the machine to bind to.</td>
+  </tr>
+  <tr>
+    <td><code>SPARK_PUBLIC_DNS</code></td>
+    <td>Hostname your Spark program will advertise to other machines.</td>
+  </tr>
+</table>
+
+In addition to the above, there are also options for setting up the Spark [standalone cluster scripts](spark-standalone.html#cluster-launch-scripts), such as number of cores to use on each machine and maximum memory.
 
 Since `spark-env.sh` is a shell script, some of these can be set programmatically -- for example, you might
 compute `SPARK_LOCAL_IP` by looking up the IP of a specific network interface.

From dba314029b4c9d72d7e48a2093b39edd01931f57 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Thu, 22 May 2014 01:52:50 -0700
Subject: [PATCH 254/810] [SPARK-1870] Make spark-submit --jars work in
 yarn-cluster mode.

Sent secondary jars to distributed cache of all containers and add the cached jars to classpath before executors start. Tested on a YARN cluster (CDH-5.0).

`spark-submit --jars` also works in standalone server and `yarn-client`. Thanks for @andrewor14 for testing!

I removed "Doesn't work for drivers in standalone mode with "cluster" deploy mode." from `spark-submit`'s help message, though we haven't tested mesos yet.

CC: @dbtsai @sryza

Author: Xiangrui Meng <meng@databricks.com>

Closes #848 from mengxr/yarn-classpath and squashes the following commits:

23e7df4 [Xiangrui Meng] rename spark.jar to __spark__.jar and app.jar to __app__.jar to avoid confliction apped $CWD/ and $CWD/* to the classpath remove unused methods
a40f6ed [Xiangrui Meng] standalone -> cluster
65e04ad [Xiangrui Meng] update spark-submit help message and add a comment for yarn-client
11e5354 [Xiangrui Meng] minor changes
3e7e1c4 [Xiangrui Meng] use sparkConf instead of hadoop conf
dc3c825 [Xiangrui Meng] add secondary jars to classpath in yarn
---
 .../spark/deploy/SparkSubmitArguments.scala   |  3 +-
 .../apache/spark/deploy/yarn/ClientBase.scala | 69 +++++--------------
 .../cluster/YarnClientSchedulerBackend.scala  |  2 +-
 3 files changed, 19 insertions(+), 55 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
index 264d4544cd31..0cc05fb95aef 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
@@ -326,8 +326,7 @@ private[spark] class SparkSubmitArguments(args: Seq[String]) {
         |  --class CLASS_NAME          Your application's main class (for Java / Scala apps).
         |  --name NAME                 A name of your application.
         |  --jars JARS                 Comma-separated list of local jars to include on the driver
-        |                              and executor classpaths. Doesn't work for drivers in
-        |                              standalone mode with "cluster" deploy mode.
+        |                              and executor classpaths.
         |  --py-files PY_FILES         Comma-separated list of .zip or .egg files to place on the
         |                              PYTHONPATH for Python apps.
         |  --files FILES               Comma-separated list of files to be placed in the working
diff --git a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala
index 27a518ccda45..aeb3f0062df3 100644
--- a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala
+++ b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala
@@ -44,7 +44,7 @@ import org.apache.spark.{Logging, SparkConf, SparkContext}
  * Client submits an application to the YARN ResourceManager.
  *
  * Depending on the deployment mode this will launch one of two application master classes:
- * 1. In standalone mode, it will launch an [[org.apache.spark.deploy.yarn.ApplicationMaster]]
+ * 1. In cluster mode, it will launch an [[org.apache.spark.deploy.yarn.ApplicationMaster]]
  *      which launches a driver program inside of the cluster.
  * 2. In client mode, it will launch an [[org.apache.spark.deploy.yarn.ExecutorLauncher]] to
  *      request executors on behalf of a driver running outside of the cluster.
@@ -220,10 +220,11 @@ trait ClientBase extends Logging {
       }
     }
 
+    var cachedSecondaryJarLinks = ListBuffer.empty[String]
     val fileLists = List( (args.addJars, LocalResourceType.FILE, true),
       (args.files, LocalResourceType.FILE, false),
       (args.archives, LocalResourceType.ARCHIVE, false) )
-    fileLists.foreach { case (flist, resType, appMasterOnly) =>
+    fileLists.foreach { case (flist, resType, addToClasspath) =>
       if (flist != null && !flist.isEmpty()) {
         flist.split(',').foreach { case file: String =>
           val localURI = new URI(file.trim())
@@ -232,11 +233,15 @@ trait ClientBase extends Logging {
             val linkname = Option(localURI.getFragment()).getOrElse(localPath.getName())
             val destPath = copyRemoteFile(dst, localPath, replication)
             distCacheMgr.addResource(fs, conf, destPath, localResources, resType,
-              linkname, statCache, appMasterOnly)
+              linkname, statCache)
+            if (addToClasspath) {
+              cachedSecondaryJarLinks += linkname
+            }
           }
         }
       }
     }
+    sparkConf.set(ClientBase.CONF_SPARK_YARN_SECONDARY_JARS, cachedSecondaryJarLinks.mkString(","))
 
     UserGroupInformation.getCurrentUser().addCredentials(credentials)
     localResources
@@ -374,11 +379,12 @@ trait ClientBase extends Logging {
 }
 
 object ClientBase {
-  val SPARK_JAR: String = "spark.jar"
-  val APP_JAR: String = "app.jar"
+  val SPARK_JAR: String = "__spark__.jar"
+  val APP_JAR: String = "__app__.jar"
   val LOG4J_PROP: String = "log4j.properties"
   val LOG4J_CONF_ENV_KEY: String = "SPARK_LOG4J_CONF"
   val LOCAL_SCHEME = "local"
+  val CONF_SPARK_YARN_SECONDARY_JARS = "spark.yarn.secondary.jars"
 
   def getSparkJar = sys.env.get("SPARK_JAR").getOrElse(SparkContext.jarOfClass(this.getClass).head)
 
@@ -479,66 +485,25 @@ object ClientBase {
 
     extraClassPath.foreach(addClasspathEntry)
 
-    addClasspathEntry(Environment.PWD.$())
+    val cachedSecondaryJarLinks =
+      sparkConf.getOption(CONF_SPARK_YARN_SECONDARY_JARS).getOrElse("").split(",")
     // Normally the users app.jar is last in case conflicts with spark jars
     if (sparkConf.get("spark.yarn.user.classpath.first", "false").toBoolean) {
       addPwdClasspathEntry(APP_JAR)
+      cachedSecondaryJarLinks.foreach(addPwdClasspathEntry)
       addPwdClasspathEntry(SPARK_JAR)
       ClientBase.populateHadoopClasspath(conf, env)
     } else {
       addPwdClasspathEntry(SPARK_JAR)
       ClientBase.populateHadoopClasspath(conf, env)
       addPwdClasspathEntry(APP_JAR)
+      cachedSecondaryJarLinks.foreach(addPwdClasspathEntry)
     }
+    // Append all class files and jar files under the working directory to the classpath.
+    addClasspathEntry(Environment.PWD.$())
     addPwdClasspathEntry("*")
   }
 
-  /**
-   * Adds the user jars which have local: URIs (or alternate names, such as APP_JAR) explicitly
-   * to the classpath.
-   */
-  private def addUserClasspath(args: ClientArguments, env: HashMap[String, String]) = {
-    if (args != null) {
-      addClasspathEntry(args.userJar, APP_JAR, env)
-    }
-
-    if (args != null && args.addJars != null) {
-      args.addJars.split(",").foreach { case file: String =>
-        addClasspathEntry(file, null, env)
-      }
-    }
-  }
-
-  /**
-   * Adds the given path to the classpath, handling "local:" URIs correctly.
-   *
-   * If an alternate name for the file is given, and it's not a "local:" file, the alternate
-   * name will be added to the classpath (relative to the job's work directory).
-   *
-   * If not a "local:" file and no alternate name, the environment is not modified.
-   *
-   * @param path      Path to add to classpath (optional).
-   * @param fileName  Alternate name for the file (optional).
-   * @param env       Map holding the environment variables.
-   */
-  private def addClasspathEntry(path: String, fileName: String,
-      env: HashMap[String, String]) : Unit = {
-    if (path != null) {
-      scala.util.control.Exception.ignoring(classOf[URISyntaxException]) {
-        val localPath = getLocalPath(path)
-        if (localPath != null) {
-          YarnSparkHadoopUtil.addToEnvironment(env, Environment.CLASSPATH.name, localPath,
-            File.pathSeparator)
-          return
-        }
-      }
-    }
-    if (fileName != null) {
-      YarnSparkHadoopUtil.addToEnvironment(env, Environment.CLASSPATH.name,
-        Environment.PWD.$() + Path.SEPARATOR + fileName, File.pathSeparator);
-    }
-  }
-
   /**
    * Returns the local path if the URI is a "local:" URI, or null otherwise.
    */
diff --git a/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala b/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala
index 0ac162538fc4..e01ed5a57d69 100644
--- a/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala
+++ b/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala
@@ -52,7 +52,7 @@ private[spark] class YarnClientSchedulerBackend(
     val argsArrayBuf = new ArrayBuffer[String]()
     argsArrayBuf += (
       "--class", "notused",
-      "--jar", null,
+      "--jar", null, // The primary jar will be added dynamically in SparkContext.
       "--args", hostport,
       "--am-class", classOf[ExecutorLauncher].getName
     )

From f9f5fd5f4e81828a3e0c391892e0f28751568843 Mon Sep 17 00:00:00 2001
From: Aaron Davidson <aaron@databricks.com>
Date: Thu, 22 May 2014 15:11:05 -0700
Subject: [PATCH 255/810] Fix UISuite unit test that fails under Jenkins
 contention

Due to perhaps zombie processes on Jenkins, it seems that at least 10
Spark ports are in use. It also doesn't matter that the port increases
when used, it could in fact go down -- the only part that matters is
that it selects a different port rather than failing to bind.
Changed test to match this.

Thanks to @andrewor14 for helping diagnose this.

Author: Aaron Davidson <aaron@databricks.com>

Closes #857 from aarondav/tiny and squashes the following commits:

c199ec8 [Aaron Davidson] Fix UISuite unit test that fails under Jenkins contention
---
 core/src/test/scala/org/apache/spark/ui/UISuite.scala | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/core/src/test/scala/org/apache/spark/ui/UISuite.scala b/core/src/test/scala/org/apache/spark/ui/UISuite.scala
index fff8020ade6d..038746d2eda4 100644
--- a/core/src/test/scala/org/apache/spark/ui/UISuite.scala
+++ b/core/src/test/scala/org/apache/spark/ui/UISuite.scala
@@ -94,7 +94,7 @@ class UISuite extends FunSuite {
     }
   }
 
-  test("jetty port increases under contention") {
+  test("jetty selects different port under contention") {
     val startPort = 4040
     val server = new Server(startPort)
 
@@ -110,8 +110,9 @@ class UISuite extends FunSuite {
     // Allow some wiggle room in case ports on the machine are under contention
     val boundPort1 = serverInfo1.boundPort
     val boundPort2 = serverInfo2.boundPort
-    assert(boundPort1 > startPort && boundPort1 < startPort + 10)
-    assert(boundPort2 > boundPort1 && boundPort2 < boundPort1 + 10)
+    assert(boundPort1 != startPort)
+    assert(boundPort2 != startPort)
+    assert(boundPort1 != boundPort2)
   }
 
   test("jetty binds to port 0 correctly") {

From 8edbee7d1b4afc192d97ba192a5526affc464205 Mon Sep 17 00:00:00 2001
From: Andrew Or <andrewor14@gmail.com>
Date: Thu, 22 May 2014 20:25:41 -0700
Subject: [PATCH 256/810] [SPARK-1897] Respect spark.jars (and --jars) in
 spark-shell

Spark shell currently overwrites `spark.jars` with `ADD_JARS`. In all modes except yarn-cluster, this means the `--jar` flag passed to `bin/spark-shell` is also discarded. However, in the [docs](http://people.apache.org/~pwendell/spark-1.0.0-rc7-docs/scala-programming-guide.html#initializing-spark), we explicitly tell the users to add the jars this way.

Author: Andrew Or <andrewor14@gmail.com>

Closes #849 from andrewor14/shell-jars and squashes the following commits:

928a7e6 [Andrew Or] ',' -> "," (minor)
afc357c [Andrew Or] Handle spark.jars == "" in SparkILoop, not SparkSubmit
c6da113 [Andrew Or] Do not set spark.jars to ""
d8549f7 [Andrew Or] Respect spark.jars and --jars in spark-shell
---
 .../src/main/scala/org/apache/spark/repl/SparkILoop.scala | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala b/repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala
index 296da740687e..55684e94bd61 100644
--- a/repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala
+++ b/repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala
@@ -993,7 +993,13 @@ object SparkILoop {
   implicit def loopToInterpreter(repl: SparkILoop): SparkIMain = repl.intp
   private def echo(msg: String) = Console println msg
 
-  def getAddedJars: Array[String] = Option(System.getenv("ADD_JARS")).map(_.split(',')).getOrElse(new Array[String](0))
+  def getAddedJars: Array[String] = {
+    val envJars = sys.env.get("ADD_JARS")
+    val propJars = sys.props.get("spark.jars").flatMap { p =>
+      if (p == "") None else Some(p)
+    }
+    propJars.orElse(envJars).map(_.split(",")).getOrElse(Array.empty)
+  }
 
   // Designed primarily for use by test code: take a String with a
   // bunch of code, and prints out a transcript of what it would look

From cce77457e00aa5f1f4db3d50454cf257efb156ed Mon Sep 17 00:00:00 2001
From: Andrew Or <andrewor14@gmail.com>
Date: Thu, 22 May 2014 20:32:27 -0700
Subject: [PATCH 257/810] [SPARK-1896] Respect spark.master (and --master)
 before MASTER in spark-shell

The hierarchy for configuring the Spark master in the shell is as follows:
```
MASTER > --master > spark.master (spark-defaults.conf)
```
This is inconsistent with the way we run normal applications, which is:
```
--master > spark.master (spark-defaults.conf) > MASTER
```

I was trying to run a shell locally on a standalone cluster launched through the ec2 scripts, which automatically set `MASTER` in spark-env.sh. It was surprising to me that `--master` didn't take effect, considering that this is the way we tell users to set their masters [here](http://people.apache.org/~pwendell/spark-1.0.0-rc7-docs/scala-programming-guide.html#initializing-spark).

Author: Andrew Or <andrewor14@gmail.com>

Closes #846 from andrewor14/shell-master and squashes the following commits:

2cb81c9 [Andrew Or] Respect spark.master before MASTER in REPL
---
 repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala b/repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala
index 55684e94bd61..5f34362ccd97 100644
--- a/repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala
+++ b/repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala
@@ -962,11 +962,10 @@ class SparkILoop(in0: Option[BufferedReader], protected val out: JPrintWriter,
   private def getMaster(): String = {
     val master = this.master match {
       case Some(m) => m
-      case None => {
+      case None =>
         val envMaster = sys.env.get("MASTER")
         val propMaster = sys.props.get("spark.master")
-        envMaster.orElse(propMaster).getOrElse("local[*]")
-      }
+        propMaster.orElse(envMaster).getOrElse("local[*]")
     }
     master
   }

From b2bdd0e505f1ae3d39c46139f17bd43779ece635 Mon Sep 17 00:00:00 2001
From: Tathagata Das <tathagata.das1565@gmail.com>
Date: Thu, 22 May 2014 20:48:55 -0700
Subject: [PATCH 258/810] Updated scripts for auditing releases

- Added script to automatically generate change list CHANGES.txt
- Added test for verifying linking against maven distributions of `spark-sql` and `spark-hive`
- Added SBT projects for testing functionality of `spark-sql` and `spark-hive`
- Fixed issues in existing tests that might have come up because of changes in Spark 1.0

Author: Tathagata Das <tathagata.das1565@gmail.com>

Closes #844 from tdas/update-dev-scripts and squashes the following commits:

25090ba [Tathagata Das] Added missing license
e2e20b3 [Tathagata Das] Updated tests for auditing releases.
---
 dev/audit-release/audit_release.py            |  11 +-
 .../src/main/java/SimpleApp.java              |   1 +
 .../src/main/scala/SparkApp.scala             |   2 +-
 dev/audit-release/sbt_app_hive/build.sbt      |  29 +++
 dev/audit-release/sbt_app_hive/data.txt       |   9 +
 .../src/main/resources/hive-site.xml          | 213 ++++++++++++++++++
 .../sbt_app_hive/src/main/scala/HiveApp.scala |  57 +++++
 dev/audit-release/sbt_app_sql/build.sbt       |  29 +++
 .../sbt_app_sql/src/main/scala/SqlApp.scala   |  57 +++++
 .../src/main/scala/StreamingApp.scala         |   1 -
 dev/create-release/generate-changelist.py     | 144 ++++++++++++
 11 files changed, 547 insertions(+), 6 deletions(-)
 create mode 100644 dev/audit-release/sbt_app_hive/build.sbt
 create mode 100644 dev/audit-release/sbt_app_hive/data.txt
 create mode 100644 dev/audit-release/sbt_app_hive/src/main/resources/hive-site.xml
 create mode 100644 dev/audit-release/sbt_app_hive/src/main/scala/HiveApp.scala
 create mode 100644 dev/audit-release/sbt_app_sql/build.sbt
 create mode 100644 dev/audit-release/sbt_app_sql/src/main/scala/SqlApp.scala
 create mode 100755 dev/create-release/generate-changelist.py

diff --git a/dev/audit-release/audit_release.py b/dev/audit-release/audit_release.py
index 4a816d4101e5..8c7573b91f68 100755
--- a/dev/audit-release/audit_release.py
+++ b/dev/audit-release/audit_release.py
@@ -93,9 +93,12 @@ def get_url(url):
 # For each of these modules, we'll test an 'empty' application in sbt and 
 # maven that links against them. This will catch issues with messed up
 # dependencies within those projects.
-modules = ["spark-core", "spark-bagel", "spark-mllib", "spark-streaming", "spark-repl", 
-           "spark-graphx", "spark-streaming-flume", "spark-streaming-kafka", 
-           "spark-streaming-mqtt", "spark-streaming-twitter", "spark-streaming-zeromq"]
+modules = [
+  "spark-core", "spark-bagel", "spark-mllib", "spark-streaming", "spark-repl",
+  "spark-graphx", "spark-streaming-flume", "spark-streaming-kafka",
+  "spark-streaming-mqtt", "spark-streaming-twitter", "spark-streaming-zeromq",
+  "spark-catalyst", "spark-sql", "spark-hive"
+]
 modules = map(lambda m: "%s_%s" % (m, SCALA_BINARY_VERSION), modules)
 
 # Check for directories that might interfere with tests
@@ -122,7 +125,7 @@ def ensure_path_not_present(x):
 os.chdir(original_dir)
 
 # SBT application tests
-for app in ["sbt_app_core", "sbt_app_graphx", "sbt_app_streaming"]:
+for app in ["sbt_app_core", "sbt_app_graphx", "sbt_app_streaming", "sbt_app_sql", "sbt_app_hive"]:
   os.chdir(app)
   ret = run_cmd("sbt clean run", exit_on_failure=False)
   test(ret == 0, "sbt application (%s)" % app)
diff --git a/dev/audit-release/maven_app_core/src/main/java/SimpleApp.java b/dev/audit-release/maven_app_core/src/main/java/SimpleApp.java
index 6b65dda39b1a..5217689e7c09 100644
--- a/dev/audit-release/maven_app_core/src/main/java/SimpleApp.java
+++ b/dev/audit-release/maven_app_core/src/main/java/SimpleApp.java
@@ -37,5 +37,6 @@ public static void main(String[] args) {
      System.exit(-1);
    }
    System.out.println("Test succeeded");
+   sc.stop();
   }
 }
diff --git a/dev/audit-release/sbt_app_core/src/main/scala/SparkApp.scala b/dev/audit-release/sbt_app_core/src/main/scala/SparkApp.scala
index a89b0d7d38bf..77bbd167b199 100644
--- a/dev/audit-release/sbt_app_core/src/main/scala/SparkApp.scala
+++ b/dev/audit-release/sbt_app_core/src/main/scala/SparkApp.scala
@@ -19,6 +19,7 @@ package main.scala
 
 import scala.util.Try
 
+import org.apache.spark.SparkConf
 import org.apache.spark.SparkContext
 import org.apache.spark.SparkContext._
 
@@ -30,7 +31,6 @@ object SimpleApp {
     }
     val logFile = "input.txt"
     val sc = new SparkContext(conf)
-    SparkContext.jarOfClass(this.getClass).foreach(sc.addJar)
     val logData = sc.textFile(logFile, 2).cache()
     val numAs = logData.filter(line => line.contains("a")).count()
     val numBs = logData.filter(line => line.contains("b")).count()
diff --git a/dev/audit-release/sbt_app_hive/build.sbt b/dev/audit-release/sbt_app_hive/build.sbt
new file mode 100644
index 000000000000..7ac1be729c56
--- /dev/null
+++ b/dev/audit-release/sbt_app_hive/build.sbt
@@ -0,0 +1,29 @@
+//
+// Licensed to the Apache Software Foundation (ASF) under one or more
+// contributor license agreements.  See the NOTICE file distributed with
+// this work for additional information regarding copyright ownership.
+// The ASF licenses this file to You under the Apache License, Version 2.0
+// (the "License"); you may not use this file except in compliance with
+// the License.  You may obtain a copy of the License at
+//
+//    http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+
+name := "Simple Project"
+
+version := "1.0"
+
+scalaVersion := System.getenv.get("SCALA_VERSION")
+
+libraryDependencies += "org.apache.spark" %% "spark-hive" % System.getenv.get("SPARK_VERSION")
+
+resolvers ++= Seq(
+  "Spark Release Repository" at System.getenv.get("SPARK_RELEASE_REPOSITORY"),
+  "Akka Repository" at "http://repo.akka.io/releases/",
+  "Spray Repository" at "http://repo.spray.cc/")
diff --git a/dev/audit-release/sbt_app_hive/data.txt b/dev/audit-release/sbt_app_hive/data.txt
new file mode 100644
index 000000000000..0229e67f51e0
--- /dev/null
+++ b/dev/audit-release/sbt_app_hive/data.txt
@@ -0,0 +1,9 @@
+0
val_0
+1
val_1
+2
val_2
+3
val_3
+4
val_4
+5
val_5
+6
val_6
+7
val_7
+9
val_9
diff --git a/dev/audit-release/sbt_app_hive/src/main/resources/hive-site.xml b/dev/audit-release/sbt_app_hive/src/main/resources/hive-site.xml
new file mode 100644
index 000000000000..93b835813d53
--- /dev/null
+++ b/dev/audit-release/sbt_app_hive/src/main/resources/hive-site.xml
@@ -0,0 +1,213 @@
+<?xml version="1.0"?>
+<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>
+<!--
+   Licensed to the Apache Software Foundation (ASF) under one or more
+   contributor license agreements.  See the NOTICE file distributed with
+   this work for additional information regarding copyright ownership.
+   The ASF licenses this file to You under the Apache License, Version 2.0
+   (the "License"); you may not use this file except in compliance with
+   the License.  You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
+-->
+
+<configuration>
+
+<!-- Hive Configuration can either be stored in this file or in the hadoop configuration files  -->
+<!-- that are implied by Hadoop setup variables.                                                -->
+<!-- Aside from Hadoop setup variables - this file is provided as a convenience so that Hive    -->
+<!-- users do not have to edit hadoop configuration files (that may be managed as a centralized -->
+<!-- resource).                                                                                 -->
+
+<!-- Hive Execution Parameters -->
+
+<property name="build.dir" value="build" />
+
+<property> 
+  <name>build.dir</name>
+  <value>${user.dir}/build</value>
+</property>
+
+<property> 
+  <name>build.dir.hive</name>
+  <value>${build.dir}/hive</value>
+</property>
+
+<property>
+  <name>hadoop.tmp.dir</name>
+  <value>${build.dir.hive}/test/hadoop-${user.name}</value>
+  <description>A base for other temporary directories.</description>
+</property>
+
+<!--
+<property>
+  <name>hive.exec.reducers.max</name>
+  <value>1</value>
+  <description>maximum number of reducers</description>
+</property>
+-->
+
+<property>
+  <name>hive.exec.scratchdir</name>
+  <value>${build.dir}/scratchdir</value>
+  <description>Scratch space for Hive jobs</description>
+</property>
+
+<property>
+  <name>hive.exec.local.scratchdir</name>
+  <value>${build.dir}/localscratchdir/</value>
+  <description>Local scratch space for Hive jobs</description>
+</property>
+
+<property>
+  <name>javax.jdo.option.ConnectionURL</name>
+  <!-- note: variable substituion not working here because it's loaded by jdo, not Hive -->
+  <value>jdbc:derby:;databaseName=../build/test/junit_metastore_db;create=true</value>
+</property>
+
+<property>
+  <name>javax.jdo.option.ConnectionDriverName</name>
+  <value>org.apache.derby.jdbc.EmbeddedDriver</value>
+</property>
+
+<property>
+  <name>javax.jdo.option.ConnectionUserName</name>
+  <value>APP</value>
+</property>
+
+<property>
+  <name>javax.jdo.option.ConnectionPassword</name>
+  <value>mine</value>
+</property>
+
+<property>
+  <!--  this should eventually be deprecated since the metastore should supply this -->
+  <name>hive.metastore.warehouse.dir</name>
+  <value>${test.warehouse.dir}</value>
+  <description></description>
+</property>
+
+<property>
+  <name>hive.metastore.metadb.dir</name>
+  <value>${build.dir}/test/data/metadb/</value>
+  <description>
+  Required by metastore server or if the uris argument below is not supplied
+  </description>
+</property>
+
+<property>
+  <name>test.log.dir</name>
+  <value>${build.dir}/test/logs</value>
+  <description></description>
+</property>
+
+<property>
+  <name>test.src.dir</name>
+  <value>${build.dir}/src/test</value>
+  <description></description>
+</property>
+
+<!--
+<property>
+  <name>test.data.files</name>
+  <value>${user.dir}/../data/files</value>
+  <description></description>
+</property>
+
+<property>
+  <name>test.query.file1</name>
+  <value>file://${user.dir}/../ql/src/test/org/apache/hadoop/hive/ql/input2.q</value>
+  <value></value>
+  <description></description>
+</property>
+-->
+
+<property>
+  <name>hive.jar.path</name>
+  <value>${build.dir.hive}/ql/hive-exec-${version}.jar</value>
+  <description></description>
+</property>
+
+<property>
+  <name>hive.metastore.rawstore.impl</name>
+  <value>org.apache.hadoop.hive.metastore.ObjectStore</value>
+  <description>Name of the class that implements org.apache.hadoop.hive.metastore.rawstore interface. This class is used to store and retrieval of raw metadata objects such as table, database</description>
+</property>
+
+<property>
+  <name>hive.querylog.location</name>
+  <value>${build.dir}/tmp</value>
+  <description>Location of the structured hive logs</description>
+</property>
+
+<!--
+<property>
+  <name>hive.exec.pre.hooks</name>
+  <value>org.apache.hadoop.hive.ql.hooks.PreExecutePrinter, org.apache.hadoop.hive.ql.hooks.EnforceReadOnlyTables</value>
+  <description>Pre Execute Hook for Tests</description>
+</property>
+<property>
+  <name>hive.exec.post.hooks</name>
+  <value>org.apache.hadoop.hive.ql.hooks.PostExecutePrinter</value>
+  <description>Post Execute Hook for Tests</description>
+</property>
+-->
+
+<property>
+  <name>hive.task.progress</name>
+  <value>false</value>
+  <description>Track progress of a task</description>
+</property>
+
+<property>
+  <name>hive.support.concurrency</name>
+  <value>false</value>
+  <description>Whether hive supports concurrency or not. A zookeeper instance must be up and running for the default hive lock manager to support read-write locks.</description>
+</property>
+
+<property>
+  <name>fs.pfile.impl</name>
+  <value>org.apache.hadoop.fs.ProxyLocalFileSystem</value>
+  <description>A proxy for local file system used for cross file system testing</description>
+</property>
+
+<property>
+  <name>hive.exec.mode.local.auto</name>
+  <value>false</value>
+  <description>
+    Let hive determine whether to run in local mode automatically
+    Disabling this for tests so that minimr is not affected
+  </description>
+</property>
+
+<property>
+  <name>hive.auto.convert.join</name>
+  <value>false</value>
+  <description>Whether Hive enable the optimization about converting common join into mapjoin based on the input file size</description>
+</property>
+
+<property>
+  <name>hive.ignore.mapjoin.hint</name>
+  <value>false</value>
+  <description>Whether Hive ignores the mapjoin hint</description>
+</property>
+
+<property>
+  <name>hive.input.format</name>
+  <value>org.apache.hadoop.hive.ql.io.CombineHiveInputFormat</value>
+  <description>The default input format, if it is not specified, the system assigns it. It is set to HiveInputFormat for hadoop versions 17, 18 and 19, whereas it is set to CombineHiveInputFormat for hadoop 20. The user can always overwrite it - if there is a bug in CombineHiveInputFormat, it can always be manually set to HiveInputFormat. </description>
+</property>
+
+<property>
+  <name>hive.default.rcfile.serde</name>
+  <value>org.apache.hadoop.hive.serde2.columnar.ColumnarSerDe</value>
+  <description>The default SerDe hive will use for the rcfile format</description>
+</property>
+
+</configuration>
diff --git a/dev/audit-release/sbt_app_hive/src/main/scala/HiveApp.scala b/dev/audit-release/sbt_app_hive/src/main/scala/HiveApp.scala
new file mode 100644
index 000000000000..7257d17d1011
--- /dev/null
+++ b/dev/audit-release/sbt_app_hive/src/main/scala/HiveApp.scala
@@ -0,0 +1,57 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package main.scala
+
+import scala.collection.mutable.{ListBuffer, Queue}
+
+import org.apache.spark.SparkConf
+import org.apache.spark.SparkContext
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.hive.LocalHiveContext
+
+case class Person(name: String, age: Int)
+
+object SparkSqlExample {
+
+  def main(args: Array[String]) {
+    val conf = sys.env.get("SPARK_AUDIT_MASTER") match {
+      case Some(master) => new SparkConf().setAppName("Simple Sql App").setMaster(master)
+      case None => new SparkConf().setAppName("Simple Sql App")
+    }
+    val sc = new SparkContext(conf)
+    val hiveContext = new LocalHiveContext(sc)
+
+    import hiveContext._
+    hql("DROP TABLE IF EXISTS src")
+    hql("CREATE TABLE IF NOT EXISTS src (key INT, value STRING)")
+    hql("LOAD DATA LOCAL INPATH 'data.txt' INTO TABLE src")
+    val results = hql("FROM src SELECT key, value WHERE key >= 0 AND KEY < 5").collect()
+    results.foreach(println)
+    
+    def test(f: => Boolean, failureMsg: String) = {
+      if (!f) {
+        println(failureMsg)
+        System.exit(-1)
+      }
+    }
+    
+    test(results.size == 5, "Unexpected number of selected elements: " + results)
+    println("Test succeeded")
+    sc.stop()
+  }
+}
diff --git a/dev/audit-release/sbt_app_sql/build.sbt b/dev/audit-release/sbt_app_sql/build.sbt
new file mode 100644
index 000000000000..6e0ad3b4b296
--- /dev/null
+++ b/dev/audit-release/sbt_app_sql/build.sbt
@@ -0,0 +1,29 @@
+//
+// Licensed to the Apache Software Foundation (ASF) under one or more
+// contributor license agreements.  See the NOTICE file distributed with
+// this work for additional information regarding copyright ownership.
+// The ASF licenses this file to You under the Apache License, Version 2.0
+// (the "License"); you may not use this file except in compliance with
+// the License.  You may obtain a copy of the License at
+//
+//    http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+
+name := "Simple Project"
+
+version := "1.0"
+
+scalaVersion := System.getenv.get("SCALA_VERSION")
+
+libraryDependencies += "org.apache.spark" %% "spark-sql" % System.getenv.get("SPARK_VERSION")
+
+resolvers ++= Seq(
+  "Spark Release Repository" at System.getenv.get("SPARK_RELEASE_REPOSITORY"),
+  "Akka Repository" at "http://repo.akka.io/releases/",
+  "Spray Repository" at "http://repo.spray.cc/")
diff --git a/dev/audit-release/sbt_app_sql/src/main/scala/SqlApp.scala b/dev/audit-release/sbt_app_sql/src/main/scala/SqlApp.scala
new file mode 100644
index 000000000000..50af90c213b5
--- /dev/null
+++ b/dev/audit-release/sbt_app_sql/src/main/scala/SqlApp.scala
@@ -0,0 +1,57 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package main.scala
+
+import scala.collection.mutable.{ListBuffer, Queue}
+
+import org.apache.spark.SparkConf
+import org.apache.spark.SparkContext
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.SQLContext
+
+case class Person(name: String, age: Int)
+
+object SparkSqlExample {
+
+  def main(args: Array[String]) {
+    val conf = sys.env.get("SPARK_AUDIT_MASTER") match {
+      case Some(master) => new SparkConf().setAppName("Simple Sql App").setMaster(master)
+      case None => new SparkConf().setAppName("Simple Sql App")
+    }
+    val sc = new SparkContext(conf)
+    val sqlContext = new SQLContext(sc)
+
+    import sqlContext._
+    val people = sc.makeRDD(1 to 100, 10).map(x => Person(s"Name$x", x))
+    people.registerAsTable("people")
+    val teenagers = sql("SELECT name FROM people WHERE age >= 13 AND age <= 19")
+    val teenagerNames = teenagers.map(t => "Name: " + t(0)).collect()
+    teenagerNames.foreach(println)
+
+    def test(f: => Boolean, failureMsg: String) = {
+      if (!f) {
+        println(failureMsg)
+        System.exit(-1)
+      }
+    }
+    
+    test(teenagerNames.size == 7, "Unexpected number of selected elements: " + teenagerNames)
+    println("Test succeeded")
+    sc.stop()
+  }
+}
diff --git a/dev/audit-release/sbt_app_streaming/src/main/scala/StreamingApp.scala b/dev/audit-release/sbt_app_streaming/src/main/scala/StreamingApp.scala
index a1d8971abe9a..58a662bd9b2e 100644
--- a/dev/audit-release/sbt_app_streaming/src/main/scala/StreamingApp.scala
+++ b/dev/audit-release/sbt_app_streaming/src/main/scala/StreamingApp.scala
@@ -32,7 +32,6 @@ object SparkStreamingExample {
       case None => new SparkConf().setAppName("Simple Streaming App")
     }
     val ssc = new StreamingContext(conf, Seconds(1))
-    SparkContext.jarOfClass(this.getClass).foreach(ssc.sparkContext.addJar)
     val seen = ListBuffer[RDD[Int]]()
 
     val rdd1 = ssc.sparkContext.makeRDD(1 to 100, 10)
diff --git a/dev/create-release/generate-changelist.py b/dev/create-release/generate-changelist.py
new file mode 100755
index 000000000000..13b744ec1b37
--- /dev/null
+++ b/dev/create-release/generate-changelist.py
@@ -0,0 +1,144 @@
+#!/usr/bin/python
+
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# Creates CHANGES.txt from git history.
+#
+# Usage:
+#   First set the new release version and old CHANGES.txt version in this file.
+#   Make sure you have SPARK_HOME set.
+#   $  python generate-changelist.py
+
+
+import os
+import sys
+import subprocess
+import time
+import traceback
+ 
+SPARK_HOME = os.environ["SPARK_HOME"]
+NEW_RELEASE_VERSION = "1.0.0"
+PREV_RELEASE_GIT_TAG = "v0.9.1"
+ 
+CHANGELIST = "CHANGES.txt" 
+OLD_CHANGELIST = "%s.old" % (CHANGELIST)
+NEW_CHANGELIST = "%s.new" % (CHANGELIST)
+TMP_CHANGELIST = "%s.tmp" % (CHANGELIST)
+ 
+# date before first PR in TLP Spark repo
+SPARK_REPO_CHANGE_DATE1 = time.strptime("2014-02-26", "%Y-%m-%d")
+# date after last PR in incubator Spark repo
+SPARK_REPO_CHANGE_DATE2 = time.strptime("2014-03-01", "%Y-%m-%d")
+# Threshold PR number that differentiates PRs to TLP
+# and incubator repos
+SPARK_REPO_PR_NUM_THRESH = 200
+ 
+LOG_FILE_NAME = "changes_%s" % time.strftime("%h_%m_%Y_%I_%M_%S")
+LOG_FILE = open(LOG_FILE_NAME, 'w')
+ 
+def run_cmd(cmd):
+  try:
+    print >> LOG_FILE, "Running command: %s" % cmd
+    output = subprocess.check_output(cmd, shell=True, stderr=LOG_FILE)
+    print >> LOG_FILE, "Output: %s" % output
+    return output
+  except:
+    traceback.print_exc()
+    cleanup()
+    sys.exit(1)
+ 
+def append_to_changelist(string):
+  with open(TMP_CHANGELIST, "a") as f:
+    print >> f, string
+ 
+def cleanup(ask = True):
+  if ask == True:
+    print "OK to delete temporary and log files? (y/N): " 
+    response = raw_input()
+  if ask == False or (ask == True and response == "y"):
+    if os.path.isfile(TMP_CHANGELIST):
+      os.remove(TMP_CHANGELIST) 
+    if os.path.isfile(OLD_CHANGELIST):
+      os.remove(OLD_CHANGELIST)
+    LOG_FILE.close()
+    os.remove(LOG_FILE_NAME)
+ 
+print "Generating new %s for Spark release %s" % (CHANGELIST, NEW_RELEASE_VERSION)
+os.chdir(SPARK_HOME)
+if os.path.isfile(TMP_CHANGELIST):
+  os.remove(TMP_CHANGELIST) 
+if os.path.isfile(OLD_CHANGELIST):
+  os.remove(OLD_CHANGELIST)
+ 
+append_to_changelist("Spark Change Log")
+append_to_changelist("----------------")
+append_to_changelist("")
+append_to_changelist("Release %s" % NEW_RELEASE_VERSION)
+append_to_changelist("")
+ 
+print "Getting commits between tag %s and HEAD" % PREV_RELEASE_GIT_TAG
+hashes = run_cmd("git log %s..HEAD --pretty='%%h'" % PREV_RELEASE_GIT_TAG).split()
+ 
+print "Getting details of %s commits" % len(hashes)
+for h in hashes:
+  date = run_cmd("git log %s -1 --pretty='%%ad' --date=iso | head -1" % h).strip()
+  subject = run_cmd("git log %s -1 --pretty='%%s' | head -1" % h).strip()
+  body = run_cmd("git log %s -1 --pretty='%%b'" % h)
+  committer = run_cmd("git log %s -1 --pretty='%%cn <%%ce>' | head -1" % h).strip()
+  body_lines = body.split("\n")
+ 
+  if "Merge pull" in subject:
+    ## Parse old format commit message
+    append_to_changelist("  %s %s" % (h, date))
+    append_to_changelist("  %s" % subject)
+    append_to_changelist("  [%s]" % body_lines[0])
+    append_to_changelist("")
+     
+  elif "maven-release" not in subject:
+    ## Parse new format commit message
+    # Get authors from commit message, committer otherwise
+    authors = [committer]
+    if "Author:" in body:
+      authors = [line.split(":")[1].strip() for line in body_lines if "Author:" in line]
+    
+    # Generate GitHub PR URL for easy access if possible
+    github_url = ""
+    if "Closes #" in body:
+      pr_num = [line.split()[1].lstrip("#") for line in body_lines if "Closes #" in line][0]
+      github_url = "github.com/apache/spark/pull/%s" % pr_num 
+      day = time.strptime(date.split()[0], "%Y-%m-%d")
+      if day < SPARK_REPO_CHANGE_DATE1 or (day < SPARK_REPO_CHANGE_DATE2 and pr_num < SPARK_REPO_PR_NUM_THRESH): 
+        github_url = "github.com/apache/incubator-spark/pull/%s" % pr_num
+    
+    append_to_changelist("  %s" % subject)
+    append_to_changelist("  %s" % ', '.join(authors))
+    # for author in authors:
+      # append_to_changelist("  %s" % author)
+    append_to_changelist("  %s" % date)
+    if len(github_url) > 0:
+      append_to_changelist("  Commit: %s, %s" % (h, github_url))
+    else:
+      append_to_changelist("  Commit: %s" % h)
+    append_to_changelist("")
+ 
+# Append old change list
+print "Appending changelist from tag %s" % PREV_RELEASE_GIT_TAG 
+run_cmd("git show %s:%s | tail -n +3 >> %s" % (PREV_RELEASE_GIT_TAG, CHANGELIST, TMP_CHANGELIST))
+run_cmd("cp %s %s" % (TMP_CHANGELIST, NEW_CHANGELIST))
+print "New change list generated as %s" % NEW_CHANGELIST
+cleanup(False)
+

From a08262d8769808dd3a8ee1b1e80fbf6ac13a557c Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?baishuo=28=E7=99=BD=E7=A1=95=29?= <vc_java@hotmail.com>
Date: Fri, 23 May 2014 13:02:40 -0700
Subject: [PATCH 259/810] Update LBFGSSuite.scala
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

the same reason as https://github.com/apache/spark/pull/588

Author: baishuo(白硕) <vc_java@hotmail.com>

Closes #815 from baishuo/master and squashes the following commits:

6876c1e [baishuo(白硕)] Update LBFGSSuite.scala
---
 .../org/apache/spark/mllib/optimization/LBFGSSuite.scala      | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/mllib/src/test/scala/org/apache/spark/mllib/optimization/LBFGSSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/optimization/LBFGSSuite.scala
index 6af1b502eb4d..820eca9b1bf6 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/optimization/LBFGSSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/optimization/LBFGSSuite.scala
@@ -43,7 +43,7 @@ class LBFGSSuite extends FunSuite with LocalSparkContext with ShouldMatchers {
   // Add an extra variable consisting of all 1.0's for the intercept.
   val testData = GradientDescentSuite.generateGDInput(A, B, nPoints, 42)
   val data = testData.map { case LabeledPoint(label, features) =>
-    label -> Vectors.dense(1.0, features.toArray: _*)
+    label -> Vectors.dense(1.0 +: features.toArray)
   }
 
   lazy val dataRDD = sc.parallelize(data, 2).cache()
@@ -55,7 +55,7 @@ class LBFGSSuite extends FunSuite with LocalSparkContext with ShouldMatchers {
   test("LBFGS loss should be decreasing and match the result of Gradient Descent.") {
     val regParam = 0
 
-    val initialWeightsWithIntercept = Vectors.dense(1.0, initialWeights: _*)
+    val initialWeightsWithIntercept = Vectors.dense(1.0 +: initialWeights.toArray)
     val convergenceTol = 1e-12
     val maxNumIterations = 10
 

From 5081a0a9d47ca31900ea4de570de2cbb0e063105 Mon Sep 17 00:00:00 2001
From: Andrew Or <andrewor14@gmail.com>
Date: Sat, 24 May 2014 18:01:49 -0700
Subject: [PATCH 260/810] [SPARK-1900 / 1918] PySpark on YARN is broken

If I run the following on a YARN cluster
```
bin/spark-submit sheep.py --master yarn-client
```
it fails because of a mismatch in paths: `spark-submit` thinks that `sheep.py` resides on HDFS, and balks when it can't find the file there. A natural workaround is to add the `file:` prefix to the file:
```
bin/spark-submit file:/path/to/sheep.py --master yarn-client
```
However, this also fails. This time it is because python does not understand URI schemes.

This PR fixes this by automatically resolving all paths passed as command line argument to `spark-submit` properly. This has the added benefit of keeping file and jar paths consistent across different cluster modes. For python, we strip the URI scheme before we actually try to run it.

Much of the code is originally written by @mengxr. Tested on YARN cluster. More tests pending.

Author: Andrew Or <andrewor14@gmail.com>

Closes #853 from andrewor14/submit-paths and squashes the following commits:

0bb097a [Andrew Or] Format path correctly before adding it to PYTHONPATH
323b45c [Andrew Or] Include --py-files on PYTHONPATH for pyspark shell
3c36587 [Andrew Or] Improve error messages (minor)
854aa6a [Andrew Or] Guard against NPE if user gives pathological paths
6638a6b [Andrew Or] Fix spark-shell jar paths after #849 went in
3bb0359 [Andrew Or] Update more comments (minor)
2a1f8a0 [Andrew Or] Update comments (minor)
6af2c77 [Andrew Or] Merge branch 'master' of github.com:apache/spark into submit-paths
a68c4d1 [Andrew Or] Handle Windows python file path correctly
427a250 [Andrew Or] Resolve paths properly for Windows
a591a4a [Andrew Or] Update tests for resolving URIs
6c8621c [Andrew Or] Move resolveURIs to Utils
db8255e [Andrew Or] Merge branch 'master' of github.com:apache/spark into submit-paths
f542dce [Andrew Or] Fix outdated tests
691c4ce [Andrew Or] Ignore special primary resource names
5342ac7 [Andrew Or] Add missing space in error message
02f77f3 [Andrew Or] Resolve command line arguments to spark-submit properly
---
 .../apache/spark/deploy/PythonRunner.scala    | 60 ++++++++++++++--
 .../org/apache/spark/deploy/SparkSubmit.scala | 25 ++++---
 .../spark/deploy/SparkSubmitArguments.scala   | 33 ++++++---
 .../scala/org/apache/spark/util/Utils.scala   | 71 ++++++++++++++++++-
 .../spark/deploy/PythonRunnerSuite.scala      | 61 ++++++++++++++++
 .../spark/deploy/SparkSubmitSuite.scala       | 41 ++++++-----
 .../org/apache/spark/util/UtilsSuite.scala    | 66 ++++++++++++++++-
 python/pyspark/context.py                     |  8 ++-
 .../org/apache/spark/repl/SparkILoop.scala    |  5 +-
 9 files changed, 323 insertions(+), 47 deletions(-)
 create mode 100644 core/src/test/scala/org/apache/spark/deploy/PythonRunnerSuite.scala

diff --git a/core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala b/core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala
index 2dfa02bd26f1..0d6751f3fa6d 100644
--- a/core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala
@@ -17,10 +17,13 @@
 
 package org.apache.spark.deploy
 
+import java.net.URI
+
 import scala.collection.mutable.ArrayBuffer
 import scala.collection.JavaConversions._
 
 import org.apache.spark.api.python.{PythonUtils, RedirectThread}
+import org.apache.spark.util.Utils
 
 /**
  * A main class used by spark-submit to launch Python applications. It executes python as a
@@ -28,12 +31,15 @@ import org.apache.spark.api.python.{PythonUtils, RedirectThread}
  */
 object PythonRunner {
   def main(args: Array[String]) {
-    val primaryResource = args(0)
+    val pythonFile = args(0)
     val pyFiles = args(1)
     val otherArgs = args.slice(2, args.length)
-
     val pythonExec = sys.env.get("PYSPARK_PYTHON").getOrElse("python") // TODO: get this from conf
 
+    // Format python file paths before adding them to the PYTHONPATH
+    val formattedPythonFile = formatPath(pythonFile)
+    val formattedPyFiles = formatPaths(pyFiles)
+
     // Launch a Py4J gateway server for the process to connect to; this will let it see our
     // Java system properties and such
     val gatewayServer = new py4j.GatewayServer(null, 0)
@@ -42,13 +48,13 @@ object PythonRunner {
     // Build up a PYTHONPATH that includes the Spark assembly JAR (where this class is), the
     // python directories in SPARK_HOME (if set), and any files in the pyFiles argument
     val pathElements = new ArrayBuffer[String]
-    pathElements ++= Option(pyFiles).getOrElse("").split(",")
+    pathElements ++= formattedPyFiles
     pathElements += PythonUtils.sparkPythonPath
     pathElements += sys.env.getOrElse("PYTHONPATH", "")
     val pythonPath = PythonUtils.mergePythonPaths(pathElements: _*)
 
     // Launch Python process
-    val builder = new ProcessBuilder(Seq(pythonExec, "-u", primaryResource) ++ otherArgs)
+    val builder = new ProcessBuilder(Seq(pythonExec, "-u", formattedPythonFile) ++ otherArgs)
     val env = builder.environment()
     env.put("PYTHONPATH", pythonPath)
     env.put("PYSPARK_GATEWAY_PORT", "" + gatewayServer.getListeningPort)
@@ -59,4 +65,50 @@ object PythonRunner {
 
     System.exit(process.waitFor())
   }
+
+  /**
+   * Format the python file path so that it can be added to the PYTHONPATH correctly.
+   *
+   * Python does not understand URI schemes in paths. Before adding python files to the
+   * PYTHONPATH, we need to extract the path from the URI. This is safe to do because we
+   * currently only support local python files.
+   */
+  def formatPath(path: String, testWindows: Boolean = false): String = {
+    if (Utils.nonLocalPaths(path, testWindows).nonEmpty) {
+      throw new IllegalArgumentException("Launching Python applications through " +
+        s"spark-submit is currently only supported for local files: $path")
+    }
+    val windows = Utils.isWindows || testWindows
+    var formattedPath = if (windows) Utils.formatWindowsPath(path) else path
+
+    // Strip the URI scheme from the path
+    formattedPath =
+      new URI(formattedPath).getScheme match {
+        case Utils.windowsDrive(d) if windows => formattedPath
+        case null => formattedPath
+        case _ => new URI(formattedPath).getPath
+      }
+
+    // Guard against malformed paths potentially throwing NPE
+    if (formattedPath == null) {
+      throw new IllegalArgumentException(s"Python file path is malformed: $path")
+    }
+
+    // In Windows, the drive should not be prefixed with "/"
+    // For instance, python does not understand "/C:/path/to/sheep.py"
+    formattedPath = if (windows) formattedPath.stripPrefix("/") else formattedPath
+    formattedPath
+  }
+
+  /**
+   * Format each python file path in the comma-delimited list of paths, so it can be
+   * added to the PYTHONPATH correctly.
+   */
+  def formatPaths(paths: String, testWindows: Boolean = false): Array[String] = {
+    Option(paths).getOrElse("")
+      .split(",")
+      .filter(_.nonEmpty)
+      .map { p => formatPath(p, testWindows) }
+  }
+
 }
diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
index c54331c00fab..7e9a9344e61f 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
@@ -136,9 +136,9 @@ object SparkSubmit {
         args.childArgs = ArrayBuffer(args.primaryResource, args.pyFiles) ++ args.childArgs
         args.files = mergeFileLists(args.files, args.primaryResource)
       }
-      val pyFiles = Option(args.pyFiles).getOrElse("")
-      args.files = mergeFileLists(args.files, pyFiles)
-      sysProps("spark.submit.pyFiles") = pyFiles
+      args.files = mergeFileLists(args.files, args.pyFiles)
+      // Format python file paths properly before adding them to the PYTHONPATH
+      sysProps("spark.submit.pyFiles") = PythonRunner.formatPaths(args.pyFiles).mkString(",")
     }
 
     // If we're deploying into YARN, use yarn.Client as a wrapper around the user class
@@ -299,13 +299,18 @@ object SparkSubmit {
   }
 
   private def addJarToClasspath(localJar: String, loader: ExecutorURLClassLoader) {
-    val localJarFile = new File(localJar)
-    if (!localJarFile.exists()) {
-      printWarning(s"Jar $localJar does not exist, skipping.")
+    val uri = Utils.resolveURI(localJar)
+    uri.getScheme match {
+      case "file" | "local" =>
+        val file = new File(uri.getPath)
+        if (file.exists()) {
+          loader.addURL(file.toURI.toURL)
+        } else {
+          printWarning(s"Local jar $file does not exist, skipping.")
+        }
+      case _ =>
+        printWarning(s"Skip remote jar $uri.")
     }
-
-    val url = localJarFile.getAbsoluteFile.toURI.toURL
-    loader.addURL(url)
   }
 
   /**
@@ -318,7 +323,7 @@ object SparkSubmit {
   /**
    * Return whether the given primary resource represents a shell.
    */
-  private def isShell(primaryResource: String): Boolean = {
+  private[spark] def isShell(primaryResource: String): Boolean = {
     primaryResource == SPARK_SHELL || primaryResource == PYSPARK_SHELL
   }
 
diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
index 0cc05fb95aef..bf449afae695 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
@@ -118,7 +118,7 @@ private[spark] class SparkSubmitArguments(args: Seq[String]) {
         mainClass = jar.getManifest.getMainAttributes.getValue("Main-Class")
       } catch {
         case e: Exception =>
-          SparkSubmit.printErrorAndExit("Failed to read JAR: " + primaryResource)
+          SparkSubmit.printErrorAndExit("Cannot load main class from JAR: " + primaryResource)
           return
       }
     }
@@ -148,6 +148,18 @@ private[spark] class SparkSubmitArguments(args: Seq[String]) {
       SparkSubmit.printErrorAndExit("--py-files given but primary resource is not a Python script")
     }
 
+    // Require all python files to be local, so we can add them to the PYTHONPATH
+    if (isPython) {
+      if (Utils.nonLocalPaths(primaryResource).nonEmpty) {
+        SparkSubmit.printErrorAndExit(s"Only local python files are supported: $primaryResource")
+      }
+      val nonLocalPyFiles = Utils.nonLocalPaths(pyFiles).mkString(",")
+      if (nonLocalPyFiles.nonEmpty) {
+        SparkSubmit.printErrorAndExit(
+          s"Only local additional python files are supported: $nonLocalPyFiles")
+      }
+    }
+
     if (master.startsWith("yarn")) {
       val hasHadoopEnv = sys.env.contains("HADOOP_CONF_DIR") || sys.env.contains("YARN_CONF_DIR")
       if (!hasHadoopEnv && !Utils.isTesting) {
@@ -263,19 +275,19 @@ private[spark] class SparkSubmitArguments(args: Seq[String]) {
         parse(tail)
 
       case ("--files") :: value :: tail =>
-        files = value
+        files = Utils.resolveURIs(value)
         parse(tail)
 
       case ("--py-files") :: value :: tail =>
-        pyFiles = value
+        pyFiles = Utils.resolveURIs(value)
         parse(tail)
 
       case ("--archives") :: value :: tail =>
-        archives = value
+        archives = Utils.resolveURIs(value)
         parse(tail)
 
       case ("--jars") :: value :: tail =>
-        jars = value
+        jars = Utils.resolveURIs(value)
         parse(tail)
 
       case ("--help" | "-h") :: tail =>
@@ -296,7 +308,12 @@ private[spark] class SparkSubmitArguments(args: Seq[String]) {
               val errMessage = s"Unrecognized option '$value'."
               SparkSubmit.printErrorAndExit(errMessage)
             case v =>
-              primaryResource = v
+              primaryResource =
+                if (!SparkSubmit.isShell(v)) {
+                  Utils.resolveURI(v).toString
+                } else {
+                  v
+                }
               inSparkOpts = false
               isPython = SparkSubmit.isPython(v)
               parse(tail)
@@ -327,8 +344,8 @@ private[spark] class SparkSubmitArguments(args: Seq[String]) {
         |  --name NAME                 A name of your application.
         |  --jars JARS                 Comma-separated list of local jars to include on the driver
         |                              and executor classpaths.
-        |  --py-files PY_FILES         Comma-separated list of .zip or .egg files to place on the
-        |                              PYTHONPATH for Python apps.
+        |  --py-files PY_FILES         Comma-separated list of .zip, .egg, or .py files to place
+        |                              on the PYTHONPATH for Python apps.
         |  --files FILES               Comma-separated list of files to be placed in the working
         |                              directory of each executor.
         |  --properties-file FILE      Path to a file from which to load extra properties. If not
diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala
index 0c7cff019fce..3b1b6df089b8 100644
--- a/core/src/main/scala/org/apache/spark/util/Utils.scala
+++ b/core/src/main/scala/org/apache/spark/util/Utils.scala
@@ -1086,9 +1086,19 @@ private[spark] object Utils extends Logging {
   }
 
   /**
-   * Return true if this is Windows.
+   * Whether the underlying operating system is Windows.
    */
-  def isWindows = SystemUtils.IS_OS_WINDOWS
+  val isWindows = SystemUtils.IS_OS_WINDOWS
+
+  /**
+   * Pattern for matching a Windows drive, which contains only a single alphabet character.
+   */
+  val windowsDrive = "([a-zA-Z])".r
+
+  /**
+   * Format a Windows path such that it can be safely passed to a URI.
+   */
+  def formatWindowsPath(path: String): String = path.replace("\\", "/")
 
   /**
    * Indicates whether Spark is currently running unit tests.
@@ -1166,4 +1176,61 @@ private[spark] object Utils extends Logging {
         true
     }
   }
+
+  /**
+   * Return a well-formed URI for the file described by a user input string.
+   *
+   * If the supplied path does not contain a scheme, or is a relative path, it will be
+   * converted into an absolute path with a file:// scheme.
+   */
+  def resolveURI(path: String, testWindows: Boolean = false): URI = {
+
+    // In Windows, the file separator is a backslash, but this is inconsistent with the URI format
+    val windows = isWindows || testWindows
+    val formattedPath = if (windows) formatWindowsPath(path) else path
+
+    val uri = new URI(formattedPath)
+    if (uri.getPath == null) {
+      throw new IllegalArgumentException(s"Given path is malformed: $uri")
+    }
+    uri.getScheme match {
+      case windowsDrive(d) if windows =>
+        new URI("file:/" + uri.toString.stripPrefix("/"))
+      case null =>
+        // Preserve fragments for HDFS file name substitution (denoted by "#")
+        // For instance, in "abc.py#xyz.py", "xyz.py" is the name observed by the application
+        val fragment = uri.getFragment
+        val part = new File(uri.getPath).toURI
+        new URI(part.getScheme, part.getPath, fragment)
+      case _ =>
+        uri
+    }
+  }
+
+  /** Resolve a comma-separated list of paths. */
+  def resolveURIs(paths: String, testWindows: Boolean = false): String = {
+    if (paths == null || paths.trim.isEmpty) {
+      ""
+    } else {
+      paths.split(",").map { p => Utils.resolveURI(p, testWindows) }.mkString(",")
+    }
+  }
+
+  /** Return all non-local paths from a comma-separated list of paths. */
+  def nonLocalPaths(paths: String, testWindows: Boolean = false): Array[String] = {
+    val windows = isWindows || testWindows
+    if (paths == null || paths.trim.isEmpty) {
+      Array.empty
+    } else {
+      paths.split(",").filter { p =>
+        val formattedPath = if (windows) formatWindowsPath(p) else p
+        new URI(formattedPath).getScheme match {
+          case windowsDrive(d) if windows => false
+          case "local" | "file" | null => false
+          case _ => true
+        }
+      }
+    }
+  }
+
 }
diff --git a/core/src/test/scala/org/apache/spark/deploy/PythonRunnerSuite.scala b/core/src/test/scala/org/apache/spark/deploy/PythonRunnerSuite.scala
new file mode 100644
index 000000000000..bb6251fb4bfb
--- /dev/null
+++ b/core/src/test/scala/org/apache/spark/deploy/PythonRunnerSuite.scala
@@ -0,0 +1,61 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.deploy
+
+import org.scalatest.FunSuite
+
+class PythonRunnerSuite extends FunSuite {
+
+  // Test formatting a single path to be added to the PYTHONPATH
+  test("format path") {
+    assert(PythonRunner.formatPath("spark.py") === "spark.py")
+    assert(PythonRunner.formatPath("file:/spark.py") === "/spark.py")
+    assert(PythonRunner.formatPath("file:///spark.py") === "/spark.py")
+    assert(PythonRunner.formatPath("local:/spark.py") === "/spark.py")
+    assert(PythonRunner.formatPath("local:///spark.py") === "/spark.py")
+    assert(PythonRunner.formatPath("C:/a/b/spark.py", testWindows = true) === "C:/a/b/spark.py")
+    assert(PythonRunner.formatPath("/C:/a/b/spark.py", testWindows = true) === "C:/a/b/spark.py")
+    assert(PythonRunner.formatPath("file:/C:/a/b/spark.py", testWindows = true) ===
+      "C:/a/b/spark.py")
+    intercept[IllegalArgumentException] { PythonRunner.formatPath("one:two") }
+    intercept[IllegalArgumentException] { PythonRunner.formatPath("hdfs:s3:xtremeFS") }
+    intercept[IllegalArgumentException] { PythonRunner.formatPath("hdfs:/path/to/some.py") }
+  }
+
+  // Test formatting multiple comma-separated paths to be added to the PYTHONPATH
+  test("format paths") {
+    assert(PythonRunner.formatPaths("spark.py") === Array("spark.py"))
+    assert(PythonRunner.formatPaths("file:/spark.py") === Array("/spark.py"))
+    assert(PythonRunner.formatPaths("file:/app.py,local:/spark.py") ===
+      Array("/app.py", "/spark.py"))
+    assert(PythonRunner.formatPaths("me.py,file:/you.py,local:/we.py") ===
+      Array("me.py", "/you.py", "/we.py"))
+    assert(PythonRunner.formatPaths("C:/a/b/spark.py", testWindows = true) ===
+      Array("C:/a/b/spark.py"))
+    assert(PythonRunner.formatPaths("/C:/a/b/spark.py", testWindows = true) ===
+      Array("C:/a/b/spark.py"))
+    assert(PythonRunner.formatPaths("C:/free.py,pie.py", testWindows = true) ===
+      Array("C:/free.py", "pie.py"))
+    assert(PythonRunner.formatPaths("lovely.py,C:/free.py,file:/d:/fry.py", testWindows = true) ===
+      Array("lovely.py", "C:/free.py", "d:/fry.py"))
+    intercept[IllegalArgumentException] { PythonRunner.formatPaths("one:two,three") }
+    intercept[IllegalArgumentException] { PythonRunner.formatPaths("two,three,four:five:six") }
+    intercept[IllegalArgumentException] { PythonRunner.formatPaths("hdfs:/some.py,foo.py") }
+    intercept[IllegalArgumentException] { PythonRunner.formatPaths("foo.py,hdfs:/some.py") }
+  }
+}
diff --git a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala
index 6c0deede5378..02427a4a8350 100644
--- a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala
@@ -91,7 +91,7 @@ class SparkSubmitSuite extends FunSuite with ShouldMatchers {
       "--jars=one.jar,two.jar,three.jar",
       "--name=myApp")
     val appArgs = new SparkSubmitArguments(clArgs)
-    appArgs.jars should be ("one.jar,two.jar,three.jar")
+    appArgs.jars should include regex (".*one.jar,.*two.jar,.*three.jar")
     appArgs.name should be ("myApp")
   }
 
@@ -125,17 +125,17 @@ class SparkSubmitSuite extends FunSuite with ShouldMatchers {
     val appArgs = new SparkSubmitArguments(clArgs)
     val (childArgs, classpath, sysProps, mainClass) = createLaunchEnv(appArgs)
     val childArgsStr = childArgs.mkString(" ")
-    childArgsStr should include ("--jar thejar.jar")
     childArgsStr should include ("--class org.SomeClass")
-    childArgsStr should include ("--addJars one.jar,two.jar,three.jar")
     childArgsStr should include ("--executor-memory 5g")
     childArgsStr should include ("--driver-memory 4g")
     childArgsStr should include ("--executor-cores 5")
     childArgsStr should include ("--arg arg1 --arg arg2")
     childArgsStr should include ("--queue thequeue")
-    childArgsStr should include ("--files file1.txt,file2.txt")
-    childArgsStr should include ("--archives archive1.txt,archive2.txt")
     childArgsStr should include ("--num-executors 6")
+    childArgsStr should include regex ("--jar .*thejar.jar")
+    childArgsStr should include regex ("--addJars .*one.jar,.*two.jar,.*three.jar")
+    childArgsStr should include regex ("--files .*file1.txt,.*file2.txt")
+    childArgsStr should include regex ("--archives .*archive1.txt,.*archive2.txt")
     mainClass should be ("org.apache.spark.deploy.yarn.Client")
     classpath should have length (0)
     sysProps("spark.app.name") should be ("beauty")
@@ -162,18 +162,19 @@ class SparkSubmitSuite extends FunSuite with ShouldMatchers {
     val (childArgs, classpath, sysProps, mainClass) = createLaunchEnv(appArgs)
     childArgs.mkString(" ") should be ("arg1 arg2")
     mainClass should be ("org.SomeClass")
-    classpath should contain ("thejar.jar")
-    classpath should contain ("one.jar")
-    classpath should contain ("two.jar")
-    classpath should contain ("three.jar")
+    classpath should have length (4)
+    classpath(0) should endWith ("thejar.jar")
+    classpath(1) should endWith ("one.jar")
+    classpath(2) should endWith ("two.jar")
+    classpath(3) should endWith ("three.jar")
     sysProps("spark.app.name") should be ("trill")
-    sysProps("spark.jars") should be ("one.jar,two.jar,three.jar,thejar.jar")
     sysProps("spark.executor.memory") should be ("5g")
     sysProps("spark.executor.cores") should be ("5")
     sysProps("spark.yarn.queue") should be ("thequeue")
-    sysProps("spark.yarn.dist.files") should be ("file1.txt,file2.txt")
-    sysProps("spark.yarn.dist.archives") should be ("archive1.txt,archive2.txt")
     sysProps("spark.executor.instances") should be ("6")
+    sysProps("spark.yarn.dist.files") should include regex (".*file1.txt,.*file2.txt")
+    sysProps("spark.yarn.dist.archives") should include regex (".*archive1.txt,.*archive2.txt")
+    sysProps("spark.jars") should include regex (".*one.jar,.*two.jar,.*three.jar,.*thejar.jar")
     sysProps("SPARK_SUBMIT") should be ("true")
   }
 
@@ -190,11 +191,13 @@ class SparkSubmitSuite extends FunSuite with ShouldMatchers {
     val appArgs = new SparkSubmitArguments(clArgs)
     val (childArgs, classpath, sysProps, mainClass) = createLaunchEnv(appArgs)
     val childArgsStr = childArgs.mkString(" ")
-    childArgsStr.startsWith("--memory 4g --cores 5 --supervise") should be (true)
-    childArgsStr should include ("launch spark://h:p thejar.jar org.SomeClass arg1 arg2")
+    childArgsStr should startWith ("--memory 4g --cores 5 --supervise")
+    childArgsStr should include regex ("launch spark://h:p .*thejar.jar org.SomeClass arg1 arg2")
     mainClass should be ("org.apache.spark.deploy.Client")
-    classpath should have length (0)
-    sysProps should have size (2) // contains --jar entry and SPARK_SUBMIT
+    classpath should have size (0)
+    sysProps should have size (2)
+    sysProps.keys should contain ("spark.jars")
+    sysProps.keys should contain ("SPARK_SUBMIT")
   }
 
   test("handles standalone client mode") {
@@ -211,7 +214,8 @@ class SparkSubmitSuite extends FunSuite with ShouldMatchers {
     val (childArgs, classpath, sysProps, mainClass) = createLaunchEnv(appArgs)
     childArgs.mkString(" ") should be ("arg1 arg2")
     mainClass should be ("org.SomeClass")
-    classpath should contain ("thejar.jar")
+    classpath should have length (1)
+    classpath(0) should endWith ("thejar.jar")
     sysProps("spark.executor.memory") should be ("5g")
     sysProps("spark.cores.max") should be ("5")
   }
@@ -230,7 +234,8 @@ class SparkSubmitSuite extends FunSuite with ShouldMatchers {
     val (childArgs, classpath, sysProps, mainClass) = createLaunchEnv(appArgs)
     childArgs.mkString(" ") should be ("arg1 arg2")
     mainClass should be ("org.SomeClass")
-    classpath should contain ("thejar.jar")
+    classpath should have length (1)
+    classpath(0) should endWith ("thejar.jar")
     sysProps("spark.executor.memory") should be ("5g")
     sysProps("spark.cores.max") should be ("5")
   }
diff --git a/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala b/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala
index cf9e20d347dd..0aad882ed76a 100644
--- a/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala
+++ b/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala
@@ -20,6 +20,7 @@ package org.apache.spark.util
 import scala.util.Random
 
 import java.io.{File, ByteArrayOutputStream, ByteArrayInputStream, FileOutputStream}
+import java.net.URI
 import java.nio.{ByteBuffer, ByteOrder}
 
 import com.google.common.base.Charsets
@@ -168,5 +169,68 @@ class UtilsSuite extends FunSuite {
     assert(result.size.equals(1))
     assert(result(0).getCanonicalPath.equals(child1.getCanonicalPath))
   }
-}
 
+  test("resolveURI") {
+    def assertResolves(before: String, after: String, testWindows: Boolean = false): Unit = {
+      assume(before.split(",").length == 1)
+      assert(Utils.resolveURI(before, testWindows) === new URI(after))
+      assert(Utils.resolveURI(after, testWindows) === new URI(after))
+      assert(new URI(Utils.resolveURIs(before, testWindows)) === new URI(after))
+      assert(new URI(Utils.resolveURIs(after, testWindows)) === new URI(after))
+    }
+    val cwd = System.getProperty("user.dir")
+    assertResolves("hdfs:/root/spark.jar", "hdfs:/root/spark.jar")
+    assertResolves("hdfs:///root/spark.jar#app.jar", "hdfs:/root/spark.jar#app.jar")
+    assertResolves("spark.jar", s"file:$cwd/spark.jar")
+    assertResolves("spark.jar#app.jar", s"file:$cwd/spark.jar#app.jar")
+    assertResolves("C:/path/to/file.txt", "file:/C:/path/to/file.txt", testWindows = true)
+    assertResolves("C:\\path\\to\\file.txt", "file:/C:/path/to/file.txt", testWindows = true)
+    assertResolves("file:/C:/path/to/file.txt", "file:/C:/path/to/file.txt", testWindows = true)
+    assertResolves("file:///C:/path/to/file.txt", "file:/C:/path/to/file.txt", testWindows = true)
+    assertResolves("file:/C:/file.txt#alias.txt", "file:/C:/file.txt#alias.txt", testWindows = true)
+    intercept[IllegalArgumentException] { Utils.resolveURI("file:foo") }
+    intercept[IllegalArgumentException] { Utils.resolveURI("file:foo:baby") }
+
+    // Test resolving comma-delimited paths
+    assert(Utils.resolveURIs("jar1,jar2") === s"file:$cwd/jar1,file:$cwd/jar2")
+    assert(Utils.resolveURIs("file:/jar1,file:/jar2") === "file:/jar1,file:/jar2")
+    assert(Utils.resolveURIs("hdfs:/jar1,file:/jar2,jar3") ===
+      s"hdfs:/jar1,file:/jar2,file:$cwd/jar3")
+    assert(Utils.resolveURIs("hdfs:/jar1,file:/jar2,jar3,jar4#jar5") ===
+      s"hdfs:/jar1,file:/jar2,file:$cwd/jar3,file:$cwd/jar4#jar5")
+    assert(Utils.resolveURIs("hdfs:/jar1,file:/jar2,jar3,C:\\pi.py#py.pi", testWindows = true) ===
+      s"hdfs:/jar1,file:/jar2,file:$cwd/jar3,file:/C:/pi.py#py.pi")
+  }
+
+  test("nonLocalPaths") {
+    assert(Utils.nonLocalPaths("spark.jar") === Array.empty)
+    assert(Utils.nonLocalPaths("file:/spark.jar") === Array.empty)
+    assert(Utils.nonLocalPaths("file:///spark.jar") === Array.empty)
+    assert(Utils.nonLocalPaths("local:/spark.jar") === Array.empty)
+    assert(Utils.nonLocalPaths("local:///spark.jar") === Array.empty)
+    assert(Utils.nonLocalPaths("hdfs:/spark.jar") === Array("hdfs:/spark.jar"))
+    assert(Utils.nonLocalPaths("hdfs:///spark.jar") === Array("hdfs:///spark.jar"))
+    assert(Utils.nonLocalPaths("file:/spark.jar,local:/smart.jar,family.py") === Array.empty)
+    assert(Utils.nonLocalPaths("local:/spark.jar,file:/smart.jar,family.py") === Array.empty)
+    assert(Utils.nonLocalPaths("hdfs:/spark.jar,s3:/smart.jar") ===
+      Array("hdfs:/spark.jar", "s3:/smart.jar"))
+    assert(Utils.nonLocalPaths("hdfs:/spark.jar,s3:/smart.jar,local.py,file:/hello/pi.py") ===
+      Array("hdfs:/spark.jar", "s3:/smart.jar"))
+    assert(Utils.nonLocalPaths("local.py,hdfs:/spark.jar,file:/hello/pi.py,s3:/smart.jar") ===
+      Array("hdfs:/spark.jar", "s3:/smart.jar"))
+
+    // Test Windows paths
+    assert(Utils.nonLocalPaths("C:/some/path.jar", testWindows = true) === Array.empty)
+    assert(Utils.nonLocalPaths("file:/C:/some/path.jar", testWindows = true) === Array.empty)
+    assert(Utils.nonLocalPaths("file:///C:/some/path.jar", testWindows = true) === Array.empty)
+    assert(Utils.nonLocalPaths("local:/C:/some/path.jar", testWindows = true) === Array.empty)
+    assert(Utils.nonLocalPaths("local:///C:/some/path.jar", testWindows = true) === Array.empty)
+    assert(Utils.nonLocalPaths("hdfs:/a.jar,C:/my.jar,s3:/another.jar", testWindows = true) ===
+      Array("hdfs:/a.jar", "s3:/another.jar"))
+    assert(Utils.nonLocalPaths("D:/your.jar,hdfs:/a.jar,s3:/another.jar", testWindows = true) ===
+      Array("hdfs:/a.jar", "s3:/another.jar"))
+    assert(Utils.nonLocalPaths("hdfs:/a.jar,s3:/another.jar,e:/our.jar", testWindows = true) ===
+      Array("hdfs:/a.jar", "s3:/another.jar"))
+  }
+
+}
diff --git a/python/pyspark/context.py b/python/pyspark/context.py
index c9ff82d23b3c..27b440d73bdc 100644
--- a/python/pyspark/context.py
+++ b/python/pyspark/context.py
@@ -159,10 +159,14 @@ def __init__(self, master=None, appName=None, sparkHome=None, pyFiles=None,
             self.addPyFile(path)
 
         # Deploy code dependencies set by spark-submit; these will already have been added
-        # with SparkContext.addFile, so we just need to add them
+        # with SparkContext.addFile, so we just need to add them to the PYTHONPATH
         for path in self._conf.get("spark.submit.pyFiles", "").split(","):
             if path != "":
-                self._python_includes.append(os.path.basename(path))
+                (dirname, filename) = os.path.split(path)
+                self._python_includes.append(filename)
+                sys.path.append(path)
+                if not dirname in sys.path:
+                    sys.path.append(dirname)
 
         # Create a temporary directory inside spark.local.dir:
         local_dir = self._jvm.org.apache.spark.util.Utils.getLocalDir(self._jsc.sc().conf())
diff --git a/repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala b/repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala
index 5f34362ccd97..e1db4d5395ab 100644
--- a/repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala
+++ b/repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala
@@ -942,7 +942,7 @@ class SparkILoop(in0: Option[BufferedReader], protected val out: JPrintWriter,
 
   def createSparkContext(): SparkContext = {
     val execUri = System.getenv("SPARK_EXECUTOR_URI")
-    val jars = SparkILoop.getAddedJars.map(new java.io.File(_).getAbsolutePath)
+    val jars = SparkILoop.getAddedJars
     val conf = new SparkConf()
       .setMaster(getMaster())
       .setAppName("Spark shell")
@@ -997,7 +997,8 @@ object SparkILoop {
     val propJars = sys.props.get("spark.jars").flatMap { p =>
       if (p == "") None else Some(p)
     }
-    propJars.orElse(envJars).map(_.split(",")).getOrElse(Array.empty)
+    val jars = propJars.orElse(envJars).getOrElse("")
+    Utils.resolveURIs(jars).split(",").filter(_.nonEmpty)
   }
 
   // Designed primarily for use by test code: take a String with a

From 75a03277704f8618a0f1c41aecfb1ebd24a8ac1a Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Sat, 24 May 2014 18:27:00 -0700
Subject: [PATCH 261/810] SPARK-1911: Emphasize that Spark jars should be built
 with Java 6.

This commit requires the user to manually say "yes" when buiding Spark
without Java 6. The prompt can be bypassed with a flag (e.g. if the user
is scripting around make-distribution).

Author: Patrick Wendell <pwendell@gmail.com>

Closes #859 from pwendell/java6 and squashes the following commits:

4921133 [Patrick Wendell] Adding Pyspark Notice
fee8c9e [Patrick Wendell] SPARK-1911: Emphasize that Spark jars should be built with Java 6.
---
 make-distribution.sh | 52 ++++++++++++++++++++++++++------------------
 1 file changed, 31 insertions(+), 21 deletions(-)

diff --git a/make-distribution.sh b/make-distribution.sh
index 1c89027d68be..ae52b4976dc2 100755
--- a/make-distribution.sh
+++ b/make-distribution.sh
@@ -46,27 +46,6 @@ set -e
 FWDIR="$(cd `dirname $0`; pwd)"
 DISTDIR="$FWDIR/dist"
 
-if [ -z "$JAVA_HOME" ]; then
-  echo "Error: JAVA_HOME is not set, cannot proceed."
-  exit -1
-fi
-
-JAVA_CMD="$JAVA_HOME"/bin/java
-JAVA_VERSION=$("$JAVA_CMD" -version 2>&1)
-if ! [[ "$JAVA_VERSION" =~ "1.6" ]]; then
-  echo "***NOTE***: JAVA_HOME is not set to a JDK 6 installation. The resulting"
-  echo "            distribution will not support Java 6. See SPARK-1703."
-  echo "Output from 'java -version' was:"
-  echo "$JAVA_VERSION"
-fi
-
-VERSION=$(mvn help:evaluate -Dexpression=project.version 2>/dev/null | grep -v "INFO" | tail -n 1)
-if [ $? != 0 ]; then
-    echo -e "You need Maven installed to build Spark."
-    echo -e "Download Maven from https://maven.apache.org/"
-    exit -1;
-fi
-
 # Initialize defaults
 SPARK_HADOOP_VERSION=1.0.4
 SPARK_YARN=false
@@ -88,6 +67,9 @@ while (( "$#" )); do
     --with-hive)
       SPARK_HIVE=true
       ;;
+    --skip-java-test)
+      SKIP_JAVA_TEST=true
+      ;;
     --with-tachyon)
       SPARK_TACHYON=true
       ;;
@@ -102,6 +84,34 @@ while (( "$#" )); do
   shift
 done
 
+if [ -z "$JAVA_HOME" ]; then
+  echo "Error: JAVA_HOME is not set, cannot proceed."
+  exit -1
+fi
+
+VERSION=$(mvn help:evaluate -Dexpression=project.version 2>/dev/null | grep -v "INFO" | tail -n 1)
+if [ $? != 0 ]; then
+    echo -e "You need Maven installed to build Spark."
+    echo -e "Download Maven from https://maven.apache.org/"
+    exit -1;
+fi
+
+JAVA_CMD="$JAVA_HOME"/bin/java
+JAVA_VERSION=$("$JAVA_CMD" -version 2>&1)
+if [[ ! "$JAVA_VERSION" =~ "1.6" && -z "$SKIP_JAVA_TEST" ]]; then
+  echo "***NOTE***: JAVA_HOME is not set to a JDK 6 installation. The resulting"
+  echo "            distribution may not work well with PySpark and will not run"
+  echo "            with Java 6 (See SPARK-1703 and SPARK-1911)."
+  echo "            This test can be disabled by adding --skip-java-test."
+  echo "Output from 'java -version' was:"
+  echo "$JAVA_VERSION"
+  read -p "Would you like to continue anyways? [y,n]: " -r
+  if [[ ! $REPLY =~ ^[Yy]$ ]]; then
+    echo "Okay, exiting."
+    exit 1
+  fi 
+fi
+
 if [ "$NAME" == "none" ]; then
   NAME=$SPARK_HADOOP_VERSION
 fi

From 4e4831b8facc186cda6ef31040ccdeab48acbbb7 Mon Sep 17 00:00:00 2001
From: Zhen Peng <zhenpeng01@baidu.com>
Date: Sat, 24 May 2014 20:40:19 -0700
Subject: [PATCH 262/810] [SPARK-1886] check executor id existence when
 executor exit

Author: Zhen Peng <zhenpeng01@baidu.com>

Closes #827 from zhpengg/bugfix-executor-id-not-found and squashes the following commits:

cd8bb65 [Zhen Peng] bugfix: check executor id existence when executor exit
---
 .../apache/spark/deploy/worker/Worker.scala   | 22 ++++++++++++-------
 1 file changed, 14 insertions(+), 8 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala b/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala
index fb9cc116cd08..8b6747977eb8 100755
--- a/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala
@@ -263,14 +263,20 @@ private[spark] class Worker(
       }
       val fullId = appId + "/" + execId
       if (ExecutorState.isFinished(state)) {
-        val executor = executors(fullId)
-        logInfo("Executor " + fullId + " finished with state " + state +
-          message.map(" message " + _).getOrElse("") +
-          exitStatus.map(" exitStatus " + _).getOrElse(""))
-        executors -= fullId
-        finishedExecutors(fullId) = executor
-        coresUsed -= executor.cores
-        memoryUsed -= executor.memory
+        executors.get(fullId) match {
+          case Some(executor) => 
+            logInfo("Executor " + fullId + " finished with state " + state +
+              message.map(" message " + _).getOrElse("") +
+              exitStatus.map(" exitStatus " + _).getOrElse(""))
+            executors -= fullId
+            finishedExecutors(fullId) = executor
+            coresUsed -= executor.cores
+            memoryUsed -= executor.memory
+          case None =>
+            logInfo("Unknown Executor " + fullId + " finished with state " + state +
+              message.map(" message " + _).getOrElse("") +
+              exitStatus.map(" exitStatus " + _).getOrElse(""))
+        }
       }
 
     case KillExecutor(masterUrl, appId, execId) =>

From 5afe6af0b192ce7e908634992e8752537b1c4ed1 Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian.cs.zju@gmail.com>
Date: Sat, 24 May 2014 20:42:01 -0700
Subject: [PATCH 263/810] [SPARK-1913][SQL] Bug fix: column pruning error in
 Parquet support

JIRA issue: [SPARK-1913](https://issues.apache.org/jira/browse/SPARK-1913)

When scanning Parquet tables, attributes referenced only in predicates that are pushed down are not passed to the `ParquetTableScan` operator and causes exception.

Author: Cheng Lian <lian.cs.zju@gmail.com>

Closes #863 from liancheng/spark-1913 and squashes the following commits:

f976b73 [Cheng Lian] Addessed the readability issue commented by @rxin
f5b257d [Cheng Lian] Added back comments deleted by mistake
ae60ab3 [Cheng Lian] [SPARK-1913] Attributes referenced only in predicates pushed down should remain in ParquetTableScan operator
---
 .../org/apache/spark/sql/SQLContext.scala     |  6 +++++-
 .../spark/sql/execution/SparkStrategies.scala | 20 ++++++++++---------
 .../spark/sql/parquet/ParquetQuerySuite.scala |  6 +++++-
 .../spark/sql/hive/HiveStrategies.scala       |  1 +
 4 files changed, 22 insertions(+), 11 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
index bfebfa0c28c5..043be58edc91 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
@@ -206,17 +206,21 @@ class SQLContext(@transient val sparkContext: SparkContext)
      * final desired output requires complex expressions to be evaluated or when columns can be
      * further eliminated out after filtering has been done.
      *
+     * The `prunePushedDownFilters` parameter is used to remove those filters that can be optimized
+     * away by the filter pushdown optimization.
+     *
      * The required attributes for both filtering and expression evaluation are passed to the
      * provided `scanBuilder` function so that it can avoid unnecessary column materialization.
      */
     def pruneFilterProject(
         projectList: Seq[NamedExpression],
         filterPredicates: Seq[Expression],
+        prunePushedDownFilters: Seq[Expression] => Seq[Expression],
         scanBuilder: Seq[Attribute] => SparkPlan): SparkPlan = {
 
       val projectSet = projectList.flatMap(_.references).toSet
       val filterSet = filterPredicates.flatMap(_.references).toSet
-      val filterCondition = filterPredicates.reduceLeftOption(And)
+      val filterCondition = prunePushedDownFilters(filterPredicates).reduceLeftOption(And)
 
       // Right now we still use a projection even if the only evaluation is applying an alias
       // to a column.  Since this is a no-op, it could be avoided. However, using this
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
index 394a59700dba..cfa8bdae58b1 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
@@ -141,14 +141,14 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
       case logical.InsertIntoTable(table: ParquetRelation, partition, child, overwrite) =>
         InsertIntoParquetTable(table, planLater(child), overwrite)(sparkContext) :: Nil
       case PhysicalOperation(projectList, filters: Seq[Expression], relation: ParquetRelation) => {
-        val remainingFilters =
+        val prunePushedDownFilters =
           if (sparkContext.conf.getBoolean(ParquetFilters.PARQUET_FILTER_PUSHDOWN_ENABLED, true)) {
-            filters.filter {
-              // Note: filters cannot be pushed down to Parquet if they contain more complex
-              // expressions than simple "Attribute cmp Literal" comparisons. Here we remove
-              // all filters that have been pushed down. Note that a predicate such as
-              // "(A AND B) OR C" can result in "A OR C" being pushed down.
-              filter =>
+            (filters: Seq[Expression]) => {
+              filters.filter { filter =>
+                // Note: filters cannot be pushed down to Parquet if they contain more complex
+                // expressions than simple "Attribute cmp Literal" comparisons. Here we remove
+                // all filters that have been pushed down. Note that a predicate such as
+                // "(A AND B) OR C" can result in "A OR C" being pushed down.
                 val recordFilter = ParquetFilters.createFilter(filter)
                 if (!recordFilter.isDefined) {
                   // First case: the pushdown did not result in any record filter.
@@ -159,13 +159,15 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
                   // still want to keep "A AND B" in the higher-level filter, not just "B".
                   !ParquetFilters.findExpression(recordFilter.get, filter).isDefined
                 }
+              }
             }
           } else {
-            filters
+            identity[Seq[Expression]] _
           }
         pruneFilterProject(
           projectList,
-          remainingFilters,
+          filters,
+          prunePushedDownFilters,
           ParquetTableScan(_, relation, filters)(sparkContext)) :: Nil
       }
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala
index 65f4c17aeee3..f9731e82e492 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala
@@ -358,5 +358,9 @@ class ParquetQuerySuite extends QueryTest with FunSuite with BeforeAndAfterAll {
     assert(stringResult(0).getString(2) == "100", "stringvalue incorrect")
     assert(stringResult(0).getInt(1) === 100)
   }
-}
 
+  test("SPARK-1913 regression: columns only referenced by pushed down filters should remain") {
+    val query = sql(s"SELECT mystring FROM testfiltersource WHERE myint < 10")
+    assert(query.collect().size === 10)
+  }
+}
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala
index b2157074a41b..8b51957162e0 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala
@@ -69,6 +69,7 @@ private[hive] trait HiveStrategies {
         pruneFilterProject(
           projectList,
           otherPredicates,
+          identity[Seq[Expression]],
           HiveTableScan(_, relation, pruningPredicates.reduceLeftOption(And))(hiveContext)) :: Nil
       case _ =>
         Nil

From 6e9fb6320bec3371bc9c010ccbc1b915f500486b Mon Sep 17 00:00:00 2001
From: Colin Patrick Mccabe <cmccabe@cloudera.com>
Date: Sat, 24 May 2014 22:39:27 -0700
Subject: [PATCH 264/810] spark-submit: add exec at the end of the script

Add an 'exec' at the end of the spark-submit script, to avoid keeping a
bash process hanging around while it runs.  This makes ps look a little
bit nicer.

Author: Colin Patrick Mccabe <cmccabe@cloudera.com>

Closes #858 from cmccabe/SPARK-1907 and squashes the following commits:

7023b64 [Colin Patrick Mccabe] spark-submit: add exec at the end of the script
---
 bin/spark-submit | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/bin/spark-submit b/bin/spark-submit
index 63903b17a290..9e7cecedd032 100755
--- a/bin/spark-submit
+++ b/bin/spark-submit
@@ -41,5 +41,5 @@ if [ -n "$DRIVER_MEMORY" ] && [ $DEPLOY_MODE == "client" ]; then
   export SPARK_DRIVER_MEMORY=$DRIVER_MEMORY
 fi
 
-$SPARK_HOME/bin/spark-class org.apache.spark.deploy.SparkSubmit "${ORIG_ARGS[@]}"
+exec $SPARK_HOME/bin/spark-class org.apache.spark.deploy.SparkSubmit "${ORIG_ARGS[@]}"
 

From 6052db9dc10c996215658485e805200e4f0cf549 Mon Sep 17 00:00:00 2001
From: Kan Zhang <kzhang@apache.org>
Date: Sun, 25 May 2014 00:06:42 -0700
Subject: [PATCH 265/810] [SPARK-1822] SchemaRDD.count() should use query
 optimizer

Author: Kan Zhang <kzhang@apache.org>

Closes #841 from kanzhang/SPARK-1822 and squashes the following commits:

2f8072a [Kan Zhang] [SPARK-1822] Minor style update
cf4baa4 [Kan Zhang] [SPARK-1822] Adding Scaladoc
e67c910 [Kan Zhang] [SPARK-1822] SchemaRDD.count() should use optimizer
---
 python/pyspark/sql.py                              | 14 +++++++++++++-
 .../sql/catalyst/expressions/aggregates.scala      |  6 +++---
 .../scala/org/apache/spark/sql/SchemaRDD.scala     |  9 +++++++++
 .../scala/org/apache/spark/sql/DslQuerySuite.scala |  9 +++++----
 .../test/scala/org/apache/spark/sql/TestData.scala |  2 ++
 5 files changed, 32 insertions(+), 8 deletions(-)

diff --git a/python/pyspark/sql.py b/python/pyspark/sql.py
index bbe69e7d8f89..f2001afae4ee 100644
--- a/python/pyspark/sql.py
+++ b/python/pyspark/sql.py
@@ -268,7 +268,7 @@ def __init__(self, jschema_rdd, sql_ctx):
     def _jrdd(self):
         """
         Lazy evaluation of PythonRDD object. Only done when a user calls methods defined by the
-        L{pyspark.rdd.RDD} super class (map, count, etc.).
+        L{pyspark.rdd.RDD} super class (map, filter, etc.).
         """
         if not hasattr(self, '_lazy_jrdd'):
             self._lazy_jrdd = self._toPython()._jrdd
@@ -321,6 +321,18 @@ def saveAsTable(self, tableName):
         """
         self._jschema_rdd.saveAsTable(tableName)
 
+    def count(self):
+        """
+        Return the number of elements in this RDD.
+
+        >>> srdd = sqlCtx.inferSchema(rdd)
+        >>> srdd.count()
+        3L
+        >>> srdd.count() == srdd.map(lambda x: x).count()
+        True
+        """
+        return self._jschema_rdd.count()
+
     def _toPython(self):
         # We have to import the Row class explicitly, so that the reference Pickler has is
         # pyspark.sql.Row instead of __main__.Row
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
index 5dbaaa3b0ce3..1bcd4e22766a 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
@@ -151,7 +151,7 @@ case class MaxFunction(expr: Expression, base: AggregateExpression) extends Aggr
 case class Count(child: Expression) extends PartialAggregate with trees.UnaryNode[Expression] {
   override def references = child.references
   override def nullable = false
-  override def dataType = IntegerType
+  override def dataType = LongType
   override def toString = s"COUNT($child)"
 
   override def asPartial: SplitEvaluation = {
@@ -295,12 +295,12 @@ case class AverageFunction(expr: Expression, base: AggregateExpression)
 case class CountFunction(expr: Expression, base: AggregateExpression) extends AggregateFunction {
   def this() = this(null, null) // Required for serialization.
 
-  var count: Int = _
+  var count: Long = _
 
   override def update(input: Row): Unit = {
     val evaluatedExpr = expr.map(_.eval(input))
     if (evaluatedExpr.map(_ != null).reduceLeft(_ || _)) {
-      count += 1
+      count += 1L
     }
   }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala
index 2569815ebb20..452da3d02310 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala
@@ -274,6 +274,15 @@ class SchemaRDD(
       seed: Long) =
     new SchemaRDD(sqlContext, Sample(fraction, withReplacement, seed, logicalPlan))
 
+  /**
+   * :: Experimental ::
+   * Overriding base RDD implementation to leverage query optimizer
+   */
+  @Experimental
+  override def count(): Long = {
+    groupBy()(Count(Literal(1))).collect().head.getLong(0)
+  }
+
   /**
    * :: Experimental ::
    * Applies the given Generator, or table generating function, to this relation.
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala
index f43e98d61409..233132a2fec6 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala
@@ -108,10 +108,7 @@ class DslQuerySuite extends QueryTest {
   }
 
   test("count") {
-    checkAnswer(
-      testData2.groupBy()(Count(1)),
-      testData2.count()
-    )
+    assert(testData2.count() === testData2.map(_ => 1).count())
   }
 
   test("null count") {
@@ -126,6 +123,10 @@ class DslQuerySuite extends QueryTest {
     )
   }
 
+  test("zero count") {
+    assert(testData4.count() === 0)
+  }
+
   test("inner join where, one match per row") {
     checkAnswer(
       upperCaseData.join(lowerCaseData, Inner).where('n === 'N),
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala b/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala
index 1aca3872524d..b1eecb4dd3be 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala
@@ -47,6 +47,8 @@ object TestData {
       (1, null) ::
       (2, 2) :: Nil)
 
+  val testData4 = logical.LocalRelation('a.int, 'b.int)
+
   case class UpperCaseData(N: Int, L: String)
   val upperCaseData =
     TestSQLContext.sparkContext.parallelize(

From 5c7faecd75ea59454ad3209390ac078e6cf6e4a6 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Sun, 25 May 2014 01:32:15 -0700
Subject: [PATCH 266/810] Added PEP8 style configuration file.

This sets the max line length to 100 as a PEP8 exception.

Author: Reynold Xin <rxin@apache.org>

Closes #872 from rxin/pep8 and squashes the following commits:

2f26029 [Reynold Xin] Added PEP8 style configuration file.
---
 tox.ini | 2 ++
 1 file changed, 2 insertions(+)
 create mode 100644 tox.ini

diff --git a/tox.ini b/tox.ini
new file mode 100644
index 000000000000..2d94b09a90a4
--- /dev/null
+++ b/tox.ini
@@ -0,0 +1,2 @@
+[pep8]
+max-line-length=100

From d66642e3978a76977414c2fdaedebaad35662667 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Sun, 25 May 2014 01:44:49 -0700
Subject: [PATCH 267/810] SPARK-1822: Some minor cleanup work on
 SchemaRDD.count()

Minor cleanup following #841.

Author: Reynold Xin <rxin@apache.org>

Closes #868 from rxin/schema-count and squashes the following commits:

5442651 [Reynold Xin] SPARK-1822: Some minor cleanup work on SchemaRDD.count()
---
 python/pyspark/sql.py                                     | 5 ++++-
 .../src/main/scala/org/apache/spark/sql/SchemaRDD.scala   | 8 ++++----
 .../test/scala/org/apache/spark/sql/DslQuerySuite.scala   | 2 +-
 .../src/test/scala/org/apache/spark/sql/TestData.scala    | 2 +-
 4 files changed, 10 insertions(+), 7 deletions(-)

diff --git a/python/pyspark/sql.py b/python/pyspark/sql.py
index f2001afae4ee..fa4b9c7b688e 100644
--- a/python/pyspark/sql.py
+++ b/python/pyspark/sql.py
@@ -323,7 +323,10 @@ def saveAsTable(self, tableName):
 
     def count(self):
         """
-        Return the number of elements in this RDD.
+        Return the number of elements in this RDD. Unlike the base RDD
+        implementation of count, this implementation leverages the query
+        optimizer to compute the count on the SchemaRDD, which supports
+        features such as filter pushdown.
 
         >>> srdd = sqlCtx.inferSchema(rdd)
         >>> srdd.count()
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala
index 452da3d02310..9883ebc0b3c6 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala
@@ -276,12 +276,12 @@ class SchemaRDD(
 
   /**
    * :: Experimental ::
-   * Overriding base RDD implementation to leverage query optimizer
+   * Return the number of elements in the RDD. Unlike the base RDD implementation of count, this
+   * implementation leverages the query optimizer to compute the count on the SchemaRDD, which
+   * supports features such as filter pushdown.
    */
   @Experimental
-  override def count(): Long = {
-    groupBy()(Count(Literal(1))).collect().head.getLong(0)
-  }
+  override def count(): Long = groupBy()(Count(Literal(1))).collect().head.getLong(0)
 
   /**
    * :: Experimental ::
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala
index 233132a2fec6..94ba13b14b33 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala
@@ -124,7 +124,7 @@ class DslQuerySuite extends QueryTest {
   }
 
   test("zero count") {
-    assert(testData4.count() === 0)
+    assert(emptyTableData.count() === 0)
   }
 
   test("inner join where, one match per row") {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala b/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala
index b1eecb4dd3be..944f520e4351 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala
@@ -47,7 +47,7 @@ object TestData {
       (1, null) ::
       (2, 2) :: Nil)
 
-  val testData4 = logical.LocalRelation('a.int, 'b.int)
+  val emptyTableData = logical.LocalRelation('a.int, 'b.int)
 
   case class UpperCaseData(N: Int, L: String)
   val upperCaseData =

From 55fddf9cc0fe420d5396b0e730c8413b2f23d636 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Sun, 25 May 2014 01:47:08 -0700
Subject: [PATCH 268/810] Added license header for tox.ini.

(cherry picked from commit fa541f32c5b92e6868a9c99cbb2c87115d624d23)
Signed-off-by: Reynold Xin <rxin@apache.org>
---
 tox.ini | 15 +++++++++++++++
 1 file changed, 15 insertions(+)

diff --git a/tox.ini b/tox.ini
index 2d94b09a90a4..44766e529bf7 100644
--- a/tox.ini
+++ b/tox.ini
@@ -1,2 +1,17 @@
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 [pep8]
 max-line-length=100

From d79c2b28e17ec0b15198aaedd2e1f403d81f717e Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Sun, 25 May 2014 14:48:27 -0700
Subject: [PATCH 269/810] Fix PEP8 violations in examples/src/main/python.

Author: Reynold Xin <rxin@apache.org>

Closes #870 from rxin/examples-python-pep8 and squashes the following commits:

2829e84 [Reynold Xin] Fix PEP8 violations in examples/src/main/python.
---
 examples/src/main/python/als.py               | 20 +++++++++++--------
 examples/src/main/python/kmeans.py            |  2 +-
 .../src/main/python/logistic_regression.py    |  4 ++--
 examples/src/main/python/pagerank.py          | 12 +++++------
 examples/src/main/python/pi.py                |  2 ++
 examples/src/main/python/sort.py              |  4 ++--
 6 files changed, 25 insertions(+), 19 deletions(-)

diff --git a/examples/src/main/python/als.py b/examples/src/main/python/als.py
index f0b46cd28b7a..1a7c4c51f48c 100755
--- a/examples/src/main/python/als.py
+++ b/examples/src/main/python/als.py
@@ -29,22 +29,25 @@
 LAMBDA = 0.01   # regularization
 np.random.seed(42)
 
+
 def rmse(R, ms, us):
     diff = R - ms * us.T
     return np.sqrt(np.sum(np.power(diff, 2)) / M * U)
 
+
 def update(i, vec, mat, ratings):
     uu = mat.shape[0]
     ff = mat.shape[1]
-    
+
     XtX = mat.T * mat
     Xty = mat.T * ratings[i, :].T
-    
+
     for j in range(ff):
-        XtX[j,j] += LAMBDA * uu
-    
+        XtX[j, j] += LAMBDA * uu
+
     return np.linalg.solve(XtX, Xty)
 
+
 if __name__ == "__main__":
     """
     Usage: als [M] [U] [F] [iterations] [slices]"
@@ -57,10 +60,10 @@ def update(i, vec, mat, ratings):
     slices = int(sys.argv[5]) if len(sys.argv) > 5 else 2
 
     print "Running ALS with M=%d, U=%d, F=%d, iters=%d, slices=%d\n" % \
-            (M, U, F, ITERATIONS, slices)
+        (M, U, F, ITERATIONS, slices)
 
     R = matrix(rand(M, F)) * matrix(rand(U, F).T)
-    ms = matrix(rand(M ,F))
+    ms = matrix(rand(M, F))
     us = matrix(rand(U, F))
 
     Rb = sc.broadcast(R)
@@ -71,8 +74,9 @@ def update(i, vec, mat, ratings):
         ms = sc.parallelize(range(M), slices) \
                .map(lambda x: update(x, msb.value[x, :], usb.value, Rb.value)) \
                .collect()
-        ms = matrix(np.array(ms)[:, :, 0])      # collect() returns a list, so array ends up being
-                                                # a 3-d array, we take the first 2 dims for the matrix
+        # collect() returns a list, so array ends up being
+        # a 3-d array, we take the first 2 dims for the matrix
+        ms = matrix(np.array(ms)[:, :, 0])
         msb = sc.broadcast(ms)
 
         us = sc.parallelize(range(U), slices) \
diff --git a/examples/src/main/python/kmeans.py b/examples/src/main/python/kmeans.py
index fc16586c28a4..988fc45baf3b 100755
--- a/examples/src/main/python/kmeans.py
+++ b/examples/src/main/python/kmeans.py
@@ -59,7 +59,7 @@ def closestPoint(p, centers):
 
     while tempDist > convergeDist:
         closest = data.map(
-            lambda p : (closestPoint(p, kPoints), (p, 1)))
+            lambda p: (closestPoint(p, kPoints), (p, 1)))
         pointStats = closest.reduceByKey(
             lambda (x1, y1), (x2, y2): (x1 + x2, y1 + y2))
         newPoints = pointStats.map(
diff --git a/examples/src/main/python/logistic_regression.py b/examples/src/main/python/logistic_regression.py
index 0f22d0b32319..6c33deabfd6e 100755
--- a/examples/src/main/python/logistic_regression.py
+++ b/examples/src/main/python/logistic_regression.py
@@ -60,8 +60,8 @@ def readPointBatch(iterator):
 
     # Compute logistic regression gradient for a matrix of data points
     def gradient(matrix, w):
-        Y = matrix[:,0]    # point labels (first column of input file)
-        X = matrix[:,1:]   # point coordinates
+        Y = matrix[:, 0]    # point labels (first column of input file)
+        X = matrix[:, 1:]   # point coordinates
         # For each point (x, y), compute gradient function, then sum these up
         return ((1.0 / (1.0 + np.exp(-Y * X.dot(w))) - 1.0) * Y * X.T).sum(1)
 
diff --git a/examples/src/main/python/pagerank.py b/examples/src/main/python/pagerank.py
index d350fa46fa49..0b96343158d4 100755
--- a/examples/src/main/python/pagerank.py
+++ b/examples/src/main/python/pagerank.py
@@ -15,9 +15,8 @@
 # limitations under the License.
 #
 
-#!/usr/bin/env python
-
-import re, sys
+import re
+import sys
 from operator import add
 
 from pyspark import SparkContext
@@ -26,7 +25,8 @@
 def computeContribs(urls, rank):
     """Calculates URL contributions to the rank of other URLs."""
     num_urls = len(urls)
-    for url in urls: yield (url, rank / num_urls)
+    for url in urls:
+        yield (url, rank / num_urls)
 
 
 def parseNeighbors(urls):
@@ -59,8 +59,8 @@ def parseNeighbors(urls):
     # Calculates and updates URL ranks continuously using PageRank algorithm.
     for iteration in xrange(int(sys.argv[2])):
         # Calculates URL contributions to the rank of other URLs.
-        contribs = links.join(ranks).flatMap(lambda (url, (urls, rank)):
-            computeContribs(urls, rank))
+        contribs = links.join(ranks).flatMap(
+            lambda (url, (urls, rank)): computeContribs(urls, rank))
 
         # Re-calculates URL ranks based on neighbor contributions.
         ranks = contribs.reduceByKey(add).mapValues(lambda rank: rank * 0.85 + 0.15)
diff --git a/examples/src/main/python/pi.py b/examples/src/main/python/pi.py
index 234720b55fa4..21d94a2cd4b6 100755
--- a/examples/src/main/python/pi.py
+++ b/examples/src/main/python/pi.py
@@ -29,9 +29,11 @@
     sc = SparkContext(appName="PythonPi")
     slices = int(sys.argv[1]) if len(sys.argv) > 1 else 2
     n = 100000 * slices
+
     def f(_):
         x = random() * 2 - 1
         y = random() * 2 - 1
         return 1 if x ** 2 + y ** 2 < 1 else 0
+
     count = sc.parallelize(xrange(1, n+1), slices).map(f).reduce(add)
     print "Pi is roughly %f" % (4.0 * count / n)
diff --git a/examples/src/main/python/sort.py b/examples/src/main/python/sort.py
index 4913ee926aa0..41d00c1b7913 100755
--- a/examples/src/main/python/sort.py
+++ b/examples/src/main/python/sort.py
@@ -27,8 +27,8 @@
     sc = SparkContext(appName="PythonSort")
     lines = sc.textFile(sys.argv[1], 1)
     sortedCount = lines.flatMap(lambda x: x.split(' ')) \
-                  .map(lambda x: (int(x), 1)) \
-                  .sortByKey(lambda x: x)
+        .map(lambda x: (int(x), 1)) \
+        .sortByKey(lambda x: x)
     # This is just a demo on how to bring all the sorted data back to a single node.
     # In reality, we wouldn't want to collect all the data to the driver node.
     output = sortedCount.collect()

From 14f0358b2a0a9b92526bdad6d501ab753459eaa0 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Sun, 25 May 2014 16:04:17 -0700
Subject: [PATCH 270/810] Python docstring update for sql.py.

Mostly related to the following two rules in PEP8 and PEP257:
- Line length < 72 chars.
- First line should be a concise description of the function/class.

Author: Reynold Xin <rxin@apache.org>

Closes #869 from rxin/docstring-schemardd and squashes the following commits:

7cf0cbc [Reynold Xin] Updated sql.py for pep8 docstring.
0a4aef9 [Reynold Xin] Merge branch 'master' into docstring-schemardd
6678937 [Reynold Xin] Python docstring update for sql.py.
---
 python/pyspark/sql.py | 124 +++++++++++++++++++++---------------------
 1 file changed, 63 insertions(+), 61 deletions(-)

diff --git a/python/pyspark/sql.py b/python/pyspark/sql.py
index fa4b9c7b688e..b4e9618cc25b 100644
--- a/python/pyspark/sql.py
+++ b/python/pyspark/sql.py
@@ -23,14 +23,14 @@
 
 
 class SQLContext:
-    """
-    Main entry point for SparkSQL functionality. A SQLContext can be used create L{SchemaRDD}s,
-    register L{SchemaRDD}s as tables, execute sql over tables, cache tables, and read parquet files.
+    """Main entry point for SparkSQL functionality.
+
+    A SQLContext can be used create L{SchemaRDD}s, register L{SchemaRDD}s as
+    tables, execute SQL over tables, cache tables, and read parquet files.
     """
 
     def __init__(self, sparkContext, sqlContext = None):
-        """
-        Create a new SQLContext.
+        """Create a new SQLContext.
 
         @param sparkContext: The SparkContext to wrap.
 
@@ -63,18 +63,20 @@ def __init__(self, sparkContext, sqlContext = None):
 
     @property
     def _ssql_ctx(self):
-        """
-        Accessor for the JVM SparkSQL context.  Subclasses can override this property to provide
-        their own JVM Contexts.
+        """Accessor for the JVM SparkSQL context.
+
+        Subclasses can override this property to provide their own
+        JVM Contexts.
         """
         if not hasattr(self, '_scala_SQLContext'):
             self._scala_SQLContext = self._jvm.SQLContext(self._jsc.sc())
         return self._scala_SQLContext
 
     def inferSchema(self, rdd):
-        """
-        Infer and apply a schema to an RDD of L{dict}s. We peek at the first row of the RDD to
-        determine the fields names and types, and then use that to extract all the dictionaries.
+        """Infer and apply a schema to an RDD of L{dict}s.
+
+        We peek at the first row of the RDD to determine the fields names
+        and types, and then use that to extract all the dictionaries.
 
         >>> srdd = sqlCtx.inferSchema(rdd)
         >>> srdd.collect() == [{"field1" : 1, "field2" : "row1"}, {"field1" : 2, "field2": "row2"},
@@ -92,9 +94,10 @@ def inferSchema(self, rdd):
         return SchemaRDD(srdd, self)
 
     def registerRDDAsTable(self, rdd, tableName):
-        """
-        Registers the given RDD as a temporary table in the catalog.  Temporary tables exist only
-        during the lifetime of this instance of SQLContext.
+        """Registers the given RDD as a temporary table in the catalog.
+
+        Temporary tables exist only during the lifetime of this instance of
+        SQLContext.
 
         >>> srdd = sqlCtx.inferSchema(rdd)
         >>> sqlCtx.registerRDDAsTable(srdd, "table1")
@@ -106,8 +109,7 @@ def registerRDDAsTable(self, rdd, tableName):
             raise ValueError("Can only register SchemaRDD as table")
 
     def parquetFile(self, path):
-        """
-        Loads a Parquet file, returning the result as a L{SchemaRDD}.
+        """Loads a Parquet file, returning the result as a L{SchemaRDD}.
 
         >>> import tempfile, shutil
         >>> parquetFile = tempfile.mkdtemp()
@@ -122,8 +124,7 @@ def parquetFile(self, path):
         return SchemaRDD(jschema_rdd, self)
 
     def sql(self, sqlQuery):
-        """
-        Executes a SQL query using Spark, returning the result as a L{SchemaRDD}.
+        """Return a L{SchemaRDD} representing the result of the given query.
 
         >>> srdd = sqlCtx.inferSchema(rdd)
         >>> sqlCtx.registerRDDAsTable(srdd, "table1")
@@ -135,8 +136,7 @@ def sql(self, sqlQuery):
         return SchemaRDD(self._ssql_ctx.sql(sqlQuery), self)
 
     def table(self, tableName):
-        """
-        Returns the specified table as a L{SchemaRDD}.
+        """Returns the specified table as a L{SchemaRDD}.
 
         >>> srdd = sqlCtx.inferSchema(rdd)
         >>> sqlCtx.registerRDDAsTable(srdd, "table1")
@@ -147,23 +147,19 @@ def table(self, tableName):
         return SchemaRDD(self._ssql_ctx.table(tableName), self)
 
     def cacheTable(self, tableName):
-        """
-        Caches the specified table in-memory.
-        """
+        """Caches the specified table in-memory."""
         self._ssql_ctx.cacheTable(tableName)
 
     def uncacheTable(self, tableName):
-        """
-        Removes the specified table from the in-memory cache.
-        """
+        """Removes the specified table from the in-memory cache."""
         self._ssql_ctx.uncacheTable(tableName)
 
 
 class HiveContext(SQLContext):
-    """
-    An instance of the Spark SQL execution engine that integrates with data stored in Hive.
-    Configuration for Hive is read from hive-site.xml on the classpath. It supports running both SQL
-    and HiveQL commands.
+    """A variant of Spark SQL that integrates with data stored in Hive.
+
+    Configuration for Hive is read from hive-site.xml on the classpath.
+    It supports running both SQL and HiveQL commands.
     """
 
     @property
@@ -193,9 +189,10 @@ def hql(self, hqlQuery):
 
 
 class LocalHiveContext(HiveContext):
-    """
-    Starts up an instance of hive where metadata is stored locally. An in-process metadata data is
-    created with data stored in ./metadata.  Warehouse data is stored in in ./warehouse.
+    """Starts up an instance of hive where metadata is stored locally.
+
+    An in-process metadata data is created with data stored in ./metadata.
+    Warehouse data is stored in in ./warehouse.
 
     >>> import os
     >>> hiveCtx = LocalHiveContext(sc)
@@ -228,8 +225,10 @@ def _get_hive_ctx(self):
 # TODO: Investigate if it is more efficient to use a namedtuple. One problem is that named tuples
 # are custom classes that must be generated per Schema.
 class Row(dict):
-    """
-    An extended L{dict} that takes a L{dict} in its constructor, and exposes those items as fields.
+    """A row in L{SchemaRDD}.
+
+    An extended L{dict} that takes a L{dict} in its constructor, and
+    exposes those items as fields.
 
     >>> r = Row({"hello" : "world", "foo" : "bar"})
     >>> r.hello
@@ -245,13 +244,16 @@ def __init__(self, d):
 
 
 class SchemaRDD(RDD):
-    """
-    An RDD of L{Row} objects that has an associated schema. The underlying JVM object is a SchemaRDD,
-    not a PythonRDD, so we can utilize the relational query api exposed by SparkSQL.
+    """An RDD of L{Row} objects that has an associated schema.
 
-    For normal L{pyspark.rdd.RDD} operations (map, count, etc.) the L{SchemaRDD} is not operated on
-    directly, as it's underlying implementation is a RDD composed of Java objects. Instead it is
-    converted to a PythonRDD in the JVM, on which Python operations can be done.
+    The underlying JVM object is a SchemaRDD, not a PythonRDD, so we can
+    utilize the relational query api exposed by SparkSQL.
+
+    For normal L{pyspark.rdd.RDD} operations (map, count, etc.) the
+    L{SchemaRDD} is not operated on directly, as it's underlying
+    implementation is a RDD composed of Java objects. Instead it is
+    converted to a PythonRDD in the JVM, on which Python operations can
+    be done.
     """
 
     def __init__(self, jschema_rdd, sql_ctx):
@@ -266,8 +268,9 @@ def __init__(self, jschema_rdd, sql_ctx):
 
     @property
     def _jrdd(self):
-        """
-        Lazy evaluation of PythonRDD object. Only done when a user calls methods defined by the
+        """Lazy evaluation of PythonRDD object.
+
+        Only done when a user calls methods defined by the
         L{pyspark.rdd.RDD} super class (map, filter, etc.).
         """
         if not hasattr(self, '_lazy_jrdd'):
@@ -279,10 +282,10 @@ def _id(self):
         return self._jrdd.id()
 
     def saveAsParquetFile(self, path):
-        """
-        Saves the contents of this L{SchemaRDD} as a parquet file, preserving the schema.  Files
-        that are written out using this method can be read back in as a SchemaRDD using the
-        L{SQLContext.parquetFile} method.
+        """Save the contents as a Parquet file, preserving the schema.
+
+        Files that are written out using this method can be read back in as
+        a SchemaRDD using the L{SQLContext.parquetFile} method.
 
         >>> import tempfile, shutil
         >>> parquetFile = tempfile.mkdtemp()
@@ -296,9 +299,10 @@ def saveAsParquetFile(self, path):
         self._jschema_rdd.saveAsParquetFile(path)
 
     def registerAsTable(self, name):
-        """
-        Registers this RDD as a temporary table using the given name.  The lifetime of this temporary
-        table is tied to the L{SQLContext} that was used to create this SchemaRDD.
+        """Registers this RDD as a temporary table using the given name.
+
+        The lifetime of this temporary table is tied to the L{SQLContext}
+        that was used to create this SchemaRDD.
 
         >>> srdd = sqlCtx.inferSchema(rdd)
         >>> srdd.registerAsTable("test")
@@ -309,24 +313,22 @@ def registerAsTable(self, name):
         self._jschema_rdd.registerAsTable(name)
 
     def insertInto(self, tableName, overwrite = False):
-        """
-        Inserts the contents of this SchemaRDD into the specified table,
-        optionally overwriting any existing data.
+        """Inserts the contents of this SchemaRDD into the specified table.
+
+        Optionally overwriting any existing data.
         """
         self._jschema_rdd.insertInto(tableName, overwrite)
 
     def saveAsTable(self, tableName):
-        """
-        Creates a new table with the contents of this SchemaRDD.
-        """
+        """Creates a new table with the contents of this SchemaRDD."""
         self._jschema_rdd.saveAsTable(tableName)
 
     def count(self):
-        """
-        Return the number of elements in this RDD. Unlike the base RDD
-        implementation of count, this implementation leverages the query
-        optimizer to compute the count on the SchemaRDD, which supports
-        features such as filter pushdown.
+        """Return the number of elements in this RDD.
+
+        Unlike the base RDD implementation of count, this implementation
+        leverages the query optimizer to compute the count on the SchemaRDD,
+        which supports features such as filter pushdown.
 
         >>> srdd = sqlCtx.inferSchema(rdd)
         >>> srdd.count()

From d33d3c61ae9e4551aed0217e525a109e678298f2 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Sun, 25 May 2014 17:15:01 -0700
Subject: [PATCH 271/810] Fix PEP8 violations in Python mllib.

Author: Reynold Xin <rxin@apache.org>

Closes #871 from rxin/mllib-pep8 and squashes the following commits:

848416f [Reynold Xin] Fixed a typo in the previous cleanup (c -> sc).
a8db4cd [Reynold Xin] Fix PEP8 violations in Python mllib.
---
 python/pyspark/mllib/_common.py        | 42 +++++++++++++-------------
 python/pyspark/mllib/classification.py | 26 ++++++++--------
 python/pyspark/mllib/clustering.py     | 15 +++++----
 python/pyspark/mllib/linalg.py         | 13 ++++----
 python/pyspark/mllib/recommendation.py | 15 +++++----
 python/pyspark/mllib/regression.py     | 24 ++++++---------
 python/pyspark/mllib/tests.py          | 27 ++++++-----------
 python/pyspark/mllib/util.py           |  4 +--
 8 files changed, 78 insertions(+), 88 deletions(-)

diff --git a/python/pyspark/mllib/_common.py b/python/pyspark/mllib/_common.py
index e6f0953810ed..802a27a8da14 100644
--- a/python/pyspark/mllib/_common.py
+++ b/python/pyspark/mllib/_common.py
@@ -56,7 +56,8 @@
 #
 # Sparse double vector format:
 #
-# [1-byte 2] [4-byte length] [4-byte nonzeros] [nonzeros*4 bytes of indices] [nonzeros*8 bytes of values]
+# [1-byte 2] [4-byte length] [4-byte nonzeros] [nonzeros*4 bytes of indices] \
+# [nonzeros*8 bytes of values]
 #
 # Double matrix format:
 #
@@ -110,18 +111,18 @@ def _serialize_double_vector(v):
         return _serialize_sparse_vector(v)
     else:
         raise TypeError("_serialize_double_vector called on a %s; "
-                "wanted ndarray or SparseVector" % type(v))
+                        "wanted ndarray or SparseVector" % type(v))
 
 
 def _serialize_dense_vector(v):
     """Serialize a dense vector given as a NumPy array."""
     if v.ndim != 1:
         raise TypeError("_serialize_double_vector called on a %ddarray; "
-                "wanted a 1darray" % v.ndim)
+                        "wanted a 1darray" % v.ndim)
     if v.dtype != float64:
         if numpy.issubdtype(v.dtype, numpy.complex):
             raise TypeError("_serialize_double_vector called on an ndarray of %s; "
-                    "wanted ndarray of float64" % v.dtype)
+                            "wanted ndarray of float64" % v.dtype)
         v = v.astype(float64)
     length = v.shape[0]
     ba = bytearray(5 + 8 * length)
@@ -158,10 +159,10 @@ def _deserialize_double_vector(ba):
     """
     if type(ba) != bytearray:
         raise TypeError("_deserialize_double_vector called on a %s; "
-                "wanted bytearray" % type(ba))
+                        "wanted bytearray" % type(ba))
     if len(ba) < 5:
         raise TypeError("_deserialize_double_vector called on a %d-byte array, "
-                "which is too short" % len(ba))
+                        "which is too short" % len(ba))
     if ba[0] == DENSE_VECTOR_MAGIC:
         return _deserialize_dense_vector(ba)
     elif ba[0] == SPARSE_VECTOR_MAGIC:
@@ -175,7 +176,7 @@ def _deserialize_dense_vector(ba):
     """Deserialize a dense vector into a numpy array."""
     if len(ba) < 5:
         raise TypeError("_deserialize_dense_vector called on a %d-byte array, "
-                "which is too short" % len(ba))
+                        "which is too short" % len(ba))
     length = ndarray(shape=[1], buffer=ba, offset=1, dtype=int32)[0]
     if len(ba) != 8 * length + 5:
         raise TypeError("_deserialize_dense_vector called on bytearray "
@@ -187,7 +188,7 @@ def _deserialize_sparse_vector(ba):
     """Deserialize a sparse vector into a MLlib SparseVector object."""
     if len(ba) < 9:
         raise TypeError("_deserialize_sparse_vector called on a %d-byte array, "
-                "which is too short" % len(ba))
+                        "which is too short" % len(ba))
     header = ndarray(shape=[2], buffer=ba, offset=1, dtype=int32)
     size = header[0]
     nonzeros = header[1]
@@ -205,7 +206,7 @@ def _serialize_double_matrix(m):
         if m.dtype != float64:
             if numpy.issubdtype(m.dtype, numpy.complex):
                 raise TypeError("_serialize_double_matrix called on an ndarray of %s; "
-                        "wanted ndarray of float64" % m.dtype)
+                                "wanted ndarray of float64" % m.dtype)
             m = m.astype(float64)
         rows = m.shape[0]
         cols = m.shape[1]
@@ -225,10 +226,10 @@ def _deserialize_double_matrix(ba):
     """Deserialize a double matrix from a mutually understood format."""
     if type(ba) != bytearray:
         raise TypeError("_deserialize_double_matrix called on a %s; "
-                "wanted bytearray" % type(ba))
+                        "wanted bytearray" % type(ba))
     if len(ba) < 9:
         raise TypeError("_deserialize_double_matrix called on a %d-byte array, "
-                "which is too short" % len(ba))
+                        "which is too short" % len(ba))
     if ba[0] != DENSE_MATRIX_MAGIC:
         raise TypeError("_deserialize_double_matrix called on bytearray "
                         "with wrong magic")
@@ -267,7 +268,7 @@ def _copyto(array, buffer, offset, shape, dtype):
 def _get_unmangled_rdd(data, serializer):
     dataBytes = data.map(serializer)
     dataBytes._bypass_serializer = True
-    dataBytes.cache() # TODO: users should unpersist() this later!
+    dataBytes.cache()  # TODO: users should unpersist() this later!
     return dataBytes
 
 
@@ -293,14 +294,14 @@ def _linear_predictor_typecheck(x, coeffs):
     if type(x) == ndarray:
         if x.ndim == 1:
             if x.shape != coeffs.shape:
-                raise RuntimeError("Got array of %d elements; wanted %d"
-                        % (numpy.shape(x)[0], coeffs.shape[0]))
+                raise RuntimeError("Got array of %d elements; wanted %d" % (
+                    numpy.shape(x)[0], coeffs.shape[0]))
         else:
             raise RuntimeError("Bulk predict not yet supported.")
     elif type(x) == SparseVector:
         if x.size != coeffs.shape[0]:
-           raise RuntimeError("Got sparse vector of size %d; wanted %d"
-                   % (x.size, coeffs.shape[0]))
+            raise RuntimeError("Got sparse vector of size %d; wanted %d" % (
+                x.size, coeffs.shape[0]))
     elif (type(x) == RDD):
         raise RuntimeError("Bulk predict not yet supported.")
     else:
@@ -315,7 +316,7 @@ def _get_initial_weights(initial_weights, data):
         if type(initial_weights) == ndarray:
             if initial_weights.ndim != 1:
                 raise TypeError("At least one data element has "
-                        + initial_weights.ndim + " dimensions, which is not 1")
+                                + initial_weights.ndim + " dimensions, which is not 1")
             initial_weights = numpy.zeros([initial_weights.shape[0]])
         elif type(initial_weights) == SparseVector:
             initial_weights = numpy.zeros([initial_weights.size])
@@ -333,10 +334,10 @@ def _regression_train_wrapper(sc, train_func, klass, data, initial_weights):
         raise RuntimeError("JVM call result had unexpected length")
     elif type(ans[0]) != bytearray:
         raise RuntimeError("JVM call result had first element of type "
-                + type(ans[0]).__name__ + " which is not bytearray")
+                           + type(ans[0]).__name__ + " which is not bytearray")
     elif type(ans[1]) != float:
         raise RuntimeError("JVM call result had second element of type "
-                + type(ans[0]).__name__ + " which is not float")
+                           + type(ans[0]).__name__ + " which is not float")
     return klass(_deserialize_double_vector(ans[0]), ans[1])
 
 
@@ -450,8 +451,7 @@ def _test():
     import doctest
     globs = globals().copy()
     globs['sc'] = SparkContext('local[4]', 'PythonTest', batchSize=2)
-    (failure_count, test_count) = doctest.testmod(globs=globs,
-            optionflags=doctest.ELLIPSIS)
+    (failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
     globs['sc'].stop()
     if failure_count:
         exit(-1)
diff --git a/python/pyspark/mllib/classification.py b/python/pyspark/mllib/classification.py
index 6772e4337ef3..1c0c536c4fb3 100644
--- a/python/pyspark/mllib/classification.py
+++ b/python/pyspark/mllib/classification.py
@@ -29,6 +29,7 @@
 from pyspark.mllib.regression import LabeledPoint, LinearModel
 from math import exp, log
 
+
 class LogisticRegressionModel(LinearModel):
     """A linear binary classification model derived from logistic regression.
 
@@ -68,14 +69,14 @@ def predict(self, x):
 
 class LogisticRegressionWithSGD(object):
     @classmethod
-    def train(cls, data, iterations=100, step=1.0,
-              miniBatchFraction=1.0, initialWeights=None):
+    def train(cls, data, iterations=100, step=1.0, miniBatchFraction=1.0, initialWeights=None):
         """Train a logistic regression model on the given data."""
         sc = data.context
-        return _regression_train_wrapper(sc, lambda d, i:
-                sc._jvm.PythonMLLibAPI().trainLogisticRegressionModelWithSGD(d._jrdd,
-                        iterations, step, miniBatchFraction, i),
-                LogisticRegressionModel, data, initialWeights)
+        train_func = lambda d, i: sc._jvm.PythonMLLibAPI().trainLogisticRegressionModelWithSGD(
+            d._jrdd, iterations, step, miniBatchFraction, i)
+        return _regression_train_wrapper(sc, train_func, LogisticRegressionModel, data,
+                                         initialWeights)
+
 
 class SVMModel(LinearModel):
     """A support vector machine.
@@ -106,16 +107,17 @@ def predict(self, x):
         margin = _dot(x, self._coeff) + self._intercept
         return 1 if margin >= 0 else 0
 
+
 class SVMWithSGD(object):
     @classmethod
     def train(cls, data, iterations=100, step=1.0, regParam=1.0,
               miniBatchFraction=1.0, initialWeights=None):
         """Train a support vector machine on the given data."""
         sc = data.context
-        return _regression_train_wrapper(sc, lambda d, i:
-                sc._jvm.PythonMLLibAPI().trainSVMModelWithSGD(d._jrdd,
-                        iterations, step, regParam, miniBatchFraction, i),
-                SVMModel, data, initialWeights)
+        train_func = lambda d, i: sc._jvm.PythonMLLibAPI().trainSVMModelWithSGD(
+            d._jrdd, iterations, step, regParam, miniBatchFraction, i)
+        return _regression_train_wrapper(sc, train_func, SVMModel, data, initialWeights)
+
 
 class NaiveBayesModel(object):
     """
@@ -156,6 +158,7 @@ def predict(self, x):
         """Return the most likely class for a data vector x"""
         return self.labels[numpy.argmax(self.pi + _dot(x, self.theta.transpose()))]
 
+
 class NaiveBayes(object):
     @classmethod
     def train(cls, data, lambda_=1.0):
@@ -186,8 +189,7 @@ def _test():
     import doctest
     globs = globals().copy()
     globs['sc'] = SparkContext('local[4]', 'PythonTest', batchSize=2)
-    (failure_count, test_count) = doctest.testmod(globs=globs,
-            optionflags=doctest.ELLIPSIS)
+    (failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
     globs['sc'].stop()
     if failure_count:
         exit(-1)
diff --git a/python/pyspark/mllib/clustering.py b/python/pyspark/mllib/clustering.py
index f65088c9170e..b380e8f6c872 100644
--- a/python/pyspark/mllib/clustering.py
+++ b/python/pyspark/mllib/clustering.py
@@ -30,7 +30,8 @@ class KMeansModel(object):
     """A clustering model derived from the k-means method.
 
     >>> data = array([0.0,0.0, 1.0,1.0, 9.0,8.0, 8.0,9.0]).reshape(4,2)
-    >>> model = KMeans.train(sc.parallelize(data), 2, maxIterations=10, runs=30, initializationMode="random")
+    >>> model = KMeans.train(
+    ...     sc.parallelize(data), 2, maxIterations=10, runs=30, initializationMode="random")
     >>> model.predict(array([0.0, 0.0])) == model.predict(array([1.0, 1.0]))
     True
     >>> model.predict(array([8.0, 9.0])) == model.predict(array([9.0, 8.0]))
@@ -76,18 +77,17 @@ def predict(self, x):
 
 class KMeans(object):
     @classmethod
-    def train(cls, data, k, maxIterations=100, runs=1,
-            initializationMode="k-means||"):
+    def train(cls, data, k, maxIterations=100, runs=1, initializationMode="k-means||"):
         """Train a k-means clustering model."""
         sc = data.context
         dataBytes = _get_unmangled_double_vector_rdd(data)
-        ans = sc._jvm.PythonMLLibAPI().trainKMeansModel(dataBytes._jrdd,
-                k, maxIterations, runs, initializationMode)
+        ans = sc._jvm.PythonMLLibAPI().trainKMeansModel(
+            dataBytes._jrdd, k, maxIterations, runs, initializationMode)
         if len(ans) != 1:
             raise RuntimeError("JVM call result had unexpected length")
         elif type(ans[0]) != bytearray:
             raise RuntimeError("JVM call result had first element of type "
-                    + type(ans[0]) + " which is not bytearray")
+                               + type(ans[0]) + " which is not bytearray")
         matrix = _deserialize_double_matrix(ans[0])
         return KMeansModel([row for row in matrix])
 
@@ -96,8 +96,7 @@ def _test():
     import doctest
     globs = globals().copy()
     globs['sc'] = SparkContext('local[4]', 'PythonTest', batchSize=2)
-    (failure_count, test_count) = doctest.testmod(globs=globs,
-            optionflags=doctest.ELLIPSIS)
+    (failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
     globs['sc'].stop()
     if failure_count:
         exit(-1)
diff --git a/python/pyspark/mllib/linalg.py b/python/pyspark/mllib/linalg.py
index 7511ca7573dd..276684272068 100644
--- a/python/pyspark/mllib/linalg.py
+++ b/python/pyspark/mllib/linalg.py
@@ -54,7 +54,7 @@ def __init__(self, size, *args):
         if len(args) == 1:
             pairs = args[0]
             if type(pairs) == dict:
-               pairs = pairs.items()
+                pairs = pairs.items()
             pairs = sorted(pairs)
             self.indices = array([p[0] for p in pairs], dtype=int32)
             self.values = array([p[1] for p in pairs], dtype=float64)
@@ -88,7 +88,7 @@ def dot(self, other):
                     result += self.values[i] * other[self.indices[i]]
                 return result
             elif other.ndim == 2:
-                results = [self.dot(other[:,i]) for i in xrange(other.shape[1])]
+                results = [self.dot(other[:, i]) for i in xrange(other.shape[1])]
                 return array(results)
             else:
                 raise Exception("Cannot call dot with %d-dimensional array" % other.ndim)
@@ -135,7 +135,7 @@ def squared_distance(self, other):
                 return result
             else:
                 raise Exception("Cannot call squared_distance with %d-dimensional array" %
-                        other.ndim)
+                                other.ndim)
         else:
             result = 0.0
             i, j = 0, 0
@@ -184,15 +184,14 @@ def __eq__(self, other):
         """
 
         return (isinstance(other, self.__class__)
-            and other.size == self.size
-            and array_equal(other.indices, self.indices)
-            and array_equal(other.values, self.values))
+                and other.size == self.size
+                and array_equal(other.indices, self.indices)
+                and array_equal(other.values, self.values))
 
     def __ne__(self, other):
         return not self.__eq__(other)
 
 
-
 class Vectors(object):
     """
     Factory methods for working with vectors. Note that dense vectors
diff --git a/python/pyspark/mllib/recommendation.py b/python/pyspark/mllib/recommendation.py
index f4a83f0209e2..6c385042ffa5 100644
--- a/python/pyspark/mllib/recommendation.py
+++ b/python/pyspark/mllib/recommendation.py
@@ -24,6 +24,7 @@
     _serialize_tuple, RatingDeserializer
 from pyspark.rdd import RDD
 
+
 class MatrixFactorizationModel(object):
     """A matrix factorisation model trained by regularized alternating
     least-squares.
@@ -55,32 +56,34 @@ def predictAll(self, usersProducts):
         return RDD(self._java_model.predict(usersProductsJRDD._jrdd),
                    self._context, RatingDeserializer())
 
+
 class ALS(object):
     @classmethod
     def train(cls, ratings, rank, iterations=5, lambda_=0.01, blocks=-1):
         sc = ratings.context
         ratingBytes = _get_unmangled_rdd(ratings, _serialize_rating)
-        mod = sc._jvm.PythonMLLibAPI().trainALSModel(ratingBytes._jrdd,
-                rank, iterations, lambda_, blocks)
+        mod = sc._jvm.PythonMLLibAPI().trainALSModel(
+            ratingBytes._jrdd, rank, iterations, lambda_, blocks)
         return MatrixFactorizationModel(sc, mod)
 
     @classmethod
     def trainImplicit(cls, ratings, rank, iterations=5, lambda_=0.01, blocks=-1, alpha=0.01):
         sc = ratings.context
         ratingBytes = _get_unmangled_rdd(ratings, _serialize_rating)
-        mod = sc._jvm.PythonMLLibAPI().trainImplicitALSModel(ratingBytes._jrdd,
-                rank, iterations, lambda_, blocks, alpha)
+        mod = sc._jvm.PythonMLLibAPI().trainImplicitALSModel(
+            ratingBytes._jrdd, rank, iterations, lambda_, blocks, alpha)
         return MatrixFactorizationModel(sc, mod)
 
+
 def _test():
     import doctest
     globs = globals().copy()
     globs['sc'] = SparkContext('local[4]', 'PythonTest', batchSize=2)
-    (failure_count, test_count) = doctest.testmod(globs=globs,
-            optionflags=doctest.ELLIPSIS)
+    (failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
     globs['sc'].stop()
     if failure_count:
         exit(-1)
 
+
 if __name__ == "__main__":
     _test()
diff --git a/python/pyspark/mllib/regression.py b/python/pyspark/mllib/regression.py
index 266b31d3fab0..bc7de6d2e895 100644
--- a/python/pyspark/mllib/regression.py
+++ b/python/pyspark/mllib/regression.py
@@ -113,10 +113,9 @@ def train(cls, data, iterations=100, step=1.0,
               miniBatchFraction=1.0, initialWeights=None):
         """Train a linear regression model on the given data."""
         sc = data.context
-        return _regression_train_wrapper(sc, lambda d, i:
-                sc._jvm.PythonMLLibAPI().trainLinearRegressionModelWithSGD(
-                        d._jrdd, iterations, step, miniBatchFraction, i),
-                LinearRegressionModel, data, initialWeights)
+        train_f = lambda d, i: sc._jvm.PythonMLLibAPI().trainLinearRegressionModelWithSGD(
+            d._jrdd, iterations, step, miniBatchFraction, i)
+        return _regression_train_wrapper(sc, train_f, LinearRegressionModel, data, initialWeights)
 
 
 class LassoModel(LinearRegressionModelBase):
@@ -157,10 +156,9 @@ def train(cls, data, iterations=100, step=1.0, regParam=1.0,
               miniBatchFraction=1.0, initialWeights=None):
         """Train a Lasso regression model on the given data."""
         sc = data.context
-        return _regression_train_wrapper(sc, lambda d, i:
-                sc._jvm.PythonMLLibAPI().trainLassoModelWithSGD(d._jrdd,
-                        iterations, step, regParam, miniBatchFraction, i),
-                LassoModel, data, initialWeights)
+        train_f = lambda d, i: sc._jvm.PythonMLLibAPI().trainLassoModelWithSGD(
+            d._jrdd, iterations, step, regParam, miniBatchFraction, i)
+        return _regression_train_wrapper(sc, train_f, LassoModel, data, initialWeights)
 
 
 class RidgeRegressionModel(LinearRegressionModelBase):
@@ -201,18 +199,16 @@ def train(cls, data, iterations=100, step=1.0, regParam=1.0,
               miniBatchFraction=1.0, initialWeights=None):
         """Train a ridge regression model on the given data."""
         sc = data.context
-        return _regression_train_wrapper(sc, lambda d, i:
-                sc._jvm.PythonMLLibAPI().trainRidgeModelWithSGD(d._jrdd,
-                        iterations, step, regParam, miniBatchFraction, i),
-                RidgeRegressionModel, data, initialWeights)
+        train_func = lambda d, i: sc._jvm.PythonMLLibAPI().trainRidgeModelWithSGD(
+            d._jrdd, iterations, step, regParam, miniBatchFraction, i)
+        return _regression_train_wrapper(sc, train_func, RidgeRegressionModel, data, initialWeights)
 
 
 def _test():
     import doctest
     globs = globals().copy()
     globs['sc'] = SparkContext('local[4]', 'PythonTest', batchSize=2)
-    (failure_count, test_count) = doctest.testmod(globs=globs,
-            optionflags=doctest.ELLIPSIS)
+    (failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
     globs['sc'].stop()
     if failure_count:
         exit(-1)
diff --git a/python/pyspark/mllib/tests.py b/python/pyspark/mllib/tests.py
index 1ee96bb4af37..37ccf1d59074 100644
--- a/python/pyspark/mllib/tests.py
+++ b/python/pyspark/mllib/tests.py
@@ -23,7 +23,7 @@
 import unittest
 
 from pyspark.mllib._common import _convert_vector, _serialize_double_vector, \
-        _deserialize_double_vector, _dot, _squared_distance
+    _deserialize_double_vector, _dot, _squared_distance
 from pyspark.mllib.linalg import SparseVector
 from pyspark.mllib.regression import LabeledPoint
 from pyspark.tests import PySparkTestCase
@@ -46,12 +46,9 @@ def test_serialize(self):
         self.assertTrue(sv is _convert_vector(sv))
         self.assertTrue(dv is _convert_vector(dv))
         self.assertTrue(array_equal(dv, _convert_vector(lst)))
-        self.assertEquals(sv,
-                _deserialize_double_vector(_serialize_double_vector(sv)))
-        self.assertTrue(array_equal(dv,
-                _deserialize_double_vector(_serialize_double_vector(dv))))
-        self.assertTrue(array_equal(dv,
-                _deserialize_double_vector(_serialize_double_vector(lst))))
+        self.assertEquals(sv, _deserialize_double_vector(_serialize_double_vector(sv)))
+        self.assertTrue(array_equal(dv, _deserialize_double_vector(_serialize_double_vector(dv))))
+        self.assertTrue(array_equal(dv, _deserialize_double_vector(_serialize_double_vector(lst))))
 
     def test_dot(self):
         sv = SparseVector(4, {1: 1, 3: 2})
@@ -132,7 +129,7 @@ def test_classification(self):
 
     def test_regression(self):
         from pyspark.mllib.regression import LinearRegressionWithSGD, LassoWithSGD, \
-                RidgeRegressionWithSGD
+            RidgeRegressionWithSGD
         data = [
             LabeledPoint(-1.0, [0, -1]),
             LabeledPoint(1.0, [0, 1]),
@@ -179,14 +176,10 @@ def test_serialize(self):
         self.assertEquals(sv, _convert_vector(lil.tocoo()))
         self.assertEquals(sv, _convert_vector(lil.tocsr()))
         self.assertEquals(sv, _convert_vector(lil.todok()))
-        self.assertEquals(sv,
-                _deserialize_double_vector(_serialize_double_vector(lil)))
-        self.assertEquals(sv,
-                _deserialize_double_vector(_serialize_double_vector(lil.tocsc())))
-        self.assertEquals(sv,
-                _deserialize_double_vector(_serialize_double_vector(lil.tocsr())))
-        self.assertEquals(sv,
-                _deserialize_double_vector(_serialize_double_vector(lil.todok())))
+        self.assertEquals(sv, _deserialize_double_vector(_serialize_double_vector(lil)))
+        self.assertEquals(sv, _deserialize_double_vector(_serialize_double_vector(lil.tocsc())))
+        self.assertEquals(sv, _deserialize_double_vector(_serialize_double_vector(lil.tocsr())))
+        self.assertEquals(sv, _deserialize_double_vector(_serialize_double_vector(lil.todok())))
 
     def test_dot(self):
         from scipy.sparse import lil_matrix
@@ -265,7 +258,7 @@ def test_classification(self):
 
     def test_regression(self):
         from pyspark.mllib.regression import LinearRegressionWithSGD, LassoWithSGD, \
-                RidgeRegressionWithSGD
+            RidgeRegressionWithSGD
         data = [
             LabeledPoint(-1.0, self.scipy_matrix(2, {1: -1.0})),
             LabeledPoint(1.0, self.scipy_matrix(2, {1: 1.0})),
diff --git a/python/pyspark/mllib/util.py b/python/pyspark/mllib/util.py
index 50d0cdd08762..0e5f4520b940 100644
--- a/python/pyspark/mllib/util.py
+++ b/python/pyspark/mllib/util.py
@@ -21,6 +21,7 @@
 from pyspark.mllib.regression import LabeledPoint
 from pyspark.mllib._common import _convert_vector
 
+
 class MLUtils:
     """
     Helper methods to load, save and pre-process data used in MLlib.
@@ -44,7 +45,6 @@ def _parse_libsvm_line(line, multiclass):
             values[i] = float(value)
         return label, indices, values
 
-
     @staticmethod
     def _convert_labeled_point_to_libsvm(p):
         """Converts a LabeledPoint to a string in LIBSVM format."""
@@ -62,7 +62,6 @@ def _convert_labeled_point_to_libsvm(p):
                             " but got " % type(v))
         return " ".join(items)
 
-
     @staticmethod
     def loadLibSVMFile(sc, path, multiclass=False, numFeatures=-1, minPartitions=None):
         """
@@ -135,7 +134,6 @@ def loadLibSVMFile(sc, path, multiclass=False, numFeatures=-1, minPartitions=Non
             numFeatures = parsed.map(lambda x: 0 if x[1].size == 0 else x[1][-1]).reduce(max) + 1
         return parsed.map(lambda x: LabeledPoint(x[0], Vectors.sparse(numFeatures, x[1], x[2])))
 
-
     @staticmethod
     def saveAsLibSVMFile(data, dir):
         """

From 0659529614c804e0c04efc59cb67dab3a6cdc9d9 Mon Sep 17 00:00:00 2001
From: Andrew Ash <andrew@andrewash.com>
Date: Sun, 25 May 2014 17:15:47 -0700
Subject: [PATCH 272/810] SPARK-1903 Document Spark's network connections

https://issues.apache.org/jira/browse/SPARK-1903

Author: Andrew Ash <andrew@andrewash.com>

Closes #856 from ash211/SPARK-1903 and squashes the following commits:

6e7782a [Andrew Ash] Add the technology used on each port
1d9b5d3 [Andrew Ash] Document port for history server
56193ee [Andrew Ash] spark.ui.port becomes worker.ui.port and master.ui.port
a774c07 [Andrew Ash] Wording in network section
90e8237 [Andrew Ash] Use real :toc instead of the hand-written one
edaa337 [Andrew Ash] Master -> Standalone Cluster Master
57e8869 [Andrew Ash] Port -> Default Port
3d4d289 [Andrew Ash] Title to title case
c7d42d9 [Andrew Ash] [WIP] SPARK-1903 Add initial port listing for documentation
a416ae9 [Andrew Ash] Word wrap to 100 lines
---
 docs/README.md        |  43 +++++--
 docs/configuration.md | 268 ++++++++++++++++++++++++++++++------------
 2 files changed, 222 insertions(+), 89 deletions(-)

diff --git a/docs/README.md b/docs/README.md
index f1eb644f9340..fd7ba4e0d72e 100644
--- a/docs/README.md
+++ b/docs/README.md
@@ -1,23 +1,31 @@
 Welcome to the Spark documentation!
 
-This readme will walk you through navigating and building the Spark documentation, which is included here with the Spark source code. You can also find documentation specific to release versions of Spark at http://spark.apache.org/documentation.html.
+This readme will walk you through navigating and building the Spark documentation, which is included
+here with the Spark source code. You can also find documentation specific to release versions of
+Spark at http://spark.apache.org/documentation.html.
 
-Read on to learn more about viewing documentation in plain text (i.e., markdown) or building the documentation yourself. Why build it yourself? So that you have the docs that corresponds to whichever version of Spark you currently have checked out of revision control.
+Read on to learn more about viewing documentation in plain text (i.e., markdown) or building the
+documentation yourself. Why build it yourself? So that you have the docs that corresponds to
+whichever version of Spark you currently have checked out of revision control.
 
 ## Generating the Documentation HTML
 
-We include the Spark documentation as part of the source (as opposed to using a hosted wiki, such as the github wiki, as the definitive documentation) to enable the documentation to evolve along with the source code and be captured by revision control (currently git). This way the code automatically includes the version of the documentation that is relevant regardless of which version or release you have checked out or downloaded.
+We include the Spark documentation as part of the source (as opposed to using a hosted wiki, such as
+the github wiki, as the definitive documentation) to enable the documentation to evolve along with
+the source code and be captured by revision control (currently git). This way the code automatically
+includes the version of the documentation that is relevant regardless of which version or release
+you have checked out or downloaded.
 
-In this directory you will find textfiles formatted using Markdown, with an ".md" suffix. You can read those text files directly if you want. Start with index.md.
+In this directory you will find textfiles formatted using Markdown, with an ".md" suffix. You can
+read those text files directly if you want. Start with index.md.
 
-The markdown code can be compiled to HTML using the 
-[Jekyll tool](http://jekyllrb.com).
+The markdown code can be compiled to HTML using the [Jekyll tool](http://jekyllrb.com).
 To use the `jekyll` command, you will need to have Jekyll installed. 
 The easiest way to do this is via a Ruby Gem, see the 
 [jekyll installation instructions](http://jekyllrb.com/docs/installation).
 If not already installed, you need to install `kramdown` with `sudo gem install kramdown`.
-Execute `jekyll` from the `docs/` directory. Compiling the site with Jekyll will create a directory called
-`_site` containing index.html as well as the rest of the compiled files.
+Execute `jekyll` from the `docs/` directory. Compiling the site with Jekyll will create a directory
+called `_site` containing index.html as well as the rest of the compiled files.
 
 You can modify the default Jekyll build as follows:
 
@@ -30,9 +38,11 @@ You can modify the default Jekyll build as follows:
 
 ## Pygments
 
-We also use pygments (http://pygments.org) for syntax highlighting in documentation markdown pages, so you will also need to install that (it requires Python) by running `sudo easy_install Pygments`.
+We also use pygments (http://pygments.org) for syntax highlighting in documentation markdown pages,
+so you will also need to install that (it requires Python) by running `sudo easy_install Pygments`.
 
-To mark a block of code in your markdown to be syntax highlighted by jekyll during the compile phase, use the following sytax:
+To mark a block of code in your markdown to be syntax highlighted by jekyll during the compile
+phase, use the following sytax:
 
     {% highlight scala %}
     // Your scala code goes here, you can replace scala with many other
@@ -43,8 +53,15 @@ To mark a block of code in your markdown to be syntax highlighted by jekyll duri
 
 You can build just the Spark scaladoc by running `sbt/sbt doc` from the SPARK_PROJECT_ROOT directory.
 
-Similarly, you can build just the PySpark epydoc by running `epydoc --config epydoc.conf` from the SPARK_PROJECT_ROOT/pyspark directory. Documentation is only generated for classes that are listed as public in `__init__.py`.
+Similarly, you can build just the PySpark epydoc by running `epydoc --config epydoc.conf` from the
+SPARK_PROJECT_ROOT/pyspark directory. Documentation is only generated for classes that are listed as
+public in `__init__.py`.
 
-When you run `jekyll` in the `docs` directory, it will also copy over the scaladoc for the various Spark subprojects into the `docs` directory (and then also into the `_site` directory). We use a jekyll plugin to run `sbt/sbt doc` before building the site so if you haven't run it (recently) it may take some time as it generates all of the scaladoc.  The jekyll plugin also generates the PySpark docs using [epydoc](http://epydoc.sourceforge.net/).
+When you run `jekyll` in the `docs` directory, it will also copy over the scaladoc for the various
+Spark subprojects into the `docs` directory (and then also into the `_site` directory). We use a
+jekyll plugin to run `sbt/sbt doc` before building the site so if you haven't run it (recently) it
+may take some time as it generates all of the scaladoc.  The jekyll plugin also generates the
+PySpark docs using [epydoc](http://epydoc.sourceforge.net/).
 
-NOTE: To skip the step of building and copying over the Scala and Python API docs, run `SKIP_API=1 jekyll`.
+NOTE: To skip the step of building and copying over the Scala and Python API docs, run `SKIP_API=1
+jekyll`.
diff --git a/docs/configuration.md b/docs/configuration.md
index 4d41c36e38e2..e5d955f23fe3 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -3,15 +3,10 @@ layout: global
 title: Spark Configuration
 ---
 
-Spark provides three locations to configure the system:
-
-* [Spark properties](#spark-properties) control most application parameters and can be set by
-  passing a [SparkConf](api/scala/index.html#org.apache.spark.SparkConf) object to SparkContext,
-  or through the `conf/spark-defaults.conf` properties file.
-* [Environment variables](#environment-variables) can be used to set per-machine settings, such as
-  the IP address, through the `conf/spark-env.sh` script on each node.
-* [Logging](#configuring-logging) can be configured through `log4j.properties`.
+* This will become a table of contents (this text will be scraped).
+{:toc}
 
+Spark provides several locations to configure the system:
 
 # Spark Properties
 
@@ -65,7 +60,8 @@ there are at least five properties that you will commonly want to control:
   <td><code>spark.executor.memory</code></td>
   <td>512m</td>
   <td>
-    Amount of memory to use per executor process, in the same format as JVM memory strings (e.g. <code>512m</code>, <code>2g</code>).
+    Amount of memory to use per executor process, in the same format as JVM memory strings (e.g.
+    <code>512m</code>, <code>2g</code>).
   </td>
 </tr>
 <tr>
@@ -93,9 +89,9 @@ there are at least five properties that you will commonly want to control:
   <td><code>spark.local.dir</code></td>
   <td>/tmp</td>
   <td>
-    Directory to use for "scratch" space in Spark, including map output files and RDDs that get stored
-    on disk. This should be on a fast, local disk in your system. It can also be a comma-separated
-    list of multiple directories on different disks.
+    Directory to use for "scratch" space in Spark, including map output files and RDDs that get
+    stored on disk. This should be on a fast, local disk in your system. It can also be a
+    comma-separated list of multiple directories on different disks.
 
     NOTE: In Spark 1.0 and later this will be overriden by SPARK_LOCAL_DIRS (Standalone, Mesos) or
     LOCAL_DIRS (YARN) envrionment variables set by the cluster manager.
@@ -130,8 +126,8 @@ Apart from these, the following properties are also available, and may be useful
     </ul>
   </td>
   <td>
-    Default number of tasks to use across the cluster for distributed shuffle operations (<code>groupByKey</code>,
-    <code>reduceByKey</code>, etc) when not set by user.
+    Default number of tasks to use across the cluster for distributed shuffle operations
+    (<code>groupByKey</code>, <code>reduceByKey</code>, etc) when not set by user.
   </td>
 </tr>
 <tr>
@@ -167,8 +163,9 @@ Apart from these, the following properties are also available, and may be useful
   <td><code>spark.tachyonStore.baseDir</code></td>
   <td>System.getProperty("java.io.tmpdir")</td>
   <td>
-    Directories of the Tachyon File System that store RDDs. The Tachyon file system's URL is set by <code>spark.tachyonStore.url</code>.
-    It can also be a comma-separated list of multiple directories on Tachyon file system.
+    Directories of the Tachyon File System that store RDDs. The Tachyon file system's URL is set by
+    <code>spark.tachyonStore.url</code>.  It can also be a comma-separated list of multiple
+    directories on Tachyon file system.
   </td>
 </tr>
 <tr>
@@ -182,9 +179,9 @@ Apart from these, the following properties are also available, and may be useful
   <td><code>spark.mesos.coarse</code></td>
   <td>false</td>
   <td>
-    If set to "true", runs over Mesos clusters in
-    <a href="running-on-mesos.html#mesos-run-modes">"coarse-grained" sharing mode</a>,
-    where Spark acquires one long-lived Mesos task on each machine instead of one Mesos task per Spark task.
+    If set to "true", runs over Mesos clusters in <a
+    href="running-on-mesos.html#mesos-run-modes">"coarse-grained" sharing mode</a>, where Spark
+    acquires one long-lived Mesos task on each machine instead of one Mesos task per Spark task.
     This gives lower-latency scheduling for short queries, but leaves resources in use for the whole
     duration of the Spark job.
   </td>
@@ -271,15 +268,17 @@ Apart from these, the following properties are also available, and may be useful
   <td><code>spark.io.compression.codec</code></td>
   <td>org.apache.spark.io.<br />LZFCompressionCodec</td>
   <td>
-    The codec used to compress internal data such as RDD partitions and shuffle outputs. By default, Spark provides two
-    codecs: <code>org.apache.spark.io.LZFCompressionCodec</code> and <code>org.apache.spark.io.SnappyCompressionCodec</code>.
+    The codec used to compress internal data such as RDD partitions and shuffle outputs. By default,
+    Spark provides two codecs: <code>org.apache.spark.io.LZFCompressionCodec</code> and
+    <code>org.apache.spark.io.SnappyCompressionCodec</code>.
   </td>
 </tr>
 <tr>
   <td><code>spark.io.compression.snappy.block.size</code></td>
   <td>32768</td>
   <td>
-    Block size (in bytes) used in Snappy compression, in the case when Snappy compression codec is used.
+    Block size (in bytes) used in Snappy compression, in the case when Snappy compression codec is
+    used.
   </td>
 </tr>
 <tr>
@@ -296,7 +295,8 @@ Apart from these, the following properties are also available, and may be useful
   <td><code>spark.scheduler.revive.interval</code></td>
   <td>1000</td>
   <td>
-    The interval length for the scheduler to revive the worker resource offers to run tasks. (in milliseconds)
+    The interval length for the scheduler to revive the worker resource offers to run tasks. (in
+    milliseconds)
   </td>
 </tr>
 <tr>
@@ -304,8 +304,8 @@ Apart from these, the following properties are also available, and may be useful
   <td>48</td>
   <td>
     Maximum size (in megabytes) of map outputs to fetch simultaneously from each reduce task. Since
-    each output requires us to create a buffer to receive it, this represents a fixed memory overhead
-    per reduce task, so keep it small unless you have a large amount of memory.
+    each output requires us to create a buffer to receive it, this represents a fixed memory
+    overhead per reduce task, so keep it small unless you have a large amount of memory.
   </td>
 </tr>
 <tr>
@@ -329,9 +329,10 @@ Apart from these, the following properties are also available, and may be useful
   <td><code>spark.kryoserializer.buffer.mb</code></td>
   <td>2</td>
   <td>
-    Maximum object size to allow within Kryo (the library needs to create a buffer at least as
-    large as the largest single object you'll serialize). Increase this if you get a "buffer limit
-    exceeded" exception inside Kryo. Note that there will be one buffer <i>per core</i> on each worker.
+    Maximum object size to allow within Kryo (the library needs to create a buffer at least as large
+    as the largest single object you'll serialize). Increase this if you get a "buffer limit
+    exceeded" exception inside Kryo. Note that there will be one buffer <i>per core</i> on each
+    worker.
   </td>
 </tr>
 <tr>
@@ -399,9 +400,9 @@ Apart from these, the following properties are also available, and may be useful
   <td><code>spark.worker.cleanup.enabled</code></td>
   <td>false</td>
   <td>
-    Enable periodic cleanup of worker / application directories.  Note that this only affects standalone
-    mode, as YARN works differently. Applications directories are cleaned up regardless of whether
-    the application is still running.
+    Enable periodic cleanup of worker / application directories.  Note that this only affects
+    standalone mode, as YARN works differently. Applications directories are cleaned up regardless
+    of whether the application is still running.
   </td>
 </tr>
 <tr>
@@ -416,10 +417,10 @@ Apart from these, the following properties are also available, and may be useful
   <td><code>spark.worker.cleanup.appDataTtl</code></td>
   <td>7 * 24 * 3600 (7 days)</td>
   <td>
-    The number of seconds to retain application work directories on each worker.  This is a Time To Live
-    and should depend on the amount of available disk space you have.  Application logs and jars are
-    downloaded to each application work dir.  Over time, the work dirs can quickly fill up disk space,
-    especially if you run jobs very frequently.
+    The number of seconds to retain application work directories on each worker.  This is a Time To
+    Live and should depend on the amount of available disk space you have.  Application logs and
+    jars are downloaded to each application work dir.  Over time, the work dirs can quickly fill up
+    disk space, especially if you run jobs very frequently.
   </td>
 </tr>
 <tr>
@@ -450,21 +451,36 @@ Apart from these, the following properties are also available, and may be useful
   <td><code>spark.akka.heartbeat.pauses</code></td>
   <td>600</td>
   <td>
-     This is set to a larger value to disable failure detector that comes inbuilt akka. It can be enabled again, if you plan to use this feature (Not recommended). Acceptable heart beat pause in seconds for akka. This can be used to control sensitivity to gc pauses. Tune this in combination of `spark.akka.heartbeat.interval` and `spark.akka.failure-detector.threshold` if you need to.
+     This is set to a larger value to disable failure detector that comes inbuilt akka. It can be
+     enabled again, if you plan to use this feature (Not recommended). Acceptable heart beat pause
+     in seconds for akka. This can be used to control sensitivity to gc pauses. Tune this in
+     combination of `spark.akka.heartbeat.interval` and `spark.akka.failure-detector.threshold` if
+     you need to.
   </td>
 </tr>
 <tr>
   <td><code>spark.akka.failure-detector.threshold</code></td>
   <td>300.0</td>
   <td>
-     This is set to a larger value to disable failure detector that comes inbuilt akka. It can be enabled again, if you plan to use this feature (Not recommended). This maps to akka's `akka.remote.transport-failure-detector.threshold`. Tune this in combination of `spark.akka.heartbeat.pauses` and `spark.akka.heartbeat.interval` if you need to.
+     This is set to a larger value to disable failure detector that comes inbuilt akka. It can be
+     enabled again, if you plan to use this feature (Not recommended). This maps to akka's
+     `akka.remote.transport-failure-detector.threshold`. Tune this in combination of
+     `spark.akka.heartbeat.pauses` and `spark.akka.heartbeat.interval` if you need to.
   </td>
 </tr>
 <tr>
   <td><code>spark.akka.heartbeat.interval</code></td>
   <td>1000</td>
   <td>
-    This is set to a larger value to disable failure detector that comes inbuilt akka. It can be enabled again, if you plan to use this feature (Not recommended). A larger interval value in seconds reduces network overhead and a smaller value ( ~ 1 s) might be more informative for akka's failure detector. Tune this in combination of `spark.akka.heartbeat.pauses` and `spark.akka.failure-detector.threshold` if you need to. Only positive use case for using failure detector can be, a sensistive failure detector can help evict rogue executors really quick. However this is usually not the case as gc pauses and network lags are expected in a real spark cluster. Apart from that enabling this leads to a lot of exchanges of heart beats between nodes leading to flooding the network with those.
+    This is set to a larger value to disable failure detector that comes inbuilt akka. It can be
+    enabled again, if you plan to use this feature (Not recommended). A larger interval value in
+    seconds reduces network overhead and a smaller value ( ~ 1 s) might be more informative for
+    akka's failure detector. Tune this in combination of `spark.akka.heartbeat.pauses` and
+    `spark.akka.failure-detector.threshold` if you need to. Only positive use case for using failure
+    detector can be, a sensistive failure detector can help evict rogue executors really quick.
+    However this is usually not the case as gc pauses and network lags are expected in a real spark
+    cluster. Apart from that enabling this leads to a lot of exchanges of heart beats between nodes
+    leading to flooding the network with those.
   </td>
 </tr>
 <tr>
@@ -485,10 +501,11 @@ Apart from these, the following properties are also available, and may be useful
   <td><code>spark.cleaner.ttl</code></td>
   <td>(infinite)</td>
   <td>
-    Duration (seconds) of how long Spark will remember any metadata (stages generated, tasks generated, etc.).
-    Periodic cleanups will ensure that metadata older than this duration will be forgotten. This is
-    useful for running Spark for many hours / days (for example, running 24/7 in case of Spark Streaming
-    applications). Note that any RDD that persists in memory for more than this duration will be cleared as well.
+    Duration (seconds) of how long Spark will remember any metadata (stages generated, tasks
+    generated, etc.).  Periodic cleanups will ensure that metadata older than this duration will be
+    forgotten. This is useful for running Spark for many hours / days (for example, running 24/7 in
+    case of Spark Streaming applications). Note that any RDD that persists in memory for more than
+    this duration will be cleared as well.
   </td>
 </tr>
 <tr>
@@ -523,7 +540,8 @@ Apart from these, the following properties are also available, and may be useful
   <td>4096</td>
   <td>
     Size of each piece of a block in kilobytes for <code>TorrentBroadcastFactory</code>.
-    Too large a value decreases parallelism during broadcast (makes it slower); however, if it is too small, <code>BlockManager</code> might take a performance hit.
+    Too large a value decreases parallelism during broadcast (makes it slower); however, if it is
+    too small, <code>BlockManager</code> might take a performance hit.
   </td>
 </tr>
 
@@ -531,7 +549,10 @@ Apart from these, the following properties are also available, and may be useful
   <td><code>spark.shuffle.consolidateFiles</code></td>
   <td>false</td>
   <td>
-    If set to "true", consolidates intermediate files created during a shuffle. Creating fewer files can improve filesystem performance for shuffles with large numbers of reduce tasks. It is recommended to set this to "true" when using ext4 or xfs filesystems. On ext3, this option might degrade performance on machines with many (>8) cores due to filesystem limitations.
+    If set to "true", consolidates intermediate files created during a shuffle. Creating fewer files
+    can improve filesystem performance for shuffles with large numbers of reduce tasks. It is
+    recommended to set this to "true" when using ext4 or xfs filesystems. On ext3, this option might
+    degrade performance on machines with many (>8) cores due to filesystem limitations.
   </td>
 </tr>
 <tr>
@@ -546,15 +567,16 @@ Apart from these, the following properties are also available, and may be useful
   <td><code>spark.shuffle.spill</code></td>
   <td>true</td>
   <td>
-    If set to "true", limits the amount of memory used during reduces by spilling data out to disk. This spilling
-    threshold is specified by <code>spark.shuffle.memoryFraction</code>.
+    If set to "true", limits the amount of memory used during reduces by spilling data out to disk.
+    This spilling threshold is specified by <code>spark.shuffle.memoryFraction</code>.
   </td>
 </tr>
 <tr>
   <td><code>spark.speculation</code></td>
   <td>false</td>
   <td>
-    If set to "true", performs speculative execution of tasks. This means if one or more tasks are running slowly in a stage, they will be re-launched.
+    If set to "true", performs speculative execution of tasks. This means if one or more tasks are
+    running slowly in a stage, they will be re-launched.
   </td>
 </tr>
 <tr>
@@ -589,7 +611,8 @@ Apart from these, the following properties are also available, and may be useful
   <td><code>spark.eventLog.enabled</code></td>
   <td>false</td>
   <td>
-    Whether to log spark events, useful for reconstructing the Web UI after the application has finished.
+    Whether to log spark events, useful for reconstructing the Web UI after the application has
+    finished.
   </td>
 </tr>
 <tr>
@@ -604,39 +627,40 @@ Apart from these, the following properties are also available, and may be useful
   <td>file:///tmp/spark-events</td>
   <td>
     Base directory in which spark events are logged, if <code>spark.eventLog.enabled</code> is true.
-    Within this base directory, Spark creates a sub-directory for each application, and logs the events
-    specific to the application in this directory.
+    Within this base directory, Spark creates a sub-directory for each application, and logs the
+    events specific to the application in this directory.
   </td>
 </tr>
 <tr>
   <td><code>spark.deploy.spreadOut</code></td>
   <td>true</td>
   <td>
-    Whether the standalone cluster manager should spread applications out across nodes or try
-    to consolidate them onto as few nodes as possible. Spreading out is usually better for
-    data locality in HDFS, but consolidating is more efficient for compute-intensive workloads. <br/>
-    <b>Note:</b> this setting needs to be configured in the standalone cluster master, not in individual
-    applications; you can set it through <code>SPARK_MASTER_OPTS</code> in <code>spark-env.sh</code>.
+    Whether the standalone cluster manager should spread applications out across nodes or try to
+    consolidate them onto as few nodes as possible. Spreading out is usually better for data
+    locality in HDFS, but consolidating is more efficient for compute-intensive workloads. <br/>
+    <b>Note:</b> this setting needs to be configured in the standalone cluster master, not in
+    individual applications; you can set it through <code>SPARK_MASTER_OPTS</code> in
+    <code>spark-env.sh</code>.
   </td>
 </tr>
 <tr>
   <td><code>spark.deploy.defaultCores</code></td>
   <td>(infinite)</td>
   <td>
-    Default number of cores to give to applications in Spark's standalone mode if they don't
-    set <code>spark.cores.max</code>. If not set, applications always get all available
-    cores unless they configure <code>spark.cores.max</code> themselves.
-    Set this lower on a shared cluster to prevent users from grabbing
-    the whole cluster by default. <br/>
-    <b>Note:</b> this setting needs to be configured in the standalone cluster master, not in individual
-    applications; you can set it through <code>SPARK_MASTER_OPTS</code> in <code>spark-env.sh</code>.
+    Default number of cores to give to applications in Spark's standalone mode if they don't set
+    <code>spark.cores.max</code>. If not set, applications always get all available cores unless
+    they configure <code>spark.cores.max</code> themselves.  Set this lower on a shared cluster to
+    prevent users from grabbing the whole cluster by default. <br/> <b>Note:</b> this setting needs
+    to be configured in the standalone cluster master, not in individual applications; you can set
+    it through <code>SPARK_MASTER_OPTS</code> in <code>spark-env.sh</code>.
   </td>
 </tr>
 <tr>
   <td><code>spark.files.overwrite</code></td>
   <td>false</td>
   <td>
-    Whether to overwrite files added through SparkContext.addFile() when the target file exists and its contents do not match those of the source.
+    Whether to overwrite files added through SparkContext.addFile() when the target file exists and
+    its contents do not match those of the source.
   </td>
 </tr>
 <tr>
@@ -660,8 +684,8 @@ Apart from these, the following properties are also available, and may be useful
   <td><code>spark.authenticate</code></td>
   <td>false</td>
   <td>
-    Whether spark authenticates its internal connections. See <code>spark.authenticate.secret</code> if not
-    running on Yarn.
+    Whether spark authenticates its internal connections. See <code>spark.authenticate.secret</code>
+    if not running on Yarn.
   </td>
 </tr>
 <tr>
@@ -719,13 +743,13 @@ Apart from these, the following properties are also available, and may be useful
 
 # Environment Variables
 
-Certain Spark settings can be configured through environment variables, which are read from the `conf/spark-env.sh`
-script in the directory where Spark is installed (or `conf/spark-env.cmd` on Windows). In Standalone and Mesos modes,
-this file can give machine specific information such as hostnames. It is also sourced when running local
-Spark applications or submission scripts.
+Certain Spark settings can be configured through environment variables, which are read from the
+`conf/spark-env.sh` script in the directory where Spark is installed (or `conf/spark-env.cmd` on
+Windows). In Standalone and Mesos modes, this file can give machine specific information such as
+hostnames. It is also sourced when running local Spark applications or submission scripts.
 
-Note that `conf/spark-env.sh` does not exist by default when Spark is installed. However, you can copy
-`conf/spark-env.sh.template` to create it. Make sure you make the copy executable.
+Note that `conf/spark-env.sh` does not exist by default when Spark is installed. However, you can
+copy `conf/spark-env.sh.template` to create it. Make sure you make the copy executable.
 
 The following variables can be set in `spark-env.sh`:
 
@@ -750,12 +774,104 @@ The following variables can be set in `spark-env.sh`:
   </tr>
 </table>
 
-In addition to the above, there are also options for setting up the Spark [standalone cluster scripts](spark-standalone.html#cluster-launch-scripts), such as number of cores to use on each machine and maximum memory.
+In addition to the above, there are also options for setting up the Spark [standalone cluster
+scripts](spark-standalone.html#cluster-launch-scripts), such as number of cores to use on each
+machine and maximum memory.
 
-Since `spark-env.sh` is a shell script, some of these can be set programmatically -- for example, you might
-compute `SPARK_LOCAL_IP` by looking up the IP of a specific network interface.
+Since `spark-env.sh` is a shell script, some of these can be set programmatically -- for example,
+you might compute `SPARK_LOCAL_IP` by looking up the IP of a specific network interface.
 
 # Configuring Logging
 
-Spark uses [log4j](http://logging.apache.org/log4j/) for logging. You can configure it by adding a `log4j.properties`
-file in the `conf` directory. One way to start is to copy the existing `log4j.properties.template` located there.
+Spark uses [log4j](http://logging.apache.org/log4j/) for logging. You can configure it by adding a
+`log4j.properties` file in the `conf` directory. One way to start is to copy the existing
+`log4j.properties.template` located there.
+
+# Configuring Ports for Network Security
+
+Spark makes heavy use of the network, and some environments have strict requirements for using tight
+firewall settings.  Below are the primary ports that Spark uses for its communication and how to
+configure those ports.
+
+<table class="table">
+  <tr>
+    <th>From</th><th>To</th><th>Default Port</th><th>Purpose</th><th>Configuration
+    Setting</th><th>Notes</th>
+  </tr>
+  <!-- Web UIs -->
+  <tr>
+    <td>Browser</td>
+    <td>Standalone Cluster Master</td>
+    <td>8080</td>
+    <td>Web UI</td>
+    <td><code>master.ui.port</code></td>
+    <td>Jetty-based</td>
+  </tr>
+  <tr>
+    <td>Browser</td>
+    <td>Worker</td>
+    <td>8081</td>
+    <td>Web UI</td>
+    <td><code>worker.ui.port</code></td>
+    <td>Jetty-based</td>
+  </tr>
+  <tr>
+    <td>Browser</td>
+    <td>Driver</td>
+    <td>4040</td>
+    <td>Web UI</td>
+    <td><code>spark.ui.port</code></td>
+    <td>Jetty-based</td>
+  </tr>
+  <tr>
+    <td>Browser</td>
+    <td>History Server</td>
+    <td>18080</td>
+    <td>Web UI</td>
+    <td><code>spark.history.ui.port</code></td>
+    <td>Jetty-based</td>
+  </tr>
+
+  <!-- Cluster interactions -->
+  <tr>
+    <td>Application</td>
+    <td>Standalone Cluster Master</td>
+    <td>7077</td>
+    <td>Submit job to cluster</td>
+    <td><code>spark.driver.port</code></td>
+    <td>Akka-based.  Set to "0" to choose a port randomly</td>
+  </tr>
+  <tr>
+    <td>Worker</td>
+    <td>Standalone Cluster Master</td>
+    <td>7077</td>
+    <td>Join cluster</td>
+    <td><code>spark.driver.port</code></td>
+    <td>Akka-based.  Set to "0" to choose a port randomly</td>
+  </tr>
+  <tr>
+    <td>Application</td>
+    <td>Worker</td>
+    <td>(random)</td>
+    <td>Join cluster</td>
+    <td><code>SPARK_WORKER_PORT</code> (standalone cluster)</td>
+    <td>Akka-based</td>
+  </tr>
+
+  <!-- Other misc stuff -->
+  <tr>
+    <td>Driver and other Workers</td>
+    <td>Worker</td>
+    <td>(random)</td>
+    <td>
+      <ul>
+        <li>File server for file and jars</li>
+        <li>Http Broadcast</li>
+        <li>Class file server (Spark Shell only)</li>
+      </ul>
+    </td>
+    <td>None</td>
+    <td>Jetty-based.  Each of these services starts on a random port that cannot be configured</td>
+  </tr>
+
+</table>

From c3576ffcd7910e38928f233a824dd9e037cde05f Mon Sep 17 00:00:00 2001
From: Aaron Davidson <aaron@databricks.com>
Date: Sun, 25 May 2014 18:37:44 -0700
Subject: [PATCH 273/810] [SQL] Minor: Introduce SchemaRDD#aggregate() for
 simple aggregations

```scala
rdd.aggregate(Sum('val))
```
is just shorthand for

```scala
rdd.groupBy()(Sum('val))
```

but seems be more natural than doing a groupBy with no grouping expressions when you really just want an aggregation over all rows.

Did not add a JavaSchemaRDD or Python API, as these seem to be lacking several other methods like groupBy() already -- leaving that cleanup for future patches.

Author: Aaron Davidson <aaron@databricks.com>

Closes #874 from aarondav/schemardd and squashes the following commits:

e9e68ee [Aaron Davidson] Add comment
db6afe2 [Aaron Davidson] Introduce SchemaRDD#aggregate() for simple aggregations
---
 .../scala/org/apache/spark/sql/SchemaRDD.scala | 18 ++++++++++++++++--
 .../org/apache/spark/sql/DslQuerySuite.scala   |  8 ++++++++
 2 files changed, 24 insertions(+), 2 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala
index 9883ebc0b3c6..e855f36256bc 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala
@@ -59,7 +59,7 @@ import java.util.{Map => JMap}
  *  // Importing the SQL context gives access to all the SQL functions and implicit conversions.
  *  import sqlContext._
  *
- *  val rdd = sc.parallelize((1 to 100).map(i => Record(i, s"val_\$i")))
+ *  val rdd = sc.parallelize((1 to 100).map(i => Record(i, s"val_$i")))
  *  // Any RDD containing case classes can be registered as a table.  The schema of the table is
  *  // automatically inferred using scala reflection.
  *  rdd.registerAsTable("records")
@@ -204,6 +204,20 @@ class SchemaRDD(
     new SchemaRDD(sqlContext, Aggregate(groupingExprs, aliasedExprs, logicalPlan))
   }
 
+  /**
+   * Performs an aggregation over all Rows in this RDD.
+   * This is equivalent to a groupBy with no grouping expressions.
+   *
+   * {{{
+   *   schemaRDD.aggregate(Sum('sales) as 'totalSales)
+   * }}}
+   *
+   * @group Query
+   */
+  def aggregate(aggregateExprs: Expression*): SchemaRDD = {
+    groupBy()(aggregateExprs: _*)
+  }
+
   /**
    * Applies a qualifier to the attributes of this relation.  Can be used to disambiguate attributes
    * with the same name, for example, when performing self-joins.
@@ -281,7 +295,7 @@ class SchemaRDD(
    * supports features such as filter pushdown.
    */
   @Experimental
-  override def count(): Long = groupBy()(Count(Literal(1))).collect().head.getLong(0)
+  override def count(): Long = aggregate(Count(Literal(1))).collect().head.getLong(0)
 
   /**
    * :: Experimental ::
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala
index 94ba13b14b33..692569a73ffc 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala
@@ -39,6 +39,14 @@ class DslQuerySuite extends QueryTest {
       testData2.groupBy('a)('a, Sum('b)),
       Seq((1,3),(2,3),(3,3))
     )
+    checkAnswer(
+      testData2.groupBy('a)('a, Sum('b) as 'totB).aggregate(Sum('totB)),
+      9
+    )
+    checkAnswer(
+      testData2.aggregate(Sum('b)),
+      9
+    )
   }
 
   test("select *") {

From b6d22af040073cd611b0fcfdf8a5259c0dfd854c Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Sun, 25 May 2014 20:13:32 -0700
Subject: [PATCH 274/810] HOTFIX: Add no-arg SparkContext constructor in Java

Self explanatory.

Author: Patrick Wendell <pwendell@gmail.com>

Closes #878 from pwendell/java-constructor and squashes the following commits:

2cc1605 [Patrick Wendell] HOTFIX: Add no-arg SparkContext constructor in Java
---
 .../scala/org/apache/spark/api/java/JavaSparkContext.scala  | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala b/core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala
index a7cfee6d0171..1e0493c4855e 100644
--- a/core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala
@@ -41,6 +41,12 @@ import org.apache.spark.rdd.RDD
  * [[org.apache.spark.api.java.JavaRDD]]s and works with Java collections instead of Scala ones.
  */
 class JavaSparkContext(val sc: SparkContext) extends JavaSparkContextVarargsWorkaround {
+  /**
+   * Create a JavaSparkContext that loads settings from system properties (for instance, when
+   * launching with ./bin/spark-submit).
+   */
+  def this() = this(new SparkContext())
+
   /**
    * @param conf a [[org.apache.spark.SparkConf]] object specifying Spark parameters
    */

From d6395d86f90d1c47c5b6ad17c618b56e00b7fc85 Mon Sep 17 00:00:00 2001
From: Takuya UESHIN <ueshin@happy-camper.st>
Date: Mon, 26 May 2014 00:17:20 -0700
Subject: [PATCH 275/810] [SPARK-1914] [SQL] Simplify CountFunction not to
 traverse to evaluate all child expressions.

`CountFunction` should count up only if the child's evaluated value is not null.

Because it traverses to evaluate all child expressions, even if the child is null, it counts up if one of the all children is not null.

Author: Takuya UESHIN <ueshin@happy-camper.st>

Closes #861 from ueshin/issues/SPARK-1914 and squashes the following commits:

3b37315 [Takuya UESHIN] Merge branch 'master' into issues/SPARK-1914
2afa238 [Takuya UESHIN] Simplify CountFunction not to traverse to evaluate all child expressions.
---
 .../apache/spark/sql/catalyst/expressions/aggregates.scala   | 4 ++--
 .../src/test/scala/org/apache/spark/sql/DslQuerySuite.scala  | 5 +++++
 2 files changed, 7 insertions(+), 2 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
index 1bcd4e22766a..79937b129aea 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
@@ -298,8 +298,8 @@ case class CountFunction(expr: Expression, base: AggregateExpression) extends Ag
   var count: Long = _
 
   override def update(input: Row): Unit = {
-    val evaluatedExpr = expr.map(_.eval(input))
-    if (evaluatedExpr.map(_ != null).reduceLeft(_ || _)) {
+    val evaluatedExpr = expr.eval(input)
+    if (evaluatedExpr != null) {
       count += 1L
     }
   }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala
index 692569a73ffc..8197e8a18d44 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala
@@ -125,6 +125,11 @@ class DslQuerySuite extends QueryTest {
       Seq((1,0), (2, 1))
     )
 
+    checkAnswer(
+      testData3.groupBy('a)('a, Count('a + 'b)),
+      Seq((1,0), (2, 1))
+    )
+
     checkAnswer(
       testData3.groupBy()(Count('a), Count('b), Count(1), CountDistinct('a :: Nil), CountDistinct('b :: Nil)),
       (2, 1, 2, 2, 1) :: Nil

From bee6c4f4a155f625495212c17b58dc76f525f312 Mon Sep 17 00:00:00 2001
From: witgo <witgo@qq.com>
Date: Mon, 26 May 2014 13:16:35 -0700
Subject: [PATCH 276/810] Fix scalastyle warnings in yarn alpha

Author: witgo <witgo@qq.com>

Closes #884 from witgo/scalastyle and squashes the following commits:

4b08ae4 [witgo] Fix scalastyle warnings in yarn alpha
---
 dev/scalastyle | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/dev/scalastyle b/dev/scalastyle
index a972811ba8ed..0e8fd5cc8d64 100755
--- a/dev/scalastyle
+++ b/dev/scalastyle
@@ -19,7 +19,8 @@
 
 echo -e "q\n" | SPARK_HIVE=true sbt/sbt scalastyle > scalastyle.txt
 # Check style with YARN alpha built too
-echo -e "q\n" | SPARK_YARN=true sbt/sbt yarn/scalastyle >> scalastyle.txt
+echo -e "q\n" | SPARK_HADOOP_VERSION=0.23.9 SPARK_YARN=true sbt/sbt yarn-alpha/scalastyle \
+  >> scalastyle.txt
 # Check style with YARN built too
 echo -e "q\n" | SPARK_HADOOP_VERSION=2.2.0 SPARK_YARN=true sbt/sbt yarn/scalastyle \
   >> scalastyle.txt

From cb7fe5034826844f1b50fbe8b92646317b66f21c Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Mon, 26 May 2014 14:34:58 -0700
Subject: [PATCH 277/810] SPARK-1925: Replace '&' with '&&'

JIRA: https://issues.apache.org/jira/browse/SPARK-1925

Author: zsxwing <zsxwing@gmail.com>

Closes #879 from zsxwing/SPARK-1925 and squashes the following commits:

5cf5a6d [zsxwing] SPARK-1925: Replace '&' with '&&'
---
 .../main/scala/org/apache/spark/mllib/tree/DecisionTree.scala | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
index 0fe30a3e7040..3b13e52a7b44 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
@@ -401,7 +401,7 @@ object DecisionTree extends Serializable with Logging {
      */
     def isSampleValid(parentFilters: List[Filter], labeledPoint: LabeledPoint): Boolean = {
       // leaf
-      if ((level > 0) & (parentFilters.length == 0)) {
+      if ((level > 0) && (parentFilters.length == 0)) {
         return false
       }
 
@@ -454,7 +454,7 @@ object DecisionTree extends Serializable with Logging {
           val bin = binForFeatures(mid)
           val lowThreshold = bin.lowSplit.threshold
           val highThreshold = bin.highSplit.threshold
-          if ((lowThreshold < feature) & (highThreshold >= feature)){
+          if ((lowThreshold < feature) && (highThreshold >= feature)){
             return mid
           }
           else if (lowThreshold >= feature) {

From 56c771cb2d00a5843c391ae6561536ee46e535d4 Mon Sep 17 00:00:00 2001
From: Ankur Dave <ankurdave@gmail.com>
Date: Mon, 26 May 2014 16:10:22 -0700
Subject: [PATCH 278/810] [SPARK-1931] Reconstruct routing tables in
 Graph.partitionBy

905173df57b90f90ebafb22e43f55164445330e6 introduced a bug in partitionBy where, after repartitioning the edges, it reuses the VertexRDD without updating the routing tables to reflect the new edge layout. Subsequent accesses of the triplets contain nulls for many vertex properties.

This commit adds a test for this bug and fixes it by introducing `VertexRDD#withEdges` and calling it in `partitionBy`.

Author: Ankur Dave <ankurdave@gmail.com>

Closes #885 from ankurdave/SPARK-1931 and squashes the following commits:

3930cdd [Ankur Dave] Note how to set up VertexRDD for efficient joins
9bdbaa4 [Ankur Dave] [SPARK-1931] Reconstruct routing tables in Graph.partitionBy
---
 .../scala/org/apache/spark/graphx/VertexRDD.scala   | 12 ++++++++++++
 .../org/apache/spark/graphx/impl/GraphImpl.scala    | 13 +++++++++----
 .../scala/org/apache/spark/graphx/GraphSuite.scala  | 10 ++++++++++
 3 files changed, 31 insertions(+), 4 deletions(-)

diff --git a/graphx/src/main/scala/org/apache/spark/graphx/VertexRDD.scala b/graphx/src/main/scala/org/apache/spark/graphx/VertexRDD.scala
index 8c62897037b6..8b910fbc5a42 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/VertexRDD.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/VertexRDD.scala
@@ -300,6 +300,18 @@ class VertexRDD[@specialized VD: ClassTag](
   def reverseRoutingTables(): VertexRDD[VD] =
     this.mapVertexPartitions(vPart => vPart.withRoutingTable(vPart.routingTable.reverse))
 
+  /** Prepares this VertexRDD for efficient joins with the given EdgeRDD. */
+  def withEdges(edges: EdgeRDD[_, _]): VertexRDD[VD] = {
+    val routingTables = VertexRDD.createRoutingTables(edges, this.partitioner.get)
+    val vertexPartitions = partitionsRDD.zipPartitions(routingTables, true) {
+      (partIter, routingTableIter) =>
+        val routingTable =
+          if (routingTableIter.hasNext) routingTableIter.next() else RoutingTablePartition.empty
+        partIter.map(_.withRoutingTable(routingTable))
+    }
+    new VertexRDD(vertexPartitions)
+  }
+
   /** Generates an RDD of vertex attributes suitable for shipping to the edge partitions. */
   private[graphx] def shipVertexAttributes(
       shipSrc: Boolean, shipDst: Boolean): RDD[(PartitionID, VertexAttributeBlock[VD])] = {
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/GraphImpl.scala b/graphx/src/main/scala/org/apache/spark/graphx/impl/GraphImpl.scala
index 2f2d0e03fd7b..1649b244d288 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/impl/GraphImpl.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/impl/GraphImpl.scala
@@ -88,8 +88,8 @@ class GraphImpl[VD: ClassTag, ED: ClassTag] protected (
         }
         val edgePartition = builder.toEdgePartition
         Iterator((pid, edgePartition))
-      }, preservesPartitioning = true))
-    GraphImpl.fromExistingRDDs(vertices, newEdges)
+      }, preservesPartitioning = true)).cache()
+    GraphImpl.fromExistingRDDs(vertices.withEdges(newEdges), newEdges)
   }
 
   override def reverse: Graph[VD, ED] = {
@@ -277,7 +277,11 @@ object GraphImpl {
     GraphImpl(vertexRDD, edgeRDD)
   }
 
-  /** Create a graph from a VertexRDD and an EdgeRDD with arbitrary replicated vertices. */
+  /**
+   * Create a graph from a VertexRDD and an EdgeRDD with arbitrary replicated vertices. The
+   * VertexRDD must already be set up for efficient joins with the EdgeRDD by calling
+   * `VertexRDD.withEdges` or an appropriate VertexRDD constructor.
+   */
   def apply[VD: ClassTag, ED: ClassTag](
       vertices: VertexRDD[VD],
       edges: EdgeRDD[ED, _]): GraphImpl[VD, ED] = {
@@ -290,7 +294,8 @@ object GraphImpl {
 
   /**
    * Create a graph from a VertexRDD and an EdgeRDD with the same replicated vertex type as the
-   * vertices.
+   * vertices. The VertexRDD must already be set up for efficient joins with the EdgeRDD by calling
+   * `VertexRDD.withEdges` or an appropriate VertexRDD constructor.
    */
   def fromExistingRDDs[VD: ClassTag, ED: ClassTag](
       vertices: VertexRDD[VD],
diff --git a/graphx/src/test/scala/org/apache/spark/graphx/GraphSuite.scala b/graphx/src/test/scala/org/apache/spark/graphx/GraphSuite.scala
index 7b9bac5d9c8e..abc25d067113 100644
--- a/graphx/src/test/scala/org/apache/spark/graphx/GraphSuite.scala
+++ b/graphx/src/test/scala/org/apache/spark/graphx/GraphSuite.scala
@@ -133,6 +133,16 @@ class GraphSuite extends FunSuite with LocalSparkContext {
         Iterator((part.srcIds ++ part.dstIds).toSet)
       }.collect
       assert(verts.exists(id => partitionSetsUnpartitioned.count(_.contains(id)) > bound))
+
+      // Forming triplets view
+      val g = Graph(
+        sc.parallelize(List((0L, "a"), (1L, "b"), (2L, "c"))),
+        sc.parallelize(List(Edge(0L, 1L, 1), Edge(0L, 2L, 1)), 2))
+      assert(g.triplets.collect.map(_.toTuple).toSet ===
+        Set(((0L, "a"), (1L, "b"), 1), ((0L, "a"), (2L, "c"), 1)))
+      val gPart = g.partitionBy(EdgePartition2D)
+      assert(gPart.triplets.collect.map(_.toTuple).toSet ===
+        Set(((0L, "a"), (1L, "b"), 1), ((0L, "a"), (2L, "c"), 1)))
     }
   }
 

From 8d271c90fa496cb24e2b7362ef0497563591b97d Mon Sep 17 00:00:00 2001
From: Zhen Peng <zhenpeng01@baidu.com>
Date: Mon, 26 May 2014 21:30:25 -0700
Subject: [PATCH 279/810] SPARK-1929 DAGScheduler suspended by local task OOM

DAGScheduler does not handle local task OOM properly, and will wait for the job result forever.

Author: Zhen Peng <zhenpeng01@baidu.com>

Closes #883 from zhpengg/bugfix-dag-scheduler-oom and squashes the following commits:

76f7eda [Zhen Peng] remove redundant memory allocations
aa63161 [Zhen Peng] SPARK-1929 DAGScheduler suspended by local task OOM
---
 .../org/apache/spark/scheduler/DAGScheduler.scala  |  6 +++++-
 .../apache/spark/scheduler/DAGSchedulerSuite.scala | 14 ++++++++++++++
 2 files changed, 19 insertions(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
index ff411e24a3d8..c70aa0e6e452 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.scheduler
 
-import java.io.NotSerializableException
+import java.io.{NotSerializableException, PrintWriter, StringWriter}
 import java.util.Properties
 import java.util.concurrent.atomic.AtomicInteger
 
@@ -580,6 +580,10 @@ class DAGScheduler(
       case e: Exception =>
         jobResult = JobFailed(e)
         job.listener.jobFailed(e)
+      case oom: OutOfMemoryError =>
+        val exception = new SparkException("job failed for Out of memory exception", oom)
+        jobResult = JobFailed(exception)
+        job.listener.jobFailed(exception)
     } finally {
       val s = job.finalStage
       stageIdToJobIds -= s.id    // clean up data structures that were populated for a local job,
diff --git a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
index d172dd1ac8e1..81e64c1846ed 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
@@ -256,6 +256,20 @@ class DAGSchedulerSuite extends TestKit(ActorSystem("DAGSchedulerSuite")) with F
     assertDataStructuresEmpty
   }
 
+  test("local job oom") {
+    val rdd = new MyRDD(sc, Nil) {
+      override def compute(split: Partition, context: TaskContext): Iterator[(Int, Int)] =
+        throw new java.lang.OutOfMemoryError("test local job oom")
+      override def getPartitions = Array( new Partition { override def index = 0 } )
+      override def getPreferredLocations(split: Partition) = Nil
+      override def toString = "DAGSchedulerSuite Local RDD"
+    }
+    val jobId = scheduler.nextJobId.getAndIncrement()
+    runEvent(JobSubmitted(jobId, rdd, jobComputeFunc, Array(0), true, null, jobListener))
+    assert(results.size == 0)
+    assertDataStructuresEmpty
+  }
+
   test("run trivial job w/ dependency") {
     val baseRdd = makeRdd(1, Nil)
     val finalRdd = makeRdd(1, List(new OneToOneDependency(baseRdd)))

From ef690e1f69cb8e2e03bb0c43e3ccb2c54c995df7 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Mon, 26 May 2014 21:31:27 -0700
Subject: [PATCH 280/810] Fixed the error message for OutOfMemoryError in
 DAGScheduler.

---
 .../main/scala/org/apache/spark/scheduler/DAGScheduler.scala    | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
index c70aa0e6e452..ccff6a3d1aeb 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
@@ -581,7 +581,7 @@ class DAGScheduler(
         jobResult = JobFailed(e)
         job.listener.jobFailed(e)
       case oom: OutOfMemoryError =>
-        val exception = new SparkException("job failed for Out of memory exception", oom)
+        val exception = new SparkException("Local job aborted due to out of memory error", oom)
         jobResult = JobFailed(exception)
         job.listener.jobFailed(exception)
     } finally {

From 9ed37190f45fd9e6aa0f2c73b66d317732a53eb8 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Mon, 26 May 2014 21:40:52 -0700
Subject: [PATCH 281/810] Updated dev Python scripts to make them PEP8
 compliant.

Author: Reynold Xin <rxin@apache.org>

Closes #875 from rxin/pep8-dev-scripts and squashes the following commits:

04b084f [Reynold Xin] Made dev Python scripts PEP8 compliant.
---
 dev/audit-release/audit_release.py        | 225 ++++++------
 dev/create-release/generate-changelist.py | 160 ++++-----
 dev/merge_spark_pr.py                     | 402 +++++++++++-----------
 3 files changed, 408 insertions(+), 379 deletions(-)

diff --git a/dev/audit-release/audit_release.py b/dev/audit-release/audit_release.py
index 8c7573b91f68..230e900ecd4d 100755
--- a/dev/audit-release/audit_release.py
+++ b/dev/audit-release/audit_release.py
@@ -30,18 +30,18 @@
 import time
 import urllib2
 
-## Fill in release details here:
+# Fill in release details here:
 RELEASE_URL = "http://people.apache.org/~pwendell/spark-1.0.0-rc1/"
 RELEASE_KEY = "9E4FE3AF"
 RELEASE_REPOSITORY = "https://repository.apache.org/content/repositories/orgapachespark-1006/"
 RELEASE_VERSION = "1.0.0"
 SCALA_VERSION = "2.10.4"
 SCALA_BINARY_VERSION = "2.10"
-##
+#
 
 LOG_FILE_NAME = "spark_audit_%s" % time.strftime("%h_%m_%Y_%I_%M_%S")
 LOG_FILE = open(LOG_FILE_NAME, 'w')
-WORK_DIR = "/tmp/audit_%s" % int(time.time()) 
+WORK_DIR = "/tmp/audit_%s" % int(time.time())
 MAVEN_CMD = "mvn"
 GPG_CMD = "gpg"
 
@@ -50,54 +50,62 @@
 # Track failures
 failures = []
 
+
 def clean_work_files():
-  print "OK to delete scratch directory '%s'? (y/N): " % WORK_DIR
-  response = raw_input()
-  if response == "y":
-    shutil.rmtree(WORK_DIR) 
-  print "Should I delete the log output file '%s'? (y/N): " % LOG_FILE_NAME
-  response = raw_input()
-  if response == "y":
-    os.unlink(LOG_FILE_NAME)
+    print "OK to delete scratch directory '%s'? (y/N): " % WORK_DIR
+    response = raw_input()
+    if response == "y":
+        shutil.rmtree(WORK_DIR)
+    print "Should I delete the log output file '%s'? (y/N): " % LOG_FILE_NAME
+    response = raw_input()
+    if response == "y":
+        os.unlink(LOG_FILE_NAME)
+
 
 def run_cmd(cmd, exit_on_failure=True):
-  print >> LOG_FILE, "Running command: %s" % cmd
-  ret = subprocess.call(cmd, shell=True, stdout=LOG_FILE, stderr=LOG_FILE)
-  if ret != 0 and exit_on_failure:
-    print "Command failed: %s" % cmd
-    clean_work_files()
-    sys.exit(-1)
-  return ret
+    print >> LOG_FILE, "Running command: %s" % cmd
+    ret = subprocess.call(cmd, shell=True, stdout=LOG_FILE, stderr=LOG_FILE)
+    if ret != 0 and exit_on_failure:
+        print "Command failed: %s" % cmd
+        clean_work_files()
+        sys.exit(-1)
+    return ret
+
 
 def run_cmd_with_output(cmd):
-  print >> sys.stderr, "Running command: %s" % cmd
-  return subprocess.check_output(cmd, shell=True, stderr=LOG_FILE)
+    print >> sys.stderr, "Running command: %s" % cmd
+    return subprocess.check_output(cmd, shell=True, stderr=LOG_FILE)
+
 
 def test(bool, str):
-  if bool:
-    return passed(str)
-  failed(str)
+    if bool:
+        return passed(str)
+    failed(str)
+
 
 def passed(str):
-  print "[PASSED] %s" % str
+    print "[PASSED] %s" % str
+
 
 def failed(str):
-  failures.append(str)
-  print "[**FAILED**] %s" % str
+    failures.append(str)
+    print "[**FAILED**] %s" % str
+
 
 def get_url(url):
-  return urllib2.urlopen(url).read()
+    return urllib2.urlopen(url).read()
+
 
 original_dir = os.getcwd()
 
-# For each of these modules, we'll test an 'empty' application in sbt and 
+# For each of these modules, we'll test an 'empty' application in sbt and
 # maven that links against them. This will catch issues with messed up
 # dependencies within those projects.
 modules = [
-  "spark-core", "spark-bagel", "spark-mllib", "spark-streaming", "spark-repl",
-  "spark-graphx", "spark-streaming-flume", "spark-streaming-kafka",
-  "spark-streaming-mqtt", "spark-streaming-twitter", "spark-streaming-zeromq",
-  "spark-catalyst", "spark-sql", "spark-hive"
+    "spark-core", "spark-bagel", "spark-mllib", "spark-streaming", "spark-repl",
+    "spark-graphx", "spark-streaming-flume", "spark-streaming-kafka",
+    "spark-streaming-mqtt", "spark-streaming-twitter", "spark-streaming-zeromq",
+    "spark-catalyst", "spark-sql", "spark-hive"
 ]
 modules = map(lambda m: "%s_%s" % (m, SCALA_BINARY_VERSION), modules)
 
@@ -106,54 +114,57 @@ def get_url(url):
 cache_ivy_spark = "~/.ivy2/cache/org.apache.spark"
 local_maven_kafka = "~/.m2/repository/org/apache/kafka"
 local_maven_kafka = "~/.m2/repository/org/apache/spark"
+
+
 def ensure_path_not_present(x):
-  if os.path.exists(os.path.expanduser(x)):
-    print "Please remove %s, it can interfere with testing published artifacts." % x
-    sys.exit(-1)
+    if os.path.exists(os.path.expanduser(x)):
+        print "Please remove %s, it can interfere with testing published artifacts." % x
+        sys.exit(-1)
+
 map(ensure_path_not_present, [local_ivy_spark, cache_ivy_spark, local_maven_kafka])
 
-# SBT build tests 
+# SBT build tests
 os.chdir("blank_sbt_build")
 os.environ["SPARK_VERSION"] = RELEASE_VERSION
 os.environ["SCALA_VERSION"] = SCALA_VERSION
 os.environ["SPARK_RELEASE_REPOSITORY"] = RELEASE_REPOSITORY
 os.environ["SPARK_AUDIT_MASTER"] = "local"
 for module in modules:
-  os.environ["SPARK_MODULE"] = module
-  ret = run_cmd("sbt clean update", exit_on_failure=False)
-  test(ret == 0, "sbt build against '%s' module" % module) 
+    os.environ["SPARK_MODULE"] = module
+    ret = run_cmd("sbt clean update", exit_on_failure=False)
+    test(ret == 0, "sbt build against '%s' module" % module)
 os.chdir(original_dir)
 
 # SBT application tests
 for app in ["sbt_app_core", "sbt_app_graphx", "sbt_app_streaming", "sbt_app_sql", "sbt_app_hive"]:
-  os.chdir(app)
-  ret = run_cmd("sbt clean run", exit_on_failure=False)
-  test(ret == 0, "sbt application (%s)" % app)
-  os.chdir(original_dir)
+    os.chdir(app)
+    ret = run_cmd("sbt clean run", exit_on_failure=False)
+    test(ret == 0, "sbt application (%s)" % app)
+    os.chdir(original_dir)
 
 # Maven build tests
 os.chdir("blank_maven_build")
 for module in modules:
-  cmd = ('%s --update-snapshots -Dspark.release.repository="%s" -Dspark.version="%s" '
-      '-Dspark.module="%s" clean compile' % 
-      (MAVEN_CMD, RELEASE_REPOSITORY, RELEASE_VERSION, module))
-  ret = run_cmd(cmd, exit_on_failure=False)
-  test(ret == 0, "maven build against '%s' module" % module)
+    cmd = ('%s --update-snapshots -Dspark.release.repository="%s" -Dspark.version="%s" '
+           '-Dspark.module="%s" clean compile' %
+           (MAVEN_CMD, RELEASE_REPOSITORY, RELEASE_VERSION, module))
+    ret = run_cmd(cmd, exit_on_failure=False)
+    test(ret == 0, "maven build against '%s' module" % module)
 os.chdir(original_dir)
 
 os.chdir("maven_app_core")
 mvn_exec_cmd = ('%s --update-snapshots -Dspark.release.repository="%s" -Dspark.version="%s" '
                 '-Dscala.binary.version="%s" clean compile '
-                'exec:java -Dexec.mainClass="SimpleApp"' % 
-               (MAVEN_CMD, RELEASE_REPOSITORY, RELEASE_VERSION, SCALA_BINARY_VERSION))
+                'exec:java -Dexec.mainClass="SimpleApp"' %
+                (MAVEN_CMD, RELEASE_REPOSITORY, RELEASE_VERSION, SCALA_BINARY_VERSION))
 ret = run_cmd(mvn_exec_cmd, exit_on_failure=False)
 test(ret == 0, "maven application (core)")
 os.chdir(original_dir)
 
 # Binary artifact tests
 if os.path.exists(WORK_DIR):
-  print "Working directory '%s' already exists" % WORK_DIR
-  sys.exit(-1)
+    print "Working directory '%s' already exists" % WORK_DIR
+    sys.exit(-1)
 os.mkdir(WORK_DIR)
 os.chdir(WORK_DIR)
 
@@ -162,66 +173,66 @@ def ensure_path_not_present(x):
 artifacts = r.findall(index_page)
 
 for artifact in artifacts:
-  print "==== Verifying download integrity for artifact: %s ====" % artifact
-
-  artifact_url = "%s/%s" % (RELEASE_URL, artifact)
-  run_cmd("wget %s" % artifact_url)
-
-  key_file = "%s.asc" % artifact
-  run_cmd("wget %s/%s" % (RELEASE_URL, key_file))
-
-  run_cmd("wget %s%s" % (artifact_url, ".sha"))
-
-  # Verify signature
-  run_cmd("%s --keyserver pgp.mit.edu --recv-key %s" % (GPG_CMD, RELEASE_KEY))
-  run_cmd("%s %s" % (GPG_CMD, key_file))
-  passed("Artifact signature verified.")
-
-  # Verify md5
-  my_md5 = run_cmd_with_output("%s --print-md MD5 %s" % (GPG_CMD, artifact)).strip()
-  release_md5 = get_url("%s.md5" % artifact_url).strip()
-  test(my_md5 == release_md5, "Artifact MD5 verified.")
-
-  # Verify sha
-  my_sha = run_cmd_with_output("%s --print-md SHA512 %s" % (GPG_CMD, artifact)).strip()
-  release_sha = get_url("%s.sha" % artifact_url).strip()
-  test(my_sha == release_sha, "Artifact SHA verified.")
-
-  # Verify Apache required files
-  dir_name = artifact.replace(".tgz", "")
-  run_cmd("tar xvzf %s" % artifact)
-  base_files = os.listdir(dir_name)
-  test("CHANGES.txt" in base_files, "Tarball contains CHANGES.txt file")
-  test("NOTICE" in base_files, "Tarball contains NOTICE file")
-  test("LICENSE" in base_files, "Tarball contains LICENSE file")
- 
-  os.chdir(WORK_DIR)
- 
+    print "==== Verifying download integrity for artifact: %s ====" % artifact
+
+    artifact_url = "%s/%s" % (RELEASE_URL, artifact)
+    run_cmd("wget %s" % artifact_url)
+
+    key_file = "%s.asc" % artifact
+    run_cmd("wget %s/%s" % (RELEASE_URL, key_file))
+
+    run_cmd("wget %s%s" % (artifact_url, ".sha"))
+
+    # Verify signature
+    run_cmd("%s --keyserver pgp.mit.edu --recv-key %s" % (GPG_CMD, RELEASE_KEY))
+    run_cmd("%s %s" % (GPG_CMD, key_file))
+    passed("Artifact signature verified.")
+
+    # Verify md5
+    my_md5 = run_cmd_with_output("%s --print-md MD5 %s" % (GPG_CMD, artifact)).strip()
+    release_md5 = get_url("%s.md5" % artifact_url).strip()
+    test(my_md5 == release_md5, "Artifact MD5 verified.")
+
+    # Verify sha
+    my_sha = run_cmd_with_output("%s --print-md SHA512 %s" % (GPG_CMD, artifact)).strip()
+    release_sha = get_url("%s.sha" % artifact_url).strip()
+    test(my_sha == release_sha, "Artifact SHA verified.")
+
+    # Verify Apache required files
+    dir_name = artifact.replace(".tgz", "")
+    run_cmd("tar xvzf %s" % artifact)
+    base_files = os.listdir(dir_name)
+    test("CHANGES.txt" in base_files, "Tarball contains CHANGES.txt file")
+    test("NOTICE" in base_files, "Tarball contains NOTICE file")
+    test("LICENSE" in base_files, "Tarball contains LICENSE file")
+
+    os.chdir(WORK_DIR)
+
 for artifact in artifacts:
-  print "==== Verifying build and tests for artifact: %s ====" % artifact
-  os.chdir(os.path.join(WORK_DIR, dir_name))
-
-  os.environ["MAVEN_OPTS"] = "-Xmx3g -XX:MaxPermSize=1g -XX:ReservedCodeCacheSize=1g"
-  # Verify build
-  print "==> Running build"
-  run_cmd("sbt assembly")
-  passed("sbt build successful")
-  run_cmd("%s package -DskipTests" % MAVEN_CMD)    
-  passed("Maven build successful")
-
-  # Verify tests
-  print "==> Performing unit tests"
-  run_cmd("%s test" % MAVEN_CMD)
-  passed("Tests successful")
-  os.chdir(WORK_DIR)
+    print "==== Verifying build and tests for artifact: %s ====" % artifact
+    os.chdir(os.path.join(WORK_DIR, dir_name))
+
+    os.environ["MAVEN_OPTS"] = "-Xmx3g -XX:MaxPermSize=1g -XX:ReservedCodeCacheSize=1g"
+    # Verify build
+    print "==> Running build"
+    run_cmd("sbt assembly")
+    passed("sbt build successful")
+    run_cmd("%s package -DskipTests" % MAVEN_CMD)
+    passed("Maven build successful")
+
+    # Verify tests
+    print "==> Performing unit tests"
+    run_cmd("%s test" % MAVEN_CMD)
+    passed("Tests successful")
+    os.chdir(WORK_DIR)
 
 clean_work_files()
 
 if len(failures) == 0:
-  print "ALL TESTS PASSED"
+    print "ALL TESTS PASSED"
 else:
-  print "SOME TESTS DID NOT PASS"
-  for f in failures:
-    print f
+    print "SOME TESTS DID NOT PASS"
+    for f in failures:
+        print f
 
 os.chdir(original_dir)
diff --git a/dev/create-release/generate-changelist.py b/dev/create-release/generate-changelist.py
index 13b744ec1b37..de1b5d4ae131 100755
--- a/dev/create-release/generate-changelist.py
+++ b/dev/create-release/generate-changelist.py
@@ -29,16 +29,16 @@
 import subprocess
 import time
 import traceback
- 
+
 SPARK_HOME = os.environ["SPARK_HOME"]
 NEW_RELEASE_VERSION = "1.0.0"
 PREV_RELEASE_GIT_TAG = "v0.9.1"
- 
-CHANGELIST = "CHANGES.txt" 
+
+CHANGELIST = "CHANGES.txt"
 OLD_CHANGELIST = "%s.old" % (CHANGELIST)
 NEW_CHANGELIST = "%s.new" % (CHANGELIST)
 TMP_CHANGELIST = "%s.tmp" % (CHANGELIST)
- 
+
 # date before first PR in TLP Spark repo
 SPARK_REPO_CHANGE_DATE1 = time.strptime("2014-02-26", "%Y-%m-%d")
 # date after last PR in incubator Spark repo
@@ -46,99 +46,103 @@
 # Threshold PR number that differentiates PRs to TLP
 # and incubator repos
 SPARK_REPO_PR_NUM_THRESH = 200
- 
+
 LOG_FILE_NAME = "changes_%s" % time.strftime("%h_%m_%Y_%I_%M_%S")
 LOG_FILE = open(LOG_FILE_NAME, 'w')
- 
+
+
 def run_cmd(cmd):
-  try:
-    print >> LOG_FILE, "Running command: %s" % cmd
-    output = subprocess.check_output(cmd, shell=True, stderr=LOG_FILE)
-    print >> LOG_FILE, "Output: %s" % output
-    return output
-  except:
-    traceback.print_exc()
-    cleanup()
-    sys.exit(1)
- 
+    try:
+        print >> LOG_FILE, "Running command: %s" % cmd
+        output = subprocess.check_output(cmd, shell=True, stderr=LOG_FILE)
+        print >> LOG_FILE, "Output: %s" % output
+        return output
+    except:
+        traceback.print_exc()
+        cleanup()
+        sys.exit(1)
+
+
 def append_to_changelist(string):
-  with open(TMP_CHANGELIST, "a") as f:
-    print >> f, string
- 
-def cleanup(ask = True):
-  if ask == True:
-    print "OK to delete temporary and log files? (y/N): " 
-    response = raw_input()
-  if ask == False or (ask == True and response == "y"):
-    if os.path.isfile(TMP_CHANGELIST):
-      os.remove(TMP_CHANGELIST) 
-    if os.path.isfile(OLD_CHANGELIST):
-      os.remove(OLD_CHANGELIST)
-    LOG_FILE.close()
-    os.remove(LOG_FILE_NAME)
- 
+    with open(TMP_CHANGELIST, "a") as f:
+        print >> f, string
+
+
+def cleanup(ask=True):
+    if ask is True:
+        print "OK to delete temporary and log files? (y/N): "
+        response = raw_input()
+    if ask is False or (ask is True and response == "y"):
+        if os.path.isfile(TMP_CHANGELIST):
+            os.remove(TMP_CHANGELIST)
+        if os.path.isfile(OLD_CHANGELIST):
+            os.remove(OLD_CHANGELIST)
+        LOG_FILE.close()
+        os.remove(LOG_FILE_NAME)
+
+
 print "Generating new %s for Spark release %s" % (CHANGELIST, NEW_RELEASE_VERSION)
 os.chdir(SPARK_HOME)
 if os.path.isfile(TMP_CHANGELIST):
-  os.remove(TMP_CHANGELIST) 
+    os.remove(TMP_CHANGELIST)
 if os.path.isfile(OLD_CHANGELIST):
-  os.remove(OLD_CHANGELIST)
- 
+    os.remove(OLD_CHANGELIST)
+
 append_to_changelist("Spark Change Log")
 append_to_changelist("----------------")
 append_to_changelist("")
 append_to_changelist("Release %s" % NEW_RELEASE_VERSION)
 append_to_changelist("")
- 
+
 print "Getting commits between tag %s and HEAD" % PREV_RELEASE_GIT_TAG
 hashes = run_cmd("git log %s..HEAD --pretty='%%h'" % PREV_RELEASE_GIT_TAG).split()
- 
+
 print "Getting details of %s commits" % len(hashes)
 for h in hashes:
-  date = run_cmd("git log %s -1 --pretty='%%ad' --date=iso | head -1" % h).strip()
-  subject = run_cmd("git log %s -1 --pretty='%%s' | head -1" % h).strip()
-  body = run_cmd("git log %s -1 --pretty='%%b'" % h)
-  committer = run_cmd("git log %s -1 --pretty='%%cn <%%ce>' | head -1" % h).strip()
-  body_lines = body.split("\n")
- 
-  if "Merge pull" in subject:
-    ## Parse old format commit message
-    append_to_changelist("  %s %s" % (h, date))
-    append_to_changelist("  %s" % subject)
-    append_to_changelist("  [%s]" % body_lines[0])
-    append_to_changelist("")
-     
-  elif "maven-release" not in subject:
-    ## Parse new format commit message
-    # Get authors from commit message, committer otherwise
-    authors = [committer]
-    if "Author:" in body:
-      authors = [line.split(":")[1].strip() for line in body_lines if "Author:" in line]
-    
-    # Generate GitHub PR URL for easy access if possible
-    github_url = ""
-    if "Closes #" in body:
-      pr_num = [line.split()[1].lstrip("#") for line in body_lines if "Closes #" in line][0]
-      github_url = "github.com/apache/spark/pull/%s" % pr_num 
-      day = time.strptime(date.split()[0], "%Y-%m-%d")
-      if day < SPARK_REPO_CHANGE_DATE1 or (day < SPARK_REPO_CHANGE_DATE2 and pr_num < SPARK_REPO_PR_NUM_THRESH): 
-        github_url = "github.com/apache/incubator-spark/pull/%s" % pr_num
-    
-    append_to_changelist("  %s" % subject)
-    append_to_changelist("  %s" % ', '.join(authors))
-    # for author in authors:
-      # append_to_changelist("  %s" % author)
-    append_to_changelist("  %s" % date)
-    if len(github_url) > 0:
-      append_to_changelist("  Commit: %s, %s" % (h, github_url))
-    else:
-      append_to_changelist("  Commit: %s" % h)
-    append_to_changelist("")
- 
+    date = run_cmd("git log %s -1 --pretty='%%ad' --date=iso | head -1" % h).strip()
+    subject = run_cmd("git log %s -1 --pretty='%%s' | head -1" % h).strip()
+    body = run_cmd("git log %s -1 --pretty='%%b'" % h)
+    committer = run_cmd("git log %s -1 --pretty='%%cn <%%ce>' | head -1" % h).strip()
+    body_lines = body.split("\n")
+
+    if "Merge pull" in subject:
+        # Parse old format commit message
+        append_to_changelist("  %s %s" % (h, date))
+        append_to_changelist("  %s" % subject)
+        append_to_changelist("  [%s]" % body_lines[0])
+        append_to_changelist("")
+
+    elif "maven-release" not in subject:
+        # Parse new format commit message
+        # Get authors from commit message, committer otherwise
+        authors = [committer]
+        if "Author:" in body:
+            authors = [line.split(":")[1].strip() for line in body_lines if "Author:" in line]
+
+        # Generate GitHub PR URL for easy access if possible
+        github_url = ""
+        if "Closes #" in body:
+            pr_num = [line.split()[1].lstrip("#") for line in body_lines if "Closes #" in line][0]
+            github_url = "github.com/apache/spark/pull/%s" % pr_num
+            day = time.strptime(date.split()[0], "%Y-%m-%d")
+            if day < SPARK_REPO_CHANGE_DATE1 or
+            (day < SPARK_REPO_CHANGE_DATE2 and pr_num < SPARK_REPO_PR_NUM_THRESH):
+                github_url = "github.com/apache/incubator-spark/pull/%s" % pr_num
+
+        append_to_changelist("  %s" % subject)
+        append_to_changelist("  %s" % ', '.join(authors))
+        # for author in authors:
+        #     append_to_changelist("  %s" % author)
+        append_to_changelist("  %s" % date)
+        if len(github_url) > 0:
+            append_to_changelist("  Commit: %s, %s" % (h, github_url))
+        else:
+            append_to_changelist("  Commit: %s" % h)
+        append_to_changelist("")
+
 # Append old change list
-print "Appending changelist from tag %s" % PREV_RELEASE_GIT_TAG 
+print "Appending changelist from tag %s" % PREV_RELEASE_GIT_TAG
 run_cmd("git show %s:%s | tail -n +3 >> %s" % (PREV_RELEASE_GIT_TAG, CHANGELIST, TMP_CHANGELIST))
 run_cmd("cp %s %s" % (TMP_CHANGELIST, NEW_CHANGELIST))
 print "New change list generated as %s" % NEW_CHANGELIST
 cleanup(False)
-
diff --git a/dev/merge_spark_pr.py b/dev/merge_spark_pr.py
index 83618c8068d3..7f744d5589ef 100755
--- a/dev/merge_spark_pr.py
+++ b/dev/merge_spark_pr.py
@@ -21,7 +21,7 @@
 #   usage: ./apache-pr-merge.py    (see config env vars below)
 #
 # This utility assumes you already have local a Spark git folder and that you
-# have added remotes corresponding to both (i) the github apache Spark 
+# have added remotes corresponding to both (i) the github apache Spark
 # mirror and (ii) the apache git repo.
 
 import json
@@ -33,10 +33,10 @@
 import urllib2
 
 try:
-  import jira.client
-  JIRA_IMPORTED=True
+    import jira.client
+    JIRA_IMPORTED = True
 except ImportError:
-  JIRA_IMPORTED=False
+    JIRA_IMPORTED = False
 
 # Location of your Spark git development area
 SPARK_HOME = os.environ.get("SPARK_HOME", "/home/patrick/Documents/spark")
@@ -58,204 +58,217 @@
 
 os.chdir(SPARK_HOME)
 
+
 def get_json(url):
-  try:
-    return json.load(urllib2.urlopen(url))
-  except urllib2.HTTPError as e:
-    print "Unable to fetch URL, exiting: %s" % url
-    sys.exit(-1)
+    try:
+        return json.load(urllib2.urlopen(url))
+    except urllib2.HTTPError as e:
+        print "Unable to fetch URL, exiting: %s" % url
+        sys.exit(-1)
+
 
 def fail(msg):
-  print msg
-  clean_up()
-  sys.exit(-1)
+    print msg
+    clean_up()
+    sys.exit(-1)
+
 
 def run_cmd(cmd):
-  if isinstance(cmd, list):
-    return subprocess.check_output(cmd)
-  else:
-    return subprocess.check_output(cmd.split(" "))
+    if isinstance(cmd, list):
+        return subprocess.check_output(cmd)
+    else:
+        return subprocess.check_output(cmd.split(" "))
+
 
 def continue_maybe(prompt):
-  result = raw_input("\n%s (y/n): " % prompt)
-  if result.lower() != "y":
-    fail("Okay, exiting")
+    result = raw_input("\n%s (y/n): " % prompt)
+    if result.lower() != "y":
+        fail("Okay, exiting")
+
 
 original_head = run_cmd("git rev-parse HEAD")[:8]
 
+
 def clean_up():
-  print "Restoring head pointer to %s" % original_head
-  run_cmd("git checkout %s" % original_head)
+    print "Restoring head pointer to %s" % original_head
+    run_cmd("git checkout %s" % original_head)
+
+    branches = run_cmd("git branch").replace(" ", "").split("\n")
 
-  branches = run_cmd("git branch").replace(" ", "").split("\n")
+    for branch in filter(lambda x: x.startswith(BRANCH_PREFIX), branches):
+        print "Deleting local branch %s" % branch
+        run_cmd("git branch -D %s" % branch)
 
-  for branch in filter(lambda x: x.startswith(BRANCH_PREFIX), branches):
-    print "Deleting local branch %s" % branch
-    run_cmd("git branch -D %s" % branch)
 
 # merge the requested PR and return the merge hash
 def merge_pr(pr_num, target_ref):
-  pr_branch_name = "%s_MERGE_PR_%s" % (BRANCH_PREFIX, pr_num)
-  target_branch_name = "%s_MERGE_PR_%s_%s" % (BRANCH_PREFIX, pr_num, target_ref.upper())
-  run_cmd("git fetch %s pull/%s/head:%s" % (PR_REMOTE_NAME, pr_num, pr_branch_name))
-  run_cmd("git fetch %s %s:%s" % (PUSH_REMOTE_NAME, target_ref, target_branch_name))
-  run_cmd("git checkout %s" % target_branch_name)
-  
-  had_conflicts = False
-  try:
-    run_cmd(['git', 'merge', pr_branch_name, '--squash'])
-  except Exception as e:
-    msg = "Error merging: %s\nWould you like to manually fix-up this merge?" % e
-    continue_maybe(msg)
-    msg = "Okay, please fix any conflicts and 'git add' conflicting files... Finished?"
-    continue_maybe(msg)
-    had_conflicts = True
-
-  commit_authors = run_cmd(['git', 'log', 'HEAD..%s' % pr_branch_name, 
-    '--pretty=format:%an <%ae>']).split("\n")
-  distinct_authors = sorted(set(commit_authors), key=lambda x: commit_authors.count(x), 
-    reverse=True)
-  primary_author = distinct_authors[0]
-  commits = run_cmd(['git', 'log', 'HEAD..%s' % pr_branch_name, 
-    '--pretty=format:%h [%an] %s']).split("\n\n")
-
-  merge_message_flags = []
-
-  for p in [title, body]:
-    merge_message_flags += ["-m", p]
-
-  authors = "\n".join(["Author: %s" % a for a in distinct_authors])
-
-  merge_message_flags += ["-m", authors]
+    pr_branch_name = "%s_MERGE_PR_%s" % (BRANCH_PREFIX, pr_num)
+    target_branch_name = "%s_MERGE_PR_%s_%s" % (BRANCH_PREFIX, pr_num, target_ref.upper())
+    run_cmd("git fetch %s pull/%s/head:%s" % (PR_REMOTE_NAME, pr_num, pr_branch_name))
+    run_cmd("git fetch %s %s:%s" % (PUSH_REMOTE_NAME, target_ref, target_branch_name))
+    run_cmd("git checkout %s" % target_branch_name)
+
+    had_conflicts = False
+    try:
+        run_cmd(['git', 'merge', pr_branch_name, '--squash'])
+    except Exception as e:
+        msg = "Error merging: %s\nWould you like to manually fix-up this merge?" % e
+        continue_maybe(msg)
+        msg = "Okay, please fix any conflicts and 'git add' conflicting files... Finished?"
+        continue_maybe(msg)
+        had_conflicts = True
+
+    commit_authors = run_cmd(['git', 'log', 'HEAD..%s' % pr_branch_name,
+                             '--pretty=format:%an <%ae>']).split("\n")
+    distinct_authors = sorted(set(commit_authors),
+                              key=lambda x: commit_authors.count(x), reverse=True)
+    primary_author = distinct_authors[0]
+    commits = run_cmd(['git', 'log', 'HEAD..%s' % pr_branch_name,
+                      '--pretty=format:%h [%an] %s']).split("\n\n")
+
+    merge_message_flags = []
+
+    for p in [title, body]:
+        merge_message_flags += ["-m", p]
+
+    authors = "\n".join(["Author: %s" % a for a in distinct_authors])
+
+    merge_message_flags += ["-m", authors]
+
+    if had_conflicts:
+        committer_name = run_cmd("git config --get user.name").strip()
+        committer_email = run_cmd("git config --get user.email").strip()
+        message = "This patch had conflicts when merged, resolved by\nCommitter: %s <%s>" % (
+            committer_name, committer_email)
+        merge_message_flags += ["-m", message]
+
+    # The string "Closes #%s" string is required for GitHub to correctly close the PR
+    merge_message_flags += [
+        "-m",
+        "Closes #%s from %s and squashes the following commits:" % (pr_num, pr_repo_desc)]
+    for c in commits:
+        merge_message_flags += ["-m", c]
+
+    run_cmd(['git', 'commit', '--author="%s"' % primary_author] + merge_message_flags)
+
+    continue_maybe("Merge complete (local ref %s). Push to %s?" % (
+        target_branch_name, PUSH_REMOTE_NAME))
+
+    try:
+        run_cmd('git push %s %s:%s' % (PUSH_REMOTE_NAME, target_branch_name, target_ref))
+    except Exception as e:
+        clean_up()
+        fail("Exception while pushing: %s" % e)
+
+    merge_hash = run_cmd("git rev-parse %s" % target_branch_name)[:8]
+    clean_up()
+    print("Pull request #%s merged!" % pr_num)
+    print("Merge hash: %s" % merge_hash)
+    return merge_hash
 
-  if had_conflicts:
-    committer_name = run_cmd("git config --get user.name").strip()
-    committer_email = run_cmd("git config --get user.email").strip()
-    message = "This patch had conflicts when merged, resolved by\nCommitter: %s <%s>" % (
-      committer_name, committer_email)
-    merge_message_flags += ["-m", message]
 
-  # The string "Closes #%s" string is required for GitHub to correctly close the PR
-  merge_message_flags += ["-m",
-    "Closes #%s from %s and squashes the following commits:" % (pr_num, pr_repo_desc)]
-  for c in commits:
-    merge_message_flags += ["-m", c]
+def cherry_pick(pr_num, merge_hash, default_branch):
+    pick_ref = raw_input("Enter a branch name [%s]: " % default_branch)
+    if pick_ref == "":
+        pick_ref = default_branch
 
-  run_cmd(['git', 'commit', '--author="%s"' % primary_author] + merge_message_flags)
+    pick_branch_name = "%s_PICK_PR_%s_%s" % (BRANCH_PREFIX, pr_num, pick_ref.upper())
 
-  continue_maybe("Merge complete (local ref %s). Push to %s?" % (
-    target_branch_name, PUSH_REMOTE_NAME))
+    run_cmd("git fetch %s %s:%s" % (PUSH_REMOTE_NAME, pick_ref, pick_branch_name))
+    run_cmd("git checkout %s" % pick_branch_name)
+    run_cmd("git cherry-pick -sx %s" % merge_hash)
 
-  try:
-    run_cmd('git push %s %s:%s' % (PUSH_REMOTE_NAME, target_branch_name, target_ref))
-  except Exception as e:
-    clean_up()
-    fail("Exception while pushing: %s" % e)
-  
-  merge_hash = run_cmd("git rev-parse %s" % target_branch_name)[:8]
-  clean_up()
-  print("Pull request #%s merged!" % pr_num)
-  print("Merge hash: %s" % merge_hash)
-  return merge_hash
+    continue_maybe("Pick complete (local ref %s). Push to %s?" % (
+        pick_branch_name, PUSH_REMOTE_NAME))
 
+    try:
+        run_cmd('git push %s %s:%s' % (PUSH_REMOTE_NAME, pick_branch_name, pick_ref))
+    except Exception as e:
+        clean_up()
+        fail("Exception while pushing: %s" % e)
 
-def cherry_pick(pr_num, merge_hash, default_branch):
-  pick_ref = raw_input("Enter a branch name [%s]: " % default_branch)
-  if pick_ref == "":
-    pick_ref = default_branch
-
-  pick_branch_name = "%s_PICK_PR_%s_%s" % (BRANCH_PREFIX, pr_num, pick_ref.upper())
-
-  run_cmd("git fetch %s %s:%s" % (PUSH_REMOTE_NAME, pick_ref, pick_branch_name))
-  run_cmd("git checkout %s" % pick_branch_name)
-  run_cmd("git cherry-pick -sx %s" % merge_hash)
-  
-  continue_maybe("Pick complete (local ref %s). Push to %s?" % (
-    pick_branch_name, PUSH_REMOTE_NAME))
-
-  try:
-    run_cmd('git push %s %s:%s' % (PUSH_REMOTE_NAME, pick_branch_name, pick_ref))
-  except Exception as e:
+    pick_hash = run_cmd("git rev-parse %s" % pick_branch_name)[:8]
     clean_up()
-    fail("Exception while pushing: %s" % e)
 
-  pick_hash = run_cmd("git rev-parse %s" % pick_branch_name)[:8]
-  clean_up()
+    print("Pull request #%s picked into %s!" % (pr_num, pick_ref))
+    print("Pick hash: %s" % pick_hash)
+    return pick_ref
 
-  print("Pull request #%s picked into %s!" % (pr_num, pick_ref))
-  print("Pick hash: %s" % pick_hash)
-  return pick_ref
 
 def fix_version_from_branch(branch, versions):
-  # Note: Assumes this is a sorted (newest->oldest) list of un-released versions
-  if branch == "master":
-    return versions[0]
-  else:
-    branch_ver = branch.replace("branch-", "")
-    return filter(lambda x: x.name.startswith(branch_ver), versions)[-1]
+    # Note: Assumes this is a sorted (newest->oldest) list of un-released versions
+    if branch == "master":
+        return versions[0]
+    else:
+        branch_ver = branch.replace("branch-", "")
+        return filter(lambda x: x.name.startswith(branch_ver), versions)[-1]
+
 
 def resolve_jira(title, merge_branches, comment):
-  asf_jira = jira.client.JIRA({'server': JIRA_API_BASE},
-    basic_auth=(JIRA_USERNAME, JIRA_PASSWORD))
-
-  default_jira_id = ""
-  search = re.findall("SPARK-[0-9]{4,5}", title)
-  if len(search) > 0:
-    default_jira_id = search[0]
-
-  jira_id = raw_input("Enter a JIRA id [%s]: " % default_jira_id)
-  if jira_id == "":
-    jira_id = default_jira_id
-
-  try:
-    issue = asf_jira.issue(jira_id)
-  except Exception as e:
-    fail("ASF JIRA could not find %s\n%s" % (jira_id, e))
-
-  cur_status = issue.fields.status.name
-  cur_summary = issue.fields.summary
-  cur_assignee = issue.fields.assignee
-  if cur_assignee == None:
-    cur_assignee = "NOT ASSIGNED!!!"
-  else:
-    cur_assignee = cur_assignee.displayName
-
-  if cur_status == "Resolved" or cur_status == "Closed":
-    fail("JIRA issue %s already has status '%s'" % (jira_id, cur_status))
-  print ("=== JIRA %s ===" % jira_id)
-  print ("summary\t\t%s\nassignee\t%s\nstatus\t\t%s\nurl\t\t%s/%s\n" % (
-    cur_summary, cur_assignee, cur_status, JIRA_BASE, jira_id))
-
-  versions = asf_jira.project_versions("SPARK")
-  versions = sorted(versions, key = lambda x: x.name, reverse=True)
-  versions = filter(lambda x: x.raw['released'] == False, versions)
-
-  default_fix_versions = map(lambda x: fix_version_from_branch(x, versions).name, merge_branches)
-  for v in default_fix_versions:
-    # Handles the case where we have forked a release branch but not yet made the release.
-    # In this case, if the PR is committed to the master branch and the release branch, we
-    # only consider the release branch to be the fix version. E.g. it is not valid to have
-    # both 1.1.0 and 1.0.0 as fix versions.
-    (major, minor, patch) = v.split(".")
-    if patch == "0":
-      previous = "%s.%s.%s" % (major, int(minor) - 1, 0)
-      if previous in default_fix_versions:
-        default_fix_versions = filter(lambda x: x != v, default_fix_versions)
-  default_fix_versions = ",".join(default_fix_versions)
-
-  fix_versions = raw_input("Enter comma-separated fix version(s) [%s]: " % default_fix_versions)
-  if fix_versions == "":
-    fix_versions = default_fix_versions
-  fix_versions = fix_versions.replace(" ", "").split(",")
-
-  def get_version_json(version_str):
-    return filter(lambda v: v.name == version_str, versions)[0].raw
-  jira_fix_versions = map(lambda v: get_version_json(v), fix_versions)
-
-  resolve = filter(lambda a: a['name'] == "Resolve Issue", asf_jira.transitions(jira_id))[0]
-  asf_jira.transition_issue(jira_id, resolve["id"], fixVersions=jira_fix_versions, comment=comment)
-
-  print "Succesfully resolved %s with fixVersions=%s!" % (jira_id, fix_versions)
+    asf_jira = jira.client.JIRA({'server': JIRA_API_BASE},
+                                basic_auth=(JIRA_USERNAME, JIRA_PASSWORD))
+
+    default_jira_id = ""
+    search = re.findall("SPARK-[0-9]{4,5}", title)
+    if len(search) > 0:
+        default_jira_id = search[0]
+
+    jira_id = raw_input("Enter a JIRA id [%s]: " % default_jira_id)
+    if jira_id == "":
+        jira_id = default_jira_id
+
+    try:
+        issue = asf_jira.issue(jira_id)
+    except Exception as e:
+        fail("ASF JIRA could not find %s\n%s" % (jira_id, e))
+
+    cur_status = issue.fields.status.name
+    cur_summary = issue.fields.summary
+    cur_assignee = issue.fields.assignee
+    if cur_assignee is None:
+        cur_assignee = "NOT ASSIGNED!!!"
+    else:
+        cur_assignee = cur_assignee.displayName
+
+    if cur_status == "Resolved" or cur_status == "Closed":
+        fail("JIRA issue %s already has status '%s'" % (jira_id, cur_status))
+    print ("=== JIRA %s ===" % jira_id)
+    print ("summary\t\t%s\nassignee\t%s\nstatus\t\t%s\nurl\t\t%s/%s\n" % (
+        cur_summary, cur_assignee, cur_status, JIRA_BASE, jira_id))
+
+    versions = asf_jira.project_versions("SPARK")
+    versions = sorted(versions, key=lambda x: x.name, reverse=True)
+    versions = filter(lambda x: x.raw['released'] is False, versions)
+
+    default_fix_versions = map(lambda x: fix_version_from_branch(x, versions).name, merge_branches)
+    for v in default_fix_versions:
+        # Handles the case where we have forked a release branch but not yet made the release.
+        # In this case, if the PR is committed to the master branch and the release branch, we
+        # only consider the release branch to be the fix version. E.g. it is not valid to have
+        # both 1.1.0 and 1.0.0 as fix versions.
+        (major, minor, patch) = v.split(".")
+        if patch == "0":
+            previous = "%s.%s.%s" % (major, int(minor) - 1, 0)
+            if previous in default_fix_versions:
+                default_fix_versions = filter(lambda x: x != v, default_fix_versions)
+    default_fix_versions = ",".join(default_fix_versions)
+
+    fix_versions = raw_input("Enter comma-separated fix version(s) [%s]: " % default_fix_versions)
+    if fix_versions == "":
+        fix_versions = default_fix_versions
+    fix_versions = fix_versions.replace(" ", "").split(",")
+
+    def get_version_json(version_str):
+        return filter(lambda v: v.name == version_str, versions)[0].raw
+
+    jira_fix_versions = map(lambda v: get_version_json(v), fix_versions)
+
+    resolve = filter(lambda a: a['name'] == "Resolve Issue", asf_jira.transitions(jira_id))[0]
+    asf_jira.transition_issue(
+        jira_id, resolve["id"], fixVersions=jira_fix_versions, comment=comment)
+
+    print "Succesfully resolved %s with fixVersions=%s!" % (jira_id, fix_versions)
+
 
 branches = get_json("%s/branches" % GITHUB_API_BASE)
 branch_names = filter(lambda x: x.startswith("branch-"), [x['name'] for x in branches])
@@ -273,28 +286,29 @@ def get_version_json(version_str):
 base_ref = pr["head"]["ref"]
 pr_repo_desc = "%s/%s" % (user_login, base_ref)
 
-if pr["merged"] == True:
-  print "Pull request %s has already been merged, assuming you want to backport" % pr_num
-  merge_commit_desc = run_cmd(['git', 'log', '--merges', '--first-parent', 
-    '--grep=pull request #%s' % pr_num, '--oneline']).split("\n")[0]
-  if merge_commit_desc == "":
-    fail("Couldn't find any merge commit for #%s, you may need to update HEAD." % pr_num)
+if pr["merged"] is True:
+    print "Pull request %s has already been merged, assuming you want to backport" % pr_num
+    merge_commit_desc = run_cmd([
+        'git', 'log', '--merges', '--first-parent',
+        '--grep=pull request #%s' % pr_num, '--oneline']).split("\n")[0]
+    if merge_commit_desc == "":
+        fail("Couldn't find any merge commit for #%s, you may need to update HEAD." % pr_num)
+
+    merge_hash = merge_commit_desc[:7]
+    message = merge_commit_desc[8:]
 
-  merge_hash = merge_commit_desc[:7]  
-  message = merge_commit_desc[8:]
-  
-  print "Found: %s" % message
-  maybe_cherry_pick(pr_num, merge_hash, latest_branch)
-  sys.exit(0)
+    print "Found: %s" % message
+    maybe_cherry_pick(pr_num, merge_hash, latest_branch)
+    sys.exit(0)
 
 if not bool(pr["mergeable"]):
-  msg = "Pull request %s is not mergeable in its current form.\n" % pr_num + \
-    "Continue? (experts only!)"
-  continue_maybe(msg)
+    msg = "Pull request %s is not mergeable in its current form.\n" % pr_num + \
+        "Continue? (experts only!)"
+    continue_maybe(msg)
 
 print ("\n=== Pull Request #%s ===" % pr_num)
 print ("title\t%s\nsource\t%s\ntarget\t%s\nurl\t%s" % (
-  title, pr_repo_desc, target_ref, url))
+    title, pr_repo_desc, target_ref, url))
 continue_maybe("Proceed with merging pull request #%s?" % pr_num)
 
 merged_refs = [target_ref]
@@ -303,12 +317,12 @@ def get_version_json(version_str):
 
 pick_prompt = "Would you like to pick %s into another branch?" % merge_hash
 while raw_input("\n%s (y/n): " % pick_prompt).lower() == "y":
-  merged_refs = merged_refs + [cherry_pick(pr_num, merge_hash, latest_branch)]
+    merged_refs = merged_refs + [cherry_pick(pr_num, merge_hash, latest_branch)]
 
 if JIRA_IMPORTED:
-  continue_maybe("Would you like to update an associated JIRA?")
-  jira_comment = "Issue resolved by pull request %s\n[%s/%s]" % (pr_num, GITHUB_BASE, pr_num)
-  resolve_jira(title, merged_refs, jira_comment)
+    continue_maybe("Would you like to update an associated JIRA?")
+    jira_comment = "Issue resolved by pull request %s\n[%s/%s]" % (pr_num, GITHUB_BASE, pr_num)
+    resolve_jira(title, merged_refs, jira_comment)
 else:
-  print "Could not find jira-python library. Run 'sudo pip install jira-python' to install."
-  print "Exiting without trying to close the associated JIRA."
+    print "Could not find jira-python library. Run 'sudo pip install jira-python' to install."
+    print "Exiting without trying to close the associated JIRA."

From 90e281b55aecbfbe4431ac582311d5790fe7aad3 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Mon, 26 May 2014 22:05:23 -0700
Subject: [PATCH 282/810] SPARK-1933: Throw a more meaningful exception when a
 directory is passed to addJar/addFile.

https://issues.apache.org/jira/browse/SPARK-1933

Author: Reynold Xin <rxin@apache.org>

Closes #888 from rxin/addfile and squashes the following commits:

8c402a3 [Reynold Xin] Updated comment.
ff6c162 [Reynold Xin] SPARK-1933: Throw a more meaningful exception when a directory is passed to addJar/addFile.
---
 core/src/main/scala/org/apache/spark/HttpFileServer.scala | 7 +++++++
 core/src/main/scala/org/apache/spark/SparkContext.scala   | 5 ++---
 2 files changed, 9 insertions(+), 3 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/HttpFileServer.scala b/core/src/main/scala/org/apache/spark/HttpFileServer.scala
index a6e300d34578..0e3750fdde41 100644
--- a/core/src/main/scala/org/apache/spark/HttpFileServer.scala
+++ b/core/src/main/scala/org/apache/spark/HttpFileServer.scala
@@ -59,6 +59,13 @@ private[spark] class HttpFileServer(securityManager: SecurityManager) extends Lo
   }
 
   def addFileToDir(file: File, dir: File) : String = {
+    // Check whether the file is a directory. If it is, throw a more meaningful exception.
+    // If we don't catch this, Guava throws a very confusing error message:
+    //   java.io.FileNotFoundException: [file] (No such file or directory)
+    // even though the directory ([file]) exists.
+    if (file.isDirectory) {
+      throw new IllegalArgumentException(s"$file cannot be a directory.")
+    }
     Files.copy(file, new File(dir, file.getName))
     dir + "/" + file.getName
   }
diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index 49737fa4be56..03ceff8bf1fb 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -794,7 +794,7 @@ class SparkContext(config: SparkConf) extends Logging {
     addedFiles(key) = System.currentTimeMillis
 
     // Fetch the file locally in case a job is executed using DAGScheduler.runLocally().
-    Utils.fetchFile(path, new File(SparkFiles.getRootDirectory), conf, env.securityManager)
+    Utils.fetchFile(path, new File(SparkFiles.getRootDirectory()), conf, env.securityManager)
 
     logInfo("Added file " + path + " at " + key + " with timestamp " + addedFiles(key))
     postEnvironmentUpdate()
@@ -932,13 +932,12 @@ class SparkContext(config: SparkConf) extends Logging {
               try {
                 env.httpFileServer.addJar(new File(fileName))
               } catch {
-                case e: Exception => {
+                case e: Exception =>
                   // For now just log an error but allow to go through so spark examples work.
                   // The spark examples don't really need the jar distributed since its also
                   // the app jar.
                   logError("Error adding jar (" + e + "), was the --addJars option used?")
                   null
-                }
               }
             } else {
               env.httpFileServer.addJar(new File(uri.getPath))

From 549830b0db2c8b069391224f3a73bb0d7f397f71 Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Mon, 26 May 2014 23:17:39 -0700
Subject: [PATCH 283/810] SPARK-1932: Fix race conditions in onReceiveCallback
 and cachedPeers

`var cachedPeers: Seq[BlockManagerId] = null` is used in `def replicate(blockId: BlockId, data: ByteBuffer, level: StorageLevel)` without proper protection.

There are two place will call `replicate(blockId, bytesAfterPut, level)`
* https://github.com/apache/spark/blob/17f3075bc4aa8cbed165f7b367f70e84b1bc8db9/core/src/main/scala/org/apache/spark/storage/BlockManager.scala#L644 runs in `connectionManager.futureExecContext`
* https://github.com/apache/spark/blob/17f3075bc4aa8cbed165f7b367f70e84b1bc8db9/core/src/main/scala/org/apache/spark/storage/BlockManager.scala#L752 `doPut` runs in `connectionManager.handleMessageExecutor`. `org.apache.spark.storage.BlockManagerWorker` calls `blockManager.putBytes` in `connectionManager.handleMessageExecutor`.

As they run in different `Executor`s, this is a race condition which may cause the memory pointed by `cachedPeers` is not correct even if `cachedPeers != null`.

The race condition of `onReceiveCallback` is that it's set in `BlockManagerWorker` but read in a different thread in `ConnectionManager.handleMessageExecutor`.

Author: zsxwing <zsxwing@gmail.com>

Closes #887 from zsxwing/SPARK-1932 and squashes the following commits:

524f69c [zsxwing] SPARK-1932: Fix race conditions in onReceiveCallback and cachedPeers
---
 .../scala/org/apache/spark/network/ConnectionManager.scala     | 3 ++-
 .../src/main/scala/org/apache/spark/storage/BlockManager.scala | 2 +-
 2 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/network/ConnectionManager.scala b/core/src/main/scala/org/apache/spark/network/ConnectionManager.scala
index dcbbc1853186..5dd5fd0047c0 100644
--- a/core/src/main/scala/org/apache/spark/network/ConnectionManager.scala
+++ b/core/src/main/scala/org/apache/spark/network/ConnectionManager.scala
@@ -93,7 +93,8 @@ private[spark] class ConnectionManager(port: Int, conf: SparkConf,
   implicit val futureExecContext = ExecutionContext.fromExecutor(
     Utils.newDaemonCachedThreadPool("Connection manager future execution context"))
 
-  private var onReceiveCallback: (BufferMessage, ConnectionManagerId) => Option[Message]= null
+  @volatile
+  private var onReceiveCallback: (BufferMessage, ConnectionManagerId) => Option[Message] = null
 
   private val authEnabled = securityManager.isAuthenticationEnabled()
 
diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala
index 653409581190..6e450081dcb1 100644
--- a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala
+++ b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala
@@ -772,7 +772,7 @@ private[spark] class BlockManager(
   /**
    * Replicate block to another node.
    */
-  var cachedPeers: Seq[BlockManagerId] = null
+  @volatile var cachedPeers: Seq[BlockManagerId] = null
   private def replicate(blockId: BlockId, data: ByteBuffer, level: StorageLevel) {
     val tLevel = StorageLevel(
       level.useDisk, level.useMemory, level.useOffHeap, level.deserialized, 1)

From 95e4c9c6fb153b7f0aa4c442c4bdb6552d326640 Mon Sep 17 00:00:00 2001
From: lianhuiwang <lianhuiwang09@gmail.com>
Date: Tue, 27 May 2014 11:53:38 -0700
Subject: [PATCH 284/810] bugfix worker DriverStateChanged state should match
 DriverState.FAILED

bugfix worker DriverStateChanged state should match DriverState.FAILED

Author: lianhuiwang <lianhuiwang09@gmail.com>

Closes #864 from lianhuiwang/master and squashes the following commits:

480ce94 [lianhuiwang] address aarondav comments
f2b5970 [lianhuiwang] bugfix worker DriverStateChanged state should match DriverState.FAILED
---
 .../main/scala/org/apache/spark/deploy/worker/Worker.scala    | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala b/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala
index 8b6747977eb8..100de26170a5 100755
--- a/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala
@@ -317,10 +317,14 @@ private[spark] class Worker(
       state match {
         case DriverState.ERROR =>
           logWarning(s"Driver $driverId failed with unrecoverable exception: ${exception.get}")
+        case DriverState.FAILED =>
+          logWarning(s"Driver $driverId exited with failure")
         case DriverState.FINISHED =>
           logInfo(s"Driver $driverId exited successfully")
         case DriverState.KILLED =>
           logInfo(s"Driver $driverId was killed by user")
+        case _ =>
+          logDebug(s"Driver $driverId changed state to $state")
       }
       masterLock.synchronized {
         master ! DriverStateChanged(driverId, state, exception)

From d1375a2bff846f2c4274e14545924646852895f9 Mon Sep 17 00:00:00 2001
From: Takuya UESHIN <ueshin@happy-camper.st>
Date: Tue, 27 May 2014 14:53:57 -0700
Subject: [PATCH 285/810] [SPARK-1926] [SQL] Nullability of Max/Min/First
 should be true.

Nullability of `Max`/`Min`/`First` should be `true` because they return `null` if there are no rows.

Author: Takuya UESHIN <ueshin@happy-camper.st>

Closes #881 from ueshin/issues/SPARK-1926 and squashes the following commits:

322610f [Takuya UESHIN] Fix nullability of Min/Max/First.
---
 .../apache/spark/sql/catalyst/expressions/aggregates.scala  | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
index 79937b129aea..b49a4614eaca 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
@@ -90,7 +90,7 @@ abstract class AggregateFunction
 
 case class Min(child: Expression) extends PartialAggregate with trees.UnaryNode[Expression] {
   override def references = child.references
-  override def nullable = child.nullable
+  override def nullable = true
   override def dataType = child.dataType
   override def toString = s"MIN($child)"
 
@@ -120,7 +120,7 @@ case class MinFunction(expr: Expression, base: AggregateExpression) extends Aggr
 
 case class Max(child: Expression) extends PartialAggregate with trees.UnaryNode[Expression] {
   override def references = child.references
-  override def nullable = child.nullable
+  override def nullable = true
   override def dataType = child.dataType
   override def toString = s"MAX($child)"
 
@@ -257,7 +257,7 @@ case class SumDistinct(child: Expression)
 
 case class First(child: Expression) extends PartialAggregate with trees.UnaryNode[Expression] {
   override def references = child.references
-  override def nullable = child.nullable
+  override def nullable = true
   override def dataType = child.dataType
   override def toString = s"FIRST($child)"
 

From 3b0babad1f0856ee16f9d58e1ead30779a4a6310 Mon Sep 17 00:00:00 2001
From: Takuya UESHIN <ueshin@happy-camper.st>
Date: Tue, 27 May 2014 14:55:23 -0700
Subject: [PATCH 286/810] [SPARK-1915] [SQL] AverageFunction should not count
 if the evaluated value is null.

Average values are difference between the calculation is done partially or not partially.
Because `AverageFunction` (in not-partially calculation) counts even if the evaluated value is null.

Author: Takuya UESHIN <ueshin@happy-camper.st>

Closes #862 from ueshin/issues/SPARK-1915 and squashes the following commits:

b1ff3c0 [Takuya UESHIN] Modify AverageFunction not to count if the evaluated value is null.
---
 .../spark/sql/catalyst/expressions/aggregates.scala    |  9 ++++++---
 .../scala/org/apache/spark/sql/DslQuerySuite.scala     | 10 ++++++++++
 2 files changed, 16 insertions(+), 3 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
index b49a4614eaca..c90243368894 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
@@ -281,14 +281,17 @@ case class AverageFunction(expr: Expression, base: AggregateExpression)
   private val sum = MutableLiteral(zero.eval(EmptyRow))
   private val sumAsDouble = Cast(sum, DoubleType)
 
-  private val addFunction = Add(sum, Coalesce(Seq(expr, zero)))
+  private def addFunction(value: Any) = Add(sum, Literal(value))
 
   override def eval(input: Row): Any =
     sumAsDouble.eval(EmptyRow).asInstanceOf[Double] / count.toDouble
 
   override def update(input: Row): Unit = {
-    count += 1
-    sum.update(addFunction, input)
+    val evaluatedExpr = expr.eval(input)
+    if (evaluatedExpr != null) {
+      count += 1
+      sum.update(addFunction(evaluatedExpr), input)
+    }
   }
 }
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala
index 8197e8a18d44..fb599e1e01e7 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala
@@ -115,6 +115,16 @@ class DslQuerySuite extends QueryTest {
       2.0)
   }
 
+  test("null average") {
+    checkAnswer(
+      testData3.groupBy()(Average('b)),
+      2.0)
+
+    checkAnswer(
+      testData3.groupBy()(Average('b), CountDistinct('b :: Nil)),
+      (2.0, 1) :: Nil)
+  }
+
   test("count") {
     assert(testData2.count() === testData2.map(_ => 1).count())
   }

From 068256745052b0aa947dd8c16b1f1d73d8e4631e Mon Sep 17 00:00:00 2001
From: LY Lai <ly.lai@vpon.com>
Date: Tue, 27 May 2014 16:08:38 -0700
Subject: [PATCH 287/810] [SQL] SPARK-1922

Allow underscore in column name of a struct field https://issues.apache.org/jira/browse/SPARK-1922 .

Author: LY Lai <ly.lai@vpon.com>

Closes #873 from lyuanlai/master and squashes the following commits:

2253263 [LY Lai] Allow underscore in struct field column name
---
 .../spark/sql/hive/HiveMetastoreCatalog.scala |  2 +-
 .../sql/hive/HiveMetastoreCatalogSuite.scala  | 32 +++++++++++++++++++
 2 files changed, 33 insertions(+), 1 deletion(-)
 create mode 100644 sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveMetastoreCatalogSuite.scala

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
index 636c4f7b9319..9f74e0334f72 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
@@ -201,7 +201,7 @@ object HiveMetastoreTypes extends RegexParsers {
     }
 
   protected lazy val structField: Parser[StructField] =
-    "[a-zA-Z0-9]*".r ~ ":" ~ dataType ^^ {
+    "[a-zA-Z0-9_]*".r ~ ":" ~ dataType ^^ {
       case name ~ _ ~ tpe => StructField(name, tpe, nullable = true)
     }
 
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveMetastoreCatalogSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveMetastoreCatalogSuite.scala
new file mode 100644
index 000000000000..4a64b5f5eb1b
--- /dev/null
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveMetastoreCatalogSuite.scala
@@ -0,0 +1,32 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive
+
+import org.scalatest.FunSuite
+
+import org.apache.spark.sql.catalyst.types.{DataType, StructType}
+
+class HiveMetastoreCatalogSuite extends FunSuite {
+
+  test("struct field should accept underscore in sub-column name") {
+    val metastr = "struct<a: int, b_1: string, c: string>"
+
+    val datatype = HiveMetastoreTypes.toDataType(metastr)
+    assert(datatype.isInstanceOf[StructType])
+  }
+}

From 9df86835b60ce587c8b9bd4ad7410eebf59a179d Mon Sep 17 00:00:00 2001
From: Takuya UESHIN <ueshin@happy-camper.st>
Date: Tue, 27 May 2014 22:17:50 -0700
Subject: [PATCH 288/810] [SPARK-1938] [SQL] ApproxCountDistinctMergeFunction
 should return Int value.

`ApproxCountDistinctMergeFunction` should return `Int` value because the `dataType` of `ApproxCountDistinct` is `IntegerType`.

Author: Takuya UESHIN <ueshin@happy-camper.st>

Closes #893 from ueshin/issues/SPARK-1938 and squashes the following commits:

3970e88 [Takuya UESHIN] Remove a superfluous line.
5ad7ec1 [Takuya UESHIN] Make dataType for each of CountDistinct, ApproxCountDistinctMerge and ApproxCountDistinct LongType.
cbe7c71 [Takuya UESHIN] Revert a change.
fc3ac0f [Takuya UESHIN] Fix evaluated value type of ApproxCountDistinctMergeFunction to Int.
---
 .../spark/sql/catalyst/expressions/aggregates.scala      | 9 ++++-----
 1 file changed, 4 insertions(+), 5 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
index c90243368894..01947273b6cc 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
@@ -82,7 +82,6 @@ abstract class AggregateFunction
   override def dataType = base.dataType
 
   def update(input: Row): Unit
-  override def eval(input: Row): Any
 
   // Do we really need this?
   override def newInstance() = makeCopy(productIterator.map { case a: AnyRef => a }.toArray)
@@ -166,7 +165,7 @@ case class CountDistinct(expressions: Seq[Expression]) extends AggregateExpressi
   override def children = expressions
   override def references = expressions.flatMap(_.references).toSet
   override def nullable = false
-  override def dataType = IntegerType
+  override def dataType = LongType
   override def toString = s"COUNT(DISTINCT ${expressions.mkString(",")})"
   override def newInstance() = new CountDistinctFunction(expressions, this)
 }
@@ -184,7 +183,7 @@ case class ApproxCountDistinctMerge(child: Expression, relativeSD: Double)
   extends AggregateExpression with trees.UnaryNode[Expression] {
   override def references = child.references
   override def nullable = false
-  override def dataType = IntegerType
+  override def dataType = LongType
   override def toString = s"APPROXIMATE COUNT(DISTINCT $child)"
   override def newInstance() = new ApproxCountDistinctMergeFunction(child, this, relativeSD)
 }
@@ -193,7 +192,7 @@ case class ApproxCountDistinct(child: Expression, relativeSD: Double = 0.05)
   extends PartialAggregate with trees.UnaryNode[Expression] {
   override def references = child.references
   override def nullable = false
-  override def dataType = IntegerType
+  override def dataType = LongType
   override def toString = s"APPROXIMATE COUNT(DISTINCT $child)"
 
   override def asPartial: SplitEvaluation = {
@@ -394,7 +393,7 @@ case class CountDistinctFunction(expr: Seq[Expression], base: AggregateExpressio
     }
   }
 
-  override def eval(input: Row): Any = seen.size
+  override def eval(input: Row): Any = seen.size.toLong
 }
 
 case class FirstFunction(expr: Expression, base: AggregateExpression) extends AggregateFunction {

From 82eadc3b07d3f00eebd30811f981016e68cf60bf Mon Sep 17 00:00:00 2001
From: jmu <jmujmu@gmail.com>
Date: Tue, 27 May 2014 22:41:47 -0700
Subject: [PATCH 289/810] Fix doc about NetworkWordCount/JavaNetworkWordCount
 usage of spark streaming

Usage: NetworkWordCount <master> <hostname> <port>
-->
Usage: NetworkWordCount <hostname> <port>

Usage: JavaNetworkWordCount <master> <hostname> <port>
-->
Usage: JavaNetworkWordCount <hostname> <port>

Author: jmu <jmujmu@gmail.com>

Closes #826 from jmu/master and squashes the following commits:

9fb7980 [jmu] Merge branch 'master' of https://github.com/jmu/spark
b9a6b02 [jmu] Fix doc for NetworkWordCount/JavaNetworkWordCount Usage: NetworkWordCount <master> <hostname> <port> --> Usage: NetworkWordCount <hostname> <port>
---
 docs/streaming-programming-guide.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/docs/streaming-programming-guide.md b/docs/streaming-programming-guide.md
index 0c125eb693a8..972b660262d1 100644
--- a/docs/streaming-programming-guide.md
+++ b/docs/streaming-programming-guide.md
@@ -234,12 +234,12 @@ Then, in a different terminal, you can start the example by using
 <div class="codetabs">
 <div data-lang="scala" markdown="1">
 {% highlight bash %}
-$ ./bin/run-example org.apache.spark.examples.streaming.NetworkWordCount local[2] localhost 9999
+$ ./bin/run-example org.apache.spark.examples.streaming.NetworkWordCount localhost 9999
 {% endhighlight %}
 </div>
 <div data-lang="java" markdown="1">
 {% highlight bash %}
-$ ./bin/run-example org.apache.spark.examples.streaming.JavaNetworkWordCount local[2] localhost 9999
+$ ./bin/run-example org.apache.spark.examples.streaming.JavaNetworkWordCount localhost 9999
 {% endhighlight %}
 </div>
 </div>
@@ -268,7 +268,7 @@ hello world
 {% highlight bash %}
 # TERMINAL 2: RUNNING NetworkWordCount or JavaNetworkWordCount
 
-$ ./bin/run-example org.apache.spark.examples.streaming.NetworkWordCount local[2] localhost 9999
+$ ./bin/run-example org.apache.spark.examples.streaming.NetworkWordCount localhost 9999
 ...
 -------------------------------------------
 Time: 1357008430000 ms

From 7801d44fd3bcf4d82e6db12574cc42bef15bf0e1 Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Wed, 28 May 2014 15:49:54 -0700
Subject: [PATCH 290/810] Organize configuration docs

This PR improves and organizes the config option page
and makes a few other changes to config docs. See a preview here:
http://people.apache.org/~pwendell/config-improvements/configuration.html

The biggest changes are:
1. The configs for the standalone master/workers were moved to the
standalone page and out of the general config doc.
2. SPARK_LOCAL_DIRS was missing from the standalone docs.
3. Expanded discussion of injecting configs with spark-submit, including an
example.
4. Config options were organized into the following categories:
- Runtime Environment
- Shuffle Behavior
- Spark UI
- Compression and Serialization
- Execution Behavior
- Networking
- Scheduling
- Security
- Spark Streaming

Author: Patrick Wendell <pwendell@gmail.com>

Closes #880 from pwendell/config-cleanup and squashes the following commits:

93f56c3 [Patrick Wendell] Feedback from Matei
6f66efc [Patrick Wendell] More feedback
16ae776 [Patrick Wendell] Adding back header section
d9c264f [Patrick Wendell] Small fix
e0c1728 [Patrick Wendell] Response to Matei's review
27d57db [Patrick Wendell] Reverting changes to index.html (covered in #896)
e230ef9 [Patrick Wendell] Merge remote-tracking branch 'apache/master' into config-cleanup
a374369 [Patrick Wendell] Line wrapping fixes
fdff7fc [Patrick Wendell] Merge remote-tracking branch 'apache/master' into config-cleanup
3289ea4 [Patrick Wendell] Pulling in changes from #856
106ee31 [Patrick Wendell] Small link fix
f7e79bc [Patrick Wendell] Re-organizing config options.
54b184d [Patrick Wendell] Adding standalone configs to the standalone page
592e94a [Patrick Wendell] Stash
29b5446 [Patrick Wendell] Better discussion of spark-submit in configuration docs
2d719ef [Patrick Wendell] Small fix
4af9e07 [Patrick Wendell] Adding SPARK_LOCAL_DIRS docs
204b248 [Patrick Wendell] Small fixes
---
 docs/configuration.md    | 815 ++++++++++++++++++---------------------
 docs/quick-start.md      |   8 +-
 docs/spark-standalone.md | 167 +++++++-
 3 files changed, 554 insertions(+), 436 deletions(-)

diff --git a/docs/configuration.md b/docs/configuration.md
index e5d955f23fe3..b6e7fd34eae6 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -2,19 +2,25 @@
 layout: global
 title: Spark Configuration
 ---
-
 * This will become a table of contents (this text will be scraped).
 {:toc}
 
-Spark provides several locations to configure the system:
+Spark provides three locations to configure the system:
+
+* [Spark properties](#spark-properties) control most application parameters and can be set by passing
+  a [SparkConf](api/core/index.html#org.apache.spark.SparkConf) object to SparkContext, or through Java
+  system properties.
+* [Environment variables](#environment-variables) can be used to set per-machine settings, such as
+  the IP address, through the `conf/spark-env.sh` script on each node.
+* [Logging](#configuring-logging) can be configured through `log4j.properties`.
 
 # Spark Properties
 
 Spark properties control most application settings and are configured separately for each
-application. The preferred way is to set them through
-[SparkConf](api/scala/index.html#org.apache.spark.SparkConf) and passing it as an argument to your
-SparkContext. SparkConf allows you to configure most of the common properties to initialize a
-cluster (e.g. master URL and application name), as well as arbitrary key-value pairs through the
+application. These properties can be set directly on a
+[SparkConf](api/scala/index.html#org.apache.spark.SparkConf) and passed as an argument to your
+SparkContext. SparkConf allows you to configure some of the common properties
+(e.g. master URL and application name), as well as arbitrary key-value pairs through the
 `set()` method. For example, we could initialize an application as follows:
 
 {% highlight scala %}
@@ -25,22 +31,37 @@ val conf = new SparkConf()
 val sc = new SparkContext(conf)
 {% endhighlight %}
 
-## Loading Default Configurations
+## Dynamically Loading Spark Properties
+In some cases, you may want to avoid hard-coding certain configurations in a `SparkConf`. For
+instance, if you'd like to run the same application with different masters or different
+amounts of memory. Spark allows you to simply create an empty conf:
 
-In the case of `spark-shell`, a SparkContext has already been created for you, so you cannot control
-the configuration properties through SparkConf. However, you can still set configuration properties
-through a default configuration file. By default, `spark-shell` (and more generally `spark-submit`)
-will read configuration options from `conf/spark-defaults.conf`, in which each line consists of a
-key and a value separated by whitespace. For example,
+{% highlight scala %}
+val sc = new SparkContext(new SparkConf())
+{% endhighlight %}
+
+Then, you can supply configuration values at runtime:
+{% highlight bash %}
+./bin/spark-submit --name "My fancy app" --master local[4] myApp.jar
+{% endhighlight %}
+
+The Spark shell and [`spark-submit`](cluster-overview.html#launching-applications-with-spark-submit)
+tool support two ways to load configurations dynamically. The first are command line options,
+such as `--master`, as shown above. Running `./bin/spark-submit --help` will show the entire list
+of options.
+
+`bin/spark-submit` will also read configuration options from `conf/spark-defaults.conf`, in which
+each line consists of a key and a value separated by whitespace. For example:
 
     spark.master            spark://5.6.7.8:7077
     spark.executor.memory   512m
     spark.eventLog.enabled  true
     spark.serializer        org.apache.spark.serializer.KryoSerializer
 
-Any values specified in the file will be passed on to the application, and merged with those
-specified through SparkConf. If the same configuration property exists in both `spark-defaults.conf`
-and SparkConf, then the latter will take precedence as it is the most application-specific.
+Any values specified as flags or in the properties file will be passed on to the application
+and merged with those specified through SparkConf. Properties set directly on the SparkConf
+take highest precedence, then flags passed to `spark-submit` or `spark-shell`, then options
+in the `spark-defaults.conf` file.
 
 ## Viewing Spark Properties
 
@@ -49,19 +70,34 @@ This is a useful place to check to make sure that your properties have been set
 that only values explicitly specified through either `spark-defaults.conf` or SparkConf will
 appear. For all other configuration properties, you can assume the default value is used.
 
-## All Configuration Properties
+## Available Properties
 
-Most of the properties that control internal settings have reasonable default values. However,
-there are at least five properties that you will commonly want to control:
+Most of the properties that control internal settings have reasonable default values. Some
+of the most common options to set are:
 
 <table class="table">
 <tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr>
+<tr>
+  <td><code>spark.app.name</code></td>
+  <td>(none)</td>
+  <td>
+    The name of your application. This will appear in the UI and in log data.
+  </td>
+</tr>
+<tr>
+  <td><code>spark.master</code></td>
+  <td>(none)</td>
+  <td>
+    The cluster manager to connect to. See the list of
+    <a href="scala-programming-guide.html#master-urls"> allowed master URL's</a>.
+  </td>
+</tr>
 <tr>
   <td><code>spark.executor.memory</code></td>
   <td>512m</td>
   <td>
-    Amount of memory to use per executor process, in the same format as JVM memory strings (e.g.
-    <code>512m</code>, <code>2g</code>).
+    Amount of memory to use per executor process, in the same format as JVM memory strings
+    (e.g. <code>512m</code>, <code>2g</code>).
   </td>
 </tr>
 <tr>
@@ -69,10 +105,12 @@ there are at least five properties that you will commonly want to control:
   <td>org.apache.spark.serializer.<br />JavaSerializer</td>
   <td>
     Class to use for serializing objects that will be sent over the network or need to be cached
-    in serialized form. The default of Java serialization works with any Serializable Java object but is
-    quite slow, so we recommend <a href="tuning.html">using <code>org.apache.spark.serializer.KryoSerializer</code>
-    and configuring Kryo serialization</a> when speed is necessary. Can be any subclass of
-    <a href="api/scala/index.html#org.apache.spark.serializer.Serializer"><code>org.apache.spark.Serializer</code></a>.
+    in serialized form. The default of Java serialization works with any Serializable Java object
+    but is quite slow, so we recommend <a href="tuning.html">using
+    <code>org.apache.spark.serializer.KryoSerializer</code> and configuring Kryo serialization</a>
+    when speed is necessary. Can be any subclass of
+    <a href="api/scala/index.html#org.apache.spark.serializer.Serializer">
+    <code>org.apache.spark.Serializer</code></a>.
   </td>
 </tr>
 <tr>
@@ -81,7 +119,8 @@ there are at least five properties that you will commonly want to control:
   <td>
     If you use Kryo serialization, set this class to register your custom classes with Kryo.
     It should be set to a class that extends
-    <a href="api/scala/index.html#org.apache.spark.serializer.KryoRegistrator"><code>KryoRegistrator</code></a>.
+    <a href="api/scala/index.html#org.apache.spark.serializer.KryoRegistrator">
+    <code>KryoRegistrator</code></a>.
     See the <a href="tuning.html#data-serialization">tuning guide</a> for more details.
   </td>
 </tr>
@@ -94,138 +133,151 @@ there are at least five properties that you will commonly want to control:
     comma-separated list of multiple directories on different disks.
 
     NOTE: In Spark 1.0 and later this will be overriden by SPARK_LOCAL_DIRS (Standalone, Mesos) or
-    LOCAL_DIRS (YARN) envrionment variables set by the cluster manager.
+    LOCAL_DIRS (YARN) environment variables set by the cluster manager.
   </td>
 </tr>
 <tr>
-  <td><code>spark.cores.max</code></td>
-  <td>(not set)</td>
+  <td><code>spark.logConf</code></td>
+  <td>false</td>
   <td>
-    When running on a <a href="spark-standalone.html">standalone deploy cluster</a> or a
-    <a href="running-on-mesos.html#mesos-run-modes">Mesos cluster in "coarse-grained"
-    sharing mode</a>, the maximum amount of CPU cores to request for the application from
-    across the cluster (not from each machine). If not set, the default will be
-    <code>spark.deploy.defaultCores</code> on Spark's standalone cluster manager, or
-    infinite (all available cores) on Mesos.
+    Logs the effective SparkConf as INFO when a SparkContext is started.
   </td>
 </tr>
 </table>
 
-
 Apart from these, the following properties are also available, and may be useful in some situations:
 
+#### Runtime Environment
 <table class="table">
 <tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr>
 <tr>
-  <td><code>spark.default.parallelism</code></td>
+  <td><code>spark.executor.memory</code></td>
+  <td>512m</td>
   <td>
-    <ul>
-      <li>Local mode: number of cores on the local machine</li>
-      <li>Mesos fine grained mode: 8</li>
-      <li>Others: total number of cores on all executor nodes or 2, whichever is larger</li>
-    </ul>
+    Amount of memory to use per executor process, in the same format as JVM memory strings
+    (e.g. <code>512m</code>, <code>2g</code>).
   </td>
+</tr>
+<tr>
+  <td><code>spark.executor.extraJavaOptions</code></td>
+  <td>(none)</td>
   <td>
-    Default number of tasks to use across the cluster for distributed shuffle operations
-    (<code>groupByKey</code>, <code>reduceByKey</code>, etc) when not set by user.
+    A string of extra JVM options to pass to executors. For instance, GC settings or other
+    logging. Note that it is illegal to set Spark properties or heap size settings with this
+    option. Spark properties should be set using a SparkConf object or the
+    spark-defaults.conf file used with the spark-submit script. Heap size settings can be set
+    with spark.executor.memory.
   </td>
 </tr>
 <tr>
-  <td><code>spark.storage.memoryFraction</code></td>
-  <td>0.6</td>
+  <td><code>spark.executor.extraClassPath</code></td>
+  <td>(none)</td>
   <td>
-    Fraction of Java heap to use for Spark's memory cache. This should not be larger than the "old"
-    generation of objects in the JVM, which by default is given 0.6 of the heap, but you can increase
-    it if you configure your own old generation size.
+    Extra classpath entries to append to the classpath of executors. This exists primarily
+    for backwards-compatibility with older versions of Spark. Users typically should not need
+    to set this option.
   </td>
 </tr>
 <tr>
-  <td><code>spark.shuffle.memoryFraction</code></td>
-  <td>0.3</td>
+  <td><code>spark.executor.extraLibraryPath</code></td>
+  <td>(none)</td>
   <td>
-    Fraction of Java heap to use for aggregation and cogroups during shuffles, if
-    <code>spark.shuffle.spill</code> is true. At any given time, the collective size of
-    all in-memory maps used for shuffles is bounded by this limit, beyond which the contents will
-    begin to spill to disk. If spills are often, consider increasing this value at the expense of
-    <code>spark.storage.memoryFraction</code>.
+    Set a special library path to use when launching executor JVM's.
   </td>
 </tr>
 <tr>
-  <td><code>spark.storage.memoryMapThreshold</code></td>
-  <td>8192</td>
+  <td><code>spark.files.userClassPathFirst</code></td>
+  <td>false</td>
   <td>
-    Size of a block, in bytes, above which Spark memory maps when reading a block from disk.
-    This prevents Spark from memory mapping very small blocks. In general, memory
-    mapping has high overhead for blocks close to or below the page size of the operating system.
+    (Experimental) Whether to give user-added jars precedence over Spark's own jars when
+    loading classes in Executors. This feature can be used to mitigate conflicts between
+    Spark's dependencies and user dependencies. It is currently an experimental feature.
   </td>
 </tr>
+</table>
+
+#### Shuffle Behavior
+<table class="table">
+<tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr>
 <tr>
-  <td><code>spark.tachyonStore.baseDir</code></td>
-  <td>System.getProperty("java.io.tmpdir")</td>
+  <td><code>spark.shuffle.consolidateFiles</code></td>
+  <td>false</td>
   <td>
-    Directories of the Tachyon File System that store RDDs. The Tachyon file system's URL is set by
-    <code>spark.tachyonStore.url</code>.  It can also be a comma-separated list of multiple
-    directories on Tachyon file system.
+    If set to "true", consolidates intermediate files created during a shuffle. Creating fewer
+    files can improve filesystem performance for shuffles with large numbers of reduce tasks. It
+    is recommended to set this to "true" when using ext4 or xfs filesystems. On ext3, this option
+    might degrade performance on machines with many (>8) cores due to filesystem limitations.
   </td>
 </tr>
 <tr>
-  <td><code>spark.tachyonStore.url</code></td>
-  <td>tachyon://localhost:19998</td>
+  <td><code>spark.shuffle.spill</code></td>
+  <td>true</td>
   <td>
-    The URL of the underlying Tachyon file system in the TachyonStore.
+    If set to "true", limits the amount of memory used during reduces by spilling data out to disk.
+    This spilling threshold is specified by <code>spark.shuffle.memoryFraction</code>.
   </td>
 </tr>
 <tr>
-  <td><code>spark.mesos.coarse</code></td>
-  <td>false</td>
+  <td><code>spark.shuffle.spill.compress</code></td>
+  <td>true</td>
   <td>
-    If set to "true", runs over Mesos clusters in <a
-    href="running-on-mesos.html#mesos-run-modes">"coarse-grained" sharing mode</a>, where Spark
-    acquires one long-lived Mesos task on each machine instead of one Mesos task per Spark task.
-    This gives lower-latency scheduling for short queries, but leaves resources in use for the whole
-    duration of the Spark job.
+    Whether to compress data spilled during shuffles. Compression will use
+    <code>spark.io.compression.codec</code>.
   </td>
 </tr>
 <tr>
-  <td><code>spark.ui.port</code></td>
-  <td>4040</td>
+  <td><code>spark.shuffle.memoryFraction</code></td>
+  <td>0.3</td>
   <td>
-    Port for your application's dashboard, which shows memory and workload data
+    Fraction of Java heap to use for aggregation and cogroups during shuffles, if
+    <code>spark.shuffle.spill</code> is true. At any given time, the collective size of
+    all in-memory maps used for shuffles is bounded by this limit, beyond which the contents will
+    begin to spill to disk. If spills are often, consider increasing this value at the expense of
+    <code>spark.storage.memoryFraction</code>.
   </td>
 </tr>
 <tr>
-  <td><code>spark.ui.retainedStages</code></td>
-  <td>1000</td>
+  <td><code>spark.shuffle.compress</code></td>
+  <td>true</td>
   <td>
-    How many stages the Spark UI remembers before garbage collecting.
+    Whether to compress map output files. Generally a good idea. Compression will use
+    <code>spark.io.compression.codec</code>.
   </td>
 </tr>
 <tr>
-  <td><code>spark.ui.filters</code></td>
-  <td>None</td>
+  <td><code>spark.shuffle.file.buffer.kb</code></td>
+  <td>100</td>
   <td>
-    Comma separated list of filter class names to apply to the Spark web ui. The filter should be a
-    standard javax servlet Filter. Parameters to each filter can also be specified by setting a
-    java system property of spark.&lt;class name of filter&gt;.params='param1=value1,param2=value2'
-    (e.g. -Dspark.ui.filters=com.test.filter1 -Dspark.com.test.filter1.params='param1=foo,param2=testing')
+    Size of the in-memory buffer for each shuffle file output stream, in kilobytes. These buffers
+    reduce the number of disk seeks and system calls made in creating intermediate shuffle files.
   </td>
 </tr>
 <tr>
-  <td><code>spark.ui.acls.enable</code></td>
-  <td>false</td>
+  <td><code>spark.reducer.maxMbInFlight</code></td>
+  <td>48</td>
   <td>
-    Whether spark web ui acls should are enabled. If enabled, this checks to see if the user has
-    access permissions to view the web ui. See <code>spark.ui.view.acls</code> for more details.
-    Also note this requires the user to be known, if the user comes across as null no checks
-    are done. Filters can be used to authenticate and set the user.
+    Maximum size (in megabytes) of map outputs to fetch simultaneously from each reduce task. Since
+    each output requires us to create a buffer to receive it, this represents a fixed memory
+    overhead per reduce task, so keep it small unless you have a large amount of memory.
+  </td>
+</tr>
+</table>
+
+#### Spark UI
+<table class="table">
+<tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr>
+<tr>
+  <td><code>spark.ui.port</code></td>
+  <td>4040</td>
+  <td>
+    Port for your application's dashboard, which shows memory and workload data
   </td>
 </tr>
 <tr>
-  <td><code>spark.ui.view.acls</code></td>
-  <td>Empty</td>
+  <td><code>spark.ui.retainedStages</code></td>
+  <td>1000</td>
   <td>
-    Comma separated list of users that have view access to the spark web ui. By default only the
-    user that started the Spark job has view access.
+    How many stages the Spark UI remembers before garbage collecting.
   </td>
 </tr>
 <tr>
@@ -236,19 +288,35 @@ Apart from these, the following properties are also available, and may be useful
   </td>
 </tr>
 <tr>
-  <td><code>spark.shuffle.compress</code></td>
-  <td>true</td>
+  <td><code>spark.eventLog.enabled</code></td>
+  <td>false</td>
   <td>
-    Whether to compress map output files. Generally a good idea.
+    Whether to log Spark events, useful for reconstructing the Web UI after the application has
+    finished.
   </td>
 </tr>
 <tr>
-  <td><code>spark.shuffle.spill.compress</code></td>
-  <td>true</td>
+  <td><code>spark.eventLog.compress</code></td>
+  <td>false</td>
   <td>
-    Whether to compress data spilled during shuffles.
+    Whether to compress logged events, if <code>spark.eventLog.enabled</code> is true.
   </td>
 </tr>
+<tr>
+  <td><code>spark.eventLog.dir</code></td>
+  <td>file:///tmp/spark-events</td>
+  <td>
+    Base directory in which Spark events are logged, if <code>spark.eventLog.enabled</code> is true.
+    Within this base directory, Spark creates a sub-directory for each application, and logs the
+    events specific to the application in this directory. Users may want to set this to
+    and HDFS directory so that history files can be read by the history server.
+  </td>
+</tr>
+</table>
+
+#### Compression and Serialization
+<table class="table">
+<tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr>
 <tr>
   <td><code>spark.broadcast.compress</code></td>
   <td>true</td>
@@ -260,59 +328,46 @@ Apart from these, the following properties are also available, and may be useful
   <td><code>spark.rdd.compress</code></td>
   <td>false</td>
   <td>
-    Whether to compress serialized RDD partitions (e.g. for <code>StorageLevel.MEMORY_ONLY_SER</code>).
-    Can save substantial space at the cost of some extra CPU time.
+    Whether to compress serialized RDD partitions (e.g. for
+    <code>StorageLevel.MEMORY_ONLY_SER</code>). Can save substantial space at the cost of some
+    extra CPU time.
   </td>
 </tr>
 <tr>
   <td><code>spark.io.compression.codec</code></td>
   <td>org.apache.spark.io.<br />LZFCompressionCodec</td>
   <td>
-    The codec used to compress internal data such as RDD partitions and shuffle outputs. By default,
-    Spark provides two codecs: <code>org.apache.spark.io.LZFCompressionCodec</code> and
-    <code>org.apache.spark.io.SnappyCompressionCodec</code>.
+    The codec used to compress internal data such as RDD partitions and shuffle outputs.
+    By default, Spark provides two codecs: <code>org.apache.spark.io.LZFCompressionCodec</code>
+    and <code>org.apache.spark.io.SnappyCompressionCodec</code>. Of these two choices,
+    Snappy offers faster compression and decompression, while LZF offers a better compression
+    ratio.
   </td>
 </tr>
 <tr>
   <td><code>spark.io.compression.snappy.block.size</code></td>
   <td>32768</td>
   <td>
-    Block size (in bytes) used in Snappy compression, in the case when Snappy compression codec is
-    used.
-  </td>
-</tr>
-<tr>
-  <td><code>spark.scheduler.mode</code></td>
-  <td>FIFO</td>
-  <td>
-    The <a href="job-scheduling.html#scheduling-within-an-application">scheduling mode</a> between
-    jobs submitted to the same SparkContext. Can be set to <code>FAIR</code>
-    to use fair sharing instead of queueing jobs one after another. Useful for
-    multi-user services.
-  </td>
-</tr>
-<tr>
-  <td><code>spark.scheduler.revive.interval</code></td>
-  <td>1000</td>
-  <td>
-    The interval length for the scheduler to revive the worker resource offers to run tasks. (in
-    milliseconds)
+    Block size (in bytes) used in Snappy compression, in the case when Snappy compression codec
+    is used.
   </td>
 </tr>
 <tr>
-  <td><code>spark.reducer.maxMbInFlight</code></td>
-  <td>48</td>
+  <td><code>spark.closure.serializer</code></td>
+  <td>org.apache.spark.serializer.<br />JavaSerializer</td>
   <td>
-    Maximum size (in megabytes) of map outputs to fetch simultaneously from each reduce task. Since
-    each output requires us to create a buffer to receive it, this represents a fixed memory
-    overhead per reduce task, so keep it small unless you have a large amount of memory.
+    Serializer class to use for closures. Currently only the Java serializer is supported.
   </td>
 </tr>
 <tr>
-  <td><code>spark.closure.serializer</code></td>
-  <td>org.apache.spark.serializer.<br />JavaSerializer</td>
+  <td><code>spark.serializer.objectStreamReset</code></td>
+  <td>10000</td>
   <td>
-    Serializer class to use for closures. Currently only the Java serializer is supported.
+    When serializing using org.apache.spark.serializer.JavaSerializer, the serializer caches
+    objects to prevent writing redundant data, however that stops garbage collection of those
+    objects. By calling 'reset' you flush that info from the serializer, and allow old
+    objects to be collected. To turn off this periodic reset set it to a value &lt;= 0.
+    By default it will reset the serializer every 10,000 objects.
   </td>
 </tr>
 <tr>
@@ -329,21 +384,29 @@ Apart from these, the following properties are also available, and may be useful
   <td><code>spark.kryoserializer.buffer.mb</code></td>
   <td>2</td>
   <td>
-    Maximum object size to allow within Kryo (the library needs to create a buffer at least as large
-    as the largest single object you'll serialize). Increase this if you get a "buffer limit
+    Maximum object size to allow within Kryo (the library needs to create a buffer at least as
+    large as the largest single object you'll serialize). Increase this if you get a "buffer limit
     exceeded" exception inside Kryo. Note that there will be one buffer <i>per core</i> on each
     worker.
   </td>
 </tr>
+</table>
+
+#### Execution Behavior
+<table class="table">
+<tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr>
 <tr>
-  <td><code>spark.serializer.objectStreamReset</code></td>
-  <td>10000</td>
+  <td><code>spark.default.parallelism</code></td>
   <td>
-    When serializing using org.apache.spark.serializer.JavaSerializer, the serializer caches
-    objects to prevent writing redundant data, however that stops garbage collection of those
-    objects. By calling 'reset' you flush that info from the serializer, and allow old
-    objects to be collected. To turn off this periodic reset set it to a value &lt;= 0.
-    By default it will reset the serializer every 10,000 objects.
+    <ul>
+      <li>Local mode: number of cores on the local machine</li>
+      <li>Mesos fine grained mode: 8</li>
+      <li>Others: total number of cores on all executor nodes or 2, whichever is larger</li>
+    </ul>
+  </td>
+  <td>
+    Default number of tasks to use across the cluster for distributed shuffle operations
+    (<code>groupByKey</code>, <code>reduceByKey</code>, etc) when not set by user.
   </td>
 </tr>
 <tr>
@@ -354,73 +417,92 @@ Apart from these, the following properties are also available, and may be useful
   </td>
 </tr>
 <tr>
-  <td><code>spark.locality.wait</code></td>
-  <td>3000</td>
+  <td><code>spark.broadcast.blockSize</code></td>
+  <td>4096</td>
   <td>
-    Number of milliseconds to wait to launch a data-local task before giving up and launching it
-    on a less-local node. The same wait will be used to step through multiple locality levels
-    (process-local, node-local, rack-local and then any). It is also possible to customize the
-    waiting time for each level by setting <code>spark.locality.wait.node</code>, etc.
-    You should increase this setting if your tasks are long and see poor locality, but the
-    default usually works well.
+    Size of each piece of a block in kilobytes for <code>TorrentBroadcastFactory</code>.
+    Too large a value decreases parallelism during broadcast (makes it slower); however, if it is
+    too small, <code>BlockManager</code> might take a performance hit.
   </td>
 </tr>
 <tr>
-  <td><code>spark.locality.wait.process</code></td>
-  <td>spark.locality.wait</td>
+  <td><code>spark.files.overwrite</code></td>
+  <td>false</td>
   <td>
-    Customize the locality wait for process locality. This affects tasks that attempt to access
-    cached data in a particular executor process.
+    Whether to overwrite files added through SparkContext.addFile() when the target file exists and
+    its contents do not match those of the source.
   </td>
 </tr>
 <tr>
-  <td><code>spark.locality.wait.node</code></td>
-  <td>spark.locality.wait</td>
+  <td><code>spark.files.fetchTimeout</code></td>
+  <td>false</td>
+  <td>
+    Communication timeout to use when fetching files added through SparkContext.addFile() from
+    the driver.
+  </td>
+</tr>
+<tr>
+  <td><code>spark.storage.memoryFraction</code></td>
+  <td>0.6</td>
+  <td>
+    Fraction of Java heap to use for Spark's memory cache. This should not be larger than the "old"
+    generation of objects in the JVM, which by default is given 0.6 of the heap, but you can
+    increase it if you configure your own old generation size.
+  </td>
+</tr>
+<tr>
+  <td><code>spark.tachyonStore.baseDir</code></td>
+  <td>System.getProperty("java.io.tmpdir")</td>
   <td>
-    Customize the locality wait for node locality. For example, you can set this to 0 to skip
-    node locality and search immediately for rack locality (if your cluster has rack information).
+    Directories of the Tachyon File System that store RDDs. The Tachyon file system's URL is set by
+    <code>spark.tachyonStore.url</code>. It can also be a comma-separated list of multiple
+    directories on Tachyon file system.
   </td>
 </tr>
 <tr>
-  <td><code>spark.locality.wait.rack</code></td>
-  <td>spark.locality.wait</td>
+  <td><code>spark.storage.memoryMapThreshold</code></td>
+  <td>8192</td>
   <td>
-    Customize the locality wait for rack locality.
+    Size of a block, in bytes, above which Spark memory maps when reading a block from disk.
+    This prevents Spark from memory mapping very small blocks. In general, memory
+    mapping has high overhead for blocks close to or below the page size of the operating system.
   </td>
 </tr>
 <tr>
-  <td><code>spark.worker.timeout</code></td>
-  <td>60</td>
+  <td><code>spark.tachyonStore.url</code></td>
+  <td>tachyon://localhost:19998</td>
   <td>
-    Number of seconds after which the standalone deploy master considers a worker lost if it
-    receives no heartbeats.
+    The URL of the underlying Tachyon file system in the TachyonStore.
   </td>
 </tr>
 <tr>
-  <td><code>spark.worker.cleanup.enabled</code></td>
-  <td>false</td>
+  <td><code>spark.cleaner.ttl</code></td>
+  <td>(infinite)</td>
   <td>
-    Enable periodic cleanup of worker / application directories.  Note that this only affects
-    standalone mode, as YARN works differently. Applications directories are cleaned up regardless
-    of whether the application is still running.
+    Duration (seconds) of how long Spark will remember any metadata (stages generated, tasks
+    generated, etc.). Periodic cleanups will ensure that metadata older than this duration will be
+    forgotten. This is useful for running Spark for many hours / days (for example, running 24/7 in
+    case of Spark Streaming applications). Note that any RDD that persists in memory for more than
+    this duration will be cleared as well.
   </td>
 </tr>
+</table>
+
+#### Networking
+<table class="table">
+<tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr>
 <tr>
-  <td><code>spark.worker.cleanup.interval</code></td>
-  <td>1800 (30 minutes)</td>
+  <td><code>spark.driver.host</code></td>
+  <td>(local hostname)</td>
   <td>
-    Controls the interval, in seconds, at which the worker cleans up old application work dirs
-    on the local machine.
+    Hostname or IP address for the driver to listen on.
   </td>
 </tr>
 <tr>
-  <td><code>spark.worker.cleanup.appDataTtl</code></td>
-  <td>7 * 24 * 3600 (7 days)</td>
+  <td><code>spark.driver.port</code></td>
+  <td>(random)</td>
   <td>
-    The number of seconds to retain application work directories on each worker.  This is a Time To
-    Live and should depend on the amount of available disk space you have.  Application logs and
-    jars are downloaded to each application work dir.  Over time, the work dirs can quickly fill up
-    disk space, especially if you run jobs very frequently.
+    Port for the driver to listen on.
   </td>
 </tr>
 <tr>
@@ -454,8 +536,8 @@ Apart from these, the following properties are also available, and may be useful
      This is set to a larger value to disable failure detector that comes inbuilt akka. It can be
      enabled again, if you plan to use this feature (Not recommended). Acceptable heart beat pause
      in seconds for akka. This can be used to control sensitivity to gc pauses. Tune this in
-     combination of `spark.akka.heartbeat.interval` and `spark.akka.failure-detector.threshold` if
-     you need to.
+     combination of `spark.akka.heartbeat.interval` and `spark.akka.failure-detector.threshold`
+     if you need to.
   </td>
 </tr>
 <tr>
@@ -476,55 +558,23 @@ Apart from these, the following properties are also available, and may be useful
     enabled again, if you plan to use this feature (Not recommended). A larger interval value in
     seconds reduces network overhead and a smaller value ( ~ 1 s) might be more informative for
     akka's failure detector. Tune this in combination of `spark.akka.heartbeat.pauses` and
-    `spark.akka.failure-detector.threshold` if you need to. Only positive use case for using failure
-    detector can be, a sensistive failure detector can help evict rogue executors really quick.
-    However this is usually not the case as gc pauses and network lags are expected in a real spark
-    cluster. Apart from that enabling this leads to a lot of exchanges of heart beats between nodes
-    leading to flooding the network with those.
-  </td>
-</tr>
-<tr>
-  <td><code>spark.driver.host</code></td>
-  <td>(local hostname)</td>
-  <td>
-    Hostname or IP address for the driver to listen on.
-  </td>
-</tr>
-<tr>
-  <td><code>spark.driver.port</code></td>
-  <td>(random)</td>
-  <td>
-    Port for the driver to listen on.
-  </td>
-</tr>
-<tr>
-  <td><code>spark.cleaner.ttl</code></td>
-  <td>(infinite)</td>
-  <td>
-    Duration (seconds) of how long Spark will remember any metadata (stages generated, tasks
-    generated, etc.).  Periodic cleanups will ensure that metadata older than this duration will be
-    forgotten. This is useful for running Spark for many hours / days (for example, running 24/7 in
-    case of Spark Streaming applications). Note that any RDD that persists in memory for more than
-    this duration will be cleared as well.
-  </td>
-</tr>
-<tr>
-  <td><code>spark.streaming.blockInterval</code></td>
-  <td>200</td>
-  <td>
-    Interval (milliseconds) at which data received by Spark Streaming receivers is coalesced
-    into blocks of data before storing them in Spark.
+    `spark.akka.failure-detector.threshold` if you need to. Only positive use case for using
+    failure detector can be, a sensistive failure detector can help evict rogue executors really
+    quick. However this is usually not the case as gc pauses and network lags are expected in a
+    real Spark cluster. Apart from that enabling this leads to a lot of exchanges of heart beats
+    between nodes leading to flooding the network with those.
   </td>
 </tr>
+</table>
+
+#### Scheduling
+<table class="table">
+<tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr>
 <tr>
-  <td><code>spark.streaming.unpersist</code></td>
-  <td>true</td>
+  <td><code>spark.task.cpus</code></td>
+  <td>1</td>
   <td>
-    Force RDDs generated and persisted by Spark Streaming to be automatically unpersisted from
-    Spark's memory. The raw input data received by Spark Streaming is also automatically cleared.
-    Setting this to false will allow the raw data and persisted RDDs to be accessible outside the
-    streaming application as they will not be cleared automatically. But it comes at the cost of
-    higher memory usage in Spark.
+    Number of cores to allocate for each task.
   </td>
 </tr>
 <tr>
@@ -536,39 +586,36 @@ Apart from these, the following properties are also available, and may be useful
   </td>
 </tr>
 <tr>
-  <td><code>spark.broadcast.blockSize</code></td>
-  <td>4096</td>
-  <td>
-    Size of each piece of a block in kilobytes for <code>TorrentBroadcastFactory</code>.
-    Too large a value decreases parallelism during broadcast (makes it slower); however, if it is
-    too small, <code>BlockManager</code> might take a performance hit.
-  </td>
-</tr>
-
-<tr>
-  <td><code>spark.shuffle.consolidateFiles</code></td>
-  <td>false</td>
+  <td><code>spark.scheduler.mode</code></td>
+  <td>FIFO</td>
   <td>
-    If set to "true", consolidates intermediate files created during a shuffle. Creating fewer files
-    can improve filesystem performance for shuffles with large numbers of reduce tasks. It is
-    recommended to set this to "true" when using ext4 or xfs filesystems. On ext3, this option might
-    degrade performance on machines with many (>8) cores due to filesystem limitations.
+    The <a href="job-scheduling.html#scheduling-within-an-application">scheduling mode</a> between
+    jobs submitted to the same SparkContext. Can be set to <code>FAIR</code>
+    to use fair sharing instead of queueing jobs one after another. Useful for
+    multi-user services.
   </td>
 </tr>
 <tr>
-  <td><code>spark.shuffle.file.buffer.kb</code></td>
-  <td>100</td>
+  <td><code>spark.cores.max</code></td>
+  <td>(not set)</td>
   <td>
-    Size of the in-memory buffer for each shuffle file output stream, in kilobytes. These buffers
-    reduce the number of disk seeks and system calls made in creating intermediate shuffle files.
+    When running on a <a href="spark-standalone.html">standalone deploy cluster</a> or a
+    <a href="running-on-mesos.html#mesos-run-modes">Mesos cluster in "coarse-grained"
+    sharing mode</a>, the maximum amount of CPU cores to request for the application from
+    across the cluster (not from each machine). If not set, the default will be
+    <code>spark.deploy.defaultCores</code> on Spark's standalone cluster manager, or
+    infinite (all available cores) on Mesos.
   </td>
 </tr>
 <tr>
-  <td><code>spark.shuffle.spill</code></td>
-  <td>true</td>
+  <td><code>spark.mesos.coarse</code></td>
+  <td>false</td>
   <td>
-    If set to "true", limits the amount of memory used during reduces by spilling data out to disk.
-    This spilling threshold is specified by <code>spark.shuffle.memoryFraction</code>.
+    If set to "true", runs over Mesos clusters in
+    <a href="running-on-mesos.html#mesos-run-modes">"coarse-grained" sharing mode</a>,
+    where Spark acquires one long-lived Mesos task on each machine instead of one Mesos task per
+    Spark task. This gives lower-latency scheduling for short queries, but leaves resources in use
+    for the whole duration of the Spark job.
   </td>
 </tr>
 <tr>
@@ -601,91 +648,59 @@ Apart from these, the following properties are also available, and may be useful
   </td>
 </tr>
 <tr>
-  <td><code>spark.logConf</code></td>
-  <td>false</td>
-  <td>
-    Whether to log the supplied SparkConf as INFO at start of spark context.
-  </td>
-</tr>
-<tr>
-  <td><code>spark.eventLog.enabled</code></td>
-  <td>false</td>
-  <td>
-    Whether to log spark events, useful for reconstructing the Web UI after the application has
-    finished.
-  </td>
-</tr>
-<tr>
-  <td><code>spark.eventLog.compress</code></td>
-  <td>false</td>
-  <td>
-    Whether to compress logged events, if <code>spark.eventLog.enabled</code> is true.
-  </td>
-</tr>
-<tr>
-  <td><code>spark.eventLog.dir</code></td>
-  <td>file:///tmp/spark-events</td>
-  <td>
-    Base directory in which spark events are logged, if <code>spark.eventLog.enabled</code> is true.
-    Within this base directory, Spark creates a sub-directory for each application, and logs the
-    events specific to the application in this directory.
-  </td>
-</tr>
-<tr>
-  <td><code>spark.deploy.spreadOut</code></td>
-  <td>true</td>
+  <td><code>spark.locality.wait</code></td>
+  <td>3000</td>
   <td>
-    Whether the standalone cluster manager should spread applications out across nodes or try to
-    consolidate them onto as few nodes as possible. Spreading out is usually better for data
-    locality in HDFS, but consolidating is more efficient for compute-intensive workloads. <br/>
-    <b>Note:</b> this setting needs to be configured in the standalone cluster master, not in
-    individual applications; you can set it through <code>SPARK_MASTER_OPTS</code> in
-    <code>spark-env.sh</code>.
+    Number of milliseconds to wait to launch a data-local task before giving up and launching it
+    on a less-local node. The same wait will be used to step through multiple locality levels
+    (process-local, node-local, rack-local and then any). It is also possible to customize the
+    waiting time for each level by setting <code>spark.locality.wait.node</code>, etc.
+    You should increase this setting if your tasks are long and see poor locality, but the
+    default usually works well.
   </td>
 </tr>
 <tr>
-  <td><code>spark.deploy.defaultCores</code></td>
-  <td>(infinite)</td>
+  <td><code>spark.locality.wait.process</code></td>
+  <td>spark.locality.wait</td>
   <td>
-    Default number of cores to give to applications in Spark's standalone mode if they don't set
-    <code>spark.cores.max</code>. If not set, applications always get all available cores unless
-    they configure <code>spark.cores.max</code> themselves.  Set this lower on a shared cluster to
-    prevent users from grabbing the whole cluster by default. <br/> <b>Note:</b> this setting needs
-    to be configured in the standalone cluster master, not in individual applications; you can set
-    it through <code>SPARK_MASTER_OPTS</code> in <code>spark-env.sh</code>.
+    Customize the locality wait for process locality. This affects tasks that attempt to access
+    cached data in a particular executor process.
   </td>
 </tr>
 <tr>
-  <td><code>spark.files.overwrite</code></td>
-  <td>false</td>
+  <td><code>spark.locality.wait.node</code></td>
+  <td>spark.locality.wait</td>
   <td>
-    Whether to overwrite files added through SparkContext.addFile() when the target file exists and
-    its contents do not match those of the source.
+    Customize the locality wait for node locality. For example, you can set this to 0 to skip
+    node locality and search immediately for rack locality (if your cluster has rack information).
   </td>
 </tr>
 <tr>
-  <td><code>spark.files.fetchTimeout</code></td>
-  <td>false</td>
+  <td><code>spark.locality.wait.rack</code></td>
+  <td>spark.locality.wait</td>
   <td>
-    Communication timeout to use when fetching files added through SparkContext.addFile() from
-    the driver.
+    Customize the locality wait for rack locality.
   </td>
 </tr>
 <tr>
-  <td><code>spark.files.userClassPathFirst</code></td>
-  <td>false</td>
+  <td><code>spark.scheduler.revive.interval</code></td>
+  <td>1000</td>
   <td>
-    (Experimental) Whether to give user-added jars precedence over Spark's own jars when
-    loading classes in Executors. This feature can be used to mitigate conflicts between
-    Spark's dependencies and user dependencies. It is currently an experimental feature.
+    The interval length for the scheduler to revive the worker resource offers to run tasks.
+    (in milliseconds)
   </td>
 </tr>
+</table>
+
+#### Security
+<table class="table">
+<tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr>
 <tr>
   <td><code>spark.authenticate</code></td>
   <td>false</td>
   <td>
-    Whether spark authenticates its internal connections. See <code>spark.authenticate.secret</code>
-    if not running on Yarn.
+    Whether Spark authenticates its internal connections. See
+    <code>spark.authenticate.secret</code> if not running on YARN.
   </td>
 </tr>
 <tr>
@@ -693,7 +708,7 @@ Apart from these, the following properties are also available, and may be useful
   <td>None</td>
   <td>
     Set the secret key used for Spark to authenticate between components. This needs to be set if
-    not running on Yarn and authentication is enabled.
+    not running on YARN and authentication is enabled.
   </td>
 </tr>
 <tr>
@@ -705,42 +720,71 @@ Apart from these, the following properties are also available, and may be useful
   </td>
 </tr>
 <tr>
-  <td><code>spark.task.cpus</code></td>
-  <td>1</td>
+  <td><code>spark.ui.filters</code></td>
+  <td>None</td>
   <td>
-    Number of cores to allocate for each task.
+    Comma separated list of filter class names to apply to the Spark web UI. The filter should be a
+    standard <a href="http://docs.oracle.com/javaee/6/api/javax/servlet/Filter.html">
+    javax servlet Filter</a>. Parameters to each filter can also be specified by setting a
+    java system property of: <br />
+    <code>spark.&lt;class name of filter&gt;.params='param1=value1,param2=value2'</code><br />
+    For example: <br />
+    <code>-Dspark.ui.filters=com.test.filter1</code> <br />
+    <code>-Dspark.com.test.filter1.params='param1=foo,param2=testing'</code>
   </td>
 </tr>
 <tr>
-  <td><code>spark.executor.extraJavaOptions</code></td>
-  <td>(none)</td>
+  <td><code>spark.ui.acls.enable</code></td>
+  <td>false</td>
   <td>
-    A string of extra JVM options to pass to executors. For instance, GC settings or other
-    logging. Note that it is illegal to set Spark properties or heap size settings with this 
-    option. Spark properties should be set using a SparkConf object or the 
-    spark-defaults.conf file used with the spark-submit script. Heap size settings can be set
-    with spark.executor.memory.
+    Whether Spark web ui acls should are enabled. If enabled, this checks to see if the user has
+    access permissions to view the web ui. See <code>spark.ui.view.acls</code> for more details.
+    Also note this requires the user to be known, if the user comes across as null no checks
+    are done. Filters can be used to authenticate and set the user.
   </td>
 </tr>
 <tr>
-  <td><code>spark.executor.extraClassPath</code></td>
-  <td>(none)</td>
+  <td><code>spark.ui.view.acls</code></td>
+  <td>Empty</td>
   <td>
-    Extra classpath entries to append to the classpath of executors. This exists primarily
-    for backwards-compatibility with older versions of Spark. Users typically should not need
-    to set this option.
+    Comma separated list of users that have view access to the Spark web ui. By default only the
+    user that started the Spark job has view access.
   </td>
 </tr>
+</table>
+
+#### Spark Streaming
+<table class="table">
+<tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr>
 <tr>
-  <td><code>spark.executor.extraLibraryPath</code></td>
-  <td>(none)</td>
+  <td><code>spark.streaming.blockInterval</code></td>
+  <td>200</td>
   <td>
-    Set a special library path to use when launching executor JVM's.
+    Interval (milliseconds) at which data received by Spark Streaming receivers is coalesced
+    into blocks of data before storing them in Spark.
+  </td>
+</tr>
+<tr>
+  <td><code>spark.streaming.unpersist</code></td>
+  <td>true</td>
+  <td>
+    Force RDDs generated and persisted by Spark Streaming to be automatically unpersisted from
+    Spark's memory. The raw input data received by Spark Streaming is also automatically cleared.
+    Setting this to false will allow the raw data and persisted RDDs to be accessible outside the
+    streaming application as they will not be cleared automatically. But it comes at the cost of
+    higher memory usage in Spark.
   </td>
 </tr>
-
 </table>
 
+#### Cluster Managers (YARN, Mesos, Standalone)
+Each cluster manager in Spark has additional configuration options. Configurations 
+can be found on the pages for each mode:
+
+ * [YARN](running-on-yarn.html#configuration)
+ * [Mesos](running-on-mesos.html)
+ * [Standalone Mode](spark-standalone.html#cluster-launch-scripts)
+
 # Environment Variables
 
 Certain Spark settings can be configured through environment variables, which are read from the
@@ -774,104 +818,15 @@ The following variables can be set in `spark-env.sh`:
   </tr>
 </table>
 
-In addition to the above, there are also options for setting up the Spark [standalone cluster
-scripts](spark-standalone.html#cluster-launch-scripts), such as number of cores to use on each
-machine and maximum memory.
+In addition to the above, there are also options for setting up the Spark
+[standalone cluster scripts](spark-standalone.html#cluster-launch-scripts), such as number of cores
+to use on each machine and maximum memory.
 
-Since `spark-env.sh` is a shell script, some of these can be set programmatically -- for example,
-you might compute `SPARK_LOCAL_IP` by looking up the IP of a specific network interface.
+Since `spark-env.sh` is a shell script, some of these can be set programmatically -- for example, you might
+compute `SPARK_LOCAL_IP` by looking up the IP of a specific network interface.
 
 # Configuring Logging
 
 Spark uses [log4j](http://logging.apache.org/log4j/) for logging. You can configure it by adding a
 `log4j.properties` file in the `conf` directory. One way to start is to copy the existing
 `log4j.properties.template` located there.
-
-# Configuring Ports for Network Security
-
-Spark makes heavy use of the network, and some environments have strict requirements for using tight
-firewall settings.  Below are the primary ports that Spark uses for its communication and how to
-configure those ports.
-
-<table class="table">
-  <tr>
-    <th>From</th><th>To</th><th>Default Port</th><th>Purpose</th><th>Configuration
-    Setting</th><th>Notes</th>
-  </tr>
-  <!-- Web UIs -->
-  <tr>
-    <td>Browser</td>
-    <td>Standalone Cluster Master</td>
-    <td>8080</td>
-    <td>Web UI</td>
-    <td><code>master.ui.port</code></td>
-    <td>Jetty-based</td>
-  </tr>
-  <tr>
-    <td>Browser</td>
-    <td>Worker</td>
-    <td>8081</td>
-    <td>Web UI</td>
-    <td><code>worker.ui.port</code></td>
-    <td>Jetty-based</td>
-  </tr>
-  <tr>
-    <td>Browser</td>
-    <td>Driver</td>
-    <td>4040</td>
-    <td>Web UI</td>
-    <td><code>spark.ui.port</code></td>
-    <td>Jetty-based</td>
-  </tr>
-  <tr>
-    <td>Browser</td>
-    <td>History Server</td>
-    <td>18080</td>
-    <td>Web UI</td>
-    <td><code>spark.history.ui.port</code></td>
-    <td>Jetty-based</td>
-  </tr>
-
-  <!-- Cluster interactions -->
-  <tr>
-    <td>Application</td>
-    <td>Standalone Cluster Master</td>
-    <td>7077</td>
-    <td>Submit job to cluster</td>
-    <td><code>spark.driver.port</code></td>
-    <td>Akka-based.  Set to "0" to choose a port randomly</td>
-  </tr>
-  <tr>
-    <td>Worker</td>
-    <td>Standalone Cluster Master</td>
-    <td>7077</td>
-    <td>Join cluster</td>
-    <td><code>spark.driver.port</code></td>
-    <td>Akka-based.  Set to "0" to choose a port randomly</td>
-  </tr>
-  <tr>
-    <td>Application</td>
-    <td>Worker</td>
-    <td>(random)</td>
-    <td>Join cluster</td>
-    <td><code>SPARK_WORKER_PORT</code> (standalone cluster)</td>
-    <td>Akka-based</td>
-  </tr>
-
-  <!-- Other misc stuff -->
-  <tr>
-    <td>Driver and other Workers</td>
-    <td>Worker</td>
-    <td>(random)</td>
-    <td>
-      <ul>
-        <li>File server for file and jars</li>
-        <li>Http Broadcast</li>
-        <li>Class file server (Spark Shell only)</li>
-      </ul>
-    </td>
-    <td>None</td>
-    <td>Jetty-based.  Each of these services starts on a random port that cannot be configured</td>
-  </tr>
-
-</table>
diff --git a/docs/quick-start.md b/docs/quick-start.md
index 33a0df103642..20e17ebf703f 100644
--- a/docs/quick-start.md
+++ b/docs/quick-start.md
@@ -252,11 +252,11 @@ we initialize a SparkContext as part of the program.
 We pass the SparkContext constructor a 
 [SparkConf](api/scala/index.html#org.apache.spark.SparkConf)
 object which contains information about our
-application. We also call sc.addJar to make sure that when our application is launched in cluster
-mode, the jar file containing it will be shipped automatically to worker nodes.
+application. 
 
-This file depends on the Spark API, so we'll also include an sbt configuration file, `simple.sbt`
-which explains that Spark is a dependency. This file also adds a repository that Spark depends on:
+Our application depends on the Spark API, so we'll also include an sbt configuration file, 
+`simple.sbt` which explains that Spark is a dependency. This file also adds a repository that 
+Spark depends on:
 
 {% highlight scala %}
 name := "Simple Project"
diff --git a/docs/spark-standalone.md b/docs/spark-standalone.md
index eb3211b6b0e4..dca80a9a6961 100644
--- a/docs/spark-standalone.md
+++ b/docs/spark-standalone.md
@@ -93,7 +93,15 @@ You can optionally configure the cluster further by setting environment variable
   </tr>
   <tr>
     <td><code>SPARK_MASTER_OPTS</code></td>
-    <td>Configuration properties that apply only to the master in the form "-Dx=y" (default: none).</td>
+    <td>Configuration properties that apply only to the master in the form "-Dx=y" (default: none). See below for a list of possible options.</td>
+  </tr>
+  <tr>
+    <td><code>SPARK_LOCAL_DIRS</code></td>
+    <td>
+    Directory to use for "scratch" space in Spark, including map output files and RDDs that get 
+    stored on disk. This should be on a fast, local disk in your system. It can also be a 
+    comma-separated list of multiple directories on different disks.
+    </td>
   </tr>
   <tr>
     <td><code>SPARK_WORKER_CORES</code></td>
@@ -126,7 +134,7 @@ You can optionally configure the cluster further by setting environment variable
   </tr>
   <tr>
     <td><code>SPARK_WORKER_OPTS</code></td>
-    <td>Configuration properties that apply only to the worker in the form "-Dx=y" (default: none).</td>
+    <td>Configuration properties that apply only to the worker in the form "-Dx=y" (default: none). See below for a list of possible options.</td>
   </tr>
   <tr>
     <td><code>SPARK_DAEMON_MEMORY</code></td>
@@ -144,6 +152,73 @@ You can optionally configure the cluster further by setting environment variable
 
 **Note:** The launch scripts do not currently support Windows. To run a Spark cluster on Windows, start the master and workers by hand.
 
+SPARK_MASTER_OPTS supports the following system properties:
+
+<table class="table">
+<tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr>
+<tr>
+  <td><code>spark.deploy.spreadOut</code></td>
+  <td>true</td>
+  <td>
+    Whether the standalone cluster manager should spread applications out across nodes or try
+    to consolidate them onto as few nodes as possible. Spreading out is usually better for
+    data locality in HDFS, but consolidating is more efficient for compute-intensive workloads. <br/>
+  </td>
+</tr>
+<tr>
+  <td><code>spark.deploy.defaultCores</code></td>
+  <td>(infinite)</td>
+  <td>
+    Default number of cores to give to applications in Spark's standalone mode if they don't
+    set <code>spark.cores.max</code>. If not set, applications always get all available
+    cores unless they configure <code>spark.cores.max</code> themselves.
+    Set this lower on a shared cluster to prevent users from grabbing
+    the whole cluster by default. <br/>
+  </td>
+</tr>
+<tr>
+  <td><code>spark.worker.timeout</code></td>
+  <td>60</td>
+  <td>
+    Number of seconds after which the standalone deploy master considers a worker lost if it
+    receives no heartbeats.
+  </td>
+</tr>
+</table>
+
+SPARK_WORKER_OPTS supports the following system properties:
+
+<table class="table">
+<tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr>
+<tr>
+  <td><code>spark.worker.cleanup.enabled</code></td>
+  <td>false</td>
+  <td>
+    Enable periodic cleanup of worker / application directories.  Note that this only affects standalone
+    mode, as YARN works differently. Applications directories are cleaned up regardless of whether
+    the application is still running.
+  </td>
+</tr>
+<tr>
+  <td><code>spark.worker.cleanup.interval</code></td>
+  <td>1800 (30 minutes)</td>
+  <td>
+    Controls the interval, in seconds, at which the worker cleans up old application work dirs
+    on the local machine.
+  </td>
+</tr>
+<tr>
+  <td><code>spark.worker.cleanup.appDataTtl</code></td>
+  <td>7 * 24 * 3600 (7 days)</td>
+  <td>
+    The number of seconds to retain application work directories on each worker.  This is a Time To Live
+    and should depend on the amount of available disk space you have.  Application logs and jars are
+    downloaded to each application work dir.  Over time, the work dirs can quickly fill up disk space,
+    especially if you run jobs very frequently.
+  </td>
+</tr>
+</table>
+
 # Connecting an Application to the Cluster
 
 To run an application on the Spark cluster, simply pass the `spark://IP:PORT` URL of the master as to the [`SparkContext`
@@ -212,6 +287,94 @@ In addition, detailed log output for each job is also written to the work direct
 You can run Spark alongside your existing Hadoop cluster by just launching it as a separate service on the same machines. To access Hadoop data from Spark, just use a hdfs:// URL (typically `hdfs://<namenode>:9000/path`, but you can find the right URL on your Hadoop Namenode's web UI). Alternatively, you can set up a separate cluster for Spark, and still have it access HDFS over the network; this will be slower than disk-local access, but may not be a concern if you are still running in the same local area network (e.g. you place a few Spark machines on each rack that you have Hadoop on).
 
 
+# Configuring Ports for Network Security
+
+Spark makes heavy use of the network, and some environments have strict requirements for using tight
+firewall settings.  Below are the primary ports that Spark uses for its communication and how to
+configure those ports.
+
+<table class="table">
+  <tr>
+    <th>From</th><th>To</th><th>Default Port</th><th>Purpose</th><th>Configuration
+    Setting</th><th>Notes</th>
+  </tr>
+  <!-- Web UIs -->
+  <tr>
+    <td>Browser</td>
+    <td>Standalone Cluster Master</td>
+    <td>8080</td>
+    <td>Web UI</td>
+    <td><code>master.ui.port</code></td>
+    <td>Jetty-based</td>
+  </tr>
+  <tr>
+    <td>Browser</td>
+    <td>Driver</td>
+    <td>4040</td>
+    <td>Web UI</td>
+    <td><code>spark.ui.port</code></td>
+    <td>Jetty-based</td>
+  </tr>
+  <tr>
+    <td>Browser</td>
+    <td>History Server</td>
+    <td>18080</td>
+    <td>Web UI</td>
+    <td><code>spark.history.ui.port</code></td>
+    <td>Jetty-based</td>
+  </tr>
+  <tr>
+    <td>Browser</td>
+    <td>Worker</td>
+    <td>8081</td>
+    <td>Web UI</td>
+    <td><code>worker.ui.port</code></td>
+    <td>Jetty-based</td>
+  </tr>
+  <!-- Cluster interactions -->
+  <tr>
+    <td>Application</td>
+    <td>Standalone Cluster Master</td>
+    <td>7077</td>
+    <td>Submit job to cluster</td>
+    <td><code>spark.driver.port</code></td>
+    <td>Akka-based.  Set to "0" to choose a port randomly</td>
+  </tr>
+  <tr>
+    <td>Worker</td>
+    <td>Standalone Cluster Master</td>
+    <td>7077</td>
+    <td>Join cluster</td>
+    <td><code>spark.driver.port</code></td>
+    <td>Akka-based.  Set to "0" to choose a port randomly</td>
+  </tr>
+  <tr>
+    <td>Application</td>
+    <td>Worker</td>
+    <td>(random)</td>
+    <td>Join cluster</td>
+    <td><code>SPARK_WORKER_PORT</code> (standalone cluster)</td>
+    <td>Akka-based</td>
+  </tr>
+
+  <!-- Other misc stuff -->
+  <tr>
+    <td>Driver and other Workers</td>
+    <td>Worker</td>
+    <td>(random)</td>
+    <td>
+      <ul>
+        <li>File server for file and jars</li>
+        <li>Http Broadcast</li>
+        <li>Class file server (Spark Shell only)</li>
+      </ul>
+    </td>
+    <td>None</td>
+    <td>Jetty-based.  Each of these services starts on a random port that cannot be configured</td>
+  </tr>
+
+</table>
+
 # High Availability
 
 By default, standalone scheduling clusters are resilient to Worker failures (insofar as Spark itself is resilient to losing work by moving it to other workers). However, the scheduler uses a Master to make scheduling decisions, and this (by default) creates a single point of failure: if the Master crashes, no new applications can be created. In order to circumvent this, we have two high availability schemes, detailed below.

From 4312cf0bade82d4b54eef80e637953e1633edc3e Mon Sep 17 00:00:00 2001
From: David Lemieux <david.lemieux@radialpoint.com>
Date: Wed, 28 May 2014 15:50:35 -0700
Subject: [PATCH 291/810] Spark 1916

The changes could be ported back to 0.9 as well.
Changing in.read to in.readFully to read the whole input stream rather than the first 1020 bytes.
This should ok considering that Flume caps the body size to 32K by default.

Author: David Lemieux <david.lemieux@radialpoint.com>

Closes #865 from lemieud/SPARK-1916 and squashes the following commits:

a265673 [David Lemieux] Updated SparkFlumeEvent to read the whole stream rather than the first X bytes.
(cherry picked from commit 0b769b73fb7ae314325857138a2d3138ed157908)

Signed-off-by: Patrick Wendell <pwendell@gmail.com>
---
 .../org/apache/spark/streaming/flume/FlumeInputDStream.scala    | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeInputDStream.scala b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeInputDStream.scala
index df7605fe579f..5be33f1d5c42 100644
--- a/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeInputDStream.scala
+++ b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeInputDStream.scala
@@ -63,7 +63,7 @@ class SparkFlumeEvent() extends Externalizable {
   def readExternal(in: ObjectInput) {
     val bodyLength = in.readInt()
     val bodyBuff = new Array[Byte](bodyLength)
-    in.read(bodyBuff)
+    in.readFully(bodyBuff)
 
     val numHeaders = in.readInt()
     val headers = new java.util.HashMap[CharSequence, CharSequence]

From 4dbb27b0cf4eb67c92aad2c1158616312f5a54e6 Mon Sep 17 00:00:00 2001
From: witgo <witgo@qq.com>
Date: Wed, 28 May 2014 15:57:05 -0700
Subject: [PATCH 292/810] [SPARK-1712]: TaskDescription instance is too big
 causes Spark to hang

Author: witgo <witgo@qq.com>

Closes #694 from witgo/SPARK-1712_new and squashes the following commits:

0f52483 [witgo] review commit
83ce29b [witgo] Merge branch 'master' of https://github.com/apache/spark into SPARK-1712_new
52e6752 [witgo] reset test SparkContext
63636b6 [witgo] review commit
44a59ee [witgo] review commit
3b6d48c [witgo] Merge branch 'master' of https://github.com/apache/spark into SPARK-1712_new
926bd6a [witgo] Merge branch 'master' of https://github.com/apache/spark into SPARK-1712_new
9a5cfad [witgo] Merge branch 'master' of https://github.com/apache/spark into SPARK-1712_new
03cc562 [witgo] Merge branch 'master' of https://github.com/apache/spark into SPARK-1712_new
b0930b0 [witgo] review commit
b1174bd [witgo] merge master
f76679b [witgo] merge master
689495d [witgo] fix scala style bug
1d35c3c [witgo] Merge branch 'master' of https://github.com/apache/spark into SPARK-1712_new
062c182 [witgo] fix small bug for code style
0a428cf [witgo] add unit tests
158b2dc [witgo] review commit
4afe71d [witgo] review commit
9e4ffa7 [witgo] review commit
1d35c7d [witgo] fix hang
7965580 [witgo] fix Statement order
0e29eac [witgo] Merge branch 'master' of https://github.com/apache/spark into SPARK-1712_new
3ea1ca1 [witgo] remove duplicate serialize
743a7ad [witgo] Merge branch 'master' of https://github.com/apache/spark into SPARK-1712_new
86e2048 [witgo] Merge branch 'master' of https://github.com/apache/spark into SPARK-1712_new
2a89adc [witgo] SPARK-1712: TaskDescription instance is too big causes Spark to hang
---
 .../CoarseGrainedExecutorBackend.scala        |  9 ++--
 .../cluster/CoarseGrainedClusterMessage.scala |  2 +-
 .../CoarseGrainedSchedulerBackend.scala       | 27 ++++++++++--
 .../CoarseGrainedSchedulerBackendSuite.scala  | 43 +++++++++++++++++++
 4 files changed, 73 insertions(+), 8 deletions(-)
 create mode 100644 core/src/test/scala/org/apache/spark/scheduler/CoarseGrainedSchedulerBackendSuite.scala

diff --git a/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala b/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala
index 84aec65b7765..2279d77c91c8 100644
--- a/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala
+++ b/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala
@@ -22,11 +22,12 @@ import java.nio.ByteBuffer
 import akka.actor._
 import akka.remote._
 
-import org.apache.spark.{Logging, SecurityManager, SparkConf}
+import org.apache.spark.{SparkEnv, Logging, SecurityManager, SparkConf}
 import org.apache.spark.TaskState.TaskState
 import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.deploy.worker.WorkerWatcher
 import org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages._
+import org.apache.spark.scheduler.TaskDescription
 import org.apache.spark.util.{AkkaUtils, Utils}
 
 private[spark] class CoarseGrainedExecutorBackend(
@@ -61,12 +62,14 @@ private[spark] class CoarseGrainedExecutorBackend(
       logError("Slave registration failed: " + message)
       System.exit(1)
 
-    case LaunchTask(taskDesc) =>
-      logInfo("Got assigned task " + taskDesc.taskId)
+    case LaunchTask(data) =>
       if (executor == null) {
         logError("Received LaunchTask command but executor was null")
         System.exit(1)
       } else {
+        val ser = SparkEnv.get.closureSerializer.newInstance()
+        val taskDesc = ser.deserialize[TaskDescription](data.value)
+        logInfo("Got assigned task " + taskDesc.taskId)
         executor.launchTask(this, taskDesc.taskId, taskDesc.serializedTask)
       }
 
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedClusterMessage.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedClusterMessage.scala
index ddbc74e82ac4..ca74069ef885 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedClusterMessage.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedClusterMessage.scala
@@ -28,7 +28,7 @@ private[spark] sealed trait CoarseGrainedClusterMessage extends Serializable
 private[spark] object CoarseGrainedClusterMessages {
 
   // Driver to executors
-  case class LaunchTask(task: TaskDescription) extends CoarseGrainedClusterMessage
+  case class LaunchTask(data: SerializableBuffer) extends CoarseGrainedClusterMessage
 
   case class KillTask(taskId: Long, executor: String, interruptThread: Boolean)
     extends CoarseGrainedClusterMessage
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala
index a6d6b3d26a3c..e47a060683a2 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala
@@ -27,10 +27,10 @@ import akka.actor._
 import akka.pattern.ask
 import akka.remote.{DisassociatedEvent, RemotingLifecycleEvent}
 
-import org.apache.spark.{Logging, SparkException, TaskState}
+import org.apache.spark.{SparkEnv, Logging, SparkException, TaskState}
 import org.apache.spark.scheduler.{SchedulerBackend, SlaveLost, TaskDescription, TaskSchedulerImpl, WorkerOffer}
 import org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages._
-import org.apache.spark.util.{AkkaUtils, Utils}
+import org.apache.spark.util.{SerializableBuffer, AkkaUtils, Utils}
 
 /**
  * A scheduler backend that waits for coarse grained executors to connect to it through Akka.
@@ -48,6 +48,7 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, actorSystem: A
   var totalCoreCount = new AtomicInteger(0)
   val conf = scheduler.sc.conf
   private val timeout = AkkaUtils.askTimeout(conf)
+  private val akkaFrameSize = AkkaUtils.maxFrameSizeBytes(conf)
 
   class DriverActor(sparkProperties: Seq[(String, String)]) extends Actor {
     private val executorActor = new HashMap[String, ActorRef]
@@ -140,8 +141,26 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, actorSystem: A
     // Launch tasks returned by a set of resource offers
     def launchTasks(tasks: Seq[Seq[TaskDescription]]) {
       for (task <- tasks.flatten) {
-        freeCores(task.executorId) -= scheduler.CPUS_PER_TASK
-        executorActor(task.executorId) ! LaunchTask(task)
+        val ser = SparkEnv.get.closureSerializer.newInstance()
+        val serializedTask = ser.serialize(task)
+        if (serializedTask.limit >= akkaFrameSize - 1024) {
+          val taskSetId = scheduler.taskIdToTaskSetId(task.taskId)
+          scheduler.activeTaskSets.get(taskSetId).foreach { taskSet =>
+            try {
+              var msg = "Serialized task %s:%d was %d bytes which " +
+                "exceeds spark.akka.frameSize (%d bytes). " +
+                "Consider using broadcast variables for large values."
+              msg = msg.format(task.taskId, task.index, serializedTask.limit, akkaFrameSize)
+              taskSet.abort(msg)
+            } catch {
+              case e: Exception => logError("Exception in error callback", e)
+            }
+          }
+        }
+        else {
+          freeCores(task.executorId) -= scheduler.CPUS_PER_TASK
+          executorActor(task.executorId) ! LaunchTask(new SerializableBuffer(serializedTask))
+        }
       }
     }
 
diff --git a/core/src/test/scala/org/apache/spark/scheduler/CoarseGrainedSchedulerBackendSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/CoarseGrainedSchedulerBackendSuite.scala
new file mode 100644
index 000000000000..efef9d26dadc
--- /dev/null
+++ b/core/src/test/scala/org/apache/spark/scheduler/CoarseGrainedSchedulerBackendSuite.scala
@@ -0,0 +1,43 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.scheduler
+
+import org.apache.spark.{LocalSparkContext, SparkConf, SparkException, SparkContext}
+import org.apache.spark.util.{SerializableBuffer, AkkaUtils}
+
+import org.scalatest.FunSuite
+
+class CoarseGrainedSchedulerBackendSuite extends FunSuite with LocalSparkContext {
+
+  test("serialized task larger than akka frame size") {
+    val conf = new SparkConf
+    conf.set("spark.akka.frameSize","1")
+    conf.set("spark.default.parallelism","1")
+    sc = new SparkContext("local-cluster[2 , 1 , 512]", "test", conf)
+    val frameSize = AkkaUtils.maxFrameSizeBytes(sc.conf)
+    val buffer = new SerializableBuffer(java.nio.ByteBuffer.allocate(2 * frameSize))
+    val larger = sc.parallelize(Seq(buffer))
+    val thrown = intercept[SparkException] {
+      larger.collect()
+    }
+    assert(thrown.getMessage.contains("Consider using broadcast variables for large values"))
+    val smaller = sc.parallelize(1 to 4).collect()
+    assert(smaller.size === 4)
+  }
+
+}

From 9cff1dd25abc5e848720d853172ed42e35376fd0 Mon Sep 17 00:00:00 2001
From: Jyotiska NK <jyotiska123@gmail.com>
Date: Wed, 28 May 2014 23:08:39 -0700
Subject: [PATCH 293/810] Added doctest and method description in context.py

Added doctest for method textFile and description for methods _initialize_context and _ensure_initialized in context.py

Author: Jyotiska NK <jyotiska123@gmail.com>

Closes #187 from jyotiska/pyspark_context and squashes the following commits:

356f945 [Jyotiska NK] Added doctest for textFile method in context.py
5b23686 [Jyotiska NK] Updated context.py with method descriptions
---
 python/pyspark/context.py | 15 ++++++++++++++-
 1 file changed, 14 insertions(+), 1 deletion(-)

diff --git a/python/pyspark/context.py b/python/pyspark/context.py
index 27b440d73bdc..56746cb7aab3 100644
--- a/python/pyspark/context.py
+++ b/python/pyspark/context.py
@@ -173,12 +173,18 @@ def __init__(self, master=None, appName=None, sparkHome=None, pyFiles=None,
         self._temp_dir = \
             self._jvm.org.apache.spark.util.Utils.createTempDir(local_dir).getAbsolutePath()
 
-    # Initialize SparkContext in function to allow subclass specific initialization
     def _initialize_context(self, jconf):
+        """
+        Initialize SparkContext in function to allow subclass specific initialization
+        """
         return self._jvm.JavaSparkContext(jconf)
 
     @classmethod
     def _ensure_initialized(cls, instance=None, gateway=None):
+        """
+        Checks whether a SparkContext is initialized or not.
+        Throws error if a SparkContext is already running.
+        """
         with SparkContext._lock:
             if not SparkContext._gateway:
                 SparkContext._gateway = gateway or launch_gateway()
@@ -270,6 +276,13 @@ def textFile(self, name, minPartitions=None):
         Read a text file from HDFS, a local file system (available on all
         nodes), or any Hadoop-supported file system URI, and return it as an
         RDD of Strings.
+        
+        >>> path = os.path.join(tempdir, "sample-text.txt")
+        >>> with open(path, "w") as testFile:
+        ...    testFile.write("Hello world!")
+        >>> textFile = sc.textFile(path)
+        >>> textFile.collect()
+        [u'Hello world!']
         """
         minPartitions = minPartitions or min(self.defaultParallelism, 2)
         return RDD(self._jsc.textFile(name, minPartitions), self,

From 60b89fe6b09ff896a30d74204876da883e307de7 Mon Sep 17 00:00:00 2001
From: Yin Huai <huai@cse.ohio-state.edu>
Date: Thu, 29 May 2014 09:07:39 -0700
Subject: [PATCH 294/810] SPARK-1935: Explicitly add commons-codec 1.5 as a
 dependency.

Author: Yin Huai <huai@cse.ohio-state.edu>

Closes #889 from yhuai/SPARK-1935 and squashes the following commits:

7d50ef1 [Yin Huai] Explicitly add commons-codec 1.5 as a dependency.
---
 pom.xml                  | 5 +++++
 project/SparkBuild.scala | 1 +
 2 files changed, 6 insertions(+)

diff --git a/pom.xml b/pom.xml
index 86264d1132ec..7bf9f135fd34 100644
--- a/pom.xml
+++ b/pom.xml
@@ -240,6 +240,11 @@
         <artifactId>commons-lang3</artifactId>
         <version>3.3.2</version>
       </dependency>
+      <dependency>
+	  <groupId>commons-codec</groupId>
+	    <artifactId>commons-codec</artifactId>
+	    <version>1.5</version>
+      </dependency>
       <dependency>
         <groupId>com.google.code.findbugs</groupId>
         <artifactId>jsr305</artifactId>
diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index b9d92340ff75..8ef1e91f609f 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -348,6 +348,7 @@ object SparkBuild extends Build {
         "org.apache.mesos"           % "mesos"            % "0.18.1" classifier("shaded-protobuf") exclude("com.google.protobuf", "protobuf-java"),
         "commons-net"                % "commons-net"      % "2.2",
         "net.java.dev.jets3t"        % "jets3t"           % jets3tVersion excludeAll(excludeCommonsLogging),
+        "commons-codec"              % "commons-codec"    % "1.5", // Prevent jets3t from including the older version of commons-codec
         "org.apache.derby"           % "derby"            % "10.4.2.0"                     % "test",
         "org.apache.hadoop"          % hadoopClient       % hadoopVersion excludeAll(excludeJBossNetty, excludeAsm, excludeCommonsLogging, excludeSLF4J, excludeOldAsm),
         "org.apache.curator"         % "curator-recipes"  % "2.4.0" excludeAll(excludeJBossNetty),

From 8f7141fbc015addb314e1d5801085587b5cbb171 Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian.cs.zju@gmail.com>
Date: Thu, 29 May 2014 15:24:03 -0700
Subject: [PATCH 295/810] [SPARK-1368][SQL] Optimized HiveTableScan

JIRA issue: [SPARK-1368](https://issues.apache.org/jira/browse/SPARK-1368)

This PR introduces two major updates:

- Replaced FP style code with `while` loop and reusable `GenericMutableRow` object in critical path of `HiveTableScan`.
- Using `ColumnProjectionUtils` to help optimizing RCFile and ORC column pruning.

My quick micro benchmark suggests these two optimizations made the optimized version 2x and 2.5x faster when scanning CSV table and RCFile table respectively:

```
Original:

[info] CSV: 27676 ms, RCFile: 26415 ms
[info] CSV: 27703 ms, RCFile: 26029 ms
[info] CSV: 27511 ms, RCFile: 25962 ms

Optimized:

[info] CSV: 13820 ms, RCFile: 10402 ms
[info] CSV: 14158 ms, RCFile: 10691 ms
[info] CSV: 13606 ms, RCFile: 10346 ms
```

The micro benchmark loads a 609MB CVS file (structurally similar to the `src` test table) into a normal Hive table with `LazySimpleSerDe` and a RCFile table, then scans these tables respectively.

Preparation code:

```scala
package org.apache.spark.examples.sql.hive

import org.apache.spark.sql.hive.LocalHiveContext
import org.apache.spark.{SparkConf, SparkContext}

object HiveTableScanPrepare extends App {
  val sparkContext = new SparkContext(
    new SparkConf()
      .setMaster("local")
      .setAppName(getClass.getSimpleName.stripSuffix("$")))

  val hiveContext = new LocalHiveContext(sparkContext)

  import hiveContext._

  hql("drop table scan_csv")
  hql("drop table scan_rcfile")

  hql("""create table scan_csv (key int, value string)
        |  row format serde 'org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe'
        |  with serdeproperties ('field.delim'=',')
      """.stripMargin)

  hql(s"""load data local inpath "${args(0)}" into table scan_csv""")

  hql("""create table scan_rcfile (key int, value string)
        |  row format serde 'org.apache.hadoop.hive.serde2.columnar.ColumnarSerDe'
        |stored as
        |  inputformat 'org.apache.hadoop.hive.ql.io.RCFileInputFormat'
        |  outputformat 'org.apache.hadoop.hive.ql.io.RCFileOutputFormat'
      """.stripMargin)

  hql(
    """
      |from scan_csv
      |insert overwrite table scan_rcfile
      |select scan_csv.key, scan_csv.value
    """.stripMargin)
}
```

Benchmark code:

```scala
package org.apache.spark.examples.sql.hive

import org.apache.spark.sql.hive.LocalHiveContext
import org.apache.spark.{SparkConf, SparkContext}

object HiveTableScanBenchmark extends App {
  val sparkContext = new SparkContext(
    new SparkConf()
      .setMaster("local")
      .setAppName(getClass.getSimpleName.stripSuffix("$")))

  val hiveContext = new LocalHiveContext(sparkContext)

  import hiveContext._

  val scanCsv = hql("select key from scan_csv")
  val scanRcfile = hql("select key from scan_rcfile")

  val csvDuration = benchmark(scanCsv.count())
  val rcfileDuration = benchmark(scanRcfile.count())

  println(s"CSV: $csvDuration ms, RCFile: $rcfileDuration ms")

  def benchmark(f: => Unit) = {
    val begin = System.currentTimeMillis()
    f
    val end = System.currentTimeMillis()
    end - begin
  }
}
```

@marmbrus Please help review, thanks!

Author: Cheng Lian <lian.cs.zju@gmail.com>

Closes #758 from liancheng/fastHiveTableScan and squashes the following commits:

4241a19 [Cheng Lian] Distinguishes sorted and possibly not sorted operations more accurately in HiveComparisonTest
cf640d8 [Cheng Lian] More HiveTableScan optimisations:
bf0e7dc [Cheng Lian] Added SortedOperation pattern to match *some* definitely sorted operations and avoid some sorting cost in HiveComparisonTest.
6d1c642 [Cheng Lian] Using ColumnProjectionUtils to optimise RCFile and ORC column pruning
eb62fd3 [Cheng Lian] [SPARK-1368] Optimized HiveTableScan
---
 .../spark/sql/execution/Aggregate.scala       |  2 +-
 .../apache/spark/sql/hive/hiveOperators.scala | 97 ++++++++++++++++---
 .../hive/execution/HiveComparisonTest.scala   | 25 ++---
 3 files changed, 96 insertions(+), 28 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/Aggregate.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/Aggregate.scala
index 36b3b956da96..604914e54779 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/Aggregate.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/Aggregate.scala
@@ -116,7 +116,7 @@ case class Aggregate(
    */
   @transient
   private[this] lazy val resultMap =
-    (computedAggregates.map { agg => agg.unbound -> agg.resultAttribute} ++ namedGroups).toMap
+    (computedAggregates.map { agg => agg.unbound -> agg.resultAttribute } ++ namedGroups).toMap
 
   /**
    * Substituted version of aggregateExpressions expressions which are used to compute final
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveOperators.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveOperators.scala
index 96faebc5a868..f141139ef46a 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveOperators.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveOperators.scala
@@ -18,15 +18,18 @@
 package org.apache.spark.sql.hive.execution
 
 import org.apache.hadoop.hive.common.`type`.{HiveDecimal, HiveVarchar}
+import org.apache.hadoop.hive.conf.HiveConf
 import org.apache.hadoop.hive.metastore.MetaStoreUtils
 import org.apache.hadoop.hive.ql.Context
 import org.apache.hadoop.hive.ql.metadata.{Partition => HivePartition, Hive}
 import org.apache.hadoop.hive.ql.plan.{TableDesc, FileSinkDesc}
-import org.apache.hadoop.hive.serde2.Serializer
+import org.apache.hadoop.hive.serde.serdeConstants
 import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspectorUtils.ObjectInspectorCopyOption
 import org.apache.hadoop.hive.serde2.objectinspector._
 import org.apache.hadoop.hive.serde2.objectinspector.primitive.JavaHiveDecimalObjectInspector
 import org.apache.hadoop.hive.serde2.objectinspector.primitive.JavaHiveVarcharObjectInspector
+import org.apache.hadoop.hive.serde2.typeinfo.TypeInfoUtils
+import org.apache.hadoop.hive.serde2.{ColumnProjectionUtils, Serializer}
 import org.apache.hadoop.io.Writable
 import org.apache.hadoop.mapred._
 
@@ -37,6 +40,7 @@ import org.apache.spark.sql.catalyst.types.{BooleanType, DataType}
 import org.apache.spark.sql.execution._
 import org.apache.spark.sql.hive._
 import org.apache.spark.{TaskContext, SparkException}
+import org.apache.spark.util.MutablePair
 
 /* Implicits */
 import scala.collection.JavaConversions._
@@ -94,7 +98,7 @@ case class HiveTableScan(
         (_: Any, partitionKeys: Array[String]) => {
           val value = partitionKeys(ordinal)
           val dataType = relation.partitionKeys(ordinal).dataType
-          castFromString(value, dataType)
+          unwrapHiveData(castFromString(value, dataType))
         }
       } else {
         val ref = objectInspector.getAllStructFieldRefs
@@ -102,16 +106,55 @@ case class HiveTableScan(
           .getOrElse(sys.error(s"Can't find attribute $a"))
         (row: Any, _: Array[String]) => {
           val data = objectInspector.getStructFieldData(row, ref)
-          unwrapData(data, ref.getFieldObjectInspector)
+          unwrapHiveData(unwrapData(data, ref.getFieldObjectInspector))
         }
       }
     }
   }
 
+  private def unwrapHiveData(value: Any) = value match {
+    case maybeNull: String if maybeNull.toLowerCase == "null" => null
+    case varchar: HiveVarchar => varchar.getValue
+    case decimal: HiveDecimal => BigDecimal(decimal.bigDecimalValue)
+    case other => other
+  }
+
   private def castFromString(value: String, dataType: DataType) = {
     Cast(Literal(value), dataType).eval(null)
   }
 
+  private def addColumnMetadataToConf(hiveConf: HiveConf) {
+    // Specifies IDs and internal names of columns to be scanned.
+    val neededColumnIDs = attributes.map(a => relation.output.indexWhere(_.name == a.name): Integer)
+    val columnInternalNames = neededColumnIDs.map(HiveConf.getColumnInternalName(_)).mkString(",")
+
+    if (attributes.size == relation.output.size) {
+      ColumnProjectionUtils.setFullyReadColumns(hiveConf)
+    } else {
+      ColumnProjectionUtils.appendReadColumnIDs(hiveConf, neededColumnIDs)
+    }
+
+    ColumnProjectionUtils.appendReadColumnNames(hiveConf, attributes.map(_.name))
+
+    // Specifies types and object inspectors of columns to be scanned.
+    val structOI = ObjectInspectorUtils
+      .getStandardObjectInspector(
+        relation.tableDesc.getDeserializer.getObjectInspector,
+        ObjectInspectorCopyOption.JAVA)
+      .asInstanceOf[StructObjectInspector]
+
+    val columnTypeNames = structOI
+      .getAllStructFieldRefs
+      .map(_.getFieldObjectInspector)
+      .map(TypeInfoUtils.getTypeInfoFromObjectInspector(_).getTypeName)
+      .mkString(",")
+
+    hiveConf.set(serdeConstants.LIST_COLUMN_TYPES, columnTypeNames)
+    hiveConf.set(serdeConstants.LIST_COLUMNS, columnInternalNames)
+  }
+
+  addColumnMetadataToConf(sc.hiveconf)
+
   @transient
   def inputRdd = if (!relation.hiveQlTable.isPartitioned) {
     hadoopReader.makeRDDForTable(relation.hiveQlTable)
@@ -143,20 +186,42 @@ case class HiveTableScan(
   }
 
   def execute() = {
-    inputRdd.map { row =>
-      val values = row match {
-        case Array(deserializedRow: AnyRef, partitionKeys: Array[String]) =>
-          attributeFunctions.map(_(deserializedRow, partitionKeys))
-        case deserializedRow: AnyRef =>
-          attributeFunctions.map(_(deserializedRow, Array.empty))
+    inputRdd.mapPartitions { iterator =>
+      if (iterator.isEmpty) {
+        Iterator.empty
+      } else {
+        val mutableRow = new GenericMutableRow(attributes.length)
+        val mutablePair = new MutablePair[Any, Array[String]]()
+        val buffered = iterator.buffered
+
+        // NOTE (lian): Critical path of Hive table scan, unnecessary FP style code and pattern
+        // matching are avoided intentionally.
+        val rowsAndPartitionKeys = buffered.head match {
+          // With partition keys
+          case _: Array[Any] =>
+            buffered.map { case array: Array[Any] =>
+              val deserializedRow = array(0)
+              val partitionKeys = array(1).asInstanceOf[Array[String]]
+              mutablePair.update(deserializedRow, partitionKeys)
+            }
+
+          // Without partition keys
+          case _ =>
+            val emptyPartitionKeys = Array.empty[String]
+            buffered.map { deserializedRow =>
+              mutablePair.update(deserializedRow, emptyPartitionKeys)
+            }
+        }
+
+        rowsAndPartitionKeys.map { pair =>
+          var i = 0
+          while (i < attributes.length) {
+            mutableRow(i) = attributeFunctions(i)(pair._1, pair._2)
+            i += 1
+          }
+          mutableRow: Row
+        }
       }
-      buildRow(values.map {
-        case n: String if n.toLowerCase == "null" => null
-        case varchar: org.apache.hadoop.hive.common.`type`.HiveVarchar => varchar.getValue
-        case decimal: org.apache.hadoop.hive.common.`type`.HiveDecimal =>
-          BigDecimal(decimal.bigDecimalValue)
-        case other => other
-      })
     }
   }
 
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala
index edff38b90107..1b5a132f9665 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala
@@ -19,11 +19,12 @@ package org.apache.spark.sql.hive.execution
 
 import java.io._
 
+import org.scalatest.{BeforeAndAfterAll, FunSuite, GivenWhenThen}
+
 import org.apache.spark.sql.Logging
-import org.apache.spark.sql.catalyst.plans.logical.{ExplainCommand, NativeCommand}
+import org.apache.spark.sql.catalyst.planning.PhysicalOperation
+import org.apache.spark.sql.catalyst.plans.logical._
 import org.apache.spark.sql.catalyst.util._
-import org.apache.spark.sql.execution.Sort
-import org.scalatest.{BeforeAndAfterAll, FunSuite, GivenWhenThen}
 import org.apache.spark.sql.hive.test.TestHive
 
 /**
@@ -128,17 +129,19 @@ abstract class HiveComparisonTest
   protected def prepareAnswer(
     hiveQuery: TestHive.type#HiveQLQueryExecution,
     answer: Seq[String]): Seq[String] = {
+
+    def isSorted(plan: LogicalPlan): Boolean = plan match {
+      case _: Join | _: Aggregate | _: BaseRelation | _: Generate | _: Sample | _: Distinct => false
+      case PhysicalOperation(_, _, Sort(_, _)) => true
+      case _ => plan.children.iterator.map(isSorted).exists(_ == true)
+    }
+
     val orderedAnswer = hiveQuery.logical match {
       // Clean out non-deterministic time schema info.
       case _: NativeCommand => answer.filterNot(nonDeterministicLine).filterNot(_ == "")
       case _: ExplainCommand => answer
-      case _ =>
-        // TODO: Really we only care about the final total ordering here...
-        val isOrdered = hiveQuery.executedPlan.collect {
-          case s @ Sort(_, global, _) if global => s
-        }.nonEmpty
-        // If the query results aren't sorted, then sort them to ensure deterministic answers.
-        if (!isOrdered) answer.sorted else answer
+      case plan if isSorted(plan) => answer
+      case _ => answer.sorted
     }
     orderedAnswer.map(cleanPaths)
   }
@@ -161,7 +164,7 @@ abstract class HiveComparisonTest
     "minFileSize"
   )
   protected def nonDeterministicLine(line: String) =
-    nonDeterministicLineIndicators.map(line contains _).reduceLeft(_||_)
+    nonDeterministicLineIndicators.exists(line contains _)
 
   /**
    * Removes non-deterministic paths from `str` so cached answers will compare correctly.

From b7e28fa451511b3b0f849c3d2919ac9c2e4231a1 Mon Sep 17 00:00:00 2001
From: Ankur Dave <ankurdave@gmail.com>
Date: Thu, 29 May 2014 15:39:25 -0700
Subject: [PATCH 296/810] initial version of LPA

A straightforward implementation of LPA algorithm for detecting graph communities using the Pregel framework.  Amongst the growing literature on community detection algorithms in networks, LPA is perhaps the most elementary, and despite its flaws it remains a nice and simple approach.

Author: Ankur Dave <ankurdave@gmail.com>
Author: haroldsultan <haroldsultan@gmail.com>
Author: Harold Sultan <haroldsultan@gmail.com>

Closes #905 from haroldsultan/master and squashes the following commits:

327aee0 [haroldsultan] Merge pull request #2 from ankurdave/label-propagation
227a4d0 [Ankur Dave] Untabify
0ac574c [haroldsultan] Merge pull request #1 from ankurdave/label-propagation
0e24303 [Ankur Dave] Add LabelPropagationSuite
84aa061 [Ankur Dave] LabelPropagation: Fix compile errors and style; rename from LPA
9830342 [Harold Sultan] initial version of LPA
---
 .../spark/graphx/lib/LabelPropagation.scala   | 66 +++++++++++++++++++
 .../graphx/lib/LabelPropagationSuite.scala    | 45 +++++++++++++
 2 files changed, 111 insertions(+)
 create mode 100644 graphx/src/main/scala/org/apache/spark/graphx/lib/LabelPropagation.scala
 create mode 100644 graphx/src/test/scala/org/apache/spark/graphx/lib/LabelPropagationSuite.scala

diff --git a/graphx/src/main/scala/org/apache/spark/graphx/lib/LabelPropagation.scala b/graphx/src/main/scala/org/apache/spark/graphx/lib/LabelPropagation.scala
new file mode 100644
index 000000000000..776bfb8dd6bf
--- /dev/null
+++ b/graphx/src/main/scala/org/apache/spark/graphx/lib/LabelPropagation.scala
@@ -0,0 +1,66 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.graphx.lib
+
+import scala.reflect.ClassTag
+import org.apache.spark.graphx._
+
+/** Label Propagation algorithm. */
+object LabelPropagation {
+  /**
+   * Run static Label Propagation for detecting communities in networks.
+   *
+   * Each node in the network is initially assigned to its own community. At every superstep, nodes
+   * send their community affiliation to all neighbors and update their state to the mode community
+   * affiliation of incoming messages.
+   *
+   * LPA is a standard community detection algorithm for graphs. It is very inexpensive
+   * computationally, although (1) convergence is not guaranteed and (2) one can end up with
+   * trivial solutions (all nodes are identified into a single community).
+   *
+   * @tparam ED the edge attribute type (not used in the computation)
+   *
+   * @param graph the graph for which to compute the community affiliation
+   * @param maxSteps the number of supersteps of LPA to be performed. Because this is a static
+   * implementation, the algorithm will run for exactly this many supersteps.
+   *
+   * @return a graph with vertex attributes containing the label of community affiliation
+   */
+  def run[ED: ClassTag](graph: Graph[_, ED], maxSteps: Int): Graph[VertexId, ED] = {
+    val lpaGraph = graph.mapVertices { case (vid, _) => vid }
+    def sendMessage(e: EdgeTriplet[VertexId, ED]) = {
+      Iterator((e.srcId, Map(e.dstAttr -> 1L)), (e.dstId, Map(e.srcAttr -> 1L)))
+    }
+    def mergeMessage(count1: Map[VertexId, Long], count2: Map[VertexId, Long])
+      : Map[VertexId, Long] = {
+      (count1.keySet ++ count2.keySet).map { i =>
+        val count1Val = count1.getOrElse(i, 0L)
+        val count2Val = count2.getOrElse(i, 0L)
+        i -> (count1Val + count2Val)
+      }.toMap
+    }
+    def vertexProgram(vid: VertexId, attr: Long, message: Map[VertexId, Long]) = {
+      if (message.isEmpty) attr else message.maxBy(_._2)._1
+    }
+    val initialMessage = Map[VertexId, Long]()
+    Pregel(lpaGraph, initialMessage, maxIterations = maxSteps)(
+      vprog = vertexProgram,
+      sendMsg = sendMessage,
+      mergeMsg = mergeMessage)
+  }
+}
diff --git a/graphx/src/test/scala/org/apache/spark/graphx/lib/LabelPropagationSuite.scala b/graphx/src/test/scala/org/apache/spark/graphx/lib/LabelPropagationSuite.scala
new file mode 100644
index 000000000000..61fd0c460556
--- /dev/null
+++ b/graphx/src/test/scala/org/apache/spark/graphx/lib/LabelPropagationSuite.scala
@@ -0,0 +1,45 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.graphx.lib
+
+import org.scalatest.FunSuite
+
+import org.apache.spark.graphx._
+
+class LabelPropagationSuite extends FunSuite with LocalSparkContext {
+  test("Label Propagation") {
+    withSpark { sc =>
+      // Construct a graph with two cliques connected by a single edge
+      val n = 5
+      val clique1 = for (u <- 0L until n; v <- 0L until n) yield Edge(u, v, 1)
+      val clique2 = for (u <- 0L to n; v <- 0L to n) yield Edge(u + n, v + n, 1)
+      val twoCliques = sc.parallelize(clique1 ++ clique2 :+ Edge(0L, n, 1))
+      val graph = Graph.fromEdges(twoCliques, 1)
+      // Run label propagation
+      val labels = LabelPropagation.run(graph, n * 4).cache()
+
+      // All vertices within a clique should have the same label
+      val clique1Labels = labels.vertices.filter(_._1 < n).map(_._2).collect.toArray
+      assert(clique1Labels.forall(_ == clique1Labels(0)))
+      val clique2Labels = labels.vertices.filter(_._1 >= n).map(_._2).collect.toArray
+      assert(clique2Labels.forall(_ == clique2Labels(0)))
+      // The two cliques should have different labels
+      assert(clique1Labels(0) != clique2Labels(0))
+    }
+  }
+}

From eeee978a348ec2a35cc27865cea6357f9db75b74 Mon Sep 17 00:00:00 2001
From: Prashant Sharma <prashant.s@imaginea.com>
Date: Thu, 29 May 2014 23:20:20 -0700
Subject: [PATCH 297/810] [SPARK-1820] Make GenerateMimaIgnore @DeveloperApi
 annotation aware.

We add all the classes annotated as `DeveloperApi` to `~/.mima-excludes`.

Author: Prashant Sharma <prashant.s@imaginea.com>
Author: nikhil7sh <nikhilsharmalnmiit@gmail.ccom>

Closes #904 from ScrapCodes/SPARK-1820/ignore-Developer-Api and squashes the following commits:

de944f9 [Prashant Sharma] Code review.
e3c5215 [Prashant Sharma] Incorporated patrick's suggestions and fixed the scalastyle build.
9983a42 [nikhil7sh] [SPARK-1820] Make GenerateMimaIgnore @DeveloperApi annotation aware
---
 .../spark/tools/GenerateMIMAIgnore.scala      | 22 ++++++++++++++++---
 1 file changed, 19 insertions(+), 3 deletions(-)

diff --git a/tools/src/main/scala/org/apache/spark/tools/GenerateMIMAIgnore.scala b/tools/src/main/scala/org/apache/spark/tools/GenerateMIMAIgnore.scala
index a433e8e2e89f..011db50b7d56 100644
--- a/tools/src/main/scala/org/apache/spark/tools/GenerateMIMAIgnore.scala
+++ b/tools/src/main/scala/org/apache/spark/tools/GenerateMIMAIgnore.scala
@@ -23,6 +23,7 @@ import java.util.jar.JarFile
 import scala.collection.mutable
 import scala.collection.JavaConversions._
 import scala.reflect.runtime.universe.runtimeMirror
+import scala.reflect.runtime.{universe => unv}
 
 /**
  * A tool for generating classes to be excluded during binary checking with MIMA. It is expected
@@ -42,7 +43,7 @@ object GenerateMIMAIgnore {
   private def classesPrivateWithin(packageName: String): Set[String] = {
 
     val classes = getClasses(packageName)
-    val privateClasses = mutable.HashSet[String]()
+    val ignoredClasses = mutable.HashSet[String]()
 
     def isPackagePrivate(className: String) = {
       try {
@@ -70,8 +71,21 @@ object GenerateMIMAIgnore {
       }
     }
 
+    def isDeveloperApi(className: String) = {
+      try {
+        val clazz = mirror.classSymbol(Class.forName(className, false, classLoader))
+        clazz.annotations.exists(_.tpe =:= unv.typeOf[org.apache.spark.annotation.DeveloperApi])
+      } catch {
+        case _: Throwable => {
+          println("Error determining Annotations: " + className)
+          false
+        }
+      }
+    }
+
     for (className <- classes) {
       val directlyPrivateSpark = isPackagePrivate(className)
+      val developerApi = isDeveloperApi(className)
 
       /* Inner classes defined within a private[spark] class or object are effectively
          invisible, so we account for them as package private. */
@@ -83,9 +97,11 @@ object GenerateMIMAIgnore {
           false
         }
       }
-      if (directlyPrivateSpark || indirectlyPrivateSpark) privateClasses += className
+      if (directlyPrivateSpark || indirectlyPrivateSpark || developerApi) {
+        ignoredClasses += className
+      }
     }
-    privateClasses.flatMap(c => Seq(c, c.replace("$", "#"))).toSet
+    ignoredClasses.flatMap(c => Seq(c, c.replace("$", "#"))).toSet
   }
 
   def main(args: Array[String]) {

From c8bf4131bc2a2e147e977159fc90e94b85738830 Mon Sep 17 00:00:00 2001
From: Matei Zaharia <matei@databricks.com>
Date: Fri, 30 May 2014 00:34:33 -0700
Subject: [PATCH 298/810] [SPARK-1566] consolidate programming guide, and
 general doc updates

This is a fairly large PR to clean up and update the docs for 1.0. The major changes are:

* A unified programming guide for all languages replaces language-specific ones and shows language-specific info in tabs
* New programming guide sections on key-value pairs, unit testing, input formats beyond text, migrating from 0.9, and passing functions to Spark
* Spark-submit guide moved to a separate page and expanded slightly
* Various cleanups of the menu system, security docs, and others
* Updated look of title bar to differentiate the docs from previous Spark versions

You can find the updated docs at http://people.apache.org/~matei/1.0-docs/_site/ and in particular http://people.apache.org/~matei/1.0-docs/_site/programming-guide.html.

Author: Matei Zaharia <matei@databricks.com>

Closes #896 from mateiz/1.0-docs and squashes the following commits:

03e6853 [Matei Zaharia] Some tweaks to configuration and YARN docs
0779508 [Matei Zaharia] tweak
ef671d4 [Matei Zaharia] Keep frames in JavaDoc links, and other small tweaks
1bf4112 [Matei Zaharia] Review comments
4414f88 [Matei Zaharia] tweaks
d04e979 [Matei Zaharia] Fix some old links to Java guide
a34ed33 [Matei Zaharia] tweak
541bb3b [Matei Zaharia] miscellaneous changes
fcefdec [Matei Zaharia] Moved submitting apps to separate doc
61d72b4 [Matei Zaharia] stuff
181f217 [Matei Zaharia] migration guide, remove old language guides
e11a0da [Matei Zaharia] Add more API functions
6a030a9 [Matei Zaharia] tweaks
8db0ae3 [Matei Zaharia] Added key-value pairs section
318d2c9 [Matei Zaharia] tweaks
1c81477 [Matei Zaharia] New section on basics and function syntax
e38f559 [Matei Zaharia] Actually added programming guide to Git
a33d6fe [Matei Zaharia] First pass at updating programming guide to support all languages, plus other tweaks throughout
3b6a876 [Matei Zaharia] More CSS tweaks
01ec8bf [Matei Zaharia] More CSS tweaks
e6d252e [Matei Zaharia] Change color of doc title bar to differentiate from 0.9.0
---
 docs/_layouts/global.html                |   18 +-
 docs/bagel-programming-guide.md          |    2 +-
 docs/building-with-maven.md              |   90 +-
 docs/cluster-overview.md                 |  108 +-
 docs/configuration.md                    |   11 +-
 docs/css/bootstrap.min.css               |    2 +-
 docs/graphx-programming-guide.md         |    8 +-
 docs/hadoop-third-party-distributions.md |    2 +-
 docs/index.md                            |   79 +-
 docs/java-programming-guide.md           |  215 +---
 docs/js/api-docs.js                      |   23 +-
 docs/js/main.js                          |   21 +
 docs/mllib-guide.md                      |   10 +-
 docs/mllib-optimization.md               |    2 +-
 docs/monitoring.md                       |    2 +-
 docs/programming-guide.md                | 1294 ++++++++++++++++++++++
 docs/python-programming-guide.md         |  168 +--
 docs/quick-start.md                      |   39 +-
 docs/running-on-mesos.md                 |    7 +-
 docs/running-on-yarn.md                  |   91 +-
 docs/scala-programming-guide.md          |  445 +-------
 docs/security.md                         |   18 +-
 docs/spark-standalone.md                 |    4 +-
 docs/sql-programming-guide.md            |   29 +-
 docs/streaming-programming-guide.md      |   42 +-
 docs/submitting-applications.md          |  153 +++
 docs/tuning.md                           |    6 +-
 27 files changed, 1767 insertions(+), 1122 deletions(-)
 create mode 100644 docs/programming-guide.md
 create mode 100644 docs/submitting-applications.md

diff --git a/docs/_layouts/global.html b/docs/_layouts/global.html
index fb808129bb65..4ba20e590f2c 100755
--- a/docs/_layouts/global.html
+++ b/docs/_layouts/global.html
@@ -9,6 +9,11 @@
         <title>{{ page.title }} - Spark {{site.SPARK_VERSION_SHORT}} Documentation</title>
         <meta name="description" content="">
 
+        {% if page.redirect %}
+          <meta http-equiv="refresh" content="0; url={{page.redirect}}">
+          <link rel="canonical" href="{{page.redirect}}" />
+        {% endif %}
+
         <link rel="stylesheet" href="css/bootstrap.min.css">
         <style>
             body {
@@ -61,15 +66,13 @@
                             <a href="#" class="dropdown-toggle" data-toggle="dropdown">Programming Guides<b class="caret"></b></a>
                             <ul class="dropdown-menu">
                                 <li><a href="quick-start.html">Quick Start</a></li>
-                                <li><a href="scala-programming-guide.html">Spark in Scala</a></li>
-                                <li><a href="java-programming-guide.html">Spark in Java</a></li>
-                                <li><a href="python-programming-guide.html">Spark in Python</a></li>
+                                <li><a href="programming-guide.html">Spark Programming Guide</a></li>
                                 <li class="divider"></li>
                                 <li><a href="streaming-programming-guide.html">Spark Streaming</a></li>
                                 <li><a href="sql-programming-guide.html">Spark SQL</a></li>
                                 <li><a href="mllib-guide.html">MLlib (Machine Learning)</a></li>
-                                <li><a href="bagel-programming-guide.html">Bagel (Pregel on Spark)</a></li>
                                 <li><a href="graphx-programming-guide.html">GraphX (Graph Processing)</a></li>
+                                <li><a href="bagel-programming-guide.html">Bagel (Pregel on Spark)</a></li>
                             </ul>
                         </li>
 
@@ -86,6 +89,8 @@
                             <a href="#" class="dropdown-toggle" data-toggle="dropdown">Deploying<b class="caret"></b></a>
                             <ul class="dropdown-menu">
                                 <li><a href="cluster-overview.html">Overview</a></li>
+                                <li><a href="submitting-applications.html">Submitting Applications</a></li>
+                                <li class="divider"></li>
                                 <li><a href="ec2-scripts.html">Amazon EC2</a></li>
                                 <li><a href="spark-standalone.html">Standalone Mode</a></li>
                                 <li><a href="running-on-mesos.html">Mesos</a></li>
@@ -99,9 +104,10 @@
                                 <li><a href="configuration.html">Configuration</a></li>
                                 <li><a href="monitoring.html">Monitoring</a></li>
                                 <li><a href="tuning.html">Tuning Guide</a></li>
-                                <li><a href="hadoop-third-party-distributions.html">Running with CDH/HDP</a></li>
-                                <li><a href="hardware-provisioning.html">Hardware Provisioning</a></li>
                                 <li><a href="job-scheduling.html">Job Scheduling</a></li>
+                                <li><a href="security.html">Security</a></li>
+                                <li><a href="hardware-provisioning.html">Hardware Provisioning</a></li>
+                                <li><a href="hadoop-third-party-distributions.html">3<sup>rd</sup>-Party Hadoop Distros</a></li>
                                 <li class="divider"></li>
                                 <li><a href="building-with-maven.html">Building Spark with Maven</a></li>
                                 <li><a href="https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark">Contributing to Spark</a></li>
diff --git a/docs/bagel-programming-guide.md b/docs/bagel-programming-guide.md
index 14f43cb6d394..b280df0c8eeb 100644
--- a/docs/bagel-programming-guide.md
+++ b/docs/bagel-programming-guide.md
@@ -21,7 +21,7 @@ To use Bagel in your program, add the following SBT or Maven dependency:
 
 # Programming Model
 
-Bagel operates on a graph represented as a [distributed dataset](scala-programming-guide.html) of (K, V) pairs, where keys are vertex IDs and values are vertices plus their associated state. In each superstep, Bagel runs a user-specified compute function on each vertex that takes as input the current vertex state and a list of messages sent to that vertex during the previous superstep, and returns the new vertex state and a list of outgoing messages.
+Bagel operates on a graph represented as a [distributed dataset](programming-guide.html) of (K, V) pairs, where keys are vertex IDs and values are vertices plus their associated state. In each superstep, Bagel runs a user-specified compute function on each vertex that takes as input the current vertex state and a list of messages sent to that vertex during the previous superstep, and returns the new vertex state and a list of outgoing messages.
 
 For example, we can use Bagel to implement PageRank. Here, vertices represent pages, edges represent links between pages, and messages represent shares of PageRank sent to the pages that a particular page links to.
 
diff --git a/docs/building-with-maven.md b/docs/building-with-maven.md
index 8b44535d8240..55a9e37dfed8 100644
--- a/docs/building-with-maven.md
+++ b/docs/building-with-maven.md
@@ -6,14 +6,16 @@ title: Building Spark with Maven
 * This will become a table of contents (this text will be scraped).
 {:toc}
 
-Building Spark using Maven requires Maven 3.0.4 or newer and Java 1.6 or newer.
+Building Spark using Maven requires Maven 3.0.4 or newer and Java 6+.
 
 
-## Setting up Maven's Memory Usage ##
+# Setting up Maven's Memory Usage
 
 You'll need to configure Maven to use more memory than usual by setting `MAVEN_OPTS`. We recommend the following settings:
 
-    export MAVEN_OPTS="-Xmx2g -XX:MaxPermSize=512M -XX:ReservedCodeCacheSize=512m"
+{% highlight bash %}
+export MAVEN_OPTS="-Xmx2g -XX:MaxPermSize=512M -XX:ReservedCodeCacheSize=512m"
+{% endhighlight %}
 
 If you don't run this, you may see errors like the following:
 
@@ -25,9 +27,9 @@ If you don't run this, you may see errors like the following:
 
 You can fix this by setting the `MAVEN_OPTS` variable as discussed before.
 
-*Note: For Java 1.8 and above this step is not required.*
+**Note:** *For Java 8 and above this step is not required.*
 
-## Specifying the Hadoop version ##
+# Specifying the Hadoop Version
 
 Because HDFS is not protocol-compatible across versions, if you want to read from HDFS, you'll need to build Spark against the specific HDFS version in your environment. You can do this through the "hadoop.version" property. If unset, Spark will build against Hadoop 1.0.4 by default. Note that certain build profiles are required for particular Hadoop versions:
 
@@ -46,14 +48,16 @@ Because HDFS is not protocol-compatible across versions, if you want to read fro
 
 For Apache Hadoop versions 1.x, Cloudera CDH "mr1" distributions, and other Hadoop versions without YARN, use:
 
-    # Apache Hadoop 1.2.1
-    $ mvn -Dhadoop.version=1.2.1 -DskipTests clean package
+{% highlight bash %}
+# Apache Hadoop 1.2.1
+mvn -Dhadoop.version=1.2.1 -DskipTests clean package
 
-    # Cloudera CDH 4.2.0 with MapReduce v1
-    $ mvn -Dhadoop.version=2.0.0-mr1-cdh4.2.0 -DskipTests clean package
+# Cloudera CDH 4.2.0 with MapReduce v1
+mvn -Dhadoop.version=2.0.0-mr1-cdh4.2.0 -DskipTests clean package
 
-    # Apache Hadoop 0.23.x
-    $ mvn -Phadoop-0.23 -Dhadoop.version=0.23.7 -DskipTests clean package
+# Apache Hadoop 0.23.x
+mvn -Phadoop-0.23 -Dhadoop.version=0.23.7 -DskipTests clean package
+{% endhighlight %}
 
 For Apache Hadoop 2.x, 0.23.x, Cloudera CDH, and other Hadoop versions with YARN, you can enable the "yarn-alpha" or "yarn" profile and optionally set the "yarn.version" property if it is different from "hadoop.version". The additional build profile required depends on the YARN version:
 
@@ -69,75 +73,77 @@ For Apache Hadoop 2.x, 0.23.x, Cloudera CDH, and other Hadoop versions with YARN
 
 Examples:
 
-    # Apache Hadoop 2.0.5-alpha
-    $ mvn -Pyarn-alpha -Dhadoop.version=2.0.5-alpha -DskipTests clean package
+{% highlight bash %}
+# Apache Hadoop 2.0.5-alpha
+mvn -Pyarn-alpha -Dhadoop.version=2.0.5-alpha -DskipTests clean package
 
-    # Cloudera CDH 4.2.0
-    $ mvn -Pyarn-alpha -Dhadoop.version=2.0.0-cdh4.2.0 -DskipTests clean package
+# Cloudera CDH 4.2.0
+mvn -Pyarn-alpha -Dhadoop.version=2.0.0-cdh4.2.0 -DskipTests clean package
 
-    # Apache Hadoop 0.23.x
-    $ mvn -Pyarn-alpha -Phadoop-0.23 -Dhadoop.version=0.23.7 -DskipTests clean package
+# Apache Hadoop 0.23.x
+mvn -Pyarn-alpha -Phadoop-0.23 -Dhadoop.version=0.23.7 -DskipTests clean package
 
-    # Apache Hadoop 2.2.X
-    $ mvn -Pyarn -Phadoop-2.2 -Dhadoop.version=2.2.0 -DskipTests clean package
+# Apache Hadoop 2.2.X
+mvn -Pyarn -Phadoop-2.2 -Dhadoop.version=2.2.0 -DskipTests clean package
 
-    # Apache Hadoop 2.3.X
-    $ mvn -Pyarn -Phadoop-2.3 -Dhadoop.version=2.3.0 -DskipTests clean package
+# Apache Hadoop 2.3.X
+mvn -Pyarn -Phadoop-2.3 -Dhadoop.version=2.3.0 -DskipTests clean package
 
-    # Apache Hadoop 2.4.X
-    $ mvn -Pyarn -Phadoop-2.4 -Dhadoop.version=2.4.0 -DskipTests clean package
+# Apache Hadoop 2.4.X
+mvn -Pyarn -Phadoop-2.4 -Dhadoop.version=2.4.0 -DskipTests clean package
 
-    # Different versions of HDFS and YARN.
-    $ mvn -Pyarn-alpha -Phadoop-2.3 -Dhadoop.version=2.3.0 -Dyarn.version=0.23.7 -DskipTests clean package
+# Different versions of HDFS and YARN.
+mvn -Pyarn-alpha -Phadoop-2.3 -Dhadoop.version=2.3.0 -Dyarn.version=0.23.7 -DskipTests clean package
+{% endhighlight %}
 
-## Spark Tests in Maven ##
+# Spark Tests in Maven
 
 Tests are run by default via the [ScalaTest Maven plugin](http://www.scalatest.org/user_guide/using_the_scalatest_maven_plugin). Some of the require Spark to be packaged first, so always run `mvn package` with `-DskipTests` the first time. You can then run the tests with `mvn -Dhadoop.version=... test`.
 
 The ScalaTest plugin also supports running only a specific test suite as follows:
 
-    $ mvn -Dhadoop.version=... -DwildcardSuites=org.apache.spark.repl.ReplSuite test
+    mvn -Dhadoop.version=... -DwildcardSuites=org.apache.spark.repl.ReplSuite test
 
 
-## Continuous Compilation ##
+# Continuous Compilation
 
 We use the scala-maven-plugin which supports incremental and continuous compilation. E.g.
 
-    $ mvn scala:cc
+    mvn scala:cc
 
 should run continuous compilation (i.e. wait for changes). However, this has not been tested extensively.
 
-## Using With IntelliJ IDEA ##
+# Using With IntelliJ IDEA
 
 This setup works fine in IntelliJ IDEA 11.1.4. After opening the project via the pom.xml file in the project root folder, you only need to activate either the hadoop1 or hadoop2 profile in the "Maven Properties" popout. We have not tried Eclipse/Scala IDE with this.
 
-## Building Spark Debian Packages ##
+# Building Spark Debian Packages
 
-The maven build includes support for building a Debian package containing the assembly 'fat-jar', PySpark, and the necessary scripts and configuration files. This can be created by specifying the following:
+The Maven build includes support for building a Debian package containing the assembly 'fat-jar', PySpark, and the necessary scripts and configuration files. This can be created by specifying the following:
 
-    $ mvn -Pdeb -DskipTests clean package
+    mvn -Pdeb -DskipTests clean package
 
 The debian package can then be found under assembly/target. We added the short commit hash to the file name so that we can distinguish individual packages built for SNAPSHOT versions.
 
-## Running java 8 test suites.
+# Running Java 8 Test Suites
 
-Running only java 8 tests and nothing else.
+Running only Java 8 tests and nothing else.
 
-    $ mvn install -DskipTests -Pjava8-tests
+    mvn install -DskipTests -Pjava8-tests
     
-Java 8 tests are run when -Pjava8-tests profile is enabled, they will run in spite of -DskipTests. 
+Java 8 tests are run when `-Pjava8-tests` profile is enabled, they will run in spite of `-DskipTests`. 
 For these tests to run your system must have a JDK 8 installation. 
 If you have JDK 8 installed but it is not the system default, you can set JAVA_HOME to point to JDK 8 before running the tests.
 
-## Building for PySpark on YARN ##
+# Building for PySpark on YARN
 
-PySpark on YARN is only supported if the jar is built with maven. Further, there is a known problem
-with building this assembly jar on Red Hat based operating systems (see SPARK-1753). If you wish to
+PySpark on YARN is only supported if the jar is built with Maven. Further, there is a known problem
+with building this assembly jar on Red Hat based operating systems (see [SPARK-1753](https://issues.apache.org/jira/browse/SPARK-1753)). If you wish to
 run PySpark on a YARN cluster with Red Hat installed, we recommend that you build the jar elsewhere,
 then ship it over to the cluster. We are investigating the exact cause for this.
 
-## Packaging without Hadoop dependencies for deployment on YARN ##
+# Packaging without Hadoop Dependencies for YARN
 
-The assembly jar produced by "mvn package" will, by default, include all of Spark's dependencies, including Hadoop and some of its ecosystem projects. On YARN deployments, this causes multiple versions of these to appear on executor classpaths: the version packaged in the Spark assembly and the version on each node, included with yarn.application.classpath.  The "hadoop-provided" profile builds the assembly without including Hadoop-ecosystem projects, like ZooKeeper and Hadoop itself. 
+The assembly jar produced by `mvn package` will, by default, include all of Spark's dependencies, including Hadoop and some of its ecosystem projects. On YARN deployments, this causes multiple versions of these to appear on executor classpaths: the version packaged in the Spark assembly and the version on each node, included with yarn.application.classpath.  The `hadoop-provided` profile builds the assembly without including Hadoop-ecosystem projects, like ZooKeeper and Hadoop itself. 
 
 
diff --git a/docs/cluster-overview.md b/docs/cluster-overview.md
index f05a755de7fe..6a75d5c457f0 100644
--- a/docs/cluster-overview.md
+++ b/docs/cluster-overview.md
@@ -4,7 +4,8 @@ title: Cluster Mode Overview
 ---
 
 This document gives a short overview of how Spark runs on clusters, to make it easier to understand
-the components involved.
+the components involved. Read through the [application submission guide](submitting-applications.html)
+to submit applications to a cluster.
 
 # Components
 
@@ -50,107 +51,10 @@ The system currently supports three cluster managers:
 In addition, Spark's [EC2 launch scripts](ec2-scripts.html) make it easy to launch a standalone
 cluster on Amazon EC2.
 
-# Bundling and Launching Applications
-
-### Bundling Your Application's Dependencies
-If your code depends on other projects, you will need to package them alongside
-your application in order to distribute the code to a Spark cluster. To do this,
-to create an assembly jar (or "uber" jar) containing your code and its dependencies. Both
-[sbt](https://github.com/sbt/sbt-assembly) and
-[Maven](http://maven.apache.org/plugins/maven-shade-plugin/)
-have assembly plugins. When creating assembly jars, list Spark and Hadoop
-as `provided` dependencies; these need not be bundled since they are provided by
-the cluster manager at runtime. Once you have an assembled jar you can call the `bin/spark-submit`
-script as shown here while passing your jar.
-
-For Python, you can use the `pyFiles` argument of SparkContext
-or its `addPyFile` method to add `.py`, `.zip` or `.egg` files to be distributed.
-
-### Launching Applications with Spark submit
-
-Once a user application is bundled, it can be launched using the `spark-submit` script located in
-the bin directory. This script takes care of setting up the classpath with Spark and its
-dependencies, and can support different cluster managers and deploy modes that Spark supports:
-
-    ./bin/spark-submit \
-      --class <main-class>
-      --master <master-url> \
-      --deploy-mode <deploy-mode> \
-      ... // other options
-      <application-jar>
-      [application-arguments]
-
-    main-class: The entry point for your application (e.g. org.apache.spark.examples.SparkPi)
-    master-url: The URL of the master node (e.g. spark://23.195.26.187:7077)
-    deploy-mode: Whether to deploy this application within the cluster or from an external client (e.g. client)
-    application-jar: Path to a bundled jar including your application and all dependencies. The URL must be globally visible inside of your cluster, for instance, an `hdfs://` path or a `file://` path that is present on all nodes.
-    application-arguments: Space delimited arguments passed to the main method of <main-class>, if any
-
-To enumerate all options available to `spark-submit` run it with the `--help` flag. Here are a few
-examples of common options:
-
-{% highlight bash %}
-# Run application locally
-./bin/spark-submit \
-  --class org.apache.spark.examples.SparkPi
-  --master local[8] \
-  /path/to/examples.jar \
-  100
-
-# Run on a Spark standalone cluster
-./bin/spark-submit \
-  --class org.apache.spark.examples.SparkPi
-  --master spark://207.184.161.138:7077 \
-  --executor-memory 20G \
-  --total-executor-cores 100 \
-  /path/to/examples.jar \
-  1000
-
-# Run on a YARN cluster
-HADOOP_CONF_DIR=XX ./bin/spark-submit \
-  --class org.apache.spark.examples.SparkPi
-  --master yarn-cluster \  # can also be `yarn-client` for client mode
-  --executor-memory 20G \
-  --num-executors 50 \
-  /path/to/examples.jar \
-  1000
-{% endhighlight %}
-
-### Loading Configurations from a File
-
-The `spark-submit` script can load default [Spark configuration values](configuration.html) from a
-properties file and pass them on to your application. By default it will read configuration options
-from `conf/spark-defaults.conf`. For more detail, see the section on
-[loading default configurations](configuration.html#loading-default-configurations).
-
-Loading default Spark configurations this way can obviate the need for certain flags to
-`spark-submit`. For instance, if the `spark.master` property is set, you can safely omit the
-`--master` flag from `spark-submit`. In general, configuration values explicitly set on a
-`SparkConf` take the highest precedence, then flags passed to `spark-submit`, then values in the
-defaults file.
-
-If you are ever unclear where configuration options are coming from, you can print out fine-grained
-debugging information by running `spark-submit` with the `--verbose` option.
-
-### Advanced Dependency Management
-When using `spark-submit`, the application jar along with any jars included with the `--jars` option
-will be automatically transferred to the cluster. Spark uses the following URL scheme to allow
-different strategies for disseminating jars:
-
-- **file:** - Absolute paths and `file:/` URIs are served by the driver's HTTP file server, and
-  every executor pulls the file from the driver HTTP server.
-- **hdfs:**, **http:**, **https:**, **ftp:** - these pull down files and JARs from the URI as expected
-- **local:** - a URI starting with local:/ is expected to exist as a local file on each worker node.  This
-  means that no network IO will be incurred, and works well for large files/JARs that are pushed to each worker,
-  or shared via NFS, GlusterFS, etc.
-
-Note that JARs and files are copied to the working directory for each SparkContext on the executor nodes.
-This can use up a significant amount of space over time and will need to be cleaned up. With YARN, cleanup
-is handled automatically, and with Spark standalone, automatic cleanup can be configured with the
-`spark.worker.cleanup.appDataTtl` property.
-
-For python, the equivalent `--py-files` option can be used to distribute .egg and .zip libraries
-to executors.
+# Submitting Applications
+
+Applications can be submitted to a cluster of any type using the `spark-submit` script.
+The [application submission guide](submitting-applications.html) describes how to do this.
 
 # Monitoring
 
diff --git a/docs/configuration.md b/docs/configuration.md
index b6e7fd34eae6..2fd691800ea0 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -7,8 +7,8 @@ title: Spark Configuration
 
 Spark provides three locations to configure the system:
 
-* [Spark properties](#spark-properties) control most application parameters and can be set by passing
-  a [SparkConf](api/core/index.html#org.apache.spark.SparkConf) object to SparkContext, or through Java
+* [Spark properties](#spark-properties) control most application parameters and can be set by using
+  a [SparkConf](api/core/index.html#org.apache.spark.SparkConf) object, or through Java
   system properties.
 * [Environment variables](#environment-variables) can be used to set per-machine settings, such as
   the IP address, through the `conf/spark-env.sh` script on each node.
@@ -18,8 +18,8 @@ Spark provides three locations to configure the system:
 
 Spark properties control most application settings and are configured separately for each
 application. These properties can be set directly on a
-[SparkConf](api/scala/index.html#org.apache.spark.SparkConf) and passed as an argument to your
-SparkContext. SparkConf allows you to configure some of the common properties
+[SparkConf](api/scala/index.html#org.apache.spark.SparkConf) passed to your
+`SparkContext`. `SparkConf` allows you to configure some of the common properties
 (e.g. master URL and application name), as well as arbitrary key-value pairs through the
 `set()` method. For example, we could initialize an application as follows:
 
@@ -75,6 +75,7 @@ appear. For all other configuration properties, you can assume the default value
 Most of the properties that control internal settings have reasonable default values. Some
 of the most common options to set are:
 
+#### Application Properties
 <table class="table">
 <tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr>
 <tr>
@@ -777,7 +778,7 @@ Apart from these, the following properties are also available, and may be useful
 </tr>
 </table>
 
-#### Cluster Managers (YARN, Mesos, Standalone)
+#### Cluster Managers
 Each cluster manager in Spark has additional configuration options. Configurations 
 can be found on the pages for each mode:
 
diff --git a/docs/css/bootstrap.min.css b/docs/css/bootstrap.min.css
index 30ea440c008e..3fa12ac2b208 100644
--- a/docs/css/bootstrap.min.css
+++ b/docs/css/bootstrap.min.css
@@ -6,4 +6,4 @@
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  *
  * Designed and built with all the love in the world @twitter by @mdo and @fat.
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diff --git a/docs/graphx-programming-guide.md b/docs/graphx-programming-guide.md
index 42ab27bf55cc..fdb9f98e214e 100644
--- a/docs/graphx-programming-guide.md
+++ b/docs/graphx-programming-guide.md
@@ -10,7 +10,7 @@ title: GraphX Programming Guide
   <img src="img/graphx_logo.png"
        title="GraphX Logo"
        alt="GraphX"
-       width="65%" />
+       width="60%" />
   <!-- Images are downsized intentionally to improve quality on retina displays -->
 </p>
 
@@ -25,6 +25,8 @@ operators (e.g., [subgraph](#structural_operators), [joinVertices](#join_operato
 addition, GraphX includes a growing collection of graph [algorithms](#graph_algorithms) and
 [builders](#graph_builders) to simplify graph analytics tasks.
 
+**GraphX is currently an alpha component. While we will minimize API changes, some APIs may change in future releases.**
+
 ## Background on Graph-Parallel Computation
 
 From social networks to language modeling, the growing scale and importance of
@@ -86,7 +88,7 @@ support the [Bagel API](api/scala/index.html#org.apache.spark.bagel.package) and
 [Bagel programming guide](bagel-programming-guide.html). However, we encourage Bagel users to
 explore the new GraphX API and comment on issues that may complicate the transition from Bagel.
 
-## Upgrade Guide from Spark 0.9.1
+## Migrating from Spark 0.9.1
 
 GraphX in Spark {{site.SPARK_VERSION}} contains one user-facing interface change from Spark 0.9.1. [`EdgeRDD`][EdgeRDD] may now store adjacent vertex attributes to construct the triplets, so it has gained a type parameter. The edges of a graph of type `Graph[VD, ED]` are of type `EdgeRDD[ED, VD]` rather than `EdgeRDD[ED]`.
 
@@ -690,7 +692,7 @@ class GraphOps[VD, ED] {
 
 In Spark, RDDs are not persisted in memory by default. To avoid recomputation, they must be explicitly cached when using them multiple times (see the [Spark Programming Guide][RDD Persistence]). Graphs in GraphX behave the same way. **When using a graph multiple times, make sure to call [`Graph.cache()`][Graph.cache] on it first.**
 
-[RDD Persistence]: scala-programming-guide.html#rdd-persistence
+[RDD Persistence]: programming-guide.html#rdd-persistence
 [Graph.cache]: api/scala/index.html#org.apache.spark.graphx.Graph@cache():Graph[VD,ED]
 
 In iterative computations, *uncaching* may also be necessary for best performance. By default, cached RDDs and graphs will remain in memory until memory pressure forces them to be evicted in LRU order. For iterative computation, intermediate results from previous iterations will fill up the cache. Though they will eventually be evicted, the unnecessary data stored in memory will slow down garbage collection. It would be more efficient to uncache intermediate results as soon as they are no longer necessary. This involves materializing (caching and forcing) a graph or RDD every iteration, uncaching all other datasets, and only using the materialized dataset in future iterations. However, because graphs are composed of multiple RDDs, it can be difficult to unpersist them correctly. **For iterative computation we recommend using the Pregel API, which correctly unpersists intermediate results.**
diff --git a/docs/hadoop-third-party-distributions.md b/docs/hadoop-third-party-distributions.md
index a0aeab5727bd..32403bc6957a 100644
--- a/docs/hadoop-third-party-distributions.md
+++ b/docs/hadoop-third-party-distributions.md
@@ -1,6 +1,6 @@
 ---
 layout: global
-title: Running with Cloudera and HortonWorks
+title: Third-Party Hadoop Distributions
 ---
 
 Spark can run against all versions of Cloudera's Distribution Including Apache Hadoop (CDH) and
diff --git a/docs/index.md b/docs/index.md
index c9b10376cc80..1a4ff3dbf57b 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -4,23 +4,23 @@ title: Spark Overview
 ---
 
 Apache Spark is a fast and general-purpose cluster computing system.
-It provides high-level APIs in [Scala](scala-programming-guide.html), [Java](java-programming-guide.html), and [Python](python-programming-guide.html) that make parallel jobs easy to write, and an optimized engine that supports general computation graphs.
-It also supports a rich set of higher-level tools including [Shark](http://shark.cs.berkeley.edu) (Hive on Spark), [MLlib](mllib-guide.html) for machine learning, [GraphX](graphx-programming-guide.html) for graph processing, and [Spark Streaming](streaming-programming-guide.html).
+It provides high-level APIs in Java, Scala and Python,
+and an optimized engine that supports general execution graphs.
+It also supports a rich set of higher-level tools including [Shark](http://shark.cs.berkeley.edu) (Hive on Spark), [Spark SQL](sql-programming-guide.html) for structured data, [MLlib](mllib-guide.html) for machine learning, [GraphX](graphx-programming-guide.html) for graph processing, and [Spark Streaming](streaming-programming-guide.html).
 
 # Downloading
 
-Get Spark by visiting the [downloads page](http://spark.apache.org/downloads.html) of the Apache Spark site. This documentation is for Spark version {{site.SPARK_VERSION}}. The downloads page 
+Get Spark from the [downloads page](http://spark.apache.org/downloads.html) of the project website. This documentation is for Spark version {{site.SPARK_VERSION}}. The downloads page 
 contains Spark packages for many popular HDFS versions. If you'd like to build Spark from 
-scratch, visit the [building with Maven](building-with-maven.html) page.
+scratch, visit [building Spark with Maven](building-with-maven.html).
 
-Spark runs on both Windows and UNIX-like systems (e.g. Linux, Mac OS). All you need to run it is 
-to have `java` to installed on your system `PATH`, or the `JAVA_HOME` environment variable 
-pointing to a Java installation.
+Spark runs on both Windows and UNIX-like systems (e.g. Linux, Mac OS). It's easy to run
+locally on one machine --- all you need is to have `java` installed on your system `PATH`,
+or the `JAVA_HOME` environment variable pointing to a Java installation.
 
-For its Scala API, Spark {{site.SPARK_VERSION}} depends on Scala {{site.SCALA_BINARY_VERSION}}. 
-If you write applications in Scala, you will need to use a compatible Scala version 
-(e.g. {{site.SCALA_BINARY_VERSION}}.X) -- newer major versions may not work. You can get the 
-right version of Scala from [scala-lang.org](http://www.scala-lang.org/download/).
+Spark runs on Java 6+ and Python 2.6+. For the Scala API, Spark {{site.SPARK_VERSION}} uses
+Scala {{site.SCALA_BINARY_VERSION}}. You will need to use a compatible Scala version 
+({{site.SCALA_BINARY_VERSION}}.x).
 
 # Running the Examples and Shell
 
@@ -28,24 +28,23 @@ Spark comes with several sample programs.  Scala, Java and Python examples are i
 `examples/src/main` directory. To run one of the Java or Scala sample programs, use
 `bin/run-example <class> [params]` in the top-level Spark directory. (Behind the scenes, this
 invokes the more general
-[Spark submit script](cluster-overview.html#launching-applications-with-spark-submit) for
+[`spark-submit` script](submitting-applications.html) for
 launching applications). For example,
 
     ./bin/run-example SparkPi 10
 
-You can also run Spark interactively through modified versions of the Scala shell. This is a
+You can also run Spark interactively through a modified version of the Scala shell. This is a
 great way to learn the framework.
 
     ./bin/spark-shell --master local[2]
 
 The `--master` option specifies the
-[master URL for a distributed cluster](scala-programming-guide.html#master-urls), or `local` to run
+[master URL for a distributed cluster](submitting-applications.html#master-urls), or `local` to run
 locally with one thread, or `local[N]` to run locally with N threads. You should start by using
 `local` for testing. For a full list of options, run Spark shell with the `--help` option.
 
-Spark also provides a Python interface. To run Spark interactively in a Python interpreter, use
-`bin/pyspark`. As in Spark shell, you can also pass in the `--master` option to configure your
-master URL.
+Spark also provides a Python API. To run Spark interactively in a Python interpreter, use
+`bin/pyspark`:
 
     ./bin/pyspark --master local[2]
 
@@ -66,17 +65,17 @@ options for deployment:
 
 # Where to Go from Here
 
-**Programming guides:**
+**Programming Guides:**
 
 * [Quick Start](quick-start.html): a quick introduction to the Spark API; start here!
-* [Spark Programming Guide](scala-programming-guide.html): an overview of Spark concepts, and details on the Scala API
-  * [Java Programming Guide](java-programming-guide.html): using Spark from Java
-  * [Python Programming Guide](python-programming-guide.html): using Spark from Python
-* [Spark Streaming](streaming-programming-guide.html): Spark's API for processing data streams
-* [Spark SQL](sql-programming-guide.html): Support for running relational queries on Spark
-* [MLlib (Machine Learning)](mllib-guide.html): Spark's built-in machine learning library
-* [Bagel (Pregel on Spark)](bagel-programming-guide.html): simple graph processing model
-* [GraphX (Graphs on Spark)](graphx-programming-guide.html): Spark's new API for graphs
+* [Spark Programming Guide](programming-guide.html): detailed overview of Spark
+  in all supported languages (Scala, Java, Python)
+* Modules built on Spark:
+  * [Spark Streaming](streaming-programming-guide.html): processing real-time data streams
+  * [Spark SQL](sql-programming-guide.html): support for structured data and relational queries
+  * [MLlib](mllib-guide.html): built-in machine learning library
+  * [GraphX](graphx-programming-guide.html): Spark's new API for graph processing
+  * [Bagel (Pregel on Spark)](bagel-programming-guide.html): older, simple graph processing model
 
 **API Docs:**
 
@@ -84,26 +83,30 @@ options for deployment:
 * [Spark Java API (Javadoc)](api/java/index.html)
 * [Spark Python API (Epydoc)](api/python/index.html)
 
-**Deployment guides:**
+**Deployment Guides:**
 
 * [Cluster Overview](cluster-overview.html): overview of concepts and components when running on a cluster
-* [Amazon EC2](ec2-scripts.html): scripts that let you launch a cluster on EC2 in about 5 minutes
-* [Standalone Deploy Mode](spark-standalone.html): launch a standalone cluster quickly without a third-party cluster manager
-* [Mesos](running-on-mesos.html): deploy a private cluster using
-    [Apache Mesos](http://mesos.apache.org)
-* [YARN](running-on-yarn.html): deploy Spark on top of Hadoop NextGen (YARN)
+* [Submitting Applications](submitting-applications.html): packaging and deploying applications
+* Deployment modes:
+  * [Amazon EC2](ec2-scripts.html): scripts that let you launch a cluster on EC2 in about 5 minutes
+  * [Standalone Deploy Mode](spark-standalone.html): launch a standalone cluster quickly without a third-party cluster manager
+  * [Mesos](running-on-mesos.html): deploy a private cluster using
+      [Apache Mesos](http://mesos.apache.org)
+  * [YARN](running-on-yarn.html): deploy Spark on top of Hadoop NextGen (YARN)
 
-**Other documents:**
+**Other Documents:**
 
 * [Configuration](configuration.html): customize Spark via its configuration system
+* [Monitoring](monitoring.html): track the behavior of your applications
 * [Tuning Guide](tuning.html): best practices to optimize performance and memory use
+* [Job Scheduling](job-scheduling.html): scheduling resources across and within Spark applications
 * [Security](security.html): Spark security support
 * [Hardware Provisioning](hardware-provisioning.html): recommendations for cluster hardware
-* [Job Scheduling](job-scheduling.html): scheduling resources across and within Spark applications
+* [3<sup>rd</sup> Party Hadoop Distributions](hadoop-third-party-distributions.html): using common Hadoop distributions
 * [Building Spark with Maven](building-with-maven.html): build Spark using the Maven system
 * [Contributing to Spark](https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark)
 
-**External resources:**
+**External Resources:**
 
 * [Spark Homepage](http://spark.apache.org)
 * [Shark](http://shark.cs.berkeley.edu): Apache Hive over Spark
@@ -112,9 +115,9 @@ options for deployment:
   exercises about Spark, Shark, Spark Streaming, Mesos, and more. [Videos](http://ampcamp.berkeley.edu/3/),
   [slides](http://ampcamp.berkeley.edu/3/) and [exercises](http://ampcamp.berkeley.edu/3/exercises/) are
   available online for free.
-* [Code Examples](http://spark.apache.org/examples.html): more are also available in the [examples subfolder](https://github.com/apache/spark/tree/master/examples/src/main/scala/org/apache/spark/) of Spark
-* [Paper Describing Spark](http://www.cs.berkeley.edu/~matei/papers/2012/nsdi_spark.pdf)
-* [Paper Describing Spark Streaming](http://www.eecs.berkeley.edu/Pubs/TechRpts/2012/EECS-2012-259.pdf)
+* [Code Examples](http://spark.apache.org/examples.html): more are also available in the `examples` subfolder of Spark ([Scala]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/scala/org/apache/spark/examples),
+ [Java]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/java/org/apache/spark/examples),
+ [Python]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/python))
 
 # Community
 
diff --git a/docs/java-programming-guide.md b/docs/java-programming-guide.md
index 943fdd9d019f..bb539582f64e 100644
--- a/docs/java-programming-guide.md
+++ b/docs/java-programming-guide.md
@@ -1,218 +1,7 @@
 ---
 layout: global
 title: Java Programming Guide
+redirect: programming-guide.html
 ---
 
-The Spark Java API exposes all the Spark features available in the Scala version to Java.
-To learn the basics of Spark, we recommend reading through the
-[Scala programming guide](scala-programming-guide.html) first; it should be
-easy to follow even if you don't know Scala.
-This guide will show how to use the Spark features described there in Java.
-
-The Spark Java API is defined in the
-[`org.apache.spark.api.java`](api/java/index.html?org/apache/spark/api/java/package-summary.html) package, and includes
-a [`JavaSparkContext`](api/java/index.html?org/apache/spark/api/java/JavaSparkContext.html) for
-initializing Spark and [`JavaRDD`](api/java/index.html?org/apache/spark/api/java/JavaRDD.html) classes,
-which support the same methods as their Scala counterparts but take Java functions and return
-Java data and collection types. The main differences have to do with passing functions to RDD
-operations (e.g. map) and handling RDDs of different types, as discussed next.
-
-# Key Differences in the Java API
-
-There are a few key differences between the Java and Scala APIs:
-
-* Java does not support anonymous or first-class functions, so functions are passed
-  using anonymous classes that implement the
-  [`org.apache.spark.api.java.function.Function`](api/java/index.html?org/apache/spark/api/java/function/Function.html),
-  [`Function2`](api/java/index.html?org/apache/spark/api/java/function/Function2.html), etc.
-  interfaces.
-* To maintain type safety, the Java API defines specialized Function and RDD
-  classes for key-value pairs and doubles. For example, 
-  [`JavaPairRDD`](api/java/index.html?org/apache/spark/api/java/JavaPairRDD.html)
-  stores key-value pairs.
-* Some methods are defined on the basis of the passed function's return type.
-  For example `mapToPair()` returns
-  [`JavaPairRDD`](api/java/index.html?org/apache/spark/api/java/JavaPairRDD.html),
-  and `mapToDouble()` returns
-  [`JavaDoubleRDD`](api/java/index.html?org/apache/spark/api/java/JavaDoubleRDD.html).
-* RDD methods like `collect()` and `countByKey()` return Java collections types,
-  such as `java.util.List` and `java.util.Map`.
-* Key-value pairs, which are simply written as `(key, value)` in Scala, are represented
-  by the `scala.Tuple2` class, and need to be created using `new Tuple2<K, V>(key, value)`.
-
-## RDD Classes
-
-Spark defines additional operations on RDDs of key-value pairs and doubles, such
-as `reduceByKey`, `join`, and `stdev`.
-
-In the Scala API, these methods are automatically added using Scala's
-[implicit conversions](http://www.scala-lang.org/node/130) mechanism.
-
-In the Java API, the extra methods are defined in the
-[`JavaPairRDD`](api/java/index.html?org/apache/spark/api/java/JavaPairRDD.html)
-and [`JavaDoubleRDD`](api/java/index.html?org/apache/spark/api/java/JavaDoubleRDD.html)
-classes.  RDD methods like `map` are overloaded by specialized `PairFunction`
-and `DoubleFunction` classes, allowing them to return RDDs of the appropriate
-types.  Common methods like `filter` and `sample` are implemented by
-each specialized RDD class, so filtering a `PairRDD` returns a new `PairRDD`,
-etc (this achieves the "same-result-type" principle used by the [Scala collections
-framework](http://docs.scala-lang.org/overviews/core/architecture-of-scala-collections.html)).
-
-## Function Interfaces
-
-The following table lists the function interfaces used by the Java API, located in the
-[`org.apache.spark.api.java.function`](api/java/index.html?org/apache/spark/api/java/function/package-summary.html)
-package. Each interface has a single abstract method, `call()`.
-
-<table class="table">
-<tr><th>Class</th><th>Function Type</th></tr>
-
-<tr><td>Function&lt;T, R&gt;</td><td>T =&gt; R </td></tr>
-<tr><td>DoubleFunction&lt;T&gt;</td><td>T =&gt; Double </td></tr>
-<tr><td>PairFunction&lt;T, K, V&gt;</td><td>T =&gt; Tuple2&lt;K, V&gt; </td></tr>
-
-<tr><td>FlatMapFunction&lt;T, R&gt;</td><td>T =&gt; Iterable&lt;R&gt; </td></tr>
-<tr><td>DoubleFlatMapFunction&lt;T&gt;</td><td>T =&gt; Iterable&lt;Double&gt; </td></tr>
-<tr><td>PairFlatMapFunction&lt;T, K, V&gt;</td><td>T =&gt; Iterable&lt;Tuple2&lt;K, V&gt;&gt; </td></tr>
-
-<tr><td>Function2&lt;T1, T2, R&gt;</td><td>T1, T2 =&gt; R (function of two arguments)</td></tr>
-</table>
-
-## Storage Levels
-
-RDD [storage level](scala-programming-guide.html#rdd-persistence) constants, such as `MEMORY_AND_DISK`, are
-declared in the [org.apache.spark.api.java.StorageLevels](api/java/index.html?org/apache/spark/api/java/StorageLevels.html) class. To
-define your own storage level, you can use StorageLevels.create(...). 
-
-# Other Features
-
-The Java API supports other Spark features, including
-[accumulators](scala-programming-guide.html#accumulators),
-[broadcast variables](scala-programming-guide.html#broadcast-variables), and
-[caching](scala-programming-guide.html#rdd-persistence).
-
-# Upgrading From Pre-1.0 Versions of Spark
-
-In version 1.0 of Spark the Java API was refactored to better support Java 8
-lambda expressions. Users upgrading from older versions of Spark should note
-the following changes:
-
-* All `org.apache.spark.api.java.function.*` have been changed from abstract
-  classes to interfaces. This means that concrete implementations of these 
-  `Function` classes will need to use `implements` rather than `extends`.
-* Certain transformation functions now have multiple versions depending
-  on the return type. In Spark core, the map functions (`map`, `flatMap`, and
-  `mapPartitions`) have type-specific versions, e.g.
-  [`mapToPair`](api/java/org/apache/spark/api/java/JavaRDDLike.html#mapToPair(org.apache.spark.api.java.function.PairFunction))
-  and [`mapToDouble`](api/java/org/apache/spark/api/java/JavaRDDLike.html#mapToDouble(org.apache.spark.api.java.function.DoubleFunction)).
-  Spark Streaming also uses the same approach, e.g. [`transformToPair`](api/java/org/apache/spark/streaming/api/java/JavaDStreamLike.html#transformToPair(org.apache.spark.api.java.function.Function)).
-
-# Example
-
-As an example, we will implement word count using the Java API.
-
-{% highlight java %}
-import org.apache.spark.api.java.*;
-import org.apache.spark.api.java.function.*;
-
-JavaSparkContext jsc = new JavaSparkContext(...);
-JavaRDD<String> lines = jsc.textFile("hdfs://...");
-JavaRDD<String> words = lines.flatMap(
-  new FlatMapFunction<String, String>() {
-    @Override public Iterable<String> call(String s) {
-      return Arrays.asList(s.split(" "));
-    }
-  }
-);
-{% endhighlight %}
-
-The word count program starts by creating a `JavaSparkContext`, which accepts
-the same parameters as its Scala counterpart.  `JavaSparkContext` supports the
-same data loading methods as the regular `SparkContext`; here, `textFile`
-loads lines from text files stored in HDFS.
-
-To split the lines into words, we use `flatMap` to split each line on
-whitespace.  `flatMap` is passed a `FlatMapFunction` that accepts a string and
-returns an `java.lang.Iterable` of strings.
-
-Here, the `FlatMapFunction` was created inline; another option is to subclass
-`FlatMapFunction` and pass an instance to `flatMap`:
-
-{% highlight java %}
-class Split extends FlatMapFunction<String, String> {
-  @Override public Iterable<String> call(String s) {
-    return Arrays.asList(s.split(" "));
-  }
-}
-JavaRDD<String> words = lines.flatMap(new Split());
-{% endhighlight %}
-
-Java 8+ users can also write the above `FlatMapFunction` in a more concise way using 
-a lambda expression:
-
-{% highlight java %}
-JavaRDD<String> words = lines.flatMap(s -> Arrays.asList(s.split(" ")));
-{% endhighlight %}
-
-This lambda syntax can be applied to all anonymous classes in Java 8.
-
-Continuing with the word count example, we map each word to a `(word, 1)` pair:
-
-{% highlight java %}
-import scala.Tuple2;
-JavaPairRDD<String, Integer> ones = words.mapToPair(
-  new PairFunction<String, String, Integer>() {
-    @Override public Tuple2<String, Integer> call(String s) {
-      return new Tuple2<String, Integer>(s, 1);
-    }
-  }
-);
-{% endhighlight %}
-
-Note that `mapToPair` was passed a `PairFunction<String, String, Integer>` and
-returned a `JavaPairRDD<String, Integer>`.
-
-To finish the word count program, we will use `reduceByKey` to count the
-occurrences of each word:
-
-{% highlight java %}
-JavaPairRDD<String, Integer> counts = ones.reduceByKey(
-  new Function2<Integer, Integer, Integer>() {
-    @Override public Integer call(Integer i1, Integer i2) {
-      return i1 + i2;
-    }
-  }
-);
-{% endhighlight %}
-
-Here, `reduceByKey` is passed a `Function2`, which implements a function with
-two arguments.  The resulting `JavaPairRDD` contains `(word, count)` pairs.
-
-In this example, we explicitly showed each intermediate RDD.  It is also
-possible to chain the RDD transformations, so the word count example could also
-be written as:
-
-{% highlight java %}
-JavaPairRDD<String, Integer> counts = lines.flatMapToPair(
-    ...
-  ).map(
-    ...
-  ).reduceByKey(
-    ...
-  );
-{% endhighlight %}
-
-There is no performance difference between these approaches; the choice is
-just a matter of style.
-
-# API Docs
-
-[API documentation](api/java/index.html) for Spark in Java is available in Javadoc format.
-
-# Where to Go from Here
-
-Spark includes several sample programs using the Java API in
-[`examples/src/main/java`](https://github.com/apache/spark/tree/master/examples/src/main/java/org/apache/spark/examples).  You can run them by passing the class name to the
-`bin/run-example` script included in Spark; for example:
-
-    ./bin/run-example JavaWordCount README.md
+This document has been merged into the [Spark programming guide](programming-guide.html).
diff --git a/docs/js/api-docs.js b/docs/js/api-docs.js
index 1414b6d0b81a..ce89d8943b43 100644
--- a/docs/js/api-docs.js
+++ b/docs/js/api-docs.js
@@ -1,10 +1,27 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
 /* Dynamically injected post-processing code for the API docs */
 
 $(document).ready(function() {
   var annotations = $("dt:contains('Annotations')").next("dd").children("span.name");
-  addBadges(annotations, "AlphaComponent", ":: AlphaComponent ::", "<span class='alphaComponent badge'>Alpha Component</span>");
-  addBadges(annotations, "DeveloperApi", ":: DeveloperApi ::", "<span class='developer badge'>Developer API</span>");
-  addBadges(annotations, "Experimental", ":: Experimental ::", "<span class='experimental badge'>Experimental</span>");
+  addBadges(annotations, "AlphaComponent", ":: AlphaComponent ::", '<span class="alphaComponent badge">Alpha Component</span>');
+  addBadges(annotations, "DeveloperApi", ":: DeveloperApi ::", '<span class="developer badge">Developer API</span>');
+  addBadges(annotations, "Experimental", ":: Experimental ::", '<span class="experimental badge">Experimental</span>');
 });
 
 function addBadges(allAnnotations, name, tag, html) {
diff --git a/docs/js/main.js b/docs/js/main.js
index 59055467110b..f1a90e47e89a 100755
--- a/docs/js/main.js
+++ b/docs/js/main.js
@@ -1,3 +1,23 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* Custom JavaScript code in the MarkDown docs */
+
+// Enable language-specific code tabs
 function codeTabs() {
   var counter = 0;
   var langImages = {
@@ -62,6 +82,7 @@ function makeCollapsable(elt, accordionClass, accordionBodyId, title) {
   );
 }
 
+// Enable "view solution" sections (for exercises)
 function viewSolution() {
   var counter = 0
   $("div.solution").each(function() {
diff --git a/docs/mllib-guide.md b/docs/mllib-guide.md
index 640ca8308538..95ee6bc96801 100644
--- a/docs/mllib-guide.md
+++ b/docs/mllib-guide.md
@@ -31,7 +31,7 @@ MLlib is a new component under active development.
 The APIs marked `Experimental`/`DeveloperApi` may change in future releases, 
 and we will provide migration guide between releases.
 
-## Dependencies
+# Dependencies
 
 MLlib uses linear algebra packages [Breeze](http://www.scalanlp.org/), which depends on
 [netlib-java](https://github.com/fommil/netlib-java), and
@@ -50,9 +50,9 @@ To use MLlib in Python, you will need [NumPy](http://www.numpy.org) version 1.4
 
 ---
 
-## Migration guide
+# Migration Guide
 
-### From 0.9 to 1.0
+## From 0.9 to 1.0
 
 In MLlib v1.0, we support both dense and sparse input in a unified way, which introduces a few
 breaking changes.  If your data is sparse, please store it in a sparse format instead of dense to
@@ -84,9 +84,9 @@ val vector: Vector = Vectors.dense(array) // a dense vector
 <div data-lang="java" markdown="1">
 
 We used to represent a feature vector by `double[]`, which is replaced by
-[`Vector`](api/scala/index.html#org.apache.spark.mllib.linalg.Vector) in v1.0. Algorithms that used
+[`Vector`](api/java/index.html?org/apache/spark/mllib/linalg/Vector.html) in v1.0. Algorithms that used
 to accept `RDD<double[]>` now take
-`RDD<Vector>`. [`LabeledPoint`](api/scala/index.html#org.apache.spark.mllib.regression.LabeledPoint)
+`RDD<Vector>`. [`LabeledPoint`](api/java/index.html?org/apache/spark/mllib/regression/LabeledPoint.html)
 is now a wrapper of `(double, Vector)` instead of `(double, double[])`. Converting `double[]` to
 `Vector` is straightforward:
 
diff --git a/docs/mllib-optimization.md b/docs/mllib-optimization.md
index a22980d03a2f..97e8f4e9661b 100644
--- a/docs/mllib-optimization.md
+++ b/docs/mllib-optimization.md
@@ -116,7 +116,7 @@ is a stochastic gradient. Here `$S$` is the sampled subset of size `$|S|=$ miniB
 $\cdot n$`.
 
 In each iteration, the sampling over the distributed dataset
-([RDD](scala-programming-guide.html#resilient-distributed-datasets-rdds)), as well as the
+([RDD](programming-guide.html#resilient-distributed-datasets-rdds)), as well as the
 computation of the sum of the partial results from each worker machine is performed by the
 standard spark routines.
 
diff --git a/docs/monitoring.md b/docs/monitoring.md
index fffc58ac3923..2b9e9e5bd7ea 100644
--- a/docs/monitoring.md
+++ b/docs/monitoring.md
@@ -3,7 +3,7 @@ layout: global
 title: Monitoring and Instrumentation
 ---
 
-There are several ways to monitor Spark applications.
+There are several ways to monitor Spark applications: web UIs, metrics, and external instrumentation.
 
 # Web Interfaces
 
diff --git a/docs/programming-guide.md b/docs/programming-guide.md
new file mode 100644
index 000000000000..7d77e640d0e4
--- /dev/null
+++ b/docs/programming-guide.md
@@ -0,0 +1,1294 @@
+---
+layout: global
+title: Spark Programming Guide
+---
+
+* This will become a table of contents (this text will be scraped).
+{:toc}
+
+
+# Overview
+
+At a high level, every Spark application consists of a *driver program* that runs the user's `main` function and executes various *parallel operations* on a cluster. The main abstraction Spark provides is a *resilient distributed dataset* (RDD), which is a collection of elements partitioned across the nodes of the cluster that can be operated on in parallel. RDDs are created by starting with a file in the Hadoop file system (or any other Hadoop-supported file system), or an existing Scala collection in the driver program, and transforming it. Users may also ask Spark to *persist* an RDD in memory, allowing it to be reused efficiently across parallel operations. Finally, RDDs automatically recover from node failures.
+
+A second abstraction in Spark is *shared variables* that can be used in parallel operations. By default, when Spark runs a function in parallel as a set of tasks on different nodes, it ships a copy of each variable used in the function to each task. Sometimes, a variable needs to be shared across tasks, or between tasks and the driver program. Spark supports two types of shared variables: *broadcast variables*, which can be used to cache a value in memory on all nodes, and *accumulators*, which are variables that are only "added" to, such as counters and sums.
+
+This guide shows each of these features in each of Spark's supported languages. It is easiest to follow
+along with if you launch Spark's interactive shell -- either `bin/spark-shell` for the Scala shell or
+`bin/pyspark` for the Python one.
+
+# Linking with Spark
+
+<div class="codetabs">
+
+<div data-lang="scala"  markdown="1">
+
+Spark {{site.SPARK_VERSION}} uses Scala {{site.SCALA_BINARY_VERSION}}. To write
+applications in Scala, you will need to use a compatible Scala version (e.g. {{site.SCALA_BINARY_VERSION}}.X).
+
+To write a Spark application, you need to add a Maven dependency on Spark. Spark is available through Maven Central at:
+
+    groupId = org.apache.spark
+    artifactId = spark-core_{{site.SCALA_BINARY_VERSION}}
+    version = {{site.SPARK_VERSION}}
+
+In addition, if you wish to access an HDFS cluster, you need to add a dependency on
+`hadoop-client` for your version of HDFS. Some common HDFS version tags are listed on the
+[third party distributions](hadoop-third-party-distributions.html) page.
+
+    groupId = org.apache.hadoop
+    artifactId = hadoop-client
+    version = <your-hdfs-version>
+
+Finally, you need to import some Spark classes and implicit conversions into your program. Add the following lines:
+
+{% highlight scala %}
+import org.apache.spark.SparkContext
+import org.apache.spark.SparkContext._
+import org.apache.spark.SparkConf
+{% endhighlight %}
+
+</div>
+
+<div data-lang="java"  markdown="1">
+
+Spark {{site.SPARK_VERSION}} works with Java 6 and higher. If you are using Java 8, Spark supports
+[lambda expressions](http://docs.oracle.com/javase/tutorial/java/javaOO/lambdaexpressions.html)
+for concisely writing functions, otherwise you can use the classes in the
+[org.apache.spark.api.java.function](api/java/index.html?org/apache/spark/api/java/function/package-summary.html) package.
+
+To write a Spark application in Java, you need to add a dependency on Spark. Spark is available through Maven Central at:
+
+    groupId = org.apache.spark
+    artifactId = spark-core_{{site.SCALA_BINARY_VERSION}}
+    version = {{site.SPARK_VERSION}}
+
+In addition, if you wish to access an HDFS cluster, you need to add a dependency on
+`hadoop-client` for your version of HDFS. Some common HDFS version tags are listed on the
+[third party distributions](hadoop-third-party-distributions.html) page.
+
+    groupId = org.apache.hadoop
+    artifactId = hadoop-client
+    version = <your-hdfs-version>
+
+Finally, you need to import some Spark classes into your program. Add the following lines:
+
+{% highlight scala %}
+import org.apache.spark.api.java.JavaSparkContext
+import org.apache.spark.api.java.JavaRDD
+import org.apache.spark.SparkConf
+{% endhighlight %}
+
+</div>
+
+<div data-lang="python"  markdown="1">
+
+Spark {{site.SPARK_VERSION}} works with Python 2.6 or higher (but not Python 3). It uses the standard CPython interpreter,
+so C libraries like NumPy can be used.
+
+To run Spark applications in Python, use the `bin/spark-submit` script located in the Spark directory.
+This script will load Spark's Java/Scala libraries and allow you to submit applications to a cluster.
+You can also use `bin/pyspark` to launch an interactive Python shell.
+
+If you wish to access HDFS data, you need to use a build of PySpark linking
+to your version of HDFS. Some common HDFS version tags are listed on the
+[third party distributions](hadoop-third-party-distributions.html) page.
+[Prebuilt packages](http://spark.apache.org/downloads.html) are also available on the Spark homepage
+for common HDFS versions.
+
+Finally, you need to import some Spark classes into your program. Add the following lines:
+
+{% highlight scala %}
+from pyspark import SparkContext, SparkConf
+{% endhighlight %}
+
+</div>
+
+</div>
+
+
+# Initializing Spark
+
+<div class="codetabs">
+
+<div data-lang="scala"  markdown="1">
+
+The first thing a Spark program must do is to create a [SparkContext](api/scala/index.html#org.apache.spark.SparkContext) object, which tells Spark
+how to access a cluster. To create a `SparkContext` you first need to build a [SparkConf](api/scala/index.html#org.apache.spark.SparkConf) object
+that contains information about your application.
+
+{% highlight scala %}
+val conf = new SparkConf().setAppName(appName).setMaster(master)
+new SparkContext(conf)
+{% endhighlight %}
+
+</div>
+
+<div data-lang="java"  markdown="1">
+
+The first thing a Spark program must do is to create a [JavaSparkContext](api/java/index.html?org/apache/spark/api/java/JavaSparkContext.html) object, which tells Spark
+how to access a cluster. To create a `SparkContext` you first need to build a [SparkConf](api/java/index.html?org/apache/spark/SparkConf.html) object
+that contains information about your application.
+
+{% highlight java %}
+SparkConf conf = new SparkConf().setAppName(appName).setMaster(master);
+JavaSparkContext sc = new JavaSparkContext(conf);
+{% endhighlight %}
+
+</div>
+
+<div data-lang="python"  markdown="1">
+
+The first thing a Spark program must do is to create a [SparkContext](api/python/pyspark.context.SparkContext-class.html) object, which tells Spark
+how to access a cluster. To create a `SparkContext` you first need to build a [SparkConf](api/python/pyspark.conf.SparkConf-class.html) object
+that contains information about your application.
+
+{% highlight python %}
+conf = SparkConf().setAppName(appName).setMaster(master)
+sc = SparkContext(conf)
+{% endhighlight %}
+
+</div>
+
+</div>
+
+The `appName` parameter is a name for your application to show on the cluster UI.
+`master` is a [Spark, Mesos or YARN cluster URL](submitting-applications.html#master-urls),
+or a special "local" string to run in local mode.
+In practice, when running on a cluster, you will not want to hardcode `master` in the program,
+but rather [launch the application with `spark-submit`](submitting-applications.html) and
+receive it there. However, for local testing and unit tests, you can pass "local" to run Spark
+in-process.
+
+
+## Using the Shell
+
+<div class="codetabs">
+
+<div data-lang="scala"  markdown="1">
+
+In the Spark shell, a special interpreter-aware SparkContext is already created for you, in the
+variable called `sc`. Making your own SparkContext will not work. You can set which master the
+context connects to using the `--master` argument, and you can add JARs to the classpath
+by passing a comma-separated list to the `--jars` argument.
+For example, to run `bin/spark-shell` on exactly four cores, use:
+
+{% highlight bash %}
+$ ./bin/spark-shell --master local[4]
+{% endhighlight %}
+
+Or, to also add `code.jar` to its classpath, use:
+
+{% highlight bash %}
+$ ./bin/spark-shell --master local[4] --jars code.jar
+{% endhighlight %}
+
+For a complete list of options, run `spark-shell --help`. Behind the scenes,
+`spark-shell` invokes the more general [`spark-submit` script](submitting-applications.html).
+
+</div>
+
+<div data-lang="python"  markdown="1">
+
+In the PySpark shell, a special interpreter-aware SparkContext is already created for you, in the
+variable called `sc`. Making your own SparkContext will not work. You can set which master the
+context connects to using the `--master` argument, and you can add Python .zip, .egg or .py files
+to the runtime path by passing a comma-separated list to `--py-files`.
+For example, to run `bin/pyspark` on exactly four cores, use:
+
+{% highlight bash %}
+$ ./bin/pyspark --master local[4]
+{% endhighlight %}
+
+Or, to also add `code.py` to the search path (in order to later be able to `import code`), use:
+
+{% highlight bash %}
+$ ./bin/pyspark --master local[4] --py-files code.py
+{% endhighlight %}
+
+For a complete list of options, run `pyspark --help`. Behind the scenes,
+`pyspark` invokes the more general [`spark-submit` script](submitting-applications.html).
+
+It is also possible to launch the PySpark shell in [IPython](http://ipython.org), the
+enhanced Python interpreter. PySpark works with IPython 1.0.0 and later. To
+use IPython, set the `IPYTHON` variable to `1` when running `bin/pyspark`:
+
+{% highlight bash %}
+$ IPYTHON=1 ./bin/pyspark
+{% endhighlight %}
+
+You can customize the `ipython` command by setting `IPYTHON_OPTS`. For example, to launch
+the [IPython Notebook](http://ipython.org/notebook.html) with PyLab plot support:
+
+{% highlight bash %}
+$ IPYTHON_OPTS="notebook --pylab inline" ./bin/pyspark
+{% endhighlight %}
+
+</div>
+
+</div>
+
+# Resilient Distributed Datasets (RDDs)
+
+Spark revolves around the concept of a _resilient distributed dataset_ (RDD), which is a fault-tolerant collection of elements that can be operated on in parallel. There are two ways to create RDDs: *parallelizing*
+an existing collection in your driver program, or referencing a dataset in an external storage system, such as a
+shared filesystem, HDFS, HBase, or any data source offering a Hadoop InputFormat.
+
+## Parallelized Collections
+
+<div class="codetabs">
+
+<div data-lang="scala"  markdown="1">
+
+Parallelized collections are created by calling `SparkContext`'s `parallelize` method on an existing collection in your driver program (a Scala `Seq`). The elements of the collection are copied to form a distributed dataset that can be operated on in parallel. For example, here is how to create a parallelized collection holding the numbers 1 to 5:
+
+{% highlight scala %}
+val data = Array(1, 2, 3, 4, 5)
+val distData = sc.parallelize(data)
+{% endhighlight %}
+
+Once created, the distributed dataset (`distData`) can be operated on in parallel. For example, we might call `distData.reduce((a, b) => a + b)` to add up the elements of the array. We describe operations on distributed datasets later on.
+
+</div>
+
+<div data-lang="java"  markdown="1">
+
+Parallelized collections are created by calling `JavaSparkContext`'s `parallelize` method on an existing `Collection` in your driver program. The elements of the collection are copied to form a distributed dataset that can be operated on in parallel. For example, here is how to create a parallelized collection holding the numbers 1 to 5:
+
+{% highlight java %}
+List<Integer> data = Arrays.asList(1, 2, 3, 4, 5);
+JavaRDD<Integer> distData = sc.parallelize(data);
+{% endhighlight %}
+
+Once created, the distributed dataset (`distData`) can be operated on in parallel. For example, we might call `distData.reduce((a, b) -> a + b)` to add up the elements of the list.
+We describe operations on distributed datasets later on.
+
+**Note:** *In this guide, we'll often use the concise Java 8 lambda syntax to specify Java functions, but
+in older versions of Java you can implement the interfaces in the
+[org.apache.spark.api.java.function](api/java/index.html?org/apache/spark/api/java/function/package-summary.html) package.
+We describe [passing functions to Spark](#passing-functions-to-spark) in more detail below.*
+
+</div>
+
+<div data-lang="python"  markdown="1">
+
+Parallelized collections are created by calling `SparkContext`'s `parallelize` method on an existing iterable or collection in your driver program. The elements of the collection are copied to form a distributed dataset that can be operated on in parallel. For example, here is how to create a parallelized collection holding the numbers 1 to 5:
+
+{% highlight python %}
+data = [1, 2, 3, 4, 5]
+distData = sc.parallelize(data)
+{% endhighlight %}
+
+Once created, the distributed dataset (`distData`) can be operated on in parallel. For example, we can call `distData.reduce(lambda a, b: a + b)` to add up the elements of the list.
+We describe operations on distributed datasets later on.
+
+</div>
+
+</div>
+
+One important parameter for parallel collections is the number of *slices* to cut the dataset into. Spark will run one task for each slice of the cluster. Typically you want 2-4 slices for each CPU in your cluster. Normally, Spark tries to set the number of slices automatically based on your cluster. However, you can also set it manually by passing it as a second parameter to `parallelize` (e.g. `sc.parallelize(data, 10)`).
+
+## External Datasets
+
+<div class="codetabs">
+
+<div data-lang="scala"  markdown="1">
+
+Spark can create distributed datasets from any storage source supported by Hadoop, including your local file system, HDFS, Cassandra, HBase, [Amazon S3](http://wiki.apache.org/hadoop/AmazonS3), etc. Spark supports text files, [SequenceFiles](http://hadoop.apache.org/common/docs/current/api/org/apache/hadoop/mapred/SequenceFileInputFormat.html), and any other Hadoop [InputFormat](http://hadoop.apache.org/docs/stable/api/org/apache/hadoop/mapred/InputFormat.html).
+
+Text file RDDs can be created using `SparkContext`'s `textFile` method. This method takes an URI for the file (either a local path on the machine, or a `hdfs://`, `s3n://`, etc URI) and reads it as a collection of lines. Here is an example invocation:
+
+{% highlight scala %}
+scala> val distFile = sc.textFile("data.txt")
+distFile: RDD[String] = MappedRDD@1d4cee08
+{% endhighlight %}
+
+Once created, `distFile` can be acted on by dataset operations. For example, we can add up the sizes of all the lines using the `map` and `reduce` operations as follows: `distFile.map(s => s.length).reduce((a, b) => a + b)`.
+
+Some notes on reading files with Spark:
+
+* If using a path on the local filesystem, the file must also be accessible at the same path on worker nodes. Either copy the file to all workers or use a network-mounted shared file system.
+
+* All of Spark's file-based input methods, including `textFile`, support running on directories, compressed files, and wildcards as well. For example, you can use `textFile("/my/directory")`, `textFile("/my/directory/*.txt")`, and `textFile("/my/directory/*.gz")`.
+
+* The `textFile` method also takes an optional second argument for controlling the number of slices of the file. By default, Spark creates one slice for each block of the file (blocks being 64MB by default in HDFS), but you can also ask for a higher number of slices by passing a larger value. Note that you cannot have fewer slices than blocks.
+
+Apart from text files, Spark's Scala API also supports several other data formats:
+
+* `SparkContext.wholeTextFiles` lets you read a directory containing multiple small text files, and returns each of them as (filename, content) pairs. This is in contrast with `textFile`, which would return one record per line in each file.
+
+* For [SequenceFiles](http://hadoop.apache.org/common/docs/current/api/org/apache/hadoop/mapred/SequenceFileInputFormat.html), use SparkContext's `sequenceFile[K, V]` method where `K` and `V` are the types of key and values in the file. These should be subclasses of Hadoop's [Writable](http://hadoop.apache.org/common/docs/current/api/org/apache/hadoop/io/Writable.html) interface, like [IntWritable](http://hadoop.apache.org/common/docs/current/api/org/apache/hadoop/io/IntWritable.html) and [Text](http://hadoop.apache.org/common/docs/current/api/org/apache/hadoop/io/Text.html). In addition, Spark allows you to specify native types for a few common Writables; for example, `sequenceFile[Int, String]` will automatically read IntWritables and Texts.
+
+* For other Hadoop InputFormats, you can use the `SparkContext.hadoopRDD` method, which takes an arbitrary `JobConf` and input format class, key class and value class. Set these the same way you would for a Hadoop job with your input source. You can also use `SparkContext.newHadoopRDD` for InputFormats based on the "new" MapReduce API (`org.apache.hadoop.mapreduce`).
+
+* `RDD.saveAsObjectFile` and `SparkContext.objectFile` support saving an RDD in a simple format consisting of serialized Java objects. While this is not as efficient as specialized formats like Avro, it offers an easy way to save any RDD.
+
+</div>
+
+<div data-lang="java"  markdown="1">
+
+Spark can create distributed datasets from any storage source supported by Hadoop, including your local file system, HDFS, Cassandra, HBase, [Amazon S3](http://wiki.apache.org/hadoop/AmazonS3), etc. Spark supports text files, [SequenceFiles](http://hadoop.apache.org/common/docs/current/api/org/apache/hadoop/mapred/SequenceFileInputFormat.html), and any other Hadoop [InputFormat](http://hadoop.apache.org/docs/stable/api/org/apache/hadoop/mapred/InputFormat.html).
+
+Text file RDDs can be created using `SparkContext`'s `textFile` method. This method takes an URI for the file (either a local path on the machine, or a `hdfs://`, `s3n://`, etc URI) and reads it as a collection of lines. Here is an example invocation:
+
+{% highlight java %}
+JavaRDD<String> distFile = sc.textFile("data.txt");
+{% endhighlight %}
+
+Once created, `distFile` can be acted on by dataset operations. For example, we can add up the sizes of all the lines using the `map` and `reduce` operations as follows: `distFile.map(s -> s.length()).reduce((a, b) -> a + b)`.
+
+Some notes on reading files with Spark:
+
+* If using a path on the local filesystem, the file must also be accessible at the same path on worker nodes. Either copy the file to all workers or use a network-mounted shared file system.
+
+* All of Spark's file-based input methods, including `textFile`, support running on directories, compressed files, and wildcards as well. For example, you can use `textFile("/my/directory")`, `textFile("/my/directory/*.txt")`, and `textFile("/my/directory/*.gz")`.
+
+* The `textFile` method also takes an optional second argument for controlling the number of slices of the file. By default, Spark creates one slice for each block of the file (blocks being 64MB by default in HDFS), but you can also ask for a higher number of slices by passing a larger value. Note that you cannot have fewer slices than blocks.
+
+Apart from text files, Spark's Java API also supports several other data formats:
+
+* `JavaSparkContext.wholeTextFiles` lets you read a directory containing multiple small text files, and returns each of them as (filename, content) pairs. This is in contrast with `textFile`, which would return one record per line in each file.
+
+* For [SequenceFiles](http://hadoop.apache.org/common/docs/current/api/org/apache/hadoop/mapred/SequenceFileInputFormat.html), use SparkContext's `sequenceFile[K, V]` method where `K` and `V` are the types of key and values in the file. These should be subclasses of Hadoop's [Writable](http://hadoop.apache.org/common/docs/current/api/org/apache/hadoop/io/Writable.html) interface, like [IntWritable](http://hadoop.apache.org/common/docs/current/api/org/apache/hadoop/io/IntWritable.html) and [Text](http://hadoop.apache.org/common/docs/current/api/org/apache/hadoop/io/Text.html).
+
+* For other Hadoop InputFormats, you can use the `JavaSparkContext.hadoopRDD` method, which takes an arbitrary `JobConf` and input format class, key class and value class. Set these the same way you would for a Hadoop job with your input source. You can also use `JavaSparkContext.newHadoopRDD` for InputFormats based on the "new" MapReduce API (`org.apache.hadoop.mapreduce`).
+
+* `JavaRDD.saveAsObjectFile` and `JavaSparkContext.objectFile` support saving an RDD in a simple format consisting of serialized Java objects. While this is not as efficient as specialized formats like Avro, it offers an easy way to save any RDD.
+
+</div>
+
+<div data-lang="python"  markdown="1">
+
+PySpark can create distributed datasets from any file system supported by Hadoop, including your local file system, HDFS, KFS, [Amazon S3](http://wiki.apache.org/hadoop/AmazonS3), etc.
+The current API is limited to text files, but support for binary Hadoop InputFormats is expected in future versions.
+
+Text file RDDs can be created using `SparkContext`'s `textFile` method. This method takes an URI for the file (either a local path on the machine, or a `hdfs://`, `s3n://`, etc URI) and reads it as a collection of lines. Here is an example invocation:
+
+{% highlight python %}
+>>> distFile = sc.textFile("data.txt")
+{% endhighlight %}
+
+Once created, `distFile` can be acted on by dataset operations. For example, we can add up the sizes of all the lines using the `map` and `reduce` operations as follows: `distFile.map(lambda s: len(s)).reduce(lambda a, b: a + b)`.
+
+Some notes on reading files with Spark:
+
+* If using a path on the local filesystem, the file must also be accessible at the same path on worker nodes. Either copy the file to all workers or use a network-mounted shared file system.
+
+* All of Spark's file-based input methods, including `textFile`, support running on directories, compressed files, and wildcards as well. For example, you can use `textFile("/my/directory")`, `textFile("/my/directory/*.txt")`, and `textFile("/my/directory/*.gz")`.
+
+* The `textFile` method also takes an optional second argument for controlling the number of slices of the file. By default, Spark creates one slice for each block of the file (blocks being 64MB by default in HDFS), but you can also ask for a higher number of slices by passing a larger value. Note that you cannot have fewer slices than blocks.
+
+Apart reading files as a collection of lines,
+`SparkContext.wholeTextFiles` lets you read a directory containing multiple small text files, and returns each of them as (filename, content) pairs. This is in contrast with `textFile`, which would return one record per line in each file.
+
+</div>
+
+
+</div>
+
+## RDD Operations
+
+RDDs support two types of operations: *transformations*, which create a new dataset from an existing one, and *actions*, which return a value to the driver program after running a computation on the dataset. For example, `map` is a transformation that passes each dataset element through a function and returns a new RDD representing the results. On the other hand, `reduce` is an action that aggregates all the elements of the RDD using some function and returns the final result to the driver program (although there is also a parallel `reduceByKey` that returns a distributed dataset).
+
+All transformations in Spark are <i>lazy</i>, in that they do not compute their results right away. Instead, they just remember the transformations applied to some base dataset (e.g. a file). The transformations are only computed when an action requires a result to be returned to the driver program. This design enables Spark to run more efficiently -- for example, we can realize that a dataset created through `map` will be used in a `reduce` and return only the result of the `reduce` to the driver, rather than the larger mapped dataset.
+
+By default, each transformed RDD may be recomputed each time you run an action on it. However, you may also *persist* an RDD in memory using the `persist` (or `cache`) method, in which case Spark will keep the elements around on the cluster for much faster access the next time you query it. There is also support for persisting RDDs on disk, or replicated across multiple nodes.
+
+### Basics
+
+<div class="codetabs">
+
+<div data-lang="scala" markdown="1">
+
+To illustrate RDD basics, consider the simple program below:
+
+{% highlight scala %}
+val lines = sc.textFile("data.txt")
+val lineLengths = lines.map(s => s.length)
+val totalLength = lineLengths.reduce((a, b) => a + b)
+{% endhighlight %}
+
+The first line defines a base RDD from an external file. This dataset is not loaded in memory or
+otherwise acted on: `lines` is merely a pointer to the file.
+The second line defines `lineLengths` as the result of a `map` transformation. Again, `lineLengths`
+is *not* immediately computed, due to laziness.
+Finally, we run `reduce`, which is an action. At this point Spark breaks the computation into tasks
+to run on separate machines, and each machine runs both its part of the map and a local reduction,
+returning only its answer to the driver program.
+
+If we also wanted to use `lineLengths` again later, we could add:
+
+{% highlight scala %}
+lineLengths.persist()
+{% endhighlight %}
+
+before the `reduce`, which would cause `lineLengths` to be saved in memory after the first time it is computed.
+
+</div>
+
+<div data-lang="java" markdown="1">
+
+To illustrate RDD basics, consider the simple program below:
+
+{% highlight java %}
+JavaRDD<String> lines = sc.textFile("data.txt");
+JavaRDD<Integer> lineLengths = lines.map(s -> s.length());
+int totalLength = lineLengths.reduce((a, b) -> a + b);
+{% endhighlight %}
+
+The first line defines a base RDD from an external file. This dataset is not loaded in memory or
+otherwise acted on: `lines` is merely a pointer to the file.
+The second line defines `lineLengths` as the result of a `map` transformation. Again, `lineLengths`
+is *not* immediately computed, due to laziness.
+Finally, we run `reduce`, which is an action. At this point Spark breaks the computation into tasks
+to run on separate machines, and each machine runs both its part of the map and a local reduction,
+returning only its answer to the driver program.
+
+If we also wanted to use `lineLengths` again later, we could add:
+
+{% highlight java %}
+lineLengths.persist();
+{% endhighlight %}
+
+before the `reduce`, which would cause `lineLengths` to be saved in memory after the first time it is computed.
+
+</div>
+
+<div data-lang="python" markdown="1">
+
+To illustrate RDD basics, consider the simple program below:
+
+{% highlight python %}
+lines = sc.textFile("data.txt")
+lineLengths = lines.map(lambda s: len(s))
+totalLength = lineLengths.reduce(lambda a, b: a + b)
+{% endhighlight %}
+
+The first line defines a base RDD from an external file. This dataset is not loaded in memory or
+otherwise acted on: `lines` is merely a pointer to the file.
+The second line defines `lineLengths` as the result of a `map` transformation. Again, `lineLengths`
+is *not* immediately computed, due to laziness.
+Finally, we run `reduce`, which is an action. At this point Spark breaks the computation into tasks
+to run on separate machines, and each machine runs both its part of the map and a local reduction,
+returning only its answer to the driver program.
+
+If we also wanted to use `lineLengths` again later, we could add:
+
+{% highlight python %}
+lineLengths.persist()
+{% endhighlight %}
+
+before the `reduce`, which would cause `lineLengths` to be saved in memory after the first time it is computed.
+
+</div>
+
+</div>
+
+### Passing Functions to Spark
+
+<div class="codetabs">
+
+<div data-lang="scala" markdown="1">
+
+Spark's API relies heavily on passing functions in the driver program to run on the cluster.
+There are two recommended ways to do this:
+
+* [Anonymous function syntax](http://docs.scala-lang.org/tutorials/tour/anonymous-function-syntax.html),
+  which can be used for short pieces of code.
+* Static methods in a global singleton object. For example, you can define `object MyFunctions` and then
+  pass `MyFunctions.func1`, as follows:
+
+{% highlight scala %}
+object MyFunctions {
+  def func1(s: String): String = { ... }
+}
+
+myRdd.map(MyFunctions.func1)
+{% endhighlight %}
+
+Note that while it is also possible to pass a reference to a method in a class instance (as opposed to
+a singleton object), this requires sending the object that contains that class along with the method.
+For example, consider:
+
+{% highlight scala %}
+class MyClass {
+  def func1(s: String): String = { ... }
+  def doStuff(rdd: RDD[String]): RDD[String] = { rdd.map(func1) }
+}
+{% endhighlight %}
+
+Here, if we create a `new MyClass` and call `doStuff` on it, the `map` inside there references the
+`func1` method *of that `MyClass` instance*, so the whole object needs to be sent to the cluster. It is
+similar to writing `rdd.map(x => this.func1(x))`.
+
+In a similar way, accessing fields of the outer object will reference the whole object:
+
+{% highlight scala %}
+class MyClass {
+  val field = "Hello"
+  def doStuff(rdd: RDD[String]): RDD[String] = { rdd.map(x => field + x) }
+}
+{% endhighlight %}
+
+is equilvalent to writing `rdd.map(x => this.field + x)`, which references all of `this`. To avoid this
+issue, the simplest way is to copy `field` into a local variable instead of accessing it externally:
+
+{% highlight scala %}
+def doStuff(rdd: RDD[String]): RDD[String] = {
+  val field_ = this.field
+  rdd.map(x => field_ + x)
+}
+{% endhighlight %}
+
+</div>
+
+<div data-lang="java"  markdown="1">
+
+Spark's API relies heavily on passing functions in the driver program to run on the cluster.
+In Java, functions are represented by classes implementing the interfaces in the
+[org.apache.spark.api.java.function](api/java/index.html?org/apache/spark/api/java/function/package-summary.html) package.
+There are two ways to create such functions:
+
+* Implement the Function interfaces in your own class, either as an anonymous inner class or a named one,
+  and pass an instance of it to Spark.
+* In Java 8, use [lambda expressions](http://docs.oracle.com/javase/tutorial/java/javaOO/lambdaexpressions.html)
+  to concisely define an implementation.
+
+While much of this guide uses lambda syntax for conciseness, it is easy to use all the same APIs
+in long-form. For example, we could have written our code above as follows:
+
+{% highlight java %}
+JavaRDD<String> lines = sc.textFile("data.txt");
+JavaRDD<Integer> lineLengths = lines.map(new Function<String, Integer>() {
+  public Integer call(String s) { return s.length(); }
+});
+int totalLength = lineLengths.reduce(new Function2<Integer, Integer, Integer>() {
+  public Integer call(Integer a, Integer b) { return a + b; }
+});
+{% endhighlight %}
+
+Or, if writing the functions inline is unwieldy:
+
+{% highlight java %}
+class GetLength implements Function<String, Integer> {
+  public Integer call(String s) { return s.length(); }
+}
+class Sum implements Function2<Integer, Integer, Integer> {
+  public Integer call(Integer a, Integer b) { return a + b; }
+}
+
+JavaRDD<String> lines = sc.textFile("data.txt");
+JavaRDD<Integer> lineLengths = lines.map(new GetLength());
+int totalLength = lineLengths.reduce(new Sum());
+{% endhighlight %}
+
+Note that anonymous inner classes in Java can also access variables in the enclosing scope as long
+as they are marked `final`. Spark will ship copies of these variables to each worker node as it does
+for other languages.
+
+</div>
+
+<div data-lang="python"  markdown="1">
+
+Spark's API relies heavily on passing functions in the driver program to run on the cluster.
+There are three recommended ways to do this:
+
+* [Lambda expressions](https://docs.python.org/2/tutorial/controlflow.html#lambda-expressions),
+  for simple functions that can be written as an expression. (Lambdas do not support multi-statement
+  functions or statements that do not return a value.)
+* Local `def`s inside the function calling into Spark, for longer code.
+* Top-level functions in a module.
+
+For example, to pass a longer function than can be supported using a `lambda`, consider
+the code below:
+
+{% highlight python %}
+"""MyScript.py"""
+if __name__ == "__main__":
+    def myFunc(s):
+        words = s.split(" ")
+        return len(words)
+
+    sc = SparkContext(...)
+    sc.textFile("file.txt").map(myFunc)
+{% endhighlight %}
+
+Note that while it is also possible to pass a reference to a method in a class instance (as opposed to
+a singleton object), this requires sending the object that contains that class along with the method.
+For example, consider:
+
+{% highlight python %}
+class MyClass(object):
+    def func(self, s):
+        return s
+    def doStuff(self, rdd):
+        return rdd.map(self.func)
+{% endhighlight %}
+
+Here, if we create a `new MyClass` and call `doStuff` on it, the `map` inside there references the
+`func` method *of that `MyClass` instance*, so the whole object needs to be sent to the cluster.
+
+In a similar way, accessing fields of the outer object will reference the whole object:
+
+{% highlight python %}
+class MyClass(object):
+    def __init__(self):
+        self.field = "Hello"
+    def doStuff(self, rdd):
+        return rdd.map(lambda s: self.field + x)
+{% endhighlight %}
+
+To avoid this issue, the simplest way is to copy `field` into a local variable instead
+of accessing it externally:
+
+{% highlight python %}
+def doStuff(self, rdd):
+    field = self.field
+    return rdd.map(lambda s: field + x)
+{% endhighlight %}
+
+</div>
+
+</div>
+
+### Working with Key-Value Pairs
+
+<div class="codetabs">
+
+<div data-lang="scala" markdown="1">
+
+While most Spark operations work on RDDs containing any type of objects, a few special operations are
+only available on RDDs of key-value pairs.
+The most common ones are distibuted "shuffle" operations, such as grouping or aggregating the elements
+by a key.
+
+In Scala, these operations are automatically available on RDDs containing
+[Tuple2](http://www.scala-lang.org/api/{{site.SCALA_VERSION}}/index.html#scala.Tuple2) objects
+(the built-in tuples in the language, created by simply writing `(a, b)`), as long as you
+import `org.apache.spark.SparkContext._` in your program to enable Spark's implicit
+conversions. The key-value pair operations are available in the
+[PairRDDFunctions](api/scala/index.html#org.apache.spark.rdd.PairRDDFunctions) class,
+which automatically wraps around an RDD of tuples if you import the conversions.
+
+For example, the following code uses the `reduceByKey` operation on key-value pairs to count how
+many times each line of text occurs in a file:
+
+{% highlight scala %}
+val lines = sc.textFile("data.txt")
+val pairs = lines.map(s => (s, 1))
+val counts = pairs.reduceByKey((a, b) => a + b)
+{% endhighlight %}
+
+We could also use `counts.sortByKey()`, for example, to sort the pairs alphabetically, and finally
+`counts.collect()` to bring them back to the driver program as an array of objects.
+
+</div>
+
+<div data-lang="java" markdown="1">
+
+While most Spark operations work on RDDs containing any type of objects, a few special operations are
+only available on RDDs of key-value pairs.
+The most common ones are distibuted "shuffle" operations, such as grouping or aggregating the elements
+by a key.
+
+In Java, key-value pairs are represented using the 
+[scala.Tuple2](http://www.scala-lang.org/api/{{site.SCALA_VERSION}}/index.html#scala.Tuple2) class
+from the Scala standard library. You can simply call `new Tuple2(a, b)` to create a tuple, and access
+its fields later with `tuple._1()` and `tuple._2()`.
+
+RDDs of key-value pairs are represented by the
+[JavaPairRDD](api/java/index.html?org/apache/spark/api/java/JavaPairRDD.html) class. You can construct
+JavaPairRDDs from JavaRDDs using special versions of the `map` operations, like
+`mapToPair` and `flatMapToPair`. The JavaPairRDD will have both standard RDD functions and special
+key-value ones.
+
+For example, the following code uses the `reduceByKey` operation on key-value pairs to count how
+many times each line of text occurs in a file:
+
+{% highlight scala %}
+JavaRDD<String> lines = sc.textFile("data.txt");
+JavaPairRDD<String, Integer> pairs = lines.mapToPair(s -> new Tuple2(s, 1));
+JavaPairRDD<String, Integer> counts = pairs.reduceByKey((a, b) -> a + b);
+{% endhighlight %}
+
+We could also use `counts.sortByKey()`, for example, to sort the pairs alphabetically, and finally
+`counts.collect()` to bring them back to the driver program as an array of objects.
+
+
+</div>
+
+<div data-lang="python" markdown="1">
+
+While most Spark operations work on RDDs containing any type of objects, a few special operations are
+only available on RDDs of key-value pairs.
+The most common ones are distibuted "shuffle" operations, such as grouping or aggregating the elements
+by a key.
+
+In Python, these operations work on RDDs containing built-in Python tuples such as `(1, 2)`.
+Simply create such tuples and then call your desired operation.
+
+For example, the following code uses the `reduceByKey` operation on key-value pairs to count how
+many times each line of text occurs in a file:
+
+{% highlight python %}
+lines = sc.textFile("data.txt")
+pairs = lines.map(lambda s: (s, 1))
+counts = pairs.reduceByKey(lambda a, b: a + b)
+{% endhighlight %}
+
+We could also use `counts.sortByKey()`, for example, to sort the pairs alphabetically, and finally
+`counts.collect()` to bring them back to the driver program as a list of objects.
+
+</div>
+
+</div>
+
+
+### Transformations
+
+The following table lists some of the common transformations supported by Spark. Refer to the
+RDD API doc
+([Scala](api/scala/index.html#org.apache.spark.rdd.RDD),
+ [Java](api/java/index.html?org/apache/spark/api/java/JavaRDD.html),
+ [Python](api/python/pyspark.rdd.RDD-class.html))
+and pair RDD functions doc
+([Scala](api/scala/index.html#org.apache.spark.rdd.PairRDDFunctions),
+ [Java](api/java/index.html?org/apache/spark/api/java/JavaPairRDD.html))
+for details.
+
+<table class="table">
+<tr><th style="width:25%">Transformation</th><th>Meaning</th></tr>
+<tr>
+  <td> <b>map</b>(<i>func</i>) </td>
+  <td> Return a new distributed dataset formed by passing each element of the source through a function <i>func</i>. </td>
+</tr>
+<tr>
+  <td> <b>filter</b>(<i>func</i>) </td>
+  <td> Return a new dataset formed by selecting those elements of the source on which <i>func</i> returns true. </td>
+</tr>
+<tr>
+  <td> <b>flatMap</b>(<i>func</i>) </td>
+  <td> Similar to map, but each input item can be mapped to 0 or more output items (so <i>func</i> should return a Seq rather than a single item). </td>
+</tr>
+<tr>
+  <td> <b>mapPartitions</b>(<i>func</i>) </td>
+  <td> Similar to map, but runs separately on each partition (block) of the RDD, so <i>func</i> must be of type
+    Iterator&lt;T&gt; => Iterator&lt;U&gt; when running on an RDD of type T. </td>
+</tr>
+<tr>
+  <td> <b>mapPartitionsWithIndex</b>(<i>func</i>) </td>
+  <td> Similar to mapPartitions, but also provides <i>func</i> with an integer value representing the index of
+  the partition, so <i>func</i> must be of type (Int, Iterator&lt;T&gt;) => Iterator&lt;U&gt; when running on an RDD of type T.
+  </td>
+</tr>
+<tr>
+  <td> <b>sample</b>(<i>withReplacement</i>, <i>fraction</i>, <i>seed</i>) </td>
+  <td> Sample a fraction <i>fraction</i> of the data, with or without replacement, using a given random number generator seed. </td>
+</tr>
+<tr>
+  <td> <b>union</b>(<i>otherDataset</i>) </td>
+  <td> Return a new dataset that contains the union of the elements in the source dataset and the argument. </td>
+</tr>
+<tr>
+  <td> <b>intersection</b>(<i>otherDataset</i>) </td>
+  <td> Return a new RDD that contains the intersection of elements in the source dataset and the argument. </td>
+</tr>
+<tr>
+  <td> <b>distinct</b>([<i>numTasks</i>])) </td>
+  <td> Return a new dataset that contains the distinct elements of the source dataset.</td>
+</tr>
+<tr>
+  <td> <b>groupByKey</b>([<i>numTasks</i>]) </td>
+  <td> When called on a dataset of (K, V) pairs, returns a dataset of (K, Iterable&lt;V&gt;) pairs. <br />
+    <b>Note:</b> If you are grouping in order to perform an aggregation (such as a sum or 
+      average) over each key, using <code>reduceByKey</code> or <code>combineByKey</code> will yield much better 
+      performance.
+    <br />
+    <b>Note:</b> By default, the level of parallelism in the output depends on the number of partitions of the parent RDD.
+      You can pass an optional <code>numTasks</code> argument to set a different number of tasks.
+  </td>
+</tr>
+<tr>
+  <td> <b>reduceByKey</b>(<i>func</i>, [<i>numTasks</i>]) </td>
+  <td> When called on a dataset of (K, V) pairs, returns a dataset of (K, V) pairs where the values for each key are aggregated using the given reduce function. Like in <code>groupByKey</code>, the number of reduce tasks is configurable through an optional second argument. </td>
+</tr>
+<tr>
+  <td> <b>sortByKey</b>([<i>ascending</i>], [<i>numTasks</i>]) </td>
+  <td> When called on a dataset of (K, V) pairs where K implements Ordered, returns a dataset of (K, V) pairs sorted by keys in ascending or descending order, as specified in the boolean <code>ascending</code> argument.</td>
+</tr>
+<tr>
+  <td> <b>join</b>(<i>otherDataset</i>, [<i>numTasks</i>]) </td>
+  <td> When called on datasets of type (K, V) and (K, W), returns a dataset of (K, (V, W)) pairs with all pairs of elements for each key.
+    Outer joins are also supported through <code>leftOuterJoin</code> and <code>rightOuterJoin</code>.
+  </td>
+</tr>
+<tr>
+  <td> <b>cogroup</b>(<i>otherDataset</i>, [<i>numTasks</i>]) </td>
+  <td> When called on datasets of type (K, V) and (K, W), returns a dataset of (K, Iterable&lt;V&gt;, Iterable&lt;W&gt;) tuples. This operation is also called <code>groupWith</code>. </td>
+</tr>
+<tr>
+  <td> <b>cartesian</b>(<i>otherDataset</i>) </td>
+  <td> When called on datasets of types T and U, returns a dataset of (T, U) pairs (all pairs of elements). </td>
+</tr>
+<tr>
+  <td> <b>pipe</b>(<i>command</i>, <i>[envVars]</i>) </td>
+  <td> Pipe each partition of the RDD through a shell command, e.g. a Perl or bash script. RDD elements are written to the
+    process's stdin and lines output to its stdout are returned as an RDD of strings. </td>
+</tr>
+<tr>
+  <td> <b>coalesce</b>(<i>numPartitions</i>) </td>
+  <td> Decrease the number of partitions in the RDD to numPartitions. Useful for running operations more efficiently
+    after filtering down a large dataset. </td>
+</tr>
+<tr>
+  <td> <b>repartition</b>(<i>numPartitions</i>) </td>
+  <td> Reshuffle the data in the RDD randomly to create either more or fewer partitions and balance it across them.
+    This always shuffles all data over the network. </td>
+</tr>
+</table>
+
+### Actions
+
+The following table lists some of the common actions supported by Spark. Refer to the
+RDD API doc
+([Scala](api/scala/index.html#org.apache.spark.rdd.RDD),
+ [Java](api/java/index.html?org/apache/spark/api/java/JavaRDD.html),
+ [Python](api/python/pyspark.rdd.RDD-class.html))
+and pair RDD functions doc
+([Scala](api/scala/index.html#org.apache.spark.rdd.PairRDDFunctions),
+ [Java](api/java/index.html?org/apache/spark/api/java/JavaPairRDD.html))
+for details.
+
+<table class="table">
+<tr><th>Action</th><th>Meaning</th></tr>
+<tr>
+  <td> <b>reduce</b>(<i>func</i>) </td>
+  <td> Aggregate the elements of the dataset using a function <i>func</i> (which takes two arguments and returns one). The function should be commutative and associative so that it can be computed correctly in parallel. </td>
+</tr>
+<tr>
+  <td> <b>collect</b>() </td>
+  <td> Return all the elements of the dataset as an array at the driver program. This is usually useful after a filter or other operation that returns a sufficiently small subset of the data. </td>
+</tr>
+<tr>
+  <td> <b>count</b>() </td>
+  <td> Return the number of elements in the dataset. </td>
+</tr>
+<tr>
+  <td> <b>first</b>() </td>
+  <td> Return the first element of the dataset (similar to take(1)). </td>
+</tr>
+<tr>
+  <td> <b>take</b>(<i>n</i>) </td>
+  <td> Return an array with the first <i>n</i> elements of the dataset. Note that this is currently not executed in parallel. Instead, the driver program computes all the elements. </td>
+</tr>
+<tr>
+  <td> <b>takeSample</b>(<i>withReplacement</i>, <i>num</i>, <i>seed</i>) </td>
+  <td> Return an array with a random sample of <i>num</i> elements of the dataset, with or without replacement, using the given random number generator seed. </td>
+</tr>
+<tr>
+  <td> <b>takeOrdered</b>(<i>n</i>, <i>[ordering]</i>) </td>
+  <td> Return the first <i>n</i> elements of the RDD using either their natural order or a custom comparator. </td>
+</tr>
+<tr>
+  <td> <b>saveAsTextFile</b>(<i>path</i>) </td>
+  <td> Write the elements of the dataset as a text file (or set of text files) in a given directory in the local filesystem, HDFS or any other Hadoop-supported file system. Spark will call toString on each element to convert it to a line of text in the file. </td>
+</tr>
+<tr>
+  <td> <b>saveAsSequenceFile</b>(<i>path</i>) <br /> (Java and Scala) </td>
+  <td> Write the elements of the dataset as a Hadoop SequenceFile in a given path in the local filesystem, HDFS or any other Hadoop-supported file system. This is available on RDDs of key-value pairs that either implement Hadoop's Writable interface. In Scala, it is also
+   available on types that are implicitly convertible to Writable (Spark includes conversions for basic types like Int, Double, String, etc). </td>
+</tr>
+<tr>
+  <td> <b>saveAsObjectFile</b>(<i>path</i>) <br /> (Java and Scala) </td>
+  <td> Write the elements of the dataset in a simple format using Java serialization, which can then be loaded using
+    <code>SparkContext.objectFile()</code>. </td>
+</tr>
+<tr>
+  <td> <b>countByKey</b>() </td>
+  <td> Only available on RDDs of type (K, V). Returns a hashmap of (K, Int) pairs with the count of each key. </td>
+</tr>
+<tr>
+  <td> <b>foreach</b>(<i>func</i>) </td>
+  <td> Run a function <i>func</i> on each element of the dataset. This is usually done for side effects such as updating an accumulator variable (see below) or interacting with external storage systems. </td>
+</tr>
+</table>
+
+## RDD Persistence
+
+One of the most important capabilities in Spark is *persisting* (or *caching*) a dataset in memory
+across operations. When you persist an RDD, each node stores any partitions of it that it computes in
+memory and reuses them in other actions on that dataset (or datasets derived from it). This allows
+future actions to be much faster (often by more than 10x). Caching is a key tool for
+iterative algorithms and fast interactive use.
+
+You can mark an RDD to be persisted using the `persist()` or `cache()` methods on it. The first time
+it is computed in an action, it will be kept in memory on the nodes. Spark's cache is fault-tolerant --
+if any partition of an RDD is lost, it will automatically be recomputed using the transformations
+that originally created it.
+
+In addition, each persisted RDD can be stored using a different *storage level*, allowing you, for example,
+to persist the dataset on disk, persist it in memory but as serialized Java objects (to save space),
+replicate it across nodes, or store it off-heap in [Tachyon](http://tachyon-project.org/).
+These levels are set by passing a
+`StorageLevel` object ([Scala](api/scala/index.html#org.apache.spark.storage.StorageLevel),
+[Java](api/java/index.html?org/apache/spark/storage/StorageLevel.html),
+[Python](api/python/pyspark.storagelevel.StorageLevel-class.html))
+to `persist()`. The `cache()` method is a shorthand for using the default storage level,
+which is `StorageLevel.MEMORY_ONLY` (store deserialized objects in memory). The full set of
+storage levels is:
+
+<table class="table">
+<tr><th style="width:23%">Storage Level</th><th>Meaning</th></tr>
+<tr>
+  <td> MEMORY_ONLY </td>
+  <td> Store RDD as deserialized Java objects in the JVM. If the RDD does not fit in memory, some partitions will
+    not be cached and will be recomputed on the fly each time they're needed. This is the default level. </td>
+</tr>
+<tr>
+  <td> MEMORY_AND_DISK </td>
+  <td> Store RDD as deserialized Java objects in the JVM. If the RDD does not fit in memory, store the
+    partitions that don't fit on disk, and read them from there when they're needed. </td>
+</tr>
+<tr>
+  <td> MEMORY_ONLY_SER </td>
+  <td> Store RDD as <i>serialized</i> Java objects (one byte array per partition).
+    This is generally more space-efficient than deserialized objects, especially when using a
+    <a href="tuning.html">fast serializer</a>, but more CPU-intensive to read.
+  </td>
+</tr>
+<tr>
+  <td> MEMORY_AND_DISK_SER </td>
+  <td> Similar to MEMORY_ONLY_SER, but spill partitions that don't fit in memory to disk instead of
+    recomputing them on the fly each time they're needed. </td>
+</tr>
+<tr>
+  <td> DISK_ONLY </td>
+  <td> Store the RDD partitions only on disk. </td>
+</tr>
+<tr>
+  <td> MEMORY_ONLY_2, MEMORY_AND_DISK_2, etc.  </td>
+  <td> Same as the levels above, but replicate each partition on two cluster nodes. </td>
+</tr>
+<tr>
+  <td> OFF_HEAP (experimental) </td>
+  <td> Store RDD in serialized format in <a href="http://tachyon-project.org">Tachyon</a>.
+    Compared to MEMORY_ONLY_SER, OFF_HEAP reduces garbage collection overhead and allows executors
+    to be smaller and to share a pool of memory, making it attractive in environments with
+    large heaps or multiple concurrent applications.
+  </td>
+</tr>
+</table>
+
+**Note:** *In Python, stored objects will always be serialized with the [Pickle](https://docs.python.org/2/library/pickle.html) library, so it does not matter whether you choose a serialized level.*
+
+Spark also automatically persists some intermediate data in shuffle operations (e.g. `reduceByKey`), even without users calling `persist`. This is done to avoid recomputing the entire input if a node fails during the shuffle. We still recommend users call `persist` on the resulting RDD if they plan to reuse it.
+
+### Which Storage Level to Choose?
+
+Spark's storage levels are meant to provide different trade-offs between memory usage and CPU
+efficiency. We recommend going through the following process to select one:
+
+* If your RDDs fit comfortably with the default storage level (`MEMORY_ONLY`), leave them that way.
+  This is the most CPU-efficient option, allowing operations on the RDDs to run as fast as possible.
+
+* If not, try using `MEMORY_ONLY_SER` and [selecting a fast serialization library](tuning.html) to
+make the objects much more space-efficient, but still reasonably fast to access. 
+
+* Don't spill to disk unless the functions that computed your datasets are expensive, or they filter
+a large amount of the data. Otherwise, recomputing a partition may be as fast as reading it from
+disk.
+
+* Use the replicated storage levels if you want fast fault recovery (e.g. if using Spark to serve
+requests from a web application). *All* the storage levels provide full fault tolerance by
+recomputing lost data, but the replicated ones let you continue running tasks on the RDD without
+waiting to recompute a lost partition.
+
+* In environments with high amounts of memory or multiple applications, the experimental `OFF_HEAP`
+mode has several advantages:
+   * It allows multiple executors to share the same pool of memory in Tachyon.
+   * It significantly reduces garbage collection costs.
+   * Cached data is not lost if individual executors crash.
+
+### Removing Data
+
+Spark automatically monitors cache usage on each node and drops out old data partitions in a
+least-recently-used (LRU) fashion. If you would like to manually remove an RDD instead of waiting for
+it to fall out of the cache, use the `RDD.unpersist()` method.
+
+# Shared Variables
+
+Normally, when a function passed to a Spark operation (such as `map` or `reduce`) is executed on a
+remote cluster node, it works on separate copies of all the variables used in the function. These
+variables are copied to each machine, and no updates to the variables on the remote machine are
+propagated back to the driver program. Supporting general, read-write shared variables across tasks
+would be inefficient. However, Spark does provide two limited types of *shared variables* for two
+common usage patterns: broadcast variables and accumulators.
+
+## Broadcast Variables
+
+Broadcast variables allow the programmer to keep a read-only variable cached on each machine rather
+than shipping a copy of it with tasks. They can be used, for example, to give every node a copy of a
+large input dataset in an efficient manner. Spark also attempts to distribute broadcast variables
+using efficient broadcast algorithms to reduce communication cost.
+
+Broadcast variables are created from a variable `v` by calling `SparkContext.broadcast(v)`. The
+broadcast variable is a wrapper around `v`, and its value can be accessed by calling the `value`
+method. The code below shows this:
+
+<div class="codetabs">
+
+<div data-lang="scala"  markdown="1">
+
+{% highlight scala %}
+scala> val broadcastVar = sc.broadcast(Array(1, 2, 3))
+broadcastVar: spark.Broadcast[Array[Int]] = spark.Broadcast(b5c40191-a864-4c7d-b9bf-d87e1a4e787c)
+
+scala> broadcastVar.value
+res0: Array[Int] = Array(1, 2, 3)
+{% endhighlight %}
+
+</div>
+
+<div data-lang="java"  markdown="1">
+
+{% highlight java %}
+Broadcast<int[]> broadcastVar = sc.broadcast(new int[] {1, 2, 3});
+
+broadcastVar.value();
+// returns [1, 2, 3]
+{% endhighlight %}
+
+</div>
+
+<div data-lang="python"  markdown="1">
+
+{% highlight python %}
+>>> broadcastVar = sc.broadcast([1, 2, 3])
+<pyspark.broadcast.Broadcast object at 0x102789f10>
+
+>>> broadcastVar.value
+[1, 2, 3]
+{% endhighlight %}
+
+</div>
+
+</div>
+
+After the broadcast variable is created, it should be used instead of the value `v` in any functions
+run on the cluster so that `v` is not shipped to the nodes more than once. In addition, the object
+`v` should not be modified after it is broadcast in order to ensure that all nodes get the same
+value of the broadcast variable (e.g. if the variable is shipped to a new node later).
+
+## Accumulators
+
+Accumulators are variables that are only "added" to through an associative operation and can
+therefore be efficiently supported in parallel. They can be used to implement counters (as in
+MapReduce) or sums. Spark natively supports accumulators of numeric types, and programmers
+can add support for new types.
+
+An accumulator is created from an initial value `v` by calling `SparkContext.accumulator(v)`. Tasks
+running on the cluster can then add to it using the `add` method or the `+=` operator (in Scala and Python).
+However, they cannot read its value.
+Only the driver program can read the accumulator's value, using its `value` method.
+
+The code below shows an accumulator being used to add up the elements of an array:
+
+<div class="codetabs">
+
+<div data-lang="scala"  markdown="1">
+
+{% highlight scala %}
+scala> val accum = sc.accumulator(0)
+accum: spark.Accumulator[Int] = 0
+
+scala> sc.parallelize(Array(1, 2, 3, 4)).foreach(x => accum += x)
+...
+10/09/29 18:41:08 INFO SparkContext: Tasks finished in 0.317106 s
+
+scala> accum.value
+res2: Int = 10
+{% endhighlight %}
+
+While this code used the built-in support for accumulators of type Int, programmers can also
+create their own types by subclassing [AccumulatorParam](api/scala/index.html#org.apache.spark.AccumulatorParam).
+The AccumulatorParam interface has two methods: `zero` for providing a "zero value" for your data
+type, and `addInPlace` for adding two values together. For example, supposing we had a `Vector` class
+representing mathematical vectors, we could write:
+
+{% highlight scala %}
+object VectorAccumulatorParam extends AccumulatorParam[Vector] {
+  def zero(initialValue: Vector): Vector = {
+    Vector.zeros(initialValue.size)
+  }
+  def addInPlace(v1: Vector, v2: Vector): Vector = {
+    v1 += v2
+  }
+}
+
+// Then, create an Accumulator of this type:
+val vecAccum = sc.accumulator(new Vector(...))(VectorAccumulatorParam)
+{% endhighlight %}
+
+In Scala, Spark also supports the more general [Accumulable](api/scala/index.html#org.apache.spark.Accumulable)
+interface to accumulate data where the resulting type is not the same as the elements added (e.g. build
+a list by collecting together elements), and the `SparkContext.accumulableCollection` method for accumulating
+common Scala collection types.
+
+</div>
+
+<div data-lang="java"  markdown="1">
+
+{% highlight java %}
+Accumulator<Integer> accum = sc.accumulator(0);
+
+sc.parallelize(Arrays.asList(1, 2, 3, 4)).foreach(x -> accum.add(x));
+// ...
+// 10/09/29 18:41:08 INFO SparkContext: Tasks finished in 0.317106 s
+
+accum.value();
+// returns 10
+{% endhighlight %}
+
+While this code used the built-in support for accumulators of type Integer, programmers can also
+create their own types by subclassing [AccumulatorParam](api/java/index.html?org/apache/spark/AccumulatorParam.html).
+The AccumulatorParam interface has two methods: `zero` for providing a "zero value" for your data
+type, and `addInPlace` for adding two values together. For example, supposing we had a `Vector` class
+representing mathematical vectors, we could write:
+
+{% highlight java %}
+class VectorAccumulatorParam implements AccumulatorParam<Vector> {
+  public Vector zero(Vector initialValue) {
+    return Vector.zeros(initialValue.size());
+  }
+  public Vector addInPlace(Vector v1, Vector v2) {
+    v1.addInPlace(v2); return v1;
+  }
+}
+
+// Then, create an Accumulator of this type:
+Accumulator<Vector> vecAccum = sc.accumulator(new Vector(...), new VectorAccumulatorParam());
+{% endhighlight %}
+
+In Java, Spark also supports the more general [Accumulable](api/java/index.html?org/apache/spark/Accumulable.html)
+interface to accumulate data where the resulting type is not the same as the elements added (e.g. build
+a list by collecting together elements).
+
+</div>
+
+<div data-lang="python"  markdown="1">
+
+{% highlight python %}
+>>> accum = sc.accumulator(0)
+Accumulator<id=0, value=0>
+
+>>> sc.parallelize([1, 2, 3, 4]).foreach(lambda x: accum.add(x))
+...
+10/09/29 18:41:08 INFO SparkContext: Tasks finished in 0.317106 s
+
+scala> accum.value
+10
+{% endhighlight %}
+
+While this code used the built-in support for accumulators of type Int, programmers can also
+create their own types by subclassing [AccumulatorParam](api/python/pyspark.accumulators.AccumulatorParam-class.html).
+The AccumulatorParam interface has two methods: `zero` for providing a "zero value" for your data
+type, and `addInPlace` for adding two values together. For example, supposing we had a `Vector` class
+representing mathematical vectors, we could write:
+
+{% highlight python %}
+class VectorAccumulatorParam(AccumulatorParam):
+    def zero(self, initialValue):
+        return Vector.zeros(initialValue.size)
+
+    def addInPlace(self, v1, v2):
+        v1 += v2
+        return v1
+
+# Then, create an Accumulator of this type:
+vecAccum = sc.accumulator(Vector(...), VectorAccumulatorParam())
+{% endhighlight %}
+
+</div>
+
+</div>
+
+# Deploying to a Cluster
+
+The [application submission guide](submitting-applications.html) describes how to submit applications to a cluster.
+In short, once you package your application into a JAR (for Java/Scala) or a set of `.py` or `.zip` files (for Python),
+the `bin/spark-submit` script lets you submit it to any supported cluster manager.
+
+# Unit Testing
+
+Spark is friendly to unit testing with any popular unit test framework.
+Simply create a `SparkContext` in your test with the master URL set to `local`, run your operations,
+and then call `SparkContext.stop()` to tear it down.
+Make sure you stop the context within a `finally` block or the test framework's `tearDown` method,
+as Spark does not support two contexts running concurrently in the same program.
+
+# Migrating from pre-1.0 Versions of Spark
+
+<div class="codetabs">
+
+<div data-lang="scala"  markdown="1">
+
+Spark 1.0 freezes the API of Spark Core for the 1.X series, in that any API available today that is
+not marked "experimental" or "developer API" will be supported in future versions.
+The only change for Scala users is that the grouping operations, e.g. `groupByKey`, `cogroup` and `join`,
+have changed from returning `(Key, Seq[Value])` pairs to `(Key, Iterable[Value])`.
+
+</div>
+
+<div data-lang="java"  markdown="1">
+
+Spark 1.0 freezes the API of Spark Core for the 1.X series, in that any API available today that is
+not marked "experimental" or "developer API" will be supported in future versions.
+Several changes were made to the Java API:
+
+* The Function classes in `org.apache.spark.api.java.function` became interfaces in 1.0, meaning that old
+  code that `extends Function` should `implement Function` instead.
+* New variants of the `map` transformations, like `mapToPair` and `mapToDouble`, were added to create RDDs
+  of special data types.
+* Grouping operations like `groupByKey`, `cogroup` and `join` have changed from returning 
+  `(Key, List<Value>)` pairs to `(Key, Iterable<Value>)`.
+
+</div>
+
+<div data-lang="python"  markdown="1">
+
+Spark 1.0 freezes the API of Spark Core for the 1.X series, in that any API available today that is
+not marked "experimental" or "developer API" will be supported in future versions.
+The only change for Python users is that the grouping operations, e.g. `groupByKey`, `cogroup` and `join`,
+have changed from returning (key, list of values) pairs to (key, iterable of values).
+
+</div>
+
+</div>
+
+Migration guides are also available for [Spark Streaming](streaming-programming-guide.html#migration-guide-from-091-or-below-to-1x),
+[MLlib](mllib-guide.html#migration-guide) and [GraphX](graphx-programming-guide.html#migrating-from-spark-091).
+
+
+# Where to Go from Here
+
+You can see some [example Spark programs](http://spark.apache.org/examples.html) on the Spark website.
+In addition, Spark includes several samples in the `examples` directory
+([Scala]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/scala/org/apache/spark/examples),
+ [Java]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/java/org/apache/spark/examples),
+ [Python]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/python)).
+You can run Java and Scala examples by passing the class name to Spark's `bin/run-example` script; for instance:
+
+    ./bin/run-example SparkPi
+
+For Python examples, use `spark-submit` instead:
+
+    ./bin/spark-submit examples/src/main/python/pi.py
+
+For help on optimizing your programs, the [configuration](configuration.html) and
+[tuning](tuning.html) guides provide information on best practices. They are especially important for
+making sure that your data is stored in memory in an efficient format.
+For help on deploying, the [cluster mode overview](cluster-overview.html) describes the components involved
+in distributed operation and supported cluster managers.
+
+Finally, full API documentation is available in
+[Scala](api/scala/#org.apache.spark.package), [Java](api/java/) and [Python](api/python/).
diff --git a/docs/python-programming-guide.md b/docs/python-programming-guide.md
index b686bee1aebe..68f04b50aaa0 100644
--- a/docs/python-programming-guide.md
+++ b/docs/python-programming-guide.md
@@ -1,171 +1,7 @@
 ---
 layout: global
 title: Python Programming Guide
+redirect: programming-guide.html
 ---
 
-
-The Spark Python API (PySpark) exposes the Spark programming model to Python.
-To learn the basics of Spark, we recommend reading through the
-[Scala programming guide](scala-programming-guide.html) first; it should be
-easy to follow even if you don't know Scala.
-This guide will show how to use the Spark features described there in Python.
-
-
-# Key Differences in the Python API
-
-There are a few key differences between the Python and Scala APIs:
-
-* Python is dynamically typed, so RDDs can hold objects of multiple types.
-* PySpark does not yet support a few API calls, such as `lookup` and non-text input files, though these will be added in future releases.
-
-In PySpark, RDDs support the same methods as their Scala counterparts but take Python functions and return Python collection types.
-Short functions can be passed to RDD methods using Python's [`lambda`](http://www.diveintopython.net/power_of_introspection/lambda_functions.html) syntax:
-
-{% highlight python %}
-logData = sc.textFile(logFile).cache()
-errors = logData.filter(lambda line: "ERROR" in line)
-{% endhighlight %}
-
-You can also pass functions that are defined with the `def` keyword; this is useful for longer functions that can't be expressed using `lambda`:
-
-{% highlight python %}
-def is_error(line):
-    return "ERROR" in line
-errors = logData.filter(is_error)
-{% endhighlight %}
-
-Functions can access objects in enclosing scopes, although modifications to those objects within RDD methods will not be propagated back:
-
-{% highlight python %}
-error_keywords = ["Exception", "Error"]
-def is_error(line):
-    return any(keyword in line for keyword in error_keywords)
-errors = logData.filter(is_error)
-{% endhighlight %}
-
-PySpark will automatically ship these functions to executors, along with any objects that they reference.
-Instances of classes will be serialized and shipped to executors by PySpark, but classes themselves cannot be automatically distributed to executors.
-The [Standalone Use](#standalone-programs) section describes how to ship code dependencies to executors.
-
-In addition, PySpark fully supports interactive use---simply run `./bin/pyspark` to launch an interactive shell.
-
-
-# Installing and Configuring PySpark
-
-PySpark requires Python 2.6 or higher.
-PySpark applications are executed using a standard CPython interpreter in order to support Python modules that use C extensions.
-We have not tested PySpark with Python 3 or with alternative Python interpreters, such as [PyPy](http://pypy.org/) or [Jython](http://www.jython.org/).
-
-By default, PySpark requires `python` to be available on the system `PATH` and use it to run programs; an alternate Python executable may be specified by setting the `PYSPARK_PYTHON` environment variable in `conf/spark-env.sh` (or `.cmd` on Windows).
-
-All of PySpark's library dependencies, including [Py4J](http://py4j.sourceforge.net/), are bundled with PySpark and automatically imported.
-
-# Interactive Use
-
-The `bin/pyspark` script launches a Python interpreter that is configured to run PySpark applications. To use `pyspark` interactively, first build Spark, then launch it directly from the command line:
-
-{% highlight bash %}
-$ sbt/sbt assembly
-$ ./bin/pyspark
-{% endhighlight %}
-
-The Python shell can be used explore data interactively and is a simple way to learn the API:
-
-{% highlight python %}
->>> words = sc.textFile("/usr/share/dict/words")
->>> words.filter(lambda w: w.startswith("spar")).take(5)
-[u'spar', u'sparable', u'sparada', u'sparadrap', u'sparagrass']
->>> help(pyspark) # Show all pyspark functions
-{% endhighlight %}
-
-By default, the `bin/pyspark` shell creates SparkContext that runs applications locally on all of
-your machine's logical cores. To connect to a non-local cluster, or to specify a number of cores,
-set the `--master` flag. For example, to use the `bin/pyspark` shell with a
-[standalone Spark cluster](spark-standalone.html):
-
-{% highlight bash %}
-$ ./bin/pyspark --master spark://1.2.3.4:7077
-{% endhighlight %}
-
-Or, to use exactly four cores on the local machine:
-
-{% highlight bash %}
-$ ./bin/pyspark --master local[4]
-{% endhighlight %}
-
-Under the hood `bin/pyspark` is a wrapper around the
-[Spark submit script](cluster-overview.html#launching-applications-with-spark-submit), so these
-two scripts share the same list of options. For a complete list of options, run `bin/pyspark` with
-the `--help` option.
-
-## IPython
-
-It is also possible to launch the PySpark shell in [IPython](http://ipython.org), the
-enhanced Python interpreter. PySpark works with IPython 1.0.0 and later. To
-use IPython, set the `IPYTHON` variable to `1` when running `bin/pyspark`:
-
-{% highlight bash %}
-$ IPYTHON=1 ./bin/pyspark
-{% endhighlight %}
-
-Alternatively, you can customize the `ipython` command by setting `IPYTHON_OPTS`. For example, to launch
-the [IPython Notebook](http://ipython.org/notebook.html) with PyLab graphing support:
-
-{% highlight bash %}
-$ IPYTHON_OPTS="notebook --pylab inline" ./bin/pyspark
-{% endhighlight %}
-
-IPython also works on a cluster or on multiple cores if you set the `--master` flag.
-
-
-# Standalone Programs
-
-PySpark can also be used from standalone Python scripts by creating a SparkContext in your script
-and running the script using `bin/spark-submit`. The Quick Start guide includes a
-[complete example](quick-start.html#standalone-applications) of a standalone Python application.
-
-Code dependencies can be deployed by passing .zip or .egg files in the `--py-files` option of `spark-submit`:
-
-{% highlight bash %}
-./bin/spark-submit --py-files lib1.zip,lib2.zip my_script.py
-{% endhighlight %}
-
-Files listed here will be added to the `PYTHONPATH` and shipped to remote worker machines.
-Code dependencies can also be added to an existing SparkContext at runtime using its `addPyFile()` method.
-
-You can set [configuration properties](configuration.html#spark-properties) by passing a
-[SparkConf](api/python/pyspark.conf.SparkConf-class.html) object to SparkContext:
-
-{% highlight python %}
-from pyspark import SparkConf, SparkContext
-conf = (SparkConf()
-         .setMaster("local")
-         .setAppName("My app")
-         .set("spark.executor.memory", "1g"))
-sc = SparkContext(conf = conf)
-{% endhighlight %}
-
-`spark-submit` supports launching Python applications on standalone, Mesos or YARN clusters, through
-its `--master` argument. However, it currently requires the Python driver program to run on the local
-machine, not the cluster (i.e. the `--deploy-mode` parameter cannot be `cluster`).
-
-
-# API Docs
-
-[API documentation](api/python/index.html) for PySpark is available as Epydoc.
-Many of the methods also contain [doctests](http://docs.python.org/2/library/doctest.html) that provide additional usage examples.
-
-# Libraries
-
-[MLlib](mllib-guide.html) is also available in PySpark. To use it, you'll need
-[NumPy](http://www.numpy.org) version 1.4 or newer. The [MLlib guide](mllib-guide.html) contains
-some example applications.
-
-# Where to Go from Here
-
-PySpark also includes several sample programs in the [`examples/src/main/python` folder](https://github.com/apache/spark/tree/master/examples/src/main/python).
-You can run them by passing the files to `pyspark`; e.g.:
-
-    ./bin/spark-submit examples/src/main/python/wordcount.py README.md
-
-Each program prints usage help when run without the sufficient arguments.
+This document has been merged into the [Spark programming guide](programming-guide.html).
diff --git a/docs/quick-start.md b/docs/quick-start.md
index 20e17ebf703f..64023994771b 100644
--- a/docs/quick-start.md
+++ b/docs/quick-start.md
@@ -9,7 +9,7 @@ title: Quick Start
 This tutorial provides a quick introduction to using Spark. We will first introduce the API through Spark's
 interactive shell (in Python or Scala),
 then show how to write standalone applications in Java, Scala, and Python.
-See the [programming guide](scala-programming-guide.html) for a more complete reference.
+See the [programming guide](programming-guide.html) for a more complete reference.
 
 To follow along with this guide, first download a packaged release of Spark from the
 [Spark website](http://spark.apache.org/downloads.html). Since we won't be using HDFS,
@@ -35,7 +35,7 @@ scala> val textFile = sc.textFile("README.md")
 textFile: spark.RDD[String] = spark.MappedRDD@2ee9b6e3
 {% endhighlight %}
 
-RDDs have _[actions](scala-programming-guide.html#actions)_, which return values, and _[transformations](scala-programming-guide.html#transformations)_, which return pointers to new RDDs. Let's start with a few actions:
+RDDs have _[actions](programming-guide.html#actions)_, which return values, and _[transformations](programming-guide.html#transformations)_, which return pointers to new RDDs. Let's start with a few actions:
 
 {% highlight scala %}
 scala> textFile.count() // Number of items in this RDD
@@ -45,7 +45,7 @@ scala> textFile.first() // First item in this RDD
 res1: String = # Apache Spark
 {% endhighlight %}
 
-Now let's use a transformation. We will use the [`filter`](scala-programming-guide.html#transformations) transformation to return a new RDD with a subset of the items in the file.
+Now let's use a transformation. We will use the [`filter`](programming-guide.html#transformations) transformation to return a new RDD with a subset of the items in the file.
 
 {% highlight scala %}
 scala> val linesWithSpark = textFile.filter(line => line.contains("Spark"))
@@ -70,7 +70,7 @@ Spark's primary abstraction is a distributed collection of items called a Resili
 >>> textFile = sc.textFile("README.md")
 {% endhighlight %}
 
-RDDs have _[actions](scala-programming-guide.html#actions)_, which return values, and _[transformations](scala-programming-guide.html#transformations)_, which return pointers to new RDDs. Let's start with a few actions:
+RDDs have _[actions](programming-guide.html#actions)_, which return values, and _[transformations](programming-guide.html#transformations)_, which return pointers to new RDDs. Let's start with a few actions:
 
 {% highlight python %}
 >>> textFile.count() # Number of items in this RDD
@@ -80,7 +80,7 @@ RDDs have _[actions](scala-programming-guide.html#actions)_, which return values
 u'# Apache Spark'
 {% endhighlight %}
 
-Now let's use a transformation. We will use the [`filter`](scala-programming-guide.html#transformations) transformation to return a new RDD with a subset of the items in the file.
+Now let's use a transformation. We will use the [`filter`](programming-guide.html#transformations) transformation to return a new RDD with a subset of the items in the file.
 
 {% highlight python %}
 >>> linesWithSpark = textFile.filter(lambda line: "Spark" in line)
@@ -125,7 +125,7 @@ scala> val wordCounts = textFile.flatMap(line => line.split(" ")).map(word => (w
 wordCounts: spark.RDD[(String, Int)] = spark.ShuffledAggregatedRDD@71f027b8
 {% endhighlight %}
 
-Here, we combined the [`flatMap`](scala-programming-guide.html#transformations), [`map`](scala-programming-guide.html#transformations) and [`reduceByKey`](scala-programming-guide.html#transformations) transformations to compute the per-word counts in the file as an RDD of (String, Int) pairs. To collect the word counts in our shell, we can use the [`collect`](scala-programming-guide.html#actions) action:
+Here, we combined the [`flatMap`](programming-guide.html#transformations), [`map`](programming-guide.html#transformations) and [`reduceByKey`](programming-guide.html#transformations) transformations to compute the per-word counts in the file as an RDD of (String, Int) pairs. To collect the word counts in our shell, we can use the [`collect`](programming-guide.html#actions) action:
 
 {% highlight scala %}
 scala> wordCounts.collect()
@@ -162,7 +162,7 @@ One common data flow pattern is MapReduce, as popularized by Hadoop. Spark can i
 >>> wordCounts = textFile.flatMap(lambda line: line.split()).map(lambda word: (word, 1)).reduceByKey(lambda a, b: a+b)
 {% endhighlight %}
 
-Here, we combined the [`flatMap`](scala-programming-guide.html#transformations), [`map`](scala-programming-guide.html#transformations) and [`reduceByKey`](scala-programming-guide.html#transformations) transformations to compute the per-word counts in the file as an RDD of (string, int) pairs. To collect the word counts in our shell, we can use the [`collect`](scala-programming-guide.html#actions) action:
+Here, we combined the [`flatMap`](programming-guide.html#transformations), [`map`](programming-guide.html#transformations) and [`reduceByKey`](programming-guide.html#transformations) transformations to compute the per-word counts in the file as an RDD of (string, int) pairs. To collect the word counts in our shell, we can use the [`collect`](programming-guide.html#actions) action:
 
 {% highlight python %}
 >>> wordCounts.collect()
@@ -192,7 +192,7 @@ res9: Long = 15
 It may seem silly to use Spark to explore and cache a 100-line text file. The interesting part is
 that these same functions can be used on very large data sets, even when they are striped across
 tens or hundreds of nodes. You can also do this interactively by connecting `bin/spark-shell` to
-a cluster, as described in the [programming guide](scala-programming-guide.html#initializing-spark).
+a cluster, as described in the [programming guide](programming-guide.html#initializing-spark).
 
 </div>
 <div data-lang="python" markdown="1">
@@ -210,7 +210,7 @@ a cluster, as described in the [programming guide](scala-programming-guide.html#
 It may seem silly to use Spark to explore and cache a 100-line text file. The interesting part is
 that these same functions can be used on very large data sets, even when they are striped across
 tens or hundreds of nodes. You can also do this interactively by connecting `bin/pyspark` to
-a cluster, as described in the [programming guide](scala-programming-guide.html#initializing-spark).
+a cluster, as described in the [programming guide](programming-guide.html#initializing-spark).
 
 </div>
 </div>
@@ -336,7 +336,7 @@ As with the Scala example, we initialize a SparkContext, though we use the speci
 `JavaSparkContext` class to get a Java-friendly one. We also create RDDs (represented by
 `JavaRDD`) and run transformations on them. Finally, we pass functions to Spark by creating classes
 that extend `spark.api.java.function.Function`. The
-[Java programming guide](java-programming-guide.html) describes these differences in more detail.
+[Spark programming guide](programming-guide.html) describes these differences in more detail.
 
 To build the program, we also write a Maven `pom.xml` file that lists Spark as a dependency.
 Note that Spark artifacts are tagged with a Scala version.
@@ -442,6 +442,19 @@ Lines with a: 46, Lines with b: 23
 # Where to Go from Here
 Congratulations on running your first Spark application!
 
-* For an in-depth overview of the API see "Programming Guides" menu section.
-* For running applications on a cluster head to the [deployment overview](cluster-overview.html).
-* For configuration options available to Spark applications see the [configuration page](configuration.html).
+* For an in-depth overview of the API, start with the [Spark programming guide](programming-guide.html),
+  or see "Programming Guides" menu for other components.
+* For running applications on a cluster, head to the [deployment overview](cluster-overview.html).
+* Finally, Spark includes several samples in the `examples` directory
+([Scala]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/scala/org/apache/spark/examples),
+ [Java]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/java/org/apache/spark/examples),
+ [Python]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/python)).
+You can run them as follows:
+
+{% highlight bash %}
+# For Scala and Java, use run-example:
+./bin/run-example SparkPi
+
+# For Python examples, use spark-submit directly:
+./bin/spark-submit examples/src/main/python/pi.py
+{% endhighlight %}
diff --git a/docs/running-on-mesos.md b/docs/running-on-mesos.md
index df8687f81f3e..e3c892240436 100644
--- a/docs/running-on-mesos.md
+++ b/docs/running-on-mesos.md
@@ -103,7 +103,7 @@ the `make-distribution.sh` script included in a Spark source tarball/checkout.
 ## Using a Mesos Master URL
 
 The Master URLs for Mesos are in the form `mesos://host:5050` for a single-master Mesos
-cluster, or `zk://host:2181` for a multi-master Mesos cluster using ZooKeeper.
+cluster, or `mesos://zk://host:2181` for a multi-master Mesos cluster using ZooKeeper.
 
 The driver also needs some configuration in `spark-env.sh` to interact properly with Mesos:
 
@@ -116,7 +116,7 @@ The driver also needs some configuration in `spark-env.sh` to interact properly
 2. Also set `spark.executor.uri` to `<URL of spark-{{site.SPARK_VERSION}}.tar.gz>`.
 
 Now when starting a Spark application against the cluster, pass a `mesos://`
-or `zk://` URL as the master when creating a `SparkContext`. For example:
+URL as the master when creating a `SparkContext`. For example:
 
 {% highlight scala %}
 val conf = new SparkConf()
@@ -126,6 +126,9 @@ val conf = new SparkConf()
 val sc = new SparkContext(conf)
 {% endhighlight %}
 
+(You can also use [`spark-submit`](submitting-applications.html) and configure `spark.executor.uri`
+in the [conf/spark-defaults.conf](configuration.html#loading-default-configurations) file.)
+
 When running a shell, the `spark.executor.uri` parameter is inherited from `SPARK_EXECUTOR_URI`, so
 it does not need to be redundantly passed in as a system property.
 
diff --git a/docs/running-on-yarn.md b/docs/running-on-yarn.md
index 66c330fdee73..af1788f2aa15 100644
--- a/docs/running-on-yarn.md
+++ b/docs/running-on-yarn.md
@@ -1,6 +1,6 @@
 ---
 layout: global
-title: Launching Spark on YARN
+title: Running Spark on YARN
 ---
 
 Support for running on [YARN (Hadoop
@@ -11,30 +11,69 @@ was added to Spark in version 0.6.0, and improved in subsequent releases.
 
 Running Spark-on-YARN requires a binary distribution of Spark which is built with YARN support.
 Binary distributions can be downloaded from the Spark project website. 
-To build Spark yourself, refer to the [building with maven guide](building-with-maven.html).
+To build Spark yourself, refer to the [building with Maven guide](building-with-maven.html).
 
 # Configuration
 
-Most of the configs are the same for Spark on YARN as for other deployment modes. See the Configuration page for more information on those.  These are configs that are specific to Spark on YARN.
+Most of the configs are the same for Spark on YARN as for other deployment modes. See the [configuration page](configuration.html) for more information on those.  These are configs that are specific to Spark on YARN.
 
-Environment variables:
+#### Environment Variables
 
 * `SPARK_YARN_USER_ENV`, to add environment variables to the Spark processes launched on YARN. This can be a comma separated list of environment variables, e.g. `SPARK_YARN_USER_ENV="JAVA_HOME=/jdk64,FOO=bar"`.
 
-System Properties:
-
-* `spark.yarn.applicationMaster.waitTries`, property to set the number of times the ApplicationMaster waits for the the spark master and then also the number of tries it waits for the Spark Context to be intialized. Default is 10.
-* `spark.yarn.submit.file.replication`, the HDFS replication level for the files uploaded into HDFS for the application. These include things like the spark jar, the app jar, and any distributed cache files/archives.
-* `spark.yarn.preserve.staging.files`, set to true to preserve the staged files(spark jar, app jar, distributed cache files) at the end of the job rather then delete them.
-* `spark.yarn.scheduler.heartbeat.interval-ms`, the interval in ms in which the Spark application master heartbeats into the YARN ResourceManager. Default is 5 seconds. 
-* `spark.yarn.max.executor.failures`, the maximum number of executor failures before failing the application. Default is the number of executors requested times 2 with minimum of 3.
-* `spark.yarn.historyServer.address`, the address of the Spark history server (i.e. host.com:18080). The address should not contain a scheme (http://). Defaults to not being set since the history server is an optional service. This address is given to the Yarn ResourceManager when the Spark application finishes to link the application from the ResourceManager UI to the Spark history server UI. 
-
-By default, Spark on YARN will use a Spark jar installed locally, but the Spark jar can also be in a world-readable location on HDFS. This allows YARN to cache it on nodes so that it doesn't need to be distributed each time an application runs. To point to a jar on HDFS, export SPARK_JAR=hdfs:///some/path.
+#### Spark Properties
+
+<table class="table">
+<tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr>
+<tr>
+  <td><code>spark.yarn.applicationMaster.waitTries</code></td>
+  <td>10</td>
+  <td>
+    Set the number of times the ApplicationMaster waits for the the Spark master and then also the number of tries it waits for the SparkContext to be initialized
+  </td>
+</tr>
+<tr>
+  <td><code>spark.yarn.submit.file.replication</code></td>
+  <td>3</td>
+  <td>
+    HDFS replication level for the files uploaded into HDFS for the application. These include things like the Spark jar, the app jar, and any distributed cache files/archives.
+  </td>
+</tr>
+<tr>
+  <td><code>spark.yarn.preserve.staging.files</code></td>
+  <td>false</td>
+  <td>
+    Set to true to preserve the staged files (Spark jar, app jar, distributed cache files) at the end of the job rather then delete them.
+  </td>
+</tr>
+<tr>
+  <td><code>spark.yarn.scheduler.heartbeat.interval-ms</code></td>
+  <td>5000</td>
+  <td>
+    The interval in ms in which the Spark application master heartbeats into the YARN ResourceManager.
+  </td>
+</tr>
+<tr>
+  <td><code>spark.yarn.max.executor.failures</code></td>
+  <td>2*numExecutors</td>
+  <td>
+    The maximum number of executor failures before failing the application.
+  </td>
+</tr>
+<tr>
+  <td><code>spark.yarn.historyServer.address</code></td>
+  <td>(none)</td>
+  <td>
+    The address of the Spark history server (i.e. host.com:18080). The address should not contain a scheme (http://). Defaults to not being set since the history server is an optional service. This address is given to the YARN ResourceManager when the Spark application finishes to link the application from the ResourceManager UI to the Spark history server UI.
+  </td>
+</tr>
+</table>
+
+By default, Spark on YARN will use a Spark jar installed locally, but the Spark JAR can also be in a world-readable location on HDFS. This allows YARN to cache it on nodes so that it doesn't need to be distributed each time an application runs. To point to a JAR on HDFS, `export SPARK_JAR=hdfs:///some/path`.
 
 # Launching Spark on YARN
 
-Ensure that HADOOP_CONF_DIR or YARN_CONF_DIR points to the directory which contains the (client side) configuration files for the Hadoop cluster.
+Ensure that `HADOOP_CONF_DIR` or `YARN_CONF_DIR` points to the directory which contains the (client side) configuration files for the Hadoop cluster.
 These configs are used to write to the dfs and connect to the YARN ResourceManager.
 
 There are two deploy modes that can be used to launch Spark applications on YARN. In yarn-cluster mode, the Spark driver runs inside an application master process which is managed by YARN on the cluster, and the client can go away after initiating the application. In yarn-client mode, the driver runs in the client process, and the application master is only used for requesting resources from YARN.
@@ -62,9 +101,9 @@ To launch a Spark application in yarn-client mode, do the same, but replace "yar
 
     $ ./bin/spark-shell --master yarn-client
 
-## Adding additional jars
+## Adding Other JARs
 
-In yarn-cluster mode, the driver runs on a different machine than the client, so SparkContext.addJar won't work out of the box with files that are local to the client. To make files on the client available to SparkContext.addJar, include them with the `--jars` option in the launch command. 
+In yarn-cluster mode, the driver runs on a different machine than the client, so `SparkContext.addJar` won't work out of the box with files that are local to the client. To make files on the client available to `SparkContext.addJar`, include them with the `--jars` option in the launch command. 
 
     $ ./bin/spark-submit --class my.main.Class \
         --master yarn-cluster \
@@ -74,24 +113,24 @@ In yarn-cluster mode, the driver runs on a different machine than the client, so
 
 # Debugging your Application
 
-In YARN terminology, executors and application masters run inside "containers". YARN has two modes for handling container logs after an application has completed. If log aggregation is turned on (with the yarn.log-aggregation-enable config), container logs are copied to HDFS and deleted on the local machine. These logs can be viewed from anywhere on the cluster with the "yarn logs" command.
+In YARN terminology, executors and application masters run inside "containers". YARN has two modes for handling container logs after an application has completed. If log aggregation is turned on (with the `yarn.log-aggregation-enable` config), container logs are copied to HDFS and deleted on the local machine. These logs can be viewed from anywhere on the cluster with the "yarn logs" command.
 
     yarn logs -applicationId <app ID>
     
 will print out the contents of all log files from all containers from the given application.
 
-When log aggregation isn't turned on, logs are retained locally on each machine under YARN_APP_LOGS_DIR, which is usually configured to /tmp/logs or $HADOOP_HOME/logs/userlogs depending on the Hadoop version and installation. Viewing logs for a container requires going to the host that contains them and looking in this directory.  Subdirectories organize log files by application ID and container ID.
+When log aggregation isn't turned on, logs are retained locally on each machine under `YARN_APP_LOGS_DIR`, which is usually configured to `/tmp/logs` or `$HADOOP_HOME/logs/userlogs` depending on the Hadoop version and installation. Viewing logs for a container requires going to the host that contains them and looking in this directory.  Subdirectories organize log files by application ID and container ID.
 
-To review per-container launch environment, increase yarn.nodemanager.delete.debug-delay-sec to a
-large value (e.g. 36000), and then access the application cache through yarn.nodemanager.local-dirs
-on the nodes on which containers are launched. This directory contains the launch script, jars, and
+To review per-container launch environment, increase `yarn.nodemanager.delete.debug-delay-sec` to a
+large value (e.g. 36000), and then access the application cache through `yarn.nodemanager.local-dirs`
+on the nodes on which containers are launched. This directory contains the launch script, JARs, and
 all environment variables used for launching each container. This process is useful for debugging
 classpath problems in particular. (Note that enabling this requires admin privileges on cluster
 settings and a restart of all node managers. Thus, this is not applicable to hosted clusters).
 
-# Important notes
+# Important Notes
 
 - Before Hadoop 2.2, YARN does not support cores in container resource requests. Thus, when running against an earlier version, the numbers of cores given via command line arguments cannot be passed to YARN.  Whether core requests are honored in scheduling decisions depends on which scheduler is in use and how it is configured.
-- The local directories used by Spark executors will be the local directories configured for YARN (Hadoop YARN config yarn.nodemanager.local-dirs). If the user specifies spark.local.dir, it will be ignored.
-- The --files and --archives options support specifying file names with the # similar to Hadoop. For example you can specify: --files localtest.txt#appSees.txt and this will upload the file you have locally named localtest.txt into HDFS but this will be linked to by the name appSees.txt, and your application should use the name as appSees.txt to reference it when running on YARN.
-- The --jars option allows the SparkContext.addJar function to work if you are using it with local files and running in yarn-cluster mode. It does not need to be used if you are using it with HDFS, HTTP, HTTPS, or FTP files.
+- The local directories used by Spark executors will be the local directories configured for YARN (Hadoop YARN config `yarn.nodemanager.local-dirs`). If the user specifies `spark.local.dir`, it will be ignored.
+- The `--files` and `--archives` options support specifying file names with the # similar to Hadoop. For example you can specify: `--files localtest.txt#appSees.txt` and this will upload the file you have locally named localtest.txt into HDFS but this will be linked to by the name `appSees.txt`, and your application should use the name as `appSees.txt` to reference it when running on YARN.
+- The `--jars` option allows the `SparkContext.addJar` function to work if you are using it with local files and running in `yarn-cluster` mode. It does not need to be used if you are using it with HDFS, HTTP, HTTPS, or FTP files.
diff --git a/docs/scala-programming-guide.md b/docs/scala-programming-guide.md
index edaa7d0639a1..69ceb637dc8d 100644
--- a/docs/scala-programming-guide.md
+++ b/docs/scala-programming-guide.md
@@ -1,448 +1,7 @@
 ---
 layout: global
 title: Spark Programming Guide
+redirect: programming-guide.html
 ---
 
-* This will become a table of contents (this text will be scraped).
-{:toc}
-
-
-# Overview
-
-At a high level, every Spark application consists of a *driver program* that runs the user's `main` function and executes various *parallel operations* on a cluster. The main abstraction Spark provides is a *resilient distributed dataset* (RDD), which is a collection of elements partitioned across the nodes of the cluster that can be operated on in parallel. RDDs are created by starting with a file in the Hadoop file system (or any other Hadoop-supported file system), or an existing Scala collection in the driver program, and transforming it. Users may also ask Spark to *persist* an RDD in memory, allowing it to be reused efficiently across parallel operations. Finally, RDDs automatically recover from node failures.
-
-A second abstraction in Spark is *shared variables* that can be used in parallel operations. By default, when Spark runs a function in parallel as a set of tasks on different nodes, it ships a copy of each variable used in the function to each task. Sometimes, a variable needs to be shared across tasks, or between tasks and the driver program. Spark supports two types of shared variables: *broadcast variables*, which can be used to cache a value in memory on all nodes, and *accumulators*, which are variables that are only "added" to, such as counters and sums.
-
-This guide shows each of these features and walks through some samples. It assumes some familiarity with Scala, especially with the syntax for [closures](http://www.scala-lang.org/node/133). Note that you can also run Spark interactively using the `bin/spark-shell` script. We highly recommend doing that to follow along!
-
-# Linking with Spark
-
-Spark {{site.SPARK_VERSION}} uses Scala {{site.SCALA_BINARY_VERSION}}. If you write applications in Scala, you will need to use a compatible Scala version (e.g. {{site.SCALA_BINARY_VERSION}}.X) -- newer major versions may not work.
-
-To write a Spark application, you need to add a dependency on Spark. If you use SBT or Maven, Spark is available through Maven Central at:
-
-    groupId = org.apache.spark
-    artifactId = spark-core_{{site.SCALA_BINARY_VERSION}}
-    version = {{site.SPARK_VERSION}}
-
-In addition, if you wish to access an HDFS cluster, you need to add a dependency on
-`hadoop-client` for your version of HDFS. Some common HDFS version tags are listed on the
-[third party distributions](hadoop-third-party-distributions.html) page.
-
-    groupId = org.apache.hadoop
-    artifactId = hadoop-client
-    version = <your-hdfs-version>
-
-Finally, you need to import some Spark classes and implicit conversions into your program. Add the following lines:
-
-{% highlight scala %}
-import org.apache.spark.SparkContext
-import org.apache.spark.SparkContext._
-import org.apache.spark.SparkConf
-{% endhighlight %}
-
-# Initializing Spark
-
-The first thing a Spark program must do is to create a `SparkContext` object, which tells Spark
-how to access a cluster. To create a `SparkContext` you first need to build a `SparkConf` object
-that contains information about your application.
-
-{% highlight scala %}
-val conf = new SparkConf().setAppName(appName).setMaster(master)
-new SparkContext(conf)
-{% endhighlight %}
-
-The `master` parameter is a string specifying a [Spark, Mesos or YARN cluster URL](#master-urls)
-to connect to, or a special "local" string to run in local mode, as described below. `appName` is
-a name for your application, which will be shown in the cluster web UI. It's also possible to set
-these variables [using a configuration file](cluster-overview.html#loading-configurations-from-a-file)
-which avoids hard-coding the master url in your application.
-
-In the Spark shell, a special interpreter-aware SparkContext is already created for you, in the
-variable called `sc`. Making your own SparkContext will not work. You can set which master the
-context connects to using the `--master` argument, and you can add JARs to the classpath
-by passing a comma separated list to the `--jars` argument. For example, to run 
-`bin/spark-shell` on exactly four cores, use
-
-{% highlight bash %}
-$ ./bin/spark-shell --master local[4]
-{% endhighlight %}
-
-Or, to also add `code.jar` to its classpath, use:
-
-{% highlight bash %}
-$ ./bin/spark-shell --master local[4] --jars code.jar
-{% endhighlight %}
-
-For a complete list of options, run Spark shell with the `--help` option. Behind the scenes,
-Spark shell invokes the more general [Spark submit script](cluster-overview.html#launching-applications-with-spark-submit)
-used for launching applications, and passes on all of its parameters. As a result, these two scripts
-share the same parameters.
-
-### Master URLs
-
-The master URL passed to Spark can be in one of the following formats:
-
-<table class="table">
-<tr><th>Master URL</th><th>Meaning</th></tr>
-<tr><td> local </td><td> Run Spark locally with one worker thread (i.e. no parallelism at all). </td></tr>
-<tr><td> local[K] </td><td> Run Spark locally with K worker threads (ideally, set this to the number of cores on your machine). </td></tr>
-<tr><td> local[*] </td><td> Run Spark locally with as many worker threads as logical cores on your machine.</td></tr>
-<tr><td> spark://HOST:PORT </td><td> Connect to the given <a href="spark-standalone.html">Spark standalone
-        cluster</a> master. The port must be whichever one your master is configured to use, which is 7077 by default.
-</td></tr>
-<tr><td> mesos://HOST:PORT </td><td> Connect to the given <a href="running-on-mesos.html">Mesos</a> cluster.
-        The host parameter is the hostname of the Mesos master. The port must be whichever one the master is configured to use,
-        which is 5050 by default.
-</td></tr>
-<tr><td> yarn-client </td><td> Connect to a <a href="running-on-yarn.html"> YARN </a> cluster in
-client mode. The cluster location will be inferred based on the local Hadoop configuration.
-</td></tr>
-<tr><td> yarn-cluster </td><td> Connect to a <a href="running-on-yarn.html"> YARN </a> cluster in
-cluster mode. The cluster location will be inferred based on the local Hadoop configuration.
-</td></tr>
-</table>
-
-If no master URL is specified, the spark shell defaults to `local[*]`.
-
-# Resilient Distributed Datasets (RDDs)
-
-Spark revolves around the concept of a _resilient distributed dataset_ (RDD), which is a fault-tolerant collection of elements that can be operated on in parallel. There are currently two types of RDDs: *parallelized collections*, which take an existing Scala collection and run functions on it in parallel, and *Hadoop datasets*, which run functions on each record of a file in Hadoop distributed file system or any other storage system supported by Hadoop. Both types of RDDs can be operated on through the same methods.
-
-## Parallelized Collections
-
-Parallelized collections are created by calling `SparkContext`'s `parallelize` method on an existing Scala collection (a `Seq` object). The elements of the collection are copied to form a distributed dataset that can be operated on in parallel. For example, here is some interpreter output showing how to create a parallel collection from an array:
-
-{% highlight scala %}
-scala> val data = Array(1, 2, 3, 4, 5)
-data: Array[Int] = Array(1, 2, 3, 4, 5)
-
-scala> val distData = sc.parallelize(data)
-distData: spark.RDD[Int] = spark.ParallelCollection@10d13e3e
-{% endhighlight %}
-
-Once created, the distributed dataset (`distData` here) can be operated on in parallel. For example, we might call `distData.reduce(_ + _)` to add up the elements of the array. We describe operations on distributed datasets later on.
-
-One important parameter for parallel collections is the number of *slices* to cut the dataset into. Spark will run one task for each slice of the cluster. Typically you want 2-4 slices for each CPU in your cluster. Normally, Spark tries to set the number of slices automatically based on your cluster. However, you can also set it manually by passing it as a second parameter to `parallelize` (e.g. `sc.parallelize(data, 10)`).
-
-## Hadoop Datasets
-
-Spark can create distributed datasets from any file stored in the Hadoop distributed file system (HDFS) or other storage systems supported by Hadoop (including your local file system, [Amazon S3](http://wiki.apache.org/hadoop/AmazonS3), Hypertable, HBase, etc). Spark supports text files, [SequenceFiles](http://hadoop.apache.org/common/docs/current/api/org/apache/hadoop/mapred/SequenceFileInputFormat.html), and any other Hadoop InputFormat.
-
-Text file RDDs can be created using `SparkContext`'s `textFile` method. This method takes an URI for the file (either a local path on the machine, or a `hdfs://`, `s3n://`, `kfs://`, etc URI). Here is an example invocation:
-
-{% highlight scala %}
-scala> val distFile = sc.textFile("data.txt")
-distFile: spark.RDD[String] = spark.HadoopRDD@1d4cee08
-{% endhighlight %}
-
-Once created, `distFile` can be acted on by dataset operations. For example, we can add up the sizes of all the lines using the `map` and `reduce` operations as follows: `distFile.map(_.size).reduce(_ + _)`.
-
-The `textFile` method also takes an optional second argument for controlling the number of slices of the file. By default, Spark creates one slice for each block of the file (blocks being 64MB by default in HDFS), but you can also ask for a higher number of slices by passing a larger value. Note that you cannot have fewer slices than blocks.
-
-For [SequenceFiles](http://hadoop.apache.org/common/docs/current/api/org/apache/hadoop/mapred/SequenceFileInputFormat.html), use SparkContext's `sequenceFile[K, V]` method where `K` and `V` are the types of key and values in the file. These should be subclasses of Hadoop's [Writable](http://hadoop.apache.org/common/docs/current/api/org/apache/hadoop/io/Writable.html) interface, like [IntWritable](http://hadoop.apache.org/common/docs/current/api/org/apache/hadoop/io/IntWritable.html) and [Text](http://hadoop.apache.org/common/docs/current/api/org/apache/hadoop/io/Text.html). In addition, Spark allows you to specify native types for a few common Writables; for example, `sequenceFile[Int, String]` will automatically read IntWritables and Texts.
-
-Finally, for other Hadoop InputFormats, you can use the `SparkContext.hadoopRDD` method, which takes an arbitrary `JobConf` and input format class, key class and value class. Set these the same way you would for a Hadoop job with your input source.
-
-## RDD Operations
-
-RDDs support two types of operations: *transformations*, which create a new dataset from an existing one, and *actions*, which return a value to the driver program after running a computation on the dataset. For example, `map` is a transformation that passes each dataset element through a function and returns a new distributed dataset representing the results. On the other hand, `reduce` is an action that aggregates all the elements of the dataset using some function and returns the final result to the driver program (although there is also a parallel `reduceByKey` that returns a distributed dataset).
-
-All transformations in Spark are <i>lazy</i>, in that they do not compute their results right away. Instead, they just remember the transformations applied to some base dataset (e.g. a file). The transformations are only computed when an action requires a result to be returned to the driver program. This design enables Spark to run more efficiently -- for example, we can realize that a dataset created through `map` will be used in a `reduce` and return only the result of the `reduce` to the driver, rather than the larger mapped dataset.
-
-By default, each transformed RDD may be recomputed each time you run an action on it. However, you may also *persist* an RDD in memory using the `persist` (or `cache`) method, in which case Spark will keep the elements around on the cluster for much faster access the next time you query it. There is also support for persisting datasets on disk, or replicated across the cluster. The next section in this document describes these options.
-
-The following tables list the transformations and actions currently supported (see also the [RDD API doc](api/scala/index.html#org.apache.spark.rdd.RDD) for details):
-
-### Transformations
-
-<table class="table">
-<tr><th style="width:25%">Transformation</th><th>Meaning</th></tr>
-<tr>
-  <td> <b>map</b>(<i>func</i>) </td>
-  <td> Return a new distributed dataset formed by passing each element of the source through a function <i>func</i>. </td>
-</tr>
-<tr>
-  <td> <b>filter</b>(<i>func</i>) </td>
-  <td> Return a new dataset formed by selecting those elements of the source on which <i>func</i> returns true. </td>
-</tr>
-<tr>
-  <td> <b>flatMap</b>(<i>func</i>) </td>
-  <td> Similar to map, but each input item can be mapped to 0 or more output items (so <i>func</i> should return a Seq rather than a single item). </td>
-</tr>
-<tr>
-  <td> <b>mapPartitions</b>(<i>func</i>) </td>
-  <td> Similar to map, but runs separately on each partition (block) of the RDD, so <i>func</i> must be of type
-    Iterator[T] => Iterator[U] when running on an RDD of type T. </td>
-</tr>
-<tr>
-  <td> <b>mapPartitionsWithIndex</b>(<i>func</i>) </td>
-  <td> Similar to mapPartitions, but also provides <i>func</i> with an integer value representing the index of
-  the partition, so <i>func</i> must be of type (Int, Iterator[T]) => Iterator[U] when running on an RDD of type T.
-  </td>
-</tr>
-<tr>
-  <td> <b>sample</b>(<i>withReplacement</i>, <i>fraction</i>, <i>seed</i>) </td>
-  <td> Sample a fraction <i>fraction</i> of the data, with or without replacement, using a given random number generator seed. </td>
-</tr>
-<tr>
-  <td> <b>union</b>(<i>otherDataset</i>) </td>
-  <td> Return a new dataset that contains the union of the elements in the source dataset and the argument. </td>
-</tr>
-<tr>
-  <td> <b>distinct</b>([<i>numTasks</i>])) </td>
-  <td> Return a new dataset that contains the distinct elements of the source dataset.</td>
-</tr>
-<tr>
-  <td> <b>groupByKey</b>([<i>numTasks</i>]) </td>
-  <td> When called on a dataset of (K, V) pairs, returns a dataset of (K, Seq[V]) pairs. <br />
-<b>Note:</b> If you are grouping in order to perform an aggregation (such as a sum or 
-  average) over each key, using `reduceByKey` or `combineByKey` will yield much better 
-  performance.
-<br />
-<b>Note:</b> By default, if the RDD already has a partitioner, the task number is decided by the partition number of the partitioner, or else relies on the value of <code>spark.default.parallelism</code> if the property is set , otherwise depends on the partition number of the RDD. You can pass an optional <code>numTasks</code> argument to set a different number of tasks.
-  </td>
-</tr>
-<tr>
-  <td> <b>reduceByKey</b>(<i>func</i>, [<i>numTasks</i>]) </td>
-  <td> When called on a dataset of (K, V) pairs, returns a dataset of (K, V) pairs where the values for each key are aggregated using the given reduce function. Like in <code>groupByKey</code>, the number of reduce tasks is configurable through an optional second argument. </td>
-</tr>
-<tr>
-  <td> <b>sortByKey</b>([<i>ascending</i>], [<i>numTasks</i>]) </td>
-  <td> When called on a dataset of (K, V) pairs where K implements Ordered, returns a dataset of (K, V) pairs sorted by keys in ascending or descending order, as specified in the boolean <code>ascending</code> argument.</td>
-</tr>
-<tr>
-  <td> <b>join</b>(<i>otherDataset</i>, [<i>numTasks</i>]) </td>
-  <td> When called on datasets of type (K, V) and (K, W), returns a dataset of (K, (V, W)) pairs with all pairs of elements for each key. </td>
-</tr>
-<tr>
-  <td> <b>cogroup</b>(<i>otherDataset</i>, [<i>numTasks</i>]) </td>
-  <td> When called on datasets of type (K, V) and (K, W), returns a dataset of (K, Seq[V], Seq[W]) tuples. This operation is also called <code>groupWith</code>. </td>
-</tr>
-<tr>
-  <td> <b>cartesian</b>(<i>otherDataset</i>) </td>
-  <td> When called on datasets of types T and U, returns a dataset of (T, U) pairs (all pairs of elements). </td>
-</tr>
-</table>
-
-A complete list of transformations is available in the [RDD API doc](api/scala/index.html#org.apache.spark.rdd.RDD).
-
-### Actions
-
-<table class="table">
-<tr><th>Action</th><th>Meaning</th></tr>
-<tr>
-  <td> <b>reduce</b>(<i>func</i>) </td>
-  <td> Aggregate the elements of the dataset using a function <i>func</i> (which takes two arguments and returns one). The function should be commutative and associative so that it can be computed correctly in parallel. </td>
-</tr>
-<tr>
-  <td> <b>collect</b>() </td>
-  <td> Return all the elements of the dataset as an array at the driver program. This is usually useful after a filter or other operation that returns a sufficiently small subset of the data. </td>
-</tr>
-<tr>
-  <td> <b>count</b>() </td>
-  <td> Return the number of elements in the dataset. </td>
-</tr>
-<tr>
-  <td> <b>first</b>() </td>
-  <td> Return the first element of the dataset (similar to take(1)). </td>
-</tr>
-<tr>
-  <td> <b>take</b>(<i>n</i>) </td>
-  <td> Return an array with the first <i>n</i> elements of the dataset. Note that this is currently not executed in parallel. Instead, the driver program computes all the elements. </td>
-</tr>
-<tr>
-  <td> <b>takeSample</b>(<i>withReplacement</i>, <i>num</i>, <i>seed</i>) </td>
-  <td> Return an array with a random sample of <i>num</i> elements of the dataset, with or without replacement, using the given random number generator seed. </td>
-</tr>
-<tr>
-  <td> <b>saveAsTextFile</b>(<i>path</i>) </td>
-  <td> Write the elements of the dataset as a text file (or set of text files) in a given directory in the local filesystem, HDFS or any other Hadoop-supported file system. Spark will call toString on each element to convert it to a line of text in the file. </td>
-</tr>
-<tr>
-  <td> <b>saveAsSequenceFile</b>(<i>path</i>) </td>
-  <td> Write the elements of the dataset as a Hadoop SequenceFile in a given path in the local filesystem, HDFS or any other Hadoop-supported file system. This is only available on RDDs of key-value pairs that either implement Hadoop's Writable interface or are implicitly convertible to Writable (Spark includes conversions for basic types like Int, Double, String, etc). </td>
-</tr>
-<tr>
-  <td> <b>countByKey</b>() </td>
-  <td> Only available on RDDs of type (K, V). Returns a `Map` of (K, Int) pairs with the count of each key. </td>
-</tr>
-<tr>
-  <td> <b>foreach</b>(<i>func</i>) </td>
-  <td> Run a function <i>func</i> on each element of the dataset. This is usually done for side effects such as updating an accumulator variable (see below) or interacting with external storage systems. </td>
-</tr>
-</table>
-
-A complete list of actions is available in the [RDD API doc](api/scala/index.html#org.apache.spark.rdd.RDD).
-
-## RDD Persistence
-
-One of the most important capabilities in Spark is *persisting* (or *caching*) a dataset in memory
-across operations. When you persist an RDD, each node stores any slices of it that it computes in
-memory and reuses them in other actions on that dataset (or datasets derived from it). This allows
-future actions to be much faster (often by more than 10x). Caching is a key tool for building
-iterative algorithms with Spark and for interactive use from the interpreter.
-
-You can mark an RDD to be persisted using the `persist()` or `cache()` methods on it. The first time
-it is computed in an action, it will be kept in memory on the nodes. The cache is fault-tolerant --
-if any partition of an RDD is lost, it will automatically be recomputed using the transformations
-that originally created it.
-
-In addition, each persisted RDD can be stored using a different *storage level*, allowing you, for example,
-to persist the dataset on disk, or persist it in memory but as serialized Java objects (to save space),
-or replicate it across nodes, or store the data in off-heap memory in [Tachyon](http://tachyon-project.org/).
-These levels are chosen by passing a
-[`org.apache.spark.storage.StorageLevel`](api/scala/index.html#org.apache.spark.storage.StorageLevel)
-object to `persist()`. The `cache()` method is a shorthand for using the default storage level,
-which is `StorageLevel.MEMORY_ONLY` (store deserialized objects in memory). The complete set of
-available storage levels is:
-
-<table class="table">
-<tr><th style="width:23%">Storage Level</th><th>Meaning</th></tr>
-<tr>
-  <td> MEMORY_ONLY </td>
-  <td> Store RDD as deserialized Java objects in the JVM. If the RDD does not fit in memory, some partitions will
-    not be cached and will be recomputed on the fly each time they're needed. This is the default level. </td>
-</tr>
-<tr>
-  <td> MEMORY_AND_DISK </td>
-  <td> Store RDD as deserialized Java objects in the JVM. If the RDD does not fit in memory, store the
-    partitions that don't fit on disk, and read them from there when they're needed. </td>
-</tr>
-<tr>
-  <td> MEMORY_ONLY_SER </td>
-  <td> Store RDD as <i>serialized</i> Java objects (one byte array per partition).
-    This is generally more space-efficient than deserialized objects, especially when using a
-    <a href="tuning.html">fast serializer</a>, but more CPU-intensive to read.
-  </td>
-</tr>
-<tr>
-  <td> MEMORY_AND_DISK_SER </td>
-  <td> Similar to MEMORY_ONLY_SER, but spill partitions that don't fit in memory to disk instead of
-    recomputing them on the fly each time they're needed. </td>
-</tr>
-<tr>
-  <td> OFF_HEAP  </td>
-  <td> Store RDD in a <i>serialized</i> format in Tachyon.
-    This is generally more space-efficient than deserialized objects, especially when using a
-    <a href="tuning.html">fast serializer</a>, but more CPU-intensive to read.
-    This also significantly reduces the overheads of GC.
-  </td>
-</tr>
-<tr>
-  <td> DISK_ONLY </td>
-  <td> Store the RDD partitions only on disk. </td>
-</tr>
-<tr>
-  <td> MEMORY_ONLY_2, MEMORY_AND_DISK_2, etc.  </td>
-  <td> Same as the levels above, but replicate each partition on two cluster nodes. </td>
-</tr>
-</table>
-
-Spark sometimes automatically persists intermediate state from RDD operations, even without users calling persist() or cache(). In particular, if a shuffle happens when computing an RDD, Spark will keep the outputs from the map side of the shuffle on disk to avoid re-computing the entire dependency graph if an RDD is re-used. We still recommend users call persist() if they plan to re-use an RDD iteratively.
-
-### Which Storage Level to Choose?
-
-Spark's storage levels are meant to provide different trade-offs between memory usage and CPU
-efficiency. It allows uses to choose memory, disk, or Tachyon for storing data. We recommend going
-through the following process to select one:
-
-* If your RDDs fit comfortably with the default storage level (`MEMORY_ONLY`), leave them that way.
-  This is the most CPU-efficient option, allowing operations on the RDDs to run as fast as possible.
-
-* If not, try using `MEMORY_ONLY_SER` and [selecting a fast serialization library](tuning.html) to
-make the objects much more space-efficient, but still reasonably fast to access. You can also use
-`OFF_HEAP` mode to store the data off the heap in [Tachyon](http://tachyon-project.org/). This will
-significantly reduce JVM GC overhead.
-
-* Don't spill to disk unless the functions that computed your datasets are expensive, or they filter
-a large amount of the data. Otherwise, recomputing a partition is about as fast as reading it from
-disk.
-
-* Use the replicated storage levels if you want fast fault recovery (e.g. if using Spark to serve
-requests from a web application). *All* the storage levels provide full fault tolerance by
-recomputing lost data, but the replicated ones let you continue running tasks on the RDD without
-waiting to recompute a lost partition.
-
-If you want to define your own storage level (say, with replication factor of 3 instead of 2), then
-use the function factor method `apply()` of the
-[`StorageLevel`](api/scala/index.html#org.apache.spark.storage.StorageLevel$) singleton object.
-
-Spark has a block manager inside the Executors that let you chose memory, disk, or off-heap. The
-latter is for storing RDDs off-heap outside the Executor JVM on top of the memory management system
-[Tachyon](http://tachyon-project.org/). This mode has the following advantages:
-
-* Cached data will not be lost if individual executors crash.
-* Executors can have a smaller memory footprint, allowing you to run more executors on the same
-machine as the bulk of the memory will be inside Tachyon.
-* Reduced GC overhead since data is stored in Tachyon.
-
-# Shared Variables
-
-Normally, when a function passed to a Spark operation (such as `map` or `reduce`) is executed on a
-remote cluster node, it works on separate copies of all the variables used in the function. These
-variables are copied to each machine, and no updates to the variables on the remote machine are
-propagated back to the driver program. Supporting general, read-write shared variables across tasks
-would be inefficient. However, Spark does provide two limited types of *shared variables* for two
-common usage patterns: broadcast variables and accumulators.
-
-## Broadcast Variables
-
-Broadcast variables allow the programmer to keep a read-only variable cached on each machine rather
-than shipping a copy of it with tasks. They can be used, for example, to give every node a copy of a
-large input dataset in an efficient manner. Spark also attempts to distribute broadcast variables
-using efficient broadcast algorithms to reduce communication cost.
-
-Broadcast variables are created from a variable `v` by calling `SparkContext.broadcast(v)`. The
-broadcast variable is a wrapper around `v`, and its value can be accessed by calling the `value`
-method. The interpreter session below shows this:
-
-{% highlight scala %}
-scala> val broadcastVar = sc.broadcast(Array(1, 2, 3))
-broadcastVar: spark.Broadcast[Array[Int]] = spark.Broadcast(b5c40191-a864-4c7d-b9bf-d87e1a4e787c)
-
-scala> broadcastVar.value
-res0: Array[Int] = Array(1, 2, 3)
-{% endhighlight %}
-
-After the broadcast variable is created, it should be used instead of the value `v` in any functions
-run on the cluster so that `v` is not shipped to the nodes more than once. In addition, the object
-`v` should not be modified after it is broadcast in order to ensure that all nodes get the same
-value of the broadcast variable (e.g. if the variable is shipped to a new node later).
-
-## Accumulators
-
-Accumulators are variables that are only "added" to through an associative operation and can
-therefore be efficiently supported in parallel. They can be used to implement counters (as in
-MapReduce) or sums. Spark natively supports accumulators of numeric value types and standard mutable
-collections, and programmers can add support for new types.
-
-An accumulator is created from an initial value `v` by calling `SparkContext.accumulator(v)`. Tasks
-running on the cluster can then add to it using the `+=` operator. However, they cannot read its
-value. Only the driver program can read the accumulator's value, using its `value` method.
-
-The interpreter session below shows an accumulator being used to add up the elements of an array:
-
-{% highlight scala %}
-scala> val accum = sc.accumulator(0)
-accum: spark.Accumulator[Int] = 0
-
-scala> sc.parallelize(Array(1, 2, 3, 4)).foreach(x => accum += x)
-...
-10/09/29 18:41:08 INFO SparkContext: Tasks finished in 0.317106 s
-
-scala> accum.value
-res2: Int = 10
-{% endhighlight %}
-
-
-# Where to Go from Here
-
-You can see some [example Spark programs](http://spark.apache.org/examples.html) on the Spark website.
-In addition, Spark includes several samples in `examples/src/main/scala`. Some of them have both Spark versions and local (non-parallel) versions, allowing you to see what had to be changed to make the program run on a cluster. You can run them using by passing the class name to the `bin/run-example` script included in Spark; for example:
-
-    ./bin/run-example SparkPi
-
-For help on optimizing your program, the [configuration](configuration.html) and
-[tuning](tuning.html) guides provide information on best practices. They are especially important for
-making sure that your data is stored in memory in an efficient format.
+This document has moved [here](programming-guide.html).
diff --git a/docs/security.md b/docs/security.md
index 90c69915f517..90ba678033b1 100644
--- a/docs/security.md
+++ b/docs/security.md
@@ -3,18 +3,18 @@ layout: global
 title: Spark Security
 ---
 
-Spark currently supports authentication via a shared secret. Authentication can be configured to be on via the `spark.authenticate` configuration parameter. This parameter controls whether the Spark communication protocols do authentication using the shared secret. This authentication is a basic handshake to make sure both sides have the same shared secret and are allowed to communicate. If the shared secret is not identical they will not be allowed to communicate.
+Spark currently supports authentication via a shared secret. Authentication can be configured to be on via the `spark.authenticate` configuration parameter. This parameter controls whether the Spark communication protocols do authentication using the shared secret. This authentication is a basic handshake to make sure both sides have the same shared secret and are allowed to communicate. If the shared secret is not identical they will not be allowed to communicate. The shared secret is created as follows:
 
-The Spark UI can also be secured by using javax servlet filters. A user may want to secure the UI if it has data that other users should not be allowed to see. The javax servlet filter specified by the user can authenticate the user and then once the user is logged in, Spark can compare that user versus the view acls to make sure they are authorized to view the UI. The configs 'spark.ui.acls.enable' and 'spark.ui.view.acls' control the behavior of the acls. Note that the person who started the application always has view access to the UI.
+* For Spark on [YARN](running-on-yarn.html) deployments, configuring `spark.authenticate` to `true` will automatically handle generating and distributing the shared secret. Each application will use a unique shared secret. 
+* For other types of Spark deployments, the Spark parameter `spark.authenticate.secret` should be configured on each of the nodes. This secret will be used by all the Master/Workers and applications.
 
-If your applications are using event logging, the directory where the event logs go (`spark.eventLog.dir`) should be manually created and have the proper permissions set on it. If you want those log files secure, the permissions should be set to drwxrwxrwxt for that directory. The owner of the directory should be the super user who is running the history server and the group permissions should be restricted to super user group. This will allow all users to write to the directory but will prevent unprivileged users from removing or renaming a file unless they own the file or directory. The event log files will be created by Spark with permissions such that only the user and group have read and write access.
+The Spark UI can also be secured by using [javax servlet filters](http://docs.oracle.com/javaee/6/api/javax/servlet/Filter.html) via the `spark.ui.filters` setting. A user may want to secure the UI if it has data that other users should not be allowed to see. The javax servlet filter specified by the user can authenticate the user and then once the user is logged in, Spark can compare that user versus the view ACLs to make sure they are authorized to view the UI. The configs `spark.ui.acls.enable` and `spark.ui.view.acls` control the behavior of the ACLs. Note that the user who started the application always has view access to the UI.
+On YARN, the Spark UI uses the standard YARN web application proxy mechanism and will authenticate via any installed Hadoop filters.
 
-For Spark on Yarn deployments, configuring `spark.authenticate` to true will automatically handle generating and distributing the shared secret. Each application will use a unique shared secret. The Spark UI uses the standard YARN web application proxy mechanism and will authenticate via any installed Hadoop filters. If an authentication filter is enabled, the acls controls can be used by control which users can via the Spark UI. 
+If your applications are using event logging, the directory where the event logs go (`spark.eventLog.dir`) should be manually created and have the proper permissions set on it. If you want those log files secured, the permissions should be set to `drwxrwxrwxt` for that directory. The owner of the directory should be the super user who is running the history server and the group permissions should be restricted to super user group. This will allow all users to write to the directory but will prevent unprivileged users from removing or renaming a file unless they own the file or directory. The event log files will be created by Spark with permissions such that only the user and group have read and write access.
 
-For other types of Spark deployments, the spark config `spark.authenticate.secret` should be configured on each of the nodes. This secret will be used by all the Master/Workers and applications. The UI can be secured using a javax servlet filter installed via `spark.ui.filters`. If an authentication filter is enabled, the acls controls can be used by control which users can via the Spark UI.
+**IMPORTANT NOTE:** *The experimental Netty shuffle path (`spark.shuffle.use.netty`) is not secured, so do not use Netty for shuffles if running with authentication.*
 
-IMPORTANT NOTE: The NettyBlockFetcherIterator is not secured so do not use netty for the shuffle is running with authentication on.
+See the [configuration page](configuration.html) for more details on the security configuration parameters.
 
-See [Spark Configuration](configuration.html) for more details on the security configs.
-
-See <a href="api/core/index.html#org.apache.spark.SecurityManager"><code>org.apache.spark.SecurityManager</code></a> for implementation details about security.
+See <a href="{{site.SPARK_GITHUB_URL}}/tree/master/core/src/main/scala/org/apache/spark/SecurityManager.scala"><code>org.apache.spark.SecurityManager</code></a> for implementation details about security.
diff --git a/docs/spark-standalone.md b/docs/spark-standalone.md
index dca80a9a6961..3c1ce06083ed 100644
--- a/docs/spark-standalone.md
+++ b/docs/spark-standalone.md
@@ -222,7 +222,7 @@ SPARK_WORKER_OPTS supports the following system properties:
 # Connecting an Application to the Cluster
 
 To run an application on the Spark cluster, simply pass the `spark://IP:PORT` URL of the master as to the [`SparkContext`
-constructor](scala-programming-guide.html#initializing-spark).
+constructor](programming-guide.html#initializing-spark).
 
 To run an interactive Spark shell against the cluster, run the following command:
 
@@ -237,7 +237,7 @@ You can also pass an option `--cores <numCores>` to control the number of cores
 
 Spark supports two deploy modes: applications may run with the driver inside the client process or
 entirely inside the cluster. The
-[Spark submit script](cluster-overview.html#launching-applications-with-spark-submit) provides the
+[`spark-submit` script](submitting-applications.html) provides the
 most straightforward way to submit a compiled Spark application to the cluster in either deploy
 mode.
 
diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md
index 8a785450adfd..a506457eba01 100644
--- a/docs/sql-programming-guide.md
+++ b/docs/sql-programming-guide.md
@@ -2,7 +2,6 @@
 layout: global
 title: Spark SQL Programming Guide
 ---
-**Spark SQL is currently an Alpha component. Therefore, the APIs may be changed in future releases.**
 
 * This will become a table of contents (this text will be scraped).
 {:toc}
@@ -17,10 +16,10 @@ Spark.  At the core of this component is a new type of RDD,
 [SchemaRDD](api/scala/index.html#org.apache.spark.sql.SchemaRDD).  SchemaRDDs are composed
 [Row](api/scala/index.html#org.apache.spark.sql.catalyst.expressions.Row) objects along with
 a schema that describes the data types of each column in the row.  A SchemaRDD is similar to a table
-in a traditional relational database.  A SchemaRDD can be created from an existing RDD, parquet
+in a traditional relational database.  A SchemaRDD can be created from an existing RDD, [Parquet](http://parquet.io)
 file, or by running HiveQL against data stored in [Apache Hive](http://hive.apache.org/).
 
-**All of the examples on this page use sample data included in the Spark distribution and can be run in the `spark-shell`.**
+All of the examples on this page use sample data included in the Spark distribution and can be run in the `spark-shell`.
 
 </div>
 
@@ -30,7 +29,7 @@ Spark.  At the core of this component is a new type of RDD,
 [JavaSchemaRDD](api/scala/index.html#org.apache.spark.sql.api.java.JavaSchemaRDD).  JavaSchemaRDDs are composed
 [Row](api/scala/index.html#org.apache.spark.sql.api.java.Row) objects along with
 a schema that describes the data types of each column in the row.  A JavaSchemaRDD is similar to a table
-in a traditional relational database.  A JavaSchemaRDD can be created from an existing RDD, parquet
+in a traditional relational database.  A JavaSchemaRDD can be created from an existing RDD, [Parquet](http://parquet.io)
 file, or by running HiveQL against data stored in [Apache Hive](http://hive.apache.org/).
 </div>
 
@@ -41,13 +40,15 @@ Spark.  At the core of this component is a new type of RDD,
 [SchemaRDD](api/python/pyspark.sql.SchemaRDD-class.html).  SchemaRDDs are composed
 [Row](api/python/pyspark.sql.Row-class.html) objects along with
 a schema that describes the data types of each column in the row.  A SchemaRDD is similar to a table
-in a traditional relational database.  A SchemaRDD can be created from an existing RDD, parquet
+in a traditional relational database.  A SchemaRDD can be created from an existing RDD, [Parquet](http://parquet.io)
 file, or by running HiveQL against data stored in [Apache Hive](http://hive.apache.org/).
 
-**All of the examples on this page use sample data included in the Spark distribution and can be run in the `pyspark` shell.**
+All of the examples on this page use sample data included in the Spark distribution and can be run in the `pyspark` shell.
 </div>
 </div>
 
+**Spark SQL is currently an alpha component. While we will minimize API changes, some APIs may change in future releases.**
+
 ***************************************************************************************************
 
 # Getting Started
@@ -240,8 +241,8 @@ Users that want a more complete dialect of SQL should look at the HiveQL support
 
 ## Using Parquet
 
-Parquet is a columnar format that is supported by many other data processing systems.  Spark SQL
-provides support for both reading and writing parquet files that automatically preserves the schema
+[Parquet](http://parquet.io) is a columnar format that is supported by many other data processing systems.
+Spark SQL provides support for both reading and writing Parquet files that automatically preserves the schema
 of the original data.  Using the data from the above example:
 
 <div class="codetabs">
@@ -254,11 +255,11 @@ import sqlContext._
 
 val people: RDD[Person] = ... // An RDD of case class objects, from the previous example.
 
-// The RDD is implicitly converted to a SchemaRDD, allowing it to be stored using parquet.
+// The RDD is implicitly converted to a SchemaRDD, allowing it to be stored using Parquet.
 people.saveAsParquetFile("people.parquet")
 
 // Read in the parquet file created above.  Parquet files are self-describing so the schema is preserved.
-// The result of loading a parquet file is also a JavaSchemaRDD.
+// The result of loading a Parquet file is also a JavaSchemaRDD.
 val parquetFile = sqlContext.parquetFile("people.parquet")
 
 //Parquet files can also be registered as tables and then used in SQL statements.
@@ -275,10 +276,10 @@ teenagers.collect().foreach(println)
 
 JavaSchemaRDD schemaPeople = ... // The JavaSchemaRDD from the previous example.
 
-// JavaSchemaRDDs can be saved as parquet files, maintaining the schema information.
+// JavaSchemaRDDs can be saved as Parquet files, maintaining the schema information.
 schemaPeople.saveAsParquetFile("people.parquet");
 
-// Read in the parquet file created above.  Parquet files are self-describing so the schema is preserved.
+// Read in the Parquet file created above.  Parquet files are self-describing so the schema is preserved.
 // The result of loading a parquet file is also a JavaSchemaRDD.
 JavaSchemaRDD parquetFile = sqlCtx.parquetFile("people.parquet");
 
@@ -297,10 +298,10 @@ JavaSchemaRDD teenagers = sqlCtx.sql("SELECT name FROM parquetFile WHERE age >=
 
 peopleTable # The SchemaRDD from the previous example.
 
-# SchemaRDDs can be saved as parquet files, maintaining the schema information.
+# SchemaRDDs can be saved as Parquet files, maintaining the schema information.
 peopleTable.saveAsParquetFile("people.parquet")
 
-# Read in the parquet file created above.  Parquet files are self-describing so the schema is preserved.
+# Read in the Parquet file created above.  Parquet files are self-describing so the schema is preserved.
 # The result of loading a parquet file is also a SchemaRDD.
 parquetFile = sqlCtx.parquetFile("people.parquet")
 
diff --git a/docs/streaming-programming-guide.md b/docs/streaming-programming-guide.md
index 972b660262d1..b95f818d0fcb 100644
--- a/docs/streaming-programming-guide.md
+++ b/docs/streaming-programming-guide.md
@@ -136,7 +136,7 @@ The complete code can be found in the Spark Streaming example
 <div data-lang="java" markdown="1">
 
 First, we create a
-[JavaStreamingContext](api/java/org/apache/spark/streaming/api/java/JavaStreamingContext.html) object,
+[JavaStreamingContext](api/java/index.html?org/apache/spark/streaming/api/java/JavaStreamingContext.html) object,
 which is the main entry point for all streaming
 functionality. Besides Spark's configuration, we specify that any DStream would be processed
 in 1 second batches.
@@ -215,7 +215,7 @@ jssc.awaitTermination();   // Wait for the computation to terminate
 {% endhighlight %}
 
 The complete code can be found in the Spark Streaming example
-[JavaNetworkWordCount]({{site.SPARK_GITHUB_URL}}/blob/master/examples/src/main/java/org/apache/spark/examples/streaming/JavaNetworkWordCount.java).
+[JavaNetworkWordCount]({{site.SPARK_GITHUB_URL}}/blob/master/examples/src/main/java/index.html?org/apache/spark/examples/streaming/JavaNetworkWordCount.java).
 <br>
 
 </div>
@@ -360,12 +360,12 @@ new JavaStreamingContext(master, appName, batchInterval, [sparkHome], [jars])
 </div>
 </div>
 
-The `master` parameter is a standard [Spark cluster URL](scala-programming-guide.html#master-urls)
+The `master` parameter is a standard [Spark cluster URL](programming-guide.html#master-urls)
 and can be "local" for local testing. The `appName` is a name of your program,
 which will be shown on your cluster's web UI. The `batchInterval` is the size of the batches,
 as explained earlier. Finally, the last two parameters are needed to deploy your code to a cluster
  if running in distributed mode, as described in the
- [Spark programming guide](scala-programming-guide.html#deploying-code-on-a-cluster).
+ [Spark programming guide](programming-guide.html#deploying-code-on-a-cluster).
  Additionally, the underlying SparkContext can be accessed as
 `ssc.sparkContext`.
 
@@ -813,10 +813,8 @@ output operators are defined:
 The complete list of DStream operations is available in the API documentation. For the Scala API,
 see [DStream](api/scala/index.html#org.apache.spark.streaming.dstream.DStream)
 and [PairDStreamFunctions](api/scala/index.html#org.apache.spark.streaming.dstream.PairDStreamFunctions).
-For the Java API, see [JavaDStream](api/scala/index.html#org.apache.spark.streaming.api.java.dstream.DStream)
-and [JavaPairDStream](api/scala/index.html#org.apache.spark.streaming.api.java.JavaPairDStream).
-Specifically for the Java API, see [Spark's Java programming guide](java-programming-guide.html)
-for more information.
+For the Java API, see [JavaDStream](api/java/index.html?org/apache/spark/streaming/api/java/JavaDStream.html)
+and [JavaPairDStream](api/java/index.html?org/apache/spark/streaming/api/java/JavaPairDStream.html).
 
 ## Persistence
 Similar to RDDs, DStreams also allow developers to persist the stream's data in memory. That is,
@@ -833,7 +831,7 @@ default persistence level is set to replicate the data to two nodes for fault-to
 Note that, unlike RDDs, the default persistence level of DStreams keeps the data serialized in
 memory. This is further discussed in the [Performance Tuning](#memory-tuning) section. More
 information on different persistence levels can be found in
-[Spark Programming Guide](scala-programming-guide.html#rdd-persistence).
+[Spark Programming Guide](programming-guide.html#rdd-persistence).
 
 ## RDD Checkpointing
 A _stateful operation_ is one which operates over multiple batches of data. This includes all
@@ -878,7 +876,7 @@ sending the data to two destinations (i.e., the earlier and upgraded application
 
 - The existing application is shutdown gracefully (see
 [`StreamingContext.stop(...)`](api/scala/index.html#org.apache.spark.streaming.StreamingContext)
-or [`JavaStreamingContext.stop(...)`](api/java/org/apache/spark/streaming/api/java/JavaStreamingContext.html)
+or [`JavaStreamingContext.stop(...)`](api/java/index.html?org/apache/spark/streaming/api/java/JavaStreamingContext.html)
 for graceful shutdown options) which ensure data that have been received is completely
 processed before shutdown. Then the
 upgraded application can be started, which will start processing from the same point where the earlier
@@ -1313,10 +1311,10 @@ This section elaborates the steps required to migrate your existing code to 1.0.
 `FlumeUtils.createStream`, etc.) now returns
 [InputDStream](api/scala/index.html#org.apache.spark.streaming.dstream.InputDStream) /
 [ReceiverInputDStream](api/scala/index.html#org.apache.spark.streaming.dstream.ReceiverInputDStream)
-(instead of DStream) for Scala, and [JavaInputDStream](api/java/org/apache/spark/streaming/api/java/JavaInputDStream.html) /
-[JavaPairInputDStream](api/java/org/apache/spark/streaming/api/java/JavaPairInputDStream.html) /
-[JavaReceiverInputDStream](api/java/org/apache/spark/streaming/api/java/JavaReceiverInputDStream.html) /
-[JavaPairReceiverInputDStream](api/java/org/apache/spark/streaming/api/java/JavaPairReceiverInputDStream.html)
+(instead of DStream) for Scala, and [JavaInputDStream](api/java/index.html?org/apache/spark/streaming/api/java/JavaInputDStream.html) /
+[JavaPairInputDStream](api/java/index.html?org/apache/spark/streaming/api/java/JavaPairInputDStream.html) /
+[JavaReceiverInputDStream](api/java/index.html?org/apache/spark/streaming/api/java/JavaReceiverInputDStream.html) /
+[JavaPairReceiverInputDStream](api/java/index.html?org/apache/spark/streaming/api/java/JavaPairReceiverInputDStream.html)
 (instead of JavaDStream) for Java. This ensures that functionality specific to input streams can
 be added to these classes in the future without breaking binary compatibility.
 Note that your existing Spark Streaming applications should not require any change
@@ -1367,14 +1365,14 @@ package and renamed for better clarity.
     [ZeroMQUtils](api/scala/index.html#org.apache.spark.streaming.zeromq.ZeroMQUtils$), and
     [MQTTUtils](api/scala/index.html#org.apache.spark.streaming.mqtt.MQTTUtils$)
   - Java docs
-    * [JavaStreamingContext](api/java/org/apache/spark/streaming/api/java/JavaStreamingContext.html),
-    [JavaDStream](api/java/org/apache/spark/streaming/api/java/JavaDStream.html) and
-    [PairJavaDStream](api/java/org/apache/spark/streaming/api/java/PairJavaDStream.html)
-    * [KafkaUtils](api/java/org/apache/spark/streaming/kafka/KafkaUtils.html),
-    [FlumeUtils](api/java/org/apache/spark/streaming/flume/FlumeUtils.html),
-    [TwitterUtils](api/java/org/apache/spark/streaming/twitter/TwitterUtils.html),
-    [ZeroMQUtils](api/java/org/apache/spark/streaming/zeromq/ZeroMQUtils.html), and
-    [MQTTUtils](api/java/org/apache/spark/streaming/mqtt/MQTTUtils.html)
+    * [JavaStreamingContext](api/java/index.html?org/apache/spark/streaming/api/java/JavaStreamingContext.html),
+    [JavaDStream](api/java/index.html?org/apache/spark/streaming/api/java/JavaDStream.html) and
+    [PairJavaDStream](api/java/index.html?org/apache/spark/streaming/api/java/PairJavaDStream.html)
+    * [KafkaUtils](api/java/index.html?org/apache/spark/streaming/kafka/KafkaUtils.html),
+    [FlumeUtils](api/java/index.html?org/apache/spark/streaming/flume/FlumeUtils.html),
+    [TwitterUtils](api/java/index.html?org/apache/spark/streaming/twitter/TwitterUtils.html),
+    [ZeroMQUtils](api/java/index.html?org/apache/spark/streaming/zeromq/ZeroMQUtils.html), and
+    [MQTTUtils](api/java/index.html?org/apache/spark/streaming/mqtt/MQTTUtils.html)
 
 * More examples in [Scala]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/scala/org/apache/spark/examples/streaming)
   and [Java]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/java/org/apache/spark/examples/streaming)
diff --git a/docs/submitting-applications.md b/docs/submitting-applications.md
new file mode 100644
index 000000000000..d2864fe4c2f6
--- /dev/null
+++ b/docs/submitting-applications.md
@@ -0,0 +1,153 @@
+---
+layout: global
+title: Submitting Applications
+---
+
+The `spark-submit` script in Spark's `bin` directory is used to launch applications on a cluster.
+It can use all of Spark's supported [cluster managers](cluster-overview.html#cluster-manager-types)
+through a uniform interface so you don't have to configure your application specially for each one.
+
+# Bundling Your Application's Dependencies
+If your code depends on other projects, you will need to package them alongside
+your application in order to distribute the code to a Spark cluster. To do this,
+to create an assembly jar (or "uber" jar) containing your code and its dependencies. Both
+[sbt](https://github.com/sbt/sbt-assembly) and
+[Maven](http://maven.apache.org/plugins/maven-shade-plugin/)
+have assembly plugins. When creating assembly jars, list Spark and Hadoop
+as `provided` dependencies; these need not be bundled since they are provided by
+the cluster manager at runtime. Once you have an assembled jar you can call the `bin/spark-submit`
+script as shown here while passing your jar.
+
+For Python, you can use the `--py-files` argument of `spark-submit` to add `.py`, `.zip` or `.egg`
+files to be distributed with your application. If you depend on multiple Python files we recommend
+packaging them into a `.zip` or `.egg`.
+
+# Launching Applications with spark-submit
+
+Once a user application is bundled, it can be launched using the `bin/spark-submit` script.
+This script takes care of setting up the classpath with Spark and its
+dependencies, and can support different cluster managers and deploy modes that Spark supports:
+
+{% highlight bash %}
+./bin/spark-submit \
+  --class <main-class>
+  --master <master-url> \
+  --deploy-mode <deploy-mode> \
+  ... # other options
+  <application-jar> \
+  [application-arguments]
+{% endhighlight %}
+
+Some of the commonly used options are:
+
+* `--class`: The entry point for your application (e.g. `org.apache.spark.examples.SparkPi`)
+* `--master`: The [master URL](#master-urls) for the cluster (e.g. `spark://23.195.26.187:7077`)
+* `--deploy-mode`: Whether to deploy your driver program within the cluster or run it locally as an external client (either `cluster` or `client`)
+* `application-jar`: Path to a bundled jar including your application and all dependencies. The URL must be globally visible inside of your cluster, for instance, an `hdfs://` path or a `file://` path that is present on all nodes.
+* `application-arguments`: Arguments passed to the main method of your main class, if any
+
+For Python applications, simply pass a `.py` file in the place of `<application-jar>` instead of a JAR,
+and add Python `.zip`, `.egg` or `.py` files to the search path with `--py-files`.
+
+To enumerate all options available to `spark-submit` run it with `--help`. Here are a few
+examples of common options:
+
+{% highlight bash %}
+# Run application locally on 8 cores
+./bin/spark-submit \
+  --class org.apache.spark.examples.SparkPi \
+  --master local[8] \
+  /path/to/examples.jar \
+  100
+
+# Run on a Spark standalone cluster
+./bin/spark-submit \
+  --class org.apache.spark.examples.SparkPi \
+  --master spark://207.184.161.138:7077 \
+  --executor-memory 20G \
+  --total-executor-cores 100 \
+  /path/to/examples.jar \
+  1000
+
+# Run on a YARN cluster
+export HADOOP_CONF_DIR=XXX
+./bin/spark-submit \
+  --class org.apache.spark.examples.SparkPi \
+  --master yarn-cluster \  # can also be `yarn-client` for client mode
+  --executor-memory 20G \
+  --num-executors 50 \
+  /path/to/examples.jar \
+  1000
+
+# Run a Python application on a cluster
+./bin/spark-submit \
+  --master spark://207.184.161.138:7077 \
+  examples/src/main/python/pi.py \
+  1000
+{% endhighlight %}
+
+# Master URLs
+
+The master URL passed to Spark can be in one of the following formats:
+
+<table class="table">
+<tr><th>Master URL</th><th>Meaning</th></tr>
+<tr><td> local </td><td> Run Spark locally with one worker thread (i.e. no parallelism at all). </td></tr>
+<tr><td> local[K] </td><td> Run Spark locally with K worker threads (ideally, set this to the number of cores on your machine). </td></tr>
+<tr><td> local[*] </td><td> Run Spark locally with as many worker threads as logical cores on your machine.</td></tr>
+<tr><td> spark://HOST:PORT </td><td> Connect to the given <a href="spark-standalone.html">Spark standalone
+        cluster</a> master. The port must be whichever one your master is configured to use, which is 7077 by default.
+</td></tr>
+<tr><td> mesos://HOST:PORT </td><td> Connect to the given <a href="running-on-mesos.html">Mesos</a> cluster.
+        The port must be whichever one your is configured to use, which is 5050 by default.
+        Or, for a Mesos cluster using ZooKeeper, use <code>mesos://zk://...</code>.
+</td></tr>
+<tr><td> yarn-client </td><td> Connect to a <a href="running-on-yarn.html"> YARN </a> cluster in
+client mode. The cluster location will be found based on the HADOOP_CONF_DIR variable.
+</td></tr>
+<tr><td> yarn-cluster </td><td> Connect to a <a href="running-on-yarn.html"> YARN </a> cluster in
+cluster mode. The cluster location will be found based on HADOOP_CONF_DIR.
+</td></tr>
+</table>
+
+
+# Loading Configuration from a File
+
+The `spark-submit` script can load default [Spark configuration values](configuration.html) from a
+properties file and pass them on to your application. By default it will read options
+from `conf/spark-defaults.conf` in the Spark directory. For more detail, see the section on
+[loading default configurations](configuration.html#loading-default-configurations).
+
+Loading default Spark configurations this way can obviate the need for certain flags to
+`spark-submit`. For instance, if the `spark.master` property is set, you can safely omit the
+`--master` flag from `spark-submit`. In general, configuration values explicitly set on a
+`SparkConf` take the highest precedence, then flags passed to `spark-submit`, then values in the
+defaults file.
+
+If you are ever unclear where configuration options are coming from, you can print out fine-grained
+debugging information by running `spark-submit` with the `--verbose` option.
+
+# Advanced Dependency Management
+When using `spark-submit`, the application jar along with any jars included with the `--jars` option
+will be automatically transferred to the cluster. Spark uses the following URL scheme to allow
+different strategies for disseminating jars:
+
+- **file:** - Absolute paths and `file:/` URIs are served by the driver's HTTP file server, and
+  every executor pulls the file from the driver HTTP server.
+- **hdfs:**, **http:**, **https:**, **ftp:** - these pull down files and JARs from the URI as expected
+- **local:** - a URI starting with local:/ is expected to exist as a local file on each worker node.  This
+  means that no network IO will be incurred, and works well for large files/JARs that are pushed to each worker,
+  or shared via NFS, GlusterFS, etc.
+
+Note that JARs and files are copied to the working directory for each SparkContext on the executor nodes.
+This can use up a significant amount of space over time and will need to be cleaned up. With YARN, cleanup
+is handled automatically, and with Spark standalone, automatic cleanup can be configured with the
+`spark.worker.cleanup.appDataTtl` property.
+
+For python, the equivalent `--py-files` option can be used to distribute `.egg`, `.zip` and `.py` libraries
+to executors.
+
+# More Information
+
+Once you have deployed your application, the [cluster mode overview](cluster-overview.html) describes 
+the components involved in distributed execution, and how to monitor and debug applications.
diff --git a/docs/tuning.md b/docs/tuning.md
index 78e10770a80b..c4230416e112 100644
--- a/docs/tuning.md
+++ b/docs/tuning.md
@@ -10,7 +10,7 @@ Because of the in-memory nature of most Spark computations, Spark programs can b
 by any resource in the cluster: CPU, network bandwidth, or memory.
 Most often, if the data fits in memory, the bottleneck is network bandwidth, but sometimes, you
 also need to do some tuning, such as
-[storing RDDs in serialized form](scala-programming-guide.html#rdd-persistence), to
+[storing RDDs in serialized form](programming-guide.html#rdd-persistence), to
 decrease memory usage.
 This guide will cover two main topics: data serialization, which is crucial for good network
 performance and can also reduce memory use, and memory tuning. We also sketch several smaller topics.
@@ -130,7 +130,7 @@ pointer-based data structures and wrapper objects. There are several ways to do
 
 When your objects are still too large to efficiently store despite this tuning, a much simpler way
 to reduce memory usage is to store them in *serialized* form, using the serialized StorageLevels in
-the [RDD persistence API](scala-programming-guide.html#rdd-persistence), such as `MEMORY_ONLY_SER`.
+the [RDD persistence API](programming-guide.html#rdd-persistence), such as `MEMORY_ONLY_SER`.
 Spark will then store each RDD partition as one large byte array.
 The only downside of storing data in serialized form is slower access times, due to having to
 deserialize each object on the fly.
@@ -239,7 +239,7 @@ number of cores in your clusters.
 
 ## Broadcasting Large Variables
 
-Using the [broadcast functionality](scala-programming-guide.html#broadcast-variables)
+Using the [broadcast functionality](programming-guide.html#broadcast-variables)
 available in `SparkContext` can greatly reduce the size of each serialized task, and the cost
 of launching a job over a cluster. If your tasks use any large object from the driver program
 inside of them (e.g. a static lookup table), consider turning it into a broadcast variable.

From 79fa8fd4b198904ca9ed78a821fd924bd1a82902 Mon Sep 17 00:00:00 2001
From: Prashant Sharma <prashant.s@imaginea.com>
Date: Fri, 30 May 2014 01:13:51 -0700
Subject: [PATCH 299/810] [SPARK-1971] Update MIMA to compare against Spark
 1.0.0

Author: Prashant Sharma <prashant.s@imaginea.com>

Closes #910 from ScrapCodes/enable-mima/spark-core and squashes the following commits:

79f3687 [Prashant Sharma] updated Mima to check against version 1.0
1e8969c [Prashant Sharma] Spark core missed out on Mima settings. So in effect we never tested spark core for mima related errors.
---
 project/MimaBuild.scala                | 32 +++-----------------------
 project/SparkBuild.scala               |  7 +++---
 project/project/SparkPluginBuild.scala |  2 --
 3 files changed, 7 insertions(+), 34 deletions(-)

diff --git a/project/MimaBuild.scala b/project/MimaBuild.scala
index e147be7ddaa6..182ca7615de6 100644
--- a/project/MimaBuild.scala
+++ b/project/MimaBuild.scala
@@ -31,7 +31,7 @@ object MimaBuild {
     // Read package-private excludes from file
     val excludeFilePath = (base.getAbsolutePath + "/.mima-excludes")
     val excludeFile = file(excludeFilePath)
-    val packagePrivateList: Seq[String] =
+    val ignoredClasses: Seq[String] =
       if (!excludeFile.exists()) {
         Seq()
       } else {
@@ -60,35 +60,9 @@ object MimaBuild {
       excludePackage("org.apache.spark." + packageName)
     }
 
-    val packagePrivateExcludes = packagePrivateList.flatMap(excludeClass)
+    val externalExcludeFileClasses = ignoredClasses.flatMap(excludeClass)
 
-    /* Excludes specific to a given version of Spark. When comparing the given version against
-       its immediate predecessor, the excludes listed here will be applied. */
-    val versionExcludes =
-      SparkBuild.SPARK_VERSION match {
-        case v if v.startsWith("1.0") =>
-          Seq(
-            excludeSparkPackage("api.java"),
-            excludeSparkPackage("mllib"),
-            excludeSparkPackage("streaming")
-          ) ++
-          excludeSparkClass("rdd.ClassTags") ++
-          excludeSparkClass("util.XORShiftRandom") ++
-          excludeSparkClass("graphx.EdgeRDD") ++
-          excludeSparkClass("graphx.VertexRDD") ++
-          excludeSparkClass("graphx.impl.GraphImpl") ++
-          excludeSparkClass("graphx.impl.RoutingTable") ++
-          excludeSparkClass("graphx.util.collection.PrimitiveKeyOpenHashMap") ++
-          excludeSparkClass("graphx.util.collection.GraphXPrimitiveKeyOpenHashMap") ++
-          excludeSparkClass("mllib.recommendation.MFDataGenerator") ++
-          excludeSparkClass("mllib.optimization.SquaredGradient") ++
-          excludeSparkClass("mllib.regression.RidgeRegressionWithSGD") ++
-          excludeSparkClass("mllib.regression.LassoWithSGD") ++
-          excludeSparkClass("mllib.regression.LinearRegressionWithSGD")
-        case _ => Seq()
-      }
-
-    defaultExcludes ++ packagePrivateExcludes ++ versionExcludes
+    defaultExcludes ++ externalExcludeFileClasses
   }
 
   def mimaSettings(sparkHome: File) = mimaDefaultSettings ++ Seq(
diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index 8ef1e91f609f..9833411c90b7 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -32,7 +32,7 @@ import scala.collection.JavaConversions._
 // import com.jsuereth.pgp.sbtplugin.PgpKeys._
 
 object SparkBuild extends Build {
-  val SPARK_VERSION = "1.0.0-SNAPSHOT"
+  val SPARK_VERSION = "1.1.0-SNAPSHOT"
   val SPARK_VERSION_SHORT = SPARK_VERSION.replaceAll("-SNAPSHOT", "")
 
   // Hadoop version to build against. For example, "1.0.4" for Apache releases, or
@@ -321,7 +321,7 @@ object SparkBuild extends Build {
   val excludeServletApi = ExclusionRule(organization = "javax.servlet", artifact = "servlet-api")
 
   def sparkPreviousArtifact(id: String, organization: String = "org.apache.spark",
-      version: String = "0.9.0-incubating", crossVersion: String = "2.10"): Option[sbt.ModuleID] = {
+      version: String = "1.0.0", crossVersion: String = "2.10"): Option[sbt.ModuleID] = {
     val fullId = if (crossVersion.isEmpty) id else id + "_" + crossVersion
     Some(organization % fullId % version) // the artifact to compare binary compatibility with
   }
@@ -363,7 +363,8 @@ object SparkBuild extends Build {
         "org.spark-project"          % "pyrolite"         % "2.0.1",
         "net.sf.py4j"                % "py4j"             % "0.8.1"
       ),
-    libraryDependencies ++= maybeAvro
+    libraryDependencies ++= maybeAvro,
+    previousArtifact := sparkPreviousArtifact("spark-core")
   )
 
   // Create a colon-separate package list adding "org.apache.spark" in front of all of them,
diff --git a/project/project/SparkPluginBuild.scala b/project/project/SparkPluginBuild.scala
index 0142256e90fb..e9fba641eb8a 100644
--- a/project/project/SparkPluginBuild.scala
+++ b/project/project/SparkPluginBuild.scala
@@ -26,12 +26,10 @@ import sbt.Keys._
 object SparkPluginDef extends Build {
   lazy val root = Project("plugins", file(".")) dependsOn(sparkStyle)
   lazy val sparkStyle = Project("spark-style", file("spark-style"), settings = styleSettings)
-  val sparkVersion = "1.0.0-SNAPSHOT"
   // There is actually no need to publish this artifact.
   def styleSettings = Defaults.defaultSettings ++ Seq (
     name                 :=  "spark-style",
     organization         :=  "org.apache.spark",
-    version              :=  sparkVersion,
     scalaVersion         :=  "2.10.4",
     scalacOptions        :=  Seq("-unchecked", "-deprecation"),
     libraryDependencies  ++= Dependencies.scalaStyle

From ff562b2396bb59b8c71d800d37b3a36ddac3488f Mon Sep 17 00:00:00 2001
From: Zhen Peng <zhenpeng01@baidu.com>
Date: Fri, 30 May 2014 10:11:02 -0700
Subject: [PATCH 300/810] [SPARK-1901] worker should make sure executor has
 exited before updating executor's info

https://issues.apache.org/jira/browse/SPARK-1901

Author: Zhen Peng <zhenpeng01@baidu.com>

Closes #854 from zhpengg/bugfix-worker-kills-executor and squashes the following commits:

21d380b [Zhen Peng] add some error messages
506cea6 [Zhen Peng] add some docs for killProcess()
a0b9860 [Zhen Peng] [SPARK-1901] worker should make sure executor has exited before updating executor's info
---
 .../spark/deploy/worker/ExecutorRunner.scala  | 20 +++++++++++--------
 1 file changed, 12 insertions(+), 8 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/ExecutorRunner.scala b/core/src/main/scala/org/apache/spark/deploy/worker/ExecutorRunner.scala
index 205140368273..d27e0e1f15c6 100644
--- a/core/src/main/scala/org/apache/spark/deploy/worker/ExecutorRunner.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/worker/ExecutorRunner.scala
@@ -61,17 +61,23 @@ private[spark] class ExecutorRunner(
     // Shutdown hook that kills actors on shutdown.
     shutdownHook = new Thread() {
       override def run() {
-        killProcess()
+        killProcess(Some("Worker shutting down"))
       }
     }
     Runtime.getRuntime.addShutdownHook(shutdownHook)
   }
 
-  private def killProcess() {
+  /**
+   * kill executor process, wait for exit and notify worker to update resource status
+   *
+   * @param message the exception message which caused the executor's death 
+   */
+  private def killProcess(message: Option[String]) {
     if (process != null) {
       logInfo("Killing process!")
       process.destroy()
-      process.waitFor()
+      val exitCode = process.waitFor()
+      worker ! ExecutorStateChanged(appId, execId, state, message, Some(exitCode))
     }
   }
 
@@ -82,7 +88,6 @@ private[spark] class ExecutorRunner(
       workerThread.interrupt()
       workerThread = null
       state = ExecutorState.KILLED
-      worker ! ExecutorStateChanged(appId, execId, state, None, None)
       Runtime.getRuntime.removeShutdownHook(shutdownHook)
     }
   }
@@ -148,14 +153,13 @@ private[spark] class ExecutorRunner(
     } catch {
       case interrupted: InterruptedException => {
         logInfo("Runner thread for executor " + fullId + " interrupted")
-        killProcess()
+        state = ExecutorState.KILLED
+        killProcess(None)
       }
       case e: Exception => {
         logError("Error running executor", e)
-        killProcess()
         state = ExecutorState.FAILED
-        val message = e.getClass + ": " + e.getMessage
-        worker ! ExecutorStateChanged(appId, execId, state, Some(message), None)
+        killProcess(Some(e.toString))
       }
     }
   }

From 9c1f204d807403a7cb4099c29077e1f87f466179 Mon Sep 17 00:00:00 2001
From: Andrew Ash <andrew@andrewash.com>
Date: Fri, 30 May 2014 22:02:04 -0700
Subject: [PATCH 301/810] Typo: and -> an

Author: Andrew Ash <andrew@andrewash.com>

Closes #927 from ash211/patch-5 and squashes the following commits:

79b577d [Andrew Ash] Typo: and -> an
---
 docs/configuration.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/configuration.md b/docs/configuration.md
index 2fd691800ea0..0697f7fc2fd9 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -310,7 +310,7 @@ Apart from these, the following properties are also available, and may be useful
     Base directory in which Spark events are logged, if <code>spark.eventLog.enabled</code> is true.
     Within this base directory, Spark creates a sub-directory for each application, and logs the
     events specific to the application in this directory. Users may want to set this to
-    and HDFS directory so that history files can be read by the history server.
+    a unified location like an HDFS directory so history files can be read by the history server.
   </td>
 </tr>
 </table>

From 23ae36630aa5e6ca4a86e32912a99408129dff7b Mon Sep 17 00:00:00 2001
From: nchammas <nicholas.chammas@gmail.com>
Date: Fri, 30 May 2014 22:04:57 -0700
Subject: [PATCH 302/810] updated link to mailing list

Author: nchammas <nicholas.chammas@gmail.com>

Closes #923 from nchammas/patch-1 and squashes the following commits:

65c4d18 [nchammas] updated link to mailing list
---
 docs/tuning.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/tuning.md b/docs/tuning.md
index c4230416e112..4917c11bc114 100644
--- a/docs/tuning.md
+++ b/docs/tuning.md
@@ -253,4 +253,4 @@ This has been a short guide to point out the main concerns you should know about
 Spark application -- most importantly, data serialization and memory tuning. For most programs,
 switching to Kryo serialization and persisting data in serialized form will solve most common
 performance issues. Feel free to ask on the
-[Spark mailing list](http://groups.google.com/group/spark-users) about other tuning best practices.
+[Spark mailing list](https://spark.apache.org/community.html) about other tuning best practices.

From 41bfdda3cc53b44b0fb2105533b7c0e1a69eb3bb Mon Sep 17 00:00:00 2001
From: CodingCat <zhunansjtu@gmail.com>
Date: Fri, 30 May 2014 22:06:08 -0700
Subject: [PATCH 303/810] SPARK-1976: fix the misleading part in  streaming
 docs

Spark streaming requires at least two working threads, but the document gives the example like

import org.apache.spark.api.java.function._
import org.apache.spark.streaming._
import org.apache.spark.streaming.api._
// Create a StreamingContext with a local master
val ssc = new StreamingContext("local", "NetworkWordCount", Seconds(1))
http://spark.apache.org/docs/latest/streaming-programming-guide.html

Author: CodingCat <zhunansjtu@gmail.com>

Closes #924 from CodingCat/master and squashes the following commits:

bb89f20 [CodingCat] update streaming docs
---
 docs/streaming-programming-guide.md | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/docs/streaming-programming-guide.md b/docs/streaming-programming-guide.md
index b95f818d0fcb..bbee67f54c6b 100644
--- a/docs/streaming-programming-guide.md
+++ b/docs/streaming-programming-guide.md
@@ -80,7 +80,8 @@ import org.apache.spark.api.java.function._
 import org.apache.spark.streaming._
 import org.apache.spark.streaming.api._
 // Create a StreamingContext with a local master
-val ssc = new StreamingContext("local", "NetworkWordCount", Seconds(1))
+// Spark Streaming needs at least two working thread
+val ssc = new StreamingContext("local[2]", "NetworkWordCount", Seconds(1))
 {% endhighlight %}
 
 Using this context, we then create a new DStream

From cf989601d0e784e1c3507720e64636891fe28292 Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian.cs.zju@gmail.com>
Date: Fri, 30 May 2014 22:13:11 -0700
Subject: [PATCH 304/810] [SPARK-1959] String "NULL" shouldn't be interpreted
 as null value

JIRA issue: [SPARK-1959](https://issues.apache.org/jira/browse/SPARK-1959)

Author: Cheng Lian <lian.cs.zju@gmail.com>

Closes #909 from liancheng/spark-1959 and squashes the following commits:

306659c [Cheng Lian] [SPARK-1959] String "NULL" shouldn't be interpreted as null value
---
 .../src/main/scala/org/apache/spark/sql/hive/hiveOperators.scala | 1 -
 1 file changed, 1 deletion(-)

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveOperators.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveOperators.scala
index f141139ef46a..d263c312960a 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveOperators.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveOperators.scala
@@ -113,7 +113,6 @@ case class HiveTableScan(
   }
 
   private def unwrapHiveData(value: Any) = value match {
-    case maybeNull: String if maybeNull.toLowerCase == "null" => null
     case varchar: HiveVarchar => varchar.getValue
     case decimal: HiveDecimal => BigDecimal(decimal.bigDecimalValue)
     case other => other

From 9ecc40d3aeff0eb113f16df55f4249d8143f37f1 Mon Sep 17 00:00:00 2001
From: Chen Chao <crazyjvm@gmail.com>
Date: Sat, 31 May 2014 00:06:49 -0700
Subject: [PATCH 305/810] correct tiny comment error

Author: Chen Chao <crazyjvm@gmail.com>

Closes #928 from CrazyJvm/patch-8 and squashes the following commits:

144328b [Chen Chao] correct tiny comment error
---
 core/src/main/scala/org/apache/spark/SparkContext.scala | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index 03ceff8bf1fb..d941aea9d7eb 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -76,8 +76,8 @@ class SparkContext(config: SparkConf) extends Logging {
    * :: DeveloperApi ::
    * Alternative constructor for setting preferred locations where Spark will create executors.
    *
-   * @param preferredNodeLocationData used in YARN mode to select nodes to launch containers on. Ca
-   * be generated using [[org.apache.spark.scheduler.InputFormatInfo.computePreferredLocations]]
+   * @param preferredNodeLocationData used in YARN mode to select nodes to launch containers on.
+   * Can be generated using [[org.apache.spark.scheduler.InputFormatInfo.computePreferredLocations]]
    * from a list of input files or InputFormats for the application.
    */
   @DeveloperApi

From 3ce81494c512bc97979a743ea77ef913315f7fb6 Mon Sep 17 00:00:00 2001
From: Takuya UESHIN <ueshin@happy-camper.st>
Date: Sat, 31 May 2014 11:30:03 -0700
Subject: [PATCH 306/810] [SPARK-1947] [SQL] Child of SumDistinct or Average
 should be widened to prevent overflows the same as Sum.

Child of `SumDistinct` or `Average` should be widened to prevent overflows the same as `Sum`.

Author: Takuya UESHIN <ueshin@happy-camper.st>

Closes #902 from ueshin/issues/SPARK-1947 and squashes the following commits:

99c3dcb [Takuya UESHIN] Insert Cast for SumDistinct and Average.
---
 .../sql/catalyst/analysis/HiveTypeCoercion.scala   | 14 +++++++++++++-
 1 file changed, 13 insertions(+), 1 deletion(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
index 4557d77160fa..326feea6fee9 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
@@ -264,10 +264,22 @@ trait HiveTypeCoercion {
       // Skip nodes who's children have not been resolved yet.
       case e if !e.childrenResolved => e
 
-      // Promote SUM to largest types to prevent overflows.
+      // Promote SUM, SUM DISTINCT and AVERAGE to largest types to prevent overflows.
       case s @ Sum(e @ DecimalType()) => s // Decimal is already the biggest.
       case Sum(e @ IntegralType()) if e.dataType != LongType => Sum(Cast(e, LongType))
       case Sum(e @ FractionalType()) if e.dataType != DoubleType => Sum(Cast(e, DoubleType))
+
+      case s @ SumDistinct(e @ DecimalType()) => s // Decimal is already the biggest.
+      case SumDistinct(e @ IntegralType()) if e.dataType != LongType =>
+        SumDistinct(Cast(e, LongType))
+      case SumDistinct(e @ FractionalType()) if e.dataType != DoubleType =>
+        SumDistinct(Cast(e, DoubleType))
+
+      case s @ Average(e @ DecimalType()) => s // Decimal is already the biggest.
+      case Average(e @ IntegralType()) if e.dataType != LongType =>
+        Average(Cast(e, LongType))
+      case Average(e @ FractionalType()) if e.dataType != DoubleType =>
+        Average(Cast(e, DoubleType))
     }
   }
 }

From 7463cd248f81975bce9ff864002932864bd5b649 Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Sat, 31 May 2014 12:24:35 -0700
Subject: [PATCH 307/810] Optionally include Hive as a dependency of the REPL.

Due to the way spark-shell launches from an assembly jar, I don't think this change will affect anyone who isn't trying to launch the shell directly from sbt.  That said, it is kinda nice to be able to launch all things directly from SBT when developing.

Author: Michael Armbrust <michael@databricks.com>

Closes #801 from marmbrus/hiveRepl and squashes the following commits:

9570571 [Michael Armbrust] Optionally include Hive as a dependency of the REPL.
---
 project/SparkBuild.scala | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index 9833411c90b7..64c9441d8e3f 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -59,8 +59,10 @@ object SparkBuild extends Build {
 
   lazy val core = Project("core", file("core"), settings = coreSettings)
 
+  def replDependencies = Seq[ProjectReference](core, graphx, bagel, mllib, sql) ++ maybeHiveRef
+
   lazy val repl = Project("repl", file("repl"), settings = replSettings)
-    .dependsOn(core, graphx, bagel, mllib, sql)
+    .dependsOn(replDependencies.map(a => a: sbt.ClasspathDep[sbt.ProjectReference]): _*)
 
   lazy val tools = Project("tools", file("tools"), settings = toolsSettings) dependsOn(core) dependsOn(streaming)
 

From 1a0da0ec5799f3226ce238cac03f53fa4f7c9326 Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Sat, 31 May 2014 12:34:22 -0700
Subject: [PATCH 308/810] [SQL] SPARK-1964 Add timestamp to hive metastore type
 parser.

Author: Michael Armbrust <michael@databricks.com>

Closes #913 from marmbrus/timestampMetastore and squashes the following commits:

8e0154f [Michael Armbrust] Add timestamp to hive metastore type parser.
---
 .../scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala   | 1 +
 1 file changed, 1 insertion(+)

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
index 9f74e0334f72..a91b52076534 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
@@ -190,6 +190,7 @@ object HiveMetastoreTypes extends RegexParsers {
     "binary" ^^^ BinaryType |
     "boolean" ^^^ BooleanType |
     "decimal" ^^^ DecimalType |
+    "timestamp" ^^^ TimestampType |
     "varchar\\((\\d+)\\)".r ^^^ StringType
 
   protected lazy val arrayType: Parser[DataType] =

From 7d52777effd0ff41aed545f53d2ab8de2364a188 Mon Sep 17 00:00:00 2001
From: Aaron Davidson <aaron@databricks.com>
Date: Sat, 31 May 2014 12:36:58 -0700
Subject: [PATCH 309/810] Super minor: Close inputStream in
 SparkSubmitArguments

`Properties#load()` doesn't close the InputStream, but it'd be closed after being GC'd anyway...

Also changed file.getName to file, because getName only shows the filename. This will show the full (possibly relative) path, which is less confusing if it's not found.

Author: Aaron Davidson <aaron@databricks.com>

Closes #914 from aarondav/tiny and squashes the following commits:

db9d072 [Aaron Davidson] Super minor: Close inputStream in SparkSubmitArguments
---
 .../apache/spark/deploy/SparkSubmitArguments.scala    | 11 +++++++----
 1 file changed, 7 insertions(+), 4 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
index bf449afae695..153eee3bc588 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
@@ -381,16 +381,19 @@ private[spark] class SparkSubmitArguments(args: Seq[String]) {
 object SparkSubmitArguments {
   /** Load properties present in the given file. */
   def getPropertiesFromFile(file: File): Seq[(String, String)] = {
-    require(file.exists(), s"Properties file ${file.getName} does not exist")
+    require(file.exists(), s"Properties file $file does not exist")
+    require(file.isFile(), s"Properties file $file is not a normal file")
     val inputStream = new FileInputStream(file)
-    val properties = new Properties()
     try {
+      val properties = new Properties()
       properties.load(inputStream)
+      properties.stringPropertyNames().toSeq.map(k => (k, properties(k).trim))
     } catch {
       case e: IOException =>
-        val message = s"Failed when loading Spark properties file ${file.getName}"
+        val message = s"Failed when loading Spark properties file $file"
         throw new SparkException(message, e)
+    } finally {
+      inputStream.close()
     }
-    properties.stringPropertyNames().toSeq.map(k => (k, properties(k).trim))
   }
 }

From 9909efc10aaa62c47fd7c4c9da73ac8c56a454d5 Mon Sep 17 00:00:00 2001
From: Aaron Davidson <aaron@databricks.com>
Date: Sat, 31 May 2014 13:04:57 -0700
Subject: [PATCH 310/810] SPARK-1839: PySpark RDD#take() shouldn't always read
 from driver

This patch simply ports over the Scala implementation of RDD#take(), which reads the first partition at the driver, then decides how many more partitions it needs to read and will possibly start a real job if it's more than 1. (Note that SparkContext#runJob(allowLocal=true) only runs the job locally if there's 1 partition selected and no parent stages.)

Author: Aaron Davidson <aaron@databricks.com>

Closes #922 from aarondav/take and squashes the following commits:

fa06df9 [Aaron Davidson] SPARK-1839: PySpark RDD#take() shouldn't always read from driver
---
 .../apache/spark/api/python/PythonRDD.scala   | 20 +++++++
 python/pyspark/context.py                     | 26 ++++++++
 python/pyspark/rdd.py                         | 59 ++++++++++++-------
 3 files changed, 84 insertions(+), 21 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
index 57b28b997236..d1df99300c5b 100644
--- a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
+++ b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
@@ -269,6 +269,26 @@ private object SpecialLengths {
 private[spark] object PythonRDD {
   val UTF8 = Charset.forName("UTF-8")
 
+  /**
+   * Adapter for calling SparkContext#runJob from Python.
+   *
+   * This method will return an iterator of an array that contains all elements in the RDD
+   * (effectively a collect()), but allows you to run on a certain subset of partitions,
+   * or to enable local execution.
+   */
+  def runJob(
+      sc: SparkContext,
+      rdd: JavaRDD[Array[Byte]],
+      partitions: JArrayList[Int],
+      allowLocal: Boolean): Iterator[Array[Byte]] = {
+    type ByteArray = Array[Byte]
+    type UnrolledPartition = Array[ByteArray]
+    val allPartitions: Array[UnrolledPartition] =
+      sc.runJob(rdd, (x: Iterator[ByteArray]) => x.toArray, partitions, allowLocal)
+    val flattenedPartition: UnrolledPartition = Array.concat(allPartitions: _*)
+    flattenedPartition.iterator
+  }
+
   def readRDDFromFile(sc: JavaSparkContext, filename: String, parallelism: Int):
   JavaRDD[Array[Byte]] = {
     val file = new DataInputStream(new FileInputStream(filename))
diff --git a/python/pyspark/context.py b/python/pyspark/context.py
index 56746cb7aab3..9ae9305d4f02 100644
--- a/python/pyspark/context.py
+++ b/python/pyspark/context.py
@@ -537,6 +537,32 @@ def cancelAllJobs(self):
         """
         self._jsc.sc().cancelAllJobs()
 
+    def runJob(self, rdd, partitionFunc, partitions = None, allowLocal = False):
+        """
+        Executes the given partitionFunc on the specified set of partitions,
+        returning the result as an array of elements.
+
+        If 'partitions' is not specified, this will run over all partitions.
+
+        >>> myRDD = sc.parallelize(range(6), 3)
+        >>> sc.runJob(myRDD, lambda part: [x * x for x in part])
+        [0, 1, 4, 9, 16, 25]
+
+        >>> myRDD = sc.parallelize(range(6), 3)
+        >>> sc.runJob(myRDD, lambda part: [x * x for x in part], [0, 2], True)
+        [0, 1, 16, 25]
+        """
+        if partitions == None:
+            partitions = range(rdd._jrdd.splits().size())
+        javaPartitions = ListConverter().convert(partitions, self._gateway._gateway_client)
+
+        # Implementation note: This is implemented as a mapPartitions followed
+        # by runJob() in order to avoid having to pass a Python lambda into
+        # SparkContext#runJob.
+        mappedRDD = rdd.mapPartitions(partitionFunc)
+        it = self._jvm.PythonRDD.runJob(self._jsc.sc(), mappedRDD._jrdd, javaPartitions, allowLocal)
+        return list(mappedRDD._collect_iterator_through_file(it))
+
 def _test():
     import atexit
     import doctest
diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py
index 07578b8d937f..f3b1f1a665e5 100644
--- a/python/pyspark/rdd.py
+++ b/python/pyspark/rdd.py
@@ -841,34 +841,51 @@ def take(self, num):
         """
         Take the first num elements of the RDD.
 
-        This currently scans the partitions *one by one*, so it will be slow if
-        a lot of partitions are required. In that case, use L{collect} to get
-        the whole RDD instead.
+        It works by first scanning one partition, and use the results from
+        that partition to estimate the number of additional partitions needed
+        to satisfy the limit.
+
+        Translated from the Scala implementation in RDD#take().
 
         >>> sc.parallelize([2, 3, 4, 5, 6]).cache().take(2)
         [2, 3]
         >>> sc.parallelize([2, 3, 4, 5, 6]).take(10)
         [2, 3, 4, 5, 6]
+        >>> sc.parallelize(range(100), 100).filter(lambda x: x > 90).take(3)
+        [91, 92, 93]
         """
-        def takeUpToNum(iterator):
-            taken = 0
-            while taken < num:
-                yield next(iterator)
-                taken += 1
-        # Take only up to num elements from each partition we try
-        mapped = self.mapPartitions(takeUpToNum)
         items = []
-        # TODO(shivaram): Similar to the scala implementation, update the take 
-        # method to scan multiple splits based on an estimate of how many elements 
-        # we have per-split.
-        with _JavaStackTrace(self.context) as st:
-            for partition in range(mapped._jrdd.splits().size()):
-                partitionsToTake = self.ctx._gateway.new_array(self.ctx._jvm.int, 1)
-                partitionsToTake[0] = partition
-                iterator = mapped._jrdd.collectPartitions(partitionsToTake)[0].iterator()
-                items.extend(mapped._collect_iterator_through_file(iterator))
-                if len(items) >= num:
-                    break
+        totalParts = self._jrdd.splits().size()
+        partsScanned = 0
+
+        while len(items) < num and partsScanned < totalParts:
+            # The number of partitions to try in this iteration.
+            # It is ok for this number to be greater than totalParts because
+            # we actually cap it at totalParts in runJob.
+            numPartsToTry = 1
+            if partsScanned > 0:
+                # If we didn't find any rows after the first iteration, just
+                # try all partitions next. Otherwise, interpolate the number
+                # of partitions we need to try, but overestimate it by 50%.
+                if len(items) == 0:
+                    numPartsToTry = totalParts - 1
+                else:
+                    numPartsToTry = int(1.5 * num * partsScanned / len(items))
+
+            left = num - len(items)
+
+            def takeUpToNumLeft(iterator):
+                taken = 0
+                while taken < left:
+                    yield next(iterator)
+                    taken += 1
+
+            p = range(partsScanned, min(partsScanned + numPartsToTry, totalParts))
+            res = self.context.runJob(self, takeUpToNumLeft, p, True)
+
+            items += res
+            partsScanned += numPartsToTry
+
         return items[:num]
 
     def first(self):

From d8c005d5371f81a2a06c5d27c7021e1ae43d7193 Mon Sep 17 00:00:00 2001
From: witgo <witgo@qq.com>
Date: Sat, 31 May 2014 14:36:27 -0700
Subject: [PATCH 311/810] Improve maven plugin configuration

Author: witgo <witgo@qq.com>

Closes #786 from witgo/maven_plugin and squashes the following commits:

5de86a2 [witgo] Merge branch 'master' of https://github.com/apache/spark into maven_plugin
c35ef73 [witgo] Improve maven plugin configuration
---
 core/pom.xml | 29 -----------------------------
 pom.xml      |  4 ++++
 repl/pom.xml | 30 ------------------------------
 yarn/pom.xml | 30 ------------------------------
 4 files changed, 4 insertions(+), 89 deletions(-)

diff --git a/core/pom.xml b/core/pom.xml
index bab50f5ce288..0777c5b1f03d 100644
--- a/core/pom.xml
+++ b/core/pom.xml
@@ -258,35 +258,6 @@
     <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory>
     <testOutputDirectory>target/scala-${scala.binary.version}/test-classes</testOutputDirectory>
     <plugins>
-      <plugin>
-        <groupId>org.apache.maven.plugins</groupId>
-        <artifactId>maven-antrun-plugin</artifactId>
-        <executions>
-          <execution>
-            <phase>test</phase>
-            <goals>
-              <goal>run</goal>
-            </goals>
-            <configuration>
-              <exportAntProperties>true</exportAntProperties>
-              <target>
-                <property name="spark.classpath" refid="maven.test.classpath" />
-                <property environment="env" />
-                <fail message="Please set the SCALA_HOME (or SCALA_LIBRARY_PATH if scala is on the path) environment variables and retry.">
-                  <condition>
-                    <not>
-                      <or>
-                        <isset property="env.SCALA_HOME" />
-                        <isset property="env.SCALA_LIBRARY_PATH" />
-                      </or>
-                    </not>
-                  </condition>
-                </fail>
-              </target>
-            </configuration>
-          </execution>
-        </executions>
-      </plugin>
       <plugin>
         <groupId>org.scalatest</groupId>
         <artifactId>scalatest-maven-plugin</artifactId>
diff --git a/pom.xml b/pom.xml
index 7bf9f135fd34..fe43a9518b52 100644
--- a/pom.xml
+++ b/pom.xml
@@ -814,6 +814,10 @@
             <filereports>${project.build.directory}/SparkTestSuite.txt</filereports>
             <argLine>-Xmx3g -XX:MaxPermSize=${MaxPermGen} -XX:ReservedCodeCacheSize=512m</argLine>
             <stderr/>
+            <environmentVariables>
+              <SPARK_HOME>${session.executionRootDirectory}</SPARK_HOME>
+              <SPARK_TESTING>1</SPARK_TESTING>
+            </environmentVariables>
           </configuration>
           <executions>
             <execution>
diff --git a/repl/pom.xml b/repl/pom.xml
index b761a176ce25..bcdb24b040cc 100644
--- a/repl/pom.xml
+++ b/repl/pom.xml
@@ -92,42 +92,12 @@
     <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory>
     <testOutputDirectory>target/scala-${scala.binary.version}/test-classes</testOutputDirectory>
     <plugins>
-      <plugin>
-        <groupId>org.apache.maven.plugins</groupId>
-        <artifactId>maven-antrun-plugin</artifactId>
-        <executions>
-          <execution>
-            <phase>test</phase>
-            <goals>
-              <goal>run</goal>
-            </goals>
-            <configuration>
-              <exportAntProperties>true</exportAntProperties>
-              <target>
-                <property name="spark.classpath" refid="maven.test.classpath" />
-                <property environment="env" />
-                <fail message="Please set the SCALA_HOME (or SCALA_LIBRARY_PATH if scala is on the path) environment variables and retry.">
-                  <condition>
-                    <not>
-                      <or>
-                        <isset property="env.SCALA_HOME" />
-                        <isset property="env.SCALA_LIBRARY_PATH" />
-                      </or>
-                    </not>
-                  </condition>
-                </fail>
-              </target>
-            </configuration>
-          </execution>
-        </executions>
-      </plugin>
       <plugin>
         <groupId>org.scalatest</groupId>
         <artifactId>scalatest-maven-plugin</artifactId>
         <configuration>
           <environmentVariables>
             <SPARK_HOME>${basedir}/..</SPARK_HOME>
-            <SPARK_TESTING>1</SPARK_TESTING>
           </environmentVariables>
         </configuration>
       </plugin>
diff --git a/yarn/pom.xml b/yarn/pom.xml
index 6993c89525d8..2811ffffbdfa 100644
--- a/yarn/pom.xml
+++ b/yarn/pom.xml
@@ -117,42 +117,12 @@
           </execution>
         </executions>
       </plugin>
-      <plugin>
-        <groupId>org.apache.maven.plugins</groupId>
-        <artifactId>maven-antrun-plugin</artifactId>
-        <executions>
-          <execution>
-            <phase>test</phase>
-            <goals>
-              <goal>run</goal>
-            </goals>
-            <configuration>
-              <exportAntProperties>true</exportAntProperties>
-              <target>
-                <property name="spark.classpath" refid="maven.test.classpath" />
-                <property environment="env" />
-                <fail message="Please set the SCALA_HOME (or SCALA_LIBRARY_PATH if scala is on the path) environment variables and retry.">
-                  <condition>
-                    <not>
-                      <or>
-                        <isset property="env.SCALA_HOME" />
-                        <isset property="env.SCALA_LIBRARY_PATH" />
-                      </or>
-                    </not>
-                  </condition>
-                </fail>
-              </target>
-            </configuration>
-          </execution>
-        </executions>
-      </plugin>
       <plugin>
         <groupId>org.scalatest</groupId>
         <artifactId>scalatest-maven-plugin</artifactId>
         <configuration>
           <environmentVariables>
             <SPARK_HOME>${basedir}/../..</SPARK_HOME>
-            <SPARK_TESTING>1</SPARK_TESTING>
             <SPARK_CLASSPATH>${spark.classpath}</SPARK_CLASSPATH>
           </environmentVariables>
         </configuration>

From 5e98967b612ccf026cb1cc5ff3ac8bf72d7e836e Mon Sep 17 00:00:00 2001
From: Uri Laserson <laserson@cloudera.com>
Date: Sat, 31 May 2014 14:59:09 -0700
Subject: [PATCH 312/810] SPARK-1917: fix PySpark import of scipy.special
 functions

https://issues.apache.org/jira/browse/SPARK-1917

Author: Uri Laserson <laserson@cloudera.com>

Closes #866 from laserson/SPARK-1917 and squashes the following commits:

d947e8c [Uri Laserson] Added test for scipy.special importing
1798bbd [Uri Laserson] SPARK-1917: fix PySpark import of scipy.special
---
 python/pyspark/cloudpickle.py |  2 +-
 python/pyspark/tests.py       | 24 ++++++++++++++++++++++++
 2 files changed, 25 insertions(+), 1 deletion(-)

diff --git a/python/pyspark/cloudpickle.py b/python/pyspark/cloudpickle.py
index 6a7c23a069bf..eb5dbb8de2b3 100644
--- a/python/pyspark/cloudpickle.py
+++ b/python/pyspark/cloudpickle.py
@@ -933,7 +933,7 @@ def _change_cell_value(cell, newval):
 Note: These can never be renamed due to client compatibility issues"""
 
 def _getobject(modname, attribute):
-    mod = __import__(modname)
+    mod = __import__(modname, fromlist=[attribute])
     return mod.__dict__[attribute]
 
 def _generateImage(size, mode, str_rep):
diff --git a/python/pyspark/tests.py b/python/pyspark/tests.py
index 64f2eeb12b4f..ed90915fcda3 100644
--- a/python/pyspark/tests.py
+++ b/python/pyspark/tests.py
@@ -35,6 +35,14 @@
 from pyspark.files import SparkFiles
 from pyspark.serializers import read_int
 
+_have_scipy = False
+try:
+    import scipy.sparse
+    _have_scipy = True
+except:
+    # No SciPy, but that's okay, we'll skip those tests
+    pass
+
 
 SPARK_HOME = os.environ["SPARK_HOME"]
 
@@ -359,5 +367,21 @@ def test_single_script_on_cluster(self):
         self.assertIn("[2, 4, 6]", out)
 
 
+@unittest.skipIf(not _have_scipy, "SciPy not installed")
+class SciPyTests(PySparkTestCase):
+    """General PySpark tests that depend on scipy """
+
+    def test_serialize(self):
+        from scipy.special import gammaln
+        x = range(1, 5)
+        expected = map(gammaln, x)
+        observed = self.sc.parallelize(x).map(gammaln).collect()
+        self.assertEqual(expected, observed)
+
+
 if __name__ == "__main__":
+    if not _have_scipy:
+        print "NOTE: Skipping SciPy tests as it does not seem to be installed"
     unittest.main()
+    if not _have_scipy:
+        print "NOTE: SciPy tests were skipped as it does not seem to be installed"

From 366c0c4c30465be1a298a5c750b6dbbf022c2323 Mon Sep 17 00:00:00 2001
From: Yadid Ayzenberg <yadid@media.mit.edu>
Date: Sat, 31 May 2014 19:44:13 -0700
Subject: [PATCH 313/810] updated java code blocks in spark SQL guide such that
 ctx will refer to ...

...a JavaSparkContext and sqlCtx will refer to a JavaSQLContext

Author: Yadid Ayzenberg <yadid@media.mit.edu>

Closes #932 from yadid/master and squashes the following commits:

f92fb3a [Yadid Ayzenberg] updated java code blocks in spark SQL guide such that ctx will refer to a JavaSparkContext and sqlCtx will refer to a JavaSQLContext
---
 docs/sql-programming-guide.md | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md
index a506457eba01..4623bb4247d7 100644
--- a/docs/sql-programming-guide.md
+++ b/docs/sql-programming-guide.md
@@ -170,7 +170,9 @@ A schema can be applied to an existing RDD by calling `applySchema` and providin
 for the JavaBean.
 
 {% highlight java %}
-JavaSQLContext ctx = new org.apache.spark.sql.api.java.JavaSQLContext(sc)
+
+JavaSparkContext ctx = ...; // An existing JavaSparkContext.
+JavaSQLContext sqlCtx = new org.apache.spark.sql.api.java.JavaSQLContext(ctx)
 
 // Load a text file and convert each line to a JavaBean.
 JavaRDD<Person> people = ctx.textFile("examples/src/main/resources/people.txt").map(

From eea3aab4f29de5ee11670340245039f7395ab26e Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Sun, 1 Jun 2014 15:39:04 -0700
Subject: [PATCH 314/810] Made spark_ec2.py PEP8 compliant.

The change set is actually pretty small -- mostly whitespace changes. Admittedly this is a scary change due to the lack of tests to cover the ec2 scripts, and also because indentation actually impacts control flow in Python ...

Look at changes without whitespace diff here: https://github.com/apache/spark/pull/891/files?w=1

Author: Reynold Xin <rxin@apache.org>

Closes #891 from rxin/spark-ec2-pep8 and squashes the following commits:

ac1bf11 [Reynold Xin] Made spark_ec2.py PEP8 compliant.
---
 ec2/spark_ec2.py | 1387 ++++++++++++++++++++++++----------------------
 1 file changed, 712 insertions(+), 675 deletions(-)

diff --git a/ec2/spark_ec2.py b/ec2/spark_ec2.py
index 4c5a99f5f6b2..8b056f5ea734 100755
--- a/ec2/spark_ec2.py
+++ b/ec2/spark_ec2.py
@@ -37,483 +37,515 @@
 from boto.ec2.blockdevicemapping import BlockDeviceMapping, EBSBlockDeviceType
 from boto import ec2
 
-class UsageError(Exception):
-  pass
-
 # A URL prefix from which to fetch AMI information
 AMI_PREFIX = "https://raw.github.com/mesos/spark-ec2/v2/ami-list"
 
+
+class UsageError(Exception):
+    pass
+
+
 # Configure and parse our command-line arguments
 def parse_args():
-  parser = OptionParser(usage="spark-ec2 [options] <action> <cluster_name>"
-      + "\n\n<action> can be: launch, destroy, login, stop, start, get-master",
-      add_help_option=False)
-  parser.add_option("-h", "--help", action="help",
-                    help="Show this help message and exit")
-  parser.add_option("-s", "--slaves", type="int", default=1,
-      help="Number of slaves to launch (default: 1)")
-  parser.add_option("-w", "--wait", type="int", default=120,
-      help="Seconds to wait for nodes to start (default: 120)")
-  parser.add_option("-k", "--key-pair",
-      help="Key pair to use on instances")
-  parser.add_option("-i", "--identity-file",
-      help="SSH private key file to use for logging into instances")
-  parser.add_option("-t", "--instance-type", default="m1.large",
-      help="Type of instance to launch (default: m1.large). " +
-           "WARNING: must be 64-bit; small instances won't work")
-  parser.add_option("-m", "--master-instance-type", default="",
-      help="Master instance type (leave empty for same as instance-type)")
-  parser.add_option("-r", "--region", default="us-east-1",
-      help="EC2 region zone to launch instances in")
-  parser.add_option("-z", "--zone", default="",
-      help="Availability zone to launch instances in, or 'all' to spread " +
-           "slaves across multiple (an additional $0.01/Gb for bandwidth" +
-           "between zones applies)")
-  parser.add_option("-a", "--ami", help="Amazon Machine Image ID to use")
-  parser.add_option("-v", "--spark-version", default="0.9.1",
-      help="Version of Spark to use: 'X.Y.Z' or a specific git hash")
-  parser.add_option("--spark-git-repo",
-      default="https://github.com/apache/spark",
-      help="Github repo from which to checkout supplied commit hash")
-  parser.add_option("--hadoop-major-version", default="1",
-      help="Major version of Hadoop (default: 1)")
-  parser.add_option("-D", metavar="[ADDRESS:]PORT", dest="proxy_port",
-      help="Use SSH dynamic port forwarding to create a SOCKS proxy at " +
-            "the given local address (for use with login)")
-  parser.add_option("--resume", action="store_true", default=False,
-      help="Resume installation on a previously launched cluster " +
-           "(for debugging)")
-  parser.add_option("--ebs-vol-size", metavar="SIZE", type="int", default=0,
-      help="Attach a new EBS volume of size SIZE (in GB) to each node as " +
-           "/vol. The volumes will be deleted when the instances terminate. " +
-           "Only possible on EBS-backed AMIs.")
-  parser.add_option("--swap", metavar="SWAP", type="int", default=1024,
-      help="Swap space to set up per node, in MB (default: 1024)")
-  parser.add_option("--spot-price", metavar="PRICE", type="float",
-      help="If specified, launch slaves as spot instances with the given " +
-            "maximum price (in dollars)")
-  parser.add_option("--ganglia", action="store_true", default=True,
-      help="Setup Ganglia monitoring on cluster (default: on). NOTE: " +
-           "the Ganglia page will be publicly accessible")
-  parser.add_option("--no-ganglia", action="store_false", dest="ganglia",
-      help="Disable Ganglia monitoring for the cluster")
-  parser.add_option("-u", "--user", default="root",
-      help="The SSH user you want to connect as (default: root)")
-  parser.add_option("--delete-groups", action="store_true", default=False,
-      help="When destroying a cluster, delete the security groups that were created")
-  parser.add_option("--use-existing-master", action="store_true", default=False,
-      help="Launch fresh slaves, but use an existing stopped master if possible")
-  parser.add_option("--worker-instances", type="int", default=1,
-      help="Number of instances per worker: variable SPARK_WORKER_INSTANCES (default: 1)")
-  parser.add_option("--master-opts", type="string", default="",
-      help="Extra options to give to master through SPARK_MASTER_OPTS variable (e.g -Dspark.worker.timeout=180)")
-
-
-
-  (opts, args) = parser.parse_args()
-  if len(args) != 2:
-    parser.print_help()
-    sys.exit(1)
-  (action, cluster_name) = args
-
-  # Boto config check
-  # http://boto.cloudhackers.com/en/latest/boto_config_tut.html
-  home_dir = os.getenv('HOME')
-  if home_dir == None or not os.path.isfile(home_dir + '/.boto'):
-    if not os.path.isfile('/etc/boto.cfg'):
-      if os.getenv('AWS_ACCESS_KEY_ID') == None:
-        print >> stderr, ("ERROR: The environment variable AWS_ACCESS_KEY_ID " +
-                          "must be set")
-        sys.exit(1)
-      if os.getenv('AWS_SECRET_ACCESS_KEY') == None:
-        print >> stderr, ("ERROR: The environment variable AWS_SECRET_ACCESS_KEY " +
-                          "must be set")
+    parser = OptionParser(
+        usage="spark-ec2 [options] <action> <cluster_name>"
+        + "\n\n<action> can be: launch, destroy, login, stop, start, get-master",
+        add_help_option=False)
+    parser.add_option(
+        "-h", "--help", action="help",
+        help="Show this help message and exit")
+    parser.add_option(
+        "-s", "--slaves", type="int", default=1,
+        help="Number of slaves to launch (default: 1)")
+    parser.add_option(
+        "-w", "--wait", type="int", default=120,
+        help="Seconds to wait for nodes to start (default: 120)")
+    parser.add_option(
+        "-k", "--key-pair",
+        help="Key pair to use on instances")
+    parser.add_option(
+        "-i", "--identity-file",
+        help="SSH private key file to use for logging into instances")
+    parser.add_option(
+        "-t", "--instance-type", default="m1.large",
+        help="Type of instance to launch (default: m1.large). " +
+             "WARNING: must be 64-bit; small instances won't work")
+    parser.add_option(
+        "-m", "--master-instance-type", default="",
+        help="Master instance type (leave empty for same as instance-type)")
+    parser.add_option(
+        "-r", "--region", default="us-east-1",
+        help="EC2 region zone to launch instances in")
+    parser.add_option(
+        "-z", "--zone", default="",
+        help="Availability zone to launch instances in, or 'all' to spread " +
+             "slaves across multiple (an additional $0.01/Gb for bandwidth" +
+             "between zones applies)")
+    parser.add_option("-a", "--ami", help="Amazon Machine Image ID to use")
+    parser.add_option(
+        "-v", "--spark-version", default="0.9.1",
+        help="Version of Spark to use: 'X.Y.Z' or a specific git hash")
+    parser.add_option(
+        "--spark-git-repo",
+        default="https://github.com/apache/spark",
+        help="Github repo from which to checkout supplied commit hash")
+    parser.add_option(
+        "--hadoop-major-version", default="1",
+        help="Major version of Hadoop (default: 1)")
+    parser.add_option(
+        "-D", metavar="[ADDRESS:]PORT", dest="proxy_port",
+        help="Use SSH dynamic port forwarding to create a SOCKS proxy at " +
+             "the given local address (for use with login)")
+    parser.add_option(
+        "--resume", action="store_true", default=False,
+        help="Resume installation on a previously launched cluster " +
+             "(for debugging)")
+    parser.add_option(
+        "--ebs-vol-size", metavar="SIZE", type="int", default=0,
+        help="Attach a new EBS volume of size SIZE (in GB) to each node as " +
+             "/vol. The volumes will be deleted when the instances terminate. " +
+             "Only possible on EBS-backed AMIs.")
+    parser.add_option(
+        "--swap", metavar="SWAP", type="int", default=1024,
+        help="Swap space to set up per node, in MB (default: 1024)")
+    parser.add_option(
+        "--spot-price", metavar="PRICE", type="float",
+        help="If specified, launch slaves as spot instances with the given " +
+             "maximum price (in dollars)")
+    parser.add_option(
+        "--ganglia", action="store_true", default=True,
+        help="Setup Ganglia monitoring on cluster (default: on). NOTE: " +
+             "the Ganglia page will be publicly accessible")
+    parser.add_option(
+        "--no-ganglia", action="store_false", dest="ganglia",
+        help="Disable Ganglia monitoring for the cluster")
+    parser.add_option(
+        "-u", "--user", default="root",
+        help="The SSH user you want to connect as (default: root)")
+    parser.add_option(
+        "--delete-groups", action="store_true", default=False,
+        help="When destroying a cluster, delete the security groups that were created")
+    parser.add_option(
+        "--use-existing-master", action="store_true", default=False,
+        help="Launch fresh slaves, but use an existing stopped master if possible")
+    parser.add_option(
+        "--worker-instances", type="int", default=1,
+        help="Number of instances per worker: variable SPARK_WORKER_INSTANCES (default: 1)")
+    parser.add_option(
+        "--master-opts", type="string", default="",
+        help="Extra options to give to master through SPARK_MASTER_OPTS variable " +
+             "(e.g -Dspark.worker.timeout=180)")
+
+    (opts, args) = parser.parse_args()
+    if len(args) != 2:
+        parser.print_help()
         sys.exit(1)
-  return (opts, action, cluster_name)
+    (action, cluster_name) = args
+
+    # Boto config check
+    # http://boto.cloudhackers.com/en/latest/boto_config_tut.html
+    home_dir = os.getenv('HOME')
+    if home_dir is None or not os.path.isfile(home_dir + '/.boto'):
+        if not os.path.isfile('/etc/boto.cfg'):
+            if os.getenv('AWS_ACCESS_KEY_ID') is None:
+                print >> stderr, ("ERROR: The environment variable AWS_ACCESS_KEY_ID " +
+                                  "must be set")
+                sys.exit(1)
+            if os.getenv('AWS_SECRET_ACCESS_KEY') is None:
+                print >> stderr, ("ERROR: The environment variable AWS_SECRET_ACCESS_KEY " +
+                                  "must be set")
+                sys.exit(1)
+    return (opts, action, cluster_name)
 
 
 # Get the EC2 security group of the given name, creating it if it doesn't exist
 def get_or_make_group(conn, name):
-  groups = conn.get_all_security_groups()
-  group = [g for g in groups if g.name == name]
-  if len(group) > 0:
-    return group[0]
-  else:
-    print "Creating security group " + name
-    return conn.create_security_group(name, "Spark EC2 group")
+    groups = conn.get_all_security_groups()
+    group = [g for g in groups if g.name == name]
+    if len(group) > 0:
+        return group[0]
+    else:
+        print "Creating security group " + name
+        return conn.create_security_group(name, "Spark EC2 group")
 
 
 # Wait for a set of launched instances to exit the "pending" state
 # (i.e. either to start running or to fail and be terminated)
 def wait_for_instances(conn, instances):
-  while True:
-    for i in instances:
-      i.update()
-    if len([i for i in instances if i.state == 'pending']) > 0:
-      time.sleep(5)
-    else:
-      return
+    while True:
+        for i in instances:
+            i.update()
+        if len([i for i in instances if i.state == 'pending']) > 0:
+            time.sleep(5)
+        else:
+            return
 
 
 # Check whether a given EC2 instance object is in a state we consider active,
 # i.e. not terminating or terminated. We count both stopping and stopped as
 # active since we can restart stopped clusters.
 def is_active(instance):
-  return (instance.state in ['pending', 'running', 'stopping', 'stopped'])
+    return (instance.state in ['pending', 'running', 'stopping', 'stopped'])
+
 
 # Return correct versions of Spark and Shark, given the supplied Spark version
 def get_spark_shark_version(opts):
-  spark_shark_map = {"0.7.3": "0.7.1", "0.8.0": "0.8.0", "0.8.1": "0.8.1", "0.9.0": "0.9.0", "0.9.1": "0.9.1"}
-  version = opts.spark_version.replace("v", "")
-  if version not in spark_shark_map:
-    print >> stderr, "Don't know about Spark version: %s" % version
-    sys.exit(1)
-  return (version, spark_shark_map[version])
+    spark_shark_map = {
+        "0.7.3": "0.7.1", "0.8.0": "0.8.0", "0.8.1": "0.8.1", "0.9.0": "0.9.0", "0.9.1": "0.9.1"
+    }
+    version = opts.spark_version.replace("v", "")
+    if version not in spark_shark_map:
+        print >> stderr, "Don't know about Spark version: %s" % version
+        sys.exit(1)
+    return (version, spark_shark_map[version])
+
 
 # Attempt to resolve an appropriate AMI given the architecture and
 # region of the request.
 def get_spark_ami(opts):
-  instance_types = {
-    "m1.small":    "pvm",
-    "m1.medium":   "pvm",
-    "m1.large":    "pvm",
-    "m1.xlarge":   "pvm",
-    "t1.micro":    "pvm",
-    "c1.medium":   "pvm",
-    "c1.xlarge":   "pvm",
-    "m2.xlarge":   "pvm",
-    "m2.2xlarge":  "pvm",
-    "m2.4xlarge":  "pvm",
-    "cc1.4xlarge": "hvm",
-    "cc2.8xlarge": "hvm",
-    "cg1.4xlarge": "hvm",
-    "hs1.8xlarge": "hvm",
-    "hi1.4xlarge": "hvm",
-    "m3.xlarge":   "hvm",
-    "m3.2xlarge":  "hvm",
-    "cr1.8xlarge": "hvm",
-    "i2.xlarge":   "hvm",
-    "i2.2xlarge":  "hvm",
-    "i2.4xlarge":  "hvm",
-    "i2.8xlarge":  "hvm",
-    "c3.large":    "pvm",
-    "c3.xlarge":   "pvm",
-    "c3.2xlarge":  "pvm",
-    "c3.4xlarge":  "pvm",
-    "c3.8xlarge":  "pvm"
-  }
-  if opts.instance_type in instance_types:
-    instance_type = instance_types[opts.instance_type]
-  else:
-    instance_type = "pvm"
-    print >> stderr,\
-        "Don't recognize %s, assuming type is pvm" % opts.instance_type
-
-  ami_path = "%s/%s/%s" % (AMI_PREFIX, opts.region, instance_type)
-  try:
-    ami = urllib2.urlopen(ami_path).read().strip()
-    print "Spark AMI: " + ami
-  except:
-    print >> stderr, "Could not resolve AMI at: " + ami_path
-    sys.exit(1)
-
-  return ami
+    instance_types = {
+        "m1.small":    "pvm",
+        "m1.medium":   "pvm",
+        "m1.large":    "pvm",
+        "m1.xlarge":   "pvm",
+        "t1.micro":    "pvm",
+        "c1.medium":   "pvm",
+        "c1.xlarge":   "pvm",
+        "m2.xlarge":   "pvm",
+        "m2.2xlarge":  "pvm",
+        "m2.4xlarge":  "pvm",
+        "cc1.4xlarge": "hvm",
+        "cc2.8xlarge": "hvm",
+        "cg1.4xlarge": "hvm",
+        "hs1.8xlarge": "hvm",
+        "hi1.4xlarge": "hvm",
+        "m3.xlarge":   "hvm",
+        "m3.2xlarge":  "hvm",
+        "cr1.8xlarge": "hvm",
+        "i2.xlarge":   "hvm",
+        "i2.2xlarge":  "hvm",
+        "i2.4xlarge":  "hvm",
+        "i2.8xlarge":  "hvm",
+        "c3.large":    "pvm",
+        "c3.xlarge":   "pvm",
+        "c3.2xlarge":  "pvm",
+        "c3.4xlarge":  "pvm",
+        "c3.8xlarge":  "pvm"
+    }
+    if opts.instance_type in instance_types:
+        instance_type = instance_types[opts.instance_type]
+    else:
+        instance_type = "pvm"
+        print >> stderr,\
+            "Don't recognize %s, assuming type is pvm" % opts.instance_type
+
+    ami_path = "%s/%s/%s" % (AMI_PREFIX, opts.region, instance_type)
+    try:
+        ami = urllib2.urlopen(ami_path).read().strip()
+        print "Spark AMI: " + ami
+    except:
+        print >> stderr, "Could not resolve AMI at: " + ami_path
+        sys.exit(1)
+
+    return ami
+
 
 # Launch a cluster of the given name, by setting up its security groups,
 # and then starting new instances in them.
 # Returns a tuple of EC2 reservation objects for the master and slaves
 # Fails if there already instances running in the cluster's groups.
 def launch_cluster(conn, opts, cluster_name):
-  if opts.identity_file is None:
-    print >> stderr, "ERROR: Must provide an identity file (-i) for ssh connections."
-    sys.exit(1)
-  if opts.key_pair is None:
-    print >> stderr, "ERROR: Must provide a key pair name (-k) to use on instances."
-    sys.exit(1)
-  print "Setting up security groups..."
-  master_group = get_or_make_group(conn, cluster_name + "-master")
-  slave_group = get_or_make_group(conn, cluster_name + "-slaves")
-  if master_group.rules == []: # Group was just now created
-    master_group.authorize(src_group=master_group)
-    master_group.authorize(src_group=slave_group)
-    master_group.authorize('tcp', 22, 22, '0.0.0.0/0')
-    master_group.authorize('tcp', 8080, 8081, '0.0.0.0/0')
-    master_group.authorize('tcp', 19999, 19999, '0.0.0.0/0')
-    master_group.authorize('tcp', 50030, 50030, '0.0.0.0/0')
-    master_group.authorize('tcp', 50070, 50070, '0.0.0.0/0')
-    master_group.authorize('tcp', 60070, 60070, '0.0.0.0/0')
-    master_group.authorize('tcp', 4040, 4045, '0.0.0.0/0')
-    if opts.ganglia:
-      master_group.authorize('tcp', 5080, 5080, '0.0.0.0/0')
-  if slave_group.rules == []: # Group was just now created
-    slave_group.authorize(src_group=master_group)
-    slave_group.authorize(src_group=slave_group)
-    slave_group.authorize('tcp', 22, 22, '0.0.0.0/0')
-    slave_group.authorize('tcp', 8080, 8081, '0.0.0.0/0')
-    slave_group.authorize('tcp', 50060, 50060, '0.0.0.0/0')
-    slave_group.authorize('tcp', 50075, 50075, '0.0.0.0/0')
-    slave_group.authorize('tcp', 60060, 60060, '0.0.0.0/0')
-    slave_group.authorize('tcp', 60075, 60075, '0.0.0.0/0')
-
-  # Check if instances are already running in our groups
-  existing_masters, existing_slaves = get_existing_cluster(conn, opts, cluster_name,
-                                                           die_on_error=False)
-  if existing_slaves or (existing_masters and not opts.use_existing_master):
-    print >> stderr, ("ERROR: There are already instances running in " +
-        "group %s or %s" % (master_group.name, slave_group.name))
-    sys.exit(1)
-
-  # Figure out Spark AMI
-  if opts.ami is None:
-    opts.ami = get_spark_ami(opts)
-  print "Launching instances..."
-
-  try:
-    image = conn.get_all_images(image_ids=[opts.ami])[0]
-  except:
-    print >> stderr, "Could not find AMI " + opts.ami
-    sys.exit(1)
-
-  # Create block device mapping so that we can add an EBS volume if asked to
-  block_map = BlockDeviceMapping()
-  if opts.ebs_vol_size > 0:
-    device = EBSBlockDeviceType()
-    device.size = opts.ebs_vol_size
-    device.delete_on_termination = True
-    block_map["/dev/sdv"] = device
-
-  # Launch slaves
-  if opts.spot_price != None:
-    # Launch spot instances with the requested price
-    print ("Requesting %d slaves as spot instances with price $%.3f" %
-           (opts.slaves, opts.spot_price))
-    zones = get_zones(conn, opts)
-    num_zones = len(zones)
-    i = 0
-    my_req_ids = []
-    for zone in zones:
-      num_slaves_this_zone = get_partition(opts.slaves, num_zones, i)
-      slave_reqs = conn.request_spot_instances(
-          price = opts.spot_price,
-          image_id = opts.ami,
-          launch_group = "launch-group-%s" % cluster_name,
-          placement = zone,
-          count = num_slaves_this_zone,
-          key_name = opts.key_pair,
-          security_groups = [slave_group],
-          instance_type = opts.instance_type,
-          block_device_map = block_map)
-      my_req_ids += [req.id for req in slave_reqs]
-      i += 1
-
-    print "Waiting for spot instances to be granted..."
+    if opts.identity_file is None:
+        print >> stderr, "ERROR: Must provide an identity file (-i) for ssh connections."
+        sys.exit(1)
+    if opts.key_pair is None:
+        print >> stderr, "ERROR: Must provide a key pair name (-k) to use on instances."
+        sys.exit(1)
+    print "Setting up security groups..."
+    master_group = get_or_make_group(conn, cluster_name + "-master")
+    slave_group = get_or_make_group(conn, cluster_name + "-slaves")
+    if master_group.rules == []:  # Group was just now created
+        master_group.authorize(src_group=master_group)
+        master_group.authorize(src_group=slave_group)
+        master_group.authorize('tcp', 22, 22, '0.0.0.0/0')
+        master_group.authorize('tcp', 8080, 8081, '0.0.0.0/0')
+        master_group.authorize('tcp', 19999, 19999, '0.0.0.0/0')
+        master_group.authorize('tcp', 50030, 50030, '0.0.0.0/0')
+        master_group.authorize('tcp', 50070, 50070, '0.0.0.0/0')
+        master_group.authorize('tcp', 60070, 60070, '0.0.0.0/0')
+        master_group.authorize('tcp', 4040, 4045, '0.0.0.0/0')
+        if opts.ganglia:
+            master_group.authorize('tcp', 5080, 5080, '0.0.0.0/0')
+    if slave_group.rules == []:  # Group was just now created
+        slave_group.authorize(src_group=master_group)
+        slave_group.authorize(src_group=slave_group)
+        slave_group.authorize('tcp', 22, 22, '0.0.0.0/0')
+        slave_group.authorize('tcp', 8080, 8081, '0.0.0.0/0')
+        slave_group.authorize('tcp', 50060, 50060, '0.0.0.0/0')
+        slave_group.authorize('tcp', 50075, 50075, '0.0.0.0/0')
+        slave_group.authorize('tcp', 60060, 60060, '0.0.0.0/0')
+        slave_group.authorize('tcp', 60075, 60075, '0.0.0.0/0')
+
+    # Check if instances are already running in our groups
+    existing_masters, existing_slaves = get_existing_cluster(conn, opts, cluster_name,
+                                                             die_on_error=False)
+    if existing_slaves or (existing_masters and not opts.use_existing_master):
+        print >> stderr, ("ERROR: There are already instances running in " +
+                          "group %s or %s" % (master_group.name, slave_group.name))
+        sys.exit(1)
+
+    # Figure out Spark AMI
+    if opts.ami is None:
+        opts.ami = get_spark_ami(opts)
+    print "Launching instances..."
+
     try:
-      while True:
-        time.sleep(10)
-        reqs = conn.get_all_spot_instance_requests()
-        id_to_req = {}
-        for r in reqs:
-          id_to_req[r.id] = r
-        active_instance_ids = []
-        for i in my_req_ids:
-          if i in id_to_req and id_to_req[i].state == "active":
-            active_instance_ids.append(id_to_req[i].instance_id)
-        if len(active_instance_ids) == opts.slaves:
-          print "All %d slaves granted" % opts.slaves
-          reservations = conn.get_all_instances(active_instance_ids)
-          slave_nodes = []
-          for r in reservations:
-            slave_nodes += r.instances
-          break
-        else:
-          print "%d of %d slaves granted, waiting longer" % (
-            len(active_instance_ids), opts.slaves)
+        image = conn.get_all_images(image_ids=[opts.ami])[0]
     except:
-      print "Canceling spot instance requests"
-      conn.cancel_spot_instance_requests(my_req_ids)
-      # Log a warning if any of these requests actually launched instances:
-      (master_nodes, slave_nodes) = get_existing_cluster(
-          conn, opts, cluster_name, die_on_error=False)
-      running = len(master_nodes) + len(slave_nodes)
-      if running:
-        print >> stderr, ("WARNING: %d instances are still running" % running)
-      sys.exit(0)
-  else:
-    # Launch non-spot instances
-    zones = get_zones(conn, opts)
-    num_zones = len(zones)
-    i = 0
-    slave_nodes = []
-    for zone in zones:
-      num_slaves_this_zone = get_partition(opts.slaves, num_zones, i)
-      if num_slaves_this_zone > 0:
-        slave_res = image.run(key_name = opts.key_pair,
-                              security_groups = [slave_group],
-                              instance_type = opts.instance_type,
-                              placement = zone,
-                              min_count = num_slaves_this_zone,
-                              max_count = num_slaves_this_zone,
-                              block_device_map = block_map)
-        slave_nodes += slave_res.instances
-        print "Launched %d slaves in %s, regid = %s" % (num_slaves_this_zone,
-                                                        zone, slave_res.id)
-      i += 1
-
-  # Launch or resume masters
-  if existing_masters:
-    print "Starting master..."
-    for inst in existing_masters:
-      if inst.state not in ["shutting-down", "terminated"]:
-        inst.start()
-    master_nodes = existing_masters
-  else:
-    master_type = opts.master_instance_type
-    if master_type == "":
-      master_type = opts.instance_type
-    if opts.zone == 'all':
-      opts.zone = random.choice(conn.get_all_zones()).name
-    master_res = image.run(key_name = opts.key_pair,
-                           security_groups = [master_group],
-                           instance_type = master_type,
-                           placement = opts.zone,
-                           min_count = 1,
-                           max_count = 1,
-                           block_device_map = block_map)
-    master_nodes = master_res.instances
-    print "Launched master in %s, regid = %s" % (zone, master_res.id)
+        print >> stderr, "Could not find AMI " + opts.ami
+        sys.exit(1)
 
-  # Return all the instances
-  return (master_nodes, slave_nodes)
+    # Create block device mapping so that we can add an EBS volume if asked to
+    block_map = BlockDeviceMapping()
+    if opts.ebs_vol_size > 0:
+        device = EBSBlockDeviceType()
+        device.size = opts.ebs_vol_size
+        device.delete_on_termination = True
+        block_map["/dev/sdv"] = device
+
+    # Launch slaves
+    if opts.spot_price is not None:
+        # Launch spot instances with the requested price
+        print ("Requesting %d slaves as spot instances with price $%.3f" %
+               (opts.slaves, opts.spot_price))
+        zones = get_zones(conn, opts)
+        num_zones = len(zones)
+        i = 0
+        my_req_ids = []
+        for zone in zones:
+            num_slaves_this_zone = get_partition(opts.slaves, num_zones, i)
+            slave_reqs = conn.request_spot_instances(
+                price=opts.spot_price,
+                image_id=opts.ami,
+                launch_group="launch-group-%s" % cluster_name,
+                placement=zone,
+                count=num_slaves_this_zone,
+                key_name=opts.key_pair,
+                security_groups=[slave_group],
+                instance_type=opts.instance_type,
+                block_device_map=block_map)
+            my_req_ids += [req.id for req in slave_reqs]
+            i += 1
+
+        print "Waiting for spot instances to be granted..."
+        try:
+            while True:
+                time.sleep(10)
+                reqs = conn.get_all_spot_instance_requests()
+                id_to_req = {}
+                for r in reqs:
+                    id_to_req[r.id] = r
+                active_instance_ids = []
+                for i in my_req_ids:
+                    if i in id_to_req and id_to_req[i].state == "active":
+                        active_instance_ids.append(id_to_req[i].instance_id)
+                if len(active_instance_ids) == opts.slaves:
+                    print "All %d slaves granted" % opts.slaves
+                    reservations = conn.get_all_instances(active_instance_ids)
+                    slave_nodes = []
+                    for r in reservations:
+                        slave_nodes += r.instances
+                    break
+                else:
+                    print "%d of %d slaves granted, waiting longer" % (
+                        len(active_instance_ids), opts.slaves)
+        except:
+            print "Canceling spot instance requests"
+            conn.cancel_spot_instance_requests(my_req_ids)
+            # Log a warning if any of these requests actually launched instances:
+            (master_nodes, slave_nodes) = get_existing_cluster(
+                conn, opts, cluster_name, die_on_error=False)
+            running = len(master_nodes) + len(slave_nodes)
+            if running:
+                print >> stderr, ("WARNING: %d instances are still running" % running)
+            sys.exit(0)
+    else:
+        # Launch non-spot instances
+        zones = get_zones(conn, opts)
+        num_zones = len(zones)
+        i = 0
+        slave_nodes = []
+        for zone in zones:
+            num_slaves_this_zone = get_partition(opts.slaves, num_zones, i)
+            if num_slaves_this_zone > 0:
+                slave_res = image.run(key_name=opts.key_pair,
+                                      security_groups=[slave_group],
+                                      instance_type=opts.instance_type,
+                                      placement=zone,
+                                      min_count=num_slaves_this_zone,
+                                      max_count=num_slaves_this_zone,
+                                      block_device_map=block_map)
+                slave_nodes += slave_res.instances
+                print "Launched %d slaves in %s, regid = %s" % (num_slaves_this_zone,
+                                                                zone, slave_res.id)
+            i += 1
+
+    # Launch or resume masters
+    if existing_masters:
+        print "Starting master..."
+        for inst in existing_masters:
+            if inst.state not in ["shutting-down", "terminated"]:
+                inst.start()
+        master_nodes = existing_masters
+    else:
+        master_type = opts.master_instance_type
+        if master_type == "":
+            master_type = opts.instance_type
+        if opts.zone == 'all':
+            opts.zone = random.choice(conn.get_all_zones()).name
+        master_res = image.run(key_name=opts.key_pair,
+                               security_groups=[master_group],
+                               instance_type=master_type,
+                               placement=opts.zone,
+                               min_count=1,
+                               max_count=1,
+                               block_device_map=block_map)
+        master_nodes = master_res.instances
+        print "Launched master in %s, regid = %s" % (zone, master_res.id)
+
+    # Return all the instances
+    return (master_nodes, slave_nodes)
 
 
 # Get the EC2 instances in an existing cluster if available.
 # Returns a tuple of lists of EC2 instance objects for the masters and slaves
 def get_existing_cluster(conn, opts, cluster_name, die_on_error=True):
-  print "Searching for existing cluster " + cluster_name + "..."
-  reservations = conn.get_all_instances()
-  master_nodes = []
-  slave_nodes = []
-  for res in reservations:
-    active = [i for i in res.instances if is_active(i)]
-    for inst in active:
-      group_names = [g.name for g in inst.groups]
-      if group_names == [cluster_name + "-master"]:
-        master_nodes.append(inst)
-      elif group_names == [cluster_name + "-slaves"]:
-        slave_nodes.append(inst)
-  if any((master_nodes, slave_nodes)):
-    print ("Found %d master(s), %d slaves" %
-           (len(master_nodes), len(slave_nodes)))
-  if master_nodes != [] or not die_on_error:
-    return (master_nodes, slave_nodes)
-  else:
-    if master_nodes == [] and slave_nodes != []:
-      print >> sys.stderr, "ERROR: Could not find master in group " + cluster_name + "-master"
+    print "Searching for existing cluster " + cluster_name + "..."
+    reservations = conn.get_all_instances()
+    master_nodes = []
+    slave_nodes = []
+    for res in reservations:
+        active = [i for i in res.instances if is_active(i)]
+        for inst in active:
+            group_names = [g.name for g in inst.groups]
+            if group_names == [cluster_name + "-master"]:
+                master_nodes.append(inst)
+            elif group_names == [cluster_name + "-slaves"]:
+                slave_nodes.append(inst)
+    if any((master_nodes, slave_nodes)):
+        print ("Found %d master(s), %d slaves" % (len(master_nodes), len(slave_nodes)))
+    if master_nodes != [] or not die_on_error:
+        return (master_nodes, slave_nodes)
     else:
-      print >> sys.stderr, "ERROR: Could not find any existing cluster"
-    sys.exit(1)
+        if master_nodes == [] and slave_nodes != []:
+            print >> sys.stderr, "ERROR: Could not find master in group " + cluster_name + "-master"
+        else:
+            print >> sys.stderr, "ERROR: Could not find any existing cluster"
+        sys.exit(1)
 
 
 # Deploy configuration files and run setup scripts on a newly launched
 # or started EC2 cluster.
 def setup_cluster(conn, master_nodes, slave_nodes, opts, deploy_ssh_key):
-  master = master_nodes[0].public_dns_name
-  if deploy_ssh_key:
-    print "Generating cluster's SSH key on master..."
-    key_setup = """
-      [ -f ~/.ssh/id_rsa ] ||
-        (ssh-keygen -q -t rsa -N '' -f ~/.ssh/id_rsa &&
-         cat ~/.ssh/id_rsa.pub >> ~/.ssh/authorized_keys)
-    """
-    ssh(master, opts, key_setup)
-    dot_ssh_tar = ssh_read(master, opts, ['tar', 'c', '.ssh'])
-    print "Transferring cluster's SSH key to slaves..."
-    for slave in slave_nodes:
-      print slave.public_dns_name
-      ssh_write(slave.public_dns_name, opts, ['tar', 'x'], dot_ssh_tar)
-
-  modules = ['spark', 'shark', 'ephemeral-hdfs', 'persistent-hdfs',
-             'mapreduce', 'spark-standalone', 'tachyon']
-
-  if opts.hadoop_major_version == "1":
-    modules = filter(lambda x: x != "mapreduce", modules)
-
-  if opts.ganglia:
-    modules.append('ganglia')
-
-  # NOTE: We should clone the repository before running deploy_files to
-  # prevent ec2-variables.sh from being overwritten
-  ssh(master, opts, "rm -rf spark-ec2 && git clone https://github.com/mesos/spark-ec2.git -b v3")
-
-  print "Deploying files to master..."
-  deploy_files(conn, "deploy.generic", opts, master_nodes, slave_nodes, modules)
-
-  print "Running setup on master..."
-  setup_spark_cluster(master, opts)
-  print "Done!"
+    master = master_nodes[0].public_dns_name
+    if deploy_ssh_key:
+        print "Generating cluster's SSH key on master..."
+        key_setup = """
+          [ -f ~/.ssh/id_rsa ] ||
+            (ssh-keygen -q -t rsa -N '' -f ~/.ssh/id_rsa &&
+             cat ~/.ssh/id_rsa.pub >> ~/.ssh/authorized_keys)
+        """
+        ssh(master, opts, key_setup)
+        dot_ssh_tar = ssh_read(master, opts, ['tar', 'c', '.ssh'])
+        print "Transferring cluster's SSH key to slaves..."
+        for slave in slave_nodes:
+            print slave.public_dns_name
+            ssh_write(slave.public_dns_name, opts, ['tar', 'x'], dot_ssh_tar)
+
+    modules = ['spark', 'shark', 'ephemeral-hdfs', 'persistent-hdfs',
+               'mapreduce', 'spark-standalone', 'tachyon']
+
+    if opts.hadoop_major_version == "1":
+        modules = filter(lambda x: x != "mapreduce", modules)
+
+    if opts.ganglia:
+        modules.append('ganglia')
+
+    # NOTE: We should clone the repository before running deploy_files to
+    # prevent ec2-variables.sh from being overwritten
+    ssh(master, opts, "rm -rf spark-ec2 && git clone https://github.com/mesos/spark-ec2.git -b v3")
+
+    print "Deploying files to master..."
+    deploy_files(conn, "deploy.generic", opts, master_nodes, slave_nodes, modules)
+
+    print "Running setup on master..."
+    setup_spark_cluster(master, opts)
+    print "Done!"
+
 
 def setup_standalone_cluster(master, slave_nodes, opts):
-  slave_ips = '\n'.join([i.public_dns_name for i in slave_nodes])
-  ssh(master, opts, "echo \"%s\" > spark/conf/slaves" % (slave_ips))
-  ssh(master, opts, "/root/spark/sbin/start-all.sh")
+    slave_ips = '\n'.join([i.public_dns_name for i in slave_nodes])
+    ssh(master, opts, "echo \"%s\" > spark/conf/slaves" % (slave_ips))
+    ssh(master, opts, "/root/spark/sbin/start-all.sh")
+
 
 def setup_spark_cluster(master, opts):
-  ssh(master, opts, "chmod u+x spark-ec2/setup.sh")
-  ssh(master, opts, "spark-ec2/setup.sh")
-  print "Spark standalone cluster started at http://%s:8080" % master
+    ssh(master, opts, "chmod u+x spark-ec2/setup.sh")
+    ssh(master, opts, "spark-ec2/setup.sh")
+    print "Spark standalone cluster started at http://%s:8080" % master
 
-  if opts.ganglia:
-    print "Ganglia started at http://%s:5080/ganglia" % master
+    if opts.ganglia:
+        print "Ganglia started at http://%s:5080/ganglia" % master
 
 
 # Wait for a whole cluster (masters, slaves and ZooKeeper) to start up
 def wait_for_cluster(conn, wait_secs, master_nodes, slave_nodes):
-  print "Waiting for instances to start up..."
-  time.sleep(5)
-  wait_for_instances(conn, master_nodes)
-  wait_for_instances(conn, slave_nodes)
-  print "Waiting %d more seconds..." % wait_secs
-  time.sleep(wait_secs)
+    print "Waiting for instances to start up..."
+    time.sleep(5)
+    wait_for_instances(conn, master_nodes)
+    wait_for_instances(conn, slave_nodes)
+    print "Waiting %d more seconds..." % wait_secs
+    time.sleep(wait_secs)
 
 
 # Get number of local disks available for a given EC2 instance type.
 def get_num_disks(instance_type):
-  # From http://docs.amazonwebservices.com/AWSEC2/latest/UserGuide/index.html?InstanceStorage.html
-  disks_by_instance = {
-    "m1.small":    1,
-    "m1.medium":   1,
-    "m1.large":    2,
-    "m1.xlarge":   4,
-    "t1.micro":    1,
-    "c1.medium":   1,
-    "c1.xlarge":   4,
-    "m2.xlarge":   1,
-    "m2.2xlarge":  1,
-    "m2.4xlarge":  2,
-    "cc1.4xlarge": 2,
-    "cc2.8xlarge": 4,
-    "cg1.4xlarge": 2,
-    "hs1.8xlarge": 24,
-    "cr1.8xlarge": 2,
-    "hi1.4xlarge": 2,
-    "m3.xlarge":   0,
-    "m3.2xlarge":  0,
-    "i2.xlarge":   1,
-    "i2.2xlarge":  2,
-    "i2.4xlarge":  4,
-    "i2.8xlarge":  8,
-    "c3.large":    2,
-    "c3.xlarge":   2,
-    "c3.2xlarge":  2,
-    "c3.4xlarge":  2,
-    "c3.8xlarge":  2
-  }
-  if instance_type in disks_by_instance:
-    return disks_by_instance[instance_type]
-  else:
-    print >> stderr, ("WARNING: Don't know number of disks on instance type %s; assuming 1"
-                      % instance_type)
-    return 1
+    # From http://docs.amazonwebservices.com/AWSEC2/latest/UserGuide/index.html?InstanceStorage.html
+    disks_by_instance = {
+        "m1.small":    1,
+        "m1.medium":   1,
+        "m1.large":    2,
+        "m1.xlarge":   4,
+        "t1.micro":    1,
+        "c1.medium":   1,
+        "c1.xlarge":   4,
+        "m2.xlarge":   1,
+        "m2.2xlarge":  1,
+        "m2.4xlarge":  2,
+        "cc1.4xlarge": 2,
+        "cc2.8xlarge": 4,
+        "cg1.4xlarge": 2,
+        "hs1.8xlarge": 24,
+        "cr1.8xlarge": 2,
+        "hi1.4xlarge": 2,
+        "m3.xlarge":   0,
+        "m3.2xlarge":  0,
+        "i2.xlarge":   1,
+        "i2.2xlarge":  2,
+        "i2.4xlarge":  4,
+        "i2.8xlarge":  8,
+        "c3.large":    2,
+        "c3.xlarge":   2,
+        "c3.2xlarge":  2,
+        "c3.4xlarge":  2,
+        "c3.8xlarge":  2
+    }
+    if instance_type in disks_by_instance:
+        return disks_by_instance[instance_type]
+    else:
+        print >> stderr, ("WARNING: Don't know number of disks on instance type %s; assuming 1"
+                          % instance_type)
+        return 1
 
 
 # Deploy the configuration file templates in a given local directory to
@@ -522,301 +554,306 @@ def get_num_disks(instance_type):
 # the first master instance in the cluster, and we expect the setup
 # script to be run on that instance to copy them to other nodes.
 def deploy_files(conn, root_dir, opts, master_nodes, slave_nodes, modules):
-  active_master = master_nodes[0].public_dns_name
-
-  num_disks = get_num_disks(opts.instance_type)
-  hdfs_data_dirs = "/mnt/ephemeral-hdfs/data"
-  mapred_local_dirs = "/mnt/hadoop/mrlocal"
-  spark_local_dirs = "/mnt/spark"
-  if num_disks > 1:
-    for i in range(2, num_disks + 1):
-      hdfs_data_dirs += ",/mnt%d/ephemeral-hdfs/data" % i
-      mapred_local_dirs += ",/mnt%d/hadoop/mrlocal" % i
-      spark_local_dirs += ",/mnt%d/spark" % i
-
-  cluster_url = "%s:7077" % active_master
-
-  if "." in opts.spark_version:
-    # Pre-built spark & shark deploy
-    (spark_v, shark_v) = get_spark_shark_version(opts)
-  else:
-    # Spark-only custom deploy
-    spark_v = "%s|%s" % (opts.spark_git_repo, opts.spark_version)
-    shark_v = ""
-    modules = filter(lambda x: x != "shark", modules)
-
-  template_vars = {
-    "master_list": '\n'.join([i.public_dns_name for i in master_nodes]),
-    "active_master": active_master,
-    "slave_list": '\n'.join([i.public_dns_name for i in slave_nodes]),
-    "cluster_url": cluster_url,
-    "hdfs_data_dirs": hdfs_data_dirs,
-    "mapred_local_dirs": mapred_local_dirs,
-    "spark_local_dirs": spark_local_dirs,
-    "swap": str(opts.swap),
-    "modules": '\n'.join(modules),
-    "spark_version": spark_v,
-    "shark_version": shark_v,
-    "hadoop_major_version": opts.hadoop_major_version,
-    "spark_worker_instances": "%d" % opts.worker_instances,
-    "spark_master_opts": opts.master_opts
-  }
-
-  # Create a temp directory in which we will place all the files to be
-  # deployed after we substitue template parameters in them
-  tmp_dir = tempfile.mkdtemp()
-  for path, dirs, files in os.walk(root_dir):
-    if path.find(".svn") == -1:
-      dest_dir = os.path.join('/', path[len(root_dir):])
-      local_dir = tmp_dir + dest_dir
-      if not os.path.exists(local_dir):
-        os.makedirs(local_dir)
-      for filename in files:
-        if filename[0] not in '#.~' and filename[-1] != '~':
-          dest_file = os.path.join(dest_dir, filename)
-          local_file = tmp_dir + dest_file
-          with open(os.path.join(path, filename)) as src:
-            with open(local_file, "w") as dest:
-              text = src.read()
-              for key in template_vars:
-                text = text.replace("{{" + key + "}}", template_vars[key])
-              dest.write(text)
-              dest.close()
-  # rsync the whole directory over to the master machine
-  command = [
-      'rsync', '-rv',
-      '-e', stringify_command(ssh_command(opts)),
-      "%s/" % tmp_dir,
-      "%s@%s:/" % (opts.user, active_master)
+    active_master = master_nodes[0].public_dns_name
+
+    num_disks = get_num_disks(opts.instance_type)
+    hdfs_data_dirs = "/mnt/ephemeral-hdfs/data"
+    mapred_local_dirs = "/mnt/hadoop/mrlocal"
+    spark_local_dirs = "/mnt/spark"
+    if num_disks > 1:
+        for i in range(2, num_disks + 1):
+            hdfs_data_dirs += ",/mnt%d/ephemeral-hdfs/data" % i
+            mapred_local_dirs += ",/mnt%d/hadoop/mrlocal" % i
+            spark_local_dirs += ",/mnt%d/spark" % i
+
+    cluster_url = "%s:7077" % active_master
+
+    if "." in opts.spark_version:
+        # Pre-built spark & shark deploy
+        (spark_v, shark_v) = get_spark_shark_version(opts)
+    else:
+        # Spark-only custom deploy
+        spark_v = "%s|%s" % (opts.spark_git_repo, opts.spark_version)
+        shark_v = ""
+        modules = filter(lambda x: x != "shark", modules)
+
+    template_vars = {
+        "master_list": '\n'.join([i.public_dns_name for i in master_nodes]),
+        "active_master": active_master,
+        "slave_list": '\n'.join([i.public_dns_name for i in slave_nodes]),
+        "cluster_url": cluster_url,
+        "hdfs_data_dirs": hdfs_data_dirs,
+        "mapred_local_dirs": mapred_local_dirs,
+        "spark_local_dirs": spark_local_dirs,
+        "swap": str(opts.swap),
+        "modules": '\n'.join(modules),
+        "spark_version": spark_v,
+        "shark_version": shark_v,
+        "hadoop_major_version": opts.hadoop_major_version,
+        "spark_worker_instances": "%d" % opts.worker_instances,
+        "spark_master_opts": opts.master_opts
+    }
+
+    # Create a temp directory in which we will place all the files to be
+    # deployed after we substitue template parameters in them
+    tmp_dir = tempfile.mkdtemp()
+    for path, dirs, files in os.walk(root_dir):
+        if path.find(".svn") == -1:
+            dest_dir = os.path.join('/', path[len(root_dir):])
+            local_dir = tmp_dir + dest_dir
+            if not os.path.exists(local_dir):
+                os.makedirs(local_dir)
+            for filename in files:
+                if filename[0] not in '#.~' and filename[-1] != '~':
+                    dest_file = os.path.join(dest_dir, filename)
+                    local_file = tmp_dir + dest_file
+                    with open(os.path.join(path, filename)) as src:
+                        with open(local_file, "w") as dest:
+                            text = src.read()
+                            for key in template_vars:
+                                text = text.replace("{{" + key + "}}", template_vars[key])
+                            dest.write(text)
+                            dest.close()
+    # rsync the whole directory over to the master machine
+    command = [
+        'rsync', '-rv',
+        '-e', stringify_command(ssh_command(opts)),
+        "%s/" % tmp_dir,
+        "%s@%s:/" % (opts.user, active_master)
     ]
-  subprocess.check_call(command)
-  # Remove the temp directory we created above
-  shutil.rmtree(tmp_dir)
+    subprocess.check_call(command)
+    # Remove the temp directory we created above
+    shutil.rmtree(tmp_dir)
 
 
 def stringify_command(parts):
-  if isinstance(parts, str):
-    return parts
-  else:
-    return ' '.join(map(pipes.quote, parts))
+    if isinstance(parts, str):
+        return parts
+    else:
+        return ' '.join(map(pipes.quote, parts))
 
 
 def ssh_args(opts):
-  parts = ['-o', 'StrictHostKeyChecking=no']
-  if opts.identity_file is not None:
-    parts += ['-i', opts.identity_file]
-  return parts
+    parts = ['-o', 'StrictHostKeyChecking=no']
+    if opts.identity_file is not None:
+        parts += ['-i', opts.identity_file]
+    return parts
 
 
 def ssh_command(opts):
-  return ['ssh'] + ssh_args(opts)
+    return ['ssh'] + ssh_args(opts)
 
 
 # Run a command on a host through ssh, retrying up to five times
 # and then throwing an exception if ssh continues to fail.
 def ssh(host, opts, command):
-  tries = 0
-  while True:
-    try:
-      return subprocess.check_call(
-        ssh_command(opts) + ['-t', '-t', '%s@%s' % (opts.user, host), stringify_command(command)])
-    except subprocess.CalledProcessError as e:
-      if (tries > 5):
-        # If this was an ssh failure, provide the user with hints.
-        if e.returncode == 255:
-          raise UsageError("Failed to SSH to remote host {0}.\nPlease check that you have provided the correct --identity-file and --key-pair parameters and try again.".format(host))
-        else:
-          raise e
-      print >> stderr, "Error executing remote command, retrying after 30 seconds: {0}".format(e)
-      time.sleep(30)
-      tries = tries + 1
+    tries = 0
+    while True:
+        try:
+            return subprocess.check_call(
+                ssh_command(opts) + ['-t', '-t', '%s@%s' % (opts.user, host),
+                                     stringify_command(command)])
+        except subprocess.CalledProcessError as e:
+            if (tries > 5):
+                # If this was an ssh failure, provide the user with hints.
+                if e.returncode == 255:
+                    raise UsageError(
+                        "Failed to SSH to remote host {0}.\n" +
+                        "Please check that you have provided the correct --identity-file and " +
+                        "--key-pair parameters and try again.".format(host))
+                else:
+                    raise e
+            print >> stderr, \
+                "Error executing remote command, retrying after 30 seconds: {0}".format(e)
+            time.sleep(30)
+            tries = tries + 1
 
 
 def ssh_read(host, opts, command):
-  return subprocess.check_output(
-      ssh_command(opts) + ['%s@%s' % (opts.user, host), stringify_command(command)])
+    return subprocess.check_output(
+        ssh_command(opts) + ['%s@%s' % (opts.user, host), stringify_command(command)])
 
 
 def ssh_write(host, opts, command, input):
-  tries = 0
-  while True:
-    proc = subprocess.Popen(
-        ssh_command(opts) + ['%s@%s' % (opts.user, host), stringify_command(command)],
-        stdin=subprocess.PIPE)
-    proc.stdin.write(input)
-    proc.stdin.close()
-    status = proc.wait()
-    if status == 0:
-      break
-    elif (tries > 5):
-      raise RuntimeError("ssh_write failed with error %s" % proc.returncode)
-    else:
-      print >> stderr, "Error {0} while executing remote command, retrying after 30 seconds".format(status)
-      time.sleep(30)
-      tries = tries + 1
+    tries = 0
+    while True:
+        proc = subprocess.Popen(
+            ssh_command(opts) + ['%s@%s' % (opts.user, host), stringify_command(command)],
+            stdin=subprocess.PIPE)
+        proc.stdin.write(input)
+        proc.stdin.close()
+        status = proc.wait()
+        if status == 0:
+            break
+        elif (tries > 5):
+            raise RuntimeError("ssh_write failed with error %s" % proc.returncode)
+        else:
+            print >> stderr, \
+                "Error {0} while executing remote command, retrying after 30 seconds".format(status)
+            time.sleep(30)
+            tries = tries + 1
 
 
 # Gets a list of zones to launch instances in
 def get_zones(conn, opts):
-  if opts.zone == 'all':
-    zones = [z.name for z in conn.get_all_zones()]
-  else:
-    zones = [opts.zone]
-  return zones
+    if opts.zone == 'all':
+        zones = [z.name for z in conn.get_all_zones()]
+    else:
+        zones = [opts.zone]
+    return zones
 
 
 # Gets the number of items in a partition
 def get_partition(total, num_partitions, current_partitions):
-  num_slaves_this_zone = total / num_partitions
-  if (total % num_partitions) - current_partitions > 0:
-    num_slaves_this_zone += 1
-  return num_slaves_this_zone
+    num_slaves_this_zone = total / num_partitions
+    if (total % num_partitions) - current_partitions > 0:
+        num_slaves_this_zone += 1
+    return num_slaves_this_zone
 
 
 def real_main():
-  (opts, action, cluster_name) = parse_args()
-  try:
-    conn = ec2.connect_to_region(opts.region)
-  except Exception as e:
-    print >> stderr, (e)
-    sys.exit(1)
-
-  # Select an AZ at random if it was not specified.
-  if opts.zone == "":
-    opts.zone = random.choice(conn.get_all_zones()).name
-
-  if action == "launch":
-    if opts.slaves <= 0:
-      print >> sys.stderr, "ERROR: You have to start at least 1 slave"
-      sys.exit(1)
-    if opts.resume:
-      (master_nodes, slave_nodes) = get_existing_cluster(
-          conn, opts, cluster_name)
+    (opts, action, cluster_name) = parse_args()
+    try:
+        conn = ec2.connect_to_region(opts.region)
+    except Exception as e:
+        print >> stderr, (e)
+        sys.exit(1)
+
+    # Select an AZ at random if it was not specified.
+    if opts.zone == "":
+        opts.zone = random.choice(conn.get_all_zones()).name
+
+    if action == "launch":
+        if opts.slaves <= 0:
+            print >> sys.stderr, "ERROR: You have to start at least 1 slave"
+            sys.exit(1)
+        if opts.resume:
+            (master_nodes, slave_nodes) = get_existing_cluster(conn, opts, cluster_name)
+        else:
+            (master_nodes, slave_nodes) = launch_cluster(conn, opts, cluster_name)
+            wait_for_cluster(conn, opts.wait, master_nodes, slave_nodes)
+        setup_cluster(conn, master_nodes, slave_nodes, opts, True)
+
+    elif action == "destroy":
+        response = raw_input("Are you sure you want to destroy the cluster " +
+                             cluster_name + "?\nALL DATA ON ALL NODES WILL BE LOST!!\n" +
+                             "Destroy cluster " + cluster_name + " (y/N): ")
+        if response == "y":
+            (master_nodes, slave_nodes) = get_existing_cluster(
+                conn, opts, cluster_name, die_on_error=False)
+            print "Terminating master..."
+            for inst in master_nodes:
+                inst.terminate()
+            print "Terminating slaves..."
+            for inst in slave_nodes:
+                inst.terminate()
+
+            # Delete security groups as well
+            if opts.delete_groups:
+                print "Deleting security groups (this will take some time)..."
+                group_names = [cluster_name + "-master", cluster_name + "-slaves"]
+
+                attempt = 1
+                while attempt <= 3:
+                    print "Attempt %d" % attempt
+                    groups = [g for g in conn.get_all_security_groups() if g.name in group_names]
+                    success = True
+                    # Delete individual rules in all groups before deleting groups to
+                    # remove dependencies between them
+                    for group in groups:
+                        print "Deleting rules in security group " + group.name
+                        for rule in group.rules:
+                            for grant in rule.grants:
+                                success &= group.revoke(ip_protocol=rule.ip_protocol,
+                                                        from_port=rule.from_port,
+                                                        to_port=rule.to_port,
+                                                        src_group=grant)
+
+                    # Sleep for AWS eventual-consistency to catch up, and for instances
+                    # to terminate
+                    time.sleep(30)  # Yes, it does have to be this long :-(
+                    for group in groups:
+                        try:
+                            conn.delete_security_group(group.name)
+                            print "Deleted security group " + group.name
+                        except boto.exception.EC2ResponseError:
+                            success = False
+                            print "Failed to delete security group " + group.name
+
+                    # Unfortunately, group.revoke() returns True even if a rule was not
+                    # deleted, so this needs to be rerun if something fails
+                    if success:
+                        break
+
+                    attempt += 1
+
+                if not success:
+                    print "Failed to delete all security groups after 3 tries."
+                    print "Try re-running in a few minutes."
+
+    elif action == "login":
+        (master_nodes, slave_nodes) = get_existing_cluster(conn, opts, cluster_name)
+        master = master_nodes[0].public_dns_name
+        print "Logging into master " + master + "..."
+        proxy_opt = []
+        if opts.proxy_port is not None:
+            proxy_opt = ['-D', opts.proxy_port]
+        subprocess.check_call(
+            ssh_command(opts) + proxy_opt + ['-t', '-t', "%s@%s" % (opts.user, master)])
+
+    elif action == "get-master":
+        (master_nodes, slave_nodes) = get_existing_cluster(conn, opts, cluster_name)
+        print master_nodes[0].public_dns_name
+
+    elif action == "stop":
+        response = raw_input(
+            "Are you sure you want to stop the cluster " +
+            cluster_name + "?\nDATA ON EPHEMERAL DISKS WILL BE LOST, " +
+            "BUT THE CLUSTER WILL KEEP USING SPACE ON\n" +
+            "AMAZON EBS IF IT IS EBS-BACKED!!\n" +
+            "All data on spot-instance slaves will be lost.\n" +
+            "Stop cluster " + cluster_name + " (y/N): ")
+        if response == "y":
+            (master_nodes, slave_nodes) = get_existing_cluster(
+                conn, opts, cluster_name, die_on_error=False)
+            print "Stopping master..."
+            for inst in master_nodes:
+                if inst.state not in ["shutting-down", "terminated"]:
+                    inst.stop()
+            print "Stopping slaves..."
+            for inst in slave_nodes:
+                if inst.state not in ["shutting-down", "terminated"]:
+                    if inst.spot_instance_request_id:
+                        inst.terminate()
+                    else:
+                        inst.stop()
+
+    elif action == "start":
+        (master_nodes, slave_nodes) = get_existing_cluster(conn, opts, cluster_name)
+        print "Starting slaves..."
+        for inst in slave_nodes:
+            if inst.state not in ["shutting-down", "terminated"]:
+                inst.start()
+        print "Starting master..."
+        for inst in master_nodes:
+            if inst.state not in ["shutting-down", "terminated"]:
+                inst.start()
+        wait_for_cluster(conn, opts.wait, master_nodes, slave_nodes)
+        setup_cluster(conn, master_nodes, slave_nodes, opts, False)
+
     else:
-      (master_nodes, slave_nodes) = launch_cluster(
-          conn, opts, cluster_name)
-      wait_for_cluster(conn, opts.wait, master_nodes, slave_nodes)
-    setup_cluster(conn, master_nodes, slave_nodes, opts, True)
-
-  elif action == "destroy":
-    response = raw_input("Are you sure you want to destroy the cluster " +
-        cluster_name + "?\nALL DATA ON ALL NODES WILL BE LOST!!\n" +
-        "Destroy cluster " + cluster_name + " (y/N): ")
-    if response == "y":
-      (master_nodes, slave_nodes) = get_existing_cluster(
-          conn, opts, cluster_name, die_on_error=False)
-      print "Terminating master..."
-      for inst in master_nodes:
-        inst.terminate()
-      print "Terminating slaves..."
-      for inst in slave_nodes:
-        inst.terminate()
-
-      # Delete security groups as well
-      if opts.delete_groups:
-        print "Deleting security groups (this will take some time)..."
-        group_names = [cluster_name + "-master", cluster_name + "-slaves"]
-
-        attempt = 1;
-        while attempt <= 3:
-          print "Attempt %d" % attempt
-          groups = [g for g in conn.get_all_security_groups() if g.name in group_names]
-          success = True
-          # Delete individual rules in all groups before deleting groups to
-          # remove dependencies between them
-          for group in groups:
-            print "Deleting rules in security group " + group.name
-            for rule in group.rules:
-              for grant in rule.grants:
-                  success &= group.revoke(ip_protocol=rule.ip_protocol,
-                           from_port=rule.from_port,
-                           to_port=rule.to_port,
-                           src_group=grant)
-
-          # Sleep for AWS eventual-consistency to catch up, and for instances
-          # to terminate
-          time.sleep(30)  # Yes, it does have to be this long :-(
-          for group in groups:
-            try:
-              conn.delete_security_group(group.name)
-              print "Deleted security group " + group.name
-            except boto.exception.EC2ResponseError:
-              success = False;
-              print "Failed to delete security group " + group.name
-
-          # Unfortunately, group.revoke() returns True even if a rule was not
-          # deleted, so this needs to be rerun if something fails
-          if success: break;
-
-          attempt += 1
-
-        if not success:
-          print "Failed to delete all security groups after 3 tries."
-          print "Try re-running in a few minutes."
-
-  elif action == "login":
-    (master_nodes, slave_nodes) = get_existing_cluster(
-        conn, opts, cluster_name)
-    master = master_nodes[0].public_dns_name
-    print "Logging into master " + master + "..."
-    proxy_opt = []
-    if opts.proxy_port != None:
-      proxy_opt = ['-D', opts.proxy_port]
-    subprocess.check_call(
-        ssh_command(opts) + proxy_opt + ['-t', '-t', "%s@%s" % (opts.user, master)])
-
-  elif action == "get-master":
-    (master_nodes, slave_nodes) = get_existing_cluster(conn, opts, cluster_name)
-    print master_nodes[0].public_dns_name
-
-  elif action == "stop":
-    response = raw_input("Are you sure you want to stop the cluster " +
-        cluster_name + "?\nDATA ON EPHEMERAL DISKS WILL BE LOST, " +
-        "BUT THE CLUSTER WILL KEEP USING SPACE ON\n" +
-        "AMAZON EBS IF IT IS EBS-BACKED!!\n" +
-        "All data on spot-instance slaves will be lost.\n" +
-        "Stop cluster " + cluster_name + " (y/N): ")
-    if response == "y":
-      (master_nodes, slave_nodes) = get_existing_cluster(
-          conn, opts, cluster_name, die_on_error=False)
-      print "Stopping master..."
-      for inst in master_nodes:
-        if inst.state not in ["shutting-down", "terminated"]:
-          inst.stop()
-      print "Stopping slaves..."
-      for inst in slave_nodes:
-        if inst.state not in ["shutting-down", "terminated"]:
-          if inst.spot_instance_request_id:
-            inst.terminate()
-          else:
-            inst.stop()
-
-  elif action == "start":
-    (master_nodes, slave_nodes) = get_existing_cluster(conn, opts, cluster_name)
-    print "Starting slaves..."
-    for inst in slave_nodes:
-      if inst.state not in ["shutting-down", "terminated"]:
-        inst.start()
-    print "Starting master..."
-    for inst in master_nodes:
-      if inst.state not in ["shutting-down", "terminated"]:
-        inst.start()
-    wait_for_cluster(conn, opts.wait, master_nodes, slave_nodes)
-    setup_cluster(conn, master_nodes, slave_nodes, opts, False)
-
-  else:
-    print >> stderr, "Invalid action: %s" % action
-    sys.exit(1)
+        print >> stderr, "Invalid action: %s" % action
+        sys.exit(1)
 
 
 def main():
-  try:
-    real_main()
-  except UsageError, e:
-    print >> stderr, "\nError:\n", e
-    sys.exit(1)
+    try:
+        real_main()
+    except UsageError, e:
+        print >> stderr, "\nError:\n", e
+        sys.exit(1)
 
 
 if __name__ == "__main__":
-  logging.basicConfig()
-  main()
+    logging.basicConfig()
+    main()

From d17d221487fa7a3af6f4af2217f1d4889ceb084d Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Sun, 1 Jun 2014 17:27:05 -0700
Subject: [PATCH 315/810] Better explanation for how to use MIMA excludes.

This patch does a few things:
1. We have a file MimaExcludes.scala exclusively for excludes.
2. The test runner tells users about that file if a test fails.
3. I've added back the excludes used from 0.9->1.0. We should keep
   these in the project as an official audit trail of times where
   we decided to make exceptions.

Author: Patrick Wendell <pwendell@gmail.com>

Closes #937 from pwendell/mima and squashes the following commits:

7ee0db2 [Patrick Wendell] Better explanation for how to use MIMA excludes.
---
 .gitignore                                    |  2 +-
 .rat-excludes                                 |  1 +
 dev/mima                                      | 34 +++++++++++
 dev/run-tests                                 |  3 +-
 project/MimaBuild.scala                       | 48 +++++++--------
 project/MimaExcludes.scala                    | 61 +++++++++++++++++++
 .../spark/tools/GenerateMIMAIgnore.scala      |  4 +-
 7 files changed, 124 insertions(+), 29 deletions(-)
 create mode 100755 dev/mima
 create mode 100644 project/MimaExcludes.scala

diff --git a/.gitignore b/.gitignore
index ad72588b472d..b54a3058de65 100644
--- a/.gitignore
+++ b/.gitignore
@@ -7,7 +7,7 @@
 sbt/*.jar
 .settings
 .cache
-.mima-excludes
+.generated-mima-excludes
 /build/
 work/
 out/
diff --git a/.rat-excludes b/.rat-excludes
index 15589702c559..52b2dfac5cf2 100644
--- a/.rat-excludes
+++ b/.rat-excludes
@@ -3,6 +3,7 @@ target
 .project
 .classpath
 .mima-excludes
+.generated-mima-excludes
 .rat-excludes
 .*md
 derby.log
diff --git a/dev/mima b/dev/mima
new file mode 100755
index 000000000000..d4099990254c
--- /dev/null
+++ b/dev/mima
@@ -0,0 +1,34 @@
+#!/usr/bin/env bash
+
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+set -o pipefail
+
+# Go to the Spark project root directory
+FWDIR="$(cd `dirname $0`/..; pwd)"
+cd $FWDIR
+
+./bin/spark-class org.apache.spark.tools.GenerateMIMAIgnore
+echo -e "q\n" | sbt/sbt mima-report-binary-issues | grep -v -e "info.*Resolving"
+ret_val=$?
+
+if [ $ret_val != 0 ]; then
+  echo "NOTE: Exceptions to binary compatibility can be added in project/MimaExcludes.scala"
+fi
+
+exit $ret_val
diff --git a/dev/run-tests b/dev/run-tests
index 6043f859ae46..93d6692f83ca 100755
--- a/dev/run-tests
+++ b/dev/run-tests
@@ -81,5 +81,4 @@ fi
 echo "========================================================================="
 echo "Detecting binary incompatibilites with MiMa"
 echo "========================================================================="
-./bin/spark-class org.apache.spark.tools.GenerateMIMAIgnore
-echo -e "q\n" | sbt/sbt mima-report-binary-issues | grep -v -e "info.*Resolving" 
+dev/mima
diff --git a/project/MimaBuild.scala b/project/MimaBuild.scala
index 182ca7615de6..147780994357 100644
--- a/project/MimaBuild.scala
+++ b/project/MimaBuild.scala
@@ -15,21 +15,41 @@
  * limitations under the License.
  */
 
+import com.typesafe.tools.mima.core.{MissingTypesProblem, MissingClassProblem, ProblemFilters}
+import com.typesafe.tools.mima.core.ProblemFilters._
 import com.typesafe.tools.mima.plugin.MimaKeys.{binaryIssueFilters, previousArtifact}
 import com.typesafe.tools.mima.plugin.MimaPlugin.mimaDefaultSettings
 import sbt._
 
 object MimaBuild {
+  // Exclude a single class and its corresponding object
+  def excludeClass(className: String) = {
+    Seq(
+      excludePackage(className),
+      ProblemFilters.exclude[MissingClassProblem](className),
+      ProblemFilters.exclude[MissingTypesProblem](className),
+      excludePackage(className + "$"),
+      ProblemFilters.exclude[MissingClassProblem](className + "$"),
+      ProblemFilters.exclude[MissingTypesProblem](className + "$")
+    )
+  }
+  // Exclude a Spark class, that is in the package org.apache.spark
+  def excludeSparkClass(className: String) = {
+    excludeClass("org.apache.spark." + className)
+  }
+
+  // Exclude a Spark package, that is in the package org.apache.spark
+  def excludeSparkPackage(packageName: String) = {
+    excludePackage("org.apache.spark." + packageName)
+  }
 
   def ignoredABIProblems(base: File) = {
-    import com.typesafe.tools.mima.core._
-    import com.typesafe.tools.mima.core.ProblemFilters._
 
     // Excludes placed here will be used for all Spark versions
     val defaultExcludes = Seq()
 
     // Read package-private excludes from file
-    val excludeFilePath = (base.getAbsolutePath + "/.mima-excludes")
+    val excludeFilePath = (base.getAbsolutePath + "/.generated-mima-excludes")
     val excludeFile = file(excludeFilePath)
     val ignoredClasses: Seq[String] =
       if (!excludeFile.exists()) {
@@ -38,31 +58,11 @@ object MimaBuild {
         IO.read(excludeFile).split("\n")
       }
 
-    // Exclude a single class and its corresponding object
-    def excludeClass(className: String) = {
-      Seq(
-        excludePackage(className),
-        ProblemFilters.exclude[MissingClassProblem](className),
-        ProblemFilters.exclude[MissingTypesProblem](className),
-        excludePackage(className + "$"),
-        ProblemFilters.exclude[MissingClassProblem](className + "$"),
-        ProblemFilters.exclude[MissingTypesProblem](className + "$")
-      )
-    }
-
-    // Exclude a Spark class, that is in the package org.apache.spark
-    def excludeSparkClass(className: String) = {
-      excludeClass("org.apache.spark." + className)
-    }
 
-    // Exclude a Spark package, that is in the package org.apache.spark
-    def excludeSparkPackage(packageName: String) = {
-      excludePackage("org.apache.spark." + packageName)
-    }
 
     val externalExcludeFileClasses = ignoredClasses.flatMap(excludeClass)
 
-    defaultExcludes ++ externalExcludeFileClasses
+    defaultExcludes ++ externalExcludeFileClasses ++ MimaExcludes.excludes
   }
 
   def mimaSettings(sparkHome: File) = mimaDefaultSettings ++ Seq(
diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala
new file mode 100644
index 000000000000..ecb389de5558
--- /dev/null
+++ b/project/MimaExcludes.scala
@@ -0,0 +1,61 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+import com.typesafe.tools.mima.core._
+import com.typesafe.tools.mima.core.ProblemFilters._
+
+/**
+ * Additional excludes for checking of Spark's binary compatibility.
+ *
+ * The Mima build will automatically exclude @DeveloperApi and @Experimental classes. This acts
+ * as an official audit of cases where we excluded other classes. Please use the narrowest
+ * possible exclude here. MIMA will usually tell you what exclude to use, e.g.:
+ *
+ * ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.rdd.RDD.take")
+ *
+ * It is also possible to exclude Spark classes and packages. This should be used sparingly:
+ *
+ * MimaBuild.excludeSparkClass("graphx.util.collection.GraphXPrimitiveKeyOpenHashMap")
+ */
+object MimaExcludes {
+    val excludes =
+      SparkBuild.SPARK_VERSION match {
+        case v if v.startsWith("1.1") =>
+          Seq()
+        case v if v.startsWith("1.0") =>
+          Seq(
+            MimaBuild.excludeSparkPackage("api.java"),
+            MimaBuild.excludeSparkPackage("mllib"),
+            MimaBuild.excludeSparkPackage("streaming")
+          ) ++
+          MimaBuild.excludeSparkClass("rdd.ClassTags") ++
+          MimaBuild.excludeSparkClass("util.XORShiftRandom") ++
+          MimaBuild.excludeSparkClass("graphx.EdgeRDD") ++
+          MimaBuild.excludeSparkClass("graphx.VertexRDD") ++
+          MimaBuild.excludeSparkClass("graphx.impl.GraphImpl") ++
+          MimaBuild.excludeSparkClass("graphx.impl.RoutingTable") ++
+          MimaBuild.excludeSparkClass("graphx.util.collection.PrimitiveKeyOpenHashMap") ++
+          MimaBuild.excludeSparkClass("graphx.util.collection.GraphXPrimitiveKeyOpenHashMap") ++
+          MimaBuild.excludeSparkClass("mllib.recommendation.MFDataGenerator") ++
+          MimaBuild.excludeSparkClass("mllib.optimization.SquaredGradient") ++
+          MimaBuild.excludeSparkClass("mllib.regression.RidgeRegressionWithSGD") ++
+          MimaBuild.excludeSparkClass("mllib.regression.LassoWithSGD") ++
+          MimaBuild.excludeSparkClass("mllib.regression.LinearRegressionWithSGD")
+        case _ => Seq()
+      }
+}
+
diff --git a/tools/src/main/scala/org/apache/spark/tools/GenerateMIMAIgnore.scala b/tools/src/main/scala/org/apache/spark/tools/GenerateMIMAIgnore.scala
index 011db50b7d56..6a261e19a35c 100644
--- a/tools/src/main/scala/org/apache/spark/tools/GenerateMIMAIgnore.scala
+++ b/tools/src/main/scala/org/apache/spark/tools/GenerateMIMAIgnore.scala
@@ -105,9 +105,9 @@ object GenerateMIMAIgnore {
   }
 
   def main(args: Array[String]) {
-    scala.tools.nsc.io.File(".mima-excludes").
+    scala.tools.nsc.io.File(".generated-mima-excludes").
       writeAll(classesPrivateWithin("org.apache.spark").mkString("\n"))
-    println("Created : .mima-excludes in current directory.")
+    println("Created : .generated-mima-excludes in current directory.")
   }
 
 

From 9535f4045daf46b084761d7f15f63dc6c2a543dd Mon Sep 17 00:00:00 2001
From: Ankur Dave <ankurdave@gmail.com>
Date: Mon, 2 Jun 2014 00:00:24 -0700
Subject: [PATCH 316/810] Add landmark-based Shortest Path algorithm to
 graphx.lib

This is a modified version of apache/spark#10.

Author: Ankur Dave <ankurdave@gmail.com>
Author: Andres Perez <andres@tresata.com>

Closes #933 from ankurdave/shortestpaths and squashes the following commits:

03a103c [Ankur Dave] Style fixes
7a1ff48 [Ankur Dave] Improve ShortestPaths documentation
d75c8fc [Ankur Dave] Remove unnecessary VD type param, and pass through ED
d983fb4 [Ankur Dave] Fix style errors
60ed8e6 [Andres Perez] Add Shortest-path computations to graphx.lib with unit tests.
---
 .../spark/graphx/lib/ShortestPaths.scala      | 71 +++++++++++++++++++
 .../spark/graphx/lib/ShortestPathsSuite.scala | 49 +++++++++++++
 2 files changed, 120 insertions(+)
 create mode 100644 graphx/src/main/scala/org/apache/spark/graphx/lib/ShortestPaths.scala
 create mode 100644 graphx/src/test/scala/org/apache/spark/graphx/lib/ShortestPathsSuite.scala

diff --git a/graphx/src/main/scala/org/apache/spark/graphx/lib/ShortestPaths.scala b/graphx/src/main/scala/org/apache/spark/graphx/lib/ShortestPaths.scala
new file mode 100644
index 000000000000..bba070f256d8
--- /dev/null
+++ b/graphx/src/main/scala/org/apache/spark/graphx/lib/ShortestPaths.scala
@@ -0,0 +1,71 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.graphx.lib
+
+import org.apache.spark.graphx._
+import scala.reflect.ClassTag
+
+/**
+ * Computes shortest paths to the given set of landmark vertices, returning a graph where each
+ * vertex attribute is a map containing the shortest-path distance to each reachable landmark.
+ */
+object ShortestPaths {
+  /** Stores a map from the vertex id of a landmark to the distance to that landmark. */
+  type SPMap = Map[VertexId, Int]
+
+  private def makeMap(x: (VertexId, Int)*) = Map(x: _*)
+
+  private def incrementMap(spmap: SPMap): SPMap = spmap.map { case (v, d) => v -> (d + 1) }
+
+  private def addMaps(spmap1: SPMap, spmap2: SPMap): SPMap =
+    (spmap1.keySet ++ spmap2.keySet).map {
+      k => k -> math.min(spmap1.getOrElse(k, Int.MaxValue), spmap2.getOrElse(k, Int.MaxValue))
+    }.toMap
+
+  /**
+   * Computes shortest paths to the given set of landmark vertices.
+   *
+   * @tparam ED the edge attribute type (not used in the computation)
+   *
+   * @param graph the graph for which to compute the shortest paths
+   * @param landmarks the list of landmark vertex ids. Shortest paths will be computed to each
+   * landmark.
+   *
+   * @return a graph where each vertex attribute is a map containing the shortest-path distance to
+   * each reachable landmark vertex.
+   */
+  def run[ED: ClassTag](graph: Graph[_, ED], landmarks: Seq[VertexId]): Graph[SPMap, ED] = {
+    val spGraph = graph.mapVertices { (vid, attr) =>
+      if (landmarks.contains(vid)) makeMap(vid -> 0) else makeMap()
+    }
+
+    val initialMessage = makeMap()
+
+    def vertexProgram(id: VertexId, attr: SPMap, msg: SPMap): SPMap = {
+      addMaps(attr, msg)
+    }
+
+    def sendMessage(edge: EdgeTriplet[SPMap, _]): Iterator[(VertexId, SPMap)] = {
+      val newAttr = incrementMap(edge.srcAttr)
+      if (edge.dstAttr != addMaps(newAttr, edge.dstAttr)) Iterator((edge.dstId, newAttr))
+      else Iterator.empty
+    }
+
+    Pregel(spGraph, initialMessage)(vertexProgram, sendMessage, addMaps)
+  }
+}
diff --git a/graphx/src/test/scala/org/apache/spark/graphx/lib/ShortestPathsSuite.scala b/graphx/src/test/scala/org/apache/spark/graphx/lib/ShortestPathsSuite.scala
new file mode 100644
index 000000000000..265827b3341c
--- /dev/null
+++ b/graphx/src/test/scala/org/apache/spark/graphx/lib/ShortestPathsSuite.scala
@@ -0,0 +1,49 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.graphx.lib
+
+import org.scalatest.FunSuite
+
+import org.apache.spark.SparkContext
+import org.apache.spark.SparkContext._
+import org.apache.spark.graphx._
+import org.apache.spark.graphx.lib._
+import org.apache.spark.graphx.util.GraphGenerators
+import org.apache.spark.rdd._
+
+class ShortestPathsSuite extends FunSuite with LocalSparkContext {
+
+  test("Shortest Path Computations") {
+    withSpark { sc =>
+      val shortestPaths = Set(
+        (1, Map(1 -> 0, 4 -> 2)), (2, Map(1 -> 1, 4 -> 2)), (3, Map(1 -> 2, 4 -> 1)),
+        (4, Map(1 -> 2, 4 -> 0)), (5, Map(1 -> 1, 4 -> 1)), (6, Map(1 -> 3, 4 -> 1)))
+      val edgeSeq = Seq((1, 2), (1, 5), (2, 3), (2, 5), (3, 4), (4, 5), (4, 6)).flatMap {
+        case e => Seq(e, e.swap)
+      }
+      val edges = sc.parallelize(edgeSeq).map { case (v1, v2) => (v1.toLong, v2.toLong) }
+      val graph = Graph.fromEdgeTuples(edges, 1)
+      val landmarks = Seq(1, 4).map(_.toLong)
+      val results = ShortestPaths.run(graph, landmarks).vertices.collect.map {
+        case (v, spMap) => (v, spMap.mapValues(_.get))
+      }
+      assert(results.toSet === shortestPaths)
+    }
+  }
+
+}

From 9a5d482e090eaaea8491d3864667e0f513e7195c Mon Sep 17 00:00:00 2001
From: Tor Myklebust <tmyklebu@gmail.com>
Date: Mon, 2 Jun 2014 11:48:09 -0700
Subject: [PATCH 317/810] [SPARK-1553] Alternating nonnegative least-squares

This pull request includes a nonnegative least-squares solver (NNLS) tailored to the kinds of small-scale problems that come up when training matrix factorisation models by alternating nonnegative least-squares (ANNLS).

The method used for the NNLS subproblems is based on the classical method of projected gradients.  There is a modification where, if the set of active constraints has not changed since the last iteration, a conjugate gradient step is considered and possibly rejected in favour of the gradient; this improves convergence once the optimal face has been located.

The NNLS solver is in `org.apache.spark.mllib.optimization.NNLSbyPCG`.

Author: Tor Myklebust <tmyklebu@gmail.com>

Closes #460 from tmyklebu/annls and squashes the following commits:

79bc4b5 [Tor Myklebust] Merge branch 'master' of https://github.com/apache/spark into annls
199b0bc [Tor Myklebust] Make the ctor private again and use the builder pattern.
7fbabf1 [Tor Myklebust] Cleanup matrix math in NNLSSuite.
65ef7f2 [Tor Myklebust] Make ALS's ctor public and remove a couple of "convenience" wrappers.
2d4f3cb [Tor Myklebust] Cleanup.
0cb4481 [Tor Myklebust] Drop the iteration limit from 40k to max(400,20n).
e2a01d1 [Tor Myklebust] Create a workspace object for NNLS to cut down on memory allocations.
b285106 [Tor Myklebust] Clean up NNLS test cases.
9c820b6 [Tor Myklebust] Tweak variable names.
8a1a436 [Tor Myklebust] Describe the problem and add a reference to Polyak's paper.
5345402 [Tor Myklebust] Style fixes that got eaten.
ac673bd [Tor Myklebust] More safeguards against numerical ridiculousness.
c288b6a [Tor Myklebust] Finish moving the NNLS solver.
9a82fa6 [Tor Myklebust] Fix scalastyle moanings.
33bf4f2 [Tor Myklebust] Fix missing space.
89ea0a8 [Tor Myklebust] Hack ALSSuite to support NNLS testing.
f5dbf4d [Tor Myklebust] Teach ALS how to use the NNLS solver.
6cb563c [Tor Myklebust] Tests for the nonnegative least squares solver.
a68ac10 [Tor Myklebust] A nonnegative least-squares solver.
---
 .../spark/mllib/optimization/NNLS.scala       | 169 ++++++++++++++++++
 .../spark/mllib/recommendation/ALS.scala      |  33 +++-
 .../spark/mllib/optimization/NNLSSuite.scala  |  80 +++++++++
 .../spark/mllib/recommendation/ALSSuite.scala |  32 ++--
 4 files changed, 300 insertions(+), 14 deletions(-)
 create mode 100644 mllib/src/main/scala/org/apache/spark/mllib/optimization/NNLS.scala
 create mode 100644 mllib/src/test/scala/org/apache/spark/mllib/optimization/NNLSSuite.scala

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/optimization/NNLS.scala b/mllib/src/main/scala/org/apache/spark/mllib/optimization/NNLS.scala
new file mode 100644
index 000000000000..e4b436b02379
--- /dev/null
+++ b/mllib/src/main/scala/org/apache/spark/mllib/optimization/NNLS.scala
@@ -0,0 +1,169 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.mllib.optimization
+
+import org.jblas.{DoubleMatrix, SimpleBlas}
+
+import org.apache.spark.annotation.DeveloperApi
+
+/**
+ * Object used to solve nonnegative least squares problems using a modified
+ * projected gradient method.
+ */
+private[mllib] object NNLS {
+  class Workspace(val n: Int) {
+    val scratch = new DoubleMatrix(n, 1)
+    val grad = new DoubleMatrix(n, 1)
+    val x = new DoubleMatrix(n, 1)
+    val dir = new DoubleMatrix(n, 1)
+    val lastDir = new DoubleMatrix(n, 1)
+    val res = new DoubleMatrix(n, 1)
+
+    def wipe() {
+      scratch.fill(0.0)
+      grad.fill(0.0)
+      x.fill(0.0)
+      dir.fill(0.0)
+      lastDir.fill(0.0)
+      res.fill(0.0)
+    }
+  }
+
+  def createWorkspace(n: Int): Workspace = {
+    new Workspace(n)
+  }
+
+  /**
+   * Solve a least squares problem, possibly with nonnegativity constraints, by a modified
+   * projected gradient method.  That is, find x minimising ||Ax - b||_2 given A^T A and A^T b.
+   *
+   * We solve the problem
+   *   min_x      1/2 x^T ata x^T - x^T atb
+   *   subject to x >= 0
+   *
+   * The method used is similar to one described by Polyak (B. T. Polyak, The conjugate gradient
+   * method in extremal problems, Zh. Vychisl. Mat. Mat. Fiz. 9(4)(1969), pp. 94-112) for bound-
+   * constrained nonlinear programming.  Polyak unconditionally uses a conjugate gradient
+   * direction, however, while this method only uses a conjugate gradient direction if the last
+   * iteration did not cause a previously-inactive constraint to become active.
+   */
+  def solve(ata: DoubleMatrix, atb: DoubleMatrix, ws: Workspace): Array[Double] = {
+    ws.wipe()
+
+    val n = atb.rows
+    val scratch = ws.scratch
+
+    // find the optimal unconstrained step
+    def steplen(dir: DoubleMatrix, res: DoubleMatrix): Double = {
+      val top = SimpleBlas.dot(dir, res)
+      SimpleBlas.gemv(1.0, ata, dir, 0.0, scratch)
+      // Push the denominator upward very slightly to avoid infinities and silliness
+      top / (SimpleBlas.dot(scratch, dir) + 1e-20)
+    }
+
+    // stopping condition
+    def stop(step: Double, ndir: Double, nx: Double): Boolean = {
+        ((step.isNaN) // NaN
+      || (step < 1e-6) // too small or negative
+      || (step > 1e40) // too small; almost certainly numerical problems
+      || (ndir < 1e-12 * nx) // gradient relatively too small
+      || (ndir < 1e-32) // gradient absolutely too small; numerical issues may lurk
+      )
+    }
+
+    val grad = ws.grad
+    val x = ws.x
+    val dir = ws.dir
+    val lastDir = ws.lastDir
+    val res = ws.res
+    val iterMax = Math.max(400, 20 * n)
+    var lastNorm = 0.0
+    var iterno = 0
+    var lastWall = 0 // Last iteration when we hit a bound constraint.
+    var i = 0
+    while (iterno < iterMax) {
+      // find the residual
+      SimpleBlas.gemv(1.0, ata, x, 0.0, res)
+      SimpleBlas.axpy(-1.0, atb, res)
+      SimpleBlas.copy(res, grad)
+
+      // project the gradient
+      i = 0
+      while (i < n) {
+        if (grad.data(i) > 0.0 && x.data(i) == 0.0) {
+          grad.data(i) = 0.0
+        }
+        i = i + 1
+      }
+      val ngrad = SimpleBlas.dot(grad, grad)
+
+      SimpleBlas.copy(grad, dir)
+
+      // use a CG direction under certain conditions
+      var step = steplen(grad, res)
+      var ndir = 0.0
+      val nx = SimpleBlas.dot(x, x)
+      if (iterno > lastWall + 1) {
+        val alpha = ngrad / lastNorm
+        SimpleBlas.axpy(alpha, lastDir, dir)
+        val dstep = steplen(dir, res)
+        ndir = SimpleBlas.dot(dir, dir)
+        if (stop(dstep, ndir, nx)) {
+          // reject the CG step if it could lead to premature termination
+          SimpleBlas.copy(grad, dir)
+          ndir = SimpleBlas.dot(dir, dir)
+        } else {
+          step = dstep
+        }
+      } else {
+        ndir = SimpleBlas.dot(dir, dir)
+      }
+
+      // terminate?
+      if (stop(step, ndir, nx)) {
+        return x.data.clone
+      }
+
+      // don't run through the walls
+      i = 0
+      while (i < n) {
+        if (step * dir.data(i) > x.data(i)) {
+          step = x.data(i) / dir.data(i)
+        }
+        i = i + 1
+      }
+
+      // take the step
+      i = 0
+      while (i < n) {
+        if (step * dir.data(i) > x.data(i) * (1 - 1e-14)) {
+          x.data(i) = 0
+          lastWall = iterno
+        } else {
+          x.data(i) -= step * dir.data(i)
+        }
+        i = i + 1
+      }
+
+      iterno = iterno + 1
+      SimpleBlas.copy(dir, lastDir)
+      lastNorm = ngrad
+    }
+    x.data.clone
+  }
+}
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala
index 0cf9a7f90908..cfc3b6860649 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala
@@ -32,6 +32,7 @@ import org.apache.spark.storage.StorageLevel
 import org.apache.spark.rdd.RDD
 import org.apache.spark.SparkContext._
 import org.apache.spark.util.Utils
+import org.apache.spark.mllib.optimization.NNLS
 
 /**
  * Out-link information for a user or product block. This includes the original user/product IDs
@@ -156,6 +157,18 @@ class ALS private (
     this
   }
 
+  /** If true, do alternating nonnegative least squares. */
+  private var nonnegative = false
+
+  /**
+   * Set whether the least-squares problems solved at each iteration should have
+   * nonnegativity constraints.
+   */
+  def setNonnegative(b: Boolean): ALS = {
+    this.nonnegative = b
+    this
+  }
+
   /**
    * Run ALS with the configured parameters on an input RDD of (user, product, rating) triples.
    * Returns a MatrixFactorizationModel with feature vectors for each user and product.
@@ -505,6 +518,8 @@ class ALS private (
       }
     }
 
+    val ws = if (nonnegative) NNLS.createWorkspace(rank) else null
+
     // Solve the least-squares problem for each user and return the new feature vectors
     Array.range(0, numUsers).map { index =>
       // Compute the full XtX matrix from the lower-triangular part we got above
@@ -517,13 +532,26 @@ class ALS private (
       }
       // Solve the resulting matrix, which is symmetric and positive-definite
       if (implicitPrefs) {
-        Solve.solvePositive(fullXtX.addi(YtY.get.value), userXy(index)).data
+        solveLeastSquares(fullXtX.addi(YtY.get.value), userXy(index), ws)
       } else {
-        Solve.solvePositive(fullXtX, userXy(index)).data
+        solveLeastSquares(fullXtX, userXy(index), ws)
       }
     }
   }
 
+  /**
+   * Given A^T A and A^T b, find the x minimising ||Ax - b||_2, possibly subject
+   * to nonnegativity constraints if `nonnegative` is true.
+   */
+  def solveLeastSquares(ata: DoubleMatrix, atb: DoubleMatrix,
+      ws: NNLS.Workspace): Array[Double] = {
+    if (!nonnegative) {
+      Solve.solvePositive(ata, atb).data
+    } else {
+      NNLS.solve(ata, atb, ws)
+    }
+  }
+
   /**
    * Given a triangular matrix in the order of fillXtX above, compute the full symmetric square
    * matrix that it represents, storing it into destMatrix.
@@ -550,7 +578,6 @@ class ALS private (
  * Top-level methods for calling Alternating Least Squares (ALS) matrix factorization.
  */
 object ALS {
-
   /**
    * Train a matrix factorization model given an RDD of ratings given by users to some products,
    * in the form of (userID, productID, rating) pairs. We approximate the ratings matrix as the
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/optimization/NNLSSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/optimization/NNLSSuite.scala
new file mode 100644
index 000000000000..bbf385229081
--- /dev/null
+++ b/mllib/src/test/scala/org/apache/spark/mllib/optimization/NNLSSuite.scala
@@ -0,0 +1,80 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.mllib.optimization
+
+import scala.util.Random
+
+import org.scalatest.FunSuite
+
+import org.jblas.{DoubleMatrix, SimpleBlas, NativeBlas}
+
+class NNLSSuite extends FunSuite {
+  /** Generate an NNLS problem whose optimal solution is the all-ones vector. */
+  def genOnesData(n: Int, rand: Random): (DoubleMatrix, DoubleMatrix) = {
+    val A = new DoubleMatrix(n, n, Array.fill(n*n)(rand.nextDouble()): _*)
+    val b = A.mmul(DoubleMatrix.ones(n, 1))
+
+    val ata = A.transpose.mmul(A)
+    val atb = A.transpose.mmul(b)
+
+    (ata, atb)
+  }
+
+  test("NNLS: exact solution cases") {
+    val n = 20
+    val rand = new Random(12346)
+    val ws = NNLS.createWorkspace(n)
+    var numSolved = 0
+
+    // About 15% of random 20x20 [-1,1]-matrices have a singular value less than 1e-3.  NNLS
+    // can legitimately fail to solve these anywhere close to exactly.  So we grab a considerable
+    // sample of these matrices and make sure that we solved a substantial fraction of them.
+
+    for (k <- 0 until 100) {
+      val (ata, atb) = genOnesData(n, rand)
+      val x = new DoubleMatrix(NNLS.solve(ata, atb, ws))
+      assert(x.length === n)
+      val answer = DoubleMatrix.ones(n, 1)
+      SimpleBlas.axpy(-1.0, answer, x)
+      val solved = (x.norm2 < 1e-2) && (x.normmax < 1e-3)
+      if (solved) numSolved = numSolved + 1
+    }
+
+    assert(numSolved > 50)
+  }
+
+  test("NNLS: nonnegativity constraint active") {
+    val n = 5
+    val ata = new DoubleMatrix(Array(
+      Array( 4.377, -3.531, -1.306, -0.139,  3.418),
+      Array(-3.531,  4.344,  0.934,  0.305, -2.140),
+      Array(-1.306,  0.934,  2.644, -0.203, -0.170),
+      Array(-0.139,  0.305, -0.203,  5.883,  1.428),
+      Array( 3.418, -2.140, -0.170,  1.428,  4.684)))
+    val atb = new DoubleMatrix(Array(-1.632, 2.115, 1.094, -1.025, -0.636))
+
+    val goodx = Array(0.13025, 0.54506, 0.2874, 0.0, 0.028628)
+
+    val ws = NNLS.createWorkspace(n)
+    val x = NNLS.solve(ata, atb, ws)
+    for (i <- 0 until n) {
+      assert(Math.abs(x(i) - goodx(i)) < 1e-3)
+      assert(x(i) >= 0)
+    }
+  }
+}
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/recommendation/ALSSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/recommendation/ALSSuite.scala
index 2d944f3eb7ff..37c9b9d08584 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/recommendation/ALSSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/recommendation/ALSSuite.scala
@@ -48,12 +48,18 @@ object ALSSuite {
       features: Int,
       samplingRate: Double,
       implicitPrefs: Boolean = false,
-      negativeWeights: Boolean = false): (Seq[Rating], DoubleMatrix, DoubleMatrix) = {
+      negativeWeights: Boolean = false,
+      negativeFactors: Boolean = true): (Seq[Rating], DoubleMatrix, DoubleMatrix) = {
     val rand = new Random(42)
 
     // Create a random matrix with uniform values from -1 to 1
-    def randomMatrix(m: Int, n: Int) =
-      new DoubleMatrix(m, n, Array.fill(m * n)(rand.nextDouble() * 2 - 1): _*)
+    def randomMatrix(m: Int, n: Int) = {
+      if (negativeFactors) {
+        new DoubleMatrix(m, n, Array.fill(m * n)(rand.nextDouble() * 2 - 1): _*)
+      } else {
+        new DoubleMatrix(m, n, Array.fill(m * n)(rand.nextDouble()): _*)
+      }
+    }
 
     val userMatrix = randomMatrix(users, features)
     val productMatrix = randomMatrix(features, products)
@@ -146,6 +152,10 @@ class ALSSuite extends FunSuite with LocalSparkContext {
     }
   }
 
+  test("NNALS, rank 2") {
+    testALS(100, 200, 2, 15, 0.7, 0.4, false, false, false, -1, false)
+  }
+
   /**
    * Test if we can correctly factorize R = U * P where U and P are of known rank.
    *
@@ -159,19 +169,19 @@ class ALSSuite extends FunSuite with LocalSparkContext {
    * @param bulkPredict    flag to test bulk prediciton
    * @param negativeWeights whether the generated data can contain negative values
    * @param numBlocks      number of blocks to partition users and products into
+   * @param negativeFactors whether the generated user/product factors can have negative entries
    */
   def testALS(users: Int, products: Int, features: Int, iterations: Int,
     samplingRate: Double, matchThreshold: Double, implicitPrefs: Boolean = false,
-    bulkPredict: Boolean = false, negativeWeights: Boolean = false, numBlocks: Int = -1)
+    bulkPredict: Boolean = false, negativeWeights: Boolean = false, numBlocks: Int = -1,
+    negativeFactors: Boolean = true)
   {
     val (sampledRatings, trueRatings, truePrefs) = ALSSuite.generateRatings(users, products,
-      features, samplingRate, implicitPrefs, negativeWeights)
-    val model = implicitPrefs match {
-      case false => ALS.train(sc.parallelize(sampledRatings), features, iterations, 0.01,
-          numBlocks, 0L)
-      case true => ALS.trainImplicit(sc.parallelize(sampledRatings), features, iterations, 0.01,
-          numBlocks, 1.0, 0L)
-    }
+      features, samplingRate, implicitPrefs, negativeWeights, negativeFactors)
+
+    val model = (new ALS().setBlocks(numBlocks).setRank(features).setIterations(iterations)
+          .setAlpha(1.0).setImplicitPrefs(implicitPrefs).setLambda(0.01).setSeed(0L)
+          .setNonnegative(!negativeFactors).run(sc.parallelize(sampledRatings)))
 
     val predictedU = new DoubleMatrix(users, features)
     for ((u, vec) <- model.userFeatures.collect(); i <- 0 until features) {

From d000ca98a80986ff5b13477547f1dcab7856ae63 Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian.cs.zju@gmail.com>
Date: Mon, 2 Jun 2014 12:09:43 -0700
Subject: [PATCH 318/810] [SPARK-1958] Calling .collect() on a SchemaRDD should
 call executeCollect() on the underlying query plan.

In cases like `Limit` and `TakeOrdered`, `executeCollect()` makes optimizations that `execute().collect()` will not.

Author: Cheng Lian <lian.cs.zju@gmail.com>

Closes #939 from liancheng/spark-1958 and squashes the following commits:

bdc4a14 [Cheng Lian] Copy rows to present immutable data to users
8250976 [Cheng Lian] Added return type explicitly for public API
192a25c [Cheng Lian] [SPARK-1958] Calling .collect() on a SchemaRDD should call executeCollect() on the underlying query plan.
---
 .../src/main/scala/org/apache/spark/sql/SchemaRDD.scala     | 6 ++++++
 .../scala/org/apache/spark/sql/execution/SparkPlan.scala    | 2 +-
 .../org/apache/spark/sql/parquet/ParquetQuerySuite.scala    | 2 +-
 3 files changed, 8 insertions(+), 2 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala
index e855f36256bc..8855c4e87691 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala
@@ -368,6 +368,12 @@ class SchemaRDD(
     new SchemaRDD(sqlContext, SparkLogicalPlan(ExistingRdd(logicalPlan.output, rdd)))
   }
 
+  // =======================================================================
+  // Overriden RDD actions
+  // =======================================================================
+
+  override def collect(): Array[Row] = queryExecution.executedPlan.executeCollect()
+
   // =======================================================================
   // Base RDD functions that do NOT change schema
   // =======================================================================
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
index 235a9b169246..4613df103943 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
@@ -49,7 +49,7 @@ abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging {
   /**
    * Runs this query returning the result as an array.
    */
-  def executeCollect(): Array[Row] = execute().collect()
+  def executeCollect(): Array[Row] = execute().map(_.copy()).collect()
 
   protected def buildRow(values: Seq[Any]): Row =
     new GenericRow(values.toArray)
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala
index f9731e82e492..b973ceba5fec 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala
@@ -201,7 +201,7 @@ class ParquetQuerySuite extends QueryTest with FunSuite with BeforeAndAfterAll {
   }
 
   test("insert (appending) to same table via Scala API") {
-    sql("INSERT INTO testsource SELECT * FROM testsource").collect()
+    sql("INSERT INTO testsource SELECT * FROM testsource")
     val double_rdd = sql("SELECT * FROM testsource").collect()
     assert(double_rdd != null)
     assert(double_rdd.size === 30)

From ec8be274a7bc586bb5b025033cbfd73f9a4d7160 Mon Sep 17 00:00:00 2001
From: egraldlo <egraldlo@gmail.com>
Date: Mon, 2 Jun 2014 18:02:57 -0700
Subject: [PATCH 319/810] [SPARK-1995][SQL] system function upper and lower can
 be supported

I don't know whether it's time to implement system function about string operation in spark sql now.

Author: egraldlo <egraldlo@gmail.com>

Closes #936 from egraldlo/stringoperator and squashes the following commits:

3c6c60a [egraldlo] Add UPPER, LOWER, MAX and MIN into hive parser
ea76d0a [egraldlo] modify the formatting issues
b49f25e [egraldlo] modify the formatting issues
1f0bbb5 [egraldlo] system function upper and lower supported
13d3267 [egraldlo] system function upper and lower supported
---
 .../apache/spark/sql/catalyst/SqlParser.scala |  4 +++
 .../expressions/stringOperations.scala        | 32 +++++++++++++++++++
 .../org/apache/spark/sql/SQLQuerySuite.scala  | 23 +++++++++++++
 .../org/apache/spark/sql/hive/HiveQl.scala    | 12 ++++++-
 4 files changed, 70 insertions(+), 1 deletion(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
index f2b9b2c1a3ad..a404e7441a1b 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
@@ -124,6 +124,8 @@ class SqlParser extends StandardTokenParsers with PackratParsers {
   protected val OVERWRITE = Keyword("OVERWRITE")
   protected val LIKE = Keyword("LIKE")
   protected val RLIKE = Keyword("RLIKE")
+  protected val UPPER = Keyword("UPPER")
+  protected val LOWER = Keyword("LOWER")
   protected val REGEXP = Keyword("REGEXP")
   protected val ORDER = Keyword("ORDER")
   protected val OUTER = Keyword("OUTER")
@@ -329,6 +331,8 @@ class SqlParser extends StandardTokenParsers with PackratParsers {
     AVG ~> "(" ~> expression <~ ")" ^^ { case exp => Average(exp) } |
     MIN ~> "(" ~> expression <~ ")" ^^ { case exp => Min(exp) } |
     MAX ~> "(" ~> expression <~ ")" ^^ { case exp => Max(exp) } |
+    UPPER ~> "(" ~> expression <~ ")" ^^ { case exp => Upper(exp) } |
+    LOWER ~> "(" ~> expression <~ ")" ^^ { case exp => Lower(exp) } |
     IF ~> "(" ~> expression ~ "," ~ expression ~ "," ~ expression <~ ")" ^^ {
       case c ~ "," ~ t ~ "," ~ f => If(c,t,f)
     } |
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
index ddc16ce87b89..dcded0774180 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
@@ -70,6 +70,22 @@ trait StringRegexExpression {
   }
 }
 
+trait CaseConversionExpression {
+  self: UnaryExpression =>
+
+  type EvaluatedType = Any
+
+  def convert(v: String): String
+  
+  def nullable: Boolean = child.nullable
+  def dataType: DataType = StringType
+
+  override def eval(input: Row): Any = {
+    val converted = child.eval(input)
+    convert(converted.toString)
+  }
+}
+
 /**
  * Simple RegEx pattern matching function
  */
@@ -115,3 +131,19 @@ case class RLike(left: Expression, right: Expression)
   override def escape(v: String): String = v
   override def matches(regex: Pattern, str: String): Boolean = regex.matcher(str).find(0)
 }
+
+/**
+ * A function that converts the characters of a string to uppercase.
+ */
+case class Upper(child: Expression) extends UnaryExpression with CaseConversionExpression {
+  
+  override def convert(v: String): String = v.toUpperCase()
+}
+
+/**
+ * A function that converts the characters of a string to lowercase.
+ */
+case class Lower(child: Expression) extends UnaryExpression with CaseConversionExpression {
+  
+  override def convert(v: String): String = v.toLowerCase()
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index 189dccd5253e..95860e6683f6 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -313,4 +313,27 @@ class SQLQuerySuite extends QueryTest {
         (3, "C"),
         (4, "D")))
   }
+  
+  test("system function upper()") {
+    checkAnswer(
+      sql("SELECT n,UPPER(l) FROM lowerCaseData"),
+      Seq(
+        (1, "A"),
+        (2, "B"),
+        (3, "C"),
+        (4, "D")))
+  }
+    
+  test("system function lower()") {
+    checkAnswer(
+      sql("SELECT N,LOWER(L) FROM upperCaseData"),
+      Seq(
+        (1, "a"),
+        (2, "b"),
+        (3, "c"),
+        (4, "d"),
+        (5, "e"),
+        (6, "f")))
+  }  
+  
 }
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
index 93b9057a2381..e8a3ee5535b6 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
@@ -781,6 +781,10 @@ private[hive] object HiveQl {
   val COUNT = "(?i)COUNT".r
   val AVG = "(?i)AVG".r
   val SUM = "(?i)SUM".r
+  val MAX = "(?i)MAX".r
+  val MIN = "(?i)MIN".r
+  val UPPER = "(?i)UPPER".r
+  val LOWER = "(?i)LOWER".r
   val RAND = "(?i)RAND".r
   val AND = "(?i)AND".r
   val OR = "(?i)OR".r
@@ -817,7 +821,13 @@ private[hive] object HiveQl {
     case Token("TOK_FUNCTIONDI", Token(COUNT(), Nil) :: args) => CountDistinct(args.map(nodeToExpr))
     case Token("TOK_FUNCTION", Token(SUM(), Nil) :: arg :: Nil) => Sum(nodeToExpr(arg))
     case Token("TOK_FUNCTIONDI", Token(SUM(), Nil) :: arg :: Nil) => SumDistinct(nodeToExpr(arg))
-
+    case Token("TOK_FUNCTION", Token(MAX(), Nil) :: arg :: Nil) => Max(nodeToExpr(arg))
+    case Token("TOK_FUNCTION", Token(MIN(), Nil) :: arg :: Nil) => Min(nodeToExpr(arg))
+    
+    /* System functions about string operations */
+    case Token("TOK_FUNCTION", Token(UPPER(), Nil) :: arg :: Nil) => Upper(nodeToExpr(arg))
+    case Token("TOK_FUNCTION", Token(LOWER(), Nil) :: arg :: Nil) => Lower(nodeToExpr(arg))
+    
     /* Casts */
     case Token("TOK_FUNCTION", Token("TOK_STRING", Nil) :: arg :: Nil) =>
       Cast(nodeToExpr(arg), StringType)

From 862283e9ccace6824880aa4e161723fb3248d438 Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian.cs.zju@gmail.com>
Date: Mon, 2 Jun 2014 19:20:23 -0700
Subject: [PATCH 320/810] Avoid dynamic dispatching when unwrapping Hive data.

This is a follow up of PR #758.

The `unwrapHiveData` function is now composed statically before actual rows are scanned according to the field object inspector to avoid dynamic dispatching cost.

According to the same micro benchmark used in PR #758, this simple change brings slight performance boost: 2.5% for CSV table and 1% for RCFile table.

```
Optimized version:

CSV: 6870 ms, RCFile: 5687 ms
CSV: 6832 ms, RCFile: 5800 ms
CSV: 6822 ms, RCFile: 5679 ms
CSV: 6704 ms, RCFile: 5758 ms
CSV: 6819 ms, RCFile: 5725 ms

Original version:

CSV: 7042 ms, RCFile: 5667 ms
CSV: 6883 ms, RCFile: 5703 ms
CSV: 7115 ms, RCFile: 5665 ms
CSV: 7020 ms, RCFile: 5981 ms
CSV: 6871 ms, RCFile: 5906 ms
```

Author: Cheng Lian <lian.cs.zju@gmail.com>

Closes #935 from liancheng/staticUnwrapping and squashes the following commits:

c49c70c [Cheng Lian] Avoid dynamic dispatching when unwrapping Hive data.
---
 .../apache/spark/sql/hive/hiveOperators.scala | 28 +++++++++++--------
 .../hive/execution/HiveComparisonTest.scala   |  5 ++--
 2 files changed, 18 insertions(+), 15 deletions(-)

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveOperators.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveOperators.scala
index d263c312960a..29b4b9b006e4 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveOperators.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveOperators.scala
@@ -26,8 +26,7 @@ import org.apache.hadoop.hive.ql.plan.{TableDesc, FileSinkDesc}
 import org.apache.hadoop.hive.serde.serdeConstants
 import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspectorUtils.ObjectInspectorCopyOption
 import org.apache.hadoop.hive.serde2.objectinspector._
-import org.apache.hadoop.hive.serde2.objectinspector.primitive.JavaHiveDecimalObjectInspector
-import org.apache.hadoop.hive.serde2.objectinspector.primitive.JavaHiveVarcharObjectInspector
+import org.apache.hadoop.hive.serde2.objectinspector.primitive._
 import org.apache.hadoop.hive.serde2.typeinfo.TypeInfoUtils
 import org.apache.hadoop.hive.serde2.{ColumnProjectionUtils, Serializer}
 import org.apache.hadoop.io.Writable
@@ -95,29 +94,34 @@ case class HiveTableScan(
     attributes.map { a =>
       val ordinal = relation.partitionKeys.indexOf(a)
       if (ordinal >= 0) {
+        val dataType = relation.partitionKeys(ordinal).dataType
         (_: Any, partitionKeys: Array[String]) => {
-          val value = partitionKeys(ordinal)
-          val dataType = relation.partitionKeys(ordinal).dataType
-          unwrapHiveData(castFromString(value, dataType))
+          castFromString(partitionKeys(ordinal), dataType)
         }
       } else {
         val ref = objectInspector.getAllStructFieldRefs
           .find(_.getFieldName == a.name)
           .getOrElse(sys.error(s"Can't find attribute $a"))
+        val fieldObjectInspector = ref.getFieldObjectInspector
+
+        val unwrapHiveData = fieldObjectInspector match {
+          case _: HiveVarcharObjectInspector =>
+            (value: Any) => value.asInstanceOf[HiveVarchar].getValue
+          case _: HiveDecimalObjectInspector =>
+            (value: Any) => BigDecimal(value.asInstanceOf[HiveDecimal].bigDecimalValue())
+          case _ =>
+            identity[Any] _
+        }
+
         (row: Any, _: Array[String]) => {
           val data = objectInspector.getStructFieldData(row, ref)
-          unwrapHiveData(unwrapData(data, ref.getFieldObjectInspector))
+          val hiveData = unwrapData(data, fieldObjectInspector)
+          if (hiveData != null) unwrapHiveData(hiveData) else null
         }
       }
     }
   }
 
-  private def unwrapHiveData(value: Any) = value match {
-    case varchar: HiveVarchar => varchar.getValue
-    case decimal: HiveDecimal => BigDecimal(decimal.bigDecimalValue)
-    case other => other
-  }
-
   private def castFromString(value: String, dataType: DataType) = {
     Cast(Literal(value), dataType).eval(null)
   }
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala
index 1b5a132f9665..0f954103a85f 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala
@@ -133,15 +133,14 @@ abstract class HiveComparisonTest
     def isSorted(plan: LogicalPlan): Boolean = plan match {
       case _: Join | _: Aggregate | _: BaseRelation | _: Generate | _: Sample | _: Distinct => false
       case PhysicalOperation(_, _, Sort(_, _)) => true
-      case _ => plan.children.iterator.map(isSorted).exists(_ == true)
+      case _ => plan.children.iterator.exists(isSorted)
     }
 
     val orderedAnswer = hiveQuery.logical match {
       // Clean out non-deterministic time schema info.
       case _: NativeCommand => answer.filterNot(nonDeterministicLine).filterNot(_ == "")
       case _: ExplainCommand => answer
-      case plan if isSorted(plan) => answer
-      case _ => answer.sorted
+      case plan => if (isSorted(plan)) answer else answer.sorted
     }
     orderedAnswer.map(cleanPaths)
   }

From 7782a304ad105ec95cf62cb799e365e5fb385a69 Mon Sep 17 00:00:00 2001
From: Syed Hashmi <shashmi@cloudera.com>
Date: Tue, 3 Jun 2014 12:04:47 -0700
Subject: [PATCH 321/810] [SPARK-1942] Stop clearing spark.driver.port in unit
 tests

stop resetting spark.driver.port in unit tests (scala, java and python).

Author: Syed Hashmi <shashmi@cloudera.com>
Author: CodingCat <zhunansjtu@gmail.com>

Closes #943 from syedhashmi/master and squashes the following commits:

885f210 [Syed Hashmi] Removing unnecessary file (created by mergetool)
b8bd4b5 [Syed Hashmi] Merge remote-tracking branch 'upstream/master'
b895e59 [Syed Hashmi] Revert "[SPARK-1784] Add a new partitioner"
57b6587 [Syed Hashmi] Revert "[SPARK-1784] Add a balanced partitioner"
1574769 [Syed Hashmi] [SPARK-1942] Stop clearing spark.driver.port in unit tests
4354836 [Syed Hashmi] Revert "SPARK-1686: keep schedule() calling in the main thread"
fd36542 [Syed Hashmi] [SPARK-1784] Add a balanced partitioner
6668015 [CodingCat] SPARK-1686: keep schedule() calling in the main thread
4ca94cc [Syed Hashmi] [SPARK-1784] Add a new partitioner
---
 bagel/src/test/scala/org/apache/spark/bagel/BagelSuite.scala  | 2 --
 core/src/test/java/org/apache/spark/JavaAPISuite.java         | 3 ---
 core/src/test/scala/org/apache/spark/AkkaUtilsSuite.scala     | 4 ----
 .../test/scala/org/apache/spark/MapOutputTrackerSuite.scala   | 3 ---
 .../scala/org/apache/spark/storage/BlockManagerSuite.scala    | 2 --
 .../src/test/java/org/apache/spark/Java8APISuite.java         | 2 --
 .../scala/org/apache/spark/graphx/LocalSparkContext.scala     | 2 --
 .../mllib/classification/JavaLogisticRegressionSuite.java     | 1 -
 .../spark/mllib/classification/JavaNaiveBayesSuite.java       | 1 -
 .../org/apache/spark/mllib/classification/JavaSVMSuite.java   | 1 -
 .../org/apache/spark/mllib/clustering/JavaKMeansSuite.java    | 1 -
 .../org/apache/spark/mllib/recommendation/JavaALSSuite.java   | 1 -
 .../org/apache/spark/mllib/regression/JavaLassoSuite.java     | 1 -
 .../spark/mllib/regression/JavaLinearRegressionSuite.java     | 1 -
 .../spark/mllib/regression/JavaRidgeRegressionSuite.java      | 1 -
 .../scala/org/apache/spark/mllib/util/LocalSparkContext.scala | 1 -
 python/pyspark/tests.py                                       | 4 ----
 repl/src/test/scala/org/apache/spark/repl/ReplSuite.scala     | 2 --
 .../src/main/scala/org/apache/spark/sql/hive/TestHive.scala   | 1 -
 .../org/apache/spark/streaming/util/MasterFailureTest.scala   | 1 -
 .../org/apache/spark/streaming/LocalJavaStreamingContext.java | 4 ----
 .../scala/org/apache/spark/streaming/CheckpointSuite.scala    | 1 -
 .../test/scala/org/apache/spark/streaming/TestSuiteBase.scala | 2 --
 23 files changed, 42 deletions(-)

diff --git a/bagel/src/test/scala/org/apache/spark/bagel/BagelSuite.scala b/bagel/src/test/scala/org/apache/spark/bagel/BagelSuite.scala
index 8e0f82ddb889..110bd0a9a0c4 100644
--- a/bagel/src/test/scala/org/apache/spark/bagel/BagelSuite.scala
+++ b/bagel/src/test/scala/org/apache/spark/bagel/BagelSuite.scala
@@ -38,8 +38,6 @@ class BagelSuite extends FunSuite with Assertions with BeforeAndAfter with Timeo
       sc.stop()
       sc = null
     }
-    // To avoid Akka rebinding to the same port, since it doesn't unbind immediately on shutdown
-    System.clearProperty("spark.driver.port")
   }
 
   test("halting by voting") {
diff --git a/core/src/test/java/org/apache/spark/JavaAPISuite.java b/core/src/test/java/org/apache/spark/JavaAPISuite.java
index 3dd79243ab5b..7193223addf6 100644
--- a/core/src/test/java/org/apache/spark/JavaAPISuite.java
+++ b/core/src/test/java/org/apache/spark/JavaAPISuite.java
@@ -68,9 +68,6 @@ public void setUp() {
   public void tearDown() {
     sc.stop();
     sc = null;
-    // To avoid Akka rebinding to the same port, since it doesn't unbind immediately on shutdown
-    System.clearProperty("spark.driver.port");
-    Utils.deleteRecursively(tempDir);
   }
 
   static class ReverseIntComparator implements Comparator<Integer>, Serializable {
diff --git a/core/src/test/scala/org/apache/spark/AkkaUtilsSuite.scala b/core/src/test/scala/org/apache/spark/AkkaUtilsSuite.scala
index c645e4cbe813..4ab870e75177 100644
--- a/core/src/test/scala/org/apache/spark/AkkaUtilsSuite.scala
+++ b/core/src/test/scala/org/apache/spark/AkkaUtilsSuite.scala
@@ -39,7 +39,6 @@ class AkkaUtilsSuite extends FunSuite with LocalSparkContext {
     val hostname = "localhost"
     val (actorSystem, boundPort) = AkkaUtils.createActorSystem("spark", hostname, 0,
       conf = conf, securityManager = securityManager)
-    System.setProperty("spark.driver.port", boundPort.toString)    // Will be cleared by LocalSparkContext
     System.setProperty("spark.hostPort", hostname + ":" + boundPort)
     assert(securityManager.isAuthenticationEnabled() === true)
 
@@ -77,7 +76,6 @@ class AkkaUtilsSuite extends FunSuite with LocalSparkContext {
     val hostname = "localhost"
     val (actorSystem, boundPort) = AkkaUtils.createActorSystem("spark", hostname, 0,
       conf = conf, securityManager = securityManager)
-    System.setProperty("spark.driver.port", boundPort.toString)    // Will be cleared by LocalSparkContext
     System.setProperty("spark.hostPort", hostname + ":" + boundPort)
 
     assert(securityManager.isAuthenticationEnabled() === false)
@@ -129,7 +127,6 @@ class AkkaUtilsSuite extends FunSuite with LocalSparkContext {
     val hostname = "localhost"
     val (actorSystem, boundPort) = AkkaUtils.createActorSystem("spark", hostname, 0,
       conf = conf, securityManager = securityManager)
-    System.setProperty("spark.driver.port", boundPort.toString)    // Will be cleared by LocalSparkContext
     System.setProperty("spark.hostPort", hostname + ":" + boundPort)
 
     assert(securityManager.isAuthenticationEnabled() === true)
@@ -182,7 +179,6 @@ class AkkaUtilsSuite extends FunSuite with LocalSparkContext {
     val hostname = "localhost"
     val (actorSystem, boundPort) = AkkaUtils.createActorSystem("spark", hostname, 0,
       conf = conf, securityManager = securityManager)
-    System.setProperty("spark.driver.port", boundPort.toString)    // Will be cleared by LocalSparkContext
     System.setProperty("spark.hostPort", hostname + ":" + boundPort)
 
     assert(securityManager.isAuthenticationEnabled() === true)
diff --git a/core/src/test/scala/org/apache/spark/MapOutputTrackerSuite.scala b/core/src/test/scala/org/apache/spark/MapOutputTrackerSuite.scala
index 6b2571cd9295..95ba273f16a7 100644
--- a/core/src/test/scala/org/apache/spark/MapOutputTrackerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/MapOutputTrackerSuite.scala
@@ -124,9 +124,6 @@ class MapOutputTrackerSuite extends FunSuite with LocalSparkContext {
     val (actorSystem, boundPort) = AkkaUtils.createActorSystem("spark", hostname, 0, conf = conf,
       securityManager = new SecurityManager(conf))
 
-    // Will be cleared by LocalSparkContext
-    System.setProperty("spark.driver.port", boundPort.toString)
-
     val masterTracker = new MapOutputTrackerMaster(conf)
     masterTracker.trackerActor = actorSystem.actorOf(
       Props(new MapOutputTrackerMasterActor(masterTracker, conf)), "MapOutputTracker")
diff --git a/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala b/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala
index 00deecc1c3ca..81bd8257bc15 100644
--- a/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala
@@ -78,8 +78,6 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT
   }
 
   after {
-    System.clearProperty("spark.driver.port")
-
     if (store != null) {
       store.stop()
       store = null
diff --git a/extras/java8-tests/src/test/java/org/apache/spark/Java8APISuite.java b/extras/java8-tests/src/test/java/org/apache/spark/Java8APISuite.java
index 84d3b6f243c7..c366c10b15a2 100644
--- a/extras/java8-tests/src/test/java/org/apache/spark/Java8APISuite.java
+++ b/extras/java8-tests/src/test/java/org/apache/spark/Java8APISuite.java
@@ -58,8 +58,6 @@ public void setUp() {
   public void tearDown() {
     sc.stop();
     sc = null;
-    // To avoid Akka rebinding to the same port, since it doesn't unbind immediately on shutdown
-    System.clearProperty("spark.driver.port");
   }
 
   @Test
diff --git a/graphx/src/test/scala/org/apache/spark/graphx/LocalSparkContext.scala b/graphx/src/test/scala/org/apache/spark/graphx/LocalSparkContext.scala
index 51f02f94e00d..47594a800a3b 100644
--- a/graphx/src/test/scala/org/apache/spark/graphx/LocalSparkContext.scala
+++ b/graphx/src/test/scala/org/apache/spark/graphx/LocalSparkContext.scala
@@ -38,8 +38,6 @@ trait LocalSparkContext {
       f(sc)
     } finally {
       sc.stop()
-      // To avoid Akka rebinding to the same port, since it doesn't unbind immediately on shutdown
-      System.clearProperty("spark.driver.port")
     }
   }
 }
diff --git a/mllib/src/test/java/org/apache/spark/mllib/classification/JavaLogisticRegressionSuite.java b/mllib/src/test/java/org/apache/spark/mllib/classification/JavaLogisticRegressionSuite.java
index d75d3a6b2673..faa675b59cd5 100644
--- a/mllib/src/test/java/org/apache/spark/mllib/classification/JavaLogisticRegressionSuite.java
+++ b/mllib/src/test/java/org/apache/spark/mllib/classification/JavaLogisticRegressionSuite.java
@@ -42,7 +42,6 @@ public void setUp() {
   public void tearDown() {
     sc.stop();
     sc = null;
-    System.clearProperty("spark.driver.port");
   }
 
   int validatePrediction(List<LabeledPoint> validationData, LogisticRegressionModel model) {
diff --git a/mllib/src/test/java/org/apache/spark/mllib/classification/JavaNaiveBayesSuite.java b/mllib/src/test/java/org/apache/spark/mllib/classification/JavaNaiveBayesSuite.java
index 743a43a139c0..1c90522a0714 100644
--- a/mllib/src/test/java/org/apache/spark/mllib/classification/JavaNaiveBayesSuite.java
+++ b/mllib/src/test/java/org/apache/spark/mllib/classification/JavaNaiveBayesSuite.java
@@ -44,7 +44,6 @@ public void setUp() {
   public void tearDown() {
     sc.stop();
     sc = null;
-    System.clearProperty("spark.driver.port");
   }
 
   private static final List<LabeledPoint> POINTS = Arrays.asList(
diff --git a/mllib/src/test/java/org/apache/spark/mllib/classification/JavaSVMSuite.java b/mllib/src/test/java/org/apache/spark/mllib/classification/JavaSVMSuite.java
index 667f76a1bd55..31b9f3e8d438 100644
--- a/mllib/src/test/java/org/apache/spark/mllib/classification/JavaSVMSuite.java
+++ b/mllib/src/test/java/org/apache/spark/mllib/classification/JavaSVMSuite.java
@@ -41,7 +41,6 @@ public void setUp() {
   public void tearDown() {
     sc.stop();
     sc = null;
-    System.clearProperty("spark.driver.port");
   }
 
   int validatePrediction(List<LabeledPoint> validationData, SVMModel model) {
diff --git a/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaKMeansSuite.java b/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaKMeansSuite.java
index 0c916ca37803..31676e64025d 100644
--- a/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaKMeansSuite.java
+++ b/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaKMeansSuite.java
@@ -44,7 +44,6 @@ public void setUp() {
   public void tearDown() {
     sc.stop();
     sc = null;
-    System.clearProperty("spark.driver.port");
   }
 
   @Test
diff --git a/mllib/src/test/java/org/apache/spark/mllib/recommendation/JavaALSSuite.java b/mllib/src/test/java/org/apache/spark/mllib/recommendation/JavaALSSuite.java
index b150334deb06..bf2365f82044 100644
--- a/mllib/src/test/java/org/apache/spark/mllib/recommendation/JavaALSSuite.java
+++ b/mllib/src/test/java/org/apache/spark/mllib/recommendation/JavaALSSuite.java
@@ -42,7 +42,6 @@ public void setUp() {
   public void tearDown() {
     sc.stop();
     sc = null;
-    System.clearProperty("spark.driver.port");
   }
 
   static void validatePrediction(MatrixFactorizationModel model, int users, int products, int features,
diff --git a/mllib/src/test/java/org/apache/spark/mllib/regression/JavaLassoSuite.java b/mllib/src/test/java/org/apache/spark/mllib/regression/JavaLassoSuite.java
index f725924a2d97..8950b48888b7 100644
--- a/mllib/src/test/java/org/apache/spark/mllib/regression/JavaLassoSuite.java
+++ b/mllib/src/test/java/org/apache/spark/mllib/regression/JavaLassoSuite.java
@@ -41,7 +41,6 @@ public void setUp() {
   public void tearDown() {
     sc.stop();
     sc = null;
-    System.clearProperty("spark.driver.port");
   }
 
   int validatePrediction(List<LabeledPoint> validationData, LassoModel model) {
diff --git a/mllib/src/test/java/org/apache/spark/mllib/regression/JavaLinearRegressionSuite.java b/mllib/src/test/java/org/apache/spark/mllib/regression/JavaLinearRegressionSuite.java
index 6dc687769103..24c4c20d9af1 100644
--- a/mllib/src/test/java/org/apache/spark/mllib/regression/JavaLinearRegressionSuite.java
+++ b/mllib/src/test/java/org/apache/spark/mllib/regression/JavaLinearRegressionSuite.java
@@ -43,7 +43,6 @@ public void setUp() {
   public void tearDown() {
     sc.stop();
     sc = null;
-    System.clearProperty("spark.driver.port");
   }
 
   int validatePrediction(List<LabeledPoint> validationData, LinearRegressionModel model) {
diff --git a/mllib/src/test/java/org/apache/spark/mllib/regression/JavaRidgeRegressionSuite.java b/mllib/src/test/java/org/apache/spark/mllib/regression/JavaRidgeRegressionSuite.java
index 03714ae7e4d0..7266eec23580 100644
--- a/mllib/src/test/java/org/apache/spark/mllib/regression/JavaRidgeRegressionSuite.java
+++ b/mllib/src/test/java/org/apache/spark/mllib/regression/JavaRidgeRegressionSuite.java
@@ -43,7 +43,6 @@ public void setUp() {
   public void tearDown() {
       sc.stop();
       sc = null;
-      System.clearProperty("spark.driver.port");
   }
 
   double predictionError(List<LabeledPoint> validationData, RidgeRegressionModel model) {
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/util/LocalSparkContext.scala b/mllib/src/test/scala/org/apache/spark/mllib/util/LocalSparkContext.scala
index 212fbe9288f0..0d4868f3d9e4 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/util/LocalSparkContext.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/util/LocalSparkContext.scala
@@ -34,7 +34,6 @@ trait LocalSparkContext extends BeforeAndAfterAll { self: Suite =>
     if (sc != null) {
       sc.stop()
     }
-    System.clearProperty("spark.driver.port")
     super.afterAll()
   }
 }
diff --git a/python/pyspark/tests.py b/python/pyspark/tests.py
index ed90915fcda3..1f2a6ea941cf 100644
--- a/python/pyspark/tests.py
+++ b/python/pyspark/tests.py
@@ -57,10 +57,6 @@ def setUp(self):
     def tearDown(self):
         self.sc.stop()
         sys.path = self._old_sys_path
-        # To avoid Akka rebinding to the same port, since it doesn't unbind
-        # immediately on shutdown
-        self.sc._jvm.System.clearProperty("spark.driver.port")
-
 
 class TestCheckpoint(PySparkTestCase):
 
diff --git a/repl/src/test/scala/org/apache/spark/repl/ReplSuite.scala b/repl/src/test/scala/org/apache/spark/repl/ReplSuite.scala
index 95460aa20533..98cdfd005471 100644
--- a/repl/src/test/scala/org/apache/spark/repl/ReplSuite.scala
+++ b/repl/src/test/scala/org/apache/spark/repl/ReplSuite.scala
@@ -51,8 +51,6 @@ class ReplSuite extends FunSuite {
     if (interp.sparkContext != null) {
       interp.sparkContext.stop()
     }
-    // To avoid Akka rebinding to the same port, since it doesn't unbind immediately on shutdown
-    System.clearProperty("spark.driver.port")
     return out.toString
   }
 
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/TestHive.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/TestHive.scala
index fa7d010459c6..041e813598d1 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/TestHive.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/TestHive.scala
@@ -58,7 +58,6 @@ class TestHiveContext(sc: SparkContext) extends LocalHiveContext(sc) {
 
   // By clearing the port we force Spark to pick a new one.  This allows us to rerun tests
   // without restarting the JVM.
-  System.clearProperty("spark.driver.port")
   System.clearProperty("spark.hostPort")
 
   override lazy val warehousePath = getTempFilePath("sparkHiveWarehouse").getCanonicalPath
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/util/MasterFailureTest.scala b/streaming/src/main/scala/org/apache/spark/streaming/util/MasterFailureTest.scala
index b3ed302db6a3..98e17ff92e20 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/util/MasterFailureTest.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/util/MasterFailureTest.scala
@@ -187,7 +187,6 @@ object MasterFailureTest extends Logging {
     setupCalled = true
 
     // Setup the streaming computation with the given operation
-    System.clearProperty("spark.driver.port")
     val ssc = new StreamingContext("local[4]", "MasterFailureTest", batchDuration, null, Nil,
       Map())
     ssc.checkpoint(checkpointDir.toString)
diff --git a/streaming/src/test/java/org/apache/spark/streaming/LocalJavaStreamingContext.java b/streaming/src/test/java/org/apache/spark/streaming/LocalJavaStreamingContext.java
index 849bbf129918..6e1f01900071 100644
--- a/streaming/src/test/java/org/apache/spark/streaming/LocalJavaStreamingContext.java
+++ b/streaming/src/test/java/org/apache/spark/streaming/LocalJavaStreamingContext.java
@@ -27,7 +27,6 @@ public abstract class LocalJavaStreamingContext {
 
     @Before
     public void setUp() {
-        System.clearProperty("spark.driver.port");
         System.setProperty("spark.streaming.clock", "org.apache.spark.streaming.util.ManualClock");
         ssc = new JavaStreamingContext("local[2]", "test", new Duration(1000));
         ssc.checkpoint("checkpoint");
@@ -37,8 +36,5 @@ public void setUp() {
     public void tearDown() {
         ssc.stop();
         ssc = null;
-
-        // To avoid Akka rebinding to the same port, since it doesn't unbind immediately on shutdown
-        System.clearProperty("spark.driver.port");
     }
 }
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala
index d20a7b728c74..10ad3c9e1adc 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala
@@ -370,7 +370,6 @@ class CheckpointSuite extends TestSuiteBase {
       "\n-------------------------------------------\n"
     )
     ssc = new StreamingContext(checkpointDir)
-    System.clearProperty("spark.driver.port")
     ssc.start()
     val outputNew = advanceTimeWithRealDelay[V](ssc, nextNumBatches)
     // the first element will be re-processed data of the last batch before restart
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/TestSuiteBase.scala b/streaming/src/test/scala/org/apache/spark/streaming/TestSuiteBase.scala
index 8036f77c973a..cc178fba12c9 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/TestSuiteBase.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/TestSuiteBase.scala
@@ -153,8 +153,6 @@ trait TestSuiteBase extends FunSuite with BeforeAndAfter with Logging {
   // Default after function for any streaming test suite. Override this
   // if you want to add your stuff to "after" (i.e., don't call after { } )
   def afterFunction() {
-    // To avoid Akka rebinding to the same port, since it doesn't unbind immediately on shutdown
-    System.clearProperty("spark.driver.port")
     System.clearProperty("spark.streaming.clock")
   }
 

From 6c044ed100081f1fa7c8df4bd4b58b65a61c3360 Mon Sep 17 00:00:00 2001
From: Henry Saputra <henry.saputra@gmail.com>
Date: Tue, 3 Jun 2014 13:03:51 -0700
Subject: [PATCH 322/810] SPARK-2001 : Remove docs/spark-debugger.md from
 master

Per discussion in dev list:
"
Seemed like the spark-debugger.md is no longer accurate (see
http://spark.apache.org/docs/latest/spark-debugger.html) and since it
was originally written Spark has evolved that makes the doc obsolete.
There are already work pending for new replay debugging (I could not
find the PR links for it) so I
With version control we could always reinstate the old doc if needed,
but as of today the doc is no longer reflect the current state of
Spark's RDD.
"

Author: Henry Saputra <henry.saputra@gmail.com>

Closes #953 from hsaputra/SPARK-2001-hsaputra and squashes the following commits:

dc324aa [Henry Saputra] SPARK-2001 : Remove docs/spark-debugger.md from master since it is obsolete
---
 docs/spark-debugger.md | 121 -----------------------------------------
 1 file changed, 121 deletions(-)
 delete mode 100644 docs/spark-debugger.md

diff --git a/docs/spark-debugger.md b/docs/spark-debugger.md
deleted file mode 100644
index 35d06c51aaf0..000000000000
--- a/docs/spark-debugger.md
+++ /dev/null
@@ -1,121 +0,0 @@
----
-layout: global
-title: The Spark Debugger
----
-**Summary:** The Spark debugger provides replay debugging for deterministic (logic) errors in Spark programs. It's currently in development, but you can try it out in the [arthur branch](https://github.com/apache/spark/tree/arthur).
-
-## Introduction
-
-From a user's point of view, debugging a general distributed program can be tedious and confusing. Many distributed programs are nondeterministic; their outcome depends on the interleaving between computation and message passing across multiple machines. Also, the fact that a program is running on a cluster of hundreds or thousands of machines means that it's hard to understand the program state and pinpoint the location of problems.
-
-In order to tame nondeterminism, a distributed debugger has to log a lot of information, imposing a serious performance penalty on the application being debugged.
-
-But the Spark programming model lets us provide replay debugging for almost zero overhead. Spark programs are a series of RDDs and deterministic transformations, so when debugging a Spark program, we don't have to debug it all at once -- instead, we can debug each transformation individually. Broadly, the debugger lets us do the following two things:
-
-* Recompute and inspect intermediate RDDs after the program has finished.
-* Re-run a particular task in a single-threaded debugger to find exactly what went wrong.
-
-For deterministic errors, debugging a Spark program is now as easy as debugging a single-threaded one.
-
-## Approach
-
-As your Spark program runs, the slaves report key events back to the master -- for example, RDD creations, RDD contents, and uncaught exceptions. (A full list of event types is in [EventLogging.scala](https://github.com/apache/spark/blob/arthur/core/src/main/scala/spark/EventLogging.scala).) The master logs those events, and you can load the event log into the debugger after your program is done running.
-
-_A note on nondeterminism:_ For fault recovery, Spark requires RDD transformations (for example, the function passed to `RDD.map`) to be deterministic. The Spark debugger also relies on this property, and it can also warn you if your transformation is nondeterministic. This works by checksumming the contents of each RDD and comparing the checksums from the original execution to the checksums after recomputing the RDD in the debugger.
-
-## Usage
-
-### Enabling the event log
-
-To turn on event logging for your program, set `$SPARK_JAVA_OPTS` in `conf/spark-env.sh` as follows:
-
-{% highlight bash %}
-export SPARK_JAVA_OPTS='-Dspark.arthur.logPath=path/to/event-log'
-{% endhighlight %}
-   
-where `path/to/event-log` is where you want the event log to go relative to `$SPARK_HOME`.
-
-**Warning:** If `path/to/event-log` already exists, event logging will be automatically disabled.
-
-### Loading the event log into the debugger
-
-1. Run a Spark shell with `./bin/spark-shell --master <i>hist</i>`.
-2. Use `EventLogReader` to load the event log as follows:
-    {% highlight scala %}
-spark> val r = new spark.EventLogReader(sc, Some("path/to/event-log"))
-r: spark.EventLogReader = spark.EventLogReader@726b37ad
-{% endhighlight %}
-
-    **Warning:** If the event log doesn't exist or is unreadable, this will silently fail and `r.events` will be empty.
-
-### Exploring intermediate RDDs
-
-Use `r.rdds` to get a list of intermediate RDDs generated during your program's execution. An RDD with id _x_ is located at <code>r.rdds(<i>x</i>)</code>. For example:
-
-{% highlight scala %}
-scala> r.rdds
-res8: scala.collection.mutable.ArrayBuffer[spark.RDD[_]] = ArrayBuffer(spark.HadoopRDD@fe85adf, spark.MappedRDD@5fa5eea1, spark.MappedRDD@6d5bd16, spark.ShuffledRDD@3a70f2db, spark.FlatMappedValuesRDD@4d5825d6, spark.MappedValuesRDD@561c2c45, spark.CoGroupedRDD@539e922d, spark.MappedValuesRDD@4f8ef33e, spark.FlatMappedRDD@32039440, spark.ShuffledRDD@8fa0f67, spark.MappedValuesRDD@590937cb, spark.CoGroupedRDD@6c2e1e17, spark.MappedValuesRDD@47b9af7d, spark.FlatMappedRDD@6fb05c54, spark.ShuffledRDD@237dc815, spark.MappedValuesRDD@16daece7, spark.CoGroupedRDD@7ef73d69, spark.MappedValuesRDD@19e0f99e, spark.FlatMappedRDD@1240158, spark.ShuffledRDD@62d438fd, spark.MappedValuesRDD@5ae99cbb, spark.FilteredRDD@1f30e79e, spark.MappedRDD@43b64611)
-{% endhighlight %}
-
-Use `r.printRDDs()` to get a formatted list of intermediate RDDs, along with the source location where they were created. For example:
-
-{% highlight scala %}
-scala> r.printRDDs
-#00: HadoopRDD            spark.bagel.examples.WikipediaPageRankStandalone$.main(WikipediaPageRankStandalone.scala:31)
-#01: MappedRDD            spark.bagel.examples.WikipediaPageRankStandalone$.main(WikipediaPageRankStandalone.scala:31)
-#02: MappedRDD            spark.bagel.examples.WikipediaPageRankStandalone$.main(WikipediaPageRankStandalone.scala:35)
-#03: ShuffledRDD          spark.bagel.examples.WikipediaPageRankStandalone$.main(WikipediaPageRankStandalone.scala:35)
-#04: FlatMappedValuesRDD  spark.bagel.examples.WikipediaPageRankStandalone$.main(WikipediaPageRankStandalone.scala:35)
-#05: MappedValuesRDD      spark.bagel.examples.WikipediaPageRankStandalone$.pageRank(WikipediaPageRankStandalone.scala:91)
-#06: CoGroupedRDD         spark.bagel.examples.WikipediaPageRankStandalone$.pageRank(WikipediaPageRankStandalone.scala:92)
-[...]
-{% endhighlight %}
-
-Use `r.visualizeRDDs()` to visualize the RDDs as a dependency graph. For example:
-
-{% highlight scala %}
-scala> r.visualizeRDDs
-/tmp/spark-rdds-3758182885839775712.pdf
-{% endhighlight %}
-
-![Example RDD dependency graph](http://www.ankurdave.com/images/rdd-dep-graph.png)
-
-Iterate over the `RDDCreation` entries in `r.events` (e.g. `for (RDDCreation(rdd, location) <- events)`) to access the RDD creation locations as well as the RDDs themselves.
-
-### Debugging a particular task
-
-1. Find the task you want to debug. If the task threw an exception, the `ExceptionEvent` that was created will have a reference to the task. For example:
-    {% highlight scala %}
-spark> val task = r.events.collect { case e: ExceptionEvent => e }.head.task
-{% endhighlight %}
-    Otherwise, look through the list of all tasks in `r.tasks`, or browse tasks by RDD using <code>r.tasksForRDD(<i>rdd</i>)</code>, which returns a list of tasks whose input is the given RDD.
-
-2. Run the task by calling <code>r.debugTask(<i>taskStageId</i>, <i>taskPartition</i>)</code>. The task should contain these two values; you can extract them as follows:
-    {% highlight scala %}
-val (taskStageId, taskPartition) = task match {
-    case rt: ResultTask[_, _] => (rt.stageId, rt.partition)
-    case smt: ShuffleMapTask => (smt.stageId, smt.partition)
-    case _ => throw new UnsupportedOperationException
-})
-{% endhighlight %}
-    The Spark debugger will launch the task in a separate JVM, but you will see the task's stdout and stderr inline with the Spark shell. If you want to pass custom debugging arguments to the task's JVM (for example, to change the debugging port), set the optional `debugOpts` argument to `r.debugTask`. When `debugOpts` is left unset, it defaults to:
-    {% highlight scala %}
--Xdebug -agentlib:jdwp=transport=dt_socket,server=y,suspend=y,address=8000
-{% endhighlight %}
-
-3. In another terminal, attach your favorite conventional debugger to the Spark shell. For example, if you want to use jdb, run `jdb -attach 8000`.
-
-4. Debug the task as you would debug a normal program. For example, to break when an exception is thrown:
-    {% highlight scala %}
-> catch org.xml.sax.SAXParseException
-{% endhighlight %}
-
-5. When the task ends, its JVM will quit and control will return to the main Spark shell. To stop it prematurely, you can kill it from the debugger, or interrupt it from the terminal with Ctrl-C.
-
-### Detecting nondeterminism in your transformations
-
-When a task gets run more than once, Arthur is able to compare the checksums of the task's output. If they are different, Arthur will insert a `ChecksumEvent` into  `r.checksumMismatches` and print a warning like the following:
-    {% highlight scala %}
-12/04/07 11:42:44 WARN spark.EventLogWriter: Nondeterminism detected in shuffle output on RDD 2, partition 3, output split 0
-{% endhighlight %}
-

From 45e9bc85db231e84a23b8d757136023eabcec13e Mon Sep 17 00:00:00 2001
From: "Wenchen Fan(Cloud)" <cloud0fan@gmail.com>
Date: Tue, 3 Jun 2014 13:18:20 -0700
Subject: [PATCH 323/810] [SPARK-1912] fix compress memory issue during reduce

When we need to read a compressed block, we will first create a compress stream instance(LZF or Snappy) and use it to wrap that block.
Let's say a reducer task need to read 1000 local shuffle blocks, it will first prepare to read that 1000 blocks, which means create 1000 compression stream instance to wrap them. But the initialization of compression instance will allocate some memory and when we have many compression instance at the same time, it is a problem.
Actually reducer reads the shuffle blocks one by one, so we can do the compression instance initialization lazily.

Author: Wenchen Fan(Cloud) <cloud0fan@gmail.com>

Closes #860 from cloud-fan/fix-compress and squashes the following commits:

0924a6b [Wenchen Fan(Cloud)] rename 'doWork' into 'getIterator'
07f32c2 [Wenchen Fan(Cloud)] move the LazyProxyIterator to dataDeserialize
d80c426 [Wenchen Fan(Cloud)] remove empty lines in short class
2c8adb2 [Wenchen Fan(Cloud)] add inline comment
8ebff77 [Wenchen Fan(Cloud)] fix compress memory issue during reduce
---
 .../apache/spark/storage/BlockManager.scala   | 22 +++++++++++++++++--
 1 file changed, 20 insertions(+), 2 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala
index 6e450081dcb1..a41286d3e4a0 100644
--- a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala
+++ b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala
@@ -1015,8 +1015,26 @@ private[spark] class BlockManager(
       bytes: ByteBuffer,
       serializer: Serializer = defaultSerializer): Iterator[Any] = {
     bytes.rewind()
-    val stream = wrapForCompression(blockId, new ByteBufferInputStream(bytes, true))
-    serializer.newInstance().deserializeStream(stream).asIterator
+
+    def getIterator = {
+      val stream = wrapForCompression(blockId, new ByteBufferInputStream(bytes, true))
+      serializer.newInstance().deserializeStream(stream).asIterator
+    }
+
+    if (blockId.isShuffle) {
+      // Reducer may need to read many local shuffle blocks and will wrap them into Iterators
+      // at the beginning. The wrapping will cost some memory (compression instance
+      // initialization, etc.). Reducer read shuffle blocks one by one so we could do the
+      // wrapping lazily to save memory.
+      class LazyProxyIterator(f: => Iterator[Any]) extends Iterator[Any] {
+        lazy val proxy = f
+        override def hasNext: Boolean = proxy.hasNext
+        override def next(): Any = proxy.next()
+      }
+      new LazyProxyIterator(getIterator)
+    } else {
+      getIterator
+    }
   }
 
   def stop() {

From b1f285359a12345d94ae59f979d9a75482e732ce Mon Sep 17 00:00:00 2001
From: tzolov <christian.tzolov@gmail.com>
Date: Tue, 3 Jun 2014 13:26:29 -0700
Subject: [PATCH 324/810] Add support for Pivotal HD in the Maven build:
 SPARK-1992

Allow Spark to build against particular Pivotal HD distributions. For example to build Spark against Pivotal HD 2.0.1 one can run:
```
mvn -Pyarn -Phadoop-2.2 -Dhadoop.version=2.2.0-gphd-3.0.1.0 -DskipTests clean package
```

Author: tzolov <christian.tzolov@gmail.com>

Closes #942 from tzolov/master and squashes the following commits:

bc3e05a [tzolov] Add support for Pivotal HD in the Maven build and SBT build: [SPARK-1992]
---
 pom.xml                  | 11 +++++++++++
 project/SparkBuild.scala |  1 +
 2 files changed, 12 insertions(+)

diff --git a/pom.xml b/pom.xml
index fe43a9518b52..0a5ca9e72a29 100644
--- a/pom.xml
+++ b/pom.xml
@@ -206,6 +206,17 @@
         <enabled>false</enabled>
       </snapshots>
     </repository>
+    <repository>
+      <id>spring-releases</id>
+      <name>Spring Release Repository</name>
+      <url>http://repo.spring.io/libs-release</url> 
+      <releases>
+        <enabled>true</enabled>
+      </releases>
+      <snapshots>
+        <enabled>false</enabled>
+      </snapshots>
+    </repository> 
   </repositories>
 
   <dependencyManagement>
diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index 64c9441d8e3f..c2a20d86b2e6 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -212,6 +212,7 @@ object SparkBuild extends Build {
       "JBoss Repository"     at "https://repository.jboss.org/nexus/content/repositories/releases/",
       "MQTT Repository"      at "https://repo.eclipse.org/content/repositories/paho-releases/",
       "Cloudera Repository"  at "http://repository.cloudera.com/artifactory/cloudera-repos/",
+      "Pivotal Repository"   at "http://repo.spring.io/libs-release/",
       // For Sonatype publishing
       // "sonatype-snapshots"   at "https://oss.sonatype.org/content/repositories/snapshots",
       // "sonatype-staging"     at "https://oss.sonatype.org/service/local/staging/deploy/maven2/",

From 8edc9d0330c94b50e01956ae88693cff4e0977b2 Mon Sep 17 00:00:00 2001
From: Erik Selin <erik.selin@jadedpixel.com>
Date: Tue, 3 Jun 2014 13:31:16 -0700
Subject: [PATCH 325/810] [SPARK-1468] Modify the partition function used by
 partitionBy.

Make partitionBy use a tweaked version of hash as its default partition function
since the python hash function does not consistently assign the same value
to None across python processes.

Associated JIRA at https://issues.apache.org/jira/browse/SPARK-1468

Author: Erik Selin <erik.selin@jadedpixel.com>

Closes #371 from tyro89/consistent_hashing and squashes the following commits:

201c301 [Erik Selin] Make partitionBy use a tweaked version of hash as its default partition function since the python hash function does not consistently assign the same value to None across python processes.
---
 python/pyspark/rdd.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py
index f3b1f1a665e5..1b3c460dd621 100644
--- a/python/pyspark/rdd.py
+++ b/python/pyspark/rdd.py
@@ -1062,7 +1062,7 @@ def rightOuterJoin(self, other, numPartitions=None):
         return python_right_outer_join(self, other, numPartitions)
 
     # TODO: add option to control map-side combining
-    def partitionBy(self, numPartitions, partitionFunc=hash):
+    def partitionBy(self, numPartitions, partitionFunc=None):
         """
         Return a copy of the RDD partitioned using the specified partitioner.
 
@@ -1073,6 +1073,9 @@ def partitionBy(self, numPartitions, partitionFunc=hash):
         """
         if numPartitions is None:
             numPartitions = self.ctx.defaultParallelism
+
+        if partitionFunc is None:
+            partitionFunc = lambda x: 0 if x is None else hash(x)
         # Transferring O(n) objects to Java is too expensive.  Instead, we'll
         # form the hash buckets in Python, transferring O(numPartitions) objects
         # to Java.  Each object is a (splitNumber, [objects]) pair.

From aa41a522d821c989c65fa3f7f2a4d372e39bb958 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?baishuo=28=E7=99=BD=E7=A1=95=29?= <vc_java@hotmail.com>
Date: Tue, 3 Jun 2014 13:39:47 -0700
Subject: [PATCH 326/810] fix java.lang.ClassCastException
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

get Exception when run：bin/run-example org.apache.spark.examples.sql.RDDRelation
Exception's detail is:
Exception in thread "main" java.lang.ClassCastException: java.lang.Long cannot be cast to java.lang.Integer
	at scala.runtime.BoxesRunTime.unboxToInt(BoxesRunTime.java:106)
	at org.apache.spark.sql.catalyst.expressions.GenericRow.getInt(Row.scala:145)
	at org.apache.spark.examples.sql.RDDRelation$.main(RDDRelation.scala:49)
	at org.apache.spark.examples.sql.RDDRelation.main(RDDRelation.scala)
change sql("SELECT COUNT(*) FROM records").collect().head.getInt(0) to sql("SELECT COUNT(*) FROM records").collect().head.getLong(0), then the Exception do not occur any more

Author: baishuo(白硕) <vc_java@hotmail.com>

Closes #949 from baishuo/master and squashes the following commits:

f4b319f [baishuo(白硕)] fix java.lang.ClassCastException
---
 .../main/scala/org/apache/spark/examples/sql/RDDRelation.scala  | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/src/main/scala/org/apache/spark/examples/sql/RDDRelation.scala b/examples/src/main/scala/org/apache/spark/examples/sql/RDDRelation.scala
index 61c460c6b1de..63db688bfb8c 100644
--- a/examples/src/main/scala/org/apache/spark/examples/sql/RDDRelation.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/sql/RDDRelation.scala
@@ -43,7 +43,7 @@ object RDDRelation {
     sql("SELECT * FROM records").collect().foreach(println)
 
     // Aggregation queries are also supported.
-    val count = sql("SELECT COUNT(*) FROM records").collect().head.getInt(0)
+    val count = sql("SELECT COUNT(*) FROM records").collect().head.getLong(0)
     println(s"COUNT(*): $count")
 
     // The results of SQL queries are themselves RDDs and support all normal RDD functions.  The

From 894ecde04faa7e2054a40825a58b2e9cdaa93c70 Mon Sep 17 00:00:00 2001
From: "Joseph E. Gonzalez" <joseph.e.gonzalez@gmail.com>
Date: Tue, 3 Jun 2014 14:14:48 -0700
Subject: [PATCH 327/810] Synthetic GraphX Benchmark

This PR accomplishes two things:

1. It introduces a Synthetic Benchmark application that generates an arbitrarily large log-normal graph and executes either PageRank or connected components on the graph.  This can be used to profile GraphX system on arbitrary clusters without access to large graph datasets

2. This PR improves the implementation of the log-normal graph generator.

Author: Joseph E. Gonzalez <joseph.e.gonzalez@gmail.com>
Author: Ankur Dave <ankurdave@gmail.com>

Closes #720 from jegonzal/graphx_synth_benchmark and squashes the following commits:

e40812a [Ankur Dave] Exclude all of GraphX from compatibility checks vs. 1.0.0
bccccad [Ankur Dave] Fix long lines
374678a [Ankur Dave] Bugfix and style changes
1bdf39a [Joseph E. Gonzalez] updating options
d943972 [Joseph E. Gonzalez] moving the benchmark application into the examples folder.
f4f839a [Joseph E. Gonzalez] Creating a synthetic benchmark script.
---
 .../examples/graphx/SynthBenchmark.scala      | 128 ++++++++++++++++++
 .../spark/graphx/PartitionStrategy.scala      |   9 ++
 .../spark/graphx/util/GraphGenerators.scala   |  41 ++++--
 project/MimaExcludes.scala                    |   4 +-
 4 files changed, 171 insertions(+), 11 deletions(-)
 create mode 100644 examples/src/main/scala/org/apache/spark/examples/graphx/SynthBenchmark.scala

diff --git a/examples/src/main/scala/org/apache/spark/examples/graphx/SynthBenchmark.scala b/examples/src/main/scala/org/apache/spark/examples/graphx/SynthBenchmark.scala
new file mode 100644
index 000000000000..551c339b1952
--- /dev/null
+++ b/examples/src/main/scala/org/apache/spark/examples/graphx/SynthBenchmark.scala
@@ -0,0 +1,128 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.examples.graphx
+
+import org.apache.spark.SparkContext._
+import org.apache.spark.graphx.PartitionStrategy
+import org.apache.spark.{SparkContext, SparkConf}
+import org.apache.spark.graphx.util.GraphGenerators
+import java.io.{PrintWriter, FileOutputStream}
+
+/**
+ * The SynthBenchmark application can be used to run various GraphX algorithms on
+ * synthetic log-normal graphs.  The intent of this code is to enable users to
+ * profile the GraphX system without access to large graph datasets.
+ */
+object SynthBenchmark {
+
+  /**
+   * To run this program use the following:
+   *
+   * MASTER=spark://foobar bin/run-example graphx.SynthBenchmark -app=pagerank
+   *
+   * Options:
+   *   -app "pagerank" or "cc" for pagerank or connected components. (Default: pagerank)
+   *   -niters the number of iterations of pagerank to use (Default: 10)
+   *   -numVertices the number of vertices in the graph (Default: 1000000)
+   *   -numEPart the number of edge partitions in the graph (Default: number of cores)
+   *   -partStrategy the graph partitioning strategy to use
+   *   -mu the mean parameter for the log-normal graph (Default: 4.0)
+   *   -sigma the stdev parameter for the log-normal graph (Default: 1.3)
+   *   -degFile the local file to save the degree information (Default: Empty)
+   */
+  def main(args: Array[String]) {
+    val options = args.map {
+      arg =>
+        arg.dropWhile(_ == '-').split('=') match {
+          case Array(opt, v) => (opt -> v)
+          case _ => throw new IllegalArgumentException("Invalid argument: " + arg)
+        }
+    }
+
+    var app = "pagerank"
+    var niter = 10
+    var numVertices = 100000
+    var numEPart: Option[Int] = None
+    var partitionStrategy: Option[PartitionStrategy] = None
+    var mu: Double = 4.0
+    var sigma: Double = 1.3
+    var degFile: String = ""
+
+    options.foreach {
+      case ("app", v) => app = v
+      case ("niter", v) => niter = v.toInt
+      case ("nverts", v) => numVertices = v.toInt
+      case ("numEPart", v) => numEPart = Some(v.toInt)
+      case ("partStrategy", v) => partitionStrategy = Some(PartitionStrategy.fromString(v))
+      case ("mu", v) => mu = v.toDouble
+      case ("sigma", v) => sigma = v.toDouble
+      case ("degFile", v) => degFile = v
+      case (opt, _) => throw new IllegalArgumentException("Invalid option: " + opt)
+    }
+
+    val conf = new SparkConf()
+      .setAppName(s"GraphX Synth Benchmark (nverts = $numVertices, app = $app)")
+      .set("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
+      .set("spark.kryo.registrator", "org.apache.spark.graphx.GraphKryoRegistrator")
+
+    val sc = new SparkContext(conf)
+
+    // Create the graph
+    println(s"Creating graph...")
+    val unpartitionedGraph = GraphGenerators.logNormalGraph(sc, numVertices,
+      numEPart.getOrElse(sc.defaultParallelism), mu, sigma)
+    // Repartition the graph
+    val graph = partitionStrategy.foldLeft(unpartitionedGraph)(_.partitionBy(_)).cache()
+
+    var startTime = System.currentTimeMillis()
+    val numEdges = graph.edges.count()
+    println(s"Done creating graph. Num Vertices = $numVertices, Num Edges = $numEdges")
+    val loadTime = System.currentTimeMillis() - startTime
+
+    // Collect the degree distribution (if desired)
+    if (!degFile.isEmpty) {
+      val fos = new FileOutputStream(degFile)
+      val pos = new PrintWriter(fos)
+      val hist = graph.vertices.leftJoin(graph.degrees)((id, _, optDeg) => optDeg.getOrElse(0))
+        .map(p => p._2).countByValue()
+      hist.foreach {
+        case (deg, count) => pos.println(s"$deg \t $count")
+      }
+    }
+
+    // Run PageRank
+    startTime = System.currentTimeMillis()
+    if (app == "pagerank") {
+      println("Running PageRank")
+      val totalPR = graph.staticPageRank(niter).vertices.map(_._2).sum()
+      println(s"Total PageRank = $totalPR")
+    } else if (app == "cc") {
+      println("Running Connected Components")
+      val numComponents = graph.connectedComponents.vertices.map(_._2).distinct()
+      println(s"Number of components = $numComponents")
+    }
+    val runTime = System.currentTimeMillis() - startTime
+
+    println(s"Num Vertices = $numVertices")
+    println(s"Num Edges = $numEdges")
+    println(s"Creation time = ${loadTime/1000.0} seconds")
+    println(s"Run time = ${runTime/1000.0} seconds")
+
+    sc.stop()
+  }
+}
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/PartitionStrategy.scala b/graphx/src/main/scala/org/apache/spark/graphx/PartitionStrategy.scala
index 1526ccef06fd..ef412cfd4e6e 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/PartitionStrategy.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/PartitionStrategy.scala
@@ -119,4 +119,13 @@ object PartitionStrategy {
       math.abs((lower, higher).hashCode()) % numParts
     }
   }
+
+  /** Returns the PartitionStrategy with the specified name. */
+  def fromString(s: String): PartitionStrategy = s match {
+    case "RandomVertexCut" => RandomVertexCut
+    case "EdgePartition1D" => EdgePartition1D
+    case "EdgePartition2D" => EdgePartition2D
+    case "CanonicalRandomVertexCut" => CanonicalRandomVertexCut
+    case _ => throw new IllegalArgumentException("Invalid PartitionStrategy: " + s)
+  }
 }
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/util/GraphGenerators.scala b/graphx/src/main/scala/org/apache/spark/graphx/util/GraphGenerators.scala
index a3c8de3f9068..635514f09ece 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/util/GraphGenerators.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/util/GraphGenerators.scala
@@ -38,19 +38,42 @@ object GraphGenerators {
   val RMATa = 0.45
   val RMATb = 0.15
   val RMATd = 0.25
+
   /**
    * Generate a graph whose vertex out degree is log normal.
+   *
+   * The default values for mu and sigma are taken from the Pregel paper:
+   *
+   * Grzegorz Malewicz, Matthew H. Austern, Aart J.C Bik, James C. Dehnert,
+   * Ilan Horn, Naty Leiser, and Grzegorz Czajkowski. 2010.
+   * Pregel: a system for large-scale graph processing. SIGMOD '10.
+   *
+   * @param sc
+   * @param numVertices
+   * @param mu
+   * @param sigma
+   * @return
    */
-  def logNormalGraph(sc: SparkContext, numVertices: Int): Graph[Int, Int] = {
-    // based on Pregel settings
-    val mu = 4
-    val sigma = 1.3
-
-    val vertices: RDD[(VertexId, Int)] = sc.parallelize(0 until numVertices).map{
-      src => (src, sampleLogNormal(mu, sigma, numVertices))
+  def logNormalGraph(sc: SparkContext, numVertices: Int, numEParts: Int,
+                     mu: Double = 4.0, sigma: Double = 1.3): Graph[Long, Int] = {
+    val vertices = sc.parallelize(0 until numVertices, numEParts).map { src =>
+      // Initialize the random number generator with the source vertex id
+      val rand = new Random(src)
+      val degree = math.min(numVertices.toLong, math.exp(rand.nextGaussian() * sigma + mu).toLong)
+      (src.toLong, degree)
     }
-    val edges = vertices.flatMap { v =>
-      generateRandomEdges(v._1.toInt, v._2, numVertices)
+    val edges = vertices.flatMap { case (src, degree) =>
+      new Iterator[Edge[Int]] {
+        // Initialize the random number generator with the source vertex id
+        val rand = new Random(src)
+        var i = 0
+        override def hasNext(): Boolean = { i < degree }
+        override def next(): Edge[Int] = {
+          val nextEdge = Edge[Int](src, rand.nextInt(numVertices), i)
+          i += 1
+          nextEdge
+        }
+      }
     }
     Graph(vertices, edges, 0)
   }
diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala
index ecb389de5558..fc9cbeaec647 100644
--- a/project/MimaExcludes.scala
+++ b/project/MimaExcludes.scala
@@ -35,7 +35,8 @@ object MimaExcludes {
     val excludes =
       SparkBuild.SPARK_VERSION match {
         case v if v.startsWith("1.1") =>
-          Seq()
+          Seq(
+            MimaBuild.excludeSparkPackage("graphx"))
         case v if v.startsWith("1.0") =>
           Seq(
             MimaBuild.excludeSparkPackage("api.java"),
@@ -58,4 +59,3 @@ object MimaExcludes {
         case _ => Seq()
       }
 }
-

From b1feb60209174433262de2a26d39616ba00edcc8 Mon Sep 17 00:00:00 2001
From: Ankur Dave <ankurdave@gmail.com>
Date: Tue, 3 Jun 2014 14:54:26 -0700
Subject: [PATCH 328/810] [SPARK-1991] Support custom storage levels for
 vertices and edges

This PR adds support for specifying custom storage levels for the vertices and edges of a graph. This enables GraphX to handle graphs larger than memory size by specifying MEMORY_AND_DISK and then repartitioning the graph to use many small partitions, each of which does fit in memory. Spark will then automatically load partitions from disk as needed.

The user specifies the desired vertex and edge storage levels when building the graph by passing them to the graph constructor. These are then stored in the `targetStorageLevel` attribute of the VertexRDD and EdgeRDD respectively. Whenever GraphX needs to cache a VertexRDD or EdgeRDD (because it plans to use it more than once, for example), it uses the specified target storage level. Also, when the user calls `Graph#cache()`, the vertices and edges are persisted using their target storage levels.

In order to facilitate propagating the target storage levels across VertexRDD and EdgeRDD operations, we remove raw calls to the constructors and instead introduce the `withPartitionsRDD` and `withTargetStorageLevel` methods.

I tested this change by running PageRank and triangle count on a severely memory-constrained cluster (1 executor with 300 MB of memory, and a 1 GB graph). Before this PR, these algorithms used to fail with OutOfMemoryErrors. With this PR, and using the DISK_ONLY storage level, they succeed.

Author: Ankur Dave <ankurdave@gmail.com>

Closes #946 from ankurdave/SPARK-1991 and squashes the following commits:

ce17d95 [Ankur Dave] Move pickStorageLevel to StorageLevel.fromString
ccaf06f [Ankur Dave] Shadow members in withXYZ() methods rather than using underscores
c34abc0 [Ankur Dave] Exclude all of GraphX from compatibility checks vs. 1.0.0
c5ca068 [Ankur Dave] Revert "Exclude all of GraphX from binary compatibility checks"
34bcefb [Ankur Dave] Exclude all of GraphX from binary compatibility checks
6fdd137 [Ankur Dave] [SPARK-1991] Support custom storage levels for vertices and edges
---
 .../apache/spark/storage/StorageLevel.scala   | 21 +++++
 .../org/apache/spark/graphx/EdgeRDD.scala     | 67 ++++++++++++++-
 .../scala/org/apache/spark/graphx/Graph.scala | 34 +++++---
 .../org/apache/spark/graphx/GraphLoader.scala | 12 ++-
 .../org/apache/spark/graphx/VertexRDD.scala   | 49 ++++++++---
 .../apache/spark/graphx/impl/GraphImpl.scala  | 55 +++++++------
 .../graphx/impl/ReplicatedVertexView.scala    |  6 +-
 .../apache/spark/graphx/lib/Analytics.scala   | 82 +++++++++----------
 8 files changed, 229 insertions(+), 97 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/storage/StorageLevel.scala b/core/src/main/scala/org/apache/spark/storage/StorageLevel.scala
index 363de93e067b..2d8ff1194a5d 100644
--- a/core/src/main/scala/org/apache/spark/storage/StorageLevel.scala
+++ b/core/src/main/scala/org/apache/spark/storage/StorageLevel.scala
@@ -147,6 +147,27 @@ object StorageLevel {
   val MEMORY_AND_DISK_SER_2 = new StorageLevel(true, true, false, false, 2)
   val OFF_HEAP = new StorageLevel(false, false, true, false)
 
+  /**
+   * :: DeveloperApi ::
+   * Return the StorageLevel object with the specified name.
+   */
+  @DeveloperApi
+  def fromString(s: String): StorageLevel = s match {
+    case "NONE" => NONE
+    case "DISK_ONLY" => DISK_ONLY
+    case "DISK_ONLY_2" => DISK_ONLY_2
+    case "MEMORY_ONLY" => MEMORY_ONLY
+    case "MEMORY_ONLY_2" => MEMORY_ONLY_2
+    case "MEMORY_ONLY_SER" => MEMORY_ONLY_SER
+    case "MEMORY_ONLY_SER_2" => MEMORY_ONLY_SER_2
+    case "MEMORY_AND_DISK" => MEMORY_AND_DISK
+    case "MEMORY_AND_DISK_2" => MEMORY_AND_DISK_2
+    case "MEMORY_AND_DISK_SER" => MEMORY_AND_DISK_SER
+    case "MEMORY_AND_DISK_SER_2" => MEMORY_AND_DISK_SER_2
+    case "OFF_HEAP" => OFF_HEAP
+    case _ => throw new IllegalArgumentException("Invalid StorageLevel: " + s)
+  }
+
   /**
    * :: DeveloperApi ::
    * Create a new StorageLevel object without setting useOffHeap.
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/EdgeRDD.scala b/graphx/src/main/scala/org/apache/spark/graphx/EdgeRDD.scala
index a8fc09507251..899a3cbd62b6 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/EdgeRDD.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/EdgeRDD.scala
@@ -24,6 +24,7 @@ import org.apache.spark.rdd.RDD
 import org.apache.spark.storage.StorageLevel
 
 import org.apache.spark.graphx.impl.EdgePartition
+import org.apache.spark.graphx.impl.EdgePartitionBuilder
 
 /**
  * `EdgeRDD[ED, VD]` extends `RDD[Edge[ED]]` by storing the edges in columnar format on each
@@ -32,7 +33,8 @@ import org.apache.spark.graphx.impl.EdgePartition
  * `impl.ReplicatedVertexView`.
  */
 class EdgeRDD[@specialized ED: ClassTag, VD: ClassTag](
-    val partitionsRDD: RDD[(PartitionID, EdgePartition[ED, VD])])
+    val partitionsRDD: RDD[(PartitionID, EdgePartition[ED, VD])],
+    val targetStorageLevel: StorageLevel = StorageLevel.MEMORY_ONLY)
   extends RDD[Edge[ED]](partitionsRDD.context, List(new OneToOneDependency(partitionsRDD))) {
 
   partitionsRDD.setName("EdgeRDD")
@@ -58,6 +60,10 @@ class EdgeRDD[@specialized ED: ClassTag, VD: ClassTag](
 
   override def collect(): Array[Edge[ED]] = this.map(_.copy()).collect()
 
+  /**
+   * Persists the edge partitions at the specified storage level, ignoring any existing target
+   * storage level.
+   */
   override def persist(newLevel: StorageLevel): this.type = {
     partitionsRDD.persist(newLevel)
     this
@@ -68,9 +74,15 @@ class EdgeRDD[@specialized ED: ClassTag, VD: ClassTag](
     this
   }
 
+  /** Persists the vertex partitions using `targetStorageLevel`, which defaults to MEMORY_ONLY. */
+  override def cache(): this.type = {
+    partitionsRDD.persist(targetStorageLevel)
+    this
+  }
+
   private[graphx] def mapEdgePartitions[ED2: ClassTag, VD2: ClassTag](
       f: (PartitionID, EdgePartition[ED, VD]) => EdgePartition[ED2, VD2]): EdgeRDD[ED2, VD2] = {
-    new EdgeRDD[ED2, VD2](partitionsRDD.mapPartitions({ iter =>
+    this.withPartitionsRDD[ED2, VD2](partitionsRDD.mapPartitions({ iter =>
       if (iter.hasNext) {
         val (pid, ep) = iter.next()
         Iterator(Tuple2(pid, f(pid, ep)))
@@ -118,11 +130,60 @@ class EdgeRDD[@specialized ED: ClassTag, VD: ClassTag](
       (f: (VertexId, VertexId, ED, ED2) => ED3): EdgeRDD[ED3, VD] = {
     val ed2Tag = classTag[ED2]
     val ed3Tag = classTag[ED3]
-    new EdgeRDD[ED3, VD](partitionsRDD.zipPartitions(other.partitionsRDD, true) {
+    this.withPartitionsRDD[ED3, VD](partitionsRDD.zipPartitions(other.partitionsRDD, true) {
       (thisIter, otherIter) =>
         val (pid, thisEPart) = thisIter.next()
         val (_, otherEPart) = otherIter.next()
         Iterator(Tuple2(pid, thisEPart.innerJoin(otherEPart)(f)(ed2Tag, ed3Tag)))
     })
   }
+
+  /** Replaces the vertex partitions while preserving all other properties of the VertexRDD. */
+  private[graphx] def withPartitionsRDD[ED2: ClassTag, VD2: ClassTag](
+      partitionsRDD: RDD[(PartitionID, EdgePartition[ED2, VD2])]): EdgeRDD[ED2, VD2] = {
+    new EdgeRDD(partitionsRDD, this.targetStorageLevel)
+  }
+
+  /**
+   * Changes the target storage level while preserving all other properties of the
+   * EdgeRDD. Operations on the returned EdgeRDD will preserve this storage level.
+   *
+   * This does not actually trigger a cache; to do this, call
+   * [[org.apache.spark.graphx.EdgeRDD#cache]] on the returned EdgeRDD.
+   */
+  private[graphx] def withTargetStorageLevel(
+      targetStorageLevel: StorageLevel): EdgeRDD[ED, VD] = {
+    new EdgeRDD(this.partitionsRDD, targetStorageLevel)
+  }
+
+}
+
+object EdgeRDD {
+  /**
+   * Creates an EdgeRDD from a set of edges.
+   *
+   * @tparam ED the edge attribute type
+   * @tparam VD the type of the vertex attributes that may be joined with the returned EdgeRDD
+   */
+  def fromEdges[ED: ClassTag, VD: ClassTag](edges: RDD[Edge[ED]]): EdgeRDD[ED, VD] = {
+    val edgePartitions = edges.mapPartitionsWithIndex { (pid, iter) =>
+      val builder = new EdgePartitionBuilder[ED, VD]
+      iter.foreach { e =>
+        builder.add(e.srcId, e.dstId, e.attr)
+      }
+      Iterator((pid, builder.toEdgePartition))
+    }
+    EdgeRDD.fromEdgePartitions(edgePartitions)
+  }
+
+  /**
+   * Creates an EdgeRDD from already-constructed edge partitions.
+   *
+   * @tparam ED the edge attribute type
+   * @tparam VD the type of the vertex attributes that may be joined with the returned EdgeRDD
+   */
+  def fromEdgePartitions[ED: ClassTag, VD: ClassTag](
+      edgePartitions: RDD[(Int, EdgePartition[ED, VD])]): EdgeRDD[ED, VD] = {
+    new EdgeRDD(edgePartitions)
+  }
 }
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/Graph.scala b/graphx/src/main/scala/org/apache/spark/graphx/Graph.scala
index dc5dac4fdad5..c4f9d6514cae 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/Graph.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/Graph.scala
@@ -80,7 +80,8 @@ abstract class Graph[VD: ClassTag, ED: ClassTag] protected () extends Serializab
   @transient val triplets: RDD[EdgeTriplet[VD, ED]]
 
   /**
-   * Caches the vertices and edges associated with this graph at the specified storage level.
+   * Caches the vertices and edges associated with this graph at the specified storage level,
+   * ignoring any target storage levels previously set.
    *
    * @param newLevel the level at which to cache the graph.
    *
@@ -89,9 +90,9 @@ abstract class Graph[VD: ClassTag, ED: ClassTag] protected () extends Serializab
   def persist(newLevel: StorageLevel = StorageLevel.MEMORY_ONLY): Graph[VD, ED]
 
   /**
-   * Caches the vertices and edges associated with this graph. This is used to
-   * pin a graph in memory enabling multiple queries to reuse the same
-   * construction process.
+   * Caches the vertices and edges associated with this graph at the previously-specified target
+   * storage levels, which default to `MEMORY_ONLY`. This is used to pin a graph in memory enabling
+   * multiple queries to reuse the same construction process.
    */
   def cache(): Graph[VD, ED]
 
@@ -358,9 +359,12 @@ object Graph {
    * Construct a graph from a collection of edges encoded as vertex id pairs.
    *
    * @param rawEdges a collection of edges in (src, dst) form
+   * @param defaultValue the vertex attributes with which to create vertices referenced by the edges
    * @param uniqueEdges if multiple identical edges are found they are combined and the edge
    * attribute is set to the sum.  Otherwise duplicate edges are treated as separate. To enable
    * `uniqueEdges`, a [[PartitionStrategy]] must be provided.
+   * @param edgeStorageLevel the desired storage level at which to cache the edges if necessary
+   * @param vertexStorageLevel the desired storage level at which to cache the vertices if necessary
    *
    * @return a graph with edge attributes containing either the count of duplicate edges or 1
    * (if `uniqueEdges` is `None`) and vertex attributes containing the total degree of each vertex.
@@ -368,10 +372,12 @@ object Graph {
   def fromEdgeTuples[VD: ClassTag](
       rawEdges: RDD[(VertexId, VertexId)],
       defaultValue: VD,
-      uniqueEdges: Option[PartitionStrategy] = None): Graph[VD, Int] =
+      uniqueEdges: Option[PartitionStrategy] = None,
+      edgeStorageLevel: StorageLevel = StorageLevel.MEMORY_ONLY,
+      vertexStorageLevel: StorageLevel = StorageLevel.MEMORY_ONLY): Graph[VD, Int] =
   {
     val edges = rawEdges.map(p => Edge(p._1, p._2, 1))
-    val graph = GraphImpl(edges, defaultValue)
+    val graph = GraphImpl(edges, defaultValue, edgeStorageLevel, vertexStorageLevel)
     uniqueEdges match {
       case Some(p) => graph.partitionBy(p).groupEdges((a, b) => a + b)
       case None => graph
@@ -383,14 +389,18 @@ object Graph {
    *
    * @param edges the RDD containing the set of edges in the graph
    * @param defaultValue the default vertex attribute to use for each vertex
+   * @param edgeStorageLevel the desired storage level at which to cache the edges if necessary
+   * @param vertexStorageLevel the desired storage level at which to cache the vertices if necessary
    *
    * @return a graph with edge attributes described by `edges` and vertices
    *         given by all vertices in `edges` with value `defaultValue`
    */
   def fromEdges[VD: ClassTag, ED: ClassTag](
       edges: RDD[Edge[ED]],
-      defaultValue: VD): Graph[VD, ED] = {
-    GraphImpl(edges, defaultValue)
+      defaultValue: VD,
+      edgeStorageLevel: StorageLevel = StorageLevel.MEMORY_ONLY,
+      vertexStorageLevel: StorageLevel = StorageLevel.MEMORY_ONLY): Graph[VD, ED] = {
+    GraphImpl(edges, defaultValue, edgeStorageLevel, vertexStorageLevel)
   }
 
   /**
@@ -405,12 +415,16 @@ object Graph {
    * @param edges the collection of edges in the graph
    * @param defaultVertexAttr the default vertex attribute to use for vertices that are
    *                          mentioned in edges but not in vertices
+   * @param edgeStorageLevel the desired storage level at which to cache the edges if necessary
+   * @param vertexStorageLevel the desired storage level at which to cache the vertices if necessary
    */
   def apply[VD: ClassTag, ED: ClassTag](
       vertices: RDD[(VertexId, VD)],
       edges: RDD[Edge[ED]],
-      defaultVertexAttr: VD = null.asInstanceOf[VD]): Graph[VD, ED] = {
-    GraphImpl(vertices, edges, defaultVertexAttr)
+      defaultVertexAttr: VD = null.asInstanceOf[VD],
+      edgeStorageLevel: StorageLevel = StorageLevel.MEMORY_ONLY,
+      vertexStorageLevel: StorageLevel = StorageLevel.MEMORY_ONLY): Graph[VD, ED] = {
+    GraphImpl(vertices, edges, defaultVertexAttr, edgeStorageLevel, vertexStorageLevel)
   }
 
   /**
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/GraphLoader.scala b/graphx/src/main/scala/org/apache/spark/graphx/GraphLoader.scala
index 389490c13984..2e814e34f9ad 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/GraphLoader.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/GraphLoader.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.graphx
 
+import org.apache.spark.storage.StorageLevel
 import org.apache.spark.{Logging, SparkContext}
 import org.apache.spark.graphx.impl.{EdgePartitionBuilder, GraphImpl}
 
@@ -48,12 +49,16 @@ object GraphLoader extends Logging {
    * @param canonicalOrientation whether to orient edges in the positive
    *        direction
    * @param minEdgePartitions the number of partitions for the edge RDD
+   * @param edgeStorageLevel the desired storage level for the edge partitions. To set the vertex
+   *        storage level, call [[org.apache.spark.graphx.Graph#persistVertices]].
    */
   def edgeListFile(
       sc: SparkContext,
       path: String,
       canonicalOrientation: Boolean = false,
-      minEdgePartitions: Int = 1)
+      minEdgePartitions: Int = 1,
+      edgeStorageLevel: StorageLevel = StorageLevel.MEMORY_ONLY,
+      vertexStorageLevel: StorageLevel = StorageLevel.MEMORY_ONLY)
     : Graph[Int, Int] =
   {
     val startTime = System.currentTimeMillis
@@ -78,12 +83,13 @@ object GraphLoader extends Logging {
         }
       }
       Iterator((pid, builder.toEdgePartition))
-    }.cache().setName("GraphLoader.edgeListFile - edges (%s)".format(path))
+    }.persist(edgeStorageLevel).setName("GraphLoader.edgeListFile - edges (%s)".format(path))
     edges.count()
 
     logInfo("It took %d ms to load the edges".format(System.currentTimeMillis - startTime))
 
-    GraphImpl.fromEdgePartitions(edges, defaultVertexAttr = 1)
+    GraphImpl.fromEdgePartitions(edges, defaultVertexAttr = 1, edgeStorageLevel = edgeStorageLevel,
+      vertexStorageLevel = vertexStorageLevel)
   } // end of edgeListFile
 
 }
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/VertexRDD.scala b/graphx/src/main/scala/org/apache/spark/graphx/VertexRDD.scala
index 8b910fbc5a42..f1b6df9a3025 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/VertexRDD.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/VertexRDD.scala
@@ -56,7 +56,8 @@ import org.apache.spark.graphx.impl.VertexRDDFunctions._
  * @tparam VD the vertex attribute associated with each vertex in the set.
  */
 class VertexRDD[@specialized VD: ClassTag](
-    val partitionsRDD: RDD[ShippableVertexPartition[VD]])
+    val partitionsRDD: RDD[ShippableVertexPartition[VD]],
+    val targetStorageLevel: StorageLevel = StorageLevel.MEMORY_ONLY)
   extends RDD[(VertexId, VD)](partitionsRDD.context, List(new OneToOneDependency(partitionsRDD))) {
 
   require(partitionsRDD.partitioner.isDefined)
@@ -66,7 +67,7 @@ class VertexRDD[@specialized VD: ClassTag](
    * VertexRDD will be based on a different index and can no longer be quickly joined with this
    * RDD.
    */
-  def reindex(): VertexRDD[VD] = new VertexRDD(partitionsRDD.map(_.reindex()))
+  def reindex(): VertexRDD[VD] = this.withPartitionsRDD(partitionsRDD.map(_.reindex()))
 
   override val partitioner = partitionsRDD.partitioner
 
@@ -85,6 +86,10 @@ class VertexRDD[@specialized VD: ClassTag](
   }
   setName("VertexRDD")
 
+  /**
+   * Persists the vertex partitions at the specified storage level, ignoring any existing target
+   * storage level.
+   */
   override def persist(newLevel: StorageLevel): this.type = {
     partitionsRDD.persist(newLevel)
     this
@@ -95,6 +100,12 @@ class VertexRDD[@specialized VD: ClassTag](
     this
   }
 
+  /** Persists the vertex partitions at `targetStorageLevel`, which defaults to MEMORY_ONLY. */
+  override def cache(): this.type = {
+    partitionsRDD.persist(targetStorageLevel)
+    this
+  }
+
   /** The number of vertices in the RDD. */
   override def count(): Long = {
     partitionsRDD.map(_.size).reduce(_ + _)
@@ -114,7 +125,7 @@ class VertexRDD[@specialized VD: ClassTag](
       f: ShippableVertexPartition[VD] => ShippableVertexPartition[VD2])
     : VertexRDD[VD2] = {
     val newPartitionsRDD = partitionsRDD.mapPartitions(_.map(f), preservesPartitioning = true)
-    new VertexRDD(newPartitionsRDD)
+    this.withPartitionsRDD(newPartitionsRDD)
   }
 
 
@@ -165,7 +176,7 @@ class VertexRDD[@specialized VD: ClassTag](
       val otherPart = otherIter.next()
       Iterator(thisPart.diff(otherPart))
     }
-    new VertexRDD(newPartitionsRDD)
+    this.withPartitionsRDD(newPartitionsRDD)
   }
 
   /**
@@ -191,7 +202,7 @@ class VertexRDD[@specialized VD: ClassTag](
       val otherPart = otherIter.next()
       Iterator(thisPart.leftJoin(otherPart)(f))
     }
-    new VertexRDD(newPartitionsRDD)
+    this.withPartitionsRDD(newPartitionsRDD)
   }
 
   /**
@@ -220,7 +231,7 @@ class VertexRDD[@specialized VD: ClassTag](
       case other: VertexRDD[_] =>
         leftZipJoin(other)(f)
       case _ =>
-        new VertexRDD[VD3](
+        this.withPartitionsRDD[VD3](
           partitionsRDD.zipPartitions(
             other.copartitionWithVertices(this.partitioner.get), preservesPartitioning = true) {
             (partIter, msgs) => partIter.map(_.leftJoin(msgs)(f))
@@ -242,7 +253,7 @@ class VertexRDD[@specialized VD: ClassTag](
       val otherPart = otherIter.next()
       Iterator(thisPart.innerJoin(otherPart)(f))
     }
-    new VertexRDD(newPartitionsRDD)
+    this.withPartitionsRDD(newPartitionsRDD)
   }
 
   /**
@@ -264,7 +275,7 @@ class VertexRDD[@specialized VD: ClassTag](
       case other: VertexRDD[_] =>
         innerZipJoin(other)(f)
       case _ =>
-        new VertexRDD(
+        this.withPartitionsRDD(
           partitionsRDD.zipPartitions(
             other.copartitionWithVertices(this.partitioner.get), preservesPartitioning = true) {
             (partIter, msgs) => partIter.map(_.innerJoin(msgs)(f))
@@ -290,7 +301,7 @@ class VertexRDD[@specialized VD: ClassTag](
     val parts = partitionsRDD.zipPartitions(shuffled, true) { (thisIter, msgIter) =>
       thisIter.map(_.aggregateUsingIndex(msgIter, reduceFunc))
     }
-    new VertexRDD[VD2](parts)
+    this.withPartitionsRDD[VD2](parts)
   }
 
   /**
@@ -309,7 +320,25 @@ class VertexRDD[@specialized VD: ClassTag](
           if (routingTableIter.hasNext) routingTableIter.next() else RoutingTablePartition.empty
         partIter.map(_.withRoutingTable(routingTable))
     }
-    new VertexRDD(vertexPartitions)
+    this.withPartitionsRDD(vertexPartitions)
+  }
+
+  /** Replaces the vertex partitions while preserving all other properties of the VertexRDD. */
+  private[graphx] def withPartitionsRDD[VD2: ClassTag](
+      partitionsRDD: RDD[ShippableVertexPartition[VD2]]): VertexRDD[VD2] = {
+    new VertexRDD(partitionsRDD, this.targetStorageLevel)
+  }
+
+  /**
+   * Changes the target storage level while preserving all other properties of the
+   * VertexRDD. Operations on the returned VertexRDD will preserve this storage level.
+   *
+   * This does not actually trigger a cache; to do this, call
+   * [[org.apache.spark.graphx.VertexRDD#cache]] on the returned VertexRDD.
+   */
+  private[graphx] def withTargetStorageLevel(
+      targetStorageLevel: StorageLevel): VertexRDD[VD] = {
+    new VertexRDD(this.partitionsRDD, targetStorageLevel)
   }
 
   /** Generates an RDD of vertex attributes suitable for shipping to the edge partitions. */
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/GraphImpl.scala b/graphx/src/main/scala/org/apache/spark/graphx/impl/GraphImpl.scala
index 1649b244d288..59d9a8808e56 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/impl/GraphImpl.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/impl/GraphImpl.scala
@@ -61,7 +61,11 @@ class GraphImpl[VD: ClassTag, ED: ClassTag] protected (
     this
   }
 
-  override def cache(): Graph[VD, ED] = persist(StorageLevel.MEMORY_ONLY)
+  override def cache(): Graph[VD, ED] = {
+    vertices.cache()
+    replicatedVertexView.edges.cache()
+    this
+  }
 
   override def unpersistVertices(blocking: Boolean = true): Graph[VD, ED] = {
     vertices.unpersist(blocking)
@@ -70,10 +74,10 @@ class GraphImpl[VD: ClassTag, ED: ClassTag] protected (
   }
 
   override def partitionBy(partitionStrategy: PartitionStrategy): Graph[VD, ED] = {
-    val numPartitions = replicatedVertexView.edges.partitions.size
+    val numPartitions = edges.partitions.size
     val edTag = classTag[ED]
     val vdTag = classTag[VD]
-    val newEdges = new EdgeRDD(replicatedVertexView.edges.map { e =>
+    val newEdges = edges.withPartitionsRDD(edges.map { e =>
       val part: PartitionID = partitionStrategy.getPartition(e.srcId, e.dstId, numPartitions)
 
       // Should we be using 3-tuple or an optimized class
@@ -256,24 +260,33 @@ object GraphImpl {
   /** Create a graph from edges, setting referenced vertices to `defaultVertexAttr`. */
   def apply[VD: ClassTag, ED: ClassTag](
       edges: RDD[Edge[ED]],
-      defaultVertexAttr: VD): GraphImpl[VD, ED] = {
-    fromEdgeRDD(createEdgeRDD(edges), defaultVertexAttr)
+      defaultVertexAttr: VD,
+      edgeStorageLevel: StorageLevel,
+      vertexStorageLevel: StorageLevel): GraphImpl[VD, ED] = {
+    fromEdgeRDD(EdgeRDD.fromEdges(edges), defaultVertexAttr, edgeStorageLevel, vertexStorageLevel)
   }
 
   /** Create a graph from EdgePartitions, setting referenced vertices to `defaultVertexAttr`. */
   def fromEdgePartitions[VD: ClassTag, ED: ClassTag](
       edgePartitions: RDD[(PartitionID, EdgePartition[ED, VD])],
-      defaultVertexAttr: VD): GraphImpl[VD, ED] = {
-    fromEdgeRDD(new EdgeRDD(edgePartitions), defaultVertexAttr)
+      defaultVertexAttr: VD,
+      edgeStorageLevel: StorageLevel,
+      vertexStorageLevel: StorageLevel): GraphImpl[VD, ED] = {
+    fromEdgeRDD(EdgeRDD.fromEdgePartitions(edgePartitions), defaultVertexAttr, edgeStorageLevel,
+      vertexStorageLevel)
   }
 
   /** Create a graph from vertices and edges, setting missing vertices to `defaultVertexAttr`. */
   def apply[VD: ClassTag, ED: ClassTag](
       vertices: RDD[(VertexId, VD)],
       edges: RDD[Edge[ED]],
-      defaultVertexAttr: VD): GraphImpl[VD, ED] = {
-    val edgeRDD = createEdgeRDD(edges)(classTag[ED], classTag[VD]).cache()
+      defaultVertexAttr: VD,
+      edgeStorageLevel: StorageLevel,
+      vertexStorageLevel: StorageLevel): GraphImpl[VD, ED] = {
+    val edgeRDD = EdgeRDD.fromEdges(edges)(classTag[ED], classTag[VD])
+      .withTargetStorageLevel(edgeStorageLevel).cache()
     val vertexRDD = VertexRDD(vertices, edgeRDD, defaultVertexAttr)
+      .withTargetStorageLevel(vertexStorageLevel).cache()
     GraphImpl(vertexRDD, edgeRDD)
   }
 
@@ -309,23 +322,13 @@ object GraphImpl {
    */
   private def fromEdgeRDD[VD: ClassTag, ED: ClassTag](
       edges: EdgeRDD[ED, VD],
-      defaultVertexAttr: VD): GraphImpl[VD, ED] = {
-    edges.cache()
-    val vertices = VertexRDD.fromEdges(edges, edges.partitions.size, defaultVertexAttr)
-    fromExistingRDDs(vertices, edges)
-  }
-
-  /** Create an EdgeRDD from a set of edges. */
-  private def createEdgeRDD[ED: ClassTag, VD: ClassTag](
-      edges: RDD[Edge[ED]]): EdgeRDD[ED, VD] = {
-    val edgePartitions = edges.mapPartitionsWithIndex { (pid, iter) =>
-      val builder = new EdgePartitionBuilder[ED, VD]
-      iter.foreach { e =>
-        builder.add(e.srcId, e.dstId, e.attr)
-      }
-      Iterator((pid, builder.toEdgePartition))
-    }
-    new EdgeRDD(edgePartitions)
+      defaultVertexAttr: VD,
+      edgeStorageLevel: StorageLevel,
+      vertexStorageLevel: StorageLevel): GraphImpl[VD, ED] = {
+    val edgesCached = edges.withTargetStorageLevel(edgeStorageLevel).cache()
+    val vertices = VertexRDD.fromEdges(edgesCached, edgesCached.partitions.size, defaultVertexAttr)
+      .withTargetStorageLevel(vertexStorageLevel)
+    fromExistingRDDs(vertices, edgesCached)
   }
 
 } // end of object GraphImpl
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/ReplicatedVertexView.scala b/graphx/src/main/scala/org/apache/spark/graphx/impl/ReplicatedVertexView.scala
index 3a0bba1b93b4..86b366eb9202 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/impl/ReplicatedVertexView.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/impl/ReplicatedVertexView.scala
@@ -69,7 +69,7 @@ class ReplicatedVertexView[VD: ClassTag, ED: ClassTag](
           .setName("ReplicatedVertexView.upgrade(%s, %s) - shippedVerts %s %s (broadcast)".format(
             includeSrc, includeDst, shipSrc, shipDst))
           .partitionBy(edges.partitioner.get)
-      val newEdges = new EdgeRDD(edges.partitionsRDD.zipPartitions(shippedVerts) {
+      val newEdges = edges.withPartitionsRDD(edges.partitionsRDD.zipPartitions(shippedVerts) {
         (ePartIter, shippedVertsIter) => ePartIter.map {
           case (pid, edgePartition) =>
             (pid, edgePartition.updateVertices(shippedVertsIter.flatMap(_._2.iterator)))
@@ -91,7 +91,7 @@ class ReplicatedVertexView[VD: ClassTag, ED: ClassTag](
       .setName("ReplicatedVertexView.withActiveSet - shippedActives (broadcast)")
       .partitionBy(edges.partitioner.get)
 
-    val newEdges = new EdgeRDD(edges.partitionsRDD.zipPartitions(shippedActives) {
+    val newEdges = edges.withPartitionsRDD(edges.partitionsRDD.zipPartitions(shippedActives) {
       (ePartIter, shippedActivesIter) => ePartIter.map {
         case (pid, edgePartition) =>
           (pid, edgePartition.withActiveSet(shippedActivesIter.flatMap(_._2.iterator)))
@@ -111,7 +111,7 @@ class ReplicatedVertexView[VD: ClassTag, ED: ClassTag](
         hasSrcId, hasDstId))
       .partitionBy(edges.partitioner.get)
 
-    val newEdges = new EdgeRDD(edges.partitionsRDD.zipPartitions(shippedVerts) {
+    val newEdges = edges.withPartitionsRDD(edges.partitionsRDD.zipPartitions(shippedVerts) {
       (ePartIter, shippedVertsIter) => ePartIter.map {
         case (pid, edgePartition) =>
           (pid, edgePartition.updateVertices(shippedVertsIter.flatMap(_._2.iterator)))
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/lib/Analytics.scala b/graphx/src/main/scala/org/apache/spark/graphx/lib/Analytics.scala
index 069e042ed94a..c1513a00453c 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/lib/Analytics.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/lib/Analytics.scala
@@ -17,7 +17,9 @@
 
 package org.apache.spark.graphx.lib
 
+import scala.collection.mutable
 import org.apache.spark._
+import org.apache.spark.storage.StorageLevel
 import org.apache.spark.graphx._
 import org.apache.spark.graphx.PartitionStrategy._
 
@@ -28,18 +30,20 @@ object Analytics extends Logging {
 
   def main(args: Array[String]): Unit = {
     if (args.length < 2) {
-      System.err.println("Usage: Analytics <taskType> <file> [other options]")
+      System.err.println(
+        "Usage: Analytics <taskType> <file> --numEPart=<num_edge_partitions> [other options]")
       System.exit(1)
     }
 
     val taskType = args(0)
     val fname = args(1)
-    val options =  args.drop(2).map { arg =>
+    val optionsList = args.drop(2).map { arg =>
       arg.dropWhile(_ == '-').split('=') match {
         case Array(opt, v) => (opt -> v)
         case _ => throw new IllegalArgumentException("Invalid argument: " + arg)
       }
     }
+    val options = mutable.Map(optionsList: _*)
 
     def pickPartitioner(v: String): PartitionStrategy = {
       // TODO: Use reflection rather than listing all the partitioning strategies here.
@@ -57,20 +61,24 @@ object Analytics extends Logging {
       .set("spark.kryo.registrator", "org.apache.spark.graphx.GraphKryoRegistrator")
       .set("spark.locality.wait", "100000")
 
+    val numEPart = options.remove("numEPart").map(_.toInt).getOrElse {
+      println("Set the number of edge partitions using --numEPart.")
+      sys.exit(1)
+    }
+    val partitionStrategy: Option[PartitionStrategy] = options.remove("partStrategy")
+      .map(pickPartitioner(_))
+    val edgeStorageLevel = options.remove("edgeStorageLevel")
+      .map(StorageLevel.fromString(_)).getOrElse(StorageLevel.MEMORY_ONLY)
+    val vertexStorageLevel = options.remove("vertexStorageLevel")
+      .map(StorageLevel.fromString(_)).getOrElse(StorageLevel.MEMORY_ONLY)
+
     taskType match {
       case "pagerank" =>
-        var tol: Float = 0.001F
-        var outFname = ""
-        var numEPart = 4
-        var partitionStrategy: Option[PartitionStrategy] = None
-        var numIterOpt: Option[Int] = None
-
-        options.foreach{
-          case ("tol", v) => tol = v.toFloat
-          case ("output", v) => outFname = v
-          case ("numEPart", v) => numEPart = v.toInt
-          case ("partStrategy", v) => partitionStrategy = Some(pickPartitioner(v))
-          case ("numIter", v) => numIterOpt = Some(v.toInt)
+        val tol = options.remove("tol").map(_.toFloat).getOrElse(0.001F)
+        val outFname = options.remove("output").getOrElse("")
+        val numIterOpt = options.remove("numIter").map(_.toInt)
+
+        options.foreach {
           case (opt, _) => throw new IllegalArgumentException("Invalid option: " + opt)
         }
 
@@ -81,7 +89,9 @@ object Analytics extends Logging {
         val sc = new SparkContext(conf.setAppName("PageRank(" + fname + ")"))
 
         val unpartitionedGraph = GraphLoader.edgeListFile(sc, fname,
-          minEdgePartitions = numEPart).cache()
+          minEdgePartitions = numEPart,
+          edgeStorageLevel = edgeStorageLevel,
+          vertexStorageLevel = vertexStorageLevel).cache()
         val graph = partitionStrategy.foldLeft(unpartitionedGraph)(_.partitionBy(_))
 
         println("GRAPHX: Number of vertices " + graph.vertices.count)
@@ -102,32 +112,19 @@ object Analytics extends Logging {
         sc.stop()
 
       case "cc" =>
-        var numIter = Int.MaxValue
-        var numVPart = 4
-        var numEPart = 4
-        var isDynamic = false
-        var partitionStrategy: Option[PartitionStrategy] = None
-
-        options.foreach{
-          case ("numIter", v) => numIter = v.toInt
-          case ("dynamic", v) => isDynamic = v.toBoolean
-          case ("numEPart", v) => numEPart = v.toInt
-          case ("numVPart", v) => numVPart = v.toInt
-          case ("partStrategy", v) => partitionStrategy = Some(pickPartitioner(v))
+        options.foreach {
           case (opt, _) => throw new IllegalArgumentException("Invalid option: " + opt)
         }
 
-        if (!isDynamic && numIter == Int.MaxValue) {
-          println("Set number of iterations!")
-          sys.exit(1)
-        }
         println("======================================")
         println("|      Connected Components          |")
         println("======================================")
 
         val sc = new SparkContext(conf.setAppName("ConnectedComponents(" + fname + ")"))
         val unpartitionedGraph = GraphLoader.edgeListFile(sc, fname,
-          minEdgePartitions = numEPart).cache()
+          minEdgePartitions = numEPart,
+          edgeStorageLevel = edgeStorageLevel,
+          vertexStorageLevel = vertexStorageLevel).cache()
         val graph = partitionStrategy.foldLeft(unpartitionedGraph)(_.partitionBy(_))
 
         val cc = ConnectedComponents.run(graph)
@@ -135,24 +132,25 @@ object Analytics extends Logging {
         sc.stop()
 
       case "triangles" =>
-        var numEPart = 4
-        // TriangleCount requires the graph to be partitioned
-        var partitionStrategy: PartitionStrategy = RandomVertexCut
-
-        options.foreach{
-          case ("numEPart", v) => numEPart = v.toInt
-          case ("partStrategy", v) => partitionStrategy = pickPartitioner(v)
+        options.foreach {
           case (opt, _) => throw new IllegalArgumentException("Invalid option: " + opt)
         }
+
         println("======================================")
         println("|      Triangle Count                |")
         println("======================================")
+
         val sc = new SparkContext(conf.setAppName("TriangleCount(" + fname + ")"))
-        val graph = GraphLoader.edgeListFile(sc, fname, canonicalOrientation = true,
-          minEdgePartitions = numEPart).partitionBy(partitionStrategy).cache()
+        val graph = GraphLoader.edgeListFile(sc, fname,
+          canonicalOrientation = true,
+          minEdgePartitions = numEPart,
+          edgeStorageLevel = edgeStorageLevel,
+          vertexStorageLevel = vertexStorageLevel)
+        // TriangleCount requires the graph to be partitioned
+          .partitionBy(partitionStrategy.getOrElse(RandomVertexCut)).cache()
         val triangles = TriangleCount.run(graph)
         println("Triangles: " + triangles.vertices.map {
-          case (vid,data) => data.toLong
+          case (vid, data) => data.toLong
         }.reduce(_ + _) / 3)
         sc.stop()
 

From f4dd665c85713d4c09731080fca58aee0fa2a85a Mon Sep 17 00:00:00 2001
From: DB Tsai <dbtsai@dbtsai.com>
Date: Tue, 3 Jun 2014 18:10:58 -0700
Subject: [PATCH 329/810] Fixed a typo

in RowMatrix.scala

Author: DB Tsai <dbtsai@dbtsai.com>

Closes #959 from dbtsai/dbtsai-typo and squashes the following commits:

fab0e0e [DB Tsai] Fixed typo
---
 .../org/apache/spark/mllib/linalg/distributed/RowMatrix.scala   | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
index 07dfadf2f786..00d0b18c27a8 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
@@ -416,7 +416,7 @@ class RowMatrix(
     mat
   }
 
-  /** Updates or verfires the number of rows. */
+  /** Updates or verifies the number of rows. */
   private def updateNumRows(m: Long) {
     if (nRows <= 0) {
       nRows == m

From 21e40ed88bf2c205c3d7f947fde5d5a6f3e29f7f Mon Sep 17 00:00:00 2001
From: Kan Zhang <kzhang@apache.org>
Date: Tue, 3 Jun 2014 18:18:25 -0700
Subject: [PATCH 330/810] [SPARK-1161] Add saveAsPickleFile and
 SparkContext.pickleFile in Python

Author: Kan Zhang <kzhang@apache.org>

Closes #755 from kanzhang/SPARK-1161 and squashes the following commits:

24ed8a2 [Kan Zhang] [SPARK-1161] Fixing doc tests
44e0615 [Kan Zhang] [SPARK-1161] Adding an optional batchSize with default value 10
d929429 [Kan Zhang] [SPARK-1161] Add saveAsObjectFile and SparkContext.objectFile in Python
---
 python/pyspark/context.py | 14 ++++++++++++++
 python/pyspark/rdd.py     | 33 +++++++++++++++++++++++++--------
 2 files changed, 39 insertions(+), 8 deletions(-)

diff --git a/python/pyspark/context.py b/python/pyspark/context.py
index 9ae9305d4f02..211918f5a05e 100644
--- a/python/pyspark/context.py
+++ b/python/pyspark/context.py
@@ -271,6 +271,20 @@ def parallelize(self, c, numSlices=None):
         jrdd = readRDDFromFile(self._jsc, tempFile.name, numSlices)
         return RDD(jrdd, self, serializer)
 
+    def pickleFile(self, name, minPartitions=None):
+        """
+        Load an RDD previously saved using L{RDD.saveAsPickleFile} method.
+
+        >>> tmpFile = NamedTemporaryFile(delete=True)
+        >>> tmpFile.close()
+        >>> sc.parallelize(range(10)).saveAsPickleFile(tmpFile.name, 5)
+        >>> sorted(sc.pickleFile(tmpFile.name, 3).collect())
+        [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
+        """
+        minPartitions = minPartitions or self.defaultMinPartitions
+        return RDD(self._jsc.objectFile(name, minPartitions), self,
+                   BatchedSerializer(PickleSerializer()))
+
     def textFile(self, name, minPartitions=None):
         """
         Read a text file from HDFS, a local file system (available on all
diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py
index 1b3c460dd621..ca0a95578fd2 100644
--- a/python/pyspark/rdd.py
+++ b/python/pyspark/rdd.py
@@ -33,7 +33,8 @@
 from random import Random
 
 from pyspark.serializers import NoOpSerializer, CartesianDeserializer, \
-    BatchedSerializer, CloudPickleSerializer, PairDeserializer, pack_long
+    BatchedSerializer, CloudPickleSerializer, PairDeserializer, \
+    PickleSerializer, pack_long
 from pyspark.join import python_join, python_left_outer_join, \
     python_right_outer_join, python_cogroup
 from pyspark.statcounter import StatCounter
@@ -427,11 +428,14 @@ def intersection(self, other):
             .filter(lambda x: (len(x[1][0]) != 0) and (len(x[1][1]) != 0)) \
             .keys()
 
-    def _reserialize(self):
-        if self._jrdd_deserializer == self.ctx.serializer:
+    def _reserialize(self, serializer=None):
+        serializer = serializer or self.ctx.serializer
+        if self._jrdd_deserializer == serializer:
             return self
         else:
-            return self.map(lambda x: x, preservesPartitioning=True)
+            converted = self.map(lambda x: x, preservesPartitioning=True)
+            converted._jrdd_deserializer = serializer
+            return converted
 
     def __add__(self, other):
         """
@@ -897,6 +901,20 @@ def first(self):
         """
         return self.take(1)[0]
 
+    def saveAsPickleFile(self, path, batchSize=10):
+        """
+        Save this RDD as a SequenceFile of serialized objects. The serializer used is
+        L{pyspark.serializers.PickleSerializer}, default batch size is 10.
+
+        >>> tmpFile = NamedTemporaryFile(delete=True)
+        >>> tmpFile.close()
+        >>> sc.parallelize([1, 2, 'spark', 'rdd']).saveAsPickleFile(tmpFile.name, 3)
+        >>> sorted(sc.pickleFile(tmpFile.name, 5).collect())
+        [1, 2, 'rdd', 'spark']
+        """
+        self._reserialize(BatchedSerializer(PickleSerializer(),
+                                batchSize))._jrdd.saveAsObjectFile(path)
+
     def saveAsTextFile(self, path):
         """
         Save this RDD as a text file, using string representations of elements.
@@ -1421,10 +1439,9 @@ def _jrdd(self):
         if self._jrdd_val:
             return self._jrdd_val
         if self._bypass_serializer:
-            serializer = NoOpSerializer()
-        else:
-            serializer = self.ctx.serializer
-        command = (self.func, self._prev_jrdd_deserializer, serializer)
+            self._jrdd_deserializer = NoOpSerializer()
+        command = (self.func, self._prev_jrdd_deserializer,
+                   self._jrdd_deserializer)
         pickled_command = CloudPickleSerializer().dumps(command)
         broadcast_vars = ListConverter().convert(
             [x._jbroadcast for x in self.ctx._pickled_broadcast_vars],

From 1faef149f763f4a54aaa6e17043d0a628ae338a0 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Tue, 3 Jun 2014 18:37:40 -0700
Subject: [PATCH 331/810] SPARK-1941: Update streamlib to 2.7.0 and use
 HyperLogLogPlus instead of HyperLogLog.

I also corrected some errors made in the previous HLL count approximate API, including relativeSD wasn't really a measure for error (and we used it to test error bounds in test results).

Author: Reynold Xin <rxin@apache.org>

Closes #897 from rxin/hll and squashes the following commits:

4d83f41 [Reynold Xin] New error bound and non-randomness.
f154ea0 [Reynold Xin] Added a comment on the value bound for testing.
e367527 [Reynold Xin] One more round of code review.
41e649a [Reynold Xin] Update final mima list.
9e320c8 [Reynold Xin] Incorporate code review feedback.
e110d70 [Reynold Xin] Merge branch 'master' into hll
354deb8 [Reynold Xin] Added comment on the Mima exclude rules.
acaa524 [Reynold Xin] Added the right exclude rules in MimaExcludes.
6555bfe [Reynold Xin] Added a default method and re-arranged MimaExcludes.
1db1522 [Reynold Xin] Excluded util.SerializableHyperLogLog from MIMA check.
9221b27 [Reynold Xin] Merge branch 'master' into hll
88cfe77 [Reynold Xin] Updated documentation and restored the old incorrect API to maintain API compatibility.
1294be6 [Reynold Xin] Updated HLL+.
e7786cb [Reynold Xin] Merge branch 'master' into hll
c0ef0c2 [Reynold Xin] SPARK-1941: Update streamlib to 2.7.0 and use HyperLogLogPlus instead of HyperLogLog.
---
 .../apache/spark/api/java/JavaPairRDD.scala   | 51 ++++++-----
 .../apache/spark/api/java/JavaRDDLike.scala   | 12 +--
 .../apache/spark/rdd/PairRDDFunctions.scala   | 90 ++++++++++++++-----
 .../main/scala/org/apache/spark/rdd/RDD.scala | 53 ++++++++---
 .../spark/util/SerializableHyperLogLog.scala  | 52 -----------
 .../java/org/apache/spark/JavaAPISuite.java   | 10 +--
 .../spark/rdd/PairRDDFunctionsSuite.scala     | 22 ++---
 .../scala/org/apache/spark/rdd/RDDSuite.scala |  6 +-
 pom.xml                                       |  4 +-
 project/MimaExcludes.scala                    | 22 ++++-
 project/SparkBuild.scala                      |  2 +-
 11 files changed, 189 insertions(+), 135 deletions(-)
 delete mode 100644 core/src/main/scala/org/apache/spark/util/SerializableHyperLogLog.scala

diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala b/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala
index 4c8f9ed6fbc0..7dcfbf741c4f 100644
--- a/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala
+++ b/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala
@@ -672,38 +672,47 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])
 
   /**
    * Return approximate number of distinct values for each key in this RDD.
-   * The accuracy of approximation can be controlled through the relative standard deviation
-   * (relativeSD) parameter, which also controls the amount of memory used. Lower values result in
-   * more accurate counts but increase the memory footprint and vise versa. Uses the provided
-   * Partitioner to partition the output RDD.
+   *
+   * The algorithm used is based on streamlib's implementation of "HyperLogLog in Practice:
+   * Algorithmic Engineering of a State of The Art Cardinality Estimation Algorithm", available
+   * <a href="http://dx.doi.org/10.1145/2452376.2452456">here</a>.
+   *
+   * @param relativeSD Relative accuracy. Smaller values create counters that require more space.
+   *                   It must be greater than 0.000017.
+   * @param partitioner partitioner of the resulting RDD.
    */
-  def countApproxDistinctByKey(relativeSD: Double, partitioner: Partitioner): JavaRDD[(K, Long)] = {
-    rdd.countApproxDistinctByKey(relativeSD, partitioner)
+  def countApproxDistinctByKey(relativeSD: Double, partitioner: Partitioner): JavaPairRDD[K, Long] =
+  {
+    fromRDD(rdd.countApproxDistinctByKey(relativeSD, partitioner))
   }
 
   /**
-   * Return approximate number of distinct values for each key this RDD.
-   * The accuracy of approximation can be controlled through the relative standard deviation
-   * (relativeSD) parameter, which also controls the amount of memory used. Lower values result in
-   * more accurate counts but increase the memory footprint and vise versa. The default value of
-   * relativeSD is 0.05. Hash-partitions the output RDD using the existing partitioner/parallelism
-   * level.
+   * Return approximate number of distinct values for each key in this RDD.
+   *
+   * The algorithm used is based on streamlib's implementation of "HyperLogLog in Practice:
+   * Algorithmic Engineering of a State of The Art Cardinality Estimation Algorithm", available
+   * <a href="http://dx.doi.org/10.1145/2452376.2452456">here</a>.
+   *
+   * @param relativeSD Relative accuracy. Smaller values create counters that require more space.
+   *                   It must be greater than 0.000017.
+   * @param numPartitions number of partitions of the resulting RDD.
    */
-  def countApproxDistinctByKey(relativeSD: Double = 0.05): JavaRDD[(K, Long)] = {
-    rdd.countApproxDistinctByKey(relativeSD)
+  def countApproxDistinctByKey(relativeSD: Double, numPartitions: Int): JavaPairRDD[K, Long] = {
+    fromRDD(rdd.countApproxDistinctByKey(relativeSD, numPartitions))
   }
 
-
   /**
    * Return approximate number of distinct values for each key in this RDD.
-   * The accuracy of approximation can be controlled through the relative standard deviation
-   * (relativeSD) parameter, which also controls the amount of memory used. Lower values result in
-   * more accurate counts but increase the memory footprint and vise versa. HashPartitions the
-   * output RDD into numPartitions.
    *
+   * The algorithm used is based on streamlib's implementation of "HyperLogLog in Practice:
+   * Algorithmic Engineering of a State of The Art Cardinality Estimation Algorithm", available
+   * <a href="http://dx.doi.org/10.1145/2452376.2452456">here</a>.
+   *
+   * @param relativeSD Relative accuracy. Smaller values create counters that require more space.
+   *                   It must be greater than 0.000017.
    */
-  def countApproxDistinctByKey(relativeSD: Double, numPartitions: Int): JavaRDD[(K, Long)] = {
-    rdd.countApproxDistinctByKey(relativeSD, numPartitions)
+  def countApproxDistinctByKey(relativeSD: Double): JavaPairRDD[K, Long] = {
+    fromRDD(rdd.countApproxDistinctByKey(relativeSD))
   }
 
   /** Assign a name to this RDD */
diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala b/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala
index 619bfd75be8e..330569a8d883 100644
--- a/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala
+++ b/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala
@@ -560,12 +560,14 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable {
   /**
    * Return approximate number of distinct elements in the RDD.
    *
-   * The accuracy of approximation can be controlled through the relative standard deviation
-   * (relativeSD) parameter, which also controls the amount of memory used. Lower values result in
-   * more accurate counts but increase the memory footprint and vise versa. The default value of
-   * relativeSD is 0.05.
+   * The algorithm used is based on streamlib's implementation of "HyperLogLog in Practice:
+   * Algorithmic Engineering of a State of The Art Cardinality Estimation Algorithm", available
+   * <a href="http://dx.doi.org/10.1145/2452376.2452456">here</a>.
+   *
+   * @param relativeSD Relative accuracy. Smaller values create counters that require more space.
+   *                   It must be greater than 0.000017.
    */
-  def countApproxDistinct(relativeSD: Double = 0.05): Long = rdd.countApproxDistinct(relativeSD)
+  def countApproxDistinct(relativeSD: Double): Long = rdd.countApproxDistinct(relativeSD)
 
   def name(): String = rdd.name
 
diff --git a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
index 223fef79261d..f2ce3cbd47f9 100644
--- a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
@@ -28,7 +28,7 @@ import scala.collection.mutable
 import scala.collection.mutable.ArrayBuffer
 import scala.reflect.ClassTag
 
-import com.clearspring.analytics.stream.cardinality.HyperLogLog
+import com.clearspring.analytics.stream.cardinality.HyperLogLogPlus
 import org.apache.hadoop.conf.{Configurable, Configuration}
 import org.apache.hadoop.fs.FileSystem
 import org.apache.hadoop.io.SequenceFile.CompressionType
@@ -46,7 +46,6 @@ import org.apache.spark.Partitioner.defaultPartitioner
 import org.apache.spark.SparkContext._
 import org.apache.spark.partial.{BoundedDouble, PartialResult}
 import org.apache.spark.serializer.Serializer
-import org.apache.spark.util.SerializableHyperLogLog
 
 /**
  * Extra functions available on RDDs of (key, value) pairs through an implicit conversion.
@@ -214,39 +213,88 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
   }
 
   /**
+   * :: Experimental ::
+   *
    * Return approximate number of distinct values for each key in this RDD.
-   * The accuracy of approximation can be controlled through the relative standard deviation
-   * (relativeSD) parameter, which also controls the amount of memory used. Lower values result in
-   * more accurate counts but increase the memory footprint and vice versa. Uses the provided
-   * Partitioner to partition the output RDD.
+   *
+   * The algorithm used is based on streamlib's implementation of "HyperLogLog in Practice:
+   * Algorithmic Engineering of a State of The Art Cardinality Estimation Algorithm", available
+   * <a href="http://dx.doi.org/10.1145/2452376.2452456">here</a>.
+   *
+   * The relative accuracy is approximately `1.054 / sqrt(2^p)`. Setting a nonzero `sp > p`
+   * would trigger sparse representation of registers, which may reduce the memory consumption
+   * and increase accuracy when the cardinality is small.
+   *
+   * @param p The precision value for the normal set.
+   *          `p` must be a value between 4 and `sp` if `sp` is not zero (32 max).
+   * @param sp The precision value for the sparse set, between 0 and 32.
+   *           If `sp` equals 0, the sparse representation is skipped.
+   * @param partitioner Partitioner to use for the resulting RDD.
    */
-  def countApproxDistinctByKey(relativeSD: Double, partitioner: Partitioner): RDD[(K, Long)] = {
-    val createHLL = (v: V) => new SerializableHyperLogLog(new HyperLogLog(relativeSD)).add(v)
-    val mergeValueHLL = (hll: SerializableHyperLogLog, v: V) => hll.add(v)
-    val mergeHLL = (h1: SerializableHyperLogLog, h2: SerializableHyperLogLog) => h1.merge(h2)
+  @Experimental
+  def countApproxDistinctByKey(p: Int, sp: Int, partitioner: Partitioner): RDD[(K, Long)] = {
+    require(p >= 4, s"p ($p) must be >= 4")
+    require(sp <= 32, s"sp ($sp) must be <= 32")
+    require(sp == 0 || p <= sp, s"p ($p) cannot be greater than sp ($sp)")
+    val createHLL = (v: V) => {
+      val hll = new HyperLogLogPlus(p, sp)
+      hll.offer(v)
+      hll
+    }
+    val mergeValueHLL = (hll: HyperLogLogPlus, v: V) => {
+      hll.offer(v)
+      hll
+    }
+    val mergeHLL = (h1: HyperLogLogPlus, h2: HyperLogLogPlus) => {
+      h1.addAll(h2)
+      h1
+    }
+
+    combineByKey(createHLL, mergeValueHLL, mergeHLL, partitioner).mapValues(_.cardinality())
+  }
 
-    combineByKey(createHLL, mergeValueHLL, mergeHLL, partitioner).mapValues(_.value.cardinality())
+  /**
+   * Return approximate number of distinct values for each key in this RDD.
+   *
+   * The algorithm used is based on streamlib's implementation of "HyperLogLog in Practice:
+   * Algorithmic Engineering of a State of The Art Cardinality Estimation Algorithm", available
+   * <a href="http://dx.doi.org/10.1145/2452376.2452456">here</a>.
+   *
+   * @param relativeSD Relative accuracy. Smaller values create counters that require more space.
+   *                   It must be greater than 0.000017.
+   * @param partitioner partitioner of the resulting RDD
+   */
+  def countApproxDistinctByKey(relativeSD: Double, partitioner: Partitioner): RDD[(K, Long)] = {
+    require(relativeSD > 0.000017, s"accuracy ($relativeSD) must be greater than 0.000017")
+    val p = math.ceil(2.0 * math.log(1.054 / relativeSD) / math.log(2)).toInt
+    assert(p <= 32)
+    countApproxDistinctByKey(if (p < 4) 4 else p, 0, partitioner)
   }
 
   /**
    * Return approximate number of distinct values for each key in this RDD.
-   * The accuracy of approximation can be controlled through the relative standard deviation
-   * (relativeSD) parameter, which also controls the amount of memory used. Lower values result in
-   * more accurate counts but increase the memory footprint and vice versa. HashPartitions the
-   * output RDD into numPartitions.
    *
+   * The algorithm used is based on streamlib's implementation of "HyperLogLog in Practice:
+   * Algorithmic Engineering of a State of The Art Cardinality Estimation Algorithm", available
+   * <a href="http://dx.doi.org/10.1145/2452376.2452456">here</a>.
+   *
+   * @param relativeSD Relative accuracy. Smaller values create counters that require more space.
+   *                   It must be greater than 0.000017.
+   * @param numPartitions number of partitions of the resulting RDD
    */
   def countApproxDistinctByKey(relativeSD: Double, numPartitions: Int): RDD[(K, Long)] = {
     countApproxDistinctByKey(relativeSD, new HashPartitioner(numPartitions))
   }
 
   /**
-   * Return approximate number of distinct values for each key this RDD.
-   * The accuracy of approximation can be controlled through the relative standard deviation
-   * (relativeSD) parameter, which also controls the amount of memory used. Lower values result in
-   * more accurate counts but increase the memory footprint and vice versa. The default value of
-   * relativeSD is 0.05. Hash-partitions the output RDD using the existing partitioner/parallelism
-   * level.
+   * Return approximate number of distinct values for each key in this RDD.
+   *
+   * The algorithm used is based on streamlib's implementation of "HyperLogLog in Practice:
+   * Algorithmic Engineering of a State of The Art Cardinality Estimation Algorithm", available
+   * <a href="http://dx.doi.org/10.1145/2452376.2452456">here</a>.
+   *
+   * @param relativeSD Relative accuracy. Smaller values create counters that require more space.
+   *                   It must be greater than 0.000017.
    */
   def countApproxDistinctByKey(relativeSD: Double = 0.05): RDD[(K, Long)] = {
     countApproxDistinctByKey(relativeSD, defaultPartitioner(self))
diff --git a/core/src/main/scala/org/apache/spark/rdd/RDD.scala b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
index aa03e9276fb3..585b2f76afa6 100644
--- a/core/src/main/scala/org/apache/spark/rdd/RDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
@@ -19,12 +19,11 @@ package org.apache.spark.rdd
 
 import java.util.Random
 
-import scala.collection.Map
-import scala.collection.mutable
+import scala.collection.{mutable, Map}
 import scala.collection.mutable.ArrayBuffer
 import scala.reflect.{classTag, ClassTag}
 
-import com.clearspring.analytics.stream.cardinality.HyperLogLog
+import com.clearspring.analytics.stream.cardinality.HyperLogLogPlus
 import org.apache.hadoop.io.BytesWritable
 import org.apache.hadoop.io.compress.CompressionCodec
 import org.apache.hadoop.io.NullWritable
@@ -41,7 +40,7 @@ import org.apache.spark.partial.CountEvaluator
 import org.apache.spark.partial.GroupedCountEvaluator
 import org.apache.spark.partial.PartialResult
 import org.apache.spark.storage.StorageLevel
-import org.apache.spark.util.{BoundedPriorityQueue, SerializableHyperLogLog, Utils}
+import org.apache.spark.util.{BoundedPriorityQueue, Utils}
 import org.apache.spark.util.collection.OpenHashMap
 import org.apache.spark.util.random.{BernoulliSampler, PoissonSampler}
 
@@ -921,15 +920,49 @@ abstract class RDD[T: ClassTag](
    * :: Experimental ::
    * Return approximate number of distinct elements in the RDD.
    *
-   * The accuracy of approximation can be controlled through the relative standard deviation
-   * (relativeSD) parameter, which also controls the amount of memory used. Lower values result in
-   * more accurate counts but increase the memory footprint and vise versa. The default value of
-   * relativeSD is 0.05.
+   * The algorithm used is based on streamlib's implementation of "HyperLogLog in Practice:
+   * Algorithmic Engineering of a State of The Art Cardinality Estimation Algorithm", available
+   * <a href="http://dx.doi.org/10.1145/2452376.2452456">here</a>.
+   *
+   * The relative accuracy is approximately `1.054 / sqrt(2^p)`. Setting a nonzero `sp > p`
+   * would trigger sparse representation of registers, which may reduce the memory consumption
+   * and increase accuracy when the cardinality is small.
+   *
+   * @param p The precision value for the normal set.
+   *          `p` must be a value between 4 and `sp` if `sp` is not zero (32 max).
+   * @param sp The precision value for the sparse set, between 0 and 32.
+   *           If `sp` equals 0, the sparse representation is skipped.
    */
   @Experimental
+  def countApproxDistinct(p: Int, sp: Int): Long = {
+    require(p >= 4, s"p ($p) must be greater than 0")
+    require(sp <= 32, s"sp ($sp) cannot be greater than 32")
+    require(sp == 0 || p <= sp, s"p ($p) cannot be greater than sp ($sp)")
+    val zeroCounter = new HyperLogLogPlus(p, sp)
+    aggregate(zeroCounter)(
+      (hll: HyperLogLogPlus, v: T) => {
+        hll.offer(v)
+        hll
+      },
+      (h1: HyperLogLogPlus, h2: HyperLogLogPlus) => {
+        h1.addAll(h2)
+        h2
+      }).cardinality()
+  }
+
+  /**
+   * Return approximate number of distinct elements in the RDD.
+   *
+   * The algorithm used is based on streamlib's implementation of "HyperLogLog in Practice:
+   * Algorithmic Engineering of a State of The Art Cardinality Estimation Algorithm", available
+   * <a href="http://dx.doi.org/10.1145/2452376.2452456">here</a>.
+   *
+   * @param relativeSD Relative accuracy. Smaller values create counters that require more space.
+   *                   It must be greater than 0.000017.
+   */
   def countApproxDistinct(relativeSD: Double = 0.05): Long = {
-    val zeroCounter = new SerializableHyperLogLog(new HyperLogLog(relativeSD))
-    aggregate(zeroCounter)(_.add(_), _.merge(_)).value.cardinality()
+    val p = math.ceil(2.0 * math.log(1.054 / relativeSD) / math.log(2)).toInt
+    countApproxDistinct(p, 0)
   }
 
   /**
diff --git a/core/src/main/scala/org/apache/spark/util/SerializableHyperLogLog.scala b/core/src/main/scala/org/apache/spark/util/SerializableHyperLogLog.scala
deleted file mode 100644
index 21a88eea3bbc..000000000000
--- a/core/src/main/scala/org/apache/spark/util/SerializableHyperLogLog.scala
+++ /dev/null
@@ -1,52 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.util
-
-import java.io.{Externalizable, ObjectInput, ObjectOutput}
-
-import com.clearspring.analytics.stream.cardinality.{HyperLogLog, ICardinality}
-
-/**
- * A wrapper around [[com.clearspring.analytics.stream.cardinality.HyperLogLog]] that is
- * serializable.
- */
-private[spark]
-class SerializableHyperLogLog(var value: ICardinality) extends Externalizable {
-
-  def this() = this(null)  // For deserialization
-
-  def merge(other: SerializableHyperLogLog) = new SerializableHyperLogLog(value.merge(other.value))
-
-  def add[T](elem: T) = {
-    this.value.offer(elem)
-    this
-  }
-
-  def readExternal(in: ObjectInput) {
-    val byteLength = in.readInt()
-    val bytes = new Array[Byte](byteLength)
-    in.readFully(bytes)
-    value = HyperLogLog.Builder.build(bytes)
-  }
-
-  def writeExternal(out: ObjectOutput) {
-    val bytes = value.getBytes()
-    out.writeInt(bytes.length)
-    out.write(bytes)
-  }
-}
diff --git a/core/src/test/java/org/apache/spark/JavaAPISuite.java b/core/src/test/java/org/apache/spark/JavaAPISuite.java
index 7193223addf6..b78309f81cb8 100644
--- a/core/src/test/java/org/apache/spark/JavaAPISuite.java
+++ b/core/src/test/java/org/apache/spark/JavaAPISuite.java
@@ -1028,27 +1028,23 @@ public void countApproxDistinct() {
       arrayData.add(i % size);
     }
     JavaRDD<Integer> simpleRdd = sc.parallelize(arrayData, 10);
-    Assert.assertTrue(Math.abs((simpleRdd.countApproxDistinct(0.2) - size) / (size * 1.0)) < 0.2);
-    Assert.assertTrue(Math.abs((simpleRdd.countApproxDistinct(0.05) - size) / (size * 1.0)) <= 0.05);
-    Assert.assertTrue(Math.abs((simpleRdd.countApproxDistinct(0.01) - size) / (size * 1.0)) <= 0.01);
+    Assert.assertTrue(Math.abs((simpleRdd.countApproxDistinct(0.05) - size) / (size * 1.0)) <= 0.1);
   }
 
   @Test
   public void countApproxDistinctByKey() {
-    double relativeSD = 0.001;
-
     List<Tuple2<Integer, Integer>> arrayData = new ArrayList<Tuple2<Integer, Integer>>();
     for (int i = 10; i < 100; i++)
       for (int j = 0; j < i; j++)
         arrayData.add(new Tuple2<Integer, Integer>(i, j));
 
     JavaPairRDD<Integer, Integer> pairRdd = sc.parallelizePairs(arrayData);
-    List<Tuple2<Integer, Object>> res =  pairRdd.countApproxDistinctByKey(relativeSD).collect();
+    List<Tuple2<Integer, Object>> res =  pairRdd.countApproxDistinctByKey(8, 0).collect();
     for (Tuple2<Integer, Object> resItem : res) {
       double count = (double)resItem._1();
       Long resCount = (Long)resItem._2();
       Double error = Math.abs((resCount - count) / count);
-      Assert.assertTrue(error < relativeSD);
+      Assert.assertTrue(error < 0.1);
     }
 
   }
diff --git a/core/src/test/scala/org/apache/spark/rdd/PairRDDFunctionsSuite.scala b/core/src/test/scala/org/apache/spark/rdd/PairRDDFunctionsSuite.scala
index 1230565ea5b7..9ddafc451878 100644
--- a/core/src/test/scala/org/apache/spark/rdd/PairRDDFunctionsSuite.scala
+++ b/core/src/test/scala/org/apache/spark/rdd/PairRDDFunctionsSuite.scala
@@ -119,28 +119,30 @@ class PairRDDFunctionsSuite extends FunSuite with SharedSparkContext {
      * relatively tight error bounds to check correctness of functionality rather than checking
      * whether the approximation conforms with the requested bound.
      */
-    val relativeSD = 0.001
+    val p = 20
+    val sp = 0
+    // When p = 20, the relative accuracy is about 0.001. So with high probability, the
+    // relative error should be smaller than the threshold 0.01 we use here.
+    val relativeSD = 0.01
 
     // For each value i, there are i tuples with first element equal to i.
     // Therefore, the expected count for key i would be i.
     val stacked = (1 to 100).flatMap(i => (1 to i).map(j => (i, j)))
     val rdd1 = sc.parallelize(stacked)
-    val counted1 = rdd1.countApproxDistinctByKey(relativeSD).collect()
-    counted1.foreach{
-      case(k, count) => assert(error(count, k) < relativeSD)
-    }
+    val counted1 = rdd1.countApproxDistinctByKey(p, sp).collect()
+    counted1.foreach { case (k, count) => assert(error(count, k) < relativeSD) }
 
-    val rnd = new Random()
+    val rnd = new Random(42)
 
     // The expected count for key num would be num
     val randStacked = (1 to 100).flatMap { i =>
-      val num = rnd.nextInt % 500
+      val num = rnd.nextInt() % 500
       (1 to num).map(j => (num, j))
     }
     val rdd2 = sc.parallelize(randStacked)
-    val counted2 = rdd2.countApproxDistinctByKey(relativeSD, 4).collect()
-    counted2.foreach{
-      case(k, count) => assert(error(count, k) < relativeSD)
+    val counted2 = rdd2.countApproxDistinctByKey(relativeSD).collect()
+    counted2.foreach { case (k, count) =>
+      assert(error(count, k) < relativeSD, s"${error(count, k)} < $relativeSD")
     }
   }
 
diff --git a/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala b/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala
index e686068f7a99..bbd0c1417836 100644
--- a/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala
+++ b/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala
@@ -73,10 +73,8 @@ class RDDSuite extends FunSuite with SharedSparkContext {
     val size = 100
     val uniformDistro = for (i <- 1 to 100000) yield i % size
     val simpleRdd = sc.makeRDD(uniformDistro)
-    assert(error(simpleRdd.countApproxDistinct(0.2), size) < 0.2)
-    assert(error(simpleRdd.countApproxDistinct(0.05), size) < 0.05)
-    assert(error(simpleRdd.countApproxDistinct(0.01), size) < 0.01)
-    assert(error(simpleRdd.countApproxDistinct(0.001), size) < 0.001)
+    assert(error(simpleRdd.countApproxDistinct(4, 0), size) < 0.4)
+    assert(error(simpleRdd.countApproxDistinct(8, 0), size) < 0.1)
   }
 
   test("SparkContext.union") {
diff --git a/pom.xml b/pom.xml
index 0a5ca9e72a29..fcd6f66b4414 100644
--- a/pom.xml
+++ b/pom.xml
@@ -300,9 +300,9 @@
       <dependency>
         <groupId>com.clearspring.analytics</groupId>
         <artifactId>stream</artifactId>
-        <version>2.5.1</version>
+        <version>2.7.0</version>
         <exclusions>
-          <!-- Only HyperLogLog is used, which doesn't depend on fastutil -->
+          <!-- Only HyperLogLogPlus is used, which doesn't depend on fastutil -->
           <exclusion>
             <groupId>it.unimi.dsi</groupId>
             <artifactId>fastutil</artifactId>
diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala
index fc9cbeaec647..fadf6a4d8bb4 100644
--- a/project/MimaExcludes.scala
+++ b/project/MimaExcludes.scala
@@ -16,7 +16,6 @@
  */
 
 import com.typesafe.tools.mima.core._
-import com.typesafe.tools.mima.core.ProblemFilters._
 
 /**
  * Additional excludes for checking of Spark's binary compatibility.
@@ -35,8 +34,27 @@ object MimaExcludes {
     val excludes =
       SparkBuild.SPARK_VERSION match {
         case v if v.startsWith("1.1") =>
+          Seq(MimaBuild.excludeSparkPackage("graphx")) ++
           Seq(
-            MimaBuild.excludeSparkPackage("graphx"))
+            // We made a mistake earlier (ed06500d3) in the Java API to use default parameter values
+            // for countApproxDistinct* functions, which does not work in Java. We later removed
+            // them, and use the following to tell Mima to not care about them.
+            ProblemFilters.exclude[IncompatibleResultTypeProblem](
+              "org.apache.spark.api.java.JavaPairRDD.countApproxDistinctByKey"),
+            ProblemFilters.exclude[IncompatibleResultTypeProblem](
+              "org.apache.spark.api.java.JavaPairRDD.countApproxDistinctByKey"),
+            ProblemFilters.exclude[MissingMethodProblem](
+              "org.apache.spark.api.java.JavaPairRDD.countApproxDistinct$default$1"),
+            ProblemFilters.exclude[MissingMethodProblem](
+              "org.apache.spark.api.java.JavaPairRDD.countApproxDistinctByKey$default$1"),
+            ProblemFilters.exclude[MissingMethodProblem](
+              "org.apache.spark.api.java.JavaRDD.countApproxDistinct$default$1"),
+            ProblemFilters.exclude[MissingMethodProblem](
+              "org.apache.spark.api.java.JavaRDDLike.countApproxDistinct$default$1"),
+            ProblemFilters.exclude[MissingMethodProblem](
+              "org.apache.spark.api.java.JavaDoubleRDD.countApproxDistinct$default$1")
+          ) ++
+          MimaBuild.excludeSparkClass("util.SerializableHyperLogLog")
         case v if v.startsWith("1.0") =>
           Seq(
             MimaBuild.excludeSparkPackage("api.java"),
diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index c2a20d86b2e6..efb0b9319be1 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -362,7 +362,7 @@ object SparkBuild extends Build {
         "com.twitter"               %% "chill"            % chillVersion excludeAll(excludeAsm),
         "com.twitter"                % "chill-java"       % chillVersion excludeAll(excludeAsm),
         "org.tachyonproject"         % "tachyon"          % "0.4.1-thrift" excludeAll(excludeHadoop, excludeCurator, excludeEclipseJetty, excludePowermock),
-        "com.clearspring.analytics"  % "stream"           % "2.5.1" excludeAll(excludeFastutil),
+        "com.clearspring.analytics"  % "stream"           % "2.7.0" excludeAll(excludeFastutil), // Only HyperLogLogPlus is used, which does not depend on fastutil.
         "org.spark-project"          % "pyrolite"         % "2.0.1",
         "net.sf.py4j"                % "py4j"             % "0.8.1"
       ),

From e8d93ee5284cb6a1d4551effe91ee8d233323329 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Tue, 3 Jun 2014 18:53:13 -0700
Subject: [PATCH 332/810] use env default python in merge_spark_pr.py

A minor change to use env default python instead of fixed `/usr/bin/python`.

Author: Xiangrui Meng <meng@databricks.com>

Closes #965 from mengxr/merge-pr-python and squashes the following commits:

1ae0013 [Xiangrui Meng] use env default python in merge_spark_pr.py
---
 dev/merge_spark_pr.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/dev/merge_spark_pr.py b/dev/merge_spark_pr.py
index 7f744d5589ef..e3ac32ef1a12 100755
--- a/dev/merge_spark_pr.py
+++ b/dev/merge_spark_pr.py
@@ -1,4 +1,4 @@
-#!/usr/bin/python
+#!/usr/bin/env python
 
 #
 # Licensed to the Apache Software Foundation (ASF) under one or more

From 5284ca78d17fb4de9a7019f3bbecf86484c13763 Mon Sep 17 00:00:00 2001
From: "Joseph E. Gonzalez" <joseph.e.gonzalez@gmail.com>
Date: Tue, 3 Jun 2014 20:49:14 -0700
Subject: [PATCH 333/810] Enable repartitioning of graph over different number
 of partitions

It is currently very difficult to repartition a graph over a different number of partitions.  This PR adds an additional `partitionBy` function that takes the number of partitions.

Author: Joseph E. Gonzalez <joseph.e.gonzalez@gmail.com>

Closes #719 from jegonzal/graph_partitioning_options and squashes the following commits:

730b405 [Joseph E. Gonzalez] adding an additional number of partitions option to partitionBy
---
 .../src/main/scala/org/apache/spark/graphx/Graph.scala | 10 ++++++++++
 .../org/apache/spark/graphx/PartitionStrategy.scala    |  8 +++++---
 .../scala/org/apache/spark/graphx/impl/GraphImpl.scala |  6 +++++-
 3 files changed, 20 insertions(+), 4 deletions(-)

diff --git a/graphx/src/main/scala/org/apache/spark/graphx/Graph.scala b/graphx/src/main/scala/org/apache/spark/graphx/Graph.scala
index c4f9d6514cae..14ae50e6657f 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/Graph.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/Graph.scala
@@ -106,9 +106,19 @@ abstract class Graph[VD: ClassTag, ED: ClassTag] protected () extends Serializab
 
   /**
    * Repartitions the edges in the graph according to `partitionStrategy`.
+   *
+   * @param the partitioning strategy to use when partitioning the edges in the graph.
    */
   def partitionBy(partitionStrategy: PartitionStrategy): Graph[VD, ED]
 
+  /**
+   * Repartitions the edges in the graph according to `partitionStrategy`.
+   *
+   * @param the partitioning strategy to use when partitioning the edges in the graph.
+   * @param numPartitions the number of edge partitions in the new graph.
+   */
+  def partitionBy(partitionStrategy: PartitionStrategy, numPartitions: Int): Graph[VD, ED]
+
   /**
    * Transforms each vertex attribute in the graph using the map function.
    *
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/PartitionStrategy.scala b/graphx/src/main/scala/org/apache/spark/graphx/PartitionStrategy.scala
index ef412cfd4e6e..5e7e72a764cc 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/PartitionStrategy.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/PartitionStrategy.scala
@@ -114,9 +114,11 @@ object PartitionStrategy {
    */
   case object CanonicalRandomVertexCut extends PartitionStrategy {
     override def getPartition(src: VertexId, dst: VertexId, numParts: PartitionID): PartitionID = {
-      val lower = math.min(src, dst)
-      val higher = math.max(src, dst)
-      math.abs((lower, higher).hashCode()) % numParts
+      if (src < dst) {
+        math.abs((src, dst).hashCode()) % numParts
+      } else {
+        math.abs((dst, src).hashCode()) % numParts
+      }
     }
   }
 
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/GraphImpl.scala b/graphx/src/main/scala/org/apache/spark/graphx/impl/GraphImpl.scala
index 59d9a8808e56..15ea05cbe281 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/impl/GraphImpl.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/impl/GraphImpl.scala
@@ -74,7 +74,11 @@ class GraphImpl[VD: ClassTag, ED: ClassTag] protected (
   }
 
   override def partitionBy(partitionStrategy: PartitionStrategy): Graph[VD, ED] = {
-    val numPartitions = edges.partitions.size
+    partitionBy(partitionStrategy, edges.partitions.size)
+  }
+
+  override def partitionBy(
+      partitionStrategy: PartitionStrategy, numPartitions: Int): Graph[VD, ED] = {
     val edTag = classTag[ED]
     val vdTag = classTag[VD]
     val newEdges = edges.withPartitionsRDD(edges.map { e =>

From ab7c62d57300c4033292d06ba840dad02f5552d7 Mon Sep 17 00:00:00 2001
From: Aaron Davidson <aaron@databricks.com>
Date: Tue, 3 Jun 2014 22:33:04 -0700
Subject: [PATCH 334/810] Update spark-ec2 scripts for 1.0.0 on master

The change was previously committed only to branch-1.0 as part of https://github.com/apache/spark/commit/a34e6fda1d6fb8e769c21db70845f1a6dde968d8

Author: Aaron Davidson <aaron@databricks.com>

This patch had conflicts when merged, resolved by
Committer: Patrick Wendell <pwendell@gmail.com>

Closes #938 from aarondav/sparkec2 and squashes the following commits:

067cc31 [Aaron Davidson] Update spark-ec2 scripts for 1.0.0 on master
---
 ec2/spark_ec2.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/ec2/spark_ec2.py b/ec2/spark_ec2.py
index 8b056f5ea734..3af9f66e17dc 100755
--- a/ec2/spark_ec2.py
+++ b/ec2/spark_ec2.py
@@ -83,7 +83,7 @@ def parse_args():
              "between zones applies)")
     parser.add_option("-a", "--ami", help="Amazon Machine Image ID to use")
     parser.add_option(
-        "-v", "--spark-version", default="0.9.1",
+        "-v", "--spark-version", default="1.0.0",
         help="Version of Spark to use: 'X.Y.Z' or a specific git hash")
     parser.add_option(
         "--spark-git-repo",
@@ -191,7 +191,8 @@ def is_active(instance):
 # Return correct versions of Spark and Shark, given the supplied Spark version
 def get_spark_shark_version(opts):
     spark_shark_map = {
-        "0.7.3": "0.7.1", "0.8.0": "0.8.0", "0.8.1": "0.8.1", "0.9.0": "0.9.0", "0.9.1": "0.9.1"
+        "0.7.3": "0.7.1", "0.8.0": "0.8.0", "0.8.1": "0.8.1", "0.9.0": "0.9.0", "0.9.1": "0.9.1",
+        "1.0.0": "1.0.0"
     }
     version = opts.spark_version.replace("v", "")
     if version not in spark_shark_map:
@@ -199,7 +200,6 @@ def get_spark_shark_version(opts):
         sys.exit(1)
     return (version, spark_shark_map[version])
 
-
 # Attempt to resolve an appropriate AMI given the architecture and
 # region of the request.
 def get_spark_ami(opts):

From 4ca06256690c5e03058dd179c2fc6437a917cfee Mon Sep 17 00:00:00 2001
From: Sean Owen <sowen@cloudera.com>
Date: Tue, 3 Jun 2014 22:37:20 -0700
Subject: [PATCH 335/810] SPARK-1806 (addendum) Use non-deprecated methods in
 Mesos 0.18

The update to Mesos 0.18 caused some deprecation warnings in the build. The change to the non-deprecated version is straightforward as it emulates what the Mesos driver does with the deprecated method anyway (https://github.com/apache/mesos/blob/c5aa1dd22155d79c5a7c33076319299a40fd63b3/src/sched/sched.cpp#L1354)

Author: Sean Owen <sowen@cloudera.com>

Closes #920 from srowen/SPARK-1806 and squashes the following commits:

8d76b6a [Sean Owen] Use non-deprecated methods in Mesos 0.18
---
 .../cluster/mesos/CoarseMesosSchedulerBackend.scala         | 6 ++++--
 .../scheduler/cluster/mesos/MesosSchedulerBackend.scala     | 2 +-
 2 files changed, 5 insertions(+), 3 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala
index cbe9bb093d1c..9f45400bcf85 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala
@@ -207,10 +207,12 @@ private[spark] class CoarseMesosSchedulerBackend(
             .addResources(createResource("cpus", cpusToUse))
             .addResources(createResource("mem", sc.executorMemory))
             .build()
-          d.launchTasks(offer.getId, Collections.singletonList(task), filters)
+          d.launchTasks(
+            Collections.singleton(offer.getId),  Collections.singletonList(task), filters)
         } else {
           // Filter it out
-          d.launchTasks(offer.getId, Collections.emptyList[MesosTaskInfo](), filters)
+          d.launchTasks(
+            Collections.singleton(offer.getId), Collections.emptyList[MesosTaskInfo](), filters)
         }
       }
     }
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala
index f08b19e6782e..a089a02d4217 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala
@@ -223,7 +223,7 @@ private[spark] class MesosSchedulerBackend(
         // Reply to the offers
         val filters = Filters.newBuilder().setRefuseSeconds(1).build() // TODO: lower timeout?
         for (i <- 0 until offers.size) {
-          d.launchTasks(offers(i).getId, mesosTasks(i), filters)
+          d.launchTasks(Collections.singleton(offers(i).getId), mesosTasks(i), filters)
         }
       }
     } finally {

From c402a4a685721d05932bbc578d997f330ff65a49 Mon Sep 17 00:00:00 2001
From: Kan Zhang <kzhang@apache.org>
Date: Tue, 3 Jun 2014 22:47:18 -0700
Subject: [PATCH 336/810] [SPARK-1817] RDD.zip() should verify partition sizes
 for each partition

RDD.zip() will throw an exception if it finds partition sizes are not the same.

Author: Kan Zhang <kzhang@apache.org>

Closes #944 from kanzhang/SPARK-1817 and squashes the following commits:

c073848 [Kan Zhang] [SPARK-1817] Cosmetic updates
524c670 [Kan Zhang] [SPARK-1817] RDD.zip() should verify partition sizes for each partition
---
 .../main/scala/org/apache/spark/rdd/RDD.scala | 14 ++-
 .../org/apache/spark/rdd/ZippedRDD.scala      | 87 -------------------
 .../org/apache/spark/CheckpointSuite.scala    | 26 +++---
 .../scala/org/apache/spark/rdd/RDDSuite.scala |  4 +
 project/MimaExcludes.scala                    |  2 +
 5 files changed, 33 insertions(+), 100 deletions(-)
 delete mode 100644 core/src/main/scala/org/apache/spark/rdd/ZippedRDD.scala

diff --git a/core/src/main/scala/org/apache/spark/rdd/RDD.scala b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
index 585b2f76afa6..54bdc3e7cbc7 100644
--- a/core/src/main/scala/org/apache/spark/rdd/RDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
@@ -654,7 +654,19 @@ abstract class RDD[T: ClassTag](
    * partitions* and the *same number of elements in each partition* (e.g. one was made through
    * a map on the other).
    */
-  def zip[U: ClassTag](other: RDD[U]): RDD[(T, U)] = new ZippedRDD(sc, this, other)
+  def zip[U: ClassTag](other: RDD[U]): RDD[(T, U)] = {
+    zipPartitions(other, true) { (thisIter, otherIter) =>
+      new Iterator[(T, U)] {
+        def hasNext = (thisIter.hasNext, otherIter.hasNext) match {
+          case (true, true) => true
+          case (false, false) => false
+          case _ => throw new SparkException("Can only zip RDDs with " +
+            "same number of elements in each partition")
+        }
+        def next = (thisIter.next, otherIter.next)
+      }
+    }
+  }
 
   /**
    * Zip this RDD's partitions with one (or more) RDD(s) and return a new RDD by
diff --git a/core/src/main/scala/org/apache/spark/rdd/ZippedRDD.scala b/core/src/main/scala/org/apache/spark/rdd/ZippedRDD.scala
deleted file mode 100644
index b8110ffc42f2..000000000000
--- a/core/src/main/scala/org/apache/spark/rdd/ZippedRDD.scala
+++ /dev/null
@@ -1,87 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.rdd
-
-import java.io.{IOException, ObjectOutputStream}
-
-import scala.reflect.ClassTag
-
-import org.apache.spark.{OneToOneDependency, Partition, SparkContext, TaskContext}
-
-private[spark] class ZippedPartition[T: ClassTag, U: ClassTag](
-    idx: Int,
-    @transient rdd1: RDD[T],
-    @transient rdd2: RDD[U]
-  ) extends Partition {
-
-  var partition1 = rdd1.partitions(idx)
-  var partition2 = rdd2.partitions(idx)
-  override val index: Int = idx
-
-  def partitions = (partition1, partition2)
-
-  @throws(classOf[IOException])
-  private def writeObject(oos: ObjectOutputStream) {
-    // Update the reference to parent partition at the time of task serialization
-    partition1 = rdd1.partitions(idx)
-    partition2 = rdd2.partitions(idx)
-    oos.defaultWriteObject()
-  }
-}
-
-private[spark] class ZippedRDD[T: ClassTag, U: ClassTag](
-    sc: SparkContext,
-    var rdd1: RDD[T],
-    var rdd2: RDD[U])
-  extends RDD[(T, U)](sc, List(new OneToOneDependency(rdd1), new OneToOneDependency(rdd2))) {
-
-  override def getPartitions: Array[Partition] = {
-    if (rdd1.partitions.size != rdd2.partitions.size) {
-      throw new IllegalArgumentException("Can't zip RDDs with unequal numbers of partitions")
-    }
-    val array = new Array[Partition](rdd1.partitions.size)
-    for (i <- 0 until rdd1.partitions.size) {
-      array(i) = new ZippedPartition(i, rdd1, rdd2)
-    }
-    array
-  }
-
-  override def compute(s: Partition, context: TaskContext): Iterator[(T, U)] = {
-    val (partition1, partition2) = s.asInstanceOf[ZippedPartition[T, U]].partitions
-    rdd1.iterator(partition1, context).zip(rdd2.iterator(partition2, context))
-  }
-
-  override def getPreferredLocations(s: Partition): Seq[String] = {
-    val (partition1, partition2) = s.asInstanceOf[ZippedPartition[T, U]].partitions
-    val pref1 = rdd1.preferredLocations(partition1)
-    val pref2 = rdd2.preferredLocations(partition2)
-    // Check whether there are any hosts that match both RDDs; otherwise return the union
-    val exactMatchLocations = pref1.intersect(pref2)
-    if (!exactMatchLocations.isEmpty) {
-      exactMatchLocations
-    } else {
-      (pref1 ++ pref2).distinct
-    }
-  }
-
-  override def clearDependencies() {
-    super.clearDependencies()
-    rdd1 = null
-    rdd2 = null
-  }
-}
diff --git a/core/src/test/scala/org/apache/spark/CheckpointSuite.scala b/core/src/test/scala/org/apache/spark/CheckpointSuite.scala
index 64933f4b1046..f64f3c903603 100644
--- a/core/src/test/scala/org/apache/spark/CheckpointSuite.scala
+++ b/core/src/test/scala/org/apache/spark/CheckpointSuite.scala
@@ -167,26 +167,28 @@ class CheckpointSuite extends FunSuite with LocalSparkContext with Logging {
     })
   }
 
-  test("ZippedRDD") {
-    testRDD(rdd => new ZippedRDD(sc, rdd, rdd.map(x => x)))
-    testRDDPartitions(rdd => new ZippedRDD(sc, rdd, rdd.map(x => x)))
+  test("ZippedPartitionsRDD") {
+    testRDD(rdd => rdd.zip(rdd.map(x => x)))
+    testRDDPartitions(rdd => rdd.zip(rdd.map(x => x)))
 
-    // Test that the ZippedPartition updates parent partitions
-    // after the parent RDD has been checkpointed and parent partitions have been changed.
-    // Note that this test is very specific to the current implementation of ZippedRDD.
+    // Test that ZippedPartitionsRDD updates parent partitions after parent RDDs have
+    // been checkpointed and parent partitions have been changed.
+    // Note that this test is very specific to the implementation of ZippedPartitionsRDD.
     val rdd = generateFatRDD()
-    val zippedRDD = new ZippedRDD(sc, rdd, rdd.map(x => x))
+    val zippedRDD = rdd.zip(rdd.map(x => x)).asInstanceOf[ZippedPartitionsRDD2[_, _, _]]
     zippedRDD.rdd1.checkpoint()
     zippedRDD.rdd2.checkpoint()
     val partitionBeforeCheckpoint =
-      serializeDeserialize(zippedRDD.partitions.head.asInstanceOf[ZippedPartition[_, _]])
+      serializeDeserialize(zippedRDD.partitions.head.asInstanceOf[ZippedPartitionsPartition])
     zippedRDD.count()
     val partitionAfterCheckpoint =
-      serializeDeserialize(zippedRDD.partitions.head.asInstanceOf[ZippedPartition[_, _]])
+      serializeDeserialize(zippedRDD.partitions.head.asInstanceOf[ZippedPartitionsPartition])
     assert(
-      partitionAfterCheckpoint.partition1.getClass != partitionBeforeCheckpoint.partition1.getClass &&
-        partitionAfterCheckpoint.partition2.getClass != partitionBeforeCheckpoint.partition2.getClass,
-      "ZippedRDD.partition1 and ZippedRDD.partition2 not updated after parent RDD is checkpointed"
+      partitionAfterCheckpoint.partitions(0).getClass !=
+        partitionBeforeCheckpoint.partitions(0).getClass &&
+      partitionAfterCheckpoint.partitions(1).getClass !=
+        partitionBeforeCheckpoint.partitions(1).getClass,
+      "ZippedPartitionsRDD partition 0 (or 1) not updated after parent RDDs are checkpointed"
     )
   }
 
diff --git a/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala b/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala
index bbd0c1417836..286e221e33b7 100644
--- a/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala
+++ b/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala
@@ -350,6 +350,10 @@ class RDDSuite extends FunSuite with SharedSparkContext {
     intercept[IllegalArgumentException] {
       nums.zip(sc.parallelize(1 to 4, 1)).collect()
     }
+
+    intercept[SparkException] {
+      nums.zip(sc.parallelize(1 to 5, 2)).collect()
+    }
   }
 
   test("partition pruning") {
diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala
index fadf6a4d8bb4..dd7efceb23c9 100644
--- a/project/MimaExcludes.scala
+++ b/project/MimaExcludes.scala
@@ -54,6 +54,8 @@ object MimaExcludes {
             ProblemFilters.exclude[MissingMethodProblem](
               "org.apache.spark.api.java.JavaDoubleRDD.countApproxDistinct$default$1")
           ) ++
+          MimaBuild.excludeSparkClass("rdd.ZippedRDD") ++
+          MimaBuild.excludeSparkClass("rdd.ZippedPartition") ++
           MimaBuild.excludeSparkClass("util.SerializableHyperLogLog")
         case v if v.startsWith("1.0") =>
           Seq(

From b8d25800393d0208a76813bcd94509ac24a3add5 Mon Sep 17 00:00:00 2001
From: Neville Li <neville@spotify.com>
Date: Wed, 4 Jun 2014 01:51:34 -0700
Subject: [PATCH 337/810] [MLLIB] set RDD names in ALS

This is very useful when debugging & fine tuning jobs with large data sets.

Author: Neville Li <neville@spotify.com>

Closes #966 from nevillelyh/master and squashes the following commits:

6747764 [Neville Li] [MLLIB] use string interpolation for RDD names
3b15d34 [Neville Li] [MLLIB] set RDD names in ALS
---
 .../apache/spark/mllib/recommendation/ALS.scala  | 16 +++++++++++-----
 1 file changed, 11 insertions(+), 5 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala
index cfc3b6860649..d743bd7dd182 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala
@@ -201,6 +201,10 @@ class ALS private (
     val (userInLinks, userOutLinks) = makeLinkRDDs(numBlocks, ratingsByUserBlock, partitioner)
     val (productInLinks, productOutLinks) =
         makeLinkRDDs(numBlocks, ratingsByProductBlock, partitioner)
+    userInLinks.setName("userInLinks")
+    userOutLinks.setName("userOutLinks")
+    productInLinks.setName("productInLinks")
+    productOutLinks.setName("productOutLinks")
 
     // Initialize user and product factors randomly, but use a deterministic seed for each
     // partition so that fault recovery works
@@ -225,14 +229,14 @@ class ALS private (
         // perform ALS update
         logInfo("Re-computing I given U (Iteration %d/%d)".format(iter, iterations))
         // Persist users because it will be called twice.
-        users.persist()
+        users.setName(s"users-$iter").persist()
         val YtY = Some(sc.broadcast(computeYtY(users)))
         val previousProducts = products
         products = updateFeatures(users, userOutLinks, productInLinks, partitioner, rank, lambda,
           alpha, YtY)
         previousProducts.unpersist()
         logInfo("Re-computing U given I (Iteration %d/%d)".format(iter, iterations))
-        products.persist()
+        products.setName(s"products-$iter").persist()
         val XtX = Some(sc.broadcast(computeYtY(products)))
         val previousUsers = users
         users = updateFeatures(products, productOutLinks, userInLinks, partitioner, rank, lambda,
@@ -245,22 +249,24 @@ class ALS private (
         logInfo("Re-computing I given U (Iteration %d/%d)".format(iter, iterations))
         products = updateFeatures(users, userOutLinks, productInLinks, partitioner, rank, lambda,
           alpha, YtY = None)
+        products.setName(s"products-$iter")
         logInfo("Re-computing U given I (Iteration %d/%d)".format(iter, iterations))
         users = updateFeatures(products, productOutLinks, userInLinks, partitioner, rank, lambda,
           alpha, YtY = None)
+        users.setName(s"users-$iter")
       }
     }
 
     // The last `products` will be used twice. One to generate the last `users` and the other to
     // generate `productsOut`. So we cache it for better performance.
-    products.persist()
+    products.setName("products").persist()
 
     // Flatten and cache the two final RDDs to un-block them
     val usersOut = unblockFactors(users, userOutLinks)
     val productsOut = unblockFactors(products, productOutLinks)
 
-    usersOut.persist()
-    productsOut.persist()
+    usersOut.setName("usersOut").persist()
+    productsOut.setName("productsOut").persist()
 
     // Materialize usersOut and productsOut.
     usersOut.count()

From d341b17c2a0a4fce04045e13fb4a3b0621296320 Mon Sep 17 00:00:00 2001
From: Sean Owen <sowen@cloudera.com>
Date: Wed, 4 Jun 2014 11:27:08 -0700
Subject: [PATCH 338/810] SPARK-1973. Add randomSplit to JavaRDD (with tests,
 and tidy Java tests)

I'd like to use randomSplit through the Java API, and would like to add a convenience wrapper for this method to JavaRDD. This is fairly trivial. (In fact, is the intent that JavaRDD not wrap every RDD method? and that sometimes users should just use JavaRDD.wrapRDD()?)

Along the way, I added tests for it, and also touched up the Java API test style and behavior. This is maybe the more useful part of this small change.

Author: Sean Owen <sowen@cloudera.com>
Author: Xiangrui Meng <meng@databricks.com>

This patch had conflicts when merged, resolved by
Committer: Xiangrui Meng <meng@databricks.com>

Closes #919 from srowen/SPARK-1973 and squashes the following commits:

148cb7b [Sean Owen] Some final Java test polish, while we are at it
1fc3f3e [Xiangrui Meng] more cleaning on Java 8 tests
9ebc57f [Sean Owen] Use accumulator instead of temp files to test foreach
5efb0be [Sean Owen] Add Java randomSplit, and unit tests (including for sample)
5dcc158 [Sean Owen] Simplified Java 8 test with new language features, and fixed the name of MLB's greatest team
91a1769 [Sean Owen] Touch up minor style issues in existing Java API suite test
---
 .../org/apache/spark/api/java/JavaRDD.scala   |  22 +
 .../java/org/apache/spark/JavaAPISuite.java   | 193 ++++-----
 .../java/org/apache/spark/Java8APISuite.java  |  96 +++--
 .../apache/spark/streaming/Java8APISuite.java | 381 +++++++++---------
 4 files changed, 358 insertions(+), 334 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaRDD.scala b/core/src/main/scala/org/apache/spark/api/java/JavaRDD.scala
index dc698dea75e4..23d13710794a 100644
--- a/core/src/main/scala/org/apache/spark/api/java/JavaRDD.scala
+++ b/core/src/main/scala/org/apache/spark/api/java/JavaRDD.scala
@@ -108,6 +108,28 @@ class JavaRDD[T](val rdd: RDD[T])(implicit val classTag: ClassTag[T])
   def sample(withReplacement: Boolean, fraction: Double, seed: Long): JavaRDD[T] =
     wrapRDD(rdd.sample(withReplacement, fraction, seed))
 
+
+  /**
+   * Randomly splits this RDD with the provided weights.
+   *
+   * @param weights weights for splits, will be normalized if they don't sum to 1
+   *
+   * @return split RDDs in an array
+   */
+  def randomSplit(weights: Array[Double]): Array[JavaRDD[T]] =
+    randomSplit(weights, Utils.random.nextLong)
+
+  /**
+   * Randomly splits this RDD with the provided weights.
+   *
+   * @param weights weights for splits, will be normalized if they don't sum to 1
+   * @param seed random seed
+   *
+   * @return split RDDs in an array
+   */
+  def randomSplit(weights: Array[Double], seed: Long): Array[JavaRDD[T]] =
+    rdd.randomSplit(weights, seed).map(wrapRDD)
+
   /**
    * Return the union of this RDD and another one. Any identical elements will appear multiple
    * times (use `.distinct()` to eliminate them).
diff --git a/core/src/test/java/org/apache/spark/JavaAPISuite.java b/core/src/test/java/org/apache/spark/JavaAPISuite.java
index b78309f81cb8..50a62129116f 100644
--- a/core/src/test/java/org/apache/spark/JavaAPISuite.java
+++ b/core/src/test/java/org/apache/spark/JavaAPISuite.java
@@ -23,6 +23,7 @@
 import scala.Tuple2;
 
 import com.google.common.collect.Iterables;
+import com.google.common.collect.Iterators;
 import com.google.common.collect.Lists;
 import com.google.common.base.Optional;
 import com.google.common.base.Charsets;
@@ -48,7 +49,6 @@
 import org.apache.spark.partial.PartialResult;
 import org.apache.spark.storage.StorageLevel;
 import org.apache.spark.util.StatCounter;
-import org.apache.spark.util.Utils;
 
 // The test suite itself is Serializable so that anonymous Function implementations can be
 // serialized, as an alternative to converting these anonymous classes to static inner classes;
@@ -70,16 +70,6 @@ public void tearDown() {
     sc = null;
   }
 
-  static class ReverseIntComparator implements Comparator<Integer>, Serializable {
-
-    @Override
-    public int compare(Integer a, Integer b) {
-      if (a > b) return -1;
-      else if (a < b) return 1;
-      else return 0;
-    }
-  }
-
   @SuppressWarnings("unchecked")
   @Test
   public void sparkContextUnion() {
@@ -124,7 +114,7 @@ public void intersection() {
     JavaRDD<Integer> intersections = s1.intersection(s2);
     Assert.assertEquals(3, intersections.count());
 
-    ArrayList<Integer> list = new ArrayList<Integer>();
+    List<Integer> list = new ArrayList<Integer>();
     JavaRDD<Integer> empty = sc.parallelize(list);
     JavaRDD<Integer> emptyIntersection = empty.intersection(s2);
     Assert.assertEquals(0, emptyIntersection.count());
@@ -144,6 +134,28 @@ public void intersection() {
     Assert.assertEquals(2, pIntersection.count());
   }
 
+  @Test
+  public void sample() {
+    List<Integer> ints = Arrays.asList(1, 2, 3, 4, 5, 6, 7, 8, 9, 10);
+    JavaRDD<Integer> rdd = sc.parallelize(ints);
+    JavaRDD<Integer> sample20 = rdd.sample(true, 0.2, 11);
+    // expected 2 but of course result varies randomly a bit
+    Assert.assertEquals(3, sample20.count());
+    JavaRDD<Integer> sample20NoReplacement = rdd.sample(false, 0.2, 11);
+    Assert.assertEquals(2, sample20NoReplacement.count());
+  }
+
+  @Test
+  public void randomSplit() {
+    List<Integer> ints = Arrays.asList(1, 2, 3, 4, 5, 6, 7, 8, 9, 10);
+    JavaRDD<Integer> rdd = sc.parallelize(ints);
+    JavaRDD<Integer>[] splits = rdd.randomSplit(new double[] { 0.4, 0.6, 1.0 }, 11);
+    Assert.assertEquals(3, splits.length);
+    Assert.assertEquals(2, splits[0].count());
+    Assert.assertEquals(3, splits[1].count());
+    Assert.assertEquals(5, splits[2].count());
+  }
+
   @Test
   public void sortByKey() {
     List<Tuple2<Integer, Integer>> pairs = new ArrayList<Tuple2<Integer, Integer>>();
@@ -161,26 +173,24 @@ public void sortByKey() {
     Assert.assertEquals(new Tuple2<Integer, Integer>(3, 2), sortedPairs.get(2));
 
     // Custom comparator
-    sortedRDD = rdd.sortByKey(new ReverseIntComparator(), false);
+    sortedRDD = rdd.sortByKey(Collections.<Integer>reverseOrder(), false);
     Assert.assertEquals(new Tuple2<Integer, Integer>(-1, 1), sortedRDD.first());
     sortedPairs = sortedRDD.collect();
     Assert.assertEquals(new Tuple2<Integer, Integer>(0, 4), sortedPairs.get(1));
     Assert.assertEquals(new Tuple2<Integer, Integer>(3, 2), sortedPairs.get(2));
   }
 
-  static int foreachCalls = 0;
-
   @Test
   public void foreach() {
-    foreachCalls = 0;
+    final Accumulator<Integer> accum = sc.accumulator(0);
     JavaRDD<String> rdd = sc.parallelize(Arrays.asList("Hello", "World"));
     rdd.foreach(new VoidFunction<String>() {
       @Override
-      public void call(String s) {
-        foreachCalls++;
+      public void call(String s) throws IOException {
+        accum.add(1);
       }
     });
-    Assert.assertEquals(2, foreachCalls);
+    Assert.assertEquals(2, accum.value().intValue());
   }
 
   @Test
@@ -188,7 +198,7 @@ public void toLocalIterator() {
     List<Integer> correct = Arrays.asList(1, 2, 3, 4);
     JavaRDD<Integer> rdd = sc.parallelize(correct);
     List<Integer> result = Lists.newArrayList(rdd.toLocalIterator());
-    Assert.assertTrue(correct.equals(result));
+    Assert.assertEquals(correct, result);
   }
 
   @Test
@@ -196,7 +206,7 @@ public void zipWithUniqueId() {
     List<Integer> dataArray = Arrays.asList(1, 2, 3, 4);
     JavaPairRDD<Integer, Long> zip = sc.parallelize(dataArray).zipWithUniqueId();
     JavaRDD<Long> indexes = zip.values();
-    Assert.assertTrue(new HashSet<Long>(indexes.collect()).size() == 4);
+    Assert.assertEquals(4, new HashSet<Long>(indexes.collect()).size());
   }
 
   @Test
@@ -205,7 +215,7 @@ public void zipWithIndex() {
     JavaPairRDD<Integer, Long> zip = sc.parallelize(dataArray).zipWithIndex();
     JavaRDD<Long> indexes = zip.values();
     List<Long> correctIndexes = Arrays.asList(0L, 1L, 2L, 3L);
-    Assert.assertTrue(indexes.collect().equals(correctIndexes));
+    Assert.assertEquals(correctIndexes, indexes.collect());
   }
 
   @SuppressWarnings("unchecked")
@@ -252,8 +262,10 @@ public void cogroup() {
       new Tuple2<String, Integer>("Oranges", 2),
       new Tuple2<String, Integer>("Apples", 3)
     ));
-    JavaPairRDD<String, Tuple2<Iterable<String>, Iterable<Integer>>> cogrouped = categories.cogroup(prices);
-    Assert.assertEquals("[Fruit, Citrus]", Iterables.toString(cogrouped.lookup("Oranges").get(0)._1()));
+    JavaPairRDD<String, Tuple2<Iterable<String>, Iterable<Integer>>> cogrouped =
+        categories.cogroup(prices);
+    Assert.assertEquals("[Fruit, Citrus]",
+                        Iterables.toString(cogrouped.lookup("Oranges").get(0)._1()));
     Assert.assertEquals("[2]", Iterables.toString(cogrouped.lookup("Oranges").get(0)._2()));
 
     cogrouped.collect();
@@ -281,8 +293,7 @@ public void leftOuterJoin() {
       rdd1.leftOuterJoin(rdd2).filter(
         new Function<Tuple2<Integer, Tuple2<Integer, Optional<Character>>>, Boolean>() {
           @Override
-          public Boolean call(Tuple2<Integer, Tuple2<Integer, Optional<Character>>> tup)
-            throws Exception {
+          public Boolean call(Tuple2<Integer, Tuple2<Integer, Optional<Character>>> tup) {
             return !tup._2()._2().isPresent();
           }
       }).first();
@@ -356,8 +367,7 @@ public Integer call(Integer a, Integer b) {
     Assert.assertEquals(2, localCounts.get(2).intValue());
     Assert.assertEquals(3, localCounts.get(3).intValue());
 
-   localCounts = rdd.reduceByKeyLocally(new Function2<Integer, Integer,
-      Integer>() {
+    localCounts = rdd.reduceByKeyLocally(new Function2<Integer, Integer, Integer>() {
       @Override
       public Integer call(Integer a, Integer b) {
         return a + b;
@@ -448,16 +458,17 @@ public void map() {
     JavaDoubleRDD doubles = rdd.mapToDouble(new DoubleFunction<Integer>() {
       @Override
       public double call(Integer x) {
-        return 1.0 * x;
+        return x.doubleValue();
       }
     }).cache();
     doubles.collect();
-    JavaPairRDD<Integer, Integer> pairs = rdd.mapToPair(new PairFunction<Integer, Integer, Integer>() {
-      @Override
-      public Tuple2<Integer, Integer> call(Integer x) {
-        return new Tuple2<Integer, Integer>(x, x);
-      }
-    }).cache();
+    JavaPairRDD<Integer, Integer> pairs = rdd.mapToPair(
+        new PairFunction<Integer, Integer, Integer>() {
+          @Override
+          public Tuple2<Integer, Integer> call(Integer x) {
+            return new Tuple2<Integer, Integer>(x, x);
+          }
+        }).cache();
     pairs.collect();
     JavaRDD<String> strings = rdd.map(new Function<Integer, String>() {
       @Override
@@ -487,7 +498,9 @@ public Iterable<String> call(String x) {
         @Override
         public Iterable<Tuple2<String, String>> call(String s) {
           List<Tuple2<String, String>> pairs = new LinkedList<Tuple2<String, String>>();
-          for (String word : s.split(" ")) pairs.add(new Tuple2<String, String>(word, word));
+          for (String word : s.split(" ")) {
+            pairs.add(new Tuple2<String, String>(word, word));
+          }
           return pairs;
         }
       }
@@ -499,7 +512,9 @@ public Iterable<Tuple2<String, String>> call(String s) {
       @Override
       public Iterable<Double> call(String s) {
         List<Double> lengths = new LinkedList<Double>();
-        for (String word : s.split(" ")) lengths.add(word.length() * 1.0);
+        for (String word : s.split(" ")) {
+          lengths.add((double) word.length());
+        }
         return lengths;
       }
     });
@@ -521,7 +536,7 @@ public void mapsFromPairsToPairs() {
       JavaPairRDD<String, Integer> swapped = pairRDD.flatMapToPair(
           new PairFlatMapFunction<Tuple2<Integer, String>, String, Integer>() {
           @Override
-          public Iterable<Tuple2<String, Integer>> call(Tuple2<Integer, String> item) throws Exception {
+          public Iterable<Tuple2<String, Integer>> call(Tuple2<Integer, String> item) {
               return Collections.singletonList(item.swap());
           }
       });
@@ -530,7 +545,7 @@ public Iterable<Tuple2<String, Integer>> call(Tuple2<Integer, String> item) thro
       // There was never a bug here, but it's worth testing:
       pairRDD.mapToPair(new PairFunction<Tuple2<Integer, String>, String, Integer>() {
           @Override
-          public Tuple2<String, Integer> call(Tuple2<Integer, String> item) throws Exception {
+          public Tuple2<String, Integer> call(Tuple2<Integer, String> item) {
               return item.swap();
           }
       }).collect();
@@ -631,14 +646,10 @@ public void wholeTextFiles() throws IOException {
     byte[] content2 = "spark is also easy to use.\n".getBytes("utf-8");
 
     String tempDirName = tempDir.getAbsolutePath();
-    DataOutputStream ds = new DataOutputStream(new FileOutputStream(tempDirName + "/part-00000"));
-    ds.write(content1);
-    ds.close();
-    ds = new DataOutputStream(new FileOutputStream(tempDirName + "/part-00001"));
-    ds.write(content2);
-    ds.close();
-
-    HashMap<String, String> container = new HashMap<String, String>();
+    Files.write(content1, new File(tempDirName + "/part-00000"));
+    Files.write(content2, new File(tempDirName + "/part-00001"));
+
+    Map<String, String> container = new HashMap<String, String>();
     container.put(tempDirName+"/part-00000", new Text(content1).toString());
     container.put(tempDirName+"/part-00001", new Text(content2).toString());
 
@@ -844,7 +855,7 @@ public void zip() {
     JavaDoubleRDD doubles = rdd.mapToDouble(new DoubleFunction<Integer>() {
       @Override
       public double call(Integer x) {
-        return 1.0 * x;
+        return x.doubleValue();
       }
     });
     JavaPairRDD<Integer, Double> zipped = rdd.zip(doubles);
@@ -859,17 +870,7 @@ public void zipPartitions() {
       new FlatMapFunction2<Iterator<Integer>, Iterator<String>, Integer>() {
         @Override
         public Iterable<Integer> call(Iterator<Integer> i, Iterator<String> s) {
-          int sizeI = 0;
-          int sizeS = 0;
-          while (i.hasNext()) {
-            sizeI += 1;
-            i.next();
-          }
-          while (s.hasNext()) {
-            sizeS += 1;
-            s.next();
-          }
-          return Arrays.asList(sizeI, sizeS);
+          return Arrays.asList(Iterators.size(i), Iterators.size(s));
         }
       };
 
@@ -883,6 +884,7 @@ public void accumulators() {
 
     final Accumulator<Integer> intAccum = sc.intAccumulator(10);
     rdd.foreach(new VoidFunction<Integer>() {
+      @Override
       public void call(Integer x) {
         intAccum.add(x);
       }
@@ -891,6 +893,7 @@ public void call(Integer x) {
 
     final Accumulator<Double> doubleAccum = sc.doubleAccumulator(10.0);
     rdd.foreach(new VoidFunction<Integer>() {
+      @Override
       public void call(Integer x) {
         doubleAccum.add((double) x);
       }
@@ -899,14 +902,17 @@ public void call(Integer x) {
 
     // Try a custom accumulator type
     AccumulatorParam<Float> floatAccumulatorParam = new AccumulatorParam<Float>() {
+      @Override
       public Float addInPlace(Float r, Float t) {
         return r + t;
       }
 
+      @Override
       public Float addAccumulator(Float r, Float t) {
         return r + t;
       }
 
+      @Override
       public Float zero(Float initialValue) {
         return 0.0f;
       }
@@ -914,6 +920,7 @@ public Float zero(Float initialValue) {
 
     final Accumulator<Float> floatAccum = sc.accumulator(10.0f, floatAccumulatorParam);
     rdd.foreach(new VoidFunction<Integer>() {
+      @Override
       public void call(Integer x) {
         floatAccum.add((float) x);
       }
@@ -929,7 +936,8 @@ public void call(Integer x) {
   public void keyBy() {
     JavaRDD<Integer> rdd = sc.parallelize(Arrays.asList(1, 2));
     List<Tuple2<String, Integer>> s = rdd.keyBy(new Function<Integer, String>() {
-      public String call(Integer t) throws Exception {
+      @Override
+      public String call(Integer t) {
         return t.toString();
       }
     }).collect();
@@ -941,10 +949,10 @@ public String call(Integer t) throws Exception {
   public void checkpointAndComputation() {
     JavaRDD<Integer> rdd = sc.parallelize(Arrays.asList(1, 2, 3, 4, 5));
     sc.setCheckpointDir(tempDir.getAbsolutePath());
-    Assert.assertEquals(false, rdd.isCheckpointed());
+    Assert.assertFalse(rdd.isCheckpointed());
     rdd.checkpoint();
     rdd.count(); // Forces the DAG to cause a checkpoint
-    Assert.assertEquals(true, rdd.isCheckpointed());
+    Assert.assertTrue(rdd.isCheckpointed());
     Assert.assertEquals(Arrays.asList(1, 2, 3, 4, 5), rdd.collect());
   }
 
@@ -952,10 +960,10 @@ public void checkpointAndComputation() {
   public void checkpointAndRestore() {
     JavaRDD<Integer> rdd = sc.parallelize(Arrays.asList(1, 2, 3, 4, 5));
     sc.setCheckpointDir(tempDir.getAbsolutePath());
-    Assert.assertEquals(false, rdd.isCheckpointed());
+    Assert.assertFalse(rdd.isCheckpointed());
     rdd.checkpoint();
     rdd.count(); // Forces the DAG to cause a checkpoint
-    Assert.assertEquals(true, rdd.isCheckpointed());
+    Assert.assertTrue(rdd.isCheckpointed());
 
     Assert.assertTrue(rdd.getCheckpointFile().isPresent());
     JavaRDD<Integer> recovered = sc.checkpointFile(rdd.getCheckpointFile().get());
@@ -966,16 +974,17 @@ public void checkpointAndRestore() {
   @Test
   public void mapOnPairRDD() {
     JavaRDD<Integer> rdd1 = sc.parallelize(Arrays.asList(1,2,3,4));
-    JavaPairRDD<Integer, Integer> rdd2 = rdd1.mapToPair(new PairFunction<Integer, Integer, Integer>() {
-      @Override
-      public Tuple2<Integer, Integer> call(Integer i) throws Exception {
-        return new Tuple2<Integer, Integer>(i, i % 2);
-      }
-    });
+    JavaPairRDD<Integer, Integer> rdd2 = rdd1.mapToPair(
+        new PairFunction<Integer, Integer, Integer>() {
+          @Override
+          public Tuple2<Integer, Integer> call(Integer i) {
+            return new Tuple2<Integer, Integer>(i, i % 2);
+          }
+        });
     JavaPairRDD<Integer, Integer> rdd3 = rdd2.mapToPair(
         new PairFunction<Tuple2<Integer, Integer>, Integer, Integer>() {
       @Override
-      public Tuple2<Integer, Integer> call(Tuple2<Integer, Integer> in) throws Exception {
+      public Tuple2<Integer, Integer> call(Tuple2<Integer, Integer> in) {
         return new Tuple2<Integer, Integer>(in._2(), in._1());
       }
     });
@@ -992,14 +1001,15 @@ public Tuple2<Integer, Integer> call(Tuple2<Integer, Integer> in) throws Excepti
   public void collectPartitions() {
     JavaRDD<Integer> rdd1 = sc.parallelize(Arrays.asList(1, 2, 3, 4, 5, 6, 7), 3);
 
-    JavaPairRDD<Integer, Integer> rdd2 = rdd1.mapToPair(new PairFunction<Integer, Integer, Integer>() {
-      @Override
-      public Tuple2<Integer, Integer> call(Integer i) throws Exception {
-        return new Tuple2<Integer, Integer>(i, i % 2);
-      }
-    });
+    JavaPairRDD<Integer, Integer> rdd2 = rdd1.mapToPair(
+        new PairFunction<Integer, Integer, Integer>() {
+          @Override
+          public Tuple2<Integer, Integer> call(Integer i) {
+            return new Tuple2<Integer, Integer>(i, i % 2);
+          }
+        });
 
-    List[] parts = rdd1.collectPartitions(new int[] {0});
+    List<Integer>[] parts = rdd1.collectPartitions(new int[] {0});
     Assert.assertEquals(Arrays.asList(1, 2), parts[0]);
 
     parts = rdd1.collectPartitions(new int[] {1, 2});
@@ -1010,14 +1020,14 @@ public Tuple2<Integer, Integer> call(Integer i) throws Exception {
                                       new Tuple2<Integer, Integer>(2, 0)),
                         rdd2.collectPartitions(new int[] {0})[0]);
 
-    parts = rdd2.collectPartitions(new int[] {1, 2});
+    List<Tuple2<Integer,Integer>>[] parts2 = rdd2.collectPartitions(new int[] {1, 2});
     Assert.assertEquals(Arrays.asList(new Tuple2<Integer, Integer>(3, 1),
                                       new Tuple2<Integer, Integer>(4, 0)),
-                        parts[0]);
+                        parts2[0]);
     Assert.assertEquals(Arrays.asList(new Tuple2<Integer, Integer>(5, 1),
                                       new Tuple2<Integer, Integer>(6, 0),
                                       new Tuple2<Integer, Integer>(7, 1)),
-                        parts[1]);
+                        parts2[1]);
   }
 
   @Test
@@ -1034,10 +1044,12 @@ public void countApproxDistinct() {
   @Test
   public void countApproxDistinctByKey() {
     List<Tuple2<Integer, Integer>> arrayData = new ArrayList<Tuple2<Integer, Integer>>();
-    for (int i = 10; i < 100; i++)
-      for (int j = 0; j < i; j++)
+    for (int i = 10; i < 100; i++) {
+      for (int j = 0; j < i; j++) {
         arrayData.add(new Tuple2<Integer, Integer>(i, j));
-
+      }
+    }
+    double relativeSD = 0.001;
     JavaPairRDD<Integer, Integer> pairRdd = sc.parallelizePairs(arrayData);
     List<Tuple2<Integer, Object>> res =  pairRdd.countApproxDistinctByKey(8, 0).collect();
     for (Tuple2<Integer, Object> resItem : res) {
@@ -1053,12 +1065,13 @@ public void countApproxDistinctByKey() {
   public void collectAsMapWithIntArrayValues() {
     // Regression test for SPARK-1040
     JavaRDD<Integer> rdd = sc.parallelize(Arrays.asList(1));
-    JavaPairRDD<Integer, int[]> pairRDD = rdd.mapToPair(new PairFunction<Integer, Integer, int[]>() {
-      @Override
-      public Tuple2<Integer, int[]> call(Integer x) throws Exception {
-        return new Tuple2<Integer, int[]>(x, new int[] { x });
-      }
-    });
+    JavaPairRDD<Integer, int[]> pairRDD = rdd.mapToPair(
+        new PairFunction<Integer, Integer, int[]>() {
+          @Override
+          public Tuple2<Integer, int[]> call(Integer x) {
+            return new Tuple2<Integer, int[]>(x, new int[] { x });
+          }
+        });
     pairRDD.collect();  // Works fine
     pairRDD.collectAsMap();  // Used to crash with ClassCastException
   }
diff --git a/extras/java8-tests/src/test/java/org/apache/spark/Java8APISuite.java b/extras/java8-tests/src/test/java/org/apache/spark/Java8APISuite.java
index c366c10b15a2..729bc0459ce5 100644
--- a/extras/java8-tests/src/test/java/org/apache/spark/Java8APISuite.java
+++ b/extras/java8-tests/src/test/java/org/apache/spark/Java8APISuite.java
@@ -99,16 +99,16 @@ public void groupBy() {
   @Test
   public void leftOuterJoin() {
     JavaPairRDD<Integer, Integer> rdd1 = sc.parallelizePairs(Arrays.asList(
-      new Tuple2<Integer, Integer>(1, 1),
-      new Tuple2<Integer, Integer>(1, 2),
-      new Tuple2<Integer, Integer>(2, 1),
-      new Tuple2<Integer, Integer>(3, 1)
+      new Tuple2<>(1, 1),
+      new Tuple2<>(1, 2),
+      new Tuple2<>(2, 1),
+      new Tuple2<>(3, 1)
     ));
     JavaPairRDD<Integer, Character> rdd2 = sc.parallelizePairs(Arrays.asList(
-      new Tuple2<Integer, Character>(1, 'x'),
-      new Tuple2<Integer, Character>(2, 'y'),
-      new Tuple2<Integer, Character>(2, 'z'),
-      new Tuple2<Integer, Character>(4, 'w')
+      new Tuple2<>(1, 'x'),
+      new Tuple2<>(2, 'y'),
+      new Tuple2<>(2, 'z'),
+      new Tuple2<>(4, 'w')
     ));
     List<Tuple2<Integer, Tuple2<Integer, Optional<Character>>>> joined =
       rdd1.leftOuterJoin(rdd2).collect();
@@ -133,11 +133,11 @@ public void foldReduce() {
   @Test
   public void foldByKey() {
     List<Tuple2<Integer, Integer>> pairs = Arrays.asList(
-      new Tuple2<Integer, Integer>(2, 1),
-      new Tuple2<Integer, Integer>(2, 1),
-      new Tuple2<Integer, Integer>(1, 1),
-      new Tuple2<Integer, Integer>(3, 2),
-      new Tuple2<Integer, Integer>(3, 1)
+      new Tuple2<>(2, 1),
+      new Tuple2<>(2, 1),
+      new Tuple2<>(1, 1),
+      new Tuple2<>(3, 2),
+      new Tuple2<>(3, 1)
     );
     JavaPairRDD<Integer, Integer> rdd = sc.parallelizePairs(pairs);
     JavaPairRDD<Integer, Integer> sums = rdd.foldByKey(0, (a, b) -> a + b);
@@ -149,11 +149,11 @@ public void foldByKey() {
   @Test
   public void reduceByKey() {
     List<Tuple2<Integer, Integer>> pairs = Arrays.asList(
-      new Tuple2<Integer, Integer>(2, 1),
-      new Tuple2<Integer, Integer>(2, 1),
-      new Tuple2<Integer, Integer>(1, 1),
-      new Tuple2<Integer, Integer>(3, 2),
-      new Tuple2<Integer, Integer>(3, 1)
+      new Tuple2<>(2, 1),
+      new Tuple2<>(2, 1),
+      new Tuple2<>(1, 1),
+      new Tuple2<>(3, 2),
+      new Tuple2<>(3, 1)
     );
     JavaPairRDD<Integer, Integer> rdd = sc.parallelizePairs(pairs);
     JavaPairRDD<Integer, Integer> counts = rdd.reduceByKey((a, b) -> a + b);
@@ -177,7 +177,7 @@ public void map() {
     JavaRDD<Integer> rdd = sc.parallelize(Arrays.asList(1, 2, 3, 4, 5));
     JavaDoubleRDD doubles = rdd.mapToDouble(x -> 1.0 * x).cache();
     doubles.collect();
-    JavaPairRDD<Integer, Integer> pairs = rdd.mapToPair(x -> new Tuple2<Integer, Integer>(x, x))
+    JavaPairRDD<Integer, Integer> pairs = rdd.mapToPair(x -> new Tuple2<>(x, x))
       .cache();
     pairs.collect();
     JavaRDD<String> strings = rdd.map(x -> x.toString()).cache();
@@ -194,31 +194,31 @@ public void flatMap() {
     Assert.assertEquals(11, words.count());
 
     JavaPairRDD<String, String> pairs = rdd.flatMapToPair(s -> {
-      List<Tuple2<String, String>> pairs2 = new LinkedList<Tuple2<String, String>>();
-      for (String word : s.split(" ")) pairs2.add(new Tuple2<String, String>(word, word));
+      List<Tuple2<String, String>> pairs2 = new LinkedList<>();
+      for (String word : s.split(" ")) pairs2.add(new Tuple2<>(word, word));
       return pairs2;
     });
 
-    Assert.assertEquals(new Tuple2<String, String>("Hello", "Hello"), pairs.first());
+    Assert.assertEquals(new Tuple2<>("Hello", "Hello"), pairs.first());
     Assert.assertEquals(11, pairs.count());
 
     JavaDoubleRDD doubles = rdd.flatMapToDouble(s -> {
-      List<Double> lengths = new LinkedList<Double>();
+      List<Double> lengths = new LinkedList<>();
       for (String word : s.split(" ")) lengths.add(word.length() * 1.0);
       return lengths;
     });
 
     Double x = doubles.first();
-    Assert.assertEquals(5.0, doubles.first().doubleValue(), 0.01);
+    Assert.assertEquals(5.0, doubles.first(), 0.01);
     Assert.assertEquals(11, pairs.count());
   }
 
   @Test
   public void mapsFromPairsToPairs() {
     List<Tuple2<Integer, String>> pairs = Arrays.asList(
-      new Tuple2<Integer, String>(1, "a"),
-      new Tuple2<Integer, String>(2, "aa"),
-      new Tuple2<Integer, String>(3, "aaa")
+      new Tuple2<>(1, "a"),
+      new Tuple2<>(2, "aa"),
+      new Tuple2<>(3, "aaa")
     );
     JavaPairRDD<Integer, String> pairRDD = sc.parallelizePairs(pairs);
 
@@ -251,19 +251,18 @@ public void sequenceFile() {
     tempDir.deleteOnExit();
     String outputDir = new File(tempDir, "output").getAbsolutePath();
     List<Tuple2<Integer, String>> pairs = Arrays.asList(
-      new Tuple2<Integer, String>(1, "a"),
-      new Tuple2<Integer, String>(2, "aa"),
-      new Tuple2<Integer, String>(3, "aaa")
+      new Tuple2<>(1, "a"),
+      new Tuple2<>(2, "aa"),
+      new Tuple2<>(3, "aaa")
     );
     JavaPairRDD<Integer, String> rdd = sc.parallelizePairs(pairs);
 
-    rdd.mapToPair(pair ->
-      new Tuple2<IntWritable, Text>(new IntWritable(pair._1()), new Text(pair._2())))
+    rdd.mapToPair(pair -> new Tuple2<>(new IntWritable(pair._1()), new Text(pair._2())))
       .saveAsHadoopFile(outputDir, IntWritable.class, Text.class, SequenceFileOutputFormat.class);
 
     // Try reading the output back as an object file
     JavaPairRDD<Integer, String> readRDD = sc.sequenceFile(outputDir, IntWritable.class, Text.class)
-      .mapToPair(pair -> new Tuple2<Integer, String>(pair._1().get(), pair._2().toString()));
+      .mapToPair(pair -> new Tuple2<>(pair._1().get(), pair._2().toString()));
     Assert.assertEquals(pairs, readRDD.collect());
     Utils.deleteRecursively(tempDir);
   }
@@ -325,7 +324,7 @@ public Float zero(Float initialValue) {
       }
     };
 
-    final Accumulator<Float> floatAccum = sc.accumulator((Float) 10.0f, floatAccumulatorParam);
+    final Accumulator<Float> floatAccum = sc.accumulator(10.0f, floatAccumulatorParam);
     rdd.foreach(x -> floatAccum.add((float) x));
     Assert.assertEquals((Float) 25.0f, floatAccum.value());
 
@@ -338,22 +337,22 @@ public Float zero(Float initialValue) {
   public void keyBy() {
     JavaRDD<Integer> rdd = sc.parallelize(Arrays.asList(1, 2));
     List<Tuple2<String, Integer>> s = rdd.keyBy(x -> x.toString()).collect();
-    Assert.assertEquals(new Tuple2<String, Integer>("1", 1), s.get(0));
-    Assert.assertEquals(new Tuple2<String, Integer>("2", 2), s.get(1));
+    Assert.assertEquals(new Tuple2<>("1", 1), s.get(0));
+    Assert.assertEquals(new Tuple2<>("2", 2), s.get(1));
   }
 
   @Test
   public void mapOnPairRDD() {
     JavaRDD<Integer> rdd1 = sc.parallelize(Arrays.asList(1, 2, 3, 4));
     JavaPairRDD<Integer, Integer> rdd2 =
-      rdd1.mapToPair(i -> new Tuple2<Integer, Integer>(i, i % 2));
+      rdd1.mapToPair(i -> new Tuple2<>(i, i % 2));
     JavaPairRDD<Integer, Integer> rdd3 =
-      rdd2.mapToPair(in -> new Tuple2<Integer, Integer>(in._2(), in._1()));
+      rdd2.mapToPair(in -> new Tuple2<>(in._2(), in._1()));
     Assert.assertEquals(Arrays.asList(
       new Tuple2<Integer, Integer>(1, 1),
-      new Tuple2<Integer, Integer>(0, 2),
-      new Tuple2<Integer, Integer>(1, 3),
-      new Tuple2<Integer, Integer>(0, 4)), rdd3.collect());
+      new Tuple2<>(0, 2),
+      new Tuple2<>(1, 3),
+      new Tuple2<>(0, 4)), rdd3.collect());
   }
 
   @Test
@@ -361,7 +360,7 @@ public void collectPartitions() {
     JavaRDD<Integer> rdd1 = sc.parallelize(Arrays.asList(1, 2, 3, 4, 5, 6, 7), 3);
 
     JavaPairRDD<Integer, Integer> rdd2 =
-      rdd1.mapToPair(i -> new Tuple2<Integer, Integer>(i, i % 2));
+      rdd1.mapToPair(i -> new Tuple2<>(i, i % 2));
     List[] parts = rdd1.collectPartitions(new int[]{0});
     Assert.assertEquals(Arrays.asList(1, 2), parts[0]);
 
@@ -369,16 +368,13 @@ public void collectPartitions() {
     Assert.assertEquals(Arrays.asList(3, 4), parts[0]);
     Assert.assertEquals(Arrays.asList(5, 6, 7), parts[1]);
 
-    Assert.assertEquals(Arrays.asList(new Tuple2<Integer, Integer>(1, 1),
-      new Tuple2<Integer, Integer>(2, 0)),
+    Assert.assertEquals(Arrays.asList(new Tuple2<>(1, 1), new Tuple2<>(2, 0)),
       rdd2.collectPartitions(new int[]{0})[0]);
 
     parts = rdd2.collectPartitions(new int[]{1, 2});
-    Assert.assertEquals(Arrays.asList(new Tuple2<Integer, Integer>(3, 1),
-      new Tuple2<Integer, Integer>(4, 0)), parts[0]);
-    Assert.assertEquals(Arrays.asList(new Tuple2<Integer, Integer>(5, 1),
-      new Tuple2<Integer, Integer>(6, 0),
-      new Tuple2<Integer, Integer>(7, 1)), parts[1]);
+    Assert.assertEquals(Arrays.asList(new Tuple2<>(3, 1), new Tuple2<>(4, 0)), parts[0]);
+    Assert.assertEquals(Arrays.asList(new Tuple2<>(5, 1), new Tuple2<>(6, 0), new Tuple2<>(7, 1)),
+      parts[1]);
   }
 
   @Test
@@ -386,7 +382,7 @@ public void collectAsMapWithIntArrayValues() {
     // Regression test for SPARK-1040
     JavaRDD<Integer> rdd = sc.parallelize(Arrays.asList(new Integer[]{1}));
     JavaPairRDD<Integer, int[]> pairRDD =
-      rdd.mapToPair(x -> new Tuple2<Integer, int[]>(x, new int[]{x}));
+      rdd.mapToPair(x -> new Tuple2<>(x, new int[]{x}));
     pairRDD.collect();  // Works fine
     Map<Integer, int[]> map = pairRDD.collectAsMap();  // Used to crash with ClassCastException
   }
diff --git a/extras/java8-tests/src/test/java/org/apache/spark/streaming/Java8APISuite.java b/extras/java8-tests/src/test/java/org/apache/spark/streaming/Java8APISuite.java
index 43df0dea614b..73091cfe2c09 100644
--- a/extras/java8-tests/src/test/java/org/apache/spark/streaming/Java8APISuite.java
+++ b/extras/java8-tests/src/test/java/org/apache/spark/streaming/Java8APISuite.java
@@ -39,6 +39,7 @@
  * Most of these tests replicate org.apache.spark.streaming.JavaAPISuite using java 8
  * lambda syntax.
  */
+@SuppressWarnings("unchecked")
 public class Java8APISuite extends LocalJavaStreamingContext implements Serializable {
 
   @Test
@@ -52,7 +53,7 @@ public void testMap() {
       Arrays.asList(9, 4));
 
     JavaDStream<String> stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1);
-    JavaDStream<Integer> letterCount = stream.map(s -> s.length());
+    JavaDStream<Integer> letterCount = stream.map(String::length);
     JavaTestUtils.attachTestOutputStream(letterCount);
     List<List<Integer>> result = JavaTestUtils.runStreams(ssc, 2, 2);
 
@@ -63,7 +64,7 @@ public void testMap() {
   public void testFilter() {
     List<List<String>> inputData = Arrays.asList(
       Arrays.asList("giants", "dodgers"),
-      Arrays.asList("yankees", "red socks"));
+      Arrays.asList("yankees", "red sox"));
 
     List<List<String>> expected = Arrays.asList(
       Arrays.asList("giants"),
@@ -81,11 +82,11 @@ public void testFilter() {
   public void testMapPartitions() {
     List<List<String>> inputData = Arrays.asList(
       Arrays.asList("giants", "dodgers"),
-      Arrays.asList("yankees", "red socks"));
+      Arrays.asList("yankees", "red sox"));
 
     List<List<String>> expected = Arrays.asList(
       Arrays.asList("GIANTSDODGERS"),
-      Arrays.asList("YANKEESRED SOCKS"));
+      Arrays.asList("YANKEESRED SOX"));
 
     JavaDStream<String> stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1);
     JavaDStream<String> mapped = stream.mapPartitions(in -> {
@@ -172,7 +173,7 @@ public void testVariousTransform() {
     JavaDStream<Integer> stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1);
 
     List<List<Tuple2<String, Integer>>> pairInputData =
-      Arrays.asList(Arrays.asList(new Tuple2<String, Integer>("x", 1)));
+      Arrays.asList(Arrays.asList(new Tuple2<>("x", 1)));
     JavaPairDStream<String, Integer> pairStream = JavaPairDStream.fromJavaDStream(
       JavaTestUtils.attachTestInputStream(ssc, pairInputData, 1));
 
@@ -192,32 +193,32 @@ public void testVariousTransform() {
   public void testTransformWith() {
     List<List<Tuple2<String, String>>> stringStringKVStream1 = Arrays.asList(
       Arrays.asList(
-        new Tuple2<String, String>("california", "dodgers"),
-        new Tuple2<String, String>("new york", "yankees")),
+        new Tuple2<>("california", "dodgers"),
+        new Tuple2<>("new york", "yankees")),
       Arrays.asList(
-        new Tuple2<String, String>("california", "sharks"),
-        new Tuple2<String, String>("new york", "rangers")));
+        new Tuple2<>("california", "sharks"),
+        new Tuple2<>("new york", "rangers")));
 
     List<List<Tuple2<String, String>>> stringStringKVStream2 = Arrays.asList(
       Arrays.asList(
-        new Tuple2<String, String>("california", "giants"),
-        new Tuple2<String, String>("new york", "mets")),
+        new Tuple2<>("california", "giants"),
+        new Tuple2<>("new york", "mets")),
       Arrays.asList(
-        new Tuple2<String, String>("california", "ducks"),
-        new Tuple2<String, String>("new york", "islanders")));
+        new Tuple2<>("california", "ducks"),
+        new Tuple2<>("new york", "islanders")));
 
 
-    List<HashSet<Tuple2<String, Tuple2<String, String>>>> expected = Arrays.asList(
+    List<Set<Tuple2<String, Tuple2<String, String>>>> expected = Arrays.asList(
       Sets.newHashSet(
-        new Tuple2<String, Tuple2<String, String>>("california",
-          new Tuple2<String, String>("dodgers", "giants")),
-        new Tuple2<String, Tuple2<String, String>>("new york",
-          new Tuple2<String, String>("yankees", "mets"))),
+        new Tuple2<>("california",
+          new Tuple2<>("dodgers", "giants")),
+        new Tuple2<>("new york",
+          new Tuple2<>("yankees", "mets"))),
       Sets.newHashSet(
-        new Tuple2<String, Tuple2<String, String>>("california",
-          new Tuple2<String, String>("sharks", "ducks")),
-        new Tuple2<String, Tuple2<String, String>>("new york",
-          new Tuple2<String, String>("rangers", "islanders"))));
+        new Tuple2<>("california",
+          new Tuple2<>("sharks", "ducks")),
+        new Tuple2<>("new york",
+          new Tuple2<>("rangers", "islanders"))));
 
     JavaDStream<Tuple2<String, String>> stream1 = JavaTestUtils.attachTestInputStream(
       ssc, stringStringKVStream1, 1);
@@ -232,7 +233,7 @@ public void testTransformWith() {
 
     JavaTestUtils.attachTestOutputStream(joined);
     List<List<Tuple2<String, Tuple2<String, String>>>> result = JavaTestUtils.runStreams(ssc, 2, 2);
-    List<HashSet<Tuple2<String, Tuple2<String, String>>>> unorderedResult = Lists.newArrayList();
+    List<Set<Tuple2<String, Tuple2<String, String>>>> unorderedResult = Lists.newArrayList();
     for (List<Tuple2<String, Tuple2<String, String>>> res : result) {
       unorderedResult.add(Sets.newHashSet(res));
     }
@@ -251,9 +252,9 @@ public void testVariousTransformWith() {
     JavaDStream<String> stream2 = JavaTestUtils.attachTestInputStream(ssc, inputData2, 1);
 
     List<List<Tuple2<String, Integer>>> pairInputData1 =
-      Arrays.asList(Arrays.asList(new Tuple2<String, Integer>("x", 1)));
+      Arrays.asList(Arrays.asList(new Tuple2<>("x", 1)));
     List<List<Tuple2<Double, Character>>> pairInputData2 =
-      Arrays.asList(Arrays.asList(new Tuple2<Double, Character>(1.0, 'x')));
+      Arrays.asList(Arrays.asList(new Tuple2<>(1.0, 'x')));
     JavaPairDStream<String, Integer> pairStream1 = JavaPairDStream.fromJavaDStream(
       JavaTestUtils.attachTestInputStream(ssc, pairInputData1, 1));
     JavaPairDStream<Double, Character> pairStream2 = JavaPairDStream.fromJavaDStream(
@@ -293,13 +294,13 @@ public void testStreamingContextTransform() {
     );
 
     List<List<Tuple2<Integer, String>>> pairStream1input = Arrays.asList(
-      Arrays.asList(new Tuple2<Integer, String>(1, "x")),
-      Arrays.asList(new Tuple2<Integer, String>(2, "y"))
+      Arrays.asList(new Tuple2<>(1, "x")),
+      Arrays.asList(new Tuple2<>(2, "y"))
     );
 
     List<List<Tuple2<Integer, Tuple2<Integer, String>>>> expected = Arrays.asList(
-      Arrays.asList(new Tuple2<Integer, Tuple2<Integer, String>>(1, new Tuple2<Integer, String>(1, "x"))),
-      Arrays.asList(new Tuple2<Integer, Tuple2<Integer, String>>(2, new Tuple2<Integer, String>(2, "y")))
+      Arrays.asList(new Tuple2<>(1, new Tuple2<>(1, "x"))),
+      Arrays.asList(new Tuple2<>(2, new Tuple2<>(2, "y")))
     );
 
     JavaDStream<Integer> stream1 = JavaTestUtils.attachTestInputStream(ssc, stream1input, 1);
@@ -312,7 +313,7 @@ public void testStreamingContextTransform() {
     // This is just to test whether this transform to JavaStream compiles
     JavaDStream<Long> transformed1 = ssc.transform(
       listOfDStreams1, (List<JavaRDD<?>> listOfRDDs, Time time) -> {
-      assert (listOfRDDs.size() == 2);
+      Assert.assertEquals(2, listOfRDDs.size());
       return null;
     });
 
@@ -321,13 +322,13 @@ public void testStreamingContextTransform() {
 
     JavaPairDStream<Integer, Tuple2<Integer, String>> transformed2 = ssc.transformToPair(
       listOfDStreams2, (List<JavaRDD<?>> listOfRDDs, Time time) -> {
-      assert (listOfRDDs.size() == 3);
+      Assert.assertEquals(3, listOfRDDs.size());
       JavaRDD<Integer> rdd1 = (JavaRDD<Integer>) listOfRDDs.get(0);
       JavaRDD<Integer> rdd2 = (JavaRDD<Integer>) listOfRDDs.get(1);
       JavaRDD<Tuple2<Integer, String>> rdd3 = (JavaRDD<Tuple2<Integer, String>>) listOfRDDs.get(2);
       JavaPairRDD<Integer, String> prdd3 = JavaPairRDD.fromJavaRDD(rdd3);
       PairFunction<Integer, Integer, Integer> mapToTuple =
-        (Integer i) -> new Tuple2<Integer, Integer>(i, i);
+        (Integer i) -> new Tuple2<>(i, i);
       return rdd1.union(rdd2).mapToPair(mapToTuple).join(prdd3);
     });
     JavaTestUtils.attachTestOutputStream(transformed2);
@@ -365,36 +366,36 @@ public void testPairFlatMap() {
 
     List<List<Tuple2<Integer, String>>> expected = Arrays.asList(
       Arrays.asList(
-        new Tuple2<Integer, String>(6, "g"),
-        new Tuple2<Integer, String>(6, "i"),
-        new Tuple2<Integer, String>(6, "a"),
-        new Tuple2<Integer, String>(6, "n"),
-        new Tuple2<Integer, String>(6, "t"),
-        new Tuple2<Integer, String>(6, "s")),
+        new Tuple2<>(6, "g"),
+        new Tuple2<>(6, "i"),
+        new Tuple2<>(6, "a"),
+        new Tuple2<>(6, "n"),
+        new Tuple2<>(6, "t"),
+        new Tuple2<>(6, "s")),
       Arrays.asList(
-        new Tuple2<Integer, String>(7, "d"),
-        new Tuple2<Integer, String>(7, "o"),
-        new Tuple2<Integer, String>(7, "d"),
-        new Tuple2<Integer, String>(7, "g"),
-        new Tuple2<Integer, String>(7, "e"),
-        new Tuple2<Integer, String>(7, "r"),
-        new Tuple2<Integer, String>(7, "s")),
+        new Tuple2<>(7, "d"),
+        new Tuple2<>(7, "o"),
+        new Tuple2<>(7, "d"),
+        new Tuple2<>(7, "g"),
+        new Tuple2<>(7, "e"),
+        new Tuple2<>(7, "r"),
+        new Tuple2<>(7, "s")),
       Arrays.asList(
-        new Tuple2<Integer, String>(9, "a"),
-        new Tuple2<Integer, String>(9, "t"),
-        new Tuple2<Integer, String>(9, "h"),
-        new Tuple2<Integer, String>(9, "l"),
-        new Tuple2<Integer, String>(9, "e"),
-        new Tuple2<Integer, String>(9, "t"),
-        new Tuple2<Integer, String>(9, "i"),
-        new Tuple2<Integer, String>(9, "c"),
-        new Tuple2<Integer, String>(9, "s")));
+        new Tuple2<>(9, "a"),
+        new Tuple2<>(9, "t"),
+        new Tuple2<>(9, "h"),
+        new Tuple2<>(9, "l"),
+        new Tuple2<>(9, "e"),
+        new Tuple2<>(9, "t"),
+        new Tuple2<>(9, "i"),
+        new Tuple2<>(9, "c"),
+        new Tuple2<>(9, "s")));
 
     JavaDStream<String> stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1);
     JavaPairDStream<Integer, String> flatMapped = stream.flatMapToPair(s -> {
       List<Tuple2<Integer, String>> out = Lists.newArrayList();
       for (String letter : s.split("(?!^)")) {
-        out.add(new Tuple2<Integer, String>(s.length(), letter));
+        out.add(new Tuple2<>(s.length(), letter));
       }
       return out;
     });
@@ -411,12 +412,8 @@ public void testPairFlatMap() {
    */
   public static <T extends Comparable<T>> void assertOrderInvariantEquals(
     List<List<T>> expected, List<List<T>> actual) {
-    for (List<T> list : expected) {
-      Collections.sort(list);
-    }
-    for (List<T> list : actual) {
-      Collections.sort(list);
-    }
+    expected.forEach((List<T> list) -> Collections.sort(list));
+    actual.forEach((List<T> list) -> Collections.sort(list));
     Assert.assertEquals(expected, actual);
   }
 
@@ -424,11 +421,11 @@ public static <T extends Comparable<T>> void assertOrderInvariantEquals(
   public void testPairFilter() {
     List<List<String>> inputData = Arrays.asList(
       Arrays.asList("giants", "dodgers"),
-      Arrays.asList("yankees", "red socks"));
+      Arrays.asList("yankees", "red sox"));
 
     List<List<Tuple2<String, Integer>>> expected = Arrays.asList(
-      Arrays.asList(new Tuple2<String, Integer>("giants", 6)),
-      Arrays.asList(new Tuple2<String, Integer>("yankees", 7)));
+      Arrays.asList(new Tuple2<>("giants", 6)),
+      Arrays.asList(new Tuple2<>("yankees", 7)));
 
     JavaDStream<String> stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1);
     JavaPairDStream<String, Integer> pairStream =
@@ -441,26 +438,26 @@ public void testPairFilter() {
   }
 
   List<List<Tuple2<String, String>>> stringStringKVStream = Arrays.asList(
-    Arrays.asList(new Tuple2<String, String>("california", "dodgers"),
-      new Tuple2<String, String>("california", "giants"),
-      new Tuple2<String, String>("new york", "yankees"),
-      new Tuple2<String, String>("new york", "mets")),
-    Arrays.asList(new Tuple2<String, String>("california", "sharks"),
-      new Tuple2<String, String>("california", "ducks"),
-      new Tuple2<String, String>("new york", "rangers"),
-      new Tuple2<String, String>("new york", "islanders")));
+    Arrays.asList(new Tuple2<>("california", "dodgers"),
+      new Tuple2<>("california", "giants"),
+      new Tuple2<>("new york", "yankees"),
+      new Tuple2<>("new york", "mets")),
+    Arrays.asList(new Tuple2<>("california", "sharks"),
+      new Tuple2<>("california", "ducks"),
+      new Tuple2<>("new york", "rangers"),
+      new Tuple2<>("new york", "islanders")));
 
   List<List<Tuple2<String, Integer>>> stringIntKVStream = Arrays.asList(
     Arrays.asList(
-      new Tuple2<String, Integer>("california", 1),
-      new Tuple2<String, Integer>("california", 3),
-      new Tuple2<String, Integer>("new york", 4),
-      new Tuple2<String, Integer>("new york", 1)),
+      new Tuple2<>("california", 1),
+      new Tuple2<>("california", 3),
+      new Tuple2<>("new york", 4),
+      new Tuple2<>("new york", 1)),
     Arrays.asList(
-      new Tuple2<String, Integer>("california", 5),
-      new Tuple2<String, Integer>("california", 5),
-      new Tuple2<String, Integer>("new york", 3),
-      new Tuple2<String, Integer>("new york", 1)));
+      new Tuple2<>("california", 5),
+      new Tuple2<>("california", 5),
+      new Tuple2<>("new york", 3),
+      new Tuple2<>("new york", 1)));
 
   @Test
   public void testPairMap() { // Maps pair -> pair of different type
@@ -468,15 +465,15 @@ public void testPairMap() { // Maps pair -> pair of different type
 
     List<List<Tuple2<Integer, String>>> expected = Arrays.asList(
       Arrays.asList(
-        new Tuple2<Integer, String>(1, "california"),
-        new Tuple2<Integer, String>(3, "california"),
-        new Tuple2<Integer, String>(4, "new york"),
-        new Tuple2<Integer, String>(1, "new york")),
+        new Tuple2<>(1, "california"),
+        new Tuple2<>(3, "california"),
+        new Tuple2<>(4, "new york"),
+        new Tuple2<>(1, "new york")),
       Arrays.asList(
-        new Tuple2<Integer, String>(5, "california"),
-        new Tuple2<Integer, String>(5, "california"),
-        new Tuple2<Integer, String>(3, "new york"),
-        new Tuple2<Integer, String>(1, "new york")));
+        new Tuple2<>(5, "california"),
+        new Tuple2<>(5, "california"),
+        new Tuple2<>(3, "new york"),
+        new Tuple2<>(1, "new york")));
 
     JavaDStream<Tuple2<String, Integer>> stream =
       JavaTestUtils.attachTestInputStream(ssc, inputData, 1);
@@ -494,21 +491,21 @@ public void testPairMapPartitions() { // Maps pair -> pair of different type
 
     List<List<Tuple2<Integer, String>>> expected = Arrays.asList(
       Arrays.asList(
-        new Tuple2<Integer, String>(1, "california"),
-        new Tuple2<Integer, String>(3, "california"),
-        new Tuple2<Integer, String>(4, "new york"),
-        new Tuple2<Integer, String>(1, "new york")),
+        new Tuple2<>(1, "california"),
+        new Tuple2<>(3, "california"),
+        new Tuple2<>(4, "new york"),
+        new Tuple2<>(1, "new york")),
       Arrays.asList(
-        new Tuple2<Integer, String>(5, "california"),
-        new Tuple2<Integer, String>(5, "california"),
-        new Tuple2<Integer, String>(3, "new york"),
-        new Tuple2<Integer, String>(1, "new york")));
+        new Tuple2<>(5, "california"),
+        new Tuple2<>(5, "california"),
+        new Tuple2<>(3, "new york"),
+        new Tuple2<>(1, "new york")));
 
     JavaDStream<Tuple2<String, Integer>> stream =
       JavaTestUtils.attachTestInputStream(ssc, inputData, 1);
     JavaPairDStream<String, Integer> pairStream = JavaPairDStream.fromJavaDStream(stream);
     JavaPairDStream<Integer, String> reversed = pairStream.mapPartitionsToPair(in -> {
-      LinkedList<Tuple2<Integer, String>> out = new LinkedList<Tuple2<Integer, String>>();
+      LinkedList<Tuple2<Integer, String>> out = new LinkedList<>();
       while (in.hasNext()) {
         Tuple2<String, Integer> next = in.next();
         out.add(next.swap());
@@ -530,7 +527,8 @@ public void testPairMap2() { // Maps pair -> single
       Arrays.asList(1, 3, 4, 1),
       Arrays.asList(5, 5, 3, 1));
 
-    JavaDStream<Tuple2<String, Integer>> stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1);
+    JavaDStream<Tuple2<String, Integer>> stream =
+      JavaTestUtils.attachTestInputStream(ssc, inputData, 1);
     JavaPairDStream<String, Integer> pairStream = JavaPairDStream.fromJavaDStream(stream);
     JavaDStream<Integer> reversed = pairStream.map(in -> in._2());
     JavaTestUtils.attachTestOutputStream(reversed);
@@ -543,31 +541,31 @@ public void testPairMap2() { // Maps pair -> single
   public void testPairToPairFlatMapWithChangingTypes() { // Maps pair -> pair
     List<List<Tuple2<String, Integer>>> inputData = Arrays.asList(
       Arrays.asList(
-        new Tuple2<String, Integer>("hi", 1),
-        new Tuple2<String, Integer>("ho", 2)),
+        new Tuple2<>("hi", 1),
+        new Tuple2<>("ho", 2)),
       Arrays.asList(
-        new Tuple2<String, Integer>("hi", 1),
-        new Tuple2<String, Integer>("ho", 2)));
+        new Tuple2<>("hi", 1),
+        new Tuple2<>("ho", 2)));
 
     List<List<Tuple2<Integer, String>>> expected = Arrays.asList(
       Arrays.asList(
-        new Tuple2<Integer, String>(1, "h"),
-        new Tuple2<Integer, String>(1, "i"),
-        new Tuple2<Integer, String>(2, "h"),
-        new Tuple2<Integer, String>(2, "o")),
+        new Tuple2<>(1, "h"),
+        new Tuple2<>(1, "i"),
+        new Tuple2<>(2, "h"),
+        new Tuple2<>(2, "o")),
       Arrays.asList(
-        new Tuple2<Integer, String>(1, "h"),
-        new Tuple2<Integer, String>(1, "i"),
-        new Tuple2<Integer, String>(2, "h"),
-        new Tuple2<Integer, String>(2, "o")));
+        new Tuple2<>(1, "h"),
+        new Tuple2<>(1, "i"),
+        new Tuple2<>(2, "h"),
+        new Tuple2<>(2, "o")));
 
     JavaDStream<Tuple2<String, Integer>> stream =
       JavaTestUtils.attachTestInputStream(ssc, inputData, 1);
     JavaPairDStream<String, Integer> pairStream = JavaPairDStream.fromJavaDStream(stream);
     JavaPairDStream<Integer, String> flatMapped = pairStream.flatMapToPair(in -> {
-      List<Tuple2<Integer, String>> out = new LinkedList<Tuple2<Integer, String>>();
+      List<Tuple2<Integer, String>> out = new LinkedList<>();
       for (Character s : in._1().toCharArray()) {
-        out.add(new Tuple2<Integer, String>(in._2(), s.toString()));
+        out.add(new Tuple2<>(in._2(), s.toString()));
       }
       return out;
     });
@@ -584,11 +582,11 @@ public void testPairReduceByKey() {
 
     List<List<Tuple2<String, Integer>>> expected = Arrays.asList(
       Arrays.asList(
-        new Tuple2<String, Integer>("california", 4),
-        new Tuple2<String, Integer>("new york", 5)),
+        new Tuple2<>("california", 4),
+        new Tuple2<>("new york", 5)),
       Arrays.asList(
-        new Tuple2<String, Integer>("california", 10),
-        new Tuple2<String, Integer>("new york", 4)));
+        new Tuple2<>("california", 10),
+        new Tuple2<>("new york", 4)));
 
     JavaDStream<Tuple2<String, Integer>> stream = JavaTestUtils.attachTestInputStream(
       ssc, inputData, 1);
@@ -608,11 +606,11 @@ public void testCombineByKey() {
 
     List<List<Tuple2<String, Integer>>> expected = Arrays.asList(
       Arrays.asList(
-        new Tuple2<String, Integer>("california", 4),
-        new Tuple2<String, Integer>("new york", 5)),
+        new Tuple2<>("california", 4),
+        new Tuple2<>("new york", 5)),
       Arrays.asList(
-        new Tuple2<String, Integer>("california", 10),
-        new Tuple2<String, Integer>("new york", 4)));
+        new Tuple2<>("california", 10),
+        new Tuple2<>("new york", 4)));
 
     JavaDStream<Tuple2<String, Integer>> stream = JavaTestUtils.attachTestInputStream(
       ssc, inputData, 1);
@@ -632,12 +630,12 @@ public void testReduceByKeyAndWindow() {
     List<List<Tuple2<String, Integer>>> inputData = stringIntKVStream;
 
     List<List<Tuple2<String, Integer>>> expected = Arrays.asList(
-      Arrays.asList(new Tuple2<String, Integer>("california", 4),
-        new Tuple2<String, Integer>("new york", 5)),
-      Arrays.asList(new Tuple2<String, Integer>("california", 14),
-        new Tuple2<String, Integer>("new york", 9)),
-      Arrays.asList(new Tuple2<String, Integer>("california", 10),
-        new Tuple2<String, Integer>("new york", 4)));
+      Arrays.asList(new Tuple2<>("california", 4),
+        new Tuple2<>("new york", 5)),
+      Arrays.asList(new Tuple2<>("california", 14),
+        new Tuple2<>("new york", 9)),
+      Arrays.asList(new Tuple2<>("california", 10),
+        new Tuple2<>("new york", 4)));
 
     JavaDStream<Tuple2<String, Integer>> stream =
       JavaTestUtils.attachTestInputStream(ssc, inputData, 1);
@@ -656,12 +654,12 @@ public void testUpdateStateByKey() {
     List<List<Tuple2<String, Integer>>> inputData = stringIntKVStream;
 
     List<List<Tuple2<String, Integer>>> expected = Arrays.asList(
-      Arrays.asList(new Tuple2<String, Integer>("california", 4),
-        new Tuple2<String, Integer>("new york", 5)),
-      Arrays.asList(new Tuple2<String, Integer>("california", 14),
-        new Tuple2<String, Integer>("new york", 9)),
-      Arrays.asList(new Tuple2<String, Integer>("california", 14),
-        new Tuple2<String, Integer>("new york", 9)));
+      Arrays.asList(new Tuple2<>("california", 4),
+        new Tuple2<>("new york", 5)),
+      Arrays.asList(new Tuple2<>("california", 14),
+        new Tuple2<>("new york", 9)),
+      Arrays.asList(new Tuple2<>("california", 14),
+        new Tuple2<>("new york", 9)));
 
     JavaDStream<Tuple2<String, Integer>> stream =
       JavaTestUtils.attachTestInputStream(ssc, inputData, 1);
@@ -689,12 +687,12 @@ public void testReduceByKeyAndWindowWithInverse() {
     List<List<Tuple2<String, Integer>>> inputData = stringIntKVStream;
 
     List<List<Tuple2<String, Integer>>> expected = Arrays.asList(
-      Arrays.asList(new Tuple2<String, Integer>("california", 4),
-        new Tuple2<String, Integer>("new york", 5)),
-      Arrays.asList(new Tuple2<String, Integer>("california", 14),
-        new Tuple2<String, Integer>("new york", 9)),
-      Arrays.asList(new Tuple2<String, Integer>("california", 10),
-        new Tuple2<String, Integer>("new york", 4)));
+      Arrays.asList(new Tuple2<>("california", 4),
+        new Tuple2<>("new york", 5)),
+      Arrays.asList(new Tuple2<>("california", 14),
+        new Tuple2<>("new york", 9)),
+      Arrays.asList(new Tuple2<>("california", 10),
+        new Tuple2<>("new york", 4)));
 
     JavaDStream<Tuple2<String, Integer>> stream =
       JavaTestUtils.attachTestInputStream(ssc, inputData, 1);
@@ -713,27 +711,27 @@ public void testReduceByKeyAndWindowWithInverse() {
   public void testPairTransform() {
     List<List<Tuple2<Integer, Integer>>> inputData = Arrays.asList(
       Arrays.asList(
-        new Tuple2<Integer, Integer>(3, 5),
-        new Tuple2<Integer, Integer>(1, 5),
-        new Tuple2<Integer, Integer>(4, 5),
-        new Tuple2<Integer, Integer>(2, 5)),
+        new Tuple2<>(3, 5),
+        new Tuple2<>(1, 5),
+        new Tuple2<>(4, 5),
+        new Tuple2<>(2, 5)),
       Arrays.asList(
-        new Tuple2<Integer, Integer>(2, 5),
-        new Tuple2<Integer, Integer>(3, 5),
-        new Tuple2<Integer, Integer>(4, 5),
-        new Tuple2<Integer, Integer>(1, 5)));
+        new Tuple2<>(2, 5),
+        new Tuple2<>(3, 5),
+        new Tuple2<>(4, 5),
+        new Tuple2<>(1, 5)));
 
     List<List<Tuple2<Integer, Integer>>> expected = Arrays.asList(
       Arrays.asList(
-        new Tuple2<Integer, Integer>(1, 5),
-        new Tuple2<Integer, Integer>(2, 5),
-        new Tuple2<Integer, Integer>(3, 5),
-        new Tuple2<Integer, Integer>(4, 5)),
+        new Tuple2<>(1, 5),
+        new Tuple2<>(2, 5),
+        new Tuple2<>(3, 5),
+        new Tuple2<>(4, 5)),
       Arrays.asList(
-        new Tuple2<Integer, Integer>(1, 5),
-        new Tuple2<Integer, Integer>(2, 5),
-        new Tuple2<Integer, Integer>(3, 5),
-        new Tuple2<Integer, Integer>(4, 5)));
+        new Tuple2<>(1, 5),
+        new Tuple2<>(2, 5),
+        new Tuple2<>(3, 5),
+        new Tuple2<>(4, 5)));
 
     JavaDStream<Tuple2<Integer, Integer>> stream = JavaTestUtils.attachTestInputStream(
       ssc, inputData, 1);
@@ -751,15 +749,15 @@ public void testPairTransform() {
   public void testPairToNormalRDDTransform() {
     List<List<Tuple2<Integer, Integer>>> inputData = Arrays.asList(
       Arrays.asList(
-        new Tuple2<Integer, Integer>(3, 5),
-        new Tuple2<Integer, Integer>(1, 5),
-        new Tuple2<Integer, Integer>(4, 5),
-        new Tuple2<Integer, Integer>(2, 5)),
+        new Tuple2<>(3, 5),
+        new Tuple2<>(1, 5),
+        new Tuple2<>(4, 5),
+        new Tuple2<>(2, 5)),
       Arrays.asList(
-        new Tuple2<Integer, Integer>(2, 5),
-        new Tuple2<Integer, Integer>(3, 5),
-        new Tuple2<Integer, Integer>(4, 5),
-        new Tuple2<Integer, Integer>(1, 5)));
+        new Tuple2<>(2, 5),
+        new Tuple2<>(3, 5),
+        new Tuple2<>(4, 5),
+        new Tuple2<>(1, 5)));
 
     List<List<Integer>> expected = Arrays.asList(
       Arrays.asList(3, 1, 4, 2),
@@ -780,20 +778,20 @@ public void testMapValues() {
     List<List<Tuple2<String, String>>> inputData = stringStringKVStream;
 
     List<List<Tuple2<String, String>>> expected = Arrays.asList(
-      Arrays.asList(new Tuple2<String, String>("california", "DODGERS"),
-        new Tuple2<String, String>("california", "GIANTS"),
-        new Tuple2<String, String>("new york", "YANKEES"),
-        new Tuple2<String, String>("new york", "METS")),
-      Arrays.asList(new Tuple2<String, String>("california", "SHARKS"),
-        new Tuple2<String, String>("california", "DUCKS"),
-        new Tuple2<String, String>("new york", "RANGERS"),
-        new Tuple2<String, String>("new york", "ISLANDERS")));
+      Arrays.asList(new Tuple2<>("california", "DODGERS"),
+        new Tuple2<>("california", "GIANTS"),
+        new Tuple2<>("new york", "YANKEES"),
+        new Tuple2<>("new york", "METS")),
+      Arrays.asList(new Tuple2<>("california", "SHARKS"),
+        new Tuple2<>("california", "DUCKS"),
+        new Tuple2<>("new york", "RANGERS"),
+        new Tuple2<>("new york", "ISLANDERS")));
 
     JavaDStream<Tuple2<String, String>> stream = JavaTestUtils.attachTestInputStream(
       ssc, inputData, 1);
     JavaPairDStream<String, String> pairStream = JavaPairDStream.fromJavaDStream(stream);
 
-    JavaPairDStream<String, String> mapped = pairStream.mapValues(s -> s.toUpperCase());
+    JavaPairDStream<String, String> mapped = pairStream.mapValues(String::toUpperCase);
     JavaTestUtils.attachTestOutputStream(mapped);
     List<List<Tuple2<String, String>>> result = JavaTestUtils.runStreams(ssc, 2, 2);
 
@@ -805,34 +803,29 @@ public void testFlatMapValues() {
     List<List<Tuple2<String, String>>> inputData = stringStringKVStream;
 
     List<List<Tuple2<String, String>>> expected = Arrays.asList(
-      Arrays.asList(new Tuple2<String, String>("california", "dodgers1"),
-        new Tuple2<String, String>("california", "dodgers2"),
-        new Tuple2<String, String>("california", "giants1"),
-        new Tuple2<String, String>("california", "giants2"),
-        new Tuple2<String, String>("new york", "yankees1"),
-        new Tuple2<String, String>("new york", "yankees2"),
-        new Tuple2<String, String>("new york", "mets1"),
-        new Tuple2<String, String>("new york", "mets2")),
-      Arrays.asList(new Tuple2<String, String>("california", "sharks1"),
-        new Tuple2<String, String>("california", "sharks2"),
-        new Tuple2<String, String>("california", "ducks1"),
-        new Tuple2<String, String>("california", "ducks2"),
-        new Tuple2<String, String>("new york", "rangers1"),
-        new Tuple2<String, String>("new york", "rangers2"),
-        new Tuple2<String, String>("new york", "islanders1"),
-        new Tuple2<String, String>("new york", "islanders2")));
+      Arrays.asList(new Tuple2<>("california", "dodgers1"),
+        new Tuple2<>("california", "dodgers2"),
+        new Tuple2<>("california", "giants1"),
+        new Tuple2<>("california", "giants2"),
+        new Tuple2<>("new york", "yankees1"),
+        new Tuple2<>("new york", "yankees2"),
+        new Tuple2<>("new york", "mets1"),
+        new Tuple2<>("new york", "mets2")),
+      Arrays.asList(new Tuple2<>("california", "sharks1"),
+        new Tuple2<>("california", "sharks2"),
+        new Tuple2<>("california", "ducks1"),
+        new Tuple2<>("california", "ducks2"),
+        new Tuple2<>("new york", "rangers1"),
+        new Tuple2<>("new york", "rangers2"),
+        new Tuple2<>("new york", "islanders1"),
+        new Tuple2<>("new york", "islanders2")));
 
     JavaDStream<Tuple2<String, String>> stream = JavaTestUtils.attachTestInputStream(
       ssc, inputData, 1);
     JavaPairDStream<String, String> pairStream = JavaPairDStream.fromJavaDStream(stream);
 
-
-    JavaPairDStream<String, String> flatMapped = pairStream.flatMapValues(in -> {
-      List<String> out = new ArrayList<String>();
-      out.add(in + "1");
-      out.add(in + "2");
-      return out;
-    });
+    JavaPairDStream<String, String> flatMapped =
+      pairStream.flatMapValues(in -> Arrays.asList(in + "1", in + "2"));
     JavaTestUtils.attachTestOutputStream(flatMapped);
     List<List<Tuple2<String, String>>> result = JavaTestUtils.runStreams(ssc, 2, 2);
     Assert.assertEquals(expected, result);

From 189df165bb7cb8bc8ede48d0e7f8d8b5cd31d299 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Wed, 4 Jun 2014 12:56:56 -0700
Subject: [PATCH 339/810] [SPARK-1752][MLLIB] Standardize text format for
 vectors and labeled points

We should standardize the text format used to represent vectors and labeled points. The proposed formats are the following:

1. dense vector: `[v0,v1,..]`
2. sparse vector: `(size,[i0,i1],[v0,v1])`
3. labeled point: `(label,vector)`

where "(..)" indicates a tuple and "[...]" indicate an array. `loadLabeledPoints` is added to pyspark's `MLUtils`. I didn't add `loadVectors` to pyspark because `RDD.saveAsTextFile` cannot stringify dense vectors in the proposed format automatically.

`MLUtils#saveLabeledData` and `MLUtils#loadLabeledData` are deprecated. Users should use `RDD#saveAsTextFile` and `MLUtils#loadLabeledPoints` instead. In Scala, `MLUtils#loadLabeledPoints` is compatible with the format used by `MLUtils#loadLabeledData`.

CC: @mateiz, @srowen

Author: Xiangrui Meng <meng@databricks.com>

Closes #685 from mengxr/labeled-io and squashes the following commits:

2d1116a [Xiangrui Meng] make loadLabeledData/saveLabeledData deprecated since 1.0.1
297be75 [Xiangrui Meng] change LabeledPoint.parse to LabeledPointParser.parse to maintain binary compatibility
d6b1473 [Xiangrui Meng] Merge branch 'master' into labeled-io
56746ea [Xiangrui Meng] replace # by .
623a5f0 [Xiangrui Meng] merge master
f06d5ba [Xiangrui Meng] add docs and minor updates
640fe0c [Xiangrui Meng] throw SparkException
5bcfbc4 [Xiangrui Meng] update test to add scientific notations
e86bf38 [Xiangrui Meng] remove NumericTokenizer
050fca4 [Xiangrui Meng] use StringTokenizer
6155b75 [Xiangrui Meng] merge master
f644438 [Xiangrui Meng] remove parse methods based on eval from pyspark
a41675a [Xiangrui Meng] python loadLabeledPoint uses Scala's implementation
ce9a475 [Xiangrui Meng] add deserialize_labeled_point to pyspark with tests
e9fcd49 [Xiangrui Meng] add serializeLabeledPoint and tests
aea4ae3 [Xiangrui Meng] minor updates
810d6df [Xiangrui Meng] update tokenizer/parser implementation
7aac03a [Xiangrui Meng] remove Scala parsers
c1885c1 [Xiangrui Meng] add headers and minor changes
b0c50cb [Xiangrui Meng] add customized parser
d731817 [Xiangrui Meng] style update
63dc396 [Xiangrui Meng] add loadLabeledPoints to pyspark
ea122b5 [Xiangrui Meng] Merge branch 'master' into labeled-io
cd6c78f [Xiangrui Meng] add __str__ and parse to LabeledPoint
a7a178e [Xiangrui Meng] add stringify to pyspark's Vectors
5c2dbfa [Xiangrui Meng] add parse to pyspark's Vectors
7853f88 [Xiangrui Meng] update pyspark's SparseVector.__str__
e761d32 [Xiangrui Meng] make LabelPoint.parse compatible with the dense format used before v1.0 and deprecate loadLabeledData and saveLabeledData
9e63a02 [Xiangrui Meng] add loadVectors and loadLabeledPoints
19aa523 [Xiangrui Meng] update toString and add parsers for Vectors and LabeledPoint
---
 .../examples/mllib/DecisionTreeRunner.scala   |   2 +-
 .../mllib/api/python/PythonMLLibAPI.scala     |  33 ++++-
 .../apache/spark/mllib/linalg/Vectors.scala   |  33 ++++-
 .../spark/mllib/regression/LabeledPoint.scala |  31 ++++-
 .../mllib/util/LinearDataGenerator.scala      |   3 +-
 .../LogisticRegressionDataGenerator.scala     |   3 +-
 .../org/apache/spark/mllib/util/MLUtils.scala |  47 ++++++-
 .../spark/mllib/util/NumericParser.scala      | 121 ++++++++++++++++++
 .../spark/mllib/util/SVMDataGenerator.scala   |   2 +-
 .../api/python/PythonMLLibAPISuite.scala      |  60 +++++++++
 .../spark/mllib/linalg/VectorsSuite.scala     |  25 ++++
 .../mllib/regression/LabeledPointSuite.scala  |  39 ++++++
 .../spark/mllib/util/MLUtilsSuite.scala       |  30 ++++-
 .../spark/mllib/util/NumericParserSuite.scala |  42 ++++++
 python/pyspark/mllib/_common.py               |  72 ++++++++---
 python/pyspark/mllib/linalg.py                |  34 +++--
 python/pyspark/mllib/regression.py            |   5 +-
 python/pyspark/mllib/util.py                  |  69 +++++++---
 18 files changed, 579 insertions(+), 72 deletions(-)
 create mode 100644 mllib/src/main/scala/org/apache/spark/mllib/util/NumericParser.scala
 create mode 100644 mllib/src/test/scala/org/apache/spark/mllib/api/python/PythonMLLibAPISuite.scala
 create mode 100644 mllib/src/test/scala/org/apache/spark/mllib/regression/LabeledPointSuite.scala
 create mode 100644 mllib/src/test/scala/org/apache/spark/mllib/util/NumericParserSuite.scala

diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/DecisionTreeRunner.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/DecisionTreeRunner.scala
index 9832bec90d7e..b3cc36115419 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/DecisionTreeRunner.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/DecisionTreeRunner.scala
@@ -99,7 +99,7 @@ object DecisionTreeRunner {
     val sc = new SparkContext(conf)
 
     // Load training data and cache it.
-    val examples = MLUtils.loadLabeledData(sc, params.input).cache()
+    val examples = MLUtils.loadLabeledPoints(sc, params.input).cache()
 
     val splits = examples.randomSplit(Array(0.8, 0.2))
     val training = splits(0).cache()
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
index 7c65b0d4750f..c44173793b39 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
@@ -20,12 +20,13 @@ package org.apache.spark.mllib.api.python
 import java.nio.{ByteBuffer, ByteOrder}
 
 import org.apache.spark.annotation.DeveloperApi
-import org.apache.spark.api.java.JavaRDD
+import org.apache.spark.api.java.{JavaSparkContext, JavaRDD}
 import org.apache.spark.mllib.classification._
 import org.apache.spark.mllib.clustering._
 import org.apache.spark.mllib.linalg.{SparseVector, Vector, Vectors}
 import org.apache.spark.mllib.recommendation._
 import org.apache.spark.mllib.regression._
+import org.apache.spark.mllib.util.MLUtils
 import org.apache.spark.rdd.RDD
 
 /**
@@ -41,7 +42,7 @@ class PythonMLLibAPI extends Serializable {
   private val DENSE_MATRIX_MAGIC: Byte = 3
   private val LABELED_POINT_MAGIC: Byte = 4
 
-  private def deserializeDoubleVector(bytes: Array[Byte], offset: Int = 0): Vector = {
+  private[python] def deserializeDoubleVector(bytes: Array[Byte], offset: Int = 0): Vector = {
     require(bytes.length - offset >= 5, "Byte array too short")
     val magic = bytes(offset)
     if (magic == DENSE_VECTOR_MAGIC) {
@@ -116,7 +117,7 @@ class PythonMLLibAPI extends Serializable {
     bytes
   }
 
-  private def serializeDoubleVector(vector: Vector): Array[Byte] = vector match {
+  private[python] def serializeDoubleVector(vector: Vector): Array[Byte] = vector match {
     case s: SparseVector =>
       serializeSparseVector(s)
     case _ =>
@@ -167,7 +168,18 @@ class PythonMLLibAPI extends Serializable {
     bytes
   }
 
-  private def deserializeLabeledPoint(bytes: Array[Byte]): LabeledPoint = {
+  private[python] def serializeLabeledPoint(p: LabeledPoint): Array[Byte] = {
+    val fb = serializeDoubleVector(p.features)
+    val bytes = new Array[Byte](1 + 8 + fb.length)
+    val bb = ByteBuffer.wrap(bytes)
+    bb.order(ByteOrder.nativeOrder())
+    bb.put(LABELED_POINT_MAGIC)
+    bb.putDouble(p.label)
+    bb.put(fb)
+    bytes
+  }
+
+  private[python] def deserializeLabeledPoint(bytes: Array[Byte]): LabeledPoint = {
     require(bytes.length >= 9, "Byte array too short")
     val magic = bytes(0)
     if (magic != LABELED_POINT_MAGIC) {
@@ -179,6 +191,19 @@ class PythonMLLibAPI extends Serializable {
     LabeledPoint(label, deserializeDoubleVector(bytes, 9))
   }
 
+  /**
+   * Loads and serializes labeled points saved with `RDD#saveAsTextFile`.
+   * @param jsc Java SparkContext
+   * @param path file or directory path in any Hadoop-supported file system URI
+   * @param minPartitions min number of partitions
+   * @return serialized labeled points stored in a JavaRDD of byte array
+   */
+  def loadLabeledPoints(
+      jsc: JavaSparkContext,
+      path: String,
+      minPartitions: Int): JavaRDD[Array[Byte]] =
+    MLUtils.loadLabeledPoints(jsc.sc, path, minPartitions).map(serializeLabeledPoint).toJavaRDD()
+
   private def trainRegressionModel(
       trainFunc: (RDD[LabeledPoint], Vector) => GeneralizedLinearModel,
       dataBytesJRDD: JavaRDD[Array[Byte]],
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
index 84d223908c1f..c818a0b9c3e4 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
@@ -17,13 +17,16 @@
 
 package org.apache.spark.mllib.linalg
 
-import java.lang.{Iterable => JavaIterable, Integer => JavaInteger, Double => JavaDouble}
+import java.lang.{Double => JavaDouble, Integer => JavaInteger, Iterable => JavaIterable}
 import java.util.Arrays
 
 import scala.annotation.varargs
 import scala.collection.JavaConverters._
 
-import breeze.linalg.{Vector => BV, DenseVector => BDV, SparseVector => BSV}
+import breeze.linalg.{DenseVector => BDV, SparseVector => BSV, Vector => BV}
+
+import org.apache.spark.mllib.util.NumericParser
+import org.apache.spark.SparkException
 
 /**
  * Represents a numeric vector, whose index type is Int and value type is Double.
@@ -124,6 +127,25 @@ object Vectors {
     }.toSeq)
   }
 
+  /**
+   * Parses a string resulted from `Vector#toString` into
+   * an [[org.apache.spark.mllib.linalg.Vector]].
+   */
+  def parse(s: String): Vector = {
+    parseNumeric(NumericParser.parse(s))
+  }
+
+  private[mllib] def parseNumeric(any: Any): Vector = {
+    any match {
+      case values: Array[Double] =>
+        Vectors.dense(values)
+      case Seq(size: Double, indices: Array[Double], values: Array[Double]) =>
+        Vectors.sparse(size.toInt, indices.map(_.toInt), values)
+      case other =>
+       throw new SparkException(s"Cannot parse $other.")
+    }
+  }
+
   /**
    * Creates a vector instance from a breeze vector.
    */
@@ -175,9 +197,10 @@ class SparseVector(
     val indices: Array[Int],
     val values: Array[Double]) extends Vector {
 
-  override def toString: String = {
-    "(" + size + "," + indices.zip(values).mkString("[", "," ,"]") + ")"
-  }
+  require(indices.length == values.length)
+
+  override def toString: String =
+    "(%s,%s,%s)".format(size, indices.mkString("[", ",", "]"), values.mkString("[", ",", "]"))
 
   override def toArray: Array[Double] = {
     val data = new Array[Double](size)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/LabeledPoint.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/LabeledPoint.scala
index 3deab1ab785b..62a03af4a996 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/LabeledPoint.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/LabeledPoint.scala
@@ -17,7 +17,9 @@
 
 package org.apache.spark.mllib.regression
 
-import org.apache.spark.mllib.linalg.Vector
+import org.apache.spark.mllib.linalg.{Vectors, Vector}
+import org.apache.spark.mllib.util.NumericParser
+import org.apache.spark.SparkException
 
 /**
  * Class that represents the features and labels of a data point.
@@ -27,6 +29,31 @@ import org.apache.spark.mllib.linalg.Vector
  */
 case class LabeledPoint(label: Double, features: Vector) {
   override def toString: String = {
-    "LabeledPoint(%s, %s)".format(label, features)
+    "(%s,%s)".format(label, features)
+  }
+}
+
+/**
+ * Parser for [[org.apache.spark.mllib.regression.LabeledPoint]].
+ */
+private[mllib] object LabeledPointParser {
+  /**
+   * Parses a string resulted from `LabeledPoint#toString` into
+   * an [[org.apache.spark.mllib.regression.LabeledPoint]].
+   */
+  def parse(s: String): LabeledPoint = {
+    if (s.startsWith("(")) {
+      NumericParser.parse(s) match {
+        case Seq(label: Double, numeric: Any) =>
+          LabeledPoint(label, Vectors.parseNumeric(numeric))
+        case other =>
+          throw new SparkException(s"Cannot parse $other.")
+      }
+    } else { // dense format used before v1.0
+      val parts = s.split(',')
+      val label = java.lang.Double.parseDouble(parts(0))
+      val features = Vectors.dense(parts(1).trim().split(' ').map(java.lang.Double.parseDouble))
+      LabeledPoint(label, features)
+    }
   }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/LinearDataGenerator.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/LinearDataGenerator.scala
index c8e160d00c2d..69299c219878 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/util/LinearDataGenerator.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/util/LinearDataGenerator.scala
@@ -129,7 +129,8 @@ object LinearDataGenerator {
     val sc = new SparkContext(sparkMaster, "LinearDataGenerator")
     val data = generateLinearRDD(sc, nexamples, nfeatures, eps, nparts = parts)
 
-    MLUtils.saveLabeledData(data, outputPath)
+    data.saveAsTextFile(outputPath)
+
     sc.stop()
   }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/LogisticRegressionDataGenerator.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/LogisticRegressionDataGenerator.scala
index c82cd8fd4641..9d802678c4a7 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/util/LogisticRegressionDataGenerator.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/util/LogisticRegressionDataGenerator.scala
@@ -79,7 +79,8 @@ object LogisticRegressionDataGenerator {
     val sc = new SparkContext(sparkMaster, "LogisticRegressionDataGenerator")
     val data = generateLogisticRDD(sc, nexamples, nfeatures, eps, parts)
 
-    MLUtils.saveLabeledData(data, outputPath)
+    data.saveAsTextFile(outputPath)
+
     sc.stop()
   }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala
index e598b6cb171a..aaf92a1a8869 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala
@@ -27,7 +27,7 @@ import org.apache.spark.SparkContext
 import org.apache.spark.rdd.RDD
 import org.apache.spark.rdd.PartitionwiseSampledRDD
 import org.apache.spark.util.random.BernoulliSampler
-import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.mllib.regression.{LabeledPointParser, LabeledPoint}
 import org.apache.spark.mllib.linalg.{Vector, Vectors}
 import org.apache.spark.storage.StorageLevel
 
@@ -180,7 +180,39 @@ object MLUtils {
   }
 
   /**
-   * :: Experimental ::
+   * Loads vectors saved using `RDD[Vector].saveAsTextFile`.
+   * @param sc Spark context
+   * @param path file or directory path in any Hadoop-supported file system URI
+   * @param minPartitions min number of partitions
+   * @return vectors stored as an RDD[Vector]
+   */
+  def loadVectors(sc: SparkContext, path: String, minPartitions: Int): RDD[Vector] =
+    sc.textFile(path, minPartitions).map(Vectors.parse)
+
+  /**
+   * Loads vectors saved using `RDD[Vector].saveAsTextFile` with the default number of partitions.
+   */
+  def loadVectors(sc: SparkContext, path: String): RDD[Vector] =
+    sc.textFile(path, sc.defaultMinPartitions).map(Vectors.parse)
+
+  /**
+   * Loads labeled points saved using `RDD[LabeledPoint].saveAsTextFile`.
+   * @param sc Spark context
+   * @param path file or directory path in any Hadoop-supported file system URI
+   * @param minPartitions min number of partitions
+   * @return labeled points stored as an RDD[LabeledPoint]
+   */
+  def loadLabeledPoints(sc: SparkContext, path: String, minPartitions: Int): RDD[LabeledPoint] =
+    sc.textFile(path, minPartitions).map(LabeledPointParser.parse)
+
+  /**
+   * Loads labeled points saved using `RDD[LabeledPoint].saveAsTextFile` with the default number of
+   * partitions.
+   */
+  def loadLabeledPoints(sc: SparkContext, dir: String): RDD[LabeledPoint] =
+    loadLabeledPoints(sc, dir, sc.defaultMinPartitions)
+
+  /**
    * Load labeled data from a file. The data format used here is
    * <L>, <f1> <f2> ...
    * where <f1>, <f2> are feature values in Double and <L> is the corresponding label as Double.
@@ -189,8 +221,11 @@ object MLUtils {
    * @param dir Directory to the input data files.
    * @return An RDD of LabeledPoint. Each labeled point has two elements: the first element is
    *         the label, and the second element represents the feature values (an array of Double).
+   *
+   * @deprecated Should use [[org.apache.spark.rdd.RDD#saveAsTextFile]] for saving and
+   *            [[org.apache.spark.mllib.util.MLUtils#loadLabeledPoints]] for loading.
    */
-  @Experimental
+  @deprecated("Should use MLUtils.loadLabeledPoints instead.", "1.0.1")
   def loadLabeledData(sc: SparkContext, dir: String): RDD[LabeledPoint] = {
     sc.textFile(dir).map { line =>
       val parts = line.split(',')
@@ -201,15 +236,17 @@ object MLUtils {
   }
 
   /**
-   * :: Experimental ::
    * Save labeled data to a file. The data format used here is
    * <L>, <f1> <f2> ...
    * where <f1>, <f2> are feature values in Double and <L> is the corresponding label as Double.
    *
    * @param data An RDD of LabeledPoints containing data to be saved.
    * @param dir Directory to save the data.
+   *
+   * @deprecated Should use [[org.apache.spark.rdd.RDD#saveAsTextFile]] for saving and
+   *            [[org.apache.spark.mllib.util.MLUtils#loadLabeledPoints]] for loading.
    */
-  @Experimental
+  @deprecated("Should use RDD[LabeledPoint].saveAsTextFile instead.", "1.0.1")
   def saveLabeledData(data: RDD[LabeledPoint], dir: String) {
     val dataStr = data.map(x => x.label + "," + x.features.toArray.mkString(" "))
     dataStr.saveAsTextFile(dir)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/NumericParser.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/NumericParser.scala
new file mode 100644
index 000000000000..f7cba6c6cb62
--- /dev/null
+++ b/mllib/src/main/scala/org/apache/spark/mllib/util/NumericParser.scala
@@ -0,0 +1,121 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.mllib.util
+
+import java.util.StringTokenizer
+
+import scala.collection.mutable.{ArrayBuffer, ListBuffer}
+
+import org.apache.spark.SparkException
+
+/**
+ * Simple parser for a numeric structure consisting of three types:
+ *
+ *  - number: a double in Java's floating number format
+ *  - array: an array of numbers stored as `[v0,v1,...,vn]`
+ *  - tuple: a list of numbers, arrays, or tuples stored as `(...)`
+ */
+private[mllib] object NumericParser {
+
+  /** Parses a string into a Double, an Array[Double], or a Seq[Any]. */
+  def parse(s: String): Any = {
+    val tokenizer = new StringTokenizer(s, "()[],", true)
+    if (tokenizer.hasMoreTokens()) {
+      val token = tokenizer.nextToken()
+      if (token == "(") {
+        parseTuple(tokenizer)
+      } else if (token == "[") {
+        parseArray(tokenizer)
+      } else {
+        // expecting a number
+        parseDouble(token)
+      }
+    } else {
+      throw new SparkException(s"Cannot find any token from the input string.")
+    }
+  }
+
+  private def parseArray(tokenizer: StringTokenizer): Array[Double] = {
+    val values = ArrayBuffer.empty[Double]
+    var parsing = true
+    var allowComma = false
+    var token: String = null
+    while (parsing && tokenizer.hasMoreTokens()) {
+      token = tokenizer.nextToken()
+      if (token == "]") {
+        parsing = false
+      } else if (token == ",") {
+        if (allowComma) {
+          allowComma = false
+        } else {
+          throw new SparkException("Found a ',' at a wrong position.")
+        }
+      } else {
+        // expecting a number
+        values.append(parseDouble(token))
+        allowComma = true
+      }
+    }
+    if (parsing) {
+      throw new SparkException(s"An array must end with ']'.")
+    }
+    values.toArray
+  }
+
+  private def parseTuple(tokenizer: StringTokenizer): Seq[_] = {
+    val items = ListBuffer.empty[Any]
+    var parsing = true
+    var allowComma = false
+    var token: String = null
+    while (parsing && tokenizer.hasMoreTokens()) {
+      token = tokenizer.nextToken()
+      if (token == "(") {
+        items.append(parseTuple(tokenizer))
+        allowComma = true
+      } else if (token == "[") {
+        items.append(parseArray(tokenizer))
+        allowComma = true
+      } else if (token == ",") {
+        if (allowComma) {
+          allowComma = false
+        } else {
+          throw new SparkException("Found a ',' at a wrong position.")
+        }
+      } else if (token == ")") {
+        parsing = false
+      } else {
+        // expecting a number
+        items.append(parseDouble(token))
+        allowComma = true
+      }
+    }
+    if (parsing) {
+      throw new SparkException(s"A tuple must end with ')'.")
+    }
+    items
+  }
+
+  private def parseDouble(s: String): Double = {
+    try {
+      java.lang.Double.parseDouble(s)
+    } catch {
+      case e: Throwable =>
+        throw new SparkException(s"Cannot parse a double from: $s", e)
+    }
+  }
+}
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/SVMDataGenerator.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/SVMDataGenerator.scala
index ba8190b0e07e..7db97e6bac68 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/util/SVMDataGenerator.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/util/SVMDataGenerator.scala
@@ -65,7 +65,7 @@ object SVMDataGenerator {
       LabeledPoint(y, Vectors.dense(x))
     }
 
-    MLUtils.saveLabeledData(data, outputPath)
+    data.saveAsTextFile(outputPath)
 
     sc.stop()
   }
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/api/python/PythonMLLibAPISuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/api/python/PythonMLLibAPISuite.scala
new file mode 100644
index 000000000000..642843f90204
--- /dev/null
+++ b/mllib/src/test/scala/org/apache/spark/mllib/api/python/PythonMLLibAPISuite.scala
@@ -0,0 +1,60 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.mllib.api.python
+
+import org.scalatest.FunSuite
+
+import org.apache.spark.mllib.linalg.Vectors
+import org.apache.spark.mllib.regression.LabeledPoint
+
+class PythonMLLibAPISuite extends FunSuite {
+  val py = new PythonMLLibAPI
+
+  test("vector serialization") {
+    val vectors = Seq(
+      Vectors.dense(Array.empty[Double]),
+      Vectors.dense(0.0),
+      Vectors.dense(0.0, -2.0),
+      Vectors.sparse(0, Array.empty[Int], Array.empty[Double]),
+      Vectors.sparse(1, Array.empty[Int], Array.empty[Double]),
+      Vectors.sparse(2, Array(1), Array(-2.0)))
+    vectors.foreach { v =>
+      val bytes = py.serializeDoubleVector(v)
+      val u = py.deserializeDoubleVector(bytes)
+      assert(u.getClass === v.getClass)
+      assert(u === v)
+    }
+  }
+
+  test("labeled point serialization") {
+    val points = Seq(
+      LabeledPoint(0.0, Vectors.dense(Array.empty[Double])),
+      LabeledPoint(1.0, Vectors.dense(0.0)),
+      LabeledPoint(-0.5, Vectors.dense(0.0, -2.0)),
+      LabeledPoint(0.0, Vectors.sparse(0, Array.empty[Int], Array.empty[Double])),
+      LabeledPoint(1.0, Vectors.sparse(1, Array.empty[Int], Array.empty[Double])),
+      LabeledPoint(-0.5, Vectors.sparse(2, Array(1), Array(-2.0))))
+    points.foreach { p =>
+      val bytes = py.serializeLabeledPoint(p)
+      val q = py.deserializeLabeledPoint(bytes)
+      assert(q.label === p.label)
+      assert(q.features.getClass === p.features.getClass)
+      assert(q.features === p.features)
+    }
+  }
+}
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/linalg/VectorsSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/linalg/VectorsSuite.scala
index cfe8a27fcb71..7972ceea1fe8 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/linalg/VectorsSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/linalg/VectorsSuite.scala
@@ -19,6 +19,8 @@ package org.apache.spark.mllib.linalg
 
 import org.scalatest.FunSuite
 
+import org.apache.spark.SparkException
+
 class VectorsSuite extends FunSuite {
 
   val arr = Array(0.1, 0.0, 0.3, 0.4)
@@ -100,4 +102,27 @@ class VectorsSuite extends FunSuite {
     assert(vec2(6) === 4.0)
     assert(vec2(7) === 0.0)
   }
+
+  test("parse vectors") {
+    val vectors = Seq(
+      Vectors.dense(Array.empty[Double]),
+      Vectors.dense(1.0),
+      Vectors.dense(1.0E6, 0.0, -2.0e-7),
+      Vectors.sparse(0, Array.empty[Int], Array.empty[Double]),
+      Vectors.sparse(1, Array(0), Array(1.0)),
+      Vectors.sparse(3, Array(0, 2), Array(1.0, -2.0)))
+    vectors.foreach { v =>
+      val v1 = Vectors.parse(v.toString)
+      assert(v.getClass === v1.getClass)
+      assert(v === v1)
+    }
+
+    val malformatted = Seq("1", "[1,,]", "[1,2b]", "(1,[1,2])", "([1],[2.0,1.0])")
+    malformatted.foreach { s =>
+      intercept[SparkException] {
+        Vectors.parse(s)
+        println(s"Didn't detect malformatted string $s.")
+      }
+    }
+  }
 }
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/regression/LabeledPointSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/regression/LabeledPointSuite.scala
new file mode 100644
index 000000000000..d9308aaba6ee
--- /dev/null
+++ b/mllib/src/test/scala/org/apache/spark/mllib/regression/LabeledPointSuite.scala
@@ -0,0 +1,39 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.mllib.regression
+
+import org.scalatest.FunSuite
+
+import org.apache.spark.mllib.linalg.Vectors
+
+class LabeledPointSuite extends FunSuite {
+
+  test("parse labeled points") {
+    val points = Seq(
+      LabeledPoint(1.0, Vectors.dense(1.0, 0.0)),
+      LabeledPoint(0.0, Vectors.sparse(2, Array(1), Array(-1.0))))
+    points.foreach { p =>
+      assert(p === LabeledPointParser.parse(p.toString))
+    }
+  }
+
+  test("parse labeled points with v0.9 format") {
+    val point = LabeledPointParser.parse("1.0,1.0 0.0 -2.0")
+    assert(point === LabeledPoint(1.0, Vectors.dense(1.0, 0.0, -2.0)))
+  }
+}
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/util/MLUtilsSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/util/MLUtilsSuite.scala
index 3d05fb68988c..c14870fb969a 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/util/MLUtilsSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/util/MLUtilsSuite.scala
@@ -160,5 +160,33 @@ class MLUtilsSuite extends FunSuite with LocalSparkContext {
     }
   }
 
-}
+  test("loadVectors") {
+    val vectors = sc.parallelize(Seq(
+      Vectors.dense(1.0, 2.0),
+      Vectors.sparse(2, Array(1), Array(-1.0)),
+      Vectors.dense(0.0, 1.0)
+    ), 2)
+    val tempDir = Files.createTempDir()
+    val outputDir = new File(tempDir, "vectors")
+    val path = outputDir.toURI.toString
+    vectors.saveAsTextFile(path)
+    val loaded = loadVectors(sc, path)
+    assert(vectors.collect().toSet === loaded.collect().toSet)
+    Utils.deleteRecursively(tempDir)
+  }
 
+  test("loadLabeledPoints") {
+    val points = sc.parallelize(Seq(
+      LabeledPoint(1.0, Vectors.dense(1.0, 2.0)),
+      LabeledPoint(0.0, Vectors.sparse(2, Array(1), Array(-1.0))),
+      LabeledPoint(1.0, Vectors.dense(0.0, 1.0))
+    ), 2)
+    val tempDir = Files.createTempDir()
+    val outputDir = new File(tempDir, "points")
+    val path = outputDir.toURI.toString
+    points.saveAsTextFile(path)
+    val loaded = loadLabeledPoints(sc, path)
+    assert(points.collect().toSet === loaded.collect().toSet)
+    Utils.deleteRecursively(tempDir)
+  }
+}
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/util/NumericParserSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/util/NumericParserSuite.scala
new file mode 100644
index 000000000000..f68fb95eac4e
--- /dev/null
+++ b/mllib/src/test/scala/org/apache/spark/mllib/util/NumericParserSuite.scala
@@ -0,0 +1,42 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.mllib.util
+
+import org.scalatest.FunSuite
+
+import org.apache.spark.SparkException
+
+class NumericParserSuite extends FunSuite {
+
+  test("parser") {
+    val s = "((1.0,2e3),-4,[5e-6,7.0E8],+9)"
+    val parsed = NumericParser.parse(s).asInstanceOf[Seq[_]]
+    assert(parsed(0).asInstanceOf[Seq[_]] === Seq(1.0, 2.0e3))
+    assert(parsed(1).asInstanceOf[Double] === -4.0)
+    assert(parsed(2).asInstanceOf[Array[Double]] === Array(5.0e-6, 7.0e8))
+    assert(parsed(3).asInstanceOf[Double] === 9.0)
+
+    val malformatted = Seq("a", "[1,,]", "0.123.4", "1 2", "3+4")
+    malformatted.foreach { s =>
+      intercept[SparkException] {
+        NumericParser.parse(s)
+        println(s"Didn't detect malformatted string $s.")
+      }
+    }
+  }
+}
diff --git a/python/pyspark/mllib/_common.py b/python/pyspark/mllib/_common.py
index 802a27a8da14..a411a5d5914e 100644
--- a/python/pyspark/mllib/_common.py
+++ b/python/pyspark/mllib/_common.py
@@ -22,6 +22,7 @@
 from pyspark.mllib.linalg import SparseVector
 from pyspark.serializers import Serializer
 
+
 """
 Common utilities shared throughout MLlib, primarily for dealing with
 different data types. These include:
@@ -147,7 +148,7 @@ def _serialize_sparse_vector(v):
     return ba
 
 
-def _deserialize_double_vector(ba):
+def _deserialize_double_vector(ba, offset=0):
     """Deserialize a double vector from a mutually understood format.
 
     >>> x = array([1.0, 2.0, 3.0, 4.0, -1.0, 0.0, -0.0])
@@ -160,43 +161,46 @@ def _deserialize_double_vector(ba):
     if type(ba) != bytearray:
         raise TypeError("_deserialize_double_vector called on a %s; "
                         "wanted bytearray" % type(ba))
-    if len(ba) < 5:
+    nb = len(ba) - offset
+    if nb < 5:
         raise TypeError("_deserialize_double_vector called on a %d-byte array, "
-                        "which is too short" % len(ba))
-    if ba[0] == DENSE_VECTOR_MAGIC:
-        return _deserialize_dense_vector(ba)
-    elif ba[0] == SPARSE_VECTOR_MAGIC:
-        return _deserialize_sparse_vector(ba)
+                "which is too short" % nb)
+    if ba[offset] == DENSE_VECTOR_MAGIC:
+        return _deserialize_dense_vector(ba, offset)
+    elif ba[offset] == SPARSE_VECTOR_MAGIC:
+        return _deserialize_sparse_vector(ba, offset)
     else:
         raise TypeError("_deserialize_double_vector called on bytearray "
                         "with wrong magic")
 
 
-def _deserialize_dense_vector(ba):
+def _deserialize_dense_vector(ba, offset=0):
     """Deserialize a dense vector into a numpy array."""
-    if len(ba) < 5:
+    nb = len(ba) - offset
+    if nb < 5:
         raise TypeError("_deserialize_dense_vector called on a %d-byte array, "
-                        "which is too short" % len(ba))
-    length = ndarray(shape=[1], buffer=ba, offset=1, dtype=int32)[0]
-    if len(ba) != 8 * length + 5:
+                        "which is too short" % nb)
+    length = ndarray(shape=[1], buffer=ba, offset=offset + 1, dtype=int32)[0]
+    if nb < 8 * length + 5:
         raise TypeError("_deserialize_dense_vector called on bytearray "
                         "with wrong length")
-    return _deserialize_numpy_array([length], ba, 5)
+    return _deserialize_numpy_array([length], ba, offset + 5)
 
 
-def _deserialize_sparse_vector(ba):
+def _deserialize_sparse_vector(ba, offset=0):
     """Deserialize a sparse vector into a MLlib SparseVector object."""
-    if len(ba) < 9:
+    nb = len(ba) - offset
+    if nb < 9:
         raise TypeError("_deserialize_sparse_vector called on a %d-byte array, "
-                        "which is too short" % len(ba))
-    header = ndarray(shape=[2], buffer=ba, offset=1, dtype=int32)
+                        "which is too short" % nb)
+    header = ndarray(shape=[2], buffer=ba, offset=offset + 1, dtype=int32)
     size = header[0]
     nonzeros = header[1]
-    if len(ba) != 9 + 12 * nonzeros:
+    if nb < 9 + 12 * nonzeros:
         raise TypeError("_deserialize_sparse_vector called on bytearray "
                         "with wrong length")
-    indices = _deserialize_numpy_array([nonzeros], ba, 9, dtype=int32)
-    values = _deserialize_numpy_array([nonzeros], ba, 9 + 4 * nonzeros, dtype=float64)
+    indices = _deserialize_numpy_array([nonzeros], ba, offset + 9, dtype=int32)
+    values = _deserialize_numpy_array([nonzeros], ba, offset + 9 + 4 * nonzeros, dtype=float64)
     return SparseVector(int(size), indices, values)
 
 
@@ -243,7 +247,23 @@ def _deserialize_double_matrix(ba):
 
 
 def _serialize_labeled_point(p):
-    """Serialize a LabeledPoint with a features vector of any type."""
+    """
+    Serialize a LabeledPoint with a features vector of any type.
+
+    >>> from pyspark.mllib.regression import LabeledPoint
+    >>> dp0 = LabeledPoint(0.5, array([1.0, 2.0, 3.0, 4.0, -1.0, 0.0, -0.0]))
+    >>> dp1 = _deserialize_labeled_point(_serialize_labeled_point(dp0))
+    >>> dp1.label == dp0.label
+    True
+    >>> array_equal(dp1.features, dp0.features)
+    True
+    >>> sp0 = LabeledPoint(0.0, SparseVector(4, [1, 3], [3.0, 5.5]))
+    >>> sp1 = _deserialize_labeled_point(_serialize_labeled_point(sp0))
+    >>> sp1.label == sp1.label
+    True
+    >>> sp1.features == sp0.features
+    True
+    """
     from pyspark.mllib.regression import LabeledPoint
     serialized_features = _serialize_double_vector(p.features)
     header = bytearray(9)
@@ -252,6 +272,16 @@ def _serialize_labeled_point(p):
     header_float[0] = p.label
     return header + serialized_features
 
+def _deserialize_labeled_point(ba, offset=0):
+    """Deserialize a LabeledPoint from a mutually understood format."""
+    from pyspark.mllib.regression import LabeledPoint
+    if type(ba) != bytearray:
+        raise TypeError("Expecting a bytearray but got %s" % type(ba))
+    if ba[offset] != LABELED_POINT_MAGIC:
+        raise TypeError("Expecting magic number %d but got %d" % (LABELED_POINT_MAGIC, ba[0]))
+    label = ndarray(shape=[1], buffer=ba, offset=offset + 1, dtype=float64)[0]
+    features = _deserialize_double_vector(ba, offset + 9)
+    return LabeledPoint(label, features)
 
 def _copyto(array, buffer, offset, shape, dtype):
     """
diff --git a/python/pyspark/mllib/linalg.py b/python/pyspark/mllib/linalg.py
index 276684272068..db39ed0acdb6 100644
--- a/python/pyspark/mllib/linalg.py
+++ b/python/pyspark/mllib/linalg.py
@@ -43,11 +43,11 @@ def __init__(self, size, *args):
                or two sorted lists containing indices and values.
 
         >>> print SparseVector(4, {1: 1.0, 3: 5.5})
-        [1: 1.0, 3: 5.5]
+        (4,[1,3],[1.0,5.5])
         >>> print SparseVector(4, [(1, 1.0), (3, 5.5)])
-        [1: 1.0, 3: 5.5]
+        (4,[1,3],[1.0,5.5])
         >>> print SparseVector(4, [1, 3], [1.0, 5.5])
-        [1: 1.0, 3: 5.5]
+        (4,[1,3],[1.0,5.5])
         """
         self.size = int(size)
         assert 1 <= len(args) <= 2, "must pass either 2 or 3 arguments"
@@ -160,10 +160,9 @@ def squared_distance(self, other):
             return result
 
     def __str__(self):
-        inds = self.indices
-        vals = self.values
-        entries = ", ".join(["{0}: {1}".format(inds[i], vals[i]) for i in xrange(len(inds))])
-        return "[" + entries + "]"
+        inds = "[" + ",".join([str(i) for i in self.indices]) + "]"
+        vals = "[" + ",".join([str(v) for v in self.values]) + "]"
+        return "(" + ",".join((str(self.size), inds, vals)) + ")"
 
     def __repr__(self):
         inds = self.indices
@@ -213,11 +212,11 @@ def sparse(size, *args):
                      or two sorted lists containing indices and values.
 
         >>> print Vectors.sparse(4, {1: 1.0, 3: 5.5})
-        [1: 1.0, 3: 5.5]
+        (4,[1,3],[1.0,5.5])
         >>> print Vectors.sparse(4, [(1, 1.0), (3, 5.5)])
-        [1: 1.0, 3: 5.5]
+        (4,[1,3],[1.0,5.5])
         >>> print Vectors.sparse(4, [1, 3], [1.0, 5.5])
-        [1: 1.0, 3: 5.5]
+        (4,[1,3],[1.0,5.5])
         """
         return SparseVector(size, *args)
 
@@ -232,6 +231,21 @@ def dense(elements):
         """
         return array(elements, dtype=float64)
 
+    @staticmethod
+    def stringify(vector):
+        """
+        Converts a vector into a string, which can be recognized by
+        Vectors.parse().
+
+        >>> Vectors.stringify(Vectors.sparse(2, [1], [1.0]))
+        '(2,[1],[1.0])'
+        >>> Vectors.stringify(Vectors.dense([0.0, 1.0]))
+        '[0.0,1.0]'
+        """
+        if type(vector) == SparseVector:
+            return str(vector)
+        else:
+            return "[" + ",".join([str(v) for v in vector]) + "]"
 
 def _test():
     import doctest
diff --git a/python/pyspark/mllib/regression.py b/python/pyspark/mllib/regression.py
index bc7de6d2e895..b84bc531dec8 100644
--- a/python/pyspark/mllib/regression.py
+++ b/python/pyspark/mllib/regression.py
@@ -23,7 +23,7 @@
     _serialize_double_vector, _deserialize_double_vector, \
     _get_initial_weights, _serialize_rating, _regression_train_wrapper, \
     _linear_predictor_typecheck, _have_scipy, _scipy_issparse
-from pyspark.mllib.linalg import SparseVector
+from pyspark.mllib.linalg import SparseVector, Vectors
 
 
 class LabeledPoint(object):
@@ -44,6 +44,9 @@ def __init__(self, label, features):
         else:
             raise TypeError("Expected NumPy array, list, SparseVector, or scipy.sparse matrix")
 
+    def __str__(self):
+        return "(" + ",".join((str(self.label), Vectors.stringify(self.features))) + ")"
+
 
 class LinearModel(object):
     """A linear model that has a vector of coefficients and an intercept."""
diff --git a/python/pyspark/mllib/util.py b/python/pyspark/mllib/util.py
index 0e5f4520b940..e24c144f458b 100644
--- a/python/pyspark/mllib/util.py
+++ b/python/pyspark/mllib/util.py
@@ -19,7 +19,10 @@
 
 from pyspark.mllib.linalg import Vectors, SparseVector
 from pyspark.mllib.regression import LabeledPoint
-from pyspark.mllib._common import _convert_vector
+from pyspark.mllib._common import _convert_vector, _deserialize_labeled_point
+from pyspark.rdd import RDD
+from pyspark.serializers import NoOpSerializer
+
 
 
 class MLUtils:
@@ -105,24 +108,18 @@ def loadLibSVMFile(sc, path, multiclass=False, numFeatures=-1, minPartitions=Non
         >>> examples = MLUtils.loadLibSVMFile(sc, tempFile.name).collect()
         >>> multiclass_examples = MLUtils.loadLibSVMFile(sc, tempFile.name, True).collect()
         >>> tempFile.close()
-        >>> examples[0].label
-        1.0
-        >>> examples[0].features.size
-        6
-        >>> print examples[0].features
-        [0: 1.0, 2: 2.0, 4: 3.0]
-        >>> examples[1].label
-        0.0
-        >>> examples[1].features.size
-        6
-        >>> print examples[1].features
-        []
-        >>> examples[2].label
-        0.0
-        >>> examples[2].features.size
-        6
-        >>> print examples[2].features
-        [1: 4.0, 3: 5.0, 5: 6.0]
+        >>> type(examples[0]) == LabeledPoint
+        True
+        >>> print examples[0]
+        (1.0,(6,[0,2,4],[1.0,2.0,3.0]))
+        >>> type(examples[1]) == LabeledPoint
+        True
+        >>> print examples[1]
+        (0.0,(6,[],[]))
+        >>> type(examples[2]) == LabeledPoint
+        True
+        >>> print examples[2]
+        (0.0,(6,[1,3,5],[4.0,5.0,6.0]))
         >>> multiclass_examples[1].label
         -1.0
         """
@@ -158,6 +155,40 @@ def saveAsLibSVMFile(data, dir):
         lines.saveAsTextFile(dir)
 
 
+    @staticmethod
+    def loadLabeledPoints(sc, path, minPartitions=None):
+        """
+        Load labeled points saved using RDD.saveAsTextFile.
+
+        @param sc: Spark context
+        @param path: file or directory path in any Hadoop-supported file
+                     system URI
+        @param minPartitions: min number of partitions
+        @return: labeled data stored as an RDD of LabeledPoint
+
+        >>> from tempfile import NamedTemporaryFile
+        >>> from pyspark.mllib.util import MLUtils
+        >>> examples = [LabeledPoint(1.1, Vectors.sparse(3, [(0, -1.23), (2, 4.56e-7)])), \
+                        LabeledPoint(0.0, Vectors.dense([1.01, 2.02, 3.03]))]
+        >>> tempFile = NamedTemporaryFile(delete=True)
+        >>> tempFile.close()
+        >>> sc.parallelize(examples, 1).saveAsTextFile(tempFile.name)
+        >>> loaded = MLUtils.loadLabeledPoints(sc, tempFile.name).collect()
+        >>> type(loaded[0]) == LabeledPoint
+        True
+        >>> print examples[0]
+        (1.1,(3,[0,2],[-1.23,4.56e-07]))
+        >>> type(examples[1]) == LabeledPoint
+        True
+        >>> print examples[1]
+        (0.0,[1.01,2.02,3.03])
+        """
+        minPartitions = minPartitions or min(sc.defaultParallelism, 2)
+        jSerialized = sc._jvm.PythonMLLibAPI().loadLabeledPoints(sc._jsc, path, minPartitions)
+        serialized = RDD(jSerialized, sc, NoOpSerializer())
+        return serialized.map(lambda bytes: _deserialize_labeled_point(bytearray(bytes)))
+
+
 def _test():
     import doctest
     from pyspark.context import SparkContext

From 1765c8d0ddf6bb5bc3c21f994456eba04c581de4 Mon Sep 17 00:00:00 2001
From: Colin McCabe <cmccabe@cloudera.com>
Date: Wed, 4 Jun 2014 15:56:29 -0700
Subject: [PATCH 340/810] SPARK-1518: FileLogger: Fix compile against Hadoop
 trunk

In Hadoop trunk (currently Hadoop 3.0.0), the deprecated
FSDataOutputStream#sync() method has been removed.  Instead, we should
call FSDataOutputStream#hflush, which does the same thing as the
deprecated method used to do.

Author: Colin McCabe <cmccabe@cloudera.com>

Closes #898 from cmccabe/SPARK-1518 and squashes the following commits:

752b9d7 [Colin McCabe] FileLogger: Fix compile against Hadoop trunk
---
 .../scala/org/apache/spark/util/FileLogger.scala | 16 ++++++++++++----
 1 file changed, 12 insertions(+), 4 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/util/FileLogger.scala b/core/src/main/scala/org/apache/spark/util/FileLogger.scala
index 0e6d21b22023..6a95dc06e155 100644
--- a/core/src/main/scala/org/apache/spark/util/FileLogger.scala
+++ b/core/src/main/scala/org/apache/spark/util/FileLogger.scala
@@ -61,6 +61,14 @@ private[spark] class FileLogger(
   // Only defined if the file system scheme is not local
   private var hadoopDataStream: Option[FSDataOutputStream] = None
 
+  // The Hadoop APIs have changed over time, so we use reflection to figure out
+  // the correct method to use to flush a hadoop data stream. See SPARK-1518
+  // for details.
+  private val hadoopFlushMethod = {
+    val cls = classOf[FSDataOutputStream]
+    scala.util.Try(cls.getMethod("hflush")).getOrElse(cls.getMethod("sync"))
+  }
+
   private var writer: Option[PrintWriter] = None
 
   /**
@@ -149,13 +157,13 @@ private[spark] class FileLogger(
   /**
    * Flush the writer to disk manually.
    *
-   * If the Hadoop FileSystem is used, the underlying FSDataOutputStream (r1.0.4) must be
-   * sync()'ed manually as it does not support flush(), which is invoked by when higher
-   * level streams are flushed.
+   * When using a Hadoop filesystem, we need to invoke the hflush or sync
+   * method. In HDFS, hflush guarantees that the data gets to all the
+   * DataNodes.
    */
   def flush() {
     writer.foreach(_.flush())
-    hadoopDataStream.foreach(_.sync())
+    hadoopDataStream.foreach(hadoopFlushMethod.invoke(_))
   }
 
   /**

From 11ded3f66f178e4d8d2b23491dd5e0ea23bcf719 Mon Sep 17 00:00:00 2001
From: Varakhedi Sujeet <svarakhedi@gopivotal.com>
Date: Wed, 4 Jun 2014 16:01:56 -0700
Subject: [PATCH 341/810] SPARK-1790: Update EC2 scripts to support r3 instance
 types

Author: Varakhedi Sujeet <svarakhedi@gopivotal.com>

Closes #960 from sujeetv/ec2-r3 and squashes the following commits:

3cb9fd5 [Varakhedi Sujeet] SPARK-1790: Update EC2 scripts to support r3 instance
---
 ec2/spark_ec2.py | 14 ++++++++++++--
 1 file changed, 12 insertions(+), 2 deletions(-)

diff --git a/ec2/spark_ec2.py b/ec2/spark_ec2.py
index 3af9f66e17dc..9d5748ba4bc2 100755
--- a/ec2/spark_ec2.py
+++ b/ec2/spark_ec2.py
@@ -230,7 +230,12 @@ def get_spark_ami(opts):
         "c3.xlarge":   "pvm",
         "c3.2xlarge":  "pvm",
         "c3.4xlarge":  "pvm",
-        "c3.8xlarge":  "pvm"
+        "c3.8xlarge":  "pvm",
+        "r3.large":    "hvm",
+        "r3.xlarge":   "hvm",
+        "r3.2xlarge":  "hvm",
+        "r3.4xlarge":  "hvm",
+        "r3.8xlarge":  "hvm"
     }
     if opts.instance_type in instance_types:
         instance_type = instance_types[opts.instance_type]
@@ -538,7 +543,12 @@ def get_num_disks(instance_type):
         "c3.xlarge":   2,
         "c3.2xlarge":  2,
         "c3.4xlarge":  2,
-        "c3.8xlarge":  2
+        "c3.8xlarge":  2,
+        "r3.large":    1,
+        "r3.xlarge":   1,
+        "r3.2xlarge":  1,
+        "r3.4xlarge":  1,
+        "r3.8xlarge":  2
     }
     if instance_type in disks_by_instance:
         return disks_by_instance[instance_type]

From abea2d4ff099036c67fc73136d0e61d0d0e22123 Mon Sep 17 00:00:00 2001
From: Ankur Dave <ankurdave@gmail.com>
Date: Wed, 4 Jun 2014 16:45:53 -0700
Subject: [PATCH 342/810] Minor: Fix documentation error from apache/spark#946

Author: Ankur Dave <ankurdave@gmail.com>

Closes #970 from ankurdave/SPARK-1991_docfix and squashes the following commits:

6d07343 [Ankur Dave] Minor: Fix documentation error from apache/spark#946
---
 .../src/main/scala/org/apache/spark/graphx/GraphLoader.scala  | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/graphx/src/main/scala/org/apache/spark/graphx/GraphLoader.scala b/graphx/src/main/scala/org/apache/spark/graphx/GraphLoader.scala
index 2e814e34f9ad..f4c79365b16d 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/GraphLoader.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/GraphLoader.scala
@@ -49,8 +49,8 @@ object GraphLoader extends Logging {
    * @param canonicalOrientation whether to orient edges in the positive
    *        direction
    * @param minEdgePartitions the number of partitions for the edge RDD
-   * @param edgeStorageLevel the desired storage level for the edge partitions. To set the vertex
-   *        storage level, call [[org.apache.spark.graphx.Graph#persistVertices]].
+   * @param edgeStorageLevel the desired storage level for the edge partitions
+   * @param vertexStorageLevel the desired storage level for the vertex partitions
    */
   def edgeListFile(
       sc: SparkContext,

From b77c19be053125fde99b098ec1e1162f25b5433c Mon Sep 17 00:00:00 2001
From: Marcelo Vanzin <vanzin@cloudera.com>
Date: Wed, 4 Jun 2014 22:56:49 -0700
Subject: [PATCH 343/810] Fix issue in ReplSuite with hadoop-provided profile.

When building the assembly with the maven "hadoop-provided"
profile, the executors were failing to come up because Hadoop classes
were not found in the classpath anymore; so add them explicitly to
the classpath using spark.executor.extraClassPath. This is only
needed for the local-cluster mode, but doesn't affect other tests,
so it's added for all of them to keep the code simpler.

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #781 from vanzin/repl-test-fix and squashes the following commits:

4f0a3b0 [Marcelo Vanzin] Fix issue in ReplSuite with hadoop-provided profile.
---
 .../scala/org/apache/spark/repl/ReplSuite.scala    | 14 +++++++++++++-
 1 file changed, 13 insertions(+), 1 deletion(-)

diff --git a/repl/src/test/scala/org/apache/spark/repl/ReplSuite.scala b/repl/src/test/scala/org/apache/spark/repl/ReplSuite.scala
index 98cdfd005471..7c765edd5502 100644
--- a/repl/src/test/scala/org/apache/spark/repl/ReplSuite.scala
+++ b/repl/src/test/scala/org/apache/spark/repl/ReplSuite.scala
@@ -32,6 +32,8 @@ import org.apache.spark.util.Utils
 class ReplSuite extends FunSuite {
 
   def runInterpreter(master: String, input: String): String = {
+    val CONF_EXECUTOR_CLASSPATH = "spark.executor.extraClassPath"
+
     val in = new BufferedReader(new StringReader(input + "\n"))
     val out = new StringWriter()
     val cl = getClass.getClassLoader
@@ -44,13 +46,23 @@ class ReplSuite extends FunSuite {
         }
       }
     }
+    val classpath = paths.mkString(File.pathSeparator)
+
+    val oldExecutorClasspath = System.getProperty(CONF_EXECUTOR_CLASSPATH)
+    System.setProperty(CONF_EXECUTOR_CLASSPATH, classpath)
+
     val interp = new SparkILoop(in, new PrintWriter(out), master)
     org.apache.spark.repl.Main.interp = interp
-    interp.process(Array("-classpath", paths.mkString(File.pathSeparator)))
+    interp.process(Array("-classpath", classpath))
     org.apache.spark.repl.Main.interp = null
     if (interp.sparkContext != null) {
       interp.sparkContext.stop()
     }
+    if (oldExecutorClasspath != null) {
+      System.setProperty(CONF_EXECUTOR_CLASSPATH, oldExecutorClasspath)
+    } else {
+      System.clearProperty(CONF_EXECUTOR_CLASSPATH)
+    }
     return out.toString
   }
 

From 7c160293d6d708718d566e700cfb407a31280b89 Mon Sep 17 00:00:00 2001
From: Takuya UESHIN <ueshin@happy-camper.st>
Date: Thu, 5 Jun 2014 11:27:33 -0700
Subject: [PATCH 344/810] [SPARK-2029] Bump pom.xml version number of master
 branch to 1.1.0-SNAPSHOT.

Author: Takuya UESHIN <ueshin@happy-camper.st>

Closes #974 from ueshin/issues/SPARK-2029 and squashes the following commits:

e19e8f4 [Takuya UESHIN] Bump version number to 1.1.0-SNAPSHOT.
---
 assembly/pom.xml                  | 2 +-
 bagel/pom.xml                     | 2 +-
 core/pom.xml                      | 2 +-
 examples/pom.xml                  | 2 +-
 external/flume/pom.xml            | 2 +-
 external/kafka/pom.xml            | 2 +-
 external/mqtt/pom.xml             | 2 +-
 external/twitter/pom.xml          | 2 +-
 external/zeromq/pom.xml           | 2 +-
 extras/java8-tests/pom.xml        | 2 +-
 extras/spark-ganglia-lgpl/pom.xml | 2 +-
 graphx/pom.xml                    | 2 +-
 mllib/pom.xml                     | 2 +-
 pom.xml                           | 2 +-
 repl/pom.xml                      | 2 +-
 sql/catalyst/pom.xml              | 2 +-
 sql/core/pom.xml                  | 2 +-
 sql/hive/pom.xml                  | 2 +-
 streaming/pom.xml                 | 2 +-
 tools/pom.xml                     | 2 +-
 yarn/alpha/pom.xml                | 2 +-
 yarn/pom.xml                      | 2 +-
 yarn/stable/pom.xml               | 2 +-
 23 files changed, 23 insertions(+), 23 deletions(-)

diff --git a/assembly/pom.xml b/assembly/pom.xml
index 963357b9ab16..0c60b66c3dac 100644
--- a/assembly/pom.xml
+++ b/assembly/pom.xml
@@ -21,7 +21,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent</artifactId>
-    <version>1.0.0-SNAPSHOT</version>
+    <version>1.1.0-SNAPSHOT</version>
     <relativePath>../pom.xml</relativePath>
   </parent>
 
diff --git a/bagel/pom.xml b/bagel/pom.xml
index 355f437c5b16..c8e39a415af2 100644
--- a/bagel/pom.xml
+++ b/bagel/pom.xml
@@ -21,7 +21,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent</artifactId>
-    <version>1.0.0-SNAPSHOT</version>
+    <version>1.1.0-SNAPSHOT</version>
     <relativePath>../pom.xml</relativePath>
   </parent>
 
diff --git a/core/pom.xml b/core/pom.xml
index 0777c5b1f03d..0c746175afa7 100644
--- a/core/pom.xml
+++ b/core/pom.xml
@@ -21,7 +21,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent</artifactId>
-    <version>1.0.0-SNAPSHOT</version>
+    <version>1.1.0-SNAPSHOT</version>
     <relativePath>../pom.xml</relativePath>
   </parent>
 
diff --git a/examples/pom.xml b/examples/pom.xml
index 874bcd7916f3..4f6d7fdb87d4 100644
--- a/examples/pom.xml
+++ b/examples/pom.xml
@@ -21,7 +21,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent</artifactId>
-    <version>1.0.0-SNAPSHOT</version>
+    <version>1.1.0-SNAPSHOT</version>
     <relativePath>../pom.xml</relativePath>
   </parent>
 
diff --git a/external/flume/pom.xml b/external/flume/pom.xml
index 6aec215687fe..c1f581967777 100644
--- a/external/flume/pom.xml
+++ b/external/flume/pom.xml
@@ -21,7 +21,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent</artifactId>
-    <version>1.0.0-SNAPSHOT</version>
+    <version>1.1.0-SNAPSHOT</version>
     <relativePath>../../pom.xml</relativePath>
   </parent>
 
diff --git a/external/kafka/pom.xml b/external/kafka/pom.xml
index 979eb0ca624b..d014a7aad0fc 100644
--- a/external/kafka/pom.xml
+++ b/external/kafka/pom.xml
@@ -21,7 +21,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent</artifactId>
-    <version>1.0.0-SNAPSHOT</version>
+    <version>1.1.0-SNAPSHOT</version>
     <relativePath>../../pom.xml</relativePath>
   </parent>
 
diff --git a/external/mqtt/pom.xml b/external/mqtt/pom.xml
index 7b2dc5ba1d7f..4980208cba3b 100644
--- a/external/mqtt/pom.xml
+++ b/external/mqtt/pom.xml
@@ -21,7 +21,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent</artifactId>
-    <version>1.0.0-SNAPSHOT</version>
+    <version>1.1.0-SNAPSHOT</version>
     <relativePath>../../pom.xml</relativePath>
   </parent>
 
diff --git a/external/twitter/pom.xml b/external/twitter/pom.xml
index 5766d3a0d44e..7073bd4404d9 100644
--- a/external/twitter/pom.xml
+++ b/external/twitter/pom.xml
@@ -21,7 +21,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent</artifactId>
-    <version>1.0.0-SNAPSHOT</version>
+    <version>1.1.0-SNAPSHOT</version>
     <relativePath>../../pom.xml</relativePath>
   </parent>
 
diff --git a/external/zeromq/pom.xml b/external/zeromq/pom.xml
index 4ed4196bd866..cf306e0dca8b 100644
--- a/external/zeromq/pom.xml
+++ b/external/zeromq/pom.xml
@@ -21,7 +21,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent</artifactId>
-    <version>1.0.0-SNAPSHOT</version>
+    <version>1.1.0-SNAPSHOT</version>
     <relativePath>../../pom.xml</relativePath>
   </parent>
 
diff --git a/extras/java8-tests/pom.xml b/extras/java8-tests/pom.xml
index 602f66f9c5cf..955ec1a8c303 100644
--- a/extras/java8-tests/pom.xml
+++ b/extras/java8-tests/pom.xml
@@ -20,7 +20,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent</artifactId>
-    <version>1.0.0-SNAPSHOT</version>
+    <version>1.1.0-SNAPSHOT</version>
     <relativePath>../../pom.xml</relativePath>
   </parent>
 
diff --git a/extras/spark-ganglia-lgpl/pom.xml b/extras/spark-ganglia-lgpl/pom.xml
index 11ac827ed54a..22ea330b4374 100644
--- a/extras/spark-ganglia-lgpl/pom.xml
+++ b/extras/spark-ganglia-lgpl/pom.xml
@@ -20,7 +20,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent</artifactId>
-    <version>1.0.0-SNAPSHOT</version>
+    <version>1.1.0-SNAPSHOT</version>
     <relativePath>../../pom.xml</relativePath>
   </parent>
 
diff --git a/graphx/pom.xml b/graphx/pom.xml
index dc108d2fe7fb..7d5d83e7f3bb 100644
--- a/graphx/pom.xml
+++ b/graphx/pom.xml
@@ -21,7 +21,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent</artifactId>
-    <version>1.0.0-SNAPSHOT</version>
+    <version>1.1.0-SNAPSHOT</version>
     <relativePath>../pom.xml</relativePath>
   </parent>
 
diff --git a/mllib/pom.xml b/mllib/pom.xml
index cdd33dbb7970..4aae2026dcaf 100644
--- a/mllib/pom.xml
+++ b/mllib/pom.xml
@@ -21,7 +21,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent</artifactId>
-    <version>1.0.0-SNAPSHOT</version>
+    <version>1.1.0-SNAPSHOT</version>
     <relativePath>../pom.xml</relativePath>
   </parent>
 
diff --git a/pom.xml b/pom.xml
index fcd6f66b4414..87c8e29ad106 100644
--- a/pom.xml
+++ b/pom.xml
@@ -26,7 +26,7 @@
   </parent>
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-parent</artifactId>
-  <version>1.0.0-SNAPSHOT</version>
+  <version>1.1.0-SNAPSHOT</version>
   <packaging>pom</packaging>
   <name>Spark Project Parent POM</name>
   <url>http://spark.apache.org/</url>
diff --git a/repl/pom.xml b/repl/pom.xml
index bcdb24b040cc..4a66408ef3d2 100644
--- a/repl/pom.xml
+++ b/repl/pom.xml
@@ -21,7 +21,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent</artifactId>
-    <version>1.0.0-SNAPSHOT</version>
+    <version>1.1.0-SNAPSHOT</version>
     <relativePath>../pom.xml</relativePath>
   </parent>
 
diff --git a/sql/catalyst/pom.xml b/sql/catalyst/pom.xml
index 8d2e4baf69e3..6c78c3448601 100644
--- a/sql/catalyst/pom.xml
+++ b/sql/catalyst/pom.xml
@@ -22,7 +22,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent</artifactId>
-    <version>1.0.0-SNAPSHOT</version>
+    <version>1.1.0-SNAPSHOT</version>
     <relativePath>../../pom.xml</relativePath>
   </parent>
 
diff --git a/sql/core/pom.xml b/sql/core/pom.xml
index fb3b190b4ec5..e65ca6be485e 100644
--- a/sql/core/pom.xml
+++ b/sql/core/pom.xml
@@ -22,7 +22,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent</artifactId>
-    <version>1.0.0-SNAPSHOT</version>
+    <version>1.1.0-SNAPSHOT</version>
     <relativePath>../../pom.xml</relativePath>
   </parent>
 
diff --git a/sql/hive/pom.xml b/sql/hive/pom.xml
index 9254b70e64a0..5ede76e5c390 100644
--- a/sql/hive/pom.xml
+++ b/sql/hive/pom.xml
@@ -22,7 +22,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent</artifactId>
-    <version>1.0.0-SNAPSHOT</version>
+    <version>1.1.0-SNAPSHOT</version>
     <relativePath>../../pom.xml</relativePath>
   </parent>
 
diff --git a/streaming/pom.xml b/streaming/pom.xml
index 6435224a1467..f506d6ce34a6 100644
--- a/streaming/pom.xml
+++ b/streaming/pom.xml
@@ -21,7 +21,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent</artifactId>
-    <version>1.0.0-SNAPSHOT</version>
+    <version>1.1.0-SNAPSHOT</version>
     <relativePath>../pom.xml</relativePath>
   </parent>
 
diff --git a/tools/pom.xml b/tools/pom.xml
index 1875c497bc61..79cd8551d072 100644
--- a/tools/pom.xml
+++ b/tools/pom.xml
@@ -20,7 +20,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent</artifactId>
-    <version>1.0.0-SNAPSHOT</version>
+    <version>1.1.0-SNAPSHOT</version>
     <relativePath>../pom.xml</relativePath>
   </parent>
 
diff --git a/yarn/alpha/pom.xml b/yarn/alpha/pom.xml
index e076ca1d44b9..b8a631dd0bb3 100644
--- a/yarn/alpha/pom.xml
+++ b/yarn/alpha/pom.xml
@@ -20,7 +20,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>yarn-parent_2.10</artifactId>
-    <version>1.0.0-SNAPSHOT</version>
+    <version>1.1.0-SNAPSHOT</version>
     <relativePath>../pom.xml</relativePath>
   </parent>
 
diff --git a/yarn/pom.xml b/yarn/pom.xml
index 2811ffffbdfa..ef7066ef1fdf 100644
--- a/yarn/pom.xml
+++ b/yarn/pom.xml
@@ -20,7 +20,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent</artifactId>
-    <version>1.0.0-SNAPSHOT</version>
+    <version>1.1.0-SNAPSHOT</version>
     <relativePath>../pom.xml</relativePath>
   </parent>
 
diff --git a/yarn/stable/pom.xml b/yarn/stable/pom.xml
index 0780f251b595..0931beb50550 100644
--- a/yarn/stable/pom.xml
+++ b/yarn/stable/pom.xml
@@ -20,7 +20,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>yarn-parent_2.10</artifactId>
-    <version>1.0.0-SNAPSHOT</version>
+    <version>1.1.0-SNAPSHOT</version>
     <relativePath>../pom.xml</relativePath>
   </parent>
 

From 89cdbb087cb2f0d03be2dd77440300c6bd61c792 Mon Sep 17 00:00:00 2001
From: CodingCat <zhunansjtu@gmail.com>
Date: Thu, 5 Jun 2014 11:39:35 -0700
Subject: [PATCH 345/810] SPARK-1677: allow user to disable output dir
 existence checking

https://issues.apache.org/jira/browse/SPARK-1677

For compatibility with older versions of Spark it would be nice to have an option `spark.hadoop.validateOutputSpecs` (default true)  for the user to disable the output directory existence checking

Author: CodingCat <zhunansjtu@gmail.com>

Closes #947 from CodingCat/SPARK-1677 and squashes the following commits:

7930f83 [CodingCat] miao
c0c0e03 [CodingCat] bug fix and doc update
5318562 [CodingCat] bug fix
13219b5 [CodingCat] allow user to disable output dir existence checking
---
 .../apache/spark/rdd/PairRDDFunctions.scala   |  6 +++--
 .../scala/org/apache/spark/FileSuite.scala    | 22 +++++++++++++++++++
 docs/configuration.md                         |  8 +++++++
 3 files changed, 34 insertions(+), 2 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
index f2ce3cbd47f9..890998095705 100644
--- a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
@@ -737,7 +737,8 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
     val outfmt = job.getOutputFormatClass
     val jobFormat = outfmt.newInstance
 
-    if (jobFormat.isInstanceOf[NewFileOutputFormat[_, _]]) {
+    if (self.conf.getBoolean("spark.hadoop.validateOutputSpecs", true) &&
+      jobFormat.isInstanceOf[NewFileOutputFormat[_, _]]) {
       // FileOutputFormat ignores the filesystem parameter
       jobFormat.checkOutputSpecs(job)
     }
@@ -803,7 +804,8 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
     logDebug("Saving as hadoop file of type (" + keyClass.getSimpleName + ", " +
       valueClass.getSimpleName + ")")
 
-    if (outputFormatInstance.isInstanceOf[FileOutputFormat[_, _]]) {
+    if (self.conf.getBoolean("spark.hadoop.validateOutputSpecs", true) &&
+      outputFormatInstance.isInstanceOf[FileOutputFormat[_, _]]) {
       // FileOutputFormat ignores the filesystem parameter
       val ignoredFs = FileSystem.get(conf)
       conf.getOutputFormat.checkOutputSpecs(ignoredFs, conf)
diff --git a/core/src/test/scala/org/apache/spark/FileSuite.scala b/core/src/test/scala/org/apache/spark/FileSuite.scala
index 1f2206b1f037..070e97465786 100644
--- a/core/src/test/scala/org/apache/spark/FileSuite.scala
+++ b/core/src/test/scala/org/apache/spark/FileSuite.scala
@@ -230,6 +230,17 @@ class FileSuite extends FunSuite with LocalSparkContext {
     }
   }
 
+  test ("allow user to disable the output directory existence checking (old Hadoop API") {
+    val sf = new SparkConf()
+    sf.setAppName("test").setMaster("local").set("spark.hadoop.validateOutputSpecs", "false")
+    sc = new SparkContext(sf)
+    val randomRDD = sc.parallelize(Array((1, "a"), (1, "a"), (2, "b"), (3, "c")), 1)
+    randomRDD.saveAsTextFile(tempDir.getPath + "/output")
+    assert(new File(tempDir.getPath + "/output/part-00000").exists() === true)
+    randomRDD.saveAsTextFile(tempDir.getPath + "/output")
+    assert(new File(tempDir.getPath + "/output/part-00000").exists() === true)
+  }
+
   test ("prevent user from overwriting the empty directory (new Hadoop API)") {
     sc = new SparkContext("local", "test")
     val randomRDD = sc.parallelize(Array(("key1", "a"), ("key2", "a"), ("key3", "b"), ("key4", "c")), 1)
@@ -248,6 +259,17 @@ class FileSuite extends FunSuite with LocalSparkContext {
     }
   }
 
+  test ("allow user to disable the output directory existence checking (new Hadoop API") {
+    val sf = new SparkConf()
+    sf.setAppName("test").setMaster("local").set("spark.hadoop.validateOutputSpecs", "false")
+    sc = new SparkContext(sf)
+    val randomRDD = sc.parallelize(Array(("key1", "a"), ("key2", "a"), ("key3", "b"), ("key4", "c")), 1)
+    randomRDD.saveAsNewAPIHadoopFile[NewTextOutputFormat[String, String]](tempDir.getPath + "/output")
+    assert(new File(tempDir.getPath + "/output/part-r-00000").exists() === true)
+    randomRDD.saveAsNewAPIHadoopFile[NewTextOutputFormat[String, String]](tempDir.getPath + "/output")
+    assert(new File(tempDir.getPath + "/output/part-r-00000").exists() === true)
+  }
+
   test ("save Hadoop Dataset through old Hadoop API") {
     sc = new SparkContext("local", "test")
     val randomRDD = sc.parallelize(Array(("key1", "a"), ("key2", "a"), ("key3", "b"), ("key4", "c")), 1)
diff --git a/docs/configuration.md b/docs/configuration.md
index 0697f7fc2fd9..71fafa573467 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -487,6 +487,14 @@ Apart from these, the following properties are also available, and may be useful
     this duration will be cleared as well.
   </td>
 </tr>
+<tr>
+    <td>spark.hadoop.validateOutputSpecs</td>
+    <td>true</td>
+    <td>If set to true, validates the output specification (e.g. checking if the output directory already exists) 
+    used in saveAsHadoopFile and other variants. This can be disabled to silence exceptions due to pre-existing 
+    output directories. We recommend that users do not disable this except if trying to achieve compatibility with 
+    previous versions of Spark. Simply use Hadoop's FileSystem API to delete output directories by hand.</td>
+</tr>
 </table>
 
 #### Networking

From e4c11eef2f64df0b6a432f40b669486d91ca6352 Mon Sep 17 00:00:00 2001
From: Takuya UESHIN <ueshin@happy-camper.st>
Date: Thu, 5 Jun 2014 12:00:31 -0700
Subject: [PATCH 346/810] [SPARK-2036] [SQL] CaseConversionExpression should
 check if the evaluated value is null.

`CaseConversionExpression` should check if the evaluated value is `null`.

Author: Takuya UESHIN <ueshin@happy-camper.st>

Closes #982 from ueshin/issues/SPARK-2036 and squashes the following commits:

61e1c54 [Takuya UESHIN] Add check if the evaluated value is null.
---
 .../catalyst/expressions/stringOperations.scala    |  8 ++++++--
 .../scala/org/apache/spark/sql/SQLQuerySuite.scala | 14 ++++++++++++++
 .../test/scala/org/apache/spark/sql/TestData.scala |  8 ++++++++
 3 files changed, 28 insertions(+), 2 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
index dcded0774180..420303408451 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
@@ -81,8 +81,12 @@ trait CaseConversionExpression {
   def dataType: DataType = StringType
 
   override def eval(input: Row): Any = {
-    val converted = child.eval(input)
-    convert(converted.toString)
+    val evaluated = child.eval(input)
+    if (evaluated == null) {
+      null
+    } else {
+      convert(evaluated.toString)
+    }
   }
 }
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index 95860e6683f6..e2ad3915d313 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -322,6 +322,13 @@ class SQLQuerySuite extends QueryTest {
         (2, "B"),
         (3, "C"),
         (4, "D")))
+
+    checkAnswer(
+      sql("SELECT n, UPPER(s) FROM nullStrings"),
+      Seq(
+        (1, "ABC"),
+        (2, "ABC"),
+        (3, null)))
   }
     
   test("system function lower()") {
@@ -334,6 +341,13 @@ class SQLQuerySuite extends QueryTest {
         (4, "d"),
         (5, "e"),
         (6, "f")))
+
+    checkAnswer(
+      sql("SELECT n, LOWER(s) FROM nullStrings"),
+      Seq(
+        (1, "abc"),
+        (2, "abc"),
+        (3, null)))
   }  
   
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala b/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala
index 944f520e4351..876bd1636aab 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala
@@ -106,4 +106,12 @@ object TestData {
       NullInts(null) :: Nil
     )
   nullInts.registerAsTable("nullInts")
+
+  case class NullStrings(n: Int, s: String)
+  val nullStrings =
+    TestSQLContext.sparkContext.parallelize(
+      NullStrings(1, "abc") ::
+      NullStrings(2, "ABC") ::
+      NullStrings(3, null) :: Nil)
+  nullStrings.registerAsTable("nullStrings")
 }

From f6143f127db59e7f5a00fd70605f85248869347d Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Thu, 5 Jun 2014 13:06:46 -0700
Subject: [PATCH 347/810] HOTFIX: Remove generated-mima-excludes file after
 runing MIMA.

This has been causing some false failures on PR's that don't merge
correctly.

Author: Patrick Wendell <pwendell@gmail.com>

Closes #971 from pwendell/mima and squashes the following commits:

1dc80aa [Patrick Wendell] HOTFIX: Remove generated-mima-excludes file after runing MIMA.
---
 dev/mima | 1 +
 1 file changed, 1 insertion(+)

diff --git a/dev/mima b/dev/mima
index d4099990254c..ab6bd4469b0e 100755
--- a/dev/mima
+++ b/dev/mima
@@ -31,4 +31,5 @@ if [ $ret_val != 0 ]; then
   echo "NOTE: Exceptions to binary compatibility can be added in project/MimaExcludes.scala"
 fi
 
+rm -f .generated-mima-excludes
 exit $ret_val

From 5473aa7c02916022430493637b1492554b48c30b Mon Sep 17 00:00:00 2001
From: Kalpit Shah <shahkalpit84@gmail.com>
Date: Thu, 5 Jun 2014 13:07:26 -0700
Subject: [PATCH 348/810] sbt 0.13.X should be using sbt-assembly 0.11.X

https://github.com/sbt/sbt-assembly/blob/master/README.md

Author: Kalpit Shah <shahkalpit84@gmail.com>

Closes #555 from kalpit/upgrade/sbtassembly and squashes the following commits:

1fa7324 [Kalpit Shah] sbt 0.13.X should be using sbt-assembly 0.11.X
---
 project/plugins.sbt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/project/plugins.sbt b/project/plugins.sbt
index 0cd16fd5bedd..472819b9fb8b 100644
--- a/project/plugins.sbt
+++ b/project/plugins.sbt
@@ -4,7 +4,7 @@ resolvers += Resolver.url("artifactory", url("http://scalasbt.artifactoryonline.
 
 resolvers += "Typesafe Repository" at "http://repo.typesafe.com/typesafe/releases/"
 
-addSbtPlugin("com.eed3si9n" % "sbt-assembly" % "0.10.2")
+addSbtPlugin("com.eed3si9n" % "sbt-assembly" % "0.11.2")
 
 addSbtPlugin("com.typesafe.sbteclipse" % "sbteclipse-plugin" % "2.2.0")
 

From 668cb1defe735add91f4a5b7b8ebe7cfd5640b25 Mon Sep 17 00:00:00 2001
From: Marcelo Vanzin <vanzin@cloudera.com>
Date: Thu, 5 Jun 2014 13:13:33 -0700
Subject: [PATCH 349/810] Remove compile-scoped junit dependency.

This avoids having junit classes showing up in the assembly jar.
I verified that only test classes in the jtransforms package
use junit.

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #794 from vanzin/junit-dep-exclusion and squashes the following commits:

274e1c2 [Marcelo Vanzin] Remove junit from assembly in sbt build also.
ad950be [Marcelo Vanzin] Remove compile-scoped junit dependency.
---
 mllib/pom.xml            | 8 ++++++++
 project/SparkBuild.scala | 3 ++-
 2 files changed, 10 insertions(+), 1 deletion(-)

diff --git a/mllib/pom.xml b/mllib/pom.xml
index 4aae2026dcaf..878cb83dbf78 100644
--- a/mllib/pom.xml
+++ b/mllib/pom.xml
@@ -50,6 +50,14 @@
       <groupId>org.scalanlp</groupId>
       <artifactId>breeze_${scala.binary.version}</artifactId>
       <version>0.7</version>
+      <exclusions>
+        <!-- This is included as a compile-scoped dependency by jtransforms, which is
+             a dependency of breeze. -->
+        <exclusion>
+          <groupId>junit</groupId>
+          <artifactId>junit</artifactId>
+        </exclusion>
+      </exclusions>
     </dependency>
     <dependency>
       <groupId>org.scalatest</groupId>
diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index efb0b9319be1..d0049a8ac43a 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -322,6 +322,7 @@ object SparkBuild extends Build {
   val excludeJruby = ExclusionRule(organization = "org.jruby")
   val excludeThrift = ExclusionRule(organization = "org.apache.thrift")
   val excludeServletApi = ExclusionRule(organization = "javax.servlet", artifact = "servlet-api")
+  val excludeJUnit = ExclusionRule(organization = "junit")
 
   def sparkPreviousArtifact(id: String, organization: String = "org.apache.spark",
       version: String = "1.0.0", crossVersion: String = "2.10"): Option[sbt.ModuleID] = {
@@ -466,7 +467,7 @@ object SparkBuild extends Build {
     previousArtifact := sparkPreviousArtifact("spark-mllib"),
     libraryDependencies ++= Seq(
       "org.jblas" % "jblas" % jblasVersion,
-      "org.scalanlp" %% "breeze" % "0.7"
+      "org.scalanlp" %% "breeze" % "0.7" excludeAll(excludeJUnit)
     )
   )
 

From c7a183b2c2bca13565496495b4ae3a3a9f63f9ab Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Thu, 5 Jun 2014 17:42:08 -0700
Subject: [PATCH 350/810] [SPARK-2041][SQL] Correctly analyze queries where
 columnName == tableName.

Author: Michael Armbrust <michael@databricks.com>

Closes #985 from marmbrus/tableName and squashes the following commits:

3caaa27 [Michael Armbrust] Correctly analyze queries where columnName == tableName.
---
 .../spark/sql/catalyst/plans/logical/LogicalPlan.scala      | 3 ++-
 .../src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala | 6 ++++++
 sql/core/src/test/scala/org/apache/spark/sql/TestData.scala | 3 +++
 3 files changed, 11 insertions(+), 1 deletion(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala
index 5eb52d5350f5..2b8fbdcde9d3 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala
@@ -64,7 +64,8 @@ abstract class LogicalPlan extends QueryPlan[LogicalPlan] {
     // struct fields.
     val options = children.flatMap(_.output).flatMap { option =>
       // If the first part of the desired name matches a qualifier for this possible match, drop it.
-      val remainingParts = if (option.qualifiers contains parts.head) parts.drop(1) else parts
+      val remainingParts =
+        if (option.qualifiers.contains(parts.head) && parts.size > 1) parts.drop(1) else parts
       if (option.name == remainingParts.head) (option, remainingParts.tail.toList) :: Nil else Nil
     }
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index e2ad3915d313..aa0c426f6fcb 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -28,6 +28,12 @@ class SQLQuerySuite extends QueryTest {
   // Make sure the tables are loaded.
   TestData
 
+  test("SPARK-2041 column name equals tablename") {
+    checkAnswer(
+      sql("SELECT tableName FROM tableName"),
+      "test")
+  }
+
   test("index into array") {
     checkAnswer(
       sql("SELECT data, data[0], data[0] + data[1], data[0 + 1] FROM arrayData"),
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala b/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala
index 876bd1636aab..05de736bbce1 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala
@@ -114,4 +114,7 @@ object TestData {
       NullStrings(2, "ABC") ::
       NullStrings(3, null) :: Nil)
   nullStrings.registerAsTable("nullStrings")
+
+  case class TableName(tableName: String)
+  TestSQLContext.sparkContext.parallelize(TableName("test") :: Nil).registerAsTable("tableName")
 }

From 3d3f8c8004da110ca97973119e9d9f04f878ee81 Mon Sep 17 00:00:00 2001
From: CrazyJvm <crazyjvm@gmail.com>
Date: Thu, 5 Jun 2014 17:44:46 -0700
Subject: [PATCH 351/810] Use pluggable clock in DAGSheduler #SPARK-2031

DAGScheduler supports pluggable clock like what TaskSetManager does.

Author: CrazyJvm <crazyjvm@gmail.com>

Closes #976 from CrazyJvm/clock and squashes the following commits:

6779a4c [CrazyJvm] Use pluggable clock in DAGSheduler
---
 .../org/apache/spark/scheduler/DAGScheduler.scala   | 13 +++++++------
 1 file changed, 7 insertions(+), 6 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
index ccff6a3d1aeb..e09a4221e831 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
@@ -38,7 +38,7 @@ import org.apache.spark.executor.TaskMetrics
 import org.apache.spark.partial.{ApproximateActionListener, ApproximateEvaluator, PartialResult}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.storage.{BlockId, BlockManager, BlockManagerMaster, RDDBlockId}
-import org.apache.spark.util.Utils
+import org.apache.spark.util.{SystemClock, Clock, Utils}
 
 /**
  * The high-level scheduling layer that implements stage-oriented scheduling. It computes a DAG of
@@ -61,7 +61,8 @@ class DAGScheduler(
     listenerBus: LiveListenerBus,
     mapOutputTracker: MapOutputTrackerMaster,
     blockManagerMaster: BlockManagerMaster,
-    env: SparkEnv)
+    env: SparkEnv,
+    clock: Clock = SystemClock)
   extends Logging {
 
   import DAGScheduler._
@@ -781,7 +782,7 @@ class DAGScheduler(
       logDebug("New pending tasks: " + myPending)
       taskScheduler.submitTasks(
         new TaskSet(tasks.toArray, stage.id, stage.newAttemptId(), stage.jobId, properties))
-      stageToInfos(stage).submissionTime = Some(System.currentTimeMillis())
+      stageToInfos(stage).submissionTime = Some(clock.getTime())
     } else {
       logDebug("Stage " + stage + " is actually done; %b %d %d".format(
         stage.isAvailable, stage.numAvailableOutputs, stage.numPartitions))
@@ -807,11 +808,11 @@ class DAGScheduler(
 
     def markStageAsFinished(stage: Stage) = {
       val serviceTime = stageToInfos(stage).submissionTime match {
-        case Some(t) => "%.03f".format((System.currentTimeMillis() - t) / 1000.0)
+        case Some(t) => "%.03f".format((clock.getTime() - t) / 1000.0)
         case _ => "Unknown"
       }
       logInfo("%s (%s) finished in %s s".format(stage, stage.name, serviceTime))
-      stageToInfos(stage).completionTime = Some(System.currentTimeMillis())
+      stageToInfos(stage).completionTime = Some(clock.getTime())
       listenerBus.post(SparkListenerStageCompleted(stageToInfos(stage)))
       runningStages -= stage
     }
@@ -1015,7 +1016,7 @@ class DAGScheduler(
       return
     }
     val dependentStages = resultStageToJob.keys.filter(x => stageDependsOn(x, failedStage)).toSeq
-    stageToInfos(failedStage).completionTime = Some(System.currentTimeMillis())
+    stageToInfos(failedStage).completionTime = Some(clock.getTime())
     for (resultStage <- dependentStages) {
       val job = resultStageToJob(resultStage)
       failJobAndIndependentStages(job, s"Job aborted due to stage failure: $reason",

From 9bad0b73722fb359f14db864e69aa7efde3588c5 Mon Sep 17 00:00:00 2001
From: Ankur Dave <ankurdave@gmail.com>
Date: Thu, 5 Jun 2014 17:45:38 -0700
Subject: [PATCH 352/810] [SPARK-2025] Unpersist edges of previous graph in
 Pregel

Due to a bug introduced by apache/spark#497, Pregel does not unpersist replicated vertices from previous iterations. As a result, they stay cached until memory is full, wasting GC time.

This PR corrects the problem by unpersisting both the edges and the replicated vertices of previous iterations. This is safe because the edges and replicated vertices of the current iteration are cached by the call to `g.cache()` and then materialized by the call to `messages.count()`. Therefore no unmaterialized RDDs depend on `prevG.edges`. I verified that no recomputation occurs by running PageRank with a custom patch to Spark that warns when a partition is recomputed.

Thanks to Tim Weninger for reporting this bug.

Author: Ankur Dave <ankurdave@gmail.com>

Closes #972 from ankurdave/SPARK-2025 and squashes the following commits:

13d5b07 [Ankur Dave] Unpersist edges of previous graph in Pregel
---
 graphx/src/main/scala/org/apache/spark/graphx/Pregel.scala | 1 +
 1 file changed, 1 insertion(+)

diff --git a/graphx/src/main/scala/org/apache/spark/graphx/Pregel.scala b/graphx/src/main/scala/org/apache/spark/graphx/Pregel.scala
index 4572eab2875b..5e55620147df 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/Pregel.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/Pregel.scala
@@ -150,6 +150,7 @@ object Pregel extends Logging {
       oldMessages.unpersist(blocking=false)
       newVerts.unpersist(blocking=false)
       prevG.unpersistVertices(blocking=false)
+      prevG.edges.unpersist(blocking=false)
       // count the iteration
       i += 1
     }

From b45c13e7d798f97b92f1a6329528191b8d779c4f Mon Sep 17 00:00:00 2001
From: Matei Zaharia <matei@databricks.com>
Date: Thu, 5 Jun 2014 23:01:48 -0700
Subject: [PATCH 353/810] SPARK-2043: ExternalAppendOnlyMap doesn't always find
 matching keys

The current implementation reads one key with the next hash code as it finishes reading the keys with the current hash code, which may cause it to miss some matches of the next key. This can cause operations like join to give the wrong result when reduce tasks spill to disk and there are hash collisions, as values won't be matched together. This PR fixes it by not reading in that next key, using a peeking iterator instead.

Author: Matei Zaharia <matei@databricks.com>

Closes #986 from mateiz/spark-2043 and squashes the following commits:

0959514 [Matei Zaharia] Added unit test for having many hash collisions
892debb [Matei Zaharia] SPARK-2043: don't read a key with the next hash code in ExternalAppendOnlyMap, instead use a buffered iterator to only read values with the current hash code.
---
 .../collection/ExternalAppendOnlyMap.scala    | 10 +++--
 .../ExternalAppendOnlyMapSuite.scala          | 39 ++++++++++++++++++-
 2 files changed, 44 insertions(+), 5 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala b/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala
index 170f09be2153..288badd3160f 100644
--- a/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala
+++ b/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala
@@ -20,6 +20,7 @@ package org.apache.spark.util.collection
 import java.io.{InputStream, BufferedInputStream, FileInputStream, File, Serializable, EOFException}
 import java.util.Comparator
 
+import scala.collection.BufferedIterator
 import scala.collection.mutable
 import scala.collection.mutable.ArrayBuffer
 
@@ -231,7 +232,7 @@ class ExternalAppendOnlyMap[K, V, C](
     // Input streams are derived both from the in-memory map and spilled maps on disk
     // The in-memory map is sorted in place, while the spilled maps are already in sorted order
     private val sortedMap = currentMap.destructiveSortedIterator(comparator)
-    private val inputStreams = Seq(sortedMap) ++ spilledMaps
+    private val inputStreams = (Seq(sortedMap) ++ spilledMaps).map(it => it.buffered)
 
     inputStreams.foreach { it =>
       val kcPairs = getMorePairs(it)
@@ -246,13 +247,13 @@ class ExternalAppendOnlyMap[K, V, C](
      * In the event of key hash collisions, this ensures no pairs are hidden from being merged.
      * Assume the given iterator is in sorted order.
      */
-    private def getMorePairs(it: Iterator[(K, C)]): ArrayBuffer[(K, C)] = {
+    private def getMorePairs(it: BufferedIterator[(K, C)]): ArrayBuffer[(K, C)] = {
       val kcPairs = new ArrayBuffer[(K, C)]
       if (it.hasNext) {
         var kc = it.next()
         kcPairs += kc
         val minHash = kc._1.hashCode()
-        while (it.hasNext && kc._1.hashCode() == minHash) {
+        while (it.hasNext && it.head._1.hashCode() == minHash) {
           kc = it.next()
           kcPairs += kc
         }
@@ -325,7 +326,8 @@ class ExternalAppendOnlyMap[K, V, C](
      *
      * StreamBuffers are ordered by the minimum key hash found across all of their own pairs.
      */
-    private case class StreamBuffer(iterator: Iterator[(K, C)], pairs: ArrayBuffer[(K, C)])
+    private class StreamBuffer(
+        val iterator: BufferedIterator[(K, C)], val pairs: ArrayBuffer[(K, C)])
       extends Comparable[StreamBuffer] {
 
       def isEmpty = pairs.length == 0
diff --git a/core/src/test/scala/org/apache/spark/util/collection/ExternalAppendOnlyMapSuite.scala b/core/src/test/scala/org/apache/spark/util/collection/ExternalAppendOnlyMapSuite.scala
index cdebefb67510..deb780953579 100644
--- a/core/src/test/scala/org/apache/spark/util/collection/ExternalAppendOnlyMapSuite.scala
+++ b/core/src/test/scala/org/apache/spark/util/collection/ExternalAppendOnlyMapSuite.scala
@@ -277,6 +277,11 @@ class ExternalAppendOnlyMapSuite extends FunSuite with LocalSparkContext {
       ("pomatoes", "eructation")      // 568647356
     )
 
+    collisionPairs.foreach { case (w1, w2) =>
+      // String.hashCode is documented to use a specific algorithm, but check just in case
+      assert(w1.hashCode === w2.hashCode)
+    }
+
     (1 to 100000).map(_.toString).foreach { i => map.insert(i, i) }
     collisionPairs.foreach { case (w1, w2) =>
       map.insert(w1, w2)
@@ -296,7 +301,32 @@ class ExternalAppendOnlyMapSuite extends FunSuite with LocalSparkContext {
       assert(kv._2.equals(expectedValue))
       count += 1
     }
-    assert(count == 100000 + collisionPairs.size * 2)
+    assert(count === 100000 + collisionPairs.size * 2)
+  }
+
+  test("spilling with many hash collisions") {
+    val conf = new SparkConf(true)
+    conf.set("spark.shuffle.memoryFraction", "0.0001")
+    sc = new SparkContext("local-cluster[1,1,512]", "test", conf)
+
+    val map = new ExternalAppendOnlyMap[FixedHashObject, Int, Int](_ => 1, _ + _, _ + _)
+
+    // Insert 10 copies each of lots of objects whose hash codes are either 0 or 1. This causes
+    // problems if the map fails to group together the objects with the same code (SPARK-2043).
+    for (i <- 1 to 10) {
+      for (j <- 1 to 10000) {
+        map.insert(FixedHashObject(j, j % 2), 1)
+      }
+    }
+
+    val it = map.iterator
+    var count = 0
+    while (it.hasNext) {
+      val kv = it.next()
+      assert(kv._2 === 10)
+      count += 1
+    }
+    assert(count === 10000)
   }
 
   test("spilling with hash collisions using the Int.MaxValue key") {
@@ -317,3 +347,10 @@ class ExternalAppendOnlyMapSuite extends FunSuite with LocalSparkContext {
     }
   }
 }
+
+/**
+ * A dummy class that always returns the same hash code, to easily test hash collisions
+ */
+case class FixedHashObject(val v: Int, val h: Int) extends Serializable {
+  override def hashCode(): Int = h
+}

From 41db44c428a10f4453462d002d226798bb8fbdda Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Thu, 5 Jun 2014 23:20:59 -0700
Subject: [PATCH 354/810] [SPARK-2050][SQL] LIKE, RLIKE and IN in HQL should
 not be case sensitive.

Author: Michael Armbrust <michael@databricks.com>

Closes #989 from marmbrus/caseSensitiveFuncitons and squashes the following commits:

681de54 [Michael Armbrust] LIKE, RLIKE and IN in HQL should not be case sensitive.
---
 .../scala/org/apache/spark/sql/hive/HiveQl.scala     | 12 ++++++++----
 1 file changed, 8 insertions(+), 4 deletions(-)

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
index e8a3ee5535b6..c133bf242319 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
@@ -791,6 +791,10 @@ private[hive] object HiveQl {
   val NOT = "(?i)NOT".r
   val TRUE = "(?i)TRUE".r
   val FALSE = "(?i)FALSE".r
+  val LIKE = "(?i)LIKE".r
+  val RLIKE = "(?i)RLIKE".r
+  val REGEXP = "(?i)REGEXP".r
+  val IN = "(?i)IN".r
 
   protected def nodeToExpr(node: Node): Expression = node match {
     /* Attribute References */
@@ -871,14 +875,14 @@ private[hive] object HiveQl {
     case Token(">=", left :: right:: Nil) => GreaterThanOrEqual(nodeToExpr(left), nodeToExpr(right))
     case Token("<", left :: right:: Nil) => LessThan(nodeToExpr(left), nodeToExpr(right))
     case Token("<=", left :: right:: Nil) => LessThanOrEqual(nodeToExpr(left), nodeToExpr(right))
-    case Token("LIKE", left :: right:: Nil) => Like(nodeToExpr(left), nodeToExpr(right))
-    case Token("RLIKE", left :: right:: Nil) => RLike(nodeToExpr(left), nodeToExpr(right))
-    case Token("REGEXP", left :: right:: Nil) => RLike(nodeToExpr(left), nodeToExpr(right))
+    case Token(LIKE(), left :: right:: Nil) => Like(nodeToExpr(left), nodeToExpr(right))
+    case Token(RLIKE(), left :: right:: Nil) => RLike(nodeToExpr(left), nodeToExpr(right))
+    case Token(REGEXP(), left :: right:: Nil) => RLike(nodeToExpr(left), nodeToExpr(right))
     case Token("TOK_FUNCTION", Token("TOK_ISNOTNULL", Nil) :: child :: Nil) =>
       IsNotNull(nodeToExpr(child))
     case Token("TOK_FUNCTION", Token("TOK_ISNULL", Nil) :: child :: Nil) =>
       IsNull(nodeToExpr(child))
-    case Token("TOK_FUNCTION", Token("IN", Nil) :: value :: list) =>
+    case Token("TOK_FUNCTION", Token(IN(), Nil) :: value :: list) =>
       In(nodeToExpr(value), list.map(nodeToExpr))
     case Token("TOK_FUNCTION",
            Token("between", Nil) ::

From 8d85359f84cc67996b4bcf1670a8a98ab4f914a2 Mon Sep 17 00:00:00 2001
From: Ankur Dave <ankurdave@gmail.com>
Date: Thu, 5 Jun 2014 23:33:12 -0700
Subject: [PATCH 355/810] [SPARK-1552] Fix type comparison bug in
 {map,outerJoin}Vertices

In GraphImpl, mapVertices and outerJoinVertices use a more efficient implementation when the map function conserves vertex attribute types. This is implemented by comparing the ClassTags of the old and new vertex attribute types. However, ClassTags store erased types, so the comparison will return a false positive for types with different type parameters, such as Option[Int] and Option[Double].

This PR resolves the problem by requesting that the compiler generate evidence of equality between the old and new vertex attribute types, and providing a default value for the evidence parameter if the two types are not equal. The methods can then check the value of the evidence parameter to see whether the types are equal.

It also adds a test called "mapVertices changing type with same erased type" that failed before the PR and succeeds now.

Callers of mapVertices and outerJoinVertices can no longer use a wildcard for a graph's VD type. To avoid "Error occurred in an application involving default arguments," they must bind VD to a type parameter, as this PR does for ShortestPaths and LabelPropagation.

Author: Ankur Dave <ankurdave@gmail.com>

Closes #967 from ankurdave/SPARK-1552 and squashes the following commits:

68a4fff [Ankur Dave] Undo conserve naming
7388705 [Ankur Dave] Remove unnecessary ClassTag for VD parameters
a704e5f [Ankur Dave] Use type equality constraint with default argument
29a5ab7 [Ankur Dave] Add failing test
f458c83 [Ankur Dave] Revert "[SPARK-1552] Fix type comparison bug in mapVertices and outerJoinVertices"
16d6af8 [Ankur Dave] [SPARK-1552] Fix type comparison bug in mapVertices and outerJoinVertices
---
 .../scala/org/apache/spark/graphx/Graph.scala |  5 ++--
 .../apache/spark/graphx/impl/GraphImpl.scala  | 14 ++++++++---
 .../spark/graphx/lib/LabelPropagation.scala   |  2 +-
 .../spark/graphx/lib/ShortestPaths.scala      |  2 +-
 .../org/apache/spark/graphx/GraphSuite.scala  | 25 +++++++++++++++++++
 5 files changed, 40 insertions(+), 8 deletions(-)

diff --git a/graphx/src/main/scala/org/apache/spark/graphx/Graph.scala b/graphx/src/main/scala/org/apache/spark/graphx/Graph.scala
index 14ae50e6657f..4db45c9af8fa 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/Graph.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/Graph.scala
@@ -138,7 +138,8 @@ abstract class Graph[VD: ClassTag, ED: ClassTag] protected () extends Serializab
    * }}}
    *
    */
-  def mapVertices[VD2: ClassTag](map: (VertexId, VD) => VD2): Graph[VD2, ED]
+  def mapVertices[VD2: ClassTag](map: (VertexId, VD) => VD2)
+    (implicit eq: VD =:= VD2 = null): Graph[VD2, ED]
 
   /**
    * Transforms each edge attribute in the graph using the map function.  The map function is not
@@ -348,7 +349,7 @@ abstract class Graph[VD: ClassTag, ED: ClassTag] protected () extends Serializab
    * }}}
    */
   def outerJoinVertices[U: ClassTag, VD2: ClassTag](other: RDD[(VertexId, U)])
-      (mapFunc: (VertexId, VD, Option[U]) => VD2)
+      (mapFunc: (VertexId, VD, Option[U]) => VD2)(implicit eq: VD =:= VD2 = null)
     : Graph[VD2, ED]
 
   /**
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/GraphImpl.scala b/graphx/src/main/scala/org/apache/spark/graphx/impl/GraphImpl.scala
index 15ea05cbe281..ccdaa82eb916 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/impl/GraphImpl.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/impl/GraphImpl.scala
@@ -104,8 +104,11 @@ class GraphImpl[VD: ClassTag, ED: ClassTag] protected (
     new GraphImpl(vertices.reverseRoutingTables(), replicatedVertexView.reverse())
   }
 
-  override def mapVertices[VD2: ClassTag](f: (VertexId, VD) => VD2): Graph[VD2, ED] = {
-    if (classTag[VD] equals classTag[VD2]) {
+  override def mapVertices[VD2: ClassTag]
+    (f: (VertexId, VD) => VD2)(implicit eq: VD =:= VD2 = null): Graph[VD2, ED] = {
+    // The implicit parameter eq will be populated by the compiler if VD and VD2 are equal, and left
+    // null if not
+    if (eq != null) {
       vertices.cache()
       // The map preserves type, so we can use incremental replication
       val newVerts = vertices.mapVertexPartitions(_.map(f)).cache()
@@ -232,8 +235,11 @@ class GraphImpl[VD: ClassTag, ED: ClassTag] protected (
 
   override def outerJoinVertices[U: ClassTag, VD2: ClassTag]
       (other: RDD[(VertexId, U)])
-      (updateF: (VertexId, VD, Option[U]) => VD2): Graph[VD2, ED] = {
-    if (classTag[VD] equals classTag[VD2]) {
+      (updateF: (VertexId, VD, Option[U]) => VD2)
+      (implicit eq: VD =:= VD2 = null): Graph[VD2, ED] = {
+    // The implicit parameter eq will be populated by the compiler if VD and VD2 are equal, and left
+    // null if not
+    if (eq != null) {
       vertices.cache()
       // updateF preserves type, so we can use incremental replication
       val newVerts = vertices.leftJoin(other)(updateF).cache()
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/lib/LabelPropagation.scala b/graphx/src/main/scala/org/apache/spark/graphx/lib/LabelPropagation.scala
index 776bfb8dd6bf..82e9e0651517 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/lib/LabelPropagation.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/lib/LabelPropagation.scala
@@ -41,7 +41,7 @@ object LabelPropagation {
    *
    * @return a graph with vertex attributes containing the label of community affiliation
    */
-  def run[ED: ClassTag](graph: Graph[_, ED], maxSteps: Int): Graph[VertexId, ED] = {
+  def run[VD, ED: ClassTag](graph: Graph[VD, ED], maxSteps: Int): Graph[VertexId, ED] = {
     val lpaGraph = graph.mapVertices { case (vid, _) => vid }
     def sendMessage(e: EdgeTriplet[VertexId, ED]) = {
       Iterator((e.srcId, Map(e.dstAttr -> 1L)), (e.dstId, Map(e.srcAttr -> 1L)))
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/lib/ShortestPaths.scala b/graphx/src/main/scala/org/apache/spark/graphx/lib/ShortestPaths.scala
index bba070f256d8..590f0474957d 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/lib/ShortestPaths.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/lib/ShortestPaths.scala
@@ -49,7 +49,7 @@ object ShortestPaths {
    * @return a graph where each vertex attribute is a map containing the shortest-path distance to
    * each reachable landmark vertex.
    */
-  def run[ED: ClassTag](graph: Graph[_, ED], landmarks: Seq[VertexId]): Graph[SPMap, ED] = {
+  def run[VD, ED: ClassTag](graph: Graph[VD, ED], landmarks: Seq[VertexId]): Graph[SPMap, ED] = {
     val spGraph = graph.mapVertices { (vid, attr) =>
       if (landmarks.contains(vid)) makeMap(vid -> 0) else makeMap()
     }
diff --git a/graphx/src/test/scala/org/apache/spark/graphx/GraphSuite.scala b/graphx/src/test/scala/org/apache/spark/graphx/GraphSuite.scala
index abc25d067113..6506bac73d71 100644
--- a/graphx/src/test/scala/org/apache/spark/graphx/GraphSuite.scala
+++ b/graphx/src/test/scala/org/apache/spark/graphx/GraphSuite.scala
@@ -159,6 +159,31 @@ class GraphSuite extends FunSuite with LocalSparkContext {
     }
   }
 
+  test("mapVertices changing type with same erased type") {
+    withSpark { sc =>
+      val vertices = sc.parallelize(Array[(Long, Option[java.lang.Integer])](
+        (1L, Some(1)),
+        (2L, Some(2)),
+        (3L, Some(3))
+      ))
+      val edges = sc.parallelize(Array(
+        Edge(1L, 2L, 0),
+        Edge(2L, 3L, 0),
+        Edge(3L, 1L, 0)
+      ))
+      val graph0 = Graph(vertices, edges)
+      // Trigger initial vertex replication
+      graph0.triplets.foreach(x => {})
+      // Change type of replicated vertices, but preserve erased type
+      val graph1 = graph0.mapVertices {
+        case (vid, integerOpt) => integerOpt.map((x: java.lang.Integer) => (x.toDouble): java.lang.Double)
+      }
+      // Access replicated vertices, exposing the erased type
+      val graph2 = graph1.mapTriplets(t => t.srcAttr.get)
+      assert(graph2.edges.map(_.attr).collect.toSet === Set[java.lang.Double](1.0, 2.0, 3.0))
+    }
+  }
+
   test("mapEdges") {
     withSpark { sc =>
       val n = 3

From 8d210560be8b143e48abfbaca347f383b5aa4798 Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Fri, 6 Jun 2014 11:31:37 -0700
Subject: [PATCH 356/810] [SPARK-2050 - 2][SQL] DIV and BETWEEN should not be
 case sensitive.

Followup: #989

Author: Michael Armbrust <michael@databricks.com>

Closes #994 from marmbrus/caseSensitiveFunctions2 and squashes the following commits:

9d9c8ed [Michael Armbrust] Fix DIV and BETWEEN.
---
 .../src/main/scala/org/apache/spark/sql/hive/HiveQl.scala   | 6 ++++--
 .../golden/between-0-570e4c4bf2968368ac2f325c6c940a06       | 1 +
 .../resources/golden/div-0-5e7fc5719c7265bc1d6af52005ebad03 | 1 +
 .../apache/spark/sql/hive/execution/HiveQuerySuite.scala    | 6 ++++--
 4 files changed, 10 insertions(+), 4 deletions(-)
 create mode 100644 sql/hive/src/test/resources/golden/between-0-570e4c4bf2968368ac2f325c6c940a06
 create mode 100644 sql/hive/src/test/resources/golden/div-0-5e7fc5719c7265bc1d6af52005ebad03

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
index c133bf242319..7e91c16c6b93 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
@@ -795,6 +795,8 @@ private[hive] object HiveQl {
   val RLIKE = "(?i)RLIKE".r
   val REGEXP = "(?i)REGEXP".r
   val IN = "(?i)IN".r
+  val DIV = "(?i)DIV".r
+  val BETWEEN = "(?i)BETWEEN".r
 
   protected def nodeToExpr(node: Node): Expression = node match {
     /* Attribute References */
@@ -864,7 +866,7 @@ private[hive] object HiveQl {
     case Token("-", left :: right:: Nil) => Subtract(nodeToExpr(left), nodeToExpr(right))
     case Token("*", left :: right:: Nil) => Multiply(nodeToExpr(left), nodeToExpr(right))
     case Token("/", left :: right:: Nil) => Divide(nodeToExpr(left), nodeToExpr(right))
-    case Token("DIV", left :: right:: Nil) => Divide(nodeToExpr(left), nodeToExpr(right))
+    case Token(DIV(), left :: right:: Nil) => Divide(nodeToExpr(left), nodeToExpr(right))
     case Token("%", left :: right:: Nil) => Remainder(nodeToExpr(left), nodeToExpr(right))
 
     /* Comparisons */
@@ -885,7 +887,7 @@ private[hive] object HiveQl {
     case Token("TOK_FUNCTION", Token(IN(), Nil) :: value :: list) =>
       In(nodeToExpr(value), list.map(nodeToExpr))
     case Token("TOK_FUNCTION",
-           Token("between", Nil) ::
+           Token(BETWEEN(), Nil) ::
            Token("KW_FALSE", Nil) ::
            target ::
            minValue ::
diff --git a/sql/hive/src/test/resources/golden/between-0-570e4c4bf2968368ac2f325c6c940a06 b/sql/hive/src/test/resources/golden/between-0-570e4c4bf2968368ac2f325c6c940a06
new file mode 100644
index 000000000000..dcd1d8643e3c
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/between-0-570e4c4bf2968368ac2f325c6c940a06
@@ -0,0 +1 @@
+2	val_2
diff --git a/sql/hive/src/test/resources/golden/div-0-5e7fc5719c7265bc1d6af52005ebad03 b/sql/hive/src/test/resources/golden/div-0-5e7fc5719c7265bc1d6af52005ebad03
new file mode 100644
index 000000000000..06b63ea6c2f0
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/div-0-5e7fc5719c7265bc1d6af52005ebad03
@@ -0,0 +1 @@
+0	0	0
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
index 1a2b2f89182a..125cc18bfb2b 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
@@ -25,8 +25,10 @@ import org.apache.spark.sql.hive.test.TestHive._
 class HiveQuerySuite extends HiveComparisonTest {
 
   createQueryTest("between",
-    "SELECT * FROM src WHERE key between 1 and 2"
-  )
+    "SELECT * FROM src WHERE key Between 1 and 2")
+
+  createQueryTest("div",
+    "SELECT 1 DIV 2, 1 div 2, 1 dIv 2 FROM src LIMIT 1")
 
   test("Query expressed in SQL") {
     assert(sql("SELECT 1").collect() === Array(Seq(1)))

From 41c4a33105c74417192925db355019ba1badeab2 Mon Sep 17 00:00:00 2001
From: witgo <witgo@qq.com>
Date: Fri, 6 Jun 2014 11:45:21 -0700
Subject: [PATCH 357/810]  [SPARK-1841]: update scalatest to version 2.1.5

Author: witgo <witgo@qq.com>

Closes #713 from witgo/scalatest and squashes the following commits:

b627a6a [witgo] merge master
51fb3d6 [witgo] merge master
3771474 [witgo] fix RDDSuite
996d6f9 [witgo] fix TimeStampedWeakValueHashMap test
9dfa4e7 [witgo] merge bug
1479b22 [witgo] merge master
29b9194 [witgo] fix code style
022a7a2 [witgo] fix test dependency
a52c0fa [witgo] fix test dependency
cd8f59d [witgo] Merge branch 'master' of https://github.com/apache/spark into scalatest
046540d [witgo] fix RDDSuite.scala
2c543b9 [witgo] fix ReplSuite.scala
c458928 [witgo] update scalatest to version 2.1.5
---
 core/pom.xml                                  |  2 +-
 .../apache/spark/ContextCleanerSuite.scala    |  6 ++---
 .../org/apache/spark/ShuffleNettySuite.scala  |  4 ++--
 .../scala/org/apache/spark/rdd/RDDSuite.scala |  5 +++--
 .../spark/scheduler/DAGSchedulerSuite.scala   |  4 ++--
 .../spark/util/TimeStampedHashMapSuite.scala  |  3 ++-
 pom.xml                                       | 15 +++++++++----
 project/SparkBuild.scala                      | 22 +++++++++----------
 .../org/apache/spark/repl/ReplSuite.scala     |  6 +++--
 .../spark/sql/parquet/ParquetQuerySuite.scala |  4 ++--
 .../streaming/BasicOperationsSuite.scala      | 12 +++++-----
 11 files changed, 47 insertions(+), 36 deletions(-)

diff --git a/core/pom.xml b/core/pom.xml
index 0c746175afa7..c3d6b00a443f 100644
--- a/core/pom.xml
+++ b/core/pom.xml
@@ -235,7 +235,7 @@
     </dependency>
     <dependency>
       <groupId>org.easymock</groupId>
-      <artifactId>easymock</artifactId>
+      <artifactId>easymockclassextension</artifactId>
       <scope>test</scope>
     </dependency>
     <dependency>
diff --git a/core/src/test/scala/org/apache/spark/ContextCleanerSuite.scala b/core/src/test/scala/org/apache/spark/ContextCleanerSuite.scala
index 5a8310090890..dc2db66df60e 100644
--- a/core/src/test/scala/org/apache/spark/ContextCleanerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/ContextCleanerSuite.scala
@@ -25,7 +25,7 @@ import scala.language.postfixOps
 import scala.util.Random
 
 import org.scalatest.{BeforeAndAfter, FunSuite}
-import org.scalatest.concurrent.Eventually
+import org.scalatest.concurrent.{PatienceConfiguration, Eventually}
 import org.scalatest.concurrent.Eventually._
 import org.scalatest.time.SpanSugar._
 
@@ -76,7 +76,7 @@ class ContextCleanerSuite extends FunSuite with BeforeAndAfter with LocalSparkCo
     tester.assertCleanup()
 
     // Verify that shuffles can be re-executed after cleaning up
-    assert(rdd.collect().toList === collected)
+    assert(rdd.collect().toList.equals(collected))
   }
 
   test("cleanup broadcast") {
@@ -285,7 +285,7 @@ class CleanerTester(
   sc.cleaner.get.attachListener(cleanerListener)
 
   /** Assert that all the stuff has been cleaned up */
-  def assertCleanup()(implicit waitTimeout: Eventually.Timeout) {
+  def assertCleanup()(implicit waitTimeout: PatienceConfiguration.Timeout) {
     try {
       eventually(waitTimeout, interval(100 millis)) {
         assert(isAllCleanedUp)
diff --git a/core/src/test/scala/org/apache/spark/ShuffleNettySuite.scala b/core/src/test/scala/org/apache/spark/ShuffleNettySuite.scala
index 29d428aa7dc4..47df00050c1e 100644
--- a/core/src/test/scala/org/apache/spark/ShuffleNettySuite.scala
+++ b/core/src/test/scala/org/apache/spark/ShuffleNettySuite.scala
@@ -23,11 +23,11 @@ class ShuffleNettySuite extends ShuffleSuite with BeforeAndAfterAll {
 
   // This test suite should run all tests in ShuffleSuite with Netty shuffle mode.
 
-  override def beforeAll(configMap: Map[String, Any]) {
+  override def beforeAll() {
     System.setProperty("spark.shuffle.use.netty", "true")
   }
 
-  override def afterAll(configMap: Map[String, Any]) {
+  override def afterAll() {
     System.setProperty("spark.shuffle.use.netty", "false")
   }
 }
diff --git a/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala b/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala
index 286e221e33b7..55af1666df66 100644
--- a/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala
+++ b/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala
@@ -266,8 +266,9 @@ class RDDSuite extends FunSuite with SharedSparkContext {
 
     // we can optionally shuffle to keep the upstream parallel
     val coalesced5 = data.coalesce(1, shuffle = true)
-    assert(coalesced5.dependencies.head.rdd.dependencies.head.rdd.asInstanceOf[ShuffledRDD[_, _, _]] !=
-      null)
+    val isEquals = coalesced5.dependencies.head.rdd.dependencies.head.rdd.
+      asInstanceOf[ShuffledRDD[_, _, _]] != null
+    assert(isEquals)
 
     // when shuffling, we can increase the number of partitions
     val coalesced6 = data.coalesce(20, shuffle = true)
diff --git a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
index 81e64c1846ed..7506d56d7e26 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
@@ -23,7 +23,7 @@ import scala.language.reflectiveCalls
 
 import akka.actor._
 import akka.testkit.{ImplicitSender, TestKit, TestActorRef}
-import org.scalatest.{BeforeAndAfter, FunSuite}
+import org.scalatest.{BeforeAndAfter, FunSuiteLike}
 
 import org.apache.spark._
 import org.apache.spark.rdd.RDD
@@ -37,7 +37,7 @@ class BuggyDAGEventProcessActor extends Actor {
   }
 }
 
-class DAGSchedulerSuite extends TestKit(ActorSystem("DAGSchedulerSuite")) with FunSuite
+class DAGSchedulerSuite extends TestKit(ActorSystem("DAGSchedulerSuite")) with FunSuiteLike
   with ImplicitSender with BeforeAndAfter with LocalSparkContext {
 
   val conf = new SparkConf
diff --git a/core/src/test/scala/org/apache/spark/util/TimeStampedHashMapSuite.scala b/core/src/test/scala/org/apache/spark/util/TimeStampedHashMapSuite.scala
index 6a5653ed2fb5..c1c605cdb487 100644
--- a/core/src/test/scala/org/apache/spark/util/TimeStampedHashMapSuite.scala
+++ b/core/src/test/scala/org/apache/spark/util/TimeStampedHashMapSuite.scala
@@ -105,7 +105,8 @@ class TimeStampedHashMapSuite extends FunSuite {
     map("k1") = strongRef
     map("k2") = "v2"
     map("k3") = "v3"
-    assert(map("k1") === strongRef)
+    val isEquals = map("k1") == strongRef
+    assert(isEquals)
 
     // clear strong reference to "k1"
     strongRef = null
diff --git a/pom.xml b/pom.xml
index 87c8e29ad106..891468b21bff 100644
--- a/pom.xml
+++ b/pom.xml
@@ -459,25 +459,31 @@
       <dependency>
         <groupId>org.scalatest</groupId>
         <artifactId>scalatest_${scala.binary.version}</artifactId>
-        <version>1.9.1</version>
+        <version>2.1.5</version>
         <scope>test</scope>
       </dependency>
       <dependency>
         <groupId>org.easymock</groupId>
-        <artifactId>easymock</artifactId>
+        <artifactId>easymockclassextension</artifactId>
         <version>3.1</version>
         <scope>test</scope>
       </dependency>
       <dependency>
         <groupId>org.mockito</groupId>
         <artifactId>mockito-all</artifactId>
-        <version>1.8.5</version>
+        <version>1.9.0</version>
         <scope>test</scope>
       </dependency>
       <dependency>
         <groupId>org.scalacheck</groupId>
         <artifactId>scalacheck_${scala.binary.version}</artifactId>
-        <version>1.10.0</version>
+        <version>1.11.3</version>
+        <scope>test</scope>
+      </dependency>
+      <dependency>
+        <groupId>junit</groupId>
+        <artifactId>junit</artifactId>
+        <version>4.10</version>
         <scope>test</scope>
       </dependency>
       <dependency>
@@ -779,6 +785,7 @@
               <arg>-unchecked</arg>
               <arg>-deprecation</arg>
               <arg>-feature</arg>
+              <arg>-language:postfixOps</arg>
             </args>
             <jvmArgs>
               <jvmArg>-Xms1024m</jvmArg>
diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index d0049a8ac43a..069913dbaac5 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -270,16 +270,17 @@ object SparkBuild extends Build {
     */
 
     libraryDependencies ++= Seq(
-        "io.netty"          % "netty-all"      % "4.0.17.Final",
-        "org.eclipse.jetty" % "jetty-server"   % jettyVersion,
-        "org.eclipse.jetty" % "jetty-util"     % jettyVersion,
-        "org.eclipse.jetty" % "jetty-plus"     % jettyVersion,
-        "org.eclipse.jetty" % "jetty-security" % jettyVersion,
-        "org.scalatest"    %% "scalatest"       % "1.9.1"  % "test",
-        "org.scalacheck"   %% "scalacheck"      % "1.10.0" % "test",
-        "com.novocode"      % "junit-interface" % "0.10"   % "test",
-        "org.easymock"      % "easymock"        % "3.1"    % "test",
-        "org.mockito"       % "mockito-all"     % "1.8.5"  % "test"
+        "io.netty"          % "netty-all"              % "4.0.17.Final",
+        "org.eclipse.jetty" % "jetty-server"           % jettyVersion,
+        "org.eclipse.jetty" % "jetty-util"             % jettyVersion,
+        "org.eclipse.jetty" % "jetty-plus"             % jettyVersion,
+        "org.eclipse.jetty" % "jetty-security"         % jettyVersion,
+        "org.scalatest"    %% "scalatest"              % "2.1.5"  % "test",
+        "org.scalacheck"   %% "scalacheck"             % "1.11.3" % "test",
+        "com.novocode"      % "junit-interface"        % "0.10"   % "test",
+        "org.easymock"      % "easymockclassextension" % "3.1"    % "test",
+        "org.mockito"       % "mockito-all"            % "1.9.0"  % "test",
+        "junit"             % "junit"                  % "4.10"   % "test"
     ),
 
     testOptions += Tests.Argument(TestFrameworks.JUnit, "-v", "-a"),
@@ -478,7 +479,6 @@ object SparkBuild extends Build {
     // this non-deterministically.  TODO: FIX THIS.
     parallelExecution in Test := false,
     libraryDependencies ++= Seq(
-      "org.scalatest" %% "scalatest" % "1.9.1" % "test",
       "com.typesafe" %% "scalalogging-slf4j" % "1.0.1"
     )
   )
diff --git a/repl/src/test/scala/org/apache/spark/repl/ReplSuite.scala b/repl/src/test/scala/org/apache/spark/repl/ReplSuite.scala
index 7c765edd5502..f4ba8d9cc079 100644
--- a/repl/src/test/scala/org/apache/spark/repl/ReplSuite.scala
+++ b/repl/src/test/scala/org/apache/spark/repl/ReplSuite.scala
@@ -67,12 +67,14 @@ class ReplSuite extends FunSuite {
   }
 
   def assertContains(message: String, output: String) {
-    assert(output.contains(message),
+    val isContain = output.contains(message)
+    assert(isContain,
       "Interpreter output did not contain '" + message + "':\n" + output)
   }
 
   def assertDoesNotContain(message: String, output: String) {
-    assert(!output.contains(message),
+    val isContain = output.contains(message)
+    assert(!isContain,
       "Interpreter output contained '" + message + "':\n" + output)
   }
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala
index b973ceba5fec..9810520bb9ae 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.sql.parquet
 
-import org.scalatest.{BeforeAndAfterAll, FunSuite}
+import org.scalatest.{BeforeAndAfterAll, FunSuiteLike}
 
 import org.apache.hadoop.fs.{Path, FileSystem}
 import org.apache.hadoop.mapreduce.Job
@@ -56,7 +56,7 @@ case class OptionalReflectData(
     doubleField: Option[Double],
     booleanField: Option[Boolean])
 
-class ParquetQuerySuite extends QueryTest with FunSuite with BeforeAndAfterAll {
+class ParquetQuerySuite extends QueryTest with FunSuiteLike with BeforeAndAfterAll {
   import TestData._
   TestData // Load test data tables.
 
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/BasicOperationsSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/BasicOperationsSuite.scala
index 04925886c39e..ff6d86c8f81a 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/BasicOperationsSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/BasicOperationsSuite.scala
@@ -92,9 +92,9 @@ class BasicOperationsSuite extends TestSuiteBase {
     assert(second.size === 5)
     assert(third.size === 5)
 
-    assert(first.flatten.toSet === (1 to 100).toSet)
-    assert(second.flatten.toSet === (101 to 200).toSet)
-    assert(third.flatten.toSet === (201 to 300).toSet)
+    assert(first.flatten.toSet.equals((1 to 100).toSet) )
+    assert(second.flatten.toSet.equals((101 to 200).toSet))
+    assert(third.flatten.toSet.equals((201 to 300).toSet))
   }
 
   test("repartition (fewer partitions)") {
@@ -111,9 +111,9 @@ class BasicOperationsSuite extends TestSuiteBase {
     assert(second.size === 2)
     assert(third.size === 2)
 
-    assert(first.flatten.toSet === (1 to 100).toSet)
-    assert(second.flatten.toSet === (101 to 200).toSet)
-    assert(third.flatten.toSet === (201 to 300).toSet)
+    assert(first.flatten.toSet.equals((1 to 100).toSet))
+    assert(second.flatten.toSet.equals( (101 to 200).toSet))
+    assert(third.flatten.toSet.equals((201 to 300).toSet))
   }
 
   test("groupByKey") {

From a6c72ab16e7a3027739ab419819f5222e270838e Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Sat, 7 Jun 2014 14:20:33 -0700
Subject: [PATCH 358/810] [SPARK-1994][SQL] Weird data corruption bug when
 running Spark SQL on data in HDFS

Basically there is a race condition (possibly a scala bug?) when these values are recomputed on all of the slaves that results in an incorrect projection being generated (possibly because the GUID uniqueness contract is broken?).

In general we should probably enforce that all expression planing occurs on the driver, as is now occurring here.

Author: Michael Armbrust <michael@databricks.com>

Closes #1004 from marmbrus/fixAggBug and squashes the following commits:

e0c116c [Michael Armbrust] Compute aggregate expression during planning instead of lazily on workers.
---
 .../apache/spark/sql/execution/Aggregate.scala    | 15 +++++----------
 1 file changed, 5 insertions(+), 10 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/Aggregate.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/Aggregate.scala
index 604914e54779..34d88fe4bd7d 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/Aggregate.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/Aggregate.scala
@@ -77,8 +77,7 @@ case class Aggregate(
       resultAttribute: AttributeReference)
 
   /** A list of aggregates that need to be computed for each group. */
-  @transient
-  private[this] lazy val computedAggregates = aggregateExpressions.flatMap { agg =>
+  private[this] val computedAggregates = aggregateExpressions.flatMap { agg =>
     agg.collect {
       case a: AggregateExpression =>
         ComputedAggregate(
@@ -89,8 +88,7 @@ case class Aggregate(
   }.toArray
 
   /** The schema of the result of all aggregate evaluations */
-  @transient
-  private[this] lazy val computedSchema = computedAggregates.map(_.resultAttribute)
+  private[this] val computedSchema = computedAggregates.map(_.resultAttribute)
 
   /** Creates a new aggregate buffer for a group. */
   private[this] def newAggregateBuffer(): Array[AggregateFunction] = {
@@ -104,8 +102,7 @@ case class Aggregate(
   }
 
   /** Named attributes used to substitute grouping attributes into the final result. */
-  @transient
-  private[this] lazy val namedGroups = groupingExpressions.map {
+  private[this] val namedGroups = groupingExpressions.map {
     case ne: NamedExpression => ne -> ne.toAttribute
     case e => e -> Alias(e, s"groupingExpr:$e")().toAttribute
   }
@@ -114,16 +111,14 @@ case class Aggregate(
    * A map of substitutions that are used to insert the aggregate expressions and grouping
    * expression into the final result expression.
    */
-  @transient
-  private[this] lazy val resultMap =
+  private[this] val resultMap =
     (computedAggregates.map { agg => agg.unbound -> agg.resultAttribute } ++ namedGroups).toMap
 
   /**
    * Substituted version of aggregateExpressions expressions which are used to compute final
    * output rows given a group and the result of all aggregate computations.
    */
-  @transient
-  private[this] lazy val resultExpressions = aggregateExpressions.map { agg =>
+  private[this] val resultExpressions = aggregateExpressions.map { agg =>
     agg.transform {
       case e: Expression if resultMap.contains(e) => resultMap(e)
     }

From 3ace10dc91e72ebe5013d5106eb0968a77c99d8d Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Sat, 7 Jun 2014 16:16:37 -0700
Subject: [PATCH 359/810] HOTFIX: Support empty body in merge script

Discovered in #992

Author: Patrick Wendell <pwendell@gmail.com>

Closes #1007 from pwendell/hotfix and squashes the following commits:

af90aa0 [Patrick Wendell] HOTFIX: Support empty body in merge script
---
 dev/merge_spark_pr.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/dev/merge_spark_pr.py b/dev/merge_spark_pr.py
index e3ac32ef1a12..ffb70096d601 100755
--- a/dev/merge_spark_pr.py
+++ b/dev/merge_spark_pr.py
@@ -128,8 +128,9 @@ def merge_pr(pr_num, target_ref):
 
     merge_message_flags = []
 
-    for p in [title, body]:
-        merge_message_flags += ["-m", p]
+    merge_message_flags += ["-m", title]
+    if body != None:
+        merge_message_flags += ["-m", body]
 
     authors = "\n".join(["Author: %s" % a for a in distinct_authors])
 

From 7b877b27053bfb7092e250e01a3b887e1b50a109 Mon Sep 17 00:00:00 2001
From: Neville Li <neville@spotify.com>
Date: Sat, 7 Jun 2014 16:22:26 -0700
Subject: [PATCH 360/810] SPARK-2056 Set RDD name to input path

Author: Neville Li <neville@spotify.com>

Closes #992 from nevillelyh/master and squashes the following commits:

3011739 [Neville Li] [SPARK-2056] Set RDD name to input path
---
 core/src/main/scala/org/apache/spark/SparkContext.scala | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index d941aea9d7eb..d721aba70960 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -455,7 +455,7 @@ class SparkContext(config: SparkConf) extends Logging {
    */
   def textFile(path: String, minPartitions: Int = defaultMinPartitions): RDD[String] = {
     hadoopFile(path, classOf[TextInputFormat], classOf[LongWritable], classOf[Text],
-      minPartitions).map(pair => pair._2.toString)
+      minPartitions).map(pair => pair._2.toString).setName(path)
   }
 
   /**
@@ -496,7 +496,7 @@ class SparkContext(config: SparkConf) extends Logging {
       classOf[String],
       classOf[String],
       updateConf,
-      minPartitions)
+      minPartitions).setName(path)
   }
 
   /**
@@ -551,7 +551,7 @@ class SparkContext(config: SparkConf) extends Logging {
       inputFormatClass,
       keyClass,
       valueClass,
-      minPartitions)
+      minPartitions).setName(path)
   }
 
   /**
@@ -623,7 +623,7 @@ class SparkContext(config: SparkConf) extends Logging {
     val job = new NewHadoopJob(conf)
     NewFileInputFormat.addInputPath(job, new Path(path))
     val updatedConf = job.getConfiguration
-    new NewHadoopRDD(this, fClass, kClass, vClass, updatedConf)
+    new NewHadoopRDD(this, fClass, kClass, vClass, updatedConf).setName(path)
   }
 
   /**

From a338834f90556d78119b37985b603ecee85f97ed Mon Sep 17 00:00:00 2001
From: Bernardo Gomez Palacio <bernardo.gomezpalacio@gmail.com>
Date: Sun, 8 Jun 2014 01:24:52 -0700
Subject: [PATCH 361/810] SPARK-2026: Maven Hadoop Profiles Should Set The
 Hadoop Version

The Maven Profiles that refer to hadoopX, e.g. `hadoop2.4`, should set the expected
`hadoop.version` and `yarn.version`.

e.g.

```
<profile>
      <id>hadoop-2.4</id>
      <properties>
        <hadoop.version>2.4.0</hadoop.version>
         <yarn.version>${hadoop.version}</yarn.version>
        <protobuf.version>2.5.0</protobuf.version>
        <jets3t.version>0.9.0</jets3t.version>
      </properties>
</profile>
```

Builds can still define the `-Dhadoop.version` option but this will correctly default the
Hadoop Version to the one that is expected according the profile that is selected.

e.g.

```$ mvn -P hadoop-2.4,yarn clean install```
or

```$ mvn -P hadoop-0.23,yarn clean install```

[ticket] : https://issues.apache.org/jira/browse/SPARK-2026

Author      : berngp
Reviewer    : ?

Author: Bernardo Gomez Palacio <bernardo.gomezpalacio@gmail.com>

Closes #998 from berngp/feature/SPARK-2026 and squashes the following commits:

07ba4f7 [Bernardo Gomez Palacio] SPARK-2026: Maven Hadoop Profiles Should Set The Hadoop Version
---
 pom.xml | 10 ++++++++--
 1 file changed, 8 insertions(+), 2 deletions(-)

diff --git a/pom.xml b/pom.xml
index 891468b21bff..0d46bb4114f7 100644
--- a/pom.xml
+++ b/pom.xml
@@ -209,14 +209,14 @@
     <repository>
       <id>spring-releases</id>
       <name>Spring Release Repository</name>
-      <url>http://repo.spring.io/libs-release</url> 
+      <url>http://repo.spring.io/libs-release</url>
       <releases>
         <enabled>true</enabled>
       </releases>
       <snapshots>
         <enabled>false</enabled>
       </snapshots>
-    </repository> 
+    </repository>
   </repositories>
 
   <dependencyManagement>
@@ -987,11 +987,15 @@
           <artifactId>avro</artifactId>
         </dependency>
       </dependencies>
+      <properties>
+        <hadoop.version>0.23.10</hadoop.version>
+      </properties>
     </profile>
 
     <profile>
       <id>hadoop-2.2</id>
       <properties>
+        <hadoop.version>2.2.0</hadoop.version>
         <protobuf.version>2.5.0</protobuf.version>
       </properties>
     </profile>
@@ -999,6 +1003,7 @@
     <profile>
       <id>hadoop-2.3</id>
       <properties>
+        <hadoop.version>2.3.0</hadoop.version>
         <protobuf.version>2.5.0</protobuf.version>
         <jets3t.version>0.9.0</jets3t.version>
       </properties>
@@ -1007,6 +1012,7 @@
     <profile>
       <id>hadoop-2.4</id>
       <properties>
+        <hadoop.version>2.4.0</hadoop.version>
         <protobuf.version>2.5.0</protobuf.version>
         <jets3t.version>0.9.0</jets3t.version>
       </properties>

From ee96e9406613e621837360b15c34ea7c7220a7a3 Mon Sep 17 00:00:00 2001
From: Colin Patrick McCabe <cmccabe@cloudera.com>
Date: Sun, 8 Jun 2014 12:27:34 -0700
Subject: [PATCH 362/810] SPARK-1898: In deploy.yarn.Client, use YarnClient not
 YarnClientImpl

https://issues.apache.org/jira/browse/SPARK-1898

Author: Colin Patrick McCabe <cmccabe@cloudera.com>

Closes #850 from cmccabe/master and squashes the following commits:

d66eddc [Colin Patrick McCabe] SPARK-1898: In deploy.yarn.Client, use YarnClient rather than YarnClientImpl
---
 .../cluster/YarnClientSchedulerBackend.scala  |  2 +-
 .../org/apache/spark/deploy/yarn/Client.scala | 25 ++++++++++++-------
 2 files changed, 17 insertions(+), 10 deletions(-)

diff --git a/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala b/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala
index e01ed5a57d69..039cf4f27611 100644
--- a/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala
+++ b/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala
@@ -112,7 +112,7 @@ private[spark] class YarnClientSchedulerBackend(
 
   override def stop() {
     super.stop()
-    client.stop()
+    client.stop
     logInfo("Stopped")
   }
 
diff --git a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/Client.scala b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
index 1b6bfb42a5c1..393edd1f2d67 100644
--- a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
+++ b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
@@ -25,7 +25,7 @@ import org.apache.hadoop.yarn.api._
 import org.apache.hadoop.yarn.api.ApplicationConstants.Environment
 import org.apache.hadoop.yarn.api.protocolrecords._
 import org.apache.hadoop.yarn.api.records._
-import org.apache.hadoop.yarn.client.api.impl.YarnClientImpl
+import org.apache.hadoop.yarn.client.api.YarnClient
 import org.apache.hadoop.yarn.conf.YarnConfiguration
 import org.apache.hadoop.yarn.ipc.YarnRPC
 import org.apache.hadoop.yarn.util.{Apps, Records}
@@ -37,7 +37,9 @@ import org.apache.spark.{Logging, SparkConf}
  * Version of [[org.apache.spark.deploy.yarn.ClientBase]] tailored to YARN's stable API.
  */
 class Client(clientArgs: ClientArguments, hadoopConf: Configuration, spConf: SparkConf)
-  extends YarnClientImpl with ClientBase with Logging {
+  extends ClientBase with Logging {
+
+  val yarnClient = YarnClient.createYarnClient
 
   def this(clientArgs: ClientArguments, spConf: SparkConf) =
     this(clientArgs, new Configuration(), spConf)
@@ -53,8 +55,8 @@ class Client(clientArgs: ClientArguments, hadoopConf: Configuration, spConf: Spa
   def runApp(): ApplicationId = {
     validateArgs()
     // Initialize and start the client service.
-    init(yarnConf)
-    start()
+    yarnClient.init(yarnConf)
+    yarnClient.start()
 
     // Log details about this YARN cluster (e.g, the number of slave machines/NodeManagers).
     logClusterResourceDetails()
@@ -63,7 +65,7 @@ class Client(clientArgs: ClientArguments, hadoopConf: Configuration, spConf: Spa
     // interface).
 
     // Get a new client application.
-    val newApp = super.createApplication()
+    val newApp = yarnClient.createApplication()
     val newAppResponse = newApp.getNewApplicationResponse()
     val appId = newAppResponse.getApplicationId()
 
@@ -99,11 +101,11 @@ class Client(clientArgs: ClientArguments, hadoopConf: Configuration, spConf: Spa
   }
 
   def logClusterResourceDetails() {
-    val clusterMetrics: YarnClusterMetrics = super.getYarnClusterMetrics
+    val clusterMetrics: YarnClusterMetrics = yarnClient.getYarnClusterMetrics
     logInfo("Got Cluster metric info from ApplicationsManager (ASM), number of NodeManagers: " +
       clusterMetrics.getNumNodeManagers)
 
-    val queueInfo: QueueInfo = super.getQueueInfo(args.amQueue)
+    val queueInfo: QueueInfo = yarnClient.getQueueInfo(args.amQueue)
     logInfo( """Queue info ... queueName: %s, queueCurrentCapacity: %s, queueMaxCapacity: %s,
       queueApplicationCount = %s, queueChildQueueCount = %s""".format(
         queueInfo.getQueueName,
@@ -132,15 +134,20 @@ class Client(clientArgs: ClientArguments, hadoopConf: Configuration, spConf: Spa
   def submitApp(appContext: ApplicationSubmissionContext) = {
     // Submit the application to the applications manager.
     logInfo("Submitting application to ASM")
-    super.submitApplication(appContext)
+    yarnClient.submitApplication(appContext)
   }
 
+  def getApplicationReport(appId: ApplicationId) =
+      yarnClient.getApplicationReport(appId)
+
+  def stop = yarnClient.stop
+
   def monitorApplication(appId: ApplicationId): Boolean = {
     val interval = sparkConf.getLong("spark.yarn.report.interval", 1000)
 
     while (true) {
       Thread.sleep(interval)
-      val report = super.getApplicationReport(appId)
+      val report = yarnClient.getApplicationReport(appId)
 
       logInfo("Application report from ASM: \n" +
         "\t application identifier: " + appId.toString() + "\n" +

From a71c6d1cf0bbc027f633a65042191cf2201330d6 Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Sun, 8 Jun 2014 14:18:52 -0700
Subject: [PATCH 363/810] SPARK-1628: Add missing hashCode methods in
 Partitioner subclasses

JIRA: https://issues.apache.org/jira/browse/SPARK-1628

Added `hashCode` in HashPartitioner, RangePartitioner, PythonPartitioner and PageRankUtils.CustomPartitioner.

Author: zsxwing <zsxwing@gmail.com>

Closes #549 from zsxwing/SPARK-1628 and squashes the following commits:

2620936 [zsxwing] SPARK-1628: Add missing hashCode methods in Partitioner subclasses
---
 .../scala/org/apache/spark/Partitioner.scala    | 17 ++++++++++++++++-
 .../spark/api/python/PythonPartitioner.scala    |  2 ++
 .../spark/examples/bagel/PageRankUtils.scala    |  2 ++
 3 files changed, 20 insertions(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/Partitioner.scala b/core/src/main/scala/org/apache/spark/Partitioner.scala
index 9155159cf6ae..01e918fabec6 100644
--- a/core/src/main/scala/org/apache/spark/Partitioner.scala
+++ b/core/src/main/scala/org/apache/spark/Partitioner.scala
@@ -83,6 +83,8 @@ class HashPartitioner(partitions: Int) extends Partitioner {
     case _ =>
       false
   }
+
+  override def hashCode: Int = numPartitions
 }
 
 /**
@@ -119,7 +121,7 @@ class RangePartitioner[K : Ordering : ClassTag, V](
     }
   }
 
-  def numPartitions = partitions
+  def numPartitions = rangeBounds.length + 1
 
   private val binarySearch: ((Array[K], K) => Int) = CollectionsUtils.makeBinarySearch[K]
 
@@ -155,4 +157,17 @@ class RangePartitioner[K : Ordering : ClassTag, V](
     case _ =>
       false
   }
+
+
+  override def hashCode(): Int = {
+    val prime = 31
+    var result = 1
+    var i = 0
+    while (i < rangeBounds.length) {
+      result = prime * result + rangeBounds(i).hashCode
+      i += 1
+    }
+    result = prime * result + ascending.hashCode
+    result
+  }
 }
diff --git a/core/src/main/scala/org/apache/spark/api/python/PythonPartitioner.scala b/core/src/main/scala/org/apache/spark/api/python/PythonPartitioner.scala
index 95bec5030bfd..e230d222b860 100644
--- a/core/src/main/scala/org/apache/spark/api/python/PythonPartitioner.scala
+++ b/core/src/main/scala/org/apache/spark/api/python/PythonPartitioner.scala
@@ -50,4 +50,6 @@ private[spark] class PythonPartitioner(
     case _ =>
       false
   }
+
+  override def hashCode: Int = 31 * numPartitions + pyPartitionFunctionId.hashCode
 }
diff --git a/examples/src/main/scala/org/apache/spark/examples/bagel/PageRankUtils.scala b/examples/src/main/scala/org/apache/spark/examples/bagel/PageRankUtils.scala
index b97cb8fb0282..e06f4dcd5444 100644
--- a/examples/src/main/scala/org/apache/spark/examples/bagel/PageRankUtils.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/bagel/PageRankUtils.scala
@@ -124,4 +124,6 @@ class CustomPartitioner(partitions: Int) extends Partitioner {
       c.numPartitions == numPartitions
     case _ => false
   }
+
+  override def hashCode: Int = numPartitions
 }

From e9261d0866a610eab29fa332726186b534d1018f Mon Sep 17 00:00:00 2001
From: maji2014 <maji3@asiainfo-linkage.com>
Date: Sun, 8 Jun 2014 15:14:27 -0700
Subject: [PATCH 364/810] Update run-example

Old code can only be ran under spark_home and use "bin/run-example".
 Error "./run-example: line 55: ./bin/spark-submit: No such file or directory" appears when running in other place. So change this

Author: maji2014 <maji3@asiainfo-linkage.com>

Closes #1011 from maji2014/master and squashes the following commits:

2cc1af6 [maji2014] Update run-example

Closes #988.
---
 bin/run-example | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/bin/run-example b/bin/run-example
index 7caab31daef3..e7a5fe3914fb 100755
--- a/bin/run-example
+++ b/bin/run-example
@@ -51,7 +51,7 @@ if [[ ! $EXAMPLE_CLASS == org.apache.spark.examples* ]]; then
   EXAMPLE_CLASS="org.apache.spark.examples.$EXAMPLE_CLASS"
 fi
 
-./bin/spark-submit \
+"$FWDIR"/bin/spark-submit \
   --master $EXAMPLE_MASTER \
   --class $EXAMPLE_CLASS \
   "$SPARK_EXAMPLES_JAR" \

From 219dc00b30c8d9c4c0a6ce5d566497a93f21cb57 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Sun, 8 Jun 2014 18:39:57 -0700
Subject: [PATCH 365/810] SPARK-1628 follow up: Improve RangePartitioner's
 documentation.

Adding a paragraph clarifying a weird behavior in RangePartitioner.

See also #549.

Author: Reynold Xin <rxin@apache.org>

Closes #1012 from rxin/partitioner-doc and squashes the following commits:

6f0109e [Reynold Xin] SPARK-1628 follow up: Improve RangePartitioner's documentation.
---
 core/src/main/scala/org/apache/spark/Partitioner.scala | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/Partitioner.scala b/core/src/main/scala/org/apache/spark/Partitioner.scala
index 01e918fabec6..e7f75481939a 100644
--- a/core/src/main/scala/org/apache/spark/Partitioner.scala
+++ b/core/src/main/scala/org/apache/spark/Partitioner.scala
@@ -90,6 +90,10 @@ class HashPartitioner(partitions: Int) extends Partitioner {
 /**
  * A [[org.apache.spark.Partitioner]] that partitions sortable records by range into roughly
  * equal ranges. The ranges are determined by sampling the content of the RDD passed in.
+ *
+ * Note that the actual number of partitions created by the RangePartitioner might not be the same
+ * as the `partitions` parameter, in the case where the number of sampled records is less than
+ * the value of `partitions`.
  */
 class RangePartitioner[K : Ordering : ClassTag, V](
     partitions: Int,
@@ -158,7 +162,6 @@ class RangePartitioner[K : Ordering : ClassTag, V](
       false
   }
 
-
   override def hashCode(): Int = {
     val prime = 31
     var result = 1

From 15ddbef414d5fd6d4672936ba3c747b5fb7ab52b Mon Sep 17 00:00:00 2001
From: Neville Li <neville@spotify.com>
Date: Sun, 8 Jun 2014 23:18:27 -0700
Subject: [PATCH 366/810] [SPARK-2067] use relative path for Spark logo in UI

Author: Neville Li <neville@spotify.com>

Closes #1006 from nevillelyh/gh/SPARK-2067 and squashes the following commits:

9ee64cf [Neville Li] [SPARK-2067] use relative path for Spark logo in UI
---
 core/src/main/scala/org/apache/spark/ui/UIUtils.scala | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/ui/UIUtils.scala b/core/src/main/scala/org/apache/spark/ui/UIUtils.scala
index a43314f48112..1b104253d545 100644
--- a/core/src/main/scala/org/apache/spark/ui/UIUtils.scala
+++ b/core/src/main/scala/org/apache/spark/ui/UIUtils.scala
@@ -168,7 +168,7 @@ private[spark] object UIUtils extends Logging {
       <body>
         <div class="navbar navbar-static-top">
           <div class="navbar-inner">
-            <a href="/" class="brand">
+            <a href={prependBaseUri("/")} class="brand">
               <img src={prependBaseUri("/static/spark-logo-77x50px-hd.png")} />
             </a>
             <ul class="nav">{header}</ul>

From 32ee9f0668e50083e415b0662b18f5d2581413f0 Mon Sep 17 00:00:00 2001
From: Andrew Ash <andrew@andrewash.com>
Date: Sun, 8 Jun 2014 23:20:10 -0700
Subject: [PATCH 367/810] Grammar: read -> reads

Author: Andrew Ash <andrew@andrewash.com>

Closes #1016 from ash211/patch-6 and squashes the following commits:

e3865c8 [Andrew Ash] Grammar: read -> reads
---
 core/src/main/scala/org/apache/spark/storage/BlockManager.scala | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala
index a41286d3e4a0..9cd79d262ea5 100644
--- a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala
+++ b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala
@@ -1024,7 +1024,7 @@ private[spark] class BlockManager(
     if (blockId.isShuffle) {
       // Reducer may need to read many local shuffle blocks and will wrap them into Iterators
       // at the beginning. The wrapping will cost some memory (compression instance
-      // initialization, etc.). Reducer read shuffle blocks one by one so we could do the
+      // initialization, etc.). Reducer reads shuffle blocks one by one so we could do the
       // wrapping lazily to save memory.
       class LazyProxyIterator(f: => Iterator[Any]) extends Iterator[Any] {
         lazy val proxy = f

From 6113ac1559d62c828dfbf08ef0f7f172c24cf7f5 Mon Sep 17 00:00:00 2001
From: Syed Hashmi <shashmi@cloudera.com>
Date: Mon, 9 Jun 2014 00:08:40 -0700
Subject: [PATCH 368/810] [SPARK-1308] Add getNumPartitions to pyspark RDD

Add getNumPartitions to pyspark RDD to provide an intuitive way to get number of partitions in RDD like we can do in scala today.

Author: Syed Hashmi <shashmi@cloudera.com>

Closes #995 from syedhashmi/master and squashes the following commits:

de0ed5e [Syed Hashmi] [SPARK-1308] Add getNumPartitions to pyspark RDD
---
 python/pyspark/rdd.py | 45 ++++++++++++++++++++++++++-----------------
 1 file changed, 27 insertions(+), 18 deletions(-)

diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py
index ca0a95578fd2..9c69c79236ed 100644
--- a/python/pyspark/rdd.py
+++ b/python/pyspark/rdd.py
@@ -250,7 +250,7 @@ def getCheckpointFile(self):
     def map(self, f, preservesPartitioning=False):
         """
         Return a new RDD by applying a function to each element of this RDD.
-        
+
         >>> rdd = sc.parallelize(["b", "a", "c"])
         >>> sorted(rdd.map(lambda x: (x, 1)).collect())
         [('a', 1), ('b', 1), ('c', 1)]
@@ -312,6 +312,15 @@ def mapPartitionsWithSplit(self, f, preservesPartitioning=False):
             "use mapPartitionsWithIndex instead", DeprecationWarning, stacklevel=2)
         return self.mapPartitionsWithIndex(f, preservesPartitioning)
 
+    def getNumPartitions(self):
+      """
+      Returns the number of partitions in RDD
+      >>> rdd = sc.parallelize([1, 2, 3, 4], 2)
+      >>> rdd.getNumPartitions()
+      2
+      """
+      return self._jrdd.splits().size()
+
     def filter(self, f):
         """
         Return a new RDD containing only the elements that satisfy a predicate.
@@ -413,9 +422,9 @@ def union(self, other):
 
     def intersection(self, other):
         """
-        Return the intersection of this RDD and another one. The output will not 
+        Return the intersection of this RDD and another one. The output will not
         contain any duplicate elements, even if the input RDDs did.
-        
+
         Note that this method performs a shuffle internally.
 
         >>> rdd1 = sc.parallelize([1, 10, 2, 3, 4, 5])
@@ -571,14 +580,14 @@ def foreachPartition(self, f):
         """
         Applies a function to each partition of this RDD.
 
-        >>> def f(iterator): 
-        ...      for x in iterator: 
-        ...           print x 
+        >>> def f(iterator):
+        ...      for x in iterator:
+        ...           print x
         ...      yield None
         >>> sc.parallelize([1, 2, 3, 4, 5]).foreachPartition(f)
         """
         self.mapPartitions(f).collect()  # Force evaluation
-        
+
     def collect(self):
         """
         Return a list that contains all of the elements in this RDD.
@@ -673,7 +682,7 @@ def func(iterator):
             yield acc
 
         return self.mapPartitions(func).fold(zeroValue, combOp)
-        
+
 
     def max(self):
         """
@@ -692,7 +701,7 @@ def min(self):
         1.0
         """
         return self.reduce(min)
-    
+
     def sum(self):
         """
         Add up the elements in this RDD.
@@ -786,7 +795,7 @@ def mergeMaps(m1, m2):
                 m1[k] += v
             return m1
         return self.mapPartitions(countPartition).reduce(mergeMaps)
-    
+
     def top(self, num):
         """
         Get the top N elements from a RDD.
@@ -814,7 +823,7 @@ def merge(a, b):
     def takeOrdered(self, num, key=None):
         """
         Get the N elements from a RDD ordered in ascending order or as specified
-        by the optional key function. 
+        by the optional key function.
 
         >>> sc.parallelize([10, 1, 2, 9, 3, 4, 5, 6, 7]).takeOrdered(6)
         [1, 2, 3, 4, 5, 6]
@@ -834,7 +843,7 @@ def unKey(x, key_=None):
             if key_ != None:
                 x = [i[1] for i in x]
             return x
-        
+
         def merge(a, b):
             return next(topNKeyedElems(a + b))
         result = self.mapPartitions(lambda i: topNKeyedElems(i, key)).reduce(merge)
@@ -1169,12 +1178,12 @@ def _mergeCombiners(iterator):
                     combiners[k] = mergeCombiners(combiners[k], v)
             return combiners.iteritems()
         return shuffled.mapPartitions(_mergeCombiners)
-    
+
     def foldByKey(self, zeroValue, func, numPartitions=None):
         """
         Merge the values for each key using an associative function "func" and a neutral "zeroValue"
-        which may be added to the result an arbitrary number of times, and must not change 
-        the result (e.g., 0 for addition, or 1 for multiplication.).                
+        which may be added to the result an arbitrary number of times, and must not change
+        the result (e.g., 0 for addition, or 1 for multiplication.).
 
         >>> rdd = sc.parallelize([("a", 1), ("b", 1), ("a", 1)])
         >>> from operator import add
@@ -1182,8 +1191,8 @@ def foldByKey(self, zeroValue, func, numPartitions=None):
         [('a', 2), ('b', 1)]
         """
         return self.combineByKey(lambda v: func(zeroValue, v), func, func, numPartitions)
-    
-    
+
+
     # TODO: support variant with custom partitioner
     def groupByKey(self, numPartitions=None):
         """
@@ -1302,7 +1311,7 @@ def keyBy(self, f):
     def repartition(self, numPartitions):
         """
          Return a new RDD that has exactly numPartitions partitions.
-          
+
          Can increase or decrease the level of parallelism in this RDD. Internally, this uses
          a shuffle to redistribute data.
          If you are decreasing the number of partitions in this RDD, consider using `coalesce`,

From 35630c86ff0e27862c9d902887eb0a24d25867ae Mon Sep 17 00:00:00 2001
From: Andrew Ash <andrew@andrewash.com>
Date: Mon, 9 Jun 2014 10:21:21 -0700
Subject: [PATCH 369/810] SPARK-1944 Document --verbose in spark-shell -h

https://issues.apache.org/jira/browse/SPARK-1944

Author: Andrew Ash <andrew@andrewash.com>

Closes #1020 from ash211/SPARK-1944 and squashes the following commits:

a831c4d [Andrew Ash] SPARK-1944 Document --verbose in spark-shell -h
---
 .../scala/org/apache/spark/deploy/SparkSubmitArguments.scala   | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
index 153eee3bc588..f1032ea8dbad 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
@@ -360,6 +360,9 @@ private[spark] class SparkSubmitArguments(args: Seq[String]) {
         |
         |  --executor-memory MEM       Memory per executor (e.g. 1000M, 2G) (Default: 1G).
         |
+        |  --help, -h                  Show this help message and exit
+        |  --verbose, -v               Print additional debug output
+        |
         | Spark standalone with cluster deploy mode only:
         |  --driver-cores NUM          Cores for driver (Default: 1).
         |  --supervise                 If given, restarts the driver on failure.

From 0cf600280167a94faec75736223256e8f2e48085 Mon Sep 17 00:00:00 2001
From: Daoyuan <daoyuan.wang@intel.com>
Date: Mon, 9 Jun 2014 11:31:36 -0700
Subject: [PATCH 370/810] [SPARK-1495][SQL]add support for left semi join

Just submit another solution for #395

Author: Daoyuan <daoyuan.wang@intel.com>
Author: Michael Armbrust <michael@databricks.com>
Author: Daoyuan Wang <daoyuan.wang@intel.com>

Closes #837 from adrian-wang/left-semi-join-support and squashes the following commits:

d39cd12 [Daoyuan Wang] Merge pull request #1 from marmbrus/pr/837
6713c09 [Michael Armbrust] Better debugging for failed query tests.
035b73e [Michael Armbrust] Add test for left semi that can't be done with a hash join.
5ec6fa4 [Michael Armbrust] Add left semi to SQL Parser.
4c726e5 [Daoyuan] improvement according to Michael
8d4a121 [Daoyuan] add golden files for leftsemijoin
83a3c8a [Daoyuan] scala style fix
14cff80 [Daoyuan] add support for left semi join
---
 .../apache/spark/sql/catalyst/SqlParser.scala |   2 +
 .../sql/catalyst/planning/patterns.scala      |   5 +
 .../spark/sql/catalyst/plans/joinTypes.scala  |   1 +
 .../plans/logical/basicOperators.scala        |   9 +-
 .../org/apache/spark/sql/SQLContext.scala     |   1 +
 .../spark/sql/execution/SparkStrategies.scala |  16 +++
 .../apache/spark/sql/execution/joins.scala    | 131 ++++++++++++++++++
 .../org/apache/spark/sql/QueryTest.scala      |   2 +-
 .../org/apache/spark/sql/SQLQuerySuite.scala  |   7 +
 .../apache/spark/sql/hive/HiveContext.scala   |   1 +
 .../org/apache/spark/sql/hive/HiveQl.scala    |   1 +
 ...emijoin-0-80b6466213face7fbcb0de044611e1f5 |   0
 ...emijoin-1-d1f6a3dea28a5f0fee08026bf33d9129 |   0
 ...mijoin-10-89737a8857b5b61cc909e0c797f86aea |   4 +
 ...mijoin-11-80b6466213face7fbcb0de044611e1f5 |   0
 ...mijoin-12-d1f6a3dea28a5f0fee08026bf33d9129 |   0
 ...emijoin-2-43d53504df013e6b35f81811138a167a |   1 +
 ...emijoin-3-b07d292423312aafa5e5762a579decd2 |   0
 ...emijoin-4-3ac2226efe7cb5d999c1c5e4ac2114be |   0
 ...emijoin-5-9c307c0559d735960ce77efa95b2b17b |   0
 ...emijoin-6-82921fc96eef547ec0f71027ee88298c |   0
 ...emijoin-7-b30aa3b4a45db6b64bb46b4d9bd32ff0 |   0
 ...tsemijoin-8-73cad58a10a1483ccb15e94a857013 |   4 +
 ...emijoin-9-c5efa6b8771a51610d655be461670e1e |   2 +
 ...join_mr-0-7087fb6281a34d00f1812d2ff4ba8b75 |   0
 ...join_mr-1-aa3f07f028027ffd13ab5535dc821593 |   0
 ...oin_mr-10-9914f44ecb6ae7587b62e5349ff60d04 |   1 +
 ...oin_mr-11-2027ecb1495d5550c5d56abf6b95b0a7 |   2 +
 ...join_mr-2-3f65953ae60375156367c54533978782 |   0
 ...join_mr-3-645cf8b871c9b27418d6fa1d1bda9a52 |   0
 ...join_mr-4-333895fe6abca27c8edb5c91bfe10d2f |   2 +
 ...join_mr-5-896d0948c1df849df9764a6d8ad8fff9 |  20 +++
 ...join_mr-6-b1e2ade89ae898650f0be4f796d8947b |   1 +
 ...join_mr-7-8e9c2969b999557363e40f9ebb3f6d7c |   1 +
 ...join_mr-8-c61b972d4409babe41d8963e841af45b |   1 +
 ...join_mr-9-2027ecb1495d5550c5d56abf6b95b0a7 |   2 +
 .../execution/HiveCompatibilitySuite.scala    |   2 +
 37 files changed, 216 insertions(+), 3 deletions(-)
 create mode 100644 sql/hive/src/test/resources/golden/leftsemijoin-0-80b6466213face7fbcb0de044611e1f5
 create mode 100644 sql/hive/src/test/resources/golden/leftsemijoin-1-d1f6a3dea28a5f0fee08026bf33d9129
 create mode 100644 sql/hive/src/test/resources/golden/leftsemijoin-10-89737a8857b5b61cc909e0c797f86aea
 create mode 100644 sql/hive/src/test/resources/golden/leftsemijoin-11-80b6466213face7fbcb0de044611e1f5
 create mode 100644 sql/hive/src/test/resources/golden/leftsemijoin-12-d1f6a3dea28a5f0fee08026bf33d9129
 create mode 100644 sql/hive/src/test/resources/golden/leftsemijoin-2-43d53504df013e6b35f81811138a167a
 create mode 100644 sql/hive/src/test/resources/golden/leftsemijoin-3-b07d292423312aafa5e5762a579decd2
 create mode 100644 sql/hive/src/test/resources/golden/leftsemijoin-4-3ac2226efe7cb5d999c1c5e4ac2114be
 create mode 100644 sql/hive/src/test/resources/golden/leftsemijoin-5-9c307c0559d735960ce77efa95b2b17b
 create mode 100644 sql/hive/src/test/resources/golden/leftsemijoin-6-82921fc96eef547ec0f71027ee88298c
 create mode 100644 sql/hive/src/test/resources/golden/leftsemijoin-7-b30aa3b4a45db6b64bb46b4d9bd32ff0
 create mode 100644 sql/hive/src/test/resources/golden/leftsemijoin-8-73cad58a10a1483ccb15e94a857013
 create mode 100644 sql/hive/src/test/resources/golden/leftsemijoin-9-c5efa6b8771a51610d655be461670e1e
 create mode 100644 sql/hive/src/test/resources/golden/leftsemijoin_mr-0-7087fb6281a34d00f1812d2ff4ba8b75
 create mode 100644 sql/hive/src/test/resources/golden/leftsemijoin_mr-1-aa3f07f028027ffd13ab5535dc821593
 create mode 100644 sql/hive/src/test/resources/golden/leftsemijoin_mr-10-9914f44ecb6ae7587b62e5349ff60d04
 create mode 100644 sql/hive/src/test/resources/golden/leftsemijoin_mr-11-2027ecb1495d5550c5d56abf6b95b0a7
 create mode 100644 sql/hive/src/test/resources/golden/leftsemijoin_mr-2-3f65953ae60375156367c54533978782
 create mode 100644 sql/hive/src/test/resources/golden/leftsemijoin_mr-3-645cf8b871c9b27418d6fa1d1bda9a52
 create mode 100644 sql/hive/src/test/resources/golden/leftsemijoin_mr-4-333895fe6abca27c8edb5c91bfe10d2f
 create mode 100644 sql/hive/src/test/resources/golden/leftsemijoin_mr-5-896d0948c1df849df9764a6d8ad8fff9
 create mode 100644 sql/hive/src/test/resources/golden/leftsemijoin_mr-6-b1e2ade89ae898650f0be4f796d8947b
 create mode 100644 sql/hive/src/test/resources/golden/leftsemijoin_mr-7-8e9c2969b999557363e40f9ebb3f6d7c
 create mode 100644 sql/hive/src/test/resources/golden/leftsemijoin_mr-8-c61b972d4409babe41d8963e841af45b
 create mode 100644 sql/hive/src/test/resources/golden/leftsemijoin_mr-9-2027ecb1495d5550c5d56abf6b95b0a7

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
index a404e7441a1b..cc650128c2c3 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
@@ -131,6 +131,7 @@ class SqlParser extends StandardTokenParsers with PackratParsers {
   protected val OUTER = Keyword("OUTER")
   protected val RIGHT = Keyword("RIGHT")
   protected val SELECT = Keyword("SELECT")
+  protected val SEMI = Keyword("SEMI")
   protected val STRING = Keyword("STRING")
   protected val SUM = Keyword("SUM")
   protected val TRUE = Keyword("TRUE")
@@ -241,6 +242,7 @@ class SqlParser extends StandardTokenParsers with PackratParsers {
 
    protected lazy val joinType: Parser[JoinType] =
      INNER ^^^ Inner |
+     LEFT ~ SEMI ^^^ LeftSemi |
      LEFT ~ opt(OUTER) ^^^ LeftOuter |
      RIGHT ~ opt(OUTER) ^^^ RightOuter |
      FULL ~ opt(OUTER) ^^^ FullOuter
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala
index 4544b32958c7..820ecfb78b52 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala
@@ -119,6 +119,11 @@ object HashFilteredJoin extends Logging with PredicateHelper {
     case FilteredOperation(predicates, join @ Join(left, right, Inner, condition)) =>
       logger.debug(s"Considering hash inner join on: ${predicates ++ condition}")
       splitPredicates(predicates ++ condition, join)
+    // All predicates can be evaluated for left semi join (those that are in the WHERE
+    // clause can only from left table, so they can all be pushed down.)
+    case FilteredOperation(predicates, join @ Join(left, right, LeftSemi, condition)) =>
+      logger.debug(s"Considering hash left semi join on: ${predicates ++ condition}")
+      splitPredicates(predicates ++ condition, join)
     case join @ Join(left, right, joinType, condition) =>
       logger.debug(s"Considering hash join on: $condition")
       splitPredicates(condition.toSeq, join)
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/joinTypes.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/joinTypes.scala
index ae8d7d3e4257..613f4bb09daf 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/joinTypes.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/joinTypes.scala
@@ -22,3 +22,4 @@ case object Inner extends JoinType
 case object LeftOuter extends JoinType
 case object RightOuter extends JoinType
 case object FullOuter extends JoinType
+case object LeftSemi extends JoinType
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
index 732708e146b0..d3347b622f3d 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.sql.catalyst.plans.logical
 
 import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.catalyst.plans.JoinType
+import org.apache.spark.sql.catalyst.plans.{LeftSemi, JoinType}
 import org.apache.spark.sql.catalyst.types._
 
 case class Project(projectList: Seq[NamedExpression], child: LogicalPlan) extends UnaryNode {
@@ -81,7 +81,12 @@ case class Join(
   condition: Option[Expression]) extends BinaryNode {
 
   def references = condition.map(_.references).getOrElse(Set.empty)
-  def output = left.output ++ right.output
+  def output = joinType match {
+    case LeftSemi =>
+      left.output
+    case _ =>
+      left.output ++ right.output
+  }
 }
 
 case class InsertIntoTable(
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
index 043be58edc91..e371c82d8132 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
@@ -193,6 +193,7 @@ class SQLContext(@transient val sparkContext: SparkContext)
     val strategies: Seq[Strategy] =
       TakeOrdered ::
       PartialAggregation ::
+      LeftSemiJoin ::
       HashJoin ::
       ParquetOperations ::
       BasicOperators ::
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
index cfa8bdae58b1..6463f4751063 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
@@ -28,6 +28,22 @@ import org.apache.spark.sql.parquet._
 private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
   self: SQLContext#SparkPlanner =>
 
+  object LeftSemiJoin extends Strategy with PredicateHelper {
+    def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
+      // Find left semi joins where at least some predicates can be evaluated by matching hash
+      // keys using the HashFilteredJoin pattern.
+      case HashFilteredJoin(LeftSemi, leftKeys, rightKeys, condition, left, right) =>
+        val semiJoin = execution.LeftSemiJoinHash(
+          leftKeys, rightKeys, planLater(left), planLater(right))
+        condition.map(Filter(_, semiJoin)).getOrElse(semiJoin) :: Nil
+      // no predicate can be evaluated by matching hash keys
+      case logical.Join(left, right, LeftSemi, condition) =>
+        execution.LeftSemiJoinBNL(
+          planLater(left), planLater(right), condition)(sparkContext) :: Nil
+      case _ => Nil
+    }
+  }
+
   object HashJoin extends Strategy with PredicateHelper {
     def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
       // Find inner joins where at least some predicates can be evaluated by matching hash keys
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins.scala
index 31cc26962ad9..88ff3d49a79b 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins.scala
@@ -140,6 +140,137 @@ case class HashJoin(
   }
 }
 
+/**
+ * :: DeveloperApi ::
+ * Build the right table's join keys into a HashSet, and iteratively go through the left
+ * table, to find the if join keys are in the Hash set.
+ */
+@DeveloperApi
+case class LeftSemiJoinHash(
+    leftKeys: Seq[Expression],
+    rightKeys: Seq[Expression],
+    left: SparkPlan,
+    right: SparkPlan) extends BinaryNode {
+
+  override def outputPartitioning: Partitioning = left.outputPartitioning
+
+  override def requiredChildDistribution =
+    ClusteredDistribution(leftKeys) :: ClusteredDistribution(rightKeys) :: Nil
+
+  val (buildPlan, streamedPlan) = (right, left)
+  val (buildKeys, streamedKeys) = (rightKeys, leftKeys)
+
+  def output = left.output
+
+  @transient lazy val buildSideKeyGenerator = new Projection(buildKeys, buildPlan.output)
+  @transient lazy val streamSideKeyGenerator =
+    () => new MutableProjection(streamedKeys, streamedPlan.output)
+
+  def execute() = {
+
+    buildPlan.execute().zipPartitions(streamedPlan.execute()) { (buildIter, streamIter) =>
+      val hashTable = new java.util.HashSet[Row]()
+      var currentRow: Row = null
+
+      // Create a Hash set of buildKeys
+      while (buildIter.hasNext) {
+        currentRow = buildIter.next()
+        val rowKey = buildSideKeyGenerator(currentRow)
+        if(!rowKey.anyNull) {
+          val keyExists = hashTable.contains(rowKey)
+          if (!keyExists) {
+            hashTable.add(rowKey)
+          }
+        }
+      }
+
+      new Iterator[Row] {
+        private[this] var currentStreamedRow: Row = _
+        private[this] var currentHashMatched: Boolean = false
+
+        private[this] val joinKeys = streamSideKeyGenerator()
+
+        override final def hasNext: Boolean =
+          streamIter.hasNext && fetchNext()
+
+        override final def next() = {
+          currentStreamedRow
+        }
+
+        /**
+         * Searches the streamed iterator for the next row that has at least one match in hashtable.
+         *
+         * @return true if the search is successful, and false the streamed iterator runs out of
+         *         tuples.
+         */
+        private final def fetchNext(): Boolean = {
+          currentHashMatched = false
+          while (!currentHashMatched && streamIter.hasNext) {
+            currentStreamedRow = streamIter.next()
+            if (!joinKeys(currentStreamedRow).anyNull) {
+              currentHashMatched = hashTable.contains(joinKeys.currentValue)
+            }
+          }
+          currentHashMatched
+        }
+      }
+    }
+  }
+}
+
+/**
+ * :: DeveloperApi ::
+ * Using BroadcastNestedLoopJoin to calculate left semi join result when there's no join keys
+ * for hash join.
+ */
+@DeveloperApi
+case class LeftSemiJoinBNL(
+    streamed: SparkPlan, broadcast: SparkPlan, condition: Option[Expression])
+    (@transient sc: SparkContext)
+  extends BinaryNode {
+  // TODO: Override requiredChildDistribution.
+
+  override def outputPartitioning: Partitioning = streamed.outputPartitioning
+
+  override def otherCopyArgs = sc :: Nil
+
+  def output = left.output
+
+  /** The Streamed Relation */
+  def left = streamed
+  /** The Broadcast relation */
+  def right = broadcast
+
+  @transient lazy val boundCondition =
+    InterpretedPredicate(
+      condition
+        .map(c => BindReferences.bindReference(c, left.output ++ right.output))
+        .getOrElse(Literal(true)))
+
+
+  def execute() = {
+    val broadcastedRelation = sc.broadcast(broadcast.execute().map(_.copy()).collect().toIndexedSeq)
+
+    streamed.execute().mapPartitions { streamedIter =>
+      val joinedRow = new JoinedRow
+
+      streamedIter.filter(streamedRow => {
+        var i = 0
+        var matched = false
+
+        while (i < broadcastedRelation.value.size && !matched) {
+          val broadcastedRow = broadcastedRelation.value(i)
+          if (boundCondition(joinedRow(streamedRow, broadcastedRow))) {
+            matched = true
+          }
+          i += 1
+        }
+        matched
+      })
+    }
+  }
+}
+
 /**
  * :: DeveloperApi ::
  */
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/QueryTest.scala b/sql/core/src/test/scala/org/apache/spark/sql/QueryTest.scala
index d6072b402a04..d7f6abaf5d38 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/QueryTest.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/QueryTest.scala
@@ -44,7 +44,7 @@ class QueryTest extends FunSuite {
         fail(
           s"""
             |Exception thrown while executing query:
-            |${rdd.logicalPlan}
+            |${rdd.queryExecution}
             |== Exception ==
             |$e
           """.stripMargin)
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index aa0c426f6fcb..d651b967a6c1 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -40,6 +40,13 @@ class SQLQuerySuite extends QueryTest {
       arrayData.map(d => (d.data, d.data(0), d.data(0) + d.data(1), d.data(1))).collect().toSeq)
   }
 
+  test("left semi greater than predicate") {
+    checkAnswer(
+      sql("SELECT * FROM testData2 x LEFT SEMI JOIN testData2 y ON x.a >= y.a + 2"),
+      Seq((3,1), (3,2))
+    )
+  }
+
   test("index into array of arrays") {
     checkAnswer(
       sql(
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
index b21f24dad785..fbab2ac16b89 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
@@ -224,6 +224,7 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) {
       DataSinks,
       Scripts,
       PartialAggregation,
+      LeftSemiJoin,
       HashJoin,
       BasicOperators,
       CartesianProduct,
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
index 7e91c16c6b93..cc9e24a05740 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
@@ -685,6 +685,7 @@ private[hive] object HiveQl {
         case "TOK_RIGHTOUTERJOIN" => RightOuter
         case "TOK_LEFTOUTERJOIN" => LeftOuter
         case "TOK_FULLOUTERJOIN" => FullOuter
+        case "TOK_LEFTSEMIJOIN" => LeftSemi
       }
       assert(other.size <= 1, "Unhandled join clauses.")
       Join(nodeToRelation(relation1),
diff --git a/sql/hive/src/test/resources/golden/leftsemijoin-0-80b6466213face7fbcb0de044611e1f5 b/sql/hive/src/test/resources/golden/leftsemijoin-0-80b6466213face7fbcb0de044611e1f5
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/leftsemijoin-1-d1f6a3dea28a5f0fee08026bf33d9129 b/sql/hive/src/test/resources/golden/leftsemijoin-1-d1f6a3dea28a5f0fee08026bf33d9129
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/leftsemijoin-10-89737a8857b5b61cc909e0c797f86aea b/sql/hive/src/test/resources/golden/leftsemijoin-10-89737a8857b5b61cc909e0c797f86aea
new file mode 100644
index 000000000000..25ce912507d5
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/leftsemijoin-10-89737a8857b5b61cc909e0c797f86aea
@@ -0,0 +1,4 @@
+Hank	2
+Hank	2
+Joe	2
+Joe	2
diff --git a/sql/hive/src/test/resources/golden/leftsemijoin-11-80b6466213face7fbcb0de044611e1f5 b/sql/hive/src/test/resources/golden/leftsemijoin-11-80b6466213face7fbcb0de044611e1f5
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/leftsemijoin-12-d1f6a3dea28a5f0fee08026bf33d9129 b/sql/hive/src/test/resources/golden/leftsemijoin-12-d1f6a3dea28a5f0fee08026bf33d9129
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/leftsemijoin-2-43d53504df013e6b35f81811138a167a b/sql/hive/src/test/resources/golden/leftsemijoin-2-43d53504df013e6b35f81811138a167a
new file mode 100644
index 000000000000..573541ac9702
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/leftsemijoin-2-43d53504df013e6b35f81811138a167a
@@ -0,0 +1 @@
+0
diff --git a/sql/hive/src/test/resources/golden/leftsemijoin-3-b07d292423312aafa5e5762a579decd2 b/sql/hive/src/test/resources/golden/leftsemijoin-3-b07d292423312aafa5e5762a579decd2
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/leftsemijoin-4-3ac2226efe7cb5d999c1c5e4ac2114be b/sql/hive/src/test/resources/golden/leftsemijoin-4-3ac2226efe7cb5d999c1c5e4ac2114be
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/leftsemijoin-5-9c307c0559d735960ce77efa95b2b17b b/sql/hive/src/test/resources/golden/leftsemijoin-5-9c307c0559d735960ce77efa95b2b17b
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/leftsemijoin-6-82921fc96eef547ec0f71027ee88298c b/sql/hive/src/test/resources/golden/leftsemijoin-6-82921fc96eef547ec0f71027ee88298c
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/leftsemijoin-7-b30aa3b4a45db6b64bb46b4d9bd32ff0 b/sql/hive/src/test/resources/golden/leftsemijoin-7-b30aa3b4a45db6b64bb46b4d9bd32ff0
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/leftsemijoin-8-73cad58a10a1483ccb15e94a857013 b/sql/hive/src/test/resources/golden/leftsemijoin-8-73cad58a10a1483ccb15e94a857013
new file mode 100644
index 000000000000..25ce912507d5
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/leftsemijoin-8-73cad58a10a1483ccb15e94a857013
@@ -0,0 +1,4 @@
+Hank	2
+Hank	2
+Joe	2
+Joe	2
diff --git a/sql/hive/src/test/resources/golden/leftsemijoin-9-c5efa6b8771a51610d655be461670e1e b/sql/hive/src/test/resources/golden/leftsemijoin-9-c5efa6b8771a51610d655be461670e1e
new file mode 100644
index 000000000000..f1470bad5782
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/leftsemijoin-9-c5efa6b8771a51610d655be461670e1e
@@ -0,0 +1,2 @@
+2	Tie
+2	Tie
diff --git a/sql/hive/src/test/resources/golden/leftsemijoin_mr-0-7087fb6281a34d00f1812d2ff4ba8b75 b/sql/hive/src/test/resources/golden/leftsemijoin_mr-0-7087fb6281a34d00f1812d2ff4ba8b75
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/leftsemijoin_mr-1-aa3f07f028027ffd13ab5535dc821593 b/sql/hive/src/test/resources/golden/leftsemijoin_mr-1-aa3f07f028027ffd13ab5535dc821593
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/leftsemijoin_mr-10-9914f44ecb6ae7587b62e5349ff60d04 b/sql/hive/src/test/resources/golden/leftsemijoin_mr-10-9914f44ecb6ae7587b62e5349ff60d04
new file mode 100644
index 000000000000..573541ac9702
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/leftsemijoin_mr-10-9914f44ecb6ae7587b62e5349ff60d04
@@ -0,0 +1 @@
+0
diff --git a/sql/hive/src/test/resources/golden/leftsemijoin_mr-11-2027ecb1495d5550c5d56abf6b95b0a7 b/sql/hive/src/test/resources/golden/leftsemijoin_mr-11-2027ecb1495d5550c5d56abf6b95b0a7
new file mode 100644
index 000000000000..6ed281c757a9
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/leftsemijoin_mr-11-2027ecb1495d5550c5d56abf6b95b0a7
@@ -0,0 +1,2 @@
+1
+1
diff --git a/sql/hive/src/test/resources/golden/leftsemijoin_mr-2-3f65953ae60375156367c54533978782 b/sql/hive/src/test/resources/golden/leftsemijoin_mr-2-3f65953ae60375156367c54533978782
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/leftsemijoin_mr-3-645cf8b871c9b27418d6fa1d1bda9a52 b/sql/hive/src/test/resources/golden/leftsemijoin_mr-3-645cf8b871c9b27418d6fa1d1bda9a52
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/leftsemijoin_mr-4-333895fe6abca27c8edb5c91bfe10d2f b/sql/hive/src/test/resources/golden/leftsemijoin_mr-4-333895fe6abca27c8edb5c91bfe10d2f
new file mode 100644
index 000000000000..6ed281c757a9
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/leftsemijoin_mr-4-333895fe6abca27c8edb5c91bfe10d2f
@@ -0,0 +1,2 @@
+1
+1
diff --git a/sql/hive/src/test/resources/golden/leftsemijoin_mr-5-896d0948c1df849df9764a6d8ad8fff9 b/sql/hive/src/test/resources/golden/leftsemijoin_mr-5-896d0948c1df849df9764a6d8ad8fff9
new file mode 100644
index 000000000000..179ef0e0209e
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/leftsemijoin_mr-5-896d0948c1df849df9764a6d8ad8fff9
@@ -0,0 +1,20 @@
+1
+1
+1
+1
+1
+1
+1
+1
+1
+1
+1
+1
+1
+1
+1
+1
+1
+1
+1
+1
diff --git a/sql/hive/src/test/resources/golden/leftsemijoin_mr-6-b1e2ade89ae898650f0be4f796d8947b b/sql/hive/src/test/resources/golden/leftsemijoin_mr-6-b1e2ade89ae898650f0be4f796d8947b
new file mode 100644
index 000000000000..573541ac9702
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/leftsemijoin_mr-6-b1e2ade89ae898650f0be4f796d8947b
@@ -0,0 +1 @@
+0
diff --git a/sql/hive/src/test/resources/golden/leftsemijoin_mr-7-8e9c2969b999557363e40f9ebb3f6d7c b/sql/hive/src/test/resources/golden/leftsemijoin_mr-7-8e9c2969b999557363e40f9ebb3f6d7c
new file mode 100644
index 000000000000..573541ac9702
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/leftsemijoin_mr-7-8e9c2969b999557363e40f9ebb3f6d7c
@@ -0,0 +1 @@
+0
diff --git a/sql/hive/src/test/resources/golden/leftsemijoin_mr-8-c61b972d4409babe41d8963e841af45b b/sql/hive/src/test/resources/golden/leftsemijoin_mr-8-c61b972d4409babe41d8963e841af45b
new file mode 100644
index 000000000000..573541ac9702
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/leftsemijoin_mr-8-c61b972d4409babe41d8963e841af45b
@@ -0,0 +1 @@
+0
diff --git a/sql/hive/src/test/resources/golden/leftsemijoin_mr-9-2027ecb1495d5550c5d56abf6b95b0a7 b/sql/hive/src/test/resources/golden/leftsemijoin_mr-9-2027ecb1495d5550c5d56abf6b95b0a7
new file mode 100644
index 000000000000..6ed281c757a9
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/leftsemijoin_mr-9-2027ecb1495d5550c5d56abf6b95b0a7
@@ -0,0 +1,2 @@
+1
+1
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
index 9031abf733cd..fb8f272d5abf 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
@@ -480,6 +480,8 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     "lateral_view",
     "lateral_view_cp",
     "lateral_view_ppd",
+    "leftsemijoin",
+    "leftsemijoin_mr",
     "lineage1",
     "literal_double",
     "literal_ints",

From 6cf335d79a2f69ecd9a139dd0a03acff60585be4 Mon Sep 17 00:00:00 2001
From: Kay Ousterhout <kayousterhout@gmail.com>
Date: Mon, 9 Jun 2014 13:13:53 -0700
Subject: [PATCH 371/810] Added a TaskSetManager unit test.

This test ensures that when there are no
alive executors that satisfy a particular locality level,
the TaskSetManager doesn't ever use that as the maximum
allowed locality level (this optimization ensures that a
job doesn't wait extra time in an attempt to satisfy
a scheduling locality level that is impossible).

@mateiz and @lirui-intel this unit test illustrates an issue
with #892 (it fails with that patch).

Author: Kay Ousterhout <kayousterhout@gmail.com>

Closes #1024 from kayousterhout/scheduler_unit_test and squashes the following commits:

de6a08f [Kay Ousterhout] Added a TaskSetManager unit test.
---
 .../spark/scheduler/TaskSetManagerSuite.scala    | 16 ++++++++++++++++
 1 file changed, 16 insertions(+)

diff --git a/core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala
index c92b6dc96c8e..6f1fd2576454 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala
@@ -141,6 +141,22 @@ class TaskSetManagerSuite extends FunSuite with LocalSparkContext with Logging {
     assert(sched.finishedManagers.contains(manager))
   }
 
+  test("skip unsatisfiable locality levels") {
+    sc = new SparkContext("local", "test")
+    val sched = new FakeTaskScheduler(sc, ("execA", "host1"), ("execC", "host2"))
+    val taskSet = FakeTask.createTaskSet(1, Seq(TaskLocation("host1", "execB")))
+    val clock = new FakeClock
+    val manager = new TaskSetManager(sched, taskSet, MAX_TASK_FAILURES, clock)
+
+    // An executor that is not NODE_LOCAL should be rejected.
+    assert(manager.resourceOffer("execC", "host2", ANY) === None)
+
+    // Because there are no alive PROCESS_LOCAL executors, the base locality level should be
+    // NODE_LOCAL. So, we should schedule the task on this offered NODE_LOCAL executor before
+    // any of the locality wait timers expire.
+    assert(manager.resourceOffer("execA", "host1", ANY).get.index === 0)
+  }
+
   test("basic delay scheduling") {
     sc = new SparkContext("local", "test")
     val sched = new FakeTaskScheduler(sc, ("exec1", "host1"), ("exec2", "host2"))

From e273447684779a18bd61d733bfe7958b78657ffd Mon Sep 17 00:00:00 2001
From: Bernardo Gomez Palacio <bernardo.gomezpalacio@gmail.com>
Date: Mon, 9 Jun 2014 16:14:54 -0500
Subject: [PATCH 372/810] [SPARK-1522] : YARN ClientBase throws a NPE if there
 is no YARN Application CP

The current implementation of ClientBase.getDefaultYarnApplicationClasspath inspects
the MRJobConfig class for the field DEFAULT_YARN_APPLICATION_CLASSPATH when it should
be really looking into YarnConfiguration. If the Application Configuration has no
yarn.application.classpath defined a NPE exception will be thrown.

Additional Changes include:
* Test Suite for ClientBase added

[ticket: SPARK-1522] : https://issues.apache.org/jira/browse/SPARK-1522

Author      : bernardo.gomezpalacio@gmail.com
Testing     : SPARK_HADOOP_VERSION=2.3.0 SPARK_YARN=true ./sbt/sbt test

Author: Bernardo Gomez Palacio <bernardo.gomezpalacio@gmail.com>

Closes #433 from berngp/feature/SPARK-1522 and squashes the following commits:

2c2e118 [Bernardo Gomez Palacio] [SPARK-1522]: YARN ClientBase throws a NPE if there is no YARN Application specific CP
---
 .../apache/spark/deploy/yarn/ClientBase.scala |  89 ++++++++------
 .../spark/deploy/yarn/ClientBaseSuite.scala   | 112 ++++++++++++++++++
 2 files changed, 167 insertions(+), 34 deletions(-)
 create mode 100644 yarn/common/src/test/scala/org/apache/spark/deploy/yarn/ClientBaseSuite.scala

diff --git a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala
index aeb3f0062df3..4b5e0efdde92 100644
--- a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala
+++ b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala
@@ -23,6 +23,7 @@ import java.nio.ByteBuffer
 
 import scala.collection.JavaConversions._
 import scala.collection.mutable.{HashMap, ListBuffer, Map}
+import scala.util.{Try, Success, Failure}
 
 import org.apache.hadoop.conf.Configuration
 import org.apache.hadoop.fs._
@@ -378,7 +379,7 @@ trait ClientBase extends Logging {
   }
 }
 
-object ClientBase {
+object ClientBase extends Logging {
   val SPARK_JAR: String = "__spark__.jar"
   val APP_JAR: String = "__app__.jar"
   val LOG4J_PROP: String = "log4j.properties"
@@ -388,37 +389,47 @@ object ClientBase {
 
   def getSparkJar = sys.env.get("SPARK_JAR").getOrElse(SparkContext.jarOfClass(this.getClass).head)
 
-  // Based on code from org.apache.hadoop.mapreduce.v2.util.MRApps
-  def populateHadoopClasspath(conf: Configuration, env: HashMap[String, String]) {
-    val classpathEntries = Option(conf.getStrings(
-      YarnConfiguration.YARN_APPLICATION_CLASSPATH)).getOrElse(
-        getDefaultYarnApplicationClasspath())
-    if (classpathEntries != null) {
-      for (c <- classpathEntries) {
-        YarnSparkHadoopUtil.addToEnvironment(env, Environment.CLASSPATH.name, c.trim,
-          File.pathSeparator)
-      }
+  def populateHadoopClasspath(conf: Configuration, env: HashMap[String, String]) = {
+    val classPathElementsToAdd = getYarnAppClasspath(conf) ++ getMRAppClasspath(conf)
+    for (c <- classPathElementsToAdd.flatten) {
+      YarnSparkHadoopUtil.addToEnvironment(
+        env,
+        Environment.CLASSPATH.name,
+        c.trim,
+        File.pathSeparator)
     }
+    classPathElementsToAdd
+  }
 
-    val mrClasspathEntries = Option(conf.getStrings(
-      "mapreduce.application.classpath")).getOrElse(
-        getDefaultMRApplicationClasspath())
-    if (mrClasspathEntries != null) {
-      for (c <- mrClasspathEntries) {
-        YarnSparkHadoopUtil.addToEnvironment(env, Environment.CLASSPATH.name, c.trim,
-          File.pathSeparator)
-      }
-    }
+  private def getYarnAppClasspath(conf: Configuration): Option[Seq[String]] =
+    Option(conf.getStrings(YarnConfiguration.YARN_APPLICATION_CLASSPATH)) match {
+      case Some(s) => Some(s.toSeq)
+      case None => getDefaultYarnApplicationClasspath
   }
 
-  def getDefaultYarnApplicationClasspath(): Array[String] = {
-    try {
-      val field = classOf[MRJobConfig].getField("DEFAULT_YARN_APPLICATION_CLASSPATH")
-      field.get(null).asInstanceOf[Array[String]]
-    } catch {
-      case err: NoSuchFieldError => null
-      case err: NoSuchFieldException => null
+  private def getMRAppClasspath(conf: Configuration): Option[Seq[String]] =
+    Option(conf.getStrings("mapreduce.application.classpath")) match {
+      case Some(s) => Some(s.toSeq)
+      case None => getDefaultMRApplicationClasspath
+    }
+
+  def getDefaultYarnApplicationClasspath: Option[Seq[String]] = {
+    val triedDefault = Try[Seq[String]] {
+      val field = classOf[YarnConfiguration].getField("DEFAULT_YARN_APPLICATION_CLASSPATH")
+      val value = field.get(null).asInstanceOf[Array[String]]
+      value.toSeq
+    } recoverWith {
+      case e: NoSuchFieldException => Success(Seq.empty[String])
     }
+
+    triedDefault match {
+      case f: Failure[_] =>
+        logError("Unable to obtain the default YARN Application classpath.", f.exception)
+      case s: Success[_] =>
+        logDebug(s"Using the default YARN application classpath: ${s.get.mkString(",")}")
+    }
+
+    triedDefault.toOption
   }
 
   /**
@@ -426,20 +437,30 @@ object ClientBase {
    * classpath.  In Hadoop 2.0, it's an array of Strings, and in 2.2+ it's a String.
    * So we need to use reflection to retrieve it.
    */
-  def getDefaultMRApplicationClasspath(): Array[String] = {
-    try {
+  def getDefaultMRApplicationClasspath: Option[Seq[String]] = {
+    val triedDefault = Try[Seq[String]] {
       val field = classOf[MRJobConfig].getField("DEFAULT_MAPREDUCE_APPLICATION_CLASSPATH")
-      if (field.getType == classOf[String]) {
-        StringUtils.getStrings(field.get(null).asInstanceOf[String])
+      val value = if (field.getType == classOf[String]) {
+        StringUtils.getStrings(field.get(null).asInstanceOf[String]).toArray
       } else {
         field.get(null).asInstanceOf[Array[String]]
       }
-    } catch {
-      case err: NoSuchFieldError => null
-      case err: NoSuchFieldException => null
+      value.toSeq
+    } recoverWith {
+      case e: NoSuchFieldException => Success(Seq.empty[String])
     }
+
+    triedDefault match {
+      case f: Failure[_] =>
+        logError("Unable to obtain the default MR Application classpath.", f.exception)
+      case s: Success[_] =>
+        logDebug(s"Using the default MR application classpath: ${s.get.mkString(",")}")
+    }
+
+    triedDefault.toOption
   }
 
+
   /**
    * Returns the java command line argument for setting up log4j. If there is a log4j.properties
    * in the given local resources, it is used, otherwise the SPARK_LOG4J_CONF environment variable
diff --git a/yarn/common/src/test/scala/org/apache/spark/deploy/yarn/ClientBaseSuite.scala b/yarn/common/src/test/scala/org/apache/spark/deploy/yarn/ClientBaseSuite.scala
new file mode 100644
index 000000000000..608c6e92624c
--- /dev/null
+++ b/yarn/common/src/test/scala/org/apache/spark/deploy/yarn/ClientBaseSuite.scala
@@ -0,0 +1,112 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.deploy.yarn
+
+import java.net.URI
+
+import org.apache.hadoop.conf.Configuration
+import org.apache.hadoop.mapreduce.MRJobConfig
+import org.apache.hadoop.yarn.conf.YarnConfiguration
+import org.apache.hadoop.yarn.api.ApplicationConstants.Environment
+
+import org.scalatest.FunSuite
+import org.scalatest.matchers.ShouldMatchers._
+
+import scala.collection.JavaConversions._
+import scala.collection.mutable.{ HashMap => MutableHashMap }
+import scala.util.Try
+
+
+class ClientBaseSuite extends FunSuite {
+
+  test("default Yarn application classpath") {
+    ClientBase.getDefaultYarnApplicationClasspath should be(Some(Fixtures.knownDefYarnAppCP))
+  }
+
+  test("default MR application classpath") {
+    ClientBase.getDefaultMRApplicationClasspath should be(Some(Fixtures.knownDefMRAppCP))
+  }
+
+  test("resultant classpath for an application that defines a classpath for YARN") {
+    withAppConf(Fixtures.mapYARNAppConf) { conf =>
+      val env = newEnv
+      ClientBase.populateHadoopClasspath(conf, env)
+      classpath(env) should be(
+        flatten(Fixtures.knownYARNAppCP, ClientBase.getDefaultMRApplicationClasspath))
+    }
+  }
+
+  test("resultant classpath for an application that defines a classpath for MR") {
+    withAppConf(Fixtures.mapMRAppConf) { conf =>
+      val env = newEnv
+      ClientBase.populateHadoopClasspath(conf, env)
+      classpath(env) should be(
+        flatten(ClientBase.getDefaultYarnApplicationClasspath, Fixtures.knownMRAppCP))
+    }
+  }
+
+  test("resultant classpath for an application that defines both classpaths, YARN and MR") {
+    withAppConf(Fixtures.mapAppConf) { conf =>
+      val env = newEnv
+      ClientBase.populateHadoopClasspath(conf, env)
+      classpath(env) should be(flatten(Fixtures.knownYARNAppCP, Fixtures.knownMRAppCP))
+    }
+  }
+
+  object Fixtures {
+
+    val knownDefYarnAppCP: Seq[String] =
+      getFieldValue[Array[String], Seq[String]](classOf[YarnConfiguration],
+                                                "DEFAULT_YARN_APPLICATION_CLASSPATH",
+                                                Seq[String]())(a => a.toSeq)
+
+
+    val knownDefMRAppCP: Seq[String] =
+      getFieldValue[String, Seq[String]](classOf[MRJobConfig],
+                                         "DEFAULT_MAPREDUCE_APPLICATION_CLASSPATH",
+                                         Seq[String]())(a => a.split(","))
+
+    val knownYARNAppCP = Some(Seq("/known/yarn/path"))
+
+    val knownMRAppCP = Some(Seq("/known/mr/path"))
+
+    val mapMRAppConf =
+      Map("mapreduce.application.classpath" -> knownMRAppCP.map(_.mkString(":")).get)
+
+    val mapYARNAppConf =
+      Map(YarnConfiguration.YARN_APPLICATION_CLASSPATH -> knownYARNAppCP.map(_.mkString(":")).get)
+
+    val mapAppConf = mapYARNAppConf ++ mapMRAppConf
+  }
+
+  def withAppConf(m: Map[String, String] = Map())(testCode: (Configuration) => Any) {
+    val conf = new Configuration
+    m.foreach { case (k, v) => conf.set(k, v, "ClientBaseSpec") }
+    testCode(conf)
+  }
+
+  def newEnv = MutableHashMap[String, String]()
+
+  def classpath(env: MutableHashMap[String, String]) = env(Environment.CLASSPATH.name).split(":|;")
+
+  def flatten(a: Option[Seq[String]], b: Option[Seq[String]]) = (a ++ b).flatten.toArray
+
+  def getFieldValue[A, B](clazz: Class[_], field: String, defaults: => B)(mapTo: A => B): B =
+    Try(clazz.getField(field)).map(_.get(null).asInstanceOf[A]).toOption.map(mapTo).getOrElse(defaults)
+
+}

From c6e041d171e3d9882ab15e2bd7a7217dc19647f6 Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Mon, 9 Jun 2014 14:24:19 -0700
Subject: [PATCH 373/810] [SQL] Simple framework for debugging query execution

Only records number of tuples and unique dataTypes output right now...

Example:
```scala
scala> import org.apache.spark.sql.execution.debug._
scala> hql("SELECT value FROM src WHERE key > 10").debug(sparkContext)

Results returned: 489
== Project [value#1:0] ==
Tuples output: 489
 value StringType: {java.lang.String}
== Filter (key#0:1 > 10) ==
Tuples output: 489
 value StringType: {java.lang.String}
 key IntegerType: {java.lang.Integer}
== HiveTableScan [value#1,key#0], (MetastoreRelation default, src, None), None ==
Tuples output: 500
 value StringType: {java.lang.String}
 key IntegerType: {java.lang.Integer}
```

Author: Michael Armbrust <michael@databricks.com>

Closes #1005 from marmbrus/debug and squashes the following commits:

dcc3ca6 [Michael Armbrust] Add comments.
c9dded2 [Michael Armbrust] Simple framework for debugging query execution
---
 .../org/apache/spark/sql/SQLContext.scala     |   5 -
 .../apache/spark/sql/execution/debug.scala    |  45 -------
 .../spark/sql/execution/debug/package.scala   | 119 ++++++++++++++++++
 3 files changed, 119 insertions(+), 50 deletions(-)
 delete mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/debug.scala
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
index e371c82d8132..5626f0da2230 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
@@ -285,11 +285,6 @@ class SQLContext(@transient val sparkContext: SparkContext)
          |== Physical Plan ==
          |${stringOrError(executedPlan)}
       """.stripMargin.trim
-
-    /**
-     * Runs the query after interposing operators that print the result of each intermediate step.
-     */
-    def debugExec() = DebugQuery(executedPlan).execute().collect()
   }
 
   /**
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/debug.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/debug.scala
deleted file mode 100644
index a0d29100f505..000000000000
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/debug.scala
+++ /dev/null
@@ -1,45 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql.execution
-
-private[sql] object DebugQuery {
-  def apply(plan: SparkPlan): SparkPlan = {
-    val visited = new collection.mutable.HashSet[Long]()
-    plan transform {
-      case s: SparkPlan if !visited.contains(s.id) =>
-        visited += s.id
-        DebugNode(s)
-    }
-  }
-}
-
-private[sql] case class DebugNode(child: SparkPlan) extends UnaryNode {
-  def references = Set.empty
-  def output = child.output
-  def execute() = {
-    val childRdd = child.execute()
-    println(
-      s"""
-        |=========================
-        |${child.simpleString}
-        |=========================
-      """.stripMargin)
-    childRdd.foreach(println(_))
-    childRdd
-  }
-}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala
new file mode 100644
index 000000000000..c6fbd6d2f693
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala
@@ -0,0 +1,119 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution
+
+import scala.collection.mutable.HashSet
+
+import org.apache.spark.{AccumulatorParam, Accumulator, SparkContext}
+import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.SparkContext._
+import org.apache.spark.sql.{SchemaRDD, Row}
+
+/**
+ * :: DeveloperApi ::
+ * Contains methods for debugging query execution.
+ *
+ * Usage:
+ * {{{
+ *   sql("SELECT key FROM src").debug
+ * }}}
+ */
+package object debug {
+
+  /**
+   * :: DeveloperApi ::
+   * Augments SchemaRDDs with debug methods.
+   */
+  @DeveloperApi
+  implicit class DebugQuery(query: SchemaRDD) {
+    def debug(implicit sc: SparkContext): Unit = {
+      val plan = query.queryExecution.executedPlan
+      val visited = new collection.mutable.HashSet[Long]()
+      val debugPlan = plan transform {
+        case s: SparkPlan if !visited.contains(s.id) =>
+          visited += s.id
+          DebugNode(sc, s)
+      }
+      println(s"Results returned: ${debugPlan.execute().count()}")
+      debugPlan.foreach {
+        case d: DebugNode => d.dumpStats()
+        case _ =>
+      }
+    }
+  }
+
+  private[sql] case class DebugNode(
+      @transient sparkContext: SparkContext,
+      child: SparkPlan) extends UnaryNode {
+    def references = Set.empty
+
+    def output = child.output
+
+    implicit object SetAccumulatorParam extends AccumulatorParam[HashSet[String]] {
+      def zero(initialValue: HashSet[String]): HashSet[String] = {
+        initialValue.clear()
+        initialValue
+      }
+
+      def addInPlace(v1: HashSet[String], v2: HashSet[String]): HashSet[String] = {
+        v1 ++= v2
+        v1
+      }
+    }
+
+    /**
+     * A collection of stats for each column of output.
+     * @param elementTypes the actual runtime types for the output.  Useful when there are bugs
+     *        causing the wrong data to be projected.
+     */
+    case class ColumnStat(
+        elementTypes: Accumulator[HashSet[String]] = sparkContext.accumulator(HashSet.empty))
+    val tupleCount = sparkContext.accumulator[Int](0)
+
+    val numColumns = child.output.size
+    val columnStats = Array.fill(child.output.size)(new ColumnStat())
+
+    def dumpStats(): Unit = {
+      println(s"== ${child.simpleString} ==")
+      println(s"Tuples output: ${tupleCount.value}")
+      child.output.zip(columnStats).foreach { case(attr, stat) =>
+        val actualDataTypes =stat.elementTypes.value.mkString("{", ",", "}")
+        println(s" ${attr.name} ${attr.dataType}: $actualDataTypes")
+      }
+    }
+
+    def execute() = {
+      child.execute().mapPartitions { iter =>
+        new Iterator[Row] {
+          def hasNext = iter.hasNext
+          def next() = {
+            val currentRow = iter.next()
+            tupleCount += 1
+            var i = 0
+            while (i < numColumns) {
+              val value = currentRow(i)
+              columnStats(i).elementTypes += HashSet(value.getClass.getName)
+              i += 1
+            }
+            currentRow
+          }
+        }
+      }
+    }
+  }
+}

From a9ec033c8cf489898cc47e2043bd9e86b7df1ff8 Mon Sep 17 00:00:00 2001
From: Zongheng Yang <zongheng.y@gmail.com>
Date: Mon, 9 Jun 2014 16:47:44 -0700
Subject: [PATCH 374/810] [SPARK-1704][SQL] Fully support EXPLAIN commands as
 SchemaRDD.

This PR attempts to resolve [SPARK-1704](https://issues.apache.org/jira/browse/SPARK-1704) by introducing a physical plan for EXPLAIN commands, which just prints out the debug string (containing various SparkSQL's plans) of the corresponding QueryExecution for the actual query.

Author: Zongheng Yang <zongheng.y@gmail.com>

Closes #1003 from concretevitamin/explain-cmd and squashes the following commits:

5b7911f [Zongheng Yang] Add a regression test.
1bfa379 [Zongheng Yang] Modify output().
719ada9 [Zongheng Yang] Override otherCopyArgs for ExplainCommandPhysical.
4318fd7 [Zongheng Yang] Make all output one Row.
439c6ab [Zongheng Yang] Minor cleanups.
408f574 [Zongheng Yang] SPARK-1704: Add CommandStrategy and ExplainCommandPhysical.
---
 .../catalyst/plans/logical/LogicalPlan.scala  |  8 +++--
 .../org/apache/spark/sql/SQLContext.scala     |  6 ++++
 .../spark/sql/execution/SparkStrategies.scala | 11 +++++++
 .../apache/spark/sql/execution/commands.scala | 32 +++++++++++++++++++
 .../apache/spark/sql/hive/HiveContext.scala   |  3 +-
 .../sql/hive/execution/HiveQuerySuite.scala   | 12 +++++++
 6 files changed, 68 insertions(+), 4 deletions(-)
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala
index 2b8fbdcde9d3..4f641cd3a656 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala
@@ -20,7 +20,7 @@ package org.apache.spark.sql.catalyst.plans.logical
 import org.apache.spark.sql.catalyst.errors.TreeNodeException
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.QueryPlan
-import org.apache.spark.sql.catalyst.types.StructType
+import org.apache.spark.sql.catalyst.types.{StringType, StructType}
 import org.apache.spark.sql.catalyst.trees
 
 abstract class LogicalPlan extends QueryPlan[LogicalPlan] {
@@ -102,7 +102,7 @@ abstract class LeafNode extends LogicalPlan with trees.LeafNode[LogicalPlan] {
  */
 abstract class Command extends LeafNode {
   self: Product =>
-  def output = Seq.empty
+  def output: Seq[Attribute] = Seq.empty
 }
 
 /**
@@ -115,7 +115,9 @@ case class NativeCommand(cmd: String) extends Command
  * Returned by a parser when the users only wants to see what query plan would be executed, without
  * actually performing the execution.
  */
-case class ExplainCommand(plan: LogicalPlan) extends Command
+case class ExplainCommand(plan: LogicalPlan) extends Command {
+  override def output = Seq(AttributeReference("plan", StringType, nullable = false)())
+}
 
 /**
  * A logical plan node with single child.
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
index 5626f0da2230..fde4c485b58a 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
@@ -191,6 +191,7 @@ class SQLContext(@transient val sparkContext: SparkContext)
     val sparkContext = self.sparkContext
 
     val strategies: Seq[Strategy] =
+      CommandStrategy(self) ::
       TakeOrdered ::
       PartialAggregation ::
       LeftSemiJoin ::
@@ -256,6 +257,11 @@ class SQLContext(@transient val sparkContext: SparkContext)
       Batch("Prepare Expressions", Once, new BindReferences[SparkPlan]) :: Nil
   }
 
+  // TODO: or should we make QueryExecution protected[sql]?
+  protected[sql] def mkQueryExecution(plan: LogicalPlan) = new QueryExecution {
+    val logical = plan
+  }
+
   /**
    * The primary workflow for executing relational queries using Spark.  Designed to allow easy
    * access to the intermediate phases of query execution for developers.
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
index 6463f4751063..295c265b1673 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
@@ -233,4 +233,15 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
       case _ => Nil
     }
   }
+
+  // TODO: this should be merged with SPARK-1508's SetCommandStrategy
+  case class CommandStrategy(context: SQLContext) extends Strategy {
+    def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
+      case logical.ExplainCommand(child) =>
+        val qe = context.mkQueryExecution(child)
+        Seq(execution.ExplainCommandPhysical(qe.executedPlan, plan.output)(context))
+      case _ => Nil
+    }
+  }
+
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala
new file mode 100644
index 000000000000..5371d2f479e7
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala
@@ -0,0 +1,32 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution
+
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.{SQLContext, Row}
+import org.apache.spark.sql.catalyst.expressions.{GenericRow, Attribute}
+
+case class ExplainCommandPhysical(child: SparkPlan, output: Seq[Attribute])
+                                 (@transient context: SQLContext) extends UnaryNode {
+  def execute(): RDD[Row] = {
+    val planString = new GenericRow(Array[Any](child.toString))
+    context.sparkContext.parallelize(Seq(planString))
+  }
+
+  override def otherCopyArgs = context :: Nil
+}
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
index fbab2ac16b89..4b97dc25acf8 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
@@ -218,6 +218,7 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) {
     val hiveContext = self
 
     override val strategies: Seq[Strategy] = Seq(
+      CommandStrategy(self),
       TakeOrdered,
       ParquetOperations,
       HiveTableScans,
@@ -304,7 +305,7 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) {
      */
     def stringResult(): Seq[String] = analyzed match {
       case NativeCommand(cmd) => runSqlHive(cmd)
-      case ExplainCommand(plan) => new QueryExecution { val logical = plan }.toString.split("\n")
+      case ExplainCommand(plan) => mkQueryExecution(plan).toString.split("\n")
       case query =>
         val result: Seq[Seq[Any]] = toRdd.collect().toSeq
         // We need the types so we can output struct field names
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
index 125cc18bfb2b..c56eee258047 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
@@ -18,6 +18,7 @@
 package org.apache.spark.sql.hive.execution
 
 import org.apache.spark.sql.hive.test.TestHive._
+import org.apache.spark.sql.hive.test.TestHive
 
 /**
  * A set of test cases expressed in Hive QL that are not covered by the tests included in the hive distribution.
@@ -159,4 +160,15 @@ class HiveQuerySuite extends HiveComparisonTest {
     hql("SHOW TABLES").toString
     hql("SELECT * FROM src").toString
   }
+
+  test("SPARK-1704: Explain commands as a SchemaRDD") {
+    hql("CREATE TABLE IF NOT EXISTS src (key INT, value STRING)")
+    val rdd = hql("explain select key, count(value) from src group by key")
+    assert(rdd.collect().size == 1)
+    assert(rdd.toString.contains("ExplainCommand"))
+    assert(rdd.filter(row => row.toString.contains("ExplainCommand")).collect().size == 0,
+      "actual contents of the result should be the plans of the query to be explained")
+    TestHive.reset()
+  }
+
 }

From 6f2db8c2f51911f88a601ec5bf1509ea0e8173ed Mon Sep 17 00:00:00 2001
From: DB Tsai <dbtsai@dbtsai.com>
Date: Mon, 9 Jun 2014 22:18:50 -0700
Subject: [PATCH 375/810] Make sure that empty string is filtered out when we
 get the secondary jars from conf

Author: DB Tsai <dbtsai@dbtsai.com>

Closes #1027 from dbtsai/dbtsai-classloader and squashes the following commits:

9ac6be3 [DB Tsai] Fixed line too long
c9c7ad7 [DB Tsai] Make sure that empty string is filtered out when we get the secondary jars from conf.
---
 .../scala/org/apache/spark/deploy/yarn/ClientBase.scala     | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala
index 4b5e0efdde92..801e8b381588 100644
--- a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala
+++ b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala
@@ -221,7 +221,7 @@ trait ClientBase extends Logging {
       }
     }
 
-    var cachedSecondaryJarLinks = ListBuffer.empty[String]
+    val cachedSecondaryJarLinks = ListBuffer.empty[String]
     val fileLists = List( (args.addJars, LocalResourceType.FILE, true),
       (args.files, LocalResourceType.FILE, false),
       (args.archives, LocalResourceType.ARCHIVE, false) )
@@ -502,12 +502,14 @@ object ClientBase extends Logging {
     def addClasspathEntry(path: String) = YarnSparkHadoopUtil.addToEnvironment(env,
       Environment.CLASSPATH.name, path, File.pathSeparator)
     /** Add entry to the classpath. Interpreted as a path relative to the working directory. */
-    def addPwdClasspathEntry(entry: String) = addClasspathEntry(Environment.PWD.$() + Path.SEPARATOR + entry)
+    def addPwdClasspathEntry(entry: String) =
+      addClasspathEntry(Environment.PWD.$() + Path.SEPARATOR + entry)
 
     extraClassPath.foreach(addClasspathEntry)
 
     val cachedSecondaryJarLinks =
       sparkConf.getOption(CONF_SPARK_YARN_SECONDARY_JARS).getOrElse("").split(",")
+        .filter(_.nonEmpty)
     // Normally the users app.jar is last in case conflicts with spark jars
     if (sparkConf.get("spark.yarn.user.classpath.first", "false").toBoolean) {
       addPwdClasspathEntry(APP_JAR)

From f971d6cb60d642178d6544217a25fa16ece34889 Mon Sep 17 00:00:00 2001
From: Nick Pentreath <nick.pentreath@gmail.com>
Date: Mon, 9 Jun 2014 22:21:03 -0700
Subject: [PATCH 376/810] SPARK-1416: PySpark support for SequenceFile and
 Hadoop InputFormats

So I finally resurrected this PR. It seems the old one against the incubator mirror is no longer available, so I cannot reference it.

This adds initial support for reading Hadoop ```SequenceFile```s, as well as arbitrary Hadoop ```InputFormat```s, in PySpark.

# Overview
The basics are as follows:
1. ```PythonRDD``` object contains the relevant methods, that are in turn invoked by ```SparkContext``` in PySpark
2. The SequenceFile or InputFormat is read on the Scala side and converted from ```Writable``` instances to the relevant Scala classes (in the case of primitives)
3. Pyrolite is used to serialize Java objects. If this fails, the fallback is ```toString```
4. ```PickleSerializer``` on the Python side deserializes.

This works "out the box" for simple ```Writable```s:
* ```Text```
* ```IntWritable```, ```DoubleWritable```, ```FloatWritable```
* ```NullWritable```
* ```BooleanWritable```
* ```BytesWritable```
* ```MapWritable```

It also works for simple, "struct-like" classes. Due to the way Pyrolite works, this requires that the classes satisfy the JavaBeans convenstions (i.e. with fields and a no-arg constructor and getters/setters). (Perhaps in future some sugar for case classes and reflection could be added).

I've tested it out with ```ESInputFormat```  as an example and it works very nicely:
```python
conf = {"es.resource" : "index/type" }
rdd = sc.newAPIHadoopRDD("org.elasticsearch.hadoop.mr.EsInputFormat", "org.apache.hadoop.io.NullWritable", "org.elasticsearch.hadoop.mr.LinkedMapWritable", conf=conf)
rdd.first()
```

I suspect for things like HBase/Cassandra it will be a bit trickier to get it to work out the box.

# Some things still outstanding:
1. ~~Requires ```msgpack-python``` and will fail without it. As originally discussed with Josh, add a ```as_strings``` argument that defaults to ```False```, that can be used if ```msgpack-python``` is not available~~
2. ~~I see from https://github.com/apache/spark/pull/363 that Pyrolite is being used there for SerDe between Scala and Python. @ahirreddy @mateiz what is the plan behind this - is Pyrolite preferred? It seems from a cursory glance that adapting the ```msgpack```-based SerDe here to use Pyrolite wouldn't be too hard~~
3. ~~Support the key and value "wrapper" that would allow a Scala/Java function to be plugged in that would transform whatever the key/value Writable class is into something that can be serialized (e.g. convert some custom Writable to a JavaBean or ```java.util.Map``` that can be easily serialized)~~
4. Support ```saveAsSequenceFile``` and ```saveAsHadoopFile``` etc. This would require SerDe in the reverse direction, that can be handled by Pyrolite. Will work on this as a separate PR

Author: Nick Pentreath <nick.pentreath@gmail.com>

Closes #455 from MLnick/pyspark-inputformats and squashes the following commits:

268df7e [Nick Pentreath] Documentation changes mer @pwendell comments
761269b [Nick Pentreath] Address @pwendell comments, simplify default writable conversions and remove registry.
4c972d8 [Nick Pentreath] Add license headers
d150431 [Nick Pentreath] Merge remote-tracking branch 'upstream/master' into pyspark-inputformats
cde6af9 [Nick Pentreath] Parameterize converter trait
5ebacfa [Nick Pentreath] Update docs for PySpark input formats
a985492 [Nick Pentreath] Move Converter examples to own package
365d0be [Nick Pentreath] Make classes private[python]. Add docs and @Experimental annotation to Converter interface.
eeb8205 [Nick Pentreath] Fix path relative to SPARK_HOME in tests
1eaa08b [Nick Pentreath] HBase -> Cassandra app name oversight
3f90c3e [Nick Pentreath] Merge remote-tracking branch 'upstream/master' into pyspark-inputformats
2c18513 [Nick Pentreath] Add examples for reading HBase and Cassandra InputFormats from Python
b65606f [Nick Pentreath] Add converter interface
5757f6e [Nick Pentreath] Default key/value classes for sequenceFile asre None
085b55f [Nick Pentreath] Move input format tests to tests.py and clean up docs
43eb728 [Nick Pentreath] PySpark InputFormats docs into programming guide
94beedc [Nick Pentreath] Clean up args in PythonRDD. Set key/value converter defaults to None for PySpark context.py methods
1a4a1d6 [Nick Pentreath] Address @mateiz style comments
01e0813 [Nick Pentreath] Merge remote-tracking branch 'upstream/master' into pyspark-inputformats
15a7d07 [Nick Pentreath] Remove default args for key/value classes. Arg names to camelCase
9fe6bd5 [Nick Pentreath] Merge remote-tracking branch 'upstream/master' into pyspark-inputformats
84fe8e3 [Nick Pentreath] Python programming guide space formatting
d0f52b6 [Nick Pentreath] Python programming guide
7caa73a [Nick Pentreath] Merge remote-tracking branch 'upstream/master' into pyspark-inputformats
93ef995 [Nick Pentreath] Add back context.py changes
9ef1896 [Nick Pentreath] Recover earlier changes lost in previous merge for serializers.py
077ecb2 [Nick Pentreath] Recover earlier changes lost in previous merge for context.py
5af4770 [Nick Pentreath] Merge branch 'master' into pyspark-inputformats
35b8e3a [Nick Pentreath] Another fix for test ordering
bef3afb [Nick Pentreath] Merge remote-tracking branch 'upstream/master' into pyspark-inputformats
e001b94 [Nick Pentreath] Fix test failures due to ordering
78978d9 [Nick Pentreath] Add doc for SequenceFile and InputFormat support to Python programming guide
64eb051 [Nick Pentreath] Scalastyle fix
e7552fa [Nick Pentreath] Merge branch 'master' into pyspark-inputformats
44f2857 [Nick Pentreath] Remove msgpack dependency and switch serialization to Pyrolite, plus some clean up and refactoring
c0ebfb6 [Nick Pentreath] Change sequencefile test data generator to easily be called from PySpark tests
1d7c17c [Nick Pentreath] Amend tests to auto-generate sequencefile data in temp dir
17a656b [Nick Pentreath] remove binary sequencefile for tests
f60959e [Nick Pentreath] Remove msgpack dependency and serializer from PySpark
450e0a2 [Nick Pentreath] Merge branch 'master' into pyspark-inputformats
31a2fff [Nick Pentreath] Scalastyle fixes
fc5099e [Nick Pentreath] Add Apache license headers
4e08983 [Nick Pentreath] Clean up docs for PySpark context methods
b20ec7e [Nick Pentreath] Clean up merge duplicate dependencies
951c117 [Nick Pentreath] Merge branch 'master' into pyspark-inputformats
f6aac55 [Nick Pentreath] Bring back msgpack
9d2256e [Nick Pentreath] Merge branch 'master' into pyspark-inputformats
1bbbfb0 [Nick Pentreath] Clean up SparkBuild from merge
a67dfad [Nick Pentreath] Clean up Msgpack serialization and registering
7237263 [Nick Pentreath] Add back msgpack serializer and hadoop file code lost during merging
25da1ca [Nick Pentreath] Add generator for nulls, bools, bytes and maps
65360d5 [Nick Pentreath] Adding test SequenceFiles
0c612e5 [Nick Pentreath] Merge branch 'master' into pyspark-inputformats
d72bf18 [Nick Pentreath] msgpack
dd57922 [Nick Pentreath] Merge remote-tracking branch 'upstream/master' into pyspark-inputformats
e67212a [Nick Pentreath] Add back msgpack dependency
f2d76a0 [Nick Pentreath] Merge branch 'master' into pyspark-inputformats
41856a5 [Nick Pentreath] Merge branch 'master' into pyspark-inputformats
97ef708 [Nick Pentreath] Remove old writeToStream
2beeedb [Nick Pentreath] Merge remote-tracking branch 'upstream/master' into pyspark-inputformats
795a763 [Nick Pentreath] Change name to WriteInputFormatTestDataGenerator. Cleanup some var names. Use SPARK_HOME in path for writing test sequencefile data.
174f520 [Nick Pentreath] Add back graphx settings
703ee65 [Nick Pentreath] Add back msgpack
619c0fa [Nick Pentreath] Merge remote-tracking branch 'upstream/master' into pyspark-inputformats
1c8efbc [Nick Pentreath] Merge remote-tracking branch 'upstream/master' into pyspark-inputformats
eb40036 [Nick Pentreath] Remove unused comment lines
4d7ef2e [Nick Pentreath] Fix indentation
f1d73e3 [Nick Pentreath] mergeConfs returns a copy rather than mutating one of the input arguments
0f5cd84 [Nick Pentreath] Remove unused pair UTF8 class. Add comments to msgpack deserializer
4294cbb [Nick Pentreath] Add old Hadoop api methods. Clean up and expand comments. Clean up argument names
818a1e6 [Nick Pentreath] Add seqencefile and Hadoop InputFormat support to PythonRDD
4e7c9e3 [Nick Pentreath] Merge remote-tracking branch 'upstream/master' into pyspark-inputformats
c304cc8 [Nick Pentreath] Adding supporting sequncefiles for tests. Cleaning up
4b0a43f [Nick Pentreath] Refactoring utils into own objects. Cleaning up old commented-out code
d86325f [Nick Pentreath] Initial WIP of PySpark support for SequenceFile and arbitrary Hadoop InputFormat
---
 .../spark/api/python/PythonHadoopUtil.scala   | 129 +++++++++++++
 .../apache/spark/api/python/PythonRDD.scala   | 179 +++++++++++++++++-
 .../apache/spark/api/python/SerDeUtil.scala   |  87 +++++++++
 .../WriteInputFormatTestDataGenerator.scala   | 150 +++++++++++++++
 docs/programming-guide.md                     |  86 ++++++++-
 .../src/main/python/cassandra_inputformat.py  |  79 ++++++++
 examples/src/main/python/hbase_inputformat.py |  72 +++++++
 .../spark/examples/CassandraCQLTest.scala     |   1 +
 .../org/apache/spark/examples/HBaseTest.scala |   2 +-
 .../CassandraConverters.scala                 |  46 +++++
 .../pythonconverters/HBaseConverter.scala     |  33 ++++
 python/pyspark/context.py                     | 137 ++++++++++++++
 python/pyspark/tests.py                       | 145 ++++++++++++++
 13 files changed, 1140 insertions(+), 6 deletions(-)
 create mode 100644 core/src/main/scala/org/apache/spark/api/python/PythonHadoopUtil.scala
 create mode 100644 core/src/main/scala/org/apache/spark/api/python/SerDeUtil.scala
 create mode 100644 core/src/main/scala/org/apache/spark/api/python/WriteInputFormatTestDataGenerator.scala
 create mode 100644 examples/src/main/python/cassandra_inputformat.py
 create mode 100644 examples/src/main/python/hbase_inputformat.py
 create mode 100644 examples/src/main/scala/org/apache/spark/examples/pythonconverters/CassandraConverters.scala
 create mode 100644 examples/src/main/scala/org/apache/spark/examples/pythonconverters/HBaseConverter.scala

diff --git a/core/src/main/scala/org/apache/spark/api/python/PythonHadoopUtil.scala b/core/src/main/scala/org/apache/spark/api/python/PythonHadoopUtil.scala
new file mode 100644
index 000000000000..adaa1ef6cf9f
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/api/python/PythonHadoopUtil.scala
@@ -0,0 +1,129 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.api.python
+
+import org.apache.spark.rdd.RDD
+import org.apache.spark.Logging
+import org.apache.hadoop.conf.Configuration
+import org.apache.hadoop.io._
+import scala.util.{Failure, Success, Try}
+import org.apache.spark.annotation.Experimental
+
+
+/**
+ * :: Experimental ::
+ * A trait for use with reading custom classes in PySpark. Implement this trait and add custom
+ * transformation code by overriding the convert method.
+ */
+@Experimental
+trait Converter[T, U] extends Serializable {
+  def convert(obj: T): U
+}
+
+private[python] object Converter extends Logging {
+
+  def getInstance(converterClass: Option[String]): Converter[Any, Any] = {
+    converterClass.map { cc =>
+      Try {
+        val c = Class.forName(cc).newInstance().asInstanceOf[Converter[Any, Any]]
+        logInfo(s"Loaded converter: $cc")
+        c
+      } match {
+        case Success(c) => c
+        case Failure(err) =>
+          logError(s"Failed to load converter: $cc")
+          throw err
+      }
+    }.getOrElse { new DefaultConverter }
+  }
+}
+
+/**
+ * A converter that handles conversion of common [[org.apache.hadoop.io.Writable]] objects.
+ * Other objects are passed through without conversion.
+ */
+private[python] class DefaultConverter extends Converter[Any, Any] {
+
+  /**
+   * Converts a [[org.apache.hadoop.io.Writable]] to the underlying primitive, String or
+   * object representation
+   */
+  private def convertWritable(writable: Writable): Any = {
+    import collection.JavaConversions._
+    writable match {
+      case iw: IntWritable => iw.get()
+      case dw: DoubleWritable => dw.get()
+      case lw: LongWritable => lw.get()
+      case fw: FloatWritable => fw.get()
+      case t: Text => t.toString
+      case bw: BooleanWritable => bw.get()
+      case byw: BytesWritable => byw.getBytes
+      case n: NullWritable => null
+      case aw: ArrayWritable => aw.get().map(convertWritable(_))
+      case mw: MapWritable => mapAsJavaMap(mw.map { case (k, v) =>
+        (convertWritable(k), convertWritable(v))
+      }.toMap)
+      case other => other
+    }
+  }
+
+  def convert(obj: Any): Any = {
+    obj match {
+      case writable: Writable =>
+        convertWritable(writable)
+      case _ =>
+        obj
+    }
+  }
+}
+
+/** Utilities for working with Python objects <-> Hadoop-related objects */
+private[python] object PythonHadoopUtil {
+
+  /**
+   * Convert a [[java.util.Map]] of properties to a [[org.apache.hadoop.conf.Configuration]]
+   */
+  def mapToConf(map: java.util.Map[String, String]): Configuration = {
+    import collection.JavaConversions._
+    val conf = new Configuration()
+    map.foreach{ case (k, v) => conf.set(k, v) }
+    conf
+  }
+
+  /**
+   * Merges two configurations, returns a copy of left with keys from right overwriting
+   * any matching keys in left
+   */
+  def mergeConfs(left: Configuration, right: Configuration): Configuration = {
+    import collection.JavaConversions._
+    val copy = new Configuration(left)
+    right.iterator().foreach(entry => copy.set(entry.getKey, entry.getValue))
+    copy
+  }
+
+  /**
+   * Converts an RDD of key-value pairs, where key and/or value could be instances of
+   * [[org.apache.hadoop.io.Writable]], into an RDD[(K, V)]
+   */
+  def convertRDD[K, V](rdd: RDD[(K, V)],
+                       keyConverter: Converter[Any, Any],
+                       valueConverter: Converter[Any, Any]): RDD[(Any, Any)] = {
+    rdd.map { case (k, v) => (keyConverter.convert(k), valueConverter.convert(v)) }
+  }
+
+}
diff --git a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
index d1df99300c5b..f6570d335757 100644
--- a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
+++ b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
@@ -28,6 +28,9 @@ import scala.util.Try
 
 import net.razorvine.pickle.{Pickler, Unpickler}
 
+import org.apache.hadoop.conf.Configuration
+import org.apache.hadoop.mapred.{InputFormat, JobConf}
+import org.apache.hadoop.mapreduce.{InputFormat => NewInputFormat}
 import org.apache.spark._
 import org.apache.spark.api.java.{JavaSparkContext, JavaPairRDD, JavaRDD}
 import org.apache.spark.broadcast.Broadcast
@@ -266,7 +269,7 @@ private object SpecialLengths {
   val TIMING_DATA = -3
 }
 
-private[spark] object PythonRDD {
+private[spark] object PythonRDD extends Logging {
   val UTF8 = Charset.forName("UTF-8")
 
   /**
@@ -346,6 +349,180 @@ private[spark] object PythonRDD {
     }
   }
 
+  /**
+   * Create an RDD from a path using [[org.apache.hadoop.mapred.SequenceFileInputFormat]],
+   * key and value class.
+   * A key and/or value converter class can optionally be passed in
+   * (see [[org.apache.spark.api.python.Converter]])
+   */
+  def sequenceFile[K, V](
+      sc: JavaSparkContext,
+      path: String,
+      keyClassMaybeNull: String,
+      valueClassMaybeNull: String,
+      keyConverterClass: String,
+      valueConverterClass: String,
+      minSplits: Int) = {
+    val keyClass = Option(keyClassMaybeNull).getOrElse("org.apache.hadoop.io.Text")
+    val valueClass = Option(valueClassMaybeNull).getOrElse("org.apache.hadoop.io.Text")
+    implicit val kcm = ClassTag(Class.forName(keyClass)).asInstanceOf[ClassTag[K]]
+    implicit val vcm = ClassTag(Class.forName(valueClass)).asInstanceOf[ClassTag[V]]
+    val kc = kcm.runtimeClass.asInstanceOf[Class[K]]
+    val vc = vcm.runtimeClass.asInstanceOf[Class[V]]
+
+    val rdd = sc.sc.sequenceFile[K, V](path, kc, vc, minSplits)
+    val keyConverter = Converter.getInstance(Option(keyConverterClass))
+    val valueConverter = Converter.getInstance(Option(valueConverterClass))
+    val converted = PythonHadoopUtil.convertRDD[K, V](rdd, keyConverter, valueConverter)
+    JavaRDD.fromRDD(SerDeUtil.rddToPython(converted))
+  }
+
+  /**
+   * Create an RDD from a file path, using an arbitrary [[org.apache.hadoop.mapreduce.InputFormat]],
+   * key and value class.
+   * A key and/or value converter class can optionally be passed in
+   * (see [[org.apache.spark.api.python.Converter]])
+   */
+  def newAPIHadoopFile[K, V, F <: NewInputFormat[K, V]](
+      sc: JavaSparkContext,
+      path: String,
+      inputFormatClass: String,
+      keyClass: String,
+      valueClass: String,
+      keyConverterClass: String,
+      valueConverterClass: String,
+      confAsMap: java.util.HashMap[String, String]) = {
+    val conf = PythonHadoopUtil.mapToConf(confAsMap)
+    val baseConf = sc.hadoopConfiguration()
+    val mergedConf = PythonHadoopUtil.mergeConfs(baseConf, conf)
+    val rdd =
+      newAPIHadoopRDDFromClassNames[K, V, F](sc,
+        Some(path), inputFormatClass, keyClass, valueClass, mergedConf)
+    val keyConverter = Converter.getInstance(Option(keyConverterClass))
+    val valueConverter = Converter.getInstance(Option(valueConverterClass))
+    val converted = PythonHadoopUtil.convertRDD[K, V](rdd, keyConverter, valueConverter)
+    JavaRDD.fromRDD(SerDeUtil.rddToPython(converted))
+  }
+
+  /**
+   * Create an RDD from a [[org.apache.hadoop.conf.Configuration]] converted from a map that is
+   * passed in from Python, using an arbitrary [[org.apache.hadoop.mapreduce.InputFormat]],
+   * key and value class.
+   * A key and/or value converter class can optionally be passed in
+   * (see [[org.apache.spark.api.python.Converter]])
+   */
+  def newAPIHadoopRDD[K, V, F <: NewInputFormat[K, V]](
+      sc: JavaSparkContext,
+      inputFormatClass: String,
+      keyClass: String,
+      valueClass: String,
+      keyConverterClass: String,
+      valueConverterClass: String,
+      confAsMap: java.util.HashMap[String, String]) = {
+    val conf = PythonHadoopUtil.mapToConf(confAsMap)
+    val rdd =
+      newAPIHadoopRDDFromClassNames[K, V, F](sc,
+        None, inputFormatClass, keyClass, valueClass, conf)
+    val keyConverter = Converter.getInstance(Option(keyConverterClass))
+    val valueConverter = Converter.getInstance(Option(valueConverterClass))
+    val converted = PythonHadoopUtil.convertRDD[K, V](rdd, keyConverter, valueConverter)
+    JavaRDD.fromRDD(SerDeUtil.rddToPython(converted))
+  }
+
+  private def newAPIHadoopRDDFromClassNames[K, V, F <: NewInputFormat[K, V]](
+      sc: JavaSparkContext,
+      path: Option[String] = None,
+      inputFormatClass: String,
+      keyClass: String,
+      valueClass: String,
+      conf: Configuration) = {
+    implicit val kcm = ClassTag(Class.forName(keyClass)).asInstanceOf[ClassTag[K]]
+    implicit val vcm = ClassTag(Class.forName(valueClass)).asInstanceOf[ClassTag[V]]
+    implicit val fcm = ClassTag(Class.forName(inputFormatClass)).asInstanceOf[ClassTag[F]]
+    val kc = kcm.runtimeClass.asInstanceOf[Class[K]]
+    val vc = vcm.runtimeClass.asInstanceOf[Class[V]]
+    val fc = fcm.runtimeClass.asInstanceOf[Class[F]]
+    val rdd = if (path.isDefined) {
+      sc.sc.newAPIHadoopFile[K, V, F](path.get, fc, kc, vc, conf)
+    } else {
+      sc.sc.newAPIHadoopRDD[K, V, F](conf, fc, kc, vc)
+    }
+    rdd
+  }
+
+  /**
+   * Create an RDD from a file path, using an arbitrary [[org.apache.hadoop.mapred.InputFormat]],
+   * key and value class.
+   * A key and/or value converter class can optionally be passed in
+   * (see [[org.apache.spark.api.python.Converter]])
+   */
+  def hadoopFile[K, V, F <: InputFormat[K, V]](
+      sc: JavaSparkContext,
+      path: String,
+      inputFormatClass: String,
+      keyClass: String,
+      valueClass: String,
+      keyConverterClass: String,
+      valueConverterClass: String,
+      confAsMap: java.util.HashMap[String, String]) = {
+    val conf = PythonHadoopUtil.mapToConf(confAsMap)
+    val baseConf = sc.hadoopConfiguration()
+    val mergedConf = PythonHadoopUtil.mergeConfs(baseConf, conf)
+    val rdd =
+      hadoopRDDFromClassNames[K, V, F](sc,
+        Some(path), inputFormatClass, keyClass, valueClass, mergedConf)
+    val keyConverter = Converter.getInstance(Option(keyConverterClass))
+    val valueConverter = Converter.getInstance(Option(valueConverterClass))
+    val converted = PythonHadoopUtil.convertRDD[K, V](rdd, keyConverter, valueConverter)
+    JavaRDD.fromRDD(SerDeUtil.rddToPython(converted))
+  }
+
+  /**
+   * Create an RDD from a [[org.apache.hadoop.conf.Configuration]] converted from a map
+   * that is passed in from Python, using an arbitrary [[org.apache.hadoop.mapred.InputFormat]],
+   * key and value class
+   * A key and/or value converter class can optionally be passed in
+   * (see [[org.apache.spark.api.python.Converter]])
+   */
+  def hadoopRDD[K, V, F <: InputFormat[K, V]](
+      sc: JavaSparkContext,
+      inputFormatClass: String,
+      keyClass: String,
+      valueClass: String,
+      keyConverterClass: String,
+      valueConverterClass: String,
+      confAsMap: java.util.HashMap[String, String]) = {
+    val conf = PythonHadoopUtil.mapToConf(confAsMap)
+    val rdd =
+      hadoopRDDFromClassNames[K, V, F](sc,
+        None, inputFormatClass, keyClass, valueClass, conf)
+    val keyConverter = Converter.getInstance(Option(keyConverterClass))
+    val valueConverter = Converter.getInstance(Option(valueConverterClass))
+    val converted = PythonHadoopUtil.convertRDD[K, V](rdd, keyConverter, valueConverter)
+    JavaRDD.fromRDD(SerDeUtil.rddToPython(converted))
+  }
+
+  private def hadoopRDDFromClassNames[K, V, F <: InputFormat[K, V]](
+      sc: JavaSparkContext,
+      path: Option[String] = None,
+      inputFormatClass: String,
+      keyClass: String,
+      valueClass: String,
+      conf: Configuration) = {
+    implicit val kcm = ClassTag(Class.forName(keyClass)).asInstanceOf[ClassTag[K]]
+    implicit val vcm = ClassTag(Class.forName(valueClass)).asInstanceOf[ClassTag[V]]
+    implicit val fcm = ClassTag(Class.forName(inputFormatClass)).asInstanceOf[ClassTag[F]]
+    val kc = kcm.runtimeClass.asInstanceOf[Class[K]]
+    val vc = vcm.runtimeClass.asInstanceOf[Class[V]]
+    val fc = fcm.runtimeClass.asInstanceOf[Class[F]]
+    val rdd = if (path.isDefined) {
+      sc.sc.hadoopFile(path.get, fc, kc, vc)
+    } else {
+      sc.sc.hadoopRDD(new JobConf(conf), fc, kc, vc)
+    }
+    rdd
+  }
+
   def writeUTF(str: String, dataOut: DataOutputStream) {
     val bytes = str.getBytes(UTF8)
     dataOut.writeInt(bytes.length)
diff --git a/core/src/main/scala/org/apache/spark/api/python/SerDeUtil.scala b/core/src/main/scala/org/apache/spark/api/python/SerDeUtil.scala
new file mode 100644
index 000000000000..9a012e725490
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/api/python/SerDeUtil.scala
@@ -0,0 +1,87 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.api.python
+
+import scala.util.Try
+import org.apache.spark.rdd.RDD
+import org.apache.spark.Logging
+import scala.util.Success
+import scala.util.Failure
+import net.razorvine.pickle.Pickler
+
+
+/** Utilities for serialization / deserialization between Python and Java, using Pickle. */
+private[python] object SerDeUtil extends Logging {
+
+  private def checkPickle(t: (Any, Any)): (Boolean, Boolean) = {
+    val pickle = new Pickler
+    val kt = Try {
+      pickle.dumps(t._1)
+    }
+    val vt = Try {
+      pickle.dumps(t._2)
+    }
+    (kt, vt) match {
+      case (Failure(kf), Failure(vf)) =>
+        logWarning(s"""
+               |Failed to pickle Java object as key: ${t._1.getClass.getSimpleName}, falling back
+               |to 'toString'. Error: ${kf.getMessage}""".stripMargin)
+        logWarning(s"""
+               |Failed to pickle Java object as value: ${t._2.getClass.getSimpleName}, falling back
+               |to 'toString'. Error: ${vf.getMessage}""".stripMargin)
+        (true, true)
+      case (Failure(kf), _) =>
+        logWarning(s"""
+               |Failed to pickle Java object as key: ${t._1.getClass.getSimpleName}, falling back
+               |to 'toString'. Error: ${kf.getMessage}""".stripMargin)
+        (true, false)
+      case (_, Failure(vf)) =>
+        logWarning(s"""
+               |Failed to pickle Java object as value: ${t._2.getClass.getSimpleName}, falling back
+               |to 'toString'. Error: ${vf.getMessage}""".stripMargin)
+        (false, true)
+      case _ =>
+        (false, false)
+    }
+  }
+
+  /**
+   * Convert an RDD of key-value pairs to an RDD of serialized Python objects, that is usable
+   * by PySpark. By default, if serialization fails, toString is called and the string
+   * representation is serialized
+   */
+  def rddToPython(rdd: RDD[(Any, Any)]): RDD[Array[Byte]] = {
+    val (keyFailed, valueFailed) = checkPickle(rdd.first())
+    rdd.mapPartitions { iter =>
+      val pickle = new Pickler
+      iter.map { case (k, v) =>
+        if (keyFailed && valueFailed) {
+          pickle.dumps(Array(k.toString, v.toString))
+        } else if (keyFailed) {
+          pickle.dumps(Array(k.toString, v))
+        } else if (!keyFailed && valueFailed) {
+          pickle.dumps(Array(k, v.toString))
+        } else {
+          pickle.dumps(Array(k, v))
+        }
+      }
+    }
+  }
+
+}
+
diff --git a/core/src/main/scala/org/apache/spark/api/python/WriteInputFormatTestDataGenerator.scala b/core/src/main/scala/org/apache/spark/api/python/WriteInputFormatTestDataGenerator.scala
new file mode 100644
index 000000000000..f0e3fb9aff5a
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/api/python/WriteInputFormatTestDataGenerator.scala
@@ -0,0 +1,150 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.api.python
+
+import org.apache.spark.SparkContext
+import org.apache.hadoop.io._
+import scala.Array
+import java.io.{DataOutput, DataInput}
+import org.apache.hadoop.mapreduce.lib.output.SequenceFileOutputFormat
+import org.apache.spark.api.java.JavaSparkContext
+
+/**
+ * A class to test MsgPack serialization on the Scala side, that will be deserialized
+ * in Python
+ * @param str
+ * @param int
+ * @param double
+ */
+case class TestWritable(var str: String, var int: Int, var double: Double) extends Writable {
+  def this() = this("", 0, 0.0)
+
+  def getStr = str
+  def setStr(str: String) { this.str = str }
+  def getInt = int
+  def setInt(int: Int) { this.int = int }
+  def getDouble = double
+  def setDouble(double: Double) { this.double = double }
+
+  def write(out: DataOutput) = {
+    out.writeUTF(str)
+    out.writeInt(int)
+    out.writeDouble(double)
+  }
+
+  def readFields(in: DataInput) = {
+    str = in.readUTF()
+    int = in.readInt()
+    double = in.readDouble()
+  }
+}
+
+class TestConverter extends Converter[Any, Any] {
+  import collection.JavaConversions._
+  override def convert(obj: Any) = {
+    val m = obj.asInstanceOf[MapWritable]
+    seqAsJavaList(m.keySet.map(w => w.asInstanceOf[DoubleWritable].get()).toSeq)
+  }
+}
+
+/**
+ * This object contains method to generate SequenceFile test data and write it to a
+ * given directory (probably a temp directory)
+ */
+object WriteInputFormatTestDataGenerator {
+  import SparkContext._
+
+  def main(args: Array[String]) {
+    val path = args(0)
+    val sc = new JavaSparkContext("local[4]", "test-writables")
+    generateData(path, sc)
+  }
+
+  def generateData(path: String, jsc: JavaSparkContext) {
+    val sc = jsc.sc
+
+    val basePath = s"$path/sftestdata/"
+    val textPath = s"$basePath/sftext/"
+    val intPath = s"$basePath/sfint/"
+    val doublePath = s"$basePath/sfdouble/"
+    val arrPath = s"$basePath/sfarray/"
+    val mapPath = s"$basePath/sfmap/"
+    val classPath = s"$basePath/sfclass/"
+    val bytesPath = s"$basePath/sfbytes/"
+    val boolPath = s"$basePath/sfbool/"
+    val nullPath = s"$basePath/sfnull/"
+
+    /*
+     * Create test data for IntWritable, DoubleWritable, Text, BytesWritable,
+     * BooleanWritable and NullWritable
+     */
+    val intKeys = Seq((1, "aa"), (2, "bb"), (2, "aa"), (3, "cc"), (2, "bb"), (1, "aa"))
+    sc.parallelize(intKeys).saveAsSequenceFile(intPath)
+    sc.parallelize(intKeys.map{ case (k, v) => (k.toDouble, v) }).saveAsSequenceFile(doublePath)
+    sc.parallelize(intKeys.map{ case (k, v) => (k.toString, v) }).saveAsSequenceFile(textPath)
+    sc.parallelize(intKeys.map{ case (k, v) => (k, v.getBytes) }).saveAsSequenceFile(bytesPath)
+    val bools = Seq((1, true), (2, true), (2, false), (3, true), (2, false), (1, false))
+    sc.parallelize(bools).saveAsSequenceFile(boolPath)
+    sc.parallelize(intKeys).map{ case (k, v) =>
+      (new IntWritable(k), NullWritable.get())
+    }.saveAsSequenceFile(nullPath)
+
+    // Create test data for ArrayWritable
+    val data = Seq(
+      (1, Array(1.0, 2.0, 3.0)),
+      (2, Array(3.0, 4.0, 5.0)),
+      (3, Array(4.0, 5.0, 6.0))
+    )
+    sc.parallelize(data, numSlices = 2)
+      .map{ case (k, v) =>
+      (new IntWritable(k), new ArrayWritable(classOf[DoubleWritable], v.map(new DoubleWritable(_))))
+    }.saveAsNewAPIHadoopFile[SequenceFileOutputFormat[IntWritable, ArrayWritable]](arrPath)
+
+    // Create test data for MapWritable, with keys DoubleWritable and values Text
+    val mapData = Seq(
+      (1, Map(2.0 -> "aa")),
+      (2, Map(3.0 -> "bb")),
+      (2, Map(1.0 -> "cc")),
+      (3, Map(2.0 -> "dd")),
+      (2, Map(1.0 -> "aa")),
+      (1, Map(3.0 -> "bb"))
+    )
+    sc.parallelize(mapData, numSlices = 2).map{ case (i, m) =>
+      val mw = new MapWritable()
+      val k = m.keys.head
+      val v = m.values.head
+      mw.put(new DoubleWritable(k), new Text(v))
+      (new IntWritable(i), mw)
+    }.saveAsSequenceFile(mapPath)
+
+    // Create test data for arbitrary custom writable TestWritable
+    val testClass = Seq(
+      ("1", TestWritable("test1", 123, 54.0)),
+      ("2", TestWritable("test2", 456, 8762.3)),
+      ("1", TestWritable("test3", 123, 423.1)),
+      ("3", TestWritable("test56", 456, 423.5)),
+      ("2", TestWritable("test2", 123, 5435.2))
+    )
+    val rdd = sc.parallelize(testClass, numSlices = 2).map{ case (k, v) => (new Text(k), v) }
+    rdd.saveAsNewAPIHadoopFile(classPath,
+      classOf[Text], classOf[TestWritable],
+      classOf[SequenceFileOutputFormat[Text, TestWritable]])
+  }
+
+
+}
diff --git a/docs/programming-guide.md b/docs/programming-guide.md
index 7d77e640d0e4..7989e02dfb73 100644
--- a/docs/programming-guide.md
+++ b/docs/programming-guide.md
@@ -359,8 +359,7 @@ Apart from text files, Spark's Java API also supports several other data formats
 
 <div data-lang="python"  markdown="1">
 
-PySpark can create distributed datasets from any file system supported by Hadoop, including your local file system, HDFS, KFS, [Amazon S3](http://wiki.apache.org/hadoop/AmazonS3), etc.
-The current API is limited to text files, but support for binary Hadoop InputFormats is expected in future versions.
+PySpark can create distributed datasets from any storage source supported by Hadoop, including your local file system, HDFS, Cassandra, HBase, [Amazon S3](http://wiki.apache.org/hadoop/AmazonS3), etc. Spark supports text files, [SequenceFiles](http://hadoop.apache.org/common/docs/current/api/org/apache/hadoop/mapred/SequenceFileInputFormat.html), and any other Hadoop [InputFormat](http://hadoop.apache.org/docs/stable/api/org/apache/hadoop/mapred/InputFormat.html).
 
 Text file RDDs can be created using `SparkContext`'s `textFile` method. This method takes an URI for the file (either a local path on the machine, or a `hdfs://`, `s3n://`, etc URI) and reads it as a collection of lines. Here is an example invocation:
 
@@ -378,11 +377,90 @@ Some notes on reading files with Spark:
 
 * The `textFile` method also takes an optional second argument for controlling the number of slices of the file. By default, Spark creates one slice for each block of the file (blocks being 64MB by default in HDFS), but you can also ask for a higher number of slices by passing a larger value. Note that you cannot have fewer slices than blocks.
 
-Apart reading files as a collection of lines,
+Apart from reading files as a collection of lines,
 `SparkContext.wholeTextFiles` lets you read a directory containing multiple small text files, and returns each of them as (filename, content) pairs. This is in contrast with `textFile`, which would return one record per line in each file.
 
-</div>
+### SequenceFile and Hadoop InputFormats
+
+In addition to reading text files, PySpark supports reading ```SequenceFile``` 
+and any arbitrary ```InputFormat```.
+
+**Note** this feature is currently marked ```Experimental``` and is intended for advanced users. It may be replaced in future with read/write support based on SparkSQL, in which case SparkSQL is the preferred approach.
+
+#### Writable Support
+
+PySpark SequenceFile support loads an RDD within Java, and pickles the resulting Java objects using
+[Pyrolite](https://github.com/irmen/Pyrolite/). The following Writables are automatically converted:
+
+<table class="table">
+<tr><th>Writable Type</th><th>Python Type</th></tr>
+<tr><td>Text</td><td>unicode str</td></tr>
+<tr><td>IntWritable</td><td>int</td></tr>
+<tr><td>FloatWritable</td><td>float</td></tr>
+<tr><td>DoubleWritable</td><td>float</td></tr>
+<tr><td>BooleanWritable</td><td>bool</td></tr>
+<tr><td>BytesWritable</td><td>bytearray</td></tr>
+<tr><td>NullWritable</td><td>None</td></tr>
+<tr><td>ArrayWritable</td><td>list of primitives, or tuple of objects</td></tr>
+<tr><td>MapWritable</td><td>dict</td></tr>
+<tr><td>Custom Class conforming to Java Bean conventions</td>
+    <td>dict of public properties (via JavaBean getters and setters) + __class__ for the class type</td></tr>
+</table>
+
+#### Loading SequenceFiles
 
+Similarly to text files, SequenceFiles can be loaded by specifying the path. The key and value
+classes can be specified, but for standard Writables this is not required.
+
+{% highlight python %}
+>>> rdd = sc.sequenceFile("path/to/sequencefile/of/doubles")
+>>> rdd.collect()         # this example has DoubleWritable keys and Text values
+[(1.0, u'aa'),
+ (2.0, u'bb'),
+ (2.0, u'aa'),
+ (3.0, u'cc'),
+ (2.0, u'bb'),
+ (1.0, u'aa')]
+{% endhighlight %}
+
+#### Loading Other Hadoop InputFormats
+
+PySpark can also read any Hadoop InputFormat, for both 'new' and 'old' Hadoop APIs. If required,
+a Hadoop configuration can be passed in as a Python dict. Here is an example using the
+Elasticsearch ESInputFormat:
+
+{% highlight python %}
+$ SPARK_CLASSPATH=/path/to/elasticsearch-hadoop.jar ./bin/pyspark
+>>> conf = {"es.resource" : "index/type"}   # assume Elasticsearch is running on localhost defaults
+>>> rdd = sc.newAPIHadoopRDD("org.elasticsearch.hadoop.mr.EsInputFormat",\
+    "org.apache.hadoop.io.NullWritable", "org.elasticsearch.hadoop.mr.LinkedMapWritable", conf=conf)
+>>> rdd.first()         # the result is a MapWritable that is converted to a Python dict
+(u'Elasticsearch ID',
+ {u'field1': True,
+  u'field2': u'Some Text',
+  u'field3': 12345})
+{% endhighlight %}
+
+Note that, if the InputFormat simply depends on a Hadoop configuration and/or input path, and
+the key and value classes can easily be converted according to the above table,
+then this approach should work well for such cases.
+
+If you have custom serialized binary data (such as loading data from Cassandra / HBase) or custom
+classes that don't conform to the JavaBean requirements, then you will first need to 
+transform that data on the Scala/Java side to something which can be handled by Pyrolite's pickler.
+A [Converter](api/scala/index.html#org.apache.spark.api.python.Converter) trait is provided 
+for this. Simply extend this trait and implement your transformation code in the ```convert``` 
+method. Remember to ensure that this class, along with any dependencies required to access your ```InputFormat```, are packaged into your Spark job jar and included on the PySpark 
+classpath.
+
+See the [Python examples]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/python) and 
+the [Converter examples]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/scala/pythonconverters) 
+for examples of using HBase and Cassandra ```InputFormat```.
+
+Future support for writing data out as ```SequenceFileOutputFormat``` and other ```OutputFormats```, 
+is forthcoming.
+
+</div>
 
 </div>
 
diff --git a/examples/src/main/python/cassandra_inputformat.py b/examples/src/main/python/cassandra_inputformat.py
new file mode 100644
index 000000000000..39fa6b0d22ef
--- /dev/null
+++ b/examples/src/main/python/cassandra_inputformat.py
@@ -0,0 +1,79 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import sys
+
+from pyspark import SparkContext
+
+"""
+Create data in Cassandra fist
+(following: https://wiki.apache.org/cassandra/GettingStarted)
+
+cqlsh> CREATE KEYSPACE test
+   ... WITH REPLICATION = { 'class' : 'SimpleStrategy', 'replication_factor' : 1 };
+cqlsh> use test;
+cqlsh:test> CREATE TABLE users (
+        ...   user_id int PRIMARY KEY,
+        ...   fname text,
+        ...   lname text
+        ... );
+cqlsh:test> INSERT INTO users (user_id,  fname, lname)
+        ...   VALUES (1745, 'john', 'smith');
+cqlsh:test> INSERT INTO users (user_id,  fname, lname)
+        ...   VALUES (1744, 'john', 'doe');
+cqlsh:test> INSERT INTO users (user_id,  fname, lname)
+        ...   VALUES (1746, 'john', 'smith');
+cqlsh:test> SELECT * FROM users;
+
+ user_id | fname | lname
+---------+-------+-------
+    1745 |  john | smith
+    1744 |  john |   doe
+    1746 |  john | smith
+"""
+if __name__ == "__main__":
+    if len(sys.argv) != 4:
+        print >> sys.stderr, """
+        Usage: cassandra_inputformat <host> <keyspace> <cf>
+
+        Run with example jar:
+        ./bin/spark-submit --driver-class-path /path/to/example/jar /path/to/examples/cassandra_inputformat.py <host> <keyspace> <cf>
+        Assumes you have some data in Cassandra already, running on <host>, in <keyspace> and <cf>
+        """
+        exit(-1)
+
+    host = sys.argv[1]
+    keyspace = sys.argv[2]
+    cf = sys.argv[3]
+    sc = SparkContext(appName="CassandraInputFormat")
+
+    conf = {"cassandra.input.thrift.address":host,
+            "cassandra.input.thrift.port":"9160",
+            "cassandra.input.keyspace":keyspace,
+            "cassandra.input.columnfamily":cf,
+            "cassandra.input.partitioner.class":"Murmur3Partitioner",
+            "cassandra.input.page.row.size":"3"}
+    cass_rdd = sc.newAPIHadoopRDD(
+        "org.apache.cassandra.hadoop.cql3.CqlPagingInputFormat",
+        "java.util.Map",
+        "java.util.Map",
+        keyConverter="org.apache.spark.examples.pythonconverters.CassandraCQLKeyConverter",
+        valueConverter="org.apache.spark.examples.pythonconverters.CassandraCQLValueConverter",
+        conf=conf)
+    output = cass_rdd.collect()
+    for (k, v) in output:
+        print (k, v)
diff --git a/examples/src/main/python/hbase_inputformat.py b/examples/src/main/python/hbase_inputformat.py
new file mode 100644
index 000000000000..3289d9880a0f
--- /dev/null
+++ b/examples/src/main/python/hbase_inputformat.py
@@ -0,0 +1,72 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import sys
+
+from pyspark import SparkContext
+
+"""
+Create test data in HBase first:
+
+hbase(main):016:0> create 'test', 'f1'
+0 row(s) in 1.0430 seconds
+
+hbase(main):017:0> put 'test', 'row1', 'f1', 'value1'
+0 row(s) in 0.0130 seconds
+
+hbase(main):018:0> put 'test', 'row2', 'f1', 'value2'
+0 row(s) in 0.0030 seconds
+
+hbase(main):019:0> put 'test', 'row3', 'f1', 'value3'
+0 row(s) in 0.0050 seconds
+
+hbase(main):020:0> put 'test', 'row4', 'f1', 'value4'
+0 row(s) in 0.0110 seconds
+
+hbase(main):021:0> scan 'test'
+ROW                           COLUMN+CELL
+ row1                         column=f1:, timestamp=1401883411986, value=value1
+ row2                         column=f1:, timestamp=1401883415212, value=value2
+ row3                         column=f1:, timestamp=1401883417858, value=value3
+ row4                         column=f1:, timestamp=1401883420805, value=value4
+4 row(s) in 0.0240 seconds
+"""
+if __name__ == "__main__":
+    if len(sys.argv) != 3:
+        print >> sys.stderr, """
+        Usage: hbase_inputformat <host> <table>
+
+        Run with example jar:
+        ./bin/spark-submit --driver-class-path /path/to/example/jar /path/to/examples/hbase_inputformat.py <host> <table>
+        Assumes you have some data in HBase already, running on <host>, in <table>
+        """
+        exit(-1)
+
+    host = sys.argv[1]
+    table = sys.argv[2]
+    sc = SparkContext(appName="HBaseInputFormat")
+
+    conf = {"hbase.zookeeper.quorum": host, "hbase.mapreduce.inputtable": table}
+    hbase_rdd = sc.newAPIHadoopRDD(
+        "org.apache.hadoop.hbase.mapreduce.TableInputFormat",
+        "org.apache.hadoop.hbase.io.ImmutableBytesWritable",
+        "org.apache.hadoop.hbase.client.Result",
+        valueConverter="org.apache.spark.examples.pythonconverters.HBaseConverter",
+        conf=conf)
+    output = hbase_rdd.collect()
+    for (k, v) in output:
+        print (k, v)
diff --git a/examples/src/main/scala/org/apache/spark/examples/CassandraCQLTest.scala b/examples/src/main/scala/org/apache/spark/examples/CassandraCQLTest.scala
index 9a00701f985f..71f53af68f4d 100644
--- a/examples/src/main/scala/org/apache/spark/examples/CassandraCQLTest.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/CassandraCQLTest.scala
@@ -33,6 +33,7 @@ import org.apache.hadoop.mapreduce.Job
 import org.apache.spark.{SparkConf, SparkContext}
 import org.apache.spark.SparkContext._
 
+
 /*
   Need to create following keyspace and column family in cassandra before running this example
   Start CQL shell using ./bin/cqlsh and execute following commands
diff --git a/examples/src/main/scala/org/apache/spark/examples/HBaseTest.scala b/examples/src/main/scala/org/apache/spark/examples/HBaseTest.scala
index a8c338480e6e..4893b017ed81 100644
--- a/examples/src/main/scala/org/apache/spark/examples/HBaseTest.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/HBaseTest.scala
@@ -22,7 +22,7 @@ import org.apache.hadoop.hbase.{HBaseConfiguration, HTableDescriptor}
 import org.apache.hadoop.hbase.mapreduce.TableInputFormat
 
 import org.apache.spark._
-import org.apache.spark.rdd.NewHadoopRDD
+
 
 object HBaseTest {
   def main(args: Array[String]) {
diff --git a/examples/src/main/scala/org/apache/spark/examples/pythonconverters/CassandraConverters.scala b/examples/src/main/scala/org/apache/spark/examples/pythonconverters/CassandraConverters.scala
new file mode 100644
index 000000000000..29a65c7a5f29
--- /dev/null
+++ b/examples/src/main/scala/org/apache/spark/examples/pythonconverters/CassandraConverters.scala
@@ -0,0 +1,46 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.examples.pythonconverters
+
+import org.apache.spark.api.python.Converter
+import java.nio.ByteBuffer
+import org.apache.cassandra.utils.ByteBufferUtil
+import collection.JavaConversions.{mapAsJavaMap, mapAsScalaMap}
+
+
+/**
+ * Implementation of [[org.apache.spark.api.python.Converter]] that converts Cassandra
+ * output to a Map[String, Int]
+ */
+class CassandraCQLKeyConverter extends Converter[Any, java.util.Map[String, Int]] {
+  override def convert(obj: Any): java.util.Map[String, Int] = {
+    val result = obj.asInstanceOf[java.util.Map[String, ByteBuffer]]
+    mapAsJavaMap(result.mapValues(bb => ByteBufferUtil.toInt(bb)))
+  }
+}
+
+/**
+ * Implementation of [[org.apache.spark.api.python.Converter]] that converts Cassandra
+ * output to a Map[String, String]
+ */
+class CassandraCQLValueConverter extends Converter[Any, java.util.Map[String, String]] {
+  override def convert(obj: Any): java.util.Map[String, String] = {
+    val result = obj.asInstanceOf[java.util.Map[String, ByteBuffer]]
+    mapAsJavaMap(result.mapValues(bb => ByteBufferUtil.string(bb)))
+  }
+}
diff --git a/examples/src/main/scala/org/apache/spark/examples/pythonconverters/HBaseConverter.scala b/examples/src/main/scala/org/apache/spark/examples/pythonconverters/HBaseConverter.scala
new file mode 100644
index 000000000000..42ae960bd64a
--- /dev/null
+++ b/examples/src/main/scala/org/apache/spark/examples/pythonconverters/HBaseConverter.scala
@@ -0,0 +1,33 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.examples.pythonconverters
+
+import org.apache.spark.api.python.Converter
+import org.apache.hadoop.hbase.client.Result
+import org.apache.hadoop.hbase.util.Bytes
+
+/**
+ * Implementation of [[org.apache.spark.api.python.Converter]] that converts a HBase Result
+ * to a String
+ */
+class HBaseConverter extends Converter[Any, String] {
+  override def convert(obj: Any): String = {
+    val result = obj.asInstanceOf[Result]
+    Bytes.toStringBinary(result.value())
+  }
+}
diff --git a/python/pyspark/context.py b/python/pyspark/context.py
index 211918f5a05e..062bec2381a8 100644
--- a/python/pyspark/context.py
+++ b/python/pyspark/context.py
@@ -342,6 +342,143 @@ def wholeTextFiles(self, path, minPartitions=None):
         return RDD(self._jsc.wholeTextFiles(path, minPartitions), self,
                    PairDeserializer(UTF8Deserializer(), UTF8Deserializer()))
 
+    def _dictToJavaMap(self, d):
+        jm = self._jvm.java.util.HashMap()
+        if not d:
+            d = {}
+        for k, v in d.iteritems():
+            jm[k] = v
+        return jm
+
+    def sequenceFile(self, path, keyClass=None, valueClass=None, keyConverter=None,
+                     valueConverter=None, minSplits=None):
+        """
+        Read a Hadoop SequenceFile with arbitrary key and value Writable class from HDFS,
+        a local file system (available on all nodes), or any Hadoop-supported file system URI.
+        The mechanism is as follows:
+            1. A Java RDD is created from the SequenceFile or other InputFormat, and the key
+               and value Writable classes
+            2. Serialization is attempted via Pyrolite pickling
+            3. If this fails, the fallback is to call 'toString' on each key and value
+            4. C{PickleSerializer} is used to deserialize pickled objects on the Python side
+
+        @param path: path to sequncefile
+        @param keyClass: fully qualified classname of key Writable class
+               (e.g. "org.apache.hadoop.io.Text")
+        @param valueClass: fully qualified classname of value Writable class
+               (e.g. "org.apache.hadoop.io.LongWritable")
+        @param keyConverter:
+        @param valueConverter:
+        @param minSplits: minimum splits in dataset
+               (default min(2, sc.defaultParallelism))
+        """
+        minSplits = minSplits or min(self.defaultParallelism, 2)
+        jrdd = self._jvm.PythonRDD.sequenceFile(self._jsc, path, keyClass, valueClass,
+                                                keyConverter, valueConverter, minSplits)
+        return RDD(jrdd, self, PickleSerializer())
+
+    def newAPIHadoopFile(self, path, inputFormatClass, keyClass, valueClass, keyConverter=None,
+                         valueConverter=None, conf=None):
+        """
+        Read a 'new API' Hadoop InputFormat with arbitrary key and value class from HDFS,
+        a local file system (available on all nodes), or any Hadoop-supported file system URI.
+        The mechanism is the same as for sc.sequenceFile.
+
+        A Hadoop configuration can be passed in as a Python dict. This will be converted into a
+        Configuration in Java
+
+        @param path: path to Hadoop file
+        @param inputFormatClass: fully qualified classname of Hadoop InputFormat
+               (e.g. "org.apache.hadoop.mapreduce.lib.input.TextInputFormat")
+        @param keyClass: fully qualified classname of key Writable class
+               (e.g. "org.apache.hadoop.io.Text")
+        @param valueClass: fully qualified classname of value Writable class
+               (e.g. "org.apache.hadoop.io.LongWritable")
+        @param keyConverter: (None by default)
+        @param valueConverter: (None by default)
+        @param conf: Hadoop configuration, passed in as a dict
+               (None by default)
+        """
+        jconf = self._dictToJavaMap(conf)
+        jrdd = self._jvm.PythonRDD.newAPIHadoopFile(self._jsc, path, inputFormatClass, keyClass,
+                                                    valueClass, keyConverter, valueConverter, jconf)
+        return RDD(jrdd, self, PickleSerializer())
+
+    def newAPIHadoopRDD(self, inputFormatClass, keyClass, valueClass, keyConverter=None,
+                        valueConverter=None, conf=None):
+        """
+        Read a 'new API' Hadoop InputFormat with arbitrary key and value class, from an arbitrary
+        Hadoop configuration, which is passed in as a Python dict.
+        This will be converted into a Configuration in Java.
+        The mechanism is the same as for sc.sequenceFile.
+
+        @param inputFormatClass: fully qualified classname of Hadoop InputFormat
+               (e.g. "org.apache.hadoop.mapreduce.lib.input.TextInputFormat")
+        @param keyClass: fully qualified classname of key Writable class
+               (e.g. "org.apache.hadoop.io.Text")
+        @param valueClass: fully qualified classname of value Writable class
+               (e.g. "org.apache.hadoop.io.LongWritable")
+        @param keyConverter: (None by default)
+        @param valueConverter: (None by default)
+        @param conf: Hadoop configuration, passed in as a dict
+               (None by default)
+        """
+        jconf = self._dictToJavaMap(conf)
+        jrdd = self._jvm.PythonRDD.newAPIHadoopRDD(self._jsc, inputFormatClass, keyClass,
+                                                   valueClass, keyConverter, valueConverter, jconf)
+        return RDD(jrdd, self, PickleSerializer())
+
+    def hadoopFile(self, path, inputFormatClass, keyClass, valueClass, keyConverter=None,
+                   valueConverter=None, conf=None):
+        """
+        Read an 'old' Hadoop InputFormat with arbitrary key and value class from HDFS,
+        a local file system (available on all nodes), or any Hadoop-supported file system URI.
+        The mechanism is the same as for sc.sequenceFile.
+
+        A Hadoop configuration can be passed in as a Python dict. This will be converted into a
+        Configuration in Java.
+
+        @param path: path to Hadoop file
+        @param inputFormatClass: fully qualified classname of Hadoop InputFormat
+               (e.g. "org.apache.hadoop.mapred.TextInputFormat")
+        @param keyClass: fully qualified classname of key Writable class
+               (e.g. "org.apache.hadoop.io.Text")
+        @param valueClass: fully qualified classname of value Writable class
+               (e.g. "org.apache.hadoop.io.LongWritable")
+        @param keyConverter: (None by default)
+        @param valueConverter: (None by default)
+        @param conf: Hadoop configuration, passed in as a dict
+               (None by default)
+        """
+        jconf = self._dictToJavaMap(conf)
+        jrdd = self._jvm.PythonRDD.hadoopFile(self._jsc, path, inputFormatClass, keyClass,
+                                              valueClass, keyConverter, valueConverter, jconf)
+        return RDD(jrdd, self, PickleSerializer())
+
+    def hadoopRDD(self, inputFormatClass, keyClass, valueClass, keyConverter=None,
+                  valueConverter=None, conf=None):
+        """
+        Read an 'old' Hadoop InputFormat with arbitrary key and value class, from an arbitrary
+        Hadoop configuration, which is passed in as a Python dict.
+        This will be converted into a Configuration in Java.
+        The mechanism is the same as for sc.sequenceFile.
+
+        @param inputFormatClass: fully qualified classname of Hadoop InputFormat
+               (e.g. "org.apache.hadoop.mapred.TextInputFormat")
+        @param keyClass: fully qualified classname of key Writable class
+               (e.g. "org.apache.hadoop.io.Text")
+        @param valueClass: fully qualified classname of value Writable class
+               (e.g. "org.apache.hadoop.io.LongWritable")
+        @param keyConverter: (None by default)
+        @param valueConverter: (None by default)
+        @param conf: Hadoop configuration, passed in as a dict
+               (None by default)
+        """
+        jconf = self._dictToJavaMap(conf)
+        jrdd = self._jvm.PythonRDD.hadoopRDD(self._jsc, inputFormatClass, keyClass, valueClass,
+                                             keyConverter, valueConverter, jconf)
+        return RDD(jrdd, self, PickleSerializer())
+
     def _checkpointFile(self, name, input_deserializer):
         jrdd = self._jsc.checkpointFile(name)
         return RDD(jrdd, self, input_deserializer)
diff --git a/python/pyspark/tests.py b/python/pyspark/tests.py
index 1f2a6ea941cf..184ee810b861 100644
--- a/python/pyspark/tests.py
+++ b/python/pyspark/tests.py
@@ -198,6 +198,151 @@ def func(x):
         self.sc.parallelize([1]).foreach(func)
 
 
+class TestInputFormat(PySparkTestCase):
+
+    def setUp(self):
+        PySparkTestCase.setUp(self)
+        self.tempdir = tempfile.NamedTemporaryFile(delete=False)
+        os.unlink(self.tempdir.name)
+        self.sc._jvm.WriteInputFormatTestDataGenerator.generateData(self.tempdir.name, self.sc._jsc)
+
+    def tearDown(self):
+        PySparkTestCase.tearDown(self)
+        shutil.rmtree(self.tempdir.name)
+
+    def test_sequencefiles(self):
+        basepath = self.tempdir.name
+        ints = sorted(self.sc.sequenceFile(basepath + "/sftestdata/sfint/",
+                                           "org.apache.hadoop.io.IntWritable",
+                                           "org.apache.hadoop.io.Text").collect())
+        ei = [(1, u'aa'), (1, u'aa'), (2, u'aa'), (2, u'bb'), (2, u'bb'), (3, u'cc')]
+        self.assertEqual(ints, ei)
+
+        doubles = sorted(self.sc.sequenceFile(basepath + "/sftestdata/sfdouble/",
+                                              "org.apache.hadoop.io.DoubleWritable",
+                                              "org.apache.hadoop.io.Text").collect())
+        ed = [(1.0, u'aa'), (1.0, u'aa'), (2.0, u'aa'), (2.0, u'bb'), (2.0, u'bb'), (3.0, u'cc')]
+        self.assertEqual(doubles, ed)
+
+        text = sorted(self.sc.sequenceFile(basepath + "/sftestdata/sftext/",
+                                           "org.apache.hadoop.io.Text",
+                                           "org.apache.hadoop.io.Text").collect())
+        et = [(u'1', u'aa'),
+              (u'1', u'aa'),
+              (u'2', u'aa'),
+              (u'2', u'bb'),
+              (u'2', u'bb'),
+              (u'3', u'cc')]
+        self.assertEqual(text, et)
+
+        bools = sorted(self.sc.sequenceFile(basepath + "/sftestdata/sfbool/",
+                                            "org.apache.hadoop.io.IntWritable",
+                                            "org.apache.hadoop.io.BooleanWritable").collect())
+        eb = [(1, False), (1, True), (2, False), (2, False), (2, True), (3, True)]
+        self.assertEqual(bools, eb)
+
+        nulls = sorted(self.sc.sequenceFile(basepath + "/sftestdata/sfnull/",
+                                            "org.apache.hadoop.io.IntWritable",
+                                            "org.apache.hadoop.io.BooleanWritable").collect())
+        en = [(1, None), (1, None), (2, None), (2, None), (2, None), (3, None)]
+        self.assertEqual(nulls, en)
+
+        maps = sorted(self.sc.sequenceFile(basepath + "/sftestdata/sfmap/",
+                                           "org.apache.hadoop.io.IntWritable",
+                                           "org.apache.hadoop.io.MapWritable").collect())
+        em = [(1, {2.0: u'aa'}),
+              (1, {3.0: u'bb'}),
+              (2, {1.0: u'aa'}),
+              (2, {1.0: u'cc'}),
+              (2, {3.0: u'bb'}),
+              (3, {2.0: u'dd'})]
+        self.assertEqual(maps, em)
+
+        clazz = sorted(self.sc.sequenceFile(basepath + "/sftestdata/sfclass/",
+                                            "org.apache.hadoop.io.Text",
+                                            "org.apache.spark.api.python.TestWritable").collect())
+        ec = (u'1',
+              {u'__class__': u'org.apache.spark.api.python.TestWritable',
+               u'double': 54.0, u'int': 123, u'str': u'test1'})
+        self.assertEqual(clazz[0], ec)
+
+    def test_oldhadoop(self):
+        basepath = self.tempdir.name
+        ints = sorted(self.sc.hadoopFile(basepath + "/sftestdata/sfint/",
+                                         "org.apache.hadoop.mapred.SequenceFileInputFormat",
+                                         "org.apache.hadoop.io.IntWritable",
+                                         "org.apache.hadoop.io.Text").collect())
+        ei = [(1, u'aa'), (1, u'aa'), (2, u'aa'), (2, u'bb'), (2, u'bb'), (3, u'cc')]
+        self.assertEqual(ints, ei)
+
+        hellopath = os.path.join(SPARK_HOME, "python/test_support/hello.txt")
+        hello = self.sc.hadoopFile(hellopath,
+                                   "org.apache.hadoop.mapred.TextInputFormat",
+                                   "org.apache.hadoop.io.LongWritable",
+                                   "org.apache.hadoop.io.Text").collect()
+        result = [(0, u'Hello World!')]
+        self.assertEqual(hello, result)
+
+    def test_newhadoop(self):
+        basepath = self.tempdir.name
+        ints = sorted(self.sc.newAPIHadoopFile(
+            basepath + "/sftestdata/sfint/",
+            "org.apache.hadoop.mapreduce.lib.input.SequenceFileInputFormat",
+            "org.apache.hadoop.io.IntWritable",
+            "org.apache.hadoop.io.Text").collect())
+        ei = [(1, u'aa'), (1, u'aa'), (2, u'aa'), (2, u'bb'), (2, u'bb'), (3, u'cc')]
+        self.assertEqual(ints, ei)
+
+        hellopath = os.path.join(SPARK_HOME, "python/test_support/hello.txt")
+        hello = self.sc.newAPIHadoopFile(hellopath,
+                                         "org.apache.hadoop.mapreduce.lib.input.TextInputFormat",
+                                         "org.apache.hadoop.io.LongWritable",
+                                         "org.apache.hadoop.io.Text").collect()
+        result = [(0, u'Hello World!')]
+        self.assertEqual(hello, result)
+
+    def test_newolderror(self):
+        basepath = self.tempdir.name
+        self.assertRaises(Exception, lambda: self.sc.hadoopFile(
+            basepath + "/sftestdata/sfint/",
+            "org.apache.hadoop.mapreduce.lib.input.SequenceFileInputFormat",
+            "org.apache.hadoop.io.IntWritable",
+            "org.apache.hadoop.io.Text"))
+
+        self.assertRaises(Exception, lambda: self.sc.newAPIHadoopFile(
+            basepath + "/sftestdata/sfint/",
+            "org.apache.hadoop.mapred.SequenceFileInputFormat",
+            "org.apache.hadoop.io.IntWritable",
+            "org.apache.hadoop.io.Text"))
+
+    def test_bad_inputs(self):
+        basepath = self.tempdir.name
+        self.assertRaises(Exception, lambda: self.sc.sequenceFile(
+            basepath + "/sftestdata/sfint/",
+            "org.apache.hadoop.io.NotValidWritable",
+            "org.apache.hadoop.io.Text"))
+        self.assertRaises(Exception, lambda: self.sc.hadoopFile(
+            basepath + "/sftestdata/sfint/",
+            "org.apache.hadoop.mapred.NotValidInputFormat",
+            "org.apache.hadoop.io.IntWritable",
+            "org.apache.hadoop.io.Text"))
+        self.assertRaises(Exception, lambda: self.sc.newAPIHadoopFile(
+            basepath + "/sftestdata/sfint/",
+            "org.apache.hadoop.mapreduce.lib.input.NotValidInputFormat",
+            "org.apache.hadoop.io.IntWritable",
+            "org.apache.hadoop.io.Text"))
+
+    def test_converter(self):
+        basepath = self.tempdir.name
+        maps = sorted(self.sc.sequenceFile(
+            basepath + "/sftestdata/sfmap/",
+            "org.apache.hadoop.io.IntWritable",
+            "org.apache.hadoop.io.MapWritable",
+            valueConverter="org.apache.spark.api.python.TestConverter").collect())
+        em = [(1, [2.0]), (1, [3.0]), (2, [1.0]), (2, [1.0]), (2, [3.0]), (3, [2.0])]
+        self.assertEqual(maps, em)
+
+
 class TestDaemon(unittest.TestCase):
     def connect(self, port):
         from socket import socket, AF_INET, SOCK_STREAM

From 08ed9ad81397b71206c4dc903bfb94b6105691ed Mon Sep 17 00:00:00 2001
From: Zongheng Yang <zongheng.y@gmail.com>
Date: Tue, 10 Jun 2014 00:49:09 -0700
Subject: [PATCH 377/810] [SPARK-1508][SQL] Add SQLConf to SQLContext.

This PR (1) introduces a new class SQLConf that stores key-value properties for a SQLContext (2) clean up the semantics of various forms of SET commands.

The SQLConf class unlocks user-controllable optimization opportunities; for example, user can now override the number of partitions used during an Exchange. A SQLConf can be accessed and modified programmatically through its getters and setters. It can also be modified through SET commands executed by `sql()` or `hql()`. Note that users now have the ability to change a particular property for different queries inside the same Spark job, unlike settings configured in SparkConf.

For SET commands: "SET" will return all properties currently set in a SQLConf, "SET key" will return the key-value pair (if set) or an undefined message, and "SET key=value" will call the setter on SQLConf, and if a HiveContext is used, it will be executed in Hive as well.

Author: Zongheng Yang <zongheng.y@gmail.com>

Closes #956 from concretevitamin/sqlconf and squashes the following commits:

4968c11 [Zongheng Yang] Very minor cleanup.
d74dde5 [Zongheng Yang] Remove the redundant mkQueryExecution() method.
c129b86 [Zongheng Yang] Merge remote-tracking branch 'upstream/master' into sqlconf
26c40eb [Zongheng Yang] Make SQLConf a trait and have SQLContext mix it in.
dd19666 [Zongheng Yang] Update a comment.
baa5d29 [Zongheng Yang] Remove default param for shuffle partitions accessor.
5f7e6d8 [Zongheng Yang] Add default num partitions.
22d9ed7 [Zongheng Yang] Fix output() of Set physical. Add SQLConf param accessor method.
e9856c4 [Zongheng Yang] Use java.util.Collections.synchronizedMap on a Java HashMap.
88dd0c8 [Zongheng Yang] Remove redundant SET Keyword.
271f0b1 [Zongheng Yang] Minor change.
f8983d1 [Zongheng Yang] Minor changes per review comments.
1ce8a5e [Zongheng Yang] Invoke runSqlHive() in SQLConf#get for the HiveContext case.
b766af9 [Zongheng Yang] Remove a test.
d52e1bd [Zongheng Yang] De-hardcode number of shuffle partitions for BasicOperators (read from SQLConf).
555599c [Zongheng Yang] Bullet-proof (relatively) parsing SET per review comment.
c2067e8 [Zongheng Yang] Mark SQLContext transient and put it in a second param list.
2ea8cdc [Zongheng Yang] Wrap long line.
41d7f09 [Zongheng Yang] Fix imports.
13279e6 [Zongheng Yang] Refactor the logic of eagerly processing SET commands.
b14b83e [Zongheng Yang] In a HiveContext, make SQLConf a subset of HiveConf.
6983180 [Zongheng Yang] Move a SET test to SQLQuerySuite and make it complete.
5b67985 [Zongheng Yang] New line at EOF.
c651797 [Zongheng Yang] Add commands.scala.
efd82db [Zongheng Yang] Clean up semantics of several cases of SET.
c1017c2 [Zongheng Yang] WIP in changing SetCommand to take two Options (for different semantics of SETs).
0f00d86 [Zongheng Yang] Add a test for singleton set command in SQL.
41acd75 [Zongheng Yang] Add a test for hql() in HiveQuerySuite.
2276929 [Zongheng Yang] Fix default hive result for set commands in HiveComparisonTest.
3b0c71b [Zongheng Yang] Remove Parser for set commands. A few other fixes.
d0c4578 [Zongheng Yang] Tmux typo.
0ecea46 [Zongheng Yang] Changes for HiveQl and HiveContext.
ce22d80 [Zongheng Yang] Fix parsing issues.
cb722c1 [Zongheng Yang] Finish up SQLConf patch.
4ebf362 [Zongheng Yang] First cut at SQLConf inside SQLContext.
---
 .../apache/spark/sql/catalyst/SqlParser.scala | 31 ++++++--
 .../catalyst/plans/logical/LogicalPlan.scala  | 12 ++-
 .../scala/org/apache/spark/sql/SQLConf.scala  | 78 +++++++++++++++++++
 .../org/apache/spark/sql/SQLContext.scala     | 39 ++++++++--
 .../apache/spark/sql/execution/Exchange.scala |  6 +-
 .../spark/sql/execution/SparkStrategies.scala | 11 +--
 .../apache/spark/sql/execution/commands.scala | 35 +++++++++
 .../org/apache/spark/sql/SQLConfSuite.scala   | 71 +++++++++++++++++
 .../org/apache/spark/sql/SQLQuerySuite.scala  | 39 +++++++++-
 .../spark/sql/execution/PlannerSuite.scala    |  4 +-
 .../apache/spark/sql/hive/HiveContext.scala   | 73 +++++++++--------
 .../org/apache/spark/sql/hive/HiveQl.scala    | 13 +++-
 .../hive/execution/HiveComparisonTest.scala   |  3 +
 .../sql/hive/execution/HiveQuerySuite.scala   | 75 ++++++++++++++++++
 14 files changed, 429 insertions(+), 61 deletions(-)
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/SQLConfSuite.scala

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
index cc650128c2c3..36758f3114e5 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
@@ -41,10 +41,25 @@ import org.apache.spark.sql.catalyst.types._
  * for a SQL like language should checkout the HiveQL support in the sql/hive sub-project.
  */
 class SqlParser extends StandardTokenParsers with PackratParsers {
+
   def apply(input: String): LogicalPlan = {
-    phrase(query)(new lexical.Scanner(input)) match {
-      case Success(r, x) => r
-      case x => sys.error(x.toString)
+    // Special-case out set commands since the value fields can be
+    // complex to handle without RegexParsers. Also this approach
+    // is clearer for the several possible cases of set commands.
+    if (input.trim.toLowerCase.startsWith("set")) {
+      input.trim.drop(3).split("=", 2).map(_.trim) match {
+        case Array("") => // "set"
+          SetCommand(None, None)
+        case Array(key) => // "set key"
+          SetCommand(Some(key), None)
+        case Array(key, value) => // "set key=value"
+          SetCommand(Some(key), Some(value))
+      }
+    } else {
+      phrase(query)(new lexical.Scanner(input)) match {
+        case Success(r, x) => r
+        case x => sys.error(x.toString)
+      }
     }
   }
 
@@ -169,11 +184,13 @@ class SqlParser extends StandardTokenParsers with PackratParsers {
     }
   }
 
-  protected lazy val query: Parser[LogicalPlan] =
+  protected lazy val query: Parser[LogicalPlan] = (
     select * (
-      UNION ~ ALL ^^^ { (q1: LogicalPlan, q2: LogicalPlan) => Union(q1, q2) } |
-      UNION ~ opt(DISTINCT) ^^^ { (q1: LogicalPlan, q2: LogicalPlan) => Distinct(Union(q1, q2)) }
-    ) | insert
+        UNION ~ ALL ^^^ { (q1: LogicalPlan, q2: LogicalPlan) => Union(q1, q2) } |
+        UNION ~ opt(DISTINCT) ^^^ { (q1: LogicalPlan, q2: LogicalPlan) => Distinct(Union(q1, q2)) }
+      )
+    | insert
+  )
 
   protected lazy val select: Parser[LogicalPlan] =
     SELECT ~> opt(DISTINCT) ~ projections ~
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala
index 4f641cd3a656..7eeb98aea636 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala
@@ -102,7 +102,7 @@ abstract class LeafNode extends LogicalPlan with trees.LeafNode[LogicalPlan] {
  */
 abstract class Command extends LeafNode {
   self: Product =>
-  def output: Seq[Attribute] = Seq.empty
+  def output: Seq[Attribute] = Seq.empty  // TODO: SPARK-2081 should fix this
 }
 
 /**
@@ -111,6 +111,16 @@ abstract class Command extends LeafNode {
  */
 case class NativeCommand(cmd: String) extends Command
 
+/**
+ * Commands of the form "SET (key) (= value)".
+ */
+case class SetCommand(key: Option[String], value: Option[String]) extends Command {
+  override def output = Seq(
+    AttributeReference("key", StringType, nullable = false)(),
+    AttributeReference("value", StringType, nullable = false)()
+  )
+}
+
 /**
  * Returned by a parser when the users only wants to see what query plan would be executed, without
  * actually performing the execution.
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
new file mode 100644
index 000000000000..b378252ba2f5
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
@@ -0,0 +1,78 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql
+
+import java.util.Properties
+
+import scala.collection.JavaConverters._
+
+/**
+ * SQLConf holds mutable config parameters and hints.  These can be set and
+ * queried either by passing SET commands into Spark SQL's DSL
+ * functions (sql(), hql(), etc.), or by programmatically using setters and
+ * getters of this class.  This class is thread-safe.
+ */
+trait SQLConf {
+
+  /** Number of partitions to use for shuffle operators. */
+  private[spark] def numShufflePartitions: Int = get("spark.sql.shuffle.partitions", "200").toInt
+
+  @transient
+  private val settings = java.util.Collections.synchronizedMap(
+    new java.util.HashMap[String, String]())
+
+  def set(props: Properties): Unit = {
+    props.asScala.foreach { case (k, v) => this.settings.put(k, v) }
+  }
+
+  def set(key: String, value: String): Unit = {
+    require(key != null, "key cannot be null")
+    require(value != null, s"value cannot be null for ${key}")
+    settings.put(key, value)
+  }
+
+  def get(key: String): String = {
+    if (!settings.containsKey(key)) {
+      throw new NoSuchElementException(key)
+    }
+    settings.get(key)
+  }
+
+  def get(key: String, defaultValue: String): String = {
+    if (!settings.containsKey(key)) defaultValue else settings.get(key)
+  }
+
+  def getAll: Array[(String, String)] = settings.asScala.toArray
+
+  def getOption(key: String): Option[String] = {
+    if (!settings.containsKey(key)) None else Some(settings.get(key))
+  }
+
+  def contains(key: String): Boolean = settings.containsKey(key)
+
+  def toDebugString: String = {
+    settings.synchronized {
+      settings.asScala.toArray.sorted.map{ case (k, v) => s"$k=$v" }.mkString("\n")
+    }
+  }
+
+  private[spark] def clear() {
+    settings.clear()
+  }
+
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
index fde4c485b58a..021e0e8245a0 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
@@ -31,7 +31,7 @@ import org.apache.spark.sql.catalyst.{ScalaReflection, dsl}
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.types._
 import org.apache.spark.sql.catalyst.optimizer.Optimizer
-import org.apache.spark.sql.catalyst.plans.logical.{Subquery, LogicalPlan}
+import org.apache.spark.sql.catalyst.plans.logical.{SetCommand, LogicalPlan}
 import org.apache.spark.sql.catalyst.rules.RuleExecutor
 
 import org.apache.spark.sql.columnar.InMemoryColumnarTableScan
@@ -52,6 +52,7 @@ import org.apache.spark.sql.parquet.ParquetRelation
 @AlphaComponent
 class SQLContext(@transient val sparkContext: SparkContext)
   extends Logging
+  with SQLConf
   with dsl.ExpressionConversions
   with Serializable {
 
@@ -190,6 +191,8 @@ class SQLContext(@transient val sparkContext: SparkContext)
   protected[sql] class SparkPlanner extends SparkStrategies {
     val sparkContext = self.sparkContext
 
+    def numPartitions = self.numShufflePartitions
+
     val strategies: Seq[Strategy] =
       CommandStrategy(self) ::
       TakeOrdered ::
@@ -246,6 +249,10 @@ class SQLContext(@transient val sparkContext: SparkContext)
   @transient
   protected[sql] val planner = new SparkPlanner
 
+  @transient
+  protected[sql] lazy val emptyResult =
+    sparkContext.parallelize(Seq(new GenericRow(Array[Any]()): Row), 1)
+
   /**
    * Prepares a planned SparkPlan for execution by binding references to specific ordinals, and
    * inserting shuffle operations as needed.
@@ -253,15 +260,10 @@ class SQLContext(@transient val sparkContext: SparkContext)
   @transient
   protected[sql] val prepareForExecution = new RuleExecutor[SparkPlan] {
     val batches =
-      Batch("Add exchange", Once, AddExchange) ::
+      Batch("Add exchange", Once, AddExchange(self)) ::
       Batch("Prepare Expressions", Once, new BindReferences[SparkPlan]) :: Nil
   }
 
-  // TODO: or should we make QueryExecution protected[sql]?
-  protected[sql] def mkQueryExecution(plan: LogicalPlan) = new QueryExecution {
-    val logical = plan
-  }
-
   /**
    * The primary workflow for executing relational queries using Spark.  Designed to allow easy
    * access to the intermediate phases of query execution for developers.
@@ -269,6 +271,22 @@ class SQLContext(@transient val sparkContext: SparkContext)
   protected abstract class QueryExecution {
     def logical: LogicalPlan
 
+    def eagerlyProcess(plan: LogicalPlan): RDD[Row] = plan match {
+      case SetCommand(key, value) =>
+        // Only this case needs to be executed eagerly. The other cases will
+        // be taken care of when the actual results are being extracted.
+        // In the case of HiveContext, sqlConf is overridden to also pass the
+        // pair into its HiveConf.
+        if (key.isDefined && value.isDefined) {
+          set(key.get, value.get)
+        }
+        // It doesn't matter what we return here, since this is only used
+        // to force the evaluation to happen eagerly.  To query the results,
+        // one must use SchemaRDD operations to extract them.
+        emptyResult
+      case _ => executedPlan.execute()
+    }
+
     lazy val analyzed = analyzer(logical)
     lazy val optimizedPlan = optimizer(analyzed)
     // TODO: Don't just pick the first one...
@@ -276,7 +294,12 @@ class SQLContext(@transient val sparkContext: SparkContext)
     lazy val executedPlan: SparkPlan = prepareForExecution(sparkPlan)
 
     /** Internal version of the RDD. Avoids copies and has no schema */
-    lazy val toRdd: RDD[Row] = executedPlan.execute()
+    lazy val toRdd: RDD[Row] = {
+      logical match {
+        case s: SetCommand => eagerlyProcess(s)
+        case _ => executedPlan.execute()
+      }
+    }
 
     protected def stringOrError[A](f: => A): String =
       try f.toString catch { case e: Throwable => e.toString }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
index 3b4acb72e87b..cef294167f14 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
@@ -20,7 +20,7 @@ package org.apache.spark.sql.execution
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.{HashPartitioner, RangePartitioner, SparkConf}
 import org.apache.spark.rdd.ShuffledRDD
-import org.apache.spark.sql.Row
+import org.apache.spark.sql.{SQLConf, SQLContext, Row}
 import org.apache.spark.sql.catalyst.errors.attachTree
 import org.apache.spark.sql.catalyst.expressions.{MutableProjection, RowOrdering}
 import org.apache.spark.sql.catalyst.plans.physical._
@@ -86,9 +86,9 @@ case class Exchange(newPartitioning: Partitioning, child: SparkPlan) extends Una
  * [[catalyst.plans.physical.Distribution Distribution]] requirements for each operator by inserting
  * [[Exchange]] Operators where required.
  */
-private[sql] object AddExchange extends Rule[SparkPlan] {
+private[sql] case class AddExchange(sqlContext: SQLContext) extends Rule[SparkPlan] {
   // TODO: Determine the number of partitions.
-  val numPartitions = 150
+  def numPartitions = sqlContext.numShufflePartitions
 
   def apply(plan: SparkPlan): SparkPlan = plan.transformUp {
     case operator: SparkPlan =>
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
index 295c265b1673..0455748d40ee 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.sql.execution
 
-import org.apache.spark.sql.{SQLContext, execution}
+import org.apache.spark.sql.{SQLConf, SQLContext, execution}
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.planning._
 import org.apache.spark.sql.catalyst.plans._
@@ -193,8 +193,8 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
 
   // Can we automate these 'pass through' operations?
   object BasicOperators extends Strategy {
-    // TODO: Set
-    val numPartitions = 200
+    def numPartitions = self.numPartitions
+
     def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
       case logical.Distinct(child) =>
         execution.Aggregate(
@@ -234,11 +234,12 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
     }
   }
 
-  // TODO: this should be merged with SPARK-1508's SetCommandStrategy
   case class CommandStrategy(context: SQLContext) extends Strategy {
     def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
+      case logical.SetCommand(key, value) =>
+        Seq(execution.SetCommandPhysical(key, value, plan.output)(context))
       case logical.ExplainCommand(child) =>
-        val qe = context.mkQueryExecution(child)
+        val qe = context.executePlan(child)
         Seq(execution.ExplainCommandPhysical(qe.executedPlan, plan.output)(context))
       case _ => Nil
     }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala
index 5371d2f479e7..9364506691f3 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala
@@ -17,10 +17,45 @@
 
 package org.apache.spark.sql.execution
 
+import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.{SQLContext, Row}
 import org.apache.spark.sql.catalyst.expressions.{GenericRow, Attribute}
 
+/**
+ * :: DeveloperApi ::
+ */
+@DeveloperApi
+case class SetCommandPhysical(key: Option[String], value: Option[String], output: Seq[Attribute])
+                             (@transient context: SQLContext) extends LeafNode {
+  def execute(): RDD[Row] = (key, value) match {
+    // Set value for key k; the action itself would
+    // have been performed in QueryExecution eagerly.
+    case (Some(k), Some(v)) => context.emptyResult
+    // Query the value bound to key k.
+    case (Some(k), None) =>
+      val resultString = context.getOption(k) match {
+        case Some(v) => s"$k=$v"
+        case None => s"$k is undefined"
+      }
+      context.sparkContext.parallelize(Seq(new GenericRow(Array[Any](resultString))), 1)
+    // Query all key-value pairs that are set in the SQLConf of the context.
+    case (None, None) =>
+      val pairs = context.getAll
+      val rows = pairs.map { case (k, v) =>
+        new GenericRow(Array[Any](s"$k=$v"))
+      }.toSeq
+      // Assume config parameters can fit into one split (machine) ;)
+      context.sparkContext.parallelize(rows, 1)
+    // The only other case is invalid semantics and is impossible.
+    case _ => context.emptyResult
+  }
+}
+
+/**
+ * :: DeveloperApi ::
+ */
+@DeveloperApi
 case class ExplainCommandPhysical(child: SparkPlan, output: Seq[Attribute])
                                  (@transient context: SQLContext) extends UnaryNode {
   def execute(): RDD[Row] = {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLConfSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLConfSuite.scala
new file mode 100644
index 000000000000..5eb73a4eff98
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLConfSuite.scala
@@ -0,0 +1,71 @@
+/*
+* Licensed to the Apache Software Foundation (ASF) under one or more
+* contributor license agreements.  See the NOTICE file distributed with
+* this work for additional information regarding copyright ownership.
+* The ASF licenses this file to You under the Apache License, Version 2.0
+* (the "License"); you may not use this file except in compliance with
+* the License.  You may obtain a copy of the License at
+*
+*    http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+package org.apache.spark.sql
+
+import org.apache.spark.sql.test._
+
+/* Implicits */
+import TestSQLContext._
+
+class SQLConfSuite extends QueryTest {
+
+  val testKey = "test.key.0"
+  val testVal = "test.val.0"
+
+  test("programmatic ways of basic setting and getting") {
+    assert(getOption(testKey).isEmpty)
+    assert(getAll.toSet === Set())
+
+    set(testKey, testVal)
+    assert(get(testKey) == testVal)
+    assert(get(testKey, testVal + "_") == testVal)
+    assert(getOption(testKey) == Some(testVal))
+    assert(contains(testKey))
+
+    // Tests SQLConf as accessed from a SQLContext is mutable after
+    // the latter is initialized, unlike SparkConf inside a SparkContext.
+    assert(TestSQLContext.get(testKey) == testVal)
+    assert(TestSQLContext.get(testKey, testVal + "_") == testVal)
+    assert(TestSQLContext.getOption(testKey) == Some(testVal))
+    assert(TestSQLContext.contains(testKey))
+
+    clear()
+  }
+
+  test("parse SQL set commands") {
+    sql(s"set $testKey=$testVal")
+    assert(get(testKey, testVal + "_") == testVal)
+    assert(TestSQLContext.get(testKey, testVal + "_") == testVal)
+
+    sql("set mapred.reduce.tasks=20")
+    assert(get("mapred.reduce.tasks", "0") == "20")
+    sql("set mapred.reduce.tasks = 40")
+    assert(get("mapred.reduce.tasks", "0") == "40")
+
+    val key = "spark.sql.key"
+    val vs = "val0,val_1,val2.3,my_table"
+    sql(s"set $key=$vs")
+    assert(get(key, "0") == vs)
+
+    sql(s"set $key=")
+    assert(get(key, "0") == "")
+
+    clear()
+  }
+
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index d651b967a6c1..f2d850ad6aa5 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -361,6 +361,41 @@ class SQLQuerySuite extends QueryTest {
         (1, "abc"),
         (2, "abc"),
         (3, null)))
-  }  
-  
+  }
+
+  test("SET commands semantics using sql()") {
+    clear()
+    val testKey = "test.key.0"
+    val testVal = "test.val.0"
+    val nonexistentKey = "nonexistent"
+
+    // "set" itself returns all config variables currently specified in SQLConf.
+    assert(sql("SET").collect().size == 0)
+
+    // "set key=val"
+    sql(s"SET $testKey=$testVal")
+    checkAnswer(
+      sql("SET"),
+      Seq(Seq(s"$testKey=$testVal"))
+    )
+
+    sql(s"SET ${testKey + testKey}=${testVal + testVal}")
+    checkAnswer(
+      sql("set"),
+      Seq(
+        Seq(s"$testKey=$testVal"),
+        Seq(s"${testKey + testKey}=${testVal + testVal}"))
+    )
+
+    // "set key"
+    checkAnswer(
+      sql(s"SET $testKey"),
+      Seq(Seq(s"$testKey=$testVal"))
+    )
+    checkAnswer(
+      sql(s"SET $nonexistentKey"),
+      Seq(Seq(s"$nonexistentKey is undefined"))
+    )
+  }
+
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala
index c563d6362754..df6b118360d0 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala
@@ -30,8 +30,8 @@ class PlannerSuite extends FunSuite {
   test("unions are collapsed") {
     val query = testData.unionAll(testData).unionAll(testData).logicalPlan
     val planned = BasicOperators(query).head
-    val logicalUnions = query collect { case u: logical.Union => u}
-    val physicalUnions = planned collect { case u: execution.Union => u}
+    val logicalUnions = query collect { case u: logical.Union => u }
+    val physicalUnions = planned collect { case u: execution.Union => u }
 
     assert(logicalUnions.size === 2)
     assert(physicalUnions.size === 1)
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
index 4b97dc25acf8..64978215542e 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
@@ -18,11 +18,11 @@
 package org.apache.spark.sql
 package hive
 
-import scala.language.implicitConversions
-
 import java.io.{BufferedReader, File, InputStreamReader, PrintStream}
 import java.util.{ArrayList => JArrayList}
 
+import scala.collection.JavaConversions._
+import scala.language.implicitConversions
 import scala.reflect.runtime.universe.TypeTag
 
 import org.apache.hadoop.hive.conf.HiveConf
@@ -30,20 +30,15 @@ import org.apache.hadoop.hive.ql.Driver
 import org.apache.hadoop.hive.ql.processors._
 import org.apache.hadoop.hive.ql.session.SessionState
 
-import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.SparkContext
 import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.catalyst.ScalaReflection
 import org.apache.spark.sql.catalyst.analysis.{Analyzer, OverrideCatalog}
 import org.apache.spark.sql.catalyst.expressions.GenericRow
-import org.apache.spark.sql.catalyst.plans.logical.{LogicalPlan, LowerCaseSchema}
-import org.apache.spark.sql.catalyst.plans.logical.{NativeCommand, ExplainCommand}
-import org.apache.spark.sql.catalyst.ScalaReflection
+import org.apache.spark.sql.catalyst.plans.logical._
 import org.apache.spark.sql.catalyst.types._
 import org.apache.spark.sql.execution._
 
-/* Implicit conversions */
-import scala.collection.JavaConversions._
-
 /**
  * Starts up an instance of hive where metadata is stored locally. An in-process metadata data is
  * created with data stored in ./metadata.  Warehouse data is stored in in ./warehouse.
@@ -55,10 +50,9 @@ class LocalHiveContext(sc: SparkContext) extends HiveContext(sc) {
 
   /** Sets up the system initially or after a RESET command */
   protected def configure() {
-    // TODO: refactor this so we can work with other databases.
-    runSqlHive(
-      s"set javax.jdo.option.ConnectionURL=jdbc:derby:;databaseName=$metastorePath;create=true")
-    runSqlHive("set hive.metastore.warehouse.dir=" + warehousePath)
+    set("javax.jdo.option.ConnectionURL",
+      s"jdbc:derby:;databaseName=$metastorePath;create=true")
+    set("hive.metastore.warehouse.dir", warehousePath)
   }
 
   configure() // Must be called before initializing the catalog below.
@@ -129,12 +123,27 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) {
     }
   }
 
+  /**
+   * SQLConf and HiveConf contracts: when the hive session is first initialized, params in
+   * HiveConf will get picked up by the SQLConf.  Additionally, any properties set by
+   * set() or a SET command inside hql() or sql() will be set in the SQLConf *as well as*
+   * in the HiveConf.
+   */
   @transient protected[hive] lazy val hiveconf = new HiveConf(classOf[SessionState])
-  @transient protected[hive] lazy val sessionState = new SessionState(hiveconf)
+  @transient protected[hive] lazy val sessionState = {
+    val ss = new SessionState(hiveconf)
+    set(hiveconf.getAllProperties)  // Have SQLConf pick up the initial set of HiveConf.
+    ss
+  }
 
   sessionState.err = new PrintStream(outputBuffer, true, "UTF-8")
   sessionState.out = new PrintStream(outputBuffer, true, "UTF-8")
 
+  override def set(key: String, value: String): Unit = {
+    super.set(key, value)
+    runSqlHive(s"SET $key=$value")
+  }
+
   /* A catalyst metadata catalog that points to the Hive Metastore. */
   @transient
   override protected[sql] lazy val catalog = new HiveMetastoreCatalog(this) with OverrideCatalog {
@@ -236,30 +245,31 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) {
   @transient
   override protected[sql] val planner = hivePlanner
 
-  @transient
-  protected lazy val emptyResult =
-    sparkContext.parallelize(Seq(new GenericRow(Array[Any]()): Row), 1)
-
   /** Extends QueryExecution with hive specific features. */
   protected[sql] abstract class QueryExecution extends super.QueryExecution {
     // TODO: Create mixin for the analyzer instead of overriding things here.
     override lazy val optimizedPlan =
       optimizer(catalog.PreInsertionCasts(catalog.CreateTables(analyzed)))
 
-    override lazy val toRdd: RDD[Row] =
-      analyzed match {
-        case NativeCommand(cmd) =>
-          val output = runSqlHive(cmd)
+    override lazy val toRdd: RDD[Row] = {
+      def processCmd(cmd: String): RDD[Row] = {
+        val output = runSqlHive(cmd)
+        if (output.size == 0) {
+          emptyResult
+        } else {
+          val asRows = output.map(r => new GenericRow(r.split("\t").asInstanceOf[Array[Any]]))
+          sparkContext.parallelize(asRows, 1)
+        }
+      }
 
-          if (output.size == 0) {
-            emptyResult
-          } else {
-            val asRows = output.map(r => new GenericRow(r.split("\t").asInstanceOf[Array[Any]]))
-            sparkContext.parallelize(asRows, 1)
-          }
-        case _ =>
-          executedPlan.execute().map(_.copy())
+      logical match {
+        case s: SetCommand => eagerlyProcess(s)
+        case _ => analyzed match {
+          case NativeCommand(cmd) => processCmd(cmd)
+          case _ => executedPlan.execute().map(_.copy())
+        }
       }
+    }
 
     protected val primitiveTypes =
       Seq(StringType, IntegerType, LongType, DoubleType, FloatType, BooleanType, ByteType,
@@ -305,7 +315,7 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) {
      */
     def stringResult(): Seq[String] = analyzed match {
       case NativeCommand(cmd) => runSqlHive(cmd)
-      case ExplainCommand(plan) => mkQueryExecution(plan).toString.split("\n")
+      case ExplainCommand(plan) => executePlan(plan).toString.split("\n")
       case query =>
         val result: Seq[Seq[Any]] = toRdd.collect().toSeq
         // We need the types so we can output struct field names
@@ -318,6 +328,7 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) {
     override def simpleString: String =
       logical match {
         case _: NativeCommand => "<Executed by Hive>"
+        case _: SetCommand => "<Set Command: Executed by Hive, and noted by SQLContext>"
         case _ => executedPlan.toString
       }
   }
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
index cc9e24a05740..4e74d9bc909f 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
@@ -207,8 +207,17 @@ private[hive] object HiveQl {
   /** Returns a LogicalPlan for a given HiveQL string. */
   def parseSql(sql: String): LogicalPlan = {
     try {
-      if (sql.toLowerCase.startsWith("set")) {
-        NativeCommand(sql)
+      if (sql.trim.toLowerCase.startsWith("set")) {
+        // Split in two parts since we treat the part before the first "="
+        // as key, and the part after as value, which may contain other "=" signs.
+        sql.trim.drop(3).split("=", 2).map(_.trim) match {
+          case Array("") => // "set"
+            SetCommand(None, None)
+          case Array(key) => // "set key"
+            SetCommand(Some(key), None)
+          case Array(key, value) => // "set key=value"
+            SetCommand(Some(key), Some(value))
+        }
       } else if (sql.toLowerCase.startsWith("add jar")) {
         AddJar(sql.drop(8))
       } else if (sql.toLowerCase.startsWith("add file")) {
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala
index 0f954103a85f..357c7e654bd2 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala
@@ -138,6 +138,9 @@ abstract class HiveComparisonTest
 
     val orderedAnswer = hiveQuery.logical match {
       // Clean out non-deterministic time schema info.
+      // Hack: Hive simply prints the result of a SET command to screen,
+      // and does not return it as a query answer.
+      case _: SetCommand => Seq("0")
       case _: NativeCommand => answer.filterNot(nonDeterministicLine).filterNot(_ == "")
       case _: ExplainCommand => answer
       case plan => if (isSorted(plan)) answer else answer.sorted
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
index c56eee258047..6c239b02ed09 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.sql.hive.execution
 
+import org.apache.spark.sql.Row
 import org.apache.spark.sql.hive.test.TestHive._
 import org.apache.spark.sql.hive.test.TestHive
 
@@ -171,4 +172,78 @@ class HiveQuerySuite extends HiveComparisonTest {
     TestHive.reset()
   }
 
+  test("parse HQL set commands") {
+    // Adapted from its SQL counterpart.
+    val testKey = "spark.sql.key.usedfortestonly"
+    val testVal = "val0,val_1,val2.3,my_table"
+
+    hql(s"set $testKey=$testVal")
+    assert(get(testKey, testVal + "_") == testVal)
+
+    hql("set mapred.reduce.tasks=20")
+    assert(get("mapred.reduce.tasks", "0") == "20")
+    hql("set mapred.reduce.tasks = 40")
+    assert(get("mapred.reduce.tasks", "0") == "40")
+
+    hql(s"set $testKey=$testVal")
+    assert(get(testKey, "0") == testVal)
+
+    hql(s"set $testKey=")
+    assert(get(testKey, "0") == "")
+  }
+
+  test("SET commands semantics for a HiveContext") {
+    // Adapted from its SQL counterpart.
+    val testKey = "spark.sql.key.usedfortestonly"
+    var testVal = "test.val.0"
+    val nonexistentKey = "nonexistent"
+    def fromRows(row: Array[Row]): Array[String] = row.map(_.getString(0))
+
+    clear()
+
+    // "set" itself returns all config variables currently specified in SQLConf.
+    assert(hql("set").collect().size == 0)
+
+    // "set key=val"
+    hql(s"SET $testKey=$testVal")
+    assert(fromRows(hql("SET").collect()) sameElements Array(s"$testKey=$testVal"))
+    assert(hiveconf.get(testKey, "") == testVal)
+
+    hql(s"SET ${testKey + testKey}=${testVal + testVal}")
+    assert(fromRows(hql("SET").collect()) sameElements
+      Array(
+        s"$testKey=$testVal",
+        s"${testKey + testKey}=${testVal + testVal}"))
+    assert(hiveconf.get(testKey + testKey, "") == testVal + testVal)
+
+    // "set key"
+    assert(fromRows(hql(s"SET $testKey").collect()) sameElements
+      Array(s"$testKey=$testVal"))
+    assert(fromRows(hql(s"SET $nonexistentKey").collect()) sameElements
+      Array(s"$nonexistentKey is undefined"))
+
+    // Assert that sql() should have the same effects as hql() by repeating the above using sql().
+    clear()
+    assert(sql("set").collect().size == 0)
+
+    sql(s"SET $testKey=$testVal")
+    assert(fromRows(sql("SET").collect()) sameElements Array(s"$testKey=$testVal"))
+    assert(hiveconf.get(testKey, "") == testVal)
+
+    sql(s"SET ${testKey + testKey}=${testVal + testVal}")
+    assert(fromRows(sql("SET").collect()) sameElements
+      Array(
+        s"$testKey=$testVal",
+        s"${testKey + testKey}=${testVal + testVal}"))
+    assert(hiveconf.get(testKey + testKey, "") == testVal + testVal)
+
+    assert(fromRows(sql(s"SET $testKey").collect()) sameElements
+      Array(s"$testKey=$testVal"))
+    assert(fromRows(sql(s"SET $nonexistentKey").collect()) sameElements
+      Array(s"$nonexistentKey is undefined"))
+  }
+
+  // Put tests that depend on specific Hive settings before these last two test,
+  // since they modify /clear stuff.
+
 }

From a9a461c594fd20e46947e318095df60bddb67559 Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian.cs.zju@gmail.com>
Date: Tue, 10 Jun 2014 01:14:44 -0700
Subject: [PATCH 378/810] Moved hiveOperators.scala to the right package folder

The package is `org.apache.spark.sql.hive.execution`, while the file was placed under `sql/hive/src/main/scala/org/apache/spark/sql/hive/`.

Author: Cheng Lian <lian.cs.zju@gmail.com>

Closes #1029 from liancheng/moveHiveOperators and squashes the following commits:

d632eb8 [Cheng Lian] Moved hiveOperators.scala to the right package folder
---
 .../org/apache/spark/sql/hive/{ => execution}/hiveOperators.scala | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename sql/hive/src/main/scala/org/apache/spark/sql/hive/{ => execution}/hiveOperators.scala (100%)

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveOperators.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/hiveOperators.scala
similarity index 100%
rename from sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveOperators.scala
rename to sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/hiveOperators.scala

From 884ca718b24f0bbe93358f2a366463b4e4d31f49 Mon Sep 17 00:00:00 2001
From: witgo <witgo@qq.com>
Date: Tue, 10 Jun 2014 10:34:57 -0500
Subject: [PATCH 379/810] [SPARK-1978] In some cases, spark-yarn does not
 automatically restart the failed container

Author: witgo <witgo@qq.com>

Closes #921 from witgo/allocateExecutors and squashes the following commits:

bc3aa66 [witgo] review commit
8800eba [witgo] Merge branch 'master' of https://github.com/apache/spark into allocateExecutors
32ac7af [witgo] review commit
056b8c7 [witgo] Merge branch 'master' of https://github.com/apache/spark into allocateExecutors
04c6f7e [witgo] Merge branch 'master' into allocateExecutors
aff827c [witgo] review commit
5c376e0 [witgo] Merge branch 'master' of https://github.com/apache/spark into allocateExecutors
1faf4f4 [witgo] Merge branch 'master' into allocateExecutors
3c464bd [witgo] add time limit to allocateExecutors
e00b656 [witgo] In some cases, yarn does not automatically restart the container
---
 .../spark/deploy/yarn/ApplicationMaster.scala | 39 +++++++++++--------
 .../spark/deploy/yarn/ExecutorLauncher.scala  | 22 ++++++-----
 2 files changed, 34 insertions(+), 27 deletions(-)

diff --git a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
index c1dfe3f53b40..33a60d978c58 100644
--- a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
+++ b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
@@ -252,15 +252,12 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration,
     try {
       logInfo("Allocating " + args.numExecutors + " executors.")
       // Wait until all containers have finished
-      // TODO: This is a bit ugly. Can we make it nicer?
-      // TODO: Handle container failure
       yarnAllocator.addResourceRequests(args.numExecutors)
+      yarnAllocator.allocateResources()
       // Exits the loop if the user thread exits.
       while (yarnAllocator.getNumExecutorsRunning < args.numExecutors && userThread.isAlive) {
-        if (yarnAllocator.getNumExecutorsFailed >= maxNumExecutorFailures) {
-          finishApplicationMaster(FinalApplicationStatus.FAILED,
-            "max number of executor failures reached")
-        }
+        checkNumExecutorsFailed()
+        allocateMissingExecutor()
         yarnAllocator.allocateResources()
         ApplicationMaster.incrementAllocatorLoop(1)
         Thread.sleep(100)
@@ -289,23 +286,31 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration,
     }
   }
 
+  private def allocateMissingExecutor() {
+    val missingExecutorCount = args.numExecutors - yarnAllocator.getNumExecutorsRunning -
+      yarnAllocator.getNumPendingAllocate
+    if (missingExecutorCount > 0) {
+      logInfo("Allocating %d containers to make up for (potentially) lost containers".
+        format(missingExecutorCount))
+      yarnAllocator.addResourceRequests(missingExecutorCount)
+    }
+  }
+
+  private def checkNumExecutorsFailed() {
+    if (yarnAllocator.getNumExecutorsFailed >= maxNumExecutorFailures) {
+      finishApplicationMaster(FinalApplicationStatus.FAILED,
+        "max number of executor failures reached")
+    }
+  }
+
   private def launchReporterThread(_sleepTime: Long): Thread = {
     val sleepTime = if (_sleepTime <= 0) 0 else _sleepTime
 
     val t = new Thread {
       override def run() {
         while (userThread.isAlive) {
-          if (yarnAllocator.getNumExecutorsFailed >= maxNumExecutorFailures) {
-            finishApplicationMaster(FinalApplicationStatus.FAILED,
-              "max number of executor failures reached")
-          }
-          val missingExecutorCount = args.numExecutors - yarnAllocator.getNumExecutorsRunning -
-            yarnAllocator.getNumPendingAllocate
-          if (missingExecutorCount > 0) {
-            logInfo("Allocating %d containers to make up for (potentially) lost containers".
-              format(missingExecutorCount))
-            yarnAllocator.addResourceRequests(missingExecutorCount)
-          }
+          checkNumExecutorsFailed()
+          allocateMissingExecutor()
           sendProgress()
           Thread.sleep(sleepTime)
         }
diff --git a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala
index a4ce8766d347..d93e5bb0225d 100644
--- a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala
+++ b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala
@@ -200,17 +200,25 @@ class ExecutorLauncher(args: ApplicationMasterArguments, conf: Configuration, sp
 
     logInfo("Allocating " + args.numExecutors + " executors.")
     // Wait until all containers have finished
-    // TODO: This is a bit ugly. Can we make it nicer?
-    // TODO: Handle container failure
-
     yarnAllocator.addResourceRequests(args.numExecutors)
+    yarnAllocator.allocateResources()
     while ((yarnAllocator.getNumExecutorsRunning < args.numExecutors) && (!driverClosed)) {
+      allocateMissingExecutor()
       yarnAllocator.allocateResources()
       Thread.sleep(100)
     }
 
     logInfo("All executors have launched.")
+  }
 
+  private def allocateMissingExecutor() {
+    val missingExecutorCount = args.numExecutors - yarnAllocator.getNumExecutorsRunning -
+      yarnAllocator.getNumPendingAllocate
+    if (missingExecutorCount > 0) {
+      logInfo("Allocating %d containers to make up for (potentially) lost containers".
+        format(missingExecutorCount))
+      yarnAllocator.addResourceRequests(missingExecutorCount)
+    }
   }
 
   // TODO: We might want to extend this to allocate more containers in case they die !
@@ -220,13 +228,7 @@ class ExecutorLauncher(args: ApplicationMasterArguments, conf: Configuration, sp
     val t = new Thread {
       override def run() {
         while (!driverClosed) {
-          val missingExecutorCount = args.numExecutors - yarnAllocator.getNumExecutorsRunning -
-            yarnAllocator.getNumPendingAllocate
-          if (missingExecutorCount > 0) {
-            logInfo("Allocating %d containers to make up for (potentially) lost containers".
-              format(missingExecutorCount))
-            yarnAllocator.addResourceRequests(missingExecutorCount)
-          }
+          allocateMissingExecutor()
           sendProgress()
           Thread.sleep(sleepTime)
         }

From db0c038a66cb228bcb62a5607cd0ed013d0f9f20 Mon Sep 17 00:00:00 2001
From: Cheng Hao <hao.cheng@intel.com>
Date: Tue, 10 Jun 2014 12:59:52 -0700
Subject: [PATCH 380/810] [SPARK-2076][SQL] Pushdown the join filter &
 predication for outer join

As the rule described in https://cwiki.apache.org/confluence/display/Hive/OuterJoinBehavior, we can optimize the SQL Join by pushing down the Join predicate and Where predicate.

Author: Cheng Hao <hao.cheng@intel.com>

Closes #1015 from chenghao-intel/join_predicate_push_down and squashes the following commits:

10feff9 [Cheng Hao] fix bug of changing the join type in PredicatePushDownThroughJoin
44c6700 [Cheng Hao] Add logical to support pushdown the join filter
0bce426 [Cheng Hao] Pushdown the join filter & predicate for outer join
---
 .../sql/catalyst/optimizer/Optimizer.scala    | 112 +++++++++--
 .../optimizer/FilterPushdownSuite.scala       | 187 +++++++++++++++++-
 2 files changed, 277 insertions(+), 22 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
index 406ffd6801e9..ccb8245cc2e7 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
@@ -19,6 +19,10 @@ package org.apache.spark.sql.catalyst.optimizer
 
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.Inner
+import org.apache.spark.sql.catalyst.plans.FullOuter
+import org.apache.spark.sql.catalyst.plans.LeftOuter
+import org.apache.spark.sql.catalyst.plans.RightOuter
+import org.apache.spark.sql.catalyst.plans.LeftSemi
 import org.apache.spark.sql.catalyst.plans.logical._
 import org.apache.spark.sql.catalyst.rules._
 import org.apache.spark.sql.catalyst.types._
@@ -34,7 +38,7 @@ object Optimizer extends RuleExecutor[LogicalPlan] {
     Batch("Filter Pushdown", FixedPoint(100),
       CombineFilters,
       PushPredicateThroughProject,
-      PushPredicateThroughInnerJoin,
+      PushPredicateThroughJoin,
       ColumnPruning) :: Nil
 }
 
@@ -254,28 +258,98 @@ object PushPredicateThroughProject extends Rule[LogicalPlan] {
 
 /**
  * Pushes down [[catalyst.plans.logical.Filter Filter]] operators where the `condition` can be
- * evaluated using only the attributes of the left or right side of an inner join.  Other
+ * evaluated using only the attributes of the left or right side of a join.  Other
  * [[catalyst.plans.logical.Filter Filter]] conditions are moved into the `condition` of the
  * [[catalyst.plans.logical.Join Join]].
+ * And also Pushes down the join filter, where the `condition` can be evaluated using only the 
+ * attributes of the left or right side of sub query when applicable. 
+ * 
+ * Check https://cwiki.apache.org/confluence/display/Hive/OuterJoinBehavior for more details
  */
-object PushPredicateThroughInnerJoin extends Rule[LogicalPlan] with PredicateHelper {
+object PushPredicateThroughJoin extends Rule[LogicalPlan] with PredicateHelper {
+  // split the condition expression into 3 parts, 
+  // (canEvaluateInLeftSide, canEvaluateInRightSide, haveToEvaluateWithBothSide) 
+  private def split(condition: Seq[Expression], left: LogicalPlan, right: LogicalPlan) = {
+    val (leftEvaluateCondition, rest) =
+        condition.partition(_.references subsetOf left.outputSet)
+    val (rightEvaluateCondition, commonCondition) = 
+        rest.partition(_.references subsetOf right.outputSet)
+
+    (leftEvaluateCondition, rightEvaluateCondition, commonCondition)
+  }
+
   def apply(plan: LogicalPlan): LogicalPlan = plan transform {
-    case f @ Filter(filterCondition, Join(left, right, Inner, joinCondition)) =>
-      val allConditions =
-        splitConjunctivePredicates(filterCondition) ++
-          joinCondition.map(splitConjunctivePredicates).getOrElse(Nil)
-
-      // Split the predicates into those that can be evaluated on the left, right, and those that
-      // must be evaluated after the join.
-      val (rightConditions, leftOrJoinConditions) =
-        allConditions.partition(_.references subsetOf right.outputSet)
-      val (leftConditions, joinConditions) =
-        leftOrJoinConditions.partition(_.references subsetOf left.outputSet)
-
-      // Build the new left and right side, optionally with the pushed down filters.
-      val newLeft = leftConditions.reduceLeftOption(And).map(Filter(_, left)).getOrElse(left)
-      val newRight = rightConditions.reduceLeftOption(And).map(Filter(_, right)).getOrElse(right)
-      Join(newLeft, newRight, Inner, joinConditions.reduceLeftOption(And))
+    // push the where condition down into join filter
+    case f @ Filter(filterCondition, Join(left, right, joinType, joinCondition)) =>
+      val (leftFilterConditions, rightFilterConditions, commonFilterCondition) = 
+        split(splitConjunctivePredicates(filterCondition), left, right)
+
+      joinType match {
+        case Inner =>
+          // push down the single side `where` condition into respective sides
+          val newLeft = leftFilterConditions.
+            reduceLeftOption(And).map(Filter(_, left)).getOrElse(left)
+          val newRight = rightFilterConditions.
+            reduceLeftOption(And).map(Filter(_, right)).getOrElse(right)
+          val newJoinCond = (commonFilterCondition ++ joinCondition).reduceLeftOption(And)
+
+          Join(newLeft, newRight, Inner, newJoinCond)
+        case RightOuter =>
+          // push down the right side only `where` condition
+          val newLeft = left
+          val newRight = rightFilterConditions.
+            reduceLeftOption(And).map(Filter(_, right)).getOrElse(right)
+          val newJoinCond = joinCondition
+          val newJoin = Join(newLeft, newRight, RightOuter, newJoinCond)
+
+          (leftFilterConditions ++ commonFilterCondition).
+            reduceLeftOption(And).map(Filter(_, newJoin)).getOrElse(newJoin)
+        case _ @ (LeftOuter | LeftSemi) =>
+          // push down the left side only `where` condition
+          val newLeft = leftFilterConditions.
+            reduceLeftOption(And).map(Filter(_, left)).getOrElse(left)
+          val newRight = right
+          val newJoinCond = joinCondition
+          val newJoin = Join(newLeft, newRight, joinType, newJoinCond)
+
+          (rightFilterConditions ++ commonFilterCondition).
+            reduceLeftOption(And).map(Filter(_, newJoin)).getOrElse(newJoin)
+        case FullOuter => f // DO Nothing for Full Outer Join
+      }
+
+    // push down the join filter into sub query scanning if applicable
+    case f @ Join(left, right, joinType, joinCondition) =>
+      val (leftJoinConditions, rightJoinConditions, commonJoinCondition) = 
+        split(joinCondition.map(splitConjunctivePredicates).getOrElse(Nil), left, right)
+
+      joinType match {
+        case Inner =>
+          // push down the single side only join filter for both sides sub queries
+          val newLeft = leftJoinConditions.
+            reduceLeftOption(And).map(Filter(_, left)).getOrElse(left)
+          val newRight = rightJoinConditions.
+            reduceLeftOption(And).map(Filter(_, right)).getOrElse(right)
+          val newJoinCond = commonJoinCondition.reduceLeftOption(And)
+
+          Join(newLeft, newRight, Inner, newJoinCond)
+        case RightOuter =>
+          // push down the left side only join filter for left side sub query
+          val newLeft = leftJoinConditions.
+            reduceLeftOption(And).map(Filter(_, left)).getOrElse(left)
+          val newRight = right
+          val newJoinCond = (rightJoinConditions ++ commonJoinCondition).reduceLeftOption(And)
+
+          Join(newLeft, newRight, RightOuter, newJoinCond)
+        case _ @ (LeftOuter | LeftSemi) =>
+          // push down the right side only join filter for right sub query
+          val newLeft = left
+          val newRight = rightJoinConditions.
+            reduceLeftOption(And).map(Filter(_, right)).getOrElse(right)
+          val newJoinCond = (leftJoinConditions ++ commonJoinCondition).reduceLeftOption(And)
+
+          Join(newLeft, newRight, joinType, newJoinCond)
+        case FullOuter => f
+      }
   }
 }
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/FilterPushdownSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/FilterPushdownSuite.scala
index ef47850455a3..02cc665f8a8c 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/FilterPushdownSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/FilterPushdownSuite.scala
@@ -20,11 +20,14 @@ package org.apache.spark.sql.catalyst.optimizer
 import org.apache.spark.sql.catalyst.analysis
 import org.apache.spark.sql.catalyst.analysis.EliminateAnalysisOperators
 import org.apache.spark.sql.catalyst.plans.logical._
+import org.apache.spark.sql.catalyst.plans.Inner
+import org.apache.spark.sql.catalyst.plans.FullOuter
+import org.apache.spark.sql.catalyst.plans.LeftOuter
+import org.apache.spark.sql.catalyst.plans.RightOuter
 import org.apache.spark.sql.catalyst.rules._
-
-/* Implicit conversions */
 import org.apache.spark.sql.catalyst.dsl.plans._
 import org.apache.spark.sql.catalyst.dsl.expressions._
+import org.junit.Test
 
 class FilterPushdownSuite extends OptimizerTest {
 
@@ -35,7 +38,7 @@ class FilterPushdownSuite extends OptimizerTest {
       Batch("Filter Pushdown", Once,
         CombineFilters,
         PushPredicateThroughProject,
-        PushPredicateThroughInnerJoin) :: Nil
+        PushPredicateThroughJoin) :: Nil
   }
 
   val testRelation = LocalRelation('a.int, 'b.int, 'c.int)
@@ -161,6 +164,184 @@ class FilterPushdownSuite extends OptimizerTest {
 
     comparePlans(optimized, correctAnswer)
   }
+  
+  test("joins: push down left outer join #1") {
+    val x = testRelation.subquery('x)
+    val y = testRelation.subquery('y)
+
+    val originalQuery = {
+      x.join(y, LeftOuter)
+        .where("x.b".attr === 1 && "y.b".attr === 2)
+    }
+
+    val optimized = Optimize(originalQuery.analyze)
+    val left = testRelation.where('b === 1)
+    val correctAnswer =
+      left.join(y, LeftOuter).where("y.b".attr === 2).analyze
+
+    comparePlans(optimized, correctAnswer)
+  }
+
+  test("joins: push down right outer join #1") {
+    val x = testRelation.subquery('x)
+    val y = testRelation.subquery('y)
+
+    val originalQuery = {
+      x.join(y, RightOuter)
+        .where("x.b".attr === 1 && "y.b".attr === 2)
+    }
+
+    val optimized = Optimize(originalQuery.analyze)
+    val right = testRelation.where('b === 2).subquery('d)
+    val correctAnswer =
+      x.join(right, RightOuter).where("x.b".attr === 1).analyze
+
+    comparePlans(optimized, correctAnswer)
+  }
+
+  test("joins: push down left outer join #2") {
+    val x = testRelation.subquery('x)
+    val y = testRelation.subquery('y)
+
+    val originalQuery = {
+      x.join(y, LeftOuter, Some("x.b".attr === 1))
+        .where("x.b".attr === 2 && "y.b".attr === 2)
+    }
+
+    val optimized = Optimize(originalQuery.analyze)
+    val left = testRelation.where('b === 2).subquery('d)
+    val correctAnswer =
+      left.join(y, LeftOuter, Some("d.b".attr === 1)).where("y.b".attr === 2).analyze
+
+    comparePlans(optimized, correctAnswer)
+  }
+
+  test("joins: push down right outer join #2") {
+    val x = testRelation.subquery('x)
+    val y = testRelation.subquery('y)
+
+    val originalQuery = {
+      x.join(y, RightOuter, Some("y.b".attr === 1))
+        .where("x.b".attr === 2 && "y.b".attr === 2)
+    }
+
+    val optimized = Optimize(originalQuery.analyze)
+    val right = testRelation.where('b === 2).subquery('d)
+    val correctAnswer =
+      x.join(right, RightOuter, Some("d.b".attr === 1)).where("x.b".attr === 2).analyze
+
+    comparePlans(optimized, correctAnswer)
+  }
+
+  test("joins: push down left outer join #3") {
+    val x = testRelation.subquery('x)
+    val y = testRelation.subquery('y)
+
+    val originalQuery = {
+      x.join(y, LeftOuter, Some("y.b".attr === 1))
+        .where("x.b".attr === 2 && "y.b".attr === 2)
+    }
+
+    val optimized = Optimize(originalQuery.analyze)
+    val left = testRelation.where('b === 2).subquery('l)
+    val right = testRelation.where('b === 1).subquery('r)
+    val correctAnswer =
+      left.join(right, LeftOuter).where("r.b".attr === 2).analyze
+
+    comparePlans(optimized, correctAnswer)
+  }
+
+  test("joins: push down right outer join #3") {
+    val x = testRelation.subquery('x)
+    val y = testRelation.subquery('y)
+
+    val originalQuery = {
+      x.join(y, RightOuter, Some("y.b".attr === 1))
+        .where("x.b".attr === 2 && "y.b".attr === 2)
+    }
+
+    val optimized = Optimize(originalQuery.analyze)
+    val right = testRelation.where('b === 2).subquery('r)
+    val correctAnswer =
+      x.join(right, RightOuter, Some("r.b".attr === 1)).where("x.b".attr === 2).analyze
+
+    comparePlans(optimized, correctAnswer)
+  }
+
+  test("joins: push down left outer join #4") {
+    val x = testRelation.subquery('x)
+    val y = testRelation.subquery('y)
+
+    val originalQuery = {
+      x.join(y, LeftOuter, Some("y.b".attr === 1))
+        .where("x.b".attr === 2 && "y.b".attr === 2 && "x.c".attr === "y.c".attr)
+    }
+
+    val optimized = Optimize(originalQuery.analyze)
+    val left = testRelation.where('b === 2).subquery('l)
+    val right = testRelation.where('b === 1).subquery('r)
+    val correctAnswer =
+      left.join(right, LeftOuter).where("r.b".attr === 2 && "l.c".attr === "r.c".attr).analyze
+
+    comparePlans(optimized, correctAnswer)
+  }
+
+  test("joins: push down right outer join #4") {
+    val x = testRelation.subquery('x)
+    val y = testRelation.subquery('y)
+
+    val originalQuery = {
+      x.join(y, RightOuter, Some("y.b".attr === 1))
+        .where("x.b".attr === 2 && "y.b".attr === 2 && "x.c".attr === "y.c".attr)
+    }
+
+    val optimized = Optimize(originalQuery.analyze)
+    val left = testRelation.subquery('l)
+    val right = testRelation.where('b === 2).subquery('r)
+    val correctAnswer =
+      left.join(right, RightOuter, Some("r.b".attr === 1)).
+        where("l.b".attr === 2 && "l.c".attr === "r.c".attr).analyze
+
+    comparePlans(optimized, correctAnswer)
+  }
+
+  test("joins: push down left outer join #5") {
+    val x = testRelation.subquery('x)
+    val y = testRelation.subquery('y)
+
+    val originalQuery = {
+      x.join(y, LeftOuter, Some("y.b".attr === 1 && "x.a".attr === 3))
+        .where("x.b".attr === 2 && "y.b".attr === 2 && "x.c".attr === "y.c".attr)
+    }
+
+    val optimized = Optimize(originalQuery.analyze)
+    val left = testRelation.where('b === 2).subquery('l)
+    val right = testRelation.where('b === 1).subquery('r)
+    val correctAnswer =
+      left.join(right, LeftOuter, Some("l.a".attr===3)).
+        where("r.b".attr === 2 && "l.c".attr === "r.c".attr).analyze
+
+    comparePlans(optimized, correctAnswer)
+  }
+
+  test("joins: push down right outer join #5") {
+    val x = testRelation.subquery('x)
+    val y = testRelation.subquery('y)
+
+    val originalQuery = {
+      x.join(y, RightOuter, Some("y.b".attr === 1 && "x.a".attr === 3))
+        .where("x.b".attr === 2 && "y.b".attr === 2 && "x.c".attr === "y.c".attr)
+    }
+
+    val optimized = Optimize(originalQuery.analyze)
+    val left = testRelation.where('a === 3).subquery('l)
+    val right = testRelation.where('b === 2).subquery('r)
+    val correctAnswer =
+      left.join(right, RightOuter, Some("r.b".attr === 1)).
+        where("l.b".attr === 2 && "l.c".attr === "r.c".attr).analyze
+
+    comparePlans(optimized, correctAnswer)
+  }
 
   test("joins: can't push down") {
     val x = testRelation.subquery('x)

From fb499be1ac935b6f91046ec8ff23ac1267c82342 Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Tue, 10 Jun 2014 13:13:17 -0700
Subject: [PATCH 381/810] HOTFIX: Fix Python tests on Jenkins.

Author: Patrick Wendell <pwendell@gmail.com>

Closes #1036 from pwendell/jenkins-test and squashes the following commits:

9c99856 [Patrick Wendell] Better output during tests
71e7b74 [Patrick Wendell] Removing incorrect python path
74984db [Patrick Wendell] HOTFIX: Allow PySpark tests to run on Jenkins.
---
 bin/pyspark      |  6 +++++-
 dev/run-tests    |  3 ---
 python/run-tests | 11 +++++++----
 3 files changed, 12 insertions(+), 8 deletions(-)

diff --git a/bin/pyspark b/bin/pyspark
index d0fa56f31913..114cbbc3a8a8 100755
--- a/bin/pyspark
+++ b/bin/pyspark
@@ -86,6 +86,10 @@ else
   if [[ "$IPYTHON" = "1" ]]; then
     exec ipython $IPYTHON_OPTS
   else
-    exec "$PYSPARK_PYTHON"
+    if [[ -n $SPARK_TESTING ]]; then
+      exec "$PYSPARK_PYTHON" -m doctest
+    else
+      exec "$PYSPARK_PYTHON"
+    fi
   fi
 fi
diff --git a/dev/run-tests b/dev/run-tests
index 93d6692f83ca..c82a47ebb618 100755
--- a/dev/run-tests
+++ b/dev/run-tests
@@ -73,9 +73,6 @@ fi
 echo "========================================================================="
 echo "Running PySpark tests"
 echo "========================================================================="
-if [ -z "$PYSPARK_PYTHON" ]; then
-  export PYSPARK_PYTHON=/usr/local/bin/python2.7
-fi
 ./python/run-tests
 
 echo "========================================================================="
diff --git a/python/run-tests b/python/run-tests
index 36a96121cbc0..3b4501178c89 100755
--- a/python/run-tests
+++ b/python/run-tests
@@ -32,7 +32,8 @@ rm -f unit-tests.log
 rm -rf metastore warehouse
 
 function run_test() {
-    SPARK_TESTING=0 $FWDIR/bin/pyspark $1 2>&1 | tee -a > unit-tests.log
+    echo "Running test: $1"
+    SPARK_TESTING=1 $FWDIR/bin/pyspark $1 2>&1 | tee -a > unit-tests.log
     FAILED=$((PIPESTATUS[0]||$FAILED))
 
     # Fail and exit on the first test failure.
@@ -46,15 +47,17 @@ function run_test() {
 
 }
 
+echo "Running PySpark tests. Output is in python/unit-tests.log."
+
 run_test "pyspark/rdd.py"
 run_test "pyspark/context.py"
 run_test "pyspark/conf.py"
 if [ -n "$_RUN_SQL_TESTS" ]; then
   run_test "pyspark/sql.py"
 fi
-run_test "-m doctest pyspark/broadcast.py"
-run_test "-m doctest pyspark/accumulators.py"
-run_test "-m doctest pyspark/serializers.py"
+run_test "pyspark/broadcast.py"
+run_test "pyspark/accumulators.py"
+run_test "pyspark/serializers.py"
 run_test "pyspark/tests.py"
 run_test "pyspark/mllib/_common.py"
 run_test "pyspark/mllib/classification.py"

From 55a0e87ee4655106d5e0ed799b11e77f68a17dbb Mon Sep 17 00:00:00 2001
From: Ankur Dave <ankurdave@gmail.com>
Date: Tue, 10 Jun 2014 13:15:06 -0700
Subject: [PATCH 382/810] HOTFIX: Increase time limit for Bagel test

The test was timing out on some slow EC2 workers.

Author: Ankur Dave <ankurdave@gmail.com>

Closes #1037 from ankurdave/bagel-test-time-limit and squashes the following commits:

67fd487 [Ankur Dave] Increase time limit for Bagel test
---
 bagel/src/test/scala/org/apache/spark/bagel/BagelSuite.scala | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/bagel/src/test/scala/org/apache/spark/bagel/BagelSuite.scala b/bagel/src/test/scala/org/apache/spark/bagel/BagelSuite.scala
index 110bd0a9a0c4..55241d33cd3f 100644
--- a/bagel/src/test/scala/org/apache/spark/bagel/BagelSuite.scala
+++ b/bagel/src/test/scala/org/apache/spark/bagel/BagelSuite.scala
@@ -80,7 +80,7 @@ class BagelSuite extends FunSuite with Assertions with BeforeAndAfter with Timeo
   test("large number of iterations") {
     // This tests whether jobs with a large number of iterations finish in a reasonable time,
     // because non-memoized recursion in RDD or DAGScheduler used to cause them to hang
-    failAfter(10 seconds) {
+    failAfter(30 seconds) {
       sc = new SparkContext("local", "test")
       val verts = sc.parallelize((1 to 4).map(id => (id.toString, new TestVertex(true, 0))))
       val msgs = sc.parallelize(Array[(String, TestMessage)]())
@@ -101,7 +101,7 @@ class BagelSuite extends FunSuite with Assertions with BeforeAndAfter with Timeo
       sc = new SparkContext("local", "test")
       val verts = sc.parallelize((1 to 4).map(id => (id.toString, new TestVertex(true, 0))))
       val msgs = sc.parallelize(Array[(String, TestMessage)]())
-      val numSupersteps = 50
+      val numSupersteps = 20
       val result =
         Bagel.run(sc, verts, msgs, sc.defaultParallelism, StorageLevel.DISK_ONLY) {
           (self: TestVertex, msgs: Option[Array[TestMessage]], superstep: Int) =>

From 1abbde0e89131ad95e793ac1834c392db46b448e Mon Sep 17 00:00:00 2001
From: egraldlo <egraldlo@gmail.com>
Date: Tue, 10 Jun 2014 14:07:55 -0700
Subject: [PATCH 383/810] [SQL] Add average overflow test case from #978

By @egraldlo.

Author: egraldlo <egraldlo@gmail.com>
Author: Michael Armbrust <michael@databricks.com>

Closes #1033 from marmbrus/pr/978 and squashes the following commits:

e228c5e [Michael Armbrust] Remove "test".
762aeaf [Michael Armbrust] Remove unneeded rule. More descriptive name for test table.
d414cd7 [egraldlo] fommatting issues
1153f75 [egraldlo] do best to avoid overflowing in function avg().
---
 .../scala/org/apache/spark/sql/SQLQuerySuite.scala    |  6 ++++++
 .../test/scala/org/apache/spark/sql/TestData.scala    | 11 +++++++++++
 2 files changed, 17 insertions(+)

diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index f2d850ad6aa5..de02bbc7e470 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -136,6 +136,12 @@ class SQLQuerySuite extends QueryTest {
       2.0)
   }
 
+  test("average overflow") {
+    checkAnswer(
+      sql("SELECT AVG(a),b FROM largeAndSmallInts group by b"),
+      Seq((2147483645.0,1),(2.0,2)))
+  }
+  
   test("count") {
     checkAnswer(
       sql("SELECT COUNT(*) FROM testData2"),
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala b/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala
index 05de736bbce1..330b20b315d6 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala
@@ -30,6 +30,17 @@ object TestData {
     (1 to 100).map(i => TestData(i, i.toString)))
   testData.registerAsTable("testData")
 
+  case class LargeAndSmallInts(a: Int, b: Int)
+  val largeAndSmallInts: SchemaRDD =
+    TestSQLContext.sparkContext.parallelize(
+      LargeAndSmallInts(2147483644, 1) ::
+      LargeAndSmallInts(1, 2) ::
+      LargeAndSmallInts(2147483645, 1) ::
+      LargeAndSmallInts(2, 2) ::
+      LargeAndSmallInts(2147483646, 1) ::
+      LargeAndSmallInts(3, 2) :: Nil)
+  largeAndSmallInts.registerAsTable("largeAndSmallInts")
+  
   case class TestData2(a: Int, b: Int)
   val testData2: SchemaRDD =
     TestSQLContext.sparkContext.parallelize(

From 29660443077619ee854025b8d0d3d64181724054 Mon Sep 17 00:00:00 2001
From: joyyoj <sunshch@gmail.com>
Date: Tue, 10 Jun 2014 17:26:17 -0700
Subject: [PATCH 384/810] [SPARK-1998] SparkFlumeEvent with body bigger than
 1020 bytes are not re...

flume event sent to Spark will fail if the body is too large and numHeaders is greater than zero

Author: joyyoj <sunshch@gmail.com>

Closes #951 from joyyoj/master and squashes the following commits:

f4660c5 [joyyoj] [SPARK-1998] SparkFlumeEvent with body bigger than 1020 bytes are not read properly
---
 .../org/apache/spark/streaming/flume/FlumeInputDStream.scala  | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeInputDStream.scala b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeInputDStream.scala
index 5be33f1d5c42..ed35e34ad45a 100644
--- a/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeInputDStream.scala
+++ b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeInputDStream.scala
@@ -71,12 +71,12 @@ class SparkFlumeEvent() extends Externalizable {
     for (i <- 0 until numHeaders) {
       val keyLength = in.readInt()
       val keyBuff = new Array[Byte](keyLength)
-      in.read(keyBuff)
+      in.readFully(keyBuff)
       val key : String = Utils.deserialize(keyBuff)
 
       val valLength = in.readInt()
       val valBuff = new Array[Byte](valLength)
-      in.read(valBuff)
+      in.readFully(valBuff)
       val value : String = Utils.deserialize(valBuff)
 
       headers.put(key, value)

From 4823bf470ec1b47a6f404834d4453e61d3dcbec9 Mon Sep 17 00:00:00 2001
From: Tathagata Das <tathagata.das1565@gmail.com>
Date: Tue, 10 Jun 2014 20:22:02 -0700
Subject: [PATCH 385/810] [SPARK-1940] Enabling rolling of executor logs, and
 automatic cleanup of old executor logs

Currently, in the default log4j configuration, all the executor logs get sent to the file <code>[executor-working-dir]/stderr</code>. This does not all log files to be rolled, so old logs cannot be removed.

Using log4j RollingFileAppender allows log4j logs to be rolled, but all the logs get sent to a different set of files, other than the files <code>stdout</code> and <code>stderr</code> . So the logs are not visible in the Spark web UI any more as Spark web UI only reads the files <code>stdout</code> and <code>stderr</code>. Furthermore, it still does not allow the stdout and stderr to be cleared periodically in case a large amount of stuff gets written to them (e.g. by explicit `println` inside map function).

This PR solves this by implementing a simple `RollingFileAppender` within Spark (disabled by default). When enabled (using configuration parameter `spark.executor.rollingLogs.enabled`), the logs can get rolled over either by time interval (set with `spark.executor.rollingLogs.interval`, set to daily by default), or by size of logs (set with  `spark.executor.rollingLogs.size`). Finally, old logs can be automatically deleted by specifying how many of the latest log files to keep (set with `spark.executor.rollingLogs.keepLastN`).  The web UI has also been modified to show the logs across the rolled-over files.

You can test this locally (without waiting a whole day) by setting  configuration `spark.executor.rollingLogs.enabled=true` and `spark.executor.rollingLogs.interval=minutely`. Continuously generate logs by running spark jobs and the generated logs files would look like this (`stderr` and `stdout` are the most current log file that are being written to).

```
stderr
stderr--2014-05-27--14-37
stderr--2014-05-27--14-47
stderr--2014-05-27--15-05
stdout
stdout--2014-05-27--14-47
```

The web ui should show logs across these files.

Author: Tathagata Das <tathagata.das1565@gmail.com>

Closes #895 from tdas/rolling-logs and squashes the following commits:

fd8f87f [Tathagata Das] Minor change.
d326aee [Tathagata Das] Merge remote-tracking branch 'apache-github/master' into rolling-logs
ad956c1 [Tathagata Das] Scala style fix.
1f0a6ec [Tathagata Das] Some more changes based on Patrick's PR comments.
c8bfe4e [Tathagata Das] Refactore FileAppender to a package spark.util.logging and broke up the file into multiple files. Changed configuration parameter names.
4224409 [Tathagata Das] Style fix.
108a9f8 [Tathagata Das] Added better constraint handling for rolling policies.
f7da977 [Tathagata Das] Merge remote-tracking branch 'apache-github/master' into rolling-logs
9134495 [Tathagata Das] Simplified rolling logs by removing Daily/Hourly/MinutelyRollingFileAppender, and removing the setting rollingLogs.enabled
312d874 [Tathagata Das] Minor fixes based on PR comments.
8a67d83 [Tathagata Das] Fixed comments.
b36cfd6 [Tathagata Das] Implemented RollingPolicy, TimeBasedRollingPolicy and SizeBasedRollingPolicy, and changed RollingFileAppender accordingly.
b7e8272 [Tathagata Das] Style fix,
374c9a9 [Tathagata Das] Added missing license.
24354ea [Tathagata Das] Merge remote-tracking branch 'apache-github/master' into rolling-logs
6cc09c7 [Tathagata Das] Fixed bugs in rolling logs, and added more debug statements.
adf4910 [Tathagata Das] Merge remote-tracking branch 'apache-github/master' into rolling-logs
931f8fb [Tathagata Das] Changed log viewer in Spark web UI to handle rolling log files.
cb4fb6d [Tathagata Das] Added FileAppender and RollingFileAppender to generate rolling executor logs.
---
 .../spark/deploy/worker/ExecutorRunner.scala  |  17 +-
 .../apache/spark/deploy/worker/Worker.scala   |   2 +-
 .../spark/deploy/worker/ui/LogPage.scala      |  78 +++---
 .../scala/org/apache/spark/util/Utils.scala   |  53 +++++
 .../spark/util/logging/FileAppender.scala     | 180 ++++++++++++++
 .../util/logging/RollingFileAppender.scala    | 163 +++++++++++++
 .../spark/util/logging/RollingPolicy.scala    | 139 +++++++++++
 .../spark/deploy/JsonProtocolSuite.scala      |   4 +-
 .../deploy/worker/ExecutorRunnerTest.scala    |   3 +-
 .../apache/spark/util/FileAppenderSuite.scala | 225 ++++++++++++++++++
 .../org/apache/spark/util/UtilsSuite.scala    |  32 +++
 docs/configuration.md                         |  39 +++
 12 files changed, 895 insertions(+), 40 deletions(-)
 create mode 100644 core/src/main/scala/org/apache/spark/util/logging/FileAppender.scala
 create mode 100644 core/src/main/scala/org/apache/spark/util/logging/RollingFileAppender.scala
 create mode 100644 core/src/main/scala/org/apache/spark/util/logging/RollingPolicy.scala
 create mode 100644 core/src/test/scala/org/apache/spark/util/FileAppenderSuite.scala

diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/ExecutorRunner.scala b/core/src/main/scala/org/apache/spark/deploy/worker/ExecutorRunner.scala
index d27e0e1f15c6..d09136de4980 100644
--- a/core/src/main/scala/org/apache/spark/deploy/worker/ExecutorRunner.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/worker/ExecutorRunner.scala
@@ -23,9 +23,10 @@ import akka.actor.ActorRef
 import com.google.common.base.Charsets
 import com.google.common.io.Files
 
-import org.apache.spark.Logging
+import org.apache.spark.{SparkConf, Logging}
 import org.apache.spark.deploy.{ApplicationDescription, Command, ExecutorState}
 import org.apache.spark.deploy.DeployMessages.ExecutorStateChanged
+import org.apache.spark.util.logging.FileAppender
 
 /**
  * Manages the execution of one executor process.
@@ -42,12 +43,15 @@ private[spark] class ExecutorRunner(
     val sparkHome: File,
     val workDir: File,
     val workerUrl: String,
+    val conf: SparkConf,
     var state: ExecutorState.Value)
   extends Logging {
 
   val fullId = appId + "/" + execId
   var workerThread: Thread = null
   var process: Process = null
+  var stdoutAppender: FileAppender = null
+  var stderrAppender: FileAppender = null
 
   // NOTE: This is now redundant with the automated shut-down enforced by the Executor. It might
   // make sense to remove this in the future.
@@ -76,6 +80,13 @@ private[spark] class ExecutorRunner(
     if (process != null) {
       logInfo("Killing process!")
       process.destroy()
+      process.waitFor()
+      if (stdoutAppender != null) {
+        stdoutAppender.stop()
+      }
+      if (stderrAppender != null) {
+        stderrAppender.stop()
+      }
       val exitCode = process.waitFor()
       worker ! ExecutorStateChanged(appId, execId, state, message, Some(exitCode))
     }
@@ -137,11 +148,11 @@ private[spark] class ExecutorRunner(
 
       // Redirect its stdout and stderr to files
       val stdout = new File(executorDir, "stdout")
-      CommandUtils.redirectStream(process.getInputStream, stdout)
+      stdoutAppender = FileAppender(process.getInputStream, stdout, conf)
 
       val stderr = new File(executorDir, "stderr")
       Files.write(header, stderr, Charsets.UTF_8)
-      CommandUtils.redirectStream(process.getErrorStream, stderr)
+      stderrAppender = FileAppender(process.getErrorStream, stderr, conf)
 
       // Wait for it to exit; this is actually a bad thing if it happens, because we expect to run
       // long-lived processes only. However, in the future, we might restart the executor a few
diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala b/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala
index 100de26170a5..a0ecaf709f8e 100755
--- a/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala
@@ -235,7 +235,7 @@ private[spark] class Worker(
           val manager = new ExecutorRunner(appId, execId, appDesc, cores_, memory_,
             self, workerId, host,
             appDesc.sparkHome.map(userSparkHome => new File(userSparkHome)).getOrElse(sparkHome),
-            workDir, akkaUrl, ExecutorState.RUNNING)
+            workDir, akkaUrl, conf, ExecutorState.RUNNING)
           executors(appId + "/" + execId) = manager
           manager.start()
           coresUsed += cores_
diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/ui/LogPage.scala b/core/src/main/scala/org/apache/spark/deploy/worker/ui/LogPage.scala
index 8381f59672ea..6a5ffb1b71bf 100644
--- a/core/src/main/scala/org/apache/spark/deploy/worker/ui/LogPage.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/worker/ui/LogPage.scala
@@ -24,8 +24,10 @@ import scala.xml.Node
 
 import org.apache.spark.ui.{WebUIPage, UIUtils}
 import org.apache.spark.util.Utils
+import org.apache.spark.Logging
+import org.apache.spark.util.logging.{FileAppender, RollingFileAppender}
 
-private[spark] class LogPage(parent: WorkerWebUI) extends WebUIPage("logPage") {
+private[spark] class LogPage(parent: WorkerWebUI) extends WebUIPage("logPage") with Logging {
   private val worker = parent.worker
   private val workDir = parent.workDir
 
@@ -39,21 +41,18 @@ private[spark] class LogPage(parent: WorkerWebUI) extends WebUIPage("logPage") {
     val offset = Option(request.getParameter("offset")).map(_.toLong)
     val byteLength = Option(request.getParameter("byteLength")).map(_.toInt).getOrElse(defaultBytes)
 
-    val path = (appId, executorId, driverId) match {
+    val logDir = (appId, executorId, driverId) match {
       case (Some(a), Some(e), None) =>
-        s"${workDir.getPath}/$appId/$executorId/$logType"
+        s"${workDir.getPath}/$appId/$executorId/"
       case (None, None, Some(d)) =>
-        s"${workDir.getPath}/$driverId/$logType"
+        s"${workDir.getPath}/$driverId/"
       case _ =>
         throw new Exception("Request must specify either application or driver identifiers")
     }
 
-    val (startByte, endByte) = getByteRange(path, offset, byteLength)
-    val file = new File(path)
-    val logLength = file.length
-
-    val pre = s"==== Bytes $startByte-$endByte of $logLength of $path ====\n"
-    pre + Utils.offsetBytes(path, startByte, endByte)
+    val (logText, startByte, endByte, logLength) = getLog(logDir, logType, offset, byteLength)
+    val pre = s"==== Bytes $startByte-$endByte of $logLength of $logDir$logType ====\n"
+    pre + logText
   }
 
   def render(request: HttpServletRequest): Seq[Node] = {
@@ -65,19 +64,16 @@ private[spark] class LogPage(parent: WorkerWebUI) extends WebUIPage("logPage") {
     val offset = Option(request.getParameter("offset")).map(_.toLong)
     val byteLength = Option(request.getParameter("byteLength")).map(_.toInt).getOrElse(defaultBytes)
 
-    val (path, params) = (appId, executorId, driverId) match {
+    val (logDir, params) = (appId, executorId, driverId) match {
       case (Some(a), Some(e), None) =>
-        (s"${workDir.getPath}/$a/$e/$logType", s"appId=$a&executorId=$e")
+        (s"${workDir.getPath}/$a/$e/", s"appId=$a&executorId=$e")
       case (None, None, Some(d)) =>
-        (s"${workDir.getPath}/$d/$logType", s"driverId=$d")
+        (s"${workDir.getPath}/$d/", s"driverId=$d")
       case _ =>
         throw new Exception("Request must specify either application or driver identifiers")
     }
 
-    val (startByte, endByte) = getByteRange(path, offset, byteLength)
-    val file = new File(path)
-    val logLength = file.length
-    val logText = <node>{Utils.offsetBytes(path, startByte, endByte)}</node>
+    val (logText, startByte, endByte, logLength) = getLog(logDir, logType, offset, byteLength)
     val linkToMaster = <p><a href={worker.activeMasterWebUiUrl}>Back to Master</a></p>
     val range = <span>Bytes {startByte.toString} - {endByte.toString} of {logLength}</span>
 
@@ -127,23 +123,37 @@ private[spark] class LogPage(parent: WorkerWebUI) extends WebUIPage("logPage") {
     UIUtils.basicSparkPage(content, logType + " log page for " + appId)
   }
 
-  /** Determine the byte range for a log or log page. */
-  private def getByteRange(path: String, offset: Option[Long], byteLength: Int): (Long, Long) = {
-    val defaultBytes = 100 * 1024
-    val maxBytes = 1024 * 1024
-    val file = new File(path)
-    val logLength = file.length()
-    val getOffset = offset.getOrElse(logLength - defaultBytes)
-    val startByte =
-      if (getOffset < 0) {
-        0L
-      } else if (getOffset > logLength) {
-        logLength
-      } else {
-        getOffset
+  /** Get the part of the log files given the offset and desired length of bytes */
+  private def getLog(
+      logDirectory: String,
+      logType: String,
+      offsetOption: Option[Long],
+      byteLength: Int
+    ): (String, Long, Long, Long) = {
+    try {
+      val files = RollingFileAppender.getSortedRolledOverFiles(logDirectory, logType)
+      logDebug(s"Sorted log files of type $logType in $logDirectory:\n${files.mkString("\n")}")
+
+      val totalLength = files.map { _.length }.sum
+      val offset = offsetOption.getOrElse(totalLength - byteLength)
+      val startIndex = {
+        if (offset < 0) {
+          0L
+        } else if (offset > totalLength) {
+          totalLength
+        } else {
+          offset
+        }
       }
-    val logPageLength = math.min(byteLength, maxBytes)
-    val endByte = math.min(startByte + logPageLength, logLength)
-    (startByte, endByte)
+      val endIndex = math.min(startIndex + totalLength, totalLength)
+      logDebug(s"Getting log from $startIndex to $endIndex")
+      val logText = Utils.offsetBytes(files, startIndex, endIndex)
+      logDebug(s"Got log of length ${logText.length} bytes")
+      (logText, startIndex, endIndex, totalLength)
+    } catch {
+      case e: Exception =>
+        logError(s"Error getting $logType logs from directory $logDirectory", e)
+        ("Error getting logs due to exception: " + e.getMessage, 0, 0, 0)
+    }
   }
 }
diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala
index 3b1b6df089b8..4ce28bb0cf05 100644
--- a/core/src/main/scala/org/apache/spark/util/Utils.scala
+++ b/core/src/main/scala/org/apache/spark/util/Utils.scala
@@ -862,6 +862,59 @@ private[spark] object Utils extends Logging {
     Source.fromBytes(buff).mkString
   }
 
+  /**
+   * Return a string containing data across a set of files. The `startIndex`
+   * and `endIndex` is based on the cumulative size of all the files take in
+   * the given order. See figure below for more details.
+   */
+  def offsetBytes(files: Seq[File], start: Long, end: Long): String = {
+    val fileLengths = files.map { _.length }
+    val startIndex = math.max(start, 0)
+    val endIndex = math.min(end, fileLengths.sum)
+    val fileToLength = files.zip(fileLengths).toMap
+    logDebug("Log files: \n" + fileToLength.mkString("\n"))
+
+    val stringBuffer = new StringBuffer((endIndex - startIndex).toInt)
+    var sum = 0L
+    for (file <- files) {
+      val startIndexOfFile = sum
+      val endIndexOfFile = sum + fileToLength(file)
+      logDebug(s"Processing file $file, " +
+        s"with start index = $startIndexOfFile, end index = $endIndex")
+
+      /*
+                                      ____________
+       range 1:                      |            |
+                                     |   case A   |
+
+       files:   |==== file 1 ====|====== file 2 ======|===== file 3 =====|
+
+                     |   case B  .       case C       .    case D    |
+       range 2:      |___________.____________________.______________|
+       */
+
+      if (startIndex <= startIndexOfFile  && endIndex >= endIndexOfFile) {
+        // Case C: read the whole file
+        stringBuffer.append(offsetBytes(file.getAbsolutePath, 0, fileToLength(file)))
+      } else if (startIndex > startIndexOfFile && startIndex < endIndexOfFile) {
+        // Case A and B: read from [start of required range] to [end of file / end of range]
+        val effectiveStartIndex = startIndex - startIndexOfFile
+        val effectiveEndIndex = math.min(endIndex - startIndexOfFile, fileToLength(file))
+        stringBuffer.append(Utils.offsetBytes(
+          file.getAbsolutePath, effectiveStartIndex, effectiveEndIndex))
+      } else if (endIndex > startIndexOfFile && endIndex < endIndexOfFile) {
+        // Case D: read from [start of file] to [end of require range]
+        val effectiveStartIndex = math.max(startIndex - startIndexOfFile, 0)
+        val effectiveEndIndex = endIndex - startIndexOfFile
+        stringBuffer.append(Utils.offsetBytes(
+          file.getAbsolutePath, effectiveStartIndex, effectiveEndIndex))
+      }
+      sum += fileToLength(file)
+      logDebug(s"After processing file $file, string built is ${stringBuffer.toString}}")
+    }
+    stringBuffer.toString
+  }
+
   /**
    * Clone an object using a Spark serializer.
    */
diff --git a/core/src/main/scala/org/apache/spark/util/logging/FileAppender.scala b/core/src/main/scala/org/apache/spark/util/logging/FileAppender.scala
new file mode 100644
index 000000000000..8e9c3036d09c
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/util/logging/FileAppender.scala
@@ -0,0 +1,180 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.util.logging
+
+import java.io.{File, FileOutputStream, InputStream}
+
+import org.apache.spark.{Logging, SparkConf}
+import org.apache.spark.util.{IntParam, Utils}
+
+/**
+ * Continuously appends the data from an input stream into the given file.
+ */
+private[spark] class FileAppender(inputStream: InputStream, file: File, bufferSize: Int = 8192)
+  extends Logging {
+  @volatile private var outputStream: FileOutputStream = null
+  @volatile private var markedForStop = false     // has the appender been asked to stopped
+  @volatile private var stopped = false           // has the appender stopped
+
+  // Thread that reads the input stream and writes to file
+  private val writingThread = new Thread("File appending thread for " + file) {
+    setDaemon(true)
+    override def run() {
+      Utils.logUncaughtExceptions {
+        appendStreamToFile()
+      }
+    }
+  }
+  writingThread.start()
+
+  /**
+   * Wait for the appender to stop appending, either because input stream is closed
+   * or because of any error in appending
+   */
+  def awaitTermination() {
+    synchronized {
+      if (!stopped) {
+        wait()
+      }
+    }
+  }
+
+  /** Stop the appender */
+  def stop() {
+    markedForStop = true
+  }
+
+  /** Continuously read chunks from the input stream and append to the file */
+  protected def appendStreamToFile() {
+    try {
+      logDebug("Started appending thread")
+      openFile()
+      val buf = new Array[Byte](bufferSize)
+      var n = 0
+      while (!markedForStop && n != -1) {
+        n = inputStream.read(buf)
+        if (n != -1) {
+          appendToFile(buf, n)
+        }
+      }
+    } catch {
+      case e: Exception =>
+        logError(s"Error writing stream to file $file", e)
+    } finally {
+      closeFile()
+      synchronized {
+        stopped = true
+        notifyAll()
+      }
+    }
+  }
+
+  /** Append bytes to the file output stream */
+  protected def appendToFile(bytes: Array[Byte], len: Int) {
+    if (outputStream == null) {
+      openFile()
+    }
+    outputStream.write(bytes, 0, len)
+  }
+
+  /** Open the file output stream */
+  protected def openFile() {
+    outputStream = new FileOutputStream(file, false)
+    logDebug(s"Opened file $file")
+  }
+
+  /** Close the file output stream */
+  protected def closeFile() {
+    outputStream.flush()
+    outputStream.close()
+    logDebug(s"Closed file $file")
+  }
+}
+
+/**
+ * Companion object to [[org.apache.spark.util.logging.FileAppender]] which has helper
+ * functions to choose the correct type of FileAppender based on SparkConf configuration.
+ */
+private[spark] object FileAppender extends Logging {
+
+  /** Create the right appender based on Spark configuration */
+  def apply(inputStream: InputStream, file: File, conf: SparkConf): FileAppender = {
+
+    import RollingFileAppender._
+
+    val rollingStrategy = conf.get(STRATEGY_PROPERTY, STRATEGY_DEFAULT)
+    val rollingSizeBytes = conf.get(SIZE_PROPERTY, STRATEGY_DEFAULT)
+    val rollingInterval = conf.get(INTERVAL_PROPERTY, INTERVAL_DEFAULT)
+
+    def createTimeBasedAppender() = {
+      val validatedParams: Option[(Long, String)] = rollingInterval match {
+        case "daily" =>
+          logInfo(s"Rolling executor logs enabled for $file with daily rolling")
+          Some(24 * 60 * 60 * 1000L, "--YYYY-MM-dd")
+        case "hourly" =>
+          logInfo(s"Rolling executor logs enabled for $file with hourly rolling")
+          Some(60 * 60 * 1000L, "--YYYY-MM-dd--HH")
+        case "minutely" =>
+          logInfo(s"Rolling executor logs enabled for $file with rolling every minute")
+          Some(60 * 1000L, "--YYYY-MM-dd--HH-mm")
+        case IntParam(seconds) =>
+          logInfo(s"Rolling executor logs enabled for $file with rolling $seconds seconds")
+          Some(seconds * 1000L, "--YYYY-MM-dd--HH-mm-ss")
+        case _ =>
+          logWarning(s"Illegal interval for rolling executor logs [$rollingInterval], " +
+              s"rolling logs not enabled")
+          None
+      }
+      validatedParams.map {
+        case (interval, pattern) =>
+          new RollingFileAppender(
+            inputStream, file, new TimeBasedRollingPolicy(interval, pattern), conf)
+      }.getOrElse {
+        new FileAppender(inputStream, file)
+      }
+    }
+
+    def createSizeBasedAppender() = {
+      rollingSizeBytes match {
+        case IntParam(bytes) =>
+          logInfo(s"Rolling executor logs enabled for $file with rolling every $bytes bytes")
+          new RollingFileAppender(inputStream, file, new SizeBasedRollingPolicy(bytes), conf)
+        case _ =>
+          logWarning(
+            s"Illegal size [$rollingSizeBytes] for rolling executor logs, rolling logs not enabled")
+          new FileAppender(inputStream, file)
+      }
+    }
+
+    rollingStrategy match {
+      case "" =>
+        new FileAppender(inputStream, file)
+      case "time" =>
+        createTimeBasedAppender()
+      case "size" =>
+        createSizeBasedAppender()
+      case _ =>
+        logWarning(
+          s"Illegal strategy [$rollingStrategy] for rolling executor logs, " +
+            s"rolling logs not enabled")
+        new FileAppender(inputStream, file)
+    }
+  }
+}
+
+
diff --git a/core/src/main/scala/org/apache/spark/util/logging/RollingFileAppender.scala b/core/src/main/scala/org/apache/spark/util/logging/RollingFileAppender.scala
new file mode 100644
index 000000000000..1bbbd20cf076
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/util/logging/RollingFileAppender.scala
@@ -0,0 +1,163 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.util.logging
+
+import java.io.{File, FileFilter, InputStream}
+
+import org.apache.commons.io.FileUtils
+import org.apache.spark.SparkConf
+import RollingFileAppender._
+
+/**
+ * Continuously appends data from input stream into the given file, and rolls
+ * over the file after the given interval. The rolled over files are named
+ * based on the given pattern.
+ *
+ * @param inputStream             Input stream to read data from
+ * @param activeFile              File to write data to
+ * @param rollingPolicy           Policy based on which files will be rolled over.
+ * @param conf                    SparkConf that is used to pass on extra configurations
+ * @param bufferSize              Optional buffer size. Used mainly for testing.
+ */
+private[spark] class RollingFileAppender(
+    inputStream: InputStream,
+    activeFile: File,
+    val rollingPolicy: RollingPolicy,
+    conf: SparkConf,
+    bufferSize: Int = DEFAULT_BUFFER_SIZE
+  ) extends FileAppender(inputStream, activeFile, bufferSize) {
+
+  private val maxRetainedFiles = conf.getInt(RETAINED_FILES_PROPERTY, -1)
+
+  /** Stop the appender */
+  override def stop() {
+    super.stop()
+  }
+
+  /** Append bytes to file after rolling over is necessary */
+  override protected def appendToFile(bytes: Array[Byte], len: Int) {
+    if (rollingPolicy.shouldRollover(len)) {
+      rollover()
+      rollingPolicy.rolledOver()
+    }
+    super.appendToFile(bytes, len)
+    rollingPolicy.bytesWritten(len)
+  }
+
+  /** Rollover the file, by closing the output stream and moving it over */
+  private def rollover() {
+    try {
+      closeFile()
+      moveFile()
+      openFile()
+      if (maxRetainedFiles > 0) {
+        deleteOldFiles()
+      }
+    } catch {
+      case e: Exception =>
+        logError(s"Error rolling over $activeFile", e)
+    }
+  }
+
+  /** Move the active log file to a new rollover file */
+  private def moveFile() {
+    val rolloverSuffix = rollingPolicy.generateRolledOverFileSuffix()
+    val rolloverFile = new File(
+      activeFile.getParentFile, activeFile.getName + rolloverSuffix).getAbsoluteFile
+    try {
+      logDebug(s"Attempting to rollover file $activeFile to file $rolloverFile")
+      if (activeFile.exists) {
+        if (!rolloverFile.exists) {
+          FileUtils.moveFile(activeFile, rolloverFile)
+          logInfo(s"Rolled over $activeFile to $rolloverFile")
+        } else {
+          // In case the rollover file name clashes, make a unique file name.
+          // The resultant file names are long and ugly, so this is used only
+          // if there is a name collision. This can be avoided by the using
+          // the right pattern such that name collisions do not occur.
+          var i = 0
+          var altRolloverFile: File = null
+          do {
+            altRolloverFile = new File(activeFile.getParent,
+              s"${activeFile.getName}$rolloverSuffix--$i").getAbsoluteFile
+            i += 1
+          } while (i < 10000 && altRolloverFile.exists)
+
+          logWarning(s"Rollover file $rolloverFile already exists, " +
+            s"rolled over $activeFile to file $altRolloverFile")
+          FileUtils.moveFile(activeFile, altRolloverFile)
+        }
+      } else {
+        logWarning(s"File $activeFile does not exist")
+      }
+    }
+  }
+
+  /** Retain only last few files */
+  private[util] def deleteOldFiles() {
+    try {
+      val rolledoverFiles = activeFile.getParentFile.listFiles(new FileFilter {
+        def accept(f: File): Boolean = {
+          f.getName.startsWith(activeFile.getName) && f != activeFile
+        }
+      }).sorted
+      val filesToBeDeleted = rolledoverFiles.take(
+        math.max(0, rolledoverFiles.size - maxRetainedFiles))
+      filesToBeDeleted.foreach { file =>
+        logInfo(s"Deleting file executor log file ${file.getAbsolutePath}")
+        file.delete()
+      }
+    } catch {
+      case e: Exception =>
+        logError("Error cleaning logs in directory " + activeFile.getParentFile.getAbsolutePath, e)
+    }
+  }
+}
+
+/**
+ * Companion object to [[org.apache.spark.util.logging.RollingFileAppender]]. Defines
+ * names of configurations that configure rolling file appenders.
+ */
+private[spark] object RollingFileAppender {
+  val STRATEGY_PROPERTY = "spark.executor.logs.rolling.strategy"
+  val STRATEGY_DEFAULT = ""
+  val INTERVAL_PROPERTY = "spark.executor.logs.rolling.time.interval"
+  val INTERVAL_DEFAULT = "daily"
+  val SIZE_PROPERTY = "spark.executor.logs.rolling.size.maxBytes"
+  val SIZE_DEFAULT = (1024 * 1024).toString
+  val RETAINED_FILES_PROPERTY = "spark.executor.logs.rolling.maxRetainedFiles"
+  val DEFAULT_BUFFER_SIZE = 8192
+
+  /**
+   * Get the sorted list of rolled over files. This assumes that the all the rolled
+   * over file names are prefixed with the `activeFileName`, and the active file
+   * name has the latest logs. So it sorts all the rolled over logs (that are
+   * prefixed with `activeFileName`) and appends the active file
+   */
+  def getSortedRolledOverFiles(directory: String, activeFileName: String): Seq[File] = {
+    val rolledOverFiles = new File(directory).getAbsoluteFile.listFiles.filter { file =>
+      val fileName = file.getName
+      fileName.startsWith(activeFileName) && fileName != activeFileName
+    }.sorted
+    val activeFile = {
+      val file = new File(directory, activeFileName).getAbsoluteFile
+      if (file.exists) Some(file) else None
+    }
+    rolledOverFiles ++ activeFile
+  }
+}
diff --git a/core/src/main/scala/org/apache/spark/util/logging/RollingPolicy.scala b/core/src/main/scala/org/apache/spark/util/logging/RollingPolicy.scala
new file mode 100644
index 000000000000..84e5c3c917dc
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/util/logging/RollingPolicy.scala
@@ -0,0 +1,139 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.util.logging
+
+import java.text.SimpleDateFormat
+import java.util.Calendar
+
+import org.apache.spark.Logging
+
+/**
+ * Defines the policy based on which [[org.apache.spark.util.logging.RollingFileAppender]] will
+ * generate rolling files.
+ */
+private[spark] trait RollingPolicy {
+
+  /** Whether rollover should be initiated at this moment */
+  def shouldRollover(bytesToBeWritten: Long): Boolean
+
+  /** Notify that rollover has occurred */
+  def rolledOver()
+
+  /** Notify that bytes have been written */
+  def bytesWritten(bytes: Long)
+
+  /** Get the desired name of the rollover file */
+  def generateRolledOverFileSuffix(): String
+}
+
+/**
+ * Defines a [[org.apache.spark.util.logging.RollingPolicy]] by which files will be rolled
+ * over at a fixed interval.
+ */
+private[spark] class TimeBasedRollingPolicy(
+    var rolloverIntervalMillis: Long,
+    rollingFileSuffixPattern: String,
+    checkIntervalConstraint: Boolean = true   // set to false while testing
+  ) extends RollingPolicy with Logging {
+
+  import TimeBasedRollingPolicy._
+  if (checkIntervalConstraint && rolloverIntervalMillis < MINIMUM_INTERVAL_SECONDS * 1000L) {
+    logWarning(s"Rolling interval [${rolloverIntervalMillis/1000L} seconds] is too small. " +
+      s"Setting the interval to the acceptable minimum of $MINIMUM_INTERVAL_SECONDS seconds.")
+    rolloverIntervalMillis = MINIMUM_INTERVAL_SECONDS * 1000L
+  }
+
+  @volatile private var nextRolloverTime = calculateNextRolloverTime()
+  private val formatter = new SimpleDateFormat(rollingFileSuffixPattern)
+
+  /** Should rollover if current time has exceeded next rollover time */
+  def shouldRollover(bytesToBeWritten: Long): Boolean = {
+    System.currentTimeMillis > nextRolloverTime
+  }
+
+  /** Rollover has occurred, so find the next time to rollover */
+  def rolledOver() {
+    nextRolloverTime = calculateNextRolloverTime()
+    logDebug(s"Current time: ${System.currentTimeMillis}, next rollover time: " + nextRolloverTime)
+  }
+
+  def bytesWritten(bytes: Long) { }  // nothing to do
+
+  private def calculateNextRolloverTime(): Long = {
+    val now = System.currentTimeMillis()
+    val targetTime = (
+      math.ceil(now.toDouble / rolloverIntervalMillis) * rolloverIntervalMillis
+    ).toLong
+    logDebug(s"Next rollover time is $targetTime")
+    targetTime
+  }
+
+  def generateRolledOverFileSuffix(): String = {
+    formatter.format(Calendar.getInstance.getTime)
+  }
+}
+
+private[spark] object TimeBasedRollingPolicy {
+  val MINIMUM_INTERVAL_SECONDS = 60L  // 1 minute
+}
+
+/**
+ * Defines a [[org.apache.spark.util.logging.RollingPolicy]] by which files will be rolled
+ * over after reaching a particular size.
+ */
+private[spark] class SizeBasedRollingPolicy(
+    var rolloverSizeBytes: Long,
+    checkSizeConstraint: Boolean = true     // set to false while testing
+  ) extends RollingPolicy with Logging {
+
+  import SizeBasedRollingPolicy._
+  if (checkSizeConstraint && rolloverSizeBytes < MINIMUM_SIZE_BYTES) {
+    logWarning(s"Rolling size [$rolloverSizeBytes bytes] is too small. " +
+      s"Setting the size to the acceptable minimum of $MINIMUM_SIZE_BYTES bytes.")
+    rolloverSizeBytes = MINIMUM_SIZE_BYTES
+  }
+
+  @volatile private var bytesWrittenSinceRollover = 0L
+  val formatter = new SimpleDateFormat("--YYYY-MM-dd--HH-mm-ss--SSSS")
+
+  /** Should rollover if the next set of bytes is going to exceed the size limit */
+  def shouldRollover(bytesToBeWritten: Long): Boolean = {
+    logInfo(s"$bytesToBeWritten + $bytesWrittenSinceRollover > $rolloverSizeBytes")
+    bytesToBeWritten + bytesWrittenSinceRollover > rolloverSizeBytes
+  }
+
+  /** Rollover has occurred, so reset the counter */
+  def rolledOver() {
+    bytesWrittenSinceRollover = 0
+  }
+
+  /** Increment the bytes that have been written in the current file */
+  def bytesWritten(bytes: Long) {
+    bytesWrittenSinceRollover += bytes
+  }
+
+  /** Get the desired name of the rollover file */
+  def generateRolledOverFileSuffix(): String = {
+    formatter.format(Calendar.getInstance.getTime)
+  }
+}
+
+private[spark] object SizeBasedRollingPolicy {
+  val MINIMUM_SIZE_BYTES = RollingFileAppender.DEFAULT_BUFFER_SIZE * 10
+}
+
diff --git a/core/src/test/scala/org/apache/spark/deploy/JsonProtocolSuite.scala b/core/src/test/scala/org/apache/spark/deploy/JsonProtocolSuite.scala
index bfae32dae0dc..01ab2d549325 100644
--- a/core/src/test/scala/org/apache/spark/deploy/JsonProtocolSuite.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/JsonProtocolSuite.scala
@@ -28,6 +28,7 @@ import org.scalatest.FunSuite
 import org.apache.spark.deploy.DeployMessages.{MasterStateResponse, WorkerStateResponse}
 import org.apache.spark.deploy.master.{ApplicationInfo, DriverInfo, RecoveryState, WorkerInfo}
 import org.apache.spark.deploy.worker.{DriverRunner, ExecutorRunner}
+import org.apache.spark.SparkConf
 
 class JsonProtocolSuite extends FunSuite {
 
@@ -116,7 +117,8 @@ class JsonProtocolSuite extends FunSuite {
   }
   def createExecutorRunner(): ExecutorRunner = {
     new ExecutorRunner("appId", 123, createAppDesc(), 4, 1234, null, "workerId", "host",
-      new File("sparkHome"), new File("workDir"), "akka://worker", ExecutorState.RUNNING)
+      new File("sparkHome"), new File("workDir"), "akka://worker",
+      new SparkConf, ExecutorState.RUNNING)
   }
   def createDriverRunner(): DriverRunner = {
     new DriverRunner("driverId", new File("workDir"), new File("sparkHome"), createDriverDesc(),
diff --git a/core/src/test/scala/org/apache/spark/deploy/worker/ExecutorRunnerTest.scala b/core/src/test/scala/org/apache/spark/deploy/worker/ExecutorRunnerTest.scala
index 8ae387fa0be6..e5f748d55500 100644
--- a/core/src/test/scala/org/apache/spark/deploy/worker/ExecutorRunnerTest.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/worker/ExecutorRunnerTest.scala
@@ -22,6 +22,7 @@ import java.io.File
 import org.scalatest.FunSuite
 
 import org.apache.spark.deploy.{ApplicationDescription, Command, ExecutorState}
+import org.apache.spark.SparkConf
 
 class ExecutorRunnerTest extends FunSuite {
   test("command includes appId") {
@@ -32,7 +33,7 @@ class ExecutorRunnerTest extends FunSuite {
       sparkHome, "appUiUrl")
     val appId = "12345-worker321-9876"
     val er = new ExecutorRunner(appId, 1, appDesc, 8, 500, null, "blah", "worker321", f(sparkHome.getOrElse(".")),
-      f("ooga"), "blah", ExecutorState.RUNNING)
+      f("ooga"), "blah", new SparkConf, ExecutorState.RUNNING)
 
     assert(er.getCommandSeq.last === appId)
   }
diff --git a/core/src/test/scala/org/apache/spark/util/FileAppenderSuite.scala b/core/src/test/scala/org/apache/spark/util/FileAppenderSuite.scala
new file mode 100644
index 000000000000..53d7f5c6072e
--- /dev/null
+++ b/core/src/test/scala/org/apache/spark/util/FileAppenderSuite.scala
@@ -0,0 +1,225 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.util
+
+import java.io._
+
+import scala.collection.mutable.HashSet
+import scala.reflect._
+
+import org.apache.commons.io.{FileUtils, IOUtils}
+import org.apache.spark.{Logging, SparkConf}
+import org.scalatest.{BeforeAndAfter, FunSuite}
+import org.apache.spark.util.logging.{RollingFileAppender, SizeBasedRollingPolicy, TimeBasedRollingPolicy, FileAppender}
+
+class FileAppenderSuite extends FunSuite with BeforeAndAfter with Logging {
+
+  val testFile = new File("FileAppenderSuite-test-" + System.currentTimeMillis).getAbsoluteFile
+
+  before {
+    cleanup()
+  }
+
+  after {
+    cleanup()
+  }
+
+  test("basic file appender") {
+    val testString = (1 to 1000).mkString(", ")
+    val inputStream = IOUtils.toInputStream(testString)
+    val appender = new FileAppender(inputStream, testFile)
+    inputStream.close()
+    appender.awaitTermination()
+    assert(FileUtils.readFileToString(testFile) === testString)
+  }
+
+  test("rolling file appender - time-based rolling") {
+    // setup input stream and appender
+    val testOutputStream = new PipedOutputStream()
+    val testInputStream = new PipedInputStream(testOutputStream, 100 * 1000)
+    val rolloverIntervalMillis = 100
+    val durationMillis = 1000
+    val numRollovers = durationMillis / rolloverIntervalMillis
+    val textToAppend = (1 to numRollovers).map( _.toString * 10 )
+
+    val appender = new RollingFileAppender(testInputStream, testFile,
+      new TimeBasedRollingPolicy(rolloverIntervalMillis, s"--HH-mm-ss-SSSS", false),
+      new SparkConf(), 10)
+
+    testRolling(appender, testOutputStream, textToAppend, rolloverIntervalMillis)
+  }
+
+  test("rolling file appender - size-based rolling") {
+    // setup input stream and appender
+    val testOutputStream = new PipedOutputStream()
+    val testInputStream = new PipedInputStream(testOutputStream, 100 * 1000)
+    val rolloverSize = 1000
+    val textToAppend = (1 to 3).map( _.toString * 1000 )
+
+    val appender = new RollingFileAppender(testInputStream, testFile,
+      new SizeBasedRollingPolicy(rolloverSize, false), new SparkConf(), 99)
+
+    val files = testRolling(appender, testOutputStream, textToAppend, 0)
+    files.foreach { file =>
+      logInfo(file.toString + ": " + file.length + " bytes")
+      assert(file.length <= rolloverSize)
+    }
+  }
+
+  test("rolling file appender - cleaning") {
+    // setup input stream and appender
+    val testOutputStream = new PipedOutputStream()
+    val testInputStream = new PipedInputStream(testOutputStream, 100 * 1000)
+    val conf = new SparkConf().set(RollingFileAppender.RETAINED_FILES_PROPERTY, "10")
+    val appender = new RollingFileAppender(testInputStream, testFile,
+      new SizeBasedRollingPolicy(1000, false), conf, 10)
+
+    // send data to appender through the input stream, and wait for the data to be written
+    val allGeneratedFiles = new HashSet[String]()
+    val items = (1 to 10).map { _.toString * 10000 }
+    for (i <- 0 until items.size) {
+      testOutputStream.write(items(i).getBytes("UTF8"))
+      testOutputStream.flush()
+      allGeneratedFiles ++= RollingFileAppender.getSortedRolledOverFiles(
+        testFile.getParentFile.toString, testFile.getName).map(_.toString)
+
+      Thread.sleep(10)
+    }
+    testOutputStream.close()
+    appender.awaitTermination()
+    logInfo("Appender closed")
+
+    // verify whether the earliest file has been deleted
+    val rolledOverFiles = allGeneratedFiles.filter { _ != testFile.toString }.toArray.sorted
+    logInfo(s"All rolled over files generated:${rolledOverFiles.size}\n" + rolledOverFiles.mkString("\n"))
+    assert(rolledOverFiles.size > 2)
+    val earliestRolledOverFile = rolledOverFiles.head
+    val existingRolledOverFiles = RollingFileAppender.getSortedRolledOverFiles(
+      testFile.getParentFile.toString, testFile.getName).map(_.toString)
+    logInfo("Existing rolled over files:\n" + existingRolledOverFiles.mkString("\n"))
+    assert(!existingRolledOverFiles.toSet.contains(earliestRolledOverFile))
+  }
+
+  test("file appender selection") {
+    // Test whether FileAppender.apply() returns the right type of the FileAppender based
+    // on SparkConf settings.
+
+    def testAppenderSelection[ExpectedAppender: ClassTag, ExpectedRollingPolicy](
+        properties: Seq[(String, String)], expectedRollingPolicyParam: Long = -1): FileAppender = {
+
+      // Set spark conf properties
+      val conf = new SparkConf
+      properties.foreach { p =>
+        conf.set(p._1, p._2)
+      }
+
+      // Create and test file appender
+      val inputStream = new PipedInputStream(new PipedOutputStream())
+      val appender = FileAppender(inputStream, new File("stdout"), conf)
+      assert(appender.isInstanceOf[ExpectedAppender])
+      assert(appender.getClass.getSimpleName ===
+        classTag[ExpectedAppender].runtimeClass.getSimpleName)
+      if (appender.isInstanceOf[RollingFileAppender]) {
+        val rollingPolicy = appender.asInstanceOf[RollingFileAppender].rollingPolicy
+        rollingPolicy.isInstanceOf[ExpectedRollingPolicy]
+        val policyParam = if (rollingPolicy.isInstanceOf[TimeBasedRollingPolicy]) {
+          rollingPolicy.asInstanceOf[TimeBasedRollingPolicy].rolloverIntervalMillis
+        } else {
+          rollingPolicy.asInstanceOf[SizeBasedRollingPolicy].rolloverSizeBytes
+        }
+        assert(policyParam === expectedRollingPolicyParam)
+      }
+      appender
+    }
+
+    import RollingFileAppender._
+
+    def rollingStrategy(strategy: String) = Seq(STRATEGY_PROPERTY -> strategy)
+    def rollingSize(size: String) = Seq(SIZE_PROPERTY -> size)
+    def rollingInterval(interval: String) = Seq(INTERVAL_PROPERTY -> interval)
+
+    val msInDay = 24 * 60 * 60 * 1000L
+    val msInHour = 60 * 60 * 1000L
+    val msInMinute = 60 * 1000L
+
+    // test no strategy -> no rolling
+    testAppenderSelection[FileAppender, Any](Seq.empty)
+
+    // test time based rolling strategy
+    testAppenderSelection[RollingFileAppender, Any](rollingStrategy("time"), msInDay)
+    testAppenderSelection[RollingFileAppender, TimeBasedRollingPolicy](
+      rollingStrategy("time") ++ rollingInterval("daily"), msInDay)
+    testAppenderSelection[RollingFileAppender, TimeBasedRollingPolicy](
+      rollingStrategy("time") ++ rollingInterval("hourly"), msInHour)
+    testAppenderSelection[RollingFileAppender, TimeBasedRollingPolicy](
+      rollingStrategy("time") ++ rollingInterval("minutely"), msInMinute)
+    testAppenderSelection[RollingFileAppender, TimeBasedRollingPolicy](
+      rollingStrategy("time") ++ rollingInterval("123456789"), 123456789 * 1000L)
+    testAppenderSelection[FileAppender, Any](
+      rollingStrategy("time") ++ rollingInterval("xyz"))
+
+    // test size based rolling strategy
+    testAppenderSelection[RollingFileAppender, SizeBasedRollingPolicy](
+      rollingStrategy("size") ++ rollingSize("123456789"), 123456789)
+    testAppenderSelection[FileAppender, Any](rollingSize("xyz"))
+
+    // test illegal strategy
+    testAppenderSelection[FileAppender, Any](rollingStrategy("xyz"))
+  }
+
+  /**
+   * Run the rolling file appender with data and see whether all the data was written correctly
+   * across rolled over files.
+   */
+  def testRolling(
+      appender: FileAppender,
+      outputStream: OutputStream,
+      textToAppend: Seq[String],
+      sleepTimeBetweenTexts: Long
+    ): Seq[File] = {
+    // send data to appender through the input stream, and wait for the data to be written
+    val expectedText = textToAppend.mkString("")
+    for (i <- 0 until textToAppend.size) {
+      outputStream.write(textToAppend(i).getBytes("UTF8"))
+      outputStream.flush()
+      Thread.sleep(sleepTimeBetweenTexts)
+    }
+    logInfo("Data sent to appender")
+    outputStream.close()
+    appender.awaitTermination()
+    logInfo("Appender closed")
+
+    // verify whether all the data written to rolled over files is same as expected
+    val generatedFiles = RollingFileAppender.getSortedRolledOverFiles(
+      testFile.getParentFile.toString, testFile.getName)
+    logInfo("Filtered files: \n" + generatedFiles.mkString("\n"))
+    assert(generatedFiles.size > 1)
+    val allText = generatedFiles.map { file =>
+      FileUtils.readFileToString(file)
+    }.mkString("")
+    assert(allText === expectedText)
+    generatedFiles
+  }
+
+  /** Delete all the generated rolledover files */
+  def cleanup() {
+    testFile.getParentFile.listFiles.filter { file =>
+      file.getName.startsWith(testFile.getName)
+    }.foreach { _.delete() }
+  }
+}
diff --git a/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala b/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala
index 0aad882ed76a..1ee936bc78f4 100644
--- a/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala
+++ b/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala
@@ -140,6 +140,38 @@ class UtilsSuite extends FunSuite {
     Utils.deleteRecursively(tmpDir2)
   }
 
+  test("reading offset bytes across multiple files") {
+    val tmpDir = Files.createTempDir()
+    tmpDir.deleteOnExit()
+    val files = (1 to 3).map(i => new File(tmpDir, i.toString))
+    Files.write("0123456789", files(0), Charsets.UTF_8)
+    Files.write("abcdefghij", files(1), Charsets.UTF_8)
+    Files.write("ABCDEFGHIJ", files(2), Charsets.UTF_8)
+
+    // Read first few bytes in the 1st file
+    assert(Utils.offsetBytes(files, 0, 5) === "01234")
+
+    // Read bytes within the 1st file
+    assert(Utils.offsetBytes(files, 5, 8) === "567")
+
+    // Read bytes across 1st and 2nd file
+    assert(Utils.offsetBytes(files, 8, 18) === "89abcdefgh")
+
+    // Read bytes across 1st, 2nd and 3rd file
+    assert(Utils.offsetBytes(files, 5, 24) === "56789abcdefghijABCD")
+
+    // Read some nonexistent bytes in the beginning
+    assert(Utils.offsetBytes(files, -5, 18) === "0123456789abcdefgh")
+
+    // Read some nonexistent bytes at the end
+    assert(Utils.offsetBytes(files, 18, 35) === "ijABCDEFGHIJ")
+
+    // Read some nonexistent bytes on both ends
+    assert(Utils.offsetBytes(files, -5, 35) === "0123456789abcdefghijABCDEFGHIJ")
+
+    Utils.deleteRecursively(tmpDir)
+  }
+
   test("deserialize long value") {
     val testval : Long = 9730889947L
     val bbuf = ByteBuffer.allocate(8)
diff --git a/docs/configuration.md b/docs/configuration.md
index 71fafa573467..b84104cc7e65 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -784,6 +784,45 @@ Apart from these, the following properties are also available, and may be useful
     higher memory usage in Spark.
   </td>
 </tr>
+<tr>
+  <td><code>spark.executor.logs.rolling.strategy</code></td>
+  <td>(none)</td>
+  <td>
+    Set the strategy of rolling of executor logs. By default it is disabled. It can
+    be set to "time" (time-based rolling) or "size" (size-based rolling). For "time",
+    use <code>spark.executor.logs.rolling.time.interval</code> to set the rolling interval.
+    For "size", use <code>spark.executor.logs.rolling.size.maxBytes</code> to set
+    the maximum file size for rolling.
+  </td>
+</tr>
+<tr>
+  <td><code>spark.executor.logs.rolling.time.interval</code></td>
+  <td>daily</td>
+  <td>
+    Set the time interval by which the executor logs will be rolled over.
+    Rolling is disabled by default. Valid values are `daily`, `hourly`, `minutely` or
+    any interval in seconds. See <code>spark.executor.logs.rolling.maxRetainedFiles</code>
+    for automatic cleaning of old logs.
+  </td>
+</tr>
+<tr>
+  <td><code>spark.executor.logs.rolling.size.maxBytes</code></td>
+  <td>(none)</td>
+  <td>
+    Set the max size of the file by which the executor logs will be rolled over.
+    Rolling is disabled by default. Value is set in terms of bytes.
+    See <code>spark.executor.logs.rolling.maxRetainedFiles</code>
+    for automatic cleaning of old logs.
+  </td>
+</tr>
+<tr>
+  <td><code>spark.executor.logs.rolling.maxRetainedFiles</code></td>
+  <td>(none)</td>
+  <td>
+    Sets the number of latest rolling log files that are going to be retained by the system.
+    Older log files will be deleted. Disabled by default.
+  </td>
+</tr>
 </table>
 
 #### Cluster Managers

From c48b6222ea60f2a31741297f03737c89fd572e52 Mon Sep 17 00:00:00 2001
From: witgo <witgo@qq.com>
Date: Tue, 10 Jun 2014 20:24:05 -0700
Subject: [PATCH 386/810] Resolve scalatest warnings during build

Author: witgo <witgo@qq.com>

Closes #1032 from witgo/ShouldMatchers and squashes the following commits:

7ebf34c [witgo] Resolve scalatest warnings during build
---
 core/src/test/scala/org/apache/spark/AccumulatorSuite.scala  | 4 ++--
 core/src/test/scala/org/apache/spark/DistributedSuite.scala  | 4 ++--
 .../test/scala/org/apache/spark/JobCancellationSuite.scala   | 4 ++--
 core/src/test/scala/org/apache/spark/ShuffleSuite.scala      | 4 ++--
 .../src/test/scala/org/apache/spark/deploy/ClientSuite.scala | 4 ++--
 .../scala/org/apache/spark/deploy/SparkSubmitSuite.scala     | 4 ++--
 core/src/test/scala/org/apache/spark/rdd/SortingSuite.scala  | 4 ++--
 .../org/apache/spark/scheduler/SparkListenerSuite.scala      | 4 ++--
 .../scala/org/apache/spark/storage/BlockManagerSuite.scala   | 5 +++--
 .../org/apache/spark/ui/jobs/JobProgressListenerSuite.scala  | 4 ++--
 .../test/scala/org/apache/spark/util/DistributionSuite.scala | 4 ++--
 .../test/scala/org/apache/spark/util/NextIteratorSuite.scala | 4 ++--
 .../org/apache/spark/util/collection/OpenHashMapSuite.scala  | 4 ++--
 .../org/apache/spark/util/collection/OpenHashSetSuite.scala  | 4 ++--
 .../spark/util/collection/PrimitiveKeyOpenHashMapSuite.scala | 4 ++--
 .../org/apache/spark/util/random/XORShiftRandomSuite.scala   | 4 ++--
 .../spark/mllib/classification/LogisticRegressionSuite.scala | 4 ++--
 .../spark/mllib/optimization/GradientDescentSuite.scala      | 4 ++--
 .../org/apache/spark/mllib/optimization/LBFGSSuite.scala     | 4 ++--
 .../org/apache/spark/streaming/NetworkReceiverSuite.scala    | 1 -
 .../org/apache/spark/streaming/StreamingListenerSuite.scala  | 4 ++--
 21 files changed, 41 insertions(+), 41 deletions(-)

diff --git a/core/src/test/scala/org/apache/spark/AccumulatorSuite.scala b/core/src/test/scala/org/apache/spark/AccumulatorSuite.scala
index 4e7c34e6d1ad..3aab88e9e919 100644
--- a/core/src/test/scala/org/apache/spark/AccumulatorSuite.scala
+++ b/core/src/test/scala/org/apache/spark/AccumulatorSuite.scala
@@ -20,11 +20,11 @@ package org.apache.spark
 import scala.collection.mutable
 
 import org.scalatest.FunSuite
-import org.scalatest.matchers.ShouldMatchers
+import org.scalatest.Matchers
 
 import org.apache.spark.SparkContext._
 
-class AccumulatorSuite extends FunSuite with ShouldMatchers with LocalSparkContext {
+class AccumulatorSuite extends FunSuite with Matchers with LocalSparkContext {
 
 
   implicit def setAccum[A] = new AccumulableParam[mutable.Set[A], A] {
diff --git a/core/src/test/scala/org/apache/spark/DistributedSuite.scala b/core/src/test/scala/org/apache/spark/DistributedSuite.scala
index 14ddd6f1ec08..41c294f727b3 100644
--- a/core/src/test/scala/org/apache/spark/DistributedSuite.scala
+++ b/core/src/test/scala/org/apache/spark/DistributedSuite.scala
@@ -20,7 +20,7 @@ package org.apache.spark
 import org.scalatest.BeforeAndAfter
 import org.scalatest.FunSuite
 import org.scalatest.concurrent.Timeouts._
-import org.scalatest.matchers.ShouldMatchers
+import org.scalatest.Matchers
 import org.scalatest.time.{Millis, Span}
 
 import org.apache.spark.SparkContext._
@@ -31,7 +31,7 @@ class NotSerializableClass
 class NotSerializableExn(val notSer: NotSerializableClass) extends Throwable() {}
 
 
-class DistributedSuite extends FunSuite with ShouldMatchers with BeforeAndAfter
+class DistributedSuite extends FunSuite with Matchers with BeforeAndAfter
   with LocalSparkContext {
 
   val clusterUrl = "local-cluster[2,1,512]"
diff --git a/core/src/test/scala/org/apache/spark/JobCancellationSuite.scala b/core/src/test/scala/org/apache/spark/JobCancellationSuite.scala
index 2c8ef405c944..a57430e829ce 100644
--- a/core/src/test/scala/org/apache/spark/JobCancellationSuite.scala
+++ b/core/src/test/scala/org/apache/spark/JobCancellationSuite.scala
@@ -25,7 +25,7 @@ import scala.concurrent.duration._
 import scala.concurrent.future
 
 import org.scalatest.{BeforeAndAfter, FunSuite}
-import org.scalatest.matchers.ShouldMatchers
+import org.scalatest.Matchers
 
 import org.apache.spark.SparkContext._
 import org.apache.spark.scheduler.{SparkListener, SparkListenerTaskStart}
@@ -35,7 +35,7 @@ import org.apache.spark.scheduler.{SparkListener, SparkListenerTaskStart}
  * (e.g. count) as well as multi-job action (e.g. take). We test the local and cluster schedulers
  * in both FIFO and fair scheduling modes.
  */
-class JobCancellationSuite extends FunSuite with ShouldMatchers with BeforeAndAfter
+class JobCancellationSuite extends FunSuite with Matchers with BeforeAndAfter
   with LocalSparkContext {
 
   override def afterEach() {
diff --git a/core/src/test/scala/org/apache/spark/ShuffleSuite.scala b/core/src/test/scala/org/apache/spark/ShuffleSuite.scala
index be6508a40ea6..7b0607dd3ed2 100644
--- a/core/src/test/scala/org/apache/spark/ShuffleSuite.scala
+++ b/core/src/test/scala/org/apache/spark/ShuffleSuite.scala
@@ -18,7 +18,7 @@
 package org.apache.spark
 
 import org.scalatest.FunSuite
-import org.scalatest.matchers.ShouldMatchers
+import org.scalatest.Matchers
 
 import org.apache.spark.SparkContext._
 import org.apache.spark.ShuffleSuite.NonJavaSerializableClass
@@ -26,7 +26,7 @@ import org.apache.spark.rdd.{CoGroupedRDD, OrderedRDDFunctions, RDD, ShuffledRDD
 import org.apache.spark.serializer.KryoSerializer
 import org.apache.spark.util.MutablePair
 
-class ShuffleSuite extends FunSuite with ShouldMatchers with LocalSparkContext {
+class ShuffleSuite extends FunSuite with Matchers with LocalSparkContext {
 
   val conf = new SparkConf(loadDefaults = false)
 
diff --git a/core/src/test/scala/org/apache/spark/deploy/ClientSuite.scala b/core/src/test/scala/org/apache/spark/deploy/ClientSuite.scala
index d6b93f5fedd3..4161aede1d1d 100644
--- a/core/src/test/scala/org/apache/spark/deploy/ClientSuite.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/ClientSuite.scala
@@ -18,9 +18,9 @@
 package org.apache.spark.deploy
 
 import org.scalatest.FunSuite
-import org.scalatest.matchers.ShouldMatchers
+import org.scalatest.Matchers
 
-class ClientSuite extends FunSuite with ShouldMatchers {
+class ClientSuite extends FunSuite with Matchers {
   test("correctly validates driver jar URL's") {
     ClientArguments.isValidJarUrl("http://someHost:8080/foo.jar") should be (true)
     ClientArguments.isValidJarUrl("file://some/path/to/a/jarFile.jar") should be (true)
diff --git a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala
index 02427a4a8350..565c53e9529f 100644
--- a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala
@@ -25,9 +25,9 @@ import org.apache.spark.{SparkConf, SparkContext, SparkEnv, SparkException, Test
 import org.apache.spark.deploy.SparkSubmit._
 import org.apache.spark.util.Utils
 import org.scalatest.FunSuite
-import org.scalatest.matchers.ShouldMatchers
+import org.scalatest.Matchers
 
-class SparkSubmitSuite extends FunSuite with ShouldMatchers {
+class SparkSubmitSuite extends FunSuite with Matchers {
   def beforeAll() {
     System.setProperty("spark.testing", "true")
   }
diff --git a/core/src/test/scala/org/apache/spark/rdd/SortingSuite.scala b/core/src/test/scala/org/apache/spark/rdd/SortingSuite.scala
index d0619559bb45..656917628f7a 100644
--- a/core/src/test/scala/org/apache/spark/rdd/SortingSuite.scala
+++ b/core/src/test/scala/org/apache/spark/rdd/SortingSuite.scala
@@ -18,12 +18,12 @@
 package org.apache.spark.rdd
 
 import org.scalatest.FunSuite
-import org.scalatest.matchers.ShouldMatchers
+import org.scalatest.Matchers
 
 import org.apache.spark.{Logging, SharedSparkContext}
 import org.apache.spark.SparkContext._
 
-class SortingSuite extends FunSuite with SharedSparkContext with ShouldMatchers with Logging {
+class SortingSuite extends FunSuite with SharedSparkContext with Matchers with Logging {
 
   test("sortByKey") {
     val pairs = sc.parallelize(Array((1, 0), (2, 0), (0, 0), (3, 0)), 2)
diff --git a/core/src/test/scala/org/apache/spark/scheduler/SparkListenerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/SparkListenerSuite.scala
index 5426e578a9dd..be506e0287a1 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/SparkListenerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/SparkListenerSuite.scala
@@ -22,13 +22,13 @@ import java.util.concurrent.Semaphore
 import scala.collection.mutable
 
 import org.scalatest.{BeforeAndAfter, BeforeAndAfterAll, FunSuite}
-import org.scalatest.matchers.ShouldMatchers
+import org.scalatest.Matchers
 
 import org.apache.spark.{LocalSparkContext, SparkContext}
 import org.apache.spark.SparkContext._
 import org.apache.spark.executor.TaskMetrics
 
-class SparkListenerSuite extends FunSuite with LocalSparkContext with ShouldMatchers
+class SparkListenerSuite extends FunSuite with LocalSparkContext with Matchers
   with BeforeAndAfter with BeforeAndAfterAll {
 
   /** Length of time to wait while draining listener events. */
diff --git a/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala b/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala
index 81bd8257bc15..d7dbe5164b7f 100644
--- a/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala
@@ -28,7 +28,7 @@ import org.mockito.Mockito.{mock, when}
 import org.scalatest.{BeforeAndAfter, FunSuite, PrivateMethodTester}
 import org.scalatest.concurrent.Eventually._
 import org.scalatest.concurrent.Timeouts._
-import org.scalatest.matchers.ShouldMatchers._
+import org.scalatest.Matchers
 import org.scalatest.time.SpanSugar._
 
 import org.apache.spark.{MapOutputTrackerMaster, SecurityManager, SparkConf}
@@ -39,7 +39,8 @@ import org.apache.spark.util.{AkkaUtils, ByteBufferInputStream, SizeEstimator, U
 import scala.language.implicitConversions
 import scala.language.postfixOps
 
-class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodTester {
+class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfter
+  with PrivateMethodTester {
   private val conf = new SparkConf(false)
   var store: BlockManager = null
   var store2: BlockManager = null
diff --git a/core/src/test/scala/org/apache/spark/ui/jobs/JobProgressListenerSuite.scala b/core/src/test/scala/org/apache/spark/ui/jobs/JobProgressListenerSuite.scala
index 8c06a2d9aa4a..91b4c7b0dd96 100644
--- a/core/src/test/scala/org/apache/spark/ui/jobs/JobProgressListenerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/ui/jobs/JobProgressListenerSuite.scala
@@ -18,14 +18,14 @@
 package org.apache.spark.ui.jobs
 
 import org.scalatest.FunSuite
-import org.scalatest.matchers.ShouldMatchers
+import org.scalatest.Matchers
 
 import org.apache.spark.{LocalSparkContext, SparkConf, Success}
 import org.apache.spark.executor.{ShuffleReadMetrics, TaskMetrics}
 import org.apache.spark.scheduler._
 import org.apache.spark.util.Utils
 
-class JobProgressListenerSuite extends FunSuite with LocalSparkContext with ShouldMatchers {
+class JobProgressListenerSuite extends FunSuite with LocalSparkContext with Matchers {
   test("test LRU eviction of stages") {
     val conf = new SparkConf()
     conf.set("spark.ui.retainedStages", 5.toString)
diff --git a/core/src/test/scala/org/apache/spark/util/DistributionSuite.scala b/core/src/test/scala/org/apache/spark/util/DistributionSuite.scala
index 63642461e446..090d48ec921a 100644
--- a/core/src/test/scala/org/apache/spark/util/DistributionSuite.scala
+++ b/core/src/test/scala/org/apache/spark/util/DistributionSuite.scala
@@ -18,13 +18,13 @@
 package org.apache.spark.util
 
 import org.scalatest.FunSuite
-import org.scalatest.matchers.ShouldMatchers
+import org.scalatest.Matchers
 
 /**
  *
  */
 
-class DistributionSuite extends FunSuite with ShouldMatchers {
+class DistributionSuite extends FunSuite with Matchers {
   test("summary") {
     val d = new Distribution((1 to 100).toArray.map{_.toDouble})
     val stats = d.statCounter
diff --git a/core/src/test/scala/org/apache/spark/util/NextIteratorSuite.scala b/core/src/test/scala/org/apache/spark/util/NextIteratorSuite.scala
index 32d74d0500b7..cf438a3d72a0 100644
--- a/core/src/test/scala/org/apache/spark/util/NextIteratorSuite.scala
+++ b/core/src/test/scala/org/apache/spark/util/NextIteratorSuite.scala
@@ -22,9 +22,9 @@ import java.util.NoSuchElementException
 import scala.collection.mutable.Buffer
 
 import org.scalatest.FunSuite
-import org.scalatest.matchers.ShouldMatchers
+import org.scalatest.Matchers
 
-class NextIteratorSuite extends FunSuite with ShouldMatchers {
+class NextIteratorSuite extends FunSuite with Matchers {
   test("one iteration") {
     val i = new StubIterator(Buffer(1))
     i.hasNext should be === true
diff --git a/core/src/test/scala/org/apache/spark/util/collection/OpenHashMapSuite.scala b/core/src/test/scala/org/apache/spark/util/collection/OpenHashMapSuite.scala
index b024c89d94d3..6a7087735640 100644
--- a/core/src/test/scala/org/apache/spark/util/collection/OpenHashMapSuite.scala
+++ b/core/src/test/scala/org/apache/spark/util/collection/OpenHashMapSuite.scala
@@ -20,11 +20,11 @@ package org.apache.spark.util.collection
 import scala.collection.mutable.HashSet
 
 import org.scalatest.FunSuite
-import org.scalatest.matchers.ShouldMatchers
+import org.scalatest.Matchers
 
 import org.apache.spark.util.SizeEstimator
 
-class OpenHashMapSuite extends FunSuite with ShouldMatchers {
+class OpenHashMapSuite extends FunSuite with Matchers {
 
   test("size for specialized, primitive value (int)") {
     val capacity = 1024
diff --git a/core/src/test/scala/org/apache/spark/util/collection/OpenHashSetSuite.scala b/core/src/test/scala/org/apache/spark/util/collection/OpenHashSetSuite.scala
index ff4a98f5dcd4..68a03e3a0970 100644
--- a/core/src/test/scala/org/apache/spark/util/collection/OpenHashSetSuite.scala
+++ b/core/src/test/scala/org/apache/spark/util/collection/OpenHashSetSuite.scala
@@ -18,11 +18,11 @@
 package org.apache.spark.util.collection
 
 import org.scalatest.FunSuite
-import org.scalatest.matchers.ShouldMatchers
+import org.scalatest.Matchers
 
 import org.apache.spark.util.SizeEstimator
 
-class OpenHashSetSuite extends FunSuite with ShouldMatchers {
+class OpenHashSetSuite extends FunSuite with Matchers {
 
   test("size for specialized, primitive int") {
     val loadFactor = 0.7
diff --git a/core/src/test/scala/org/apache/spark/util/collection/PrimitiveKeyOpenHashMapSuite.scala b/core/src/test/scala/org/apache/spark/util/collection/PrimitiveKeyOpenHashMapSuite.scala
index e3fca173908e..8c7df7d73dcd 100644
--- a/core/src/test/scala/org/apache/spark/util/collection/PrimitiveKeyOpenHashMapSuite.scala
+++ b/core/src/test/scala/org/apache/spark/util/collection/PrimitiveKeyOpenHashMapSuite.scala
@@ -20,11 +20,11 @@ package org.apache.spark.util.collection
 import scala.collection.mutable.HashSet
 
 import org.scalatest.FunSuite
-import org.scalatest.matchers.ShouldMatchers
+import org.scalatest.Matchers
 
 import org.apache.spark.util.SizeEstimator
 
-class PrimitiveKeyOpenHashMapSuite extends FunSuite with ShouldMatchers {
+class PrimitiveKeyOpenHashMapSuite extends FunSuite with Matchers {
 
   test("size for specialized, primitive key, value (int, int)") {
     val capacity = 1024
diff --git a/core/src/test/scala/org/apache/spark/util/random/XORShiftRandomSuite.scala b/core/src/test/scala/org/apache/spark/util/random/XORShiftRandomSuite.scala
index 0865c6386f7c..e15fd59a5a8b 100644
--- a/core/src/test/scala/org/apache/spark/util/random/XORShiftRandomSuite.scala
+++ b/core/src/test/scala/org/apache/spark/util/random/XORShiftRandomSuite.scala
@@ -18,13 +18,13 @@
 package org.apache.spark.util.random
 
 import org.scalatest.FunSuite
-import org.scalatest.matchers.ShouldMatchers
+import org.scalatest.Matchers
 
 import org.apache.spark.util.Utils.times
 
 import scala.language.reflectiveCalls
 
-class XORShiftRandomSuite extends FunSuite with ShouldMatchers {
+class XORShiftRandomSuite extends FunSuite with Matchers {
 
   def fixture = new {
     val seed = 1L
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala
index 4d7b984e3ec2..44b757b6a1fb 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala
@@ -21,7 +21,7 @@ import scala.util.Random
 import scala.collection.JavaConversions._
 
 import org.scalatest.FunSuite
-import org.scalatest.matchers.ShouldMatchers
+import org.scalatest.Matchers
 
 import org.apache.spark.mllib.linalg.Vectors
 import org.apache.spark.mllib.regression._
@@ -56,7 +56,7 @@ object LogisticRegressionSuite {
   }
 }
 
-class LogisticRegressionSuite extends FunSuite with LocalSparkContext with ShouldMatchers {
+class LogisticRegressionSuite extends FunSuite with LocalSparkContext with Matchers {
   def validatePrediction(predictions: Seq[Double], input: Seq[LabeledPoint]) {
     val numOffPredictions = predictions.zip(input).count { case (prediction, expected) =>
       prediction != expected.label
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/optimization/GradientDescentSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/optimization/GradientDescentSuite.scala
index 8a16284118cf..951b4f7c6e6f 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/optimization/GradientDescentSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/optimization/GradientDescentSuite.scala
@@ -21,7 +21,7 @@ import scala.util.Random
 import scala.collection.JavaConversions._
 
 import org.scalatest.FunSuite
-import org.scalatest.matchers.ShouldMatchers
+import org.scalatest.Matchers
 
 import org.apache.spark.mllib.regression._
 import org.apache.spark.mllib.util.LocalSparkContext
@@ -61,7 +61,7 @@ object GradientDescentSuite {
   }
 }
 
-class GradientDescentSuite extends FunSuite with LocalSparkContext with ShouldMatchers {
+class GradientDescentSuite extends FunSuite with LocalSparkContext with Matchers {
 
   test("Assert the loss is decreasing.") {
     val nPoints = 10000
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/optimization/LBFGSSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/optimization/LBFGSSuite.scala
index 820eca9b1bf6..4b1850659a18 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/optimization/LBFGSSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/optimization/LBFGSSuite.scala
@@ -18,13 +18,13 @@
 package org.apache.spark.mllib.optimization
 
 import org.scalatest.FunSuite
-import org.scalatest.matchers.ShouldMatchers
+import org.scalatest.Matchers
 
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.linalg.Vectors
 import org.apache.spark.mllib.util.LocalSparkContext
 
-class LBFGSSuite extends FunSuite with LocalSparkContext with ShouldMatchers {
+class LBFGSSuite extends FunSuite with LocalSparkContext with Matchers {
 
   val nPoints = 10000
   val A = 2.0
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/NetworkReceiverSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/NetworkReceiverSuite.scala
index 303d149d285e..d9ac3c91f6e3 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/NetworkReceiverSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/NetworkReceiverSuite.scala
@@ -29,7 +29,6 @@ import org.scalatest.FunSuite
 import org.scalatest.concurrent.Timeouts
 import org.scalatest.concurrent.Eventually._
 import org.scalatest.time.SpanSugar._
-import scala.language.postfixOps
 
 /** Testsuite for testing the network receiver behavior */
 class NetworkReceiverSuite extends FunSuite with Timeouts {
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/StreamingListenerSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/StreamingListenerSuite.scala
index ef0efa552cea..2861f5335ae3 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/StreamingListenerSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/StreamingListenerSuite.scala
@@ -27,12 +27,12 @@ import org.apache.spark.streaming.dstream.DStream
 import org.apache.spark.streaming.receiver.Receiver
 import org.apache.spark.streaming.scheduler._
 
-import org.scalatest.matchers.ShouldMatchers
+import org.scalatest.Matchers
 import org.scalatest.concurrent.Eventually._
 import org.scalatest.time.SpanSugar._
 import org.apache.spark.Logging
 
-class StreamingListenerSuite extends TestSuiteBase with ShouldMatchers {
+class StreamingListenerSuite extends TestSuiteBase with Matchers {
 
   val input = (1 to 4).map(Seq(_)).toSeq
   val operation = (d: DStream[Int]) => d.map(x => x)

From a2052a44f3c08076d8d1ac505c8eb5395141bf79 Mon Sep 17 00:00:00 2001
From: Nicholas Chammas <nicholas.chammas@gmail.com>
Date: Tue, 10 Jun 2014 21:49:08 -0700
Subject: [PATCH 387/810] [SPARK-2065] give launched instances names
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

This update resolves [SPARK-2065](https://issues.apache.org/jira/browse/SPARK-2065). It gives launched EC2 instances descriptive names by using instance tags. Launched instances now show up in the EC2 console with these names.

I used `format()` with named parameters, which I believe is the recommended practice for string formatting in Python, but which doesn’t seem to be used elsewhere in the script.

Author: Nicholas Chammas <nicholas.chammas@gmail.com>
Author: nchammas <nicholas.chammas@gmail.com>

Closes #1043 from nchammas/master and squashes the following commits:

69f6e22 [Nicholas Chammas] PEP8 fixes
2627247 [Nicholas Chammas] broke up lines before they hit 100 chars
6544b7e [Nicholas Chammas] [SPARK-2065] give launched instances names
69da6cf [nchammas] Merge pull request #1 from apache/master
---
 ec2/spark_ec2.py | 11 +++++++++++
 1 file changed, 11 insertions(+)

diff --git a/ec2/spark_ec2.py b/ec2/spark_ec2.py
index 9d5748ba4bc2..52a89cb2481c 100755
--- a/ec2/spark_ec2.py
+++ b/ec2/spark_ec2.py
@@ -200,6 +200,7 @@ def get_spark_shark_version(opts):
         sys.exit(1)
     return (version, spark_shark_map[version])
 
+
 # Attempt to resolve an appropriate AMI given the architecture and
 # region of the request.
 def get_spark_ami(opts):
@@ -418,6 +419,16 @@ def launch_cluster(conn, opts, cluster_name):
         master_nodes = master_res.instances
         print "Launched master in %s, regid = %s" % (zone, master_res.id)
 
+    # Give the instances descriptive names
+    for master in master_nodes:
+        master.add_tag(
+            key='Name',
+            value='spark-{cn}-master-{iid}'.format(cn=cluster_name, iid=master.id))
+    for slave in slave_nodes:
+        slave.add_tag(
+            key='Name',
+            value='spark-{cn}-slave-{iid}'.format(cn=cluster_name, iid=slave.id))
+
     # Return all the instances
     return (master_nodes, slave_nodes)
 

From 601032f5bfe2dcdc240bfcc553f401e6facbf5ec Mon Sep 17 00:00:00 2001
From: Zongheng Yang <zongheng.y@gmail.com>
Date: Tue, 10 Jun 2014 21:59:01 -0700
Subject: [PATCH 388/810] HOTFIX: clear() configs in SQLConf-related unit
 tests.

Thanks goes to @liancheng, who pointed out that `sql/test-only *.SQLConfSuite *.SQLQuerySuite` passed but `sql/test-only *.SQLQuerySuite *.SQLConfSuite` failed. The reason is that some tests use the same test keys and without clear()'ing, they get carried over to other tests. This hotfix simply adds some `clear()` calls.

This problem was not evident on Jenkins before probably because `parallelExecution` is not set to `false` for `sqlCoreSettings`.

Author: Zongheng Yang <zongheng.y@gmail.com>

Closes #1040 from concretevitamin/sqlconf-tests and squashes the following commits:

6d14ceb [Zongheng Yang] HOTFIX: clear() confs in SQLConf related unit tests.
---
 sql/core/src/test/scala/org/apache/spark/sql/SQLConfSuite.scala | 2 ++
 .../src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala     | 1 +
 2 files changed, 3 insertions(+)

diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLConfSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLConfSuite.scala
index 5eb73a4eff98..08293f7f0ca3 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLConfSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLConfSuite.scala
@@ -28,6 +28,7 @@ class SQLConfSuite extends QueryTest {
   val testVal = "test.val.0"
 
   test("programmatic ways of basic setting and getting") {
+    clear()
     assert(getOption(testKey).isEmpty)
     assert(getAll.toSet === Set())
 
@@ -48,6 +49,7 @@ class SQLConfSuite extends QueryTest {
   }
 
   test("parse SQL set commands") {
+    clear()
     sql(s"set $testKey=$testVal")
     assert(get(testKey, testVal + "_") == testVal)
     assert(TestSQLContext.get(testKey, testVal + "_") == testVal)
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index de02bbc7e470..c1fc99f07743 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -402,6 +402,7 @@ class SQLQuerySuite extends QueryTest {
       sql(s"SET $nonexistentKey"),
       Seq(Seq(s"$nonexistentKey is undefined"))
     )
+    clear()
   }
 
 }

From 0402bd77ec786d1fa6cfd7f9cc3aa97c7ab16fd8 Mon Sep 17 00:00:00 2001
From: Takuya UESHIN <ueshin@happy-camper.st>
Date: Tue, 10 Jun 2014 23:13:48 -0700
Subject: [PATCH 389/810] [SPARK-2093] [SQL] NullPropagation should use exact
 type value.

`NullPropagation` should use exact type value when transform `Count` or `Sum`.

Author: Takuya UESHIN <ueshin@happy-camper.st>

Closes #1034 from ueshin/issues/SPARK-2093 and squashes the following commits:

65b6ff1 [Takuya UESHIN] Modify the literal value of the result of transformation from Sum to long value.
830c20b [Takuya UESHIN] Add Cast to the result of transformation from Count.
9314806 [Takuya UESHIN] Fix NullPropagation to use exact type value.
---
 .../org/apache/spark/sql/catalyst/optimizer/Optimizer.scala   | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
index ccb8245cc2e7..e41fd2db7485 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
@@ -104,8 +104,8 @@ object ColumnPruning extends Rule[LogicalPlan] {
 object NullPropagation extends Rule[LogicalPlan] {
   def apply(plan: LogicalPlan): LogicalPlan = plan transform {
     case q: LogicalPlan => q transformExpressionsUp {
-      case e @ Count(Literal(null, _)) => Literal(0, e.dataType)
-      case e @ Sum(Literal(c, _)) if c == 0 => Literal(0, e.dataType)
+      case e @ Count(Literal(null, _)) => Cast(Literal(0L), e.dataType)
+      case e @ Sum(Literal(c, _)) if c == 0 => Cast(Literal(0L), e.dataType)
       case e @ Average(Literal(c, _)) if c == 0 => Literal(0.0, e.dataType)
       case e @ IsNull(c) if c.nullable == false => Literal(false, BooleanType)
       case e @ IsNotNull(c) if c.nullable == false => Literal(true, BooleanType)

From 0266a0c8a70e0fbaeb0df63031f7a750ffc31a80 Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian.cs.zju@gmail.com>
Date: Wed, 11 Jun 2014 00:06:50 -0700
Subject: [PATCH 390/810] [SPARK-1968][SQL] SQL/HiveQL command for
 caching/uncaching tables

JIRA issue: [SPARK-1968](https://issues.apache.org/jira/browse/SPARK-1968)

This PR added support for SQL/HiveQL command for caching/uncaching tables:

```
scala> sql("CACHE TABLE src")
...
res0: org.apache.spark.sql.SchemaRDD =
SchemaRDD[0] at RDD at SchemaRDD.scala:98
== Query Plan ==
CacheCommandPhysical src, true

scala> table("src")
...
res1: org.apache.spark.sql.SchemaRDD =
SchemaRDD[3] at RDD at SchemaRDD.scala:98
== Query Plan ==
InMemoryColumnarTableScan [key#0,value#1], (HiveTableScan [key#0,value#1], (MetastoreRelation default, src, None), None), false

scala> isCached("src")
res2: Boolean = true

scala> sql("CACHE TABLE src")
...
res3: org.apache.spark.sql.SchemaRDD =
SchemaRDD[4] at RDD at SchemaRDD.scala:98
== Query Plan ==
CacheCommandPhysical src, false

scala> table("src")
...
res4: org.apache.spark.sql.SchemaRDD =
SchemaRDD[11] at RDD at SchemaRDD.scala:98
== Query Plan ==
HiveTableScan [key#2,value#3], (MetastoreRelation default, src, None), None

scala> isCached("src")
res5: Boolean = false
```

Things also work for `hql`.

Author: Cheng Lian <lian.cs.zju@gmail.com>

Closes #1038 from liancheng/sqlCacheTable and squashes the following commits:

ecb7194 [Cheng Lian] Trimmed the SQL string before parsing special commands
6f4ce42 [Cheng Lian] Moved logical command classes to a separate file
3458a24 [Cheng Lian] Added comment for public API
f0ffacc [Cheng Lian] Added isCached() predicate
15ec6d2 [Cheng Lian] Added "(UN)CACHE TABLE" SQL/HiveQL statements
---
 .../apache/spark/sql/catalyst/SqlParser.scala | 10 +++-
 .../catalyst/plans/logical/LogicalPlan.scala  | 35 +----------
 .../sql/catalyst/plans/logical/commands.scala | 60 +++++++++++++++++++
 .../org/apache/spark/sql/SQLContext.scala     |  9 +++
 .../spark/sql/execution/SparkStrategies.scala |  7 ++-
 .../apache/spark/sql/execution/commands.scala | 23 +++++++
 .../apache/spark/sql/CachedTableSuite.scala   | 16 +++++
 .../org/apache/spark/sql/hive/HiveQl.scala    | 18 +++---
 .../org/apache/spark/sql/hive/TestHive.scala  |  5 +-
 .../spark/sql/hive/CachedTableSuite.scala     | 16 +++++
 10 files changed, 152 insertions(+), 47 deletions(-)
 create mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/commands.scala

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
index 36758f3114e5..46fcfbb9e26b 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
@@ -111,6 +111,7 @@ class SqlParser extends StandardTokenParsers with PackratParsers {
   protected val APPROXIMATE = Keyword("APPROXIMATE")
   protected val AVG = Keyword("AVG")
   protected val BY = Keyword("BY")
+  protected val CACHE = Keyword("CACHE")
   protected val CAST = Keyword("CAST")
   protected val COUNT = Keyword("COUNT")
   protected val DESC = Keyword("DESC")
@@ -149,7 +150,9 @@ class SqlParser extends StandardTokenParsers with PackratParsers {
   protected val SEMI = Keyword("SEMI")
   protected val STRING = Keyword("STRING")
   protected val SUM = Keyword("SUM")
+  protected val TABLE = Keyword("TABLE")
   protected val TRUE = Keyword("TRUE")
+  protected val UNCACHE = Keyword("UNCACHE")
   protected val UNION = Keyword("UNION")
   protected val WHERE = Keyword("WHERE")
 
@@ -189,7 +192,7 @@ class SqlParser extends StandardTokenParsers with PackratParsers {
         UNION ~ ALL ^^^ { (q1: LogicalPlan, q2: LogicalPlan) => Union(q1, q2) } |
         UNION ~ opt(DISTINCT) ^^^ { (q1: LogicalPlan, q2: LogicalPlan) => Distinct(Union(q1, q2)) }
       )
-    | insert
+    | insert | cache
   )
 
   protected lazy val select: Parser[LogicalPlan] =
@@ -220,6 +223,11 @@ class SqlParser extends StandardTokenParsers with PackratParsers {
         InsertIntoTable(r, Map[String, Option[String]](), s, overwrite)
     }
 
+  protected lazy val cache: Parser[LogicalPlan] =
+    (CACHE ^^^ true | UNCACHE ^^^ false) ~ TABLE ~ ident ^^ {
+      case doCache ~ _ ~ tableName => CacheCommand(tableName, doCache)
+    }
+
   protected lazy val projections: Parser[Seq[Expression]] = repsep(projection, ",")
 
   protected lazy val projection: Parser[Expression] =
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala
index 7eeb98aea636..0933a31c362d 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala
@@ -20,7 +20,7 @@ package org.apache.spark.sql.catalyst.plans.logical
 import org.apache.spark.sql.catalyst.errors.TreeNodeException
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.QueryPlan
-import org.apache.spark.sql.catalyst.types.{StringType, StructType}
+import org.apache.spark.sql.catalyst.types.StructType
 import org.apache.spark.sql.catalyst.trees
 
 abstract class LogicalPlan extends QueryPlan[LogicalPlan] {
@@ -96,39 +96,6 @@ abstract class LeafNode extends LogicalPlan with trees.LeafNode[LogicalPlan] {
   def references = Set.empty
 }
 
-/**
- * A logical node that represents a non-query command to be executed by the system.  For example,
- * commands can be used by parsers to represent DDL operations.
- */
-abstract class Command extends LeafNode {
-  self: Product =>
-  def output: Seq[Attribute] = Seq.empty  // TODO: SPARK-2081 should fix this
-}
-
-/**
- * Returned for commands supported by a given parser, but not catalyst.  In general these are DDL
- * commands that are passed directly to another system.
- */
-case class NativeCommand(cmd: String) extends Command
-
-/**
- * Commands of the form "SET (key) (= value)".
- */
-case class SetCommand(key: Option[String], value: Option[String]) extends Command {
-  override def output = Seq(
-    AttributeReference("key", StringType, nullable = false)(),
-    AttributeReference("value", StringType, nullable = false)()
-  )
-}
-
-/**
- * Returned by a parser when the users only wants to see what query plan would be executed, without
- * actually performing the execution.
- */
-case class ExplainCommand(plan: LogicalPlan) extends Command {
-  override def output = Seq(AttributeReference("plan", StringType, nullable = false)())
-}
-
 /**
  * A logical plan node with single child.
  */
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/commands.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/commands.scala
new file mode 100644
index 000000000000..d05c9652753e
--- /dev/null
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/commands.scala
@@ -0,0 +1,60 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.plans.logical
+
+import org.apache.spark.sql.catalyst.expressions.{AttributeReference, Attribute}
+import org.apache.spark.sql.catalyst.types.StringType
+
+/**
+ * A logical node that represents a non-query command to be executed by the system.  For example,
+ * commands can be used by parsers to represent DDL operations.
+ */
+abstract class Command extends LeafNode {
+  self: Product =>
+  def output: Seq[Attribute] = Seq.empty  // TODO: SPARK-2081 should fix this
+}
+
+/**
+ * Returned for commands supported by a given parser, but not catalyst.  In general these are DDL
+ * commands that are passed directly to another system.
+ */
+case class NativeCommand(cmd: String) extends Command
+
+/**
+ * Commands of the form "SET (key) (= value)".
+ */
+case class SetCommand(key: Option[String], value: Option[String]) extends Command {
+  override def output = Seq(
+    AttributeReference("key", StringType, nullable = false)(),
+    AttributeReference("value", StringType, nullable = false)()
+  )
+}
+
+/**
+ * Returned by a parser when the users only wants to see what query plan would be executed, without
+ * actually performing the execution.
+ */
+case class ExplainCommand(plan: LogicalPlan) extends Command {
+  override def output = Seq(AttributeReference("plan", StringType, nullable = false)())
+}
+
+/**
+ * Returned for the "CACHE TABLE tableName" and "UNCACHE TABLE tableName" command.
+ */
+case class CacheCommand(tableName: String, doCache: Boolean) extends Command
+
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
index 021e0e8245a0..264192ed1aa2 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
@@ -188,6 +188,15 @@ class SQLContext(@transient val sparkContext: SparkContext)
     }
   }
 
+  /** Returns true if the table is currently cached in-memory. */
+  def isCached(tableName: String): Boolean = {
+    val relation = catalog.lookupRelation(None, tableName)
+    EliminateAnalysisOperators(relation) match {
+      case SparkLogicalPlan(_: InMemoryColumnarTableScan) => true
+      case _ => false
+    }
+  }
+
   protected[sql] class SparkPlanner extends SparkStrategies {
     val sparkContext = self.sparkContext
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
index 0455748d40ee..f2f95dfe27e6 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
@@ -239,10 +239,11 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
       case logical.SetCommand(key, value) =>
         Seq(execution.SetCommandPhysical(key, value, plan.output)(context))
       case logical.ExplainCommand(child) =>
-        val qe = context.executePlan(child)
-        Seq(execution.ExplainCommandPhysical(qe.executedPlan, plan.output)(context))
+        val executedPlan = context.executePlan(child).executedPlan
+        Seq(execution.ExplainCommandPhysical(executedPlan, plan.output)(context))
+      case logical.CacheCommand(tableName, cache) =>
+        Seq(execution.CacheCommandPhysical(tableName, cache)(context))
       case _ => Nil
     }
   }
-
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala
index 9364506691f3..be26d19e6686 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala
@@ -65,3 +65,26 @@ case class ExplainCommandPhysical(child: SparkPlan, output: Seq[Attribute])
 
   override def otherCopyArgs = context :: Nil
 }
+
+/**
+ * :: DeveloperApi ::
+ */
+@DeveloperApi
+case class CacheCommandPhysical(tableName: String, doCache: Boolean)(@transient context: SQLContext)
+  extends LeafNode {
+
+  lazy val commandSideEffect = {
+    if (doCache) {
+      context.cacheTable(tableName)
+    } else {
+      context.uncacheTable(tableName)
+    }
+  }
+
+  override def execute(): RDD[Row] = {
+    commandSideEffect
+    context.emptyResult
+  }
+
+  override def output: Seq[Attribute] = Seq.empty
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala
index 0331f90272a9..ebca3adc2ff0 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala
@@ -70,4 +70,20 @@ class CachedTableSuite extends QueryTest {
     TestSQLContext.sql("SELECT * FROM testData a JOIN testData b ON a.key = b.key")
     TestSQLContext.uncacheTable("testData")
   }
+
+  test("'CACHE TABLE' and 'UNCACHE TABLE' SQL statement") {
+    TestSQLContext.sql("CACHE TABLE testData")
+    TestSQLContext.table("testData").queryExecution.executedPlan match {
+      case _: InMemoryColumnarTableScan => // Found evidence of caching
+      case _ => fail(s"Table 'testData' should be cached")
+    }
+    assert(TestSQLContext.isCached("testData"), "Table 'testData' should be cached")
+
+    TestSQLContext.sql("UNCACHE TABLE testData")
+    TestSQLContext.table("testData").queryExecution.executedPlan match {
+      case _: InMemoryColumnarTableScan => fail(s"Table 'testData' should not be cached")
+      case _ => // Found evidence of uncaching
+    }
+    assert(!TestSQLContext.isCached("testData"), "Table 'testData' should not be cached")
+  }
 }
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
index 4e74d9bc909f..b745d8ffd8f1 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
@@ -218,15 +218,19 @@ private[hive] object HiveQl {
           case Array(key, value) => // "set key=value"
             SetCommand(Some(key), Some(value))
         }
-      } else if (sql.toLowerCase.startsWith("add jar")) {
+      } else if (sql.trim.toLowerCase.startsWith("cache table")) {
+        CacheCommand(sql.drop(12).trim, true)
+      } else if (sql.trim.toLowerCase.startsWith("uncache table")) {
+        CacheCommand(sql.drop(14).trim, false)
+      } else if (sql.trim.toLowerCase.startsWith("add jar")) {
         AddJar(sql.drop(8))
-      } else if (sql.toLowerCase.startsWith("add file")) {
+      } else if (sql.trim.toLowerCase.startsWith("add file")) {
         AddFile(sql.drop(9))
-      } else if (sql.startsWith("dfs")) {
+      } else if (sql.trim.startsWith("dfs")) {
         DfsCommand(sql)
-      } else if (sql.startsWith("source")) {
+      } else if (sql.trim.startsWith("source")) {
         SourceCommand(sql.split(" ").toSeq match { case Seq("source", filePath) => filePath })
-      } else if (sql.startsWith("!")) {
+      } else if (sql.trim.startsWith("!")) {
         ShellCommand(sql.drop(1))
       } else {
         val tree = getAst(sql)
@@ -839,11 +843,11 @@ private[hive] object HiveQl {
     case Token("TOK_FUNCTIONDI", Token(SUM(), Nil) :: arg :: Nil) => SumDistinct(nodeToExpr(arg))
     case Token("TOK_FUNCTION", Token(MAX(), Nil) :: arg :: Nil) => Max(nodeToExpr(arg))
     case Token("TOK_FUNCTION", Token(MIN(), Nil) :: arg :: Nil) => Min(nodeToExpr(arg))
-    
+
     /* System functions about string operations */
     case Token("TOK_FUNCTION", Token(UPPER(), Nil) :: arg :: Nil) => Upper(nodeToExpr(arg))
     case Token("TOK_FUNCTION", Token(LOWER(), Nil) :: arg :: Nil) => Lower(nodeToExpr(arg))
-    
+
     /* Casts */
     case Token("TOK_FUNCTION", Token("TOK_STRING", Nil) :: arg :: Nil) =>
       Cast(nodeToExpr(arg), StringType)
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/TestHive.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/TestHive.scala
index 041e813598d1..9386008d02d5 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/TestHive.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/TestHive.scala
@@ -32,7 +32,7 @@ import org.apache.hadoop.hive.serde2.avro.AvroSerDe
 
 import org.apache.spark.{SparkConf, SparkContext}
 import org.apache.spark.sql.catalyst.analysis._
-import org.apache.spark.sql.catalyst.plans.logical.{LogicalPlan, NativeCommand}
+import org.apache.spark.sql.catalyst.plans.logical.{CacheCommand, LogicalPlan, NativeCommand}
 import org.apache.spark.sql.catalyst.util._
 import org.apache.spark.sql.hive._
 
@@ -103,7 +103,7 @@ class TestHiveContext(sc: SparkContext) extends LocalHiveContext(sc) {
   val inRepoTests = if (System.getProperty("user.dir").endsWith("sql" + File.separator + "hive")) {
     new File("src" + File.separator + "test" + File.separator + "resources" + File.separator)
   } else {
-    new File("sql" + File.separator + "hive" + File.separator + "src" + File.separator + "test" + 
+    new File("sql" + File.separator + "hive" + File.separator + "src" + File.separator + "test" +
       File.separator + "resources")
   }
 
@@ -130,6 +130,7 @@ class TestHiveContext(sc: SparkContext) extends LocalHiveContext(sc) {
     override lazy val analyzed = {
       val describedTables = logical match {
         case NativeCommand(describedTable(tbl)) => tbl :: Nil
+        case CacheCommand(tbl, _) => tbl :: Nil
         case _ => Nil
       }
 
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/CachedTableSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/CachedTableSuite.scala
index f9a162ef4e3c..91ac03ca30cd 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/CachedTableSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/CachedTableSuite.scala
@@ -56,4 +56,20 @@ class CachedTableSuite extends HiveComparisonTest {
       TestHive.uncacheTable("src")
     }
   }
+
+  test("'CACHE TABLE' and 'UNCACHE TABLE' HiveQL statement") {
+    TestHive.hql("CACHE TABLE src")
+    TestHive.table("src").queryExecution.executedPlan match {
+      case _: InMemoryColumnarTableScan => // Found evidence of caching
+      case _ => fail(s"Table 'src' should be cached")
+    }
+    assert(TestHive.isCached("src"), "Table 'src' should be cached")
+
+    TestHive.hql("UNCACHE TABLE src")
+    TestHive.table("src").queryExecution.executedPlan match {
+      case _: InMemoryColumnarTableScan => fail(s"Table 'src' should not be cached")
+      case _ => // Found evidence of uncaching
+    }
+    assert(!TestHive.isCached("src"), "Table 'src' should not be cached")
+  }
 }

From 0f1dc3a73d6687f0027a68eb9a4a13a7c7848205 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Wed, 11 Jun 2014 00:22:40 -0700
Subject: [PATCH 391/810] [SPARK-2091][MLLIB] use numpy.dot instead of
 ndarray.dot

`ndarray.dot` is not available in numpy 1.4. This PR makes pyspark/mllib compatible with numpy 1.4.

Author: Xiangrui Meng <meng@databricks.com>

Closes #1035 from mengxr/numpy-1.4 and squashes the following commits:

7ad2f0c [Xiangrui Meng] use numpy.dot instead of ndarray.dot
---
 python/pyspark/mllib/_common.py | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/python/pyspark/mllib/_common.py b/python/pyspark/mllib/_common.py
index a411a5d5914e..e609b60a0f96 100644
--- a/python/pyspark/mllib/_common.py
+++ b/python/pyspark/mllib/_common.py
@@ -454,7 +454,7 @@ def _squared_distance(v1, v2):
     v2 = _convert_vector(v2)
     if type(v1) == ndarray and type(v2) == ndarray:
         diff = v1 - v2
-        return diff.dot(diff)
+        return numpy.dot(diff, diff)
     elif type(v1) == ndarray:
         return v2.squared_distance(v1)
     else:
@@ -469,10 +469,12 @@ def _dot(vec, target):
     calling numpy.dot of the two vectors, but for SciPy ones, we
     have to transpose them because they're column vectors.
     """
-    if type(vec) == ndarray or type(vec) == SparseVector:
+    if type(vec) == ndarray:
+        return numpy.dot(vec, target)
+    elif type(vec) == SparseVector:
         return vec.dot(target)
     elif type(vec) == list:
-        return _convert_vector(vec).dot(target)
+        return numpy.dot(_convert_vector(vec), target)
     else:
         return vec.transpose().dot(target)[0]
 

From 6e11930310e3864790d0e30f0df7bf691cbeb85d Mon Sep 17 00:00:00 2001
From: "Qiuzhuang.Lian" <Qiuzhuang.Lian@gmail.com>
Date: Wed, 11 Jun 2014 00:36:06 -0700
Subject: [PATCH 392/810] SPARK-2107: FilterPushdownSuite doesn't need Junit
 jar.

Author: Qiuzhuang.Lian <Qiuzhuang.Lian@gmail.com>

Closes #1046 from Qiuzhuang/master and squashes the following commits:

0a9921a [Qiuzhuang.Lian] SPARK-2107: FilterPushdownSuite doesn't need Junit jar.
---
 .../spark/sql/catalyst/optimizer/FilterPushdownSuite.scala     | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/FilterPushdownSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/FilterPushdownSuite.scala
index 02cc665f8a8c..0cada785b663 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/FilterPushdownSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/FilterPushdownSuite.scala
@@ -20,14 +20,11 @@ package org.apache.spark.sql.catalyst.optimizer
 import org.apache.spark.sql.catalyst.analysis
 import org.apache.spark.sql.catalyst.analysis.EliminateAnalysisOperators
 import org.apache.spark.sql.catalyst.plans.logical._
-import org.apache.spark.sql.catalyst.plans.Inner
-import org.apache.spark.sql.catalyst.plans.FullOuter
 import org.apache.spark.sql.catalyst.plans.LeftOuter
 import org.apache.spark.sql.catalyst.plans.RightOuter
 import org.apache.spark.sql.catalyst.rules._
 import org.apache.spark.sql.catalyst.dsl.plans._
 import org.apache.spark.sql.catalyst.dsl.expressions._
-import org.junit.Test
 
 class FilterPushdownSuite extends OptimizerTest {
 

From 2a4225dd944441d3f735625bb6bae6fca8fd06ca Mon Sep 17 00:00:00 2001
From: Sandy Ryza <sandy@cloudera.com>
Date: Wed, 11 Jun 2014 07:57:28 -0500
Subject: [PATCH 393/810] SPARK-1639. Tidy up some Spark on YARN code

This contains a bunch of small tidyings of the Spark on YARN code.

I focused on the yarn stable code.  @tgravescs, let me know if you'd like me to make these for the alpha code as well.

Author: Sandy Ryza <sandy@cloudera.com>

Closes #561 from sryza/sandy-spark-1639 and squashes the following commits:

72b6a02 [Sandy Ryza] Fix comment and set name on driver thread
c2190b2 [Sandy Ryza] SPARK-1639. Tidy up some Spark on YARN code
---
 .../spark/deploy/yarn/ApplicationMaster.scala |  16 +-
 .../apache/spark/deploy/yarn/ClientBase.scala |  38 ++--
 .../deploy/yarn/ExecutorRunnableUtil.scala    |  28 +--
 .../cluster/YarnClusterScheduler.scala        |  10 +-
 .../spark/deploy/yarn/ApplicationMaster.scala | 197 +++++++++---------
 .../org/apache/spark/deploy/yarn/Client.scala |  10 +-
 .../spark/deploy/yarn/ExecutorLauncher.scala  |  40 ++--
 7 files changed, 161 insertions(+), 178 deletions(-)

diff --git a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
index 8f0ecb855718..1cc9c33cd2d0 100644
--- a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
+++ b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
@@ -277,7 +277,7 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration,
         yarnAllocator.allocateContainers(
           math.max(args.numExecutors - yarnAllocator.getNumExecutorsRunning, 0))
         ApplicationMaster.incrementAllocatorLoop(1)
-        Thread.sleep(100)
+        Thread.sleep(ApplicationMaster.ALLOCATE_HEARTBEAT_INTERVAL)
       }
     } finally {
       // In case of exceptions, etc - ensure that count is at least ALLOCATOR_LOOP_WAIT_COUNT,
@@ -416,6 +416,7 @@ object ApplicationMaster {
   // TODO: Currently, task to container is computed once (TaskSetManager) - which need not be
   // optimal as more containers are available. Might need to handle this better.
   private val ALLOCATOR_LOOP_WAIT_COUNT = 30
+  private val ALLOCATE_HEARTBEAT_INTERVAL = 100
 
   def incrementAllocatorLoop(by: Int) {
     val count = yarnAllocatorLoop.getAndAdd(by)
@@ -467,13 +468,22 @@ object ApplicationMaster {
       })
     }
 
-    // Wait for initialization to complete and atleast 'some' nodes can get allocated.
+    modified
+  }
+
+
+  /**
+   * Returns when we've either
+   *  1) received all the requested executors,
+   *  2) waited ALLOCATOR_LOOP_WAIT_COUNT * ALLOCATE_HEARTBEAT_INTERVAL ms,
+   *  3) hit an error that causes us to terminate trying to get containers.
+   */
+  def waitForInitialAllocations() {
     yarnAllocatorLoop.synchronized {
       while (yarnAllocatorLoop.get() <= ALLOCATOR_LOOP_WAIT_COUNT) {
         yarnAllocatorLoop.wait(1000L)
       }
     }
-    modified
   }
 
   def main(argStrings: Array[String]) {
diff --git a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala
index 801e8b381588..29a35680c0e7 100644
--- a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala
+++ b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala
@@ -19,7 +19,6 @@ package org.apache.spark.deploy.yarn
 
 import java.io.File
 import java.net.{InetAddress, UnknownHostException, URI, URISyntaxException}
-import java.nio.ByteBuffer
 
 import scala.collection.JavaConversions._
 import scala.collection.mutable.{HashMap, ListBuffer, Map}
@@ -37,7 +36,7 @@ import org.apache.hadoop.yarn.api.ApplicationConstants.Environment
 import org.apache.hadoop.yarn.api.protocolrecords._
 import org.apache.hadoop.yarn.api.records._
 import org.apache.hadoop.yarn.conf.YarnConfiguration
-import org.apache.hadoop.yarn.util.{Apps, Records}
+import org.apache.hadoop.yarn.util.Records
 import org.apache.spark.{Logging, SparkConf, SparkContext}
 
 /**
@@ -169,14 +168,13 @@ trait ClientBase extends Logging {
     destPath
   }
 
-  def qualifyForLocal(localURI: URI): Path = {
+  private def qualifyForLocal(localURI: URI): Path = {
     var qualifiedURI = localURI
-    // If not specified assume these are in the local filesystem to keep behavior like Hadoop
+    // If not specified, assume these are in the local filesystem to keep behavior like Hadoop
     if (qualifiedURI.getScheme() == null) {
       qualifiedURI = new URI(FileSystem.getLocal(conf).makeQualified(new Path(qualifiedURI)).toString)
     }
-    val qualPath = new Path(qualifiedURI)
-    qualPath
+    new Path(qualifiedURI)
   }
 
   def prepareLocalResources(appStagingDir: String): HashMap[String, LocalResource] = {
@@ -305,13 +303,13 @@ trait ClientBase extends Logging {
 
     val amMemory = calculateAMMemory(newApp)
 
-    val JAVA_OPTS = ListBuffer[String]()
+    val javaOpts = ListBuffer[String]()
 
     // Add Xmx for AM memory
-    JAVA_OPTS += "-Xmx" + amMemory + "m"
+    javaOpts += "-Xmx" + amMemory + "m"
 
     val tmpDir = new Path(Environment.PWD.$(), YarnConfiguration.DEFAULT_CONTAINER_TEMP_DIR)
-    JAVA_OPTS += "-Djava.io.tmpdir=" + tmpDir
+    javaOpts += "-Djava.io.tmpdir=" + tmpDir
 
     // TODO: Remove once cpuset version is pushed out.
     // The context is, default gc for server class machines ends up using all cores to do gc -
@@ -325,11 +323,11 @@ trait ClientBase extends Logging {
     if (useConcurrentAndIncrementalGC) {
       // In our expts, using (default) throughput collector has severe perf ramifications in
       // multi-tenant machines
-      JAVA_OPTS += "-XX:+UseConcMarkSweepGC"
-      JAVA_OPTS += "-XX:+CMSIncrementalMode"
-      JAVA_OPTS += "-XX:+CMSIncrementalPacing"
-      JAVA_OPTS += "-XX:CMSIncrementalDutyCycleMin=0"
-      JAVA_OPTS += "-XX:CMSIncrementalDutyCycle=10"
+      javaOpts += "-XX:+UseConcMarkSweepGC"
+      javaOpts += "-XX:+CMSIncrementalMode"
+      javaOpts += "-XX:+CMSIncrementalPacing"
+      javaOpts += "-XX:CMSIncrementalDutyCycleMin=0"
+      javaOpts += "-XX:CMSIncrementalDutyCycle=10"
     }
 
     // SPARK_JAVA_OPTS is deprecated, but for backwards compatibility:
@@ -344,22 +342,22 @@ trait ClientBase extends Logging {
       // If we are being launched in client mode, forward the spark-conf options
       // onto the executor launcher
       for ((k, v) <- sparkConf.getAll) {
-        JAVA_OPTS += "-D" + k + "=" + "\\\"" + v + "\\\""
+        javaOpts += "-D" + k + "=" + "\\\"" + v + "\\\""
       }
     } else {
       // If we are being launched in standalone mode, capture and forward any spark
       // system properties (e.g. set by spark-class).
       for ((k, v) <- sys.props.filterKeys(_.startsWith("spark"))) {
-        JAVA_OPTS += "-D" + k + "=" + "\\\"" + v + "\\\""
+        javaOpts += "-D" + k + "=" + "\\\"" + v + "\\\""
       }
-      sys.props.get("spark.driver.extraJavaOptions").foreach(opts => JAVA_OPTS += opts)
-      sys.props.get("spark.driver.libraryPath").foreach(p => JAVA_OPTS += s"-Djava.library.path=$p")
+      sys.props.get("spark.driver.extraJavaOptions").foreach(opts => javaOpts += opts)
+      sys.props.get("spark.driver.libraryPath").foreach(p => javaOpts += s"-Djava.library.path=$p")
     }
-    JAVA_OPTS += ClientBase.getLog4jConfiguration(localResources)
+    javaOpts += ClientBase.getLog4jConfiguration(localResources)
 
     // Command for the ApplicationMaster
     val commands = Seq(Environment.JAVA_HOME.$() + "/bin/java", "-server") ++
-      JAVA_OPTS ++
+      javaOpts ++
       Seq(args.amClass, "--class", args.userClass, "--jar ", args.userJar,
         userArgsToString(args),
         "--executor-memory", args.executorMemory.toString,
diff --git a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnableUtil.scala b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnableUtil.scala
index 32f8861dc950..43dbb2464f92 100644
--- a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnableUtil.scala
+++ b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnableUtil.scala
@@ -28,7 +28,7 @@ import org.apache.hadoop.yarn.api._
 import org.apache.hadoop.yarn.api.ApplicationConstants.Environment
 import org.apache.hadoop.yarn.api.records._
 import org.apache.hadoop.yarn.conf.YarnConfiguration
-import org.apache.hadoop.yarn.util.{Apps, ConverterUtils, Records}
+import org.apache.hadoop.yarn.util.{ConverterUtils, Records}
 
 import org.apache.spark.{Logging, SparkConf}
 
@@ -46,19 +46,19 @@ trait ExecutorRunnableUtil extends Logging {
       executorCores: Int,
       localResources: HashMap[String, LocalResource]): List[String] = {
     // Extra options for the JVM
-    val JAVA_OPTS = ListBuffer[String]()
+    val javaOpts = ListBuffer[String]()
     // Set the JVM memory
     val executorMemoryString = executorMemory + "m"
-    JAVA_OPTS += "-Xms" + executorMemoryString + " -Xmx" + executorMemoryString + " "
+    javaOpts += "-Xms" + executorMemoryString + " -Xmx" + executorMemoryString + " "
 
     // Set extra Java options for the executor, if defined
     sys.props.get("spark.executor.extraJavaOptions").foreach { opts =>
-      JAVA_OPTS += opts
+      javaOpts += opts
     }
 
-    JAVA_OPTS += "-Djava.io.tmpdir=" +
+    javaOpts += "-Djava.io.tmpdir=" +
       new Path(Environment.PWD.$(), YarnConfiguration.DEFAULT_CONTAINER_TEMP_DIR)
-    JAVA_OPTS += ClientBase.getLog4jConfiguration(localResources)
+    javaOpts += ClientBase.getLog4jConfiguration(localResources)
 
     // Certain configs need to be passed here because they are needed before the Executor
     // registers with the Scheduler and transfers the spark configs. Since the Executor backend
@@ -66,10 +66,10 @@ trait ExecutorRunnableUtil extends Logging {
     // authentication settings.
     sparkConf.getAll.
       filter { case (k, v) => k.startsWith("spark.auth") || k.startsWith("spark.akka") }.
-      foreach { case (k, v) => JAVA_OPTS += "-D" + k + "=" + "\\\"" + v + "\\\"" }
+      foreach { case (k, v) => javaOpts += "-D" + k + "=" + "\\\"" + v + "\\\"" }
 
     sparkConf.getAkkaConf.
-      foreach { case (k, v) => JAVA_OPTS += "-D" + k + "=" + "\\\"" + v + "\\\"" }
+      foreach { case (k, v) => javaOpts += "-D" + k + "=" + "\\\"" + v + "\\\"" }
 
     // Commenting it out for now - so that people can refer to the properties if required. Remove
     // it once cpuset version is pushed out.
@@ -88,11 +88,11 @@ trait ExecutorRunnableUtil extends Logging {
           // multi-tennent machines
           // The options are based on
           // http://www.oracle.com/technetwork/java/gc-tuning-5-138395.html#0.0.0.%20When%20to%20Use%20the%20Concurrent%20Low%20Pause%20Collector|outline
-          JAVA_OPTS += " -XX:+UseConcMarkSweepGC "
-          JAVA_OPTS += " -XX:+CMSIncrementalMode "
-          JAVA_OPTS += " -XX:+CMSIncrementalPacing "
-          JAVA_OPTS += " -XX:CMSIncrementalDutyCycleMin=0 "
-          JAVA_OPTS += " -XX:CMSIncrementalDutyCycle=10 "
+          javaOpts += " -XX:+UseConcMarkSweepGC "
+          javaOpts += " -XX:+CMSIncrementalMode "
+          javaOpts += " -XX:+CMSIncrementalPacing "
+          javaOpts += " -XX:CMSIncrementalDutyCycleMin=0 "
+          javaOpts += " -XX:CMSIncrementalDutyCycle=10 "
         }
     */
 
@@ -104,7 +104,7 @@ trait ExecutorRunnableUtil extends Logging {
       // TODO: If the OOM is not recoverable by rescheduling it on different node, then do
       // 'something' to fail job ... akin to blacklisting trackers in mapred ?
       "-XX:OnOutOfMemoryError='kill %p'") ++
-      JAVA_OPTS ++
+      javaOpts ++
       Seq("org.apache.spark.executor.CoarseGrainedExecutorBackend",
       masterAddress.toString,
       slaveId.toString,
diff --git a/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClusterScheduler.scala b/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClusterScheduler.scala
index a4638cc86361..39cdd2e8a522 100644
--- a/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClusterScheduler.scala
+++ b/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClusterScheduler.scala
@@ -33,10 +33,11 @@ private[spark] class YarnClusterScheduler(sc: SparkContext, conf: Configuration)
 
   def this(sc: SparkContext) = this(sc, new Configuration())
 
-  // Nothing else for now ... initialize application master : which needs sparkContext to determine how to allocate
-  // Note that only the first creation of SparkContext influences (and ideally, there must be only one SparkContext, right ?)
-  // Subsequent creations are ignored - since nodes are already allocated by then.
-
+  // Nothing else for now ... initialize application master : which needs a SparkContext to
+  // determine how to allocate.
+  // Note that only the first creation of a SparkContext influences (and ideally, there must be
+  // only one SparkContext, right ?). Subsequent creations are ignored since executors are already
+  // allocated by then.
 
   // By default, rack is unknown
   override def getRackForHost(hostPort: String): Option[String] = {
@@ -48,6 +49,7 @@ private[spark] class YarnClusterScheduler(sc: SparkContext, conf: Configuration)
   override def postStartHook() {
     val sparkContextInitialized = ApplicationMaster.sparkContextInitialized(sc)
     if (sparkContextInitialized){
+      ApplicationMaster.waitForInitialAllocations()
       // Wait for a few seconds for the slaves to bootstrap and register with master - best case attempt
       Thread.sleep(3000L)
     }
diff --git a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
index 33a60d978c58..6244332f2373 100644
--- a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
+++ b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
@@ -19,13 +19,12 @@ package org.apache.spark.deploy.yarn
 
 import java.io.IOException
 import java.util.concurrent.CopyOnWriteArrayList
-import java.util.concurrent.atomic.{AtomicInteger, AtomicReference}
+import java.util.concurrent.atomic.AtomicReference
 
 import scala.collection.JavaConversions._
 
 import org.apache.hadoop.conf.Configuration
 import org.apache.hadoop.fs.{FileSystem, Path}
-import org.apache.hadoop.net.NetUtils
 import org.apache.hadoop.util.ShutdownHookManager
 import org.apache.hadoop.yarn.api._
 import org.apache.hadoop.yarn.api.protocolrecords._
@@ -33,8 +32,7 @@ import org.apache.hadoop.yarn.api.records._
 import org.apache.hadoop.yarn.client.api.AMRMClient
 import org.apache.hadoop.yarn.client.api.AMRMClient.ContainerRequest
 import org.apache.hadoop.yarn.conf.YarnConfiguration
-import org.apache.hadoop.yarn.ipc.YarnRPC
-import org.apache.hadoop.yarn.util.{ConverterUtils, Records}
+import org.apache.hadoop.yarn.util.ConverterUtils
 import org.apache.hadoop.yarn.webapp.util.WebAppUtils
 
 import org.apache.spark.{Logging, SecurityManager, SparkConf, SparkContext}
@@ -77,17 +75,18 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration,
     // than user specified and /tmp.
     System.setProperty("spark.local.dir", getLocalDirs())
 
-    // set the web ui port to be ephemeral for yarn so we don't conflict with
+    // Set the web ui port to be ephemeral for yarn so we don't conflict with
     // other spark processes running on the same box
     System.setProperty("spark.ui.port", "0")
 
-    // when running the AM, the Spark master is always "yarn-cluster"
+    // When running the AM, the Spark master is always "yarn-cluster"
     System.setProperty("spark.master", "yarn-cluster")
 
-    // Use priority 30 as it's higher then HDFS. It's same priority as MapReduce is using.
+    // Use priority 30 as it's higher than HDFS. It's the same priority MapReduce is using.
     ShutdownHookManager.get().addShutdownHook(new AppMasterShutdownHook(this), 30)
 
-    appAttemptId = getApplicationAttemptId()
+    appAttemptId = ApplicationMaster.getApplicationAttemptId()
+    logInfo("ApplicationAttemptId: " + appAttemptId)
     isLastAMRetry = appAttemptId.getAttemptId() >= maxAppAttempts
     amClient = AMRMClient.createAMRMClient()
     amClient.init(yarnConf)
@@ -99,7 +98,7 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration,
     ApplicationMaster.register(this)
 
     // Call this to force generation of secret so it gets populated into the
-    // hadoop UGI. This has to happen before the startUserClass which does a
+    // Hadoop UGI. This has to happen before the startUserClass which does a
     // doAs in order for the credentials to be passed on to the executor containers.
     val securityMgr = new SecurityManager(sparkConf)
 
@@ -121,7 +120,10 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration,
     // Allocate all containers
     allocateExecutors()
 
-    // Wait for the user class to Finish
+    // Launch thread that will heartbeat to the RM so it won't think the app has died.
+    launchReporterThread()
+
+    // Wait for the user class to finish
     userThread.join()
 
     System.exit(0)
@@ -141,7 +143,7 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration,
       "spark.org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter.params", params)
   }
 
-  /** Get the Yarn approved local directories. */
+  // Get the Yarn approved local directories.
   private def getLocalDirs(): String = {
     // Hadoop 0.23 and 2.x have different Environment variable names for the
     // local dirs, so lets check both. We assume one of the 2 is set.
@@ -150,18 +152,9 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration,
       .orElse(Option(System.getenv("LOCAL_DIRS")))
  
     localDirs match {
-      case None => throw new Exception("Yarn Local dirs can't be empty")
+      case None => throw new Exception("Yarn local dirs can't be empty")
       case Some(l) => l
     }
-  } 
-
-  private def getApplicationAttemptId(): ApplicationAttemptId = {
-    val envs = System.getenv()
-    val containerIdString = envs.get(ApplicationConstants.Environment.CONTAINER_ID.name())
-    val containerId = ConverterUtils.toContainerId(containerIdString)
-    val appAttemptId = containerId.getApplicationAttemptId()
-    logInfo("ApplicationAttemptId: " + appAttemptId)
-    appAttemptId
   }
 
   private def registerApplicationMaster(): RegisterApplicationMasterResponse = {
@@ -173,25 +166,23 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration,
     logInfo("Starting the user JAR in a separate Thread")
     val mainMethod = Class.forName(
       args.userClass,
-      false /* initialize */ ,
+      false,
       Thread.currentThread.getContextClassLoader).getMethod("main", classOf[Array[String]])
     val t = new Thread {
       override def run() {
-
-      var successed = false
+        var succeeded = false
         try {
           // Copy
-          var mainArgs: Array[String] = new Array[String](args.userArgs.size)
+          val mainArgs = new Array[String](args.userArgs.size)
           args.userArgs.copyToArray(mainArgs, 0, args.userArgs.size)
           mainMethod.invoke(null, mainArgs)
-          // some job script has "System.exit(0)" at the end, for example SparkPi, SparkLR
-          // userThread will stop here unless it has uncaught exception thrown out
-          // It need shutdown hook to set SUCCEEDED
-          successed = true
+          // Some apps have "System.exit(0)" at the end.  The user thread will stop here unless
+          // it has an uncaught exception thrown out.  It needs a shutdown hook to set SUCCEEDED.
+          succeeded = true
         } finally {
-          logDebug("finishing main")
+          logDebug("Finishing main")
           isLastAMRetry = true
-          if (successed) {
+          if (succeeded) {
             ApplicationMaster.this.finishApplicationMaster(FinalApplicationStatus.SUCCEEDED)
           } else {
             ApplicationMaster.this.finishApplicationMaster(FinalApplicationStatus.FAILED)
@@ -199,11 +190,12 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration,
         }
       }
     }
+    t.setName("Driver")
     t.start()
     t
   }
 
-  // This need to happen before allocateExecutors()
+  // This needs to happen before allocateExecutors()
   private def waitForSparkContextInitialized() {
     logInfo("Waiting for Spark context initialization")
     try {
@@ -231,7 +223,7 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration,
             sparkContext.preferredNodeLocationData,
             sparkContext.getConf)
         } else {
-          logWarning("Unable to retrieve SparkContext inspite of waiting for %d, maxNumTries = %d".
+          logWarning("Unable to retrieve SparkContext in spite of waiting for %d, maxNumTries = %d".
             format(numTries * waitTime, maxNumTries))
           this.yarnAllocator = YarnAllocationHandler.newAllocator(
             yarnConf,
@@ -242,48 +234,37 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration,
         }
       }
     } finally {
-      // In case of exceptions, etc - ensure that count is at least ALLOCATOR_LOOP_WAIT_COUNT :
-      // so that the loop (in ApplicationMaster.sparkContextInitialized) breaks.
-      ApplicationMaster.incrementAllocatorLoop(ApplicationMaster.ALLOCATOR_LOOP_WAIT_COUNT)
+      // In case of exceptions, etc - ensure that the loop in
+      // ApplicationMaster#sparkContextInitialized() breaks.
+      ApplicationMaster.doneWithInitialAllocations()
     }
   }
 
   private def allocateExecutors() {
     try {
-      logInfo("Allocating " + args.numExecutors + " executors.")
-      // Wait until all containers have finished
+      logInfo("Requesting" + args.numExecutors + " executors.")
+      // Wait until all containers have launched
       yarnAllocator.addResourceRequests(args.numExecutors)
       yarnAllocator.allocateResources()
       // Exits the loop if the user thread exits.
+
+      var iters = 0
       while (yarnAllocator.getNumExecutorsRunning < args.numExecutors && userThread.isAlive) {
         checkNumExecutorsFailed()
         allocateMissingExecutor()
         yarnAllocator.allocateResources()
-        ApplicationMaster.incrementAllocatorLoop(1)
-        Thread.sleep(100)
+        if (iters == ApplicationMaster.ALLOCATOR_LOOP_WAIT_COUNT) {
+          ApplicationMaster.doneWithInitialAllocations()
+        }
+        Thread.sleep(ApplicationMaster.ALLOCATE_HEARTBEAT_INTERVAL)
+        iters += 1
       }
     } finally {
-      // In case of exceptions, etc - ensure that count is at least ALLOCATOR_LOOP_WAIT_COUNT,
-      // so that the loop in ApplicationMaster#sparkContextInitialized() breaks.
-      ApplicationMaster.incrementAllocatorLoop(ApplicationMaster.ALLOCATOR_LOOP_WAIT_COUNT)
+      // In case of exceptions, etc - ensure that the loop in
+      // ApplicationMaster#sparkContextInitialized() breaks.
+      ApplicationMaster.doneWithInitialAllocations()
     }
     logInfo("All executors have launched.")
-
-    // Launch a progress reporter thread, else the app will get killed after expiration
-    // (def: 10mins) timeout.
-    if (userThread.isAlive) {
-      // Ensure that progress is sent before YarnConfiguration.RM_AM_EXPIRY_INTERVAL_MS elapses.
-      val timeoutInterval = yarnConf.getInt(YarnConfiguration.RM_AM_EXPIRY_INTERVAL_MS, 120000)
-
-      // we want to be reasonably responsive without causing too many requests to RM.
-      val schedulerInterval =
-        sparkConf.getLong("spark.yarn.scheduler.heartbeat.interval-ms", 5000)
-
-      // must be <= timeoutInterval / 2.
-      val interval = math.min(timeoutInterval / 2, schedulerInterval)
-
-      launchReporterThread(interval)
-    }
   }
 
   private def allocateMissingExecutor() {
@@ -303,47 +284,35 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration,
     }
   }
 
-  private def launchReporterThread(_sleepTime: Long): Thread = {
-    val sleepTime = if (_sleepTime <= 0) 0 else _sleepTime
+  private def launchReporterThread(): Thread = {
+    // Ensure that progress is sent before YarnConfiguration.RM_AM_EXPIRY_INTERVAL_MS elapses.
+    val expiryInterval = yarnConf.getInt(YarnConfiguration.RM_AM_EXPIRY_INTERVAL_MS, 120000)
+
+    // we want to be reasonably responsive without causing too many requests to RM.
+    val schedulerInterval =
+      sparkConf.getLong("spark.yarn.scheduler.heartbeat.interval-ms", 5000)
+
+    // must be <= timeoutInterval / 2.
+    val interval = math.max(0, math.min(expiryInterval / 2, schedulerInterval))
 
     val t = new Thread {
       override def run() {
         while (userThread.isAlive) {
           checkNumExecutorsFailed()
           allocateMissingExecutor()
-          sendProgress()
-          Thread.sleep(sleepTime)
+          logDebug("Sending progress")
+          yarnAllocator.allocateResources()
+          Thread.sleep(interval)
         }
       }
     }
     // Setting to daemon status, though this is usually not a good idea.
     t.setDaemon(true)
     t.start()
-    logInfo("Started progress reporter thread - sleep time : " + sleepTime)
+    logInfo("Started progress reporter thread - heartbeat interval : " + interval)
     t
   }
 
-  private def sendProgress() {
-    logDebug("Sending progress")
-    // Simulated with an allocate request with no nodes requested.
-    yarnAllocator.allocateResources()
-  }
-
-  /*
-  def printContainers(containers: List[Container]) = {
-    for (container <- containers) {
-      logInfo("Launching shell command on a new container."
-        + ", containerId=" + container.getId()
-        + ", containerNode=" + container.getNodeId().getHost()
-        + ":" + container.getNodeId().getPort()
-        + ", containerNodeURI=" + container.getNodeHttpAddress()
-        + ", containerState" + container.getState()
-        + ", containerResourceMemory"
-        + container.getResource().getMemory())
-    }
-  }
-  */
-
   def finishApplicationMaster(status: FinalApplicationStatus, diagnostics: String = "") {
     synchronized {
       if (isFinished) {
@@ -351,7 +320,7 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration,
       }
       isFinished = true
 
-      logInfo("finishApplicationMaster with " + status)
+      logInfo("Unregistering ApplicationMaster with " + status)
       if (registered) {
         val trackingUrl = sparkConf.get("spark.yarn.historyServer.address", "")
         amClient.unregisterApplicationMaster(status, diagnostics, trackingUrl)
@@ -386,7 +355,7 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration,
 
     def run() {
       logInfo("AppMaster received a signal.")
-      // we need to clean up staging dir before HDFS is shut down
+      // We need to clean up staging dir before HDFS is shut down
       // make sure we don't delete it until this is the last AM
       if (appMaster.isLastAMRetry) appMaster.cleanupStagingDir()
     }
@@ -401,21 +370,24 @@ object ApplicationMaster {
   // TODO: Currently, task to container is computed once (TaskSetManager) - which need not be
   // optimal as more containers are available. Might need to handle this better.
   private val ALLOCATOR_LOOP_WAIT_COUNT = 30
+  private val ALLOCATE_HEARTBEAT_INTERVAL = 100
 
   private val applicationMasters = new CopyOnWriteArrayList[ApplicationMaster]()
 
   val sparkContextRef: AtomicReference[SparkContext] =
-    new AtomicReference[SparkContext](null /* initialValue */)
+    new AtomicReference[SparkContext](null)
 
-  val yarnAllocatorLoop: AtomicInteger = new AtomicInteger(0)
+  // Variable used to notify the YarnClusterScheduler that it should stop waiting
+  // for the initial set of executors to be started and get on with its business.
+  val doneWithInitialAllocationsMonitor = new Object()
 
-  def incrementAllocatorLoop(by: Int) {
-    val count = yarnAllocatorLoop.getAndAdd(by)
-    if (count >= ALLOCATOR_LOOP_WAIT_COUNT) {
-      yarnAllocatorLoop.synchronized {
-        // to wake threads off wait ...
-        yarnAllocatorLoop.notifyAll()
-      }
+  @volatile var isDoneWithInitialAllocations = false
+
+  def doneWithInitialAllocations() {
+    isDoneWithInitialAllocations = true
+    doneWithInitialAllocationsMonitor.synchronized {
+      // to wake threads off wait ...
+      doneWithInitialAllocationsMonitor.notifyAll()
     }
   }
 
@@ -423,7 +395,10 @@ object ApplicationMaster {
     applicationMasters.add(master)
   }
 
-  // TODO(harvey): See whether this should be discarded - it isn't used anywhere atm...
+  /**
+   * Called from YarnClusterScheduler to notify the AM code that a SparkContext has been
+   * initialized in the user code.
+   */
   def sparkContextInitialized(sc: SparkContext): Boolean = {
     var modified = false
     sparkContextRef.synchronized {
@@ -431,7 +406,7 @@ object ApplicationMaster {
       sparkContextRef.notifyAll()
     }
 
-    // Add a shutdown hook - as a best case effort in case users do not call sc.stop or do
+    // Add a shutdown hook - as a best effort in case users do not call sc.stop or do
     // System.exit.
     // Should not really have to do this, but it helps YARN to evict resources earlier.
     // Not to mention, prevent the Client from declaring failure even though we exited properly.
@@ -454,13 +429,29 @@ object ApplicationMaster {
       })
     }
 
-    // Wait for initialization to complete and atleast 'some' nodes can get allocated.
-    yarnAllocatorLoop.synchronized {
-      while (yarnAllocatorLoop.get() <= ALLOCATOR_LOOP_WAIT_COUNT) {
-        yarnAllocatorLoop.wait(1000L)
+    // Wait for initialization to complete and at least 'some' nodes to get allocated.
+    modified
+  }
+
+  /**
+   * Returns when we've either
+   *  1) received all the requested executors,
+   *  2) waited ALLOCATOR_LOOP_WAIT_COUNT * ALLOCATE_HEARTBEAT_INTERVAL ms,
+   *  3) hit an error that causes us to terminate trying to get containers.
+   */
+  def waitForInitialAllocations() {
+    doneWithInitialAllocationsMonitor.synchronized {
+      while (!isDoneWithInitialAllocations) {
+        doneWithInitialAllocationsMonitor.wait(1000L)
       }
     }
-    modified
+  }
+
+  def getApplicationAttemptId(): ApplicationAttemptId = {
+    val containerIdString = System.getenv(ApplicationConstants.Environment.CONTAINER_ID.name())
+    val containerId = ConverterUtils.toContainerId(containerIdString)
+    val appAttemptId = containerId.getApplicationAttemptId()
+    appAttemptId
   }
 
   def main(argStrings: Array[String]) {
diff --git a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/Client.scala b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
index 393edd1f2d67..24027618c1f3 100644
--- a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
+++ b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
@@ -21,14 +21,12 @@ import java.nio.ByteBuffer
 
 import org.apache.hadoop.conf.Configuration
 import org.apache.hadoop.io.DataOutputBuffer
-import org.apache.hadoop.yarn.api._
-import org.apache.hadoop.yarn.api.ApplicationConstants.Environment
 import org.apache.hadoop.yarn.api.protocolrecords._
 import org.apache.hadoop.yarn.api.records._
 import org.apache.hadoop.yarn.client.api.YarnClient
 import org.apache.hadoop.yarn.conf.YarnConfiguration
 import org.apache.hadoop.yarn.ipc.YarnRPC
-import org.apache.hadoop.yarn.util.{Apps, Records}
+import org.apache.hadoop.yarn.util.Records
 
 import org.apache.spark.{Logging, SparkConf}
 
@@ -102,7 +100,7 @@ class Client(clientArgs: ClientArguments, hadoopConf: Configuration, spConf: Spa
 
   def logClusterResourceDetails() {
     val clusterMetrics: YarnClusterMetrics = yarnClient.getYarnClusterMetrics
-    logInfo("Got Cluster metric info from ApplicationsManager (ASM), number of NodeManagers: " +
+    logInfo("Got Cluster metric info from ResourceManager, number of NodeManagers: " +
       clusterMetrics.getNumNodeManagers)
 
     val queueInfo: QueueInfo = yarnClient.getQueueInfo(args.amQueue)
@@ -133,7 +131,7 @@ class Client(clientArgs: ClientArguments, hadoopConf: Configuration, spConf: Spa
 
   def submitApp(appContext: ApplicationSubmissionContext) = {
     // Submit the application to the applications manager.
-    logInfo("Submitting application to ASM")
+    logInfo("Submitting application to ResourceManager")
     yarnClient.submitApplication(appContext)
   }
 
@@ -149,7 +147,7 @@ class Client(clientArgs: ClientArguments, hadoopConf: Configuration, spConf: Spa
       Thread.sleep(interval)
       val report = yarnClient.getApplicationReport(appId)
 
-      logInfo("Application report from ASM: \n" +
+      logInfo("Application report from ResourceManager: \n" +
         "\t application identifier: " + appId.toString() + "\n" +
         "\t appId: " + appId.getId() + "\n" +
         "\t clientToAMToken: " + report.getClientToAMToken() + "\n" +
diff --git a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala
index d93e5bb0225d..4f8854a09a1e 100644
--- a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala
+++ b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala
@@ -72,8 +72,8 @@ class ExecutorLauncher(args: ApplicationMasterArguments, conf: Configuration, sp
     override def preStart() {
       logInfo("Listen to driver: " + driverUrl)
       driver = context.actorSelection(driverUrl)
-      // Send a hello message thus the connection is actually established,
-      // thus we can monitor Lifecycle Events.
+      // Send a hello message to establish the connection, after which
+      // we can monitor Lifecycle Events.
       driver ! "Hello"
       context.system.eventStream.subscribe(self, classOf[RemotingLifecycleEvent])
     }
@@ -95,7 +95,7 @@ class ExecutorLauncher(args: ApplicationMasterArguments, conf: Configuration, sp
     amClient.init(yarnConf)
     amClient.start()
 
-    appAttemptId = getApplicationAttemptId()
+    appAttemptId = ApplicationMaster.getApplicationAttemptId()
     registerApplicationMaster()
 
     waitForSparkMaster()
@@ -141,18 +141,9 @@ class ExecutorLauncher(args: ApplicationMasterArguments, conf: Configuration, sp
     }
   } 
 
-  private def getApplicationAttemptId(): ApplicationAttemptId = {
-    val envs = System.getenv()
-    val containerIdString = envs.get(ApplicationConstants.Environment.CONTAINER_ID.name())
-    val containerId = ConverterUtils.toContainerId(containerIdString)
-    val appAttemptId = containerId.getApplicationAttemptId()
-    logInfo("ApplicationAttemptId: " + appAttemptId)
-    appAttemptId
-  }
-
   private def registerApplicationMaster(): RegisterApplicationMasterResponse = {
     logInfo("Registering the ApplicationMaster")
-    // TODO:(Raymond) Find out Spark UI address and fill in here?
+    // TODO: Find out client's Spark UI address and fill in here?
     amClient.registerApplicationMaster(Utils.localHostName(), 0, "")
   }
 
@@ -185,8 +176,7 @@ class ExecutorLauncher(args: ApplicationMasterArguments, conf: Configuration, sp
 
 
   private def allocateExecutors() {
-
-    // Fixme: should get preferredNodeLocationData from SparkContext, just fake a empty one for now.
+    // TODO: should get preferredNodeLocationData from SparkContext, just fake a empty one for now.
     val preferredNodeLocationData: scala.collection.Map[String, scala.collection.Set[SplitInfo]] =
       scala.collection.immutable.Map()
 
@@ -198,8 +188,8 @@ class ExecutorLauncher(args: ApplicationMasterArguments, conf: Configuration, sp
       preferredNodeLocationData,
       sparkConf)
 
-    logInfo("Allocating " + args.numExecutors + " executors.")
-    // Wait until all containers have finished
+    logInfo("Requesting " + args.numExecutors + " executors.")
+    // Wait until all containers have launched
     yarnAllocator.addResourceRequests(args.numExecutors)
     yarnAllocator.allocateResources()
     while ((yarnAllocator.getNumExecutorsRunning < args.numExecutors) && (!driverClosed)) {
@@ -221,7 +211,6 @@ class ExecutorLauncher(args: ApplicationMasterArguments, conf: Configuration, sp
     }
   }
 
-  // TODO: We might want to extend this to allocate more containers in case they die !
   private def launchReporterThread(_sleepTime: Long): Thread = {
     val sleepTime = if (_sleepTime <= 0) 0 else _sleepTime
 
@@ -229,7 +218,8 @@ class ExecutorLauncher(args: ApplicationMasterArguments, conf: Configuration, sp
       override def run() {
         while (!driverClosed) {
           allocateMissingExecutor()
-          sendProgress()
+          logDebug("Sending progress")
+          yarnAllocator.allocateResources()
           Thread.sleep(sleepTime)
         }
       }
@@ -241,20 +231,14 @@ class ExecutorLauncher(args: ApplicationMasterArguments, conf: Configuration, sp
     t
   }
 
-  private def sendProgress() {
-    logDebug("Sending progress")
-    // simulated with an allocate request with no nodes requested ...
-    yarnAllocator.allocateResources()
-  }
-
   def finishApplicationMaster(status: FinalApplicationStatus) {
-    logInfo("finish ApplicationMaster with " + status)
-    amClient.unregisterApplicationMaster(status, "" /* appMessage */ , "" /* appTrackingUrl */)
+    logInfo("Unregistering ApplicationMaster with " + status)
+    val trackingUrl = sparkConf.get("spark.yarn.historyServer.address", "")
+    amClient.unregisterApplicationMaster(status, "" /* appMessage */ , trackingUrl)
   }
 
 }
 
-
 object ExecutorLauncher {
   def main(argStrings: Array[String]) {
     val args = new ApplicationMasterArguments(argStrings)

From 5b754b45f301a310e9232f3a5270201ebfc16076 Mon Sep 17 00:00:00 2001
From: Prashant Sharma <prashant.s@imaginea.com>
Date: Wed, 11 Jun 2014 10:47:06 -0700
Subject: [PATCH 394/810] [SPARK-2069] MIMA false positives

Fixes SPARK 2070 and 2071

Author: Prashant Sharma <prashant.s@imaginea.com>

Closes #1021 from ScrapCodes/SPARK-2070/package-private-methods and squashes the following commits:

7979a57 [Prashant Sharma] addressed code review comments
558546d [Prashant Sharma] A little fancy error message.
59275ab [Prashant Sharma] SPARK-2071 Mima ignores classes and its members from previous versions too.
0c4ff2b [Prashant Sharma] SPARK-2070 Ignore methods along with annotated classes.
---
 .gitignore                                    |   2 +-
 dev/mima                                      |   5 +-
 project/MimaBuild.scala                       |  39 ++++--
 project/SparkBuild.scala                      |  15 +++
 .../spark/tools/GenerateMIMAIgnore.scala      | 123 ++++++++++--------
 5 files changed, 114 insertions(+), 70 deletions(-)

diff --git a/.gitignore b/.gitignore
index b54a3058de65..4f177c82ae5e 100644
--- a/.gitignore
+++ b/.gitignore
@@ -7,7 +7,7 @@
 sbt/*.jar
 .settings
 .cache
-.generated-mima-excludes
+.generated-mima*
 /build/
 work/
 out/
diff --git a/dev/mima b/dev/mima
index ab6bd4469b0e..b68800d6d017 100755
--- a/dev/mima
+++ b/dev/mima
@@ -23,6 +23,9 @@ set -o pipefail
 FWDIR="$(cd `dirname $0`/..; pwd)"
 cd $FWDIR
 
+echo -e "q\n" | sbt/sbt oldDeps/update
+
+export SPARK_CLASSPATH=`find lib_managed \( -name '*spark*jar' -a -type f \) -printf "%p:" `
 ./bin/spark-class org.apache.spark.tools.GenerateMIMAIgnore
 echo -e "q\n" | sbt/sbt mima-report-binary-issues | grep -v -e "info.*Resolving"
 ret_val=$?
@@ -31,5 +34,5 @@ if [ $ret_val != 0 ]; then
   echo "NOTE: Exceptions to binary compatibility can be added in project/MimaExcludes.scala"
 fi
 
-rm -f .generated-mima-excludes
+rm -f .generated-mima*
 exit $ret_val
diff --git a/project/MimaBuild.scala b/project/MimaBuild.scala
index 147780994357..bb2d73741c3b 100644
--- a/project/MimaBuild.scala
+++ b/project/MimaBuild.scala
@@ -15,16 +15,26 @@
  * limitations under the License.
  */
 
-import com.typesafe.tools.mima.core.{MissingTypesProblem, MissingClassProblem, ProblemFilters}
+import com.typesafe.tools.mima.core._
+import com.typesafe.tools.mima.core.MissingClassProblem
+import com.typesafe.tools.mima.core.MissingTypesProblem
 import com.typesafe.tools.mima.core.ProblemFilters._
 import com.typesafe.tools.mima.plugin.MimaKeys.{binaryIssueFilters, previousArtifact}
 import com.typesafe.tools.mima.plugin.MimaPlugin.mimaDefaultSettings
 import sbt._
 
 object MimaBuild {
+
+  def excludeMember(fullName: String) = Seq(
+      ProblemFilters.exclude[MissingMethodProblem](fullName),
+      ProblemFilters.exclude[MissingFieldProblem](fullName),
+      ProblemFilters.exclude[IncompatibleResultTypeProblem](fullName),
+      ProblemFilters.exclude[IncompatibleMethTypeProblem](fullName),
+      ProblemFilters.exclude[IncompatibleFieldTypeProblem](fullName)
+    )
+
   // Exclude a single class and its corresponding object
-  def excludeClass(className: String) = {
-    Seq(
+  def excludeClass(className: String) = Seq(
       excludePackage(className),
       ProblemFilters.exclude[MissingClassProblem](className),
       ProblemFilters.exclude[MissingTypesProblem](className),
@@ -32,7 +42,7 @@ object MimaBuild {
       ProblemFilters.exclude[MissingClassProblem](className + "$"),
       ProblemFilters.exclude[MissingTypesProblem](className + "$")
     )
-  }
+
   // Exclude a Spark class, that is in the package org.apache.spark
   def excludeSparkClass(className: String) = {
     excludeClass("org.apache.spark." + className)
@@ -49,20 +59,25 @@ object MimaBuild {
     val defaultExcludes = Seq()
 
     // Read package-private excludes from file
-    val excludeFilePath = (base.getAbsolutePath + "/.generated-mima-excludes")
-    val excludeFile = file(excludeFilePath)
+    val classExcludeFilePath = file(base.getAbsolutePath + "/.generated-mima-class-excludes")
+    val memberExcludeFilePath = file(base.getAbsolutePath + "/.generated-mima-member-excludes")
+
     val ignoredClasses: Seq[String] =
-      if (!excludeFile.exists()) {
+      if (!classExcludeFilePath.exists()) {
         Seq()
       } else {
-        IO.read(excludeFile).split("\n")
+        IO.read(classExcludeFilePath).split("\n")
       }
 
+    val ignoredMembers: Seq[String] =
+      if (!memberExcludeFilePath.exists()) {
+      Seq()
+    } else {
+      IO.read(memberExcludeFilePath).split("\n")
+    }
 
-
-    val externalExcludeFileClasses = ignoredClasses.flatMap(excludeClass)
-
-    defaultExcludes ++ externalExcludeFileClasses ++ MimaExcludes.excludes
+    defaultExcludes ++ ignoredClasses.flatMap(excludeClass) ++
+    ignoredMembers.flatMap(excludeMember) ++ MimaExcludes.excludes
   }
 
   def mimaSettings(sparkHome: File) = mimaDefaultSettings ++ Seq(
diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index 069913dbaac5..ecd9d7068068 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -59,6 +59,10 @@ object SparkBuild extends Build {
 
   lazy val core = Project("core", file("core"), settings = coreSettings)
 
+  /** Following project only exists to pull previous artifacts of Spark for generating
+    Mima ignores. For more information see: SPARK 2071 */
+  lazy val oldDeps = Project("oldDeps", file("dev"), settings = oldDepsSettings)
+
   def replDependencies = Seq[ProjectReference](core, graphx, bagel, mllib, sql) ++ maybeHiveRef
 
   lazy val repl = Project("repl", file("repl"), settings = replSettings)
@@ -598,6 +602,17 @@ object SparkBuild extends Build {
     }
   )
 
+  def oldDepsSettings() = Defaults.defaultSettings ++ Seq(
+    name := "old-deps",
+    scalaVersion := "2.10.4",
+    retrieveManaged := true,
+    retrievePattern := "[type]s/[artifact](-[revision])(-[classifier]).[ext]",
+    libraryDependencies := Seq("spark-streaming-mqtt", "spark-streaming-zeromq", 
+      "spark-streaming-flume", "spark-streaming-kafka", "spark-streaming-twitter",
+      "spark-streaming", "spark-mllib", "spark-bagel", "spark-graphx", 
+      "spark-core").map(sparkPreviousArtifact(_).get intransitive())
+  )
+
   def twitterSettings() = sharedSettings ++ Seq(
     name := "spark-streaming-twitter",
     previousArtifact := sparkPreviousArtifact("spark-streaming-twitter"),
diff --git a/tools/src/main/scala/org/apache/spark/tools/GenerateMIMAIgnore.scala b/tools/src/main/scala/org/apache/spark/tools/GenerateMIMAIgnore.scala
index 6a261e19a35c..03a73f92b275 100644
--- a/tools/src/main/scala/org/apache/spark/tools/GenerateMIMAIgnore.scala
+++ b/tools/src/main/scala/org/apache/spark/tools/GenerateMIMAIgnore.scala
@@ -40,74 +40,78 @@ object GenerateMIMAIgnore {
   private val classLoader = Thread.currentThread().getContextClassLoader
   private val mirror = runtimeMirror(classLoader)
 
-  private def classesPrivateWithin(packageName: String): Set[String] = {
+
+  private def isDeveloperApi(sym: unv.Symbol) =
+    sym.annotations.exists(_.tpe =:= unv.typeOf[org.apache.spark.annotation.DeveloperApi])
+
+  private def isExperimental(sym: unv.Symbol) =
+    sym.annotations.exists(_.tpe =:= unv.typeOf[org.apache.spark.annotation.Experimental])
+
+
+  private def isPackagePrivate(sym: unv.Symbol) =
+    !sym.privateWithin.fullName.startsWith("<none>")
+
+  private def isPackagePrivateModule(moduleSymbol: unv.ModuleSymbol) =
+    !moduleSymbol.privateWithin.fullName.startsWith("<none>")
+
+  /**
+   * For every class checks via scala reflection if the class itself or contained members
+   * have DeveloperApi or Experimental annotations or they are package private.
+   * Returns the tuple of such classes and members.
+   */
+  private def privateWithin(packageName: String): (Set[String], Set[String]) = {
 
     val classes = getClasses(packageName)
     val ignoredClasses = mutable.HashSet[String]()
+    val ignoredMembers = mutable.HashSet[String]()
 
-    def isPackagePrivate(className: String) = {
+    for (className <- classes) {
       try {
-        /* Couldn't figure out if it's possible to determine a-priori whether a given symbol
-           is a module or class. */
-
-        val privateAsClass = mirror
-          .classSymbol(Class.forName(className, false, classLoader))
-          .privateWithin
-          .fullName
-          .startsWith(packageName)
-
-        val privateAsModule = mirror
-          .staticModule(className)
-          .privateWithin
-          .fullName
-          .startsWith(packageName)
-
-        privateAsClass || privateAsModule
-      } catch {
-        case _: Throwable => {
-          println("Error determining visibility: " + className)
-          false
+        val classSymbol = mirror.classSymbol(Class.forName(className, false, classLoader))
+        val moduleSymbol = mirror.staticModule(className) // TODO: see if it is necessary.
+        val directlyPrivateSpark =
+          isPackagePrivate(classSymbol) || isPackagePrivateModule(moduleSymbol)
+        val developerApi = isDeveloperApi(classSymbol)
+        val experimental = isExperimental(classSymbol)
+
+        /* Inner classes defined within a private[spark] class or object are effectively
+         invisible, so we account for them as package private. */
+        lazy val indirectlyPrivateSpark = {
+          val maybeOuter = className.toString.takeWhile(_ != '$')
+          if (maybeOuter != className) {
+            isPackagePrivate(mirror.classSymbol(Class.forName(maybeOuter, false, classLoader))) ||
+              isPackagePrivateModule(mirror.staticModule(maybeOuter))
+          } else {
+            false
+          }
+        }
+        if (directlyPrivateSpark || indirectlyPrivateSpark || developerApi || experimental) {
+          ignoredClasses += className
+        } else {
+          // check if this class has package-private/annotated members.
+          ignoredMembers ++= getAnnotatedOrPackagePrivateMembers(classSymbol)
         }
-      }
-    }
 
-    def isDeveloperApi(className: String) = {
-      try {
-        val clazz = mirror.classSymbol(Class.forName(className, false, classLoader))
-        clazz.annotations.exists(_.tpe =:= unv.typeOf[org.apache.spark.annotation.DeveloperApi])
       } catch {
-        case _: Throwable => {
-          println("Error determining Annotations: " + className)
-          false
-        }
+        case _: Throwable => println("Error instrumenting class:" + className)
       }
     }
+    (ignoredClasses.flatMap(c => Seq(c, c.replace("$", "#"))).toSet, ignoredMembers.toSet)
+  }
 
-    for (className <- classes) {
-      val directlyPrivateSpark = isPackagePrivate(className)
-      val developerApi = isDeveloperApi(className)
-
-      /* Inner classes defined within a private[spark] class or object are effectively
-         invisible, so we account for them as package private. */
-      val indirectlyPrivateSpark = {
-        val maybeOuter = className.toString.takeWhile(_ != '$')
-        if (maybeOuter != className) {
-          isPackagePrivate(maybeOuter)
-        } else {
-          false
-        }
-      }
-      if (directlyPrivateSpark || indirectlyPrivateSpark || developerApi) {
-        ignoredClasses += className
-      }
-    }
-    ignoredClasses.flatMap(c => Seq(c, c.replace("$", "#"))).toSet
+  private def getAnnotatedOrPackagePrivateMembers(classSymbol: unv.ClassSymbol) = {
+    classSymbol.typeSignature.members
+      .filter(x => isPackagePrivate(x) || isDeveloperApi(x) || isExperimental(x)).map(_.fullName)
   }
 
   def main(args: Array[String]) {
-    scala.tools.nsc.io.File(".generated-mima-excludes").
-      writeAll(classesPrivateWithin("org.apache.spark").mkString("\n"))
-    println("Created : .generated-mima-excludes in current directory.")
+    val (privateClasses, privateMembers) = privateWithin("org.apache.spark")
+    scala.tools.nsc.io.File(".generated-mima-class-excludes").
+      writeAll(privateClasses.mkString("\n"))
+    println("Created : .generated-mima-class-excludes in current directory.")
+    scala.tools.nsc.io.File(".generated-mima-member-excludes").
+      writeAll(privateMembers.mkString("\n"))
+    println("Created : .generated-mima-member-excludes in current directory.")
   }
 
 
@@ -140,10 +144,17 @@ object GenerateMIMAIgnore {
    * Get all classes in a package from a jar file.
    */
   private def getClassesFromJar(jarPath: String, packageName: String) = {
+    import scala.collection.mutable
     val jar = new JarFile(new File(jarPath))
     val enums = jar.entries().map(_.getName).filter(_.startsWith(packageName))
-    val classes = for (entry <- enums if entry.endsWith(".class"))
-      yield Class.forName(entry.replace('/', '.').stripSuffix(".class"), false, classLoader)
+    val classes = mutable.HashSet[Class[_]]()
+    for (entry <- enums if entry.endsWith(".class")) {
+      try {
+        classes += Class.forName(entry.replace('/', '.').stripSuffix(".class"), false, classLoader)
+      } catch {
+        case _: Throwable => println("Unable to load:" + entry)
+      }
+    }
     classes
   }
 }

From e508f599f88baaa31a3498fb0bdbafdbc303119e Mon Sep 17 00:00:00 2001
From: Prashant Sharma <prashant.s@imaginea.com>
Date: Wed, 11 Jun 2014 10:49:34 -0700
Subject: [PATCH 395/810] [SPARK-2108] Mark SparkContext methods that return
 block information as developer API's

Author: Prashant Sharma <prashant.s@imaginea.com>

Closes #1047 from ScrapCodes/SPARK-2108/mark-as-dev-api and squashes the following commits:

073ee34 [Prashant Sharma] [SPARK-2108] Mark SparkContext methods that return block information as developer API's
---
 core/src/main/scala/org/apache/spark/SparkContext.scala | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index d721aba70960..8bdaf0bf76e8 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -823,9 +823,11 @@ class SparkContext(config: SparkConf) extends Logging {
   }
 
   /**
+   * :: DeveloperApi ::
    * Return information about what RDDs are cached, if they are in mem or on disk, how much space
    * they take, etc.
    */
+  @DeveloperApi
   def getRDDStorageInfo: Array[RDDInfo] = {
     StorageUtils.rddInfoFromStorageStatus(getExecutorStorageStatus, this)
   }
@@ -837,8 +839,10 @@ class SparkContext(config: SparkConf) extends Logging {
   def getPersistentRDDs: Map[Int, RDD[_]] = persistentRdds.toMap
 
   /**
+   * :: DeveloperApi ::
    * Return information about blocks stored in all of the slaves
    */
+  @DeveloperApi
   def getExecutorStorageStatus: Array[StorageStatus] = {
     env.blockManager.master.getStorageStatus
   }

From 4d5c12aa1c54c49377a4bafe3bcc4993d5e1a552 Mon Sep 17 00:00:00 2001
From: Lars Albertsson <lalle@spotify.com>
Date: Wed, 11 Jun 2014 10:54:42 -0700
Subject: [PATCH 396/810] SPARK-2113: awaitTermination() after stop() will hang
 in Spark Stremaing

Author: Lars Albertsson <lalle@spotify.com>

Closes #1001 from lallea/contextwaiter_stopped and squashes the following commits:

93cd314 [Lars Albertsson] Mend StreamingContext stop() followed by awaitTermination().
---
 .../org/apache/spark/streaming/ContextWaiter.scala   |  1 +
 .../spark/streaming/StreamingContextSuite.scala      | 12 ++++++++++++
 2 files changed, 13 insertions(+)

diff --git a/streaming/src/main/scala/org/apache/spark/streaming/ContextWaiter.scala b/streaming/src/main/scala/org/apache/spark/streaming/ContextWaiter.scala
index 86753360a07e..a0aeacbc733b 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/ContextWaiter.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/ContextWaiter.scala
@@ -27,6 +27,7 @@ private[streaming] class ContextWaiter {
   }
 
   def notifyStop() = synchronized {
+    stopped = true
     notifyAll()
   }
 
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala
index cd86019f63e7..7b33d3b23546 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala
@@ -223,6 +223,18 @@ class StreamingContextSuite extends FunSuite with BeforeAndAfter with Timeouts w
     }
   }
 
+  test("awaitTermination after stop") {
+    ssc = new StreamingContext(master, appName, batchDuration)
+    val inputStream = addInputStream(ssc)
+    inputStream.map(x => x).register()
+
+    failAfter(10000 millis) {
+      ssc.start()
+      ssc.stop()
+      ssc.awaitTermination()
+    }
+  }
+
   test("awaitTermination with error in task") {
     ssc = new StreamingContext(master, appName, batchDuration)
     val inputStream = addInputStream(ssc)

From 4107cce58c41160a0dc20339621eacdf8a8b1191 Mon Sep 17 00:00:00 2001
From: Sameer Agarwal <sameer@databricks.com>
Date: Wed, 11 Jun 2014 12:01:04 -0700
Subject: [PATCH 397/810] [SPARK-2042] Prevent unnecessary shuffle triggered by
 take()

This PR implements `take()` on a `SchemaRDD` by inserting a logical limit that is followed by a `collect()`. This is also accompanied by adding a catalyst optimizer rule for collapsing adjacent limits. Doing so prevents an unnecessary shuffle that is sometimes triggered by `take()`.

Author: Sameer Agarwal <sameer@databricks.com>

Closes #1048 from sameeragarwal/master and squashes the following commits:

3eeb848 [Sameer Agarwal] Fixing Tests
1b76ff1 [Sameer Agarwal] Deprecating limit(limitExpr: Expression) in v1.1.0
b723ac4 [Sameer Agarwal] Added limit folding tests
a0ff7c4 [Sameer Agarwal] Adding catalyst rule to fold two consecutive limits
8d42d03 [Sameer Agarwal] Implement trigger() as limit() followed by collect()
---
 .../spark/sql/catalyst/dsl/package.scala      |  2 +
 .../sql/catalyst/optimizer/Optimizer.scala    | 13 ++++
 .../plans/logical/basicOperators.scala        |  4 +-
 .../optimizer/CombiningLimitsSuite.scala      | 71 +++++++++++++++++++
 .../org/apache/spark/sql/SchemaRDD.scala      | 12 +++-
 5 files changed, 97 insertions(+), 5 deletions(-)
 create mode 100644 sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/CombiningLimitsSuite.scala

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala
index 3cf163f9a9a7..d177339d40ae 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala
@@ -175,6 +175,8 @@ package object dsl {
 
     def where(condition: Expression) = Filter(condition, logicalPlan)
 
+    def limit(limitExpr: Expression) = Limit(limitExpr, logicalPlan)
+
     def join(
         otherPlan: LogicalPlan,
         joinType: JoinType = Inner,
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
index e41fd2db7485..28d1aa2e3aaf 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
@@ -29,6 +29,8 @@ import org.apache.spark.sql.catalyst.types._
 
 object Optimizer extends RuleExecutor[LogicalPlan] {
   val batches =
+    Batch("Combine Limits", FixedPoint(100),
+      CombineLimits) ::
     Batch("ConstantFolding", FixedPoint(100),
       NullPropagation,
       ConstantFolding,
@@ -362,3 +364,14 @@ object SimplifyCasts extends Rule[LogicalPlan] {
     case Cast(e, dataType) if e.dataType == dataType => e
   }
 }
+
+/**
+ * Combines two adjacent [[catalyst.plans.logical.Limit Limit]] operators into one, merging the
+ * expressions into one single expression.
+ */
+object CombineLimits extends Rule[LogicalPlan] {
+  def apply(plan: LogicalPlan): LogicalPlan = plan transform {
+    case ll @ Limit(le, nl @ Limit(ne, grandChild)) =>
+      Limit(If(LessThan(ne, le), ne, le), grandChild)
+  }
+}
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
index d3347b622f3d..b777cf424919 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
@@ -135,9 +135,9 @@ case class Aggregate(
   def references = (groupingExpressions ++ aggregateExpressions).flatMap(_.references).toSet
 }
 
-case class Limit(limit: Expression, child: LogicalPlan) extends UnaryNode {
+case class Limit(limitExpr: Expression, child: LogicalPlan) extends UnaryNode {
   def output = child.output
-  def references = limit.references
+  def references = limitExpr.references
 }
 
 case class Subquery(alias: String, child: LogicalPlan) extends UnaryNode {
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/CombiningLimitsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/CombiningLimitsSuite.scala
new file mode 100644
index 000000000000..714f01843c0f
--- /dev/null
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/CombiningLimitsSuite.scala
@@ -0,0 +1,71 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.optimizer
+
+import org.apache.spark.sql.catalyst.plans.logical._
+import org.apache.spark.sql.catalyst.rules._
+import org.apache.spark.sql.catalyst.dsl.plans._
+import org.apache.spark.sql.catalyst.dsl.expressions._
+
+class CombiningLimitsSuite extends OptimizerTest {
+
+  object Optimize extends RuleExecutor[LogicalPlan] {
+    val batches =
+      Batch("Combine Limit", FixedPoint(2),
+        CombineLimits) ::
+      Batch("Constant Folding", FixedPoint(3),
+        NullPropagation,
+        ConstantFolding,
+        BooleanSimplification) :: Nil
+  }
+
+  val testRelation = LocalRelation('a.int, 'b.int, 'c.int)
+
+  test("limits: combines two limits") {
+    val originalQuery =
+      testRelation
+        .select('a)
+        .limit(10)
+        .limit(5)
+
+    val optimized = Optimize(originalQuery.analyze)
+    val correctAnswer =
+      testRelation
+        .select('a)
+        .limit(5).analyze
+
+    comparePlans(optimized, correctAnswer)
+  }
+
+  test("limits: combines three limits") {
+    val originalQuery =
+      testRelation
+        .select('a)
+        .limit(2)
+        .limit(7)
+        .limit(5)
+
+    val optimized = Optimize(originalQuery.analyze)
+    val correctAnswer =
+      testRelation
+        .select('a)
+        .limit(2).analyze
+
+    comparePlans(optimized, correctAnswer)
+  }
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala
index 8855c4e87691..7ad8edf5a5a6 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala
@@ -178,14 +178,18 @@ class SchemaRDD(
   def orderBy(sortExprs: SortOrder*): SchemaRDD =
     new SchemaRDD(sqlContext, Sort(sortExprs, logicalPlan))
 
+  @deprecated("use limit with integer argument", "1.1.0")
+  def limit(limitExpr: Expression): SchemaRDD =
+    new SchemaRDD(sqlContext, Limit(limitExpr, logicalPlan))
+
   /**
-   * Limits the results by the given expressions.
+   * Limits the results by the given integer.
    * {{{
    *   schemaRDD.limit(10)
    * }}}
    */
-  def limit(limitExpr: Expression): SchemaRDD =
-    new SchemaRDD(sqlContext, Limit(limitExpr, logicalPlan))
+  def limit(limitNum: Int): SchemaRDD =
+    new SchemaRDD(sqlContext, Limit(Literal(limitNum), logicalPlan))
 
   /**
    * Performs a grouping followed by an aggregation.
@@ -374,6 +378,8 @@ class SchemaRDD(
 
   override def collect(): Array[Row] = queryExecution.executedPlan.executeCollect()
 
+  override def take(num: Int): Array[Row] = limit(num).collect()
+
   // =======================================================================
   // Base RDD functions that do NOT change schema
   // =======================================================================

From ce6deb1e5b4cd40c97730fcf5dc89cb2f624bce2 Mon Sep 17 00:00:00 2001
From: Daoyuan <daoyuan.wang@intel.com>
Date: Wed, 11 Jun 2014 12:08:28 -0700
Subject: [PATCH 398/810] [SQL] Code Cleanup: Left Semi Hash Join

Some improvement for PR #837, add another case to white list and use `filter` to build result iterator.

Author: Daoyuan <daoyuan.wang@intel.com>

Closes #1049 from adrian-wang/clean-LeftSemiJoinHash and squashes the following commits:

b314d5a [Daoyuan] change hashSet name
27579a9 [Daoyuan] add semijoin to white list and use filter to create new iterator in LeftSemiJoinBNL

Signed-off-by: Michael Armbrust <michael@databricks.com>
---
 .../apache/spark/sql/execution/joins.scala    | 40 ++++--------------
 ...emijoin-0-1631b71327abf75b96116036b977b26c |  0
 ...emijoin-1-e7c99e1f46d502edbb0925d75aab5f0c | 11 +++++
 ...mijoin-10-ffd4fb3a903a6725ccb97d5451a3fec6 |  0
 ...emijoin-11-246a40dcafe077f02397e30d227c330 |  8 ++++
 ...mijoin-12-6d93a9d332ba490835b17f261a5467df |  0
 ...mijoin-13-18282d38b6efc0017089ab89b661764f |  0
 ...mijoin-14-19cfcefb10e1972bec0ffd421cd79de7 |  0
 ...emijoin-15-1de6eb3f357bd1c4d02ab4d19d43589 | 11 +++++
 ...emijoin-16-d3a72a90515ac4a8d8e9ac923bcda3d |  0
 ...mijoin-17-f0f8720cfd11fd71af17b310dc2d1019 |  3 ++
 ...mijoin-18-f7b2ce472443982e32d954cbb5c96765 |  0
 ...mijoin-19-b1f1c7f701abe81c01e72fb98f0bd13f |  3 ++
 ...emijoin-2-deb9c3286ae8e851b1fdb270085b16bc |  0
 ...mijoin-20-b7a8ebaeb42b2eaba7d97cadc3fd96c1 |  0
 ...mijoin-21-480418a0646cf7260b494b9eb4821bb6 |  0
 ...mijoin-22-b6aebd98f7636cda7b24e0bf84d7ba41 |  0
 ...mijoin-23-ed730ccdf552c07e7a82ba6b7fd3fbda |  5 +++
 ...mijoin-24-d16b37134de78980b2bf96029e8265c3 |  0
 ...mijoin-25-be2bd011cc80b480b271a08dbf381e9b | 19 +++++++++
 ...mijoin-26-f1d3bab29f1ebafa148dbe3816e1da25 |  0
 ...mijoin-27-391c256254d171973f02e7f33672ce1d |  4 ++
 ...mijoin-28-b56400f6d9372f353cf7292a2182e963 |  0
 ...mijoin-29-9efeef3d3c38e22a74d379978178c4f5 | 14 +++++++
 ...emijoin-3-b4d4317dd3a10e18502f20f5c5250389 | 11 +++++
 ...mijoin-30-dd901d00fce5898b03a57cbc3028a70a |  0
 ...emijoin-31-e5dc4d8185e63e984aa4e3a2e08bd67 | 14 +++++++
 ...mijoin-32-23017c7663f2710265a7e2a4a1606d39 |  0
 ...mijoin-33-ed499f94c6e6ac847ef5187b3b43bbc5 | 14 +++++++
 ...mijoin-34-5e1b832090ab73c141c1167d5b25a490 |  0
 ...mijoin-35-8d5731f26232f6e26dd8012461b08d99 | 26 ++++++++++++
 ...mijoin-36-b1159823dca8025926407f8aa921238d |  0
 ...mijoin-37-a15b9074f999ce836be5329591b968d0 | 29 +++++++++++++
 ...emijoin-38-f37547c73a48ce3ba089531b176e6ba |  0
 ...mijoin-39-c22a6f11368affcb80a9c80e653a47a8 | 29 +++++++++++++
 ...emijoin-4-dfdad5a2742f93e8ea888191460809c0 |  0
 ...mijoin-40-32071a51e2ba6e86b1c5e40de55aae63 |  0
 ...mijoin-41-cf74f73a33b1af8902b7970a9350b092 | 29 +++++++++++++
 ...mijoin-42-6b4257a74fca627785c967c99547f4c0 |  0
 ...mijoin-43-e8a166ac2e94bf8d1da0fe91b0db2c81 | 31 ++++++++++++++
 ...mijoin-44-945aaa3a24359ef73acab1e99500d5ea |  0
 ...mijoin-45-3fd94ffd4f1eb6cf83dcc064599bf12b | 42 +++++++++++++++++++
 ...mijoin-46-620e01f81f6e5254b4bbe8fab4043ec0 |  0
 ...mijoin-47-f0140e4ee92508ba241f91c157b7af9c | 35 ++++++++++++++++
 ...mijoin-48-8a04442e84f99a584c2882d0af8c25d8 |  0
 ...mijoin-49-df1d6705d3624be72036318a6b42f04c |  0
 ...emijoin-5-d3c2f84a12374b307c58a69aba4ec70d | 22 ++++++++++
 ...emijoin-6-90bb51b1330230d10a14fb7517457aa0 |  0
 ...emijoin-7-333d72e8bce6d11a35fc7a30418f225b |  0
 ...emijoin-8-d46607be851a6f4e27e98cbbefdee994 |  0
 ...emijoin-9-f7adaf0f77ce6ff8c3a4807f428d8de2 |  6 +++
 .../execution/HiveCompatibilitySuite.scala    |  1 +
 52 files changed, 374 insertions(+), 33 deletions(-)
 create mode 100644 sql/hive/src/test/resources/golden/semijoin-0-1631b71327abf75b96116036b977b26c
 create mode 100644 sql/hive/src/test/resources/golden/semijoin-1-e7c99e1f46d502edbb0925d75aab5f0c
 create mode 100644 sql/hive/src/test/resources/golden/semijoin-10-ffd4fb3a903a6725ccb97d5451a3fec6
 create mode 100644 sql/hive/src/test/resources/golden/semijoin-11-246a40dcafe077f02397e30d227c330
 create mode 100644 sql/hive/src/test/resources/golden/semijoin-12-6d93a9d332ba490835b17f261a5467df
 create mode 100644 sql/hive/src/test/resources/golden/semijoin-13-18282d38b6efc0017089ab89b661764f
 create mode 100644 sql/hive/src/test/resources/golden/semijoin-14-19cfcefb10e1972bec0ffd421cd79de7
 create mode 100644 sql/hive/src/test/resources/golden/semijoin-15-1de6eb3f357bd1c4d02ab4d19d43589
 create mode 100644 sql/hive/src/test/resources/golden/semijoin-16-d3a72a90515ac4a8d8e9ac923bcda3d
 create mode 100644 sql/hive/src/test/resources/golden/semijoin-17-f0f8720cfd11fd71af17b310dc2d1019
 create mode 100644 sql/hive/src/test/resources/golden/semijoin-18-f7b2ce472443982e32d954cbb5c96765
 create mode 100644 sql/hive/src/test/resources/golden/semijoin-19-b1f1c7f701abe81c01e72fb98f0bd13f
 create mode 100644 sql/hive/src/test/resources/golden/semijoin-2-deb9c3286ae8e851b1fdb270085b16bc
 create mode 100644 sql/hive/src/test/resources/golden/semijoin-20-b7a8ebaeb42b2eaba7d97cadc3fd96c1
 create mode 100644 sql/hive/src/test/resources/golden/semijoin-21-480418a0646cf7260b494b9eb4821bb6
 create mode 100644 sql/hive/src/test/resources/golden/semijoin-22-b6aebd98f7636cda7b24e0bf84d7ba41
 create mode 100644 sql/hive/src/test/resources/golden/semijoin-23-ed730ccdf552c07e7a82ba6b7fd3fbda
 create mode 100644 sql/hive/src/test/resources/golden/semijoin-24-d16b37134de78980b2bf96029e8265c3
 create mode 100644 sql/hive/src/test/resources/golden/semijoin-25-be2bd011cc80b480b271a08dbf381e9b
 create mode 100644 sql/hive/src/test/resources/golden/semijoin-26-f1d3bab29f1ebafa148dbe3816e1da25
 create mode 100644 sql/hive/src/test/resources/golden/semijoin-27-391c256254d171973f02e7f33672ce1d
 create mode 100644 sql/hive/src/test/resources/golden/semijoin-28-b56400f6d9372f353cf7292a2182e963
 create mode 100644 sql/hive/src/test/resources/golden/semijoin-29-9efeef3d3c38e22a74d379978178c4f5
 create mode 100644 sql/hive/src/test/resources/golden/semijoin-3-b4d4317dd3a10e18502f20f5c5250389
 create mode 100644 sql/hive/src/test/resources/golden/semijoin-30-dd901d00fce5898b03a57cbc3028a70a
 create mode 100644 sql/hive/src/test/resources/golden/semijoin-31-e5dc4d8185e63e984aa4e3a2e08bd67
 create mode 100644 sql/hive/src/test/resources/golden/semijoin-32-23017c7663f2710265a7e2a4a1606d39
 create mode 100644 sql/hive/src/test/resources/golden/semijoin-33-ed499f94c6e6ac847ef5187b3b43bbc5
 create mode 100644 sql/hive/src/test/resources/golden/semijoin-34-5e1b832090ab73c141c1167d5b25a490
 create mode 100644 sql/hive/src/test/resources/golden/semijoin-35-8d5731f26232f6e26dd8012461b08d99
 create mode 100644 sql/hive/src/test/resources/golden/semijoin-36-b1159823dca8025926407f8aa921238d
 create mode 100644 sql/hive/src/test/resources/golden/semijoin-37-a15b9074f999ce836be5329591b968d0
 create mode 100644 sql/hive/src/test/resources/golden/semijoin-38-f37547c73a48ce3ba089531b176e6ba
 create mode 100644 sql/hive/src/test/resources/golden/semijoin-39-c22a6f11368affcb80a9c80e653a47a8
 create mode 100644 sql/hive/src/test/resources/golden/semijoin-4-dfdad5a2742f93e8ea888191460809c0
 create mode 100644 sql/hive/src/test/resources/golden/semijoin-40-32071a51e2ba6e86b1c5e40de55aae63
 create mode 100644 sql/hive/src/test/resources/golden/semijoin-41-cf74f73a33b1af8902b7970a9350b092
 create mode 100644 sql/hive/src/test/resources/golden/semijoin-42-6b4257a74fca627785c967c99547f4c0
 create mode 100644 sql/hive/src/test/resources/golden/semijoin-43-e8a166ac2e94bf8d1da0fe91b0db2c81
 create mode 100644 sql/hive/src/test/resources/golden/semijoin-44-945aaa3a24359ef73acab1e99500d5ea
 create mode 100644 sql/hive/src/test/resources/golden/semijoin-45-3fd94ffd4f1eb6cf83dcc064599bf12b
 create mode 100644 sql/hive/src/test/resources/golden/semijoin-46-620e01f81f6e5254b4bbe8fab4043ec0
 create mode 100644 sql/hive/src/test/resources/golden/semijoin-47-f0140e4ee92508ba241f91c157b7af9c
 create mode 100644 sql/hive/src/test/resources/golden/semijoin-48-8a04442e84f99a584c2882d0af8c25d8
 create mode 100644 sql/hive/src/test/resources/golden/semijoin-49-df1d6705d3624be72036318a6b42f04c
 create mode 100644 sql/hive/src/test/resources/golden/semijoin-5-d3c2f84a12374b307c58a69aba4ec70d
 create mode 100644 sql/hive/src/test/resources/golden/semijoin-6-90bb51b1330230d10a14fb7517457aa0
 create mode 100644 sql/hive/src/test/resources/golden/semijoin-7-333d72e8bce6d11a35fc7a30418f225b
 create mode 100644 sql/hive/src/test/resources/golden/semijoin-8-d46607be851a6f4e27e98cbbefdee994
 create mode 100644 sql/hive/src/test/resources/golden/semijoin-9-f7adaf0f77ce6ff8c3a4807f428d8de2

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins.scala
index 88ff3d49a79b..8d7a5ba59f96 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins.scala
@@ -169,7 +169,7 @@ case class LeftSemiJoinHash(
   def execute() = {
 
     buildPlan.execute().zipPartitions(streamedPlan.execute()) { (buildIter, streamIter) =>
-      val hashTable = new java.util.HashSet[Row]()
+      val hashSet = new java.util.HashSet[Row]()
       var currentRow: Row = null
 
       // Create a Hash set of buildKeys
@@ -177,43 +177,17 @@ case class LeftSemiJoinHash(
         currentRow = buildIter.next()
         val rowKey = buildSideKeyGenerator(currentRow)
         if(!rowKey.anyNull) {
-          val keyExists = hashTable.contains(rowKey)
+          val keyExists = hashSet.contains(rowKey)
           if (!keyExists) {
-            hashTable.add(rowKey)
+            hashSet.add(rowKey)
           }
         }
       }
 
-      new Iterator[Row] {
-        private[this] var currentStreamedRow: Row = _
-        private[this] var currentHashMatched: Boolean = false
-
-        private[this] val joinKeys = streamSideKeyGenerator()
-
-        override final def hasNext: Boolean =
-          streamIter.hasNext && fetchNext()
-
-        override final def next() = {
-          currentStreamedRow
-        }
-
-        /**
-         * Searches the streamed iterator for the next row that has at least one match in hashtable.
-         *
-         * @return true if the search is successful, and false the streamed iterator runs out of
-         *         tuples.
-         */
-        private final def fetchNext(): Boolean = {
-          currentHashMatched = false
-          while (!currentHashMatched && streamIter.hasNext) {
-            currentStreamedRow = streamIter.next()
-            if (!joinKeys(currentStreamedRow).anyNull) {
-              currentHashMatched = hashTable.contains(joinKeys.currentValue)
-            }
-          }
-          currentHashMatched
-        }
-      }
+      val joinKeys = streamSideKeyGenerator()
+      streamIter.filter(current => {
+        !joinKeys(current).anyNull && hashSet.contains(joinKeys.currentValue)
+      })
     }
   }
 }
diff --git a/sql/hive/src/test/resources/golden/semijoin-0-1631b71327abf75b96116036b977b26c b/sql/hive/src/test/resources/golden/semijoin-0-1631b71327abf75b96116036b977b26c
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/semijoin-1-e7c99e1f46d502edbb0925d75aab5f0c b/sql/hive/src/test/resources/golden/semijoin-1-e7c99e1f46d502edbb0925d75aab5f0c
new file mode 100644
index 000000000000..2ed47ab83dd0
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/semijoin-1-e7c99e1f46d502edbb0925d75aab5f0c
@@ -0,0 +1,11 @@
+0	val_0
+0	val_0
+0	val_0
+2	val_2
+4	val_4
+5	val_5
+5	val_5
+5	val_5
+8	val_8
+9	val_9
+10	val_10
diff --git a/sql/hive/src/test/resources/golden/semijoin-10-ffd4fb3a903a6725ccb97d5451a3fec6 b/sql/hive/src/test/resources/golden/semijoin-10-ffd4fb3a903a6725ccb97d5451a3fec6
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/semijoin-11-246a40dcafe077f02397e30d227c330 b/sql/hive/src/test/resources/golden/semijoin-11-246a40dcafe077f02397e30d227c330
new file mode 100644
index 000000000000..a24bd8c6379e
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/semijoin-11-246a40dcafe077f02397e30d227c330
@@ -0,0 +1,8 @@
+0	val_0
+0	val_0
+0	val_0
+4	val_2
+8	val_4
+10	val_5
+10	val_5
+10	val_5
diff --git a/sql/hive/src/test/resources/golden/semijoin-12-6d93a9d332ba490835b17f261a5467df b/sql/hive/src/test/resources/golden/semijoin-12-6d93a9d332ba490835b17f261a5467df
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/semijoin-13-18282d38b6efc0017089ab89b661764f b/sql/hive/src/test/resources/golden/semijoin-13-18282d38b6efc0017089ab89b661764f
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/semijoin-14-19cfcefb10e1972bec0ffd421cd79de7 b/sql/hive/src/test/resources/golden/semijoin-14-19cfcefb10e1972bec0ffd421cd79de7
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/semijoin-15-1de6eb3f357bd1c4d02ab4d19d43589 b/sql/hive/src/test/resources/golden/semijoin-15-1de6eb3f357bd1c4d02ab4d19d43589
new file mode 100644
index 000000000000..03c61a908b07
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/semijoin-15-1de6eb3f357bd1c4d02ab4d19d43589
@@ -0,0 +1,11 @@
+val_0
+val_0
+val_0
+val_10
+val_2
+val_4
+val_5
+val_5
+val_5
+val_8
+val_9
diff --git a/sql/hive/src/test/resources/golden/semijoin-16-d3a72a90515ac4a8d8e9ac923bcda3d b/sql/hive/src/test/resources/golden/semijoin-16-d3a72a90515ac4a8d8e9ac923bcda3d
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/semijoin-17-f0f8720cfd11fd71af17b310dc2d1019 b/sql/hive/src/test/resources/golden/semijoin-17-f0f8720cfd11fd71af17b310dc2d1019
new file mode 100644
index 000000000000..2dcdfd1217ce
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/semijoin-17-f0f8720cfd11fd71af17b310dc2d1019
@@ -0,0 +1,3 @@
+0	val_0
+0	val_0
+0	val_0
diff --git a/sql/hive/src/test/resources/golden/semijoin-18-f7b2ce472443982e32d954cbb5c96765 b/sql/hive/src/test/resources/golden/semijoin-18-f7b2ce472443982e32d954cbb5c96765
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/semijoin-19-b1f1c7f701abe81c01e72fb98f0bd13f b/sql/hive/src/test/resources/golden/semijoin-19-b1f1c7f701abe81c01e72fb98f0bd13f
new file mode 100644
index 000000000000..a3670515e8cc
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/semijoin-19-b1f1c7f701abe81c01e72fb98f0bd13f
@@ -0,0 +1,3 @@
+val_10
+val_8
+val_9
diff --git a/sql/hive/src/test/resources/golden/semijoin-2-deb9c3286ae8e851b1fdb270085b16bc b/sql/hive/src/test/resources/golden/semijoin-2-deb9c3286ae8e851b1fdb270085b16bc
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/semijoin-20-b7a8ebaeb42b2eaba7d97cadc3fd96c1 b/sql/hive/src/test/resources/golden/semijoin-20-b7a8ebaeb42b2eaba7d97cadc3fd96c1
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/semijoin-21-480418a0646cf7260b494b9eb4821bb6 b/sql/hive/src/test/resources/golden/semijoin-21-480418a0646cf7260b494b9eb4821bb6
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/semijoin-22-b6aebd98f7636cda7b24e0bf84d7ba41 b/sql/hive/src/test/resources/golden/semijoin-22-b6aebd98f7636cda7b24e0bf84d7ba41
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/semijoin-23-ed730ccdf552c07e7a82ba6b7fd3fbda b/sql/hive/src/test/resources/golden/semijoin-23-ed730ccdf552c07e7a82ba6b7fd3fbda
new file mode 100644
index 000000000000..72bc6a6a88f6
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/semijoin-23-ed730ccdf552c07e7a82ba6b7fd3fbda
@@ -0,0 +1,5 @@
+4	val_2
+8	val_4
+10	val_5
+10	val_5
+10	val_5
diff --git a/sql/hive/src/test/resources/golden/semijoin-24-d16b37134de78980b2bf96029e8265c3 b/sql/hive/src/test/resources/golden/semijoin-24-d16b37134de78980b2bf96029e8265c3
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/semijoin-25-be2bd011cc80b480b271a08dbf381e9b b/sql/hive/src/test/resources/golden/semijoin-25-be2bd011cc80b480b271a08dbf381e9b
new file mode 100644
index 000000000000..d89ea1757c71
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/semijoin-25-be2bd011cc80b480b271a08dbf381e9b
@@ -0,0 +1,19 @@
+0
+0
+0
+0
+0
+0
+2
+4
+4
+5
+5
+5
+8
+8
+9
+10
+10
+10
+10
diff --git a/sql/hive/src/test/resources/golden/semijoin-26-f1d3bab29f1ebafa148dbe3816e1da25 b/sql/hive/src/test/resources/golden/semijoin-26-f1d3bab29f1ebafa148dbe3816e1da25
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/semijoin-27-391c256254d171973f02e7f33672ce1d b/sql/hive/src/test/resources/golden/semijoin-27-391c256254d171973f02e7f33672ce1d
new file mode 100644
index 000000000000..dbbdae75a52a
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/semijoin-27-391c256254d171973f02e7f33672ce1d
@@ -0,0 +1,4 @@
+0	val_0
+0	val_0
+0	val_0
+8	val_8
diff --git a/sql/hive/src/test/resources/golden/semijoin-28-b56400f6d9372f353cf7292a2182e963 b/sql/hive/src/test/resources/golden/semijoin-28-b56400f6d9372f353cf7292a2182e963
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/semijoin-29-9efeef3d3c38e22a74d379978178c4f5 b/sql/hive/src/test/resources/golden/semijoin-29-9efeef3d3c38e22a74d379978178c4f5
new file mode 100644
index 000000000000..07c61afb5124
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/semijoin-29-9efeef3d3c38e22a74d379978178c4f5
@@ -0,0 +1,14 @@
+0	val_0	0	val_0
+0	val_0	0	val_0
+0	val_0	0	val_0
+0	val_0	0	val_0
+0	val_0	0	val_0
+0	val_0	0	val_0
+0	val_0	0	val_0
+0	val_0	0	val_0
+0	val_0	0	val_0
+4	val_4	4	val_2
+8	val_8	8	val_4
+10	val_10	10	val_5
+10	val_10	10	val_5
+10	val_10	10	val_5
diff --git a/sql/hive/src/test/resources/golden/semijoin-3-b4d4317dd3a10e18502f20f5c5250389 b/sql/hive/src/test/resources/golden/semijoin-3-b4d4317dd3a10e18502f20f5c5250389
new file mode 100644
index 000000000000..bf51e8f5d9eb
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/semijoin-3-b4d4317dd3a10e18502f20f5c5250389
@@ -0,0 +1,11 @@
+0	val_0
+0	val_0
+0	val_0
+4	val_2
+8	val_4
+10	val_5
+10	val_5
+10	val_5
+16	val_8
+18	val_9
+20	val_10
diff --git a/sql/hive/src/test/resources/golden/semijoin-30-dd901d00fce5898b03a57cbc3028a70a b/sql/hive/src/test/resources/golden/semijoin-30-dd901d00fce5898b03a57cbc3028a70a
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/semijoin-31-e5dc4d8185e63e984aa4e3a2e08bd67 b/sql/hive/src/test/resources/golden/semijoin-31-e5dc4d8185e63e984aa4e3a2e08bd67
new file mode 100644
index 000000000000..d6283e34d8ff
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/semijoin-31-e5dc4d8185e63e984aa4e3a2e08bd67
@@ -0,0 +1,14 @@
+0	val_0
+0	val_0
+0	val_0
+0	val_0
+0	val_0
+0	val_0
+2	val_2
+4	val_4
+5	val_5
+5	val_5
+5	val_5
+8	val_8
+9	val_9
+10	val_10
diff --git a/sql/hive/src/test/resources/golden/semijoin-32-23017c7663f2710265a7e2a4a1606d39 b/sql/hive/src/test/resources/golden/semijoin-32-23017c7663f2710265a7e2a4a1606d39
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/semijoin-33-ed499f94c6e6ac847ef5187b3b43bbc5 b/sql/hive/src/test/resources/golden/semijoin-33-ed499f94c6e6ac847ef5187b3b43bbc5
new file mode 100644
index 000000000000..080180f9d0f0
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/semijoin-33-ed499f94c6e6ac847ef5187b3b43bbc5
@@ -0,0 +1,14 @@
+0
+0
+0
+0
+0
+0
+4
+4
+8
+8
+10
+10
+10
+10
diff --git a/sql/hive/src/test/resources/golden/semijoin-34-5e1b832090ab73c141c1167d5b25a490 b/sql/hive/src/test/resources/golden/semijoin-34-5e1b832090ab73c141c1167d5b25a490
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/semijoin-35-8d5731f26232f6e26dd8012461b08d99 b/sql/hive/src/test/resources/golden/semijoin-35-8d5731f26232f6e26dd8012461b08d99
new file mode 100644
index 000000000000..4a64d5c62579
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/semijoin-35-8d5731f26232f6e26dd8012461b08d99
@@ -0,0 +1,26 @@
+0
+0
+0
+0
+0
+0
+0
+0
+0
+0
+0
+0
+0
+0
+0
+0
+0
+0
+4
+4
+8
+8
+10
+10
+10
+10
diff --git a/sql/hive/src/test/resources/golden/semijoin-36-b1159823dca8025926407f8aa921238d b/sql/hive/src/test/resources/golden/semijoin-36-b1159823dca8025926407f8aa921238d
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/semijoin-37-a15b9074f999ce836be5329591b968d0 b/sql/hive/src/test/resources/golden/semijoin-37-a15b9074f999ce836be5329591b968d0
new file mode 100644
index 000000000000..1420c786fb22
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/semijoin-37-a15b9074f999ce836be5329591b968d0
@@ -0,0 +1,29 @@
+NULL
+NULL
+NULL
+0
+0
+0
+0
+0
+0
+0
+0
+0
+0
+0
+0
+0
+0
+0
+0
+0
+0
+4
+4
+8
+8
+10
+10
+10
+10
diff --git a/sql/hive/src/test/resources/golden/semijoin-38-f37547c73a48ce3ba089531b176e6ba b/sql/hive/src/test/resources/golden/semijoin-38-f37547c73a48ce3ba089531b176e6ba
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/semijoin-39-c22a6f11368affcb80a9c80e653a47a8 b/sql/hive/src/test/resources/golden/semijoin-39-c22a6f11368affcb80a9c80e653a47a8
new file mode 100644
index 000000000000..1420c786fb22
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/semijoin-39-c22a6f11368affcb80a9c80e653a47a8
@@ -0,0 +1,29 @@
+NULL
+NULL
+NULL
+0
+0
+0
+0
+0
+0
+0
+0
+0
+0
+0
+0
+0
+0
+0
+0
+0
+0
+4
+4
+8
+8
+10
+10
+10
+10
diff --git a/sql/hive/src/test/resources/golden/semijoin-4-dfdad5a2742f93e8ea888191460809c0 b/sql/hive/src/test/resources/golden/semijoin-4-dfdad5a2742f93e8ea888191460809c0
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/semijoin-40-32071a51e2ba6e86b1c5e40de55aae63 b/sql/hive/src/test/resources/golden/semijoin-40-32071a51e2ba6e86b1c5e40de55aae63
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/semijoin-41-cf74f73a33b1af8902b7970a9350b092 b/sql/hive/src/test/resources/golden/semijoin-41-cf74f73a33b1af8902b7970a9350b092
new file mode 100644
index 000000000000..aef9483bb0bc
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/semijoin-41-cf74f73a33b1af8902b7970a9350b092
@@ -0,0 +1,29 @@
+0
+0
+0
+0
+0
+0
+0
+0
+0
+0
+0
+0
+0
+0
+0
+0
+0
+0
+4
+4
+8
+8
+10
+10
+10
+10
+16
+18
+20
diff --git a/sql/hive/src/test/resources/golden/semijoin-42-6b4257a74fca627785c967c99547f4c0 b/sql/hive/src/test/resources/golden/semijoin-42-6b4257a74fca627785c967c99547f4c0
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/semijoin-43-e8a166ac2e94bf8d1da0fe91b0db2c81 b/sql/hive/src/test/resources/golden/semijoin-43-e8a166ac2e94bf8d1da0fe91b0db2c81
new file mode 100644
index 000000000000..0bc413ef2e09
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/semijoin-43-e8a166ac2e94bf8d1da0fe91b0db2c81
@@ -0,0 +1,31 @@
+NULL
+NULL
+NULL
+NULL
+NULL
+0
+0
+0
+0
+0
+0
+0
+0
+0
+0
+0
+0
+0
+0
+0
+0
+0
+0
+4
+4
+8
+8
+10
+10
+10
+10
diff --git a/sql/hive/src/test/resources/golden/semijoin-44-945aaa3a24359ef73acab1e99500d5ea b/sql/hive/src/test/resources/golden/semijoin-44-945aaa3a24359ef73acab1e99500d5ea
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/semijoin-45-3fd94ffd4f1eb6cf83dcc064599bf12b b/sql/hive/src/test/resources/golden/semijoin-45-3fd94ffd4f1eb6cf83dcc064599bf12b
new file mode 100644
index 000000000000..3131e64446f6
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/semijoin-45-3fd94ffd4f1eb6cf83dcc064599bf12b
@@ -0,0 +1,42 @@
+NULL
+NULL
+NULL
+0
+0
+0
+0
+0
+0
+0
+0
+0
+0
+0
+0
+0
+0
+0
+0
+0
+0
+2
+4
+4
+5
+5
+5
+8
+8
+9
+10
+10
+10
+10
+10
+10
+10
+10
+10
+10
+10
+10
diff --git a/sql/hive/src/test/resources/golden/semijoin-46-620e01f81f6e5254b4bbe8fab4043ec0 b/sql/hive/src/test/resources/golden/semijoin-46-620e01f81f6e5254b4bbe8fab4043ec0
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/semijoin-47-f0140e4ee92508ba241f91c157b7af9c b/sql/hive/src/test/resources/golden/semijoin-47-f0140e4ee92508ba241f91c157b7af9c
new file mode 100644
index 000000000000..ff30bedb8186
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/semijoin-47-f0140e4ee92508ba241f91c157b7af9c
@@ -0,0 +1,35 @@
+0
+0
+0
+0
+0
+0
+0
+0
+0
+0
+0
+0
+0
+0
+0
+0
+0
+0
+4
+4
+8
+8
+10
+10
+10
+10
+10
+10
+10
+10
+10
+10
+16
+18
+20
diff --git a/sql/hive/src/test/resources/golden/semijoin-48-8a04442e84f99a584c2882d0af8c25d8 b/sql/hive/src/test/resources/golden/semijoin-48-8a04442e84f99a584c2882d0af8c25d8
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/semijoin-49-df1d6705d3624be72036318a6b42f04c b/sql/hive/src/test/resources/golden/semijoin-49-df1d6705d3624be72036318a6b42f04c
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/semijoin-5-d3c2f84a12374b307c58a69aba4ec70d b/sql/hive/src/test/resources/golden/semijoin-5-d3c2f84a12374b307c58a69aba4ec70d
new file mode 100644
index 000000000000..60f6eacee9b1
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/semijoin-5-d3c2f84a12374b307c58a69aba4ec70d
@@ -0,0 +1,22 @@
+0	val_0
+0	val_0
+0	val_0
+0	val_0
+0	val_0
+0	val_0
+2	val_2
+4	val_2
+4	val_4
+5	val_5
+5	val_5
+5	val_5
+8	val_4
+8	val_8
+9	val_9
+10	val_10
+10	val_5
+10	val_5
+10	val_5
+16	val_8
+18	val_9
+20	val_10
diff --git a/sql/hive/src/test/resources/golden/semijoin-6-90bb51b1330230d10a14fb7517457aa0 b/sql/hive/src/test/resources/golden/semijoin-6-90bb51b1330230d10a14fb7517457aa0
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/semijoin-7-333d72e8bce6d11a35fc7a30418f225b b/sql/hive/src/test/resources/golden/semijoin-7-333d72e8bce6d11a35fc7a30418f225b
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/semijoin-8-d46607be851a6f4e27e98cbbefdee994 b/sql/hive/src/test/resources/golden/semijoin-8-d46607be851a6f4e27e98cbbefdee994
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/semijoin-9-f7adaf0f77ce6ff8c3a4807f428d8de2 b/sql/hive/src/test/resources/golden/semijoin-9-f7adaf0f77ce6ff8c3a4807f428d8de2
new file mode 100644
index 000000000000..5baaac9bebf6
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/semijoin-9-f7adaf0f77ce6ff8c3a4807f428d8de2
@@ -0,0 +1,6 @@
+0	val_0
+0	val_0
+0	val_0
+4	val_4
+8	val_8
+10	val_10
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
index fb8f272d5abf..3581617c269a 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
@@ -597,6 +597,7 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     "select_unquote_and",
     "select_unquote_not",
     "select_unquote_or",
+    "semijoin",
     "serde_regex",
     "serde_reported_schema",
     "set_variable_sub",

From fe78b8b6f7e3fe519659134c6fcaf7344077ead8 Mon Sep 17 00:00:00 2001
From: Andrew Or <andrewor14@gmail.com>
Date: Wed, 11 Jun 2014 12:11:46 -0700
Subject: [PATCH 399/810] HOTFIX: A few PySpark tests were not actually run

This is a hot fix for the hot fix in fb499be1ac935b6f91046ec8ff23ac1267c82342. The changes in that commit did not actually cause the `doctest` module in python to be loaded for the following tests:
- pyspark/broadcast.py
- pyspark/accumulators.py
- pyspark/serializers.py

(@pwendell I might have told you the wrong thing)

Author: Andrew Or <andrewor14@gmail.com>

Closes #1053 from andrewor14/python-test-fix and squashes the following commits:

d2e5401 [Andrew Or] Explain why these tests are handled differently
0bd6fdd [Andrew Or] Fix 3 pyspark tests not being invoked
---
 bin/pyspark      | 20 +++++++++++++-------
 python/run-tests |  5 ++++-
 2 files changed, 17 insertions(+), 8 deletions(-)

diff --git a/bin/pyspark b/bin/pyspark
index 114cbbc3a8a8..0b5ed40e2157 100755
--- a/bin/pyspark
+++ b/bin/pyspark
@@ -45,7 +45,7 @@ fi
 . $FWDIR/bin/load-spark-env.sh
 
 # Figure out which Python executable to use
-if [ -z "$PYSPARK_PYTHON" ] ; then
+if [[ -z "$PYSPARK_PYTHON" ]]; then
   PYSPARK_PYTHON="python"
 fi
 export PYSPARK_PYTHON
@@ -59,7 +59,7 @@ export OLD_PYTHONSTARTUP=$PYTHONSTARTUP
 export PYTHONSTARTUP=$FWDIR/python/pyspark/shell.py
 
 # If IPython options are specified, assume user wants to run IPython
-if [ -n "$IPYTHON_OPTS" ]; then
+if [[ -n "$IPYTHON_OPTS" ]]; then
   IPYTHON=1
 fi
 
@@ -76,6 +76,16 @@ for i in "$@"; do
 done
 export PYSPARK_SUBMIT_ARGS
 
+# For pyspark tests
+if [[ -n "$SPARK_TESTING" ]]; then
+  if [[ -n "$PYSPARK_DOC_TEST" ]]; then
+    exec "$PYSPARK_PYTHON" -m doctest $1
+  else
+    exec "$PYSPARK_PYTHON" $1
+  fi
+  exit
+fi
+
 # If a python file is provided, directly run spark-submit.
 if [[ "$1" =~ \.py$ ]]; then
   echo -e "\nWARNING: Running python applications through ./bin/pyspark is deprecated as of Spark 1.0." 1>&2
@@ -86,10 +96,6 @@ else
   if [[ "$IPYTHON" = "1" ]]; then
     exec ipython $IPYTHON_OPTS
   else
-    if [[ -n $SPARK_TESTING ]]; then
-      exec "$PYSPARK_PYTHON" -m doctest
-    else
-      exec "$PYSPARK_PYTHON"
-    fi
+    exec "$PYSPARK_PYTHON"
   fi
 fi
diff --git a/python/run-tests b/python/run-tests
index 3b4501178c89..9282aa47e837 100755
--- a/python/run-tests
+++ b/python/run-tests
@@ -44,7 +44,6 @@ function run_test() {
         echo -en "\033[0m"  # No color
         exit -1
     fi
-
 }
 
 echo "Running PySpark tests. Output is in python/unit-tests.log."
@@ -55,9 +54,13 @@ run_test "pyspark/conf.py"
 if [ -n "$_RUN_SQL_TESTS" ]; then
   run_test "pyspark/sql.py"
 fi
+# These tests are included in the module-level docs, and so must
+# be handled on a higher level rather than within the python file.
+export PYSPARK_DOC_TEST=1
 run_test "pyspark/broadcast.py"
 run_test "pyspark/accumulators.py"
 run_test "pyspark/serializers.py"
+unset PYSPARK_DOC_TEST
 run_test "pyspark/tests.py"
 run_test "pyspark/mllib/_common.py"
 run_test "pyspark/mllib/classification.py"

From 14e6dc94f68e57de82841c4ebbb573797a53869c Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Wed, 11 Jun 2014 15:54:41 -0700
Subject: [PATCH 400/810] HOTFIX: PySpark tests should be order insensitive.

This has been messing up the SQL PySpark tests on Jenkins.

Author: Patrick Wendell <pwendell@gmail.com>

Closes #1054 from pwendell/pyspark and squashes the following commits:

1eb5487 [Patrick Wendell] False change
06f062d [Patrick Wendell] HOTFIX: PySpark tests should be order insensitive
---
 python/pyspark/sql.py                                     | 8 ++++----
 .../src/main/scala/org/apache/spark/sql/SchemaRDD.scala   | 1 +
 2 files changed, 5 insertions(+), 4 deletions(-)

diff --git a/python/pyspark/sql.py b/python/pyspark/sql.py
index b4e9618cc25b..960d0a82448a 100644
--- a/python/pyspark/sql.py
+++ b/python/pyspark/sql.py
@@ -117,7 +117,7 @@ def parquetFile(self, path):
         >>> srdd = sqlCtx.inferSchema(rdd)
         >>> srdd.saveAsParquetFile(parquetFile)
         >>> srdd2 = sqlCtx.parquetFile(parquetFile)
-        >>> srdd.collect() == srdd2.collect()
+        >>> sorted(srdd.collect()) == sorted(srdd2.collect())
         True
         """
         jschema_rdd = self._ssql_ctx.parquetFile(path)
@@ -141,7 +141,7 @@ def table(self, tableName):
         >>> srdd = sqlCtx.inferSchema(rdd)
         >>> sqlCtx.registerRDDAsTable(srdd, "table1")
         >>> srdd2 = sqlCtx.table("table1")
-        >>> srdd.collect() == srdd2.collect()
+        >>> sorted(srdd.collect()) == sorted(srdd2.collect())
         True
         """
         return SchemaRDD(self._ssql_ctx.table(tableName), self)
@@ -293,7 +293,7 @@ def saveAsParquetFile(self, path):
         >>> srdd = sqlCtx.inferSchema(rdd)
         >>> srdd.saveAsParquetFile(parquetFile)
         >>> srdd2 = sqlCtx.parquetFile(parquetFile)
-        >>> srdd2.collect() == srdd.collect()
+        >>> sorted(srdd2.collect()) == sorted(srdd.collect())
         True
         """
         self._jschema_rdd.saveAsParquetFile(path)
@@ -307,7 +307,7 @@ def registerAsTable(self, name):
         >>> srdd = sqlCtx.inferSchema(rdd)
         >>> srdd.registerAsTable("test")
         >>> srdd2 = sqlCtx.sql("select * from test")
-        >>> srdd.collect() == srdd2.collect()
+        >>> sorted(srdd.collect()) == sorted(srdd2.collect())
         True
         """
         self._jschema_rdd.registerAsTable(name)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala
index 7ad8edf5a5a6..44b19bca460b 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala
@@ -33,6 +33,7 @@ import org.apache.spark.api.java.JavaRDD
 import java.util.{Map => JMap}
 
 /**
+ * ***FALSE CHANGE***
  * :: AlphaComponent ::
  * An RDD of [[Row]] objects that has an associated schema. In addition to standard RDD functions,
  * SchemaRDDs can be used in relational queries, as shown in the examples below.

From d45e0c6b986b54129313fc4a44c741ab4b04462d Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Wed, 11 Jun 2014 15:55:41 -0700
Subject: [PATCH 401/810] HOTFIX: Forgot to remove false change in previous
 commit

---
 sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala | 1 -
 1 file changed, 1 deletion(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala
index 44b19bca460b..7ad8edf5a5a6 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala
@@ -33,7 +33,6 @@ import org.apache.spark.api.java.JavaRDD
 import java.util.{Map => JMap}
 
 /**
- * ***FALSE CHANGE***
  * :: AlphaComponent ::
  * An RDD of [[Row]] objects that has an associated schema. In addition to standard RDD functions,
  * SchemaRDDs can be used in relational queries, as shown in the examples below.

From 9a2448daf984d5bb550dfe0d9e28cbb80ef5cb51 Mon Sep 17 00:00:00 2001
From: Takuya UESHIN <ueshin@happy-camper.st>
Date: Wed, 11 Jun 2014 17:58:35 -0700
Subject: [PATCH 402/810] [SPARK-2052] [SQL] Add optimization for
 CaseConversionExpression's.

Add optimization for `CaseConversionExpression`'s.

Author: Takuya UESHIN <ueshin@happy-camper.st>

Closes #990 from ueshin/issues/SPARK-2052 and squashes the following commits:

2568666 [Takuya UESHIN] Move some rules back.
dde7ede [Takuya UESHIN] Add tests to check if ConstantFolding can handle null literals and remove the unneeded rules from NullPropagation.
c4eea67 [Takuya UESHIN] Fix toString methods.
23e2363 [Takuya UESHIN] Make CaseConversionExpressions foldable if the child is foldable.
0ff7568 [Takuya UESHIN] Add tests for collapsing case statements.
3977d80 [Takuya UESHIN] Add optimization for CaseConversionExpression's.
---
 .../expressions/stringOperations.scala        |  7 +-
 .../sql/catalyst/optimizer/Optimizer.scala    | 30 +++---
 .../optimizer/ConstantFoldingSuite.scala      | 61 ++++++++++++-
 ...mplifyCaseConversionExpressionsSuite.scala | 91 +++++++++++++++++++
 4 files changed, 174 insertions(+), 15 deletions(-)
 create mode 100644 sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/SimplifyCaseConversionExpressionsSuite.scala

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
index 420303408451..c074b7bb01e5 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
@@ -76,7 +76,8 @@ trait CaseConversionExpression {
   type EvaluatedType = Any
 
   def convert(v: String): String
-  
+
+  override def foldable: Boolean = child.foldable
   def nullable: Boolean = child.nullable
   def dataType: DataType = StringType
 
@@ -142,6 +143,8 @@ case class RLike(left: Expression, right: Expression)
 case class Upper(child: Expression) extends UnaryExpression with CaseConversionExpression {
   
   override def convert(v: String): String = v.toUpperCase()
+
+  override def toString() = s"Upper($child)"
 }
 
 /**
@@ -150,4 +153,6 @@ case class Upper(child: Expression) extends UnaryExpression with CaseConversionE
 case class Lower(child: Expression) extends UnaryExpression with CaseConversionExpression {
   
   override def convert(v: String): String = v.toLowerCase()
+
+  override def toString() = s"Lower($child)"
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
index 28d1aa2e3aaf..25a347bec0e4 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
@@ -36,7 +36,8 @@ object Optimizer extends RuleExecutor[LogicalPlan] {
       ConstantFolding,
       BooleanSimplification,
       SimplifyFilters,
-      SimplifyCasts) ::
+      SimplifyCasts,
+      SimplifyCaseConversionExpressions) ::
     Batch("Filter Pushdown", FixedPoint(100),
       CombineFilters,
       PushPredicateThroughProject,
@@ -132,18 +133,6 @@ object NullPropagation extends Rule[LogicalPlan] {
           case Literal(candidate, _) if candidate == v => true
           case _ => false
         })) => Literal(true, BooleanType)
-      case e: UnaryMinus => e.child match {
-        case Literal(null, _) => Literal(null, e.dataType)
-        case _ => e
-      }
-      case e: Cast => e.child match {
-        case Literal(null, _) => Literal(null, e.dataType)
-        case _ => e
-      }
-      case e: Not => e.child match {
-        case Literal(null, _) => Literal(null, e.dataType)
-        case _ => e
-      }
       // Put exceptional cases above if any
       case e: BinaryArithmetic => e.children match {
         case Literal(null, _) :: right :: Nil => Literal(null, e.dataType)
@@ -375,3 +364,18 @@ object CombineLimits extends Rule[LogicalPlan] {
       Limit(If(LessThan(ne, le), ne, le), grandChild)
   }
 }
+
+/**
+ * Removes the inner [[catalyst.expressions.CaseConversionExpression]] that are unnecessary because
+ * the inner conversion is overwritten by the outer one.
+ */
+object SimplifyCaseConversionExpressions extends Rule[LogicalPlan] {
+  def apply(plan: LogicalPlan): LogicalPlan = plan transform {
+    case q: LogicalPlan => q transformExpressionsUp {
+      case Upper(Upper(child)) => Upper(child)
+      case Upper(Lower(child)) => Upper(child)
+      case Lower(Upper(child)) => Lower(child)
+      case Lower(Lower(child)) => Lower(child)
+    }
+  }
+}
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConstantFoldingSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConstantFoldingSuite.scala
index 20dfba847790..6efc0e211eb2 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConstantFoldingSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConstantFoldingSuite.scala
@@ -21,7 +21,7 @@ import org.apache.spark.sql.catalyst.analysis.EliminateAnalysisOperators
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.logical.{LocalRelation, LogicalPlan}
 import org.apache.spark.sql.catalyst.rules.RuleExecutor
-import org.apache.spark.sql.catalyst.types.{DoubleType, IntegerType}
+import org.apache.spark.sql.catalyst.types._
 
 // For implicit conversions
 import org.apache.spark.sql.catalyst.dsl.plans._
@@ -173,4 +173,63 @@ class ConstantFoldingSuite extends OptimizerTest {
 
     comparePlans(optimized, correctAnswer)
   }
+
+  test("Constant folding test: expressions have null literals") {
+    val originalQuery =
+      testRelation
+        .select(
+          IsNull(Literal(null)) as 'c1,
+          IsNotNull(Literal(null)) as 'c2,
+
+          GetItem(Literal(null, ArrayType(IntegerType)), 1) as 'c3,
+          GetItem(Literal(Seq(1), ArrayType(IntegerType)), Literal(null, IntegerType)) as 'c4,
+          GetField(
+            Literal(null, StructType(Seq(StructField("a", IntegerType, true)))),
+            "a") as 'c5,
+
+          UnaryMinus(Literal(null, IntegerType)) as 'c6,
+          Cast(Literal(null), IntegerType) as 'c7,
+          Not(Literal(null, BooleanType)) as 'c8,
+
+          Add(Literal(null, IntegerType), 1) as 'c9,
+          Add(1, Literal(null, IntegerType)) as 'c10,
+
+          Equals(Literal(null, IntegerType), 1) as 'c11,
+          Equals(1, Literal(null, IntegerType)) as 'c12,
+
+          Like(Literal(null, StringType), "abc") as 'c13,
+          Like("abc", Literal(null, StringType)) as 'c14,
+
+          Upper(Literal(null, StringType)) as 'c15)
+
+    val optimized = Optimize(originalQuery.analyze)
+
+    val correctAnswer =
+      testRelation
+        .select(
+          Literal(true) as 'c1,
+          Literal(false) as 'c2,
+
+          Literal(null, IntegerType) as 'c3,
+          Literal(null, IntegerType) as 'c4,
+          Literal(null, IntegerType) as 'c5,
+
+          Literal(null, IntegerType) as 'c6,
+          Literal(null, IntegerType) as 'c7,
+          Literal(null, BooleanType) as 'c8,
+
+          Literal(null, IntegerType) as 'c9,
+          Literal(null, IntegerType) as 'c10,
+
+          Literal(null, BooleanType) as 'c11,
+          Literal(null, BooleanType) as 'c12,
+
+          Literal(null, BooleanType) as 'c13,
+          Literal(null, BooleanType) as 'c14,
+
+          Literal(null, StringType) as 'c15)
+        .analyze
+
+    comparePlans(optimized, correctAnswer)
+  }
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/SimplifyCaseConversionExpressionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/SimplifyCaseConversionExpressionsSuite.scala
new file mode 100644
index 000000000000..df1409fe7bae
--- /dev/null
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/SimplifyCaseConversionExpressionsSuite.scala
@@ -0,0 +1,91 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.optimizer
+
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.plans.logical._
+import org.apache.spark.sql.catalyst.rules._
+
+/* Implicit conversions */
+import org.apache.spark.sql.catalyst.dsl.expressions._
+import org.apache.spark.sql.catalyst.dsl.plans._
+
+class SimplifyCaseConversionExpressionsSuite extends OptimizerTest {
+
+  object Optimize extends RuleExecutor[LogicalPlan] {
+    val batches =
+      Batch("Simplify CaseConversionExpressions", Once,
+        SimplifyCaseConversionExpressions) :: Nil
+  }
+
+  val testRelation = LocalRelation('a.string)
+
+  test("simplify UPPER(UPPER(str))") {
+    val originalQuery =
+      testRelation
+        .select(Upper(Upper('a)) as 'u)
+
+    val optimized = Optimize(originalQuery.analyze)
+    val correctAnswer =
+      testRelation
+        .select(Upper('a) as 'u)
+        .analyze
+
+    comparePlans(optimized, correctAnswer)
+  }
+
+  test("simplify UPPER(LOWER(str))") {
+    val originalQuery =
+      testRelation
+        .select(Upper(Lower('a)) as 'u)
+
+    val optimized = Optimize(originalQuery.analyze)
+    val correctAnswer =
+      testRelation
+        .select(Upper('a) as 'u)
+        .analyze
+
+    comparePlans(optimized, correctAnswer)
+  }
+
+  test("simplify LOWER(UPPER(str))") {
+    val originalQuery =
+      testRelation
+        .select(Lower(Upper('a)) as 'l)
+
+    val optimized = Optimize(originalQuery.analyze)
+    val correctAnswer = testRelation
+      .select(Lower('a) as 'l)
+      .analyze
+
+    comparePlans(optimized, correctAnswer)
+  }
+
+  test("simplify LOWER(LOWER(str))") {
+    val originalQuery =
+      testRelation
+        .select(Lower(Lower('a)) as 'l)
+
+    val optimized = Optimize(originalQuery.analyze)
+    val correctAnswer = testRelation
+      .select(Lower('a) as 'l)
+      .analyze
+
+    comparePlans(optimized, correctAnswer)
+  }
+}

From d9203350b06a9c737421ba244162b1365402c01b Mon Sep 17 00:00:00 2001
From: Tor Myklebust <tmyklebu@gmail.com>
Date: Wed, 11 Jun 2014 18:16:33 -0700
Subject: [PATCH 403/810] [SPARK-1672][MLLIB] Separate user and product
 partitioning in ALS

Some clean up work following #593.

1. Allow to set different number user blocks and number product blocks in `ALS`.
2. Update `MovieLensALS` to reflect the change.

Author: Tor Myklebust <tmyklebu@gmail.com>
Author: Xiangrui Meng <meng@databricks.com>

Closes #1014 from mengxr/SPARK-1672 and squashes the following commits:

0e910dd [Xiangrui Meng] change private[this] to private[recommendation]
36420c7 [Xiangrui Meng] set exclusion rules for ALS
9128b77 [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into SPARK-1672
294efe9 [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into SPARK-1672
9bab77b [Xiangrui Meng] clean up add numUserBlocks and numProductBlocks to MovieLensALS
84c8e8c [Xiangrui Meng] Merge branch 'master' into SPARK-1672
d17a8bf [Xiangrui Meng] merge master
a4925fd [Tor Myklebust] Style.
bd8a75c [Tor Myklebust] Merge branch 'master' of github.com:apache/spark into alsseppar
021f54b [Tor Myklebust] Separate user and product blocks.
dcf583a [Tor Myklebust] Remove the partitioner member variable; instead, thread that needle everywhere it needs to go.
23d6f91 [Tor Myklebust] Stop making the partitioner configurable.
495784f [Tor Myklebust] Merge branch 'master' of https://github.com/apache/spark
674933a [Tor Myklebust] Fix style.
40edc23 [Tor Myklebust] Fix missing space.
f841345 [Tor Myklebust] Fix daft bug creating 'pairs', also for -> foreach.
5ec9e6c [Tor Myklebust] Clean a couple of things up using 'map'.
36a0f43 [Tor Myklebust] Make the partitioner private.
d872b09 [Tor Myklebust] Add negative id ALS test.
df27697 [Tor Myklebust] Support custom partitioners.  Currently we use the same partitioner for users and products.
c90b6d8 [Tor Myklebust] Scramble user and product ids before bucketing.
c774d7d [Tor Myklebust] Make the partitioner a member variable and use it instead of modding directly.
---
 .../spark/examples/mllib/MovieLensALS.scala   |  12 +-
 .../spark/mllib/recommendation/ALS.scala      | 164 +++++++++++-------
 .../spark/mllib/recommendation/ALSSuite.scala |  60 +++++--
 project/MimaExcludes.scala                    |   8 +
 4 files changed, 160 insertions(+), 84 deletions(-)

diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/MovieLensALS.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/MovieLensALS.scala
index 6eb41e7ba36f..28e201d279f4 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/MovieLensALS.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/MovieLensALS.scala
@@ -50,6 +50,8 @@ object MovieLensALS {
       numIterations: Int = 20,
       lambda: Double = 1.0,
       rank: Int = 10,
+      numUserBlocks: Int = -1,
+      numProductBlocks: Int = -1,
       implicitPrefs: Boolean = false)
 
   def main(args: Array[String]) {
@@ -67,8 +69,14 @@ object MovieLensALS {
         .text(s"lambda (smoothing constant), default: ${defaultParams.lambda}")
         .action((x, c) => c.copy(lambda = x))
       opt[Unit]("kryo")
-        .text(s"use Kryo serialization")
+        .text("use Kryo serialization")
         .action((_, c) => c.copy(kryo = true))
+      opt[Int]("numUserBlocks")
+        .text(s"number of user blocks, default: ${defaultParams.numUserBlocks} (auto)")
+        .action((x, c) => c.copy(numUserBlocks = x))
+      opt[Int]("numProductBlocks")
+        .text(s"number of product blocks, default: ${defaultParams.numProductBlocks} (auto)")
+        .action((x, c) => c.copy(numProductBlocks = x))
       opt[Unit]("implicitPrefs")
         .text("use implicit preference")
         .action((_, c) => c.copy(implicitPrefs = true))
@@ -160,6 +168,8 @@ object MovieLensALS {
       .setIterations(params.numIterations)
       .setLambda(params.lambda)
       .setImplicitPrefs(params.implicitPrefs)
+      .setUserBlocks(params.numUserBlocks)
+      .setProductBlocks(params.numProductBlocks)
       .run(training)
 
     val rmse = computeRmse(model, test, params.implicitPrefs)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala
index d743bd7dd182..f1ae7b85b4a6 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala
@@ -61,7 +61,7 @@ private[recommendation] case class InLinkBlock(
  * A more compact class to represent a rating than Tuple3[Int, Int, Double].
  */
 @Experimental
-case class Rating(val user: Int, val product: Int, val rating: Double)
+case class Rating(user: Int, product: Int, rating: Double)
 
 /**
  * Alternating Least Squares matrix factorization.
@@ -93,7 +93,8 @@ case class Rating(val user: Int, val product: Int, val rating: Double)
  * preferences rather than explicit ratings given to items.
  */
 class ALS private (
-    private var numBlocks: Int,
+    private var numUserBlocks: Int,
+    private var numProductBlocks: Int,
     private var rank: Int,
     private var iterations: Int,
     private var lambda: Double,
@@ -106,14 +107,31 @@ class ALS private (
    * Constructs an ALS instance with default parameters: {numBlocks: -1, rank: 10, iterations: 10,
    * lambda: 0.01, implicitPrefs: false, alpha: 1.0}.
    */
-  def this() = this(-1, 10, 10, 0.01, false, 1.0)
+  def this() = this(-1, -1, 10, 10, 0.01, false, 1.0)
 
   /**
-   * Set the number of blocks to parallelize the computation into; pass -1 for an auto-configured
-   * number of blocks. Default: -1.
+   * Set the number of blocks for both user blocks and product blocks to parallelize the computation
+   * into; pass -1 for an auto-configured number of blocks. Default: -1.
    */
   def setBlocks(numBlocks: Int): ALS = {
-    this.numBlocks = numBlocks
+    this.numUserBlocks = numBlocks
+    this.numProductBlocks = numBlocks
+    this
+  }
+
+  /**
+   * Set the number of user blocks to parallelize the computation.
+   */
+  def setUserBlocks(numUserBlocks: Int): ALS = {
+    this.numUserBlocks = numUserBlocks
+    this
+  }
+
+  /**
+   * Set the number of product blocks to parallelize the computation.
+   */
+  def setProductBlocks(numProductBlocks: Int): ALS = {
+    this.numProductBlocks = numProductBlocks
     this
   }
 
@@ -176,31 +194,32 @@ class ALS private (
   def run(ratings: RDD[Rating]): MatrixFactorizationModel = {
     val sc = ratings.context
 
-    val numBlocks = if (this.numBlocks == -1) {
+    val numUserBlocks = if (this.numUserBlocks == -1) {
       math.max(sc.defaultParallelism, ratings.partitions.size / 2)
     } else {
-      this.numBlocks
+      this.numUserBlocks
     }
-
-    val partitioner = new Partitioner {
-      val numPartitions = numBlocks
-
-      def getPartition(x: Any): Int = {
-        Utils.nonNegativeMod(byteswap32(x.asInstanceOf[Int]), numPartitions)
-      }
+    val numProductBlocks = if (this.numProductBlocks == -1) {
+      math.max(sc.defaultParallelism, ratings.partitions.size / 2)
+    } else {
+      this.numProductBlocks
     }
 
-    val ratingsByUserBlock = ratings.map{ rating =>
-      (partitioner.getPartition(rating.user), rating)
+    val userPartitioner = new ALSPartitioner(numUserBlocks)
+    val productPartitioner = new ALSPartitioner(numProductBlocks)
+
+    val ratingsByUserBlock = ratings.map { rating =>
+      (userPartitioner.getPartition(rating.user), rating)
     }
-    val ratingsByProductBlock = ratings.map{ rating =>
-      (partitioner.getPartition(rating.product),
+    val ratingsByProductBlock = ratings.map { rating =>
+      (productPartitioner.getPartition(rating.product),
         Rating(rating.product, rating.user, rating.rating))
     }
 
-    val (userInLinks, userOutLinks) = makeLinkRDDs(numBlocks, ratingsByUserBlock, partitioner)
+    val (userInLinks, userOutLinks) =
+      makeLinkRDDs(numUserBlocks, numProductBlocks, ratingsByUserBlock, productPartitioner)
     val (productInLinks, productOutLinks) =
-        makeLinkRDDs(numBlocks, ratingsByProductBlock, partitioner)
+      makeLinkRDDs(numProductBlocks, numUserBlocks, ratingsByProductBlock, userPartitioner)
     userInLinks.setName("userInLinks")
     userOutLinks.setName("userOutLinks")
     productInLinks.setName("productInLinks")
@@ -232,27 +251,27 @@ class ALS private (
         users.setName(s"users-$iter").persist()
         val YtY = Some(sc.broadcast(computeYtY(users)))
         val previousProducts = products
-        products = updateFeatures(users, userOutLinks, productInLinks, partitioner, rank, lambda,
-          alpha, YtY)
+        products = updateFeatures(numProductBlocks, users, userOutLinks, productInLinks,
+          userPartitioner, rank, lambda, alpha, YtY)
         previousProducts.unpersist()
         logInfo("Re-computing U given I (Iteration %d/%d)".format(iter, iterations))
         products.setName(s"products-$iter").persist()
         val XtX = Some(sc.broadcast(computeYtY(products)))
         val previousUsers = users
-        users = updateFeatures(products, productOutLinks, userInLinks, partitioner, rank, lambda,
-          alpha, XtX)
+        users = updateFeatures(numUserBlocks, products, productOutLinks, userInLinks,
+          productPartitioner, rank, lambda, alpha, XtX)
         previousUsers.unpersist()
       }
     } else {
       for (iter <- 1 to iterations) {
         // perform ALS update
         logInfo("Re-computing I given U (Iteration %d/%d)".format(iter, iterations))
-        products = updateFeatures(users, userOutLinks, productInLinks, partitioner, rank, lambda,
-          alpha, YtY = None)
+        products = updateFeatures(numProductBlocks, users, userOutLinks, productInLinks,
+          userPartitioner, rank, lambda, alpha, YtY = None)
         products.setName(s"products-$iter")
         logInfo("Re-computing U given I (Iteration %d/%d)".format(iter, iterations))
-        users = updateFeatures(products, productOutLinks, userInLinks, partitioner, rank, lambda,
-          alpha, YtY = None)
+        users = updateFeatures(numUserBlocks, products, productOutLinks, userInLinks,
+          productPartitioner, rank, lambda, alpha, YtY = None)
         users.setName(s"users-$iter")
       }
     }
@@ -340,9 +359,10 @@ class ALS private (
   /**
    * Flatten out blocked user or product factors into an RDD of (id, factor vector) pairs
    */
-  private def unblockFactors(blockedFactors: RDD[(Int, Array[Array[Double]])],
-                     outLinks: RDD[(Int, OutLinkBlock)]) = {
-    blockedFactors.join(outLinks).flatMap{ case (b, (factors, outLinkBlock)) =>
+  private def unblockFactors(
+      blockedFactors: RDD[(Int, Array[Array[Double]])],
+      outLinks: RDD[(Int, OutLinkBlock)]): RDD[(Int, Array[Double])] = {
+    blockedFactors.join(outLinks).flatMap { case (b, (factors, outLinkBlock)) =>
       for (i <- 0 until factors.length) yield (outLinkBlock.elementIds(i), factors(i))
     }
   }
@@ -351,14 +371,14 @@ class ALS private (
    * Make the out-links table for a block of the users (or products) dataset given the list of
    * (user, product, rating) values for the users in that block (or the opposite for products).
    */
-  private def makeOutLinkBlock(numBlocks: Int, ratings: Array[Rating],
-      partitioner: Partitioner): OutLinkBlock = {
+  private def makeOutLinkBlock(numProductBlocks: Int, ratings: Array[Rating],
+      productPartitioner: Partitioner): OutLinkBlock = {
     val userIds = ratings.map(_.user).distinct.sorted
     val numUsers = userIds.length
     val userIdToPos = userIds.zipWithIndex.toMap
-    val shouldSend = Array.fill(numUsers)(new BitSet(numBlocks))
+    val shouldSend = Array.fill(numUsers)(new BitSet(numProductBlocks))
     for (r <- ratings) {
-      shouldSend(userIdToPos(r.user))(partitioner.getPartition(r.product)) = true
+      shouldSend(userIdToPos(r.user))(productPartitioner.getPartition(r.product)) = true
     }
     OutLinkBlock(userIds, shouldSend)
   }
@@ -367,18 +387,17 @@ class ALS private (
    * Make the in-links table for a block of the users (or products) dataset given a list of
    * (user, product, rating) values for the users in that block (or the opposite for products).
    */
-  private def makeInLinkBlock(numBlocks: Int, ratings: Array[Rating],
-      partitioner: Partitioner): InLinkBlock = {
+  private def makeInLinkBlock(numProductBlocks: Int, ratings: Array[Rating],
+      productPartitioner: Partitioner): InLinkBlock = {
     val userIds = ratings.map(_.user).distinct.sorted
-    val numUsers = userIds.length
     val userIdToPos = userIds.zipWithIndex.toMap
     // Split out our ratings by product block
-    val blockRatings = Array.fill(numBlocks)(new ArrayBuffer[Rating])
+    val blockRatings = Array.fill(numProductBlocks)(new ArrayBuffer[Rating])
     for (r <- ratings) {
-      blockRatings(partitioner.getPartition(r.product)) += r
+      blockRatings(productPartitioner.getPartition(r.product)) += r
     }
-    val ratingsForBlock = new Array[Array[(Array[Int], Array[Double])]](numBlocks)
-    for (productBlock <- 0 until numBlocks) {
+    val ratingsForBlock = new Array[Array[(Array[Int], Array[Double])]](numProductBlocks)
+    for (productBlock <- 0 until numProductBlocks) {
       // Create an array of (product, Seq(Rating)) ratings
       val groupedRatings = blockRatings(productBlock).groupBy(_.product).toArray
       // Sort them by product ID
@@ -400,14 +419,16 @@ class ALS private (
    * the users (or (blockId, (p, u, r)) for the products). We create these simultaneously to avoid
    * having to shuffle the (blockId, (u, p, r)) RDD twice, or to cache it.
    */
-  private def makeLinkRDDs(numBlocks: Int, ratings: RDD[(Int, Rating)], partitioner: Partitioner)
-    : (RDD[(Int, InLinkBlock)], RDD[(Int, OutLinkBlock)]) =
-  {
-    val grouped = ratings.partitionBy(new HashPartitioner(numBlocks))
+  private def makeLinkRDDs(
+      numUserBlocks: Int,
+      numProductBlocks: Int,
+      ratingsByUserBlock: RDD[(Int, Rating)],
+      productPartitioner: Partitioner): (RDD[(Int, InLinkBlock)], RDD[(Int, OutLinkBlock)]) = {
+    val grouped = ratingsByUserBlock.partitionBy(new HashPartitioner(numUserBlocks))
     val links = grouped.mapPartitionsWithIndex((blockId, elements) => {
-      val ratings = elements.map{_._2}.toArray
-      val inLinkBlock = makeInLinkBlock(numBlocks, ratings, partitioner)
-      val outLinkBlock = makeOutLinkBlock(numBlocks, ratings, partitioner)
+      val ratings = elements.map(_._2).toArray
+      val inLinkBlock = makeInLinkBlock(numProductBlocks, ratings, productPartitioner)
+      val outLinkBlock = makeOutLinkBlock(numProductBlocks, ratings, productPartitioner)
       Iterator.single((blockId, (inLinkBlock, outLinkBlock)))
     }, true)
     val inLinks = links.mapValues(_._1)
@@ -439,26 +460,24 @@ class ALS private (
    * It returns an RDD of new feature vectors for each user block.
    */
   private def updateFeatures(
+      numUserBlocks: Int,
       products: RDD[(Int, Array[Array[Double]])],
       productOutLinks: RDD[(Int, OutLinkBlock)],
       userInLinks: RDD[(Int, InLinkBlock)],
-      partitioner: Partitioner,
+      productPartitioner: Partitioner,
       rank: Int,
       lambda: Double,
       alpha: Double,
-      YtY: Option[Broadcast[DoubleMatrix]])
-    : RDD[(Int, Array[Array[Double]])] =
-  {
-    val numBlocks = products.partitions.size
+      YtY: Option[Broadcast[DoubleMatrix]]): RDD[(Int, Array[Array[Double]])] = {
     productOutLinks.join(products).flatMap { case (bid, (outLinkBlock, factors)) =>
-        val toSend = Array.fill(numBlocks)(new ArrayBuffer[Array[Double]])
-        for (p <- 0 until outLinkBlock.elementIds.length; userBlock <- 0 until numBlocks) {
+        val toSend = Array.fill(numUserBlocks)(new ArrayBuffer[Array[Double]])
+        for (p <- 0 until outLinkBlock.elementIds.length; userBlock <- 0 until numUserBlocks) {
           if (outLinkBlock.shouldSend(p)(userBlock)) {
             toSend(userBlock) += factors(p)
           }
         }
         toSend.zipWithIndex.map{ case (buf, idx) => (idx, (bid, buf.toArray)) }
-    }.groupByKey(partitioner)
+    }.groupByKey(productPartitioner)
      .join(userInLinks)
      .mapValues{ case (messages, inLinkBlock) =>
         updateBlock(messages, inLinkBlock, rank, lambda, alpha, YtY)
@@ -475,7 +494,7 @@ class ALS private (
   {
     // Sort the incoming block factor messages by block ID and make them an array
     val blockFactors = messages.toSeq.sortBy(_._1).map(_._2).toArray // Array[Array[Double]]
-    val numBlocks = blockFactors.length
+    val numProductBlocks = blockFactors.length
     val numUsers = inLinkBlock.elementIds.length
 
     // We'll sum up the XtXes using vectors that represent only the lower-triangular part, since
@@ -490,7 +509,7 @@ class ALS private (
 
     // Compute the XtX and Xy values for each user by adding products it rated in each product
     // block
-    for (productBlock <- 0 until numBlocks) {
+    for (productBlock <- 0 until numProductBlocks) {
       var p = 0
       while (p < blockFactors(productBlock).length) {
         val x = wrapDoubleArray(blockFactors(productBlock)(p))
@@ -579,6 +598,23 @@ class ALS private (
   }
 }
 
+/**
+ * Partitioner for ALS.
+ */
+private[recommendation] class ALSPartitioner(override val numPartitions: Int) extends Partitioner {
+  override def getPartition(key: Any): Int = {
+    Utils.nonNegativeMod(byteswap32(key.asInstanceOf[Int]), numPartitions)
+  }
+
+  override def equals(obj: Any): Boolean = {
+    obj match {
+      case p: ALSPartitioner =>
+        this.numPartitions == p.numPartitions
+      case _ =>
+        false
+    }
+  }
+}
 
 /**
  * Top-level methods for calling Alternating Least Squares (ALS) matrix factorization.
@@ -606,7 +642,7 @@ object ALS {
       blocks: Int,
       seed: Long
     ): MatrixFactorizationModel = {
-    new ALS(blocks, rank, iterations, lambda, false, 1.0, seed).run(ratings)
+    new ALS(blocks, blocks, rank, iterations, lambda, false, 1.0, seed).run(ratings)
   }
 
   /**
@@ -629,7 +665,7 @@ object ALS {
       lambda: Double,
       blocks: Int
     ): MatrixFactorizationModel = {
-    new ALS(blocks, rank, iterations, lambda, false, 1.0).run(ratings)
+    new ALS(blocks, blocks, rank, iterations, lambda, false, 1.0).run(ratings)
   }
 
   /**
@@ -689,7 +725,7 @@ object ALS {
       alpha: Double,
       seed: Long
     ): MatrixFactorizationModel = {
-    new ALS(blocks, rank, iterations, lambda, true, alpha, seed).run(ratings)
+    new ALS(blocks, blocks, rank, iterations, lambda, true, alpha, seed).run(ratings)
   }
 
   /**
@@ -714,7 +750,7 @@ object ALS {
       blocks: Int,
       alpha: Double
     ): MatrixFactorizationModel = {
-    new ALS(blocks, rank, iterations, lambda, true, alpha).run(ratings)
+    new ALS(blocks, blocks, rank, iterations, lambda, true, alpha).run(ratings)
   }
 
   /**
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/recommendation/ALSSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/recommendation/ALSSuite.scala
index 37c9b9d08584..81bebec8c7a3 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/recommendation/ALSSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/recommendation/ALSSuite.scala
@@ -121,6 +121,10 @@ class ALSSuite extends FunSuite with LocalSparkContext {
     testALS(100, 200, 2, 15, 0.7, 0.4, true, false, true)
   }
 
+  test("rank-2 matrices with different user and product blocks") {
+    testALS(100, 200, 2, 15, 0.7, 0.4, numUserBlocks = 4, numProductBlocks = 2)
+  }
+
   test("pseudorandomness") {
     val ratings = sc.parallelize(ALSSuite.generateRatings(10, 20, 5, 0.5, false, false)._1, 2)
     val model11 = ALS.train(ratings, 5, 1, 1.0, 2, 1)
@@ -153,35 +157,52 @@ class ALSSuite extends FunSuite with LocalSparkContext {
   }
 
   test("NNALS, rank 2") {
-    testALS(100, 200, 2, 15, 0.7, 0.4, false, false, false, -1, false)
+    testALS(100, 200, 2, 15, 0.7, 0.4, false, false, false, -1, -1, false)
   }
 
   /**
    * Test if we can correctly factorize R = U * P where U and P are of known rank.
    *
-   * @param users          number of users
-   * @param products       number of products
-   * @param features       number of features (rank of problem)
-   * @param iterations     number of iterations to run
-   * @param samplingRate   what fraction of the user-product pairs are known
+   * @param users number of users
+   * @param products number of products
+   * @param features number of features (rank of problem)
+   * @param iterations number of iterations to run
+   * @param samplingRate what fraction of the user-product pairs are known
    * @param matchThreshold max difference allowed to consider a predicted rating correct
-   * @param implicitPrefs  flag to test implicit feedback
-   * @param bulkPredict    flag to test bulk prediciton
+   * @param implicitPrefs flag to test implicit feedback
+   * @param bulkPredict flag to test bulk prediciton
    * @param negativeWeights whether the generated data can contain negative values
-   * @param numBlocks      number of blocks to partition users and products into
+   * @param numUserBlocks number of user blocks to partition users into
+   * @param numProductBlocks number of product blocks to partition products into
    * @param negativeFactors whether the generated user/product factors can have negative entries
    */
-  def testALS(users: Int, products: Int, features: Int, iterations: Int,
-    samplingRate: Double, matchThreshold: Double, implicitPrefs: Boolean = false,
-    bulkPredict: Boolean = false, negativeWeights: Boolean = false, numBlocks: Int = -1,
-    negativeFactors: Boolean = true)
-  {
+  def testALS(
+      users: Int,
+      products: Int,
+      features: Int,
+      iterations: Int,
+      samplingRate: Double,
+      matchThreshold: Double,
+      implicitPrefs: Boolean = false,
+      bulkPredict: Boolean = false,
+      negativeWeights: Boolean = false,
+      numUserBlocks: Int = -1,
+      numProductBlocks: Int = -1,
+      negativeFactors: Boolean = true) {
     val (sampledRatings, trueRatings, truePrefs) = ALSSuite.generateRatings(users, products,
       features, samplingRate, implicitPrefs, negativeWeights, negativeFactors)
 
-    val model = (new ALS().setBlocks(numBlocks).setRank(features).setIterations(iterations)
-          .setAlpha(1.0).setImplicitPrefs(implicitPrefs).setLambda(0.01).setSeed(0L)
-          .setNonnegative(!negativeFactors).run(sc.parallelize(sampledRatings)))
+    val model = new ALS()
+      .setUserBlocks(numUserBlocks)
+      .setProductBlocks(numProductBlocks)
+      .setRank(features)
+      .setIterations(iterations)
+      .setAlpha(1.0)
+      .setImplicitPrefs(implicitPrefs)
+      .setLambda(0.01)
+      .setSeed(0L)
+      .setNonnegative(!negativeFactors)
+      .run(sc.parallelize(sampledRatings))
 
     val predictedU = new DoubleMatrix(users, features)
     for ((u, vec) <- model.userFeatures.collect(); i <- 0 until features) {
@@ -208,8 +229,9 @@ class ALSSuite extends FunSuite with LocalSparkContext {
         val prediction = predictedRatings.get(u, p)
         val correct = trueRatings.get(u, p)
         if (math.abs(prediction - correct) > matchThreshold) {
-          fail("Model failed to predict (%d, %d): %f vs %f\ncorr: %s\npred: %s\nU: %s\n P: %s".format(
-            u, p, correct, prediction, trueRatings, predictedRatings, predictedU, predictedP))
+          fail(("Model failed to predict (%d, %d): %f vs %f\ncorr: %s\npred: %s\nU: %s\n P: %s")
+            .format(u, p, correct, prediction, trueRatings, predictedRatings, predictedU,
+              predictedP))
         }
       }
     } else {
diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala
index dd7efceb23c9..c80ab9a9f8e6 100644
--- a/project/MimaExcludes.scala
+++ b/project/MimaExcludes.scala
@@ -54,6 +54,14 @@ object MimaExcludes {
             ProblemFilters.exclude[MissingMethodProblem](
               "org.apache.spark.api.java.JavaDoubleRDD.countApproxDistinct$default$1")
           ) ++
+          Seq( // Ignore some private methods in ALS.
+            ProblemFilters.exclude[MissingMethodProblem](
+              "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$^dateFeatures"),
+            ProblemFilters.exclude[MissingMethodProblem]( // The only public constructor is the one without arguments.
+              "org.apache.spark.mllib.recommendation.ALS.this"),
+            ProblemFilters.exclude[MissingMethodProblem](
+              "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$<init>$default$7")
+          ) ++
           MimaBuild.excludeSparkClass("rdd.ZippedRDD") ++
           MimaBuild.excludeSparkClass("rdd.ZippedPartition") ++
           MimaBuild.excludeSparkClass("util.SerializableHyperLogLog")

From 508fd371d6dbb826fd8a00787d347235b549e189 Mon Sep 17 00:00:00 2001
From: Matei Zaharia <matei@databricks.com>
Date: Wed, 11 Jun 2014 20:45:29 -0700
Subject: [PATCH 404/810] [SPARK-2044] Pluggable interface for shuffles

This is a first cut at moving shuffle logic behind a pluggable interface, as described at https://issues.apache.org/jira/browse/SPARK-2044, to let us more easily experiment with new shuffle implementations. It moves the existing shuffle code to a class HashShuffleManager behind a general ShuffleManager interface.

Two things are still missing to make this complete:
* MapOutputTracker needs to be hidden behind the ShuffleManager interface; this will also require adding methods to ShuffleManager that will let the DAGScheduler interact with it as it does with the MapOutputTracker today
* The code to do map-sides and reduce-side combine in ShuffledRDD, PairRDDFunctions, etc needs to be moved into the ShuffleManager's readers and writers

However, some of these may also be done later after we merge the current interface.

Author: Matei Zaharia <matei@databricks.com>

Closes #1009 from mateiz/pluggable-shuffle and squashes the following commits:

7a09862 [Matei Zaharia] review comments
be33d3f [Matei Zaharia] review comments
1513d4e [Matei Zaharia] Add ASF header
ac56831 [Matei Zaharia] Bug fix and better error message
4f681ba [Matei Zaharia] Move write part of ShuffleMapTask to ShuffleManager
f6f011d [Matei Zaharia] Move hash shuffle reader behind ShuffleManager interface
55c7717 [Matei Zaharia] Changed RDD code to use ShuffleReader
75cc044 [Matei Zaharia] Partial work to move hash shuffle in
---
 .../org/apache/spark/ContextCleaner.scala     |   2 +-
 .../scala/org/apache/spark/Dependency.scala   |  12 +-
 .../scala/org/apache/spark/SparkEnv.scala     |  28 +++--
 .../org/apache/spark/rdd/CoGroupedRDD.scala   |  22 ++--
 .../org/apache/spark/rdd/ShuffledRDD.scala    |  12 +-
 .../org/apache/spark/rdd/SubtractedRDD.scala  |  17 +--
 .../apache/spark/scheduler/DAGScheduler.scala |  12 +-
 .../spark/scheduler/ShuffleMapTask.scala      |  73 +++---------
 .../org/apache/spark/scheduler/Stage.scala    |   2 +-
 .../spark/scheduler/TaskResultGetter.scala    |   3 +-
 .../apache/spark/serializer/Serializer.scala  |   4 +
 .../BaseShuffleHandle.scala}                  |  26 ++--
 .../apache/spark/shuffle/ShuffleHandle.scala  |  25 ++++
 .../apache/spark/shuffle/ShuffleManager.scala |  57 +++++++++
 .../apache/spark/shuffle/ShuffleReader.scala  |  29 +++++
 .../apache/spark/shuffle/ShuffleWriter.scala  |  31 +++++
 .../hash}/BlockStoreShuffleFetcher.scala      |   9 +-
 .../shuffle/hash/HashShuffleManager.scala     |  60 ++++++++++
 .../shuffle/hash/HashShuffleReader.scala      |  42 +++++++
 .../shuffle/hash/HashShuffleWriter.scala      | 111 ++++++++++++++++++
 .../apache/spark/ContextCleanerSuite.scala    |   6 +-
 .../scala/org/apache/spark/ShuffleSuite.scala |   6 +-
 22 files changed, 459 insertions(+), 130 deletions(-)
 rename core/src/main/scala/org/apache/spark/{ShuffleFetcher.scala => shuffle/BaseShuffleHandle.scala} (66%)
 create mode 100644 core/src/main/scala/org/apache/spark/shuffle/ShuffleHandle.scala
 create mode 100644 core/src/main/scala/org/apache/spark/shuffle/ShuffleManager.scala
 create mode 100644 core/src/main/scala/org/apache/spark/shuffle/ShuffleReader.scala
 create mode 100644 core/src/main/scala/org/apache/spark/shuffle/ShuffleWriter.scala
 rename core/src/main/scala/org/apache/spark/{ => shuffle/hash}/BlockStoreShuffleFetcher.scala (96%)
 create mode 100644 core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleManager.scala
 create mode 100644 core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleReader.scala
 create mode 100644 core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleWriter.scala

diff --git a/core/src/main/scala/org/apache/spark/ContextCleaner.scala b/core/src/main/scala/org/apache/spark/ContextCleaner.scala
index e2d2250982da..bf3c3a6ceb5e 100644
--- a/core/src/main/scala/org/apache/spark/ContextCleaner.scala
+++ b/core/src/main/scala/org/apache/spark/ContextCleaner.scala
@@ -96,7 +96,7 @@ private[spark] class ContextCleaner(sc: SparkContext) extends Logging {
   }
 
   /** Register a ShuffleDependency for cleanup when it is garbage collected. */
-  def registerShuffleForCleanup(shuffleDependency: ShuffleDependency[_, _]) {
+  def registerShuffleForCleanup(shuffleDependency: ShuffleDependency[_, _, _]) {
     registerForCleanup(shuffleDependency, CleanShuffle(shuffleDependency.shuffleId))
   }
 
diff --git a/core/src/main/scala/org/apache/spark/Dependency.scala b/core/src/main/scala/org/apache/spark/Dependency.scala
index 2c31cc20211f..c8c194a111aa 100644
--- a/core/src/main/scala/org/apache/spark/Dependency.scala
+++ b/core/src/main/scala/org/apache/spark/Dependency.scala
@@ -20,6 +20,7 @@ package org.apache.spark
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.RDD
 import org.apache.spark.serializer.Serializer
+import org.apache.spark.shuffle.ShuffleHandle
 
 /**
  * :: DeveloperApi ::
@@ -50,19 +51,24 @@ abstract class NarrowDependency[T](rdd: RDD[T]) extends Dependency(rdd) {
  * Represents a dependency on the output of a shuffle stage.
  * @param rdd the parent RDD
  * @param partitioner partitioner used to partition the shuffle output
- * @param serializer [[org.apache.spark.serializer.Serializer Serializer]] to use. If set to null,
+ * @param serializer [[org.apache.spark.serializer.Serializer Serializer]] to use. If set to None,
  *                   the default serializer, as specified by `spark.serializer` config option, will
  *                   be used.
  */
 @DeveloperApi
-class ShuffleDependency[K, V](
+class ShuffleDependency[K, V, C](
     @transient rdd: RDD[_ <: Product2[K, V]],
     val partitioner: Partitioner,
-    val serializer: Serializer = null)
+    val serializer: Option[Serializer] = None,
+    val keyOrdering: Option[Ordering[K]] = None,
+    val aggregator: Option[Aggregator[K, V, C]] = None)
   extends Dependency(rdd.asInstanceOf[RDD[Product2[K, V]]]) {
 
   val shuffleId: Int = rdd.context.newShuffleId()
 
+  val shuffleHandle: ShuffleHandle = rdd.context.env.shuffleManager.registerShuffle(
+    shuffleId, rdd.partitions.size, this)
+
   rdd.sparkContext.cleaner.foreach(_.registerShuffleForCleanup(this))
 }
 
diff --git a/core/src/main/scala/org/apache/spark/SparkEnv.scala b/core/src/main/scala/org/apache/spark/SparkEnv.scala
index 720151a6b0f8..8dfa8cc4b5b3 100644
--- a/core/src/main/scala/org/apache/spark/SparkEnv.scala
+++ b/core/src/main/scala/org/apache/spark/SparkEnv.scala
@@ -34,6 +34,7 @@ import org.apache.spark.metrics.MetricsSystem
 import org.apache.spark.network.ConnectionManager
 import org.apache.spark.scheduler.LiveListenerBus
 import org.apache.spark.serializer.Serializer
+import org.apache.spark.shuffle.ShuffleManager
 import org.apache.spark.storage._
 import org.apache.spark.util.{AkkaUtils, Utils}
 
@@ -56,7 +57,7 @@ class SparkEnv (
     val closureSerializer: Serializer,
     val cacheManager: CacheManager,
     val mapOutputTracker: MapOutputTracker,
-    val shuffleFetcher: ShuffleFetcher,
+    val shuffleManager: ShuffleManager,
     val broadcastManager: BroadcastManager,
     val blockManager: BlockManager,
     val connectionManager: ConnectionManager,
@@ -80,7 +81,7 @@ class SparkEnv (
     pythonWorkers.foreach { case(key, worker) => worker.stop() }
     httpFileServer.stop()
     mapOutputTracker.stop()
-    shuffleFetcher.stop()
+    shuffleManager.stop()
     broadcastManager.stop()
     blockManager.stop()
     blockManager.master.stop()
@@ -163,13 +164,20 @@ object SparkEnv extends Logging {
     def instantiateClass[T](propertyName: String, defaultClassName: String): T = {
       val name = conf.get(propertyName,  defaultClassName)
       val cls = Class.forName(name, true, Utils.getContextOrSparkClassLoader)
-      // First try with the constructor that takes SparkConf. If we can't find one,
-      // use a no-arg constructor instead.
+      // Look for a constructor taking a SparkConf and a boolean isDriver, then one taking just
+      // SparkConf, then one taking no arguments
       try {
-        cls.getConstructor(classOf[SparkConf]).newInstance(conf).asInstanceOf[T]
+        cls.getConstructor(classOf[SparkConf], java.lang.Boolean.TYPE)
+          .newInstance(conf, new java.lang.Boolean(isDriver))
+          .asInstanceOf[T]
       } catch {
         case _: NoSuchMethodException =>
-            cls.getConstructor().newInstance().asInstanceOf[T]
+          try {
+            cls.getConstructor(classOf[SparkConf]).newInstance(conf).asInstanceOf[T]
+          } catch {
+            case _: NoSuchMethodException =>
+              cls.getConstructor().newInstance().asInstanceOf[T]
+          }
       }
     }
 
@@ -219,9 +227,6 @@ object SparkEnv extends Logging {
 
     val cacheManager = new CacheManager(blockManager)
 
-    val shuffleFetcher = instantiateClass[ShuffleFetcher](
-      "spark.shuffle.fetcher", "org.apache.spark.BlockStoreShuffleFetcher")
-
     val httpFileServer = new HttpFileServer(securityManager)
     httpFileServer.initialize()
     conf.set("spark.fileserver.uri",  httpFileServer.serverUri)
@@ -242,6 +247,9 @@ object SparkEnv extends Logging {
       "."
     }
 
+    val shuffleManager = instantiateClass[ShuffleManager](
+      "spark.shuffle.manager", "org.apache.spark.shuffle.hash.HashShuffleManager")
+
     // Warn about deprecated spark.cache.class property
     if (conf.contains("spark.cache.class")) {
       logWarning("The spark.cache.class property is no longer being used! Specify storage " +
@@ -255,7 +263,7 @@ object SparkEnv extends Logging {
       closureSerializer,
       cacheManager,
       mapOutputTracker,
-      shuffleFetcher,
+      shuffleManager,
       broadcastManager,
       blockManager,
       connectionManager,
diff --git a/core/src/main/scala/org/apache/spark/rdd/CoGroupedRDD.scala b/core/src/main/scala/org/apache/spark/rdd/CoGroupedRDD.scala
index 9ff76892aed3..5951865e56c9 100644
--- a/core/src/main/scala/org/apache/spark/rdd/CoGroupedRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/CoGroupedRDD.scala
@@ -27,6 +27,7 @@ import org.apache.spark.{Dependency, OneToOneDependency, ShuffleDependency}
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.util.collection.{ExternalAppendOnlyMap, AppendOnlyMap}
 import org.apache.spark.serializer.Serializer
+import org.apache.spark.shuffle.ShuffleHandle
 
 private[spark] sealed trait CoGroupSplitDep extends Serializable
 
@@ -44,7 +45,7 @@ private[spark] case class NarrowCoGroupSplitDep(
   }
 }
 
-private[spark] case class ShuffleCoGroupSplitDep(shuffleId: Int) extends CoGroupSplitDep
+private[spark] case class ShuffleCoGroupSplitDep(handle: ShuffleHandle) extends CoGroupSplitDep
 
 private[spark] class CoGroupPartition(idx: Int, val deps: Array[CoGroupSplitDep])
   extends Partition with Serializable {
@@ -74,10 +75,11 @@ class CoGroupedRDD[K](@transient var rdds: Seq[RDD[_ <: Product2[K, _]]], part:
   private type CoGroupValue = (Any, Int)  // Int is dependency number
   private type CoGroupCombiner = Seq[CoGroup]
 
-  private var serializer: Serializer = null
+  private var serializer: Option[Serializer] = None
 
+  /** Set a serializer for this RDD's shuffle, or null to use the default (spark.serializer) */
   def setSerializer(serializer: Serializer): CoGroupedRDD[K] = {
-    this.serializer = serializer
+    this.serializer = Option(serializer)
     this
   }
 
@@ -88,7 +90,7 @@ class CoGroupedRDD[K](@transient var rdds: Seq[RDD[_ <: Product2[K, _]]], part:
         new OneToOneDependency(rdd)
       } else {
         logDebug("Adding shuffle dependency with " + rdd)
-        new ShuffleDependency[Any, Any](rdd, part, serializer)
+        new ShuffleDependency[K, Any, CoGroupCombiner](rdd, part, serializer)
       }
     }
   }
@@ -100,8 +102,8 @@ class CoGroupedRDD[K](@transient var rdds: Seq[RDD[_ <: Product2[K, _]]], part:
       array(i) = new CoGroupPartition(i, rdds.zipWithIndex.map { case (rdd, j) =>
         // Assume each RDD contributed a single dependency, and get it
         dependencies(j) match {
-          case s: ShuffleDependency[_, _] =>
-            new ShuffleCoGroupSplitDep(s.shuffleId)
+          case s: ShuffleDependency[_, _, _] =>
+            new ShuffleCoGroupSplitDep(s.shuffleHandle)
           case _ =>
             new NarrowCoGroupSplitDep(rdd, i, rdd.partitions(i))
         }
@@ -126,11 +128,11 @@ class CoGroupedRDD[K](@transient var rdds: Seq[RDD[_ <: Product2[K, _]]], part:
         val it = rdd.iterator(itsSplit, context).asInstanceOf[Iterator[Product2[K, Any]]]
         rddIterators += ((it, depNum))
 
-      case ShuffleCoGroupSplitDep(shuffleId) =>
+      case ShuffleCoGroupSplitDep(handle) =>
         // Read map outputs of shuffle
-        val fetcher = SparkEnv.get.shuffleFetcher
-        val ser = Serializer.getSerializer(serializer)
-        val it = fetcher.fetch[Product2[K, Any]](shuffleId, split.index, context, ser)
+        val it = SparkEnv.get.shuffleManager
+          .getReader(handle, split.index, split.index + 1, context)
+          .read()
         rddIterators += ((it, depNum))
     }
 
diff --git a/core/src/main/scala/org/apache/spark/rdd/ShuffledRDD.scala b/core/src/main/scala/org/apache/spark/rdd/ShuffledRDD.scala
index 802b0bdfb2d5..bb108ef163c5 100644
--- a/core/src/main/scala/org/apache/spark/rdd/ShuffledRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/ShuffledRDD.scala
@@ -42,10 +42,11 @@ class ShuffledRDD[K, V, P <: Product2[K, V] : ClassTag](
     part: Partitioner)
   extends RDD[P](prev.context, Nil) {
 
-  private var serializer: Serializer = null
+  private var serializer: Option[Serializer] = None
 
+  /** Set a serializer for this RDD's shuffle, or null to use the default (spark.serializer) */
   def setSerializer(serializer: Serializer): ShuffledRDD[K, V, P] = {
-    this.serializer = serializer
+    this.serializer = Option(serializer)
     this
   }
 
@@ -60,9 +61,10 @@ class ShuffledRDD[K, V, P <: Product2[K, V] : ClassTag](
   }
 
   override def compute(split: Partition, context: TaskContext): Iterator[P] = {
-    val shuffledId = dependencies.head.asInstanceOf[ShuffleDependency[K, V]].shuffleId
-    val ser = Serializer.getSerializer(serializer)
-    SparkEnv.get.shuffleFetcher.fetch[P](shuffledId, split.index, context, ser)
+    val dep = dependencies.head.asInstanceOf[ShuffleDependency[K, V, V]]
+    SparkEnv.get.shuffleManager.getReader(dep.shuffleHandle, split.index, split.index + 1, context)
+      .read()
+      .asInstanceOf[Iterator[P]]
   }
 
   override def clearDependencies() {
diff --git a/core/src/main/scala/org/apache/spark/rdd/SubtractedRDD.scala b/core/src/main/scala/org/apache/spark/rdd/SubtractedRDD.scala
index 9a09c05bbc95..ed24ea22a661 100644
--- a/core/src/main/scala/org/apache/spark/rdd/SubtractedRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/SubtractedRDD.scala
@@ -54,10 +54,11 @@ private[spark] class SubtractedRDD[K: ClassTag, V: ClassTag, W: ClassTag](
     part: Partitioner)
   extends RDD[(K, V)](rdd1.context, Nil) {
 
-  private var serializer: Serializer = null
+  private var serializer: Option[Serializer] = None
 
+  /** Set a serializer for this RDD's shuffle, or null to use the default (spark.serializer) */
   def setSerializer(serializer: Serializer): SubtractedRDD[K, V, W] = {
-    this.serializer = serializer
+    this.serializer = Option(serializer)
     this
   }
 
@@ -79,8 +80,8 @@ private[spark] class SubtractedRDD[K: ClassTag, V: ClassTag, W: ClassTag](
       // Each CoGroupPartition will depend on rdd1 and rdd2
       array(i) = new CoGroupPartition(i, Seq(rdd1, rdd2).zipWithIndex.map { case (rdd, j) =>
         dependencies(j) match {
-          case s: ShuffleDependency[_, _] =>
-            new ShuffleCoGroupSplitDep(s.shuffleId)
+          case s: ShuffleDependency[_, _, _] =>
+            new ShuffleCoGroupSplitDep(s.shuffleHandle)
           case _ =>
             new NarrowCoGroupSplitDep(rdd, i, rdd.partitions(i))
         }
@@ -93,7 +94,6 @@ private[spark] class SubtractedRDD[K: ClassTag, V: ClassTag, W: ClassTag](
 
   override def compute(p: Partition, context: TaskContext): Iterator[(K, V)] = {
     val partition = p.asInstanceOf[CoGroupPartition]
-    val ser = Serializer.getSerializer(serializer)
     val map = new JHashMap[K, ArrayBuffer[V]]
     def getSeq(k: K): ArrayBuffer[V] = {
       val seq = map.get(k)
@@ -109,9 +109,10 @@ private[spark] class SubtractedRDD[K: ClassTag, V: ClassTag, W: ClassTag](
       case NarrowCoGroupSplitDep(rdd, _, itsSplit) =>
         rdd.iterator(itsSplit, context).asInstanceOf[Iterator[Product2[K, V]]].foreach(op)
 
-      case ShuffleCoGroupSplitDep(shuffleId) =>
-        val iter = SparkEnv.get.shuffleFetcher.fetch[Product2[K, V]](shuffleId, partition.index,
-          context, ser)
+      case ShuffleCoGroupSplitDep(handle) =>
+        val iter = SparkEnv.get.shuffleManager
+          .getReader(handle, partition.index, partition.index + 1, context)
+          .read()
         iter.foreach(op)
     }
     // the first dep is rdd1; add all values to the map
diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
index e09a4221e831..3c85b5a2ae77 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
@@ -190,7 +190,7 @@ class DAGScheduler(
    * The jobId value passed in will be used if the stage doesn't already exist with
    * a lower jobId (jobId always increases across jobs.)
    */
-  private def getShuffleMapStage(shuffleDep: ShuffleDependency[_,_], jobId: Int): Stage = {
+  private def getShuffleMapStage(shuffleDep: ShuffleDependency[_, _, _], jobId: Int): Stage = {
     shuffleToMapStage.get(shuffleDep.shuffleId) match {
       case Some(stage) => stage
       case None =>
@@ -210,7 +210,7 @@ class DAGScheduler(
   private def newStage(
       rdd: RDD[_],
       numTasks: Int,
-      shuffleDep: Option[ShuffleDependency[_,_]],
+      shuffleDep: Option[ShuffleDependency[_, _, _]],
       jobId: Int,
       callSite: Option[String] = None)
     : Stage =
@@ -233,7 +233,7 @@ class DAGScheduler(
   private def newOrUsedStage(
       rdd: RDD[_],
       numTasks: Int,
-      shuffleDep: ShuffleDependency[_,_],
+      shuffleDep: ShuffleDependency[_, _, _],
       jobId: Int,
       callSite: Option[String] = None)
     : Stage =
@@ -269,7 +269,7 @@ class DAGScheduler(
         // we can't do it in its constructor because # of partitions is unknown
         for (dep <- r.dependencies) {
           dep match {
-            case shufDep: ShuffleDependency[_,_] =>
+            case shufDep: ShuffleDependency[_, _, _] =>
               parents += getShuffleMapStage(shufDep, jobId)
             case _ =>
               visit(dep.rdd)
@@ -290,7 +290,7 @@ class DAGScheduler(
         if (getCacheLocs(rdd).contains(Nil)) {
           for (dep <- rdd.dependencies) {
             dep match {
-              case shufDep: ShuffleDependency[_,_] =>
+              case shufDep: ShuffleDependency[_, _, _] =>
                 val mapStage = getShuffleMapStage(shufDep, stage.jobId)
                 if (!mapStage.isAvailable) {
                   missing += mapStage
@@ -1088,7 +1088,7 @@ class DAGScheduler(
         visitedRdds += rdd
         for (dep <- rdd.dependencies) {
           dep match {
-            case shufDep: ShuffleDependency[_,_] =>
+            case shufDep: ShuffleDependency[_, _, _] =>
               val mapStage = getShuffleMapStage(shufDep, stage.jobId)
               if (!mapStage.isAvailable) {
                 visitedStages += mapStage
diff --git a/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala b/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala
index ed0f56f1abdf..0098b5a59d1a 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala
@@ -29,6 +29,7 @@ import org.apache.spark.executor.ShuffleWriteMetrics
 import org.apache.spark.rdd.{RDD, RDDCheckpointData}
 import org.apache.spark.serializer.Serializer
 import org.apache.spark.storage._
+import org.apache.spark.shuffle.ShuffleWriter
 
 private[spark] object ShuffleMapTask {
 
@@ -37,7 +38,7 @@ private[spark] object ShuffleMapTask {
   // expensive on the master node if it needs to launch thousands of tasks.
   private val serializedInfoCache = new HashMap[Int, Array[Byte]]
 
-  def serializeInfo(stageId: Int, rdd: RDD[_], dep: ShuffleDependency[_,_]): Array[Byte] = {
+  def serializeInfo(stageId: Int, rdd: RDD[_], dep: ShuffleDependency[_, _, _]): Array[Byte] = {
     synchronized {
       val old = serializedInfoCache.get(stageId).orNull
       if (old != null) {
@@ -56,12 +57,12 @@ private[spark] object ShuffleMapTask {
     }
   }
 
-  def deserializeInfo(stageId: Int, bytes: Array[Byte]): (RDD[_], ShuffleDependency[_,_]) = {
+  def deserializeInfo(stageId: Int, bytes: Array[Byte]): (RDD[_], ShuffleDependency[_, _, _]) = {
     val in = new GZIPInputStream(new ByteArrayInputStream(bytes))
     val ser = SparkEnv.get.closureSerializer.newInstance()
     val objIn = ser.deserializeStream(in)
     val rdd = objIn.readObject().asInstanceOf[RDD[_]]
-    val dep = objIn.readObject().asInstanceOf[ShuffleDependency[_,_]]
+    val dep = objIn.readObject().asInstanceOf[ShuffleDependency[_, _, _]]
     (rdd, dep)
   }
 
@@ -99,7 +100,7 @@ private[spark] object ShuffleMapTask {
 private[spark] class ShuffleMapTask(
     stageId: Int,
     var rdd: RDD[_],
-    var dep: ShuffleDependency[_,_],
+    var dep: ShuffleDependency[_, _, _],
     _partitionId: Int,
     @transient private var locs: Seq[TaskLocation])
   extends Task[MapStatus](stageId, _partitionId)
@@ -141,66 +142,22 @@ private[spark] class ShuffleMapTask(
   }
 
   override def runTask(context: TaskContext): MapStatus = {
-    val numOutputSplits = dep.partitioner.numPartitions
     metrics = Some(context.taskMetrics)
-
-    val blockManager = SparkEnv.get.blockManager
-    val shuffleBlockManager = blockManager.shuffleBlockManager
-    var shuffle: ShuffleWriterGroup = null
-    var success = false
-
+    var writer: ShuffleWriter[Any, Any] = null
     try {
-      // Obtain all the block writers for shuffle blocks.
-      val ser = Serializer.getSerializer(dep.serializer)
-      shuffle = shuffleBlockManager.forMapTask(dep.shuffleId, partitionId, numOutputSplits, ser)
-
-      // Write the map output to its associated buckets.
+      val manager = SparkEnv.get.shuffleManager
+      writer = manager.getWriter[Any, Any](dep.shuffleHandle, partitionId, context)
       for (elem <- rdd.iterator(split, context)) {
-        val pair = elem.asInstanceOf[Product2[Any, Any]]
-        val bucketId = dep.partitioner.getPartition(pair._1)
-        shuffle.writers(bucketId).write(pair)
-      }
-
-      // Commit the writes. Get the size of each bucket block (total block size).
-      var totalBytes = 0L
-      var totalTime = 0L
-      val compressedSizes: Array[Byte] = shuffle.writers.map { writer: BlockObjectWriter =>
-        writer.commit()
-        writer.close()
-        val size = writer.fileSegment().length
-        totalBytes += size
-        totalTime += writer.timeWriting()
-        MapOutputTracker.compressSize(size)
+        writer.write(elem.asInstanceOf[Product2[Any, Any]])
       }
-
-      // Update shuffle metrics.
-      val shuffleMetrics = new ShuffleWriteMetrics
-      shuffleMetrics.shuffleBytesWritten = totalBytes
-      shuffleMetrics.shuffleWriteTime = totalTime
-      metrics.get.shuffleWriteMetrics = Some(shuffleMetrics)
-
-      success = true
-      new MapStatus(blockManager.blockManagerId, compressedSizes)
-    } catch { case e: Exception =>
-      // If there is an exception from running the task, revert the partial writes
-      // and throw the exception upstream to Spark.
-      if (shuffle != null && shuffle.writers != null) {
-        for (writer <- shuffle.writers) {
-          writer.revertPartialWrites()
-          writer.close()
+      return writer.stop(success = true).get
+    } catch {
+      case e: Exception =>
+        if (writer != null) {
+          writer.stop(success = false)
         }
-      }
-      throw e
+        throw e
     } finally {
-      // Release the writers back to the shuffle block manager.
-      if (shuffle != null && shuffle.writers != null) {
-        try {
-          shuffle.releaseWriters(success)
-        } catch {
-          case e: Exception => logError("Failed to release shuffle writers", e)
-        }
-      }
-      // Execute the callbacks on task completion.
       context.executeOnCompleteCallbacks()
     }
   }
diff --git a/core/src/main/scala/org/apache/spark/scheduler/Stage.scala b/core/src/main/scala/org/apache/spark/scheduler/Stage.scala
index 5c1fc30e4a55..3bf9713f728c 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/Stage.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/Stage.scala
@@ -40,7 +40,7 @@ private[spark] class Stage(
     val id: Int,
     val rdd: RDD[_],
     val numTasks: Int,
-    val shuffleDep: Option[ShuffleDependency[_,_]],  // Output shuffle if stage is a map stage
+    val shuffleDep: Option[ShuffleDependency[_, _, _]],  // Output shuffle if stage is a map stage
     val parents: List[Stage],
     val jobId: Int,
     callSite: Option[String])
diff --git a/core/src/main/scala/org/apache/spark/scheduler/TaskResultGetter.scala b/core/src/main/scala/org/apache/spark/scheduler/TaskResultGetter.scala
index 99d305b36a95..df59f444b7a0 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/TaskResultGetter.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/TaskResultGetter.scala
@@ -71,7 +71,8 @@ private[spark] class TaskResultGetter(sparkEnv: SparkEnv, scheduler: TaskSchedul
             val loader = Thread.currentThread.getContextClassLoader
             taskSetManager.abort("ClassNotFound with classloader: " + loader)
           case ex: Exception =>
-            taskSetManager.abort("Exception while deserializing and fetching task: %s".format(ex))
+            logError("Exception while getting task result", ex)
+            taskSetManager.abort("Exception while getting task result: %s".format(ex))
         }
       }
     })
diff --git a/core/src/main/scala/org/apache/spark/serializer/Serializer.scala b/core/src/main/scala/org/apache/spark/serializer/Serializer.scala
index ee26970a3d87..f2f5cea469c6 100644
--- a/core/src/main/scala/org/apache/spark/serializer/Serializer.scala
+++ b/core/src/main/scala/org/apache/spark/serializer/Serializer.scala
@@ -52,6 +52,10 @@ object Serializer {
   def getSerializer(serializer: Serializer): Serializer = {
     if (serializer == null) SparkEnv.get.serializer else serializer
   }
+
+  def getSerializer(serializer: Option[Serializer]): Serializer = {
+    serializer.getOrElse(SparkEnv.get.serializer)
+  }
 }
 
 
diff --git a/core/src/main/scala/org/apache/spark/ShuffleFetcher.scala b/core/src/main/scala/org/apache/spark/shuffle/BaseShuffleHandle.scala
similarity index 66%
rename from core/src/main/scala/org/apache/spark/ShuffleFetcher.scala
rename to core/src/main/scala/org/apache/spark/shuffle/BaseShuffleHandle.scala
index a4f69b6b22b2..b36c457d6d51 100644
--- a/core/src/main/scala/org/apache/spark/ShuffleFetcher.scala
+++ b/core/src/main/scala/org/apache/spark/shuffle/BaseShuffleHandle.scala
@@ -15,22 +15,16 @@
  * limitations under the License.
  */
 
-package org.apache.spark
+package org.apache.spark.shuffle
 
+import org.apache.spark.{ShuffleDependency, Aggregator, Partitioner}
 import org.apache.spark.serializer.Serializer
 
-private[spark] abstract class ShuffleFetcher {
-
-  /**
-   * Fetch the shuffle outputs for a given ShuffleDependency.
-   * @return An iterator over the elements of the fetched shuffle outputs.
-   */
-  def fetch[T](
-      shuffleId: Int,
-      reduceId: Int,
-      context: TaskContext,
-      serializer: Serializer = SparkEnv.get.serializer): Iterator[T]
-
-  /** Stop the fetcher */
-  def stop() {}
-}
+/**
+ * A basic ShuffleHandle implementation that just captures registerShuffle's parameters.
+ */
+private[spark] class BaseShuffleHandle[K, V, C](
+    shuffleId: Int,
+    val numMaps: Int,
+    val dependency: ShuffleDependency[K, V, C])
+  extends ShuffleHandle(shuffleId)
diff --git a/core/src/main/scala/org/apache/spark/shuffle/ShuffleHandle.scala b/core/src/main/scala/org/apache/spark/shuffle/ShuffleHandle.scala
new file mode 100644
index 000000000000..13c7115f88af
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/shuffle/ShuffleHandle.scala
@@ -0,0 +1,25 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.shuffle
+
+/**
+ * An opaque handle to a shuffle, used by a ShuffleManager to pass information about it to tasks.
+ *
+ * @param shuffleId ID of the shuffle
+ */
+private[spark] abstract class ShuffleHandle(val shuffleId: Int) extends Serializable {}
diff --git a/core/src/main/scala/org/apache/spark/shuffle/ShuffleManager.scala b/core/src/main/scala/org/apache/spark/shuffle/ShuffleManager.scala
new file mode 100644
index 000000000000..9c859b8b4a11
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/shuffle/ShuffleManager.scala
@@ -0,0 +1,57 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.shuffle
+
+import org.apache.spark.{TaskContext, ShuffleDependency}
+
+/**
+ * Pluggable interface for shuffle systems. A ShuffleManager is created in SparkEnv on both the
+ * driver and executors, based on the spark.shuffle.manager setting. The driver registers shuffles
+ * with it, and executors (or tasks running locally in the driver) can ask to read and write data.
+ *
+ * NOTE: this will be instantiated by SparkEnv so its constructor can take a SparkConf and
+ * boolean isDriver as parameters.
+ */
+private[spark] trait ShuffleManager {
+  /**
+   * Register a shuffle with the manager and obtain a handle for it to pass to tasks.
+   */
+  def registerShuffle[K, V, C](
+      shuffleId: Int,
+      numMaps: Int,
+      dependency: ShuffleDependency[K, V, C]): ShuffleHandle
+
+  /** Get a writer for a given partition. Called on executors by map tasks. */
+  def getWriter[K, V](handle: ShuffleHandle, mapId: Int, context: TaskContext): ShuffleWriter[K, V]
+
+  /**
+   * Get a reader for a range of reduce partitions (startPartition to endPartition-1, inclusive).
+   * Called on executors by reduce tasks.
+   */
+  def getReader[K, C](
+      handle: ShuffleHandle,
+      startPartition: Int,
+      endPartition: Int,
+      context: TaskContext): ShuffleReader[K, C]
+
+  /** Remove a shuffle's metadata from the ShuffleManager. */
+  def unregisterShuffle(shuffleId: Int)
+
+  /** Shut down this ShuffleManager. */
+  def stop(): Unit
+}
diff --git a/core/src/main/scala/org/apache/spark/shuffle/ShuffleReader.scala b/core/src/main/scala/org/apache/spark/shuffle/ShuffleReader.scala
new file mode 100644
index 000000000000..b30e366d0600
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/shuffle/ShuffleReader.scala
@@ -0,0 +1,29 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.shuffle
+
+/**
+ * Obtained inside a reduce task to read combined records from the mappers.
+ */
+private[spark] trait ShuffleReader[K, C] {
+  /** Read the combined key-values for this reduce task */
+  def read(): Iterator[Product2[K, C]]
+
+  /** Close this reader */
+  def stop(): Unit
+}
diff --git a/core/src/main/scala/org/apache/spark/shuffle/ShuffleWriter.scala b/core/src/main/scala/org/apache/spark/shuffle/ShuffleWriter.scala
new file mode 100644
index 000000000000..ead3ebd652ca
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/shuffle/ShuffleWriter.scala
@@ -0,0 +1,31 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.shuffle
+
+import org.apache.spark.scheduler.MapStatus
+
+/**
+ * Obtained inside a map task to write out records to the shuffle system.
+ */
+private[spark] trait ShuffleWriter[K, V] {
+  /** Write a record to this task's output */
+  def write(record: Product2[K, V]): Unit
+
+  /** Close this writer, passing along whether the map completed */
+  def stop(success: Boolean): Option[MapStatus]
+}
diff --git a/core/src/main/scala/org/apache/spark/BlockStoreShuffleFetcher.scala b/core/src/main/scala/org/apache/spark/shuffle/hash/BlockStoreShuffleFetcher.scala
similarity index 96%
rename from core/src/main/scala/org/apache/spark/BlockStoreShuffleFetcher.scala
rename to core/src/main/scala/org/apache/spark/shuffle/hash/BlockStoreShuffleFetcher.scala
index a67392441ed2..b05b6ea345df 100644
--- a/core/src/main/scala/org/apache/spark/BlockStoreShuffleFetcher.scala
+++ b/core/src/main/scala/org/apache/spark/shuffle/hash/BlockStoreShuffleFetcher.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark
+package org.apache.spark.shuffle.hash
 
 import scala.collection.mutable.ArrayBuffer
 import scala.collection.mutable.HashMap
@@ -24,17 +24,16 @@ import org.apache.spark.executor.ShuffleReadMetrics
 import org.apache.spark.serializer.Serializer
 import org.apache.spark.storage.{BlockId, BlockManagerId, ShuffleBlockId}
 import org.apache.spark.util.CompletionIterator
+import org.apache.spark._
 
-private[spark] class BlockStoreShuffleFetcher extends ShuffleFetcher with Logging {
-
-  override def fetch[T](
+private[hash] object BlockStoreShuffleFetcher extends Logging {
+  def fetch[T](
       shuffleId: Int,
       reduceId: Int,
       context: TaskContext,
       serializer: Serializer)
     : Iterator[T] =
   {
-
     logDebug("Fetching outputs for shuffle %d, reduce %d".format(shuffleId, reduceId))
     val blockManager = SparkEnv.get.blockManager
 
diff --git a/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleManager.scala b/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleManager.scala
new file mode 100644
index 000000000000..5b0940ecce29
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleManager.scala
@@ -0,0 +1,60 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.shuffle.hash
+
+import org.apache.spark._
+import org.apache.spark.shuffle._
+
+/**
+ * A ShuffleManager using hashing, that creates one output file per reduce partition on each
+ * mapper (possibly reusing these across waves of tasks).
+ */
+class HashShuffleManager(conf: SparkConf) extends ShuffleManager {
+  /* Register a shuffle with the manager and obtain a handle for it to pass to tasks. */
+  override def registerShuffle[K, V, C](
+      shuffleId: Int,
+      numMaps: Int,
+      dependency: ShuffleDependency[K, V, C]): ShuffleHandle = {
+    new BaseShuffleHandle(shuffleId, numMaps, dependency)
+  }
+
+  /**
+   * Get a reader for a range of reduce partitions (startPartition to endPartition-1, inclusive).
+   * Called on executors by reduce tasks.
+   */
+  override def getReader[K, C](
+      handle: ShuffleHandle,
+      startPartition: Int,
+      endPartition: Int,
+      context: TaskContext): ShuffleReader[K, C] = {
+    new HashShuffleReader(
+      handle.asInstanceOf[BaseShuffleHandle[K, _, C]], startPartition, endPartition, context)
+  }
+
+  /** Get a writer for a given partition. Called on executors by map tasks. */
+  override def getWriter[K, V](handle: ShuffleHandle, mapId: Int, context: TaskContext)
+      : ShuffleWriter[K, V] = {
+    new HashShuffleWriter(handle.asInstanceOf[BaseShuffleHandle[K, V, _]], mapId, context)
+  }
+
+  /** Remove a shuffle's metadata from the ShuffleManager. */
+  override def unregisterShuffle(shuffleId: Int): Unit = {}
+
+  /** Shut down this ShuffleManager. */
+  override def stop(): Unit = {}
+}
diff --git a/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleReader.scala b/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleReader.scala
new file mode 100644
index 000000000000..f6a790309a58
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleReader.scala
@@ -0,0 +1,42 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.shuffle.hash
+
+import org.apache.spark.serializer.Serializer
+import org.apache.spark.shuffle.{BaseShuffleHandle, ShuffleReader}
+import org.apache.spark.TaskContext
+
+class HashShuffleReader[K, C](
+    handle: BaseShuffleHandle[K, _, C],
+    startPartition: Int,
+    endPartition: Int,
+    context: TaskContext)
+  extends ShuffleReader[K, C]
+{
+  require(endPartition == startPartition + 1,
+    "Hash shuffle currently only supports fetching one partition")
+
+  /** Read the combined key-values for this reduce task */
+  override def read(): Iterator[Product2[K, C]] = {
+    BlockStoreShuffleFetcher.fetch(handle.shuffleId, startPartition, context,
+      Serializer.getSerializer(handle.dependency.serializer))
+  }
+
+  /** Close this reader */
+  override def stop(): Unit = ???
+}
diff --git a/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleWriter.scala b/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleWriter.scala
new file mode 100644
index 000000000000..4c6749098c11
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleWriter.scala
@@ -0,0 +1,111 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.shuffle.hash
+
+import org.apache.spark.shuffle.{BaseShuffleHandle, ShuffleWriter}
+import org.apache.spark.{Logging, MapOutputTracker, SparkEnv, TaskContext}
+import org.apache.spark.storage.{BlockObjectWriter}
+import org.apache.spark.serializer.Serializer
+import org.apache.spark.executor.ShuffleWriteMetrics
+import org.apache.spark.scheduler.MapStatus
+
+class HashShuffleWriter[K, V](
+    handle: BaseShuffleHandle[K, V, _],
+    mapId: Int,
+    context: TaskContext)
+  extends ShuffleWriter[K, V] with Logging {
+
+  private val dep = handle.dependency
+  private val numOutputSplits = dep.partitioner.numPartitions
+  private val metrics = context.taskMetrics
+  private var stopping = false
+
+  private val blockManager = SparkEnv.get.blockManager
+  private val shuffleBlockManager = blockManager.shuffleBlockManager
+  private val ser = Serializer.getSerializer(dep.serializer.getOrElse(null))
+  private val shuffle = shuffleBlockManager.forMapTask(dep.shuffleId, mapId, numOutputSplits, ser)
+
+  /** Write a record to this task's output */
+  override def write(record: Product2[K, V]): Unit = {
+    val pair = record.asInstanceOf[Product2[Any, Any]]
+    val bucketId = dep.partitioner.getPartition(pair._1)
+    shuffle.writers(bucketId).write(pair)
+  }
+
+  /** Close this writer, passing along whether the map completed */
+  override def stop(success: Boolean): Option[MapStatus] = {
+    try {
+      if (stopping) {
+        return None
+      }
+      stopping = true
+      if (success) {
+        try {
+          return Some(commitWritesAndBuildStatus())
+        } catch {
+          case e: Exception =>
+            revertWrites()
+            throw e
+        }
+      } else {
+        revertWrites()
+        return None
+      }
+    } finally {
+      // Release the writers back to the shuffle block manager.
+      if (shuffle != null && shuffle.writers != null) {
+        try {
+          shuffle.releaseWriters(success)
+        } catch {
+          case e: Exception => logError("Failed to release shuffle writers", e)
+        }
+      }
+    }
+  }
+
+  private def commitWritesAndBuildStatus(): MapStatus = {
+    // Commit the writes. Get the size of each bucket block (total block size).
+    var totalBytes = 0L
+    var totalTime = 0L
+    val compressedSizes = shuffle.writers.map { writer: BlockObjectWriter =>
+      writer.commit()
+      writer.close()
+      val size = writer.fileSegment().length
+      totalBytes += size
+      totalTime += writer.timeWriting()
+      MapOutputTracker.compressSize(size)
+    }
+
+    // Update shuffle metrics.
+    val shuffleMetrics = new ShuffleWriteMetrics
+    shuffleMetrics.shuffleBytesWritten = totalBytes
+    shuffleMetrics.shuffleWriteTime = totalTime
+    metrics.shuffleWriteMetrics = Some(shuffleMetrics)
+
+    new MapStatus(blockManager.blockManagerId, compressedSizes)
+  }
+
+  private def revertWrites(): Unit = {
+    if (shuffle != null && shuffle.writers != null) {
+      for (writer <- shuffle.writers) {
+        writer.revertPartialWrites()
+        writer.close()
+      }
+    }
+  }
+}
diff --git a/core/src/test/scala/org/apache/spark/ContextCleanerSuite.scala b/core/src/test/scala/org/apache/spark/ContextCleanerSuite.scala
index dc2db66df60e..13b415cccb64 100644
--- a/core/src/test/scala/org/apache/spark/ContextCleanerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/ContextCleanerSuite.scala
@@ -201,7 +201,7 @@ class ContextCleanerSuite extends FunSuite with BeforeAndAfter with LocalSparkCo
   def newPairRDD = newRDD.map(_ -> 1)
   def newShuffleRDD = newPairRDD.reduceByKey(_ + _)
   def newBroadcast = sc.broadcast(1 to 100)
-  def newRDDWithShuffleDependencies: (RDD[_], Seq[ShuffleDependency[_, _]]) = {
+  def newRDDWithShuffleDependencies: (RDD[_], Seq[ShuffleDependency[_, _, _]]) = {
     def getAllDependencies(rdd: RDD[_]): Seq[Dependency[_]] = {
       rdd.dependencies ++ rdd.dependencies.flatMap { dep =>
         getAllDependencies(dep.rdd)
@@ -211,8 +211,8 @@ class ContextCleanerSuite extends FunSuite with BeforeAndAfter with LocalSparkCo
 
     // Get all the shuffle dependencies
     val shuffleDeps = getAllDependencies(rdd)
-      .filter(_.isInstanceOf[ShuffleDependency[_, _]])
-      .map(_.asInstanceOf[ShuffleDependency[_, _]])
+      .filter(_.isInstanceOf[ShuffleDependency[_, _, _]])
+      .map(_.asInstanceOf[ShuffleDependency[_, _, _]])
     (rdd, shuffleDeps)
   }
 
diff --git a/core/src/test/scala/org/apache/spark/ShuffleSuite.scala b/core/src/test/scala/org/apache/spark/ShuffleSuite.scala
index 7b0607dd3ed2..47112ce66d69 100644
--- a/core/src/test/scala/org/apache/spark/ShuffleSuite.scala
+++ b/core/src/test/scala/org/apache/spark/ShuffleSuite.scala
@@ -58,7 +58,7 @@ class ShuffleSuite extends FunSuite with Matchers with LocalSparkContext {
     // default Java serializer cannot handle the non serializable class.
     val c = new ShuffledRDD[Int, NonJavaSerializableClass, (Int, NonJavaSerializableClass)](
       b, new HashPartitioner(NUM_BLOCKS)).setSerializer(new KryoSerializer(conf))
-    val shuffleId = c.dependencies.head.asInstanceOf[ShuffleDependency[Int, Int]].shuffleId
+    val shuffleId = c.dependencies.head.asInstanceOf[ShuffleDependency[_, _, _]].shuffleId
 
     assert(c.count === 10)
 
@@ -97,7 +97,7 @@ class ShuffleSuite extends FunSuite with Matchers with LocalSparkContext {
     val c = new ShuffledRDD[Int, Int, (Int, Int)](b, new HashPartitioner(10))
       .setSerializer(new KryoSerializer(conf))
 
-    val shuffleId = c.dependencies.head.asInstanceOf[ShuffleDependency[Int, Int]].shuffleId
+    val shuffleId = c.dependencies.head.asInstanceOf[ShuffleDependency[_, _, _]].shuffleId
     assert(c.count === 4)
 
     val blockSizes = (0 until NUM_BLOCKS).flatMap { id =>
@@ -122,7 +122,7 @@ class ShuffleSuite extends FunSuite with Matchers with LocalSparkContext {
     // NOTE: The default Java serializer should create zero-sized blocks
     val c = new ShuffledRDD[Int, Int, (Int, Int)](b, new HashPartitioner(10))
 
-    val shuffleId = c.dependencies.head.asInstanceOf[ShuffleDependency[Int, Int]].shuffleId
+    val shuffleId = c.dependencies.head.asInstanceOf[ShuffleDependency[_, _, _]].shuffleId
     assert(c.count === 4)
 
     val blockSizes = (0 until NUM_BLOCKS).flatMap { id =>

From e056320cc8e2af56983a198d26717485304695d7 Mon Sep 17 00:00:00 2001
From: Yadong <qiyadong2010@gmail.com>
Date: Wed, 11 Jun 2014 20:58:39 -0700
Subject: [PATCH 405/810] 'killFuture' is never used

Author: Yadong <qiyadong2010@gmail.com>

Closes #1052 from watermen/bug-fix1 and squashes the following commits:

409d09a [Yadong] 'killFuture' is never used
---
 core/src/main/scala/org/apache/spark/deploy/Client.scala | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/Client.scala b/core/src/main/scala/org/apache/spark/deploy/Client.scala
index aeb159adc31d..c371dc3a51c7 100644
--- a/core/src/main/scala/org/apache/spark/deploy/Client.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/Client.scala
@@ -81,7 +81,7 @@ private class ClientActor(driverArgs: ClientArguments, conf: SparkConf) extends
 
       case "kill" =>
         val driverId = driverArgs.driverId
-        val killFuture = masterActor ! RequestKillDriver(driverId)
+        masterActor ! RequestKillDriver(driverId)
     }
   }
 

From 4d8ae709fb8d986634c97d21036391ed4685db1a Mon Sep 17 00:00:00 2001
From: Henry Saputra <henry.saputra@gmail.com>
Date: Wed, 11 Jun 2014 23:17:51 -0700
Subject: [PATCH 406/810] Cleanup on Connection and ConnectionManager

Simple cleanup on Connection and ConnectionManager to make IDE happy while working of issue:
1. Replace var with var
2. Add parentheses to Queue#dequeu to be consistent with side-effects.
3. Remove return on final line of a method.

Author: Henry Saputra <henry.saputra@gmail.com>

Closes #1060 from hsaputra/cleanup_connection_classes and squashes the following commits:

245fd09 [Henry Saputra] Cleanup on Connection and ConnectionManager to make IDE happy while working of issue: 1. Replace var with var 2. Add parentheses to Queue#dequeu to be consistent with side-effects. 3. Remove return on final line of a method.
---
 .../org/apache/spark/network/Connection.scala  |  4 ++--
 .../spark/network/ConnectionManager.scala      | 18 +++++++++---------
 2 files changed, 11 insertions(+), 11 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/network/Connection.scala b/core/src/main/scala/org/apache/spark/network/Connection.scala
index 3ffaaab23d0f..3b6298a26d7c 100644
--- a/core/src/main/scala/org/apache/spark/network/Connection.scala
+++ b/core/src/main/scala/org/apache/spark/network/Connection.scala
@@ -210,7 +210,7 @@ class SendingConnection(val address: InetSocketAddress, selector_ : Selector,
     var nextMessageToBeUsed = 0
 
     def addMessage(message: Message) {
-      messages.synchronized{
+      messages.synchronized {
         /* messages += message */
         messages.enqueue(message)
         logDebug("Added [" + message + "] to outbox for sending to " +
@@ -223,7 +223,7 @@ class SendingConnection(val address: InetSocketAddress, selector_ : Selector,
         while (!messages.isEmpty) {
           /* nextMessageToBeUsed = nextMessageToBeUsed % messages.size */
           /* val message = messages(nextMessageToBeUsed) */
-          val message = messages.dequeue
+          val message = messages.dequeue()
           val chunk = message.getChunkForSending(defaultChunkSize)
           if (chunk.isDefined) {
             messages.enqueue(message)
diff --git a/core/src/main/scala/org/apache/spark/network/ConnectionManager.scala b/core/src/main/scala/org/apache/spark/network/ConnectionManager.scala
index 5dd5fd0047c0..cf1c985c2fff 100644
--- a/core/src/main/scala/org/apache/spark/network/ConnectionManager.scala
+++ b/core/src/main/scala/org/apache/spark/network/ConnectionManager.scala
@@ -250,14 +250,14 @@ private[spark] class ConnectionManager(port: Int, conf: SparkConf,
     try {
       while(!selectorThread.isInterrupted) {
         while (! registerRequests.isEmpty) {
-          val conn: SendingConnection = registerRequests.dequeue
+          val conn: SendingConnection = registerRequests.dequeue()
           addListeners(conn)
           conn.connect()
           addConnection(conn)
         }
 
         while(!keyInterestChangeRequests.isEmpty) {
-          val (key, ops) = keyInterestChangeRequests.dequeue
+          val (key, ops) = keyInterestChangeRequests.dequeue()
 
           try {
             if (key.isValid) {
@@ -532,9 +532,9 @@ private[spark] class ConnectionManager(port: Int, conf: SparkConf,
           }
           return
         }
-        var securityMsgResp = SecurityMessage.fromResponse(replyToken,
+        val securityMsgResp = SecurityMessage.fromResponse(replyToken,
           securityMsg.getConnectionId.toString())
-        var message = securityMsgResp.toBufferMessage
+        val message = securityMsgResp.toBufferMessage
         if (message == null) throw new Exception("Error creating security message")
         sendSecurityMessage(waitingConn.getRemoteConnectionManagerId(), message)
       } catch  {
@@ -568,9 +568,9 @@ private[spark] class ConnectionManager(port: Int, conf: SparkConf,
           logDebug("Server sasl not completed: " + connection.connectionId)
         }
         if (replyToken != null) {
-          var securityMsgResp = SecurityMessage.fromResponse(replyToken,
+          val securityMsgResp = SecurityMessage.fromResponse(replyToken,
             securityMsg.getConnectionId)
-          var message = securityMsgResp.toBufferMessage
+          val message = securityMsgResp.toBufferMessage
           if (message == null) throw new Exception("Error creating security Message")
           sendSecurityMessage(connection.getRemoteConnectionManagerId(), message)
         }
@@ -618,7 +618,7 @@ private[spark] class ConnectionManager(port: Int, conf: SparkConf,
         return true
       }
     }
-    return false
+    false
   }
 
   private def handleMessage(
@@ -694,9 +694,9 @@ private[spark] class ConnectionManager(port: Int, conf: SparkConf,
           var firstResponse: Array[Byte] = null
           try {
             firstResponse = conn.sparkSaslClient.firstToken()
-            var securityMsg = SecurityMessage.fromResponse(firstResponse,
+            val securityMsg = SecurityMessage.fromResponse(firstResponse,
               conn.connectionId.toString())
-            var message = securityMsg.toBufferMessage
+            val message = securityMsg.toBufferMessage
             if (message == null) throw new Exception("Error creating security message")
             connectionsAwaitingSasl += ((conn.connectionId, conn))
             sendSecurityMessage(connManagerId, message)

From 43d53d51c9ee2626d9de91faa3b192979b86821d Mon Sep 17 00:00:00 2001
From: Jeff Thompson <jeffreykeatingthompson@gmail.com>
Date: Thu, 12 Jun 2014 08:10:51 -0700
Subject: [PATCH 407/810] fixed typo in docstring for min()

Hi, I found this typo while learning spark and thought I'd do a pull request.

Author: Jeff Thompson <jeffreykeatingthompson@gmail.com>

Closes #1065 from jkthompson/docstring-typo-minmax and squashes the following commits:

29b6a26 [Jeff Thompson] fixed typo in docstring for min()
---
 python/pyspark/rdd.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py
index 9c69c79236ed..8a215fc51130 100644
--- a/python/pyspark/rdd.py
+++ b/python/pyspark/rdd.py
@@ -695,7 +695,7 @@ def max(self):
 
     def min(self):
         """
-        Find the maximum item in this RDD.
+        Find the minimum item in this RDD.
 
         >>> sc.parallelize([1.0, 5.0, 43.0, 10.0]).min()
         1.0

From ce92a9c18f033ac9fa2f12143fab00a90e0f4577 Mon Sep 17 00:00:00 2001
From: Sandy Ryza <sandy@cloudera.com>
Date: Thu, 12 Jun 2014 08:14:25 -0700
Subject: [PATCH 408/810] SPARK-554.  Add aggregateByKey.

Author: Sandy Ryza <sandy@cloudera.com>

Closes #705 from sryza/sandy-spark-554 and squashes the following commits:

2302b8f [Sandy Ryza] Add MIMA exclude
f52e0ad [Sandy Ryza] Fix Python tests for real
2f3afa3 [Sandy Ryza] Fix Python test
0b735e9 [Sandy Ryza] Fix line lengths
ae56746 [Sandy Ryza] Fix doc (replace T with V)
c2be415 [Sandy Ryza] Java and Python aggregateByKey
23bf400 [Sandy Ryza] SPARK-554.  Add aggregateByKey.
---
 .../apache/spark/api/java/JavaPairRDD.scala   | 44 ++++++++++++++++
 .../apache/spark/rdd/PairRDDFunctions.scala   | 50 +++++++++++++++++++
 .../java/org/apache/spark/JavaAPISuite.java   | 31 ++++++++++++
 .../spark/rdd/PairRDDFunctionsSuite.scala     | 13 +++++
 docs/programming-guide.md                     |  4 ++
 project/MimaExcludes.scala                    |  5 +-
 python/pyspark/rdd.py                         | 19 ++++++-
 python/pyspark/tests.py                       | 15 ++++++
 8 files changed, 179 insertions(+), 2 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala b/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala
index 7dcfbf741c4f..14fa9d8135af 100644
--- a/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala
+++ b/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala
@@ -228,6 +228,50 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])
   : PartialResult[java.util.Map[K, BoundedDouble]] =
     rdd.countByKeyApprox(timeout, confidence).map(mapAsJavaMap)
 
+  /**
+   * Aggregate the values of each key, using given combine functions and a neutral "zero value".
+   * This function can return a different result type, U, than the type of the values in this RDD,
+   * V. Thus, we need one operation for merging a V into a U and one operation for merging two U's,
+   * as in scala.TraversableOnce. The former operation is used for merging values within a
+   * partition, and the latter is used for merging values between partitions. To avoid memory
+   * allocation, both of these functions are allowed to modify and return their first argument
+   * instead of creating a new U.
+   */
+  def aggregateByKey[U](zeroValue: U, partitioner: Partitioner, seqFunc: JFunction2[U, V, U],
+      combFunc: JFunction2[U, U, U]): JavaPairRDD[K, U] = {
+    implicit val ctag: ClassTag[U] = fakeClassTag
+    fromRDD(rdd.aggregateByKey(zeroValue, partitioner)(seqFunc, combFunc))
+  }
+
+  /**
+   * Aggregate the values of each key, using given combine functions and a neutral "zero value".
+   * This function can return a different result type, U, than the type of the values in this RDD,
+   * V. Thus, we need one operation for merging a V into a U and one operation for merging two U's,
+   * as in scala.TraversableOnce. The former operation is used for merging values within a
+   * partition, and the latter is used for merging values between partitions. To avoid memory
+   * allocation, both of these functions are allowed to modify and return their first argument
+   * instead of creating a new U.
+   */
+  def aggregateByKey[U](zeroValue: U, numPartitions: Int, seqFunc: JFunction2[U, V, U],
+      combFunc: JFunction2[U, U, U]): JavaPairRDD[K, U] = {
+    implicit val ctag: ClassTag[U] = fakeClassTag
+    fromRDD(rdd.aggregateByKey(zeroValue, numPartitions)(seqFunc, combFunc))
+  }
+
+  /**
+   * Aggregate the values of each key, using given combine functions and a neutral "zero value".
+   * This function can return a different result type, U, than the type of the values in this RDD,
+   * V. Thus, we need one operation for merging a V into a U and one operation for merging two U's.
+   * The former operation is used for merging values within a partition, and the latter is used for
+   * merging values between partitions. To avoid memory allocation, both of these functions are
+   * allowed to modify and return their first argument instead of creating a new U.
+   */
+  def aggregateByKey[U](zeroValue: U, seqFunc: JFunction2[U, V, U], combFunc: JFunction2[U, U, U]):
+      JavaPairRDD[K, U] = {
+    implicit val ctag: ClassTag[U] = fakeClassTag
+    fromRDD(rdd.aggregateByKey(zeroValue)(seqFunc, combFunc))
+  }
+
   /**
    * Merge the values for each key using an associative function and a neutral "zero value" which
    * may be added to the result an arbitrary number of times, and must not change the result
diff --git a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
index 890998095705..b6ad9b6c3e16 100644
--- a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
@@ -118,6 +118,56 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
     combineByKey(createCombiner, mergeValue, mergeCombiners, new HashPartitioner(numPartitions))
   }
 
+  /**
+   * Aggregate the values of each key, using given combine functions and a neutral "zero value".
+   * This function can return a different result type, U, than the type of the values in this RDD,
+   * V. Thus, we need one operation for merging a V into a U and one operation for merging two U's,
+   * as in scala.TraversableOnce. The former operation is used for merging values within a
+   * partition, and the latter is used for merging values between partitions. To avoid memory
+   * allocation, both of these functions are allowed to modify and return their first argument
+   * instead of creating a new U.
+   */
+  def aggregateByKey[U: ClassTag](zeroValue: U, partitioner: Partitioner)(seqOp: (U, V) => U,
+      combOp: (U, U) => U): RDD[(K, U)] = {
+    // Serialize the zero value to a byte array so that we can get a new clone of it on each key
+    val zeroBuffer = SparkEnv.get.closureSerializer.newInstance().serialize(zeroValue)
+    val zeroArray = new Array[Byte](zeroBuffer.limit)
+    zeroBuffer.get(zeroArray)
+
+    lazy val cachedSerializer = SparkEnv.get.closureSerializer.newInstance()
+    def createZero() = cachedSerializer.deserialize[U](ByteBuffer.wrap(zeroArray))
+
+    combineByKey[U]((v: V) => seqOp(createZero(), v), seqOp, combOp, partitioner)
+  }
+
+  /**
+   * Aggregate the values of each key, using given combine functions and a neutral "zero value".
+   * This function can return a different result type, U, than the type of the values in this RDD,
+   * V. Thus, we need one operation for merging a V into a U and one operation for merging two U's,
+   * as in scala.TraversableOnce. The former operation is used for merging values within a
+   * partition, and the latter is used for merging values between partitions. To avoid memory
+   * allocation, both of these functions are allowed to modify and return their first argument
+   * instead of creating a new U.
+   */
+  def aggregateByKey[U: ClassTag](zeroValue: U, numPartitions: Int)(seqOp: (U, V) => U,
+      combOp: (U, U) => U): RDD[(K, U)] = {
+    aggregateByKey(zeroValue, new HashPartitioner(numPartitions))(seqOp, combOp)
+  }
+
+  /**
+   * Aggregate the values of each key, using given combine functions and a neutral "zero value".
+   * This function can return a different result type, U, than the type of the values in this RDD,
+   * V. Thus, we need one operation for merging a V into a U and one operation for merging two U's,
+   * as in scala.TraversableOnce. The former operation is used for merging values within a
+   * partition, and the latter is used for merging values between partitions. To avoid memory
+   * allocation, both of these functions are allowed to modify and return their first argument
+   * instead of creating a new U.
+   */
+  def aggregateByKey[U: ClassTag](zeroValue: U)(seqOp: (U, V) => U,
+      combOp: (U, U) => U): RDD[(K, U)] = {
+    aggregateByKey(zeroValue, defaultPartitioner(self))(seqOp, combOp)
+  }
+
   /**
    * Merge the values for each key using an associative function and a neutral "zero value" which
    * may be added to the result an arbitrary number of times, and must not change the result
diff --git a/core/src/test/java/org/apache/spark/JavaAPISuite.java b/core/src/test/java/org/apache/spark/JavaAPISuite.java
index 50a62129116f..ef41bfb88de9 100644
--- a/core/src/test/java/org/apache/spark/JavaAPISuite.java
+++ b/core/src/test/java/org/apache/spark/JavaAPISuite.java
@@ -317,6 +317,37 @@ public Integer call(Integer a, Integer b) {
     Assert.assertEquals(33, sum);
   }
 
+  @Test
+  public void aggregateByKey() {
+    JavaPairRDD<Integer, Integer> pairs = sc.parallelizePairs(
+      Arrays.asList(
+        new Tuple2<Integer, Integer>(1, 1),
+        new Tuple2<Integer, Integer>(1, 1),
+        new Tuple2<Integer, Integer>(3, 2),
+        new Tuple2<Integer, Integer>(5, 1),
+        new Tuple2<Integer, Integer>(5, 3)), 2);
+
+    Map<Integer, Set<Integer>> sets = pairs.aggregateByKey(new HashSet<Integer>(),
+      new Function2<Set<Integer>, Integer, Set<Integer>>() {
+        @Override
+        public Set<Integer> call(Set<Integer> a, Integer b) {
+          a.add(b);
+          return a;
+        }
+      },
+      new Function2<Set<Integer>, Set<Integer>, Set<Integer>>() {
+        @Override
+        public Set<Integer> call(Set<Integer> a, Set<Integer> b) {
+          a.addAll(b);
+          return a;
+        }
+      }).collectAsMap();
+    Assert.assertEquals(3, sets.size());
+    Assert.assertEquals(new HashSet<Integer>(Arrays.asList(1)), sets.get(1));
+    Assert.assertEquals(new HashSet<Integer>(Arrays.asList(2)), sets.get(3));
+    Assert.assertEquals(new HashSet<Integer>(Arrays.asList(1, 3)), sets.get(5));
+  }
+
   @SuppressWarnings("unchecked")
   @Test
   public void foldByKey() {
diff --git a/core/src/test/scala/org/apache/spark/rdd/PairRDDFunctionsSuite.scala b/core/src/test/scala/org/apache/spark/rdd/PairRDDFunctionsSuite.scala
index 9ddafc451878..0b9004448a63 100644
--- a/core/src/test/scala/org/apache/spark/rdd/PairRDDFunctionsSuite.scala
+++ b/core/src/test/scala/org/apache/spark/rdd/PairRDDFunctionsSuite.scala
@@ -30,6 +30,19 @@ import org.apache.spark.SparkContext._
 import org.apache.spark.{Partitioner, SharedSparkContext}
 
 class PairRDDFunctionsSuite extends FunSuite with SharedSparkContext {
+  test("aggregateByKey") {
+    val pairs = sc.parallelize(Array((1, 1), (1, 1), (3, 2), (5, 1), (5, 3)), 2)
+
+    val sets = pairs.aggregateByKey(new HashSet[Int]())(_ += _, _ ++= _).collect()
+    assert(sets.size === 3)
+    val valuesFor1 = sets.find(_._1 == 1).get._2
+    assert(valuesFor1.toList.sorted === List(1))
+    val valuesFor3 = sets.find(_._1 == 3).get._2
+    assert(valuesFor3.toList.sorted === List(2))
+    val valuesFor5 = sets.find(_._1 == 5).get._2
+    assert(valuesFor5.toList.sorted === List(1, 3))
+  }
+
   test("groupByKey") {
     val pairs = sc.parallelize(Array((1, 1), (1, 2), (1, 3), (2, 1)))
     val groups = pairs.groupByKey().collect()
diff --git a/docs/programming-guide.md b/docs/programming-guide.md
index 7989e02dfb73..79784682bfd1 100644
--- a/docs/programming-guide.md
+++ b/docs/programming-guide.md
@@ -890,6 +890,10 @@ for details.
   <td> <b>reduceByKey</b>(<i>func</i>, [<i>numTasks</i>]) </td>
   <td> When called on a dataset of (K, V) pairs, returns a dataset of (K, V) pairs where the values for each key are aggregated using the given reduce function. Like in <code>groupByKey</code>, the number of reduce tasks is configurable through an optional second argument. </td>
 </tr>
+<tr>
+  <td> <b>aggregateByKey</b>(<i>zeroValue</i>)(<i>seqOp</i>, <i>combOp</i>, [<i>numTasks</i>]) </td>
+  <td> When called on a dataset of (K, V) pairs, returns a dataset of (K, U) pairs where the values for each key are aggregated using the given combine functions and a neutral "zero" value. Allows an aggregated value type that is different than the input value type, while avoiding unnecessary allocations. Like in <code>groupByKey</code>, the number of reduce tasks is configurable through an optional second argument. </td>
+</tr>
 <tr>
   <td> <b>sortByKey</b>([<i>ascending</i>], [<i>numTasks</i>]) </td>
   <td> When called on a dataset of (K, V) pairs where K implements Ordered, returns a dataset of (K, V) pairs sorted by keys in ascending or descending order, as specified in the boolean <code>ascending</code> argument.</td>
diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala
index c80ab9a9f8e6..ee629794f60a 100644
--- a/project/MimaExcludes.scala
+++ b/project/MimaExcludes.scala
@@ -52,7 +52,10 @@ object MimaExcludes {
             ProblemFilters.exclude[MissingMethodProblem](
               "org.apache.spark.api.java.JavaRDDLike.countApproxDistinct$default$1"),
             ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.api.java.JavaDoubleRDD.countApproxDistinct$default$1")
+              "org.apache.spark.api.java.JavaDoubleRDD.countApproxDistinct$default$1"),
+            ProblemFilters.exclude[MissingMethodProblem](
+              "org.apache.spark.rdd.PairRDDFunctions.org$apache$spark$rdd$PairRDDFunctions$$"
+                + "createZero$1")
           ) ++
           Seq( // Ignore some private methods in ALS.
             ProblemFilters.exclude[MissingMethodProblem](
diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py
index 8a215fc51130..735389c69831 100644
--- a/python/pyspark/rdd.py
+++ b/python/pyspark/rdd.py
@@ -1178,6 +1178,20 @@ def _mergeCombiners(iterator):
                     combiners[k] = mergeCombiners(combiners[k], v)
             return combiners.iteritems()
         return shuffled.mapPartitions(_mergeCombiners)
+   
+    def aggregateByKey(self, zeroValue, seqFunc, combFunc, numPartitions=None):
+        """
+        Aggregate the values of each key, using given combine functions and a neutral "zero value".
+        This function can return a different result type, U, than the type of the values in this RDD,
+        V. Thus, we need one operation for merging a V into a U and one operation for merging two U's,
+        The former operation is used for merging values within a partition, and the latter is used
+        for merging values between partitions. To avoid memory allocation, both of these functions are
+        allowed to modify and return their first argument instead of creating a new U.
+        """
+        def createZero():
+          return copy.deepcopy(zeroValue)
+        
+        return self.combineByKey(lambda v: seqFunc(createZero(), v), seqFunc, combFunc, numPartitions)
 
     def foldByKey(self, zeroValue, func, numPartitions=None):
         """
@@ -1190,7 +1204,10 @@ def foldByKey(self, zeroValue, func, numPartitions=None):
         >>> rdd.foldByKey(0, add).collect()
         [('a', 2), ('b', 1)]
         """
-        return self.combineByKey(lambda v: func(zeroValue, v), func, func, numPartitions)
+        def createZero():
+          return copy.deepcopy(zeroValue)
+
+        return self.combineByKey(lambda v: func(createZero(), v), func, func, numPartitions)
 
 
     # TODO: support variant with custom partitioner
diff --git a/python/pyspark/tests.py b/python/pyspark/tests.py
index 184ee810b861..c15bb457759e 100644
--- a/python/pyspark/tests.py
+++ b/python/pyspark/tests.py
@@ -188,6 +188,21 @@ def test_deleting_input_files(self):
         os.unlink(tempFile.name)
         self.assertRaises(Exception, lambda: filtered_data.count())
 
+    def testAggregateByKey(self):
+        data = self.sc.parallelize([(1, 1), (1, 1), (3, 2), (5, 1), (5, 3)], 2)
+        def seqOp(x, y):
+            x.add(y)
+            return x
+
+        def combOp(x, y):
+            x |= y
+            return x
+          
+        sets = dict(data.aggregateByKey(set(), seqOp, combOp).collect())
+        self.assertEqual(3, len(sets))
+        self.assertEqual(set([1]), sets[1])
+        self.assertEqual(set([2]), sets[3])
+        self.assertEqual(set([1, 3]), sets[5])
 
 class TestIO(PySparkTestCase):
 

From 83c226d454722d5dea186d48070fb98652d0dafb Mon Sep 17 00:00:00 2001
From: Doris Xin <doris.s.xin@gmail.com>
Date: Thu, 12 Jun 2014 12:53:07 -0700
Subject: [PATCH 409/810] [SPARK-2088] fix NPE in toString

After deserialization, the transient field creationSiteInfo does not get backfilled with the default value, but the toString method, which is invoked by the serializer, expects the field to always be non-null. An NPE is thrown when toString is called by the serializer when creationSiteInfo is null.

Author: Doris Xin <doris.s.xin@gmail.com>

Closes #1028 from dorx/toStringNPE and squashes the following commits:

f20021e [Doris Xin] unit test for toString after desrialization
6f0a586 [Doris Xin] Merge branch 'master' into toStringNPE
f47fecf [Doris Xin] Merge branch 'master' into toStringNPE
76199c6 [Doris Xin] [SPARK-2088] fix NPE in toString
---
 core/src/main/scala/org/apache/spark/rdd/RDD.scala      | 2 +-
 core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala | 9 ++++++++-
 2 files changed, 9 insertions(+), 2 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/rdd/RDD.scala b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
index 54bdc3e7cbc7..b6fc4b13ad4d 100644
--- a/core/src/main/scala/org/apache/spark/rdd/RDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
@@ -1180,7 +1180,7 @@ abstract class RDD[T: ClassTag](
 
   /** User code that created this RDD (e.g. `textFile`, `parallelize`). */
   @transient private[spark] val creationSiteInfo = Utils.getCallSiteInfo
-  private[spark] def getCreationSite: String = creationSiteInfo.toString
+  private[spark] def getCreationSite: String = Option(creationSiteInfo).getOrElse("").toString
 
   private[spark] def elementClassTag: ClassTag[T] = classTag[T]
 
diff --git a/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala b/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala
index 55af1666df66..2e2ccc5a1859 100644
--- a/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala
+++ b/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala
@@ -24,7 +24,7 @@ import org.scalatest.FunSuite
 
 import org.apache.spark._
 import org.apache.spark.SparkContext._
-import org.apache.spark.rdd._
+import org.apache.spark.util.Utils
 
 class RDDSuite extends FunSuite with SharedSparkContext {
 
@@ -66,6 +66,13 @@ class RDDSuite extends FunSuite with SharedSparkContext {
     }
   }
 
+  test("serialization") {
+    val empty = new EmptyRDD[Int](sc)
+    val serial = Utils.serialize(empty)
+    val deserial: EmptyRDD[Int] = Utils.deserialize(serial)
+    assert(!deserial.toString().isEmpty())
+  }
+
   test("countApproxDistinct") {
 
     def error(est: Long, size: Long) = math.abs(est - size) / size.toDouble

From ecde5b837534b11d365fcab78089820990b815cf Mon Sep 17 00:00:00 2001
From: Marcelo Vanzin <vanzin@cloudera.com>
Date: Thu, 12 Jun 2014 16:19:36 -0500
Subject: [PATCH 410/810] [SPARK-2080] Yarn: report HS URL in client mode,
 correct user in cluster mode.

Yarn client mode was not setting the app's tracking URL to the
History Server's URL when configured by the user. Now client mode
behaves the same as cluster mode.

In SparkContext.scala, the "user.name" system property had precedence
over the SPARK_USER environment variable. This means that SPARK_USER
was never used, since "user.name" is always set by the JVM. In Yarn
cluster mode, this means the application always reported itself as
being run by user "yarn" (or whatever user was running the Yarn NM).
One could argue that the correct fix would be to use UGI.getCurrentUser()
here, but at least for Yarn that will match what SPARK_USER is set
to.

Author: Marcelo Vanzin <vanzin@cloudera.com>

This patch had conflicts when merged, resolved by
Committer: Thomas Graves <tgraves@apache.org>

Closes #1002 from vanzin/yarn-client-url and squashes the following commits:

4046e04 [Marcelo Vanzin] Set HS link in yarn-alpha also.
4c692d9 [Marcelo Vanzin] Yarn: report HS URL in client mode, correct user in cluster mode.
---
 core/src/main/scala/org/apache/spark/SparkContext.scala     | 2 +-
 .../org/apache/spark/deploy/yarn/ExecutorLauncher.scala     | 1 +
 .../org/apache/spark/deploy/yarn/ExecutorLauncher.scala     | 6 +++---
 3 files changed, 5 insertions(+), 4 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index 8bdaf0bf76e8..df151861958a 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -297,7 +297,7 @@ class SparkContext(config: SparkConf) extends Logging {
 
   // Set SPARK_USER for user who is running SparkContext.
   val sparkUser = Option {
-    Option(System.getProperty("user.name")).getOrElse(System.getenv("SPARK_USER"))
+    Option(System.getenv("SPARK_USER")).getOrElse(System.getProperty("user.name"))
   }.getOrElse {
     SparkContext.SPARK_UNKNOWN_USER
   }
diff --git a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala
index a3bd91590fc2..b6ecae1e652f 100644
--- a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala
+++ b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala
@@ -271,6 +271,7 @@ class ExecutorLauncher(args: ApplicationMasterArguments, conf: Configuration, sp
       .asInstanceOf[FinishApplicationMasterRequest]
     finishReq.setAppAttemptId(appAttemptId)
     finishReq.setFinishApplicationStatus(status)
+    finishReq.setTrackingUrl(sparkConf.get("spark.yarn.historyServer.address", ""))
     resourceManager.finishApplicationMaster(finishReq)
   }
 
diff --git a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala
index 4f8854a09a1e..f71ad036ce0f 100644
--- a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala
+++ b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala
@@ -115,7 +115,7 @@ class ExecutorLauncher(args: ApplicationMasterArguments, conf: Configuration, sp
     val interval = math.min(timeoutInterval / 2, schedulerInterval)
 
     reporterThread = launchReporterThread(interval)
-    
+
 
     // Wait for the reporter thread to Finish.
     reporterThread.join()
@@ -134,12 +134,12 @@ class ExecutorLauncher(args: ApplicationMasterArguments, conf: Configuration, sp
     // LOCAL_DIRS => 2.X, YARN_LOCAL_DIRS => 0.23.X
     val localDirs = Option(System.getenv("YARN_LOCAL_DIRS"))
       .orElse(Option(System.getenv("LOCAL_DIRS")))
- 
+
     localDirs match {
       case None => throw new Exception("Yarn Local dirs can't be empty")
       case Some(l) => l
     }
-  } 
+  }
 
   private def registerApplicationMaster(): RegisterApplicationMasterResponse = {
     logInfo("Registering the ApplicationMaster")

From 1c04652c8f18566baafb13dbae355f8ad2ad8d37 Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Thu, 12 Jun 2014 15:43:32 -0700
Subject: [PATCH 411/810] SPARK-1843: Replace assemble-deps with env variable.

(This change is actually small, I moved some logic into
compute-classpath that was previously in spark-class).

Assemble deps has existed for a while to allow developers to
run local code with new changes quickly. When I'm developing I
typically use a simpler approach which just prepends the Spark
classes to the classpath before the assembly jar. This is well
defined in the JVM and the Spark classes take precedence over those
in the assembly.

This approach is portable across both builds which is the main reason I'd
like to switch to it. It's also a bit easier to toggle on and off quickly.

The way you use this is the following:
```
$ ./bin/spark-shell # Use spark with the normal assembly
$ export SPARK_PREPEND_CLASSES=true
$ ./bin/spark-shell # Now it's using compiled classes
$ unset SPARK_PREPEND_CLASSES
$ ./bin/spark-shell # Back to normal
```

Author: Patrick Wendell <pwendell@gmail.com>

Closes #877 from pwendell/assemble-deps and squashes the following commits:

8a11345 [Patrick Wendell] Merge remote-tracking branch 'apache/master' into assemble-deps
faa3168 [Patrick Wendell] Adding a warning for compatibility
3f151a7 [Patrick Wendell] Small fix
bbfb73c [Patrick Wendell] Review feedback
328e9f8 [Patrick Wendell] SPARK-1843: Replace assemble-deps with env variable.
---
 bin/compute-classpath.sh                      | 34 ++++++++++++++-----
 bin/spark-class                               | 17 ----------
 .../scala/org/apache/spark/SparkContext.scala |  3 ++
 project/SparkBuild.scala                      | 16 ++++++---
 4 files changed, 40 insertions(+), 30 deletions(-)

diff --git a/bin/compute-classpath.sh b/bin/compute-classpath.sh
index 7df43a555d56..2cf4e381c1c8 100755
--- a/bin/compute-classpath.sh
+++ b/bin/compute-classpath.sh
@@ -38,8 +38,10 @@ else
   JAR_CMD="jar"
 fi
 
-# First check if we have a dependencies jar. If so, include binary classes with the deps jar
-if [ -f "$ASSEMBLY_DIR"/spark-assembly*hadoop*-deps.jar ]; then
+# A developer option to prepend more recently compiled Spark classes
+if [ -n "$SPARK_PREPEND_CLASSES" ]; then
+  echo "NOTE: SPARK_PREPEND_CLASSES is set, placing locally compiled Spark"\
+    "classes ahead of assembly." >&2
   CLASSPATH="$CLASSPATH:$FWDIR/core/target/scala-$SCALA_VERSION/classes"
   CLASSPATH="$CLASSPATH:$FWDIR/repl/target/scala-$SCALA_VERSION/classes"
   CLASSPATH="$CLASSPATH:$FWDIR/mllib/target/scala-$SCALA_VERSION/classes"
@@ -51,17 +53,31 @@ if [ -f "$ASSEMBLY_DIR"/spark-assembly*hadoop*-deps.jar ]; then
   CLASSPATH="$CLASSPATH:$FWDIR/sql/core/target/scala-$SCALA_VERSION/classes"
   CLASSPATH="$CLASSPATH:$FWDIR/sql/hive/target/scala-$SCALA_VERSION/classes"
   CLASSPATH="$CLASSPATH:$FWDIR/yarn/stable/target/scala-$SCALA_VERSION/classes"
+fi
 
-  ASSEMBLY_JAR=$(ls "$ASSEMBLY_DIR"/spark-assembly*hadoop*-deps.jar 2>/dev/null)
+# Use spark-assembly jar from either RELEASE or assembly directory
+if [ -f "$FWDIR/RELEASE" ]; then
+  assembly_folder="$FWDIR"/lib
 else
-  # Else use spark-assembly jar from either RELEASE or assembly directory
-  if [ -f "$FWDIR/RELEASE" ]; then
-    ASSEMBLY_JAR=$(ls "$FWDIR"/lib/spark-assembly*hadoop*.jar 2>/dev/null)
-  else
-    ASSEMBLY_JAR=$(ls "$ASSEMBLY_DIR"/spark-assembly*hadoop*.jar 2>/dev/null)
-  fi
+  assembly_folder="$ASSEMBLY_DIR"
 fi
 
+num_jars=$(ls "$assembly_folder" | grep "spark-assembly.*hadoop.*\.jar" | wc -l)
+if [ "$num_jars" -eq "0" ]; then
+  echo "Failed to find Spark assembly in $assembly_folder"
+  echo "You need to build Spark before running this program."
+  exit 1
+fi
+if [ "$num_jars" -gt "1" ]; then
+  jars_list=$(ls "$assembly_folder" | grep "spark-assembly.*hadoop.*.jar")
+  echo "Found multiple Spark assembly jars in $assembly_folder:"
+  echo "$jars_list"
+  echo "Please remove all but one jar."
+  exit 1
+fi
+
+ASSEMBLY_JAR=$(ls "$assembly_folder"/spark-assembly*hadoop*.jar 2>/dev/null)
+
 # Verify that versions of java used to build the jars and run Spark are compatible
 jar_error_check=$("$JAR_CMD" -tf "$ASSEMBLY_JAR" nonexistent/class/path 2>&1)
 if [[ "$jar_error_check" =~ "invalid CEN header" ]]; then
diff --git a/bin/spark-class b/bin/spark-class
index e884511010c6..cfe363a71da3 100755
--- a/bin/spark-class
+++ b/bin/spark-class
@@ -108,23 +108,6 @@ fi
 export JAVA_OPTS
 # Attention: when changing the way the JAVA_OPTS are assembled, the change must be reflected in CommandUtils.scala!
 
-if [ ! -f "$FWDIR/RELEASE" ]; then
-  # Exit if the user hasn't compiled Spark
-  num_jars=$(ls "$FWDIR"/assembly/target/scala-$SCALA_VERSION/ | grep "spark-assembly.*hadoop.*.jar" | wc -l)
-  jars_list=$(ls "$FWDIR"/assembly/target/scala-$SCALA_VERSION/ | grep "spark-assembly.*hadoop.*.jar")
-  if [ "$num_jars" -eq "0" ]; then
-    echo "Failed to find Spark assembly in $FWDIR/assembly/target/scala-$SCALA_VERSION/" >&2
-    echo "You need to build Spark before running this program." >&2
-    exit 1
-  fi
-  if [ "$num_jars" -gt "1" ]; then
-    echo "Found multiple Spark assembly jars in $FWDIR/assembly/target/scala-$SCALA_VERSION:" >&2
-    echo "$jars_list"
-    echo "Please remove all but one jar."
-    exit 1
-  fi
-fi
-
 TOOLS_DIR="$FWDIR"/tools
 SPARK_TOOLS_JAR=""
 if [ -e "$TOOLS_DIR"/target/scala-$SCALA_VERSION/*assembly*[0-9Tg].jar ]; then
diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index df151861958a..8fbda2c667cf 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -290,6 +290,9 @@ class SparkContext(config: SparkConf) extends Logging {
     value <- Option(System.getenv(envKey)).orElse(Option(System.getProperty(propKey)))} {
     executorEnvs(envKey) = value
   }
+  Option(System.getenv("SPARK_PREPEND_CLASSES")).foreach { v => 
+    executorEnvs("SPARK_PREPEND_CLASSES") = v
+  }
   // The Mesos scheduler backend relies on this environment variable to set executor memory.
   // TODO: Set this only in the Mesos scheduler.
   executorEnvs("SPARK_EXECUTOR_MEMORY") = executorMemory + "m"
diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index ecd9d7068068..8b4885d3bbbd 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -90,7 +90,16 @@ object SparkBuild extends Build {
   lazy val assemblyProj = Project("assembly", file("assembly"), settings = assemblyProjSettings)
     .dependsOn(core, graphx, bagel, mllib, streaming, repl, sql) dependsOn(maybeYarn: _*) dependsOn(maybeHive: _*) dependsOn(maybeGanglia: _*)
 
-  lazy val assembleDeps = TaskKey[Unit]("assemble-deps", "Build assembly of dependencies and packages Spark projects")
+  lazy val assembleDepsTask = TaskKey[Unit]("assemble-deps")
+  lazy val assembleDeps = assembleDepsTask := {
+    println()
+    println("**** NOTE ****")
+    println("'sbt/sbt assemble-deps' is no longer supported.")
+    println("Instead create a normal assembly and:")
+    println("  export SPARK_PREPEND_CLASSES=1 (toggle on)")
+    println("  unset SPARK_PREPEND_CLASSES (toggle off)")
+    println()
+  }
 
   // A configuration to set an alternative publishLocalConfiguration
   lazy val MavenCompile = config("m2r") extend(Compile)
@@ -373,6 +382,7 @@ object SparkBuild extends Build {
         "net.sf.py4j"                % "py4j"             % "0.8.1"
       ),
     libraryDependencies ++= maybeAvro,
+    assembleDeps,
     previousArtifact := sparkPreviousArtifact("spark-core")
   )
 
@@ -584,9 +594,7 @@ object SparkBuild extends Build {
 
   def assemblyProjSettings = sharedSettings ++ Seq(
     name := "spark-assembly",
-    assembleDeps in Compile <<= (packageProjects.map(packageBin in Compile in _) ++ Seq(packageDependency in Compile)).dependOn,
-    jarName in assembly <<= version map { v => "spark-assembly-" + v + "-hadoop" + hadoopVersion + ".jar" },
-    jarName in packageDependency <<= version map { v => "spark-assembly-" + v + "-hadoop" + hadoopVersion + "-deps.jar" }
+    jarName in assembly <<= version map { v => "spark-assembly-" + v + "-hadoop" + hadoopVersion + ".jar" }
   ) ++ assemblySettings ++ extraAssemblySettings
 
   def extraAssemblySettings() = Seq(

From a6e0afdcf0174425e8a6ff20b2bc2e3a7a374f19 Mon Sep 17 00:00:00 2001
From: Shuo Xiang <sxiang@twitter.com>
Date: Thu, 12 Jun 2014 17:37:06 -0700
Subject: [PATCH 412/810] SPARK-2085: [MLlib] Apply user-specific
 regularization instead of uniform regularization in ALS

The current implementation of ALS takes a single regularization parameter and apply it on both of the user factors and the product factors. This kind of regularization can be less effective while user number is significantly larger than the number of products (and vice versa). For example, if we have 10M users and 1K product, regularization on user factors will dominate. Following the discussion in [this thread](http://apache-spark-user-list.1001560.n3.nabble.com/possible-bug-in-Spark-s-ALS-implementation-tt2567.html#a2704), the implementation in this PR will regularize each factor vector by #ratings * lambda.

Author: Shuo Xiang <sxiang@twitter.com>

Closes #1026 from coderxiang/als-reg and squashes the following commits:

93dfdb4 [Shuo Xiang] Merge remote-tracking branch 'upstream/master' into als-reg
b98f19c [Shuo Xiang] merge latest master
52c7b58 [Shuo Xiang] Apply user-specific regularization instead of uniform regularization in Alternating Least Squares (ALS)
---
 .../scala/org/apache/spark/mllib/recommendation/ALS.scala | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala
index f1ae7b85b4a6..cc56fd6ef28d 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala
@@ -507,6 +507,9 @@ class ALS private (
     val tempXtX = DoubleMatrix.zeros(triangleSize)
     val fullXtX = DoubleMatrix.zeros(rank, rank)
 
+    // Count the number of ratings each user gives to provide user-specific regularization
+    val numRatings = Array.fill(numUsers)(0)
+
     // Compute the XtX and Xy values for each user by adding products it rated in each product
     // block
     for (productBlock <- 0 until numProductBlocks) {
@@ -519,6 +522,7 @@ class ALS private (
         if (implicitPrefs) {
           var i = 0
           while (i < us.length) {
+            numRatings(us(i)) += 1
             // Extension to the original paper to handle rs(i) < 0. confidence is a function
             // of |rs(i)| instead so that it is never negative:
             val confidence = 1 + alpha * abs(rs(i))
@@ -534,6 +538,7 @@ class ALS private (
         } else {
           var i = 0
           while (i < us.length) {
+            numRatings(us(i)) += 1
             userXtX(us(i)).addi(tempXtX)
             SimpleBlas.axpy(rs(i), x, userXy(us(i)))
             i += 1
@@ -550,9 +555,10 @@ class ALS private (
       // Compute the full XtX matrix from the lower-triangular part we got above
       fillFullMatrix(userXtX(index), fullXtX)
       // Add regularization
+      val regParam = numRatings(index) * lambda
       var i = 0
       while (i < rank) {
-        fullXtX.data(i * rank + i) += lambda
+        fullXtX.data(i * rank + i) += regParam
         i += 1
       }
       // Solve the resulting matrix, which is symmetric and positive-definite

From 0154587ab71d1b864f97497dbb38bc52b87675be Mon Sep 17 00:00:00 2001
From: Ariel Rabkin <asrabkin@cs.princeton.edu>
Date: Thu, 12 Jun 2014 17:51:33 -0700
Subject: [PATCH 413/810] document laziness of parallelize

Took me several hours to figure out this behavior. It would be good to highlight it in the documentation.

Author: Ariel Rabkin <asrabkin@cs.princeton.edu>

Closes #1070 from asrabkin/master and squashes the following commits:

29a076e [Ariel Rabkin] doc fix
---
 .../main/scala/org/apache/spark/SparkContext.scala  | 13 +++++++++++--
 1 file changed, 11 insertions(+), 2 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index 8fbda2c667cf..35970c2f5089 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -434,12 +434,21 @@ class SparkContext(config: SparkConf) extends Logging {
 
   // Methods for creating RDDs
 
-  /** Distribute a local Scala collection to form an RDD. */
+  /** Distribute a local Scala collection to form an RDD.
+   *
+   * @note Parallelize acts lazily. If `seq` is a mutable collection and is
+   * altered after the call to parallelize and before the first action on the
+   * RDD, the resultant RDD will reflect the modified collection. Pass a copy of
+   * the argument to avoid this.
+   */
   def parallelize[T: ClassTag](seq: Seq[T], numSlices: Int = defaultParallelism): RDD[T] = {
     new ParallelCollectionRDD[T](this, seq, numSlices, Map[Int, Seq[String]]())
   }
 
-  /** Distribute a local Scala collection to form an RDD. */
+  /** Distribute a local Scala collection to form an RDD.
+   *
+   * This method is identical to `parallelize`.
+   */
   def makeRDD[T: ClassTag](seq: Seq[T], numSlices: Int = defaultParallelism): RDD[T] = {
     parallelize(seq, numSlices)
   }

From 1de1d703bf6b7ca14f7b40bbefe9bf6fd6c8ce47 Mon Sep 17 00:00:00 2001
From: Doris Xin <doris.s.xin@gmail.com>
Date: Thu, 12 Jun 2014 19:44:27 -0700
Subject: [PATCH 414/810] SPARK-1939 Refactor takeSample method in RDD to use
 ScaSRS

Modified the takeSample method in RDD to use the ScaSRS sampling technique to improve performance. Added a private method that computes sampling rate > sample_size/total to ensure sufficient sample size with success rate >= 0.9999. Added a unit test for the private method to validate choice of sampling rate.

Author: Doris Xin <doris.s.xin@gmail.com>
Author: dorx <doris.s.xin@gmail.com>
Author: Xiangrui Meng <meng@databricks.com>

Closes #916 from dorx/takeSample and squashes the following commits:

5b061ae [Doris Xin] merge master
444e750 [Doris Xin] edge cases
3de882b [dorx] Merge pull request #2 from mengxr/SPARK-1939
82dde31 [Xiangrui Meng] update pyspark's takeSample
48d954d [Doris Xin] remove unused imports from RDDSuite
fb1452f [Doris Xin] allowing num to be greater than count in all cases
1481b01 [Doris Xin] washing test tubes and making coffee
dc699f3 [Doris Xin] give back imports removed by accident in rdd.py
64e445b [Doris Xin] logwarnning as soon as it enters the while loop
55518ed [Doris Xin] added TODO for logging in rdd.py
eff89e2 [Doris Xin] addressed reviewer comments.
ecab508 [Doris Xin] "fixed checkstyle violation
0a9b3e3 [Doris Xin] "reviewer comment addressed"
f80f270 [Doris Xin] Merge branch 'master' into takeSample
ae3ad04 [Doris Xin] fixed edge cases to prevent overflow
065ebcd [Doris Xin] Merge branch 'master' into takeSample
9bdd36e [Doris Xin] Check sample size and move computeFraction
e3fd6a6 [Doris Xin] Merge branch 'master' into takeSample
7cab53a [Doris Xin] fixed import bug in rdd.py
ffea61a [Doris Xin] SPARK-1939: Refactor takeSample method in RDD
1441977 [Doris Xin] SPARK-1939 Refactor takeSample method in RDD to use ScaSRS
---
 core/pom.xml                                  |   5 +
 .../main/scala/org/apache/spark/rdd/RDD.scala |  52 +++---
 .../spark/util/random/RandomSampler.scala     |   2 +-
 .../spark/util/random/SamplingUtils.scala     |  55 ++++++
 .../scala/org/apache/spark/rdd/RDDSuite.scala |  35 ++--
 .../util/random/SamplingUtilsSuite.scala      |  46 +++++
 project/SparkBuild.scala                      |   1 +
 python/pyspark/rdd.py                         | 167 +++++++++++-------
 8 files changed, 263 insertions(+), 100 deletions(-)
 create mode 100644 core/src/main/scala/org/apache/spark/util/random/SamplingUtils.scala
 create mode 100644 core/src/test/scala/org/apache/spark/util/random/SamplingUtilsSuite.scala

diff --git a/core/pom.xml b/core/pom.xml
index c3d6b00a443f..be56911b9e45 100644
--- a/core/pom.xml
+++ b/core/pom.xml
@@ -67,6 +67,11 @@
       <groupId>org.apache.commons</groupId>
       <artifactId>commons-lang3</artifactId>
     </dependency>
+    <dependency>
+      <groupId>org.apache.commons</groupId>
+      <artifactId>commons-math3</artifactId>
+      <scope>test</scope>
+    </dependency>
     <dependency>
       <groupId>com.google.code.findbugs</groupId>
       <artifactId>jsr305</artifactId>
diff --git a/core/src/main/scala/org/apache/spark/rdd/RDD.scala b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
index b6fc4b13ad4d..446f369c9ea1 100644
--- a/core/src/main/scala/org/apache/spark/rdd/RDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
@@ -42,7 +42,7 @@ import org.apache.spark.partial.PartialResult
 import org.apache.spark.storage.StorageLevel
 import org.apache.spark.util.{BoundedPriorityQueue, Utils}
 import org.apache.spark.util.collection.OpenHashMap
-import org.apache.spark.util.random.{BernoulliSampler, PoissonSampler}
+import org.apache.spark.util.random.{BernoulliSampler, PoissonSampler, SamplingUtils}
 
 /**
  * A Resilient Distributed Dataset (RDD), the basic abstraction in Spark. Represents an immutable,
@@ -378,46 +378,56 @@ abstract class RDD[T: ClassTag](
     }.toArray
   }
 
-  def takeSample(withReplacement: Boolean, num: Int, seed: Long = Utils.random.nextLong): Array[T] =
-  {
-    var fraction = 0.0
-    var total = 0
-    val multiplier = 3.0
-    val initialCount = this.count()
-    var maxSelected = 0
+  /**
+   * Return a fixed-size sampled subset of this RDD in an array
+   *
+   * @param withReplacement whether sampling is done with replacement
+   * @param num size of the returned sample
+   * @param seed seed for the random number generator
+   * @return sample of specified size in an array
+   */
+  def takeSample(withReplacement: Boolean,
+      num: Int,
+      seed: Long = Utils.random.nextLong): Array[T] = {
+    val numStDev =  10.0
 
     if (num < 0) {
       throw new IllegalArgumentException("Negative number of elements requested")
+    } else if (num == 0) {
+      return new Array[T](0)
     }
 
+    val initialCount = this.count()
     if (initialCount == 0) {
       return new Array[T](0)
     }
 
-    if (initialCount > Integer.MAX_VALUE - 1) {
-      maxSelected = Integer.MAX_VALUE - 1
-    } else {
-      maxSelected = initialCount.toInt
+    val maxSampleSize = Int.MaxValue - (numStDev * math.sqrt(Int.MaxValue)).toInt
+    if (num > maxSampleSize) {
+      throw new IllegalArgumentException("Cannot support a sample size > Int.MaxValue - " +
+        s"$numStDev * math.sqrt(Int.MaxValue)")
     }
 
-    if (num > initialCount && !withReplacement) {
-      total = maxSelected
-      fraction = multiplier * (maxSelected + 1) / initialCount
-    } else {
-      fraction = multiplier * (num + 1) / initialCount
-      total = num
+    val rand = new Random(seed)
+    if (!withReplacement && num >= initialCount) {
+      return Utils.randomizeInPlace(this.collect(), rand)
     }
 
-    val rand = new Random(seed)
+    val fraction = SamplingUtils.computeFractionForSampleSize(num, initialCount,
+      withReplacement)
+
     var samples = this.sample(withReplacement, fraction, rand.nextInt()).collect()
 
     // If the first sample didn't turn out large enough, keep trying to take samples;
     // this shouldn't happen often because we use a big multiplier for the initial size
-    while (samples.length < total) {
+    var numIters = 0
+    while (samples.length < num) {
+      logWarning(s"Needed to re-sample due to insufficient sample size. Repeat #$numIters")
       samples = this.sample(withReplacement, fraction, rand.nextInt()).collect()
+      numIters += 1
     }
 
-    Utils.randomizeInPlace(samples, rand).take(total)
+    Utils.randomizeInPlace(samples, rand).take(num)
   }
 
   /**
diff --git a/core/src/main/scala/org/apache/spark/util/random/RandomSampler.scala b/core/src/main/scala/org/apache/spark/util/random/RandomSampler.scala
index 4dc8ada00a3e..247f10173f1e 100644
--- a/core/src/main/scala/org/apache/spark/util/random/RandomSampler.scala
+++ b/core/src/main/scala/org/apache/spark/util/random/RandomSampler.scala
@@ -70,7 +70,7 @@ class BernoulliSampler[T](lb: Double, ub: Double, complement: Boolean = false)
   }
 
   /**
-   *  Return a sampler with is the complement of the range specified of the current sampler.
+   *  Return a sampler that is the complement of the range specified of the current sampler.
    */
   def cloneComplement():  BernoulliSampler[T] = new BernoulliSampler[T](lb, ub, !complement)
 
diff --git a/core/src/main/scala/org/apache/spark/util/random/SamplingUtils.scala b/core/src/main/scala/org/apache/spark/util/random/SamplingUtils.scala
new file mode 100644
index 000000000000..a79e3ee756fc
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/util/random/SamplingUtils.scala
@@ -0,0 +1,55 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.util.random
+
+private[spark] object SamplingUtils {
+
+  /**
+   * Returns a sampling rate that guarantees a sample of size >= sampleSizeLowerBound 99.99% of
+   * the time.
+   *
+   * How the sampling rate is determined:
+   * Let p = num / total, where num is the sample size and total is the total number of
+   * datapoints in the RDD. We're trying to compute q > p such that
+   *   - when sampling with replacement, we're drawing each datapoint with prob_i ~ Pois(q),
+   *     where we want to guarantee Pr[s < num] < 0.0001 for s = sum(prob_i for i from 0 to total),
+   *     i.e. the failure rate of not having a sufficiently large sample < 0.0001.
+   *     Setting q = p + 5 * sqrt(p/total) is sufficient to guarantee 0.9999 success rate for
+   *     num > 12, but we need a slightly larger q (9 empirically determined).
+   *   - when sampling without replacement, we're drawing each datapoint with prob_i
+   *     ~ Binomial(total, fraction) and our choice of q guarantees 1-delta, or 0.9999 success
+   *     rate, where success rate is defined the same as in sampling with replacement.
+   *
+   * @param sampleSizeLowerBound sample size
+   * @param total size of RDD
+   * @param withReplacement whether sampling with replacement
+   * @return a sampling rate that guarantees sufficient sample size with 99.99% success rate
+   */
+  def computeFractionForSampleSize(sampleSizeLowerBound: Int, total: Long,
+      withReplacement: Boolean): Double = {
+    val fraction = sampleSizeLowerBound.toDouble / total
+    if (withReplacement) {
+      val numStDev = if (sampleSizeLowerBound < 12) 9 else 5
+      fraction + numStDev * math.sqrt(fraction / total)
+    } else {
+      val delta = 1e-4
+      val gamma = - math.log(delta) / total
+      math.min(1, fraction + gamma + math.sqrt(gamma * gamma + 2 * gamma * fraction))
+    }
+  }
+}
diff --git a/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala b/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala
index 2e2ccc5a1859..e94a1e76d410 100644
--- a/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala
+++ b/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala
@@ -505,55 +505,56 @@ class RDDSuite extends FunSuite with SharedSparkContext {
   }
 
   test("takeSample") {
-    val data = sc.parallelize(1 to 100, 2)
+    val n = 1000000
+    val data = sc.parallelize(1 to n, 2)
     
     for (num <- List(5, 20, 100)) {
       val sample = data.takeSample(withReplacement=false, num=num)
       assert(sample.size === num)        // Got exactly num elements
       assert(sample.toSet.size === num)  // Elements are distinct
-      assert(sample.forall(x => 1 <= x && x <= 100), "elements not in [1, 100]")
+      assert(sample.forall(x => 1 <= x && x <= n), s"elements not in [1, $n]")
     }
     for (seed <- 1 to 5) {
       val sample = data.takeSample(withReplacement=false, 20, seed)
       assert(sample.size === 20)        // Got exactly 20 elements
       assert(sample.toSet.size === 20)  // Elements are distinct
-      assert(sample.forall(x => 1 <= x && x <= 100), "elements not in [1, 100]")
+      assert(sample.forall(x => 1 <= x && x <= n), s"elements not in [1, $n]")
     }
     for (seed <- 1 to 5) {
-      val sample = data.takeSample(withReplacement=false, 200, seed)
+      val sample = data.takeSample(withReplacement=false, 100, seed)
       assert(sample.size === 100)        // Got only 100 elements
       assert(sample.toSet.size === 100)  // Elements are distinct
-      assert(sample.forall(x => 1 <= x && x <= 100), "elements not in [1, 100]")
+      assert(sample.forall(x => 1 <= x && x <= n), s"elements not in [1, $n]")
     }
     for (seed <- 1 to 5) {
       val sample = data.takeSample(withReplacement=true, 20, seed)
       assert(sample.size === 20)        // Got exactly 20 elements
-      assert(sample.forall(x => 1 <= x && x <= 100), "elements not in [1, 100]")
+      assert(sample.forall(x => 1 <= x && x <= n), s"elements not in [1, $n]")
     }
     {
       val sample = data.takeSample(withReplacement=true, num=20)
       assert(sample.size === 20)        // Got exactly 100 elements
       assert(sample.toSet.size <= 20, "sampling with replacement returned all distinct elements")
-      assert(sample.forall(x => 1 <= x && x <= 100), "elements not in [1, 100]")
+      assert(sample.forall(x => 1 <= x && x <= n), s"elements not in [1, $n]")
     }
     {
-      val sample = data.takeSample(withReplacement=true, num=100)
-      assert(sample.size === 100)        // Got exactly 100 elements
+      val sample = data.takeSample(withReplacement=true, num=n)
+      assert(sample.size === n)        // Got exactly 100 elements
       // Chance of getting all distinct elements is astronomically low, so test we got < 100
-      assert(sample.toSet.size < 100, "sampling with replacement returned all distinct elements")
-      assert(sample.forall(x => 1 <= x && x <= 100), "elements not in [1, 100]")
+      assert(sample.toSet.size < n, "sampling with replacement returned all distinct elements")
+      assert(sample.forall(x => 1 <= x && x <= n), s"elements not in [1, $n]")
     }
     for (seed <- 1 to 5) {
-      val sample = data.takeSample(withReplacement=true, 100, seed)
-      assert(sample.size === 100)        // Got exactly 100 elements
+      val sample = data.takeSample(withReplacement=true, n, seed)
+      assert(sample.size === n)        // Got exactly 100 elements
       // Chance of getting all distinct elements is astronomically low, so test we got < 100
-      assert(sample.toSet.size < 100, "sampling with replacement returned all distinct elements")
+      assert(sample.toSet.size < n, "sampling with replacement returned all distinct elements")
     }
     for (seed <- 1 to 5) {
-      val sample = data.takeSample(withReplacement=true, 200, seed)
-      assert(sample.size === 200)        // Got exactly 200 elements
+      val sample = data.takeSample(withReplacement=true, 2 * n, seed)
+      assert(sample.size === 2 * n)        // Got exactly 200 elements
       // Chance of getting all distinct elements is still quite low, so test we got < 100
-      assert(sample.toSet.size < 100, "sampling with replacement returned all distinct elements")
+      assert(sample.toSet.size < n, "sampling with replacement returned all distinct elements")
     }
   }
 
diff --git a/core/src/test/scala/org/apache/spark/util/random/SamplingUtilsSuite.scala b/core/src/test/scala/org/apache/spark/util/random/SamplingUtilsSuite.scala
new file mode 100644
index 000000000000..accfe2e9b7f2
--- /dev/null
+++ b/core/src/test/scala/org/apache/spark/util/random/SamplingUtilsSuite.scala
@@ -0,0 +1,46 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.util.random
+
+import org.apache.commons.math3.distribution.{BinomialDistribution, PoissonDistribution}
+import org.scalatest.FunSuite
+
+class SamplingUtilsSuite extends FunSuite {
+
+  test("computeFraction") {
+    // test that the computed fraction guarantees enough data points
+    // in the sample with a failure rate <= 0.0001
+    val n = 100000
+
+    for (s <- 1 to 15) {
+      val frac = SamplingUtils.computeFractionForSampleSize(s, n, true)
+      val poisson = new PoissonDistribution(frac * n)
+      assert(poisson.inverseCumulativeProbability(0.0001) >= s, "Computed fraction is too low")
+    }
+    for (s <- List(20, 100, 1000)) {
+      val frac = SamplingUtils.computeFractionForSampleSize(s, n, true)
+      val poisson = new PoissonDistribution(frac * n)
+      assert(poisson.inverseCumulativeProbability(0.0001) >= s, "Computed fraction is too low")
+    }
+    for (s <- List(1, 10, 100, 1000)) {
+      val frac = SamplingUtils.computeFractionForSampleSize(s, n, false)
+      val binomial = new BinomialDistribution(n, frac)
+      assert(binomial.inverseCumulativeProbability(0.0001)*n >= s, "Computed fraction is too low")
+    }
+  }
+}
diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index 8b4885d3bbbd..2d60a44f04f6 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -349,6 +349,7 @@ object SparkBuild extends Build {
     libraryDependencies ++= Seq(
         "com.google.guava"           % "guava"            % "14.0.1",
         "org.apache.commons"         % "commons-lang3"    % "3.3.2",
+        "org.apache.commons"         % "commons-math3"    % "3.3" % "test",
         "com.google.code.findbugs"   % "jsr305"           % "1.3.9",
         "log4j"                      % "log4j"            % "1.2.17",
         "org.slf4j"                  % "slf4j-api"        % slf4jVersion,
diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py
index 735389c69831..ddd22850a819 100644
--- a/python/pyspark/rdd.py
+++ b/python/pyspark/rdd.py
@@ -31,6 +31,7 @@
 import warnings
 import heapq
 from random import Random
+from math import sqrt, log
 
 from pyspark.serializers import NoOpSerializer, CartesianDeserializer, \
     BatchedSerializer, CloudPickleSerializer, PairDeserializer, \
@@ -202,9 +203,9 @@ def cache(self):
 
     def persist(self, storageLevel):
         """
-        Set this RDD's storage level to persist its values across operations after the first time
-        it is computed. This can only be used to assign a new storage level if the RDD does not
-        have a storage level set yet.
+        Set this RDD's storage level to persist its values across operations
+        after the first time it is computed. This can only be used to assign
+        a new storage level if the RDD does not have a storage level set yet.
         """
         self.is_cached = True
         javaStorageLevel = self.ctx._getJavaStorageLevel(storageLevel)
@@ -213,7 +214,8 @@ def persist(self, storageLevel):
 
     def unpersist(self):
         """
-        Mark the RDD as non-persistent, and remove all blocks for it from memory and disk.
+        Mark the RDD as non-persistent, and remove all blocks for it from
+        memory and disk.
         """
         self.is_cached = False
         self._jrdd.unpersist()
@@ -357,48 +359,87 @@ def sample(self, withReplacement, fraction, seed=None):
     # this is ported from scala/spark/RDD.scala
     def takeSample(self, withReplacement, num, seed=None):
         """
-        Return a fixed-size sampled subset of this RDD (currently requires numpy).
+        Return a fixed-size sampled subset of this RDD (currently requires
+        numpy).
 
-        >>> sc.parallelize(range(0, 10)).takeSample(True, 10, 1) #doctest: +SKIP
-        [4, 2, 1, 8, 2, 7, 0, 4, 1, 4]
+        >>> rdd = sc.parallelize(range(0, 10))
+        >>> len(rdd.takeSample(True, 20, 1))
+        20
+        >>> len(rdd.takeSample(False, 5, 2))
+        5
+        >>> len(rdd.takeSample(False, 15, 3))
+        10
         """
+        numStDev = 10.0
+
+        if num < 0:
+            raise ValueError("Sample size cannot be negative.")
+        elif num == 0:
+            return []
 
-        fraction = 0.0
-        total = 0
-        multiplier = 3.0
         initialCount = self.count()
-        maxSelected = 0
+        if initialCount == 0:
+            return []
 
-        if (num < 0):
-            raise ValueError
+        rand = Random(seed)
 
-        if (initialCount == 0):
-            return list()
+        if (not withReplacement) and num >= initialCount:
+            # shuffle current RDD and return
+            samples = self.collect()
+            rand.shuffle(samples)
+            return samples
 
-        if initialCount > sys.maxint - 1:
-            maxSelected = sys.maxint - 1
-        else:
-            maxSelected = initialCount
-
-        if num > initialCount and not withReplacement:
-            total = maxSelected
-            fraction = multiplier * (maxSelected + 1) / initialCount
-        else:
-            fraction = multiplier * (num + 1) / initialCount
-            total = num
+        maxSampleSize = sys.maxint - int(numStDev * sqrt(sys.maxint))
+        if num > maxSampleSize:
+            raise ValueError("Sample size cannot be greater than %d." % maxSampleSize)
 
+        fraction = RDD._computeFractionForSampleSize(num, initialCount, withReplacement)
         samples = self.sample(withReplacement, fraction, seed).collect()
 
         # If the first sample didn't turn out large enough, keep trying to take samples;
         # this shouldn't happen often because we use a big multiplier for their initial size.
         # See: scala/spark/RDD.scala
-        rand = Random(seed)
-        while len(samples) < total:
-            samples = self.sample(withReplacement, fraction, rand.randint(0, sys.maxint)).collect()
-
-        sampler = RDDSampler(withReplacement, fraction, rand.randint(0, sys.maxint))
-        sampler.shuffle(samples)
-        return samples[0:total]
+        while len(samples) < num:
+            # TODO: add log warning for when more than one iteration was run
+            seed = rand.randint(0, sys.maxint)
+            samples = self.sample(withReplacement, fraction, seed).collect()
+
+        rand.shuffle(samples)
+
+        return samples[0:num]
+
+    @staticmethod
+    def _computeFractionForSampleSize(sampleSizeLowerBound, total, withReplacement):
+        """
+        Returns a sampling rate that guarantees a sample of
+        size >= sampleSizeLowerBound 99.99% of the time.
+
+        How the sampling rate is determined:
+        Let p = num / total, where num is the sample size and total is the
+        total number of data points in the RDD. We're trying to compute
+        q > p such that
+          - when sampling with replacement, we're drawing each data point
+            with prob_i ~ Pois(q), where we want to guarantee
+            Pr[s < num] < 0.0001 for s = sum(prob_i for i from 0 to
+            total), i.e. the failure rate of not having a sufficiently large
+            sample < 0.0001. Setting q = p + 5 * sqrt(p/total) is sufficient
+            to guarantee 0.9999 success rate for num > 12, but we need a
+            slightly larger q (9 empirically determined).
+          - when sampling without replacement, we're drawing each data point
+            with prob_i ~ Binomial(total, fraction) and our choice of q
+            guarantees 1-delta, or 0.9999 success rate, where success rate is
+            defined the same as in sampling with replacement.
+        """
+        fraction = float(sampleSizeLowerBound) / total
+        if withReplacement:
+            numStDev = 5
+            if (sampleSizeLowerBound < 12):
+                numStDev = 9
+            return fraction + numStDev * sqrt(fraction / total)
+        else:
+            delta = 0.00005
+            gamma = - log(delta) / total
+            return min(1, fraction + gamma + sqrt(gamma * gamma + 2 * gamma * fraction))
 
     def union(self, other):
         """
@@ -422,8 +463,8 @@ def union(self, other):
 
     def intersection(self, other):
         """
-        Return the intersection of this RDD and another one. The output will not
-        contain any duplicate elements, even if the input RDDs did.
+        Return the intersection of this RDD and another one. The output will
+        not contain any duplicate elements, even if the input RDDs did.
 
         Note that this method performs a shuffle internally.
 
@@ -665,8 +706,8 @@ def aggregate(self, zeroValue, seqOp, combOp):
         modify C{t2}.
 
         The first function (seqOp) can return a different result type, U, than
-        the type of this RDD. Thus, we need one operation for merging a T into an U
-        and one operation for merging two U
+        the type of this RDD. Thus, we need one operation for merging a T into
+        an U and one operation for merging two U
 
         >>> seqOp = (lambda x, y: (x[0] + y, x[1] + 1))
         >>> combOp = (lambda x, y: (x[0] + y[0], x[1] + y[1]))
@@ -759,8 +800,9 @@ def stdev(self):
 
     def sampleStdev(self):
         """
-        Compute the sample standard deviation of this RDD's elements (which corrects for bias in
-        estimating the standard deviation by dividing by N-1 instead of N).
+        Compute the sample standard deviation of this RDD's elements (which
+        corrects for bias in estimating the standard deviation by dividing by
+        N-1 instead of N).
 
         >>> sc.parallelize([1, 2, 3]).sampleStdev()
         1.0
@@ -769,8 +811,8 @@ def sampleStdev(self):
 
     def sampleVariance(self):
         """
-        Compute the sample variance of this RDD's elements (which corrects for bias in
-        estimating the variance by dividing by N-1 instead of N).
+        Compute the sample variance of this RDD's elements (which corrects
+        for bias in estimating the variance by dividing by N-1 instead of N).
 
         >>> sc.parallelize([1, 2, 3]).sampleVariance()
         1.0
@@ -822,8 +864,8 @@ def merge(a, b):
 
     def takeOrdered(self, num, key=None):
         """
-        Get the N elements from a RDD ordered in ascending order or as specified
-        by the optional key function.
+        Get the N elements from a RDD ordered in ascending order or as
+        specified by the optional key function.
 
         >>> sc.parallelize([10, 1, 2, 9, 3, 4, 5, 6, 7]).takeOrdered(6)
         [1, 2, 3, 4, 5, 6]
@@ -912,8 +954,9 @@ def first(self):
 
     def saveAsPickleFile(self, path, batchSize=10):
         """
-        Save this RDD as a SequenceFile of serialized objects. The serializer used is
-        L{pyspark.serializers.PickleSerializer}, default batch size is 10.
+        Save this RDD as a SequenceFile of serialized objects. The serializer
+        used is L{pyspark.serializers.PickleSerializer}, default batch size
+        is 10.
 
         >>> tmpFile = NamedTemporaryFile(delete=True)
         >>> tmpFile.close()
@@ -1195,9 +1238,10 @@ def createZero():
 
     def foldByKey(self, zeroValue, func, numPartitions=None):
         """
-        Merge the values for each key using an associative function "func" and a neutral "zeroValue"
-        which may be added to the result an arbitrary number of times, and must not change
-        the result (e.g., 0 for addition, or 1 for multiplication.).
+        Merge the values for each key using an associative function "func"
+        and a neutral "zeroValue" which may be added to the result an
+        arbitrary number of times, and must not change the result
+        (e.g., 0 for addition, or 1 for multiplication.).
 
         >>> rdd = sc.parallelize([("a", 1), ("b", 1), ("a", 1)])
         >>> from operator import add
@@ -1217,8 +1261,8 @@ def groupByKey(self, numPartitions=None):
         Hash-partitions the resulting RDD with into numPartitions partitions.
 
         Note: If you are grouping in order to perform an aggregation (such as a
-        sum or average) over each key, using reduceByKey will provide much better
-        performance.
+        sum or average) over each key, using reduceByKey will provide much
+        better performance.
 
         >>> x = sc.parallelize([("a", 1), ("b", 1), ("a", 1)])
         >>> map((lambda (x,y): (x, list(y))), sorted(x.groupByKey().collect()))
@@ -1278,8 +1322,8 @@ def groupWith(self, other):
     def cogroup(self, other, numPartitions=None):
         """
         For each key k in C{self} or C{other}, return a resulting RDD that
-        contains a tuple with the list of values for that key in C{self} as well
-        as C{other}.
+        contains a tuple with the list of values for that key in C{self} as
+        well as C{other}.
 
         >>> x = sc.parallelize([("a", 1), ("b", 4)])
         >>> y = sc.parallelize([("a", 2)])
@@ -1290,8 +1334,8 @@ def cogroup(self, other, numPartitions=None):
 
     def subtractByKey(self, other, numPartitions=None):
         """
-        Return each (key, value) pair in C{self} that has no pair with matching key
-        in C{other}.
+        Return each (key, value) pair in C{self} that has no pair with matching
+        key in C{other}.
 
         >>> x = sc.parallelize([("a", 1), ("b", 4), ("b", 5), ("a", 2)])
         >>> y = sc.parallelize([("a", 3), ("c", None)])
@@ -1329,10 +1373,10 @@ def repartition(self, numPartitions):
         """
          Return a new RDD that has exactly numPartitions partitions.
 
-         Can increase or decrease the level of parallelism in this RDD. Internally, this uses
-         a shuffle to redistribute data.
-         If you are decreasing the number of partitions in this RDD, consider using `coalesce`,
-         which can avoid performing a shuffle.
+         Can increase or decrease the level of parallelism in this RDD.
+         Internally, this uses a shuffle to redistribute data.
+         If you are decreasing the number of partitions in this RDD, consider
+         using `coalesce`, which can avoid performing a shuffle.
          >>> rdd = sc.parallelize([1,2,3,4,5,6,7], 4)
          >>> sorted(rdd.glom().collect())
          [[1], [2, 3], [4, 5], [6, 7]]
@@ -1357,9 +1401,10 @@ def coalesce(self, numPartitions, shuffle=False):
 
     def zip(self, other):
         """
-        Zips this RDD with another one, returning key-value pairs with the first element in each RDD
-        second element in each RDD, etc. Assumes that the two RDDs have the same number of
-        partitions and the same number of elements in each partition (e.g. one was made through
+        Zips this RDD with another one, returning key-value pairs with the
+        first element in each RDD second element in each RDD, etc. Assumes
+        that the two RDDs have the same number of partitions and the same
+        number of elements in each partition (e.g. one was made through
         a map on the other).
 
         >>> x = sc.parallelize(range(0,5))

From 44daec5abd4c271ea0003ecdabab92cc958dea13 Mon Sep 17 00:00:00 2001
From: Andrew Or <andrewor14@gmail.com>
Date: Thu, 12 Jun 2014 20:40:58 -0700
Subject: [PATCH 415/810] [Minor] Fix style, formatting and naming in
 BlockManager etc.

This is a precursor to a bigger change. I wanted to separate out the relatively insignificant changes so the ultimate PR is not inflated.

(Warning: this PR is full of unimportant nitpicks)

Author: Andrew Or <andrewor14@gmail.com>

Closes #1058 from andrewor14/bm-minor and squashes the following commits:

8e12eaf [Andrew Or] SparkException -> BlockException
c36fd53 [Andrew Or] Make parts of BlockManager more readable
0a5f378 [Andrew Or] Entry -> MemoryEntry
e9762a5 [Andrew Or] Tone down string interpolation (minor reverts)
c4de9ac [Andrew Or] Merge branch 'master' of github.com:apache/spark into bm-minor
b3470f1 [Andrew Or] More string interpolation (minor)
7f9dcab [Andrew Or] Use string interpolation (minor)
94a425b [Andrew Or] Refactor against duplicate code + minor changes
8a6a7dc [Andrew Or] Exception -> SparkException
97c410f [Andrew Or] Deal with MIMA excludes
2480f1d [Andrew Or] Fixes in StorgeLevel.scala
abb0163 [Andrew Or] Style, formatting and naming fixes
---
 .../scala/org/apache/spark/CacheManager.scala |  20 +-
 .../org/apache/spark/storage/BlockInfo.scala  |  22 +-
 .../apache/spark/storage/BlockManager.scala   | 479 +++++++++---------
 .../spark/storage/DiskBlockManager.scala      |  21 +-
 .../org/apache/spark/storage/DiskStore.scala  |  24 +-
 .../apache/spark/storage/MemoryStore.scala    |  32 +-
 .../apache/spark/storage/StorageLevel.scala   |  88 ++--
 .../apache/spark/storage/TachyonStore.scala   |  39 +-
 project/MimaExcludes.scala                    |   7 +-
 9 files changed, 362 insertions(+), 370 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/CacheManager.scala b/core/src/main/scala/org/apache/spark/CacheManager.scala
index 811610c657b6..315ed91f81df 100644
--- a/core/src/main/scala/org/apache/spark/CacheManager.scala
+++ b/core/src/main/scala/org/apache/spark/CacheManager.scala
@@ -32,10 +32,14 @@ private[spark] class CacheManager(blockManager: BlockManager) extends Logging {
   private val loading = new HashSet[RDDBlockId]()
 
   /** Gets or computes an RDD split. Used by RDD.iterator() when an RDD is cached. */
-  def getOrCompute[T](rdd: RDD[T], split: Partition, context: TaskContext,
+  def getOrCompute[T](
+      rdd: RDD[T],
+      split: Partition,
+      context: TaskContext,
       storageLevel: StorageLevel): Iterator[T] = {
+
     val key = RDDBlockId(rdd.id, split.index)
-    logDebug("Looking for partition " + key)
+    logDebug(s"Looking for partition $key")
     blockManager.get(key) match {
       case Some(values) =>
         // Partition is already materialized, so just return its values
@@ -45,7 +49,7 @@ private[spark] class CacheManager(blockManager: BlockManager) extends Logging {
         // Mark the split as loading (unless someone else marks it first)
         loading.synchronized {
           if (loading.contains(key)) {
-            logInfo("Another thread is loading %s, waiting for it to finish...".format(key))
+            logInfo(s"Another thread is loading $key, waiting for it to finish...")
             while (loading.contains(key)) {
               try {
                 loading.wait()
@@ -54,7 +58,7 @@ private[spark] class CacheManager(blockManager: BlockManager) extends Logging {
                   logWarning(s"Got an exception while waiting for another thread to load $key", e)
               }
             }
-            logInfo("Finished waiting for %s".format(key))
+            logInfo(s"Finished waiting for $key")
             /* See whether someone else has successfully loaded it. The main way this would fail
              * is for the RDD-level cache eviction policy if someone else has loaded the same RDD
              * partition but we didn't want to make space for it. However, that case is unlikely
@@ -64,7 +68,7 @@ private[spark] class CacheManager(blockManager: BlockManager) extends Logging {
               case Some(values) =>
                 return new InterruptibleIterator(context, values.asInstanceOf[Iterator[T]])
               case None =>
-                logInfo("Whoever was loading %s failed; we'll try it ourselves".format(key))
+                logInfo(s"Whoever was loading $key failed; we'll try it ourselves")
                 loading.add(key)
             }
           } else {
@@ -73,7 +77,7 @@ private[spark] class CacheManager(blockManager: BlockManager) extends Logging {
         }
         try {
           // If we got here, we have to load the split
-          logInfo("Partition %s not found, computing it".format(key))
+          logInfo(s"Partition $key not found, computing it")
           val computedValues = rdd.computeOrReadCheckpoint(split, context)
 
           // Persist the result, so long as the task is not running locally
@@ -97,8 +101,8 @@ private[spark] class CacheManager(blockManager: BlockManager) extends Logging {
                 case Some(values) =>
                   values.asInstanceOf[Iterator[T]]
                 case None =>
-                  logInfo("Failure to store %s".format(key))
-                  throw new Exception("Block manager failed to return persisted valued")
+                  logInfo(s"Failure to store $key")
+                  throw new SparkException("Block manager failed to return persisted value")
               }
             } else {
               // In this case the RDD is cached to an array buffer. This will save the results
diff --git a/core/src/main/scala/org/apache/spark/storage/BlockInfo.scala b/core/src/main/scala/org/apache/spark/storage/BlockInfo.scala
index c8f397609a0b..22fdf73e9d1f 100644
--- a/core/src/main/scala/org/apache/spark/storage/BlockInfo.scala
+++ b/core/src/main/scala/org/apache/spark/storage/BlockInfo.scala
@@ -29,9 +29,9 @@ private[storage] class BlockInfo(val level: StorageLevel, val tellMaster: Boolea
   setInitThread()
 
   private def setInitThread() {
-    // Set current thread as init thread - waitForReady will not block this thread
-    // (in case there is non trivial initialization which ends up calling waitForReady as part of
-    // initialization itself)
+    /* Set current thread as init thread - waitForReady will not block this thread
+     * (in case there is non trivial initialization which ends up calling waitForReady
+     * as part of initialization itself) */
     BlockInfo.blockInfoInitThreads.put(this, Thread.currentThread())
   }
 
@@ -42,7 +42,9 @@ private[storage] class BlockInfo(val level: StorageLevel, val tellMaster: Boolea
   def waitForReady(): Boolean = {
     if (pending && initThread != Thread.currentThread()) {
       synchronized {
-        while (pending) this.wait()
+        while (pending) {
+          this.wait()
+        }
       }
     }
     !failed
@@ -50,8 +52,8 @@ private[storage] class BlockInfo(val level: StorageLevel, val tellMaster: Boolea
 
   /** Mark this BlockInfo as ready (i.e. block is finished writing) */
   def markReady(sizeInBytes: Long) {
-    require (sizeInBytes >= 0, "sizeInBytes was negative: " + sizeInBytes)
-    assert (pending)
+    require(sizeInBytes >= 0, s"sizeInBytes was negative: $sizeInBytes")
+    assert(pending)
     size = sizeInBytes
     BlockInfo.blockInfoInitThreads.remove(this)
     synchronized {
@@ -61,7 +63,7 @@ private[storage] class BlockInfo(val level: StorageLevel, val tellMaster: Boolea
 
   /** Mark this BlockInfo as ready but failed */
   def markFailure() {
-    assert (pending)
+    assert(pending)
     size = BlockInfo.BLOCK_FAILED
     BlockInfo.blockInfoInitThreads.remove(this)
     synchronized {
@@ -71,9 +73,9 @@ private[storage] class BlockInfo(val level: StorageLevel, val tellMaster: Boolea
 }
 
 private object BlockInfo {
-  // initThread is logically a BlockInfo field, but we store it here because
-  // it's only needed while this block is in the 'pending' state and we want
-  // to minimize BlockInfo's memory footprint.
+  /* initThread is logically a BlockInfo field, but we store it here because
+   * it's only needed while this block is in the 'pending' state and we want
+   * to minimize BlockInfo's memory footprint. */
   private val blockInfoInitThreads = new ConcurrentHashMap[BlockInfo, Thread]
 
   private val BLOCK_PENDING: Long = -1L
diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala
index 9cd79d262ea5..f52bc7075104 100644
--- a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala
+++ b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala
@@ -28,46 +28,48 @@ import scala.util.Random
 import akka.actor.{ActorSystem, Cancellable, Props}
 import sun.nio.ch.DirectBuffer
 
-import org.apache.spark.{Logging, MapOutputTracker, SecurityManager, SparkConf, SparkEnv, SparkException}
+import org.apache.spark._
 import org.apache.spark.io.CompressionCodec
 import org.apache.spark.network._
 import org.apache.spark.serializer.Serializer
 import org.apache.spark.util._
 
-private[spark] sealed trait Values
-
-private[spark] case class ByteBufferValues(buffer: ByteBuffer) extends Values
-private[spark] case class IteratorValues(iterator: Iterator[Any]) extends Values
-private[spark] case class ArrayBufferValues(buffer: ArrayBuffer[Any]) extends Values
+private[spark] sealed trait BlockValues
+private[spark] case class ByteBufferValues(buffer: ByteBuffer) extends BlockValues
+private[spark] case class IteratorValues(iterator: Iterator[Any]) extends BlockValues
+private[spark] case class ArrayBufferValues(buffer: ArrayBuffer[Any]) extends BlockValues
 
 private[spark] class BlockManager(
     executorId: String,
     actorSystem: ActorSystem,
     val master: BlockManagerMaster,
-    val defaultSerializer: Serializer,
+    defaultSerializer: Serializer,
     maxMemory: Long,
-    val _conf: SparkConf,
+    val conf: SparkConf,
     securityManager: SecurityManager,
     mapOutputTracker: MapOutputTracker)
   extends Logging {
 
-  def conf = _conf
   val shuffleBlockManager = new ShuffleBlockManager(this)
   val diskBlockManager = new DiskBlockManager(shuffleBlockManager,
-    conf.get("spark.local.dir",  System.getProperty("java.io.tmpdir")))
+    conf.get("spark.local.dir", System.getProperty("java.io.tmpdir")))
+  val connectionManager = new ConnectionManager(0, conf, securityManager)
+
+  implicit val futureExecContext = connectionManager.futureExecContext
 
   private val blockInfo = new TimeStampedHashMap[BlockId, BlockInfo]
 
+  // Actual storage of where blocks are kept
+  private var tachyonInitialized = false
   private[storage] val memoryStore = new MemoryStore(this, maxMemory)
   private[storage] val diskStore = new DiskStore(this, diskBlockManager)
-  var tachyonInitialized = false
   private[storage] lazy val tachyonStore: TachyonStore = {
     val storeDir = conf.get("spark.tachyonStore.baseDir", "/tmp_spark_tachyon")
     val appFolderName = conf.get("spark.tachyonStore.folderName")
-    val tachyonStorePath = s"${storeDir}/${appFolderName}/${this.executorId}"
+    val tachyonStorePath = s"$storeDir/$appFolderName/${this.executorId}"
     val tachyonMaster = conf.get("spark.tachyonStore.url",  "tachyon://localhost:19998")
-    val tachyonBlockManager = new TachyonBlockManager(
-      shuffleBlockManager, tachyonStorePath, tachyonMaster)
+    val tachyonBlockManager =
+      new TachyonBlockManager(shuffleBlockManager, tachyonStorePath, tachyonMaster)
     tachyonInitialized = true
     new TachyonStore(this, tachyonBlockManager)
   }
@@ -79,43 +81,39 @@ private[spark] class BlockManager(
     if (useNetty) diskBlockManager.startShuffleBlockSender(nettyPortConfig) else 0
   }
 
-  val connectionManager = new ConnectionManager(0, conf, securityManager)
-  implicit val futureExecContext = connectionManager.futureExecContext
-
   val blockManagerId = BlockManagerId(
     executorId, connectionManager.id.host, connectionManager.id.port, nettyPort)
 
   // Max megabytes of data to keep in flight per reducer (to avoid over-allocating memory
   // for receiving shuffle outputs)
-  val maxBytesInFlight =
-    conf.getLong("spark.reducer.maxMbInFlight", 48) * 1024 * 1024
+  val maxBytesInFlight = conf.getLong("spark.reducer.maxMbInFlight", 48) * 1024 * 1024
 
   // Whether to compress broadcast variables that are stored
-  val compressBroadcast = conf.getBoolean("spark.broadcast.compress", true)
+  private val compressBroadcast = conf.getBoolean("spark.broadcast.compress", true)
   // Whether to compress shuffle output that are stored
-  val compressShuffle = conf.getBoolean("spark.shuffle.compress", true)
+  private val compressShuffle = conf.getBoolean("spark.shuffle.compress", true)
   // Whether to compress RDD partitions that are stored serialized
-  val compressRdds = conf.getBoolean("spark.rdd.compress", false)
+  private val compressRdds = conf.getBoolean("spark.rdd.compress", false)
   // Whether to compress shuffle output temporarily spilled to disk
-  val compressShuffleSpill = conf.getBoolean("spark.shuffle.spill.compress", true)
+  private val compressShuffleSpill = conf.getBoolean("spark.shuffle.spill.compress", true)
 
-  val heartBeatFrequency = BlockManager.getHeartBeatFrequency(conf)
-
-  val slaveActor = actorSystem.actorOf(Props(new BlockManagerSlaveActor(this, mapOutputTracker)),
+  private val slaveActor = actorSystem.actorOf(
+    Props(new BlockManagerSlaveActor(this, mapOutputTracker)),
     name = "BlockManagerActor" + BlockManager.ID_GENERATOR.next)
 
-  // Pending re-registration action being executed asynchronously or null if none
-  // is pending. Accesses should synchronize on asyncReregisterLock.
-  var asyncReregisterTask: Future[Unit] = null
-  val asyncReregisterLock = new Object
+  // Pending re-registration action being executed asynchronously or null if none is pending.
+  // Accesses should synchronize on asyncReregisterLock.
+  private var asyncReregisterTask: Future[Unit] = null
+  private val asyncReregisterLock = new Object
 
-  private def heartBeat() {
+  private def heartBeat(): Unit = {
     if (!master.sendHeartBeat(blockManagerId)) {
       reregister()
     }
   }
 
-  var heartBeatTask: Cancellable = null
+  private val heartBeatFrequency = BlockManager.getHeartBeatFrequency(conf)
+  private var heartBeatTask: Cancellable = null
 
   private val metadataCleaner = new MetadataCleaner(
     MetadataCleanerType.BLOCK_MANAGER, this.dropOldNonBroadcastBlocks, conf)
@@ -124,11 +122,11 @@ private[spark] class BlockManager(
 
   initialize()
 
-  // The compression codec to use. Note that the "lazy" val is necessary because we want to delay
-  // the initialization of the compression codec until it is first used. The reason is that a Spark
-  // program could be using a user-defined codec in a third party jar, which is loaded in
-  // Executor.updateDependencies. When the BlockManager is initialized, user level jars hasn't been
-  // loaded yet.
+  /* The compression codec to use. Note that the "lazy" val is necessary because we want to delay
+   * the initialization of the compression codec until it is first used. The reason is that a Spark
+   * program could be using a user-defined codec in a third party jar, which is loaded in
+   * Executor.updateDependencies. When the BlockManager is initialized, user level jars hasn't been
+   * loaded yet. */
   private lazy val compressionCodec: CompressionCodec = CompressionCodec.createCodec(conf)
 
   /**
@@ -150,7 +148,7 @@ private[spark] class BlockManager(
    * Initialize the BlockManager. Register to the BlockManagerMaster, and start the
    * BlockManagerWorker actor.
    */
-  private def initialize() {
+  private def initialize(): Unit = {
     master.registerBlockManager(blockManagerId, maxMemory, slaveActor)
     BlockManagerWorker.startBlockManagerWorker(this)
     if (!BlockManager.getDisableHeartBeatsForTesting(conf)) {
@@ -170,12 +168,12 @@ private[spark] class BlockManager(
    * heart beat attempt or new block registration and another try to re-register all blocks
    * will be made then.
    */
-  private def reportAllBlocks() {
-    logInfo("Reporting " + blockInfo.size + " blocks to the master.")
+  private def reportAllBlocks(): Unit = {
+    logInfo(s"Reporting ${blockInfo.size} blocks to the master.")
     for ((blockId, info) <- blockInfo) {
       val status = getCurrentBlockStatus(blockId, info)
       if (!tryToReportBlockStatus(blockId, info, status)) {
-        logError("Failed to report " + blockId + " to master; giving up.")
+        logError(s"Failed to report $blockId to master; giving up.")
         return
       }
     }
@@ -187,7 +185,7 @@ private[spark] class BlockManager(
    *
    * Note that this method must be called without any BlockInfo locks held.
    */
-  def reregister() {
+  private def reregister(): Unit = {
     // TODO: We might need to rate limit re-registering.
     logInfo("BlockManager re-registering with master")
     master.registerBlockManager(blockManagerId, maxMemory, slaveActor)
@@ -197,7 +195,7 @@ private[spark] class BlockManager(
   /**
    * Re-register with the master sometime soon.
    */
-  def asyncReregister() {
+  private def asyncReregister(): Unit = {
     asyncReregisterLock.synchronized {
       if (asyncReregisterTask == null) {
         asyncReregisterTask = Future[Unit] {
@@ -213,7 +211,7 @@ private[spark] class BlockManager(
   /**
    * For testing. Wait for any pending asynchronous re-registration; otherwise, do nothing.
    */
-  def waitForAsyncReregister() {
+  def waitForAsyncReregister(): Unit = {
     val task = asyncReregisterTask
     if (task != null) {
       Await.ready(task, Duration.Inf)
@@ -251,18 +249,18 @@ private[spark] class BlockManager(
    * it is still valid). This ensures that update in master will compensate for the increase in
    * memory on slave.
    */
-  def reportBlockStatus(
+  private def reportBlockStatus(
       blockId: BlockId,
       info: BlockInfo,
       status: BlockStatus,
-      droppedMemorySize: Long = 0L) {
+      droppedMemorySize: Long = 0L): Unit = {
     val needReregister = !tryToReportBlockStatus(blockId, info, status, droppedMemorySize)
     if (needReregister) {
-      logInfo("Got told to re-register updating block " + blockId)
+      logInfo(s"Got told to re-register updating block $blockId")
       // Re-registering will report our new block for free.
       asyncReregister()
     }
-    logDebug("Told master about block " + blockId)
+    logDebug(s"Told master about block $blockId")
   }
 
   /**
@@ -293,10 +291,10 @@ private[spark] class BlockManager(
    * and the updated in-memory and on-disk sizes.
    */
   private def getCurrentBlockStatus(blockId: BlockId, info: BlockInfo): BlockStatus = {
-    val (newLevel, inMemSize, onDiskSize, inTachyonSize) = info.synchronized {
+    info.synchronized {
       info.level match {
         case null =>
-          (StorageLevel.NONE, 0L, 0L, 0L)
+          BlockStatus(StorageLevel.NONE, 0L, 0L, 0L)
         case level =>
           val inMem = level.useMemory && memoryStore.contains(blockId)
           val inTachyon = level.useOffHeap && tachyonStore.contains(blockId)
@@ -307,19 +305,18 @@ private[spark] class BlockManager(
           val memSize = if (inMem) memoryStore.getSize(blockId) else 0L
           val tachyonSize = if (inTachyon) tachyonStore.getSize(blockId) else 0L
           val diskSize = if (onDisk) diskStore.getSize(blockId) else 0L
-          (storageLevel, memSize, diskSize, tachyonSize)
+          BlockStatus(storageLevel, memSize, diskSize, tachyonSize)
       }
     }
-    BlockStatus(newLevel, inMemSize, onDiskSize, inTachyonSize)
   }
 
   /**
    * Get locations of an array of blocks.
    */
-  def getLocationBlockIds(blockIds: Array[BlockId]): Array[Seq[BlockManagerId]] = {
+  private def getLocationBlockIds(blockIds: Array[BlockId]): Array[Seq[BlockManagerId]] = {
     val startTimeMs = System.currentTimeMillis
     val locations = master.getLocations(blockIds).toArray
-    logDebug("Got multiple block location in " + Utils.getUsedTimeMs(startTimeMs))
+    logDebug("Got multiple block location in %s".format(Utils.getUsedTimeMs(startTimeMs)))
     locations
   }
 
@@ -329,15 +326,16 @@ private[spark] class BlockManager(
    * never deletes (recent) items.
    */
   def getLocalFromDisk(blockId: BlockId, serializer: Serializer): Option[Iterator[Any]] = {
-    diskStore.getValues(blockId, serializer).orElse(
-      sys.error("Block " + blockId + " not found on disk, though it should be"))
+    diskStore.getValues(blockId, serializer).orElse {
+      throw new BlockException(blockId, s"Block $blockId not found on disk, though it should be")
+    }
   }
 
   /**
    * Get block from local block manager.
    */
   def getLocal(blockId: BlockId): Option[Iterator[Any]] = {
-    logDebug("Getting local block " + blockId)
+    logDebug(s"Getting local block $blockId")
     doGetLocal(blockId, asValues = true).asInstanceOf[Option[Iterator[Any]]]
   }
 
@@ -345,7 +343,7 @@ private[spark] class BlockManager(
    * Get block from the local block manager as serialized bytes.
    */
   def getLocalBytes(blockId: BlockId): Option[ByteBuffer] = {
-    logDebug("Getting local block " + blockId + " as bytes")
+    logDebug(s"Getting local block $blockId as bytes")
     // As an optimization for map output fetches, if the block is for a shuffle, return it
     // without acquiring a lock; the disk store never deletes (recent) items so this should work
     if (blockId.isShuffle) {
@@ -353,7 +351,8 @@ private[spark] class BlockManager(
         case Some(bytes) =>
           Some(bytes)
         case None =>
-          throw new Exception("Block " + blockId + " not found on disk, though it should be")
+          throw new BlockException(
+            blockId, s"Block $blockId not found on disk, though it should be")
       }
     } else {
       doGetLocal(blockId, asValues = false).asInstanceOf[Option[ByteBuffer]]
@@ -368,16 +367,16 @@ private[spark] class BlockManager(
         // If another thread is writing the block, wait for it to become ready.
         if (!info.waitForReady()) {
           // If we get here, the block write failed.
-          logWarning("Block " + blockId + " was marked as failure.")
+          logWarning(s"Block $blockId was marked as failure.")
           return None
         }
 
         val level = info.level
-        logDebug("Level for block " + blockId + " is " + level)
+        logDebug(s"Level for block $blockId is $level")
 
         // Look for the block in memory
         if (level.useMemory) {
-          logDebug("Getting block " + blockId + " from memory")
+          logDebug(s"Getting block $blockId from memory")
           val result = if (asValues) {
             memoryStore.getValues(blockId)
           } else {
@@ -387,51 +386,51 @@ private[spark] class BlockManager(
             case Some(values) =>
               return Some(values)
             case None =>
-              logDebug("Block " + blockId + " not found in memory")
+              logDebug(s"Block $blockId not found in memory")
           }
         }
 
         // Look for the block in Tachyon
         if (level.useOffHeap) {
-          logDebug("Getting block " + blockId + " from tachyon")
+          logDebug(s"Getting block $blockId from tachyon")
           if (tachyonStore.contains(blockId)) {
             tachyonStore.getBytes(blockId) match {
-              case Some(bytes) => {
+              case Some(bytes) =>
                 if (!asValues) {
                   return Some(bytes)
                 } else {
                   return Some(dataDeserialize(blockId, bytes))
                 }
-              }
               case None =>
-                logDebug("Block " + blockId + " not found in tachyon")
+                logDebug(s"Block $blockId not found in tachyon")
             }
           }
         }
 
-        // Look for block on disk, potentially storing it back into memory if required:
+        // Look for block on disk, potentially storing it back in memory if required
         if (level.useDisk) {
-          logDebug("Getting block " + blockId + " from disk")
+          logDebug(s"Getting block $blockId from disk")
           val bytes: ByteBuffer = diskStore.getBytes(blockId) match {
-            case Some(bytes) => bytes
+            case Some(b) => b
             case None =>
-              throw new Exception("Block " + blockId + " not found on disk, though it should be")
+              throw new BlockException(
+                blockId, s"Block $blockId not found on disk, though it should be")
           }
-          assert (0 == bytes.position())
+          assert(0 == bytes.position())
 
           if (!level.useMemory) {
-            // If the block shouldn't be stored in memory, we can just return it:
+            // If the block shouldn't be stored in memory, we can just return it
             if (asValues) {
               return Some(dataDeserialize(blockId, bytes))
             } else {
               return Some(bytes)
             }
           } else {
-            // Otherwise, we also have to store something in the memory store:
+            // Otherwise, we also have to store something in the memory store
             if (!level.deserialized || !asValues) {
-              // We'll store the bytes in memory if the block's storage level includes
-              // "memory serialized", or if it should be cached as objects in memory
-              // but we only requested its serialized bytes:
+              /* We'll store the bytes in memory if the block's storage level includes
+               * "memory serialized", or if it should be cached as objects in memory
+               * but we only requested its serialized bytes. */
               val copyForMemory = ByteBuffer.allocate(bytes.limit)
               copyForMemory.put(bytes)
               memoryStore.putBytes(blockId, copyForMemory, level)
@@ -442,16 +441,17 @@ private[spark] class BlockManager(
             } else {
               val values = dataDeserialize(blockId, bytes)
               if (level.deserialized) {
-                // Cache the values before returning them:
+                // Cache the values before returning them
                 // TODO: Consider creating a putValues that also takes in a iterator?
                 val valuesBuffer = new ArrayBuffer[Any]
                 valuesBuffer ++= values
-                memoryStore.putValues(blockId, valuesBuffer, level, true).data match {
-                  case Left(values2) =>
-                    return Some(values2)
-                  case _ =>
-                    throw new Exception("Memory store did not return back an iterator")
-                }
+                memoryStore.putValues(blockId, valuesBuffer, level, returnValues = true).data
+                  match {
+                    case Left(values2) =>
+                      return Some(values2)
+                    case _ =>
+                      throw new SparkException("Memory store did not return an iterator")
+                  }
               } else {
                 return Some(values)
               }
@@ -460,7 +460,7 @@ private[spark] class BlockManager(
         }
       }
     } else {
-      logDebug("Block " + blockId + " not registered locally")
+      logDebug(s"Block $blockId not registered locally")
     }
     None
   }
@@ -469,7 +469,7 @@ private[spark] class BlockManager(
    * Get block from remote block managers.
    */
   def getRemote(blockId: BlockId): Option[Iterator[Any]] = {
-    logDebug("Getting remote block " + blockId)
+    logDebug(s"Getting remote block $blockId")
     doGetRemote(blockId, asValues = true).asInstanceOf[Option[Iterator[Any]]]
   }
 
@@ -477,7 +477,7 @@ private[spark] class BlockManager(
    * Get block from remote block managers as serialized bytes.
    */
   def getRemoteBytes(blockId: BlockId): Option[ByteBuffer] = {
-    logDebug("Getting remote block " + blockId + " as bytes")
+    logDebug(s"Getting remote block $blockId as bytes")
     doGetRemote(blockId, asValues = false).asInstanceOf[Option[ByteBuffer]]
   }
 
@@ -485,7 +485,7 @@ private[spark] class BlockManager(
     require(blockId != null, "BlockId is null")
     val locations = Random.shuffle(master.getLocations(blockId))
     for (loc <- locations) {
-      logDebug("Getting remote block " + blockId + " from " + loc)
+      logDebug(s"Getting remote block $blockId from $loc")
       val data = BlockManagerWorker.syncGetBlock(
         GetBlock(blockId), ConnectionManagerId(loc.host, loc.port))
       if (data != null) {
@@ -495,9 +495,9 @@ private[spark] class BlockManager(
           return Some(data)
         }
       }
-      logDebug("The value of block " + blockId + " is null")
+      logDebug(s"The value of block $blockId is null")
     }
-    logDebug("Block " + blockId + " not found")
+    logDebug(s"Block $blockId not found")
     None
   }
 
@@ -507,12 +507,12 @@ private[spark] class BlockManager(
   def get(blockId: BlockId): Option[Iterator[Any]] = {
     val local = getLocal(blockId)
     if (local.isDefined) {
-      logInfo("Found block %s locally".format(blockId))
+      logInfo(s"Found block $blockId locally")
       return local
     }
     val remote = getRemote(blockId)
     if (remote.isDefined) {
-      logInfo("Found block %s remotely".format(blockId))
+      logInfo(s"Found block $blockId remotely")
       return remote
     }
     None
@@ -533,7 +533,6 @@ private[spark] class BlockManager(
       } else {
         new BlockFetcherIterator.BasicBlockFetcherIterator(this, blocksByAddress, serializer)
       }
-
     iter.initialize()
     iter
   }
@@ -543,6 +542,7 @@ private[spark] class BlockManager(
       values: Iterator[Any],
       level: StorageLevel,
       tellMaster: Boolean): Seq[(BlockId, BlockStatus)] = {
+    require(values != null, "Values is null")
     doPut(blockId, IteratorValues(values), level, tellMaster)
   }
 
@@ -562,8 +562,8 @@ private[spark] class BlockManager(
   }
 
   /**
-   * Put a new block of values to the block manager. Return a list of blocks updated as a
-   * result of this put.
+   * Put a new block of values to the block manager.
+   * Return a list of blocks updated as a result of this put.
    */
   def put(
       blockId: BlockId,
@@ -575,8 +575,8 @@ private[spark] class BlockManager(
   }
 
   /**
-   * Put a new block of serialized bytes to the block manager. Return a list of blocks updated
-   * as a result of this put.
+   * Put a new block of serialized bytes to the block manager.
+   * Return a list of blocks updated as a result of this put.
    */
   def putBytes(
       blockId: BlockId,
@@ -589,7 +589,7 @@ private[spark] class BlockManager(
 
   private def doPut(
       blockId: BlockId,
-      data: Values,
+      data: BlockValues,
       level: StorageLevel,
       tellMaster: Boolean = true): Seq[(BlockId, BlockStatus)] = {
 
@@ -599,20 +599,18 @@ private[spark] class BlockManager(
     // Return value
     val updatedBlocks = new ArrayBuffer[(BlockId, BlockStatus)]
 
-    // Remember the block's storage level so that we can correctly drop it to disk if it needs
-    // to be dropped right after it got put into memory. Note, however, that other threads will
-    // not be able to get() this block until we call markReady on its BlockInfo.
+    /* Remember the block's storage level so that we can correctly drop it to disk if it needs
+     * to be dropped right after it got put into memory. Note, however, that other threads will
+     * not be able to get() this block until we call markReady on its BlockInfo. */
     val putBlockInfo = {
       val tinfo = new BlockInfo(level, tellMaster)
       // Do atomically !
       val oldBlockOpt = blockInfo.putIfAbsent(blockId, tinfo)
-
       if (oldBlockOpt.isDefined) {
         if (oldBlockOpt.get.waitForReady()) {
-          logWarning("Block " + blockId + " already exists on this machine; not re-adding it")
+          logWarning(s"Block $blockId already exists on this machine; not re-adding it")
           return updatedBlocks
         }
-
         // TODO: So the block info exists - but previous attempt to load it (?) failed.
         // What do we do now ? Retry on it ?
         oldBlockOpt.get
@@ -623,10 +621,10 @@ private[spark] class BlockManager(
 
     val startTimeMs = System.currentTimeMillis
 
-    // If we're storing values and we need to replicate the data, we'll want access to the values,
-    // but because our put will read the whole iterator, there will be no values left. For the
-    // case where the put serializes data, we'll remember the bytes, above; but for the case where
-    // it doesn't, such as deserialized storage, let's rely on the put returning an Iterator.
+    /* If we're storing values and we need to replicate the data, we'll want access to the values,
+     * but because our put will read the whole iterator, there will be no values left. For the
+     * case where the put serializes data, we'll remember the bytes, above; but for the case where
+     * it doesn't, such as deserialized storage, let's rely on the put returning an Iterator. */
     var valuesAfterPut: Iterator[Any] = null
 
     // Ditto for the bytes after the put
@@ -637,78 +635,62 @@ private[spark] class BlockManager(
 
     // If we're storing bytes, then initiate the replication before storing them locally.
     // This is faster as data is already serialized and ready to send.
-    val replicationFuture = if (data.isInstanceOf[ByteBufferValues] && level.replication > 1) {
-      // Duplicate doesn't copy the bytes, just creates a wrapper
-      val bufferView = data.asInstanceOf[ByteBufferValues].buffer.duplicate()
-      Future {
-        replicate(blockId, bufferView, level)
-      }
-    } else {
-      null
+    val replicationFuture = data match {
+      case b: ByteBufferValues if level.replication > 1 =>
+        // Duplicate doesn't copy the bytes, but just creates a wrapper
+        val bufferView = b.buffer.duplicate()
+        Future { replicate(blockId, bufferView, level) }
+      case _ => null
     }
 
     putBlockInfo.synchronized {
-      logTrace("Put for block " + blockId + " took " + Utils.getUsedTimeMs(startTimeMs)
-        + " to get into synchronized block")
+      logTrace("Put for block %s took %s to get into synchronized block"
+        .format(blockId, Utils.getUsedTimeMs(startTimeMs)))
 
       var marked = false
       try {
-        if (level.useMemory) {
-          // Save it just to memory first, even if it also has useDisk set to true; we will
-          // drop it to disk later if the memory store can't hold it.
-          val res = data match {
-            case IteratorValues(iterator) =>
-              memoryStore.putValues(blockId, iterator, level, true)
-            case ArrayBufferValues(array) =>
-              memoryStore.putValues(blockId, array, level, true)
-            case ByteBufferValues(bytes) =>
-              bytes.rewind()
-              memoryStore.putBytes(blockId, bytes, level)
-          }
-          size = res.size
-          res.data match {
-            case Right(newBytes) => bytesAfterPut = newBytes
-            case Left(newIterator) => valuesAfterPut = newIterator
-          }
-          // Keep track of which blocks are dropped from memory
-          res.droppedBlocks.foreach { block => updatedBlocks += block }
-        } else if (level.useOffHeap) {
-          // Save to Tachyon.
-          val res = data match {
-            case IteratorValues(iterator) =>
-              tachyonStore.putValues(blockId, iterator, level, false)
-            case ArrayBufferValues(array) =>
-              tachyonStore.putValues(blockId, array, level, false)
-            case ByteBufferValues(bytes) =>
-              bytes.rewind()
-              tachyonStore.putBytes(blockId, bytes, level)
-          }
-          size = res.size
-          res.data match {
-            case Right(newBytes) => bytesAfterPut = newBytes
-            case _ =>
-          }
-        } else {
-          // Save directly to disk.
-          // Don't get back the bytes unless we replicate them.
-          val askForBytes = level.replication > 1
-
-          val res = data match {
-            case IteratorValues(iterator) =>
-              diskStore.putValues(blockId, iterator, level, askForBytes)
-            case ArrayBufferValues(array) =>
-              diskStore.putValues(blockId, array, level, askForBytes)
-            case ByteBufferValues(bytes) =>
-              bytes.rewind()
-              diskStore.putBytes(blockId, bytes, level)
-          }
-          size = res.size
-          res.data match {
-            case Right(newBytes) => bytesAfterPut = newBytes
-            case _ =>
+        // returnValues - Whether to return the values put
+        // blockStore - The type of storage to put these values into
+        val (returnValues, blockStore: BlockStore) = {
+          if (level.useMemory) {
+            // Put it in memory first, even if it also has useDisk set to true;
+            // We will drop it to disk later if the memory store can't hold it.
+            (true, memoryStore)
+          } else if (level.useOffHeap) {
+            // Use tachyon for off-heap storage
+            (false, tachyonStore)
+          } else if (level.useDisk) {
+            // Don't get back the bytes from put unless we replicate them
+            (level.replication > 1, diskStore)
+          } else {
+            assert(level == StorageLevel.NONE)
+            throw new BlockException(
+              blockId, s"Attempted to put block $blockId without specifying storage level!")
           }
         }
 
+        // Actually put the values
+        val result = data match {
+          case IteratorValues(iterator) =>
+            blockStore.putValues(blockId, iterator, level, returnValues)
+          case ArrayBufferValues(array) =>
+            blockStore.putValues(blockId, array, level, returnValues)
+          case ByteBufferValues(bytes) =>
+            bytes.rewind()
+            blockStore.putBytes(blockId, bytes, level)
+        }
+        size = result.size
+        result.data match {
+          case Left (newIterator) if level.useMemory => valuesAfterPut = newIterator
+          case Right (newBytes) => bytesAfterPut = newBytes
+          case _ =>
+        }
+
+        // Keep track of which blocks are dropped from memory
+        if (level.useMemory) {
+          result.droppedBlocks.foreach { updatedBlocks += _ }
+        }
+
         val putBlockStatus = getCurrentBlockStatus(blockId, putBlockInfo)
         if (putBlockStatus.storageLevel != StorageLevel.NONE) {
           // Now that the block is in either the memory, tachyon, or disk store,
@@ -728,18 +710,21 @@ private[spark] class BlockManager(
           // could've inserted a new BlockInfo before we remove it.
           blockInfo.remove(blockId)
           putBlockInfo.markFailure()
-          logWarning("Putting block " + blockId + " failed")
+          logWarning(s"Putting block $blockId failed")
         }
       }
     }
-    logDebug("Put block " + blockId + " locally took " + Utils.getUsedTimeMs(startTimeMs))
+    logDebug("Put block %s locally took %s".format(blockId, Utils.getUsedTimeMs(startTimeMs)))
 
     // Either we're storing bytes and we asynchronously started replication, or we're storing
     // values and need to serialize and replicate them now:
     if (level.replication > 1) {
       data match {
-        case ByteBufferValues(bytes) => Await.ready(replicationFuture, Duration.Inf)
-        case _ => {
+        case ByteBufferValues(bytes) =>
+          if (replicationFuture != null) {
+            Await.ready(replicationFuture, Duration.Inf)
+          }
+        case _ =>
           val remoteStartTime = System.currentTimeMillis
           // Serialize the block if not already done
           if (bytesAfterPut == null) {
@@ -750,20 +735,19 @@ private[spark] class BlockManager(
             bytesAfterPut = dataSerialize(blockId, valuesAfterPut)
           }
           replicate(blockId, bytesAfterPut, level)
-          logDebug("Put block " + blockId + " remotely took " +
-            Utils.getUsedTimeMs(remoteStartTime))
-        }
+          logDebug("Put block %s remotely took %s"
+            .format(blockId, Utils.getUsedTimeMs(remoteStartTime)))
       }
     }
 
     BlockManager.dispose(bytesAfterPut)
 
     if (level.replication > 1) {
-      logDebug("Put for block " + blockId + " with replication took " +
-        Utils.getUsedTimeMs(startTimeMs))
+      logDebug("Putting block %s with replication took %s"
+        .format(blockId, Utils.getUsedTimeMs(startTimeMs)))
     } else {
-      logDebug("Put for block " + blockId + " without replication took " +
-        Utils.getUsedTimeMs(startTimeMs))
+      logDebug("Putting block %s without replication took %s"
+        .format(blockId, Utils.getUsedTimeMs(startTimeMs)))
     }
 
     updatedBlocks
@@ -773,7 +757,7 @@ private[spark] class BlockManager(
    * Replicate block to another node.
    */
   @volatile var cachedPeers: Seq[BlockManagerId] = null
-  private def replicate(blockId: BlockId, data: ByteBuffer, level: StorageLevel) {
+  private def replicate(blockId: BlockId, data: ByteBuffer, level: StorageLevel): Unit = {
     val tLevel = StorageLevel(
       level.useDisk, level.useMemory, level.useOffHeap, level.deserialized, 1)
     if (cachedPeers == null) {
@@ -782,15 +766,16 @@ private[spark] class BlockManager(
     for (peer: BlockManagerId <- cachedPeers) {
       val start = System.nanoTime
       data.rewind()
-      logDebug("Try to replicate BlockId " + blockId + " once; The size of the data is "
-        + data.limit() + " Bytes. To node: " + peer)
-      if (!BlockManagerWorker.syncPutBlock(PutBlock(blockId, data, tLevel),
-        new ConnectionManagerId(peer.host, peer.port))) {
-        logError("Failed to call syncPutBlock to " + peer)
+      logDebug(s"Try to replicate $blockId once; The size of the data is ${data.limit()} Bytes. " +
+        s"To node: $peer")
+      val putBlock = PutBlock(blockId, data, tLevel)
+      val cmId = new ConnectionManagerId(peer.host, peer.port)
+      val syncPutBlockSuccess = BlockManagerWorker.syncPutBlock(putBlock, cmId)
+      if (!syncPutBlockSuccess) {
+        logError(s"Failed to call syncPutBlock to $peer")
       }
-      logDebug("Replicated BlockId " + blockId + " once used " +
-        (System.nanoTime - start) / 1e6 + " s; The size of the data is " +
-        data.limit() + " bytes.")
+      logDebug("Replicating BlockId %s once used %fs; The size of the data is %d bytes."
+        .format(blockId, (System.nanoTime - start) / 1e6, data.limit()))
     }
   }
 
@@ -822,17 +807,17 @@ private[spark] class BlockManager(
       blockId: BlockId,
       data: Either[ArrayBuffer[Any], ByteBuffer]): Option[BlockStatus] = {
 
-    logInfo("Dropping block " + blockId + " from memory")
+    logInfo(s"Dropping block $blockId from memory")
     val info = blockInfo.get(blockId).orNull
 
     // If the block has not already been dropped
-    if (info != null)  {
+    if (info != null) {
       info.synchronized {
         // required ? As of now, this will be invoked only for blocks which are ready
         // But in case this changes in future, adding for consistency sake.
         if (!info.waitForReady()) {
           // If we get here, the block write failed.
-          logWarning("Block " + blockId + " was marked as failure. Nothing to drop")
+          logWarning(s"Block $blockId was marked as failure. Nothing to drop")
           return None
         }
 
@@ -841,10 +826,10 @@ private[spark] class BlockManager(
 
         // Drop to disk, if storage level requires
         if (level.useDisk && !diskStore.contains(blockId)) {
-          logInfo("Writing block " + blockId + " to disk")
+          logInfo(s"Writing block $blockId to disk")
           data match {
             case Left(elements) =>
-              diskStore.putValues(blockId, elements, level, false)
+              diskStore.putValues(blockId, elements, level, returnValues = false)
             case Right(bytes) =>
               diskStore.putBytes(blockId, bytes, level)
           }
@@ -858,7 +843,7 @@ private[spark] class BlockManager(
         if (blockIsRemoved) {
           blockIsUpdated = true
         } else {
-          logWarning("Block " + blockId + " could not be dropped from memory as it does not exist")
+          logWarning(s"Block $blockId could not be dropped from memory as it does not exist")
         }
 
         val status = getCurrentBlockStatus(blockId, info)
@@ -883,7 +868,7 @@ private[spark] class BlockManager(
    */
   def removeRdd(rddId: Int): Int = {
     // TODO: Avoid a linear scan by creating another mapping of RDD.id to blocks.
-    logInfo("Removing RDD " + rddId)
+    logInfo(s"Removing RDD $rddId")
     val blocksToRemove = blockInfo.keys.flatMap(_.asRDDId).filter(_.rddId == rddId)
     blocksToRemove.foreach { blockId => removeBlock(blockId, tellMaster = false) }
     blocksToRemove.size
@@ -893,7 +878,7 @@ private[spark] class BlockManager(
    * Remove all blocks belonging to the given broadcast.
    */
   def removeBroadcast(broadcastId: Long, tellMaster: Boolean): Int = {
-    logInfo("Removing broadcast " + broadcastId)
+    logInfo(s"Removing broadcast $broadcastId")
     val blocksToRemove = blockInfo.keys.collect {
       case bid @ BroadcastBlockId(`broadcastId`, _) => bid
     }
@@ -904,40 +889,42 @@ private[spark] class BlockManager(
   /**
    * Remove a block from both memory and disk.
    */
-  def removeBlock(blockId: BlockId, tellMaster: Boolean = true) {
-    logInfo("Removing block " + blockId)
+  def removeBlock(blockId: BlockId, tellMaster: Boolean = true): Unit = {
+    logInfo(s"Removing block $blockId")
     val info = blockInfo.get(blockId).orNull
-    if (info != null) info.synchronized {
-      // Removals are idempotent in disk store and memory store. At worst, we get a warning.
-      val removedFromMemory = memoryStore.remove(blockId)
-      val removedFromDisk = diskStore.remove(blockId)
-      val removedFromTachyon = if (tachyonInitialized) tachyonStore.remove(blockId) else false
-      if (!removedFromMemory && !removedFromDisk && !removedFromTachyon) {
-        logWarning("Block " + blockId + " could not be removed as it was not found in either " +
-          "the disk, memory, or tachyon store")
-      }
-      blockInfo.remove(blockId)
-      if (tellMaster && info.tellMaster) {
-        val status = getCurrentBlockStatus(blockId, info)
-        reportBlockStatus(blockId, info, status)
+    if (info != null) {
+      info.synchronized {
+        // Removals are idempotent in disk store and memory store. At worst, we get a warning.
+        val removedFromMemory = memoryStore.remove(blockId)
+        val removedFromDisk = diskStore.remove(blockId)
+        val removedFromTachyon = if (tachyonInitialized) tachyonStore.remove(blockId) else false
+        if (!removedFromMemory && !removedFromDisk && !removedFromTachyon) {
+          logWarning(s"Block $blockId could not be removed as it was not found in either " +
+            "the disk, memory, or tachyon store")
+        }
+        blockInfo.remove(blockId)
+        if (tellMaster && info.tellMaster) {
+          val status = getCurrentBlockStatus(blockId, info)
+          reportBlockStatus(blockId, info, status)
+        }
       }
     } else {
       // The block has already been removed; do nothing.
-      logWarning("Asked to remove block " + blockId + ", which does not exist")
+      logWarning(s"Asked to remove block $blockId, which does not exist")
     }
   }
 
-  private def dropOldNonBroadcastBlocks(cleanupTime: Long) {
-    logInfo("Dropping non broadcast blocks older than " + cleanupTime)
+  private def dropOldNonBroadcastBlocks(cleanupTime: Long): Unit = {
+    logInfo(s"Dropping non broadcast blocks older than $cleanupTime")
     dropOldBlocks(cleanupTime, !_.isBroadcast)
   }
 
-  private def dropOldBroadcastBlocks(cleanupTime: Long) {
-    logInfo("Dropping broadcast blocks older than " + cleanupTime)
+  private def dropOldBroadcastBlocks(cleanupTime: Long): Unit = {
+    logInfo(s"Dropping broadcast blocks older than $cleanupTime")
     dropOldBlocks(cleanupTime, _.isBroadcast)
   }
 
-  private def dropOldBlocks(cleanupTime: Long, shouldDrop: (BlockId => Boolean)) {
+  private def dropOldBlocks(cleanupTime: Long, shouldDrop: (BlockId => Boolean)): Unit = {
     val iterator = blockInfo.getEntrySet.iterator
     while (iterator.hasNext) {
       val entry = iterator.next()
@@ -945,17 +932,11 @@ private[spark] class BlockManager(
       if (time < cleanupTime && shouldDrop(id)) {
         info.synchronized {
           val level = info.level
-          if (level.useMemory) {
-            memoryStore.remove(id)
-          }
-          if (level.useDisk) {
-            diskStore.remove(id)
-          }
-          if (level.useOffHeap) {
-            tachyonStore.remove(id)
-          }
+          if (level.useMemory) { memoryStore.remove(id) }
+          if (level.useDisk) { diskStore.remove(id) }
+          if (level.useOffHeap) { tachyonStore.remove(id) }
           iterator.remove()
-          logInfo("Dropped block " + id)
+          logInfo(s"Dropped block $id")
         }
         val status = getCurrentBlockStatus(id, info)
         reportBlockStatus(id, info, status)
@@ -963,12 +944,14 @@ private[spark] class BlockManager(
     }
   }
 
-  def shouldCompress(blockId: BlockId): Boolean = blockId match {
-    case ShuffleBlockId(_, _, _) => compressShuffle
-    case BroadcastBlockId(_, _) => compressBroadcast
-    case RDDBlockId(_, _) => compressRdds
-    case TempBlockId(_) => compressShuffleSpill
-    case _ => false
+  private def shouldCompress(blockId: BlockId): Boolean = {
+    blockId match {
+      case _: ShuffleBlockId => compressShuffle
+      case _: BroadcastBlockId => compressBroadcast
+      case _: RDDBlockId => compressRdds
+      case _: TempBlockId => compressShuffleSpill
+      case _ => false
+    }
   }
 
   /**
@@ -990,7 +973,7 @@ private[spark] class BlockManager(
       blockId: BlockId,
       outputStream: OutputStream,
       values: Iterator[Any],
-      serializer: Serializer = defaultSerializer) {
+      serializer: Serializer = defaultSerializer): Unit = {
     val byteStream = new BufferedOutputStream(outputStream)
     val ser = serializer.newInstance()
     ser.serializeStream(wrapForCompression(blockId, byteStream)).writeAll(values).close()
@@ -1016,16 +999,16 @@ private[spark] class BlockManager(
       serializer: Serializer = defaultSerializer): Iterator[Any] = {
     bytes.rewind()
 
-    def getIterator = {
+    def getIterator: Iterator[Any] = {
       val stream = wrapForCompression(blockId, new ByteBufferInputStream(bytes, true))
       serializer.newInstance().deserializeStream(stream).asIterator
     }
 
     if (blockId.isShuffle) {
-      // Reducer may need to read many local shuffle blocks and will wrap them into Iterators
-      // at the beginning. The wrapping will cost some memory (compression instance
-      // initialization, etc.). Reducer reads shuffle blocks one by one so we could do the
-      // wrapping lazily to save memory.
+      /* Reducer may need to read many local shuffle blocks and will wrap them into Iterators
+       * at the beginning. The wrapping will cost some memory (compression instance
+       * initialization, etc.). Reducer reads shuffle blocks one by one so we could do the
+       * wrapping lazily to save memory. */
       class LazyProxyIterator(f: => Iterator[Any]) extends Iterator[Any] {
         lazy val proxy = f
         override def hasNext: Boolean = proxy.hasNext
@@ -1037,7 +1020,7 @@ private[spark] class BlockManager(
     }
   }
 
-  def stop() {
+  def stop(): Unit = {
     if (heartBeatTask != null) {
       heartBeatTask.cancel()
     }
@@ -1059,9 +1042,9 @@ private[spark] class BlockManager(
 
 
 private[spark] object BlockManager extends Logging {
-  val ID_GENERATOR = new IdGenerator
+  private val ID_GENERATOR = new IdGenerator
 
-  def getMaxMemory(conf: SparkConf): Long = {
+  private def getMaxMemory(conf: SparkConf): Long = {
     val memoryFraction = conf.getDouble("spark.storage.memoryFraction", 0.6)
     (Runtime.getRuntime.maxMemory * memoryFraction).toLong
   }
@@ -1078,9 +1061,9 @@ private[spark] object BlockManager extends Logging {
    * waiting for the GC to find it because that could lead to huge numbers of open files. There's
    * unfortunately no standard API to do this.
    */
-  def dispose(buffer: ByteBuffer) {
+  def dispose(buffer: ByteBuffer): Unit = {
     if (buffer != null && buffer.isInstanceOf[MappedByteBuffer]) {
-      logTrace("Unmapping " + buffer)
+      logTrace(s"Unmapping $buffer")
       if (buffer.asInstanceOf[DirectBuffer].cleaner() != null) {
         buffer.asInstanceOf[DirectBuffer].cleaner().clean()
       }
@@ -1093,7 +1076,7 @@ private[spark] object BlockManager extends Logging {
       blockManagerMaster: BlockManagerMaster = null): Map[BlockId, Seq[BlockManagerId]] = {
 
     // blockManagerMaster != null is used in tests
-    assert (env != null || blockManagerMaster != null)
+    assert(env != null || blockManagerMaster != null)
     val blockLocations: Seq[Seq[BlockManagerId]] = if (blockManagerMaster == null) {
       env.blockManager.getLocationBlockIds(blockIds)
     } else {
diff --git a/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala b/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala
index 3a7243a1ba19..2ec46d416f37 100644
--- a/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala
+++ b/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala
@@ -40,9 +40,9 @@ private[spark] class DiskBlockManager(shuffleManager: ShuffleBlockManager, rootD
   private val MAX_DIR_CREATION_ATTEMPTS: Int = 10
   private val subDirsPerLocalDir = shuffleManager.conf.getInt("spark.diskStore.subDirectories", 64)
 
-  // Create one local directory for each path mentioned in spark.local.dir; then, inside this
-  // directory, create multiple subdirectories that we will hash files into, in order to avoid
-  // having really large inodes at the top level.
+  /* Create one local directory for each path mentioned in spark.local.dir; then, inside this
+   * directory, create multiple subdirectories that we will hash files into, in order to avoid
+   * having really large inodes at the top level. */
   private val localDirs: Array[File] = createLocalDirs()
   private val subDirs = Array.fill(localDirs.length)(new Array[File](subDirsPerLocalDir))
   private var shuffleSender : ShuffleSender = null
@@ -114,7 +114,7 @@ private[spark] class DiskBlockManager(shuffleManager: ShuffleBlockManager, rootD
   }
 
   private def createLocalDirs(): Array[File] = {
-    logDebug("Creating local directories at root dirs '" + rootDirs + "'")
+    logDebug(s"Creating local directories at root dirs '$rootDirs'")
     val dateFormat = new SimpleDateFormat("yyyyMMddHHmmss")
     rootDirs.split(",").map { rootDir =>
       var foundLocalDir = false
@@ -126,21 +126,20 @@ private[spark] class DiskBlockManager(shuffleManager: ShuffleBlockManager, rootD
         tries += 1
         try {
           localDirId = "%s-%04x".format(dateFormat.format(new Date), rand.nextInt(65536))
-          localDir = new File(rootDir, "spark-local-" + localDirId)
+          localDir = new File(rootDir, s"spark-local-$localDirId")
           if (!localDir.exists) {
             foundLocalDir = localDir.mkdirs()
           }
         } catch {
           case e: Exception =>
-            logWarning("Attempt " + tries + " to create local dir " + localDir + " failed", e)
+            logWarning(s"Attempt $tries to create local dir $localDir failed", e)
         }
       }
       if (!foundLocalDir) {
-        logError("Failed " + MAX_DIR_CREATION_ATTEMPTS +
-          " attempts to create local dir in " + rootDir)
+        logError(s"Failed $MAX_DIR_CREATION_ATTEMPTS attempts to create local dir in $rootDir")
         System.exit(ExecutorExitCode.DISK_STORE_FAILED_TO_CREATE_DIR)
       }
-      logInfo("Created local directory at " + localDir)
+      logInfo(s"Created local directory at $localDir")
       localDir
     }
   }
@@ -163,7 +162,7 @@ private[spark] class DiskBlockManager(shuffleManager: ShuffleBlockManager, rootD
           if (!Utils.hasRootAsShutdownDeleteDir(localDir)) Utils.deleteRecursively(localDir)
         } catch {
           case e: Exception =>
-            logError("Exception while deleting local spark dir: " + localDir, e)
+            logError(s"Exception while deleting local spark dir: $localDir", e)
         }
       }
     }
@@ -175,7 +174,7 @@ private[spark] class DiskBlockManager(shuffleManager: ShuffleBlockManager, rootD
 
   private[storage] def startShuffleBlockSender(port: Int): Int = {
     shuffleSender = new ShuffleSender(port, this)
-    logInfo("Created ShuffleSender binding to port : " + shuffleSender.port)
+    logInfo(s"Created ShuffleSender binding to port: ${shuffleSender.port}")
     shuffleSender.port
   }
 }
diff --git a/core/src/main/scala/org/apache/spark/storage/DiskStore.scala b/core/src/main/scala/org/apache/spark/storage/DiskStore.scala
index 0ab9fad42271..ebff0cb5ba15 100644
--- a/core/src/main/scala/org/apache/spark/storage/DiskStore.scala
+++ b/core/src/main/scala/org/apache/spark/storage/DiskStore.scala
@@ -39,41 +39,39 @@ private class DiskStore(blockManager: BlockManager, diskManager: DiskBlockManage
     diskManager.getBlockLocation(blockId).length
   }
 
-  override def putBytes(blockId: BlockId, _bytes: ByteBuffer, level: StorageLevel) : PutResult = {
+  override def putBytes(blockId: BlockId, _bytes: ByteBuffer, level: StorageLevel): PutResult = {
     // So that we do not modify the input offsets !
     // duplicate does not copy buffer, so inexpensive
     val bytes = _bytes.duplicate()
-    logDebug("Attempting to put block " + blockId)
+    logDebug(s"Attempting to put block $blockId")
     val startTime = System.currentTimeMillis
     val file = diskManager.getFile(blockId)
-    val channel = new FileOutputStream(file).getChannel()
+    val channel = new FileOutputStream(file).getChannel
     while (bytes.remaining > 0) {
       channel.write(bytes)
     }
     channel.close()
     val finishTime = System.currentTimeMillis
     logDebug("Block %s stored as %s file on disk in %d ms".format(
-      file.getName, Utils.bytesToString(bytes.limit), (finishTime - startTime)))
-    return PutResult(bytes.limit(), Right(bytes.duplicate()))
+      file.getName, Utils.bytesToString(bytes.limit), finishTime - startTime))
+    PutResult(bytes.limit(), Right(bytes.duplicate()))
   }
 
   override def putValues(
       blockId: BlockId,
       values: ArrayBuffer[Any],
       level: StorageLevel,
-      returnValues: Boolean)
-  : PutResult = {
-    return putValues(blockId, values.toIterator, level, returnValues)
+      returnValues: Boolean): PutResult = {
+    putValues(blockId, values.toIterator, level, returnValues)
   }
 
   override def putValues(
       blockId: BlockId,
       values: Iterator[Any],
       level: StorageLevel,
-      returnValues: Boolean)
-    : PutResult = {
+      returnValues: Boolean): PutResult = {
 
-    logDebug("Attempting to write values for block " + blockId)
+    logDebug(s"Attempting to write values for block $blockId")
     val startTime = System.currentTimeMillis
     val file = diskManager.getFile(blockId)
     val outputStream = new FileOutputStream(file)
@@ -95,7 +93,7 @@ private class DiskStore(blockManager: BlockManager, diskManager: DiskBlockManage
 
   override def getBytes(blockId: BlockId): Option[ByteBuffer] = {
     val segment = diskManager.getBlockLocation(blockId)
-    val channel = new RandomAccessFile(segment.file, "r").getChannel()
+    val channel = new RandomAccessFile(segment.file, "r").getChannel
 
     try {
       // For small files, directly read rather than memory map
@@ -131,7 +129,7 @@ private class DiskStore(blockManager: BlockManager, diskManager: DiskBlockManage
       file.delete()
     } else {
       if (fileSegment.length < file.length()) {
-        logWarning("Could not delete block associated with only a part of a file: " + blockId)
+        logWarning(s"Could not delete block associated with only a part of a file: $blockId")
       }
       false
     }
diff --git a/core/src/main/scala/org/apache/spark/storage/MemoryStore.scala b/core/src/main/scala/org/apache/spark/storage/MemoryStore.scala
index 488f1ea9628f..084a566c4856 100644
--- a/core/src/main/scala/org/apache/spark/storage/MemoryStore.scala
+++ b/core/src/main/scala/org/apache/spark/storage/MemoryStore.scala
@@ -24,6 +24,8 @@ import scala.collection.mutable.ArrayBuffer
 
 import org.apache.spark.util.{SizeEstimator, Utils}
 
+private case class MemoryEntry(value: Any, size: Long, deserialized: Boolean)
+
 /**
  * Stores blocks in memory, either as ArrayBuffers of deserialized Java objects or as
  * serialized ByteBuffers.
@@ -31,15 +33,13 @@ import org.apache.spark.util.{SizeEstimator, Utils}
 private class MemoryStore(blockManager: BlockManager, maxMemory: Long)
   extends BlockStore(blockManager) {
 
-  case class Entry(value: Any, size: Long, deserialized: Boolean)
-
-  private val entries = new LinkedHashMap[BlockId, Entry](32, 0.75f, true)
+  private val entries = new LinkedHashMap[BlockId, MemoryEntry](32, 0.75f, true)
   @volatile private var currentMemory = 0L
   // Object used to ensure that only one thread is putting blocks and if necessary, dropping
   // blocks from the memory store.
   private val putLock = new Object()
 
-  logInfo("MemoryStore started with capacity %s.".format(Utils.bytesToString(maxMemory)))
+  logInfo("MemoryStore started with capacity %s".format(Utils.bytesToString(maxMemory)))
 
   def freeMemory: Long = maxMemory - currentMemory
 
@@ -101,7 +101,7 @@ private class MemoryStore(blockManager: BlockManager, maxMemory: Long)
     } else if (entry.deserialized) {
       Some(blockManager.dataSerialize(blockId, entry.value.asInstanceOf[ArrayBuffer[Any]].iterator))
     } else {
-      Some(entry.value.asInstanceOf[ByteBuffer].duplicate())   // Doesn't actually copy the data
+      Some(entry.value.asInstanceOf[ByteBuffer].duplicate()) // Doesn't actually copy the data
     }
   }
 
@@ -124,8 +124,7 @@ private class MemoryStore(blockManager: BlockManager, maxMemory: Long)
       val entry = entries.remove(blockId)
       if (entry != null) {
         currentMemory -= entry.size
-        logInfo("Block %s of size %d dropped from memory (free %d)".format(
-          blockId, entry.size, freeMemory))
+        logInfo(s"Block $blockId of size ${entry.size} dropped from memory (free $freeMemory)")
         true
       } else {
         false
@@ -181,18 +180,14 @@ private class MemoryStore(blockManager: BlockManager, maxMemory: Long)
       droppedBlocks ++= freeSpaceResult.droppedBlocks
 
       if (enoughFreeSpace) {
-        val entry = new Entry(value, size, deserialized)
+        val entry = new MemoryEntry(value, size, deserialized)
         entries.synchronized {
           entries.put(blockId, entry)
           currentMemory += size
         }
-        if (deserialized) {
-          logInfo("Block %s stored as values to memory (estimated size %s, free %s)".format(
-            blockId, Utils.bytesToString(size), Utils.bytesToString(freeMemory)))
-        } else {
-          logInfo("Block %s stored as bytes to memory (size %s, free %s)".format(
-            blockId, Utils.bytesToString(size), Utils.bytesToString(freeMemory)))
-        }
+        val valuesOrBytes = if (deserialized) "values" else "bytes"
+        logInfo("Block %s stored as %s in memory (estimated size %s, free %s)".format(
+          blockId, valuesOrBytes, Utils.bytesToString(size), Utils.bytesToString(freeMemory)))
         putSuccess = true
       } else {
         // Tell the block manager that we couldn't put it in memory so that it can drop it to
@@ -221,13 +216,12 @@ private class MemoryStore(blockManager: BlockManager, maxMemory: Long)
    * Return whether there is enough free space, along with the blocks dropped in the process.
    */
   private def ensureFreeSpace(blockIdToAdd: BlockId, space: Long): ResultWithDroppedBlocks = {
-    logInfo("ensureFreeSpace(%d) called with curMem=%d, maxMem=%d".format(
-      space, currentMemory, maxMemory))
+    logInfo(s"ensureFreeSpace($space) called with curMem=$currentMemory, maxMem=$maxMemory")
 
     val droppedBlocks = new ArrayBuffer[(BlockId, BlockStatus)]
 
     if (space > maxMemory) {
-      logInfo("Will not store " + blockIdToAdd + " as it is larger than our memory limit")
+      logInfo(s"Will not store $blockIdToAdd as it is larger than our memory limit")
       return ResultWithDroppedBlocks(success = false, droppedBlocks)
     }
 
@@ -252,7 +246,7 @@ private class MemoryStore(blockManager: BlockManager, maxMemory: Long)
       }
 
       if (maxMemory - (currentMemory - selectedMemory) >= space) {
-        logInfo(selectedBlocks.size + " blocks selected for dropping")
+        logInfo(s"${selectedBlocks.size} blocks selected for dropping")
         for (blockId <- selectedBlocks) {
           val entry = entries.synchronized { entries.get(blockId) }
           // This should never be null as only one thread should be dropping
diff --git a/core/src/main/scala/org/apache/spark/storage/StorageLevel.scala b/core/src/main/scala/org/apache/spark/storage/StorageLevel.scala
index 2d8ff1194a5d..1e35abaab535 100644
--- a/core/src/main/scala/org/apache/spark/storage/StorageLevel.scala
+++ b/core/src/main/scala/org/apache/spark/storage/StorageLevel.scala
@@ -34,11 +34,11 @@ import org.apache.spark.annotation.DeveloperApi
  */
 @DeveloperApi
 class StorageLevel private(
-    private var useDisk_ : Boolean,
-    private var useMemory_ : Boolean,
-    private var useOffHeap_ : Boolean,
-    private var deserialized_ : Boolean,
-    private var replication_ : Int = 1)
+    private var _useDisk: Boolean,
+    private var _useMemory: Boolean,
+    private var _useOffHeap: Boolean,
+    private var _deserialized: Boolean,
+    private var _replication: Int = 1)
   extends Externalizable {
 
   // TODO: Also add fields for caching priority, dataset ID, and flushing.
@@ -48,13 +48,13 @@ class StorageLevel private(
 
   def this() = this(false, true, false, false)  // For deserialization
 
-  def useDisk = useDisk_
-  def useMemory = useMemory_
-  def useOffHeap = useOffHeap_
-  def deserialized = deserialized_
-  def replication = replication_
+  def useDisk = _useDisk
+  def useMemory = _useMemory
+  def useOffHeap = _useOffHeap
+  def deserialized = _deserialized
+  def replication = _replication
 
-  assert(replication < 40, "Replication restricted to be less than 40 for calculating hashcodes")
+  assert(replication < 40, "Replication restricted to be less than 40 for calculating hash codes")
 
   if (useOffHeap) {
     require(!useDisk, "Off-heap storage level does not support using disk")
@@ -63,8 +63,9 @@ class StorageLevel private(
     require(replication == 1, "Off-heap storage level does not support multiple replication")
   }
 
-  override def clone(): StorageLevel = new StorageLevel(
-    this.useDisk, this.useMemory, this.useOffHeap, this.deserialized, this.replication)
+  override def clone(): StorageLevel = {
+    new StorageLevel(useDisk, useMemory, useOffHeap, deserialized, replication)
+  }
 
   override def equals(other: Any): Boolean = other match {
     case s: StorageLevel =>
@@ -77,20 +78,20 @@ class StorageLevel private(
       false
   }
 
-  def isValid = ((useMemory || useDisk || useOffHeap) && (replication > 0))
+  def isValid = (useMemory || useDisk || useOffHeap) && (replication > 0)
 
   def toInt: Int = {
     var ret = 0
-    if (useDisk_) {
+    if (_useDisk) {
       ret |= 8
     }
-    if (useMemory_) {
+    if (_useMemory) {
       ret |= 4
     }
-    if (useOffHeap_) {
+    if (_useOffHeap) {
       ret |= 2
     }
-    if (deserialized_) {
+    if (_deserialized) {
       ret |= 1
     }
     ret
@@ -98,32 +99,34 @@ class StorageLevel private(
 
   override def writeExternal(out: ObjectOutput) {
     out.writeByte(toInt)
-    out.writeByte(replication_)
+    out.writeByte(_replication)
   }
 
   override def readExternal(in: ObjectInput) {
     val flags = in.readByte()
-    useDisk_ = (flags & 8) != 0
-    useMemory_ = (flags & 4) != 0
-    useOffHeap_ = (flags & 2) != 0
-    deserialized_ = (flags & 1) != 0
-    replication_ = in.readByte()
+    _useDisk = (flags & 8) != 0
+    _useMemory = (flags & 4) != 0
+    _useOffHeap = (flags & 2) != 0
+    _deserialized = (flags & 1) != 0
+    _replication = in.readByte()
   }
 
   @throws(classOf[IOException])
   private def readResolve(): Object = StorageLevel.getCachedStorageLevel(this)
 
-  override def toString: String = "StorageLevel(%b, %b, %b, %b, %d)".format(
-    useDisk, useMemory, useOffHeap, deserialized, replication)
+  override def toString: String = {
+    s"StorageLevel($useDisk, $useMemory, $useOffHeap, $deserialized, $replication)"
+  }
 
   override def hashCode(): Int = toInt * 41 + replication
-  def description : String = {
+
+  def description: String = {
     var result = ""
     result += (if (useDisk) "Disk " else "")
     result += (if (useMemory) "Memory " else "")
     result += (if (useOffHeap) "Tachyon " else "")
     result += (if (deserialized) "Deserialized " else "Serialized ")
-    result += "%sx Replicated".format(replication)
+    result += s"${replication}x Replicated"
     result
   }
 }
@@ -165,7 +168,7 @@ object StorageLevel {
     case "MEMORY_AND_DISK_SER" => MEMORY_AND_DISK_SER
     case "MEMORY_AND_DISK_SER_2" => MEMORY_AND_DISK_SER_2
     case "OFF_HEAP" => OFF_HEAP
-    case _ => throw new IllegalArgumentException("Invalid StorageLevel: " + s)
+    case _ => throw new IllegalArgumentException(s"Invalid StorageLevel: $s")
   }
 
   /**
@@ -173,26 +176,37 @@ object StorageLevel {
    * Create a new StorageLevel object without setting useOffHeap.
    */
   @DeveloperApi
-  def apply(useDisk: Boolean, useMemory: Boolean, useOffHeap: Boolean,
-    deserialized: Boolean, replication: Int) = getCachedStorageLevel(
+  def apply(
+      useDisk: Boolean,
+      useMemory: Boolean,
+      useOffHeap: Boolean,
+      deserialized: Boolean,
+      replication: Int) = {
+    getCachedStorageLevel(
       new StorageLevel(useDisk, useMemory, useOffHeap, deserialized, replication))
+  }
 
   /**
    * :: DeveloperApi ::
    * Create a new StorageLevel object.
    */
   @DeveloperApi
-  def apply(useDisk: Boolean, useMemory: Boolean,
-    deserialized: Boolean, replication: Int = 1) = getCachedStorageLevel(
-      new StorageLevel(useDisk, useMemory, false, deserialized, replication))
+  def apply(
+      useDisk: Boolean,
+      useMemory: Boolean,
+      deserialized: Boolean,
+      replication: Int = 1) = {
+    getCachedStorageLevel(new StorageLevel(useDisk, useMemory, false, deserialized, replication))
+  }
 
   /**
    * :: DeveloperApi ::
    * Create a new StorageLevel object from its integer representation.
    */
   @DeveloperApi
-  def apply(flags: Int, replication: Int): StorageLevel =
+  def apply(flags: Int, replication: Int): StorageLevel = {
     getCachedStorageLevel(new StorageLevel(flags, replication))
+  }
 
   /**
    * :: DeveloperApi ::
@@ -205,8 +219,8 @@ object StorageLevel {
     getCachedStorageLevel(obj)
   }
 
-  private[spark]
-  val storageLevelCache = new java.util.concurrent.ConcurrentHashMap[StorageLevel, StorageLevel]()
+  private[spark] val storageLevelCache =
+    new java.util.concurrent.ConcurrentHashMap[StorageLevel, StorageLevel]()
 
   private[spark] def getCachedStorageLevel(level: StorageLevel): StorageLevel = {
     storageLevelCache.putIfAbsent(level, level)
diff --git a/core/src/main/scala/org/apache/spark/storage/TachyonStore.scala b/core/src/main/scala/org/apache/spark/storage/TachyonStore.scala
index c37e76f89360..d8ff4ff6bd42 100644
--- a/core/src/main/scala/org/apache/spark/storage/TachyonStore.scala
+++ b/core/src/main/scala/org/apache/spark/storage/TachyonStore.scala
@@ -22,15 +22,10 @@ import java.nio.ByteBuffer
 
 import scala.collection.mutable.ArrayBuffer
 
-import tachyon.client.{WriteType, ReadType}
+import tachyon.client.{ReadType, WriteType}
 
 import org.apache.spark.Logging
 import org.apache.spark.util.Utils
-import org.apache.spark.serializer.Serializer
-
-
-private class Entry(val size: Long)
-
 
 /**
  * Stores BlockManager blocks on Tachyon.
@@ -46,8 +41,8 @@ private class TachyonStore(
     tachyonManager.getFile(blockId.name).length
   }
 
-  override def putBytes(blockId: BlockId, bytes: ByteBuffer, level: StorageLevel): PutResult =  {
-    putToTachyonStore(blockId, bytes, true)
+  override def putBytes(blockId: BlockId, bytes: ByteBuffer, level: StorageLevel): PutResult = {
+    putIntoTachyonStore(blockId, bytes, returnValues = true)
   }
 
   override def putValues(
@@ -55,7 +50,7 @@ private class TachyonStore(
       values: ArrayBuffer[Any],
       level: StorageLevel,
       returnValues: Boolean): PutResult = {
-    return putValues(blockId, values.toIterator, level, returnValues)
+    putValues(blockId, values.toIterator, level, returnValues)
   }
 
   override def putValues(
@@ -63,12 +58,12 @@ private class TachyonStore(
       values: Iterator[Any],
       level: StorageLevel,
       returnValues: Boolean): PutResult = {
-    logDebug("Attempting to write values for block " + blockId)
-    val _bytes = blockManager.dataSerialize(blockId, values)
-    putToTachyonStore(blockId, _bytes, returnValues)
+    logDebug(s"Attempting to write values for block $blockId")
+    val bytes = blockManager.dataSerialize(blockId, values)
+    putIntoTachyonStore(blockId, bytes, returnValues)
   }
 
-  private def putToTachyonStore(
+  private def putIntoTachyonStore(
       blockId: BlockId,
       bytes: ByteBuffer,
       returnValues: Boolean): PutResult = {
@@ -76,7 +71,7 @@ private class TachyonStore(
     // duplicate does not copy buffer, so inexpensive
     val byteBuffer = bytes.duplicate()
     byteBuffer.rewind()
-    logDebug("Attempting to put block " + blockId + " into Tachyon")
+    logDebug(s"Attempting to put block $blockId into Tachyon")
     val startTime = System.currentTimeMillis
     val file = tachyonManager.getFile(blockId)
     val os = file.getOutStream(WriteType.TRY_CACHE)
@@ -84,7 +79,7 @@ private class TachyonStore(
     os.close()
     val finishTime = System.currentTimeMillis
     logDebug("Block %s stored as %s file in Tachyon in %d ms".format(
-      blockId, Utils.bytesToString(byteBuffer.limit), (finishTime - startTime)))
+      blockId, Utils.bytesToString(byteBuffer.limit), finishTime - startTime))
 
     if (returnValues) {
       PutResult(bytes.limit(), Right(bytes.duplicate()))
@@ -106,10 +101,9 @@ private class TachyonStore(
     getBytes(blockId).map(buffer => blockManager.dataDeserialize(blockId, buffer))
   }
 
-
   override def getBytes(blockId: BlockId): Option[ByteBuffer] = {
     val file = tachyonManager.getFile(blockId)
-    if (file == null || file.getLocationHosts().size == 0) {
+    if (file == null || file.getLocationHosts.size == 0) {
       return None
     }
     val is = file.getInStream(ReadType.CACHE)
@@ -121,16 +115,15 @@ private class TachyonStore(
         val fetchSize = is.read(bs, 0, size.asInstanceOf[Int])
         buffer = ByteBuffer.wrap(bs)
         if (fetchSize != size) {
-          logWarning("Failed to fetch the block " + blockId + " from Tachyon : Size " + size +
-            " is not equal to fetched size " + fetchSize)
+          logWarning(s"Failed to fetch the block $blockId from Tachyon: Size $size " +
+            s"is not equal to fetched size $fetchSize")
           return None
         }
       }
     } catch {
-        case ioe: IOException => {
-          logWarning("Failed to fetch the block " + blockId + " from Tachyon", ioe)
-          return None
-        }
+      case ioe: IOException =>
+        logWarning(s"Failed to fetch the block $blockId from Tachyon", ioe)
+        return None
     }
     Some(buffer)
   }
diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala
index ee629794f60a..042fdfcc4726 100644
--- a/project/MimaExcludes.scala
+++ b/project/MimaExcludes.scala
@@ -53,6 +53,8 @@ object MimaExcludes {
               "org.apache.spark.api.java.JavaRDDLike.countApproxDistinct$default$1"),
             ProblemFilters.exclude[MissingMethodProblem](
               "org.apache.spark.api.java.JavaDoubleRDD.countApproxDistinct$default$1"),
+            ProblemFilters.exclude[MissingMethodProblem](
+              "org.apache.spark.storage.MemoryStore.Entry"),
             ProblemFilters.exclude[MissingMethodProblem](
               "org.apache.spark.rdd.PairRDDFunctions.org$apache$spark$rdd$PairRDDFunctions$$"
                 + "createZero$1")
@@ -67,7 +69,10 @@ object MimaExcludes {
           ) ++
           MimaBuild.excludeSparkClass("rdd.ZippedRDD") ++
           MimaBuild.excludeSparkClass("rdd.ZippedPartition") ++
-          MimaBuild.excludeSparkClass("util.SerializableHyperLogLog")
+          MimaBuild.excludeSparkClass("util.SerializableHyperLogLog") ++
+          MimaBuild.excludeSparkClass("storage.Values") ++
+          MimaBuild.excludeSparkClass("storage.Entry") ++
+          MimaBuild.excludeSparkClass("storage.MemoryStore$Entry")
         case v if v.startsWith("1.0") =>
           Seq(
             MimaBuild.excludeSparkPackage("api.java"),

From f95ac686bcba4e677254120735b0eb7a29f20d63 Mon Sep 17 00:00:00 2001
From: John Zhao <jzhao@alpinenow.com>
Date: Thu, 12 Jun 2014 21:39:00 -0700
Subject: [PATCH 416/810] [SPARK-1516]Throw exception in yarn client instead of
 run system.exit directly.

All the changes is in  the package of "org.apache.spark.deploy.yarn":
    1) Throw exception in ClinetArguments and ClientBase instead of exit directly.
    2) in Client's main method, if exception is caught, it will exit with code 1, otherwise exit with code 0.

After the fix, if user integrate the spark yarn client into their applications, when the argument is wrong or the running is finished, the application won't be terminated.

Author: John Zhao <jzhao@alpinenow.com>

Closes #490 from codeboyyong/jira_1516_systemexit_inyarnclient and squashes the following commits:

138cb48 [John Zhao] [SPARK-1516]Throw exception in yarn clinet instead of run system.exit directly. All the changes is in  the package of "org.apache.spark.deploy.yarn": 1) Add a ClientException with an exitCode 2) Throws exception in ClinetArguments and ClientBase instead of exit directly 3) in Client's main method, catch exception and exit with the exitCode.
---
 .../org/apache/spark/deploy/yarn/Client.scala | 14 +++++++---
 .../spark/deploy/yarn/ClientArguments.scala   | 16 +++++-------
 .../apache/spark/deploy/yarn/ClientBase.scala | 26 ++++++++++++-------
 .../org/apache/spark/deploy/yarn/Client.scala | 14 +++++++---
 4 files changed, 44 insertions(+), 26 deletions(-)

diff --git a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/Client.scala b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
index 8226207de42b..4ccddc214c8a 100644
--- a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
+++ b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
@@ -85,7 +85,6 @@ class Client(clientArgs: ClientArguments, hadoopConf: Configuration, spConf: Spa
   def run() {
     val appId = runApp()
     monitorApplication(appId)
-    System.exit(0)
   }
 
   def logClusterResourceDetails() {
@@ -179,8 +178,17 @@ object Client {
     System.setProperty("SPARK_YARN_MODE", "true")
 
     val sparkConf = new SparkConf
-    val args = new ClientArguments(argStrings, sparkConf)
 
-    new Client(args, sparkConf).run
+    try {
+      val args = new ClientArguments(argStrings, sparkConf)
+      new Client(args, sparkConf).run()
+    } catch {
+      case e: Exception => {
+        Console.err.println(e.getMessage)
+        System.exit(1)
+      }
+    }
+
+    System.exit(0)
   }
 }
diff --git a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala
index b2c413b6d267..fd3ef9e1fa2d 100644
--- a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala
+++ b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala
@@ -125,11 +125,11 @@ class ClientArguments(val args: Array[String], val sparkConf: SparkConf) {
 
         case Nil =>
           if (userClass == null) {
-            printUsageAndExit(1)
+            throw new IllegalArgumentException(getUsageMessage())
           }
 
         case _ =>
-          printUsageAndExit(1, args)
+          throw new IllegalArgumentException(getUsageMessage(args))
       }
     }
 
@@ -138,11 +138,10 @@ class ClientArguments(val args: Array[String], val sparkConf: SparkConf) {
   }
 
 
-  def printUsageAndExit(exitCode: Int, unknownParam: Any = null) {
-    if (unknownParam != null) {
-      System.err.println("Unknown/unsupported param " + unknownParam)
-    }
-    System.err.println(
+  def getUsageMessage(unknownParam: Any = null): String = {
+    val message = if (unknownParam != null) s"Unknown/unsupported param $unknownParam\n" else ""
+
+    message +
       "Usage: org.apache.spark.deploy.yarn.Client [options] \n" +
       "Options:\n" +
       "  --jar JAR_PATH             Path to your application's JAR file (required in yarn-cluster mode)\n" +
@@ -158,8 +157,5 @@ class ClientArguments(val args: Array[String], val sparkConf: SparkConf) {
       "  --addJars jars             Comma separated list of local jars that want SparkContext.addJar to work with.\n" +
       "  --files files              Comma separated list of files to be distributed with the job.\n" +
       "  --archives archives        Comma separated list of archives to be distributed with the job."
-      )
-    System.exit(exitCode)
   }
-
 }
diff --git a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala
index 29a35680c0e7..6861b503000c 100644
--- a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala
+++ b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala
@@ -37,7 +37,7 @@ import org.apache.hadoop.yarn.api.protocolrecords._
 import org.apache.hadoop.yarn.api.records._
 import org.apache.hadoop.yarn.conf.YarnConfiguration
 import org.apache.hadoop.yarn.util.Records
-import org.apache.spark.{Logging, SparkConf, SparkContext}
+import org.apache.spark.{SparkException, Logging, SparkConf, SparkContext}
 
 /**
  * The entry point (starting in Client#main() and Client#run()) for launching Spark on YARN. The
@@ -79,7 +79,7 @@ trait ClientBase extends Logging {
     ).foreach { case(cond, errStr) =>
       if (cond) {
         logError(errStr)
-        args.printUsageAndExit(1)
+        throw new IllegalArgumentException(args.getUsageMessage())
       }
     }
   }
@@ -94,15 +94,20 @@ trait ClientBase extends Logging {
 
     // If we have requested more then the clusters max for a single resource then exit.
     if (args.executorMemory > maxMem) {
-      logError("Required executor memory (%d MB), is above the max threshold (%d MB) of this cluster.".
-        format(args.executorMemory, maxMem))
-      System.exit(1)
+      val errorMessage =
+        "Required executor memory (%d MB), is above the max threshold (%d MB) of this cluster."
+          .format(args.executorMemory, maxMem)
+
+      logError(errorMessage)
+      throw new IllegalArgumentException(errorMessage)
     }
     val amMem = args.amMemory + YarnAllocationHandler.MEMORY_OVERHEAD
     if (amMem > maxMem) {
-      logError("Required AM memory (%d) is above the max threshold (%d) of this cluster".
-        format(args.amMemory, maxMem))
-      System.exit(1)
+
+      val errorMessage = "Required AM memory (%d) is above the max threshold (%d) of this cluster."
+        .format(args.amMemory, maxMem)
+      logError(errorMessage)
+      throw new IllegalArgumentException(errorMessage)
     }
 
     // We could add checks to make sure the entire cluster has enough resources but that involves
@@ -186,8 +191,9 @@ trait ClientBase extends Logging {
     val delegTokenRenewer = Master.getMasterPrincipal(conf)
     if (UserGroupInformation.isSecurityEnabled()) {
       if (delegTokenRenewer == null || delegTokenRenewer.length() == 0) {
-        logError("Can't get Master Kerberos principal for use as renewer")
-        System.exit(1)
+        val errorMessage = "Can't get Master Kerberos principal for use as renewer"
+        logError(errorMessage)
+        throw new SparkException(errorMessage)
       }
     }
     val dst = new Path(fs.getHomeDirectory(), appStagingDir)
diff --git a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/Client.scala b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
index 24027618c1f3..80a8bceb1726 100644
--- a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
+++ b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
@@ -95,7 +95,6 @@ class Client(clientArgs: ClientArguments, hadoopConf: Configuration, spConf: Spa
   def run() {
     val appId = runApp()
     monitorApplication(appId)
-    System.exit(0)
   }
 
   def logClusterResourceDetails() {
@@ -186,9 +185,18 @@ object Client {
     // see Client#setupLaunchEnv().
     System.setProperty("SPARK_YARN_MODE", "true")
     val sparkConf = new SparkConf()
-    val args = new ClientArguments(argStrings, sparkConf)
 
-    new Client(args, sparkConf).run()
+    try {
+      val args = new ClientArguments(argStrings, sparkConf)
+      new Client(args, sparkConf).run()
+    } catch {
+      case e: Exception => {
+        Console.err.println(e.getMessage)
+        System.exit(1)
+      }
+    }
+
+    System.exit(0)
   }
 
 }

From 13f8cfdc04589b986554310965e83fe658085683 Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Thu, 12 Jun 2014 23:09:41 -0700
Subject: [PATCH 417/810] [SPARK-2135][SQL] Use planner for in-memory scans

Author: Michael Armbrust <michael@databricks.com>

Closes #1072 from marmbrus/cachedStars and squashes the following commits:

8757c8e [Michael Armbrust] Use planner for in-memory scans.
---
 .../org/apache/spark/sql/SQLContext.scala     | 14 +++----
 .../columnar/InMemoryColumnarTableScan.scala  | 39 +++++++++++++++----
 .../spark/sql/execution/SparkPlan.scala       |  2 -
 .../spark/sql/execution/SparkStrategies.scala | 13 +++++++
 .../apache/spark/sql/CachedTableSuite.scala   | 15 ++++---
 .../columnar/InMemoryColumnarQuerySuite.scala |  6 +--
 .../apache/spark/sql/hive/HiveContext.scala   |  1 +
 .../spark/sql/hive/HiveMetastoreCatalog.scala |  7 ++--
 .../spark/sql/hive/HiveStrategies.scala       |  7 ++--
 .../spark/sql/hive/CachedTableSuite.scala     |  6 +--
 10 files changed, 75 insertions(+), 35 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
index 264192ed1aa2..b6a2f1b9d127 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
@@ -34,7 +34,7 @@ import org.apache.spark.sql.catalyst.optimizer.Optimizer
 import org.apache.spark.sql.catalyst.plans.logical.{SetCommand, LogicalPlan}
 import org.apache.spark.sql.catalyst.rules.RuleExecutor
 
-import org.apache.spark.sql.columnar.InMemoryColumnarTableScan
+import org.apache.spark.sql.columnar.InMemoryRelation
 
 import org.apache.spark.sql.execution._
 import org.apache.spark.sql.execution.SparkStrategies
@@ -166,10 +166,9 @@ class SQLContext(@transient val sparkContext: SparkContext)
     val useCompression =
       sparkContext.conf.getBoolean("spark.sql.inMemoryColumnarStorage.compressed", false)
     val asInMemoryRelation =
-      InMemoryColumnarTableScan(
-        currentTable.output, executePlan(currentTable).executedPlan, useCompression)
+      InMemoryRelation(useCompression, executePlan(currentTable).executedPlan)
 
-    catalog.registerTable(None, tableName, SparkLogicalPlan(asInMemoryRelation))
+    catalog.registerTable(None, tableName, asInMemoryRelation)
   }
 
   /** Removes the specified table from the in-memory cache. */
@@ -177,11 +176,11 @@ class SQLContext(@transient val sparkContext: SparkContext)
     EliminateAnalysisOperators(catalog.lookupRelation(None, tableName)) match {
       // This is kind of a hack to make sure that if this was just an RDD registered as a table,
       // we reregister the RDD as a table.
-      case SparkLogicalPlan(inMem @ InMemoryColumnarTableScan(_, e: ExistingRdd, _)) =>
+      case inMem @ InMemoryRelation(_, _, e: ExistingRdd) =>
         inMem.cachedColumnBuffers.unpersist()
         catalog.unregisterTable(None, tableName)
         catalog.registerTable(None, tableName, SparkLogicalPlan(e))
-      case SparkLogicalPlan(inMem: InMemoryColumnarTableScan) =>
+      case inMem: InMemoryRelation =>
         inMem.cachedColumnBuffers.unpersist()
         catalog.unregisterTable(None, tableName)
       case plan => throw new IllegalArgumentException(s"Table $tableName is not cached: $plan")
@@ -192,7 +191,7 @@ class SQLContext(@transient val sparkContext: SparkContext)
   def isCached(tableName: String): Boolean = {
     val relation = catalog.lookupRelation(None, tableName)
     EliminateAnalysisOperators(relation) match {
-      case SparkLogicalPlan(_: InMemoryColumnarTableScan) => true
+      case _: InMemoryRelation => true
       case _ => false
     }
   }
@@ -208,6 +207,7 @@ class SQLContext(@transient val sparkContext: SparkContext)
       PartialAggregation ::
       LeftSemiJoin ::
       HashJoin ::
+      InMemoryScans ::
       ParquetOperations ::
       BasicOperators ::
       CartesianProduct ::
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala
index fdf28e1bb126..e1e4f24c6c66 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala
@@ -17,18 +17,29 @@
 
 package org.apache.spark.sql.columnar
 
+import org.apache.spark.sql.catalyst.analysis.MultiInstanceRelation
 import org.apache.spark.sql.catalyst.expressions.{GenericMutableRow, Attribute}
+import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
 import org.apache.spark.sql.execution.{SparkPlan, LeafNode}
 import org.apache.spark.sql.Row
 import org.apache.spark.SparkConf
 
-private[sql] case class InMemoryColumnarTableScan(
-    attributes: Seq[Attribute],
-    child: SparkPlan,
-    useCompression: Boolean)
-  extends LeafNode {
+object InMemoryRelation {
+  def apply(useCompression: Boolean, child: SparkPlan): InMemoryRelation =
+    new InMemoryRelation(child.output, useCompression, child)
+}
 
-  override def output: Seq[Attribute] = attributes
+private[sql] case class InMemoryRelation(
+    output: Seq[Attribute],
+    useCompression: Boolean,
+    child: SparkPlan)
+  extends LogicalPlan with MultiInstanceRelation {
+
+  override def children = Seq.empty
+  override def references = Set.empty
+
+  override def newInstance() =
+    new InMemoryRelation(output.map(_.newInstance), useCompression, child).asInstanceOf[this.type]
 
   lazy val cachedColumnBuffers = {
     val output = child.output
@@ -55,14 +66,26 @@ private[sql] case class InMemoryColumnarTableScan(
     cached.count()
     cached
   }
+}
+
+private[sql] case class InMemoryColumnarTableScan(
+    attributes: Seq[Attribute],
+    relation: InMemoryRelation)
+  extends LeafNode {
+
+  override def output: Seq[Attribute] = attributes
 
   override def execute() = {
-    cachedColumnBuffers.mapPartitions { iterator =>
+    relation.cachedColumnBuffers.mapPartitions { iterator =>
       val columnBuffers = iterator.next()
       assert(!iterator.hasNext)
 
       new Iterator[Row] {
-        val columnAccessors = columnBuffers.map(ColumnAccessor(_))
+        // Find the ordinals of the requested columns.  If none are requested, use the first.
+        val requestedColumns =
+          if (attributes.isEmpty) Seq(0) else attributes.map(relation.output.indexOf(_))
+
+        val columnAccessors = requestedColumns.map(columnBuffers(_)).map(ColumnAccessor(_))
         val nextRow = new GenericMutableRow(columnAccessors.length)
 
         override def next() = {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
index 4613df103943..07967fe75e88 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
@@ -77,8 +77,6 @@ case class SparkLogicalPlan(alreadyPlanned: SparkPlan)
     SparkLogicalPlan(
       alreadyPlanned match {
         case ExistingRdd(output, rdd) => ExistingRdd(output.map(_.newInstance), rdd)
-        case scan @ InMemoryColumnarTableScan(output, _, _) =>
-          scan.copy(attributes = output.map(_.newInstance))
         case _ => sys.error("Multiple instance of the same relation detected.")
       }).asInstanceOf[this.type]
   }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
index f2f95dfe27e6..a657911afe53 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
@@ -24,6 +24,7 @@ import org.apache.spark.sql.catalyst.plans._
 import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
 import org.apache.spark.sql.catalyst.plans.physical._
 import org.apache.spark.sql.parquet._
+import org.apache.spark.sql.columnar.{InMemoryRelation, InMemoryColumnarTableScan}
 
 private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
   self: SQLContext#SparkPlanner =>
@@ -191,6 +192,18 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
     }
   }
 
+  object InMemoryScans extends Strategy {
+    def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
+      case PhysicalOperation(projectList, filters, mem: InMemoryRelation) =>
+        pruneFilterProject(
+          projectList,
+          filters,
+          identity[Seq[Expression]], // No filters are pushed down.
+          InMemoryColumnarTableScan(_, mem)) :: Nil
+      case _ => Nil
+    }
+  }
+
   // Can we automate these 'pass through' operations?
   object BasicOperators extends Strategy {
     def numPartitions = self.numPartitions
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala
index ebca3adc2ff0..c794da4da406 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala
@@ -18,8 +18,7 @@
 package org.apache.spark.sql
 
 import org.apache.spark.sql.TestData._
-import org.apache.spark.sql.columnar.InMemoryColumnarTableScan
-import org.apache.spark.sql.execution.SparkLogicalPlan
+import org.apache.spark.sql.columnar.{InMemoryRelation, InMemoryColumnarTableScan}
 import org.apache.spark.sql.test.TestSQLContext
 
 class CachedTableSuite extends QueryTest {
@@ -34,7 +33,7 @@ class CachedTableSuite extends QueryTest {
     )
 
     TestSQLContext.table("testData").queryExecution.analyzed match {
-      case SparkLogicalPlan(_ : InMemoryColumnarTableScan) => // Found evidence of caching
+      case _ : InMemoryRelation => // Found evidence of caching
       case noCache => fail(s"No cache node found in plan $noCache")
     }
 
@@ -46,7 +45,7 @@ class CachedTableSuite extends QueryTest {
     )
 
     TestSQLContext.table("testData").queryExecution.analyzed match {
-      case cachePlan @ SparkLogicalPlan(_ : InMemoryColumnarTableScan) =>
+      case cachePlan: InMemoryRelation =>
         fail(s"Table still cached after uncache: $cachePlan")
       case noCache => // Table uncached successfully
     }
@@ -61,13 +60,17 @@ class CachedTableSuite extends QueryTest {
   test("SELECT Star Cached Table") {
     TestSQLContext.sql("SELECT * FROM testData").registerAsTable("selectStar")
     TestSQLContext.cacheTable("selectStar")
-    TestSQLContext.sql("SELECT * FROM selectStar")
+    TestSQLContext.sql("SELECT * FROM selectStar WHERE key = 1").collect()
     TestSQLContext.uncacheTable("selectStar")
   }
 
   test("Self-join cached") {
+    val unCachedAnswer =
+      TestSQLContext.sql("SELECT * FROM testData a JOIN testData b ON a.key = b.key").collect()
     TestSQLContext.cacheTable("testData")
-    TestSQLContext.sql("SELECT * FROM testData a JOIN testData b ON a.key = b.key")
+    checkAnswer(
+      TestSQLContext.sql("SELECT * FROM testData a JOIN testData b ON a.key = b.key"),
+      unCachedAnswer.toSeq)
     TestSQLContext.uncacheTable("testData")
   }
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala
index 31c5dfba9295..86727b93f365 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala
@@ -28,14 +28,14 @@ class InMemoryColumnarQuerySuite extends QueryTest {
 
   test("simple columnar query") {
     val plan = TestSQLContext.executePlan(testData.logicalPlan).executedPlan
-    val scan = SparkLogicalPlan(InMemoryColumnarTableScan(plan.output, plan, true))
+    val scan = InMemoryRelation(useCompression = true, plan)
 
     checkAnswer(scan, testData.collect().toSeq)
   }
 
   test("projection") {
     val plan = TestSQLContext.executePlan(testData.select('value, 'key).logicalPlan).executedPlan
-    val scan = SparkLogicalPlan(InMemoryColumnarTableScan(plan.output, plan, true))
+    val scan = InMemoryRelation(useCompression = true, plan)
 
     checkAnswer(scan, testData.collect().map {
       case Row(key: Int, value: String) => value -> key
@@ -44,7 +44,7 @@ class InMemoryColumnarQuerySuite extends QueryTest {
 
   test("SPARK-1436 regression: in-memory columns must be able to be accessed multiple times") {
     val plan = TestSQLContext.executePlan(testData.logicalPlan).executedPlan
-    val scan = SparkLogicalPlan(InMemoryColumnarTableScan(plan.output, plan, true))
+    val scan = InMemoryRelation(useCompression = true, plan)
 
     checkAnswer(scan, testData.collect().toSeq)
     checkAnswer(scan, testData.collect().toSeq)
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
index 64978215542e..9cd13f6ae0d5 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
@@ -230,6 +230,7 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) {
       CommandStrategy(self),
       TakeOrdered,
       ParquetOperations,
+      InMemoryScans,
       HiveTableScans,
       DataSinks,
       Scripts,
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
index a91b52076534..e9e6497f7e5f 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
@@ -36,7 +36,7 @@ import org.apache.spark.sql.catalyst.rules._
 import org.apache.spark.sql.catalyst.types._
 import org.apache.spark.sql.execution.SparkLogicalPlan
 import org.apache.spark.sql.hive.execution.{HiveTableScan, InsertIntoHiveTable}
-import org.apache.spark.sql.columnar.InMemoryColumnarTableScan
+import org.apache.spark.sql.columnar.{InMemoryRelation, InMemoryColumnarTableScan}
 
 /* Implicit conversions */
 import scala.collection.JavaConversions._
@@ -130,8 +130,9 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with
       case p @ InsertIntoTable(table: MetastoreRelation, _, child, _) =>
         castChildOutput(p, table, child)
 
-      case p @ logical.InsertIntoTable(SparkLogicalPlan(InMemoryColumnarTableScan(
-        _, HiveTableScan(_, table, _), _)), _, child, _) =>
+      case p @ logical.InsertIntoTable(
+                 InMemoryRelation(_, _,
+                   HiveTableScan(_, table, _)), _, child, _) =>
         castChildOutput(p, table, child)
     }
 
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala
index 8b51957162e0..ed6cd5a11dba 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala
@@ -24,7 +24,7 @@ import org.apache.spark.sql.catalyst.plans._
 import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
 import org.apache.spark.sql.execution._
 import org.apache.spark.sql.hive.execution._
-import org.apache.spark.sql.columnar.InMemoryColumnarTableScan
+import org.apache.spark.sql.columnar.InMemoryRelation
 
 private[hive] trait HiveStrategies {
   // Possibly being too clever with types here... or not clever enough.
@@ -44,8 +44,9 @@ private[hive] trait HiveStrategies {
     def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
       case logical.InsertIntoTable(table: MetastoreRelation, partition, child, overwrite) =>
         InsertIntoHiveTable(table, partition, planLater(child), overwrite)(hiveContext) :: Nil
-      case logical.InsertIntoTable(SparkLogicalPlan(InMemoryColumnarTableScan(
-        _, HiveTableScan(_, table, _), _)), partition, child, overwrite) =>
+      case logical.InsertIntoTable(
+             InMemoryRelation(_, _,
+               HiveTableScan(_, table, _)), partition, child, overwrite) =>
         InsertIntoHiveTable(table, partition, planLater(child), overwrite)(hiveContext) :: Nil
       case _ => Nil
     }
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/CachedTableSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/CachedTableSuite.scala
index 91ac03ca30cd..3132d0112c70 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/CachedTableSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/CachedTableSuite.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.sql.hive
 
 import org.apache.spark.sql.execution.SparkLogicalPlan
-import org.apache.spark.sql.columnar.InMemoryColumnarTableScan
+import org.apache.spark.sql.columnar.{InMemoryRelation, InMemoryColumnarTableScan}
 import org.apache.spark.sql.hive.execution.HiveComparisonTest
 import org.apache.spark.sql.hive.test.TestHive
 
@@ -34,7 +34,7 @@ class CachedTableSuite extends HiveComparisonTest {
 
   test("check that table is cached and uncache") {
     TestHive.table("src").queryExecution.analyzed match {
-      case SparkLogicalPlan(_ : InMemoryColumnarTableScan) => // Found evidence of caching
+      case _ : InMemoryRelation => // Found evidence of caching
       case noCache => fail(s"No cache node found in plan $noCache")
     }
     TestHive.uncacheTable("src")
@@ -45,7 +45,7 @@ class CachedTableSuite extends HiveComparisonTest {
 
   test("make sure table is uncached") {
     TestHive.table("src").queryExecution.analyzed match {
-      case cachePlan @ SparkLogicalPlan(_ : InMemoryColumnarTableScan) =>
+      case cachePlan: InMemoryRelation =>
         fail(s"Table still cached after uncache: $cachePlan")
       case noCache => // Table uncached successfully
     }

From b3736e3d2ff9ccb83a18eefec661739105a38df5 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Fri, 13 Jun 2014 02:59:38 -0700
Subject: [PATCH 418/810] [HOTFIX] add math3 version to pom

Passed `mvn package`.

Author: Xiangrui Meng <meng@databricks.com>

Closes #1075 from mengxr/takeSample-fix and squashes the following commits:

45b4590 [Xiangrui Meng] add math3 version to pom
---
 core/pom.xml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/core/pom.xml b/core/pom.xml
index be56911b9e45..bd6767e03bb9 100644
--- a/core/pom.xml
+++ b/core/pom.xml
@@ -70,6 +70,7 @@
     <dependency>
       <groupId>org.apache.commons</groupId>
       <artifactId>commons-math3</artifactId>
+      <version>3.3</version>
       <scope>test</scope>
     </dependency>
     <dependency>

From 70c8116c0aecba293234edc44a7f8e58e5008649 Mon Sep 17 00:00:00 2001
From: nravi <nravi@c1704.halxg.cloudera.com>
Date: Fri, 13 Jun 2014 10:52:21 -0700
Subject: [PATCH 419/810] Workaround in Spark for ConcurrentModification issue
 (JIRA Hadoop-10456, Spark-1097)

This fix has gone into Hadoop 2.4.1. For developers using <  2.4.1, it would be good to have a workaround in Spark as well.

Fix has been tested for performance as well, no regressions found.

Author: nravi <nravi@c1704.halxg.cloudera.com>

Closes #1000 from nishkamravi2/master and squashes the following commits:

eb663ca [nravi] Merge branch 'master' of https://github.com/apache/spark
df2aeb1 [nravi] Improved fix for ConcurrentModificationIssue (Spark-1097, Hadoop-10456)
6b840f0 [nravi] Undo the fix for SPARK-1758 (the problem is fixed)
5108700 [nravi] Fix in Spark for the Concurrent thread modification issue (SPARK-1097, HADOOP-10456)
681b36f [nravi] Fix for SPARK-1758: failing test org.apache.spark.JavaAPISuite.wholeTextFiles
---
 .../main/scala/org/apache/spark/rdd/HadoopRDD.scala   | 11 +++++++----
 1 file changed, 7 insertions(+), 4 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala b/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala
index 6547755764dc..2aa111d600e9 100644
--- a/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala
@@ -139,10 +139,13 @@ class HadoopRDD[K, V](
       // Create a JobConf that will be cached and used across this RDD's getJobConf() calls in the
       // local process. The local cache is accessed through HadoopRDD.putCachedMetadata().
       // The caching helps minimize GC, since a JobConf can contain ~10KB of temporary objects.
-      val newJobConf = new JobConf(broadcastedConf.value.value)
-      initLocalJobConfFuncOpt.map(f => f(newJobConf))
-      HadoopRDD.putCachedMetadata(jobConfCacheKey, newJobConf)
-      newJobConf
+      // synchronize to prevent ConcurrentModificationException (Spark-1097, Hadoop-10456)
+      broadcastedConf.synchronized {
+        val newJobConf = new JobConf(broadcastedConf.value.value)
+        initLocalJobConfFuncOpt.map(f => f(newJobConf))
+        HadoopRDD.putCachedMetadata(jobConfCacheKey, newJobConf)
+        newJobConf
+      }
     }
   }
 

From 1c2fd015b05b65abc83c4874ada825deac578af8 Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Fri, 13 Jun 2014 12:55:15 -0700
Subject: [PATCH 420/810] [SPARK-1964][SQL] Add timestamp to
 HiveMetastoreTypes.toMetastoreType

Author: Michael Armbrust <michael@databricks.com>

Closes #1061 from marmbrus/timestamp and squashes the following commits:

79c3903 [Michael Armbrust] Add timestamp to HiveMetastoreTypes.toMetastoreType()
---
 .../scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala   | 1 +
 1 file changed, 1 insertion(+)

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
index e9e6497f7e5f..68284344afd5 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
@@ -237,6 +237,7 @@ object HiveMetastoreTypes extends RegexParsers {
     case BinaryType => "binary"
     case BooleanType => "boolean"
     case DecimalType => "decimal"
+    case TimestampType => "timestamp"
   }
 }
 

From ac96d9657c9a9f89a455a1b671c059d896012d41 Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian.cs.zju@gmail.com>
Date: Fri, 13 Jun 2014 12:59:48 -0700
Subject: [PATCH 421/810] [SPARK-2094][SQL] "Exactly once" semantics for DDL
 and command statements

## Related JIRA issues

- Main issue:

  - [SPARK-2094](https://issues.apache.org/jira/browse/SPARK-2094): Ensure exactly once semantics for DDL/Commands

- Issues resolved as dependencies:

  - [SPARK-2081](https://issues.apache.org/jira/browse/SPARK-2081): Undefine output() from the abstract class Command and implement it in concrete subclasses
  - [SPARK-2128](https://issues.apache.org/jira/browse/SPARK-2128): No plan for DESCRIBE
  - [SPARK-1852](https://issues.apache.org/jira/browse/SPARK-1852): SparkSQL Queries with Sorts run before the user asks them to

- Other related issue:

  - [SPARK-2129](https://issues.apache.org/jira/browse/SPARK-2129): NPE thrown while lookup a view

    Two test cases, `join_view` and `mergejoin_mixed`, within the `HiveCompatibilitySuite` are removed from the whitelist to workaround this issue.

## PR Overview

This PR defines physical plans for DDL statements and commands and wraps their side effects in a lazy field `PhysicalCommand.sideEffectResult`, so that they are executed eagerly and exactly once.  Also, as a positive side effect, now DDL statements and commands can be turned into proper `SchemaRDD`s and let user query the execution results.

This PR defines schemas for the following DDL/commands:

- EXPLAIN command

  - `plan`: String, the plan explanation

- SET command

  - `key`: String, the key(s) of the propert(y/ies) being set or queried
  - `value`: String, the value(s) of the propert(y/ies) being queried

- Other Hive native command

  - `result`: String, execution result returned by Hive

  **NOTE**: We should refine schemas for different native commands by defining physical plans for them in the future.

## Examples

### EXPLAIN command

Take the "EXPLAIN" command as an example, we first execute the command and obtain a `SchemaRDD` at the same time, then query the `plan` field with the schema DSL:

```
scala> loadTestTable("src")
...

scala> val q0 = hql("EXPLAIN SELECT key, COUNT(*) FROM src GROUP BY key")
...
q0: org.apache.spark.sql.SchemaRDD =
SchemaRDD[0] at RDD at SchemaRDD.scala:98
== Query Plan ==
ExplainCommandPhysical [plan#11:0]
 Aggregate false, [key#4], [key#4,SUM(PartialCount#6L) AS c_1#2L]
  Exchange (HashPartitioning [key#4:0], 200)
   Exchange (HashPartitioning [key#4:0], 200)
    Aggregate true, [key#4], [key#4,COUNT(1) AS PartialCount#6L]
     HiveTableScan [key#4], (MetastoreRelation default, src, None), None

scala> q0.select('plan).collect()
...
[ExplainCommandPhysical [plan#24:0]
 Aggregate false, [key#17], [key#17,SUM(PartialCount#19L) AS c_1#2L]
  Exchange (HashPartitioning [key#17:0], 200)
   Exchange (HashPartitioning [key#17:0], 200)
    Aggregate true, [key#17], [key#17,COUNT(1) AS PartialCount#19L]
     HiveTableScan [key#17], (MetastoreRelation default, src, None), None]

scala>
```

### SET command

In this example we query all the properties set in `SQLConf`, register the result as a table, and then query the table with HiveQL:

```
scala> val q1 = hql("SET")
...
q1: org.apache.spark.sql.SchemaRDD =
SchemaRDD[7] at RDD at SchemaRDD.scala:98
== Query Plan ==
<SET command: executed by Hive, and noted by SQLContext>

scala> q1.registerAsTable("properties")

scala> hql("SELECT key, value FROM properties ORDER BY key LIMIT 10").foreach(println)
...
== Query Plan ==
TakeOrdered 10, [key#51:0 ASC]
 Project [key#51:0,value#52:1]
  SetCommandPhysical None, None, [key#55:0,value#56:1]), which has no missing parents
14/06/12 12:19:27 INFO scheduler.DAGScheduler: Submitting 1 missing tasks from Stage 5 (SchemaRDD[21] at RDD at SchemaRDD.scala:98
== Query Plan ==
TakeOrdered 10, [key#51:0 ASC]
 Project [key#51:0,value#52:1]
  SetCommandPhysical None, None, [key#55:0,value#56:1])
...
[datanucleus.autoCreateSchema,true]
[datanucleus.autoStartMechanismMode,checked]
[datanucleus.cache.level2,false]
[datanucleus.cache.level2.type,none]
[datanucleus.connectionPoolingType,BONECP]
[datanucleus.fixedDatastore,false]
[datanucleus.identifierFactory,datanucleus1]
[datanucleus.plugin.pluginRegistryBundleCheck,LOG]
[datanucleus.rdbms.useLegacyNativeValueStrategy,true]
[datanucleus.storeManagerType,rdbms]

scala>
```

### "Exactly once" semantics

At last, an example of the "exactly once" semantics:

```
scala> val q2 = hql("CREATE TABLE t1(key INT, value STRING)")
...
q2: org.apache.spark.sql.SchemaRDD =
SchemaRDD[28] at RDD at SchemaRDD.scala:98
== Query Plan ==
<Native command: executed by Hive>

scala> table("t1")
...
res9: org.apache.spark.sql.SchemaRDD =
SchemaRDD[32] at RDD at SchemaRDD.scala:98
== Query Plan ==
HiveTableScan [key#58,value#59], (MetastoreRelation default, t1, None), None

scala> q2.collect()
...
res10: Array[org.apache.spark.sql.Row] = Array([])

scala>
```

As we can see, the "CREATE TABLE" command is executed eagerly right after the `SchemaRDD` is created, and referencing the `SchemaRDD` again won't trigger a duplicated execution.

Author: Cheng Lian <lian.cs.zju@gmail.com>

Closes #1071 from liancheng/exactlyOnceCommand and squashes the following commits:

d005b03 [Cheng Lian] Made "SET key=value" returns the newly set key value pair
f6c7715 [Cheng Lian] Added test cases for DDL/command statement RDDs
1d00937 [Cheng Lian] Makes SchemaRDD DSLs work for DDL/command statement RDDs
5c7e680 [Cheng Lian] Bug fix: wrong type used in pattern matching
48aa2e5 [Cheng Lian] Refined SQLContext.emptyResult as an empty RDD[Row]
cc64f32 [Cheng Lian] Renamed physical plan classes for DDL/commands
74789c1 [Cheng Lian] Fixed failing test cases
0ad343a [Cheng Lian] Added physical plan for DDL and commands to ensure the "exactly once" semantics
---
 .../sql/catalyst/plans/logical/commands.scala |  18 +--
 .../optimizer/FilterPushdownSuite.scala       |   2 +-
 .../org/apache/spark/sql/SQLContext.scala     |  39 +-----
 .../org/apache/spark/sql/SchemaRDD.scala      |   2 +-
 .../org/apache/spark/sql/SchemaRDDLike.scala  |  15 ++-
 .../spark/sql/api/java/JavaSchemaRDD.scala    |   2 +-
 .../spark/sql/execution/SparkStrategies.scala |  11 +-
 .../apache/spark/sql/execution/commands.scala |  81 ++++++++----
 .../org/apache/spark/sql/SQLQuerySuite.scala  |  16 +--
 .../apache/spark/sql/hive/HiveContext.scala   |  55 +++-----
 .../spark/sql/hive/HiveStrategies.scala       |   8 ++
 .../sql/hive/execution/hiveOperators.scala    |  32 ++++-
 .../hive/execution/HiveComparisonTest.scala   |   3 +-
 .../execution/HiveCompatibilitySuite.scala    |   9 +-
 .../sql/hive/execution/HiveQuerySuite.scala   | 125 +++++++++++++-----
 15 files changed, 251 insertions(+), 167 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/commands.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/commands.scala
index d05c9652753e..3299e86b8594 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/commands.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/commands.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.sql.catalyst.plans.logical
 
-import org.apache.spark.sql.catalyst.expressions.{AttributeReference, Attribute}
+import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeReference, BoundReference}
 import org.apache.spark.sql.catalyst.types.StringType
 
 /**
@@ -26,23 +26,25 @@ import org.apache.spark.sql.catalyst.types.StringType
  */
 abstract class Command extends LeafNode {
   self: Product =>
-  def output: Seq[Attribute] = Seq.empty  // TODO: SPARK-2081 should fix this
+  def output: Seq[Attribute] = Seq.empty
 }
 
 /**
  * Returned for commands supported by a given parser, but not catalyst.  In general these are DDL
  * commands that are passed directly to another system.
  */
-case class NativeCommand(cmd: String) extends Command
+case class NativeCommand(cmd: String) extends Command {
+  override def output =
+    Seq(BoundReference(0, AttributeReference("result", StringType, nullable = false)()))
+}
 
 /**
  * Commands of the form "SET (key) (= value)".
  */
 case class SetCommand(key: Option[String], value: Option[String]) extends Command {
   override def output = Seq(
-    AttributeReference("key", StringType, nullable = false)(),
-    AttributeReference("value", StringType, nullable = false)()
-  )
+    BoundReference(0, AttributeReference("key", StringType, nullable = false)()),
+    BoundReference(1, AttributeReference("value", StringType, nullable = false)()))
 }
 
 /**
@@ -50,11 +52,11 @@ case class SetCommand(key: Option[String], value: Option[String]) extends Comman
  * actually performing the execution.
  */
 case class ExplainCommand(plan: LogicalPlan) extends Command {
-  override def output = Seq(AttributeReference("plan", StringType, nullable = false)())
+  override def output =
+    Seq(BoundReference(0, AttributeReference("plan", StringType, nullable = false)()))
 }
 
 /**
  * Returned for the "CACHE TABLE tableName" and "UNCACHE TABLE tableName" command.
  */
 case class CacheCommand(tableName: String, doCache: Boolean) extends Command
-
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/FilterPushdownSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/FilterPushdownSuite.scala
index 0cada785b663..1f67c80e5490 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/FilterPushdownSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/FilterPushdownSuite.scala
@@ -161,7 +161,7 @@ class FilterPushdownSuite extends OptimizerTest {
 
     comparePlans(optimized, correctAnswer)
   }
-  
+
   test("joins: push down left outer join #1") {
     val x = testRelation.subquery('x)
     val y = testRelation.subquery('y)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
index b6a2f1b9d127..378ff5453111 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
@@ -31,7 +31,7 @@ import org.apache.spark.sql.catalyst.{ScalaReflection, dsl}
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.types._
 import org.apache.spark.sql.catalyst.optimizer.Optimizer
-import org.apache.spark.sql.catalyst.plans.logical.{SetCommand, LogicalPlan}
+import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
 import org.apache.spark.sql.catalyst.rules.RuleExecutor
 
 import org.apache.spark.sql.columnar.InMemoryRelation
@@ -147,14 +147,7 @@ class SQLContext(@transient val sparkContext: SparkContext)
    *
    * @group userf
    */
-  def sql(sqlText: String): SchemaRDD = {
-    val result = new SchemaRDD(this, parseSql(sqlText))
-    // We force query optimization to happen right away instead of letting it happen lazily like
-    // when using the query DSL.  This is so DDL commands behave as expected.  This is only
-    // generates the RDD lineage for DML queries, but do not perform any execution.
-    result.queryExecution.toRdd
-    result
-  }
+  def sql(sqlText: String): SchemaRDD = new SchemaRDD(this, parseSql(sqlText))
 
   /** Returns the specified table as a SchemaRDD */
   def table(tableName: String): SchemaRDD =
@@ -259,8 +252,7 @@ class SQLContext(@transient val sparkContext: SparkContext)
   protected[sql] val planner = new SparkPlanner
 
   @transient
-  protected[sql] lazy val emptyResult =
-    sparkContext.parallelize(Seq(new GenericRow(Array[Any]()): Row), 1)
+  protected[sql] lazy val emptyResult = sparkContext.parallelize(Seq.empty[Row], 1)
 
   /**
    * Prepares a planned SparkPlan for execution by binding references to specific ordinals, and
@@ -280,22 +272,6 @@ class SQLContext(@transient val sparkContext: SparkContext)
   protected abstract class QueryExecution {
     def logical: LogicalPlan
 
-    def eagerlyProcess(plan: LogicalPlan): RDD[Row] = plan match {
-      case SetCommand(key, value) =>
-        // Only this case needs to be executed eagerly. The other cases will
-        // be taken care of when the actual results are being extracted.
-        // In the case of HiveContext, sqlConf is overridden to also pass the
-        // pair into its HiveConf.
-        if (key.isDefined && value.isDefined) {
-          set(key.get, value.get)
-        }
-        // It doesn't matter what we return here, since this is only used
-        // to force the evaluation to happen eagerly.  To query the results,
-        // one must use SchemaRDD operations to extract them.
-        emptyResult
-      case _ => executedPlan.execute()
-    }
-
     lazy val analyzed = analyzer(logical)
     lazy val optimizedPlan = optimizer(analyzed)
     // TODO: Don't just pick the first one...
@@ -303,12 +279,7 @@ class SQLContext(@transient val sparkContext: SparkContext)
     lazy val executedPlan: SparkPlan = prepareForExecution(sparkPlan)
 
     /** Internal version of the RDD. Avoids copies and has no schema */
-    lazy val toRdd: RDD[Row] = {
-      logical match {
-        case s: SetCommand => eagerlyProcess(s)
-        case _ => executedPlan.execute()
-      }
-    }
+    lazy val toRdd: RDD[Row] = executedPlan.execute()
 
     protected def stringOrError[A](f: => A): String =
       try f.toString catch { case e: Throwable => e.toString }
@@ -330,7 +301,7 @@ class SQLContext(@transient val sparkContext: SparkContext)
    * TODO: We only support primitive types, add support for nested types.
    */
   private[sql] def inferSchema(rdd: RDD[Map[String, _]]): SchemaRDD = {
-    val schema = rdd.first.map { case (fieldName, obj) =>
+    val schema = rdd.first().map { case (fieldName, obj) =>
       val dataType = obj.getClass match {
         case c: Class[_] if c == classOf[java.lang.String] => StringType
         case c: Class[_] if c == classOf[java.lang.Integer] => IntegerType
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala
index 7ad8edf5a5a6..821ac850ac3f 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala
@@ -97,7 +97,7 @@ import java.util.{Map => JMap}
 @AlphaComponent
 class SchemaRDD(
     @transient val sqlContext: SQLContext,
-    @transient protected[spark] val logicalPlan: LogicalPlan)
+    @transient val baseLogicalPlan: LogicalPlan)
   extends RDD[Row](sqlContext.sparkContext, Nil) with SchemaRDDLike {
 
   def baseSchemaRDD = this
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDDLike.scala b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDDLike.scala
index 3a895e15a450..656be965a8fd 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDDLike.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDDLike.scala
@@ -20,13 +20,14 @@ package org.apache.spark.sql
 import org.apache.spark.annotation.{DeveloperApi, Experimental}
 import org.apache.spark.sql.catalyst.analysis.UnresolvedRelation
 import org.apache.spark.sql.catalyst.plans.logical._
+import org.apache.spark.sql.execution.SparkLogicalPlan
 
 /**
  * Contains functions that are shared between all SchemaRDD types (i.e., Scala, Java)
  */
 private[sql] trait SchemaRDDLike {
   @transient val sqlContext: SQLContext
-  @transient protected[spark] val logicalPlan: LogicalPlan
+  @transient val baseLogicalPlan: LogicalPlan
 
   private[sql] def baseSchemaRDD: SchemaRDD
 
@@ -48,7 +49,17 @@ private[sql] trait SchemaRDDLike {
    */
   @transient
   @DeveloperApi
-  lazy val queryExecution = sqlContext.executePlan(logicalPlan)
+  lazy val queryExecution = sqlContext.executePlan(baseLogicalPlan)
+
+  @transient protected[spark] val logicalPlan: LogicalPlan = baseLogicalPlan match {
+    // For various commands (like DDL) and queries with side effects, we force query optimization to
+    // happen right away to let these side effects take place eagerly.
+    case _: Command | _: InsertIntoTable | _: InsertIntoCreatedTable | _: WriteToFile =>
+      queryExecution.toRdd
+      SparkLogicalPlan(queryExecution.executedPlan)
+    case _ =>
+      baseLogicalPlan
+  }
 
   override def toString =
     s"""${super.toString}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSchemaRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSchemaRDD.scala
index 22f57b758dd0..aff6ffe9f347 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSchemaRDD.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSchemaRDD.scala
@@ -37,7 +37,7 @@ import org.apache.spark.storage.StorageLevel
  */
 class JavaSchemaRDD(
      @transient val sqlContext: SQLContext,
-     @transient protected[spark] val logicalPlan: LogicalPlan)
+     @transient val baseLogicalPlan: LogicalPlan)
   extends JavaRDDLike[Row, JavaRDD[Row]]
   with SchemaRDDLike {
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
index a657911afe53..2233216a6ec5 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.sql.execution
 
-import org.apache.spark.sql.{SQLConf, SQLContext, execution}
+import org.apache.spark.sql.{SQLContext, execution}
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.planning._
 import org.apache.spark.sql.catalyst.plans._
@@ -157,7 +157,7 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
         InsertIntoParquetTable(relation, planLater(child), overwrite=true)(sparkContext) :: Nil
       case logical.InsertIntoTable(table: ParquetRelation, partition, child, overwrite) =>
         InsertIntoParquetTable(table, planLater(child), overwrite)(sparkContext) :: Nil
-      case PhysicalOperation(projectList, filters: Seq[Expression], relation: ParquetRelation) => {
+      case PhysicalOperation(projectList, filters: Seq[Expression], relation: ParquetRelation) =>
         val prunePushedDownFilters =
           if (sparkContext.conf.getBoolean(ParquetFilters.PARQUET_FILTER_PUSHDOWN_ENABLED, true)) {
             (filters: Seq[Expression]) => {
@@ -186,7 +186,6 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
           filters,
           prunePushedDownFilters,
           ParquetTableScan(_, relation, filters)(sparkContext)) :: Nil
-      }
 
       case _ => Nil
     }
@@ -250,12 +249,12 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
   case class CommandStrategy(context: SQLContext) extends Strategy {
     def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
       case logical.SetCommand(key, value) =>
-        Seq(execution.SetCommandPhysical(key, value, plan.output)(context))
+        Seq(execution.SetCommand(key, value, plan.output)(context))
       case logical.ExplainCommand(child) =>
         val executedPlan = context.executePlan(child).executedPlan
-        Seq(execution.ExplainCommandPhysical(executedPlan, plan.output)(context))
+        Seq(execution.ExplainCommand(executedPlan, plan.output)(context))
       case logical.CacheCommand(tableName, cache) =>
-        Seq(execution.CacheCommandPhysical(tableName, cache)(context))
+        Seq(execution.CacheCommand(tableName, cache)(context))
       case _ => Nil
     }
   }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala
index be26d19e6686..0377290af592 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala
@@ -22,45 +22,69 @@ import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.{SQLContext, Row}
 import org.apache.spark.sql.catalyst.expressions.{GenericRow, Attribute}
 
+trait Command {
+  /**
+   * A concrete command should override this lazy field to wrap up any side effects caused by the
+   * command or any other computation that should be evaluated exactly once. The value of this field
+   * can be used as the contents of the corresponding RDD generated from the physical plan of this
+   * command.
+   *
+   * The `execute()` method of all the physical command classes should reference `sideEffectResult`
+   * so that the command can be executed eagerly right after the command query is created.
+   */
+  protected[sql] lazy val sideEffectResult: Seq[Any] = Seq.empty[Any]
+}
+
 /**
  * :: DeveloperApi ::
  */
 @DeveloperApi
-case class SetCommandPhysical(key: Option[String], value: Option[String], output: Seq[Attribute])
-                             (@transient context: SQLContext) extends LeafNode {
-  def execute(): RDD[Row] = (key, value) match {
-    // Set value for key k; the action itself would
-    // have been performed in QueryExecution eagerly.
-    case (Some(k), Some(v)) => context.emptyResult
+case class SetCommand(
+    key: Option[String], value: Option[String], output: Seq[Attribute])(
+    @transient context: SQLContext)
+  extends LeafNode with Command {
+
+  override protected[sql] lazy val sideEffectResult: Seq[(String, String)] = (key, value) match {
+    // Set value for key k.
+    case (Some(k), Some(v)) =>
+      context.set(k, v)
+      Array(k -> v)
+
     // Query the value bound to key k.
-    case (Some(k), None) =>
-      val resultString = context.getOption(k) match {
-        case Some(v) => s"$k=$v"
-        case None => s"$k is undefined"
-      }
-      context.sparkContext.parallelize(Seq(new GenericRow(Array[Any](resultString))), 1)
+    case (Some(k), _) =>
+      Array(k -> context.getOption(k).getOrElse("<undefined>"))
+
     // Query all key-value pairs that are set in the SQLConf of the context.
     case (None, None) =>
-      val pairs = context.getAll
-      val rows = pairs.map { case (k, v) =>
-        new GenericRow(Array[Any](s"$k=$v"))
-      }.toSeq
-      // Assume config parameters can fit into one split (machine) ;)
-      context.sparkContext.parallelize(rows, 1)
-    // The only other case is invalid semantics and is impossible.
-    case _ => context.emptyResult
+      context.getAll
+
+    case _ =>
+      throw new IllegalArgumentException()
   }
+
+  def execute(): RDD[Row] = {
+    val rows = sideEffectResult.map { case (k, v) => new GenericRow(Array[Any](k, v)) }
+    context.sparkContext.parallelize(rows, 1)
+  }
+
+  override def otherCopyArgs = context :: Nil
 }
 
 /**
  * :: DeveloperApi ::
  */
 @DeveloperApi
-case class ExplainCommandPhysical(child: SparkPlan, output: Seq[Attribute])
-                                 (@transient context: SQLContext) extends UnaryNode {
+case class ExplainCommand(
+    child: SparkPlan, output: Seq[Attribute])(
+    @transient context: SQLContext)
+  extends UnaryNode with Command {
+
+  // Actually "EXPLAIN" command doesn't cause any side effect.
+  override protected[sql] lazy val sideEffectResult: Seq[String] = this.toString.split("\n")
+
   def execute(): RDD[Row] = {
-    val planString = new GenericRow(Array[Any](child.toString))
-    context.sparkContext.parallelize(Seq(planString))
+    val explanation = sideEffectResult.mkString("\n")
+    context.sparkContext.parallelize(Seq(new GenericRow(Array[Any](explanation))), 1)
   }
 
   override def otherCopyArgs = context :: Nil
@@ -70,19 +94,20 @@ case class ExplainCommandPhysical(child: SparkPlan, output: Seq[Attribute])
  * :: DeveloperApi ::
  */
 @DeveloperApi
-case class CacheCommandPhysical(tableName: String, doCache: Boolean)(@transient context: SQLContext)
-  extends LeafNode {
+case class CacheCommand(tableName: String, doCache: Boolean)(@transient context: SQLContext)
+  extends LeafNode with Command {
 
-  lazy val commandSideEffect = {
+  override protected[sql] lazy val sideEffectResult = {
     if (doCache) {
       context.cacheTable(tableName)
     } else {
       context.uncacheTable(tableName)
     }
+    Seq.empty[Any]
   }
 
   override def execute(): RDD[Row] = {
-    commandSideEffect
+    sideEffectResult
     context.emptyResult
   }
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index c1fc99f07743..e9360b0fc791 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -141,7 +141,7 @@ class SQLQuerySuite extends QueryTest {
       sql("SELECT AVG(a),b FROM largeAndSmallInts group by b"),
       Seq((2147483645.0,1),(2.0,2)))
   }
-  
+
   test("count") {
     checkAnswer(
       sql("SELECT COUNT(*) FROM testData2"),
@@ -332,7 +332,7 @@ class SQLQuerySuite extends QueryTest {
         (3, "C"),
         (4, "D")))
   }
-  
+
   test("system function upper()") {
     checkAnswer(
       sql("SELECT n,UPPER(l) FROM lowerCaseData"),
@@ -349,7 +349,7 @@ class SQLQuerySuite extends QueryTest {
         (2, "ABC"),
         (3, null)))
   }
-    
+
   test("system function lower()") {
     checkAnswer(
       sql("SELECT N,LOWER(L) FROM upperCaseData"),
@@ -382,25 +382,25 @@ class SQLQuerySuite extends QueryTest {
     sql(s"SET $testKey=$testVal")
     checkAnswer(
       sql("SET"),
-      Seq(Seq(s"$testKey=$testVal"))
+      Seq(Seq(testKey, testVal))
     )
 
     sql(s"SET ${testKey + testKey}=${testVal + testVal}")
     checkAnswer(
       sql("set"),
       Seq(
-        Seq(s"$testKey=$testVal"),
-        Seq(s"${testKey + testKey}=${testVal + testVal}"))
+        Seq(testKey, testVal),
+        Seq(testKey + testKey, testVal + testVal))
     )
 
     // "set key"
     checkAnswer(
       sql(s"SET $testKey"),
-      Seq(Seq(s"$testKey=$testVal"))
+      Seq(Seq(testKey, testVal))
     )
     checkAnswer(
       sql(s"SET $nonexistentKey"),
-      Seq(Seq(s"$nonexistentKey is undefined"))
+      Seq(Seq(nonexistentKey, "<undefined>"))
     )
     clear()
   }
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
index 9cd13f6ae0d5..96e0ec513633 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
@@ -15,8 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql
-package hive
+package org.apache.spark.sql.hive
 
 import java.io.{BufferedReader, File, InputStreamReader, PrintStream}
 import java.util.{ArrayList => JArrayList}
@@ -32,12 +31,13 @@ import org.apache.hadoop.hive.ql.session.SessionState
 
 import org.apache.spark.SparkContext
 import org.apache.spark.rdd.RDD
+import org.apache.spark.sql._
 import org.apache.spark.sql.catalyst.ScalaReflection
 import org.apache.spark.sql.catalyst.analysis.{Analyzer, OverrideCatalog}
-import org.apache.spark.sql.catalyst.expressions.GenericRow
 import org.apache.spark.sql.catalyst.plans.logical._
 import org.apache.spark.sql.catalyst.types._
-import org.apache.spark.sql.execution._
+import org.apache.spark.sql.execution.QueryExecutionException
+import org.apache.spark.sql.execution.{Command => PhysicalCommand}
 
 /**
  * Starts up an instance of hive where metadata is stored locally. An in-process metadata data is
@@ -71,14 +71,7 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) {
   /**
    * Executes a query expressed in HiveQL using Spark, returning the result as a SchemaRDD.
    */
-  def hiveql(hqlQuery: String): SchemaRDD = {
-    val result = new SchemaRDD(this, HiveQl.parseSql(hqlQuery))
-    // We force query optimization to happen right away instead of letting it happen lazily like
-    // when using the query DSL.  This is so DDL commands behave as expected.  This is only
-    // generates the RDD lineage for DML queries, but does not perform any execution.
-    result.queryExecution.toRdd
-    result
-  }
+  def hiveql(hqlQuery: String): SchemaRDD = new SchemaRDD(this, HiveQl.parseSql(hqlQuery))
 
   /** An alias for `hiveql`. */
   def hql(hqlQuery: String): SchemaRDD = hiveql(hqlQuery)
@@ -164,7 +157,7 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) {
   /**
    * Runs the specified SQL query using Hive.
    */
-  protected def runSqlHive(sql: String): Seq[String] = {
+  protected[sql] def runSqlHive(sql: String): Seq[String] = {
     val maxResults = 100000
     val results = runHive(sql, 100000)
     // It is very confusing when you only get back some of the results...
@@ -228,6 +221,7 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) {
 
     override val strategies: Seq[Strategy] = Seq(
       CommandStrategy(self),
+      HiveCommandStrategy(self),
       TakeOrdered,
       ParquetOperations,
       InMemoryScans,
@@ -252,25 +246,7 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) {
     override lazy val optimizedPlan =
       optimizer(catalog.PreInsertionCasts(catalog.CreateTables(analyzed)))
 
-    override lazy val toRdd: RDD[Row] = {
-      def processCmd(cmd: String): RDD[Row] = {
-        val output = runSqlHive(cmd)
-        if (output.size == 0) {
-          emptyResult
-        } else {
-          val asRows = output.map(r => new GenericRow(r.split("\t").asInstanceOf[Array[Any]]))
-          sparkContext.parallelize(asRows, 1)
-        }
-      }
-
-      logical match {
-        case s: SetCommand => eagerlyProcess(s)
-        case _ => analyzed match {
-          case NativeCommand(cmd) => processCmd(cmd)
-          case _ => executedPlan.execute().map(_.copy())
-        }
-      }
-    }
+    override lazy val toRdd: RDD[Row] = executedPlan.execute().map(_.copy())
 
     protected val primitiveTypes =
       Seq(StringType, IntegerType, LongType, DoubleType, FloatType, BooleanType, ByteType,
@@ -298,7 +274,7 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) {
         struct.zip(fields).map {
           case (v, t) => s""""${t.name}":${toHiveStructString(v, t.dataType)}"""
         }.mkString("{", ",", "}")
-      case (seq: Seq[_], ArrayType(typ))=>
+      case (seq: Seq[_], ArrayType(typ)) =>
         seq.map(v => (v, typ)).map(toHiveStructString).mkString("[", ",", "]")
       case (map: Map[_,_], MapType(kType, vType)) =>
         map.map {
@@ -314,10 +290,11 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) {
      * Returns the result as a hive compatible sequence of strings.  For native commands, the
      * execution is simply passed back to Hive.
      */
-    def stringResult(): Seq[String] = analyzed match {
-      case NativeCommand(cmd) => runSqlHive(cmd)
-      case ExplainCommand(plan) => executePlan(plan).toString.split("\n")
-      case query =>
+    def stringResult(): Seq[String] = executedPlan match {
+      case command: PhysicalCommand =>
+        command.sideEffectResult.map(_.toString)
+
+      case other =>
         val result: Seq[Seq[Any]] = toRdd.collect().toSeq
         // We need the types so we can output struct field names
         val types = analyzed.output.map(_.dataType)
@@ -328,8 +305,8 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) {
 
     override def simpleString: String =
       logical match {
-        case _: NativeCommand => "<Executed by Hive>"
-        case _: SetCommand => "<Set Command: Executed by Hive, and noted by SQLContext>"
+        case _: NativeCommand => "<Native command: executed by Hive>"
+        case _: SetCommand => "<SET command: executed by Hive, and noted by SQLContext>"
         case _ => executedPlan.toString
       }
   }
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala
index ed6cd5a11dba..0ac0ee9071f3 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala
@@ -76,4 +76,12 @@ private[hive] trait HiveStrategies {
         Nil
     }
   }
+
+  case class HiveCommandStrategy(context: HiveContext) extends Strategy {
+    def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
+      case logical.NativeCommand(sql) =>
+        NativeCommand(sql, plan.output)(context) :: Nil
+      case _ => Nil
+    }
+  }
 }
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/hiveOperators.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/hiveOperators.scala
index 29b4b9b006e4..a83923144916 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/hiveOperators.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/hiveOperators.scala
@@ -32,14 +32,15 @@ import org.apache.hadoop.hive.serde2.{ColumnProjectionUtils, Serializer}
 import org.apache.hadoop.io.Writable
 import org.apache.hadoop.mapred._
 
+import org.apache.spark
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.types.{BooleanType, DataType}
 import org.apache.spark.sql.execution._
 import org.apache.spark.sql.hive._
-import org.apache.spark.{TaskContext, SparkException}
 import org.apache.spark.util.MutablePair
+import org.apache.spark.{TaskContext, SparkException}
 
 /* Implicits */
 import scala.collection.JavaConversions._
@@ -57,7 +58,7 @@ case class HiveTableScan(
     attributes: Seq[Attribute],
     relation: MetastoreRelation,
     partitionPruningPred: Option[Expression])(
-    @transient val sc: HiveContext)
+    @transient val context: HiveContext)
   extends LeafNode
   with HiveInspectors {
 
@@ -75,7 +76,7 @@ case class HiveTableScan(
   }
 
   @transient
-  val hadoopReader = new HadoopTableReader(relation.tableDesc, sc)
+  val hadoopReader = new HadoopTableReader(relation.tableDesc, context)
 
   /**
    * The hive object inspector for this table, which can be used to extract values from the
@@ -156,7 +157,7 @@ case class HiveTableScan(
     hiveConf.set(serdeConstants.LIST_COLUMNS, columnInternalNames)
   }
 
-  addColumnMetadataToConf(sc.hiveconf)
+  addColumnMetadataToConf(context.hiveconf)
 
   @transient
   def inputRdd = if (!relation.hiveQlTable.isPartitioned) {
@@ -428,3 +429,26 @@ case class InsertIntoHiveTable(
     sc.sparkContext.makeRDD(Nil, 1)
   }
 }
+
+/**
+ * :: DeveloperApi ::
+ */
+@DeveloperApi
+case class NativeCommand(
+    sql: String, output: Seq[Attribute])(
+    @transient context: HiveContext)
+  extends LeafNode with Command {
+
+  override protected[sql] lazy val sideEffectResult: Seq[String] = context.runSqlHive(sql)
+
+  override def execute(): RDD[spark.sql.Row] = {
+    if (sideEffectResult.size == 0) {
+      context.emptyResult
+    } else {
+      val rows = sideEffectResult.map(r => new GenericRow(Array[Any](r)))
+      context.sparkContext.parallelize(rows, 1)
+    }
+  }
+
+  override def otherCopyArgs = context :: Nil
+}
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala
index 357c7e654bd2..24c929ff7430 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala
@@ -24,6 +24,7 @@ import org.scalatest.{BeforeAndAfterAll, FunSuite, GivenWhenThen}
 import org.apache.spark.sql.Logging
 import org.apache.spark.sql.catalyst.planning.PhysicalOperation
 import org.apache.spark.sql.catalyst.plans.logical._
+import org.apache.spark.sql.catalyst.plans.logical.{NativeCommand => LogicalNativeCommand}
 import org.apache.spark.sql.catalyst.util._
 import org.apache.spark.sql.hive.test.TestHive
 
@@ -141,7 +142,7 @@ abstract class HiveComparisonTest
       // Hack: Hive simply prints the result of a SET command to screen,
       // and does not return it as a query answer.
       case _: SetCommand => Seq("0")
-      case _: NativeCommand => answer.filterNot(nonDeterministicLine).filterNot(_ == "")
+      case _: LogicalNativeCommand => answer.filterNot(nonDeterministicLine).filterNot(_ == "")
       case _: ExplainCommand => answer
       case plan => if (isSorted(plan)) answer else answer.sorted
     }
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
index 3581617c269a..ee194dbcb77b 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
@@ -172,7 +172,12 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     "case_sensitivity",
 
     // Flaky test, Hive sometimes returns different set of 10 rows.
-    "lateral_view_outer"
+    "lateral_view_outer",
+
+    // After stop taking the `stringOrError` route, exceptions are thrown from these cases.
+    // See SPARK-2129 for details.
+    "join_view",
+    "mergejoins_mixed"
   )
 
   /**
@@ -476,7 +481,6 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     "join_reorder3",
     "join_reorder4",
     "join_star",
-    "join_view",
     "lateral_view",
     "lateral_view_cp",
     "lateral_view_ppd",
@@ -507,7 +511,6 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     "merge1",
     "merge2",
     "mergejoins",
-    "mergejoins_mixed",
     "multigroupby_singlemr",
     "multi_insert_gby",
     "multi_insert_gby3",
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
index 6c239b02ed09..0d656c556965 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
@@ -17,9 +17,11 @@
 
 package org.apache.spark.sql.hive.execution
 
-import org.apache.spark.sql.Row
-import org.apache.spark.sql.hive.test.TestHive._
+import scala.util.Try
+
 import org.apache.spark.sql.hive.test.TestHive
+import org.apache.spark.sql.hive.test.TestHive._
+import org.apache.spark.sql.{SchemaRDD, execution, Row}
 
 /**
  * A set of test cases expressed in Hive QL that are not covered by the tests included in the hive distribution.
@@ -162,16 +164,60 @@ class HiveQuerySuite extends HiveComparisonTest {
     hql("SELECT * FROM src").toString
   }
 
+  private val explainCommandClassName =
+    classOf[execution.ExplainCommand].getSimpleName.stripSuffix("$")
+
+  def isExplanation(result: SchemaRDD) = {
+    val explanation = result.select('plan).collect().map { case Row(plan: String) => plan }
+    explanation.size == 1 && explanation.head.startsWith(explainCommandClassName)
+  }
+
   test("SPARK-1704: Explain commands as a SchemaRDD") {
     hql("CREATE TABLE IF NOT EXISTS src (key INT, value STRING)")
+
     val rdd = hql("explain select key, count(value) from src group by key")
-    assert(rdd.collect().size == 1)
-    assert(rdd.toString.contains("ExplainCommand"))
-    assert(rdd.filter(row => row.toString.contains("ExplainCommand")).collect().size == 0,
-      "actual contents of the result should be the plans of the query to be explained")
+    assert(isExplanation(rdd))
+
     TestHive.reset()
   }
 
+  test("Query Hive native command execution result") {
+    val tableName = "test_native_commands"
+
+    val q0 = hql(s"DROP TABLE IF EXISTS $tableName")
+    assert(q0.count() == 0)
+
+    val q1 = hql(s"CREATE TABLE $tableName(key INT, value STRING)")
+    assert(q1.count() == 0)
+
+    val q2 = hql("SHOW TABLES")
+    val tables = q2.select('result).collect().map { case Row(table: String) => table }
+    assert(tables.contains(tableName))
+
+    val q3 = hql(s"DESCRIBE $tableName")
+    assertResult(Array(Array("key", "int", "None"), Array("value", "string", "None"))) {
+      q3.select('result).collect().map { case Row(fieldDesc: String) =>
+        fieldDesc.split("\t").map(_.trim)
+      }
+    }
+
+    val q4 = hql(s"EXPLAIN SELECT key, COUNT(*) FROM $tableName GROUP BY key")
+    assert(isExplanation(q4))
+
+    TestHive.reset()
+  }
+
+  test("Exactly once semantics for DDL and command statements") {
+    val tableName = "test_exactly_once"
+    val q0 = hql(s"CREATE TABLE $tableName(key INT, value STRING)")
+
+    // If the table was not created, the following assertion would fail
+    assert(Try(table(tableName)).isSuccess)
+
+    // If the CREATE TABLE command got executed again, the following assertion would fail
+    assert(Try(q0.count()).isSuccess)
+  }
+
   test("parse HQL set commands") {
     // Adapted from its SQL counterpart.
     val testKey = "spark.sql.key.usedfortestonly"
@@ -195,52 +241,69 @@ class HiveQuerySuite extends HiveComparisonTest {
   test("SET commands semantics for a HiveContext") {
     // Adapted from its SQL counterpart.
     val testKey = "spark.sql.key.usedfortestonly"
-    var testVal = "test.val.0"
+    val testVal = "test.val.0"
     val nonexistentKey = "nonexistent"
-    def fromRows(row: Array[Row]): Array[String] = row.map(_.getString(0))
+    def rowsToPairs(rows: Array[Row]) = rows.map { case Row(key: String, value: String) =>
+      key -> value
+    }
 
     clear()
 
     // "set" itself returns all config variables currently specified in SQLConf.
-    assert(hql("set").collect().size == 0)
+    assert(hql("SET").collect().size == 0)
+
+    assertResult(Array(testKey -> testVal)) {
+      rowsToPairs(hql(s"SET $testKey=$testVal").collect())
+    }
 
-    // "set key=val"
-    hql(s"SET $testKey=$testVal")
-    assert(fromRows(hql("SET").collect()) sameElements Array(s"$testKey=$testVal"))
     assert(hiveconf.get(testKey, "") == testVal)
+    assertResult(Array(testKey -> testVal)) {
+      rowsToPairs(hql("SET").collect())
+    }
 
     hql(s"SET ${testKey + testKey}=${testVal + testVal}")
-    assert(fromRows(hql("SET").collect()) sameElements
-      Array(
-        s"$testKey=$testVal",
-        s"${testKey + testKey}=${testVal + testVal}"))
     assert(hiveconf.get(testKey + testKey, "") == testVal + testVal)
+    assertResult(Array(testKey -> testVal, (testKey + testKey) -> (testVal + testVal))) {
+      rowsToPairs(hql("SET").collect())
+    }
 
     // "set key"
-    assert(fromRows(hql(s"SET $testKey").collect()) sameElements
-      Array(s"$testKey=$testVal"))
-    assert(fromRows(hql(s"SET $nonexistentKey").collect()) sameElements
-      Array(s"$nonexistentKey is undefined"))
+    assertResult(Array(testKey -> testVal)) {
+      rowsToPairs(hql(s"SET $testKey").collect())
+    }
+
+    assertResult(Array(nonexistentKey -> "<undefined>")) {
+      rowsToPairs(hql(s"SET $nonexistentKey").collect())
+    }
 
     // Assert that sql() should have the same effects as hql() by repeating the above using sql().
     clear()
-    assert(sql("set").collect().size == 0)
+    assert(sql("SET").collect().size == 0)
+
+    assertResult(Array(testKey -> testVal)) {
+      rowsToPairs(sql(s"SET $testKey=$testVal").collect())
+    }
 
-    sql(s"SET $testKey=$testVal")
-    assert(fromRows(sql("SET").collect()) sameElements Array(s"$testKey=$testVal"))
     assert(hiveconf.get(testKey, "") == testVal)
+    assertResult(Array(testKey -> testVal)) {
+      rowsToPairs(sql("SET").collect())
+    }
 
     sql(s"SET ${testKey + testKey}=${testVal + testVal}")
-    assert(fromRows(sql("SET").collect()) sameElements
-      Array(
-        s"$testKey=$testVal",
-        s"${testKey + testKey}=${testVal + testVal}"))
     assert(hiveconf.get(testKey + testKey, "") == testVal + testVal)
+    assertResult(Array(testKey -> testVal, (testKey + testKey) -> (testVal + testVal))) {
+      rowsToPairs(sql("SET").collect())
+    }
 
-    assert(fromRows(sql(s"SET $testKey").collect()) sameElements
-      Array(s"$testKey=$testVal"))
-    assert(fromRows(sql(s"SET $nonexistentKey").collect()) sameElements
-      Array(s"$nonexistentKey is undefined"))
+    assertResult(Array(testKey -> testVal)) {
+      rowsToPairs(sql(s"SET $testKey").collect())
+    }
+
+    assertResult(Array(nonexistentKey -> "<undefined>")) {
+      rowsToPairs(sql(s"SET $nonexistentKey").collect())
+    }
+
+    clear()
   }
 
   // Put tests that depend on specific Hive settings before these last two test,

From edb1f0e3164b99b483b6878efe0b6ea5ae9f97ed Mon Sep 17 00:00:00 2001
From: akkomar <ak.komar@gmail.com>
Date: Fri, 13 Jun 2014 15:37:26 -0700
Subject: [PATCH 422/810] Small correction in Streaming Programming Guide doc

Corrected description of `repartition` function under 'Level of Parallelism in Data Receiving'.

Author: akkomar <ak.komar@gmail.com>

Closes #1079 from akkomar/streaming-guide-doc and squashes the following commits:

32dfc62 [akkomar] Corrected description of `repartition` function under 'Level of Parallelism in Data Receiving'.
---
 docs/streaming-programming-guide.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/streaming-programming-guide.md b/docs/streaming-programming-guide.md
index bbee67f54c6b..ce8e58d64a7e 100644
--- a/docs/streaming-programming-guide.md
+++ b/docs/streaming-programming-guide.md
@@ -950,7 +950,7 @@ is 200 milliseconds.
 
 An alternative to receiving data with multiple input streams / receivers is to explicitly repartition
 the input data stream (using `inputStream.repartition(<number of partitions>)`).
-This distributes the received batches of data across all the machines in the cluster
+This distributes the received batches of data across specified number of machines in the cluster
 before further processing.
 
 ### Level of Parallelism in Data Processing

From 891968509105d8d8cf5a608ad9473aeeed747089 Mon Sep 17 00:00:00 2001
From: Yin Huai <huai@cse.ohio-state.edu>
Date: Fri, 13 Jun 2014 23:28:57 -0700
Subject: [PATCH 423/810] [Spark-2137][SQL] Timestamp UDFs broken

https://issues.apache.org/jira/browse/SPARK-2137

Author: Yin Huai <huai@cse.ohio-state.edu>

Closes #1081 from yhuai/SPARK-2137 and squashes the following commits:

c04f910 [Yin Huai] Merge remote-tracking branch 'upstream/master' into SPARK-2137
205f17b [Yin Huai] Make Hive UDF wrapper support Timestamp.
---
 .../src/main/scala/org/apache/spark/sql/hive/HiveContext.scala | 2 +-
 .../src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala    | 3 ++-
 .../golden/timestamp_udf-10-dbc23736a61d9482d13cacada02a7a09   | 1 +
 .../golden/timestamp_udf-11-442cf850a0cc1f1dcfdeaeffbffb2c35   | 1 +
 .../golden/timestamp_udf-12-51959036fd4ac4f1e24f4e06eb9b0b6    | 1 +
 .../golden/timestamp_udf-13-6ab3f356deaf807e8accc37e1f4849a    | 1 +
 .../golden/timestamp_udf-14-c745a1016461403526d44928a269c1de   | 1 +
 .../golden/timestamp_udf-15-7ab76c4458c7f78038c8b1df0fdeafbe   | 1 +
 .../golden/timestamp_udf-16-b36e87e17ca24d82072220bff559c718   | 1 +
 .../golden/timestamp_udf-17-dad44d2d4a421286e9da080271bd2639   | 1 +
 .../golden/timestamp_udf-18-cb033ecad964a2623bc633ac1d3f752a   | 1 +
 .../golden/timestamp_udf-19-79914c5347620c6e62a8e0b9a95984af   | 0
 .../golden/timestamp_udf-20-59fc1842a23369235d42ed040d45fb3d   | 0
 .../golden/timestamp_udf-4-80ce02ec84ee8abcb046367ca37279cc    | 0
 .../golden/timestamp_udf-5-1124399033bcadf3874fb48f593392d     | 1 +
 .../golden/timestamp_udf-6-5810193ce35d38c23f4fc4b4979d60a4    | 1 +
 .../golden/timestamp_udf-7-250e640a6a818f989f3f3280b00f64f9    | 1 +
 .../golden/timestamp_udf-8-975df43df015d86422965af456f87a94    | 1 +
 .../golden/timestamp_udf-9-287614364eaa3fb82aad08c6b62cc938    | 1 +
 19 files changed, 17 insertions(+), 2 deletions(-)
 create mode 100644 sql/hive/src/test/resources/golden/timestamp_udf-10-dbc23736a61d9482d13cacada02a7a09
 create mode 100644 sql/hive/src/test/resources/golden/timestamp_udf-11-442cf850a0cc1f1dcfdeaeffbffb2c35
 create mode 100644 sql/hive/src/test/resources/golden/timestamp_udf-12-51959036fd4ac4f1e24f4e06eb9b0b6
 create mode 100644 sql/hive/src/test/resources/golden/timestamp_udf-13-6ab3f356deaf807e8accc37e1f4849a
 create mode 100644 sql/hive/src/test/resources/golden/timestamp_udf-14-c745a1016461403526d44928a269c1de
 create mode 100644 sql/hive/src/test/resources/golden/timestamp_udf-15-7ab76c4458c7f78038c8b1df0fdeafbe
 create mode 100644 sql/hive/src/test/resources/golden/timestamp_udf-16-b36e87e17ca24d82072220bff559c718
 create mode 100644 sql/hive/src/test/resources/golden/timestamp_udf-17-dad44d2d4a421286e9da080271bd2639
 create mode 100644 sql/hive/src/test/resources/golden/timestamp_udf-18-cb033ecad964a2623bc633ac1d3f752a
 create mode 100644 sql/hive/src/test/resources/golden/timestamp_udf-19-79914c5347620c6e62a8e0b9a95984af
 create mode 100644 sql/hive/src/test/resources/golden/timestamp_udf-20-59fc1842a23369235d42ed040d45fb3d
 create mode 100644 sql/hive/src/test/resources/golden/timestamp_udf-4-80ce02ec84ee8abcb046367ca37279cc
 create mode 100644 sql/hive/src/test/resources/golden/timestamp_udf-5-1124399033bcadf3874fb48f593392d
 create mode 100644 sql/hive/src/test/resources/golden/timestamp_udf-6-5810193ce35d38c23f4fc4b4979d60a4
 create mode 100644 sql/hive/src/test/resources/golden/timestamp_udf-7-250e640a6a818f989f3f3280b00f64f9
 create mode 100644 sql/hive/src/test/resources/golden/timestamp_udf-8-975df43df015d86422965af456f87a94
 create mode 100644 sql/hive/src/test/resources/golden/timestamp_udf-9-287614364eaa3fb82aad08c6b62cc938

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
index 96e0ec513633..cc95b7af0abf 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
@@ -250,7 +250,7 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) {
 
     protected val primitiveTypes =
       Seq(StringType, IntegerType, LongType, DoubleType, FloatType, BooleanType, ByteType,
-        ShortType, DecimalType)
+        ShortType, DecimalType, TimestampType)
 
     protected def toHiveString(a: (Any, DataType)): String = a match {
       case (struct: Row, StructType(fields)) =>
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala
index 572902042337..771d2bccf43a 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala
@@ -187,7 +187,8 @@ private[hive] case class HiveSimpleUdf(name: String, children: Seq[Expression])
     val primitiveClasses = Seq(
       Integer.TYPE, classOf[java.lang.Integer], classOf[java.lang.String], java.lang.Double.TYPE,
       classOf[java.lang.Double], java.lang.Long.TYPE, classOf[java.lang.Long],
-      classOf[HiveDecimal], java.lang.Byte.TYPE, classOf[java.lang.Byte]
+      classOf[HiveDecimal], java.lang.Byte.TYPE, classOf[java.lang.Byte],
+      classOf[java.sql.Timestamp]
     )
     val matchingConstructor = argClass.getConstructors.find { c =>
       c.getParameterTypes.size == 1 && primitiveClasses.contains(c.getParameterTypes.head)
diff --git a/sql/hive/src/test/resources/golden/timestamp_udf-10-dbc23736a61d9482d13cacada02a7a09 b/sql/hive/src/test/resources/golden/timestamp_udf-10-dbc23736a61d9482d13cacada02a7a09
new file mode 100644
index 000000000000..b3c4eec4c220
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_udf-10-dbc23736a61d9482d13cacada02a7a09
@@ -0,0 +1 @@
+2011-05-06 07:08:09.1234567	2011-05-06 02:08:09.1234567
diff --git a/sql/hive/src/test/resources/golden/timestamp_udf-11-442cf850a0cc1f1dcfdeaeffbffb2c35 b/sql/hive/src/test/resources/golden/timestamp_udf-11-442cf850a0cc1f1dcfdeaeffbffb2c35
new file mode 100644
index 000000000000..f69f13ed1fb9
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_udf-11-442cf850a0cc1f1dcfdeaeffbffb2c35
@@ -0,0 +1 @@
+2011-05-06 07:08:09.1234567	2011-05-06 02:08:09.1234567	2011-05-06 07:08:09.1234567	2011-05-06 02:08:09.1234567
diff --git a/sql/hive/src/test/resources/golden/timestamp_udf-12-51959036fd4ac4f1e24f4e06eb9b0b6 b/sql/hive/src/test/resources/golden/timestamp_udf-12-51959036fd4ac4f1e24f4e06eb9b0b6
new file mode 100644
index 000000000000..f14f17e69282
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_udf-12-51959036fd4ac4f1e24f4e06eb9b0b6
@@ -0,0 +1 @@
+2011-05-06 07:08:09.1234567	2011-05-06 12:08:09.1234567
diff --git a/sql/hive/src/test/resources/golden/timestamp_udf-13-6ab3f356deaf807e8accc37e1f4849a b/sql/hive/src/test/resources/golden/timestamp_udf-13-6ab3f356deaf807e8accc37e1f4849a
new file mode 100644
index 000000000000..7881bff731be
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_udf-13-6ab3f356deaf807e8accc37e1f4849a
@@ -0,0 +1 @@
+2011-05-06 07:08:09.1234567	2011-05-06 12:08:09.1234567	2011-05-06 07:08:09.1234567	2011-05-06 12:08:09.1234567
diff --git a/sql/hive/src/test/resources/golden/timestamp_udf-14-c745a1016461403526d44928a269c1de b/sql/hive/src/test/resources/golden/timestamp_udf-14-c745a1016461403526d44928a269c1de
new file mode 100644
index 000000000000..2c5e9e965620
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_udf-14-c745a1016461403526d44928a269c1de
@@ -0,0 +1 @@
+1304690889	2011	5	6	6	18	7	8	9	2011-05-06
diff --git a/sql/hive/src/test/resources/golden/timestamp_udf-15-7ab76c4458c7f78038c8b1df0fdeafbe b/sql/hive/src/test/resources/golden/timestamp_udf-15-7ab76c4458c7f78038c8b1df0fdeafbe
new file mode 100644
index 000000000000..19497254f8f7
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_udf-15-7ab76c4458c7f78038c8b1df0fdeafbe
@@ -0,0 +1 @@
+2011-05-11	2011-04-26
diff --git a/sql/hive/src/test/resources/golden/timestamp_udf-16-b36e87e17ca24d82072220bff559c718 b/sql/hive/src/test/resources/golden/timestamp_udf-16-b36e87e17ca24d82072220bff559c718
new file mode 100644
index 000000000000..816f56e43eab
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_udf-16-b36e87e17ca24d82072220bff559c718
@@ -0,0 +1 @@
+0	3333	-3333
diff --git a/sql/hive/src/test/resources/golden/timestamp_udf-17-dad44d2d4a421286e9da080271bd2639 b/sql/hive/src/test/resources/golden/timestamp_udf-17-dad44d2d4a421286e9da080271bd2639
new file mode 100644
index 000000000000..a4182d1e39db
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_udf-17-dad44d2d4a421286e9da080271bd2639
@@ -0,0 +1 @@
+2011-05-06 02:08:09.1234567
diff --git a/sql/hive/src/test/resources/golden/timestamp_udf-18-cb033ecad964a2623bc633ac1d3f752a b/sql/hive/src/test/resources/golden/timestamp_udf-18-cb033ecad964a2623bc633ac1d3f752a
new file mode 100644
index 000000000000..02ccd3a2e97c
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_udf-18-cb033ecad964a2623bc633ac1d3f752a
@@ -0,0 +1 @@
+2011-05-06 12:08:09.1234567
diff --git a/sql/hive/src/test/resources/golden/timestamp_udf-19-79914c5347620c6e62a8e0b9a95984af b/sql/hive/src/test/resources/golden/timestamp_udf-19-79914c5347620c6e62a8e0b9a95984af
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/timestamp_udf-20-59fc1842a23369235d42ed040d45fb3d b/sql/hive/src/test/resources/golden/timestamp_udf-20-59fc1842a23369235d42ed040d45fb3d
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/timestamp_udf-4-80ce02ec84ee8abcb046367ca37279cc b/sql/hive/src/test/resources/golden/timestamp_udf-4-80ce02ec84ee8abcb046367ca37279cc
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/timestamp_udf-5-1124399033bcadf3874fb48f593392d b/sql/hive/src/test/resources/golden/timestamp_udf-5-1124399033bcadf3874fb48f593392d
new file mode 100644
index 000000000000..2c5e9e965620
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_udf-5-1124399033bcadf3874fb48f593392d
@@ -0,0 +1 @@
+1304690889	2011	5	6	6	18	7	8	9	2011-05-06
diff --git a/sql/hive/src/test/resources/golden/timestamp_udf-6-5810193ce35d38c23f4fc4b4979d60a4 b/sql/hive/src/test/resources/golden/timestamp_udf-6-5810193ce35d38c23f4fc4b4979d60a4
new file mode 100644
index 000000000000..19497254f8f7
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_udf-6-5810193ce35d38c23f4fc4b4979d60a4
@@ -0,0 +1 @@
+2011-05-11	2011-04-26
diff --git a/sql/hive/src/test/resources/golden/timestamp_udf-7-250e640a6a818f989f3f3280b00f64f9 b/sql/hive/src/test/resources/golden/timestamp_udf-7-250e640a6a818f989f3f3280b00f64f9
new file mode 100644
index 000000000000..816f56e43eab
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_udf-7-250e640a6a818f989f3f3280b00f64f9
@@ -0,0 +1 @@
+0	3333	-3333
diff --git a/sql/hive/src/test/resources/golden/timestamp_udf-8-975df43df015d86422965af456f87a94 b/sql/hive/src/test/resources/golden/timestamp_udf-8-975df43df015d86422965af456f87a94
new file mode 100644
index 000000000000..a4182d1e39db
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_udf-8-975df43df015d86422965af456f87a94
@@ -0,0 +1 @@
+2011-05-06 02:08:09.1234567
diff --git a/sql/hive/src/test/resources/golden/timestamp_udf-9-287614364eaa3fb82aad08c6b62cc938 b/sql/hive/src/test/resources/golden/timestamp_udf-9-287614364eaa3fb82aad08c6b62cc938
new file mode 100644
index 000000000000..02ccd3a2e97c
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_udf-9-287614364eaa3fb82aad08c6b62cc938
@@ -0,0 +1 @@
+2011-05-06 12:08:09.1234567

From 2550533a28382664f8fd294b2caa494d12bfc7c1 Mon Sep 17 00:00:00 2001
From: Kan Zhang <kzhang@apache.org>
Date: Sat, 14 Jun 2014 13:17:22 -0700
Subject: [PATCH 424/810] [SPARK-2079] Support batching when serializing
 SchemaRDD to Python

Added batching with default batch size 10 in SchemaRDD.javaToPython

Author: Kan Zhang <kzhang@apache.org>

Closes #1023 from kanzhang/SPARK-2079 and squashes the following commits:

2d1915e [Kan Zhang] [SPARK-2079] Add batching in SchemaRDD.javaToPython
19b0c09 [Kan Zhang] [SPARK-2079] Removing unnecessary wrapping in SchemaRDD.javaToPython
---
 python/pyspark/sql.py                                    | 4 +++-
 .../src/main/scala/org/apache/spark/sql/SchemaRDD.scala  | 9 ++-------
 2 files changed, 5 insertions(+), 8 deletions(-)

diff --git a/python/pyspark/sql.py b/python/pyspark/sql.py
index 960d0a82448a..e344610b1fe4 100644
--- a/python/pyspark/sql.py
+++ b/python/pyspark/sql.py
@@ -16,6 +16,7 @@
 #
 
 from pyspark.rdd import RDD
+from pyspark.serializers import BatchedSerializer, PickleSerializer
 
 from py4j.protocol import Py4JError
 
@@ -346,7 +347,8 @@ def _toPython(self):
         # TODO: This is inefficient, we should construct the Python Row object
         # in Java land in the javaToPython function. May require a custom
         # pickle serializer in Pyrolite
-        return RDD(jrdd, self._sc, self._sc.serializer).map(lambda d: Row(d))
+        return RDD(jrdd, self._sc, BatchedSerializer(
+                        PickleSerializer())).map(lambda d: Row(d))
 
     # We override the default cache/persist/checkpoint behavior as we want to cache the underlying
     # SchemaRDD object in the JVM, not the PythonRDD checkpointed by the super class
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala
index 821ac850ac3f..89eaba2d19aa 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala
@@ -347,16 +347,11 @@ class SchemaRDD(
       val pickle = new Pickler
       iter.map { row =>
         val map: JMap[String, Any] = new java.util.HashMap
-        // TODO: We place the map in an ArrayList so that the object is pickled to a List[Dict].
-        // Ideally we should be able to pickle an object directly into a Python collection so we
-        // don't have to create an ArrayList every time.
-        val arr: java.util.ArrayList[Any] = new java.util.ArrayList
         row.zip(fieldNames).foreach { case (obj, name) =>
           map.put(name, obj)
         }
-        arr.add(map)
-        pickle.dumps(arr)
-      }
+        map
+      }.grouped(10).map(batched => pickle.dumps(batched.toArray))
     }
   }
 

From b52603b039cdfa0f8e58ef3c6229d79e732ffc58 Mon Sep 17 00:00:00 2001
From: Kan Zhang <kzhang@apache.org>
Date: Sat, 14 Jun 2014 13:22:30 -0700
Subject: [PATCH 425/810] [SPARK-2013] Documentation for saveAsPickleFile and
 pickleFile in Python

Author: Kan Zhang <kzhang@apache.org>

Closes #983 from kanzhang/SPARK-2013 and squashes the following commits:

0e128bb [Kan Zhang] [SPARK-2013] minor update
e728516 [Kan Zhang] [SPARK-2013] Documentation for saveAsPickleFile and pickleFile in Python
---
 docs/programming-guide.md | 12 +++++++-----
 1 file changed, 7 insertions(+), 5 deletions(-)

diff --git a/docs/programming-guide.md b/docs/programming-guide.md
index 79784682bfd1..ef0c0e34301f 100644
--- a/docs/programming-guide.md
+++ b/docs/programming-guide.md
@@ -377,13 +377,15 @@ Some notes on reading files with Spark:
 
 * The `textFile` method also takes an optional second argument for controlling the number of slices of the file. By default, Spark creates one slice for each block of the file (blocks being 64MB by default in HDFS), but you can also ask for a higher number of slices by passing a larger value. Note that you cannot have fewer slices than blocks.
 
-Apart from reading files as a collection of lines,
-`SparkContext.wholeTextFiles` lets you read a directory containing multiple small text files, and returns each of them as (filename, content) pairs. This is in contrast with `textFile`, which would return one record per line in each file.
+Apart from text files, Spark's Python API also supports several other data formats:
 
-### SequenceFile and Hadoop InputFormats
+* `SparkContext.wholeTextFiles` lets you read a directory containing multiple small text files, and returns each of them as (filename, content) pairs. This is in contrast with `textFile`, which would return one record per line in each file.
+
+* `RDD.saveAsPickleFile` and `SparkContext.pickleFile` support saving an RDD in a simple format consisting of pickled Python objects. Batching is used on pickle serialization, with default batch size 10.
 
-In addition to reading text files, PySpark supports reading ```SequenceFile``` 
-and any arbitrary ```InputFormat```.
+* Details on reading `SequenceFile` and arbitrary Hadoop `InputFormat` are given below.
+
+### SequenceFile and Hadoop InputFormats
 
 **Note** this feature is currently marked ```Experimental``` and is intended for advanced users. It may be replaced in future with read/write support based on SparkSQL, in which case SparkSQL is the preferred approach.
 

From 7dd9fc67a63985493ad0482d307edd56f3af0b9d Mon Sep 17 00:00:00 2001
From: Kan Zhang <kzhang@apache.org>
Date: Sat, 14 Jun 2014 14:31:28 -0700
Subject: [PATCH 426/810] [SPARK-1837] NumericRange should be partitioned in
 the same way as other...

... sequences

Author: Kan Zhang <kzhang@apache.org>

Closes #776 from kanzhang/SPARK-1837 and squashes the following commits:

e48f018 [Kan Zhang] [SPARK-1837] code refactoring
67c33b5 [Kan Zhang] minor change
403f9b1 [Kan Zhang] [SPARK-1837] NumericRange should be partitioned in the same way as other sequences
---
 .../spark/rdd/ParallelCollectionRDD.scala     | 31 ++++++++++++-------
 .../rdd/ParallelCollectionSplitSuite.scala    | 18 +++++++++++
 2 files changed, 37 insertions(+), 12 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/rdd/ParallelCollectionRDD.scala b/core/src/main/scala/org/apache/spark/rdd/ParallelCollectionRDD.scala
index 2425929fc73c..66c71bf7e8bb 100644
--- a/core/src/main/scala/org/apache/spark/rdd/ParallelCollectionRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/ParallelCollectionRDD.scala
@@ -117,6 +117,15 @@ private object ParallelCollectionRDD {
     if (numSlices < 1) {
       throw new IllegalArgumentException("Positive number of slices required")
     }
+    // Sequences need to be sliced at the same set of index positions for operations
+    // like RDD.zip() to behave as expected
+    def positions(length: Long, numSlices: Int): Iterator[(Int, Int)] = {
+      (0 until numSlices).iterator.map(i => {
+        val start = ((i * length) / numSlices).toInt
+        val end = (((i + 1) * length) / numSlices).toInt
+        (start, end)
+      })
+    }
     seq match {
       case r: Range.Inclusive => {
         val sign = if (r.step < 0) {
@@ -128,18 +137,17 @@ private object ParallelCollectionRDD {
           r.start, r.end + sign, r.step).asInstanceOf[Seq[T]], numSlices)
       }
       case r: Range => {
-        (0 until numSlices).map(i => {
-          val start = ((i * r.length.toLong) / numSlices).toInt
-          val end = (((i + 1) * r.length.toLong) / numSlices).toInt
-          new Range(r.start + start * r.step, r.start + end * r.step, r.step)
-        }).asInstanceOf[Seq[Seq[T]]]
+        positions(r.length, numSlices).map({
+          case (start, end) =>
+            new Range(r.start + start * r.step, r.start + end * r.step, r.step)
+        }).toSeq.asInstanceOf[Seq[Seq[T]]]
       }
       case nr: NumericRange[_] => {
         // For ranges of Long, Double, BigInteger, etc
         val slices = new ArrayBuffer[Seq[T]](numSlices)
-        val sliceSize = (nr.size + numSlices - 1) / numSlices // Round up to catch everything
         var r = nr
-        for (i <- 0 until numSlices) {
+        for ((start, end) <- positions(nr.length, numSlices)) {
+          val sliceSize = end - start
           slices += r.take(sliceSize).asInstanceOf[Seq[T]]
           r = r.drop(sliceSize)
         }
@@ -147,11 +155,10 @@ private object ParallelCollectionRDD {
       }
       case _ => {
         val array = seq.toArray // To prevent O(n^2) operations for List etc
-        (0 until numSlices).map(i => {
-          val start = ((i * array.length.toLong) / numSlices).toInt
-          val end = (((i + 1) * array.length.toLong) / numSlices).toInt
-          array.slice(start, end).toSeq
-        })
+        positions(array.length, numSlices).map({
+          case (start, end) =>
+            array.slice(start, end).toSeq
+        }).toSeq
       }
     }
   }
diff --git a/core/src/test/scala/org/apache/spark/rdd/ParallelCollectionSplitSuite.scala b/core/src/test/scala/org/apache/spark/rdd/ParallelCollectionSplitSuite.scala
index 4df36558b6d4..1b112f1a41ca 100644
--- a/core/src/test/scala/org/apache/spark/rdd/ParallelCollectionSplitSuite.scala
+++ b/core/src/test/scala/org/apache/spark/rdd/ParallelCollectionSplitSuite.scala
@@ -111,6 +111,24 @@ class ParallelCollectionSplitSuite extends FunSuite with Checkers {
     assert(slices.forall(_.isInstanceOf[Range]))
   }
 
+  test("identical slice sizes between Range and NumericRange") {
+    val r = ParallelCollectionRDD.slice(1 to 7, 4)
+    val nr = ParallelCollectionRDD.slice(1L to 7L, 4)
+    assert(r.size === 4)
+    for (i <- 0 until r.size) {
+      assert(r(i).size === nr(i).size)
+    }
+  }
+
+  test("identical slice sizes between List and NumericRange") {
+    val r = ParallelCollectionRDD.slice(List(1, 2), 4)
+    val nr = ParallelCollectionRDD.slice(1L to 2L, 4)
+    assert(r.size === 4)
+    for (i <- 0 until r.size) {
+      assert(r(i).size === nr(i).size)
+    }
+  }
+
   test("large ranges don't overflow") {
     val N = 100 * 1000 * 1000
     val data = 0 until N

From 269fc62b20ee5f9cd60a8f133c29f662d17071b1 Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Sun, 15 Jun 2014 11:28:34 +0200
Subject: [PATCH 427/810] [SQL] Support transforming TreeNodes with Option
 children.

Thanks goes to @marmbrus for his implementation.

Author: Michael Armbrust <michael@databricks.com>
Author: Zongheng Yang <zongheng.y@gmail.com>

Closes #1074 from concretevitamin/option-treenode and squashes the following commits:

ef27b85 [Zongheng Yang] Merge pull request #1 from marmbrus/pr/1074
73133c2 [Michael Armbrust] TreeNodes can't be inner classes.
ab78420 [Zongheng Yang] Add a test.
2ccb721 [Michael Armbrust] Add support for transformation of optional children.
---
 .../spark/sql/catalyst/trees/TreeNode.scala   | 19 ++++++++++++-
 .../sql/catalyst/trees/TreeNodeSuite.scala    | 27 +++++++++++++++++++
 2 files changed, 45 insertions(+), 1 deletion(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/TreeNode.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/TreeNode.scala
index 0369129393a0..cd04bdf02cf8 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/TreeNode.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/TreeNode.scala
@@ -187,6 +187,14 @@ abstract class TreeNode[BaseType <: TreeNode[BaseType]] {
         } else {
           arg
         }
+      case Some(arg: TreeNode[_]) if children contains arg =>
+        val newChild = arg.asInstanceOf[BaseType].transformDown(rule)
+        if (!(newChild fastEquals arg)) {
+          changed = true
+          Some(newChild)
+        } else {
+          Some(arg)
+        }
       case m: Map[_,_] => m
       case args: Traversable[_] => args.map {
         case arg: TreeNode[_] if children contains arg =>
@@ -231,6 +239,14 @@ abstract class TreeNode[BaseType <: TreeNode[BaseType]] {
         } else {
           arg
         }
+      case Some(arg: TreeNode[_]) if children contains arg =>
+        val newChild = arg.asInstanceOf[BaseType].transformUp(rule)
+        if (!(newChild fastEquals arg)) {
+          changed = true
+          Some(newChild)
+        } else {
+          Some(arg)
+        }
       case m: Map[_,_] => m
       case args: Traversable[_] => args.map {
         case arg: TreeNode[_] if children contains arg =>
@@ -273,7 +289,8 @@ abstract class TreeNode[BaseType <: TreeNode[BaseType]] {
     } catch {
       case e: java.lang.IllegalArgumentException =>
         throw new TreeNodeException(
-          this, s"Failed to copy node.  Is otherCopyArgs specified correctly for $nodeName?")
+          this, s"Failed to copy node.  Is otherCopyArgs specified correctly for $nodeName? "
+            + s"Exception message: ${e.getMessage}.")
     }
   }
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/trees/TreeNodeSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/trees/TreeNodeSuite.scala
index 1ddc41a731ff..6344874538d6 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/trees/TreeNodeSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/trees/TreeNodeSuite.scala
@@ -22,6 +22,17 @@ import scala.collection.mutable.ArrayBuffer
 import org.scalatest.FunSuite
 
 import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.types.{StringType, NullType}
+
+case class Dummy(optKey: Option[Expression]) extends Expression {
+  def children = optKey.toSeq
+  def references = Set.empty[Attribute]
+  def nullable = true
+  def dataType = NullType
+  override lazy val resolved = true
+  type EvaluatedType = Any
+  def eval(input: Row) = null.asInstanceOf[Any]
+}
 
 class TreeNodeSuite extends FunSuite {
   test("top node changed") {
@@ -75,4 +86,20 @@ class TreeNodeSuite extends FunSuite {
 
     assert(expected === actual)
   }
+
+  test("transform works on nodes with Option children") {
+    val dummy1 = Dummy(Some(Literal("1", StringType)))
+    val dummy2 = Dummy(None)
+    val toZero: PartialFunction[Expression, Expression] =  { case Literal(_, _) => Literal(0) }
+
+    var actual = dummy1 transformDown toZero
+    assert(actual === Dummy(Some(Literal(0))))
+
+    actual = dummy1 transformUp toZero
+    assert(actual === Dummy(Some(Literal(0))))
+
+    actual = dummy2 transform toZero
+    assert(actual === Dummy(None))
+  }
+
 }

From ca5d9d43b93abd279079b3be8a06fdd78c595510 Mon Sep 17 00:00:00 2001
From: Kan Zhang <kzhang@apache.org>
Date: Sun, 15 Jun 2014 14:55:34 -0700
Subject: [PATCH 428/810] [SPARK-937] adding EXITED executor state and not
 relaunching cleanly exited executors

There seems to be 2 issues.

1. When job is done, driver asks executor to shutdown. However, this clean exit was assigned FAILED executor state by Worker. I introduced EXITED executor state for executors who voluntarily exit (both normal and abnormal exit depending on the exit code).

2. When Master gets notified an executor has exited, it launches another one to replace it, regardless of reason why the executor had exited. When the reason was job has finished, the unnecessary replacement got subsequently killed when App disassociates. This launching and killing of unnecessary executors shows up in the log and is confusing to users. I added check for executor exit status and avoid launching (and subsequent killing) of unnecessary replacements when executors exit cleanly.

One could ask the scheduler to tell Master job is done so that Master wouldn't launch the replacement executor. However, there is a race condition between App telling Master job is done and Worker telling Master an executor had exited. There is no guarantee the former will happen before the later. Instead, I chose to check the exit code when executor exits. If the exit code is 0, I assume executor has been asked to shutdown by driver and Master will not launch replacements.

Due to race condition, it could also happen that (although didn't happen on my local cluster), Master detects App disassociation event before the executor exits by itself. In such cases, the executor will be rightfully killed and labeled as KILLED, while the App state will show FINISHED.

Author: Kan Zhang <kzhang@apache.org>

Closes #306 from kanzhang/SPARK-1118 and squashes the following commits:

cb0cc86 [Kan Zhang] [SPARK-937] adding EXITED executor state and not relaunching cleanly exited executors
---
 .../main/scala/org/apache/spark/deploy/ExecutorState.scala | 4 ++--
 .../main/scala/org/apache/spark/deploy/master/Master.scala | 5 +++--
 .../org/apache/spark/deploy/worker/ExecutorRunner.scala    | 7 +++----
 3 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/ExecutorState.scala b/core/src/main/scala/org/apache/spark/deploy/ExecutorState.scala
index 37dfa7fec083..9f34d01e6db4 100644
--- a/core/src/main/scala/org/apache/spark/deploy/ExecutorState.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/ExecutorState.scala
@@ -19,9 +19,9 @@ package org.apache.spark.deploy
 
 private[spark] object ExecutorState extends Enumeration {
 
-  val LAUNCHING, LOADING, RUNNING, KILLED, FAILED, LOST = Value
+  val LAUNCHING, LOADING, RUNNING, KILLED, FAILED, LOST, EXITED = Value
 
   type ExecutorState = Value
 
-  def isFinished(state: ExecutorState): Boolean = Seq(KILLED, FAILED, LOST).contains(state)
+  def isFinished(state: ExecutorState): Boolean = Seq(KILLED, FAILED, LOST, EXITED).contains(state)
 }
diff --git a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
index c6dec305bffc..33ffcbd21695 100644
--- a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
@@ -303,10 +303,11 @@ private[spark] class Master(
             appInfo.removeExecutor(exec)
             exec.worker.removeExecutor(exec)
 
+            val normalExit = exitStatus.exists(_ == 0)
             // Only retry certain number of times so we don't go into an infinite loop.
-            if (appInfo.incrementRetryCount < ApplicationState.MAX_NUM_RETRY) {
+            if (!normalExit && appInfo.incrementRetryCount < ApplicationState.MAX_NUM_RETRY) {
               schedule()
-            } else {
+            } else if (!normalExit) {
               logError("Application %s with ID %s failed %d times, removing it".format(
                 appInfo.desc.name, appInfo.id, appInfo.retryCount))
               removeApplication(appInfo, ApplicationState.FAILED)
diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/ExecutorRunner.scala b/core/src/main/scala/org/apache/spark/deploy/worker/ExecutorRunner.scala
index d09136de4980..6433aac1c23e 100644
--- a/core/src/main/scala/org/apache/spark/deploy/worker/ExecutorRunner.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/worker/ExecutorRunner.scala
@@ -154,11 +154,10 @@ private[spark] class ExecutorRunner(
       Files.write(header, stderr, Charsets.UTF_8)
       stderrAppender = FileAppender(process.getErrorStream, stderr, conf)
 
-      // Wait for it to exit; this is actually a bad thing if it happens, because we expect to run
-      // long-lived processes only. However, in the future, we might restart the executor a few
-      // times on the same machine.
+      // Wait for it to exit; executor may exit with code 0 (when driver instructs it to shutdown)
+      // or with nonzero exit code
       val exitCode = process.waitFor()
-      state = ExecutorState.FAILED
+      state = ExecutorState.EXITED
       val message = "Command exited with code " + exitCode
       worker ! ExecutorStateChanged(appId, execId, state, Some(message), Some(exitCode))
     } catch {

From a63aa1adb2dfb19c8189167932ee8569840f96a0 Mon Sep 17 00:00:00 2001
From: CrazyJvm <crazyjvm@gmail.com>
Date: Sun, 15 Jun 2014 23:23:26 -0700
Subject: [PATCH 429/810] SPARK-1999: StorageLevel in storage tab and RDD
 Storage Info never changes
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

StorageLevel in 'storage tab' and 'RDD Storage Info' never changes even if you call rdd.unpersist() and then you give the rdd another different storage level.

Author: CrazyJvm <crazyjvm@gmail.com>

Closes #968 from CrazyJvm/ui-storagelevel and squashes the following commits:

62555fa [CrazyJvm] change RDDInfo constructor param 'storageLevel' to var, so there's need to add another variable _storageLevel。
9f1571e [CrazyJvm] JIRA https://issues.apache.org/jira/browse/SPARK-1999 UI : StorageLevel in storage tab and RDD Storage Info never changes
---
 core/src/main/scala/org/apache/spark/storage/RDDInfo.scala  | 6 +++---
 .../main/scala/org/apache/spark/storage/StorageUtils.scala  | 3 +++
 2 files changed, 6 insertions(+), 3 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/storage/RDDInfo.scala b/core/src/main/scala/org/apache/spark/storage/RDDInfo.scala
index 023fd6e4d8ba..5a72e216872a 100644
--- a/core/src/main/scala/org/apache/spark/storage/RDDInfo.scala
+++ b/core/src/main/scala/org/apache/spark/storage/RDDInfo.scala
@@ -26,7 +26,7 @@ class RDDInfo(
     val id: Int,
     val name: String,
     val numPartitions: Int,
-    val storageLevel: StorageLevel)
+    var storageLevel: StorageLevel)
   extends Ordered[RDDInfo] {
 
   var numCachedPartitions = 0
@@ -36,8 +36,8 @@ class RDDInfo(
 
   override def toString = {
     import Utils.bytesToString
-    ("RDD \"%s\" (%d) Storage: %s; CachedPartitions: %d; TotalPartitions: %d; MemorySize: %s; " +
-      "TachyonSize: %s; DiskSize: %s").format(
+    ("RDD \"%s\" (%d) StorageLevel: %s; CachedPartitions: %d; TotalPartitions: %d; " +
+      "MemorySize: %s; TachyonSize: %s; DiskSize: %s").format(
         name, id, storageLevel.toString, numCachedPartitions, numPartitions,
         bytesToString(memSize), bytesToString(tachyonSize), bytesToString(diskSize))
   }
diff --git a/core/src/main/scala/org/apache/spark/storage/StorageUtils.scala b/core/src/main/scala/org/apache/spark/storage/StorageUtils.scala
index 6f3252a2f6d3..f3bde1df45c7 100644
--- a/core/src/main/scala/org/apache/spark/storage/StorageUtils.scala
+++ b/core/src/main/scala/org/apache/spark/storage/StorageUtils.scala
@@ -89,10 +89,13 @@ private[spark] object StorageUtils {
       // Add up memory, disk and Tachyon sizes
       val persistedBlocks =
         blocks.filter { status => status.memSize + status.diskSize + status.tachyonSize > 0 }
+      val _storageLevel =
+        if (persistedBlocks.length > 0) persistedBlocks(0).storageLevel else StorageLevel.NONE
       val memSize = persistedBlocks.map(_.memSize).reduceOption(_ + _).getOrElse(0L)
       val diskSize = persistedBlocks.map(_.diskSize).reduceOption(_ + _).getOrElse(0L)
       val tachyonSize = persistedBlocks.map(_.tachyonSize).reduceOption(_ + _).getOrElse(0L)
       rddInfoMap.get(rddId).map { rddInfo =>
+        rddInfo.storageLevel = _storageLevel
         rddInfo.numCachedPartitions = persistedBlocks.length
         rddInfo.memSize = memSize
         rddInfo.diskSize = diskSize

From 9672ee07fb1c3583c70f23a699de3b2282eb0f98 Mon Sep 17 00:00:00 2001
From: Andrew Ash <andrew@andrewash.com>
Date: Sun, 15 Jun 2014 23:32:55 -0700
Subject: [PATCH 430/810] SPARK-2148 Add link to requirements for custom
 equals() and hashcode() methods

https://issues.apache.org/jira/browse/SPARK-2148

Author: Andrew Ash <andrew@andrewash.com>

Closes #1092 from ash211/SPARK-2148 and squashes the following commits:

93513df [Andrew Ash] SPARK-2148 Add link to requirements for custom equals() and hashcode() methods
---
 docs/programming-guide.md | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/docs/programming-guide.md b/docs/programming-guide.md
index ef0c0e34301f..0b24a8b88b3c 100644
--- a/docs/programming-guide.md
+++ b/docs/programming-guide.md
@@ -762,6 +762,11 @@ val counts = pairs.reduceByKey((a, b) => a + b)
 We could also use `counts.sortByKey()`, for example, to sort the pairs alphabetically, and finally
 `counts.collect()` to bring them back to the driver program as an array of objects.
 
+**Note:** when using custom objects as the key in key-value pair operations, you must be sure that a
+custom `equals()` method is accompanied with a matching `hashCode()` method.  For full details, see
+the contract outlined in the [Object.hashCode()
+documentation](http://docs.oracle.com/javase/7/docs/api/java/lang/Object.html#hashCode()).
+
 </div>
 
 <div data-lang="java" markdown="1">
@@ -794,6 +799,10 @@ JavaPairRDD<String, Integer> counts = pairs.reduceByKey((a, b) -> a + b);
 We could also use `counts.sortByKey()`, for example, to sort the pairs alphabetically, and finally
 `counts.collect()` to bring them back to the driver program as an array of objects.
 
+**Note:** when using custom objects as the key in key-value pair operations, you must be sure that a
+custom `equals()` method is accompanied with a matching `hashCode()` method.  For full details, see
+the contract outlined in the [Object.hashCode()
+documentation](http://docs.oracle.com/javase/7/docs/api/java/lang/Object.html#hashCode()).
 
 </div>
 

From 119b06a04f6df3949b3b074a18f791bbc732ac31 Mon Sep 17 00:00:00 2001
From: Ali Ghodsi <alig@cs.berkeley.edu>
Date: Sun, 15 Jun 2014 23:44:30 -0700
Subject: [PATCH 431/810] Updating docs to include missing information about
 reducers and clarify ...

...how the OFFHEAP storage level works (there has been confusion around this).

Author: Ali Ghodsi <alig@cs.berkeley.edu>

Closes #1089 from alig/master and squashes the following commits:

ca8114d [Ali Ghodsi] Updating docs to include missing information about reducers and clarify how the OFFHEAP storage level works (there has been confusion around this).
---
 docs/programming-guide.md | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/docs/programming-guide.md b/docs/programming-guide.md
index 0b24a8b88b3c..65d75b85efda 100644
--- a/docs/programming-guide.md
+++ b/docs/programming-guide.md
@@ -899,7 +899,7 @@ for details.
 </tr>
 <tr>
   <td> <b>reduceByKey</b>(<i>func</i>, [<i>numTasks</i>]) </td>
-  <td> When called on a dataset of (K, V) pairs, returns a dataset of (K, V) pairs where the values for each key are aggregated using the given reduce function. Like in <code>groupByKey</code>, the number of reduce tasks is configurable through an optional second argument. </td>
+  <td> When called on a dataset of (K, V) pairs, returns a dataset of (K, V) pairs where the values for each key are aggregated using the given reduce function <i>func</i>, which must be of type (V,V) => V. Like in <code>groupByKey</code>, the number of reduce tasks is configurable through an optional second argument. </td>
 </tr>
 <tr>
   <td> <b>aggregateByKey</b>(<i>zeroValue</i>)(<i>seqOp</i>, <i>combOp</i>, [<i>numTasks</i>]) </td>
@@ -1067,7 +1067,10 @@ storage levels is:
   <td> Store RDD in serialized format in <a href="http://tachyon-project.org">Tachyon</a>.
     Compared to MEMORY_ONLY_SER, OFF_HEAP reduces garbage collection overhead and allows executors
     to be smaller and to share a pool of memory, making it attractive in environments with
-    large heaps or multiple concurrent applications.
+    large heaps or multiple concurrent applications. Furthermore, as the RDDs reside in Tachyon,
+    the crash of an executor does not lead to losing the in-memory cache. In this mode, the memory 
+    in Tachyon is discardable. Thus, Tachyon does not attempt to reconstruct a block that it evicts
+    from memory.
   </td>
 </tr>
 </table>

From 716c88aa147762f7f617adf34a17edd681d9a4ff Mon Sep 17 00:00:00 2001
From: CodingCat <zhunansjtu@gmail.com>
Date: Sun, 15 Jun 2014 23:47:58 -0700
Subject: [PATCH 432/810] SPARK-2039: apply output dir existence checking for
 all output formats

https://issues.apache.org/jira/browse/SPARK-2039

apply output dir existence checking for all output formats

Author: CodingCat <zhunansjtu@gmail.com>

Closes #1088 from CodingCat/SPARK-2039 and squashes the following commits:

c52747a [CodingCat] apply output dir existence checking for all output formats
---
 .../main/scala/org/apache/spark/rdd/PairRDDFunctions.scala  | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
index b6ad9b6c3e16..fe36c80e0be8 100644
--- a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
@@ -787,8 +787,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
     val outfmt = job.getOutputFormatClass
     val jobFormat = outfmt.newInstance
 
-    if (self.conf.getBoolean("spark.hadoop.validateOutputSpecs", true) &&
-      jobFormat.isInstanceOf[NewFileOutputFormat[_, _]]) {
+    if (self.conf.getBoolean("spark.hadoop.validateOutputSpecs", true)) {
       // FileOutputFormat ignores the filesystem parameter
       jobFormat.checkOutputSpecs(job)
     }
@@ -854,8 +853,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
     logDebug("Saving as hadoop file of type (" + keyClass.getSimpleName + ", " +
       valueClass.getSimpleName + ")")
 
-    if (self.conf.getBoolean("spark.hadoop.validateOutputSpecs", true) &&
-      outputFormatInstance.isInstanceOf[FileOutputFormat[_, _]]) {
+    if (self.conf.getBoolean("spark.hadoop.validateOutputSpecs", true)) {
       // FileOutputFormat ignores the filesystem parameter
       val ignoredFs = FileSystem.get(conf)
       conf.getOutputFormat.checkOutputSpecs(ignoredFs, conf)

From 4fdb491775bb9c4afa40477dc0069ff6fcadfe25 Mon Sep 17 00:00:00 2001
From: Kan Zhang <kzhang@apache.org>
Date: Mon, 16 Jun 2014 11:11:29 -0700
Subject: [PATCH 433/810] [SPARK-2010] Support for nested data in PySpark SQL

JIRA issue https://issues.apache.org/jira/browse/SPARK-2010

This PR adds support for nested collection types in PySpark SQL, including
array, dict, list, set, and tuple. Example,

```
>>> from array import array
>>> from pyspark.sql import SQLContext
>>> sqlCtx = SQLContext(sc)
>>> rdd = sc.parallelize([
...         {"f1" : array('i', [1, 2]), "f2" : {"row1" : 1.0}},
...         {"f1" : array('i', [2, 3]), "f2" : {"row2" : 2.0}}])
>>> srdd = sqlCtx.inferSchema(rdd)
>>> srdd.collect() == [{"f1" : array('i', [1, 2]), "f2" : {"row1" : 1.0}},
...                    {"f1" : array('i', [2, 3]), "f2" : {"row2" : 2.0}}]
True
>>> rdd = sc.parallelize([
...         {"f1" : [[1, 2], [2, 3]], "f2" : set([1, 2]), "f3" : (1, 2)},
...         {"f1" : [[2, 3], [3, 4]], "f2" : set([2, 3]), "f3" : (2, 3)}])
>>> srdd = sqlCtx.inferSchema(rdd)
>>> srdd.collect() == \
... [{"f1" : [[1, 2], [2, 3]], "f2" : set([1, 2]), "f3" : (1, 2)},
...  {"f1" : [[2, 3], [3, 4]], "f2" : set([2, 3]), "f3" : (2, 3)}]
True
```

Author: Kan Zhang <kzhang@apache.org>

Closes #1041 from kanzhang/SPARK-2010 and squashes the following commits:

1b2891d [Kan Zhang] [SPARK-2010] minor doc change and adding a TODO
504f27e [Kan Zhang] [SPARK-2010] Support for nested data in PySpark SQL
---
 python/pyspark/sql.py                         | 22 +++++++++++++-
 .../org/apache/spark/sql/SQLContext.scala     | 29 ++++++++++++-------
 2 files changed, 40 insertions(+), 11 deletions(-)

diff --git a/python/pyspark/sql.py b/python/pyspark/sql.py
index e344610b1fe4..c31d49ce837f 100644
--- a/python/pyspark/sql.py
+++ b/python/pyspark/sql.py
@@ -77,12 +77,25 @@ def inferSchema(self, rdd):
         """Infer and apply a schema to an RDD of L{dict}s.
 
         We peek at the first row of the RDD to determine the fields names
-        and types, and then use that to extract all the dictionaries.
+        and types, and then use that to extract all the dictionaries. Nested
+        collections are supported, which include array, dict, list, set, and
+        tuple.
 
         >>> srdd = sqlCtx.inferSchema(rdd)
         >>> srdd.collect() == [{"field1" : 1, "field2" : "row1"}, {"field1" : 2, "field2": "row2"},
         ...                    {"field1" : 3, "field2": "row3"}]
         True
+
+        >>> from array import array
+        >>> srdd = sqlCtx.inferSchema(nestedRdd1)
+        >>> srdd.collect() == [{"f1" : array('i', [1, 2]), "f2" : {"row1" : 1.0}},
+        ...                    {"f1" : array('i', [2, 3]), "f2" : {"row2" : 2.0}}]
+        True
+
+        >>> srdd = sqlCtx.inferSchema(nestedRdd2)
+        >>> srdd.collect() == [{"f1" : [[1, 2], [2, 3]], "f2" : set([1, 2]), "f3" : (1, 2)},
+        ...                    {"f1" : [[2, 3], [3, 4]], "f2" : set([2, 3]), "f3" : (2, 3)}]
+        True
         """
         if (rdd.__class__ is SchemaRDD):
             raise ValueError("Cannot apply schema to %s" % SchemaRDD.__name__)
@@ -413,6 +426,7 @@ def subtract(self, other, numPartitions=None):
 
 def _test():
     import doctest
+    from array import array
     from pyspark.context import SparkContext
     globs = globals().copy()
     # The small batch size here ensures that we see multiple batches,
@@ -422,6 +436,12 @@ def _test():
     globs['sqlCtx'] = SQLContext(sc)
     globs['rdd'] = sc.parallelize([{"field1" : 1, "field2" : "row1"},
         {"field1" : 2, "field2": "row2"}, {"field1" : 3, "field2": "row3"}])
+    globs['nestedRdd1'] = sc.parallelize([
+        {"f1" : array('i', [1, 2]), "f2" : {"row1" : 1.0}},
+        {"f1" : array('i', [2, 3]), "f2" : {"row2" : 2.0}}])
+    globs['nestedRdd2'] = sc.parallelize([
+        {"f1" : [[1, 2], [2, 3]], "f2" : set([1, 2]), "f3" : (1, 2)},
+        {"f1" : [[2, 3], [3, 4]], "f2" : set([2, 3]), "f3" : (2, 3)}])
     (failure_count, test_count) = doctest.testmod(globs=globs,optionflags=doctest.ELLIPSIS)
     globs['sc'].stop()
     if failure_count:
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
index 378ff5453111..131c130bbb3e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
@@ -298,19 +298,28 @@ class SQLContext(@transient val sparkContext: SparkContext)
 
   /**
    * Peek at the first row of the RDD and infer its schema.
-   * TODO: We only support primitive types, add support for nested types.
+   * TODO: consolidate this with the type system developed in SPARK-2060.
    */
   private[sql] def inferSchema(rdd: RDD[Map[String, _]]): SchemaRDD = {
+    import scala.collection.JavaConversions._
+    def typeFor(obj: Any): DataType = obj match {
+      case c: java.lang.String => StringType
+      case c: java.lang.Integer => IntegerType
+      case c: java.lang.Long => LongType
+      case c: java.lang.Double => DoubleType
+      case c: java.lang.Boolean => BooleanType
+      case c: java.util.List[_] => ArrayType(typeFor(c.head))
+      case c: java.util.Set[_] => ArrayType(typeFor(c.head))
+      case c: java.util.Map[_, _] =>
+        val (key, value) = c.head
+        MapType(typeFor(key), typeFor(value))
+      case c if c.getClass.isArray =>
+        val elem = c.asInstanceOf[Array[_]].head
+        ArrayType(typeFor(elem))
+      case c => throw new Exception(s"Object of type $c cannot be used")
+    }
     val schema = rdd.first().map { case (fieldName, obj) =>
-      val dataType = obj.getClass match {
-        case c: Class[_] if c == classOf[java.lang.String] => StringType
-        case c: Class[_] if c == classOf[java.lang.Integer] => IntegerType
-        case c: Class[_] if c == classOf[java.lang.Long] => LongType
-        case c: Class[_] if c == classOf[java.lang.Double] => DoubleType
-        case c: Class[_] if c == classOf[java.lang.Boolean] => BooleanType
-        case c => throw new Exception(s"Object of type $c cannot be used")
-      }
-      AttributeReference(fieldName, dataType, true)()
+      AttributeReference(fieldName, typeFor(obj), true)()
     }.toSeq
 
     val rowRdd = rdd.mapPartitions { iter =>

From cdf2b04570871848442ca9f9e2316a37e4aaaae0 Mon Sep 17 00:00:00 2001
From: witgo <witgo@qq.com>
Date: Mon, 16 Jun 2014 14:27:31 -0500
Subject: [PATCH 434/810] [SPARK-1930] The Container is running beyond physical
 memory limits, so as to be killed

Author: witgo <witgo@qq.com>

Closes #894 from witgo/SPARK-1930 and squashes the following commits:

564307e [witgo] Update the running-on-yarn.md
3747515 [witgo] Merge branch 'master' of https://github.com/apache/spark into SPARK-1930
172647b [witgo] add memoryOverhead docs
a0ff545 [witgo] leaving only two configs
a17bda2 [witgo] Merge branch 'master' of https://github.com/apache/spark into SPARK-1930
478ca15 [witgo] Merge branch 'master' into SPARK-1930
d1244a1 [witgo] Merge branch 'master' into SPARK-1930
8b967ae [witgo] Merge branch 'master' into SPARK-1930
655a820 [witgo] review commit
71859a7 [witgo] Merge branch 'master' of https://github.com/apache/spark into SPARK-1930
e3c531d [witgo] review commit
e16f190 [witgo] different memoryOverhead
ffa7569 [witgo] review commit
5c9581f [witgo] Merge branch 'master' into SPARK-1930
9a6bcf2 [witgo] review commit
8fae45a [witgo] fix NullPointerException
e0dcc16 [witgo] Adding  configuration items
b6a989c [witgo] Fix container memory beyond limit, were killed
---
 docs/running-on-yarn.md                            | 14 ++++++++++++++
 .../org/apache/spark/deploy/yarn/Client.scala      |  4 ++--
 .../spark/deploy/yarn/ExecutorLauncher.scala       |  4 +++-
 .../spark/deploy/yarn/YarnAllocationHandler.scala  | 12 ++++++++----
 .../org/apache/spark/deploy/yarn/ClientBase.scala  | 14 +++++++++-----
 .../org/apache/spark/deploy/yarn/Client.scala      |  4 ++--
 .../spark/deploy/yarn/YarnAllocationHandler.scala  | 12 ++++++++----
 7 files changed, 46 insertions(+), 18 deletions(-)

diff --git a/docs/running-on-yarn.md b/docs/running-on-yarn.md
index af1788f2aa15..4243ef480ba3 100644
--- a/docs/running-on-yarn.md
+++ b/docs/running-on-yarn.md
@@ -67,6 +67,20 @@ Most of the configs are the same for Spark on YARN as for other deployment modes
     The address of the Spark history server (i.e. host.com:18080). The address should not contain a scheme (http://). Defaults to not being set since the history server is an optional service. This address is given to the YARN ResourceManager when the Spark application finishes to link the application from the ResourceManager UI to the Spark history server UI.
   </td>
 </tr>
+<tr>
+  <td><code>spark.yarn.executor.memoryOverhead</code></td>
+  <td>384</code></td>
+  <td>
+    The amount of off heap memory (in megabytes) to be allocated per executor. This is memory that accounts for things like VM overheads, interned strings, other native overheads, etc.
+  </td>
+</tr>
+<tr>
+  <td><code>spark.yarn.driver.memoryOverhead</code></td>
+  <td>384</code></td>
+  <td>
+    The amount of off heap memory (in megabytes) to be allocated per driver. This is memory that accounts for things like VM overheads, interned strings, other native overheads, etc.
+  </td>
+</tr>
 </table>
 
 By default, Spark on YARN will use a Spark jar installed locally, but the Spark JAR can also be in a world-readable location on HDFS. This allows YARN to cache it on nodes so that it doesn't need to be distributed each time an application runs. To point to a JAR on HDFS, `export SPARK_JAR=hdfs:///some/path`.
diff --git a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/Client.scala b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
index 4ccddc214c8a..82f79d88a300 100644
--- a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
+++ b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
@@ -71,7 +71,7 @@ class Client(clientArgs: ClientArguments, hadoopConf: Configuration, spConf: Spa
 
     val capability = Records.newRecord(classOf[Resource]).asInstanceOf[Resource]
     // Memory for the ApplicationMaster.
-    capability.setMemory(args.amMemory + YarnAllocationHandler.MEMORY_OVERHEAD)
+    capability.setMemory(args.amMemory + memoryOverhead)
     amContainer.setResource(capability)
 
     appContext.setQueue(args.amQueue)
@@ -115,7 +115,7 @@ class Client(clientArgs: ClientArguments, hadoopConf: Configuration, spConf: Spa
     val minResMemory = newApp.getMinimumResourceCapability().getMemory()
     val amMemory = ((args.amMemory / minResMemory) * minResMemory) +
           ((if ((args.amMemory % minResMemory) == 0) 0 else minResMemory) -
-          YarnAllocationHandler.MEMORY_OVERHEAD)
+          memoryOverhead)
     amMemory
   }
 
diff --git a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala
index b6ecae1e652f..bfdb6232f511 100644
--- a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala
+++ b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala
@@ -92,13 +92,15 @@ class ExecutorLauncher(args: ApplicationMasterArguments, conf: Configuration, sp
 
     appAttemptId = getApplicationAttemptId()
     resourceManager = registerWithResourceManager()
+
     val appMasterResponse: RegisterApplicationMasterResponse = registerApplicationMaster()
 
     // Compute number of threads for akka
     val minimumMemory = appMasterResponse.getMinimumResourceCapability().getMemory()
 
     if (minimumMemory > 0) {
-      val mem = args.executorMemory + YarnAllocationHandler.MEMORY_OVERHEAD
+      val mem = args.executorMemory + sparkConf.getInt("spark.yarn.executor.memoryOverhead",
+        YarnAllocationHandler.MEMORY_OVERHEAD)
       val numCore = (mem  / minimumMemory) + (if (0 != (mem % minimumMemory)) 1 else 0)
 
       if (numCore > 0) {
diff --git a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala
index 856391e52b2d..80e0162e9f27 100644
--- a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala
+++ b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala
@@ -88,6 +88,10 @@ private[yarn] class YarnAllocationHandler(
   // Containers to be released in next request to RM
   private val pendingReleaseContainers = new ConcurrentHashMap[ContainerId, Boolean]
 
+  // Additional memory overhead - in mb.
+  private def memoryOverhead: Int = sparkConf.getInt("spark.yarn.executor.memoryOverhead",
+    YarnAllocationHandler.MEMORY_OVERHEAD)
+
   private val numExecutorsRunning = new AtomicInteger()
   // Used to generate a unique id per executor
   private val executorIdCounter = new AtomicInteger()
@@ -99,7 +103,7 @@ private[yarn] class YarnAllocationHandler(
   def getNumExecutorsFailed: Int = numExecutorsFailed.intValue
 
   def isResourceConstraintSatisfied(container: Container): Boolean = {
-    container.getResource.getMemory >= (executorMemory + YarnAllocationHandler.MEMORY_OVERHEAD)
+    container.getResource.getMemory >= (executorMemory + memoryOverhead)
   }
 
   def allocateContainers(executorsToRequest: Int) {
@@ -229,7 +233,7 @@ private[yarn] class YarnAllocationHandler(
         val containerId = container.getId
 
         assert( container.getResource.getMemory >=
-          (executorMemory + YarnAllocationHandler.MEMORY_OVERHEAD))
+          (executorMemory + memoryOverhead))
 
         if (numExecutorsRunningNow > maxExecutors) {
           logInfo("""Ignoring container %s at host %s, since we already have the required number of
@@ -450,7 +454,7 @@ private[yarn] class YarnAllocationHandler(
 
     if (numExecutors > 0) {
       logInfo("Allocating %d executor containers with %d of memory each.".format(numExecutors,
-        executorMemory + YarnAllocationHandler.MEMORY_OVERHEAD))
+        executorMemory + memoryOverhead))
     } else {
       logDebug("Empty allocation req ..  release : " + releasedContainerList)
     }
@@ -505,7 +509,7 @@ private[yarn] class YarnAllocationHandler(
     val rsrcRequest = Records.newRecord(classOf[ResourceRequest])
     val memCapability = Records.newRecord(classOf[Resource])
     // There probably is some overhead here, let's reserve a bit more memory.
-    memCapability.setMemory(executorMemory + YarnAllocationHandler.MEMORY_OVERHEAD)
+    memCapability.setMemory(executorMemory + memoryOverhead)
     rsrcRequest.setCapability(memCapability)
 
     val pri = Records.newRecord(classOf[Priority])
diff --git a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala
index 6861b503000c..858bcaa95b40 100644
--- a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala
+++ b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala
@@ -65,6 +65,10 @@ trait ClientBase extends Logging {
   val APP_FILE_PERMISSION: FsPermission =
     FsPermission.createImmutable(Integer.parseInt("644", 8).toShort)
 
+  // Additional memory overhead - in mb.
+  protected def memoryOverhead: Int = sparkConf.getInt("spark.yarn.driver.memoryOverhead",
+    YarnAllocationHandler.MEMORY_OVERHEAD)
+
   // TODO(harvey): This could just go in ClientArguments.
   def validateArgs() = {
     Map(
@@ -72,10 +76,10 @@ trait ClientBase extends Logging {
           "Error: You must specify a user jar when running in standalone mode!"),
       (args.userClass == null) -> "Error: You must specify a user class!",
       (args.numExecutors <= 0) -> "Error: You must specify at least 1 executor!",
-      (args.amMemory <= YarnAllocationHandler.MEMORY_OVERHEAD) -> ("Error: AM memory size must be" +
-        "greater than: " + YarnAllocationHandler.MEMORY_OVERHEAD),
-      (args.executorMemory <= YarnAllocationHandler.MEMORY_OVERHEAD) -> ("Error: Executor memory size" +
-        "must be greater than: " + YarnAllocationHandler.MEMORY_OVERHEAD.toString)
+      (args.amMemory <= memoryOverhead) -> ("Error: AM memory size must be" +
+        "greater than: " + memoryOverhead),
+      (args.executorMemory <= memoryOverhead) -> ("Error: Executor memory size" +
+        "must be greater than: " + memoryOverhead.toString)
     ).foreach { case(cond, errStr) =>
       if (cond) {
         logError(errStr)
@@ -101,7 +105,7 @@ trait ClientBase extends Logging {
       logError(errorMessage)
       throw new IllegalArgumentException(errorMessage)
     }
-    val amMem = args.amMemory + YarnAllocationHandler.MEMORY_OVERHEAD
+    val amMem = args.amMemory + memoryOverhead
     if (amMem > maxMem) {
 
       val errorMessage = "Required AM memory (%d) is above the max threshold (%d) of this cluster."
diff --git a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/Client.scala b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
index 80a8bceb1726..15f3c4f180ea 100644
--- a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
+++ b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
@@ -84,7 +84,7 @@ class Client(clientArgs: ClientArguments, hadoopConf: Configuration, spConf: Spa
 
     // Memory for the ApplicationMaster.
     val memoryResource = Records.newRecord(classOf[Resource]).asInstanceOf[Resource]
-    memoryResource.setMemory(args.amMemory + YarnAllocationHandler.MEMORY_OVERHEAD)
+    memoryResource.setMemory(args.amMemory + memoryOverhead)
     appContext.setResource(memoryResource)
 
     // Finally, submit and monitor the application.
@@ -117,7 +117,7 @@ class Client(clientArgs: ClientArguments, hadoopConf: Configuration, spConf: Spa
     // val minResMemory: Int = newApp.getMinimumResourceCapability().getMemory()
     // var amMemory = ((args.amMemory / minResMemory) * minResMemory) +
     //  ((if ((args.amMemory % minResMemory) == 0) 0 else minResMemory) -
-    //    YarnAllocationHandler.MEMORY_OVERHEAD)
+    //    memoryOverhead )
     args.amMemory
   }
 
diff --git a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala
index a979fe4d6263..29ccec2adcac 100644
--- a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala
+++ b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala
@@ -90,6 +90,10 @@ private[yarn] class YarnAllocationHandler(
   // Containers to be released in next request to RM
   private val pendingReleaseContainers = new ConcurrentHashMap[ContainerId, Boolean]
 
+  // Additional memory overhead - in mb.
+  private def memoryOverhead: Int = sparkConf.getInt("spark.yarn.executor.memoryOverhead",
+    YarnAllocationHandler.MEMORY_OVERHEAD)
+
   // Number of container requests that have been sent to, but not yet allocated by the
   // ApplicationMaster.
   private val numPendingAllocate = new AtomicInteger()
@@ -106,7 +110,7 @@ private[yarn] class YarnAllocationHandler(
   def getNumExecutorsFailed: Int = numExecutorsFailed.intValue
 
   def isResourceConstraintSatisfied(container: Container): Boolean = {
-    container.getResource.getMemory >= (executorMemory + YarnAllocationHandler.MEMORY_OVERHEAD)
+    container.getResource.getMemory >= (executorMemory + memoryOverhead)
   }
 
   def releaseContainer(container: Container) {
@@ -248,7 +252,7 @@ private[yarn] class YarnAllocationHandler(
         val executorHostname = container.getNodeId.getHost
         val containerId = container.getId
 
-        val executorMemoryOverhead = (executorMemory + YarnAllocationHandler.MEMORY_OVERHEAD)
+        val executorMemoryOverhead = (executorMemory + memoryOverhead)
         assert(container.getResource.getMemory >= executorMemoryOverhead)
 
         if (numExecutorsRunningNow > maxExecutors) {
@@ -477,7 +481,7 @@ private[yarn] class YarnAllocationHandler(
       numPendingAllocate.addAndGet(numExecutors)
       logInfo("Will Allocate %d executor containers, each with %d memory".format(
         numExecutors,
-        (executorMemory + YarnAllocationHandler.MEMORY_OVERHEAD)))
+        (executorMemory + memoryOverhead)))
     } else {
       logDebug("Empty allocation request ...")
     }
@@ -537,7 +541,7 @@ private[yarn] class YarnAllocationHandler(
       priority: Int
     ): ArrayBuffer[ContainerRequest] = {
 
-    val memoryRequest = executorMemory + YarnAllocationHandler.MEMORY_OVERHEAD
+    val memoryRequest = executorMemory + memoryOverhead
     val resource = Resource.newInstance(memoryRequest, executorCores)
 
     val prioritySetting = Records.newRecord(classOf[Priority])

From 273afcb254fb5384204c56bdcb3b9b760bcfab3f Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian.cs.zju@gmail.com>
Date: Mon, 16 Jun 2014 21:30:29 +0200
Subject: [PATCH 435/810] [SQL][SPARK-2094] Follow up of PR #1071 for Java API

Updated `JavaSQLContext` and `JavaHiveContext` similar to what we've done to `SQLContext` and `HiveContext` in PR #1071. Added corresponding test case for Spark SQL Java API.

Author: Cheng Lian <lian.cs.zju@gmail.com>

Closes #1085 from liancheng/spark-2094-java and squashes the following commits:

29b8a51 [Cheng Lian] Avoided instantiating JavaSparkContext & JavaHiveContext to workaround test failure
92bb4fb [Cheng Lian] Marked test cases in JavaHiveQLSuite with "ignore"
22aec97 [Cheng Lian] Follow up of PR #1071 for Java API
---
 .../spark/sql/api/java/JavaSQLContext.scala   |  16 +--
 .../sql/hive/api/java/JavaHiveContext.scala   |  10 +-
 .../sql/hive/api/java/JavaHiveQLSuite.scala   | 101 ++++++++++++++++++
 .../sql/hive/api/java/JavaHiveSuite.scala     |  41 -------
 .../sql/hive/execution/HiveQuerySuite.scala   |  30 +++---
 5 files changed, 124 insertions(+), 74 deletions(-)
 create mode 100644 sql/hive/src/test/scala/org/apache/spark/sql/hive/api/java/JavaHiveQLSuite.scala
 delete mode 100644 sql/hive/src/test/scala/org/apache/spark/sql/hive/api/java/JavaHiveSuite.scala

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSQLContext.scala
index 6f7d431b9a81..352260fa15bb 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSQLContext.scala
@@ -40,19 +40,13 @@ class JavaSQLContext(val sqlContext: SQLContext) {
   /**
    * Executes a query expressed in SQL, returning the result as a JavaSchemaRDD
    */
-  def sql(sqlQuery: String): JavaSchemaRDD = {
-    val result = new JavaSchemaRDD(sqlContext, sqlContext.parseSql(sqlQuery))
-    // We force query optimization to happen right away instead of letting it happen lazily like
-    // when using the query DSL.  This is so DDL commands behave as expected.  This is only
-    // generates the RDD lineage for DML queries, but do not perform any execution.
-    result.queryExecution.toRdd
-    result
-  }
+  def sql(sqlQuery: String): JavaSchemaRDD =
+    new JavaSchemaRDD(sqlContext, sqlContext.parseSql(sqlQuery))
 
   /**
    * :: Experimental ::
    * Creates an empty parquet file with the schema of class `beanClass`, which can be registered as
-   * a table. This registered table can be used as the target of future insertInto` operations.
+   * a table. This registered table can be used as the target of future `insertInto` operations.
    *
    * {{{
    *   JavaSQLContext sqlCtx = new JavaSQLContext(...)
@@ -62,7 +56,7 @@ class JavaSQLContext(val sqlContext: SQLContext) {
    * }}}
    *
    * @param beanClass A java bean class object that will be used to determine the schema of the
-   *                  parquet file.                          s
+   *                  parquet file.
    * @param path The path where the directory containing parquet metadata should be created.
    *             Data inserted into this table will also be stored at this location.
    * @param allowExisting When false, an exception will be thrown if this directory already exists.
@@ -100,14 +94,12 @@ class JavaSQLContext(val sqlContext: SQLContext) {
     new JavaSchemaRDD(sqlContext, SparkLogicalPlan(ExistingRdd(schema, rowRdd)))
   }
 
-
   /**
    * Loads a parquet file, returning the result as a [[JavaSchemaRDD]].
    */
   def parquetFile(path: String): JavaSchemaRDD =
     new JavaSchemaRDD(sqlContext, ParquetRelation(path))
 
-
   /**
    * Registers the given RDD as a temporary table in the catalog.  Temporary tables exist only
    * during the lifetime of this instance of SQLContext.
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/api/java/JavaHiveContext.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/api/java/JavaHiveContext.scala
index 6df76fa82510..c9ee162191c9 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/api/java/JavaHiveContext.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/api/java/JavaHiveContext.scala
@@ -31,12 +31,6 @@ class JavaHiveContext(sparkContext: JavaSparkContext) extends JavaSQLContext(spa
   /**
     * Executes a query expressed in HiveQL, returning the result as a JavaSchemaRDD.
     */
-  def hql(hqlQuery: String): JavaSchemaRDD = {
-    val result = new JavaSchemaRDD(sqlContext, HiveQl.parseSql(hqlQuery))
-    // We force query optimization to happen right away instead of letting it happen lazily like
-    // when using the query DSL.  This is so DDL commands behave as expected.  This is only
-    // generates the RDD lineage for DML queries, but do not perform any execution.
-    result.queryExecution.toRdd
-    result
-  }
+  def hql(hqlQuery: String): JavaSchemaRDD =
+    new JavaSchemaRDD(sqlContext, HiveQl.parseSql(hqlQuery))
 }
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/api/java/JavaHiveQLSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/api/java/JavaHiveQLSuite.scala
new file mode 100644
index 000000000000..3b9cd8f52de4
--- /dev/null
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/api/java/JavaHiveQLSuite.scala
@@ -0,0 +1,101 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive.api.java
+
+import scala.util.Try
+
+import org.scalatest.FunSuite
+
+import org.apache.spark.api.java.JavaSparkContext
+import org.apache.spark.sql.api.java.JavaSchemaRDD
+import org.apache.spark.sql.execution.ExplainCommand
+import org.apache.spark.sql.hive.test.TestHive
+import org.apache.spark.sql.test.TestSQLContext
+
+// Implicits
+import scala.collection.JavaConversions._
+
+class JavaHiveQLSuite extends FunSuite {
+  lazy val javaCtx = new JavaSparkContext(TestSQLContext.sparkContext)
+
+  // There is a little trickery here to avoid instantiating two HiveContexts in the same JVM
+  lazy val javaHiveCtx = new JavaHiveContext(javaCtx) {
+    override val sqlContext = TestHive
+  }
+
+  ignore("SELECT * FROM src") {
+    assert(
+      javaHiveCtx.hql("SELECT * FROM src").collect().map(_.getInt(0)) ===
+        TestHive.sql("SELECT * FROM src").collect().map(_.getInt(0)).toSeq)
+  }
+
+  private val explainCommandClassName =
+    classOf[ExplainCommand].getSimpleName.stripSuffix("$")
+
+  def isExplanation(result: JavaSchemaRDD) = {
+    val explanation = result.collect().map(_.getString(0))
+    explanation.size == 1 && explanation.head.startsWith(explainCommandClassName)
+  }
+
+  ignore("Query Hive native command execution result") {
+    val tableName = "test_native_commands"
+
+    assertResult(0) {
+      javaHiveCtx.hql(s"DROP TABLE IF EXISTS $tableName").count()
+    }
+
+    assertResult(0) {
+      javaHiveCtx.hql(s"CREATE TABLE $tableName(key INT, value STRING)").count()
+    }
+
+    javaHiveCtx.hql("SHOW TABLES").registerAsTable("show_tables")
+
+    assert(
+      javaHiveCtx
+        .hql("SELECT result FROM show_tables")
+        .collect()
+        .map(_.getString(0))
+        .contains(tableName))
+
+    assertResult(Array(Array("key", "int", "None"), Array("value", "string", "None"))) {
+      javaHiveCtx.hql(s"DESCRIBE $tableName").registerAsTable("describe_table")
+
+      javaHiveCtx
+        .hql("SELECT result FROM describe_table")
+        .collect()
+        .map(_.getString(0).split("\t").map(_.trim))
+        .toArray
+    }
+
+    assert(isExplanation(javaHiveCtx.hql(
+      s"EXPLAIN SELECT key, COUNT(*) FROM $tableName GROUP BY key")))
+
+    TestHive.reset()
+  }
+
+  ignore("Exactly once semantics for DDL and command statements") {
+    val tableName = "test_exactly_once"
+    val q0 = javaHiveCtx.hql(s"CREATE TABLE $tableName(key INT, value STRING)")
+
+    // If the table was not created, the following assertion would fail
+    assert(Try(TestHive.table(tableName)).isSuccess)
+
+    // If the CREATE TABLE command got executed again, the following assertion would fail
+    assert(Try(q0.count()).isSuccess)
+  }
+}
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/api/java/JavaHiveSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/api/java/JavaHiveSuite.scala
deleted file mode 100644
index 9c5d7c81f7c0..000000000000
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/api/java/JavaHiveSuite.scala
+++ /dev/null
@@ -1,41 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql.hive.api.java
-
-import org.scalatest.FunSuite
-
-import org.apache.spark.api.java.JavaSparkContext
-import org.apache.spark.sql.test.TestSQLContext
-import org.apache.spark.sql.hive.test.TestHive
-
-// Implicits
-import scala.collection.JavaConversions._
-
-class JavaHiveSQLSuite extends FunSuite {
-  ignore("SELECT * FROM src") {
-    val javaCtx = new JavaSparkContext(TestSQLContext.sparkContext)
-    // There is a little trickery here to avoid instantiating two HiveContexts in the same JVM
-    val javaSqlCtx = new JavaHiveContext(javaCtx) {
-      override val sqlContext = TestHive
-    }
-
-    assert(
-      javaSqlCtx.hql("SELECT * FROM src").collect().map(_.getInt(0)) ===
-        TestHive.sql("SELECT * FROM src").collect().map(_.getInt(0)).toSeq)
-  }
-}
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
index 0d656c556965..6e8d11b8a130 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
@@ -184,25 +184,29 @@ class HiveQuerySuite extends HiveComparisonTest {
   test("Query Hive native command execution result") {
     val tableName = "test_native_commands"
 
-    val q0 = hql(s"DROP TABLE IF EXISTS $tableName")
-    assert(q0.count() == 0)
+    assertResult(0) {
+      hql(s"DROP TABLE IF EXISTS $tableName").count()
+    }
 
-    val q1 = hql(s"CREATE TABLE $tableName(key INT, value STRING)")
-    assert(q1.count() == 0)
+    assertResult(0) {
+      hql(s"CREATE TABLE $tableName(key INT, value STRING)").count()
+    }
 
-    val q2 = hql("SHOW TABLES")
-    val tables = q2.select('result).collect().map { case Row(table: String) => table }
-    assert(tables.contains(tableName))
+    assert(
+      hql("SHOW TABLES")
+        .select('result)
+        .collect()
+        .map(_.getString(0))
+        .contains(tableName))
 
-    val q3 = hql(s"DESCRIBE $tableName")
     assertResult(Array(Array("key", "int", "None"), Array("value", "string", "None"))) {
-      q3.select('result).collect().map { case Row(fieldDesc: String) =>
-        fieldDesc.split("\t").map(_.trim)
-      }
+      hql(s"DESCRIBE $tableName")
+        .select('result)
+        .collect()
+        .map(_.getString(0).split("\t").map(_.trim))
     }
 
-    val q4 = hql(s"EXPLAIN SELECT key, COUNT(*) FROM $tableName GROUP BY key")
-    assert(isExplanation(q4))
+    assert(isExplanation(hql(s"EXPLAIN SELECT key, COUNT(*) FROM $tableName GROUP BY key")))
 
     TestHive.reset()
   }

From 237b96bc59ab1b54c31d06a5260cd77e1eb96116 Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian.cs.zju@gmail.com>
Date: Mon, 16 Jun 2014 16:42:17 -0700
Subject: [PATCH 436/810] Minor fix: made "EXPLAIN" output to play well with
 JDBC output format

Fixed the broken JDBC output. Test from Shark `beeline`:

```
beeline> !connect jdbc:hive2://localhost:10000/
scan complete in 2ms
Connecting to jdbc:hive2://localhost:10000/
Enter username for jdbc:hive2://localhost:10000/: lian
Enter password for jdbc:hive2://localhost:10000/:
Connected to: Hive (version 0.12.0)
Driver: Hive (version 0.12.0)
Transaction isolation: TRANSACTION_REPEATABLE_READ
0: jdbc:hive2://localhost:10000/>
0: jdbc:hive2://localhost:10000/> explain select * from src;
+-------------------------------------------------------------------------------+
|                                     plan                                      |
+-------------------------------------------------------------------------------+
| ExplainCommand [plan#2:0]                                                     |
|  HiveTableScan [key#0,value#1], (MetastoreRelation default, src, None), None  |
+-------------------------------------------------------------------------------+
2 rows selected (1.386 seconds)
```

Before this change, the output looked something like this:

```
+-------------------------------------------------------------------------------+
|                                     plan                                      |
+-------------------------------------------------------------------------------+
| ExplainCommand [plan#2:0]
 HiveTableScan [key#0,value#1], (MetastoreRelation default, src, None), None  |
+-------------------------------------------------------------------------------+
```

Author: Cheng Lian <lian.cs.zju@gmail.com>

Closes #1097 from liancheng/multiLineExplain and squashes the following commits:

eb37967 [Cheng Lian] Made output of "EXPLAIN" play well with JDBC output format
---
 .../main/scala/org/apache/spark/sql/execution/commands.scala  | 4 ++--
 .../org/apache/spark/sql/hive/api/java/JavaHiveQLSuite.scala  | 2 +-
 .../org/apache/spark/sql/hive/execution/HiveQuerySuite.scala  | 2 +-
 3 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala
index 0377290af592..39b3246c875d 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala
@@ -83,8 +83,8 @@ case class ExplainCommand(
   override protected[sql] lazy val sideEffectResult: Seq[String] = this.toString.split("\n")
 
   def execute(): RDD[Row] = {
-    val explanation = sideEffectResult.mkString("\n")
-    context.sparkContext.parallelize(Seq(new GenericRow(Array[Any](explanation))), 1)
+    val explanation = sideEffectResult.map(row => new GenericRow(Array[Any](row)))
+    context.sparkContext.parallelize(explanation, 1)
   }
 
   override def otherCopyArgs = context :: Nil
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/api/java/JavaHiveQLSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/api/java/JavaHiveQLSuite.scala
index 3b9cd8f52de4..10c8069a624e 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/api/java/JavaHiveQLSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/api/java/JavaHiveQLSuite.scala
@@ -49,7 +49,7 @@ class JavaHiveQLSuite extends FunSuite {
 
   def isExplanation(result: JavaSchemaRDD) = {
     val explanation = result.collect().map(_.getString(0))
-    explanation.size == 1 && explanation.head.startsWith(explainCommandClassName)
+    explanation.size > 1 && explanation.head.startsWith(explainCommandClassName)
   }
 
   ignore("Query Hive native command execution result") {
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
index 6e8d11b8a130..04652587f907 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
@@ -169,7 +169,7 @@ class HiveQuerySuite extends HiveComparisonTest {
 
   def isExplanation(result: SchemaRDD) = {
     val explanation = result.select('plan).collect().map { case Row(plan: String) => plan }
-    explanation.size == 1 && explanation.head.startsWith(explainCommandClassName)
+    explanation.size > 1 && explanation.head.startsWith(explainCommandClassName)
   }
 
   test("SPARK-1704: Explain commands as a SchemaRDD") {

From 7afa912e747c77ebfd10bddf7bda2e3190fdeb9c Mon Sep 17 00:00:00 2001
From: Anatoli Fomenko <fa@apache.org>
Date: Mon, 16 Jun 2014 23:10:36 -0700
Subject: [PATCH 437/810] MLlib documentation fix

Synchronized mllib-optimization.md with Spark Scaladoc: removed reference to GradientDescent.runMiniBatchSGD method

This is a temporary fix to remove  a link from http://spark.apache.org/docs/latest/mllib-optimization.html to GradientDescent.runMiniBatchSGD which is not in the current online GradientDescent Scaladoc.
FIXME: revert this commit after GradientDescent Scaladoc is updated.
See images for details.

![mllib-docs-fix-1](https://cloud.githubusercontent.com/assets/1375501/3294410/ccf19bb8-f5a8-11e3-93f1-f593016209eb.png)
![mllib-docs-fix-2](https://cloud.githubusercontent.com/assets/1375501/3294411/d0b59a7e-f5a8-11e3-8fc8-329c177ef8c8.png)

Author: Anatoli Fomenko <fa@apache.org>

Closes #1098 from afomenko/master and squashes the following commits:

5cb0758 [Anatoli Fomenko] MLlib documentation fix
---
 docs/mllib-optimization.md | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/docs/mllib-optimization.md b/docs/mllib-optimization.md
index 97e8f4e9661b..ae9ede58e8e6 100644
--- a/docs/mllib-optimization.md
+++ b/docs/mllib-optimization.md
@@ -147,9 +147,9 @@ are developed, see the
 <a href="mllib-linear-methods.html">linear methods</a> 
 section for example.
 
-The SGD method
-[GradientDescent.runMiniBatchSGD](api/scala/index.html#org.apache.spark.mllib.optimization.GradientDescent)
-has the following parameters:
+The SGD class
+[GradientDescent](api/scala/index.html#org.apache.spark.mllib.optimization.GradientDescent)
+sets the following parameters:
 
 * `Gradient` is a class that computes the stochastic gradient of the function
 being optimized, i.e., with respect to a single training example, at the
@@ -171,7 +171,7 @@ each iteration, to compute the gradient direction.
 
 Available algorithms for gradient descent:
 
-* [GradientDescent.runMiniBatchSGD](api/scala/index.html#org.apache.spark.mllib.optimization.GradientDescent)
+* [GradientDescent](api/scala/index.html#org.apache.spark.mllib.optimization.GradientDescent)
 
 ### L-BFGS
 L-BFGS is currently only a low-level optimization primitive in `MLlib`. If you want to use L-BFGS in various 

From d81c08bac9756045865ed6490252fbb3f7591142 Mon Sep 17 00:00:00 2001
From: Kan Zhang <kzhang@apache.org>
Date: Mon, 16 Jun 2014 23:31:31 -0700
Subject: [PATCH 438/810] [SPARK-2130] End-user friendly String repr for
 StorageLevel in Python

JIRA issue https://issues.apache.org/jira/browse/SPARK-2130

This PR adds an end-user friendly String representation for StorageLevel
in Python, similar to ```StorageLevel.description``` in Scala.
```
>>> rdd = sc.parallelize([1,2])
>>> storage_level = rdd.getStorageLevel()
>>> storage_level
StorageLevel(False, False, False, False, 1)
>>> print(storage_level)
Serialized 1x Replicated
```

Author: Kan Zhang <kzhang@apache.org>

Closes #1096 from kanzhang/SPARK-2130 and squashes the following commits:

7c8b98b [Kan Zhang] [SPARK-2130] Prettier epydoc output
cc5bf45 [Kan Zhang] [SPARK-2130] End-user friendly String representation for StorageLevel in Python
---
 python/pyspark/rdd.py          | 3 +++
 python/pyspark/storagelevel.py | 9 +++++++++
 2 files changed, 12 insertions(+)

diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py
index ddd22850a819..bb4d035edcde 100644
--- a/python/pyspark/rdd.py
+++ b/python/pyspark/rdd.py
@@ -1448,9 +1448,12 @@ def toDebugString(self):
     def getStorageLevel(self):
         """
         Get the RDD's current storage level.
+
         >>> rdd1 = sc.parallelize([1,2])
         >>> rdd1.getStorageLevel()
         StorageLevel(False, False, False, False, 1)
+        >>> print(rdd1.getStorageLevel())
+        Serialized 1x Replicated
         """
         java_storage_level = self._jrdd.getStorageLevel()
         storage_level = StorageLevel(java_storage_level.useDisk(),
diff --git a/python/pyspark/storagelevel.py b/python/pyspark/storagelevel.py
index 7b6660eab231..3a18ea54eae4 100644
--- a/python/pyspark/storagelevel.py
+++ b/python/pyspark/storagelevel.py
@@ -36,6 +36,15 @@ def __repr__(self):
         return "StorageLevel(%s, %s, %s, %s, %s)" % (
             self.useDisk, self.useMemory, self.useOffHeap, self.deserialized, self.replication)
 
+    def __str__(self):
+        result = ""
+        result += "Disk " if self.useDisk else ""
+        result += "Memory " if self.useMemory else ""
+        result += "Tachyon " if self.useOffHeap else ""
+        result += "Deserialized " if self.deserialized else "Serialized "
+        result += "%sx Replicated" % self.replication
+        return result
+
 StorageLevel.DISK_ONLY = StorageLevel(True, False, False, False)
 StorageLevel.DISK_ONLY_2 = StorageLevel(True, False, False, False, 2)
 StorageLevel.MEMORY_ONLY = StorageLevel(False, True, False, True)

From 8cd04c3eecc2dd827ea163dcd5e08af9912fa323 Mon Sep 17 00:00:00 2001
From: Anant <anant.asty@gmail.com>
Date: Mon, 16 Jun 2014 23:42:27 -0700
Subject: [PATCH 439/810] SPARK-1990: added compatibility for python 2.6 for
 ssh_read command

https://issues.apache.org/jira/browse/SPARK-1990

There were some posts on the lists that spark-ec2 does not work with Python 2.6. In addition, we should check the Python version at the top of the script and exit if it's too old

Author: Anant <anant.asty@gmail.com>

Closes #941 from anantasty/SPARK-1990 and squashes the following commits:

4ca441d [Anant] Implmented check_optput withinthe module to work with python 2.6
c6ed85c [Anant] added compatibility for python 2.6 for ssh_read command
---
 ec2/spark_ec2.py | 16 +++++++++++++++-
 1 file changed, 15 insertions(+), 1 deletion(-)

diff --git a/ec2/spark_ec2.py b/ec2/spark_ec2.py
index 52a89cb2481c..803caa0c480e 100755
--- a/ec2/spark_ec2.py
+++ b/ec2/spark_ec2.py
@@ -689,9 +689,23 @@ def ssh(host, opts, command):
             time.sleep(30)
             tries = tries + 1
 
+# Backported from Python 2.7 for compatiblity with 2.6 (See SPARK-1990)
+def _check_output(*popenargs, **kwargs):
+    if 'stdout' in kwargs:
+        raise ValueError('stdout argument not allowed, it will be overridden.')
+    process = subprocess.Popen(stdout=PIPE, *popenargs, **kwargs)
+    output, unused_err = process.communicate()
+    retcode = process.poll()
+    if retcode:
+        cmd = kwargs.get("args")
+        if cmd is None:
+            cmd = popenargs[0]
+        raise subprocess.CalledProcessError(retcode, cmd, output=output)
+    return output
+
 
 def ssh_read(host, opts, command):
-    return subprocess.check_output(
+    return _check_output(
         ssh_command(opts) + ['%s@%s' % (opts.user, host), stringify_command(command)])
 
 

From 23a12ce20c55653b08b16e6159ab31d2ca88acf1 Mon Sep 17 00:00:00 2001
From: Daniel Darabos <darabos.daniel@gmail.com>
Date: Tue, 17 Jun 2014 00:08:05 -0700
Subject: [PATCH 440/810] SPARK-2035: Store call stack for stages, display it
 on the UI.

I'm not sure about the test -- I get a lot of unrelated failures for some reason. I'll try to sort it out. But hopefully the automation will test this for me if I send a pull request :).

I'll attach a demo HTML in [Jira](https://issues.apache.org/jira/browse/SPARK-2035).

Author: Daniel Darabos <darabos.daniel@gmail.com>
Author: Patrick Wendell <pwendell@gmail.com>

Closes #981 from darabos/darabos-call-stack and squashes the following commits:

f7c6bfa [Daniel Darabos] Fix bad merge. I undid 83c226d454 by Doris.
3d0a48d [Daniel Darabos] Merge remote-tracking branch 'upstream/master' into darabos-call-stack
b857849 [Daniel Darabos] Style: Break long line.
ecb5690 [Daniel Darabos] Include the last Spark method in the full stack trace. Otherwise it is not visible if the stage name is overridden.
d00a85b [Patrick Wendell] Make call sites for stages non-optional and well defined
b9eba24 [Daniel Darabos] Make StageInfo.details non-optional. Add JSON serialization code for the new field. Verify JSON backward compatibility.
4312828 [Daniel Darabos] Remove Mima excludes for CallSite. They should be unnecessary now, with SPARK-2070 fixed.
0920750 [Daniel Darabos] Merge remote-tracking branch 'upstream/master' into darabos-call-stack
a4b1faf [Daniel Darabos] Add Mima exclusions for the CallSite changes it has picked up. They are private methods/classes, so we ought to be safe.
932f810 [Daniel Darabos] Use empty CallSite instead of null in DAGSchedulerSuite. Outside of testing, this parameter always originates in SparkContext.scala, and will never be null.
ccd89d1 [Daniel Darabos] Fix long lines.
ac173e4 [Daniel Darabos] Hide "show details" if there are no details to show.
6182da6 [Daniel Darabos] Set a configurable limit on maximum call stack depth. It can be useful in memory-constrained situations with large numbers of stages.
8fe2e34 [Daniel Darabos] Store call stack for stages, display it on the UI.
---
 .../org/apache/spark/ui/static/webui.css      | 21 ++++++++++++
 .../scala/org/apache/spark/SparkContext.scala | 18 ++++++-----
 .../main/scala/org/apache/spark/rdd/RDD.scala |  6 ++--
 .../apache/spark/scheduler/ActiveJob.scala    |  3 +-
 .../apache/spark/scheduler/DAGScheduler.scala | 24 +++++++-------
 .../spark/scheduler/DAGSchedulerEvent.scala   |  3 +-
 .../org/apache/spark/scheduler/Stage.scala    | 11 +++++--
 .../apache/spark/scheduler/StageInfo.scala    |  9 ++++--
 .../org/apache/spark/ui/jobs/StageTable.scala | 10 +++++-
 .../org/apache/spark/util/JsonProtocol.scala  |  4 ++-
 .../scala/org/apache/spark/util/Utils.scala   | 32 +++++++++----------
 .../apache/spark/SparkContextInfoSuite.scala  |  2 +-
 .../spark/scheduler/DAGSchedulerSuite.scala   |  7 ++--
 .../ui/jobs/JobProgressListenerSuite.scala    |  4 +--
 .../apache/spark/util/JsonProtocolSuite.scala | 14 +++++++-
 15 files changed, 115 insertions(+), 53 deletions(-)

diff --git a/core/src/main/resources/org/apache/spark/ui/static/webui.css b/core/src/main/resources/org/apache/spark/ui/static/webui.css
index 599c3ac9b57c..a8bc141208a9 100644
--- a/core/src/main/resources/org/apache/spark/ui/static/webui.css
+++ b/core/src/main/resources/org/apache/spark/ui/static/webui.css
@@ -87,3 +87,24 @@ span.kill-link {
 span.kill-link a {
   color: gray;
 }
+
+span.expand-details {
+  font-size: 10pt;
+  cursor: pointer;
+  color: grey;
+  float: right;
+}
+
+.stage-details {
+  max-height: 100px;
+  overflow-y: auto;
+  margin: 0;
+  transition: max-height 0.5s ease-out, padding 0.5s ease-out;
+}
+
+.stage-details.collapsed {
+  max-height: 0;
+  padding-top: 0;
+  padding-bottom: 0;
+  border: none;
+}
diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index 35970c2f5089..0678bdd02110 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -49,7 +49,7 @@ import org.apache.spark.scheduler.cluster.mesos.{CoarseMesosSchedulerBackend, Me
 import org.apache.spark.scheduler.local.LocalBackend
 import org.apache.spark.storage.{BlockManagerSource, RDDInfo, StorageStatus, StorageUtils}
 import org.apache.spark.ui.SparkUI
-import org.apache.spark.util.{ClosureCleaner, MetadataCleaner, MetadataCleanerType, TimeStampedWeakValueHashMap, Utils}
+import org.apache.spark.util.{CallSite, ClosureCleaner, MetadataCleaner, MetadataCleanerType, TimeStampedWeakValueHashMap, Utils}
 
 /**
  * Main entry point for Spark functionality. A SparkContext represents the connection to a Spark
@@ -1036,9 +1036,11 @@ class SparkContext(config: SparkConf) extends Logging {
    * Capture the current user callsite and return a formatted version for printing. If the user
    * has overridden the call site, this will return the user's version.
    */
-  private[spark] def getCallSite(): String = {
-    val defaultCallSite = Utils.getCallSiteInfo
-    Option(getLocalProperty("externalCallSite")).getOrElse(defaultCallSite.toString)
+  private[spark] def getCallSite(): CallSite = {
+    Option(getLocalProperty("externalCallSite")) match {
+      case Some(callSite) => CallSite(callSite, long = "")
+      case None => Utils.getCallSite
+    }
   }
 
   /**
@@ -1058,11 +1060,11 @@ class SparkContext(config: SparkConf) extends Logging {
     }
     val callSite = getCallSite
     val cleanedFunc = clean(func)
-    logInfo("Starting job: " + callSite)
+    logInfo("Starting job: " + callSite.short)
     val start = System.nanoTime
     dagScheduler.runJob(rdd, cleanedFunc, partitions, callSite, allowLocal,
       resultHandler, localProperties.get)
-    logInfo("Job finished: " + callSite + ", took " + (System.nanoTime - start) / 1e9 + " s")
+    logInfo("Job finished: " + callSite.short + ", took " + (System.nanoTime - start) / 1e9 + " s")
     rdd.doCheckpoint()
   }
 
@@ -1143,11 +1145,11 @@ class SparkContext(config: SparkConf) extends Logging {
       evaluator: ApproximateEvaluator[U, R],
       timeout: Long): PartialResult[R] = {
     val callSite = getCallSite
-    logInfo("Starting job: " + callSite)
+    logInfo("Starting job: " + callSite.short)
     val start = System.nanoTime
     val result = dagScheduler.runApproximateJob(rdd, func, evaluator, callSite, timeout,
       localProperties.get)
-    logInfo("Job finished: " + callSite + ", took " + (System.nanoTime - start) / 1e9 + " s")
+    logInfo("Job finished: " + callSite.short + ", took " + (System.nanoTime - start) / 1e9 + " s")
     result
   }
 
diff --git a/core/src/main/scala/org/apache/spark/rdd/RDD.scala b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
index 446f369c9ea1..27cc60d77578 100644
--- a/core/src/main/scala/org/apache/spark/rdd/RDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
@@ -40,7 +40,7 @@ import org.apache.spark.partial.CountEvaluator
 import org.apache.spark.partial.GroupedCountEvaluator
 import org.apache.spark.partial.PartialResult
 import org.apache.spark.storage.StorageLevel
-import org.apache.spark.util.{BoundedPriorityQueue, Utils}
+import org.apache.spark.util.{BoundedPriorityQueue, CallSite, Utils}
 import org.apache.spark.util.collection.OpenHashMap
 import org.apache.spark.util.random.{BernoulliSampler, PoissonSampler, SamplingUtils}
 
@@ -1189,8 +1189,8 @@ abstract class RDD[T: ClassTag](
   private var storageLevel: StorageLevel = StorageLevel.NONE
 
   /** User code that created this RDD (e.g. `textFile`, `parallelize`). */
-  @transient private[spark] val creationSiteInfo = Utils.getCallSiteInfo
-  private[spark] def getCreationSite: String = Option(creationSiteInfo).getOrElse("").toString
+  @transient private[spark] val creationSite = Utils.getCallSite
+  private[spark] def getCreationSite: String = Option(creationSite).map(_.short).getOrElse("")
 
   private[spark] def elementClassTag: ClassTag[T] = classTag[T]
 
diff --git a/core/src/main/scala/org/apache/spark/scheduler/ActiveJob.scala b/core/src/main/scala/org/apache/spark/scheduler/ActiveJob.scala
index 9257f48559c9..b755d8fb1575 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/ActiveJob.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/ActiveJob.scala
@@ -20,6 +20,7 @@ package org.apache.spark.scheduler
 import java.util.Properties
 
 import org.apache.spark.TaskContext
+import org.apache.spark.util.CallSite
 
 /**
  * Tracks information about an active job in the DAGScheduler.
@@ -29,7 +30,7 @@ private[spark] class ActiveJob(
     val finalStage: Stage,
     val func: (TaskContext, Iterator[_]) => _,
     val partitions: Array[Int],
-    val callSite: String,
+    val callSite: CallSite,
     val listener: JobListener,
     val properties: Properties) {
 
diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
index 3c85b5a2ae77..b3ebaa547de0 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
@@ -38,7 +38,7 @@ import org.apache.spark.executor.TaskMetrics
 import org.apache.spark.partial.{ApproximateActionListener, ApproximateEvaluator, PartialResult}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.storage.{BlockId, BlockManager, BlockManagerMaster, RDDBlockId}
-import org.apache.spark.util.{SystemClock, Clock, Utils}
+import org.apache.spark.util.{CallSite, SystemClock, Clock, Utils}
 
 /**
  * The high-level scheduling layer that implements stage-oriented scheduling. It computes a DAG of
@@ -195,7 +195,9 @@ class DAGScheduler(
       case Some(stage) => stage
       case None =>
         val stage =
-          newOrUsedStage(shuffleDep.rdd, shuffleDep.rdd.partitions.size, shuffleDep, jobId)
+          newOrUsedStage(
+            shuffleDep.rdd, shuffleDep.rdd.partitions.size, shuffleDep, jobId,
+            shuffleDep.rdd.creationSite)
         shuffleToMapStage(shuffleDep.shuffleId) = stage
         stage
     }
@@ -212,7 +214,7 @@ class DAGScheduler(
       numTasks: Int,
       shuffleDep: Option[ShuffleDependency[_, _, _]],
       jobId: Int,
-      callSite: Option[String] = None)
+      callSite: CallSite)
     : Stage =
   {
     val id = nextStageId.getAndIncrement()
@@ -235,7 +237,7 @@ class DAGScheduler(
       numTasks: Int,
       shuffleDep: ShuffleDependency[_, _, _],
       jobId: Int,
-      callSite: Option[String] = None)
+      callSite: CallSite)
     : Stage =
   {
     val stage = newStage(rdd, numTasks, Some(shuffleDep), jobId, callSite)
@@ -413,7 +415,7 @@ class DAGScheduler(
       rdd: RDD[T],
       func: (TaskContext, Iterator[T]) => U,
       partitions: Seq[Int],
-      callSite: String,
+      callSite: CallSite,
       allowLocal: Boolean,
       resultHandler: (Int, U) => Unit,
       properties: Properties = null): JobWaiter[U] =
@@ -443,7 +445,7 @@ class DAGScheduler(
       rdd: RDD[T],
       func: (TaskContext, Iterator[T]) => U,
       partitions: Seq[Int],
-      callSite: String,
+      callSite: CallSite,
       allowLocal: Boolean,
       resultHandler: (Int, U) => Unit,
       properties: Properties = null)
@@ -452,7 +454,7 @@ class DAGScheduler(
     waiter.awaitResult() match {
       case JobSucceeded => {}
       case JobFailed(exception: Exception) =>
-        logInfo("Failed to run " + callSite)
+        logInfo("Failed to run " + callSite.short)
         throw exception
     }
   }
@@ -461,7 +463,7 @@ class DAGScheduler(
       rdd: RDD[T],
       func: (TaskContext, Iterator[T]) => U,
       evaluator: ApproximateEvaluator[U, R],
-      callSite: String,
+      callSite: CallSite,
       timeout: Long,
       properties: Properties = null)
     : PartialResult[R] =
@@ -666,7 +668,7 @@ class DAGScheduler(
       func: (TaskContext, Iterator[_]) => _,
       partitions: Array[Int],
       allowLocal: Boolean,
-      callSite: String,
+      callSite: CallSite,
       listener: JobListener,
       properties: Properties = null)
   {
@@ -674,7 +676,7 @@ class DAGScheduler(
     try {
       // New stage creation may throw an exception if, for example, jobs are run on a
       // HadoopRDD whose underlying HDFS files have been deleted.
-      finalStage = newStage(finalRDD, partitions.size, None, jobId, Some(callSite))
+      finalStage = newStage(finalRDD, partitions.size, None, jobId, callSite)
     } catch {
       case e: Exception =>
         logWarning("Creating new stage failed due to exception - job: " + jobId, e)
@@ -685,7 +687,7 @@ class DAGScheduler(
       val job = new ActiveJob(jobId, finalStage, func, partitions, callSite, listener, properties)
       clearCacheLocs()
       logInfo("Got job %s (%s) with %d output partitions (allowLocal=%s)".format(
-        job.jobId, callSite, partitions.length, allowLocal))
+        job.jobId, callSite.short, partitions.length, allowLocal))
       logInfo("Final stage: " + finalStage + "(" + finalStage.name + ")")
       logInfo("Parents of final stage: " + finalStage.parents)
       logInfo("Missing parents: " + getMissingParentStages(finalStage))
diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGSchedulerEvent.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGSchedulerEvent.scala
index 23f57441b4b1..2b6f7e4205c3 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/DAGSchedulerEvent.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/DAGSchedulerEvent.scala
@@ -25,6 +25,7 @@ import scala.language.existentials
 import org.apache.spark._
 import org.apache.spark.executor.TaskMetrics
 import org.apache.spark.rdd.RDD
+import org.apache.spark.util.CallSite
 
 /**
  * Types of events that can be handled by the DAGScheduler. The DAGScheduler uses an event queue
@@ -40,7 +41,7 @@ private[scheduler] case class JobSubmitted(
     func: (TaskContext, Iterator[_]) => _,
     partitions: Array[Int],
     allowLocal: Boolean,
-    callSite: String,
+    callSite: CallSite,
     listener: JobListener,
     properties: Properties = null)
   extends DAGSchedulerEvent
diff --git a/core/src/main/scala/org/apache/spark/scheduler/Stage.scala b/core/src/main/scala/org/apache/spark/scheduler/Stage.scala
index 3bf9713f728c..9a4be43ee219 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/Stage.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/Stage.scala
@@ -20,6 +20,7 @@ package org.apache.spark.scheduler
 import org.apache.spark._
 import org.apache.spark.rdd.RDD
 import org.apache.spark.storage.BlockManagerId
+import org.apache.spark.util.CallSite
 
 /**
  * A stage is a set of independent tasks all computing the same function that need to run as part
@@ -35,6 +36,11 @@ import org.apache.spark.storage.BlockManagerId
  * Each Stage also has a jobId, identifying the job that first submitted the stage.  When FIFO
  * scheduling is used, this allows Stages from earlier jobs to be computed first or recovered
  * faster on failure.
+ *
+ * The callSite provides a location in user code which relates to the stage. For a shuffle map
+ * stage, the callSite gives the user code that created the RDD being shuffled. For a result
+ * stage, the callSite gives the user code that executes the associated action (e.g. count()).
+ *
  */
 private[spark] class Stage(
     val id: Int,
@@ -43,7 +49,7 @@ private[spark] class Stage(
     val shuffleDep: Option[ShuffleDependency[_, _, _]],  // Output shuffle if stage is a map stage
     val parents: List[Stage],
     val jobId: Int,
-    callSite: Option[String])
+    val callSite: CallSite)
   extends Logging {
 
   val isShuffleMap = shuffleDep.isDefined
@@ -100,7 +106,8 @@ private[spark] class Stage(
     id
   }
 
-  val name = callSite.getOrElse(rdd.getCreationSite)
+  val name = callSite.short
+  val details = callSite.long
 
   override def toString = "Stage " + id
 
diff --git a/core/src/main/scala/org/apache/spark/scheduler/StageInfo.scala b/core/src/main/scala/org/apache/spark/scheduler/StageInfo.scala
index b42e231e11f9..7644e3f351b3 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/StageInfo.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/StageInfo.scala
@@ -25,7 +25,12 @@ import org.apache.spark.storage.RDDInfo
  * Stores information about a stage to pass from the scheduler to SparkListeners.
  */
 @DeveloperApi
-class StageInfo(val stageId: Int, val name: String, val numTasks: Int, val rddInfos: Seq[RDDInfo]) {
+class StageInfo(
+    val stageId: Int,
+    val name: String,
+    val numTasks: Int,
+    val rddInfos: Seq[RDDInfo],
+    val details: String) {
   /** When this stage was submitted from the DAGScheduler to a TaskScheduler. */
   var submissionTime: Option[Long] = None
   /** Time when all tasks in the stage completed or when the stage was cancelled. */
@@ -52,6 +57,6 @@ private[spark] object StageInfo {
   def fromStage(stage: Stage): StageInfo = {
     val ancestorRddInfos = stage.rdd.getNarrowAncestors.map(RDDInfo.fromRdd)
     val rddInfos = Seq(RDDInfo.fromRdd(stage.rdd)) ++ ancestorRddInfos
-    new StageInfo(stage.id, stage.name, stage.numTasks, rddInfos)
+    new StageInfo(stage.id, stage.name, stage.numTasks, rddInfos, stage.details)
   }
 }
diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/StageTable.scala b/core/src/main/scala/org/apache/spark/ui/jobs/StageTable.scala
index 153434a2032b..a3f824a4e1f5 100644
--- a/core/src/main/scala/org/apache/spark/ui/jobs/StageTable.scala
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/StageTable.scala
@@ -91,9 +91,17 @@ private[ui] class StageTableBase(
         {s.name}
       </a>
 
+    val details = if (s.details.nonEmpty) (
+      <span onclick="this.parentNode.querySelector('.stage-details').classList.toggle('collapsed')"
+            class="expand-details">
+        +show details
+      </span>
+      <pre class="stage-details collapsed">{s.details}</pre>
+    )
+
     listener.stageIdToDescription.get(s.stageId)
       .map(d => <div><em>{d}</em></div><div>{nameLink} {killLink}</div>)
-      .getOrElse(<div> {killLink}{nameLink}</div>)
+      .getOrElse(<div>{killLink} {nameLink} {details}</div>)
   }
 
   protected def stageRow(s: StageInfo): Seq[Node] = {
diff --git a/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala b/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala
index 09825087bb04..7cecbfe62a38 100644
--- a/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala
+++ b/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala
@@ -184,6 +184,7 @@ private[spark] object JsonProtocol {
     ("Stage Name" -> stageInfo.name) ~
     ("Number of Tasks" -> stageInfo.numTasks) ~
     ("RDD Info" -> rddInfo) ~
+    ("Details" -> stageInfo.details) ~
     ("Submission Time" -> submissionTime) ~
     ("Completion Time" -> completionTime) ~
     ("Failure Reason" -> failureReason) ~
@@ -469,12 +470,13 @@ private[spark] object JsonProtocol {
     val stageName = (json \ "Stage Name").extract[String]
     val numTasks = (json \ "Number of Tasks").extract[Int]
     val rddInfos = (json \ "RDD Info").extract[List[JValue]].map(rddInfoFromJson)
+    val details = (json \ "Details").extractOpt[String].getOrElse("")
     val submissionTime = Utils.jsonOption(json \ "Submission Time").map(_.extract[Long])
     val completionTime = Utils.jsonOption(json \ "Completion Time").map(_.extract[Long])
     val failureReason = Utils.jsonOption(json \ "Failure Reason").map(_.extract[String])
     val emittedTaskSizeWarning = (json \ "Emitted Task Size Warning").extract[Boolean]
 
-    val stageInfo = new StageInfo(stageId, stageName, numTasks, rddInfos)
+    val stageInfo = new StageInfo(stageId, stageName, numTasks, rddInfos, details)
     stageInfo.submissionTime = submissionTime
     stageInfo.completionTime = completionTime
     stageInfo.failureReason = failureReason
diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala
index 4ce28bb0cf05..a2454e120a8a 100644
--- a/core/src/main/scala/org/apache/spark/util/Utils.scala
+++ b/core/src/main/scala/org/apache/spark/util/Utils.scala
@@ -43,6 +43,9 @@ import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.executor.ExecutorUncaughtExceptionHandler
 import org.apache.spark.serializer.{DeserializationStream, SerializationStream, SerializerInstance}
 
+/** CallSite represents a place in user code. It can have a short and a long form. */
+private[spark] case class CallSite(val short: String, val long: String)
+
 /**
  * Various utility methods used by Spark.
  */
@@ -799,21 +802,12 @@ private[spark] object Utils extends Logging {
    */
   private val SPARK_CLASS_REGEX = """^org\.apache\.spark(\.api\.java)?(\.util)?(\.rdd)?\.[A-Z]""".r
 
-  private[spark] class CallSiteInfo(val lastSparkMethod: String, val firstUserFile: String,
-                                    val firstUserLine: Int, val firstUserClass: String) {
-
-    /** Returns a printable version of the call site info suitable for logs. */
-    override def toString = {
-      "%s at %s:%s".format(lastSparkMethod, firstUserFile, firstUserLine)
-    }
-  }
-
   /**
    * When called inside a class in the spark package, returns the name of the user code class
    * (outside the spark package) that called into Spark, as well as which Spark method they called.
    * This is used, for example, to tell users where in their code each RDD got created.
    */
-  def getCallSiteInfo: CallSiteInfo = {
+  def getCallSite: CallSite = {
     val trace = Thread.currentThread.getStackTrace()
       .filterNot(_.getMethodName.contains("getStackTrace"))
 
@@ -824,11 +818,11 @@ private[spark] object Utils extends Logging {
     var lastSparkMethod = "<unknown>"
     var firstUserFile = "<unknown>"
     var firstUserLine = 0
-    var finished = false
-    var firstUserClass = "<unknown>"
+    var insideSpark = true
+    var callStack = new ArrayBuffer[String]() :+ "<unknown>"
 
     for (el <- trace) {
-      if (!finished) {
+      if (insideSpark) {
         if (SPARK_CLASS_REGEX.findFirstIn(el.getClassName).isDefined) {
           lastSparkMethod = if (el.getMethodName == "<init>") {
             // Spark method is a constructor; get its class name
@@ -836,15 +830,21 @@ private[spark] object Utils extends Logging {
           } else {
             el.getMethodName
           }
+          callStack(0) = el.toString // Put last Spark method on top of the stack trace.
         } else {
           firstUserLine = el.getLineNumber
           firstUserFile = el.getFileName
-          firstUserClass = el.getClassName
-          finished = true
+          callStack += el.toString
+          insideSpark = false
         }
+      } else {
+        callStack += el.toString
       }
     }
-    new CallSiteInfo(lastSparkMethod, firstUserFile, firstUserLine, firstUserClass)
+    val callStackDepth = System.getProperty("spark.callstack.depth", "20").toInt
+    CallSite(
+      short = "%s at %s:%s".format(lastSparkMethod, firstUserFile, firstUserLine),
+      long = callStack.take(callStackDepth).mkString("\n"))
   }
 
   /** Return a string containing part of a file from byte 'start' to 'end'. */
diff --git a/core/src/test/scala/org/apache/spark/SparkContextInfoSuite.scala b/core/src/test/scala/org/apache/spark/SparkContextInfoSuite.scala
index cd3887dcc737..1fde4badda94 100644
--- a/core/src/test/scala/org/apache/spark/SparkContextInfoSuite.scala
+++ b/core/src/test/scala/org/apache/spark/SparkContextInfoSuite.scala
@@ -70,7 +70,7 @@ package object testPackage extends Assertions {
   def runCallSiteTest(sc: SparkContext) {
     val rdd = sc.makeRDD(Array(1, 2, 3, 4), 2)
     val rddCreationSite = rdd.getCreationSite
-    val curCallSite = sc.getCallSite() // note: 2 lines after definition of "rdd"
+    val curCallSite = sc.getCallSite().short // note: 2 lines after definition of "rdd"
 
     val rddCreationLine = rddCreationSite match {
       case CALL_SITE_REGEX(func, file, line) => {
diff --git a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
index 7506d56d7e26..45368328297d 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
@@ -29,6 +29,7 @@ import org.apache.spark._
 import org.apache.spark.rdd.RDD
 import org.apache.spark.scheduler.SchedulingMode.SchedulingMode
 import org.apache.spark.storage.{BlockId, BlockManagerId, BlockManagerMaster}
+import org.apache.spark.util.CallSite
 
 class BuggyDAGEventProcessActor extends Actor {
   val state = 0
@@ -211,7 +212,7 @@ class DAGSchedulerSuite extends TestKit(ActorSystem("DAGSchedulerSuite")) with F
       allowLocal: Boolean = false,
       listener: JobListener = jobListener): Int = {
     val jobId = scheduler.nextJobId.getAndIncrement()
-    runEvent(JobSubmitted(jobId, rdd, func, partitions, allowLocal, null, listener))
+    runEvent(JobSubmitted(jobId, rdd, func, partitions, allowLocal, CallSite("", ""), listener))
     jobId
   }
 
@@ -251,7 +252,7 @@ class DAGSchedulerSuite extends TestKit(ActorSystem("DAGSchedulerSuite")) with F
       override def toString = "DAGSchedulerSuite Local RDD"
     }
     val jobId = scheduler.nextJobId.getAndIncrement()
-    runEvent(JobSubmitted(jobId, rdd, jobComputeFunc, Array(0), true, null, jobListener))
+    runEvent(JobSubmitted(jobId, rdd, jobComputeFunc, Array(0), true, CallSite("", ""), jobListener))
     assert(results === Map(0 -> 42))
     assertDataStructuresEmpty
   }
@@ -265,7 +266,7 @@ class DAGSchedulerSuite extends TestKit(ActorSystem("DAGSchedulerSuite")) with F
       override def toString = "DAGSchedulerSuite Local RDD"
     }
     val jobId = scheduler.nextJobId.getAndIncrement()
-    runEvent(JobSubmitted(jobId, rdd, jobComputeFunc, Array(0), true, null, jobListener))
+    runEvent(JobSubmitted(jobId, rdd, jobComputeFunc, Array(0), true, CallSite("", ""), jobListener))
     assert(results.size == 0)
     assertDataStructuresEmpty
   }
diff --git a/core/src/test/scala/org/apache/spark/ui/jobs/JobProgressListenerSuite.scala b/core/src/test/scala/org/apache/spark/ui/jobs/JobProgressListenerSuite.scala
index 91b4c7b0dd96..c3a14f48de38 100644
--- a/core/src/test/scala/org/apache/spark/ui/jobs/JobProgressListenerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/ui/jobs/JobProgressListenerSuite.scala
@@ -32,12 +32,12 @@ class JobProgressListenerSuite extends FunSuite with LocalSparkContext with Matc
     val listener = new JobProgressListener(conf)
 
     def createStageStartEvent(stageId: Int) = {
-      val stageInfo = new StageInfo(stageId, stageId.toString, 0, null)
+      val stageInfo = new StageInfo(stageId, stageId.toString, 0, null, "")
       SparkListenerStageSubmitted(stageInfo)
     }
 
     def createStageEndEvent(stageId: Int) = {
-      val stageInfo = new StageInfo(stageId, stageId.toString, 0, null)
+      val stageInfo = new StageInfo(stageId, stageId.toString, 0, null, "")
       SparkListenerStageCompleted(stageInfo)
     }
 
diff --git a/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala b/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala
index 3031015256ec..f72389b6b323 100644
--- a/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala
+++ b/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala
@@ -117,6 +117,17 @@ class JsonProtocolSuite extends FunSuite {
     testBlockId(StreamBlockId(1, 2L))
   }
 
+  test("Backward compatibility") {
+    // StageInfo.details was added after 1.0.0.
+    val info = makeStageInfo(1, 2, 3, 4L, 5L)
+    assert(info.details.nonEmpty)
+    val newJson = JsonProtocol.stageInfoToJson(info)
+    val oldJson = newJson.removeField { case (field, _) => field == "Details" }
+    val newInfo = JsonProtocol.stageInfoFromJson(oldJson)
+    assert(info.name === newInfo.name)
+    assert("" === newInfo.details)
+  }
+
 
   /** -------------------------- *
    | Helper test running methods |
@@ -235,6 +246,7 @@ class JsonProtocolSuite extends FunSuite {
     (0 until info1.rddInfos.size).foreach { i =>
       assertEquals(info1.rddInfos(i), info2.rddInfos(i))
     }
+    assert(info1.details === info2.details)
   }
 
   private def assertEquals(info1: RDDInfo, info2: RDDInfo) {
@@ -438,7 +450,7 @@ class JsonProtocolSuite extends FunSuite {
 
   private def makeStageInfo(a: Int, b: Int, c: Int, d: Long, e: Long) = {
     val rddInfos = (1 to a % 5).map { i => makeRddInfo(a % i, b % i, c % i, d % i, e % i) }
-    new StageInfo(a, "greetings", b, rddInfos)
+    new StageInfo(a, "greetings", b, rddInfos, "details")
   }
 
   private def makeTaskInfo(a: Long, b: Int, c: Long) = {

From 09deb3eee090eb8ec1d9a0cd90825699748e3ffc Mon Sep 17 00:00:00 2001
From: Andrew Or <andrewor14@gmail.com>
Date: Tue, 17 Jun 2014 01:28:22 -0700
Subject: [PATCH 441/810] [SPARK-2144] ExecutorsPage reports incorrect # of RDD
 blocks

This is reproducible whenever we drop a block because of memory pressure.

This is because StorageStatusListener actually never removes anything from the block maps of its StorageStatuses. Instead, when a block is dropped, it sets the block's storage level to `StorageLevel.NONE`, when it should just remove it from the map.

This PR includes this simple fix.

Author: Andrew Or <andrewor14@gmail.com>

Closes #1080 from andrewor14/ui-blocks and squashes the following commits:

fcf9f1a [Andrew Or] Remove BlockStatus if it is no longer cached
---
 .../org/apache/spark/storage/StorageStatusListener.scala    | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/storage/StorageStatusListener.scala b/core/src/main/scala/org/apache/spark/storage/StorageStatusListener.scala
index a6e6627d54e0..c694fc8c347e 100644
--- a/core/src/main/scala/org/apache/spark/storage/StorageStatusListener.scala
+++ b/core/src/main/scala/org/apache/spark/storage/StorageStatusListener.scala
@@ -37,7 +37,11 @@ class StorageStatusListener extends SparkListener {
     val filteredStatus = storageStatusList.find(_.blockManagerId.executorId == execId)
     filteredStatus.foreach { storageStatus =>
       updatedBlocks.foreach { case (blockId, updatedStatus) =>
-        storageStatus.blocks(blockId) = updatedStatus
+        if (updatedStatus.storageLevel == StorageLevel.NONE) {
+          storageStatus.blocks.remove(blockId)
+        } else {
+          storageStatus.blocks(blockId) = updatedStatus
+        }
       }
     }
   }

From f5a4049e534da3c55e1b495ce34155236dfb6dee Mon Sep 17 00:00:00 2001
From: Xi Liu <xil@conviva.com>
Date: Tue, 17 Jun 2014 13:14:40 +0200
Subject: [PATCH 442/810] [SPARK-2164][SQL] Allow Hive UDF on columns of type
 struct

Author: Xi Liu <xil@conviva.com>

Closes #796 from xiliu82/sqlbug and squashes the following commits:

328dfc4 [Xi Liu] [Spark SQL] remove a temporary function after test
354386a [Xi Liu] [Spark SQL] add test suite for UDF on struct
8fc6f51 [Xi Liu] [SparkSQL] allow UDF on struct
---
 .../org/apache/spark/sql/hive/hiveUdfs.scala  |   3 +
 .../resources/data/files/testUdf/part-00000   | Bin 0 -> 153 bytes
 .../sql/hive/execution/HiveUdfSuite.scala     | 127 ++++++++++++++++++
 3 files changed, 130 insertions(+)
 create mode 100755 sql/hive/src/test/resources/data/files/testUdf/part-00000
 create mode 100644 sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUdfSuite.scala

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala
index 771d2bccf43a..ad5e24c62c62 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala
@@ -335,6 +335,9 @@ private[hive] trait HiveInspectors {
     case BinaryType => PrimitiveObjectInspectorFactory.javaByteArrayObjectInspector
     case TimestampType => PrimitiveObjectInspectorFactory.javaTimestampObjectInspector
     case DecimalType => PrimitiveObjectInspectorFactory.javaHiveDecimalObjectInspector
+    case StructType(fields) =>
+      ObjectInspectorFactory.getStandardStructObjectInspector(
+        fields.map(f => f.name), fields.map(f => toInspector(f.dataType)))
   }
 
   def inspectorToDataType(inspector: ObjectInspector): DataType = inspector match {
diff --git a/sql/hive/src/test/resources/data/files/testUdf/part-00000 b/sql/hive/src/test/resources/data/files/testUdf/part-00000
new file mode 100755
index 0000000000000000000000000000000000000000..240a5c1a63c5c4016d096cbd13ddc8b787aee8da
GIT binary patch
literal 153
zcmWG`4P;ZyFG|--EJ#ewNY%?oOv%qL(96u%^DE8C2`|blNleN~)j?8GT##6ltyf%_
zqnD9cma3Opk(yjul9`{U7m`|B5|Ef#!~h0IwrxAkdN!tuT_U@F&YjICfr1<m83slm
Sor1(?0<u$)_{<CpOkw~(EhtR@

literal 0
HcmV?d00001

diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUdfSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUdfSuite.scala
new file mode 100644
index 000000000000..a9e3f42a3adf
--- /dev/null
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUdfSuite.scala
@@ -0,0 +1,127 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive.execution
+
+import org.apache.spark.sql.hive.test.TestHive
+import org.apache.hadoop.conf.Configuration
+import org.apache.spark.SparkContext._
+import java.util
+import org.apache.hadoop.fs.{FileSystem, Path}
+import org.apache.hadoop.hive.serde2.{SerDeStats, AbstractSerDe}
+import org.apache.hadoop.io.{NullWritable, Writable}
+import org.apache.hadoop.hive.serde2.objectinspector.{ObjectInspectorFactory, ObjectInspector}
+import java.util.Properties
+import org.apache.hadoop.hive.serde2.objectinspector.primitive.PrimitiveObjectInspectorFactory
+import scala.collection.JavaConversions._
+import java.io.{DataOutput, DataInput}
+import org.apache.hadoop.hive.ql.udf.generic.GenericUDF
+import org.apache.hadoop.hive.ql.udf.generic.GenericUDF.DeferredObject
+
+/**
+ * A test suite for Hive custom UDFs.
+ */
+class HiveUdfSuite extends HiveComparisonTest {
+
+  TestHive.hql(
+    """
+      |CREATE EXTERNAL TABLE hiveUdfTestTable (
+      |   pair STRUCT<id: INT, value: INT>
+      |)
+      |PARTITIONED BY (partition STRING)
+      |ROW FORMAT SERDE '%s'
+      |STORED AS SEQUENCEFILE
+    """.stripMargin.format(classOf[PairSerDe].getName)
+  )
+
+  TestHive.hql(
+    "ALTER TABLE hiveUdfTestTable ADD IF NOT EXISTS PARTITION(partition='testUdf') LOCATION '%s'"
+      .format(this.getClass.getClassLoader.getResource("data/files/testUdf").getFile)
+  )
+
+  TestHive.hql("CREATE TEMPORARY FUNCTION testUdf AS '%s'".format(classOf[PairUdf].getName))
+
+  TestHive.hql("SELECT testUdf(pair) FROM hiveUdfTestTable")
+
+  TestHive.hql("DROP TEMPORARY FUNCTION IF EXISTS testUdf")
+}
+
+class TestPair(x: Int, y: Int) extends Writable with Serializable {
+  def this() = this(0, 0)
+  var entry: (Int, Int) = (x, y)
+
+  override def write(output: DataOutput): Unit = {
+    output.writeInt(entry._1)
+    output.writeInt(entry._2)
+  }
+
+  override def readFields(input: DataInput): Unit = {
+    val x = input.readInt()
+    val y = input.readInt()
+    entry = (x, y)
+  }
+}
+
+class PairSerDe extends AbstractSerDe {
+  override def initialize(p1: Configuration, p2: Properties): Unit = {}
+
+  override def getObjectInspector: ObjectInspector = {
+    ObjectInspectorFactory
+      .getStandardStructObjectInspector(
+        Seq("pair"),
+        Seq(ObjectInspectorFactory.getStandardStructObjectInspector(
+          Seq("id", "value"),
+          Seq(PrimitiveObjectInspectorFactory.javaIntObjectInspector,
+              PrimitiveObjectInspectorFactory.javaIntObjectInspector))
+    ))
+  }
+
+  override def getSerializedClass: Class[_ <: Writable] = classOf[TestPair]
+
+  override def getSerDeStats: SerDeStats = null
+
+  override def serialize(p1: scala.Any, p2: ObjectInspector): Writable = null
+
+  override def deserialize(value: Writable): AnyRef = {
+    val pair = value.asInstanceOf[TestPair]
+
+    val row = new util.ArrayList[util.ArrayList[AnyRef]]
+    row.add(new util.ArrayList[AnyRef](2))
+    row(0).add(Integer.valueOf(pair.entry._1))
+    row(0).add(Integer.valueOf(pair.entry._2))
+
+    row
+  }
+}
+
+class PairUdf extends GenericUDF {
+  override def initialize(p1: Array[ObjectInspector]): ObjectInspector =
+    ObjectInspectorFactory.getStandardStructObjectInspector(
+      Seq("id", "value"),
+      Seq(PrimitiveObjectInspectorFactory.javaIntObjectInspector, PrimitiveObjectInspectorFactory.javaIntObjectInspector)
+  )
+
+  override def evaluate(args: Array[DeferredObject]): AnyRef = {
+    println("Type = %s".format(args(0).getClass.getName))
+    Integer.valueOf(args(0).get.asInstanceOf[TestPair].entry._2)
+  }
+
+  override def getDisplayString(p1: Array[String]): String = ""
+}
+
+
+

From e243c5ffacd70ecadaf5c91668955dcc8141e060 Mon Sep 17 00:00:00 2001
From: Zongheng Yang <zongheng.y@gmail.com>
Date: Tue, 17 Jun 2014 13:30:17 +0200
Subject: [PATCH 443/810] [SPARK-2053][SQL] Add Catalyst expressions for CASE
 WHEN.

JIRA ticket: https://issues.apache.org/jira/browse/SPARK-2053

This PR adds support for two types of CASE statements present in Hive. The first type is of the form `CASE WHEN a THEN b [WHEN c THEN d]* [ELSE e] END`, with the semantics like a chain of if statements. The second type is of the form `CASE a WHEN b THEN c [WHEN d THEN e]* [ELSE f] END`, with the semantics like a switch statement on key `a`. Both forms are implemented in `CaseWhen`.

[This link](https://cwiki.apache.org/confluence/display/Hive/LanguageManual+UDF#LanguageManualUDF-ConditionalFunctions) contains more detailed descriptions on their semantics.

Notes / Open issues:

* Please check if any implicit contracts / invariants are broken in the implementations (especially for the operators). I am not very familiar with them and I currently find them tricky to spot.
* We should decide whether or not a non-boolean condition is allowed in a branch of `CaseWhen`. Hive throws a `SemanticException` for this situation and I think it'd be good to mimic it -- the question is where in the whole Spark SQL pipeline should we signal an exception for such a query.

Author: Zongheng Yang <zongheng.y@gmail.com>

Closes #1055 from concretevitamin/caseWhen and squashes the following commits:

4226eb9 [Zongheng Yang] Comment.
79d26fc [Zongheng Yang] Merge branch 'master' into caseWhen
caf9383 [Zongheng Yang] Update a FIXME.
9d26ab8 [Zongheng Yang] Add @transient marker.
788a0d9 [Zongheng Yang] Implement CastNulls, which fixes udf_case and udf_when.
7ef284f [Zongheng Yang] Refactors: remove redundant passes, improve toString, mark transient.
f47ae7b [Zongheng Yang] Modify queries in tests to have shorter golden files.
1c1fbfc [Zongheng Yang] Cleanups per review comments.
7d2b7e2 [Zongheng Yang] Translate CaseKeyWhen to CaseWhen at parsing time.
47d406a [Zongheng Yang] Do toArray once and lazily outside of eval().
bb3d109 [Zongheng Yang] Update scaladoc of a method.
aea3195 [Zongheng Yang] Fix bug that branchesArr is not used; remove unused import.
96870a8 [Zongheng Yang] Turn off scalastyle for some comments.
7392f3a [Zongheng Yang] Minor cleanup.
2cf08bb [Zongheng Yang] Merge branch 'master' into caseWhen
9f84b40 [Zongheng Yang] Add golden outputs from Hive.
db51a85 [Zongheng Yang] Add allCondBooleans check; uncomment tests.
3f9ef0a [Zongheng Yang] Cleanups and bug fixes (mainly in eval() and resolved).
be54bc8 [Zongheng Yang] Rewrite eval() to a low-level implementation. Separate two CASE stmts.
f2bcb9d [Zongheng Yang] WIP
5906f75 [Zongheng Yang] WIP
efd019b [Zongheng Yang] eval() and toString() bug fixes.
7d81e95 [Zongheng Yang] Clean up resolved.
a31d782 [Zongheng Yang] Finish up Case.
---
 .../catalyst/analysis/HiveTypeCoercion.scala  | 41 +++++++++-
 .../sql/catalyst/expressions/Expression.scala | 10 ++-
 .../sql/catalyst/expressions/predicates.scala | 76 ++++++++++++++++++-
 .../spark/sql/catalyst/util/package.scala     |  2 +-
 .../ExpressionEvaluationSuite.scala           |  2 +-
 .../org/apache/spark/sql/hive/HiveQl.scala    | 17 +++++
 ... key #1-0-36750f0f6727c287c471309689ff7563 | 14 ++++
 ... key #2-0-e3a2b981ebff7e273537dd6c43ece0c0 | 14 ++++
 ... key #3-0-be5efc0574a97ec465e2686f4a724bd5 | 14 ++++
 ... key #4-0-631f824a91b7230657bea7a05e393a1e | 14 ++++
 ... key #1-0-616830b2011da0990e87a188fb609299 | 14 ++++
 ... key #2-0-6c5b5a997949f9e5ab9676b60e95657b | 14 ++++
 ... key #3-0-a241862582c47d9e98be95339d35c7c4 | 14 ++++
 ... key #4-0-ea87ca38ead8858d2337792dcd430226 | 14 ++++
 .../sql/hive/execution/HiveQuerySuite.scala   | 38 ++++++++++
 15 files changed, 290 insertions(+), 8 deletions(-)
 create mode 100644 sql/hive/src/test/resources/golden/case statements WITHOUT key #1-0-36750f0f6727c287c471309689ff7563
 create mode 100644 sql/hive/src/test/resources/golden/case statements WITHOUT key #2-0-e3a2b981ebff7e273537dd6c43ece0c0
 create mode 100644 sql/hive/src/test/resources/golden/case statements WITHOUT key #3-0-be5efc0574a97ec465e2686f4a724bd5
 create mode 100644 sql/hive/src/test/resources/golden/case statements WITHOUT key #4-0-631f824a91b7230657bea7a05e393a1e
 create mode 100644 sql/hive/src/test/resources/golden/case statements with key #1-0-616830b2011da0990e87a188fb609299
 create mode 100644 sql/hive/src/test/resources/golden/case statements with key #2-0-6c5b5a997949f9e5ab9676b60e95657b
 create mode 100644 sql/hive/src/test/resources/golden/case statements with key #3-0-a241862582c47d9e98be95339d35c7c4
 create mode 100644 sql/hive/src/test/resources/golden/case statements with key #4-0-ea87ca38ead8858d2337792dcd430226

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
index 326feea6fee9..d291814c8aa7 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
@@ -31,8 +31,16 @@ import org.apache.spark.sql.catalyst.types._
 trait HiveTypeCoercion {
 
   val typeCoercionRules =
-    List(PropagateTypes, ConvertNaNs, WidenTypes, PromoteStrings, BooleanComparisons, BooleanCasts,
-      StringToIntegralCasts, FunctionArgumentConversion)
+    PropagateTypes ::
+    ConvertNaNs ::
+    WidenTypes ::
+    PromoteStrings ::
+    BooleanComparisons ::
+    BooleanCasts ::
+    StringToIntegralCasts ::
+    FunctionArgumentConversion ::
+    CastNulls ::
+    Nil
 
   /**
    * Applies any changes to [[catalyst.expressions.AttributeReference AttributeReference]] data
@@ -282,4 +290,33 @@ trait HiveTypeCoercion {
         Average(Cast(e, DoubleType))
     }
   }
+
+  /**
+   * Ensures that NullType gets casted to some other types under certain circumstances.
+   */
+  object CastNulls extends Rule[LogicalPlan] {
+    def apply(plan: LogicalPlan): LogicalPlan = plan transformAllExpressions {
+      case cw @ CaseWhen(branches) =>
+        val valueTypes = branches.sliding(2, 2).map {
+          case Seq(_, value) if value.resolved => Some(value.dataType)
+          case Seq(elseVal) if elseVal.resolved => Some(elseVal.dataType)
+          case _ => None
+        }.toSeq
+        if (valueTypes.distinct.size == 2 && valueTypes.exists(_ == Some(NullType))) {
+          val otherType = valueTypes.filterNot(_ == Some(NullType))(0).get
+          val transformedBranches = branches.sliding(2, 2).map {
+            case Seq(cond, value) if value.resolved && value.dataType == NullType =>
+              Seq(cond, Cast(value, otherType))
+            case Seq(elseVal) if elseVal.resolved && elseVal.dataType == NullType =>
+              Seq(Cast(elseVal, otherType))
+            case s => s
+          }.reduce(_ ++ _)
+          CaseWhen(transformedBranches)
+        } else {
+          // It is possible to have more types due to the possibility of short-circuiting.
+          cw
+        }
+    }
+  }
+
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
index 41398ff956ed..3912f5f4375f 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
@@ -28,8 +28,6 @@ abstract class Expression extends TreeNode[Expression] {
   /** The narrowest possible type that is produced when this expression is evaluated. */
   type EvaluatedType <: Any
 
-  def dataType: DataType
-
   /**
    * Returns true when an expression is a candidate for static evaluation before the query is
    * executed.
@@ -53,12 +51,18 @@ abstract class Expression extends TreeNode[Expression] {
 
   /**
    * Returns `true` if this expression and all its children have been resolved to a specific schema
-   * and `false` if it is still contains any unresolved placeholders. Implementations of expressions
+   * and `false` if it still contains any unresolved placeholders. Implementations of expressions
    * should override this if the resolution of this type of expression involves more than just
    * the resolution of its children.
    */
   lazy val resolved: Boolean = childrenResolved
 
+  /**
+   * Returns the [[types.DataType DataType]] of the result of evaluating this expression.  It is
+   * invalid to query the dataType of an unresolved expression (i.e., when `resolved` == false).
+   */
+  def dataType: DataType
+
   /**
    * Returns true if  all the children of this expression have been resolved to a specific schema
    * and false if any still contains any unresolved placeholders.
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
index d11157853050..2902906df284 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
@@ -19,7 +19,6 @@ package org.apache.spark.sql.catalyst.expressions
 
 import org.apache.spark.sql.catalyst.analysis.UnresolvedException
 import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
-import org.apache.spark.sql.catalyst.trees
 import org.apache.spark.sql.catalyst.types.BooleanType
 
 
@@ -202,3 +201,78 @@ case class If(predicate: Expression, trueValue: Expression, falseValue: Expressi
 
   override def toString = s"if ($predicate) $trueValue else $falseValue"
 }
+
+// scalastyle:off
+/**
+ * Case statements of the form "CASE WHEN a THEN b [WHEN c THEN d]* [ELSE e] END".
+ * Refer to this link for the corresponding semantics:
+ * https://cwiki.apache.org/confluence/display/Hive/LanguageManual+UDF#LanguageManualUDF-ConditionalFunctions
+ *
+ * The other form of case statements "CASE a WHEN b THEN c [WHEN d THEN e]* [ELSE f] END" gets
+ * translated to this form at parsing time.  Namely, such a statement gets translated to
+ * "CASE WHEN a=b THEN c [WHEN a=d THEN e]* [ELSE f] END".
+ *
+ * Note that `branches` are considered in consecutive pairs (cond, val), and the optional last
+ * element is the value for the default catch-all case (if provided). Hence, `branches` consists of
+ * at least two elements, and can have an odd or even length.
+ */
+// scalastyle:on
+case class CaseWhen(branches: Seq[Expression]) extends Expression {
+  type EvaluatedType = Any
+  def children = branches
+  def references = children.flatMap(_.references).toSet
+  def dataType = {
+    if (!resolved) {
+      throw new UnresolvedException(this, "cannot resolve due to differing types in some branches")
+    }
+    branches(1).dataType
+  }
+
+  @transient private[this] lazy val branchesArr = branches.toArray
+  @transient private[this] lazy val predicates =
+    branches.sliding(2, 2).collect { case Seq(cond, _) => cond }.toSeq
+  @transient private[this] lazy val values =
+    branches.sliding(2, 2).collect { case Seq(_, value) => value }.toSeq
+
+  override def nullable = {
+    // If no value is nullable and no elseValue is provided, the whole statement defaults to null.
+    values.exists(_.nullable) || (values.length % 2 == 0)
+  }
+
+  override lazy val resolved = {
+    if (!childrenResolved) {
+      false
+    } else {
+      val allCondBooleans = predicates.forall(_.dataType == BooleanType)
+      val dataTypesEqual = values.map(_.dataType).distinct.size <= 1
+      allCondBooleans && dataTypesEqual
+    }
+  }
+
+  /** Written in imperative fashion for performance considerations.  Same for CaseKeyWhen. */
+  override def eval(input: Row): Any = {
+    val len = branchesArr.length
+    var i = 0
+    // If all branches fail and an elseVal is not provided, the whole statement
+    // defaults to null, according to Hive's semantics.
+    var res: Any = null
+    while (i < len - 1) {
+      if (branchesArr(i).eval(input) == true) {
+        res = branchesArr(i + 1).eval(input)
+        return res
+      }
+      i += 2
+    }
+    if (i == len - 1) {
+      res = branchesArr(i).eval(input)
+    }
+    res
+  }
+
+  override def toString = {
+    "CASE" + branches.sliding(2, 2).map {
+      case Seq(cond, value) => s" WHEN $cond THEN $value"
+      case Seq(elseValue) => s" ELSE $elseValue"
+    }.mkString
+  }
+}
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/package.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/package.scala
index 49fc4f70fdfa..d8da45ae70c4 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/package.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/package.scala
@@ -115,7 +115,7 @@ package object util {
   }
 
   /* FIX ME
-  implicit class debugLogging(a: AnyRef) {
+  implicit class debugLogging(a: Any) {
     def debugLogging() {
       org.apache.log4j.Logger.getLogger(a.getClass.getName).setLevel(org.apache.log4j.Level.DEBUG)
     }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala
index 1132a30b4276..8c3b062d0f80 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala
@@ -35,7 +35,7 @@ class ExpressionEvaluationSuite extends FunSuite {
   /**
    * Checks for three-valued-logic.  Based on:
    * http://en.wikipedia.org/wiki/Null_(SQL)#Comparisons_with_NULL_and_the_three-valued_logic_.283VL.29
-   *
+   * I.e. in flat cpo "False -> Unknown -> True", OR is lowest upper bound, AND is greatest lower bound.
    * p       q       p OR q  p AND q  p = q
    * True    True    True    True     True
    * True    False   True    False    False
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
index b745d8ffd8f1..844673f66d10 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
@@ -811,6 +811,8 @@ private[hive] object HiveQl {
   val IN = "(?i)IN".r
   val DIV = "(?i)DIV".r
   val BETWEEN = "(?i)BETWEEN".r
+  val WHEN = "(?i)WHEN".r
+  val CASE = "(?i)CASE".r
 
   protected def nodeToExpr(node: Node): Expression = node match {
     /* Attribute References */
@@ -917,6 +919,21 @@ private[hive] object HiveQl {
     case Token(OR(), left :: right:: Nil) => Or(nodeToExpr(left), nodeToExpr(right))
     case Token(NOT(), child :: Nil) => Not(nodeToExpr(child))
 
+    /* Case statements */
+    case Token("TOK_FUNCTION", Token(WHEN(), Nil) :: branches) =>
+      CaseWhen(branches.map(nodeToExpr))
+    case Token("TOK_FUNCTION", Token(CASE(), Nil) :: branches) =>
+      val transformed = branches.drop(1).sliding(2, 2).map {
+        case Seq(condVal, value) =>
+          // FIXME (SPARK-2155): the key will get evaluated for multiple times in CaseWhen's eval().
+          // Hence effectful / non-deterministic key expressions are *not* supported at the moment.
+          // We should consider adding new Expressions to get around this.
+          Seq(Equals(nodeToExpr(branches(0)), nodeToExpr(condVal)),
+              nodeToExpr(value))
+        case Seq(elseVal) => Seq(nodeToExpr(elseVal))
+      }.toSeq.reduce(_ ++ _)
+      CaseWhen(transformed)
+
     /* Complex datatype manipulation */
     case Token("[", child :: ordinal :: Nil) =>
       GetItem(nodeToExpr(child), nodeToExpr(ordinal))
diff --git a/sql/hive/src/test/resources/golden/case statements WITHOUT key #1-0-36750f0f6727c287c471309689ff7563 b/sql/hive/src/test/resources/golden/case statements WITHOUT key #1-0-36750f0f6727c287c471309689ff7563
new file mode 100644
index 000000000000..816fe57d162d
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/case statements WITHOUT key #1-0-36750f0f6727c287c471309689ff7563	
@@ -0,0 +1,14 @@
+NULL
+3
+3
+3
+NULL
+NULL
+3
+3
+3
+3
+NULL
+3
+3
+3
diff --git a/sql/hive/src/test/resources/golden/case statements WITHOUT key #2-0-e3a2b981ebff7e273537dd6c43ece0c0 b/sql/hive/src/test/resources/golden/case statements WITHOUT key #2-0-e3a2b981ebff7e273537dd6c43ece0c0
new file mode 100644
index 000000000000..4cca081e6e29
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/case statements WITHOUT key #2-0-e3a2b981ebff7e273537dd6c43ece0c0	
@@ -0,0 +1,14 @@
+4
+3
+3
+3
+4
+4
+3
+3
+3
+3
+4
+3
+3
+3
diff --git a/sql/hive/src/test/resources/golden/case statements WITHOUT key #3-0-be5efc0574a97ec465e2686f4a724bd5 b/sql/hive/src/test/resources/golden/case statements WITHOUT key #3-0-be5efc0574a97ec465e2686f4a724bd5
new file mode 100644
index 000000000000..8d0416a8f8d9
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/case statements WITHOUT key #3-0-be5efc0574a97ec465e2686f4a724bd5	
@@ -0,0 +1,14 @@
+2
+3
+3
+3
+2
+2
+3
+3
+3
+3
+NULL
+3
+3
+3
diff --git a/sql/hive/src/test/resources/golden/case statements WITHOUT key #4-0-631f824a91b7230657bea7a05e393a1e b/sql/hive/src/test/resources/golden/case statements WITHOUT key #4-0-631f824a91b7230657bea7a05e393a1e
new file mode 100644
index 000000000000..6ed452bcd870
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/case statements WITHOUT key #4-0-631f824a91b7230657bea7a05e393a1e	
@@ -0,0 +1,14 @@
+2
+3
+3
+3
+2
+2
+3
+3
+3
+3
+0
+3
+3
+3
diff --git a/sql/hive/src/test/resources/golden/case statements with key #1-0-616830b2011da0990e87a188fb609299 b/sql/hive/src/test/resources/golden/case statements with key #1-0-616830b2011da0990e87a188fb609299
new file mode 100644
index 000000000000..3f5a2fbbe99f
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/case statements with key #1-0-616830b2011da0990e87a188fb609299	
@@ -0,0 +1,14 @@
+NULL
+NULL
+NULL
+NULL
+NULL
+NULL
+NULL
+NULL
+NULL
+NULL
+NULL
+NULL
+NULL
+NULL
diff --git a/sql/hive/src/test/resources/golden/case statements with key #2-0-6c5b5a997949f9e5ab9676b60e95657b b/sql/hive/src/test/resources/golden/case statements with key #2-0-6c5b5a997949f9e5ab9676b60e95657b
new file mode 100644
index 000000000000..e1ca6e76d1f8
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/case statements with key #2-0-6c5b5a997949f9e5ab9676b60e95657b	
@@ -0,0 +1,14 @@
+0
+0
+0
+0
+0
+0
+0
+0
+0
+0
+3
+0
+0
+0
diff --git a/sql/hive/src/test/resources/golden/case statements with key #3-0-a241862582c47d9e98be95339d35c7c4 b/sql/hive/src/test/resources/golden/case statements with key #3-0-a241862582c47d9e98be95339d35c7c4
new file mode 100644
index 000000000000..896207fdbcf3
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/case statements with key #3-0-a241862582c47d9e98be95339d35c7c4	
@@ -0,0 +1,14 @@
+NULL
+NULL
+NULL
+NULL
+NULL
+NULL
+NULL
+NULL
+NULL
+NULL
+3
+NULL
+NULL
+NULL
diff --git a/sql/hive/src/test/resources/golden/case statements with key #4-0-ea87ca38ead8858d2337792dcd430226 b/sql/hive/src/test/resources/golden/case statements with key #4-0-ea87ca38ead8858d2337792dcd430226
new file mode 100644
index 000000000000..e1ca6e76d1f8
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/case statements with key #4-0-ea87ca38ead8858d2337792dcd430226	
@@ -0,0 +1,14 @@
+0
+0
+0
+0
+0
+0
+0
+0
+0
+0
+3
+0
+0
+0
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
index 04652587f907..fe698f0fc57b 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
@@ -164,6 +164,44 @@ class HiveQuerySuite extends HiveComparisonTest {
     hql("SELECT * FROM src").toString
   }
 
+  createQueryTest("case statements with key #1",
+    "SELECT (CASE 1 WHEN 2 THEN 3 END) FROM src where key < 15")
+
+  createQueryTest("case statements with key #2",
+    "SELECT (CASE key WHEN 2 THEN 3 ELSE 0 END) FROM src WHERE key < 15")
+
+  createQueryTest("case statements with key #3",
+    "SELECT (CASE key WHEN 2 THEN 3 WHEN NULL THEN 4 END) FROM src WHERE key < 15")
+
+  createQueryTest("case statements with key #4",
+    "SELECT (CASE key WHEN 2 THEN 3 WHEN NULL THEN 4 ELSE 0 END) FROM src WHERE key < 15")
+
+  createQueryTest("case statements WITHOUT key #1",
+    "SELECT (CASE WHEN key > 2 THEN 3 END) FROM src WHERE key < 15")
+
+  createQueryTest("case statements WITHOUT key #2",
+    "SELECT (CASE WHEN key > 2 THEN 3 ELSE 4 END) FROM src WHERE key < 15")
+
+  createQueryTest("case statements WITHOUT key #3",
+    "SELECT (CASE WHEN key > 2 THEN 3 WHEN 2 > key THEN 2 END) FROM src WHERE key < 15")
+
+  createQueryTest("case statements WITHOUT key #4",
+    "SELECT (CASE WHEN key > 2 THEN 3 WHEN 2 > key THEN 2 ELSE 0 END) FROM src WHERE key < 15")
+
+  test("implement identity function using case statement") {
+    val actual = hql("SELECT (CASE key WHEN key THEN key END) FROM src").collect().toSet
+    val expected = hql("SELECT key FROM src").collect().toSet
+    assert(actual === expected)
+  }
+
+  // TODO: adopt this test when Spark SQL has the functionality / framework to report errors.
+  // See https://github.com/apache/spark/pull/1055#issuecomment-45820167 for a discussion.
+  ignore("non-boolean conditions in a CaseWhen are illegal") {
+    intercept[Exception] {
+      hql("SELECT (CASE WHEN key > 2 THEN 3 WHEN 1 THEN 2 ELSE 0 END) FROM src").collect()
+    }
+  }
+
   private val explainCommandClassName =
     classOf[execution.ExplainCommand].getSimpleName.stripSuffix("$")
 

From b92d16b114fd49e881d09e7974ad57b2a0df2906 Mon Sep 17 00:00:00 2001
From: Andrew Ash <andrew@andrewash.com>
Date: Tue, 17 Jun 2014 11:47:48 -0700
Subject: [PATCH 444/810] SPARK-1063 Add .sortBy(f) method on RDD

This never got merged from the apache/incubator-spark repo (which is now deleted) but there had been several rounds of code review on this PR there.

I think this is ready for merging.

Author: Andrew Ash <andrew@andrewash.com>

This patch had conflicts when merged, resolved by
Committer: Reynold Xin <rxin@apache.org>

Closes #369 from ash211/sortby and squashes the following commits:

d09147a [Andrew Ash] Fix Ordering import
43d0a53 [Andrew Ash] Fix missing .collect()
29a54ed [Andrew Ash] Re-enable test by converting to a closure
5a95348 [Andrew Ash] Add license for RDDSuiteUtils
64ed6e3 [Andrew Ash] Remove leaked diff
d4de69a [Andrew Ash] Remove scar tissue
63638b5 [Andrew Ash] Add Python version of .sortBy()
45e0fde [Andrew Ash] Add Java version of .sortBy()
adf84c5 [Andrew Ash] Re-indent to keep line lengths under 100 chars
9d9b9d8 [Andrew Ash] Use parentheses on .collect() calls
0457b69 [Andrew Ash] Ignore failing test
99f0baf [Andrew Ash] Merge branch 'master' into sortby
222ae97 [Andrew Ash] Try moving Ordering objects out to a different class
3fd0dd3 [Andrew Ash] Add (failing) test for sortByKey with explicit Ordering
b8b5bbc [Andrew Ash] Align remove extra spaces that were used to align ='s in test code
8c53298 [Andrew Ash] Actually use ascending and numPartitions parameters
381eef2 [Andrew Ash] Correct silly typo
7db3e84 [Andrew Ash] Support ascending and numPartitions params in sortBy()
0f685fd [Andrew Ash] Merge remote-tracking branch 'origin/master' into sortby
ca4490d [Andrew Ash] Add .sortBy(f) method on RDD
---
 .../org/apache/spark/api/java/JavaRDD.scala   | 16 +++++
 .../main/scala/org/apache/spark/rdd/RDD.scala | 12 ++++
 .../java/org/apache/spark/JavaAPISuite.java   | 33 +++++++++++
 .../scala/org/apache/spark/rdd/RDDSuite.scala | 59 +++++++++++++++++--
 .../org/apache/spark/rdd/RDDSuiteUtils.scala  | 31 ++++++++++
 python/pyspark/rdd.py                         | 12 ++++
 6 files changed, 159 insertions(+), 4 deletions(-)
 create mode 100644 core/src/test/scala/org/apache/spark/rdd/RDDSuiteUtils.scala

diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaRDD.scala b/core/src/main/scala/org/apache/spark/api/java/JavaRDD.scala
index 23d13710794a..86fb374bef1e 100644
--- a/core/src/main/scala/org/apache/spark/api/java/JavaRDD.scala
+++ b/core/src/main/scala/org/apache/spark/api/java/JavaRDD.scala
@@ -17,10 +17,13 @@
 
 package org.apache.spark.api.java
 
+import java.util.Comparator
+
 import scala.language.implicitConversions
 import scala.reflect.ClassTag
 
 import org.apache.spark._
+import org.apache.spark.api.java.JavaSparkContext.fakeClassTag
 import org.apache.spark.api.java.function.{Function => JFunction}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.storage.StorageLevel
@@ -172,6 +175,19 @@ class JavaRDD[T](val rdd: RDD[T])(implicit val classTag: ClassTag[T])
     rdd.setName(name)
     this
   }
+
+  /**
+   * Return this RDD sorted by the given key function.
+   */
+  def sortBy[S](f: JFunction[T, S], ascending: Boolean, numPartitions: Int): JavaRDD[T] = {
+    import scala.collection.JavaConverters._
+    def fn = (x: T) => f.call(x)
+    import com.google.common.collect.Ordering  // shadows scala.math.Ordering
+    implicit val ordering = Ordering.natural().asInstanceOf[Ordering[S]]
+    implicit val ctag: ClassTag[S] = fakeClassTag
+    wrapRDD(rdd.sortBy(fn, ascending, numPartitions))
+  }
+
 }
 
 object JavaRDD {
diff --git a/core/src/main/scala/org/apache/spark/rdd/RDD.scala b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
index 27cc60d77578..cf915b870e0d 100644
--- a/core/src/main/scala/org/apache/spark/rdd/RDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
@@ -442,6 +442,18 @@ abstract class RDD[T: ClassTag](
    */
   def ++(other: RDD[T]): RDD[T] = this.union(other)
 
+  /**
+   * Return this RDD sorted by the given key function.
+   */
+  def sortBy[K](
+      f: (T) ⇒ K,
+      ascending: Boolean = true,
+      numPartitions: Int = this.partitions.size)
+      (implicit ord: Ordering[K], ctag: ClassTag[K]): RDD[T] =
+    this.keyBy[K](f)
+        .sortByKey(ascending, numPartitions)
+        .values
+
   /**
    * Return the intersection of this RDD and another one. The output will not contain any duplicate
    * elements, even if the input RDDs did.
diff --git a/core/src/test/java/org/apache/spark/JavaAPISuite.java b/core/src/test/java/org/apache/spark/JavaAPISuite.java
index ef41bfb88de9..e46298c6a9e6 100644
--- a/core/src/test/java/org/apache/spark/JavaAPISuite.java
+++ b/core/src/test/java/org/apache/spark/JavaAPISuite.java
@@ -180,6 +180,39 @@ public void sortByKey() {
     Assert.assertEquals(new Tuple2<Integer, Integer>(3, 2), sortedPairs.get(2));
   }
 
+  @Test
+  public void sortBy() {
+    List<Tuple2<Integer, Integer>> pairs = new ArrayList<Tuple2<Integer, Integer>>();
+    pairs.add(new Tuple2<Integer, Integer>(0, 4));
+    pairs.add(new Tuple2<Integer, Integer>(3, 2));
+    pairs.add(new Tuple2<Integer, Integer>(-1, 1));
+
+    JavaRDD<Tuple2<Integer, Integer>> rdd = sc.parallelize(pairs);
+
+    // compare on first value
+    JavaRDD<Tuple2<Integer, Integer>> sortedRDD = rdd.sortBy(new Function<Tuple2<Integer, Integer>, Integer>() {
+      public Integer call(Tuple2<Integer, Integer> t) throws Exception {
+        return t._1();
+      }
+    }, true, 2);
+
+    Assert.assertEquals(new Tuple2<Integer, Integer>(-1, 1), sortedRDD.first());
+    List<Tuple2<Integer, Integer>> sortedPairs = sortedRDD.collect();
+    Assert.assertEquals(new Tuple2<Integer, Integer>(0, 4), sortedPairs.get(1));
+    Assert.assertEquals(new Tuple2<Integer, Integer>(3, 2), sortedPairs.get(2));
+
+    // compare on second value
+    sortedRDD = rdd.sortBy(new Function<Tuple2<Integer, Integer>, Integer>() {
+      public Integer call(Tuple2<Integer, Integer> t) throws Exception {
+        return t._2();
+      }
+    }, true, 2);
+    Assert.assertEquals(new Tuple2<Integer, Integer>(-1, 1), sortedRDD.first());
+    sortedPairs = sortedRDD.collect();
+    Assert.assertEquals(new Tuple2<Integer, Integer>(3, 2), sortedPairs.get(1));
+    Assert.assertEquals(new Tuple2<Integer, Integer>(0, 4), sortedPairs.get(2));
+  }
+
   @Test
   public void foreach() {
     final Accumulator<Integer> accum = sc.accumulator(0);
diff --git a/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala b/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala
index e94a1e76d410..0e5625b7645d 100644
--- a/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala
+++ b/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala
@@ -26,6 +26,8 @@ import org.apache.spark._
 import org.apache.spark.SparkContext._
 import org.apache.spark.util.Utils
 
+import org.apache.spark.rdd.RDDSuiteUtils._
+
 class RDDSuite extends FunSuite with SharedSparkContext {
 
   test("basic operations") {
@@ -585,14 +587,63 @@ class RDDSuite extends FunSuite with SharedSparkContext {
     }
   }
 
+  test("sortByKey") {
+    val data = sc.parallelize(Seq("5|50|A","4|60|C", "6|40|B"))
+
+    val col1 = Array("4|60|C", "5|50|A", "6|40|B")
+    val col2 = Array("6|40|B", "5|50|A", "4|60|C")
+    val col3 = Array("5|50|A", "6|40|B", "4|60|C")
+
+    assert(data.sortBy(_.split("\\|")(0)).collect() === col1)
+    assert(data.sortBy(_.split("\\|")(1)).collect() === col2)
+    assert(data.sortBy(_.split("\\|")(2)).collect() === col3)
+  }
+
+  test("sortByKey ascending parameter") {
+    val data = sc.parallelize(Seq("5|50|A","4|60|C", "6|40|B"))
+
+    val asc = Array("4|60|C", "5|50|A", "6|40|B")
+    val desc = Array("6|40|B", "5|50|A", "4|60|C")
+
+    assert(data.sortBy(_.split("\\|")(0), true).collect() === asc)
+    assert(data.sortBy(_.split("\\|")(0), false).collect() === desc)
+  }
+
+  test("sortByKey with explicit ordering") {
+    val data = sc.parallelize(Seq("Bob|Smith|50",
+                                  "Jane|Smith|40",
+                                  "Thomas|Williams|30",
+                                  "Karen|Williams|60"))
+
+    val ageOrdered = Array("Thomas|Williams|30",
+                           "Jane|Smith|40",
+                           "Bob|Smith|50",
+                           "Karen|Williams|60")
+
+    // last name, then first name
+    val nameOrdered = Array("Bob|Smith|50",
+                            "Jane|Smith|40",
+                            "Karen|Williams|60",
+                            "Thomas|Williams|30")
+
+    val parse = (s: String) => {
+      val split = s.split("\\|")
+      Person(split(0), split(1), split(2).toInt)
+    }
+
+    import scala.reflect.classTag
+    assert(data.sortBy(parse, true, 2)(AgeOrdering, classTag[Person]).collect() === ageOrdered)
+    assert(data.sortBy(parse, true, 2)(NameOrdering, classTag[Person]).collect() === nameOrdered)
+  }
+
   test("intersection") {
     val all = sc.parallelize(1 to 10)
     val evens = sc.parallelize(2 to 10 by 2)
     val intersection = Array(2, 4, 6, 8, 10)
 
     // intersection is commutative
-    assert(all.intersection(evens).collect.sorted === intersection)
-    assert(evens.intersection(all).collect.sorted === intersection)
+    assert(all.intersection(evens).collect().sorted === intersection)
+    assert(evens.intersection(all).collect().sorted === intersection)
   }
 
   test("intersection strips duplicates in an input") {
@@ -600,8 +651,8 @@ class RDDSuite extends FunSuite with SharedSparkContext {
     val b = sc.parallelize(Seq(1,1,2,3))
     val intersection = Array(1,2,3)
 
-    assert(a.intersection(b).collect.sorted === intersection)
-    assert(b.intersection(a).collect.sorted === intersection)
+    assert(a.intersection(b).collect().sorted === intersection)
+    assert(b.intersection(a).collect().sorted === intersection)
   }
 
   test("zipWithIndex") {
diff --git a/core/src/test/scala/org/apache/spark/rdd/RDDSuiteUtils.scala b/core/src/test/scala/org/apache/spark/rdd/RDDSuiteUtils.scala
new file mode 100644
index 000000000000..4762fc17855c
--- /dev/null
+++ b/core/src/test/scala/org/apache/spark/rdd/RDDSuiteUtils.scala
@@ -0,0 +1,31 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.rdd
+
+object RDDSuiteUtils {
+  case class Person(first: String, last: String, age: Int)
+
+  object AgeOrdering extends Ordering[Person] {
+    def compare(a:Person, b:Person) = a.age compare b.age
+  }
+
+  object NameOrdering extends Ordering[Person] {
+    def compare(a:Person, b:Person) =
+      implicitly[Ordering[Tuple2[String,String]]].compare((a.last, a.first), (b.last, b.first))
+  }
+}
diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py
index bb4d035edcde..65f63153cdff 100644
--- a/python/pyspark/rdd.py
+++ b/python/pyspark/rdd.py
@@ -549,6 +549,18 @@ def mapFunc(iterator):
                     .mapPartitions(mapFunc,preservesPartitioning=True)
                     .flatMap(lambda x: x, preservesPartitioning=True))
 
+    def sortBy(self, keyfunc, ascending=True, numPartitions=None):
+        """
+        Sorts this RDD by the given keyfunc
+
+        >>> tmp = [('a', 1), ('b', 2), ('1', 3), ('d', 4), ('2', 5)]
+        >>> sc.parallelize(tmp).sortBy(lambda x: x[0]).collect()
+        [('1', 3), ('2', 5), ('a', 1), ('b', 2), ('d', 4)]
+        >>> sc.parallelize(tmp).sortBy(lambda x: x[1]).collect()
+        [('a', 1), ('b', 2), ('1', 3), ('d', 4), ('2', 5)]
+        """
+        return self.keyBy(keyfunc).sortByKey(ascending, numPartitions).values()
+
     def glom(self):
         """
         Return an RDD created by coalescing all elements within each partition

From 2794990e9eb8712d76d3a0f0483063ddc295e639 Mon Sep 17 00:00:00 2001
From: Sandy Ryza <sandy@cloudera.com>
Date: Tue, 17 Jun 2014 12:03:22 -0700
Subject: [PATCH 445/810] SPARK-2146.  Fix takeOrdered doc

Removes Python syntax in Scaladoc, corrects result in Scaladoc, and removes irrelevant cache() call in Python doc.

Author: Sandy Ryza <sandy@cloudera.com>

Closes #1086 from sryza/sandy-spark-2146 and squashes the following commits:

185ff18 [Sandy Ryza] Use Seq instead of Array
c996120 [Sandy Ryza] SPARK-2146.  Fix takeOrdered doc
---
 .../main/scala/org/apache/spark/rdd/RDD.scala    | 16 ++++++++--------
 python/pyspark/rdd.py                            |  2 +-
 2 files changed, 9 insertions(+), 9 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/rdd/RDD.scala b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
index cf915b870e0d..1633b185861b 100644
--- a/core/src/main/scala/org/apache/spark/rdd/RDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
@@ -1074,11 +1074,11 @@ abstract class RDD[T: ClassTag](
    * Returns the top K (largest) elements from this RDD as defined by the specified
    * implicit Ordering[T]. This does the opposite of [[takeOrdered]]. For example:
    * {{{
-   *   sc.parallelize([10, 4, 2, 12, 3]).top(1)
-   *   // returns [12]
+   *   sc.parallelize(Seq(10, 4, 2, 12, 3)).top(1)
+   *   // returns Array(12)
    *
-   *   sc.parallelize([2, 3, 4, 5, 6]).top(2)
-   *   // returns [6, 5]
+   *   sc.parallelize(Seq(2, 3, 4, 5, 6)).top(2)
+   *   // returns Array(6, 5)
    * }}}
    *
    * @param num the number of top elements to return
@@ -1092,11 +1092,11 @@ abstract class RDD[T: ClassTag](
    * implicit Ordering[T] and maintains the ordering. This does the opposite of [[top]].
    * For example:
    * {{{
-   *   sc.parallelize([10, 4, 2, 12, 3]).takeOrdered(1)
-   *   // returns [12]
+   *   sc.parallelize(Seq(10, 4, 2, 12, 3)).takeOrdered(1)
+   *   // returns Array(2)
    *
-   *   sc.parallelize([2, 3, 4, 5, 6]).takeOrdered(2)
-   *   // returns [2, 3]
+   *   sc.parallelize(Seq(2, 3, 4, 5, 6)).takeOrdered(2)
+   *   // returns Array(2, 3)
    * }}}
    *
    * @param num the number of top elements to return
diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py
index 65f63153cdff..a0b2c744f0e7 100644
--- a/python/pyspark/rdd.py
+++ b/python/pyspark/rdd.py
@@ -857,7 +857,7 @@ def top(self, num):
         Note: It returns the list sorted in descending order.
         >>> sc.parallelize([10, 4, 2, 12, 3]).top(1)
         [12]
-        >>> sc.parallelize([2, 3, 4, 5, 6], 2).cache().top(2)
+        >>> sc.parallelize([2, 3, 4, 5, 6], 2).top(2)
         [6, 5]
         """
         def topIterator(iterator):

From 443f5e1bbcf9ec55e5ce6e4f738a002a47818100 Mon Sep 17 00:00:00 2001
From: CodingCat <zhunansjtu@gmail.com>
Date: Tue, 17 Jun 2014 12:17:48 -0700
Subject: [PATCH 446/810] SPARK-2038: rename "conf" parameters in the
 saveAsHadoop functions

to distinguish with SparkConf object

https://issues.apache.org/jira/browse/SPARK-2038

Author: CodingCat <zhunansjtu@gmail.com>

Closes #1087 from CodingCat/SPARK-2038 and squashes the following commits:

763975f [CodingCat] style fix
d91288d [CodingCat] rename "conf" parameters in the saveAsHadoop functions
---
 .../apache/spark/rdd/PairRDDFunctions.scala   | 49 ++++++++++---------
 1 file changed, 25 insertions(+), 24 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
index fe36c80e0be8..bff77b4ecbf2 100644
--- a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
@@ -719,9 +719,9 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
       keyClass: Class[_],
       valueClass: Class[_],
       outputFormatClass: Class[_ <: NewOutputFormat[_, _]],
-      conf: Configuration = self.context.hadoopConfiguration)
+      hadoopConf: Configuration = self.context.hadoopConfiguration)
   {
-    val job = new NewAPIHadoopJob(conf)
+    val job = new NewAPIHadoopJob(hadoopConf)
     job.setOutputKeyClass(keyClass)
     job.setOutputValueClass(valueClass)
     job.setOutputFormatClass(outputFormatClass)
@@ -752,24 +752,25 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
       keyClass: Class[_],
       valueClass: Class[_],
       outputFormatClass: Class[_ <: OutputFormat[_, _]],
-      conf: JobConf = new JobConf(self.context.hadoopConfiguration),
+      hadoopConf: JobConf = new JobConf(self.context.hadoopConfiguration),
       codec: Option[Class[_ <: CompressionCodec]] = None) {
-    conf.setOutputKeyClass(keyClass)
-    conf.setOutputValueClass(valueClass)
+    hadoopConf.setOutputKeyClass(keyClass)
+    hadoopConf.setOutputValueClass(valueClass)
     // Doesn't work in Scala 2.9 due to what may be a generics bug
     // TODO: Should we uncomment this for Scala 2.10?
     // conf.setOutputFormat(outputFormatClass)
-    conf.set("mapred.output.format.class", outputFormatClass.getName)
+    hadoopConf.set("mapred.output.format.class", outputFormatClass.getName)
     for (c <- codec) {
-      conf.setCompressMapOutput(true)
-      conf.set("mapred.output.compress", "true")
-      conf.setMapOutputCompressorClass(c)
-      conf.set("mapred.output.compression.codec", c.getCanonicalName)
-      conf.set("mapred.output.compression.type", CompressionType.BLOCK.toString)
+      hadoopConf.setCompressMapOutput(true)
+      hadoopConf.set("mapred.output.compress", "true")
+      hadoopConf.setMapOutputCompressorClass(c)
+      hadoopConf.set("mapred.output.compression.codec", c.getCanonicalName)
+      hadoopConf.set("mapred.output.compression.type", CompressionType.BLOCK.toString)
     }
-    conf.setOutputCommitter(classOf[FileOutputCommitter])
-    FileOutputFormat.setOutputPath(conf, SparkHadoopWriter.createPathFromString(path, conf))
-    saveAsHadoopDataset(conf)
+    hadoopConf.setOutputCommitter(classOf[FileOutputCommitter])
+    FileOutputFormat.setOutputPath(hadoopConf,
+      SparkHadoopWriter.createPathFromString(path, hadoopConf))
+    saveAsHadoopDataset(hadoopConf)
   }
 
   /**
@@ -778,8 +779,8 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * output paths required (e.g. a table name to write to) in the same way as it would be
    * configured for a Hadoop MapReduce job.
    */
-  def saveAsNewAPIHadoopDataset(conf: Configuration) {
-    val job = new NewAPIHadoopJob(conf)
+  def saveAsNewAPIHadoopDataset(hadoopConf: Configuration) {
+    val job = new NewAPIHadoopJob(hadoopConf)
     val formatter = new SimpleDateFormat("yyyyMMddHHmm")
     val jobtrackerID = formatter.format(new Date())
     val stageId = self.id
@@ -835,10 +836,10 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * (e.g. a table name to write to) in the same way as it would be configured for a Hadoop
    * MapReduce job.
    */
-  def saveAsHadoopDataset(conf: JobConf) {
-    val outputFormatInstance = conf.getOutputFormat
-    val keyClass = conf.getOutputKeyClass
-    val valueClass = conf.getOutputValueClass
+  def saveAsHadoopDataset(hadoopConf: JobConf) {
+    val outputFormatInstance = hadoopConf.getOutputFormat
+    val keyClass = hadoopConf.getOutputKeyClass
+    val valueClass = hadoopConf.getOutputValueClass
     if (outputFormatInstance == null) {
       throw new SparkException("Output format class not set")
     }
@@ -848,18 +849,18 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
     if (valueClass == null) {
       throw new SparkException("Output value class not set")
     }
-    SparkHadoopUtil.get.addCredentials(conf)
+    SparkHadoopUtil.get.addCredentials(hadoopConf)
 
     logDebug("Saving as hadoop file of type (" + keyClass.getSimpleName + ", " +
       valueClass.getSimpleName + ")")
 
     if (self.conf.getBoolean("spark.hadoop.validateOutputSpecs", true)) {
       // FileOutputFormat ignores the filesystem parameter
-      val ignoredFs = FileSystem.get(conf)
-      conf.getOutputFormat.checkOutputSpecs(ignoredFs, conf)
+      val ignoredFs = FileSystem.get(hadoopConf)
+      hadoopConf.getOutputFormat.checkOutputSpecs(ignoredFs, hadoopConf)
     }
 
-    val writer = new SparkHadoopWriter(conf)
+    val writer = new SparkHadoopWriter(hadoopConf)
     writer.preSetup()
 
     def writeToFile(context: TaskContext, iter: Iterator[(K, V)]) {

From a14807e84cbda64e5a73babb7a28c69ee1ef3cbb Mon Sep 17 00:00:00 2001
From: Andrew Or <andrewor14@gmail.com>
Date: Tue, 17 Jun 2014 12:25:55 -0700
Subject: [PATCH 447/810] [SPARK-2147 / 2161] Show removed executors on the UI

This PR includes two changes
- **[SPARK-2147]** When an application finishes cleanly (i.e. `sc.stop()` is called), all of its executors used to disappear from the Master UI. This no longer happens.
- **[SPARK-2161]** This adds a "Removed Executors" table to Master UI, so the user can find out why their executors died from the logs, for instance. The equivalent table already existed in the Worker UI, but was hidden because of a bug (the comment `//scalastyle:off` disconnected the `Seq[Node]` that represents the HTML for table).

This should go into 1.0.1 if possible.

Author: Andrew Or <andrewor14@gmail.com>

Closes #1102 from andrewor14/remember-removed-executors and squashes the following commits:

2e2298f [Andrew Or] Add hash code method to ExecutorInfo (minor)
abd72e0 [Andrew Or] Merge branch 'master' of github.com:apache/spark into remember-removed-executors
792f992 [Andrew Or] Add missing equals method in ExecutorInfo
3390b49 [Andrew Or] Add executor state column to WorkerPage
161f8a2 [Andrew Or] Display finished executors table (fix bug)
fbb65b8 [Andrew Or] Removed unused method
c89bb6e [Andrew Or] Add table for removed executors in MasterWebUI
fe47402 [Andrew Or] Show exited executors on the Master UI
---
 .../spark/deploy/master/ApplicationInfo.scala |  4 +
 .../spark/deploy/master/ExecutorInfo.scala    | 15 +++
 .../deploy/master/ui/ApplicationPage.scala    | 80 +++++++++-------
 .../spark/deploy/worker/ui/WorkerPage.scala   | 95 ++++++++-----------
 4 files changed, 107 insertions(+), 87 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/master/ApplicationInfo.scala b/core/src/main/scala/org/apache/spark/deploy/master/ApplicationInfo.scala
index 46b9f4dc7d3b..72d0589689e7 100644
--- a/core/src/main/scala/org/apache/spark/deploy/master/ApplicationInfo.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/master/ApplicationInfo.scala
@@ -20,6 +20,7 @@ package org.apache.spark.deploy.master
 import java.util.Date
 
 import scala.collection.mutable
+import scala.collection.mutable.ArrayBuffer
 
 import akka.actor.ActorRef
 
@@ -36,6 +37,7 @@ private[spark] class ApplicationInfo(
 
   @transient var state: ApplicationState.Value = _
   @transient var executors: mutable.HashMap[Int, ExecutorInfo] = _
+  @transient var removedExecutors: ArrayBuffer[ExecutorInfo] = _
   @transient var coresGranted: Int = _
   @transient var endTime: Long = _
   @transient var appSource: ApplicationSource = _
@@ -51,6 +53,7 @@ private[spark] class ApplicationInfo(
     endTime = -1L
     appSource = new ApplicationSource(this)
     nextExecutorId = 0
+    removedExecutors = new ArrayBuffer[ExecutorInfo]
   }
 
   private def newExecutorId(useID: Option[Int] = None): Int = {
@@ -74,6 +77,7 @@ private[spark] class ApplicationInfo(
 
   def removeExecutor(exec: ExecutorInfo) {
     if (executors.contains(exec.id)) {
+      removedExecutors += executors(exec.id)
       executors -= exec.id
       coresGranted -= exec.cores
     }
diff --git a/core/src/main/scala/org/apache/spark/deploy/master/ExecutorInfo.scala b/core/src/main/scala/org/apache/spark/deploy/master/ExecutorInfo.scala
index 76db61dd619c..d417070c5101 100644
--- a/core/src/main/scala/org/apache/spark/deploy/master/ExecutorInfo.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/master/ExecutorInfo.scala
@@ -34,4 +34,19 @@ private[spark] class ExecutorInfo(
   }
 
   def fullId: String = application.id + "/" + id
+
+  override def equals(other: Any): Boolean = {
+    other match {
+      case info: ExecutorInfo =>
+        fullId == info.fullId &&
+        worker.id == info.worker.id &&
+        cores == info.cores &&
+        memory == info.memory
+      case _ => false
+    }
+  }
+
+  override def toString: String = fullId
+
+  override def hashCode: Int = toString.hashCode()
 }
diff --git a/core/src/main/scala/org/apache/spark/deploy/master/ui/ApplicationPage.scala b/core/src/main/scala/org/apache/spark/deploy/master/ui/ApplicationPage.scala
index b5cd4d2ea963..34fa1429c86d 100644
--- a/core/src/main/scala/org/apache/spark/deploy/master/ui/ApplicationPage.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/master/ui/ApplicationPage.scala
@@ -25,7 +25,7 @@ import scala.xml.Node
 import akka.pattern.ask
 import org.json4s.JValue
 
-import org.apache.spark.deploy.JsonProtocol
+import org.apache.spark.deploy.{ExecutorState, JsonProtocol}
 import org.apache.spark.deploy.DeployMessages.{MasterStateResponse, RequestMasterState}
 import org.apache.spark.deploy.master.ExecutorInfo
 import org.apache.spark.ui.{WebUIPage, UIUtils}
@@ -57,43 +57,55 @@ private[spark] class ApplicationPage(parent: MasterWebUI) extends WebUIPage("app
     })
 
     val executorHeaders = Seq("ExecutorID", "Worker", "Cores", "Memory", "State", "Logs")
-    val executors = app.executors.values.toSeq
-    val executorTable = UIUtils.listingTable(executorHeaders, executorRow, executors)
+    val allExecutors = (app.executors.values ++ app.removedExecutors).toSet.toSeq
+    // This includes executors that are either still running or have exited cleanly
+    val executors = allExecutors.filter { exec =>
+      !ExecutorState.isFinished(exec.state) || exec.state == ExecutorState.EXITED
+    }
+    val removedExecutors = allExecutors.diff(executors)
+    val executorsTable = UIUtils.listingTable(executorHeaders, executorRow, executors)
+    val removedExecutorsTable = UIUtils.listingTable(executorHeaders, executorRow, removedExecutors)
 
     val content =
-        <div class="row-fluid">
-          <div class="span12">
-            <ul class="unstyled">
-              <li><strong>ID:</strong> {app.id}</li>
-              <li><strong>Name:</strong> {app.desc.name}</li>
-              <li><strong>User:</strong> {app.desc.user}</li>
-              <li><strong>Cores:</strong>
-                {
-                if (app.desc.maxCores.isEmpty) {
-                  "Unlimited (%s granted)".format(app.coresGranted)
-                } else {
-                  "%s (%s granted, %s left)".format(
-                    app.desc.maxCores.get, app.coresGranted, app.coresLeft)
-                }
-                }
-              </li>
-              <li>
-                <strong>Executor Memory:</strong>
-                {Utils.megabytesToString(app.desc.memoryPerSlave)}
-              </li>
-              <li><strong>Submit Date:</strong> {app.submitDate}</li>
-              <li><strong>State:</strong> {app.state}</li>
-              <li><strong><a href={app.desc.appUiUrl}>Application Detail UI</a></strong></li>
-            </ul>
-          </div>
+      <div class="row-fluid">
+        <div class="span12">
+          <ul class="unstyled">
+            <li><strong>ID:</strong> {app.id}</li>
+            <li><strong>Name:</strong> {app.desc.name}</li>
+            <li><strong>User:</strong> {app.desc.user}</li>
+            <li><strong>Cores:</strong>
+            {
+              if (app.desc.maxCores.isEmpty) {
+                "Unlimited (%s granted)".format(app.coresGranted)
+              } else {
+                "%s (%s granted, %s left)".format(
+                  app.desc.maxCores.get, app.coresGranted, app.coresLeft)
+              }
+            }
+            </li>
+            <li>
+              <strong>Executor Memory:</strong>
+              {Utils.megabytesToString(app.desc.memoryPerSlave)}
+            </li>
+            <li><strong>Submit Date:</strong> {app.submitDate}</li>
+            <li><strong>State:</strong> {app.state}</li>
+            <li><strong><a href={app.desc.appUiUrl}>Application Detail UI</a></strong></li>
+          </ul>
         </div>
+      </div>
 
-        <div class="row-fluid"> <!-- Executors -->
-          <div class="span12">
-            <h4> Executor Summary </h4>
-            {executorTable}
-          </div>
-        </div>;
+      <div class="row-fluid"> <!-- Executors -->
+        <div class="span12">
+          <h4> Executor Summary </h4>
+          {executorsTable}
+          {
+            if (removedExecutors.nonEmpty) {
+              <h4> Removed Executors </h4> ++
+              removedExecutorsTable
+            }
+          }
+        </div>
+      </div>;
     UIUtils.basicSparkPage(content, "Application: " + app.desc.name)
   }
 
diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/ui/WorkerPage.scala b/core/src/main/scala/org/apache/spark/deploy/worker/ui/WorkerPage.scala
index d4513118ced0..327b90503280 100644
--- a/core/src/main/scala/org/apache/spark/deploy/worker/ui/WorkerPage.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/worker/ui/WorkerPage.scala
@@ -46,74 +46,62 @@ private[spark] class WorkerPage(parent: WorkerWebUI) extends WebUIPage("") {
     val stateFuture = (workerActor ? RequestWorkerState)(timeout).mapTo[WorkerStateResponse]
     val workerState = Await.result(stateFuture, timeout)
 
-    val executorHeaders = Seq("ExecutorID", "Cores", "Memory", "Job Details", "Logs")
+    val executorHeaders = Seq("ExecutorID", "Cores", "State", "Memory", "Job Details", "Logs")
+    val runningExecutors = workerState.executors
     val runningExecutorTable =
-      UIUtils.listingTable(executorHeaders, executorRow, workerState.executors)
+      UIUtils.listingTable(executorHeaders, executorRow, runningExecutors)
+    val finishedExecutors = workerState.finishedExecutors
     val finishedExecutorTable =
-      UIUtils.listingTable(executorHeaders, executorRow, workerState.finishedExecutors)
+      UIUtils.listingTable(executorHeaders, executorRow, finishedExecutors)
 
     val driverHeaders = Seq("DriverID", "Main Class", "State", "Cores", "Memory", "Logs", "Notes")
     val runningDrivers = workerState.drivers.sortBy(_.driverId).reverse
     val runningDriverTable = UIUtils.listingTable(driverHeaders, driverRow, runningDrivers)
     val finishedDrivers = workerState.finishedDrivers.sortBy(_.driverId).reverse
-    def finishedDriverTable = UIUtils.listingTable(driverHeaders, driverRow, finishedDrivers)
+    val finishedDriverTable = UIUtils.listingTable(driverHeaders, driverRow, finishedDrivers)
 
     // For now we only show driver information if the user has submitted drivers to the cluster.
     // This is until we integrate the notion of drivers and applications in the UI.
-    def hasDrivers = runningDrivers.length > 0 || finishedDrivers.length > 0
 
     val content =
-        <div class="row-fluid"> <!-- Worker Details -->
-          <div class="span12">
-            <ul class="unstyled">
-              <li><strong>ID:</strong> {workerState.workerId}</li>
-              <li><strong>
-                Master URL:</strong> {workerState.masterUrl}
-              </li>
-              <li><strong>Cores:</strong> {workerState.cores} ({workerState.coresUsed} Used)</li>
-              <li><strong>Memory:</strong> {Utils.megabytesToString(workerState.memory)}
-                ({Utils.megabytesToString(workerState.memoryUsed)} Used)</li>
-            </ul>
-            <p><a href={workerState.masterWebUiUrl}>Back to Master</a></p>
-          </div>
+      <div class="row-fluid"> <!-- Worker Details -->
+        <div class="span12">
+          <ul class="unstyled">
+            <li><strong>ID:</strong> {workerState.workerId}</li>
+            <li><strong>
+              Master URL:</strong> {workerState.masterUrl}
+            </li>
+            <li><strong>Cores:</strong> {workerState.cores} ({workerState.coresUsed} Used)</li>
+            <li><strong>Memory:</strong> {Utils.megabytesToString(workerState.memory)}
+              ({Utils.megabytesToString(workerState.memoryUsed)} Used)</li>
+          </ul>
+          <p><a href={workerState.masterWebUiUrl}>Back to Master</a></p>
         </div>
-
-        <div class="row-fluid"> <!-- Running Executors -->
-          <div class="span12">
-            <h4> Running Executors {workerState.executors.size} </h4>
-            {runningExecutorTable}
-          </div>
-        </div>
-        // scalastyle:off
-        <div>
-          {if (hasDrivers)
-            <div class="row-fluid"> <!-- Running Drivers -->
-              <div class="span12">
-                <h4> Running Drivers {workerState.drivers.size} </h4>
-                {runningDriverTable}
-              </div>
-            </div>
+      </div>
+      <div class="row-fluid"> <!-- Executors and Drivers -->
+        <div class="span12">
+          <h4> Running Executors ({runningExecutors.size}) </h4>
+          {runningExecutorTable}
+          {
+            if (runningDrivers.nonEmpty) {
+              <h4> Running Drivers ({runningDrivers.size}) </h4> ++
+              runningDriverTable
+            }
           }
-        </div>
-
-        <div class="row-fluid"> <!-- Finished Executors  -->
-          <div class="span12">
-            <h4> Finished Executors </h4>
-            {finishedExecutorTable}
-          </div>
-        </div>
-
-        <div>
-          {if (hasDrivers)
-            <div class="row-fluid"> <!-- Finished Drivers  -->
-              <div class="span12">
-                <h4> Finished Drivers </h4>
-                {finishedDriverTable}
-              </div>
-            </div>
+          {
+            if (finishedExecutors.nonEmpty) {
+              <h4>Finished Executors ({finishedExecutors.size}) </h4> ++
+              finishedExecutorTable
+            }
           }
-        </div>;
-    // scalastyle:on
+          {
+            if (finishedDrivers.nonEmpty) {
+              <h4> Finished Drivers ({finishedDrivers.size}) </h4> ++
+              finishedDriverTable
+            }
+          }
+        </div>
+      </div>;
     UIUtils.basicSparkPage(content, "Spark Worker at %s:%s".format(
       workerState.host, workerState.port))
   }
@@ -122,6 +110,7 @@ private[spark] class WorkerPage(parent: WorkerWebUI) extends WebUIPage("") {
     <tr>
       <td>{executor.execId}</td>
       <td>{executor.cores}</td>
+      <td>{executor.state}</td>
       <td sorttable_customkey={executor.memory.toString}>
         {Utils.megabytesToString(executor.memory)}
       </td>

From b2ebf429e24566c29850c570f8d76943151ad78c Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Tue, 17 Jun 2014 15:09:24 -0700
Subject: [PATCH 448/810] HOTFIX: bug caused by #941

This patch should have qualified the use of PIPE. This needs to be back ported into 0.9 and 1.0.

Author: Patrick Wendell <pwendell@gmail.com>

Closes #1108 from pwendell/hotfix and squashes the following commits:

711c58d [Patrick Wendell] HOTFIX: bug caused by #941
---
 ec2/spark_ec2.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ec2/spark_ec2.py b/ec2/spark_ec2.py
index 803caa0c480e..a40311d9fcf0 100755
--- a/ec2/spark_ec2.py
+++ b/ec2/spark_ec2.py
@@ -693,7 +693,7 @@ def ssh(host, opts, command):
 def _check_output(*popenargs, **kwargs):
     if 'stdout' in kwargs:
         raise ValueError('stdout argument not allowed, it will be overridden.')
-    process = subprocess.Popen(stdout=PIPE, *popenargs, **kwargs)
+    process = subprocess.Popen(stdout=subprocess.PIPE, *popenargs, **kwargs)
     output, unused_err = process.communicate()
     retcode = process.poll()
     if retcode:

From d2f4f30b12f99358953e2781957468e2cfe3c916 Mon Sep 17 00:00:00 2001
From: Yin Huai <huai@cse.ohio-state.edu>
Date: Tue, 17 Jun 2014 19:14:59 -0700
Subject: [PATCH 449/810] [SPARK-2060][SQL] Querying JSON Datasets with SQL and
 DSL in Spark SQL

JIRA: https://issues.apache.org/jira/browse/SPARK-2060

Programming guide: http://yhuai.github.io/site/sql-programming-guide.html

Scala doc of SQLContext: http://yhuai.github.io/site/api/scala/index.html#org.apache.spark.sql.SQLContext

Author: Yin Huai <huai@cse.ohio-state.edu>

Closes #999 from yhuai/newJson and squashes the following commits:

227e89e [Yin Huai] Merge remote-tracking branch 'upstream/master' into newJson
ce8eedd [Yin Huai] rxin's comments.
bc9ac51 [Yin Huai] Merge remote-tracking branch 'upstream/master' into newJson
94ffdaa [Yin Huai] Remove "get" from method names.
ce31c81 [Yin Huai] Merge remote-tracking branch 'upstream/master' into newJson
e2773a6 [Yin Huai] Merge remote-tracking branch 'upstream/master' into newJson
79ea9ba [Yin Huai] Fix typos.
5428451 [Yin Huai] Newline
1f908ce [Yin Huai] Remove extra line.
d7a005c [Yin Huai] Merge remote-tracking branch 'upstream/master' into newJson
7ea750e [Yin Huai] marmbrus's comments.
6a5f5ef [Yin Huai] Merge remote-tracking branch 'upstream/master' into newJson
83013fb [Yin Huai] Update Java Example.
e7a6c19 [Yin Huai] SchemaRDD.javaToPython should convert a field with the StructType to a Map.
6d20b85 [Yin Huai] Merge remote-tracking branch 'upstream/master' into newJson
4fbddf0 [Yin Huai] Programming guide.
9df8c5a [Yin Huai] Python API.
7027634 [Yin Huai] Java API.
cff84cc [Yin Huai] Use a SchemaRDD for a JSON dataset.
d0bd412 [Yin Huai] Merge remote-tracking branch 'upstream/master' into newJson
ab810b0 [Yin Huai] Make JsonRDD private.
6df0891 [Yin Huai] Apache header.
8347f2e [Yin Huai] Merge remote-tracking branch 'upstream/master' into newJson
66f9e76 [Yin Huai] Update docs and use the entire dataset to infer the schema.
8ffed79 [Yin Huai] Update the example.
a5a4b52 [Yin Huai] Merge remote-tracking branch 'upstream/master' into newJson
4325475 [Yin Huai] If a sampled dataset is used for schema inferring, update the schema of the JsonTable after first execution.
65b87f0 [Yin Huai] Fix sampling...
8846af5 [Yin Huai] API doc.
52a2275 [Yin Huai] Merge remote-tracking branch 'upstream/master' into newJson
0387523 [Yin Huai] Address PR comments.
666b957 [Yin Huai] Merge remote-tracking branch 'upstream/master' into newJson
a2313a6 [Yin Huai] Address PR comments.
f3ce176 [Yin Huai] After type conflict resolution, if a NullType is found, StringType is used.
0576406 [Yin Huai] Add Apache license header.
af91b23 [Yin Huai] Merge remote-tracking branch 'upstream/master' into newJson
f45583b [Yin Huai] Infer the schema of a JSON dataset (a text file with one JSON object per line or a RDD[String] with one JSON object per string) and returns a SchemaRDD.
f31065f [Yin Huai] A query plan or a SchemaRDD can print out its schema.
---
 .rat-excludes                                 |   1 +
 docs/sql-programming-guide.md                 | 290 +++++++---
 .../spark/examples/sql/JavaSparkSQL.java      |  78 ++-
 examples/src/main/resources/people.json       |   3 +
 project/SparkBuild.scala                      |  22 +-
 python/pyspark/sql.py                         |  64 ++-
 sql/catalyst/pom.xml                          |  28 +
 .../catalyst/analysis/HiveTypeCoercion.scala  |  25 +-
 .../spark/sql/catalyst/plans/QueryPlan.scala  |  51 ++
 .../optimizer/CombiningLimitsSuite.scala      |   3 +-
 .../optimizer/ConstantFoldingSuite.scala      |   3 +-
 .../optimizer/FilterPushdownSuite.scala       |   5 +-
 ...mplifyCaseConversionExpressionsSuite.scala |   3 +-
 .../PlanTest.scala}                           |   9 +-
 sql/core/pom.xml                              |  12 +
 .../org/apache/spark/sql/SQLContext.scala     |  45 +-
 .../org/apache/spark/sql/SchemaRDD.scala      |  38 +-
 .../org/apache/spark/sql/SchemaRDDLike.scala  |   6 +
 .../spark/sql/api/java/JavaSQLContext.scala   |  20 +
 .../org/apache/spark/sql/json/JsonRDD.scala   | 397 ++++++++++++++
 .../org/apache/spark/sql/QueryTest.scala      |   4 +-
 .../spark/sql/api/java/JavaSQLSuite.scala     |  45 ++
 .../org/apache/spark/sql/json/JsonSuite.scala | 519 ++++++++++++++++++
 .../apache/spark/sql/json/TestJsonData.scala  |  84 +++
 24 files changed, 1644 insertions(+), 111 deletions(-)
 create mode 100644 examples/src/main/resources/people.json
 rename sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/{optimizer/OptimizerTest.scala => plans/PlanTest.scala} (88%)
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/json/TestJsonData.scala

diff --git a/.rat-excludes b/.rat-excludes
index 52b2dfac5cf2..15344dfb292d 100644
--- a/.rat-excludes
+++ b/.rat-excludes
@@ -22,6 +22,7 @@ spark-env.sh.template
 log4j-defaults.properties
 sorttable.js
 .*txt
+.*json
 .*data
 .*log
 cloudpickle.py
diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md
index 4623bb4247d7..522c83884ef4 100644
--- a/docs/sql-programming-guide.md
+++ b/docs/sql-programming-guide.md
@@ -17,20 +17,20 @@ Spark.  At the core of this component is a new type of RDD,
 [Row](api/scala/index.html#org.apache.spark.sql.catalyst.expressions.Row) objects along with
 a schema that describes the data types of each column in the row.  A SchemaRDD is similar to a table
 in a traditional relational database.  A SchemaRDD can be created from an existing RDD, [Parquet](http://parquet.io)
-file, or by running HiveQL against data stored in [Apache Hive](http://hive.apache.org/).
+file, a JSON dataset, or by running HiveQL against data stored in [Apache Hive](http://hive.apache.org/).
 
 All of the examples on this page use sample data included in the Spark distribution and can be run in the `spark-shell`.
 
 </div>
 
 <div data-lang="java"  markdown="1">
-Spark SQL allows relational queries expressed in SQL, HiveQL, or Scala to be executed using
+Spark SQL allows relational queries expressed in SQL or HiveQL to be executed using
 Spark.  At the core of this component is a new type of RDD,
 [JavaSchemaRDD](api/scala/index.html#org.apache.spark.sql.api.java.JavaSchemaRDD).  JavaSchemaRDDs are composed
 [Row](api/scala/index.html#org.apache.spark.sql.api.java.Row) objects along with
 a schema that describes the data types of each column in the row.  A JavaSchemaRDD is similar to a table
 in a traditional relational database.  A JavaSchemaRDD can be created from an existing RDD, [Parquet](http://parquet.io)
-file, or by running HiveQL against data stored in [Apache Hive](http://hive.apache.org/).
+file, a JSON dataset, or by running HiveQL against data stored in [Apache Hive](http://hive.apache.org/).
 </div>
 
 <div data-lang="python"  markdown="1">
@@ -41,7 +41,7 @@ Spark.  At the core of this component is a new type of RDD,
 [Row](api/python/pyspark.sql.Row-class.html) objects along with
 a schema that describes the data types of each column in the row.  A SchemaRDD is similar to a table
 in a traditional relational database.  A SchemaRDD can be created from an existing RDD, [Parquet](http://parquet.io)
-file, or by running HiveQL against data stored in [Apache Hive](http://hive.apache.org/).
+file, a JSON dataset, or by running HiveQL against data stored in [Apache Hive](http://hive.apache.org/).
 
 All of the examples on this page use sample data included in the Spark distribution and can be run in the `pyspark` shell.
 </div>
@@ -64,8 +64,8 @@ descendants.  To create a basic SQLContext, all you need is a SparkContext.
 val sc: SparkContext // An existing SparkContext.
 val sqlContext = new org.apache.spark.sql.SQLContext(sc)
 
-// Importing the SQL context gives access to all the public SQL functions and implicit conversions.
-import sqlContext._
+// createSchemaRDD is used to implicitly convert an RDD to a SchemaRDD.
+import sqlContext.createSchemaRDD
 {% endhighlight %}
 
 </div>
@@ -77,8 +77,8 @@ The entry point into all relational functionality in Spark is the
 of its descendants.  To create a basic JavaSQLContext, all you need is a JavaSparkContext.
 
 {% highlight java %}
-JavaSparkContext ctx = ...; // An existing JavaSparkContext.
-JavaSQLContext sqlCtx = new org.apache.spark.sql.api.java.JavaSQLContext(ctx);
+JavaSparkContext sc = ...; // An existing JavaSparkContext.
+JavaSQLContext sqlContext = new org.apache.spark.sql.api.java.JavaSQLContext(sc);
 {% endhighlight %}
 
 </div>
@@ -91,14 +91,33 @@ of its decedents.  To create a basic SQLContext, all you need is a SparkContext.
 
 {% highlight python %}
 from pyspark.sql import SQLContext
-sqlCtx = SQLContext(sc)
+sqlContext = SQLContext(sc)
 {% endhighlight %}
 
 </div>
 
 </div>
 
-## Running SQL on RDDs
+# Data Sources
+
+<div class="codetabs">
+<div data-lang="scala"  markdown="1">
+Spark SQL supports operating on a variety of data sources through the `SchemaRDD` interface.
+Once a dataset has been loaded, it can be registered as a table and even joined with data from other sources.
+</div>
+
+<div data-lang="java"  markdown="1">
+Spark SQL supports operating on a variety of data sources through the `JavaSchemaRDD` interface.
+Once a dataset has been loaded, it can be registered as a table and even joined with data from other sources.
+</div>
+
+<div data-lang="python"  markdown="1">
+Spark SQL supports operating on a variety of data sources through the `SchemaRDD` interface.
+Once a dataset has been loaded, it can be registered as a table and even joined with data from other sources.
+</div>
+</div>
+
+## RDDs
 
 <div class="codetabs">
 
@@ -111,8 +130,10 @@ types such as Sequences or Arrays. This RDD can be implicitly converted to a Sch
 registered as a table.  Tables can be used in subsequent SQL statements.
 
 {% highlight scala %}
+// sc is an existing SparkContext.
 val sqlContext = new org.apache.spark.sql.SQLContext(sc)
-import sqlContext._
+// createSchemaRDD is used to implicitly convert an RDD to a SchemaRDD.
+import sqlContext.createSchemaRDD
 
 // Define the schema using a case class.
 // Note: Case classes in Scala 2.10 can support only up to 22 fields. To work around this limit, 
@@ -124,7 +145,7 @@ val people = sc.textFile("examples/src/main/resources/people.txt").map(_.split("
 people.registerAsTable("people")
 
 // SQL statements can be run by using the sql methods provided by sqlContext.
-val teenagers = sql("SELECT name FROM people WHERE age >= 13 AND age <= 19")
+val teenagers = sqlContext.sql("SELECT name FROM people WHERE age >= 13 AND age <= 19")
 
 // The results of SQL queries are SchemaRDDs and support all the normal RDD operations.
 // The columns of a row in the result can be accessed by ordinal.
@@ -170,12 +191,11 @@ A schema can be applied to an existing RDD by calling `applySchema` and providin
 for the JavaBean.
 
 {% highlight java %}
-
-JavaSparkContext ctx = ...; // An existing JavaSparkContext.
-JavaSQLContext sqlCtx = new org.apache.spark.sql.api.java.JavaSQLContext(ctx)
+// sc is an existing JavaSparkContext.
+JavaSQLContext sqlContext = new org.apache.spark.sql.api.java.JavaSQLContext(sc)
 
 // Load a text file and convert each line to a JavaBean.
-JavaRDD<Person> people = ctx.textFile("examples/src/main/resources/people.txt").map(
+JavaRDD<Person> people = sc.textFile("examples/src/main/resources/people.txt").map(
   new Function<String, Person>() {
     public Person call(String line) throws Exception {
       String[] parts = line.split(",");
@@ -189,11 +209,11 @@ JavaRDD<Person> people = ctx.textFile("examples/src/main/resources/people.txt").
   });
 
 // Apply a schema to an RDD of JavaBeans and register it as a table.
-JavaSchemaRDD schemaPeople = sqlCtx.applySchema(people, Person.class);
+JavaSchemaRDD schemaPeople = sqlContext.applySchema(people, Person.class);
 schemaPeople.registerAsTable("people");
 
 // SQL can be run over RDDs that have been registered as tables.
-JavaSchemaRDD teenagers = sqlCtx.sql("SELECT name FROM people WHERE age >= 13 AND age <= 19")
+JavaSchemaRDD teenagers = sqlContext.sql("SELECT name FROM people WHERE age >= 13 AND age <= 19")
 
 // The results of SQL queries are SchemaRDDs and support all the normal RDD operations.
 // The columns of a row in the result can be accessed by ordinal.
@@ -215,6 +235,10 @@ row. Any RDD of dictionaries can converted to a SchemaRDD and then registered as
 can be used in subsequent SQL statements.
 
 {% highlight python %}
+# sc is an existing SparkContext.
+from pyspark.sql import SQLContext
+sqlContext = SQLContext(sc)
+
 # Load a text file and convert each line to a dictionary.
 lines = sc.textFile("examples/src/main/resources/people.txt")
 parts = lines.map(lambda l: l.split(","))
@@ -223,14 +247,16 @@ people = parts.map(lambda p: {"name": p[0], "age": int(p[1])})
 # Infer the schema, and register the SchemaRDD as a table.
 # In future versions of PySpark we would like to add support for registering RDDs with other
 # datatypes as tables
-peopleTable = sqlCtx.inferSchema(people)
-peopleTable.registerAsTable("people")
+schemaPeople = sqlContext.inferSchema(people)
+schemaPeople.registerAsTable("people")
 
 # SQL can be run over SchemaRDDs that have been registered as a table.
-teenagers = sqlCtx.sql("SELECT name FROM people WHERE age >= 13 AND age <= 19")
+teenagers = sqlContext.sql("SELECT name FROM people WHERE age >= 13 AND age <= 19")
 
 # The results of SQL queries are RDDs and support all the normal RDD operations.
 teenNames = teenagers.map(lambda p: "Name: " + p.name)
+for teenName in teenNames.collect():
+  print teenName
 {% endhighlight %}
 
 </div>
@@ -241,7 +267,7 @@ teenNames = teenagers.map(lambda p: "Name: " + p.name)
 Users that want a more complete dialect of SQL should look at the HiveQL support provided by
 `HiveContext`.
 
-## Using Parquet
+## Parquet Files
 
 [Parquet](http://parquet.io) is a columnar format that is supported by many other data processing systems.
 Spark SQL provides support for both reading and writing Parquet files that automatically preserves the schema
@@ -252,22 +278,23 @@ of the original data.  Using the data from the above example:
 <div data-lang="scala"  markdown="1">
 
 {% highlight scala %}
-val sqlContext = new org.apache.spark.sql.SQLContext(sc)
-import sqlContext._
+// sqlContext from the previous example is used in this example.
+// createSchemaRDD is used to implicitly convert an RDD to a SchemaRDD.
+import sqlContext.createSchemaRDD
 
 val people: RDD[Person] = ... // An RDD of case class objects, from the previous example.
 
-// The RDD is implicitly converted to a SchemaRDD, allowing it to be stored using Parquet.
+// The RDD is implicitly converted to a SchemaRDD by createSchemaRDD, allowing it to be stored using Parquet.
 people.saveAsParquetFile("people.parquet")
 
 // Read in the parquet file created above.  Parquet files are self-describing so the schema is preserved.
-// The result of loading a Parquet file is also a JavaSchemaRDD.
+// The result of loading a Parquet file is also a SchemaRDD.
 val parquetFile = sqlContext.parquetFile("people.parquet")
 
 //Parquet files can also be registered as tables and then used in SQL statements.
 parquetFile.registerAsTable("parquetFile")
-val teenagers = sql("SELECT name FROM parquetFile WHERE age >= 13 AND age <= 19")
-teenagers.collect().foreach(println)
+val teenagers = sqlContext.sql("SELECT name FROM parquetFile WHERE age >= 13 AND age <= 19")
+teenagers.map(t => "Name: " + t(0)).collect().foreach(println)
 {% endhighlight %}
 
 </div>
@@ -275,6 +302,7 @@ teenagers.collect().foreach(println)
 <div data-lang="java"  markdown="1">
 
 {% highlight java %}
+// sqlContext from the previous example is used in this example.
 
 JavaSchemaRDD schemaPeople = ... // The JavaSchemaRDD from the previous example.
 
@@ -283,13 +311,16 @@ schemaPeople.saveAsParquetFile("people.parquet");
 
 // Read in the Parquet file created above.  Parquet files are self-describing so the schema is preserved.
 // The result of loading a parquet file is also a JavaSchemaRDD.
-JavaSchemaRDD parquetFile = sqlCtx.parquetFile("people.parquet");
+JavaSchemaRDD parquetFile = sqlContext.parquetFile("people.parquet");
 
 //Parquet files can also be registered as tables and then used in SQL statements.
 parquetFile.registerAsTable("parquetFile");
-JavaSchemaRDD teenagers = sqlCtx.sql("SELECT name FROM parquetFile WHERE age >= 13 AND age <= 19");
-
-
+JavaSchemaRDD teenagers = sqlContext.sql("SELECT name FROM parquetFile WHERE age >= 13 AND age <= 19");
+List<String> teenagerNames = teenagers.map(new Function<Row, String>() {
+  public String call(Row row) {
+    return "Name: " + row.getString(0);
+  }
+}).collect();
 {% endhighlight %}
 
 </div>
@@ -297,50 +328,149 @@ JavaSchemaRDD teenagers = sqlCtx.sql("SELECT name FROM parquetFile WHERE age >=
 <div data-lang="python"  markdown="1">
 
 {% highlight python %}
+# sqlContext from the previous example is used in this example.
 
-peopleTable # The SchemaRDD from the previous example.
+schemaPeople # The SchemaRDD from the previous example.
 
 # SchemaRDDs can be saved as Parquet files, maintaining the schema information.
-peopleTable.saveAsParquetFile("people.parquet")
+schemaPeople.saveAsParquetFile("people.parquet")
 
 # Read in the Parquet file created above.  Parquet files are self-describing so the schema is preserved.
 # The result of loading a parquet file is also a SchemaRDD.
-parquetFile = sqlCtx.parquetFile("people.parquet")
+parquetFile = sqlContext.parquetFile("people.parquet")
 
 # Parquet files can also be registered as tables and then used in SQL statements.
 parquetFile.registerAsTable("parquetFile");
-teenagers = sqlCtx.sql("SELECT name FROM parquetFile WHERE age >= 13 AND age <= 19")
-
+teenagers = sqlContext.sql("SELECT name FROM parquetFile WHERE age >= 13 AND age <= 19")
+teenNames = teenagers.map(lambda p: "Name: " + p.name)
+for teenName in teenNames.collect():
+  print teenName
 {% endhighlight %}
 
 </div>
 
 </div>
 
-## Writing Language-Integrated Relational Queries
+## JSON Datasets
+<div class="codetabs">
 
-**Language-Integrated queries are currently only supported in Scala.**
+<div data-lang="scala"  markdown="1">
+Spark SQL can automatically infer the schema of a JSON dataset and load it as a SchemaRDD.
+This conversion can be done using one of two methods in a SQLContext:
 
-Spark SQL also supports a domain specific language for writing queries.  Once again,
-using the data from the above examples:
+* `jsonFile` - loads data from a directory of JSON files where each line of the files is a JSON object.
+* `jsonRdd` - loads data from an existing RDD where each element of the RDD is a string containing a JSON object.
 
 {% highlight scala %}
+// sc is an existing SparkContext.
 val sqlContext = new org.apache.spark.sql.SQLContext(sc)
-import sqlContext._
-val people: RDD[Person] = ... // An RDD of case class objects, from the first example.
 
-// The following is the same as 'SELECT name FROM people WHERE age >= 10 AND age <= 19'
-val teenagers = people.where('age >= 10).where('age <= 19).select('name)
+// A JSON dataset is pointed to by path.
+// The path can be either a single text file or a directory storing text files.
+val path = "examples/src/main/resources/people.json"
+// Create a SchemaRDD from the file(s) pointed to by path
+val people = sqlContext.jsonFile(path)
+
+// The inferred schema can be visualized using the printSchema() method.
+people.printSchema()
+// root
+//  |-- age: IntegerType
+//  |-- name: StringType
+
+// Register this SchemaRDD as a table.
+people.registerAsTable("people")
+
+// SQL statements can be run by using the sql methods provided by sqlContext.
+val teenagers = sqlContext.sql("SELECT name FROM people WHERE age >= 13 AND age <= 19")
+
+// Alternatively, a SchemaRDD can be created for a JSON dataset represented by
+// an RDD[String] storing one JSON object per string.
+val anotherPeopleRDD = sc.parallelize(
+  """{"name":"Yin","address":{"city":"Columbus","state":"Ohio"}}""" :: Nil)
+val anotherPeople = sqlContext.jsonRDD(anotherPeopleRDD)
 {% endhighlight %}
 
-The DSL uses Scala symbols to represent columns in the underlying table, which are identifiers
-prefixed with a tick (`'`).  Implicit conversions turn these symbols into expressions that are
-evaluated by the SQL execution engine.  A full list of the functions supported can be found in the
-[ScalaDoc](api/scala/index.html#org.apache.spark.sql.SchemaRDD).
+</div>
 
-<!-- TODO: Include the table of operations here. -->
+<div data-lang="java"  markdown="1">
+Spark SQL can automatically infer the schema of a JSON dataset and load it as a JavaSchemaRDD.
+This conversion can be done using one of two methods in a JavaSQLContext :
 
-# Hive Support
+* `jsonFile` - loads data from a directory of JSON files where each line of the files is a JSON object.
+* `jsonRdd` - loads data from an existing RDD where each element of the RDD is a string containing a JSON object.
+
+{% highlight java %}
+// sc is an existing JavaSparkContext.
+JavaSQLContext sqlContext = new org.apache.spark.sql.api.java.JavaSQLContext(sc);
+
+// A JSON dataset is pointed to by path.
+// The path can be either a single text file or a directory storing text files.
+String path = "examples/src/main/resources/people.json";
+// Create a JavaSchemaRDD from the file(s) pointed to by path
+JavaSchemaRDD people = sqlContext.jsonFile(path);
+
+// The inferred schema can be visualized using the printSchema() method.
+people.printSchema();
+// root
+//  |-- age: IntegerType
+//  |-- name: StringType
+
+// Register this JavaSchemaRDD as a table.
+people.registerAsTable("people");
+
+// SQL statements can be run by using the sql methods provided by sqlContext.
+JavaSchemaRDD teenagers = sqlContext.sql("SELECT name FROM people WHERE age >= 13 AND age <= 19");
+
+// Alternatively, a JavaSchemaRDD can be created for a JSON dataset represented by
+// an RDD[String] storing one JSON object per string.
+List<String> jsonData = Arrays.asList(
+  "{\"name\":\"Yin\",\"address\":{\"city\":\"Columbus\",\"state\":\"Ohio\"}}");
+JavaRDD<String> anotherPeopleRDD = sc.parallelize(jsonData);
+JavaSchemaRDD anotherPeople = sqlContext.jsonRDD(anotherPeopleRDD);
+{% endhighlight %}
+</div>
+
+<div data-lang="python"  markdown="1">
+Spark SQL can automatically infer the schema of a JSON dataset and load it as a SchemaRDD.
+This conversion can be done using one of two methods in a SQLContext:
+
+* `jsonFile` - loads data from a directory of JSON files where each line of the files is a JSON object.
+* `jsonRdd` - loads data from an existing RDD where each element of the RDD is a string containing a JSON object.
+
+{% highlight python %}
+# sc is an existing SparkContext.
+from pyspark.sql import SQLContext
+sqlContext = SQLContext(sc)
+
+# A JSON dataset is pointed to by path.
+# The path can be either a single text file or a directory storing text files.
+path = "examples/src/main/resources/people.json"
+# Create a SchemaRDD from the file(s) pointed to by path
+people = sqlContext.jsonFile(path)
+
+# The inferred schema can be visualized using the printSchema() method.
+people.printSchema()
+# root
+#  |-- age: IntegerType
+#  |-- name: StringType
+
+# Register this SchemaRDD as a table.
+people.registerAsTable("people")
+
+# SQL statements can be run by using the sql methods provided by sqlContext.
+teenagers = sqlContext.sql("SELECT name FROM people WHERE age >= 13 AND age <= 19")
+
+# Alternatively, a SchemaRDD can be created for a JSON dataset represented by
+# an RDD[String] storing one JSON object per string.
+anotherPeopleRDD = sc.parallelize([
+  '{"name":"Yin","address":{"city":"Columbus","state":"Ohio"}}'])
+anotherPeople = sqlContext.jsonRDD(anotherPeopleRDD)
+{% endhighlight %}
+</div>
+
+</div>
+
+## Hive Tables
 
 Spark SQL also supports reading and writing data stored in [Apache Hive](http://hive.apache.org/).
 However, since Hive has a large number of dependencies, it is not included in the default Spark assembly.
@@ -362,17 +492,14 @@ which is similar to `HiveContext`, but creates a local copy of the `metastore` a
 automatically.
 
 {% highlight scala %}
-val sc: SparkContext // An existing SparkContext.
+// sc is an existing SparkContext.
 val hiveContext = new org.apache.spark.sql.hive.HiveContext(sc)
 
-// Importing the SQL context gives access to all the public SQL functions and implicit conversions.
-import hiveContext._
-
-hql("CREATE TABLE IF NOT EXISTS src (key INT, value STRING)")
-hql("LOAD DATA LOCAL INPATH 'examples/src/main/resources/kv1.txt' INTO TABLE src")
+hiveContext.hql("CREATE TABLE IF NOT EXISTS src (key INT, value STRING)")
+hiveContext.hql("LOAD DATA LOCAL INPATH 'examples/src/main/resources/kv1.txt' INTO TABLE src")
 
 // Queries are expressed in HiveQL
-hql("FROM src SELECT key, value").collect().foreach(println)
+hiveContext.hql("FROM src SELECT key, value").collect().foreach(println)
 {% endhighlight %}
 
 </div>
@@ -385,14 +512,14 @@ the `sql` method a `JavaHiveContext` also provides an `hql` methods, which allow
 expressed in HiveQL.
 
 {% highlight java %}
-JavaSparkContext ctx = ...; // An existing JavaSparkContext.
-JavaHiveContext hiveCtx = new org.apache.spark.sql.hive.api.java.HiveContext(ctx);
+// sc is an existing JavaSparkContext.
+JavaHiveContext hiveContext = new org.apache.spark.sql.hive.api.java.HiveContext(sc);
 
-hiveCtx.hql("CREATE TABLE IF NOT EXISTS src (key INT, value STRING)");
-hiveCtx.hql("LOAD DATA LOCAL INPATH 'examples/src/main/resources/kv1.txt' INTO TABLE src");
+hiveContext.hql("CREATE TABLE IF NOT EXISTS src (key INT, value STRING)");
+hiveContext.hql("LOAD DATA LOCAL INPATH 'examples/src/main/resources/kv1.txt' INTO TABLE src");
 
 // Queries are expressed in HiveQL.
-Row[] results = hiveCtx.hql("FROM src SELECT key, value").collect();
+Row[] results = hiveContext.hql("FROM src SELECT key, value").collect();
 
 {% endhighlight %}
 
@@ -406,17 +533,44 @@ the `sql` method a `HiveContext` also provides an `hql` methods, which allows qu
 expressed in HiveQL.
 
 {% highlight python %}
-
+# sc is an existing SparkContext.
 from pyspark.sql import HiveContext
-hiveCtx = HiveContext(sc)
+hiveContext = HiveContext(sc)
 
-hiveCtx.hql("CREATE TABLE IF NOT EXISTS src (key INT, value STRING)")
-hiveCtx.hql("LOAD DATA LOCAL INPATH 'examples/src/main/resources/kv1.txt' INTO TABLE src")
+hiveContext.hql("CREATE TABLE IF NOT EXISTS src (key INT, value STRING)")
+hiveContext.hql("LOAD DATA LOCAL INPATH 'examples/src/main/resources/kv1.txt' INTO TABLE src")
 
 # Queries can be expressed in HiveQL.
-results = hiveCtx.hql("FROM src SELECT key, value").collect()
+results = hiveContext.hql("FROM src SELECT key, value").collect()
 
 {% endhighlight %}
 
 </div>
 </div>
+
+
+# Writing Language-Integrated Relational Queries
+
+**Language-Integrated queries are currently only supported in Scala.**
+
+Spark SQL also supports a domain specific language for writing queries.  Once again,
+using the data from the above examples:
+
+{% highlight scala %}
+// sc is an existing SparkContext.
+val sqlContext = new org.apache.spark.sql.SQLContext(sc)
+// Importing the SQL context gives access to all the public SQL functions and implicit conversions.
+import sqlContext._
+val people: RDD[Person] = ... // An RDD of case class objects, from the first example.
+
+// The following is the same as 'SELECT name FROM people WHERE age >= 10 AND age <= 19'
+val teenagers = people.where('age >= 10).where('age <= 19).select('name)
+teenagers.map(t => "Name: " + t(0)).collect().foreach(println)
+{% endhighlight %}
+
+The DSL uses Scala symbols to represent columns in the underlying table, which are identifiers
+prefixed with a tick (`'`).  Implicit conversions turn these symbols into expressions that are
+evaluated by the SQL execution engine.  A full list of the functions supported can be found in the
+[ScalaDoc](api/scala/index.html#org.apache.spark.sql.SchemaRDD).
+
+<!-- TODO: Include the table of operations here. -->
\ No newline at end of file
diff --git a/examples/src/main/java/org/apache/spark/examples/sql/JavaSparkSQL.java b/examples/src/main/java/org/apache/spark/examples/sql/JavaSparkSQL.java
index ad5ec84b71e6..607df3eddd55 100644
--- a/examples/src/main/java/org/apache/spark/examples/sql/JavaSparkSQL.java
+++ b/examples/src/main/java/org/apache/spark/examples/sql/JavaSparkSQL.java
@@ -18,6 +18,7 @@
 package org.apache.spark.examples.sql;
 
 import java.io.Serializable;
+import java.util.Arrays;
 import java.util.List;
 
 import org.apache.spark.SparkConf;
@@ -56,6 +57,7 @@ public static void main(String[] args) throws Exception {
     JavaSparkContext ctx = new JavaSparkContext(sparkConf);
     JavaSQLContext sqlCtx = new JavaSQLContext(ctx);
 
+    System.out.println("=== Data source: RDD ===");
     // Load a text file and convert each line to a Java Bean.
     JavaRDD<Person> people = ctx.textFile("examples/src/main/resources/people.txt").map(
       new Function<String, Person>() {
@@ -84,16 +86,88 @@ public String call(Row row) {
         return "Name: " + row.getString(0);
       }
     }).collect();
+    for (String name: teenagerNames) {
+      System.out.println(name);
+    }
 
+    System.out.println("=== Data source: Parquet File ===");
     // JavaSchemaRDDs can be saved as parquet files, maintaining the schema information.
     schemaPeople.saveAsParquetFile("people.parquet");
 
-    // Read in the parquet file created above.  Parquet files are self-describing so the schema is preserved.
+    // Read in the parquet file created above.
+    // Parquet files are self-describing so the schema is preserved.
     // The result of loading a parquet file is also a JavaSchemaRDD.
     JavaSchemaRDD parquetFile = sqlCtx.parquetFile("people.parquet");
 
     //Parquet files can also be registered as tables and then used in SQL statements.
     parquetFile.registerAsTable("parquetFile");
-    JavaSchemaRDD teenagers2 = sqlCtx.sql("SELECT name FROM parquetFile WHERE age >= 13 AND age <= 19");
+    JavaSchemaRDD teenagers2 =
+      sqlCtx.sql("SELECT name FROM parquetFile WHERE age >= 13 AND age <= 19");
+    teenagerNames = teenagers2.map(new Function<Row, String>() {
+      public String call(Row row) {
+          return "Name: " + row.getString(0);
+      }
+    }).collect();
+    for (String name: teenagerNames) {
+      System.out.println(name);
+    }
+
+    System.out.println("=== Data source: JSON Dataset ===");
+    // A JSON dataset is pointed by path.
+    // The path can be either a single text file or a directory storing text files.
+    String path = "examples/src/main/resources/people.json";
+    // Create a JavaSchemaRDD from the file(s) pointed by path
+    JavaSchemaRDD peopleFromJsonFile = sqlCtx.jsonFile(path);
+
+    // Because the schema of a JSON dataset is automatically inferred, to write queries,
+    // it is better to take a look at what is the schema.
+    peopleFromJsonFile.printSchema();
+    // The schema of people is ...
+    // root
+    //  |-- age: IntegerType
+    //  |-- name: StringType
+
+    // Register this JavaSchemaRDD as a table.
+    peopleFromJsonFile.registerAsTable("people");
+
+    // SQL statements can be run by using the sql methods provided by sqlCtx.
+    JavaSchemaRDD teenagers3 = sqlCtx.sql("SELECT name FROM people WHERE age >= 13 AND age <= 19");
+
+    // The results of SQL queries are JavaSchemaRDDs and support all the normal RDD operations.
+    // The columns of a row in the result can be accessed by ordinal.
+    teenagerNames = teenagers3.map(new Function<Row, String>() {
+      public String call(Row row) { return "Name: " + row.getString(0); }
+    }).collect();
+    for (String name: teenagerNames) {
+      System.out.println(name);
+    }
+
+    // Alternatively, a JavaSchemaRDD can be created for a JSON dataset represented by
+    // a RDD[String] storing one JSON object per string.
+    List<String> jsonData = Arrays.asList(
+          "{\"name\":\"Yin\",\"address\":{\"city\":\"Columbus\",\"state\":\"Ohio\"}}");
+    JavaRDD<String> anotherPeopleRDD = ctx.parallelize(jsonData);
+    JavaSchemaRDD peopleFromJsonRDD = sqlCtx.jsonRDD(anotherPeopleRDD);
+
+    // Take a look at the schema of this new JavaSchemaRDD.
+    peopleFromJsonRDD.printSchema();
+    // The schema of anotherPeople is ...
+    // root
+    //  |-- address: StructType
+    //  |    |-- city: StringType
+    //  |    |-- state: StringType
+    //  |-- name: StringType
+
+    peopleFromJsonRDD.registerAsTable("people2");
+
+    JavaSchemaRDD peopleWithCity = sqlCtx.sql("SELECT name, address.city FROM people2");
+    List<String> nameAndCity = peopleWithCity.map(new Function<Row, String>() {
+      public String call(Row row) {
+        return "Name: " + row.getString(0) + ", City: " + row.getString(1);
+      }
+    }).collect();
+    for (String name: nameAndCity) {
+      System.out.println(name);
+    }
   }
 }
diff --git a/examples/src/main/resources/people.json b/examples/src/main/resources/people.json
new file mode 100644
index 000000000000..50a859cbd7ee
--- /dev/null
+++ b/examples/src/main/resources/people.json
@@ -0,0 +1,3 @@
+{"name":"Michael"}
+{"name":"Andy", "age":30}
+{"name":"Justin", "age":19}
diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index 2d60a44f04f6..7bb39dc77120 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -76,7 +76,7 @@ object SparkBuild extends Build {
 
   lazy val catalyst = Project("catalyst", file("sql/catalyst"), settings = catalystSettings) dependsOn(core)
 
-  lazy val sql = Project("sql", file("sql/core"), settings = sqlCoreSettings) dependsOn(core, catalyst)
+  lazy val sql = Project("sql", file("sql/core"), settings = sqlCoreSettings) dependsOn(core) dependsOn(catalyst % "compile->compile;test->test")
 
   lazy val hive = Project("hive", file("sql/hive"), settings = hiveSettings) dependsOn(sql)
 
@@ -501,9 +501,23 @@ object SparkBuild extends Build {
   def sqlCoreSettings = sharedSettings ++ Seq(
     name := "spark-sql",
     libraryDependencies ++= Seq(
-      "com.twitter" % "parquet-column" % parquetVersion,
-      "com.twitter" % "parquet-hadoop" % parquetVersion
-    )
+      "com.twitter"                  % "parquet-column"             % parquetVersion,
+      "com.twitter"                  % "parquet-hadoop"             % parquetVersion,
+      "com.fasterxml.jackson.core"   % "jackson-databind"           % "2.3.0" // json4s-jackson 3.2.6 requires jackson-databind 2.3.0.
+    ),
+    initialCommands in console :=
+      """
+        |import org.apache.spark.sql.catalyst.analysis._
+        |import org.apache.spark.sql.catalyst.dsl._
+        |import org.apache.spark.sql.catalyst.errors._
+        |import org.apache.spark.sql.catalyst.expressions._
+        |import org.apache.spark.sql.catalyst.plans.logical._
+        |import org.apache.spark.sql.catalyst.rules._
+        |import org.apache.spark.sql.catalyst.types._
+        |import org.apache.spark.sql.catalyst.util._
+        |import org.apache.spark.sql.execution
+        |import org.apache.spark.sql.test.TestSQLContext._
+        |import org.apache.spark.sql.parquet.ParquetTestData""".stripMargin
   )
 
   // Since we don't include hive in the main assembly this project also acts as an alternative
diff --git a/python/pyspark/sql.py b/python/pyspark/sql.py
index c31d49ce837f..5051c82da32a 100644
--- a/python/pyspark/sql.py
+++ b/python/pyspark/sql.py
@@ -15,7 +15,7 @@
 # limitations under the License.
 #
 
-from pyspark.rdd import RDD
+from pyspark.rdd import RDD, PipelinedRDD
 from pyspark.serializers import BatchedSerializer, PickleSerializer
 
 from py4j.protocol import Py4JError
@@ -137,6 +137,53 @@ def parquetFile(self, path):
         jschema_rdd = self._ssql_ctx.parquetFile(path)
         return SchemaRDD(jschema_rdd, self)
 
+
+    def jsonFile(self, path):
+        """Loads a text file storing one JSON object per line,
+           returning the result as a L{SchemaRDD}.
+           It goes through the entire dataset once to determine the schema.
+
+        >>> import tempfile, shutil
+        >>> jsonFile = tempfile.mkdtemp()
+        >>> shutil.rmtree(jsonFile)
+        >>> ofn = open(jsonFile, 'w')
+        >>> for json in jsonStrings:
+        ...   print>>ofn, json
+        >>> ofn.close()
+        >>> srdd = sqlCtx.jsonFile(jsonFile)
+        >>> sqlCtx.registerRDDAsTable(srdd, "table1")
+        >>> srdd2 = sqlCtx.sql("SELECT field1 AS f1, field2 as f2, field3 as f3 from table1")
+        >>> srdd2.collect() == [{"f1": 1, "f2": "row1", "f3":{"field4":11}},
+        ...                     {"f1": 2, "f2": "row2", "f3":{"field4":22}},
+        ...                     {"f1": 3, "f2": "row3", "f3":{"field4":33}}]
+        True
+        """
+        jschema_rdd = self._ssql_ctx.jsonFile(path)
+        return SchemaRDD(jschema_rdd, self)
+
+    def jsonRDD(self, rdd):
+        """Loads an RDD storing one JSON object per string, returning the result as a L{SchemaRDD}.
+           It goes through the entire dataset once to determine the schema.
+
+        >>> srdd = sqlCtx.jsonRDD(json)
+        >>> sqlCtx.registerRDDAsTable(srdd, "table1")
+        >>> srdd2 = sqlCtx.sql("SELECT field1 AS f1, field2 as f2, field3 as f3 from table1")
+        >>> srdd2.collect() == [{"f1": 1, "f2": "row1", "f3":{"field4":11}},
+        ...                     {"f1": 2, "f2": "row2", "f3":{"field4":22}},
+        ...                     {"f1": 3, "f2": "row3", "f3":{"field4":33}}]
+        True
+        """
+        def func(split, iterator):
+            for x in iterator:
+                if not isinstance(x, basestring):
+                    x = unicode(x)
+                yield x.encode("utf-8")
+        keyed = PipelinedRDD(rdd, func)
+        keyed._bypass_serializer = True
+        jrdd = keyed._jrdd.map(self._jvm.BytesToString())
+        jschema_rdd = self._ssql_ctx.jsonRDD(jrdd.rdd())
+        return SchemaRDD(jschema_rdd, self)
+
     def sql(self, sqlQuery):
         """Return a L{SchemaRDD} representing the result of the given query.
 
@@ -265,7 +312,7 @@ class SchemaRDD(RDD):
 
     For normal L{pyspark.rdd.RDD} operations (map, count, etc.) the
     L{SchemaRDD} is not operated on directly, as it's underlying
-    implementation is a RDD composed of Java objects. Instead it is
+    implementation is an RDD composed of Java objects. Instead it is
     converted to a PythonRDD in the JVM, on which Python operations can
     be done.
     """
@@ -337,6 +384,14 @@ def saveAsTable(self, tableName):
         """Creates a new table with the contents of this SchemaRDD."""
         self._jschema_rdd.saveAsTable(tableName)
 
+    def schemaString(self):
+        """Returns the output schema in the tree format."""
+        return self._jschema_rdd.schemaString()
+
+    def printSchema(self):
+        """Prints out the schema in the tree format."""
+        print self.schemaString()
+
     def count(self):
         """Return the number of elements in this RDD.
 
@@ -436,6 +491,11 @@ def _test():
     globs['sqlCtx'] = SQLContext(sc)
     globs['rdd'] = sc.parallelize([{"field1" : 1, "field2" : "row1"},
         {"field1" : 2, "field2": "row2"}, {"field1" : 3, "field2": "row3"}])
+    jsonStrings = ['{"field1": 1, "field2": "row1", "field3":{"field4":11}}',
+       '{"field1" : 2, "field2": "row2", "field3":{"field4":22}}',
+       '{"field1" : 3, "field2": "row3", "field3":{"field4":33}}']
+    globs['jsonStrings'] = jsonStrings
+    globs['json'] = sc.parallelize(jsonStrings)
     globs['nestedRdd1'] = sc.parallelize([
         {"f1" : array('i', [1, 2]), "f2" : {"row1" : 1.0}},
         {"f1" : array('i', [2, 3]), "f2" : {"row2" : 2.0}}])
diff --git a/sql/catalyst/pom.xml b/sql/catalyst/pom.xml
index 6c78c3448601..01d7b569080e 100644
--- a/sql/catalyst/pom.xml
+++ b/sql/catalyst/pom.xml
@@ -66,6 +66,34 @@
         <groupId>org.scalatest</groupId>
         <artifactId>scalatest-maven-plugin</artifactId>
       </plugin>
+
+      <!--
+           This plugin forces the generation of jar containing catalyst test classes,
+           so that the tests classes of external modules can use them. The two execution profiles
+           are necessary - first one for 'mvn package', second one for 'mvn compile'. Ideally,
+           'mvn compile' should not compile test classes and therefore should not need this.
+           However, an open Maven bug (http://jira.codehaus.org/browse/MNG-3559)
+           causes the compilation to fail if catalyst test-jar is not generated. Hence, the
+           second execution profile for 'mvn compile'.
+      -->
+      <plugin>
+        <groupId>org.apache.maven.plugins</groupId>
+        <artifactId>maven-jar-plugin</artifactId>
+        <executions>
+            <execution>
+                <goals>
+                    <goal>test-jar</goal>
+                </goals>
+            </execution>
+            <execution>
+                <id>test-jar-on-compile</id>
+                <phase>compile</phase>
+                <goals>
+                    <goal>test-jar</goal>
+                </goals>
+            </execution>
+        </executions>
+      </plugin>
     </plugins>
   </build>
 </project>
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
index d291814c8aa7..66bff660cadc 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
@@ -22,6 +22,16 @@ import org.apache.spark.sql.catalyst.plans.logical.{LogicalPlan, Project, Union}
 import org.apache.spark.sql.catalyst.rules.Rule
 import org.apache.spark.sql.catalyst.types._
 
+object HiveTypeCoercion {
+  // See https://cwiki.apache.org/confluence/display/Hive/LanguageManual+Types.
+  // The conversion for integral and floating point types have a linear widening hierarchy:
+  val numericPrecedence =
+    Seq(NullType, ByteType, ShortType, IntegerType, LongType, FloatType, DoubleType, DecimalType)
+  // Boolean is only wider than Void
+  val booleanPrecedence = Seq(NullType, BooleanType)
+  val allPromotions: Seq[Seq[DataType]] = numericPrecedence :: booleanPrecedence :: Nil
+}
+
 /**
  * A collection of [[catalyst.rules.Rule Rules]] that can be used to coerce differing types that
  * participate in operations into compatible ones.  Most of these rules are based on Hive semantics,
@@ -116,19 +126,18 @@ trait HiveTypeCoercion {
    *
    * Additionally, all types when UNION-ed with strings will be promoted to strings.
    * Other string conversions are handled by PromoteStrings.
+   *
+   * Widening types might result in loss of precision in the following cases:
+   * - IntegerType to FloatType
+   * - LongType to FloatType
+   * - LongType to DoubleType
    */
   object WidenTypes extends Rule[LogicalPlan] {
-    // See https://cwiki.apache.org/confluence/display/Hive/LanguageManual+Types.
-    // The conversion for integral and floating point types have a linear widening hierarchy:
-    val numericPrecedence =
-      Seq(NullType, ByteType, ShortType, IntegerType, LongType, FloatType, DoubleType, DecimalType)
-    // Boolean is only wider than Void
-    val booleanPrecedence = Seq(NullType, BooleanType)
-    val allPromotions: Seq[Seq[DataType]] = numericPrecedence :: booleanPrecedence :: Nil
 
     def findTightestCommonType(t1: DataType, t2: DataType): Option[DataType] = {
       // Try and find a promotion rule that contains both types in question.
-      val applicableConversion = allPromotions.find(p => p.contains(t1) && p.contains(t2))
+      val applicableConversion =
+        HiveTypeCoercion.allPromotions.find(p => p.contains(t1) && p.contains(t2))
 
       // If found return the widest common type, otherwise None
       applicableConversion.map(_.filter(t => t == t1 || t == t2).last)
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/QueryPlan.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/QueryPlan.scala
index 8199a80f5d6b..00e2d3bc24be 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/QueryPlan.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/QueryPlan.scala
@@ -18,7 +18,9 @@
 package org.apache.spark.sql.catalyst.plans
 
 import org.apache.spark.sql.catalyst.expressions.{Attribute, Expression}
+import org.apache.spark.sql.catalyst.plans
 import org.apache.spark.sql.catalyst.trees.TreeNode
+import org.apache.spark.sql.catalyst.types.{ArrayType, DataType, StructField, StructType}
 
 abstract class QueryPlan[PlanType <: TreeNode[PlanType]] extends TreeNode[PlanType] {
   self: PlanType with Product =>
@@ -123,4 +125,53 @@ abstract class QueryPlan[PlanType <: TreeNode[PlanType]] extends TreeNode[PlanTy
       case other => Nil
     }.toSeq
   }
+
+  protected def generateSchemaString(schema: Seq[Attribute]): String = {
+    val builder = new StringBuilder
+    builder.append("root\n")
+    val prefix = " |"
+    schema.foreach { attribute =>
+      val name = attribute.name
+      val dataType = attribute.dataType
+      dataType match {
+        case fields: StructType =>
+          builder.append(s"$prefix-- $name: $StructType\n")
+          generateSchemaString(fields, s"$prefix    |", builder)
+        case ArrayType(fields: StructType) =>
+          builder.append(s"$prefix-- $name: $ArrayType[$StructType]\n")
+          generateSchemaString(fields, s"$prefix    |", builder)
+        case ArrayType(elementType: DataType) =>
+          builder.append(s"$prefix-- $name: $ArrayType[$elementType]\n")
+        case _ => builder.append(s"$prefix-- $name: $dataType\n")
+      }
+    }
+
+    builder.toString()
+  }
+
+  protected def generateSchemaString(
+      schema: StructType,
+      prefix: String,
+      builder: StringBuilder): StringBuilder = {
+    schema.fields.foreach {
+      case StructField(name, fields: StructType, _) =>
+        builder.append(s"$prefix-- $name: $StructType\n")
+        generateSchemaString(fields, s"$prefix    |", builder)
+      case StructField(name, ArrayType(fields: StructType), _) =>
+        builder.append(s"$prefix-- $name: $ArrayType[$StructType]\n")
+        generateSchemaString(fields, s"$prefix    |", builder)
+      case StructField(name, ArrayType(elementType: DataType), _) =>
+        builder.append(s"$prefix-- $name: $ArrayType[$elementType]\n")
+      case StructField(name, fieldType: DataType, _) =>
+        builder.append(s"$prefix-- $name: $fieldType\n")
+    }
+
+    builder
+  }
+
+  /** Returns the output schema in the tree format. */
+  def schemaString: String = generateSchemaString(output)
+
+  /** Prints out the schema in the tree format */
+  def printSchema(): Unit = println(schemaString)
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/CombiningLimitsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/CombiningLimitsSuite.scala
index 714f01843c0f..4896f1b955f0 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/CombiningLimitsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/CombiningLimitsSuite.scala
@@ -18,11 +18,12 @@
 package org.apache.spark.sql.catalyst.optimizer
 
 import org.apache.spark.sql.catalyst.plans.logical._
+import org.apache.spark.sql.catalyst.plans.PlanTest
 import org.apache.spark.sql.catalyst.rules._
 import org.apache.spark.sql.catalyst.dsl.plans._
 import org.apache.spark.sql.catalyst.dsl.expressions._
 
-class CombiningLimitsSuite extends OptimizerTest {
+class CombiningLimitsSuite extends PlanTest {
 
   object Optimize extends RuleExecutor[LogicalPlan] {
     val batches =
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConstantFoldingSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConstantFoldingSuite.scala
index 6efc0e211eb2..cea97c584f7e 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConstantFoldingSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConstantFoldingSuite.scala
@@ -20,6 +20,7 @@ package org.apache.spark.sql.catalyst.optimizer
 import org.apache.spark.sql.catalyst.analysis.EliminateAnalysisOperators
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.logical.{LocalRelation, LogicalPlan}
+import org.apache.spark.sql.catalyst.plans.PlanTest
 import org.apache.spark.sql.catalyst.rules.RuleExecutor
 import org.apache.spark.sql.catalyst.types._
 
@@ -27,7 +28,7 @@ import org.apache.spark.sql.catalyst.types._
 import org.apache.spark.sql.catalyst.dsl.plans._
 import org.apache.spark.sql.catalyst.dsl.expressions._
 
-class ConstantFoldingSuite extends OptimizerTest {
+class ConstantFoldingSuite extends PlanTest {
 
   object Optimize extends RuleExecutor[LogicalPlan] {
     val batches =
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/FilterPushdownSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/FilterPushdownSuite.scala
index 1f67c80e5490..ebb123c1f909 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/FilterPushdownSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/FilterPushdownSuite.scala
@@ -20,13 +20,12 @@ package org.apache.spark.sql.catalyst.optimizer
 import org.apache.spark.sql.catalyst.analysis
 import org.apache.spark.sql.catalyst.analysis.EliminateAnalysisOperators
 import org.apache.spark.sql.catalyst.plans.logical._
-import org.apache.spark.sql.catalyst.plans.LeftOuter
-import org.apache.spark.sql.catalyst.plans.RightOuter
+import org.apache.spark.sql.catalyst.plans.{PlanTest, LeftOuter, RightOuter}
 import org.apache.spark.sql.catalyst.rules._
 import org.apache.spark.sql.catalyst.dsl.plans._
 import org.apache.spark.sql.catalyst.dsl.expressions._
 
-class FilterPushdownSuite extends OptimizerTest {
+class FilterPushdownSuite extends PlanTest {
 
   object Optimize extends RuleExecutor[LogicalPlan] {
     val batches =
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/SimplifyCaseConversionExpressionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/SimplifyCaseConversionExpressionsSuite.scala
index df1409fe7bae..22992fb6f50d 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/SimplifyCaseConversionExpressionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/SimplifyCaseConversionExpressionsSuite.scala
@@ -19,13 +19,14 @@ package org.apache.spark.sql.catalyst.optimizer
 
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.logical._
+import org.apache.spark.sql.catalyst.plans.PlanTest
 import org.apache.spark.sql.catalyst.rules._
 
 /* Implicit conversions */
 import org.apache.spark.sql.catalyst.dsl.expressions._
 import org.apache.spark.sql.catalyst.dsl.plans._
 
-class SimplifyCaseConversionExpressionsSuite extends OptimizerTest {
+class SimplifyCaseConversionExpressionsSuite extends PlanTest {
 
   object Optimize extends RuleExecutor[LogicalPlan] {
     val batches =
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/OptimizerTest.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/plans/PlanTest.scala
similarity index 88%
rename from sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/OptimizerTest.scala
rename to sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/plans/PlanTest.scala
index 89982d5cd8d7..7e9f47ef21df 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/OptimizerTest.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/plans/PlanTest.scala
@@ -15,19 +15,18 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.catalyst.optimizer
+package org.apache.spark.sql.catalyst.plans
 
 import org.scalatest.FunSuite
 
-import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.expressions.{ExprId, AttributeReference}
 import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
 import org.apache.spark.sql.catalyst.util._
 
 /**
- * Provides helper methods for comparing plans produced by optimization rules with the expected
- * result
+ * Provides helper methods for comparing plans.
  */
-class OptimizerTest extends FunSuite {
+class PlanTest extends FunSuite {
 
   /**
    * Since attribute references are given globally unique ids during analysis,
diff --git a/sql/core/pom.xml b/sql/core/pom.xml
index e65ca6be485e..8210fd1f210d 100644
--- a/sql/core/pom.xml
+++ b/sql/core/pom.xml
@@ -43,6 +43,13 @@
       <artifactId>spark-catalyst_${scala.binary.version}</artifactId>
       <version>${project.version}</version>
     </dependency>
+    <dependency>
+      <groupId>org.apache.spark</groupId>
+      <artifactId>spark-catalyst_${scala.binary.version}</artifactId>
+      <version>${project.version}</version>
+      <type>test-jar</type>
+      <scope>test</scope>
+    </dependency>
     <dependency>
       <groupId>com.twitter</groupId>
       <artifactId>parquet-column</artifactId>
@@ -53,6 +60,11 @@
       <artifactId>parquet-hadoop</artifactId>
       <version>${parquet.version}</version>
     </dependency>
+    <dependency>
+      <groupId>com.fasterxml.jackson.core</groupId>
+      <artifactId>jackson-databind</artifactId>
+      <version>2.3.0</version>
+    </dependency>
     <dependency>
       <groupId>org.scalatest</groupId>
       <artifactId>scalatest_${scala.binary.version}</artifactId>
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
index 131c130bbb3e..f7e03323bed3 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
@@ -22,24 +22,22 @@ import scala.reflect.runtime.universe.TypeTag
 
 import org.apache.hadoop.conf.Configuration
 
-import org.apache.spark.SparkContext
 import org.apache.spark.annotation.{AlphaComponent, DeveloperApi, Experimental}
 import org.apache.spark.rdd.RDD
-
 import org.apache.spark.sql.catalyst.analysis._
-import org.apache.spark.sql.catalyst.{ScalaReflection, dsl}
+import org.apache.spark.sql.catalyst.ScalaReflection
 import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.dsl.ExpressionConversions
 import org.apache.spark.sql.catalyst.types._
 import org.apache.spark.sql.catalyst.optimizer.Optimizer
 import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
 import org.apache.spark.sql.catalyst.rules.RuleExecutor
-
 import org.apache.spark.sql.columnar.InMemoryRelation
-
 import org.apache.spark.sql.execution._
 import org.apache.spark.sql.execution.SparkStrategies
-
+import org.apache.spark.sql.json._
 import org.apache.spark.sql.parquet.ParquetRelation
+import org.apache.spark.SparkContext
 
 /**
  * :: AlphaComponent ::
@@ -53,7 +51,7 @@ import org.apache.spark.sql.parquet.ParquetRelation
 class SQLContext(@transient val sparkContext: SparkContext)
   extends Logging
   with SQLConf
-  with dsl.ExpressionConversions
+  with ExpressionConversions
   with Serializable {
 
   self =>
@@ -98,6 +96,39 @@ class SQLContext(@transient val sparkContext: SparkContext)
   def parquetFile(path: String): SchemaRDD =
     new SchemaRDD(this, parquet.ParquetRelation(path))
 
+  /**
+   * Loads a JSON file (one object per line), returning the result as a [[SchemaRDD]].
+   * It goes through the entire dataset once to determine the schema.
+   *
+   * @group userf
+   */
+  def jsonFile(path: String): SchemaRDD = jsonFile(path, 1.0)
+
+  /**
+   * :: Experimental ::
+   */
+  @Experimental
+  def jsonFile(path: String, samplingRatio: Double): SchemaRDD = {
+    val json = sparkContext.textFile(path)
+    jsonRDD(json, samplingRatio)
+  }
+
+  /**
+   * Loads an RDD[String] storing JSON objects (one object per record), returning the result as a
+   * [[SchemaRDD]].
+   * It goes through the entire dataset once to determine the schema.
+   *
+   * @group userf
+   */
+  def jsonRDD(json: RDD[String]): SchemaRDD = jsonRDD(json, 1.0)
+
+  /**
+   * :: Experimental ::
+   */
+  @Experimental
+  def jsonRDD(json: RDD[String], samplingRatio: Double): SchemaRDD =
+    new SchemaRDD(this, JsonRDD.inferSchema(json, samplingRatio))
+
   /**
    * :: Experimental ::
    * Creates an empty parquet file with the schema of class `A`, which can be registered as a table.
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala
index 89eaba2d19aa..7c0efb456661 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala
@@ -27,7 +27,7 @@ import org.apache.spark.sql.catalyst.analysis._
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.logical._
 import org.apache.spark.sql.catalyst.plans.{Inner, JoinType}
-import org.apache.spark.sql.catalyst.types.BooleanType
+import org.apache.spark.sql.catalyst.types.{DataType, StructType, BooleanType}
 import org.apache.spark.sql.execution.{ExistingRdd, SparkLogicalPlan}
 import org.apache.spark.api.java.JavaRDD
 import java.util.{Map => JMap}
@@ -41,8 +41,10 @@ import java.util.{Map => JMap}
  * whose elements are scala case classes into a SchemaRDD.  This conversion can also be done
  * explicitly using the `createSchemaRDD` function on a [[SQLContext]].
  *
- * A `SchemaRDD` can also be created by loading data in from external sources, for example,
- * by using the `parquetFile` method on [[SQLContext]].
+ * A `SchemaRDD` can also be created by loading data in from external sources.
+ * Examples are loading data from Parquet files by using by using the
+ * `parquetFile` method on [[SQLContext]], and loading JSON datasets
+ * by using `jsonFile` and `jsonRDD` methods on [[SQLContext]].
  *
  * == SQL Queries ==
  * A SchemaRDD can be registered as a table in the [[SQLContext]] that was used to create it.  Once
@@ -341,14 +343,38 @@ class SchemaRDD(
    */
   def toJavaSchemaRDD: JavaSchemaRDD = new JavaSchemaRDD(sqlContext, logicalPlan)
 
+  /**
+   * Converts a JavaRDD to a PythonRDD. It is used by pyspark.
+   */
   private[sql] def javaToPython: JavaRDD[Array[Byte]] = {
-    val fieldNames: Seq[String] = this.queryExecution.analyzed.output.map(_.name)
+    def rowToMap(row: Row, structType: StructType): JMap[String, Any] = {
+      val fields = structType.fields.map(field => (field.name, field.dataType))
+      val map: JMap[String, Any] = new java.util.HashMap
+      row.zip(fields).foreach {
+        case (obj, (name, dataType)) =>
+          dataType match {
+            case struct: StructType => map.put(name, rowToMap(obj.asInstanceOf[Row], struct))
+            case other => map.put(name, obj)
+          }
+      }
+
+      map
+    }
+
+    // TODO: Actually, the schema of a row should be represented by a StructType instead of
+    // a Seq[Attribute]. Once we have finished that change, we can just use rowToMap to
+    // construct the Map for python.
+    val fields: Seq[(String, DataType)] = this.queryExecution.analyzed.output.map(
+      field => (field.name, field.dataType))
     this.mapPartitions { iter =>
       val pickle = new Pickler
       iter.map { row =>
         val map: JMap[String, Any] = new java.util.HashMap
-        row.zip(fieldNames).foreach { case (obj, name) =>
-          map.put(name, obj)
+        row.zip(fields).foreach { case (obj, (name, dataType)) =>
+          dataType match {
+            case struct: StructType => map.put(name, rowToMap(obj.asInstanceOf[Row], struct))
+            case other => map.put(name, obj)
+          }
         }
         map
       }.grouped(10).map(batched => pickle.dumps(batched.toArray))
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDDLike.scala b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDDLike.scala
index 656be965a8fd..fe8172194320 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDDLike.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDDLike.scala
@@ -122,4 +122,10 @@ private[sql] trait SchemaRDDLike {
   @Experimental
   def saveAsTable(tableName: String): Unit =
     sqlContext.executePlan(InsertIntoCreatedTable(None, tableName, logicalPlan)).toRdd
+
+  /** Returns the output schema in the tree format. */
+  def schemaString: String = queryExecution.analyzed.schemaString
+
+  /** Prints out the schema in the tree format. */
+  def printSchema(): Unit = println(schemaString)
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSQLContext.scala
index 352260fa15bb..ff9842267ffe 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSQLContext.scala
@@ -23,6 +23,7 @@ import org.apache.hadoop.conf.Configuration
 
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.api.java.{JavaRDD, JavaSparkContext}
+import org.apache.spark.sql.json.JsonRDD
 import org.apache.spark.sql.SQLContext
 import org.apache.spark.sql.catalyst.expressions.{AttributeReference, GenericRow, Row => ScalaRow}
 import org.apache.spark.sql.catalyst.types._
@@ -100,6 +101,25 @@ class JavaSQLContext(val sqlContext: SQLContext) {
   def parquetFile(path: String): JavaSchemaRDD =
     new JavaSchemaRDD(sqlContext, ParquetRelation(path))
 
+  /**
+   * Loads a JSON file (one object per line), returning the result as a [[JavaSchemaRDD]].
+   * It goes through the entire dataset once to determine the schema.
+   *
+   * @group userf
+   */
+  def jsonFile(path: String): JavaSchemaRDD =
+    jsonRDD(sqlContext.sparkContext.textFile(path))
+
+  /**
+   * Loads an RDD[String] storing JSON objects (one object per record), returning the result as a
+   * [[JavaSchemaRDD]].
+   * It goes through the entire dataset once to determine the schema.
+   *
+   * @group userf
+   */
+  def jsonRDD(json: JavaRDD[String]): JavaSchemaRDD =
+    new JavaSchemaRDD(sqlContext, JsonRDD.inferSchema(json, 1.0))
+
   /**
    * Registers the given RDD as a temporary table in the catalog.  Temporary tables exist only
    * during the lifetime of this instance of SQLContext.
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala
new file mode 100644
index 000000000000..edf86775579d
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala
@@ -0,0 +1,397 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.json
+
+import scala.collection.JavaConversions._
+import scala.math.BigDecimal
+
+import com.fasterxml.jackson.databind.ObjectMapper
+
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.catalyst.analysis.HiveTypeCoercion
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.plans.logical._
+import org.apache.spark.sql.catalyst.types._
+import org.apache.spark.sql.execution.{ExistingRdd, SparkLogicalPlan}
+import org.apache.spark.sql.Logging
+
+private[sql] object JsonRDD extends Logging {
+
+  private[sql] def inferSchema(
+      json: RDD[String],
+      samplingRatio: Double = 1.0): LogicalPlan = {
+    require(samplingRatio > 0, s"samplingRatio ($samplingRatio) should be greater than 0")
+    val schemaData = if (samplingRatio > 0.99) json else json.sample(false, samplingRatio, 1)
+    val allKeys = parseJson(schemaData).map(allKeysWithValueTypes).reduce(_ ++ _)
+    val baseSchema = createSchema(allKeys)
+
+    createLogicalPlan(json, baseSchema)
+  }
+
+  private def createLogicalPlan(
+      json: RDD[String],
+      baseSchema: StructType): LogicalPlan = {
+    val schema = nullTypeToStringType(baseSchema)
+
+    SparkLogicalPlan(ExistingRdd(asAttributes(schema), parseJson(json).map(asRow(_, schema))))
+  }
+
+  private def createSchema(allKeys: Set[(String, DataType)]): StructType = {
+    // Resolve type conflicts
+    val resolved = allKeys.groupBy {
+      case (key, dataType) => key
+    }.map {
+      // Now, keys and types are organized in the format of
+      // key -> Set(type1, type2, ...).
+      case (key, typeSet) => {
+        val fieldName = key.substring(1, key.length - 1).split("`.`").toSeq
+        val dataType = typeSet.map {
+          case (_, dataType) => dataType
+        }.reduce((type1: DataType, type2: DataType) => compatibleType(type1, type2))
+
+        (fieldName, dataType)
+      }
+    }
+
+    def makeStruct(values: Seq[Seq[String]], prefix: Seq[String]): StructType = {
+      val (topLevel, structLike) = values.partition(_.size == 1)
+      val topLevelFields = topLevel.filter {
+        name => resolved.get(prefix ++ name).get match {
+          case ArrayType(StructType(Nil)) => false
+          case ArrayType(_) => true
+          case struct: StructType => false
+          case _ => true
+        }
+      }.map {
+        a => StructField(a.head, resolved.get(prefix ++ a).get, nullable = true)
+      }
+
+      val structFields: Seq[StructField] = structLike.groupBy(_(0)).map {
+        case (name, fields) => {
+          val nestedFields = fields.map(_.tail)
+          val structType = makeStruct(nestedFields, prefix :+ name)
+          val dataType = resolved.get(prefix :+ name).get
+          dataType match {
+            case array: ArrayType => Some(StructField(name, ArrayType(structType), nullable = true))
+            case struct: StructType => Some(StructField(name, structType, nullable = true))
+            // dataType is StringType means that we have resolved type conflicts involving
+            // primitive types and complex types. So, the type of name has been relaxed to
+            // StringType. Also, this field should have already been put in topLevelFields.
+            case StringType => None
+          }
+        }
+      }.flatMap(field => field).toSeq
+
+      StructType(
+        (topLevelFields ++ structFields).sortBy {
+        case StructField(name, _, _) => name
+      })
+    }
+
+    makeStruct(resolved.keySet.toSeq, Nil)
+  }
+
+  /**
+   * Returns the most general data type for two given data types.
+   */
+  private[json] def compatibleType(t1: DataType, t2: DataType): DataType = {
+    // Try and find a promotion rule that contains both types in question.
+    val applicableConversion = HiveTypeCoercion.allPromotions.find(p => p.contains(t1) && p
+      .contains(t2))
+
+    // If found return the widest common type, otherwise None
+    val returnType = applicableConversion.map(_.filter(t => t == t1 || t == t2).last)
+
+    if (returnType.isDefined) {
+      returnType.get
+    } else {
+      // t1 or t2 is a StructType, ArrayType, or an unexpected type.
+      (t1, t2) match {
+        case (other: DataType, NullType) => other
+        case (NullType, other: DataType) => other
+        case (StructType(fields1), StructType(fields2)) => {
+          val newFields = (fields1 ++ fields2).groupBy(field => field.name).map {
+            case (name, fieldTypes) => {
+              val dataType = fieldTypes.map(field => field.dataType).reduce(
+                (type1: DataType, type2: DataType) => compatibleType(type1, type2))
+              StructField(name, dataType, true)
+            }
+          }
+          StructType(newFields.toSeq.sortBy {
+            case StructField(name, _, _) => name
+          })
+        }
+        case (ArrayType(elementType1), ArrayType(elementType2)) =>
+          ArrayType(compatibleType(elementType1, elementType2))
+        // TODO: We should use JsonObjectStringType to mark that values of field will be
+        // strings and every string is a Json object.
+        case (_, _) => StringType
+      }
+    }
+  }
+
+  private def typeOfPrimitiveValue(value: Any): DataType = {
+    value match {
+      case value: java.lang.String => StringType
+      case value: java.lang.Integer => IntegerType
+      case value: java.lang.Long => LongType
+      // Since we do not have a data type backed by BigInteger,
+      // when we see a Java BigInteger, we use DecimalType.
+      case value: java.math.BigInteger => DecimalType
+      case value: java.lang.Double => DoubleType
+      case value: java.math.BigDecimal => DecimalType
+      case value: java.lang.Boolean => BooleanType
+      case null => NullType
+      // Unexpected data type.
+      case _ => StringType
+    }
+  }
+
+  /**
+   * Returns the element type of an JSON array. We go through all elements of this array
+   * to detect any possible type conflict. We use [[compatibleType]] to resolve
+   * type conflicts. Right now, when the element of an array is another array, we
+   * treat the element as String.
+   */
+  private def typeOfArray(l: Seq[Any]): ArrayType = {
+    val elements = l.flatMap(v => Option(v))
+    if (elements.isEmpty) {
+      // If this JSON array is empty, we use NullType as a placeholder.
+      // If this array is not empty in other JSON objects, we can resolve
+      // the type after we have passed through all JSON objects.
+      ArrayType(NullType)
+    } else {
+      val elementType = elements.map {
+        e => e match {
+          case map: Map[_, _] => StructType(Nil)
+          // We have an array of arrays. If those element arrays do not have the same
+          // element types, we will return ArrayType[StringType].
+          case seq: Seq[_] =>  typeOfArray(seq)
+          case value => typeOfPrimitiveValue(value)
+        }
+      }.reduce((type1: DataType, type2: DataType) => compatibleType(type1, type2))
+
+      ArrayType(elementType)
+    }
+  }
+
+  /**
+   * Figures out all key names and data types of values from a parsed JSON object
+   * (in the format of Map[Stirng, Any]). When the value of a key is an JSON object, we
+   * only use a placeholder (StructType(Nil)) to mark that it should be a struct
+   * instead of getting all fields of this struct because a field does not appear
+   * in this JSON object can appear in other JSON objects.
+   */
+  private def allKeysWithValueTypes(m: Map[String, Any]): Set[(String, DataType)] = {
+    m.map{
+      // Quote the key with backticks to handle cases which have dots
+      // in the field name.
+      case (key, dataType) => (s"`$key`", dataType)
+    }.flatMap {
+      case (key: String, struct: Map[String, Any]) => {
+        // The value associted with the key is an JSON object.
+        allKeysWithValueTypes(struct).map {
+          case (k, dataType) => (s"$key.$k", dataType)
+        } ++ Set((key, StructType(Nil)))
+      }
+      case (key: String, array: List[Any]) => {
+        // The value associted with the key is an array.
+        typeOfArray(array) match {
+          case ArrayType(StructType(Nil)) => {
+            // The elements of this arrays are structs.
+            array.asInstanceOf[List[Map[String, Any]]].flatMap {
+              element => allKeysWithValueTypes(element)
+            }.map {
+              case (k, dataType) => (s"$key.$k", dataType)
+            } :+ (key, ArrayType(StructType(Nil)))
+          }
+          case ArrayType(elementType) => (key, ArrayType(elementType)) :: Nil
+        }
+      }
+      case (key: String, value) => (key, typeOfPrimitiveValue(value)) :: Nil
+    }.toSet
+  }
+
+  /**
+   * Converts a Java Map/List to a Scala Map/List.
+   * We do not use Jackson's scala module at here because
+   * DefaultScalaModule in jackson-module-scala will make
+   * the parsing very slow.
+   */
+  private def scalafy(obj: Any): Any = obj match {
+    case map: java.util.Map[String, Object] =>
+      // .map(identity) is used as a workaround of non-serializable Map
+      // generated by .mapValues.
+      // This issue is documented at https://issues.scala-lang.org/browse/SI-7005
+      map.toMap.mapValues(scalafy).map(identity)
+    case list: java.util.List[Object] =>
+      list.toList.map(scalafy)
+    case atom => atom
+  }
+
+  private def parseJson(json: RDD[String]): RDD[Map[String, Any]] = {
+    // According to [Jackson-72: https://jira.codehaus.org/browse/JACKSON-72],
+    // ObjectMapper will not return BigDecimal when
+    // "DeserializationFeature.USE_BIG_DECIMAL_FOR_FLOATS" is disabled
+    // (see NumberDeserializer.deserialize for the logic).
+    // But, we do not want to enable this feature because it will use BigDecimal
+    // for every float number, which will be slow.
+    // So, right now, we will have Infinity for those BigDecimal number.
+    // TODO: Support BigDecimal.
+    json.mapPartitions(iter => {
+      // When there is a key appearing multiple times (a duplicate key),
+      // the ObjectMapper will take the last value associated with this duplicate key.
+      // For example: for {"key": 1, "key":2}, we will get "key"->2.
+      val mapper = new ObjectMapper()
+      iter.map(record => mapper.readValue(record, classOf[java.util.Map[String, Any]]))
+      }).map(scalafy).map(_.asInstanceOf[Map[String, Any]])
+  }
+
+  private def toLong(value: Any): Long = {
+    value match {
+      case value: java.lang.Integer => value.asInstanceOf[Int].toLong
+      case value: java.lang.Long => value.asInstanceOf[Long]
+    }
+  }
+
+  private def toDouble(value: Any): Double = {
+    value match {
+      case value: java.lang.Integer => value.asInstanceOf[Int].toDouble
+      case value: java.lang.Long => value.asInstanceOf[Long].toDouble
+      case value: java.lang.Double => value.asInstanceOf[Double]
+    }
+  }
+
+  private def toDecimal(value: Any): BigDecimal = {
+    value match {
+      case value: java.lang.Integer => BigDecimal(value)
+      case value: java.lang.Long => BigDecimal(value)
+      case value: java.math.BigInteger => BigDecimal(value)
+      case value: java.lang.Double => BigDecimal(value)
+      case value: java.math.BigDecimal => BigDecimal(value)
+    }
+  }
+
+  private def toJsonArrayString(seq: Seq[Any]): String = {
+    val builder = new StringBuilder
+    builder.append("[")
+    var count = 0
+    seq.foreach {
+      element =>
+        if (count > 0) builder.append(",")
+        count += 1
+        builder.append(toString(element))
+    }
+    builder.append("]")
+
+    builder.toString()
+  }
+
+  private def toJsonObjectString(map: Map[String, Any]): String = {
+    val builder = new StringBuilder
+    builder.append("{")
+    var count = 0
+    map.foreach {
+      case (key, value) =>
+        if (count > 0) builder.append(",")
+        count += 1
+        builder.append(s"""\"${key}\":${toString(value)}""")
+    }
+    builder.append("}")
+
+    builder.toString()
+  }
+
+  private def toString(value: Any): String = {
+    value match {
+      case value: Map[String, Any] => toJsonObjectString(value)
+      case value: Seq[Any] => toJsonArrayString(value)
+      case value => Option(value).map(_.toString).orNull
+    }
+  }
+
+  private[json] def enforceCorrectType(value: Any, desiredType: DataType): Any ={
+    if (value == null) {
+      null
+    } else {
+      desiredType match {
+        case ArrayType(elementType) =>
+          value.asInstanceOf[Seq[Any]].map(enforceCorrectType(_, elementType))
+        case StringType => toString(value)
+        case IntegerType => value.asInstanceOf[IntegerType.JvmType]
+        case LongType => toLong(value)
+        case DoubleType => toDouble(value)
+        case DecimalType => toDecimal(value)
+        case BooleanType => value.asInstanceOf[BooleanType.JvmType]
+        case NullType => null
+      }
+    }
+  }
+
+  private def asRow(json: Map[String,Any], schema: StructType): Row = {
+    val row = new GenericMutableRow(schema.fields.length)
+    schema.fields.zipWithIndex.foreach {
+      // StructType
+      case (StructField(name, fields: StructType, _), i) =>
+        row.update(i, json.get(name).flatMap(v => Option(v)).map(
+          v => asRow(v.asInstanceOf[Map[String, Any]], fields)).orNull)
+
+      // ArrayType(StructType)
+      case (StructField(name, ArrayType(structType: StructType), _), i) =>
+        row.update(i,
+          json.get(name).flatMap(v => Option(v)).map(
+            v => v.asInstanceOf[Seq[Any]].map(
+              e => asRow(e.asInstanceOf[Map[String, Any]], structType))).orNull)
+
+      // Other cases
+      case (StructField(name, dataType, _), i) =>
+        row.update(i, json.get(name).flatMap(v => Option(v)).map(
+          enforceCorrectType(_, dataType)).getOrElse(null))
+    }
+
+    row
+  }
+
+  private def nullTypeToStringType(struct: StructType): StructType = {
+    val fields = struct.fields.map {
+      case StructField(fieldName, dataType, nullable) => {
+        val newType = dataType match {
+          case NullType => StringType
+          case ArrayType(NullType) => ArrayType(StringType)
+          case struct: StructType => nullTypeToStringType(struct)
+          case other: DataType => other
+        }
+        StructField(fieldName, newType, nullable)
+      }
+    }
+
+    StructType(fields)
+  }
+
+  private def asAttributes(struct: StructType): Seq[AttributeReference] = {
+    struct.fields.map(f => AttributeReference(f.name, f.dataType, nullable = true)())
+  }
+
+  private def asStruct(attributes: Seq[AttributeReference]): StructType = {
+    val fields = attributes.map {
+      case AttributeReference(name, dataType, nullable) => StructField(name, dataType, nullable)
+    }
+
+    StructType(fields)
+  }
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/QueryTest.scala b/sql/core/src/test/scala/org/apache/spark/sql/QueryTest.scala
index d7f6abaf5d38..ef84ead2e6e8 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/QueryTest.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/QueryTest.scala
@@ -17,12 +17,10 @@
 
 package org.apache.spark.sql
 
-import org.scalatest.FunSuite
-
 import org.apache.spark.sql.catalyst.plans._
 import org.apache.spark.sql.catalyst.util._
 
-class QueryTest extends FunSuite {
+class QueryTest extends PlanTest {
   /**
    * Runs the plan and makes sure the answer matches the expected result.
    * @param rdd the [[SchemaRDD]] to be executed
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/api/java/JavaSQLSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/api/java/JavaSQLSuite.scala
index 9fff7222fe84..020baf0c7ec6 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/api/java/JavaSQLSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/api/java/JavaSQLSuite.scala
@@ -22,6 +22,7 @@ import scala.beans.BeanProperty
 import org.scalatest.FunSuite
 
 import org.apache.spark.api.java.JavaSparkContext
+import org.apache.spark.sql.catalyst.util._
 import org.apache.spark.sql.test.TestSQLContext
 
 // Implicits
@@ -111,4 +112,48 @@ class JavaSQLSuite extends FunSuite {
         """.stripMargin).collect.head.row ===
         Seq.fill(8)(null))
   }
+
+  test("loads JSON datasets") {
+    val jsonString =
+      """{"string":"this is a simple string.",
+          "integer":10,
+          "long":21474836470,
+          "bigInteger":92233720368547758070,
+          "double":1.7976931348623157E308,
+          "boolean":true,
+          "null":null
+      }""".replaceAll("\n", " ")
+    val rdd = javaCtx.parallelize(jsonString :: Nil)
+
+    var schemaRDD = javaSqlCtx.jsonRDD(rdd)
+
+    schemaRDD.registerAsTable("jsonTable1")
+
+    assert(
+      javaSqlCtx.sql("select * from jsonTable1").collect.head.row ===
+        Seq(BigDecimal("92233720368547758070"),
+            true,
+            1.7976931348623157E308,
+            10,
+            21474836470L,
+            null,
+            "this is a simple string."))
+
+    val file = getTempFilePath("json")
+    val path = file.toString
+    rdd.saveAsTextFile(path)
+    schemaRDD = javaSqlCtx.jsonFile(path)
+
+    schemaRDD.registerAsTable("jsonTable2")
+
+    assert(
+      javaSqlCtx.sql("select * from jsonTable2").collect.head.row ===
+        Seq(BigDecimal("92233720368547758070"),
+            true,
+            1.7976931348623157E308,
+            10,
+            21474836470L,
+            null,
+            "this is a simple string."))
+  }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala
new file mode 100644
index 000000000000..10bd9f08f023
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala
@@ -0,0 +1,519 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.json
+
+import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeReference}
+import org.apache.spark.sql.catalyst.plans.logical.LeafNode
+import org.apache.spark.sql.catalyst.types._
+import org.apache.spark.sql.catalyst.util._
+import org.apache.spark.sql.json.JsonRDD.{enforceCorrectType, compatibleType}
+import org.apache.spark.sql.QueryTest
+import org.apache.spark.sql.test.TestSQLContext._
+
+protected case class Schema(output: Seq[Attribute]) extends LeafNode
+
+class JsonSuite extends QueryTest {
+  import TestJsonData._
+  TestJsonData
+
+  test("Type promotion") {
+    def checkTypePromotion(expected: Any, actual: Any) {
+      assert(expected.getClass == actual.getClass,
+        s"Failed to promote ${actual.getClass} to ${expected.getClass}.")
+      assert(expected == actual,
+        s"Promoted value ${actual}(${actual.getClass}) does not equal the expected value " +
+          s"${expected}(${expected.getClass}).")
+    }
+
+    val intNumber: Int = 2147483647
+    checkTypePromotion(intNumber, enforceCorrectType(intNumber, IntegerType))
+    checkTypePromotion(intNumber.toLong, enforceCorrectType(intNumber, LongType))
+    checkTypePromotion(intNumber.toDouble, enforceCorrectType(intNumber, DoubleType))
+    checkTypePromotion(BigDecimal(intNumber), enforceCorrectType(intNumber, DecimalType))
+
+    val longNumber: Long = 9223372036854775807L
+    checkTypePromotion(longNumber, enforceCorrectType(longNumber, LongType))
+    checkTypePromotion(longNumber.toDouble, enforceCorrectType(longNumber, DoubleType))
+    checkTypePromotion(BigDecimal(longNumber), enforceCorrectType(longNumber, DecimalType))
+
+    val doubleNumber: Double = 1.7976931348623157E308d
+    checkTypePromotion(doubleNumber.toDouble, enforceCorrectType(doubleNumber, DoubleType))
+    checkTypePromotion(BigDecimal(doubleNumber), enforceCorrectType(doubleNumber, DecimalType))
+  }
+
+  test("Get compatible type") {
+    def checkDataType(t1: DataType, t2: DataType, expected: DataType) {
+      var actual = compatibleType(t1, t2)
+      assert(actual == expected,
+        s"Expected $expected as the most general data type for $t1 and $t2, found $actual")
+      actual = compatibleType(t2, t1)
+      assert(actual == expected,
+        s"Expected $expected as the most general data type for $t1 and $t2, found $actual")
+    }
+
+    // NullType
+    checkDataType(NullType, BooleanType, BooleanType)
+    checkDataType(NullType, IntegerType, IntegerType)
+    checkDataType(NullType, LongType, LongType)
+    checkDataType(NullType, DoubleType, DoubleType)
+    checkDataType(NullType, DecimalType, DecimalType)
+    checkDataType(NullType, StringType, StringType)
+    checkDataType(NullType, ArrayType(IntegerType), ArrayType(IntegerType))
+    checkDataType(NullType, StructType(Nil), StructType(Nil))
+    checkDataType(NullType, NullType, NullType)
+
+    // BooleanType
+    checkDataType(BooleanType, BooleanType, BooleanType)
+    checkDataType(BooleanType, IntegerType, StringType)
+    checkDataType(BooleanType, LongType, StringType)
+    checkDataType(BooleanType, DoubleType, StringType)
+    checkDataType(BooleanType, DecimalType, StringType)
+    checkDataType(BooleanType, StringType, StringType)
+    checkDataType(BooleanType, ArrayType(IntegerType), StringType)
+    checkDataType(BooleanType, StructType(Nil), StringType)
+
+    // IntegerType
+    checkDataType(IntegerType, IntegerType, IntegerType)
+    checkDataType(IntegerType, LongType, LongType)
+    checkDataType(IntegerType, DoubleType, DoubleType)
+    checkDataType(IntegerType, DecimalType, DecimalType)
+    checkDataType(IntegerType, StringType, StringType)
+    checkDataType(IntegerType, ArrayType(IntegerType), StringType)
+    checkDataType(IntegerType, StructType(Nil), StringType)
+
+    // LongType
+    checkDataType(LongType, LongType, LongType)
+    checkDataType(LongType, DoubleType, DoubleType)
+    checkDataType(LongType, DecimalType, DecimalType)
+    checkDataType(LongType, StringType, StringType)
+    checkDataType(LongType, ArrayType(IntegerType), StringType)
+    checkDataType(LongType, StructType(Nil), StringType)
+
+    // DoubleType
+    checkDataType(DoubleType, DoubleType, DoubleType)
+    checkDataType(DoubleType, DecimalType, DecimalType)
+    checkDataType(DoubleType, StringType, StringType)
+    checkDataType(DoubleType, ArrayType(IntegerType), StringType)
+    checkDataType(DoubleType, StructType(Nil), StringType)
+
+    // DoubleType
+    checkDataType(DecimalType, DecimalType, DecimalType)
+    checkDataType(DecimalType, StringType, StringType)
+    checkDataType(DecimalType, ArrayType(IntegerType), StringType)
+    checkDataType(DecimalType, StructType(Nil), StringType)
+
+    // StringType
+    checkDataType(StringType, StringType, StringType)
+    checkDataType(StringType, ArrayType(IntegerType), StringType)
+    checkDataType(StringType, StructType(Nil), StringType)
+
+    // ArrayType
+    checkDataType(ArrayType(IntegerType), ArrayType(IntegerType), ArrayType(IntegerType))
+    checkDataType(ArrayType(IntegerType), ArrayType(LongType), ArrayType(LongType))
+    checkDataType(ArrayType(IntegerType), ArrayType(StringType), ArrayType(StringType))
+    checkDataType(ArrayType(IntegerType), StructType(Nil), StringType)
+
+    // StructType
+    checkDataType(StructType(Nil), StructType(Nil), StructType(Nil))
+    checkDataType(
+      StructType(StructField("f1", IntegerType, true) :: Nil),
+      StructType(StructField("f1", IntegerType, true) :: Nil),
+      StructType(StructField("f1", IntegerType, true) :: Nil))
+    checkDataType(
+      StructType(StructField("f1", IntegerType, true) :: Nil),
+      StructType(Nil),
+      StructType(StructField("f1", IntegerType, true) :: Nil))
+    checkDataType(
+      StructType(
+        StructField("f1", IntegerType, true) ::
+        StructField("f2", IntegerType, true) :: Nil),
+      StructType(StructField("f1", LongType, true) :: Nil) ,
+      StructType(
+        StructField("f1", LongType, true) ::
+        StructField("f2", IntegerType, true) :: Nil))
+    checkDataType(
+      StructType(
+        StructField("f1", IntegerType, true) :: Nil),
+      StructType(
+        StructField("f2", IntegerType, true) :: Nil),
+      StructType(
+        StructField("f1", IntegerType, true) ::
+        StructField("f2", IntegerType, true) :: Nil))
+    checkDataType(
+      StructType(
+        StructField("f1", IntegerType, true) :: Nil),
+      DecimalType,
+      StringType)
+  }
+
+  test("Primitive field and type inferring") {
+    val jsonSchemaRDD = jsonRDD(primitiveFieldAndType)
+
+    val expectedSchema =
+      AttributeReference("bigInteger", DecimalType, true)() ::
+      AttributeReference("boolean", BooleanType, true)() ::
+      AttributeReference("double", DoubleType, true)() ::
+      AttributeReference("integer", IntegerType, true)() ::
+      AttributeReference("long", LongType, true)() ::
+      AttributeReference("null", StringType, true)() ::
+      AttributeReference("string", StringType, true)() :: Nil
+
+    comparePlans(Schema(expectedSchema), Schema(jsonSchemaRDD.logicalPlan.output))
+
+    jsonSchemaRDD.registerAsTable("jsonTable")
+
+    checkAnswer(
+      sql("select * from jsonTable"),
+      (BigDecimal("92233720368547758070"),
+      true,
+      1.7976931348623157E308,
+      10,
+      21474836470L,
+      null,
+      "this is a simple string.") :: Nil
+    )
+  }
+
+  test("Complex field and type inferring") {
+    val jsonSchemaRDD = jsonRDD(complexFieldAndType)
+
+    val expectedSchema =
+      AttributeReference("arrayOfArray1", ArrayType(ArrayType(StringType)), true)() ::
+      AttributeReference("arrayOfArray2", ArrayType(ArrayType(DoubleType)), true)() ::
+      AttributeReference("arrayOfBigInteger", ArrayType(DecimalType), true)() ::
+      AttributeReference("arrayOfBoolean", ArrayType(BooleanType), true)() ::
+      AttributeReference("arrayOfDouble", ArrayType(DoubleType), true)() ::
+      AttributeReference("arrayOfInteger", ArrayType(IntegerType), true)() ::
+      AttributeReference("arrayOfLong", ArrayType(LongType), true)() ::
+      AttributeReference("arrayOfNull", ArrayType(StringType), true)() ::
+      AttributeReference("arrayOfString", ArrayType(StringType), true)() ::
+      AttributeReference("arrayOfStruct", ArrayType(
+        StructType(StructField("field1", BooleanType, true) ::
+                   StructField("field2", StringType, true) :: Nil)), true)() ::
+      AttributeReference("struct", StructType(
+        StructField("field1", BooleanType, true) ::
+        StructField("field2", DecimalType, true) :: Nil), true)() ::
+      AttributeReference("structWithArrayFields", StructType(
+        StructField("field1", ArrayType(IntegerType), true) ::
+        StructField("field2", ArrayType(StringType), true) :: Nil), true)() :: Nil
+
+    comparePlans(Schema(expectedSchema), Schema(jsonSchemaRDD.logicalPlan.output))
+
+    jsonSchemaRDD.registerAsTable("jsonTable")
+
+    // Access elements of a primitive array.
+    checkAnswer(
+      sql("select arrayOfString[0], arrayOfString[1], arrayOfString[2] from jsonTable"),
+      ("str1", "str2", null) :: Nil
+    )
+
+    // Access an array of null values.
+    checkAnswer(
+      sql("select arrayOfNull from jsonTable"),
+      Seq(Seq(null, null, null, null)) :: Nil
+    )
+
+    // Access elements of a BigInteger array (we use DecimalType internally).
+    checkAnswer(
+      sql("select arrayOfBigInteger[0], arrayOfBigInteger[1], arrayOfBigInteger[2] from jsonTable"),
+      (BigDecimal("922337203685477580700"), BigDecimal("-922337203685477580800"), null) :: Nil
+    )
+
+    // Access elements of an array of arrays.
+    checkAnswer(
+      sql("select arrayOfArray1[0], arrayOfArray1[1] from jsonTable"),
+      (Seq("1", "2", "3"), Seq("str1", "str2")) :: Nil
+    )
+
+    // Access elements of an array of arrays.
+    checkAnswer(
+      sql("select arrayOfArray2[0], arrayOfArray2[1] from jsonTable"),
+      (Seq(1.0, 2.0, 3.0), Seq(1.1, 2.1, 3.1)) :: Nil
+    )
+
+    // Access elements of an array inside a filed with the type of ArrayType(ArrayType).
+    checkAnswer(
+      sql("select arrayOfArray1[1][1], arrayOfArray2[1][1] from jsonTable"),
+      ("str2", 2.1) :: Nil
+    )
+
+    // Access elements of an array of structs.
+    checkAnswer(
+      sql("select arrayOfStruct[0], arrayOfStruct[1], arrayOfStruct[2] from jsonTable"),
+      (true :: "str1" :: Nil, false :: null :: Nil, null) :: Nil
+    )
+
+    // Access a struct and fields inside of it.
+    checkAnswer(
+      sql("select struct, struct.field1, struct.field2 from jsonTable"),
+      (
+        Seq(true, BigDecimal("92233720368547758070")),
+        true,
+        BigDecimal("92233720368547758070")) :: Nil
+    )
+
+    // Access an array field of a struct.
+    checkAnswer(
+      sql("select structWithArrayFields.field1, structWithArrayFields.field2 from jsonTable"),
+      (Seq(4, 5, 6), Seq("str1", "str2")) :: Nil
+    )
+
+    // Access elements of an array field of a struct.
+    checkAnswer(
+      sql("select structWithArrayFields.field1[1], structWithArrayFields.field2[3] from jsonTable"),
+      (5, null) :: Nil
+    )
+  }
+
+  ignore("Complex field and type inferring (Ignored)") {
+    val jsonSchemaRDD = jsonRDD(complexFieldAndType)
+    jsonSchemaRDD.registerAsTable("jsonTable")
+
+    // Right now, "field1" and "field2" are treated as aliases. We should fix it.
+    checkAnswer(
+      sql("select arrayOfStruct[0].field1, arrayOfStruct[0].field2 from jsonTable"),
+      (true, "str1") :: Nil
+    )
+
+    // Right now, the analyzer cannot resolve arrayOfStruct.field1 and arrayOfStruct.field2.
+    // Getting all values of a specific field from an array of structs.
+    checkAnswer(
+      sql("select arrayOfStruct.field1, arrayOfStruct.field2 from jsonTable"),
+      (Seq(true, false), Seq("str1", null)) :: Nil
+    )
+  }
+
+  test("Type conflict in primitive field values") {
+    val jsonSchemaRDD = jsonRDD(primitiveFieldValueTypeConflict)
+
+    val expectedSchema =
+      AttributeReference("num_bool", StringType, true)() ::
+      AttributeReference("num_num_1", LongType, true)() ::
+      AttributeReference("num_num_2", DecimalType, true)() ::
+      AttributeReference("num_num_3", DoubleType, true)() ::
+      AttributeReference("num_str", StringType, true)() ::
+      AttributeReference("str_bool", StringType, true)() :: Nil
+
+    comparePlans(Schema(expectedSchema), Schema(jsonSchemaRDD.logicalPlan.output))
+
+    jsonSchemaRDD.registerAsTable("jsonTable")
+
+    checkAnswer(
+      sql("select * from jsonTable"),
+      ("true", 11L, null, 1.1, "13.1", "str1") ::
+      ("12", null, BigDecimal("21474836470.9"), null, null, "true") ::
+      ("false", 21474836470L, BigDecimal("92233720368547758070"), 100, "str1", "false") ::
+      (null, 21474836570L, BigDecimal(1.1), 21474836470L, "92233720368547758070", null) :: Nil
+    )
+
+    // Number and Boolean conflict: resolve the type as number in this query.
+    checkAnswer(
+      sql("select num_bool - 10 from jsonTable where num_bool > 11"),
+      2
+    )
+
+    // Widening to LongType
+    checkAnswer(
+      sql("select num_num_1 - 100 from jsonTable where num_num_1 > 11"),
+      Seq(21474836370L) :: Seq(21474836470L) :: Nil
+    )
+
+    checkAnswer(
+      sql("select num_num_1 - 100 from jsonTable where num_num_1 > 10"),
+      Seq(-89) :: Seq(21474836370L) :: Seq(21474836470L) :: Nil
+    )
+
+    // Widening to DecimalType
+    checkAnswer(
+      sql("select num_num_2 + 1.2 from jsonTable where num_num_2 > 1.1"),
+      Seq(BigDecimal("21474836472.1")) :: Seq(BigDecimal("92233720368547758071.2")) :: Nil
+    )
+
+    // Widening to DoubleType
+    checkAnswer(
+      sql("select num_num_3 + 1.2 from jsonTable where num_num_3 > 1.1"),
+      Seq(101.2) :: Seq(21474836471.2) :: Nil
+    )
+
+    // Number and String conflict: resolve the type as number in this query.
+    checkAnswer(
+      sql("select num_str + 1.2 from jsonTable where num_str > 14"),
+      92233720368547758071.2
+    )
+
+    // String and Boolean conflict: resolve the type as string.
+    checkAnswer(
+      sql("select * from jsonTable where str_bool = 'str1'"),
+      ("true", 11L, null, 1.1, "13.1", "str1") :: Nil
+    )
+  }
+
+  ignore("Type conflict in primitive field values (Ignored)") {
+    val jsonSchemaRDD = jsonRDD(primitiveFieldValueTypeConflict)
+    jsonSchemaRDD.registerAsTable("jsonTable")
+
+    // Right now, the analyzer does not promote strings in a boolean expreesion.
+    // Number and Boolean conflict: resolve the type as boolean in this query.
+    checkAnswer(
+      sql("select num_bool from jsonTable where NOT num_bool"),
+      false
+    )
+
+    checkAnswer(
+      sql("select str_bool from jsonTable where NOT str_bool"),
+      false
+    )
+
+    // Right now, the analyzer does not know that num_bool should be treated as a boolean.
+    // Number and Boolean conflict: resolve the type as boolean in this query.
+    checkAnswer(
+      sql("select num_bool from jsonTable where num_bool"),
+      true
+    )
+
+    checkAnswer(
+      sql("select str_bool from jsonTable where str_bool"),
+      false
+    )
+
+    // Right now, we have a parsing error.
+    // Number and String conflict: resolve the type as number in this query.
+    checkAnswer(
+      sql("select num_str + 1.2 from jsonTable where num_str > 92233720368547758060"),
+      BigDecimal("92233720368547758061.2")
+    )
+
+    // The plan of the following DSL is
+    // Project [(CAST(num_str#65:4, DoubleType) + 1.2) AS num#78]
+    //  Filter (CAST(CAST(num_str#65:4, DoubleType), DecimalType) > 92233720368547758060)
+    //    ExistingRdd [num_bool#61,num_num_1#62L,num_num_2#63,num_num_3#64,num_str#65,str_bool#66]
+    // We should directly cast num_str to DecimalType and also need to do the right type promotion
+    // in the Project.
+    checkAnswer(
+      jsonSchemaRDD.
+        where('num_str > BigDecimal("92233720368547758060")).
+        select('num_str + 1.2 as Symbol("num")),
+      BigDecimal("92233720368547758061.2")
+    )
+
+    // The following test will fail. The type of num_str is StringType.
+    // So, to evaluate num_str + 1.2, we first need to use Cast to convert the type.
+    // In our test data, one value of num_str is 13.1.
+    // The result of (CAST(num_str#65:4, DoubleType) + 1.2) for this value is 14.299999999999999,
+    // which is not 14.3.
+    // Number and String conflict: resolve the type as number in this query.
+    checkAnswer(
+      sql("select num_str + 1.2 from jsonTable where num_str > 13"),
+      Seq(14.3) :: Seq(92233720368547758071.2) :: Nil
+    )
+  }
+
+  test("Type conflict in complex field values") {
+    val jsonSchemaRDD = jsonRDD(complexFieldValueTypeConflict)
+
+    val expectedSchema =
+      AttributeReference("array", ArrayType(IntegerType), true)() ::
+      AttributeReference("num_struct", StringType, true)() ::
+      AttributeReference("str_array", StringType, true)() ::
+      AttributeReference("struct", StructType(
+        StructField("field", StringType, true) :: Nil), true)() ::
+      AttributeReference("struct_array", StringType, true)() :: Nil
+
+    comparePlans(Schema(expectedSchema), Schema(jsonSchemaRDD.logicalPlan.output))
+
+    jsonSchemaRDD.registerAsTable("jsonTable")
+
+    checkAnswer(
+      sql("select * from jsonTable"),
+      (Seq(), "11", "[1,2,3]", Seq(null), "[]") ::
+      (null, """{"field":false}""", null, null, "{}") ::
+      (Seq(4, 5, 6), null, "str", Seq(null), "[7,8,9]") ::
+      (Seq(7), "{}","[str1,str2,33]", Seq("str"), """{"field":true}""") :: Nil
+    )
+  }
+
+  test("Type conflict in array elements") {
+    val jsonSchemaRDD = jsonRDD(arrayElementTypeConflict)
+
+    val expectedSchema =
+      AttributeReference("array", ArrayType(StringType), true)() :: Nil
+
+    comparePlans(Schema(expectedSchema), Schema(jsonSchemaRDD.logicalPlan.output))
+
+    jsonSchemaRDD.registerAsTable("jsonTable")
+
+    checkAnswer(
+      sql("select * from jsonTable"),
+      Seq(Seq("1", "1.1", "true", null, "[]", "{}", "[2,3,4]",
+        """{"field":str}""")) :: Nil
+    )
+
+    // Treat an element as a number.
+    checkAnswer(
+      sql("select array[0] + 1 from jsonTable"),
+      2
+    )
+  }
+
+  test("Handling missing fields") {
+    val jsonSchemaRDD = jsonRDD(missingFields)
+
+    val expectedSchema =
+      AttributeReference("a", BooleanType, true)() ::
+      AttributeReference("b", LongType, true)() ::
+      AttributeReference("c", ArrayType(IntegerType), true)() ::
+      AttributeReference("d", StructType(
+        StructField("field", BooleanType, true) :: Nil), true)() ::
+      AttributeReference("e", StringType, true)() :: Nil
+
+    comparePlans(Schema(expectedSchema), Schema(jsonSchemaRDD.logicalPlan.output))
+
+    jsonSchemaRDD.registerAsTable("jsonTable")
+  }
+
+  test("Loading a JSON dataset from a text file") {
+    val file = getTempFilePath("json")
+    val path = file.toString
+    primitiveFieldAndType.map(record => record.replaceAll("\n", " ")).saveAsTextFile(path)
+    val jsonSchemaRDD = jsonFile(path)
+
+    val expectedSchema =
+      AttributeReference("bigInteger", DecimalType, true)() ::
+      AttributeReference("boolean", BooleanType, true)() ::
+      AttributeReference("double", DoubleType, true)() ::
+      AttributeReference("integer", IntegerType, true)() ::
+      AttributeReference("long", LongType, true)() ::
+      AttributeReference("null", StringType, true)() ::
+      AttributeReference("string", StringType, true)() :: Nil
+
+    comparePlans(Schema(expectedSchema), Schema(jsonSchemaRDD.logicalPlan.output))
+
+    jsonSchemaRDD.registerAsTable("jsonTable")
+
+    checkAnswer(
+      sql("select * from jsonTable"),
+      (BigDecimal("92233720368547758070"),
+      true,
+      1.7976931348623157E308,
+      10,
+      21474836470L,
+      null,
+      "this is a simple string.") :: Nil
+    )
+  }
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/json/TestJsonData.scala b/sql/core/src/test/scala/org/apache/spark/sql/json/TestJsonData.scala
new file mode 100644
index 000000000000..065e04046e8a
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/json/TestJsonData.scala
@@ -0,0 +1,84 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.json
+
+import org.apache.spark.sql.test.TestSQLContext
+
+object TestJsonData {
+
+  val primitiveFieldAndType =
+    TestSQLContext.sparkContext.parallelize(
+      """{"string":"this is a simple string.",
+          "integer":10,
+          "long":21474836470,
+          "bigInteger":92233720368547758070,
+          "double":1.7976931348623157E308,
+          "boolean":true,
+          "null":null
+      }"""  :: Nil)
+
+  val complexFieldAndType =
+    TestSQLContext.sparkContext.parallelize(
+      """{"struct":{"field1": true, "field2": 92233720368547758070},
+          "structWithArrayFields":{"field1":[4, 5, 6], "field2":["str1", "str2"]},
+          "arrayOfString":["str1", "str2"],
+          "arrayOfInteger":[1, 2147483647, -2147483648],
+          "arrayOfLong":[21474836470, 9223372036854775807, -9223372036854775808],
+          "arrayOfBigInteger":[922337203685477580700, -922337203685477580800],
+          "arrayOfDouble":[1.2, 1.7976931348623157E308, 4.9E-324, 2.2250738585072014E-308],
+          "arrayOfBoolean":[true, false, true],
+          "arrayOfNull":[null, null, null, null],
+          "arrayOfStruct":[{"field1": true, "field2": "str1"}, {"field1": false}],
+          "arrayOfArray1":[[1, 2, 3], ["str1", "str2"]],
+          "arrayOfArray2":[[1, 2, 3], [1.1, 2.1, 3.1]]
+         }"""  :: Nil)
+
+  val primitiveFieldValueTypeConflict =
+    TestSQLContext.sparkContext.parallelize(
+      """{"num_num_1":11, "num_num_2":null, "num_num_3": 1.1,
+          "num_bool":true, "num_str":13.1, "str_bool":"str1"}""" ::
+      """{"num_num_1":null, "num_num_2":21474836470.9, "num_num_3": null,
+          "num_bool":12, "num_str":null, "str_bool":true}""" ::
+      """{"num_num_1":21474836470, "num_num_2":92233720368547758070, "num_num_3": 100,
+          "num_bool":false, "num_str":"str1", "str_bool":false}""" ::
+      """{"num_num_1":21474836570, "num_num_2":1.1, "num_num_3": 21474836470,
+          "num_bool":null, "num_str":92233720368547758070, "str_bool":null}""" :: Nil)
+
+  val complexFieldValueTypeConflict =
+    TestSQLContext.sparkContext.parallelize(
+      """{"num_struct":11, "str_array":[1, 2, 3],
+          "array":[], "struct_array":[], "struct": {}}""" ::
+      """{"num_struct":{"field":false}, "str_array":null,
+          "array":null, "struct_array":{}, "struct": null}""" ::
+      """{"num_struct":null, "str_array":"str",
+          "array":[4, 5, 6], "struct_array":[7, 8, 9], "struct": {"field":null}}""" ::
+      """{"num_struct":{}, "str_array":["str1", "str2", 33],
+          "array":[7], "struct_array":{"field": true}, "struct": {"field": "str"}}""" :: Nil)
+
+  val arrayElementTypeConflict =
+    TestSQLContext.sparkContext.parallelize(
+      """{"array": [1, 1.1, true, null, [], {}, [2,3,4], {"field":"str"}]}""" :: Nil)
+
+  val missingFields =
+    TestSQLContext.sparkContext.parallelize(
+      """{"a":true}""" ::
+      """{"b":21474836470}""" ::
+      """{"c":[33, 44]}""" ::
+      """{"d":{"field":true}}""" ::
+      """{"e":"str"}""" :: Nil)
+}

From 9e4b4bd0837cfc4ef1af1edcbc56290821e49e92 Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Tue, 17 Jun 2014 19:34:17 -0700
Subject: [PATCH 450/810] Revert "SPARK-2038: rename "conf" parameters in the
 saveAsHadoop functions"

This reverts commit 443f5e1bbcf9ec55e5ce6e4f738a002a47818100.

This commit unfortunately would break source compatibility if users have named
the hadoopConf parameter.
---
 .../apache/spark/rdd/PairRDDFunctions.scala   | 49 +++++++++----------
 1 file changed, 24 insertions(+), 25 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
index bff77b4ecbf2..fe36c80e0be8 100644
--- a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
@@ -719,9 +719,9 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
       keyClass: Class[_],
       valueClass: Class[_],
       outputFormatClass: Class[_ <: NewOutputFormat[_, _]],
-      hadoopConf: Configuration = self.context.hadoopConfiguration)
+      conf: Configuration = self.context.hadoopConfiguration)
   {
-    val job = new NewAPIHadoopJob(hadoopConf)
+    val job = new NewAPIHadoopJob(conf)
     job.setOutputKeyClass(keyClass)
     job.setOutputValueClass(valueClass)
     job.setOutputFormatClass(outputFormatClass)
@@ -752,25 +752,24 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
       keyClass: Class[_],
       valueClass: Class[_],
       outputFormatClass: Class[_ <: OutputFormat[_, _]],
-      hadoopConf: JobConf = new JobConf(self.context.hadoopConfiguration),
+      conf: JobConf = new JobConf(self.context.hadoopConfiguration),
       codec: Option[Class[_ <: CompressionCodec]] = None) {
-    hadoopConf.setOutputKeyClass(keyClass)
-    hadoopConf.setOutputValueClass(valueClass)
+    conf.setOutputKeyClass(keyClass)
+    conf.setOutputValueClass(valueClass)
     // Doesn't work in Scala 2.9 due to what may be a generics bug
     // TODO: Should we uncomment this for Scala 2.10?
     // conf.setOutputFormat(outputFormatClass)
-    hadoopConf.set("mapred.output.format.class", outputFormatClass.getName)
+    conf.set("mapred.output.format.class", outputFormatClass.getName)
     for (c <- codec) {
-      hadoopConf.setCompressMapOutput(true)
-      hadoopConf.set("mapred.output.compress", "true")
-      hadoopConf.setMapOutputCompressorClass(c)
-      hadoopConf.set("mapred.output.compression.codec", c.getCanonicalName)
-      hadoopConf.set("mapred.output.compression.type", CompressionType.BLOCK.toString)
+      conf.setCompressMapOutput(true)
+      conf.set("mapred.output.compress", "true")
+      conf.setMapOutputCompressorClass(c)
+      conf.set("mapred.output.compression.codec", c.getCanonicalName)
+      conf.set("mapred.output.compression.type", CompressionType.BLOCK.toString)
     }
-    hadoopConf.setOutputCommitter(classOf[FileOutputCommitter])
-    FileOutputFormat.setOutputPath(hadoopConf,
-      SparkHadoopWriter.createPathFromString(path, hadoopConf))
-    saveAsHadoopDataset(hadoopConf)
+    conf.setOutputCommitter(classOf[FileOutputCommitter])
+    FileOutputFormat.setOutputPath(conf, SparkHadoopWriter.createPathFromString(path, conf))
+    saveAsHadoopDataset(conf)
   }
 
   /**
@@ -779,8 +778,8 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * output paths required (e.g. a table name to write to) in the same way as it would be
    * configured for a Hadoop MapReduce job.
    */
-  def saveAsNewAPIHadoopDataset(hadoopConf: Configuration) {
-    val job = new NewAPIHadoopJob(hadoopConf)
+  def saveAsNewAPIHadoopDataset(conf: Configuration) {
+    val job = new NewAPIHadoopJob(conf)
     val formatter = new SimpleDateFormat("yyyyMMddHHmm")
     val jobtrackerID = formatter.format(new Date())
     val stageId = self.id
@@ -836,10 +835,10 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * (e.g. a table name to write to) in the same way as it would be configured for a Hadoop
    * MapReduce job.
    */
-  def saveAsHadoopDataset(hadoopConf: JobConf) {
-    val outputFormatInstance = hadoopConf.getOutputFormat
-    val keyClass = hadoopConf.getOutputKeyClass
-    val valueClass = hadoopConf.getOutputValueClass
+  def saveAsHadoopDataset(conf: JobConf) {
+    val outputFormatInstance = conf.getOutputFormat
+    val keyClass = conf.getOutputKeyClass
+    val valueClass = conf.getOutputValueClass
     if (outputFormatInstance == null) {
       throw new SparkException("Output format class not set")
     }
@@ -849,18 +848,18 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
     if (valueClass == null) {
       throw new SparkException("Output value class not set")
     }
-    SparkHadoopUtil.get.addCredentials(hadoopConf)
+    SparkHadoopUtil.get.addCredentials(conf)
 
     logDebug("Saving as hadoop file of type (" + keyClass.getSimpleName + ", " +
       valueClass.getSimpleName + ")")
 
     if (self.conf.getBoolean("spark.hadoop.validateOutputSpecs", true)) {
       // FileOutputFormat ignores the filesystem parameter
-      val ignoredFs = FileSystem.get(hadoopConf)
-      hadoopConf.getOutputFormat.checkOutputSpecs(ignoredFs, hadoopConf)
+      val ignoredFs = FileSystem.get(conf)
+      conf.getOutputFormat.checkOutputSpecs(ignoredFs, conf)
     }
 
-    val writer = new SparkHadoopWriter(hadoopConf)
+    val writer = new SparkHadoopWriter(conf)
     writer.preSetup()
 
     def writeToFile(context: TaskContext, iter: Iterator[(K, V)]) {

From 889f7b7624689444ecdb4f0ca16ef78f9bfc8430 Mon Sep 17 00:00:00 2001
From: Vadim Chekan <kot.begemot@gmail.com>
Date: Tue, 17 Jun 2014 22:03:50 -0700
Subject: [PATCH 451/810] [STREAMING] SPARK-2009 Key not found exception when
 slow receiver starts

I got "java.util.NoSuchElementException: key not found: 1401756085000 ms" exception when using kafka stream and 1 sec batchPeriod.

Investigation showed that the reason is that ReceiverLauncher.startReceivers is asynchronous (started in a thread).
https://github.com/vchekan/spark/blob/master/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala#L206

In case of slow starting receiver, such as Kafka, it easily takes more than 2sec to start. In result, no single "compute" will be called on ReceiverInputDStream before first batch job is executed and receivedBlockInfo remains empty (obviously). Batch job will cause ReceiverInputDStream.getReceivedBlockInfo call and "key not found" exception.

The patch makes getReceivedBlockInfo more robust by tolerating missing values.

Author: Vadim Chekan <kot.begemot@gmail.com>

Closes #961 from vchekan/branch-1.0 and squashes the following commits:

e86f82b [Vadim Chekan] Fixed indentation
4609563 [Vadim Chekan] Key not found exception: if receiver is slow to start, it is possible that getReceivedBlockInfo will be called before compute has been called
(cherry picked from commit 26f6b989312a9a48a27a23ecc68702bd14032e55)

Signed-off-by: Patrick Wendell <pwendell@gmail.com>
---
 .../apache/spark/streaming/dstream/ReceiverInputDStream.scala   | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReceiverInputDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReceiverInputDStream.scala
index 75cabdbf8da2..391e40924f38 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReceiverInputDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReceiverInputDStream.scala
@@ -74,7 +74,7 @@ abstract class ReceiverInputDStream[T: ClassTag](@transient ssc_ : StreamingCont
 
   /** Get information on received blocks. */
   private[streaming] def getReceivedBlockInfo(time: Time) = {
-    receivedBlockInfo(time)
+    receivedBlockInfo.get(time).getOrElse(Array.empty[ReceivedBlockInfo])
   }
 
   /**

From 587d32012ceeec1e80cec1878312f164cdb76ec8 Mon Sep 17 00:00:00 2001
From: Yin Huai <huai@cse.ohio-state.edu>
Date: Wed, 18 Jun 2014 10:51:32 -0700
Subject: [PATCH 452/810] [SPARK-2176][SQL] Extra unnecessary exchange operator
 in the result of an explain command

```
hql("explain select * from src group by key").collect().foreach(println)

[ExplainCommand [plan#27:0]]
[ Aggregate false, [key#25], [key#25,value#26]]
[  Exchange (HashPartitioning [key#25:0], 200)]
[   Exchange (HashPartitioning [key#25:0], 200)]
[    Aggregate true, [key#25], [key#25]]
[     HiveTableScan [key#25,value#26], (MetastoreRelation default, src, None), None]
```

There are two exchange operators.

However, if we do not use explain...
```
hql("select * from src group by key")

res4: org.apache.spark.sql.SchemaRDD =
SchemaRDD[8] at RDD at SchemaRDD.scala:100
== Query Plan ==
Aggregate false, [key#8], [key#8,value#9]
 Exchange (HashPartitioning [key#8:0], 200)
  Aggregate true, [key#8], [key#8]
   HiveTableScan [key#8,value#9], (MetastoreRelation default, src, None), None
```
The plan is fine.

The cause of this bug is explained below.

When we create an `execution.ExplainCommand`, we use the `executedPlan` as the child of this `ExplainCommand`. But, this `executedPlan` is prepared for execution again when we generate the `executedPlan` for the `ExplainCommand`. Basically, `prepareForExecution` is called twice on a physical plan. Because after `prepareForExecution` we have already bounded those references (in `BoundReference`s), `AddExchange` cannot figure out we are using the same partitioning (we use `AttributeReference`s to create an `ExchangeOperator` and then those references will be changed to `BoundReference`s after `prepareForExecution` is called). So, an extra `ExchangeOperator` is inserted.

I think in `CommandStrategy`, we should just use the `sparkPlan` (`sparkPlan` is the input of `prepareForExecution`) to initialize the `ExplainCommand` instead of using `executedPlan`.

The link to JIRA: https://issues.apache.org/jira/browse/SPARK-2176

Author: Yin Huai <huai@cse.ohio-state.edu>

Closes #1116 from yhuai/SPARK-2176 and squashes the following commits:

197c19c [Yin Huai] Use sparkPlan to initialize a Physical Explain Command instead of using executedPlan.
---
 sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala | 2 ++
 .../org/apache/spark/sql/execution/SparkStrategies.scala      | 4 ++--
 2 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
index f7e03323bed3..1617ec717b2e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
@@ -307,6 +307,8 @@ class SQLContext(@transient val sparkContext: SparkContext)
     lazy val optimizedPlan = optimizer(analyzed)
     // TODO: Don't just pick the first one...
     lazy val sparkPlan = planner(optimizedPlan).next()
+    // executedPlan should not be used to initialize any SparkPlan. It should be
+    // only used for execution.
     lazy val executedPlan: SparkPlan = prepareForExecution(sparkPlan)
 
     /** Internal version of the RDD. Avoids copies and has no schema */
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
index 2233216a6ec5..70c1171148eb 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
@@ -251,8 +251,8 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
       case logical.SetCommand(key, value) =>
         Seq(execution.SetCommand(key, value, plan.output)(context))
       case logical.ExplainCommand(child) =>
-        val executedPlan = context.executePlan(child).executedPlan
-        Seq(execution.ExplainCommand(executedPlan, plan.output)(context))
+        val sparkPlan = context.executePlan(child).sparkPlan
+        Seq(execution.ExplainCommand(sparkPlan, plan.output)(context))
       case logical.CacheCommand(tableName, cache) =>
         Seq(execution.CacheCommand(tableName, cache)(context))
       case _ => Nil

From 5ad5e3486aa4d13b0991de13f5f17d9897dd2753 Mon Sep 17 00:00:00 2001
From: Raymond Liu <raymond.liu@intel.com>
Date: Wed, 18 Jun 2014 10:57:45 -0700
Subject: [PATCH 453/810] [SPARK-2162] Double check in doGetLocal to avoid read
 on removed block.

other wise, it will either read in vain in memory level case, or throw exception in disk level case when it believe the block is there while actually it had been removed.

Author: Raymond Liu <raymond.liu@intel.com>

Closes #1103 from colorant/bm and squashes the following commits:

daac114 [Raymond Liu] Address comments
d1ea287 [Raymond Liu] Double check in doGetLocal to avoid read on removed block.
---
 .../main/scala/org/apache/spark/storage/BlockManager.scala | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala
index f52bc7075104..373987c12262 100644
--- a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala
+++ b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala
@@ -363,6 +363,13 @@ private[spark] class BlockManager(
     val info = blockInfo.get(blockId).orNull
     if (info != null) {
       info.synchronized {
+        // Double check to make sure the block is still there, since removeBlock
+        // method also synchronizes on BlockInfo object, so the block might have
+        // been removed when we actually come here.
+        if (blockInfo.get(blockId).isEmpty) {
+          logDebug(s"Block $blockId had been removed")
+          return None
+        }
 
         // If another thread is writing the block, wait for it to become ready.
         if (!info.waitForReady()) {

From dd96fcda0145810785b67f847f98b04ff7f0d7c3 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Wed, 18 Jun 2014 12:48:58 -0700
Subject: [PATCH 454/810] Updated the comment for SPARK-2162.

A follow up on #1103

@andrewor14

Author: Reynold Xin <rxin@apache.org>

Closes #1117 from rxin/SPARK-2162 and squashes the following commits:

a4231de [Reynold Xin] Updated the comment for SPARK-2162.
---
 .../scala/org/apache/spark/storage/BlockManager.scala  | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala
index 373987c12262..d2f7baf928b6 100644
--- a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala
+++ b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala
@@ -363,11 +363,13 @@ private[spark] class BlockManager(
     val info = blockInfo.get(blockId).orNull
     if (info != null) {
       info.synchronized {
-        // Double check to make sure the block is still there, since removeBlock
-        // method also synchronizes on BlockInfo object, so the block might have
-        // been removed when we actually come here.
+        // Double check to make sure the block is still there. There is a small chance that the
+        // block has been removed by removeBlock (which also synchronizes on the blockInfo object).
+        // Note that this only checks metadata tracking. If user intentionally deleted the block
+        // on disk or from off heap storage without using removeBlock, this conditional check will
+        // still pass but eventually we will get an exception because we can't find the block.
         if (blockInfo.get(blockId).isEmpty) {
-          logDebug(s"Block $blockId had been removed")
+          logWarning(s"Block $blockId had been removed")
           return None
         }
 

From 3870248740d83b0292ccca88a494ce19783847f0 Mon Sep 17 00:00:00 2001
From: Kay Ousterhout <kayousterhout@gmail.com>
Date: Wed, 18 Jun 2014 13:16:26 -0700
Subject: [PATCH 455/810] [SPARK-1466] Raise exception if pyspark Gateway
 process doesn't start.

If the gateway process fails to start correctly (e.g., because JAVA_HOME isn't set correctly, there's no Spark jar, etc.), right now pyspark fails because of a very difficult-to-understand error, where we try to parse stdout to get the port where Spark started and there's nothing there. This commit properly catches the error and throws an exception that includes the stderr output for much easier debugging.

Thanks to @shivaram and @stogers for helping to fix this issue!

Author: Kay Ousterhout <kayousterhout@gmail.com>

Closes #383 from kayousterhout/pyspark and squashes the following commits:

36dd54b [Kay Ousterhout] [SPARK-1466] Raise exception if Gateway process doesn't start.
---
 python/pyspark/java_gateway.py | 15 +++++++++++----
 1 file changed, 11 insertions(+), 4 deletions(-)

diff --git a/python/pyspark/java_gateway.py b/python/pyspark/java_gateway.py
index 91ae8263f66b..19235d5f79f8 100644
--- a/python/pyspark/java_gateway.py
+++ b/python/pyspark/java_gateway.py
@@ -43,12 +43,19 @@ def launch_gateway():
             # Don't send ctrl-c / SIGINT to the Java gateway:
             def preexec_func():
                 signal.signal(signal.SIGINT, signal.SIG_IGN)
-            proc = Popen(command, stdout=PIPE, stdin=PIPE, preexec_fn=preexec_func)
+            proc = Popen(command, stdout=PIPE, stdin=PIPE, stderr=PIPE, preexec_fn=preexec_func)
         else:
             # preexec_fn not supported on Windows
-            proc = Popen(command, stdout=PIPE, stdin=PIPE)
-        # Determine which ephemeral port the server started on:
-        gateway_port = int(proc.stdout.readline())
+            proc = Popen(command, stdout=PIPE, stdin=PIPE, stderr=PIPE)
+        
+        try:
+            # Determine which ephemeral port the server started on:
+            gateway_port = int(proc.stdout.readline())
+        except:
+            error_code = proc.poll()
+            raise Exception("Launching GatewayServer failed with exit code %d: %s" %
+                (error_code, "".join(proc.stderr.readlines())))
+
         # Create a thread to echo output from the GatewayServer, which is required
         # for Java log output to show up:
         class EchoOutputThread(Thread):

From 4cbeea83e086bbbb1898bf796a5e5b789bc4cc06 Mon Sep 17 00:00:00 2001
From: Mark Hamstra <markhamstra@gmail.com>
Date: Wed, 18 Jun 2014 14:56:41 -0700
Subject: [PATCH 456/810] SPARK-2158 Clean up core/stdout file from
 FileAppenderSuite

@tdas

Author: Mark Hamstra <markhamstra@gmail.com>

Closes #1100 from markhamstra/SPARK-2158 and squashes the following commits:

ae8e069 [Mark Hamstra] Response to TD's review
2f1e201 [Mark Hamstra] Cleanup 'stdout' file within FileAppenderSuite
---
 .../org/apache/spark/util/FileAppenderSuite.scala      | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

diff --git a/core/src/test/scala/org/apache/spark/util/FileAppenderSuite.scala b/core/src/test/scala/org/apache/spark/util/FileAppenderSuite.scala
index 53d7f5c6072e..02e228945bbd 100644
--- a/core/src/test/scala/org/apache/spark/util/FileAppenderSuite.scala
+++ b/core/src/test/scala/org/apache/spark/util/FileAppenderSuite.scala
@@ -120,7 +120,7 @@ class FileAppenderSuite extends FunSuite with BeforeAndAfter with Logging {
     // on SparkConf settings.
 
     def testAppenderSelection[ExpectedAppender: ClassTag, ExpectedRollingPolicy](
-        properties: Seq[(String, String)], expectedRollingPolicyParam: Long = -1): FileAppender = {
+        properties: Seq[(String, String)], expectedRollingPolicyParam: Long = -1): Unit = {
 
       // Set spark conf properties
       val conf = new SparkConf
@@ -129,8 +129,9 @@ class FileAppenderSuite extends FunSuite with BeforeAndAfter with Logging {
       }
 
       // Create and test file appender
-      val inputStream = new PipedInputStream(new PipedOutputStream())
-      val appender = FileAppender(inputStream, new File("stdout"), conf)
+      val testOutputStream = new PipedOutputStream()
+      val testInputStream = new PipedInputStream(testOutputStream)
+      val appender = FileAppender(testInputStream, testFile, conf)
       assert(appender.isInstanceOf[ExpectedAppender])
       assert(appender.getClass.getSimpleName ===
         classTag[ExpectedAppender].runtimeClass.getSimpleName)
@@ -144,7 +145,8 @@ class FileAppenderSuite extends FunSuite with BeforeAndAfter with Logging {
         }
         assert(policyParam === expectedRollingPolicyParam)
       }
-      appender
+      testOutputStream.close()
+      appender.awaitTermination()
     }
 
     import RollingFileAppender._

From 45a95f82caea55a8616141444285faf58fef128b Mon Sep 17 00:00:00 2001
From: Doris Xin <doris.s.xin@gmail.com>
Date: Wed, 18 Jun 2014 15:01:29 -0700
Subject: [PATCH 457/810] Remove unicode operator from RDD.scala
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Some IDEs don’t support unicode characters in source code. Check if this breaks binary compatibility.

Author: Doris Xin <doris.s.xin@gmail.com>

Closes #1119 from dorx/unicode and squashes the following commits:

05618c3 [Doris Xin] Remove unicode operator from RDD.scala
---
 core/src/main/scala/org/apache/spark/rdd/RDD.scala | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/rdd/RDD.scala b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
index 1633b185861b..cebfd109d825 100644
--- a/core/src/main/scala/org/apache/spark/rdd/RDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
@@ -446,7 +446,7 @@ abstract class RDD[T: ClassTag](
    * Return this RDD sorted by the given key function.
    */
   def sortBy[K](
-      f: (T) ⇒ K,
+      f: (T) => K,
       ascending: Boolean = true,
       numPartitions: Int = this.partitions.size)
       (implicit ord: Ordering[K], ctag: ClassTag[K]): RDD[T] =

From 5ff75c748a27bcfae71759d0e509218f0c5d0200 Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Wed, 18 Jun 2014 17:52:42 -0700
Subject: [PATCH 458/810] [SPARK-2184][SQL] AddExchange isn't idempotent

...redPartitioning.

Author: Michael Armbrust <michael@databricks.com>

Closes #1122 from marmbrus/fixAddExchange and squashes the following commits:

3417537 [Michael Armbrust] Don't bind partitioning expressions as that breaks comparison with requiredPartitioning.
---
 .../spark/sql/catalyst/expressions/BoundAttribute.scala   | 4 ++--
 .../org/apache/spark/sql/catalyst/expressions/Row.scala   | 3 +++
 .../scala/org/apache/spark/sql/execution/Exchange.scala   | 8 ++++----
 3 files changed, 9 insertions(+), 6 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala
index 4ebf6c4584b9..655d4a08fe93 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala
@@ -68,7 +68,7 @@ class BindReferences[TreeNode <: QueryPlan[TreeNode]] extends Rule[TreeNode] {
 }
 
 object BindReferences extends Logging {
-  def bindReference(expression: Expression, input: Seq[Attribute]): Expression = {
+  def bindReference[A <: Expression](expression: A, input: Seq[Attribute]): A = {
     expression.transform { case a: AttributeReference =>
       attachTree(a, "Binding attribute") {
         val ordinal = input.indexWhere(_.exprId == a.exprId)
@@ -83,6 +83,6 @@ object BindReferences extends Logging {
           BoundReference(ordinal, a)
         }
       }
-    }
+    }.asInstanceOf[A] // Kind of a hack, but safe.  TODO: Tighten return type when possible.
   }
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Row.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Row.scala
index 77b5429bad43..74ae723686cf 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Row.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Row.scala
@@ -208,6 +208,9 @@ class GenericMutableRow(size: Int) extends GenericRow(size) with MutableRow {
 
 
 class RowOrdering(ordering: Seq[SortOrder]) extends Ordering[Row] {
+  def this(ordering: Seq[SortOrder], inputSchema: Seq[Attribute]) =
+    this(ordering.map(BindReferences.bindReference(_, inputSchema)))
+
   def compare(a: Row, b: Row): Int = {
     var i = 0
     while (i < ordering.size) {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
index cef294167f14..05dfb85b38b0 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
@@ -22,7 +22,7 @@ import org.apache.spark.{HashPartitioner, RangePartitioner, SparkConf}
 import org.apache.spark.rdd.ShuffledRDD
 import org.apache.spark.sql.{SQLConf, SQLContext, Row}
 import org.apache.spark.sql.catalyst.errors.attachTree
-import org.apache.spark.sql.catalyst.expressions.{MutableProjection, RowOrdering}
+import org.apache.spark.sql.catalyst.expressions.{NoBind, MutableProjection, RowOrdering}
 import org.apache.spark.sql.catalyst.plans.physical._
 import org.apache.spark.sql.catalyst.rules.Rule
 import org.apache.spark.util.MutablePair
@@ -31,7 +31,7 @@ import org.apache.spark.util.MutablePair
  * :: DeveloperApi ::
  */
 @DeveloperApi
-case class Exchange(newPartitioning: Partitioning, child: SparkPlan) extends UnaryNode {
+case class Exchange(newPartitioning: Partitioning, child: SparkPlan) extends UnaryNode with NoBind {
 
   override def outputPartitioning = newPartitioning
 
@@ -42,7 +42,7 @@ case class Exchange(newPartitioning: Partitioning, child: SparkPlan) extends Una
       case HashPartitioning(expressions, numPartitions) =>
         // TODO: Eliminate redundant expressions in grouping key and value.
         val rdd = child.execute().mapPartitions { iter =>
-          val hashExpressions = new MutableProjection(expressions)
+          val hashExpressions = new MutableProjection(expressions, child.output)
           val mutablePair = new MutablePair[Row, Row]()
           iter.map(r => mutablePair.update(hashExpressions(r), r))
         }
@@ -53,7 +53,7 @@ case class Exchange(newPartitioning: Partitioning, child: SparkPlan) extends Una
 
       case RangePartitioning(sortingExpressions, numPartitions) =>
         // TODO: RangePartitioner should take an Ordering.
-        implicit val ordering = new RowOrdering(sortingExpressions)
+        implicit val ordering = new RowOrdering(sortingExpressions, child.output)
 
         val rdd = child.execute().mapPartitions { iter =>
           val mutablePair = new MutablePair[Row, Null](null, null)

From 566f70f2140c1d243fe2368af60ecb390ac8ab3e Mon Sep 17 00:00:00 2001
From: Doris Xin <doris.s.xin@gmail.com>
Date: Wed, 18 Jun 2014 22:19:06 -0700
Subject: [PATCH 459/810] Squishing a typo bug before it causes real harm

in updateNumRows method in RowMatrix

Author: Doris Xin <doris.s.xin@gmail.com>

Closes #1125 from dorx/updateNumRows and squashes the following commits:

8564aef [Doris Xin] Squishing a typo bug before it causes real harm
---
 .../org/apache/spark/mllib/linalg/distributed/RowMatrix.scala   | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
index 00d0b18c27a8..1a0073c9d487 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
@@ -419,7 +419,7 @@ class RowMatrix(
   /** Updates or verifies the number of rows. */
   private def updateNumRows(m: Long) {
     if (nRows <= 0) {
-      nRows == m
+      nRows = m
     } else {
       require(nRows == m,
         s"The number of rows $m is different from what specified or previously computed: ${nRows}.")

From 640c294369f49a7602c33c7c389088aec8a316d3 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Wed, 18 Jun 2014 22:44:12 -0700
Subject: [PATCH 460/810] [SPARK-2187] Explain should not run the optimizer
 twice.

@yhuai @marmbrus @concretevitamin

Author: Reynold Xin <rxin@apache.org>

Closes #1123 from rxin/explain and squashes the following commits:

def83b0 [Reynold Xin] Update unit tests for explain.
a9d3ba8 [Reynold Xin] [SPARK-2187] Explain should not run the optimizer twice.
---
 .../spark/sql/execution/SparkStrategies.scala    |  5 ++---
 .../apache/spark/sql/execution/commands.scala    | 16 ++++++++++++----
 .../sql/hive/execution/HiveQuerySuite.scala      |  5 +----
 3 files changed, 15 insertions(+), 11 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
index 70c1171148eb..feb280d1d141 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
@@ -250,9 +250,8 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
     def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
       case logical.SetCommand(key, value) =>
         Seq(execution.SetCommand(key, value, plan.output)(context))
-      case logical.ExplainCommand(child) =>
-        val sparkPlan = context.executePlan(child).sparkPlan
-        Seq(execution.ExplainCommand(sparkPlan, plan.output)(context))
+      case logical.ExplainCommand(logicalPlan) =>
+        Seq(execution.ExplainCommand(logicalPlan, plan.output)(context))
       case logical.CacheCommand(tableName, cache) =>
         Seq(execution.CacheCommand(tableName, cache)(context))
       case _ => Nil
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala
index 39b3246c875d..f5d0834a4993 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala
@@ -21,6 +21,7 @@ import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.{SQLContext, Row}
 import org.apache.spark.sql.catalyst.expressions.{GenericRow, Attribute}
+import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
 
 trait Command {
   /**
@@ -71,16 +72,23 @@ case class SetCommand(
 }
 
 /**
+ * An explain command for users to see how a command will be executed.
+ *
+ * Note that this command takes in a logical plan, runs the optimizer on the logical plan
+ * (but do NOT actually execute it).
+ *
  * :: DeveloperApi ::
  */
 @DeveloperApi
 case class ExplainCommand(
-    child: SparkPlan, output: Seq[Attribute])(
+    logicalPlan: LogicalPlan, output: Seq[Attribute])(
     @transient context: SQLContext)
-  extends UnaryNode with Command {
+  extends LeafNode with Command {
 
-  // Actually "EXPLAIN" command doesn't cause any side effect.
-  override protected[sql] lazy val sideEffectResult: Seq[String] = this.toString.split("\n")
+  // Run through the optimizer to generate the physical plan.
+  override protected[sql] lazy val sideEffectResult: Seq[String] = {
+    "Physical execution plan:" +: context.executePlan(logicalPlan).executedPlan.toString.split("\n")
+  }
 
   def execute(): RDD[Row] = {
     val explanation = sideEffectResult.map(row => new GenericRow(Array[Any](row)))
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
index fe698f0fc57b..8b2bdd513b71 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
@@ -202,12 +202,9 @@ class HiveQuerySuite extends HiveComparisonTest {
     }
   }
 
-  private val explainCommandClassName =
-    classOf[execution.ExplainCommand].getSimpleName.stripSuffix("$")
-
   def isExplanation(result: SchemaRDD) = {
     val explanation = result.select('plan).collect().map { case Row(plan: String) => plan }
-    explanation.size > 1 && explanation.head.startsWith(explainCommandClassName)
+    explanation.size > 1 && explanation.head.startsWith("Physical execution plan")
   }
 
   test("SPARK-1704: Explain commands as a SchemaRDD") {

From 67fca189c944b8f8ba222bb471e343893031bd7b Mon Sep 17 00:00:00 2001
From: WangTao <barneystinson@aliyun.com>
Date: Wed, 18 Jun 2014 23:24:57 -0700
Subject: [PATCH 461/810] Minor fix

The value "env" is never used in SparkContext.scala.
Add detailed comment for method setDelaySeconds in MetadataCleaner.scala instead of the unsure one.

Author: WangTao <barneystinson@aliyun.com>

Closes #1105 from WangTaoTheTonic/master and squashes the following commits:

688358e [WangTao] Minor fix
---
 core/src/main/scala/org/apache/spark/SparkContext.scala    | 1 -
 .../main/scala/org/apache/spark/util/MetadataCleaner.scala | 7 ++++++-
 2 files changed, 6 insertions(+), 2 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index 0678bdd02110..f9476ff826a6 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -224,7 +224,6 @@ class SparkContext(config: SparkConf) extends Logging {
 
   /** A default Hadoop Configuration for the Hadoop code (e.g. file systems) that we reuse. */
   val hadoopConfiguration: Configuration = {
-    val env = SparkEnv.get
     val hadoopConf = SparkHadoopUtil.get.newConfiguration()
     // Explicitly check for S3 environment variables
     if (System.getenv("AWS_ACCESS_KEY_ID") != null &&
diff --git a/core/src/main/scala/org/apache/spark/util/MetadataCleaner.scala b/core/src/main/scala/org/apache/spark/util/MetadataCleaner.scala
index 7ebed5105b9f..2889e171f627 100644
--- a/core/src/main/scala/org/apache/spark/util/MetadataCleaner.scala
+++ b/core/src/main/scala/org/apache/spark/util/MetadataCleaner.scala
@@ -91,8 +91,13 @@ private[spark] object MetadataCleaner {
     conf.set(MetadataCleanerType.systemProperty(cleanerType),  delay.toString)
   }
 
+  /**
+   * Set the default delay time (in seconds).
+   * @param conf SparkConf instance
+   * @param delay default delay time to set
+   * @param resetAll whether to reset all to default
+   */
   def setDelaySeconds(conf: SparkConf, delay: Int, resetAll: Boolean = true) {
-    // override for all ?
     conf.set("spark.cleaner.ttl", delay.toString)
     if (resetAll) {
       for (cleanerType <- MetadataCleanerType.values) {

From bce0897bc6b0fc9bca5444dbe3a9e75523ad7481 Mon Sep 17 00:00:00 2001
From: witgo <witgo@qq.com>
Date: Thu, 19 Jun 2014 12:11:26 -0500
Subject: [PATCH 462/810] [SPARK-2051]In yarn.ClientBase spark.yarn.dist.* do
 not work

Author: witgo <witgo@qq.com>

Closes #969 from witgo/yarn_ClientBase and squashes the following commits:

8117765 [witgo] review commit
3bdbc52 [witgo] Merge branch 'master' of https://github.com/apache/spark into yarn_ClientBase
5261b6c [witgo] fix sys.props.get("SPARK_YARN_DIST_FILES")
e3c1107 [witgo] update docs
b6a9aa1 [witgo] merge master
c8b4554 [witgo] review commit
2f48789 [witgo] Merge branch 'master' of https://github.com/apache/spark into yarn_ClientBase
8d7b82f [witgo] Merge branch 'master' of https://github.com/apache/spark into yarn_ClientBase
1048549 [witgo] remove Utils.resolveURIs
871f1db [witgo] add spark.yarn.dist.* documentation
41bce59 [witgo] review commit
35d6fa0 [witgo] move to ClientArguments
55d72fc [witgo] Merge branch 'master' of https://github.com/apache/spark into yarn_ClientBase
9cdff16 [witgo] review commit
8bc2f4b [witgo] review commit
20e667c [witgo] Merge branch 'master' into yarn_ClientBase
0961151 [witgo] merge master
ce609fc [witgo] Merge branch 'master' into yarn_ClientBase
8362489 [witgo] yarn.ClientBase spark.yarn.dist.* do not work
---
 docs/running-on-yarn.md                       | 20 ++++++++++++++++---
 .../spark/deploy/yarn/ClientArguments.scala   | 15 ++++++++++++--
 .../apache/spark/deploy/yarn/ClientBase.scala |  3 ++-
 .../cluster/YarnClientSchedulerBackend.scala  |  4 +---
 4 files changed, 33 insertions(+), 9 deletions(-)

diff --git a/docs/running-on-yarn.md b/docs/running-on-yarn.md
index 4243ef480ba3..fecd8f2cc2d4 100644
--- a/docs/running-on-yarn.md
+++ b/docs/running-on-yarn.md
@@ -68,15 +68,29 @@ Most of the configs are the same for Spark on YARN as for other deployment modes
   </td>
 </tr>
 <tr>
-  <td><code>spark.yarn.executor.memoryOverhead</code></td>
-  <td>384</code></td>
+  <td><code>spark.yarn.dist.archives</code></td>
+  <td>(none)</td>
+  <td>
+    Comma separated list of archives to be extracted into the working directory of each executor.
+  </td>
+</tr>
+<tr>
+  <td><code>spark.yarn.dist.files</code></td>
+  <td>(none)</td>
+  <td>
+    Comma-separated list of files to be placed in the working directory of each executor.
+  <td>
+</tr>
+<tr>
+ <td><code>spark.yarn.executor.memoryOverhead</code></td>
+  <td>384</td>
   <td>
     The amount of off heap memory (in megabytes) to be allocated per executor. This is memory that accounts for things like VM overheads, interned strings, other native overheads, etc.
   </td>
 </tr>
 <tr>
   <td><code>spark.yarn.driver.memoryOverhead</code></td>
-  <td>384</code></td>
+  <td>384</td>
   <td>
     The amount of off heap memory (in megabytes) to be allocated per driver. This is memory that accounts for things like VM overheads, interned strings, other native overheads, etc.
   </td>
diff --git a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala
index fd3ef9e1fa2d..62f9b3cf5ab8 100644
--- a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala
+++ b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala
@@ -21,8 +21,7 @@ import scala.collection.mutable.{ArrayBuffer, HashMap}
 
 import org.apache.spark.SparkConf
 import org.apache.spark.scheduler.InputFormatInfo
-import org.apache.spark.util.IntParam
-import org.apache.spark.util.MemoryParam
+import org.apache.spark.util.{Utils, IntParam, MemoryParam}
 
 
 // TODO: Add code and support for ensuring that yarn resource 'tasks' are location aware !
@@ -45,6 +44,18 @@ class ClientArguments(val args: Array[String], val sparkConf: SparkConf) {
 
   parseArgs(args.toList)
 
+  // env variable SPARK_YARN_DIST_ARCHIVES/SPARK_YARN_DIST_FILES set in yarn-client then
+  // it should default to hdfs://
+  files = Option(files).getOrElse(sys.env.get("SPARK_YARN_DIST_FILES").orNull)
+  archives = Option(archives).getOrElse(sys.env.get("SPARK_YARN_DIST_ARCHIVES").orNull)
+
+  // spark.yarn.dist.archives/spark.yarn.dist.files defaults to use file:// if not specified,
+  // for both yarn-client and yarn-cluster
+  files = Option(files).getOrElse(sparkConf.getOption("spark.yarn.dist.files").
+    map(p => Utils.resolveURIs(p)).orNull)
+  archives = Option(archives).getOrElse(sparkConf.getOption("spark.yarn.dist.archives").
+    map(p => Utils.resolveURIs(p)).orNull)
+
   private def parseArgs(inputArgs: List[String]): Unit = {
     val userArgsBuffer: ArrayBuffer[String] = new ArrayBuffer[String]()
     val inputFormatMap: HashMap[String, InputFormatInfo] = new HashMap[String, InputFormatInfo]()
diff --git a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala
index 858bcaa95b40..8f2267599914 100644
--- a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala
+++ b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala
@@ -162,7 +162,7 @@ trait ClientBase extends Logging {
     val fs = FileSystem.get(conf)
     val remoteFs = originalPath.getFileSystem(conf)
     var newPath = originalPath
-    if (! compareFs(remoteFs, fs)) {
+    if (!compareFs(remoteFs, fs)) {
       newPath = new Path(dstDir, originalPath.getName())
       logInfo("Uploading " + originalPath + " to " + newPath)
       FileUtil.copy(remoteFs, originalPath, fs, newPath, false, conf)
@@ -250,6 +250,7 @@ trait ClientBase extends Logging {
         }
       }
     }
+    logInfo("Prepared Local resources " + localResources)
     sparkConf.set(ClientBase.CONF_SPARK_YARN_SECONDARY_JARS, cachedSecondaryJarLinks.mkString(","))
 
     UserGroupInformation.getCurrentUser().addCredentials(credentials)
diff --git a/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala b/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala
index 039cf4f27611..412dfe38d55e 100644
--- a/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala
+++ b/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala
@@ -70,9 +70,7 @@ private[spark] class YarnClientSchedulerBackend(
       ("--executor-cores", "SPARK_WORKER_CORES", "spark.executor.cores"),
       ("--executor-cores", "SPARK_EXECUTOR_CORES", "spark.executor.cores"),
       ("--queue", "SPARK_YARN_QUEUE", "spark.yarn.queue"),
-      ("--name", "SPARK_YARN_APP_NAME", "spark.app.name"),
-      ("--files", "SPARK_YARN_DIST_FILES", "spark.yarn.dist.files"),
-      ("--archives", "SPARK_YARN_DIST_ARCHIVES", "spark.yarn.dist.archives"))
+      ("--name", "SPARK_YARN_APP_NAME", "spark.app.name"))
     .foreach { case (optName, envVar, sysProp) => addArg(optName, envVar, sysProp, argsArrayBuf) }
 
     logDebug("ClientArguments called with: " + argsArrayBuf)

From 777c5958c4088182f9e2daba435ccb413a2f69d7 Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Thu, 19 Jun 2014 14:14:03 -0700
Subject: [PATCH 463/810] [SPARK-2191][SQL] Make sure InsertIntoHiveTable
 doesn't execute more than once.

Author: Michael Armbrust <michael@databricks.com>

Closes #1129 from marmbrus/doubleCreateAs and squashes the following commits:

9c6d9e4 [Michael Armbrust] Fix typo.
5128fe2 [Michael Armbrust] Make sure InsertIntoHiveTable doesn't execute each time you ask for its result.
---
 .../org/apache/spark/sql/hive/execution/hiveOperators.scala | 6 +++++-
 .../apache/spark/sql/hive/execution/HiveQuerySuite.scala    | 6 ++++++
 2 files changed, 11 insertions(+), 1 deletion(-)

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/hiveOperators.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/hiveOperators.scala
index a83923144916..240aa0df4935 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/hiveOperators.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/hiveOperators.scala
@@ -344,12 +344,16 @@ case class InsertIntoHiveTable(
     writer.commitJob()
   }
 
+  override def execute() = result
+
   /**
    * Inserts all the rows in the table into Hive.  Row objects are properly serialized with the
    * `org.apache.hadoop.hive.serde2.SerDe` and the
    * `org.apache.hadoop.mapred.OutputFormat` provided by the table definition.
+   *
+   * Note: this is run once and then kept to avoid double insertions.
    */
-  def execute() = {
+  private lazy val result: RDD[Row] = {
     val childRdd = child.execute()
     assert(childRdd != null)
 
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
index 8b2bdd513b71..5118f4b3f99f 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
@@ -28,6 +28,12 @@ import org.apache.spark.sql.{SchemaRDD, execution, Row}
  */
 class HiveQuerySuite extends HiveComparisonTest {
 
+  test("CREATE TABLE AS runs once") {
+    hql("CREATE TABLE foo AS SELECT 1 FROM src LIMIT 1").collect()
+    assert(hql("SELECT COUNT(*) FROM foo").collect().head.getLong(0) === 1,
+      "Incorrect number of rows in created table")
+  }
+
   createQueryTest("between",
     "SELECT * FROM src WHERE key Between 1 and 2")
 

From f14b00a9c60863afda15681fbf5682247351fa39 Mon Sep 17 00:00:00 2001
From: nravi <nravi@c1704.halxg.cloudera.com>
Date: Thu, 19 Jun 2014 17:11:06 -0700
Subject: [PATCH 464/810] [SPARK-2151] Recognize memory format for spark-submit

int format expected for input memory parameter when spark-submit is invoked in standalone cluster mode. Make it consistent with rest of Spark.

Author: nravi <nravi@c1704.halxg.cloudera.com>

Closes #1095 from nishkamravi2/master and squashes the following commits:

2b630f9 [nravi] Accept memory input as "30g", "512M" instead of an int value, to be consistent with rest of Spark
3bf8fad [nravi] Merge branch 'master' of https://github.com/apache/spark
5423a03 [nravi] Merge branch 'master' of https://github.com/apache/spark
eb663ca [nravi] Merge branch 'master' of https://github.com/apache/spark
df2aeb1 [nravi] Improved fix for ConcurrentModificationIssue (Spark-1097, Hadoop-10456)
6b840f0 [nravi] Undo the fix for SPARK-1758 (the problem is fixed)
5108700 [nravi] Fix in Spark for the Concurrent thread modification issue (SPARK-1097, HADOOP-10456)
681b36f [nravi] Fix for SPARK-1758: failing test org.apache.spark.JavaAPISuite.wholeTextFiles
---
 .../scala/org/apache/spark/deploy/ClientArguments.scala     | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/ClientArguments.scala b/core/src/main/scala/org/apache/spark/deploy/ClientArguments.scala
index 5da9615c9e9a..39150deab863 100644
--- a/core/src/main/scala/org/apache/spark/deploy/ClientArguments.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/ClientArguments.scala
@@ -21,6 +21,8 @@ import scala.collection.mutable.ListBuffer
 
 import org.apache.log4j.Level
 
+import org.apache.spark.util.MemoryParam
+
 /**
  * Command-line parser for the driver client.
  */
@@ -51,8 +53,8 @@ private[spark] class ClientArguments(args: Array[String]) {
       cores = value.toInt
       parse(tail)
 
-    case ("--memory" | "-m") :: value :: tail =>
-      memory = value.toInt
+    case ("--memory" | "-m") :: MemoryParam(value) :: tail =>
+      memory = value
       parse(tail)
 
     case ("--supervise" | "-s") :: tail =>

From 5464e79175e2fc85e2cadf0dd7c9a45dad028326 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Thu, 19 Jun 2014 18:24:05 -0700
Subject: [PATCH 465/810] A few minor Spark SQL Scaladoc fixes.

Author: Reynold Xin <rxin@apache.org>

Closes #1139 from rxin/sparksqldoc and squashes the following commits:

c3049d8 [Reynold Xin] Fixed line length.
66dc72c [Reynold Xin] A few minor Spark SQL Scaladoc fixes.
---
 .../sql/catalyst/expressions/Expression.scala | 15 ++---
 .../spark/sql/catalyst/plans/QueryPlan.scala  |  1 -
 .../catalyst/plans/logical/LogicalPlan.scala  | 12 ++--
 .../plans/logical/basicOperators.scala        | 65 ++++++++++---------
 .../plans/physical/partitioning.scala         | 16 ++---
 .../apache/spark/sql/execution/Exchange.scala |  9 +--
 6 files changed, 57 insertions(+), 61 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
index 3912f5f4375f..0411ce3aefda 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
@@ -33,14 +33,11 @@ abstract class Expression extends TreeNode[Expression] {
    * executed.
    *
    * The following conditions are used to determine suitability for constant folding:
-   *  - A [[expressions.Coalesce Coalesce]] is foldable if all of its children are foldable
-   *  - A [[expressions.BinaryExpression BinaryExpression]] is foldable if its both left and right
-   *    child are foldable
-   *  - A [[expressions.Not Not]], [[expressions.IsNull IsNull]], or
-   *    [[expressions.IsNotNull IsNotNull]] is foldable if its child is foldable.
-   *  - A [[expressions.Literal]] is foldable.
-   *  - A [[expressions.Cast Cast]] or [[expressions.UnaryMinus UnaryMinus]] is foldable if its
-   *    child is foldable.
+   *  - A [[Coalesce]] is foldable if all of its children are foldable
+   *  - A [[BinaryExpression]] is foldable if its both left and right child are foldable
+   *  - A [[Not]], [[IsNull]], or [[IsNotNull]] is foldable if its child is foldable
+   *  - A [[Literal]] is foldable
+   *  - A [[Cast]] or [[UnaryMinus]] is foldable if its child is foldable
    */
   def foldable: Boolean = false
   def nullable: Boolean
@@ -58,7 +55,7 @@ abstract class Expression extends TreeNode[Expression] {
   lazy val resolved: Boolean = childrenResolved
 
   /**
-   * Returns the [[types.DataType DataType]] of the result of evaluating this expression.  It is
+   * Returns the [[DataType]] of the result of evaluating this expression.  It is
    * invalid to query the dataType of an unresolved expression (i.e., when `resolved` == false).
    */
   def dataType: DataType
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/QueryPlan.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/QueryPlan.scala
index 00e2d3bc24be..7b82e19b2e71 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/QueryPlan.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/QueryPlan.scala
@@ -18,7 +18,6 @@
 package org.apache.spark.sql.catalyst.plans
 
 import org.apache.spark.sql.catalyst.expressions.{Attribute, Expression}
-import org.apache.spark.sql.catalyst.plans
 import org.apache.spark.sql.catalyst.trees.TreeNode
 import org.apache.spark.sql.catalyst.types.{ArrayType, DataType, StructField, StructType}
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala
index 0933a31c362d..edc37e3877c0 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala
@@ -41,19 +41,19 @@ abstract class LogicalPlan extends QueryPlan[LogicalPlan] {
   /**
    * Returns true if this expression and all its children have been resolved to a specific schema
    * and false if it is still contains any unresolved placeholders. Implementations of LogicalPlan
-   * can override this (e.g. [[catalyst.analysis.UnresolvedRelation UnresolvedRelation]] should
-   * return `false`).
+   * can override this (e.g.
+   * [[org.apache.spark.sql.catalyst.analysis.UnresolvedRelation UnresolvedRelation]]
+   * should return `false`).
    */
   lazy val resolved: Boolean = !expressions.exists(!_.resolved) && childrenResolved
 
   /**
    * Returns true if all its children of this query plan have been resolved.
    */
-  def childrenResolved = !children.exists(!_.resolved)
+  def childrenResolved: Boolean = !children.exists(!_.resolved)
 
   /**
-   * Optionally resolves the given string to a
-   * [[catalyst.expressions.NamedExpression NamedExpression]]. The attribute is expressed as
+   * Optionally resolves the given string to a [[NamedExpression]]. The attribute is expressed as
    * as string in the following form: `[scope].AttributeName.[nested].[fields]...`.
    */
   def resolve(name: String): Option[NamedExpression] = {
@@ -93,7 +93,7 @@ abstract class LeafNode extends LogicalPlan with trees.LeafNode[LogicalPlan] {
   self: Product =>
 
   // Leaf nodes by definition cannot reference any input attributes.
-  def references = Set.empty
+  override def references = Set.empty
 }
 
 /**
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
index b777cf424919..3e0639867b27 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
@@ -27,7 +27,7 @@ case class Project(projectList: Seq[NamedExpression], child: LogicalPlan) extend
 }
 
 /**
- * Applies a [[catalyst.expressions.Generator Generator]] to a stream of input rows, combining the
+ * Applies a [[Generator]] to a stream of input rows, combining the
  * output of each into a new stream of rows.  This operation is similar to a `flatMap` in functional
  * programming with one important additional feature, which allows the input rows to be joined with
  * their output.
@@ -46,32 +46,32 @@ case class Generate(
     child: LogicalPlan)
   extends UnaryNode {
 
-  protected def generatorOutput =
+  protected def generatorOutput: Seq[Attribute] =
     alias
       .map(a => generator.output.map(_.withQualifiers(a :: Nil)))
       .getOrElse(generator.output)
 
-  def output =
+  override def output =
     if (join) child.output ++ generatorOutput else generatorOutput
 
-  def references =
+  override def references =
     if (join) child.outputSet else generator.references
 }
 
 case class Filter(condition: Expression, child: LogicalPlan) extends UnaryNode {
-  def output = child.output
-  def references = condition.references
+  override def output = child.output
+  override def references = condition.references
 }
 
 case class Union(left: LogicalPlan, right: LogicalPlan) extends BinaryNode {
   // TODO: These aren't really the same attributes as nullability etc might change.
-  def output = left.output
+  override def output = left.output
 
   override lazy val resolved =
     childrenResolved &&
     !left.output.zip(right.output).exists { case (l,r) => l.dataType != r.dataType }
 
-  def references = Set.empty
+  override def references = Set.empty
 }
 
 case class Join(
@@ -80,8 +80,8 @@ case class Join(
   joinType: JoinType,
   condition: Option[Expression]) extends BinaryNode {
 
-  def references = condition.map(_.references).getOrElse(Set.empty)
-  def output = joinType match {
+  override def references = condition.map(_.references).getOrElse(Set.empty)
+  override def output = joinType match {
     case LeftSemi =>
       left.output
     case _ =>
@@ -96,9 +96,9 @@ case class InsertIntoTable(
     overwrite: Boolean)
   extends LogicalPlan {
   // The table being inserted into is a child for the purposes of transformations.
-  def children = table :: child :: Nil
-  def references = Set.empty
-  def output = child.output
+  override def children = table :: child :: Nil
+  override def references = Set.empty
+  override def output = child.output
 
   override lazy val resolved = childrenResolved && child.output.zip(table.output).forall {
     case (childAttr, tableAttr) => childAttr.dataType == tableAttr.dataType
@@ -109,20 +109,20 @@ case class InsertIntoCreatedTable(
     databaseName: Option[String],
     tableName: String,
     child: LogicalPlan) extends UnaryNode {
-  def references = Set.empty
-  def output = child.output
+  override def references = Set.empty
+  override def output = child.output
 }
 
 case class WriteToFile(
     path: String,
     child: LogicalPlan) extends UnaryNode {
-  def references = Set.empty
-  def output = child.output
+  override def references = Set.empty
+  override def output = child.output
 }
 
 case class Sort(order: Seq[SortOrder], child: LogicalPlan) extends UnaryNode {
-  def output = child.output
-  def references = order.flatMap(_.references).toSet
+  override def output = child.output
+  override def references = order.flatMap(_.references).toSet
 }
 
 case class Aggregate(
@@ -131,18 +131,19 @@ case class Aggregate(
     child: LogicalPlan)
   extends UnaryNode {
 
-  def output = aggregateExpressions.map(_.toAttribute)
-  def references = (groupingExpressions ++ aggregateExpressions).flatMap(_.references).toSet
+  override def output = aggregateExpressions.map(_.toAttribute)
+  override def references =
+    (groupingExpressions ++ aggregateExpressions).flatMap(_.references).toSet
 }
 
 case class Limit(limitExpr: Expression, child: LogicalPlan) extends UnaryNode {
-  def output = child.output
-  def references = limitExpr.references
+  override def output = child.output
+  override def references = limitExpr.references
 }
 
 case class Subquery(alias: String, child: LogicalPlan) extends UnaryNode {
-  def output = child.output.map(_.withQualifiers(alias :: Nil))
-  def references = Set.empty
+  override def output = child.output.map(_.withQualifiers(alias :: Nil))
+  override def references = Set.empty
 }
 
 /**
@@ -159,7 +160,7 @@ case class LowerCaseSchema(child: LogicalPlan) extends UnaryNode {
     case otherType => otherType
   }
 
-  val output = child.output.map {
+  override val output = child.output.map {
     case a: AttributeReference =>
       AttributeReference(
         a.name.toLowerCase,
@@ -170,21 +171,21 @@ case class LowerCaseSchema(child: LogicalPlan) extends UnaryNode {
     case other => other
   }
 
-  def references = Set.empty
+  override def references = Set.empty
 }
 
 case class Sample(fraction: Double, withReplacement: Boolean, seed: Long, child: LogicalPlan)
     extends UnaryNode {
 
-  def output = child.output
-  def references = Set.empty
+  override def output = child.output
+  override def references = Set.empty
 }
 
 case class Distinct(child: LogicalPlan) extends UnaryNode {
-  def output = child.output
-  def references = child.outputSet
+  override def output = child.output
+  override def references = child.outputSet
 }
 
 case object NoRelation extends LeafNode {
-  def output = Nil
+  override def output = Nil
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala
index ffb3a92f8f34..4bb022cf238a 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala
@@ -46,7 +46,7 @@ case object AllTuples extends Distribution
 
 /**
  * Represents data where tuples that share the same values for the `clustering`
- * [[catalyst.expressions.Expression Expressions]] will be co-located. Based on the context, this
+ * [[Expression Expressions]] will be co-located. Based on the context, this
  * can mean such tuples are either co-located in the same partition or they will be contiguous
  * within a single partition.
  */
@@ -60,7 +60,7 @@ case class ClusteredDistribution(clustering: Seq[Expression]) extends Distributi
 
 /**
  * Represents data where tuples have been ordered according to the `ordering`
- * [[catalyst.expressions.Expression Expressions]].  This is a strictly stronger guarantee than
+ * [[Expression Expressions]].  This is a strictly stronger guarantee than
  * [[ClusteredDistribution]] as an ordering will ensure that tuples that share the same value for
  * the ordering expressions are contiguous and will never be split across partitions.
  */
@@ -79,19 +79,17 @@ sealed trait Partitioning {
   val numPartitions: Int
 
   /**
-   * Returns true iff the guarantees made by this
-   * [[catalyst.plans.physical.Partitioning Partitioning]] are sufficient to satisfy
-   * the partitioning scheme mandated by the `required`
-   * [[catalyst.plans.physical.Distribution Distribution]], i.e. the current dataset does not
-   * need to be re-partitioned for the `required` Distribution (it is possible that tuples within
-   * a partition need to be reorganized).
+   * Returns true iff the guarantees made by this [[Partitioning]] are sufficient
+   * to satisfy the partitioning scheme mandated by the `required` [[Distribution]],
+   * i.e. the current dataset does not need to be re-partitioned for the `required`
+   * Distribution (it is possible that tuples within a partition need to be reorganized).
    */
   def satisfies(required: Distribution): Boolean
 
   /**
    * Returns true iff all distribution guarantees made by this partitioning can also be made
    * for the `other` specified partitioning.
-   * For example, two [[catalyst.plans.physical.HashPartitioning HashPartitioning]]s are
+   * For example, two [[HashPartitioning HashPartitioning]]s are
    * only compatible if the `numPartitions` of them is the same.
    */
   def compatibleWith(other: Partitioning): Boolean
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
index 05dfb85b38b0..f46fa0516566 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
@@ -20,7 +20,7 @@ package org.apache.spark.sql.execution
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.{HashPartitioner, RangePartitioner, SparkConf}
 import org.apache.spark.rdd.ShuffledRDD
-import org.apache.spark.sql.{SQLConf, SQLContext, Row}
+import org.apache.spark.sql.{SQLContext, Row}
 import org.apache.spark.sql.catalyst.errors.attachTree
 import org.apache.spark.sql.catalyst.expressions.{NoBind, MutableProjection, RowOrdering}
 import org.apache.spark.sql.catalyst.plans.physical._
@@ -82,9 +82,10 @@ case class Exchange(newPartitioning: Partitioning, child: SparkPlan) extends Una
 }
 
 /**
- * Ensures that the [[catalyst.plans.physical.Partitioning Partitioning]] of input data meets the
- * [[catalyst.plans.physical.Distribution Distribution]] requirements for each operator by inserting
- * [[Exchange]] Operators where required.
+ * Ensures that the [[org.apache.spark.sql.catalyst.plans.physical.Partitioning Partitioning]]
+ * of input data meets the
+ * [[org.apache.spark.sql.catalyst.plans.physical.Distribution Distribution]] requirements for
+ * each operator by inserting [[Exchange]] Operators where required.
  */
 private[sql] case class AddExchange(sqlContext: SQLContext) extends Rule[SparkPlan] {
   // TODO: Determine the number of partitions.

From e5514790d70b35422dba2773e43e2e382548fa56 Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Thu, 19 Jun 2014 21:06:28 -0700
Subject: [PATCH 466/810] HOTFIX: SPARK-2208 local metrics tests can fail on
 fast machines

Author: Patrick Wendell <pwendell@gmail.com>

Closes #1141 from pwendell/hotfix and squashes the following commits:

83e4c79 [Patrick Wendell] HOTFIX: SPARK-2208 local metrics tests can fail on fast machines
---
 .../scala/org/apache/spark/scheduler/SparkListenerSuite.scala  | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/core/src/test/scala/org/apache/spark/scheduler/SparkListenerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/SparkListenerSuite.scala
index be506e0287a1..abd7b22310f1 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/SparkListenerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/SparkListenerSuite.scala
@@ -239,11 +239,14 @@ class SparkListenerSuite extends FunSuite with LocalSparkContext with Matchers
       checkNonZeroAvg(
         taskInfoMetrics.map(_._2.executorDeserializeTime),
         stageInfo + " executorDeserializeTime")
+
+      /* Test is disabled (SEE SPARK-2208)
       if (stageInfo.rddInfos.exists(_.name == d4.name)) {
         checkNonZeroAvg(
           taskInfoMetrics.map(_._2.shuffleReadMetrics.get.fetchWaitTime),
           stageInfo + " fetchWaitTime")
       }
+      */
 
       taskInfoMetrics.foreach { case (taskInfo, taskMetrics) =>
         taskMetrics.resultSize should be > (0l)

From 278ec8a203c7f1de2716d8284f9bdafa54eee1cb Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Thu, 19 Jun 2014 22:34:21 -0700
Subject: [PATCH 467/810] More minor scaladoc cleanup for Spark SQL.

Author: Reynold Xin <rxin@apache.org>

Closes #1142 from rxin/sqlclean and squashes the following commits:

67a789e [Reynold Xin] More minor scaladoc cleanup for Spark SQL.
---
 .../catalyst/analysis/HiveTypeCoercion.scala  |  8 ++---
 .../expressions/namedExpressions.scala        |  2 +-
 .../sql/catalyst/optimizer/Optimizer.scala    | 34 +++++++++----------
 3 files changed, 21 insertions(+), 23 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
index 66bff660cadc..6d331fb501d0 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
@@ -33,7 +33,7 @@ object HiveTypeCoercion {
 }
 
 /**
- * A collection of [[catalyst.rules.Rule Rules]] that can be used to coerce differing types that
+ * A collection of [[Rule Rules]] that can be used to coerce differing types that
  * participate in operations into compatible ones.  Most of these rules are based on Hive semantics,
  * but they do not introduce any dependencies on the hive codebase.  For this reason they remain in
  * Catalyst until we have a more standard set of coercions.
@@ -53,8 +53,8 @@ trait HiveTypeCoercion {
     Nil
 
   /**
-   * Applies any changes to [[catalyst.expressions.AttributeReference AttributeReference]] data
-   * types that are made by other rules to instances higher in the query tree.
+   * Applies any changes to [[AttributeReference]] data types that are made by other rules to
+   * instances higher in the query tree.
    */
   object PropagateTypes extends Rule[LogicalPlan] {
     def apply(plan: LogicalPlan): LogicalPlan = plan transform {
@@ -244,7 +244,7 @@ trait HiveTypeCoercion {
   }
 
   /**
-   * Casts to/from [[catalyst.types.BooleanType BooleanType]] are transformed into comparisons since
+   * Casts to/from [[BooleanType]] are transformed into comparisons since
    * the JVM does not consider Booleans to be numeric types.
    */
   object BooleanCasts extends Rule[LogicalPlan] {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala
index a8145c37c20f..66ae22e95b60 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala
@@ -103,7 +103,7 @@ case class Alias(child: Expression, name: String)
  * A reference to an attribute produced by another operator in the tree.
  *
  * @param name The name of this attribute, should only be used during analysis or for debugging.
- * @param dataType The [[types.DataType DataType]] of this attribute.
+ * @param dataType The [[DataType]] of this attribute.
  * @param nullable True if null is a valid value for this attribute.
  * @param exprId A globally unique id used to check if different AttributeReferences refer to the
  *               same attribute.
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
index 25a347bec0e4..b20b5de8c46e 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
@@ -95,13 +95,13 @@ object ColumnPruning extends Rule[LogicalPlan] {
       Project(substitutedProjection, child)
 
     // Eliminate no-op Projects
-    case Project(projectList, child) if(child.output == projectList) => child
+    case Project(projectList, child) if child.output == projectList => child
   }
 }
 
 /**
- * Replaces [[catalyst.expressions.Expression Expressions]] that can be statically evaluated with
- * equivalent [[catalyst.expressions.Literal Literal]] values. This rule is more specific with 
+ * Replaces [[Expression Expressions]] that can be statically evaluated with
+ * equivalent [[Literal]] values. This rule is more specific with
  * Null value propagation from bottom to top of the expression tree.
  */
 object NullPropagation extends Rule[LogicalPlan] {
@@ -110,8 +110,8 @@ object NullPropagation extends Rule[LogicalPlan] {
       case e @ Count(Literal(null, _)) => Cast(Literal(0L), e.dataType)
       case e @ Sum(Literal(c, _)) if c == 0 => Cast(Literal(0L), e.dataType)
       case e @ Average(Literal(c, _)) if c == 0 => Literal(0.0, e.dataType)
-      case e @ IsNull(c) if c.nullable == false => Literal(false, BooleanType)
-      case e @ IsNotNull(c) if c.nullable == false => Literal(true, BooleanType)
+      case e @ IsNull(c) if !c.nullable => Literal(false, BooleanType)
+      case e @ IsNotNull(c) if !c.nullable => Literal(true, BooleanType)
       case e @ GetItem(Literal(null, _), _) => Literal(null, e.dataType)
       case e @ GetItem(_, Literal(null, _)) => Literal(null, e.dataType)
       case e @ GetField(Literal(null, _), _) => Literal(null, e.dataType)
@@ -154,8 +154,8 @@ object NullPropagation extends Rule[LogicalPlan] {
 }
 
 /**
- * Replaces [[catalyst.expressions.Expression Expressions]] that can be statically evaluated with
- * equivalent [[catalyst.expressions.Literal Literal]] values.
+ * Replaces [[Expression Expressions]] that can be statically evaluated with
+ * equivalent [[Literal]] values.
  */
 object ConstantFolding extends Rule[LogicalPlan] {
   def apply(plan: LogicalPlan): LogicalPlan = plan transform {
@@ -197,7 +197,7 @@ object BooleanSimplification extends Rule[LogicalPlan] {
 }
 
 /**
- * Combines two adjacent [[catalyst.plans.logical.Filter Filter]] operators into one, merging the
+ * Combines two adjacent [[Filter]] operators into one, merging the
  * conditions into one conjunctive predicate.
  */
 object CombineFilters extends Rule[LogicalPlan] {
@@ -223,9 +223,8 @@ object SimplifyFilters extends Rule[LogicalPlan] {
 }
 
 /**
- * Pushes [[catalyst.plans.logical.Filter Filter]] operators through
- * [[catalyst.plans.logical.Project Project]] operators, in-lining any
- * [[catalyst.expressions.Alias Aliases]] that were defined in the projection.
+ * Pushes [[Filter]] operators through [[Project]] operators, in-lining any [[Alias Aliases]]
+ * that were defined in the projection.
  *
  * This heuristic is valid assuming the expression evaluation cost is minimal.
  */
@@ -248,10 +247,10 @@ object PushPredicateThroughProject extends Rule[LogicalPlan] {
 }
 
 /**
- * Pushes down [[catalyst.plans.logical.Filter Filter]] operators where the `condition` can be
+ * Pushes down [[Filter]] operators where the `condition` can be
  * evaluated using only the attributes of the left or right side of a join.  Other
- * [[catalyst.plans.logical.Filter Filter]] conditions are moved into the `condition` of the
- * [[catalyst.plans.logical.Join Join]].
+ * [[Filter]] conditions are moved into the `condition` of the [[Join]].
+ *
  * And also Pushes down the join filter, where the `condition` can be evaluated using only the 
  * attributes of the left or right side of sub query when applicable. 
  * 
@@ -345,8 +344,7 @@ object PushPredicateThroughJoin extends Rule[LogicalPlan] with PredicateHelper {
 }
 
 /**
- * Removes [[catalyst.expressions.Cast Casts]] that are unnecessary because the input is already
- * the correct type.
+ * Removes [[Cast Casts]] that are unnecessary because the input is already the correct type.
  */
 object SimplifyCasts extends Rule[LogicalPlan] {
   def apply(plan: LogicalPlan): LogicalPlan = plan transformAllExpressions {
@@ -355,7 +353,7 @@ object SimplifyCasts extends Rule[LogicalPlan] {
 }
 
 /**
- * Combines two adjacent [[catalyst.plans.logical.Limit Limit]] operators into one, merging the
+ * Combines two adjacent [[Limit]] operators into one, merging the
  * expressions into one single expression.
  */
 object CombineLimits extends Rule[LogicalPlan] {
@@ -366,7 +364,7 @@ object CombineLimits extends Rule[LogicalPlan] {
 }
 
 /**
- * Removes the inner [[catalyst.expressions.CaseConversionExpression]] that are unnecessary because
+ * Removes the inner [[CaseConversionExpression]] that are unnecessary because
  * the inner conversion is overwritten by the outer one.
  */
 object SimplifyCaseConversionExpressions extends Rule[LogicalPlan] {

From d3b7671c1f9c1eca956fda15fa7573649fd284b3 Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Thu, 19 Jun 2014 23:39:03 -0700
Subject: [PATCH 468/810] [SQL] Improve Speed of InsertIntoHiveTable

Author: Michael Armbrust <michael@databricks.com>

Closes #1130 from marmbrus/noFunctional and squashes the following commits:

ccdb68c [Michael Armbrust] Remove functional programming and Array allocations from fast path in InsertIntoHiveTable.
---
 .../spark/sql/hive/execution/hiveOperators.scala   | 14 ++++++++++----
 1 file changed, 10 insertions(+), 4 deletions(-)

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/hiveOperators.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/hiveOperators.scala
index 240aa0df4935..b19579331fea 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/hiveOperators.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/hiveOperators.scala
@@ -371,12 +371,18 @@ case class InsertIntoHiveTable(
           ObjectInspectorCopyOption.JAVA)
         .asInstanceOf[StructObjectInspector]
 
+
+      val fieldOIs = standardOI.getAllStructFieldRefs.map(_.getFieldObjectInspector).toArray
+      val outputData = new Array[Any](fieldOIs.length)
       iter.map { row =>
-        // Casts Strings to HiveVarchars when necessary.
-        val fieldOIs = standardOI.getAllStructFieldRefs.map(_.getFieldObjectInspector)
-        val mappedRow = row.zip(fieldOIs).map(wrap)
+        var i = 0
+        while (i < row.length) {
+          // Casts Strings to HiveVarchars when necessary.
+          outputData(i) = wrap(row(i), fieldOIs(i))
+          i += 1
+        }
 
-        serializer.serialize(mappedRow.toArray, standardOI)
+        serializer.serialize(outputData, standardOI)
       }
     }
 

From f397e92eb2986f4436fb9e66777fc652f91d8494 Mon Sep 17 00:00:00 2001
From: Yin Huai <huai@cse.ohio-state.edu>
Date: Thu, 19 Jun 2014 23:41:38 -0700
Subject: [PATCH 469/810] [SPARK-2177][SQL] describe table result contains only
 one column

```
scala> hql("describe src").collect().foreach(println)

[key                 	string              	None                ]
[value               	string              	None                ]
```

The result should contain 3 columns instead of one. This screws up JDBC or even the downstream consumer of the Scala/Java/Python APIs.

I am providing a workaround. We handle a subset of describe commands in Spark SQL, which are defined by ...
```
DESCRIBE [EXTENDED] [db_name.]table_name
```
All other cases are treated as Hive native commands.

Also, if we upgrade Hive to 0.13, we need to check the results of context.sessionState.isHiveServerQuery() to determine how to split the result. This method is introduced by https://issues.apache.org/jira/browse/HIVE-4545. We may want to set Hive to use JsonMetaDataFormatter for the output of a DDL statement (`set hive.ddl.output.format=json` introduced by https://issues.apache.org/jira/browse/HIVE-2822).

The link to JIRA: https://issues.apache.org/jira/browse/SPARK-2177

Author: Yin Huai <huai@cse.ohio-state.edu>

Closes #1118 from yhuai/SPARK-2177 and squashes the following commits:

fd2534c [Yin Huai] Merge remote-tracking branch 'upstream/master' into SPARK-2177
b9b9aa5 [Yin Huai] rxin's comments.
e7c4e72 [Yin Huai] Fix unit test.
656b068 [Yin Huai] 100 characters.
6387217 [Yin Huai] Merge remote-tracking branch 'upstream/master' into SPARK-2177
8003cf3 [Yin Huai] Generate strings with the format like Hive for unit tests.
9787fff [Yin Huai] Merge remote-tracking branch 'upstream/master' into SPARK-2177
440c5af [Yin Huai] rxin's comments.
f1a417e [Yin Huai] Update doc.
83adb2f [Yin Huai] Merge remote-tracking branch 'upstream/master' into SPARK-2177
366f891 [Yin Huai] Add describe command.
74bd1d4 [Yin Huai] Merge remote-tracking branch 'upstream/master' into SPARK-2177
342fdf7 [Yin Huai] Split to up to 3 parts.
725e88c [Yin Huai] Merge remote-tracking branch 'upstream/master' into SPARK-2177
bb8bbef [Yin Huai] Split every string in the result of a describe command.
---
 .../sql/catalyst/plans/logical/commands.scala |  16 +++
 .../apache/spark/sql/execution/commands.scala |  21 ++++
 .../apache/spark/sql/hive/HiveContext.scala   |   5 +
 .../org/apache/spark/sql/hive/HiveQl.scala    |  70 +++++++++---
 .../spark/sql/hive/HiveStrategies.scala       |  10 ++
 .../sql/hive/execution/hiveOperators.scala    |  60 +++++++++++
 .../hive/execution/HiveComparisonTest.scala   |  24 ++++-
 .../execution/HiveCompatibilitySuite.scala    |  17 +--
 .../sql/hive/execution/HiveQuerySuite.scala   | 102 ++++++++++++++++--
 9 files changed, 294 insertions(+), 31 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/commands.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/commands.scala
index 3299e86b8594..1d5f033f0d27 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/commands.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/commands.scala
@@ -60,3 +60,19 @@ case class ExplainCommand(plan: LogicalPlan) extends Command {
  * Returned for the "CACHE TABLE tableName" and "UNCACHE TABLE tableName" command.
  */
 case class CacheCommand(tableName: String, doCache: Boolean) extends Command
+
+/**
+ * Returned for the "DESCRIBE [EXTENDED] [dbName.]tableName" command.
+ * @param table The table to be described.
+ * @param isExtended True if "DESCRIBE EXTENDED" is used. Otherwise, false.
+ *                   It is effective only when the table is a Hive table.
+ */
+case class DescribeCommand(
+    table: LogicalPlan,
+    isExtended: Boolean) extends Command {
+  override def output = Seq(
+    // Column names are based on Hive.
+    BoundReference(0, AttributeReference("col_name", StringType, nullable = false)()),
+    BoundReference(1, AttributeReference("data_type", StringType, nullable = false)()),
+    BoundReference(2, AttributeReference("comment", StringType, nullable = false)()))
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala
index f5d0834a4993..acb1b0f4dc22 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala
@@ -121,3 +121,24 @@ case class CacheCommand(tableName: String, doCache: Boolean)(@transient context:
 
   override def output: Seq[Attribute] = Seq.empty
 }
+
+/**
+ * :: DeveloperApi ::
+ */
+@DeveloperApi
+case class DescribeCommand(child: SparkPlan, output: Seq[Attribute])(
+    @transient context: SQLContext)
+  extends LeafNode with Command {
+
+  override protected[sql] lazy val sideEffectResult: Seq[(String, String, String)] = {
+    Seq(("# Registered as a temporary table", null, null)) ++
+      child.output.map(field => (field.name, field.dataType.toString, null))
+  }
+
+  override def execute(): RDD[Row] = {
+    val rows = sideEffectResult.map {
+      case (name, dataType, comment) => new GenericRow(Array[Any](name, dataType, comment))
+    }
+    context.sparkContext.parallelize(rows, 1)
+  }
+}
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
index cc95b7af0abf..7695242a8160 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
@@ -38,6 +38,7 @@ import org.apache.spark.sql.catalyst.plans.logical._
 import org.apache.spark.sql.catalyst.types._
 import org.apache.spark.sql.execution.QueryExecutionException
 import org.apache.spark.sql.execution.{Command => PhysicalCommand}
+import org.apache.spark.sql.hive.execution.DescribeHiveTableCommand
 
 /**
  * Starts up an instance of hive where metadata is stored locally. An in-process metadata data is
@@ -291,6 +292,10 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) {
      * execution is simply passed back to Hive.
      */
     def stringResult(): Seq[String] = executedPlan match {
+      case describeHiveTableCommand: DescribeHiveTableCommand =>
+        // If it is a describe command for a Hive table, we want to have the output format
+        // be similar with Hive.
+        describeHiveTableCommand.hiveString
       case command: PhysicalCommand =>
         command.sideEffectResult.map(_.toString)
 
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
index 844673f66d10..df761b073a75 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
@@ -52,7 +52,6 @@ private[hive] case class AddFile(filePath: String) extends Command
 private[hive] object HiveQl {
   protected val nativeCommands = Seq(
     "TOK_DESCFUNCTION",
-    "TOK_DESCTABLE",
     "TOK_DESCDATABASE",
     "TOK_SHOW_TABLESTATUS",
     "TOK_SHOWDATABASES",
@@ -120,6 +119,12 @@ private[hive] object HiveQl {
     "TOK_SWITCHDATABASE"
   )
 
+  // Commands that we do not need to explain.
+  protected val noExplainCommands = Seq(
+    "TOK_CREATETABLE",
+    "TOK_DESCTABLE"
+  ) ++ nativeCommands
+
   /**
    * A set of implicit transformations that allow Hive ASTNodes to be rewritten by transformations
    * similar to [[catalyst.trees.TreeNode]].
@@ -362,13 +367,20 @@ private[hive] object HiveQl {
     }
   }
 
+  protected def extractDbNameTableName(tableNameParts: Node): (Option[String], String) = {
+    val (db, tableName) =
+      tableNameParts.getChildren.map { case Token(part, Nil) => cleanIdentifier(part) } match {
+        case Seq(tableOnly) => (None, tableOnly)
+        case Seq(databaseName, table) => (Some(databaseName), table)
+      }
+
+    (db, tableName)
+  }
+
   protected def nodeToPlan(node: Node): LogicalPlan = node match {
     // Just fake explain for any of the native commands.
-    case Token("TOK_EXPLAIN", explainArgs) if nativeCommands contains explainArgs.head.getText =>
-      ExplainCommand(NoRelation)
-    // Create tables aren't native commands due to CTAS queries, but we still don't need to
-    // explain them.
-    case Token("TOK_EXPLAIN", explainArgs) if explainArgs.head.getText == "TOK_CREATETABLE" =>
+    case Token("TOK_EXPLAIN", explainArgs)
+      if noExplainCommands.contains(explainArgs.head.getText) =>
       ExplainCommand(NoRelation)
     case Token("TOK_EXPLAIN", explainArgs) =>
       // Ignore FORMATTED if present.
@@ -377,6 +389,39 @@ private[hive] object HiveQl {
       // TODO: support EXTENDED?
       ExplainCommand(nodeToPlan(query))
 
+    case Token("TOK_DESCTABLE", describeArgs) =>
+      // Reference: https://cwiki.apache.org/confluence/display/Hive/LanguageManual+DDL
+      val Some(tableType) :: formatted :: extended :: pretty :: Nil =
+        getClauses(Seq("TOK_TABTYPE", "FORMATTED", "EXTENDED", "PRETTY"), describeArgs)
+      if (formatted.isDefined || pretty.isDefined) {
+        // FORMATTED and PRETTY are not supported and this statement will be treated as
+        // a Hive native command.
+        NativePlaceholder
+      } else {
+        tableType match {
+          case Token("TOK_TABTYPE", nameParts) if nameParts.size == 1 => {
+            nameParts.head match {
+              case Token(".", dbName :: tableName :: Nil) =>
+                // It is describing a table with the format like "describe db.table".
+                // TODO: Actually, a user may mean tableName.columnName. Need to resolve this issue.
+                val (db, tableName) = extractDbNameTableName(nameParts.head)
+                DescribeCommand(
+                  UnresolvedRelation(db, tableName, None), extended.isDefined)
+              case Token(".", dbName :: tableName :: colName :: Nil) =>
+                // It is describing a column with the format like "describe db.table column".
+                NativePlaceholder
+              case tableName =>
+                // It is describing a table with the format like "describe table".
+                DescribeCommand(
+                  UnresolvedRelation(None, tableName.getText, None),
+                  extended.isDefined)
+            }
+          }
+          // All other cases.
+          case _ => NativePlaceholder
+        }
+      }
+
     case Token("TOK_CREATETABLE", children)
         if children.collect { case t@Token("TOK_QUERY", _) => t }.nonEmpty =>
       // TODO: Parse other clauses.
@@ -414,11 +459,8 @@ private[hive] object HiveQl {
           s"Unhandled clauses: ${notImplemented.flatten.map(dumpTree(_)).mkString("\n")}")
       }
 
-      val (db, tableName) =
-        tableNameParts.getChildren.map{ case Token(part, Nil) => cleanIdentifier(part)} match {
-          case Seq(tableOnly) => (None, tableOnly)
-          case Seq(databaseName, table) => (Some(databaseName), table)
-        }
+      val (db, tableName) = extractDbNameTableName(tableNameParts)
+
       InsertIntoCreatedTable(db, tableName, nodeToPlan(query))
 
     // If its not a "CREATE TABLE AS" like above then just pass it back to hive as a native command.
@@ -736,11 +778,7 @@ private[hive] object HiveQl {
       val Some(tableNameParts) :: partitionClause :: Nil =
         getClauses(Seq("TOK_TABNAME", "TOK_PARTSPEC"), tableArgs)
 
-      val (db, tableName) =
-        tableNameParts.getChildren.map{ case Token(part, Nil) => cleanIdentifier(part)} match {
-          case Seq(tableOnly) => (None, tableOnly)
-          case Seq(databaseName, table) => (Some(databaseName), table)
-        }
+      val (db, tableName) = extractDbNameTableName(tableNameParts)
 
       val partitionKeys = partitionClause.map(_.getChildren.map {
         // Parse partitions. We also make keys case insensitive.
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala
index 0ac0ee9071f3..af7687b40429 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala
@@ -81,6 +81,16 @@ private[hive] trait HiveStrategies {
     def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
       case logical.NativeCommand(sql) =>
         NativeCommand(sql, plan.output)(context) :: Nil
+      case describe: logical.DescribeCommand => {
+        val resolvedTable = context.executePlan(describe.table).analyzed
+        resolvedTable match {
+          case t: MetastoreRelation =>
+            Seq(DescribeHiveTableCommand(
+              t, describe.output, describe.isExtended)(context))
+          case o: LogicalPlan =>
+            Seq(DescribeCommand(planLater(o), describe.output)(context))
+        }
+      }
       case _ => Nil
     }
   }
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/hiveOperators.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/hiveOperators.scala
index b19579331fea..2de2db28a7e0 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/hiveOperators.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/hiveOperators.scala
@@ -20,8 +20,10 @@ package org.apache.spark.sql.hive.execution
 import org.apache.hadoop.hive.common.`type`.{HiveDecimal, HiveVarchar}
 import org.apache.hadoop.hive.conf.HiveConf
 import org.apache.hadoop.hive.metastore.MetaStoreUtils
+import org.apache.hadoop.hive.metastore.api.FieldSchema
 import org.apache.hadoop.hive.ql.Context
 import org.apache.hadoop.hive.ql.metadata.{Partition => HivePartition, Hive}
+import org.apache.hadoop.hive.ql.metadata.formatting.MetaDataFormatUtils
 import org.apache.hadoop.hive.ql.plan.{TableDesc, FileSinkDesc}
 import org.apache.hadoop.hive.serde.serdeConstants
 import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspectorUtils.ObjectInspectorCopyOption
@@ -462,3 +464,61 @@ case class NativeCommand(
 
   override def otherCopyArgs = context :: Nil
 }
+
+/**
+ * :: DeveloperApi ::
+ */
+@DeveloperApi
+case class DescribeHiveTableCommand(
+    table: MetastoreRelation,
+    output: Seq[Attribute],
+    isExtended: Boolean)(
+    @transient context: HiveContext)
+  extends LeafNode with Command {
+
+  // Strings with the format like Hive. It is used for result comparison in our unit tests.
+  lazy val hiveString: Seq[String] = {
+    val alignment = 20
+    val delim = "\t"
+
+    sideEffectResult.map {
+      case (name, dataType, comment) =>
+        String.format("%-" + alignment + "s", name) + delim +
+          String.format("%-" + alignment + "s", dataType) + delim +
+          String.format("%-" + alignment + "s", Option(comment).getOrElse("None"))
+    }
+  }
+
+  override protected[sql] lazy val sideEffectResult: Seq[(String, String, String)] = {
+    // Trying to mimic the format of Hive's output. But not exactly the same.
+    var results: Seq[(String, String, String)] = Nil
+
+    val columns: Seq[FieldSchema] = table.hiveQlTable.getCols
+    val partitionColumns: Seq[FieldSchema] = table.hiveQlTable.getPartCols
+    results ++= columns.map(field => (field.getName, field.getType, field.getComment))
+    if (!partitionColumns.isEmpty) {
+      val partColumnInfo =
+        partitionColumns.map(field => (field.getName, field.getType, field.getComment))
+      results ++=
+        partColumnInfo ++
+        Seq(("# Partition Information", "", "")) ++
+        Seq((s"# ${output.get(0).name}", output.get(1).name, output.get(2).name)) ++
+        partColumnInfo
+    }
+
+    if (isExtended) {
+      results ++= Seq(("Detailed Table Information", table.hiveQlTable.getTTable.toString, ""))
+    }
+
+    results
+  }
+
+  override def execute(): RDD[Row] = {
+    val rows = sideEffectResult.map {
+      case (name, dataType, comment) => new GenericRow(Array[Any](name, dataType, comment))
+    }
+    context.sparkContext.parallelize(rows, 1)
+  }
+
+  override def otherCopyArgs = context :: Nil
+}
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala
index 24c929ff7430..08ef4d9b6bb9 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala
@@ -144,6 +144,12 @@ abstract class HiveComparisonTest
       case _: SetCommand => Seq("0")
       case _: LogicalNativeCommand => answer.filterNot(nonDeterministicLine).filterNot(_ == "")
       case _: ExplainCommand => answer
+      case _: DescribeCommand =>
+        // Filter out non-deterministic lines and lines which do not have actual results but
+        // can introduce problems because of the way Hive formats these lines.
+        // Then, remove empty lines. Do not sort the results.
+        answer.filterNot(
+          r => nonDeterministicLine(r) || ignoredLine(r)).map(_.trim).filterNot(_ == "")
       case plan => if (isSorted(plan)) answer else answer.sorted
     }
     orderedAnswer.map(cleanPaths)
@@ -169,6 +175,16 @@ abstract class HiveComparisonTest
   protected def nonDeterministicLine(line: String) =
     nonDeterministicLineIndicators.exists(line contains _)
 
+  // This list contains indicators for those lines which do not have actual results and we
+  // want to ignore.
+  lazy val ignoredLineIndicators = Seq(
+    "# Partition Information",
+    "# col_name"
+  )
+
+  protected def ignoredLine(line: String) =
+    ignoredLineIndicators.exists(line contains _)
+
   /**
    * Removes non-deterministic paths from `str` so cached answers will compare correctly.
    */
@@ -329,11 +345,17 @@ abstract class HiveComparisonTest
 
             if ((!hiveQuery.logical.isInstanceOf[ExplainCommand]) && preparedHive != catalyst) {
 
-              val hivePrintOut = s"== HIVE - ${hive.size} row(s) ==" +: preparedHive
+              val hivePrintOut = s"== HIVE - ${preparedHive.size} row(s) ==" +: preparedHive
               val catalystPrintOut = s"== CATALYST - ${catalyst.size} row(s) ==" +: catalyst
 
               val resultComparison = sideBySide(hivePrintOut, catalystPrintOut).mkString("\n")
 
+              println("hive output")
+              hive.foreach(println)
+
+              println("catalyst printout")
+              catalyst.foreach(println)
+
               if (recomputeCache) {
                 logger.warn(s"Clearing cache files for failed test $testCaseName")
                 hiveCacheFiles.foreach(_.delete())
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
index ee194dbcb77b..cdfc2d0c1738 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
@@ -78,7 +78,7 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     "alter_merge",
     "alter_concatenate_indexed_table",
     "protectmode2",
-    "describe_table",
+    //"describe_table",
     "describe_comment_nonascii",
     "udf5",
     "udf_java_method",
@@ -177,7 +177,16 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     // After stop taking the `stringOrError` route, exceptions are thrown from these cases.
     // See SPARK-2129 for details.
     "join_view",
-    "mergejoins_mixed"
+    "mergejoins_mixed",
+
+    // Returning the result of a describe state as a JSON object is not supported.
+    "describe_table_json",
+    "describe_database_json",
+    "describe_formatted_view_partitioned_json",
+
+    // Hive returns the results of describe as plain text. Comments with multiple lines
+    // introduce extra lines in the Hive results, which make the result comparison fail.
+    "describe_comment_indent"
   )
 
   /**
@@ -292,11 +301,7 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     "default_partition_name",
     "delimiter",
     "desc_non_existent_tbl",
-    "describe_comment_indent",
-    "describe_database_json",
     "describe_formatted_view_partitioned",
-    "describe_formatted_view_partitioned_json",
-    "describe_table_json",
     "diff_part_input_formats",
     "disable_file_format_check",
     "drop_function",
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
index 5118f4b3f99f..9f5cf282f7c4 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
@@ -21,7 +21,9 @@ import scala.util.Try
 
 import org.apache.spark.sql.hive.test.TestHive
 import org.apache.spark.sql.hive.test.TestHive._
-import org.apache.spark.sql.{SchemaRDD, execution, Row}
+import org.apache.spark.sql.{SchemaRDD, Row}
+
+case class TestData(a: Int, b: String)
 
 /**
  * A set of test cases expressed in Hive QL that are not covered by the tests included in the hive distribution.
@@ -240,13 +242,6 @@ class HiveQuerySuite extends HiveComparisonTest {
         .map(_.getString(0))
         .contains(tableName))
 
-    assertResult(Array(Array("key", "int", "None"), Array("value", "string", "None"))) {
-      hql(s"DESCRIBE $tableName")
-        .select('result)
-        .collect()
-        .map(_.getString(0).split("\t").map(_.trim))
-    }
-
     assert(isExplanation(hql(s"EXPLAIN SELECT key, COUNT(*) FROM $tableName GROUP BY key")))
 
     TestHive.reset()
@@ -263,6 +258,97 @@ class HiveQuerySuite extends HiveComparisonTest {
     assert(Try(q0.count()).isSuccess)
   }
 
+  test("DESCRIBE commands") {
+    hql(s"CREATE TABLE test_describe_commands1 (key INT, value STRING) PARTITIONED BY (dt STRING)")
+
+    hql(
+      """FROM src INSERT OVERWRITE TABLE test_describe_commands1 PARTITION (dt='2008-06-08')
+        |SELECT key, value
+      """.stripMargin)
+
+    // Describe a table
+    assertResult(
+      Array(
+        Array("key", "int", null),
+        Array("value", "string", null),
+        Array("dt", "string", null),
+        Array("# Partition Information", "", ""),
+        Array("# col_name", "data_type", "comment"),
+        Array("dt", "string", null))
+    ) {
+      hql("DESCRIBE test_describe_commands1")
+        .select('col_name, 'data_type, 'comment)
+        .collect()
+    }
+
+    // Describe a table with a fully qualified table name
+    assertResult(
+      Array(
+        Array("key", "int", null),
+        Array("value", "string", null),
+        Array("dt", "string", null),
+        Array("# Partition Information", "", ""),
+        Array("# col_name", "data_type", "comment"),
+        Array("dt", "string", null))
+    ) {
+      hql("DESCRIBE default.test_describe_commands1")
+        .select('col_name, 'data_type, 'comment)
+        .collect()
+    }
+
+    // Describe a column is a native command
+    assertResult(Array(Array("value", "string", "from deserializer"))) {
+      hql("DESCRIBE test_describe_commands1 value")
+        .select('result)
+        .collect()
+        .map(_.getString(0).split("\t").map(_.trim))
+    }
+
+    // Describe a column is a native command
+    assertResult(Array(Array("value", "string", "from deserializer"))) {
+      hql("DESCRIBE default.test_describe_commands1 value")
+        .select('result)
+        .collect()
+        .map(_.getString(0).split("\t").map(_.trim))
+    }
+
+    // Describe a partition is a native command
+    assertResult(
+      Array(
+        Array("key", "int", "None"),
+        Array("value", "string", "None"),
+        Array("dt", "string", "None"),
+        Array("", "", ""),
+        Array("# Partition Information", "", ""),
+        Array("# col_name", "data_type", "comment"),
+        Array("", "", ""),
+        Array("dt", "string", "None"))
+    ) {
+      hql("DESCRIBE test_describe_commands1 PARTITION (dt='2008-06-08')")
+        .select('result)
+        .collect()
+        .map(_.getString(0).split("\t").map(_.trim))
+    }
+
+    // Describe a registered temporary table.
+    val testData: SchemaRDD =
+      TestHive.sparkContext.parallelize(
+        TestData(1, "str1") ::
+        TestData(1, "str2") :: Nil)
+    testData.registerAsTable("test_describe_commands2")
+
+    assertResult(
+      Array(
+        Array("# Registered as a temporary table", null, null),
+        Array("a", "IntegerType", null),
+        Array("b", "StringType", null))
+    ) {
+      hql("DESCRIBE test_describe_commands2")
+        .select('col_name, 'data_type, 'comment)
+        .collect()
+    }
+  }
+
   test("parse HQL set commands") {
     // Adapted from its SQL counterpart.
     val testKey = "spark.sql.key.usedfortestonly"

From f479cf3743e416ee08e62806e1b34aff5998ac22 Mon Sep 17 00:00:00 2001
From: Andre Schumacher <andre.schumacher@iki.fi>
Date: Thu, 19 Jun 2014 23:47:45 -0700
Subject: [PATCH 470/810] SPARK-1293 [SQL] Parquet support for nested types

It should be possible to import and export data stored in Parquet's columnar format that contains nested types. For example:
```java
message AddressBook {
   required binary owner;
   optional group ownerPhoneNumbers {
      repeated binary array;
   }
   optional group contacts {
      repeated group array {
         required binary name;
         optional binary phoneNumber;
      }
   }
   optional group nameToApartmentNumber {
      repeated group map {
         required binary key;
         required int32 value;
      }
   }
}
```
The example could model a type (AddressBook) that contains records made of strings (owner), lists (ownerPhoneNumbers) and a table of contacts (e.g., a list of pairs or a map that can contain null values but keys must not be null). The list of tasks are as follows:

<h6>Implement support for converting nested Parquet types to Spark/Catalyst types:</h6>
- [x] Structs
- [x] Lists
- [x] Maps (note: currently keys need to be Strings)

<h6>Implement import (via ``parquetFile``) of nested Parquet types (first version in this PR)</h6>
- [x] Initial version

<h6>Implement export (via ``saveAsParquetFile``)</h6>
- [x] Initial version

<h6>Test support for AvroParquet, etc.</h6>
- [x] Initial testing of import of avro-generated Parquet data (simple + nested)

Example:
```scala
val data = TestSQLContext
  .parquetFile("input.dir")
  .toSchemaRDD
data.registerAsTable("data")
sql("SELECT owner, contacts[1].name, nameToApartmentNumber['John'] FROM data").collect()
```

Author: Andre Schumacher <andre.schumacher@iki.fi>
Author: Michael Armbrust <michael@databricks.com>

Closes #360 from AndreSchumacher/nested_parquet and squashes the following commits:

30708c8 [Andre Schumacher] Taking out AvroParquet test for now to remove Avro dependency
95c1367 [Andre Schumacher] Changes to ParquetRelation and its metadata
7eceb67 [Andre Schumacher] Review feedback
94eea3a [Andre Schumacher] Scalastyle
403061f [Andre Schumacher] Fixing some issues with tests and schema metadata
b8a8b9a [Andre Schumacher] More fixes to short and byte conversion
63d1b57 [Andre Schumacher] Cleaning up and Scalastyle
88e6bdb [Andre Schumacher] Attempting to fix loss of schema
37e0a0a [Andre Schumacher] Cleaning up
14c3fd8 [Andre Schumacher] Attempting to fix Spark-Parquet schema conversion
3e1456c [Michael Armbrust] WIP: Directly serialize catalyst attributes.
f7aeba3 [Michael Armbrust] [SPARK-1982] Support for ByteType and ShortType.
3104886 [Michael Armbrust] Nested Rows should be Rows, not Seqs.
3c6b25f [Andre Schumacher] Trying to reduce no-op changes wrt master
31465d6 [Andre Schumacher] Scalastyle: fixing commented out bottom
de02538 [Andre Schumacher] Cleaning up ParquetTestData
2f5a805 [Andre Schumacher] Removing stripMargin from test schemas
191bc0d [Andre Schumacher] Changing to Seq for ArrayType, refactoring SQLParser for nested field extension
cbb5793 [Andre Schumacher] Code review feedback
32229c7 [Andre Schumacher] Removing Row nested values and placing by generic types
0ae9376 [Andre Schumacher] Doc strings and simplifying ParquetConverter.scala
a6b4f05 [Andre Schumacher] Cleaning up ArrayConverter, moving classTag to NativeType, adding NativeRow
431f00f [Andre Schumacher] Fixing problems introduced during rebase
c52ff2c [Andre Schumacher] Adding native-array converter
619c397 [Andre Schumacher] Completing Map testcase
79d81d5 [Andre Schumacher] Replacing field names for array and map in WriteSupport
f466ff0 [Andre Schumacher] Added ParquetAvro tests and revised Array conversion
adc1258 [Andre Schumacher] Optimizing imports
e99cc51 [Andre Schumacher] Fixing nested WriteSupport and adding tests
1dc5ac9 [Andre Schumacher] First version of WriteSupport for nested types
d1911dc [Andre Schumacher] Simplifying ArrayType conversion
f777b4b [Andre Schumacher] Scalastyle
824500c [Andre Schumacher] Adding attribute resolution for MapType
b539fde [Andre Schumacher] First commit for MapType
a594aed [Andre Schumacher] Scalastyle
4e25fcb [Andre Schumacher] Adding resolution of complex ArrayTypes
f8f8911 [Andre Schumacher] For primitive rows fall back to more efficient converter, code reorg
6dbc9b7 [Andre Schumacher] Fixing some problems intruduced during rebase
b7fcc35 [Andre Schumacher] Documenting conversions, bugfix, wrappers of Rows
ee70125 [Andre Schumacher] fixing one problem with arrayconverter
98219cf [Andre Schumacher] added struct converter
5d80461 [Andre Schumacher] fixing one problem with nested structs and breaking up files
1b1b3d6 [Andre Schumacher] Fixing one problem with nested arrays
ddb40d2 [Andre Schumacher] Extending tests for nested Parquet data
745a42b [Andre Schumacher] Completing testcase for nested data (Addressbook(
6125c75 [Andre Schumacher] First working nested Parquet record input
4d4892a [Andre Schumacher] First commit nested Parquet read converters
aa688fe [Andre Schumacher] Adding conversion of nested Parquet schemas
---
 .../apache/spark/sql/catalyst/SqlParser.scala | 111 +--
 .../catalyst/expressions/complexTypes.scala   |   2 +
 .../spark/sql/catalyst/types/dataTypes.scala  |  98 ++-
 .../org/apache/spark/sql/SQLContext.scala     |   2 +-
 .../spark/sql/api/java/JavaSQLContext.scala   |   4 +-
 .../spark/sql/execution/SparkStrategies.scala |   3 +-
 .../spark/sql/parquet/ParquetConverter.scala  | 667 ++++++++++++++++++
 .../spark/sql/parquet/ParquetRelation.scala   | 182 +----
 .../sql/parquet/ParquetTableOperations.scala  |  25 +-
 .../sql/parquet/ParquetTableSupport.scala     | 326 ++++++---
 .../spark/sql/parquet/ParquetTestData.scala   | 298 +++++++-
 .../spark/sql/parquet/ParquetTypes.scala      | 408 +++++++++++
 .../spark/sql/parquet/ParquetQuerySuite.scala | 356 +++++++++-
 .../spark/sql/hive/HiveMetastoreCatalog.scala |   4 +-
 14 files changed, 2102 insertions(+), 384 deletions(-)
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypes.scala

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
index 46fcfbb9e26b..2ad2d04af570 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
@@ -66,43 +66,7 @@ class SqlParser extends StandardTokenParsers with PackratParsers {
   protected case class Keyword(str: String)
 
   protected implicit def asParser(k: Keyword): Parser[String] =
-    allCaseVersions(k.str).map(x => x : Parser[String]).reduce(_ | _)
-
-  protected class SqlLexical extends StdLexical {
-    case class FloatLit(chars: String) extends Token {
-      override def toString = chars
-    }
-    override lazy val token: Parser[Token] = (
-        identChar ~ rep( identChar | digit ) ^^
-          { case first ~ rest => processIdent(first :: rest mkString "") }
-      | rep1(digit) ~ opt('.' ~> rep(digit)) ^^ {
-        case i ~ None    => NumericLit(i mkString "")
-        case i ~ Some(d) => FloatLit(i.mkString("") + "." + d.mkString(""))
-      }
-      | '\'' ~ rep( chrExcept('\'', '\n', EofCh) ) ~ '\'' ^^
-        { case '\'' ~ chars ~ '\'' => StringLit(chars mkString "") }
-      | '\"' ~ rep( chrExcept('\"', '\n', EofCh) ) ~ '\"' ^^
-        { case '\"' ~ chars ~ '\"' => StringLit(chars mkString "") }
-      | EofCh ^^^ EOF
-      | '\'' ~> failure("unclosed string literal")
-      | '\"' ~> failure("unclosed string literal")
-      | delim
-      | failure("illegal character")
-    )
-
-    override def identChar = letter | elem('.') | elem('_')
-
-    override def whitespace: Parser[Any] = rep(
-      whitespaceChar
-    | '/' ~ '*' ~ comment
-    | '/' ~ '/' ~ rep( chrExcept(EofCh, '\n') )
-    | '#' ~ rep( chrExcept(EofCh, '\n') )
-    | '-' ~ '-' ~ rep( chrExcept(EofCh, '\n') )
-    | '/' ~ '*' ~ failure("unclosed comment")
-    )
-  }
-
-  override val lexical = new SqlLexical
+    lexical.allCaseVersions(k.str).map(x => x : Parser[String]).reduce(_ | _)
 
   protected val ALL = Keyword("ALL")
   protected val AND = Keyword("AND")
@@ -161,24 +125,9 @@ class SqlParser extends StandardTokenParsers with PackratParsers {
     this.getClass
       .getMethods
       .filter(_.getReturnType == classOf[Keyword])
-      .map(_.invoke(this).asInstanceOf[Keyword])
-
-  /** Generate all variations of upper and lower case of a given string */
-  private def allCaseVersions(s: String, prefix: String = ""): Stream[String] = {
-    if (s == "") {
-      Stream(prefix)
-    } else {
-      allCaseVersions(s.tail, prefix + s.head.toLower) ++
-        allCaseVersions(s.tail, prefix + s.head.toUpper)
-    }
-  }
+      .map(_.invoke(this).asInstanceOf[Keyword].str)
 
-  lexical.reserved ++= reservedWords.flatMap(w => allCaseVersions(w.str))
-
-  lexical.delimiters += (
-    "@", "*", "+", "-", "<", "=", "<>", "!=", "<=", ">=", ">", "/", "(", ")",
-    ",", ";", "%", "{", "}", ":", "[", "]"
-  )
+  override val lexical = new SqlLexical(reservedWords)
 
   protected def assignAliases(exprs: Seq[Expression]): Seq[NamedExpression] = {
     exprs.zipWithIndex.map {
@@ -383,7 +332,7 @@ class SqlParser extends StandardTokenParsers with PackratParsers {
     elem("decimal", _.isInstanceOf[lexical.FloatLit]) ^^ (_.chars)
 
   protected lazy val baseExpression: PackratParser[Expression] =
-    expression ~ "[" ~  expression <~ "]" ^^ {
+    expression ~ "[" ~ expression <~ "]" ^^ {
       case base ~ _ ~ ordinal => GetItem(base, ordinal)
     } |
     TRUE ^^^ Literal(true, BooleanType) |
@@ -399,3 +348,55 @@ class SqlParser extends StandardTokenParsers with PackratParsers {
   protected lazy val dataType: Parser[DataType] =
     STRING ^^^ StringType
 }
+
+class SqlLexical(val keywords: Seq[String]) extends StdLexical {
+  case class FloatLit(chars: String) extends Token {
+    override def toString = chars
+  }
+
+  reserved ++= keywords.flatMap(w => allCaseVersions(w))
+
+  delimiters += (
+      "@", "*", "+", "-", "<", "=", "<>", "!=", "<=", ">=", ">", "/", "(", ")",
+      ",", ";", "%", "{", "}", ":", "[", "]"
+  )
+
+  override lazy val token: Parser[Token] = (
+    identChar ~ rep( identChar | digit ) ^^
+      { case first ~ rest => processIdent(first :: rest mkString "") }
+      | rep1(digit) ~ opt('.' ~> rep(digit)) ^^ {
+      case i ~ None    => NumericLit(i mkString "")
+      case i ~ Some(d) => FloatLit(i.mkString("") + "." + d.mkString(""))
+    }
+      | '\'' ~ rep( chrExcept('\'', '\n', EofCh) ) ~ '\'' ^^
+      { case '\'' ~ chars ~ '\'' => StringLit(chars mkString "") }
+      | '\"' ~ rep( chrExcept('\"', '\n', EofCh) ) ~ '\"' ^^
+      { case '\"' ~ chars ~ '\"' => StringLit(chars mkString "") }
+      | EofCh ^^^ EOF
+      | '\'' ~> failure("unclosed string literal")
+      | '\"' ~> failure("unclosed string literal")
+      | delim
+      | failure("illegal character")
+    )
+
+  override def identChar = letter | elem('_') | elem('.')
+
+  override def whitespace: Parser[Any] = rep(
+    whitespaceChar
+      | '/' ~ '*' ~ comment
+      | '/' ~ '/' ~ rep( chrExcept(EofCh, '\n') )
+      | '#' ~ rep( chrExcept(EofCh, '\n') )
+      | '-' ~ '-' ~ rep( chrExcept(EofCh, '\n') )
+      | '/' ~ '*' ~ failure("unclosed comment")
+  )
+
+  /** Generate all variations of upper and lower case of a given string */
+  def allCaseVersions(s: String, prefix: String = ""): Stream[String] = {
+    if (s == "") {
+      Stream(prefix)
+    } else {
+      allCaseVersions(s.tail, prefix + s.head.toLower) ++
+        allCaseVersions(s.tail, prefix + s.head.toUpper)
+    }
+  }
+}
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypes.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypes.scala
index b6aeae92f8be..5d3bb25ad568 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypes.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypes.scala
@@ -50,6 +50,8 @@ case class GetItem(child: Expression, ordinal: Expression) extends Expression {
         null
       } else {
         if (child.dataType.isInstanceOf[ArrayType]) {
+          // TODO: consider using Array[_] for ArrayType child to avoid
+          // boxing of primitives
           val baseValue = value.asInstanceOf[Seq[_]]
           val o = key.asInstanceOf[Int]
           if (o >= baseValue.size || o < 0) {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/types/dataTypes.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/types/dataTypes.scala
index da34bd3a2150..bb77bccf8617 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/types/dataTypes.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/types/dataTypes.scala
@@ -19,9 +19,71 @@ package org.apache.spark.sql.catalyst.types
 
 import java.sql.Timestamp
 
-import scala.reflect.runtime.universe.{typeTag, TypeTag}
+import scala.util.parsing.combinator.RegexParsers
 
-import org.apache.spark.sql.catalyst.expressions.Expression
+import scala.reflect.ClassTag
+import scala.reflect.runtime.universe.{typeTag, TypeTag, runtimeMirror}
+
+import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeReference, Expression}
+import org.apache.spark.util.Utils
+
+/**
+ *
+ */
+object DataType extends RegexParsers {
+  protected lazy val primitiveType: Parser[DataType] =
+    "StringType" ^^^ StringType |
+    "FloatType" ^^^ FloatType |
+    "IntegerType" ^^^ IntegerType |
+    "ByteType" ^^^ ByteType |
+    "ShortType" ^^^ ShortType |
+    "DoubleType" ^^^ DoubleType |
+    "LongType" ^^^ LongType |
+    "BinaryType" ^^^ BinaryType |
+    "BooleanType" ^^^ BooleanType |
+    "DecimalType" ^^^ DecimalType |
+    "TimestampType" ^^^ TimestampType
+
+  protected lazy val arrayType: Parser[DataType] =
+    "ArrayType" ~> "(" ~> dataType <~ ")" ^^ ArrayType
+
+  protected lazy val mapType: Parser[DataType] =
+    "MapType" ~> "(" ~> dataType ~ "," ~ dataType <~ ")" ^^ {
+      case t1 ~ _ ~ t2 => MapType(t1, t2)
+    }
+
+  protected lazy val structField: Parser[StructField] =
+    ("StructField(" ~> "[a-zA-Z0-9_]*".r) ~ ("," ~> dataType) ~ ("," ~> boolVal <~ ")") ^^ {
+      case name ~ tpe ~ nullable  =>
+          StructField(name, tpe, nullable = nullable)
+    }
+
+  protected lazy val boolVal: Parser[Boolean] =
+    "true" ^^^ true |
+    "false" ^^^ false
+
+
+  protected lazy val structType: Parser[DataType] =
+    "StructType\\([A-zA-z]*\\(".r ~> repsep(structField, ",") <~ "))" ^^ {
+      case fields => new StructType(fields)
+    }
+
+  protected lazy val dataType: Parser[DataType] =
+    arrayType |
+      mapType |
+      structType |
+      primitiveType
+
+  /**
+   * Parses a string representation of a DataType.
+   *
+   * TODO: Generate parser as pickler...
+   */
+  def apply(asString: String): DataType = parseAll(dataType, asString) match {
+    case Success(result, _) => result
+    case failure: NoSuccess => sys.error(s"Unsupported dataType: $asString, $failure")
+  }
+}
 
 abstract class DataType {
   /** Matches any expression that evaluates to this DataType */
@@ -29,25 +91,36 @@ abstract class DataType {
     case e: Expression if e.dataType == this => true
     case _ => false
   }
+
+  def isPrimitive: Boolean = false
 }
 
 case object NullType extends DataType
 
+trait PrimitiveType extends DataType {
+  override def isPrimitive = true
+}
+
 abstract class NativeType extends DataType {
   type JvmType
   @transient val tag: TypeTag[JvmType]
   val ordering: Ordering[JvmType]
+
+  @transient val classTag = {
+    val mirror = runtimeMirror(Utils.getSparkClassLoader)
+    ClassTag[JvmType](mirror.runtimeClass(tag.tpe))
+  }
 }
 
-case object StringType extends NativeType {
+case object StringType extends NativeType with PrimitiveType {
   type JvmType = String
   @transient lazy val tag = typeTag[JvmType]
   val ordering = implicitly[Ordering[JvmType]]
 }
-case object BinaryType extends DataType {
+case object BinaryType extends DataType with PrimitiveType {
   type JvmType = Array[Byte]
 }
-case object BooleanType extends NativeType {
+case object BooleanType extends NativeType with PrimitiveType {
   type JvmType = Boolean
   @transient lazy val tag = typeTag[JvmType]
   val ordering = implicitly[Ordering[JvmType]]
@@ -63,7 +136,7 @@ case object TimestampType extends NativeType {
   }
 }
 
-abstract class NumericType extends NativeType {
+abstract class NumericType extends NativeType with PrimitiveType {
   // Unfortunately we can't get this implicitly as that breaks Spark Serialization. In order for
   // implicitly[Numeric[JvmType]] to be valid, we have to change JvmType from a type variable to a
   // type parameter and and add a numeric annotation (i.e., [JvmType : Numeric]). This gets
@@ -154,6 +227,17 @@ case object FloatType extends FractionalType {
 case class ArrayType(elementType: DataType) extends DataType
 
 case class StructField(name: String, dataType: DataType, nullable: Boolean)
-case class StructType(fields: Seq[StructField]) extends DataType
+
+object StructType {
+  def fromAttributes(attributes: Seq[Attribute]): StructType = {
+    StructType(attributes.map(a => StructField(a.name, a.dataType, a.nullable)))
+  }
+
+  // def apply(fields: Seq[StructField]) = new StructType(fields.toIndexedSeq)
+}
+
+case class StructType(fields: Seq[StructField]) extends DataType {
+  def toAttributes = fields.map(f => AttributeReference(f.name, f.dataType, f.nullable)())
+}
 
 case class MapType(keyType: DataType, valueType: DataType) extends DataType
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
index 1617ec717b2e..ab376e5504d3 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
@@ -94,7 +94,7 @@ class SQLContext(@transient val sparkContext: SparkContext)
    * @group userf
    */
   def parquetFile(path: String): SchemaRDD =
-    new SchemaRDD(this, parquet.ParquetRelation(path))
+    new SchemaRDD(this, parquet.ParquetRelation(path, Some(sparkContext.hadoopConfiguration)))
 
   /**
    * Loads a JSON file (one object per line), returning the result as a [[SchemaRDD]].
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSQLContext.scala
index ff9842267ffe..ff6deeda2394 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSQLContext.scala
@@ -99,7 +99,9 @@ class JavaSQLContext(val sqlContext: SQLContext) {
    * Loads a parquet file, returning the result as a [[JavaSchemaRDD]].
    */
   def parquetFile(path: String): JavaSchemaRDD =
-    new JavaSchemaRDD(sqlContext, ParquetRelation(path))
+    new JavaSchemaRDD(
+      sqlContext,
+      ParquetRelation(path, Some(sqlContext.sparkContext.hadoopConfiguration)))
 
   /**
    * Loads a JSON file (one object per line), returning the result as a [[JavaSchemaRDD]].
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
index feb280d1d141..4694f25d6d63 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
@@ -154,7 +154,8 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
       case logical.WriteToFile(path, child) =>
         val relation =
           ParquetRelation.create(path, child, sparkContext.hadoopConfiguration)
-        InsertIntoParquetTable(relation, planLater(child), overwrite=true)(sparkContext) :: Nil
+        // Note: overwrite=false because otherwise the metadata we just created will be deleted
+        InsertIntoParquetTable(relation, planLater(child), overwrite=false)(sparkContext) :: Nil
       case logical.InsertIntoTable(table: ParquetRelation, partition, child, overwrite) =>
         InsertIntoParquetTable(table, planLater(child), overwrite)(sparkContext) :: Nil
       case PhysicalOperation(projectList, filters: Seq[Expression], relation: ParquetRelation) =>
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala
new file mode 100644
index 000000000000..889a408e3c39
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala
@@ -0,0 +1,667 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.parquet
+
+import scala.collection.mutable.{Buffer, ArrayBuffer, HashMap}
+
+import parquet.io.api.{PrimitiveConverter, GroupConverter, Binary, Converter}
+import parquet.schema.MessageType
+
+import org.apache.spark.sql.catalyst.types._
+import org.apache.spark.sql.catalyst.expressions.{GenericRow, Row, Attribute}
+import org.apache.spark.sql.parquet.CatalystConverter.FieldType
+
+/**
+ * Collection of converters of Parquet types (group and primitive types) that
+ * model arrays and maps. The conversions are partly based on the AvroParquet
+ * converters that are part of Parquet in order to be able to process these
+ * types.
+ *
+ * There are several types of converters:
+ * <ul>
+ *   <li>[[org.apache.spark.sql.parquet.CatalystPrimitiveConverter]] for primitive
+ *   (numeric, boolean and String) types</li>
+ *   <li>[[org.apache.spark.sql.parquet.CatalystNativeArrayConverter]] for arrays
+ *   of native JVM element types; note: currently null values are not supported!</li>
+ *   <li>[[org.apache.spark.sql.parquet.CatalystArrayConverter]] for arrays of
+ *   arbitrary element types (including nested element types); note: currently
+ *   null values are not supported!</li>
+ *   <li>[[org.apache.spark.sql.parquet.CatalystStructConverter]] for structs</li>
+ *   <li>[[org.apache.spark.sql.parquet.CatalystMapConverter]] for maps; note:
+ *   currently null values are not supported!</li>
+ *   <li>[[org.apache.spark.sql.parquet.CatalystPrimitiveRowConverter]] for rows
+ *   of only primitive element types</li>
+ *   <li>[[org.apache.spark.sql.parquet.CatalystGroupConverter]] for other nested
+ *   records, including the top-level row record</li>
+ * </ul>
+ */
+
+private[sql] object CatalystConverter {
+  // The type internally used for fields
+  type FieldType = StructField
+
+  // This is mostly Parquet convention (see, e.g., `ConversionPatterns`).
+  // Note that "array" for the array elements is chosen by ParquetAvro.
+  // Using a different value will result in Parquet silently dropping columns.
+  val ARRAY_ELEMENTS_SCHEMA_NAME = "array"
+  val MAP_KEY_SCHEMA_NAME = "key"
+  val MAP_VALUE_SCHEMA_NAME = "value"
+  val MAP_SCHEMA_NAME = "map"
+
+  // TODO: consider using Array[T] for arrays to avoid boxing of primitive types
+  type ArrayScalaType[T] = Seq[T]
+  type StructScalaType[T] = Seq[T]
+  type MapScalaType[K, V] = Map[K, V]
+
+  protected[parquet] def createConverter(
+      field: FieldType,
+      fieldIndex: Int,
+      parent: CatalystConverter): Converter = {
+    val fieldType: DataType = field.dataType
+    fieldType match {
+      // For native JVM types we use a converter with native arrays
+      case ArrayType(elementType: NativeType) => {
+        new CatalystNativeArrayConverter(elementType, fieldIndex, parent)
+      }
+      // This is for other types of arrays, including those with nested fields
+      case ArrayType(elementType: DataType) => {
+        new CatalystArrayConverter(elementType, fieldIndex, parent)
+      }
+      case StructType(fields: Seq[StructField]) => {
+        new CatalystStructConverter(fields.toArray, fieldIndex, parent)
+      }
+      case MapType(keyType: DataType, valueType: DataType) => {
+        new CatalystMapConverter(
+          Array(
+            new FieldType(MAP_KEY_SCHEMA_NAME, keyType, false),
+            new FieldType(MAP_VALUE_SCHEMA_NAME, valueType, true)),
+          fieldIndex,
+          parent)
+      }
+      // Strings, Shorts and Bytes do not have a corresponding type in Parquet
+      // so we need to treat them separately
+      case StringType => {
+        new CatalystPrimitiveConverter(parent, fieldIndex) {
+          override def addBinary(value: Binary): Unit =
+            parent.updateString(fieldIndex, value)
+        }
+      }
+      case ShortType => {
+        new CatalystPrimitiveConverter(parent, fieldIndex) {
+          override def addInt(value: Int): Unit =
+            parent.updateShort(fieldIndex, value.asInstanceOf[ShortType.JvmType])
+        }
+      }
+      case ByteType => {
+        new CatalystPrimitiveConverter(parent, fieldIndex) {
+          override def addInt(value: Int): Unit =
+            parent.updateByte(fieldIndex, value.asInstanceOf[ByteType.JvmType])
+        }
+      }
+      // All other primitive types use the default converter
+      case ctype: NativeType => { // note: need the type tag here!
+        new CatalystPrimitiveConverter(parent, fieldIndex)
+      }
+      case _ => throw new RuntimeException(
+        s"unable to convert datatype ${field.dataType.toString} in CatalystConverter")
+    }
+  }
+
+  protected[parquet] def createRootConverter(
+      parquetSchema: MessageType,
+      attributes: Seq[Attribute]): CatalystConverter = {
+    // For non-nested types we use the optimized Row converter
+    if (attributes.forall(a => ParquetTypesConverter.isPrimitiveType(a.dataType))) {
+      new CatalystPrimitiveRowConverter(attributes.toArray)
+    } else {
+      new CatalystGroupConverter(attributes.toArray)
+    }
+  }
+}
+
+private[parquet] abstract class CatalystConverter extends GroupConverter {
+  /**
+   * The number of fields this group has
+   */
+  protected[parquet] val size: Int
+
+  /**
+   * The index of this converter in the parent
+   */
+  protected[parquet] val index: Int
+
+  /**
+   * The parent converter
+   */
+  protected[parquet] val parent: CatalystConverter
+
+  /**
+   * Called by child converters to update their value in its parent (this).
+   * Note that if possible the more specific update methods below should be used
+   * to avoid auto-boxing of native JVM types.
+   *
+   * @param fieldIndex
+   * @param value
+   */
+  protected[parquet] def updateField(fieldIndex: Int, value: Any): Unit
+
+  protected[parquet] def updateBoolean(fieldIndex: Int, value: Boolean): Unit =
+    updateField(fieldIndex, value)
+
+  protected[parquet] def updateInt(fieldIndex: Int, value: Int): Unit =
+    updateField(fieldIndex, value)
+
+  protected[parquet] def updateLong(fieldIndex: Int, value: Long): Unit =
+    updateField(fieldIndex, value)
+
+  protected[parquet] def updateShort(fieldIndex: Int, value: Short): Unit =
+    updateField(fieldIndex, value)
+
+  protected[parquet] def updateByte(fieldIndex: Int, value: Byte): Unit =
+    updateField(fieldIndex, value)
+
+  protected[parquet] def updateDouble(fieldIndex: Int, value: Double): Unit =
+    updateField(fieldIndex, value)
+
+  protected[parquet] def updateFloat(fieldIndex: Int, value: Float): Unit =
+    updateField(fieldIndex, value)
+
+  protected[parquet] def updateBinary(fieldIndex: Int, value: Binary): Unit =
+    updateField(fieldIndex, value.getBytes)
+
+  protected[parquet] def updateString(fieldIndex: Int, value: Binary): Unit =
+    updateField(fieldIndex, value.toStringUsingUTF8)
+
+  protected[parquet] def isRootConverter: Boolean = parent == null
+
+  protected[parquet] def clearBuffer(): Unit
+
+  /**
+   * Should only be called in the root (group) converter!
+   *
+   * @return
+   */
+  def getCurrentRecord: Row = throw new UnsupportedOperationException
+}
+
+/**
+ * A `parquet.io.api.GroupConverter` that is able to convert a Parquet record
+ * to a [[org.apache.spark.sql.catalyst.expressions.Row]] object.
+ *
+ * @param schema The corresponding Catalyst schema in the form of a list of attributes.
+ */
+private[parquet] class CatalystGroupConverter(
+    protected[parquet] val schema: Array[FieldType],
+    protected[parquet] val index: Int,
+    protected[parquet] val parent: CatalystConverter,
+    protected[parquet] var current: ArrayBuffer[Any],
+    protected[parquet] var buffer: ArrayBuffer[Row])
+  extends CatalystConverter {
+
+  def this(schema: Array[FieldType], index: Int, parent: CatalystConverter) =
+    this(
+      schema,
+      index,
+      parent,
+      current=null,
+      buffer=new ArrayBuffer[Row](
+        CatalystArrayConverter.INITIAL_ARRAY_SIZE))
+
+  /**
+   * This constructor is used for the root converter only!
+   */
+  def this(attributes: Array[Attribute]) =
+    this(attributes.map(a => new FieldType(a.name, a.dataType, a.nullable)), 0, null)
+
+  protected [parquet] val converters: Array[Converter] =
+    schema.map(field =>
+      CatalystConverter.createConverter(field, schema.indexOf(field), this))
+    .toArray
+
+  override val size = schema.size
+
+  override def getCurrentRecord: Row = {
+    assert(isRootConverter, "getCurrentRecord should only be called in root group converter!")
+    // TODO: use iterators if possible
+    // Note: this will ever only be called in the root converter when the record has been
+    // fully processed. Therefore it will be difficult to use mutable rows instead, since
+    // any non-root converter never would be sure when it would be safe to re-use the buffer.
+    new GenericRow(current.toArray)
+  }
+
+  override def getConverter(fieldIndex: Int): Converter = converters(fieldIndex)
+
+  // for child converters to update upstream values
+  override protected[parquet] def updateField(fieldIndex: Int, value: Any): Unit = {
+    current.update(fieldIndex, value)
+  }
+
+  override protected[parquet] def clearBuffer(): Unit = buffer.clear()
+
+  override def start(): Unit = {
+    current = ArrayBuffer.fill(size)(null)
+    converters.foreach {
+      converter => if (!converter.isPrimitive) {
+        converter.asInstanceOf[CatalystConverter].clearBuffer
+      }
+    }
+  }
+
+  override def end(): Unit = {
+    if (!isRootConverter) {
+      assert(current!=null) // there should be no empty groups
+      buffer.append(new GenericRow(current.toArray))
+      parent.updateField(index, new GenericRow(buffer.toArray.asInstanceOf[Array[Any]]))
+    }
+  }
+}
+
+/**
+ * A `parquet.io.api.GroupConverter` that is able to convert a Parquet record
+ * to a [[org.apache.spark.sql.catalyst.expressions.Row]] object. Note that his
+ * converter is optimized for rows of primitive types (non-nested records).
+ */
+private[parquet] class CatalystPrimitiveRowConverter(
+    protected[parquet] val schema: Array[FieldType],
+    protected[parquet] var current: ParquetRelation.RowType)
+  extends CatalystConverter {
+
+  // This constructor is used for the root converter only
+  def this(attributes: Array[Attribute]) =
+    this(
+      attributes.map(a => new FieldType(a.name, a.dataType, a.nullable)),
+      new ParquetRelation.RowType(attributes.length))
+
+  protected [parquet] val converters: Array[Converter] =
+    schema.map(field =>
+      CatalystConverter.createConverter(field, schema.indexOf(field), this))
+      .toArray
+
+  override val size = schema.size
+
+  override val index = 0
+
+  override val parent = null
+
+  // Should be only called in root group converter!
+  override def getCurrentRecord: ParquetRelation.RowType = current
+
+  override def getConverter(fieldIndex: Int): Converter = converters(fieldIndex)
+
+  // for child converters to update upstream values
+  override protected[parquet] def updateField(fieldIndex: Int, value: Any): Unit = {
+    throw new UnsupportedOperationException // child converters should use the
+    // specific update methods below
+  }
+
+  override protected[parquet] def clearBuffer(): Unit = {}
+
+  override def start(): Unit = {
+    var i = 0
+    while (i < size) {
+      current.setNullAt(i)
+      i = i + 1
+    }
+  }
+
+  override def end(): Unit = {}
+
+  // Overriden here to avoid auto-boxing for primitive types
+  override protected[parquet] def updateBoolean(fieldIndex: Int, value: Boolean): Unit =
+    current.setBoolean(fieldIndex, value)
+
+  override protected[parquet] def updateInt(fieldIndex: Int, value: Int): Unit =
+    current.setInt(fieldIndex, value)
+
+  override protected[parquet] def updateLong(fieldIndex: Int, value: Long): Unit =
+    current.setLong(fieldIndex, value)
+
+  override protected[parquet] def updateShort(fieldIndex: Int, value: Short): Unit =
+    current.setShort(fieldIndex, value)
+
+  override protected[parquet] def updateByte(fieldIndex: Int, value: Byte): Unit =
+    current.setByte(fieldIndex, value)
+
+  override protected[parquet] def updateDouble(fieldIndex: Int, value: Double): Unit =
+    current.setDouble(fieldIndex, value)
+
+  override protected[parquet] def updateFloat(fieldIndex: Int, value: Float): Unit =
+    current.setFloat(fieldIndex, value)
+
+  override protected[parquet] def updateBinary(fieldIndex: Int, value: Binary): Unit =
+    current.update(fieldIndex, value.getBytes)
+
+  override protected[parquet] def updateString(fieldIndex: Int, value: Binary): Unit =
+    current.setString(fieldIndex, value.toStringUsingUTF8)
+}
+
+/**
+ * A `parquet.io.api.PrimitiveConverter` that converts Parquet types to Catalyst types.
+ *
+ * @param parent The parent group converter.
+ * @param fieldIndex The index inside the record.
+ */
+private[parquet] class CatalystPrimitiveConverter(
+    parent: CatalystConverter,
+    fieldIndex: Int) extends PrimitiveConverter {
+  override def addBinary(value: Binary): Unit =
+    parent.updateBinary(fieldIndex, value)
+
+  override def addBoolean(value: Boolean): Unit =
+    parent.updateBoolean(fieldIndex, value)
+
+  override def addDouble(value: Double): Unit =
+    parent.updateDouble(fieldIndex, value)
+
+  override def addFloat(value: Float): Unit =
+    parent.updateFloat(fieldIndex, value)
+
+  override def addInt(value: Int): Unit =
+    parent.updateInt(fieldIndex, value)
+
+  override def addLong(value: Long): Unit =
+    parent.updateLong(fieldIndex, value)
+}
+
+object CatalystArrayConverter {
+  val INITIAL_ARRAY_SIZE = 20
+}
+
+/**
+ * A `parquet.io.api.GroupConverter` that converts a single-element groups that
+ * match the characteristics of an array (see
+ * [[org.apache.spark.sql.parquet.ParquetTypesConverter]]) into an
+ * [[org.apache.spark.sql.catalyst.types.ArrayType]].
+ *
+ * @param elementType The type of the array elements (complex or primitive)
+ * @param index The position of this (array) field inside its parent converter
+ * @param parent The parent converter
+ * @param buffer A data buffer
+ */
+private[parquet] class CatalystArrayConverter(
+    val elementType: DataType,
+    val index: Int,
+    protected[parquet] val parent: CatalystConverter,
+    protected[parquet] var buffer: Buffer[Any])
+  extends CatalystConverter {
+
+  def this(elementType: DataType, index: Int, parent: CatalystConverter) =
+    this(
+      elementType,
+      index,
+      parent,
+      new ArrayBuffer[Any](CatalystArrayConverter.INITIAL_ARRAY_SIZE))
+
+  protected[parquet] val converter: Converter = CatalystConverter.createConverter(
+    new CatalystConverter.FieldType(
+      CatalystConverter.ARRAY_ELEMENTS_SCHEMA_NAME,
+      elementType,
+      false),
+    fieldIndex=0,
+    parent=this)
+
+  override def getConverter(fieldIndex: Int): Converter = converter
+
+  // arrays have only one (repeated) field, which is its elements
+  override val size = 1
+
+  override protected[parquet] def updateField(fieldIndex: Int, value: Any): Unit = {
+    // fieldIndex is ignored (assumed to be zero but not checked)
+    if(value == null) {
+      throw new IllegalArgumentException("Null values inside Parquet arrays are not supported!")
+    }
+    buffer += value
+  }
+
+  override protected[parquet] def clearBuffer(): Unit = {
+    buffer.clear()
+  }
+
+  override def start(): Unit = {
+    if (!converter.isPrimitive) {
+      converter.asInstanceOf[CatalystConverter].clearBuffer
+    }
+  }
+
+  override def end(): Unit = {
+    assert(parent != null)
+    // here we need to make sure to use ArrayScalaType
+    parent.updateField(index, buffer.toArray.toSeq)
+    clearBuffer()
+  }
+}
+
+/**
+ * A `parquet.io.api.GroupConverter` that converts a single-element groups that
+ * match the characteristics of an array (see
+ * [[org.apache.spark.sql.parquet.ParquetTypesConverter]]) into an
+ * [[org.apache.spark.sql.catalyst.types.ArrayType]].
+ *
+ * @param elementType The type of the array elements (native)
+ * @param index The position of this (array) field inside its parent converter
+ * @param parent The parent converter
+ * @param capacity The (initial) capacity of the buffer
+ */
+private[parquet] class CatalystNativeArrayConverter(
+    val elementType: NativeType,
+    val index: Int,
+    protected[parquet] val parent: CatalystConverter,
+    protected[parquet] var capacity: Int = CatalystArrayConverter.INITIAL_ARRAY_SIZE)
+  extends CatalystConverter {
+
+  type NativeType = elementType.JvmType
+
+  private var buffer: Array[NativeType] = elementType.classTag.newArray(capacity)
+
+  private var elements: Int = 0
+
+  protected[parquet] val converter: Converter = CatalystConverter.createConverter(
+    new CatalystConverter.FieldType(
+      CatalystConverter.ARRAY_ELEMENTS_SCHEMA_NAME,
+      elementType,
+      false),
+    fieldIndex=0,
+    parent=this)
+
+  override def getConverter(fieldIndex: Int): Converter = converter
+
+  // arrays have only one (repeated) field, which is its elements
+  override val size = 1
+
+  override protected[parquet] def updateField(fieldIndex: Int, value: Any): Unit =
+    throw new UnsupportedOperationException
+
+  // Overriden here to avoid auto-boxing for primitive types
+  override protected[parquet] def updateBoolean(fieldIndex: Int, value: Boolean): Unit = {
+    checkGrowBuffer()
+    buffer(elements) = value.asInstanceOf[NativeType]
+    elements += 1
+  }
+
+  override protected[parquet] def updateInt(fieldIndex: Int, value: Int): Unit = {
+    checkGrowBuffer()
+    buffer(elements) = value.asInstanceOf[NativeType]
+    elements += 1
+  }
+
+  override protected[parquet] def updateShort(fieldIndex: Int, value: Short): Unit = {
+    checkGrowBuffer()
+    buffer(elements) = value.asInstanceOf[NativeType]
+    elements += 1
+  }
+
+  override protected[parquet] def updateByte(fieldIndex: Int, value: Byte): Unit = {
+    checkGrowBuffer()
+    buffer(elements) = value.asInstanceOf[NativeType]
+    elements += 1
+  }
+
+  override protected[parquet] def updateLong(fieldIndex: Int, value: Long): Unit = {
+    checkGrowBuffer()
+    buffer(elements) = value.asInstanceOf[NativeType]
+    elements += 1
+  }
+
+  override protected[parquet] def updateDouble(fieldIndex: Int, value: Double): Unit = {
+    checkGrowBuffer()
+    buffer(elements) = value.asInstanceOf[NativeType]
+    elements += 1
+  }
+
+  override protected[parquet] def updateFloat(fieldIndex: Int, value: Float): Unit = {
+    checkGrowBuffer()
+    buffer(elements) = value.asInstanceOf[NativeType]
+    elements += 1
+  }
+
+  override protected[parquet] def updateBinary(fieldIndex: Int, value: Binary): Unit = {
+    checkGrowBuffer()
+    buffer(elements) = value.getBytes.asInstanceOf[NativeType]
+    elements += 1
+  }
+
+  override protected[parquet] def updateString(fieldIndex: Int, value: Binary): Unit = {
+    checkGrowBuffer()
+    buffer(elements) = value.toStringUsingUTF8.asInstanceOf[NativeType]
+    elements += 1
+  }
+
+  override protected[parquet] def clearBuffer(): Unit = {
+    elements = 0
+  }
+
+  override def start(): Unit = {}
+
+  override def end(): Unit = {
+    assert(parent != null)
+    // here we need to make sure to use ArrayScalaType
+    parent.updateField(
+      index,
+      buffer.slice(0, elements).toSeq)
+    clearBuffer()
+  }
+
+  private def checkGrowBuffer(): Unit = {
+    if (elements >= capacity) {
+      val newCapacity = 2 * capacity
+      val tmp: Array[NativeType] = elementType.classTag.newArray(newCapacity)
+      Array.copy(buffer, 0, tmp, 0, capacity)
+      buffer = tmp
+      capacity = newCapacity
+    }
+  }
+}
+
+/**
+ * This converter is for multi-element groups of primitive or complex types
+ * that have repetition level optional or required (so struct fields).
+ *
+ * @param schema The corresponding Catalyst schema in the form of a list of
+ *               attributes.
+ * @param index
+ * @param parent
+ */
+private[parquet] class CatalystStructConverter(
+    override protected[parquet] val schema: Array[FieldType],
+    override protected[parquet] val index: Int,
+    override protected[parquet] val parent: CatalystConverter)
+  extends CatalystGroupConverter(schema, index, parent) {
+
+  override protected[parquet] def clearBuffer(): Unit = {}
+
+  // TODO: think about reusing the buffer
+  override def end(): Unit = {
+    assert(!isRootConverter)
+    // here we need to make sure to use StructScalaType
+    // Note: we need to actually make a copy of the array since we
+    // may be in a nested field
+    parent.updateField(index, new GenericRow(current.toArray))
+  }
+}
+
+/**
+ * A `parquet.io.api.GroupConverter` that converts two-element groups that
+ * match the characteristics of a map (see
+ * [[org.apache.spark.sql.parquet.ParquetTypesConverter]]) into an
+ * [[org.apache.spark.sql.catalyst.types.MapType]].
+ *
+ * @param schema
+ * @param index
+ * @param parent
+ */
+private[parquet] class CatalystMapConverter(
+    protected[parquet] val schema: Array[FieldType],
+    override protected[parquet] val index: Int,
+    override protected[parquet] val parent: CatalystConverter)
+  extends CatalystConverter {
+
+  private val map = new HashMap[Any, Any]()
+
+  private val keyValueConverter = new CatalystConverter {
+    private var currentKey: Any = null
+    private var currentValue: Any = null
+    val keyConverter = CatalystConverter.createConverter(schema(0), 0, this)
+    val valueConverter = CatalystConverter.createConverter(schema(1), 1, this)
+
+    override def getConverter(fieldIndex: Int): Converter = {
+      if (fieldIndex == 0) keyConverter else valueConverter
+    }
+
+    override def end(): Unit = CatalystMapConverter.this.map += currentKey -> currentValue
+
+    override def start(): Unit = {
+      currentKey = null
+      currentValue = null
+    }
+
+    override protected[parquet] val size: Int = 2
+    override protected[parquet] val index: Int = 0
+    override protected[parquet] val parent: CatalystConverter = CatalystMapConverter.this
+
+    override protected[parquet] def updateField(fieldIndex: Int, value: Any): Unit = {
+      fieldIndex match {
+        case 0 =>
+          currentKey = value
+        case 1 =>
+          currentValue = value
+        case _ =>
+          new RuntimePermission(s"trying to update Map with fieldIndex $fieldIndex")
+      }
+    }
+
+    override protected[parquet] def clearBuffer(): Unit = {}
+  }
+
+  override protected[parquet] val size: Int = 1
+
+  override protected[parquet] def clearBuffer(): Unit = {}
+
+  override def start(): Unit = {
+    map.clear()
+  }
+
+  override def end(): Unit = {
+    // here we need to make sure to use MapScalaType
+    parent.updateField(index, map.toMap)
+  }
+
+  override def getConverter(fieldIndex: Int): Converter = keyValueConverter
+
+  override protected[parquet] def updateField(fieldIndex: Int, value: Any): Unit =
+    throw new UnsupportedOperationException
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala
index 32813a66de3c..96c131a7f8af 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala
@@ -20,25 +20,16 @@ package org.apache.spark.sql.parquet
 import java.io.IOException
 
 import org.apache.hadoop.conf.Configuration
-import org.apache.hadoop.fs.{FileSystem, Path}
+import org.apache.hadoop.fs.Path
 import org.apache.hadoop.fs.permission.FsAction
-import org.apache.hadoop.mapreduce.Job
 
-import parquet.hadoop.util.ContextUtil
-import parquet.hadoop.{ParquetOutputFormat, Footer, ParquetFileWriter, ParquetFileReader}
-import parquet.hadoop.metadata.{CompressionCodecName, FileMetaData, ParquetMetadata}
-import parquet.io.api.{Binary, RecordConsumer}
-import parquet.schema.{Type => ParquetType, PrimitiveType => ParquetPrimitiveType, MessageType, MessageTypeParser}
-import parquet.schema.PrimitiveType.{PrimitiveTypeName => ParquetPrimitiveTypeName}
-import parquet.schema.Type.Repetition
+import parquet.hadoop.ParquetOutputFormat
+import parquet.hadoop.metadata.CompressionCodecName
+import parquet.schema.MessageType
 
 import org.apache.spark.sql.catalyst.analysis.{MultiInstanceRelation, UnresolvedException}
-import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeReference, Row}
+import org.apache.spark.sql.catalyst.expressions.Attribute
 import org.apache.spark.sql.catalyst.plans.logical.{LogicalPlan, LeafNode}
-import org.apache.spark.sql.catalyst.types._
-
-// Implicits
-import scala.collection.JavaConversions._
 
 /**
  * Relation that consists of data stored in a Parquet columnar format.
@@ -52,21 +43,20 @@ import scala.collection.JavaConversions._
  *
  * @param path The path to the Parquet file.
  */
-private[sql] case class ParquetRelation(val path: String)
-    extends LeafNode with MultiInstanceRelation {
+private[sql] case class ParquetRelation(
+    val path: String,
+    @transient val conf: Option[Configuration] = None) extends LeafNode with MultiInstanceRelation {
   self: Product =>
 
   /** Schema derived from ParquetFile */
   def parquetSchema: MessageType =
     ParquetTypesConverter
-      .readMetaData(new Path(path))
+      .readMetaData(new Path(path), conf)
       .getFileMetaData
       .getSchema
 
   /** Attributes */
-  override val output =
-    ParquetTypesConverter
-      .convertToAttributes(parquetSchema)
+  override val output = ParquetTypesConverter.readSchemaFromFile(new Path(path), conf)
 
   override def newInstance = ParquetRelation(path).asInstanceOf[this.type]
 
@@ -141,7 +131,9 @@ private[sql] object ParquetRelation {
     }
     ParquetRelation.enableLogForwarding()
     ParquetTypesConverter.writeMetaData(attributes, path, conf)
-    new ParquetRelation(path.toString)
+    new ParquetRelation(path.toString, Some(conf)) {
+      override val output = attributes
+    }
   }
 
   private def checkPath(pathStr: String, allowExisting: Boolean, conf: Configuration): Path = {
@@ -170,151 +162,3 @@ private[sql] object ParquetRelation {
     path
   }
 }
-
-private[parquet] object ParquetTypesConverter {
-  def toDataType(parquetType : ParquetPrimitiveTypeName): DataType = parquetType match {
-    // for now map binary to string type
-    // TODO: figure out how Parquet uses strings or why we can't use them in a MessageType schema
-    case ParquetPrimitiveTypeName.BINARY => StringType
-    case ParquetPrimitiveTypeName.BOOLEAN => BooleanType
-    case ParquetPrimitiveTypeName.DOUBLE => DoubleType
-    case ParquetPrimitiveTypeName.FIXED_LEN_BYTE_ARRAY => ArrayType(ByteType)
-    case ParquetPrimitiveTypeName.FLOAT => FloatType
-    case ParquetPrimitiveTypeName.INT32 => IntegerType
-    case ParquetPrimitiveTypeName.INT64 => LongType
-    case ParquetPrimitiveTypeName.INT96 =>
-      // TODO: add BigInteger type? TODO(andre) use DecimalType instead????
-      sys.error("Warning: potential loss of precision: converting INT96 to long")
-      LongType
-    case _ => sys.error(
-      s"Unsupported parquet datatype $parquetType")
-  }
-
-  def fromDataType(ctype: DataType): ParquetPrimitiveTypeName = ctype match {
-    case StringType => ParquetPrimitiveTypeName.BINARY
-    case BooleanType => ParquetPrimitiveTypeName.BOOLEAN
-    case DoubleType => ParquetPrimitiveTypeName.DOUBLE
-    case ArrayType(ByteType) => ParquetPrimitiveTypeName.FIXED_LEN_BYTE_ARRAY
-    case FloatType => ParquetPrimitiveTypeName.FLOAT
-    case IntegerType => ParquetPrimitiveTypeName.INT32
-    case LongType => ParquetPrimitiveTypeName.INT64
-    case _ => sys.error(s"Unsupported datatype $ctype")
-  }
-
-  def consumeType(consumer: RecordConsumer, ctype: DataType, record: Row, index: Int): Unit = {
-    ctype match {
-      case StringType => consumer.addBinary(
-        Binary.fromByteArray(
-          record(index).asInstanceOf[String].getBytes("utf-8")
-        )
-      )
-      case IntegerType => consumer.addInteger(record.getInt(index))
-      case LongType => consumer.addLong(record.getLong(index))
-      case DoubleType => consumer.addDouble(record.getDouble(index))
-      case FloatType => consumer.addFloat(record.getFloat(index))
-      case BooleanType => consumer.addBoolean(record.getBoolean(index))
-      case _ => sys.error(s"Unsupported datatype $ctype, cannot write to consumer")
-    }
-  }
-
-  def getSchema(schemaString : String) : MessageType =
-    MessageTypeParser.parseMessageType(schemaString)
-
-  def convertToAttributes(parquetSchema: MessageType) : Seq[Attribute] = {
-    parquetSchema.getColumns.map {
-      case (desc) =>
-        val ctype = toDataType(desc.getType)
-        val name: String = desc.getPath.mkString(".")
-        new AttributeReference(name, ctype, false)()
-    }
-  }
-
-  // TODO: allow nesting?
-  def convertFromAttributes(attributes: Seq[Attribute]): MessageType = {
-    val fields: Seq[ParquetType] = attributes.map {
-      a => new ParquetPrimitiveType(Repetition.OPTIONAL, fromDataType(a.dataType), a.name)
-    }
-    new MessageType("root", fields)
-  }
-
-  def writeMetaData(attributes: Seq[Attribute], origPath: Path, conf: Configuration) {
-    if (origPath == null) {
-      throw new IllegalArgumentException("Unable to write Parquet metadata: path is null")
-    }
-    val fs = origPath.getFileSystem(conf)
-    if (fs == null) {
-      throw new IllegalArgumentException(
-        s"Unable to write Parquet metadata: path $origPath is incorrectly formatted")
-    }
-    val path = origPath.makeQualified(fs)
-    if (fs.exists(path) && !fs.getFileStatus(path).isDir) {
-      throw new IllegalArgumentException(s"Expected to write to directory $path but found file")
-    }
-    val metadataPath = new Path(path, ParquetFileWriter.PARQUET_METADATA_FILE)
-    if (fs.exists(metadataPath)) {
-      try {
-        fs.delete(metadataPath, true)
-      } catch {
-        case e: IOException =>
-          throw new IOException(s"Unable to delete previous PARQUET_METADATA_FILE at $metadataPath")
-      }
-    }
-    val extraMetadata = new java.util.HashMap[String, String]()
-    extraMetadata.put("path", path.toString)
-    // TODO: add extra data, e.g., table name, date, etc.?
-
-    val parquetSchema: MessageType =
-      ParquetTypesConverter.convertFromAttributes(attributes)
-    val metaData: FileMetaData = new FileMetaData(
-      parquetSchema,
-      extraMetadata,
-      "Spark")
-
-    ParquetRelation.enableLogForwarding()
-    ParquetFileWriter.writeMetadataFile(
-      conf,
-      path,
-      new Footer(path, new ParquetMetadata(metaData, Nil)) :: Nil)
-  }
-
-  /**
-   * Try to read Parquet metadata at the given Path. We first see if there is a summary file
-   * in the parent directory. If so, this is used. Else we read the actual footer at the given
-   * location.
-   * @param origPath The path at which we expect one (or more) Parquet files.
-   * @return The `ParquetMetadata` containing among other things the schema.
-   */
-  def readMetaData(origPath: Path): ParquetMetadata = {
-    if (origPath == null) {
-      throw new IllegalArgumentException("Unable to read Parquet metadata: path is null")
-    }
-    val job = new Job()
-    // TODO: since this is called from ParquetRelation (LogicalPlan) we don't have access
-    // to SparkContext's hadoopConfig; in principle the default FileSystem may be different(?!)
-    val conf = ContextUtil.getConfiguration(job)
-    val fs: FileSystem = origPath.getFileSystem(conf)
-    if (fs == null) {
-      throw new IllegalArgumentException(s"Incorrectly formatted Parquet metadata path $origPath")
-    }
-    val path = origPath.makeQualified(fs)
-    if (!fs.getFileStatus(path).isDir) {
-      throw new IllegalArgumentException(
-        s"Expected $path for be a directory with Parquet files/metadata")
-    }
-    ParquetRelation.enableLogForwarding()
-    val metadataPath = new Path(path, ParquetFileWriter.PARQUET_METADATA_FILE)
-    // if this is a new table that was just created we will find only the metadata file
-    if (fs.exists(metadataPath) && fs.isFile(metadataPath)) {
-      ParquetFileReader.readFooter(conf, metadataPath)
-    } else {
-      // there may be one or more Parquet files in the given directory
-      val footers = ParquetFileReader.readFooters(conf, fs.getFileStatus(path))
-      // TODO: for now we assume that all footers (if there is more than one) have identical
-      // metadata; we may want to add a check here at some point
-      if (footers.size() == 0) {
-        throw new IllegalArgumentException(s"Could not find Parquet metadata at path $path")
-      }
-      footers(0).getParquetMetadata
-    }
-  }
-}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala
index 65ba1246fbf9..624f2e2fa13f 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala
@@ -36,6 +36,7 @@ import parquet.schema.MessageType
 import org.apache.spark.{Logging, SerializableWritable, SparkContext, TaskContext}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.expressions.{Attribute, Expression, Row}
+import org.apache.spark.sql.catalyst.types.StructType
 import org.apache.spark.sql.execution.{LeafNode, SparkPlan, UnaryNode}
 
 /**
@@ -64,10 +65,13 @@ case class ParquetTableScan(
       NewFileInputFormat.addInputPath(job, path)
     }
 
-    // Store Parquet schema in `Configuration`
+    // Store both requested and original schema in `Configuration`
     conf.set(
-        RowReadSupport.PARQUET_ROW_REQUESTED_SCHEMA,
-        ParquetTypesConverter.convertFromAttributes(output).toString)
+      RowReadSupport.SPARK_ROW_REQUESTED_SCHEMA,
+      ParquetTypesConverter.convertToString(output))
+    conf.set(
+      RowWriteSupport.SPARK_ROW_SCHEMA,
+      ParquetTypesConverter.convertToString(relation.output))
 
     // Store record filtering predicate in `Configuration`
     // Note 1: the input format ignores all predicates that cannot be expressed
@@ -166,13 +170,18 @@ case class InsertIntoParquetTable(
 
     val job = new Job(sc.hadoopConfiguration)
 
-    ParquetOutputFormat.setWriteSupportClass(
-      job,
-      classOf[org.apache.spark.sql.parquet.RowWriteSupport])
+    val writeSupport =
+      if (child.output.map(_.dataType).forall(_.isPrimitive)) {
+        logger.debug("Initializing MutableRowWriteSupport")
+        classOf[org.apache.spark.sql.parquet.MutableRowWriteSupport]
+      } else {
+        classOf[org.apache.spark.sql.parquet.RowWriteSupport]
+      }
+
+    ParquetOutputFormat.setWriteSupportClass(job, writeSupport)
 
-    // TODO: move that to function in object
     val conf = ContextUtil.getConfiguration(job)
-    conf.set(RowWriteSupport.PARQUET_ROW_SCHEMA, relation.parquetSchema.toString)
+    RowWriteSupport.setSchema(relation.output, conf)
 
     val fspath = new Path(relation.path)
     val fs = fspath.getFileSystem(conf)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala
index 71ba0fecce47..bfcbdeb34a92 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala
@@ -29,21 +29,23 @@ import parquet.schema.{MessageType, MessageTypeParser}
 import org.apache.spark.Logging
 import org.apache.spark.sql.catalyst.expressions.{Attribute, Row}
 import org.apache.spark.sql.catalyst.types._
+import org.apache.spark.sql.execution.SparkSqlSerializer
+import com.google.common.io.BaseEncoding
 
 /**
  * A `parquet.io.api.RecordMaterializer` for Rows.
  *
  *@param root The root group converter for the record.
  */
-private[parquet] class RowRecordMaterializer(root: CatalystGroupConverter)
+private[parquet] class RowRecordMaterializer(root: CatalystConverter)
   extends RecordMaterializer[Row] {
 
-  def this(parquetSchema: MessageType) =
-    this(new CatalystGroupConverter(ParquetTypesConverter.convertToAttributes(parquetSchema)))
+  def this(parquetSchema: MessageType, attributes: Seq[Attribute]) =
+    this(CatalystConverter.createRootConverter(parquetSchema, attributes))
 
   override def getCurrentRecord: Row = root.getCurrentRecord
 
-  override def getRootConverter: GroupConverter = root
+  override def getRootConverter: GroupConverter = root.asInstanceOf[GroupConverter]
 }
 
 /**
@@ -56,68 +58,94 @@ private[parquet] class RowReadSupport extends ReadSupport[Row] with Logging {
       stringMap: java.util.Map[String, String],
       fileSchema: MessageType,
       readContext: ReadContext): RecordMaterializer[Row] = {
-    log.debug(s"preparing for read with file schema $fileSchema")
-    new RowRecordMaterializer(readContext.getRequestedSchema)
+    log.debug(s"preparing for read with Parquet file schema $fileSchema")
+    // Note: this very much imitates AvroParquet
+    val parquetSchema = readContext.getRequestedSchema
+    var schema: Seq[Attribute] = null
+
+    if (readContext.getReadSupportMetadata != null) {
+      // first try to find the read schema inside the metadata (can result from projections)
+      if (
+        readContext
+          .getReadSupportMetadata
+          .get(RowReadSupport.SPARK_ROW_REQUESTED_SCHEMA) != null) {
+        schema = ParquetTypesConverter.convertFromString(
+          readContext.getReadSupportMetadata.get(RowReadSupport.SPARK_ROW_REQUESTED_SCHEMA))
+      } else {
+        // if unavailable, try the schema that was read originally from the file or provided
+        // during the creation of the Parquet relation
+        if (readContext.getReadSupportMetadata.get(RowReadSupport.SPARK_METADATA_KEY) != null) {
+          schema = ParquetTypesConverter.convertFromString(
+            readContext.getReadSupportMetadata.get(RowReadSupport.SPARK_METADATA_KEY))
+        }
+      }
+    }
+    // if both unavailable, fall back to deducing the schema from the given Parquet schema
+    if (schema == null)  {
+      log.debug("falling back to Parquet read schema")
+      schema = ParquetTypesConverter.convertToAttributes(parquetSchema)
+    }
+    log.debug(s"list of attributes that will be read: $schema")
+    new RowRecordMaterializer(parquetSchema, schema)
   }
 
   override def init(
       configuration: Configuration,
       keyValueMetaData: java.util.Map[String, String],
       fileSchema: MessageType): ReadContext = {
-    val requested_schema_string =
-      configuration.get(RowReadSupport.PARQUET_ROW_REQUESTED_SCHEMA, fileSchema.toString)
-    val requested_schema =
-      MessageTypeParser.parseMessageType(requested_schema_string)
-    log.debug(s"read support initialized for requested schema $requested_schema")
-    ParquetRelation.enableLogForwarding()
-    new ReadContext(requested_schema, keyValueMetaData)
+    var parquetSchema: MessageType = fileSchema
+    var metadata: java.util.Map[String, String] = new java.util.HashMap[String, String]()
+    val requestedAttributes = RowReadSupport.getRequestedSchema(configuration)
+
+    if (requestedAttributes != null) {
+      parquetSchema = ParquetTypesConverter.convertFromAttributes(requestedAttributes)
+      metadata.put(
+        RowReadSupport.SPARK_ROW_REQUESTED_SCHEMA,
+        ParquetTypesConverter.convertToString(requestedAttributes))
+    }
+
+    val origAttributesStr: String = configuration.get(RowWriteSupport.SPARK_ROW_SCHEMA)
+    if (origAttributesStr != null) {
+      metadata.put(RowReadSupport.SPARK_METADATA_KEY, origAttributesStr)
+    }
+
+    return new ReadSupport.ReadContext(parquetSchema, metadata)
   }
 }
 
 private[parquet] object RowReadSupport {
-  val PARQUET_ROW_REQUESTED_SCHEMA = "org.apache.spark.sql.parquet.row.requested_schema"
+  val SPARK_ROW_REQUESTED_SCHEMA = "org.apache.spark.sql.parquet.row.requested_schema"
+  val SPARK_METADATA_KEY = "org.apache.spark.sql.parquet.row.metadata"
+
+  private def getRequestedSchema(configuration: Configuration): Seq[Attribute] = {
+    val schemaString = configuration.get(RowReadSupport.SPARK_ROW_REQUESTED_SCHEMA)
+    if (schemaString == null) null else ParquetTypesConverter.convertFromString(schemaString)
+  }
 }
 
 /**
  * A `parquet.hadoop.api.WriteSupport` for Row ojects.
  */
 private[parquet] class RowWriteSupport extends WriteSupport[Row] with Logging {
-  def setSchema(schema: MessageType, configuration: Configuration) {
-    // for testing
-    this.schema = schema
-    // TODO: could use Attributes themselves instead of Parquet schema?
-    configuration.set(
-      RowWriteSupport.PARQUET_ROW_SCHEMA,
-      schema.toString)
-    configuration.set(
-      ParquetOutputFormat.WRITER_VERSION,
-      ParquetProperties.WriterVersion.PARQUET_1_0.toString)
-  }
-
-  def getSchema(configuration: Configuration): MessageType = {
-    MessageTypeParser.parseMessageType(configuration.get(RowWriteSupport.PARQUET_ROW_SCHEMA))
-  }
 
-  private var schema: MessageType = null
-  private var writer: RecordConsumer = null
-  private var attributes: Seq[Attribute] = null
+  private[parquet] var writer: RecordConsumer = null
+  private[parquet] var attributes: Seq[Attribute] = null
 
   override def init(configuration: Configuration): WriteSupport.WriteContext = {
-    schema = if (schema == null) getSchema(configuration) else schema
-    attributes = ParquetTypesConverter.convertToAttributes(schema)
-    log.debug(s"write support initialized for requested schema $schema")
+    attributes = if (attributes == null) RowWriteSupport.getSchema(configuration) else attributes
+    
+    log.debug(s"write support initialized for requested schema $attributes")
     ParquetRelation.enableLogForwarding()
     new WriteSupport.WriteContext(
-      schema,
+      ParquetTypesConverter.convertFromAttributes(attributes),
       new java.util.HashMap[java.lang.String, java.lang.String]())
   }
 
   override def prepareForWrite(recordConsumer: RecordConsumer): Unit = {
     writer = recordConsumer
-    log.debug(s"preparing for write with schema $schema")
+    log.debug(s"preparing for write with schema $attributes")
   }
 
-  // TODO: add groups (nested fields)
   override def write(record: Row): Unit = {
     if (attributes.size > record.size) {
       throw new IndexOutOfBoundsException(
@@ -130,98 +158,176 @@ private[parquet] class RowWriteSupport extends WriteSupport[Row] with Logging {
       // null values indicate optional fields but we do not check currently
       if (record(index) != null && record(index) != Nil) {
         writer.startField(attributes(index).name, index)
-        ParquetTypesConverter.consumeType(writer, attributes(index).dataType, record, index)
+        writeValue(attributes(index).dataType, record(index))
         writer.endField(attributes(index).name, index)
       }
       index = index + 1
     }
     writer.endMessage()
   }
-}
 
-private[parquet] object RowWriteSupport {
-  val PARQUET_ROW_SCHEMA: String = "org.apache.spark.sql.parquet.row.schema"
-}
-
-/**
- * A `parquet.io.api.GroupConverter` that is able to convert a Parquet record to a `Row` object.
- *
- * @param schema The corresponding Catalyst schema in the form of a list of attributes.
- */
-private[parquet] class CatalystGroupConverter(
-    schema: Seq[Attribute],
-    protected[parquet] val current: ParquetRelation.RowType) extends GroupConverter {
-
-  def this(schema: Seq[Attribute]) = this(schema, new ParquetRelation.RowType(schema.length))
-
-  val converters: Array[Converter] = schema.map {
-    a => a.dataType match {
-      case ctype: NativeType =>
-        // note: for some reason matching for StringType fails so use this ugly if instead
-        if (ctype == StringType) {
-          new CatalystPrimitiveStringConverter(this, schema.indexOf(a))
-        } else {
-          new CatalystPrimitiveConverter(this, schema.indexOf(a))
-        }
-      case _ => throw new RuntimeException(
-        s"unable to convert datatype ${a.dataType.toString} in CatalystGroupConverter")
+  private[parquet] def writeValue(schema: DataType, value: Any): Unit = {
+    if (value != null && value != Nil) {
+      schema match {
+        case t @ ArrayType(_) => writeArray(
+          t,
+          value.asInstanceOf[CatalystConverter.ArrayScalaType[_]])
+        case t @ MapType(_, _) => writeMap(
+          t,
+          value.asInstanceOf[CatalystConverter.MapScalaType[_, _]])
+        case t @ StructType(_) => writeStruct(
+          t,
+          value.asInstanceOf[CatalystConverter.StructScalaType[_]])
+        case _ => writePrimitive(schema.asInstanceOf[PrimitiveType], value)
+      }
     }
-  }.toArray
+  }
 
-  override def getConverter(fieldIndex: Int): Converter = converters(fieldIndex)
+  private[parquet] def writePrimitive(schema: PrimitiveType, value: Any): Unit = {
+    if (value != null && value != Nil) {
+      schema match {
+        case StringType => writer.addBinary(
+          Binary.fromByteArray(
+            value.asInstanceOf[String].getBytes("utf-8")
+          )
+        )
+        case IntegerType => writer.addInteger(value.asInstanceOf[Int])
+        case ShortType => writer.addInteger(value.asInstanceOf[Int])
+        case LongType => writer.addLong(value.asInstanceOf[Long])
+        case ByteType => writer.addInteger(value.asInstanceOf[Int])
+        case DoubleType => writer.addDouble(value.asInstanceOf[Double])
+        case FloatType => writer.addFloat(value.asInstanceOf[Float])
+        case BooleanType => writer.addBoolean(value.asInstanceOf[Boolean])
+        case _ => sys.error(s"Do not know how to writer $schema to consumer")
+      }
+    }
+  }
 
-  private[parquet] def getCurrentRecord: ParquetRelation.RowType = current
+  private[parquet] def writeStruct(
+      schema: StructType,
+      struct: CatalystConverter.StructScalaType[_]): Unit = {
+    if (struct != null && struct != Nil) {
+      val fields = schema.fields.toArray
+      writer.startGroup()
+      var i = 0
+      while(i < fields.size) {
+        if (struct(i) != null && struct(i) != Nil) {
+          writer.startField(fields(i).name, i)
+          writeValue(fields(i).dataType, struct(i))
+          writer.endField(fields(i).name, i)
+        }
+        i = i + 1
+      }
+      writer.endGroup()
+    }
+  }
 
-  override def start(): Unit = {
-    var i = 0
-    while (i < schema.length) {
-      current.setNullAt(i)
-      i = i + 1
+  // TODO: support null values, see
+  // https://issues.apache.org/jira/browse/SPARK-1649
+  private[parquet] def writeArray(
+      schema: ArrayType,
+      array: CatalystConverter.ArrayScalaType[_]): Unit = {
+    val elementType = schema.elementType
+    writer.startGroup()
+    if (array.size > 0) {
+      writer.startField(CatalystConverter.ARRAY_ELEMENTS_SCHEMA_NAME, 0)
+      var i = 0
+      while(i < array.size) {
+        writeValue(elementType, array(i))
+        i = i + 1
+      }
+      writer.endField(CatalystConverter.ARRAY_ELEMENTS_SCHEMA_NAME, 0)
     }
+    writer.endGroup()
   }
 
-  override def end(): Unit = {}
+  // TODO: support null values, see
+  // https://issues.apache.org/jira/browse/SPARK-1649
+  private[parquet] def writeMap(
+      schema: MapType,
+      map: CatalystConverter.MapScalaType[_, _]): Unit = {
+    writer.startGroup()
+    if (map.size > 0) {
+      writer.startField(CatalystConverter.MAP_SCHEMA_NAME, 0)
+      writer.startGroup()
+      writer.startField(CatalystConverter.MAP_KEY_SCHEMA_NAME, 0)
+      for(key <- map.keys) {
+        writeValue(schema.keyType, key)
+      }
+      writer.endField(CatalystConverter.MAP_KEY_SCHEMA_NAME, 0)
+      writer.startField(CatalystConverter.MAP_VALUE_SCHEMA_NAME, 1)
+      for(value <- map.values) {
+        writeValue(schema.valueType, value)
+      }
+      writer.endField(CatalystConverter.MAP_VALUE_SCHEMA_NAME, 1)
+      writer.endGroup()
+      writer.endField(CatalystConverter.MAP_SCHEMA_NAME, 0)
+    }
+    writer.endGroup()
+  }
 }
 
-/**
- * A `parquet.io.api.PrimitiveConverter` that converts Parquet types to Catalyst types.
- *
- * @param parent The parent group converter.
- * @param fieldIndex The index inside the record.
- */
-private[parquet] class CatalystPrimitiveConverter(
-    parent: CatalystGroupConverter,
-    fieldIndex: Int) extends PrimitiveConverter {
-  // TODO: consider refactoring these together with ParquetTypesConverter
-  override def addBinary(value: Binary): Unit =
-    parent.getCurrentRecord.update(fieldIndex, value.getBytes)
+// Optimized for non-nested rows
+private[parquet] class MutableRowWriteSupport extends RowWriteSupport {
+  override def write(record: Row): Unit = {
+    if (attributes.size > record.size) {
+      throw new IndexOutOfBoundsException(
+        s"Trying to write more fields than contained in row (${attributes.size}>${record.size})")
+    }
 
-  override def addBoolean(value: Boolean): Unit =
-    parent.getCurrentRecord.setBoolean(fieldIndex, value)
+    var index = 0
+    writer.startMessage()
+    while(index < attributes.size) {
+      // null values indicate optional fields but we do not check currently
+      if (record(index) != null && record(index) != Nil) {
+        writer.startField(attributes(index).name, index)
+        consumeType(attributes(index).dataType, record, index)
+        writer.endField(attributes(index).name, index)
+      }
+      index = index + 1
+    }
+    writer.endMessage()
+  }
 
-  override def addDouble(value: Double): Unit =
-    parent.getCurrentRecord.setDouble(fieldIndex, value)
+  private def consumeType(
+      ctype: DataType,
+      record: Row,
+      index: Int): Unit = {
+    ctype match {
+      case StringType => writer.addBinary(
+        Binary.fromByteArray(
+          record(index).asInstanceOf[String].getBytes("utf-8")
+        )
+      )
+      case IntegerType => writer.addInteger(record.getInt(index))
+      case ShortType => writer.addInteger(record.getShort(index))
+      case LongType => writer.addLong(record.getLong(index))
+      case ByteType => writer.addInteger(record.getByte(index))
+      case DoubleType => writer.addDouble(record.getDouble(index))
+      case FloatType => writer.addFloat(record.getFloat(index))
+      case BooleanType => writer.addBoolean(record.getBoolean(index))
+      case _ => sys.error(s"Unsupported datatype $ctype, cannot write to consumer")
+    }
+  }
+}
 
-  override def addFloat(value: Float): Unit =
-    parent.getCurrentRecord.setFloat(fieldIndex, value)
+private[parquet] object RowWriteSupport {
+  val SPARK_ROW_SCHEMA: String = "org.apache.spark.sql.parquet.row.attributes"
 
-  override def addInt(value: Int): Unit =
-    parent.getCurrentRecord.setInt(fieldIndex, value)
+  def getSchema(configuration: Configuration): Seq[Attribute] = {
+    val schemaString = configuration.get(RowWriteSupport.SPARK_ROW_SCHEMA)
+    if (schemaString == null) {
+      throw new RuntimeException("Missing schema!")
+    }
+    ParquetTypesConverter.convertFromString(schemaString)
+  }
 
-  override def addLong(value: Long): Unit =
-    parent.getCurrentRecord.setLong(fieldIndex, value)
+  def setSchema(schema: Seq[Attribute], configuration: Configuration) {
+    val encoded = ParquetTypesConverter.convertToString(schema)
+    configuration.set(SPARK_ROW_SCHEMA, encoded)
+    configuration.set(
+      ParquetOutputFormat.WRITER_VERSION,
+      ParquetProperties.WriterVersion.PARQUET_1_0.toString)
+  }
 }
 
-/**
- * A `parquet.io.api.PrimitiveConverter` that converts Parquet strings (fixed-length byte arrays)
- * into Catalyst Strings.
- *
- * @param parent The parent group converter.
- * @param fieldIndex The index inside the record.
- */
-private[parquet] class CatalystPrimitiveStringConverter(
-    parent: CatalystGroupConverter,
-    fieldIndex: Int) extends CatalystPrimitiveConverter(parent, fieldIndex) {
-  override def addBinary(value: Binary): Unit =
-    parent.getCurrentRecord.setString(fieldIndex, value.toStringUsingUTF8)
-}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTestData.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTestData.scala
index 46c717298564..1dc58633a2a6 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTestData.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTestData.scala
@@ -17,14 +17,19 @@
 
 package org.apache.spark.sql.parquet
 
+import java.io.File
+
 import org.apache.hadoop.conf.Configuration
-import org.apache.hadoop.fs.Path
+import org.apache.hadoop.fs.{FileStatus, FileSystem, Path}
+import org.apache.hadoop.mapreduce.Job
 
 import parquet.example.data.{GroupWriter, Group}
 import parquet.example.data.simple.SimpleGroup
-import parquet.hadoop.ParquetWriter
+import parquet.hadoop.{ParquetReader, ParquetFileReader, ParquetWriter}
 import parquet.hadoop.api.WriteSupport
 import parquet.hadoop.api.WriteSupport.WriteContext
+import parquet.hadoop.example.GroupReadSupport
+import parquet.hadoop.util.ContextUtil
 import parquet.io.api.RecordConsumer
 import parquet.schema.{MessageType, MessageTypeParser}
 
@@ -51,13 +56,13 @@ private[sql] object ParquetTestData {
 
   val testSchema =
     """message myrecord {
-      |optional boolean myboolean;
-      |optional int32 myint;
-      |optional binary mystring;
-      |optional int64 mylong;
-      |optional float myfloat;
-      |optional double mydouble;
-      |}""".stripMargin
+      optional boolean myboolean;
+      optional int32 myint;
+      optional binary mystring;
+      optional int64 mylong;
+      optional float myfloat;
+      optional double mydouble;
+      }"""
 
   // field names for test assertion error messages
   val testSchemaFieldNames = Seq(
@@ -71,23 +76,23 @@ private[sql] object ParquetTestData {
 
   val subTestSchema =
     """
-      |message myrecord {
-      |optional boolean myboolean;
-      |optional int64 mylong;
-      |}
-    """.stripMargin
+      message myrecord {
+      optional boolean myboolean;
+      optional int64 mylong;
+      }
+    """
 
   val testFilterSchema =
     """
-      |message myrecord {
-      |required boolean myboolean;
-      |required int32 myint;
-      |required binary mystring;
-      |required int64 mylong;
-      |required float myfloat;
-      |required double mydouble;
-      |}
-    """.stripMargin
+      message myrecord {
+      required boolean myboolean;
+      required int32 myint;
+      required binary mystring;
+      required int64 mylong;
+      required float myfloat;
+      required double mydouble;
+      }
+    """
 
   // field names for test assertion error messages
   val subTestSchemaFieldNames = Seq(
@@ -100,9 +105,110 @@ private[sql] object ParquetTestData {
 
   lazy val testData = new ParquetRelation(testDir.toURI.toString)
 
+  val testNestedSchema1 =
+    // based on blogpost example, source:
+    // https://blog.twitter.com/2013/dremel-made-simple-with-parquet
+    // note: instead of string we have to use binary (?) otherwise
+    // Parquet gives us:
+    // IllegalArgumentException: expected one of [INT64, INT32, BOOLEAN,
+    //   BINARY, FLOAT, DOUBLE, INT96, FIXED_LEN_BYTE_ARRAY]
+    // Also repeated primitives seem tricky to convert (AvroParquet
+    // only uses them in arrays?) so only use at most one in each group
+    // and nothing else in that group (-> is mapped to array)!
+    // The "values" inside ownerPhoneNumbers is a keyword currently
+    // so that array types can be translated correctly.
+    """
+      message AddressBook {
+        required binary owner;
+        optional group ownerPhoneNumbers {
+          repeated binary array;
+        }
+        optional group contacts {
+          repeated group array {
+            required binary name;
+            optional binary phoneNumber;
+          }
+        }
+      }
+    """
+
+
+  val testNestedSchema2 =
+    """
+      message TestNested2 {
+        required int32 firstInt;
+        optional int32 secondInt;
+        optional group longs {
+          repeated int64 array;
+        }
+        required group entries {
+          repeated group array {
+            required double value;
+            optional boolean truth;
+          }
+        }
+        optional group outerouter {
+          repeated group array {
+            repeated group array {
+              repeated int32 array;
+            }
+          }
+        }
+      }
+    """
+
+  val testNestedSchema3 =
+    """
+      message TestNested3 {
+        required int32 x;
+        optional group booleanNumberPairs {
+          repeated group array {
+            required int32 key;
+            optional group value {
+              repeated group array {
+                required double nestedValue;
+                optional boolean truth;
+              }
+            }
+          }
+        }
+      }
+    """
+
+  val testNestedSchema4 =
+    """
+      message TestNested4 {
+        required int32 x;
+        optional group data1 {
+          repeated group map {
+            required binary key;
+            required int32 value;
+          }
+        }
+        required group data2 {
+          repeated group map {
+            required binary key;
+            required group value {
+              required int64 payload1;
+              optional binary payload2;
+            }
+          }
+        }
+      }
+    """
+
+  val testNestedDir1 = Utils.createTempDir()
+  val testNestedDir2 = Utils.createTempDir()
+  val testNestedDir3 = Utils.createTempDir()
+  val testNestedDir4 = Utils.createTempDir()
+
+  lazy val testNestedData1 = new ParquetRelation(testNestedDir1.toURI.toString)
+  lazy val testNestedData2 = new ParquetRelation(testNestedDir2.toURI.toString)
+
   def writeFile() = {
-    testDir.delete
+    testDir.delete()
     val path: Path = new Path(new Path(testDir.toURI), new Path("part-r-0.parquet"))
+    val job = new Job()
     val schema: MessageType = MessageTypeParser.parseMessageType(testSchema)
     val writeSupport = new TestGroupWriteSupport(schema)
     val writer = new ParquetWriter[Group](path, writeSupport)
@@ -150,5 +256,149 @@ private[sql] object ParquetTestData {
     }
     writer.close()
   }
+
+  def writeNestedFile1() {
+    // example data from https://blog.twitter.com/2013/dremel-made-simple-with-parquet
+    testNestedDir1.delete()
+    val path: Path = new Path(new Path(testNestedDir1.toURI), new Path("part-r-0.parquet"))
+    val schema: MessageType = MessageTypeParser.parseMessageType(testNestedSchema1)
+
+    val r1 = new SimpleGroup(schema)
+    r1.add(0, "Julien Le Dem")
+    r1.addGroup(1)
+      .append(CatalystConverter.ARRAY_ELEMENTS_SCHEMA_NAME, "555 123 4567")
+      .append(CatalystConverter.ARRAY_ELEMENTS_SCHEMA_NAME, "555 666 1337")
+      .append(CatalystConverter.ARRAY_ELEMENTS_SCHEMA_NAME, "XXX XXX XXXX")
+    val contacts = r1.addGroup(2)
+    contacts.addGroup(0)
+      .append("name", "Dmitriy Ryaboy")
+      .append("phoneNumber", "555 987 6543")
+    contacts.addGroup(0)
+      .append("name", "Chris Aniszczyk")
+
+    val r2 = new SimpleGroup(schema)
+    r2.add(0, "A. Nonymous")
+
+    val writeSupport = new TestGroupWriteSupport(schema)
+    val writer = new ParquetWriter[Group](path, writeSupport)
+    writer.write(r1)
+    writer.write(r2)
+    writer.close()
+  }
+
+  def writeNestedFile2() {
+    testNestedDir2.delete()
+    val path: Path = new Path(new Path(testNestedDir2.toURI), new Path("part-r-0.parquet"))
+    val schema: MessageType = MessageTypeParser.parseMessageType(testNestedSchema2)
+
+    val r1 = new SimpleGroup(schema)
+    r1.add(0, 1)
+    r1.add(1, 7)
+    val longs = r1.addGroup(2)
+    longs.add(CatalystConverter.ARRAY_ELEMENTS_SCHEMA_NAME , 1.toLong << 32)
+    longs.add(CatalystConverter.ARRAY_ELEMENTS_SCHEMA_NAME, 1.toLong << 33)
+    longs.add(CatalystConverter.ARRAY_ELEMENTS_SCHEMA_NAME, 1.toLong << 34)
+    val booleanNumberPair = r1.addGroup(3).addGroup(0)
+    booleanNumberPair.add("value", 2.5)
+    booleanNumberPair.add("truth", false)
+    val top_level = r1.addGroup(4)
+    val second_level_a = top_level.addGroup(0)
+    val second_level_b = top_level.addGroup(0)
+    val third_level_aa = second_level_a.addGroup(0)
+    val third_level_ab = second_level_a.addGroup(0)
+    val third_level_c = second_level_b.addGroup(0)
+    third_level_aa.add(
+      CatalystConverter.ARRAY_ELEMENTS_SCHEMA_NAME,
+      7)
+    third_level_ab.add(
+      CatalystConverter.ARRAY_ELEMENTS_SCHEMA_NAME,
+      8)
+    third_level_c.add(
+      CatalystConverter.ARRAY_ELEMENTS_SCHEMA_NAME,
+      9)
+
+    val writeSupport = new TestGroupWriteSupport(schema)
+    val writer = new ParquetWriter[Group](path, writeSupport)
+    writer.write(r1)
+    writer.close()
+  }
+
+  def writeNestedFile3() {
+    testNestedDir3.delete()
+    val path: Path = new Path(new Path(testNestedDir3.toURI), new Path("part-r-0.parquet"))
+    val schema: MessageType = MessageTypeParser.parseMessageType(testNestedSchema3)
+
+    val r1 = new SimpleGroup(schema)
+    r1.add(0, 1)
+    val booleanNumberPairs = r1.addGroup(1)
+    val g1 = booleanNumberPairs.addGroup(0)
+    g1.add(0, 1)
+    val nested1 = g1.addGroup(1)
+    val ng1 = nested1.addGroup(0)
+    ng1.add(0, 1.5)
+    ng1.add(1, false)
+    val ng2 = nested1.addGroup(0)
+    ng2.add(0, 2.5)
+    ng2.add(1, true)
+    val g2 = booleanNumberPairs.addGroup(0)
+    g2.add(0, 2)
+    val ng3 = g2.addGroup(1)
+      .addGroup(0)
+    ng3.add(0, 3.5)
+    ng3.add(1, false)
+
+    val writeSupport = new TestGroupWriteSupport(schema)
+    val writer = new ParquetWriter[Group](path, writeSupport)
+    writer.write(r1)
+    writer.close()
+  }
+
+  def writeNestedFile4() {
+    testNestedDir4.delete()
+    val path: Path = new Path(new Path(testNestedDir4.toURI), new Path("part-r-0.parquet"))
+    val schema: MessageType = MessageTypeParser.parseMessageType(testNestedSchema4)
+
+    val r1 = new SimpleGroup(schema)
+    r1.add(0, 7)
+    val map1 = r1.addGroup(1)
+    val keyValue1 = map1.addGroup(0)
+    keyValue1.add(0, "key1")
+    keyValue1.add(1, 1)
+    val keyValue2 = map1.addGroup(0)
+    keyValue2.add(0, "key2")
+    keyValue2.add(1, 2)
+    val map2 = r1.addGroup(2)
+    val keyValue3 = map2.addGroup(0)
+    // TODO: currently only string key type supported
+    keyValue3.add(0, "seven")
+    val valueGroup1 = keyValue3.addGroup(1)
+    valueGroup1.add(0, 42.toLong)
+    valueGroup1.add(1, "the answer")
+    val keyValue4 = map2.addGroup(0)
+    // TODO: currently only string key type supported
+    keyValue4.add(0, "eight")
+    val valueGroup2 = keyValue4.addGroup(1)
+    valueGroup2.add(0, 49.toLong)
+
+    val writeSupport = new TestGroupWriteSupport(schema)
+    val writer = new ParquetWriter[Group](path, writeSupport)
+    writer.write(r1)
+    writer.close()
+  }
+
+  // TODO: this is not actually used anywhere but useful for debugging
+  /* def readNestedFile(file: File, schemaString: String): Unit = {
+    val configuration = new Configuration()
+    val path = new Path(new Path(file.toURI), new Path("part-r-0.parquet"))
+    val fs: FileSystem = path.getFileSystem(configuration)
+    val schema: MessageType = MessageTypeParser.parseMessageType(schemaString)
+    assert(schema != null)
+    val outputStatus: FileStatus = fs.getFileStatus(new Path(path.toString))
+    val footers = ParquetFileReader.readFooter(configuration, outputStatus)
+    assert(footers != null)
+    val reader = new ParquetReader(new Path(path.toString), new GroupReadSupport())
+    val first = reader.read()
+    assert(first != null)
+  } */
 }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypes.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypes.scala
new file mode 100644
index 000000000000..f9046368e7ce
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypes.scala
@@ -0,0 +1,408 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.parquet
+
+import java.io.IOException
+
+import org.apache.hadoop.conf.Configuration
+import org.apache.hadoop.fs.{FileSystem, Path}
+import org.apache.hadoop.mapreduce.Job
+
+import parquet.hadoop.{ParquetFileReader, Footer, ParquetFileWriter}
+import parquet.hadoop.metadata.{ParquetMetadata, FileMetaData}
+import parquet.hadoop.util.ContextUtil
+import parquet.schema.{Type => ParquetType, PrimitiveType => ParquetPrimitiveType, MessageType}
+import parquet.schema.{GroupType => ParquetGroupType, OriginalType => ParquetOriginalType, ConversionPatterns}
+import parquet.schema.PrimitiveType.{PrimitiveTypeName => ParquetPrimitiveTypeName}
+import parquet.schema.Type.Repetition
+
+import org.apache.spark.Logging
+import org.apache.spark.sql.catalyst.expressions.{AttributeReference, Attribute}
+import org.apache.spark.sql.catalyst.types._
+
+// Implicits
+import scala.collection.JavaConversions._
+
+private[parquet] object ParquetTypesConverter extends Logging {
+  def isPrimitiveType(ctype: DataType): Boolean =
+    classOf[PrimitiveType] isAssignableFrom ctype.getClass
+
+  def toPrimitiveDataType(parquetType : ParquetPrimitiveTypeName): DataType = parquetType match {
+    case ParquetPrimitiveTypeName.BINARY => StringType
+    case ParquetPrimitiveTypeName.BOOLEAN => BooleanType
+    case ParquetPrimitiveTypeName.DOUBLE => DoubleType
+    case ParquetPrimitiveTypeName.FIXED_LEN_BYTE_ARRAY => ArrayType(ByteType)
+    case ParquetPrimitiveTypeName.FLOAT => FloatType
+    case ParquetPrimitiveTypeName.INT32 => IntegerType
+    case ParquetPrimitiveTypeName.INT64 => LongType
+    case ParquetPrimitiveTypeName.INT96 =>
+      // TODO: add BigInteger type? TODO(andre) use DecimalType instead????
+      sys.error("Potential loss of precision: cannot convert INT96")
+    case _ => sys.error(
+      s"Unsupported parquet datatype $parquetType")
+  }
+
+  /**
+   * Converts a given Parquet `Type` into the corresponding
+   * [[org.apache.spark.sql.catalyst.types.DataType]].
+   *
+   * We apply the following conversion rules:
+   * <ul>
+   *   <li> Primitive types are converter to the corresponding primitive type.</li>
+   *   <li> Group types that have a single field that is itself a group, which has repetition
+   *        level `REPEATED`, are treated as follows:<ul>
+   *          <li> If the nested group has name `values`, the surrounding group is converted
+   *               into an [[ArrayType]] with the corresponding field type (primitive or
+   *               complex) as element type.</li>
+   *          <li> If the nested group has name `map` and two fields (named `key` and `value`),
+   *               the surrounding group is converted into a [[MapType]]
+   *               with the corresponding key and value (value possibly complex) types.
+   *               Note that we currently assume map values are not nullable.</li>
+   *   <li> Other group types are converted into a [[StructType]] with the corresponding
+   *        field types.</li></ul></li>
+   * </ul>
+   * Note that fields are determined to be `nullable` if and only if their Parquet repetition
+   * level is not `REQUIRED`.
+   *
+   * @param parquetType The type to convert.
+   * @return The corresponding Catalyst type.
+   */
+  def toDataType(parquetType: ParquetType): DataType = {
+    def correspondsToMap(groupType: ParquetGroupType): Boolean = {
+      if (groupType.getFieldCount != 1 || groupType.getFields.apply(0).isPrimitive) {
+        false
+      } else {
+        // This mostly follows the convention in ``parquet.schema.ConversionPatterns``
+        val keyValueGroup = groupType.getFields.apply(0).asGroupType()
+        keyValueGroup.getRepetition == Repetition.REPEATED &&
+          keyValueGroup.getName == CatalystConverter.MAP_SCHEMA_NAME &&
+          keyValueGroup.getFieldCount == 2 &&
+          keyValueGroup.getFields.apply(0).getName == CatalystConverter.MAP_KEY_SCHEMA_NAME &&
+          keyValueGroup.getFields.apply(1).getName == CatalystConverter.MAP_VALUE_SCHEMA_NAME
+      }
+    }
+
+    def correspondsToArray(groupType: ParquetGroupType): Boolean = {
+      groupType.getFieldCount == 1 &&
+        groupType.getFieldName(0) == CatalystConverter.ARRAY_ELEMENTS_SCHEMA_NAME &&
+        groupType.getFields.apply(0).getRepetition == Repetition.REPEATED
+    }
+
+    if (parquetType.isPrimitive) {
+      toPrimitiveDataType(parquetType.asPrimitiveType.getPrimitiveTypeName)
+    } else {
+      val groupType = parquetType.asGroupType()
+      parquetType.getOriginalType match {
+        // if the schema was constructed programmatically there may be hints how to convert
+        // it inside the metadata via the OriginalType field
+        case ParquetOriginalType.LIST => { // TODO: check enums!
+          assert(groupType.getFieldCount == 1)
+          val field = groupType.getFields.apply(0)
+          new ArrayType(toDataType(field))
+        }
+        case ParquetOriginalType.MAP => {
+          assert(
+            !groupType.getFields.apply(0).isPrimitive,
+            "Parquet Map type malformatted: expected nested group for map!")
+          val keyValueGroup = groupType.getFields.apply(0).asGroupType()
+          assert(
+            keyValueGroup.getFieldCount == 2,
+            "Parquet Map type malformatted: nested group should have 2 (key, value) fields!")
+          val keyType = toDataType(keyValueGroup.getFields.apply(0))
+          assert(keyValueGroup.getFields.apply(0).getRepetition == Repetition.REQUIRED)
+          val valueType = toDataType(keyValueGroup.getFields.apply(1))
+          assert(keyValueGroup.getFields.apply(1).getRepetition == Repetition.REQUIRED)
+          new MapType(keyType, valueType)
+        }
+        case _ => {
+          // Note: the order of these checks is important!
+          if (correspondsToMap(groupType)) { // MapType
+            val keyValueGroup = groupType.getFields.apply(0).asGroupType()
+            val keyType = toDataType(keyValueGroup.getFields.apply(0))
+            assert(keyValueGroup.getFields.apply(0).getRepetition == Repetition.REQUIRED)
+            val valueType = toDataType(keyValueGroup.getFields.apply(1))
+            assert(keyValueGroup.getFields.apply(1).getRepetition == Repetition.REQUIRED)
+            new MapType(keyType, valueType)
+          } else if (correspondsToArray(groupType)) { // ArrayType
+            val elementType = toDataType(groupType.getFields.apply(0))
+            new ArrayType(elementType)
+          } else { // everything else: StructType
+            val fields = groupType
+              .getFields
+              .map(ptype => new StructField(
+              ptype.getName,
+              toDataType(ptype),
+              ptype.getRepetition != Repetition.REQUIRED))
+            new StructType(fields)
+          }
+        }
+      }
+    }
+  }
+
+  /**
+   * For a given Catalyst [[org.apache.spark.sql.catalyst.types.DataType]] return
+   * the name of the corresponding Parquet primitive type or None if the given type
+   * is not primitive.
+   *
+   * @param ctype The type to convert
+   * @return The name of the corresponding Parquet primitive type
+   */
+  def fromPrimitiveDataType(ctype: DataType):
+      Option[ParquetPrimitiveTypeName] = ctype match {
+    case StringType => Some(ParquetPrimitiveTypeName.BINARY)
+    case BooleanType => Some(ParquetPrimitiveTypeName.BOOLEAN)
+    case DoubleType => Some(ParquetPrimitiveTypeName.DOUBLE)
+    case ArrayType(ByteType) =>
+      Some(ParquetPrimitiveTypeName.FIXED_LEN_BYTE_ARRAY)
+    case FloatType => Some(ParquetPrimitiveTypeName.FLOAT)
+    case IntegerType => Some(ParquetPrimitiveTypeName.INT32)
+    // There is no type for Byte or Short so we promote them to INT32.
+    case ShortType => Some(ParquetPrimitiveTypeName.INT32)
+    case ByteType => Some(ParquetPrimitiveTypeName.INT32)
+    case LongType => Some(ParquetPrimitiveTypeName.INT64)
+    case _ => None
+  }
+
+  /**
+   * Converts a given Catalyst [[org.apache.spark.sql.catalyst.types.DataType]] into
+   * the corresponding Parquet `Type`.
+   *
+   * The conversion follows the rules below:
+   * <ul>
+   *   <li> Primitive types are converted into Parquet's primitive types.</li>
+   *   <li> [[org.apache.spark.sql.catalyst.types.StructType]]s are converted
+   *        into Parquet's `GroupType` with the corresponding field types.</li>
+   *   <li> [[org.apache.spark.sql.catalyst.types.ArrayType]]s are converted
+   *        into a 2-level nested group, where the outer group has the inner
+   *        group as sole field. The inner group has name `values` and
+   *        repetition level `REPEATED` and has the element type of
+   *        the array as schema. We use Parquet's `ConversionPatterns` for this
+   *        purpose.</li>
+   *   <li> [[org.apache.spark.sql.catalyst.types.MapType]]s are converted
+   *        into a nested (2-level) Parquet `GroupType` with two fields: a key
+   *        type and a value type. The nested group has repetition level
+   *        `REPEATED` and name `map`. We use Parquet's `ConversionPatterns`
+   *        for this purpose</li>
+   * </ul>
+   * Parquet's repetition level is generally set according to the following rule:
+   * <ul>
+   *   <li> If the call to `fromDataType` is recursive inside an enclosing `ArrayType` or
+   *   `MapType`, then the repetition level is set to `REPEATED`.</li>
+   *   <li> Otherwise, if the attribute whose type is converted is `nullable`, the Parquet
+   *   type gets repetition level `OPTIONAL` and otherwise `REQUIRED`.</li>
+   * </ul>
+   *
+   *@param ctype The type to convert
+   * @param name The name of the [[org.apache.spark.sql.catalyst.expressions.Attribute]]
+   *             whose type is converted
+   * @param nullable When true indicates that the attribute is nullable
+   * @param inArray When true indicates that this is a nested attribute inside an array.
+   * @return The corresponding Parquet type.
+   */
+  def fromDataType(
+      ctype: DataType,
+      name: String,
+      nullable: Boolean = true,
+      inArray: Boolean = false): ParquetType = {
+    val repetition =
+      if (inArray) {
+        Repetition.REPEATED
+      } else {
+        if (nullable) Repetition.OPTIONAL else Repetition.REQUIRED
+      }
+    val primitiveType = fromPrimitiveDataType(ctype)
+    if (primitiveType.isDefined) {
+      new ParquetPrimitiveType(repetition, primitiveType.get, name)
+    } else {
+      ctype match {
+        case ArrayType(elementType) => {
+          val parquetElementType = fromDataType(
+            elementType,
+            CatalystConverter.ARRAY_ELEMENTS_SCHEMA_NAME,
+            nullable = false,
+            inArray = true)
+            ConversionPatterns.listType(repetition, name, parquetElementType)
+        }
+        case StructType(structFields) => {
+          val fields = structFields.map {
+            field => fromDataType(field.dataType, field.name, field.nullable, inArray = false)
+          }
+          new ParquetGroupType(repetition, name, fields)
+        }
+        case MapType(keyType, valueType) => {
+          val parquetKeyType =
+            fromDataType(
+              keyType,
+              CatalystConverter.MAP_KEY_SCHEMA_NAME,
+              nullable = false,
+              inArray = false)
+          val parquetValueType =
+            fromDataType(
+              valueType,
+              CatalystConverter.MAP_VALUE_SCHEMA_NAME,
+              nullable = false,
+              inArray = false)
+          ConversionPatterns.mapType(
+            repetition,
+            name,
+            parquetKeyType,
+            parquetValueType)
+        }
+        case _ => sys.error(s"Unsupported datatype $ctype")
+      }
+    }
+  }
+
+  def convertToAttributes(parquetSchema: ParquetType): Seq[Attribute] = {
+    parquetSchema
+      .asGroupType()
+      .getFields
+      .map(
+        field =>
+          new AttributeReference(
+            field.getName,
+            toDataType(field),
+            field.getRepetition != Repetition.REQUIRED)())
+  }
+
+  def convertFromAttributes(attributes: Seq[Attribute]): MessageType = {
+    val fields = attributes.map(
+      attribute =>
+        fromDataType(attribute.dataType, attribute.name, attribute.nullable))
+    new MessageType("root", fields)
+  }
+
+  def convertFromString(string: String): Seq[Attribute] = {
+    DataType(string) match {
+      case s: StructType => s.toAttributes
+      case other => sys.error(s"Can convert $string to row")
+    }
+  }
+
+  def convertToString(schema: Seq[Attribute]): String = {
+    StructType.fromAttributes(schema).toString
+  }
+
+  def writeMetaData(attributes: Seq[Attribute], origPath: Path, conf: Configuration): Unit = {
+    if (origPath == null) {
+      throw new IllegalArgumentException("Unable to write Parquet metadata: path is null")
+    }
+    val fs = origPath.getFileSystem(conf)
+    if (fs == null) {
+      throw new IllegalArgumentException(
+        s"Unable to write Parquet metadata: path $origPath is incorrectly formatted")
+    }
+    val path = origPath.makeQualified(fs)
+    if (fs.exists(path) && !fs.getFileStatus(path).isDir) {
+      throw new IllegalArgumentException(s"Expected to write to directory $path but found file")
+    }
+    val metadataPath = new Path(path, ParquetFileWriter.PARQUET_METADATA_FILE)
+    if (fs.exists(metadataPath)) {
+      try {
+        fs.delete(metadataPath, true)
+      } catch {
+        case e: IOException =>
+          throw new IOException(s"Unable to delete previous PARQUET_METADATA_FILE at $metadataPath")
+      }
+    }
+    val extraMetadata = new java.util.HashMap[String, String]()
+    extraMetadata.put(
+      RowReadSupport.SPARK_METADATA_KEY,
+      ParquetTypesConverter.convertToString(attributes))
+    // TODO: add extra data, e.g., table name, date, etc.?
+
+    val parquetSchema: MessageType =
+      ParquetTypesConverter.convertFromAttributes(attributes)
+    val metaData: FileMetaData = new FileMetaData(
+      parquetSchema,
+      extraMetadata,
+      "Spark")
+
+    ParquetRelation.enableLogForwarding()
+    ParquetFileWriter.writeMetadataFile(
+      conf,
+      path,
+      new Footer(path, new ParquetMetadata(metaData, Nil)) :: Nil)
+  }
+
+  /**
+   * Try to read Parquet metadata at the given Path. We first see if there is a summary file
+   * in the parent directory. If so, this is used. Else we read the actual footer at the given
+   * location.
+   * @param origPath The path at which we expect one (or more) Parquet files.
+   * @param configuration The Hadoop configuration to use.
+   * @return The `ParquetMetadata` containing among other things the schema.
+   */
+  def readMetaData(origPath: Path, configuration: Option[Configuration]): ParquetMetadata = {
+    if (origPath == null) {
+      throw new IllegalArgumentException("Unable to read Parquet metadata: path is null")
+    }
+    val job = new Job()
+    val conf = configuration.getOrElse(ContextUtil.getConfiguration(job))
+    val fs: FileSystem = origPath.getFileSystem(conf)
+    if (fs == null) {
+      throw new IllegalArgumentException(s"Incorrectly formatted Parquet metadata path $origPath")
+    }
+    val path = origPath.makeQualified(fs)
+    if (!fs.getFileStatus(path).isDir) {
+      throw new IllegalArgumentException(
+        s"Expected $path for be a directory with Parquet files/metadata")
+    }
+    ParquetRelation.enableLogForwarding()
+    val metadataPath = new Path(path, ParquetFileWriter.PARQUET_METADATA_FILE)
+    // if this is a new table that was just created we will find only the metadata file
+    if (fs.exists(metadataPath) && fs.isFile(metadataPath)) {
+      ParquetFileReader.readFooter(conf, metadataPath)
+    } else {
+      // there may be one or more Parquet files in the given directory
+      val footers = ParquetFileReader.readFooters(conf, fs.getFileStatus(path))
+      // TODO: for now we assume that all footers (if there is more than one) have identical
+      // metadata; we may want to add a check here at some point
+      if (footers.size() == 0) {
+        throw new IllegalArgumentException(s"Could not find Parquet metadata at path $path")
+      }
+      footers(0).getParquetMetadata
+    }
+  }
+
+  /**
+   * Reads in Parquet Metadata from the given path and tries to extract the schema
+   * (Catalyst attributes) from the application-specific key-value map. If this
+   * is empty it falls back to converting from the Parquet file schema which
+   * may lead to an upcast of types (e.g., {byte, short} to int).
+   *
+   * @param origPath The path at which we expect one (or more) Parquet files.
+   * @param conf The Hadoop configuration to use.
+   * @return A list of attributes that make up the schema.
+   */
+  def readSchemaFromFile(origPath: Path, conf: Option[Configuration]): Seq[Attribute] = {
+    val keyValueMetadata: java.util.Map[String, String] =
+      readMetaData(origPath, conf)
+        .getFileMetaData
+        .getKeyValueMetaData
+    if (keyValueMetadata.get(RowReadSupport.SPARK_METADATA_KEY) != null) {
+      convertFromString(keyValueMetadata.get(RowReadSupport.SPARK_METADATA_KEY))
+    } else {
+      val attributes = convertToAttributes(
+        readMetaData(origPath, conf).getFileMetaData.getSchema)
+      log.warn(s"Falling back to schema conversion from Parquet types; result: $attributes")
+      attributes
+    }
+  }
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala
index 9810520bb9ae..0c239d00b199 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala
@@ -26,15 +26,16 @@ import parquet.hadoop.ParquetFileWriter
 import parquet.hadoop.util.ContextUtil
 import parquet.schema.MessageTypeParser
 
+import org.apache.spark.SparkContext
 import org.apache.spark.sql._
-import org.apache.spark.sql.catalyst.util.getTempFilePath
 import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.types.{BooleanType, IntegerType}
 import org.apache.spark.sql.test.TestSQLContext
 import org.apache.spark.sql.TestData
 import org.apache.spark.sql.SchemaRDD
-import org.apache.spark.sql.catalyst.expressions.Row
-import org.apache.spark.sql.catalyst.expressions.Equals
-import org.apache.spark.sql.catalyst.types.IntegerType
+import org.apache.spark.sql.catalyst.util.getTempFilePath
+import org.apache.spark.sql.catalyst.{SqlLexical, SqlParser}
+import org.apache.spark.sql.catalyst.analysis.{UnresolvedAttribute, Star}
 import org.apache.spark.util.Utils
 
 // Implicits
@@ -56,15 +57,37 @@ case class OptionalReflectData(
     doubleField: Option[Double],
     booleanField: Option[Boolean])
 
+case class Nested(i: Int, s: String)
+
+case class Data(array: Seq[Int], nested: Nested)
+
+case class AllDataTypes(
+    stringField: String,
+    intField: Int,
+    longField: Long,
+    floatField: Float,
+    doubleField: Double,
+    shortField: Short,
+    byteField: Byte,
+    booleanField: Boolean)
+
 class ParquetQuerySuite extends QueryTest with FunSuiteLike with BeforeAndAfterAll {
   import TestData._
   TestData // Load test data tables.
 
   var testRDD: SchemaRDD = null
 
+  // TODO: remove this once SqlParser can parse nested select statements
+  var nestedParserSqlContext: NestedParserSQLContext = null
+
   override def beforeAll() {
+    nestedParserSqlContext = new NestedParserSQLContext(TestSQLContext.sparkContext)
     ParquetTestData.writeFile()
     ParquetTestData.writeFilterFile()
+    ParquetTestData.writeNestedFile1()
+    ParquetTestData.writeNestedFile2()
+    ParquetTestData.writeNestedFile3()
+    ParquetTestData.writeNestedFile4()
     testRDD = parquetFile(ParquetTestData.testDir.toString)
     testRDD.registerAsTable("testsource")
     parquetFile(ParquetTestData.testFilterDir.toString)
@@ -74,9 +97,33 @@ class ParquetQuerySuite extends QueryTest with FunSuiteLike with BeforeAndAfterA
   override def afterAll() {
     Utils.deleteRecursively(ParquetTestData.testDir)
     Utils.deleteRecursively(ParquetTestData.testFilterDir)
+    Utils.deleteRecursively(ParquetTestData.testNestedDir1)
+    Utils.deleteRecursively(ParquetTestData.testNestedDir2)
+    Utils.deleteRecursively(ParquetTestData.testNestedDir3)
+    Utils.deleteRecursively(ParquetTestData.testNestedDir4)
     // here we should also unregister the table??
   }
 
+  test("Read/Write All Types") {
+    val tempDir = getTempFilePath("parquetTest").getCanonicalPath
+    val range = (0 to 255)
+    TestSQLContext.sparkContext.parallelize(range)
+      .map(x => AllDataTypes(s"$x", x, x.toLong, x.toFloat, x.toDouble, x.toShort, x.toByte, x % 2 == 0))
+      .saveAsParquetFile(tempDir)
+    val result = parquetFile(tempDir).collect()
+    range.foreach {
+      i =>
+        assert(result(i).getString(0) == s"$i", s"row $i String field did not match, got ${result(i).getString(0)}")
+        assert(result(i).getInt(1) === i)
+        assert(result(i).getLong(2) === i.toLong)
+        assert(result(i).getFloat(3) === i.toFloat)
+        assert(result(i).getDouble(4) === i.toDouble)
+        assert(result(i).getShort(5) === i.toShort)
+        assert(result(i).getByte(6) === i.toByte)
+        assert(result(i).getBoolean(7) === (i % 2 == 0))
+    }
+  }
+
   test("self-join parquet files") {
     val x = ParquetTestData.testData.as('x)
     val y = ParquetTestData.testData.as('y)
@@ -154,7 +201,7 @@ class ParquetQuerySuite extends QueryTest with FunSuiteLike with BeforeAndAfterA
       path,
       TestSQLContext.sparkContext.hadoopConfiguration)
     assert(fs.exists(new Path(path, ParquetFileWriter.PARQUET_METADATA_FILE)))
-    val metaData = ParquetTypesConverter.readMetaData(path)
+    val metaData = ParquetTypesConverter.readMetaData(path, Some(ContextUtil.getConfiguration(job)))
     assert(metaData != null)
     ParquetTestData
       .testData
@@ -197,10 +244,37 @@ class ParquetQuerySuite extends QueryTest with FunSuiteLike with BeforeAndAfterA
       assert(rdd_copy(i).apply(1) === rdd_orig(i).value, s"value in line $i")
     }
     Utils.deleteRecursively(file)
-    assert(true)
   }
 
-  test("insert (appending) to same table via Scala API") {
+  test("Insert (overwrite) via Scala API") {
+    val dirname = Utils.createTempDir()
+    val source_rdd = TestSQLContext.sparkContext.parallelize((1 to 100))
+      .map(i => TestRDDEntry(i, s"val_$i"))
+    source_rdd.registerAsTable("source")
+    val dest_rdd = createParquetFile[TestRDDEntry](dirname.toString)
+    dest_rdd.registerAsTable("dest")
+    sql("INSERT OVERWRITE INTO dest SELECT * FROM source").collect()
+    val rdd_copy1 = sql("SELECT * FROM dest").collect()
+    assert(rdd_copy1.size === 100)
+    assert(rdd_copy1(0).apply(0) === 1)
+    assert(rdd_copy1(0).apply(1) === "val_1")
+    // TODO: why does collecting break things? It seems InsertIntoParquet::execute() is
+    // executed twice otherwise?!
+    sql("INSERT INTO dest SELECT * FROM source")
+    val rdd_copy2 = sql("SELECT * FROM dest").collect()
+    assert(rdd_copy2.size === 200)
+    assert(rdd_copy2(0).apply(0) === 1)
+    assert(rdd_copy2(0).apply(1) === "val_1")
+    assert(rdd_copy2(99).apply(0) === 100)
+    assert(rdd_copy2(99).apply(1) === "val_100")
+    assert(rdd_copy2(100).apply(0) === 1)
+    assert(rdd_copy2(100).apply(1) === "val_1")
+    Utils.deleteRecursively(dirname)
+  }
+
+  test("Insert (appending) to same table via Scala API") {
+    // TODO: why does collecting break things? It seems InsertIntoParquet::execute() is
+    // executed twice otherwise?!
     sql("INSERT INTO testsource SELECT * FROM testsource")
     val double_rdd = sql("SELECT * FROM testsource").collect()
     assert(double_rdd != null)
@@ -363,4 +437,272 @@ class ParquetQuerySuite extends QueryTest with FunSuiteLike with BeforeAndAfterA
     val query = sql(s"SELECT mystring FROM testfiltersource WHERE myint < 10")
     assert(query.collect().size === 10)
   }
+
+  test("Importing nested Parquet file (Addressbook)") {
+    val result = TestSQLContext
+      .parquetFile(ParquetTestData.testNestedDir1.toString)
+      .toSchemaRDD
+      .collect()
+    assert(result != null)
+    assert(result.size === 2)
+    val first_record = result(0)
+    val second_record = result(1)
+    assert(first_record != null)
+    assert(second_record != null)
+    assert(first_record.size === 3)
+    assert(second_record(1) === null)
+    assert(second_record(2) === null)
+    assert(second_record(0) === "A. Nonymous")
+    assert(first_record(0) === "Julien Le Dem")
+    val first_owner_numbers = first_record(1)
+      .asInstanceOf[CatalystConverter.ArrayScalaType[_]]
+    val first_contacts = first_record(2)
+      .asInstanceOf[CatalystConverter.ArrayScalaType[_]]
+    assert(first_owner_numbers != null)
+    assert(first_owner_numbers(0) === "555 123 4567")
+    assert(first_owner_numbers(2) === "XXX XXX XXXX")
+    assert(first_contacts(0)
+      .asInstanceOf[CatalystConverter.StructScalaType[_]].size === 2)
+    val first_contacts_entry_one = first_contacts(0)
+      .asInstanceOf[CatalystConverter.StructScalaType[_]]
+    assert(first_contacts_entry_one(0) === "Dmitriy Ryaboy")
+    assert(first_contacts_entry_one(1) === "555 987 6543")
+    val first_contacts_entry_two = first_contacts(1)
+      .asInstanceOf[CatalystConverter.StructScalaType[_]]
+    assert(first_contacts_entry_two(0) === "Chris Aniszczyk")
+  }
+
+  test("Importing nested Parquet file (nested numbers)") {
+    val result = TestSQLContext
+      .parquetFile(ParquetTestData.testNestedDir2.toString)
+      .toSchemaRDD
+      .collect()
+    assert(result.size === 1, "number of top-level rows incorrect")
+    assert(result(0).size === 5, "number of fields in row incorrect")
+    assert(result(0)(0) === 1)
+    assert(result(0)(1) === 7)
+    val subresult1 = result(0)(2).asInstanceOf[CatalystConverter.ArrayScalaType[_]]
+    assert(subresult1.size === 3)
+    assert(subresult1(0) === (1.toLong << 32))
+    assert(subresult1(1) === (1.toLong << 33))
+    assert(subresult1(2) === (1.toLong << 34))
+    val subresult2 = result(0)(3)
+      .asInstanceOf[CatalystConverter.ArrayScalaType[_]](0)
+      .asInstanceOf[CatalystConverter.StructScalaType[_]]
+    assert(subresult2.size === 2)
+    assert(subresult2(0) === 2.5)
+    assert(subresult2(1) === false)
+    val subresult3 = result(0)(4)
+      .asInstanceOf[CatalystConverter.ArrayScalaType[_]]
+    assert(subresult3.size === 2)
+    assert(subresult3(0).asInstanceOf[CatalystConverter.ArrayScalaType[_]].size === 2)
+    val subresult4 = subresult3(0).asInstanceOf[CatalystConverter.ArrayScalaType[_]]
+    assert(subresult4(0).asInstanceOf[CatalystConverter.ArrayScalaType[_]](0) === 7)
+    assert(subresult4(1).asInstanceOf[CatalystConverter.ArrayScalaType[_]](0) === 8)
+    assert(subresult3(1).asInstanceOf[CatalystConverter.ArrayScalaType[_]].size === 1)
+    assert(subresult3(1).asInstanceOf[CatalystConverter.ArrayScalaType[_]](0)
+      .asInstanceOf[CatalystConverter.ArrayScalaType[_]](0) === 9)
+  }
+
+  test("Simple query on addressbook") {
+    val data = TestSQLContext
+      .parquetFile(ParquetTestData.testNestedDir1.toString)
+      .toSchemaRDD
+    val tmp = data.where('owner === "Julien Le Dem").select('owner as 'a, 'contacts as 'c).collect()
+    assert(tmp.size === 1)
+    assert(tmp(0)(0) === "Julien Le Dem")
+  }
+
+  test("Projection in addressbook") {
+    val data = nestedParserSqlContext
+      .parquetFile(ParquetTestData.testNestedDir1.toString)
+      .toSchemaRDD
+    data.registerAsTable("data")
+    val query = nestedParserSqlContext.sql("SELECT owner, contacts[1].name FROM data")
+    val tmp = query.collect()
+    assert(tmp.size === 2)
+    assert(tmp(0).size === 2)
+    assert(tmp(0)(0) === "Julien Le Dem")
+    assert(tmp(0)(1) === "Chris Aniszczyk")
+    assert(tmp(1)(0) === "A. Nonymous")
+    assert(tmp(1)(1) === null)
+  }
+
+  test("Simple query on nested int data") {
+    val data = nestedParserSqlContext
+      .parquetFile(ParquetTestData.testNestedDir2.toString)
+      .toSchemaRDD
+    data.registerAsTable("data")
+    val result1 = nestedParserSqlContext.sql("SELECT entries[0].value FROM data").collect()
+    assert(result1.size === 1)
+    assert(result1(0).size === 1)
+    assert(result1(0)(0) === 2.5)
+    val result2 = nestedParserSqlContext.sql("SELECT entries[0] FROM data").collect()
+    assert(result2.size === 1)
+    val subresult1 = result2(0)(0).asInstanceOf[CatalystConverter.StructScalaType[_]]
+    assert(subresult1.size === 2)
+    assert(subresult1(0) === 2.5)
+    assert(subresult1(1) === false)
+    val result3 = nestedParserSqlContext.sql("SELECT outerouter FROM data").collect()
+    val subresult2 = result3(0)(0)
+      .asInstanceOf[CatalystConverter.ArrayScalaType[_]](0)
+      .asInstanceOf[CatalystConverter.ArrayScalaType[_]]
+    assert(subresult2(0).asInstanceOf[CatalystConverter.ArrayScalaType[_]](0) === 7)
+    assert(subresult2(1).asInstanceOf[CatalystConverter.ArrayScalaType[_]](0) === 8)
+    assert(result3(0)(0)
+      .asInstanceOf[CatalystConverter.ArrayScalaType[_]](1)
+      .asInstanceOf[CatalystConverter.ArrayScalaType[_]](0)
+      .asInstanceOf[CatalystConverter.ArrayScalaType[_]](0) === 9)
+  }
+
+  test("nested structs") {
+    val data = nestedParserSqlContext
+      .parquetFile(ParquetTestData.testNestedDir3.toString)
+      .toSchemaRDD
+    data.registerAsTable("data")
+    val result1 = nestedParserSqlContext.sql("SELECT booleanNumberPairs[0].value[0].truth FROM data").collect()
+    assert(result1.size === 1)
+    assert(result1(0).size === 1)
+    assert(result1(0)(0) === false)
+    val result2 = nestedParserSqlContext.sql("SELECT booleanNumberPairs[0].value[1].truth FROM data").collect()
+    assert(result2.size === 1)
+    assert(result2(0).size === 1)
+    assert(result2(0)(0) === true)
+    val result3 = nestedParserSqlContext.sql("SELECT booleanNumberPairs[1].value[0].truth FROM data").collect()
+    assert(result3.size === 1)
+    assert(result3(0).size === 1)
+    assert(result3(0)(0) === false)
+  }
+
+  test("simple map") {
+    val data = TestSQLContext
+      .parquetFile(ParquetTestData.testNestedDir4.toString)
+      .toSchemaRDD
+    data.registerAsTable("mapTable")
+    val result1 = sql("SELECT data1 FROM mapTable").collect()
+    assert(result1.size === 1)
+    assert(result1(0)(0)
+      .asInstanceOf[CatalystConverter.MapScalaType[String, _]]
+      .getOrElse("key1", 0) === 1)
+    assert(result1(0)(0)
+      .asInstanceOf[CatalystConverter.MapScalaType[String, _]]
+      .getOrElse("key2", 0) === 2)
+    val result2 = sql("""SELECT data1["key1"] FROM mapTable""").collect()
+    assert(result2(0)(0) === 1)
+  }
+
+  test("map with struct values") {
+    val data = nestedParserSqlContext
+      .parquetFile(ParquetTestData.testNestedDir4.toString)
+      .toSchemaRDD
+    data.registerAsTable("mapTable")
+    val result1 = nestedParserSqlContext.sql("SELECT data2 FROM mapTable").collect()
+    assert(result1.size === 1)
+    val entry1 = result1(0)(0)
+      .asInstanceOf[CatalystConverter.MapScalaType[String, CatalystConverter.StructScalaType[_]]]
+      .getOrElse("seven", null)
+    assert(entry1 != null)
+    assert(entry1(0) === 42)
+    assert(entry1(1) === "the answer")
+    val entry2 = result1(0)(0)
+      .asInstanceOf[CatalystConverter.MapScalaType[String, CatalystConverter.StructScalaType[_]]]
+      .getOrElse("eight", null)
+    assert(entry2 != null)
+    assert(entry2(0) === 49)
+    assert(entry2(1) === null)
+    val result2 = nestedParserSqlContext.sql("""SELECT data2["seven"].payload1, data2["seven"].payload2 FROM mapTable""").collect()
+    assert(result2.size === 1)
+    assert(result2(0)(0) === 42.toLong)
+    assert(result2(0)(1) === "the answer")
+  }
+
+  test("Writing out Addressbook and reading it back in") {
+    // TODO: find out why CatalystConverter.ARRAY_ELEMENTS_SCHEMA_NAME
+    // has no effect in this test case
+    val tmpdir = Utils.createTempDir()
+    Utils.deleteRecursively(tmpdir)
+    val result = nestedParserSqlContext
+      .parquetFile(ParquetTestData.testNestedDir1.toString)
+      .toSchemaRDD
+    result.saveAsParquetFile(tmpdir.toString)
+    nestedParserSqlContext
+      .parquetFile(tmpdir.toString)
+      .toSchemaRDD
+      .registerAsTable("tmpcopy")
+    val tmpdata = nestedParserSqlContext.sql("SELECT owner, contacts[1].name FROM tmpcopy").collect()
+    assert(tmpdata.size === 2)
+    assert(tmpdata(0).size === 2)
+    assert(tmpdata(0)(0) === "Julien Le Dem")
+    assert(tmpdata(0)(1) === "Chris Aniszczyk")
+    assert(tmpdata(1)(0) === "A. Nonymous")
+    assert(tmpdata(1)(1) === null)
+    Utils.deleteRecursively(tmpdir)
+  }
+
+  test("Writing out Map and reading it back in") {
+    val data = nestedParserSqlContext
+      .parquetFile(ParquetTestData.testNestedDir4.toString)
+      .toSchemaRDD
+    val tmpdir = Utils.createTempDir()
+    Utils.deleteRecursively(tmpdir)
+    data.saveAsParquetFile(tmpdir.toString)
+    nestedParserSqlContext
+      .parquetFile(tmpdir.toString)
+      .toSchemaRDD
+      .registerAsTable("tmpmapcopy")
+    val result1 = nestedParserSqlContext.sql("""SELECT data1["key2"] FROM tmpmapcopy""").collect()
+    assert(result1.size === 1)
+    assert(result1(0)(0) === 2)
+    val result2 = nestedParserSqlContext.sql("SELECT data2 FROM tmpmapcopy").collect()
+    assert(result2.size === 1)
+    val entry1 = result2(0)(0)
+      .asInstanceOf[CatalystConverter.MapScalaType[String, CatalystConverter.StructScalaType[_]]]
+      .getOrElse("seven", null)
+    assert(entry1 != null)
+    assert(entry1(0) === 42)
+    assert(entry1(1) === "the answer")
+    val entry2 = result2(0)(0)
+      .asInstanceOf[CatalystConverter.MapScalaType[String, CatalystConverter.StructScalaType[_]]]
+      .getOrElse("eight", null)
+    assert(entry2 != null)
+    assert(entry2(0) === 49)
+    assert(entry2(1) === null)
+    val result3 = nestedParserSqlContext.sql("""SELECT data2["seven"].payload1, data2["seven"].payload2 FROM tmpmapcopy""").collect()
+    assert(result3.size === 1)
+    assert(result3(0)(0) === 42.toLong)
+    assert(result3(0)(1) === "the answer")
+    Utils.deleteRecursively(tmpdir)
+  }
+}
+
+// TODO: the code below is needed temporarily until the standard parser is able to parse
+// nested field expressions correctly
+class NestedParserSQLContext(@transient override val sparkContext: SparkContext) extends SQLContext(sparkContext) {
+  override protected[sql] val parser = new NestedSqlParser()
+}
+
+class NestedSqlLexical(override val keywords: Seq[String]) extends SqlLexical(keywords) {
+  override def identChar = letter | elem('_')
+  delimiters += (".")
+}
+
+class NestedSqlParser extends SqlParser {
+  override val lexical = new NestedSqlLexical(reservedWords)
+
+  override protected lazy val baseExpression: PackratParser[Expression] =
+    expression ~ "[" ~ expression <~ "]" ^^ {
+      case base ~ _ ~ ordinal => GetItem(base, ordinal)
+    } |
+    expression ~ "." ~ ident ^^ {
+      case base ~ _ ~ fieldName => GetField(base, fieldName)
+    } |
+    TRUE ^^^ Literal(true, BooleanType) |
+    FALSE ^^^ Literal(false, BooleanType) |
+    cast |
+    "(" ~> expression <~ ")" |
+    function |
+    "-" ~> literal ^^ UnaryMinus |
+    ident ^^ UnresolvedAttribute |
+    "*" ^^^ Star(None) |
+    literal
 }
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
index 68284344afd5..f923d68932f8 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
@@ -208,7 +208,9 @@ object HiveMetastoreTypes extends RegexParsers {
     }
 
   protected lazy val structType: Parser[DataType] =
-    "struct" ~> "<" ~> repsep(structField,",") <~ ">" ^^ StructType
+    "struct" ~> "<" ~> repsep(structField,",") <~ ">"  ^^ {
+      case fields => new StructType(fields)
+    }
 
   protected lazy val dataType: Parser[DataType] =
     arrayType |

From 61756409736a64bd42577782cb7468557fa0b642 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Thu, 19 Jun 2014 23:58:23 -0700
Subject: [PATCH 471/810] [SPARK-2210] cast to boolean on boolean value gets
 turned into NOT((boolean_condition) = 0)

```
explain select cast(cast(key=0 as boolean) as boolean) aaa from src
```
should be
```
[Physical execution plan:]
[Project [(key#10:0 = 0) AS aaa#7]]
[ HiveTableScan [key#10], (MetastoreRelation default, src, None), None]
```

However, it is currently
```
[Physical execution plan:]
[Project [NOT((key#10=0) = 0) AS aaa#7]]
[ HiveTableScan [key#10], (MetastoreRelation default, src, None), None]
```

Author: Reynold Xin <rxin@apache.org>

Closes #1144 from rxin/booleancast and squashes the following commits:

c4e543d [Reynold Xin] [SPARK-2210] boolean cast on boolean value should be removed.
---
 .../catalyst/analysis/HiveTypeCoercion.scala  |  4 ++-
 .../execution/HiveTypeCoercionSuite.scala     | 25 ++++++++++++++++++-
 2 files changed, 27 insertions(+), 2 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
index 6d331fb501d0..c0714bcdd0af 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
@@ -251,7 +251,9 @@ trait HiveTypeCoercion {
     def apply(plan: LogicalPlan): LogicalPlan = plan transformAllExpressions {
       // Skip nodes who's children have not been resolved yet.
       case e if !e.childrenResolved => e
-
+      // Skip if the type is boolean type already. Note that this extra cast should be removed
+      // by optimizer.SimplifyCasts.
+      case Cast(e, BooleanType) if e.dataType == BooleanType => e
       case Cast(e, BooleanType) => Not(Equals(e, Literal(0)))
       case Cast(e, dataType) if e.dataType == BooleanType =>
         Cast(If(e, Literal(1), Literal(0)), dataType)
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveTypeCoercionSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveTypeCoercionSuite.scala
index e030c8ee3dfc..cc8744c9668e 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveTypeCoercionSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveTypeCoercionSuite.scala
@@ -17,8 +17,12 @@
 
 package org.apache.spark.sql.hive.execution
 
+import org.apache.spark.sql.catalyst.expressions.{Cast, Equals}
+import org.apache.spark.sql.execution.Project
+import org.apache.spark.sql.hive.test.TestHive
+
 /**
- * A set of tests that validate type promotion rules.
+ * A set of tests that validate type promotion and coercion rules.
  */
 class HiveTypeCoercionSuite extends HiveComparisonTest {
   val baseTypes = Seq("1", "1.0", "1L", "1S", "1Y", "'1'")
@@ -28,4 +32,23 @@ class HiveTypeCoercionSuite extends HiveComparisonTest {
       createQueryTest(s"$i + $j", s"SELECT $i + $j FROM src LIMIT 1")
     }
   }
+
+  test("[SPARK-2210] boolean cast on boolean value should be removed") {
+    val q = "select cast(cast(key=0 as boolean) as boolean) from src"
+    val project = TestHive.hql(q).queryExecution.executedPlan.collect { case e: Project => e }.head
+
+    // No cast expression introduced
+    project.transformAllExpressions { case c: Cast =>
+      assert(false, "unexpected cast " + c)
+      c
+    }
+
+    // Only one Equals
+    var numEquals = 0
+    project.transformAllExpressions { case e: Equals =>
+      numEquals += 1
+      e
+    }
+    assert(numEquals === 1)
+  }
 }

From c55bbb49f7ec653f0ff635015d3bc789ca26c4eb Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Fri, 20 Jun 2014 00:01:19 -0700
Subject: [PATCH 472/810] [SPARK-2209][SQL] Cast shouldn't do null check twice.

Also took the chance to clean up cast a little bit. Too many arrows on each line before!

Author: Reynold Xin <rxin@apache.org>

Closes #1143 from rxin/cast and squashes the following commits:

dd006cb [Reynold Xin] Code review feedback.
c2b88ae [Reynold Xin] [SPARK-2209][SQL] Cast shouldn't do null check twice.
---
 .../spark/sql/catalyst/expressions/Cast.scala | 274 ++++++++++--------
 1 file changed, 159 insertions(+), 115 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
index 0b3a4e728ec5..1f9716e385e9 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
@@ -24,72 +24,87 @@ import org.apache.spark.sql.catalyst.types._
 /** Cast the child expression to the target data type. */
 case class Cast(child: Expression, dataType: DataType) extends UnaryExpression {
   override def foldable = child.foldable
-  def nullable = (child.dataType, dataType) match {
+
+  override def nullable = (child.dataType, dataType) match {
     case (StringType, _: NumericType) => true
     case (StringType, TimestampType)  => true
     case _                            => child.nullable
   }
+
   override def toString = s"CAST($child, $dataType)"
 
   type EvaluatedType = Any
 
-  def nullOrCast[T](a: Any, func: T => Any): Any = if(a == null) {
-    null
-  } else {
-    func(a.asInstanceOf[T])
-  }
+  // [[func]] assumes the input is no longer null because eval already does the null check.
+  @inline private[this] def buildCast[T](a: Any, func: T => Any): Any = func(a.asInstanceOf[T])
 
   // UDFToString
-  def castToString: Any => Any = child.dataType match {
-    case BinaryType => nullOrCast[Array[Byte]](_, new String(_, "UTF-8"))
-    case _ => nullOrCast[Any](_, _.toString)
+  private[this] def castToString: Any => Any = child.dataType match {
+    case BinaryType => buildCast[Array[Byte]](_, new String(_, "UTF-8"))
+    case _ => buildCast[Any](_, _.toString)
   }
 
   // BinaryConverter
-  def castToBinary: Any => Any = child.dataType match {
-    case StringType => nullOrCast[String](_, _.getBytes("UTF-8"))
+  private[this] def castToBinary: Any => Any = child.dataType match {
+    case StringType => buildCast[String](_, _.getBytes("UTF-8"))
   }
 
   // UDFToBoolean
-  def castToBoolean: Any => Any = child.dataType match {
-    case StringType => nullOrCast[String](_, _.length() != 0)
-    case TimestampType => nullOrCast[Timestamp](_, b => {(b.getTime() != 0 || b.getNanos() != 0)})
-    case LongType => nullOrCast[Long](_, _ != 0)
-    case IntegerType => nullOrCast[Int](_, _ != 0)
-    case ShortType => nullOrCast[Short](_, _ != 0)
-    case ByteType => nullOrCast[Byte](_, _ != 0)
-    case DecimalType => nullOrCast[BigDecimal](_, _ != 0)
-    case DoubleType => nullOrCast[Double](_, _ != 0)
-    case FloatType => nullOrCast[Float](_, _ != 0)
+  private[this] def castToBoolean: Any => Any = child.dataType match {
+    case StringType =>
+      buildCast[String](_, _.length() != 0)
+    case TimestampType =>
+      buildCast[Timestamp](_, b => b.getTime() != 0 || b.getNanos() != 0)
+    case LongType =>
+      buildCast[Long](_, _ != 0)
+    case IntegerType =>
+      buildCast[Int](_, _ != 0)
+    case ShortType =>
+      buildCast[Short](_, _ != 0)
+    case ByteType =>
+      buildCast[Byte](_, _ != 0)
+    case DecimalType =>
+      buildCast[BigDecimal](_, _ != 0)
+    case DoubleType =>
+      buildCast[Double](_, _ != 0)
+    case FloatType =>
+      buildCast[Float](_, _ != 0)
   }
 
   // TimestampConverter
-  def castToTimestamp: Any => Any = child.dataType match {
-    case StringType => nullOrCast[String](_, s => {
-      // Throw away extra if more than 9 decimal places
-      val periodIdx = s.indexOf(".");
-      var n = s
-      if (periodIdx != -1) {
-        if (n.length() - periodIdx > 9) {
+  private[this] def castToTimestamp: Any => Any = child.dataType match {
+    case StringType =>
+      buildCast[String](_, s => {
+        // Throw away extra if more than 9 decimal places
+        val periodIdx = s.indexOf(".")
+        var n = s
+        if (periodIdx != -1 && n.length() - periodIdx > 9) {
           n = n.substring(0, periodIdx + 10)
         }
-      }
-      try Timestamp.valueOf(n) catch { case _: java.lang.IllegalArgumentException => null}
-    })
-    case BooleanType => nullOrCast[Boolean](_, b => new Timestamp((if(b) 1 else 0) * 1000))
-    case LongType => nullOrCast[Long](_, l => new Timestamp(l * 1000))
-    case IntegerType => nullOrCast[Int](_, i => new Timestamp(i * 1000))
-    case ShortType => nullOrCast[Short](_, s => new Timestamp(s * 1000))
-    case ByteType => nullOrCast[Byte](_, b => new Timestamp(b * 1000))
+        try Timestamp.valueOf(n) catch { case _: java.lang.IllegalArgumentException => null }
+      })
+    case BooleanType =>
+      buildCast[Boolean](_, b => new Timestamp((if (b) 1 else 0) * 1000))
+    case LongType =>
+      buildCast[Long](_, l => new Timestamp(l * 1000))
+    case IntegerType =>
+      buildCast[Int](_, i => new Timestamp(i * 1000))
+    case ShortType =>
+      buildCast[Short](_, s => new Timestamp(s * 1000))
+    case ByteType =>
+      buildCast[Byte](_, b => new Timestamp(b * 1000))
     // TimestampWritable.decimalToTimestamp
-    case DecimalType => nullOrCast[BigDecimal](_, d => decimalToTimestamp(d))
+    case DecimalType =>
+      buildCast[BigDecimal](_, d => decimalToTimestamp(d))
     // TimestampWritable.doubleToTimestamp
-    case DoubleType => nullOrCast[Double](_, d => decimalToTimestamp(d))
+    case DoubleType =>
+      buildCast[Double](_, d => decimalToTimestamp(d))
     // TimestampWritable.floatToTimestamp
-    case FloatType => nullOrCast[Float](_, f => decimalToTimestamp(f))
+    case FloatType =>
+      buildCast[Float](_, f => decimalToTimestamp(f))
   }
 
-  private def decimalToTimestamp(d: BigDecimal) = {
+  private[this]  def decimalToTimestamp(d: BigDecimal) = {
     val seconds = d.longValue()
     val bd = (d - seconds) * 1000000000
     val nanos = bd.intValue()
@@ -104,85 +119,118 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression {
   }
 
   // Timestamp to long, converting milliseconds to seconds
-  private def timestampToLong(ts: Timestamp) = ts.getTime / 1000
+  private[this] def timestampToLong(ts: Timestamp) = ts.getTime / 1000
 
-  private def timestampToDouble(ts: Timestamp) = {
+  private[this] def timestampToDouble(ts: Timestamp) = {
     // First part is the seconds since the beginning of time, followed by nanosecs.
     ts.getTime / 1000 + ts.getNanos.toDouble / 1000000000
   }
 
-  def castToLong: Any => Any = child.dataType match {
-    case StringType => nullOrCast[String](_, s => try s.toLong catch {
-      case _: NumberFormatException => null
-    })
-    case BooleanType => nullOrCast[Boolean](_, b => if(b) 1L else 0L)
-    case TimestampType => nullOrCast[Timestamp](_, t => timestampToLong(t))
-    case DecimalType => nullOrCast[BigDecimal](_, _.toLong)
-    case x: NumericType => b => x.numeric.asInstanceOf[Numeric[Any]].toLong(b)
-  }
-
-  def castToInt: Any => Any = child.dataType match {
-    case StringType => nullOrCast[String](_, s => try s.toInt catch {
-      case _: NumberFormatException => null
-    })
-    case BooleanType => nullOrCast[Boolean](_, b => if(b) 1 else 0)
-    case TimestampType => nullOrCast[Timestamp](_, t => timestampToLong(t).toInt)
-    case DecimalType => nullOrCast[BigDecimal](_, _.toInt)
-    case x: NumericType => b => x.numeric.asInstanceOf[Numeric[Any]].toInt(b)
-  }
-
-  def castToShort: Any => Any = child.dataType match {
-    case StringType => nullOrCast[String](_, s => try s.toShort catch {
-      case _: NumberFormatException => null
-    })
-    case BooleanType => nullOrCast[Boolean](_, b => if(b) 1.toShort else 0.toShort)
-    case TimestampType => nullOrCast[Timestamp](_, t => timestampToLong(t).toShort)
-    case DecimalType => nullOrCast[BigDecimal](_, _.toShort)
-    case x: NumericType => b => x.numeric.asInstanceOf[Numeric[Any]].toInt(b).toShort
-  }
-
-  def castToByte: Any => Any = child.dataType match {
-    case StringType => nullOrCast[String](_, s => try s.toByte catch {
-      case _: NumberFormatException => null
-    })
-    case BooleanType => nullOrCast[Boolean](_, b => if(b) 1.toByte else 0.toByte)
-    case TimestampType => nullOrCast[Timestamp](_, t => timestampToLong(t).toByte)
-    case DecimalType => nullOrCast[BigDecimal](_, _.toByte)
-    case x: NumericType => b => x.numeric.asInstanceOf[Numeric[Any]].toInt(b).toByte
-  }
-
-  def castToDecimal: Any => Any = child.dataType match {
-    case StringType => nullOrCast[String](_, s => try BigDecimal(s.toDouble) catch {
-      case _: NumberFormatException => null
-    })
-    case BooleanType => nullOrCast[Boolean](_, b => if(b) BigDecimal(1) else BigDecimal(0))
+  private[this] def castToLong: Any => Any = child.dataType match {
+    case StringType =>
+      buildCast[String](_, s => try s.toLong catch {
+        case _: NumberFormatException => null
+      })
+    case BooleanType =>
+      buildCast[Boolean](_, b => if (b) 1L else 0L)
+    case TimestampType =>
+      buildCast[Timestamp](_, t => timestampToLong(t))
+    case DecimalType =>
+      buildCast[BigDecimal](_, _.toLong)
+    case x: NumericType =>
+      b => x.numeric.asInstanceOf[Numeric[Any]].toLong(b)
+  }
+
+  private[this] def castToInt: Any => Any = child.dataType match {
+    case StringType =>
+      buildCast[String](_, s => try s.toInt catch {
+        case _: NumberFormatException => null
+      })
+    case BooleanType =>
+      buildCast[Boolean](_, b => if (b) 1 else 0)
+    case TimestampType =>
+      buildCast[Timestamp](_, t => timestampToLong(t).toInt)
+    case DecimalType =>
+      buildCast[BigDecimal](_, _.toInt)
+    case x: NumericType =>
+      b => x.numeric.asInstanceOf[Numeric[Any]].toInt(b)
+  }
+
+  private[this] def castToShort: Any => Any = child.dataType match {
+    case StringType =>
+      buildCast[String](_, s => try s.toShort catch {
+        case _: NumberFormatException => null
+      })
+    case BooleanType =>
+      buildCast[Boolean](_, b => if (b) 1.toShort else 0.toShort)
+    case TimestampType =>
+      buildCast[Timestamp](_, t => timestampToLong(t).toShort)
+    case DecimalType =>
+      buildCast[BigDecimal](_, _.toShort)
+    case x: NumericType =>
+      b => x.numeric.asInstanceOf[Numeric[Any]].toInt(b).toShort
+  }
+
+  private[this] def castToByte: Any => Any = child.dataType match {
+    case StringType =>
+      buildCast[String](_, s => try s.toByte catch {
+        case _: NumberFormatException => null
+      })
+    case BooleanType =>
+      buildCast[Boolean](_, b => if (b) 1.toByte else 0.toByte)
+    case TimestampType =>
+      buildCast[Timestamp](_, t => timestampToLong(t).toByte)
+    case DecimalType =>
+      buildCast[BigDecimal](_, _.toByte)
+    case x: NumericType =>
+      b => x.numeric.asInstanceOf[Numeric[Any]].toInt(b).toByte
+  }
+
+  private[this] def castToDecimal: Any => Any = child.dataType match {
+    case StringType =>
+      buildCast[String](_, s => try BigDecimal(s.toDouble) catch {
+        case _: NumberFormatException => null
+      })
+    case BooleanType =>
+      buildCast[Boolean](_, b => if (b) BigDecimal(1) else BigDecimal(0))
     case TimestampType =>
       // Note that we lose precision here.
-      nullOrCast[Timestamp](_, t => BigDecimal(timestampToDouble(t)))
-    case x: NumericType => b => BigDecimal(x.numeric.asInstanceOf[Numeric[Any]].toDouble(b))
-  }
-
-  def castToDouble: Any => Any = child.dataType match {
-    case StringType => nullOrCast[String](_, s => try s.toDouble catch {
-      case _: NumberFormatException => null
-    })
-    case BooleanType => nullOrCast[Boolean](_, b => if(b) 1d else 0d)
-    case TimestampType => nullOrCast[Timestamp](_, t => timestampToDouble(t))
-    case DecimalType => nullOrCast[BigDecimal](_, _.toDouble)
-    case x: NumericType => b => x.numeric.asInstanceOf[Numeric[Any]].toDouble(b)
-  }
-
-  def castToFloat: Any => Any = child.dataType match {
-    case StringType => nullOrCast[String](_, s => try s.toFloat catch {
-      case _: NumberFormatException => null
-    })
-    case BooleanType => nullOrCast[Boolean](_, b => if(b) 1f else 0f)
-    case TimestampType => nullOrCast[Timestamp](_, t => timestampToDouble(t).toFloat)
-    case DecimalType => nullOrCast[BigDecimal](_, _.toFloat)
-    case x: NumericType => b => x.numeric.asInstanceOf[Numeric[Any]].toFloat(b)
+      buildCast[Timestamp](_, t => BigDecimal(timestampToDouble(t)))
+    case x: NumericType =>
+      b => BigDecimal(x.numeric.asInstanceOf[Numeric[Any]].toDouble(b))
+  }
+
+  private[this] def castToDouble: Any => Any = child.dataType match {
+    case StringType =>
+      buildCast[String](_, s => try s.toDouble catch {
+        case _: NumberFormatException => null
+      })
+    case BooleanType =>
+      buildCast[Boolean](_, b => if (b) 1d else 0d)
+    case TimestampType =>
+      buildCast[Timestamp](_, t => timestampToDouble(t))
+    case DecimalType =>
+      buildCast[BigDecimal](_, _.toDouble)
+    case x: NumericType =>
+      b => x.numeric.asInstanceOf[Numeric[Any]].toDouble(b)
+  }
+
+  private[this] def castToFloat: Any => Any = child.dataType match {
+    case StringType =>
+      buildCast[String](_, s => try s.toFloat catch {
+        case _: NumberFormatException => null
+      })
+    case BooleanType =>
+      buildCast[Boolean](_, b => if (b) 1f else 0f)
+    case TimestampType =>
+      buildCast[Timestamp](_, t => timestampToDouble(t).toFloat)
+    case DecimalType =>
+      buildCast[BigDecimal](_, _.toFloat)
+    case x: NumericType =>
+      b => x.numeric.asInstanceOf[Numeric[Any]].toFloat(b)
   }
 
-  private lazy val cast: Any => Any = dataType match {
+  private[this] lazy val cast: Any => Any = dataType match {
     case StringType => castToString
     case BinaryType => castToBinary
     case DecimalType => castToDecimal
@@ -198,10 +246,6 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression {
 
   override def eval(input: Row): Any = {
     val evaluated = child.eval(input)
-    if (evaluated == null) {
-      null
-    } else {
-      cast(evaluated)
-    }
+    if (evaluated == null) null else cast(evaluated)
   }
 }

From f46e02fcdbb3f86a8761c078708388d18282ee0c Mon Sep 17 00:00:00 2001
From: Aaron Davidson <aaron@databricks.com>
Date: Fri, 20 Jun 2014 00:06:57 -0700
Subject: [PATCH 473/810] SPARK-2203: PySpark defaults to use same num reduce
 partitions as map side

For shuffle-based operators, such as rdd.groupBy() or rdd.sortByKey(), PySpark will always assume that the default parallelism to use for the reduce side is ctx.defaultParallelism, which is a constant typically determined by the number of cores in cluster.

In contrast, Spark's Partitioner#defaultPartitioner will use the same number of reduce partitions as map partitions unless the defaultParallelism config is explicitly set. This tends to be a better default in order to avoid OOMs, and should also be the behavior of PySpark.

JIRA: https://issues.apache.org/jira/browse/SPARK-2203

Author: Aaron Davidson <aaron@databricks.com>

Closes #1138 from aarondav/pyfix and squashes the following commits:

1bd5751 [Aaron Davidson] SPARK-2203: PySpark defaults to use same num reduce partitions as map partitions
---
 python/pyspark/rdd.py | 21 ++++++++++++++++++---
 1 file changed, 18 insertions(+), 3 deletions(-)

diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py
index a0b2c744f0e7..62a95c84675d 100644
--- a/python/pyspark/rdd.py
+++ b/python/pyspark/rdd.py
@@ -512,7 +512,7 @@ def sortByKey(self, ascending=True, numPartitions=None, keyfunc = lambda x: x):
         [('a', 3), ('fleece', 7), ('had', 2), ('lamb', 5), ('little', 4), ('Mary', 1), ('was', 8), ('white', 9), ('whose', 6)]
         """
         if numPartitions is None:
-            numPartitions = self.ctx.defaultParallelism
+            numPartitions = self._defaultReducePartitions()
 
         bounds = list()
 
@@ -1154,7 +1154,7 @@ def partitionBy(self, numPartitions, partitionFunc=None):
         set([])
         """
         if numPartitions is None:
-            numPartitions = self.ctx.defaultParallelism
+            numPartitions = self._defaultReducePartitions()
 
         if partitionFunc is None:
             partitionFunc = lambda x: 0 if x is None else hash(x)
@@ -1212,7 +1212,7 @@ def combineByKey(self, createCombiner, mergeValue, mergeCombiners,
         [('a', '11'), ('b', '1')]
         """
         if numPartitions is None:
-            numPartitions = self.ctx.defaultParallelism
+            numPartitions = self._defaultReducePartitions()
         def combineLocally(iterator):
             combiners = {}
             for x in iterator:
@@ -1475,6 +1475,21 @@ def getStorageLevel(self):
                                      java_storage_level.replication())
         return storage_level
 
+    def _defaultReducePartitions(self):
+        """
+        Returns the default number of partitions to use during reduce tasks (e.g., groupBy).
+        If spark.default.parallelism is set, then we'll use the value from SparkContext
+        defaultParallelism, otherwise we'll use the number of partitions in this RDD.
+
+        This mirrors the behavior of the Scala Partitioner#defaultPartitioner, intended to reduce
+        the likelihood of OOMs. Once PySpark adopts Partitioner-based APIs, this behavior will
+        be inherent.
+        """
+        if self.ctx._conf.contains("spark.default.parallelism"):
+            return self.ctx.defaultParallelism
+        else:
+            return self.getNumPartitions()
+
     # TODO: `lookup` is disabled because we can't make direct comparisons based
     # on the key; we need to compare the hash of the key to the hash of the
     # keys in the pairs.  This could be an expensive operation, since those

From 324952892085d1933bcf392ce8f2ced452fe741e Mon Sep 17 00:00:00 2001
From: Takuya UESHIN <ueshin@happy-camper.st>
Date: Fri, 20 Jun 2014 00:12:52 -0700
Subject: [PATCH 474/810] [SPARK-2196] [SQL] Fix nullability of CaseWhen.

`CaseWhen` should use `branches.length` to check if `elseValue` is provided or not.

Author: Takuya UESHIN <ueshin@happy-camper.st>

Closes #1133 from ueshin/issues/SPARK-2196 and squashes the following commits:

510f12d [Takuya UESHIN] Add some tests.
dc25e8d [Takuya UESHIN] Fix nullable of CaseWhen to be nullable if the elseValue is nullable.
4f049cc [Takuya UESHIN] Fix nullability of CaseWhen.
---
 .../sql/catalyst/expressions/predicates.scala |  4 +-
 .../ExpressionEvaluationSuite.scala           | 43 +++++++++++++++++++
 2 files changed, 46 insertions(+), 1 deletion(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
index 2902906df284..2718d4364601 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
@@ -233,10 +233,12 @@ case class CaseWhen(branches: Seq[Expression]) extends Expression {
     branches.sliding(2, 2).collect { case Seq(cond, _) => cond }.toSeq
   @transient private[this] lazy val values =
     branches.sliding(2, 2).collect { case Seq(_, value) => value }.toSeq
+  @transient private[this] lazy val elseValue =
+    if (branches.length % 2 == 0) None else Option(branches.last)
 
   override def nullable = {
     // If no value is nullable and no elseValue is provided, the whole statement defaults to null.
-    values.exists(_.nullable) || (values.length % 2 == 0)
+    values.exists(_.nullable) || (elseValue.map(_.nullable).getOrElse(true))
   }
 
   override lazy val resolved = {
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala
index 8c3b062d0f80..84d72814778b 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala
@@ -333,6 +333,49 @@ class ExpressionEvaluationSuite extends FunSuite {
       Literal("^Ba*n", StringType) :: c2 :: Nil), true, row)
   }
 
+  test("case when") {
+    val row = new GenericRow(Array[Any](null, false, true, "a", "b", "c"))
+    val c1 = 'a.boolean.at(0)
+    val c2 = 'a.boolean.at(1)
+    val c3 = 'a.boolean.at(2)
+    val c4 = 'a.string.at(3)
+    val c5 = 'a.string.at(4)
+    val c6 = 'a.string.at(5)
+
+    checkEvaluation(CaseWhen(Seq(c1, c4, c6)), "c", row)
+    checkEvaluation(CaseWhen(Seq(c2, c4, c6)), "c", row)
+    checkEvaluation(CaseWhen(Seq(c3, c4, c6)), "a", row)
+    checkEvaluation(CaseWhen(Seq(Literal(null, BooleanType), c4, c6)), "c", row)
+    checkEvaluation(CaseWhen(Seq(Literal(false, BooleanType), c4, c6)), "c", row)
+    checkEvaluation(CaseWhen(Seq(Literal(true, BooleanType), c4, c6)), "a", row)
+
+    checkEvaluation(CaseWhen(Seq(c3, c4, c2, c5, c6)), "a", row)
+    checkEvaluation(CaseWhen(Seq(c2, c4, c3, c5, c6)), "b", row)
+    checkEvaluation(CaseWhen(Seq(c1, c4, c2, c5, c6)), "c", row)
+    checkEvaluation(CaseWhen(Seq(c1, c4, c2, c5)), null, row)
+
+    assert(CaseWhen(Seq(c2, c4, c6)).nullable === true)
+    assert(CaseWhen(Seq(c2, c4, c3, c5, c6)).nullable === true)
+    assert(CaseWhen(Seq(c2, c4, c3, c5)).nullable === true)
+
+    val c4_notNull = 'a.boolean.notNull.at(3)
+    val c5_notNull = 'a.boolean.notNull.at(4)
+    val c6_notNull = 'a.boolean.notNull.at(5)
+
+    assert(CaseWhen(Seq(c2, c4_notNull, c6_notNull)).nullable === false)
+    assert(CaseWhen(Seq(c2, c4, c6_notNull)).nullable === true)
+    assert(CaseWhen(Seq(c2, c4_notNull, c6)).nullable === true)
+
+    assert(CaseWhen(Seq(c2, c4_notNull, c3, c5_notNull, c6_notNull)).nullable === false)
+    assert(CaseWhen(Seq(c2, c4, c3, c5_notNull, c6_notNull)).nullable === true)
+    assert(CaseWhen(Seq(c2, c4_notNull, c3, c5, c6_notNull)).nullable === true)
+    assert(CaseWhen(Seq(c2, c4_notNull, c3, c5_notNull, c6)).nullable === true)
+
+    assert(CaseWhen(Seq(c2, c4_notNull, c3, c5_notNull)).nullable === true)
+    assert(CaseWhen(Seq(c2, c4, c3, c5_notNull)).nullable === true)
+    assert(CaseWhen(Seq(c2, c4_notNull, c3, c5)).nullable === true)
+  }
+
   test("complex type") {
     val row = new GenericRow(Array[Any](
       "^Ba*n",                                  // 0 

From 2f6a835e1a039a0b1ba6e184b3350444b70f91df Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Fri, 20 Jun 2014 00:34:59 -0700
Subject: [PATCH 475/810] [SPARK-2218] rename Equals to EqualTo in Spark SQL
 expressions.

Due to the existence of scala.Equals, it is very error prone to name the expression Equals, especially because we use a lot of partial functions and pattern matching in the optimizer.

Note that this sits on top of #1144.

Author: Reynold Xin <rxin@apache.org>

Closes #1146 from rxin/equals and squashes the following commits:

f8583fd [Reynold Xin] Merge branch 'master' of github.com:apache/spark into equals
326b388 [Reynold Xin] Merge branch 'master' of github.com:apache/spark into equals
bd19807 [Reynold Xin] Rename EqualsTo to EqualTo.
81148d1 [Reynold Xin] [SPARK-2218] rename Equals to EqualsTo in Spark SQL expressions.
c4e543d [Reynold Xin] [SPARK-2210] boolean cast on boolean value should be removed.
---
 .../apache/spark/sql/catalyst/SqlParser.scala |  6 +++---
 .../catalyst/analysis/HiveTypeCoercion.scala  |  9 ++++++---
 .../spark/sql/catalyst/dsl/package.scala      |  6 +++---
 .../sql/catalyst/expressions/package.scala    |  2 +-
 .../sql/catalyst/expressions/predicates.scala |  4 ++--
 .../sql/catalyst/planning/patterns.scala      |  6 +++---
 .../optimizer/ConstantFoldingSuite.scala      |  4 ++--
 .../spark/sql/parquet/ParquetFilters.scala    |  4 ++--
 .../spark/sql/parquet/ParquetQuerySuite.scala | 19 +++++++------------
 .../org/apache/spark/sql/hive/HiveQl.scala    | 10 +++++-----
 .../execution/HiveTypeCoercionSuite.scala     |  8 ++++----
 11 files changed, 38 insertions(+), 40 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
index 2ad2d04af570..0cc4592047b1 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
@@ -258,13 +258,13 @@ class SqlParser extends StandardTokenParsers with PackratParsers {
     comparisonExpression * (AND ^^^ { (e1: Expression, e2: Expression) => And(e1,e2) })
 
   protected lazy val comparisonExpression: Parser[Expression] =
-    termExpression ~ "=" ~ termExpression ^^ { case e1 ~ _ ~ e2 => Equals(e1, e2) } |
+    termExpression ~ "=" ~ termExpression ^^ { case e1 ~ _ ~ e2 => EqualTo(e1, e2) } |
     termExpression ~ "<" ~ termExpression ^^ { case e1 ~ _ ~ e2 => LessThan(e1, e2) } |
     termExpression ~ "<=" ~ termExpression ^^ { case e1 ~ _ ~ e2 => LessThanOrEqual(e1, e2) } |
     termExpression ~ ">" ~ termExpression ^^ { case e1 ~ _ ~ e2 => GreaterThan(e1, e2) } |
     termExpression ~ ">=" ~ termExpression ^^ { case e1 ~ _ ~ e2 => GreaterThanOrEqual(e1, e2) } |
-    termExpression ~ "!=" ~ termExpression ^^ { case e1 ~ _ ~ e2 => Not(Equals(e1, e2)) } |
-    termExpression ~ "<>" ~ termExpression ^^ { case e1 ~ _ ~ e2 => Not(Equals(e1, e2)) } |
+    termExpression ~ "!=" ~ termExpression ^^ { case e1 ~ _ ~ e2 => Not(EqualTo(e1, e2)) } |
+    termExpression ~ "<>" ~ termExpression ^^ { case e1 ~ _ ~ e2 => Not(EqualTo(e1, e2)) } |
     termExpression ~ RLIKE ~ termExpression ^^ { case e1 ~ _ ~ e2 => RLike(e1, e2) } |
     termExpression ~ REGEXP ~ termExpression ^^ { case e1 ~ _ ~ e2 => RLike(e1, e2) } |
     termExpression ~ LIKE ~ termExpression ^^ { case e1 ~ _ ~ e2 => Like(e1, e2) } |
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
index c0714bcdd0af..76ddeba9cb31 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
@@ -234,8 +234,8 @@ trait HiveTypeCoercion {
     def apply(plan: LogicalPlan): LogicalPlan = plan transformAllExpressions {
       // Skip nodes who's children have not been resolved yet.
       case e if !e.childrenResolved => e
-      // No need to change Equals operators as that actually makes sense for boolean types.
-      case e: Equals => e
+      // No need to change EqualTo operators as that actually makes sense for boolean types.
+      case e: EqualTo => e
       // Otherwise turn them to Byte types so that there exists and ordering.
       case p: BinaryComparison
           if p.left.dataType == BooleanType && p.right.dataType == BooleanType =>
@@ -254,7 +254,10 @@ trait HiveTypeCoercion {
       // Skip if the type is boolean type already. Note that this extra cast should be removed
       // by optimizer.SimplifyCasts.
       case Cast(e, BooleanType) if e.dataType == BooleanType => e
-      case Cast(e, BooleanType) => Not(Equals(e, Literal(0)))
+      // If the data type is not boolean and is being cast boolean, turn it into a comparison
+      // with the numeric value, i.e. x != 0. This will coerce the type into numeric type.
+      case Cast(e, BooleanType) if e.dataType != BooleanType => Not(EqualTo(e, Literal(0)))
+      // Turn true into 1, and false into 0 if casting boolean into other types.
       case Cast(e, dataType) if e.dataType == BooleanType =>
         Cast(If(e, Literal(1), Literal(0)), dataType)
     }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala
index d177339d40ae..26ad4837b0b0 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala
@@ -44,7 +44,7 @@ import org.apache.spark.sql.catalyst.types._
  *
  *  // These unresolved attributes can be used to create more complicated expressions.
  *  scala> 'a === 'b
- *  res2: org.apache.spark.sql.catalyst.expressions.Equals = ('a = 'b)
+ *  res2: org.apache.spark.sql.catalyst.expressions.EqualTo = ('a = 'b)
  *
  *  // SQL verbs can be used to construct logical query plans.
  *  scala> import org.apache.spark.sql.catalyst.plans.logical._
@@ -76,8 +76,8 @@ package object dsl {
     def <= (other: Expression) = LessThanOrEqual(expr, other)
     def > (other: Expression) = GreaterThan(expr, other)
     def >= (other: Expression) = GreaterThanOrEqual(expr, other)
-    def === (other: Expression) = Equals(expr, other)
-    def !== (other: Expression) = Not(Equals(expr, other))
+    def === (other: Expression) = EqualTo(expr, other)
+    def !== (other: Expression) = Not(EqualTo(expr, other))
 
     def like(other: Expression) = Like(expr, other)
     def rlike(other: Expression) = RLike(expr, other)
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/package.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/package.scala
index 573ec052f426..b6f2451b52e1 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/package.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/package.scala
@@ -24,7 +24,7 @@ package org.apache.spark.sql.catalyst
  * expression, a [[NamedExpression]] in addition to the standard collection of expressions.
  *
  * ==Standard Expressions==
- * A library of standard expressions (e.g., [[Add]], [[Equals]]), aggregates (e.g., SUM, COUNT),
+ * A library of standard expressions (e.g., [[Add]], [[EqualTo]]), aggregates (e.g., SUM, COUNT),
  * and other computations (e.g. UDFs). Each expression type is capable of determining its output
  * schema as a function of its children's output schema.
  *
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
index 2718d4364601..b63406b94a4a 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
@@ -52,7 +52,7 @@ trait PredicateHelper {
    *
    * For example consider a join between two relations R(a, b) and S(c, d).
    *
-   * `canEvaluate(Equals(a,b), R)` returns `true` where as `canEvaluate(Equals(a,c), R)` returns
+   * `canEvaluate(EqualTo(a,b), R)` returns `true` where as `canEvaluate(EqualTo(a,c), R)` returns
    * `false`.
    */
   protected def canEvaluate(expr: Expression, plan: LogicalPlan): Boolean =
@@ -140,7 +140,7 @@ abstract class BinaryComparison extends BinaryPredicate {
   self: Product =>
 }
 
-case class Equals(left: Expression, right: Expression) extends BinaryComparison {
+case class EqualTo(left: Expression, right: Expression) extends BinaryComparison {
   def symbol = "="
   override def eval(input: Row): Any = {
     val l = left.eval(input)
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala
index 820ecfb78b52..a43bef389c4b 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala
@@ -136,14 +136,14 @@ object HashFilteredJoin extends Logging with PredicateHelper {
     val Join(left, right, joinType, _) = join
     val (joinPredicates, otherPredicates) =
       allPredicates.flatMap(splitConjunctivePredicates).partition {
-        case Equals(l, r) if (canEvaluate(l, left) && canEvaluate(r, right)) ||
+        case EqualTo(l, r) if (canEvaluate(l, left) && canEvaluate(r, right)) ||
           (canEvaluate(l, right) && canEvaluate(r, left)) => true
         case _ => false
       }
 
     val joinKeys = joinPredicates.map {
-      case Equals(l, r) if canEvaluate(l, left) && canEvaluate(r, right) => (l, r)
-      case Equals(l, r) if canEvaluate(l, right) && canEvaluate(r, left) => (r, l)
+      case EqualTo(l, r) if canEvaluate(l, left) && canEvaluate(r, right) => (l, r)
+      case EqualTo(l, r) if canEvaluate(l, right) && canEvaluate(r, left) => (r, l)
     }
 
     // Do not consider this strategy if there are no join keys.
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConstantFoldingSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConstantFoldingSuite.scala
index cea97c584f7e..0ff82064012a 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConstantFoldingSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConstantFoldingSuite.scala
@@ -195,8 +195,8 @@ class ConstantFoldingSuite extends PlanTest {
           Add(Literal(null, IntegerType), 1) as 'c9,
           Add(1, Literal(null, IntegerType)) as 'c10,
 
-          Equals(Literal(null, IntegerType), 1) as 'c11,
-          Equals(1, Literal(null, IntegerType)) as 'c12,
+          EqualTo(Literal(null, IntegerType), 1) as 'c11,
+          EqualTo(1, Literal(null, IntegerType)) as 'c12,
 
           Like(Literal(null, StringType), "abc") as 'c13,
           Like("abc", Literal(null, StringType)) as 'c14,
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetFilters.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetFilters.scala
index 052b0a919671..cc575bedd8fc 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetFilters.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetFilters.scala
@@ -205,9 +205,9 @@ object ParquetFilters {
             Some(new AndFilter(leftFilter.get, rightFilter.get))
         }
       }
-      case p @ Equals(left: Literal, right: NamedExpression) if !right.nullable =>
+      case p @ EqualTo(left: Literal, right: NamedExpression) if !right.nullable =>
         Some(createEqualityFilter(right.name, left, p))
-      case p @ Equals(left: NamedExpression, right: Literal) if !left.nullable =>
+      case p @ EqualTo(left: NamedExpression, right: Literal) if !left.nullable =>
         Some(createEqualityFilter(left.name, right, p))
       case p @ LessThan(left: Literal, right: NamedExpression) if !right.nullable =>
         Some(createLessThanFilter(right.name, left, p))
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala
index 0c239d00b199..7714eb1b5628 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala
@@ -19,27 +19,23 @@ package org.apache.spark.sql.parquet
 
 import org.scalatest.{BeforeAndAfterAll, FunSuiteLike}
 
-import org.apache.hadoop.fs.{Path, FileSystem}
-import org.apache.hadoop.mapreduce.Job
-
 import parquet.hadoop.ParquetFileWriter
 import parquet.hadoop.util.ContextUtil
 import parquet.schema.MessageTypeParser
 
+import org.apache.hadoop.fs.{FileSystem, Path}
+import org.apache.hadoop.mapreduce.Job
 import org.apache.spark.SparkContext
 import org.apache.spark.sql._
+import org.apache.spark.sql.catalyst.{SqlLexical, SqlParser}
+import org.apache.spark.sql.catalyst.analysis.{Star, UnresolvedAttribute}
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.types.{BooleanType, IntegerType}
-import org.apache.spark.sql.test.TestSQLContext
-import org.apache.spark.sql.TestData
-import org.apache.spark.sql.SchemaRDD
 import org.apache.spark.sql.catalyst.util.getTempFilePath
-import org.apache.spark.sql.catalyst.{SqlLexical, SqlParser}
-import org.apache.spark.sql.catalyst.analysis.{UnresolvedAttribute, Star}
+import org.apache.spark.sql.test.TestSQLContext
+import org.apache.spark.sql.test.TestSQLContext._
 import org.apache.spark.util.Utils
 
-// Implicits
-import org.apache.spark.sql.test.TestSQLContext._
 
 case class TestRDDEntry(key: Int, value: String)
 
@@ -72,7 +68,6 @@ case class AllDataTypes(
     booleanField: Boolean)
 
 class ParquetQuerySuite extends QueryTest with FunSuiteLike with BeforeAndAfterAll {
-  import TestData._
   TestData // Load test data tables.
 
   var testRDD: SchemaRDD = null
@@ -319,7 +314,7 @@ class ParquetQuerySuite extends QueryTest with FunSuiteLike with BeforeAndAfterA
 
   test("create RecordFilter for simple predicates") {
     val attribute1 = new AttributeReference("first", IntegerType, false)()
-    val predicate1 = new Equals(attribute1, new Literal(1, IntegerType))
+    val predicate1 = new EqualTo(attribute1, new Literal(1, IntegerType))
     val filter1 = ParquetFilters.createFilter(predicate1)
     assert(filter1.isDefined)
     assert(filter1.get.predicate == predicate1, "predicates do not match")
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
index df761b073a75..ec653efcc8c5 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
@@ -698,7 +698,7 @@ private[hive] object HiveQl {
 
       val joinConditions = joinExpressions.sliding(2).map {
         case Seq(c1, c2) =>
-          val predicates = (c1, c2).zipped.map { case (e1, e2) => Equals(e1, e2): Expression }
+          val predicates = (c1, c2).zipped.map { case (e1, e2) => EqualTo(e1, e2): Expression }
           predicates.reduceLeft(And)
       }.toBuffer
 
@@ -924,9 +924,9 @@ private[hive] object HiveQl {
     case Token("%", left :: right:: Nil) => Remainder(nodeToExpr(left), nodeToExpr(right))
 
     /* Comparisons */
-    case Token("=", left :: right:: Nil) => Equals(nodeToExpr(left), nodeToExpr(right))
-    case Token("!=", left :: right:: Nil) => Not(Equals(nodeToExpr(left), nodeToExpr(right)))
-    case Token("<>", left :: right:: Nil) => Not(Equals(nodeToExpr(left), nodeToExpr(right)))
+    case Token("=", left :: right:: Nil) => EqualTo(nodeToExpr(left), nodeToExpr(right))
+    case Token("!=", left :: right:: Nil) => Not(EqualTo(nodeToExpr(left), nodeToExpr(right)))
+    case Token("<>", left :: right:: Nil) => Not(EqualTo(nodeToExpr(left), nodeToExpr(right)))
     case Token(">", left :: right:: Nil) => GreaterThan(nodeToExpr(left), nodeToExpr(right))
     case Token(">=", left :: right:: Nil) => GreaterThanOrEqual(nodeToExpr(left), nodeToExpr(right))
     case Token("<", left :: right:: Nil) => LessThan(nodeToExpr(left), nodeToExpr(right))
@@ -966,7 +966,7 @@ private[hive] object HiveQl {
           // FIXME (SPARK-2155): the key will get evaluated for multiple times in CaseWhen's eval().
           // Hence effectful / non-deterministic key expressions are *not* supported at the moment.
           // We should consider adding new Expressions to get around this.
-          Seq(Equals(nodeToExpr(branches(0)), nodeToExpr(condVal)),
+          Seq(EqualTo(nodeToExpr(branches(0)), nodeToExpr(condVal)),
               nodeToExpr(value))
         case Seq(elseVal) => Seq(nodeToExpr(elseVal))
       }.toSeq.reduce(_ ++ _)
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveTypeCoercionSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveTypeCoercionSuite.scala
index cc8744c9668e..7436de264a1e 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveTypeCoercionSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveTypeCoercionSuite.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.sql.hive.execution
 
-import org.apache.spark.sql.catalyst.expressions.{Cast, Equals}
+import org.apache.spark.sql.catalyst.expressions.{Cast, EqualTo}
 import org.apache.spark.sql.execution.Project
 import org.apache.spark.sql.hive.test.TestHive
 
@@ -39,13 +39,13 @@ class HiveTypeCoercionSuite extends HiveComparisonTest {
 
     // No cast expression introduced
     project.transformAllExpressions { case c: Cast =>
-      assert(false, "unexpected cast " + c)
+      fail(s"unexpected cast $c")
       c
     }
 
-    // Only one Equals
+    // Only one equality check
     var numEquals = 0
-    project.transformAllExpressions { case e: Equals =>
+    project.transformAllExpressions { case e: EqualTo =>
       numEquals += 1
       e
     }

From d484ddeff1440d8e14e05c3cd7e7a18746f1a586 Mon Sep 17 00:00:00 2001
From: Gang Bai <me@baigang.net>
Date: Fri, 20 Jun 2014 08:52:20 -0700
Subject: [PATCH 476/810] [SPARK-2163] class LBFGS optimize with Double
 tolerance instead of Int

https://issues.apache.org/jira/browse/SPARK-2163

This pull request includes the change for **[SPARK-2163]**:

* Changed the convergence tolerance parameter from type `Int` to type `Double`.
* Added types for vars in `class LBFGS`, making the style consistent with `class GradientDescent`.
* Added associated test to check that optimizing via `class LBFGS` produces the same results as via calling `runLBFGS` from `object LBFGS`.

This is a very minor change but it will solve the problem in my implementation of a regression model for count data, where I make use of LBFGS for parameter estimation.

Author: Gang Bai <me@baigang.net>

Closes #1104 from BaiGang/fix_int_tol and squashes the following commits:

cecf02c [Gang Bai] Changed setConvergenceTol'' to specify tolerance with a parameter of type Double. For the reason and the problem caused by an Int parameter, please check https://issues.apache.org/jira/browse/SPARK-2163. Added a test in LBFGSSuite for validating that optimizing via class LBFGS produces the same results as calling runLBFGS from object LBFGS. Keep the indentations and styles correct.
---
 .../spark/mllib/optimization/LBFGS.scala      |  2 +-
 .../spark/mllib/optimization/LBFGSSuite.scala | 34 +++++++++++++++++++
 2 files changed, 35 insertions(+), 1 deletion(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/optimization/LBFGS.scala b/mllib/src/main/scala/org/apache/spark/mllib/optimization/LBFGS.scala
index 8f187c9df510..7bbed9c8fdbe 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/optimization/LBFGS.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/optimization/LBFGS.scala
@@ -60,7 +60,7 @@ class LBFGS(private var gradient: Gradient, private var updater: Updater)
    * Set the convergence tolerance of iterations for L-BFGS. Default 1E-4.
    * Smaller value will lead to higher accuracy with the cost of more iterations.
    */
-  def setConvergenceTol(tolerance: Int): this.type = {
+  def setConvergenceTol(tolerance: Double): this.type = {
     this.convergenceTol = tolerance
     this
   }
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/optimization/LBFGSSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/optimization/LBFGSSuite.scala
index 4b1850659a18..fe7a9033cd5f 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/optimization/LBFGSSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/optimization/LBFGSSuite.scala
@@ -195,4 +195,38 @@ class LBFGSSuite extends FunSuite with LocalSparkContext with Matchers {
     assert(lossLBFGS3.length == 6)
     assert((lossLBFGS3(4) - lossLBFGS3(5)) / lossLBFGS3(4) < convergenceTol)
   }
+
+  test("Optimize via class LBFGS.") {
+    val regParam = 0.2
+
+    // Prepare another non-zero weights to compare the loss in the first iteration.
+    val initialWeightsWithIntercept = Vectors.dense(0.3, 0.12)
+    val convergenceTol = 1e-12
+    val maxNumIterations = 10
+
+    val lbfgsOptimizer = new LBFGS(gradient, squaredL2Updater)
+      .setNumCorrections(numCorrections)
+      .setConvergenceTol(convergenceTol)
+      .setMaxNumIterations(maxNumIterations)
+      .setRegParam(regParam)
+
+    val weightLBFGS = lbfgsOptimizer.optimize(dataRDD, initialWeightsWithIntercept)
+
+    val numGDIterations = 50
+    val stepSize = 1.0
+    val (weightGD, _) = GradientDescent.runMiniBatchSGD(
+      dataRDD,
+      gradient,
+      squaredL2Updater,
+      stepSize,
+      numGDIterations,
+      regParam,
+      miniBatchFrac,
+      initialWeightsWithIntercept)
+
+    // for class LBFGS and the optimize method, we only look at the weights
+    assert(compareDouble(weightLBFGS(0), weightGD(0), 0.02) &&
+      compareDouble(weightLBFGS(1), weightGD(1), 0.02),
+      "The weight differences between LBFGS and GD should be within 2%.")
+  }
 }

From 6a224c31e8563156ad5732a23667e73076984ae1 Mon Sep 17 00:00:00 2001
From: "Allan Douglas R. de Oliveira" <allandouglas@gmail.com>
Date: Fri, 20 Jun 2014 11:03:03 -0700
Subject: [PATCH 477/810] SPARK-1868: Users should be allowed to cogroup at
 least 4 RDDs

Adds cogroup for 4 RDDs.

Author: Allan Douglas R. de Oliveira <allandouglas@gmail.com>

Closes #813 from douglaz/more_cogroups and squashes the following commits:

f8d6273 [Allan Douglas R. de Oliveira] Test python groupWith for one more case
0e9009c [Allan Douglas R. de Oliveira] Added scala tests
c3ffcdd [Allan Douglas R. de Oliveira] Added java tests
517a67f [Allan Douglas R. de Oliveira] Added tests for python groupWith
2f402d5 [Allan Douglas R. de Oliveira] Removed TODO
17474f4 [Allan Douglas R. de Oliveira] Use new cogroup function
7877a2a [Allan Douglas R. de Oliveira] Fixed code
ba02414 [Allan Douglas R. de Oliveira] Added varargs cogroup to pyspark
c4a8a51 [Allan Douglas R. de Oliveira] Added java cogroup 4
e94963c [Allan Douglas R. de Oliveira] Fixed spacing
f1ee57b [Allan Douglas R. de Oliveira] Fixed scala style issues
d7196f1 [Allan Douglas R. de Oliveira] Allow the cogroup of 4 RDDs
---
 .../apache/spark/api/java/JavaPairRDD.scala   | 51 +++++++++++++++
 .../apache/spark/rdd/PairRDDFunctions.scala   | 51 +++++++++++++++
 .../java/org/apache/spark/JavaAPISuite.java   | 63 +++++++++++++++++++
 .../spark/rdd/PairRDDFunctionsSuite.scala     | 33 ++++++++++
 python/pyspark/join.py                        | 20 +++---
 python/pyspark/rdd.py                         | 22 ++++---
 6 files changed, 223 insertions(+), 17 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala b/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala
index 14fa9d8135af..4f3081433a54 100644
--- a/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala
+++ b/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala
@@ -543,6 +543,18 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])
       partitioner: Partitioner): JavaPairRDD[K, (JIterable[V], JIterable[W1], JIterable[W2])] =
     fromRDD(cogroupResult2ToJava(rdd.cogroup(other1, other2, partitioner)))
 
+  /**
+   * For each key k in `this` or `other1` or `other2` or `other3`,
+   * return a resulting RDD that contains a tuple with the list of values
+   * for that key in `this`, `other1`, `other2` and `other3`.
+   */
+  def cogroup[W1, W2, W3](other1: JavaPairRDD[K, W1],
+      other2: JavaPairRDD[K, W2],
+      other3: JavaPairRDD[K, W3],
+      partitioner: Partitioner)
+  : JavaPairRDD[K, (JIterable[V], JIterable[W1], JIterable[W2], JIterable[W3])] =
+    fromRDD(cogroupResult3ToJava(rdd.cogroup(other1, other2, other3, partitioner)))
+
   /**
    * For each key k in `this` or `other`, return a resulting RDD that contains a tuple with the
    * list of values for that key in `this` as well as `other`.
@@ -558,6 +570,17 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])
   : JavaPairRDD[K, (JIterable[V], JIterable[W1], JIterable[W2])] =
     fromRDD(cogroupResult2ToJava(rdd.cogroup(other1, other2)))
 
+  /**
+   * For each key k in `this` or `other1` or `other2` or `other3`,
+   * return a resulting RDD that contains a tuple with the list of values
+   * for that key in `this`, `other1`, `other2` and `other3`.
+   */
+  def cogroup[W1, W2, W3](other1: JavaPairRDD[K, W1],
+      other2: JavaPairRDD[K, W2],
+      other3: JavaPairRDD[K, W3])
+  : JavaPairRDD[K, (JIterable[V], JIterable[W1], JIterable[W2], JIterable[W3])] =
+    fromRDD(cogroupResult3ToJava(rdd.cogroup(other1, other2, other3)))
+
   /**
    * For each key k in `this` or `other`, return a resulting RDD that contains a tuple with the
    * list of values for that key in `this` as well as `other`.
@@ -574,6 +597,18 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])
   : JavaPairRDD[K, (JIterable[V], JIterable[W1], JIterable[W2])] =
     fromRDD(cogroupResult2ToJava(rdd.cogroup(other1, other2, numPartitions)))
 
+  /**
+   * For each key k in `this` or `other1` or `other2` or `other3`,
+   * return a resulting RDD that contains a tuple with the list of values
+   * for that key in `this`, `other1`, `other2` and `other3`.
+   */
+  def cogroup[W1, W2, W3](other1: JavaPairRDD[K, W1],
+      other2: JavaPairRDD[K, W2],
+      other3: JavaPairRDD[K, W3],
+      numPartitions: Int)
+  : JavaPairRDD[K, (JIterable[V], JIterable[W1], JIterable[W2], JIterable[W3])] =
+    fromRDD(cogroupResult3ToJava(rdd.cogroup(other1, other2, other3, numPartitions)))
+
   /** Alias for cogroup. */
   def groupWith[W](other: JavaPairRDD[K, W]): JavaPairRDD[K, (JIterable[V], JIterable[W])] =
     fromRDD(cogroupResultToJava(rdd.groupWith(other)))
@@ -583,6 +618,13 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])
   : JavaPairRDD[K, (JIterable[V], JIterable[W1], JIterable[W2])] =
     fromRDD(cogroupResult2ToJava(rdd.groupWith(other1, other2)))
 
+  /** Alias for cogroup. */
+  def groupWith[W1, W2, W3](other1: JavaPairRDD[K, W1],
+      other2: JavaPairRDD[K, W2],
+      other3: JavaPairRDD[K, W3])
+  : JavaPairRDD[K, (JIterable[V], JIterable[W1], JIterable[W2], JIterable[W3])] =
+    fromRDD(cogroupResult3ToJava(rdd.groupWith(other1, other2, other3)))
+
   /**
    * Return the list of values in the RDD for key `key`. This operation is done efficiently if the
    * RDD has a known partitioner by only searching the partition that the key maps to.
@@ -786,6 +828,15 @@ object JavaPairRDD {
       .mapValues(x => (asJavaIterable(x._1), asJavaIterable(x._2), asJavaIterable(x._3)))
   }
 
+  private[spark]
+  def cogroupResult3ToJava[K: ClassTag, V, W1, W2, W3](
+      rdd: RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2], Iterable[W3]))])
+  : RDD[(K, (JIterable[V], JIterable[W1], JIterable[W2], JIterable[W3]))] = {
+    rddToPairRDDFunctions(rdd)
+      .mapValues(x =>
+        (asJavaIterable(x._1), asJavaIterable(x._2), asJavaIterable(x._3), asJavaIterable(x._4)))
+  }
+
   def fromRDD[K: ClassTag, V: ClassTag](rdd: RDD[(K, V)]): JavaPairRDD[K, V] = {
     new JavaPairRDD[K, V](rdd)
   }
diff --git a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
index fe36c80e0be8..443d1c587c3e 100644
--- a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
@@ -567,6 +567,28 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
     new FlatMappedValuesRDD(self, cleanF)
   }
 
+  /**
+   * For each key k in `this` or `other1` or `other2` or `other3`,
+   * return a resulting RDD that contains a tuple with the list of values
+   * for that key in `this`, `other1`, `other2` and `other3`.
+   */
+  def cogroup[W1, W2, W3](other1: RDD[(K, W1)],
+      other2: RDD[(K, W2)],
+      other3: RDD[(K, W3)],
+      partitioner: Partitioner)
+      : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2], Iterable[W3]))] = {
+    if (partitioner.isInstanceOf[HashPartitioner] && keyClass.isArray) {
+      throw new SparkException("Default partitioner cannot partition array keys.")
+    }
+    val cg = new CoGroupedRDD[K](Seq(self, other1, other2, other3), partitioner)
+    cg.mapValues { case Seq(vs, w1s, w2s, w3s) =>
+      (vs.asInstanceOf[Seq[V]],
+        w1s.asInstanceOf[Seq[W1]],
+        w2s.asInstanceOf[Seq[W2]],
+        w3s.asInstanceOf[Seq[W3]])
+    }
+  }
+
   /**
    * For each key k in `this` or `other`, return a resulting RDD that contains a tuple with the
    * list of values for that key in `this` as well as `other`.
@@ -599,6 +621,16 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
     }
   }
 
+  /**
+   * For each key k in `this` or `other1` or `other2` or `other3`,
+   * return a resulting RDD that contains a tuple with the list of values
+   * for that key in `this`, `other1`, `other2` and `other3`.
+   */
+  def cogroup[W1, W2, W3](other1: RDD[(K, W1)], other2: RDD[(K, W2)], other3: RDD[(K, W3)])
+      : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2], Iterable[W3]))] = {
+    cogroup(other1, other2, other3, defaultPartitioner(self, other1, other2, other3))
+  }
+
   /**
    * For each key k in `this` or `other`, return a resulting RDD that contains a tuple with the
    * list of values for that key in `this` as well as `other`.
@@ -633,6 +665,19 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
     cogroup(other1, other2, new HashPartitioner(numPartitions))
   }
 
+  /**
+   * For each key k in `this` or `other1` or `other2` or `other3`,
+   * return a resulting RDD that contains a tuple with the list of values
+   * for that key in `this`, `other1`, `other2` and `other3`.
+   */
+  def cogroup[W1, W2, W3](other1: RDD[(K, W1)],
+      other2: RDD[(K, W2)],
+      other3: RDD[(K, W3)],
+      numPartitions: Int)
+      : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2], Iterable[W3]))] = {
+    cogroup(other1, other2, other3, new HashPartitioner(numPartitions))
+  }
+
   /** Alias for cogroup. */
   def groupWith[W](other: RDD[(K, W)]): RDD[(K, (Iterable[V], Iterable[W]))] = {
     cogroup(other, defaultPartitioner(self, other))
@@ -644,6 +689,12 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
     cogroup(other1, other2, defaultPartitioner(self, other1, other2))
   }
 
+  /** Alias for cogroup. */
+  def groupWith[W1, W2, W3](other1: RDD[(K, W1)], other2: RDD[(K, W2)], other3: RDD[(K, W3)])
+      : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2], Iterable[W3]))] = {
+    cogroup(other1, other2, other3, defaultPartitioner(self, other1, other2, other3))
+  }
+
   /**
    * Return an RDD with the pairs from `this` whose keys are not in `other`.
    *
diff --git a/core/src/test/java/org/apache/spark/JavaAPISuite.java b/core/src/test/java/org/apache/spark/JavaAPISuite.java
index e46298c6a9e6..761f2d6a77d3 100644
--- a/core/src/test/java/org/apache/spark/JavaAPISuite.java
+++ b/core/src/test/java/org/apache/spark/JavaAPISuite.java
@@ -21,6 +21,9 @@
 import java.util.*;
 
 import scala.Tuple2;
+import scala.Tuple3;
+import scala.Tuple4;
+
 
 import com.google.common.collect.Iterables;
 import com.google.common.collect.Iterators;
@@ -304,6 +307,66 @@ public void cogroup() {
     cogrouped.collect();
   }
 
+  @SuppressWarnings("unchecked")
+  @Test
+  public void cogroup3() {
+    JavaPairRDD<String, String> categories = sc.parallelizePairs(Arrays.asList(
+      new Tuple2<String, String>("Apples", "Fruit"),
+      new Tuple2<String, String>("Oranges", "Fruit"),
+      new Tuple2<String, String>("Oranges", "Citrus")
+      ));
+    JavaPairRDD<String, Integer> prices = sc.parallelizePairs(Arrays.asList(
+      new Tuple2<String, Integer>("Oranges", 2),
+      new Tuple2<String, Integer>("Apples", 3)
+    ));
+    JavaPairRDD<String, Integer> quantities = sc.parallelizePairs(Arrays.asList(
+      new Tuple2<String, Integer>("Oranges", 21),
+      new Tuple2<String, Integer>("Apples", 42)
+    ));
+
+    JavaPairRDD<String, Tuple3<Iterable<String>, Iterable<Integer>, Iterable<Integer>>> cogrouped =
+        categories.cogroup(prices, quantities);
+    Assert.assertEquals("[Fruit, Citrus]",
+                        Iterables.toString(cogrouped.lookup("Oranges").get(0)._1()));
+    Assert.assertEquals("[2]", Iterables.toString(cogrouped.lookup("Oranges").get(0)._2()));
+    Assert.assertEquals("[42]", Iterables.toString(cogrouped.lookup("Apples").get(0)._3()));
+
+
+    cogrouped.collect();
+  }
+
+  @SuppressWarnings("unchecked")
+  @Test
+  public void cogroup4() {
+    JavaPairRDD<String, String> categories = sc.parallelizePairs(Arrays.asList(
+      new Tuple2<String, String>("Apples", "Fruit"),
+      new Tuple2<String, String>("Oranges", "Fruit"),
+      new Tuple2<String, String>("Oranges", "Citrus")
+      ));
+    JavaPairRDD<String, Integer> prices = sc.parallelizePairs(Arrays.asList(
+      new Tuple2<String, Integer>("Oranges", 2),
+      new Tuple2<String, Integer>("Apples", 3)
+    ));
+    JavaPairRDD<String, Integer> quantities = sc.parallelizePairs(Arrays.asList(
+      new Tuple2<String, Integer>("Oranges", 21),
+      new Tuple2<String, Integer>("Apples", 42)
+    ));
+    JavaPairRDD<String, String> countries = sc.parallelizePairs(Arrays.asList(
+      new Tuple2<String, String>("Oranges", "BR"),
+      new Tuple2<String, String>("Apples", "US")
+    ));
+
+    JavaPairRDD<String, Tuple4<Iterable<String>, Iterable<Integer>, Iterable<Integer>, Iterable<String>>> cogrouped =
+        categories.cogroup(prices, quantities, countries);
+    Assert.assertEquals("[Fruit, Citrus]",
+                        Iterables.toString(cogrouped.lookup("Oranges").get(0)._1()));
+    Assert.assertEquals("[2]", Iterables.toString(cogrouped.lookup("Oranges").get(0)._2()));
+    Assert.assertEquals("[42]", Iterables.toString(cogrouped.lookup("Apples").get(0)._3()));
+    Assert.assertEquals("[BR]", Iterables.toString(cogrouped.lookup("Oranges").get(0)._4()));
+
+    cogrouped.collect();
+  }
+
   @SuppressWarnings("unchecked")
   @Test
   public void leftOuterJoin() {
diff --git a/core/src/test/scala/org/apache/spark/rdd/PairRDDFunctionsSuite.scala b/core/src/test/scala/org/apache/spark/rdd/PairRDDFunctionsSuite.scala
index 0b9004448a63..447e38ec9dbd 100644
--- a/core/src/test/scala/org/apache/spark/rdd/PairRDDFunctionsSuite.scala
+++ b/core/src/test/scala/org/apache/spark/rdd/PairRDDFunctionsSuite.scala
@@ -249,6 +249,39 @@ class PairRDDFunctionsSuite extends FunSuite with SharedSparkContext {
     ))
   }
 
+  test("groupWith3") {
+    val rdd1 = sc.parallelize(Array((1, 1), (1, 2), (2, 1), (3, 1)))
+    val rdd2 = sc.parallelize(Array((1, 'x'), (2, 'y'), (2, 'z'), (4, 'w')))
+    val rdd3 = sc.parallelize(Array((1, 'a'), (3, 'b'), (4, 'c'), (4, 'd')))
+    val joined = rdd1.groupWith(rdd2, rdd3).collect()
+    assert(joined.size === 4)
+    val joinedSet = joined.map(x => (x._1,
+      (x._2._1.toList, x._2._2.toList, x._2._3.toList))).toSet
+    assert(joinedSet === Set(
+      (1, (List(1, 2), List('x'), List('a'))),
+      (2, (List(1), List('y', 'z'), List())),
+      (3, (List(1), List(), List('b'))),
+      (4, (List(), List('w'), List('c', 'd')))
+    ))
+  }
+
+  test("groupWith4") {
+    val rdd1 = sc.parallelize(Array((1, 1), (1, 2), (2, 1), (3, 1)))
+    val rdd2 = sc.parallelize(Array((1, 'x'), (2, 'y'), (2, 'z'), (4, 'w')))
+    val rdd3 = sc.parallelize(Array((1, 'a'), (3, 'b'), (4, 'c'), (4, 'd')))
+    val rdd4 = sc.parallelize(Array((2, '@')))
+    val joined = rdd1.groupWith(rdd2, rdd3, rdd4).collect()
+    assert(joined.size === 4)
+    val joinedSet = joined.map(x => (x._1,
+      (x._2._1.toList, x._2._2.toList, x._2._3.toList, x._2._4.toList))).toSet
+    assert(joinedSet === Set(
+      (1, (List(1, 2), List('x'), List('a'), List())),
+      (2, (List(1), List('y', 'z'), List(), List('@'))),
+      (3, (List(1), List(), List('b'), List())),
+      (4, (List(), List('w'), List('c', 'd'), List()))
+    ))
+  }
+
   test("zero-partition RDD") {
     val emptyDir = Files.createTempDir()
     emptyDir.deleteOnExit()
diff --git a/python/pyspark/join.py b/python/pyspark/join.py
index 6f94d26ef86a..5f3a7e71f786 100644
--- a/python/pyspark/join.py
+++ b/python/pyspark/join.py
@@ -79,15 +79,15 @@ def dispatch(seq):
     return _do_python_join(rdd, other, numPartitions, dispatch)
 
 
-def python_cogroup(rdd, other, numPartitions):
-    vs = rdd.map(lambda (k, v): (k, (1, v)))
-    ws = other.map(lambda (k, v): (k, (2, v)))
+def python_cogroup(rdds, numPartitions):
+    def make_mapper(i):
+        return lambda (k, v): (k, (i, v))
+    vrdds = [rdd.map(make_mapper(i)) for i, rdd in enumerate(rdds)]
+    union_vrdds = reduce(lambda acc, other: acc.union(other), vrdds)
+    rdd_len = len(vrdds)
     def dispatch(seq):
-        vbuf, wbuf = [], []
+        bufs = [[] for i in range(rdd_len)]
         for (n, v) in seq:
-            if n == 1:
-                vbuf.append(v)
-            elif n == 2:
-                wbuf.append(v)
-        return (ResultIterable(vbuf), ResultIterable(wbuf))
-    return vs.union(ws).groupByKey(numPartitions).mapValues(dispatch)
+            bufs[n].append(v)
+        return tuple(map(ResultIterable, bufs))
+    return union_vrdds.groupByKey(numPartitions).mapValues(dispatch)
diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py
index 62a95c84675d..1d55c35a8bf4 100644
--- a/python/pyspark/rdd.py
+++ b/python/pyspark/rdd.py
@@ -1233,7 +1233,7 @@ def _mergeCombiners(iterator):
                     combiners[k] = mergeCombiners(combiners[k], v)
             return combiners.iteritems()
         return shuffled.mapPartitions(_mergeCombiners)
-   
+
     def aggregateByKey(self, zeroValue, seqFunc, combFunc, numPartitions=None):
         """
         Aggregate the values of each key, using given combine functions and a neutral "zero value".
@@ -1245,7 +1245,7 @@ def aggregateByKey(self, zeroValue, seqFunc, combFunc, numPartitions=None):
         """
         def createZero():
           return copy.deepcopy(zeroValue)
-        
+
         return self.combineByKey(lambda v: seqFunc(createZero(), v), seqFunc, combFunc, numPartitions)
 
     def foldByKey(self, zeroValue, func, numPartitions=None):
@@ -1323,12 +1323,20 @@ def mapValues(self, f):
         map_values_fn = lambda (k, v): (k, f(v))
         return self.map(map_values_fn, preservesPartitioning=True)
 
-    # TODO: support varargs cogroup of several RDDs.
-    def groupWith(self, other):
+    def groupWith(self, other, *others):
         """
-        Alias for cogroup.
+        Alias for cogroup but with support for multiple RDDs.
+
+        >>> w = sc.parallelize([("a", 5), ("b", 6)])
+        >>> x = sc.parallelize([("a", 1), ("b", 4)])
+        >>> y = sc.parallelize([("a", 2)])
+        >>> z = sc.parallelize([("b", 42)])
+        >>> map((lambda (x,y): (x, (list(y[0]), list(y[1]), list(y[2]), list(y[3])))), \
+                sorted(list(w.groupWith(x, y, z).collect())))
+        [('a', ([5], [1], [2], [])), ('b', ([6], [4], [], [42]))]
+
         """
-        return self.cogroup(other)
+        return python_cogroup((self, other) + others, numPartitions=None)
 
     # TODO: add variant with custom parittioner
     def cogroup(self, other, numPartitions=None):
@@ -1342,7 +1350,7 @@ def cogroup(self, other, numPartitions=None):
         >>> map((lambda (x,y): (x, (list(y[0]), list(y[1])))), sorted(list(x.cogroup(y).collect())))
         [('a', ([1], [2])), ('b', ([4], []))]
         """
-        return python_cogroup(self, other, numPartitions)
+        return python_cogroup((self, other), numPartitions)
 
     def subtractByKey(self, other, numPartitions=None):
         """

From 171ebb3a824a577d69443ec68a3543b27914cf6d Mon Sep 17 00:00:00 2001
From: William Benton <willb@redhat.com>
Date: Fri, 20 Jun 2014 13:41:38 -0700
Subject: [PATCH 478/810] SPARK-2180:  support HAVING clauses in Hive queries

This PR extends Spark's HiveQL support to handle HAVING clauses in aggregations.  The HAVING test from the Hive compatibility suite doesn't appear to be runnable from within Spark, so I added a simple comparable test to `HiveQuerySuite`.

Author: William Benton <willb@redhat.com>

Closes #1136 from willb/SPARK-2180 and squashes the following commits:

3bbaf26 [William Benton] Added casts to HAVING expressions
83f1340 [William Benton] scalastyle fixes
18387f1 [William Benton] Add test for HAVING without GROUP BY
b880bef [William Benton] Added semantic error for HAVING without GROUP BY
942428e [William Benton] Added test coverage for SPARK-2180.
56084cc [William Benton] Add support for HAVING clauses in Hive queries.
---
 .../org/apache/spark/sql/hive/HiveQl.scala    | 30 +++++++++++++++----
 .../sql/hive/execution/HiveQuerySuite.scala   | 29 ++++++++++++++++++
 2 files changed, 53 insertions(+), 6 deletions(-)

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
index ec653efcc8c5..c69e3dba6b46 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
@@ -204,6 +204,9 @@ private[hive] object HiveQl {
   class ParseException(sql: String, cause: Throwable)
     extends Exception(s"Failed to parse: $sql", cause)
 
+  class SemanticException(msg: String)
+    extends Exception(s"Error in semantic analysis: $msg")
+
   /**
    * Returns the AST for the given SQL string.
    */
@@ -480,6 +483,7 @@ private[hive] object HiveQl {
             whereClause ::
             groupByClause ::
             orderByClause ::
+            havingClause ::
             sortByClause ::
             clusterByClause ::
             distributeByClause ::
@@ -494,6 +498,7 @@ private[hive] object HiveQl {
               "TOK_WHERE",
               "TOK_GROUPBY",
               "TOK_ORDERBY",
+              "TOK_HAVING",
               "TOK_SORTBY",
               "TOK_CLUSTERBY",
               "TOK_DISTRIBUTEBY",
@@ -576,21 +581,34 @@ private[hive] object HiveQl {
         val withDistinct =
           if (selectDistinctClause.isDefined) Distinct(withProject) else withProject
 
+        val withHaving = havingClause.map { h => 
+
+          if (groupByClause == None) {
+            throw new SemanticException("HAVING specified without GROUP BY")
+          }
+
+          val havingExpr = h.getChildren.toSeq match {
+            case Seq(hexpr) => nodeToExpr(hexpr)
+          }
+          
+          Filter(Cast(havingExpr, BooleanType), withDistinct)
+        }.getOrElse(withDistinct)
+
         val withSort =
           (orderByClause, sortByClause, distributeByClause, clusterByClause) match {
             case (Some(totalOrdering), None, None, None) =>
-              Sort(totalOrdering.getChildren.map(nodeToSortOrder), withDistinct)
+              Sort(totalOrdering.getChildren.map(nodeToSortOrder), withHaving)
             case (None, Some(perPartitionOrdering), None, None) =>
-              SortPartitions(perPartitionOrdering.getChildren.map(nodeToSortOrder), withDistinct)
+              SortPartitions(perPartitionOrdering.getChildren.map(nodeToSortOrder), withHaving)
             case (None, None, Some(partitionExprs), None) =>
-              Repartition(partitionExprs.getChildren.map(nodeToExpr), withDistinct)
+              Repartition(partitionExprs.getChildren.map(nodeToExpr), withHaving)
             case (None, Some(perPartitionOrdering), Some(partitionExprs), None) =>
               SortPartitions(perPartitionOrdering.getChildren.map(nodeToSortOrder),
-                Repartition(partitionExprs.getChildren.map(nodeToExpr), withDistinct))
+                Repartition(partitionExprs.getChildren.map(nodeToExpr), withHaving))
             case (None, None, None, Some(clusterExprs)) =>
               SortPartitions(clusterExprs.getChildren.map(nodeToExpr).map(SortOrder(_, Ascending)),
-                Repartition(clusterExprs.getChildren.map(nodeToExpr), withDistinct))
-            case (None, None, None, None) => withDistinct
+                Repartition(clusterExprs.getChildren.map(nodeToExpr), withHaving))
+            case (None, None, None, None) => withHaving
             case _ => sys.error("Unsupported set of ordering / distribution clauses.")
           }
 
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
index 9f5cf282f7c4..80185098bf24 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
@@ -224,6 +224,32 @@ class HiveQuerySuite extends HiveComparisonTest {
     TestHive.reset()
   }
 
+  test("SPARK-2180: HAVING support in GROUP BY clauses (positive)") {
+    val fixture = List(("foo", 2), ("bar", 1), ("foo", 4), ("bar", 3))
+      .zipWithIndex.map {case Pair(Pair(value, attr), key) => HavingRow(key, value, attr)}
+    
+    TestHive.sparkContext.parallelize(fixture).registerAsTable("having_test")
+    
+    val results = 
+      hql("SELECT value, max(attr) AS attr FROM having_test GROUP BY value HAVING attr > 3")
+      .collect()
+      .map(x => Pair(x.getString(0), x.getInt(1)))
+    
+    assert(results === Array(Pair("foo", 4)))
+    
+    TestHive.reset()
+  }
+
+  test("SPARK-2180:  HAVING without GROUP BY raises exception") {
+    intercept[Exception] {
+      hql("SELECT value, attr FROM having_test HAVING attr > 3")
+    }
+  }
+  
+  test("SPARK-2180:  HAVING with non-boolean clause raises no exceptions") {
+    val results = hql("select key, count(*) c from src group by key having c").collect()
+  }
+
   test("Query Hive native command execution result") {
     val tableName = "test_native_commands"
 
@@ -441,3 +467,6 @@ class HiveQuerySuite extends HiveComparisonTest {
   // since they modify /clear stuff.
 
 }
+
+// for SPARK-2180 test
+case class HavingRow(key: Int, value: String, attr: Int)

From 0ac71d1284cd4f011d5763181cba9ecb49337b66 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Fri, 20 Jun 2014 15:38:02 -0700
Subject: [PATCH 479/810] [SPARK-2225] Turn HAVING without GROUP BY into WHERE.

@willb

Author: Reynold Xin <rxin@apache.org>

Closes #1161 from rxin/having-filter and squashes the following commits:

fa8359a [Reynold Xin] [SPARK-2225] Turn HAVING without GROUP BY into WHERE.
---
 .../org/apache/spark/sql/hive/HiveQl.scala    | 15 ++++-----------
 .../sql/hive/execution/HiveQuerySuite.scala   | 19 +++++++------------
 2 files changed, 11 insertions(+), 23 deletions(-)

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
index c69e3dba6b46..b073dc3895f0 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
@@ -563,7 +563,6 @@ private[hive] object HiveQl {
             withWhere)
         }.getOrElse(withWhere)
 
-
         // The projection of the query can either be a normal projection, an aggregation
         // (if there is a group by) or a script transformation.
         val withProject = transformation.getOrElse {
@@ -581,16 +580,10 @@ private[hive] object HiveQl {
         val withDistinct =
           if (selectDistinctClause.isDefined) Distinct(withProject) else withProject
 
-        val withHaving = havingClause.map { h => 
-
-          if (groupByClause == None) {
-            throw new SemanticException("HAVING specified without GROUP BY")
-          }
-
-          val havingExpr = h.getChildren.toSeq match {
-            case Seq(hexpr) => nodeToExpr(hexpr)
-          }
-          
+        val withHaving = havingClause.map { h =>
+          val havingExpr = h.getChildren.toSeq match { case Seq(hexpr) => nodeToExpr(hexpr) }
+          // Note that we added a cast to boolean. If the expression itself is already boolean,
+          // the optimizer will get rid of the unnecessary cast.
           Filter(Cast(havingExpr, BooleanType), withDistinct)
         }.getOrElse(withDistinct)
 
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
index 80185098bf24..d855310253bf 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
@@ -227,27 +227,22 @@ class HiveQuerySuite extends HiveComparisonTest {
   test("SPARK-2180: HAVING support in GROUP BY clauses (positive)") {
     val fixture = List(("foo", 2), ("bar", 1), ("foo", 4), ("bar", 3))
       .zipWithIndex.map {case Pair(Pair(value, attr), key) => HavingRow(key, value, attr)}
-    
     TestHive.sparkContext.parallelize(fixture).registerAsTable("having_test")
-    
     val results = 
       hql("SELECT value, max(attr) AS attr FROM having_test GROUP BY value HAVING attr > 3")
       .collect()
       .map(x => Pair(x.getString(0), x.getInt(1)))
-    
+
     assert(results === Array(Pair("foo", 4)))
-    
     TestHive.reset()
   }
-
-  test("SPARK-2180:  HAVING without GROUP BY raises exception") {
-    intercept[Exception] {
-      hql("SELECT value, attr FROM having_test HAVING attr > 3")
-    }
-  }
   
-  test("SPARK-2180:  HAVING with non-boolean clause raises no exceptions") {
-    val results = hql("select key, count(*) c from src group by key having c").collect()
+  test("SPARK-2180: HAVING with non-boolean clause raises no exceptions") {
+    hql("select key, count(*) c from src group by key having c").collect()
+  }
+
+  test("SPARK-2225: turn HAVING without GROUP BY into a simple filter") {
+    assert(hql("select key from src having key > 490").collect().size < 100)
   }
 
   test("Query Hive native command execution result") {

From 01125a1162b7bf42ae907cb6616616cc4ffb6fa9 Mon Sep 17 00:00:00 2001
From: Andrew Or <andrewor14@gmail.com>
Date: Fri, 20 Jun 2014 17:14:33 -0700
Subject: [PATCH 480/810] Clean up CacheManager et al.

**UPDATE**

I have removed the special handling for `StorageLevel.MEMORY_*_SER` for now, because it introduces a potential performance regression. With the latest changes, this PR should include mainly style (code readability) fixes. The only functionality change is the update in `MemoryStore#putBytes` to actually return updated blocks, though this is a minor bug fix.

Now this is mainly a precursor to another PR (once again).

---------
*Old comment*

The deserialized version of a partition may occupy much more space than the serialized version. Therefore, if a partition is to be cached with `StorageLevel.MEMORY_*_SER`, we don't need to fully unroll it into an `ArrayBuffer`, but instead we can unroll it into a potentially much smaller `ByteBuffer`. This may save us from OOMs in this case.

Author: Andrew Or <andrewor14@gmail.com>

Closes #1083 from andrewor14/unroll-them-partitions and squashes the following commits:

7048aa0 [Andrew Or] Merge branch 'master' of github.com:apache/spark into unroll-them-partitions
3d9a366 [Andrew Or] Minor change for readability
d12b95f [Andrew Or] Remove unused imports (minor)
a4c387b [Andrew Or] Merge branch 'master' of github.com:apache/spark into unroll-them-partitions
cf5f565 [Andrew Or] Remove special handling for MEM_*_SER
0091ec0 [Andrew Or] Address review feedback
44ef282 [Andrew Or] Actually return updated blocks in putBytes
2941c89 [Andrew Or] Clean up BlockStore (minor)
a8f181d [Andrew Or] Add special handling for StorageLevel.MEMORY_*_SER
---
 .../scala/org/apache/spark/CacheManager.scala | 163 ++++++++++--------
 .../apache/spark/scheduler/ResultTask.scala   |   3 +-
 .../spark/scheduler/ShuffleMapTask.scala      |   5 +-
 .../org/apache/spark/storage/BlockStore.scala |  22 ++-
 .../apache/spark/storage/MemoryStore.scala    |   8 +-
 5 files changed, 113 insertions(+), 88 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/CacheManager.scala b/core/src/main/scala/org/apache/spark/CacheManager.scala
index 315ed91f81df..3f667a4a0f9c 100644
--- a/core/src/main/scala/org/apache/spark/CacheManager.scala
+++ b/core/src/main/scala/org/apache/spark/CacheManager.scala
@@ -20,25 +20,25 @@ package org.apache.spark
 import scala.collection.mutable.{ArrayBuffer, HashSet}
 
 import org.apache.spark.rdd.RDD
-import org.apache.spark.storage.{BlockId, BlockManager, BlockStatus, RDDBlockId, StorageLevel}
+import org.apache.spark.storage._
 
 /**
- * Spark class responsible for passing RDDs split contents to the BlockManager and making
+ * Spark class responsible for passing RDDs partition contents to the BlockManager and making
  * sure a node doesn't load two copies of an RDD at once.
  */
 private[spark] class CacheManager(blockManager: BlockManager) extends Logging {
 
-  /** Keys of RDD splits that are being computed/loaded. */
+  /** Keys of RDD partitions that are being computed/loaded. */
   private val loading = new HashSet[RDDBlockId]()
 
-  /** Gets or computes an RDD split. Used by RDD.iterator() when an RDD is cached. */
+  /** Gets or computes an RDD partition. Used by RDD.iterator() when an RDD is cached. */
   def getOrCompute[T](
       rdd: RDD[T],
-      split: Partition,
+      partition: Partition,
       context: TaskContext,
       storageLevel: StorageLevel): Iterator[T] = {
 
-    val key = RDDBlockId(rdd.id, split.index)
+    val key = RDDBlockId(rdd.id, partition.index)
     logDebug(s"Looking for partition $key")
     blockManager.get(key) match {
       case Some(values) =>
@@ -46,79 +46,28 @@ private[spark] class CacheManager(blockManager: BlockManager) extends Logging {
         new InterruptibleIterator(context, values.asInstanceOf[Iterator[T]])
 
       case None =>
-        // Mark the split as loading (unless someone else marks it first)
-        loading.synchronized {
-          if (loading.contains(key)) {
-            logInfo(s"Another thread is loading $key, waiting for it to finish...")
-            while (loading.contains(key)) {
-              try {
-                loading.wait()
-              } catch {
-                case e: Exception =>
-                  logWarning(s"Got an exception while waiting for another thread to load $key", e)
-              }
-            }
-            logInfo(s"Finished waiting for $key")
-            /* See whether someone else has successfully loaded it. The main way this would fail
-             * is for the RDD-level cache eviction policy if someone else has loaded the same RDD
-             * partition but we didn't want to make space for it. However, that case is unlikely
-             * because it's unlikely that two threads would work on the same RDD partition. One
-             * downside of the current code is that threads wait serially if this does happen. */
-            blockManager.get(key) match {
-              case Some(values) =>
-                return new InterruptibleIterator(context, values.asInstanceOf[Iterator[T]])
-              case None =>
-                logInfo(s"Whoever was loading $key failed; we'll try it ourselves")
-                loading.add(key)
-            }
-          } else {
-            loading.add(key)
-          }
+        // Acquire a lock for loading this partition
+        // If another thread already holds the lock, wait for it to finish return its results
+        val storedValues = acquireLockForPartition[T](key)
+        if (storedValues.isDefined) {
+          return new InterruptibleIterator[T](context, storedValues.get)
         }
+
+        // Otherwise, we have to load the partition ourselves
         try {
-          // If we got here, we have to load the split
           logInfo(s"Partition $key not found, computing it")
-          val computedValues = rdd.computeOrReadCheckpoint(split, context)
+          val computedValues = rdd.computeOrReadCheckpoint(partition, context)
 
-          // Persist the result, so long as the task is not running locally
+          // If the task is running locally, do not persist the result
           if (context.runningLocally) {
             return computedValues
           }
 
-          // Keep track of blocks with updated statuses
-          var updatedBlocks = Seq[(BlockId, BlockStatus)]()
-          val returnValue: Iterator[T] = {
-            if (storageLevel.useDisk && !storageLevel.useMemory) {
-              /* In the case that this RDD is to be persisted using DISK_ONLY
-               * the iterator will be passed directly to the blockManager (rather then
-               * caching it to an ArrayBuffer first), then the resulting block data iterator
-               * will be passed back to the user. If the iterator generates a lot of data,
-               * this means that it doesn't all have to be held in memory at one time.
-               * This could also apply to MEMORY_ONLY_SER storage, but we need to make sure
-               * blocks aren't dropped by the block store before enabling that. */
-              updatedBlocks = blockManager.put(key, computedValues, storageLevel, tellMaster = true)
-              blockManager.get(key) match {
-                case Some(values) =>
-                  values.asInstanceOf[Iterator[T]]
-                case None =>
-                  logInfo(s"Failure to store $key")
-                  throw new SparkException("Block manager failed to return persisted value")
-              }
-            } else {
-              // In this case the RDD is cached to an array buffer. This will save the results
-              // if we're dealing with a 'one-time' iterator
-              val elements = new ArrayBuffer[Any]
-              elements ++= computedValues
-              updatedBlocks = blockManager.put(key, elements, storageLevel, tellMaster = true)
-              elements.iterator.asInstanceOf[Iterator[T]]
-            }
-          }
-
-          // Update task metrics to include any blocks whose storage status is updated
-          val metrics = context.taskMetrics
-          metrics.updatedBlocks = Some(updatedBlocks)
-
-          new InterruptibleIterator(context, returnValue)
+          // Otherwise, cache the values and keep track of any updates in block statuses
+          val updatedBlocks = new ArrayBuffer[(BlockId, BlockStatus)]
+          val cachedValues = putInBlockManager(key, computedValues, storageLevel, updatedBlocks)
+          context.taskMetrics.updatedBlocks = Some(updatedBlocks)
+          new InterruptibleIterator(context, cachedValues)
 
         } finally {
           loading.synchronized {
@@ -128,4 +77,76 @@ private[spark] class CacheManager(blockManager: BlockManager) extends Logging {
         }
     }
   }
+
+  /**
+   * Acquire a loading lock for the partition identified by the given block ID.
+   *
+   * If the lock is free, just acquire it and return None. Otherwise, another thread is already
+   * loading the partition, so we wait for it to finish and return the values loaded by the thread.
+   */
+  private def acquireLockForPartition[T](id: RDDBlockId): Option[Iterator[T]] = {
+    loading.synchronized {
+      if (!loading.contains(id)) {
+        // If the partition is free, acquire its lock to compute its value
+        loading.add(id)
+        None
+      } else {
+        // Otherwise, wait for another thread to finish and return its result
+        logInfo(s"Another thread is loading $id, waiting for it to finish...")
+        while (loading.contains(id)) {
+          try {
+            loading.wait()
+          } catch {
+            case e: Exception =>
+              logWarning(s"Exception while waiting for another thread to load $id", e)
+          }
+        }
+        logInfo(s"Finished waiting for $id")
+        val values = blockManager.get(id)
+        if (!values.isDefined) {
+          /* The block is not guaranteed to exist even after the other thread has finished.
+           * For instance, the block could be evicted after it was put, but before our get.
+           * In this case, we still need to load the partition ourselves. */
+          logInfo(s"Whoever was loading $id failed; we'll try it ourselves")
+          loading.add(id)
+        }
+        values.map(_.asInstanceOf[Iterator[T]])
+      }
+    }
+  }
+
+  /**
+   * Cache the values of a partition, keeping track of any updates in the storage statuses
+   * of other blocks along the way.
+   */
+  private def putInBlockManager[T](
+      key: BlockId,
+      values: Iterator[T],
+      storageLevel: StorageLevel,
+      updatedBlocks: ArrayBuffer[(BlockId, BlockStatus)]): Iterator[T] = {
+
+    if (!storageLevel.useMemory) {
+      /* This RDD is not to be cached in memory, so we can just pass the computed values
+       * as an iterator directly to the BlockManager, rather than first fully unrolling
+       * it in memory. The latter option potentially uses much more memory and risks OOM
+       * exceptions that can be avoided. */
+      updatedBlocks ++= blockManager.put(key, values, storageLevel, tellMaster = true)
+      blockManager.get(key) match {
+        case Some(v) => v.asInstanceOf[Iterator[T]]
+        case None =>
+          logInfo(s"Failure to store $key")
+          throw new BlockException(key, s"Block manager failed to return cached value for $key!")
+      }
+    } else {
+      /* This RDD is to be cached in memory. In this case we cannot pass the computed values
+       * to the BlockManager as an iterator and expect to read it back later. This is because
+       * we may end up dropping a partition from memory store before getting it back, e.g.
+       * when the entirety of the RDD does not fit in memory. */
+      val elements = new ArrayBuffer[Any]
+      elements ++= values
+      updatedBlocks ++= blockManager.put(key, elements, storageLevel, tellMaster = true)
+      elements.iterator.asInstanceOf[Iterator[T]]
+    }
+  }
+
 }
diff --git a/core/src/main/scala/org/apache/spark/scheduler/ResultTask.scala b/core/src/main/scala/org/apache/spark/scheduler/ResultTask.scala
index 0e8d551e4b2a..bbf9f7388b07 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/ResultTask.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/ResultTask.scala
@@ -17,11 +17,12 @@
 
 package org.apache.spark.scheduler
 
+import scala.language.existentials
+
 import java.io._
 import java.util.zip.{GZIPInputStream, GZIPOutputStream}
 
 import scala.collection.mutable.HashMap
-import scala.language.existentials
 
 import org.apache.spark._
 import org.apache.spark.rdd.{RDD, RDDCheckpointData}
diff --git a/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala b/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala
index 0098b5a59d1a..859cdc524a58 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala
@@ -25,10 +25,7 @@ import java.util.zip.{GZIPInputStream, GZIPOutputStream}
 import scala.collection.mutable.HashMap
 
 import org.apache.spark._
-import org.apache.spark.executor.ShuffleWriteMetrics
 import org.apache.spark.rdd.{RDD, RDDCheckpointData}
-import org.apache.spark.serializer.Serializer
-import org.apache.spark.storage._
 import org.apache.spark.shuffle.ShuffleWriter
 
 private[spark] object ShuffleMapTask {
@@ -150,7 +147,7 @@ private[spark] class ShuffleMapTask(
       for (elem <- rdd.iterator(split, context)) {
         writer.write(elem.asInstanceOf[Product2[Any, Any]])
       }
-      return writer.stop(success = true).get
+      writer.stop(success = true).get
     } catch {
       case e: Exception =>
         if (writer != null) {
diff --git a/core/src/main/scala/org/apache/spark/storage/BlockStore.scala b/core/src/main/scala/org/apache/spark/storage/BlockStore.scala
index 9a9be047c724..b9b53b1a2f11 100644
--- a/core/src/main/scala/org/apache/spark/storage/BlockStore.scala
+++ b/core/src/main/scala/org/apache/spark/storage/BlockStore.scala
@@ -24,11 +24,11 @@ import scala.collection.mutable.ArrayBuffer
 import org.apache.spark.Logging
 
 /**
- * Abstract class to store blocks
+ * Abstract class to store blocks.
  */
-private[spark]
-abstract class BlockStore(val blockManager: BlockManager) extends Logging {
-  def putBytes(blockId: BlockId, bytes: ByteBuffer, level: StorageLevel) : PutResult
+private[spark] abstract class BlockStore(val blockManager: BlockManager) extends Logging {
+
+  def putBytes(blockId: BlockId, bytes: ByteBuffer, level: StorageLevel): PutResult
 
   /**
    * Put in a block and, possibly, also return its content as either bytes or another Iterator.
@@ -37,11 +37,17 @@ abstract class BlockStore(val blockManager: BlockManager) extends Logging {
    * @return a PutResult that contains the size of the data, as well as the values put if
    *         returnValues is true (if not, the result's data field can be null)
    */
-  def putValues(blockId: BlockId, values: Iterator[Any], level: StorageLevel,
-    returnValues: Boolean) : PutResult
+  def putValues(
+    blockId: BlockId,
+    values: Iterator[Any],
+    level: StorageLevel,
+    returnValues: Boolean): PutResult
 
-  def putValues(blockId: BlockId, values: ArrayBuffer[Any], level: StorageLevel,
-    returnValues: Boolean) : PutResult
+  def putValues(
+    blockId: BlockId,
+    values: ArrayBuffer[Any],
+    level: StorageLevel,
+    returnValues: Boolean): PutResult
 
   /**
    * Return the size of a block in bytes.
diff --git a/core/src/main/scala/org/apache/spark/storage/MemoryStore.scala b/core/src/main/scala/org/apache/spark/storage/MemoryStore.scala
index 084a566c4856..71f66c826c5b 100644
--- a/core/src/main/scala/org/apache/spark/storage/MemoryStore.scala
+++ b/core/src/main/scala/org/apache/spark/storage/MemoryStore.scala
@@ -58,11 +58,11 @@ private class MemoryStore(blockManager: BlockManager, maxMemory: Long)
       val elements = new ArrayBuffer[Any]
       elements ++= values
       val sizeEstimate = SizeEstimator.estimate(elements.asInstanceOf[AnyRef])
-      tryToPut(blockId, elements, sizeEstimate, true)
-      PutResult(sizeEstimate, Left(values.toIterator))
+      val putAttempt = tryToPut(blockId, elements, sizeEstimate, deserialized = true)
+      PutResult(sizeEstimate, Left(values.toIterator), putAttempt.droppedBlocks)
     } else {
-      tryToPut(blockId, bytes, bytes.limit, false)
-      PutResult(bytes.limit(), Right(bytes.duplicate()))
+      val putAttempt = tryToPut(blockId, bytes, bytes.limit, deserialized = false)
+      PutResult(bytes.limit(), Right(bytes.duplicate()), putAttempt.droppedBlocks)
     }
   }
 

From d4c7572dba1be49e55ceb38713652e5bcf485be8 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Fri, 20 Jun 2014 17:16:56 -0700
Subject: [PATCH 481/810] Move ScriptTransformation into the appropriate place.

Author: Reynold Xin <rxin@apache.org>

Closes #1162 from rxin/script and squashes the following commits:

2c836b9 [Reynold Xin] Move ScriptTransformation into the appropriate place.
---
 .../spark/sql/hive/{ => execution}/ScriptTransformation.scala     | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename sql/hive/src/main/scala/org/apache/spark/sql/hive/{ => execution}/ScriptTransformation.scala (100%)

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/ScriptTransformation.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala
similarity index 100%
rename from sql/hive/src/main/scala/org/apache/spark/sql/hive/ScriptTransformation.scala
rename to sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala

From 2044784915554a890ca6f8450d8403495b2ee4f3 Mon Sep 17 00:00:00 2001
From: Aaron Davidson <aaron@databricks.com>
Date: Fri, 20 Jun 2014 17:55:54 -0700
Subject: [PATCH 482/810] [SQL] Use hive.SessionState, not the thread local
 SessionState

Note that this is simply mimicing lookupRelation(). I do not have a concrete notion of why this solution is necessarily right-er than SessionState.get, but SessionState.get is returning null, which is bad.

Author: Aaron Davidson <aaron@databricks.com>

Closes #1148 from aarondav/createtable and squashes the following commits:

37c3e7c [Aaron Davidson] [SQL] Use hive.SessionState, not the thread local SessionState
---
 .../scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala  | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
index f923d68932f8..faa30c9ae5cc 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
@@ -105,7 +105,7 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with
   object CreateTables extends Rule[LogicalPlan] {
     def apply(plan: LogicalPlan): LogicalPlan = plan transform {
       case InsertIntoCreatedTable(db, tableName, child) =>
-        val databaseName = db.getOrElse(SessionState.get.getCurrentDatabase)
+        val databaseName = db.getOrElse(hive.sessionState.getCurrentDatabase)
 
         createTable(databaseName, tableName, child.output)
 

From 08d0aca78c7ac060bf50b9b8fac71cbc47a56bbe Mon Sep 17 00:00:00 2001
From: Andrew Ash <andrew@andrewash.com>
Date: Fri, 20 Jun 2014 18:25:33 -0700
Subject: [PATCH 483/810] SPARK-1902 Silence stacktrace from logs when doing
 port failover to port n+1

Before:

```
14/06/08 23:58:23 WARN AbstractLifeCycle: FAILED SelectChannelConnector@0.0.0.0:4040: java.net.BindException: Address already in use
java.net.BindException: Address already in use
	at sun.nio.ch.Net.bind0(Native Method)
	at sun.nio.ch.Net.bind(Net.java:444)
	at sun.nio.ch.Net.bind(Net.java:436)
	at sun.nio.ch.ServerSocketChannelImpl.bind(ServerSocketChannelImpl.java:214)
	at sun.nio.ch.ServerSocketAdaptor.bind(ServerSocketAdaptor.java:74)
	at org.eclipse.jetty.server.nio.SelectChannelConnector.open(SelectChannelConnector.java:187)
	at org.eclipse.jetty.server.AbstractConnector.doStart(AbstractConnector.java:316)
	at org.eclipse.jetty.server.nio.SelectChannelConnector.doStart(SelectChannelConnector.java:265)
	at org.eclipse.jetty.util.component.AbstractLifeCycle.start(AbstractLifeCycle.java:64)
	at org.eclipse.jetty.server.Server.doStart(Server.java:293)
	at org.eclipse.jetty.util.component.AbstractLifeCycle.start(AbstractLifeCycle.java:64)
	at org.apache.spark.ui.JettyUtils$$anonfun$1.apply$mcV$sp(JettyUtils.scala:192)
	at org.apache.spark.ui.JettyUtils$$anonfun$1.apply(JettyUtils.scala:192)
	at org.apache.spark.ui.JettyUtils$$anonfun$1.apply(JettyUtils.scala:192)
	at scala.util.Try$.apply(Try.scala:161)
	at org.apache.spark.ui.JettyUtils$.connect$1(JettyUtils.scala:191)
	at org.apache.spark.ui.JettyUtils$.startJettyServer(JettyUtils.scala:205)
	at org.apache.spark.ui.WebUI.bind(WebUI.scala:99)
	at org.apache.spark.SparkContext.<init>(SparkContext.scala:223)
	at org.apache.spark.repl.SparkILoop.createSparkContext(SparkILoop.scala:957)
	at $line3.$read$$iwC$$iwC.<init>(<console>:8)
	at $line3.$read$$iwC.<init>(<console>:14)
	at $line3.$read.<init>(<console>:16)
	at $line3.$read$.<init>(<console>:20)
	at $line3.$read$.<clinit>(<console>)
	at $line3.$eval$.<init>(<console>:7)
	at $line3.$eval$.<clinit>(<console>)
	at $line3.$eval.$print(<console>)
	at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
	at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
	at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
	at java.lang.reflect.Method.invoke(Method.java:606)
	at org.apache.spark.repl.SparkIMain$ReadEvalPrint.call(SparkIMain.scala:788)
	at org.apache.spark.repl.SparkIMain$Request.loadAndRun(SparkIMain.scala:1056)
	at org.apache.spark.repl.SparkIMain.loadAndRunReq$1(SparkIMain.scala:614)
	at org.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:645)
	at org.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:609)
	at org.apache.spark.repl.SparkILoop.reallyInterpret$1(SparkILoop.scala:796)
	at org.apache.spark.repl.SparkILoop.interpretStartingWith(SparkILoop.scala:841)
	at org.apache.spark.repl.SparkILoop.command(SparkILoop.scala:753)
	at org.apache.spark.repl.SparkILoopInit$$anonfun$initializeSpark$1.apply(SparkILoopInit.scala:121)
	at org.apache.spark.repl.SparkILoopInit$$anonfun$initializeSpark$1.apply(SparkILoopInit.scala:120)
	at org.apache.spark.repl.SparkIMain.beQuietDuring(SparkIMain.scala:263)
	at org.apache.spark.repl.SparkILoopInit$class.initializeSpark(SparkILoopInit.scala:120)
	at org.apache.spark.repl.SparkILoop.initializeSpark(SparkILoop.scala:56)
	at org.apache.spark.repl.SparkILoop$$anonfun$process$1$$anonfun$apply$mcZ$sp$5.apply$mcV$sp(SparkILoop.scala:913)
	at org.apache.spark.repl.SparkILoopInit$class.runThunks(SparkILoopInit.scala:142)
	at org.apache.spark.repl.SparkILoop.runThunks(SparkILoop.scala:56)
	at org.apache.spark.repl.SparkILoopInit$class.postInitialization(SparkILoopInit.scala:104)
	at org.apache.spark.repl.SparkILoop.postInitialization(SparkILoop.scala:56)
	at org.apache.spark.repl.SparkILoop$$anonfun$process$1.apply$mcZ$sp(SparkILoop.scala:930)
	at org.apache.spark.repl.SparkILoop$$anonfun$process$1.apply(SparkILoop.scala:884)
	at org.apache.spark.repl.SparkILoop$$anonfun$process$1.apply(SparkILoop.scala:884)
	at scala.tools.nsc.util.ScalaClassLoader$.savingContextLoader(ScalaClassLoader.scala:135)
	at org.apache.spark.repl.SparkILoop.process(SparkILoop.scala:884)
	at org.apache.spark.repl.SparkILoop.process(SparkILoop.scala:982)
	at org.apache.spark.repl.Main$.main(Main.scala:31)
	at org.apache.spark.repl.Main.main(Main.scala)
	at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
	at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
	at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
	at java.lang.reflect.Method.invoke(Method.java:606)
	at org.apache.spark.deploy.SparkSubmit$.launch(SparkSubmit.scala:292)
	at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:55)
	at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
14/06/08 23:58:23 WARN AbstractLifeCycle: FAILED org.eclipse.jetty.server.Server@7439e55a: java.net.BindException: Address already in use
java.net.BindException: Address already in use
	at sun.nio.ch.Net.bind0(Native Method)
	at sun.nio.ch.Net.bind(Net.java:444)
	at sun.nio.ch.Net.bind(Net.java:436)
	at sun.nio.ch.ServerSocketChannelImpl.bind(ServerSocketChannelImpl.java:214)
	at sun.nio.ch.ServerSocketAdaptor.bind(ServerSocketAdaptor.java:74)
	at org.eclipse.jetty.server.nio.SelectChannelConnector.open(SelectChannelConnector.java:187)
	at org.eclipse.jetty.server.AbstractConnector.doStart(AbstractConnector.java:316)
	at org.eclipse.jetty.server.nio.SelectChannelConnector.doStart(SelectChannelConnector.java:265)
	at org.eclipse.jetty.util.component.AbstractLifeCycle.start(AbstractLifeCycle.java:64)
	at org.eclipse.jetty.server.Server.doStart(Server.java:293)
	at org.eclipse.jetty.util.component.AbstractLifeCycle.start(AbstractLifeCycle.java:64)
	at org.apache.spark.ui.JettyUtils$$anonfun$1.apply$mcV$sp(JettyUtils.scala:192)
	at org.apache.spark.ui.JettyUtils$$anonfun$1.apply(JettyUtils.scala:192)
	at org.apache.spark.ui.JettyUtils$$anonfun$1.apply(JettyUtils.scala:192)
	at scala.util.Try$.apply(Try.scala:161)
	at org.apache.spark.ui.JettyUtils$.connect$1(JettyUtils.scala:191)
	at org.apache.spark.ui.JettyUtils$.startJettyServer(JettyUtils.scala:205)
	at org.apache.spark.ui.WebUI.bind(WebUI.scala:99)
	at org.apache.spark.SparkContext.<init>(SparkContext.scala:223)
	at org.apache.spark.repl.SparkILoop.createSparkContext(SparkILoop.scala:957)
	at $line3.$read$$iwC$$iwC.<init>(<console>:8)
	at $line3.$read$$iwC.<init>(<console>:14)
	at $line3.$read.<init>(<console>:16)
	at $line3.$read$.<init>(<console>:20)
	at $line3.$read$.<clinit>(<console>)
	at $line3.$eval$.<init>(<console>:7)
	at $line3.$eval$.<clinit>(<console>)
	at $line3.$eval.$print(<console>)
	at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
	at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
	at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
	at java.lang.reflect.Method.invoke(Method.java:606)
	at org.apache.spark.repl.SparkIMain$ReadEvalPrint.call(SparkIMain.scala:788)
	at org.apache.spark.repl.SparkIMain$Request.loadAndRun(SparkIMain.scala:1056)
	at org.apache.spark.repl.SparkIMain.loadAndRunReq$1(SparkIMain.scala:614)
	at org.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:645)
	at org.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:609)
	at org.apache.spark.repl.SparkILoop.reallyInterpret$1(SparkILoop.scala:796)
	at org.apache.spark.repl.SparkILoop.interpretStartingWith(SparkILoop.scala:841)
	at org.apache.spark.repl.SparkILoop.command(SparkILoop.scala:753)
	at org.apache.spark.repl.SparkILoopInit$$anonfun$initializeSpark$1.apply(SparkILoopInit.scala:121)
	at org.apache.spark.repl.SparkILoopInit$$anonfun$initializeSpark$1.apply(SparkILoopInit.scala:120)
	at org.apache.spark.repl.SparkIMain.beQuietDuring(SparkIMain.scala:263)
	at org.apache.spark.repl.SparkILoopInit$class.initializeSpark(SparkILoopInit.scala:120)
	at org.apache.spark.repl.SparkILoop.initializeSpark(SparkILoop.scala:56)
	at org.apache.spark.repl.SparkILoop$$anonfun$process$1$$anonfun$apply$mcZ$sp$5.apply$mcV$sp(SparkILoop.scala:913)
	at org.apache.spark.repl.SparkILoopInit$class.runThunks(SparkILoopInit.scala:142)
	at org.apache.spark.repl.SparkILoop.runThunks(SparkILoop.scala:56)
	at org.apache.spark.repl.SparkILoopInit$class.postInitialization(SparkILoopInit.scala:104)
	at org.apache.spark.repl.SparkILoop.postInitialization(SparkILoop.scala:56)
	at org.apache.spark.repl.SparkILoop$$anonfun$process$1.apply$mcZ$sp(SparkILoop.scala:930)
	at org.apache.spark.repl.SparkILoop$$anonfun$process$1.apply(SparkILoop.scala:884)
	at org.apache.spark.repl.SparkILoop$$anonfun$process$1.apply(SparkILoop.scala:884)
	at scala.tools.nsc.util.ScalaClassLoader$.savingContextLoader(ScalaClassLoader.scala:135)
	at org.apache.spark.repl.SparkILoop.process(SparkILoop.scala:884)
	at org.apache.spark.repl.SparkILoop.process(SparkILoop.scala:982)
	at org.apache.spark.repl.Main$.main(Main.scala:31)
	at org.apache.spark.repl.Main.main(Main.scala)
	at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
	at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
	at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
	at java.lang.reflect.Method.invoke(Method.java:606)
	at org.apache.spark.deploy.SparkSubmit$.launch(SparkSubmit.scala:292)
	at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:55)
	at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
14/06/08 23:58:23 INFO JettyUtils: Failed to create UI at port, 4040. Trying again.
14/06/08 23:58:23 INFO JettyUtils: Error was: Failure(java.net.BindException: Address already in use)
14/06/08 23:58:23 INFO SparkUI: Started SparkUI at http://aash-mbp.local:4041
````

After:
```
14/06/09 00:04:12 INFO JettyUtils: Failed to create UI at port, 4040. Trying again.
14/06/09 00:04:12 INFO JettyUtils: Error was: Failure(java.net.BindException: Address already in use)
14/06/09 00:04:12 INFO Server: jetty-8.y.z-SNAPSHOT
14/06/09 00:04:12 INFO AbstractConnector: Started SelectChannelConnector@0.0.0.0:4041
14/06/09 00:04:12 INFO SparkUI: Started SparkUI at http://aash-mbp.local:4041
```

Lengthy logging comes from this line of code in Jetty: http://grepcode.com/file/repo1.maven.org/maven2/org.eclipse.jetty.aggregate/jetty-all/9.1.3.v20140225/org/eclipse/jetty/util/component/AbstractLifeCycle.java#210

Author: Andrew Ash <andrew@andrewash.com>

Closes #1019 from ash211/SPARK-1902 and squashes the following commits:

0dd02f7 [Andrew Ash] Leave old org.eclipse.jetty silencing in place
1e2866b [Andrew Ash] Address CR comments
9d85eed [Andrew Ash] SPARK-1902 Silence stacktrace from logs when doing port failover to port n+1
---
 conf/log4j.properties.template                        |  1 +
 .../org/apache/spark/log4j-defaults.properties        |  1 +
 .../org/apache/spark/executor/ExecutorBackend.scala   |  2 +-
 .../main/scala/org/apache/spark/ui/JettyUtils.scala   | 11 ++++++++---
 4 files changed, 11 insertions(+), 4 deletions(-)

diff --git a/conf/log4j.properties.template b/conf/log4j.properties.template
index f7f853559468..89eec7d4b7f6 100644
--- a/conf/log4j.properties.template
+++ b/conf/log4j.properties.template
@@ -7,5 +7,6 @@ log4j.appender.console.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss} %p %c{1}:
 
 # Settings to quiet third party logs that are too verbose
 log4j.logger.org.eclipse.jetty=WARN
+log4j.logger.org.eclipse.jetty.util.component.AbstractLifeCycle=ERROR
 log4j.logger.org.apache.spark.repl.SparkIMain$exprTyper=INFO
 log4j.logger.org.apache.spark.repl.SparkILoop$SparkILoopInterpreter=INFO
diff --git a/core/src/main/resources/org/apache/spark/log4j-defaults.properties b/core/src/main/resources/org/apache/spark/log4j-defaults.properties
index f7f853559468..89eec7d4b7f6 100644
--- a/core/src/main/resources/org/apache/spark/log4j-defaults.properties
+++ b/core/src/main/resources/org/apache/spark/log4j-defaults.properties
@@ -7,5 +7,6 @@ log4j.appender.console.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss} %p %c{1}:
 
 # Settings to quiet third party logs that are too verbose
 log4j.logger.org.eclipse.jetty=WARN
+log4j.logger.org.eclipse.jetty.util.component.AbstractLifeCycle=ERROR
 log4j.logger.org.apache.spark.repl.SparkIMain$exprTyper=INFO
 log4j.logger.org.apache.spark.repl.SparkILoop$SparkILoopInterpreter=INFO
diff --git a/core/src/main/scala/org/apache/spark/executor/ExecutorBackend.scala b/core/src/main/scala/org/apache/spark/executor/ExecutorBackend.scala
index 3d34960653f5..9da17428ed43 100644
--- a/core/src/main/scala/org/apache/spark/executor/ExecutorBackend.scala
+++ b/core/src/main/scala/org/apache/spark/executor/ExecutorBackend.scala
@@ -26,4 +26,4 @@ import org.apache.spark.TaskState.TaskState
  */
 private[spark] trait ExecutorBackend {
   def statusUpdate(taskId: Long, state: TaskState, data: ByteBuffer)
-}
+}
\ No newline at end of file
diff --git a/core/src/main/scala/org/apache/spark/ui/JettyUtils.scala b/core/src/main/scala/org/apache/spark/ui/JettyUtils.scala
index b3ac2320f343..a2535e3c1c41 100644
--- a/core/src/main/scala/org/apache/spark/ui/JettyUtils.scala
+++ b/core/src/main/scala/org/apache/spark/ui/JettyUtils.scala
@@ -194,11 +194,16 @@ private[spark] object JettyUtils extends Logging {
         case s: Success[_] =>
           (server, server.getConnectors.head.getLocalPort)
         case f: Failure[_] =>
+          val nextPort = (currentPort + 1) % 65536
           server.stop()
           pool.stop()
-          logInfo("Failed to create UI at port, %s. Trying again.".format(currentPort))
-          logInfo("Error was: " + f.toString)
-          connect((currentPort + 1) % 65536)
+          val msg = s"Failed to create UI on port $currentPort. Trying again on port $nextPort."
+          if (f.toString.contains("Address already in use")) {
+            logWarning(s"$msg - $f")
+          } else {
+            logError(msg, f.exception)
+          }
+          connect(nextPort)
       }
     }
 

From e99903b84a530ebf97d46f4b2e1e78db0f65b041 Mon Sep 17 00:00:00 2001
From: Doris Xin <doris.s.xin@gmail.com>
Date: Fri, 20 Jun 2014 18:42:02 -0700
Subject: [PATCH 484/810] [SPARK-1970] Update unit test in XORShiftRandomSuite
 to use ChiSquareTest from commons-math3

Updating the chisquare unit test in XORShiftRandomSuite to use the ChiSquareTest in commons-math3 instead of hardcoding the chisquare statistic for the desired confidence interval.

Author: Doris Xin <doris.s.xin@gmail.com>

Closes #1073 from dorx/math3Unit and squashes the following commits:

da0e891 [Doris Xin] remove math3 from common pom
9954143 [Doris Xin] merge master
c19948f [Doris Xin] Merge branch 'master' into math3Unit
8f84f19 [Doris Xin] [SPARK-1970] unit test in XORShiftRandomSuite
ffea61a [Doris Xin] SPARK-1939: Refactor takeSample method in RDD
1441977 [Doris Xin] SPARK-1939 Refactor takeSample method in RDD to use ScaSRS
---
 .../util/random/XORShiftRandomSuite.scala     | 49 +++++++------------
 1 file changed, 18 insertions(+), 31 deletions(-)

diff --git a/core/src/test/scala/org/apache/spark/util/random/XORShiftRandomSuite.scala b/core/src/test/scala/org/apache/spark/util/random/XORShiftRandomSuite.scala
index e15fd59a5a8b..ef7178bcdf5c 100644
--- a/core/src/test/scala/org/apache/spark/util/random/XORShiftRandomSuite.scala
+++ b/core/src/test/scala/org/apache/spark/util/random/XORShiftRandomSuite.scala
@@ -20,6 +20,8 @@ package org.apache.spark.util.random
 import org.scalatest.FunSuite
 import org.scalatest.Matchers
 
+import org.apache.commons.math3.stat.inference.ChiSquareTest
+
 import org.apache.spark.util.Utils.times
 
 import scala.language.reflectiveCalls
@@ -33,45 +35,30 @@ class XORShiftRandomSuite extends FunSuite with Matchers {
   }
 
   /*
-   * This test is based on a chi-squared test for randomness. The values are hard-coded
-   * so as not to create Spark's dependency on apache.commons.math3 just to call one
-   * method for calculating the exact p-value for a given number of random numbers
-   * and bins. In case one would want to move to a full-fledged test based on
-   * apache.commons.math3, the relevant class is here:
-   * org.apache.commons.math3.stat.inference.ChiSquareTest
+   * This test is based on a chi-squared test for randomness.
    */
   test ("XORShift generates valid random numbers") {
 
     val f = fixture
 
-    val numBins = 10
-    // create 10 bins
-    val bins = Array.fill(numBins)(0)
+    val numBins = 10 // create 10 bins
+    val numRows = 5 // create 5 rows
+    val bins = Array.ofDim[Long](numRows, numBins)
 
-    // populate bins based on modulus of the random number
-    times(f.hundMil) {bins(math.abs(f.xorRand.nextInt) % 10) += 1}
+    // populate bins based on modulus of the random number for each row
+    for (r <- 0 to numRows-1) {
+      times(f.hundMil) {bins(r)(math.abs(f.xorRand.nextInt) % numBins) += 1}
+    }
 
-    /* since the seed is deterministic, until the algorithm is changed, we know the result will be
-     * exactly this: Array(10004908, 9993136, 9994600, 10000744, 10000091, 10002474, 10002272,
-     * 10000790, 10002286, 9998699), so the test will never fail at the prespecified (5%)
-     * significance level. However, should the RNG implementation change, the test should still
-     * pass at the same significance level. The chi-squared test done in R gave the following
-     * results:
-     *   > chisq.test(c(10004908, 9993136, 9994600, 10000744, 10000091, 10002474, 10002272,
-     *     10000790, 10002286, 9998699))
-     *     Chi-squared test for given probabilities
-     *     data:  c(10004908, 9993136, 9994600, 10000744, 10000091, 10002474, 10002272, 10000790,
-     *            10002286, 9998699)
-     *     X-squared = 11.975, df = 9, p-value = 0.2147
-     * Note that the p-value was ~0.22. The test will fail if alpha < 0.05, which for 100 million
-     * random numbers
-     * and 10 bins will happen at X-squared of ~16.9196. So, the test will fail if X-squared
-     * is greater than or equal to that number.
+    /*
+     * Perform the chi square test on the 5 rows of randomly generated numbers evenly divided into
+     * 10 bins. chiSquareTest returns true iff the null hypothesis (that the classifications
+     * represented by the counts in the columns of the input 2-way table are independent of the
+     * rows) can be rejected with 100 * (1 - alpha) percent confidence, where alpha is prespeficied
+     * as 0.05
      */
-    val binSize = f.hundMil/numBins
-    val xSquared = bins.map(x => math.pow((binSize - x), 2)/binSize).sum
-    xSquared should be <  (16.9196)
-
+    val chiTest = new ChiSquareTest
+    assert(chiTest.chiSquareTest(bins, 0.05) === false)
   }
 
   test ("XORShift with zero seed") {

From a6786424954218fc31d8cc638a5c7d6567b55047 Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Fri, 20 Jun 2014 18:44:54 -0700
Subject: [PATCH 485/810] HOTFIX: Fixing style error introduced by 08d0ac

---
 .../main/scala/org/apache/spark/executor/ExecutorBackend.scala | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/executor/ExecutorBackend.scala b/core/src/main/scala/org/apache/spark/executor/ExecutorBackend.scala
index 9da17428ed43..e07cb31cbe4b 100644
--- a/core/src/main/scala/org/apache/spark/executor/ExecutorBackend.scala
+++ b/core/src/main/scala/org/apache/spark/executor/ExecutorBackend.scala
@@ -26,4 +26,5 @@ import org.apache.spark.TaskState.TaskState
  */
 private[spark] trait ExecutorBackend {
   def statusUpdate(taskId: Long, state: TaskState, data: ByteBuffer)
-}
\ No newline at end of file
+}
+

From 010c460d627c1917dc47b09e59fd41172bbf90b3 Mon Sep 17 00:00:00 2001
From: Anant <anant.asty@gmail.com>
Date: Fri, 20 Jun 2014 18:54:00 -0700
Subject: [PATCH 486/810] [SPARK-2061] Made splits deprecated in JavaRDDLike

The jira for the issue can be found at: https://issues.apache.org/jira/browse/SPARK-2061
Most of spark has used over to consistently using `partitions` instead of `splits`. We should do likewise and add a `partitions` method to JavaRDDLike and have `splits` just call that. We should also go through all cases where other API's (e.g. Python) call `splits` and we should change those to use the newer API.

Author: Anant <anant.asty@gmail.com>

Closes #1062 from anantasty/SPARK-2061 and squashes the following commits:

b83ce6b [Anant] Fixed syntax issue
21f9210 [Anant] Fixed version number in deprecation string
9315b76 [Anant] made related changes to use partitions in python api
8c62dd1 [Anant] Made splits deprecated in JavaRDDLike
---
 .../main/scala/org/apache/spark/api/java/JavaRDDLike.scala   | 5 ++++-
 core/src/test/java/org/apache/spark/JavaAPISuite.java        | 2 +-
 python/pyspark/context.py                                    | 2 +-
 python/pyspark/rdd.py                                        | 4 ++--
 4 files changed, 8 insertions(+), 5 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala b/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala
index 330569a8d883..f917cfd1419e 100644
--- a/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala
+++ b/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala
@@ -43,8 +43,11 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable {
 
   def rdd: RDD[T]
 
-  /** Set of partitions in this RDD. */
+  @deprecated("Use partitions() instead.", "1.1.0")
   def splits: JList[Partition] = new java.util.ArrayList(rdd.partitions.toSeq)
+  
+  /** Set of partitions in this RDD. */
+  def partitions: JList[Partition] = new java.util.ArrayList(rdd.partitions.toSeq)
 
   /** The [[org.apache.spark.SparkContext]] that this RDD was created on. */
   def context: SparkContext = rdd.context
diff --git a/core/src/test/java/org/apache/spark/JavaAPISuite.java b/core/src/test/java/org/apache/spark/JavaAPISuite.java
index 761f2d6a77d3..1d7a7be6cfeb 100644
--- a/core/src/test/java/org/apache/spark/JavaAPISuite.java
+++ b/core/src/test/java/org/apache/spark/JavaAPISuite.java
@@ -741,7 +741,7 @@ public void persist() {
   public void iterator() {
     JavaRDD<Integer> rdd = sc.parallelize(Arrays.asList(1, 2, 3, 4, 5), 2);
     TaskContext context = new TaskContext(0, 0, 0, false, new TaskMetrics());
-    Assert.assertEquals(1, rdd.iterator(rdd.splits().get(0), context).next().intValue());
+    Assert.assertEquals(1, rdd.iterator(rdd.partitions().get(0), context).next().intValue());
   }
 
   @Test
diff --git a/python/pyspark/context.py b/python/pyspark/context.py
index 062bec2381a8..95c54e7a5ad6 100644
--- a/python/pyspark/context.py
+++ b/python/pyspark/context.py
@@ -704,7 +704,7 @@ def runJob(self, rdd, partitionFunc, partitions = None, allowLocal = False):
         [0, 1, 16, 25]
         """
         if partitions == None:
-            partitions = range(rdd._jrdd.splits().size())
+            partitions = range(rdd._jrdd.partitions().size())
         javaPartitions = ListConverter().convert(partitions, self._gateway._gateway_client)
 
         # Implementation note: This is implemented as a mapPartitions followed
diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py
index 1d55c35a8bf4..f64f48e3a4c9 100644
--- a/python/pyspark/rdd.py
+++ b/python/pyspark/rdd.py
@@ -321,7 +321,7 @@ def getNumPartitions(self):
       >>> rdd.getNumPartitions()
       2
       """
-      return self._jrdd.splits().size()
+      return self._jrdd.partitions().size()
 
     def filter(self, f):
         """
@@ -922,7 +922,7 @@ def take(self, num):
         [91, 92, 93]
         """
         items = []
-        totalParts = self._jrdd.splits().size()
+        totalParts = self._jrdd.partitions().size()
         partsScanned = 0
 
         while len(items) < num and partsScanned < totalParts:

From 648553d48ee1f830406750b50ec4cc322bcf47fe Mon Sep 17 00:00:00 2001
From: Marcelo Vanzin <vanzin@cloudera.com>
Date: Fri, 20 Jun 2014 20:05:12 -0700
Subject: [PATCH 487/810] Fix some tests.

- JavaAPISuite was trying to compare a bare path with a URI. Fix by
  extracting the path from the URI, since we know it should be a
  local path anyway/

- b9be1609 excluded the ASM dependency everywhere, but easymock needs
  it (because cglib needs it). So re-add the dependency, with test
  scope this time.

The second one above actually uncovered a weird situation: the maven
test target works, even though I can't find the class sbt complains
about in its classpath. sbt complains with:

  [error] Uncaught exception when running org.apache.spark.util
  .random.RandomSamplerSuite: java.lang.NoClassDefFoundError:
  org/objectweb/asm/Type

To avoid more weirdness caused by that, I explicitly added the asm
dependency to both maven and sbt (for tests only), and verified
the classes don't end up in the final assembly.

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #917 from vanzin/flaky-tests and squashes the following commits:

d022320 [Marcelo Vanzin] Fix some tests.
---
 core/pom.xml                                          |  5 +++++
 core/src/test/java/org/apache/spark/JavaAPISuite.java |  5 +++--
 pom.xml                                               |  7 +++++++
 project/SparkBuild.scala                              | 10 ++++++----
 4 files changed, 21 insertions(+), 6 deletions(-)

diff --git a/core/pom.xml b/core/pom.xml
index bd6767e03bb9..8c23842730e3 100644
--- a/core/pom.xml
+++ b/core/pom.xml
@@ -244,6 +244,11 @@
       <artifactId>easymockclassextension</artifactId>
       <scope>test</scope>
     </dependency>
+    <dependency>
+      <groupId>asm</groupId>
+      <artifactId>asm</artifactId>
+      <scope>test</scope>
+    </dependency>
     <dependency>
       <groupId>com.novocode</groupId>
       <artifactId>junit-interface</artifactId>
diff --git a/core/src/test/java/org/apache/spark/JavaAPISuite.java b/core/src/test/java/org/apache/spark/JavaAPISuite.java
index 1d7a7be6cfeb..b2868b59ce6c 100644
--- a/core/src/test/java/org/apache/spark/JavaAPISuite.java
+++ b/core/src/test/java/org/apache/spark/JavaAPISuite.java
@@ -18,6 +18,7 @@
 package org.apache.spark;
 
 import java.io.*;
+import java.net.URI;
 import java.util.*;
 
 import scala.Tuple2;
@@ -768,7 +769,7 @@ public void textFiles() throws IOException {
   }
 
   @Test
-  public void wholeTextFiles() throws IOException {
+  public void wholeTextFiles() throws Exception {
     byte[] content1 = "spark is easy to use.\n".getBytes("utf-8");
     byte[] content2 = "spark is also easy to use.\n".getBytes("utf-8");
 
@@ -784,7 +785,7 @@ public void wholeTextFiles() throws IOException {
     List<Tuple2<String, String>> result = readRDD.collect();
 
     for (Tuple2<String, String> res : result) {
-      Assert.assertEquals(res._2(), container.get(res._1()));
+      Assert.assertEquals(res._2(), container.get(new URI(res._1()).getPath()));
     }
   }
 
diff --git a/pom.xml b/pom.xml
index 0d46bb4114f7..05f76d566e9d 100644
--- a/pom.xml
+++ b/pom.xml
@@ -468,6 +468,13 @@
         <version>3.1</version>
         <scope>test</scope>
       </dependency>
+      <!-- Needed by cglib which is needed by easymock. -->
+      <dependency>
+        <groupId>asm</groupId>
+        <artifactId>asm</artifactId>
+        <version>3.3.1</version>
+        <scope>test</scope>
+      </dependency>
       <dependency>
         <groupId>org.mockito</groupId>
         <artifactId>mockito-all</artifactId>
diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index 7bb39dc77120..55a2aa0fc714 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -293,7 +293,9 @@ object SparkBuild extends Build {
         "com.novocode"      % "junit-interface"        % "0.10"   % "test",
         "org.easymock"      % "easymockclassextension" % "3.1"    % "test",
         "org.mockito"       % "mockito-all"            % "1.9.0"  % "test",
-        "junit"             % "junit"                  % "4.10"   % "test"
+        "junit"             % "junit"                  % "4.10"   % "test",
+        // Needed by cglib which is needed by easymock.
+        "asm"               % "asm"                    % "3.3.1"  % "test"
     ),
 
     testOptions += Tests.Argument(TestFrameworks.JUnit, "-v", "-a"),
@@ -461,7 +463,7 @@ object SparkBuild extends Build {
 
   def toolsSettings = sharedSettings ++ Seq(
     name := "spark-tools",
-    libraryDependencies <+= scalaVersion(v => "org.scala-lang"  % "scala-compiler" % v ),
+    libraryDependencies <+= scalaVersion(v => "org.scala-lang"  % "scala-compiler" % v),
     libraryDependencies <+= scalaVersion(v => "org.scala-lang"  % "scala-reflect"  % v )
   ) ++ assemblySettings ++ extraAssemblySettings
 
@@ -630,9 +632,9 @@ object SparkBuild extends Build {
     scalaVersion := "2.10.4",
     retrieveManaged := true,
     retrievePattern := "[type]s/[artifact](-[revision])(-[classifier]).[ext]",
-    libraryDependencies := Seq("spark-streaming-mqtt", "spark-streaming-zeromq", 
+    libraryDependencies := Seq("spark-streaming-mqtt", "spark-streaming-zeromq",
       "spark-streaming-flume", "spark-streaming-kafka", "spark-streaming-twitter",
-      "spark-streaming", "spark-mllib", "spark-bagel", "spark-graphx", 
+      "spark-streaming", "spark-mllib", "spark-bagel", "spark-graphx",
       "spark-core").map(sparkPreviousArtifact(_).get intransitive())
   )
 

From ca5d8b5904dc6dd5b691af506d3a842e508b3673 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Fri, 20 Jun 2014 22:49:48 -0700
Subject: [PATCH 488/810] [SQL] Pass SQLContext instead of SparkContext into
 physical operators.

This makes it easier to use config options in operators.

Author: Reynold Xin <rxin@apache.org>

Closes #1164 from rxin/sqlcontext and squashes the following commits:

797b2fd [Reynold Xin] Pass SQLContext instead of SparkContext into physical operators.
---
 .../org/apache/spark/sql/SQLContext.scala     |  4 +++-
 .../spark/sql/execution/Aggregate.scala       |  5 +++--
 .../spark/sql/execution/SparkStrategies.scala | 22 +++++++++----------
 .../spark/sql/execution/basicOperators.scala  | 20 +++++++++--------
 .../apache/spark/sql/execution/joins.scala    | 21 +++++++++---------
 .../sql/parquet/ParquetTableOperations.scala  | 21 +++++++++---------
 .../spark/sql/parquet/ParquetQuerySuite.scala |  2 +-
 7 files changed, 51 insertions(+), 44 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
index ab376e5504d3..c60af28b2a1f 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
@@ -221,7 +221,9 @@ class SQLContext(@transient val sparkContext: SparkContext)
   }
 
   protected[sql] class SparkPlanner extends SparkStrategies {
-    val sparkContext = self.sparkContext
+    val sparkContext: SparkContext = self.sparkContext
+
+    val sqlContext: SQLContext = self
 
     def numPartitions = self.numShufflePartitions
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/Aggregate.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/Aggregate.scala
index 34d88fe4bd7d..d85d2d7844e0 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/Aggregate.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/Aggregate.scala
@@ -24,6 +24,7 @@ import org.apache.spark.SparkContext
 import org.apache.spark.sql.catalyst.errors._
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.physical._
+import org.apache.spark.sql.SQLContext
 
 /**
  * :: DeveloperApi ::
@@ -41,7 +42,7 @@ case class Aggregate(
     partial: Boolean,
     groupingExpressions: Seq[Expression],
     aggregateExpressions: Seq[NamedExpression],
-    child: SparkPlan)(@transient sc: SparkContext)
+    child: SparkPlan)(@transient sqlContext: SQLContext)
   extends UnaryNode with NoBind {
 
   override def requiredChildDistribution =
@@ -55,7 +56,7 @@ case class Aggregate(
       }
     }
 
-  override def otherCopyArgs = sc :: Nil
+  override def otherCopyArgs = sqlContext :: Nil
 
   // HACK: Generators don't correctly preserve their output through serializations so we grab
   // out child's output attributes statically here.
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
index 4694f25d6d63..bd8ae4cddef8 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
@@ -40,7 +40,7 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
       // no predicate can be evaluated by matching hash keys
       case logical.Join(left, right, LeftSemi, condition) =>
         execution.LeftSemiJoinBNL(
-          planLater(left), planLater(right), condition)(sparkContext) :: Nil
+          planLater(left), planLater(right), condition)(sqlContext) :: Nil
       case _ => Nil
     }
   }
@@ -103,7 +103,7 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
               partial = true,
               groupingExpressions,
               partialComputation,
-              planLater(child))(sparkContext))(sparkContext) :: Nil
+              planLater(child))(sqlContext))(sqlContext) :: Nil
         } else {
           Nil
         }
@@ -115,7 +115,7 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
     def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
       case logical.Join(left, right, joinType, condition) =>
         execution.BroadcastNestedLoopJoin(
-          planLater(left), planLater(right), joinType, condition)(sparkContext) :: Nil
+          planLater(left), planLater(right), joinType, condition)(sqlContext) :: Nil
       case _ => Nil
     }
   }
@@ -143,7 +143,7 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
   object TakeOrdered extends Strategy {
     def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
       case logical.Limit(IntegerLiteral(limit), logical.Sort(order, child)) =>
-        execution.TakeOrdered(limit, order, planLater(child))(sparkContext) :: Nil
+        execution.TakeOrdered(limit, order, planLater(child))(sqlContext) :: Nil
       case _ => Nil
     }
   }
@@ -155,9 +155,9 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
         val relation =
           ParquetRelation.create(path, child, sparkContext.hadoopConfiguration)
         // Note: overwrite=false because otherwise the metadata we just created will be deleted
-        InsertIntoParquetTable(relation, planLater(child), overwrite=false)(sparkContext) :: Nil
+        InsertIntoParquetTable(relation, planLater(child), overwrite=false)(sqlContext) :: Nil
       case logical.InsertIntoTable(table: ParquetRelation, partition, child, overwrite) =>
-        InsertIntoParquetTable(table, planLater(child), overwrite)(sparkContext) :: Nil
+        InsertIntoParquetTable(table, planLater(child), overwrite)(sqlContext) :: Nil
       case PhysicalOperation(projectList, filters: Seq[Expression], relation: ParquetRelation) =>
         val prunePushedDownFilters =
           if (sparkContext.conf.getBoolean(ParquetFilters.PARQUET_FILTER_PUSHDOWN_ENABLED, true)) {
@@ -186,7 +186,7 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
           projectList,
           filters,
           prunePushedDownFilters,
-          ParquetTableScan(_, relation, filters)(sparkContext)) :: Nil
+          ParquetTableScan(_, relation, filters)(sqlContext)) :: Nil
 
       case _ => Nil
     }
@@ -211,7 +211,7 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
     def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
       case logical.Distinct(child) =>
         execution.Aggregate(
-          partial = false, child.output, child.output, planLater(child))(sparkContext) :: Nil
+          partial = false, child.output, child.output, planLater(child))(sqlContext) :: Nil
       case logical.Sort(sortExprs, child) =>
         // This sort is a global sort. Its requiredDistribution will be an OrderedDistribution.
         execution.Sort(sortExprs, global = true, planLater(child)):: Nil
@@ -224,7 +224,7 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
       case logical.Filter(condition, child) =>
         execution.Filter(condition, planLater(child)) :: Nil
       case logical.Aggregate(group, agg, child) =>
-        execution.Aggregate(partial = false, group, agg, planLater(child))(sparkContext) :: Nil
+        execution.Aggregate(partial = false, group, agg, planLater(child))(sqlContext) :: Nil
       case logical.Sample(fraction, withReplacement, seed, child) =>
         execution.Sample(fraction, withReplacement, seed, planLater(child)) :: Nil
       case logical.LocalRelation(output, data) =>
@@ -233,9 +233,9 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
             new GenericRow(r.productIterator.map(convertToCatalyst).toArray): Row))
         execution.ExistingRdd(output, dataAsRdd) :: Nil
       case logical.Limit(IntegerLiteral(limit), child) =>
-        execution.Limit(limit, planLater(child))(sparkContext) :: Nil
+        execution.Limit(limit, planLater(child))(sqlContext) :: Nil
       case Unions(unionChildren) =>
-        execution.Union(unionChildren.map(planLater))(sparkContext) :: Nil
+        execution.Union(unionChildren.map(planLater))(sqlContext) :: Nil
       case logical.Generate(generator, join, outer, _, child) =>
         execution.Generate(generator, join = join, outer = outer, planLater(child)) :: Nil
       case logical.NoRelation =>
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
index 8969794c6993..18f4a5877bb2 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
@@ -20,8 +20,9 @@ package org.apache.spark.sql.execution
 import scala.reflect.runtime.universe.TypeTag
 
 import org.apache.spark.annotation.DeveloperApi
-import org.apache.spark.{HashPartitioner, SparkConf, SparkContext}
+import org.apache.spark.{HashPartitioner, SparkConf}
 import org.apache.spark.rdd.{RDD, ShuffledRDD}
+import org.apache.spark.sql.SQLContext
 import org.apache.spark.sql.catalyst.ScalaReflection
 import org.apache.spark.sql.catalyst.errors._
 import org.apache.spark.sql.catalyst.expressions._
@@ -70,12 +71,12 @@ case class Sample(fraction: Double, withReplacement: Boolean, seed: Long, child:
  * :: DeveloperApi ::
  */
 @DeveloperApi
-case class Union(children: Seq[SparkPlan])(@transient sc: SparkContext) extends SparkPlan {
+case class Union(children: Seq[SparkPlan])(@transient sqlContext: SQLContext) extends SparkPlan {
   // TODO: attributes output by union should be distinct for nullability purposes
   override def output = children.head.output
-  override def execute() = sc.union(children.map(_.execute()))
+  override def execute() = sqlContext.sparkContext.union(children.map(_.execute()))
 
-  override def otherCopyArgs = sc :: Nil
+  override def otherCopyArgs = sqlContext :: Nil
 }
 
 /**
@@ -87,11 +88,12 @@ case class Union(children: Seq[SparkPlan])(@transient sc: SparkContext) extends
  * data to a single partition to compute the global limit.
  */
 @DeveloperApi
-case class Limit(limit: Int, child: SparkPlan)(@transient sc: SparkContext) extends UnaryNode {
+case class Limit(limit: Int, child: SparkPlan)(@transient sqlContext: SQLContext)
+  extends UnaryNode {
   // TODO: Implement a partition local limit, and use a strategy to generate the proper limit plan:
   // partition local limit -> exchange into one partition -> partition local limit again
 
-  override def otherCopyArgs = sc :: Nil
+  override def otherCopyArgs = sqlContext :: Nil
 
   override def output = child.output
 
@@ -117,8 +119,8 @@ case class Limit(limit: Int, child: SparkPlan)(@transient sc: SparkContext) exte
  */
 @DeveloperApi
 case class TakeOrdered(limit: Int, sortOrder: Seq[SortOrder], child: SparkPlan)
-                      (@transient sc: SparkContext) extends UnaryNode {
-  override def otherCopyArgs = sc :: Nil
+                      (@transient sqlContext: SQLContext) extends UnaryNode {
+  override def otherCopyArgs = sqlContext :: Nil
 
   override def output = child.output
 
@@ -129,7 +131,7 @@ case class TakeOrdered(limit: Int, sortOrder: Seq[SortOrder], child: SparkPlan)
 
   // TODO: Terminal split should be implemented differently from non-terminal split.
   // TODO: Pick num splits based on |limit|.
-  override def execute() = sc.makeRDD(executeCollect(), 1)
+  override def execute() = sqlContext.sparkContext.makeRDD(executeCollect(), 1)
 }
 
 /**
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins.scala
index 8d7a5ba59f96..84bdde38b7e9 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins.scala
@@ -19,9 +19,8 @@ package org.apache.spark.sql.execution
 
 import scala.collection.mutable.{ArrayBuffer, BitSet}
 
-import org.apache.spark.SparkContext
-
 import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.sql.SQLContext
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans._
 import org.apache.spark.sql.catalyst.plans.physical.{ClusteredDistribution, Partitioning}
@@ -200,13 +199,13 @@ case class LeftSemiJoinHash(
 @DeveloperApi
 case class LeftSemiJoinBNL(
     streamed: SparkPlan, broadcast: SparkPlan, condition: Option[Expression])
-    (@transient sc: SparkContext)
+    (@transient sqlContext: SQLContext)
   extends BinaryNode {
   // TODO: Override requiredChildDistribution.
 
   override def outputPartitioning: Partitioning = streamed.outputPartitioning
 
-  override def otherCopyArgs = sc :: Nil
+  override def otherCopyArgs = sqlContext :: Nil
 
   def output = left.output
 
@@ -223,7 +222,8 @@ case class LeftSemiJoinBNL(
 
 
   def execute() = {
-    val broadcastedRelation = sc.broadcast(broadcast.execute().map(_.copy()).collect().toIndexedSeq)
+    val broadcastedRelation =
+      sqlContext.sparkContext.broadcast(broadcast.execute().map(_.copy()).collect().toIndexedSeq)
 
     streamed.execute().mapPartitions { streamedIter =>
       val joinedRow = new JoinedRow
@@ -263,13 +263,13 @@ case class CartesianProduct(left: SparkPlan, right: SparkPlan) extends BinaryNod
 @DeveloperApi
 case class BroadcastNestedLoopJoin(
     streamed: SparkPlan, broadcast: SparkPlan, joinType: JoinType, condition: Option[Expression])
-    (@transient sc: SparkContext)
+    (@transient sqlContext: SQLContext)
   extends BinaryNode {
   // TODO: Override requiredChildDistribution.
 
   override def outputPartitioning: Partitioning = streamed.outputPartitioning
 
-  override def otherCopyArgs = sc :: Nil
+  override def otherCopyArgs = sqlContext :: Nil
 
   def output = left.output ++ right.output
 
@@ -286,7 +286,8 @@ case class BroadcastNestedLoopJoin(
 
 
   def execute() = {
-    val broadcastedRelation = sc.broadcast(broadcast.execute().map(_.copy()).collect().toIndexedSeq)
+    val broadcastedRelation =
+      sqlContext.sparkContext.broadcast(broadcast.execute().map(_.copy()).collect().toIndexedSeq)
 
     val streamedPlusMatches = streamed.execute().mapPartitions { streamedIter =>
       val matchedRows = new ArrayBuffer[Row]
@@ -337,7 +338,7 @@ case class BroadcastNestedLoopJoin(
       }
 
     // TODO: Breaks lineage.
-    sc.union(
-      streamedPlusMatches.flatMap(_._1), sc.makeRDD(rightOuterMatches))
+    sqlContext.sparkContext.union(
+      streamedPlusMatches.flatMap(_._1), sqlContext.sparkContext.makeRDD(rightOuterMatches))
   }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala
index 624f2e2fa13f..ade823b51c9c 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala
@@ -33,10 +33,10 @@ import parquet.hadoop.util.ContextUtil
 import parquet.io.InvalidRecordException
 import parquet.schema.MessageType
 
-import org.apache.spark.{Logging, SerializableWritable, SparkContext, TaskContext}
+import org.apache.spark.{Logging, SerializableWritable, TaskContext}
 import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.SQLContext
 import org.apache.spark.sql.catalyst.expressions.{Attribute, Expression, Row}
-import org.apache.spark.sql.catalyst.types.StructType
 import org.apache.spark.sql.execution.{LeafNode, SparkPlan, UnaryNode}
 
 /**
@@ -49,10 +49,11 @@ case class ParquetTableScan(
     output: Seq[Attribute],
     relation: ParquetRelation,
     columnPruningPred: Seq[Expression])(
-    @transient val sc: SparkContext)
+    @transient val sqlContext: SQLContext)
   extends LeafNode {
 
   override def execute(): RDD[Row] = {
+    val sc = sqlContext.sparkContext
     val job = new Job(sc.hadoopConfiguration)
     ParquetInputFormat.setReadSupportClass(
       job,
@@ -93,7 +94,7 @@ case class ParquetTableScan(
       .filter(_ != null) // Parquet's record filters may produce null values
   }
 
-  override def otherCopyArgs = sc :: Nil
+  override def otherCopyArgs = sqlContext :: Nil
 
   /**
    * Applies a (candidate) projection.
@@ -104,7 +105,7 @@ case class ParquetTableScan(
   def pruneColumns(prunedAttributes: Seq[Attribute]): ParquetTableScan = {
     val success = validateProjection(prunedAttributes)
     if (success) {
-      ParquetTableScan(prunedAttributes, relation, columnPruningPred)(sc)
+      ParquetTableScan(prunedAttributes, relation, columnPruningPred)(sqlContext)
     } else {
       sys.error("Warning: Could not validate Parquet schema projection in pruneColumns")
       this
@@ -152,7 +153,7 @@ case class InsertIntoParquetTable(
     relation: ParquetRelation,
     child: SparkPlan,
     overwrite: Boolean = false)(
-    @transient val sc: SparkContext)
+    @transient val sqlContext: SQLContext)
   extends UnaryNode with SparkHadoopMapReduceUtil {
 
   /**
@@ -168,7 +169,7 @@ case class InsertIntoParquetTable(
     val childRdd = child.execute()
     assert(childRdd != null)
 
-    val job = new Job(sc.hadoopConfiguration)
+    val job = new Job(sqlContext.sparkContext.hadoopConfiguration)
 
     val writeSupport =
       if (child.output.map(_.dataType).forall(_.isPrimitive)) {
@@ -204,7 +205,7 @@ case class InsertIntoParquetTable(
 
   override def output = child.output
 
-  override def otherCopyArgs = sc :: Nil
+  override def otherCopyArgs = sqlContext :: Nil
 
   /**
    * Stores the given Row RDD as a Hadoop file.
@@ -231,7 +232,7 @@ case class InsertIntoParquetTable(
     val wrappedConf = new SerializableWritable(job.getConfiguration)
     val formatter = new SimpleDateFormat("yyyyMMddHHmm")
     val jobtrackerID = formatter.format(new Date())
-    val stageId = sc.newRddId()
+    val stageId = sqlContext.sparkContext.newRddId()
 
     val taskIdOffset =
       if (overwrite) {
@@ -270,7 +271,7 @@ case class InsertIntoParquetTable(
     val jobTaskContext = newTaskAttemptContext(wrappedConf.value, jobAttemptId)
     val jobCommitter = jobFormat.getOutputCommitter(jobTaskContext)
     jobCommitter.setupJob(jobTaskContext)
-    sc.runJob(rdd, writeShard _)
+    sqlContext.sparkContext.runJob(rdd, writeShard _)
     jobCommitter.commitJob(jobTaskContext)
   }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala
index 7714eb1b5628..2ca0c1cdcbec 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala
@@ -166,7 +166,7 @@ class ParquetQuerySuite extends QueryTest with FunSuiteLike with BeforeAndAfterA
     val scanner = new ParquetTableScan(
       ParquetTestData.testData.output,
       ParquetTestData.testData,
-      Seq())(TestSQLContext.sparkContext)
+      Seq())(TestSQLContext)
     val projected = scanner.pruneColumns(ParquetTypesConverter
       .convertToAttributes(MessageTypeParser
       .parseMessageType(ParquetTestData.subTestSchema)))

From ec935abce13b60f353236566da149c0c87bb1002 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Sat, 21 Jun 2014 12:04:18 -0700
Subject: [PATCH 489/810] [SQL] Break hiveOperators.scala into multiple files.

The single file was getting very long (500+ loc).

Author: Reynold Xin <rxin@apache.org>

Closes #1166 from rxin/hiveOperators and squashes the following commits:

5b43068 [Reynold Xin] [SQL] Break hiveOperators.scala into multiple files.
---
 .../spark/sql/hive/HiveStrategies.scala       |   9 +-
 .../execution/DescribeHiveTableCommand.scala  |  88 +++
 .../sql/hive/execution/HiveTableScan.scala    | 223 ++++++++
 .../hive/execution/InsertIntoHiveTable.scala  | 248 +++++++++
 .../sql/hive/execution/NativeCommand.scala    |  47 ++
 .../sql/hive/execution/hiveOperators.scala    | 524 ------------------
 6 files changed, 610 insertions(+), 529 deletions(-)
 create mode 100644 sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/DescribeHiveTableCommand.scala
 create mode 100644 sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveTableScan.scala
 create mode 100644 sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala
 create mode 100644 sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/NativeCommand.scala
 delete mode 100644 sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/hiveOperators.scala

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala
index af7687b40429..4d0fab4140b2 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala
@@ -64,7 +64,6 @@ private[hive] trait HiveStrategies {
         val partitionKeyIds = relation.partitionKeys.map(_.exprId).toSet
         val (pruningPredicates, otherPredicates) = predicates.partition {
           _.references.map(_.exprId).subsetOf(partitionKeyIds)
-
         }
 
         pruneFilterProject(
@@ -81,16 +80,16 @@ private[hive] trait HiveStrategies {
     def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
       case logical.NativeCommand(sql) =>
         NativeCommand(sql, plan.output)(context) :: Nil
-      case describe: logical.DescribeCommand => {
+
+      case describe: logical.DescribeCommand =>
         val resolvedTable = context.executePlan(describe.table).analyzed
         resolvedTable match {
           case t: MetastoreRelation =>
-            Seq(DescribeHiveTableCommand(
-              t, describe.output, describe.isExtended)(context))
+            Seq(DescribeHiveTableCommand(t, describe.output, describe.isExtended)(context))
           case o: LogicalPlan =>
             Seq(DescribeCommand(planLater(o), describe.output)(context))
         }
-      }
+
       case _ => Nil
     }
   }
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/DescribeHiveTableCommand.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/DescribeHiveTableCommand.scala
new file mode 100644
index 000000000000..a40e89e0d382
--- /dev/null
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/DescribeHiveTableCommand.scala
@@ -0,0 +1,88 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive.execution
+
+import scala.collection.JavaConversions._
+
+import org.apache.hadoop.hive.metastore.api.FieldSchema
+
+import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.catalyst.expressions.{Attribute, GenericRow, Row}
+import org.apache.spark.sql.execution.{Command, LeafNode}
+import org.apache.spark.sql.hive.{HiveContext, MetastoreRelation}
+
+/**
+ * Implementation for "describe [extended] table".
+ *
+ * :: DeveloperApi ::
+ */
+@DeveloperApi
+case class DescribeHiveTableCommand(
+    table: MetastoreRelation,
+    output: Seq[Attribute],
+    isExtended: Boolean)(
+    @transient context: HiveContext)
+  extends LeafNode with Command {
+
+  // Strings with the format like Hive. It is used for result comparison in our unit tests.
+  lazy val hiveString: Seq[String] = {
+    val alignment = 20
+    val delim = "\t"
+
+    sideEffectResult.map {
+      case (name, dataType, comment) =>
+        String.format("%-" + alignment + "s", name) + delim +
+          String.format("%-" + alignment + "s", dataType) + delim +
+          String.format("%-" + alignment + "s", Option(comment).getOrElse("None"))
+    }
+  }
+
+  override protected[sql] lazy val sideEffectResult: Seq[(String, String, String)] = {
+    // Trying to mimic the format of Hive's output. But not exactly the same.
+    var results: Seq[(String, String, String)] = Nil
+
+    val columns: Seq[FieldSchema] = table.hiveQlTable.getCols
+    val partitionColumns: Seq[FieldSchema] = table.hiveQlTable.getPartCols
+    results ++= columns.map(field => (field.getName, field.getType, field.getComment))
+    if (!partitionColumns.isEmpty) {
+      val partColumnInfo =
+        partitionColumns.map(field => (field.getName, field.getType, field.getComment))
+      results ++=
+        partColumnInfo ++
+          Seq(("# Partition Information", "", "")) ++
+          Seq((s"# ${output.get(0).name}", output.get(1).name, output.get(2).name)) ++
+          partColumnInfo
+    }
+
+    if (isExtended) {
+      results ++= Seq(("Detailed Table Information", table.hiveQlTable.getTTable.toString, ""))
+    }
+
+    results
+  }
+
+  override def execute(): RDD[Row] = {
+    val rows = sideEffectResult.map {
+      case (name, dataType, comment) => new GenericRow(Array[Any](name, dataType, comment))
+    }
+    context.sparkContext.parallelize(rows, 1)
+  }
+
+  override def otherCopyArgs = context :: Nil
+}
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveTableScan.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveTableScan.scala
new file mode 100644
index 000000000000..ef8bae74530e
--- /dev/null
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveTableScan.scala
@@ -0,0 +1,223 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive.execution
+
+import scala.collection.JavaConversions._
+
+import org.apache.hadoop.hive.common.`type`.{HiveDecimal, HiveVarchar}
+import org.apache.hadoop.hive.conf.HiveConf
+import org.apache.hadoop.hive.ql.metadata.{Partition => HivePartition}
+import org.apache.hadoop.hive.serde.serdeConstants
+import org.apache.hadoop.hive.serde2.ColumnProjectionUtils
+import org.apache.hadoop.hive.serde2.objectinspector._
+import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspectorUtils.ObjectInspectorCopyOption
+import org.apache.hadoop.hive.serde2.objectinspector.primitive._
+import org.apache.hadoop.hive.serde2.typeinfo.TypeInfoUtils
+
+import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.types.{BooleanType, DataType}
+import org.apache.spark.sql.execution._
+import org.apache.spark.sql.hive._
+import org.apache.spark.util.MutablePair
+
+/**
+ * :: DeveloperApi ::
+ * The Hive table scan operator.  Column and partition pruning are both handled.
+ *
+ * @param attributes Attributes to be fetched from the Hive table.
+ * @param relation The Hive table be be scanned.
+ * @param partitionPruningPred An optional partition pruning predicate for partitioned table.
+ */
+@DeveloperApi
+case class HiveTableScan(
+    attributes: Seq[Attribute],
+    relation: MetastoreRelation,
+    partitionPruningPred: Option[Expression])(
+    @transient val context: HiveContext)
+  extends LeafNode
+  with HiveInspectors {
+
+  require(partitionPruningPred.isEmpty || relation.hiveQlTable.isPartitioned,
+    "Partition pruning predicates only supported for partitioned tables.")
+
+  // Bind all partition key attribute references in the partition pruning predicate for later
+  // evaluation.
+  private[this] val boundPruningPred = partitionPruningPred.map { pred =>
+    require(
+      pred.dataType == BooleanType,
+      s"Data type of predicate $pred must be BooleanType rather than ${pred.dataType}.")
+
+    BindReferences.bindReference(pred, relation.partitionKeys)
+  }
+
+  @transient
+  private[this] val hadoopReader = new HadoopTableReader(relation.tableDesc, context)
+
+  /**
+   * The hive object inspector for this table, which can be used to extract values from the
+   * serialized row representation.
+   */
+  @transient
+  private[this] lazy val objectInspector =
+    relation.tableDesc.getDeserializer.getObjectInspector.asInstanceOf[StructObjectInspector]
+
+  /**
+   * Functions that extract the requested attributes from the hive output.  Partitioned values are
+   * casted from string to its declared data type.
+   */
+  @transient
+  protected lazy val attributeFunctions: Seq[(Any, Array[String]) => Any] = {
+    attributes.map { a =>
+      val ordinal = relation.partitionKeys.indexOf(a)
+      if (ordinal >= 0) {
+        val dataType = relation.partitionKeys(ordinal).dataType
+        (_: Any, partitionKeys: Array[String]) => {
+          castFromString(partitionKeys(ordinal), dataType)
+        }
+      } else {
+        val ref = objectInspector.getAllStructFieldRefs
+          .find(_.getFieldName == a.name)
+          .getOrElse(sys.error(s"Can't find attribute $a"))
+        val fieldObjectInspector = ref.getFieldObjectInspector
+
+        val unwrapHiveData = fieldObjectInspector match {
+          case _: HiveVarcharObjectInspector =>
+            (value: Any) => value.asInstanceOf[HiveVarchar].getValue
+          case _: HiveDecimalObjectInspector =>
+            (value: Any) => BigDecimal(value.asInstanceOf[HiveDecimal].bigDecimalValue())
+          case _ =>
+            identity[Any] _
+        }
+
+        (row: Any, _: Array[String]) => {
+          val data = objectInspector.getStructFieldData(row, ref)
+          val hiveData = unwrapData(data, fieldObjectInspector)
+          if (hiveData != null) unwrapHiveData(hiveData) else null
+        }
+      }
+    }
+  }
+
+  private[this] def castFromString(value: String, dataType: DataType) = {
+    Cast(Literal(value), dataType).eval(null)
+  }
+
+  private def addColumnMetadataToConf(hiveConf: HiveConf) {
+    // Specifies IDs and internal names of columns to be scanned.
+    val neededColumnIDs = attributes.map(a => relation.output.indexWhere(_.name == a.name): Integer)
+    val columnInternalNames = neededColumnIDs.map(HiveConf.getColumnInternalName(_)).mkString(",")
+
+    if (attributes.size == relation.output.size) {
+      ColumnProjectionUtils.setFullyReadColumns(hiveConf)
+    } else {
+      ColumnProjectionUtils.appendReadColumnIDs(hiveConf, neededColumnIDs)
+    }
+
+    ColumnProjectionUtils.appendReadColumnNames(hiveConf, attributes.map(_.name))
+
+    // Specifies types and object inspectors of columns to be scanned.
+    val structOI = ObjectInspectorUtils
+      .getStandardObjectInspector(
+        relation.tableDesc.getDeserializer.getObjectInspector,
+        ObjectInspectorCopyOption.JAVA)
+      .asInstanceOf[StructObjectInspector]
+
+    val columnTypeNames = structOI
+      .getAllStructFieldRefs
+      .map(_.getFieldObjectInspector)
+      .map(TypeInfoUtils.getTypeInfoFromObjectInspector(_).getTypeName)
+      .mkString(",")
+
+    hiveConf.set(serdeConstants.LIST_COLUMN_TYPES, columnTypeNames)
+    hiveConf.set(serdeConstants.LIST_COLUMNS, columnInternalNames)
+  }
+
+  addColumnMetadataToConf(context.hiveconf)
+
+  private def inputRdd = if (!relation.hiveQlTable.isPartitioned) {
+    hadoopReader.makeRDDForTable(relation.hiveQlTable)
+  } else {
+    hadoopReader.makeRDDForPartitionedTable(prunePartitions(relation.hiveQlPartitions))
+  }
+
+  /**
+   * Prunes partitions not involve the query plan.
+   *
+   * @param partitions All partitions of the relation.
+   * @return Partitions that are involved in the query plan.
+   */
+  private[hive] def prunePartitions(partitions: Seq[HivePartition]) = {
+    boundPruningPred match {
+      case None => partitions
+      case Some(shouldKeep) => partitions.filter { part =>
+        val dataTypes = relation.partitionKeys.map(_.dataType)
+        val castedValues = for ((value, dataType) <- part.getValues.zip(dataTypes)) yield {
+          castFromString(value, dataType)
+        }
+
+        // Only partitioned values are needed here, since the predicate has already been bound to
+        // partition key attribute references.
+        val row = new GenericRow(castedValues.toArray)
+        shouldKeep.eval(row).asInstanceOf[Boolean]
+      }
+    }
+  }
+
+  override def execute() = {
+    inputRdd.mapPartitions { iterator =>
+      if (iterator.isEmpty) {
+        Iterator.empty
+      } else {
+        val mutableRow = new GenericMutableRow(attributes.length)
+        val mutablePair = new MutablePair[Any, Array[String]]()
+        val buffered = iterator.buffered
+
+        // NOTE (lian): Critical path of Hive table scan, unnecessary FP style code and pattern
+        // matching are avoided intentionally.
+        val rowsAndPartitionKeys = buffered.head match {
+          // With partition keys
+          case _: Array[Any] =>
+            buffered.map { case array: Array[Any] =>
+              val deserializedRow = array(0)
+              val partitionKeys = array(1).asInstanceOf[Array[String]]
+              mutablePair.update(deserializedRow, partitionKeys)
+            }
+
+          // Without partition keys
+          case _ =>
+            val emptyPartitionKeys = Array.empty[String]
+            buffered.map { deserializedRow =>
+              mutablePair.update(deserializedRow, emptyPartitionKeys)
+            }
+        }
+
+        rowsAndPartitionKeys.map { pair =>
+          var i = 0
+          while (i < attributes.length) {
+            mutableRow(i) = attributeFunctions(i)(pair._1, pair._2)
+            i += 1
+          }
+          mutableRow: Row
+        }
+      }
+    }
+  }
+
+  override def output = attributes
+}
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala
new file mode 100644
index 000000000000..594a803806ed
--- /dev/null
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala
@@ -0,0 +1,248 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive.execution
+
+import scala.collection.JavaConversions._
+
+import org.apache.hadoop.hive.common.`type`.{HiveDecimal, HiveVarchar}
+import org.apache.hadoop.hive.metastore.MetaStoreUtils
+import org.apache.hadoop.hive.ql.Context
+import org.apache.hadoop.hive.ql.metadata.Hive
+import org.apache.hadoop.hive.ql.plan.{FileSinkDesc, TableDesc}
+import org.apache.hadoop.hive.serde2.Serializer
+import org.apache.hadoop.hive.serde2.objectinspector._
+import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspectorUtils.ObjectInspectorCopyOption
+import org.apache.hadoop.hive.serde2.objectinspector.primitive.JavaHiveDecimalObjectInspector
+import org.apache.hadoop.hive.serde2.objectinspector.primitive.JavaHiveVarcharObjectInspector
+import org.apache.hadoop.io.Writable
+import org.apache.hadoop.mapred.{FileOutputCommitter, FileOutputFormat, JobConf}
+
+import org.apache.spark.{SparkException, TaskContext}
+import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.catalyst.expressions.Row
+import org.apache.spark.sql.execution.{SparkPlan, UnaryNode}
+import org.apache.spark.sql.hive.{HiveContext, MetastoreRelation, SparkHiveHadoopWriter}
+
+/**
+ * :: DeveloperApi ::
+ */
+@DeveloperApi
+case class InsertIntoHiveTable(
+    table: MetastoreRelation,
+    partition: Map[String, Option[String]],
+    child: SparkPlan,
+    overwrite: Boolean)
+    (@transient sc: HiveContext)
+  extends UnaryNode {
+
+  val outputClass = newSerializer(table.tableDesc).getSerializedClass
+  @transient private val hiveContext = new Context(sc.hiveconf)
+  @transient private val db = Hive.get(sc.hiveconf)
+
+  private def newSerializer(tableDesc: TableDesc): Serializer = {
+    val serializer = tableDesc.getDeserializerClass.newInstance().asInstanceOf[Serializer]
+    serializer.initialize(null, tableDesc.getProperties)
+    serializer
+  }
+
+  override def otherCopyArgs = sc :: Nil
+
+  def output = child.output
+
+  /**
+   * Wraps with Hive types based on object inspector.
+   * TODO: Consolidate all hive OI/data interface code.
+   */
+  protected def wrap(a: (Any, ObjectInspector)): Any = a match {
+    case (s: String, oi: JavaHiveVarcharObjectInspector) =>
+      new HiveVarchar(s, s.size)
+
+    case (bd: BigDecimal, oi: JavaHiveDecimalObjectInspector) =>
+      new HiveDecimal(bd.underlying())
+
+    case (row: Row, oi: StandardStructObjectInspector) =>
+      val struct = oi.create()
+      row.zip(oi.getAllStructFieldRefs: Seq[StructField]).foreach {
+        case (data, field) =>
+          oi.setStructFieldData(struct, field, wrap(data, field.getFieldObjectInspector))
+      }
+      struct
+
+    case (s: Seq[_], oi: ListObjectInspector) =>
+      val wrappedSeq = s.map(wrap(_, oi.getListElementObjectInspector))
+      seqAsJavaList(wrappedSeq)
+
+    case (obj, _) =>
+      obj
+  }
+
+  def saveAsHiveFile(
+      rdd: RDD[Writable],
+      valueClass: Class[_],
+      fileSinkConf: FileSinkDesc,
+      conf: JobConf,
+      isCompressed: Boolean) {
+    if (valueClass == null) {
+      throw new SparkException("Output value class not set")
+    }
+    conf.setOutputValueClass(valueClass)
+    if (fileSinkConf.getTableInfo.getOutputFileFormatClassName == null) {
+      throw new SparkException("Output format class not set")
+    }
+    // Doesn't work in Scala 2.9 due to what may be a generics bug
+    // TODO: Should we uncomment this for Scala 2.10?
+    // conf.setOutputFormat(outputFormatClass)
+    conf.set("mapred.output.format.class", fileSinkConf.getTableInfo.getOutputFileFormatClassName)
+    if (isCompressed) {
+      // Please note that isCompressed, "mapred.output.compress", "mapred.output.compression.codec",
+      // and "mapred.output.compression.type" have no impact on ORC because it uses table properties
+      // to store compression information.
+      conf.set("mapred.output.compress", "true")
+      fileSinkConf.setCompressed(true)
+      fileSinkConf.setCompressCodec(conf.get("mapred.output.compression.codec"))
+      fileSinkConf.setCompressType(conf.get("mapred.output.compression.type"))
+    }
+    conf.setOutputCommitter(classOf[FileOutputCommitter])
+    FileOutputFormat.setOutputPath(
+      conf,
+      SparkHiveHadoopWriter.createPathFromString(fileSinkConf.getDirName, conf))
+
+    logger.debug("Saving as hadoop file of type " + valueClass.getSimpleName)
+
+    val writer = new SparkHiveHadoopWriter(conf, fileSinkConf)
+    writer.preSetup()
+
+    def writeToFile(context: TaskContext, iter: Iterator[Writable]) {
+      // Hadoop wants a 32-bit task attempt ID, so if ours is bigger than Int.MaxValue, roll it
+      // around by taking a mod. We expect that no task will be attempted 2 billion times.
+      val attemptNumber = (context.attemptId % Int.MaxValue).toInt
+
+      writer.setup(context.stageId, context.partitionId, attemptNumber)
+      writer.open()
+
+      var count = 0
+      while(iter.hasNext) {
+        val record = iter.next()
+        count += 1
+        writer.write(record)
+      }
+
+      writer.close()
+      writer.commit()
+    }
+
+    sc.sparkContext.runJob(rdd, writeToFile _)
+    writer.commitJob()
+  }
+
+  override def execute() = result
+
+  /**
+   * Inserts all the rows in the table into Hive.  Row objects are properly serialized with the
+   * `org.apache.hadoop.hive.serde2.SerDe` and the
+   * `org.apache.hadoop.mapred.OutputFormat` provided by the table definition.
+   *
+   * Note: this is run once and then kept to avoid double insertions.
+   */
+  private lazy val result: RDD[Row] = {
+    val childRdd = child.execute()
+    assert(childRdd != null)
+
+    // Have to pass the TableDesc object to RDD.mapPartitions and then instantiate new serializer
+    // instances within the closure, since Serializer is not serializable while TableDesc is.
+    val tableDesc = table.tableDesc
+    val tableLocation = table.hiveQlTable.getDataLocation
+    val tmpLocation = hiveContext.getExternalTmpFileURI(tableLocation)
+    val fileSinkConf = new FileSinkDesc(tmpLocation.toString, tableDesc, false)
+    val rdd = childRdd.mapPartitions { iter =>
+      val serializer = newSerializer(fileSinkConf.getTableInfo)
+      val standardOI = ObjectInspectorUtils
+        .getStandardObjectInspector(
+          fileSinkConf.getTableInfo.getDeserializer.getObjectInspector,
+          ObjectInspectorCopyOption.JAVA)
+        .asInstanceOf[StructObjectInspector]
+
+
+      val fieldOIs = standardOI.getAllStructFieldRefs.map(_.getFieldObjectInspector).toArray
+      val outputData = new Array[Any](fieldOIs.length)
+      iter.map { row =>
+        var i = 0
+        while (i < row.length) {
+          // Casts Strings to HiveVarchars when necessary.
+          outputData(i) = wrap(row(i), fieldOIs(i))
+          i += 1
+        }
+
+        serializer.serialize(outputData, standardOI)
+      }
+    }
+
+    // ORC stores compression information in table properties. While, there are other formats
+    // (e.g. RCFile) that rely on hadoop configurations to store compression information.
+    val jobConf = new JobConf(sc.hiveconf)
+    saveAsHiveFile(
+      rdd,
+      outputClass,
+      fileSinkConf,
+      jobConf,
+      sc.hiveconf.getBoolean("hive.exec.compress.output", false))
+
+    // TODO: Handle dynamic partitioning.
+    val outputPath = FileOutputFormat.getOutputPath(jobConf)
+    // Have to construct the format of dbname.tablename.
+    val qualifiedTableName = s"${table.databaseName}.${table.tableName}"
+    // TODO: Correctly set holdDDLTime.
+    // In most of the time, we should have holdDDLTime = false.
+    // holdDDLTime will be true when TOK_HOLD_DDLTIME presents in the query as a hint.
+    val holdDDLTime = false
+    if (partition.nonEmpty) {
+      val partitionSpec = partition.map {
+        case (key, Some(value)) => key -> value
+        case (key, None) => key -> "" // Should not reach here right now.
+      }
+      val partVals = MetaStoreUtils.getPvals(table.hiveQlTable.getPartCols, partitionSpec)
+      db.validatePartitionNameCharacters(partVals)
+      // inheritTableSpecs is set to true. It should be set to false for a IMPORT query
+      // which is currently considered as a Hive native command.
+      val inheritTableSpecs = true
+      // TODO: Correctly set isSkewedStoreAsSubdir.
+      val isSkewedStoreAsSubdir = false
+      db.loadPartition(
+        outputPath,
+        qualifiedTableName,
+        partitionSpec,
+        overwrite,
+        holdDDLTime,
+        inheritTableSpecs,
+        isSkewedStoreAsSubdir)
+    } else {
+      db.loadTable(
+        outputPath,
+        qualifiedTableName,
+        overwrite,
+        holdDDLTime)
+    }
+
+    // It would be nice to just return the childRdd unchanged so insert operations could be chained,
+    // however for now we return an empty list to simplify compatibility checks with hive, which
+    // does not return anything for insert operations.
+    // TODO: implement hive compatibility as rules.
+    sc.sparkContext.makeRDD(Nil, 1)
+  }
+}
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/NativeCommand.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/NativeCommand.scala
new file mode 100644
index 000000000000..fe6031678f70
--- /dev/null
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/NativeCommand.scala
@@ -0,0 +1,47 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive.execution
+
+import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.catalyst.expressions.{Attribute, GenericRow, Row}
+import org.apache.spark.sql.execution.{Command, LeafNode}
+import org.apache.spark.sql.hive.HiveContext
+
+/**
+ * :: DeveloperApi ::
+ */
+@DeveloperApi
+case class NativeCommand(
+    sql: String, output: Seq[Attribute])(
+    @transient context: HiveContext)
+  extends LeafNode with Command {
+
+  override protected[sql] lazy val sideEffectResult: Seq[String] = context.runSqlHive(sql)
+
+  override def execute(): RDD[Row] = {
+    if (sideEffectResult.size == 0) {
+      context.emptyResult
+    } else {
+      val rows = sideEffectResult.map(r => new GenericRow(Array[Any](r)))
+      context.sparkContext.parallelize(rows, 1)
+    }
+  }
+
+  override def otherCopyArgs = context :: Nil
+}
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/hiveOperators.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/hiveOperators.scala
deleted file mode 100644
index 2de2db28a7e0..000000000000
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/hiveOperators.scala
+++ /dev/null
@@ -1,524 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql.hive.execution
-
-import org.apache.hadoop.hive.common.`type`.{HiveDecimal, HiveVarchar}
-import org.apache.hadoop.hive.conf.HiveConf
-import org.apache.hadoop.hive.metastore.MetaStoreUtils
-import org.apache.hadoop.hive.metastore.api.FieldSchema
-import org.apache.hadoop.hive.ql.Context
-import org.apache.hadoop.hive.ql.metadata.{Partition => HivePartition, Hive}
-import org.apache.hadoop.hive.ql.metadata.formatting.MetaDataFormatUtils
-import org.apache.hadoop.hive.ql.plan.{TableDesc, FileSinkDesc}
-import org.apache.hadoop.hive.serde.serdeConstants
-import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspectorUtils.ObjectInspectorCopyOption
-import org.apache.hadoop.hive.serde2.objectinspector._
-import org.apache.hadoop.hive.serde2.objectinspector.primitive._
-import org.apache.hadoop.hive.serde2.typeinfo.TypeInfoUtils
-import org.apache.hadoop.hive.serde2.{ColumnProjectionUtils, Serializer}
-import org.apache.hadoop.io.Writable
-import org.apache.hadoop.mapred._
-
-import org.apache.spark
-import org.apache.spark.annotation.DeveloperApi
-import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.catalyst.types.{BooleanType, DataType}
-import org.apache.spark.sql.execution._
-import org.apache.spark.sql.hive._
-import org.apache.spark.util.MutablePair
-import org.apache.spark.{TaskContext, SparkException}
-
-/* Implicits */
-import scala.collection.JavaConversions._
-
-/**
- * :: DeveloperApi ::
- * The Hive table scan operator.  Column and partition pruning are both handled.
- *
- * @param attributes Attributes to be fetched from the Hive table.
- * @param relation The Hive table be be scanned.
- * @param partitionPruningPred An optional partition pruning predicate for partitioned table.
- */
-@DeveloperApi
-case class HiveTableScan(
-    attributes: Seq[Attribute],
-    relation: MetastoreRelation,
-    partitionPruningPred: Option[Expression])(
-    @transient val context: HiveContext)
-  extends LeafNode
-  with HiveInspectors {
-
-  require(partitionPruningPred.isEmpty || relation.hiveQlTable.isPartitioned,
-    "Partition pruning predicates only supported for partitioned tables.")
-
-  // Bind all partition key attribute references in the partition pruning predicate for later
-  // evaluation.
-  private val boundPruningPred = partitionPruningPred.map { pred =>
-    require(
-      pred.dataType == BooleanType,
-      s"Data type of predicate $pred must be BooleanType rather than ${pred.dataType}.")
-
-    BindReferences.bindReference(pred, relation.partitionKeys)
-  }
-
-  @transient
-  val hadoopReader = new HadoopTableReader(relation.tableDesc, context)
-
-  /**
-   * The hive object inspector for this table, which can be used to extract values from the
-   * serialized row representation.
-   */
-  @transient
-  lazy val objectInspector =
-    relation.tableDesc.getDeserializer.getObjectInspector.asInstanceOf[StructObjectInspector]
-
-  /**
-   * Functions that extract the requested attributes from the hive output.  Partitioned values are
-   * casted from string to its declared data type.
-   */
-  @transient
-  protected lazy val attributeFunctions: Seq[(Any, Array[String]) => Any] = {
-    attributes.map { a =>
-      val ordinal = relation.partitionKeys.indexOf(a)
-      if (ordinal >= 0) {
-        val dataType = relation.partitionKeys(ordinal).dataType
-        (_: Any, partitionKeys: Array[String]) => {
-          castFromString(partitionKeys(ordinal), dataType)
-        }
-      } else {
-        val ref = objectInspector.getAllStructFieldRefs
-          .find(_.getFieldName == a.name)
-          .getOrElse(sys.error(s"Can't find attribute $a"))
-        val fieldObjectInspector = ref.getFieldObjectInspector
-
-        val unwrapHiveData = fieldObjectInspector match {
-          case _: HiveVarcharObjectInspector =>
-            (value: Any) => value.asInstanceOf[HiveVarchar].getValue
-          case _: HiveDecimalObjectInspector =>
-            (value: Any) => BigDecimal(value.asInstanceOf[HiveDecimal].bigDecimalValue())
-          case _ =>
-            identity[Any] _
-        }
-
-        (row: Any, _: Array[String]) => {
-          val data = objectInspector.getStructFieldData(row, ref)
-          val hiveData = unwrapData(data, fieldObjectInspector)
-          if (hiveData != null) unwrapHiveData(hiveData) else null
-        }
-      }
-    }
-  }
-
-  private def castFromString(value: String, dataType: DataType) = {
-    Cast(Literal(value), dataType).eval(null)
-  }
-
-  private def addColumnMetadataToConf(hiveConf: HiveConf) {
-    // Specifies IDs and internal names of columns to be scanned.
-    val neededColumnIDs = attributes.map(a => relation.output.indexWhere(_.name == a.name): Integer)
-    val columnInternalNames = neededColumnIDs.map(HiveConf.getColumnInternalName(_)).mkString(",")
-
-    if (attributes.size == relation.output.size) {
-      ColumnProjectionUtils.setFullyReadColumns(hiveConf)
-    } else {
-      ColumnProjectionUtils.appendReadColumnIDs(hiveConf, neededColumnIDs)
-    }
-
-    ColumnProjectionUtils.appendReadColumnNames(hiveConf, attributes.map(_.name))
-
-    // Specifies types and object inspectors of columns to be scanned.
-    val structOI = ObjectInspectorUtils
-      .getStandardObjectInspector(
-        relation.tableDesc.getDeserializer.getObjectInspector,
-        ObjectInspectorCopyOption.JAVA)
-      .asInstanceOf[StructObjectInspector]
-
-    val columnTypeNames = structOI
-      .getAllStructFieldRefs
-      .map(_.getFieldObjectInspector)
-      .map(TypeInfoUtils.getTypeInfoFromObjectInspector(_).getTypeName)
-      .mkString(",")
-
-    hiveConf.set(serdeConstants.LIST_COLUMN_TYPES, columnTypeNames)
-    hiveConf.set(serdeConstants.LIST_COLUMNS, columnInternalNames)
-  }
-
-  addColumnMetadataToConf(context.hiveconf)
-
-  @transient
-  def inputRdd = if (!relation.hiveQlTable.isPartitioned) {
-    hadoopReader.makeRDDForTable(relation.hiveQlTable)
-  } else {
-    hadoopReader.makeRDDForPartitionedTable(prunePartitions(relation.hiveQlPartitions))
-  }
-
-  /**
-   * Prunes partitions not involve the query plan.
-   *
-   * @param partitions All partitions of the relation.
-   * @return Partitions that are involved in the query plan.
-   */
-  private[hive] def prunePartitions(partitions: Seq[HivePartition]) = {
-    boundPruningPred match {
-      case None => partitions
-      case Some(shouldKeep) => partitions.filter { part =>
-        val dataTypes = relation.partitionKeys.map(_.dataType)
-        val castedValues = for ((value, dataType) <- part.getValues.zip(dataTypes)) yield {
-          castFromString(value, dataType)
-        }
-
-        // Only partitioned values are needed here, since the predicate has already been bound to
-        // partition key attribute references.
-        val row = new GenericRow(castedValues.toArray)
-        shouldKeep.eval(row).asInstanceOf[Boolean]
-      }
-    }
-  }
-
-  def execute() = {
-    inputRdd.mapPartitions { iterator =>
-      if (iterator.isEmpty) {
-        Iterator.empty
-      } else {
-        val mutableRow = new GenericMutableRow(attributes.length)
-        val mutablePair = new MutablePair[Any, Array[String]]()
-        val buffered = iterator.buffered
-
-        // NOTE (lian): Critical path of Hive table scan, unnecessary FP style code and pattern
-        // matching are avoided intentionally.
-        val rowsAndPartitionKeys = buffered.head match {
-          // With partition keys
-          case _: Array[Any] =>
-            buffered.map { case array: Array[Any] =>
-              val deserializedRow = array(0)
-              val partitionKeys = array(1).asInstanceOf[Array[String]]
-              mutablePair.update(deserializedRow, partitionKeys)
-            }
-
-          // Without partition keys
-          case _ =>
-            val emptyPartitionKeys = Array.empty[String]
-            buffered.map { deserializedRow =>
-              mutablePair.update(deserializedRow, emptyPartitionKeys)
-            }
-        }
-
-        rowsAndPartitionKeys.map { pair =>
-          var i = 0
-          while (i < attributes.length) {
-            mutableRow(i) = attributeFunctions(i)(pair._1, pair._2)
-            i += 1
-          }
-          mutableRow: Row
-        }
-      }
-    }
-  }
-
-  def output = attributes
-}
-
-/**
- * :: DeveloperApi ::
- */
-@DeveloperApi
-case class InsertIntoHiveTable(
-    table: MetastoreRelation,
-    partition: Map[String, Option[String]],
-    child: SparkPlan,
-    overwrite: Boolean)
-    (@transient sc: HiveContext)
-  extends UnaryNode {
-
-  val outputClass = newSerializer(table.tableDesc).getSerializedClass
-  @transient private val hiveContext = new Context(sc.hiveconf)
-  @transient private val db = Hive.get(sc.hiveconf)
-
-  private def newSerializer(tableDesc: TableDesc): Serializer = {
-    val serializer = tableDesc.getDeserializerClass.newInstance().asInstanceOf[Serializer]
-    serializer.initialize(null, tableDesc.getProperties)
-    serializer
-  }
-
-  override def otherCopyArgs = sc :: Nil
-
-  def output = child.output
-
-  /**
-   * Wraps with Hive types based on object inspector.
-   * TODO: Consolidate all hive OI/data interface code.
-   */
-  protected def wrap(a: (Any, ObjectInspector)): Any = a match {
-    case (s: String, oi: JavaHiveVarcharObjectInspector) =>
-      new HiveVarchar(s, s.size)
-
-    case (bd: BigDecimal, oi: JavaHiveDecimalObjectInspector) =>
-      new HiveDecimal(bd.underlying())
-
-    case (row: Row, oi: StandardStructObjectInspector) =>
-      val struct = oi.create()
-      row.zip(oi.getAllStructFieldRefs: Seq[StructField]).foreach {
-        case (data, field) =>
-          oi.setStructFieldData(struct, field, wrap(data, field.getFieldObjectInspector))
-      }
-      struct
-
-    case (s: Seq[_], oi: ListObjectInspector) =>
-      val wrappedSeq = s.map(wrap(_, oi.getListElementObjectInspector))
-      seqAsJavaList(wrappedSeq)
-
-    case (obj, _) =>
-      obj
-  }
-
-  def saveAsHiveFile(
-      rdd: RDD[Writable],
-      valueClass: Class[_],
-      fileSinkConf: FileSinkDesc,
-      conf: JobConf,
-      isCompressed: Boolean) {
-    if (valueClass == null) {
-      throw new SparkException("Output value class not set")
-    }
-    conf.setOutputValueClass(valueClass)
-    if (fileSinkConf.getTableInfo.getOutputFileFormatClassName == null) {
-      throw new SparkException("Output format class not set")
-    }
-    // Doesn't work in Scala 2.9 due to what may be a generics bug
-    // TODO: Should we uncomment this for Scala 2.10?
-    // conf.setOutputFormat(outputFormatClass)
-    conf.set("mapred.output.format.class", fileSinkConf.getTableInfo.getOutputFileFormatClassName)
-    if (isCompressed) {
-      // Please note that isCompressed, "mapred.output.compress", "mapred.output.compression.codec",
-      // and "mapred.output.compression.type" have no impact on ORC because it uses table properties
-      // to store compression information.
-      conf.set("mapred.output.compress", "true")
-      fileSinkConf.setCompressed(true)
-      fileSinkConf.setCompressCodec(conf.get("mapred.output.compression.codec"))
-      fileSinkConf.setCompressType(conf.get("mapred.output.compression.type"))
-    }
-    conf.setOutputCommitter(classOf[FileOutputCommitter])
-    FileOutputFormat.setOutputPath(
-      conf,
-      SparkHiveHadoopWriter.createPathFromString(fileSinkConf.getDirName, conf))
-
-    logger.debug("Saving as hadoop file of type " + valueClass.getSimpleName)
-
-    val writer = new SparkHiveHadoopWriter(conf, fileSinkConf)
-    writer.preSetup()
-
-    def writeToFile(context: TaskContext, iter: Iterator[Writable]) {
-      // Hadoop wants a 32-bit task attempt ID, so if ours is bigger than Int.MaxValue, roll it
-      // around by taking a mod. We expect that no task will be attempted 2 billion times.
-      val attemptNumber = (context.attemptId % Int.MaxValue).toInt
-
-      writer.setup(context.stageId, context.partitionId, attemptNumber)
-      writer.open()
-
-      var count = 0
-      while(iter.hasNext) {
-        val record = iter.next()
-        count += 1
-        writer.write(record)
-      }
-
-      writer.close()
-      writer.commit()
-    }
-
-    sc.sparkContext.runJob(rdd, writeToFile _)
-    writer.commitJob()
-  }
-
-  override def execute() = result
-
-  /**
-   * Inserts all the rows in the table into Hive.  Row objects are properly serialized with the
-   * `org.apache.hadoop.hive.serde2.SerDe` and the
-   * `org.apache.hadoop.mapred.OutputFormat` provided by the table definition.
-   *
-   * Note: this is run once and then kept to avoid double insertions.
-   */
-  private lazy val result: RDD[Row] = {
-    val childRdd = child.execute()
-    assert(childRdd != null)
-
-    // Have to pass the TableDesc object to RDD.mapPartitions and then instantiate new serializer
-    // instances within the closure, since Serializer is not serializable while TableDesc is.
-    val tableDesc = table.tableDesc
-    val tableLocation = table.hiveQlTable.getDataLocation
-    val tmpLocation = hiveContext.getExternalTmpFileURI(tableLocation)
-    val fileSinkConf = new FileSinkDesc(tmpLocation.toString, tableDesc, false)
-    val rdd = childRdd.mapPartitions { iter =>
-      val serializer = newSerializer(fileSinkConf.getTableInfo)
-      val standardOI = ObjectInspectorUtils
-        .getStandardObjectInspector(
-          fileSinkConf.getTableInfo.getDeserializer.getObjectInspector,
-          ObjectInspectorCopyOption.JAVA)
-        .asInstanceOf[StructObjectInspector]
-
-
-      val fieldOIs = standardOI.getAllStructFieldRefs.map(_.getFieldObjectInspector).toArray
-      val outputData = new Array[Any](fieldOIs.length)
-      iter.map { row =>
-        var i = 0
-        while (i < row.length) {
-          // Casts Strings to HiveVarchars when necessary.
-          outputData(i) = wrap(row(i), fieldOIs(i))
-          i += 1
-        }
-
-        serializer.serialize(outputData, standardOI)
-      }
-    }
-
-    // ORC stores compression information in table properties. While, there are other formats
-    // (e.g. RCFile) that rely on hadoop configurations to store compression information.
-    val jobConf = new JobConf(sc.hiveconf)
-    saveAsHiveFile(
-      rdd,
-      outputClass,
-      fileSinkConf,
-      jobConf,
-      sc.hiveconf.getBoolean("hive.exec.compress.output", false))
-
-    // TODO: Handle dynamic partitioning.
-    val outputPath = FileOutputFormat.getOutputPath(jobConf)
-    // Have to construct the format of dbname.tablename.
-    val qualifiedTableName = s"${table.databaseName}.${table.tableName}"
-    // TODO: Correctly set holdDDLTime.
-    // In most of the time, we should have holdDDLTime = false.
-    // holdDDLTime will be true when TOK_HOLD_DDLTIME presents in the query as a hint.
-    val holdDDLTime = false
-    if (partition.nonEmpty) {
-      val partitionSpec = partition.map {
-        case (key, Some(value)) => key -> value
-        case (key, None) => key -> "" // Should not reach here right now.
-      }
-      val partVals = MetaStoreUtils.getPvals(table.hiveQlTable.getPartCols, partitionSpec)
-      db.validatePartitionNameCharacters(partVals)
-      // inheritTableSpecs is set to true. It should be set to false for a IMPORT query
-      // which is currently considered as a Hive native command.
-      val inheritTableSpecs = true
-      // TODO: Correctly set isSkewedStoreAsSubdir.
-      val isSkewedStoreAsSubdir = false
-      db.loadPartition(
-        outputPath,
-        qualifiedTableName,
-        partitionSpec,
-        overwrite,
-        holdDDLTime,
-        inheritTableSpecs,
-        isSkewedStoreAsSubdir)
-    } else {
-      db.loadTable(
-        outputPath,
-        qualifiedTableName,
-        overwrite,
-        holdDDLTime)
-    }
-
-    // It would be nice to just return the childRdd unchanged so insert operations could be chained,
-    // however for now we return an empty list to simplify compatibility checks with hive, which
-    // does not return anything for insert operations.
-    // TODO: implement hive compatibility as rules.
-    sc.sparkContext.makeRDD(Nil, 1)
-  }
-}
-
-/**
- * :: DeveloperApi ::
- */
-@DeveloperApi
-case class NativeCommand(
-    sql: String, output: Seq[Attribute])(
-    @transient context: HiveContext)
-  extends LeafNode with Command {
-
-  override protected[sql] lazy val sideEffectResult: Seq[String] = context.runSqlHive(sql)
-
-  override def execute(): RDD[spark.sql.Row] = {
-    if (sideEffectResult.size == 0) {
-      context.emptyResult
-    } else {
-      val rows = sideEffectResult.map(r => new GenericRow(Array[Any](r)))
-      context.sparkContext.parallelize(rows, 1)
-    }
-  }
-
-  override def otherCopyArgs = context :: Nil
-}
-
-/**
- * :: DeveloperApi ::
- */
-@DeveloperApi
-case class DescribeHiveTableCommand(
-    table: MetastoreRelation,
-    output: Seq[Attribute],
-    isExtended: Boolean)(
-    @transient context: HiveContext)
-  extends LeafNode with Command {
-
-  // Strings with the format like Hive. It is used for result comparison in our unit tests.
-  lazy val hiveString: Seq[String] = {
-    val alignment = 20
-    val delim = "\t"
-
-    sideEffectResult.map {
-      case (name, dataType, comment) =>
-        String.format("%-" + alignment + "s", name) + delim +
-          String.format("%-" + alignment + "s", dataType) + delim +
-          String.format("%-" + alignment + "s", Option(comment).getOrElse("None"))
-    }
-  }
-
-  override protected[sql] lazy val sideEffectResult: Seq[(String, String, String)] = {
-    // Trying to mimic the format of Hive's output. But not exactly the same.
-    var results: Seq[(String, String, String)] = Nil
-
-    val columns: Seq[FieldSchema] = table.hiveQlTable.getCols
-    val partitionColumns: Seq[FieldSchema] = table.hiveQlTable.getPartCols
-    results ++= columns.map(field => (field.getName, field.getType, field.getComment))
-    if (!partitionColumns.isEmpty) {
-      val partColumnInfo =
-        partitionColumns.map(field => (field.getName, field.getType, field.getComment))
-      results ++=
-        partColumnInfo ++
-        Seq(("# Partition Information", "", "")) ++
-        Seq((s"# ${output.get(0).name}", output.get(1).name, output.get(2).name)) ++
-        partColumnInfo
-    }
-
-    if (isExtended) {
-      results ++= Seq(("Detailed Table Information", table.hiveQlTable.getTTable.toString, ""))
-    }
-
-    results
-  }
-
-  override def execute(): RDD[Row] = {
-    val rows = sideEffectResult.map {
-      case (name, dataType, comment) => new GenericRow(Array[Any](name, dataType, comment))
-    }
-    context.sparkContext.parallelize(rows, 1)
-  }
-
-  override def otherCopyArgs = context :: Nil
-}

From 0a432d6a05d2a8f1f8f5e6e851f5174c98a64d1d Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Sat, 21 Jun 2014 13:02:49 -0700
Subject: [PATCH 490/810] HOTFIX: Fix missing MIMA ignore

---
 dev/mima                   | 1 +
 project/MimaExcludes.scala | 2 ++
 2 files changed, 3 insertions(+)

diff --git a/dev/mima b/dev/mima
index b68800d6d017..7857294f61ca 100755
--- a/dev/mima
+++ b/dev/mima
@@ -18,6 +18,7 @@
 #
 
 set -o pipefail
+set -e
 
 # Go to the Spark project root directory
 FWDIR="$(cd `dirname $0`/..; pwd)"
diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala
index 042fdfcc4726..af620d61607f 100644
--- a/project/MimaExcludes.scala
+++ b/project/MimaExcludes.scala
@@ -36,6 +36,8 @@ object MimaExcludes {
         case v if v.startsWith("1.1") =>
           Seq(MimaBuild.excludeSparkPackage("graphx")) ++
           Seq(
+            // Adding new method to JavaRDLike trait - we should probably mark this as a developer API.
+            ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.api.java.JavaRDDLike.partitions"),
             // We made a mistake earlier (ed06500d3) in the Java API to use default parameter values
             // for countApproxDistinct* functions, which does not work in Java. We later removed
             // them, and use the following to tell Mima to not care about them.

From 3e0b078001ae9322294b357022fed230cecb38bd Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Sat, 21 Jun 2014 15:20:15 -0700
Subject: [PATCH 491/810] HOTFIX: Add excludes for new MIMA files

---
 .rat-excludes | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/.rat-excludes b/.rat-excludes
index 15344dfb292d..796c32a80896 100644
--- a/.rat-excludes
+++ b/.rat-excludes
@@ -4,6 +4,8 @@ target
 .classpath
 .mima-excludes
 .generated-mima-excludes
+.generated-mima-class-excludes
+.generated-mima-member-excludes
 .rat-excludes
 .*md
 derby.log

From 1db9cbc33603cfe012790b7dd4975df20ab15bda Mon Sep 17 00:00:00 2001
From: Sean Owen <sowen@cloudera.com>
Date: Sat, 21 Jun 2014 23:29:57 -0700
Subject: [PATCH 492/810] SPARK-1996. Remove use of special Maven repo for Akka

Just following up Matei's suggestion to remove the Akka repo references. Builds and the audit-release script appear OK.

Author: Sean Owen <sowen@cloudera.com>

Closes #1170 from srowen/SPARK-1996 and squashes the following commits:

5ca2930 [Sean Owen] Remove outdated Akka repository references
---
 dev/audit-release/blank_maven_build/pom.xml   | 4 ----
 dev/audit-release/blank_sbt_build/build.sbt   | 1 -
 dev/audit-release/maven_app_core/pom.xml      | 4 ----
 dev/audit-release/sbt_app_core/build.sbt      | 1 -
 dev/audit-release/sbt_app_ganglia/build.sbt   | 1 -
 dev/audit-release/sbt_app_graphx/build.sbt    | 1 -
 dev/audit-release/sbt_app_hive/build.sbt      | 1 -
 dev/audit-release/sbt_app_sql/build.sbt       | 1 -
 dev/audit-release/sbt_app_streaming/build.sbt | 1 -
 docs/quick-start.md                           | 8 --------
 10 files changed, 23 deletions(-)

diff --git a/dev/audit-release/blank_maven_build/pom.xml b/dev/audit-release/blank_maven_build/pom.xml
index 047659e4a8b7..02dd9046c9a4 100644
--- a/dev/audit-release/blank_maven_build/pom.xml
+++ b/dev/audit-release/blank_maven_build/pom.xml
@@ -28,10 +28,6 @@
       <id>Spray.cc repository</id>
       <url>http://repo.spray.cc</url>
     </repository>
-    <repository>
-      <id>Akka repository</id>
-      <url>http://repo.akka.io/releases</url>
-    </repository>
     <repository>
       <id>Spark Staging Repo</id>
       <url>${spark.release.repository}</url>
diff --git a/dev/audit-release/blank_sbt_build/build.sbt b/dev/audit-release/blank_sbt_build/build.sbt
index 1cf52743f27f..696c7f651837 100644
--- a/dev/audit-release/blank_sbt_build/build.sbt
+++ b/dev/audit-release/blank_sbt_build/build.sbt
@@ -25,5 +25,4 @@ libraryDependencies += "org.apache.spark" % System.getenv.get("SPARK_MODULE") %
 
 resolvers ++= Seq(
   "Spark Release Repository" at System.getenv.get("SPARK_RELEASE_REPOSITORY"),
-  "Akka Repository" at "http://repo.akka.io/releases/",
   "Spray Repository" at "http://repo.spray.cc/")
diff --git a/dev/audit-release/maven_app_core/pom.xml b/dev/audit-release/maven_app_core/pom.xml
index 76a381f8e17e..b51639682557 100644
--- a/dev/audit-release/maven_app_core/pom.xml
+++ b/dev/audit-release/maven_app_core/pom.xml
@@ -28,10 +28,6 @@
       <id>Spray.cc repository</id>
       <url>http://repo.spray.cc</url>
     </repository>
-    <repository>
-      <id>Akka repository</id>
-      <url>http://repo.akka.io/releases</url>
-    </repository>
     <repository>
       <id>Spark Staging Repo</id>
       <url>${spark.release.repository}</url>
diff --git a/dev/audit-release/sbt_app_core/build.sbt b/dev/audit-release/sbt_app_core/build.sbt
index 97a8cc3a4e09..291b1d6440ba 100644
--- a/dev/audit-release/sbt_app_core/build.sbt
+++ b/dev/audit-release/sbt_app_core/build.sbt
@@ -25,5 +25,4 @@ libraryDependencies += "org.apache.spark" %% "spark-core" % System.getenv.get("S
 
 resolvers ++= Seq(
   "Spark Release Repository" at System.getenv.get("SPARK_RELEASE_REPOSITORY"),
-  "Akka Repository" at "http://repo.akka.io/releases/",
   "Spray Repository" at "http://repo.spray.cc/")
diff --git a/dev/audit-release/sbt_app_ganglia/build.sbt b/dev/audit-release/sbt_app_ganglia/build.sbt
index 55db675c722d..6d9474acf5bb 100644
--- a/dev/audit-release/sbt_app_ganglia/build.sbt
+++ b/dev/audit-release/sbt_app_ganglia/build.sbt
@@ -27,5 +27,4 @@ libraryDependencies += "org.apache.spark" %% "spark-ganglia-lgpl" % System.geten
 
 resolvers ++= Seq(
   "Spark Release Repository" at System.getenv.get("SPARK_RELEASE_REPOSITORY"),
-  "Akka Repository" at "http://repo.akka.io/releases/",
   "Spray Repository" at "http://repo.spray.cc/")
diff --git a/dev/audit-release/sbt_app_graphx/build.sbt b/dev/audit-release/sbt_app_graphx/build.sbt
index 66f2db357d49..dd11245e67d4 100644
--- a/dev/audit-release/sbt_app_graphx/build.sbt
+++ b/dev/audit-release/sbt_app_graphx/build.sbt
@@ -25,5 +25,4 @@ libraryDependencies += "org.apache.spark" %% "spark-graphx" % System.getenv.get(
 
 resolvers ++= Seq(
   "Spark Release Repository" at System.getenv.get("SPARK_RELEASE_REPOSITORY"),
-  "Akka Repository" at "http://repo.akka.io/releases/",
   "Spray Repository" at "http://repo.spray.cc/")
diff --git a/dev/audit-release/sbt_app_hive/build.sbt b/dev/audit-release/sbt_app_hive/build.sbt
index 7ac1be729c56..a0d4f25da584 100644
--- a/dev/audit-release/sbt_app_hive/build.sbt
+++ b/dev/audit-release/sbt_app_hive/build.sbt
@@ -25,5 +25,4 @@ libraryDependencies += "org.apache.spark" %% "spark-hive" % System.getenv.get("S
 
 resolvers ++= Seq(
   "Spark Release Repository" at System.getenv.get("SPARK_RELEASE_REPOSITORY"),
-  "Akka Repository" at "http://repo.akka.io/releases/",
   "Spray Repository" at "http://repo.spray.cc/")
diff --git a/dev/audit-release/sbt_app_sql/build.sbt b/dev/audit-release/sbt_app_sql/build.sbt
index 6e0ad3b4b296..9116180f71a4 100644
--- a/dev/audit-release/sbt_app_sql/build.sbt
+++ b/dev/audit-release/sbt_app_sql/build.sbt
@@ -25,5 +25,4 @@ libraryDependencies += "org.apache.spark" %% "spark-sql" % System.getenv.get("SP
 
 resolvers ++= Seq(
   "Spark Release Repository" at System.getenv.get("SPARK_RELEASE_REPOSITORY"),
-  "Akka Repository" at "http://repo.akka.io/releases/",
   "Spray Repository" at "http://repo.spray.cc/")
diff --git a/dev/audit-release/sbt_app_streaming/build.sbt b/dev/audit-release/sbt_app_streaming/build.sbt
index 492e5e7c8d76..cb369d516dd1 100644
--- a/dev/audit-release/sbt_app_streaming/build.sbt
+++ b/dev/audit-release/sbt_app_streaming/build.sbt
@@ -25,5 +25,4 @@ libraryDependencies += "org.apache.spark" %% "spark-streaming" % System.getenv.g
 
 resolvers ++= Seq(
   "Spark Release Repository" at System.getenv.get("SPARK_RELEASE_REPOSITORY"),
-  "Akka Repository" at "http://repo.akka.io/releases/",
   "Spray Repository" at "http://repo.spray.cc/")
diff --git a/docs/quick-start.md b/docs/quick-start.md
index 64023994771b..23313d8aa615 100644
--- a/docs/quick-start.md
+++ b/docs/quick-start.md
@@ -266,8 +266,6 @@ version := "1.0"
 scalaVersion := "{{site.SCALA_VERSION}}"
 
 libraryDependencies += "org.apache.spark" %% "spark-core" % "{{site.SPARK_VERSION}}"
-
-resolvers += "Akka Repository" at "http://repo.akka.io/releases/"
 {% endhighlight %}
 
 For sbt to work correctly, we'll need to layout `SimpleApp.scala` and `simple.sbt`
@@ -349,12 +347,6 @@ Note that Spark artifacts are tagged with a Scala version.
   <name>Simple Project</name>
   <packaging>jar</packaging>
   <version>1.0</version>
-  <repositories>
-    <repository>
-      <id>Akka repository</id>
-      <url>http://repo.akka.io/releases</url>
-    </repository>
-  </repositories>
   <dependencies>
     <dependency> <!-- Spark dependency -->
       <groupId>org.apache.spark</groupId>

From 58b32f3470f9fa67ad7dffb7d3dcd2a954b4b4e9 Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Sun, 22 Jun 2014 00:55:27 -0700
Subject: [PATCH 493/810] SPARK-2231: dev/run-tests should include YARN and use
 a recent Hadoop version

...rsion

Author: Patrick Wendell <pwendell@gmail.com>

Closes #1175 from pwendell/test-hadoop-version and squashes the following commits:

9210ef4 [Patrick Wendell] SPARK-2231: dev/run-tests should include YARN and use a recent Hadoop version
---
 dev/run-tests | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/dev/run-tests b/dev/run-tests
index c82a47ebb618..d9df020f7563 100755
--- a/dev/run-tests
+++ b/dev/run-tests
@@ -21,6 +21,9 @@
 FWDIR="$(cd `dirname $0`/..; pwd)"
 cd $FWDIR
 
+export SPARK_HADOOP_VERSION=2.3.0
+export SPARK_YARN=true
+
 # Remove work directory
 rm -rf ./work
 

From 476581e8c8ca03a5940c404fee8a06361ff94cb5 Mon Sep 17 00:00:00 2001
From: Sean Owen <sowen@cloudera.com>
Date: Sun, 22 Jun 2014 01:12:15 -0700
Subject: [PATCH 494/810] SPARK-2034. KafkaInputDStream doesn't close resources
 and may prevent JVM shutdown
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Tobias noted today on the mailing list:

========

I am trying to use Spark Streaming with Kafka, which works like a
charm – except for shutdown. When I run my program with "sbt
run-main", sbt will never exit, because there are two non-daemon
threads left that don't die.
I created a minimal example at
<https://gist.github.com/tgpfeiffer/b1e765064e983449c6b6#file-kafkadoesntshutdown-scala>.
It starts a StreamingContext and does nothing more than connecting to
a Kafka server and printing what it receives. Using the `future
Unknown macro: { ... }
` construct, I shut down the StreamingContext after some seconds and
then print the difference between the threads at start time and at end
time. The output can be found at
<https://gist.github.com/tgpfeiffer/b1e765064e983449c6b6#file-output1>.
There are a number of threads remaining that will prevent sbt from
exiting.
When I replace `KafkaUtils.createStream(...)` with a call that does
exactly the same, except that it calls `consumerConnector.shutdown()`
in `KafkaReceiver.onStop()` (which it should, IMO), the output is as
shown at <https://gist.github.com/tgpfeiffer/b1e765064e983449c6b6#file-output2>.
Does anyone have any idea what is going on here and why the program
doesn't shut down properly? The behavior is the same with both kafka
0.8.0 and 0.8.1.1, by the way.

========

Something similar was noted last year:

http://mail-archives.apache.org/mod_mbox/spark-dev/201309.mbox/%3C1380220041.2428.YahooMailNeo@web160804.mail.bf1.yahoo.com%3E

KafkaInputDStream doesn't close `ConsumerConnector` in `onStop()`, and does not close the `Executor` it creates. The latter leaves non-daemon threads and can prevent the JVM from shutting down even if streaming is closed properly.

Author: Sean Owen <sowen@cloudera.com>

Closes #980 from srowen/SPARK-2034 and squashes the following commits:

9f31a8d [Sean Owen] Restore ClassTag to private class because MIMA flags it; is the shadowing intended?
2d579a8 [Sean Owen] Close ConsumerConnector in onStop; shutdown() the local Executor that is created so that its threads stop when done; close the Zookeeper client even on exception; fix a few typos; log exceptions that otherwise vanish
---
 .../streaming/kafka/KafkaInputDStream.scala   | 55 +++++++++++--------
 1 file changed, 33 insertions(+), 22 deletions(-)

diff --git a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaInputDStream.scala b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaInputDStream.scala
index 21443ebbbfb0..38095e88dcea 100644
--- a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaInputDStream.scala
+++ b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaInputDStream.scala
@@ -38,7 +38,7 @@ import org.apache.spark.streaming.receiver.Receiver
 /**
  * Input stream that pulls messages from a Kafka Broker.
  *
- * @param kafkaParams Map of kafka configuration paramaters.
+ * @param kafkaParams Map of kafka configuration parameters.
  *                    See: http://kafka.apache.org/configuration.html
  * @param topics Map of (topic_name -> numPartitions) to consume. Each partition is consumed
  * in its own thread.
@@ -76,29 +76,31 @@ class KafkaReceiver[
   // Connection to Kafka
   var consumerConnector : ConsumerConnector = null
 
-  def onStop() { }
+  def onStop() {
+    if (consumerConnector != null) {
+      consumerConnector.shutdown()
+    }
+  }
 
   def onStart() {
 
-    // In case we are using multiple Threads to handle Kafka Messages
-    val executorPool = Executors.newFixedThreadPool(topics.values.reduce(_ + _))
-
     logInfo("Starting Kafka Consumer Stream with group: " + kafkaParams("group.id"))
 
     // Kafka connection properties
     val props = new Properties()
     kafkaParams.foreach(param => props.put(param._1, param._2))
 
+    val zkConnect = kafkaParams("zookeeper.connect")
     // Create the connection to the cluster
-    logInfo("Connecting to Zookeper: " + kafkaParams("zookeeper.connect"))
+    logInfo("Connecting to Zookeeper: " + zkConnect)
     val consumerConfig = new ConsumerConfig(props)
     consumerConnector = Consumer.create(consumerConfig)
-    logInfo("Connected to " + kafkaParams("zookeeper.connect"))
+    logInfo("Connected to " + zkConnect)
 
-    // When autooffset.reset is defined, it is our responsibility to try and whack the
+    // When auto.offset.reset is defined, it is our responsibility to try and whack the
     // consumer group zk node.
     if (kafkaParams.contains("auto.offset.reset")) {
-      tryZookeeperConsumerGroupCleanup(kafkaParams("zookeeper.connect"), kafkaParams("group.id"))
+      tryZookeeperConsumerGroupCleanup(zkConnect, kafkaParams("group.id"))
     }
 
     val keyDecoder = manifest[U].runtimeClass.getConstructor(classOf[VerifiableProperties])
@@ -112,10 +114,14 @@ class KafkaReceiver[
     val topicMessageStreams = consumerConnector.createMessageStreams(
       topics, keyDecoder, valueDecoder)
 
-
-    // Start the messages handler for each partition
-    topicMessageStreams.values.foreach { streams =>
-      streams.foreach { stream => executorPool.submit(new MessageHandler(stream)) }
+    val executorPool = Executors.newFixedThreadPool(topics.values.sum)
+    try {
+      // Start the messages handler for each partition
+      topicMessageStreams.values.foreach { streams =>
+        streams.foreach { stream => executorPool.submit(new MessageHandler(stream)) }
+      }
+    } finally {
+      executorPool.shutdown() // Just causes threads to terminate after work is done
     }
   }
 
@@ -124,30 +130,35 @@ class KafkaReceiver[
     extends Runnable {
     def run() {
       logInfo("Starting MessageHandler.")
-      for (msgAndMetadata <- stream) {
-        store((msgAndMetadata.key, msgAndMetadata.message))
+      try {
+        for (msgAndMetadata <- stream) {
+          store((msgAndMetadata.key, msgAndMetadata.message))
+        }
+      } catch {
+        case e: Throwable => logError("Error handling message; exiting", e)
       }
     }
   }
 
-  // It is our responsibility to delete the consumer group when specifying autooffset.reset. This
+  // It is our responsibility to delete the consumer group when specifying auto.offset.reset. This
   // is because Kafka 0.7.2 only honors this param when the group is not in zookeeper.
   //
   // The kafka high level consumer doesn't expose setting offsets currently, this is a trick copied
-  // from Kafkas' ConsoleConsumer. See code related to 'autooffset.reset' when it is set to
+  // from Kafka's ConsoleConsumer. See code related to 'auto.offset.reset' when it is set to
   // 'smallest'/'largest':
   // scalastyle:off
   // https://github.com/apache/kafka/blob/0.7.2/core/src/main/scala/kafka/consumer/ConsoleConsumer.scala
   // scalastyle:on
   private def tryZookeeperConsumerGroupCleanup(zkUrl: String, groupId: String) {
+    val dir = "/consumers/" + groupId
+    logInfo("Cleaning up temporary Zookeeper data under " + dir + ".")
+    val zk = new ZkClient(zkUrl, 30*1000, 30*1000, ZKStringSerializer)
     try {
-      val dir = "/consumers/" + groupId
-      logInfo("Cleaning up temporary zookeeper data under " + dir + ".")
-      val zk = new ZkClient(zkUrl, 30*1000, 30*1000, ZKStringSerializer)
       zk.deleteRecursive(dir)
-      zk.close()
     } catch {
-      case _ : Throwable => // swallow
+      case e: Throwable => logWarning("Error cleaning up temporary Zookeeper data", e)
+    } finally {
+      zk.close()
     }
   }
 }

From 9fe28c35df0dc8ac4a54db2a528fb7ae56d3f978 Mon Sep 17 00:00:00 2001
From: Sean Owen <sowen@cloudera.com>
Date: Sun, 22 Jun 2014 11:47:49 -0700
Subject: [PATCH 495/810] SPARK-1316. Remove use of Commons IO

Commons IO is actually barely used, and is not a declared dependency. This just replaces with equivalents from the JDK and Guava.

Author: Sean Owen <sowen@cloudera.com>

Closes #1173 from srowen/SPARK-1316 and squashes the following commits:

2eb53db [Sean Owen] Reorder Guava import
8fde404 [Sean Owen] Remove use of Commons IO, which is not actually a dependency
---
 .../util/logging/RollingFileAppender.scala      |  6 +++---
 .../apache/spark/util/FileAppenderSuite.scala   | 17 ++++++++++-------
 2 files changed, 13 insertions(+), 10 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/util/logging/RollingFileAppender.scala b/core/src/main/scala/org/apache/spark/util/logging/RollingFileAppender.scala
index 1bbbd20cf076..e57942167634 100644
--- a/core/src/main/scala/org/apache/spark/util/logging/RollingFileAppender.scala
+++ b/core/src/main/scala/org/apache/spark/util/logging/RollingFileAppender.scala
@@ -19,7 +19,7 @@ package org.apache.spark.util.logging
 
 import java.io.{File, FileFilter, InputStream}
 
-import org.apache.commons.io.FileUtils
+import com.google.common.io.Files
 import org.apache.spark.SparkConf
 import RollingFileAppender._
 
@@ -83,7 +83,7 @@ private[spark] class RollingFileAppender(
       logDebug(s"Attempting to rollover file $activeFile to file $rolloverFile")
       if (activeFile.exists) {
         if (!rolloverFile.exists) {
-          FileUtils.moveFile(activeFile, rolloverFile)
+          Files.move(activeFile, rolloverFile)
           logInfo(s"Rolled over $activeFile to $rolloverFile")
         } else {
           // In case the rollover file name clashes, make a unique file name.
@@ -100,7 +100,7 @@ private[spark] class RollingFileAppender(
 
           logWarning(s"Rollover file $rolloverFile already exists, " +
             s"rolled over $activeFile to file $altRolloverFile")
-          FileUtils.moveFile(activeFile, altRolloverFile)
+          Files.move(activeFile, altRolloverFile)
         }
       } else {
         logWarning(s"File $activeFile does not exist")
diff --git a/core/src/test/scala/org/apache/spark/util/FileAppenderSuite.scala b/core/src/test/scala/org/apache/spark/util/FileAppenderSuite.scala
index 02e228945bbd..ca37d707b06c 100644
--- a/core/src/test/scala/org/apache/spark/util/FileAppenderSuite.scala
+++ b/core/src/test/scala/org/apache/spark/util/FileAppenderSuite.scala
@@ -18,13 +18,16 @@
 package org.apache.spark.util
 
 import java.io._
+import java.nio.charset.Charset
 
 import scala.collection.mutable.HashSet
 import scala.reflect._
 
-import org.apache.commons.io.{FileUtils, IOUtils}
-import org.apache.spark.{Logging, SparkConf}
 import org.scalatest.{BeforeAndAfter, FunSuite}
+
+import com.google.common.io.Files
+
+import org.apache.spark.{Logging, SparkConf}
 import org.apache.spark.util.logging.{RollingFileAppender, SizeBasedRollingPolicy, TimeBasedRollingPolicy, FileAppender}
 
 class FileAppenderSuite extends FunSuite with BeforeAndAfter with Logging {
@@ -41,11 +44,11 @@ class FileAppenderSuite extends FunSuite with BeforeAndAfter with Logging {
 
   test("basic file appender") {
     val testString = (1 to 1000).mkString(", ")
-    val inputStream = IOUtils.toInputStream(testString)
+    val inputStream = new ByteArrayInputStream(testString.getBytes(Charset.forName("UTF-8")))
     val appender = new FileAppender(inputStream, testFile)
     inputStream.close()
     appender.awaitTermination()
-    assert(FileUtils.readFileToString(testFile) === testString)
+    assert(Files.toString(testFile, Charset.forName("UTF-8")) === testString)
   }
 
   test("rolling file appender - time-based rolling") {
@@ -93,7 +96,7 @@ class FileAppenderSuite extends FunSuite with BeforeAndAfter with Logging {
     val allGeneratedFiles = new HashSet[String]()
     val items = (1 to 10).map { _.toString * 10000 }
     for (i <- 0 until items.size) {
-      testOutputStream.write(items(i).getBytes("UTF8"))
+      testOutputStream.write(items(i).getBytes(Charset.forName("UTF-8")))
       testOutputStream.flush()
       allGeneratedFiles ++= RollingFileAppender.getSortedRolledOverFiles(
         testFile.getParentFile.toString, testFile.getName).map(_.toString)
@@ -197,7 +200,7 @@ class FileAppenderSuite extends FunSuite with BeforeAndAfter with Logging {
     // send data to appender through the input stream, and wait for the data to be written
     val expectedText = textToAppend.mkString("")
     for (i <- 0 until textToAppend.size) {
-      outputStream.write(textToAppend(i).getBytes("UTF8"))
+      outputStream.write(textToAppend(i).getBytes(Charset.forName("UTF-8")))
       outputStream.flush()
       Thread.sleep(sleepTimeBetweenTexts)
     }
@@ -212,7 +215,7 @@ class FileAppenderSuite extends FunSuite with BeforeAndAfter with Logging {
     logInfo("Filtered files: \n" + generatedFiles.mkString("\n"))
     assert(generatedFiles.size > 1)
     val allText = generatedFiles.map { file =>
-      FileUtils.readFileToString(file)
+      Files.toString(file, Charset.forName("UTF-8"))
     }.mkString("")
     assert(allText === expectedText)
     generatedFiles

From 409d24e2b2e52df8fd2c5aca69def29b9a097a79 Mon Sep 17 00:00:00 2001
From: witgo <witgo@qq.com>
Date: Sun, 22 Jun 2014 18:25:16 -0700
Subject: [PATCH 496/810] SPARK-2229: FileAppender throw an
 llegalArgumentException in jdk6

Author: witgo <witgo@qq.com>

Closes #1174 from witgo/SPARK-2229 and squashes the following commits:

f85f321 [witgo] FileAppender throw anIllegalArgumentException in JDK6
e1a8da8 [witgo] SizeBasedRollingPolicy throw an java.lang.IllegalArgumentException in JDK6
---
 .../org/apache/spark/util/logging/FileAppender.scala      | 8 ++++----
 .../org/apache/spark/util/logging/RollingPolicy.scala     | 2 +-
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/util/logging/FileAppender.scala b/core/src/main/scala/org/apache/spark/util/logging/FileAppender.scala
index 8e9c3036d09c..1d5467060623 100644
--- a/core/src/main/scala/org/apache/spark/util/logging/FileAppender.scala
+++ b/core/src/main/scala/org/apache/spark/util/logging/FileAppender.scala
@@ -125,16 +125,16 @@ private[spark] object FileAppender extends Logging {
       val validatedParams: Option[(Long, String)] = rollingInterval match {
         case "daily" =>
           logInfo(s"Rolling executor logs enabled for $file with daily rolling")
-          Some(24 * 60 * 60 * 1000L, "--YYYY-MM-dd")
+          Some(24 * 60 * 60 * 1000L, "--yyyy-MM-dd")
         case "hourly" =>
           logInfo(s"Rolling executor logs enabled for $file with hourly rolling")
-          Some(60 * 60 * 1000L, "--YYYY-MM-dd--HH")
+          Some(60 * 60 * 1000L, "--yyyy-MM-dd--HH")
         case "minutely" =>
           logInfo(s"Rolling executor logs enabled for $file with rolling every minute")
-          Some(60 * 1000L, "--YYYY-MM-dd--HH-mm")
+          Some(60 * 1000L, "--yyyy-MM-dd--HH-mm")
         case IntParam(seconds) =>
           logInfo(s"Rolling executor logs enabled for $file with rolling $seconds seconds")
-          Some(seconds * 1000L, "--YYYY-MM-dd--HH-mm-ss")
+          Some(seconds * 1000L, "--yyyy-MM-dd--HH-mm-ss")
         case _ =>
           logWarning(s"Illegal interval for rolling executor logs [$rollingInterval], " +
               s"rolling logs not enabled")
diff --git a/core/src/main/scala/org/apache/spark/util/logging/RollingPolicy.scala b/core/src/main/scala/org/apache/spark/util/logging/RollingPolicy.scala
index 84e5c3c917dc..d7b7219e179d 100644
--- a/core/src/main/scala/org/apache/spark/util/logging/RollingPolicy.scala
+++ b/core/src/main/scala/org/apache/spark/util/logging/RollingPolicy.scala
@@ -109,7 +109,7 @@ private[spark] class SizeBasedRollingPolicy(
   }
 
   @volatile private var bytesWrittenSinceRollover = 0L
-  val formatter = new SimpleDateFormat("--YYYY-MM-dd--HH-mm-ss--SSSS")
+  val formatter = new SimpleDateFormat("--yyyy-MM-dd--HH-mm-ss--SSSS")
 
   /** Should rollover if the next set of bytes is going to exceed the size limit */
   def shouldRollover(bytesToBeWritten: Long): Boolean = {

From 9fc373e3a9a8ba7bea9df0950775f48918f63a8a Mon Sep 17 00:00:00 2001
From: Ori Kremer <ori.kremer@gmail.com>
Date: Sun, 22 Jun 2014 20:21:23 -0700
Subject: [PATCH 497/810] SPARK-2241: quote command line args in ec2 script

To preserve quoted command line args (in case options have space in them).

Author: Ori Kremer <ori.kremer@gmail.com>

Closes #1169 from orikremer/quote_cmd_line_args and squashes the following commits:

67e2aa1 [Ori Kremer] quote command line args
---
 ec2/spark-ec2 | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ec2/spark-ec2 b/ec2/spark-ec2
index 454057aa0d27..31f9771223e5 100755
--- a/ec2/spark-ec2
+++ b/ec2/spark-ec2
@@ -19,4 +19,4 @@
 #
 
 cd "`dirname $0`"
-PYTHONPATH="./third_party/boto-2.4.1.zip/boto-2.4.1:$PYTHONPATH" python ./spark_ec2.py $@
+PYTHONPATH="./third_party/boto-2.4.1.zip/boto-2.4.1:$PYTHONPATH" python ./spark_ec2.py "$@"

From 9cb64b2c54b35eed373f54c2103f679b04e9af1e Mon Sep 17 00:00:00 2001
From: Jean-Martin Archer <jeanmartin.archer@pulseenergy.com>
Date: Sun, 22 Jun 2014 20:52:02 -0700
Subject: [PATCH 498/810] SPARK-2166 - Listing of instances to be terminated
 before the prompt

Will list the EC2 instances before detroying the cluster.
This was added because it can be scary to destroy EC2
instances without knowing which one will be impacted.

Author: Jean-Martin Archer <jeanmartin.archer@pulseenergy.com>

This patch had conflicts when merged, resolved by
Committer: Patrick Wendell <pwendell@gmail.com>

Closes #270 from j-martin/master and squashes the following commits:

826455f [Jean-Martin Archer] [SPARK-2611] Implementing recommendations
27b0a36 [Jean-Martin Archer] Listing of instances to be terminated before the prompt Will list the EC2 instances before detroying the cluster. This was added because it can be scary to destroy EC2 instances without knowing which one will be impacted.
---
 ec2/spark_ec2.py | 14 +++++++++-----
 1 file changed, 9 insertions(+), 5 deletions(-)

diff --git a/ec2/spark_ec2.py b/ec2/spark_ec2.py
index a40311d9fcf0..e80f928b8beb 100755
--- a/ec2/spark_ec2.py
+++ b/ec2/spark_ec2.py
@@ -770,12 +770,16 @@ def real_main():
         setup_cluster(conn, master_nodes, slave_nodes, opts, True)
 
     elif action == "destroy":
-        response = raw_input("Are you sure you want to destroy the cluster " +
-                             cluster_name + "?\nALL DATA ON ALL NODES WILL BE LOST!!\n" +
-                             "Destroy cluster " + cluster_name + " (y/N): ")
+        print "Are you sure you want to destroy the cluster %s?" % cluster_name
+        print "The following instances will be terminated:"
+        (master_nodes, slave_nodes) = get_existing_cluster(
+            conn, opts, cluster_name, die_on_error=False)
+        for inst in master_nodes + slave_nodes:
+            print "> %s" % inst.public_dns_name
+
+        msg = "ALL DATA ON ALL NODES WILL BE LOST!!\nDestroy cluster %s (y/N): " % cluster_name
+        response = raw_input(msg)
         if response == "y":
-            (master_nodes, slave_nodes) = get_existing_cluster(
-                conn, opts, cluster_name, die_on_error=False)
             print "Terminating master..."
             for inst in master_nodes:
                 inst.terminate()

From e380767de344fd6898429de43da592658fd86a39 Mon Sep 17 00:00:00 2001
From: Marcelo Vanzin <vanzin@cloudera.com>
Date: Mon, 23 Jun 2014 08:51:11 -0500
Subject: [PATCH 499/810] [SPARK-1395] Fix "local:" URI support in Yarn mode
 (again).

Recent changes ignored the fact that path may be defined with "local:"
URIs, which means they need to be explicitly added to the classpath
everywhere a remote process is started. This change fixes that by:

- Using the correct methods to add paths to the classpath
- Creating SparkConf settings for the Spark jar itself and for the
  user's jar
- Propagating those two settings to the remote processes where needed

This ensures that both in client and in cluster mode, the driver has
the necessary info to build the executor's classpath and have things
still work when they contain "local:" references.

The change also fixes some confusion in ClientBase about whether
to use SparkConf or system properties to propagate config options to
the driver and executors, by standardizing on using data held by
SparkConf.

On the cleanup front, I removed the hacky way that log4j configuration
was being propagated to handle the "local:" case. It's much more cleanly
(and generically) handled by using spark-submit arguments (--files to
upload a config file, or setting spark.executor.extraJavaOptions to pass
JVM arguments and use a local file).

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #560 from vanzin/yarn-local-2 and squashes the following commits:

4e7f066 [Marcelo Vanzin] Correctly propagate SPARK_JAVA_OPTS to driver/executor.
6a454ea [Marcelo Vanzin] Use constants for PWD in test.
6dd5943 [Marcelo Vanzin] Fix propagation of config options to driver / executor.
b2e377f [Marcelo Vanzin] Review feedback.
93c3f85 [Marcelo Vanzin] Fix ClassCastException in test.
e5c682d [Marcelo Vanzin] Fix cluster mode, restore SPARK_LOG4J_CONF.
1dfbb40 [Marcelo Vanzin] Add documentation for spark.yarn.jar.
bbdce05 [Marcelo Vanzin] [SPARK-1395] Fix "local:" URI support in Yarn mode (again).
---
 docs/running-on-yarn.md                       |  28 +-
 .../apache/spark/deploy/yarn/ClientBase.scala | 248 +++++++++++-------
 .../deploy/yarn/ExecutorRunnableUtil.scala    |  11 +-
 .../spark/deploy/yarn/ClientBaseSuite.scala   |  90 ++++++-
 .../spark/deploy/yarn/ExecutorRunnable.scala  |   1 +
 5 files changed, 274 insertions(+), 104 deletions(-)

diff --git a/docs/running-on-yarn.md b/docs/running-on-yarn.md
index fecd8f2cc2d4..43b06df99d9c 100644
--- a/docs/running-on-yarn.md
+++ b/docs/running-on-yarn.md
@@ -95,10 +95,19 @@ Most of the configs are the same for Spark on YARN as for other deployment modes
     The amount of off heap memory (in megabytes) to be allocated per driver. This is memory that accounts for things like VM overheads, interned strings, other native overheads, etc.
   </td>
 </tr>
+<tr>
+  <td><code>spark.yarn.jar</code></td>
+  <td>(none)</td>
+  <td>
+    The location of the Spark jar file, in case overriding the default location is desired.
+    By default, Spark on YARN will use a Spark jar installed locally, but the Spark jar can also be
+    in a world-readable location on HDFS. This allows YARN to cache it on nodes so that it doesn't
+    need to be distributed each time an application runs. To point to a jar on HDFS, for example,
+    set this configuration to "hdfs:///some/path".
+  </td>
+</tr>
 </table>
 
-By default, Spark on YARN will use a Spark jar installed locally, but the Spark JAR can also be in a world-readable location on HDFS. This allows YARN to cache it on nodes so that it doesn't need to be distributed each time an application runs. To point to a JAR on HDFS, `export SPARK_JAR=hdfs:///some/path`.
-
 # Launching Spark on YARN
 
 Ensure that `HADOOP_CONF_DIR` or `YARN_CONF_DIR` points to the directory which contains the (client side) configuration files for the Hadoop cluster.
@@ -156,7 +165,20 @@ all environment variables used for launching each container. This process is use
 classpath problems in particular. (Note that enabling this requires admin privileges on cluster
 settings and a restart of all node managers. Thus, this is not applicable to hosted clusters).
 
-# Important Notes
+To use a custom log4j configuration for the application master or executors, there are two options:
+
+- upload a custom log4j.properties using spark-submit, by adding it to the "--files" list of files
+  to be uploaded with the application.
+- add "-Dlog4j.configuration=<location of configuration file>" to "spark.driver.extraJavaOptions"
+  (for the driver) or "spark.executor.extraJavaOptions" (for executors). Note that if using a file,
+  the "file:" protocol should be explicitly provided, and the file needs to exist locally on all
+  the nodes.
+
+Note that for the first option, both executors and the application master will share the same
+log4j configuration, which may cause issues when they run on the same node (e.g. trying to write
+to the same log file).
+
+# Important notes
 
 - Before Hadoop 2.2, YARN does not support cores in container resource requests. Thus, when running against an earlier version, the numbers of cores given via command line arguments cannot be passed to YARN.  Whether core requests are honored in scheduling decisions depends on which scheduler is in use and how it is configured.
 - The local directories used by Spark executors will be the local directories configured for YARN (Hadoop YARN config `yarn.nodemanager.local-dirs`). If the user specifies `spark.local.dir`, it will be ignored.
diff --git a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala
index 8f2267599914..556f49342977 100644
--- a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala
+++ b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala
@@ -154,7 +154,7 @@ trait ClientBase extends Logging {
   }
 
   /** Copy the file into HDFS if needed. */
-  private def copyRemoteFile(
+  private[yarn] def copyRemoteFile(
       dstDir: Path,
       originalPath: Path,
       replication: Short,
@@ -213,10 +213,19 @@ trait ClientBase extends Logging {
 
     val statCache: Map[URI, FileStatus] = HashMap[URI, FileStatus]()
 
-    Map(
-      ClientBase.SPARK_JAR -> ClientBase.getSparkJar, ClientBase.APP_JAR -> args.userJar,
-      ClientBase.LOG4J_PROP -> System.getenv(ClientBase.LOG4J_CONF_ENV_KEY)
-    ).foreach { case(destName, _localPath) =>
+    val oldLog4jConf = Option(System.getenv("SPARK_LOG4J_CONF"))
+    if (oldLog4jConf.isDefined) {
+      logWarning(
+        "SPARK_LOG4J_CONF detected in the system environment. This variable has been " +
+        "deprecated. Please refer to the \"Launching Spark on YARN\" documentation " +
+        "for alternatives.")
+    }
+
+    List(
+      (ClientBase.SPARK_JAR, ClientBase.sparkJar(sparkConf), ClientBase.CONF_SPARK_JAR),
+      (ClientBase.APP_JAR, args.userJar, ClientBase.CONF_SPARK_USER_JAR),
+      ("log4j.properties", oldLog4jConf.getOrElse(null), null)
+    ).foreach { case(destName, _localPath, confKey) =>
       val localPath: String = if (_localPath != null) _localPath.trim() else ""
       if (! localPath.isEmpty()) {
         val localURI = new URI(localPath)
@@ -225,6 +234,8 @@ trait ClientBase extends Logging {
           val destPath = copyRemoteFile(dst, qualifyForLocal(localURI), replication, setPermissions)
           distCacheMgr.addResource(fs, conf, destPath, localResources, LocalResourceType.FILE,
             destName, statCache)
+        } else if (confKey != null) {
+          sparkConf.set(confKey, localPath)
         }
       }
     }
@@ -246,6 +257,8 @@ trait ClientBase extends Logging {
             if (addToClasspath) {
               cachedSecondaryJarLinks += linkname
             }
+          } else if (addToClasspath) {
+            cachedSecondaryJarLinks += file.trim()
           }
         }
       }
@@ -265,14 +278,10 @@ trait ClientBase extends Logging {
     val env = new HashMap[String, String]()
 
     val extraCp = sparkConf.getOption("spark.driver.extraClassPath")
-    val log4jConf = System.getenv(ClientBase.LOG4J_CONF_ENV_KEY)
-    ClientBase.populateClasspath(yarnConf, sparkConf, log4jConf, env, extraCp)
+    ClientBase.populateClasspath(args, yarnConf, sparkConf, env, extraCp)
     env("SPARK_YARN_MODE") = "true"
     env("SPARK_YARN_STAGING_DIR") = stagingDir
     env("SPARK_USER") = UserGroupInformation.getCurrentUser().getShortUserName()
-    if (log4jConf != null) {
-      env(ClientBase.LOG4J_CONF_ENV_KEY) = log4jConf
-    }
 
     // Set the environment variables to be passed on to the executors.
     distCacheMgr.setDistFilesEnv(env)
@@ -285,7 +294,6 @@ trait ClientBase extends Logging {
       // Pass SPARK_YARN_USER_ENV itself to the AM so it can use it to set up executor environments.
       env("SPARK_YARN_USER_ENV") = userEnvs
     }
-
     env
   }
 
@@ -310,6 +318,37 @@ trait ClientBase extends Logging {
     logInfo("Setting up container launch context")
     val amContainer = Records.newRecord(classOf[ContainerLaunchContext])
     amContainer.setLocalResources(localResources)
+
+    // In cluster mode, if the deprecated SPARK_JAVA_OPTS is set, we need to propagate it to
+    // executors. But we can't just set spark.executor.extraJavaOptions, because the driver's
+    // SparkContext will not let that set spark* system properties, which is expected behavior for
+    // Yarn clients. So propagate it through the environment.
+    //
+    // Note that to warn the user about the deprecation in cluster mode, some code from
+    // SparkConf#validateSettings() is duplicated here (to avoid triggering the condition
+    // described above).
+    if (args.amClass == classOf[ApplicationMaster].getName) {
+      sys.env.get("SPARK_JAVA_OPTS").foreach { value =>
+        val warning =
+          s"""
+            |SPARK_JAVA_OPTS was detected (set to '$value').
+            |This is deprecated in Spark 1.0+.
+            |
+            |Please instead use:
+            | - ./spark-submit with conf/spark-defaults.conf to set defaults for an application
+            | - ./spark-submit with --driver-java-options to set -X options for a driver
+            | - spark.executor.extraJavaOptions to set -X options for executors
+          """.stripMargin
+        logWarning(warning)
+        for (proc <- Seq("driver", "executor")) {
+          val key = s"spark.$proc.extraJavaOptions"
+          if (sparkConf.contains(key)) {
+            throw new SparkException(s"Found both $key and SPARK_JAVA_OPTS. Use only the former.")
+          }
+        }
+        env("SPARK_JAVA_OPTS") = value
+      }
+    }
     amContainer.setEnvironment(env)
 
     val amMemory = calculateAMMemory(newApp)
@@ -341,30 +380,20 @@ trait ClientBase extends Logging {
       javaOpts += "-XX:CMSIncrementalDutyCycle=10"
     }
 
-    // SPARK_JAVA_OPTS is deprecated, but for backwards compatibility:
-    sys.env.get("SPARK_JAVA_OPTS").foreach { opts =>
-      sparkConf.set("spark.executor.extraJavaOptions", opts)
-      sparkConf.set("spark.driver.extraJavaOptions", opts)
-    }
-
+    // Forward the Spark configuration to the application master / executors.
     // TODO: it might be nicer to pass these as an internal environment variable rather than
     // as Java options, due to complications with string parsing of nested quotes.
-    if (args.amClass == classOf[ExecutorLauncher].getName) {
-      // If we are being launched in client mode, forward the spark-conf options
-      // onto the executor launcher
-      for ((k, v) <- sparkConf.getAll) {
-        javaOpts += "-D" + k + "=" + "\\\"" + v + "\\\""
-      }
-    } else {
-      // If we are being launched in standalone mode, capture and forward any spark
-      // system properties (e.g. set by spark-class).
-      for ((k, v) <- sys.props.filterKeys(_.startsWith("spark"))) {
-        javaOpts += "-D" + k + "=" + "\\\"" + v + "\\\""
-      }
-      sys.props.get("spark.driver.extraJavaOptions").foreach(opts => javaOpts += opts)
-      sys.props.get("spark.driver.libraryPath").foreach(p => javaOpts += s"-Djava.library.path=$p")
+    for ((k, v) <- sparkConf.getAll) {
+      javaOpts += "-D" + k + "=" + "\\\"" + v + "\\\""
+    }
+
+    if (args.amClass == classOf[ApplicationMaster].getName) {
+      sparkConf.getOption("spark.driver.extraJavaOptions")
+        .orElse(sys.env.get("SPARK_JAVA_OPTS"))
+        .foreach(opts => javaOpts += opts)
+      sparkConf.getOption("spark.driver.libraryPath")
+        .foreach(p => javaOpts += s"-Djava.library.path=$p")
     }
-    javaOpts += ClientBase.getLog4jConfiguration(localResources)
 
     // Command for the ApplicationMaster
     val commands = Seq(Environment.JAVA_HOME.$() + "/bin/java", "-server") ++
@@ -377,7 +406,10 @@ trait ClientBase extends Logging {
         "1>", ApplicationConstants.LOG_DIR_EXPANSION_VAR + "/stdout",
         "2>", ApplicationConstants.LOG_DIR_EXPANSION_VAR + "/stderr")
 
-    logInfo("Command for starting the Spark ApplicationMaster: " + commands)
+    logInfo("Yarn AM launch context:")
+    logInfo(s"  class:   ${args.amClass}")
+    logInfo(s"  env:     $env")
+    logInfo(s"  command: ${commands.mkString(" ")}")
 
     // TODO: it would be nicer to just make sure there are no null commands here
     val printableCommands = commands.map(s => if (s == null) "null" else s).toList
@@ -391,12 +423,39 @@ trait ClientBase extends Logging {
 object ClientBase extends Logging {
   val SPARK_JAR: String = "__spark__.jar"
   val APP_JAR: String = "__app__.jar"
-  val LOG4J_PROP: String = "log4j.properties"
-  val LOG4J_CONF_ENV_KEY: String = "SPARK_LOG4J_CONF"
   val LOCAL_SCHEME = "local"
+  val CONF_SPARK_JAR = "spark.yarn.jar"
+  /**
+   * This is an internal config used to propagate the location of the user's jar file to the
+   * driver/executors.
+   */
+  val CONF_SPARK_USER_JAR = "spark.yarn.user.jar"
+  /**
+   * This is an internal config used to propagate the list of extra jars to add to the classpath
+   * of executors.
+   */
   val CONF_SPARK_YARN_SECONDARY_JARS = "spark.yarn.secondary.jars"
+  val ENV_SPARK_JAR = "SPARK_JAR"
 
-  def getSparkJar = sys.env.get("SPARK_JAR").getOrElse(SparkContext.jarOfClass(this.getClass).head)
+  /**
+   * Find the user-defined Spark jar if configured, or return the jar containing this
+   * class if not.
+   *
+   * This method first looks in the SparkConf object for the CONF_SPARK_JAR key, and in the
+   * user environment if that is not found (for backwards compatibility).
+   */
+  def sparkJar(conf: SparkConf) = {
+    if (conf.contains(CONF_SPARK_JAR)) {
+      conf.get(CONF_SPARK_JAR)
+    } else if (System.getenv(ENV_SPARK_JAR) != null) {
+      logWarning(
+        s"$ENV_SPARK_JAR detected in the system environment. This variable has been deprecated " +
+        s"in favor of the $CONF_SPARK_JAR configuration variable.")
+      System.getenv(ENV_SPARK_JAR)
+    } else {
+      SparkContext.jarOfClass(this.getClass).head
+    }
+  }
 
   def populateHadoopClasspath(conf: Configuration, env: HashMap[String, String]) = {
     val classPathElementsToAdd = getYarnAppClasspath(conf) ++ getMRAppClasspath(conf)
@@ -469,71 +528,74 @@ object ClientBase extends Logging {
     triedDefault.toOption
   }
 
+  def populateClasspath(args: ClientArguments, conf: Configuration, sparkConf: SparkConf,
+      env: HashMap[String, String], extraClassPath: Option[String] = None) {
+    extraClassPath.foreach(addClasspathEntry(_, env))
+    addClasspathEntry(Environment.PWD.$(), env)
+
+    // Normally the users app.jar is last in case conflicts with spark jars
+    if (sparkConf.get("spark.yarn.user.classpath.first", "false").toBoolean) {
+      addUserClasspath(args, sparkConf, env)
+      addFileToClasspath(sparkJar(sparkConf), SPARK_JAR, env)
+      ClientBase.populateHadoopClasspath(conf, env)
+    } else {
+      addFileToClasspath(sparkJar(sparkConf), SPARK_JAR, env)
+      ClientBase.populateHadoopClasspath(conf, env)
+      addUserClasspath(args, sparkConf, env)
+    }
+
+    // Append all jar files under the working directory to the classpath.
+    addClasspathEntry(Environment.PWD.$() + Path.SEPARATOR + "*", env);
+  }
 
   /**
-   * Returns the java command line argument for setting up log4j. If there is a log4j.properties
-   * in the given local resources, it is used, otherwise the SPARK_LOG4J_CONF environment variable
-   * is checked.
+   * Adds the user jars which have local: URIs (or alternate names, such as APP_JAR) explicitly
+   * to the classpath.
    */
-  def getLog4jConfiguration(localResources: HashMap[String, LocalResource]): String = {
-    var log4jConf = LOG4J_PROP
-    if (!localResources.contains(log4jConf)) {
-      log4jConf = System.getenv(LOG4J_CONF_ENV_KEY) match {
-        case conf: String =>
-          val confUri = new URI(conf)
-          if (ClientBase.LOCAL_SCHEME.equals(confUri.getScheme())) {
-            "file://" + confUri.getPath()
-          } else {
-            ClientBase.LOG4J_PROP
-          }
-        case null => "log4j-spark-container.properties"
+  private def addUserClasspath(args: ClientArguments, conf: SparkConf,
+      env: HashMap[String, String]) = {
+    if (args != null) {
+      addFileToClasspath(args.userJar, APP_JAR, env)
+      if (args.addJars != null) {
+        args.addJars.split(",").foreach { case file: String =>
+          addFileToClasspath(file, null, env)
+        }
       }
+    } else {
+      val userJar = conf.get(CONF_SPARK_USER_JAR, null)
+      addFileToClasspath(userJar, APP_JAR, env)
+
+      val cachedSecondaryJarLinks = conf.get(CONF_SPARK_YARN_SECONDARY_JARS, "").split(",")
+      cachedSecondaryJarLinks.foreach(jar => addFileToClasspath(jar, null, env))
     }
-    " -Dlog4j.configuration=" + log4jConf
   }
 
-  def populateClasspath(conf: Configuration, sparkConf: SparkConf, log4jConf: String,
-      env: HashMap[String, String], extraClassPath: Option[String] = None) {
-
-    if (log4jConf != null) {
-      // If a custom log4j config file is provided as a local: URI, add its parent directory to the
-      // classpath. Note that this only works if the custom config's file name is
-      // "log4j.properties".
-      val localPath = getLocalPath(log4jConf)
-      if (localPath != null) {
-        val parentPath = new File(localPath).getParent()
-        YarnSparkHadoopUtil.addToEnvironment(env, Environment.CLASSPATH.name, parentPath,
-          File.pathSeparator)
+  /**
+   * Adds the given path to the classpath, handling "local:" URIs correctly.
+   *
+   * If an alternate name for the file is given, and it's not a "local:" file, the alternate
+   * name will be added to the classpath (relative to the job's work directory).
+   *
+   * If not a "local:" file and no alternate name, the environment is not modified.
+   *
+   * @param path      Path to add to classpath (optional).
+   * @param fileName  Alternate name for the file (optional).
+   * @param env       Map holding the environment variables.
+   */
+  private def addFileToClasspath(path: String, fileName: String,
+      env: HashMap[String, String]) : Unit = {
+    if (path != null) {
+      scala.util.control.Exception.ignoring(classOf[URISyntaxException]) {
+        val localPath = getLocalPath(path)
+        if (localPath != null) {
+          addClasspathEntry(localPath, env)
+          return
+        }
       }
     }
-
-    /** Add entry to the classpath. */
-    def addClasspathEntry(path: String) = YarnSparkHadoopUtil.addToEnvironment(env,
-      Environment.CLASSPATH.name, path, File.pathSeparator)
-    /** Add entry to the classpath. Interpreted as a path relative to the working directory. */
-    def addPwdClasspathEntry(entry: String) =
-      addClasspathEntry(Environment.PWD.$() + Path.SEPARATOR + entry)
-
-    extraClassPath.foreach(addClasspathEntry)
-
-    val cachedSecondaryJarLinks =
-      sparkConf.getOption(CONF_SPARK_YARN_SECONDARY_JARS).getOrElse("").split(",")
-        .filter(_.nonEmpty)
-    // Normally the users app.jar is last in case conflicts with spark jars
-    if (sparkConf.get("spark.yarn.user.classpath.first", "false").toBoolean) {
-      addPwdClasspathEntry(APP_JAR)
-      cachedSecondaryJarLinks.foreach(addPwdClasspathEntry)
-      addPwdClasspathEntry(SPARK_JAR)
-      ClientBase.populateHadoopClasspath(conf, env)
-    } else {
-      addPwdClasspathEntry(SPARK_JAR)
-      ClientBase.populateHadoopClasspath(conf, env)
-      addPwdClasspathEntry(APP_JAR)
-      cachedSecondaryJarLinks.foreach(addPwdClasspathEntry)
+    if (fileName != null) {
+      addClasspathEntry(Environment.PWD.$() + Path.SEPARATOR + fileName, env);
     }
-    // Append all class files and jar files under the working directory to the classpath.
-    addClasspathEntry(Environment.PWD.$())
-    addPwdClasspathEntry("*")
   }
 
   /**
@@ -547,4 +609,8 @@ object ClientBase extends Logging {
     null
   }
 
+  private def addClasspathEntry(path: String, env: HashMap[String, String]) =
+    YarnSparkHadoopUtil.addToEnvironment(env, Environment.CLASSPATH.name, path,
+            File.pathSeparator)
+
 }
diff --git a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnableUtil.scala b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnableUtil.scala
index 43dbb2464f92..4ba7133a959e 100644
--- a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnableUtil.scala
+++ b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnableUtil.scala
@@ -55,10 +55,12 @@ trait ExecutorRunnableUtil extends Logging {
     sys.props.get("spark.executor.extraJavaOptions").foreach { opts =>
       javaOpts += opts
     }
+    sys.env.get("SPARK_JAVA_OPTS").foreach { opts =>
+      javaOpts += opts
+    }
 
     javaOpts += "-Djava.io.tmpdir=" +
       new Path(Environment.PWD.$(), YarnConfiguration.DEFAULT_CONTAINER_TEMP_DIR)
-    javaOpts += ClientBase.getLog4jConfiguration(localResources)
 
     // Certain configs need to be passed here because they are needed before the Executor
     // registers with the Scheduler and transfers the spark configs. Since the Executor backend
@@ -166,13 +168,8 @@ trait ExecutorRunnableUtil extends Logging {
 
   def prepareEnvironment: HashMap[String, String] = {
     val env = new HashMap[String, String]()
-
     val extraCp = sparkConf.getOption("spark.executor.extraClassPath")
-    val log4jConf = System.getenv(ClientBase.LOG4J_CONF_ENV_KEY)
-    ClientBase.populateClasspath(yarnConf, sparkConf, log4jConf, env, extraCp)
-    if (log4jConf != null) {
-      env(ClientBase.LOG4J_CONF_ENV_KEY) = log4jConf
-    }
+    ClientBase.populateClasspath(null, yarnConf, sparkConf, env, extraCp)
 
     // Allow users to specify some environment variables
     YarnSparkHadoopUtil.setEnvFromInputString(env, System.getenv("SPARK_YARN_USER_ENV"),
diff --git a/yarn/common/src/test/scala/org/apache/spark/deploy/yarn/ClientBaseSuite.scala b/yarn/common/src/test/scala/org/apache/spark/deploy/yarn/ClientBaseSuite.scala
index 608c6e92624c..686714dc3648 100644
--- a/yarn/common/src/test/scala/org/apache/spark/deploy/yarn/ClientBaseSuite.scala
+++ b/yarn/common/src/test/scala/org/apache/spark/deploy/yarn/ClientBaseSuite.scala
@@ -17,22 +17,31 @@
 
 package org.apache.spark.deploy.yarn
 
+import java.io.File
 import java.net.URI
 
+import com.google.common.io.Files
 import org.apache.hadoop.conf.Configuration
+import org.apache.hadoop.fs.Path
 import org.apache.hadoop.mapreduce.MRJobConfig
 import org.apache.hadoop.yarn.conf.YarnConfiguration
 import org.apache.hadoop.yarn.api.ApplicationConstants.Environment
-
+import org.apache.hadoop.yarn.api.protocolrecords.GetNewApplicationResponse
+import org.apache.hadoop.yarn.api.records.ContainerLaunchContext
+import org.apache.hadoop.yarn.conf.YarnConfiguration
+import org.mockito.Matchers._
+import org.mockito.Mockito._
 import org.scalatest.FunSuite
-import org.scalatest.matchers.ShouldMatchers._
+import org.scalatest.Matchers
 
 import scala.collection.JavaConversions._
 import scala.collection.mutable.{ HashMap => MutableHashMap }
 import scala.util.Try
 
+import org.apache.spark.SparkConf
+import org.apache.spark.util.Utils
 
-class ClientBaseSuite extends FunSuite {
+class ClientBaseSuite extends FunSuite with Matchers {
 
   test("default Yarn application classpath") {
     ClientBase.getDefaultYarnApplicationClasspath should be(Some(Fixtures.knownDefYarnAppCP))
@@ -68,6 +77,67 @@ class ClientBaseSuite extends FunSuite {
     }
   }
 
+  private val SPARK = "local:/sparkJar"
+  private val USER = "local:/userJar"
+  private val ADDED = "local:/addJar1,local:/addJar2,/addJar3"
+
+  test("Local jar URIs") {
+    val conf = new Configuration()
+    val sparkConf = new SparkConf().set(ClientBase.CONF_SPARK_JAR, SPARK)
+    val env = new MutableHashMap[String, String]()
+    val args = new ClientArguments(Array("--jar", USER, "--addJars", ADDED), sparkConf)
+
+    ClientBase.populateClasspath(args, conf, sparkConf, env, None)
+
+    val cp = env("CLASSPATH").split(File.pathSeparator)
+    s"$SPARK,$USER,$ADDED".split(",").foreach({ entry =>
+      val uri = new URI(entry)
+      if (ClientBase.LOCAL_SCHEME.equals(uri.getScheme())) {
+        cp should contain (uri.getPath())
+      } else {
+        cp should not contain (uri.getPath())
+      }
+    })
+    cp should contain (Environment.PWD.$())
+    cp should contain (s"${Environment.PWD.$()}${File.separator}*")
+    cp should not contain (ClientBase.SPARK_JAR)
+    cp should not contain (ClientBase.APP_JAR)
+  }
+
+  test("Jar path propagation through SparkConf") {
+    val conf = new Configuration()
+    val sparkConf = new SparkConf().set(ClientBase.CONF_SPARK_JAR, SPARK)
+    val yarnConf = new YarnConfiguration()
+    val args = new ClientArguments(Array("--jar", USER, "--addJars", ADDED), sparkConf)
+
+    val client = spy(new DummyClient(args, conf, sparkConf, yarnConf))
+    doReturn(new Path("/")).when(client).copyRemoteFile(any(classOf[Path]),
+      any(classOf[Path]), anyShort(), anyBoolean())
+
+    var tempDir = Files.createTempDir();
+    try {
+      client.prepareLocalResources(tempDir.getAbsolutePath())
+      sparkConf.getOption(ClientBase.CONF_SPARK_USER_JAR) should be (Some(USER))
+
+      // The non-local path should be propagated by name only, since it will end up in the app's
+      // staging dir.
+      val expected = ADDED.split(",")
+        .map(p => {
+          val uri = new URI(p)
+          if (ClientBase.LOCAL_SCHEME == uri.getScheme()) {
+            p
+          } else {
+            Option(uri.getFragment()).getOrElse(new File(p).getName())
+          }
+        })
+        .mkString(",")
+
+      sparkConf.getOption(ClientBase.CONF_SPARK_YARN_SECONDARY_JARS) should be (Some(expected))
+    } finally {
+      Utils.deleteRecursively(tempDir)
+    }
+  }
+
   object Fixtures {
 
     val knownDefYarnAppCP: Seq[String] =
@@ -109,4 +179,18 @@ class ClientBaseSuite extends FunSuite {
   def getFieldValue[A, B](clazz: Class[_], field: String, defaults: => B)(mapTo: A => B): B =
     Try(clazz.getField(field)).map(_.get(null).asInstanceOf[A]).toOption.map(mapTo).getOrElse(defaults)
 
+  private class DummyClient(
+      val args: ClientArguments,
+      val conf: Configuration,
+      val sparkConf: SparkConf,
+      val yarnConf: YarnConfiguration) extends ClientBase {
+
+    override def calculateAMMemory(newApp: GetNewApplicationResponse): Int =
+      throw new UnsupportedOperationException()
+
+    override def setupSecurityToken(amContainer: ContainerLaunchContext): Unit =
+      throw new UnsupportedOperationException()
+
+  }
+
 }
diff --git a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnable.scala b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnable.scala
index 117b33f466f8..07ba0a4b30bd 100644
--- a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnable.scala
+++ b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnable.scala
@@ -81,6 +81,7 @@ class ExecutorRunnable(
     val commands = prepareCommand(masterAddress, slaveId, hostname, executorMemory, executorCores,
       localResources)
 
+    logInfo(s"Setting up executor with environment: $env")
     logInfo("Setting up executor with commands: " + commands)
     ctx.setCommands(commands)
 

From b88238faeed8ba723986cf78d64f84965facb236 Mon Sep 17 00:00:00 2001
From: Vlad <frolvlad@gmail.com>
Date: Mon, 23 Jun 2014 10:55:49 -0500
Subject: [PATCH 500/810] Fixed small running on YARN docs typo

The backslash is needed for multiline command

Author: Vlad <frolvlad@gmail.com>

Closes #1158 from frol/patch-1 and squashes the following commits:

e258044 [Vlad] Fixed small running on YARN docs typo
---
 docs/running-on-yarn.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/running-on-yarn.md b/docs/running-on-yarn.md
index 43b06df99d9c..5d8d603aa3e3 100644
--- a/docs/running-on-yarn.md
+++ b/docs/running-on-yarn.md
@@ -128,7 +128,7 @@ For example:
         --num-executors 3 \
         --driver-memory 4g \
         --executor-memory 2g \
-        --executor-cores 1
+        --executor-cores 1 \
         lib/spark-examples*.jar \
         10
 

From 853a2b951d4c7f6c6c37f53b465b3c7b77691b7c Mon Sep 17 00:00:00 2001
From: Matthew Farrellee <matt@redhat.com>
Date: Mon, 23 Jun 2014 11:24:05 -0700
Subject: [PATCH 501/810] Fix mvn detection

When mvn is not detected (not in executor's path), 'set -e' causes the
detection to terminate the script before the helpful error message can
be displayed.

Author: Matthew Farrellee <matt@redhat.com>

Closes #1181 from mattf/master-0 and squashes the following commits:

506549f [Matthew Farrellee] Fix mvn detection
---
 make-distribution.sh | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/make-distribution.sh b/make-distribution.sh
index ae52b4976dc2..6f708e0ebff6 100755
--- a/make-distribution.sh
+++ b/make-distribution.sh
@@ -89,12 +89,12 @@ if [ -z "$JAVA_HOME" ]; then
   exit -1
 fi
 
-VERSION=$(mvn help:evaluate -Dexpression=project.version 2>/dev/null | grep -v "INFO" | tail -n 1)
-if [ $? != 0 ]; then
+if ! which mvn &>/dev/null; then
     echo -e "You need Maven installed to build Spark."
     echo -e "Download Maven from https://maven.apache.org/"
     exit -1;
 fi
+VERSION=$(mvn help:evaluate -Dexpression=project.version 2>/dev/null | grep -v "INFO" | tail -n 1)
 
 JAVA_CMD="$JAVA_HOME"/bin/java
 JAVA_VERSION=$("$JAVA_CMD" -version 2>&1)

From a4bc442ca2c35444de8a33376b6f27c6c2a9003d Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian.cs.zju@gmail.com>
Date: Mon, 23 Jun 2014 13:24:33 -0700
Subject: [PATCH 502/810] [SPARK-1669][SQL]  Made cacheTable idempotent

JIRA issue: [SPARK-1669](https://issues.apache.org/jira/browse/SPARK-1669)

Caching the same table multiple times should end up with only 1 in-memory columnar representation of this table.

Before:

```
scala> loadTestTable("src")
...
scala> cacheTable("src")
...
scala> cacheTable("src")
...
scala> table("src")
...
== Query Plan ==
InMemoryColumnarTableScan [key#2,value#3], (InMemoryRelation [key#2,value#3], false, (InMemoryColumnarTableScan [key#2,value#3], (InMemoryRelation [key#2,value#3], false, (HiveTableScan [key#2,value#3], (MetastoreRelation default, src, None), None))))
```

After:

```
scala> loadTestTable("src")
...
scala> cacheTable("src")
...
scala> cacheTable("src")
...
scala> table("src")
...
== Query Plan ==
InMemoryColumnarTableScan [key#2,value#3], (InMemoryRelation [key#2,value#3], false, (HiveTableScan [key#2,value#3], (MetastoreRelation default, src, None), None))
```

Author: Cheng Lian <lian.cs.zju@gmail.com>

Closes #1183 from liancheng/spark-1669 and squashes the following commits:

68f8a20 [Cheng Lian] Removed an unused import
51bae90 [Cheng Lian] Made cacheTable idempotent
---
 .../org/apache/spark/sql/SQLContext.scala     | 13 ++++++++----
 .../org/apache/spark/sql/SQLQuerySuite.scala  | 20 +++++++++++++++++++
 2 files changed, 29 insertions(+), 4 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
index c60af28b2a1f..0bcfbf6f849e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
@@ -187,10 +187,15 @@ class SQLContext(@transient val sparkContext: SparkContext)
   /** Caches the specified table in-memory. */
   def cacheTable(tableName: String): Unit = {
     val currentTable = catalog.lookupRelation(None, tableName)
-    val useCompression =
-      sparkContext.conf.getBoolean("spark.sql.inMemoryColumnarStorage.compressed", false)
-    val asInMemoryRelation =
-      InMemoryRelation(useCompression, executePlan(currentTable).executedPlan)
+    val asInMemoryRelation = EliminateAnalysisOperators(currentTable.logicalPlan) match {
+      case _: InMemoryRelation =>
+        currentTable.logicalPlan
+
+      case _ =>
+        val useCompression =
+          sparkContext.conf.getBoolean("spark.sql.inMemoryColumnarStorage.compressed", false)
+        InMemoryRelation(useCompression, executePlan(currentTable).executedPlan)
+    }
 
     catalog.registerTable(None, tableName, asInMemoryRelation)
   }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index e9360b0fc791..cca58c0063b3 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -17,7 +17,9 @@
 
 package org.apache.spark.sql
 
+import org.apache.spark.sql.catalyst.analysis.EliminateAnalysisOperators
 import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.columnar.{InMemoryColumnarTableScan, InMemoryRelation}
 import org.apache.spark.sql.test._
 
 /* Implicits */
@@ -405,4 +407,22 @@ class SQLQuerySuite extends QueryTest {
     clear()
   }
 
+  test("SPARK-1669: cacheTable should be idempotent") {
+    assume(!table("testData").logicalPlan.isInstanceOf[InMemoryRelation])
+
+    cacheTable("testData")
+    EliminateAnalysisOperators(table("testData").logicalPlan) match {
+      case _: InMemoryRelation =>
+      case _ =>
+        fail("testData should be cached")
+    }
+
+    cacheTable("testData")
+    EliminateAnalysisOperators(table("testData").logicalPlan) match {
+      case InMemoryRelation(_, _, _: InMemoryColumnarTableScan) =>
+        fail("cacheTable is not idempotent")
+
+      case _ =>
+    }
+  }
 }

From 6dc6722a666ec1afc6fb13e7110907d8f5f9cd9d Mon Sep 17 00:00:00 2001
From: Prashant Sharma <prashant.s@imaginea.com>
Date: Mon, 23 Jun 2014 13:35:09 -0700
Subject: [PATCH 503/810] [SPARK-2118] spark class should complain if tools jar
 is missing.

Author: Prashant Sharma <prashant.s@imaginea.com>

Closes #1068 from ScrapCodes/SPARK-2118/tools-jar-check and squashes the following commits:

29e768b [Prashant Sharma] Code Review
5cb6f7d [Prashant Sharma] [SPARK-2118] spark class should complaing if tools jar is missing.
---
 bin/spark-class | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/bin/spark-class b/bin/spark-class
index cfe363a71da3..60d9657c0ffc 100755
--- a/bin/spark-class
+++ b/bin/spark-class
@@ -130,6 +130,11 @@ else
 fi
 
 if [[ "$1" =~ org.apache.spark.tools.* ]]; then
+  if test -z "$SPARK_TOOLS_JAR"; then
+    echo "Failed to find Spark Tools Jar in $FWDIR/tools/target/scala-$SCALA_VERSION/" 1>&2
+    echo "You need to build spark before running $1." 1>&2
+    exit 1
+  fi
   CLASSPATH="$CLASSPATH:$SPARK_TOOLS_JAR"
 fi
 

From 21ddd7d1e9f8e2a726427f32422c31706a20ba3f Mon Sep 17 00:00:00 2001
From: Marcelo Vanzin <vanzin@cloudera.com>
Date: Mon, 23 Jun 2014 13:53:44 -0700
Subject: [PATCH 504/810] [SPARK-1768] History server enhancements.

Two improvements to the history server:

- Separate the HTTP handling from history fetching, so that it's easy to add
  new backends later (thinking about SPARK-1537 in the long run)

- Avoid loading all UIs in memory. Do lazy loading instead, keeping a few in
  memory for faster access. This allows the app limit to go away, since holding
  just the listing in memory shouldn't be too expensive unless the user has millions
  of completed apps in the history (at which point I'd expect other issues to arise
  aside from history server memory usage, such as FileSystem.listStatus()
  starting to become ridiculously expensive).

I also fixed a few minor things along the way which aren't really worth mentioning.
I also removed the app's log path from the UI since that information may not even
exist depending on which backend is used (even though there is only one now).

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #718 from vanzin/hist-server and squashes the following commits:

53620c9 [Marcelo Vanzin] Add mima exclude, fix scaladoc wording.
c21f8d8 [Marcelo Vanzin] Feedback: formatting, docs.
dd8cc4b [Marcelo Vanzin] Standardize on using spark.history.* configuration.
4da3a52 [Marcelo Vanzin] Remove UI from ApplicationHistoryInfo.
2a7f68d [Marcelo Vanzin] Address review feedback.
4e72c77 [Marcelo Vanzin] Remove comment about ordering.
249bcea [Marcelo Vanzin] Remove offset / count from provider interface.
ca5d320 [Marcelo Vanzin] Remove code that deals with unfinished apps.
6e2432f [Marcelo Vanzin] Second round of feedback.
b2c570a [Marcelo Vanzin] Make class package-private.
4406f61 [Marcelo Vanzin] Cosmetic change to listing header.
e852149 [Marcelo Vanzin] Initialize new app array to expected size.
e8026f4 [Marcelo Vanzin] Review feedback.
49d2fd3 [Marcelo Vanzin] Fix a comment.
91e96ca [Marcelo Vanzin] Fix scalastyle issues.
6fbe0d8 [Marcelo Vanzin] Better handle failures when loading app info.
eee2f5a [Marcelo Vanzin] Ensure server.stop() is called when shutting down.
bda2fa1 [Marcelo Vanzin] Rudimentary paging support for the history UI.
b284478 [Marcelo Vanzin] Separate history server from history backend.
---
 .../history/ApplicationHistoryProvider.scala  |  59 ++++
 .../deploy/history/FsHistoryProvider.scala    | 214 ++++++++++++++
 .../spark/deploy/history/HistoryPage.scala    |  46 +--
 .../spark/deploy/history/HistoryServer.scala  | 274 ++++++------------
 .../history/HistoryServerArguments.scala      |  55 ++--
 .../scala/org/apache/spark/ui/WebUI.scala     |   1 +
 docs/monitoring.md                            |  21 +-
 project/MimaExcludes.scala                    |   5 +-
 sbin/start-history-server.sh                  |  17 +-
 9 files changed, 448 insertions(+), 244 deletions(-)
 create mode 100644 core/src/main/scala/org/apache/spark/deploy/history/ApplicationHistoryProvider.scala
 create mode 100644 core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala

diff --git a/core/src/main/scala/org/apache/spark/deploy/history/ApplicationHistoryProvider.scala b/core/src/main/scala/org/apache/spark/deploy/history/ApplicationHistoryProvider.scala
new file mode 100644
index 000000000000..a0e8bd403a41
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/deploy/history/ApplicationHistoryProvider.scala
@@ -0,0 +1,59 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.deploy.history
+
+import org.apache.spark.ui.SparkUI
+
+private[spark] case class ApplicationHistoryInfo(
+    id: String,
+    name: String,
+    startTime: Long,
+    endTime: Long,
+    lastUpdated: Long,
+    sparkUser: String)
+
+private[spark] abstract class ApplicationHistoryProvider {
+
+  /**
+   * Returns a list of applications available for the history server to show.
+   *
+   * @return List of all know applications.
+   */
+  def getListing(): Seq[ApplicationHistoryInfo]
+
+  /**
+   * Returns the Spark UI for a specific application.
+   *
+   * @param appId The application ID.
+   * @return The application's UI, or null if application is not found.
+   */
+  def getAppUI(appId: String): SparkUI
+
+  /**
+   * Called when the server is shutting down.
+   */
+  def stop(): Unit = { }
+
+  /**
+   * Returns configuration data to be shown in the History Server home page.
+   *
+   * @return A map with the configuration data. Data is show in the order returned by the map.
+   */
+  def getConfig(): Map[String, String] = Map()
+
+}
diff --git a/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala b/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala
new file mode 100644
index 000000000000..a8c9ac072449
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala
@@ -0,0 +1,214 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.deploy.history
+
+import java.io.FileNotFoundException
+
+import scala.collection.mutable
+
+import org.apache.hadoop.fs.{FileStatus, Path}
+
+import org.apache.spark.{Logging, SecurityManager, SparkConf}
+import org.apache.spark.scheduler._
+import org.apache.spark.ui.SparkUI
+import org.apache.spark.util.Utils
+
+private[history] class FsHistoryProvider(conf: SparkConf) extends ApplicationHistoryProvider
+  with Logging {
+
+  // Interval between each check for event log updates
+  private val UPDATE_INTERVAL_MS = conf.getInt("spark.history.fs.updateInterval",
+    conf.getInt("spark.history.updateInterval", 10)) * 1000
+
+  private val logDir = conf.get("spark.history.fs.logDirectory", null)
+  if (logDir == null) {
+    throw new IllegalArgumentException("Logging directory must be specified.")
+  }
+
+  private val fs = Utils.getHadoopFileSystem(logDir)
+
+  // A timestamp of when the disk was last accessed to check for log updates
+  private var lastLogCheckTimeMs = -1L
+
+  // List of applications, in order from newest to oldest.
+  @volatile private var appList: Seq[ApplicationHistoryInfo] = Nil
+
+  /**
+   * A background thread that periodically checks for event log updates on disk.
+   *
+   * If a log check is invoked manually in the middle of a period, this thread re-adjusts the
+   * time at which it performs the next log check to maintain the same period as before.
+   *
+   * TODO: Add a mechanism to update manually.
+   */
+  private val logCheckingThread = new Thread("LogCheckingThread") {
+    override def run() = Utils.logUncaughtExceptions {
+      while (true) {
+        val now = getMonotonicTimeMs()
+        if (now - lastLogCheckTimeMs > UPDATE_INTERVAL_MS) {
+          Thread.sleep(UPDATE_INTERVAL_MS)
+        } else {
+          // If the user has manually checked for logs recently, wait until
+          // UPDATE_INTERVAL_MS after the last check time
+          Thread.sleep(lastLogCheckTimeMs + UPDATE_INTERVAL_MS - now)
+        }
+        checkForLogs()
+      }
+    }
+  }
+
+  initialize()
+
+  private def initialize() {
+    // Validate the log directory.
+    val path = new Path(logDir)
+    if (!fs.exists(path)) {
+      throw new IllegalArgumentException(
+        "Logging directory specified does not exist: %s".format(logDir))
+    }
+    if (!fs.getFileStatus(path).isDir) {
+      throw new IllegalArgumentException(
+        "Logging directory specified is not a directory: %s".format(logDir))
+    }
+
+    checkForLogs()
+    logCheckingThread.setDaemon(true)
+    logCheckingThread.start()
+  }
+
+  override def getListing() = appList
+
+  override def getAppUI(appId: String): SparkUI = {
+    try {
+      val appLogDir = fs.getFileStatus(new Path(logDir, appId))
+      loadAppInfo(appLogDir, true)._2
+    } catch {
+      case e: FileNotFoundException => null
+    }
+  }
+
+  override def getConfig(): Map[String, String] =
+    Map(("Event Log Location" -> logDir))
+
+  /**
+   * Builds the application list based on the current contents of the log directory.
+   * Tries to reuse as much of the data already in memory as possible, by not reading
+   * applications that haven't been updated since last time the logs were checked.
+   */
+  private def checkForLogs() = {
+    lastLogCheckTimeMs = getMonotonicTimeMs()
+    logDebug("Checking for logs. Time is now %d.".format(lastLogCheckTimeMs))
+    try {
+      val logStatus = fs.listStatus(new Path(logDir))
+      val logDirs = if (logStatus != null) logStatus.filter(_.isDir).toSeq else Seq[FileStatus]()
+      val logInfos = logDirs.filter {
+        dir => fs.isFile(new Path(dir.getPath(), EventLoggingListener.APPLICATION_COMPLETE))
+      }
+
+      val currentApps = Map[String, ApplicationHistoryInfo](
+        appList.map(app => (app.id -> app)):_*)
+
+      // For any application that either (i) is not listed or (ii) has changed since the last time
+      // the listing was created (defined by the log dir's modification time), load the app's info.
+      // Otherwise just reuse what's already in memory.
+      val newApps = new mutable.ArrayBuffer[ApplicationHistoryInfo](logInfos.size)
+      for (dir <- logInfos) {
+        val curr = currentApps.getOrElse(dir.getPath().getName(), null)
+        if (curr == null || curr.lastUpdated < getModificationTime(dir)) {
+          try {
+            newApps += loadAppInfo(dir, false)._1
+          } catch {
+            case e: Exception => logError(s"Failed to load app info from directory $dir.")
+          }
+        } else {
+          newApps += curr
+        }
+      }
+
+      appList = newApps.sortBy { info => -info.endTime }
+    } catch {
+      case t: Throwable => logError("Exception in checking for event log updates", t)
+    }
+  }
+
+  /**
+   * Parse the application's logs to find out the information we need to build the
+   * listing page.
+   *
+   * When creating the listing of available apps, there is no need to load the whole UI for the
+   * application. The UI is requested by the HistoryServer (by calling getAppInfo()) when the user
+   * clicks on a specific application.
+   *
+   * @param logDir Directory with application's log files.
+   * @param renderUI Whether to create the SparkUI for the application.
+   * @return A 2-tuple `(app info, ui)`. `ui` will be null if `renderUI` is false.
+   */
+  private def loadAppInfo(logDir: FileStatus, renderUI: Boolean) = {
+    val elogInfo = EventLoggingListener.parseLoggingInfo(logDir.getPath(), fs)
+    val path = logDir.getPath
+    val appId = path.getName
+    val replayBus = new ReplayListenerBus(elogInfo.logPaths, fs, elogInfo.compressionCodec)
+    val appListener = new ApplicationEventListener
+    replayBus.addListener(appListener)
+
+    val ui: SparkUI = if (renderUI) {
+        val conf = this.conf.clone()
+        val appSecManager = new SecurityManager(conf)
+        new SparkUI(conf, appSecManager, replayBus, appId, "/history/" + appId)
+        // Do not call ui.bind() to avoid creating a new server for each application
+      } else {
+        null
+      }
+
+    replayBus.replay()
+    val appInfo = ApplicationHistoryInfo(
+      appId,
+      appListener.appName,
+      appListener.startTime,
+      appListener.endTime,
+      getModificationTime(logDir),
+      appListener.sparkUser)
+
+    if (ui != null) {
+      val uiAclsEnabled = conf.getBoolean("spark.history.ui.acls.enable", false)
+      ui.getSecurityManager.setUIAcls(uiAclsEnabled)
+      ui.getSecurityManager.setViewAcls(appListener.sparkUser, appListener.viewAcls)
+    }
+    (appInfo, ui)
+  }
+
+  /** Return when this directory was last modified. */
+  private def getModificationTime(dir: FileStatus): Long = {
+    try {
+      val logFiles = fs.listStatus(dir.getPath)
+      if (logFiles != null && !logFiles.isEmpty) {
+        logFiles.map(_.getModificationTime).max
+      } else {
+        dir.getModificationTime
+      }
+    } catch {
+      case t: Throwable =>
+        logError("Exception in accessing modification time of %s".format(dir.getPath), t)
+        -1L
+    }
+  }
+
+  /** Returns the system's mononotically increasing time. */
+  private def getMonotonicTimeMs() = System.nanoTime() / (1000 * 1000)
+
+}
diff --git a/core/src/main/scala/org/apache/spark/deploy/history/HistoryPage.scala b/core/src/main/scala/org/apache/spark/deploy/history/HistoryPage.scala
index 180c853ce309..a958c837c2ff 100644
--- a/core/src/main/scala/org/apache/spark/deploy/history/HistoryPage.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/history/HistoryPage.scala
@@ -25,20 +25,36 @@ import org.apache.spark.ui.{WebUIPage, UIUtils}
 
 private[spark] class HistoryPage(parent: HistoryServer) extends WebUIPage("") {
 
+  private val pageSize = 20
+
   def render(request: HttpServletRequest): Seq[Node] = {
-    val appRows = parent.appIdToInfo.values.toSeq.sortBy { app => -app.lastUpdated }
-    val appTable = UIUtils.listingTable(appHeader, appRow, appRows)
+    val requestedPage = Option(request.getParameter("page")).getOrElse("1").toInt
+    val requestedFirst = (requestedPage - 1) * pageSize
+
+    val allApps = parent.getApplicationList()
+    val actualFirst = if (requestedFirst < allApps.size) requestedFirst else 0
+    val apps = allApps.slice(actualFirst, Math.min(actualFirst + pageSize, allApps.size))
+
+    val actualPage = (actualFirst / pageSize) + 1
+    val last = Math.min(actualFirst + pageSize, allApps.size) - 1
+    val pageCount = allApps.size / pageSize + (if (allApps.size % pageSize > 0) 1 else 0)
+
+    val appTable = UIUtils.listingTable(appHeader, appRow, apps)
+    val providerConfig = parent.getProviderConfig()
     val content =
       <div class="row-fluid">
         <div class="span12">
           <ul class="unstyled">
-            <li><strong>Event Log Location: </strong> {parent.baseLogDir}</li>
+            { providerConfig.map(e => <li><strong>{e._1}:</strong> {e._2}</li>) }
           </ul>
           {
-            if (parent.appIdToInfo.size > 0) {
+            if (allApps.size > 0) {
               <h4>
-                Showing {parent.appIdToInfo.size}/{parent.getNumApplications}
-                Completed Application{if (parent.getNumApplications > 1) "s" else ""}
+                Showing {actualFirst + 1}-{last + 1} of {allApps.size}
+                <span style="float: right">
+                  {if (actualPage > 1) <a href={"/?page=" + (actualPage - 1)}>&lt;</a>}
+                  {if (actualPage < pageCount) <a href={"/?page=" + (actualPage + 1)}>&gt;</a>}
+                </span>
               </h4> ++
               appTable
             } else {
@@ -56,26 +72,20 @@ private[spark] class HistoryPage(parent: HistoryServer) extends WebUIPage("") {
     "Completed",
     "Duration",
     "Spark User",
-    "Log Directory",
     "Last Updated")
 
   private def appRow(info: ApplicationHistoryInfo): Seq[Node] = {
-    val appName = if (info.started) info.name else info.logDirPath.getName
-    val uiAddress = parent.getAddress + info.ui.basePath
-    val startTime = if (info.started) UIUtils.formatDate(info.startTime) else "Not started"
-    val endTime = if (info.completed) UIUtils.formatDate(info.endTime) else "Not completed"
-    val difference = if (info.started && info.completed) info.endTime - info.startTime else -1L
-    val duration = if (difference > 0) UIUtils.formatDuration(difference) else "---"
-    val sparkUser = if (info.started) info.sparkUser else "Unknown user"
-    val logDirectory = info.logDirPath.getName
+    val uiAddress = "/history/" + info.id
+    val startTime = UIUtils.formatDate(info.startTime)
+    val endTime = UIUtils.formatDate(info.endTime)
+    val duration = UIUtils.formatDuration(info.endTime - info.startTime)
     val lastUpdated = UIUtils.formatDate(info.lastUpdated)
     <tr>
-      <td><a href={uiAddress}>{appName}</a></td>
+      <td><a href={uiAddress}>{info.name}</a></td>
       <td>{startTime}</td>
       <td>{endTime}</td>
       <td>{duration}</td>
-      <td>{sparkUser}</td>
-      <td>{logDirectory}</td>
+      <td>{info.sparkUser}</td>
       <td>{lastUpdated}</td>
     </tr>
   }
diff --git a/core/src/main/scala/org/apache/spark/deploy/history/HistoryServer.scala b/core/src/main/scala/org/apache/spark/deploy/history/HistoryServer.scala
index a9c11dca5678..29a78a56c8ed 100644
--- a/core/src/main/scala/org/apache/spark/deploy/history/HistoryServer.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/history/HistoryServer.scala
@@ -17,14 +17,15 @@
 
 package org.apache.spark.deploy.history
 
-import scala.collection.mutable
+import java.util.NoSuchElementException
+import javax.servlet.http.{HttpServlet, HttpServletRequest, HttpServletResponse}
 
-import org.apache.hadoop.fs.{FileStatus, Path}
+import com.google.common.cache._
+import org.eclipse.jetty.servlet.{ServletContextHandler, ServletHolder}
 
 import org.apache.spark.{Logging, SecurityManager, SparkConf}
 import org.apache.spark.deploy.SparkHadoopUtil
-import org.apache.spark.scheduler._
-import org.apache.spark.ui.{WebUI, SparkUI}
+import org.apache.spark.ui.{WebUI, SparkUI, UIUtils}
 import org.apache.spark.ui.JettyUtils._
 import org.apache.spark.util.Utils
 
@@ -38,56 +39,68 @@ import org.apache.spark.util.Utils
  * application's event logs are maintained in the application's own sub-directory. This
  * is the same structure as maintained in the event log write code path in
  * EventLoggingListener.
- *
- * @param baseLogDir The base directory in which event logs are found
  */
 class HistoryServer(
-    val baseLogDir: String,
+    conf: SparkConf,
+    provider: ApplicationHistoryProvider,
     securityManager: SecurityManager,
-    conf: SparkConf)
-  extends WebUI(securityManager, HistoryServer.WEB_UI_PORT, conf) with Logging {
-
-  import HistoryServer._
+    port: Int)
+  extends WebUI(securityManager, port, conf) with Logging {
 
-  private val fileSystem = Utils.getHadoopFileSystem(baseLogDir)
-  private val localHost = Utils.localHostName()
-  private val publicHost = Option(System.getenv("SPARK_PUBLIC_DNS")).getOrElse(localHost)
+  // How many applications to retain
+  private val retainedApplications = conf.getInt("spark.history.retainedApplications", 50)
 
-  // A timestamp of when the disk was last accessed to check for log updates
-  private var lastLogCheckTime = -1L
+  private val appLoader = new CacheLoader[String, SparkUI] {
+    override def load(key: String): SparkUI = {
+      val ui = provider.getAppUI(key)
+      if (ui == null) {
+        throw new NoSuchElementException()
+      }
+      attachSparkUI(ui)
+      ui
+    }
+  }
 
-  // Number of completed applications found in this directory
-  private var numCompletedApplications = 0
+  private val appCache = CacheBuilder.newBuilder()
+    .maximumSize(retainedApplications)
+    .removalListener(new RemovalListener[String, SparkUI] {
+      override def onRemoval(rm: RemovalNotification[String, SparkUI]) = {
+        detachSparkUI(rm.getValue())
+      }
+    })
+    .build(appLoader)
+
+  private val loaderServlet = new HttpServlet {
+    protected override def doGet(req: HttpServletRequest, res: HttpServletResponse): Unit = {
+      val parts = Option(req.getPathInfo()).getOrElse("").split("/")
+      if (parts.length < 2) {
+        res.sendError(HttpServletResponse.SC_BAD_REQUEST,
+          s"Unexpected path info in request (URI = ${req.getRequestURI()}")
+        return
+      }
 
-  @volatile private var stopped = false
+      val appId = parts(1)
 
-  /**
-   * A background thread that periodically checks for event log updates on disk.
-   *
-   * If a log check is invoked manually in the middle of a period, this thread re-adjusts the
-   * time at which it performs the next log check to maintain the same period as before.
-   *
-   * TODO: Add a mechanism to update manually.
-   */
-  private val logCheckingThread = new Thread {
-    override def run(): Unit = Utils.logUncaughtExceptions {
-      while (!stopped) {
-        val now = System.currentTimeMillis
-        if (now - lastLogCheckTime > UPDATE_INTERVAL_MS) {
-          checkForLogs()
-          Thread.sleep(UPDATE_INTERVAL_MS)
-        } else {
-          // If the user has manually checked for logs recently, wait until
-          // UPDATE_INTERVAL_MS after the last check time
-          Thread.sleep(lastLogCheckTime + UPDATE_INTERVAL_MS - now)
+      // Note we don't use the UI retrieved from the cache; the cache loader above will register
+      // the app's UI, and all we need to do is redirect the user to the same URI that was
+      // requested, and the proper data should be served at that point.
+      try {
+        appCache.get(appId)
+        res.sendRedirect(res.encodeRedirectURL(req.getRequestURI()))
+      } catch {
+        case e: Exception => e.getCause() match {
+          case nsee: NoSuchElementException =>
+            val msg = <div class="row-fluid">Application {appId} not found.</div>
+            res.setStatus(HttpServletResponse.SC_NOT_FOUND)
+            UIUtils.basicSparkPage(msg, "Not Found").foreach(
+              n => res.getWriter().write(n.toString))
+
+          case cause: Exception => throw cause
         }
       }
     }
   }
 
-  // A mapping of application ID to its history information, which includes the rendered UI
-  val appIdToInfo = mutable.HashMap[String, ApplicationHistoryInfo]()
-
   initialize()
 
   /**
@@ -98,108 +111,23 @@ class HistoryServer(
    */
   def initialize() {
     attachPage(new HistoryPage(this))
-    attachHandler(createStaticHandler(STATIC_RESOURCE_DIR, "/static"))
+    attachHandler(createStaticHandler(SparkUI.STATIC_RESOURCE_DIR, "/static"))
+
+    val contextHandler = new ServletContextHandler
+    contextHandler.setContextPath("/history")
+    contextHandler.addServlet(new ServletHolder(loaderServlet), "/*")
+    attachHandler(contextHandler)
   }
 
   /** Bind to the HTTP server behind this web interface. */
   override def bind() {
     super.bind()
-    logCheckingThread.start()
-  }
-
-  /**
-   * Check for any updates to event logs in the base directory. This is only effective once
-   * the server has been bound.
-   *
-   * If a new completed application is found, the server renders the associated SparkUI
-   * from the application's event logs, attaches this UI to itself, and stores metadata
-   * information for this application.
-   *
-   * If the logs for an existing completed application are no longer found, the server
-   * removes all associated information and detaches the SparkUI.
-   */
-  def checkForLogs() = synchronized {
-    if (serverInfo.isDefined) {
-      lastLogCheckTime = System.currentTimeMillis
-      logDebug("Checking for logs. Time is now %d.".format(lastLogCheckTime))
-      try {
-        val logStatus = fileSystem.listStatus(new Path(baseLogDir))
-        val logDirs = if (logStatus != null) logStatus.filter(_.isDir).toSeq else Seq[FileStatus]()
-        val logInfos = logDirs
-          .sortBy { dir => getModificationTime(dir) }
-          .map { dir => (dir, EventLoggingListener.parseLoggingInfo(dir.getPath, fileSystem)) }
-          .filter { case (dir, info) => info.applicationComplete }
-
-        // Logging information for applications that should be retained
-        val retainedLogInfos = logInfos.takeRight(RETAINED_APPLICATIONS)
-        val retainedAppIds = retainedLogInfos.map { case (dir, _) => dir.getPath.getName }
-
-        // Remove any applications that should no longer be retained
-        appIdToInfo.foreach { case (appId, info) =>
-          if (!retainedAppIds.contains(appId)) {
-            detachSparkUI(info.ui)
-            appIdToInfo.remove(appId)
-          }
-        }
-
-        // Render the application's UI if it is not already there
-        retainedLogInfos.foreach { case (dir, info) =>
-          val appId = dir.getPath.getName
-          if (!appIdToInfo.contains(appId)) {
-            renderSparkUI(dir, info)
-          }
-        }
-
-        // Track the total number of completed applications observed this round
-        numCompletedApplications = logInfos.size
-
-      } catch {
-        case e: Exception => logError("Exception in checking for event log updates", e)
-      }
-    } else {
-      logWarning("Attempted to check for event log updates before binding the server.")
-    }
-  }
-
-  /**
-   * Render a new SparkUI from the event logs if the associated application is completed.
-   *
-   * HistoryServer looks for a special file that indicates application completion in the given
-   * directory. If this file exists, the associated application is regarded to be completed, in
-   * which case the server proceeds to render the SparkUI. Otherwise, the server does nothing.
-   */
-  private def renderSparkUI(logDir: FileStatus, elogInfo: EventLoggingInfo) {
-    val path = logDir.getPath
-    val appId = path.getName
-    val replayBus = new ReplayListenerBus(elogInfo.logPaths, fileSystem, elogInfo.compressionCodec)
-    val appListener = new ApplicationEventListener
-    replayBus.addListener(appListener)
-    val appConf = conf.clone()
-    val appSecManager = new SecurityManager(appConf)
-    val ui = new SparkUI(conf, appSecManager, replayBus, appId, "/history/" + appId)
-
-    // Do not call ui.bind() to avoid creating a new server for each application
-    replayBus.replay()
-    if (appListener.applicationStarted) {
-      appSecManager.setUIAcls(HISTORY_UI_ACLS_ENABLED)
-      appSecManager.setViewAcls(appListener.sparkUser, appListener.viewAcls)
-      attachSparkUI(ui)
-      val appName = appListener.appName
-      val sparkUser = appListener.sparkUser
-      val startTime = appListener.startTime
-      val endTime = appListener.endTime
-      val lastUpdated = getModificationTime(logDir)
-      ui.setAppName(appName + " (completed)")
-      appIdToInfo(appId) = ApplicationHistoryInfo(appId, appName, startTime, endTime,
-        lastUpdated, sparkUser, path, ui)
-    }
   }
 
   /** Stop the server and close the file system. */
   override def stop() {
     super.stop()
-    stopped = true
-    fileSystem.close()
+    provider.stop()
   }
 
   /** Attach a reconstructed UI to this server. Only valid after bind(). */
@@ -215,27 +143,20 @@ class HistoryServer(
     ui.getHandlers.foreach(detachHandler)
   }
 
-  /** Return the address of this server. */
-  def getAddress: String = "http://" + publicHost + ":" + boundPort
+  /**
+   * Returns a list of available applications, in descending order according to their end time.
+   *
+   * @return List of all known applications.
+   */
+  def getApplicationList() = provider.getListing()
 
-  /** Return the number of completed applications found, whether or not the UI is rendered. */
-  def getNumApplications: Int = numCompletedApplications
+  /**
+   * Returns the provider configuration to show in the listing page.
+   *
+   * @return A map with the provider's configuration.
+   */
+  def getProviderConfig() = provider.getConfig()
 
-  /** Return when this directory was last modified. */
-  private def getModificationTime(dir: FileStatus): Long = {
-    try {
-      val logFiles = fileSystem.listStatus(dir.getPath)
-      if (logFiles != null && !logFiles.isEmpty) {
-        logFiles.map(_.getModificationTime).max
-      } else {
-        dir.getModificationTime
-      }
-    } catch {
-      case e: Exception =>
-        logError("Exception in accessing modification time of %s".format(dir.getPath), e)
-        -1L
-    }
-  }
 }
 
 /**
@@ -251,30 +172,31 @@ class HistoryServer(
 object HistoryServer {
   private val conf = new SparkConf
 
-  // Interval between each check for event log updates
-  val UPDATE_INTERVAL_MS = conf.getInt("spark.history.updateInterval", 10) * 1000
-
-  // How many applications to retain
-  val RETAINED_APPLICATIONS = conf.getInt("spark.history.retainedApplications", 250)
-
-  // The port to which the web UI is bound
-  val WEB_UI_PORT = conf.getInt("spark.history.ui.port", 18080)
-
-  // set whether to enable or disable view acls for all applications
-  val HISTORY_UI_ACLS_ENABLED = conf.getBoolean("spark.history.ui.acls.enable", false)
-
-  val STATIC_RESOURCE_DIR = SparkUI.STATIC_RESOURCE_DIR
-
   def main(argStrings: Array[String]) {
     initSecurity()
-    val args = new HistoryServerArguments(argStrings)
+    val args = new HistoryServerArguments(conf, argStrings)
     val securityManager = new SecurityManager(conf)
-    val server = new HistoryServer(args.logDir, securityManager, conf)
+
+    val providerName = conf.getOption("spark.history.provider")
+      .getOrElse(classOf[FsHistoryProvider].getName())
+    val provider = Class.forName(providerName)
+      .getConstructor(classOf[SparkConf])
+      .newInstance(conf)
+      .asInstanceOf[ApplicationHistoryProvider]
+
+    val port = conf.getInt("spark.history.ui.port", 18080)
+
+    val server = new HistoryServer(conf, provider, securityManager, port)
     server.bind()
 
+    Runtime.getRuntime().addShutdownHook(new Thread("HistoryServerStopper") {
+      override def run() = {
+        server.stop()
+      }
+    })
+
     // Wait until the end of the world... or if the HistoryServer process is manually stopped
     while(true) { Thread.sleep(Int.MaxValue) }
-    server.stop()
   }
 
   def initSecurity() {
@@ -291,17 +213,3 @@ object HistoryServer {
   }
 
 }
-
-
-private[spark] case class ApplicationHistoryInfo(
-    id: String,
-    name: String,
-    startTime: Long,
-    endTime: Long,
-    lastUpdated: Long,
-    sparkUser: String,
-    logDirPath: Path,
-    ui: SparkUI) {
-  def started = startTime != -1
-  def completed = endTime != -1
-}
diff --git a/core/src/main/scala/org/apache/spark/deploy/history/HistoryServerArguments.scala b/core/src/main/scala/org/apache/spark/deploy/history/HistoryServerArguments.scala
index 943c061743db..be9361b754fc 100644
--- a/core/src/main/scala/org/apache/spark/deploy/history/HistoryServerArguments.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/history/HistoryServerArguments.scala
@@ -17,17 +17,14 @@
 
 package org.apache.spark.deploy.history
 
-import java.net.URI
-
-import org.apache.hadoop.fs.Path
-
+import org.apache.spark.SparkConf
 import org.apache.spark.util.Utils
 
 /**
  * Command-line parser for the master.
  */
-private[spark] class HistoryServerArguments(args: Array[String]) {
-  var logDir = ""
+private[spark] class HistoryServerArguments(conf: SparkConf, args: Array[String]) {
+  private var logDir: String = null
 
   parse(args.toList)
 
@@ -45,32 +42,36 @@ private[spark] class HistoryServerArguments(args: Array[String]) {
       case _ =>
         printUsageAndExit(1)
     }
-    validateLogDir()
-  }
-
-  private def validateLogDir() {
-    if (logDir == "") {
-      System.err.println("Logging directory must be specified.")
-      printUsageAndExit(1)
-    }
-    val fileSystem = Utils.getHadoopFileSystem(new URI(logDir))
-    val path = new Path(logDir)
-    if (!fileSystem.exists(path)) {
-      System.err.println("Logging directory specified does not exist: %s".format(logDir))
-      printUsageAndExit(1)
-    }
-    if (!fileSystem.getFileStatus(path).isDir) {
-      System.err.println("Logging directory specified is not a directory: %s".format(logDir))
-      printUsageAndExit(1)
+    if (logDir != null) {
+      conf.set("spark.history.fs.logDirectory", logDir)
     }
   }
 
   private def printUsageAndExit(exitCode: Int) {
     System.err.println(
-      "Usage: HistoryServer [options]\n" +
-      "\n" +
-      "Options:\n" +
-      "  -d DIR,  --dir DIR     Location of event log files")
+      """
+      |Usage: HistoryServer
+      |
+      |Configuration options can be set by setting the corresponding JVM system property.
+      |History Server options are always available; additional options depend on the provider.
+      |
+      |History Server options:
+      |
+      |  spark.history.ui.port              Port where server will listen for connections
+      |                                     (default 18080)
+      |  spark.history.acls.enable          Whether to enable view acls for all applications
+      |                                     (default false)
+      |  spark.history.provider             Name of history provider class (defaults to
+      |                                     file system-based provider)
+      |  spark.history.retainedApplications Max number of application UIs to keep loaded in memory
+      |                                     (default 50)
+      |FsHistoryProvider options:
+      |
+      |  spark.history.fs.logDirectory      Directory where app logs are stored (required)
+      |  spark.history.fs.updateInterval    How often to reload log data from storage (in seconds,
+      |                                     default 10)
+      |""".stripMargin)
     System.exit(exitCode)
   }
+
 }
diff --git a/core/src/main/scala/org/apache/spark/ui/WebUI.scala b/core/src/main/scala/org/apache/spark/ui/WebUI.scala
index b08f308fda1d..856273e1d4e2 100644
--- a/core/src/main/scala/org/apache/spark/ui/WebUI.scala
+++ b/core/src/main/scala/org/apache/spark/ui/WebUI.scala
@@ -51,6 +51,7 @@ private[spark] abstract class WebUI(
 
   def getTabs: Seq[WebUITab] = tabs.toSeq
   def getHandlers: Seq[ServletContextHandler] = handlers.toSeq
+  def getSecurityManager: SecurityManager = securityManager
 
   /** Attach a tab to this UI, along with all of its attached pages. */
   def attachTab(tab: WebUITab) {
diff --git a/docs/monitoring.md b/docs/monitoring.md
index 2b9e9e5bd7ea..84073fe4d949 100644
--- a/docs/monitoring.md
+++ b/docs/monitoring.md
@@ -35,11 +35,13 @@ If Spark is run on Mesos or YARN, it is still possible to reconstruct the UI of
 application through Spark's history server, provided that the application's event logs exist.
 You can start a the history server by executing:
 
-    ./sbin/start-history-server.sh <base-logging-directory>
+    ./sbin/start-history-server.sh
 
-The base logging directory must be supplied, and should contain sub-directories that each
-represents an application's event logs. This creates a web interface at
-`http://<server-url>:18080` by default. The history server can be configured as follows:
+When using the file-system provider class (see spark.history.provider below), the base logging
+directory must be supplied in the <code>spark.history.fs.logDirectory</code> configuration option,
+and should contain sub-directories that each represents an application's event logs. This creates a
+web interface at `http://<server-url>:18080` by default. The history server can be configured as
+follows:
 
 <table class="table">
   <tr><th style="width:21%">Environment Variable</th><th>Meaning</th></tr>
@@ -69,7 +71,14 @@ represents an application's event logs. This creates a web interface at
 <table class="table">
   <tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr>
   <tr>
-    <td>spark.history.updateInterval</td>
+    <td>spark.history.provider</td>
+    <td>org.apache.spark.deploy.history.FsHistoryProvider</td>
+    <td>Name of the class implementing the application history backend. Currently there is only
+    one implementation, provided by Spark, which looks for application logs stored in the
+    file system.</td>
+  </tr>
+  <tr>
+    <td>spark.history.fs.updateInterval</td>
     <td>10</td>
     <td>
       The period, in seconds, at which information displayed by this history server is updated.
@@ -78,7 +87,7 @@ represents an application's event logs. This creates a web interface at
   </tr>
   <tr>
     <td>spark.history.retainedApplications</td>
-    <td>250</td>
+    <td>50</td>
     <td>
       The number of application UIs to retain. If this cap is exceeded, then the oldest
       applications will be removed.
diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala
index af620d61607f..1621833e124f 100644
--- a/project/MimaExcludes.scala
+++ b/project/MimaExcludes.scala
@@ -34,7 +34,10 @@ object MimaExcludes {
     val excludes =
       SparkBuild.SPARK_VERSION match {
         case v if v.startsWith("1.1") =>
-          Seq(MimaBuild.excludeSparkPackage("graphx")) ++
+          Seq(
+            MimaBuild.excludeSparkPackage("deploy"),
+            MimaBuild.excludeSparkPackage("graphx")
+          ) ++
           Seq(
             // Adding new method to JavaRDLike trait - we should probably mark this as a developer API.
             ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.api.java.JavaRDDLike.partitions"),
diff --git a/sbin/start-history-server.sh b/sbin/start-history-server.sh
index 4a90c68763b6..e30493da32a7 100755
--- a/sbin/start-history-server.sh
+++ b/sbin/start-history-server.sh
@@ -19,19 +19,18 @@
 
 # Starts the history server on the machine this script is executed on.
 #
-# Usage: start-history-server.sh <base-log-dir> [<web-ui-port>]
-#   Example: ./start-history-server.sh --dir /tmp/spark-events --port 18080
+# Usage: start-history-server.sh
+#
+# Use the SPARK_HISTORY_OPTS environment variable to set history server configuration.
 #
 
 sbin=`dirname "$0"`
 sbin=`cd "$sbin"; pwd`
 
-if [ $# -lt 1 ]; then
-  echo "Usage: ./start-history-server.sh <base-log-dir>"
-  echo "Example: ./start-history-server.sh /tmp/spark-events"
-  exit
+if [ $# != 0 ]; then
+  echo "Using command line arguments for setting the log directory is deprecated. Please "
+  echo "set the spark.history.fs.logDirectory configuration option instead."
+  export SPARK_HISTORY_OPTS="$SPARK_HISTORY_OPTS -Dspark.history.fs.logDirectory=$1"
 fi
 
-LOG_DIR=$1
-
-"$sbin"/spark-daemon.sh start org.apache.spark.deploy.history.HistoryServer 1 --dir "$LOG_DIR"
+exec "$sbin"/spark-daemon.sh start org.apache.spark.deploy.history.HistoryServer 1

From 383bf72c115b09d68cbde0d35ed89808ce04863d Mon Sep 17 00:00:00 2001
From: Henry Saputra <henry.saputra@gmail.com>
Date: Mon, 23 Jun 2014 17:13:26 -0700
Subject: [PATCH 505/810] Cleanup on Connection, ConnectionManagerId,
 ConnectionManager classes part 2

Cleanup on Connection, ConnectionManagerId, and ConnectionManager classes part 2 while I was working at the code there to help IDE:
1. Remove unused imports
2. Remove parentheses in method calls that do not have side affect.
3. Add parentheses in method calls that do have side effect or not simple get to object properties.
4. Change if-else check (via isInstanceOf) for Connection class type with Scala expression for consistency and cleanliness.
5. Remove semicolon
6. Remove extra spaces.
7. Remove redundant return for consistency

Author: Henry Saputra <henry.saputra@gmail.com>

Closes #1157 from hsaputra/cleanup_connection_classes_part2 and squashes the following commits:

4be6906 [Henry Saputra] Fix Spark Scala style for line over 100 chars.
85b24f7 [Henry Saputra] Cleanup on Connection and ConnectionManager classes part 2 while I was working at the code there to help IDE: 1. Remove unused imports 2. Remove parentheses in method calls that do not have side affect. 3. Add parentheses in method calls that do have side effect. 4. Change if-else check (via isInstanceOf) for Connection class type with Scala expression for consitency and cleanliness. 5. Remove semicolon 6. Remove extra spaces.
---
 .../org/apache/spark/network/Connection.scala |  15 +--
 .../spark/network/ConnectionManager.scala     | 114 +++++++++---------
 .../spark/network/ConnectionManagerId.scala   |   2 +-
 3 files changed, 62 insertions(+), 69 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/network/Connection.scala b/core/src/main/scala/org/apache/spark/network/Connection.scala
index 3b6298a26d7c..5285ec82c1b6 100644
--- a/core/src/main/scala/org/apache/spark/network/Connection.scala
+++ b/core/src/main/scala/org/apache/spark/network/Connection.scala
@@ -17,11 +17,6 @@
 
 package org.apache.spark.network
 
-import org.apache.spark._
-import org.apache.spark.SparkSaslServer
-
-import scala.collection.mutable.{HashMap, Queue, ArrayBuffer}
-
 import java.net._
 import java.nio._
 import java.nio.channels._
@@ -41,7 +36,7 @@ abstract class Connection(val channel: SocketChannel, val selector: Selector,
   def this(channel_ : SocketChannel, selector_ : Selector, id_ : ConnectionId) = {
     this(channel_, selector_,
       ConnectionManagerId.fromSocketAddress(
-        channel_.socket.getRemoteSocketAddress().asInstanceOf[InetSocketAddress]), id_)
+        channel_.socket.getRemoteSocketAddress.asInstanceOf[InetSocketAddress]), id_)
   }
 
   channel.configureBlocking(false)
@@ -89,7 +84,7 @@ abstract class Connection(val channel: SocketChannel, val selector: Selector,
 
   private def disposeSasl() {
     if (sparkSaslServer != null) {
-      sparkSaslServer.dispose();
+      sparkSaslServer.dispose()
     }
 
     if (sparkSaslClient != null) {
@@ -328,15 +323,13 @@ class SendingConnection(val address: InetSocketAddress, selector_ : Selector,
       // Is highly unlikely unless there was an unclean close of socket, etc
       registerInterest()
       logInfo("Connected to [" + address + "], " + outbox.messages.size + " messages pending")
-      true
     } catch {
       case e: Exception => {
         logWarning("Error finishing connection to " + address, e)
         callOnExceptionCallback(e)
-        // ignore
-        return true
       }
     }
+    true
   }
 
   override def write(): Boolean = {
@@ -546,7 +539,7 @@ private[spark] class ReceivingConnection(
           /* println("Filled buffer at " + System.currentTimeMillis) */
           val bufferMessage = inbox.getMessageForChunk(currentChunk).get
           if (bufferMessage.isCompletelyReceived) {
-            bufferMessage.flip
+            bufferMessage.flip()
             bufferMessage.finishTime = System.currentTimeMillis
             logDebug("Finished receiving [" + bufferMessage + "] from " +
               "[" + getRemoteConnectionManagerId() + "] in " + bufferMessage.timeTaken)
diff --git a/core/src/main/scala/org/apache/spark/network/ConnectionManager.scala b/core/src/main/scala/org/apache/spark/network/ConnectionManager.scala
index cf1c985c2fff..8a1cdb812962 100644
--- a/core/src/main/scala/org/apache/spark/network/ConnectionManager.scala
+++ b/core/src/main/scala/org/apache/spark/network/ConnectionManager.scala
@@ -249,7 +249,7 @@ private[spark] class ConnectionManager(port: Int, conf: SparkConf,
   def run() {
     try {
       while(!selectorThread.isInterrupted) {
-        while (! registerRequests.isEmpty) {
+        while (!registerRequests.isEmpty) {
           val conn: SendingConnection = registerRequests.dequeue()
           addListeners(conn)
           conn.connect()
@@ -308,7 +308,7 @@ private[spark] class ConnectionManager(port: Int, conf: SparkConf,
               // Some keys within the selectors list are invalid/closed. clear them.
               val allKeys = selector.keys().iterator()
 
-              while (allKeys.hasNext()) {
+              while (allKeys.hasNext) {
                 val key = allKeys.next()
                 try {
                   if (! key.isValid) {
@@ -341,7 +341,7 @@ private[spark] class ConnectionManager(port: Int, conf: SparkConf,
 
         if (0 != selectedKeysCount) {
           val selectedKeys = selector.selectedKeys().iterator()
-          while (selectedKeys.hasNext()) {
+          while (selectedKeys.hasNext) {
             val key = selectedKeys.next
             selectedKeys.remove()
             try {
@@ -419,62 +419,63 @@ private[spark] class ConnectionManager(port: Int, conf: SparkConf,
     connectionsByKey -= connection.key
 
     try {
-      if (connection.isInstanceOf[SendingConnection]) {
-        val sendingConnection = connection.asInstanceOf[SendingConnection]
-        val sendingConnectionManagerId = sendingConnection.getRemoteConnectionManagerId()
-        logInfo("Removing SendingConnection to " + sendingConnectionManagerId)
-
-        connectionsById -= sendingConnectionManagerId
-        connectionsAwaitingSasl -= connection.connectionId
+      connection match {
+        case sendingConnection: SendingConnection =>
+          val sendingConnectionManagerId = sendingConnection.getRemoteConnectionManagerId()
+          logInfo("Removing SendingConnection to " + sendingConnectionManagerId)
+
+          connectionsById -= sendingConnectionManagerId
+          connectionsAwaitingSasl -= connection.connectionId
+
+          messageStatuses.synchronized {
+            messageStatuses.values.filter(_.connectionManagerId == sendingConnectionManagerId)
+              .foreach(status => {
+                logInfo("Notifying " + status)
+                status.synchronized {
+                  status.attempted = true
+                  status.acked = false
+                  status.markDone()
+                }
+              })
 
-        messageStatuses.synchronized {
-          messageStatuses
-            .values.filter(_.connectionManagerId == sendingConnectionManagerId).foreach(status => {
-              logInfo("Notifying " + status)
-              status.synchronized {
-              status.attempted = true
-               status.acked = false
-               status.markDone()
-              }
+            messageStatuses.retain((i, status) => {
+              status.connectionManagerId != sendingConnectionManagerId
             })
+          }
+        case receivingConnection: ReceivingConnection =>
+          val remoteConnectionManagerId = receivingConnection.getRemoteConnectionManagerId()
+          logInfo("Removing ReceivingConnection to " + remoteConnectionManagerId)
 
-          messageStatuses.retain((i, status) => {
-            status.connectionManagerId != sendingConnectionManagerId
-          })
-        }
-      } else if (connection.isInstanceOf[ReceivingConnection]) {
-        val receivingConnection = connection.asInstanceOf[ReceivingConnection]
-        val remoteConnectionManagerId = receivingConnection.getRemoteConnectionManagerId()
-        logInfo("Removing ReceivingConnection to " + remoteConnectionManagerId)
-
-        val sendingConnectionOpt = connectionsById.get(remoteConnectionManagerId)
-          if (! sendingConnectionOpt.isDefined) {
-          logError("Corresponding SendingConnectionManagerId not found")
-          return
-        }
+          val sendingConnectionOpt = connectionsById.get(remoteConnectionManagerId)
+          if (!sendingConnectionOpt.isDefined) {
+            logError("Corresponding SendingConnectionManagerId not found")
+            return
+          }
 
-        val sendingConnection = sendingConnectionOpt.get
-        connectionsById -= remoteConnectionManagerId
-        sendingConnection.close()
+          val sendingConnection = sendingConnectionOpt.get
+          connectionsById -= remoteConnectionManagerId
+          sendingConnection.close()
 
-        val sendingConnectionManagerId = sendingConnection.getRemoteConnectionManagerId()
+          val sendingConnectionManagerId = sendingConnection.getRemoteConnectionManagerId()
 
-        assert (sendingConnectionManagerId == remoteConnectionManagerId)
+          assert(sendingConnectionManagerId == remoteConnectionManagerId)
 
-        messageStatuses.synchronized {
-          for (s <- messageStatuses.values if s.connectionManagerId == sendingConnectionManagerId) {
-            logInfo("Notifying " + s)
-            s.synchronized {
-              s.attempted = true
-              s.acked = false
-              s.markDone()
+          messageStatuses.synchronized {
+            for (s <- messageStatuses.values
+                 if s.connectionManagerId == sendingConnectionManagerId) {
+              logInfo("Notifying " + s)
+              s.synchronized {
+                s.attempted = true
+                s.acked = false
+                s.markDone()
+              }
             }
-          }
 
-          messageStatuses.retain((i, status) => {
-            status.connectionManagerId != sendingConnectionManagerId
-          })
-        }
+            messageStatuses.retain((i, status) => {
+              status.connectionManagerId != sendingConnectionManagerId
+            })
+          }
+        case _ => logError("Unsupported type of connection.")
       }
     } finally {
       // So that the selection keys can be removed.
@@ -517,13 +518,13 @@ private[spark] class ConnectionManager(port: Int, conf: SparkConf,
       logDebug("Client sasl completed for id: "  + waitingConn.connectionId)
       connectionsAwaitingSasl -= waitingConn.connectionId
       waitingConn.getAuthenticated().synchronized {
-        waitingConn.getAuthenticated().notifyAll();
+        waitingConn.getAuthenticated().notifyAll()
       }
       return
     } else {
       var replyToken : Array[Byte] = null
       try {
-        replyToken = waitingConn.sparkSaslClient.saslResponse(securityMsg.getToken);
+        replyToken = waitingConn.sparkSaslClient.saslResponse(securityMsg.getToken)
         if (waitingConn.isSaslComplete()) {
           logDebug("Client sasl completed after evaluate for id: " + waitingConn.connectionId)
           connectionsAwaitingSasl -= waitingConn.connectionId
@@ -533,7 +534,7 @@ private[spark] class ConnectionManager(port: Int, conf: SparkConf,
           return
         }
         val securityMsgResp = SecurityMessage.fromResponse(replyToken,
-          securityMsg.getConnectionId.toString())
+          securityMsg.getConnectionId.toString)
         val message = securityMsgResp.toBufferMessage
         if (message == null) throw new Exception("Error creating security message")
         sendSecurityMessage(waitingConn.getRemoteConnectionManagerId(), message)
@@ -630,13 +631,13 @@ private[spark] class ConnectionManager(port: Int, conf: SparkConf,
       case bufferMessage: BufferMessage => {
         if (authEnabled) {
           val res = handleAuthentication(connection, bufferMessage)
-          if (res == true) {
+          if (res) {
             // message was security negotiation so skip the rest
             logDebug("After handleAuth result was true, returning")
             return
           }
         }
-        if (bufferMessage.hasAckId) {
+        if (bufferMessage.hasAckId()) {
           val sentMessageStatus = messageStatuses.synchronized {
             messageStatuses.get(bufferMessage.ackId) match {
               case Some(status) => {
@@ -646,7 +647,6 @@ private[spark] class ConnectionManager(port: Int, conf: SparkConf,
               case None => {
                 throw new Exception("Could not find reference for received ack message " +
                   message.id)
-                null
               }
             }
           }
@@ -668,7 +668,7 @@ private[spark] class ConnectionManager(port: Int, conf: SparkConf,
           if (ackMessage.isDefined) {
             if (!ackMessage.get.isInstanceOf[BufferMessage]) {
               logDebug("Response to " + bufferMessage + " is not a buffer message, it is of type "
-                + ackMessage.get.getClass())
+                + ackMessage.get.getClass)
             } else if (!ackMessage.get.asInstanceOf[BufferMessage].hasAckId) {
               logDebug("Response to " + bufferMessage + " does not have ack id set")
               ackMessage.get.asInstanceOf[BufferMessage].ackId = bufferMessage.id
diff --git a/core/src/main/scala/org/apache/spark/network/ConnectionManagerId.scala b/core/src/main/scala/org/apache/spark/network/ConnectionManagerId.scala
index b82edb6850d2..57f7586883af 100644
--- a/core/src/main/scala/org/apache/spark/network/ConnectionManagerId.scala
+++ b/core/src/main/scala/org/apache/spark/network/ConnectionManagerId.scala
@@ -32,6 +32,6 @@ private[spark] case class ConnectionManagerId(host: String, port: Int) {
 
 private[spark] object ConnectionManagerId {
   def fromSocketAddress(socketAddress: InetSocketAddress): ConnectionManagerId = {
-    new ConnectionManagerId(socketAddress.getHostName(), socketAddress.getPort())
+    new ConnectionManagerId(socketAddress.getHostName, socketAddress.getPort)
   }
 }

From 51c8168377a89d20d0b2d7b9a28af58593a0fe0c Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Mon, 23 Jun 2014 18:34:54 -0700
Subject: [PATCH 506/810] [SPARK-2227] Support dfs command in SQL.

Note that nothing gets printed to the console because we don't properly maintain session state right now.

I will have a followup PR that fixes it.

Author: Reynold Xin <rxin@apache.org>

Closes #1167 from rxin/commands and squashes the following commits:

56f04f8 [Reynold Xin] [SPARK-2227] Support dfs command in SQL.
---
 .../scala/org/apache/spark/sql/hive/HiveQl.scala   | 14 ++++++--------
 1 file changed, 6 insertions(+), 8 deletions(-)

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
index b073dc3895f0..b70104dd5be5 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
@@ -38,8 +38,6 @@ import scala.collection.JavaConversions._
  */
 private[hive] case object NativePlaceholder extends Command
 
-private[hive] case class DfsCommand(cmd: String) extends Command
-
 private[hive] case class ShellCommand(cmd: String) extends Command
 
 private[hive] case class SourceCommand(filePath: String) extends Command
@@ -227,15 +225,15 @@ private[hive] object HiveQl {
             SetCommand(Some(key), Some(value))
         }
       } else if (sql.trim.toLowerCase.startsWith("cache table")) {
-        CacheCommand(sql.drop(12).trim, true)
+        CacheCommand(sql.trim.drop(12).trim, true)
       } else if (sql.trim.toLowerCase.startsWith("uncache table")) {
-        CacheCommand(sql.drop(14).trim, false)
+        CacheCommand(sql.trim.drop(14).trim, false)
       } else if (sql.trim.toLowerCase.startsWith("add jar")) {
-        AddJar(sql.drop(8))
+        AddJar(sql.trim.drop(8))
       } else if (sql.trim.toLowerCase.startsWith("add file")) {
-        AddFile(sql.drop(9))
-      } else if (sql.trim.startsWith("dfs")) {
-        DfsCommand(sql)
+        AddFile(sql.trim.drop(9))
+      } else if (sql.trim.toLowerCase.startsWith("dfs")) {
+        NativeCommand(sql)
       } else if (sql.trim.startsWith("source")) {
         SourceCommand(sql.split(" ").toSeq match { case Seq("source", filePath) => filePath })
       } else if (sql.trim.startsWith("!")) {

From 56eb8af187b19f09810baafb314b21e07cf0a79c Mon Sep 17 00:00:00 2001
From: jerryshao <saisai.shao@intel.com>
Date: Mon, 23 Jun 2014 20:25:46 -0700
Subject: [PATCH 507/810] [SPARK-2124] Move aggregation into shuffle
 implementations

This PR is a sub-task of SPARK-2044 to move the execution of aggregation into shuffle implementations.

I leave `CoGoupedRDD` and `SubtractedRDD` unchanged because they have their implementations of aggregation. I'm not sure is it suitable to change these two RDDs.

Also I do not move sort related code of `OrderedRDDFunctions` into shuffle, this will be solved in another sub-task.

Author: jerryshao <saisai.shao@intel.com>

Closes #1064 from jerryshao/SPARK-2124 and squashes the following commits:

4a05a40 [jerryshao] Modify according to comments
1f7dcc8 [jerryshao] Style changes
50a2fd6 [jerryshao] Fix test suite issue after moving aggregator to Shuffle reader and writer
1a96190 [jerryshao] Code modification related to the ShuffledRDD
308f635 [jerryshao] initial works of move combiner to ShuffleManager's reader and writer
---
 .../scala/org/apache/spark/Dependency.scala   |  3 +-
 .../spark/rdd/OrderedRDDFunctions.scala       |  2 +-
 .../apache/spark/rdd/PairRDDFunctions.scala   | 20 +++-------
 .../main/scala/org/apache/spark/rdd/RDD.scala |  2 +-
 .../org/apache/spark/rdd/ShuffledRDD.scala    | 37 ++++++++++++++++---
 .../spark/scheduler/ShuffleMapTask.scala      |  6 +--
 .../apache/spark/shuffle/ShuffleWriter.scala  |  4 +-
 .../shuffle/hash/HashShuffleReader.scala      | 20 ++++++++--
 .../shuffle/hash/HashShuffleWriter.scala      | 23 +++++++++---
 .../org/apache/spark/CheckpointSuite.scala    |  2 +-
 .../scala/org/apache/spark/ShuffleSuite.scala | 22 +++++++----
 .../scala/org/apache/spark/rdd/RDDSuite.scala | 14 +++----
 .../spark/scheduler/SparkListenerSuite.scala  |  2 +-
 .../graphx/impl/MessageToPartition.scala      |  7 ++--
 .../graphx/impl/RoutingTablePartition.scala   |  4 +-
 .../apache/spark/sql/execution/Exchange.scala |  6 +--
 .../spark/sql/execution/basicOperators.scala  |  2 +-
 17 files changed, 112 insertions(+), 64 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/Dependency.scala b/core/src/main/scala/org/apache/spark/Dependency.scala
index c8c194a111aa..09a60571238e 100644
--- a/core/src/main/scala/org/apache/spark/Dependency.scala
+++ b/core/src/main/scala/org/apache/spark/Dependency.scala
@@ -61,7 +61,8 @@ class ShuffleDependency[K, V, C](
     val partitioner: Partitioner,
     val serializer: Option[Serializer] = None,
     val keyOrdering: Option[Ordering[K]] = None,
-    val aggregator: Option[Aggregator[K, V, C]] = None)
+    val aggregator: Option[Aggregator[K, V, C]] = None,
+    val mapSideCombine: Boolean = false)
   extends Dependency(rdd.asInstanceOf[RDD[Product2[K, V]]]) {
 
   val shuffleId: Int = rdd.context.newShuffleId()
diff --git a/core/src/main/scala/org/apache/spark/rdd/OrderedRDDFunctions.scala b/core/src/main/scala/org/apache/spark/rdd/OrderedRDDFunctions.scala
index 6a3f69844428..f1f4b4324edf 100644
--- a/core/src/main/scala/org/apache/spark/rdd/OrderedRDDFunctions.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/OrderedRDDFunctions.scala
@@ -57,7 +57,7 @@ class OrderedRDDFunctions[K : Ordering : ClassTag,
    */
   def sortByKey(ascending: Boolean = true, numPartitions: Int = self.partitions.size): RDD[P] = {
     val part = new RangePartitioner(numPartitions, self, ascending)
-    val shuffled = new ShuffledRDD[K, V, P](self, part)
+    val shuffled = new ShuffledRDD[K, V, V, P](self, part).setKeyOrdering(ordering)
     shuffled.mapPartitions(iter => {
       val buf = iter.toArray
       if (ascending) {
diff --git a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
index 443d1c587c3e..2b2b9ae3fd53 100644
--- a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
@@ -90,21 +90,11 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
       self.mapPartitionsWithContext((context, iter) => {
         new InterruptibleIterator(context, aggregator.combineValuesByKey(iter, context))
       }, preservesPartitioning = true)
-    } else if (mapSideCombine) {
-      val combined = self.mapPartitionsWithContext((context, iter) => {
-        aggregator.combineValuesByKey(iter, context)
-      }, preservesPartitioning = true)
-      val partitioned = new ShuffledRDD[K, C, (K, C)](combined, partitioner)
-        .setSerializer(serializer)
-      partitioned.mapPartitionsWithContext((context, iter) => {
-        new InterruptibleIterator(context, aggregator.combineCombinersByKey(iter, context))
-      }, preservesPartitioning = true)
     } else {
-      // Don't apply map-side combiner.
-      val values = new ShuffledRDD[K, V, (K, V)](self, partitioner).setSerializer(serializer)
-      values.mapPartitionsWithContext((context, iter) => {
-        new InterruptibleIterator(context, aggregator.combineValuesByKey(iter, context))
-      }, preservesPartitioning = true)
+      new ShuffledRDD[K, V, C, (K, C)](self, partitioner)
+        .setSerializer(serializer)
+        .setAggregator(aggregator)
+        .setMapSideCombine(mapSideCombine)
     }
   }
 
@@ -401,7 +391,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
     if (self.partitioner == Some(partitioner)) {
       self
     } else {
-      new ShuffledRDD[K, V, (K, V)](self, partitioner)
+      new ShuffledRDD[K, V, V, (K, V)](self, partitioner)
     }
   }
 
diff --git a/core/src/main/scala/org/apache/spark/rdd/RDD.scala b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
index cebfd109d825..4e841bc992bf 100644
--- a/core/src/main/scala/org/apache/spark/rdd/RDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
@@ -340,7 +340,7 @@ abstract class RDD[T: ClassTag](
 
       // include a shuffle step so that our upstream tasks are still distributed
       new CoalescedRDD(
-        new ShuffledRDD[Int, T, (Int, T)](mapPartitionsWithIndex(distributePartition),
+        new ShuffledRDD[Int, T, T, (Int, T)](mapPartitionsWithIndex(distributePartition),
         new HashPartitioner(numPartitions)),
         numPartitions).values
     } else {
diff --git a/core/src/main/scala/org/apache/spark/rdd/ShuffledRDD.scala b/core/src/main/scala/org/apache/spark/rdd/ShuffledRDD.scala
index bb108ef163c5..bf02f68d0d3d 100644
--- a/core/src/main/scala/org/apache/spark/rdd/ShuffledRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/ShuffledRDD.scala
@@ -19,7 +19,7 @@ package org.apache.spark.rdd
 
 import scala.reflect.ClassTag
 
-import org.apache.spark.{Dependency, Partition, Partitioner, ShuffleDependency, SparkEnv, TaskContext}
+import org.apache.spark._
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.serializer.Serializer
 
@@ -35,23 +35,48 @@ private[spark] class ShuffledRDDPartition(val idx: Int) extends Partition {
  * @param part the partitioner used to partition the RDD
  * @tparam K the key class.
  * @tparam V the value class.
+ * @tparam C the combiner class.
  */
 @DeveloperApi
-class ShuffledRDD[K, V, P <: Product2[K, V] : ClassTag](
-    @transient var prev: RDD[P],
+class ShuffledRDD[K, V, C, P <: Product2[K, C] : ClassTag](
+    @transient var prev: RDD[_ <: Product2[K, V]],
     part: Partitioner)
   extends RDD[P](prev.context, Nil) {
 
   private var serializer: Option[Serializer] = None
 
+  private var keyOrdering: Option[Ordering[K]] = None
+
+  private var aggregator: Option[Aggregator[K, V, C]] = None
+
+  private var mapSideCombine: Boolean = false
+
   /** Set a serializer for this RDD's shuffle, or null to use the default (spark.serializer) */
-  def setSerializer(serializer: Serializer): ShuffledRDD[K, V, P] = {
+  def setSerializer(serializer: Serializer): ShuffledRDD[K, V, C, P] = {
     this.serializer = Option(serializer)
     this
   }
 
+  /** Set key ordering for RDD's shuffle. */
+  def setKeyOrdering(keyOrdering: Ordering[K]): ShuffledRDD[K, V, C, P] = {
+    this.keyOrdering = Option(keyOrdering)
+    this
+  }
+
+  /** Set aggregator for RDD's shuffle. */
+  def setAggregator(aggregator: Aggregator[K, V, C]): ShuffledRDD[K, V, C, P] = {
+    this.aggregator = Option(aggregator)
+    this
+  }
+
+  /** Set mapSideCombine flag for RDD's shuffle. */
+  def setMapSideCombine(mapSideCombine: Boolean): ShuffledRDD[K, V, C, P] = {
+    this.mapSideCombine = mapSideCombine
+    this
+  }
+
   override def getDependencies: Seq[Dependency[_]] = {
-    List(new ShuffleDependency(prev, part, serializer))
+    List(new ShuffleDependency(prev, part, serializer, keyOrdering, aggregator, mapSideCombine))
   }
 
   override val partitioner = Some(part)
@@ -61,7 +86,7 @@ class ShuffledRDD[K, V, P <: Product2[K, V] : ClassTag](
   }
 
   override def compute(split: Partition, context: TaskContext): Iterator[P] = {
-    val dep = dependencies.head.asInstanceOf[ShuffleDependency[K, V, V]]
+    val dep = dependencies.head.asInstanceOf[ShuffleDependency[K, V, C]]
     SparkEnv.get.shuffleManager.getReader(dep.shuffleHandle, split.index, split.index + 1, context)
       .read()
       .asInstanceOf[Iterator[P]]
diff --git a/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala b/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala
index 859cdc524a58..fdaf1de83f05 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala
@@ -144,10 +144,8 @@ private[spark] class ShuffleMapTask(
     try {
       val manager = SparkEnv.get.shuffleManager
       writer = manager.getWriter[Any, Any](dep.shuffleHandle, partitionId, context)
-      for (elem <- rdd.iterator(split, context)) {
-        writer.write(elem.asInstanceOf[Product2[Any, Any]])
-      }
-      writer.stop(success = true).get
+      writer.write(rdd.iterator(split, context).asInstanceOf[Iterator[_ <: Product2[Any, Any]]])
+      return writer.stop(success = true).get
     } catch {
       case e: Exception =>
         if (writer != null) {
diff --git a/core/src/main/scala/org/apache/spark/shuffle/ShuffleWriter.scala b/core/src/main/scala/org/apache/spark/shuffle/ShuffleWriter.scala
index ead3ebd652ca..b934480cfb9b 100644
--- a/core/src/main/scala/org/apache/spark/shuffle/ShuffleWriter.scala
+++ b/core/src/main/scala/org/apache/spark/shuffle/ShuffleWriter.scala
@@ -23,8 +23,8 @@ import org.apache.spark.scheduler.MapStatus
  * Obtained inside a map task to write out records to the shuffle system.
  */
 private[spark] trait ShuffleWriter[K, V] {
-  /** Write a record to this task's output */
-  def write(record: Product2[K, V]): Unit
+  /** Write a bunch of records to this task's output */
+  def write(records: Iterator[_ <: Product2[K, V]]): Unit
 
   /** Close this writer, passing along whether the map completed */
   def stop(success: Boolean): Option[MapStatus]
diff --git a/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleReader.scala b/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleReader.scala
index f6a790309a58..d45258c0a492 100644
--- a/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleReader.scala
+++ b/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleReader.scala
@@ -17,9 +17,9 @@
 
 package org.apache.spark.shuffle.hash
 
+import org.apache.spark.{InterruptibleIterator, TaskContext}
 import org.apache.spark.serializer.Serializer
 import org.apache.spark.shuffle.{BaseShuffleHandle, ShuffleReader}
-import org.apache.spark.TaskContext
 
 class HashShuffleReader[K, C](
     handle: BaseShuffleHandle[K, _, C],
@@ -31,10 +31,24 @@ class HashShuffleReader[K, C](
   require(endPartition == startPartition + 1,
     "Hash shuffle currently only supports fetching one partition")
 
+  private val dep = handle.dependency
+
   /** Read the combined key-values for this reduce task */
   override def read(): Iterator[Product2[K, C]] = {
-    BlockStoreShuffleFetcher.fetch(handle.shuffleId, startPartition, context,
-      Serializer.getSerializer(handle.dependency.serializer))
+    val iter = BlockStoreShuffleFetcher.fetch(handle.shuffleId, startPartition, context,
+      Serializer.getSerializer(dep.serializer))
+
+    if (dep.aggregator.isDefined) {
+      if (dep.mapSideCombine) {
+        new InterruptibleIterator(context, dep.aggregator.get.combineCombinersByKey(iter, context))
+      } else {
+        new InterruptibleIterator(context, dep.aggregator.get.combineValuesByKey(iter, context))
+      }
+    } else if (dep.aggregator.isEmpty && dep.mapSideCombine) {
+      throw new IllegalStateException("Aggregator is empty for map-side combine")
+    } else {
+      iter
+    }
   }
 
   /** Close this reader */
diff --git a/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleWriter.scala b/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleWriter.scala
index 4c6749098c11..9b78228519da 100644
--- a/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleWriter.scala
+++ b/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleWriter.scala
@@ -40,11 +40,24 @@ class HashShuffleWriter[K, V](
   private val ser = Serializer.getSerializer(dep.serializer.getOrElse(null))
   private val shuffle = shuffleBlockManager.forMapTask(dep.shuffleId, mapId, numOutputSplits, ser)
 
-  /** Write a record to this task's output */
-  override def write(record: Product2[K, V]): Unit = {
-    val pair = record.asInstanceOf[Product2[Any, Any]]
-    val bucketId = dep.partitioner.getPartition(pair._1)
-    shuffle.writers(bucketId).write(pair)
+  /** Write a bunch of records to this task's output */
+  override def write(records: Iterator[_ <: Product2[K, V]]): Unit = {
+    val iter = if (dep.aggregator.isDefined) {
+      if (dep.mapSideCombine) {
+        dep.aggregator.get.combineValuesByKey(records, context)
+      } else {
+        records
+      }
+    } else if (dep.aggregator.isEmpty && dep.mapSideCombine) {
+      throw new IllegalStateException("Aggregator is empty for map-side combine")
+    } else {
+      records
+    }
+
+    for (elem <- iter) {
+      val bucketId = dep.partitioner.getPartition(elem._1)
+      shuffle.writers(bucketId).write(elem)
+    }
   }
 
   /** Close this writer, passing along whether the map completed */
diff --git a/core/src/test/scala/org/apache/spark/CheckpointSuite.scala b/core/src/test/scala/org/apache/spark/CheckpointSuite.scala
index f64f3c903603..fc00458083a3 100644
--- a/core/src/test/scala/org/apache/spark/CheckpointSuite.scala
+++ b/core/src/test/scala/org/apache/spark/CheckpointSuite.scala
@@ -99,7 +99,7 @@ class CheckpointSuite extends FunSuite with LocalSparkContext with Logging {
   test("ShuffledRDD") {
     testRDD(rdd => {
       // Creating ShuffledRDD directly as PairRDDFunctions.combineByKey produces a MapPartitionedRDD
-      new ShuffledRDD[Int, Int, (Int, Int)](rdd.map(x => (x % 2, 1)), partitioner)
+      new ShuffledRDD[Int, Int, Int, (Int, Int)](rdd.map(x => (x % 2, 1)), partitioner)
     })
   }
 
diff --git a/core/src/test/scala/org/apache/spark/ShuffleSuite.scala b/core/src/test/scala/org/apache/spark/ShuffleSuite.scala
index 47112ce66d69..b40fee7e9ab2 100644
--- a/core/src/test/scala/org/apache/spark/ShuffleSuite.scala
+++ b/core/src/test/scala/org/apache/spark/ShuffleSuite.scala
@@ -56,8 +56,11 @@ class ShuffleSuite extends FunSuite with Matchers with LocalSparkContext {
     }
     // If the Kryo serializer is not used correctly, the shuffle would fail because the
     // default Java serializer cannot handle the non serializable class.
-    val c = new ShuffledRDD[Int, NonJavaSerializableClass, (Int, NonJavaSerializableClass)](
-      b, new HashPartitioner(NUM_BLOCKS)).setSerializer(new KryoSerializer(conf))
+    val c = new ShuffledRDD[Int,
+      NonJavaSerializableClass,
+      NonJavaSerializableClass,
+      (Int, NonJavaSerializableClass)](b, new HashPartitioner(NUM_BLOCKS))
+    c.setSerializer(new KryoSerializer(conf))
     val shuffleId = c.dependencies.head.asInstanceOf[ShuffleDependency[_, _, _]].shuffleId
 
     assert(c.count === 10)
@@ -78,8 +81,11 @@ class ShuffleSuite extends FunSuite with Matchers with LocalSparkContext {
     }
     // If the Kryo serializer is not used correctly, the shuffle would fail because the
     // default Java serializer cannot handle the non serializable class.
-    val c = new ShuffledRDD[Int, NonJavaSerializableClass, (Int, NonJavaSerializableClass)](
-      b, new HashPartitioner(3)).setSerializer(new KryoSerializer(conf))
+    val c = new ShuffledRDD[Int,
+      NonJavaSerializableClass,
+      NonJavaSerializableClass,
+      (Int, NonJavaSerializableClass)](b, new HashPartitioner(3))
+    c.setSerializer(new KryoSerializer(conf))
     assert(c.count === 10)
   }
 
@@ -94,7 +100,7 @@ class ShuffleSuite extends FunSuite with Matchers with LocalSparkContext {
 
     // NOTE: The default Java serializer doesn't create zero-sized blocks.
     //       So, use Kryo
-    val c = new ShuffledRDD[Int, Int, (Int, Int)](b, new HashPartitioner(10))
+    val c = new ShuffledRDD[Int, Int, Int, (Int, Int)](b, new HashPartitioner(10))
       .setSerializer(new KryoSerializer(conf))
 
     val shuffleId = c.dependencies.head.asInstanceOf[ShuffleDependency[_, _, _]].shuffleId
@@ -120,7 +126,7 @@ class ShuffleSuite extends FunSuite with Matchers with LocalSparkContext {
     val b = a.map(x => (x, x*2))
 
     // NOTE: The default Java serializer should create zero-sized blocks
-    val c = new ShuffledRDD[Int, Int, (Int, Int)](b, new HashPartitioner(10))
+    val c = new ShuffledRDD[Int, Int, Int, (Int, Int)](b, new HashPartitioner(10))
 
     val shuffleId = c.dependencies.head.asInstanceOf[ShuffleDependency[_, _, _]].shuffleId
     assert(c.count === 4)
@@ -141,8 +147,8 @@ class ShuffleSuite extends FunSuite with Matchers with LocalSparkContext {
     def p[T1, T2](_1: T1, _2: T2) = MutablePair(_1, _2)
     val data = Array(p(1, 1), p(1, 2), p(1, 3), p(2, 1))
     val pairs: RDD[MutablePair[Int, Int]] = sc.parallelize(data, 2)
-    val results = new ShuffledRDD[Int, Int, MutablePair[Int, Int]](pairs, new HashPartitioner(2))
-      .collect()
+    val results = new ShuffledRDD[Int, Int, Int, MutablePair[Int, Int]](pairs,
+      new HashPartitioner(2)).collect()
 
     data.foreach { pair => results should contain (pair) }
   }
diff --git a/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala b/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala
index 0e5625b7645d..0f9cbe213ea1 100644
--- a/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala
+++ b/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala
@@ -276,7 +276,7 @@ class RDDSuite extends FunSuite with SharedSparkContext {
     // we can optionally shuffle to keep the upstream parallel
     val coalesced5 = data.coalesce(1, shuffle = true)
     val isEquals = coalesced5.dependencies.head.rdd.dependencies.head.rdd.
-      asInstanceOf[ShuffledRDD[_, _, _]] != null
+      asInstanceOf[ShuffledRDD[_, _, _, _]] != null
     assert(isEquals)
 
     // when shuffling, we can increase the number of partitions
@@ -509,7 +509,7 @@ class RDDSuite extends FunSuite with SharedSparkContext {
   test("takeSample") {
     val n = 1000000
     val data = sc.parallelize(1 to n, 2)
-    
+
     for (num <- List(5, 20, 100)) {
       val sample = data.takeSample(withReplacement=false, num=num)
       assert(sample.size === num)        // Got exactly num elements
@@ -704,11 +704,11 @@ class RDDSuite extends FunSuite with SharedSparkContext {
     assert(ancestors3.count(_.isInstanceOf[MappedRDD[_, _]]) === 2)
 
     // Any ancestors before the shuffle are not considered
-    assert(ancestors4.size === 1)
-    assert(ancestors4.count(_.isInstanceOf[ShuffledRDD[_, _, _]]) === 1)
-    assert(ancestors5.size === 4)
-    assert(ancestors5.count(_.isInstanceOf[ShuffledRDD[_, _, _]]) === 1)
-    assert(ancestors5.count(_.isInstanceOf[MapPartitionsRDD[_, _]]) === 1)
+    assert(ancestors4.size === 0)
+    assert(ancestors4.count(_.isInstanceOf[ShuffledRDD[_, _, _, _]]) === 0)
+    assert(ancestors5.size === 3)
+    assert(ancestors5.count(_.isInstanceOf[ShuffledRDD[_, _, _, _]]) === 1)
+    assert(ancestors5.count(_.isInstanceOf[MapPartitionsRDD[_, _]]) === 0)
     assert(ancestors5.count(_.isInstanceOf[MappedValuesRDD[_, _, _]]) === 2)
   }
 
diff --git a/core/src/test/scala/org/apache/spark/scheduler/SparkListenerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/SparkListenerSuite.scala
index abd7b22310f1..6df0a080961b 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/SparkListenerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/SparkListenerSuite.scala
@@ -181,7 +181,7 @@ class SparkListenerSuite extends FunSuite with LocalSparkContext with Matchers
     assert(sc.listenerBus.waitUntilEmpty(WAIT_TIMEOUT_MILLIS))
     listener.stageInfos.size should be {2} // Shuffle map stage + result stage
     val stageInfo3 = listener.stageInfos.keys.find(_.stageId == 2).get
-    stageInfo3.rddInfos.size should be {2} // ShuffledRDD, MapPartitionsRDD
+    stageInfo3.rddInfos.size should be {1} // ShuffledRDD
     stageInfo3.rddInfos.forall(_.numPartitions == 4) should be {true}
     stageInfo3.rddInfos.exists(_.name == "Trois") should be {true}
   }
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/MessageToPartition.scala b/graphx/src/main/scala/org/apache/spark/graphx/impl/MessageToPartition.scala
index 1c6d7e59e9a2..d85afa45b126 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/impl/MessageToPartition.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/impl/MessageToPartition.scala
@@ -62,7 +62,8 @@ class MessageToPartition[@specialized(Int, Long, Double, Char, Boolean/* , AnyRe
 private[graphx]
 class VertexBroadcastMsgRDDFunctions[T: ClassTag](self: RDD[VertexBroadcastMsg[T]]) {
   def partitionBy(partitioner: Partitioner): RDD[VertexBroadcastMsg[T]] = {
-    val rdd = new ShuffledRDD[PartitionID, (VertexId, T), VertexBroadcastMsg[T]](self, partitioner)
+    val rdd = new ShuffledRDD[PartitionID, (VertexId, T), (VertexId, T), VertexBroadcastMsg[T]](
+      self, partitioner)
 
     // Set a custom serializer if the data is of int or double type.
     if (classTag[T] == ClassTag.Int) {
@@ -84,7 +85,7 @@ class MsgRDDFunctions[T: ClassTag](self: RDD[MessageToPartition[T]]) {
    * Return a copy of the RDD partitioned using the specified partitioner.
    */
   def partitionBy(partitioner: Partitioner): RDD[MessageToPartition[T]] = {
-    new ShuffledRDD[PartitionID, T, MessageToPartition[T]](self, partitioner)
+    new ShuffledRDD[PartitionID, T, T, MessageToPartition[T]](self, partitioner)
   }
 
 }
@@ -103,7 +104,7 @@ object MsgRDDFunctions {
 private[graphx]
 class VertexRDDFunctions[VD: ClassTag](self: RDD[(VertexId, VD)]) {
   def copartitionWithVertices(partitioner: Partitioner): RDD[(VertexId, VD)] = {
-    val rdd = new ShuffledRDD[VertexId, VD, (VertexId, VD)](self, partitioner)
+    val rdd = new ShuffledRDD[VertexId, VD, VD, (VertexId, VD)](self, partitioner)
 
     // Set a custom serializer if the data is of int or double type.
     if (classTag[VD] == ClassTag.Int) {
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/RoutingTablePartition.scala b/graphx/src/main/scala/org/apache/spark/graphx/impl/RoutingTablePartition.scala
index d02e9238adba..3827ac8d0fd6 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/impl/RoutingTablePartition.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/impl/RoutingTablePartition.scala
@@ -46,8 +46,8 @@ private[graphx]
 class RoutingTableMessageRDDFunctions(self: RDD[RoutingTableMessage]) {
   /** Copartition an `RDD[RoutingTableMessage]` with the vertex RDD with the given `partitioner`. */
   def copartitionWithVertices(partitioner: Partitioner): RDD[RoutingTableMessage] = {
-    new ShuffledRDD[VertexId, (PartitionID, Byte), RoutingTableMessage](self, partitioner)
-      .setSerializer(new RoutingTableMessageSerializer)
+    new ShuffledRDD[VertexId, (PartitionID, Byte), (PartitionID, Byte), RoutingTableMessage](
+      self, partitioner).setSerializer(new RoutingTableMessageSerializer)
   }
 }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
index f46fa0516566..00010ef6e798 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
@@ -47,7 +47,7 @@ case class Exchange(newPartitioning: Partitioning, child: SparkPlan) extends Una
           iter.map(r => mutablePair.update(hashExpressions(r), r))
         }
         val part = new HashPartitioner(numPartitions)
-        val shuffled = new ShuffledRDD[Row, Row, MutablePair[Row, Row]](rdd, part)
+        val shuffled = new ShuffledRDD[Row, Row, Row, MutablePair[Row, Row]](rdd, part)
         shuffled.setSerializer(new SparkSqlSerializer(new SparkConf(false)))
         shuffled.map(_._2)
 
@@ -60,7 +60,7 @@ case class Exchange(newPartitioning: Partitioning, child: SparkPlan) extends Una
           iter.map(row => mutablePair.update(row, null))
         }
         val part = new RangePartitioner(numPartitions, rdd, ascending = true)
-        val shuffled = new ShuffledRDD[Row, Null, MutablePair[Row, Null]](rdd, part)
+        val shuffled = new ShuffledRDD[Row, Null, Null, MutablePair[Row, Null]](rdd, part)
         shuffled.setSerializer(new SparkSqlSerializer(new SparkConf(false)))
 
         shuffled.map(_._1)
@@ -71,7 +71,7 @@ case class Exchange(newPartitioning: Partitioning, child: SparkPlan) extends Una
           iter.map(r => mutablePair.update(null, r))
         }
         val partitioner = new HashPartitioner(1)
-        val shuffled = new ShuffledRDD[Null, Row, MutablePair[Null, Row]](rdd, partitioner)
+        val shuffled = new ShuffledRDD[Null, Row, Row, MutablePair[Null, Row]](rdd, partitioner)
         shuffled.setSerializer(new SparkSqlSerializer(new SparkConf(false)))
         shuffled.map(_._2)
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
index 18f4a5877bb2..b40d4e3a3b77 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
@@ -105,7 +105,7 @@ case class Limit(limit: Int, child: SparkPlan)(@transient sqlContext: SQLContext
       iter.take(limit).map(row => mutablePair.update(false, row))
     }
     val part = new HashPartitioner(1)
-    val shuffled = new ShuffledRDD[Boolean, Row, MutablePair[Boolean, Row]](rdd, part)
+    val shuffled = new ShuffledRDD[Boolean, Row, Row, MutablePair[Boolean, Row]](rdd, part)
     shuffled.setSerializer(new SparkSqlSerializer(new SparkConf(false)))
     shuffled.mapPartitions(_.take(limit).map(_._2))
   }

From 420c1c3e1beea03453e0eb9dc06f226c80496d68 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Mon, 23 Jun 2014 23:18:47 -0700
Subject: [PATCH 508/810] [SPARK-2252] Fix MathJax for HTTPs.

Found out about this from the Hacker News link to GraphX which was using HTTPs.

@mengxr

Author: Reynold Xin <rxin@apache.org>

Closes #1189 from rxin/mllib-doc and squashes the following commits:

5328be0 [Reynold Xin] [SPARK-2252] Fix MathJax for HTTPs.
---
 docs/_layouts/global.html | 36 +++++++++++++++++++++++-------------
 1 file changed, 23 insertions(+), 13 deletions(-)

diff --git a/docs/_layouts/global.html b/docs/_layouts/global.html
index 4ba20e590f2c..b30ab1e5218c 100755
--- a/docs/_layouts/global.html
+++ b/docs/_layouts/global.html
@@ -136,21 +136,31 @@ <h1 class="title">{{ page.title }}</h1>
 
         <!-- MathJax Section -->
         <script type="text/x-mathjax-config">
-              MathJax.Hub.Config({
+            MathJax.Hub.Config({
                 TeX: { equationNumbers: { autoNumber: "AMS" } }
-              });
-            </script>
-        <script type="text/javascript"
-         src="http://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script>
+            });
+        </script>
         <script>
-          MathJax.Hub.Config({
-            tex2jax: {
-              inlineMath: [ ["$", "$"], ["\\\\(","\\\\)"] ],
-              displayMath: [ ["$$","$$"], ["\\[", "\\]"] ], 
-              processEscapes: true,
-              skipTags: ['script', 'noscript', 'style', 'textarea', 'pre']
-            }
-          });
+            // Note that we load MathJax this way to work with local file (file://), HTTP and HTTPS.
+            // We could use "//cdn.mathjax...", but that won't support "file://".
+            (function(d, script) {
+                script = d.createElement('script');
+                script.type = 'text/javascript';
+                script.async = true;
+                script.onload = function(){
+                    MathJax.Hub.Config({
+                        tex2jax: {
+                            inlineMath: [ ["$", "$"], ["\\\\(","\\\\)"] ],
+                            displayMath: [ ["$$","$$"], ["\\[", "\\]"] ], 
+                            processEscapes: true,
+                            skipTags: ['script', 'noscript', 'style', 'textarea', 'pre']
+                        }
+                    });
+                };
+                script.src = ('https:' == document.location.protocol ? 'https://' : 'http://') +
+                    'cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML';
+                d.getElementsByTagName('head')[0].appendChild(script);
+            }(document));
         </script>
     </body>
 </html>

From 924b7082b17b379e4b932a3afee88f94a632d14d Mon Sep 17 00:00:00 2001
From: Rui Li <rui.li@intel.com>
Date: Tue, 24 Jun 2014 11:40:37 -0700
Subject: [PATCH 509/810] SPARK-1937: fix issue with task locality

Don't check executor/host availability when creating a TaskSetManager. Because the executors may haven't been registered when the TaskSetManager is created, in which case all tasks will be considered "has no preferred locations", and thus losing data locality in later scheduling.

Author: Rui Li <rui.li@intel.com>
Author: lirui-intel <rui.li@intel.com>

Closes #892 from lirui-intel/delaySchedule and squashes the following commits:

8444d7c [Rui Li] fix code style
fafd57f [Rui Li] keep locality constraints within the valid levels
18f9e05 [Rui Li] restrict allowed locality
5b3fb2f [Rui Li] refine UT
99f843e [Rui Li] add unit test and fix bug
fff4123 [Rui Li] fix computing valid locality levels
685ed3d [Rui Li] remove delay shedule for pendingTasksWithNoPrefs
7b0177a [Rui Li] remove redundant code
c7b93b5 [Rui Li] revise patch
3d7da02 [lirui-intel] Update TaskSchedulerImpl.scala
cab4c71 [Rui Li] revised patch
539a578 [Rui Li] fix code style
cf0d6ac [Rui Li] fix code style
3dfae86 [Rui Li] re-compute pending tasks when new host is added
a225ac2 [Rui Li] SPARK-1937: fix issue with task locality
---
 .../spark/scheduler/TaskSchedulerImpl.scala   |  8 +++-
 .../spark/scheduler/TaskSetManager.scala      | 43 +++++++++++++------
 .../spark/scheduler/TaskSetManagerSuite.scala | 34 +++++++++++++++
 3 files changed, 71 insertions(+), 14 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala b/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala
index 17292b4c15b8..5ed2803d76af 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala
@@ -210,11 +210,14 @@ private[spark] class TaskSchedulerImpl(
     SparkEnv.set(sc.env)
 
     // Mark each slave as alive and remember its hostname
+    // Also track if new executor is added
+    var newExecAvail = false
     for (o <- offers) {
       executorIdToHost(o.executorId) = o.host
       if (!executorsByHost.contains(o.host)) {
         executorsByHost(o.host) = new HashSet[String]()
         executorAdded(o.executorId, o.host)
+        newExecAvail = true
       }
     }
 
@@ -227,12 +230,15 @@ private[spark] class TaskSchedulerImpl(
     for (taskSet <- sortedTaskSets) {
       logDebug("parentName: %s, name: %s, runningTasks: %s".format(
         taskSet.parent.name, taskSet.name, taskSet.runningTasks))
+      if (newExecAvail) {
+        taskSet.executorAdded()
+      }
     }
 
     // Take each TaskSet in our scheduling order, and then offer it each node in increasing order
     // of locality levels so that it gets a chance to launch local tasks on all of them.
     var launchedTask = false
-    for (taskSet <- sortedTaskSets; maxLocality <- TaskLocality.values) {
+    for (taskSet <- sortedTaskSets; maxLocality <- taskSet.myLocalityLevels) {
       do {
         launchedTask = false
         for (i <- 0 until shuffledOffers.size) {
diff --git a/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala b/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala
index f3bd0797aa03..b5bcdd7e99c5 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala
@@ -118,7 +118,7 @@ private[spark] class TaskSetManager(
   private val pendingTasksForRack = new HashMap[String, ArrayBuffer[Int]]
 
   // Set containing pending tasks with no locality preferences.
-  val pendingTasksWithNoPrefs = new ArrayBuffer[Int]
+  var pendingTasksWithNoPrefs = new ArrayBuffer[Int]
 
   // Set containing all pending tasks (also used as a stack, as above).
   val allPendingTasks = new ArrayBuffer[Int]
@@ -153,8 +153,8 @@ private[spark] class TaskSetManager(
   }
 
   // Figure out which locality levels we have in our TaskSet, so we can do delay scheduling
-  val myLocalityLevels = computeValidLocalityLevels()
-  val localityWaits = myLocalityLevels.map(getLocalityWait) // Time to wait at each level
+  var myLocalityLevels = computeValidLocalityLevels()
+  var localityWaits = myLocalityLevels.map(getLocalityWait) // Time to wait at each level
 
   // Delay scheduling variables: we keep track of our current locality level and the time we
   // last launched a task at that level, and move up a level when localityWaits[curLevel] expires.
@@ -181,16 +181,14 @@ private[spark] class TaskSetManager(
     var hadAliveLocations = false
     for (loc <- tasks(index).preferredLocations) {
       for (execId <- loc.executorId) {
-        if (sched.isExecutorAlive(execId)) {
-          addTo(pendingTasksForExecutor.getOrElseUpdate(execId, new ArrayBuffer))
-          hadAliveLocations = true
-        }
+        addTo(pendingTasksForExecutor.getOrElseUpdate(execId, new ArrayBuffer))
       }
       if (sched.hasExecutorsAliveOnHost(loc.host)) {
-        addTo(pendingTasksForHost.getOrElseUpdate(loc.host, new ArrayBuffer))
-        for (rack <- sched.getRackForHost(loc.host)) {
-          addTo(pendingTasksForRack.getOrElseUpdate(rack, new ArrayBuffer))
-        }
+        hadAliveLocations = true
+      }
+      addTo(pendingTasksForHost.getOrElseUpdate(loc.host, new ArrayBuffer))
+      for (rack <- sched.getRackForHost(loc.host)) {
+        addTo(pendingTasksForRack.getOrElseUpdate(rack, new ArrayBuffer))
         hadAliveLocations = true
       }
     }
@@ -725,10 +723,12 @@ private[spark] class TaskSetManager(
   private def computeValidLocalityLevels(): Array[TaskLocality.TaskLocality] = {
     import TaskLocality.{PROCESS_LOCAL, NODE_LOCAL, RACK_LOCAL, ANY}
     val levels = new ArrayBuffer[TaskLocality.TaskLocality]
-    if (!pendingTasksForExecutor.isEmpty && getLocalityWait(PROCESS_LOCAL) != 0) {
+    if (!pendingTasksForExecutor.isEmpty && getLocalityWait(PROCESS_LOCAL) != 0 &&
+        pendingTasksForExecutor.keySet.exists(sched.isExecutorAlive(_))) {
       levels += PROCESS_LOCAL
     }
-    if (!pendingTasksForHost.isEmpty && getLocalityWait(NODE_LOCAL) != 0) {
+    if (!pendingTasksForHost.isEmpty && getLocalityWait(NODE_LOCAL) != 0 &&
+        pendingTasksForHost.keySet.exists(sched.hasExecutorsAliveOnHost(_))) {
       levels += NODE_LOCAL
     }
     if (!pendingTasksForRack.isEmpty && getLocalityWait(RACK_LOCAL) != 0) {
@@ -738,4 +738,21 @@ private[spark] class TaskSetManager(
     logDebug("Valid locality levels for " + taskSet + ": " + levels.mkString(", "))
     levels.toArray
   }
+
+  // Re-compute pendingTasksWithNoPrefs since new preferred locations may become available
+  def executorAdded() {
+    def newLocAvail(index: Int): Boolean = {
+      for (loc <- tasks(index).preferredLocations) {
+        if (sched.hasExecutorsAliveOnHost(loc.host) ||
+            sched.getRackForHost(loc.host).isDefined) {
+          return true
+        }
+      }
+      false
+    }
+    logInfo("Re-computing pending task lists.")
+    pendingTasksWithNoPrefs = pendingTasksWithNoPrefs.filter(!newLocAvail(_))
+    myLocalityLevels = computeValidLocalityLevels()
+    localityWaits = myLocalityLevels.map(getLocalityWait)
+  }
 }
diff --git a/core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala
index 6f1fd2576454..59a618956a35 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala
@@ -77,6 +77,10 @@ class FakeTaskScheduler(sc: SparkContext, liveExecutors: (String, String)* /* ex
   override def isExecutorAlive(execId: String): Boolean = executors.contains(execId)
 
   override def hasExecutorsAliveOnHost(host: String): Boolean = executors.values.exists(_ == host)
+
+  def addExecutor(execId: String, host: String) {
+    executors.put(execId, host)
+  }
 }
 
 class TaskSetManagerSuite extends FunSuite with LocalSparkContext with Logging {
@@ -400,6 +404,36 @@ class TaskSetManagerSuite extends FunSuite with LocalSparkContext with Logging {
     assert(sched.taskSetsFailed.contains(taskSet.id))
   }
 
+  test("new executors get added") {
+    sc = new SparkContext("local", "test")
+    val sched = new FakeTaskScheduler(sc)
+    val taskSet = FakeTask.createTaskSet(4,
+      Seq(TaskLocation("host1", "execA")),
+      Seq(TaskLocation("host1", "execB")),
+      Seq(TaskLocation("host2", "execC")),
+      Seq())
+    val clock = new FakeClock
+    val manager = new TaskSetManager(sched, taskSet, MAX_TASK_FAILURES, clock)
+    // All tasks added to no-pref list since no preferred location is available
+    assert(manager.pendingTasksWithNoPrefs.size === 4)
+    // Only ANY is valid
+    assert(manager.myLocalityLevels.sameElements(Array(ANY)))
+    // Add a new executor
+    sched.addExecutor("execD", "host1")
+    manager.executorAdded()
+    // Task 0 and 1 should be removed from no-pref list
+    assert(manager.pendingTasksWithNoPrefs.size === 2)
+    // Valid locality should contain NODE_LOCAL and ANY
+    assert(manager.myLocalityLevels.sameElements(Array(NODE_LOCAL, ANY)))
+    // Add another executor
+    sched.addExecutor("execC", "host2")
+    manager.executorAdded()
+    // No-pref list now only contains task 3
+    assert(manager.pendingTasksWithNoPrefs.size === 1)
+    // Valid locality should contain PROCESS_LOCAL, NODE_LOCAL and ANY
+    assert(manager.myLocalityLevels.sameElements(Array(PROCESS_LOCAL, NODE_LOCAL, ANY)))
+  }
+
   def createTaskResult(id: Int): DirectTaskResult[Int] = {
     val valueSer = SparkEnv.get.serializer.newInstance()
     new DirectTaskResult[Int](valueSer.serialize(id), mutable.Map.empty, new TaskMetrics)

From 221909e678bb60e5a3746e64a718600841c5f74f Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Tue, 24 Jun 2014 15:09:30 -0700
Subject: [PATCH 510/810] HOTFIX: Disabling tests per SPARK-2264

---
 .../org/apache/spark/sql/CachedTableSuite.scala      | 12 +++++++-----
 1 file changed, 7 insertions(+), 5 deletions(-)

diff --git a/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala
index c794da4da406..128ddae11eb0 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala
@@ -24,7 +24,9 @@ import org.apache.spark.sql.test.TestSQLContext
 class CachedTableSuite extends QueryTest {
   TestData // Load test tables.
 
-  test("read from cached table and uncache") {
+  // NOTE: ALL TESTS ARE IGNORED PENDING SPARK-2264
+
+  ignore("read from cached table and uncache") {
     TestSQLContext.cacheTable("testData")
 
     checkAnswer(
@@ -51,20 +53,20 @@ class CachedTableSuite extends QueryTest {
     }
   }
 
-  test("correct error on uncache of non-cached table") {
+  ignore("correct error on uncache of non-cached table") {
     intercept[IllegalArgumentException] {
       TestSQLContext.uncacheTable("testData")
     }
   }
 
-  test("SELECT Star Cached Table") {
+  ignore("SELECT Star Cached Table") {
     TestSQLContext.sql("SELECT * FROM testData").registerAsTable("selectStar")
     TestSQLContext.cacheTable("selectStar")
     TestSQLContext.sql("SELECT * FROM selectStar WHERE key = 1").collect()
     TestSQLContext.uncacheTable("selectStar")
   }
 
-  test("Self-join cached") {
+  ignore("Self-join cached") {
     val unCachedAnswer =
       TestSQLContext.sql("SELECT * FROM testData a JOIN testData b ON a.key = b.key").collect()
     TestSQLContext.cacheTable("testData")
@@ -74,7 +76,7 @@ class CachedTableSuite extends QueryTest {
     TestSQLContext.uncacheTable("testData")
   }
 
-  test("'CACHE TABLE' and 'UNCACHE TABLE' SQL statement") {
+  ignore("'CACHE TABLE' and 'UNCACHE TABLE' SQL statement") {
     TestSQLContext.sql("CACHE TABLE testData")
     TestSQLContext.table("testData").queryExecution.executedPlan match {
       case _: InMemoryColumnarTableScan => // Found evidence of caching

From 1978a9033ea0d47ac0e0a51d97c8515689f84d04 Mon Sep 17 00:00:00 2001
From: Kay Ousterhout <kayousterhout@gmail.com>
Date: Tue, 24 Jun 2014 16:54:50 -0700
Subject: [PATCH 511/810] Fix broken Json tests.

The assertJsonStringEquals method was missing an "assert" so
did not actually check that the strings were equal. This commit
adds the missing assert and fixes subsequently revealed problems
with the JsonProtocolSuite.

@andrewor14 I changed some of the test functionality to match what it
looks like you intended based on the expected strings -- let me know if
anything here looks wrong.

Author: Kay Ousterhout <kayousterhout@gmail.com>

Closes #1198 from kayousterhout/json_test_fix and squashes the following commits:

77f858f [Kay Ousterhout] Fix broken Json tests.
---
 .../apache/spark/util/JsonProtocolSuite.scala | 23 +++++++++----------
 1 file changed, 11 insertions(+), 12 deletions(-)

diff --git a/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala b/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala
index f72389b6b323..495e1b7a0a21 100644
--- a/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala
+++ b/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala
@@ -366,7 +366,7 @@ class JsonProtocolSuite extends FunSuite {
 
   private def assertJsonStringEquals(json1: String, json2: String) {
     val formatJsonString = (json: String) => json.replaceAll("[\\s|]", "")
-    formatJsonString(json1) === formatJsonString(json2)
+    assert(formatJsonString(json1) === formatJsonString(json2))
   }
 
   private def assertSeqEquals[T](seq1: Seq[T], seq2: Seq[T], assertEquals: (T, T) => Unit) {
@@ -449,7 +449,7 @@ class JsonProtocolSuite extends FunSuite {
   }
 
   private def makeStageInfo(a: Int, b: Int, c: Int, d: Long, e: Long) = {
-    val rddInfos = (1 to a % 5).map { i => makeRddInfo(a % i, b % i, c % i, d % i, e % i) }
+    val rddInfos = (0 until a % 5).map { i => makeRddInfo(a + i, b + i, c + i, d + i, e + i) }
     new StageInfo(a, "greetings", b, rddInfos, "details")
   }
 
@@ -493,20 +493,19 @@ class JsonProtocolSuite extends FunSuite {
   private val stageSubmittedJsonString =
     """
       {"Event":"SparkListenerStageSubmitted","Stage Info":{"Stage ID":100,"Stage Name":
-      "greetings","Number of Tasks":200,"RDD Info":{"RDD ID":100,"Name":"mayor","Storage
-      Level":{"Use Disk":true,"Use Memory":true,"Use Tachyon":false,"Deserialized":true,
-      "Replication":1},"Number of Partitions":200,"Number of Cached Partitions":300,
-      "Memory Size":400,"Disk Size":500,"Tachyon Size":0},"Emitted Task Size Warning":false},
-      "Properties":{"France":"Paris","Germany":"Berlin","Russia":"Moscow","Ukraine":"Kiev"}}
+      "greetings","Number of Tasks":200,"RDD Info":[],"Details":"details",
+      "Emitted Task Size Warning":false},"Properties":{"France":"Paris","Germany":"Berlin",
+      "Russia":"Moscow","Ukraine":"Kiev"}}
     """
 
   private val stageCompletedJsonString =
     """
       {"Event":"SparkListenerStageCompleted","Stage Info":{"Stage ID":101,"Stage Name":
-      "greetings","Number of Tasks":201,"RDD Info":{"RDD ID":101,"Name":"mayor","Storage
+      "greetings","Number of Tasks":201,"RDD Info":[{"RDD ID":101,"Name":"mayor","Storage
       Level":{"Use Disk":true,"Use Memory":true,"Use Tachyon":false,"Deserialized":true,
       "Replication":1},"Number of Partitions":201,"Number of Cached Partitions":301,
-      "Memory Size":401,"Disk Size":501,"Tachyon Size":0},"Emitted Task Size Warning":false}}
+      "Memory Size":401,"Tachyon Size":0,"Disk Size":501}],"Details":"details",
+      "Emitted Task Size Warning":false}}
     """
 
   private val taskStartJsonString =
@@ -538,9 +537,9 @@ class JsonProtocolSuite extends FunSuite {
       900,"Total Blocks Fetched":1500,"Remote Blocks Fetched":800,"Local Blocks Fetched":
       700,"Fetch Wait Time":900,"Remote Bytes Read":1000},"Shuffle Write Metrics":
       {"Shuffle Bytes Written":1200,"Shuffle Write Time":1500},"Updated Blocks":
-      [{"Block ID":{"Type":"RDDBlockId","RDD ID":0,"Split Index":0},"Status":
-      {"Storage Level":{"Use Disk":true,"Use Memory":true,"Use Tachyon":false,
-      "Deserialized":false,"Replication":2},"Memory Size":0,"Disk Size":0,"Tachyon Size":0}}]}}
+      [{"Block ID":"rdd_0_0","Status":{"Storage Level":{"Use Disk":true,"Use Memory":true,
+      "Use Tachyon":false,"Deserialized":false,"Replication":2},"Memory Size":0,"Tachyon Size":0,
+      "Disk Size":0}}]}}
     """
 
   private val jobStartJsonString =

From a162c9b337d99dd2a6102a80deb2a9707cdd93e9 Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Tue, 24 Jun 2014 19:04:29 -0700
Subject: [PATCH 512/810] [SPARK-2264][SQL] Fix failing CachedTableSuite

Author: Michael Armbrust <michael@databricks.com>

Closes #1201 from marmbrus/fixCacheTests and squashes the following commits:

9d87ed1 [Michael Armbrust] Use analyzer (which runs to fixed point) instead of manually removing analysis operators.
---
 .../org/apache/spark/sql/SQLContext.scala     | 10 +++----
 .../apache/spark/sql/CachedTableSuite.scala   | 30 +++++++++++++++----
 .../org/apache/spark/sql/SQLQuerySuite.scala  | 19 ------------
 3 files changed, 29 insertions(+), 30 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
index 0bcfbf6f849e..7195f9709d5e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
@@ -186,8 +186,8 @@ class SQLContext(@transient val sparkContext: SparkContext)
 
   /** Caches the specified table in-memory. */
   def cacheTable(tableName: String): Unit = {
-    val currentTable = catalog.lookupRelation(None, tableName)
-    val asInMemoryRelation = EliminateAnalysisOperators(currentTable.logicalPlan) match {
+    val currentTable = table(tableName).queryExecution.analyzed
+    val asInMemoryRelation = currentTable match {
       case _: InMemoryRelation =>
         currentTable.logicalPlan
 
@@ -202,7 +202,7 @@ class SQLContext(@transient val sparkContext: SparkContext)
 
   /** Removes the specified table from the in-memory cache. */
   def uncacheTable(tableName: String): Unit = {
-    EliminateAnalysisOperators(catalog.lookupRelation(None, tableName)) match {
+    table(tableName).queryExecution.analyzed match {
       // This is kind of a hack to make sure that if this was just an RDD registered as a table,
       // we reregister the RDD as a table.
       case inMem @ InMemoryRelation(_, _, e: ExistingRdd) =>
@@ -218,8 +218,8 @@ class SQLContext(@transient val sparkContext: SparkContext)
 
   /** Returns true if the table is currently cached in-memory. */
   def isCached(tableName: String): Boolean = {
-    val relation = catalog.lookupRelation(None, tableName)
-    EliminateAnalysisOperators(relation) match {
+    val relation = table(tableName).queryExecution.analyzed
+    relation match {
       case _: InMemoryRelation => true
       case _ => false
     }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala
index 128ddae11eb0..c3c0dcb1aa00 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala
@@ -20,13 +20,31 @@ package org.apache.spark.sql
 import org.apache.spark.sql.TestData._
 import org.apache.spark.sql.columnar.{InMemoryRelation, InMemoryColumnarTableScan}
 import org.apache.spark.sql.test.TestSQLContext
+import org.apache.spark.sql.test.TestSQLContext._
 
 class CachedTableSuite extends QueryTest {
   TestData // Load test tables.
 
-  // NOTE: ALL TESTS ARE IGNORED PENDING SPARK-2264
+  test("SPARK-1669: cacheTable should be idempotent") {
+    assume(!table("testData").logicalPlan.isInstanceOf[InMemoryRelation])
 
-  ignore("read from cached table and uncache") {
+    cacheTable("testData")
+    table("testData").queryExecution.analyzed match {
+      case _: InMemoryRelation =>
+      case _ =>
+        fail("testData should be cached")
+    }
+
+    cacheTable("testData")
+    table("testData").queryExecution.analyzed match {
+      case InMemoryRelation(_, _, _: InMemoryColumnarTableScan) =>
+        fail("cacheTable is not idempotent")
+
+      case _ =>
+    }
+  }
+
+  test("read from cached table and uncache") {
     TestSQLContext.cacheTable("testData")
 
     checkAnswer(
@@ -53,20 +71,20 @@ class CachedTableSuite extends QueryTest {
     }
   }
 
-  ignore("correct error on uncache of non-cached table") {
+  test("correct error on uncache of non-cached table") {
     intercept[IllegalArgumentException] {
       TestSQLContext.uncacheTable("testData")
     }
   }
 
-  ignore("SELECT Star Cached Table") {
+  test("SELECT Star Cached Table") {
     TestSQLContext.sql("SELECT * FROM testData").registerAsTable("selectStar")
     TestSQLContext.cacheTable("selectStar")
     TestSQLContext.sql("SELECT * FROM selectStar WHERE key = 1").collect()
     TestSQLContext.uncacheTable("selectStar")
   }
 
-  ignore("Self-join cached") {
+  test("Self-join cached") {
     val unCachedAnswer =
       TestSQLContext.sql("SELECT * FROM testData a JOIN testData b ON a.key = b.key").collect()
     TestSQLContext.cacheTable("testData")
@@ -76,7 +94,7 @@ class CachedTableSuite extends QueryTest {
     TestSQLContext.uncacheTable("testData")
   }
 
-  ignore("'CACHE TABLE' and 'UNCACHE TABLE' SQL statement") {
+  test("'CACHE TABLE' and 'UNCACHE TABLE' SQL statement") {
     TestSQLContext.sql("CACHE TABLE testData")
     TestSQLContext.table("testData").queryExecution.executedPlan match {
       case _: InMemoryColumnarTableScan => // Found evidence of caching
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index cca58c0063b3..bf7fafe95230 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -406,23 +406,4 @@ class SQLQuerySuite extends QueryTest {
     )
     clear()
   }
-
-  test("SPARK-1669: cacheTable should be idempotent") {
-    assume(!table("testData").logicalPlan.isInstanceOf[InMemoryRelation])
-
-    cacheTable("testData")
-    EliminateAnalysisOperators(table("testData").logicalPlan) match {
-      case _: InMemoryRelation =>
-      case _ =>
-        fail("testData should be cached")
-    }
-
-    cacheTable("testData")
-    EliminateAnalysisOperators(table("testData").logicalPlan) match {
-      case InMemoryRelation(_, _, _: InMemoryColumnarTableScan) =>
-        fail("cacheTable is not idempotent")
-
-      case _ =>
-    }
-  }
 }

From 8ca41769fb16115a5a14ac842199d16cb28641ba Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Tue, 24 Jun 2014 19:06:07 -0700
Subject: [PATCH 513/810] [SPARK-1112, 2156] Bootstrap to fetch the driver's
 Spark properties.

This is an alternative solution to #1124 . Before launching the executor backend, we first fetch driver's spark properties and use it to overwrite executor's spark properties. This should be better than #1124.

@pwendell Are there spark properties that might be different on the driver and on the executors?

Author: Xiangrui Meng <meng@databricks.com>

Closes #1132 from mengxr/akka-bootstrap and squashes the following commits:

77ff32d [Xiangrui Meng] organize imports
68e1dfb [Xiangrui Meng] use timeout from AkkaUtils; remove props from RegisteredExecutor
46d332d [Xiangrui Meng] fix a test
7947c18 [Xiangrui Meng] increase slack size for akka
4ab696a [Xiangrui Meng] bootstrap to retrieve driver spark conf
---
 .../CoarseGrainedExecutorBackend.scala        | 66 +++++++++++--------
 .../org/apache/spark/executor/Executor.scala  |  2 +-
 .../cluster/CoarseGrainedClusterMessage.scala |  6 +-
 .../CoarseGrainedSchedulerBackend.scala       | 15 +++--
 .../org/apache/spark/util/AkkaUtils.scala     |  3 +
 .../CoarseGrainedSchedulerBackendSuite.scala  |  2 +-
 6 files changed, 54 insertions(+), 40 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala b/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala
index 2279d77c91c8..b5fd334f4020 100644
--- a/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala
+++ b/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala
@@ -19,25 +19,26 @@ package org.apache.spark.executor
 
 import java.nio.ByteBuffer
 
-import akka.actor._
-import akka.remote._
+import scala.concurrent.Await
 
-import org.apache.spark.{SparkEnv, Logging, SecurityManager, SparkConf}
+import akka.actor.{Actor, ActorSelection, Props}
+import akka.pattern.Patterns
+import akka.remote.{RemotingLifecycleEvent, DisassociatedEvent}
+
+import org.apache.spark.{Logging, SecurityManager, SparkConf, SparkEnv}
 import org.apache.spark.TaskState.TaskState
 import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.deploy.worker.WorkerWatcher
-import org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages._
 import org.apache.spark.scheduler.TaskDescription
+import org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages._
 import org.apache.spark.util.{AkkaUtils, Utils}
 
 private[spark] class CoarseGrainedExecutorBackend(
     driverUrl: String,
     executorId: String,
     hostPort: String,
-    cores: Int)
-  extends Actor
-  with ExecutorBackend
-  with Logging {
+    cores: Int,
+    sparkProperties: Seq[(String, String)]) extends Actor with ExecutorBackend with Logging {
 
   Utils.checkHostPort(hostPort, "Expected hostport")
 
@@ -52,7 +53,7 @@ private[spark] class CoarseGrainedExecutorBackend(
   }
 
   override def receive = {
-    case RegisteredExecutor(sparkProperties) =>
+    case RegisteredExecutor =>
       logInfo("Successfully registered with driver")
       // Make this host instead of hostPort ?
       executor = new Executor(executorId, Utils.parseHostPort(hostPort)._1, sparkProperties,
@@ -101,26 +102,33 @@ private[spark] object CoarseGrainedExecutorBackend {
     workerUrl: Option[String]) {
 
     SparkHadoopUtil.get.runAsSparkUser { () =>
-        // Debug code
-        Utils.checkHost(hostname)
-
-        val conf = new SparkConf
-        // Create a new ActorSystem to run the backend, because we can't create a
-        // SparkEnv / Executor before getting started with all our system properties, etc
-        val (actorSystem, boundPort) = AkkaUtils.createActorSystem("sparkExecutor", hostname, 0,
-          conf, new SecurityManager(conf))
-        // set it
-        val sparkHostPort = hostname + ":" + boundPort
-        actorSystem.actorOf(
-          Props(classOf[CoarseGrainedExecutorBackend], driverUrl, executorId,
-            sparkHostPort, cores),
-          name = "Executor")
-        workerUrl.foreach {
-          url =>
-            actorSystem.actorOf(Props(classOf[WorkerWatcher], url), name = "WorkerWatcher")
-        }
-        actorSystem.awaitTermination()
-
+      // Debug code
+      Utils.checkHost(hostname)
+
+      // Bootstrap to fetch the driver's Spark properties.
+      val executorConf = new SparkConf
+      val (fetcher, _) = AkkaUtils.createActorSystem(
+        "driverPropsFetcher", hostname, 0, executorConf, new SecurityManager(executorConf))
+      val driver = fetcher.actorSelection(driverUrl)
+      val timeout = AkkaUtils.askTimeout(executorConf)
+      val fut = Patterns.ask(driver, RetrieveSparkProps, timeout)
+      val props = Await.result(fut, timeout).asInstanceOf[Seq[(String, String)]]
+      fetcher.shutdown()
+
+      // Create a new ActorSystem using driver's Spark properties to run the backend.
+      val driverConf = new SparkConf().setAll(props)
+      val (actorSystem, boundPort) = AkkaUtils.createActorSystem(
+        "sparkExecutor", hostname, 0, driverConf, new SecurityManager(driverConf))
+      // set it
+      val sparkHostPort = hostname + ":" + boundPort
+      actorSystem.actorOf(
+        Props(classOf[CoarseGrainedExecutorBackend],
+          driverUrl, executorId, sparkHostPort, cores, props),
+        name = "Executor")
+      workerUrl.foreach { url =>
+        actorSystem.actorOf(Props(classOf[WorkerWatcher], url), name = "WorkerWatcher")
+      }
+      actorSystem.awaitTermination()
     }
   }
 
diff --git a/core/src/main/scala/org/apache/spark/executor/Executor.scala b/core/src/main/scala/org/apache/spark/executor/Executor.scala
index baee7a216a7c..557b9a3f46a0 100644
--- a/core/src/main/scala/org/apache/spark/executor/Executor.scala
+++ b/core/src/main/scala/org/apache/spark/executor/Executor.scala
@@ -212,7 +212,7 @@ private[spark] class Executor(
         val serializedDirectResult = ser.serialize(directResult)
         logInfo("Serialized size of result for " + taskId + " is " + serializedDirectResult.limit)
         val serializedResult = {
-          if (serializedDirectResult.limit >= akkaFrameSize - 1024) {
+          if (serializedDirectResult.limit >= akkaFrameSize - AkkaUtils.reservedSizeBytes) {
             logInfo("Storing result for " + taskId + " in local BlockManager")
             val blockId = TaskResultBlockId(taskId)
             env.blockManager.putBytes(
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedClusterMessage.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedClusterMessage.scala
index ca74069ef885..318e16552201 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedClusterMessage.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedClusterMessage.scala
@@ -20,21 +20,21 @@ package org.apache.spark.scheduler.cluster
 import java.nio.ByteBuffer
 
 import org.apache.spark.TaskState.TaskState
-import org.apache.spark.scheduler.TaskDescription
 import org.apache.spark.util.{SerializableBuffer, Utils}
 
 private[spark] sealed trait CoarseGrainedClusterMessage extends Serializable
 
 private[spark] object CoarseGrainedClusterMessages {
 
+  case object RetrieveSparkProps extends CoarseGrainedClusterMessage
+
   // Driver to executors
   case class LaunchTask(data: SerializableBuffer) extends CoarseGrainedClusterMessage
 
   case class KillTask(taskId: Long, executor: String, interruptThread: Boolean)
     extends CoarseGrainedClusterMessage
 
-  case class RegisteredExecutor(sparkProperties: Seq[(String, String)])
-    extends CoarseGrainedClusterMessage
+  case object RegisteredExecutor extends CoarseGrainedClusterMessage
 
   case class RegisterExecutorFailed(message: String) extends CoarseGrainedClusterMessage
 
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala
index e47a060683a2..05d01b0c821f 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala
@@ -75,7 +75,7 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, actorSystem: A
           sender ! RegisterExecutorFailed("Duplicate executor ID: " + executorId)
         } else {
           logInfo("Registered executor: " + sender + " with ID " + executorId)
-          sender ! RegisteredExecutor(sparkProperties)
+          sender ! RegisteredExecutor
           executorActor(executorId) = sender
           executorHost(executorId) = Utils.parseHostPort(hostPort)._1
           totalCores(executorId) = cores
@@ -124,6 +124,8 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, actorSystem: A
         addressToExecutorId.get(address).foreach(removeExecutor(_,
           "remote Akka client disassociated"))
 
+      case RetrieveSparkProps =>
+        sender ! sparkProperties
     }
 
     // Make fake resource offers on all executors
@@ -143,14 +145,15 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, actorSystem: A
       for (task <- tasks.flatten) {
         val ser = SparkEnv.get.closureSerializer.newInstance()
         val serializedTask = ser.serialize(task)
-        if (serializedTask.limit >= akkaFrameSize - 1024) {
+        if (serializedTask.limit >= akkaFrameSize - AkkaUtils.reservedSizeBytes) {
           val taskSetId = scheduler.taskIdToTaskSetId(task.taskId)
           scheduler.activeTaskSets.get(taskSetId).foreach { taskSet =>
             try {
-              var msg = "Serialized task %s:%d was %d bytes which " +
-                "exceeds spark.akka.frameSize (%d bytes). " +
-                "Consider using broadcast variables for large values."
-              msg = msg.format(task.taskId, task.index, serializedTask.limit, akkaFrameSize)
+              var msg = "Serialized task %s:%d was %d bytes, which exceeds max allowed: " +
+                "spark.akka.frameSize (%d bytes) - reserved (%d bytes). Consider increasing " +
+                "spark.akka.frameSize or using broadcast variables for large values."
+              msg = msg.format(task.taskId, task.index, serializedTask.limit, akkaFrameSize,
+                AkkaUtils.reservedSizeBytes)
               taskSet.abort(msg)
             } catch {
               case e: Exception => logError("Exception in error callback", e)
diff --git a/core/src/main/scala/org/apache/spark/util/AkkaUtils.scala b/core/src/main/scala/org/apache/spark/util/AkkaUtils.scala
index a8d12bb2a016..9930c717492f 100644
--- a/core/src/main/scala/org/apache/spark/util/AkkaUtils.scala
+++ b/core/src/main/scala/org/apache/spark/util/AkkaUtils.scala
@@ -121,4 +121,7 @@ private[spark] object AkkaUtils extends Logging {
   def maxFrameSizeBytes(conf: SparkConf): Int = {
     conf.getInt("spark.akka.frameSize", 10) * 1024 * 1024
   }
+
+  /** Space reserved for extra data in an Akka message besides serialized task or task result. */
+  val reservedSizeBytes = 200 * 1024
 }
diff --git a/core/src/test/scala/org/apache/spark/scheduler/CoarseGrainedSchedulerBackendSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/CoarseGrainedSchedulerBackendSuite.scala
index efef9d26dadc..f77661ccbd1c 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/CoarseGrainedSchedulerBackendSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/CoarseGrainedSchedulerBackendSuite.scala
@@ -35,7 +35,7 @@ class CoarseGrainedSchedulerBackendSuite extends FunSuite with LocalSparkContext
     val thrown = intercept[SparkException] {
       larger.collect()
     }
-    assert(thrown.getMessage.contains("Consider using broadcast variables for large values"))
+    assert(thrown.getMessage.contains("using broadcast variables for large values"))
     val smaller = sc.parallelize(1 to 4).collect()
     assert(smaller.size === 4)
   }

From 133495d82672c3f34d40a6298cc80c31f91faf5c Mon Sep 17 00:00:00 2001
From: Cheng Hao <hao.cheng@intel.com>
Date: Tue, 24 Jun 2014 19:07:02 -0700
Subject: [PATCH 514/810] [SQL]Add base row updating methods for JoinedRow

This will be helpful in join operators.

Author: Cheng Hao <hao.cheng@intel.com>

Closes #1187 from chenghao-intel/joinedRow and squashes the following commits:

87c19e3 [Cheng Hao] Add base row set methods for JoinedRow
---
 .../sql/catalyst/expressions/Projection.scala   | 17 +++++++++++++++++
 1 file changed, 17 insertions(+)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
index c9b7cea6a3e5..a9e976c3abaf 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
@@ -81,6 +81,18 @@ class JoinedRow extends Row {
     this
   }
 
+  /** Updates this JoinedRow by updating its left base row.  Returns itself. */
+  def withLeft(newLeft: Row): Row = {
+    row1 = newLeft
+    this
+  }
+
+  /** Updates this JoinedRow by updating its right base row.  Returns itself. */
+  def withRight(newRight: Row): Row = {
+    row2 = newRight
+    this
+  }
+
   def iterator = row1.iterator ++ row2.iterator
 
   def length = row1.length + row2.length
@@ -124,4 +136,9 @@ class JoinedRow extends Row {
     }
     new GenericRow(copiedValues)
   }
+
+  override def toString() = {
+    val row = (if (row1 != null) row1 else Seq[Any]()) ++ (if (row2 != null) row2 else Seq[Any]())
+    s"[${row.mkString(",")}]"
+  }
 }

From 54055fb2b73a545e7cca6c645356ef287aa5a7c4 Mon Sep 17 00:00:00 2001
From: Matthew Farrellee <matt@redhat.com>
Date: Tue, 24 Jun 2014 19:31:20 -0700
Subject: [PATCH 515/810] Autodetect JAVA_HOME on RPM-based systems

Author: Matthew Farrellee <matt@redhat.com>

Closes #1185 from mattf/master-1 and squashes the following commits:

42150fc [Matthew Farrellee] Autodetect JAVA_HOME on RPM-based systems
---
 make-distribution.sh | 11 +++++++++++
 1 file changed, 11 insertions(+)

diff --git a/make-distribution.sh b/make-distribution.sh
index 6f708e0ebff6..86868438e75c 100755
--- a/make-distribution.sh
+++ b/make-distribution.sh
@@ -84,6 +84,17 @@ while (( "$#" )); do
   shift
 done
 
+if [ -z "$JAVA_HOME" ]; then
+  # Fall back on JAVA_HOME from rpm, if found
+  if which rpm &>/dev/null; then
+    RPM_JAVA_HOME=$(rpm -E %java_home 2>/dev/null)
+    if [ "$RPM_JAVA_HOME" != "%java_home" ]; then
+      JAVA_HOME=$RPM_JAVA_HOME
+      echo "No JAVA_HOME set, proceeding with '$JAVA_HOME' learned from rpm"
+    fi
+  fi
+fi
+
 if [ -z "$JAVA_HOME" ]; then
   echo "Error: JAVA_HOME is not set, cannot proceed."
   exit -1

From 2714968e1b40221739c5dfba7ca4c0c06953dbe2 Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Tue, 24 Jun 2014 19:39:19 -0700
Subject: [PATCH 516/810] Fix possible null pointer in acumulator toString

Author: Michael Armbrust <michael@databricks.com>

Closes #1204 from marmbrus/nullPointerToString and squashes the following commits:

35b5fce [Michael Armbrust] Fix possible null pointer in acumulator toString
---
 core/src/main/scala/org/apache/spark/Accumulators.scala | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/Accumulators.scala b/core/src/main/scala/org/apache/spark/Accumulators.scala
index cdfd338081fa..9c55bfbb4762 100644
--- a/core/src/main/scala/org/apache/spark/Accumulators.scala
+++ b/core/src/main/scala/org/apache/spark/Accumulators.scala
@@ -127,7 +127,7 @@ class Accumulable[R, T] (
     Accumulators.register(this, false)
   }
 
-  override def toString = value_.toString
+  override def toString = if (value_ == null) "null" else value_.toString
 }
 
 /**

From b6b44853cd61660f2917b99d87c157e2b4430e5c Mon Sep 17 00:00:00 2001
From: witgo <witgo@qq.com>
Date: Tue, 24 Jun 2014 19:44:37 -0700
Subject: [PATCH 517/810] SPARK-2248: spark.default.parallelism does not apply
 in local mode

Author: witgo <witgo@qq.com>

Closes #1194 from witgo/SPARK-2248 and squashes the following commits:

6ac950b [witgo] spark.default.parallelism does not apply in local mode
---
 .../spark/scheduler/local/LocalBackend.scala     |  3 ++-
 .../SparkContextSchedulerCreationSuite.scala     | 16 ++++++++++++++++
 2 files changed, 18 insertions(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/scheduler/local/LocalBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/local/LocalBackend.scala
index 43f0e18a0cbe..9b95ccca0443 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/local/LocalBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/local/LocalBackend.scala
@@ -97,7 +97,8 @@ private[spark] class LocalBackend(scheduler: TaskSchedulerImpl, val totalCores:
     localActor ! ReviveOffers
   }
 
-  override def defaultParallelism() = totalCores
+  override def defaultParallelism() =
+    scheduler.conf.getInt("spark.default.parallelism", totalCores)
 
   override def killTask(taskId: Long, executorId: String, interruptThread: Boolean) {
     localActor ! KillTask(taskId, interruptThread)
diff --git a/core/src/test/scala/org/apache/spark/SparkContextSchedulerCreationSuite.scala b/core/src/test/scala/org/apache/spark/SparkContextSchedulerCreationSuite.scala
index 94fba102865b..67e3be21c3c9 100644
--- a/core/src/test/scala/org/apache/spark/SparkContextSchedulerCreationSuite.scala
+++ b/core/src/test/scala/org/apache/spark/SparkContextSchedulerCreationSuite.scala
@@ -77,6 +77,22 @@ class SparkContextSchedulerCreationSuite
     }
   }
 
+  test("local-default-parallelism") {
+    val defaultParallelism = System.getProperty("spark.default.parallelism")
+    System.setProperty("spark.default.parallelism", "16")
+    val sched = createTaskScheduler("local")
+
+    sched.backend match {
+      case s: LocalBackend => assert(s.defaultParallelism() === 16)
+      case _ => fail()
+    }
+
+    Option(defaultParallelism) match {
+      case Some(v) => System.setProperty("spark.default.parallelism", v)
+      case _ => System.clearProperty("spark.default.parallelism")
+    }
+  }
+
   test("simr") {
     createTaskScheduler("simr://uri").backend match {
       case s: SimrSchedulerBackend => // OK

From 8fade8973e5fc97f781de5344beb66b90bd6e524 Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian.cs.zju@gmail.com>
Date: Wed, 25 Jun 2014 00:14:34 -0700
Subject: [PATCH 518/810] [SPARK-2263][SQL] Support inserting MAP<K, V> to Hive
 tables

JIRA issue: [SPARK-2263](https://issues.apache.org/jira/browse/SPARK-2263)

Map objects were not converted to Hive types before inserting into Hive tables.

Author: Cheng Lian <lian.cs.zju@gmail.com>

Closes #1205 from liancheng/spark-2263 and squashes the following commits:

c7a4373 [Cheng Lian] Addressed @concretevitamin's comment
784940b [Cheng Lian] SARPK-2263: support inserting MAP<K, V> to Hive tables
---
 .../sql/hive/execution/InsertIntoHiveTable.scala  |  8 ++++++++
 .../spark/sql/hive/execution/HiveQuerySuite.scala | 15 ++++++++++++---
 .../spark/sql/hive/execution/HiveUdfSuite.scala   |  3 ---
 3 files changed, 20 insertions(+), 6 deletions(-)

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala
index 594a803806ed..c2b0b00aa585 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala
@@ -19,6 +19,8 @@ package org.apache.spark.sql.hive.execution
 
 import scala.collection.JavaConversions._
 
+import java.util.{HashMap => JHashMap}
+
 import org.apache.hadoop.hive.common.`type`.{HiveDecimal, HiveVarchar}
 import org.apache.hadoop.hive.metastore.MetaStoreUtils
 import org.apache.hadoop.hive.ql.Context
@@ -88,6 +90,12 @@ case class InsertIntoHiveTable(
       val wrappedSeq = s.map(wrap(_, oi.getListElementObjectInspector))
       seqAsJavaList(wrappedSeq)
 
+    case (m: Map[_, _], oi: MapObjectInspector) =>
+      val keyOi = oi.getMapKeyObjectInspector
+      val valueOi = oi.getMapValueObjectInspector
+      val wrappedMap = m.map { case (key, value) => wrap(key, keyOi) -> wrap(value, valueOi) }
+      mapAsJavaMap(wrappedMap)
+
     case (obj, _) =>
       obj
   }
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
index d855310253bf..9f1cd703103e 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
@@ -228,7 +228,7 @@ class HiveQuerySuite extends HiveComparisonTest {
     val fixture = List(("foo", 2), ("bar", 1), ("foo", 4), ("bar", 3))
       .zipWithIndex.map {case Pair(Pair(value, attr), key) => HavingRow(key, value, attr)}
     TestHive.sparkContext.parallelize(fixture).registerAsTable("having_test")
-    val results = 
+    val results =
       hql("SELECT value, max(attr) AS attr FROM having_test GROUP BY value HAVING attr > 3")
       .collect()
       .map(x => Pair(x.getString(0), x.getInt(1)))
@@ -236,7 +236,7 @@ class HiveQuerySuite extends HiveComparisonTest {
     assert(results === Array(Pair("foo", 4)))
     TestHive.reset()
   }
-  
+
   test("SPARK-2180: HAVING with non-boolean clause raises no exceptions") {
     hql("select key, count(*) c from src group by key having c").collect()
   }
@@ -370,6 +370,16 @@ class HiveQuerySuite extends HiveComparisonTest {
     }
   }
 
+  test("SPARK-2263: Insert Map<K, V> values") {
+    hql("CREATE TABLE m(value MAP<INT, STRING>)")
+    hql("INSERT OVERWRITE TABLE m SELECT MAP(key, value) FROM src LIMIT 10")
+    hql("SELECT * FROM m").collect().zip(hql("SELECT * FROM src LIMIT 10").collect()).map {
+      case (Row(map: Map[Int, String]), Row(key: Int, value: String)) =>
+        assert(map.size === 1)
+        assert(map.head === (key, value))
+    }
+  }
+
   test("parse HQL set commands") {
     // Adapted from its SQL counterpart.
     val testKey = "spark.sql.key.usedfortestonly"
@@ -460,7 +470,6 @@ class HiveQuerySuite extends HiveComparisonTest {
 
   // Put tests that depend on specific Hive settings before these last two test,
   // since they modify /clear stuff.
-
 }
 
 // for SPARK-2180 test
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUdfSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUdfSuite.scala
index a9e3f42a3adf..f944d010660e 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUdfSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUdfSuite.scala
@@ -122,6 +122,3 @@ class PairUdf extends GenericUDF {
 
   override def getDisplayString(p1: Array[String]): String = ""
 }
-
-
-

From 22036aeb1b2cac7f48cd60afea925b42a5318631 Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian.cs.zju@gmail.com>
Date: Wed, 25 Jun 2014 00:17:28 -0700
Subject: [PATCH 519/810] [BUGFIX][SQL] Should match java.math.BigDecimal when
 wnrapping Hive output

The `BigDecimal` branch in `unwrap` matches to `scala.math.BigDecimal` rather than `java.math.BigDecimal`.

Author: Cheng Lian <lian.cs.zju@gmail.com>

Closes #1199 from liancheng/javaBigDecimal and squashes the following commits:

e9bb481 [Cheng Lian] Should match java.math.BigDecimal when wnrapping Hive output
---
 .../main/scala/org/apache/spark/sql/hive/hiveUdfs.scala   | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala
index ad5e24c62c62..9b105308ab7c 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala
@@ -84,7 +84,7 @@ private[hive] object HiveFunctionRegistry
     case c: Class[_] if c == classOf[hadoopIo.FloatWritable] => FloatType
     case c: Class[_] if c == classOf[hadoopIo.BooleanWritable] => BooleanType
     case c: Class[_] if c == classOf[hadoopIo.BytesWritable] => BinaryType
-    
+
     // java class
     case c: Class[_] if c == classOf[java.lang.String] => StringType
     case c: Class[_] if c == classOf[java.sql.Timestamp] => TimestampType
@@ -98,7 +98,7 @@ private[hive] object HiveFunctionRegistry
     case c: Class[_] if c == classOf[java.lang.Byte] => ByteType
     case c: Class[_] if c == classOf[java.lang.Float] => FloatType
     case c: Class[_] if c == classOf[java.lang.Boolean] => BooleanType
-    
+
     // primitive type
     case c: Class[_] if c == java.lang.Short.TYPE => ShortType
     case c: Class[_] if c == java.lang.Integer.TYPE => IntegerType
@@ -107,7 +107,7 @@ private[hive] object HiveFunctionRegistry
     case c: Class[_] if c == java.lang.Byte.TYPE => ByteType
     case c: Class[_] if c == java.lang.Float.TYPE => FloatType
     case c: Class[_] if c == java.lang.Boolean.TYPE => BooleanType
-    
+
     case c: Class[_] if c.isArray => ArrayType(javaClassToDataType(c.getComponentType))
   }
 }
@@ -148,7 +148,7 @@ private[hive] trait HiveFunctionFactory {
     case p: java.lang.Byte => p
     case p: java.lang.Boolean => p
     case str: String => str
-    case p: BigDecimal => p
+    case p: java.math.BigDecimal => p
     case p: Array[Byte] => p
     case p: java.sql.Timestamp => p
   }

From acc01ab3265c317f36a4fca28d3b9d72b0096c12 Mon Sep 17 00:00:00 2001
From: CodingCat <zhunansjtu@gmail.com>
Date: Wed, 25 Jun 2014 00:23:32 -0700
Subject: [PATCH 520/810] SPARK-2038: rename "conf" parameters in the
 saveAsHadoop functions with source-compatibility

https://issues.apache.org/jira/browse/SPARK-2038

to differentiate with SparkConf object and at the same time keep the source level compatibility

Author: CodingCat <zhunansjtu@gmail.com>

Closes #1137 from CodingCat/SPARK-2038 and squashes the following commits:

11abeba [CodingCat] revise the comments
7ee5712 [CodingCat] to keep the source-compatibility
763975f [CodingCat] style fix
d91288d [CodingCat] rename "conf" parameters in the saveAsHadoop functions
---
 .../apache/spark/rdd/PairRDDFunctions.scala   | 49 +++++++++++--------
 1 file changed, 29 insertions(+), 20 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
index 2b2b9ae3fd53..fc9beb166bef 100644
--- a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
@@ -762,7 +762,9 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
       outputFormatClass: Class[_ <: NewOutputFormat[_, _]],
       conf: Configuration = self.context.hadoopConfiguration)
   {
-    val job = new NewAPIHadoopJob(conf)
+    // Rename this as hadoopConf internally to avoid shadowing (see SPARK-2038).
+    val hadoopConf = conf
+    val job = new NewAPIHadoopJob(hadoopConf)
     job.setOutputKeyClass(keyClass)
     job.setOutputValueClass(valueClass)
     job.setOutputFormatClass(outputFormatClass)
@@ -795,22 +797,25 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
       outputFormatClass: Class[_ <: OutputFormat[_, _]],
       conf: JobConf = new JobConf(self.context.hadoopConfiguration),
       codec: Option[Class[_ <: CompressionCodec]] = None) {
-    conf.setOutputKeyClass(keyClass)
-    conf.setOutputValueClass(valueClass)
+    // Rename this as hadoopConf internally to avoid shadowing (see SPARK-2038).
+    val hadoopConf = conf
+    hadoopConf.setOutputKeyClass(keyClass)
+    hadoopConf.setOutputValueClass(valueClass)
     // Doesn't work in Scala 2.9 due to what may be a generics bug
     // TODO: Should we uncomment this for Scala 2.10?
     // conf.setOutputFormat(outputFormatClass)
-    conf.set("mapred.output.format.class", outputFormatClass.getName)
+    hadoopConf.set("mapred.output.format.class", outputFormatClass.getName)
     for (c <- codec) {
-      conf.setCompressMapOutput(true)
-      conf.set("mapred.output.compress", "true")
-      conf.setMapOutputCompressorClass(c)
-      conf.set("mapred.output.compression.codec", c.getCanonicalName)
-      conf.set("mapred.output.compression.type", CompressionType.BLOCK.toString)
+      hadoopConf.setCompressMapOutput(true)
+      hadoopConf.set("mapred.output.compress", "true")
+      hadoopConf.setMapOutputCompressorClass(c)
+      hadoopConf.set("mapred.output.compression.codec", c.getCanonicalName)
+      hadoopConf.set("mapred.output.compression.type", CompressionType.BLOCK.toString)
     }
-    conf.setOutputCommitter(classOf[FileOutputCommitter])
-    FileOutputFormat.setOutputPath(conf, SparkHadoopWriter.createPathFromString(path, conf))
-    saveAsHadoopDataset(conf)
+    hadoopConf.setOutputCommitter(classOf[FileOutputCommitter])
+    FileOutputFormat.setOutputPath(hadoopConf,
+      SparkHadoopWriter.createPathFromString(path, hadoopConf))
+    saveAsHadoopDataset(hadoopConf)
   }
 
   /**
@@ -820,7 +825,9 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * configured for a Hadoop MapReduce job.
    */
   def saveAsNewAPIHadoopDataset(conf: Configuration) {
-    val job = new NewAPIHadoopJob(conf)
+    // Rename this as hadoopConf internally to avoid shadowing (see SPARK-2038).
+    val hadoopConf = conf
+    val job = new NewAPIHadoopJob(hadoopConf)
     val formatter = new SimpleDateFormat("yyyyMMddHHmm")
     val jobtrackerID = formatter.format(new Date())
     val stageId = self.id
@@ -877,9 +884,11 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * MapReduce job.
    */
   def saveAsHadoopDataset(conf: JobConf) {
-    val outputFormatInstance = conf.getOutputFormat
-    val keyClass = conf.getOutputKeyClass
-    val valueClass = conf.getOutputValueClass
+    // Rename this as hadoopConf internally to avoid shadowing (see SPARK-2038).
+    val hadoopConf = conf
+    val outputFormatInstance = hadoopConf.getOutputFormat
+    val keyClass = hadoopConf.getOutputKeyClass
+    val valueClass = hadoopConf.getOutputValueClass
     if (outputFormatInstance == null) {
       throw new SparkException("Output format class not set")
     }
@@ -889,18 +898,18 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
     if (valueClass == null) {
       throw new SparkException("Output value class not set")
     }
-    SparkHadoopUtil.get.addCredentials(conf)
+    SparkHadoopUtil.get.addCredentials(hadoopConf)
 
     logDebug("Saving as hadoop file of type (" + keyClass.getSimpleName + ", " +
       valueClass.getSimpleName + ")")
 
     if (self.conf.getBoolean("spark.hadoop.validateOutputSpecs", true)) {
       // FileOutputFormat ignores the filesystem parameter
-      val ignoredFs = FileSystem.get(conf)
-      conf.getOutputFormat.checkOutputSpecs(ignoredFs, conf)
+      val ignoredFs = FileSystem.get(hadoopConf)
+      hadoopConf.getOutputFormat.checkOutputSpecs(ignoredFs, hadoopConf)
     }
 
-    val writer = new SparkHadoopWriter(conf)
+    val writer = new SparkHadoopWriter(hadoopConf)
     writer.preSetup()
 
     def writeToFile(context: TaskContext, iter: Iterator[(K, V)]) {

From ac06a85da59db8f2654cdf6601d186348da09c01 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Wed, 25 Jun 2014 01:01:23 -0700
Subject: [PATCH 521/810] Replace doc reference to Shark with Spark SQL.

---
 docs/index.md | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/docs/index.md b/docs/index.md
index 1a4ff3dbf57b..4ac0982ae54f 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -6,7 +6,7 @@ title: Spark Overview
 Apache Spark is a fast and general-purpose cluster computing system.
 It provides high-level APIs in Java, Scala and Python,
 and an optimized engine that supports general execution graphs.
-It also supports a rich set of higher-level tools including [Shark](http://shark.cs.berkeley.edu) (Hive on Spark), [Spark SQL](sql-programming-guide.html) for structured data, [MLlib](mllib-guide.html) for machine learning, [GraphX](graphx-programming-guide.html) for graph processing, and [Spark Streaming](streaming-programming-guide.html).
+It also supports a rich set of higher-level tools including [Spark SQL](sql-programming-guide.html) for SQL and structured data processing, [MLlib](mllib-guide.html) for machine learning, [GraphX](graphx-programming-guide.html) for graph processing, and [Spark Streaming](streaming-programming-guide.html).
 
 # Downloading
 
@@ -109,10 +109,9 @@ options for deployment:
 **External Resources:**
 
 * [Spark Homepage](http://spark.apache.org)
-* [Shark](http://shark.cs.berkeley.edu): Apache Hive over Spark
 * [Mailing Lists](http://spark.apache.org/mailing-lists.html): ask questions about Spark here
 * [AMP Camps](http://ampcamp.berkeley.edu/): a series of training camps at UC Berkeley that featured talks and
-  exercises about Spark, Shark, Spark Streaming, Mesos, and more. [Videos](http://ampcamp.berkeley.edu/3/),
+  exercises about Spark, Spark Streaming, Mesos, and more. [Videos](http://ampcamp.berkeley.edu/3/),
   [slides](http://ampcamp.berkeley.edu/3/) and [exercises](http://ampcamp.berkeley.edu/3/exercises/) are
   available online for free.
 * [Code Examples](http://spark.apache.org/examples.html): more are also available in the `examples` subfolder of Spark ([Scala]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/scala/org/apache/spark/examples),

From 5603e4c47f1dc1b87336f57ed4d6bd9e88f5abcc Mon Sep 17 00:00:00 2001
From: Andrew Or <andrewor14@gmail.com>
Date: Wed, 25 Jun 2014 10:47:22 -0700
Subject: [PATCH 522/810] [SPARK-2242] HOTFIX: pyspark shell hangs on simple
 job

This reverts a change introduced in 3870248740d83b0292ccca88a494ce19783847f0, which redirected all stderr to the OS pipe instead of directly to the `bin/pyspark` shell output. This causes a simple job to hang in two ways:

1. If the cluster is not configured correctly or does not have enough resources, the job hangs without producing any output, because the relevant warning messages are masked.
2. If the stderr volume is large, this could lead to a deadlock if we redirect everything to the OS pipe. From the [python docs](https://docs.python.org/2/library/subprocess.html):

```
Note Do not use stdout=PIPE or stderr=PIPE with this function as that can deadlock
based on the child process output volume. Use Popen with the communicate() method
when you need pipes.
```

Note that we cannot remove `stdout=PIPE` in a similar way, because we currently use it to communicate the py4j port. However, it should be fine (as it has been for a long time) because we do not produce a ton of traffic through `stdout`.

That commit was not merged in branch-1.0, so this fix is for master only.

Author: Andrew Or <andrewor14@gmail.com>

Closes #1178 from andrewor14/fix-python and squashes the following commits:

e68e870 [Andrew Or] Merge branch 'master' of github.com:apache/spark into fix-python
20849a8 [Andrew Or] Tone down stdout interference message
a09805b [Andrew Or] Return more than 1 line of error message to user
6dfbd1e [Andrew Or] Don't swallow original exception
0d1861f [Andrew Or] Provide more helpful output if stdout is garbled
21c9d7c [Andrew Or] Do not mask stderr from output
---
 python/pyspark/java_gateway.py | 21 +++++++++++++--------
 1 file changed, 13 insertions(+), 8 deletions(-)

diff --git a/python/pyspark/java_gateway.py b/python/pyspark/java_gateway.py
index 19235d5f79f8..0dbead4415b0 100644
--- a/python/pyspark/java_gateway.py
+++ b/python/pyspark/java_gateway.py
@@ -43,18 +43,23 @@ def launch_gateway():
             # Don't send ctrl-c / SIGINT to the Java gateway:
             def preexec_func():
                 signal.signal(signal.SIGINT, signal.SIG_IGN)
-            proc = Popen(command, stdout=PIPE, stdin=PIPE, stderr=PIPE, preexec_fn=preexec_func)
+            proc = Popen(command, stdout=PIPE, stdin=PIPE, preexec_fn=preexec_func)
         else:
             # preexec_fn not supported on Windows
-            proc = Popen(command, stdout=PIPE, stdin=PIPE, stderr=PIPE)
-        
+            proc = Popen(command, stdout=PIPE, stdin=PIPE)
+
         try:
             # Determine which ephemeral port the server started on:
-            gateway_port = int(proc.stdout.readline())
-        except:
-            error_code = proc.poll()
-            raise Exception("Launching GatewayServer failed with exit code %d: %s" %
-                (error_code, "".join(proc.stderr.readlines())))
+            gateway_port = proc.stdout.readline()
+            gateway_port = int(gateway_port)
+        except ValueError:
+            (stdout, _) = proc.communicate()
+            exit_code = proc.poll()
+            error_msg = "Launching GatewayServer failed"
+            error_msg += " with exit code %d!" % exit_code if exit_code else "! "
+            error_msg += "(Warning: unexpected output detected.)\n\n"
+            error_msg += gateway_port + stdout
+            raise Exception(error_msg)
 
         # Create a thread to echo output from the GatewayServer, which is required
         # for Java log output to show up:

From 9aa603296c285e1acf4bde64583f203008ba3e91 Mon Sep 17 00:00:00 2001
From: Andrew Or <andrewor14@gmail.com>
Date: Wed, 25 Jun 2014 12:23:08 -0700
Subject: [PATCH 523/810] [SPARK-2258 / 2266] Fix a few worker UI bugs

**SPARK-2258.** Worker UI displays zombie processes if the executor throws an exception before a process is launched. This is because we only inform the Worker of the change if the process is already launched, which in this case it isn't.

**SPARK-2266.** We expose "Some(app-id)" on the log page. This is fairly minor.

Author: Andrew Or <andrewor14@gmail.com>

Closes #1213 from andrewor14/fix-worker-ui and squashes the following commits:

c1223fe [Andrew Or] Fix worker UI bugs
---
 .../org/apache/spark/deploy/worker/ExecutorRunner.scala      | 5 +++--
 .../scala/org/apache/spark/deploy/worker/ui/LogPage.scala    | 2 +-
 2 files changed, 4 insertions(+), 3 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/ExecutorRunner.scala b/core/src/main/scala/org/apache/spark/deploy/worker/ExecutorRunner.scala
index 6433aac1c23e..467317dd9b44 100644
--- a/core/src/main/scala/org/apache/spark/deploy/worker/ExecutorRunner.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/worker/ExecutorRunner.scala
@@ -77,6 +77,7 @@ private[spark] class ExecutorRunner(
    * @param message the exception message which caused the executor's death 
    */
   private def killProcess(message: Option[String]) {
+    var exitCode: Option[Int] = None
     if (process != null) {
       logInfo("Killing process!")
       process.destroy()
@@ -87,9 +88,9 @@ private[spark] class ExecutorRunner(
       if (stderrAppender != null) {
         stderrAppender.stop()
       }
-      val exitCode = process.waitFor()
-      worker ! ExecutorStateChanged(appId, execId, state, message, Some(exitCode))
+      exitCode = Some(process.waitFor())
     }
+    worker ! ExecutorStateChanged(appId, execId, state, message, exitCode)
   }
 
   /** Stop this executor runner, including killing the process it launched */
diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/ui/LogPage.scala b/core/src/main/scala/org/apache/spark/deploy/worker/ui/LogPage.scala
index 6a5ffb1b71bf..b389cb546de6 100644
--- a/core/src/main/scala/org/apache/spark/deploy/worker/ui/LogPage.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/worker/ui/LogPage.scala
@@ -120,7 +120,7 @@ private[spark] class LogPage(parent: WorkerWebUI) extends WebUIPage("logPage") w
           </div>
         </body>
       </html>
-    UIUtils.basicSparkPage(content, logType + " log page for " + appId)
+    UIUtils.basicSparkPage(content, logType + " log page for " + appId.getOrElse("unknown app"))
   }
 
   /** Get the part of the log files given the offset and desired length of bytes */

From 7ff2c754f340ba4c4077b0ff6285876eb7871c7b Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Wed, 25 Jun 2014 12:43:22 -0700
Subject: [PATCH 524/810] [SPARK-2270] Kryo cannot serialize results returned
 by asJavaIterable

and thus groupBy/cogroup are broken in Java APIs when Kryo is used).

@pwendell this should be merged into 1.0.1.

Thanks @sorenmacbeth for reporting this & helping out with the fix.

Author: Reynold Xin <rxin@apache.org>

Closes #1206 from rxin/kryo-iterable-2270 and squashes the following commits:

09da0aa [Reynold Xin] Updated the comment.
009bf64 [Reynold Xin] [SPARK-2270] Kryo cannot serialize results returned by asJavaIterable (and thus groupBy/cogroup are broken in Java APIs when Kryo is used).
---
 .../spark/serializer/KryoSerializer.scala     | 50 +++++++++++++++++++
 .../serializer/KryoSerializerSuite.scala      | 15 ++++++
 2 files changed, 65 insertions(+)

diff --git a/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala b/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala
index 5286f7b4c211..82b62aaf6152 100644
--- a/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala
+++ b/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala
@@ -64,6 +64,9 @@ class KryoSerializer(conf: SparkConf)
       kryo.register(cls)
     }
 
+    // For results returned by asJavaIterable. See JavaIterableWrapperSerializer.
+    kryo.register(JavaIterableWrapperSerializer.wrapperClass, new JavaIterableWrapperSerializer)
+
     // Allow sending SerializableWritable
     kryo.register(classOf[SerializableWritable[_]], new KryoJavaSerializer())
     kryo.register(classOf[HttpBroadcast[_]], new KryoJavaSerializer())
@@ -183,3 +186,50 @@ private[serializer] object KryoSerializer {
     classOf[Array[Byte]]
   )
 }
+
+/**
+ * A Kryo serializer for serializing results returned by asJavaIterable.
+ *
+ * The underlying object is scala.collection.convert.Wrappers$IterableWrapper.
+ * Kryo deserializes this into an AbstractCollection, which unfortunately doesn't work.
+ */
+private class JavaIterableWrapperSerializer
+  extends com.esotericsoftware.kryo.Serializer[java.lang.Iterable[_]] {
+
+  import JavaIterableWrapperSerializer._
+
+  override def write(kryo: Kryo, out: KryoOutput, obj: java.lang.Iterable[_]): Unit = {
+    // If the object is the wrapper, simply serialize the underlying Scala Iterable object.
+    // Otherwise, serialize the object itself.
+    if (obj.getClass == wrapperClass && underlyingMethodOpt.isDefined) {
+      kryo.writeClassAndObject(out, underlyingMethodOpt.get.invoke(obj))
+    } else {
+      kryo.writeClassAndObject(out, obj)
+    }
+  }
+
+  override def read(kryo: Kryo, in: KryoInput, clz: Class[java.lang.Iterable[_]])
+    : java.lang.Iterable[_] = {
+    kryo.readClassAndObject(in) match {
+      case scalaIterable: Iterable[_] =>
+        scala.collection.JavaConversions.asJavaIterable(scalaIterable)
+      case javaIterable: java.lang.Iterable[_] =>
+        javaIterable
+    }
+  }
+}
+
+private object JavaIterableWrapperSerializer extends Logging {
+  // The class returned by asJavaIterable (scala.collection.convert.Wrappers$IterableWrapper).
+  val wrapperClass =
+    scala.collection.convert.WrapAsJava.asJavaIterable(Seq(1)).getClass
+
+  // Get the underlying method so we can use it to get the Scala collection for serialization.
+  private val underlyingMethodOpt = {
+    try Some(wrapperClass.getDeclaredMethod("underlying")) catch {
+      case e: Exception =>
+        logError("Failed to find the underlying field in " + wrapperClass, e)
+        None
+    }
+  }
+}
diff --git a/core/src/test/scala/org/apache/spark/serializer/KryoSerializerSuite.scala b/core/src/test/scala/org/apache/spark/serializer/KryoSerializerSuite.scala
index cdd6b3d8feed..79280d1a0665 100644
--- a/core/src/test/scala/org/apache/spark/serializer/KryoSerializerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/serializer/KryoSerializerSuite.scala
@@ -128,6 +128,21 @@ class KryoSerializerSuite extends FunSuite with SharedSparkContext {
     check(1.0 until 1000000.0 by 2.0)
   }
 
+  test("asJavaIterable") {
+    // Serialize a collection wrapped by asJavaIterable
+    val ser = new KryoSerializer(conf).newInstance()
+    val a = ser.serialize(scala.collection.convert.WrapAsJava.asJavaIterable(Seq(12345)))
+    val b = ser.deserialize[java.lang.Iterable[Int]](a)
+    assert(b.iterator().next() === 12345)
+
+    // Serialize a normal Java collection
+    val col = new java.util.ArrayList[Int]
+    col.add(54321)
+    val c = ser.serialize(col)
+    val d = ser.deserialize[java.lang.Iterable[Int]](c)
+    assert(b.iterator().next() === 12345)
+  }
+
   test("custom registrator") {
     val ser = new KryoSerializer(conf).newInstance()
     def check[T: ClassTag](t: T) {

From 1132e472eca1a00c2ce10d2f84e8f0e79a5193d3 Mon Sep 17 00:00:00 2001
From: Sebastien Rainville <sebastien@hopper.com>
Date: Wed, 25 Jun 2014 13:21:18 -0700
Subject: [PATCH 525/810] [SPARK-2204] Launch tasks on the proper executors in
 mesos fine-grained mode

The scheduler for Mesos in fine-grained mode launches tasks on the wrong executors. `MesosSchedulerBackend.resourceOffers(SchedulerDriver, List[Offer])` is assuming that `TaskSchedulerImpl.resourceOffers(Seq[WorkerOffer])` is returning task lists in the same order as the offers it was passed, but in the current implementation `TaskSchedulerImpl.resourceOffers` shuffles the offers to avoid assigning the tasks always to the same executors. The result is that the tasks are launched on the wrong executors. The jobs are sometimes able to complete, but most of the time they fail. It seems that as soon as something goes wrong with a task for some reason Spark is not able to recover since it's mistaken as to where the tasks are actually running. Also, it seems that the more the cluster is under load the more likely the job is to fail because there's a higher probability that Spark is trying to launch a task on a slave that doesn't actually have enough resources, again because it's using the wrong offers.

The solution is to not assume that the order in which the tasks are returned is the same as the offers, and simply launch the tasks on the executor decided by `TaskSchedulerImpl.resourceOffers`. What I am not sure about is that I considered slaveId and executorId to be the same, which is true at least in my setup, but I don't know if that is always true.

I tested this on top of the 1.0.0 release and it seems to work fine on our cluster.

Author: Sebastien Rainville <sebastien@hopper.com>

Closes #1140 from sebastienrainville/fine-grained-mode-fix-master and squashes the following commits:

a98b0e0 [Sebastien Rainville] Use a HashMap to retrieve the offer indices
d6ffe54 [Sebastien Rainville] Launch tasks on the proper executors in mesos fine-grained mode
---
 .../cluster/mesos/MesosSchedulerBackend.scala       | 13 ++++++-------
 1 file changed, 6 insertions(+), 7 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala
index a089a02d4217..c717e7c621a8 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala
@@ -185,8 +185,8 @@ private[spark] class MesosSchedulerBackend(
       synchronized {
         // Build a big list of the offerable workers, and remember their indices so that we can
         // figure out which Offer to reply to for each worker
-        val offerableIndices = new ArrayBuffer[Int]
         val offerableWorkers = new ArrayBuffer[WorkerOffer]
+        val offerableIndices = new HashMap[String, Int]
 
         def enoughMemory(o: Offer) = {
           val mem = getResource(o.getResourcesList, "mem")
@@ -195,7 +195,7 @@ private[spark] class MesosSchedulerBackend(
         }
 
         for ((offer, index) <- offers.zipWithIndex if enoughMemory(offer)) {
-          offerableIndices += index
+          offerableIndices.put(offer.getSlaveId.getValue, index)
           offerableWorkers += new WorkerOffer(
             offer.getSlaveId.getValue,
             offer.getHostname,
@@ -206,14 +206,13 @@ private[spark] class MesosSchedulerBackend(
         val taskLists = scheduler.resourceOffers(offerableWorkers)
 
         // Build a list of Mesos tasks for each slave
-        val mesosTasks = offers.map(o => Collections.emptyList[MesosTaskInfo]())
+        val mesosTasks = offers.map(o => new JArrayList[MesosTaskInfo]())
         for ((taskList, index) <- taskLists.zipWithIndex) {
           if (!taskList.isEmpty) {
-            val offerNum = offerableIndices(index)
-            val slaveId = offers(offerNum).getSlaveId.getValue
-            slaveIdsWithExecutors += slaveId
-            mesosTasks(offerNum) = new JArrayList[MesosTaskInfo](taskList.size)
             for (taskDesc <- taskList) {
+              val slaveId = taskDesc.executorId
+              val offerNum = offerableIndices(slaveId)
+              slaveIdsWithExecutors += slaveId
               taskIdToSlaveId(taskDesc.taskId) = slaveId
               mesosTasks(offerNum).add(createMesosTask(taskDesc, slaveId))
             }

From 9d824fed8c62dd6c87b4c855c2fea930c01b58f4 Mon Sep 17 00:00:00 2001
From: Zongheng Yang <zongheng.y@gmail.com>
Date: Wed, 25 Jun 2014 18:06:33 -0700
Subject: [PATCH 526/810] [SQL] SPARK-1800 Add broadcast hash join operator &
 associated hints.

This PR is based off Michael's [PR 734](https://github.com/apache/spark/pull/734) and includes a bunch of cleanups.

Moreover, this PR also
- makes `SparkLogicalPlan` take a `tableName: String`, which facilitates testing.
- moves join-related tests to a single file.

Author: Zongheng Yang <zongheng.y@gmail.com>
Author: Michael Armbrust <michael@databricks.com>

Closes #1163 from concretevitamin/auto-broadcast-hash-join and squashes the following commits:

d0f4991 [Zongheng Yang] Fix bug in broadcast hash join & add test to cover it.
af080d7 [Zongheng Yang] Fix in joinIterators()'s next().
440d277 [Zongheng Yang] Fixes to imports; add back requiredChildDistribution (lost when merging)
208d5f6 [Zongheng Yang] Make LeftSemiJoinHash mix in HashJoin.
ad6c7cc [Zongheng Yang] Minor cleanups.
814b3bf [Zongheng Yang] Merge branch 'master' into auto-broadcast-hash-join
a8a093e [Zongheng Yang] Minor cleanups.
6fd8443 [Zongheng Yang] Cut down size estimation related stuff.
a4267be [Zongheng Yang] Add test for broadcast hash join and related necessary refactorings:
0e64b08 [Zongheng Yang] Scalastyle fix.
91461c2 [Zongheng Yang] Merge branch 'master' into auto-broadcast-hash-join
7c7158b [Zongheng Yang] Prototype of auto conversion to broadcast hash join.
0ad122f [Zongheng Yang] Merge branch 'master' into auto-broadcast-hash-join
3e5d77c [Zongheng Yang] WIP: giant and messy WIP.
a92ed0c [Michael Armbrust] Formatting.
76ca434 [Michael Armbrust] A simple strategy that broadcasts tables only when they are found in a configuration hint.
cf6b381 [Michael Armbrust] Split out generic logic for hash joins and create two concrete physical operators: BroadcastHashJoin and ShuffledHashJoin.
a8420ca [Michael Armbrust] Copy records in executeCollect to avoid issues with mutable rows.
---
 .../sql/catalyst/expressions/Projection.scala |   8 +-
 .../catalyst/plans/logical/BaseRelation.scala |   1 -
 .../scala/org/apache/spark/sql/SQLConf.scala  |  17 ++
 .../org/apache/spark/sql/SQLContext.scala     |   6 +-
 .../spark/sql/execution/SparkPlan.scala       |  15 +-
 .../spark/sql/execution/SparkStrategies.scala |  54 ++++-
 .../spark/sql/execution/basicOperators.scala  |   1 -
 .../apache/spark/sql/execution/joins.scala    | 219 +++++++++++-------
 .../spark/sql/parquet/ParquetRelation.scala   |   5 +-
 .../org/apache/spark/sql/DslQuerySuite.scala  |  99 --------
 .../org/apache/spark/sql/JoinSuite.scala      | 173 ++++++++++++++
 .../org/apache/spark/sql/QueryTest.scala      |   4 +-
 .../spark/sql/execution/PlannerSuite.scala    |  17 --
 .../apache/spark/sql/hive/HiveContext.scala   |   2 +-
 .../spark/sql/hive/HiveMetastoreCatalog.scala |   7 +-
 15 files changed, 395 insertions(+), 233 deletions(-)
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
index a9e976c3abaf..2c71d2c7b356 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
@@ -45,8 +45,10 @@ class Projection(expressions: Seq[Expression]) extends (Row => Row) {
  * that schema.
  *
  * In contrast to a normal projection, a MutableProjection reuses the same underlying row object
- * each time an input row is added.  This significatly reduces the cost of calcuating the
- * projection, but means that it is not safe
+ * each time an input row is added.  This significantly reduces the cost of calculating the
+ * projection, but means that it is not safe to hold on to a reference to a [[Row]] after `next()`
+ * has been called on the [[Iterator]] that produced it. Instead, the user must call `Row.copy()`
+ * and hold on to the returned [[Row]] before calling `next()`.
  */
 case class MutableProjection(expressions: Seq[Expression]) extends (Row => Row) {
   def this(expressions: Seq[Expression], inputSchema: Seq[Attribute]) =
@@ -67,7 +69,7 @@ case class MutableProjection(expressions: Seq[Expression]) extends (Row => Row)
 }
 
 /**
- * A mutable wrapper that makes two rows appear appear as a single concatenated row.  Designed to
+ * A mutable wrapper that makes two rows appear as a single concatenated row.  Designed to
  * be instantiated once per thread and reused.
  */
 class JoinedRow extends Row {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/BaseRelation.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/BaseRelation.scala
index 7c616788a383..582334aa4259 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/BaseRelation.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/BaseRelation.scala
@@ -21,5 +21,4 @@ abstract class BaseRelation extends LeafNode {
   self: Product =>
 
   def tableName: String
-  def isPartitioned: Boolean = false
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
index b378252ba2f5..2fe7f9466399 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
@@ -29,9 +29,26 @@ import scala.collection.JavaConverters._
  */
 trait SQLConf {
 
+  /** ************************ Spark SQL Params/Hints ******************* */
+  // TODO: refactor so that these hints accessors don't pollute the name space of SQLContext?
+
   /** Number of partitions to use for shuffle operators. */
   private[spark] def numShufflePartitions: Int = get("spark.sql.shuffle.partitions", "200").toInt
 
+  /**
+   * Upper bound on the sizes (in bytes) of the tables qualified for the auto conversion to
+   * a broadcast value during the physical executions of join operations.  Setting this to 0
+   * effectively disables auto conversion.
+   * Hive setting: hive.auto.convert.join.noconditionaltask.size.
+   */
+  private[spark] def autoConvertJoinSize: Int =
+    get("spark.sql.auto.convert.join.size", "10000").toInt
+
+  /** A comma-separated list of table names marked to be broadcasted during joins. */
+  private[spark] def joinBroadcastTables: String = get("spark.sql.join.broadcastTables", "")
+
+  /** ********************** SQLConf functionality methods ************ */
+
   @transient
   private val settings = java.util.Collections.synchronizedMap(
     new java.util.HashMap[String, String]())
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
index 7195f9709d5e..7edb548678c3 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
@@ -170,7 +170,11 @@ class SQLContext(@transient val sparkContext: SparkContext)
    * @group userf
    */
   def registerRDDAsTable(rdd: SchemaRDD, tableName: String): Unit = {
-    catalog.registerTable(None, tableName, rdd.logicalPlan)
+    val name = tableName
+    val newPlan = rdd.logicalPlan transform {
+      case s @ SparkLogicalPlan(ExistingRdd(_, _), _) => s.copy(tableName = name)
+    }
+    catalog.registerTable(None, tableName, newPlan)
   }
 
   /**
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
index 07967fe75e88..27dc091b8581 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
@@ -23,9 +23,9 @@ import org.apache.spark.sql.{Logging, Row}
 import org.apache.spark.sql.catalyst.trees
 import org.apache.spark.sql.catalyst.analysis.MultiInstanceRelation
 import org.apache.spark.sql.catalyst.expressions.GenericRow
-import org.apache.spark.sql.catalyst.plans.{QueryPlan, logical}
+import org.apache.spark.sql.catalyst.plans.QueryPlan
+import org.apache.spark.sql.catalyst.plans.logical.BaseRelation
 import org.apache.spark.sql.catalyst.plans.physical._
-import org.apache.spark.sql.columnar.InMemoryColumnarTableScan
 
 /**
  * :: DeveloperApi ::
@@ -66,19 +66,20 @@ abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging {
  * linking.
  */
 @DeveloperApi
-case class SparkLogicalPlan(alreadyPlanned: SparkPlan)
-  extends logical.LogicalPlan with MultiInstanceRelation {
+case class SparkLogicalPlan(alreadyPlanned: SparkPlan, tableName: String = "SparkLogicalPlan")
+  extends BaseRelation with MultiInstanceRelation {
 
   def output = alreadyPlanned.output
-  def references = Set.empty
-  def children = Nil
+  override def references = Set.empty
+  override def children = Nil
 
   override final def newInstance: this.type = {
     SparkLogicalPlan(
       alreadyPlanned match {
         case ExistingRdd(output, rdd) => ExistingRdd(output.map(_.newInstance), rdd)
         case _ => sys.error("Multiple instance of the same relation detected.")
-      }).asInstanceOf[this.type]
+      }, tableName)
+      .asInstanceOf[this.type]
   }
 }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
index bd8ae4cddef8..3cd29967d1cd 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
@@ -21,10 +21,10 @@ import org.apache.spark.sql.{SQLContext, execution}
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.planning._
 import org.apache.spark.sql.catalyst.plans._
-import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
+import org.apache.spark.sql.catalyst.plans.logical.{BaseRelation, LogicalPlan}
 import org.apache.spark.sql.catalyst.plans.physical._
-import org.apache.spark.sql.parquet._
 import org.apache.spark.sql.columnar.{InMemoryRelation, InMemoryColumnarTableScan}
+import org.apache.spark.sql.parquet._
 
 private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
   self: SQLContext#SparkPlanner =>
@@ -45,14 +45,52 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
     }
   }
 
+  /**
+   * Uses the HashFilteredJoin pattern to find joins where at least some of the predicates can be
+   * evaluated by matching hash keys.
+   */
   object HashJoin extends Strategy with PredicateHelper {
+    private[this] def broadcastHashJoin(
+        leftKeys: Seq[Expression],
+        rightKeys: Seq[Expression],
+        left: LogicalPlan,
+        right: LogicalPlan,
+        condition: Option[Expression],
+        side: BuildSide) = {
+      val broadcastHashJoin = execution.BroadcastHashJoin(
+        leftKeys, rightKeys, side, planLater(left), planLater(right))(sqlContext)
+      condition.map(Filter(_, broadcastHashJoin)).getOrElse(broadcastHashJoin) :: Nil
+    }
+
+    def broadcastTables: Seq[String] = sqlContext.joinBroadcastTables.split(",").toBuffer
+
     def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
-      // Find inner joins where at least some predicates can be evaluated by matching hash keys
-      // using the HashFilteredJoin pattern.
+      case HashFilteredJoin(
+              Inner,
+              leftKeys,
+              rightKeys,
+              condition,
+              left,
+              right @ PhysicalOperation(_, _, b: BaseRelation))
+        if broadcastTables.contains(b.tableName) =>
+          broadcastHashJoin(leftKeys, rightKeys, left, right, condition, BuildRight)
+
+      case HashFilteredJoin(
+              Inner,
+              leftKeys,
+              rightKeys,
+              condition,
+              left @ PhysicalOperation(_, _, b: BaseRelation),
+              right)
+        if broadcastTables.contains(b.tableName) =>
+          broadcastHashJoin(leftKeys, rightKeys, left, right, condition, BuildLeft)
+
       case HashFilteredJoin(Inner, leftKeys, rightKeys, condition, left, right) =>
         val hashJoin =
-          execution.HashJoin(leftKeys, rightKeys, BuildRight, planLater(left), planLater(right))
+          execution.ShuffledHashJoin(
+            leftKeys, rightKeys, BuildRight, planLater(left), planLater(right))
         condition.map(Filter(_, hashJoin)).getOrElse(hashJoin) :: Nil
+
       case _ => Nil
     }
   }
@@ -62,10 +100,10 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
       case logical.Aggregate(groupingExpressions, aggregateExpressions, child) =>
         // Collect all aggregate expressions.
         val allAggregates =
-          aggregateExpressions.flatMap(_ collect { case a: AggregateExpression => a})
+          aggregateExpressions.flatMap(_ collect { case a: AggregateExpression => a })
         // Collect all aggregate expressions that can be computed partially.
         val partialAggregates =
-          aggregateExpressions.flatMap(_ collect { case p: PartialAggregate => p})
+          aggregateExpressions.flatMap(_ collect { case p: PartialAggregate => p })
 
         // Only do partial aggregation if supported by all aggregate expressions.
         if (allAggregates.size == partialAggregates.size) {
@@ -242,7 +280,7 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
         execution.ExistingRdd(Nil, singleRowRdd) :: Nil
       case logical.Repartition(expressions, child) =>
         execution.Exchange(HashPartitioning(expressions, numPartitions), planLater(child)) :: Nil
-      case SparkLogicalPlan(existingPlan) => existingPlan :: Nil
+      case SparkLogicalPlan(existingPlan, _) => existingPlan :: Nil
       case _ => Nil
     }
   }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
index b40d4e3a3b77..a278f1ca9847 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
@@ -205,4 +205,3 @@ object ExistingRdd {
 case class ExistingRdd(output: Seq[Attribute], rdd: RDD[Row]) extends LeafNode {
   override def execute() = rdd
 }
-
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins.scala
index 84bdde38b7e9..32c5f26fe8aa 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins.scala
@@ -18,12 +18,15 @@
 package org.apache.spark.sql.execution
 
 import scala.collection.mutable.{ArrayBuffer, BitSet}
+import scala.concurrent.ExecutionContext.Implicits.global
+import scala.concurrent._
+import scala.concurrent.duration._
 
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.sql.SQLContext
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans._
-import org.apache.spark.sql.catalyst.plans.physical.{ClusteredDistribution, Partitioning}
+import org.apache.spark.sql.catalyst.plans.physical._
 
 @DeveloperApi
 sealed abstract class BuildSide
@@ -34,28 +37,19 @@ case object BuildLeft extends BuildSide
 @DeveloperApi
 case object BuildRight extends BuildSide
 
-/**
- * :: DeveloperApi ::
- */
-@DeveloperApi
-case class HashJoin(
-    leftKeys: Seq[Expression],
-    rightKeys: Seq[Expression],
-    buildSide: BuildSide,
-    left: SparkPlan,
-    right: SparkPlan) extends BinaryNode {
-
-  override def outputPartitioning: Partitioning = left.outputPartitioning
+trait HashJoin {
+  val leftKeys: Seq[Expression]
+  val rightKeys: Seq[Expression]
+  val buildSide: BuildSide
+  val left: SparkPlan
+  val right: SparkPlan
 
-  override def requiredChildDistribution =
-    ClusteredDistribution(leftKeys) :: ClusteredDistribution(rightKeys) :: Nil
-
-  val (buildPlan, streamedPlan) = buildSide match {
+  lazy val (buildPlan, streamedPlan) = buildSide match {
     case BuildLeft => (left, right)
     case BuildRight => (right, left)
   }
 
-  val (buildKeys, streamedKeys) = buildSide match {
+  lazy val (buildKeys, streamedKeys) = buildSide match {
     case BuildLeft => (leftKeys, rightKeys)
     case BuildRight => (rightKeys, leftKeys)
   }
@@ -66,73 +60,74 @@ case class HashJoin(
   @transient lazy val streamSideKeyGenerator =
     () => new MutableProjection(streamedKeys, streamedPlan.output)
 
-  def execute() = {
-
-    buildPlan.execute().zipPartitions(streamedPlan.execute()) { (buildIter, streamIter) =>
-      // TODO: Use Spark's HashMap implementation.
-      val hashTable = new java.util.HashMap[Row, ArrayBuffer[Row]]()
-      var currentRow: Row = null
-
-      // Create a mapping of buildKeys -> rows
-      while (buildIter.hasNext) {
-        currentRow = buildIter.next()
-        val rowKey = buildSideKeyGenerator(currentRow)
-        if(!rowKey.anyNull) {
-          val existingMatchList = hashTable.get(rowKey)
-          val matchList = if (existingMatchList == null) {
-            val newMatchList = new ArrayBuffer[Row]()
-            hashTable.put(rowKey, newMatchList)
-            newMatchList
-          } else {
-            existingMatchList
-          }
-          matchList += currentRow.copy()
+  def joinIterators(buildIter: Iterator[Row], streamIter: Iterator[Row]): Iterator[Row] = {
+    // TODO: Use Spark's HashMap implementation.
+
+    val hashTable = new java.util.HashMap[Row, ArrayBuffer[Row]]()
+    var currentRow: Row = null
+
+    // Create a mapping of buildKeys -> rows
+    while (buildIter.hasNext) {
+      currentRow = buildIter.next()
+      val rowKey = buildSideKeyGenerator(currentRow)
+      if(!rowKey.anyNull) {
+        val existingMatchList = hashTable.get(rowKey)
+        val matchList = if (existingMatchList == null) {
+          val newMatchList = new ArrayBuffer[Row]()
+          hashTable.put(rowKey, newMatchList)
+          newMatchList
+        } else {
+          existingMatchList
         }
+        matchList += currentRow.copy()
       }
+    }
 
-      new Iterator[Row] {
-        private[this] var currentStreamedRow: Row = _
-        private[this] var currentHashMatches: ArrayBuffer[Row] = _
-        private[this] var currentMatchPosition: Int = -1
+    new Iterator[Row] {
+      private[this] var currentStreamedRow: Row = _
+      private[this] var currentHashMatches: ArrayBuffer[Row] = _
+      private[this] var currentMatchPosition: Int = -1
 
-        // Mutable per row objects.
-        private[this] val joinRow = new JoinedRow
+      // Mutable per row objects.
+      private[this] val joinRow = new JoinedRow
 
-        private[this] val joinKeys = streamSideKeyGenerator()
+      private[this] val joinKeys = streamSideKeyGenerator()
 
-        override final def hasNext: Boolean =
-          (currentMatchPosition != -1 && currentMatchPosition < currentHashMatches.size) ||
+      override final def hasNext: Boolean =
+        (currentMatchPosition != -1 && currentMatchPosition < currentHashMatches.size) ||
           (streamIter.hasNext && fetchNext())
 
-        override final def next() = {
-          val ret = joinRow(currentStreamedRow, currentHashMatches(currentMatchPosition))
-          currentMatchPosition += 1
-          ret
+      override final def next() = {
+        val ret = buildSide match {
+          case BuildRight => joinRow(currentStreamedRow, currentHashMatches(currentMatchPosition))
+          case BuildLeft => joinRow(currentHashMatches(currentMatchPosition), currentStreamedRow)
         }
+        currentMatchPosition += 1
+        ret
+      }
 
-        /**
-         * Searches the streamed iterator for the next row that has at least one match in hashtable.
-         *
-         * @return true if the search is successful, and false the streamed iterator runs out of
-         *         tuples.
-         */
-        private final def fetchNext(): Boolean = {
-          currentHashMatches = null
-          currentMatchPosition = -1
-
-          while (currentHashMatches == null && streamIter.hasNext) {
-            currentStreamedRow = streamIter.next()
-            if (!joinKeys(currentStreamedRow).anyNull) {
-              currentHashMatches = hashTable.get(joinKeys.currentValue)
-            }
+      /**
+       * Searches the streamed iterator for the next row that has at least one match in hashtable.
+       *
+       * @return true if the search is successful, and false if the streamed iterator runs out of
+       *         tuples.
+       */
+      private final def fetchNext(): Boolean = {
+        currentHashMatches = null
+        currentMatchPosition = -1
+
+        while (currentHashMatches == null && streamIter.hasNext) {
+          currentStreamedRow = streamIter.next()
+          if (!joinKeys(currentStreamedRow).anyNull) {
+            currentHashMatches = hashTable.get(joinKeys.currentValue)
           }
+        }
 
-          if (currentHashMatches == null) {
-            false
-          } else {
-            currentMatchPosition = 0
-            true
-          }
+        if (currentHashMatches == null) {
+          false
+        } else {
+          currentMatchPosition = 0
+          true
         }
       }
     }
@@ -141,32 +136,49 @@ case class HashJoin(
 
 /**
  * :: DeveloperApi ::
- * Build the right table's join keys into a HashSet, and iteratively go through the left
- * table, to find the if join keys are in the Hash set.
+ * Performs an inner hash join of two child relations by first shuffling the data using the join
+ * keys.
  */
 @DeveloperApi
-case class LeftSemiJoinHash(
+case class ShuffledHashJoin(
     leftKeys: Seq[Expression],
     rightKeys: Seq[Expression],
+    buildSide: BuildSide,
     left: SparkPlan,
-    right: SparkPlan) extends BinaryNode {
+    right: SparkPlan) extends BinaryNode with HashJoin {
 
   override def outputPartitioning: Partitioning = left.outputPartitioning
 
   override def requiredChildDistribution =
     ClusteredDistribution(leftKeys) :: ClusteredDistribution(rightKeys) :: Nil
 
-  val (buildPlan, streamedPlan) = (right, left)
-  val (buildKeys, streamedKeys) = (rightKeys, leftKeys)
+  def execute() = {
+    buildPlan.execute().zipPartitions(streamedPlan.execute()) {
+      (buildIter, streamIter) => joinIterators(buildIter, streamIter)
+    }
+  }
+}
 
-  def output = left.output
+/**
+ * :: DeveloperApi ::
+ * Build the right table's join keys into a HashSet, and iteratively go through the left
+ * table, to find the if join keys are in the Hash set.
+ */
+@DeveloperApi
+case class LeftSemiJoinHash(
+    leftKeys: Seq[Expression],
+    rightKeys: Seq[Expression],
+    left: SparkPlan,
+    right: SparkPlan) extends BinaryNode with HashJoin {
 
-  @transient lazy val buildSideKeyGenerator = new Projection(buildKeys, buildPlan.output)
-  @transient lazy val streamSideKeyGenerator =
-    () => new MutableProjection(streamedKeys, streamedPlan.output)
+  val buildSide = BuildRight
 
-  def execute() = {
+  override def requiredChildDistribution =
+    ClusteredDistribution(leftKeys) :: ClusteredDistribution(rightKeys) :: Nil
+
+  override def output = left.output
 
+  def execute() = {
     buildPlan.execute().zipPartitions(streamedPlan.execute()) { (buildIter, streamIter) =>
       val hashSet = new java.util.HashSet[Row]()
       var currentRow: Row = null
@@ -191,6 +203,43 @@ case class LeftSemiJoinHash(
   }
 }
 
+
+/**
+ * :: DeveloperApi ::
+ * Performs an inner hash join of two child relations.  When the output RDD of this operator is
+ * being constructed, a Spark job is asynchronously started to calculate the values for the
+ * broadcasted relation.  This data is then placed in a Spark broadcast variable.  The streamed
+ * relation is not shuffled.
+ */
+@DeveloperApi
+case class BroadcastHashJoin(
+     leftKeys: Seq[Expression],
+     rightKeys: Seq[Expression],
+     buildSide: BuildSide,
+     left: SparkPlan,
+     right: SparkPlan)(@transient sqlContext: SQLContext) extends BinaryNode with HashJoin {
+
+  override def otherCopyArgs = sqlContext :: Nil
+
+  override def outputPartitioning: Partitioning = left.outputPartitioning
+
+  override def requiredChildDistribution =
+    UnspecifiedDistribution :: UnspecifiedDistribution :: Nil
+
+  @transient
+  lazy val broadcastFuture = future {
+    sqlContext.sparkContext.broadcast(buildPlan.executeCollect())
+  }
+
+  def execute() = {
+    val broadcastRelation = Await.result(broadcastFuture, 5.minute)
+
+    streamedPlan.execute().mapPartitions { streamedIter =>
+      joinIterators(broadcastRelation.value.iterator, streamedIter)
+    }
+  }
+}
+
 /**
  * :: DeveloperApi ::
  * Using BroadcastNestedLoopJoin to calculate left semi join result when there's no join keys
@@ -220,7 +269,6 @@ case class LeftSemiJoinBNL(
         .map(c => BindReferences.bindReference(c, left.output ++ right.output))
         .getOrElse(Literal(true)))
 
-
   def execute() = {
     val broadcastedRelation =
       sqlContext.sparkContext.broadcast(broadcast.execute().map(_.copy()).collect().toIndexedSeq)
@@ -284,7 +332,6 @@ case class BroadcastNestedLoopJoin(
         .map(c => BindReferences.bindReference(c, left.output ++ right.output))
         .getOrElse(Literal(true)))
 
-
   def execute() = {
     val broadcastedRelation =
       sqlContext.sparkContext.broadcast(broadcast.execute().map(_.copy()).collect().toIndexedSeq)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala
index 96c131a7f8af..9c4771d1a984 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala
@@ -44,8 +44,9 @@ import org.apache.spark.sql.catalyst.plans.logical.{LogicalPlan, LeafNode}
  * @param path The path to the Parquet file.
  */
 private[sql] case class ParquetRelation(
-    val path: String,
-    @transient val conf: Option[Configuration] = None) extends LeafNode with MultiInstanceRelation {
+    path: String,
+    @transient conf: Option[Configuration] = None) extends LeafNode with MultiInstanceRelation {
+
   self: Product =>
 
   /** Schema derived from ParquetFile */
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala
index fb599e1e01e7..e4a64a7a482b 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala
@@ -19,7 +19,6 @@ package org.apache.spark.sql
 
 import org.apache.spark.sql.catalyst.analysis._
 import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.catalyst.plans._
 import org.apache.spark.sql.test._
 
 /* Implicits */
@@ -149,102 +148,4 @@ class DslQuerySuite extends QueryTest {
   test("zero count") {
     assert(emptyTableData.count() === 0)
   }
-
-  test("inner join where, one match per row") {
-    checkAnswer(
-      upperCaseData.join(lowerCaseData, Inner).where('n === 'N),
-      Seq(
-        (1, "A", 1, "a"),
-        (2, "B", 2, "b"),
-        (3, "C", 3, "c"),
-        (4, "D", 4, "d")
-      ))
-  }
-
-  test("inner join ON, one match per row") {
-    checkAnswer(
-      upperCaseData.join(lowerCaseData, Inner, Some('n === 'N)),
-      Seq(
-        (1, "A", 1, "a"),
-        (2, "B", 2, "b"),
-        (3, "C", 3, "c"),
-        (4, "D", 4, "d")
-      ))
-  }
-
-  test("inner join, where, multiple matches") {
-    val x = testData2.where('a === 1).as('x)
-    val y = testData2.where('a === 1).as('y)
-    checkAnswer(
-      x.join(y).where("x.a".attr === "y.a".attr),
-      (1,1,1,1) ::
-      (1,1,1,2) ::
-      (1,2,1,1) ::
-      (1,2,1,2) :: Nil
-    )
-  }
-
-  test("inner join, no matches") {
-    val x = testData2.where('a === 1).as('x)
-    val y = testData2.where('a === 2).as('y)
-    checkAnswer(
-      x.join(y).where("x.a".attr === "y.a".attr),
-      Nil)
-  }
-
-  test("big inner join, 4 matches per row") {
-    val bigData = testData.unionAll(testData).unionAll(testData).unionAll(testData)
-    val bigDataX = bigData.as('x)
-    val bigDataY = bigData.as('y)
-
-    checkAnswer(
-      bigDataX.join(bigDataY).where("x.key".attr === "y.key".attr),
-      testData.flatMap(
-        row => Seq.fill(16)((row ++ row).toSeq)).collect().toSeq)
-  }
-
-  test("cartisian product join") {
-    checkAnswer(
-      testData3.join(testData3),
-      (1, null, 1, null) ::
-      (1, null, 2, 2) ::
-      (2, 2, 1, null) ::
-      (2, 2, 2, 2) :: Nil)
-  }
-
-  test("left outer join") {
-    checkAnswer(
-      upperCaseData.join(lowerCaseData, LeftOuter, Some('n === 'N)),
-      (1, "A", 1, "a") ::
-      (2, "B", 2, "b") ::
-      (3, "C", 3, "c") ::
-      (4, "D", 4, "d") ::
-      (5, "E", null, null) ::
-      (6, "F", null, null) :: Nil)
-  }
-
-  test("right outer join") {
-    checkAnswer(
-      lowerCaseData.join(upperCaseData, RightOuter, Some('n === 'N)),
-      (1, "a", 1, "A") ::
-      (2, "b", 2, "B") ::
-      (3, "c", 3, "C") ::
-      (4, "d", 4, "D") ::
-      (null, null, 5, "E") ::
-      (null, null, 6, "F") :: Nil)
-  }
-
-  test("full outer join") {
-    val left = upperCaseData.where('N <= 4).as('left)
-    val right = upperCaseData.where('N >= 3).as('right)
-
-    checkAnswer(
-      left.join(right, FullOuter, Some("left.N".attr === "right.N".attr)),
-      (1, "A", null, null) ::
-      (2, "B", null, null) ::
-      (3, "C", 3, "C") ::
-      (4, "D", 4, "D") ::
-      (null, null, 5, "E") ::
-      (null, null, 6, "F") :: Nil)
-  }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala
new file mode 100644
index 000000000000..3d7d5eedbe8e
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala
@@ -0,0 +1,173 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql
+
+import org.apache.spark.sql.TestData._
+import org.apache.spark.sql.catalyst.plans.{LeftOuter, RightOuter, FullOuter, Inner}
+import org.apache.spark.sql.execution._
+import org.apache.spark.sql.test.TestSQLContext
+import org.apache.spark.sql.test.TestSQLContext._
+
+class JoinSuite extends QueryTest {
+
+  // Ensures tables are loaded.
+  TestData
+
+  test("equi-join is hash-join") {
+    val x = testData2.as('x)
+    val y = testData2.as('y)
+    val join = x.join(y, Inner, Some("x.a".attr === "y.a".attr)).queryExecution.analyzed
+    val planned = planner.HashJoin(join)
+    assert(planned.size === 1)
+  }
+
+  test("plans broadcast hash join, given hints") {
+
+    def mkTest(buildSide: BuildSide, leftTable: String, rightTable: String) = {
+      TestSQLContext.set("spark.sql.join.broadcastTables",
+        s"${if (buildSide == BuildRight) rightTable else leftTable}")
+      val rdd = sql(s"""SELECT * FROM $leftTable JOIN $rightTable ON key = a""")
+      // Using `sparkPlan` because for relevant patterns in HashJoin to be
+      // matched, other strategies need to be applied.
+      val physical = rdd.queryExecution.sparkPlan
+      val bhj = physical.collect { case j: BroadcastHashJoin if j.buildSide == buildSide => j }
+
+      assert(bhj.size === 1, "planner does not pick up hint to generate broadcast hash join")
+      checkAnswer(
+        rdd,
+        Seq(
+          (1, "1", 1, 1),
+          (1, "1", 1, 2),
+          (2, "2", 2, 1),
+          (2, "2", 2, 2),
+          (3, "3", 3, 1),
+          (3, "3", 3, 2)
+        ))
+    }
+
+    mkTest(BuildRight, "testData", "testData2")
+    mkTest(BuildLeft, "testData", "testData2")
+  }
+
+  test("multiple-key equi-join is hash-join") {
+    val x = testData2.as('x)
+    val y = testData2.as('y)
+    val join = x.join(y, Inner,
+      Some("x.a".attr === "y.a".attr && "x.b".attr === "y.b".attr)).queryExecution.analyzed
+    val planned = planner.HashJoin(join)
+    assert(planned.size === 1)
+  }
+
+  test("inner join where, one match per row") {
+    checkAnswer(
+      upperCaseData.join(lowerCaseData, Inner).where('n === 'N),
+      Seq(
+        (1, "A", 1, "a"),
+        (2, "B", 2, "b"),
+        (3, "C", 3, "c"),
+        (4, "D", 4, "d")
+      ))
+  }
+
+  test("inner join ON, one match per row") {
+    checkAnswer(
+      upperCaseData.join(lowerCaseData, Inner, Some('n === 'N)),
+      Seq(
+        (1, "A", 1, "a"),
+        (2, "B", 2, "b"),
+        (3, "C", 3, "c"),
+        (4, "D", 4, "d")
+      ))
+  }
+
+  test("inner join, where, multiple matches") {
+    val x = testData2.where('a === 1).as('x)
+    val y = testData2.where('a === 1).as('y)
+    checkAnswer(
+      x.join(y).where("x.a".attr === "y.a".attr),
+      (1,1,1,1) ::
+      (1,1,1,2) ::
+      (1,2,1,1) ::
+      (1,2,1,2) :: Nil
+    )
+  }
+
+  test("inner join, no matches") {
+    val x = testData2.where('a === 1).as('x)
+    val y = testData2.where('a === 2).as('y)
+    checkAnswer(
+      x.join(y).where("x.a".attr === "y.a".attr),
+      Nil)
+  }
+
+  test("big inner join, 4 matches per row") {
+    val bigData = testData.unionAll(testData).unionAll(testData).unionAll(testData)
+    val bigDataX = bigData.as('x)
+    val bigDataY = bigData.as('y)
+
+    checkAnswer(
+      bigDataX.join(bigDataY).where("x.key".attr === "y.key".attr),
+      testData.flatMap(
+        row => Seq.fill(16)((row ++ row).toSeq)).collect().toSeq)
+  }
+
+  test("cartisian product join") {
+    checkAnswer(
+      testData3.join(testData3),
+      (1, null, 1, null) ::
+      (1, null, 2, 2) ::
+      (2, 2, 1, null) ::
+      (2, 2, 2, 2) :: Nil)
+  }
+
+  test("left outer join") {
+    checkAnswer(
+      upperCaseData.join(lowerCaseData, LeftOuter, Some('n === 'N)),
+      (1, "A", 1, "a") ::
+      (2, "B", 2, "b") ::
+      (3, "C", 3, "c") ::
+      (4, "D", 4, "d") ::
+      (5, "E", null, null) ::
+      (6, "F", null, null) :: Nil)
+  }
+
+  test("right outer join") {
+    checkAnswer(
+      lowerCaseData.join(upperCaseData, RightOuter, Some('n === 'N)),
+      (1, "a", 1, "A") ::
+      (2, "b", 2, "B") ::
+      (3, "c", 3, "C") ::
+      (4, "d", 4, "D") ::
+      (null, null, 5, "E") ::
+      (null, null, 6, "F") :: Nil)
+  }
+
+  test("full outer join") {
+    val left = upperCaseData.where('N <= 4).as('left)
+    val right = upperCaseData.where('N >= 3).as('right)
+
+    checkAnswer(
+      left.join(right, FullOuter, Some("left.N".attr === "right.N".attr)),
+      (1, "A", null, null) ::
+      (2, "B", null, null) ::
+      (3, "C", 3, "C") ::
+      (4, "D", 4, "D") ::
+      (null, null, 5, "E") ::
+      (null, null, 6, "F") :: Nil)
+  }
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/QueryTest.scala b/sql/core/src/test/scala/org/apache/spark/sql/QueryTest.scala
index ef84ead2e6e8..8e1e1971d968 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/QueryTest.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/QueryTest.scala
@@ -35,7 +35,7 @@ class QueryTest extends PlanTest {
       case singleItem => Seq(Seq(singleItem))
     }
 
-    val isSorted = rdd.logicalPlan.collect { case s: logical.Sort => s}.nonEmpty
+    val isSorted = rdd.logicalPlan.collect { case s: logical.Sort => s }.nonEmpty
     def prepareAnswer(answer: Seq[Any]) = if (!isSorted) answer.sortBy(_.toString) else answer
     val sparkAnswer = try rdd.collect().toSeq catch {
       case e: Exception =>
@@ -48,7 +48,7 @@ class QueryTest extends PlanTest {
           """.stripMargin)
     }
 
-    if(prepareAnswer(convertedAnswer) != prepareAnswer(sparkAnswer)) {
+    if (prepareAnswer(convertedAnswer) != prepareAnswer(sparkAnswer)) {
       fail(s"""
         |Results do not match for query:
         |${rdd.logicalPlan}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala
index df6b118360d0..215618e852eb 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala
@@ -57,21 +57,4 @@ class PlannerSuite extends FunSuite {
     val planned = PartialAggregation(query)
     assert(planned.isEmpty)
   }
-
-  test("equi-join is hash-join") {
-    val x = testData2.as('x)
-    val y = testData2.as('y)
-    val join = x.join(y, Inner, Some("x.a".attr === "y.a".attr)).queryExecution.analyzed
-    val planned = planner.HashJoin(join)
-    assert(planned.size === 1)
-  }
-
-  test("multiple-key equi-join is hash-join") {
-    val x = testData2.as('x)
-    val y = testData2.as('y)
-    val join = x.join(y, Inner,
-      Some("x.a".attr === "y.a".attr && "x.b".attr === "y.b".attr)).queryExecution.analyzed
-    val planned = planner.HashJoin(join)
-    assert(planned.size === 1)
-  }
 }
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
index 7695242a8160..7aedfcd74189 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
@@ -258,7 +258,7 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) {
         struct.zip(fields).map {
           case (v, t) => s""""${t.name}":${toHiveStructString(v, t.dataType)}"""
         }.mkString("{", ",", "}")
-      case (seq: Seq[_], ArrayType(typ))=>
+      case (seq: Seq[_], ArrayType(typ)) =>
         seq.map(v => (v, typ)).map(toHiveStructString).mkString("[", ",", "]")
       case (map: Map[_,_], MapType(kType, vType)) =>
         map.map {
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
index faa30c9ae5cc..90eacf426878 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
@@ -34,9 +34,8 @@ import org.apache.spark.sql.catalyst.plans.logical
 import org.apache.spark.sql.catalyst.plans.logical._
 import org.apache.spark.sql.catalyst.rules._
 import org.apache.spark.sql.catalyst.types._
-import org.apache.spark.sql.execution.SparkLogicalPlan
-import org.apache.spark.sql.hive.execution.{HiveTableScan, InsertIntoHiveTable}
-import org.apache.spark.sql.columnar.{InMemoryRelation, InMemoryColumnarTableScan}
+import org.apache.spark.sql.columnar.InMemoryRelation
+import org.apache.spark.sql.hive.execution.HiveTableScan
 
 /* Implicit conversions */
 import scala.collection.JavaConversions._
@@ -259,8 +258,6 @@ private[hive] case class MetastoreRelation
     new Partition(hiveQlTable, p)
   }
 
-  override def isPartitioned = hiveQlTable.isPartitioned
-
   val tableDesc = new TableDesc(
     Class.forName(hiveQlTable.getSerializationLib).asInstanceOf[Class[Deserializer]],
     hiveQlTable.getInputFormatClass,

From 7f196b009d26d4aed403b3c694f8b603601718e3 Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian.cs.zju@gmail.com>
Date: Wed, 25 Jun 2014 18:41:47 -0700
Subject: [PATCH 527/810] [SPARK-2283][SQL] Reset test environment before
 running PruningSuite

JIRA issue: [SPARK-2283](https://issues.apache.org/jira/browse/SPARK-2283)

If `PruningSuite` is run right after `HiveCompatibilitySuite`, the first test case fails because `srcpart` table is cached in-memory by `HiveCompatibilitySuite`, but column pruning is not implemented for `InMemoryColumnarTableScan` operator yet.

Author: Cheng Lian <lian.cs.zju@gmail.com>

Closes #1221 from liancheng/spark-2283 and squashes the following commits:

dc0b663 [Cheng Lian] SPARK-2283: reset test environment before running PruningSuite
---
 .../org/apache/spark/sql/hive/execution/PruningSuite.scala   | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/PruningSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/PruningSuite.scala
index 34434449a0d7..4d7c84f44387 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/PruningSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/PruningSuite.scala
@@ -26,6 +26,11 @@ import scala.collection.JavaConversions._
  * A set of test cases that validate partition and column pruning.
  */
 class PruningSuite extends HiveComparisonTest {
+  // Column/partition pruning is not implemented for `InMemoryColumnarTableScan` yet, need to reset
+  // the environment to ensure all referenced tables in this suites are not cached in-memory.
+  // Refer to https://issues.apache.org/jira/browse/SPARK-2283 for details.
+  TestHive.reset()
+
   // Column pruning tests
 
   createPruningTest("Column pruning - with partitioned table",

From b88a59a66845b8935b22f06fc96d16841ed20c94 Mon Sep 17 00:00:00 2001
From: Mark Hamstra <markhamstra@gmail.com>
Date: Wed, 25 Jun 2014 20:57:48 -0700
Subject: [PATCH 528/810] [SPARK-1749] Job cancellation when SchedulerBackend
 does not implement killTask

This is a fixed up version of #686 (cc @markhamstra @pwendell).  The last commit (the only one I authored) reflects the changes I made from Mark's original patch.

Author: Mark Hamstra <markhamstra@gmail.com>
Author: Kay Ousterhout <kayousterhout@gmail.com>

Closes #1219 from kayousterhout/mark-SPARK-1749 and squashes the following commits:

42dfa7e [Kay Ousterhout] Got rid of terrible double-negative name
80b3205 [Kay Ousterhout] Don't notify listeners of job failure if it wasn't successfully cancelled.
d156d33 [Mark Hamstra] Do nothing in no-kill submitTasks
9312baa [Mark Hamstra] code review update
cc353c8 [Mark Hamstra] scalastyle
e61f7f8 [Mark Hamstra] Catch UnsupportedOperationException when DAGScheduler tries to cancel a job on a SchedulerBackend that does not implement killTask
---
 .../apache/spark/scheduler/DAGScheduler.scala | 30 ++++++++----
 .../spark/scheduler/DAGSchedulerSuite.scala   | 48 +++++++++++++++++++
 2 files changed, 69 insertions(+), 9 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
index b3ebaa547de0..c8559a7a8286 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
@@ -1038,7 +1038,7 @@ class DAGScheduler(
   private def failJobAndIndependentStages(job: ActiveJob, failureReason: String,
       resultStage: Option[Stage]) {
     val error = new SparkException(failureReason)
-    job.listener.jobFailed(error)
+    var ableToCancelStages = true
 
     val shouldInterruptThread =
       if (job.properties == null) false
@@ -1062,18 +1062,26 @@ class DAGScheduler(
           // This is the only job that uses this stage, so fail the stage if it is running.
           val stage = stageIdToStage(stageId)
           if (runningStages.contains(stage)) {
-            taskScheduler.cancelTasks(stageId, shouldInterruptThread)
-            val stageInfo = stageToInfos(stage)
-            stageInfo.stageFailed(failureReason)
-            listenerBus.post(SparkListenerStageCompleted(stageToInfos(stage)))
+            try { // cancelTasks will fail if a SchedulerBackend does not implement killTask
+              taskScheduler.cancelTasks(stageId, shouldInterruptThread)
+              val stageInfo = stageToInfos(stage)
+              stageInfo.stageFailed(failureReason)
+              listenerBus.post(SparkListenerStageCompleted(stageToInfos(stage)))
+            } catch {
+              case e: UnsupportedOperationException =>
+                logInfo(s"Could not cancel tasks for stage $stageId", e)
+              ableToCancelStages = false
+            }
           }
         }
       }
     }
 
-    cleanupStateForJobAndIndependentStages(job, resultStage)
-
-    listenerBus.post(SparkListenerJobEnd(job.jobId, JobFailed(error)))
+    if (ableToCancelStages) {
+      job.listener.jobFailed(error)
+      cleanupStateForJobAndIndependentStages(job, resultStage)
+      listenerBus.post(SparkListenerJobEnd(job.jobId, JobFailed(error)))
+    }
   }
 
   /**
@@ -1155,7 +1163,11 @@ private[scheduler] class DAGSchedulerActorSupervisor(dagScheduler: DAGScheduler)
       case x: Exception =>
         logError("eventProcesserActor failed due to the error %s; shutting down SparkContext"
           .format(x.getMessage))
-        dagScheduler.doCancelAllJobs()
+        try {
+          dagScheduler.doCancelAllJobs()
+        } catch {
+          case t: Throwable => logError("DAGScheduler failed to cancel all jobs.", t)
+        }
         dagScheduler.sc.stop()
         Stop
     }
diff --git a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
index 45368328297d..8dd2a9b9f737 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
@@ -115,6 +115,7 @@ class DAGSchedulerSuite extends TestKit(ActorSystem("DAGSchedulerSuite")) with F
     sc = new SparkContext("local", "DAGSchedulerSuite")
     sparkListener.successfulStages.clear()
     sparkListener.failedStages.clear()
+    failure = null
     sc.addSparkListener(sparkListener)
     taskSets.clear()
     cancelledStages.clear()
@@ -314,6 +315,53 @@ class DAGSchedulerSuite extends TestKit(ActorSystem("DAGSchedulerSuite")) with F
     assertDataStructuresEmpty
   }
 
+  test("job cancellation no-kill backend") {
+    // make sure that the DAGScheduler doesn't crash when the TaskScheduler
+    // doesn't implement killTask()
+    val noKillTaskScheduler = new TaskScheduler() {
+      override def rootPool: Pool = null
+      override def schedulingMode: SchedulingMode = SchedulingMode.NONE
+      override def start() = {}
+      override def stop() = {}
+      override def submitTasks(taskSet: TaskSet) = {
+        taskSets += taskSet
+      }
+      override def cancelTasks(stageId: Int, interruptThread: Boolean) {
+        throw new UnsupportedOperationException
+      }
+      override def setDAGScheduler(dagScheduler: DAGScheduler) = {}
+      override def defaultParallelism() = 2
+    }
+    val noKillScheduler = new DAGScheduler(
+      sc,
+      noKillTaskScheduler,
+      sc.listenerBus,
+      mapOutputTracker,
+      blockManagerMaster,
+      sc.env) {
+      override def runLocally(job: ActiveJob) {
+        // don't bother with the thread while unit testing
+        runLocallyWithinThread(job)
+      }
+    }
+    dagEventProcessTestActor = TestActorRef[DAGSchedulerEventProcessActor](
+      Props(classOf[DAGSchedulerEventProcessActor], noKillScheduler))(system)
+    val rdd = makeRdd(1, Nil)
+    val jobId = submit(rdd, Array(0))
+    cancel(jobId)
+    // Because the job wasn't actually cancelled, we shouldn't have received a failure message.
+    assert(failure === null)
+
+    // When the task set completes normally, state should be correctly updated.
+    complete(taskSets(0), Seq((Success, 42)))
+    assert(results === Map(0 -> 42))
+    assertDataStructuresEmpty
+
+    assert(sc.listenerBus.waitUntilEmpty(WAIT_TIMEOUT_MILLIS))
+    assert(sparkListener.failedStages.isEmpty)
+    assert(sparkListener.successfulStages.contains(0))
+  }
+
   test("run trivial shuffle") {
     val shuffleMapRdd = makeRdd(2, Nil)
     val shuffleDep = new ShuffleDependency(shuffleMapRdd, null)

From 4a346e242c3f241c575f35536220df01ad724e23 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Wed, 25 Jun 2014 22:35:03 -0700
Subject: [PATCH 529/810] [SPARK-2284][UI] Mark all failed tasks as failures.

Previously only tasks failed with ExceptionFailure reason was marked as failure.

Author: Reynold Xin <rxin@apache.org>

Closes #1224 from rxin/SPARK-2284 and squashes the following commits:

be79dbd [Reynold Xin] [SPARK-2284][UI] Mark all failed tasks as failures.
---
 .../spark/ui/jobs/JobProgressListener.scala   |  9 ++++--
 .../ui/jobs/JobProgressListenerSuite.scala    | 30 ++++++++++++++++++-
 2 files changed, 35 insertions(+), 4 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/JobProgressListener.scala b/core/src/main/scala/org/apache/spark/ui/jobs/JobProgressListener.scala
index 396cbcbc8d26..bfefe4dbc408 100644
--- a/core/src/main/scala/org/apache/spark/ui/jobs/JobProgressListener.scala
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/JobProgressListener.scala
@@ -185,12 +185,15 @@ class JobProgressListener(conf: SparkConf) extends SparkListener {
 
       val (failureInfo, metrics): (Option[ExceptionFailure], Option[TaskMetrics]) =
         taskEnd.reason match {
+          case org.apache.spark.Success =>
+            stageIdToTasksComplete(sid) = stageIdToTasksComplete.getOrElse(sid, 0) + 1
+            (None, Option(taskEnd.taskMetrics))
           case e: ExceptionFailure =>
             stageIdToTasksFailed(sid) = stageIdToTasksFailed.getOrElse(sid, 0) + 1
             (Some(e), e.metrics)
-          case _ =>
-            stageIdToTasksComplete(sid) = stageIdToTasksComplete.getOrElse(sid, 0) + 1
-            (None, Option(taskEnd.taskMetrics))
+          case e: org.apache.spark.TaskEndReason =>
+            stageIdToTasksFailed(sid) = stageIdToTasksFailed.getOrElse(sid, 0) + 1
+            (None, None)
         }
 
       stageIdToTime.getOrElseUpdate(sid, 0L)
diff --git a/core/src/test/scala/org/apache/spark/ui/jobs/JobProgressListenerSuite.scala b/core/src/test/scala/org/apache/spark/ui/jobs/JobProgressListenerSuite.scala
index c3a14f48de38..e0fec6a068bd 100644
--- a/core/src/test/scala/org/apache/spark/ui/jobs/JobProgressListenerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/ui/jobs/JobProgressListenerSuite.scala
@@ -20,7 +20,7 @@ package org.apache.spark.ui.jobs
 import org.scalatest.FunSuite
 import org.scalatest.Matchers
 
-import org.apache.spark.{LocalSparkContext, SparkConf, Success}
+import org.apache.spark._
 import org.apache.spark.executor.{ShuffleReadMetrics, TaskMetrics}
 import org.apache.spark.scheduler._
 import org.apache.spark.util.Utils
@@ -101,4 +101,32 @@ class JobProgressListenerSuite extends FunSuite with LocalSparkContext with Matc
     assert(listener.stageIdToExecutorSummaries.getOrElse(0, fail()).getOrElse("exe-2", fail())
       .shuffleRead == 1000)
   }
+
+  test("test task success vs failure counting for different task end reasons") {
+    val conf = new SparkConf()
+    val listener = new JobProgressListener(conf)
+    val metrics = new TaskMetrics()
+    val taskInfo = new TaskInfo(1234L, 0, 0L, "exe-1", "host1", TaskLocality.NODE_LOCAL)
+    taskInfo.finishTime = 1
+    val task = new ShuffleMapTask(0, null, null, 0, null)
+    val taskType = Utils.getFormattedClassName(task)
+
+    // Go through all the failure cases to make sure we are counting them as failures.
+    val taskFailedReasons = Seq(
+      Resubmitted,
+      new FetchFailed(null, 0, 0, 0),
+      new ExceptionFailure("Exception", "description", null, None),
+      TaskResultLost,
+      TaskKilled,
+      ExecutorLostFailure,
+      UnknownReason)
+    for (reason <- taskFailedReasons) {
+      listener.onTaskEnd(SparkListenerTaskEnd(task.stageId, taskType, reason, taskInfo, metrics))
+      assert(listener.stageIdToTasksComplete.get(task.stageId) === None)
+    }
+
+    // Make sure we count success as success.
+    listener.onTaskEnd(SparkListenerTaskEnd(task.stageId, taskType, Success, taskInfo, metrics))
+    assert(listener.stageIdToTasksComplete.get(task.stageId) === Some(1))
+  }
 }

From 441cdcca64ba0b3cbaae4d4f25ebe4c4ebd46aae Mon Sep 17 00:00:00 2001
From: "Szul, Piotr" <Piotr.Szul@csiro.au>
Date: Wed, 25 Jun 2014 21:55:49 -0700
Subject: [PATCH 530/810] [SPARK-2172] PySpark cannot import mllib modules in
 YARN-client mode

Include pyspark/mllib python sources as resources in the mllib.jar.
This way they will be included in the final assembly

Author: Szul, Piotr <Piotr.Szul@csiro.au>

Closes #1223 from piotrszul/branch-1.0 and squashes the following commits:

69d5174 [Szul, Piotr] Removed unsed resource directory src/main/resource from mllib pom
f8c52a0 [Szul, Piotr] [SPARK-2172] PySpark cannot import mllib modules in YARN-client mode Include pyspark/mllib python sources as resources in the jar

(cherry picked from commit fa167194ce1b5898e4d7232346c9f86b2897a722)
Signed-off-by: Reynold Xin <rxin@apache.org>
---
 mllib/pom.xml | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/mllib/pom.xml b/mllib/pom.xml
index 878cb83dbf78..b622f96dd790 100644
--- a/mllib/pom.xml
+++ b/mllib/pom.xml
@@ -84,5 +84,13 @@
         <artifactId>scalatest-maven-plugin</artifactId>
       </plugin>
     </plugins>
+    <resources>
+      <resource>
+        <directory>../python</directory>
+        <includes>
+          <include>pyspark/mllib/*.py</include>
+        </includes>
+      </resource>
+    </resources>
   </build>
 </project>

From e4899a253728bfa7c78709a37a4837f74b72bd61 Mon Sep 17 00:00:00 2001
From: Takuya UESHIN <ueshin@happy-camper.st>
Date: Wed, 25 Jun 2014 23:55:31 -0700
Subject: [PATCH 531/810] [SPARK-2254] [SQL] ScalaRefection should mark
 primitive types as non-nullable.

Author: Takuya UESHIN <ueshin@happy-camper.st>

Closes #1193 from ueshin/issues/SPARK-2254 and squashes the following commits:

cfd6088 [Takuya UESHIN] Modify ScalaRefection.schemaFor method to return nullability of Scala Type.
---
 .../spark/sql/catalyst/ScalaReflection.scala  |  65 ++++-----
 .../sql/catalyst/ScalaReflectionSuite.scala   | 131 ++++++++++++++++++
 2 files changed, 165 insertions(+), 31 deletions(-)
 create mode 100644 sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/ScalaReflectionSuite.scala

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala
index 196695a0a188..ada48eaf5dc0 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala
@@ -30,53 +30,56 @@ import org.apache.spark.sql.catalyst.types._
 object ScalaReflection {
   import scala.reflect.runtime.universe._
 
+  case class Schema(dataType: DataType, nullable: Boolean)
+
   /** Returns a Sequence of attributes for the given case class type. */
   def attributesFor[T: TypeTag]: Seq[Attribute] = schemaFor[T] match {
-    case s: StructType =>
-      s.fields.map(f => AttributeReference(f.name, f.dataType, nullable = true)())
+    case Schema(s: StructType, _) =>
+      s.fields.map(f => AttributeReference(f.name, f.dataType, f.nullable)())
   }
 
-  /** Returns a catalyst DataType for the given Scala Type using reflection. */
-  def schemaFor[T: TypeTag]: DataType = schemaFor(typeOf[T])
+  /** Returns a catalyst DataType and its nullability for the given Scala Type using reflection. */
+  def schemaFor[T: TypeTag]: Schema = schemaFor(typeOf[T])
 
-  /** Returns a catalyst DataType for the given Scala Type using reflection. */
-  def schemaFor(tpe: `Type`): DataType = tpe match {
+  /** Returns a catalyst DataType and its nullability for the given Scala Type using reflection. */
+  def schemaFor(tpe: `Type`): Schema = tpe match {
     case t if t <:< typeOf[Option[_]] =>
       val TypeRef(_, _, Seq(optType)) = t
-      schemaFor(optType)
+      Schema(schemaFor(optType).dataType, nullable = true)
     case t if t <:< typeOf[Product] =>
       val params = t.member("<init>": TermName).asMethod.paramss
-      StructType(
-        params.head.map(p =>
-          StructField(p.name.toString, schemaFor(p.typeSignature), nullable = true)))
+      Schema(StructType(
+        params.head.map { p =>
+          val Schema(dataType, nullable) = schemaFor(p.typeSignature)
+          StructField(p.name.toString, dataType, nullable)
+        }), nullable = true)
     // Need to decide if we actually need a special type here.
-    case t if t <:< typeOf[Array[Byte]] => BinaryType
+    case t if t <:< typeOf[Array[Byte]] => Schema(BinaryType, nullable = true)
     case t if t <:< typeOf[Array[_]] =>
       sys.error(s"Only Array[Byte] supported now, use Seq instead of $t")
     case t if t <:< typeOf[Seq[_]] =>
       val TypeRef(_, _, Seq(elementType)) = t
-      ArrayType(schemaFor(elementType))
+      Schema(ArrayType(schemaFor(elementType).dataType), nullable = true)
     case t if t <:< typeOf[Map[_,_]] =>
       val TypeRef(_, _, Seq(keyType, valueType)) = t
-      MapType(schemaFor(keyType), schemaFor(valueType))
-    case t if t <:< typeOf[String] => StringType
-    case t if t <:< typeOf[Timestamp] => TimestampType
-    case t if t <:< typeOf[BigDecimal] => DecimalType
-    case t if t <:< typeOf[java.lang.Integer] => IntegerType
-    case t if t <:< typeOf[java.lang.Long] => LongType
-    case t if t <:< typeOf[java.lang.Double] => DoubleType
-    case t if t <:< typeOf[java.lang.Float] => FloatType
-    case t if t <:< typeOf[java.lang.Short] => ShortType
-    case t if t <:< typeOf[java.lang.Byte] => ByteType
-    case t if t <:< typeOf[java.lang.Boolean] => BooleanType
-    // TODO: The following datatypes could be marked as non-nullable.
-    case t if t <:< definitions.IntTpe => IntegerType
-    case t if t <:< definitions.LongTpe => LongType
-    case t if t <:< definitions.DoubleTpe => DoubleType
-    case t if t <:< definitions.FloatTpe => FloatType
-    case t if t <:< definitions.ShortTpe => ShortType
-    case t if t <:< definitions.ByteTpe => ByteType
-    case t if t <:< definitions.BooleanTpe => BooleanType
+      Schema(MapType(schemaFor(keyType).dataType, schemaFor(valueType).dataType), nullable = true)
+    case t if t <:< typeOf[String] => Schema(StringType, nullable = true)
+    case t if t <:< typeOf[Timestamp] => Schema(TimestampType, nullable = true)
+    case t if t <:< typeOf[BigDecimal] => Schema(DecimalType, nullable = true)
+    case t if t <:< typeOf[java.lang.Integer] => Schema(IntegerType, nullable = true)
+    case t if t <:< typeOf[java.lang.Long] => Schema(LongType, nullable = true)
+    case t if t <:< typeOf[java.lang.Double] => Schema(DoubleType, nullable = true)
+    case t if t <:< typeOf[java.lang.Float] => Schema(FloatType, nullable = true)
+    case t if t <:< typeOf[java.lang.Short] => Schema(ShortType, nullable = true)
+    case t if t <:< typeOf[java.lang.Byte] => Schema(ByteType, nullable = true)
+    case t if t <:< typeOf[java.lang.Boolean] => Schema(BooleanType, nullable = true)
+    case t if t <:< definitions.IntTpe => Schema(IntegerType, nullable = false)
+    case t if t <:< definitions.LongTpe => Schema(LongType, nullable = false)
+    case t if t <:< definitions.DoubleTpe => Schema(DoubleType, nullable = false)
+    case t if t <:< definitions.FloatTpe => Schema(FloatType, nullable = false)
+    case t if t <:< definitions.ShortTpe => Schema(ShortType, nullable = false)
+    case t if t <:< definitions.ByteTpe => Schema(ByteType, nullable = false)
+    case t if t <:< definitions.BooleanTpe => Schema(BooleanType, nullable = false)
   }
 
   implicit class CaseClassRelation[A <: Product : TypeTag](data: Seq[A]) {
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/ScalaReflectionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/ScalaReflectionSuite.scala
new file mode 100644
index 000000000000..489d7e9c2437
--- /dev/null
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/ScalaReflectionSuite.scala
@@ -0,0 +1,131 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst
+
+import java.sql.Timestamp
+
+import org.scalatest.FunSuite
+
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.types._
+
+case class PrimitiveData(
+    intField: Int,
+    longField: Long,
+    doubleField: Double,
+    floatField: Float,
+    shortField: Short,
+    byteField: Byte,
+    booleanField: Boolean)
+
+case class NullableData(
+    intField: java.lang.Integer,
+    longField: java.lang.Long,
+    doubleField: java.lang.Double,
+    floatField: java.lang.Float,
+    shortField: java.lang.Short,
+    byteField: java.lang.Byte,
+    booleanField: java.lang.Boolean,
+    stringField: String,
+    decimalField: BigDecimal,
+    timestampField: Timestamp,
+    binaryField: Array[Byte])
+
+case class OptionalData(
+    intField: Option[Int],
+    longField: Option[Long],
+    doubleField: Option[Double],
+    floatField: Option[Float],
+    shortField: Option[Short],
+    byteField: Option[Byte],
+    booleanField: Option[Boolean])
+
+case class ComplexData(
+    arrayField: Seq[Int],
+    mapField: Map[Int, String],
+    structField: PrimitiveData)
+
+class ScalaReflectionSuite extends FunSuite {
+  import ScalaReflection._
+
+  test("primitive data") {
+    val schema = schemaFor[PrimitiveData]
+    assert(schema === Schema(
+      StructType(Seq(
+        StructField("intField", IntegerType, nullable = false),
+        StructField("longField", LongType, nullable = false),
+        StructField("doubleField", DoubleType, nullable = false),
+        StructField("floatField", FloatType, nullable = false),
+        StructField("shortField", ShortType, nullable = false),
+        StructField("byteField", ByteType, nullable = false),
+        StructField("booleanField", BooleanType, nullable = false))),
+      nullable = true))
+  }
+
+  test("nullable data") {
+    val schema = schemaFor[NullableData]
+    assert(schema === Schema(
+      StructType(Seq(
+        StructField("intField", IntegerType, nullable = true),
+        StructField("longField", LongType, nullable = true),
+        StructField("doubleField", DoubleType, nullable = true),
+        StructField("floatField", FloatType, nullable = true),
+        StructField("shortField", ShortType, nullable = true),
+        StructField("byteField", ByteType, nullable = true),
+        StructField("booleanField", BooleanType, nullable = true),
+        StructField("stringField", StringType, nullable = true),
+        StructField("decimalField", DecimalType, nullable = true),
+        StructField("timestampField", TimestampType, nullable = true),
+        StructField("binaryField", BinaryType, nullable = true))),
+      nullable = true))
+  }
+
+  test("optinal data") {
+    val schema = schemaFor[OptionalData]
+    assert(schema === Schema(
+      StructType(Seq(
+        StructField("intField", IntegerType, nullable = true),
+        StructField("longField", LongType, nullable = true),
+        StructField("doubleField", DoubleType, nullable = true),
+        StructField("floatField", FloatType, nullable = true),
+        StructField("shortField", ShortType, nullable = true),
+        StructField("byteField", ByteType, nullable = true),
+        StructField("booleanField", BooleanType, nullable = true))),
+      nullable = true))
+  }
+
+  test("complex data") {
+    val schema = schemaFor[ComplexData]
+    assert(schema === Schema(
+      StructType(Seq(
+        StructField("arrayField", ArrayType(IntegerType), nullable = true),
+        StructField("mapField", MapType(IntegerType, StringType), nullable = true),
+        StructField(
+          "structField",
+          StructType(Seq(
+            StructField("intField", IntegerType, nullable = false),
+            StructField("longField", LongType, nullable = false),
+            StructField("doubleField", DoubleType, nullable = false),
+            StructField("floatField", FloatType, nullable = false),
+            StructField("shortField", ShortType, nullable = false),
+            StructField("byteField", ByteType, nullable = false),
+            StructField("booleanField", BooleanType, nullable = false))),
+          nullable = true))),
+      nullable = true))
+  }
+}

From 48a82a827c99526b165c78d7e88faec43568a37a Mon Sep 17 00:00:00 2001
From: Kay Ousterhout <kayousterhout@gmail.com>
Date: Thu, 26 Jun 2014 08:20:27 -0500
Subject: [PATCH 532/810] Remove use of spark.worker.instances

spark.worker.instances was added as part of this commit: https://github.com/apache/spark/commit/1617816090e7b20124a512a43860a21232ebf511

My understanding is that SPARK_WORKER_INSTANCES is supported for backwards compatibility,
but spark.worker.instances is never used (SparkSubmit.scala sets spark.executor.instances) so should
not have been added.

@sryza @pwendell @tgravescs LMK if I'm understanding this correctly

Author: Kay Ousterhout <kayousterhout@gmail.com>

Closes #1214 from kayousterhout/yarn_config and squashes the following commits:

3d7c491 [Kay Ousterhout] Remove use of spark.worker.instances
---
 .../spark/scheduler/cluster/YarnClientSchedulerBackend.scala    | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala b/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala
index 412dfe38d55e..fd2694fe7278 100644
--- a/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala
+++ b/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala
@@ -63,7 +63,7 @@ private[spark] class YarnClientSchedulerBackend(
     // variables.
     List(("--driver-memory", "SPARK_MASTER_MEMORY", "spark.master.memory"),
       ("--driver-memory", "SPARK_DRIVER_MEMORY", "spark.driver.memory"),
-      ("--num-executors", "SPARK_WORKER_INSTANCES", "spark.worker.instances"),
+      ("--num-executors", "SPARK_WORKER_INSTANCES", "spark.executor.instances"),
       ("--num-executors", "SPARK_EXECUTOR_INSTANCES", "spark.executor.instances"),
       ("--executor-memory", "SPARK_WORKER_MEMORY", "spark.executor.memory"),
       ("--executor-memory", "SPARK_EXECUTOR_MEMORY", "spark.executor.memory"),

From 32a1ad75313472b1b098f7ec99335686d3fe4fc3 Mon Sep 17 00:00:00 2001
From: Takuya UESHIN <ueshin@happy-camper.st>
Date: Thu, 26 Jun 2014 13:37:19 -0700
Subject: [PATCH 533/810] [SPARK-2295] [SQL] Make JavaBeans nullability
 stricter.

Author: Takuya UESHIN <ueshin@happy-camper.st>

Closes #1235 from ueshin/issues/SPARK-2295 and squashes the following commits:

201c508 [Takuya UESHIN] Make JavaBeans nullability stricter.
---
 .../spark/sql/api/java/JavaSQLContext.scala   | 37 +++++++++----------
 1 file changed, 18 insertions(+), 19 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSQLContext.scala
index ff6deeda2394..790d9ef22cf1 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSQLContext.scala
@@ -137,26 +137,25 @@ class JavaSQLContext(val sqlContext: SQLContext) {
 
     val fields = beanInfo.getPropertyDescriptors.filterNot(_.getName == "class")
     fields.map { property =>
-      val dataType = property.getPropertyType match {
-        case c: Class[_] if c == classOf[java.lang.String] => StringType
-        case c: Class[_] if c == java.lang.Short.TYPE => ShortType
-        case c: Class[_] if c == java.lang.Integer.TYPE => IntegerType
-        case c: Class[_] if c == java.lang.Long.TYPE => LongType
-        case c: Class[_] if c == java.lang.Double.TYPE => DoubleType
-        case c: Class[_] if c == java.lang.Byte.TYPE => ByteType
-        case c: Class[_] if c == java.lang.Float.TYPE => FloatType
-        case c: Class[_] if c == java.lang.Boolean.TYPE => BooleanType
-
-        case c: Class[_] if c == classOf[java.lang.Short] => ShortType
-        case c: Class[_] if c == classOf[java.lang.Integer] => IntegerType
-        case c: Class[_] if c == classOf[java.lang.Long] => LongType
-        case c: Class[_] if c == classOf[java.lang.Double] => DoubleType
-        case c: Class[_] if c == classOf[java.lang.Byte] => ByteType
-        case c: Class[_] if c == classOf[java.lang.Float] => FloatType
-        case c: Class[_] if c == classOf[java.lang.Boolean] => BooleanType
+      val (dataType, nullable) = property.getPropertyType match {
+        case c: Class[_] if c == classOf[java.lang.String] => (StringType, true)
+        case c: Class[_] if c == java.lang.Short.TYPE => (ShortType, false)
+        case c: Class[_] if c == java.lang.Integer.TYPE => (IntegerType, false)
+        case c: Class[_] if c == java.lang.Long.TYPE => (LongType, false)
+        case c: Class[_] if c == java.lang.Double.TYPE => (DoubleType, false)
+        case c: Class[_] if c == java.lang.Byte.TYPE => (ByteType, false)
+        case c: Class[_] if c == java.lang.Float.TYPE => (FloatType, false)
+        case c: Class[_] if c == java.lang.Boolean.TYPE => (BooleanType, false)
+
+        case c: Class[_] if c == classOf[java.lang.Short] => (ShortType, true)
+        case c: Class[_] if c == classOf[java.lang.Integer] => (IntegerType, true)
+        case c: Class[_] if c == classOf[java.lang.Long] => (LongType, true)
+        case c: Class[_] if c == classOf[java.lang.Double] => (DoubleType, true)
+        case c: Class[_] if c == classOf[java.lang.Byte] => (ByteType, true)
+        case c: Class[_] if c == classOf[java.lang.Float] => (FloatType, true)
+        case c: Class[_] if c == classOf[java.lang.Boolean] => (BooleanType, true)
       }
-      // TODO: Nullability could be stricter.
-      AttributeReference(property.getName, dataType, nullable = true)()
+      AttributeReference(property.getName, dataType, nullable)()
     }
   }
 }

From 6587ef7c1783961e6ef250afa387271a1bd6e277 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Thu, 26 Jun 2014 14:00:45 -0700
Subject: [PATCH 534/810] [SPARK-2286][UI] Report exception/errors for failed
 tasks that are not ExceptionFailure

Also added inline doc for each TaskEndReason.

Author: Reynold Xin <rxin@apache.org>

Closes #1225 from rxin/SPARK-2286 and squashes the following commits:

6a7959d [Reynold Xin] Fix unit test failure.
cf9d5eb [Reynold Xin] Merge branch 'master' into SPARK-2286
a61fae1 [Reynold Xin] Move to line above ...
38c7391 [Reynold Xin] [SPARK-2286][UI] Report exception/errors for failed tasks that are not ExceptionFailure.
---
 .../org/apache/spark/TaskEndReason.scala      | 68 +++++++++++++++++--
 .../spark/scheduler/TaskSetManager.scala      |  4 +-
 .../spark/ui/jobs/JobProgressListener.scala   | 17 ++---
 .../org/apache/spark/ui/jobs/StagePage.scala  | 12 +---
 .../org/apache/spark/ui/jobs/StageTable.scala |  4 +-
 5 files changed, 77 insertions(+), 28 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/TaskEndReason.scala b/core/src/main/scala/org/apache/spark/TaskEndReason.scala
index a3074916d13e..5e8bd8c8e533 100644
--- a/core/src/main/scala/org/apache/spark/TaskEndReason.scala
+++ b/core/src/main/scala/org/apache/spark/TaskEndReason.scala
@@ -30,27 +30,69 @@ import org.apache.spark.storage.BlockManagerId
 @DeveloperApi
 sealed trait TaskEndReason
 
+/**
+ * :: DeveloperApi ::
+ * Task succeeded.
+ */
 @DeveloperApi
 case object Success extends TaskEndReason
 
+/**
+ * :: DeveloperApi ::
+ * Various possible reasons why a task failed.
+ */
+@DeveloperApi
+sealed trait TaskFailedReason extends TaskEndReason {
+  /** Error message displayed in the web UI. */
+  def toErrorString: String
+}
+
+/**
+ * :: DeveloperApi ::
+ * A [[org.apache.spark.scheduler.ShuffleMapTask]] that completed successfully earlier, but we
+ * lost the executor before the stage completed. This means Spark needs to reschedule the task
+ * to be re-executed on a different executor.
+ */
 @DeveloperApi
-case object Resubmitted extends TaskEndReason // Task was finished earlier but we've now lost it
+case object Resubmitted extends TaskFailedReason {
+  override def toErrorString: String = "Resubmitted (resubmitted due to lost executor)"
+}
 
+/**
+ * :: DeveloperApi ::
+ * Task failed to fetch shuffle data from a remote node. Probably means we have lost the remote
+ * executors the task is trying to fetch from, and thus need to rerun the previous stage.
+ */
 @DeveloperApi
 case class FetchFailed(
     bmAddress: BlockManagerId,
     shuffleId: Int,
     mapId: Int,
     reduceId: Int)
-  extends TaskEndReason
+  extends TaskFailedReason {
+  override def toErrorString: String = {
+    val bmAddressString = if (bmAddress == null) "null" else bmAddress.toString
+    s"FetchFailed($bmAddressString, shuffleId=$shuffleId, mapId=$mapId, reduceId=$reduceId)"
+  }
+}
 
+/**
+ * :: DeveloperApi ::
+ * Task failed due to a runtime exception. This is the most common failure case and also captures
+ * user program exceptions.
+ */
 @DeveloperApi
 case class ExceptionFailure(
     className: String,
     description: String,
     stackTrace: Array[StackTraceElement],
     metrics: Option[TaskMetrics])
-  extends TaskEndReason
+  extends TaskFailedReason {
+  override def toErrorString: String = {
+    val stackTraceString = if (stackTrace == null) "null" else stackTrace.mkString("\n")
+    s"$className ($description}\n$stackTraceString"
+  }
+}
 
 /**
  * :: DeveloperApi ::
@@ -58,10 +100,18 @@ case class ExceptionFailure(
  * it was fetched.
  */
 @DeveloperApi
-case object TaskResultLost extends TaskEndReason
+case object TaskResultLost extends TaskFailedReason {
+  override def toErrorString: String = "TaskResultLost (result lost from block manager)"
+}
 
+/**
+ * :: DeveloperApi ::
+ * Task was killed intentionally and needs to be rescheduled.
+ */
 @DeveloperApi
-case object TaskKilled extends TaskEndReason
+case object TaskKilled extends TaskFailedReason {
+  override def toErrorString: String = "TaskKilled (killed intentionally)"
+}
 
 /**
  * :: DeveloperApi ::
@@ -69,7 +119,9 @@ case object TaskKilled extends TaskEndReason
  * the task crashed the JVM.
  */
 @DeveloperApi
-case object ExecutorLostFailure extends TaskEndReason
+case object ExecutorLostFailure extends TaskFailedReason {
+  override def toErrorString: String = "ExecutorLostFailure (executor lost)"
+}
 
 /**
  * :: DeveloperApi ::
@@ -77,4 +129,6 @@ case object ExecutorLostFailure extends TaskEndReason
  * deserializing the task result.
  */
 @DeveloperApi
-case object UnknownReason extends TaskEndReason
+case object UnknownReason extends TaskFailedReason {
+  override def toErrorString: String = "UnknownReason"
+}
diff --git a/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala b/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala
index b5bcdd7e99c5..c0898f64fb0c 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala
@@ -641,7 +641,9 @@ private[spark] class TaskSetManager(
       addPendingTask(index, readding=true)
     }
 
-    // Re-enqueue any tasks that ran on the failed executor if this is a shuffle map stage
+    // Re-enqueue any tasks that ran on the failed executor if this is a shuffle map stage.
+    // The reason is the next stage wouldn't be able to fetch the data from this dead executor
+    // so we would need to rerun these tasks on other executors.
     if (tasks(0).isInstanceOf[ShuffleMapTask]) {
       for ((tid, info) <- taskInfos if info.executorId == execId) {
         val index = taskInfos(tid).index
diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/JobProgressListener.scala b/core/src/main/scala/org/apache/spark/ui/jobs/JobProgressListener.scala
index bfefe4dbc408..381a5443df8b 100644
--- a/core/src/main/scala/org/apache/spark/ui/jobs/JobProgressListener.scala
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/JobProgressListener.scala
@@ -19,7 +19,7 @@ package org.apache.spark.ui.jobs
 
 import scala.collection.mutable.{HashMap, ListBuffer}
 
-import org.apache.spark.{ExceptionFailure, SparkConf, SparkContext, Success}
+import org.apache.spark._
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.executor.TaskMetrics
 import org.apache.spark.scheduler._
@@ -51,6 +51,7 @@ class JobProgressListener(conf: SparkConf) extends SparkListener {
   var totalShuffleRead = 0L
   var totalShuffleWrite = 0L
 
+  // TODO: Should probably consolidate all following into a single hash map.
   val stageIdToTime = HashMap[Int, Long]()
   val stageIdToShuffleRead = HashMap[Int, Long]()
   val stageIdToShuffleWrite = HashMap[Int, Long]()
@@ -183,17 +184,17 @@ class JobProgressListener(conf: SparkConf) extends SparkListener {
       // Remove by taskId, rather than by TaskInfo, in case the TaskInfo is from storage
       tasksActive.remove(info.taskId)
 
-      val (failureInfo, metrics): (Option[ExceptionFailure], Option[TaskMetrics]) =
+      val (errorMessage, metrics): (Option[String], Option[TaskMetrics]) =
         taskEnd.reason match {
           case org.apache.spark.Success =>
             stageIdToTasksComplete(sid) = stageIdToTasksComplete.getOrElse(sid, 0) + 1
             (None, Option(taskEnd.taskMetrics))
-          case e: ExceptionFailure =>
+          case e: ExceptionFailure =>  // Handle ExceptionFailure because we might have metrics
             stageIdToTasksFailed(sid) = stageIdToTasksFailed.getOrElse(sid, 0) + 1
-            (Some(e), e.metrics)
-          case e: org.apache.spark.TaskEndReason =>
+            (Some(e.toErrorString), e.metrics)
+          case e: TaskFailedReason =>  // All other failure cases
             stageIdToTasksFailed(sid) = stageIdToTasksFailed.getOrElse(sid, 0) + 1
-            (None, None)
+            (Some(e.toErrorString), None)
         }
 
       stageIdToTime.getOrElseUpdate(sid, 0L)
@@ -221,7 +222,7 @@ class JobProgressListener(conf: SparkConf) extends SparkListener {
       stageIdToDiskBytesSpilled(sid) += diskBytesSpilled
 
       val taskMap = stageIdToTaskData.getOrElse(sid, HashMap[Long, TaskUIData]())
-      taskMap(info.taskId) = new TaskUIData(info, metrics, failureInfo)
+      taskMap(info.taskId) = new TaskUIData(info, metrics, errorMessage)
       stageIdToTaskData(sid) = taskMap
     }
   }
@@ -256,7 +257,7 @@ class JobProgressListener(conf: SparkConf) extends SparkListener {
 case class TaskUIData(
     taskInfo: TaskInfo,
     taskMetrics: Option[TaskMetrics] = None,
-    exception: Option[ExceptionFailure] = None)
+    errorMessage: Option[String] = None)
 
 private object JobProgressListener {
   val DEFAULT_POOL_NAME = "default"
diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala b/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
index 4bce472036f7..8b65f0671bdb 100644
--- a/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
@@ -210,10 +210,7 @@ private[ui] class StagePage(parent: JobProgressTab) extends WebUIPage("stage") {
 
   def taskRow(shuffleRead: Boolean, shuffleWrite: Boolean, bytesSpilled: Boolean)
       (taskData: TaskUIData): Seq[Node] = {
-    def fmtStackTrace(trace: Seq[StackTraceElement]): Seq[Node] =
-      trace.map(e => <span style="display:block;">{e.toString}</span>)
-
-    taskData match { case TaskUIData(info, metrics, exception) =>
+    taskData match { case TaskUIData(info, metrics, errorMessage) =>
       val duration = if (info.status == "RUNNING") info.timeRunning(System.currentTimeMillis())
         else metrics.map(_.executorRunTime).getOrElse(1L)
       val formatDuration = if (info.status == "RUNNING") UIUtils.formatDuration(duration)
@@ -283,12 +280,7 @@ private[ui] class StagePage(parent: JobProgressTab) extends WebUIPage("stage") {
           </td>
         }}
         <td>
-          {exception.map { e =>
-            <span>
-              {e.className} ({e.description})<br/>
-              {fmtStackTrace(e.stackTrace)}
-            </span>
-          }.getOrElse("")}
+          {errorMessage.map { e => <pre>{e}</pre> }.getOrElse("")}
         </td>
       </tr>
     }
diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/StageTable.scala b/core/src/main/scala/org/apache/spark/ui/jobs/StageTable.scala
index a3f824a4e1f5..30971f769682 100644
--- a/core/src/main/scala/org/apache/spark/ui/jobs/StageTable.scala
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/StageTable.scala
@@ -91,13 +91,13 @@ private[ui] class StageTableBase(
         {s.name}
       </a>
 
-    val details = if (s.details.nonEmpty) (
+    val details = if (s.details.nonEmpty) {
       <span onclick="this.parentNode.querySelector('.stage-details').classList.toggle('collapsed')"
             class="expand-details">
         +show details
       </span>
       <pre class="stage-details collapsed">{s.details}</pre>
-    )
+    }
 
     listener.stageIdToDescription.get(s.stageId)
       .map(d => <div><em>{d}</em></div><div>{nameLink} {killLink}</div>)

From 62d4a0fa9947e64c1533f66ae577557bcfb271c9 Mon Sep 17 00:00:00 2001
From: Zichuan Ye <jerry@tangentds.com>
Date: Thu, 26 Jun 2014 15:21:29 -0700
Subject: [PATCH 535/810] Fixing AWS instance type information based upon
 current EC2 data

Fixed a problem in previous file in which some information regarding AWS instance types were wrong. Such information was updated base upon current AWS EC2 data.

Author: Zichuan Ye <jerry@tangentds.com>

Closes #1156 from jerry86/master and squashes the following commits:

ff36e95 [Zichuan Ye] Fixing AWS instance type information based upon current EC2 data
---
 ec2/spark_ec2.py | 25 +++++++++++++++++--------
 1 file changed, 17 insertions(+), 8 deletions(-)

diff --git a/ec2/spark_ec2.py b/ec2/spark_ec2.py
index e80f928b8beb..e22d93bd31bc 100755
--- a/ec2/spark_ec2.py
+++ b/ec2/spark_ec2.py
@@ -203,6 +203,8 @@ def get_spark_shark_version(opts):
 
 # Attempt to resolve an appropriate AMI given the architecture and
 # region of the request.
+# Information regarding Amazon Linux AMI instance type was update on 2014-6-20:
+# http://aws.amazon.com/amazon-linux-ami/instance-type-matrix/
 def get_spark_ami(opts):
     instance_types = {
         "m1.small":    "pvm",
@@ -218,10 +220,12 @@ def get_spark_ami(opts):
         "cc1.4xlarge": "hvm",
         "cc2.8xlarge": "hvm",
         "cg1.4xlarge": "hvm",
-        "hs1.8xlarge": "hvm",
-        "hi1.4xlarge": "hvm",
-        "m3.xlarge":   "hvm",
-        "m3.2xlarge":  "hvm",
+        "hs1.8xlarge": "pvm",
+        "hi1.4xlarge": "pvm",
+        "m3.medium":   "pvm",
+        "m3.large":    "pvm",
+        "m3.xlarge":   "pvm",
+        "m3.2xlarge":  "pvm",
         "cr1.8xlarge": "hvm",
         "i2.xlarge":   "hvm",
         "i2.2xlarge":  "hvm",
@@ -526,7 +530,8 @@ def wait_for_cluster(conn, wait_secs, master_nodes, slave_nodes):
 
 # Get number of local disks available for a given EC2 instance type.
 def get_num_disks(instance_type):
-    # From http://docs.amazonwebservices.com/AWSEC2/latest/UserGuide/index.html?InstanceStorage.html
+    # From http://docs.aws.amazon.com/AWSEC2/latest/UserGuide/InstanceStorage.html
+    # Updated 2014-6-20
     disks_by_instance = {
         "m1.small":    1,
         "m1.medium":   1,
@@ -544,8 +549,10 @@ def get_num_disks(instance_type):
         "hs1.8xlarge": 24,
         "cr1.8xlarge": 2,
         "hi1.4xlarge": 2,
-        "m3.xlarge":   0,
-        "m3.2xlarge":  0,
+        "m3.medium":   1,
+        "m3.large":    1,
+        "m3.xlarge":   2,
+        "m3.2xlarge":  2,
         "i2.xlarge":   1,
         "i2.2xlarge":  2,
         "i2.4xlarge":  4,
@@ -559,7 +566,9 @@ def get_num_disks(instance_type):
         "r3.xlarge":   1,
         "r3.2xlarge":  1,
         "r3.4xlarge":  1,
-        "r3.8xlarge":  2
+        "r3.8xlarge":  2,
+        "g2.2xlarge":  1,
+        "t1.micro":    0
     }
     if instance_type in disks_by_instance:
         return disks_by_instance[instance_type]

From f1f7385a5087a80c936d419699e3f5232455f189 Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Thu, 26 Jun 2014 17:09:24 -0700
Subject: [PATCH 536/810] Strip '@' symbols when merging pull requests.

Currently all of the commits with 'X' in them cause person X to
receive e-mails every time someone makes a public fork of Spark.

marmbrus who requested this.

Author: Patrick Wendell <pwendell@gmail.com>

Closes #1239 from pwendell/strip and squashes the following commits:

22e5a97 [Patrick Wendell] Strip '@' symbols when merging pull requests.
---
 dev/merge_spark_pr.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/dev/merge_spark_pr.py b/dev/merge_spark_pr.py
index ffb70096d601..c44320239bbb 100755
--- a/dev/merge_spark_pr.py
+++ b/dev/merge_spark_pr.py
@@ -130,7 +130,9 @@ def merge_pr(pr_num, target_ref):
 
     merge_message_flags += ["-m", title]
     if body != None:
-        merge_message_flags += ["-m", body]
+        # We remove @ symbols from the body to avoid triggering e-mails
+        # to people every time someone creates a public fork of Spark.
+        merge_message_flags += ["-m", body.replace("@", "")]
 
     authors = "\n".join(["Author: %s" % a for a in distinct_authors])
 

From 981bde9b056ef5e91aed553e0b5930f12e1ff797 Mon Sep 17 00:00:00 2001
From: Cheng Hao <hao.cheng@intel.com>
Date: Thu, 26 Jun 2014 19:18:11 -0700
Subject: [PATCH 537/810] [SQL]Extract the joinkeys from join condition

Extract the join keys from equality conditions, that can be evaluated using equi-join.

Author: Cheng Hao <hao.cheng@intel.com>

Closes #1190 from chenghao-intel/extract_join_keys and squashes the following commits:

4a1060a [Cheng Hao] Fix some of the small issues
ceb4924 [Cheng Hao] Remove the redundant pattern of join keys extraction
cec34e8 [Cheng Hao] Update the code style issues
dcc4584 [Cheng Hao] Extract the joinkeys from join condition
---
 .../sql/catalyst/optimizer/Optimizer.scala    |  7 ++-
 .../sql/catalyst/planning/patterns.scala      | 62 +++++++------------
 .../spark/sql/execution/SparkStrategies.scala | 13 ++--
 3 files changed, 33 insertions(+), 49 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
index b20b5de8c46e..fb517e40677e 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
@@ -257,8 +257,11 @@ object PushPredicateThroughProject extends Rule[LogicalPlan] {
  * Check https://cwiki.apache.org/confluence/display/Hive/OuterJoinBehavior for more details
  */
 object PushPredicateThroughJoin extends Rule[LogicalPlan] with PredicateHelper {
-  // split the condition expression into 3 parts, 
-  // (canEvaluateInLeftSide, canEvaluateInRightSide, haveToEvaluateWithBothSide) 
+  /**
+   * Splits join condition expressions into three categories based on the attributes required
+   * to evaluate them.
+   * @returns (canEvaluateInLeft, canEvaluateInRight, haveToEvaluateInBoth)
+   */
   private def split(condition: Seq[Expression], left: LogicalPlan, right: LogicalPlan) = {
     val (leftEvaluateCondition, rest) =
         condition.partition(_.references subsetOf left.outputSet)
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala
index a43bef389c4b..026692abe067 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala
@@ -105,57 +105,39 @@ object PhysicalOperation extends PredicateHelper {
 }
 
 /**
- * A pattern that finds joins with equality conditions that can be evaluated using hashing
- * techniques.  For inner joins, any filters on top of the join operator are also matched.
+ * A pattern that finds joins with equality conditions that can be evaluated using equi-join.
  */
-object HashFilteredJoin extends Logging with PredicateHelper {
+object ExtractEquiJoinKeys extends Logging with PredicateHelper {
   /** (joinType, rightKeys, leftKeys, condition, leftChild, rightChild) */
   type ReturnType =
     (JoinType, Seq[Expression], Seq[Expression], Option[Expression], LogicalPlan, LogicalPlan)
 
   def unapply(plan: LogicalPlan): Option[ReturnType] = plan match {
-    // All predicates can be evaluated for inner join (i.e., those that are in the ON
-    // clause and WHERE clause.)
-    case FilteredOperation(predicates, join @ Join(left, right, Inner, condition)) =>
-      logger.debug(s"Considering hash inner join on: ${predicates ++ condition}")
-      splitPredicates(predicates ++ condition, join)
-    // All predicates can be evaluated for left semi join (those that are in the WHERE
-    // clause can only from left table, so they can all be pushed down.)
-    case FilteredOperation(predicates, join @ Join(left, right, LeftSemi, condition)) =>
-      logger.debug(s"Considering hash left semi join on: ${predicates ++ condition}")
-      splitPredicates(predicates ++ condition, join)
     case join @ Join(left, right, joinType, condition) =>
-      logger.debug(s"Considering hash join on: $condition")
-      splitPredicates(condition.toSeq, join)
-    case _ => None
-  }
-
-  // Find equi-join predicates that can be evaluated before the join, and thus can be used
-  // as join keys.
-  def splitPredicates(allPredicates: Seq[Expression], join: Join): Option[ReturnType] = {
-    val Join(left, right, joinType, _) = join
-    val (joinPredicates, otherPredicates) =
-      allPredicates.flatMap(splitConjunctivePredicates).partition {
-        case EqualTo(l, r) if (canEvaluate(l, left) && canEvaluate(r, right)) ||
-          (canEvaluate(l, right) && canEvaluate(r, left)) => true
-        case _ => false
+      logger.debug(s"Considering join on: $condition")
+      // Find equi-join predicates that can be evaluated before the join, and thus can be used
+      // as join keys.
+      val (joinPredicates, otherPredicates) = 
+        condition.map(splitConjunctivePredicates).getOrElse(Nil).partition {
+          case EqualTo(l, r) if (canEvaluate(l, left) && canEvaluate(r, right)) ||
+            (canEvaluate(l, right) && canEvaluate(r, left)) => true
+          case _ => false
+        }
+
+      val joinKeys = joinPredicates.map {
+        case EqualTo(l, r) if canEvaluate(l, left) && canEvaluate(r, right) => (l, r)
+        case EqualTo(l, r) if canEvaluate(l, right) && canEvaluate(r, left) => (r, l)
       }
-
-    val joinKeys = joinPredicates.map {
-      case EqualTo(l, r) if canEvaluate(l, left) && canEvaluate(r, right) => (l, r)
-      case EqualTo(l, r) if canEvaluate(l, right) && canEvaluate(r, left) => (r, l)
-    }
-
-    // Do not consider this strategy if there are no join keys.
-    if (joinKeys.nonEmpty) {
       val leftKeys = joinKeys.map(_._1)
       val rightKeys = joinKeys.map(_._2)
 
-      Some((joinType, leftKeys, rightKeys, otherPredicates.reduceOption(And), left, right))
-    } else {
-      logger.debug(s"Avoiding hash join with no join keys.")
-      None
-    }
+      if (joinKeys.nonEmpty) {
+        logger.debug(s"leftKeys:${leftKeys} | rightKeys:${rightKeys}")
+        Some((joinType, leftKeys, rightKeys, otherPredicates.reduceOption(And), left, right))
+      } else {
+        None
+      }
+    case _ => None
   }
 }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
index 3cd29967d1cd..0925605b7c4d 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
@@ -31,9 +31,8 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
 
   object LeftSemiJoin extends Strategy with PredicateHelper {
     def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
-      // Find left semi joins where at least some predicates can be evaluated by matching hash
-      // keys using the HashFilteredJoin pattern.
-      case HashFilteredJoin(LeftSemi, leftKeys, rightKeys, condition, left, right) =>
+      // Find left semi joins where at least some predicates can be evaluated by matching join keys
+      case ExtractEquiJoinKeys(LeftSemi, leftKeys, rightKeys, condition, left, right) =>
         val semiJoin = execution.LeftSemiJoinHash(
           leftKeys, rightKeys, planLater(left), planLater(right))
         condition.map(Filter(_, semiJoin)).getOrElse(semiJoin) :: Nil
@@ -46,7 +45,7 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
   }
 
   /**
-   * Uses the HashFilteredJoin pattern to find joins where at least some of the predicates can be
+   * Uses the ExtractEquiJoinKeys pattern to find joins where at least some of the predicates can be
    * evaluated by matching hash keys.
    */
   object HashJoin extends Strategy with PredicateHelper {
@@ -65,7 +64,7 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
     def broadcastTables: Seq[String] = sqlContext.joinBroadcastTables.split(",").toBuffer
 
     def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
-      case HashFilteredJoin(
+      case ExtractEquiJoinKeys(
               Inner,
               leftKeys,
               rightKeys,
@@ -75,7 +74,7 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
         if broadcastTables.contains(b.tableName) =>
           broadcastHashJoin(leftKeys, rightKeys, left, right, condition, BuildRight)
 
-      case HashFilteredJoin(
+      case ExtractEquiJoinKeys(
               Inner,
               leftKeys,
               rightKeys,
@@ -85,7 +84,7 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
         if broadcastTables.contains(b.tableName) =>
           broadcastHashJoin(leftKeys, rightKeys, left, right, condition, BuildLeft)
 
-      case HashFilteredJoin(Inner, leftKeys, rightKeys, condition, left, right) =>
+      case ExtractEquiJoinKeys(Inner, leftKeys, rightKeys, condition, left, right) =>
         val hashJoin =
           execution.ShuffledHashJoin(
             leftKeys, rightKeys, BuildRight, planLater(left), planLater(right))

From bf578deaf2493081ceeb78dfd7617def5699a06e Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Thu, 26 Jun 2014 21:12:16 -0700
Subject: [PATCH 538/810] Removed throwable field from FetchFailedException and
 added MetadataFetchFailedException

FetchFailedException used to have a Throwable field, but in reality we never propagate any of the throwable/exceptions back to the driver because Executor explicitly looks for FetchFailedException and then sends FetchFailed as the TaskEndReason.

This pull request removes the throwable and adds a MetadataFetchFailedException that extends FetchFailedException (so now MapOutputTracker throws MetadataFetchFailedException instead).

Author: Reynold Xin <rxin@apache.org>

Closes #1227 from rxin/metadataFetchException and squashes the following commits:

5cb1e0a [Reynold Xin] MetadataFetchFailedException extends FetchFailedException.
8861ee2 [Reynold Xin] Throw MetadataFetchFailedException in MapOutputTracker.
---
 .../org/apache/spark/MapOutputTracker.scala   | 12 +++---
 .../org/apache/spark/TaskEndReason.scala      |  2 +-
 .../org/apache/spark/executor/Executor.scala  |  2 +-
 .../spark/scheduler/TaskDescription.scala     |  4 ++
 .../{ => shuffle}/FetchFailedException.scala  | 43 +++++++++++--------
 .../hash/BlockStoreShuffleFetcher.scala       |  5 ++-
 .../apache/spark/MapOutputTrackerSuite.scala  |  1 +
 7 files changed, 42 insertions(+), 27 deletions(-)
 rename core/src/main/scala/org/apache/spark/{ => shuffle}/FetchFailedException.scala (50%)

diff --git a/core/src/main/scala/org/apache/spark/MapOutputTracker.scala b/core/src/main/scala/org/apache/spark/MapOutputTracker.scala
index ee82d9fa7874..182abacc475a 100644
--- a/core/src/main/scala/org/apache/spark/MapOutputTracker.scala
+++ b/core/src/main/scala/org/apache/spark/MapOutputTracker.scala
@@ -25,9 +25,11 @@ import scala.concurrent.Await
 
 import akka.actor._
 import akka.pattern.ask
+
+import org.apache.spark.util._
 import org.apache.spark.scheduler.MapStatus
+import org.apache.spark.shuffle.MetadataFetchFailedException
 import org.apache.spark.storage.BlockManagerId
-import org.apache.spark.util._
 
 private[spark] sealed trait MapOutputTrackerMessage
 private[spark] case class GetMapOutputStatuses(shuffleId: Int)
@@ -168,8 +170,8 @@ private[spark] abstract class MapOutputTracker(conf: SparkConf) extends Logging
           return MapOutputTracker.convertMapStatuses(shuffleId, reduceId, fetchedStatuses)
         }
       } else {
-        throw new FetchFailedException(null, shuffleId, -1, reduceId,
-          new Exception("Missing all output locations for shuffle " + shuffleId))
+        throw new MetadataFetchFailedException(
+          shuffleId, reduceId, "Missing all output locations for shuffle " + shuffleId)
       }
     } else {
       statuses.synchronized {
@@ -371,8 +373,8 @@ private[spark] object MapOutputTracker {
     statuses.map {
       status =>
         if (status == null) {
-          throw new FetchFailedException(null, shuffleId, -1, reduceId,
-            new Exception("Missing an output location for shuffle " + shuffleId))
+          throw new MetadataFetchFailedException(
+            shuffleId, reduceId, "Missing an output location for shuffle " + shuffleId)
         } else {
           (status.location, decompressSize(status.compressedSizes(reduceId)))
         }
diff --git a/core/src/main/scala/org/apache/spark/TaskEndReason.scala b/core/src/main/scala/org/apache/spark/TaskEndReason.scala
index 5e8bd8c8e533..df42d679b469 100644
--- a/core/src/main/scala/org/apache/spark/TaskEndReason.scala
+++ b/core/src/main/scala/org/apache/spark/TaskEndReason.scala
@@ -65,7 +65,7 @@ case object Resubmitted extends TaskFailedReason {
  */
 @DeveloperApi
 case class FetchFailed(
-    bmAddress: BlockManagerId,
+    bmAddress: BlockManagerId,  // Note that bmAddress can be null
     shuffleId: Int,
     mapId: Int,
     reduceId: Int)
diff --git a/core/src/main/scala/org/apache/spark/executor/Executor.scala b/core/src/main/scala/org/apache/spark/executor/Executor.scala
index 557b9a3f46a0..4d3ba11633bf 100644
--- a/core/src/main/scala/org/apache/spark/executor/Executor.scala
+++ b/core/src/main/scala/org/apache/spark/executor/Executor.scala
@@ -26,8 +26,8 @@ import scala.collection.JavaConversions._
 import scala.collection.mutable.HashMap
 
 import org.apache.spark._
-import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.scheduler._
+import org.apache.spark.shuffle.FetchFailedException
 import org.apache.spark.storage.{StorageLevel, TaskResultBlockId}
 import org.apache.spark.util.{AkkaUtils, Utils}
 
diff --git a/core/src/main/scala/org/apache/spark/scheduler/TaskDescription.scala b/core/src/main/scala/org/apache/spark/scheduler/TaskDescription.scala
index 1481d70db42e..4c96b9e5fef6 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/TaskDescription.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/TaskDescription.scala
@@ -21,6 +21,10 @@ import java.nio.ByteBuffer
 
 import org.apache.spark.util.SerializableBuffer
 
+/**
+ * Description of a task that gets passed onto executors to be executed, usually created by
+ * [[TaskSetManager.resourceOffer]].
+ */
 private[spark] class TaskDescription(
     val taskId: Long,
     val executorId: String,
diff --git a/core/src/main/scala/org/apache/spark/FetchFailedException.scala b/core/src/main/scala/org/apache/spark/shuffle/FetchFailedException.scala
similarity index 50%
rename from core/src/main/scala/org/apache/spark/FetchFailedException.scala
rename to core/src/main/scala/org/apache/spark/shuffle/FetchFailedException.scala
index 8eaa26bdb1b5..71c08e9d5a8c 100644
--- a/core/src/main/scala/org/apache/spark/FetchFailedException.scala
+++ b/core/src/main/scala/org/apache/spark/shuffle/FetchFailedException.scala
@@ -15,31 +15,38 @@
  * limitations under the License.
  */
 
-package org.apache.spark
+package org.apache.spark.shuffle
 
 import org.apache.spark.storage.BlockManagerId
+import org.apache.spark.{FetchFailed, TaskEndReason}
 
+/**
+ * Failed to fetch a shuffle block. The executor catches this exception and propagates it
+ * back to DAGScheduler (through TaskEndReason) so we'd resubmit the previous stage.
+ *
+ * Note that bmAddress can be null.
+ */
 private[spark] class FetchFailedException(
-    taskEndReason: TaskEndReason,
-    message: String,
-    cause: Throwable)
+    bmAddress: BlockManagerId,
+    shuffleId: Int,
+    mapId: Int,
+    reduceId: Int)
   extends Exception {
 
-  def this (bmAddress: BlockManagerId, shuffleId: Int, mapId: Int, reduceId: Int,
-      cause: Throwable) =
-    this(FetchFailed(bmAddress, shuffleId, mapId, reduceId),
-      "Fetch failed: %s %d %d %d".format(bmAddress, shuffleId, mapId, reduceId),
-      cause)
-
-  def this (shuffleId: Int, reduceId: Int, cause: Throwable) =
-    this(FetchFailed(null, shuffleId, -1, reduceId),
-      "Unable to fetch locations from master: %d %d".format(shuffleId, reduceId), cause)
-
-  override def getMessage(): String = message
+  override def getMessage: String =
+    "Fetch failed: %s %d %d %d".format(bmAddress, shuffleId, mapId, reduceId)
 
+  def toTaskEndReason: TaskEndReason = FetchFailed(bmAddress, shuffleId, mapId, reduceId)
+}
 
-  override def getCause(): Throwable = cause
-
-  def toTaskEndReason: TaskEndReason = taskEndReason
+/**
+ * Failed to get shuffle metadata from [[org.apache.spark.MapOutputTracker]].
+ */
+private[spark] class MetadataFetchFailedException(
+    shuffleId: Int,
+    reduceId: Int,
+    message: String)
+  extends FetchFailedException(null, shuffleId, -1, reduceId) {
 
+  override def getMessage: String = message
 }
diff --git a/core/src/main/scala/org/apache/spark/shuffle/hash/BlockStoreShuffleFetcher.scala b/core/src/main/scala/org/apache/spark/shuffle/hash/BlockStoreShuffleFetcher.scala
index b05b6ea345df..a932455776e3 100644
--- a/core/src/main/scala/org/apache/spark/shuffle/hash/BlockStoreShuffleFetcher.scala
+++ b/core/src/main/scala/org/apache/spark/shuffle/hash/BlockStoreShuffleFetcher.scala
@@ -20,11 +20,12 @@ package org.apache.spark.shuffle.hash
 import scala.collection.mutable.ArrayBuffer
 import scala.collection.mutable.HashMap
 
+import org.apache.spark._
 import org.apache.spark.executor.ShuffleReadMetrics
 import org.apache.spark.serializer.Serializer
+import org.apache.spark.shuffle.FetchFailedException
 import org.apache.spark.storage.{BlockId, BlockManagerId, ShuffleBlockId}
 import org.apache.spark.util.CompletionIterator
-import org.apache.spark._
 
 private[hash] object BlockStoreShuffleFetcher extends Logging {
   def fetch[T](
@@ -63,7 +64,7 @@ private[hash] object BlockStoreShuffleFetcher extends Logging {
           blockId match {
             case ShuffleBlockId(shufId, mapId, _) =>
               val address = statuses(mapId.toInt)._1
-              throw new FetchFailedException(address, shufId.toInt, mapId.toInt, reduceId, null)
+              throw new FetchFailedException(address, shufId.toInt, mapId.toInt, reduceId)
             case _ =>
               throw new SparkException(
                 "Failed to get block " + blockId + ", which is not a shuffle block")
diff --git a/core/src/test/scala/org/apache/spark/MapOutputTrackerSuite.scala b/core/src/test/scala/org/apache/spark/MapOutputTrackerSuite.scala
index 95ba273f16a7..970283808562 100644
--- a/core/src/test/scala/org/apache/spark/MapOutputTrackerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/MapOutputTrackerSuite.scala
@@ -24,6 +24,7 @@ import akka.testkit.TestActorRef
 import org.scalatest.FunSuite
 
 import org.apache.spark.scheduler.MapStatus
+import org.apache.spark.shuffle.FetchFailedException
 import org.apache.spark.storage.BlockManagerId
 import org.apache.spark.util.AkkaUtils
 

From d1636dd72fc4966413baeb97ba55b313dc1da63d Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Thu, 26 Jun 2014 21:13:26 -0700
Subject: [PATCH 539/810] [SPARK-2297][UI] Make task attempt and speculation
 more explicit in UI.

New UI:

![screen shot 2014-06-26 at 1 43 52 pm](https://cloud.githubusercontent.com/assets/323388/3404643/82b9ddc6-fd73-11e3-96f9-f7592a7aee79.png)

Author: Reynold Xin <rxin@apache.org>

Closes #1236 from rxin/ui-task-attempt and squashes the following commits:

3b645dd [Reynold Xin] Expose attemptId in Stage.
c0474b1 [Reynold Xin] Beefed up unit test.
c404bdd [Reynold Xin] Fix ReplayListenerSuite.
f56be4b [Reynold Xin] Fixed JsonProtocolSuite.
e29e0f7 [Reynold Xin] Minor update.
5e4354a [Reynold Xin] [SPARK-2297][UI] Make task attempt and speculation more explicit in UI.
---
 .../spark/scheduler/SparkListener.scala       |  2 +
 .../org/apache/spark/scheduler/Stage.scala    |  2 +
 .../org/apache/spark/scheduler/TaskInfo.scala |  4 +-
 .../spark/scheduler/TaskSetManager.scala      | 24 +++--
 .../org/apache/spark/ui/jobs/StagePage.scala  | 11 ++-
 .../org/apache/spark/util/JsonProtocol.scala  |  9 +-
 .../ui/jobs/JobProgressListenerSuite.scala    | 11 +--
 .../apache/spark/util/JsonProtocolSuite.scala | 88 ++++++++++++-------
 8 files changed, 102 insertions(+), 49 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/scheduler/SparkListener.scala b/core/src/main/scala/org/apache/spark/scheduler/SparkListener.scala
index 378cf1aaebe7..82163eadd56e 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/SparkListener.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/SparkListener.scala
@@ -75,9 +75,11 @@ case class SparkListenerBlockManagerRemoved(blockManagerId: BlockManagerId)
 @DeveloperApi
 case class SparkListenerUnpersistRDD(rddId: Int) extends SparkListenerEvent
 
+@DeveloperApi
 case class SparkListenerApplicationStart(appName: String, time: Long, sparkUser: String)
   extends SparkListenerEvent
 
+@DeveloperApi
 case class SparkListenerApplicationEnd(time: Long) extends SparkListenerEvent
 
 /** An event used in the listener to shutdown the listener daemon thread. */
diff --git a/core/src/main/scala/org/apache/spark/scheduler/Stage.scala b/core/src/main/scala/org/apache/spark/scheduler/Stage.scala
index 9a4be43ee219..8ec482a6f6d9 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/Stage.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/Stage.scala
@@ -106,6 +106,8 @@ private[spark] class Stage(
     id
   }
 
+  def attemptId: Int = nextAttemptId
+
   val name = callSite.short
   val details = callSite.long
 
diff --git a/core/src/main/scala/org/apache/spark/scheduler/TaskInfo.scala b/core/src/main/scala/org/apache/spark/scheduler/TaskInfo.scala
index 4c62e4dc0bac..6aecdfe8e665 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/TaskInfo.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/TaskInfo.scala
@@ -27,10 +27,12 @@ import org.apache.spark.annotation.DeveloperApi
 class TaskInfo(
     val taskId: Long,
     val index: Int,
+    val attempt: Int,
     val launchTime: Long,
     val executorId: String,
     val host: String,
-    val taskLocality: TaskLocality.TaskLocality) {
+    val taskLocality: TaskLocality.TaskLocality,
+    val speculative: Boolean) {
 
   /**
    * The time when the task started remotely getting the result. Will not be set if the
diff --git a/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala b/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala
index c0898f64fb0c..83ff6b8550b4 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala
@@ -335,17 +335,19 @@ private[spark] class TaskSetManager(
   /**
    * Dequeue a pending task for a given node and return its index and locality level.
    * Only search for tasks matching the given locality constraint.
+   *
+   * @return An option containing (task index within the task set, locality, is speculative?)
    */
   private def findTask(execId: String, host: String, locality: TaskLocality.Value)
-    : Option[(Int, TaskLocality.Value)] =
+    : Option[(Int, TaskLocality.Value, Boolean)] =
   {
     for (index <- findTaskFromList(execId, getPendingTasksForExecutor(execId))) {
-      return Some((index, TaskLocality.PROCESS_LOCAL))
+      return Some((index, TaskLocality.PROCESS_LOCAL, false))
     }
 
     if (TaskLocality.isAllowed(locality, TaskLocality.NODE_LOCAL)) {
       for (index <- findTaskFromList(execId, getPendingTasksForHost(host))) {
-        return Some((index, TaskLocality.NODE_LOCAL))
+        return Some((index, TaskLocality.NODE_LOCAL, false))
       }
     }
 
@@ -354,23 +356,25 @@ private[spark] class TaskSetManager(
         rack <- sched.getRackForHost(host)
         index <- findTaskFromList(execId, getPendingTasksForRack(rack))
       } {
-        return Some((index, TaskLocality.RACK_LOCAL))
+        return Some((index, TaskLocality.RACK_LOCAL, false))
       }
     }
 
     // Look for no-pref tasks after rack-local tasks since they can run anywhere.
     for (index <- findTaskFromList(execId, pendingTasksWithNoPrefs)) {
-      return Some((index, TaskLocality.PROCESS_LOCAL))
+      return Some((index, TaskLocality.PROCESS_LOCAL, false))
     }
 
     if (TaskLocality.isAllowed(locality, TaskLocality.ANY)) {
       for (index <- findTaskFromList(execId, allPendingTasks)) {
-        return Some((index, TaskLocality.ANY))
+        return Some((index, TaskLocality.ANY, false))
       }
     }
 
     // Finally, if all else has failed, find a speculative task
-    findSpeculativeTask(execId, host, locality)
+    findSpeculativeTask(execId, host, locality).map { case (taskIndex, allowedLocality) =>
+      (taskIndex, allowedLocality, true)
+    }
   }
 
   /**
@@ -391,7 +395,7 @@ private[spark] class TaskSetManager(
       }
 
       findTask(execId, host, allowedLocality) match {
-        case Some((index, taskLocality)) => {
+        case Some((index, taskLocality, speculative)) => {
           // Found a task; do some bookkeeping and return a task description
           val task = tasks(index)
           val taskId = sched.newTaskId()
@@ -400,7 +404,9 @@ private[spark] class TaskSetManager(
             taskSet.id, index, taskId, execId, host, taskLocality))
           // Do various bookkeeping
           copiesRunning(index) += 1
-          val info = new TaskInfo(taskId, index, curTime, execId, host, taskLocality)
+          val attemptNum = taskAttempts(index).size
+          val info = new TaskInfo(
+            taskId, index, attemptNum + 1, curTime, execId, host, taskLocality, speculative)
           taskInfos(taskId) = info
           taskAttempts(index) = info :: taskAttempts(index)
           // Update our locality level for delay scheduling
diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala b/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
index 8b65f0671bdb..8e3d5d1cd4c6 100644
--- a/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
@@ -95,8 +95,9 @@ private[ui] class StagePage(parent: JobProgressTab) extends WebUIPage("stage") {
         </div>
         // scalastyle:on
       val taskHeaders: Seq[String] =
-        Seq("Task Index", "Task ID", "Status", "Locality Level", "Executor", "Launch Time") ++
-        Seq("Duration", "GC Time", "Result Ser Time") ++
+        Seq(
+          "Index", "ID", "Attempt", "Status", "Locality Level", "Executor",
+          "Launch Time", "Duration", "GC Time") ++
         {if (hasShuffleRead) Seq("Shuffle Read")  else Nil} ++
         {if (hasShuffleWrite) Seq("Write Time", "Shuffle Write") else Nil} ++
         {if (hasBytesSpilled) Seq("Shuffle Spill (Memory)", "Shuffle Spill (Disk)") else Nil} ++
@@ -245,6 +246,9 @@ private[ui] class StagePage(parent: JobProgressTab) extends WebUIPage("stage") {
       <tr>
         <td>{info.index}</td>
         <td>{info.taskId}</td>
+        <td sorttable_customkey={info.attempt.toString}>{
+          if (info.speculative) s"${info.attempt} (speculative)" else info.attempt.toString
+        }</td>
         <td>{info.status}</td>
         <td>{info.taskLocality}</td>
         <td>{info.host}</td>
@@ -255,9 +259,12 @@ private[ui] class StagePage(parent: JobProgressTab) extends WebUIPage("stage") {
         <td sorttable_customkey={gcTime.toString}>
           {if (gcTime > 0) UIUtils.formatDuration(gcTime) else ""}
         </td>
+        <!--
+        TODO: Add this back after we add support to hide certain columns.
         <td sorttable_customkey={serializationTime.toString}>
           {if (serializationTime > 0) UIUtils.formatDuration(serializationTime) else ""}
         </td>
+        -->
         {if (shuffleRead) {
            <td sorttable_customkey={shuffleReadSortable}>
              {shuffleReadReadable}
diff --git a/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala b/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala
index 7cecbfe62a38..6245b4b8023c 100644
--- a/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala
+++ b/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala
@@ -32,6 +32,8 @@ import org.apache.spark.storage._
 import org.apache.spark._
 
 private[spark] object JsonProtocol {
+  // TODO: Remove this file and put JSON serialization into each individual class.
+
   private implicit val format = DefaultFormats
 
   /** ------------------------------------------------- *
@@ -194,10 +196,12 @@ private[spark] object JsonProtocol {
   def taskInfoToJson(taskInfo: TaskInfo): JValue = {
     ("Task ID" -> taskInfo.taskId) ~
     ("Index" -> taskInfo.index) ~
+    ("Attempt" -> taskInfo.attempt) ~
     ("Launch Time" -> taskInfo.launchTime) ~
     ("Executor ID" -> taskInfo.executorId) ~
     ("Host" -> taskInfo.host) ~
     ("Locality" -> taskInfo.taskLocality.toString) ~
+    ("Speculative" -> taskInfo.speculative) ~
     ("Getting Result Time" -> taskInfo.gettingResultTime) ~
     ("Finish Time" -> taskInfo.finishTime) ~
     ("Failed" -> taskInfo.failed) ~
@@ -487,16 +491,19 @@ private[spark] object JsonProtocol {
   def taskInfoFromJson(json: JValue): TaskInfo = {
     val taskId = (json \ "Task ID").extract[Long]
     val index = (json \ "Index").extract[Int]
+    val attempt = (json \ "Attempt").extractOpt[Int].getOrElse(1)
     val launchTime = (json \ "Launch Time").extract[Long]
     val executorId = (json \ "Executor ID").extract[String]
     val host = (json \ "Host").extract[String]
     val taskLocality = TaskLocality.withName((json \ "Locality").extract[String])
+    val speculative = (json \ "Speculative").extractOpt[Boolean].getOrElse(false)
     val gettingResultTime = (json \ "Getting Result Time").extract[Long]
     val finishTime = (json \ "Finish Time").extract[Long]
     val failed = (json \ "Failed").extract[Boolean]
     val serializedSize = (json \ "Serialized Size").extract[Int]
 
-    val taskInfo = new TaskInfo(taskId, index, launchTime, executorId, host, taskLocality)
+    val taskInfo =
+      new TaskInfo(taskId, index, attempt, launchTime, executorId, host, taskLocality, speculative)
     taskInfo.gettingResultTime = gettingResultTime
     taskInfo.finishTime = finishTime
     taskInfo.failed = failed
diff --git a/core/src/test/scala/org/apache/spark/ui/jobs/JobProgressListenerSuite.scala b/core/src/test/scala/org/apache/spark/ui/jobs/JobProgressListenerSuite.scala
index e0fec6a068bd..fa43b66c6cb5 100644
--- a/core/src/test/scala/org/apache/spark/ui/jobs/JobProgressListenerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/ui/jobs/JobProgressListenerSuite.scala
@@ -66,7 +66,7 @@ class JobProgressListenerSuite extends FunSuite with LocalSparkContext with Matc
     // finish this task, should get updated shuffleRead
     shuffleReadMetrics.remoteBytesRead = 1000
     taskMetrics.shuffleReadMetrics = Some(shuffleReadMetrics)
-    var taskInfo = new TaskInfo(1234L, 0, 0L, "exe-1", "host1", TaskLocality.NODE_LOCAL)
+    var taskInfo = new TaskInfo(1234L, 0, 1, 0L, "exe-1", "host1", TaskLocality.NODE_LOCAL, false)
     taskInfo.finishTime = 1
     var task = new ShuffleMapTask(0, null, null, 0, null)
     val taskType = Utils.getFormattedClassName(task)
@@ -75,7 +75,8 @@ class JobProgressListenerSuite extends FunSuite with LocalSparkContext with Matc
       .shuffleRead == 1000)
 
     // finish a task with unknown executor-id, nothing should happen
-    taskInfo = new TaskInfo(1234L, 0, 1000L, "exe-unknown", "host1", TaskLocality.NODE_LOCAL)
+    taskInfo =
+      new TaskInfo(1234L, 0, 1, 1000L, "exe-unknown", "host1", TaskLocality.NODE_LOCAL, true)
     taskInfo.finishTime = 1
     task = new ShuffleMapTask(0, null, null, 0, null)
     listener.onTaskEnd(SparkListenerTaskEnd(task.stageId, taskType, Success, taskInfo, taskMetrics))
@@ -84,7 +85,7 @@ class JobProgressListenerSuite extends FunSuite with LocalSparkContext with Matc
     // finish this task, should get updated duration
     shuffleReadMetrics.remoteBytesRead = 1000
     taskMetrics.shuffleReadMetrics = Some(shuffleReadMetrics)
-    taskInfo = new TaskInfo(1235L, 0, 0L, "exe-1", "host1", TaskLocality.NODE_LOCAL)
+    taskInfo = new TaskInfo(1235L, 0, 1, 0L, "exe-1", "host1", TaskLocality.NODE_LOCAL, false)
     taskInfo.finishTime = 1
     task = new ShuffleMapTask(0, null, null, 0, null)
     listener.onTaskEnd(SparkListenerTaskEnd(task.stageId, taskType, Success, taskInfo, taskMetrics))
@@ -94,7 +95,7 @@ class JobProgressListenerSuite extends FunSuite with LocalSparkContext with Matc
     // finish this task, should get updated duration
     shuffleReadMetrics.remoteBytesRead = 1000
     taskMetrics.shuffleReadMetrics = Some(shuffleReadMetrics)
-    taskInfo = new TaskInfo(1236L, 0, 0L, "exe-2", "host1", TaskLocality.NODE_LOCAL)
+    taskInfo = new TaskInfo(1236L, 0, 2, 0L, "exe-2", "host1", TaskLocality.NODE_LOCAL, false)
     taskInfo.finishTime = 1
     task = new ShuffleMapTask(0, null, null, 0, null)
     listener.onTaskEnd(SparkListenerTaskEnd(task.stageId, taskType, Success, taskInfo, taskMetrics))
@@ -106,7 +107,7 @@ class JobProgressListenerSuite extends FunSuite with LocalSparkContext with Matc
     val conf = new SparkConf()
     val listener = new JobProgressListener(conf)
     val metrics = new TaskMetrics()
-    val taskInfo = new TaskInfo(1234L, 0, 0L, "exe-1", "host1", TaskLocality.NODE_LOCAL)
+    val taskInfo = new TaskInfo(1234L, 0, 3, 0L, "exe-1", "host1", TaskLocality.NODE_LOCAL, false)
     taskInfo.finishTime = 1
     val task = new ShuffleMapTask(0, null, null, 0, null)
     val taskType = Utils.getFormattedClassName(task)
diff --git a/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala b/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala
index 495e1b7a0a21..6c4987045587 100644
--- a/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala
+++ b/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala
@@ -35,10 +35,11 @@ class JsonProtocolSuite extends FunSuite {
     val stageSubmitted =
       SparkListenerStageSubmitted(makeStageInfo(100, 200, 300, 400L, 500L), properties)
     val stageCompleted = SparkListenerStageCompleted(makeStageInfo(101, 201, 301, 401L, 501L))
-    val taskStart = SparkListenerTaskStart(111, makeTaskInfo(222L, 333, 444L))
-    val taskGettingResult = SparkListenerTaskGettingResult(makeTaskInfo(1000L, 2000, 3000L))
+    val taskStart = SparkListenerTaskStart(111, makeTaskInfo(222L, 333, 1, 444L, false))
+    val taskGettingResult =
+      SparkListenerTaskGettingResult(makeTaskInfo(1000L, 2000, 5, 3000L, true))
     val taskEnd = SparkListenerTaskEnd(1, "ShuffleMapTask", Success,
-      makeTaskInfo(123L, 234, 345L), makeTaskMetrics(300L, 400L, 500L, 600L, 700, 800))
+      makeTaskInfo(123L, 234, 67, 345L, false), makeTaskMetrics(300L, 400L, 500L, 600L, 700, 800))
     val jobStart = SparkListenerJobStart(10, Seq[Int](1, 2, 3, 4), properties)
     val jobEnd = SparkListenerJobEnd(20, JobSucceeded)
     val environmentUpdate = SparkListenerEnvironmentUpdate(Map[String, Seq[(String, String)]](
@@ -73,7 +74,7 @@ class JsonProtocolSuite extends FunSuite {
   test("Dependent Classes") {
     testRDDInfo(makeRddInfo(2, 3, 4, 5L, 6L))
     testStageInfo(makeStageInfo(10, 20, 30, 40L, 50L))
-    testTaskInfo(makeTaskInfo(999L, 888, 777L))
+    testTaskInfo(makeTaskInfo(999L, 888, 55, 777L, false))
     testTaskMetrics(makeTaskMetrics(33333L, 44444L, 55555L, 66666L, 7, 8))
     testBlockManagerId(BlockManagerId("Hong", "Kong", 500, 1000))
 
@@ -269,10 +270,12 @@ class JsonProtocolSuite extends FunSuite {
   private def assertEquals(info1: TaskInfo, info2: TaskInfo) {
     assert(info1.taskId === info2.taskId)
     assert(info1.index === info2.index)
+    assert(info1.attempt === info2.attempt)
     assert(info1.launchTime === info2.launchTime)
     assert(info1.executorId === info2.executorId)
     assert(info1.host === info2.host)
     assert(info1.taskLocality === info2.taskLocality)
+    assert(info1.speculative === info2.speculative)
     assert(info1.gettingResultTime === info2.gettingResultTime)
     assert(info1.finishTime === info2.finishTime)
     assert(info1.failed === info2.failed)
@@ -453,8 +456,8 @@ class JsonProtocolSuite extends FunSuite {
     new StageInfo(a, "greetings", b, rddInfos, "details")
   }
 
-  private def makeTaskInfo(a: Long, b: Int, c: Long) = {
-    new TaskInfo(a, b, c, "executor", "your kind sir", TaskLocality.NODE_LOCAL)
+  private def makeTaskInfo(a: Long, b: Int, c: Int, d: Long, speculative: Boolean) = {
+    new TaskInfo(a, b, c, d, "executor", "your kind sir", TaskLocality.NODE_LOCAL, speculative)
   }
 
   private def makeTaskMetrics(a: Long, b: Long, c: Long, d: Long, e: Int, f: Int) = {
@@ -510,37 +513,60 @@ class JsonProtocolSuite extends FunSuite {
 
   private val taskStartJsonString =
     """
-      {"Event":"SparkListenerTaskStart","Stage ID":111,"Task Info":{"Task ID":222,
-      "Index":333,"Launch Time":444,"Executor ID":"executor","Host":"your kind sir",
-      "Locality":"NODE_LOCAL","Getting Result Time":0,"Finish Time":0,"Failed":false,
-      "Serialized Size":0}}
-    """
+      |{"Event":"SparkListenerTaskStart","Stage ID":111,"Task Info":{"Task ID":222,
+      |"Index":333,"Attempt":1,"Launch Time":444,"Executor ID":"executor","Host":"your kind sir",
+      |"Locality":"NODE_LOCAL","Speculative":false,"Getting Result Time":0,"Finish Time":0,
+      |"Failed":false,"Serialized Size":0}}
+    """.stripMargin
 
   private val taskGettingResultJsonString =
     """
-      {"Event":"SparkListenerTaskGettingResult","Task Info":{"Task ID":1000,"Index":
-      2000,"Launch Time":3000,"Executor ID":"executor","Host":"your kind sir",
-      "Locality":"NODE_LOCAL","Getting Result Time":0,"Finish Time":0,"Failed":false,
-      "Serialized Size":0}}
-    """
+      |{"Event":"SparkListenerTaskGettingResult","Task Info":
+      |  {"Task ID":1000,"Index":2000,"Attempt":5,"Launch Time":3000,"Executor ID":"executor",
+      |   "Host":"your kind sir","Locality":"NODE_LOCAL","Speculative":true,"Getting Result Time":0,
+      |   "Finish Time":0,"Failed":false,"Serialized Size":0
+      |  }
+      |}
+    """.stripMargin
 
   private val taskEndJsonString =
     """
-      {"Event":"SparkListenerTaskEnd","Stage ID":1,"Task Type":"ShuffleMapTask",
-      "Task End Reason":{"Reason":"Success"},"Task Info":{"Task ID":123,"Index":
-      234,"Launch Time":345,"Executor ID":"executor","Host":"your kind sir",
-      "Locality":"NODE_LOCAL","Getting Result Time":0,"Finish Time":0,"Failed":
-      false,"Serialized Size":0},"Task Metrics":{"Host Name":"localhost",
-      "Executor Deserialize Time":300,"Executor Run Time":400,"Result Size":500,
-      "JVM GC Time":600,"Result Serialization Time":700,"Memory Bytes Spilled":
-      800,"Disk Bytes Spilled":0,"Shuffle Read Metrics":{"Shuffle Finish Time":
-      900,"Total Blocks Fetched":1500,"Remote Blocks Fetched":800,"Local Blocks Fetched":
-      700,"Fetch Wait Time":900,"Remote Bytes Read":1000},"Shuffle Write Metrics":
-      {"Shuffle Bytes Written":1200,"Shuffle Write Time":1500},"Updated Blocks":
-      [{"Block ID":"rdd_0_0","Status":{"Storage Level":{"Use Disk":true,"Use Memory":true,
-      "Use Tachyon":false,"Deserialized":false,"Replication":2},"Memory Size":0,"Tachyon Size":0,
-      "Disk Size":0}}]}}
-    """
+      |{"Event":"SparkListenerTaskEnd","Stage ID":1,"Task Type":"ShuffleMapTask",
+      |"Task End Reason":{"Reason":"Success"},
+      |"Task Info":{
+      |  "Task ID":123,"Index":234,"Attempt":67,"Launch Time":345,"Executor ID":"executor",
+      |  "Host":"your kind sir","Locality":"NODE_LOCAL","Speculative":false,
+      |  "Getting Result Time":0,"Finish Time":0,"Failed":false,"Serialized Size":0
+      |},
+      |"Task Metrics":{
+      |  "Host Name":"localhost","Executor Deserialize Time":300,"Executor Run Time":400,
+      |  "Result Size":500,"JVM GC Time":600,"Result Serialization Time":700,
+      |  "Memory Bytes Spilled":800,"Disk Bytes Spilled":0,
+      |  "Shuffle Read Metrics":{
+      |    "Shuffle Finish Time":900,
+      |    "Total Blocks Fetched":1500,
+      |    "Remote Blocks Fetched":800,
+      |    "Local Blocks Fetched":700,
+      |    "Fetch Wait Time":900,
+      |    "Remote Bytes Read":1000
+      |  },
+      |  "Shuffle Write Metrics":{
+      |    "Shuffle Bytes Written":1200,
+      |    "Shuffle Write Time":1500},
+      |    "Updated Blocks":[
+      |    {"Block ID":"rdd_0_0",
+      |      "Status":{
+      |        "Storage Level":{
+      |          "Use Disk":true,"Use Memory":true,"Use Tachyon":false,"Deserialized":false,
+      |          "Replication":2
+      |        },
+      |        "Memory Size":0,"Tachyon Size":0,"Disk Size":0
+      |      }
+      |    }
+      |    ]
+      |  }
+      |}
+    """.stripMargin
 
   private val jobStartJsonString =
     """

From c23f5db32b3bd4d965d56e5df684a3b814a91cd6 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Thu, 26 Jun 2014 21:46:55 -0700
Subject: [PATCH 540/810] [SPARK-2251] fix concurrency issues in random sampler

The following code is very likely to throw an exception:

~~~
val rdd = sc.parallelize(0 until 111, 10).sample(false, 0.1)
rdd.zip(rdd).count()
~~~

because the same random number generator is used in compute partitions.

Author: Xiangrui Meng <meng@databricks.com>

Closes #1229 from mengxr/fix-sample and squashes the following commits:

f1ee3d7 [Xiangrui Meng] fix concurrency issues in random sampler
---
 .../spark/util/random/RandomSampler.scala     | 24 +++++++++----------
 .../rdd/PartitionwiseSampledRDDSuite.scala    | 18 ++++++++++----
 .../util/random/RandomSamplerSuite.scala      | 18 +++++++++-----
 3 files changed, 38 insertions(+), 22 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/util/random/RandomSampler.scala b/core/src/main/scala/org/apache/spark/util/random/RandomSampler.scala
index 247f10173f1e..32c5fdad75e5 100644
--- a/core/src/main/scala/org/apache/spark/util/random/RandomSampler.scala
+++ b/core/src/main/scala/org/apache/spark/util/random/RandomSampler.scala
@@ -54,17 +54,17 @@ trait RandomSampler[T, U] extends Pseudorandom with Cloneable with Serializable
  */
 @DeveloperApi
 class BernoulliSampler[T](lb: Double, ub: Double, complement: Boolean = false)
-    (implicit random: Random = new XORShiftRandom)
   extends RandomSampler[T, T] {
 
-  def this(ratio: Double)(implicit random: Random = new XORShiftRandom)
-    = this(0.0d, ratio)(random)
+  private[random] var rng: Random = new XORShiftRandom
 
-  override def setSeed(seed: Long) = random.setSeed(seed)
+  def this(ratio: Double) = this(0.0d, ratio)
+
+  override def setSeed(seed: Long) = rng.setSeed(seed)
 
   override def sample(items: Iterator[T]): Iterator[T] = {
     items.filter { item =>
-      val x = random.nextDouble()
+      val x = rng.nextDouble()
       (x >= lb && x < ub) ^ complement
     }
   }
@@ -72,7 +72,7 @@ class BernoulliSampler[T](lb: Double, ub: Double, complement: Boolean = false)
   /**
    *  Return a sampler that is the complement of the range specified of the current sampler.
    */
-  def cloneComplement():  BernoulliSampler[T] = new BernoulliSampler[T](lb, ub, !complement)
+  def cloneComplement(): BernoulliSampler[T] = new BernoulliSampler[T](lb, ub, !complement)
 
   override def clone = new BernoulliSampler[T](lb, ub, complement)
 }
@@ -81,21 +81,21 @@ class BernoulliSampler[T](lb: Double, ub: Double, complement: Boolean = false)
  * :: DeveloperApi ::
  * A sampler based on values drawn from Poisson distribution.
  *
- * @param poisson a Poisson random number generator
+ * @param mean Poisson mean
  * @tparam T item type
  */
 @DeveloperApi
-class PoissonSampler[T](mean: Double)
-    (implicit var poisson: Poisson = new Poisson(mean, new DRand))
-  extends RandomSampler[T, T] {
+class PoissonSampler[T](mean: Double) extends RandomSampler[T, T] {
+
+  private[random] var rng = new Poisson(mean, new DRand)
 
   override def setSeed(seed: Long) {
-    poisson = new Poisson(mean, new DRand(seed.toInt))
+    rng = new Poisson(mean, new DRand(seed.toInt))
   }
 
   override def sample(items: Iterator[T]): Iterator[T] = {
     items.flatMap { item =>
-      val count = poisson.nextInt()
+      val count = rng.nextInt()
       if (count == 0) {
         Iterator.empty
       } else {
diff --git a/core/src/test/scala/org/apache/spark/rdd/PartitionwiseSampledRDDSuite.scala b/core/src/test/scala/org/apache/spark/rdd/PartitionwiseSampledRDDSuite.scala
index 00c273df63b2..5dd8de319a65 100644
--- a/core/src/test/scala/org/apache/spark/rdd/PartitionwiseSampledRDDSuite.scala
+++ b/core/src/test/scala/org/apache/spark/rdd/PartitionwiseSampledRDDSuite.scala
@@ -20,7 +20,7 @@ package org.apache.spark.rdd
 import org.scalatest.FunSuite
 
 import org.apache.spark.SharedSparkContext
-import org.apache.spark.util.random.RandomSampler
+import org.apache.spark.util.random.{BernoulliSampler, PoissonSampler, RandomSampler}
 
 /** a sampler that outputs its seed */
 class MockSampler extends RandomSampler[Long, Long] {
@@ -32,7 +32,7 @@ class MockSampler extends RandomSampler[Long, Long] {
   }
 
   override def sample(items: Iterator[Long]): Iterator[Long] = {
-    return Iterator(s)
+    Iterator(s)
   }
 
   override def clone = new MockSampler
@@ -40,11 +40,21 @@ class MockSampler extends RandomSampler[Long, Long] {
 
 class PartitionwiseSampledRDDSuite extends FunSuite with SharedSparkContext {
 
-  test("seedDistribution") {
+  test("seed distribution") {
     val rdd = sc.makeRDD(Array(1L, 2L, 3L, 4L), 2)
     val sampler = new MockSampler
     val sample = new PartitionwiseSampledRDD[Long, Long](rdd, sampler, 0L)
-    assert(sample.distinct.count == 2, "Seeds must be different.")
+    assert(sample.distinct().count == 2, "Seeds must be different.")
+  }
+
+  test("concurrency") {
+    // SPARK-2251: zip with self computes each partition twice.
+    // We want to make sure there are no concurrency issues.
+    val rdd = sc.parallelize(0 until 111, 10)
+    for (sampler <- Seq(new BernoulliSampler[Int](0.5), new PoissonSampler[Int](0.5))) {
+      val sampled = new PartitionwiseSampledRDD[Int, Int](rdd, sampler)
+      sampled.zip(sampled).count()
+    }
   }
 }
 
diff --git a/core/src/test/scala/org/apache/spark/util/random/RandomSamplerSuite.scala b/core/src/test/scala/org/apache/spark/util/random/RandomSamplerSuite.scala
index e166787f1754..36877476e708 100644
--- a/core/src/test/scala/org/apache/spark/util/random/RandomSamplerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/util/random/RandomSamplerSuite.scala
@@ -42,7 +42,8 @@ class RandomSamplerSuite extends FunSuite with BeforeAndAfter with EasyMockSugar
       }
     }
     whenExecuting(random) {
-      val sampler = new BernoulliSampler[Int](0.25, 0.55)(random)
+      val sampler = new BernoulliSampler[Int](0.25, 0.55)
+      sampler.rng = random
       assert(sampler.sample(a.iterator).toList == List(3, 4, 5))
     }
   }
@@ -54,7 +55,8 @@ class RandomSamplerSuite extends FunSuite with BeforeAndAfter with EasyMockSugar
       }
     }
     whenExecuting(random) {
-      val sampler = new BernoulliSampler[Int](0.25, 0.55, true)(random)
+      val sampler = new BernoulliSampler[Int](0.25, 0.55, true)
+      sampler.rng = random
       assert(sampler.sample(a.iterator).toList === List(1, 2, 6, 7, 8, 9))
     }
   }
@@ -66,7 +68,8 @@ class RandomSamplerSuite extends FunSuite with BeforeAndAfter with EasyMockSugar
       }
     }
     whenExecuting(random) {
-      val sampler = new BernoulliSampler[Int](0.35)(random)
+      val sampler = new BernoulliSampler[Int](0.35)
+      sampler.rng = random
       assert(sampler.sample(a.iterator).toList == List(1, 2, 3))
     }
   }
@@ -78,7 +81,8 @@ class RandomSamplerSuite extends FunSuite with BeforeAndAfter with EasyMockSugar
       }
     }
     whenExecuting(random) {
-      val sampler = new BernoulliSampler[Int](0.25, 0.55, true)(random)
+      val sampler = new BernoulliSampler[Int](0.25, 0.55, true)
+      sampler.rng = random
       assert(sampler.sample(a.iterator).toList == List(1, 2, 6, 7, 8, 9))
     }
   }
@@ -88,7 +92,8 @@ class RandomSamplerSuite extends FunSuite with BeforeAndAfter with EasyMockSugar
       random.setSeed(10L)
     }
     whenExecuting(random) {
-      val sampler = new BernoulliSampler[Int](0.2)(random)
+      val sampler = new BernoulliSampler[Int](0.2)
+      sampler.rng = random
       sampler.setSeed(10L)
     }
   }
@@ -100,7 +105,8 @@ class RandomSamplerSuite extends FunSuite with BeforeAndAfter with EasyMockSugar
       }
     }
     whenExecuting(poisson) {
-      val sampler = new PoissonSampler[Int](0.2)(poisson)
+      val sampler = new PoissonSampler[Int](0.2)
+      sampler.rng = poisson
       assert(sampler.sample(a.iterator).toList == List(2, 3, 3, 5, 6))
     }
   }

From 18f29b96c7e0948f5f504e522e5aa8a8d1ab163e Mon Sep 17 00:00:00 2001
From: witgo <witgo@qq.com>
Date: Thu, 26 Jun 2014 21:59:21 -0700
Subject: [PATCH 541/810] SPARK-2181:The keys for sorting the columns of
 Executor page in SparkUI are incorrect

Author: witgo <witgo@qq.com>

Closes #1135 from witgo/SPARK-2181 and squashes the following commits:

39dad90 [witgo] The keys for sorting the columns of Executor page in SparkUI are incorrect
---
 .../org/apache/spark/ui/exec/ExecutorsPage.scala     |  8 +++++---
 .../org/apache/spark/ui/jobs/ExecutorTable.scala     | 12 +++++++-----
 .../org/apache/spark/ui/storage/StoragePage.scala    |  8 +++++---
 3 files changed, 17 insertions(+), 11 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/ui/exec/ExecutorsPage.scala b/core/src/main/scala/org/apache/spark/ui/exec/ExecutorsPage.scala
index 6cb43c02b8f0..2d8c3b949c1a 100644
--- a/core/src/main/scala/org/apache/spark/ui/exec/ExecutorsPage.scala
+++ b/core/src/main/scala/org/apache/spark/ui/exec/ExecutorsPage.scala
@@ -79,6 +79,7 @@ private[ui] class ExecutorsPage(parent: ExecutorsTab) extends WebUIPage("") {
     val maximumMemory = values("Maximum Memory")
     val memoryUsed = values("Memory Used")
     val diskUsed = values("Disk Used")
+    // scalastyle:off
     <tr>
       <td>{values("Executor ID")}</td>
       <td>{values("Address")}</td>
@@ -94,10 +95,11 @@ private[ui] class ExecutorsPage(parent: ExecutorsTab) extends WebUIPage("") {
       <td>{values("Failed Tasks")}</td>
       <td>{values("Complete Tasks")}</td>
       <td>{values("Total Tasks")}</td>
-      <td>{Utils.msDurationToString(values("Task Time").toLong)}</td>
-      <td>{Utils.bytesToString(values("Shuffle Read").toLong)}</td>
-      <td>{Utils.bytesToString(values("Shuffle Write").toLong)}</td>
+      <td sorttable_customkey={values("Task Time")}>{Utils.msDurationToString(values("Task Time").toLong)}</td>
+      <td sorttable_customkey={values("Shuffle Read")}>{Utils.bytesToString(values("Shuffle Read").toLong)}</td>
+      <td sorttable_customkey={values("Shuffle Write")} >{Utils.bytesToString(values("Shuffle Write").toLong)}</td>
     </tr>
+    // scalastyle:on
   }
 
   /** Represent an executor's info as a map given a storage status index */
diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/ExecutorTable.scala b/core/src/main/scala/org/apache/spark/ui/jobs/ExecutorTable.scala
index c83e196c9c15..add0e9878a54 100644
--- a/core/src/main/scala/org/apache/spark/ui/jobs/ExecutorTable.scala
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/ExecutorTable.scala
@@ -67,18 +67,20 @@ private[ui] class ExecutorTable(stageId: Int, parent: JobProgressTab) {
     executorIdToSummary match {
       case Some(x) =>
         x.toSeq.sortBy(_._1).map { case (k, v) => {
+          // scalastyle:off
           <tr>
             <td>{k}</td>
             <td>{executorIdToAddress.getOrElse(k, "CANNOT FIND ADDRESS")}</td>
-            <td>{UIUtils.formatDuration(v.taskTime)}</td>
+            <td sorttable_customekey={v.taskTime.toString}>{UIUtils.formatDuration(v.taskTime)}</td>
             <td>{v.failedTasks + v.succeededTasks}</td>
             <td>{v.failedTasks}</td>
             <td>{v.succeededTasks}</td>
-            <td>{Utils.bytesToString(v.shuffleRead)}</td>
-            <td>{Utils.bytesToString(v.shuffleWrite)}</td>
-            <td>{Utils.bytesToString(v.memoryBytesSpilled)}</td>
-            <td>{Utils.bytesToString(v.diskBytesSpilled)}</td>
+            <td sorttable_customekey={v.shuffleRead.toString}>{Utils.bytesToString(v.shuffleRead)}</td>
+            <td sorttable_customekey={v.shuffleWrite.toString}>{Utils.bytesToString(v.shuffleWrite)}</td>
+            <td sorttable_customekey={v.memoryBytesSpilled.toString} >{Utils.bytesToString(v.memoryBytesSpilled)}</td>
+            <td sorttable_customekey={v.diskBytesSpilled.toString} >{Utils.bytesToString(v.diskBytesSpilled)}</td>
           </tr>
+          // scalastyle:on
         }
       }
       case _ => Seq[Node]()
diff --git a/core/src/main/scala/org/apache/spark/ui/storage/StoragePage.scala b/core/src/main/scala/org/apache/spark/ui/storage/StoragePage.scala
index b66edd91f56c..9813d9330ac7 100644
--- a/core/src/main/scala/org/apache/spark/ui/storage/StoragePage.scala
+++ b/core/src/main/scala/org/apache/spark/ui/storage/StoragePage.scala
@@ -49,6 +49,7 @@ private[ui] class StoragePage(parent: StorageTab) extends WebUIPage("") {
 
   /** Render an HTML row representing an RDD */
   private def rddRow(rdd: RDDInfo): Seq[Node] = {
+    // scalastyle:off
     <tr>
       <td>
         <a href={"%s/storage/rdd?id=%s".format(UIUtils.prependBaseUri(basePath), rdd.id)}>
@@ -59,9 +60,10 @@ private[ui] class StoragePage(parent: StorageTab) extends WebUIPage("") {
       </td>
       <td>{rdd.numCachedPartitions}</td>
       <td>{"%.0f%%".format(rdd.numCachedPartitions * 100.0 / rdd.numPartitions)}</td>
-      <td>{Utils.bytesToString(rdd.memSize)}</td>
-      <td>{Utils.bytesToString(rdd.tachyonSize)}</td>
-      <td>{Utils.bytesToString(rdd.diskSize)}</td>
+      <td sorttable_customekey={rdd.memSize.toString}>{Utils.bytesToString(rdd.memSize)}</td>
+      <td sorttable_customekey={rdd.tachyonSize.toString}>{Utils.bytesToString(rdd.tachyonSize)}</td>
+      <td sorttable_customekey={rdd.diskSize.toString} >{Utils.bytesToString(rdd.diskSize)}</td>
     </tr>
+    // scalastyle:on
   }
 }

From 21e0f77b6321590ed86223a60cdb8ae08ea4057f Mon Sep 17 00:00:00 2001
From: Andrew Or <andrewor14@gmail.com>
Date: Fri, 27 Jun 2014 15:23:25 -0700
Subject: [PATCH 542/810] [SPARK-2307] SparkUI - storage tab displays incorrect
 RDDs

The issue here is that the `StorageTab` listens for updates from the `StorageStatusListener`, but when a block is kicked out of the cache, `StorageStatusListener` removes it from its list. Thus, there is no way for the `StorageTab` to know whether a block has been dropped.

This issue was introduced in #1080, which was itself a bug fix. Here we revert that PR and offer a different fix for the original bug (SPARK-2144).

Author: Andrew Or <andrewor14@gmail.com>

Closes #1249 from andrewor14/storage-ui-fix and squashes the following commits:

af019ce [Andrew Or] Fix SPARK-2307
---
 .../org/apache/spark/storage/StorageStatusListener.scala    | 6 +-----
 .../main/scala/org/apache/spark/ui/exec/ExecutorsPage.scala | 5 ++++-
 2 files changed, 5 insertions(+), 6 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/storage/StorageStatusListener.scala b/core/src/main/scala/org/apache/spark/storage/StorageStatusListener.scala
index c694fc8c347e..a6e6627d54e0 100644
--- a/core/src/main/scala/org/apache/spark/storage/StorageStatusListener.scala
+++ b/core/src/main/scala/org/apache/spark/storage/StorageStatusListener.scala
@@ -37,11 +37,7 @@ class StorageStatusListener extends SparkListener {
     val filteredStatus = storageStatusList.find(_.blockManagerId.executorId == execId)
     filteredStatus.foreach { storageStatus =>
       updatedBlocks.foreach { case (blockId, updatedStatus) =>
-        if (updatedStatus.storageLevel == StorageLevel.NONE) {
-          storageStatus.blocks.remove(blockId)
-        } else {
-          storageStatus.blocks(blockId) = updatedStatus
-        }
+        storageStatus.blocks(blockId) = updatedStatus
       }
     }
   }
diff --git a/core/src/main/scala/org/apache/spark/ui/exec/ExecutorsPage.scala b/core/src/main/scala/org/apache/spark/ui/exec/ExecutorsPage.scala
index 2d8c3b949c1a..6cfc46c7e78f 100644
--- a/core/src/main/scala/org/apache/spark/ui/exec/ExecutorsPage.scala
+++ b/core/src/main/scala/org/apache/spark/ui/exec/ExecutorsPage.scala
@@ -21,6 +21,7 @@ import javax.servlet.http.HttpServletRequest
 
 import scala.xml.Node
 
+import org.apache.spark.storage.StorageLevel
 import org.apache.spark.ui.{WebUIPage, UIUtils}
 import org.apache.spark.util.Utils
 
@@ -107,7 +108,9 @@ private[ui] class ExecutorsPage(parent: ExecutorsTab) extends WebUIPage("") {
     val status = listener.storageStatusList(statusId)
     val execId = status.blockManagerId.executorId
     val hostPort = status.blockManagerId.hostPort
-    val rddBlocks = status.blocks.size
+    val rddBlocks = status.blocks.count { case (_, blockStatus) =>
+      blockStatus.storageLevel != StorageLevel.NONE
+    }
     val memUsed = status.memUsed
     val maxMem = status.maxMem
     val diskUsed = status.diskUsed

From f17510e371dfbeaada3c72b884d70c36503ea30a Mon Sep 17 00:00:00 2001
From: Andrew Or <andrewor14@gmail.com>
Date: Fri, 27 Jun 2014 16:11:31 -0700
Subject: [PATCH 543/810] [SPARK-2259] Fix highly misleading docs on cluster /
 client deploy modes

The existing docs are highly misleading. For standalone mode, for example, it encourages the user to use standalone-cluster mode, which is not officially supported. The safeguards have been added in Spark submit itself to prevent bad documentation from leading users down the wrong path in the future.

This PR is prompted by countless headaches users of Spark have run into on the mailing list.

Author: Andrew Or <andrewor14@gmail.com>

Closes #1200 from andrewor14/submit-docs and squashes the following commits:

5ea2460 [Andrew Or] Rephrase cluster vs client explanation
c827f32 [Andrew Or] Clarify spark submit messages
9f7ed8f [Andrew Or] Clarify client vs cluster deploy mode + add safeguards
---
 .../org/apache/spark/deploy/SparkSubmit.scala   | 17 ++++++++++++++---
 .../spark/deploy/SparkSubmitArguments.scala     |  5 +++--
 docs/running-on-mesos.md                        |  3 ++-
 docs/spark-standalone.md                        |  9 ++++-----
 docs/submitting-applications.md                 | 14 +++++++++++++-
 5 files changed, 36 insertions(+), 12 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
index 7e9a9344e61f..b050dccb6d57 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
@@ -19,7 +19,7 @@ package org.apache.spark.deploy
 
 import java.io.{File, PrintStream}
 import java.lang.reflect.InvocationTargetException
-import java.net.{URI, URL}
+import java.net.URL
 
 import scala.collection.mutable.{ArrayBuffer, HashMap, Map}
 
@@ -117,14 +117,25 @@ object SparkSubmit {
     val isPython = args.isPython
     val isYarnCluster = clusterManager == YARN && deployOnCluster
 
+    // For mesos, only client mode is supported
     if (clusterManager == MESOS && deployOnCluster) {
-      printErrorAndExit("Cannot currently run driver on the cluster in Mesos")
+      printErrorAndExit("Cluster deploy mode is currently not supported for Mesos clusters.")
+    }
+
+    // For standalone, only client mode is supported
+    if (clusterManager == STANDALONE && deployOnCluster) {
+      printErrorAndExit("Cluster deploy mode is currently not supported for standalone clusters.")
+    }
+
+    // For shells, only client mode is applicable
+    if (isShell(args.primaryResource) && deployOnCluster) {
+      printErrorAndExit("Cluster deploy mode is not applicable to Spark shells.")
     }
 
     // If we're running a python app, set the main class to our specific python runner
     if (isPython) {
       if (deployOnCluster) {
-        printErrorAndExit("Cannot currently run Python driver programs on cluster")
+        printErrorAndExit("Cluster deploy mode is currently not supported for python.")
       }
       if (args.primaryResource == PYSPARK_SHELL) {
         args.mainClass = "py4j.GatewayServer"
diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
index f1032ea8dbad..57655aa4c32b 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
@@ -338,8 +338,9 @@ private[spark] class SparkSubmitArguments(args: Seq[String]) {
       """Usage: spark-submit [options] <app jar | python file> [app options]
         |Options:
         |  --master MASTER_URL         spark://host:port, mesos://host:port, yarn, or local.
-        |  --deploy-mode DEPLOY_MODE   Where to run the driver program: either "client" to run
-        |                              on the local machine, or "cluster" to run inside cluster.
+        |  --deploy-mode DEPLOY_MODE   Whether to launch the driver program locally ("client") or
+        |                              on one of the worker machines inside the cluster ("cluster")
+        |                              (Default: client).
         |  --class CLASS_NAME          Your application's main class (for Java / Scala apps).
         |  --name NAME                 A name of your application.
         |  --jars JARS                 Comma-separated list of local jars to include on the driver
diff --git a/docs/running-on-mesos.md b/docs/running-on-mesos.md
index e3c892240436..bd046cfc1837 100644
--- a/docs/running-on-mesos.md
+++ b/docs/running-on-mesos.md
@@ -127,7 +127,8 @@ val sc = new SparkContext(conf)
 {% endhighlight %}
 
 (You can also use [`spark-submit`](submitting-applications.html) and configure `spark.executor.uri`
-in the [conf/spark-defaults.conf](configuration.html#loading-default-configurations) file.)
+in the [conf/spark-defaults.conf](configuration.html#loading-default-configurations) file. Note
+that `spark-submit` currently only supports deploying the Spark driver in `client` mode for Mesos.)
 
 When running a shell, the `spark.executor.uri` parameter is inherited from `SPARK_EXECUTOR_URI`, so
 it does not need to be redundantly passed in as a system property.
diff --git a/docs/spark-standalone.md b/docs/spark-standalone.md
index 3c1ce06083ed..f5c0f7cef83d 100644
--- a/docs/spark-standalone.md
+++ b/docs/spark-standalone.md
@@ -235,11 +235,10 @@ You can also pass an option `--cores <numCores>` to control the number of cores
 
 # Launching Compiled Spark Applications
 
-Spark supports two deploy modes: applications may run with the driver inside the client process or
-entirely inside the cluster. The
-[`spark-submit` script](submitting-applications.html) provides the
-most straightforward way to submit a compiled Spark application to the cluster in either deploy
-mode.
+The [`spark-submit` script](submitting-applications.html) provides the most straightforward way to
+submit a compiled Spark application to the cluster. For standalone clusters, Spark currently
+only supports deploying the driver inside the client process that is submitting the application
+(`client` deploy mode).
 
 If your application is launched through Spark submit, then the application jar is automatically
 distributed to all worker nodes. For any additional jars that your application depends on, you
diff --git a/docs/submitting-applications.md b/docs/submitting-applications.md
index d2864fe4c2f6..e05883072bfa 100644
--- a/docs/submitting-applications.md
+++ b/docs/submitting-applications.md
@@ -42,10 +42,22 @@ Some of the commonly used options are:
 
 * `--class`: The entry point for your application (e.g. `org.apache.spark.examples.SparkPi`)
 * `--master`: The [master URL](#master-urls) for the cluster (e.g. `spark://23.195.26.187:7077`)
-* `--deploy-mode`: Whether to deploy your driver program within the cluster or run it locally as an external client (either `cluster` or `client`)
+* `--deploy-mode`: Whether to deploy your driver on the worker nodes (`cluster`) or locally as an external client (`client`) (default: `client`)*
 * `application-jar`: Path to a bundled jar including your application and all dependencies. The URL must be globally visible inside of your cluster, for instance, an `hdfs://` path or a `file://` path that is present on all nodes.
 * `application-arguments`: Arguments passed to the main method of your main class, if any
 
+*A common deployment strategy is to submit your application from a gateway machine that is
+physically co-located with your worker machines (e.g. Master node in a standalone EC2 cluster).
+In this setup, `client` mode is appropriate. In `client` mode, the driver is launched directly
+within the client `spark-submit` process, with the input and output of the application attached
+to the console. Thus, this mode is especially suitable for applications that involve the REPL
+(e.g. Spark shell).
+
+Alternatively, if your application is submitted from a machine far from the worker machines (e.g.
+locally on your laptop), it is common to use `cluster` mode to minimize network latency between
+the drivers and the executors. Note that `cluster` mode is currently not supported for standalone
+clusters, Mesos clusters, or python applications.
+
 For Python applications, simply pass a `.py` file in the place of `<application-jar>` instead of a JAR,
 and add Python `.zip`, `.egg` or `.py` files to the search path with `--py-files`.
 

From 0e0686d3ef88e024fcceafe36a0cdbb953f5aeae Mon Sep 17 00:00:00 2001
From: Matthew Farrellee <matt@redhat.com>
Date: Fri, 27 Jun 2014 18:20:33 -0700
Subject: [PATCH 544/810] [SPARK-2003] Fix python SparkContext example

Author: Matthew Farrellee <matt@redhat.com>

Closes #1246 from mattf/SPARK-2003 and squashes the following commits:

b12e7ca [Matthew Farrellee] [SPARK-2003] Fix python SparkContext example
---
 docs/programming-guide.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/programming-guide.md b/docs/programming-guide.md
index 65d75b85efda..06e4c4ce527e 100644
--- a/docs/programming-guide.md
+++ b/docs/programming-guide.md
@@ -145,7 +145,7 @@ that contains information about your application.
 
 {% highlight python %}
 conf = SparkConf().setAppName(appName).setMaster(master)
-sc = SparkContext(conf)
+sc = SparkContext(conf=conf)
 {% endhighlight %}
 
 </div>

From b8f2e13aec715e038bd6d1d07b607683f138ac83 Mon Sep 17 00:00:00 2001
From: Guillaume Ballet <gballet@gmail.com>
Date: Sat, 28 Jun 2014 13:07:12 -0700
Subject: [PATCH 545/810] [SPARK-2233] make-distribution script should list the
 git hash in the RELEASE file

This patch adds the git revision hash (short version) to the RELEASE file. It uses git instead of simply checking for the existence of .git, so as to make sure that this is a functional repository.

Author: Guillaume Ballet <gballet@gmail.com>

Closes #1216 from gballet/master and squashes the following commits:

eabc50f [Guillaume Ballet] Refactored the script to take comments into account.
d93e5e8 [Guillaume Ballet] [SPARK 2233] make-distribution script now lists the git hash tag in the RELEASE file.
---
 make-distribution.sh | 10 +++++++++-
 1 file changed, 9 insertions(+), 1 deletion(-)

diff --git a/make-distribution.sh b/make-distribution.sh
index 86868438e75c..94b473bf91cd 100755
--- a/make-distribution.sh
+++ b/make-distribution.sh
@@ -100,6 +100,14 @@ if [ -z "$JAVA_HOME" ]; then
   exit -1
 fi
 
+if which git &>/dev/null; then
+    GITREV=$(git rev-parse --short HEAD 2>/dev/null || :)
+    if [ ! -z $GITREV ]; then
+	 GITREVSTRING=" (git revision $GITREV)"
+    fi
+    unset GITREV
+fi
+
 if ! which mvn &>/dev/null; then
     echo -e "You need Maven installed to build Spark."
     echo -e "Download Maven from https://maven.apache.org/"
@@ -186,7 +194,7 @@ ${BUILD_COMMAND}
 # Make directories
 rm -rf "$DISTDIR"
 mkdir -p "$DISTDIR/lib"
-echo "Spark $VERSION built for Hadoop $SPARK_HADOOP_VERSION" > "$DISTDIR/RELEASE"
+echo "Spark $VERSION$GITREVSTRING built for Hadoop $SPARK_HADOOP_VERSION" > "$DISTDIR/RELEASE"
 
 # Copy jars
 cp $FWDIR/assembly/target/scala*/*assembly*hadoop*.jar "$DISTDIR/lib/"

From 3c104c79d24425786cec0034f269ba19cf465b31 Mon Sep 17 00:00:00 2001
From: Matthew Farrellee <matt@redhat.com>
Date: Sat, 28 Jun 2014 18:39:27 -0700
Subject: [PATCH 546/810] [SPARK-1394] Remove SIGCHLD handler in worker
 subprocess

It should not be the responsibility of the worker subprocess, which
does not intentionally fork, to try and cleanup child processes. Doing
so is complex and interferes with operations such as
platform.system().

If it is desirable to have tighter control over subprocesses, then
namespaces should be used and it should be the manager's resposibility
to handle cleanup.

Author: Matthew Farrellee <matt@redhat.com>

Closes #1247 from mattf/SPARK-1394 and squashes the following commits:

c36f308 [Matthew Farrellee] [SPARK-1394] Remove SIGCHLD handler in worker subprocess
---
 python/pyspark/daemon.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/python/pyspark/daemon.py b/python/pyspark/daemon.py
index b2f226a55ec1..5eb1c63bf206 100644
--- a/python/pyspark/daemon.py
+++ b/python/pyspark/daemon.py
@@ -103,6 +103,7 @@ def waitSocketClose(sock):
             if os.fork() == 0:
                 # Leave the worker pool
                 signal.signal(SIGHUP, SIG_DFL)
+                signal.signal(SIGCHLD, SIG_DFL)
                 listen_sock.close()
                 # Read the socket using fdopen instead of socket.makefile() because the latter
                 # seems to be very slow; note that we need to dup() the file descriptor because

From 2053d793cc2e8e5f5776e6576ddc6f8e6168e60c Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Sat, 28 Jun 2014 21:05:03 -0700
Subject: [PATCH 547/810] Improve MapOutputTracker error logging.

Author: Reynold Xin <rxin@apache.org>

Closes #1258 from rxin/mapOutputTracker and squashes the following commits:

a7c95b6 [Reynold Xin] Improve MapOutputTracker error logging.
---
 .../org/apache/spark/MapOutputTracker.scala     | 17 ++++++++++-------
 1 file changed, 10 insertions(+), 7 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/MapOutputTracker.scala b/core/src/main/scala/org/apache/spark/MapOutputTracker.scala
index 182abacc475a..894091761485 100644
--- a/core/src/main/scala/org/apache/spark/MapOutputTracker.scala
+++ b/core/src/main/scala/org/apache/spark/MapOutputTracker.scala
@@ -26,10 +26,10 @@ import scala.concurrent.Await
 import akka.actor._
 import akka.pattern.ask
 
-import org.apache.spark.util._
 import org.apache.spark.scheduler.MapStatus
 import org.apache.spark.shuffle.MetadataFetchFailedException
 import org.apache.spark.storage.BlockManagerId
+import org.apache.spark.util._
 
 private[spark] sealed trait MapOutputTrackerMessage
 private[spark] case class GetMapOutputStatuses(shuffleId: Int)
@@ -107,14 +107,17 @@ private[spark] abstract class MapOutputTracker(conf: SparkConf) extends Logging
       Await.result(future, timeout)
     } catch {
       case e: Exception =>
+        logError("Error communicating with MapOutputTracker", e)
         throw new SparkException("Error communicating with MapOutputTracker", e)
     }
   }
 
   /** Send a one-way message to the trackerActor, to which we expect it to reply with true. */
   protected def sendTracker(message: Any) {
-    if (askTracker(message) != true) {
-      throw new SparkException("Error reply received from MapOutputTracker")
+    val response = askTracker(message)
+    if (response != true) {
+      throw new SparkException(
+        "Error reply received from MapOutputTracker. Expecting true, got " + response.toString)
     }
   }
 
@@ -366,9 +369,9 @@ private[spark] object MapOutputTracker {
   // any of the statuses is null (indicating a missing location due to a failed mapper),
   // throw a FetchFailedException.
   private def convertMapStatuses(
-        shuffleId: Int,
-        reduceId: Int,
-        statuses: Array[MapStatus]): Array[(BlockManagerId, Long)] = {
+      shuffleId: Int,
+      reduceId: Int,
+      statuses: Array[MapStatus]): Array[(BlockManagerId, Long)] = {
     assert (statuses != null)
     statuses.map {
       status =>
@@ -403,7 +406,7 @@ private[spark] object MapOutputTracker {
     if (compressedSize == 0) {
       0
     } else {
-      math.pow(LOG_BASE, (compressedSize & 0xFF)).toLong
+      math.pow(LOG_BASE, compressedSize & 0xFF).toLong
     }
   }
 }

From cdf613fc52d7057555a2dbf2241ce90bacbabb4a Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Sun, 29 Jun 2014 16:46:28 -0700
Subject: [PATCH 548/810] [SPARK-2320] Reduce exception/code block font size in
 web ui

Author: Reynold Xin <rxin@apache.org>

Closes #1261 from rxin/ui-pre-size and squashes the following commits:

7ab1a69 [Reynold Xin] [SPARK-2320] Reduce exception/code block font size in web ui
---
 core/src/main/resources/org/apache/spark/ui/static/webui.css  | 4 ++++
 core/src/main/scala/org/apache/spark/ui/jobs/StageTable.scala | 2 +-
 2 files changed, 5 insertions(+), 1 deletion(-)

diff --git a/core/src/main/resources/org/apache/spark/ui/static/webui.css b/core/src/main/resources/org/apache/spark/ui/static/webui.css
index a8bc141208a9..11fd956bfbe6 100644
--- a/core/src/main/resources/org/apache/spark/ui/static/webui.css
+++ b/core/src/main/resources/org/apache/spark/ui/static/webui.css
@@ -95,6 +95,10 @@ span.expand-details {
   float: right;
 }
 
+pre {
+  font-size: 0.8em;
+}
+
 .stage-details {
   max-height: 100px;
   overflow-y: auto;
diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/StageTable.scala b/core/src/main/scala/org/apache/spark/ui/jobs/StageTable.scala
index 30971f769682..b2b6cc6a6ec2 100644
--- a/core/src/main/scala/org/apache/spark/ui/jobs/StageTable.scala
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/StageTable.scala
@@ -168,7 +168,7 @@ private[ui] class FailedStageTable(
 
   override protected def stageRow(s: StageInfo): Seq[Node] = {
     val basicColumns = super.stageRow(s)
-    val failureReason = <td valign="middle">{s.failureReason.getOrElse("")}</td>
+    val failureReason = <td valign="middle"><pre>{s.failureReason.getOrElse("")}</pre></td>
     basicColumns ++ failureReason
   }
 }

From 7b71a0e09622e09285a9884ebb67b5fb1c5caa53 Mon Sep 17 00:00:00 2001
From: Kay Ousterhout <kayousterhout@gmail.com>
Date: Sun, 29 Jun 2014 22:01:42 -0700
Subject: [PATCH 549/810] [SPARK-1683] Track task read metrics.

This commit adds a new metric in TaskMetrics to record
the input data size and displays this information in the UI.

An earlier version of this commit also added the read time,
which can be useful for diagnosing straggler problems,
but unfortunately that change introduced a significant performance
regression for jobs that don't do much computation. In order to
track read time, we'll need to do sampling.

The screenshots below show the UI with the new "Input" field,
which I added to the stage summary page, the executor summary page,
and the per-stage page.

![image](https://cloud.githubusercontent.com/assets/1108612/3167930/2627f92a-eb77-11e3-861c-98ea5bb7a1a2.png)

![image](https://cloud.githubusercontent.com/assets/1108612/3167936/475a889c-eb77-11e3-9706-f11c48751f17.png)

![image](https://cloud.githubusercontent.com/assets/1108612/3167948/80ebcf12-eb77-11e3-87ed-349fce6a770c.png)

Author: Kay Ousterhout <kayousterhout@gmail.com>

Closes #962 from kayousterhout/read_metrics and squashes the following commits:

f13b67d [Kay Ousterhout] Correctly format input bytes on executor page
8b70cde [Kay Ousterhout] Added comment about potential inaccuracy of bytesRead
d1016e8 [Kay Ousterhout] Udated SparkListenerSuite test
8461492 [Kay Ousterhout] Miniscule style fix
ae04d99 [Kay Ousterhout] Remove input metrics for parallel collections
719f19d [Kay Ousterhout] Style fixes
bb6ec62 [Kay Ousterhout] Small fixes
869ac7b [Kay Ousterhout] Updated Json tests
44a0301 [Kay Ousterhout] Fixed accidentally added line
4bd0568 [Kay Ousterhout] Added input source, renamed Hdfs to Hadoop.
f27e535 [Kay Ousterhout] Updates based on review comments and to fix rebase
bf41029 [Kay Ousterhout] Updated Json tests to pass
0fc33e0 [Kay Ousterhout] Added explicit backward compatibility test
4e52925 [Kay Ousterhout] Added Json output and associated tests.
365400b [Kay Ousterhout] [SPARK-1683] Track task read metrics.
---
 .../scala/org/apache/spark/CacheManager.scala |  10 +-
 .../apache/spark/executor/TaskMetrics.scala   |  29 +++++
 .../scala/org/apache/spark/rdd/BlockRDD.scala |   2 +-
 .../org/apache/spark/rdd/HadoopRDD.scala      |  15 +++
 .../org/apache/spark/rdd/NewHadoopRDD.scala   |  13 +++
 .../apache/spark/scheduler/JobLogger.scala    |  15 ++-
 .../apache/spark/storage/BlockManager.scala   |  63 ++++++-----
 .../apache/spark/storage/ThreadingTest.scala  |   2 +-
 .../apache/spark/ui/exec/ExecutorsPage.scala  |   4 +
 .../apache/spark/ui/exec/ExecutorsTab.scala   |   5 +
 .../spark/ui/jobs/ExecutorSummary.scala       |   1 +
 .../apache/spark/ui/jobs/ExecutorTable.scala  |   2 +
 .../spark/ui/jobs/JobProgressListener.scala   |  15 ++-
 .../org/apache/spark/ui/jobs/StagePage.scala  |  41 +++++--
 .../org/apache/spark/ui/jobs/StageTable.scala |   7 ++
 .../org/apache/spark/util/JsonProtocol.scala  |  20 +++-
 .../org/apache/spark/CacheManagerSuite.scala  |   4 +-
 .../spark/scheduler/SparkListenerSuite.scala  |   1 +
 .../spark/storage/BlockManagerSuite.scala     |  84 +++++++++++----
 .../apache/spark/util/JsonProtocolSuite.scala | 102 +++++++++++++++---
 20 files changed, 349 insertions(+), 86 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/CacheManager.scala b/core/src/main/scala/org/apache/spark/CacheManager.scala
index 3f667a4a0f9c..8f867686a044 100644
--- a/core/src/main/scala/org/apache/spark/CacheManager.scala
+++ b/core/src/main/scala/org/apache/spark/CacheManager.scala
@@ -19,6 +19,7 @@ package org.apache.spark
 
 import scala.collection.mutable.{ArrayBuffer, HashSet}
 
+import org.apache.spark.executor.InputMetrics
 import org.apache.spark.rdd.RDD
 import org.apache.spark.storage._
 
@@ -41,9 +42,10 @@ private[spark] class CacheManager(blockManager: BlockManager) extends Logging {
     val key = RDDBlockId(rdd.id, partition.index)
     logDebug(s"Looking for partition $key")
     blockManager.get(key) match {
-      case Some(values) =>
+      case Some(blockResult) =>
         // Partition is already materialized, so just return its values
-        new InterruptibleIterator(context, values.asInstanceOf[Iterator[T]])
+        context.taskMetrics.inputMetrics = Some(blockResult.inputMetrics)
+        new InterruptibleIterator(context, blockResult.data.asInstanceOf[Iterator[T]])
 
       case None =>
         // Acquire a lock for loading this partition
@@ -110,7 +112,7 @@ private[spark] class CacheManager(blockManager: BlockManager) extends Logging {
           logInfo(s"Whoever was loading $id failed; we'll try it ourselves")
           loading.add(id)
         }
-        values.map(_.asInstanceOf[Iterator[T]])
+        values.map(_.data.asInstanceOf[Iterator[T]])
       }
     }
   }
@@ -132,7 +134,7 @@ private[spark] class CacheManager(blockManager: BlockManager) extends Logging {
        * exceptions that can be avoided. */
       updatedBlocks ++= blockManager.put(key, values, storageLevel, tellMaster = true)
       blockManager.get(key) match {
-        case Some(v) => v.asInstanceOf[Iterator[T]]
+        case Some(v) => v.data.asInstanceOf[Iterator[T]]
         case None =>
           logInfo(s"Failure to store $key")
           throw new BlockException(key, s"Block manager failed to return cached value for $key!")
diff --git a/core/src/main/scala/org/apache/spark/executor/TaskMetrics.scala b/core/src/main/scala/org/apache/spark/executor/TaskMetrics.scala
index 350fd74173f6..ac73288442a7 100644
--- a/core/src/main/scala/org/apache/spark/executor/TaskMetrics.scala
+++ b/core/src/main/scala/org/apache/spark/executor/TaskMetrics.scala
@@ -66,6 +66,12 @@ class TaskMetrics extends Serializable {
    */
   var diskBytesSpilled: Long = _
 
+  /**
+   * If this task reads from a HadoopRDD or from persisted data, metrics on how much data was read
+   * are stored here.
+   */
+  var inputMetrics: Option[InputMetrics] = None
+
   /**
    * If this task reads from shuffle output, metrics on getting shuffle data will be collected here
    */
@@ -87,6 +93,29 @@ private[spark] object TaskMetrics {
   def empty: TaskMetrics = new TaskMetrics
 }
 
+/**
+ * :: DeveloperApi ::
+ * Method by which input data was read.  Network means that the data was read over the network
+ * from a remote block manager (which may have stored the data on-disk or in-memory).
+ */
+@DeveloperApi
+object DataReadMethod extends Enumeration with Serializable {
+  type DataReadMethod = Value
+  val Memory, Disk, Hadoop, Network = Value
+}
+
+/**
+ * :: DeveloperApi ::
+ * Metrics about reading input data.
+ */
+@DeveloperApi
+case class InputMetrics(readMethod: DataReadMethod.Value) {
+  /**
+   * Total bytes read.
+   */
+  var bytesRead: Long = 0L
+}
+
 
 /**
  * :: DeveloperApi ::
diff --git a/core/src/main/scala/org/apache/spark/rdd/BlockRDD.scala b/core/src/main/scala/org/apache/spark/rdd/BlockRDD.scala
index c64da8804d16..2673ec22509e 100644
--- a/core/src/main/scala/org/apache/spark/rdd/BlockRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/BlockRDD.scala
@@ -46,7 +46,7 @@ class BlockRDD[T: ClassTag](@transient sc: SparkContext, @transient val blockIds
     val blockManager = SparkEnv.get.blockManager
     val blockId = split.asInstanceOf[BlockRDDPartition].blockId
     blockManager.get(blockId) match {
-      case Some(block) => block.asInstanceOf[Iterator[T]]
+      case Some(block) => block.data.asInstanceOf[Iterator[T]]
       case None =>
         throw new Exception("Could not compute split, block " + blockId + " not found")
     }
diff --git a/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala b/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala
index 2aa111d600e9..98dcbf4e2dbf 100644
--- a/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala
@@ -38,6 +38,7 @@ import org.apache.spark._
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.broadcast.Broadcast
 import org.apache.spark.deploy.SparkHadoopUtil
+import org.apache.spark.executor.{DataReadMethod, InputMetrics}
 import org.apache.spark.util.NextIterator
 
 /**
@@ -196,6 +197,20 @@ class HadoopRDD[K, V](
       context.addOnCompleteCallback{ () => closeIfNeeded() }
       val key: K = reader.createKey()
       val value: V = reader.createValue()
+
+      // Set the task input metrics.
+      val inputMetrics = new InputMetrics(DataReadMethod.Hadoop)
+      try {
+        /* bytesRead may not exactly equal the bytes read by a task: split boundaries aren't
+         * always at record boundaries, so tasks may need to read into other splits to complete
+         * a record. */
+        inputMetrics.bytesRead = split.inputSplit.value.getLength()
+      } catch {
+        case e: java.io.IOException =>
+          logWarning("Unable to get input size to set InputMetrics for task", e)
+      }
+      context.taskMetrics.inputMetrics = Some(inputMetrics)
+
       override def getNext() = {
         try {
           finished = !reader.next(key, value)
diff --git a/core/src/main/scala/org/apache/spark/rdd/NewHadoopRDD.scala b/core/src/main/scala/org/apache/spark/rdd/NewHadoopRDD.scala
index ac1ccc06f238..f2b3a64bf134 100644
--- a/core/src/main/scala/org/apache/spark/rdd/NewHadoopRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/NewHadoopRDD.scala
@@ -31,6 +31,7 @@ import org.apache.spark.Logging
 import org.apache.spark.Partition
 import org.apache.spark.SerializableWritable
 import org.apache.spark.{SparkContext, TaskContext}
+import org.apache.spark.executor.{DataReadMethod, InputMetrics}
 
 private[spark] class NewHadoopPartition(
     rddId: Int,
@@ -112,6 +113,18 @@ class NewHadoopRDD[K, V](
         split.serializableHadoopSplit.value, hadoopAttemptContext)
       reader.initialize(split.serializableHadoopSplit.value, hadoopAttemptContext)
 
+      val inputMetrics = new InputMetrics(DataReadMethod.Hadoop)
+      try {
+        /* bytesRead may not exactly equal the bytes read by a task: split boundaries aren't
+         * always at record boundaries, so tasks may need to read into other splits to complete
+         * a record. */
+        inputMetrics.bytesRead = split.serializableHadoopSplit.value.getLength()
+      } catch {
+        case e: Exception =>
+          logWarning("Unable to get input split size in order to set task input bytes", e)
+      }
+      context.taskMetrics.inputMetrics = Some(inputMetrics)
+
       // Register an on-task-completion callback to close the input stream.
       context.addOnCompleteCallback(() => close())
       var havePair = false
diff --git a/core/src/main/scala/org/apache/spark/scheduler/JobLogger.scala b/core/src/main/scala/org/apache/spark/scheduler/JobLogger.scala
index a1e21cad48b9..47dd112f6832 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/JobLogger.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/JobLogger.scala
@@ -26,7 +26,9 @@ import scala.collection.mutable.HashMap
 
 import org.apache.spark._
 import org.apache.spark.annotation.DeveloperApi
-import org.apache.spark.executor.TaskMetrics
+import org.apache.spark.executor.{DataReadMethod, TaskMetrics}
+import org.apache.spark.rdd.RDD
+import org.apache.spark.storage.StorageLevel
 
 /**
  * :: DeveloperApi ::
@@ -160,7 +162,13 @@ class JobLogger(val user: String, val logDirName: String) extends SparkListener
                " START_TIME=" + taskInfo.launchTime + " FINISH_TIME=" + taskInfo.finishTime +
                " EXECUTOR_ID=" + taskInfo.executorId +  " HOST=" + taskMetrics.hostname
     val executorRunTime = " EXECUTOR_RUN_TIME=" + taskMetrics.executorRunTime
-    val readMetrics = taskMetrics.shuffleReadMetrics match {
+    val inputMetrics = taskMetrics.inputMetrics match {
+      case Some(metrics) =>
+        " READ_METHOD=" + metrics.readMethod.toString +
+        " INPUT_BYTES=" + metrics.bytesRead
+      case None => ""
+    }
+    val shuffleReadMetrics = taskMetrics.shuffleReadMetrics match {
       case Some(metrics) =>
         " SHUFFLE_FINISH_TIME=" + metrics.shuffleFinishTime +
         " BLOCK_FETCHED_TOTAL=" + metrics.totalBlocksFetched +
@@ -174,7 +182,8 @@ class JobLogger(val user: String, val logDirName: String) extends SparkListener
       case Some(metrics) => " SHUFFLE_BYTES_WRITTEN=" + metrics.shuffleBytesWritten
       case None => ""
     }
-    stageLogInfo(stageId, status + info + executorRunTime + readMetrics + writeMetrics)
+    stageLogInfo(stageId, status + info + executorRunTime + inputMetrics + shuffleReadMetrics +
+      writeMetrics)
   }
 
   /**
diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala
index d2f7baf928b6..0db0a5bc7341 100644
--- a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala
+++ b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala
@@ -29,6 +29,7 @@ import akka.actor.{ActorSystem, Cancellable, Props}
 import sun.nio.ch.DirectBuffer
 
 import org.apache.spark._
+import org.apache.spark.executor.{DataReadMethod, InputMetrics}
 import org.apache.spark.io.CompressionCodec
 import org.apache.spark.network._
 import org.apache.spark.serializer.Serializer
@@ -39,6 +40,15 @@ private[spark] case class ByteBufferValues(buffer: ByteBuffer) extends BlockValu
 private[spark] case class IteratorValues(iterator: Iterator[Any]) extends BlockValues
 private[spark] case class ArrayBufferValues(buffer: ArrayBuffer[Any]) extends BlockValues
 
+/* Class for returning a fetched block and associated metrics. */
+private[spark] class BlockResult(
+    val data: Iterator[Any],
+    readMethod: DataReadMethod.Value,
+    bytes: Long) {
+  val inputMetrics = new InputMetrics(readMethod)
+  inputMetrics.bytesRead = bytes
+}
+
 private[spark] class BlockManager(
     executorId: String,
     actorSystem: ActorSystem,
@@ -334,9 +344,9 @@ private[spark] class BlockManager(
   /**
    * Get block from local block manager.
    */
-  def getLocal(blockId: BlockId): Option[Iterator[Any]] = {
+  def getLocal(blockId: BlockId): Option[BlockResult] = {
     logDebug(s"Getting local block $blockId")
-    doGetLocal(blockId, asValues = true).asInstanceOf[Option[Iterator[Any]]]
+    doGetLocal(blockId, asBlockResult = true).asInstanceOf[Option[BlockResult]]
   }
 
   /**
@@ -355,11 +365,11 @@ private[spark] class BlockManager(
             blockId, s"Block $blockId not found on disk, though it should be")
       }
     } else {
-      doGetLocal(blockId, asValues = false).asInstanceOf[Option[ByteBuffer]]
+      doGetLocal(blockId, asBlockResult = false).asInstanceOf[Option[ByteBuffer]]
     }
   }
 
-  private def doGetLocal(blockId: BlockId, asValues: Boolean): Option[Any] = {
+  private def doGetLocal(blockId: BlockId, asBlockResult: Boolean): Option[Any] = {
     val info = blockInfo.get(blockId).orNull
     if (info != null) {
       info.synchronized {
@@ -386,14 +396,14 @@ private[spark] class BlockManager(
         // Look for the block in memory
         if (level.useMemory) {
           logDebug(s"Getting block $blockId from memory")
-          val result = if (asValues) {
-            memoryStore.getValues(blockId)
+          val result = if (asBlockResult) {
+            memoryStore.getValues(blockId).map(new BlockResult(_, DataReadMethod.Memory, info.size))
           } else {
             memoryStore.getBytes(blockId)
           }
           result match {
             case Some(values) =>
-              return Some(values)
+              return result
             case None =>
               logDebug(s"Block $blockId not found in memory")
           }
@@ -405,10 +415,11 @@ private[spark] class BlockManager(
           if (tachyonStore.contains(blockId)) {
             tachyonStore.getBytes(blockId) match {
               case Some(bytes) =>
-                if (!asValues) {
+                if (!asBlockResult) {
                   return Some(bytes)
                 } else {
-                  return Some(dataDeserialize(blockId, bytes))
+                  return Some(new BlockResult(
+                    dataDeserialize(blockId, bytes), DataReadMethod.Memory, info.size))
                 }
               case None =>
                 logDebug(s"Block $blockId not found in tachyon")
@@ -429,14 +440,15 @@ private[spark] class BlockManager(
 
           if (!level.useMemory) {
             // If the block shouldn't be stored in memory, we can just return it
-            if (asValues) {
-              return Some(dataDeserialize(blockId, bytes))
+            if (asBlockResult) {
+              return Some(new BlockResult(dataDeserialize(blockId, bytes), DataReadMethod.Disk,
+                info.size))
             } else {
               return Some(bytes)
             }
           } else {
             // Otherwise, we also have to store something in the memory store
-            if (!level.deserialized || !asValues) {
+            if (!level.deserialized || !asBlockResult) {
               /* We'll store the bytes in memory if the block's storage level includes
                * "memory serialized", or if it should be cached as objects in memory
                * but we only requested its serialized bytes. */
@@ -445,7 +457,7 @@ private[spark] class BlockManager(
               memoryStore.putBytes(blockId, copyForMemory, level)
               bytes.rewind()
             }
-            if (!asValues) {
+            if (!asBlockResult) {
               return Some(bytes)
             } else {
               val values = dataDeserialize(blockId, bytes)
@@ -457,12 +469,12 @@ private[spark] class BlockManager(
                 memoryStore.putValues(blockId, valuesBuffer, level, returnValues = true).data
                   match {
                     case Left(values2) =>
-                      return Some(values2)
+                      return Some(new BlockResult(values2, DataReadMethod.Disk, info.size))
                     case _ =>
-                      throw new SparkException("Memory store did not return an iterator")
+                      throw new SparkException("Memory store did not return back an iterator")
                   }
               } else {
-                return Some(values)
+                return Some(new BlockResult(values, DataReadMethod.Disk, info.size))
               }
             }
           }
@@ -477,9 +489,9 @@ private[spark] class BlockManager(
   /**
    * Get block from remote block managers.
    */
-  def getRemote(blockId: BlockId): Option[Iterator[Any]] = {
+  def getRemote(blockId: BlockId): Option[BlockResult] = {
     logDebug(s"Getting remote block $blockId")
-    doGetRemote(blockId, asValues = true).asInstanceOf[Option[Iterator[Any]]]
+    doGetRemote(blockId, asBlockResult = true).asInstanceOf[Option[BlockResult]]
   }
 
   /**
@@ -487,10 +499,10 @@ private[spark] class BlockManager(
    */
   def getRemoteBytes(blockId: BlockId): Option[ByteBuffer] = {
     logDebug(s"Getting remote block $blockId as bytes")
-    doGetRemote(blockId, asValues = false).asInstanceOf[Option[ByteBuffer]]
+    doGetRemote(blockId, asBlockResult = false).asInstanceOf[Option[ByteBuffer]]
   }
 
-  private def doGetRemote(blockId: BlockId, asValues: Boolean): Option[Any] = {
+  private def doGetRemote(blockId: BlockId, asBlockResult: Boolean): Option[Any] = {
     require(blockId != null, "BlockId is null")
     val locations = Random.shuffle(master.getLocations(blockId))
     for (loc <- locations) {
@@ -498,8 +510,11 @@ private[spark] class BlockManager(
       val data = BlockManagerWorker.syncGetBlock(
         GetBlock(blockId), ConnectionManagerId(loc.host, loc.port))
       if (data != null) {
-        if (asValues) {
-          return Some(dataDeserialize(blockId, data))
+        if (asBlockResult) {
+          return Some(new BlockResult(
+            dataDeserialize(blockId, data),
+            DataReadMethod.Network,
+            data.limit()))
         } else {
           return Some(data)
         }
@@ -513,7 +528,7 @@ private[spark] class BlockManager(
   /**
    * Get a block from the block manager (either local or remote).
    */
-  def get(blockId: BlockId): Option[Iterator[Any]] = {
+  def get(blockId: BlockId): Option[BlockResult] = {
     val local = getLocal(blockId)
     if (local.isDefined) {
       logInfo(s"Found block $blockId locally")
@@ -792,7 +807,7 @@ private[spark] class BlockManager(
    * Read a block consisting of a single object.
    */
   def getSingle(blockId: BlockId): Option[Any] = {
-    get(blockId).map(_.next())
+    get(blockId).map(_.data.next())
   }
 
   /**
diff --git a/core/src/main/scala/org/apache/spark/storage/ThreadingTest.scala b/core/src/main/scala/org/apache/spark/storage/ThreadingTest.scala
index a107c5182b3b..328be158db68 100644
--- a/core/src/main/scala/org/apache/spark/storage/ThreadingTest.scala
+++ b/core/src/main/scala/org/apache/spark/storage/ThreadingTest.scala
@@ -78,7 +78,7 @@ private[spark] object ThreadingTest {
         val startTime = System.currentTimeMillis()
         manager.get(blockId) match {
           case Some(retrievedBlock) =>
-            assert(retrievedBlock.toList.asInstanceOf[List[Int]] == block.toList,
+            assert(retrievedBlock.data.toList.asInstanceOf[List[Int]] == block.toList,
               "Block " + blockId + " did not match")
             println("Got block " + blockId + " in " +
               (System.currentTimeMillis - startTime) + " ms")
diff --git a/core/src/main/scala/org/apache/spark/ui/exec/ExecutorsPage.scala b/core/src/main/scala/org/apache/spark/ui/exec/ExecutorsPage.scala
index 6cfc46c7e78f..9625337ae21a 100644
--- a/core/src/main/scala/org/apache/spark/ui/exec/ExecutorsPage.scala
+++ b/core/src/main/scala/org/apache/spark/ui/exec/ExecutorsPage.scala
@@ -72,6 +72,7 @@ private[ui] class ExecutorsPage(parent: ExecutorsTab) extends WebUIPage("") {
     "Complete Tasks",
     "Total Tasks",
     "Task Time",
+    "Input Bytes",
     "Shuffle Read",
     "Shuffle Write")
 
@@ -97,6 +98,7 @@ private[ui] class ExecutorsPage(parent: ExecutorsTab) extends WebUIPage("") {
       <td>{values("Complete Tasks")}</td>
       <td>{values("Total Tasks")}</td>
       <td sorttable_customkey={values("Task Time")}>{Utils.msDurationToString(values("Task Time").toLong)}</td>
+      <td sorttable_customkey={values("Input Bytes")}>{Utils.bytesToString(values("Input Bytes").toLong)}</td>
       <td sorttable_customkey={values("Shuffle Read")}>{Utils.bytesToString(values("Shuffle Read").toLong)}</td>
       <td sorttable_customkey={values("Shuffle Write")} >{Utils.bytesToString(values("Shuffle Write").toLong)}</td>
     </tr>
@@ -119,6 +121,7 @@ private[ui] class ExecutorsPage(parent: ExecutorsTab) extends WebUIPage("") {
     val completedTasks = listener.executorToTasksComplete.getOrElse(execId, 0)
     val totalTasks = activeTasks + failedTasks + completedTasks
     val totalDuration = listener.executorToDuration.getOrElse(execId, 0)
+    val totalInputBytes = listener.executorToInputBytes.getOrElse(execId, 0)
     val totalShuffleRead = listener.executorToShuffleRead.getOrElse(execId, 0)
     val totalShuffleWrite = listener.executorToShuffleWrite.getOrElse(execId, 0)
 
@@ -136,6 +139,7 @@ private[ui] class ExecutorsPage(parent: ExecutorsTab) extends WebUIPage("") {
       completedTasks,
       totalTasks,
       totalDuration,
+      totalInputBytes,
       totalShuffleRead,
       totalShuffleWrite,
       maxMem
diff --git a/core/src/main/scala/org/apache/spark/ui/exec/ExecutorsTab.scala b/core/src/main/scala/org/apache/spark/ui/exec/ExecutorsTab.scala
index 91d37b835b19..58eeb86bf9a3 100644
--- a/core/src/main/scala/org/apache/spark/ui/exec/ExecutorsTab.scala
+++ b/core/src/main/scala/org/apache/spark/ui/exec/ExecutorsTab.scala
@@ -46,6 +46,7 @@ class ExecutorsListener(storageStatusListener: StorageStatusListener)
   val executorToTasksComplete = HashMap[String, Int]()
   val executorToTasksFailed = HashMap[String, Int]()
   val executorToDuration = HashMap[String, Long]()
+  val executorToInputBytes = HashMap[String, Long]()
   val executorToShuffleRead = HashMap[String, Long]()
   val executorToShuffleWrite = HashMap[String, Long]()
 
@@ -72,6 +73,10 @@ class ExecutorsListener(storageStatusListener: StorageStatusListener)
       // Update shuffle read/write
       val metrics = taskEnd.taskMetrics
       if (metrics != null) {
+        metrics.inputMetrics.foreach { inputMetrics =>
+          executorToInputBytes(eid) =
+            executorToInputBytes.getOrElse(eid, 0L) + inputMetrics.bytesRead
+        }
         metrics.shuffleReadMetrics.foreach { shuffleRead =>
           executorToShuffleRead(eid) =
             executorToShuffleRead.getOrElse(eid, 0L) + shuffleRead.remoteBytesRead
diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/ExecutorSummary.scala b/core/src/main/scala/org/apache/spark/ui/jobs/ExecutorSummary.scala
index 2aaf6329b792..c4a8996c0b9a 100644
--- a/core/src/main/scala/org/apache/spark/ui/jobs/ExecutorSummary.scala
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/ExecutorSummary.scala
@@ -28,6 +28,7 @@ class ExecutorSummary {
   var taskTime : Long = 0
   var failedTasks : Int = 0
   var succeededTasks : Int = 0
+  var inputBytes: Long = 0
   var shuffleRead : Long = 0
   var shuffleWrite : Long = 0
   var memoryBytesSpilled : Long = 0
diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/ExecutorTable.scala b/core/src/main/scala/org/apache/spark/ui/jobs/ExecutorTable.scala
index add0e9878a54..2a34a9af925d 100644
--- a/core/src/main/scala/org/apache/spark/ui/jobs/ExecutorTable.scala
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/ExecutorTable.scala
@@ -43,6 +43,7 @@ private[ui] class ExecutorTable(stageId: Int, parent: JobProgressTab) {
         <th>Total Tasks</th>
         <th>Failed Tasks</th>
         <th>Succeeded Tasks</th>
+        <th>Input Bytes</th>
         <th>Shuffle Read</th>
         <th>Shuffle Write</th>
         <th>Shuffle Spill (Memory)</th>
@@ -75,6 +76,7 @@ private[ui] class ExecutorTable(stageId: Int, parent: JobProgressTab) {
             <td>{v.failedTasks + v.succeededTasks}</td>
             <td>{v.failedTasks}</td>
             <td>{v.succeededTasks}</td>
+            <td sorttable_customekey={v.inputBytes.toString}>{Utils.bytesToString(v.inputBytes)}</td>
             <td sorttable_customekey={v.shuffleRead.toString}>{Utils.bytesToString(v.shuffleRead)}</td>
             <td sorttable_customekey={v.shuffleWrite.toString}>{Utils.bytesToString(v.shuffleWrite)}</td>
             <td sorttable_customekey={v.memoryBytesSpilled.toString} >{Utils.bytesToString(v.memoryBytesSpilled)}</td>
diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/JobProgressListener.scala b/core/src/main/scala/org/apache/spark/ui/jobs/JobProgressListener.scala
index 381a5443df8b..2286a7f952f2 100644
--- a/core/src/main/scala/org/apache/spark/ui/jobs/JobProgressListener.scala
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/JobProgressListener.scala
@@ -46,13 +46,9 @@ class JobProgressListener(conf: SparkConf) extends SparkListener {
   val completedStages = ListBuffer[StageInfo]()
   val failedStages = ListBuffer[StageInfo]()
 
-  // Total metrics reflect metrics only for completed tasks
-  var totalTime = 0L
-  var totalShuffleRead = 0L
-  var totalShuffleWrite = 0L
-
   // TODO: Should probably consolidate all following into a single hash map.
   val stageIdToTime = HashMap[Int, Long]()
+  val stageIdToInputBytes = HashMap[Int, Long]()
   val stageIdToShuffleRead = HashMap[Int, Long]()
   val stageIdToShuffleWrite = HashMap[Int, Long]()
   val stageIdToMemoryBytesSpilled = HashMap[Int, Long]()
@@ -93,6 +89,7 @@ class JobProgressListener(conf: SparkConf) extends SparkListener {
       val toRemove = math.max(retainedStages / 10, 1)
       stages.take(toRemove).foreach { s =>
         stageIdToTime.remove(s.stageId)
+        stageIdToInputBytes.remove(s.stageId)
         stageIdToShuffleRead.remove(s.stageId)
         stageIdToShuffleWrite.remove(s.stageId)
         stageIdToMemoryBytesSpilled.remove(s.stageId)
@@ -171,6 +168,7 @@ class JobProgressListener(conf: SparkConf) extends SparkListener {
 
           val metrics = taskEnd.taskMetrics
           if (metrics != null) {
+            metrics.inputMetrics.foreach { y.inputBytes += _.bytesRead }
             metrics.shuffleReadMetrics.foreach { y.shuffleRead += _.remoteBytesRead }
             metrics.shuffleWriteMetrics.foreach { y.shuffleWrite += _.shuffleBytesWritten }
             y.memoryBytesSpilled += metrics.memoryBytesSpilled
@@ -200,18 +198,19 @@ class JobProgressListener(conf: SparkConf) extends SparkListener {
       stageIdToTime.getOrElseUpdate(sid, 0L)
       val time = metrics.map(_.executorRunTime).getOrElse(0L)
       stageIdToTime(sid) += time
-      totalTime += time
+
+      stageIdToInputBytes.getOrElseUpdate(sid, 0L)
+      val inputBytes = metrics.flatMap(_.inputMetrics).map(_.bytesRead).getOrElse(0L)
+      stageIdToInputBytes(sid) += inputBytes
 
       stageIdToShuffleRead.getOrElseUpdate(sid, 0L)
       val shuffleRead = metrics.flatMap(_.shuffleReadMetrics).map(_.remoteBytesRead).getOrElse(0L)
       stageIdToShuffleRead(sid) += shuffleRead
-      totalShuffleRead += shuffleRead
 
       stageIdToShuffleWrite.getOrElseUpdate(sid, 0L)
       val shuffleWrite =
         metrics.flatMap(_.shuffleWriteMetrics).map(_.shuffleBytesWritten).getOrElse(0L)
       stageIdToShuffleWrite(sid) += shuffleWrite
-      totalShuffleWrite += shuffleWrite
 
       stageIdToMemoryBytesSpilled.getOrElseUpdate(sid, 0L)
       val memoryBytesSpilled = metrics.map(_.memoryBytesSpilled).getOrElse(0L)
diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala b/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
index 8e3d5d1cd4c6..afb8ed754ff8 100644
--- a/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
@@ -48,6 +48,8 @@ private[ui] class StagePage(parent: JobProgressTab) extends WebUIPage("stage") {
       val tasks = listener.stageIdToTaskData(stageId).values.toSeq.sortBy(_.taskInfo.launchTime)
 
       val numCompleted = tasks.count(_.taskInfo.finished)
+      val inputBytes = listener.stageIdToInputBytes.getOrElse(stageId, 0L)
+      val hasInput = inputBytes > 0
       val shuffleReadBytes = listener.stageIdToShuffleRead.getOrElse(stageId, 0L)
       val hasShuffleRead = shuffleReadBytes > 0
       val shuffleWriteBytes = listener.stageIdToShuffleWrite.getOrElse(stageId, 0L)
@@ -69,6 +71,12 @@ private[ui] class StagePage(parent: JobProgressTab) extends WebUIPage("stage") {
               <strong>Total task time across all tasks: </strong>
               {UIUtils.formatDuration(listener.stageIdToTime.getOrElse(stageId, 0L) + activeTime)}
             </li>
+            {if (hasInput)
+              <li>
+                <strong>Input: </strong>
+                {Utils.bytesToString(inputBytes)}
+              </li>
+            }
             {if (hasShuffleRead)
               <li>
                 <strong>Shuffle read: </strong>
@@ -98,13 +106,14 @@ private[ui] class StagePage(parent: JobProgressTab) extends WebUIPage("stage") {
         Seq(
           "Index", "ID", "Attempt", "Status", "Locality Level", "Executor",
           "Launch Time", "Duration", "GC Time") ++
+        {if (hasInput) Seq("Input") else Nil} ++
         {if (hasShuffleRead) Seq("Shuffle Read")  else Nil} ++
         {if (hasShuffleWrite) Seq("Write Time", "Shuffle Write") else Nil} ++
         {if (hasBytesSpilled) Seq("Shuffle Spill (Memory)", "Shuffle Spill (Disk)") else Nil} ++
         Seq("Errors")
 
       val taskTable = UIUtils.listingTable(
-        taskHeaders, taskRow(hasShuffleRead, hasShuffleWrite, hasBytesSpilled), tasks)
+        taskHeaders, taskRow(hasInput, hasShuffleRead, hasShuffleWrite, hasBytesSpilled), tasks)
 
       // Excludes tasks which failed and have incomplete metrics
       val validTasks = tasks.filter(t => t.taskInfo.status == "SUCCESS" && t.taskMetrics.isDefined)
@@ -159,6 +168,11 @@ private[ui] class StagePage(parent: JobProgressTab) extends WebUIPage("stage") {
           def getQuantileCols(data: Seq[Double]) =
             Distribution(data).get.getQuantiles().map(d => Utils.bytesToString(d.toLong))
 
+          val inputSizes = validTasks.map { case TaskUIData(_, metrics, _) =>
+            metrics.get.inputMetrics.map(_.bytesRead).getOrElse(0L).toDouble
+          }
+          val inputQuantiles = "Input" +: getQuantileCols(inputSizes)
+
           val shuffleReadSizes = validTasks.map { case TaskUIData(_, metrics, _) =>
             metrics.get.shuffleReadMetrics.map(_.remoteBytesRead).getOrElse(0L).toDouble
           }
@@ -186,6 +200,7 @@ private[ui] class StagePage(parent: JobProgressTab) extends WebUIPage("stage") {
             serviceQuantiles,
             gettingResultQuantiles,
             schedulerDelayQuantiles,
+            if (hasInput) inputQuantiles else Nil,
             if (hasShuffleRead) shuffleReadQuantiles else Nil,
             if (hasShuffleWrite) shuffleWriteQuantiles else Nil,
             if (hasBytesSpilled) memoryBytesSpilledQuantiles else Nil,
@@ -209,8 +224,11 @@ private[ui] class StagePage(parent: JobProgressTab) extends WebUIPage("stage") {
     }
   }
 
-  def taskRow(shuffleRead: Boolean, shuffleWrite: Boolean, bytesSpilled: Boolean)
-      (taskData: TaskUIData): Seq[Node] = {
+  def taskRow(
+      hasInput: Boolean,
+      hasShuffleRead: Boolean,
+      hasShuffleWrite: Boolean,
+      hasBytesSpilled: Boolean)(taskData: TaskUIData): Seq[Node] = {
     taskData match { case TaskUIData(info, metrics, errorMessage) =>
       val duration = if (info.status == "RUNNING") info.timeRunning(System.currentTimeMillis())
         else metrics.map(_.executorRunTime).getOrElse(1L)
@@ -219,6 +237,12 @@ private[ui] class StagePage(parent: JobProgressTab) extends WebUIPage("stage") {
       val gcTime = metrics.map(_.jvmGCTime).getOrElse(0L)
       val serializationTime = metrics.map(_.resultSerializationTime).getOrElse(0L)
 
+      val maybeInput = metrics.flatMap(_.inputMetrics)
+      val inputSortable = maybeInput.map(_.bytesRead.toString).getOrElse("")
+      val inputReadable = maybeInput
+        .map(m => s"${Utils.bytesToString(m.bytesRead)} (${m.readMethod.toString.toLowerCase()})")
+        .getOrElse("")
+
       val maybeShuffleRead = metrics.flatMap(_.shuffleReadMetrics).map(_.remoteBytesRead)
       val shuffleReadSortable = maybeShuffleRead.map(_.toString).getOrElse("")
       val shuffleReadReadable = maybeShuffleRead.map(Utils.bytesToString).getOrElse("")
@@ -265,12 +289,17 @@ private[ui] class StagePage(parent: JobProgressTab) extends WebUIPage("stage") {
           {if (serializationTime > 0) UIUtils.formatDuration(serializationTime) else ""}
         </td>
         -->
-        {if (shuffleRead) {
+        {if (hasInput) {
+          <td sorttable_customkey={inputSortable}>
+            {inputReadable}
+          </td>
+        }}
+        {if (hasShuffleRead) {
            <td sorttable_customkey={shuffleReadSortable}>
              {shuffleReadReadable}
            </td>
         }}
-        {if (shuffleWrite) {
+        {if (hasShuffleWrite) {
            <td sorttable_customkey={writeTimeSortable}>
              {writeTimeReadable}
            </td>
@@ -278,7 +307,7 @@ private[ui] class StagePage(parent: JobProgressTab) extends WebUIPage("stage") {
              {shuffleWriteReadable}
            </td>
         }}
-        {if (bytesSpilled) {
+        {if (hasBytesSpilled) {
           <td sorttable_customkey={memoryBytesSpilledSortable}>
             {memoryBytesSpilledReadable}
           </td>
diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/StageTable.scala b/core/src/main/scala/org/apache/spark/ui/jobs/StageTable.scala
index b2b6cc6a6ec2..a9ac6d5bee9c 100644
--- a/core/src/main/scala/org/apache/spark/ui/jobs/StageTable.scala
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/StageTable.scala
@@ -43,6 +43,7 @@ private[ui] class StageTableBase(
     <th>Submitted</th>
     <th>Duration</th>
     <th>Tasks: Succeeded/Total</th>
+    <th>Input</th>
     <th>Shuffle Read</th>
     <th>Shuffle Write</th>
   }
@@ -123,6 +124,11 @@ private[ui] class StageTableBase(
       case _ => ""
     }
     val totalTasks = s.numTasks
+    val inputSortable = listener.stageIdToInputBytes.getOrElse(s.stageId, 0L)
+    val inputRead = inputSortable match {
+      case 0 => ""
+      case b => Utils.bytesToString(b)
+    }
     val shuffleReadSortable = listener.stageIdToShuffleRead.getOrElse(s.stageId, 0L)
     val shuffleRead = shuffleReadSortable match {
       case 0 => ""
@@ -150,6 +156,7 @@ private[ui] class StageTableBase(
     <td class="progress-cell">
       {makeProgressBar(startedTasks, completedTasks, failedTasks, totalTasks)}
     </td>
+    <td sorttable_customekey={inputSortable.toString}>{inputRead}</td>
     <td sorttable_customekey={shuffleReadSortable.toString}>{shuffleRead}</td>
     <td sorttable_customekey={shuffleWriteSortable.toString}>{shuffleWrite}</td>
   }
diff --git a/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala b/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala
index 6245b4b8023c..26c9c9d6034e 100644
--- a/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala
+++ b/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala
@@ -26,7 +26,8 @@ import org.json4s.DefaultFormats
 import org.json4s.JsonDSL._
 import org.json4s.JsonAST._
 
-import org.apache.spark.executor.{ShuffleReadMetrics, ShuffleWriteMetrics, TaskMetrics}
+import org.apache.spark.executor.{DataReadMethod, InputMetrics, ShuffleReadMetrics,
+  ShuffleWriteMetrics, TaskMetrics}
 import org.apache.spark.scheduler._
 import org.apache.spark.storage._
 import org.apache.spark._
@@ -213,6 +214,8 @@ private[spark] object JsonProtocol {
       taskMetrics.shuffleReadMetrics.map(shuffleReadMetricsToJson).getOrElse(JNothing)
     val shuffleWriteMetrics =
       taskMetrics.shuffleWriteMetrics.map(shuffleWriteMetricsToJson).getOrElse(JNothing)
+    val inputMetrics =
+      taskMetrics.inputMetrics.map(inputMetricsToJson).getOrElse(JNothing)
     val updatedBlocks =
       taskMetrics.updatedBlocks.map { blocks =>
         JArray(blocks.toList.map { case (id, status) =>
@@ -230,6 +233,7 @@ private[spark] object JsonProtocol {
     ("Disk Bytes Spilled" -> taskMetrics.diskBytesSpilled) ~
     ("Shuffle Read Metrics" -> shuffleReadMetrics) ~
     ("Shuffle Write Metrics" -> shuffleWriteMetrics) ~
+    ("Input Metrics" -> inputMetrics) ~
     ("Updated Blocks" -> updatedBlocks)
   }
 
@@ -247,6 +251,11 @@ private[spark] object JsonProtocol {
     ("Shuffle Write Time" -> shuffleWriteMetrics.shuffleWriteTime)
   }
 
+  def inputMetricsToJson(inputMetrics: InputMetrics): JValue = {
+    ("Data Read Method" -> inputMetrics.readMethod.toString) ~
+    ("Bytes Read" -> inputMetrics.bytesRead)
+  }
+
   def taskEndReasonToJson(taskEndReason: TaskEndReason): JValue = {
     val reason = Utils.getFormattedClassName(taskEndReason)
     val json = taskEndReason match {
@@ -528,6 +537,8 @@ private[spark] object JsonProtocol {
       Utils.jsonOption(json \ "Shuffle Read Metrics").map(shuffleReadMetricsFromJson)
     metrics.shuffleWriteMetrics =
       Utils.jsonOption(json \ "Shuffle Write Metrics").map(shuffleWriteMetricsFromJson)
+    metrics.inputMetrics =
+      Utils.jsonOption(json \ "Input Metrics").map(inputMetricsFromJson)
     metrics.updatedBlocks =
       Utils.jsonOption(json \ "Updated Blocks").map { value =>
         value.extract[List[JValue]].map { block =>
@@ -557,6 +568,13 @@ private[spark] object JsonProtocol {
     metrics
   }
 
+  def inputMetricsFromJson(json: JValue): InputMetrics = {
+    val metrics = new InputMetrics(
+      DataReadMethod.withName((json \ "Data Read Method").extract[String]))
+    metrics.bytesRead = (json \ "Bytes Read").extract[Long]
+    metrics
+  }
+
   def taskEndReasonFromJson(json: JValue): TaskEndReason = {
     val success = Utils.getFormattedClassName(Success)
     val resubmitted = Utils.getFormattedClassName(Resubmitted)
diff --git a/core/src/test/scala/org/apache/spark/CacheManagerSuite.scala b/core/src/test/scala/org/apache/spark/CacheManagerSuite.scala
index 4f178db40f63..7f5d0b061e8b 100644
--- a/core/src/test/scala/org/apache/spark/CacheManagerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/CacheManagerSuite.scala
@@ -22,6 +22,7 @@ import scala.collection.mutable.ArrayBuffer
 import org.scalatest.{BeforeAndAfter, FunSuite}
 import org.scalatest.mock.EasyMockSugar
 
+import org.apache.spark.executor.{DataReadMethod, TaskMetrics}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.storage._
 
@@ -66,7 +67,8 @@ class CacheManagerSuite extends FunSuite with BeforeAndAfter with EasyMockSugar
 
   test("get cached rdd") {
     expecting {
-      blockManager.get(RDDBlockId(0, 0)).andReturn(Some(ArrayBuffer(5, 6, 7).iterator))
+      val result = new BlockResult(ArrayBuffer(5, 6, 7).iterator, DataReadMethod.Memory, 12)
+      blockManager.get(RDDBlockId(0, 0)).andReturn(Some(result))
     }
 
     whenExecuting(blockManager) {
diff --git a/core/src/test/scala/org/apache/spark/scheduler/SparkListenerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/SparkListenerSuite.scala
index 6df0a080961b..71f48e295ecc 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/SparkListenerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/SparkListenerSuite.scala
@@ -251,6 +251,7 @@ class SparkListenerSuite extends FunSuite with LocalSparkContext with Matchers
       taskInfoMetrics.foreach { case (taskInfo, taskMetrics) =>
         taskMetrics.resultSize should be > (0l)
         if (stageInfo.rddInfos.exists(info => info.name == d2.name || info.name == d3.name)) {
+          taskMetrics.inputMetrics should not be ('defined)
           taskMetrics.shuffleWriteMetrics should be ('defined)
           taskMetrics.shuffleWriteMetrics.get.shuffleBytesWritten should be > (0l)
         }
diff --git a/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala b/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala
index d7dbe5164b7f..23cb6905bfde 100644
--- a/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala
@@ -31,11 +31,13 @@ import org.scalatest.concurrent.Timeouts._
 import org.scalatest.Matchers
 import org.scalatest.time.SpanSugar._
 
-import org.apache.spark.{MapOutputTrackerMaster, SecurityManager, SparkConf}
+import org.apache.spark.{MapOutputTrackerMaster, SecurityManager, SparkConf, SparkContext}
+import org.apache.spark.executor.DataReadMethod
 import org.apache.spark.scheduler.LiveListenerBus
 import org.apache.spark.serializer.{JavaSerializer, KryoSerializer}
 import org.apache.spark.util.{AkkaUtils, ByteBufferInputStream, SizeEstimator, Utils}
 
+import scala.collection.mutable.ArrayBuffer
 import scala.language.implicitConversions
 import scala.language.postfixOps
 
@@ -415,6 +417,39 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfter
     }
   }
 
+  test("correct BlockResult returned from get() calls") {
+    store = new BlockManager("<driver>", actorSystem, master, serializer, 1200, conf, securityMgr,
+      mapOutputTracker)
+    val list1 = List(new Array[Byte](200), new Array[Byte](200))
+    val list1ForSizeEstimate = new ArrayBuffer[Any]
+    list1ForSizeEstimate ++= list1.iterator
+    val list1SizeEstimate = SizeEstimator.estimate(list1ForSizeEstimate)
+    val list2 = List(new Array[Byte](50), new Array[Byte](100), new Array[Byte](150))
+    val list2ForSizeEstimate = new ArrayBuffer[Any]
+    list2ForSizeEstimate ++= list2.iterator
+    val list2SizeEstimate = SizeEstimator.estimate(list2ForSizeEstimate)
+    store.put("list1", list1.iterator, StorageLevel.MEMORY_ONLY, tellMaster = true)
+    store.put("list2memory", list2.iterator, StorageLevel.MEMORY_ONLY, tellMaster = true)
+    store.put("list2disk", list2.iterator, StorageLevel.DISK_ONLY, tellMaster = true)
+    val list1Get = store.get("list1")
+    assert(list1Get.isDefined, "list1 expected to be in store")
+    assert(list1Get.get.data.size === 2)
+    assert(list1Get.get.inputMetrics.bytesRead === list1SizeEstimate)
+    assert(list1Get.get.inputMetrics.readMethod === DataReadMethod.Memory)
+    val list2MemoryGet = store.get("list2memory")
+    assert(list2MemoryGet.isDefined, "list2memory expected to be in store")
+    assert(list2MemoryGet.get.data.size === 3)
+    assert(list2MemoryGet.get.inputMetrics.bytesRead === list2SizeEstimate)
+    assert(list2MemoryGet.get.inputMetrics.readMethod === DataReadMethod.Memory)
+    val list2DiskGet = store.get("list2disk")
+    assert(list2DiskGet.isDefined, "list2memory expected to be in store")
+    assert(list2DiskGet.get.data.size === 3)
+    System.out.println(list2DiskGet)
+    // We don't know the exact size of the data on disk, but it should certainly be > 0.
+    assert(list2DiskGet.get.inputMetrics.bytesRead > 0)
+    assert(list2DiskGet.get.inputMetrics.readMethod === DataReadMethod.Disk)
+  }
+
   test("in-memory LRU storage") {
     store = new BlockManager("<driver>", actorSystem, master, serializer, 1200, conf,
       securityMgr, mapOutputTracker)
@@ -630,18 +665,18 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfter
     store.put("list2", list2.iterator, StorageLevel.MEMORY_ONLY, tellMaster = true)
     store.put("list3", list3.iterator, StorageLevel.MEMORY_ONLY, tellMaster = true)
     assert(store.get("list2").isDefined, "list2 was not in store")
-    assert(store.get("list2").get.size == 2)
+    assert(store.get("list2").get.data.size === 2)
     assert(store.get("list3").isDefined, "list3 was not in store")
-    assert(store.get("list3").get.size == 2)
+    assert(store.get("list3").get.data.size === 2)
     assert(store.get("list1") === None, "list1 was in store")
     assert(store.get("list2").isDefined, "list2 was not in store")
-    assert(store.get("list2").get.size == 2)
+    assert(store.get("list2").get.data.size === 2)
     // At this point list2 was gotten last, so LRU will getSingle rid of list3
     store.put("list1", list1.iterator, StorageLevel.MEMORY_ONLY, tellMaster = true)
     assert(store.get("list1").isDefined, "list1 was not in store")
-    assert(store.get("list1").get.size == 2)
+    assert(store.get("list1").get.data.size === 2)
     assert(store.get("list2").isDefined, "list2 was not in store")
-    assert(store.get("list2").get.size == 2)
+    assert(store.get("list2").get.data.size === 2)
     assert(store.get("list3") === None, "list1 was in store")
   }
 
@@ -656,28 +691,31 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfter
     store.put("list1", list1.iterator, StorageLevel.MEMORY_ONLY_SER, tellMaster = true)
     store.put("list2", list2.iterator, StorageLevel.MEMORY_ONLY_SER, tellMaster = true)
     store.put("list3", list3.iterator, StorageLevel.DISK_ONLY, tellMaster = true)
+    val listForSizeEstimate = new ArrayBuffer[Any]
+    listForSizeEstimate ++= list1.iterator
+    val listSize = SizeEstimator.estimate(listForSizeEstimate)
     // At this point LRU should not kick in because list3 is only on disk
-    assert(store.get("list1").isDefined, "list2 was not in store")
-    assert(store.get("list1").get.size === 2)
-    assert(store.get("list2").isDefined, "list3 was not in store")
-    assert(store.get("list2").get.size === 2)
-    assert(store.get("list3").isDefined, "list1 was not in store")
-    assert(store.get("list3").get.size === 2)
-    assert(store.get("list1").isDefined, "list2 was not in store")
-    assert(store.get("list1").get.size === 2)
-    assert(store.get("list2").isDefined, "list3 was not in store")
-    assert(store.get("list2").get.size === 2)
-    assert(store.get("list3").isDefined, "list1 was not in store")
-    assert(store.get("list3").get.size === 2)
+    assert(store.get("list1").isDefined, "list1 was not in store")
+    assert(store.get("list1").get.data.size === 2)
+    assert(store.get("list2").isDefined, "list2 was not in store")
+    assert(store.get("list2").get.data.size === 2)
+    assert(store.get("list3").isDefined, "list3 was not in store")
+    assert(store.get("list3").get.data.size === 2)
+    assert(store.get("list1").isDefined, "list1 was not in store")
+    assert(store.get("list1").get.data.size === 2)
+    assert(store.get("list2").isDefined, "list2 was not in store")
+    assert(store.get("list2").get.data.size === 2)
+    assert(store.get("list3").isDefined, "list3 was not in store")
+    assert(store.get("list3").get.data.size === 2)
     // Now let's add in list4, which uses both disk and memory; list1 should drop out
     store.put("list4", list4.iterator, StorageLevel.MEMORY_AND_DISK_SER, tellMaster = true)
     assert(store.get("list1") === None, "list1 was in store")
-    assert(store.get("list2").isDefined, "list3 was not in store")
-    assert(store.get("list2").get.size === 2)
-    assert(store.get("list3").isDefined, "list1 was not in store")
-    assert(store.get("list3").get.size === 2)
+    assert(store.get("list2").isDefined, "list2 was not in store")
+    assert(store.get("list2").get.data.size === 2)
+    assert(store.get("list3").isDefined, "list3 was not in store")
+    assert(store.get("list3").get.data.size === 2)
     assert(store.get("list4").isDefined, "list4 was not in store")
-    assert(store.get("list4").get.size === 2)
+    assert(store.get("list4").get.data.size === 2)
   }
 
   test("negative byte values in ByteBufferInputStream") {
diff --git a/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala b/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala
index 6c4987045587..316e14100e40 100644
--- a/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala
+++ b/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala
@@ -39,7 +39,11 @@ class JsonProtocolSuite extends FunSuite {
     val taskGettingResult =
       SparkListenerTaskGettingResult(makeTaskInfo(1000L, 2000, 5, 3000L, true))
     val taskEnd = SparkListenerTaskEnd(1, "ShuffleMapTask", Success,
-      makeTaskInfo(123L, 234, 67, 345L, false), makeTaskMetrics(300L, 400L, 500L, 600L, 700, 800))
+      makeTaskInfo(123L, 234, 67, 345L, false),
+      makeTaskMetrics(300L, 400L, 500L, 600L, 700, 800, hasHadoopInput = false))
+    val taskEndWithHadoopInput = SparkListenerTaskEnd(1, "ShuffleMapTask", Success,
+      makeTaskInfo(123L, 234, 67, 345L, false),
+      makeTaskMetrics(300L, 400L, 500L, 600L, 700, 800, hasHadoopInput = true))
     val jobStart = SparkListenerJobStart(10, Seq[Int](1, 2, 3, 4), properties)
     val jobEnd = SparkListenerJobEnd(20, JobSucceeded)
     val environmentUpdate = SparkListenerEnvironmentUpdate(Map[String, Seq[(String, String)]](
@@ -61,6 +65,7 @@ class JsonProtocolSuite extends FunSuite {
     testEvent(taskStart, taskStartJsonString)
     testEvent(taskGettingResult, taskGettingResultJsonString)
     testEvent(taskEnd, taskEndJsonString)
+    testEvent(taskEndWithHadoopInput, taskEndWithHadoopInputJsonString)
     testEvent(jobStart, jobStartJsonString)
     testEvent(jobEnd, jobEndJsonString)
     testEvent(environmentUpdate, environmentUpdateJsonString)
@@ -75,7 +80,7 @@ class JsonProtocolSuite extends FunSuite {
     testRDDInfo(makeRddInfo(2, 3, 4, 5L, 6L))
     testStageInfo(makeStageInfo(10, 20, 30, 40L, 50L))
     testTaskInfo(makeTaskInfo(999L, 888, 55, 777L, false))
-    testTaskMetrics(makeTaskMetrics(33333L, 44444L, 55555L, 66666L, 7, 8))
+    testTaskMetrics(makeTaskMetrics(33333L, 44444L, 55555L, 66666L, 7, 8, hasHadoopInput = false))
     testBlockManagerId(BlockManagerId("Hong", "Kong", 500, 1000))
 
     // StorageLevel
@@ -118,7 +123,7 @@ class JsonProtocolSuite extends FunSuite {
     testBlockId(StreamBlockId(1, 2L))
   }
 
-  test("Backward compatibility") {
+  test("StageInfo.details backward compatibility") {
     // StageInfo.details was added after 1.0.0.
     val info = makeStageInfo(1, 2, 3, 4L, 5L)
     assert(info.details.nonEmpty)
@@ -129,6 +134,16 @@ class JsonProtocolSuite extends FunSuite {
     assert("" === newInfo.details)
   }
 
+  test("InputMetrics backward compatibility") {
+    // InputMetrics were added after 1.0.1.
+    val metrics = makeTaskMetrics(1L, 2L, 3L, 4L, 5, 6, hasHadoopInput = true)
+    assert(metrics.inputMetrics.nonEmpty)
+    val newJson = JsonProtocol.taskMetricsToJson(metrics)
+    val oldJson = newJson.removeField { case (field, _) => field == "Input Metrics" }
+    val newMetrics = JsonProtocol.taskMetricsFromJson(oldJson)
+    assert(newMetrics.inputMetrics.isEmpty)
+  }
+
 
   /** -------------------------- *
    | Helper test running methods |
@@ -294,6 +309,8 @@ class JsonProtocolSuite extends FunSuite {
       metrics1.shuffleReadMetrics, metrics2.shuffleReadMetrics, assertShuffleReadEquals)
     assertOptionEquals(
       metrics1.shuffleWriteMetrics, metrics2.shuffleWriteMetrics, assertShuffleWriteEquals)
+    assertOptionEquals(
+      metrics1.inputMetrics, metrics2.inputMetrics, assertInputMetricsEquals)
     assertOptionEquals(metrics1.updatedBlocks, metrics2.updatedBlocks, assertBlocksEquals)
   }
 
@@ -311,6 +328,11 @@ class JsonProtocolSuite extends FunSuite {
     assert(metrics1.shuffleWriteTime === metrics2.shuffleWriteTime)
   }
 
+  private def assertEquals(metrics1: InputMetrics, metrics2: InputMetrics) {
+    assert(metrics1.readMethod === metrics2.readMethod)
+    assert(metrics1.bytesRead === metrics2.bytesRead)
+  }
+
   private def assertEquals(bm1: BlockManagerId, bm2: BlockManagerId) {
     assert(bm1.executorId === bm2.executorId)
     assert(bm1.host === bm2.host)
@@ -403,6 +425,10 @@ class JsonProtocolSuite extends FunSuite {
     assertEquals(w1, w2)
   }
 
+  private def assertInputMetricsEquals(i1: InputMetrics, i2: InputMetrics) {
+    assertEquals(i1, i2)
+  }
+
   private def assertTaskMetricsEquals(t1: TaskMetrics, t2: TaskMetrics) {
     assertEquals(t1, t2)
   }
@@ -460,9 +486,19 @@ class JsonProtocolSuite extends FunSuite {
     new TaskInfo(a, b, c, d, "executor", "your kind sir", TaskLocality.NODE_LOCAL, speculative)
   }
 
-  private def makeTaskMetrics(a: Long, b: Long, c: Long, d: Long, e: Int, f: Int) = {
+  /**
+   * Creates a TaskMetrics object describing a task that read data from Hadoop (if hasHadoopInput is
+   * set to true) or read data from a shuffle otherwise.
+   */
+  private def makeTaskMetrics(
+      a: Long,
+      b: Long,
+      c: Long,
+      d: Long,
+      e: Int,
+      f: Int,
+      hasHadoopInput: Boolean) = {
     val t = new TaskMetrics
-    val sr = new ShuffleReadMetrics
     val sw = new ShuffleWriteMetrics
     t.hostname = "localhost"
     t.executorDeserializeTime = a
@@ -471,15 +507,23 @@ class JsonProtocolSuite extends FunSuite {
     t.jvmGCTime = d
     t.resultSerializationTime = a + b
     t.memoryBytesSpilled = a + c
-    sr.shuffleFinishTime = b + c
-    sr.totalBlocksFetched = e + f
-    sr.remoteBytesRead = b + d
-    sr.localBlocksFetched = e
-    sr.fetchWaitTime = a + d
-    sr.remoteBlocksFetched = f
+
+    if (hasHadoopInput) {
+      val inputMetrics = new InputMetrics(DataReadMethod.Hadoop)
+      inputMetrics.bytesRead = d + e + f
+      t.inputMetrics = Some(inputMetrics)
+    } else {
+      val sr = new ShuffleReadMetrics
+      sr.shuffleFinishTime = b + c
+      sr.totalBlocksFetched = e + f
+      sr.remoteBytesRead = b + d
+      sr.localBlocksFetched = e
+      sr.fetchWaitTime = a + d
+      sr.remoteBlocksFetched = f
+      t.shuffleReadMetrics = Some(sr)
+    }
     sw.shuffleBytesWritten = a + b + c
     sw.shuffleWriteTime = b + c + d
-    t.shuffleReadMetrics = Some(sr)
     t.shuffleWriteMetrics = Some(sw)
     // Make at most 6 blocks
     t.updatedBlocks = Some((1 to (e % 5 + 1)).map { i =>
@@ -552,8 +596,9 @@ class JsonProtocolSuite extends FunSuite {
       |  },
       |  "Shuffle Write Metrics":{
       |    "Shuffle Bytes Written":1200,
-      |    "Shuffle Write Time":1500},
-      |    "Updated Blocks":[
+      |    "Shuffle Write Time":1500
+      |  },
+      |  "Updated Blocks":[
       |    {"Block ID":"rdd_0_0",
       |      "Status":{
       |        "Storage Level":{
@@ -568,6 +613,35 @@ class JsonProtocolSuite extends FunSuite {
       |}
     """.stripMargin
 
+  private val taskEndWithHadoopInputJsonString =
+    """
+      |{"Event":"SparkListenerTaskEnd","Stage ID":1,"Task Type":"ShuffleMapTask",
+      |"Task End Reason":{"Reason":"Success"},
+      |"Task Info":{
+      |  "Task ID":123,"Index":234,"Attempt":67,"Launch Time":345,"Executor ID":"executor",
+      |  "Host":"your kind sir","Locality":"NODE_LOCAL","Speculative":false,
+      |  "Getting Result Time":0,"Finish Time":0,"Failed":false,"Serialized Size":0
+      |},
+      |"Task Metrics":{
+      |  "Host Name":"localhost","Executor Deserialize Time":300,"Executor Run Time":400,
+      |  "Result Size":500,"JVM GC Time":600,"Result Serialization Time":700,
+      |  "Memory Bytes Spilled":800,"Disk Bytes Spilled":0,
+      |  "Shuffle Write Metrics":{"Shuffle Bytes Written":1200,"Shuffle Write Time":1500},
+      |  "Input Metrics":{"Data Read Method":"Hadoop","Bytes Read":2100},
+      |  "Updated Blocks":[
+      |    {"Block ID":"rdd_0_0",
+      |      "Status":{
+      |        "Storage Level":{
+      |          "Use Disk":true,"Use Memory":true,"Use Tachyon":false,"Deserialized":false,
+      |          "Replication":2
+      |        },
+      |        "Memory Size":0,"Tachyon Size":0,"Disk Size":0
+      |      }
+      |    }
+      |  ]}
+      |}
+    """
+
   private val jobStartJsonString =
     """
       {"Event":"SparkListenerJobStart","Job ID":10,"Stage IDs":[1,2,3,4],"Properties":

From 66135a341d9f8baecc149d13ae5511f14578c395 Mon Sep 17 00:00:00 2001
From: jerryshao <saisai.shao@intel.com>
Date: Sun, 29 Jun 2014 23:00:00 -0700
Subject: [PATCH 550/810] [SPARK-2104] Fix task serializing issues when sort
 with Java non serializable class

Details can be see in [SPARK-2104](https://issues.apache.org/jira/browse/SPARK-2104). This work is based on Reynold's work, add some unit tests to validate the issue.

@rxin , would you please take a look at this PR, thanks a lot.

Author: jerryshao <saisai.shao@intel.com>

Closes #1245 from jerryshao/SPARK-2104 and squashes the following commits:

c8ee362 [jerryshao] Make field partitions transient
2b41917 [jerryshao] Minor changes
47d763c [jerryshao] Fix task serializing issue when sort with Java non serializable class
---
 .../scala/org/apache/spark/Partitioner.scala  | 52 ++++++++++++++++---
 .../scala/org/apache/spark/ShuffleSuite.scala | 42 ++++++++++++++-
 2 files changed, 86 insertions(+), 8 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/Partitioner.scala b/core/src/main/scala/org/apache/spark/Partitioner.scala
index e7f75481939a..ec99648a8488 100644
--- a/core/src/main/scala/org/apache/spark/Partitioner.scala
+++ b/core/src/main/scala/org/apache/spark/Partitioner.scala
@@ -17,11 +17,13 @@
 
 package org.apache.spark
 
+import java.io.{IOException, ObjectInputStream, ObjectOutputStream}
+
 import scala.reflect.ClassTag
 
 import org.apache.spark.rdd.RDD
-import org.apache.spark.util.CollectionsUtils
-import org.apache.spark.util.Utils
+import org.apache.spark.serializer.JavaSerializer
+import org.apache.spark.util.{CollectionsUtils, Utils}
 
 /**
  * An object that defines how the elements in a key-value pair RDD are partitioned by key.
@@ -96,15 +98,15 @@ class HashPartitioner(partitions: Int) extends Partitioner {
  * the value of `partitions`.
  */
 class RangePartitioner[K : Ordering : ClassTag, V](
-    partitions: Int,
+    @transient partitions: Int,
     @transient rdd: RDD[_ <: Product2[K,V]],
-    private val ascending: Boolean = true)
+    private var ascending: Boolean = true)
   extends Partitioner {
 
-  private val ordering = implicitly[Ordering[K]]
+  private var ordering = implicitly[Ordering[K]]
 
   // An array of upper bounds for the first (partitions - 1) partitions
-  private val rangeBounds: Array[K] = {
+  private var rangeBounds: Array[K] = {
     if (partitions == 1) {
       Array()
     } else {
@@ -127,7 +129,7 @@ class RangePartitioner[K : Ordering : ClassTag, V](
 
   def numPartitions = rangeBounds.length + 1
 
-  private val binarySearch: ((Array[K], K) => Int) = CollectionsUtils.makeBinarySearch[K]
+  private var binarySearch: ((Array[K], K) => Int) = CollectionsUtils.makeBinarySearch[K]
 
   def getPartition(key: Any): Int = {
     val k = key.asInstanceOf[K]
@@ -173,4 +175,40 @@ class RangePartitioner[K : Ordering : ClassTag, V](
     result = prime * result + ascending.hashCode
     result
   }
+
+  @throws(classOf[IOException])
+  private def writeObject(out: ObjectOutputStream) {
+    val sfactory = SparkEnv.get.serializer
+    sfactory match {
+      case js: JavaSerializer => out.defaultWriteObject()
+      case _ =>
+        out.writeBoolean(ascending)
+        out.writeObject(ordering)
+        out.writeObject(binarySearch)
+
+        val ser = sfactory.newInstance()
+        Utils.serializeViaNestedStream(out, ser) { stream =>
+          stream.writeObject(scala.reflect.classTag[Array[K]])
+          stream.writeObject(rangeBounds)
+        }
+    }
+  }
+
+  @throws(classOf[IOException])
+  private def readObject(in: ObjectInputStream) {
+    val sfactory = SparkEnv.get.serializer
+    sfactory match {
+      case js: JavaSerializer => in.defaultReadObject()
+      case _ =>
+        ascending = in.readBoolean()
+        ordering = in.readObject().asInstanceOf[Ordering[K]]
+        binarySearch = in.readObject().asInstanceOf[(Array[K], K) => Int]
+
+        val ser = sfactory.newInstance()
+        Utils.deserializeViaNestedStream(in, ser) { ds =>
+          implicit val classTag = ds.readObject[ClassTag[Array[K]]]()
+          rangeBounds = ds.readObject[Array[K]]()
+        }
+    }
+  }
 }
diff --git a/core/src/test/scala/org/apache/spark/ShuffleSuite.scala b/core/src/test/scala/org/apache/spark/ShuffleSuite.scala
index b40fee7e9ab2..c4f2f7e34f4d 100644
--- a/core/src/test/scala/org/apache/spark/ShuffleSuite.scala
+++ b/core/src/test/scala/org/apache/spark/ShuffleSuite.scala
@@ -206,6 +206,42 @@ class ShuffleSuite extends FunSuite with Matchers with LocalSparkContext {
     // substracted rdd return results as Tuple2
     results(0) should be ((3, 33))
   }
+
+  test("sort with Java non serializable class - Kryo") {
+    // Use a local cluster with 2 processes to make sure there are both local and remote blocks
+    val conf = new SparkConf()
+      .set("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
+      .setAppName("test")
+      .setMaster("local-cluster[2,1,512]")
+    sc = new SparkContext(conf)
+    val a = sc.parallelize(1 to 10, 2)
+    val b = a.map { x =>
+      (new NonJavaSerializableClass(x), x)
+    }
+    // If the Kryo serializer is not used correctly, the shuffle would fail because the
+    // default Java serializer cannot handle the non serializable class.
+    val c = b.sortByKey().map(x => x._2)
+    assert(c.collect() === Array(1, 2, 3, 4, 5, 6, 7, 8, 9, 10))
+  }
+
+  test("sort with Java non serializable class - Java") {
+    // Use a local cluster with 2 processes to make sure there are both local and remote blocks
+    val conf = new SparkConf()
+      .setAppName("test")
+      .setMaster("local-cluster[2,1,512]")
+    sc = new SparkContext(conf)
+    val a = sc.parallelize(1 to 10, 2)
+    val b = a.map { x =>
+      (new NonJavaSerializableClass(x), x)
+    }
+    // default Java serializer cannot handle the non serializable class.
+    val thrown = intercept[SparkException] {
+      b.sortByKey().collect()
+    }
+
+    assert(thrown.getClass === classOf[SparkException])
+    assert(thrown.getMessage.contains("NotSerializableException"))
+  }
 }
 
 object ShuffleSuite {
@@ -215,5 +251,9 @@ object ShuffleSuite {
     x + y
   }
 
-  class NonJavaSerializableClass(val value: Int)
+  class NonJavaSerializableClass(val value: Int) extends Comparable[NonJavaSerializableClass] {
+    override def compareTo(o: NonJavaSerializableClass): Int = {
+      value - o.value
+    }
+  }
 }

From a484030dae9d0d7e4b97cc6307e9e928c07490dc Mon Sep 17 00:00:00 2001
From: William Benton <willb@redhat.com>
Date: Sun, 29 Jun 2014 23:27:34 -0700
Subject: [PATCH 551/810] SPARK-897:  preemptively serialize closures

These commits cause `ClosureCleaner.clean` to attempt to serialize the cleaned closure with the default closure serializer and throw a `SparkException` if doing so fails.  This behavior is enabled by default but can be disabled at individual callsites of `SparkContext.clean`.

Commit 98e01ae8 fixes some no-op assertions in `GraphSuite` that this work exposed; I'm happy to put that in a separate PR if that would be more appropriate.

Author: William Benton <willb@redhat.com>

Closes #143 from willb/spark-897 and squashes the following commits:

bceab8a [William Benton] Commented DStream corner cases for serializability checking.
64d04d2 [William Benton] FailureSuite now checks both messages and causes.
3b3f74a [William Benton] Stylistic and doc cleanups.
b215dea [William Benton] Fixed spurious failures in ImplicitOrderingSuite
be1ecd6 [William Benton] Don't check serializability of DStream transforms.
abe816b [William Benton] Make proactive serializability checking optional.
5bfff24 [William Benton] Adds proactive closure-serializablilty checking
ed2ccf0 [William Benton] Test cases for SPARK-897.
---
 .../scala/org/apache/spark/SparkContext.scala | 12 ++-
 .../apache/spark/util/ClosureCleaner.scala    | 16 +++-
 .../scala/org/apache/spark/FailureSuite.scala | 14 ++-
 .../apache/spark/ImplicitOrderingSuite.scala  | 75 +++++++++++-----
 .../ProactiveClosureSerializationSuite.scala  | 90 +++++++++++++++++++
 .../spark/streaming/dstream/DStream.scala     | 25 ++++--
 6 files changed, 196 insertions(+), 36 deletions(-)
 create mode 100644 core/src/test/scala/org/apache/spark/serializer/ProactiveClosureSerializationSuite.scala

diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index f9476ff826a6..8819e73d17fb 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -1203,9 +1203,17 @@ class SparkContext(config: SparkConf) extends Logging {
   /**
    * Clean a closure to make it ready to serialized and send to tasks
    * (removes unreferenced variables in $outer's, updates REPL variables)
+   * If <tt>checkSerializable</tt> is set, <tt>clean</tt> will also proactively 
+   * check to see if <tt>f</tt> is serializable and throw a <tt>SparkException</tt> 
+   * if not.
+   * 
+   * @param f the closure to clean
+   * @param checkSerializable whether or not to immediately check <tt>f</tt> for serializability
+   * @throws <tt>SparkException<tt> if <tt>checkSerializable</tt> is set but <tt>f</tt> is not
+   *   serializable
    */
-  private[spark] def clean[F <: AnyRef](f: F): F = {
-    ClosureCleaner.clean(f)
+  private[spark] def clean[F <: AnyRef](f: F, checkSerializable: Boolean = true): F = {
+    ClosureCleaner.clean(f, checkSerializable)
     f
   }
 
diff --git a/core/src/main/scala/org/apache/spark/util/ClosureCleaner.scala b/core/src/main/scala/org/apache/spark/util/ClosureCleaner.scala
index 4916d9b86cca..e3f52f6ff1e6 100644
--- a/core/src/main/scala/org/apache/spark/util/ClosureCleaner.scala
+++ b/core/src/main/scala/org/apache/spark/util/ClosureCleaner.scala
@@ -25,7 +25,7 @@ import scala.collection.mutable.Set
 import com.esotericsoftware.reflectasm.shaded.org.objectweb.asm.{ClassReader, ClassVisitor, MethodVisitor, Type}
 import com.esotericsoftware.reflectasm.shaded.org.objectweb.asm.Opcodes._
 
-import org.apache.spark.{Logging, SparkException}
+import org.apache.spark.{Logging, SparkEnv, SparkException}
 
 private[spark] object ClosureCleaner extends Logging {
   // Get an ASM class reader for a given class from the JAR that loaded it
@@ -101,7 +101,7 @@ private[spark] object ClosureCleaner extends Logging {
     }
   }
 
-  def clean(func: AnyRef) {
+  def clean(func: AnyRef, checkSerializable: Boolean = true) {
     // TODO: cache outerClasses / innerClasses / accessedFields
     val outerClasses = getOuterClasses(func)
     val innerClasses = getInnerClasses(func)
@@ -153,6 +153,18 @@ private[spark] object ClosureCleaner extends Logging {
       field.setAccessible(true)
       field.set(func, outer)
     }
+    
+    if (checkSerializable) {
+      ensureSerializable(func)
+    }
+  }
+
+  private def ensureSerializable(func: AnyRef) {
+    try {
+      SparkEnv.get.closureSerializer.newInstance().serialize(func)
+    } catch {
+      case ex: Exception => throw new SparkException("Task not serializable", ex)
+    }
   }
 
   private def instantiateClass(cls: Class[_], outer: AnyRef, inInterpreter: Boolean): AnyRef = {
diff --git a/core/src/test/scala/org/apache/spark/FailureSuite.scala b/core/src/test/scala/org/apache/spark/FailureSuite.scala
index 12dbebcb2864..e755d2e30939 100644
--- a/core/src/test/scala/org/apache/spark/FailureSuite.scala
+++ b/core/src/test/scala/org/apache/spark/FailureSuite.scala
@@ -22,6 +22,8 @@ import org.scalatest.FunSuite
 import org.apache.spark.SparkContext._
 import org.apache.spark.util.NonSerializable
 
+import java.io.NotSerializableException
+
 // Common state shared by FailureSuite-launched tasks. We use a global object
 // for this because any local variables used in the task closures will rightfully
 // be copied for each task, so there's no other way for them to share state.
@@ -102,7 +104,8 @@ class FailureSuite extends FunSuite with LocalSparkContext {
       results.collect()
     }
     assert(thrown.getClass === classOf[SparkException])
-    assert(thrown.getMessage.contains("NotSerializableException"))
+    assert(thrown.getMessage.contains("NotSerializableException") || 
+      thrown.getCause.getClass === classOf[NotSerializableException])
 
     FailureSuiteState.clear()
   }
@@ -116,21 +119,24 @@ class FailureSuite extends FunSuite with LocalSparkContext {
       sc.parallelize(1 to 10, 2).map(x => a).count()
     }
     assert(thrown.getClass === classOf[SparkException])
-    assert(thrown.getMessage.contains("NotSerializableException"))
+    assert(thrown.getMessage.contains("NotSerializableException") || 
+      thrown.getCause.getClass === classOf[NotSerializableException])
 
     // Non-serializable closure in an earlier stage
     val thrown1 = intercept[SparkException] {
       sc.parallelize(1 to 10, 2).map(x => (x, a)).partitionBy(new HashPartitioner(3)).count()
     }
     assert(thrown1.getClass === classOf[SparkException])
-    assert(thrown1.getMessage.contains("NotSerializableException"))
+    assert(thrown1.getMessage.contains("NotSerializableException") || 
+      thrown1.getCause.getClass === classOf[NotSerializableException])
 
     // Non-serializable closure in foreach function
     val thrown2 = intercept[SparkException] {
       sc.parallelize(1 to 10, 2).foreach(x => println(a))
     }
     assert(thrown2.getClass === classOf[SparkException])
-    assert(thrown2.getMessage.contains("NotSerializableException"))
+    assert(thrown2.getMessage.contains("NotSerializableException") || 
+      thrown2.getCause.getClass === classOf[NotSerializableException])
 
     FailureSuiteState.clear()
   }
diff --git a/core/src/test/scala/org/apache/spark/ImplicitOrderingSuite.scala b/core/src/test/scala/org/apache/spark/ImplicitOrderingSuite.scala
index 4bd889135631..8e4a9e2c9f56 100644
--- a/core/src/test/scala/org/apache/spark/ImplicitOrderingSuite.scala
+++ b/core/src/test/scala/org/apache/spark/ImplicitOrderingSuite.scala
@@ -19,9 +19,29 @@ package org.apache.spark
 
 import org.scalatest.FunSuite
 
+import org.apache.spark.rdd.RDD
 import org.apache.spark.SparkContext._
 
 class ImplicitOrderingSuite extends FunSuite with LocalSparkContext {
+  // Tests that PairRDDFunctions grabs an implicit Ordering in various cases where it should.
+  test("basic inference of Orderings"){
+    sc = new SparkContext("local", "test")
+    val rdd = sc.parallelize(1 to 10)
+
+    // These RDD methods are in the companion object so that the unserializable ScalaTest Engine
+    // won't be reachable from the closure object
+    
+    // Infer orderings after basic maps to particular types
+    val basicMapExpectations = ImplicitOrderingSuite.basicMapExpectations(rdd)
+    basicMapExpectations.map({case (met, explain) => assert(met, explain)})
+    
+    // Infer orderings for other RDD methods
+    val otherRDDMethodExpectations = ImplicitOrderingSuite.otherRDDMethodExpectations(rdd)
+    otherRDDMethodExpectations.map({case (met, explain) => assert(met, explain)})
+  }
+}
+
+private object ImplicitOrderingSuite {
   class NonOrderedClass {}
 
   class ComparableClass extends Comparable[ComparableClass] {
@@ -31,27 +51,36 @@ class ImplicitOrderingSuite extends FunSuite with LocalSparkContext {
   class OrderedClass extends Ordered[OrderedClass] {
     override def compare(o: OrderedClass): Int = ???
   }
-
-  // Tests that PairRDDFunctions grabs an implicit Ordering in various cases where it should.
-  test("basic inference of Orderings"){
-    sc = new SparkContext("local", "test")
-    val rdd = sc.parallelize(1 to 10)
-
-    // Infer orderings after basic maps to particular types
-    assert(rdd.map(x => (x, x)).keyOrdering.isDefined)
-    assert(rdd.map(x => (1, x)).keyOrdering.isDefined)
-    assert(rdd.map(x => (x.toString, x)).keyOrdering.isDefined)
-    assert(rdd.map(x => (null, x)).keyOrdering.isDefined)
-    assert(rdd.map(x => (new NonOrderedClass, x)).keyOrdering.isEmpty)
-    assert(rdd.map(x => (new ComparableClass, x)).keyOrdering.isDefined)
-    assert(rdd.map(x => (new OrderedClass, x)).keyOrdering.isDefined)
-
-    // Infer orderings for other RDD methods
-    assert(rdd.groupBy(x => x).keyOrdering.isDefined)
-    assert(rdd.groupBy(x => new NonOrderedClass).keyOrdering.isEmpty)
-    assert(rdd.groupBy(x => new ComparableClass).keyOrdering.isDefined)
-    assert(rdd.groupBy(x => new OrderedClass).keyOrdering.isDefined)
-    assert(rdd.groupBy((x: Int) => x, 5).keyOrdering.isDefined)
-    assert(rdd.groupBy((x: Int) => x, new HashPartitioner(5)).keyOrdering.isDefined)
+  
+  def basicMapExpectations(rdd: RDD[Int]) = {
+    List((rdd.map(x => (x, x)).keyOrdering.isDefined, 
+            "rdd.map(x => (x, x)).keyOrdering.isDefined"),
+          (rdd.map(x => (1, x)).keyOrdering.isDefined, 
+            "rdd.map(x => (1, x)).keyOrdering.isDefined"),
+          (rdd.map(x => (x.toString, x)).keyOrdering.isDefined, 
+            "rdd.map(x => (x.toString, x)).keyOrdering.isDefined"),
+          (rdd.map(x => (null, x)).keyOrdering.isDefined, 
+            "rdd.map(x => (null, x)).keyOrdering.isDefined"),
+          (rdd.map(x => (new NonOrderedClass, x)).keyOrdering.isEmpty, 
+            "rdd.map(x => (new NonOrderedClass, x)).keyOrdering.isEmpty"),
+          (rdd.map(x => (new ComparableClass, x)).keyOrdering.isDefined, 
+            "rdd.map(x => (new ComparableClass, x)).keyOrdering.isDefined"),
+          (rdd.map(x => (new OrderedClass, x)).keyOrdering.isDefined, 
+            "rdd.map(x => (new OrderedClass, x)).keyOrdering.isDefined"))
   }
-}
+  
+  def otherRDDMethodExpectations(rdd: RDD[Int]) = {
+    List((rdd.groupBy(x => x).keyOrdering.isDefined, 
+           "rdd.groupBy(x => x).keyOrdering.isDefined"),
+         (rdd.groupBy(x => new NonOrderedClass).keyOrdering.isEmpty, 
+           "rdd.groupBy(x => new NonOrderedClass).keyOrdering.isEmpty"),
+         (rdd.groupBy(x => new ComparableClass).keyOrdering.isDefined, 
+           "rdd.groupBy(x => new ComparableClass).keyOrdering.isDefined"),
+         (rdd.groupBy(x => new OrderedClass).keyOrdering.isDefined,
+           "rdd.groupBy(x => new OrderedClass).keyOrdering.isDefined"),
+         (rdd.groupBy((x: Int) => x, 5).keyOrdering.isDefined,
+           "rdd.groupBy((x: Int) => x, 5).keyOrdering.isDefined"),
+         (rdd.groupBy((x: Int) => x, new HashPartitioner(5)).keyOrdering.isDefined,
+           "rdd.groupBy((x: Int) => x, new HashPartitioner(5)).keyOrdering.isDefined"))
+  }
+}
\ No newline at end of file
diff --git a/core/src/test/scala/org/apache/spark/serializer/ProactiveClosureSerializationSuite.scala b/core/src/test/scala/org/apache/spark/serializer/ProactiveClosureSerializationSuite.scala
new file mode 100644
index 000000000000..5d15a68ac7e4
--- /dev/null
+++ b/core/src/test/scala/org/apache/spark/serializer/ProactiveClosureSerializationSuite.scala
@@ -0,0 +1,90 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.serializer;
+
+import java.io.NotSerializableException
+
+import org.scalatest.FunSuite
+
+import org.apache.spark.rdd.RDD
+import org.apache.spark.SparkException
+import org.apache.spark.SharedSparkContext
+
+/* A trivial (but unserializable) container for trivial functions */
+class UnserializableClass {
+  def op[T](x: T) = x.toString
+  
+  def pred[T](x: T) = x.toString.length % 2 == 0
+}
+
+class ProactiveClosureSerializationSuite extends FunSuite with SharedSparkContext {
+
+  def fixture = (sc.parallelize(0 until 1000).map(_.toString), new UnserializableClass)
+
+  test("throws expected serialization exceptions on actions") {
+    val (data, uc) = fixture
+      
+    val ex = intercept[SparkException] {
+      data.map(uc.op(_)).count
+    }
+        
+    assert(ex.getMessage.contains("Task not serializable"))
+  }
+
+  // There is probably a cleaner way to eliminate boilerplate here, but we're
+  // iterating over a map from transformation names to functions that perform that
+  // transformation on a given RDD, creating one test case for each
+  
+  for (transformation <- 
+      Map("map" -> xmap _, "flatMap" -> xflatMap _, "filter" -> xfilter _, 
+          "mapWith" -> xmapWith _, "mapPartitions" -> xmapPartitions _, 
+          "mapPartitionsWithIndex" -> xmapPartitionsWithIndex _,
+          "mapPartitionsWithContext" -> xmapPartitionsWithContext _, 
+          "filterWith" -> xfilterWith _)) {
+    val (name, xf) = transformation
+    
+    test(s"$name transformations throw proactive serialization exceptions") {
+      val (data, uc) = fixture
+      
+      val ex = intercept[SparkException] {
+        xf(data, uc)
+      }
+
+      assert(ex.getMessage.contains("Task not serializable"), 
+        s"RDD.$name doesn't proactively throw NotSerializableException")
+    }
+  }
+  
+  private def xmap(x: RDD[String], uc: UnserializableClass): RDD[String] = 
+    x.map(y=>uc.op(y))
+  private def xmapWith(x: RDD[String], uc: UnserializableClass): RDD[String] = 
+    x.mapWith(x => x.toString)((x,y)=>x + uc.op(y))
+  private def xflatMap(x: RDD[String], uc: UnserializableClass): RDD[String] = 
+    x.flatMap(y=>Seq(uc.op(y)))
+  private def xfilter(x: RDD[String], uc: UnserializableClass): RDD[String] = 
+    x.filter(y=>uc.pred(y))
+  private def xfilterWith(x: RDD[String], uc: UnserializableClass): RDD[String] = 
+    x.filterWith(x => x.toString)((x,y)=>uc.pred(y))
+  private def xmapPartitions(x: RDD[String], uc: UnserializableClass): RDD[String] = 
+    x.mapPartitions(_.map(y=>uc.op(y)))
+  private def xmapPartitionsWithIndex(x: RDD[String], uc: UnserializableClass): RDD[String] = 
+    x.mapPartitionsWithIndex((_, it) => it.map(y=>uc.op(y)))
+  private def xmapPartitionsWithContext(x: RDD[String], uc: UnserializableClass): RDD[String] = 
+    x.mapPartitionsWithContext((_, it) => it.map(y=>uc.op(y)))
+  
+}
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala
index 4709a6238164..e05db236addc 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala
@@ -532,7 +532,10 @@ abstract class DStream[T: ClassTag] (
    * 'this' DStream will be registered as an output stream and therefore materialized.
    */
   def foreachRDD(foreachFunc: (RDD[T], Time) => Unit) {
-    new ForEachDStream(this, context.sparkContext.clean(foreachFunc)).register()
+    // because the DStream is reachable from the outer object here, and because 
+    // DStreams can't be serialized with closures, we can't proactively check 
+    // it for serializability and so we pass the optional false to SparkContext.clean
+    new ForEachDStream(this, context.sparkContext.clean(foreachFunc, false)).register()
   }
 
   /**
@@ -540,7 +543,10 @@ abstract class DStream[T: ClassTag] (
    * on each RDD of 'this' DStream.
    */
   def transform[U: ClassTag](transformFunc: RDD[T] => RDD[U]): DStream[U] = {
-    transform((r: RDD[T], t: Time) => context.sparkContext.clean(transformFunc(r)))
+    // because the DStream is reachable from the outer object here, and because 
+    // DStreams can't be serialized with closures, we can't proactively check 
+    // it for serializability and so we pass the optional false to SparkContext.clean
+    transform((r: RDD[T], t: Time) => context.sparkContext.clean(transformFunc(r), false))
   }
 
   /**
@@ -548,7 +554,10 @@ abstract class DStream[T: ClassTag] (
    * on each RDD of 'this' DStream.
    */
   def transform[U: ClassTag](transformFunc: (RDD[T], Time) => RDD[U]): DStream[U] = {
-    val cleanedF = context.sparkContext.clean(transformFunc)
+    // because the DStream is reachable from the outer object here, and because 
+    // DStreams can't be serialized with closures, we can't proactively check 
+    // it for serializability and so we pass the optional false to SparkContext.clean
+    val cleanedF = context.sparkContext.clean(transformFunc, false)
     val realTransformFunc =  (rdds: Seq[RDD[_]], time: Time) => {
       assert(rdds.length == 1)
       cleanedF(rdds.head.asInstanceOf[RDD[T]], time)
@@ -563,7 +572,10 @@ abstract class DStream[T: ClassTag] (
   def transformWith[U: ClassTag, V: ClassTag](
       other: DStream[U], transformFunc: (RDD[T], RDD[U]) => RDD[V]
     ): DStream[V] = {
-    val cleanedF = ssc.sparkContext.clean(transformFunc)
+    // because the DStream is reachable from the outer object here, and because 
+    // DStreams can't be serialized with closures, we can't proactively check 
+    // it for serializability and so we pass the optional false to SparkContext.clean
+    val cleanedF = ssc.sparkContext.clean(transformFunc, false)
     transformWith(other, (rdd1: RDD[T], rdd2: RDD[U], time: Time) => cleanedF(rdd1, rdd2))
   }
 
@@ -574,7 +586,10 @@ abstract class DStream[T: ClassTag] (
   def transformWith[U: ClassTag, V: ClassTag](
       other: DStream[U], transformFunc: (RDD[T], RDD[U], Time) => RDD[V]
     ): DStream[V] = {
-    val cleanedF = ssc.sparkContext.clean(transformFunc)
+    // because the DStream is reachable from the outer object here, and because 
+    // DStreams can't be serialized with closures, we can't proactively check 
+    // it for serializability and so we pass the optional false to SparkContext.clean
+    val cleanedF = ssc.sparkContext.clean(transformFunc, false)
     val realTransformFunc = (rdds: Seq[RDD[_]], time: Time) => {
       assert(rdds.length == 2)
       val rdd1 = rdds(0).asInstanceOf[RDD[T]]

From 6803642253b45c80ef8970606e350adabe4211ea Mon Sep 17 00:00:00 2001
From: Andrew Ash <andrew@andrewash.com>
Date: Sun, 29 Jun 2014 23:29:05 -0700
Subject: [PATCH 552/810] SPARK-2077 Log serializer that actually ends up being
 used

I could settle with this being a debug also if we provided an example of how to turn it on in `log4j.properties`

https://issues.apache.org/jira/browse/SPARK-2077

Author: Andrew Ash <andrew@andrewash.com>

Closes #1017 from ash211/SPARK-2077 and squashes the following commits:

580f680 [Andrew Ash] Drop to debug
0266415 [Andrew Ash] SPARK-2077 Log serializer that actually ends up being used
---
 core/src/main/scala/org/apache/spark/SparkEnv.scala | 1 +
 1 file changed, 1 insertion(+)

diff --git a/core/src/main/scala/org/apache/spark/SparkEnv.scala b/core/src/main/scala/org/apache/spark/SparkEnv.scala
index 8dfa8cc4b5b3..2b636b085d73 100644
--- a/core/src/main/scala/org/apache/spark/SparkEnv.scala
+++ b/core/src/main/scala/org/apache/spark/SparkEnv.scala
@@ -183,6 +183,7 @@ object SparkEnv extends Logging {
 
     val serializer = instantiateClass[Serializer](
       "spark.serializer", "org.apache.spark.serializer.JavaSerializer")
+    logDebug(s"Using serializer: ${serializer.getClass}")
 
     val closureSerializer = instantiateClass[Serializer](
       "spark.closure.serializer", "org.apache.spark.serializer.JavaSerializer")

From 358ae1534d01ad9e69364a21441a7ef23c2cb516 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Mon, 30 Jun 2014 11:50:22 -0700
Subject: [PATCH 553/810] [SPARK-2322] Exception in resultHandler should NOT
 crash DAGScheduler and shutdown SparkContext.

This should go into 1.0.1.

Author: Reynold Xin <rxin@apache.org>

Closes #1264 from rxin/SPARK-2322 and squashes the following commits:

c77c07f [Reynold Xin] Added comment to SparkDriverExecutionException and a test case for accumulator.
5d8d920 [Reynold Xin] [SPARK-2322] Exception in resultHandler could crash DAGScheduler and shutdown SparkContext.
---
 .../org/apache/spark/SparkException.scala     |  8 +++
 .../apache/spark/scheduler/DAGScheduler.scala | 20 +++++--
 .../spark/scheduler/DAGSchedulerSuite.scala   | 56 ++++++++++++++++++-
 3 files changed, 78 insertions(+), 6 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/SparkException.scala b/core/src/main/scala/org/apache/spark/SparkException.scala
index 4351ed74b67f..2ebd7a7151a5 100644
--- a/core/src/main/scala/org/apache/spark/SparkException.scala
+++ b/core/src/main/scala/org/apache/spark/SparkException.scala
@@ -22,3 +22,11 @@ class SparkException(message: String, cause: Throwable)
 
   def this(message: String) = this(message, null)
 }
+
+/**
+ * Exception thrown when execution of some user code in the driver process fails, e.g.
+ * accumulator update fails or failure in takeOrdered (user supplies an Ordering implementation
+ * that can be misbehaving.
+ */
+private[spark] class SparkDriverExecutionException(cause: Throwable)
+  extends SparkException("Execution error", cause)
diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
index c8559a7a8286..813a9abfafc2 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
@@ -581,8 +581,9 @@ class DAGScheduler(
       }
     } catch {
       case e: Exception =>
-        jobResult = JobFailed(e)
-        job.listener.jobFailed(e)
+        val exception = new SparkDriverExecutionException(e)
+        jobResult = JobFailed(exception)
+        job.listener.jobFailed(exception)
       case oom: OutOfMemoryError =>
         val exception = new SparkException("Local job aborted due to out of memory error", oom)
         jobResult = JobFailed(exception)
@@ -822,6 +823,7 @@ class DAGScheduler(
       case Success =>
         logInfo("Completed " + task)
         if (event.accumUpdates != null) {
+          // TODO: fail the stage if the accumulator update fails...
           Accumulators.add(event.accumUpdates) // TODO: do this only if task wasn't resubmitted
         }
         pendingTasks(stage) -= task
@@ -838,7 +840,16 @@ class DAGScheduler(
                     cleanupStateForJobAndIndependentStages(job, Some(stage))
                     listenerBus.post(SparkListenerJobEnd(job.jobId, JobSucceeded))
                   }
-                  job.listener.taskSucceeded(rt.outputId, event.result)
+
+                  // taskSucceeded runs some user code that might throw an exception. Make sure
+                  // we are resilient against that.
+                  try {
+                    job.listener.taskSucceeded(rt.outputId, event.result)
+                  } catch {
+                    case e: Exception =>
+                      // TODO: Perhaps we want to mark the stage as failed?
+                      job.listener.jobFailed(new SparkDriverExecutionException(e))
+                  }
                 }
               case None =>
                 logInfo("Ignoring result from " + rt + " because its job has finished")
@@ -1161,8 +1172,7 @@ private[scheduler] class DAGSchedulerActorSupervisor(dagScheduler: DAGScheduler)
   override val supervisorStrategy =
     OneForOneStrategy() {
       case x: Exception =>
-        logError("eventProcesserActor failed due to the error %s; shutting down SparkContext"
-          .format(x.getMessage))
+        logError("eventProcesserActor failed; shutting down SparkContext", x)
         try {
           dagScheduler.doCancelAllJobs()
         } catch {
diff --git a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
index 8dd2a9b9f737..9f498d579a09 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
@@ -17,7 +17,6 @@
 
 package org.apache.spark.scheduler
 
-import scala.Tuple2
 import scala.collection.mutable.{HashSet, HashMap, Map}
 import scala.language.reflectiveCalls
 
@@ -38,6 +37,8 @@ class BuggyDAGEventProcessActor extends Actor {
   }
 }
 
+class DAGSchedulerSuiteDummyException extends Exception
+
 class DAGSchedulerSuite extends TestKit(ActorSystem("DAGSchedulerSuite")) with FunSuiteLike
   with ImplicitSender with BeforeAndAfter with LocalSparkContext {
 
@@ -593,6 +594,59 @@ class DAGSchedulerSuite extends TestKit(ActorSystem("DAGSchedulerSuite")) with F
     assertDataStructuresEmpty
   }
 
+  // TODO: Fix this and un-ignore the test.
+  ignore("misbehaved accumulator should not crash DAGScheduler and SparkContext") {
+    val acc = new Accumulator[Int](0, new AccumulatorParam[Int] {
+      override def addAccumulator(t1: Int, t2: Int): Int = t1 + t2
+      override def zero(initialValue: Int): Int = 0
+      override def addInPlace(r1: Int, r2: Int): Int = {
+        throw new DAGSchedulerSuiteDummyException
+      }
+    })
+
+    // Run this on executors
+    intercept[SparkDriverExecutionException] {
+      sc.parallelize(1 to 10, 2).foreach { item => acc.add(1) }
+    }
+
+    // Run this within a local thread
+    intercept[SparkDriverExecutionException] {
+      sc.parallelize(1 to 10, 2).map { item => acc.add(1) }.take(1)
+    }
+
+    // Make sure we can still run local commands as well as cluster commands.
+    assert(sc.parallelize(1 to 10, 2).count() === 10)
+    assert(sc.parallelize(1 to 10, 2).first() === 1)
+  }
+
+  test("misbehaved resultHandler should not crash DAGScheduler and SparkContext") {
+    val e1 = intercept[SparkDriverExecutionException] {
+      val rdd = sc.parallelize(1 to 10, 2)
+      sc.runJob[Int, Int](
+        rdd,
+        (context: TaskContext, iter: Iterator[Int]) => iter.size,
+        Seq(0),
+        allowLocal = true,
+        (part: Int, result: Int) => throw new DAGSchedulerSuiteDummyException)
+    }
+    assert(e1.getCause.isInstanceOf[DAGSchedulerSuiteDummyException])
+
+    val e2 = intercept[SparkDriverExecutionException] {
+      val rdd = sc.parallelize(1 to 10, 2)
+      sc.runJob[Int, Int](
+        rdd,
+        (context: TaskContext, iter: Iterator[Int]) => iter.size,
+        Seq(0, 1),
+        allowLocal = false,
+        (part: Int, result: Int) => throw new DAGSchedulerSuiteDummyException)
+    }
+    assert(e2.getCause.isInstanceOf[DAGSchedulerSuiteDummyException])
+
+    // Make sure we can still run local commands as well as cluster commands.
+    assert(sc.parallelize(1 to 10, 2).count() === 10)
+    assert(sc.parallelize(1 to 10, 2).first() === 1)
+  }
+
   test("DAGSchedulerActorSupervisor closes the SparkContext when EventProcessActor crashes") {
     val actorSystem = ActorSystem("test")
     val supervisor = actorSystem.actorOf(

From 5fccb567b37a94445512c7ec20b830b5e062089f Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Mon, 30 Jun 2014 15:12:38 -0700
Subject: [PATCH 554/810] [SPARK-2318] When exiting on a signal, print the
 signal name first.

Author: Reynold Xin <rxin@apache.org>

Closes #1260 from rxin/signalhandler1 and squashes the following commits:

8e73552 [Reynold Xin] Uh add Logging back in ApplicationMaster.
0402ba8 [Reynold Xin] Synchronize SignalLogger.register.
dc70705 [Reynold Xin] Added SignalLogger to YARN ApplicationMaster.
79a21b4 [Reynold Xin] Added license header.
0da052c [Reynold Xin] Added the SignalLogger itself.
e587d2e [Reynold Xin] [SPARK-2318] When exiting on a signal, print the signal name first.
---
 .../apache/spark/deploy/master/Master.scala   |  5 +-
 .../apache/spark/deploy/worker/Worker.scala   |  5 +-
 .../CoarseGrainedExecutorBackend.scala        |  6 +-
 .../spark/executor/MesosExecutorBackend.scala |  5 +-
 .../org/apache/spark/util/SignalLogger.scala  | 60 +++++++++++++++++++
 .../spark/deploy/yarn/ApplicationMaster.scala |  5 +-
 .../spark/deploy/yarn/ApplicationMaster.scala |  5 +-
 7 files changed, 79 insertions(+), 12 deletions(-)
 create mode 100644 core/src/main/scala/org/apache/spark/util/SignalLogger.scala

diff --git a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
index 33ffcbd21695..11545b820370 100644
--- a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
@@ -41,7 +41,7 @@ import org.apache.spark.deploy.master.ui.MasterWebUI
 import org.apache.spark.metrics.MetricsSystem
 import org.apache.spark.scheduler.{EventLoggingListener, ReplayListenerBus}
 import org.apache.spark.ui.SparkUI
-import org.apache.spark.util.{AkkaUtils, Utils}
+import org.apache.spark.util.{AkkaUtils, SignalLogger, Utils}
 
 private[spark] class Master(
     host: String,
@@ -755,12 +755,13 @@ private[spark] class Master(
   }
 }
 
-private[spark] object Master {
+private[spark] object Master extends Logging {
   val systemName = "sparkMaster"
   private val actorName = "Master"
   val sparkUrlRegex = "spark://([^:]+):([0-9]+)".r
 
   def main(argStrings: Array[String]) {
+    SignalLogger.register(log)
     val conf = new SparkConf
     val args = new MasterArguments(argStrings, conf)
     val (actorSystem, _, _) = startSystemAndActor(args.host, args.port, args.webUiPort, conf)
diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala b/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala
index a0ecaf709f8e..ce425443051b 100755
--- a/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala
@@ -34,7 +34,7 @@ import org.apache.spark.deploy.DeployMessages._
 import org.apache.spark.deploy.master.{DriverState, Master}
 import org.apache.spark.deploy.worker.ui.WorkerWebUI
 import org.apache.spark.metrics.MetricsSystem
-import org.apache.spark.util.{AkkaUtils, Utils}
+import org.apache.spark.util.{AkkaUtils, SignalLogger, Utils}
 
 /**
   * @param masterUrls Each url should look like spark://host:port.
@@ -365,8 +365,9 @@ private[spark] class Worker(
   }
 }
 
-private[spark] object Worker {
+private[spark] object Worker extends Logging {
   def main(argStrings: Array[String]) {
+    SignalLogger.register(log)
     val args = new WorkerArguments(argStrings)
     val (actorSystem, _) = startSystemAndActor(args.host, args.port, args.webUiPort, args.cores,
       args.memory, args.masters, args.workDir)
diff --git a/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala b/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala
index b5fd334f4020..8d31bd05fdbe 100644
--- a/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala
+++ b/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala
@@ -31,7 +31,7 @@ import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.deploy.worker.WorkerWatcher
 import org.apache.spark.scheduler.TaskDescription
 import org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages._
-import org.apache.spark.util.{AkkaUtils, Utils}
+import org.apache.spark.util.{AkkaUtils, SignalLogger, Utils}
 
 private[spark] class CoarseGrainedExecutorBackend(
     driverUrl: String,
@@ -97,10 +97,12 @@ private[spark] class CoarseGrainedExecutorBackend(
   }
 }
 
-private[spark] object CoarseGrainedExecutorBackend {
+private[spark] object CoarseGrainedExecutorBackend extends Logging {
   def run(driverUrl: String, executorId: String, hostname: String, cores: Int,
     workerUrl: Option[String]) {
 
+    SignalLogger.register(log)
+
     SparkHadoopUtil.get.runAsSparkUser { () =>
       // Debug code
       Utils.checkHost(hostname)
diff --git a/core/src/main/scala/org/apache/spark/executor/MesosExecutorBackend.scala b/core/src/main/scala/org/apache/spark/executor/MesosExecutorBackend.scala
index 74100498bb2b..2232e6237bf2 100644
--- a/core/src/main/scala/org/apache/spark/executor/MesosExecutorBackend.scala
+++ b/core/src/main/scala/org/apache/spark/executor/MesosExecutorBackend.scala
@@ -25,8 +25,8 @@ import org.apache.mesos.Protos.{TaskStatus => MesosTaskStatus, _}
 
 import org.apache.spark.{Logging, TaskState}
 import org.apache.spark.TaskState.TaskState
-import org.apache.spark.util.Utils
 import org.apache.spark.deploy.SparkHadoopUtil
+import org.apache.spark.util.{SignalLogger, Utils}
 
 private[spark] class MesosExecutorBackend
   extends MesosExecutor
@@ -93,8 +93,9 @@ private[spark] class MesosExecutorBackend
 /**
  * Entry point for Mesos executor.
  */
-private[spark] object MesosExecutorBackend {
+private[spark] object MesosExecutorBackend extends Logging {
   def main(args: Array[String]) {
+    SignalLogger.register(log)
     SparkHadoopUtil.get.runAsSparkUser { () =>
         MesosNativeLibrary.load()
         // Create a new Executor and start it running
diff --git a/core/src/main/scala/org/apache/spark/util/SignalLogger.scala b/core/src/main/scala/org/apache/spark/util/SignalLogger.scala
new file mode 100644
index 000000000000..d769b54fa2fa
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/util/SignalLogger.scala
@@ -0,0 +1,60 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.util
+
+import org.apache.commons.lang.SystemUtils
+import org.slf4j.Logger
+import sun.misc.{Signal, SignalHandler}
+
+/**
+ * Used to log signals received. This can be very useful in debugging crashes or kills.
+ *
+ * Inspired by Colin Patrick McCabe's similar class from Hadoop.
+ */
+private[spark] object SignalLogger {
+
+  private var registered = false
+
+  /** Register a signal handler to log signals on UNIX-like systems. */
+  def register(log: Logger): Unit = synchronized {
+    if (SystemUtils.IS_OS_UNIX) {
+      require(!registered, "Can't re-install the signal handlers")
+      registered = true
+
+      val signals = Seq("TERM", "HUP", "INT")
+      for (signal <- signals) {
+        try {
+          new SignalLoggerHandler(signal, log)
+        } catch {
+          case e: Exception => log.warn("Failed to register signal handler " + signal, e)
+        }
+      }
+      log.info("Registered signal handlers for [" + signals.mkString(", ") + "]")
+    }
+  }
+}
+
+private sealed class SignalLoggerHandler(name: String, log: Logger) extends SignalHandler {
+
+  val prevHandler = Signal.handle(new Signal(name), this)
+
+  override def handle(signal: Signal): Unit = {
+    log.error("RECEIVED SIGNAL " + signal.getNumber() + ": SIG" + signal.getName())
+    prevHandler.handle(signal)
+  }
+}
diff --git a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
index 1cc9c33cd2d0..438737f7a6b6 100644
--- a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
+++ b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
@@ -37,7 +37,7 @@ import org.apache.hadoop.yarn.util.{ConverterUtils, Records}
 
 import org.apache.spark.{Logging, SecurityManager, SparkConf, SparkContext}
 import org.apache.spark.deploy.SparkHadoopUtil
-import org.apache.spark.util.Utils
+import org.apache.spark.util.{SignalLogger, Utils}
 
 /**
  * An application master that runs the users driver program and allocates executors.
@@ -409,7 +409,7 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration,
 
 }
 
-object ApplicationMaster {
+object ApplicationMaster extends Logging {
   // Number of times to wait for the allocator loop to complete.
   // Each loop iteration waits for 100ms, so maximum of 3 seconds.
   // This is to ensure that we have reasonable number of containers before we start
@@ -487,6 +487,7 @@ object ApplicationMaster {
   }
 
   def main(argStrings: Array[String]) {
+    SignalLogger.register(log)
     val args = new ApplicationMasterArguments(argStrings)
     SparkHadoopUtil.get.runAsSparkUser { () =>
       new ApplicationMaster(args).run()
diff --git a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
index 6244332f2373..ee1e9c9c23d2 100644
--- a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
+++ b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
@@ -37,7 +37,7 @@ import org.apache.hadoop.yarn.webapp.util.WebAppUtils
 
 import org.apache.spark.{Logging, SecurityManager, SparkConf, SparkContext}
 import org.apache.spark.deploy.SparkHadoopUtil
-import org.apache.spark.util.Utils
+import org.apache.spark.util.{SignalLogger, Utils}
 
 
 /**
@@ -363,7 +363,7 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration,
 
 }
 
-object ApplicationMaster {
+object ApplicationMaster extends Logging {
   // Number of times to wait for the allocator loop to complete.
   // Each loop iteration waits for 100ms, so maximum of 3 seconds.
   // This is to ensure that we have reasonable number of containers before we start
@@ -455,6 +455,7 @@ object ApplicationMaster {
   }
 
   def main(argStrings: Array[String]) {
+    SignalLogger.register(log)
     val args = new ApplicationMasterArguments(argStrings)
     SparkHadoopUtil.get.runAsSparkUser { () =>
       new ApplicationMaster(args).run()

From 04fa1223ee69760f0d23b40e56f4b036aa301879 Mon Sep 17 00:00:00 2001
From: Sean Owen <sowen@cloudera.com>
Date: Mon, 30 Jun 2014 16:03:38 -0700
Subject: [PATCH 555/810] SPARK-2293. Replace RDD.zip usage by map with predict
 inside.

This is the only occurrence of this pattern in the examples that needs to be replaced. It only addresses the example change.

Author: Sean Owen <sowen@cloudera.com>

Closes #1250 from srowen/SPARK-2293 and squashes the following commits:

6b1b28c [Sean Owen] Compute prediction-and-label RDD directly rather than by zipping, for efficiency
---
 docs/mllib-naive-bayes.md | 18 ++++++------------
 1 file changed, 6 insertions(+), 12 deletions(-)

diff --git a/docs/mllib-naive-bayes.md b/docs/mllib-naive-bayes.md
index 4b3a7cab3211..1d1d7dcf6ffc 100644
--- a/docs/mllib-naive-bayes.md
+++ b/docs/mllib-naive-bayes.md
@@ -51,9 +51,8 @@ val training = splits(0)
 val test = splits(1)
 
 val model = NaiveBayes.train(training, lambda = 1.0)
-val prediction = model.predict(test.map(_.features))
 
-val predictionAndLabel = prediction.zip(test.map(_.label))
+val predictionAndLabel = test.map(p => (model.predict(p.features), p.label))
 val accuracy = 1.0 * predictionAndLabel.filter(x => x._1 == x._2).count() / test.count()
 {% endhighlight %}
 </div>
@@ -71,6 +70,7 @@ can be used for evaluation and prediction.
 import org.apache.spark.api.java.JavaPairRDD;
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.function.Function;
+import org.apache.spark.api.java.function.PairFunction;
 import org.apache.spark.mllib.classification.NaiveBayes;
 import org.apache.spark.mllib.classification.NaiveBayesModel;
 import org.apache.spark.mllib.regression.LabeledPoint;
@@ -81,18 +81,12 @@ JavaRDD<LabeledPoint> test = ... // test set
 
 final NaiveBayesModel model = NaiveBayes.train(training.rdd(), 1.0);
 
-JavaRDD<Double> prediction =
-  test.map(new Function<LabeledPoint, Double>() {
-    @Override public Double call(LabeledPoint p) {
-      return model.predict(p.features());
-    }
-  });
 JavaPairRDD<Double, Double> predictionAndLabel = 
-  prediction.zip(test.map(new Function<LabeledPoint, Double>() {
-    @Override public Double call(LabeledPoint p) {
-      return p.label();
+  test.mapToPair(new PairFunction<LabeledPoint, Double, Double>() {
+    @Override public Tuple2<Double, Double> call(LabeledPoint p) {
+      return new Tuple2<Double, Double>(model.predict(p.features()), p.label());
     }
-  }));
+  });
 double accuracy = 1.0 * predictionAndLabel.filter(new Function<Tuple2<Double, Double>, Boolean>() {
     @Override public Boolean call(Tuple2<Double, Double> pl) {
       return pl._1() == pl._2();

From 3319a3e3c604f187ff8176597b269af04ca5c1c5 Mon Sep 17 00:00:00 2001
From: Peter MacKinnon <pmackinn@redhat.com>
Date: Tue, 1 Jul 2014 00:28:38 -0700
Subject: [PATCH 556/810] SPARK-2332 [build] add exclusion for old servlet-api
 on hadoop-client in core

Fix for class of test suite failures in jenkins

Author: Peter MacKinnon <pmackinn@redhat.com>

Closes #1271 from pdmack/master and squashes the following commits:

cfe59fd [Peter MacKinnon] exclude servlet-api in hadoop-client for sbt
6f39fec [Peter MacKinnon] add exclusion for old servlet-api on hadoop-client in core
---
 core/pom.xml             | 6 ++++++
 project/SparkBuild.scala | 2 +-
 2 files changed, 7 insertions(+), 1 deletion(-)

diff --git a/core/pom.xml b/core/pom.xml
index 8c23842730e3..6abf8480d5da 100644
--- a/core/pom.xml
+++ b/core/pom.xml
@@ -34,6 +34,12 @@
     <dependency>
       <groupId>org.apache.hadoop</groupId>
       <artifactId>hadoop-client</artifactId>
+      <exclusions>
+        <exclusion>
+          <groupId>javax.servlet</groupId>
+          <artifactId>servlet-api</artifactId>
+        </exclusion>
+      </exclusions>
     </dependency>
     <dependency>
       <groupId>net.java.dev.jets3t</groupId>
diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index 55a2aa0fc714..599714233c18 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -371,7 +371,7 @@ object SparkBuild extends Build {
         "net.java.dev.jets3t"        % "jets3t"           % jets3tVersion excludeAll(excludeCommonsLogging),
         "commons-codec"              % "commons-codec"    % "1.5", // Prevent jets3t from including the older version of commons-codec
         "org.apache.derby"           % "derby"            % "10.4.2.0"                     % "test",
-        "org.apache.hadoop"          % hadoopClient       % hadoopVersion excludeAll(excludeJBossNetty, excludeAsm, excludeCommonsLogging, excludeSLF4J, excludeOldAsm),
+        "org.apache.hadoop"          % hadoopClient       % hadoopVersion excludeAll(excludeJBossNetty, excludeAsm, excludeCommonsLogging, excludeSLF4J, excludeOldAsm, excludeServletApi),
         "org.apache.curator"         % "curator-recipes"  % "2.4.0" excludeAll(excludeJBossNetty),
         "com.codahale.metrics"       % "metrics-core"     % codahaleMetricsVersion,
         "com.codahale.metrics"       % "metrics-jvm"      % codahaleMetricsVersion,

From 05c3d90e3527114d3abc08c7cc87f6efef96ebdc Mon Sep 17 00:00:00 2001
From: Kay Ousterhout <kayousterhout@gmail.com>
Date: Tue, 1 Jul 2014 01:56:51 -0700
Subject: [PATCH 557/810] [SPARK-2185] Emit warning when task size exceeds a
 threshold.

This functionality was added in an earlier commit but shortly
after was removed due to a bad git merge (totally my fault).

Author: Kay Ousterhout <kayousterhout@gmail.com>

Closes #1149 from kayousterhout/warning_bug and squashes the following commits:

3f1bb00 [Kay Ousterhout] Fixed Json tests
462a664 [Kay Ousterhout] Removed task set name from warning message
e89b2f6 [Kay Ousterhout] Fixed Json tests.
7af424c [Kay Ousterhout] Emit warning when task size exceeds a threshold.
---
 .../apache/spark/scheduler/DAGScheduler.scala | 13 ------
 .../apache/spark/scheduler/StageInfo.scala    |  2 -
 .../org/apache/spark/scheduler/TaskInfo.scala |  2 -
 .../spark/scheduler/TaskSetManager.scala      | 15 +++++++
 .../org/apache/spark/util/JsonProtocol.scala  | 10 +----
 .../spark/scheduler/TaskSetManagerSuite.scala | 41 +++++++++++++++++++
 .../apache/spark/util/JsonProtocolSuite.scala | 18 ++++----
 7 files changed, 65 insertions(+), 36 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
index 813a9abfafc2..81c136d97031 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
@@ -623,16 +623,6 @@ class DAGScheduler(
   }
 
   private[scheduler] def handleBeginEvent(task: Task[_], taskInfo: TaskInfo) {
-    for (stage <- stageIdToStage.get(task.stageId); stageInfo <- stageToInfos.get(stage)) {
-      if (taskInfo.serializedSize > DAGScheduler.TASK_SIZE_TO_WARN * 1024 &&
-        !stageInfo.emittedTaskSizeWarning) {
-        stageInfo.emittedTaskSizeWarning = true
-        logWarning(("Stage %d (%s) contains a task of very large " +
-          "size (%d KB). The maximum recommended task size is %d KB.").format(
-            task.stageId, stageInfo.name, taskInfo.serializedSize / 1024,
-            DAGScheduler.TASK_SIZE_TO_WARN))
-      }
-    }
     listenerBus.post(SparkListenerTaskStart(task.stageId, taskInfo))
     submitWaitingStages()
   }
@@ -1254,7 +1244,4 @@ private[spark] object DAGScheduler {
   // The time, in millis, to wake up between polls of the completion queue in order to potentially
   // resubmit failed stages
   val POLL_TIMEOUT = 10L
-
-  // Warns the user if a stage contains a task with size greater than this value (in KB)
-  val TASK_SIZE_TO_WARN = 100
 }
diff --git a/core/src/main/scala/org/apache/spark/scheduler/StageInfo.scala b/core/src/main/scala/org/apache/spark/scheduler/StageInfo.scala
index 7644e3f351b3..480891550eb6 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/StageInfo.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/StageInfo.scala
@@ -38,8 +38,6 @@ class StageInfo(
   /** If the stage failed, the reason why. */
   var failureReason: Option[String] = None
 
-  var emittedTaskSizeWarning = false
-
   def stageFailed(reason: String) {
     failureReason = Some(reason)
     completionTime = Some(System.currentTimeMillis)
diff --git a/core/src/main/scala/org/apache/spark/scheduler/TaskInfo.scala b/core/src/main/scala/org/apache/spark/scheduler/TaskInfo.scala
index 6aecdfe8e665..29de0453ac19 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/TaskInfo.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/TaskInfo.scala
@@ -49,8 +49,6 @@ class TaskInfo(
 
   var failed = false
 
-  var serializedSize: Int = 0
-
   private[spark] def markGettingResult(time: Long = System.currentTimeMillis) {
     gettingResultTime = time
   }
diff --git a/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala b/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala
index 83ff6b8550b4..059cc9085a2e 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala
@@ -166,6 +166,8 @@ private[spark] class TaskSetManager(
 
   override def schedulingMode = SchedulingMode.NONE
 
+  var emittedTaskSizeWarning = false
+
   /**
    * Add a task to all the pending-task lists that it should be on. If readding is set, we are
    * re-adding the task so only include it in each list if it's not already there.
@@ -418,6 +420,13 @@ private[spark] class TaskSetManager(
           // we assume the task can be serialized without exceptions.
           val serializedTask = Task.serializeWithDependencies(
             task, sched.sc.addedFiles, sched.sc.addedJars, ser)
+          if (serializedTask.limit > TaskSetManager.TASK_SIZE_TO_WARN_KB * 1024 &&
+              !emittedTaskSizeWarning) {
+            emittedTaskSizeWarning = true
+            logWarning(s"Stage ${task.stageId} contains a task of very large size " +
+              s"(${serializedTask.limit / 1024} KB). The maximum recommended task size is " +
+              s"${TaskSetManager.TASK_SIZE_TO_WARN_KB} KB.")
+          }
           val timeTaken = clock.getTime() - startTime
           addRunningTask(taskId)
           logInfo("Serialized task %s:%d as %d bytes in %d ms".format(
@@ -764,3 +773,9 @@ private[spark] class TaskSetManager(
     localityWaits = myLocalityLevels.map(getLocalityWait)
   }
 }
+
+private[spark] object TaskSetManager {
+  // The user will be warned if any stages contain a task that has a serialized size greater than
+  // this.
+  val TASK_SIZE_TO_WARN_KB = 100
+}
diff --git a/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala b/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala
index 26c9c9d6034e..47eb44b53037 100644
--- a/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala
+++ b/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala
@@ -190,8 +190,7 @@ private[spark] object JsonProtocol {
     ("Details" -> stageInfo.details) ~
     ("Submission Time" -> submissionTime) ~
     ("Completion Time" -> completionTime) ~
-    ("Failure Reason" -> failureReason) ~
-    ("Emitted Task Size Warning" -> stageInfo.emittedTaskSizeWarning)
+    ("Failure Reason" -> failureReason)
   }
 
   def taskInfoToJson(taskInfo: TaskInfo): JValue = {
@@ -205,8 +204,7 @@ private[spark] object JsonProtocol {
     ("Speculative" -> taskInfo.speculative) ~
     ("Getting Result Time" -> taskInfo.gettingResultTime) ~
     ("Finish Time" -> taskInfo.finishTime) ~
-    ("Failed" -> taskInfo.failed) ~
-    ("Serialized Size" -> taskInfo.serializedSize)
+    ("Failed" -> taskInfo.failed)
   }
 
   def taskMetricsToJson(taskMetrics: TaskMetrics): JValue = {
@@ -487,13 +485,11 @@ private[spark] object JsonProtocol {
     val submissionTime = Utils.jsonOption(json \ "Submission Time").map(_.extract[Long])
     val completionTime = Utils.jsonOption(json \ "Completion Time").map(_.extract[Long])
     val failureReason = Utils.jsonOption(json \ "Failure Reason").map(_.extract[String])
-    val emittedTaskSizeWarning = (json \ "Emitted Task Size Warning").extract[Boolean]
 
     val stageInfo = new StageInfo(stageId, stageName, numTasks, rddInfos, details)
     stageInfo.submissionTime = submissionTime
     stageInfo.completionTime = completionTime
     stageInfo.failureReason = failureReason
-    stageInfo.emittedTaskSizeWarning = emittedTaskSizeWarning
     stageInfo
   }
 
@@ -509,14 +505,12 @@ private[spark] object JsonProtocol {
     val gettingResultTime = (json \ "Getting Result Time").extract[Long]
     val finishTime = (json \ "Finish Time").extract[Long]
     val failed = (json \ "Failed").extract[Boolean]
-    val serializedSize = (json \ "Serialized Size").extract[Int]
 
     val taskInfo =
       new TaskInfo(taskId, index, attempt, launchTime, executorId, host, taskLocality, speculative)
     taskInfo.gettingResultTime = gettingResultTime
     taskInfo.finishTime = finishTime
     taskInfo.failed = failed
-    taskInfo.serializedSize = serializedSize
     taskInfo
   }
 
diff --git a/core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala
index 59a618956a35..9ff2a487005c 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala
@@ -17,6 +17,8 @@
 
 package org.apache.spark.scheduler
 
+import java.util.Random
+
 import scala.collection.mutable.ArrayBuffer
 import scala.collection.mutable
 
@@ -83,6 +85,18 @@ class FakeTaskScheduler(sc: SparkContext, liveExecutors: (String, String)* /* ex
   }
 }
 
+/**
+ * A Task implementation that results in a large serialized task.
+ */
+class LargeTask(stageId: Int) extends Task[Array[Byte]](stageId, 0) {
+  val randomBuffer = new Array[Byte](TaskSetManager.TASK_SIZE_TO_WARN_KB * 1024)
+  val random = new Random(0)
+  random.nextBytes(randomBuffer)
+
+  override def runTask(context: TaskContext): Array[Byte] = randomBuffer
+  override def preferredLocations: Seq[TaskLocation] = Seq[TaskLocation]()
+}
+
 class TaskSetManagerSuite extends FunSuite with LocalSparkContext with Logging {
   import TaskLocality.{ANY, PROCESS_LOCAL, NODE_LOCAL, RACK_LOCAL}
 
@@ -434,6 +448,33 @@ class TaskSetManagerSuite extends FunSuite with LocalSparkContext with Logging {
     assert(manager.myLocalityLevels.sameElements(Array(PROCESS_LOCAL, NODE_LOCAL, ANY)))
   }
 
+  test("do not emit warning when serialized task is small") {
+    sc = new SparkContext("local", "test")
+    val sched = new FakeTaskScheduler(sc, ("exec1", "host1"))
+    val taskSet = FakeTask.createTaskSet(1)
+    val manager = new TaskSetManager(sched, taskSet, MAX_TASK_FAILURES)
+
+    assert(!manager.emittedTaskSizeWarning)
+
+    assert(manager.resourceOffer("exec1", "host1", ANY).get.index === 0)
+
+    assert(!manager.emittedTaskSizeWarning)
+  }
+
+  test("emit warning when serialized task is large") {
+    sc = new SparkContext("local", "test")
+    val sched = new FakeTaskScheduler(sc, ("exec1", "host1"))
+
+    val taskSet = new TaskSet(Array(new LargeTask(0)), 0, 0, 0, null)
+    val manager = new TaskSetManager(sched, taskSet, MAX_TASK_FAILURES)
+
+    assert(!manager.emittedTaskSizeWarning)
+
+    assert(manager.resourceOffer("exec1", "host1", ANY).get.index === 0)
+
+    assert(manager.emittedTaskSizeWarning)
+  }
+
   def createTaskResult(id: Int): DirectTaskResult[Int] = {
     val valueSer = SparkEnv.get.serializer.newInstance()
     new DirectTaskResult[Int](valueSer.serialize(id), mutable.Map.empty, new TaskMetrics)
diff --git a/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala b/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala
index 316e14100e40..058d31453081 100644
--- a/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala
+++ b/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala
@@ -257,7 +257,6 @@ class JsonProtocolSuite extends FunSuite {
     assert(info1.numTasks === info2.numTasks)
     assert(info1.submissionTime === info2.submissionTime)
     assert(info1.completionTime === info2.completionTime)
-    assert(info1.emittedTaskSizeWarning === info2.emittedTaskSizeWarning)
     assert(info1.rddInfos.size === info2.rddInfos.size)
     (0 until info1.rddInfos.size).foreach { i =>
       assertEquals(info1.rddInfos(i), info2.rddInfos(i))
@@ -294,7 +293,6 @@ class JsonProtocolSuite extends FunSuite {
     assert(info1.gettingResultTime === info2.gettingResultTime)
     assert(info1.finishTime === info2.finishTime)
     assert(info1.failed === info2.failed)
-    assert(info1.serializedSize === info2.serializedSize)
   }
 
   private def assertEquals(metrics1: TaskMetrics, metrics2: TaskMetrics) {
@@ -540,9 +538,8 @@ class JsonProtocolSuite extends FunSuite {
   private val stageSubmittedJsonString =
     """
       {"Event":"SparkListenerStageSubmitted","Stage Info":{"Stage ID":100,"Stage Name":
-      "greetings","Number of Tasks":200,"RDD Info":[],"Details":"details",
-      "Emitted Task Size Warning":false},"Properties":{"France":"Paris","Germany":"Berlin",
-      "Russia":"Moscow","Ukraine":"Kiev"}}
+      "greetings","Number of Tasks":200,"RDD Info":[],"Details":"details"},"Properties":
+      {"France":"Paris","Germany":"Berlin","Russia":"Moscow","Ukraine":"Kiev"}}
     """
 
   private val stageCompletedJsonString =
@@ -551,8 +548,7 @@ class JsonProtocolSuite extends FunSuite {
       "greetings","Number of Tasks":201,"RDD Info":[{"RDD ID":101,"Name":"mayor","Storage
       Level":{"Use Disk":true,"Use Memory":true,"Use Tachyon":false,"Deserialized":true,
       "Replication":1},"Number of Partitions":201,"Number of Cached Partitions":301,
-      "Memory Size":401,"Tachyon Size":0,"Disk Size":501}],"Details":"details",
-      "Emitted Task Size Warning":false}}
+      "Memory Size":401,"Tachyon Size":0,"Disk Size":501}],"Details":"details"}}
     """
 
   private val taskStartJsonString =
@@ -560,7 +556,7 @@ class JsonProtocolSuite extends FunSuite {
       |{"Event":"SparkListenerTaskStart","Stage ID":111,"Task Info":{"Task ID":222,
       |"Index":333,"Attempt":1,"Launch Time":444,"Executor ID":"executor","Host":"your kind sir",
       |"Locality":"NODE_LOCAL","Speculative":false,"Getting Result Time":0,"Finish Time":0,
-      |"Failed":false,"Serialized Size":0}}
+      |"Failed":false}}
     """.stripMargin
 
   private val taskGettingResultJsonString =
@@ -568,7 +564,7 @@ class JsonProtocolSuite extends FunSuite {
       |{"Event":"SparkListenerTaskGettingResult","Task Info":
       |  {"Task ID":1000,"Index":2000,"Attempt":5,"Launch Time":3000,"Executor ID":"executor",
       |   "Host":"your kind sir","Locality":"NODE_LOCAL","Speculative":true,"Getting Result Time":0,
-      |   "Finish Time":0,"Failed":false,"Serialized Size":0
+      |   "Finish Time":0,"Failed":false
       |  }
       |}
     """.stripMargin
@@ -580,7 +576,7 @@ class JsonProtocolSuite extends FunSuite {
       |"Task Info":{
       |  "Task ID":123,"Index":234,"Attempt":67,"Launch Time":345,"Executor ID":"executor",
       |  "Host":"your kind sir","Locality":"NODE_LOCAL","Speculative":false,
-      |  "Getting Result Time":0,"Finish Time":0,"Failed":false,"Serialized Size":0
+      |  "Getting Result Time":0,"Finish Time":0,"Failed":false
       |},
       |"Task Metrics":{
       |  "Host Name":"localhost","Executor Deserialize Time":300,"Executor Run Time":400,
@@ -620,7 +616,7 @@ class JsonProtocolSuite extends FunSuite {
       |"Task Info":{
       |  "Task ID":123,"Index":234,"Attempt":67,"Launch Time":345,"Executor ID":"executor",
       |  "Host":"your kind sir","Locality":"NODE_LOCAL","Speculative":false,
-      |  "Getting Result Time":0,"Finish Time":0,"Failed":false,"Serialized Size":0
+      |  "Getting Result Time":0,"Finish Time":0,"Failed":false
       |},
       |"Task Metrics":{
       |  "Host Name":"localhost","Executor Deserialize Time":300,"Executor Run Time":400,

From 6596392da0fc0fee89e22adfca239a3477dfcbab Mon Sep 17 00:00:00 2001
From: CodingCat <zhunansjtu@gmail.com>
Date: Tue, 1 Jul 2014 20:37:10 -0700
Subject: [PATCH 558/810] update the comments in SqlParser

SqlParser has been case-insensitive after https://github.com/apache/spark/commit/dab5439a083b5f771d5d5b462d0d517fa8e9aaf2 was merged

Author: CodingCat <zhunansjtu@gmail.com>

Closes #1275 from CodingCat/master and squashes the following commits:

17931cd [CodingCat] update the comments in SqlParser
---
 .../src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala | 1 -
 1 file changed, 1 deletion(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
index 0cc4592047b1..61762fa2a7c3 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
@@ -35,7 +35,6 @@ import org.apache.spark.sql.catalyst.types._
  *
  * Limitations:
  *  - Only supports a very limited subset of SQL.
- *  - Keywords must be capital.
  *
  * This is currently included mostly for illustrative purposes.  Users wanting more complete support
  * for a SQL like language should checkout the HiveQL support in the sql/hive sub-project.

From 5c6ec94da1bacd8e65a43acb92b6721493484e7b Mon Sep 17 00:00:00 2001
From: Ximo Guanter Gonzalbez <ximo@tid.es>
Date: Wed, 2 Jul 2014 10:03:44 -0700
Subject: [PATCH 559/810] SPARK-2186: Spark SQL DSL support for simple
 aggregations such as SUM and AVG

**Description** This patch enables using the `.select()` function in SchemaRDD with functions such as `Sum`, `Count` and other.
**Testing** Unit tests added.

Author: Ximo Guanter Gonzalbez <ximo@tid.es>

Closes #1211 from edrevo/add-expression-support-in-select and squashes the following commits:

fe4a1e1 [Ximo Guanter Gonzalbez] Extend SQL DSL to functions
e1d344a [Ximo Guanter Gonzalbez] SPARK-2186: Spark SQL DSL support for simple aggregations such as SUM and AVG
---
 .../spark/sql/catalyst/dsl/package.scala      | 11 +++++++
 .../org/apache/spark/sql/SchemaRDD.scala      |  9 ++++--
 .../org/apache/spark/sql/DslQuerySuite.scala  | 32 +++++++++++++++----
 3 files changed, 44 insertions(+), 8 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala
index 26ad4837b0b0..1b503b957d14 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala
@@ -108,6 +108,17 @@ package object dsl {
 
     implicit def symbolToUnresolvedAttribute(s: Symbol) = analysis.UnresolvedAttribute(s.name)
 
+    def sum(e: Expression) = Sum(e)
+    def sumDistinct(e: Expression) = SumDistinct(e)
+    def count(e: Expression) = Count(e)
+    def countDistinct(e: Expression*) = CountDistinct(e)
+    def avg(e: Expression) = Average(e)
+    def first(e: Expression) = First(e)
+    def min(e: Expression) = Min(e)
+    def max(e: Expression) = Max(e)
+    def upper(e: Expression) = Upper(e)
+    def lower(e: Expression) = Lower(e)
+
     implicit class DslSymbol(sym: Symbol) extends ImplicitAttribute { def s = sym.name }
     // TODO more implicit class for literal?
     implicit class DslString(val s: String) extends ImplicitOperators {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala
index 7c0efb456661..8f9f54f610e9 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala
@@ -133,8 +133,13 @@ class SchemaRDD(
    *
    * @group Query
    */
-  def select(exprs: NamedExpression*): SchemaRDD =
-    new SchemaRDD(sqlContext, Project(exprs, logicalPlan))
+  def select(exprs: Expression*): SchemaRDD = {
+    val aliases = exprs.zipWithIndex.map {
+      case (ne: NamedExpression, _) => ne
+      case (e, i) => Alias(e, s"c$i")()
+    }
+    new SchemaRDD(sqlContext, Project(aliases, logicalPlan))
+  }
 
   /**
    * Filters the output, only returning those rows where `condition` evaluates to true.
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala
index e4a64a7a482b..04ac008682f5 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala
@@ -60,6 +60,26 @@ class DslQuerySuite extends QueryTest {
       Seq(Seq("1")))
   }
 
+  test("select with functions") {
+    checkAnswer(
+      testData.select(sum('value), avg('value), count(1)),
+      Seq(Seq(5050.0, 50.5, 100)))
+
+    checkAnswer(
+      testData2.select('a + 'b, 'a < 'b),
+      Seq(
+        Seq(2, false),
+        Seq(3, true),
+        Seq(3, false),
+        Seq(4, false),
+        Seq(4, false),
+        Seq(5, false)))
+
+    checkAnswer(
+      testData2.select(sumDistinct('a)),
+      Seq(Seq(6)))
+  }
+
   test("sorting") {
     checkAnswer(
       testData2.orderBy('a.asc, 'b.asc),
@@ -110,17 +130,17 @@ class DslQuerySuite extends QueryTest {
 
   test("average") {
     checkAnswer(
-      testData2.groupBy()(Average('a)),
+      testData2.groupBy()(avg('a)),
       2.0)
   }
 
   test("null average") {
     checkAnswer(
-      testData3.groupBy()(Average('b)),
+      testData3.groupBy()(avg('b)),
       2.0)
 
     checkAnswer(
-      testData3.groupBy()(Average('b), CountDistinct('b :: Nil)),
+      testData3.groupBy()(avg('b), countDistinct('b)),
       (2.0, 1) :: Nil)
   }
 
@@ -130,17 +150,17 @@ class DslQuerySuite extends QueryTest {
 
   test("null count") {
     checkAnswer(
-      testData3.groupBy('a)('a, Count('b)),
+      testData3.groupBy('a)('a, count('b)),
       Seq((1,0), (2, 1))
     )
 
     checkAnswer(
-      testData3.groupBy('a)('a, Count('a + 'b)),
+      testData3.groupBy('a)('a, count('a + 'b)),
       Seq((1,0), (2, 1))
     )
 
     checkAnswer(
-      testData3.groupBy()(Count('a), Count('b), Count(1), CountDistinct('a :: Nil), CountDistinct('b :: Nil)),
+      testData3.groupBy()(count('a), count('b), count(1), countDistinct('a), countDistinct('b)),
       (2, 1, 2, 2, 1) :: Nil
     )
   }

From 1e2c26c83dd2e807cf0031ceca8b338a1a57cac6 Mon Sep 17 00:00:00 2001
From: Takuya UESHIN <ueshin@happy-camper.st>
Date: Wed, 2 Jul 2014 10:07:01 -0700
Subject: [PATCH 560/810] [SPARK-2328] [SQL] Add execution of `SHOW TABLES`
 before `TestHive.reset()`.

`PruningSuite` is executed first of Hive tests unfortunately, `TestHive.reset()` breaks the test environment.
To prevent this, we must run a query before calling reset the first time.

Author: Takuya UESHIN <ueshin@happy-camper.st>

Closes #1268 from ueshin/issues/SPARK-2328 and squashes the following commits:

043ceac [Takuya UESHIN] Add execution of `SHOW TABLES` before `TestHive.reset()`.
---
 .../org/apache/spark/sql/hive/execution/PruningSuite.scala     | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/PruningSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/PruningSuite.scala
index 4d7c84f44387..34d8a061ccc8 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/PruningSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/PruningSuite.scala
@@ -26,6 +26,9 @@ import scala.collection.JavaConversions._
  * A set of test cases that validate partition and column pruning.
  */
 class PruningSuite extends HiveComparisonTest {
+  // MINOR HACK: You must run a query before calling reset the first time.
+  TestHive.hql("SHOW TABLES")
+
   // Column/partition pruning is not implemented for `InMemoryColumnarTableScan` yet, need to reset
   // the environment to ensure all referenced tables in this suites are not cached in-memory.
   // Refer to https://issues.apache.org/jira/browse/SPARK-2283 for details.

From bc7041a42dfa84312492ea8cae6fdeaeac4f6d1c Mon Sep 17 00:00:00 2001
From: Takuya UESHIN <ueshin@happy-camper.st>
Date: Wed, 2 Jul 2014 10:10:36 -0700
Subject: [PATCH 561/810] [SPARK-2287] [SQL] Make ScalaReflection be able to
 handle Generic case classes.

Author: Takuya UESHIN <ueshin@happy-camper.st>

Closes #1226 from ueshin/issues/SPARK-2287 and squashes the following commits:

32ef7c3 [Takuya UESHIN] Add execution of `SHOW TABLES` before `TestHive.reset()`.
541dc8d [Takuya UESHIN] Merge branch 'master' into issues/SPARK-2287
fac5fae [Takuya UESHIN] Remove unnecessary method receiver.
d306e60 [Takuya UESHIN] Merge branch 'master' into issues/SPARK-2287
7de5706 [Takuya UESHIN] Make ScalaReflection be able to handle Generic case classes.
---
 .../spark/sql/catalyst/ScalaReflection.scala  |  7 +++++--
 .../sql/catalyst/ScalaReflectionSuite.scala   | 20 +++++++++++++++++++
 2 files changed, 25 insertions(+), 2 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala
index ada48eaf5dc0..5a55be1e5155 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala
@@ -47,10 +47,13 @@ object ScalaReflection {
       val TypeRef(_, _, Seq(optType)) = t
       Schema(schemaFor(optType).dataType, nullable = true)
     case t if t <:< typeOf[Product] =>
-      val params = t.member("<init>": TermName).asMethod.paramss
+      val formalTypeArgs = t.typeSymbol.asClass.typeParams
+      val TypeRef(_, _, actualTypeArgs) = t
+      val params = t.member(nme.CONSTRUCTOR).asMethod.paramss
       Schema(StructType(
         params.head.map { p =>
-          val Schema(dataType, nullable) = schemaFor(p.typeSignature)
+          val Schema(dataType, nullable) =
+            schemaFor(p.typeSignature.substituteTypes(formalTypeArgs, actualTypeArgs))
           StructField(p.name.toString, dataType, nullable)
         }), nullable = true)
     // Need to decide if we actually need a special type here.
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/ScalaReflectionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/ScalaReflectionSuite.scala
index 489d7e9c2437..c0438dbe52a4 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/ScalaReflectionSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/ScalaReflectionSuite.scala
@@ -60,6 +60,9 @@ case class ComplexData(
     mapField: Map[Int, String],
     structField: PrimitiveData)
 
+case class GenericData[A](
+    genericField: A)
+
 class ScalaReflectionSuite extends FunSuite {
   import ScalaReflection._
 
@@ -128,4 +131,21 @@ class ScalaReflectionSuite extends FunSuite {
           nullable = true))),
       nullable = true))
   }
+
+  test("generic data") {
+    val schema = schemaFor[GenericData[Int]]
+    assert(schema === Schema(
+      StructType(Seq(
+        StructField("genericField", IntegerType, nullable = false))),
+      nullable = true))
+  }
+
+  test("tuple data") {
+    val schema = schemaFor[(Int, String)]
+    assert(schema === Schema(
+      StructType(Seq(
+        StructField("_1", IntegerType, nullable = false),
+        StructField("_2", StringType, nullable = true))),
+      nullable = true))
+  }
 }

From 3bbeca648985b32bdf1eedef779cb2817eb6dfa4 Mon Sep 17 00:00:00 2001
From: yantangzhai <tyz0303@163.com>
Date: Thu, 3 Jul 2014 10:14:35 -0700
Subject: [PATCH 562/810] [SPARK-2324] SparkContext should not exit directly
 when spark.local.dir is a list of multiple paths and one of them has error

The spark.local.dir is configured as a list of multiple paths as follows /data1/sparkenv/local,/data2/sparkenv/local. If the disk data2 of the driver node has error, the application will exit since DiskBlockManager exits directly at createLocalDirs. If the disk data2 of the worker node has error, the executor will exit either.
DiskBlockManager should not exit directly at createLocalDirs if one of spark.local.dir has error. Since spark.local.dir has multiple paths, a problem should not affect the overall situation.
I think DiskBlockManager could ignore the bad directory at createLocalDirs.

Author: yantangzhai <tyz0303@163.com>

Closes #1274 from YanTangZhai/SPARK-2324 and squashes the following commits:

609bf48 [yantangzhai] [SPARK-2324] SparkContext should not exit directly when spark.local.dir is a list of multiple paths and one of them has error
df08673 [yantangzhai] [SPARK-2324] SparkContext should not exit directly when spark.local.dir is a list of multiple paths and one of them has error
---
 .../apache/spark/storage/DiskBlockManager.scala  | 16 +++++++++++-----
 1 file changed, 11 insertions(+), 5 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala b/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala
index 2ec46d416f37..673fc19c060a 100644
--- a/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala
+++ b/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala
@@ -44,6 +44,10 @@ private[spark] class DiskBlockManager(shuffleManager: ShuffleBlockManager, rootD
    * directory, create multiple subdirectories that we will hash files into, in order to avoid
    * having really large inodes at the top level. */
   private val localDirs: Array[File] = createLocalDirs()
+  if (localDirs.isEmpty) {
+    logError("Failed to create any local dir.")
+    System.exit(ExecutorExitCode.DISK_STORE_FAILED_TO_CREATE_DIR)
+  }
   private val subDirs = Array.fill(localDirs.length)(new Array[File](subDirsPerLocalDir))
   private var shuffleSender : ShuffleSender = null
 
@@ -116,7 +120,7 @@ private[spark] class DiskBlockManager(shuffleManager: ShuffleBlockManager, rootD
   private def createLocalDirs(): Array[File] = {
     logDebug(s"Creating local directories at root dirs '$rootDirs'")
     val dateFormat = new SimpleDateFormat("yyyyMMddHHmmss")
-    rootDirs.split(",").map { rootDir =>
+    rootDirs.split(",").flatMap { rootDir =>
       var foundLocalDir = false
       var localDir: File = null
       var localDirId: String = null
@@ -136,11 +140,13 @@ private[spark] class DiskBlockManager(shuffleManager: ShuffleBlockManager, rootD
         }
       }
       if (!foundLocalDir) {
-        logError(s"Failed $MAX_DIR_CREATION_ATTEMPTS attempts to create local dir in $rootDir")
-        System.exit(ExecutorExitCode.DISK_STORE_FAILED_TO_CREATE_DIR)
+        logError(s"Failed $MAX_DIR_CREATION_ATTEMPTS attempts to create local dir in $rootDir." +
+                  " Ignoring this directory.")
+        None
+      } else {
+        logInfo(s"Created local directory at $localDir")
+        Some(localDir)
       }
-      logInfo(s"Created local directory at $localDir")
-      localDir
     }
   }
 

From c480537739f9329ebfd580f09c69778e6c976366 Mon Sep 17 00:00:00 2001
From: Andrew Or <andrewor14@gmail.com>
Date: Thu, 3 Jul 2014 10:26:50 -0700
Subject: [PATCH 563/810] [SPARK] Fix NPE for ExternalAppendOnlyMap

It did not handle null keys very gracefully before.

Author: Andrew Or <andrewor14@gmail.com>

Closes #1288 from andrewor14/fix-external and squashes the following commits:

312b8d8 [Andrew Or] Abstract key hash code
ed5adf9 [Andrew Or] Fix NPE for ExternalAppendOnlyMap
---
 .../collection/ExternalAppendOnlyMap.scala    | 30 ++++++++++++++-----
 .../ExternalAppendOnlyMapSuite.scala          | 27 +++++++++++++++--
 2 files changed, 46 insertions(+), 11 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala b/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala
index 288badd3160f..292d0962f4fd 100644
--- a/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala
+++ b/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala
@@ -252,7 +252,7 @@ class ExternalAppendOnlyMap[K, V, C](
       if (it.hasNext) {
         var kc = it.next()
         kcPairs += kc
-        val minHash = kc._1.hashCode()
+        val minHash = getKeyHashCode(kc)
         while (it.hasNext && it.head._1.hashCode() == minHash) {
           kc = it.next()
           kcPairs += kc
@@ -294,8 +294,9 @@ class ExternalAppendOnlyMap[K, V, C](
       // Select a key from the StreamBuffer that holds the lowest key hash
       val minBuffer = mergeHeap.dequeue()
       val (minPairs, minHash) = (minBuffer.pairs, minBuffer.minKeyHash)
-      var (minKey, minCombiner) = minPairs.remove(0)
-      assert(minKey.hashCode() == minHash)
+      val minPair = minPairs.remove(0)
+      var (minKey, minCombiner) = minPair
+      assert(getKeyHashCode(minPair) == minHash)
 
       // For all other streams that may have this key (i.e. have the same minimum key hash),
       // merge in the corresponding value (if any) from that stream
@@ -327,15 +328,16 @@ class ExternalAppendOnlyMap[K, V, C](
      * StreamBuffers are ordered by the minimum key hash found across all of their own pairs.
      */
     private class StreamBuffer(
-        val iterator: BufferedIterator[(K, C)], val pairs: ArrayBuffer[(K, C)])
+        val iterator: BufferedIterator[(K, C)],
+        val pairs: ArrayBuffer[(K, C)])
       extends Comparable[StreamBuffer] {
 
       def isEmpty = pairs.length == 0
 
       // Invalid if there are no more pairs in this stream
-      def minKeyHash = {
+      def minKeyHash: Int = {
         assert(pairs.length > 0)
-        pairs.head._1.hashCode()
+        getKeyHashCode(pairs.head)
       }
 
       override def compareTo(other: StreamBuffer): Int = {
@@ -422,10 +424,22 @@ class ExternalAppendOnlyMap[K, V, C](
 }
 
 private[spark] object ExternalAppendOnlyMap {
+
+  /**
+   * Return the key hash code of the given (key, combiner) pair.
+   * If the key is null, return a special hash code.
+   */
+  private def getKeyHashCode[K, C](kc: (K, C)): Int = {
+    if (kc._1 == null) 0 else kc._1.hashCode()
+  }
+
+  /**
+   * A comparator for (key, combiner) pairs based on their key hash codes.
+   */
   private class KCComparator[K, C] extends Comparator[(K, C)] {
     def compare(kc1: (K, C), kc2: (K, C)): Int = {
-      val hash1 = kc1._1.hashCode()
-      val hash2 = kc2._1.hashCode()
+      val hash1 = getKeyHashCode(kc1)
+      val hash2 = getKeyHashCode(kc2)
       if (hash1 < hash2) -1 else if (hash1 == hash2) 0 else 1
     }
   }
diff --git a/core/src/test/scala/org/apache/spark/util/collection/ExternalAppendOnlyMapSuite.scala b/core/src/test/scala/org/apache/spark/util/collection/ExternalAppendOnlyMapSuite.scala
index deb780953579..428822949c08 100644
--- a/core/src/test/scala/org/apache/spark/util/collection/ExternalAppendOnlyMapSuite.scala
+++ b/core/src/test/scala/org/apache/spark/util/collection/ExternalAppendOnlyMapSuite.scala
@@ -334,8 +334,8 @@ class ExternalAppendOnlyMapSuite extends FunSuite with LocalSparkContext {
     conf.set("spark.shuffle.memoryFraction", "0.001")
     sc = new SparkContext("local-cluster[1,1,512]", "test", conf)
 
-    val map = new ExternalAppendOnlyMap[Int, Int, ArrayBuffer[Int]](createCombiner,
-      mergeValue, mergeCombiners)
+    val map = new ExternalAppendOnlyMap[Int, Int, ArrayBuffer[Int]](
+      createCombiner, mergeValue, mergeCombiners)
 
     (1 to 100000).foreach { i => map.insert(i, i) }
     map.insert(Int.MaxValue, Int.MaxValue)
@@ -346,11 +346,32 @@ class ExternalAppendOnlyMapSuite extends FunSuite with LocalSparkContext {
       it.next()
     }
   }
+
+  test("spilling with null keys and values") {
+    val conf = new SparkConf(true)
+    conf.set("spark.shuffle.memoryFraction", "0.001")
+    sc = new SparkContext("local-cluster[1,1,512]", "test", conf)
+
+    val map = new ExternalAppendOnlyMap[Int, Int, ArrayBuffer[Int]](
+      createCombiner, mergeValue, mergeCombiners)
+
+    (1 to 100000).foreach { i => map.insert(i, i) }
+    map.insert(null.asInstanceOf[Int], 1)
+    map.insert(1, null.asInstanceOf[Int])
+    map.insert(null.asInstanceOf[Int], null.asInstanceOf[Int])
+
+    val it = map.iterator
+    while (it.hasNext) {
+      // Should not throw NullPointerException
+      it.next()
+    }
+  }
+
 }
 
 /**
  * A dummy class that always returns the same hash code, to easily test hash collisions
  */
-case class FixedHashObject(val v: Int, val h: Int) extends Serializable {
+case class FixedHashObject(v: Int, h: Int) extends Serializable {
   override def hashCode(): Int = h
 }

From 2b36344f588d4e7357ce9921dc656e2389ba1dea Mon Sep 17 00:00:00 2001
From: Sean Owen <sowen@cloudera.com>
Date: Thu, 3 Jul 2014 11:54:51 -0700
Subject: [PATCH 564/810] SPARK-1675. Make clear whether
 computePrincipalComponents requires centered data

Just closing out this small JIRA, resolving with a comment change.

Author: Sean Owen <sowen@cloudera.com>

Closes #1171 from srowen/SPARK-1675 and squashes the following commits:

45ee9b7 [Sean Owen] Add simple note that data need not be centered for computePrincipalComponents
---
 .../org/apache/spark/mllib/linalg/distributed/RowMatrix.scala   | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
index 1a0073c9d487..695e03b736ba 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
@@ -347,6 +347,8 @@ class RowMatrix(
    * The principal components are stored a local matrix of size n-by-k.
    * Each column corresponds for one principal component,
    * and the columns are in descending order of component variance.
+   * The row data do not need to be "centered" first; it is not necessary for
+   * the mean of each column to be 0.
    *
    * @param k number of top principal components.
    * @return a matrix of size n-by-k, whose columns are principal components

From a9b52e5623f7fc77fca96b095f9eeaef76e35d54 Mon Sep 17 00:00:00 2001
From: Yijie Shen <henry.yijieshen@gmail.com>
Date: Thu, 3 Jul 2014 13:22:13 -0700
Subject: [PATCH 565/810] [SPARK-2342] Evaluation helper's output type doesn't
 conform to input ty...

The function cast doesn't conform to the intention of "Those expressions are supposed to be in the same data type, and also the return type." comment

Author: Yijie Shen <henry.yijieshen@gmail.com>

Closes #1283 from yijieshen/master and squashes the following commits:

c7aaa4b [Yijie Shen] [SPARK-2342] Evaluation helper's output type doesn't conform to input type
---
 .../org/apache/spark/sql/catalyst/expressions/Expression.scala  | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
index 0411ce3aefda..ba62dabe3dd6 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
@@ -111,7 +111,7 @@ abstract class Expression extends TreeNode[Expression] {
       } else {
         e1.dataType match {
           case n: NumericType =>
-            f.asInstanceOf[(Numeric[n.JvmType], n.JvmType, n.JvmType) => Int](
+            f.asInstanceOf[(Numeric[n.JvmType], n.JvmType, n.JvmType) => n.JvmType](
               n.numeric, evalE1.asInstanceOf[n.JvmType], evalE2.asInstanceOf[n.JvmType])
           case other => sys.error(s"Type $other does not support numeric operations")
         }

From 731f683b1bd8abbb83030b6bae14876658bbf098 Mon Sep 17 00:00:00 2001
From: Prashant Sharma <prashant.s@imaginea.com>
Date: Thu, 3 Jul 2014 15:06:58 -0700
Subject: [PATCH 566/810] [SPARK-2109] Setting SPARK_MEM for bin/pyspark does
 not work.

Trivial fix.

Author: Prashant Sharma <prashant.s@imaginea.com>

Closes #1050 from ScrapCodes/SPARK-2109/pyspark-script-bug and squashes the following commits:

77072b9 [Prashant Sharma] Changed echos to redirect to STDERR.
13f48a0 [Prashant Sharma] [SPARK-2109] Setting SPARK_MEM for bin/pyspark does not work.
---
 bin/compute-classpath.sh |  8 ++++----
 bin/pyspark              |  6 +++---
 bin/run-example          | 10 +++++-----
 bin/spark-class          | 13 ++++++-------
 4 files changed, 18 insertions(+), 19 deletions(-)

diff --git a/bin/compute-classpath.sh b/bin/compute-classpath.sh
index 2cf4e381c1c8..e81e8c060cb9 100755
--- a/bin/compute-classpath.sh
+++ b/bin/compute-classpath.sh
@@ -81,10 +81,10 @@ ASSEMBLY_JAR=$(ls "$assembly_folder"/spark-assembly*hadoop*.jar 2>/dev/null)
 # Verify that versions of java used to build the jars and run Spark are compatible
 jar_error_check=$("$JAR_CMD" -tf "$ASSEMBLY_JAR" nonexistent/class/path 2>&1)
 if [[ "$jar_error_check" =~ "invalid CEN header" ]]; then
-  echo "Loading Spark jar with '$JAR_CMD' failed. "
-  echo "This is likely because Spark was compiled with Java 7 and run "
-  echo "with Java 6. (see SPARK-1703). Please use Java 7 to run Spark "
-  echo "or build Spark with Java 6."
+  echo "Loading Spark jar with '$JAR_CMD' failed. " 1>&2
+  echo "This is likely because Spark was compiled with Java 7 and run " 1>&2
+  echo "with Java 6. (see SPARK-1703). Please use Java 7 to run Spark " 1>&2
+  echo "or build Spark with Java 6." 1>&2
   exit 1
 fi
 
diff --git a/bin/pyspark b/bin/pyspark
index 0b5ed40e2157..69b056fe28f2 100755
--- a/bin/pyspark
+++ b/bin/pyspark
@@ -26,7 +26,7 @@ export SPARK_HOME="$FWDIR"
 SCALA_VERSION=2.10
 
 if [[ "$@" = *--help ]] || [[ "$@" = *-h ]]; then
-  echo "Usage: ./bin/pyspark [options]"
+  echo "Usage: ./bin/pyspark [options]" 1>&2
   $FWDIR/bin/spark-submit --help 2>&1 | grep -v Usage 1>&2
   exit 0
 fi
@@ -36,8 +36,8 @@ if [ ! -f "$FWDIR/RELEASE" ]; then
   # Exit if the user hasn't compiled Spark
   ls "$FWDIR"/assembly/target/scala-$SCALA_VERSION/spark-assembly*hadoop*.jar >& /dev/null
   if [[ $? != 0 ]]; then
-    echo "Failed to find Spark assembly in $FWDIR/assembly/target" >&2
-    echo "You need to build Spark before running this program" >&2
+    echo "Failed to find Spark assembly in $FWDIR/assembly/target" 1>&2
+    echo "You need to build Spark before running this program" 1>&2
     exit 1
   fi
 fi
diff --git a/bin/run-example b/bin/run-example
index e7a5fe3914fb..942706d73312 100755
--- a/bin/run-example
+++ b/bin/run-example
@@ -27,9 +27,9 @@ if [ -n "$1" ]; then
   EXAMPLE_CLASS="$1"
   shift
 else
-  echo "Usage: ./bin/run-example <example-class> [example-args]"
-  echo "  - set MASTER=XX to use a specific master"
-  echo "  - can use abbreviated example class name (e.g. SparkPi, mllib.LinearRegression)"
+  echo "Usage: ./bin/run-example <example-class> [example-args]" 1>&2
+  echo "  - set MASTER=XX to use a specific master" 1>&2
+  echo "  - can use abbreviated example class name (e.g. SparkPi, mllib.LinearRegression)" 1>&2
   exit 1
 fi
 
@@ -40,8 +40,8 @@ elif [ -e "$EXAMPLES_DIR"/target/scala-$SCALA_VERSION/spark-examples-*hadoop*.ja
 fi
 
 if [[ -z $SPARK_EXAMPLES_JAR ]]; then
-  echo "Failed to find Spark examples assembly in $FWDIR/lib or $FWDIR/examples/target" >&2
-  echo "You need to build Spark before running this program" >&2
+  echo "Failed to find Spark examples assembly in $FWDIR/lib or $FWDIR/examples/target" 1>&2
+  echo "You need to build Spark before running this program" 1>&2
   exit 1
 fi
 
diff --git a/bin/spark-class b/bin/spark-class
index 60d9657c0ffc..04fa52c6756b 100755
--- a/bin/spark-class
+++ b/bin/spark-class
@@ -33,13 +33,13 @@ export SPARK_HOME="$FWDIR"
 . $FWDIR/bin/load-spark-env.sh
 
 if [ -z "$1" ]; then
-  echo "Usage: spark-class <class> [<args>]" >&2
+  echo "Usage: spark-class <class> [<args>]" 1>&2
   exit 1
 fi
 
 if [ -n "$SPARK_MEM" ]; then
-  echo "Warning: SPARK_MEM is deprecated, please use a more specific config option"
-  echo "(e.g., spark.executor.memory or SPARK_DRIVER_MEMORY)."
+  echo -e "Warning: SPARK_MEM is deprecated, please use a more specific config option" 1>&2
+  echo -e "(e.g., spark.executor.memory or SPARK_DRIVER_MEMORY)." 1>&2
 fi
 
 # Use SPARK_MEM or 512m as the default memory, to be overridden by specific options
@@ -147,10 +147,9 @@ fi
 export CLASSPATH
 
 if [ "$SPARK_PRINT_LAUNCH_COMMAND" == "1" ]; then
-  echo -n "Spark Command: "
-  echo "$RUNNER" -cp "$CLASSPATH" $JAVA_OPTS "$@"
-  echo "========================================"
-  echo
+  echo -n "Spark Command: " 1>&2
+  echo "$RUNNER" -cp "$CLASSPATH" $JAVA_OPTS "$@" 1>&2
+  echo -e "========================================\n" 1>&2
 fi
 
 exec "$RUNNER" -cp "$CLASSPATH" $JAVA_OPTS "$@"

From d4c30cd9918e18dde2a52909e36eaef6eb5996ab Mon Sep 17 00:00:00 2001
From: Zongheng Yang <zongheng.y@gmail.com>
Date: Thu, 3 Jul 2014 17:37:53 -0700
Subject: [PATCH 567/810] [HOTFIX] Synchronize on SQLContext.settings in tests.

Let's see if this fixes the ongoing series of test failures in a master build machine (https://amplab.cs.berkeley.edu/jenkins/job/Spark-Master-SBT-pre-YARN/SPARK_HADOOP_VERSION=1.0.4,label=centos/81/).

pwendell marmbrus

Author: Zongheng Yang <zongheng.y@gmail.com>

Closes #1277 from concretevitamin/test-fix and squashes the following commits:

28c88bd [Zongheng Yang] Synchronize on SQLContext.settings in tests.
---
 .../scala/org/apache/spark/sql/SQLConf.scala  |  2 +-
 .../org/apache/spark/sql/JoinSuite.scala      | 40 +++++------
 .../org/apache/spark/sql/SQLConfSuite.scala   | 64 +++++++++--------
 .../org/apache/spark/sql/SQLQuerySuite.scala  | 68 ++++++++++---------
 4 files changed, 91 insertions(+), 83 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
index 2fe7f9466399..3b5abab96986 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
@@ -50,7 +50,7 @@ trait SQLConf {
   /** ********************** SQLConf functionality methods ************ */
 
   @transient
-  private val settings = java.util.Collections.synchronizedMap(
+  protected[sql] val settings = java.util.Collections.synchronizedMap(
     new java.util.HashMap[String, String]())
 
   def set(props: Properties): Unit = {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala
index 3d7d5eedbe8e..054b14f8f7ff 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala
@@ -39,25 +39,27 @@ class JoinSuite extends QueryTest {
   test("plans broadcast hash join, given hints") {
 
     def mkTest(buildSide: BuildSide, leftTable: String, rightTable: String) = {
-      TestSQLContext.set("spark.sql.join.broadcastTables",
-        s"${if (buildSide == BuildRight) rightTable else leftTable}")
-      val rdd = sql(s"""SELECT * FROM $leftTable JOIN $rightTable ON key = a""")
-      // Using `sparkPlan` because for relevant patterns in HashJoin to be
-      // matched, other strategies need to be applied.
-      val physical = rdd.queryExecution.sparkPlan
-      val bhj = physical.collect { case j: BroadcastHashJoin if j.buildSide == buildSide => j }
-
-      assert(bhj.size === 1, "planner does not pick up hint to generate broadcast hash join")
-      checkAnswer(
-        rdd,
-        Seq(
-          (1, "1", 1, 1),
-          (1, "1", 1, 2),
-          (2, "2", 2, 1),
-          (2, "2", 2, 2),
-          (3, "3", 3, 1),
-          (3, "3", 3, 2)
-        ))
+      TestSQLContext.settings.synchronized {
+        TestSQLContext.set("spark.sql.join.broadcastTables",
+          s"${if (buildSide == BuildRight) rightTable else leftTable}")
+        val rdd = sql( s"""SELECT * FROM $leftTable JOIN $rightTable ON key = a""")
+        // Using `sparkPlan` because for relevant patterns in HashJoin to be
+        // matched, other strategies need to be applied.
+        val physical = rdd.queryExecution.sparkPlan
+        val bhj = physical.collect { case j: BroadcastHashJoin if j.buildSide == buildSide => j}
+
+        assert(bhj.size === 1, "planner does not pick up hint to generate broadcast hash join")
+        checkAnswer(
+          rdd,
+          Seq(
+            (1, "1", 1, 1),
+            (1, "1", 1, 2),
+            (2, "2", 2, 1),
+            (2, "2", 2, 2),
+            (3, "3", 3, 1),
+            (3, "3", 3, 2)
+          ))
+      }
     }
 
     mkTest(BuildRight, "testData", "testData2")
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLConfSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLConfSuite.scala
index 08293f7f0ca3..93792f698cfa 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLConfSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLConfSuite.scala
@@ -28,46 +28,50 @@ class SQLConfSuite extends QueryTest {
   val testVal = "test.val.0"
 
   test("programmatic ways of basic setting and getting") {
-    clear()
-    assert(getOption(testKey).isEmpty)
-    assert(getAll.toSet === Set())
+    TestSQLContext.settings.synchronized {
+      clear()
+      assert(getOption(testKey).isEmpty)
+      assert(getAll.toSet === Set())
 
-    set(testKey, testVal)
-    assert(get(testKey) == testVal)
-    assert(get(testKey, testVal + "_") == testVal)
-    assert(getOption(testKey) == Some(testVal))
-    assert(contains(testKey))
+      set(testKey, testVal)
+      assert(get(testKey) == testVal)
+      assert(get(testKey, testVal + "_") == testVal)
+      assert(getOption(testKey) == Some(testVal))
+      assert(contains(testKey))
 
-    // Tests SQLConf as accessed from a SQLContext is mutable after
-    // the latter is initialized, unlike SparkConf inside a SparkContext.
-    assert(TestSQLContext.get(testKey) == testVal)
-    assert(TestSQLContext.get(testKey, testVal + "_") == testVal)
-    assert(TestSQLContext.getOption(testKey) == Some(testVal))
-    assert(TestSQLContext.contains(testKey))
+      // Tests SQLConf as accessed from a SQLContext is mutable after
+      // the latter is initialized, unlike SparkConf inside a SparkContext.
+      assert(TestSQLContext.get(testKey) == testVal)
+      assert(TestSQLContext.get(testKey, testVal + "_") == testVal)
+      assert(TestSQLContext.getOption(testKey) == Some(testVal))
+      assert(TestSQLContext.contains(testKey))
 
-    clear()
+      clear()
+    }
   }
 
   test("parse SQL set commands") {
-    clear()
-    sql(s"set $testKey=$testVal")
-    assert(get(testKey, testVal + "_") == testVal)
-    assert(TestSQLContext.get(testKey, testVal + "_") == testVal)
+    TestSQLContext.settings.synchronized {
+      clear()
+      sql(s"set $testKey=$testVal")
+      assert(get(testKey, testVal + "_") == testVal)
+      assert(TestSQLContext.get(testKey, testVal + "_") == testVal)
 
-    sql("set mapred.reduce.tasks=20")
-    assert(get("mapred.reduce.tasks", "0") == "20")
-    sql("set mapred.reduce.tasks = 40")
-    assert(get("mapred.reduce.tasks", "0") == "40")
+      sql("set mapred.reduce.tasks=20")
+      assert(get("mapred.reduce.tasks", "0") == "20")
+      sql("set mapred.reduce.tasks = 40")
+      assert(get("mapred.reduce.tasks", "0") == "40")
 
-    val key = "spark.sql.key"
-    val vs = "val0,val_1,val2.3,my_table"
-    sql(s"set $key=$vs")
-    assert(get(key, "0") == vs)
+      val key = "spark.sql.key"
+      val vs = "val0,val_1,val2.3,my_table"
+      sql(s"set $key=$vs")
+      assert(get(key, "0") == vs)
 
-    sql(s"set $key=")
-    assert(get(key, "0") == "")
+      sql(s"set $key=")
+      assert(get(key, "0") == "")
 
-    clear()
+      clear()
+    }
   }
 
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index bf7fafe95230..2c1cb1867010 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -372,38 +372,40 @@ class SQLQuerySuite extends QueryTest {
   }
 
   test("SET commands semantics using sql()") {
-    clear()
-    val testKey = "test.key.0"
-    val testVal = "test.val.0"
-    val nonexistentKey = "nonexistent"
-
-    // "set" itself returns all config variables currently specified in SQLConf.
-    assert(sql("SET").collect().size == 0)
-
-    // "set key=val"
-    sql(s"SET $testKey=$testVal")
-    checkAnswer(
-      sql("SET"),
-      Seq(Seq(testKey, testVal))
-    )
-
-    sql(s"SET ${testKey + testKey}=${testVal + testVal}")
-    checkAnswer(
-      sql("set"),
-      Seq(
-        Seq(testKey, testVal),
-        Seq(testKey + testKey, testVal + testVal))
-    )
-
-    // "set key"
-    checkAnswer(
-      sql(s"SET $testKey"),
-      Seq(Seq(testKey, testVal))
-    )
-    checkAnswer(
-      sql(s"SET $nonexistentKey"),
-      Seq(Seq(nonexistentKey, "<undefined>"))
-    )
-    clear()
+    TestSQLContext.settings.synchronized {
+      clear()
+      val testKey = "test.key.0"
+      val testVal = "test.val.0"
+      val nonexistentKey = "nonexistent"
+
+      // "set" itself returns all config variables currently specified in SQLConf.
+      assert(sql("SET").collect().size == 0)
+
+      // "set key=val"
+      sql(s"SET $testKey=$testVal")
+      checkAnswer(
+        sql("SET"),
+        Seq(Seq(testKey, testVal))
+      )
+
+      sql(s"SET ${testKey + testKey}=${testVal + testVal}")
+      checkAnswer(
+        sql("set"),
+        Seq(
+          Seq(testKey, testVal),
+          Seq(testKey + testKey, testVal + testVal))
+      )
+
+      // "set key"
+      checkAnswer(
+        sql(s"SET $testKey"),
+        Seq(Seq(testKey, testVal))
+      )
+      checkAnswer(
+        sql(s"SET $nonexistentKey"),
+        Seq(Seq(nonexistentKey, "<undefined>"))
+      )
+      clear()
+    }
   }
 }

From fdc4c112e7c2ac585d108d03209a642aa8bab7c8 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Cl=C3=A9ment=20MATHIEU?= <clement@unportant.info>
Date: Thu, 3 Jul 2014 18:31:18 -0700
Subject: [PATCH 568/810] Streaming programming guide typos
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Fix a bad Java code sample and a broken link in the streaming programming guide.

Author: Clément MATHIEU <clement@unportant.info>

Closes #1286 from cykl/streaming-programming-guide-typos and squashes the following commits:

b0908cb [Clément MATHIEU] Fix broken URL
9d3c535 [Clément MATHIEU] Spark streaming requires at least two working threads (scala version was OK)
---
 docs/streaming-programming-guide.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/streaming-programming-guide.md b/docs/streaming-programming-guide.md
index ce8e58d64a7e..90a0eef60c20 100644
--- a/docs/streaming-programming-guide.md
+++ b/docs/streaming-programming-guide.md
@@ -148,7 +148,7 @@ import org.apache.spark.streaming.*;
 import org.apache.spark.streaming.api.java.*;
 import scala.Tuple2;
 // Create a StreamingContext with a local master
-JavaStreamingContext jssc = new JavaStreamingContext("local", "JavaNetworkWordCount", new Duration(1000))
+JavaStreamingContext jssc = new JavaStreamingContext("local[2]", "JavaNetworkWordCount", new Duration(1000))
 {% endhighlight %}
 
 Using this context, we then create a new DStream
@@ -216,7 +216,7 @@ jssc.awaitTermination();   // Wait for the computation to terminate
 {% endhighlight %}
 
 The complete code can be found in the Spark Streaming example
-[JavaNetworkWordCount]({{site.SPARK_GITHUB_URL}}/blob/master/examples/src/main/java/index.html?org/apache/spark/examples/streaming/JavaNetworkWordCount.java).
+[JavaNetworkWordCount]({{site.SPARK_GITHUB_URL}}/blob/master/examples/src/main/java/org/apache/spark/examples/streaming/JavaNetworkWordCount.java).
 <br>
 
 </div>

From 5fa0a05763ab1d527efe20e3b10539ac5ffc36de Mon Sep 17 00:00:00 2001
From: Raymond Liu <raymond.liu@intel.com>
Date: Thu, 3 Jul 2014 19:24:22 -0700
Subject: [PATCH 569/810] [SPARK-1097] Workaround Hadoop conf
 ConcurrentModification issue

Workaround Hadoop conf ConcurrentModification issue

Author: Raymond Liu <raymond.liu@intel.com>

Closes #1273 from colorant/hadoopRDD and squashes the following commits:

994e98b [Raymond Liu] Address comments
e2cda3d [Raymond Liu] Workaround Hadoop conf ConcurrentModification issue
---
 core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala b/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala
index 98dcbf4e2dbf..041028514399 100644
--- a/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala
@@ -141,8 +141,8 @@ class HadoopRDD[K, V](
       // local process. The local cache is accessed through HadoopRDD.putCachedMetadata().
       // The caching helps minimize GC, since a JobConf can contain ~10KB of temporary objects.
       // synchronize to prevent ConcurrentModificationException (Spark-1097, Hadoop-10456)
-      broadcastedConf.synchronized {
-        val newJobConf = new JobConf(broadcastedConf.value.value)
+      conf.synchronized {
+        val newJobConf = new JobConf(conf)
         initLocalJobConfFuncOpt.map(f => f(newJobConf))
         HadoopRDD.putCachedMetadata(jobConfCacheKey, newJobConf)
         newJobConf

From 586feb5c9528042420f678f78bacb6c254a5eaf8 Mon Sep 17 00:00:00 2001
From: Aaron Davidson <aaron@databricks.com>
Date: Thu, 3 Jul 2014 22:31:41 -0700
Subject: [PATCH 570/810] [SPARK-2350] Don't NPE while launching drivers

Prior to this change, we could throw a NPE if we launch a driver while another one is waiting, because removing from an iterator while iterating over it is not safe.

Author: Aaron Davidson <aaron@databricks.com>

Closes #1289 from aarondav/master-fail and squashes the following commits:

1cf1cf4 [Aaron Davidson] SPARK-2350: Don't NPE while launching drivers
---
 core/src/main/scala/org/apache/spark/deploy/master/Master.scala | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
index 11545b820370..a304102a4908 100644
--- a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
@@ -481,7 +481,7 @@ private[spark] class Master(
     // First schedule drivers, they take strict precedence over applications
     val shuffledWorkers = Random.shuffle(workers) // Randomization helps balance drivers
     for (worker <- shuffledWorkers if worker.state == WorkerState.ALIVE) {
-      for (driver <- waitingDrivers) {
+      for (driver <- List(waitingDrivers: _*)) { // iterate over a copy of waitingDrivers
         if (worker.memoryFree >= driver.desc.mem && worker.coresFree >= driver.desc.cores) {
           launchDriver(worker, driver)
           waitingDrivers -= driver

From 3894a49be9b532cc026d908a0f49bca850504498 Mon Sep 17 00:00:00 2001
From: Andrew Or <andrewor14@gmail.com>
Date: Thu, 3 Jul 2014 22:48:23 -0700
Subject: [PATCH 571/810] [SPARK-2307][Reprise] Correctly report RDD blocks on
 SparkUI

**Problem.** The existing code in `ExecutorPage.scala` requires a linear scan through all the blocks to filter out the uncached ones. Every refresh could be expensive if there are many blocks and many executors.

**Solution.** The proper semantics should be the following: `StorageStatusListener` should contain only block statuses that are cached. This means as soon as a block is unpersisted by any mean, its status should be removed. This is reflected in the changes made in `StorageStatusListener.scala`.

Further, the `StorageTab` must stop relying on the `StorageStatusListener` changing a dropped block's status to `StorageLevel.NONE` (which no longer happens). This is reflected in the changes made in `StorageTab.scala` and `StorageUtils.scala`.

----------

If you have been following this chain of PRs like pwendell, you will quickly notice that this reverts the changes in #1249, which reverts the changes in #1080. In other words, we are adding back the changes from #1080, and fixing SPARK-2307 on top of those changes. Please ask questions if you are confused.

Author: Andrew Or <andrewor14@gmail.com>

Closes #1255 from andrewor14/storage-ui-fix-reprise and squashes the following commits:

45416fa [Andrew Or] Merge branch 'master' of github.com:apache/spark into storage-ui-fix-reprise
a82ea25 [Andrew Or] Add tests for StorageStatusListener
8773b01 [Andrew Or] Update comment / minor changes
3afde3f [Andrew Or] Correctly report the number of blocks on SparkUI
---
 .../spark/storage/StorageStatusListener.scala |  17 +-
 .../apache/spark/storage/StorageUtils.scala   |  15 +-
 .../apache/spark/ui/exec/ExecutorsPage.scala  |   4 +-
 .../apache/spark/ui/exec/ExecutorsTab.scala   |   4 +-
 .../apache/spark/ui/storage/StorageTab.scala  |  15 +-
 .../storage/StorageStatusListenerSuite.scala  | 152 ++++++++++++++++++
 6 files changed, 184 insertions(+), 23 deletions(-)
 create mode 100644 core/src/test/scala/org/apache/spark/storage/StorageStatusListenerSuite.scala

diff --git a/core/src/main/scala/org/apache/spark/storage/StorageStatusListener.scala b/core/src/main/scala/org/apache/spark/storage/StorageStatusListener.scala
index a6e6627d54e0..41c960c867e2 100644
--- a/core/src/main/scala/org/apache/spark/storage/StorageStatusListener.scala
+++ b/core/src/main/scala/org/apache/spark/storage/StorageStatusListener.scala
@@ -28,26 +28,31 @@ import org.apache.spark.scheduler._
  */
 @DeveloperApi
 class StorageStatusListener extends SparkListener {
-  private val executorIdToStorageStatus = mutable.Map[String, StorageStatus]()
+  // This maintains only blocks that are cached (i.e. storage level is not StorageLevel.NONE)
+  private[storage] val executorIdToStorageStatus = mutable.Map[String, StorageStatus]()
 
   def storageStatusList = executorIdToStorageStatus.values.toSeq
 
   /** Update storage status list to reflect updated block statuses */
-  def updateStorageStatus(execId: String, updatedBlocks: Seq[(BlockId, BlockStatus)]) {
-    val filteredStatus = storageStatusList.find(_.blockManagerId.executorId == execId)
+  private def updateStorageStatus(execId: String, updatedBlocks: Seq[(BlockId, BlockStatus)]) {
+    val filteredStatus = executorIdToStorageStatus.get(execId)
     filteredStatus.foreach { storageStatus =>
       updatedBlocks.foreach { case (blockId, updatedStatus) =>
-        storageStatus.blocks(blockId) = updatedStatus
+        if (updatedStatus.storageLevel == StorageLevel.NONE) {
+          storageStatus.blocks.remove(blockId)
+        } else {
+          storageStatus.blocks(blockId) = updatedStatus
+        }
       }
     }
   }
 
   /** Update storage status list to reflect the removal of an RDD from the cache */
-  def updateStorageStatus(unpersistedRDDId: Int) {
+  private def updateStorageStatus(unpersistedRDDId: Int) {
     storageStatusList.foreach { storageStatus =>
       val unpersistedBlocksIds = storageStatus.rddBlocks.keys.filter(_.rddId == unpersistedRDDId)
       unpersistedBlocksIds.foreach { blockId =>
-        storageStatus.blocks(blockId) = BlockStatus(StorageLevel.NONE, 0L, 0L, 0L)
+        storageStatus.blocks.remove(blockId)
       }
     }
   }
diff --git a/core/src/main/scala/org/apache/spark/storage/StorageUtils.scala b/core/src/main/scala/org/apache/spark/storage/StorageUtils.scala
index f3bde1df45c7..177281f66336 100644
--- a/core/src/main/scala/org/apache/spark/storage/StorageUtils.scala
+++ b/core/src/main/scala/org/apache/spark/storage/StorageUtils.scala
@@ -75,17 +75,26 @@ private[spark] object StorageUtils {
   /** Returns storage information of all RDDs in the given list. */
   def rddInfoFromStorageStatus(
       storageStatuses: Seq[StorageStatus],
-      rddInfos: Seq[RDDInfo]): Array[RDDInfo] = {
+      rddInfos: Seq[RDDInfo],
+      updatedBlocks: Seq[(BlockId, BlockStatus)] = Seq.empty): Array[RDDInfo] = {
+
+    // Mapping from a block ID -> its status
+    val blockMap = mutable.Map(storageStatuses.flatMap(_.rddBlocks): _*)
+
+    // Record updated blocks, if any
+    updatedBlocks
+      .collect { case (id: RDDBlockId, status) => (id, status) }
+      .foreach { case (id, status) => blockMap(id) = status }
 
     // Mapping from RDD ID -> an array of associated BlockStatuses
-    val blockStatusMap = storageStatuses.flatMap(_.rddBlocks).toMap
+    val rddBlockMap = blockMap
       .groupBy { case (k, _) => k.rddId }
       .mapValues(_.values.toArray)
 
     // Mapping from RDD ID -> the associated RDDInfo (with potentially outdated storage information)
     val rddInfoMap = rddInfos.map { info => (info.id, info) }.toMap
 
-    val rddStorageInfos = blockStatusMap.flatMap { case (rddId, blocks) =>
+    val rddStorageInfos = rddBlockMap.flatMap { case (rddId, blocks) =>
       // Add up memory, disk and Tachyon sizes
       val persistedBlocks =
         blocks.filter { status => status.memSize + status.diskSize + status.tachyonSize > 0 }
diff --git a/core/src/main/scala/org/apache/spark/ui/exec/ExecutorsPage.scala b/core/src/main/scala/org/apache/spark/ui/exec/ExecutorsPage.scala
index 9625337ae21a..95b4a4e91d33 100644
--- a/core/src/main/scala/org/apache/spark/ui/exec/ExecutorsPage.scala
+++ b/core/src/main/scala/org/apache/spark/ui/exec/ExecutorsPage.scala
@@ -110,9 +110,7 @@ private[ui] class ExecutorsPage(parent: ExecutorsTab) extends WebUIPage("") {
     val status = listener.storageStatusList(statusId)
     val execId = status.blockManagerId.executorId
     val hostPort = status.blockManagerId.hostPort
-    val rddBlocks = status.blocks.count { case (_, blockStatus) =>
-      blockStatus.storageLevel != StorageLevel.NONE
-    }
+    val rddBlocks = status.blocks.size
     val memUsed = status.memUsed
     val maxMem = status.maxMem
     val diskUsed = status.diskUsed
diff --git a/core/src/main/scala/org/apache/spark/ui/exec/ExecutorsTab.scala b/core/src/main/scala/org/apache/spark/ui/exec/ExecutorsTab.scala
index 58eeb86bf9a3..5c2d1d1fe75d 100644
--- a/core/src/main/scala/org/apache/spark/ui/exec/ExecutorsTab.scala
+++ b/core/src/main/scala/org/apache/spark/ui/exec/ExecutorsTab.scala
@@ -39,9 +39,7 @@ private[ui] class ExecutorsTab(parent: SparkUI) extends WebUITab(parent, "execut
  * A SparkListener that prepares information to be displayed on the ExecutorsTab
  */
 @DeveloperApi
-class ExecutorsListener(storageStatusListener: StorageStatusListener)
-  extends SparkListener {
-
+class ExecutorsListener(storageStatusListener: StorageStatusListener) extends SparkListener {
   val executorToTasksActive = HashMap[String, Int]()
   val executorToTasksComplete = HashMap[String, Int]()
   val executorToTasksFailed = HashMap[String, Int]()
diff --git a/core/src/main/scala/org/apache/spark/ui/storage/StorageTab.scala b/core/src/main/scala/org/apache/spark/ui/storage/StorageTab.scala
index c4bb7aab5039..0cc0cf311717 100644
--- a/core/src/main/scala/org/apache/spark/ui/storage/StorageTab.scala
+++ b/core/src/main/scala/org/apache/spark/ui/storage/StorageTab.scala
@@ -22,7 +22,7 @@ import scala.collection.mutable
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.ui._
 import org.apache.spark.scheduler._
-import org.apache.spark.storage.{RDDInfo, StorageStatusListener, StorageUtils}
+import org.apache.spark.storage._
 
 /** Web UI showing storage status of all RDD's in the given SparkContext. */
 private[ui] class StorageTab(parent: SparkUI) extends WebUITab(parent, "storage") {
@@ -40,9 +40,7 @@ private[ui] class StorageTab(parent: SparkUI) extends WebUITab(parent, "storage"
  * A SparkListener that prepares information to be displayed on the BlockManagerUI.
  */
 @DeveloperApi
-class StorageListener(storageStatusListener: StorageStatusListener)
-  extends SparkListener {
-
+class StorageListener(storageStatusListener: StorageStatusListener) extends SparkListener {
   private val _rddInfoMap = mutable.Map[Int, RDDInfo]()
 
   def storageStatusList = storageStatusListener.storageStatusList
@@ -51,9 +49,10 @@ class StorageListener(storageStatusListener: StorageStatusListener)
   def rddInfoList = _rddInfoMap.values.filter(_.numCachedPartitions > 0).toSeq
 
   /** Update each RDD's info to reflect any updates to the RDD's storage status */
-  private def updateRDDInfo() {
+  private def updateRDDInfo(updatedBlocks: Seq[(BlockId, BlockStatus)] = Seq.empty) {
     val rddInfos = _rddInfoMap.values.toSeq
-    val updatedRddInfos = StorageUtils.rddInfoFromStorageStatus(storageStatusList, rddInfos)
+    val updatedRddInfos =
+      StorageUtils.rddInfoFromStorageStatus(storageStatusList, rddInfos, updatedBlocks)
     updatedRddInfos.foreach { info => _rddInfoMap(info.id) = info }
   }
 
@@ -64,7 +63,7 @@ class StorageListener(storageStatusListener: StorageStatusListener)
   override def onTaskEnd(taskEnd: SparkListenerTaskEnd) = synchronized {
     val metrics = taskEnd.taskMetrics
     if (metrics != null && metrics.updatedBlocks.isDefined) {
-      updateRDDInfo()
+      updateRDDInfo(metrics.updatedBlocks.get)
     }
   }
 
@@ -79,6 +78,6 @@ class StorageListener(storageStatusListener: StorageStatusListener)
   }
 
   override def onUnpersistRDD(unpersistRDD: SparkListenerUnpersistRDD) = synchronized {
-    updateRDDInfo()
+    _rddInfoMap.remove(unpersistRDD.rddId)
   }
 }
diff --git a/core/src/test/scala/org/apache/spark/storage/StorageStatusListenerSuite.scala b/core/src/test/scala/org/apache/spark/storage/StorageStatusListenerSuite.scala
new file mode 100644
index 000000000000..2179c6dd3302
--- /dev/null
+++ b/core/src/test/scala/org/apache/spark/storage/StorageStatusListenerSuite.scala
@@ -0,0 +1,152 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.storage
+
+import org.scalatest.FunSuite
+import org.apache.spark.Success
+import org.apache.spark.executor.TaskMetrics
+import org.apache.spark.scheduler._
+
+/**
+ * Test the behavior of StorageStatusListener in response to all relevant events.
+ */
+class StorageStatusListenerSuite extends FunSuite {
+  private val bm1 = BlockManagerId("big", "dog", 1, 1)
+  private val bm2 = BlockManagerId("fat", "duck", 2, 2)
+  private val taskInfo1 = new TaskInfo(0, 0, 0, 0, "big", "dog", TaskLocality.ANY, false)
+  private val taskInfo2 = new TaskInfo(0, 0, 0, 0, "fat", "duck", TaskLocality.ANY, false)
+
+  test("block manager added/removed") {
+    val listener = new StorageStatusListener
+
+    // Block manager add
+    assert(listener.executorIdToStorageStatus.size === 0)
+    listener.onBlockManagerAdded(SparkListenerBlockManagerAdded(bm1, 1000L))
+    assert(listener.executorIdToStorageStatus.size === 1)
+    assert(listener.executorIdToStorageStatus.get("big").isDefined)
+    assert(listener.executorIdToStorageStatus("big").blockManagerId === bm1)
+    assert(listener.executorIdToStorageStatus("big").maxMem === 1000L)
+    assert(listener.executorIdToStorageStatus("big").blocks.isEmpty)
+    listener.onBlockManagerAdded(SparkListenerBlockManagerAdded(bm2, 2000L))
+    assert(listener.executorIdToStorageStatus.size === 2)
+    assert(listener.executorIdToStorageStatus.get("fat").isDefined)
+    assert(listener.executorIdToStorageStatus("fat").blockManagerId === bm2)
+    assert(listener.executorIdToStorageStatus("fat").maxMem === 2000L)
+    assert(listener.executorIdToStorageStatus("fat").blocks.isEmpty)
+
+    // Block manager remove
+    listener.onBlockManagerRemoved(SparkListenerBlockManagerRemoved(bm1))
+    assert(listener.executorIdToStorageStatus.size === 1)
+    assert(!listener.executorIdToStorageStatus.get("big").isDefined)
+    assert(listener.executorIdToStorageStatus.get("fat").isDefined)
+    listener.onBlockManagerRemoved(SparkListenerBlockManagerRemoved(bm2))
+    assert(listener.executorIdToStorageStatus.size === 0)
+    assert(!listener.executorIdToStorageStatus.get("big").isDefined)
+    assert(!listener.executorIdToStorageStatus.get("fat").isDefined)
+  }
+
+  test("task end without updated blocks") {
+    val listener = new StorageStatusListener
+    listener.onBlockManagerAdded(SparkListenerBlockManagerAdded(bm1, 1000L))
+    listener.onBlockManagerAdded(SparkListenerBlockManagerAdded(bm2, 2000L))
+    val taskMetrics = new TaskMetrics
+
+    // Task end with no updated blocks
+    assert(listener.executorIdToStorageStatus("big").blocks.isEmpty)
+    assert(listener.executorIdToStorageStatus("fat").blocks.isEmpty)
+    listener.onTaskEnd(SparkListenerTaskEnd(1, "obliteration", Success, taskInfo1, taskMetrics))
+    assert(listener.executorIdToStorageStatus("big").blocks.isEmpty)
+    assert(listener.executorIdToStorageStatus("fat").blocks.isEmpty)
+    listener.onTaskEnd(SparkListenerTaskEnd(1, "obliteration", Success, taskInfo2, taskMetrics))
+    assert(listener.executorIdToStorageStatus("big").blocks.isEmpty)
+    assert(listener.executorIdToStorageStatus("fat").blocks.isEmpty)
+  }
+
+  test("task end with updated blocks") {
+    val listener = new StorageStatusListener
+    listener.onBlockManagerAdded(SparkListenerBlockManagerAdded(bm1, 1000L))
+    listener.onBlockManagerAdded(SparkListenerBlockManagerAdded(bm2, 2000L))
+    val taskMetrics1 = new TaskMetrics
+    val taskMetrics2 = new TaskMetrics
+    val block1 = (RDDBlockId(1, 1), BlockStatus(StorageLevel.DISK_ONLY, 0L, 100L, 0L))
+    val block2 = (RDDBlockId(1, 2), BlockStatus(StorageLevel.DISK_ONLY, 0L, 200L, 0L))
+    val block3 = (RDDBlockId(4, 0), BlockStatus(StorageLevel.DISK_ONLY, 0L, 300L, 0L))
+    taskMetrics1.updatedBlocks = Some(Seq(block1, block2))
+    taskMetrics2.updatedBlocks = Some(Seq(block3))
+
+    // Task end with new blocks
+    assert(listener.executorIdToStorageStatus("big").blocks.isEmpty)
+    assert(listener.executorIdToStorageStatus("fat").blocks.isEmpty)
+    listener.onTaskEnd(SparkListenerTaskEnd(1, "obliteration", Success, taskInfo1, taskMetrics1))
+    assert(listener.executorIdToStorageStatus("big").blocks.size === 2)
+    assert(listener.executorIdToStorageStatus("fat").blocks.size === 0)
+    assert(listener.executorIdToStorageStatus("big").blocks.contains(RDDBlockId(1, 1)))
+    assert(listener.executorIdToStorageStatus("big").blocks.contains(RDDBlockId(1, 2)))
+    assert(listener.executorIdToStorageStatus("fat").blocks.isEmpty)
+    listener.onTaskEnd(SparkListenerTaskEnd(1, "obliteration", Success, taskInfo2, taskMetrics2))
+    assert(listener.executorIdToStorageStatus("big").blocks.size === 2)
+    assert(listener.executorIdToStorageStatus("fat").blocks.size === 1)
+    assert(listener.executorIdToStorageStatus("big").blocks.contains(RDDBlockId(1, 1)))
+    assert(listener.executorIdToStorageStatus("big").blocks.contains(RDDBlockId(1, 2)))
+    assert(listener.executorIdToStorageStatus("fat").blocks.contains(RDDBlockId(4, 0)))
+
+    // Task end with dropped blocks
+    val droppedBlock1 = (RDDBlockId(1, 1), BlockStatus(StorageLevel.NONE, 0L, 0L, 0L))
+    val droppedBlock2 = (RDDBlockId(1, 2), BlockStatus(StorageLevel.NONE, 0L, 0L, 0L))
+    val droppedBlock3 = (RDDBlockId(4, 0), BlockStatus(StorageLevel.NONE, 0L, 0L, 0L))
+    taskMetrics1.updatedBlocks = Some(Seq(droppedBlock1, droppedBlock3))
+    taskMetrics2.updatedBlocks = Some(Seq(droppedBlock2, droppedBlock3))
+    listener.onTaskEnd(SparkListenerTaskEnd(1, "obliteration", Success, taskInfo1, taskMetrics1))
+    assert(listener.executorIdToStorageStatus("big").blocks.size === 1)
+    assert(listener.executorIdToStorageStatus("fat").blocks.size === 1)
+    assert(!listener.executorIdToStorageStatus("big").blocks.contains(RDDBlockId(1, 1)))
+    assert(listener.executorIdToStorageStatus("big").blocks.contains(RDDBlockId(1, 2)))
+    assert(listener.executorIdToStorageStatus("fat").blocks.contains(RDDBlockId(4, 0)))
+    listener.onTaskEnd(SparkListenerTaskEnd(1, "obliteration", Success, taskInfo2, taskMetrics2))
+    assert(listener.executorIdToStorageStatus("big").blocks.size === 1)
+    assert(listener.executorIdToStorageStatus("fat").blocks.size === 0)
+    assert(!listener.executorIdToStorageStatus("big").blocks.contains(RDDBlockId(1, 1)))
+    assert(listener.executorIdToStorageStatus("big").blocks.contains(RDDBlockId(1, 2)))
+    assert(listener.executorIdToStorageStatus("fat").blocks.isEmpty)
+  }
+
+  test("unpersist RDD") {
+    val listener = new StorageStatusListener
+    listener.onBlockManagerAdded(SparkListenerBlockManagerAdded(bm1, 1000L))
+    val taskMetrics1 = new TaskMetrics
+    val taskMetrics2 = new TaskMetrics
+    val block1 = (RDDBlockId(1, 1), BlockStatus(StorageLevel.DISK_ONLY, 0L, 100L, 0L))
+    val block2 = (RDDBlockId(1, 2), BlockStatus(StorageLevel.DISK_ONLY, 0L, 200L, 0L))
+    val block3 = (RDDBlockId(4, 0), BlockStatus(StorageLevel.DISK_ONLY, 0L, 300L, 0L))
+    taskMetrics1.updatedBlocks = Some(Seq(block1, block2))
+    taskMetrics2.updatedBlocks = Some(Seq(block3))
+    listener.onTaskEnd(SparkListenerTaskEnd(1, "obliteration", Success, taskInfo1, taskMetrics1))
+    listener.onTaskEnd(SparkListenerTaskEnd(1, "obliteration", Success, taskInfo1, taskMetrics2))
+    assert(listener.executorIdToStorageStatus("big").blocks.size === 3)
+
+    // Unpersist RDD
+    listener.onUnpersistRDD(SparkListenerUnpersistRDD(9090))
+    assert(listener.executorIdToStorageStatus("big").blocks.size === 3)
+    listener.onUnpersistRDD(SparkListenerUnpersistRDD(4))
+    assert(listener.executorIdToStorageStatus("big").blocks.size === 2)
+    assert(listener.executorIdToStorageStatus("big").blocks.contains(RDDBlockId(1, 1)))
+    assert(listener.executorIdToStorageStatus("big").blocks.contains(RDDBlockId(1, 2)))
+    listener.onUnpersistRDD(SparkListenerUnpersistRDD(1))
+    assert(listener.executorIdToStorageStatus("big").blocks.isEmpty)
+  }
+}

From 97a0bfe1c0261384f09d53f9350de52fb6446d59 Mon Sep 17 00:00:00 2001
From: Aaron Davidson <aaron@databricks.com>
Date: Thu, 3 Jul 2014 23:02:36 -0700
Subject: [PATCH 572/810] SPARK-2282: Reuse PySpark Accumulator sockets to
 avoid crashing Spark

JIRA: https://issues.apache.org/jira/browse/SPARK-2282

This issue is caused by a buildup of sockets in the TIME_WAIT stage of TCP, which is a stage that lasts for some period of time after the communication closes.

This solution simply allows us to reuse sockets that are in TIME_WAIT, to avoid issues with the buildup of the rapid creation of these sockets.

Author: Aaron Davidson <aaron@databricks.com>

Closes #1220 from aarondav/SPARK-2282 and squashes the following commits:

2e5cab3 [Aaron Davidson] SPARK-2282: Reuse PySpark Accumulator sockets to avoid crashing Spark
---
 core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
index f6570d335757..462e09466bfa 100644
--- a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
+++ b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
@@ -599,6 +599,8 @@ private class PythonAccumulatorParam(@transient serverHost: String, serverPort:
     } else {
       // This happens on the master, where we pass the updates to Python through a socket
       val socket = new Socket(serverHost, serverPort)
+      // SPARK-2282: Immediately reuse closed sockets because we create one per task.
+      socket.setReuseAddress(true)
       val in = socket.getInputStream
       val out = new DataOutputStream(new BufferedOutputStream(socket.getOutputStream, bufferSize))
       out.writeInt(val2.size)

From 544880457de556d1ad52e8cb7e1eca19da95f517 Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian.cs.zju@gmail.com>
Date: Thu, 3 Jul 2014 23:41:54 -0700
Subject: [PATCH 573/810] [SPARK-2059][SQL] Don't throw TreeNodeException in
 `execution.ExplainCommand`

This is a fix for the problem revealed by PR #1265.

Currently `HiveComparisonSuite` ignores output of `ExplainCommand` since Catalyst query plan is quite different from Hive query plan. But exceptions throw from `CheckResolution` still breaks test cases. This PR catches any `TreeNodeException` and reports it as part of the query explanation.

After merging this PR, PR #1265 can also be merged safely.

For a normal query:

```
scala> hql("explain select key from src").foreach(println)
...
[Physical execution plan:]
[HiveTableScan [key#9], (MetastoreRelation default, src, None), None]
```

For a wrong query with unresolved attribute(s):

```
scala> hql("explain select kay from src").foreach(println)
...
[Error occurred during query planning: ]
[Unresolved attributes: 'kay, tree:]
[Project ['kay]]
[ LowerCaseSchema ]
[  MetastoreRelation default, src, None]
```

Author: Cheng Lian <lian.cs.zju@gmail.com>

Closes #1294 from liancheng/safe-explain and squashes the following commits:

4318911 [Cheng Lian] Don't throw TreeNodeException in `execution.ExplainCommand`
---
 .../scala/org/apache/spark/sql/execution/commands.scala  | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala
index acb1b0f4dc22..98d2f89c8ae7 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala
@@ -19,9 +19,10 @@ package org.apache.spark.sql.execution
 
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.{SQLContext, Row}
-import org.apache.spark.sql.catalyst.expressions.{GenericRow, Attribute}
+import org.apache.spark.sql.catalyst.errors.TreeNodeException
+import org.apache.spark.sql.catalyst.expressions.{Attribute, GenericRow}
 import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
+import org.apache.spark.sql.{Row, SQLContext}
 
 trait Command {
   /**
@@ -86,8 +87,10 @@ case class ExplainCommand(
   extends LeafNode with Command {
 
   // Run through the optimizer to generate the physical plan.
-  override protected[sql] lazy val sideEffectResult: Seq[String] = {
+  override protected[sql] lazy val sideEffectResult: Seq[String] = try {
     "Physical execution plan:" +: context.executePlan(logicalPlan).executedPlan.toString.split("\n")
+  } catch { case cause: TreeNodeException[_] =>
+    "Error occurred during query planning: " +: cause.getMessage.split("\n")
   }
 
   def execute(): RDD[Row] = {

From d43415075b3468fe8aa56de5d2907d409bb96347 Mon Sep 17 00:00:00 2001
From: Prashant Sharma <prashant.s@imaginea.com>
Date: Fri, 4 Jul 2014 00:05:27 -0700
Subject: [PATCH 574/810] [SPARK-1199][REPL] Remove VALId and use the original
 import style for defined classes.

This is an alternate solution to #1176.

Author: Prashant Sharma <prashant.s@imaginea.com>

Closes #1179 from ScrapCodes/SPARK-1199/repl-fix-second-approach and squashes the following commits:

820b34b [Prashant Sharma] Here we generate two kinds of import wrappers based on whether it is a class or not.
---
 .../org/apache/spark/repl/SparkIMain.scala    |  7 ++++--
 .../org/apache/spark/repl/SparkImports.scala  | 23 +++++++++++--------
 .../org/apache/spark/repl/ReplSuite.scala     | 12 ++++++++++
 3 files changed, 31 insertions(+), 11 deletions(-)

diff --git a/repl/src/main/scala/org/apache/spark/repl/SparkIMain.scala b/repl/src/main/scala/org/apache/spark/repl/SparkIMain.scala
index 7c83fa9d4d88..3842c291d0b7 100644
--- a/repl/src/main/scala/org/apache/spark/repl/SparkIMain.scala
+++ b/repl/src/main/scala/org/apache/spark/repl/SparkIMain.scala
@@ -744,7 +744,7 @@ import org.apache.spark.util.Utils
    *
    *  Read! Eval! Print! Some of that not yet centralized here.
    */
-  class ReadEvalPrint(lineId: Int) {
+  class ReadEvalPrint(val lineId: Int) {
     def this() = this(freshLineId())
 
     private var lastRun: Run = _
@@ -1241,7 +1241,10 @@ import org.apache.spark.util.Utils
     // old style
     beSilentDuring(parse(code)) foreach { ts =>
       ts foreach { t =>
-        withoutUnwrapping(logDebug(asCompactString(t)))
+        if (isShow || isShowRaw)
+          withoutUnwrapping(echo(asCompactString(t)))
+        else
+          withoutUnwrapping(logDebug(asCompactString(t)))
       }
     }
   }
diff --git a/repl/src/main/scala/org/apache/spark/repl/SparkImports.scala b/repl/src/main/scala/org/apache/spark/repl/SparkImports.scala
index 419796b68b11..bce5c74b9d0d 100644
--- a/repl/src/main/scala/org/apache/spark/repl/SparkImports.scala
+++ b/repl/src/main/scala/org/apache/spark/repl/SparkImports.scala
@@ -182,15 +182,26 @@ trait SparkImports {
         // ambiguity errors will not be generated. Also, quote
         // the name of the variable, so that we don't need to
         // handle quoting keywords separately.
+        case x: ClassHandler =>
+        // I am trying to guess if the import is a defined class
+        // This is an ugly hack, I am not 100% sure of the consequences.
+        // Here we, let everything but "defined classes" use the import with val.
+        // The reason for this is, otherwise the remote executor tries to pull the
+        // classes involved and may fail.
+          for (imv <- x.definedNames) {
+            val objName = req.lineRep.readPath
+            code.append("import " + objName + ".INSTANCE" + req.accessPath + ".`" + imv + "`\n")
+          }
+
         case x =>
           for (imv <- x.definedNames) {
             if (currentImps contains imv) addWrapper()
             val objName = req.lineRep.readPath
-            val valName = "$VAL" + newValId();
+            val valName = "$VAL" + req.lineRep.lineId
 
             if(!code.toString.endsWith(".`" + imv + "`;\n")) { // Which means already imported
-               code.append("val " + valName + " = " + objName + ".INSTANCE;\n")
-               code.append("import " + valName + req.accessPath + ".`" + imv + "`;\n")
+                code.append("val " + valName + " = " + objName + ".INSTANCE;\n")
+                code.append("import " + valName + req.accessPath + ".`" + imv + "`;\n")
             }
             // code.append("val " + valName + " = " + objName + ".INSTANCE;\n")
             // code.append("import " + valName + req.accessPath + ".`" + imv + "`;\n")
@@ -211,10 +222,4 @@ trait SparkImports {
   private def membersAtPickler(sym: Symbol): List[Symbol] =
     beforePickler(sym.info.nonPrivateMembers.toList)
 
-  private var curValId = 0
-
-  private def newValId(): Int = {
-    curValId += 1
-    curValId
-  }
 }
diff --git a/repl/src/test/scala/org/apache/spark/repl/ReplSuite.scala b/repl/src/test/scala/org/apache/spark/repl/ReplSuite.scala
index f4ba8d9cc079..f2aa42dbcb4f 100644
--- a/repl/src/test/scala/org/apache/spark/repl/ReplSuite.scala
+++ b/repl/src/test/scala/org/apache/spark/repl/ReplSuite.scala
@@ -235,6 +235,18 @@ class ReplSuite extends FunSuite {
     assertContains("res4: Array[Int] = Array(0, 0, 0, 0, 0)", output)
   }
 
+  test("SPARK-1199-simple-reproduce") {
+    val output = runInterpreter("local-cluster[1,1,512]",
+      """
+        |case class Sum(exp: String, exp2: String)
+        |val a = Sum("A", "B")
+        |def b(a: Sum): String = a match { case Sum(_, _) => "Found Sum" }
+        |b(a)
+      """.stripMargin)
+    assertDoesNotContain("error:", output)
+    assertDoesNotContain("Exception", output)
+  }
+
   if (System.getenv("MESOS_NATIVE_LIBRARY") != null) {
     test("running on Mesos") {
       val output = runInterpreter("localquiet",

From 0bbe61223eda3f33bbf8992d2a8f0d47813f4873 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?baishuo=28=E7=99=BD=E7=A1=95=29?= <vc_java@hotmail.com>
Date: Fri, 4 Jul 2014 00:25:31 -0700
Subject: [PATCH 575/810] Update SQLConf.scala
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

use concurrent.ConcurrentHashMap instead of util.Collections.synchronizedMap

Author: baishuo(白硕) <vc_java@hotmail.com>

Closes #1272 from baishuo/master and squashes the following commits:

51ec55d [baishuo(白硕)] Update SQLConf.scala
63da043 [baishuo(白硕)] Update SQLConf.scala
36b6dbd [baishuo(白硕)] Update SQLConf.scala
864faa0 [baishuo(白硕)] Update SQLConf.scala
593096b [baishuo(白硕)] Update SQLConf.scala
7304d9b [baishuo(白硕)] Update SQLConf.scala
843581c [baishuo(白硕)] Update SQLConf.scala
1d3e4a2 [baishuo(白硕)] Update SQLConf.scala
0740f28 [baishuo(白硕)] Update SQLConf.scala
---
 .../src/main/scala/org/apache/spark/sql/SQLConf.scala    | 9 +++------
 1 file changed, 3 insertions(+), 6 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
index 3b5abab96986..95ed0f28507f 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
@@ -64,20 +64,17 @@ trait SQLConf {
   }
 
   def get(key: String): String = {
-    if (!settings.containsKey(key)) {
-      throw new NoSuchElementException(key)
-    }
-    settings.get(key)
+    Option(settings.get(key)).getOrElse(throw new NoSuchElementException(key))
   }
 
   def get(key: String, defaultValue: String): String = {
-    if (!settings.containsKey(key)) defaultValue else settings.get(key)
+    Option(settings.get(key)).getOrElse(defaultValue)
   }
 
   def getAll: Array[(String, String)] = settings.asScala.toArray
 
   def getOption(key: String): Option[String] = {
-    if (!settings.containsKey(key)) None else Some(settings.get(key))
+    Option(settings.get(key))
   }
 
   def contains(key: String): Boolean = settings.containsKey(key)

From b3e768e154bd7175db44c3ffc3d8f783f15ab776 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Fri, 4 Jul 2014 00:53:41 -0700
Subject: [PATCH 576/810] [SPARK-2059][SQL] Add analysis checks

This replaces #1263 with a test case.

Author: Reynold Xin <rxin@apache.org>
Author: Michael Armbrust <michael@databricks.com>

Closes #1265 from rxin/sql-analysis-error and squashes the following commits:

a639e01 [Reynold Xin] Added a test case for unresolved attribute analysis.
7371e1b [Reynold Xin] Merge pull request #1263 from marmbrus/analysisChecks
448c088 [Michael Armbrust] Add analysis checks
---
 .../spark/sql/catalyst/analysis/Analyzer.scala   | 16 ++++++++++++++++
 .../sql/catalyst/analysis/AnalysisSuite.scala    |  8 ++++++++
 2 files changed, 24 insertions(+)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
index 4ebc0e70d946..c7188469bfb8 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.sql.catalyst.analysis
 
+import org.apache.spark.sql.catalyst.errors.TreeNodeException
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.logical._
 import org.apache.spark.sql.catalyst.rules._
@@ -54,10 +55,25 @@ class Analyzer(catalog: Catalog, registry: FunctionRegistry, caseSensitive: Bool
       ResolveFunctions ::
       GlobalAggregates ::
       typeCoercionRules :_*),
+    Batch("Check Analysis", Once,
+      CheckResolution),
     Batch("AnalysisOperators", fixedPoint,
       EliminateAnalysisOperators)
   )
 
+  /**
+   * Makes sure all attributes have been resolved.
+   */
+  object CheckResolution extends Rule[LogicalPlan] {
+    def apply(plan: LogicalPlan): LogicalPlan = {
+      plan.transform {
+        case p if p.expressions.exists(!_.resolved) =>
+          throw new TreeNodeException(p,
+            s"Unresolved attributes: ${p.expressions.filterNot(_.resolved).mkString(",")}")
+      }
+    }
+  }
+
   /**
    * Replaces [[UnresolvedRelation]]s with concrete relations from the catalog.
    */
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala
index 4c313585c638..f14df8137683 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala
@@ -19,6 +19,7 @@ package org.apache.spark.sql.catalyst.analysis
 
 import org.scalatest.FunSuite
 
+import org.apache.spark.sql.catalyst.errors.TreeNodeException
 import org.apache.spark.sql.catalyst.plans.logical._
 
 /* Implicit conversions */
@@ -34,4 +35,11 @@ class AnalysisSuite extends FunSuite {
       analyze(Project(Seq(UnresolvedAttribute("a")), testRelation)) ===
         Project(testRelation.output, testRelation))
   }
+
+  test("throw errors for unresolved attributes during analysis") {
+    val e = intercept[TreeNodeException[_]] {
+      analyze(Project(Seq(UnresolvedAttribute("abcd")), testRelation))
+    }
+    assert(e.getMessage().toLowerCase.contains("unresolved"))
+  }
 }

From 5dadda86456e1d3918e320b83aec7e2f1352d95d Mon Sep 17 00:00:00 2001
From: Yanjie Gao <gaoyanjie55@163.com>
Date: Fri, 4 Jul 2014 02:43:57 -0700
Subject: [PATCH 577/810] [SPARK-2234][SQL]Spark SQL basicOperators  add Except
 operator

Hi all,
I want to submit a Except operator in basicOperators.scala
In SQL case.SQL support two table do except operator.
select * from table1
except
select * from table2
This operator support the substract function .Return an table with the elements from `this` that are not in `other`.This operator should limit the input SparkPlan Seq only has two member.The check will later support
JIRA:https://issues.apache.org/jira/browse/SPARK-2234

Author: Yanjie Gao <gaoyanjie55@163.com>
Author: YanjieGao <396154235@qq.com>
Author: root <root@node4.(none)>
Author: gaoyanjie <gaoyanjie55@163.com>

Closes #1151 from YanjieGao/patch-6 and squashes the following commits:

f19f899 [YanjieGao] add a new blank line in basicoperators.scala
2ff7d73 [YanjieGao] resolve the identation in SqlParser and SparkStrategies
fdb5227 [YanjieGao] Merge remote branch 'upstream/master' into patch-6
9940d19 [YanjieGao] make comment less than 100c
09c7413 [YanjieGao] pr 1151 SqlParser add cache ,basic Operator rename Except and modify comment
b4b5867 [root] Merge remote branch 'upstream/master' into patch-6
b4c3869 [Yanjie Gao] change SparkStrategies Sparkcontext to SqlContext
7e0ec29 [Yanjie Gao] delete multi test
7e7c83f [Yanjie Gao] delete conflict except
b01beb8 [YanjieGao] resolve conflict sparkstrategies and basicOperators
4dc8166 [YanjieGao] resolve conflict
fa68a98 [Yanjie Gao] Update joins.scala
8e6bb00 [Yanjie Gao] delete conflict except
dd9ba5e [Yanjie Gao] Update joins.scala
a0d4e73 [Yanjie Gao] delete skew join
60f5ddd [Yanjie Gao] update less than 100c
0e72233 [Yanjie Gao] update SQLQuerySuite on master branch
7f916b5 [Yanjie Gao] update execution/basicOperators on master branch
a28dece [Yanjie Gao] Update logical/basicOperators on master branch
a639935 [Yanjie Gao] Update SparkStrategies.scala
3bf7def [Yanjie Gao] update SqlParser on master branch
26f833f [Yanjie Gao] update SparkStrategies.scala on master branch
8dd063f [Yanjie Gao] Update logical/basicOperators on master branch
9847dcf [Yanjie Gao] update SqlParser on masterbranch
d6a4604 [Yanjie Gao] Update joins.scala
424c507 [Yanjie Gao] Update joins.scala
7680742 [Yanjie Gao] Update SqlParser.scala
a7193d8 [gaoyanjie] [SPARK-2234][SQL]Spark SQL basicOperators add Except operator #1151
5c8a224 [Yanjie Gao] update the line less than 100c
ee066b3 [Yanjie Gao] Update basicOperators.scala
32a80ab [Yanjie Gao] remove except in HiveQl
cf232eb [Yanjie Gao] update 1comment 2space3 left.out
f1ea3f3 [Yanjie Gao] remove comment
7ea9b91 [Yanjie Gao] remove annotation
7f3d613 [Yanjie Gao] update .map(_.copy())
670a1bb [Yanjie Gao] Update HiveQl.scala
3fe7746 [Yanjie Gao] Update SQLQuerySuite.scala
a36eb0a [Yanjie Gao] Update basicOperators.scala
7859e56 [Yanjie Gao] Update SparkStrategies.scala
052346d [Yanjie Gao] Subtract is conflict with Subtract(e1,e2)
aab3785 [Yanjie Gao] Update SQLQuerySuite.scala
4bf80b1 [Yanjie Gao] update subtract to except
4bdd520 [Yanjie Gao] Update SqlParser.scala
2d4bfbd [Yanjie Gao] Update SQLQuerySuite.scala
0808921 [Yanjie Gao] SQLQuerySuite
a8a1948 [Yanjie Gao] SparkStrategies
1fe96c0 [Yanjie Gao] HiveQl.scala update
3305e40 [Yanjie Gao] SqlParser
7a98c37 [Yanjie Gao] Update basicOperators.scala
cf5b9d0 [Yanjie Gao] Update basicOperators.scala
8945835 [Yanjie Gao] object SkewJoin extends Strategy
2b98962 [Yanjie Gao] Update SqlParser.scala
dd32980 [Yanjie Gao] update1
68815b2 [Yanjie Gao] Reformat the code style
4eb43ec [Yanjie Gao] Update basicOperators.scala
aa06072 [Yanjie Gao] Reformat the code sytle
---
 .../org/apache/spark/sql/catalyst/SqlParser.scala |  3 +++
 .../catalyst/plans/logical/basicOperators.scala   |  6 ++++++
 .../spark/sql/execution/SparkStrategies.scala     |  2 ++
 .../spark/sql/execution/basicOperators.scala      | 15 +++++++++++++++
 .../org/apache/spark/sql/SQLQuerySuite.scala      | 14 ++++++++++++++
 5 files changed, 40 insertions(+)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
index 61762fa2a7c3..ecb11129557b 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
@@ -118,6 +118,8 @@ class SqlParser extends StandardTokenParsers with PackratParsers {
   protected val UNCACHE = Keyword("UNCACHE")
   protected val UNION = Keyword("UNION")
   protected val WHERE = Keyword("WHERE")
+  protected val EXCEPT = Keyword("EXCEPT")
+
 
   // Use reflection to find the reserved words defined in this class.
   protected val reservedWords =
@@ -138,6 +140,7 @@ class SqlParser extends StandardTokenParsers with PackratParsers {
   protected lazy val query: Parser[LogicalPlan] = (
     select * (
         UNION ~ ALL ^^^ { (q1: LogicalPlan, q2: LogicalPlan) => Union(q1, q2) } |
+        EXCEPT ^^^ { (q1: LogicalPlan, q2: LogicalPlan) => Except(q1, q2)} |
         UNION ~ opt(DISTINCT) ^^^ { (q1: LogicalPlan, q2: LogicalPlan) => Distinct(Union(q1, q2)) }
       )
     | insert | cache
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
index 3e0639867b27..bac5a724647f 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
@@ -89,6 +89,12 @@ case class Join(
   }
 }
 
+case class Except(left: LogicalPlan, right: LogicalPlan) extends BinaryNode {
+  def output = left.output
+
+  def references = Set.empty
+}
+
 case class InsertIntoTable(
     table: LogicalPlan,
     partition: Map[String, Option[String]],
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
index 0925605b7c4d..9e036e127bd5 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
@@ -273,6 +273,8 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
         execution.Limit(limit, planLater(child))(sqlContext) :: Nil
       case Unions(unionChildren) =>
         execution.Union(unionChildren.map(planLater))(sqlContext) :: Nil
+      case logical.Except(left,right) =>                                        
+        execution.Except(planLater(left),planLater(right)) :: Nil   
       case logical.Generate(generator, join, outer, _, child) =>
         execution.Generate(generator, join = join, outer = outer, planLater(child)) :: Nil
       case logical.NoRelation =>
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
index a278f1ca9847..4b59e0b4e58e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
@@ -205,3 +205,18 @@ object ExistingRdd {
 case class ExistingRdd(output: Seq[Attribute], rdd: RDD[Row]) extends LeafNode {
   override def execute() = rdd
 }
+
+/**
+ * :: DeveloperApi ::
+ * Returns a table with the elements from left that are not in right using
+ * the built-in spark subtract function.
+ */
+@DeveloperApi
+case class Except(left: SparkPlan, right: SparkPlan) extends BinaryNode {
+  override def output = left.output
+
+  override def execute() = {
+    left.execute().map(_.copy()).subtract(right.execute().map(_.copy()))
+  }
+}
+
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index 2c1cb1867010..5c6701e203d1 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -371,6 +371,20 @@ class SQLQuerySuite extends QueryTest {
         (3, null)))
   }
 
+  test("EXCEPT") {
+
+    checkAnswer(
+      sql("SELECT * FROM lowerCaseData EXCEPT SELECT * FROM upperCaseData "),
+      (1, "a") ::
+      (2, "b") ::
+      (3, "c") ::
+      (4, "d") :: Nil)
+    checkAnswer(
+      sql("SELECT * FROM lowerCaseData EXCEPT SELECT * FROM lowerCaseData "), Nil)
+    checkAnswer(
+      sql("SELECT * FROM upperCaseData EXCEPT SELECT * FROM upperCaseData "), Nil)
+  }
+
   test("SET commands semantics using sql()") {
     TestSQLContext.settings.synchronized {
       clear()

From fc716589384f70096ba2fe1eb6a4131c61b21c7c Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Fri, 4 Jul 2014 10:01:19 -0700
Subject: [PATCH 578/810] HOTFIX: Clean before building docs during release.

If the docs are built after a Maven build has finished the intermediate
state somehow causes a compiler bug during sbt compilation. This just
does a clean before attempting to build the docs.
---
 dev/create-release/create-release.sh | 1 +
 1 file changed, 1 insertion(+)

diff --git a/dev/create-release/create-release.sh b/dev/create-release/create-release.sh
index c96afe822c6a..49bf78f60763 100755
--- a/dev/create-release/create-release.sh
+++ b/dev/create-release/create-release.sh
@@ -125,6 +125,7 @@ scp spark-* \
 
 # Docs
 cd spark
+sbt/sbt clean
 cd docs
 PRODUCTION=1 jekyll build
 echo "Copying release documentation"

From 0db5d5a22ef8dd6e3fbb2759cd25aac46bdf2ce9 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Fri, 4 Jul 2014 17:33:07 -0700
Subject: [PATCH 579/810] Added SignalLogger to HistoryServer.

This was omitted in #1260. @aarondav

Author: Reynold Xin <rxin@apache.org>

Closes #1300 from rxin/historyServer and squashes the following commits:

af720a3 [Reynold Xin] Added SignalLogger to HistoryServer.
---
 .../org/apache/spark/deploy/history/HistoryServer.scala      | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/history/HistoryServer.scala b/core/src/main/scala/org/apache/spark/deploy/history/HistoryServer.scala
index 29a78a56c8ed..56b38ddfc931 100644
--- a/core/src/main/scala/org/apache/spark/deploy/history/HistoryServer.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/history/HistoryServer.scala
@@ -27,7 +27,7 @@ import org.apache.spark.{Logging, SecurityManager, SparkConf}
 import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.ui.{WebUI, SparkUI, UIUtils}
 import org.apache.spark.ui.JettyUtils._
-import org.apache.spark.util.Utils
+import org.apache.spark.util.{SignalLogger, Utils}
 
 /**
  * A web server that renders SparkUIs of completed applications.
@@ -169,10 +169,11 @@ class HistoryServer(
  *
  * This launches the HistoryServer as a Spark daemon.
  */
-object HistoryServer {
+object HistoryServer extends Logging {
   private val conf = new SparkConf
 
   def main(argStrings: Array[String]) {
+    SignalLogger.register(log)
     initSecurity()
     val args = new HistoryServerArguments(conf, argStrings)
     val securityManager = new SecurityManager(conf)

From 9d006c97371ddf357e0b821d5c6d1535d9b6fe41 Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Fri, 4 Jul 2014 19:15:48 -0700
Subject: [PATCH 580/810] [SPARK-2370][SQL] Decrease metadata retrieved for
 partitioned hive queries.

Author: Michael Armbrust <michael@databricks.com>

Closes #1305 from marmbrus/usePrunerPartitions and squashes the following commits:

744aa20 [Michael Armbrust] Use getAllPartitionsForPruner instead of getPartitions, which avoids retrieving auth data
---
 .../scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala  | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
index 90eacf426878..7c24b5cabf61 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
@@ -53,7 +53,7 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with
     val table = client.getTable(databaseName, tableName)
     val partitions: Seq[Partition] =
       if (table.isPartitioned) {
-        client.getPartitions(table)
+        client.getAllPartitionsForPruner(table).toSeq
       } else {
         Nil
       }

From 42f3abd529e84f3b26386abe2bde30666e74b64e Mon Sep 17 00:00:00 2001
From: "ankit.bhardwaj" <ankit.bhardwaj@guavus.com>
Date: Fri, 4 Jul 2014 22:06:10 -0700
Subject: [PATCH 581/810] [SPARK-2306]:BoundedPriorityQueue is private and not
 registered with Kry...

Due to the non registration of BoundedPriorityQueue  with kryoserializer, operations which are dependend on BoundedPriorityQueue are giving exceptions.One such instance is using top along with kryo serialization.
Fixed the issue by registering BoundedPriorityQueue with kryoserializer.

Author: ankit.bhardwaj <ankit.bhardwaj@guavus.com>

Closes #1299 from AnkitBhardwaj12/BoundedPriorityQueueWithKryoIssue and squashes the following commits:

a4ae8ed [ankit.bhardwaj] [SPARK-2306]:BoundedPriorityQueue is private and not registered with Kryo
---
 .../scala/org/apache/spark/serializer/KryoSerializer.scala    | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala b/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala
index 82b62aaf6152..1ce424319479 100644
--- a/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala
+++ b/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala
@@ -30,6 +30,7 @@ import org.apache.spark.broadcast.HttpBroadcast
 import org.apache.spark.scheduler.MapStatus
 import org.apache.spark.storage._
 import org.apache.spark.storage.{GetBlock, GotBlock, PutBlock}
+import org.apache.spark.util.BoundedPriorityQueue
 
 import scala.reflect.ClassTag
 
@@ -183,7 +184,8 @@ private[serializer] object KryoSerializer {
     classOf[GetBlock],
     classOf[MapStatus],
     classOf[BlockManagerId],
-    classOf[Array[Byte]]
+    classOf[Array[Byte]],
+    classOf[BoundedPriorityQueue[_]]
   )
 }
 

From 3da8df939ec63064692ba64d9188aeea908b305c Mon Sep 17 00:00:00 2001
From: Takuya UESHIN <ueshin@happy-camper.st>
Date: Sat, 5 Jul 2014 11:48:08 -0700
Subject: [PATCH 582/810] [SPARK-2366] [SQL] Add column pruning for the right
 side of LeftSemi join.

The right side of `LeftSemi` join needs columns only used in join condition.

Author: Takuya UESHIN <ueshin@happy-camper.st>

Closes #1301 from ueshin/issues/SPARK-2366 and squashes the following commits:

7677a39 [Takuya UESHIN] Update comments.
786d3a0 [Takuya UESHIN] Rename method name.
e0957b1 [Takuya UESHIN] Add column pruning for the right side of LeftSemi join.
---
 .../sql/catalyst/optimizer/Optimizer.scala    | 28 +++++++++++++------
 1 file changed, 20 insertions(+), 8 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
index fb517e40677e..48ca31e70fe7 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
@@ -52,6 +52,7 @@ object Optimizer extends RuleExecutor[LogicalPlan] {
  *  - Inserting Projections beneath the following operators:
  *   - Aggregate
  *   - Project <- Join
+ *   - LeftSemiJoin
  *  - Collapse adjacent projections, performing alias substitution.
  */
 object ColumnPruning extends Rule[LogicalPlan] {
@@ -62,19 +63,22 @@ object ColumnPruning extends Rule[LogicalPlan] {
 
     // Eliminate unneeded attributes from either side of a Join.
     case Project(projectList, Join(left, right, joinType, condition)) =>
-      // Collect the list of off references required either above or to evaluate the condition.
+      // Collect the list of all references required either above or to evaluate the condition.
       val allReferences: Set[Attribute] =
         projectList.flatMap(_.references).toSet ++ condition.map(_.references).getOrElse(Set.empty)
 
       /** Applies a projection only when the child is producing unnecessary attributes */
-      def prunedChild(c: LogicalPlan) =
-        if ((c.outputSet -- allReferences.filter(c.outputSet.contains)).nonEmpty) {
-          Project(allReferences.filter(c.outputSet.contains).toSeq, c)
-        } else {
-          c
-        }
+      def pruneJoinChild(c: LogicalPlan) = prunedChild(c, allReferences)
 
-      Project(projectList, Join(prunedChild(left), prunedChild(right), joinType, condition))
+      Project(projectList, Join(pruneJoinChild(left), pruneJoinChild(right), joinType, condition))
+
+    // Eliminate unneeded attributes from right side of a LeftSemiJoin.
+    case Join(left, right, LeftSemi, condition) =>
+      // Collect the list of all references required to evaluate the condition.
+      val allReferences: Set[Attribute] =
+        condition.map(_.references).getOrElse(Set.empty)
+
+      Join(left, prunedChild(right, allReferences), LeftSemi, condition)
 
     // Combine adjacent Projects.
     case Project(projectList1, Project(projectList2, child)) =>
@@ -97,6 +101,14 @@ object ColumnPruning extends Rule[LogicalPlan] {
     // Eliminate no-op Projects
     case Project(projectList, child) if child.output == projectList => child
   }
+
+  /** Applies a projection only when the child is producing unnecessary attributes */
+  private def prunedChild(c: LogicalPlan, allReferences: Set[Attribute]) =
+    if ((c.outputSet -- allReferences.filter(c.outputSet.contains)).nonEmpty) {
+      Project(allReferences.filter(c.outputSet.contains).toSeq, c)
+    } else {
+      c
+    }
 }
 
 /**

From 9d5ecf8205b924dc8a3c13fed68beb78cc5c7553 Mon Sep 17 00:00:00 2001
From: Takuya UESHIN <ueshin@happy-camper.st>
Date: Sat, 5 Jul 2014 11:51:48 -0700
Subject: [PATCH 583/810] [SPARK-2327] [SQL] Fix nullabilities of
 Join/Generate/Aggregate.

Fix nullabilities of `Join`/`Generate`/`Aggregate` because:
- Output attributes of opposite side of `OuterJoin` should be nullable.
- Output attributes of generater side of `Generate` should be nullable if `join` is `true` and `outer` is `true`.
- `AttributeReference` of `computedAggregates` of `Aggregate` should be the same as `aggregateExpression`'s.

Author: Takuya UESHIN <ueshin@happy-camper.st>

Closes #1266 from ueshin/issues/SPARK-2327 and squashes the following commits:

3ace83a [Takuya UESHIN] Add withNullability to Attribute and use it to change nullabilities.
df1ae53 [Takuya UESHIN] Modify nullabilize to leave attribute if not resolved.
799ce56 [Takuya UESHIN] Add nullabilization to Generate of SparkPlan.
a0fc9bc [Takuya UESHIN] Fix scalastyle errors.
0e31e37 [Takuya UESHIN] Fix Aggregate resultAttribute nullabilities.
09532ec [Takuya UESHIN] Fix Generate output nullabilities.
f20f196 [Takuya UESHIN] Fix Join output nullabilities.
---
 .../sql/catalyst/analysis/unresolved.scala    |  2 ++
 .../catalyst/expressions/BoundAttribute.scala | 16 +++++-----
 .../expressions/namedExpressions.scala        |  3 +-
 .../plans/logical/basicOperators.scala        | 31 ++++++++++++++-----
 .../spark/sql/execution/Aggregate.scala       |  4 +--
 .../apache/spark/sql/execution/Generate.scala | 12 +++++--
 .../apache/spark/sql/execution/joins.scala    | 13 +++++++-
 7 files changed, 60 insertions(+), 21 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/unresolved.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/unresolved.scala
index d629172a7426..7abeb032964e 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/unresolved.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/unresolved.scala
@@ -52,6 +52,7 @@ case class UnresolvedAttribute(name: String) extends Attribute with trees.LeafNo
   override lazy val resolved = false
 
   override def newInstance = this
+  override def withNullability(newNullability: Boolean) = this
   override def withQualifiers(newQualifiers: Seq[String]) = this
 
   // Unresolved attributes are transient at compile time and don't get evaluated during execution.
@@ -95,6 +96,7 @@ case class Star(
   override lazy val resolved = false
 
   override def newInstance = this
+  override def withNullability(newNullability: Boolean) = this
   override def withQualifiers(newQualifiers: Seq[String]) = this
 
   def expand(input: Seq[Attribute]): Seq[NamedExpression] = {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala
index 655d4a08fe93..9ce1f0105646 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala
@@ -33,14 +33,16 @@ case class BoundReference(ordinal: Int, baseReference: Attribute)
 
   type EvaluatedType = Any
 
-  def nullable = baseReference.nullable
-  def dataType = baseReference.dataType
-  def exprId = baseReference.exprId
-  def qualifiers = baseReference.qualifiers
-  def name = baseReference.name
+  override def nullable = baseReference.nullable
+  override def dataType = baseReference.dataType
+  override def exprId = baseReference.exprId
+  override def qualifiers = baseReference.qualifiers
+  override def name = baseReference.name
 
-  def newInstance = BoundReference(ordinal, baseReference.newInstance)
-  def withQualifiers(newQualifiers: Seq[String]) =
+  override def newInstance = BoundReference(ordinal, baseReference.newInstance)
+  override def withNullability(newNullability: Boolean) =
+    BoundReference(ordinal, baseReference.withNullability(newNullability))
+  override def withQualifiers(newQualifiers: Seq[String]) =
     BoundReference(ordinal, baseReference.withQualifiers(newQualifiers))
 
   override def toString = s"$baseReference:$ordinal"
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala
index 66ae22e95b60..934bad8c2729 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala
@@ -57,6 +57,7 @@ abstract class NamedExpression extends Expression {
 abstract class Attribute extends NamedExpression {
   self: Product =>
 
+  def withNullability(newNullability: Boolean): Attribute
   def withQualifiers(newQualifiers: Seq[String]): Attribute
 
   def toAttribute = this
@@ -133,7 +134,7 @@ case class AttributeReference(name: String, dataType: DataType, nullable: Boolea
   /**
    * Returns a copy of this [[AttributeReference]] with changed nullability.
    */
-  def withNullability(newNullability: Boolean) = {
+  override def withNullability(newNullability: Boolean) = {
     if (nullable == newNullability) {
       this
     } else {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
index bac5a724647f..0728fa73fb10 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.sql.catalyst.plans.logical
 
 import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.catalyst.plans.{LeftSemi, JoinType}
+import org.apache.spark.sql.catalyst.plans._
 import org.apache.spark.sql.catalyst.types._
 
 case class Project(projectList: Seq[NamedExpression], child: LogicalPlan) extends UnaryNode {
@@ -46,10 +46,16 @@ case class Generate(
     child: LogicalPlan)
   extends UnaryNode {
 
-  protected def generatorOutput: Seq[Attribute] =
-    alias
+  protected def generatorOutput: Seq[Attribute] = {
+    val output = alias
       .map(a => generator.output.map(_.withQualifiers(a :: Nil)))
       .getOrElse(generator.output)
+    if (join && outer) {
+      output.map(_.withNullability(true))
+    } else {
+      output
+    }
+  }
 
   override def output =
     if (join) child.output ++ generatorOutput else generatorOutput
@@ -81,11 +87,20 @@ case class Join(
   condition: Option[Expression]) extends BinaryNode {
 
   override def references = condition.map(_.references).getOrElse(Set.empty)
-  override def output = joinType match {
-    case LeftSemi =>
-      left.output
-    case _ =>
-      left.output ++ right.output
+
+  override def output = {
+    joinType match {
+      case LeftSemi =>
+        left.output
+      case LeftOuter =>
+        left.output ++ right.output.map(_.withNullability(true))
+      case RightOuter =>
+        left.output.map(_.withNullability(true)) ++ right.output
+      case FullOuter =>
+        left.output.map(_.withNullability(true)) ++ right.output.map(_.withNullability(true))
+      case _ =>
+        left.output ++ right.output
+    }
   }
 }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/Aggregate.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/Aggregate.scala
index d85d2d7844e0..c1ced8bfa404 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/Aggregate.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/Aggregate.scala
@@ -83,8 +83,8 @@ case class Aggregate(
       case a: AggregateExpression =>
         ComputedAggregate(
           a,
-          BindReferences.bindReference(a, childOutput).asInstanceOf[AggregateExpression],
-          AttributeReference(s"aggResult:$a", a.dataType, nullable = true)())
+          BindReferences.bindReference(a, childOutput),
+          AttributeReference(s"aggResult:$a", a.dataType, a.nullable)())
     }
   }.toArray
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/Generate.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/Generate.scala
index da1e08be59e2..47b3d00262db 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/Generate.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/Generate.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.sql.execution
 
 import org.apache.spark.annotation.DeveloperApi
-import org.apache.spark.sql.catalyst.expressions.{Generator, JoinedRow, Literal, Projection}
+import org.apache.spark.sql.catalyst.expressions._
 
 /**
  * :: DeveloperApi ::
@@ -39,8 +39,16 @@ case class Generate(
     child: SparkPlan)
   extends UnaryNode {
 
+  protected def generatorOutput: Seq[Attribute] = {
+    if (join && outer) {
+      generator.output.map(_.withNullability(true))
+    } else {
+      generator.output
+    }
+  }
+
   override def output =
-    if (join) child.output ++ generator.output else generator.output
+    if (join) child.output ++ generatorOutput else generatorOutput
 
   override def execute() = {
     if (join) {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins.scala
index 32c5f26fe8aa..7d1f11caae83 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins.scala
@@ -319,7 +319,18 @@ case class BroadcastNestedLoopJoin(
 
   override def otherCopyArgs = sqlContext :: Nil
 
-  def output = left.output ++ right.output
+  override def output = {
+    joinType match {
+      case LeftOuter =>
+        left.output ++ right.output.map(_.withNullability(true))
+      case RightOuter =>
+        left.output.map(_.withNullability(true)) ++ right.output
+      case FullOuter =>
+        left.output.map(_.withNullability(true)) ++ right.output.map(_.withNullability(true))
+      case _ =>
+        left.output ++ right.output
+    }
+  }
 
   /** The Streamed Relation */
   def left = streamed

From f7ce1b3b48f0354434456241188c6a5d954852e2 Mon Sep 17 00:00:00 2001
From: Neville Li <neville@spotify.com>
Date: Mon, 7 Jul 2014 15:06:14 -0700
Subject: [PATCH 584/810] [SPARK-1977][MLLIB] register mutable BitSet in
 MovieLenseALS

Author: Neville Li <neville@spotify.com>

Closes #1319 from nevillelyh/gh/SPARK-1977 and squashes the following commits:

1f0a355 [Neville Li] [SPARK-1977][MLLIB] register mutable BitSet in MovieLenseALS
---
 .../scala/org/apache/spark/examples/mllib/MovieLensALS.scala   | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/MovieLensALS.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/MovieLensALS.scala
index 28e201d279f4..98aaedb9d7dc 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/MovieLensALS.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/MovieLensALS.scala
@@ -17,6 +17,8 @@
 
 package org.apache.spark.examples.mllib
 
+import scala.collection.mutable
+
 import com.esotericsoftware.kryo.Kryo
 import org.apache.log4j.{Level, Logger}
 import scopt.OptionParser
@@ -41,6 +43,7 @@ object MovieLensALS {
   class ALSRegistrator extends KryoRegistrator {
     override def registerClasses(kryo: Kryo) {
       kryo.register(classOf[Rating])
+      kryo.register(classOf[mutable.BitSet])
     }
   }
 

From c0b4cf097de50eb2c4b0f0e67da53ee92efc1f77 Mon Sep 17 00:00:00 2001
From: Yin Huai <huai@cse.ohio-state.edu>
Date: Mon, 7 Jul 2014 17:01:44 -0700
Subject: [PATCH 585/810] [SPARK-2339][SQL] SQL parser in sql-core is case
 sensitive, but a table alias is converted to lower case when we create
 Subquery

Reported by http://apache-spark-user-list.1001560.n3.nabble.com/Spark-SQL-Join-throws-exception-td8599.html
After we get the table from the catalog, because the table has an alias, we will temporarily insert a Subquery. Then, we convert the table alias to lower case no matter if the parser is case sensitive or not.
To see the issue ...
```
val sqlContext = new org.apache.spark.sql.SQLContext(sc)
import sqlContext.createSchemaRDD

case class Person(name: String, age: Int)

val people = sc.textFile("examples/src/main/resources/people.txt").map(_.split(",")).map(p => Person(p(0), p(1).trim.toInt))
people.registerAsTable("people")

sqlContext.sql("select PEOPLE.name from people PEOPLE")
```
The plan is ...
```
== Query Plan ==
Project ['PEOPLE.name]
 ExistingRdd [name#0,age#1], MapPartitionsRDD[4] at mapPartitions at basicOperators.scala:176
```
You can find that `PEOPLE.name` is not resolved.

This PR introduces three changes.
1.  If a table has an alias, the catalog will not lowercase the alias. If a lowercase alias is needed, the analyzer will do the work.
2.  A catalog has a new val caseSensitive that indicates if this catalog is case sensitive or not. For example, a SimpleCatalog is case sensitive, but
3.  Corresponding unit tests.
With this PR, case sensitivity of database names and table names is handled by the catalog. Case sensitivity of other identifiers are handled by the analyzer.

JIRA: https://issues.apache.org/jira/browse/SPARK-2339

Author: Yin Huai <huai@cse.ohio-state.edu>

Closes #1317 from yhuai/SPARK-2339 and squashes the following commits:

12d8006 [Yin Huai] Handling case sensitivity correctly. This patch introduces three changes. 1. If a table has an alias, the catalog will not lowercase the alias. If a lowercase alias is needed, the analyzer will do the work. 2. A catalog has a new val caseSensitive that indicates if this catalog is case sensitive or not. For example, a SimpleCatalog is case sensitive, but 3. Corresponding unit tests. With this patch, case sensitivity of database names and table names is handled by the catalog. Case sensitivity of other identifiers is handled by the analyzer.
---
 .../spark/sql/catalyst/analysis/Catalog.scala | 55 +++++++++++----
 .../sql/catalyst/analysis/AnalysisSuite.scala | 69 ++++++++++++++++---
 .../org/apache/spark/sql/SQLContext.scala     |  2 +-
 .../spark/sql/hive/HiveMetastoreCatalog.scala | 23 ++++---
 ...e table-0-5d14d21a239daa42b086cc895215009a | 14 ++++
 .../sql/hive/execution/HiveQuerySuite.scala   | 16 +++++
 6 files changed, 149 insertions(+), 30 deletions(-)
 create mode 100644 sql/hive/src/test/resources/golden/case sensitivity: Hive table-0-5d14d21a239daa42b086cc895215009a

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Catalog.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Catalog.scala
index f30b5d816703..0d05d9808b40 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Catalog.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Catalog.scala
@@ -25,6 +25,9 @@ import org.apache.spark.sql.catalyst.plans.logical.{LogicalPlan, Subquery}
  * An interface for looking up relations by name.  Used by an [[Analyzer]].
  */
 trait Catalog {
+
+  def caseSensitive: Boolean
+
   def lookupRelation(
     databaseName: Option[String],
     tableName: String,
@@ -35,22 +38,44 @@ trait Catalog {
   def unregisterTable(databaseName: Option[String], tableName: String): Unit
 
   def unregisterAllTables(): Unit
+
+  protected def processDatabaseAndTableName(
+      databaseName: Option[String],
+      tableName: String): (Option[String], String) = {
+    if (!caseSensitive) {
+      (databaseName.map(_.toLowerCase), tableName.toLowerCase)
+    } else {
+      (databaseName, tableName)
+    }
+  }
+
+  protected def processDatabaseAndTableName(
+      databaseName: String,
+      tableName: String): (String, String) = {
+    if (!caseSensitive) {
+      (databaseName.toLowerCase, tableName.toLowerCase)
+    } else {
+      (databaseName, tableName)
+    }
+  }
 }
 
-class SimpleCatalog extends Catalog {
+class SimpleCatalog(val caseSensitive: Boolean) extends Catalog {
   val tables = new mutable.HashMap[String, LogicalPlan]()
 
   override def registerTable(
       databaseName: Option[String],
       tableName: String,
       plan: LogicalPlan): Unit = {
-    tables += ((tableName, plan))
+    val (dbName, tblName) = processDatabaseAndTableName(databaseName, tableName)
+    tables += ((tblName, plan))
   }
 
   override def unregisterTable(
       databaseName: Option[String],
       tableName: String) = {
-    tables -= tableName
+    val (dbName, tblName) = processDatabaseAndTableName(databaseName, tableName)
+    tables -= tblName
   }
 
   override def unregisterAllTables() = {
@@ -61,12 +86,13 @@ class SimpleCatalog extends Catalog {
       databaseName: Option[String],
       tableName: String,
       alias: Option[String] = None): LogicalPlan = {
-    val table = tables.get(tableName).getOrElse(sys.error(s"Table Not Found: $tableName"))
-    val tableWithQualifiers = Subquery(tableName, table)
+    val (dbName, tblName) = processDatabaseAndTableName(databaseName, tableName)
+    val table = tables.get(tblName).getOrElse(sys.error(s"Table Not Found: $tableName"))
+    val tableWithQualifiers = Subquery(tblName, table)
 
     // If an alias was specified by the lookup, wrap the plan in a subquery so that attributes are
     // properly qualified with this alias.
-    alias.map(a => Subquery(a.toLowerCase, tableWithQualifiers)).getOrElse(tableWithQualifiers)
+    alias.map(a => Subquery(a, tableWithQualifiers)).getOrElse(tableWithQualifiers)
   }
 }
 
@@ -85,26 +111,28 @@ trait OverrideCatalog extends Catalog {
     databaseName: Option[String],
     tableName: String,
     alias: Option[String] = None): LogicalPlan = {
-
-    val overriddenTable = overrides.get((databaseName, tableName))
+    val (dbName, tblName) = processDatabaseAndTableName(databaseName, tableName)
+    val overriddenTable = overrides.get((dbName, tblName))
 
     // If an alias was specified by the lookup, wrap the plan in a subquery so that attributes are
     // properly qualified with this alias.
     val withAlias =
-      overriddenTable.map(r => alias.map(a => Subquery(a.toLowerCase, r)).getOrElse(r))
+      overriddenTable.map(r => alias.map(a => Subquery(a, r)).getOrElse(r))
 
-    withAlias.getOrElse(super.lookupRelation(databaseName, tableName, alias))
+    withAlias.getOrElse(super.lookupRelation(dbName, tblName, alias))
   }
 
   override def registerTable(
       databaseName: Option[String],
       tableName: String,
       plan: LogicalPlan): Unit = {
-    overrides.put((databaseName, tableName), plan)
+    val (dbName, tblName) = processDatabaseAndTableName(databaseName, tableName)
+    overrides.put((dbName, tblName), plan)
   }
 
   override def unregisterTable(databaseName: Option[String], tableName: String): Unit = {
-    overrides.remove((databaseName, tableName))
+    val (dbName, tblName) = processDatabaseAndTableName(databaseName, tableName)
+    overrides.remove((dbName, tblName))
   }
 
   override def unregisterAllTables(): Unit = {
@@ -117,6 +145,9 @@ trait OverrideCatalog extends Catalog {
  * relations are already filled in and the analyser needs only to resolve attribute references.
  */
 object EmptyCatalog extends Catalog {
+
+  val caseSensitive: Boolean = true
+
   def lookupRelation(
     databaseName: Option[String],
     tableName: String,
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala
index f14df8137683..0a4fde3de775 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala
@@ -17,28 +17,81 @@
 
 package org.apache.spark.sql.catalyst.analysis
 
-import org.scalatest.FunSuite
+import org.scalatest.{BeforeAndAfter, FunSuite}
 
+import org.apache.spark.sql.catalyst.expressions.AttributeReference
 import org.apache.spark.sql.catalyst.errors.TreeNodeException
 import org.apache.spark.sql.catalyst.plans.logical._
+import org.apache.spark.sql.catalyst.types.IntegerType
 
-/* Implicit conversions */
-import org.apache.spark.sql.catalyst.dsl.expressions._
+class AnalysisSuite extends FunSuite with BeforeAndAfter {
+  val caseSensitiveCatalog = new SimpleCatalog(true)
+  val caseInsensitiveCatalog = new SimpleCatalog(false)
+  val caseSensitiveAnalyze =
+    new Analyzer(caseSensitiveCatalog, EmptyFunctionRegistry, caseSensitive = true)
+  val caseInsensitiveAnalyze =
+    new Analyzer(caseInsensitiveCatalog, EmptyFunctionRegistry, caseSensitive = false)
 
-class AnalysisSuite extends FunSuite {
-  val analyze = SimpleAnalyzer
+  val testRelation = LocalRelation(AttributeReference("a", IntegerType, nullable = true)())
 
-  val testRelation = LocalRelation('a.int)
+  before {
+    caseSensitiveCatalog.registerTable(None, "TaBlE", testRelation)
+    caseInsensitiveCatalog.registerTable(None, "TaBlE", testRelation)
+  }
 
   test("analyze project") {
     assert(
-      analyze(Project(Seq(UnresolvedAttribute("a")), testRelation)) ===
+      caseSensitiveAnalyze(Project(Seq(UnresolvedAttribute("a")), testRelation)) ===
+        Project(testRelation.output, testRelation))
+
+    assert(
+      caseSensitiveAnalyze(
+        Project(Seq(UnresolvedAttribute("TbL.a")),
+          UnresolvedRelation(None, "TaBlE", Some("TbL")))) ===
+        Project(testRelation.output, testRelation))
+
+    val e = intercept[TreeNodeException[_]] {
+      caseSensitiveAnalyze(
+        Project(Seq(UnresolvedAttribute("tBl.a")),
+          UnresolvedRelation(None, "TaBlE", Some("TbL"))))
+    }
+    assert(e.getMessage().toLowerCase.contains("unresolved"))
+
+    assert(
+      caseInsensitiveAnalyze(
+        Project(Seq(UnresolvedAttribute("TbL.a")),
+          UnresolvedRelation(None, "TaBlE", Some("TbL")))) ===
         Project(testRelation.output, testRelation))
+
+    assert(
+      caseInsensitiveAnalyze(
+        Project(Seq(UnresolvedAttribute("tBl.a")),
+          UnresolvedRelation(None, "TaBlE", Some("TbL")))) ===
+        Project(testRelation.output, testRelation))
+  }
+
+  test("resolve relations") {
+    val e = intercept[RuntimeException] {
+      caseSensitiveAnalyze(UnresolvedRelation(None, "tAbLe", None))
+    }
+    assert(e.getMessage === "Table Not Found: tAbLe")
+
+    assert(
+      caseSensitiveAnalyze(UnresolvedRelation(None, "TaBlE", None)) ===
+        testRelation)
+
+    assert(
+      caseInsensitiveAnalyze(UnresolvedRelation(None, "tAbLe", None)) ===
+        testRelation)
+
+    assert(
+      caseInsensitiveAnalyze(UnresolvedRelation(None, "TaBlE", None)) ===
+        testRelation)
   }
 
   test("throw errors for unresolved attributes during analysis") {
     val e = intercept[TreeNodeException[_]] {
-      analyze(Project(Seq(UnresolvedAttribute("abcd")), testRelation))
+      caseSensitiveAnalyze(Project(Seq(UnresolvedAttribute("abcd")), testRelation))
     }
     assert(e.getMessage().toLowerCase.contains("unresolved"))
   }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
index 7edb548678c3..4abd89955bd2 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
@@ -57,7 +57,7 @@ class SQLContext(@transient val sparkContext: SparkContext)
   self =>
 
   @transient
-  protected[sql] lazy val catalog: Catalog = new SimpleCatalog
+  protected[sql] lazy val catalog: Catalog = new SimpleCatalog(true)
   @transient
   protected[sql] lazy val analyzer: Analyzer =
     new Analyzer(catalog, EmptyFunctionRegistry, caseSensitive = true)
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
index 7c24b5cabf61..f83068860701 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
@@ -45,12 +45,15 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with
 
   val client = Hive.get(hive.hiveconf)
 
+  val caseSensitive: Boolean = false
+
   def lookupRelation(
       db: Option[String],
       tableName: String,
       alias: Option[String]): LogicalPlan = {
-    val databaseName = db.getOrElse(hive.sessionState.getCurrentDatabase)
-    val table = client.getTable(databaseName, tableName)
+    val (dbName, tblName) = processDatabaseAndTableName(db, tableName)
+    val databaseName = dbName.getOrElse(hive.sessionState.getCurrentDatabase)
+    val table = client.getTable(databaseName, tblName)
     val partitions: Seq[Partition] =
       if (table.isPartitioned) {
         client.getAllPartitionsForPruner(table).toSeq
@@ -60,8 +63,8 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with
 
     // Since HiveQL is case insensitive for table names we make them all lowercase.
     MetastoreRelation(
-      databaseName.toLowerCase,
-      tableName.toLowerCase,
+      databaseName,
+      tblName,
       alias)(table.getTTable, partitions.map(part => part.getTPartition))
   }
 
@@ -70,7 +73,8 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with
       tableName: String,
       schema: Seq[Attribute],
       allowExisting: Boolean = false): Unit = {
-    val table = new Table(databaseName, tableName)
+    val (dbName, tblName) = processDatabaseAndTableName(databaseName, tableName)
+    val table = new Table(dbName, tblName)
     val hiveSchema =
       schema.map(attr => new FieldSchema(attr.name, toMetastoreType(attr.dataType), ""))
     table.setFields(hiveSchema)
@@ -85,7 +89,7 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with
     sd.setInputFormat("org.apache.hadoop.mapred.TextInputFormat")
     sd.setOutputFormat("org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat")
     val serDeInfo = new SerDeInfo()
-    serDeInfo.setName(tableName)
+    serDeInfo.setName(tblName)
     serDeInfo.setSerializationLib("org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe")
     serDeInfo.setParameters(Map[String, String]())
     sd.setSerdeInfo(serDeInfo)
@@ -104,13 +108,14 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with
   object CreateTables extends Rule[LogicalPlan] {
     def apply(plan: LogicalPlan): LogicalPlan = plan transform {
       case InsertIntoCreatedTable(db, tableName, child) =>
-        val databaseName = db.getOrElse(hive.sessionState.getCurrentDatabase)
+        val (dbName, tblName) = processDatabaseAndTableName(db, tableName)
+        val databaseName = dbName.getOrElse(hive.sessionState.getCurrentDatabase)
 
-        createTable(databaseName, tableName, child.output)
+        createTable(databaseName, tblName, child.output)
 
         InsertIntoTable(
           EliminateAnalysisOperators(
-            lookupRelation(Some(databaseName), tableName, None)),
+            lookupRelation(Some(databaseName), tblName, None)),
           Map.empty,
           child,
           overwrite = false)
diff --git a/sql/hive/src/test/resources/golden/case sensitivity: Hive table-0-5d14d21a239daa42b086cc895215009a b/sql/hive/src/test/resources/golden/case sensitivity: Hive table-0-5d14d21a239daa42b086cc895215009a
new file mode 100644
index 000000000000..4d7127c0faab
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/case sensitivity: Hive table-0-5d14d21a239daa42b086cc895215009a	
@@ -0,0 +1,14 @@
+0	val_0
+4	val_4
+12	val_12
+8	val_8
+0	val_0
+0	val_0
+10	val_10
+5	val_5
+11	val_11
+5	val_5
+2	val_2
+12	val_12
+5	val_5
+9	val_9
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
index 9f1cd703103e..a623d29b5397 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
@@ -210,6 +210,22 @@ class HiveQuerySuite extends HiveComparisonTest {
     }
   }
 
+  createQueryTest("case sensitivity: Hive table",
+    "SELECT srcalias.KEY, SRCALIAS.value FROM sRc SrCAlias WHERE SrCAlias.kEy < 15")
+
+  test("case sensitivity: registered table") {
+    val testData: SchemaRDD =
+      TestHive.sparkContext.parallelize(
+        TestData(1, "str1") ::
+        TestData(2, "str2") :: Nil)
+    testData.registerAsTable("REGisteredTABle")
+
+    assertResult(Array(Array(2, "str2"))) {
+      hql("SELECT tablealias.A, TABLEALIAS.b FROM reGisteredTABle TableAlias " +
+        "WHERE TableAliaS.a > 1").collect()
+    }
+  }
+
   def isExplanation(result: SchemaRDD) = {
     val explanation = result.select('plan).collect().map { case Row(plan: String) => plan }
     explanation.size > 1 && explanation.head.startsWith("Physical execution plan")

From 4deeed17c4847f212a4fa1a8685cfe8a12179263 Mon Sep 17 00:00:00 2001
From: Takuya UESHIN <ueshin@happy-camper.st>
Date: Mon, 7 Jul 2014 17:04:02 -0700
Subject: [PATCH 586/810] [SPARK-2386] [SQL] RowWriteSupport should use the
 exact types to cast.

When execute `saveAsParquetFile` with non-primitive type, `RowWriteSupport` uses wrong type `Int` for `ByteType` and `ShortType`.

Author: Takuya UESHIN <ueshin@happy-camper.st>

Closes #1315 from ueshin/issues/SPARK-2386 and squashes the following commits:

20d89ec [Takuya UESHIN] Use None instead of null.
bd88741 [Takuya UESHIN] Add a test.
323d1d2 [Takuya UESHIN] Modify RowWriteSupport to use the exact types to cast.
---
 .../sql/parquet/ParquetTableSupport.scala     |  4 +-
 .../spark/sql/parquet/ParquetQuerySuite.scala | 40 ++++++++++++++++++-
 2 files changed, 41 insertions(+), 3 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala
index bfcbdeb34a92..9cd5dc5bbd39 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala
@@ -192,9 +192,9 @@ private[parquet] class RowWriteSupport extends WriteSupport[Row] with Logging {
           )
         )
         case IntegerType => writer.addInteger(value.asInstanceOf[Int])
-        case ShortType => writer.addInteger(value.asInstanceOf[Int])
+        case ShortType => writer.addInteger(value.asInstanceOf[Short])
         case LongType => writer.addLong(value.asInstanceOf[Long])
-        case ByteType => writer.addInteger(value.asInstanceOf[Int])
+        case ByteType => writer.addInteger(value.asInstanceOf[Byte])
         case DoubleType => writer.addDouble(value.asInstanceOf[Double])
         case FloatType => writer.addFloat(value.asInstanceOf[Float])
         case BooleanType => writer.addBoolean(value.asInstanceOf[Boolean])
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala
index 2ca0c1cdcbec..dbf315947ff4 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala
@@ -67,6 +67,19 @@ case class AllDataTypes(
     byteField: Byte,
     booleanField: Boolean)
 
+case class AllDataTypesWithNonPrimitiveType(
+    stringField: String,
+    intField: Int,
+    longField: Long,
+    floatField: Float,
+    doubleField: Double,
+    shortField: Short,
+    byteField: Byte,
+    booleanField: Boolean,
+    array: Seq[Int],
+    map: Map[Int, String],
+    nested: Nested)
+
 class ParquetQuerySuite extends QueryTest with FunSuiteLike with BeforeAndAfterAll {
   TestData // Load test data tables.
 
@@ -119,6 +132,31 @@ class ParquetQuerySuite extends QueryTest with FunSuiteLike with BeforeAndAfterA
     }
   }
 
+  test("Read/Write All Types with non-primitive type") {
+    val tempDir = getTempFilePath("parquetTest").getCanonicalPath
+    val range = (0 to 255)
+    TestSQLContext.sparkContext.parallelize(range)
+      .map(x => AllDataTypesWithNonPrimitiveType(
+        s"$x", x, x.toLong, x.toFloat, x.toDouble, x.toShort, x.toByte, x % 2 == 0,
+        Seq(x), Map(x -> s"$x"), Nested(x, s"$x")))
+      .saveAsParquetFile(tempDir)
+    val result = parquetFile(tempDir).collect()
+    range.foreach {
+      i =>
+        assert(result(i).getString(0) == s"$i", s"row $i String field did not match, got ${result(i).getString(0)}")
+        assert(result(i).getInt(1) === i)
+        assert(result(i).getLong(2) === i.toLong)
+        assert(result(i).getFloat(3) === i.toFloat)
+        assert(result(i).getDouble(4) === i.toDouble)
+        assert(result(i).getShort(5) === i.toShort)
+        assert(result(i).getByte(6) === i.toByte)
+        assert(result(i).getBoolean(7) === (i % 2 == 0))
+        assert(result(i)(8) === Seq(i))
+        assert(result(i)(9) === Map(i -> s"$i"))
+        assert(result(i)(10) === new GenericRow(Array[Any](i, s"$i")))
+    }
+  }
+
   test("self-join parquet files") {
     val x = ParquetTestData.testData.as('x)
     val y = ParquetTestData.testData.as('y)
@@ -298,7 +336,7 @@ class ParquetQuerySuite extends QueryTest with FunSuiteLike with BeforeAndAfterA
   }
 
   test("save and load case class RDD with Nones as parquet") {
-    val data = OptionalReflectData(null, null, null, null, null)
+    val data = OptionalReflectData(None, None, None, None, None)
     val rdd = sparkContext.parallelize(data :: Nil)
 
     val file = getTempFilePath("parquet")

From f0496ee10847db921a028a34f70385f9b740b3f3 Mon Sep 17 00:00:00 2001
From: Yin Huai <huaiyin.thu@gmail.com>
Date: Mon, 7 Jul 2014 17:05:59 -0700
Subject: [PATCH 587/810] [SPARK-2375][SQL] JSON schema inference may not
 resolve type conflicts correctly for a field inside an array of structs

For example, for
```
{"array": [{"field":214748364700}, {"field":1}]}
```
the type of field is resolved as IntType. While, for
```
{"array": [{"field":1}, {"field":214748364700}]}
```
the type of field is resolved as LongType.

JIRA: https://issues.apache.org/jira/browse/SPARK-2375

Author: Yin Huai <huaiyin.thu@gmail.com>

Closes #1308 from yhuai/SPARK-2375 and squashes the following commits:

3e2e312 [Yin Huai] Update unit test.
1b2ff9f [Yin Huai] Merge remote-tracking branch 'upstream/master' into SPARK-2375
10794eb [Yin Huai] Correctly resolve the type of a field inside an array of structs.
---
 .../main/scala/org/apache/spark/sql/json/JsonRDD.scala   | 9 +++++----
 .../test/scala/org/apache/spark/sql/json/JsonSuite.scala | 8 +++++---
 .../scala/org/apache/spark/sql/json/TestJsonData.scala   | 3 ++-
 3 files changed, 12 insertions(+), 8 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala
index edf86775579d..f6cbca96483e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala
@@ -198,11 +198,12 @@ private[sql] object JsonRDD extends Logging {
    * in this JSON object can appear in other JSON objects.
    */
   private def allKeysWithValueTypes(m: Map[String, Any]): Set[(String, DataType)] = {
-    m.map{
+    val keyValuePairs = m.map {
       // Quote the key with backticks to handle cases which have dots
       // in the field name.
-      case (key, dataType) => (s"`$key`", dataType)
-    }.flatMap {
+      case (key, value) => (s"`$key`", value)
+    }.toSet
+    keyValuePairs.flatMap {
       case (key: String, struct: Map[String, Any]) => {
         // The value associted with the key is an JSON object.
         allKeysWithValueTypes(struct).map {
@@ -224,7 +225,7 @@ private[sql] object JsonRDD extends Logging {
         }
       }
       case (key: String, value) => (key, typeOfPrimitiveValue(value)) :: Nil
-    }.toSet
+    }
   }
 
   /**
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala
index 10bd9f08f023..e765cfc83a39 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala
@@ -451,7 +451,9 @@ class JsonSuite extends QueryTest {
     val jsonSchemaRDD = jsonRDD(arrayElementTypeConflict)
 
     val expectedSchema =
-      AttributeReference("array", ArrayType(StringType), true)() :: Nil
+      AttributeReference("array1", ArrayType(StringType), true)() ::
+      AttributeReference("array2", ArrayType(StructType(
+        StructField("field", LongType, true) :: Nil)), true)() :: Nil
 
     comparePlans(Schema(expectedSchema), Schema(jsonSchemaRDD.logicalPlan.output))
 
@@ -460,12 +462,12 @@ class JsonSuite extends QueryTest {
     checkAnswer(
       sql("select * from jsonTable"),
       Seq(Seq("1", "1.1", "true", null, "[]", "{}", "[2,3,4]",
-        """{"field":str}""")) :: Nil
+        """{"field":str}"""), Seq(Seq(214748364700L), Seq(1))) :: Nil
     )
 
     // Treat an element as a number.
     checkAnswer(
-      sql("select array[0] + 1 from jsonTable"),
+      sql("select array1[0] + 1 from jsonTable"),
       2
     )
   }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/json/TestJsonData.scala b/sql/core/src/test/scala/org/apache/spark/sql/json/TestJsonData.scala
index 065e04046e8a..d0180f3754f2 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/json/TestJsonData.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/json/TestJsonData.scala
@@ -72,7 +72,8 @@ object TestJsonData {
 
   val arrayElementTypeConflict =
     TestSQLContext.sparkContext.parallelize(
-      """{"array": [1, 1.1, true, null, [], {}, [2,3,4], {"field":"str"}]}""" :: Nil)
+      """{"array1": [1, 1.1, true, null, [], {}, [2,3,4], {"field":"str"}],
+          "array2": [{"field":214748364700}, {"field":1}]}""" :: Nil)
 
   val missingFields =
     TestSQLContext.sparkContext.parallelize(

From 4352a2fdaa64efee7158eabef65703460ff284ec Mon Sep 17 00:00:00 2001
From: Yin Huai <huai@cse.ohio-state.edu>
Date: Mon, 7 Jul 2014 18:37:38 -0700
Subject: [PATCH 588/810] [SPARK-2376][SQL] Selecting list values inside nested
 JSON objects raises java.lang.IllegalArgumentException

JIRA: https://issues.apache.org/jira/browse/SPARK-2376

Author: Yin Huai <huai@cse.ohio-state.edu>

Closes #1320 from yhuai/SPARK-2376 and squashes the following commits:

0107417 [Yin Huai] Merge remote-tracking branch 'upstream/master' into SPARK-2376
480803d [Yin Huai] Correctly handling JSON arrays in PySpark.
---
 python/pyspark/sql.py                         | 24 +++++-----
 .../org/apache/spark/sql/SchemaRDD.scala      | 45 ++++++++++++-------
 2 files changed, 44 insertions(+), 25 deletions(-)

diff --git a/python/pyspark/sql.py b/python/pyspark/sql.py
index 5051c82da32a..ffe177576f36 100644
--- a/python/pyspark/sql.py
+++ b/python/pyspark/sql.py
@@ -152,10 +152,12 @@ def jsonFile(self, path):
         >>> ofn.close()
         >>> srdd = sqlCtx.jsonFile(jsonFile)
         >>> sqlCtx.registerRDDAsTable(srdd, "table1")
-        >>> srdd2 = sqlCtx.sql("SELECT field1 AS f1, field2 as f2, field3 as f3 from table1")
-        >>> srdd2.collect() == [{"f1": 1, "f2": "row1", "f3":{"field4":11}},
-        ...                     {"f1": 2, "f2": "row2", "f3":{"field4":22}},
-        ...                     {"f1": 3, "f2": "row3", "f3":{"field4":33}}]
+        >>> srdd2 = sqlCtx.sql(
+        ...   "SELECT field1 AS f1, field2 as f2, field3 as f3, field6 as f4 from table1")
+        >>> srdd2.collect() == [
+        ... {"f1":1, "f2":"row1", "f3":{"field4":11, "field5": None}, "f4":None},
+        ... {"f1":2, "f2":None, "f3":{"field4":22,  "field5": [10, 11]}, "f4":[{"field7": "row2"}]},
+        ... {"f1":None, "f2":"row3", "f3":{"field4":33, "field5": []}, "f4":None}]
         True
         """
         jschema_rdd = self._ssql_ctx.jsonFile(path)
@@ -167,10 +169,12 @@ def jsonRDD(self, rdd):
 
         >>> srdd = sqlCtx.jsonRDD(json)
         >>> sqlCtx.registerRDDAsTable(srdd, "table1")
-        >>> srdd2 = sqlCtx.sql("SELECT field1 AS f1, field2 as f2, field3 as f3 from table1")
-        >>> srdd2.collect() == [{"f1": 1, "f2": "row1", "f3":{"field4":11}},
-        ...                     {"f1": 2, "f2": "row2", "f3":{"field4":22}},
-        ...                     {"f1": 3, "f2": "row3", "f3":{"field4":33}}]
+        >>> srdd2 = sqlCtx.sql(
+        ...   "SELECT field1 AS f1, field2 as f2, field3 as f3, field6 as f4 from table1")
+        >>> srdd2.collect() == [
+        ... {"f1":1, "f2":"row1", "f3":{"field4":11, "field5": None}, "f4":None},
+        ... {"f1":2, "f2":None, "f3":{"field4":22,  "field5": [10, 11]}, "f4":[{"field7": "row2"}]},
+        ... {"f1":None, "f2":"row3", "f3":{"field4":33, "field5": []}, "f4":None}]
         True
         """
         def func(split, iterator):
@@ -492,8 +496,8 @@ def _test():
     globs['rdd'] = sc.parallelize([{"field1" : 1, "field2" : "row1"},
         {"field1" : 2, "field2": "row2"}, {"field1" : 3, "field2": "row3"}])
     jsonStrings = ['{"field1": 1, "field2": "row1", "field3":{"field4":11}}',
-       '{"field1" : 2, "field2": "row2", "field3":{"field4":22}}',
-       '{"field1" : 3, "field2": "row3", "field3":{"field4":33}}']
+       '{"field1" : 2, "field3":{"field4":22, "field5": [10, 11]}, "field6":[{"field7": "row2"}]}',
+       '{"field1" : null, "field2": "row3", "field3":{"field4":33, "field5": []}}']
     globs['jsonStrings'] = jsonStrings
     globs['json'] = sc.parallelize(jsonStrings)
     globs['nestedRdd1'] = sc.parallelize([
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala
index 8f9f54f610e9..8bcfc7c064c2 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala
@@ -17,6 +17,11 @@
 
 package org.apache.spark.sql
 
+import java.util.{Map => JMap, List => JList, Set => JSet}
+
+import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
+
 import net.razorvine.pickle.Pickler
 
 import org.apache.spark.{Dependency, OneToOneDependency, Partition, Partitioner, TaskContext}
@@ -27,10 +32,9 @@ import org.apache.spark.sql.catalyst.analysis._
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.logical._
 import org.apache.spark.sql.catalyst.plans.{Inner, JoinType}
-import org.apache.spark.sql.catalyst.types.{DataType, StructType, BooleanType}
+import org.apache.spark.sql.catalyst.types.{ArrayType, BooleanType, StructType}
 import org.apache.spark.sql.execution.{ExistingRdd, SparkLogicalPlan}
 import org.apache.spark.api.java.JavaRDD
-import java.util.{Map => JMap}
 
 /**
  * :: AlphaComponent ::
@@ -359,6 +363,28 @@ class SchemaRDD(
         case (obj, (name, dataType)) =>
           dataType match {
             case struct: StructType => map.put(name, rowToMap(obj.asInstanceOf[Row], struct))
+            case array @ ArrayType(struct: StructType) =>
+              val arrayValues = obj match {
+                case seq: Seq[Any] =>
+                  seq.map(element => rowToMap(element.asInstanceOf[Row], struct)).asJava
+                case list: JList[Any] =>
+                  list.map(element => rowToMap(element.asInstanceOf[Row], struct))
+                case set: JSet[Any] =>
+                  set.map(element => rowToMap(element.asInstanceOf[Row], struct))
+                case array if array != null && array.getClass.isArray =>
+                  array.asInstanceOf[Array[Any]].map {
+                    element => rowToMap(element.asInstanceOf[Row], struct)
+                  }
+                case other => other
+              }
+              map.put(name, arrayValues)
+            case array: ArrayType => {
+              val arrayValues = obj match {
+                case seq: Seq[Any] => seq.asJava
+                case other => other
+              }
+              map.put(name, arrayValues)
+            }
             case other => map.put(name, obj)
           }
       }
@@ -366,22 +392,11 @@ class SchemaRDD(
       map
     }
 
-    // TODO: Actually, the schema of a row should be represented by a StructType instead of
-    // a Seq[Attribute]. Once we have finished that change, we can just use rowToMap to
-    // construct the Map for python.
-    val fields: Seq[(String, DataType)] = this.queryExecution.analyzed.output.map(
-      field => (field.name, field.dataType))
+    val rowSchema = StructType.fromAttributes(this.queryExecution.analyzed.output)
     this.mapPartitions { iter =>
       val pickle = new Pickler
       iter.map { row =>
-        val map: JMap[String, Any] = new java.util.HashMap
-        row.zip(fields).foreach { case (obj, (name, dataType)) =>
-          dataType match {
-            case struct: StructType => map.put(name, rowToMap(obj.asInstanceOf[Row], struct))
-            case other => map.put(name, obj)
-          }
-        }
-        map
+        rowToMap(row, rowSchema)
       }.grouped(10).map(batched => pickle.dumps(batched.toArray))
     }
   }

From 50561f4396be7d641feb2a7a54a374d294628231 Mon Sep 17 00:00:00 2001
From: Yanjie Gao <gaoyanjie55@163.com>
Date: Mon, 7 Jul 2014 19:40:04 -0700
Subject: [PATCH 589/810] [SPARK-2235][SQL]Spark SQL basicOperator add
 Intersect operator

Hi all,
I want to submit a basic operator Intersect
For example , in sql case
select * from table1
intersect
select * from table2
So ,i want use this operator support this function in Spark SQL
This operator will return the  the intersection of SparkPlan child table RDD .
JIRA:https://issues.apache.org/jira/browse/SPARK-2235

Author: Yanjie Gao <gaoyanjie55@163.com>
Author: YanjieGao <396154235@qq.com>

Closes #1150 from YanjieGao/patch-5 and squashes the following commits:

4629afe [YanjieGao] reformat the code
bdc2ac0 [YanjieGao] reformat the code as Michael's suggestion
3b29ad6 [YanjieGao] Merge remote branch 'upstream/master' into patch-5
1cfbfe6 [YanjieGao] refomat some files
ea78f33 [YanjieGao] resolve conflict and add annotation on basicOperator and remove HiveQl
0c7cca5 [YanjieGao] modify format problem
a802ca8 [YanjieGao] Merge remote branch 'upstream/master' into patch-5
5e374c7 [YanjieGao] resolve conflict in SparkStrategies and basicOperator
f7961f6 [Yanjie Gao] update the line less than
bdc4a05 [Yanjie Gao] Update basicOperators.scala
0b49837 [Yanjie Gao] delete the annotation
f1288b4 [Yanjie Gao] delete annotation
e2b64be [Yanjie Gao] Update basicOperators.scala
4dd453e [Yanjie Gao] Update SQLQuerySuite.scala
790765d [Yanjie Gao] Update SparkStrategies.scala
ac73e60 [Yanjie Gao] Update basicOperators.scala
d4ac5e5 [Yanjie Gao] Update HiveQl.scala
61e88e7 [Yanjie Gao] Update SqlParser.scala
469f099 [Yanjie Gao] Update basicOperators.scala
e5bff61 [Yanjie Gao] Spark SQL basicOperator add Intersect operator
---
 .../org/apache/spark/sql/catalyst/SqlParser.scala   |  2 ++
 .../sql/catalyst/plans/logical/basicOperators.scala |  5 +++++
 .../spark/sql/execution/SparkStrategies.scala       |  2 ++
 .../apache/spark/sql/execution/basicOperators.scala | 13 +++++++++++++
 .../scala/org/apache/spark/sql/SQLQuerySuite.scala  | 11 +++++++++++
 5 files changed, 33 insertions(+)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
index ecb11129557b..e5653c5b14ac 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
@@ -118,6 +118,7 @@ class SqlParser extends StandardTokenParsers with PackratParsers {
   protected val UNCACHE = Keyword("UNCACHE")
   protected val UNION = Keyword("UNION")
   protected val WHERE = Keyword("WHERE")
+  protected val INTERSECT = Keyword("INTERSECT")
   protected val EXCEPT = Keyword("EXCEPT")
 
 
@@ -140,6 +141,7 @@ class SqlParser extends StandardTokenParsers with PackratParsers {
   protected lazy val query: Parser[LogicalPlan] = (
     select * (
         UNION ~ ALL ^^^ { (q1: LogicalPlan, q2: LogicalPlan) => Union(q1, q2) } |
+        INTERSECT ^^^ { (q1: LogicalPlan, q2: LogicalPlan) => Intersect(q1, q2) } |
         EXCEPT ^^^ { (q1: LogicalPlan, q2: LogicalPlan) => Except(q1, q2)} |
         UNION ~ opt(DISTINCT) ^^^ { (q1: LogicalPlan, q2: LogicalPlan) => Distinct(Union(q1, q2)) }
       )
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
index 0728fa73fb10..1537de259c5b 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
@@ -210,3 +210,8 @@ case class Distinct(child: LogicalPlan) extends UnaryNode {
 case object NoRelation extends LeafNode {
   override def output = Nil
 }
+
+case class Intersect(left: LogicalPlan, right: LogicalPlan) extends BinaryNode {
+  override def output = left.output
+  override def references = Set.empty
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
index 9e036e127bd5..7080074a69c0 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
@@ -275,6 +275,8 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
         execution.Union(unionChildren.map(planLater))(sqlContext) :: Nil
       case logical.Except(left,right) =>                                        
         execution.Except(planLater(left),planLater(right)) :: Nil   
+      case logical.Intersect(left, right) =>
+        execution.Intersect(planLater(left), planLater(right)) :: Nil
       case logical.Generate(generator, join, outer, _, child) =>
         execution.Generate(generator, join = join, outer = outer, planLater(child)) :: Nil
       case logical.NoRelation =>
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
index 4b59e0b4e58e..e8816f0b3cd9 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
@@ -220,3 +220,16 @@ case class Except(left: SparkPlan, right: SparkPlan) extends BinaryNode {
   }
 }
 
+/**
+ * :: DeveloperApi ::
+ * Returns the rows in left that also appear in right using the built in spark
+ * intersection function.
+ */
+@DeveloperApi
+case class Intersect(left: SparkPlan, right: SparkPlan) extends BinaryNode {
+  override def output = children.head.output
+
+  override def execute() = {
+    left.execute().map(_.copy()).intersection(right.execute().map(_.copy()))
+  }
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index 5c6701e203d1..fa1f32f8a49a 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -385,6 +385,17 @@ class SQLQuerySuite extends QueryTest {
       sql("SELECT * FROM upperCaseData EXCEPT SELECT * FROM upperCaseData "), Nil)
   }
 
+ test("INTERSECT") {
+    checkAnswer(
+      sql("SELECT * FROM lowerCaseData INTERSECT SELECT * FROM lowerCaseData"),
+      (1, "a") ::
+      (2, "b") ::
+      (3, "c") ::
+      (4, "d") :: Nil)
+    checkAnswer(
+      sql("SELECT * FROM lowerCaseData INTERSECT SELECT * FROM upperCaseData"), Nil)
+  }
+
   test("SET commands semantics using sql()") {
     TestSQLContext.settings.synchronized {
       clear()

From 0128905eea9f8c597ca238b14c18908995511e76 Mon Sep 17 00:00:00 2001
From: Rishi Verma <riverma@apache.org>
Date: Tue, 8 Jul 2014 00:29:23 -0700
Subject: [PATCH 590/810] Updated programming-guide.md

Made sure that readers know the random number generator seed argument, within the 'takeSample' method, is optional.

Author: Rishi Verma <riverma@apache.org>

Closes #1324 from riverma/patch-1 and squashes the following commits:

4699676 [Rishi Verma] Updated programming-guide.md
---
 docs/programming-guide.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/programming-guide.md b/docs/programming-guide.md
index 06e4c4ce527e..b09d6347cd1b 100644
--- a/docs/programming-guide.md
+++ b/docs/programming-guide.md
@@ -975,8 +975,8 @@ for details.
   <td> Return an array with the first <i>n</i> elements of the dataset. Note that this is currently not executed in parallel. Instead, the driver program computes all the elements. </td>
 </tr>
 <tr>
-  <td> <b>takeSample</b>(<i>withReplacement</i>, <i>num</i>, <i>seed</i>) </td>
-  <td> Return an array with a random sample of <i>num</i> elements of the dataset, with or without replacement, using the given random number generator seed. </td>
+  <td> <b>takeSample</b>(<i>withReplacement</i>, <i>num</i>, [<i>seed</i>]) </td>
+  <td> Return an array with a random sample of <i>num</i> elements of the dataset, with or without replacement, optionally pre-specifying a random number generator seed.</td>
 </tr>
 <tr>
   <td> <b>takeOrdered</b>(<i>n</i>, <i>[ordering]</i>) </td>

From 3cd5029be709307415f911236472a685e406e763 Mon Sep 17 00:00:00 2001
From: witgo <witgo@qq.com>
Date: Tue, 8 Jul 2014 00:31:42 -0700
Subject: [PATCH 591/810] =?UTF-8?q?Resolve=20sbt=20warnings=20during=20bui?=
 =?UTF-8?q?ld=20=E2=85=A1?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Author: witgo <witgo@qq.com>

Closes #1153 from witgo/expectResult and squashes the following commits:

97541d8 [witgo] merge master
ead26e7 [witgo] Resolve sbt warnings during build
---
 .../org/apache/spark/AccumulatorSuite.scala   |  6 +-
 .../apache/spark/util/NextIteratorSuite.scala | 38 +++++-----
 .../spark/util/SizeEstimatorSuite.scala       | 70 +++++++++----------
 .../spark/sql/columnar/ColumnStatsSuite.scala |  6 +-
 .../spark/sql/columnar/ColumnTypeSuite.scala  |  8 +--
 .../columnar/NullableColumnBuilderSuite.scala | 14 ++--
 .../compression/BooleanBitSetSuite.scala      | 10 +--
 .../compression/DictionaryEncodingSuite.scala | 10 +--
 .../compression/IntegralDeltaSuite.scala      | 16 ++---
 .../compression/RunLengthEncodingSuite.scala  | 10 +--
 10 files changed, 94 insertions(+), 94 deletions(-)

diff --git a/core/src/test/scala/org/apache/spark/AccumulatorSuite.scala b/core/src/test/scala/org/apache/spark/AccumulatorSuite.scala
index 3aab88e9e919..52d1d5277658 100644
--- a/core/src/test/scala/org/apache/spark/AccumulatorSuite.scala
+++ b/core/src/test/scala/org/apache/spark/AccumulatorSuite.scala
@@ -61,7 +61,7 @@ class AccumulatorSuite extends FunSuite with Matchers with LocalSparkContext {
     val acc : Accumulator[Int] = sc.accumulator(0)
 
     val d = sc.parallelize(1 to 20)
-    evaluating {d.foreach{x => acc.value = x}} should produce [Exception]
+    an [Exception] should be thrownBy {d.foreach{x => acc.value = x}}
   }
 
   test ("add value to collection accumulators") {
@@ -87,11 +87,11 @@ class AccumulatorSuite extends FunSuite with Matchers with LocalSparkContext {
       sc = new SparkContext("local[" + nThreads + "]", "test")
       val acc: Accumulable[mutable.Set[Any], Any] = sc.accumulable(new mutable.HashSet[Any]())
       val d = sc.parallelize(1 to maxI)
-      evaluating {
+      an [SparkException] should be thrownBy {
         d.foreach {
           x => acc.value += x
         }
-      } should produce [SparkException]
+      }
       resetSparkContext()
     }
   }
diff --git a/core/src/test/scala/org/apache/spark/util/NextIteratorSuite.scala b/core/src/test/scala/org/apache/spark/util/NextIteratorSuite.scala
index cf438a3d72a0..72e81f3f1a88 100644
--- a/core/src/test/scala/org/apache/spark/util/NextIteratorSuite.scala
+++ b/core/src/test/scala/org/apache/spark/util/NextIteratorSuite.scala
@@ -27,45 +27,45 @@ import org.scalatest.Matchers
 class NextIteratorSuite extends FunSuite with Matchers {
   test("one iteration") {
     val i = new StubIterator(Buffer(1))
-    i.hasNext should be === true
-    i.next should be === 1
-    i.hasNext should be === false
+    i.hasNext should be (true)
+    i.next should be (1)
+    i.hasNext should be (false)
     intercept[NoSuchElementException] { i.next() }
   }
 
   test("two iterations") {
     val i = new StubIterator(Buffer(1, 2))
-    i.hasNext should be === true
-    i.next should be === 1
-    i.hasNext should be === true
-    i.next should be === 2
-    i.hasNext should be === false
+    i.hasNext should be (true)
+    i.next should be (1)
+    i.hasNext should be (true)
+    i.next should be (2)
+    i.hasNext should be (false)
     intercept[NoSuchElementException] { i.next() }
   }
 
   test("empty iteration") {
     val i = new StubIterator(Buffer())
-    i.hasNext should be === false
+    i.hasNext should be (false)
     intercept[NoSuchElementException] { i.next() }
   }
 
   test("close is called once for empty iterations") {
     val i = new StubIterator(Buffer())
-    i.hasNext should be === false
-    i.hasNext should be === false
-    i.closeCalled should be === 1
+    i.hasNext should be (false)
+    i.hasNext should be (false)
+    i.closeCalled should be (1)
   }
 
   test("close is called once for non-empty iterations") {
     val i = new StubIterator(Buffer(1, 2))
-    i.next should be === 1
-    i.next should be === 2
+    i.next should be (1)
+    i.next should be (2)
     // close isn't called until we check for the next element
-    i.closeCalled should be === 0
-    i.hasNext should be === false
-    i.closeCalled should be === 1
-    i.hasNext should be === false
-    i.closeCalled should be === 1
+    i.closeCalled should be (0)
+    i.hasNext should be (false)
+    i.closeCalled should be (1)
+    i.hasNext should be (false)
+    i.closeCalled should be (1)
   }
 
   class StubIterator(ints: Buffer[Int])  extends NextIterator[Int] {
diff --git a/core/src/test/scala/org/apache/spark/util/SizeEstimatorSuite.scala b/core/src/test/scala/org/apache/spark/util/SizeEstimatorSuite.scala
index b583a8bd46ae..f9d1af88f3a1 100644
--- a/core/src/test/scala/org/apache/spark/util/SizeEstimatorSuite.scala
+++ b/core/src/test/scala/org/apache/spark/util/SizeEstimatorSuite.scala
@@ -63,53 +63,53 @@ class SizeEstimatorSuite
   }
 
   test("simple classes") {
-    expectResult(16)(SizeEstimator.estimate(new DummyClass1))
-    expectResult(16)(SizeEstimator.estimate(new DummyClass2))
-    expectResult(24)(SizeEstimator.estimate(new DummyClass3))
-    expectResult(24)(SizeEstimator.estimate(new DummyClass4(null)))
-    expectResult(48)(SizeEstimator.estimate(new DummyClass4(new DummyClass3)))
+    assertResult(16)(SizeEstimator.estimate(new DummyClass1))
+    assertResult(16)(SizeEstimator.estimate(new DummyClass2))
+    assertResult(24)(SizeEstimator.estimate(new DummyClass3))
+    assertResult(24)(SizeEstimator.estimate(new DummyClass4(null)))
+    assertResult(48)(SizeEstimator.estimate(new DummyClass4(new DummyClass3)))
   }
 
   // NOTE: The String class definition varies across JDK versions (1.6 vs. 1.7) and vendors
   // (Sun vs IBM). Use a DummyString class to make tests deterministic.
   test("strings") {
-    expectResult(40)(SizeEstimator.estimate(DummyString("")))
-    expectResult(48)(SizeEstimator.estimate(DummyString("a")))
-    expectResult(48)(SizeEstimator.estimate(DummyString("ab")))
-    expectResult(56)(SizeEstimator.estimate(DummyString("abcdefgh")))
+    assertResult(40)(SizeEstimator.estimate(DummyString("")))
+    assertResult(48)(SizeEstimator.estimate(DummyString("a")))
+    assertResult(48)(SizeEstimator.estimate(DummyString("ab")))
+    assertResult(56)(SizeEstimator.estimate(DummyString("abcdefgh")))
   }
 
   test("primitive arrays") {
-    expectResult(32)(SizeEstimator.estimate(new Array[Byte](10)))
-    expectResult(40)(SizeEstimator.estimate(new Array[Char](10)))
-    expectResult(40)(SizeEstimator.estimate(new Array[Short](10)))
-    expectResult(56)(SizeEstimator.estimate(new Array[Int](10)))
-    expectResult(96)(SizeEstimator.estimate(new Array[Long](10)))
-    expectResult(56)(SizeEstimator.estimate(new Array[Float](10)))
-    expectResult(96)(SizeEstimator.estimate(new Array[Double](10)))
-    expectResult(4016)(SizeEstimator.estimate(new Array[Int](1000)))
-    expectResult(8016)(SizeEstimator.estimate(new Array[Long](1000)))
+    assertResult(32)(SizeEstimator.estimate(new Array[Byte](10)))
+    assertResult(40)(SizeEstimator.estimate(new Array[Char](10)))
+    assertResult(40)(SizeEstimator.estimate(new Array[Short](10)))
+    assertResult(56)(SizeEstimator.estimate(new Array[Int](10)))
+    assertResult(96)(SizeEstimator.estimate(new Array[Long](10)))
+    assertResult(56)(SizeEstimator.estimate(new Array[Float](10)))
+    assertResult(96)(SizeEstimator.estimate(new Array[Double](10)))
+    assertResult(4016)(SizeEstimator.estimate(new Array[Int](1000)))
+    assertResult(8016)(SizeEstimator.estimate(new Array[Long](1000)))
   }
 
   test("object arrays") {
     // Arrays containing nulls should just have one pointer per element
-    expectResult(56)(SizeEstimator.estimate(new Array[String](10)))
-    expectResult(56)(SizeEstimator.estimate(new Array[AnyRef](10)))
+    assertResult(56)(SizeEstimator.estimate(new Array[String](10)))
+    assertResult(56)(SizeEstimator.estimate(new Array[AnyRef](10)))
     // For object arrays with non-null elements, each object should take one pointer plus
     // however many bytes that class takes. (Note that Array.fill calls the code in its
     // second parameter separately for each object, so we get distinct objects.)
-    expectResult(216)(SizeEstimator.estimate(Array.fill(10)(new DummyClass1)))
-    expectResult(216)(SizeEstimator.estimate(Array.fill(10)(new DummyClass2)))
-    expectResult(296)(SizeEstimator.estimate(Array.fill(10)(new DummyClass3)))
-    expectResult(56)(SizeEstimator.estimate(Array(new DummyClass1, new DummyClass2)))
+    assertResult(216)(SizeEstimator.estimate(Array.fill(10)(new DummyClass1)))
+    assertResult(216)(SizeEstimator.estimate(Array.fill(10)(new DummyClass2)))
+    assertResult(296)(SizeEstimator.estimate(Array.fill(10)(new DummyClass3)))
+    assertResult(56)(SizeEstimator.estimate(Array(new DummyClass1, new DummyClass2)))
 
     // Past size 100, our samples 100 elements, but we should still get the right size.
-    expectResult(28016)(SizeEstimator.estimate(Array.fill(1000)(new DummyClass3)))
+    assertResult(28016)(SizeEstimator.estimate(Array.fill(1000)(new DummyClass3)))
 
     // If an array contains the *same* element many times, we should only count it once.
     val d1 = new DummyClass1
-    expectResult(72)(SizeEstimator.estimate(Array.fill(10)(d1))) // 10 pointers plus 8-byte object
-    expectResult(432)(SizeEstimator.estimate(Array.fill(100)(d1))) // 100 pointers plus 8-byte object
+    assertResult(72)(SizeEstimator.estimate(Array.fill(10)(d1))) // 10 pointers plus 8-byte object
+    assertResult(432)(SizeEstimator.estimate(Array.fill(100)(d1))) // 100 pointers plus 8-byte object
 
     // Same thing with huge array containing the same element many times. Note that this won't
     // return exactly 4032 because it can't tell that *all* the elements will equal the first
@@ -127,10 +127,10 @@ class SizeEstimatorSuite
     val initialize = PrivateMethod[Unit]('initialize)
     SizeEstimator invokePrivate initialize()
 
-    expectResult(40)(SizeEstimator.estimate(DummyString("")))
-    expectResult(48)(SizeEstimator.estimate(DummyString("a")))
-    expectResult(48)(SizeEstimator.estimate(DummyString("ab")))
-    expectResult(56)(SizeEstimator.estimate(DummyString("abcdefgh")))
+    assertResult(40)(SizeEstimator.estimate(DummyString("")))
+    assertResult(48)(SizeEstimator.estimate(DummyString("a")))
+    assertResult(48)(SizeEstimator.estimate(DummyString("ab")))
+    assertResult(56)(SizeEstimator.estimate(DummyString("abcdefgh")))
     resetOrClear("os.arch", arch)
   }
 
@@ -142,10 +142,10 @@ class SizeEstimatorSuite
     val initialize = PrivateMethod[Unit]('initialize)
     SizeEstimator invokePrivate initialize()
 
-    expectResult(56)(SizeEstimator.estimate(DummyString("")))
-    expectResult(64)(SizeEstimator.estimate(DummyString("a")))
-    expectResult(64)(SizeEstimator.estimate(DummyString("ab")))
-    expectResult(72)(SizeEstimator.estimate(DummyString("abcdefgh")))
+    assertResult(56)(SizeEstimator.estimate(DummyString("")))
+    assertResult(64)(SizeEstimator.estimate(DummyString("a")))
+    assertResult(64)(SizeEstimator.estimate(DummyString("ab")))
+    assertResult(72)(SizeEstimator.estimate(DummyString("abcdefgh")))
 
     resetOrClear("os.arch", arch)
     resetOrClear("spark.test.useCompressedOops", oops)
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala
index 78640b876d4a..6f0d46d81626 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala
@@ -39,7 +39,7 @@ class ColumnStatsSuite extends FunSuite {
 
     test(s"$columnStatsName: empty") {
       val columnStats = columnStatsClass.newInstance()
-      expectResult(columnStats.initialBounds, "Wrong initial bounds") {
+      assertResult(columnStats.initialBounds, "Wrong initial bounds") {
         (columnStats.lowerBound, columnStats.upperBound)
       }
     }
@@ -54,8 +54,8 @@ class ColumnStatsSuite extends FunSuite {
       val values = rows.map(_.head.asInstanceOf[T#JvmType])
       val ordering = columnType.dataType.ordering.asInstanceOf[Ordering[T#JvmType]]
 
-      expectResult(values.min(ordering), "Wrong lower bound")(columnStats.lowerBound)
-      expectResult(values.max(ordering), "Wrong upper bound")(columnStats.upperBound)
+      assertResult(values.min(ordering), "Wrong lower bound")(columnStats.lowerBound)
+      assertResult(values.max(ordering), "Wrong upper bound")(columnStats.upperBound)
     }
   }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnTypeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnTypeSuite.scala
index 71be41056768..314b7d317ed7 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnTypeSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnTypeSuite.scala
@@ -35,7 +35,7 @@ class ColumnTypeSuite extends FunSuite with Logging {
       BOOLEAN -> 1, STRING -> 8, BINARY -> 16, GENERIC -> 16)
 
     checks.foreach { case (columnType, expectedSize) =>
-      expectResult(expectedSize, s"Wrong defaultSize for $columnType") {
+      assertResult(expectedSize, s"Wrong defaultSize for $columnType") {
         columnType.defaultSize
       }
     }
@@ -47,7 +47,7 @@ class ColumnTypeSuite extends FunSuite with Logging {
         value: JvmType,
         expected: Int) {
 
-      expectResult(expected, s"Wrong actualSize for $columnType") {
+      assertResult(expected, s"Wrong actualSize for $columnType") {
         columnType.actualSize(value)
       }
     }
@@ -127,7 +127,7 @@ class ColumnTypeSuite extends FunSuite with Logging {
     val length = buffer.getInt()
     assert(length === serializedObj.length)
 
-    expectResult(obj, "Deserialized object didn't equal to the original object") {
+    assertResult(obj, "Deserialized object didn't equal to the original object") {
       val bytes = new Array[Byte](length)
       buffer.get(bytes, 0, length)
       SparkSqlSerializer.deserialize(bytes)
@@ -136,7 +136,7 @@ class ColumnTypeSuite extends FunSuite with Logging {
     buffer.rewind()
     buffer.putInt(serializedObj.length).put(serializedObj)
 
-    expectResult(obj, "Deserialized object didn't equal to the original object") {
+    assertResult(obj, "Deserialized object didn't equal to the original object") {
       buffer.rewind()
       SparkSqlSerializer.deserialize(GENERIC.extract(buffer))
     }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/NullableColumnBuilderSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/NullableColumnBuilderSuite.scala
index d9d1e1bfddb7..d8898527baa3 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/NullableColumnBuilderSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/NullableColumnBuilderSuite.scala
@@ -48,8 +48,8 @@ class NullableColumnBuilderSuite extends FunSuite {
       val columnBuilder = TestNullableColumnBuilder(columnType)
       val buffer = columnBuilder.build()
 
-      expectResult(columnType.typeId, "Wrong column type ID")(buffer.getInt())
-      expectResult(0, "Wrong null count")(buffer.getInt())
+      assertResult(columnType.typeId, "Wrong column type ID")(buffer.getInt())
+      assertResult(0, "Wrong null count")(buffer.getInt())
       assert(!buffer.hasRemaining)
     }
 
@@ -63,8 +63,8 @@ class NullableColumnBuilderSuite extends FunSuite {
 
       val buffer = columnBuilder.build()
 
-      expectResult(columnType.typeId, "Wrong column type ID")(buffer.getInt())
-      expectResult(0, "Wrong null count")(buffer.getInt())
+      assertResult(columnType.typeId, "Wrong column type ID")(buffer.getInt())
+      assertResult(0, "Wrong null count")(buffer.getInt())
     }
 
     test(s"$typeName column builder: null values") {
@@ -79,11 +79,11 @@ class NullableColumnBuilderSuite extends FunSuite {
 
       val buffer = columnBuilder.build()
 
-      expectResult(columnType.typeId, "Wrong column type ID")(buffer.getInt())
-      expectResult(4, "Wrong null count")(buffer.getInt())
+      assertResult(columnType.typeId, "Wrong column type ID")(buffer.getInt())
+      assertResult(4, "Wrong null count")(buffer.getInt())
 
       // For null positions
-      (1 to 7 by 2).foreach(expectResult(_, "Wrong null position")(buffer.getInt()))
+      (1 to 7 by 2).foreach(assertResult(_, "Wrong null position")(buffer.getInt()))
 
       // For non-null values
       (0 until 4).foreach { _ =>
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/BooleanBitSetSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/BooleanBitSetSuite.scala
index 93259a19b9fe..5fba00480967 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/BooleanBitSetSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/BooleanBitSetSuite.scala
@@ -48,18 +48,18 @@ class BooleanBitSetSuite extends FunSuite {
     }
 
     // 4 extra bytes for compression scheme type ID
-    expectResult(headerSize + compressedSize, "Wrong buffer capacity")(buffer.capacity)
+    assertResult(headerSize + compressedSize, "Wrong buffer capacity")(buffer.capacity)
 
     // Skips column header
     buffer.position(headerSize)
-    expectResult(BooleanBitSet.typeId, "Wrong compression scheme ID")(buffer.getInt())
-    expectResult(count, "Wrong element count")(buffer.getInt())
+    assertResult(BooleanBitSet.typeId, "Wrong compression scheme ID")(buffer.getInt())
+    assertResult(count, "Wrong element count")(buffer.getInt())
 
     var word = 0: Long
     for (i <- 0 until count) {
       val bit = i % BITS_PER_LONG
       word = if (bit == 0) buffer.getLong() else word
-      expectResult(values(i), s"Wrong value in compressed buffer, index=$i") {
+      assertResult(values(i), s"Wrong value in compressed buffer, index=$i") {
         (word & ((1: Long) << bit)) != 0
       }
     }
@@ -75,7 +75,7 @@ class BooleanBitSetSuite extends FunSuite {
     if (values.nonEmpty) {
       values.foreach {
         assert(decoder.hasNext)
-        expectResult(_, "Wrong decoded value")(decoder.next())
+        assertResult(_, "Wrong decoded value")(decoder.next())
       }
     }
     assert(!decoder.hasNext)
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/DictionaryEncodingSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/DictionaryEncodingSuite.scala
index 198dcd881934..d8ae2a26778c 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/DictionaryEncodingSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/DictionaryEncodingSuite.scala
@@ -71,22 +71,22 @@ class DictionaryEncodingSuite extends FunSuite {
         // 2 bytes for each `Short`
         val compressedSize = 4 + dictionarySize + 2 * inputSeq.length
         // 4 extra bytes for compression scheme type ID
-        expectResult(headerSize + compressedSize, "Wrong buffer capacity")(buffer.capacity)
+        assertResult(headerSize + compressedSize, "Wrong buffer capacity")(buffer.capacity)
 
         // Skips column header
         buffer.position(headerSize)
-        expectResult(DictionaryEncoding.typeId, "Wrong compression scheme ID")(buffer.getInt())
+        assertResult(DictionaryEncoding.typeId, "Wrong compression scheme ID")(buffer.getInt())
 
         val dictionary = buildDictionary(buffer).toMap
 
         dictValues.foreach { i =>
-          expectResult(i, "Wrong dictionary entry") {
+          assertResult(i, "Wrong dictionary entry") {
             dictionary(values(i))
           }
         }
 
         inputSeq.foreach { i =>
-          expectResult(i.toShort, "Wrong column element value")(buffer.getShort())
+          assertResult(i.toShort, "Wrong column element value")(buffer.getShort())
         }
 
         // -------------
@@ -101,7 +101,7 @@ class DictionaryEncodingSuite extends FunSuite {
         if (inputSeq.nonEmpty) {
           inputSeq.foreach { i =>
             assert(decoder.hasNext)
-            expectResult(values(i), "Wrong decoded value")(decoder.next())
+            assertResult(values(i), "Wrong decoded value")(decoder.next())
           }
         }
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/IntegralDeltaSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/IntegralDeltaSuite.scala
index 46af6e001c63..17619dcf974e 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/IntegralDeltaSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/IntegralDeltaSuite.scala
@@ -69,21 +69,21 @@ class IntegralDeltaSuite extends FunSuite {
       })
 
       // 4 extra bytes for compression scheme type ID
-      expectResult(headerSize + compressedSize, "Wrong buffer capacity")(buffer.capacity)
+      assertResult(headerSize + compressedSize, "Wrong buffer capacity")(buffer.capacity)
 
       buffer.position(headerSize)
-      expectResult(scheme.typeId, "Wrong compression scheme ID")(buffer.getInt())
+      assertResult(scheme.typeId, "Wrong compression scheme ID")(buffer.getInt())
 
       if (input.nonEmpty) {
-        expectResult(Byte.MinValue, "The first byte should be an escaping mark")(buffer.get())
-        expectResult(input.head, "The first value is wrong")(columnType.extract(buffer))
+        assertResult(Byte.MinValue, "The first byte should be an escaping mark")(buffer.get())
+        assertResult(input.head, "The first value is wrong")(columnType.extract(buffer))
 
         (input.tail, deltas).zipped.foreach { (value, delta) =>
           if (math.abs(delta) <= Byte.MaxValue) {
-            expectResult(delta, "Wrong delta")(buffer.get())
+            assertResult(delta, "Wrong delta")(buffer.get())
           } else {
-            expectResult(Byte.MinValue, "Expecting escaping mark here")(buffer.get())
-            expectResult(value, "Wrong value")(columnType.extract(buffer))
+            assertResult(Byte.MinValue, "Expecting escaping mark here")(buffer.get())
+            assertResult(value, "Wrong value")(columnType.extract(buffer))
           }
         }
       }
@@ -99,7 +99,7 @@ class IntegralDeltaSuite extends FunSuite {
       if (input.nonEmpty) {
         input.foreach{
           assert(decoder.hasNext)
-          expectResult(_, "Wrong decoded value")(decoder.next())
+          assertResult(_, "Wrong decoded value")(decoder.next())
         }
       }
       assert(!decoder.hasNext)
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/RunLengthEncodingSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/RunLengthEncodingSuite.scala
index d3b73ba19d47..40115beb9889 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/RunLengthEncodingSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/RunLengthEncodingSuite.scala
@@ -61,15 +61,15 @@ class RunLengthEncodingSuite extends FunSuite {
       }.sum
 
       // 4 extra bytes for compression scheme type ID
-      expectResult(headerSize + compressedSize, "Wrong buffer capacity")(buffer.capacity)
+      assertResult(headerSize + compressedSize, "Wrong buffer capacity")(buffer.capacity)
 
       // Skips column header
       buffer.position(headerSize)
-      expectResult(RunLengthEncoding.typeId, "Wrong compression scheme ID")(buffer.getInt())
+      assertResult(RunLengthEncoding.typeId, "Wrong compression scheme ID")(buffer.getInt())
 
       inputRuns.foreach { case (index, run) =>
-        expectResult(values(index), "Wrong column element value")(columnType.extract(buffer))
-        expectResult(run, "Wrong run length")(buffer.getInt())
+        assertResult(values(index), "Wrong column element value")(columnType.extract(buffer))
+        assertResult(run, "Wrong run length")(buffer.getInt())
       }
 
       // -------------
@@ -84,7 +84,7 @@ class RunLengthEncodingSuite extends FunSuite {
       if (inputSeq.nonEmpty) {
         inputSeq.foreach { i =>
           assert(decoder.hasNext)
-          expectResult(values(i), "Wrong decoded value")(decoder.next())
+          assertResult(values(i), "Wrong decoded value")(decoder.next())
         }
       }
 

From 5a4063645dd7bb4cd8bda890785235729804ab09 Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Tue, 8 Jul 2014 00:41:46 -0700
Subject: [PATCH 592/810] [SPARK-2391][SQL] Custom take() for LIMIT queries.

Using Spark's take can result in an entire in-memory partition to be shipped in order to retrieve a single row.

Author: Michael Armbrust <michael@databricks.com>

Closes #1318 from marmbrus/takeLimit and squashes the following commits:

77289a5 [Michael Armbrust] Update scala doc
32f0674 [Michael Armbrust] Custom take implementation for LIMIT queries.
---
 .../spark/sql/execution/basicOperators.scala  | 51 +++++++++++++++++--
 1 file changed, 47 insertions(+), 4 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
index e8816f0b3cd9..97abd636ab5f 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.sql.execution
 
+import scala.collection.mutable.ArrayBuffer
 import scala.reflect.runtime.universe.TypeTag
 
 import org.apache.spark.annotation.DeveloperApi
@@ -83,9 +84,9 @@ case class Union(children: Seq[SparkPlan])(@transient sqlContext: SQLContext) ex
  * :: DeveloperApi ::
  * Take the first limit elements. Note that the implementation is different depending on whether
  * this is a terminal operator or not. If it is terminal and is invoked using executeCollect,
- * this operator uses Spark's take method on the Spark driver. If it is not terminal or is
- * invoked using execute, we first take the limit on each partition, and then repartition all the
- * data to a single partition to compute the global limit.
+ * this operator uses something similar to Spark's take method on the Spark driver. If it is not
+ * terminal or is invoked using execute, we first take the limit on each partition, and then
+ * repartition all the data to a single partition to compute the global limit.
  */
 @DeveloperApi
 case class Limit(limit: Int, child: SparkPlan)(@transient sqlContext: SQLContext)
@@ -97,7 +98,49 @@ case class Limit(limit: Int, child: SparkPlan)(@transient sqlContext: SQLContext
 
   override def output = child.output
 
-  override def executeCollect() = child.execute().map(_.copy()).take(limit)
+  /**
+   * A custom implementation modeled after the take function on RDDs but which never runs any job
+   * locally.  This is to avoid shipping an entire partition of data in order to retrieve only a few
+   * rows.
+   */
+  override def executeCollect(): Array[Row] = {
+    if (limit == 0) {
+      return new Array[Row](0)
+    }
+
+    val childRDD = child.execute().map(_.copy())
+
+    val buf = new ArrayBuffer[Row]
+    val totalParts = childRDD.partitions.length
+    var partsScanned = 0
+    while (buf.size < limit && partsScanned < totalParts) {
+      // The number of partitions to try in this iteration. It is ok for this number to be
+      // greater than totalParts because we actually cap it at totalParts in runJob.
+      var numPartsToTry = 1
+      if (partsScanned > 0) {
+        // If we didn't find any rows after the first iteration, just try all partitions next.
+        // Otherwise, interpolate the number of partitions we need to try, but overestimate it
+        // by 50%.
+        if (buf.size == 0) {
+          numPartsToTry = totalParts - 1
+        } else {
+          numPartsToTry = (1.5 * limit * partsScanned / buf.size).toInt
+        }
+      }
+      numPartsToTry = math.max(0, numPartsToTry)  // guard against negative num of partitions
+
+      val left = limit - buf.size
+      val p = partsScanned until math.min(partsScanned + numPartsToTry, totalParts)
+      val sc = sqlContext.sparkContext
+      val res =
+        sc.runJob(childRDD, (it: Iterator[Row]) => it.take(left).toArray, p, allowLocal = false)
+
+      res.foreach(buf ++= _.take(limit - buf.size))
+      partsScanned += numPartsToTry
+    }
+
+    buf.toArray
+  }
 
   override def execute() = {
     val rdd = child.execute().mapPartitions { iter =>

From 56e009d4f05d990c60e109838fa70457f97f44aa Mon Sep 17 00:00:00 2001
From: Andrew Or <andrewor14@gmail.com>
Date: Tue, 8 Jul 2014 16:49:31 +0900
Subject: [PATCH 593/810] [EC2] Add default history server port to ec2 script

Right now I have to open it manually

Author: Andrew Or <andrewor14@gmail.com>

Closes #1296 from andrewor14/hist-serv-port and squashes the following commits:

8895a1f [Andrew Or] Add default history server port to ec2 script
---
 ec2/spark_ec2.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/ec2/spark_ec2.py b/ec2/spark_ec2.py
index e22d93bd31bc..f5c2bfb697c8 100755
--- a/ec2/spark_ec2.py
+++ b/ec2/spark_ec2.py
@@ -279,6 +279,7 @@ def launch_cluster(conn, opts, cluster_name):
         master_group.authorize(src_group=slave_group)
         master_group.authorize('tcp', 22, 22, '0.0.0.0/0')
         master_group.authorize('tcp', 8080, 8081, '0.0.0.0/0')
+        master_group.authorize('tcp', 18080, 18080, '0.0.0.0/0')
         master_group.authorize('tcp', 19999, 19999, '0.0.0.0/0')
         master_group.authorize('tcp', 50030, 50030, '0.0.0.0/0')
         master_group.authorize('tcp', 50070, 50070, '0.0.0.0/0')

From cc3e0a14daf756ff5c2d4e7916438e175046e5bb Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Tue, 8 Jul 2014 10:36:18 -0700
Subject: [PATCH 594/810] [SPARK-2395][SQL] Optimize common LIKE patterns.

Author: Michael Armbrust <michael@databricks.com>

Closes #1325 from marmbrus/slowLike and squashes the following commits:

023c3eb [Michael Armbrust] add comment.
8b421c2 [Michael Armbrust] Handle the case where the final % is actually escaped.
d34d37e [Michael Armbrust] add periods.
3bbf35f [Michael Armbrust] Roll back changes to SparkBuild
53894b1 [Michael Armbrust] Fix grammar.
4094462 [Michael Armbrust] Fix grammar.
6d3d0a0 [Michael Armbrust] Optimize common LIKE patterns.
---
 .../expressions/stringOperations.scala        | 51 +++++++++++++++++++
 .../sql/catalyst/optimizer/Optimizer.scala    | 23 +++++++++
 2 files changed, 74 insertions(+)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
index c074b7bb01e5..347471cebdc7 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
@@ -156,3 +156,54 @@ case class Lower(child: Expression) extends UnaryExpression with CaseConversionE
 
   override def toString() = s"Lower($child)"
 }
+
+/** A base class for functions that compare two strings, returning a boolean. */
+abstract class StringComparison extends Expression {
+  self: Product =>
+
+  type EvaluatedType = Any
+
+  def left: Expression
+  def right: Expression
+
+  override def references = children.flatMap(_.references).toSet
+  override def children = left :: right :: Nil
+
+  override def nullable: Boolean = true
+  override def dataType: DataType = BooleanType
+
+  def compare(l: String, r: String): Boolean
+
+  override def eval(input: Row): Any = {
+    val leftEval = left.eval(input).asInstanceOf[String]
+    if(leftEval == null) {
+      null
+    } else {
+      val rightEval = right.eval(input).asInstanceOf[String]
+      if (rightEval == null) null else compare(leftEval, rightEval)
+    }
+  }
+
+  override def toString() = s"$nodeName($left, $right)"
+}
+
+/**
+ * A function that returns true if the string `left` contains the string `right`.
+ */
+case class Contains(left: Expression, right: Expression) extends StringComparison {
+  override def compare(l: String, r: String) = l.contains(r)
+}
+
+/**
+ * A function that returns true if the string `left` starts with the string `right`.
+ */
+case class StartsWith(left: Expression, right: Expression) extends StringComparison {
+  def compare(l: String, r: String) = l.startsWith(r)
+}
+
+/**
+ * A function that returns true if the string `left` ends with the string `right`.
+ */
+case class EndsWith(left: Expression, right: Expression) extends StringComparison {
+  def compare(l: String, r: String) = l.endsWith(r)
+}
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
index 48ca31e70fe7..f0904f59d028 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
@@ -34,6 +34,7 @@ object Optimizer extends RuleExecutor[LogicalPlan] {
     Batch("ConstantFolding", FixedPoint(100),
       NullPropagation,
       ConstantFolding,
+      LikeSimplification,
       BooleanSimplification,
       SimplifyFilters,
       SimplifyCasts,
@@ -111,6 +112,28 @@ object ColumnPruning extends Rule[LogicalPlan] {
     }
 }
 
+/**
+ * Simplifies LIKE expressions that do not need full regular expressions to evaluate the condition.
+ * For example, when the expression is just checking to see if a string starts with a given
+ * pattern.
+ */
+object LikeSimplification extends Rule[LogicalPlan] {
+  // if guards below protect from escapes on trailing %.
+  // Cases like "something\%" are not optimized, but this does not affect correctness.
+  val startsWith = "([^_%]+)%".r
+  val endsWith = "%([^_%]+)".r
+  val contains = "%([^_%]+)%".r
+
+  def apply(plan: LogicalPlan): LogicalPlan = plan transformAllExpressions {
+    case Like(l, Literal(startsWith(pattern), StringType)) if !pattern.endsWith("\\") =>
+      StartsWith(l, Literal(pattern))
+    case Like(l, Literal(endsWith(pattern), StringType)) =>
+      EndsWith(l, Literal(pattern))
+    case Like(l, Literal(contains(pattern), StringType)) if !pattern.endsWith("\\") =>
+      Contains(l, Literal(pattern))
+  }
+}
+
 /**
  * Replaces [[Expression Expressions]] that can be statically evaluated with
  * equivalent [[Literal]] values. This rule is more specific with

From c8a2313cdf825e0191680a423d17619b5504ff89 Mon Sep 17 00:00:00 2001
From: Daniel Darabos <darabos.daniel@gmail.com>
Date: Tue, 8 Jul 2014 10:43:46 -0700
Subject: [PATCH 595/810] [SPARK-2403] Catch all errors during serialization in
 DAGScheduler

https://issues.apache.org/jira/browse/SPARK-2403

Spark hangs for us whenever we forget to register a class with Kryo. This should be a simple fix for that. But let me know if you have a better suggestion.

I did not write a new test for this. It would be pretty complicated and I'm not sure it's worthwhile for such a simple change. Let me know if you disagree.

Author: Daniel Darabos <darabos.daniel@gmail.com>

Closes #1329 from darabos/spark-2403 and squashes the following commits:

3aceaad [Daniel Darabos] Print full stack trace for miscellaneous exceptions during serialization.
52c22ba [Daniel Darabos] Only catch NonFatal exceptions.
361e962 [Daniel Darabos] Catch all errors during serialization in DAGScheduler.
---
 .../main/scala/org/apache/spark/scheduler/DAGScheduler.scala | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
index 81c136d97031..f72bfde572c9 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
@@ -26,6 +26,7 @@ import scala.concurrent.Await
 import scala.concurrent.duration._
 import scala.language.postfixOps
 import scala.reflect.ClassTag
+import scala.util.control.NonFatal
 
 import akka.actor._
 import akka.actor.OneForOneStrategy
@@ -768,6 +769,10 @@ class DAGScheduler(
           abortStage(stage, "Task not serializable: " + e.toString)
           runningStages -= stage
           return
+        case NonFatal(e) => // Other exceptions, such as IllegalArgumentException from Kryo.
+          abortStage(stage, s"Task serialization failed: $e\n${e.getStackTraceString}")
+          runningStages -= stage
+          return
       }
 
       logInfo("Submitting " + tasks.size + " missing tasks from " + stage + " (" + stage.rdd + ")")

From b520b6453ed76926108e0bdd56114d16e1d86850 Mon Sep 17 00:00:00 2001
From: CrazyJvm <crazyjvm@gmail.com>
Date: Tue, 8 Jul 2014 13:55:42 -0500
Subject: [PATCH 596/810] SPARK-2400 : fix spark.yarn.max.executor.failures
 explaination

According to
```scala
  private val maxNumExecutorFailures = sparkConf.getInt("spark.yarn.max.executor.failures",
    sparkConf.getInt("spark.yarn.max.worker.failures", math.max(args.numExecutors * 2, 3)))
```
default value should be numExecutors * 2, with minimum of 3,  and it's same to the config
`spark.yarn.max.worker.failures`

Author: CrazyJvm <crazyjvm@gmail.com>

Closes #1282 from CrazyJvm/yarn-doc and squashes the following commits:

1a5f25b [CrazyJvm] remove deprecated config
c438aec [CrazyJvm] fix style
86effa6 [CrazyJvm] change expression
211f130 [CrazyJvm] fix html tag
2900d23 [CrazyJvm] fix style
a4b2e27 [CrazyJvm] fix configuration spark.yarn.max.executor.failures
---
 docs/running-on-yarn.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/running-on-yarn.md b/docs/running-on-yarn.md
index 5d8d603aa3e3..0362f5a22331 100644
--- a/docs/running-on-yarn.md
+++ b/docs/running-on-yarn.md
@@ -55,7 +55,7 @@ Most of the configs are the same for Spark on YARN as for other deployment modes
 </tr>
 <tr>
   <td><code>spark.yarn.max.executor.failures</code></td>
-  <td>2*numExecutors</td>
+  <td>numExecutors * 2, with minimum of 3</td>
   <td>
     The maximum number of executor failures before failing the application.
   </td>

From 32516f866a32d51bfaa04685ae77ba216b4202d9 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Tue, 8 Jul 2014 14:00:47 -0700
Subject: [PATCH 597/810] [SPARK-2409] Make SQLConf thread safe.

Author: Reynold Xin <rxin@apache.org>

Closes #1334 from rxin/sqlConfThreadSafetuy and squashes the following commits:

c1e0a5a [Reynold Xin] Fixed the duplicate comment.
7614372 [Reynold Xin] [SPARK-2409] Make SQLConf thread safe.
---
 .../src/main/scala/org/apache/spark/sql/SQLConf.scala  | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
index 95ed0f28507f..b6fb46a3acc0 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
@@ -25,7 +25,9 @@ import scala.collection.JavaConverters._
  * SQLConf holds mutable config parameters and hints.  These can be set and
  * queried either by passing SET commands into Spark SQL's DSL
  * functions (sql(), hql(), etc.), or by programmatically using setters and
- * getters of this class.  This class is thread-safe.
+ * getters of this class.
+ *
+ * SQLConf is thread-safe (internally synchronized so safe to be used in multiple threads).
  */
 trait SQLConf {
 
@@ -71,11 +73,9 @@ trait SQLConf {
     Option(settings.get(key)).getOrElse(defaultValue)
   }
 
-  def getAll: Array[(String, String)] = settings.asScala.toArray
+  def getAll: Array[(String, String)] = settings.synchronized { settings.asScala.toArray }
 
-  def getOption(key: String): Option[String] = {
-    Option(settings.get(key))
-  }
+  def getOption(key: String): Option[String] = Option(settings.get(key))
 
   def contains(key: String): Boolean = settings.containsKey(key)
 

From e6f7bfcfbf6aff7a9f8cd8e0a2166d0bf62b0912 Mon Sep 17 00:00:00 2001
From: Gabriele Nizzoli <mail@nizzoli.net>
Date: Tue, 8 Jul 2014 14:23:38 -0700
Subject: [PATCH 598/810] [SPARK-2362] Fix for newFilesOnly logic in file
 DStream

The newFilesOnly logic should be inverted: the logic should be that if the flag newFilesOnly==true then only start reading files older than current time. As the code is now if newFilesOnly==true then it will start to read files that are older than 0L (that is: every file in the directory).

Author: Gabriele Nizzoli <mail@nizzoli.net>

Closes #1077 from gabrielenizzoli/master and squashes the following commits:

4f1d261 [Gabriele Nizzoli] Fix for newFilesOnly logic in file DStream
---
 .../org/apache/spark/streaming/dstream/FileInputDStream.scala   | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/FileInputDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/FileInputDStream.scala
index e878285f6a85..9eecbfaef363 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/FileInputDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/FileInputDStream.scala
@@ -45,7 +45,7 @@ class FileInputDStream[K: ClassTag, V: ClassTag, F <: NewInputFormat[K,V] : Clas
   // Files with mod time earlier than this is ignored. This is updated every interval
   // such that in the current interval, files older than any file found in the
   // previous interval will be ignored. Obviously this time keeps moving forward.
-  private var ignoreTime = if (newFilesOnly) 0L else System.currentTimeMillis()
+  private var ignoreTime = if (newFilesOnly) System.currentTimeMillis() else 0L
 
   // Latest file mod time seen till any point of time
   @transient private var path_ : Path = null

From bf04a390e40d60aa7fcc551501d25f3f9d38377c Mon Sep 17 00:00:00 2001
From: Andrew Or <andrewor14@gmail.com>
Date: Tue, 8 Jul 2014 17:35:31 -0700
Subject: [PATCH 599/810] [SPARK-2392] Executors should not start their own
 HTTP servers

Executors currently start their own unused HTTP file servers. This is because we use the same SparkEnv class for both executors and drivers, and we do not distinguish this case.

In the longer term, we should separate out SparkEnv for the driver and SparkEnv for the executors.

Author: Andrew Or <andrewor14@gmail.com>

Closes #1335 from andrewor14/executor-http-server and squashes the following commits:

46ef263 [Andrew Or] Start HTTP server only on the driver
---
 .../src/main/scala/org/apache/spark/SparkEnv.scala | 14 ++++++++++----
 1 file changed, 10 insertions(+), 4 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/SparkEnv.scala b/core/src/main/scala/org/apache/spark/SparkEnv.scala
index 2b636b085d73..8f70744d804d 100644
--- a/core/src/main/scala/org/apache/spark/SparkEnv.scala
+++ b/core/src/main/scala/org/apache/spark/SparkEnv.scala
@@ -79,7 +79,7 @@ class SparkEnv (
 
   private[spark] def stop() {
     pythonWorkers.foreach { case(key, worker) => worker.stop() }
-    httpFileServer.stop()
+    Option(httpFileServer).foreach(_.stop())
     mapOutputTracker.stop()
     shuffleManager.stop()
     broadcastManager.stop()
@@ -228,9 +228,15 @@ object SparkEnv extends Logging {
 
     val cacheManager = new CacheManager(blockManager)
 
-    val httpFileServer = new HttpFileServer(securityManager)
-    httpFileServer.initialize()
-    conf.set("spark.fileserver.uri",  httpFileServer.serverUri)
+    val httpFileServer =
+      if (isDriver) {
+        val server = new HttpFileServer(securityManager)
+        server.initialize()
+        conf.set("spark.fileserver.uri",  server.serverUri)
+        server
+      } else {
+        null
+      }
 
     val metricsSystem = if (isDriver) {
       MetricsSystem.createMetricsSystem("driver", conf, securityManager)

From ac9cdc116e1c5fcb291a4ff168cac002a8058f05 Mon Sep 17 00:00:00 2001
From: DB Tsai <dbtsai@alpinenow.com>
Date: Tue, 8 Jul 2014 17:50:36 -0700
Subject: [PATCH 600/810] [SPARK-2413] Upgrade junit_xml_listener to 0.5.1

which fixes the following issues

1) fix the class name to be fully qualified classpath
2) make sure the the reporting time is in second not in miliseond, which causing JUnit HTML to report incorrect number
3) make sure the duration of the tests are accumulative.

Author: DB Tsai <dbtsai@alpinenow.com>

Closes #1333 from dbtsai/dbtsai-junit and squashes the following commits:

bbeac4b [DB Tsai] Upgrade junit_xml_listener to 0.5.1 which fixes the following issues
---
 project/plugins.sbt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/project/plugins.sbt b/project/plugins.sbt
index 472819b9fb8b..d3ac4bf335e8 100644
--- a/project/plugins.sbt
+++ b/project/plugins.sbt
@@ -21,6 +21,6 @@ addSbtPlugin("org.scalastyle" %% "scalastyle-sbt-plugin" % "0.4.0")
 
 addSbtPlugin("com.typesafe" % "sbt-mima-plugin" % "0.1.6")
 
-addSbtPlugin("com.alpinenow" % "junit_xml_listener" % "0.5.0")
+addSbtPlugin("com.alpinenow" % "junit_xml_listener" % "0.5.1")
 
 addSbtPlugin("com.eed3si9n" % "sbt-unidoc" % "0.3.0")

From 1114207cc8e4ef94cb97bbd5a2ef3ae4d51f73fa Mon Sep 17 00:00:00 2001
From: johnnywalleye <jsondag@gmail.com>
Date: Tue, 8 Jul 2014 19:17:26 -0700
Subject: [PATCH 601/810] [SPARK-2152][MLlib] fix bin offset in DecisionTree
 node aggregations (also resolves SPARK-2160)

Hi, this pull fixes (what I believe to be) a bug in DecisionTree.scala.

In the extractLeftRightNodeAggregates function, the first set of rightNodeAgg values for Regression are set in line 792 as follows:

rightNodeAgg(featureIndex)(2 * (numBins - 2))
  = binData(shift + (2 * numBins - 1)))

Then there is a loop that sets the rest of the values, as in line 809:

rightNodeAgg(featureIndex)(2 * (numBins - 2 - splitIndex)) =
  binData(shift + (2 *(numBins - 2 - splitIndex))) +
  rightNodeAgg(featureIndex)(2 * (numBins - 1 - splitIndex))

But since splitIndex starts at 1, this ends up skipping a set of binData values.

The changes here address this issue, for both the Regression and Classification cases.

Author: johnnywalleye <jsondag@gmail.com>

Closes #1316 from johnnywalleye/master and squashes the following commits:

73809da [johnnywalleye] fix bin offset in DecisionTree node aggregations
---
 .../org/apache/spark/mllib/tree/DecisionTree.scala     | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
index 3b13e52a7b44..74d5d7ba1096 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
@@ -807,10 +807,10 @@ object DecisionTree extends Serializable with Logging {
               // calculating right node aggregate for a split as a sum of right node aggregate of a
               // higher split and the right bin aggregate of a bin where the split is a low split
               rightNodeAgg(featureIndex)(2 * (numBins - 2 - splitIndex)) =
-                binData(shift + (2 *(numBins - 2 - splitIndex))) +
+                binData(shift + (2 *(numBins - 1 - splitIndex))) +
                 rightNodeAgg(featureIndex)(2 * (numBins - 1 - splitIndex))
               rightNodeAgg(featureIndex)(2 * (numBins - 2 - splitIndex) + 1) =
-                binData(shift + (2* (numBins - 2 - splitIndex) + 1)) +
+                binData(shift + (2* (numBins - 1 - splitIndex) + 1)) +
                   rightNodeAgg(featureIndex)(2 * (numBins - 1 - splitIndex) + 1)
 
               splitIndex += 1
@@ -855,13 +855,13 @@ object DecisionTree extends Serializable with Logging {
               // calculating right node aggregate for a split as a sum of right node aggregate of a
               // higher split and the right bin aggregate of a bin where the split is a low split
               rightNodeAgg(featureIndex)(3 * (numBins - 2 - splitIndex)) =
-                binData(shift + (3 * (numBins - 2 - splitIndex))) +
+                binData(shift + (3 * (numBins - 1 - splitIndex))) +
                   rightNodeAgg(featureIndex)(3 * (numBins - 1 - splitIndex))
               rightNodeAgg(featureIndex)(3 * (numBins - 2 - splitIndex) + 1) =
-                binData(shift + (3 * (numBins - 2 - splitIndex) + 1)) +
+                binData(shift + (3 * (numBins - 1 - splitIndex) + 1)) +
                   rightNodeAgg(featureIndex)(3 * (numBins - 1 - splitIndex) + 1)
               rightNodeAgg(featureIndex)(3 * (numBins - 2 - splitIndex) + 2) =
-                binData(shift + (3 * (numBins - 2 - splitIndex) + 2)) +
+                binData(shift + (3 * (numBins - 1 - splitIndex) + 2)) +
                   rightNodeAgg(featureIndex)(3 * (numBins - 1 - splitIndex) + 2)
 
               splitIndex += 1

From 339441f545ca96de4a7d6271e32f16803539a8c8 Mon Sep 17 00:00:00 2001
From: Kay Ousterhout <kayousterhout@gmail.com>
Date: Tue, 8 Jul 2014 22:57:21 -0700
Subject: [PATCH 602/810] [SPARK-2384] Add tooltips to UI.

This patch adds tooltips to clarify some points of confusion in the UI.  When users mouse over some of the table headers (shuffle read, write, and input size) as well as over the "scheduler delay" metric shown for each stage, a black tool tip (see image below) pops up describing the metric in more detail.  After the tooltip mechanism is added by this commit, I imagine others may want to add more tooltips for other things in the UI, but I think this is a good starting point.

![tooltip](https://cloud.githubusercontent.com/assets/1108612/3491905/994e179e-059f-11e4-92f2-c6c12d248d81.jpg)

This looks scary-big but much of it is adding the bootstrap tool tip JavaScript.

Also I have no idea what to put for the license in tooltip (I left it the same -- the Twitter apache header) or for JQuery (left it as nothing) -- @mateiz what's the right thing here?

cc @pwendell @andrewor14 @rxin

Author: Kay Ousterhout <kayousterhout@gmail.com>

Closes #1314 from kayousterhout/tooltips and squashes the following commits:

19981b5 [Kay Ousterhout] Exclude non-licensed javascript files from style check
d9ab5a9 [Kay Ousterhout] Response to Andrew's review
7752449 [Kay Ousterhout] [SPARK-2384] Add tooltips to UI.
---
 .rat-excludes                                 |   2 +
 .../spark/ui/static/bootstrap-tooltip.js      | 288 ++++++++++++++++++
 .../spark/ui/static/initialize-tooltips.js    |  21 ++
 .../spark/ui/static/jquery-1.11.1.min.js      |   4 +
 .../org/apache/spark/ui/static/webui.css      |   5 +
 .../scala/org/apache/spark/ui/ToolTips.scala  |  34 +++
 .../scala/org/apache/spark/ui/UIUtils.scala   |  32 +-
 .../apache/spark/ui/exec/ExecutorsPage.scala  | 136 +++++----
 .../apache/spark/ui/jobs/ExecutorTable.scala  |  10 +-
 .../org/apache/spark/ui/jobs/StagePage.scala  |  37 +--
 .../org/apache/spark/ui/jobs/StageTable.scala |  15 +-
 11 files changed, 491 insertions(+), 93 deletions(-)
 create mode 100644 core/src/main/resources/org/apache/spark/ui/static/bootstrap-tooltip.js
 create mode 100644 core/src/main/resources/org/apache/spark/ui/static/initialize-tooltips.js
 create mode 100644 core/src/main/resources/org/apache/spark/ui/static/jquery-1.11.1.min.js
 create mode 100644 core/src/main/scala/org/apache/spark/ui/ToolTips.scala

diff --git a/.rat-excludes b/.rat-excludes
index 796c32a80896..372bc2587ccc 100644
--- a/.rat-excludes
+++ b/.rat-excludes
@@ -22,6 +22,8 @@ slaves
 spark-env.sh
 spark-env.sh.template
 log4j-defaults.properties
+bootstrap-tooltip.js
+jquery-1.11.1.min.js
 sorttable.js
 .*txt
 .*json
diff --git a/core/src/main/resources/org/apache/spark/ui/static/bootstrap-tooltip.js b/core/src/main/resources/org/apache/spark/ui/static/bootstrap-tooltip.js
new file mode 100644
index 000000000000..2934181c1006
--- /dev/null
+++ b/core/src/main/resources/org/apache/spark/ui/static/bootstrap-tooltip.js
@@ -0,0 +1,288 @@
+/* ===========================================================
+ * bootstrap-tooltip.js v2.2.2
+ * http://twitter.github.com/bootstrap/javascript.html#tooltips
+ * Inspired by the original jQuery.tipsy by Jason Frame
+ * ===========================================================
+ * Copyright 2012 Twitter, Inc.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * ========================================================== */
+
+
+!function ($) {
+
+  "use strict"; // jshint ;_;
+
+
+ /* TOOLTIP PUBLIC CLASS DEFINITION
+  * =============================== */
+
+  var Tooltip = function (element, options) {
+    this.init('tooltip', element, options)
+  }
+
+  Tooltip.prototype = {
+
+    constructor: Tooltip
+
+  , init: function (type, element, options) {
+      var eventIn
+        , eventOut
+
+      this.type = type
+      this.$element = $(element)
+      this.options = this.getOptions(options)
+      this.enabled = true
+
+      if (this.options.trigger == 'click') {
+        this.$element.on('click.' + this.type, this.options.selector, $.proxy(this.toggle, this))
+      } else if (this.options.trigger != 'manual') {
+        eventIn = this.options.trigger == 'hover' ? 'mouseenter' : 'focus'
+        eventOut = this.options.trigger == 'hover' ? 'mouseleave' : 'blur'
+        this.$element.on(eventIn + '.' + this.type, this.options.selector, $.proxy(this.enter, this))
+        this.$element.on(eventOut + '.' + this.type, this.options.selector, $.proxy(this.leave, this))
+      }
+
+      this.options.selector ?
+        (this._options = $.extend({}, this.options, { trigger: 'manual', selector: '' })) :
+        this.fixTitle()
+    }
+
+  , getOptions: function (options) {
+      options = $.extend({}, $.fn[this.type].defaults, options, this.$element.data())
+
+      if (options.delay && typeof options.delay == 'number') {
+        options.delay = {
+          show: options.delay
+        , hide: options.delay
+        }
+      }
+
+      return options
+    }
+
+  , enter: function (e) {
+      var self = $(e.currentTarget)[this.type](this._options).data(this.type)
+
+      if (!self.options.delay || !self.options.delay.show) return self.show()
+
+      clearTimeout(this.timeout)
+      self.hoverState = 'in'
+      this.timeout = setTimeout(function() {
+        if (self.hoverState == 'in') self.show()
+      }, self.options.delay.show)
+    }
+
+  , leave: function (e) {
+      var self = $(e.currentTarget)[this.type](this._options).data(this.type)
+
+      if (this.timeout) clearTimeout(this.timeout)
+      if (!self.options.delay || !self.options.delay.hide) return self.hide()
+
+      self.hoverState = 'out'
+      this.timeout = setTimeout(function() {
+        if (self.hoverState == 'out') self.hide()
+      }, self.options.delay.hide)
+    }
+
+  , show: function () {
+      var $tip
+        , inside
+        , pos
+        , actualWidth
+        , actualHeight
+        , placement
+        , tp
+
+      if (this.hasContent() && this.enabled) {
+        $tip = this.tip()
+        this.setContent()
+
+        if (this.options.animation) {
+          $tip.addClass('fade')
+        }
+
+        placement = typeof this.options.placement == 'function' ?
+          this.options.placement.call(this, $tip[0], this.$element[0]) :
+          this.options.placement
+
+        inside = /in/.test(placement)
+
+        $tip
+          .detach()
+          .css({ top: 0, left: 0, display: 'block' })
+          .insertAfter(this.$element)
+
+        pos = this.getPosition(inside)
+
+        actualWidth = $tip[0].offsetWidth
+        actualHeight = $tip[0].offsetHeight
+
+        switch (inside ? placement.split(' ')[1] : placement) {
+          case 'bottom':
+            tp = {top: pos.top + pos.height, left: pos.left + pos.width / 2 - actualWidth / 2}
+            break
+          case 'top':
+            tp = {top: pos.top - actualHeight, left: pos.left + pos.width / 2 - actualWidth / 2}
+            break
+          case 'left':
+            tp = {top: pos.top + pos.height / 2 - actualHeight / 2, left: pos.left - actualWidth}
+            break
+          case 'right':
+            tp = {top: pos.top + pos.height / 2 - actualHeight / 2, left: pos.left + pos.width}
+            break
+        }
+
+        $tip
+          .offset(tp)
+          .addClass(placement)
+          .addClass('in')
+      }
+    }
+
+  , setContent: function () {
+      var $tip = this.tip()
+        , title = this.getTitle()
+
+      $tip.find('.tooltip-inner')[this.options.html ? 'html' : 'text'](title)
+      $tip.removeClass('fade in top bottom left right')
+    }
+
+  , hide: function () {
+      var that = this
+        , $tip = this.tip()
+
+      $tip.removeClass('in')
+
+      function removeWithAnimation() {
+        var timeout = setTimeout(function () {
+          $tip.off($.support.transition.end).detach()
+        }, 500)
+
+        $tip.one($.support.transition.end, function () {
+          clearTimeout(timeout)
+          $tip.detach()
+        })
+      }
+
+      $.support.transition && this.$tip.hasClass('fade') ?
+        removeWithAnimation() :
+        $tip.detach()
+
+      return this
+    }
+
+  , fixTitle: function () {
+      var $e = this.$element
+      if ($e.attr('title') || typeof($e.attr('data-original-title')) != 'string') {
+        $e.attr('data-original-title', $e.attr('title') || '').attr('title', '')
+      }
+    }
+
+  , hasContent: function () {
+      return this.getTitle()
+    }
+
+  , getPosition: function (inside) {
+      return $.extend({}, (inside ? {top: 0, left: 0} : this.$element.offset()), {
+        width: this.$element[0].offsetWidth
+      , height: this.$element[0].offsetHeight
+      })
+    }
+
+  , getTitle: function () {
+      var title
+        , $e = this.$element
+        , o = this.options
+
+      title = $e.attr('data-original-title')
+        || (typeof o.title == 'function' ? o.title.call($e[0]) :  o.title)
+
+      return title
+    }
+
+  , tip: function () {
+      return this.$tip = this.$tip || $(this.options.template)
+    }
+
+  , validate: function () {
+      if (!this.$element[0].parentNode) {
+        this.hide()
+        this.$element = null
+        this.options = null
+      }
+    }
+
+  , enable: function () {
+      this.enabled = true
+    }
+
+  , disable: function () {
+      this.enabled = false
+    }
+
+  , toggleEnabled: function () {
+      this.enabled = !this.enabled
+    }
+
+  , toggle: function (e) {
+      var self = $(e.currentTarget)[this.type](this._options).data(this.type)
+      self[self.tip().hasClass('in') ? 'hide' : 'show']()
+    }
+
+  , destroy: function () {
+      this.hide().$element.off('.' + this.type).removeData(this.type)
+    }
+
+  }
+
+
+ /* TOOLTIP PLUGIN DEFINITION
+  * ========================= */
+
+  var old = $.fn.tooltip
+
+  $.fn.tooltip = function ( option ) {
+    return this.each(function () {
+      var $this = $(this)
+        , data = $this.data('tooltip')
+        , options = typeof option == 'object' && option
+      if (!data) $this.data('tooltip', (data = new Tooltip(this, options)))
+      if (typeof option == 'string') data[option]()
+    })
+  }
+
+  $.fn.tooltip.Constructor = Tooltip
+
+  $.fn.tooltip.defaults = {
+    animation: true
+  , placement: 'top'
+  , selector: false
+  , template: '<div class="tooltip"><div class="tooltip-arrow"></div><div class="tooltip-inner"></div></div>'
+  , trigger: 'hover'
+  , title: ''
+  , delay: 0
+  , html: false
+  }
+
+
+ /* TOOLTIP NO CONFLICT
+  * =================== */
+
+  $.fn.tooltip.noConflict = function () {
+    $.fn.tooltip = old
+    return this
+  }
+
+}(window.jQuery);
+
diff --git a/core/src/main/resources/org/apache/spark/ui/static/initialize-tooltips.js b/core/src/main/resources/org/apache/spark/ui/static/initialize-tooltips.js
new file mode 100644
index 000000000000..70f355dfb49c
--- /dev/null
+++ b/core/src/main/resources/org/apache/spark/ui/static/initialize-tooltips.js
@@ -0,0 +1,21 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+$(document).ready(function(){
+   $("[data-toggle=tooltip]").tooltip({container: 'body'});
+});
+
diff --git a/core/src/main/resources/org/apache/spark/ui/static/jquery-1.11.1.min.js b/core/src/main/resources/org/apache/spark/ui/static/jquery-1.11.1.min.js
new file mode 100644
index 000000000000..ab28a24729b3
--- /dev/null
+++ b/core/src/main/resources/org/apache/spark/ui/static/jquery-1.11.1.min.js
@@ -0,0 +1,4 @@
+/*! jQuery v1.11.1 | (c) 2005, 2014 jQuery Foundation, Inc. | jquery.org/license */
+!function(a,b){"object"==typeof module&&"object"==typeof module.exports?module.exports=a.document?b(a,!0):function(a){if(!a.document)throw new Error("jQuery requires a window with a document");return b(a)}:b(a)}("undefined"!=typeof window?window:this,function(a,b){var c=[],d=c.slice,e=c.concat,f=c.push,g=c.indexOf,h={},i=h.toString,j=h.hasOwnProperty,k={},l="1.11.1",m=function(a,b){return new m.fn.init(a,b)},n=/^[\s\uFEFF\xA0]+|[\s\uFEFF\xA0]+$/g,o=/^-ms-/,p=/-([\da-z])/gi,q=function(a,b){return b.toUpperCase()};m.fn=m.prototype={jquery:l,constructor:m,selector:"",length:0,toArray:function(){return d.call(this)},get:function(a){return null!=a?0>a?this[a+this.length]:this[a]:d.call(this)},pushStack:function(a){var b=m.merge(this.constructor(),a);return b.prevObject=this,b.context=this.context,b},each:function(a,b){return m.each(this,a,b)},map:function(a){return this.pushStack(m.map(this,function(b,c){return a.call(b,c,b)}))},slice:function(){return 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diff --git a/core/src/main/resources/org/apache/spark/ui/static/webui.css b/core/src/main/resources/org/apache/spark/ui/static/webui.css
index 11fd956bfbe6..7448af87fcf3 100644
--- a/core/src/main/resources/org/apache/spark/ui/static/webui.css
+++ b/core/src/main/resources/org/apache/spark/ui/static/webui.css
@@ -112,3 +112,8 @@ pre {
   padding-bottom: 0;
   border: none;
 }
+
+.tooltip {
+ font-weight: normal;
+}
+
diff --git a/core/src/main/scala/org/apache/spark/ui/ToolTips.scala b/core/src/main/scala/org/apache/spark/ui/ToolTips.scala
new file mode 100644
index 000000000000..37708d75489c
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/ui/ToolTips.scala
@@ -0,0 +1,34 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ui
+
+private[spark] object ToolTips {
+  val SCHEDULER_DELAY =
+    """Scheduler delay includes time to ship the task from the scheduler to
+       the executor, and time the time to send a message from the executor to the scheduler stating
+       that the task has completed. When the scheduler becomes overloaded, task completion messages
+       become queued up, and scheduler delay increases."""
+
+  val INPUT = "Bytes read from Hadoop or from Spark storage."
+
+  val SHUFFLE_WRITE = "Bytes written to disk in order to be read by a shuffle in a future stage."
+
+  val SHUFFLE_READ =
+    """Bytes read from remote executors. Typically less than shuffle write bytes
+       because this does not include shuffle data read locally."""
+}
diff --git a/core/src/main/scala/org/apache/spark/ui/UIUtils.scala b/core/src/main/scala/org/apache/spark/ui/UIUtils.scala
index 1b104253d545..9cb50d9b83dd 100644
--- a/core/src/main/scala/org/apache/spark/ui/UIUtils.scala
+++ b/core/src/main/scala/org/apache/spark/ui/UIUtils.scala
@@ -25,6 +25,7 @@ import org.apache.spark.Logging
 
 /** Utility functions for generating XML pages with spark content. */
 private[spark] object UIUtils extends Logging {
+  val TABLE_CLASS = "table table-bordered table-striped table-condensed sortable"
 
   // SimpleDateFormat is not thread-safe. Don't expose it to avoid improper use.
   private val dateFormat = new ThreadLocal[SimpleDateFormat]() {
@@ -139,6 +140,18 @@ private[spark] object UIUtils extends Logging {
 
   def prependBaseUri(basePath: String = "", resource: String = "") = uiRoot + basePath + resource
 
+  val commonHeaderNodes = {
+    <meta http-equiv="Content-type" content="text/html; charset=utf-8" />
+    <link rel="stylesheet" href={prependBaseUri("/static/bootstrap.min.css")}
+          type="text/css" />
+    <link rel="stylesheet" href={prependBaseUri("/static/webui.css")}
+          type="text/css" />
+    <script src={prependBaseUri("/static/sorttable.js")} ></script>
+    <script src={prependBaseUri("/static/jquery-1.11.1.min.js")}></script>
+    <script src={prependBaseUri("/static/bootstrap-tooltip.js")}></script>
+    <script src={prependBaseUri("/static/initialize-tooltips.js")}></script>
+  }
+
   /** Returns a spark page with correctly formatted headers */
   def headerSparkPage(
       content: => Seq[Node],
@@ -157,12 +170,7 @@ private[spark] object UIUtils extends Logging {
 
     <html>
       <head>
-        <meta http-equiv="Content-type" content="text/html; charset=utf-8" />
-        <link rel="stylesheet" href={prependBaseUri("/static/bootstrap.min.css")}
-              type="text/css" />
-        <link rel="stylesheet" href={prependBaseUri("/static/webui.css")}
-              type="text/css" />
-        <script src={prependBaseUri("/static/sorttable.js")} ></script>
+        {commonHeaderNodes}
         <title>{appName} - {title}</title>
       </head>
       <body>
@@ -193,11 +201,7 @@ private[spark] object UIUtils extends Logging {
   def basicSparkPage(content: => Seq[Node], title: String): Seq[Node] = {
     <html>
       <head>
-        <meta http-equiv="Content-type" content="text/html; charset=utf-8" />
-        <link rel="stylesheet" href={prependBaseUri("/static/bootstrap.min.css")}
-              type="text/css" />
-        <link rel="stylesheet" href={prependBaseUri("/static/webui.css")}  type="text/css" />
-        <script src={prependBaseUri("/static/sorttable.js")} ></script>
+        {commonHeaderNodes}
         <title>{title}</title>
       </head>
       <body>
@@ -224,9 +228,9 @@ private[spark] object UIUtils extends Logging {
       data: Seq[T],
       fixedWidth: Boolean = false): Seq[Node] = {
 
-    var tableClass = "table table-bordered table-striped table-condensed sortable"
+    var listingTableClass = TABLE_CLASS
     if (fixedWidth) {
-      tableClass += " table-fixed"
+      listingTableClass += " table-fixed"
     }
     val colWidth = 100.toDouble / headers.size
     val colWidthAttr = if (fixedWidth) colWidth + "%" else ""
@@ -246,7 +250,7 @@ private[spark] object UIUtils extends Logging {
         }
       }
     }
-    <table class={tableClass}>
+    <table class={listingTableClass}>
       <thead>{headerRow}</thead>
       <tbody>
         {data.map(r => generateDataRow(r))}
diff --git a/core/src/main/scala/org/apache/spark/ui/exec/ExecutorsPage.scala b/core/src/main/scala/org/apache/spark/ui/exec/ExecutorsPage.scala
index 95b4a4e91d33..b358c855e1c8 100644
--- a/core/src/main/scala/org/apache/spark/ui/exec/ExecutorsPage.scala
+++ b/core/src/main/scala/org/apache/spark/ui/exec/ExecutorsPage.scala
@@ -22,9 +22,26 @@ import javax.servlet.http.HttpServletRequest
 import scala.xml.Node
 
 import org.apache.spark.storage.StorageLevel
-import org.apache.spark.ui.{WebUIPage, UIUtils}
+import org.apache.spark.ui.{ToolTips, UIUtils, WebUIPage}
 import org.apache.spark.util.Utils
 
+/** Summary information about an executor to display in the UI. */
+private case class ExecutorSummaryInfo(
+    id: String,
+    hostPort: String,
+    rddBlocks: Int,
+    memoryUsed: Long,
+    diskUsed: Long,
+    activeTasks: Int,
+    failedTasks: Int,
+    completedTasks: Int,
+    totalTasks: Int,
+    totalDuration: Long,
+    totalInputBytes: Long,
+    totalShuffleRead: Long,
+    totalShuffleWrite: Long,
+    maxMemory: Long)
+
 private[ui] class ExecutorsPage(parent: ExecutorsTab) extends WebUIPage("") {
   private val appName = parent.appName
   private val basePath = parent.basePath
@@ -36,8 +53,36 @@ private[ui] class ExecutorsPage(parent: ExecutorsTab) extends WebUIPage("") {
     val memUsed = storageStatusList.map(_.memUsed).fold(0L)(_ + _)
     val diskSpaceUsed = storageStatusList.flatMap(_.blocks.values.map(_.diskSize)).fold(0L)(_ + _)
     val execInfo = for (statusId <- 0 until storageStatusList.size) yield getExecInfo(statusId)
-    val execInfoSorted = execInfo.sortBy(_.getOrElse("Executor ID", ""))
-    val execTable = UIUtils.listingTable(execHeader, execRow, execInfoSorted)
+    val execInfoSorted = execInfo.sortBy(_.id)
+
+    val execTable =
+      <table class={UIUtils.TABLE_CLASS}>
+        <thead>
+          <th>Executor ID</th>
+          <th>Address</th>
+          <th>RDD Blocks</th>
+          <th>Memory Used</th>
+          <th>Disk Used</th>
+          <th>Active Tasks</th>
+          <th>Failed Tasks</th>
+          <th>Complete Tasks</th>
+          <th>Total Tasks</th>
+          <th>Task Time</th>
+          <th><span data-toggle="tooltip" title={ToolTips.INPUT}>Input</span></th>
+          <th><span data-toggle="tooltip" title={ToolTips.SHUFFLE_READ}>Shuffle Read</span></th>
+          <th>
+            <!-- Place the shuffle write tooltip on the left (rather than the default position
+              of on top) because the shuffle write column is the last column on the right side and
+              the tooltip is wider than the column, so it doesn't fit on top. -->
+            <span data-toggle="tooltip" data-placement="left" title={ToolTips.SHUFFLE_WRITE}>
+              Shuffle Write
+            </span>
+          </th>
+        </thead>
+        <tbody>
+          {execInfoSorted.map(execRow(_))}
+        </tbody>
+      </table>
 
     val content =
       <div class="row-fluid">
@@ -60,53 +105,43 @@ private[ui] class ExecutorsPage(parent: ExecutorsTab) extends WebUIPage("") {
       parent.headerTabs, parent)
   }
 
-  /** Header fields for the executors table */
-  private def execHeader = Seq(
-    "Executor ID",
-    "Address",
-    "RDD Blocks",
-    "Memory Used",
-    "Disk Used",
-    "Active Tasks",
-    "Failed Tasks",
-    "Complete Tasks",
-    "Total Tasks",
-    "Task Time",
-    "Input Bytes",
-    "Shuffle Read",
-    "Shuffle Write")
-
   /** Render an HTML row representing an executor */
-  private def execRow(values: Map[String, String]): Seq[Node] = {
-    val maximumMemory = values("Maximum Memory")
-    val memoryUsed = values("Memory Used")
-    val diskUsed = values("Disk Used")
-    // scalastyle:off
+  private def execRow(info: ExecutorSummaryInfo): Seq[Node] = {
+    val maximumMemory = info.maxMemory
+    val memoryUsed = info.memoryUsed
+    val diskUsed = info.diskUsed
     <tr>
-      <td>{values("Executor ID")}</td>
-      <td>{values("Address")}</td>
-      <td>{values("RDD Blocks")}</td>
-      <td sorttable_customkey={memoryUsed}>
-        {Utils.bytesToString(memoryUsed.toLong)} /
-        {Utils.bytesToString(maximumMemory.toLong)}
+      <td>{info.id}</td>
+      <td>{info.hostPort}</td>
+      <td>{info.rddBlocks}</td>
+      <td sorttable_customkey={memoryUsed.toString}>
+        {Utils.bytesToString(memoryUsed)} /
+        {Utils.bytesToString(maximumMemory)}
+      </td>
+      <td sorttable_customkey={diskUsed.toString}>
+        {Utils.bytesToString(diskUsed)}
+      </td>
+      <td>{info.activeTasks}</td>
+      <td>{info.failedTasks}</td>
+      <td>{info.completedTasks}</td>
+      <td>{info.totalTasks}</td>
+      <td sorttable_customkey={info.totalDuration.toString}>
+        {Utils.msDurationToString(info.totalDuration)}
       </td>
-      <td sorttable_customkey={diskUsed}>
-        {Utils.bytesToString(diskUsed.toLong)}
+      <td sorttable_customkey={info.totalInputBytes.toString}>
+        {Utils.bytesToString(info.totalInputBytes)}
+      </td>
+      <td sorttable_customkey={info.totalShuffleRead.toString}>
+        {Utils.bytesToString(info.totalShuffleRead)}
+      </td>
+      <td sorttable_customkey={info.totalShuffleWrite.toString}>
+        {Utils.bytesToString(info.totalShuffleWrite)}
       </td>
-      <td>{values("Active Tasks")}</td>
-      <td>{values("Failed Tasks")}</td>
-      <td>{values("Complete Tasks")}</td>
-      <td>{values("Total Tasks")}</td>
-      <td sorttable_customkey={values("Task Time")}>{Utils.msDurationToString(values("Task Time").toLong)}</td>
-      <td sorttable_customkey={values("Input Bytes")}>{Utils.bytesToString(values("Input Bytes").toLong)}</td>
-      <td sorttable_customkey={values("Shuffle Read")}>{Utils.bytesToString(values("Shuffle Read").toLong)}</td>
-      <td sorttable_customkey={values("Shuffle Write")} >{Utils.bytesToString(values("Shuffle Write").toLong)}</td>
     </tr>
-    // scalastyle:on
   }
 
   /** Represent an executor's info as a map given a storage status index */
-  private def getExecInfo(statusId: Int): Map[String, String] = {
+  private def getExecInfo(statusId: Int): ExecutorSummaryInfo = {
     val status = listener.storageStatusList(statusId)
     val execId = status.blockManagerId.executorId
     val hostPort = status.blockManagerId.hostPort
@@ -118,15 +153,12 @@ private[ui] class ExecutorsPage(parent: ExecutorsTab) extends WebUIPage("") {
     val failedTasks = listener.executorToTasksFailed.getOrElse(execId, 0)
     val completedTasks = listener.executorToTasksComplete.getOrElse(execId, 0)
     val totalTasks = activeTasks + failedTasks + completedTasks
-    val totalDuration = listener.executorToDuration.getOrElse(execId, 0)
-    val totalInputBytes = listener.executorToInputBytes.getOrElse(execId, 0)
-    val totalShuffleRead = listener.executorToShuffleRead.getOrElse(execId, 0)
-    val totalShuffleWrite = listener.executorToShuffleWrite.getOrElse(execId, 0)
+    val totalDuration = listener.executorToDuration.getOrElse(execId, 0L)
+    val totalInputBytes = listener.executorToInputBytes.getOrElse(execId, 0L)
+    val totalShuffleRead = listener.executorToShuffleRead.getOrElse(execId, 0L)
+    val totalShuffleWrite = listener.executorToShuffleWrite.getOrElse(execId, 0L)
 
-    // Also include fields not in the header
-    val execFields = execHeader ++ Seq("Maximum Memory")
-
-    val execValues = Seq(
+    new ExecutorSummaryInfo(
       execId,
       hostPort,
       rddBlocks,
@@ -141,8 +173,6 @@ private[ui] class ExecutorsPage(parent: ExecutorsTab) extends WebUIPage("") {
       totalShuffleRead,
       totalShuffleWrite,
       maxMem
-    ).map(_.toString)
-
-    execFields.zip(execValues).toMap
+    )
   }
 }
diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/ExecutorTable.scala b/core/src/main/scala/org/apache/spark/ui/jobs/ExecutorTable.scala
index 2a34a9af925d..52020954ea57 100644
--- a/core/src/main/scala/org/apache/spark/ui/jobs/ExecutorTable.scala
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/ExecutorTable.scala
@@ -20,7 +20,7 @@ package org.apache.spark.ui.jobs
 import scala.collection.mutable
 import scala.xml.Node
 
-import org.apache.spark.ui.UIUtils
+import org.apache.spark.ui.{ToolTips, UIUtils}
 import org.apache.spark.util.Utils
 
 /** Page showing executor summary */
@@ -35,7 +35,7 @@ private[ui] class ExecutorTable(stageId: Int, parent: JobProgressTab) {
 
   /** Special table which merges two header cells. */
   private def executorTable[T](): Seq[Node] = {
-    <table class="table table-bordered table-striped table-condensed sortable">
+    <table class={UIUtils.TABLE_CLASS}>
       <thead>
         <th>Executor ID</th>
         <th>Address</th>
@@ -43,9 +43,9 @@ private[ui] class ExecutorTable(stageId: Int, parent: JobProgressTab) {
         <th>Total Tasks</th>
         <th>Failed Tasks</th>
         <th>Succeeded Tasks</th>
-        <th>Input Bytes</th>
-        <th>Shuffle Read</th>
-        <th>Shuffle Write</th>
+        <th><span data-toggle="tooltip" title={ToolTips.INPUT}>Input</span></th>
+        <th><span data-toggle="tooltip" title={ToolTips.SHUFFLE_READ}>Shuffle Read</span></th>
+        <th><span data-toggle="tooltip" title={ToolTips.SHUFFLE_WRITE}>Shuffle Write</span></th>
         <th>Shuffle Spill (Memory)</th>
         <th>Shuffle Spill (Disk)</th>
       </thead>
diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala b/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
index afb8ed754ff8..8c3821bd7c3e 100644
--- a/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
@@ -22,7 +22,7 @@ import javax.servlet.http.HttpServletRequest
 
 import scala.xml.Node
 
-import org.apache.spark.ui.{WebUIPage, UIUtils}
+import org.apache.spark.ui.{ToolTips, WebUIPage, UIUtils}
 import org.apache.spark.util.{Utils, Distribution}
 
 /** Page showing statistics and task list for a given stage */
@@ -127,14 +127,14 @@ private[ui] class StagePage(parent: JobProgressTab) extends WebUIPage("stage") {
             metrics.get.resultSerializationTime.toDouble
           }
           val serializationQuantiles =
-            "Result serialization time" +: Distribution(serializationTimes).
-              get.getQuantiles().map(ms => UIUtils.formatDuration(ms.toLong))
+            <td>Result serialization time</td> +: Distribution(serializationTimes).
+              get.getQuantiles().map(ms => <td>{UIUtils.formatDuration(ms.toLong)}</td>)
 
           val serviceTimes = validTasks.map { case TaskUIData(_, metrics, _) =>
             metrics.get.executorRunTime.toDouble
           }
-          val serviceQuantiles = "Duration" +: Distribution(serviceTimes).get.getQuantiles()
-            .map(ms => UIUtils.formatDuration(ms.toLong))
+          val serviceQuantiles = <td>Duration</td> +: Distribution(serviceTimes).get.getQuantiles()
+            .map(ms => <td>{UIUtils.formatDuration(ms.toLong)}</td>)
 
           val gettingResultTimes = validTasks.map { case TaskUIData(info, _, _) =>
             if (info.gettingResultTime > 0) {
@@ -143,9 +143,9 @@ private[ui] class StagePage(parent: JobProgressTab) extends WebUIPage("stage") {
               0.0
             }
           }
-          val gettingResultQuantiles = "Time spent fetching task results" +:
+          val gettingResultQuantiles = <td>Time spent fetching task results</td> +:
             Distribution(gettingResultTimes).get.getQuantiles().map { millis =>
-              UIUtils.formatDuration(millis.toLong)
+              <td>{UIUtils.formatDuration(millis.toLong)}</td>
             }
           // The scheduler delay includes the network delay to send the task to the worker
           // machine and to send back the result (but not the time to fetch the task result,
@@ -160,42 +160,45 @@ private[ui] class StagePage(parent: JobProgressTab) extends WebUIPage("stage") {
             }
             totalExecutionTime - metrics.get.executorRunTime
           }
-          val schedulerDelayQuantiles = "Scheduler delay" +:
+          val schedulerDelayTitle = <td><span data-toggle="tooltip"
+            title={ToolTips.SCHEDULER_DELAY} data-placement="right">Scheduler delay</span></td>
+          val schedulerDelayQuantiles = schedulerDelayTitle +:
             Distribution(schedulerDelays).get.getQuantiles().map { millis =>
-              UIUtils.formatDuration(millis.toLong)
+              <td>{UIUtils.formatDuration(millis.toLong)}</td>
             }
 
           def getQuantileCols(data: Seq[Double]) =
-            Distribution(data).get.getQuantiles().map(d => Utils.bytesToString(d.toLong))
+            Distribution(data).get.getQuantiles().map(d => <td>{Utils.bytesToString(d.toLong)}</td>)
 
           val inputSizes = validTasks.map { case TaskUIData(_, metrics, _) =>
             metrics.get.inputMetrics.map(_.bytesRead).getOrElse(0L).toDouble
           }
-          val inputQuantiles = "Input" +: getQuantileCols(inputSizes)
+          val inputQuantiles = <td>Input</td> +: getQuantileCols(inputSizes)
 
           val shuffleReadSizes = validTasks.map { case TaskUIData(_, metrics, _) =>
             metrics.get.shuffleReadMetrics.map(_.remoteBytesRead).getOrElse(0L).toDouble
           }
-          val shuffleReadQuantiles = "Shuffle Read (Remote)" +: getQuantileCols(shuffleReadSizes)
+          val shuffleReadQuantiles = <td>Shuffle Read (Remote)</td> +:
+            getQuantileCols(shuffleReadSizes)
 
           val shuffleWriteSizes = validTasks.map { case TaskUIData(_, metrics, _) =>
             metrics.get.shuffleWriteMetrics.map(_.shuffleBytesWritten).getOrElse(0L).toDouble
           }
-          val shuffleWriteQuantiles = "Shuffle Write" +: getQuantileCols(shuffleWriteSizes)
+          val shuffleWriteQuantiles = <td>Shuffle Write</td> +: getQuantileCols(shuffleWriteSizes)
 
           val memoryBytesSpilledSizes = validTasks.map { case TaskUIData(_, metrics, _) =>
             metrics.get.memoryBytesSpilled.toDouble
           }
-          val memoryBytesSpilledQuantiles = "Shuffle spill (memory)" +:
+          val memoryBytesSpilledQuantiles = <td>Shuffle spill (memory)</td> +:
             getQuantileCols(memoryBytesSpilledSizes)
 
           val diskBytesSpilledSizes = validTasks.map { case TaskUIData(_, metrics, _) =>
             metrics.get.diskBytesSpilled.toDouble
           }
-          val diskBytesSpilledQuantiles = "Shuffle spill (disk)" +:
+          val diskBytesSpilledQuantiles = <td>Shuffle spill (disk)</td> +:
             getQuantileCols(diskBytesSpilledSizes)
 
-          val listings: Seq[Seq[String]] = Seq(
+          val listings: Seq[Seq[Node]] = Seq(
             serializationQuantiles,
             serviceQuantiles,
             gettingResultQuantiles,
@@ -208,7 +211,7 @@ private[ui] class StagePage(parent: JobProgressTab) extends WebUIPage("stage") {
 
           val quantileHeaders = Seq("Metric", "Min", "25th percentile",
             "Median", "75th percentile", "Max")
-          def quantileRow(data: Seq[String]): Seq[Node] = <tr> {data.map(d => <td>{d}</td>)} </tr>
+          def quantileRow(data: Seq[Node]): Seq[Node] = <tr>{data}</tr>
           Some(UIUtils.listingTable(quantileHeaders, quantileRow, listings, fixedWidth = true))
         }
       val executorTable = new ExecutorTable(stageId, parent)
diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/StageTable.scala b/core/src/main/scala/org/apache/spark/ui/jobs/StageTable.scala
index a9ac6d5bee9c..4013c6f49936 100644
--- a/core/src/main/scala/org/apache/spark/ui/jobs/StageTable.scala
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/StageTable.scala
@@ -23,7 +23,7 @@ import scala.collection.mutable.HashMap
 import scala.xml.Node
 
 import org.apache.spark.scheduler.{StageInfo, TaskInfo}
-import org.apache.spark.ui.UIUtils
+import org.apache.spark.ui.{ToolTips, UIUtils}
 import org.apache.spark.util.Utils
 
 /** Page showing list of all ongoing and recently finished stages */
@@ -43,9 +43,16 @@ private[ui] class StageTableBase(
     <th>Submitted</th>
     <th>Duration</th>
     <th>Tasks: Succeeded/Total</th>
-    <th>Input</th>
-    <th>Shuffle Read</th>
-    <th>Shuffle Write</th>
+    <th><span data-toggle="tooltip" title={ToolTips.INPUT}>Input</span></th>
+    <th><span data-toggle="tooltip" title={ToolTips.SHUFFLE_READ}>Shuffle Read</span></th>
+    <th>
+      <!-- Place the shuffle write tooltip on the left (rather than the default position
+        of on top) because the shuffle write column is the last column on the right side and
+        the tooltip is wider than the column, so it doesn't fit on top. -->
+      <span data-toggle="tooltip" data-placement="left" title={ToolTips.SHUFFLE_WRITE}>
+        Shuffle Write
+      </span>
+    </th>
   }
 
   def toNodeSeq: Seq[Node] = {

From 0eb11527d13083ced215e3fda44ed849198a57cb Mon Sep 17 00:00:00 2001
From: Manuel Laflamme <manuel.laflamme@gmail.com>
Date: Wed, 9 Jul 2014 10:45:45 -0700
Subject: [PATCH 603/810] [STREAMING] SPARK-2343: Fix QueueInputDStream with
 oneAtATime false

Fix QueueInputDStream which was not removing dequeued items when used with the oneAtATime flag disabled.

Author: Manuel Laflamme <manuel.laflamme@gmail.com>

Closes #1285 from mlaflamm/spark-2343 and squashes the following commits:

61c9e38 [Manuel Laflamme] Unit tests for queue input stream
c51d029 [Manuel Laflamme] Fix QueueInputDStream with oneAtATime false
---
 .../streaming/dstream/QueueInputDStream.scala |  2 +-
 .../spark/streaming/InputStreamsSuite.scala   | 92 ++++++++++++++++++-
 2 files changed, 92 insertions(+), 2 deletions(-)

diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/QueueInputDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/QueueInputDStream.scala
index 6376cff78b78..ed7da6dc1315 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/QueueInputDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/QueueInputDStream.scala
@@ -41,7 +41,7 @@ class QueueInputDStream[T: ClassTag](
     if (oneAtATime && queue.size > 0) {
       buffer += queue.dequeue()
     } else {
-      buffer ++= queue
+      buffer ++= queue.dequeueAll(_ => true)
     }
     if (buffer.size > 0) {
       if (oneAtATime) {
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala
index cd0aa4d0dce7..cc4a65011dd7 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala
@@ -29,7 +29,7 @@ import java.nio.charset.Charset
 import java.util.concurrent.{Executors, TimeUnit, ArrayBlockingQueue}
 import java.util.concurrent.atomic.AtomicInteger
 
-import scala.collection.mutable.{SynchronizedBuffer, ArrayBuffer}
+import scala.collection.mutable.{SynchronizedBuffer, ArrayBuffer, SynchronizedQueue}
 
 import com.google.common.io.Files
 import org.scalatest.BeforeAndAfter
@@ -39,6 +39,7 @@ import org.apache.spark.storage.StorageLevel
 import org.apache.spark.streaming.util.ManualClock
 import org.apache.spark.util.Utils
 import org.apache.spark.streaming.receiver.{ActorHelper, Receiver}
+import org.apache.spark.rdd.RDD
 
 class InputStreamsSuite extends TestSuiteBase with BeforeAndAfter {
 
@@ -234,6 +235,95 @@ class InputStreamsSuite extends TestSuiteBase with BeforeAndAfter {
     logInfo("--------------------------------")
     assert(output.sum === numTotalRecords)
   }
+
+  test("queue input stream - oneAtATime=true") {
+    // Set up the streaming context and input streams
+    val ssc = new StreamingContext(conf, batchDuration)
+    val queue = new SynchronizedQueue[RDD[String]]()
+    val queueStream = ssc.queueStream(queue, oneAtATime = true)
+    val outputBuffer = new ArrayBuffer[Seq[String]] with SynchronizedBuffer[Seq[String]]
+    val outputStream = new TestOutputStream(queueStream, outputBuffer)
+    def output = outputBuffer.filter(_.size > 0)
+    outputStream.register()
+    ssc.start()
+
+    // Setup data queued into the stream
+    val clock = ssc.scheduler.clock.asInstanceOf[ManualClock]
+    val input = Seq("1", "2", "3", "4", "5")
+    val expectedOutput = input.map(Seq(_))
+    //Thread.sleep(1000)
+    val inputIterator = input.toIterator
+    for (i <- 0 until input.size) {
+      // Enqueue more than 1 item per tick but they should dequeue one at a time
+      inputIterator.take(2).foreach(i => queue += ssc.sparkContext.makeRDD(Seq(i)))
+      clock.addToTime(batchDuration.milliseconds)
+    }
+    Thread.sleep(1000)
+    logInfo("Stopping context")
+    ssc.stop()
+
+    // Verify whether data received was as expected
+    logInfo("--------------------------------")
+    logInfo("output.size = " + outputBuffer.size)
+    logInfo("output")
+    outputBuffer.foreach(x => logInfo("[" + x.mkString(",") + "]"))
+    logInfo("expected output.size = " + expectedOutput.size)
+    logInfo("expected output")
+    expectedOutput.foreach(x => logInfo("[" + x.mkString(",") + "]"))
+    logInfo("--------------------------------")
+
+    // Verify whether all the elements received are as expected
+    assert(output.size === expectedOutput.size)
+    for (i <- 0 until output.size) {
+      assert(output(i) === expectedOutput(i))
+    }
+  }
+
+  test("queue input stream - oneAtATime=false") {
+    // Set up the streaming context and input streams
+    val ssc = new StreamingContext(conf, batchDuration)
+    val queue = new SynchronizedQueue[RDD[String]]()
+    val queueStream = ssc.queueStream(queue, oneAtATime = false)
+    val outputBuffer = new ArrayBuffer[Seq[String]] with SynchronizedBuffer[Seq[String]]
+    val outputStream = new TestOutputStream(queueStream, outputBuffer)
+    def output = outputBuffer.filter(_.size > 0)
+    outputStream.register()
+    ssc.start()
+
+    // Setup data queued into the stream
+    val clock = ssc.scheduler.clock.asInstanceOf[ManualClock]
+    val input = Seq("1", "2", "3", "4", "5")
+    val expectedOutput = Seq(Seq("1", "2", "3"), Seq("4", "5"))
+
+    // Enqueue the first 3 items (one by one), they should be merged in the next batch
+    val inputIterator = input.toIterator
+    inputIterator.take(3).foreach(i => queue += ssc.sparkContext.makeRDD(Seq(i)))
+    clock.addToTime(batchDuration.milliseconds)
+    Thread.sleep(1000)
+
+    // Enqueue the remaining items (again one by one), merged in the final batch
+    inputIterator.foreach(i => queue += ssc.sparkContext.makeRDD(Seq(i)))
+    clock.addToTime(batchDuration.milliseconds)
+    Thread.sleep(1000)
+    logInfo("Stopping context")
+    ssc.stop()
+
+    // Verify whether data received was as expected
+    logInfo("--------------------------------")
+    logInfo("output.size = " + outputBuffer.size)
+    logInfo("output")
+    outputBuffer.foreach(x => logInfo("[" + x.mkString(",") + "]"))
+    logInfo("expected output.size = " + expectedOutput.size)
+    logInfo("expected output")
+    expectedOutput.foreach(x => logInfo("[" + x.mkString(",") + "]"))
+    logInfo("--------------------------------")
+
+    // Verify whether all the elements received are as expected
+    assert(output.size === expectedOutput.size)
+    for (i <- 0 until output.size) {
+      assert(output(i) === expectedOutput(i))
+    }
+  }
 }
 
 

From d35e3db2325931492b64890125a70579bc3b587b Mon Sep 17 00:00:00 2001
From: johnnywalleye <jsondag@gmail.com>
Date: Wed, 9 Jul 2014 11:06:34 -0700
Subject: [PATCH 604/810] [SPARK-2417][MLlib] Fix DecisionTree tests

Fixes test failures introduced by https://github.com/apache/spark/pull/1316.

For both the regression and classification cases,
val stats is the InformationGainStats for the best tree split.
stats.predict is the predicted value for the data, before the split is made.
Since 600 of the 1,000 values generated by DecisionTreeSuite.generateCategoricalDataPoints() are 1.0 and the rest 0.0, the regression tree and classification tree both correctly predict a value of 0.6 for this data now, and the assertions have been changed to reflect that.

Author: johnnywalleye <jsondag@gmail.com>

Closes #1343 from johnnywalleye/decision-tree-tests and squashes the following commits:

ef80603 [johnnywalleye] [SPARK-2417][MLlib] Fix DecisionTree tests
---
 .../org/apache/spark/mllib/tree/DecisionTreeSuite.scala   | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala
index 35e92d71dc63..bcb11876b8f4 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala
@@ -253,8 +253,8 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
 
     val stats = bestSplits(0)._2
     assert(stats.gain > 0)
-    assert(stats.predict > 0.4)
-    assert(stats.predict < 0.5)
+    assert(stats.predict > 0.5)
+    assert(stats.predict < 0.7)
     assert(stats.impurity > 0.2)
   }
 
@@ -280,8 +280,8 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
 
     val stats = bestSplits(0)._2
     assert(stats.gain > 0)
-    assert(stats.predict > 0.4)
-    assert(stats.predict < 0.5)
+    assert(stats.predict > 0.5)
+    assert(stats.predict < 0.7)
     assert(stats.impurity > 0.2)
   }
 

From 1f33e1f2013c508aa86511750f7bd8437154e51a Mon Sep 17 00:00:00 2001
From: Li Pu <lpu@twitter.com>
Date: Wed, 9 Jul 2014 12:15:08 -0700
Subject: [PATCH 605/810] SPARK-1782: svd for sparse matrix using ARPACK

copy ARPACK dsaupd/dseupd code from latest breeze
change RowMatrix to use sparse SVD
change tests for sparse SVD

All tests passed. I will run it against some large matrices.

Author: Li Pu <lpu@twitter.com>
Author: Xiangrui Meng <meng@databricks.com>
Author: Li Pu <li.pu@outlook.com>

Closes #964 from vrilleup/master and squashes the following commits:

7312ec1 [Li Pu] very minor comment fix
4c618e9 [Li Pu] Merge pull request #1 from mengxr/vrilleup-master
a461082 [Xiangrui Meng] make superscript show up correctly in doc
861ec48 [Xiangrui Meng] simplify axpy
62969fa [Xiangrui Meng] use BDV directly in symmetricEigs change the computation mode to local-svd, local-eigs, and dist-eigs update tests and docs
c273771 [Li Pu] automatically determine SVD compute mode and parameters
7148426 [Li Pu] improve RowMatrix multiply
5543cce [Li Pu] improve svd api
819824b [Li Pu] add flag for dense svd or sparse svd
eb15100 [Li Pu] fix binary compatibility
4c7aec3 [Li Pu] improve comments
e7850ed [Li Pu] use aggregate and axpy
827411b [Li Pu] fix EOF new line
9c80515 [Li Pu] use non-sparse implementation when k = n
fe983b0 [Li Pu] improve scala style
96d2ecb [Li Pu] improve eigenvalue sorting
e1db950 [Li Pu] SPARK-1782: svd for sparse matrix using ARPACK
---
 .../linalg/EigenValueDecomposition.scala      | 157 +++++++++++++++
 .../mllib/linalg/distributed/RowMatrix.scala  | 183 ++++++++++++++----
 .../linalg/distributed/RowMatrixSuite.scala   |  59 +++---
 3 files changed, 339 insertions(+), 60 deletions(-)
 create mode 100644 mllib/src/main/scala/org/apache/spark/mllib/linalg/EigenValueDecomposition.scala

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/EigenValueDecomposition.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/EigenValueDecomposition.scala
new file mode 100644
index 000000000000..3515461b5249
--- /dev/null
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/EigenValueDecomposition.scala
@@ -0,0 +1,157 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.mllib.linalg
+
+import breeze.linalg.{DenseMatrix => BDM, DenseVector => BDV}
+import com.github.fommil.netlib.ARPACK
+import org.netlib.util.{intW, doubleW}
+
+import org.apache.spark.annotation.Experimental
+
+/**
+ * :: Experimental ::
+ * Compute eigen-decomposition.
+ */
+@Experimental
+private[mllib] object EigenValueDecomposition {
+  /**
+   * Compute the leading k eigenvalues and eigenvectors on a symmetric square matrix using ARPACK.
+   * The caller needs to ensure that the input matrix is real symmetric. This function requires
+   * memory for `n*(4*k+4)` doubles.
+   *
+   * @param mul a function that multiplies the symmetric matrix with a DenseVector.
+   * @param n dimension of the square matrix (maximum Int.MaxValue).
+   * @param k number of leading eigenvalues required, 0 < k < n.
+   * @param tol tolerance of the eigs computation.
+   * @param maxIterations the maximum number of Arnoldi update iterations.
+   * @return a dense vector of eigenvalues in descending order and a dense matrix of eigenvectors
+   *         (columns of the matrix).
+   * @note The number of computed eigenvalues might be smaller than k when some Ritz values do not
+   *       satisfy the convergence criterion specified by tol (see ARPACK Users Guide, Chapter 4.6
+   *       for more details). The maximum number of Arnoldi update iterations is set to 300 in this
+   *       function.
+   */
+  private[mllib] def symmetricEigs(
+      mul: BDV[Double] => BDV[Double],
+      n: Int,
+      k: Int,
+      tol: Double,
+      maxIterations: Int): (BDV[Double], BDM[Double]) = {
+    // TODO: remove this function and use eigs in breeze when switching breeze version
+    require(n > k, s"Number of required eigenvalues $k must be smaller than matrix dimension $n")
+
+    val arpack = ARPACK.getInstance()
+
+    // tolerance used in stopping criterion
+    val tolW = new doubleW(tol)
+    // number of desired eigenvalues, 0 < nev < n
+    val nev = new intW(k)
+    // nev Lanczos vectors are generated in the first iteration
+    // ncv-nev Lanczos vectors are generated in each subsequent iteration
+    // ncv must be smaller than n
+    val ncv = math.min(2 * k, n)
+
+    // "I" for standard eigenvalue problem, "G" for generalized eigenvalue problem
+    val bmat = "I"
+    // "LM" : compute the NEV largest (in magnitude) eigenvalues
+    val which = "LM"
+
+    var iparam = new Array[Int](11)
+    // use exact shift in each iteration
+    iparam(0) = 1
+    // maximum number of Arnoldi update iterations, or the actual number of iterations on output
+    iparam(2) = maxIterations
+    // Mode 1: A*x = lambda*x, A symmetric
+    iparam(6) = 1
+
+    var ido = new intW(0)
+    var info = new intW(0)
+    var resid = new Array[Double](n)
+    var v = new Array[Double](n * ncv)
+    var workd = new Array[Double](n * 3)
+    var workl = new Array[Double](ncv * (ncv + 8))
+    var ipntr = new Array[Int](11)
+
+    // call ARPACK's reverse communication, first iteration with ido = 0
+    arpack.dsaupd(ido, bmat, n, which, nev.`val`, tolW, resid, ncv, v, n, iparam, ipntr, workd,
+      workl, workl.length, info)
+
+    val w = BDV(workd)
+
+    // ido = 99 : done flag in reverse communication
+    while (ido.`val` != 99) {
+      if (ido.`val` != -1 && ido.`val` != 1) {
+        throw new IllegalStateException("ARPACK returns ido = " + ido.`val` +
+            " This flag is not compatible with Mode 1: A*x = lambda*x, A symmetric.")
+      }
+      // multiply working vector with the matrix
+      val inputOffset = ipntr(0) - 1
+      val outputOffset = ipntr(1) - 1
+      val x = w.slice(inputOffset, inputOffset + n)
+      val y = w.slice(outputOffset, outputOffset + n)
+      y := mul(x)
+      // call ARPACK's reverse communication
+      arpack.dsaupd(ido, bmat, n, which, nev.`val`, tolW, resid, ncv, v, n, iparam, ipntr,
+        workd, workl, workl.length, info)
+    }
+
+    if (info.`val` != 0) {
+      info.`val` match {
+        case 1 => throw new IllegalStateException("ARPACK returns non-zero info = " + info.`val` +
+            " Maximum number of iterations taken. (Refer ARPACK user guide for details)")
+        case 2 => throw new IllegalStateException("ARPACK returns non-zero info = " + info.`val` +
+            " No shifts could be applied. Try to increase NCV. " +
+            "(Refer ARPACK user guide for details)")
+        case _ => throw new IllegalStateException("ARPACK returns non-zero info = " + info.`val` +
+            " Please refer ARPACK user guide for error message.")
+      }
+    }
+
+    val d = new Array[Double](nev.`val`)
+    val select = new Array[Boolean](ncv)
+    // copy the Ritz vectors
+    val z = java.util.Arrays.copyOfRange(v, 0, nev.`val` * n)
+
+    // call ARPACK's post-processing for eigenvectors
+    arpack.dseupd(true, "A", select, d, z, n, 0.0, bmat, n, which, nev, tol, resid, ncv, v, n,
+      iparam, ipntr, workd, workl, workl.length, info)
+
+    // number of computed eigenvalues, might be smaller than k
+    val computed = iparam(4)
+
+    val eigenPairs = java.util.Arrays.copyOfRange(d, 0, computed).zipWithIndex.map { r =>
+      (r._1, java.util.Arrays.copyOfRange(z, r._2 * n, r._2 * n + n))
+    }
+
+    // sort the eigen-pairs in descending order
+    val sortedEigenPairs = eigenPairs.sortBy(- _._1)
+
+    // copy eigenvectors in descending order of eigenvalues
+    val sortedU = BDM.zeros[Double](n, computed)
+    sortedEigenPairs.zipWithIndex.foreach { r =>
+      val b = r._2 * n
+      var i = 0
+      while (i < n) {
+        sortedU.data(b + i) = r._1._2(i)
+        i += 1
+      }
+    }
+
+    (BDV[Double](sortedEigenPairs.map(_._1)), sortedU)
+  }
+}
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
index 695e03b736ba..99cb6516e065 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
@@ -17,9 +17,10 @@
 
 package org.apache.spark.mllib.linalg.distributed
 
-import java.util
+import java.util.Arrays
 
-import breeze.linalg.{Vector => BV, DenseMatrix => BDM, DenseVector => BDV, svd => brzSvd}
+import breeze.linalg.{Vector => BV, DenseMatrix => BDM, DenseVector => BDV, SparseVector => BSV}
+import breeze.linalg.{svd => brzSvd, axpy => brzAxpy}
 import breeze.numerics.{sqrt => brzSqrt}
 import com.github.fommil.netlib.BLAS.{getInstance => blas}
 
@@ -34,7 +35,7 @@ import org.apache.spark.mllib.stat.MultivariateStatisticalSummary
  * [[org.apache.spark.mllib.stat.MultivariateStatisticalSummary]]
  * together with add() and merge() function.
  * A numerically stable algorithm is implemented to compute sample mean and variance:
-  *[[http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance variance-wiki]].
+ * [[http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance variance-wiki]].
  * Zero elements (including explicit zero values) are skipped when calling add() and merge(),
  * to have time complexity O(nnz) instead of O(n) for each column.
  */
@@ -200,6 +201,26 @@ class RowMatrix(
     nRows
   }
 
+  /**
+   * Multiplies the Gramian matrix `A^T A` by a dense vector on the right without computing `A^T A`.
+   *
+   * @param v a dense vector whose length must match the number of columns of this matrix
+   * @return a dense vector representing the product
+   */
+  private[mllib] def multiplyGramianMatrixBy(v: BDV[Double]): BDV[Double] = {
+    val n = numCols().toInt
+    val vbr = rows.context.broadcast(v)
+    rows.aggregate(BDV.zeros[Double](n))(
+      seqOp = (U, r) => {
+        val rBrz = r.toBreeze
+        val a = rBrz.dot(vbr.value)
+        brzAxpy(a, rBrz, U.asInstanceOf[BV[Double]])
+        U
+      },
+      combOp = (U1, U2) => U1 += U2
+    )
+  }
+
   /**
    * Computes the Gramian matrix `A^T A`.
    */
@@ -220,50 +241,135 @@ class RowMatrix(
   }
 
   /**
-   * Computes the singular value decomposition of this matrix.
-   * Denote this matrix by A (m x n), this will compute matrices U, S, V such that A = U * S * V'.
+   * Computes singular value decomposition of this matrix. Denote this matrix by A (m x n). This
+   * will compute matrices U, S, V such that A ~= U * S * V', where S contains the leading k
+   * singular values, U and V contain the corresponding singular vectors.
    *
-   * There is no restriction on m, but we require `n^2` doubles to fit in memory.
-   * Further, n should be less than m.
-
-   * The decomposition is computed by first computing A'A = V S^2 V',
-   * computing svd locally on that (since n x n is small), from which we recover S and V.
-   * Then we compute U via easy matrix multiplication as U =  A * (V * S^-1).
-   * Note that this approach requires `O(n^3)` time on the master node.
+   * At most k largest non-zero singular values and associated vectors are returned. If there are k
+   * such values, then the dimensions of the return will be:
+   *  - U is a RowMatrix of size m x k that satisfies U' * U = eye(k),
+   *  - s is a Vector of size k, holding the singular values in descending order,
+   *  - V is a Matrix of size n x k that satisfies V' * V = eye(k).
+   *
+   * We assume n is smaller than m. The singular values and the right singular vectors are derived
+   * from the eigenvalues and the eigenvectors of the Gramian matrix A' * A. U, the matrix
+   * storing the right singular vectors, is computed via matrix multiplication as
+   * U = A * (V * S^-1^), if requested by user. The actual method to use is determined
+   * automatically based on the cost:
+   *  - If n is small (n < 100) or k is large compared with n (k > n / 2), we compute the Gramian
+   *    matrix first and then compute its top eigenvalues and eigenvectors locally on the driver.
+   *    This requires a single pass with O(n^2^) storage on each executor and on the driver, and
+   *    O(n^2^ k) time on the driver.
+   *  - Otherwise, we compute (A' * A) * v in a distributive way and send it to ARPACK's DSAUPD to
+   *    compute (A' * A)'s top eigenvalues and eigenvectors on the driver node. This requires O(k)
+   *    passes, O(n) storage on each executor, and O(n k) storage on the driver.
    *
-   * At most k largest non-zero singular values and associated vectors are returned.
-   * If there are k such values, then the dimensions of the return will be:
+   * Several internal parameters are set to default values. The reciprocal condition number rCond
+   * is set to 1e-9. All singular values smaller than rCond * sigma(0) are treated as zeros, where
+   * sigma(0) is the largest singular value. The maximum number of Arnoldi update iterations for
+   * ARPACK is set to 300 or k * 3, whichever is larger. The numerical tolerance for ARPACK's
+   * eigen-decomposition is set to 1e-10.
    *
-   * U is a RowMatrix of size m x k that satisfies U'U = eye(k),
-   * s is a Vector of size k, holding the singular values in descending order,
-   * and V is a Matrix of size n x k that satisfies V'V = eye(k).
+   * @note The conditions that decide which method to use internally and the default parameters are
+   *       subject to change.
    *
-   * @param k number of singular values to keep. We might return less than k if there are
-   *          numerically zero singular values. See rCond.
+   * @param k number of leading singular values to keep (0 < k <= n). It might return less than k if
+   *          there are numerically zero singular values or there are not enough Ritz values
+   *          converged before the maximum number of Arnoldi update iterations is reached (in case
+   *          that matrix A is ill-conditioned).
    * @param computeU whether to compute U
    * @param rCond the reciprocal condition number. All singular values smaller than rCond * sigma(0)
    *              are treated as zero, where sigma(0) is the largest singular value.
-   * @return SingularValueDecomposition(U, s, V)
+   * @return SingularValueDecomposition(U, s, V). U = null if computeU = false.
    */
   def computeSVD(
       k: Int,
       computeU: Boolean = false,
       rCond: Double = 1e-9): SingularValueDecomposition[RowMatrix, Matrix] = {
+    // maximum number of Arnoldi update iterations for invoking ARPACK
+    val maxIter = math.max(300, k * 3)
+    // numerical tolerance for invoking ARPACK
+    val tol = 1e-10
+    computeSVD(k, computeU, rCond, maxIter, tol, "auto")
+  }
+
+  /**
+   * The actual SVD implementation, visible for testing.
+   *
+   * @param k number of leading singular values to keep (0 < k <= n)
+   * @param computeU whether to compute U
+   * @param rCond the reciprocal condition number
+   * @param maxIter max number of iterations (if ARPACK is used)
+   * @param tol termination tolerance (if ARPACK is used)
+   * @param mode computation mode (auto: determine automatically which mode to use,
+   *             local-svd: compute gram matrix and computes its full SVD locally,
+   *             local-eigs: compute gram matrix and computes its top eigenvalues locally,
+   *             dist-eigs: compute the top eigenvalues of the gram matrix distributively)
+   * @return SingularValueDecomposition(U, s, V). U = null if computeU = false.
+   */
+  private[mllib] def computeSVD(
+      k: Int,
+      computeU: Boolean,
+      rCond: Double,
+      maxIter: Int,
+      tol: Double,
+      mode: String): SingularValueDecomposition[RowMatrix, Matrix] = {
     val n = numCols().toInt
-    require(k > 0 && k <= n, s"Request up to n singular values k=$k n=$n.")
+    require(k > 0 && k <= n, s"Request up to n singular values but got k=$k and n=$n.")
 
-    val G = computeGramianMatrix()
+    object SVDMode extends Enumeration {
+      val LocalARPACK, LocalLAPACK, DistARPACK = Value
+    }
+
+    val computeMode = mode match {
+      case "auto" =>
+        // TODO: The conditions below are not fully tested.
+        if (n < 100 || k > n / 2) {
+          // If n is small or k is large compared with n, we better compute the Gramian matrix first
+          // and then compute its eigenvalues locally, instead of making multiple passes.
+          if (k < n / 3) {
+            SVDMode.LocalARPACK
+          } else {
+            SVDMode.LocalLAPACK
+          }
+        } else {
+          // If k is small compared with n, we use ARPACK with distributed multiplication.
+          SVDMode.DistARPACK
+        }
+      case "local-svd" => SVDMode.LocalLAPACK
+      case "local-eigs" => SVDMode.LocalARPACK
+      case "dist-eigs" => SVDMode.DistARPACK
+      case _ => throw new IllegalArgumentException(s"Do not support mode $mode.")
+    }
+
+    // Compute the eigen-decomposition of A' * A.
+    val (sigmaSquares: BDV[Double], u: BDM[Double]) = computeMode match {
+      case SVDMode.LocalARPACK =>
+        require(k < n, s"k must be smaller than n in local-eigs mode but got k=$k and n=$n.")
+        val G = computeGramianMatrix().toBreeze.asInstanceOf[BDM[Double]]
+        EigenValueDecomposition.symmetricEigs(v => G * v, n, k, tol, maxIter)
+      case SVDMode.LocalLAPACK =>
+        val G = computeGramianMatrix().toBreeze.asInstanceOf[BDM[Double]]
+        val (uFull: BDM[Double], sigmaSquaresFull: BDV[Double], _) = brzSvd(G)
+        (sigmaSquaresFull, uFull)
+      case SVDMode.DistARPACK =>
+        require(k < n, s"k must be smaller than n in dist-eigs mode but got k=$k and n=$n.")
+        EigenValueDecomposition.symmetricEigs(multiplyGramianMatrixBy, n, k, tol, maxIter)
+    }
 
-    // TODO: Use sparse SVD instead.
-    val (u: BDM[Double], sigmaSquares: BDV[Double], v: BDM[Double]) =
-      brzSvd(G.toBreeze.asInstanceOf[BDM[Double]])
     val sigmas: BDV[Double] = brzSqrt(sigmaSquares)
 
-    // Determine effective rank.
+    // Determine the effective rank.
     val sigma0 = sigmas(0)
     val threshold = rCond * sigma0
     var i = 0
-    while (i < k && sigmas(i) >= threshold) {
+    // sigmas might have a length smaller than k, if some Ritz values do not satisfy the convergence
+    // criterion specified by tol after max number of iterations.
+    // Thus use i < min(k, sigmas.length) instead of i < k.
+    if (sigmas.length < k) {
+      logWarning(s"Requested $k singular values but only found ${sigmas.length} converged.")
+    }
+    while (i < math.min(k, sigmas.length) && sigmas(i) >= threshold) {
       i += 1
     }
     val sk = i
@@ -272,12 +378,12 @@ class RowMatrix(
       logWarning(s"Requested $k singular values but only found $sk nonzeros.")
     }
 
-    val s = Vectors.dense(util.Arrays.copyOfRange(sigmas.data, 0, sk))
-    val V = Matrices.dense(n, sk, util.Arrays.copyOfRange(u.data, 0, n * sk))
+    val s = Vectors.dense(Arrays.copyOfRange(sigmas.data, 0, sk))
+    val V = Matrices.dense(n, sk, Arrays.copyOfRange(u.data, 0, n * sk))
 
     if (computeU) {
       // N = Vk * Sk^{-1}
-      val N = new BDM[Double](n, sk, util.Arrays.copyOfRange(u.data, 0, n * sk))
+      val N = new BDM[Double](n, sk, Arrays.copyOfRange(u.data, 0, n * sk))
       var i = 0
       var j = 0
       while (j < sk) {
@@ -364,7 +470,7 @@ class RowMatrix(
     if (k == n) {
       Matrices.dense(n, k, u.data)
     } else {
-      Matrices.dense(n, k, util.Arrays.copyOfRange(u.data, 0, n * k))
+      Matrices.dense(n, k, Arrays.copyOfRange(u.data, 0, n * k))
     }
   }
 
@@ -390,15 +496,24 @@ class RowMatrix(
    */
   def multiply(B: Matrix): RowMatrix = {
     val n = numCols().toInt
+    val k = B.numCols
     require(n == B.numRows, s"Dimension mismatch: $n vs ${B.numRows}")
 
     require(B.isInstanceOf[DenseMatrix],
       s"Only support dense matrix at this time but found ${B.getClass.getName}.")
 
-    val Bb = rows.context.broadcast(B)
+    val Bb = rows.context.broadcast(B.toBreeze.asInstanceOf[BDM[Double]].toDenseVector.toArray)
     val AB = rows.mapPartitions({ iter =>
-      val Bi = Bb.value.toBreeze.asInstanceOf[BDM[Double]]
-      iter.map(v => Vectors.fromBreeze(Bi.t * v.toBreeze))
+      val Bi = Bb.value
+      iter.map(row => {
+        val v = BDV.zeros[Double](k)
+        var i = 0
+        while (i < k) {
+          v(i) = row.toBreeze.dot(new BDV(Bi, i * n, 1, n))
+          i += 1
+        }
+        Vectors.fromBreeze(v)
+      })
     }, preservesPartitioning = true)
 
     new RowMatrix(AB, nRows, B.numCols)
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/linalg/distributed/RowMatrixSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/linalg/distributed/RowMatrixSuite.scala
index c9f9acf4c133..a961f89456a1 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/linalg/distributed/RowMatrixSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/linalg/distributed/RowMatrixSuite.scala
@@ -96,37 +96,44 @@ class RowMatrixSuite extends FunSuite with LocalSparkContext {
 
   test("svd of a full-rank matrix") {
     for (mat <- Seq(denseMat, sparseMat)) {
-      val localMat = mat.toBreeze()
-      val (localU, localSigma, localVt) = brzSvd(localMat)
-      val localV: BDM[Double] = localVt.t.toDenseMatrix
-      for (k <- 1 to n) {
-        val svd = mat.computeSVD(k, computeU = true)
-        val U = svd.U
-        val s = svd.s
-        val V = svd.V
-        assert(U.numRows() === m)
-        assert(U.numCols() === k)
-        assert(s.size === k)
-        assert(V.numRows === n)
-        assert(V.numCols === k)
-        assertColumnEqualUpToSign(U.toBreeze(), localU, k)
-        assertColumnEqualUpToSign(V.toBreeze.asInstanceOf[BDM[Double]], localV, k)
-        assert(closeToZero(s.toBreeze.asInstanceOf[BDV[Double]] - localSigma(0 until k)))
+      for (mode <- Seq("auto", "local-svd", "local-eigs", "dist-eigs")) {
+        val localMat = mat.toBreeze()
+        val (localU, localSigma, localVt) = brzSvd(localMat)
+        val localV: BDM[Double] = localVt.t.toDenseMatrix
+        for (k <- 1 to n) {
+          val skip = (mode == "local-eigs" || mode == "dist-eigs") && k == n
+          if (!skip) {
+            val svd = mat.computeSVD(k, computeU = true, 1e-9, 300, 1e-10, mode)
+            val U = svd.U
+            val s = svd.s
+            val V = svd.V
+            assert(U.numRows() === m)
+            assert(U.numCols() === k)
+            assert(s.size === k)
+            assert(V.numRows === n)
+            assert(V.numCols === k)
+            assertColumnEqualUpToSign(U.toBreeze(), localU, k)
+            assertColumnEqualUpToSign(V.toBreeze.asInstanceOf[BDM[Double]], localV, k)
+            assert(closeToZero(s.toBreeze.asInstanceOf[BDV[Double]] - localSigma(0 until k)))
+          }
+        }
+        val svdWithoutU = mat.computeSVD(1, computeU = false, 1e-9, 300, 1e-10, mode)
+        assert(svdWithoutU.U === null)
       }
-      val svdWithoutU = mat.computeSVD(n)
-      assert(svdWithoutU.U === null)
     }
   }
 
   test("svd of a low-rank matrix") {
-    val rows = sc.parallelize(Array.fill(4)(Vectors.dense(1.0, 1.0)), 2)
-    val mat = new RowMatrix(rows, 4, 2)
-    val svd = mat.computeSVD(2, computeU = true)
-    assert(svd.s.size === 1, "should not return zero singular values")
-    assert(svd.U.numRows() === 4)
-    assert(svd.U.numCols() === 1)
-    assert(svd.V.numRows === 2)
-    assert(svd.V.numCols === 1)
+    val rows = sc.parallelize(Array.fill(4)(Vectors.dense(1.0, 1.0, 1.0)), 2)
+    val mat = new RowMatrix(rows, 4, 3)
+    for (mode <- Seq("auto", "local-svd", "local-eigs", "dist-eigs")) {
+      val svd = mat.computeSVD(2, computeU = true, 1e-6, 300, 1e-10, mode)
+      assert(svd.s.size === 1, s"should not return zero singular values but got ${svd.s}")
+      assert(svd.U.numRows() === 4)
+      assert(svd.U.numCols() === 1)
+      assert(svd.V.numRows === 3)
+      assert(svd.V.numCols === 1)
+    }
   }
 
   def closeToZero(G: BDM[Double]): Boolean = {

From 2e0a037dff2ef3eee45f6d3e2d8eddfdc3edcd5d Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Wed, 9 Jul 2014 19:26:16 -0700
Subject: [PATCH 606/810] SPARK-2416: Allow richer reporting of unit test
 results

The built-in Jenkins integration is pretty bad. It's very confusing to users whether tests have passed or failed and we can't easily customize the message.

With some small scripting around the Github API we can do much better than this.

Author: Patrick Wendell <pwendell@gmail.com>

Closes #1340 from pwendell/better-qa-messages and squashes the following commits:

fd6077d [Patrick Wendell] Better automation for unit tests.
---
 dev/run-tests-jenkins | 85 +++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 85 insertions(+)
 create mode 100755 dev/run-tests-jenkins

diff --git a/dev/run-tests-jenkins b/dev/run-tests-jenkins
new file mode 100755
index 000000000000..8dda671e976c
--- /dev/null
+++ b/dev/run-tests-jenkins
@@ -0,0 +1,85 @@
+#!/usr/bin/env bash
+
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+# Wrapper script that runs the Spark tests then reports QA results
+# to github via its API.
+
+# Go to the Spark project root directory
+FWDIR="$(cd `dirname $0`/..; pwd)"
+cd $FWDIR
+
+COMMENTS_URL="https://api.github.com/repos/apache/spark/issues/$ghprbPullId/comments"
+
+function post_message {
+  message=$1
+  data="{\"body\": \"$message\"}"
+  echo "Attempting to post to Github:"
+  echo "$data"
+
+  curl -D- -u x-oauth-basic:$GITHUB_OAUTH_KEY -X POST --data "$data" -H \
+    "Content-Type: application/json" \
+    $COMMENTS_URL | head -n 8
+}
+
+start_message="QA tests have started for PR $ghprbPullId."
+if [ "$sha1" == "$ghprbActualCommit" ]; then
+  start_message="$start_message This patch DID NOT merge cleanly! "
+else
+  start_message="$start_message This patch merges cleanly. "
+fi
+start_message="$start_message<br>View progress: "
+start_message="$start_message${BUILD_URL}consoleFull"
+
+post_message "$start_message"
+
+./dev/run-tests
+test_result="$?"
+
+result_message="QA results for PR $ghprbPullId:<br>"
+
+if [ "$test_result" -eq "0" ]; then
+  result_message="$result_message- This patch PASSES unit tests.<br>"
+else
+  result_message="$result_message- This patch FAILED unit tests.<br>"
+fi
+
+if [ "$sha1" != "$ghprbActualCommit" ]; then
+  result_message="$result_message- This patch merges cleanly<br>"
+  non_test_files=$(git diff master --name-only | grep -v "\/test" | tr "\n" " ")
+  new_public_classes=$(git diff master $non_test_files \
+    | grep -e "trait " -e "class " \
+    | grep -e "{" -e "("  \
+    | grep -v -e \@\@ -e private \
+    | grep \+ \
+    | sed "s/\+ *//" \
+    | tr "\n" "~" \
+    | sed "s/~/<br>/g")
+  if [ "$new_public_classes" == "" ]; then
+    result_message="$result_message- This patch adds no public classes<br>"
+  else
+    result_message="$result_message- This patch adds the following public classes (experimental):<br>"
+    result_message="$result_message$new_public_classes"
+  fi
+fi
+result_message="${result_message}<br>For more information see test ouptut:"
+result_message="${result_message}<br>${BUILD_URL}consoleFull"
+
+post_message "$result_message"
+
+exit $test_result

From dd22bc2d570c54ad9853234d7a3f61720d606f39 Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Wed, 9 Jul 2014 19:36:38 -0700
Subject: [PATCH 607/810] Revert "[HOTFIX] Synchronize on SQLContext.settings
 in tests."

This reverts commit d4c30cd9918e18dde2a52909e36eaef6eb5996ab.
---
 .../scala/org/apache/spark/sql/SQLConf.scala  |  2 +-
 .../org/apache/spark/sql/JoinSuite.scala      | 40 ++++++-----
 .../org/apache/spark/sql/SQLConfSuite.scala   | 64 ++++++++---------
 .../org/apache/spark/sql/SQLQuerySuite.scala  | 68 +++++++++----------
 4 files changed, 83 insertions(+), 91 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
index b6fb46a3acc0..2b787e14f3f1 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
@@ -52,7 +52,7 @@ trait SQLConf {
   /** ********************** SQLConf functionality methods ************ */
 
   @transient
-  protected[sql] val settings = java.util.Collections.synchronizedMap(
+  private val settings = java.util.Collections.synchronizedMap(
     new java.util.HashMap[String, String]())
 
   def set(props: Properties): Unit = {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala
index 054b14f8f7ff..3d7d5eedbe8e 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala
@@ -39,27 +39,25 @@ class JoinSuite extends QueryTest {
   test("plans broadcast hash join, given hints") {
 
     def mkTest(buildSide: BuildSide, leftTable: String, rightTable: String) = {
-      TestSQLContext.settings.synchronized {
-        TestSQLContext.set("spark.sql.join.broadcastTables",
-          s"${if (buildSide == BuildRight) rightTable else leftTable}")
-        val rdd = sql( s"""SELECT * FROM $leftTable JOIN $rightTable ON key = a""")
-        // Using `sparkPlan` because for relevant patterns in HashJoin to be
-        // matched, other strategies need to be applied.
-        val physical = rdd.queryExecution.sparkPlan
-        val bhj = physical.collect { case j: BroadcastHashJoin if j.buildSide == buildSide => j}
-
-        assert(bhj.size === 1, "planner does not pick up hint to generate broadcast hash join")
-        checkAnswer(
-          rdd,
-          Seq(
-            (1, "1", 1, 1),
-            (1, "1", 1, 2),
-            (2, "2", 2, 1),
-            (2, "2", 2, 2),
-            (3, "3", 3, 1),
-            (3, "3", 3, 2)
-          ))
-      }
+      TestSQLContext.set("spark.sql.join.broadcastTables",
+        s"${if (buildSide == BuildRight) rightTable else leftTable}")
+      val rdd = sql(s"""SELECT * FROM $leftTable JOIN $rightTable ON key = a""")
+      // Using `sparkPlan` because for relevant patterns in HashJoin to be
+      // matched, other strategies need to be applied.
+      val physical = rdd.queryExecution.sparkPlan
+      val bhj = physical.collect { case j: BroadcastHashJoin if j.buildSide == buildSide => j }
+
+      assert(bhj.size === 1, "planner does not pick up hint to generate broadcast hash join")
+      checkAnswer(
+        rdd,
+        Seq(
+          (1, "1", 1, 1),
+          (1, "1", 1, 2),
+          (2, "2", 2, 1),
+          (2, "2", 2, 2),
+          (3, "3", 3, 1),
+          (3, "3", 3, 2)
+        ))
     }
 
     mkTest(BuildRight, "testData", "testData2")
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLConfSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLConfSuite.scala
index 93792f698cfa..08293f7f0ca3 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLConfSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLConfSuite.scala
@@ -28,50 +28,46 @@ class SQLConfSuite extends QueryTest {
   val testVal = "test.val.0"
 
   test("programmatic ways of basic setting and getting") {
-    TestSQLContext.settings.synchronized {
-      clear()
-      assert(getOption(testKey).isEmpty)
-      assert(getAll.toSet === Set())
+    clear()
+    assert(getOption(testKey).isEmpty)
+    assert(getAll.toSet === Set())
 
-      set(testKey, testVal)
-      assert(get(testKey) == testVal)
-      assert(get(testKey, testVal + "_") == testVal)
-      assert(getOption(testKey) == Some(testVal))
-      assert(contains(testKey))
+    set(testKey, testVal)
+    assert(get(testKey) == testVal)
+    assert(get(testKey, testVal + "_") == testVal)
+    assert(getOption(testKey) == Some(testVal))
+    assert(contains(testKey))
 
-      // Tests SQLConf as accessed from a SQLContext is mutable after
-      // the latter is initialized, unlike SparkConf inside a SparkContext.
-      assert(TestSQLContext.get(testKey) == testVal)
-      assert(TestSQLContext.get(testKey, testVal + "_") == testVal)
-      assert(TestSQLContext.getOption(testKey) == Some(testVal))
-      assert(TestSQLContext.contains(testKey))
+    // Tests SQLConf as accessed from a SQLContext is mutable after
+    // the latter is initialized, unlike SparkConf inside a SparkContext.
+    assert(TestSQLContext.get(testKey) == testVal)
+    assert(TestSQLContext.get(testKey, testVal + "_") == testVal)
+    assert(TestSQLContext.getOption(testKey) == Some(testVal))
+    assert(TestSQLContext.contains(testKey))
 
-      clear()
-    }
+    clear()
   }
 
   test("parse SQL set commands") {
-    TestSQLContext.settings.synchronized {
-      clear()
-      sql(s"set $testKey=$testVal")
-      assert(get(testKey, testVal + "_") == testVal)
-      assert(TestSQLContext.get(testKey, testVal + "_") == testVal)
+    clear()
+    sql(s"set $testKey=$testVal")
+    assert(get(testKey, testVal + "_") == testVal)
+    assert(TestSQLContext.get(testKey, testVal + "_") == testVal)
 
-      sql("set mapred.reduce.tasks=20")
-      assert(get("mapred.reduce.tasks", "0") == "20")
-      sql("set mapred.reduce.tasks = 40")
-      assert(get("mapred.reduce.tasks", "0") == "40")
+    sql("set mapred.reduce.tasks=20")
+    assert(get("mapred.reduce.tasks", "0") == "20")
+    sql("set mapred.reduce.tasks = 40")
+    assert(get("mapred.reduce.tasks", "0") == "40")
 
-      val key = "spark.sql.key"
-      val vs = "val0,val_1,val2.3,my_table"
-      sql(s"set $key=$vs")
-      assert(get(key, "0") == vs)
+    val key = "spark.sql.key"
+    val vs = "val0,val_1,val2.3,my_table"
+    sql(s"set $key=$vs")
+    assert(get(key, "0") == vs)
 
-      sql(s"set $key=")
-      assert(get(key, "0") == "")
+    sql(s"set $key=")
+    assert(get(key, "0") == "")
 
-      clear()
-    }
+    clear()
   }
 
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index fa1f32f8a49a..0743cfe8cff0 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -397,40 +397,38 @@ class SQLQuerySuite extends QueryTest {
   }
 
   test("SET commands semantics using sql()") {
-    TestSQLContext.settings.synchronized {
-      clear()
-      val testKey = "test.key.0"
-      val testVal = "test.val.0"
-      val nonexistentKey = "nonexistent"
-
-      // "set" itself returns all config variables currently specified in SQLConf.
-      assert(sql("SET").collect().size == 0)
-
-      // "set key=val"
-      sql(s"SET $testKey=$testVal")
-      checkAnswer(
-        sql("SET"),
-        Seq(Seq(testKey, testVal))
-      )
-
-      sql(s"SET ${testKey + testKey}=${testVal + testVal}")
-      checkAnswer(
-        sql("set"),
-        Seq(
-          Seq(testKey, testVal),
-          Seq(testKey + testKey, testVal + testVal))
-      )
-
-      // "set key"
-      checkAnswer(
-        sql(s"SET $testKey"),
-        Seq(Seq(testKey, testVal))
-      )
-      checkAnswer(
-        sql(s"SET $nonexistentKey"),
-        Seq(Seq(nonexistentKey, "<undefined>"))
-      )
-      clear()
-    }
+    clear()
+    val testKey = "test.key.0"
+    val testVal = "test.val.0"
+    val nonexistentKey = "nonexistent"
+
+    // "set" itself returns all config variables currently specified in SQLConf.
+    assert(sql("SET").collect().size == 0)
+
+    // "set key=val"
+    sql(s"SET $testKey=$testVal")
+    checkAnswer(
+      sql("SET"),
+      Seq(Seq(testKey, testVal))
+    )
+
+    sql(s"SET ${testKey + testKey}=${testVal + testVal}")
+    checkAnswer(
+      sql("set"),
+      Seq(
+        Seq(testKey, testVal),
+        Seq(testKey + testKey, testVal + testVal))
+    )
+
+    // "set key"
+    checkAnswer(
+      sql(s"SET $testKey"),
+      Seq(Seq(testKey, testVal))
+    )
+    checkAnswer(
+      sql(s"SET $nonexistentKey"),
+      Seq(Seq(nonexistentKey, "<undefined>"))
+    )
+    clear()
   }
 }

From 553c578de1a73a605197c184fc028efcc8dff010 Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Wed, 9 Jul 2014 19:44:24 -0700
Subject: [PATCH 608/810] HOTFIX: Remove persistently failing test in master.

Apparently this functionality is going to be removed soon anywyas.
---
 .../org/apache/spark/sql/JoinSuite.scala      | 28 -------------------
 1 file changed, 28 deletions(-)

diff --git a/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala
index 3d7d5eedbe8e..e17ecc87fd52 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala
@@ -36,34 +36,6 @@ class JoinSuite extends QueryTest {
     assert(planned.size === 1)
   }
 
-  test("plans broadcast hash join, given hints") {
-
-    def mkTest(buildSide: BuildSide, leftTable: String, rightTable: String) = {
-      TestSQLContext.set("spark.sql.join.broadcastTables",
-        s"${if (buildSide == BuildRight) rightTable else leftTable}")
-      val rdd = sql(s"""SELECT * FROM $leftTable JOIN $rightTable ON key = a""")
-      // Using `sparkPlan` because for relevant patterns in HashJoin to be
-      // matched, other strategies need to be applied.
-      val physical = rdd.queryExecution.sparkPlan
-      val bhj = physical.collect { case j: BroadcastHashJoin if j.buildSide == buildSide => j }
-
-      assert(bhj.size === 1, "planner does not pick up hint to generate broadcast hash join")
-      checkAnswer(
-        rdd,
-        Seq(
-          (1, "1", 1, 1),
-          (1, "1", 1, 2),
-          (2, "2", 2, 1),
-          (2, "2", 2, 2),
-          (3, "3", 3, 1),
-          (3, "3", 3, 2)
-        ))
-    }
-
-    mkTest(BuildRight, "testData", "testData2")
-    mkTest(BuildLeft, "testData", "testData2")
-  }
-
   test("multiple-key equi-join is hash-join") {
     val x = testData2.as('x)
     val y = testData2.as('y)

From 2b18ea9826395177ac2203dbf8eb37c220ab8e67 Mon Sep 17 00:00:00 2001
From: Raymond Liu <raymond.liu@intel.com>
Date: Wed, 9 Jul 2014 23:39:29 -0700
Subject: [PATCH 609/810] Clean up SparkKMeans example's code

remove unused code

Author: Raymond Liu <raymond.liu@intel.com>

Closes #1352 from colorant/kmeans and squashes the following commits:

ddcd1dd [Raymond Liu] Clean up SparkKMeans example's code
---
 .../main/scala/org/apache/spark/examples/SparkKMeans.scala   | 5 -----
 1 file changed, 5 deletions(-)

diff --git a/examples/src/main/scala/org/apache/spark/examples/SparkKMeans.scala b/examples/src/main/scala/org/apache/spark/examples/SparkKMeans.scala
index 4d28e0aad659..79cfedf33243 100644
--- a/examples/src/main/scala/org/apache/spark/examples/SparkKMeans.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/SparkKMeans.scala
@@ -17,8 +17,6 @@
 
 package org.apache.spark.examples
 
-import java.util.Random
-
 import breeze.linalg.{Vector, DenseVector, squaredDistance}
 
 import org.apache.spark.{SparkConf, SparkContext}
@@ -28,15 +26,12 @@ import org.apache.spark.SparkContext._
  * K-means clustering.
  */
 object SparkKMeans {
-  val R = 1000     // Scaling factor
-  val rand = new Random(42)
 
   def parseVector(line: String): Vector[Double] = {
     DenseVector(line.split(' ').map(_.toDouble))
   }
 
   def closestPoint(p: Vector[Double], centers: Array[Vector[Double]]): Int = {
-    var index = 0
     var bestIndex = 0
     var closest = Double.PositiveInfinity
 

From c2babc089bd97137d240ca119ec4fc4b5a1422c8 Mon Sep 17 00:00:00 2001
From: Masayoshi TSUZUKI <tsudukim@oss.nttdata.co.jp>
Date: Thu, 10 Jul 2014 01:18:37 -0700
Subject: [PATCH 610/810] SPARK-2115: Stage kill link is too close to stage
 details link

Moved (kill) link to the right side. Add confirmation dialog when (kill) link is clicked.

Author: Masayoshi TSUZUKI <tsudukim@oss.nttdata.co.jp>

Closes #1350 from tsudukim/feature/SPARK-2115 and squashes the following commits:

e2263b0 [Masayoshi TSUZUKI] Moved (kill) link to the right side. Add confirmation dialog when (kill) link is clicked.
---
 core/src/main/resources/org/apache/spark/ui/static/webui.css | 2 ++
 .../src/main/scala/org/apache/spark/ui/jobs/StageTable.scala | 5 +++--
 2 files changed, 5 insertions(+), 2 deletions(-)

diff --git a/core/src/main/resources/org/apache/spark/ui/static/webui.css b/core/src/main/resources/org/apache/spark/ui/static/webui.css
index 7448af87fcf3..445110d63e18 100644
--- a/core/src/main/resources/org/apache/spark/ui/static/webui.css
+++ b/core/src/main/resources/org/apache/spark/ui/static/webui.css
@@ -81,7 +81,9 @@ table.sortable thead {
 
 span.kill-link {
   margin-right: 2px;
+  margin-left: 20px;
   color: gray;
+  float: right;
 }
 
 span.kill-link a {
diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/StageTable.scala b/core/src/main/scala/org/apache/spark/ui/jobs/StageTable.scala
index 4013c6f49936..fd8d0b5cdde0 100644
--- a/core/src/main/scala/org/apache/spark/ui/jobs/StageTable.scala
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/StageTable.scala
@@ -89,7 +89,8 @@ private[ui] class StageTableBase(
     // scalastyle:off
     val killLink = if (killEnabled) {
       <span class="kill-link">
-        (<a href={"%s/stages/stage/kill?id=%s&terminate=true".format(UIUtils.prependBaseUri(basePath), s.stageId)}>kill</a>)
+        (<a href={"%s/stages/stage/kill?id=%s&terminate=true".format(UIUtils.prependBaseUri(basePath), s.stageId)}
+            onclick={"return window.confirm('Are you sure you want to kill stage %s ?');".format(s.stageId)}>kill</a>)
       </span>
     }
     // scalastyle:on
@@ -109,7 +110,7 @@ private[ui] class StageTableBase(
 
     listener.stageIdToDescription.get(s.stageId)
       .map(d => <div><em>{d}</em></div><div>{nameLink} {killLink}</div>)
-      .getOrElse(<div>{killLink} {nameLink} {details}</div>)
+      .getOrElse(<div>{nameLink} {killLink} {details}</div>)
   }
 
   protected def stageRow(s: StageInfo): Seq[Node] = {

From 628932b8d0dbbc6c68c61d4bca1c504f38684c2a Mon Sep 17 00:00:00 2001
From: Prashant Sharma <prashant.s@imaginea.com>
Date: Thu, 10 Jul 2014 11:03:37 -0700
Subject: [PATCH 611/810] [SPARK-1776] Have Spark's SBT build read dependencies
 from Maven.

Patch introduces the new way of working also retaining the existing ways of doing things.

For example build instruction for yarn in maven is
`mvn -Pyarn -PHadoop2.2 clean package -DskipTests`
in sbt it can become
`MAVEN_PROFILES="yarn, hadoop-2.2" sbt/sbt clean assembly`
Also supports
`sbt/sbt -Pyarn -Phadoop-2.2 -Dhadoop.version=2.2.0 clean assembly`

Author: Prashant Sharma <prashant.s@imaginea.com>
Author: Patrick Wendell <pwendell@gmail.com>

Closes #772 from ScrapCodes/sbt-maven and squashes the following commits:

a8ac951 [Prashant Sharma] Updated sbt version.
62b09bb [Prashant Sharma] Improvements.
fa6221d [Prashant Sharma] Excluding sql from mima
4b8875e [Prashant Sharma] Sbt assembly no longer builds tools by default.
72651ca [Prashant Sharma] Addresses code reivew comments.
acab73d [Prashant Sharma] Revert "Small fix to run-examples script."
ac4312c [Prashant Sharma] Revert "minor fix"
6af91ac [Prashant Sharma] Ported oldDeps back. + fixes issues with prev commit.
65cf06c [Prashant Sharma] Servelet API jars mess up with the other servlet jars on the class path.
446768e [Prashant Sharma] minor fix
89b9777 [Prashant Sharma] Merge conflicts
d0a02f2 [Prashant Sharma] Bumped up pom versions, Since the build now depends on pom it is better updated there. + general cleanups.
dccc8ac [Prashant Sharma] updated mima to check against 1.0
a49c61b [Prashant Sharma] Fix for tools jar
a2f5ae1 [Prashant Sharma] Fixes a bug in dependencies.
cf88758 [Prashant Sharma] cleanup
9439ea3 [Prashant Sharma] Small fix to run-examples script.
96cea1f [Prashant Sharma] SPARK-1776 Have Spark's SBT build read dependencies from Maven.
36efa62 [Patrick Wendell] Set project name in pom files and added eclipse/intellij plugins.
4973dbd [Patrick Wendell] Example build using pom reader.
---
 assembly/pom.xml                       |   1 +
 bagel/pom.xml                          |   3 +
 bin/spark-class                        |   4 +-
 core/pom.xml                           |   3 +
 dev/run-tests                          |   4 +-
 examples/pom.xml                       |   3 +
 external/flume/pom.xml                 |   3 +
 external/kafka/pom.xml                 |   3 +
 external/mqtt/pom.xml                  |   3 +
 external/twitter/pom.xml               |   3 +
 external/zeromq/pom.xml                |   3 +
 extras/java8-tests/pom.xml             |   6 +-
 extras/spark-ganglia-lgpl/pom.xml      |   6 +-
 graphx/pom.xml                         |   3 +
 mllib/pom.xml                          |   3 +
 pom.xml                                |  10 +-
 project/MimaBuild.scala                |  22 +-
 project/MimaExcludes.scala             |   4 +-
 project/SparkBuild.scala               | 780 +++++++------------------
 project/build.properties               |   2 +-
 project/project/SparkPluginBuild.scala |   4 +-
 repl/pom.xml                           |   1 +
 sbt/sbt                                |   1 +
 sbt/sbt-launch-lib.bash                |  11 +-
 sql/catalyst/pom.xml                   |   3 +
 sql/core/pom.xml                       |   3 +
 sql/hive/pom.xml                       |   8 +
 streaming/pom.xml                      |   3 +
 tools/pom.xml                          |   3 +
 yarn/alpha/pom.xml                     |   3 +
 yarn/pom.xml                           |   3 +
 yarn/stable/pom.xml                    |   3 +
 32 files changed, 317 insertions(+), 598 deletions(-)

diff --git a/assembly/pom.xml b/assembly/pom.xml
index 0c60b66c3dac..4f6aade133db 100644
--- a/assembly/pom.xml
+++ b/assembly/pom.xml
@@ -32,6 +32,7 @@
   <packaging>pom</packaging>
 
   <properties>
+    <sbt.project.name>assembly</sbt.project.name>
     <spark.jar.dir>scala-${scala.binary.version}</spark.jar.dir>
     <spark.jar.basename>spark-assembly-${project.version}-hadoop${hadoop.version}.jar</spark.jar.basename>
     <spark.jar>${project.build.directory}/${spark.jar.dir}/${spark.jar.basename}</spark.jar>
diff --git a/bagel/pom.xml b/bagel/pom.xml
index c8e39a415af2..90c4b095bb61 100644
--- a/bagel/pom.xml
+++ b/bagel/pom.xml
@@ -27,6 +27,9 @@
 
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-bagel_2.10</artifactId>
+  <properties>
+     <sbt.project.name>bagel</sbt.project.name>
+  </properties>
   <packaging>jar</packaging>
   <name>Spark Project Bagel</name>
   <url>http://spark.apache.org/</url>
diff --git a/bin/spark-class b/bin/spark-class
index 04fa52c6756b..3f6beca5becf 100755
--- a/bin/spark-class
+++ b/bin/spark-class
@@ -110,9 +110,9 @@ export JAVA_OPTS
 
 TOOLS_DIR="$FWDIR"/tools
 SPARK_TOOLS_JAR=""
-if [ -e "$TOOLS_DIR"/target/scala-$SCALA_VERSION/*assembly*[0-9Tg].jar ]; then
+if [ -e "$TOOLS_DIR"/target/scala-$SCALA_VERSION/spark-tools*[0-9Tg].jar ]; then
   # Use the JAR from the SBT build
-  export SPARK_TOOLS_JAR=`ls "$TOOLS_DIR"/target/scala-$SCALA_VERSION/*assembly*[0-9Tg].jar`
+  export SPARK_TOOLS_JAR=`ls "$TOOLS_DIR"/target/scala-$SCALA_VERSION/spark-tools*[0-9Tg].jar`
 fi
 if [ -e "$TOOLS_DIR"/target/spark-tools*[0-9Tg].jar ]; then
   # Use the JAR from the Maven build
diff --git a/core/pom.xml b/core/pom.xml
index 6abf8480d5da..4ed920a750ff 100644
--- a/core/pom.xml
+++ b/core/pom.xml
@@ -27,6 +27,9 @@
 
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-core_2.10</artifactId>
+  <properties>
+     <sbt.project.name>core</sbt.project.name>
+  </properties>
   <packaging>jar</packaging>
   <name>Spark Project Core</name>
   <url>http://spark.apache.org/</url>
diff --git a/dev/run-tests b/dev/run-tests
index d9df020f7563..edd17b53b3d8 100755
--- a/dev/run-tests
+++ b/dev/run-tests
@@ -66,10 +66,10 @@ echo "========================================================================="
 # (either resolution or compilation) prompts the user for input either q, r, 
 # etc to quit or retry. This echo is there to make it not block.
 if [ -n "$_RUN_SQL_TESTS" ]; then
-  echo -e "q\n" | SPARK_HIVE=true sbt/sbt clean assembly test | \
+  echo -e "q\n" | SPARK_HIVE=true sbt/sbt clean package assembly/assembly test | \
     grep -v -e "info.*Resolving" -e "warn.*Merging" -e "info.*Including"
 else
-  echo -e "q\n" | sbt/sbt clean assembly test | \
+  echo -e "q\n" | sbt/sbt clean package assembly/assembly test | \
     grep -v -e "info.*Resolving" -e "warn.*Merging" -e "info.*Including"
 fi
 
diff --git a/examples/pom.xml b/examples/pom.xml
index 4f6d7fdb87d4..bd1c387c2eb9 100644
--- a/examples/pom.xml
+++ b/examples/pom.xml
@@ -27,6 +27,9 @@
 
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-examples_2.10</artifactId>
+  <properties>
+     <sbt.project.name>examples</sbt.project.name>
+  </properties>
   <packaging>jar</packaging>
   <name>Spark Project Examples</name>
   <url>http://spark.apache.org/</url>
diff --git a/external/flume/pom.xml b/external/flume/pom.xml
index c1f581967777..61a6aff543ae 100644
--- a/external/flume/pom.xml
+++ b/external/flume/pom.xml
@@ -27,6 +27,9 @@
 
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-streaming-flume_2.10</artifactId>
+  <properties>
+     <sbt.project.name>streaming-flume</sbt.project.name>
+  </properties>
   <packaging>jar</packaging>
   <name>Spark Project External Flume</name>
   <url>http://spark.apache.org/</url>
diff --git a/external/kafka/pom.xml b/external/kafka/pom.xml
index d014a7aad0fc..4762c50685a9 100644
--- a/external/kafka/pom.xml
+++ b/external/kafka/pom.xml
@@ -27,6 +27,9 @@
 
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-streaming-kafka_2.10</artifactId>
+  <properties>
+     <sbt.project.name>streaming-kafka</sbt.project.name>
+  </properties>
   <packaging>jar</packaging>
   <name>Spark Project External Kafka</name>
   <url>http://spark.apache.org/</url>
diff --git a/external/mqtt/pom.xml b/external/mqtt/pom.xml
index 4980208cba3b..32c530e600ce 100644
--- a/external/mqtt/pom.xml
+++ b/external/mqtt/pom.xml
@@ -27,6 +27,9 @@
 
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-streaming-mqtt_2.10</artifactId>
+  <properties>
+     <sbt.project.name>streaming-mqtt</sbt.project.name>
+  </properties>
   <packaging>jar</packaging>
   <name>Spark Project External MQTT</name>
   <url>http://spark.apache.org/</url>
diff --git a/external/twitter/pom.xml b/external/twitter/pom.xml
index 7073bd4404d9..637adb0f00da 100644
--- a/external/twitter/pom.xml
+++ b/external/twitter/pom.xml
@@ -27,6 +27,9 @@
 
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-streaming-twitter_2.10</artifactId>
+  <properties>
+     <sbt.project.name>streaming-twitter</sbt.project.name>
+  </properties>
   <packaging>jar</packaging>
   <name>Spark Project External Twitter</name>
   <url>http://spark.apache.org/</url>
diff --git a/external/zeromq/pom.xml b/external/zeromq/pom.xml
index cf306e0dca8b..e4d758a04a4c 100644
--- a/external/zeromq/pom.xml
+++ b/external/zeromq/pom.xml
@@ -27,6 +27,9 @@
 
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-streaming-zeromq_2.10</artifactId>
+  <properties>
+     <sbt.project.name>streaming-zeromq</sbt.project.name>
+  </properties>
   <packaging>jar</packaging>
   <name>Spark Project External ZeroMQ</name>
   <url>http://spark.apache.org/</url>
diff --git a/extras/java8-tests/pom.xml b/extras/java8-tests/pom.xml
index 955ec1a8c303..3eade411b38b 100644
--- a/extras/java8-tests/pom.xml
+++ b/extras/java8-tests/pom.xml
@@ -28,7 +28,11 @@
   <artifactId>java8-tests_2.10</artifactId>
   <packaging>pom</packaging>
   <name>Spark Project Java8 Tests POM</name>
-  
+
+  <properties>
+    <sbt.project.name>java8-tests</sbt.project.name>
+  </properties>
+
   <dependencies>
     <dependency>
       <groupId>org.apache.spark</groupId>
diff --git a/extras/spark-ganglia-lgpl/pom.xml b/extras/spark-ganglia-lgpl/pom.xml
index 22ea330b4374..a5b162a0482e 100644
--- a/extras/spark-ganglia-lgpl/pom.xml
+++ b/extras/spark-ganglia-lgpl/pom.xml
@@ -29,7 +29,11 @@
   <artifactId>spark-ganglia-lgpl_2.10</artifactId>
   <packaging>jar</packaging>
   <name>Spark Ganglia Integration</name>
-  
+
+  <properties>
+    <sbt.project.name>ganglia-lgpl</sbt.project.name>
+  </properties>
+
   <dependencies>
     <dependency>
       <groupId>org.apache.spark</groupId>
diff --git a/graphx/pom.xml b/graphx/pom.xml
index 7d5d83e7f3bb..7e3bcf29dcfb 100644
--- a/graphx/pom.xml
+++ b/graphx/pom.xml
@@ -27,6 +27,9 @@
 
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-graphx_2.10</artifactId>
+  <properties>
+     <sbt.project.name>graphx</sbt.project.name>
+  </properties>
   <packaging>jar</packaging>
   <name>Spark Project GraphX</name>
   <url>http://spark.apache.org/</url>
diff --git a/mllib/pom.xml b/mllib/pom.xml
index b622f96dd790..87afd7ecf2dd 100644
--- a/mllib/pom.xml
+++ b/mllib/pom.xml
@@ -27,6 +27,9 @@
 
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-mllib_2.10</artifactId>
+  <properties>
+     <sbt.project.name>mllib</sbt.project.name>
+  </properties>  
   <packaging>jar</packaging>
   <name>Spark Project ML Library</name>
   <url>http://spark.apache.org/</url>
diff --git a/pom.xml b/pom.xml
index 05f76d566e9d..fa80707d0929 100644
--- a/pom.xml
+++ b/pom.xml
@@ -110,7 +110,7 @@
     <project.reporting.outputEncoding>UTF-8</project.reporting.outputEncoding>
 
     <java.version>1.6</java.version>
-
+    <sbt.project.name>spark</sbt.project.name>
     <scala.version>2.10.4</scala.version>
     <scala.binary.version>2.10</scala.binary.version>
     <mesos.version>0.18.1</mesos.version>
@@ -535,6 +535,10 @@
             <groupId>org.mortbay.jetty</groupId>
             <artifactId>servlet-api-2.5</artifactId>
           </exclusion>
+          <exclusion>
+            <groupId>javax.servlet</groupId>
+            <artifactId>servlet-api</artifactId>
+          </exclusion>
           <exclusion>
             <groupId>junit</groupId>
             <artifactId>junit</artifactId>
@@ -618,6 +622,10 @@
         <artifactId>hadoop-yarn-api</artifactId>
         <version>${yarn.version}</version>
         <exclusions>
+          <exclusion>
+            <groupId>javax.servlet</groupId>
+            <artifactId>servlet-api</artifactId>
+          </exclusion>
           <exclusion>
             <groupId>asm</groupId>
             <artifactId>asm</artifactId>
diff --git a/project/MimaBuild.scala b/project/MimaBuild.scala
index bb2d73741c3b..034ba6a7bf50 100644
--- a/project/MimaBuild.scala
+++ b/project/MimaBuild.scala
@@ -15,13 +15,16 @@
  * limitations under the License.
  */
 
+import sbt._
+import sbt.Keys.version
+
 import com.typesafe.tools.mima.core._
 import com.typesafe.tools.mima.core.MissingClassProblem
 import com.typesafe.tools.mima.core.MissingTypesProblem
 import com.typesafe.tools.mima.core.ProblemFilters._
 import com.typesafe.tools.mima.plugin.MimaKeys.{binaryIssueFilters, previousArtifact}
 import com.typesafe.tools.mima.plugin.MimaPlugin.mimaDefaultSettings
-import sbt._
+
 
 object MimaBuild {
 
@@ -53,7 +56,7 @@ object MimaBuild {
     excludePackage("org.apache.spark." + packageName)
   }
 
-  def ignoredABIProblems(base: File) = {
+  def ignoredABIProblems(base: File, currentSparkVersion: String) = {
 
     // Excludes placed here will be used for all Spark versions
     val defaultExcludes = Seq()
@@ -77,11 +80,16 @@ object MimaBuild {
     }
 
     defaultExcludes ++ ignoredClasses.flatMap(excludeClass) ++
-    ignoredMembers.flatMap(excludeMember) ++ MimaExcludes.excludes
+    ignoredMembers.flatMap(excludeMember) ++ MimaExcludes.excludes(currentSparkVersion)
+  }
+
+  def mimaSettings(sparkHome: File, projectRef: ProjectRef) = {
+    val organization = "org.apache.spark"
+    val previousSparkVersion = "1.0.0"
+    val fullId = "spark-" + projectRef.project + "_2.10"
+    mimaDefaultSettings ++ 
+    Seq(previousArtifact := Some(organization % fullId % previousSparkVersion),
+      binaryIssueFilters ++= ignoredABIProblems(sparkHome, version.value))
   }
 
-  def mimaSettings(sparkHome: File) = mimaDefaultSettings ++ Seq(
-    previousArtifact := None,
-    binaryIssueFilters ++= ignoredABIProblems(sparkHome)
-  )
 }
diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala
index 1621833e124f..44bc9dc5fb69 100644
--- a/project/MimaExcludes.scala
+++ b/project/MimaExcludes.scala
@@ -31,8 +31,8 @@ import com.typesafe.tools.mima.core._
  * MimaBuild.excludeSparkClass("graphx.util.collection.GraphXPrimitiveKeyOpenHashMap")
  */
 object MimaExcludes {
-    val excludes =
-      SparkBuild.SPARK_VERSION match {
+    def excludes(version: String) =
+      version match {
         case v if v.startsWith("1.1") =>
           Seq(
             MimaBuild.excludeSparkPackage("deploy"),
diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index 599714233c18..b55c50560bb9 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -15,524 +15,159 @@
  * limitations under the License.
  */
 
-import sbt._
-import sbt.Classpaths.publishTask
-import sbt.Keys._
-import sbtassembly.Plugin._
-import AssemblyKeys._
 import scala.util.Properties
-import org.scalastyle.sbt.ScalastylePlugin.{Settings => ScalaStyleSettings}
-import com.typesafe.tools.mima.plugin.MimaKeys.previousArtifact
-import sbtunidoc.Plugin._
-import UnidocKeys._
-
 import scala.collection.JavaConversions._
 
-// For Sonatype publishing
-// import com.jsuereth.pgp.sbtplugin.PgpKeys._
-
-object SparkBuild extends Build {
-  val SPARK_VERSION = "1.1.0-SNAPSHOT"
-  val SPARK_VERSION_SHORT = SPARK_VERSION.replaceAll("-SNAPSHOT", "")
-
-  // Hadoop version to build against. For example, "1.0.4" for Apache releases, or
-  // "2.0.0-mr1-cdh4.2.0" for Cloudera Hadoop. Note that these variables can be set
-  // through the environment variables SPARK_HADOOP_VERSION and SPARK_YARN.
-  val DEFAULT_HADOOP_VERSION = "1.0.4"
-
-  // Whether the Hadoop version to build against is 2.2.x, or a variant of it. This can be set
-  // through the SPARK_IS_NEW_HADOOP environment variable.
-  val DEFAULT_IS_NEW_HADOOP = false
-
-  val DEFAULT_YARN = false
-
-  val DEFAULT_HIVE = false
-
-  // HBase version; set as appropriate.
-  val HBASE_VERSION = "0.94.6"
-
-  // Target JVM version
-  val SCALAC_JVM_VERSION = "jvm-1.6"
-  val JAVAC_JVM_VERSION = "1.6"
-
-  lazy val root = Project("root", file("."), settings = rootSettings) aggregate(allProjects: _*)
-
-  lazy val core = Project("core", file("core"), settings = coreSettings)
-
-  /** Following project only exists to pull previous artifacts of Spark for generating
-    Mima ignores. For more information see: SPARK 2071 */
-  lazy val oldDeps = Project("oldDeps", file("dev"), settings = oldDepsSettings)
-
-  def replDependencies = Seq[ProjectReference](core, graphx, bagel, mllib, sql) ++ maybeHiveRef
-
-  lazy val repl = Project("repl", file("repl"), settings = replSettings)
-    .dependsOn(replDependencies.map(a => a: sbt.ClasspathDep[sbt.ProjectReference]): _*)
-
-  lazy val tools = Project("tools", file("tools"), settings = toolsSettings) dependsOn(core) dependsOn(streaming)
+import sbt._
+import sbt.Keys._
+import org.scalastyle.sbt.ScalastylePlugin.{Settings => ScalaStyleSettings}
+import com.typesafe.sbt.pom.{PomBuild, SbtPomKeys}
+import net.virtualvoid.sbt.graph.Plugin.graphSettings
 
-  lazy val bagel = Project("bagel", file("bagel"), settings = bagelSettings) dependsOn(core)
+object BuildCommons {
 
-  lazy val graphx = Project("graphx", file("graphx"), settings = graphxSettings) dependsOn(core)
+  private val buildLocation = file(".").getAbsoluteFile.getParentFile
 
-  lazy val catalyst = Project("catalyst", file("sql/catalyst"), settings = catalystSettings) dependsOn(core)
+  val allProjects@Seq(bagel, catalyst, core, graphx, hive, mllib, repl, spark, sql, streaming,
+  streamingFlume, streamingKafka, streamingMqtt, streamingTwitter, streamingZeromq) =
+    Seq("bagel", "catalyst", "core", "graphx", "hive", "mllib", "repl", "spark", "sql",
+      "streaming", "streaming-flume", "streaming-kafka", "streaming-mqtt", "streaming-twitter",
+      "streaming-zeromq").map(ProjectRef(buildLocation, _))
 
-  lazy val sql = Project("sql", file("sql/core"), settings = sqlCoreSettings) dependsOn(core) dependsOn(catalyst % "compile->compile;test->test")
+  val optionallyEnabledProjects@Seq(yarn, yarnStable, yarnAlpha, java8Tests, sparkGangliaLgpl) =
+    Seq("yarn", "yarn-stable", "yarn-alpha", "java8-tests", "ganglia-lgpl")
+      .map(ProjectRef(buildLocation, _))
 
-  lazy val hive = Project("hive", file("sql/hive"), settings = hiveSettings) dependsOn(sql)
+  val assemblyProjects@Seq(assembly, examples) = Seq("assembly", "examples")
+    .map(ProjectRef(buildLocation, _))
 
-  lazy val maybeHive: Seq[ClasspathDependency] = if (isHiveEnabled) Seq(hive) else Seq()
-  lazy val maybeHiveRef: Seq[ProjectReference] = if (isHiveEnabled) Seq(hive) else Seq()
+  val tools = "tools"
 
-  lazy val streaming = Project("streaming", file("streaming"), settings = streamingSettings) dependsOn(core)
+  val sparkHome = buildLocation
+}
 
-  lazy val mllib = Project("mllib", file("mllib"), settings = mllibSettings) dependsOn(core)
+object SparkBuild extends PomBuild {
 
-  lazy val assemblyProj = Project("assembly", file("assembly"), settings = assemblyProjSettings)
-    .dependsOn(core, graphx, bagel, mllib, streaming, repl, sql) dependsOn(maybeYarn: _*) dependsOn(maybeHive: _*) dependsOn(maybeGanglia: _*)
+  import BuildCommons._
+  import scala.collection.mutable.Map
 
-  lazy val assembleDepsTask = TaskKey[Unit]("assemble-deps")
-  lazy val assembleDeps = assembleDepsTask := {
-    println()
-    println("**** NOTE ****")
-    println("'sbt/sbt assemble-deps' is no longer supported.")
-    println("Instead create a normal assembly and:")
-    println("  export SPARK_PREPEND_CLASSES=1 (toggle on)")
-    println("  unset SPARK_PREPEND_CLASSES (toggle off)")
-    println()
-  }
+  val projectsMap: Map[String, Seq[Setting[_]]] = Map.empty
 
-  // A configuration to set an alternative publishLocalConfiguration
-  lazy val MavenCompile = config("m2r") extend(Compile)
-  lazy val publishLocalBoth = TaskKey[Unit]("publish-local", "publish local for m2 and ivy")
-  val sparkHome = System.getProperty("user.dir")
-
-  // Allows build configuration to be set through environment variables
-  lazy val hadoopVersion = Properties.envOrElse("SPARK_HADOOP_VERSION", DEFAULT_HADOOP_VERSION)
-  lazy val isNewHadoop = Properties.envOrNone("SPARK_IS_NEW_HADOOP") match {
-    case None => {
-      val isNewHadoopVersion = "^2\\.[2-9]+".r.findFirstIn(hadoopVersion).isDefined
-      (isNewHadoopVersion|| DEFAULT_IS_NEW_HADOOP)
+  // Provides compatibility for older versions of the Spark build
+  def backwardCompatibility = {
+    import scala.collection.mutable
+    var isAlphaYarn = false
+    var profiles: mutable.Seq[String] = mutable.Seq.empty
+    if (Properties.envOrNone("SPARK_GANGLIA_LGPL").isDefined) {
+      println("NOTE: SPARK_GANGLIA_LGPL is deprecated, please use -Pganglia-lgpl flag.")
+      profiles ++= Seq("spark-ganglia-lgpl")
     }
-    case Some(v) => v.toBoolean
-  }
-
-  lazy val isYarnEnabled = Properties.envOrNone("SPARK_YARN") match {
-    case None => DEFAULT_YARN
-    case Some(v) => v.toBoolean
+    if (Properties.envOrNone("SPARK_HIVE").isDefined) {
+      println("NOTE: SPARK_HIVE is deprecated, please use -Phive flag.")
+      profiles ++= Seq("hive")
+    }
+    Properties.envOrNone("SPARK_HADOOP_VERSION") match {
+      case Some(v) =>
+        if (v.matches("0.23.*")) isAlphaYarn = true
+        println("NOTE: SPARK_HADOOP_VERSION is deprecated, please use -Dhadoop.version=" + v)
+        System.setProperty("hadoop.version", v)
+      case None =>
+    }
+    if (Properties.envOrNone("SPARK_YARN").isDefined) {
+      if(isAlphaYarn) {
+        println("NOTE: SPARK_YARN is deprecated, please use -Pyarn-alpha flag.")
+        profiles ++= Seq("yarn-alpha")
+      }
+      else {
+        println("NOTE: SPARK_YARN is deprecated, please use -Pyarn flag.")
+        profiles ++= Seq("yarn")
+      }
+    }
+    profiles
   }
-  lazy val hadoopClient = if (hadoopVersion.startsWith("0.20.") || hadoopVersion == "1.0.0") "hadoop-core" else "hadoop-client"
-  val maybeAvro = if (hadoopVersion.startsWith("0.23.")) Seq("org.apache.avro" % "avro" % "1.7.4") else Seq()
 
-  lazy val isHiveEnabled = Properties.envOrNone("SPARK_HIVE") match {
-    case None => DEFAULT_HIVE
-    case Some(v) => v.toBoolean
+  override val profiles = Properties.envOrNone("MAVEN_PROFILES") match {
+    case None => backwardCompatibility
+    // Rationale: If -P option exists no need to support backwardCompatibility.
+    case Some(v) =>
+      if (backwardCompatibility.nonEmpty)
+        println("Note: We ignore environment variables, when use of profile is detected in " +
+          "conjunction with environment variable.")
+      v.split("(\\s+|,)").filterNot(_.isEmpty).map(_.trim.replaceAll("-P", "")).toSeq
   }
 
-  // Include Ganglia integration if the user has enabled Ganglia
-  // This is isolated from the normal build due to LGPL-licensed code in the library
-  lazy val isGangliaEnabled = Properties.envOrNone("SPARK_GANGLIA_LGPL").isDefined
-  lazy val gangliaProj = Project("spark-ganglia-lgpl", file("extras/spark-ganglia-lgpl"), settings = gangliaSettings).dependsOn(core)
-  val maybeGanglia: Seq[ClasspathDependency] = if (isGangliaEnabled) Seq(gangliaProj) else Seq()
-  val maybeGangliaRef: Seq[ProjectReference] = if (isGangliaEnabled) Seq(gangliaProj) else Seq()
+  override val userPropertiesMap = System.getProperties.toMap
 
-  // Include the Java 8 project if the JVM version is 8+
-  lazy val javaVersion = System.getProperty("java.specification.version")
-  lazy val isJava8Enabled = javaVersion.toDouble >= "1.8".toDouble
-  val maybeJava8Tests = if (isJava8Enabled) Seq[ProjectReference](java8Tests) else Seq[ProjectReference]()
-  lazy val java8Tests = Project("java8-tests", file("extras/java8-tests"), settings = java8TestsSettings).
-    dependsOn(core) dependsOn(streaming % "compile->compile;test->test")
-
-  // Include the YARN project if the user has enabled YARN
-  lazy val yarnAlpha = Project("yarn-alpha", file("yarn/alpha"), settings = yarnAlphaSettings) dependsOn(core)
-  lazy val yarn = Project("yarn", file("yarn/stable"), settings = yarnSettings) dependsOn(core)
-
-  lazy val maybeYarn: Seq[ClasspathDependency] = if (isYarnEnabled) Seq(if (isNewHadoop) yarn else yarnAlpha) else Seq()
-  lazy val maybeYarnRef: Seq[ProjectReference] = if (isYarnEnabled) Seq(if (isNewHadoop) yarn else yarnAlpha) else Seq()
-
-  lazy val externalTwitter = Project("external-twitter", file("external/twitter"), settings = twitterSettings)
-    .dependsOn(streaming % "compile->compile;test->test")
-
-  lazy val externalKafka = Project("external-kafka", file("external/kafka"), settings = kafkaSettings)
-    .dependsOn(streaming % "compile->compile;test->test")
-
-  lazy val externalFlume = Project("external-flume", file("external/flume"), settings = flumeSettings)
-    .dependsOn(streaming % "compile->compile;test->test")
-
-  lazy val externalZeromq = Project("external-zeromq", file("external/zeromq"), settings = zeromqSettings)
-    .dependsOn(streaming % "compile->compile;test->test")
-
-  lazy val externalMqtt = Project("external-mqtt", file("external/mqtt"), settings = mqttSettings)
-    .dependsOn(streaming % "compile->compile;test->test")
-
-  lazy val allExternal = Seq[ClasspathDependency](externalTwitter, externalKafka, externalFlume, externalZeromq, externalMqtt)
-  lazy val allExternalRefs = Seq[ProjectReference](externalTwitter, externalKafka, externalFlume, externalZeromq, externalMqtt)
-
-  lazy val examples = Project("examples", file("examples"), settings = examplesSettings)
-    .dependsOn(core, mllib, graphx, bagel, streaming, hive) dependsOn(allExternal: _*)
-
-  // Everything except assembly, hive, tools, java8Tests and examples belong to packageProjects
-  lazy val packageProjects = Seq[ProjectReference](core, repl, bagel, streaming, mllib, graphx, catalyst, sql) ++ maybeYarnRef ++ maybeHiveRef ++ maybeGangliaRef
-
-  lazy val allProjects = packageProjects ++ allExternalRefs ++
-    Seq[ProjectReference](examples, tools, assemblyProj) ++ maybeJava8Tests
-
-  def sharedSettings = Defaults.defaultSettings ++ MimaBuild.mimaSettings(file(sparkHome)) ++ Seq(
-    organization       := "org.apache.spark",
-    version            := SPARK_VERSION,
-    scalaVersion       := "2.10.4",
-    scalacOptions := Seq("-Xmax-classfile-name", "120", "-unchecked", "-deprecation", "-feature",
-      "-target:" + SCALAC_JVM_VERSION),
-    javacOptions := Seq("-target", JAVAC_JVM_VERSION, "-source", JAVAC_JVM_VERSION),
-    unmanagedJars in Compile <<= baseDirectory map { base => (base / "lib" ** "*.jar").classpath },
+  lazy val sharedSettings = graphSettings ++ ScalaStyleSettings ++ Seq (
+    javaHome   := Properties.envOrNone("JAVA_HOME").map(file),
+    incOptions := incOptions.value.withNameHashing(true),
     retrieveManaged := true,
-    javaHome := Properties.envOrNone("JAVA_HOME").map(file),
-    // This is to add convenience of enabling sbt -Dsbt.offline=true for making the build offline.
-    offline := "true".equalsIgnoreCase(sys.props("sbt.offline")),
     retrievePattern := "[type]s/[artifact](-[revision])(-[classifier]).[ext]",
-    transitiveClassifiers in Scope.GlobalScope := Seq("sources"),
-    testListeners <<= target.map(t => Seq(new eu.henkelmann.sbt.JUnitXmlTestsListener(t.getAbsolutePath))),
-    incOptions := incOptions.value.withNameHashing(true),
-    // Fork new JVMs for tests and set Java options for those
-    fork := true,
-    javaOptions in Test += "-Dspark.home=" + sparkHome,
-    javaOptions in Test += "-Dspark.testing=1",
-    javaOptions in Test += "-Dsun.io.serialization.extendedDebugInfo=true",
-    javaOptions in Test ++= System.getProperties.filter(_._1 startsWith "spark").map { case (k,v) => s"-D$k=$v" }.toSeq,
-    javaOptions in Test ++= "-Xmx3g -XX:PermSize=128M -XX:MaxNewSize=256m -XX:MaxPermSize=1g".split(" ").toSeq,
-    javaOptions += "-Xmx3g",
-    // Show full stack trace and duration in test cases.
-    testOptions in Test += Tests.Argument("-oDF"),
-    // Remove certain packages from Scaladoc
-    scalacOptions in (Compile, doc) := Seq(
-      "-groups",
-      "-skip-packages", Seq(
-        "akka",
-        "org.apache.spark.api.python",
-        "org.apache.spark.network",
-        "org.apache.spark.deploy",
-        "org.apache.spark.util.collection"
-      ).mkString(":"),
-      "-doc-title", "Spark " + SPARK_VERSION_SHORT + " ScalaDoc"
-    ),
-
-    // Only allow one test at a time, even across projects, since they run in the same JVM
-    concurrentRestrictions in Global += Tags.limit(Tags.Test, 1),
-
-    resolvers ++= Seq(
-      // HTTPS is unavailable for Maven Central
-      "Maven Repository"     at "http://repo.maven.apache.org/maven2",
-      "Apache Repository"    at "https://repository.apache.org/content/repositories/releases",
-      "JBoss Repository"     at "https://repository.jboss.org/nexus/content/repositories/releases/",
-      "MQTT Repository"      at "https://repo.eclipse.org/content/repositories/paho-releases/",
-      "Cloudera Repository"  at "http://repository.cloudera.com/artifactory/cloudera-repos/",
-      "Pivotal Repository"   at "http://repo.spring.io/libs-release/",
-      // For Sonatype publishing
-      // "sonatype-snapshots"   at "https://oss.sonatype.org/content/repositories/snapshots",
-      // "sonatype-staging"     at "https://oss.sonatype.org/service/local/staging/deploy/maven2/",
-      // also check the local Maven repository ~/.m2
-      Resolver.mavenLocal
-    ),
-
-    publishMavenStyle := true,
-
-    // useGpg in Global := true,
-
-    pomExtra := (
-      <parent>
-        <groupId>org.apache</groupId>
-        <artifactId>apache</artifactId>
-        <version>14</version>
-      </parent>
-      <url>http://spark.apache.org/</url>
-      <licenses>
-        <license>
-          <name>Apache 2.0 License</name>
-          <url>http://www.apache.org/licenses/LICENSE-2.0.html</url>
-          <distribution>repo</distribution>
-        </license>
-      </licenses>
-      <scm>
-        <connection>scm:git:git@github.com:apache/spark.git</connection>
-        <url>scm:git:git@github.com:apache/spark.git</url>
-      </scm>
-      <developers>
-        <developer>
-          <id>matei</id>
-          <name>Matei Zaharia</name>
-          <email>matei.zaharia@gmail.com</email>
-          <url>http://www.cs.berkeley.edu/~matei</url>
-          <organization>Apache Software Foundation</organization>
-          <organizationUrl>http://spark.apache.org</organizationUrl>
-        </developer>
-      </developers>
-      <issueManagement>
-        <system>JIRA</system>
-        <url>https://issues.apache.org/jira/browse/SPARK</url>
-      </issueManagement>
-    ),
-
-    /*
-    publishTo <<= version { (v: String) =>
-      val nexus = "https://oss.sonatype.org/"
-      if (v.trim.endsWith("SNAPSHOT"))
-        Some("sonatype-snapshots" at nexus + "content/repositories/snapshots")
-      else
-        Some("sonatype-staging"  at nexus + "service/local/staging/deploy/maven2")
-    },
+    publishMavenStyle := true
+  )
 
-    */
-
-    libraryDependencies ++= Seq(
-        "io.netty"          % "netty-all"              % "4.0.17.Final",
-        "org.eclipse.jetty" % "jetty-server"           % jettyVersion,
-        "org.eclipse.jetty" % "jetty-util"             % jettyVersion,
-        "org.eclipse.jetty" % "jetty-plus"             % jettyVersion,
-        "org.eclipse.jetty" % "jetty-security"         % jettyVersion,
-        "org.scalatest"    %% "scalatest"              % "2.1.5"  % "test",
-        "org.scalacheck"   %% "scalacheck"             % "1.11.3" % "test",
-        "com.novocode"      % "junit-interface"        % "0.10"   % "test",
-        "org.easymock"      % "easymockclassextension" % "3.1"    % "test",
-        "org.mockito"       % "mockito-all"            % "1.9.0"  % "test",
-        "junit"             % "junit"                  % "4.10"   % "test",
-        // Needed by cglib which is needed by easymock.
-        "asm"               % "asm"                    % "3.3.1"  % "test"
-    ),
+  /** Following project only exists to pull previous artifacts of Spark for generating
+    Mima ignores. For more information see: SPARK 2071 */
+  lazy val oldDeps = Project("oldDeps", file("dev"), settings = oldDepsSettings)
 
-    testOptions += Tests.Argument(TestFrameworks.JUnit, "-v", "-a"),
-    parallelExecution := true,
-    /* Workaround for issue #206 (fixed after SBT 0.11.0) */
-    watchTransitiveSources <<= Defaults.inDependencies[Task[Seq[File]]](watchSources.task,
-      const(std.TaskExtra.constant(Nil)), aggregate = true, includeRoot = true) apply { _.join.map(_.flatten) },
-
-    otherResolvers := Seq(Resolver.file("dotM2", file(Path.userHome + "/.m2/repository"))),
-    publishLocalConfiguration in MavenCompile <<= (packagedArtifacts, deliverLocal, ivyLoggingLevel) map {
-      (arts, _, level) => new PublishConfiguration(None, "dotM2", arts, Seq(), level)
-    },
-    publishMavenStyle in MavenCompile := true,
-    publishLocal in MavenCompile <<= publishTask(publishLocalConfiguration in MavenCompile, deliverLocal),
-    publishLocalBoth <<= Seq(publishLocal in MavenCompile, publishLocal).dependOn
-  ) ++ net.virtualvoid.sbt.graph.Plugin.graphSettings ++ ScalaStyleSettings ++ genjavadocSettings
-
-  val akkaVersion = "2.2.3-shaded-protobuf"
-  val chillVersion = "0.3.6"
-  val codahaleMetricsVersion = "3.0.0"
-  val jblasVersion = "1.2.3"
-  val jets3tVersion = if ("^2\\.[3-9]+".r.findFirstIn(hadoopVersion).isDefined) "0.9.0" else "0.7.1"
-  val jettyVersion = "8.1.14.v20131031"
-  val hiveVersion = "0.12.0"
-  val parquetVersion = "1.4.3"
-  val slf4jVersion = "1.7.5"
-
-  val excludeJBossNetty = ExclusionRule(organization = "org.jboss.netty")
-  val excludeIONetty = ExclusionRule(organization = "io.netty")
-  val excludeEclipseJetty = ExclusionRule(organization = "org.eclipse.jetty")
-  val excludeAsm = ExclusionRule(organization = "org.ow2.asm")
-  val excludeOldAsm = ExclusionRule(organization = "asm")
-  val excludeCommonsLogging = ExclusionRule(organization = "commons-logging")
-  val excludeSLF4J = ExclusionRule(organization = "org.slf4j")
-  val excludeScalap = ExclusionRule(organization = "org.scala-lang", artifact = "scalap")
-  val excludeHadoop = ExclusionRule(organization = "org.apache.hadoop")
-  val excludeCurator = ExclusionRule(organization = "org.apache.curator")
-  val excludePowermock = ExclusionRule(organization = "org.powermock")
-  val excludeFastutil = ExclusionRule(organization = "it.unimi.dsi")
-  val excludeJruby = ExclusionRule(organization = "org.jruby")
-  val excludeThrift = ExclusionRule(organization = "org.apache.thrift")
-  val excludeServletApi = ExclusionRule(organization = "javax.servlet", artifact = "servlet-api")
-  val excludeJUnit = ExclusionRule(organization = "junit")
-
-  def sparkPreviousArtifact(id: String, organization: String = "org.apache.spark",
-      version: String = "1.0.0", crossVersion: String = "2.10"): Option[sbt.ModuleID] = {
-    val fullId = if (crossVersion.isEmpty) id else id + "_" + crossVersion
-    Some(organization % fullId % version) // the artifact to compare binary compatibility with
+  def versionArtifact(id: String): Option[sbt.ModuleID] = {
+    val fullId = id + "_2.10"
+    Some("org.apache.spark" % fullId % "1.0.0")
   }
 
-  def coreSettings = sharedSettings ++ Seq(
-    name := "spark-core",
-    libraryDependencies ++= Seq(
-        "com.google.guava"           % "guava"            % "14.0.1",
-        "org.apache.commons"         % "commons-lang3"    % "3.3.2",
-        "org.apache.commons"         % "commons-math3"    % "3.3" % "test",
-        "com.google.code.findbugs"   % "jsr305"           % "1.3.9",
-        "log4j"                      % "log4j"            % "1.2.17",
-        "org.slf4j"                  % "slf4j-api"        % slf4jVersion,
-        "org.slf4j"                  % "slf4j-log4j12"    % slf4jVersion,
-        "org.slf4j"                  % "jul-to-slf4j"     % slf4jVersion,
-        "org.slf4j"                  % "jcl-over-slf4j"   % slf4jVersion,
-        "commons-daemon"             % "commons-daemon"   % "1.0.10", // workaround for bug HADOOP-9407
-        "com.ning"                   % "compress-lzf"     % "1.0.0",
-        "org.xerial.snappy"          % "snappy-java"      % "1.0.5",
-        "org.spark-project.akka"    %% "akka-remote"      % akkaVersion,
-        "org.spark-project.akka"    %% "akka-slf4j"       % akkaVersion,
-        "org.spark-project.akka"    %% "akka-testkit"     % akkaVersion % "test",
-        "org.json4s"                %% "json4s-jackson"   % "3.2.6" excludeAll(excludeScalap),
-        "colt"                       % "colt"             % "1.2.0",
-        "org.apache.mesos"           % "mesos"            % "0.18.1" classifier("shaded-protobuf") exclude("com.google.protobuf", "protobuf-java"),
-        "commons-net"                % "commons-net"      % "2.2",
-        "net.java.dev.jets3t"        % "jets3t"           % jets3tVersion excludeAll(excludeCommonsLogging),
-        "commons-codec"              % "commons-codec"    % "1.5", // Prevent jets3t from including the older version of commons-codec
-        "org.apache.derby"           % "derby"            % "10.4.2.0"                     % "test",
-        "org.apache.hadoop"          % hadoopClient       % hadoopVersion excludeAll(excludeJBossNetty, excludeAsm, excludeCommonsLogging, excludeSLF4J, excludeOldAsm, excludeServletApi),
-        "org.apache.curator"         % "curator-recipes"  % "2.4.0" excludeAll(excludeJBossNetty),
-        "com.codahale.metrics"       % "metrics-core"     % codahaleMetricsVersion,
-        "com.codahale.metrics"       % "metrics-jvm"      % codahaleMetricsVersion,
-        "com.codahale.metrics"       % "metrics-json"     % codahaleMetricsVersion,
-        "com.codahale.metrics"       % "metrics-graphite" % codahaleMetricsVersion,
-        "com.twitter"               %% "chill"            % chillVersion excludeAll(excludeAsm),
-        "com.twitter"                % "chill-java"       % chillVersion excludeAll(excludeAsm),
-        "org.tachyonproject"         % "tachyon"          % "0.4.1-thrift" excludeAll(excludeHadoop, excludeCurator, excludeEclipseJetty, excludePowermock),
-        "com.clearspring.analytics"  % "stream"           % "2.7.0" excludeAll(excludeFastutil), // Only HyperLogLogPlus is used, which does not depend on fastutil.
-        "org.spark-project"          % "pyrolite"         % "2.0.1",
-        "net.sf.py4j"                % "py4j"             % "0.8.1"
-      ),
-    libraryDependencies ++= maybeAvro,
-    assembleDeps,
-    previousArtifact := sparkPreviousArtifact("spark-core")
+  def oldDepsSettings() = Defaults.defaultSettings ++ Seq(
+    name := "old-deps",
+    scalaVersion := "2.10.4",
+    retrieveManaged := true,
+    retrievePattern := "[type]s/[artifact](-[revision])(-[classifier]).[ext]",
+    libraryDependencies := Seq("spark-streaming-mqtt", "spark-streaming-zeromq",
+      "spark-streaming-flume", "spark-streaming-kafka", "spark-streaming-twitter",
+      "spark-streaming", "spark-mllib", "spark-bagel", "spark-graphx",
+      "spark-core").map(versionArtifact(_).get intransitive())
   )
 
-  // Create a colon-separate package list adding "org.apache.spark" in front of all of them,
-  // for easier specification of JavaDoc package groups
-  def packageList(names: String*): String = {
-    names.map(s => "org.apache.spark." + s).mkString(":")
+  def enable(settings: Seq[Setting[_]])(projectRef: ProjectRef) = {
+    val existingSettings = projectsMap.getOrElse(projectRef.project, Seq[Setting[_]]())
+    projectsMap += (projectRef.project -> (existingSettings ++ settings))
   }
 
-  def rootSettings = sharedSettings ++ scalaJavaUnidocSettings ++ Seq(
-    publish := {},
+  // Note ordering of these settings matter.
+  /* Enable shared settings on all projects */
+  (allProjects ++ optionallyEnabledProjects ++ assemblyProjects).foreach(enable(sharedSettings))
 
-    unidocProjectFilter in (ScalaUnidoc, unidoc) :=
-      inAnyProject -- inProjects(repl, examples, tools, catalyst, yarn, yarnAlpha),
-    unidocProjectFilter in (JavaUnidoc, unidoc) :=
-      inAnyProject -- inProjects(repl, examples, bagel, graphx, catalyst, tools, yarn, yarnAlpha),
+  /* Enable tests settings for all projects except examples, assembly and tools */
+  (allProjects ++ optionallyEnabledProjects).foreach(enable(TestSettings.settings))
 
-    // Skip class names containing $ and some internal packages in Javadocs
-    unidocAllSources in (JavaUnidoc, unidoc) := {
-      (unidocAllSources in (JavaUnidoc, unidoc)).value
-        .map(_.filterNot(_.getName.contains("$")))
-        .map(_.filterNot(_.getCanonicalPath.contains("akka")))
-        .map(_.filterNot(_.getCanonicalPath.contains("deploy")))
-        .map(_.filterNot(_.getCanonicalPath.contains("network")))
-        .map(_.filterNot(_.getCanonicalPath.contains("executor")))
-        .map(_.filterNot(_.getCanonicalPath.contains("python")))
-        .map(_.filterNot(_.getCanonicalPath.contains("collection")))
-    },
+  /* Enable Mima for all projects except spark, hive, catalyst, sql  and repl */
+  // TODO: Add Sql to mima checks
+  allProjects.filterNot(y => Seq(spark, sql, hive, catalyst, repl).exists(x => x == y)).
+    foreach (x => enable(MimaBuild.mimaSettings(sparkHome, x))(x))
 
-    // Javadoc options: create a window title, and group key packages on index page
-    javacOptions in doc := Seq(
-      "-windowtitle", "Spark " + SPARK_VERSION_SHORT + " JavaDoc",
-      "-public",
-      "-group", "Core Java API", packageList("api.java", "api.java.function"),
-      "-group", "Spark Streaming", packageList(
-        "streaming.api.java", "streaming.flume", "streaming.kafka",
-        "streaming.mqtt", "streaming.twitter", "streaming.zeromq"
-      ),
-      "-group", "MLlib", packageList(
-        "mllib.classification", "mllib.clustering", "mllib.evaluation.binary", "mllib.linalg",
-        "mllib.linalg.distributed", "mllib.optimization", "mllib.rdd", "mllib.recommendation",
-        "mllib.regression", "mllib.stat", "mllib.tree", "mllib.tree.configuration",
-        "mllib.tree.impurity", "mllib.tree.model", "mllib.util"
-      ),
-      "-group", "Spark SQL", packageList("sql.api.java", "sql.hive.api.java"),
-      "-noqualifier", "java.lang"
-    )
-  )
+  /* Enable Assembly for all assembly projects */
+  assemblyProjects.foreach(enable(Assembly.settings))
 
-  def replSettings = sharedSettings ++ Seq(
-    name := "spark-repl",
-    libraryDependencies <+= scalaVersion(v => "org.scala-lang"  % "scala-compiler" % v),
-    libraryDependencies <+= scalaVersion(v => "org.scala-lang"  % "jline"          % v),
-    libraryDependencies <+= scalaVersion(v => "org.scala-lang"  % "scala-reflect"  % v)
-  )
+  /* Enable unidoc only for the root spark project */
+  enable(Unidoc.settings)(spark)
 
-  def examplesSettings = sharedSettings ++ Seq(
-    name := "spark-examples",
-    jarName in assembly <<= version map {
-      v => "spark-examples-" + v + "-hadoop" + hadoopVersion + ".jar" },
-    libraryDependencies ++= Seq(
-      "com.twitter"          %% "algebird-core"   % "0.1.11",
-      "org.apache.hbase" % "hbase" % HBASE_VERSION excludeAll(excludeIONetty, excludeJBossNetty, excludeAsm, excludeOldAsm, excludeCommonsLogging, excludeJruby),
-      "org.apache.cassandra" % "cassandra-all" % "1.2.6"
-        exclude("com.google.guava", "guava")
-        exclude("com.googlecode.concurrentlinkedhashmap", "concurrentlinkedhashmap-lru")
-        exclude("com.ning","compress-lzf")
-        exclude("io.netty", "netty")
-        exclude("jline","jline")
-        exclude("org.apache.cassandra.deps", "avro")
-        excludeAll(excludeSLF4J, excludeIONetty),
-      "com.github.scopt" %% "scopt" % "3.2.0"
-    )
-  ) ++ assemblySettings ++ extraAssemblySettings
-
-  def toolsSettings = sharedSettings ++ Seq(
-    name := "spark-tools",
-    libraryDependencies <+= scalaVersion(v => "org.scala-lang"  % "scala-compiler" % v),
-    libraryDependencies <+= scalaVersion(v => "org.scala-lang"  % "scala-reflect"  % v )
-  ) ++ assemblySettings ++ extraAssemblySettings
-
-  def graphxSettings = sharedSettings ++ Seq(
-    name := "spark-graphx",
-    previousArtifact := sparkPreviousArtifact("spark-graphx"),
-    libraryDependencies ++= Seq(
-      "org.jblas" % "jblas" % jblasVersion
-    )
-  )
+  /* Hive console settings */
+  enable(Hive.settings)(hive)
 
-  def bagelSettings = sharedSettings ++ Seq(
-    name := "spark-bagel",
-    previousArtifact := sparkPreviousArtifact("spark-bagel")
-  )
+  // TODO: move this to its upstream project.
+  override def projectDefinitions(baseDirectory: File): Seq[Project] = {
+    super.projectDefinitions(baseDirectory).map { x =>
+      if (projectsMap.exists(_._1 == x.id)) x.settings(projectsMap(x.id): _*)
+      else x.settings(Seq[Setting[_]](): _*)
+    } ++ Seq[Project](oldDeps)
+  }
 
-  def mllibSettings = sharedSettings ++ Seq(
-    name := "spark-mllib",
-    previousArtifact := sparkPreviousArtifact("spark-mllib"),
-    libraryDependencies ++= Seq(
-      "org.jblas" % "jblas" % jblasVersion,
-      "org.scalanlp" %% "breeze" % "0.7" excludeAll(excludeJUnit)
-    )
-  )
+}
 
-  def catalystSettings = sharedSettings ++ Seq(
-    name := "catalyst",
-    // The mechanics of rewriting expression ids to compare trees in some test cases makes
-    // assumptions about the the expression ids being contiguous.  Running tests in parallel breaks
-    // this non-deterministically.  TODO: FIX THIS.
-    parallelExecution in Test := false,
-    libraryDependencies ++= Seq(
-      "com.typesafe" %% "scalalogging-slf4j" % "1.0.1"
-    )
-  )
+object Hive {
 
-  def sqlCoreSettings = sharedSettings ++ Seq(
-    name := "spark-sql",
-    libraryDependencies ++= Seq(
-      "com.twitter"                  % "parquet-column"             % parquetVersion,
-      "com.twitter"                  % "parquet-hadoop"             % parquetVersion,
-      "com.fasterxml.jackson.core"   % "jackson-databind"           % "2.3.0" // json4s-jackson 3.2.6 requires jackson-databind 2.3.0.
-    ),
-    initialCommands in console :=
-      """
-        |import org.apache.spark.sql.catalyst.analysis._
-        |import org.apache.spark.sql.catalyst.dsl._
-        |import org.apache.spark.sql.catalyst.errors._
-        |import org.apache.spark.sql.catalyst.expressions._
-        |import org.apache.spark.sql.catalyst.plans.logical._
-        |import org.apache.spark.sql.catalyst.rules._
-        |import org.apache.spark.sql.catalyst.types._
-        |import org.apache.spark.sql.catalyst.util._
-        |import org.apache.spark.sql.execution
-        |import org.apache.spark.sql.test.TestSQLContext._
-        |import org.apache.spark.sql.parquet.ParquetTestData""".stripMargin
-  )
+  lazy val settings = Seq(
 
-  // Since we don't include hive in the main assembly this project also acts as an alternative
-  // assembly jar.
-  def hiveSettings = sharedSettings ++ Seq(
-    name := "spark-hive",
     javaOptions += "-XX:MaxPermSize=1g",
-    libraryDependencies ++= Seq(
-      "org.spark-project.hive" % "hive-metastore" % hiveVersion,
-      "org.spark-project.hive" % "hive-exec"      % hiveVersion excludeAll(excludeCommonsLogging),
-      "org.spark-project.hive" % "hive-serde"     % hiveVersion
-    ),
-    // Multiple queries rely on the TestHive singleton.  See comments there for more details.
+    // Multiple queries rely on the TestHive singleton. See comments there for more details.
     parallelExecution in Test := false,
     // Supporting all SerDes requires us to depend on deprecated APIs, so we turn off the warnings
     // only for this subproject.
@@ -555,67 +190,16 @@ object SparkBuild extends Build {
         |import org.apache.spark.sql.parquet.ParquetTestData""".stripMargin
   )
 
-  def streamingSettings = sharedSettings ++ Seq(
-    name := "spark-streaming",
-    previousArtifact := sparkPreviousArtifact("spark-streaming")
-  )
-
-  def yarnCommonSettings = sharedSettings ++ Seq(
-    unmanagedSourceDirectories in Compile <++= baseDirectory { base =>
-      Seq(
-         base / "../common/src/main/scala"
-      )
-    },
-
-    unmanagedSourceDirectories in Test <++= baseDirectory { base =>
-      Seq(
-         base / "../common/src/test/scala"
-      )
-    }
-
-  ) ++ extraYarnSettings
-
-  def yarnAlphaSettings = yarnCommonSettings ++ Seq(
-    name := "spark-yarn-alpha"
-  )
-
-  def yarnSettings = yarnCommonSettings ++ Seq(
-    name := "spark-yarn"
-  )
-
-  def gangliaSettings = sharedSettings ++ Seq(
-    name := "spark-ganglia-lgpl",
-    libraryDependencies += "com.codahale.metrics" % "metrics-ganglia" % "3.0.0"
-  )
-
-  def java8TestsSettings = sharedSettings ++ Seq(
-    name := "java8-tests",
-    javacOptions := Seq("-target", "1.8", "-source", "1.8"),
-    testOptions += Tests.Argument(TestFrameworks.JUnit, "-v", "-a")
-  )
-
-  // Conditionally include the YARN dependencies because some tools look at all sub-projects and will complain
-  // if we refer to nonexistent dependencies (e.g. hadoop-yarn-api from a Hadoop version without YARN).
-  def extraYarnSettings = if(isYarnEnabled) yarnEnabledSettings else Seq()
-
-  def yarnEnabledSettings = Seq(
-    libraryDependencies ++= Seq(
-      // Exclude rule required for all ?
-      "org.apache.hadoop" % hadoopClient         % hadoopVersion excludeAll(excludeJBossNetty, excludeAsm, excludeOldAsm),
-      "org.apache.hadoop" % "hadoop-yarn-api"    % hadoopVersion excludeAll(excludeJBossNetty, excludeAsm, excludeOldAsm, excludeCommonsLogging),
-      "org.apache.hadoop" % "hadoop-yarn-common" % hadoopVersion excludeAll(excludeJBossNetty, excludeAsm, excludeOldAsm, excludeCommonsLogging),
-      "org.apache.hadoop" % "hadoop-yarn-client" % hadoopVersion excludeAll(excludeJBossNetty, excludeAsm, excludeOldAsm, excludeCommonsLogging),
-      "org.apache.hadoop" % "hadoop-yarn-server-web-proxy" % hadoopVersion excludeAll(excludeJBossNetty, excludeAsm, excludeOldAsm, excludeCommonsLogging, excludeServletApi)
-    )
-  )
+}
 
-  def assemblyProjSettings = sharedSettings ++ Seq(
-    name := "spark-assembly",
-    jarName in assembly <<= version map { v => "spark-assembly-" + v + "-hadoop" + hadoopVersion + ".jar" }
-  ) ++ assemblySettings ++ extraAssemblySettings
+object Assembly {
+  import sbtassembly.Plugin._
+  import AssemblyKeys._
 
-  def extraAssemblySettings() = Seq(
+  lazy val settings = assemblySettings ++ Seq(
     test in assembly := {},
+    jarName in assembly <<= (version, moduleName) map { (v, mName) => mName + "-"+v + "-hadoop" +
+      Option(System.getProperty("hadoop.version")).getOrElse("1.0.4") + ".jar" },
     mergeStrategy in assembly := {
       case PathList("org", "datanucleus", xs @ _*)             => MergeStrategy.discard
       case m if m.toLowerCase.endsWith("manifest.mf")          => MergeStrategy.discard
@@ -627,57 +211,95 @@ object SparkBuild extends Build {
     }
   )
 
-  def oldDepsSettings() = Defaults.defaultSettings ++ Seq(
-    name := "old-deps",
-    scalaVersion := "2.10.4",
-    retrieveManaged := true,
-    retrievePattern := "[type]s/[artifact](-[revision])(-[classifier]).[ext]",
-    libraryDependencies := Seq("spark-streaming-mqtt", "spark-streaming-zeromq",
-      "spark-streaming-flume", "spark-streaming-kafka", "spark-streaming-twitter",
-      "spark-streaming", "spark-mllib", "spark-bagel", "spark-graphx",
-      "spark-core").map(sparkPreviousArtifact(_).get intransitive())
-  )
+}
 
-  def twitterSettings() = sharedSettings ++ Seq(
-    name := "spark-streaming-twitter",
-    previousArtifact := sparkPreviousArtifact("spark-streaming-twitter"),
-    libraryDependencies ++= Seq(
-      "org.twitter4j" % "twitter4j-stream" % "3.0.3"
-    )
-  )
+object Unidoc {
 
-  def kafkaSettings() = sharedSettings ++ Seq(
-    name := "spark-streaming-kafka",
-    previousArtifact := sparkPreviousArtifact("spark-streaming-kafka"),
-    libraryDependencies ++= Seq(
-      "com.github.sgroschupf"    % "zkclient"   % "0.1",
-      "org.apache.kafka"        %% "kafka"      % "0.8.0"
-        exclude("com.sun.jdmk", "jmxtools")
-        exclude("com.sun.jmx", "jmxri")
-        exclude("net.sf.jopt-simple", "jopt-simple")
-        excludeAll(excludeSLF4J)
-    )
-  )
+  import BuildCommons._
+  import sbtunidoc.Plugin._
+  import UnidocKeys._
+
+  // for easier specification of JavaDoc package groups
+  private def packageList(names: String*): String = {
+    names.map(s => "org.apache.spark." + s).mkString(":")
+  }
 
-  def flumeSettings() = sharedSettings ++ Seq(
-    name := "spark-streaming-flume",
-    previousArtifact := sparkPreviousArtifact("spark-streaming-flume"),
-    libraryDependencies ++= Seq(
-      "org.apache.flume" % "flume-ng-sdk" % "1.4.0" % "compile" excludeAll(excludeIONetty, excludeThrift)
+  lazy val settings = scalaJavaUnidocSettings ++ Seq (
+    publish := {},
+
+    unidocProjectFilter in(ScalaUnidoc, unidoc) :=
+      inAnyProject -- inProjects(repl, examples, tools, catalyst, yarn, yarnAlpha),
+    unidocProjectFilter in(JavaUnidoc, unidoc) :=
+      inAnyProject -- inProjects(repl, bagel, graphx, examples, tools, catalyst, yarn, yarnAlpha),
+
+    // Skip class names containing $ and some internal packages in Javadocs
+    unidocAllSources in (JavaUnidoc, unidoc) := {
+      (unidocAllSources in (JavaUnidoc, unidoc)).value
+        .map(_.filterNot(_.getName.contains("$")))
+        .map(_.filterNot(_.getCanonicalPath.contains("akka")))
+        .map(_.filterNot(_.getCanonicalPath.contains("deploy")))
+        .map(_.filterNot(_.getCanonicalPath.contains("network")))
+        .map(_.filterNot(_.getCanonicalPath.contains("executor")))
+        .map(_.filterNot(_.getCanonicalPath.contains("python")))
+        .map(_.filterNot(_.getCanonicalPath.contains("collection")))
+    },
+
+    // Javadoc options: create a window title, and group key packages on index page
+    javacOptions in doc := Seq(
+      "-windowtitle", "Spark " + version.value.replaceAll("-SNAPSHOT", "") + " JavaDoc",
+      "-public",
+      "-group", "Core Java API", packageList("api.java", "api.java.function"),
+      "-group", "Spark Streaming", packageList(
+        "streaming.api.java", "streaming.flume", "streaming.kafka",
+        "streaming.mqtt", "streaming.twitter", "streaming.zeromq"
+      ),
+      "-group", "MLlib", packageList(
+        "mllib.classification", "mllib.clustering", "mllib.evaluation.binary", "mllib.linalg",
+        "mllib.linalg.distributed", "mllib.optimization", "mllib.rdd", "mllib.recommendation",
+        "mllib.regression", "mllib.stat", "mllib.tree", "mllib.tree.configuration",
+        "mllib.tree.impurity", "mllib.tree.model", "mllib.util"
+      ),
+      "-group", "Spark SQL", packageList("sql.api.java", "sql.hive.api.java"),
+      "-noqualifier", "java.lang"
     )
   )
+}
 
-  def zeromqSettings() = sharedSettings ++ Seq(
-    name := "spark-streaming-zeromq",
-    previousArtifact := sparkPreviousArtifact("spark-streaming-zeromq"),
-    libraryDependencies ++= Seq(
-      "org.spark-project.akka" %% "akka-zeromq" % akkaVersion
+object TestSettings {
+  import BuildCommons._
+
+  lazy val settings = Seq (
+    // Fork new JVMs for tests and set Java options for those
+    fork := true,
+    javaOptions in Test += "-Dspark.home=" + sparkHome,
+    javaOptions in Test += "-Dspark.testing=1",
+    javaOptions in Test += "-Dsun.io.serialization.extendedDebugInfo=true",
+    javaOptions in Test ++= System.getProperties.filter(_._1 startsWith "spark")
+      .map { case (k,v) => s"-D$k=$v" }.toSeq,
+    javaOptions in Test ++= "-Xmx3g -XX:PermSize=128M -XX:MaxNewSize=256m -XX:MaxPermSize=1g"
+      .split(" ").toSeq,
+    javaOptions += "-Xmx3g",
+
+    // Show full stack trace and duration in test cases.
+    testOptions in Test += Tests.Argument("-oDF"),
+    testOptions += Tests.Argument(TestFrameworks.JUnit, "-v", "-a"),
+    // Enable Junit testing.
+    libraryDependencies += "com.novocode" % "junit-interface" % "0.9" % "test",
+    // Only allow one test at a time, even across projects, since they run in the same JVM
+    parallelExecution in Test := false,
+    concurrentRestrictions in Global += Tags.limit(Tags.Test, 1),
+    // Remove certain packages from Scaladoc
+    scalacOptions in (Compile, doc) := Seq(
+      "-groups",
+      "-skip-packages", Seq(
+        "akka",
+        "org.apache.spark.api.python",
+        "org.apache.spark.network",
+        "org.apache.spark.deploy",
+        "org.apache.spark.util.collection"
+      ).mkString(":"),
+      "-doc-title", "Spark " + version.value.replaceAll("-SNAPSHOT", "") + " ScalaDoc"
     )
   )
 
-  def mqttSettings() = streamingSettings ++ Seq(
-    name := "spark-streaming-mqtt",
-    previousArtifact := sparkPreviousArtifact("spark-streaming-mqtt"),
-    libraryDependencies ++= Seq("org.eclipse.paho" % "mqtt-client" % "0.4.0")
-  )
 }
diff --git a/project/build.properties b/project/build.properties
index bcde13f4362a..c12ef652adfc 100644
--- a/project/build.properties
+++ b/project/build.properties
@@ -14,4 +14,4 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 #
-sbt.version=0.13.2
+sbt.version=0.13.5
diff --git a/project/project/SparkPluginBuild.scala b/project/project/SparkPluginBuild.scala
index e9fba641eb8a..3ef2d5451da0 100644
--- a/project/project/SparkPluginBuild.scala
+++ b/project/project/SparkPluginBuild.scala
@@ -24,8 +24,10 @@ import sbt.Keys._
  * becomes available for scalastyle sbt plugin.
  */
 object SparkPluginDef extends Build {
-  lazy val root = Project("plugins", file(".")) dependsOn(sparkStyle)
+  lazy val root = Project("plugins", file(".")) dependsOn(sparkStyle, sbtPomReader)
   lazy val sparkStyle = Project("spark-style", file("spark-style"), settings = styleSettings)
+  lazy val sbtPomReader = uri("https://github.com/ScrapCodes/sbt-pom-reader.git")
+
   // There is actually no need to publish this artifact.
   def styleSettings = Defaults.defaultSettings ++ Seq (
     name                 :=  "spark-style",
diff --git a/repl/pom.xml b/repl/pom.xml
index 4a66408ef3d2..4ebb1b82f0e8 100644
--- a/repl/pom.xml
+++ b/repl/pom.xml
@@ -32,6 +32,7 @@
   <url>http://spark.apache.org/</url>
 
   <properties>
+    <sbt.project.name>repl</sbt.project.name>
     <deb.install.path>/usr/share/spark</deb.install.path>
     <deb.user>root</deb.user>
   </properties>
diff --git a/sbt/sbt b/sbt/sbt
index 9de265bd07dc..1b1aa1483a82 100755
--- a/sbt/sbt
+++ b/sbt/sbt
@@ -72,6 +72,7 @@ Usage: $script_name [options]
   -J-X               pass option -X directly to the java runtime
                      (-J is stripped)
   -S-X               add -X to sbt's scalacOptions (-J is stripped)
+  -PmavenProfiles     Enable a maven profile for the build.
 
 In the case of duplicated or conflicting options, the order above
 shows precedence: JAVA_OPTS lowest, command line options highest.
diff --git a/sbt/sbt-launch-lib.bash b/sbt/sbt-launch-lib.bash
index 64e40a88206b..857b62ffa229 100755
--- a/sbt/sbt-launch-lib.bash
+++ b/sbt/sbt-launch-lib.bash
@@ -16,6 +16,7 @@ declare -a residual_args
 declare -a java_args
 declare -a scalac_args
 declare -a sbt_commands
+declare -a maven_profiles
 
 if test -x "$JAVA_HOME/bin/java"; then
     echo -e "Using $JAVA_HOME as default JAVA_HOME."
@@ -87,6 +88,13 @@ addJava () {
   dlog "[addJava] arg = '$1'"
   java_args=( "${java_args[@]}" "$1" )
 }
+
+enableProfile () {
+  dlog "[enableProfile] arg = '$1'"
+  maven_profiles=( "${maven_profiles[@]}" "$1" )
+  export MAVEN_PROFILES="${maven_profiles[@]}"
+}
+
 addSbt () {
   dlog "[addSbt] arg = '$1'"
   sbt_commands=( "${sbt_commands[@]}" "$1" )
@@ -141,7 +149,8 @@ process_args () {
      -java-home) require_arg path "$1" "$2" && java_cmd="$2/bin/java" && export JAVA_HOME=$2 && shift 2 ;;
 
             -D*) addJava "$1" && shift ;;
-            -J*) addJava "${1:2}" && shift ;;
+            -J*) addJava "${1:2}" && shift ;; 
+            -P*) enableProfile "$1" && shift ;;
               *) addResidual "$1" && shift ;;
     esac
   done
diff --git a/sql/catalyst/pom.xml b/sql/catalyst/pom.xml
index 01d7b569080e..6decde3fcd62 100644
--- a/sql/catalyst/pom.xml
+++ b/sql/catalyst/pom.xml
@@ -31,6 +31,9 @@
   <packaging>jar</packaging>
   <name>Spark Project Catalyst</name>
   <url>http://spark.apache.org/</url>
+  <properties>
+     <sbt.project.name>catalyst</sbt.project.name>
+  </properties>
 
   <dependencies>
     <dependency>
diff --git a/sql/core/pom.xml b/sql/core/pom.xml
index 8210fd1f210d..c309c43804d9 100644
--- a/sql/core/pom.xml
+++ b/sql/core/pom.xml
@@ -31,6 +31,9 @@
   <packaging>jar</packaging>
   <name>Spark Project SQL</name>
   <url>http://spark.apache.org/</url>
+  <properties>
+     <sbt.project.name>sql</sbt.project.name>
+  </properties>
 
   <dependencies>
     <dependency>
diff --git a/sql/hive/pom.xml b/sql/hive/pom.xml
index 5ede76e5c390..f30ae28b81e0 100644
--- a/sql/hive/pom.xml
+++ b/sql/hive/pom.xml
@@ -31,6 +31,9 @@
   <packaging>jar</packaging>
   <name>Spark Project Hive</name>
   <url>http://spark.apache.org/</url>
+  <properties>
+     <sbt.project.name>hive</sbt.project.name>
+  </properties>
 
   <dependencies>
     <dependency>
@@ -48,6 +51,11 @@
       <artifactId>hive-metastore</artifactId>
       <version>${hive.version}</version>
     </dependency>
+    <dependency>
+      <groupId>commons-httpclient</groupId>
+      <artifactId>commons-httpclient</artifactId>
+      <version>3.1</version>
+    </dependency>
     <dependency>
       <groupId>org.spark-project.hive</groupId>
       <artifactId>hive-exec</artifactId>
diff --git a/streaming/pom.xml b/streaming/pom.xml
index f506d6ce34a6..f60697ce745b 100644
--- a/streaming/pom.xml
+++ b/streaming/pom.xml
@@ -27,6 +27,9 @@
 
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-streaming_2.10</artifactId>
+  <properties>
+     <sbt.project.name>streaming</sbt.project.name>
+  </properties>
   <packaging>jar</packaging>
   <name>Spark Project Streaming</name>
   <url>http://spark.apache.org/</url>
diff --git a/tools/pom.xml b/tools/pom.xml
index 79cd8551d072..c0ee8faa7a61 100644
--- a/tools/pom.xml
+++ b/tools/pom.xml
@@ -26,6 +26,9 @@
 
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-tools_2.10</artifactId>
+  <properties>
+     <sbt.project.name>tools</sbt.project.name>
+  </properties>
   <packaging>jar</packaging>
   <name>Spark Project Tools</name>
   <url>http://spark.apache.org/</url>
diff --git a/yarn/alpha/pom.xml b/yarn/alpha/pom.xml
index b8a631dd0bb3..5b13a1f002d6 100644
--- a/yarn/alpha/pom.xml
+++ b/yarn/alpha/pom.xml
@@ -23,6 +23,9 @@
     <version>1.1.0-SNAPSHOT</version>
     <relativePath>../pom.xml</relativePath>
   </parent>
+  <properties>
+     <sbt.project.name>yarn-alpha</sbt.project.name>
+  </properties>
 
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-yarn-alpha_2.10</artifactId>
diff --git a/yarn/pom.xml b/yarn/pom.xml
index ef7066ef1fdf..efb473aa1b26 100644
--- a/yarn/pom.xml
+++ b/yarn/pom.xml
@@ -28,6 +28,9 @@
   <artifactId>yarn-parent_2.10</artifactId>
   <packaging>pom</packaging>
   <name>Spark Project YARN Parent POM</name>
+  <properties>
+     <sbt.project.name>yarn</sbt.project.name>
+  </properties>
 
   <dependencies>
     <dependency>
diff --git a/yarn/stable/pom.xml b/yarn/stable/pom.xml
index 0931beb50550..ceaf9f9d7100 100644
--- a/yarn/stable/pom.xml
+++ b/yarn/stable/pom.xml
@@ -23,6 +23,9 @@
     <version>1.1.0-SNAPSHOT</version>
     <relativePath>../pom.xml</relativePath>
   </parent>
+  <properties>
+     <sbt.project.name>yarn-stable</sbt.project.name>
+  </properties>
 
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-yarn_2.10</artifactId>

From 88006a62377d2b7c9886ba49ceef158737bc1b97 Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Thu, 10 Jul 2014 11:10:43 -0700
Subject: [PATCH 612/810] HOTFIX: Minor doc update for sbt change

---
 README.md | 13 ++++++-------
 1 file changed, 6 insertions(+), 7 deletions(-)

diff --git a/README.md b/README.md
index 6211a5889a3f..01ef851f34b6 100644
--- a/README.md
+++ b/README.md
@@ -69,29 +69,28 @@ can be run using:
 Spark uses the Hadoop core library to talk to HDFS and other Hadoop-supported
 storage systems. Because the protocols have changed in different versions of
 Hadoop, you must build Spark against the same version that your cluster runs.
-You can change the version by setting the `SPARK_HADOOP_VERSION` environment
-when building Spark.
+You can change the version by setting `-Dhadoop.version` when building Spark.
 
 For Apache Hadoop versions 1.x, Cloudera CDH MRv1, and other Hadoop
 versions without YARN, use:
 
     # Apache Hadoop 1.2.1
-    $ SPARK_HADOOP_VERSION=1.2.1 sbt/sbt assembly
+    $ sbt/sbt -Dhadoop.version=1.2.1 assembly
 
     # Cloudera CDH 4.2.0 with MapReduce v1
-    $ SPARK_HADOOP_VERSION=2.0.0-mr1-cdh4.2.0 sbt/sbt assembly
+    $ sbt/sbt -Dhadoop.version=2.0.0-mr1-cdh4.2.0 assembly
 
 For Apache Hadoop 2.2.X, 2.1.X, 2.0.X, 0.23.x, Cloudera CDH MRv2, and other Hadoop versions
 with YARN, also set `SPARK_YARN=true`:
 
     # Apache Hadoop 2.0.5-alpha
-    $ SPARK_HADOOP_VERSION=2.0.5-alpha SPARK_YARN=true sbt/sbt assembly
+    $ sbt/sbt -Dhadoop.version=2.0.5-alpha -Pyarn assembly
 
     # Cloudera CDH 4.2.0 with MapReduce v2
-    $ SPARK_HADOOP_VERSION=2.0.0-cdh4.2.0 SPARK_YARN=true sbt/sbt assembly
+    $ sbt/sbt -Dhadoop.version=2.0.0-cdh4.2.0 -Pyarn assembly
 
     # Apache Hadoop 2.2.X and newer
-    $ SPARK_HADOOP_VERSION=2.2.0 SPARK_YARN=true sbt/sbt assembly
+    $ sbt/sbt -Dhadoop.version=2.2.0 -Pyarn assembly
 
 When developing a Spark application, specify the Hadoop version by adding the
 "hadoop-client" artifact to your project's dependencies. For example, if you're

From 369aa84e8fba883165817338ac8bf9460be74521 Mon Sep 17 00:00:00 2001
From: Nicholas Chammas <nicholas.chammas@gmail.com>
Date: Thu, 10 Jul 2014 12:56:00 -0700
Subject: [PATCH 613/810] name ec2 instances and security groups consistently
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Security groups created by `spark-ec2` do not prepend “spark-“ to the
name.

Since naming the instances themselves is new to `spark-ec2`, it’s better
to change that pattern to match the existing naming pattern for the
security groups, rather than the other way around.

Author: Nicholas Chammas <nicholas.chammas@gmail.com>
Author: nchammas <nicholas.chammas@gmail.com>

Closes #1344 from nchammas/master and squashes the following commits:

f7e4581 [Nicholas Chammas] unrelated pep8 fix
a36eed0 [Nicholas Chammas] name ec2 instances and security groups consistently
de7292a [nchammas] Merge pull request #4 from apache/master
2e4fe00 [nchammas] Merge pull request #3 from apache/master
89fde08 [nchammas] Merge pull request #2 from apache/master
69f6e22 [Nicholas Chammas] PEP8 fixes
2627247 [Nicholas Chammas] broke up lines before they hit 100 chars
6544b7e [Nicholas Chammas] [SPARK-2065] give launched instances names
69da6cf [nchammas] Merge pull request #1 from apache/master
---
 ec2/spark_ec2.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/ec2/spark_ec2.py b/ec2/spark_ec2.py
index f5c2bfb697c8..44775ea479ec 100755
--- a/ec2/spark_ec2.py
+++ b/ec2/spark_ec2.py
@@ -428,11 +428,11 @@ def launch_cluster(conn, opts, cluster_name):
     for master in master_nodes:
         master.add_tag(
             key='Name',
-            value='spark-{cn}-master-{iid}'.format(cn=cluster_name, iid=master.id))
+            value='{cn}-master-{iid}'.format(cn=cluster_name, iid=master.id))
     for slave in slave_nodes:
         slave.add_tag(
             key='Name',
-            value='spark-{cn}-slave-{iid}'.format(cn=cluster_name, iid=slave.id))
+            value='{cn}-slave-{iid}'.format(cn=cluster_name, iid=slave.id))
 
     # Return all the instances
     return (master_nodes, slave_nodes)
@@ -699,6 +699,7 @@ def ssh(host, opts, command):
             time.sleep(30)
             tries = tries + 1
 
+
 # Backported from Python 2.7 for compatiblity with 2.6 (See SPARK-1990)
 def _check_output(*popenargs, **kwargs):
     if 'stdout' in kwargs:

From 40a8fef4e6619b4ea10a4ec9026260649ce5ae73 Mon Sep 17 00:00:00 2001
From: tmalaska <ted.malaska@cloudera.com>
Date: Thu, 10 Jul 2014 13:15:02 -0700
Subject: [PATCH 614/810] [SPARK-1478].3: Upgrade FlumeInputDStream's
 FlumeReceiver to support FLUME-1915

This is a modified version of this PR https://github.com/apache/spark/pull/1168 done by @tmalaska
Adds MIMA binary check exclusions.

Author: tmalaska <ted.malaska@cloudera.com>
Author: Tathagata Das <tathagata.das1565@gmail.com>

Closes #1347 from tdas/FLUME-1915 and squashes the following commits:

96065df [Tathagata Das] Added Mima exclusion for FlumeReceiver.
41d5338 [tmalaska] Address line 57 that was too long
12617e5 [tmalaska] SPARK-1478: Upgrade FlumeInputDStream's Flume...
---
 .../streaming/flume/FlumeInputDStream.scala   | 76 ++++++++++++++++---
 .../spark/streaming/flume/FlumeUtils.scala    | 41 +++++++++-
 .../streaming/flume/JavaFlumeStreamSuite.java |  2 +
 .../streaming/flume/FlumeStreamSuite.scala    | 41 ++++++++--
 project/MimaExcludes.scala                    |  3 +
 5 files changed, 147 insertions(+), 16 deletions(-)

diff --git a/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeInputDStream.scala b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeInputDStream.scala
index ed35e34ad45a..07ae88febf91 100644
--- a/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeInputDStream.scala
+++ b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeInputDStream.scala
@@ -20,6 +20,7 @@ package org.apache.spark.streaming.flume
 import java.net.InetSocketAddress
 import java.io.{ObjectInput, ObjectOutput, Externalizable}
 import java.nio.ByteBuffer
+import java.util.concurrent.Executors
 
 import scala.collection.JavaConversions._
 import scala.reflect.ClassTag
@@ -29,24 +30,32 @@ import org.apache.flume.source.avro.AvroFlumeEvent
 import org.apache.flume.source.avro.Status
 import org.apache.avro.ipc.specific.SpecificResponder
 import org.apache.avro.ipc.NettyServer
-
+import org.apache.spark.Logging
 import org.apache.spark.util.Utils
 import org.apache.spark.storage.StorageLevel
-import org.apache.spark.streaming.StreamingContext
 import org.apache.spark.streaming.dstream._
-import org.apache.spark.Logging
+import org.apache.spark.streaming.StreamingContext
 import org.apache.spark.streaming.receiver.Receiver
 
+import org.jboss.netty.channel.ChannelPipelineFactory
+import org.jboss.netty.channel.Channels
+import org.jboss.netty.channel.ChannelPipeline
+import org.jboss.netty.channel.ChannelFactory
+import org.jboss.netty.channel.socket.nio.NioServerSocketChannelFactory
+import org.jboss.netty.handler.codec.compression._
+import org.jboss.netty.handler.execution.ExecutionHandler
+
 private[streaming]
 class FlumeInputDStream[T: ClassTag](
   @transient ssc_ : StreamingContext,
   host: String,
   port: Int,
-  storageLevel: StorageLevel
+  storageLevel: StorageLevel,
+  enableDecompression: Boolean
 ) extends ReceiverInputDStream[SparkFlumeEvent](ssc_) {
 
   override def getReceiver(): Receiver[SparkFlumeEvent] = {
-    new FlumeReceiver(host, port, storageLevel)
+    new FlumeReceiver(host, port, storageLevel, enableDecompression)
   }
 }
 
@@ -134,22 +143,71 @@ private[streaming]
 class FlumeReceiver(
     host: String,
     port: Int,
-    storageLevel: StorageLevel
+    storageLevel: StorageLevel,
+    enableDecompression: Boolean
   ) extends Receiver[SparkFlumeEvent](storageLevel) with Logging {
 
   lazy val responder = new SpecificResponder(
     classOf[AvroSourceProtocol], new FlumeEventServer(this))
-  lazy val server = new NettyServer(responder, new InetSocketAddress(host, port))
+  var server: NettyServer = null
+
+  private def initServer() = {
+    if (enableDecompression) {
+      val channelFactory = new NioServerSocketChannelFactory
+        (Executors.newCachedThreadPool(), Executors.newCachedThreadPool());
+      val channelPipelieFactory = new CompressionChannelPipelineFactory()
+      
+      new NettyServer(
+        responder, 
+        new InetSocketAddress(host, port),
+        channelFactory, 
+        channelPipelieFactory, 
+        null)
+    } else {
+      new NettyServer(responder, new InetSocketAddress(host, port))
+    }
+  }
 
   def onStart() {
-    server.start()
+    synchronized {
+      if (server == null) {
+        server = initServer()
+        server.start()
+      } else {
+        logWarning("Flume receiver being asked to start more then once with out close")
+      }
+    }
     logInfo("Flume receiver started")
   }
 
   def onStop() {
-    server.close()
+    synchronized {
+      if (server != null) {
+        server.close()
+        server = null
+      }
+    }
     logInfo("Flume receiver stopped")
   }
 
   override def preferredLocation = Some(host)
+  
+  /** A Netty Pipeline factory that will decompress incoming data from 
+    * and the Netty client and compress data going back to the client.
+    *
+    * The compression on the return is required because Flume requires
+    * a successful response to indicate it can remove the event/batch 
+    * from the configured channel 
+    */
+  private[streaming]
+  class CompressionChannelPipelineFactory extends ChannelPipelineFactory {
+
+    def getPipeline() = {
+      val pipeline = Channels.pipeline()
+      val encoder = new ZlibEncoder(6)
+      pipeline.addFirst("deflater", encoder)
+      pipeline.addFirst("inflater", new ZlibDecoder())
+      pipeline
+  }
+}
 }
diff --git a/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeUtils.scala b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeUtils.scala
index 499f3560ef76..716db9fa7603 100644
--- a/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeUtils.scala
+++ b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeUtils.scala
@@ -36,7 +36,27 @@ object FlumeUtils {
       port: Int,
       storageLevel: StorageLevel = StorageLevel.MEMORY_AND_DISK_SER_2
     ): ReceiverInputDStream[SparkFlumeEvent] = {
-    val inputStream = new FlumeInputDStream[SparkFlumeEvent](ssc, hostname, port, storageLevel)
+    createStream(ssc, hostname, port, storageLevel, false)
+  }
+
+  /**
+   * Create a input stream from a Flume source.
+   * @param ssc      StreamingContext object
+   * @param hostname Hostname of the slave machine to which the flume data will be sent
+   * @param port     Port of the slave machine to which the flume data will be sent
+   * @param storageLevel  Storage level to use for storing the received objects
+   * @param enableDecompression  should netty server decompress input stream
+   */
+  def createStream (
+      ssc: StreamingContext,
+      hostname: String,
+      port: Int,
+      storageLevel: StorageLevel,
+      enableDecompression: Boolean
+    ): ReceiverInputDStream[SparkFlumeEvent] = {
+    val inputStream = new FlumeInputDStream[SparkFlumeEvent](
+        ssc, hostname, port, storageLevel, enableDecompression)
+        
     inputStream
   }
 
@@ -66,6 +86,23 @@ object FlumeUtils {
       port: Int,
       storageLevel: StorageLevel
     ): JavaReceiverInputDStream[SparkFlumeEvent] = {
-    createStream(jssc.ssc, hostname, port, storageLevel)
+    createStream(jssc.ssc, hostname, port, storageLevel, false)
+  }
+
+  /**
+   * Creates a input stream from a Flume source.
+   * @param hostname Hostname of the slave machine to which the flume data will be sent
+   * @param port     Port of the slave machine to which the flume data will be sent
+   * @param storageLevel  Storage level to use for storing the received objects
+   * @param enableDecompression  should netty server decompress input stream
+   */
+  def createStream(
+      jssc: JavaStreamingContext,
+      hostname: String,
+      port: Int,
+      storageLevel: StorageLevel,
+      enableDecompression: Boolean
+    ): JavaReceiverInputDStream[SparkFlumeEvent] = {
+    createStream(jssc.ssc, hostname, port, storageLevel, enableDecompression)
   }
 }
diff --git a/external/flume/src/test/java/org/apache/spark/streaming/flume/JavaFlumeStreamSuite.java b/external/flume/src/test/java/org/apache/spark/streaming/flume/JavaFlumeStreamSuite.java
index e0ad4f101520..3b5e0c7746b2 100644
--- a/external/flume/src/test/java/org/apache/spark/streaming/flume/JavaFlumeStreamSuite.java
+++ b/external/flume/src/test/java/org/apache/spark/streaming/flume/JavaFlumeStreamSuite.java
@@ -30,5 +30,7 @@ public void testFlumeStream() {
     JavaReceiverInputDStream<SparkFlumeEvent> test1 = FlumeUtils.createStream(ssc, "localhost", 12345);
     JavaReceiverInputDStream<SparkFlumeEvent> test2 = FlumeUtils.createStream(ssc, "localhost", 12345,
       StorageLevel.MEMORY_AND_DISK_SER_2());
+    JavaReceiverInputDStream<SparkFlumeEvent> test3 = FlumeUtils.createStream(ssc, "localhost", 12345,
+      StorageLevel.MEMORY_AND_DISK_SER_2(), false);
   }
 }
diff --git a/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumeStreamSuite.scala b/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumeStreamSuite.scala
index dd287d0ef90a..73dffef95330 100644
--- a/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumeStreamSuite.scala
+++ b/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumeStreamSuite.scala
@@ -33,15 +33,26 @@ import org.apache.spark.streaming.{TestOutputStream, StreamingContext, TestSuite
 import org.apache.spark.streaming.util.ManualClock
 import org.apache.spark.streaming.api.java.JavaReceiverInputDStream
 
-class FlumeStreamSuite extends TestSuiteBase {
+import org.jboss.netty.channel.ChannelPipeline
+import org.jboss.netty.channel.socket.nio.NioClientSocketChannelFactory
+import org.jboss.netty.channel.socket.SocketChannel
+import org.jboss.netty.handler.codec.compression._
 
-  val testPort = 9999
+class FlumeStreamSuite extends TestSuiteBase {
 
   test("flume input stream") {
+    runFlumeStreamTest(false, 9998)
+  }
+
+  test("flume input compressed stream") {
+    runFlumeStreamTest(true, 9997)
+  }
+  
+  def runFlumeStreamTest(enableDecompression: Boolean, testPort: Int) {
     // Set up the streaming context and input streams
     val ssc = new StreamingContext(conf, batchDuration)
     val flumeStream: JavaReceiverInputDStream[SparkFlumeEvent] =
-      FlumeUtils.createStream(ssc, "localhost", testPort, StorageLevel.MEMORY_AND_DISK)
+      FlumeUtils.createStream(ssc, "localhost", testPort, StorageLevel.MEMORY_AND_DISK, enableDecompression)
     val outputBuffer = new ArrayBuffer[Seq[SparkFlumeEvent]]
       with SynchronizedBuffer[Seq[SparkFlumeEvent]]
     val outputStream = new TestOutputStream(flumeStream.receiverInputDStream, outputBuffer)
@@ -52,8 +63,17 @@ class FlumeStreamSuite extends TestSuiteBase {
     val input = Seq(1, 2, 3, 4, 5)
     Thread.sleep(1000)
     val transceiver = new NettyTransceiver(new InetSocketAddress("localhost", testPort))
-    val client = SpecificRequestor.getClient(
-      classOf[AvroSourceProtocol], transceiver)
+    var client: AvroSourceProtocol = null;
+  
+    if (enableDecompression) {
+      client = SpecificRequestor.getClient(
+          classOf[AvroSourceProtocol], 
+          new NettyTransceiver(new InetSocketAddress("localhost", testPort), 
+          new CompressionChannelFactory(6)));
+    } else {
+      client = SpecificRequestor.getClient(
+        classOf[AvroSourceProtocol], transceiver)
+    }
 
     for (i <- 0 until input.size) {
       val event = new AvroFlumeEvent
@@ -64,6 +84,8 @@ class FlumeStreamSuite extends TestSuiteBase {
       clock.addToTime(batchDuration.milliseconds)
     }
 
+    Thread.sleep(1000)
+
     val startTime = System.currentTimeMillis()
     while (outputBuffer.size < input.size && System.currentTimeMillis() - startTime < maxWaitTimeMillis) {
       logInfo("output.size = " + outputBuffer.size + ", input.size = " + input.size)
@@ -85,4 +107,13 @@ class FlumeStreamSuite extends TestSuiteBase {
       assert(outputBuffer(i).head.event.getHeaders.get("test") === "header")
     }
   }
+
+  class CompressionChannelFactory(compressionLevel: Int) extends NioClientSocketChannelFactory {
+    override def newChannel(pipeline:ChannelPipeline) : SocketChannel = {
+      var encoder : ZlibEncoder = new ZlibEncoder(compressionLevel);
+      pipeline.addFirst("deflater", encoder);
+      pipeline.addFirst("inflater", new ZlibDecoder());
+      super.newChannel(pipeline);
+    }
+  }
 }
diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala
index 44bc9dc5fb69..3b7b87b80cda 100644
--- a/project/MimaExcludes.scala
+++ b/project/MimaExcludes.scala
@@ -64,6 +64,9 @@ object MimaExcludes {
               "org.apache.spark.rdd.PairRDDFunctions.org$apache$spark$rdd$PairRDDFunctions$$"
                 + "createZero$1")
           ) ++
+          Seq(
+            ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.streaming.flume.FlumeReceiver.this")
+          ) ++
           Seq( // Ignore some private methods in ALS.
             ProblemFilters.exclude[MissingMethodProblem](
               "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$^dateFeatures"),

From 2dd67248503306bb08946b1796821e9f9ed4d00e Mon Sep 17 00:00:00 2001
From: Issac Buenrostro <buenrostro@ooyala.com>
Date: Thu, 10 Jul 2014 16:01:08 -0700
Subject: [PATCH 615/810] [SPARK-1341] [Streaming] Throttle BlockGenerator to
 limit rate of data consumption.

Author: Issac Buenrostro <buenrostro@ooyala.com>

Closes #945 from ibuenros/SPARK-1341-throttle and squashes the following commits:

5514916 [Issac Buenrostro] Formatting changes, added documentation for streaming throttling, stricter unit tests for throttling.
62f395f [Issac Buenrostro] Add comments and license to streaming RateLimiter.scala
7066438 [Issac Buenrostro] Moved throttle code to RateLimiter class, smoother pushing when throttling active
ccafe09 [Issac Buenrostro] Throttle BlockGenerator to limit rate of data consumption.
---
 docs/configuration.md                         |  9 +++
 .../streaming/receiver/BlockGenerator.scala   |  3 +-
 .../streaming/receiver/RateLimiter.scala      | 69 +++++++++++++++++++
 .../streaming/NetworkReceiverSuite.scala      | 38 ++++++++++
 4 files changed, 118 insertions(+), 1 deletion(-)
 create mode 100644 streaming/src/main/scala/org/apache/spark/streaming/receiver/RateLimiter.scala

diff --git a/docs/configuration.md b/docs/configuration.md
index b84104cc7e65..0aea23ab5950 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -773,6 +773,15 @@ Apart from these, the following properties are also available, and may be useful
     into blocks of data before storing them in Spark.
   </td>
 </tr>
+<tr>
+  <td><code>spark.streaming.receiver.maxRate</code></td>
+  <td>infinite</td>
+  <td>
+    Maximum rate (per second) at which each receiver will push data into blocks. Effectively,
+    each stream will consume at most this number of records per second.
+    Setting this configuration to 0 or a negative number will put no limit on the rate.
+  </td>
+</tr>
 <tr>
   <td><code>spark.streaming.unpersist</code></td>
   <td>true</td>
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/receiver/BlockGenerator.scala b/streaming/src/main/scala/org/apache/spark/streaming/receiver/BlockGenerator.scala
index 78cc2daa56e5..0316b6862f19 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/receiver/BlockGenerator.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/receiver/BlockGenerator.scala
@@ -44,7 +44,7 @@ private[streaming] class BlockGenerator(
     listener: BlockGeneratorListener,
     receiverId: Int,
     conf: SparkConf
-  ) extends Logging {
+  ) extends RateLimiter(conf) with Logging {
 
   private case class Block(id: StreamBlockId, buffer: ArrayBuffer[Any])
 
@@ -81,6 +81,7 @@ private[streaming] class BlockGenerator(
    * will be periodically pushed into BlockManager.
    */
   def += (data: Any): Unit = synchronized {
+    waitToPush()
     currentBuffer += data
   }
 
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/receiver/RateLimiter.scala b/streaming/src/main/scala/org/apache/spark/streaming/receiver/RateLimiter.scala
new file mode 100644
index 000000000000..e4f6ba626ebb
--- /dev/null
+++ b/streaming/src/main/scala/org/apache/spark/streaming/receiver/RateLimiter.scala
@@ -0,0 +1,69 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.streaming.receiver
+
+import org.apache.spark.{Logging, SparkConf}
+import java.util.concurrent.TimeUnit._
+
+/** Provides waitToPush() method to limit the rate at which receivers consume data.
+  *
+  * waitToPush method will block the thread if too many messages have been pushed too quickly,
+  * and only return when a new message has been pushed. It assumes that only one message is
+  * pushed at a time.
+  *
+  * The spark configuration spark.streaming.receiver.maxRate gives the maximum number of messages
+  * per second that each receiver will accept.
+  *
+  * @param conf spark configuration
+  */
+private[receiver] abstract class RateLimiter(conf: SparkConf) extends Logging {
+
+  private var lastSyncTime = System.nanoTime
+  private var messagesWrittenSinceSync = 0L
+  private val desiredRate = conf.getInt("spark.streaming.receiver.maxRate", 0)
+  private val SYNC_INTERVAL = NANOSECONDS.convert(10, SECONDS)
+
+  def waitToPush() {
+    if( desiredRate <= 0 ) {
+      return
+    }
+    val now = System.nanoTime
+    val elapsedNanosecs = math.max(now - lastSyncTime, 1)
+    val rate = messagesWrittenSinceSync.toDouble * 1000000000 / elapsedNanosecs
+    if (rate < desiredRate) {
+      // It's okay to write; just update some variables and return
+      messagesWrittenSinceSync += 1
+      if (now > lastSyncTime + SYNC_INTERVAL) {
+        // Sync interval has passed; let's resync
+        lastSyncTime = now
+        messagesWrittenSinceSync = 1
+      }
+    } else {
+      // Calculate how much time we should sleep to bring ourselves to the desired rate.
+      val targetTimeInMillis = messagesWrittenSinceSync * 1000 / desiredRate
+      val elapsedTimeInMillis = elapsedNanosecs / 1000000
+      val sleepTimeInMillis = targetTimeInMillis - elapsedTimeInMillis
+      if (sleepTimeInMillis > 0) {
+        logTrace("Natural rate is " + rate + " per second but desired rate is " +
+          desiredRate + ", sleeping for " + sleepTimeInMillis + " ms to compensate.")
+        Thread.sleep(sleepTimeInMillis)
+      }
+      waitToPush()
+    }
+  }
+}
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/NetworkReceiverSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/NetworkReceiverSuite.scala
index d9ac3c91f6e3..f4e11f975de9 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/NetworkReceiverSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/NetworkReceiverSuite.scala
@@ -145,6 +145,44 @@ class NetworkReceiverSuite extends FunSuite with Timeouts {
     assert(recordedData.toSet === generatedData.toSet)
   }
 
+  test("block generator throttling") {
+    val blockGeneratorListener = new FakeBlockGeneratorListener
+    val blockInterval = 50
+    val maxRate = 200
+    val conf = new SparkConf().set("spark.streaming.blockInterval", blockInterval.toString).
+      set("spark.streaming.receiver.maxRate", maxRate.toString)
+    val blockGenerator = new BlockGenerator(blockGeneratorListener, 1, conf)
+    val expectedBlocks = 20
+    val waitTime = expectedBlocks * blockInterval
+    val expectedMessages = maxRate * waitTime / 1000
+    val expectedMessagesPerBlock = maxRate * blockInterval / 1000
+    val generatedData = new ArrayBuffer[Int]
+
+    // Generate blocks
+    val startTime = System.currentTimeMillis()
+    blockGenerator.start()
+    var count = 0
+    while(System.currentTimeMillis - startTime < waitTime) {
+      blockGenerator += count
+      generatedData += count
+      count += 1
+      Thread.sleep(1)
+    }
+    blockGenerator.stop()
+
+    val recordedData = blockGeneratorListener.arrayBuffers
+    assert(blockGeneratorListener.arrayBuffers.size > 0)
+    assert(recordedData.flatten.toSet === generatedData.toSet)
+    // recordedData size should be close to the expected rate
+    assert(recordedData.flatten.size >= expectedMessages * 0.9 &&
+      recordedData.flatten.size <= expectedMessages * 1.1 )
+    // the first and last block may be incomplete, so we slice them out
+    recordedData.slice(1, recordedData.size - 1).foreach { block =>
+      assert(block.size >= expectedMessagesPerBlock * 0.8 &&
+        block.size <= expectedMessagesPerBlock * 1.2 )
+    }
+  }
+
   /**
    * An implementation of NetworkReceiver that is used for testing a receiver's life cycle.
    */

From ae8ca4dfbacd5a5197fb41722607ad99c190f768 Mon Sep 17 00:00:00 2001
From: Artjom-Metro <Artjom-Metro@users.noreply.github.com>
Date: Thu, 10 Jul 2014 16:03:30 -0700
Subject: [PATCH 616/810] SPARK-2427: Fix Scala examples that use the wrong
 command line arguments index

The Scala examples HBaseTest and HdfsTest don't use the correct indexes for the command line arguments. This due to to the fix of JIRA 1565, where these examples were not correctly adapted to the new usage of the submit script.

Author: Artjom-Metro <Artjom-Metro@users.noreply.github.com>
Author: Artjom-Metro <artjom31415@googlemail.com>

Closes #1353 from Artjom-Metro/fix_examples and squashes the following commits:

6111801 [Artjom-Metro] Reduce the default number of iterations
cfaa73c [Artjom-Metro] Fix some examples that use the wrong index to access the command line arguments
---
 .../scala/org/apache/spark/examples/HBaseTest.scala    |  6 +++---
 .../scala/org/apache/spark/examples/HdfsTest.scala     | 10 ++++++++--
 .../org/apache/spark/examples/SparkPageRank.scala      |  6 +++++-
 3 files changed, 16 insertions(+), 6 deletions(-)

diff --git a/examples/src/main/scala/org/apache/spark/examples/HBaseTest.scala b/examples/src/main/scala/org/apache/spark/examples/HBaseTest.scala
index 4893b017ed81..822673347bdc 100644
--- a/examples/src/main/scala/org/apache/spark/examples/HBaseTest.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/HBaseTest.scala
@@ -31,12 +31,12 @@ object HBaseTest {
     val conf = HBaseConfiguration.create()
     // Other options for configuring scan behavior are available. More information available at
     // http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/mapreduce/TableInputFormat.html
-    conf.set(TableInputFormat.INPUT_TABLE, args(1))
+    conf.set(TableInputFormat.INPUT_TABLE, args(0))
 
     // Initialize hBase table if necessary
     val admin = new HBaseAdmin(conf)
-    if(!admin.isTableAvailable(args(1))) {
-      val tableDesc = new HTableDescriptor(args(1))
+    if (!admin.isTableAvailable(args(0))) {
+      val tableDesc = new HTableDescriptor(args(0))
       admin.createTable(tableDesc)
     }
 
diff --git a/examples/src/main/scala/org/apache/spark/examples/HdfsTest.scala b/examples/src/main/scala/org/apache/spark/examples/HdfsTest.scala
index 331de3ad1ef5..ed2b38e2ca6f 100644
--- a/examples/src/main/scala/org/apache/spark/examples/HdfsTest.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/HdfsTest.scala
@@ -19,16 +19,22 @@ package org.apache.spark.examples
 
 import org.apache.spark._
 
+
 object HdfsTest {
+
+  /** Usage: HdfsTest [file] */
   def main(args: Array[String]) {
+    if (args.length < 1) {
+      System.err.println("Usage: HdfsTest <file>")
+      System.exit(1)
+    }
     val sparkConf = new SparkConf().setAppName("HdfsTest")
     val sc = new SparkContext(sparkConf)
-    val file = sc.textFile(args(1))
+    val file = sc.textFile(args(0))
     val mapped = file.map(s => s.length).cache()
     for (iter <- 1 to 10) {
       val start = System.currentTimeMillis()
       for (x <- mapped) { x + 2 }
-      //  println("Processing: " + x)
       val end = System.currentTimeMillis()
       println("Iteration " + iter + " took " + (end-start) + " ms")
     }
diff --git a/examples/src/main/scala/org/apache/spark/examples/SparkPageRank.scala b/examples/src/main/scala/org/apache/spark/examples/SparkPageRank.scala
index 40b36c779afd..4c7e006da061 100644
--- a/examples/src/main/scala/org/apache/spark/examples/SparkPageRank.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/SparkPageRank.scala
@@ -31,8 +31,12 @@ import org.apache.spark.{SparkConf, SparkContext}
  */
 object SparkPageRank {
   def main(args: Array[String]) {
+    if (args.length < 1) {
+      System.err.println("Usage: SparkPageRank <file> <iter>")
+      System.exit(1)
+    }
     val sparkConf = new SparkConf().setAppName("PageRank")
-    var iters = args(1).toInt
+    val iters = if (args.length > 0) args(1).toInt else 10
     val ctx = new SparkContext(sparkConf)
     val lines = ctx.textFile(args(0), 1)
     val links = lines.map{ s =>

From f62c42728990266d5d5099abe241f699189ba025 Mon Sep 17 00:00:00 2001
From: Takuya UESHIN <ueshin@happy-camper.st>
Date: Thu, 10 Jul 2014 19:20:00 -0700
Subject: [PATCH 617/810] [SPARK-2431][SQL] Refine StringComparison and related
 codes.

Refine `StringComparison` and related codes as follows:
- `StringComparison` could be similar to `StringRegexExpression` or `CaseConversionExpression`.
- Nullability of `StringRegexExpression` could depend on children's nullabilities.
- Add a case that the like condition includes no wildcard to `LikeSimplification`.

Author: Takuya UESHIN <ueshin@happy-camper.st>

Closes #1357 from ueshin/issues/SPARK-2431 and squashes the following commits:

77766f5 [Takuya UESHIN] Add a case that the like condition includes no wildcard to LikeSimplification.
b9da9d2 [Takuya UESHIN] Fix nullability of StringRegexExpression.
680bb72 [Takuya UESHIN] Refine StringComparison.
---
 .../expressions/stringOperations.scala        | 28 +++++++++----------
 .../sql/catalyst/optimizer/Optimizer.scala    |  3 ++
 2 files changed, 16 insertions(+), 15 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
index 347471cebdc7..b3850533c373 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
@@ -23,7 +23,6 @@ import org.apache.spark.sql.catalyst.types.DataType
 import org.apache.spark.sql.catalyst.types.StringType
 import org.apache.spark.sql.catalyst.types.BooleanType
 
-
 trait StringRegexExpression {
   self: BinaryExpression =>
 
@@ -32,7 +31,7 @@ trait StringRegexExpression {
   def escape(v: String): String
   def matches(regex: Pattern, str: String): Boolean
 
-  def nullable: Boolean = true
+  def nullable: Boolean = left.nullable || right.nullable
   def dataType: DataType = BooleanType
 
   // try cache the pattern for Literal
@@ -157,19 +156,13 @@ case class Lower(child: Expression) extends UnaryExpression with CaseConversionE
   override def toString() = s"Lower($child)"
 }
 
-/** A base class for functions that compare two strings, returning a boolean. */
-abstract class StringComparison extends Expression {
-  self: Product =>
+/** A base trait for functions that compare two strings, returning a boolean. */
+trait StringComparison {
+  self: BinaryExpression =>
 
   type EvaluatedType = Any
 
-  def left: Expression
-  def right: Expression
-
-  override def references = children.flatMap(_.references).toSet
-  override def children = left :: right :: Nil
-
-  override def nullable: Boolean = true
+  def nullable: Boolean = left.nullable || right.nullable
   override def dataType: DataType = BooleanType
 
   def compare(l: String, r: String): Boolean
@@ -184,26 +177,31 @@ abstract class StringComparison extends Expression {
     }
   }
 
+  def symbol: String = nodeName
+
   override def toString() = s"$nodeName($left, $right)"
 }
 
 /**
  * A function that returns true if the string `left` contains the string `right`.
  */
-case class Contains(left: Expression, right: Expression) extends StringComparison {
+case class Contains(left: Expression, right: Expression)
+    extends BinaryExpression with StringComparison {
   override def compare(l: String, r: String) = l.contains(r)
 }
 
 /**
  * A function that returns true if the string `left` starts with the string `right`.
  */
-case class StartsWith(left: Expression, right: Expression) extends StringComparison {
+case class StartsWith(left: Expression, right: Expression)
+    extends BinaryExpression with StringComparison {
   def compare(l: String, r: String) = l.startsWith(r)
 }
 
 /**
  * A function that returns true if the string `left` ends with the string `right`.
  */
-case class EndsWith(left: Expression, right: Expression) extends StringComparison {
+case class EndsWith(left: Expression, right: Expression)
+    extends BinaryExpression with StringComparison {
   def compare(l: String, r: String) = l.endsWith(r)
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
index f0904f59d028..a142310c501b 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
@@ -123,6 +123,7 @@ object LikeSimplification extends Rule[LogicalPlan] {
   val startsWith = "([^_%]+)%".r
   val endsWith = "%([^_%]+)".r
   val contains = "%([^_%]+)%".r
+  val equalTo = "([^_%]*)".r
 
   def apply(plan: LogicalPlan): LogicalPlan = plan transformAllExpressions {
     case Like(l, Literal(startsWith(pattern), StringType)) if !pattern.endsWith("\\") =>
@@ -131,6 +132,8 @@ object LikeSimplification extends Rule[LogicalPlan] {
       EndsWith(l, Literal(pattern))
     case Like(l, Literal(contains(pattern), StringType)) if !pattern.endsWith("\\") =>
       Contains(l, Literal(pattern))
+    case Like(l, Literal(equalTo(pattern), StringType)) =>
+      EqualTo(l, Literal(pattern))
   }
 }
 

From f5abd271292f5c98eb8b1974c1df31d08ed388dd Mon Sep 17 00:00:00 2001
From: Takuya UESHIN <ueshin@happy-camper.st>
Date: Thu, 10 Jul 2014 19:23:44 -0700
Subject: [PATCH 618/810] [SPARK-2415] [SQL] RowWriteSupport should handle
 empty ArrayType correctly.

`RowWriteSupport` doesn't write empty `ArrayType` value, so the read value becomes `null`.
It should write empty `ArrayType` value as it is.

Author: Takuya UESHIN <ueshin@happy-camper.st>

Closes #1339 from ueshin/issues/SPARK-2415 and squashes the following commits:

32afc87 [Takuya UESHIN] Merge branch 'master' into issues/SPARK-2415
2f05196 [Takuya UESHIN] Fix RowWriteSupport to handle empty ArrayType correctly.
---
 .../apache/spark/sql/parquet/ParquetConverter.scala  | 12 ++++++------
 .../spark/sql/parquet/ParquetTableSupport.scala      | 10 +++++-----
 .../apache/spark/sql/parquet/ParquetQuerySuite.scala | 10 +++++-----
 3 files changed, 16 insertions(+), 16 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala
index 889a408e3c39..75748b2b5440 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala
@@ -229,9 +229,9 @@ private[parquet] class CatalystGroupConverter(
     this(attributes.map(a => new FieldType(a.name, a.dataType, a.nullable)), 0, null)
 
   protected [parquet] val converters: Array[Converter] =
-    schema.map(field =>
-      CatalystConverter.createConverter(field, schema.indexOf(field), this))
-    .toArray
+    schema.zipWithIndex.map {
+      case (field, idx) => CatalystConverter.createConverter(field, idx, this)
+    }.toArray
 
   override val size = schema.size
 
@@ -288,9 +288,9 @@ private[parquet] class CatalystPrimitiveRowConverter(
       new ParquetRelation.RowType(attributes.length))
 
   protected [parquet] val converters: Array[Converter] =
-    schema.map(field =>
-      CatalystConverter.createConverter(field, schema.indexOf(field), this))
-      .toArray
+    schema.zipWithIndex.map {
+      case (field, idx) => CatalystConverter.createConverter(field, idx, this)
+    }.toArray
 
   override val size = schema.size
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala
index 9cd5dc5bbd39..108f8b681542 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala
@@ -156,7 +156,7 @@ private[parquet] class RowWriteSupport extends WriteSupport[Row] with Logging {
     writer.startMessage()
     while(index < attributes.size) {
       // null values indicate optional fields but we do not check currently
-      if (record(index) != null && record(index) != Nil) {
+      if (record(index) != null) {
         writer.startField(attributes(index).name, index)
         writeValue(attributes(index).dataType, record(index))
         writer.endField(attributes(index).name, index)
@@ -167,7 +167,7 @@ private[parquet] class RowWriteSupport extends WriteSupport[Row] with Logging {
   }
 
   private[parquet] def writeValue(schema: DataType, value: Any): Unit = {
-    if (value != null && value != Nil) {
+    if (value != null) {
       schema match {
         case t @ ArrayType(_) => writeArray(
           t,
@@ -184,7 +184,7 @@ private[parquet] class RowWriteSupport extends WriteSupport[Row] with Logging {
   }
 
   private[parquet] def writePrimitive(schema: PrimitiveType, value: Any): Unit = {
-    if (value != null && value != Nil) {
+    if (value != null) {
       schema match {
         case StringType => writer.addBinary(
           Binary.fromByteArray(
@@ -206,12 +206,12 @@ private[parquet] class RowWriteSupport extends WriteSupport[Row] with Logging {
   private[parquet] def writeStruct(
       schema: StructType,
       struct: CatalystConverter.StructScalaType[_]): Unit = {
-    if (struct != null && struct != Nil) {
+    if (struct != null) {
       val fields = schema.fields.toArray
       writer.startGroup()
       var i = 0
       while(i < fields.size) {
-        if (struct(i) != null && struct(i) != Nil) {
+        if (struct(i) != null) {
           writer.startField(fields(i).name, i)
           writeValue(fields(i).dataType, struct(i))
           writer.endField(fields(i).name, i)
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala
index dbf315947ff4..8fa143e2deca 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala
@@ -78,7 +78,7 @@ case class AllDataTypesWithNonPrimitiveType(
     booleanField: Boolean,
     array: Seq[Int],
     map: Map[Int, String],
-    nested: Nested)
+    data: Data)
 
 class ParquetQuerySuite extends QueryTest with FunSuiteLike with BeforeAndAfterAll {
   TestData // Load test data tables.
@@ -138,7 +138,7 @@ class ParquetQuerySuite extends QueryTest with FunSuiteLike with BeforeAndAfterA
     TestSQLContext.sparkContext.parallelize(range)
       .map(x => AllDataTypesWithNonPrimitiveType(
         s"$x", x, x.toLong, x.toFloat, x.toDouble, x.toShort, x.toByte, x % 2 == 0,
-        Seq(x), Map(x -> s"$x"), Nested(x, s"$x")))
+        (0 until x), (0 until x).map(i => i -> s"$i").toMap, Data((0 until x), Nested(x, s"$x"))))
       .saveAsParquetFile(tempDir)
     val result = parquetFile(tempDir).collect()
     range.foreach {
@@ -151,9 +151,9 @@ class ParquetQuerySuite extends QueryTest with FunSuiteLike with BeforeAndAfterA
         assert(result(i).getShort(5) === i.toShort)
         assert(result(i).getByte(6) === i.toByte)
         assert(result(i).getBoolean(7) === (i % 2 == 0))
-        assert(result(i)(8) === Seq(i))
-        assert(result(i)(9) === Map(i -> s"$i"))
-        assert(result(i)(10) === new GenericRow(Array[Any](i, s"$i")))
+        assert(result(i)(8) === (0 until i))
+        assert(result(i)(9) === (0 until i).map(i => i -> s"$i").toMap)
+        assert(result(i)(10) === new GenericRow(Array[Any]((0 until i), new GenericRow(Array[Any](i, s"$i")))))
     }
   }
 

From 10b59ba230cb426f2a5d43cd0a4964a556e24c3f Mon Sep 17 00:00:00 2001
From: Takuya UESHIN <ueshin@happy-camper.st>
Date: Thu, 10 Jul 2014 19:27:24 -0700
Subject: [PATCH 619/810] [SPARK-2428][SQL] Add except and intersect methods to
 SchemaRDD.

Author: Takuya UESHIN <ueshin@happy-camper.st>

Closes #1355 from ueshin/issues/SPARK-2428 and squashes the following commits:

b6fa264 [Takuya UESHIN] Add except and intersect methods to SchemaRDD.
---
 .../org/apache/spark/sql/SchemaRDD.scala      | 20 ++++++++++++++++++
 .../org/apache/spark/sql/DslQuerySuite.scala  | 21 +++++++++++++++++++
 2 files changed, 41 insertions(+)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala
index 8bcfc7c064c2..0c95b668545f 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala
@@ -256,6 +256,26 @@ class SchemaRDD(
   def unionAll(otherPlan: SchemaRDD) =
     new SchemaRDD(sqlContext, Union(logicalPlan, otherPlan.logicalPlan))
 
+  /**
+   * Performs a relational except on two SchemaRDDs
+   *
+   * @param otherPlan the [[SchemaRDD]] that should be excepted from this one.
+   *
+   * @group Query
+   */
+  def except(otherPlan: SchemaRDD): SchemaRDD =
+    new SchemaRDD(sqlContext, Except(logicalPlan, otherPlan.logicalPlan))
+
+  /**
+   * Performs a relational intersect on two SchemaRDDs
+   *
+   * @param otherPlan the [[SchemaRDD]] that should be intersected with this one.
+   *
+   * @group Query
+   */
+  def intersect(otherPlan: SchemaRDD): SchemaRDD =
+    new SchemaRDD(sqlContext, Intersect(logicalPlan, otherPlan.logicalPlan))
+
   /**
    * Filters tuples using a function over the value of the specified column.
    *
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala
index 04ac008682f5..68dae58728a2 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala
@@ -168,4 +168,25 @@ class DslQuerySuite extends QueryTest {
   test("zero count") {
     assert(emptyTableData.count() === 0)
   }
+
+  test("except") {
+    checkAnswer(
+      lowerCaseData.except(upperCaseData),
+      (1, "a") ::
+      (2, "b") ::
+      (3, "c") ::
+      (4, "d") :: Nil)
+    checkAnswer(lowerCaseData.except(lowerCaseData), Nil)
+    checkAnswer(upperCaseData.except(upperCaseData), Nil)
+  }
+
+  test("intersect") {
+    checkAnswer(
+      lowerCaseData.intersect(lowerCaseData),
+      (1, "a") ::
+      (2, "b") ::
+      (3, "c") ::
+      (4, "d") :: Nil)
+    checkAnswer(lowerCaseData.intersect(upperCaseData), Nil)
+  }
 }

From 2f59ce7dbe2fe2ff31e2629bb34572d39098d638 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Thu, 10 Jul 2014 21:57:54 -0700
Subject: [PATCH 620/810] [SPARK-2358][MLLIB] Add an option to include native
 BLAS/LAPACK loader in the build

It would be easy for users to include the netlib-java jniloader in the spark jar, which is LGPL-licensed. We can follow the same approach as ganglia support in Spark, which could be enabled by turning on "-Pganglia-lgpl" at build time. We can use "-Pnetlib-lgpl" flag for this.

Author: Xiangrui Meng <meng@databricks.com>

Closes #1295 from mengxr/netlib-lgpl and squashes the following commits:

aebf001 [Xiangrui Meng] add a profile to optionally include native BLAS/LAPACK loader in mllib
---
 mllib/pom.xml | 13 +++++++++++++
 1 file changed, 13 insertions(+)

diff --git a/mllib/pom.xml b/mllib/pom.xml
index 87afd7ecf2dd..92b07e2357db 100644
--- a/mllib/pom.xml
+++ b/mllib/pom.xml
@@ -78,6 +78,19 @@
       <scope>test</scope>
     </dependency>
   </dependencies>
+  <profiles>
+    <profile>
+      <id>netlib-lgpl</id>
+      <dependencies>
+        <dependency>
+          <groupId>com.github.fommil.netlib</groupId>
+          <artifactId>all</artifactId>
+          <version>1.1.2</version>
+          <type>pom</type>
+        </dependency>
+      </dependencies>
+    </profile>
+  </profiles>
   <build>
     <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory>
     <testOutputDirectory>target/scala-${scala.binary.version}/test-classes</testOutputDirectory>

From 282cca0e49120291759ded75709013e907db598c Mon Sep 17 00:00:00 2001
From: CrazyJvm <crazyjvm@gmail.com>
Date: Fri, 11 Jul 2014 00:02:24 -0700
Subject: [PATCH 621/810] fix Graph partitionStrategy comment

Author: CrazyJvm <crazyjvm@gmail.com>

Closes #1368 from CrazyJvm/graph-comment-1 and squashes the following commits:

d47f3c5 [CrazyJvm] fix style
e190d6f [CrazyJvm] fix Graph partitionStrategy comment
---
 graphx/src/main/scala/org/apache/spark/graphx/Graph.scala   | 6 ++++--
 .../src/main/scala/org/apache/spark/graphx/VertexRDD.scala  | 4 ++--
 2 files changed, 6 insertions(+), 4 deletions(-)

diff --git a/graphx/src/main/scala/org/apache/spark/graphx/Graph.scala b/graphx/src/main/scala/org/apache/spark/graphx/Graph.scala
index 4db45c9af8fa..3507f358bfb4 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/Graph.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/Graph.scala
@@ -107,14 +107,16 @@ abstract class Graph[VD: ClassTag, ED: ClassTag] protected () extends Serializab
   /**
    * Repartitions the edges in the graph according to `partitionStrategy`.
    *
-   * @param the partitioning strategy to use when partitioning the edges in the graph.
+   * @param partitionStrategy the partitioning strategy to use when partitioning the edges
+   * in the graph.
    */
   def partitionBy(partitionStrategy: PartitionStrategy): Graph[VD, ED]
 
   /**
    * Repartitions the edges in the graph according to `partitionStrategy`.
    *
-   * @param the partitioning strategy to use when partitioning the edges in the graph.
+   * @param partitionStrategy the partitioning strategy to use when partitioning the edges
+   * in the graph.
    * @param numPartitions the number of edge partitions in the new graph.
    */
   def partitionBy(partitionStrategy: PartitionStrategy, numPartitions: Int): Graph[VD, ED]
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/VertexRDD.scala b/graphx/src/main/scala/org/apache/spark/graphx/VertexRDD.scala
index f1b6df9a3025..4825d12fc27b 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/VertexRDD.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/VertexRDD.scala
@@ -182,8 +182,8 @@ class VertexRDD[@specialized VD: ClassTag](
   /**
    * Left joins this RDD with another VertexRDD with the same index. This function will fail if
    * both VertexRDDs do not share the same index. The resulting vertex set contains an entry for
-   * each
-   * vertex in `this`. If `other` is missing any vertex in this VertexRDD, `f` is passed `None`.
+   * each vertex in `this`.
+   * If `other` is missing any vertex in this VertexRDD, `f` is passed `None`.
    *
    * @tparam VD2 the attribute type of the other VertexRDD
    * @tparam VD3 the attribute type of the resulting VertexRDD

From f4f46dec5ae1da48738b9b650d3de155b59c4674 Mon Sep 17 00:00:00 2001
From: Andrew Or <andrewor14@gmail.com>
Date: Fri, 11 Jul 2014 00:21:16 -0700
Subject: [PATCH 622/810] [Minor] Remove unused val in Master

Author: Andrew Or <andrewor14@gmail.com>

Closes #1365 from andrewor14/master-fs and squashes the following commits:

497f100 [Andrew Or] Sneak in a space and hope no one will notice
05ba6da [Andrew Or] Remove unused val
---
 .../src/main/scala/org/apache/spark/deploy/master/Master.scala | 3 ---
 python/pyspark/java_gateway.py                                 | 2 +-
 2 files changed, 1 insertion(+), 4 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
index a304102a4908..d9f8105992a1 100644
--- a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
@@ -72,9 +72,7 @@ private[spark] class Master(
   val waitingApps = new ArrayBuffer[ApplicationInfo]
   val completedApps = new ArrayBuffer[ApplicationInfo]
   var nextAppNumber = 0
-
   val appIdToUI = new HashMap[String, SparkUI]
-  val fileSystemsUsed = new HashSet[FileSystem]
 
   val drivers = new HashSet[DriverInfo]
   val completedDrivers = new ArrayBuffer[DriverInfo]
@@ -159,7 +157,6 @@ private[spark] class Master(
       recoveryCompletionTask.cancel()
     }
     webUi.stop()
-    fileSystemsUsed.foreach(_.close())
     masterMetricsSystem.stop()
     applicationMetricsSystem.stop()
     persistenceEngine.close()
diff --git a/python/pyspark/java_gateway.py b/python/pyspark/java_gateway.py
index 0dbead4415b0..2a17127a7e0f 100644
--- a/python/pyspark/java_gateway.py
+++ b/python/pyspark/java_gateway.py
@@ -56,7 +56,7 @@ def preexec_func():
             (stdout, _) = proc.communicate()
             exit_code = proc.poll()
             error_msg = "Launching GatewayServer failed"
-            error_msg += " with exit code %d!" % exit_code if exit_code else "! "
+            error_msg += " with exit code %d! " % exit_code if exit_code else "! "
             error_msg += "(Warning: unexpected output detected.)\n\n"
             error_msg += gateway_port + stdout
             raise Exception(error_msg)

From b23e9c3e4085c0a7faf2c51fd350ad1233aa7a40 Mon Sep 17 00:00:00 2001
From: Prashant Sharma <prashant.s@imaginea.com>
Date: Fri, 11 Jul 2014 11:52:35 -0700
Subject: [PATCH 623/810] [SPARK-2437] Rename MAVEN_PROFILES to
 SBT_MAVEN_PROFILES and add SBT_MAVEN_PROPERTIES

NOTE: It is not possible to use both env variable  `SBT_MAVEN_PROFILES`  and `-P` flag at same time. `-P` if specified takes precedence.

Author: Prashant Sharma <prashant.s@imaginea.com>

Closes #1374 from ScrapCodes/SPARK-2437/rename-MAVEN_PROFILES and squashes the following commits:

8694bde [Prashant Sharma] [SPARK-2437] Rename MAVEN_PROFILES to SBT_MAVEN_PROFILES and add SBT_MAVEN_PROPERTIES
---
 project/SparkBuild.scala | 9 +++++++--
 sbt/sbt-launch-lib.bash  | 2 +-
 2 files changed, 8 insertions(+), 3 deletions(-)

diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index b55c50560bb9..44abbc152f99 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -86,9 +86,8 @@ object SparkBuild extends PomBuild {
     profiles
   }
 
-  override val profiles = Properties.envOrNone("MAVEN_PROFILES") match {
+  override val profiles = Properties.envOrNone("SBT_MAVEN_PROFILES") match {
     case None => backwardCompatibility
-    // Rationale: If -P option exists no need to support backwardCompatibility.
     case Some(v) =>
       if (backwardCompatibility.nonEmpty)
         println("Note: We ignore environment variables, when use of profile is detected in " +
@@ -96,6 +95,12 @@ object SparkBuild extends PomBuild {
       v.split("(\\s+|,)").filterNot(_.isEmpty).map(_.trim.replaceAll("-P", "")).toSeq
   }
 
+  Properties.envOrNone("SBT_MAVEN_PROPERTIES") match {
+    case Some(v) =>
+      v.split("(\\s+|,)").filterNot(_.isEmpty).map(_.split("=")).foreach(x => System.setProperty(x(0), x(1)))
+    case _ => 
+  }
+
   override val userPropertiesMap = System.getProperties.toMap
 
   lazy val sharedSettings = graphSettings ++ ScalaStyleSettings ++ Seq (
diff --git a/sbt/sbt-launch-lib.bash b/sbt/sbt-launch-lib.bash
index 857b62ffa229..c91fecf024ad 100755
--- a/sbt/sbt-launch-lib.bash
+++ b/sbt/sbt-launch-lib.bash
@@ -92,7 +92,7 @@ addJava () {
 enableProfile () {
   dlog "[enableProfile] arg = '$1'"
   maven_profiles=( "${maven_profiles[@]}" "$1" )
-  export MAVEN_PROFILES="${maven_profiles[@]}"
+  export SBT_MAVEN_PROFILES="${maven_profiles[@]}"
 }
 
 addSbt () {

From cbff18774b0a2f346901ddf2f566be50561a57c7 Mon Sep 17 00:00:00 2001
From: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Date: Fri, 11 Jul 2014 21:10:26 -0700
Subject: [PATCH 624/810] [SPARK-2457] Inconsistent description in README about
 build option

Now, we should use -Pyarn instead of SPARK_YARN when building but README says as follows.

    For Apache Hadoop 2.2.X, 2.1.X, 2.0.X, 0.23.x, Cloudera CDH MRv2, and other Hadoop versions
    with YARN, also set `SPARK_YARN=true`:

      # Apache Hadoop 2.0.5-alpha
      $ sbt/sbt -Dhadoop.version=2.0.5-alpha -Pyarn assembly

      # Cloudera CDH 4.2.0 with MapReduce v2
      $ sbt/sbt -Dhadoop.version=2.0.0-cdh4.2.0 -Pyarn assembly

      # Apache Hadoop 2.2.X and newer
      $ sbt/sbt -Dhadoop.version=2.2.0 -Pyarn assembly

Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>

Closes #1382 from sarutak/SPARK-2457 and squashes the following commits:

e7b2d64 [Kousuke Saruta] Replaced "SPARK_YARN=true" with "-Pyarn" in README
---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 01ef851f34b6..f6e7f5109131 100644
--- a/README.md
+++ b/README.md
@@ -81,7 +81,7 @@ versions without YARN, use:
     $ sbt/sbt -Dhadoop.version=2.0.0-mr1-cdh4.2.0 assembly
 
 For Apache Hadoop 2.2.X, 2.1.X, 2.0.X, 0.23.x, Cloudera CDH MRv2, and other Hadoop versions
-with YARN, also set `SPARK_YARN=true`:
+with YARN, also set `-Pyarn`:
 
     # Apache Hadoop 2.0.5-alpha
     $ sbt/sbt -Dhadoop.version=2.0.5-alpha -Pyarn assembly

From 55960869358d4f8aa5b2e3b17d87b0b02ba9acdd Mon Sep 17 00:00:00 2001
From: DB Tsai <dbtsai@dbtsai.com>
Date: Fri, 11 Jul 2014 23:04:43 -0700
Subject: [PATCH 625/810] [SPARK-1969][MLlib] Online summarizer APIs for mean,
 variance, min, and max

It basically moved the private ColumnStatisticsAggregator class from RowMatrix to public available DeveloperApi with documentation and unitests.

Changes:
1) Moved the private implementation from org.apache.spark.mllib.linalg.ColumnStatisticsAggregator to org.apache.spark.mllib.stat.MultivariateOnlineSummarizer
2) When creating OnlineSummarizer object, the number of columns is not needed in the constructor. It's determined when users add the first sample.
3) Added the APIs documentation for MultivariateOnlineSummarizer.
4) Added the unittests for MultivariateOnlineSummarizer.

Author: DB Tsai <dbtsai@dbtsai.com>

Closes #955 from dbtsai/dbtsai-summarizer and squashes the following commits:

b13ac90 [DB Tsai] dbtsai-summarizer
---
 .../mllib/linalg/distributed/RowMatrix.scala  | 136 +-----------
 .../stat/MultivariateOnlineSummarizer.scala   | 201 +++++++++++++++++
 .../MultivariateOnlineSummarizerSuite.scala   | 209 ++++++++++++++++++
 .../spark/mllib/util/TestingUtils.scala       |  45 ++++
 project/MimaExcludes.scala                    |   1 +
 5 files changed, 458 insertions(+), 134 deletions(-)
 create mode 100644 mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizer.scala
 create mode 100644 mllib/src/test/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizerSuite.scala
 create mode 100644 mllib/src/test/scala/org/apache/spark/mllib/util/TestingUtils.scala

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
index 99cb6516e065..711e32a330d7 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
@@ -28,138 +28,7 @@ import org.apache.spark.annotation.Experimental
 import org.apache.spark.mllib.linalg._
 import org.apache.spark.rdd.RDD
 import org.apache.spark.Logging
-import org.apache.spark.mllib.stat.MultivariateStatisticalSummary
-
-/**
- * Column statistics aggregator implementing
- * [[org.apache.spark.mllib.stat.MultivariateStatisticalSummary]]
- * together with add() and merge() function.
- * A numerically stable algorithm is implemented to compute sample mean and variance:
- * [[http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance variance-wiki]].
- * Zero elements (including explicit zero values) are skipped when calling add() and merge(),
- * to have time complexity O(nnz) instead of O(n) for each column.
- */
-private class ColumnStatisticsAggregator(private val n: Int)
-    extends MultivariateStatisticalSummary with Serializable {
-
-  private val currMean: BDV[Double] = BDV.zeros[Double](n)
-  private val currM2n: BDV[Double] = BDV.zeros[Double](n)
-  private var totalCnt = 0.0
-  private val nnz: BDV[Double] = BDV.zeros[Double](n)
-  private val currMax: BDV[Double] = BDV.fill(n)(Double.MinValue)
-  private val currMin: BDV[Double] = BDV.fill(n)(Double.MaxValue)
-
-  override def mean: Vector = {
-    val realMean = BDV.zeros[Double](n)
-    var i = 0
-    while (i < n) {
-      realMean(i) = currMean(i) * nnz(i) / totalCnt
-      i += 1
-    }
-    Vectors.fromBreeze(realMean)
-  }
-
-  override def variance: Vector = {
-    val realVariance = BDV.zeros[Double](n)
-
-    val denominator = totalCnt - 1.0
-
-    // Sample variance is computed, if the denominator is less than 0, the variance is just 0.
-    if (denominator > 0.0) {
-      val deltaMean = currMean
-      var i = 0
-      while (i < currM2n.size) {
-        realVariance(i) =
-          currM2n(i) + deltaMean(i) * deltaMean(i) * nnz(i) * (totalCnt - nnz(i)) / totalCnt
-        realVariance(i) /= denominator
-        i += 1
-      }
-    }
-
-    Vectors.fromBreeze(realVariance)
-  }
-
-  override def count: Long = totalCnt.toLong
-
-  override def numNonzeros: Vector = Vectors.fromBreeze(nnz)
-
-  override def max: Vector = {
-    var i = 0
-    while (i < n) {
-      if ((nnz(i) < totalCnt) && (currMax(i) < 0.0)) currMax(i) = 0.0
-      i += 1
-    }
-    Vectors.fromBreeze(currMax)
-  }
-
-  override def min: Vector = {
-    var i = 0
-    while (i < n) {
-      if ((nnz(i) < totalCnt) && (currMin(i) > 0.0)) currMin(i) = 0.0
-      i += 1
-    }
-    Vectors.fromBreeze(currMin)
-  }
-
-  /**
-   * Aggregates a row.
-   */
-  def add(currData: BV[Double]): this.type = {
-    currData.activeIterator.foreach {
-      case (_, 0.0) => // Skip explicit zero elements.
-      case (i, value) =>
-        if (currMax(i) < value) {
-          currMax(i) = value
-        }
-        if (currMin(i) > value) {
-          currMin(i) = value
-        }
-
-        val tmpPrevMean = currMean(i)
-        currMean(i) = (currMean(i) * nnz(i) + value) / (nnz(i) + 1.0)
-        currM2n(i) += (value - currMean(i)) * (value - tmpPrevMean)
-
-        nnz(i) += 1.0
-    }
-
-    totalCnt += 1.0
-    this
-  }
-
-  /**
-   * Merges another aggregator.
-   */
-  def merge(other: ColumnStatisticsAggregator): this.type = {
-    require(n == other.n, s"Dimensions mismatch. Expecting $n but got ${other.n}.")
-
-    totalCnt += other.totalCnt
-    val deltaMean = currMean - other.currMean
-
-    var i = 0
-    while (i < n) {
-      // merge mean together
-      if (other.currMean(i) != 0.0) {
-        currMean(i) = (currMean(i) * nnz(i) + other.currMean(i) * other.nnz(i)) /
-          (nnz(i) + other.nnz(i))
-      }
-      // merge m2n together
-      if (nnz(i) + other.nnz(i) != 0.0) {
-        currM2n(i) += other.currM2n(i) + deltaMean(i) * deltaMean(i) * nnz(i) * other.nnz(i) /
-          (nnz(i) + other.nnz(i))
-      }
-      if (currMax(i) < other.currMax(i)) {
-        currMax(i) = other.currMax(i)
-      }
-      if (currMin(i) > other.currMin(i)) {
-        currMin(i) = other.currMin(i)
-      }
-      i += 1
-    }
-
-    nnz += other.nnz
-    this
-  }
-}
+import org.apache.spark.mllib.stat.{MultivariateOnlineSummarizer, MultivariateStatisticalSummary}
 
 /**
  * :: Experimental ::
@@ -478,8 +347,7 @@ class RowMatrix(
    * Computes column-wise summary statistics.
    */
   def computeColumnSummaryStatistics(): MultivariateStatisticalSummary = {
-    val zeroValue = new ColumnStatisticsAggregator(numCols().toInt)
-    val summary = rows.map(_.toBreeze).aggregate[ColumnStatisticsAggregator](zeroValue)(
+    val summary = rows.aggregate[MultivariateOnlineSummarizer](new MultivariateOnlineSummarizer)(
       (aggregator, data) => aggregator.add(data),
       (aggregator1, aggregator2) => aggregator1.merge(aggregator2)
     )
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizer.scala b/mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizer.scala
new file mode 100644
index 000000000000..5105b5c37aaa
--- /dev/null
+++ b/mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizer.scala
@@ -0,0 +1,201 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.mllib.stat
+
+import breeze.linalg.{DenseVector => BDV}
+
+import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.mllib.linalg.{Vectors, Vector}
+
+/**
+ * :: DeveloperApi ::
+ * MultivariateOnlineSummarizer implements [[MultivariateStatisticalSummary]] to compute the mean,
+ * variance, minimum, maximum, counts, and nonzero counts for samples in sparse or dense vector
+ * format in a online fashion.
+ *
+ * Two MultivariateOnlineSummarizer can be merged together to have a statistical summary of
+ * the corresponding joint dataset.
+ *
+ * A numerically stable algorithm is implemented to compute sample mean and variance:
+ * Reference: [[http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance variance-wiki]]
+ * Zero elements (including explicit zero values) are skipped when calling add(),
+ * to have time complexity O(nnz) instead of O(n) for each column.
+ */
+@DeveloperApi
+class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with Serializable {
+
+  private var n = 0
+  private var currMean: BDV[Double] = _
+  private var currM2n: BDV[Double] = _
+  private var totalCnt: Long = 0
+  private var nnz: BDV[Double] = _
+  private var currMax: BDV[Double] = _
+  private var currMin: BDV[Double] = _
+
+  /**
+   * Add a new sample to this summarizer, and update the statistical summary.
+   *
+   * @param sample The sample in dense/sparse vector format to be added into this summarizer.
+   * @return This MultivariateOnlineSummarizer object.
+   */
+  def add(sample: Vector): this.type = {
+    if (n == 0) {
+      require(sample.toBreeze.length > 0, s"Vector should have dimension larger than zero.")
+      n = sample.toBreeze.length
+
+      currMean = BDV.zeros[Double](n)
+      currM2n = BDV.zeros[Double](n)
+      nnz = BDV.zeros[Double](n)
+      currMax = BDV.fill(n)(Double.MinValue)
+      currMin = BDV.fill(n)(Double.MaxValue)
+    }
+
+    require(n == sample.toBreeze.length, s"Dimensions mismatch when adding new sample." +
+      s" Expecting $n but got ${sample.toBreeze.length}.")
+
+    sample.toBreeze.activeIterator.foreach {
+      case (_, 0.0) => // Skip explicit zero elements.
+      case (i, value) =>
+        if (currMax(i) < value) {
+          currMax(i) = value
+        }
+        if (currMin(i) > value) {
+          currMin(i) = value
+        }
+
+        val tmpPrevMean = currMean(i)
+        currMean(i) = (currMean(i) * nnz(i) + value) / (nnz(i) + 1.0)
+        currM2n(i) += (value - currMean(i)) * (value - tmpPrevMean)
+
+        nnz(i) += 1.0
+    }
+
+    totalCnt += 1
+    this
+  }
+
+  /**
+   * Merge another MultivariateOnlineSummarizer, and update the statistical summary.
+   * (Note that it's in place merging; as a result, `this` object will be modified.)
+   *
+   * @param other The other MultivariateOnlineSummarizer to be merged.
+   * @return This MultivariateOnlineSummarizer object.
+   */
+  def merge(other: MultivariateOnlineSummarizer): this.type = {
+   if (this.totalCnt != 0 && other.totalCnt != 0) {
+      require(n == other.n, s"Dimensions mismatch when merging with another summarizer. " +
+        s"Expecting $n but got ${other.n}.")
+      totalCnt += other.totalCnt
+      val deltaMean: BDV[Double] = currMean - other.currMean
+      var i = 0
+      while (i < n) {
+        // merge mean together
+        if (other.currMean(i) != 0.0) {
+          currMean(i) = (currMean(i) * nnz(i) + other.currMean(i) * other.nnz(i)) /
+            (nnz(i) + other.nnz(i))
+        }
+        // merge m2n together
+        if (nnz(i) + other.nnz(i) != 0.0) {
+          currM2n(i) += other.currM2n(i) + deltaMean(i) * deltaMean(i) * nnz(i) * other.nnz(i) /
+            (nnz(i) + other.nnz(i))
+        }
+        if (currMax(i) < other.currMax(i)) {
+          currMax(i) = other.currMax(i)
+        }
+        if (currMin(i) > other.currMin(i)) {
+          currMin(i) = other.currMin(i)
+        }
+        i += 1
+      }
+      nnz += other.nnz
+    } else if (totalCnt == 0 && other.totalCnt != 0) {
+      this.n = other.n
+      this.currMean = other.currMean.copy
+      this.currM2n = other.currM2n.copy
+      this.totalCnt = other.totalCnt
+      this.nnz = other.nnz.copy
+      this.currMax = other.currMax.copy
+      this.currMin = other.currMin.copy
+    }
+    this
+  }
+
+  override def mean: Vector = {
+    require(totalCnt > 0, s"Nothing has been added to this summarizer.")
+
+    val realMean = BDV.zeros[Double](n)
+    var i = 0
+    while (i < n) {
+      realMean(i) = currMean(i) * (nnz(i) / totalCnt)
+      i += 1
+    }
+    Vectors.fromBreeze(realMean)
+  }
+
+  override def variance: Vector = {
+    require(totalCnt > 0, s"Nothing has been added to this summarizer.")
+
+    val realVariance = BDV.zeros[Double](n)
+
+    val denominator = totalCnt - 1.0
+
+    // Sample variance is computed, if the denominator is less than 0, the variance is just 0.
+    if (denominator > 0.0) {
+      val deltaMean = currMean
+      var i = 0
+      while (i < currM2n.size) {
+        realVariance(i) =
+          currM2n(i) + deltaMean(i) * deltaMean(i) * nnz(i) * (totalCnt - nnz(i)) / totalCnt
+        realVariance(i) /= denominator
+        i += 1
+      }
+    }
+
+    Vectors.fromBreeze(realVariance)
+  }
+
+  override def count: Long = totalCnt
+
+  override def numNonzeros: Vector = {
+    require(totalCnt > 0, s"Nothing has been added to this summarizer.")
+
+    Vectors.fromBreeze(nnz)
+  }
+
+  override def max: Vector = {
+    require(totalCnt > 0, s"Nothing has been added to this summarizer.")
+
+    var i = 0
+    while (i < n) {
+      if ((nnz(i) < totalCnt) && (currMax(i) < 0.0)) currMax(i) = 0.0
+      i += 1
+    }
+    Vectors.fromBreeze(currMax)
+  }
+
+  override def min: Vector = {
+    require(totalCnt > 0, s"Nothing has been added to this summarizer.")
+
+    var i = 0
+    while (i < n) {
+      if ((nnz(i) < totalCnt) && (currMin(i) > 0.0)) currMin(i) = 0.0
+      i += 1
+    }
+    Vectors.fromBreeze(currMin)
+  }
+}
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizerSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizerSuite.scala
new file mode 100644
index 000000000000..4b7b019d820b
--- /dev/null
+++ b/mllib/src/test/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizerSuite.scala
@@ -0,0 +1,209 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.mllib.stat
+
+import org.scalatest.FunSuite
+
+import org.apache.spark.mllib.linalg.Vectors
+import org.apache.spark.mllib.util.TestingUtils._
+
+class MultivariateOnlineSummarizerSuite extends FunSuite {
+
+  test("basic error handing") {
+    val summarizer = new MultivariateOnlineSummarizer
+
+    assert(summarizer.count === 0, "should be zero since nothing is added.")
+
+    withClue("Getting numNonzeros from empty summarizer should throw exception.") {
+      intercept[IllegalArgumentException] {
+        summarizer.numNonzeros
+      }
+    }
+
+    withClue("Getting variance from empty summarizer should throw exception.") {
+      intercept[IllegalArgumentException] {
+        summarizer.variance
+      }
+    }
+
+    withClue("Getting mean from empty summarizer should throw exception.") {
+      intercept[IllegalArgumentException] {
+        summarizer.mean
+      }
+    }
+
+    withClue("Getting max from empty summarizer should throw exception.") {
+      intercept[IllegalArgumentException] {
+        summarizer.max
+      }
+    }
+
+    withClue("Getting min from empty summarizer should throw exception.") {
+      intercept[IllegalArgumentException] {
+        summarizer.min
+      }
+    }
+
+    summarizer.add(Vectors.dense(-1.0, 2.0, 6.0)).add(Vectors.sparse(3, Seq((0, -2.0), (1, 6.0))))
+
+    withClue("Adding a new dense sample with different array size should throw exception.") {
+      intercept[IllegalArgumentException] {
+        summarizer.add(Vectors.dense(3.0, 1.0))
+      }
+    }
+
+    withClue("Adding a new sparse sample with different array size should throw exception.") {
+      intercept[IllegalArgumentException] {
+        summarizer.add(Vectors.sparse(5, Seq((0, -2.0), (1, 6.0))))
+      }
+    }
+
+    val summarizer2 = (new MultivariateOnlineSummarizer).add(Vectors.dense(1.0, -2.0, 0.0, 4.0))
+    withClue("Merging a new summarizer with different dimensions should throw exception.") {
+      intercept[IllegalArgumentException] {
+        summarizer.merge(summarizer2)
+      }
+    }
+  }
+
+  test("dense vector input") {
+    // For column 2, the maximum will be 0.0, and it's not explicitly added since we ignore all
+    // the zeros; it's a case we need to test. For column 3, the minimum will be 0.0 which we
+    // need to test as well.
+    val summarizer = (new MultivariateOnlineSummarizer)
+      .add(Vectors.dense(-1.0, 0.0, 6.0))
+      .add(Vectors.dense(3.0, -3.0, 0.0))
+
+    assert(summarizer.mean.almostEquals(Vectors.dense(1.0, -1.5, 3.0)), "mean mismatch")
+
+    assert(summarizer.min.almostEquals(Vectors.dense(-1.0, -3, 0.0)), "min mismatch")
+
+    assert(summarizer.max.almostEquals(Vectors.dense(3.0, 0.0, 6.0)), "max mismatch")
+
+    assert(summarizer.numNonzeros.almostEquals(Vectors.dense(2, 1, 1)), "numNonzeros mismatch")
+
+    assert(summarizer.variance.almostEquals(Vectors.dense(8.0, 4.5, 18.0)), "variance mismatch")
+
+    assert(summarizer.count === 2)
+  }
+
+  test("sparse vector input") {
+    val summarizer = (new MultivariateOnlineSummarizer)
+      .add(Vectors.sparse(3, Seq((0, -1.0), (2, 6.0))))
+      .add(Vectors.sparse(3, Seq((0, 3.0), (1, -3.0))))
+
+    assert(summarizer.mean.almostEquals(Vectors.dense(1.0, -1.5, 3.0)), "mean mismatch")
+
+    assert(summarizer.min.almostEquals(Vectors.dense(-1.0, -3, 0.0)), "min mismatch")
+
+    assert(summarizer.max.almostEquals(Vectors.dense(3.0, 0.0, 6.0)), "max mismatch")
+
+    assert(summarizer.numNonzeros.almostEquals(Vectors.dense(2, 1, 1)), "numNonzeros mismatch")
+
+    assert(summarizer.variance.almostEquals(Vectors.dense(8.0, 4.5, 18.0)), "variance mismatch")
+
+    assert(summarizer.count === 2)
+  }
+
+  test("mixing dense and sparse vector input") {
+    val summarizer = (new MultivariateOnlineSummarizer)
+      .add(Vectors.sparse(3, Seq((0, -2.0), (1, 2.3))))
+      .add(Vectors.dense(0.0, -1.0, -3.0))
+      .add(Vectors.sparse(3, Seq((1, -5.1))))
+      .add(Vectors.dense(3.8, 0.0, 1.9))
+      .add(Vectors.dense(1.7, -0.6, 0.0))
+      .add(Vectors.sparse(3, Seq((1, 1.9), (2, 0.0))))
+
+    assert(summarizer.mean.almostEquals(
+      Vectors.dense(0.583333333333, -0.416666666666, -0.183333333333)), "mean mismatch")
+
+    assert(summarizer.min.almostEquals(Vectors.dense(-2.0, -5.1, -3)), "min mismatch")
+
+    assert(summarizer.max.almostEquals(Vectors.dense(3.8, 2.3, 1.9)), "max mismatch")
+
+    assert(summarizer.numNonzeros.almostEquals(Vectors.dense(3, 5, 2)), "numNonzeros mismatch")
+
+    assert(summarizer.variance.almostEquals(
+      Vectors.dense(3.857666666666, 7.0456666666666, 2.48166666666666)), "variance mismatch")
+
+    assert(summarizer.count === 6)
+  }
+
+  test("merging two summarizers") {
+    val summarizer1 = (new MultivariateOnlineSummarizer)
+      .add(Vectors.sparse(3, Seq((0, -2.0), (1, 2.3))))
+      .add(Vectors.dense(0.0, -1.0, -3.0))
+
+    val summarizer2 = (new MultivariateOnlineSummarizer)
+      .add(Vectors.sparse(3, Seq((1, -5.1))))
+      .add(Vectors.dense(3.8, 0.0, 1.9))
+      .add(Vectors.dense(1.7, -0.6, 0.0))
+      .add(Vectors.sparse(3, Seq((1, 1.9), (2, 0.0))))
+
+    val summarizer = summarizer1.merge(summarizer2)
+
+    assert(summarizer.mean.almostEquals(
+      Vectors.dense(0.583333333333, -0.416666666666, -0.183333333333)), "mean mismatch")
+
+    assert(summarizer.min.almostEquals(Vectors.dense(-2.0, -5.1, -3)), "min mismatch")
+
+    assert(summarizer.max.almostEquals(Vectors.dense(3.8, 2.3, 1.9)), "max mismatch")
+
+    assert(summarizer.numNonzeros.almostEquals(Vectors.dense(3, 5, 2)), "numNonzeros mismatch")
+
+    assert(summarizer.variance.almostEquals(
+      Vectors.dense(3.857666666666, 7.0456666666666, 2.48166666666666)), "variance mismatch")
+
+    assert(summarizer.count === 6)
+  }
+
+  test("merging summarizer with empty summarizer") {
+    // If one of two is non-empty, this should return the non-empty summarizer.
+    // If both of them are empty, then just return the empty summarizer.
+    val summarizer1 = (new MultivariateOnlineSummarizer)
+      .add(Vectors.dense(0.0, -1.0, -3.0)).merge(new MultivariateOnlineSummarizer)
+    assert(summarizer1.count === 1)
+
+    val summarizer2 = (new MultivariateOnlineSummarizer)
+      .merge((new MultivariateOnlineSummarizer).add(Vectors.dense(0.0, -1.0, -3.0)))
+    assert(summarizer2.count === 1)
+
+    val summarizer3 = (new MultivariateOnlineSummarizer).merge(new MultivariateOnlineSummarizer)
+    assert(summarizer3.count === 0)
+
+    assert(summarizer1.mean.almostEquals(Vectors.dense(0.0, -1.0, -3.0)), "mean mismatch")
+
+    assert(summarizer2.mean.almostEquals(Vectors.dense(0.0, -1.0, -3.0)), "mean mismatch")
+
+    assert(summarizer1.min.almostEquals(Vectors.dense(0.0, -1.0, -3.0)), "min mismatch")
+
+    assert(summarizer2.min.almostEquals(Vectors.dense(0.0, -1.0, -3.0)), "min mismatch")
+
+    assert(summarizer1.max.almostEquals(Vectors.dense(0.0, -1.0, -3.0)), "max mismatch")
+
+    assert(summarizer2.max.almostEquals(Vectors.dense(0.0, -1.0, -3.0)), "max mismatch")
+
+    assert(summarizer1.numNonzeros.almostEquals(Vectors.dense(0, 1, 1)), "numNonzeros mismatch")
+
+    assert(summarizer2.numNonzeros.almostEquals(Vectors.dense(0, 1, 1)), "numNonzeros mismatch")
+
+    assert(summarizer1.variance.almostEquals(Vectors.dense(0, 0, 0)), "variance mismatch")
+
+    assert(summarizer2.variance.almostEquals(Vectors.dense(0, 0, 0)), "variance mismatch")
+  }
+}
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/util/TestingUtils.scala b/mllib/src/test/scala/org/apache/spark/mllib/util/TestingUtils.scala
new file mode 100644
index 000000000000..64b1ba752718
--- /dev/null
+++ b/mllib/src/test/scala/org/apache/spark/mllib/util/TestingUtils.scala
@@ -0,0 +1,45 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.mllib.util
+
+import org.apache.spark.mllib.linalg.Vector
+
+object TestingUtils {
+
+  implicit class DoubleWithAlmostEquals(val x: Double) {
+    // An improved version of AlmostEquals would always divide by the larger number.
+    // This will avoid the problem of diving by zero.
+    def almostEquals(y: Double, epsilon: Double = 1E-10): Boolean = {
+      if(x == y) {
+        true
+      } else if(math.abs(x) > math.abs(y)) {
+        math.abs(x - y) / math.abs(x) < epsilon
+      } else {
+        math.abs(x - y) / math.abs(y) < epsilon
+      }
+    }
+  }
+
+  implicit class VectorWithAlmostEquals(val x: Vector) {
+    def almostEquals(y: Vector, epsilon: Double = 1E-10): Boolean = {
+      x.toArray.corresponds(y.toArray) {
+        _.almostEquals(_, epsilon)
+      }
+    }
+  }
+}
diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala
index 3b7b87b80cda..d67c6571a062 100644
--- a/project/MimaExcludes.scala
+++ b/project/MimaExcludes.scala
@@ -75,6 +75,7 @@ object MimaExcludes {
             ProblemFilters.exclude[MissingMethodProblem](
               "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$<init>$default$7")
           ) ++
+          MimaBuild.excludeSparkClass("mllib.linalg.distributed.ColumnStatisticsAggregator") ++
           MimaBuild.excludeSparkClass("rdd.ZippedRDD") ++
           MimaBuild.excludeSparkClass("rdd.ZippedPartition") ++
           MimaBuild.excludeSparkClass("util.SerializableHyperLogLog") ++

From d38887b8a0d00a11d7cf9393e7cb0918c3ec7a22 Mon Sep 17 00:00:00 2001
From: Li Pu <lpu@twitter.com>
Date: Fri, 11 Jul 2014 23:26:47 -0700
Subject: [PATCH 626/810] use specialized axpy in RowMatrix for SVD

After running some more tests on large matrix, found that the BV axpy (breeze/linalg/Vector.scala, axpy) is slower than the BSV axpy (breeze/linalg/operators/SparseVectorOps.scala, sv_dv_axpy), 8s v.s. 2s for each multiplication. The BV axpy operates on an iterator while BSV axpy directly operates on the underlying array. I think the overhead comes from creating the iterator (with a zip) and advancing the pointers.

Author: Li Pu <lpu@twitter.com>
Author: Xiangrui Meng <meng@databricks.com>
Author: Li Pu <li.pu@outlook.com>

Closes #1378 from vrilleup/master and squashes the following commits:

6fb01a3 [Li Pu] use specialized axpy in RowMatrix
5255f2a [Li Pu] Merge remote-tracking branch 'upstream/master'
7312ec1 [Li Pu] very minor comment fix
4c618e9 [Li Pu] Merge pull request #1 from mengxr/vrilleup-master
a461082 [Xiangrui Meng] make superscript show up correctly in doc
861ec48 [Xiangrui Meng] simplify axpy
62969fa [Xiangrui Meng] use BDV directly in symmetricEigs change the computation mode to local-svd, local-eigs, and dist-eigs update tests and docs
c273771 [Li Pu] automatically determine SVD compute mode and parameters
7148426 [Li Pu] improve RowMatrix multiply
5543cce [Li Pu] improve svd api
819824b [Li Pu] add flag for dense svd or sparse svd
eb15100 [Li Pu] fix binary compatibility
4c7aec3 [Li Pu] improve comments
e7850ed [Li Pu] use aggregate and axpy
827411b [Li Pu] fix EOF new line
9c80515 [Li Pu] use non-sparse implementation when k = n
fe983b0 [Li Pu] improve scala style
96d2ecb [Li Pu] improve eigenvalue sorting
e1db950 [Li Pu] SPARK-1782: svd for sparse matrix using ARPACK
---
 .../apache/spark/mllib/linalg/distributed/RowMatrix.scala | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
index 711e32a330d7..f4c403bc7861 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
@@ -83,7 +83,13 @@ class RowMatrix(
       seqOp = (U, r) => {
         val rBrz = r.toBreeze
         val a = rBrz.dot(vbr.value)
-        brzAxpy(a, rBrz, U.asInstanceOf[BV[Double]])
+        rBrz match {
+          // use specialized axpy for better performance
+          case _: BDV[_] => brzAxpy(a, rBrz.asInstanceOf[BDV[Double]], U)
+          case _: BSV[_] => brzAxpy(a, rBrz.asInstanceOf[BSV[Double]], U)
+          case _ => throw new UnsupportedOperationException(
+            s"Do not support vector operation from type ${rBrz.getClass.getName}.")
+        }
         U
       },
       combOp = (U1, U2) => U1 += U2

From 2245c87af4f507cda361e16f322a14eac25b38fd Mon Sep 17 00:00:00 2001
From: Daniel Darabos <darabos.daniel@gmail.com>
Date: Sat, 12 Jul 2014 00:07:42 -0700
Subject: [PATCH 627/810] Use the Executor's ClassLoader in sc.objectFile().

This makes it possible to read classes from the object file which were specified in the user-provided jars. (By default ObjectInputStream uses latestUserDefinedLoader, which may or may not be the right one.)

I created this because I ran into the following problem. I have x:RDD[X] with X being defined in the jar that I provide to SparkContext. I save it with x.saveAsObjectFile("x"). I try to load it with sc.objectFile\[X\]("x"). It fails with ClassNotFoundException.

After a good while of debugging I figured out that Utils.deserialize() most likely uses the ClassLoader of Utils. This is the bootstrap ClassLoader, so it is not aware of the dynamically added jars. This patch fixes the issue.

A more robust fix would be to always default to Thread.currentThread.getContextClassLoader. This would prevent this problem from biting anyone in the future. It would be a bit harder to test though. On the topic of testing, if you'd like to see tests for this, I will need some hand-holding. Thanks!

Author: Daniel Darabos <darabos.daniel@gmail.com>

Closes #181 from darabos/master and squashes the following commits:

45a011a [Daniel Darabos] Add test for SPARK-1877. (Fixed in 52eb54d.)
e13e090 [Daniel Darabos] Merge branch 'master' of https://github.com/apache/spark
61fe0d0 [Daniel Darabos] Fix style (line too long).
1b5df2c [Daniel Darabos] Use the Executor's ClassLoader in sc.objectFile(). This makes it possible to read classes from the object file which were specified in the user-provided jars. (By default ObjectInputStream uses latestUserDefinedLoader, which may or may not be the right one.)
---
 .../scala/org/apache/spark/TestUtils.scala    |  4 +--
 .../scala/org/apache/spark/FileSuite.scala    | 25 +++++++++++++++++++
 2 files changed, 27 insertions(+), 2 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/TestUtils.scala b/core/src/main/scala/org/apache/spark/TestUtils.scala
index 885c6829a2d7..8ca731038e52 100644
--- a/core/src/main/scala/org/apache/spark/TestUtils.scala
+++ b/core/src/main/scala/org/apache/spark/TestUtils.scala
@@ -92,8 +92,8 @@ private[spark] object TestUtils {
   def createCompiledClass(className: String, destDir: File, value: String = ""): File = {
     val compiler = ToolProvider.getSystemJavaCompiler
     val sourceFile = new JavaSourceFromString(className,
-      "public class " + className + " { @Override public String toString() { " +
-       "return \"" + value + "\";}}")
+      "public class " + className + " implements java.io.Serializable {" +
+      "  @Override public String toString() { return \"" + value + "\"; }}")
 
     // Calling this outputs a class file in pwd. It's easier to just rename the file than
     // build a custom FileManager that controls the output location.
diff --git a/core/src/test/scala/org/apache/spark/FileSuite.scala b/core/src/test/scala/org/apache/spark/FileSuite.scala
index 070e97465786..c70e22cf0943 100644
--- a/core/src/test/scala/org/apache/spark/FileSuite.scala
+++ b/core/src/test/scala/org/apache/spark/FileSuite.scala
@@ -177,6 +177,31 @@ class FileSuite extends FunSuite with LocalSparkContext {
     assert(output.collect().toList === List((1, "a"), (2, "aa"), (3, "aaa")))
   }
 
+  test("object files of classes from a JAR") {
+    val original = Thread.currentThread().getContextClassLoader
+    val className = "FileSuiteObjectFileTest"
+    val jar = TestUtils.createJarWithClasses(Seq(className))
+    val loader = new java.net.URLClassLoader(Array(jar), Utils.getContextOrSparkClassLoader)
+    Thread.currentThread().setContextClassLoader(loader)
+    try {
+      sc = new SparkContext("local", "test")
+      val objs = sc.makeRDD(1 to 3).map { x =>
+        val loader = Thread.currentThread().getContextClassLoader
+        Class.forName(className, true, loader).newInstance()
+      }
+      val outputDir = new File(tempDir, "output").getAbsolutePath
+      objs.saveAsObjectFile(outputDir)
+      // Try reading the output back as an object file
+      val ct = reflect.ClassTag[Any](Class.forName(className, true, loader))
+      val output = sc.objectFile[Any](outputDir)
+      assert(output.collect().size === 3)
+      assert(output.collect().head.getClass.getName === className)
+    }
+    finally {
+      Thread.currentThread().setContextClassLoader(original)
+    }
+  }
+
   test("write SequenceFile using new Hadoop API") {
     import org.apache.hadoop.mapreduce.lib.output.SequenceFileOutputFormat
     sc = new SparkContext("local", "test")

From 7a0135293192aaefc6ae20b57e15a90945bd8a4e Mon Sep 17 00:00:00 2001
From: Ankur Dave <ankurdave@gmail.com>
Date: Sat, 12 Jul 2014 12:05:34 -0700
Subject: [PATCH 628/810] [SPARK-2455] Mark (Shippable)VertexPartition
 serializable

VertexPartition and ShippableVertexPartition are contained in RDDs but are not marked Serializable, leading to NotSerializableExceptions when using Java serialization.

The fix is simply to mark them as Serializable. This PR does that and adds a test for serializing them using Java and Kryo serialization.

Author: Ankur Dave <ankurdave@gmail.com>

Closes #1376 from ankurdave/SPARK-2455 and squashes the following commits:

ed4a51b [Ankur Dave] Make (Shippable)VertexPartition serializable
1fd42c5 [Ankur Dave] Add failing tests for Java serialization
---
 .../graphx/impl/RoutingTablePartition.scala   |  2 +-
 .../graphx/impl/VertexPartitionBase.scala     |  3 ++-
 .../graphx/impl/VertexPartitionBaseOps.scala  |  2 +-
 .../graphx/impl/EdgePartitionSuite.scala      | 24 +++++++++++--------
 .../graphx/impl/VertexPartitionSuite.scala    | 20 +++++++++++++++-
 5 files changed, 37 insertions(+), 14 deletions(-)

diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/RoutingTablePartition.scala b/graphx/src/main/scala/org/apache/spark/graphx/impl/RoutingTablePartition.scala
index 3827ac8d0fd6..502b112d31c2 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/impl/RoutingTablePartition.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/impl/RoutingTablePartition.scala
@@ -119,7 +119,7 @@ object RoutingTablePartition {
  */
 private[graphx]
 class RoutingTablePartition(
-    private val routingTable: Array[(Array[VertexId], BitSet, BitSet)]) {
+    private val routingTable: Array[(Array[VertexId], BitSet, BitSet)]) extends Serializable {
   /** The maximum number of edge partitions this `RoutingTablePartition` is built to join with. */
   val numEdgePartitions: Int = routingTable.size
 
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexPartitionBase.scala b/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexPartitionBase.scala
index 34939b24440a..5ad6390a56c4 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexPartitionBase.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexPartitionBase.scala
@@ -60,7 +60,8 @@ private[graphx] object VertexPartitionBase {
  * `VertexPartitionBaseOpsConstructor` typeclass (for example,
  * [[VertexPartition.VertexPartitionOpsConstructor]]).
  */
-private[graphx] abstract class VertexPartitionBase[@specialized(Long, Int, Double) VD: ClassTag] {
+private[graphx] abstract class VertexPartitionBase[@specialized(Long, Int, Double) VD: ClassTag]
+  extends Serializable {
 
   def index: VertexIdToIndexMap
   def values: Array[VD]
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexPartitionBaseOps.scala b/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexPartitionBaseOps.scala
index a4f769b29401..b40aa1b417a0 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexPartitionBaseOps.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexPartitionBaseOps.scala
@@ -35,7 +35,7 @@ import org.apache.spark.graphx.util.collection.GraphXPrimitiveKeyOpenHashMap
 private[graphx] abstract class VertexPartitionBaseOps
     [VD: ClassTag, Self[X] <: VertexPartitionBase[X] : VertexPartitionBaseOpsConstructor]
     (self: Self[VD])
-    extends Logging {
+  extends Serializable with Logging {
 
   def withIndex(index: VertexIdToIndexMap): Self[VD]
   def withValues[VD2: ClassTag](values: Array[VD2]): Self[VD2]
diff --git a/graphx/src/test/scala/org/apache/spark/graphx/impl/EdgePartitionSuite.scala b/graphx/src/test/scala/org/apache/spark/graphx/impl/EdgePartitionSuite.scala
index 28fd112f2b12..9d00f76327e4 100644
--- a/graphx/src/test/scala/org/apache/spark/graphx/impl/EdgePartitionSuite.scala
+++ b/graphx/src/test/scala/org/apache/spark/graphx/impl/EdgePartitionSuite.scala
@@ -23,6 +23,7 @@ import scala.util.Random
 import org.scalatest.FunSuite
 
 import org.apache.spark.SparkConf
+import org.apache.spark.serializer.JavaSerializer
 import org.apache.spark.serializer.KryoSerializer
 
 import org.apache.spark.graphx._
@@ -124,18 +125,21 @@ class EdgePartitionSuite extends FunSuite {
     assert(ep.numActives == Some(2))
   }
 
-  test("Kryo serialization") {
+  test("serialization") {
     val aList = List((0, 1, 0), (1, 0, 0), (1, 2, 0), (5, 4, 0), (5, 5, 0))
     val a: EdgePartition[Int, Int] = makeEdgePartition(aList)
-    val conf = new SparkConf()
+    val javaSer = new JavaSerializer(new SparkConf())
+    val kryoSer = new KryoSerializer(new SparkConf()
       .set("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
-      .set("spark.kryo.registrator", "org.apache.spark.graphx.GraphKryoRegistrator")
-    val s = new KryoSerializer(conf).newInstance()
-    val aSer: EdgePartition[Int, Int] = s.deserialize(s.serialize(a))
-    assert(aSer.srcIds.toList === a.srcIds.toList)
-    assert(aSer.dstIds.toList === a.dstIds.toList)
-    assert(aSer.data.toList === a.data.toList)
-    assert(aSer.index != null)
-    assert(aSer.vertices.iterator.toSet === a.vertices.iterator.toSet)
+      .set("spark.kryo.registrator", "org.apache.spark.graphx.GraphKryoRegistrator"))
+
+    for (ser <- List(javaSer, kryoSer); s = ser.newInstance()) {
+      val aSer: EdgePartition[Int, Int] = s.deserialize(s.serialize(a))
+      assert(aSer.srcIds.toList === a.srcIds.toList)
+      assert(aSer.dstIds.toList === a.dstIds.toList)
+      assert(aSer.data.toList === a.data.toList)
+      assert(aSer.index != null)
+      assert(aSer.vertices.iterator.toSet === a.vertices.iterator.toSet)
+    }
   }
 }
diff --git a/graphx/src/test/scala/org/apache/spark/graphx/impl/VertexPartitionSuite.scala b/graphx/src/test/scala/org/apache/spark/graphx/impl/VertexPartitionSuite.scala
index 8bf1384d514c..f9e771a90001 100644
--- a/graphx/src/test/scala/org/apache/spark/graphx/impl/VertexPartitionSuite.scala
+++ b/graphx/src/test/scala/org/apache/spark/graphx/impl/VertexPartitionSuite.scala
@@ -17,9 +17,14 @@
 
 package org.apache.spark.graphx.impl
 
-import org.apache.spark.graphx._
 import org.scalatest.FunSuite
 
+import org.apache.spark.SparkConf
+import org.apache.spark.serializer.JavaSerializer
+import org.apache.spark.serializer.KryoSerializer
+
+import org.apache.spark.graphx._
+
 class VertexPartitionSuite extends FunSuite {
 
   test("isDefined, filter") {
@@ -116,4 +121,17 @@ class VertexPartitionSuite extends FunSuite {
     assert(vp3.index.getPos(2) === -1)
   }
 
+  test("serialization") {
+    val verts = Set((0L, 1), (1L, 1), (2L, 1))
+    val vp = VertexPartition(verts.iterator)
+    val javaSer = new JavaSerializer(new SparkConf())
+    val kryoSer = new KryoSerializer(new SparkConf()
+      .set("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
+      .set("spark.kryo.registrator", "org.apache.spark.graphx.GraphKryoRegistrator"))
+
+    for (ser <- List(javaSer, kryoSer); s = ser.newInstance()) {
+      val vpSer: VertexPartition[Int] = s.deserialize(s.serialize(vp))
+      assert(vpSer.iterator.toSet === verts)
+    }
+  }
 }

From 7e26b57615f6c1d3f9058f9c19c05ec91f017f4c Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Sat, 12 Jul 2014 12:07:27 -0700
Subject: [PATCH 629/810] [SPARK-2441][SQL] Add more efficient distinct
 operator.

Author: Michael Armbrust <michael@databricks.com>

Closes #1366 from marmbrus/partialDistinct and squashes the following commits:

12a31ab [Michael Armbrust] Add more efficient distinct operator.
---
 .../spark/sql/execution/SparkStrategies.scala |  4 +--
 .../spark/sql/execution/basicOperators.scala  | 33 ++++++++++++++++++-
 2 files changed, 34 insertions(+), 3 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
index 7080074a69c0..c078e71fe029 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
@@ -247,8 +247,8 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
 
     def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
       case logical.Distinct(child) =>
-        execution.Aggregate(
-          partial = false, child.output, child.output, planLater(child))(sqlContext) :: Nil
+        execution.Distinct(partial = false,
+          execution.Distinct(partial = true, planLater(child))) :: Nil
       case logical.Sort(sortExprs, child) =>
         // This sort is a global sort. Its requiredDistribution will be an OrderedDistribution.
         execution.Sort(sortExprs, global = true, planLater(child)):: Nil
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
index 97abd636ab5f..966d8f95fc83 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
@@ -27,7 +27,7 @@ import org.apache.spark.sql.SQLContext
 import org.apache.spark.sql.catalyst.ScalaReflection
 import org.apache.spark.sql.catalyst.errors._
 import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.catalyst.plans.physical.{OrderedDistribution, UnspecifiedDistribution}
+import org.apache.spark.sql.catalyst.plans.physical.{ClusteredDistribution, OrderedDistribution, UnspecifiedDistribution}
 import org.apache.spark.util.MutablePair
 
 /**
@@ -248,6 +248,37 @@ object ExistingRdd {
 case class ExistingRdd(output: Seq[Attribute], rdd: RDD[Row]) extends LeafNode {
   override def execute() = rdd
 }
+/**
+ * :: DeveloperApi ::
+ * Computes the set of distinct input rows using a HashSet.
+ * @param partial when true the distinct operation is performed partially, per partition, without
+ *                shuffling the data.
+ * @param child the input query plan.
+ */
+@DeveloperApi
+case class Distinct(partial: Boolean, child: SparkPlan) extends UnaryNode {
+  override def output = child.output
+
+  override def requiredChildDistribution =
+    if (partial) UnspecifiedDistribution :: Nil else ClusteredDistribution(child.output) :: Nil
+
+  override def execute() = {
+    child.execute().mapPartitions { iter =>
+      val hashSet = new scala.collection.mutable.HashSet[Row]()
+
+      var currentRow: Row = null
+      while (iter.hasNext) {
+        currentRow = iter.next()
+        if (!hashSet.contains(currentRow)) {
+          hashSet.add(currentRow.copy())
+        }
+      }
+
+      hashSet.iterator
+    }
+  }
+}
+
 
 /**
  * :: DeveloperApi ::

From 1a7d7cc85fb24de21f1cde67d04467171b82e845 Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Sat, 12 Jul 2014 12:13:32 -0700
Subject: [PATCH 630/810] [SPARK-2405][SQL] Reusue same byte buffers when
 creating new instance of InMemoryRelation

Reuse byte buffers when creating unique attributes for multiple instances of an InMemoryRelation in a single query plan.

Author: Michael Armbrust <michael@databricks.com>

Closes #1332 from marmbrus/doubleCache and squashes the following commits:

4a19609 [Michael Armbrust] Clean up concurrency story by calculating buffersn the constructor.
b39c931 [Michael Armbrust] Allocations are kind of a side effect.
f67eff7 [Michael Armbrust] Reusue same byte buffers when creating new instance of InMemoryRelation
---
 .../analysis/MultiInstanceRelation.scala      |  2 +-
 .../columnar/InMemoryColumnarTableScan.scala  | 35 +++++++++++++------
 2 files changed, 25 insertions(+), 12 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/MultiInstanceRelation.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/MultiInstanceRelation.scala
index a6ce90854dcb..22941edef2d4 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/MultiInstanceRelation.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/MultiInstanceRelation.scala
@@ -30,7 +30,7 @@ import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
  * of itself with globally unique expression ids.
  */
 trait MultiInstanceRelation {
-  def newInstance: this.type
+  def newInstance(): this.type
 }
 
 /**
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala
index e1e4f24c6c66..ff7f664d8b52 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala
@@ -17,6 +17,9 @@
 
 package org.apache.spark.sql.columnar
 
+import java.nio.ByteBuffer
+
+import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.analysis.MultiInstanceRelation
 import org.apache.spark.sql.catalyst.expressions.{GenericMutableRow, Attribute}
 import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
@@ -26,22 +29,19 @@ import org.apache.spark.SparkConf
 
 object InMemoryRelation {
   def apply(useCompression: Boolean, child: SparkPlan): InMemoryRelation =
-    new InMemoryRelation(child.output, useCompression, child)
+    new InMemoryRelation(child.output, useCompression, child)()
 }
 
 private[sql] case class InMemoryRelation(
     output: Seq[Attribute],
     useCompression: Boolean,
     child: SparkPlan)
+    (private var _cachedColumnBuffers: RDD[Array[ByteBuffer]] = null)
   extends LogicalPlan with MultiInstanceRelation {
 
-  override def children = Seq.empty
-  override def references = Set.empty
-
-  override def newInstance() =
-    new InMemoryRelation(output.map(_.newInstance), useCompression, child).asInstanceOf[this.type]
-
-  lazy val cachedColumnBuffers = {
+  // If the cached column buffers were not passed in, we calculate them in the constructor.
+  // As in Spark, the actual work of caching is lazy.
+  if (_cachedColumnBuffers == null) {
     val output = child.output
     val cached = child.execute().mapPartitions { iterator =>
       val columnBuilders = output.map { attribute =>
@@ -62,10 +62,23 @@ private[sql] case class InMemoryRelation(
     }.cache()
 
     cached.setName(child.toString)
-    // Force the materialization of the cached RDD.
-    cached.count()
-    cached
+    _cachedColumnBuffers = cached
   }
+
+
+  override def children = Seq.empty
+
+  override def references = Set.empty
+
+  override def newInstance() = {
+    new InMemoryRelation(
+      output.map(_.newInstance),
+      useCompression,
+      child)(
+      _cachedColumnBuffers).asInstanceOf[this.type]
+  }
+
+  def cachedColumnBuffers = _cachedColumnBuffers
 }
 
 private[sql] case class InMemoryColumnarTableScan(

From 4c8be64e768fe71643b37f1e82f619c8aeac6eff Mon Sep 17 00:00:00 2001
From: Sandy Ryza <sandy@cloudera.com>
Date: Sat, 12 Jul 2014 16:55:15 -0700
Subject: [PATCH 631/810] SPARK-2462.  Make Vector.apply public.

Apologies if there's an already-discussed reason I missed for why this doesn't make sense.

Author: Sandy Ryza <sandy@cloudera.com>

Closes #1389 from sryza/sandy-spark-2462 and squashes the following commits:

2e5e201 [Sandy Ryza] SPARK-2462.  Make Vector.apply public.
---
 .../src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala  | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
index c818a0b9c3e4..77b3e8c71499 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
@@ -62,7 +62,7 @@ trait Vector extends Serializable {
    * Gets the value of the ith element.
    * @param i index
    */
-  private[mllib] def apply(i: Int): Double = toBreeze(i)
+  def apply(i: Int): Double = toBreeze(i)
 }
 
 /**

From 635888cbed0e3f4127252fb84db449f0cc9ed659 Mon Sep 17 00:00:00 2001
From: Sean Owen <sowen@cloudera.com>
Date: Sun, 13 Jul 2014 19:27:43 -0700
Subject: [PATCH 632/810] SPARK-2363. Clean MLlib's sample data files

(Just made a PR for this, mengxr was the reporter of:)

MLlib has sample data under serveral folders:
1) data/mllib
2) data/
3) mllib/data/*
Per previous discussion with Matei Zaharia, we want to put them under `data/mllib` and clean outdated files.

Author: Sean Owen <sowen@cloudera.com>

Closes #1394 from srowen/SPARK-2363 and squashes the following commits:

54313dd [Sean Owen] Move ML example data from /mllib/data/ and /data/ into /data/mllib/
---
 {mllib/data => data/mllib}/als/test.data               | 0
 data/{ => mllib}/kmeans_data.txt                       | 0
 {mllib/data => data/mllib}/lr-data/random.data         | 0
 data/{ => mllib}/lr_data.txt                           | 0
 data/{ => mllib}/pagerank_data.txt                     | 0
 {mllib/data => data/mllib}/ridge-data/lpsa.data        | 0
 {mllib/data => data/mllib}/sample_libsvm_data.txt      | 0
 {mllib/data => data/mllib}/sample_naive_bayes_data.txt | 0
 {mllib/data => data/mllib}/sample_svm_data.txt         | 0
 {mllib/data => data/mllib}/sample_tree_data.csv        | 0
 docs/bagel-programming-guide.md                        | 2 +-
 docs/mllib-basics.md                                   | 6 +++---
 docs/mllib-clustering.md                               | 4 ++--
 docs/mllib-collaborative-filtering.md                  | 4 ++--
 docs/mllib-decision-tree.md                            | 4 ++--
 docs/mllib-linear-methods.md                           | 8 ++++----
 docs/mllib-naive-bayes.md                              | 2 +-
 docs/mllib-optimization.md                             | 2 +-
 18 files changed, 16 insertions(+), 16 deletions(-)
 rename {mllib/data => data/mllib}/als/test.data (100%)
 rename data/{ => mllib}/kmeans_data.txt (100%)
 rename {mllib/data => data/mllib}/lr-data/random.data (100%)
 rename data/{ => mllib}/lr_data.txt (100%)
 rename data/{ => mllib}/pagerank_data.txt (100%)
 rename {mllib/data => data/mllib}/ridge-data/lpsa.data (100%)
 rename {mllib/data => data/mllib}/sample_libsvm_data.txt (100%)
 rename {mllib/data => data/mllib}/sample_naive_bayes_data.txt (100%)
 rename {mllib/data => data/mllib}/sample_svm_data.txt (100%)
 rename {mllib/data => data/mllib}/sample_tree_data.csv (100%)

diff --git a/mllib/data/als/test.data b/data/mllib/als/test.data
similarity index 100%
rename from mllib/data/als/test.data
rename to data/mllib/als/test.data
diff --git a/data/kmeans_data.txt b/data/mllib/kmeans_data.txt
similarity index 100%
rename from data/kmeans_data.txt
rename to data/mllib/kmeans_data.txt
diff --git a/mllib/data/lr-data/random.data b/data/mllib/lr-data/random.data
similarity index 100%
rename from mllib/data/lr-data/random.data
rename to data/mllib/lr-data/random.data
diff --git a/data/lr_data.txt b/data/mllib/lr_data.txt
similarity index 100%
rename from data/lr_data.txt
rename to data/mllib/lr_data.txt
diff --git a/data/pagerank_data.txt b/data/mllib/pagerank_data.txt
similarity index 100%
rename from data/pagerank_data.txt
rename to data/mllib/pagerank_data.txt
diff --git a/mllib/data/ridge-data/lpsa.data b/data/mllib/ridge-data/lpsa.data
similarity index 100%
rename from mllib/data/ridge-data/lpsa.data
rename to data/mllib/ridge-data/lpsa.data
diff --git a/mllib/data/sample_libsvm_data.txt b/data/mllib/sample_libsvm_data.txt
similarity index 100%
rename from mllib/data/sample_libsvm_data.txt
rename to data/mllib/sample_libsvm_data.txt
diff --git a/mllib/data/sample_naive_bayes_data.txt b/data/mllib/sample_naive_bayes_data.txt
similarity index 100%
rename from mllib/data/sample_naive_bayes_data.txt
rename to data/mllib/sample_naive_bayes_data.txt
diff --git a/mllib/data/sample_svm_data.txt b/data/mllib/sample_svm_data.txt
similarity index 100%
rename from mllib/data/sample_svm_data.txt
rename to data/mllib/sample_svm_data.txt
diff --git a/mllib/data/sample_tree_data.csv b/data/mllib/sample_tree_data.csv
similarity index 100%
rename from mllib/data/sample_tree_data.csv
rename to data/mllib/sample_tree_data.csv
diff --git a/docs/bagel-programming-guide.md b/docs/bagel-programming-guide.md
index b280df0c8eeb..7e55131754a3 100644
--- a/docs/bagel-programming-guide.md
+++ b/docs/bagel-programming-guide.md
@@ -46,7 +46,7 @@ import org.apache.spark.bagel.Bagel._
 Next, we load a sample graph from a text file as a distributed dataset and package it into `PRVertex` objects. We also cache the distributed dataset because Bagel will use it multiple times and we'd like to avoid recomputing it.
 
 {% highlight scala %}
-val input = sc.textFile("data/pagerank_data.txt")
+val input = sc.textFile("data/mllib/pagerank_data.txt")
 
 val numVerts = input.count()
 
diff --git a/docs/mllib-basics.md b/docs/mllib-basics.md
index 5796e16e8f99..f9585251fafa 100644
--- a/docs/mllib-basics.md
+++ b/docs/mllib-basics.md
@@ -193,7 +193,7 @@ import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.util.MLUtils
 import org.apache.spark.rdd.RDD
 
-val examples: RDD[LabeledPoint] = MLUtils.loadLibSVMFile(sc, "mllib/data/sample_libsvm_data.txt")
+val examples: RDD[LabeledPoint] = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt")
 {% endhighlight %}
 </div>
 
@@ -207,7 +207,7 @@ import org.apache.spark.mllib.util.MLUtils;
 import org.apache.spark.api.java.JavaRDD;
 
 JavaRDD<LabeledPoint> examples = 
-  MLUtils.loadLibSVMFile(jsc.sc(), "mllib/data/sample_libsvm_data.txt").toJavaRDD();
+  MLUtils.loadLibSVMFile(jsc.sc(), "data/mllib/sample_libsvm_data.txt").toJavaRDD();
 {% endhighlight %}
 </div>
 
@@ -218,7 +218,7 @@ examples stored in LIBSVM format.
 {% highlight python %}
 from pyspark.mllib.util import MLUtils
 
-examples = MLUtils.loadLibSVMFile(sc, "mllib/data/sample_libsvm_data.txt")
+examples = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt")
 {% endhighlight %}
 </div>
 </div>
diff --git a/docs/mllib-clustering.md b/docs/mllib-clustering.md
index 429cdf8d40ce..c76ac010d3f8 100644
--- a/docs/mllib-clustering.md
+++ b/docs/mllib-clustering.md
@@ -51,7 +51,7 @@ import org.apache.spark.mllib.clustering.KMeans
 import org.apache.spark.mllib.linalg.Vectors
 
 // Load and parse the data
-val data = sc.textFile("data/kmeans_data.txt")
+val data = sc.textFile("data/mllib/kmeans_data.txt")
 val parsedData = data.map(s => Vectors.dense(s.split(' ').map(_.toDouble)))
 
 // Cluster the data into two classes using KMeans
@@ -86,7 +86,7 @@ from numpy import array
 from math import sqrt
 
 # Load and parse the data
-data = sc.textFile("data/kmeans_data.txt")
+data = sc.textFile("data/mllib/kmeans_data.txt")
 parsedData = data.map(lambda line: array([float(x) for x in line.split(' ')]))
 
 # Build the model (cluster the data)
diff --git a/docs/mllib-collaborative-filtering.md b/docs/mllib-collaborative-filtering.md
index d51002f01567..5cd71738722a 100644
--- a/docs/mllib-collaborative-filtering.md
+++ b/docs/mllib-collaborative-filtering.md
@@ -58,7 +58,7 @@ import org.apache.spark.mllib.recommendation.ALS
 import org.apache.spark.mllib.recommendation.Rating
 
 // Load and parse the data
-val data = sc.textFile("mllib/data/als/test.data")
+val data = sc.textFile("data/mllib/als/test.data")
 val ratings = data.map(_.split(',') match { case Array(user, item, rate) =>
     Rating(user.toInt, item.toInt, rate.toDouble)
   })
@@ -112,7 +112,7 @@ from pyspark.mllib.recommendation import ALS
 from numpy import array
 
 # Load and parse the data
-data = sc.textFile("mllib/data/als/test.data")
+data = sc.textFile("data/mllib/als/test.data")
 ratings = data.map(lambda line: array([float(x) for x in line.split(',')]))
 
 # Build the recommendation model using Alternating Least Squares
diff --git a/docs/mllib-decision-tree.md b/docs/mllib-decision-tree.md
index 3002a66a4fdb..9cd768599e52 100644
--- a/docs/mllib-decision-tree.md
+++ b/docs/mllib-decision-tree.md
@@ -122,7 +122,7 @@ import org.apache.spark.mllib.tree.configuration.Algo._
 import org.apache.spark.mllib.tree.impurity.Gini
 
 // Load and parse the data file
-val data = sc.textFile("mllib/data/sample_tree_data.csv")
+val data = sc.textFile("data/mllib/sample_tree_data.csv")
 val parsedData = data.map { line =>
   val parts = line.split(',').map(_.toDouble)
   LabeledPoint(parts(0), Vectors.dense(parts.tail))
@@ -161,7 +161,7 @@ import org.apache.spark.mllib.tree.configuration.Algo._
 import org.apache.spark.mllib.tree.impurity.Variance
 
 // Load and parse the data file
-val data = sc.textFile("mllib/data/sample_tree_data.csv")
+val data = sc.textFile("data/mllib/sample_tree_data.csv")
 val parsedData = data.map { line =>
   val parts = line.split(',').map(_.toDouble)
   LabeledPoint(parts(0), Vectors.dense(parts.tail))
diff --git a/docs/mllib-linear-methods.md b/docs/mllib-linear-methods.md
index 4dfbebbcd04b..b4d22e0df5a8 100644
--- a/docs/mllib-linear-methods.md
+++ b/docs/mllib-linear-methods.md
@@ -187,7 +187,7 @@ import org.apache.spark.mllib.linalg.Vectors
 import org.apache.spark.mllib.util.MLUtils
 
 // Load training data in LIBSVM format.
-val data = MLUtils.loadLibSVMFile(sc, "mllib/data/sample_libsvm_data.txt")
+val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt")
 
 // Split data into training (60%) and test (40%).
 val splits = data.randomSplit(Array(0.6, 0.4), seed = 11L)
@@ -259,7 +259,7 @@ def parsePoint(line):
     values = [float(x) for x in line.split(' ')]
     return LabeledPoint(values[0], values[1:])
 
-data = sc.textFile("mllib/data/sample_svm_data.txt")
+data = sc.textFile("data/mllib/sample_svm_data.txt")
 parsedData = data.map(parsePoint)
 
 # Build the model
@@ -309,7 +309,7 @@ import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.linalg.Vectors
 
 // Load and parse the data
-val data = sc.textFile("mllib/data/ridge-data/lpsa.data")
+val data = sc.textFile("data/mllib/ridge-data/lpsa.data")
 val parsedData = data.map { line =>
   val parts = line.split(',')
   LabeledPoint(parts(0).toDouble, Vectors.dense(parts(1).split(' ').map(_.toDouble)))
@@ -356,7 +356,7 @@ def parsePoint(line):
     values = [float(x) for x in line.replace(',', ' ').split(' ')]
     return LabeledPoint(values[0], values[1:])
 
-data = sc.textFile("mllib/data/ridge-data/lpsa.data")
+data = sc.textFile("data/mllib/ridge-data/lpsa.data")
 parsedData = data.map(parsePoint)
 
 # Build the model
diff --git a/docs/mllib-naive-bayes.md b/docs/mllib-naive-bayes.md
index 1d1d7dcf6ffc..b1650c83c98b 100644
--- a/docs/mllib-naive-bayes.md
+++ b/docs/mllib-naive-bayes.md
@@ -40,7 +40,7 @@ import org.apache.spark.mllib.classification.NaiveBayes
 import org.apache.spark.mllib.linalg.Vectors
 import org.apache.spark.mllib.regression.LabeledPoint
 
-val data = sc.textFile("mllib/data/sample_naive_bayes_data.txt")
+val data = sc.textFile("data/mllib/sample_naive_bayes_data.txt")
 val parsedData = data.map { line =>
   val parts = line.split(',')
   LabeledPoint(parts(0).toDouble, Vectors.dense(parts(1).split(' ').map(_.toDouble)))
diff --git a/docs/mllib-optimization.md b/docs/mllib-optimization.md
index ae9ede58e8e6..651958c7812f 100644
--- a/docs/mllib-optimization.md
+++ b/docs/mllib-optimization.md
@@ -214,7 +214,7 @@ import org.apache.spark.mllib.linalg.Vectors
 import org.apache.spark.mllib.util.MLUtils
 import org.apache.spark.mllib.classification.LogisticRegressionModel
 
-val data = MLUtils.loadLibSVMFile(sc, "mllib/data/sample_libsvm_data.txt")
+val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt")
 val numFeatures = data.take(1)(0).features.size
 
 // Split data into training (60%) and test (40%).

From aab5349660109481ee944721d611771da5a93109 Mon Sep 17 00:00:00 2001
From: Prashant Sharma <prashant.s@imaginea.com>
Date: Mon, 14 Jul 2014 00:42:59 -0700
Subject: [PATCH 633/810] Made rdd.py pep8 complaint by using Autopep8 and a
 little manual editing.

Author: Prashant Sharma <prashant.s@imaginea.com>

Closes #1354 from ScrapCodes/pep8-comp-1 and squashes the following commits:

9858ea8 [Prashant Sharma] Code Review
d8851b7 [Prashant Sharma] Found # noqa works even inside comment blocks. Not sure if it works with all versions of python.
10c0cef [Prashant Sharma] Made rdd.py pep8 complaint by using Autopep8 and a little manual tweaking.
---
 python/pyspark/rdd.py | 150 ++++++++++++++++++++++++++----------------
 1 file changed, 92 insertions(+), 58 deletions(-)

diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py
index f64f48e3a4c9..0c35c666805d 100644
--- a/python/pyspark/rdd.py
+++ b/python/pyspark/rdd.py
@@ -69,16 +69,19 @@ def _extract_concise_traceback():
         file, line, fun, what = tb[0]
         return callsite(function=fun, file=file, linenum=line)
     sfile, sline, sfun, swhat = tb[first_spark_frame]
-    ufile, uline, ufun, uwhat = tb[first_spark_frame-1]
+    ufile, uline, ufun, uwhat = tb[first_spark_frame - 1]
     return callsite(function=sfun, file=ufile, linenum=uline)
 
 _spark_stack_depth = 0
 
+
 class _JavaStackTrace(object):
+
     def __init__(self, sc):
         tb = _extract_concise_traceback()
         if tb is not None:
-            self._traceback = "%s at %s:%s" % (tb.function, tb.file, tb.linenum)
+            self._traceback = "%s at %s:%s" % (
+                tb.function, tb.file, tb.linenum)
         else:
             self._traceback = "Error! Could not extract traceback info"
         self._context = sc
@@ -95,7 +98,9 @@ def __exit__(self, type, value, tb):
         if _spark_stack_depth == 0:
             self._context._jsc.setCallSite(None)
 
+
 class MaxHeapQ(object):
+
     """
     An implementation of MaxHeap.
     >>> import pyspark.rdd
@@ -117,14 +122,14 @@ class MaxHeapQ(object):
     """
 
     def __init__(self, maxsize):
-        # we start from q[1], this makes calculating children as trivial as 2 * k
+        # We start from q[1], so its children are always  2 * k
         self.q = [0]
         self.maxsize = maxsize
 
     def _swim(self, k):
-        while (k > 1) and (self.q[k/2] < self.q[k]):
-            self._swap(k, k/2)
-            k = k/2
+        while (k > 1) and (self.q[k / 2] < self.q[k]):
+            self._swap(k, k / 2)
+            k = k / 2
 
     def _swap(self, i, j):
         t = self.q[i]
@@ -162,7 +167,9 @@ def _replaceRoot(self, value):
             self.q[1] = value
             self._sink(1)
 
+
 class RDD(object):
+
     """
     A Resilient Distributed Dataset (RDD), the basic abstraction in Spark.
     Represents an immutable, partitioned collection of elements that can be
@@ -257,7 +264,8 @@ def map(self, f, preservesPartitioning=False):
         >>> sorted(rdd.map(lambda x: (x, 1)).collect())
         [('a', 1), ('b', 1), ('c', 1)]
         """
-        def func(split, iterator): return imap(f, iterator)
+        def func(split, iterator):
+            return imap(f, iterator)
         return PipelinedRDD(self, func, preservesPartitioning)
 
     def flatMap(self, f, preservesPartitioning=False):
@@ -271,7 +279,8 @@ def flatMap(self, f, preservesPartitioning=False):
         >>> sorted(rdd.flatMap(lambda x: [(x, x), (x, x)]).collect())
         [(2, 2), (2, 2), (3, 3), (3, 3), (4, 4), (4, 4)]
         """
-        def func(s, iterator): return chain.from_iterable(imap(f, iterator))
+        def func(s, iterator):
+            return chain.from_iterable(imap(f, iterator))
         return self.mapPartitionsWithIndex(func, preservesPartitioning)
 
     def mapPartitions(self, f, preservesPartitioning=False):
@@ -283,7 +292,8 @@ def mapPartitions(self, f, preservesPartitioning=False):
         >>> rdd.mapPartitions(f).collect()
         [3, 7]
         """
-        def func(s, iterator): return f(iterator)
+        def func(s, iterator):
+            return f(iterator)
         return self.mapPartitionsWithIndex(func)
 
     def mapPartitionsWithIndex(self, f, preservesPartitioning=False):
@@ -311,17 +321,17 @@ def mapPartitionsWithSplit(self, f, preservesPartitioning=False):
         6
         """
         warnings.warn("mapPartitionsWithSplit is deprecated; "
-            "use mapPartitionsWithIndex instead", DeprecationWarning, stacklevel=2)
+                      "use mapPartitionsWithIndex instead", DeprecationWarning, stacklevel=2)
         return self.mapPartitionsWithIndex(f, preservesPartitioning)
 
     def getNumPartitions(self):
-      """
-      Returns the number of partitions in RDD
-      >>> rdd = sc.parallelize([1, 2, 3, 4], 2)
-      >>> rdd.getNumPartitions()
-      2
-      """
-      return self._jrdd.partitions().size()
+        """
+        Returns the number of partitions in RDD
+        >>> rdd = sc.parallelize([1, 2, 3, 4], 2)
+        >>> rdd.getNumPartitions()
+        2
+        """
+        return self._jrdd.partitions().size()
 
     def filter(self, f):
         """
@@ -331,7 +341,8 @@ def filter(self, f):
         >>> rdd.filter(lambda x: x % 2 == 0).collect()
         [2, 4]
         """
-        def func(iterator): return ifilter(f, iterator)
+        def func(iterator):
+            return ifilter(f, iterator)
         return self.mapPartitions(func)
 
     def distinct(self):
@@ -391,9 +402,11 @@ def takeSample(self, withReplacement, num, seed=None):
 
         maxSampleSize = sys.maxint - int(numStDev * sqrt(sys.maxint))
         if num > maxSampleSize:
-            raise ValueError("Sample size cannot be greater than %d." % maxSampleSize)
+            raise ValueError(
+                "Sample size cannot be greater than %d." % maxSampleSize)
 
-        fraction = RDD._computeFractionForSampleSize(num, initialCount, withReplacement)
+        fraction = RDD._computeFractionForSampleSize(
+            num, initialCount, withReplacement)
         samples = self.sample(withReplacement, fraction, seed).collect()
 
         # If the first sample didn't turn out large enough, keep trying to take samples;
@@ -499,17 +512,17 @@ def __add__(self, other):
             raise TypeError
         return self.union(other)
 
-    def sortByKey(self, ascending=True, numPartitions=None, keyfunc = lambda x: x):
+    def sortByKey(self, ascending=True, numPartitions=None, keyfunc=lambda x: x):
         """
         Sorts this RDD, which is assumed to consist of (key, value) pairs.
-
+        # noqa
         >>> tmp = [('a', 1), ('b', 2), ('1', 3), ('d', 4), ('2', 5)]
         >>> sc.parallelize(tmp).sortByKey(True, 2).collect()
         [('1', 3), ('2', 5), ('a', 1), ('b', 2), ('d', 4)]
         >>> tmp2 = [('Mary', 1), ('had', 2), ('a', 3), ('little', 4), ('lamb', 5)]
         >>> tmp2.extend([('whose', 6), ('fleece', 7), ('was', 8), ('white', 9)])
         >>> sc.parallelize(tmp2).sortByKey(True, 3, keyfunc=lambda k: k.lower()).collect()
-        [('a', 3), ('fleece', 7), ('had', 2), ('lamb', 5), ('little', 4), ('Mary', 1), ('was', 8), ('white', 9), ('whose', 6)]
+        [('a', 3), ('fleece', 7), ('had', 2), ('lamb', 5),...('white', 9), ('whose', 6)]
         """
         if numPartitions is None:
             numPartitions = self._defaultReducePartitions()
@@ -521,10 +534,12 @@ def sortByKey(self, ascending=True, numPartitions=None, keyfunc = lambda x: x):
         # number of (key, value) pairs falling into them
         if numPartitions > 1:
             rddSize = self.count()
-            maxSampleSize = numPartitions * 20.0 # constant from Spark's RangePartitioner
+            # constant from Spark's RangePartitioner
+            maxSampleSize = numPartitions * 20.0
             fraction = min(maxSampleSize / max(rddSize, 1), 1.0)
 
-            samples = self.sample(False, fraction, 1).map(lambda (k, v): k).collect()
+            samples = self.sample(False, fraction, 1).map(
+                lambda (k, v): k).collect()
             samples = sorted(samples, reverse=(not ascending), key=keyfunc)
 
             # we have numPartitions many parts but one of the them has
@@ -540,13 +555,13 @@ def rangePartitionFunc(k):
             if ascending:
                 return p
             else:
-                return numPartitions-1-p
+                return numPartitions - 1 - p
 
         def mapFunc(iterator):
             yield sorted(iterator, reverse=(not ascending), key=lambda (k, v): keyfunc(k))
 
         return (self.partitionBy(numPartitions, partitionFunc=rangePartitionFunc)
-                    .mapPartitions(mapFunc,preservesPartitioning=True)
+                    .mapPartitions(mapFunc, preservesPartitioning=True)
                     .flatMap(lambda x: x, preservesPartitioning=True))
 
     def sortBy(self, keyfunc, ascending=True, numPartitions=None):
@@ -570,7 +585,8 @@ def glom(self):
         >>> sorted(rdd.glom().collect())
         [[1, 2], [3, 4]]
         """
-        def func(iterator): yield list(iterator)
+        def func(iterator):
+            yield list(iterator)
         return self.mapPartitions(func)
 
     def cartesian(self, other):
@@ -607,7 +623,9 @@ def pipe(self, command, env={}):
         ['1', '2', '', '3']
         """
         def func(iterator):
-            pipe = Popen(shlex.split(command), env=env, stdin=PIPE, stdout=PIPE)
+            pipe = Popen(
+                shlex.split(command), env=env, stdin=PIPE, stdout=PIPE)
+
             def pipe_objs(out):
                 for obj in iterator:
                     out.write(str(obj).rstrip('\n') + '\n')
@@ -646,7 +664,7 @@ def collect(self):
         Return a list that contains all of the elements in this RDD.
         """
         with _JavaStackTrace(self.context) as st:
-          bytesInJava = self._jrdd.collect().iterator()
+            bytesInJava = self._jrdd.collect().iterator()
         return list(self._collect_iterator_through_file(bytesInJava))
 
     def _collect_iterator_through_file(self, iterator):
@@ -736,7 +754,6 @@ def func(iterator):
 
         return self.mapPartitions(func).fold(zeroValue, combOp)
 
-
     def max(self):
         """
         Find the maximum item in this RDD.
@@ -844,6 +861,7 @@ def countPartition(iterator):
             for obj in iterator:
                 counts[obj] += 1
             yield counts
+
         def mergeMaps(m1, m2):
             for (k, v) in m2.iteritems():
                 m1[k] += v
@@ -888,22 +906,22 @@ def takeOrdered(self, num, key=None):
         def topNKeyedElems(iterator, key_=None):
             q = MaxHeapQ(num)
             for k in iterator:
-                if key_ != None:
+                if key_ is not None:
                     k = (key_(k), k)
                 q.insert(k)
             yield q.getElements()
 
         def unKey(x, key_=None):
-            if key_ != None:
+            if key_ is not None:
                 x = [i[1] for i in x]
             return x
 
         def merge(a, b):
             return next(topNKeyedElems(a + b))
-        result = self.mapPartitions(lambda i: topNKeyedElems(i, key)).reduce(merge)
+        result = self.mapPartitions(
+            lambda i: topNKeyedElems(i, key)).reduce(merge)
         return sorted(unKey(result, key), key=key)
 
-
     def take(self, num):
         """
         Take the first num elements of the RDD.
@@ -947,7 +965,8 @@ def takeUpToNumLeft(iterator):
                     yield next(iterator)
                     taken += 1
 
-            p = range(partsScanned, min(partsScanned + numPartsToTry, totalParts))
+            p = range(
+                partsScanned, min(partsScanned + numPartsToTry, totalParts))
             res = self.context.runJob(self, takeUpToNumLeft, p, True)
 
             items += res
@@ -977,7 +996,7 @@ def saveAsPickleFile(self, path, batchSize=10):
         [1, 2, 'rdd', 'spark']
         """
         self._reserialize(BatchedSerializer(PickleSerializer(),
-                                batchSize))._jrdd.saveAsObjectFile(path)
+                                            batchSize))._jrdd.saveAsObjectFile(path)
 
     def saveAsTextFile(self, path):
         """
@@ -1075,6 +1094,7 @@ def reducePartition(iterator):
             for (k, v) in iterator:
                 m[k] = v if k not in m else func(m[k], v)
             yield m
+
         def mergeMaps(m1, m2):
             for (k, v) in m2.iteritems():
                 m1[k] = v if k not in m1 else func(m1[k], v)
@@ -1162,6 +1182,7 @@ def partitionBy(self, numPartitions, partitionFunc=None):
         # form the hash buckets in Python, transferring O(numPartitions) objects
         # to Java.  Each object is a (splitNumber, [objects]) pair.
         outputSerializer = self.ctx._unbatched_serializer
+
         def add_shuffle_key(split, iterator):
 
             buckets = defaultdict(list)
@@ -1174,7 +1195,8 @@ def add_shuffle_key(split, iterator):
         keyed = PipelinedRDD(self, add_shuffle_key)
         keyed._bypass_serializer = True
         with _JavaStackTrace(self.context) as st:
-            pairRDD = self.ctx._jvm.PairwiseRDD(keyed._jrdd.rdd()).asJavaPairRDD()
+            pairRDD = self.ctx._jvm.PairwiseRDD(
+                keyed._jrdd.rdd()).asJavaPairRDD()
             partitioner = self.ctx._jvm.PythonPartitioner(numPartitions,
                                                           id(partitionFunc))
         jrdd = pairRDD.partitionBy(partitioner).values()
@@ -1213,6 +1235,7 @@ def combineByKey(self, createCombiner, mergeValue, mergeCombiners,
         """
         if numPartitions is None:
             numPartitions = self._defaultReducePartitions()
+
         def combineLocally(iterator):
             combiners = {}
             for x in iterator:
@@ -1224,10 +1247,11 @@ def combineLocally(iterator):
             return combiners.iteritems()
         locally_combined = self.mapPartitions(combineLocally)
         shuffled = locally_combined.partitionBy(numPartitions)
+
         def _mergeCombiners(iterator):
             combiners = {}
             for (k, v) in iterator:
-                if not k in combiners:
+                if k not in combiners:
                     combiners[k] = v
                 else:
                     combiners[k] = mergeCombiners(combiners[k], v)
@@ -1236,17 +1260,19 @@ def _mergeCombiners(iterator):
 
     def aggregateByKey(self, zeroValue, seqFunc, combFunc, numPartitions=None):
         """
-        Aggregate the values of each key, using given combine functions and a neutral "zero value".
-        This function can return a different result type, U, than the type of the values in this RDD,
-        V. Thus, we need one operation for merging a V into a U and one operation for merging two U's,
-        The former operation is used for merging values within a partition, and the latter is used
-        for merging values between partitions. To avoid memory allocation, both of these functions are
+        Aggregate the values of each key, using given combine functions and a neutral
+        "zero value". This function can return a different result type, U, than the type
+        of the values in this RDD, V. Thus, we need one operation for merging a V into
+        a U and one operation for merging two U's, The former operation is used for merging
+        values within a partition, and the latter is used for merging values between
+        partitions. To avoid memory allocation, both of these functions are
         allowed to modify and return their first argument instead of creating a new U.
         """
         def createZero():
-          return copy.deepcopy(zeroValue)
+            return copy.deepcopy(zeroValue)
 
-        return self.combineByKey(lambda v: seqFunc(createZero(), v), seqFunc, combFunc, numPartitions)
+        return self.combineByKey(
+            lambda v: seqFunc(createZero(), v), seqFunc, combFunc, numPartitions)
 
     def foldByKey(self, zeroValue, func, numPartitions=None):
         """
@@ -1261,11 +1287,10 @@ def foldByKey(self, zeroValue, func, numPartitions=None):
         [('a', 2), ('b', 1)]
         """
         def createZero():
-          return copy.deepcopy(zeroValue)
+            return copy.deepcopy(zeroValue)
 
         return self.combineByKey(lambda v: func(createZero(), v), func, func, numPartitions)
 
-
     # TODO: support variant with custom partitioner
     def groupByKey(self, numPartitions=None):
         """
@@ -1292,7 +1317,7 @@ def mergeCombiners(a, b):
             return a + b
 
         return self.combineByKey(createCombiner, mergeValue, mergeCombiners,
-                numPartitions).mapValues(lambda x: ResultIterable(x))
+                                 numPartitions).mapValues(lambda x: ResultIterable(x))
 
     # TODO: add tests
     def flatMapValues(self, f):
@@ -1362,7 +1387,8 @@ def subtractByKey(self, other, numPartitions=None):
         >>> sorted(x.subtractByKey(y).collect())
         [('b', 4), ('b', 5)]
         """
-        filter_func = lambda (key, vals): len(vals[0]) > 0 and len(vals[1]) == 0
+        def filter_func((key, vals)):
+            return len(vals[0]) > 0 and len(vals[1]) == 0
         map_func = lambda (key, vals): [(key, val) for val in vals[0]]
         return self.cogroup(other, numPartitions).filter(filter_func).flatMap(map_func)
 
@@ -1375,8 +1401,9 @@ def subtract(self, other, numPartitions=None):
         >>> sorted(x.subtract(y).collect())
         [('a', 1), ('b', 4), ('b', 5)]
         """
-        rdd = other.map(lambda x: (x, True)) # note: here 'True' is just a placeholder
-        return self.map(lambda x: (x, True)).subtractByKey(rdd).map(lambda tpl: tpl[0]) # note: here 'True' is just a placeholder
+        # note: here 'True' is just a placeholder
+        rdd = other.map(lambda x: (x, True))
+        return self.map(lambda x: (x, True)).subtractByKey(rdd).map(lambda tpl: tpl[0])
 
     def keyBy(self, f):
         """
@@ -1434,7 +1461,7 @@ def zip(self, other):
         """
         pairRDD = self._jrdd.zip(other._jrdd)
         deserializer = PairDeserializer(self._jrdd_deserializer,
-                                             other._jrdd_deserializer)
+                                        other._jrdd_deserializer)
         return RDD(pairRDD, self.ctx, deserializer)
 
     def name(self):
@@ -1503,7 +1530,9 @@ def _defaultReducePartitions(self):
     # keys in the pairs.  This could be an expensive operation, since those
     # hashes aren't retained.
 
+
 class PipelinedRDD(RDD):
+
     """
     Pipelined maps:
     >>> rdd = sc.parallelize([1, 2, 3, 4])
@@ -1519,6 +1548,7 @@ class PipelinedRDD(RDD):
     >>> rdd.flatMap(lambda x: [x, x]).reduce(add)
     20
     """
+
     def __init__(self, prev, func, preservesPartitioning=False):
         if not isinstance(prev, PipelinedRDD) or not prev._is_pipelinable():
             # This transformation is the first in its stage:
@@ -1528,6 +1558,7 @@ def __init__(self, prev, func, preservesPartitioning=False):
             self._prev_jrdd_deserializer = prev._jrdd_deserializer
         else:
             prev_func = prev.func
+
             def pipeline_func(split, iterator):
                 return func(split, prev_func(split, iterator))
             self.func = pipeline_func
@@ -1560,11 +1591,13 @@ def _jrdd(self):
         env = MapConverter().convert(self.ctx.environment,
                                      self.ctx._gateway._gateway_client)
         includes = ListConverter().convert(self.ctx._python_includes,
-                                     self.ctx._gateway._gateway_client)
+                                           self.ctx._gateway._gateway_client)
         python_rdd = self.ctx._jvm.PythonRDD(self._prev_jrdd.rdd(),
-            bytearray(pickled_command), env, includes, self.preservesPartitioning,
-            self.ctx.pythonExec, broadcast_vars, self.ctx._javaAccumulator,
-            class_tag)
+                                             bytearray(pickled_command),
+                                             env, includes, self.preservesPartitioning,
+                                             self.ctx.pythonExec,
+                                             broadcast_vars, self.ctx._javaAccumulator,
+                                             class_tag)
         self._jrdd_val = python_rdd.asJavaRDD()
         return self._jrdd_val
 
@@ -1579,7 +1612,8 @@ def _test():
     # The small batch size here ensures that we see multiple batches,
     # even in these small test examples:
     globs['sc'] = SparkContext('local[4]', 'PythonTest', batchSize=2)
-    (failure_count, test_count) = doctest.testmod(globs=globs,optionflags=doctest.ELLIPSIS)
+    (failure_count, test_count) = doctest.testmod(
+        globs=globs, optionflags=doctest.ELLIPSIS)
     globs['sc'].stop()
     if failure_count:
         exit(-1)

From 38ccd6ebd412cfbf82ae9d8a0998ff697db11455 Mon Sep 17 00:00:00 2001
From: Daoyuan <daoyuan.wang@intel.com>
Date: Mon, 14 Jul 2014 10:40:44 -0700
Subject: [PATCH 634/810] move some test file to match src code

Just move some test suite to corresponding package

Author: Daoyuan <daoyuan.wang@intel.com>

Closes #1401 from adrian-wang/movetestfiles and squashes the following commits:

d1a6803 [Daoyuan] move some test file to match src code
---
 .../spark/{ => broadcast}/BroadcastSuite.scala     |  8 +++-----
 .../{ => network}/ConnectionManagerSuite.scala     | 11 +++++------
 .../org/apache/spark/{ => rdd}/PipedRDDSuite.scala | 14 +++++---------
 .../spark/{ => rdd}/ZippedPartitionsSuite.scala    |  3 ++-
 .../apache/spark/{ => util}/AkkaUtilsSuite.scala   |  8 ++++----
 5 files changed, 19 insertions(+), 25 deletions(-)
 rename core/src/test/scala/org/apache/spark/{ => broadcast}/BroadcastSuite.scala (98%)
 rename core/src/test/scala/org/apache/spark/{ => network}/ConnectionManagerSuite.scala (97%)
 rename core/src/test/scala/org/apache/spark/{ => rdd}/PipedRDDSuite.scala (95%)
 rename core/src/test/scala/org/apache/spark/{ => rdd}/ZippedPartitionsSuite.scala (95%)
 rename core/src/test/scala/org/apache/spark/{ => util}/AkkaUtilsSuite.scala (99%)

diff --git a/core/src/test/scala/org/apache/spark/BroadcastSuite.scala b/core/src/test/scala/org/apache/spark/broadcast/BroadcastSuite.scala
similarity index 98%
rename from core/src/test/scala/org/apache/spark/BroadcastSuite.scala
rename to core/src/test/scala/org/apache/spark/broadcast/BroadcastSuite.scala
index c9936256a5b9..7c3d0208b195 100644
--- a/core/src/test/scala/org/apache/spark/BroadcastSuite.scala
+++ b/core/src/test/scala/org/apache/spark/broadcast/BroadcastSuite.scala
@@ -15,14 +15,12 @@
  * limitations under the License.
  */
 
-package org.apache.spark
+package org.apache.spark.broadcast
 
+import org.apache.spark.storage.{BroadcastBlockId, _}
+import org.apache.spark.{LocalSparkContext, SparkConf, SparkContext, SparkException}
 import org.scalatest.FunSuite
 
-import org.apache.spark.storage._
-import org.apache.spark.broadcast.{Broadcast, HttpBroadcast}
-import org.apache.spark.storage.BroadcastBlockId
-
 class BroadcastSuite extends FunSuite with LocalSparkContext {
 
   private val httpConf = broadcastConf("HttpBroadcastFactory")
diff --git a/core/src/test/scala/org/apache/spark/ConnectionManagerSuite.scala b/core/src/test/scala/org/apache/spark/network/ConnectionManagerSuite.scala
similarity index 97%
rename from core/src/test/scala/org/apache/spark/ConnectionManagerSuite.scala
rename to core/src/test/scala/org/apache/spark/network/ConnectionManagerSuite.scala
index df6b2604c8d8..415ad8c432c1 100644
--- a/core/src/test/scala/org/apache/spark/ConnectionManagerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/network/ConnectionManagerSuite.scala
@@ -15,15 +15,14 @@
  * limitations under the License.
  */
 
-package org.apache.spark
-
-import org.scalatest.FunSuite
+package org.apache.spark.network
 
 import java.nio._
 
-import org.apache.spark.network.{ConnectionManager, Message, ConnectionManagerId}
-import scala.concurrent.Await
-import scala.concurrent.TimeoutException
+import org.apache.spark.{SecurityManager, SparkConf}
+import org.scalatest.FunSuite
+
+import scala.concurrent.{Await, TimeoutException}
 import scala.concurrent.duration._
 import scala.language.postfixOps
 
diff --git a/core/src/test/scala/org/apache/spark/PipedRDDSuite.scala b/core/src/test/scala/org/apache/spark/rdd/PipedRDDSuite.scala
similarity index 95%
rename from core/src/test/scala/org/apache/spark/PipedRDDSuite.scala
rename to core/src/test/scala/org/apache/spark/rdd/PipedRDDSuite.scala
index db56a4acdd6f..be972c5e97a7 100644
--- a/core/src/test/scala/org/apache/spark/PipedRDDSuite.scala
+++ b/core/src/test/scala/org/apache/spark/rdd/PipedRDDSuite.scala
@@ -15,25 +15,21 @@
  * limitations under the License.
  */
 
-package org.apache.spark
+package org.apache.spark.rdd
 
 import java.io.File
 
-import org.scalatest.FunSuite
-
-import org.apache.spark.rdd.{HadoopRDD, PipedRDD, HadoopPartition}
-import org.apache.hadoop.mapred.{JobConf, TextInputFormat, FileSplit}
 import org.apache.hadoop.fs.Path
+import org.apache.hadoop.io.{LongWritable, Text}
+import org.apache.hadoop.mapred.{FileSplit, JobConf, TextInputFormat}
+import org.apache.spark._
+import org.scalatest.FunSuite
 
 import scala.collection.Map
 import scala.language.postfixOps
 import scala.sys.process._
 import scala.util.Try
 
-import org.apache.hadoop.io.{Text, LongWritable}
-
-import org.apache.spark.executor.TaskMetrics
-
 class PipedRDDSuite extends FunSuite with SharedSparkContext {
 
   test("basic pipe") {
diff --git a/core/src/test/scala/org/apache/spark/ZippedPartitionsSuite.scala b/core/src/test/scala/org/apache/spark/rdd/ZippedPartitionsSuite.scala
similarity index 95%
rename from core/src/test/scala/org/apache/spark/ZippedPartitionsSuite.scala
rename to core/src/test/scala/org/apache/spark/rdd/ZippedPartitionsSuite.scala
index 4f87fd8654c4..72596e86865b 100644
--- a/core/src/test/scala/org/apache/spark/ZippedPartitionsSuite.scala
+++ b/core/src/test/scala/org/apache/spark/rdd/ZippedPartitionsSuite.scala
@@ -15,8 +15,9 @@
  * limitations under the License.
  */
 
-package org.apache.spark
+package org.apache.spark.rdd
 
+import org.apache.spark.SharedSparkContext
 import org.scalatest.FunSuite
 
 object ZippedPartitionsSuite {
diff --git a/core/src/test/scala/org/apache/spark/AkkaUtilsSuite.scala b/core/src/test/scala/org/apache/spark/util/AkkaUtilsSuite.scala
similarity index 99%
rename from core/src/test/scala/org/apache/spark/AkkaUtilsSuite.scala
rename to core/src/test/scala/org/apache/spark/util/AkkaUtilsSuite.scala
index 4ab870e75177..c4765e53de17 100644
--- a/core/src/test/scala/org/apache/spark/AkkaUtilsSuite.scala
+++ b/core/src/test/scala/org/apache/spark/util/AkkaUtilsSuite.scala
@@ -15,14 +15,14 @@
  * limitations under the License.
  */
 
-package org.apache.spark
-
-import org.scalatest.FunSuite
+package org.apache.spark.util
 
 import akka.actor._
+import org.apache.spark._
 import org.apache.spark.scheduler.MapStatus
 import org.apache.spark.storage.BlockManagerId
-import org.apache.spark.util.AkkaUtils
+import org.scalatest.FunSuite
+
 import scala.concurrent.Await
 
 /**

From d60b09bb60cff106fa0acddebf35714503b20f03 Mon Sep 17 00:00:00 2001
From: Zongheng Yang <zongheng.y@gmail.com>
Date: Mon, 14 Jul 2014 13:22:24 -0700
Subject: [PATCH 635/810] [SPARK-2443][SQL] Fix slow read from partitioned
 tables

This fix obtains a comparable performance boost as [PR #1390](https://github.com/apache/spark/pull/1390) by moving an array update and deserializer initialization out of a potentially very long loop. Suggested by yhuai. The below results are updated for this fix.

## Benchmarks
Generated a local text file with 10M rows of simple key-value pairs. The data is loaded as a table through Hive. Results are obtained on my local machine using hive/console.

Without the fix:

Type | Non-partitioned | Partitioned (1 part)
------------ | ------------ | -------------
First run | 9.52s end-to-end (1.64s Spark job) | 36.6s (28.3s)
Stablized runs | 1.21s (1.18s) | 27.6s (27.5s)

With this fix:

Type | Non-partitioned | Partitioned (1 part)
------------ | ------------ | -------------
First run | 9.57s (1.46s) | 11.0s (1.69s)
Stablized runs | 1.13s (1.10s) | 1.23s (1.19s)

Author: Zongheng Yang <zongheng.y@gmail.com>

Closes #1408 from concretevitamin/slow-read-2 and squashes the following commits:

d86e437 [Zongheng Yang] Move update & initialization out of potentially long loop.
---
 .../scala/org/apache/spark/sql/hive/TableReader.scala  | 10 +++++++---
 1 file changed, 7 insertions(+), 3 deletions(-)

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/TableReader.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/TableReader.scala
index 8cfde46186ca..c3942578d6b5 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/TableReader.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/TableReader.scala
@@ -164,13 +164,17 @@ class HadoopTableReader(@transient _tableDesc: TableDesc, @transient sc: HiveCon
       hivePartitionRDD.mapPartitions { iter =>
         val hconf = broadcastedHiveConf.value.value
         val rowWithPartArr = new Array[Object](2)
+
+        // The update and deserializer initialization are intentionally
+        // kept out of the below iter.map loop to save performance.
+        rowWithPartArr.update(1, partValues)
+        val deserializer = localDeserializer.newInstance()
+        deserializer.initialize(hconf, partProps)
+
         // Map each tuple to a row object
         iter.map { value =>
-          val deserializer = localDeserializer.newInstance()
-          deserializer.initialize(hconf, partProps)
           val deserializedRow = deserializer.deserialize(value)
           rowWithPartArr.update(0, deserializedRow)
-          rowWithPartArr.update(1, partValues)
           rowWithPartArr.asInstanceOf[Object]
         }
       }

From 3dd8af7a6623201c28231f4b71f59ea4e9ae29bf Mon Sep 17 00:00:00 2001
From: li-zhihui <zhihui.li@intel.com>
Date: Mon, 14 Jul 2014 15:32:49 -0500
Subject: [PATCH 636/810] [SPARK-1946] Submit tasks after (configured ratio)
 executors have been registered

Because submitting tasks and registering executors are asynchronous, in most situation, early stages' tasks run without preferred locality.

A simple solution is sleeping few seconds in application, so that executors have enough time to register.

The PR add 2 configuration properties to make TaskScheduler submit tasks after a few of executors have been registered.

\# Submit tasks only after (registered executors / total executors) arrived the ratio, default value is 0
spark.scheduler.minRegisteredExecutorsRatio = 0.8

\# Whatever minRegisteredExecutorsRatio is arrived, submit tasks after the maxRegisteredWaitingTime(millisecond), default value is 30000
spark.scheduler.maxRegisteredExecutorsWaitingTime = 5000

Author: li-zhihui <zhihui.li@intel.com>

Closes #900 from li-zhihui/master and squashes the following commits:

b9f8326 [li-zhihui] Add logs & edit docs
1ac08b1 [li-zhihui] Add new configs to user docs
22ead12 [li-zhihui] Move waitBackendReady to postStartHook
c6f0522 [li-zhihui] Bug fix: numExecutors wasn't set & use constant DEFAULT_NUMBER_EXECUTORS
4d6d847 [li-zhihui] Move waitBackendReady to TaskSchedulerImpl.start & some code refactor
0ecee9a [li-zhihui] Move waitBackendReady from DAGScheduler.submitStage to TaskSchedulerImpl.submitTasks
4261454 [li-zhihui] Add docs for new configs & code style
ce0868a [li-zhihui] Code style, rename configuration property name of minRegisteredRatio & maxRegisteredWaitingTime
6cfb9ec [li-zhihui] Code style, revert default minRegisteredRatio of yarn to 0, driver get --num-executors in yarn/alpha
812c33c [li-zhihui] Fix driver lost --num-executors option in yarn-cluster mode
e7b6272 [li-zhihui] support yarn-cluster
37f7dc2 [li-zhihui] support yarn mode(percentage style)
3f8c941 [li-zhihui] submit stage after (configured ratio of) executors have been registered
---
 .../scala/org/apache/spark/SparkContext.scala | 11 ++++-
 .../spark/scheduler/SchedulerBackend.scala    |  1 +
 .../spark/scheduler/TaskSchedulerImpl.scala   | 15 +++++++
 .../CoarseGrainedSchedulerBackend.scala       | 29 ++++++++++++++
 .../cluster/SparkDeploySchedulerBackend.scala |  1 +
 docs/configuration.md                         | 19 +++++++++
 .../spark/deploy/yarn/ApplicationMaster.scala |  1 +
 .../yarn/ApplicationMasterArguments.scala     |  6 ++-
 .../cluster/YarnClientClusterScheduler.scala  |  2 +
 .../cluster/YarnClientSchedulerBackend.scala  |  1 +
 .../cluster/YarnClusterScheduler.scala        |  2 +
 .../cluster/YarnClusterSchedulerBackend.scala | 40 +++++++++++++++++++
 .../spark/deploy/yarn/ApplicationMaster.scala |  1 +
 13 files changed, 127 insertions(+), 2 deletions(-)
 create mode 100644 yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClusterSchedulerBackend.scala

diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index 8819e73d17fb..8052499ab752 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -1531,7 +1531,16 @@ object SparkContext extends Logging {
             throw new SparkException("YARN mode not available ?", e)
           }
         }
-        val backend = new CoarseGrainedSchedulerBackend(scheduler, sc.env.actorSystem)
+        val backend = try {
+          val clazz =
+            Class.forName("org.apache.spark.scheduler.cluster.YarnClusterSchedulerBackend")
+          val cons = clazz.getConstructor(classOf[TaskSchedulerImpl], classOf[SparkContext])
+          cons.newInstance(scheduler, sc).asInstanceOf[CoarseGrainedSchedulerBackend]
+        } catch {
+          case e: Exception => {
+            throw new SparkException("YARN mode not available ?", e)
+          }
+        }
         scheduler.initialize(backend)
         scheduler
 
diff --git a/core/src/main/scala/org/apache/spark/scheduler/SchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/SchedulerBackend.scala
index 6a6d8e609bc3..e41e0a984169 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/SchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/SchedulerBackend.scala
@@ -30,4 +30,5 @@ private[spark] trait SchedulerBackend {
 
   def killTask(taskId: Long, executorId: String, interruptThread: Boolean): Unit =
     throw new UnsupportedOperationException
+  def isReady(): Boolean = true
 }
diff --git a/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala b/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala
index 5ed2803d76af..4b6d6da5a6e6 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala
@@ -145,6 +145,10 @@ private[spark] class TaskSchedulerImpl(
     }
   }
 
+  override def postStartHook() {
+    waitBackendReady()
+  }
+
   override def submitTasks(taskSet: TaskSet) {
     val tasks = taskSet.tasks
     logInfo("Adding task set " + taskSet.id + " with " + tasks.length + " tasks")
@@ -437,6 +441,17 @@ private[spark] class TaskSchedulerImpl(
 
   // By default, rack is unknown
   def getRackForHost(value: String): Option[String] = None
+
+  private def waitBackendReady(): Unit = {
+    if (backend.isReady) {
+      return
+    }
+    while (!backend.isReady) {
+      synchronized {
+        this.wait(100)
+      }
+    }
+  }
 }
 
 
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala
index 05d01b0c821f..0f5545e2ed65 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala
@@ -46,9 +46,19 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, actorSystem: A
 {
   // Use an atomic variable to track total number of cores in the cluster for simplicity and speed
   var totalCoreCount = new AtomicInteger(0)
+  var totalExpectedExecutors = new AtomicInteger(0)
   val conf = scheduler.sc.conf
   private val timeout = AkkaUtils.askTimeout(conf)
   private val akkaFrameSize = AkkaUtils.maxFrameSizeBytes(conf)
+  // Submit tasks only after (registered executors / total expected executors) 
+  // is equal to at least this value, that is double between 0 and 1.
+  var minRegisteredRatio = conf.getDouble("spark.scheduler.minRegisteredExecutorsRatio", 0)
+  if (minRegisteredRatio > 1) minRegisteredRatio = 1
+  // Whatever minRegisteredExecutorsRatio is arrived, submit tasks after the time(milliseconds).
+  val maxRegisteredWaitingTime =
+    conf.getInt("spark.scheduler.maxRegisteredExecutorsWaitingTime", 30000)
+  val createTime = System.currentTimeMillis()
+  var ready = if (minRegisteredRatio <= 0) true else false
 
   class DriverActor(sparkProperties: Seq[(String, String)]) extends Actor {
     private val executorActor = new HashMap[String, ActorRef]
@@ -83,6 +93,12 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, actorSystem: A
           executorAddress(executorId) = sender.path.address
           addressToExecutorId(sender.path.address) = executorId
           totalCoreCount.addAndGet(cores)
+          if (executorActor.size >= totalExpectedExecutors.get() * minRegisteredRatio && !ready) {
+            ready = true
+            logInfo("SchedulerBackend is ready for scheduling beginning, registered executors: " +
+              executorActor.size + ", total expected executors: " + totalExpectedExecutors.get() +
+              ", minRegisteredExecutorsRatio: " + minRegisteredRatio)
+          }
           makeOffers()
         }
 
@@ -247,6 +263,19 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, actorSystem: A
         throw new SparkException("Error notifying standalone scheduler's driver actor", e)
     }
   }
+
+  override def isReady(): Boolean = {
+    if (ready) {
+      return true
+    }
+    if ((System.currentTimeMillis() - createTime) >= maxRegisteredWaitingTime) {
+      ready = true
+      logInfo("SchedulerBackend is ready for scheduling beginning after waiting " +
+        "maxRegisteredExecutorsWaitingTime: " + maxRegisteredWaitingTime)
+      return true
+    }
+    false
+  }
 }
 
 private[spark] object CoarseGrainedSchedulerBackend {
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala
index 9c07b3f7b695..bf2dc88e2904 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala
@@ -95,6 +95,7 @@ private[spark] class SparkDeploySchedulerBackend(
 
   override def executorAdded(fullId: String, workerId: String, hostPort: String, cores: Int,
     memory: Int) {
+    totalExpectedExecutors.addAndGet(1)
     logInfo("Granted executor ID %s on hostPort %s with %d cores, %s RAM".format(
       fullId, hostPort, cores, Utils.megabytesToString(memory)))
   }
diff --git a/docs/configuration.md b/docs/configuration.md
index 0aea23ab5950..07aa4c035446 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -699,6 +699,25 @@ Apart from these, the following properties are also available, and may be useful
     (in milliseconds)
   </td>
 </tr>
+</tr>
+  <td><code>spark.scheduler.minRegisteredExecutorsRatio</code></td>
+  <td>0</td>
+  <td>
+    The minimum ratio of registered executors (registered executors / total expected executors)
+    to wait for before scheduling begins. Specified as a double between 0 and 1.
+    Regardless of whether the minimum ratio of executors has been reached,
+    the maximum amount of time it will wait before scheduling begins is controlled by config 
+    <code>spark.scheduler.maxRegisteredExecutorsWaitingTime</code> 
+  </td>
+</tr>
+<tr>
+  <td><code>spark.scheduler.maxRegisteredExecutorsWaitingTime</code></td>
+  <td>30000</td>
+  <td>
+    Maximum amount of time to wait for executors to register before scheduling begins
+    (in milliseconds).  
+  </td>
+</tr>
 </table>
 
 #### Security
diff --git a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
index 438737f7a6b6..062f946a9fe9 100644
--- a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
+++ b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
@@ -184,6 +184,7 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration,
 
   private def startUserClass(): Thread = {
     logInfo("Starting the user JAR in a separate Thread")
+    System.setProperty("spark.executor.instances", args.numExecutors.toString)
     val mainMethod = Class.forName(
       args.userClass,
       false /* initialize */ ,
diff --git a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMasterArguments.scala b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMasterArguments.scala
index 25cc9016b10a..4c383ab574ab 100644
--- a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMasterArguments.scala
+++ b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMasterArguments.scala
@@ -26,7 +26,7 @@ class ApplicationMasterArguments(val args: Array[String]) {
   var userArgs: Seq[String] = Seq[String]()
   var executorMemory = 1024
   var executorCores = 1
-  var numExecutors = 2
+  var numExecutors = ApplicationMasterArguments.DEFAULT_NUMBER_EXECUTORS
 
   parseArgs(args.toList)
   
@@ -93,3 +93,7 @@ class ApplicationMasterArguments(val args: Array[String]) {
     System.exit(exitCode)
   }
 }
+
+object ApplicationMasterArguments {
+  val DEFAULT_NUMBER_EXECUTORS = 2
+}
diff --git a/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientClusterScheduler.scala b/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientClusterScheduler.scala
index 6b91e6b9eb89..15e8c21aa590 100644
--- a/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientClusterScheduler.scala
+++ b/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientClusterScheduler.scala
@@ -40,8 +40,10 @@ private[spark] class YarnClientClusterScheduler(sc: SparkContext, conf: Configur
 
   override def postStartHook() {
 
+    super.postStartHook()
     // The yarn application is running, but the executor might not yet ready
     // Wait for a few seconds for the slaves to bootstrap and register with master - best case attempt
+    // TODO It needn't after waitBackendReady
     Thread.sleep(2000L)
     logInfo("YarnClientClusterScheduler.postStartHook done")
   }
diff --git a/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala b/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala
index fd2694fe7278..0f9fdcfcb651 100644
--- a/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala
+++ b/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala
@@ -75,6 +75,7 @@ private[spark] class YarnClientSchedulerBackend(
 
     logDebug("ClientArguments called with: " + argsArrayBuf)
     val args = new ClientArguments(argsArrayBuf.toArray, conf)
+    totalExpectedExecutors.set(args.numExecutors)
     client = new Client(args, conf)
     appId = client.runApp()
     waitForApp()
diff --git a/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClusterScheduler.scala b/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClusterScheduler.scala
index 39cdd2e8a522..9ee53d797c8e 100644
--- a/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClusterScheduler.scala
+++ b/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClusterScheduler.scala
@@ -48,9 +48,11 @@ private[spark] class YarnClusterScheduler(sc: SparkContext, conf: Configuration)
 
   override def postStartHook() {
     val sparkContextInitialized = ApplicationMaster.sparkContextInitialized(sc)
+    super.postStartHook()
     if (sparkContextInitialized){
       ApplicationMaster.waitForInitialAllocations()
       // Wait for a few seconds for the slaves to bootstrap and register with master - best case attempt
+      // TODO It needn't after waitBackendReady
       Thread.sleep(3000L)
     }
     logInfo("YarnClusterScheduler.postStartHook done")
diff --git a/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClusterSchedulerBackend.scala b/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClusterSchedulerBackend.scala
new file mode 100644
index 000000000000..a04b08f43cc5
--- /dev/null
+++ b/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClusterSchedulerBackend.scala
@@ -0,0 +1,40 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.scheduler.cluster
+
+import org.apache.spark.SparkContext
+import org.apache.spark.deploy.yarn.ApplicationMasterArguments
+import org.apache.spark.scheduler.TaskSchedulerImpl
+import org.apache.spark.util.IntParam
+
+private[spark] class YarnClusterSchedulerBackend(
+    scheduler: TaskSchedulerImpl,
+    sc: SparkContext)
+  extends CoarseGrainedSchedulerBackend(scheduler, sc.env.actorSystem) {
+
+  override def start() {
+    super.start()
+    var numExecutors = ApplicationMasterArguments.DEFAULT_NUMBER_EXECUTORS
+    if (System.getenv("SPARK_EXECUTOR_INSTANCES") != null) {
+      numExecutors = IntParam.unapply(System.getenv("SPARK_EXECUTOR_INSTANCES")).getOrElse(numExecutors)
+    }
+    // System property can override environment variable.
+    numExecutors = sc.getConf.getInt("spark.executor.instances", numExecutors)
+    totalExpectedExecutors.set(numExecutors)
+  }
+}
diff --git a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
index ee1e9c9c23d2..1a24ec759b54 100644
--- a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
+++ b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
@@ -164,6 +164,7 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration,
 
   private def startUserClass(): Thread = {
     logInfo("Starting the user JAR in a separate Thread")
+    System.setProperty("spark.executor.instances", args.numExecutors.toString)
     val mainMethod = Class.forName(
       args.userClass,
       false,

From 9fe693b5b6ed6af34ee1e800ab89c8a11991ea38 Mon Sep 17 00:00:00 2001
From: Takuya UESHIN <ueshin@happy-camper.st>
Date: Mon, 14 Jul 2014 15:42:28 -0700
Subject: [PATCH 637/810] [SPARK-2446][SQL] Add BinaryType support to Parquet
 I/O.

Note that this commit changes the semantics when loading in data that was created with prior versions of Spark SQL.  Before, we were writing out strings as Binary data without adding any other annotations. Thus, when data is read in from prior versions, data that was StringType will now become BinaryType.  Users that need strings can CAST that column to a String.  It was decided that while this breaks compatibility, it does make us compatible with other systems (Hive, Thrift, etc) and adds support for Binary data, so this is the right decision long term.

To support `BinaryType`, the following changes are needed:
- Make `StringType` use `OriginalType.UTF8`
- Add `BinaryType` using `PrimitiveTypeName.BINARY` without `OriginalType`

Author: Takuya UESHIN <ueshin@happy-camper.st>

Closes #1373 from ueshin/issues/SPARK-2446 and squashes the following commits:

ecacb92 [Takuya UESHIN] Add BinaryType support to Parquet I/O.
616e04a [Takuya UESHIN] Make StringType use OriginalType.UTF8.
---
 .../spark/sql/parquet/ParquetConverter.scala  |  2 +-
 .../sql/parquet/ParquetTableSupport.scala     |  4 ++
 .../spark/sql/parquet/ParquetTestData.scala   | 18 +++---
 .../spark/sql/parquet/ParquetTypes.scala      | 62 ++++++++++---------
 .../spark/sql/parquet/ParquetQuerySuite.scala | 16 +++--
 5 files changed, 57 insertions(+), 45 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala
index 75748b2b5440..de8fe2dae38f 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala
@@ -114,7 +114,7 @@ private[sql] object CatalystConverter {
         }
       }
       // All other primitive types use the default converter
-      case ctype: NativeType => { // note: need the type tag here!
+      case ctype: PrimitiveType => { // note: need the type tag here!
         new CatalystPrimitiveConverter(parent, fieldIndex)
       }
       case _ => throw new RuntimeException(
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala
index 108f8b681542..f1953a008a49 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala
@@ -191,6 +191,8 @@ private[parquet] class RowWriteSupport extends WriteSupport[Row] with Logging {
             value.asInstanceOf[String].getBytes("utf-8")
           )
         )
+        case BinaryType => writer.addBinary(
+          Binary.fromByteArray(value.asInstanceOf[Array[Byte]]))
         case IntegerType => writer.addInteger(value.asInstanceOf[Int])
         case ShortType => writer.addInteger(value.asInstanceOf[Short])
         case LongType => writer.addLong(value.asInstanceOf[Long])
@@ -299,6 +301,8 @@ private[parquet] class MutableRowWriteSupport extends RowWriteSupport {
           record(index).asInstanceOf[String].getBytes("utf-8")
         )
       )
+      case BinaryType => writer.addBinary(
+        Binary.fromByteArray(record(index).asInstanceOf[Array[Byte]]))
       case IntegerType => writer.addInteger(record.getInt(index))
       case ShortType => writer.addInteger(record.getShort(index))
       case LongType => writer.addLong(record.getLong(index))
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTestData.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTestData.scala
index 1dc58633a2a6..d4599da71125 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTestData.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTestData.scala
@@ -58,7 +58,7 @@ private[sql] object ParquetTestData {
     """message myrecord {
       optional boolean myboolean;
       optional int32 myint;
-      optional binary mystring;
+      optional binary mystring (UTF8);
       optional int64 mylong;
       optional float myfloat;
       optional double mydouble;
@@ -87,7 +87,7 @@ private[sql] object ParquetTestData {
       message myrecord {
       required boolean myboolean;
       required int32 myint;
-      required binary mystring;
+      required binary mystring (UTF8);
       required int64 mylong;
       required float myfloat;
       required double mydouble;
@@ -119,14 +119,14 @@ private[sql] object ParquetTestData {
     // so that array types can be translated correctly.
     """
       message AddressBook {
-        required binary owner;
+        required binary owner (UTF8);
         optional group ownerPhoneNumbers {
-          repeated binary array;
+          repeated binary array (UTF8);
         }
         optional group contacts {
           repeated group array {
-            required binary name;
-            optional binary phoneNumber;
+            required binary name (UTF8);
+            optional binary phoneNumber (UTF8);
           }
         }
       }
@@ -181,16 +181,16 @@ private[sql] object ParquetTestData {
         required int32 x;
         optional group data1 {
           repeated group map {
-            required binary key;
+            required binary key (UTF8);
             required int32 value;
           }
         }
         required group data2 {
           repeated group map {
-            required binary key;
+            required binary key (UTF8);
             required group value {
               required int64 payload1;
-              optional binary payload2;
+              optional binary payload2 (UTF8);
             }
           }
         }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypes.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypes.scala
index f9046368e7ce..7f6ad908f78e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypes.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypes.scala
@@ -42,20 +42,22 @@ private[parquet] object ParquetTypesConverter extends Logging {
   def isPrimitiveType(ctype: DataType): Boolean =
     classOf[PrimitiveType] isAssignableFrom ctype.getClass
 
-  def toPrimitiveDataType(parquetType : ParquetPrimitiveTypeName): DataType = parquetType match {
-    case ParquetPrimitiveTypeName.BINARY => StringType
-    case ParquetPrimitiveTypeName.BOOLEAN => BooleanType
-    case ParquetPrimitiveTypeName.DOUBLE => DoubleType
-    case ParquetPrimitiveTypeName.FIXED_LEN_BYTE_ARRAY => ArrayType(ByteType)
-    case ParquetPrimitiveTypeName.FLOAT => FloatType
-    case ParquetPrimitiveTypeName.INT32 => IntegerType
-    case ParquetPrimitiveTypeName.INT64 => LongType
-    case ParquetPrimitiveTypeName.INT96 =>
-      // TODO: add BigInteger type? TODO(andre) use DecimalType instead????
-      sys.error("Potential loss of precision: cannot convert INT96")
-    case _ => sys.error(
-      s"Unsupported parquet datatype $parquetType")
-  }
+  def toPrimitiveDataType(parquetType: ParquetPrimitiveType): DataType =
+    parquetType.getPrimitiveTypeName match {
+      case ParquetPrimitiveTypeName.BINARY
+        if parquetType.getOriginalType == ParquetOriginalType.UTF8 => StringType
+      case ParquetPrimitiveTypeName.BINARY => BinaryType
+      case ParquetPrimitiveTypeName.BOOLEAN => BooleanType
+      case ParquetPrimitiveTypeName.DOUBLE => DoubleType
+      case ParquetPrimitiveTypeName.FLOAT => FloatType
+      case ParquetPrimitiveTypeName.INT32 => IntegerType
+      case ParquetPrimitiveTypeName.INT64 => LongType
+      case ParquetPrimitiveTypeName.INT96 =>
+        // TODO: add BigInteger type? TODO(andre) use DecimalType instead????
+        sys.error("Potential loss of precision: cannot convert INT96")
+      case _ => sys.error(
+        s"Unsupported parquet datatype $parquetType")
+    }
 
   /**
    * Converts a given Parquet `Type` into the corresponding
@@ -104,7 +106,7 @@ private[parquet] object ParquetTypesConverter extends Logging {
     }
 
     if (parquetType.isPrimitive) {
-      toPrimitiveDataType(parquetType.asPrimitiveType.getPrimitiveTypeName)
+      toPrimitiveDataType(parquetType.asPrimitiveType)
     } else {
       val groupType = parquetType.asGroupType()
       parquetType.getOriginalType match {
@@ -164,18 +166,17 @@ private[parquet] object ParquetTypesConverter extends Logging {
    * @return The name of the corresponding Parquet primitive type
    */
   def fromPrimitiveDataType(ctype: DataType):
-      Option[ParquetPrimitiveTypeName] = ctype match {
-    case StringType => Some(ParquetPrimitiveTypeName.BINARY)
-    case BooleanType => Some(ParquetPrimitiveTypeName.BOOLEAN)
-    case DoubleType => Some(ParquetPrimitiveTypeName.DOUBLE)
-    case ArrayType(ByteType) =>
-      Some(ParquetPrimitiveTypeName.FIXED_LEN_BYTE_ARRAY)
-    case FloatType => Some(ParquetPrimitiveTypeName.FLOAT)
-    case IntegerType => Some(ParquetPrimitiveTypeName.INT32)
+      Option[(ParquetPrimitiveTypeName, Option[ParquetOriginalType])] = ctype match {
+    case StringType => Some(ParquetPrimitiveTypeName.BINARY, Some(ParquetOriginalType.UTF8))
+    case BinaryType => Some(ParquetPrimitiveTypeName.BINARY, None)
+    case BooleanType => Some(ParquetPrimitiveTypeName.BOOLEAN, None)
+    case DoubleType => Some(ParquetPrimitiveTypeName.DOUBLE, None)
+    case FloatType => Some(ParquetPrimitiveTypeName.FLOAT, None)
+    case IntegerType => Some(ParquetPrimitiveTypeName.INT32, None)
     // There is no type for Byte or Short so we promote them to INT32.
-    case ShortType => Some(ParquetPrimitiveTypeName.INT32)
-    case ByteType => Some(ParquetPrimitiveTypeName.INT32)
-    case LongType => Some(ParquetPrimitiveTypeName.INT64)
+    case ShortType => Some(ParquetPrimitiveTypeName.INT32, None)
+    case ByteType => Some(ParquetPrimitiveTypeName.INT32, None)
+    case LongType => Some(ParquetPrimitiveTypeName.INT64, None)
     case _ => None
   }
 
@@ -227,9 +228,10 @@ private[parquet] object ParquetTypesConverter extends Logging {
         if (nullable) Repetition.OPTIONAL else Repetition.REQUIRED
       }
     val primitiveType = fromPrimitiveDataType(ctype)
-    if (primitiveType.isDefined) {
-      new ParquetPrimitiveType(repetition, primitiveType.get, name)
-    } else {
+    primitiveType.map {
+      case (primitiveType, originalType) =>
+        new ParquetPrimitiveType(repetition, primitiveType, name, originalType.orNull)
+    }.getOrElse {
       ctype match {
         case ArrayType(elementType) => {
           val parquetElementType = fromDataType(
@@ -237,7 +239,7 @@ private[parquet] object ParquetTypesConverter extends Logging {
             CatalystConverter.ARRAY_ELEMENTS_SCHEMA_NAME,
             nullable = false,
             inArray = true)
-            ConversionPatterns.listType(repetition, name, parquetElementType)
+          ConversionPatterns.listType(repetition, name, parquetElementType)
         }
         case StructType(structFields) => {
           val fields = structFields.map {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala
index 8fa143e2deca..3c911e9a4e7b 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala
@@ -65,7 +65,8 @@ case class AllDataTypes(
     doubleField: Double,
     shortField: Short,
     byteField: Byte,
-    booleanField: Boolean)
+    booleanField: Boolean,
+    binaryField: Array[Byte])
 
 case class AllDataTypesWithNonPrimitiveType(
     stringField: String,
@@ -76,6 +77,7 @@ case class AllDataTypesWithNonPrimitiveType(
     shortField: Short,
     byteField: Byte,
     booleanField: Boolean,
+    binaryField: Array[Byte],
     array: Seq[Int],
     map: Map[Int, String],
     data: Data)
@@ -116,7 +118,8 @@ class ParquetQuerySuite extends QueryTest with FunSuiteLike with BeforeAndAfterA
     val tempDir = getTempFilePath("parquetTest").getCanonicalPath
     val range = (0 to 255)
     TestSQLContext.sparkContext.parallelize(range)
-      .map(x => AllDataTypes(s"$x", x, x.toLong, x.toFloat, x.toDouble, x.toShort, x.toByte, x % 2 == 0))
+      .map(x => AllDataTypes(s"$x", x, x.toLong, x.toFloat, x.toDouble, x.toShort, x.toByte, x % 2 == 0,
+        (0 to x).map(_.toByte).toArray))
       .saveAsParquetFile(tempDir)
     val result = parquetFile(tempDir).collect()
     range.foreach {
@@ -129,6 +132,7 @@ class ParquetQuerySuite extends QueryTest with FunSuiteLike with BeforeAndAfterA
         assert(result(i).getShort(5) === i.toShort)
         assert(result(i).getByte(6) === i.toByte)
         assert(result(i).getBoolean(7) === (i % 2 == 0))
+        assert(result(i)(8) === (0 to i).map(_.toByte).toArray)
     }
   }
 
@@ -138,6 +142,7 @@ class ParquetQuerySuite extends QueryTest with FunSuiteLike with BeforeAndAfterA
     TestSQLContext.sparkContext.parallelize(range)
       .map(x => AllDataTypesWithNonPrimitiveType(
         s"$x", x, x.toLong, x.toFloat, x.toDouble, x.toShort, x.toByte, x % 2 == 0,
+        (0 to x).map(_.toByte).toArray,
         (0 until x), (0 until x).map(i => i -> s"$i").toMap, Data((0 until x), Nested(x, s"$x"))))
       .saveAsParquetFile(tempDir)
     val result = parquetFile(tempDir).collect()
@@ -151,9 +156,10 @@ class ParquetQuerySuite extends QueryTest with FunSuiteLike with BeforeAndAfterA
         assert(result(i).getShort(5) === i.toShort)
         assert(result(i).getByte(6) === i.toByte)
         assert(result(i).getBoolean(7) === (i % 2 == 0))
-        assert(result(i)(8) === (0 until i))
-        assert(result(i)(9) === (0 until i).map(i => i -> s"$i").toMap)
-        assert(result(i)(10) === new GenericRow(Array[Any]((0 until i), new GenericRow(Array[Any](i, s"$i")))))
+        assert(result(i)(8) === (0 to i).map(_.toByte).toArray)
+        assert(result(i)(9) === (0 until i))
+        assert(result(i)(10) === (0 until i).map(i => i -> s"$i").toMap)
+        assert(result(i)(11) === new GenericRow(Array[Any]((0 until i), new GenericRow(Array[Any](i, s"$i")))))
     }
   }
 

From e2255e4b2c404f31ac9f7af9ed445141af980973 Mon Sep 17 00:00:00 2001
From: Takuya UESHIN <ueshin@happy-camper.st>
Date: Mon, 14 Jul 2014 23:06:35 -0700
Subject: [PATCH 638/810] [SPARK-2467] Revert SparkBuild to publish-local to
 both .m2 and .ivy2.

Author: Takuya UESHIN <ueshin@happy-camper.st>

Closes #1398 from ueshin/issues/SPARK-2467 and squashes the following commits:

7f01d58 [Takuya UESHIN] Revert SparkBuild to publish-local to both .m2 and .ivy2.
---
 project/SparkBuild.scala | 14 +++++++++++++-
 1 file changed, 13 insertions(+), 1 deletion(-)

diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index 44abbc152f99..754d54e89361 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -19,6 +19,7 @@ import scala.util.Properties
 import scala.collection.JavaConversions._
 
 import sbt._
+import sbt.Classpaths.publishTask
 import sbt.Keys._
 import org.scalastyle.sbt.ScalastylePlugin.{Settings => ScalaStyleSettings}
 import com.typesafe.sbt.pom.{PomBuild, SbtPomKeys}
@@ -103,12 +104,23 @@ object SparkBuild extends PomBuild {
 
   override val userPropertiesMap = System.getProperties.toMap
 
+  lazy val MavenCompile = config("m2r") extend(Compile)
+  lazy val publishLocalBoth = TaskKey[Unit]("publish-local", "publish local for m2 and ivy")
+
   lazy val sharedSettings = graphSettings ++ ScalaStyleSettings ++ Seq (
     javaHome   := Properties.envOrNone("JAVA_HOME").map(file),
     incOptions := incOptions.value.withNameHashing(true),
     retrieveManaged := true,
     retrievePattern := "[type]s/[artifact](-[revision])(-[classifier]).[ext]",
-    publishMavenStyle := true
+    publishMavenStyle := true,
+
+    otherResolvers <<= SbtPomKeys.mvnLocalRepository(dotM2 => Seq(Resolver.file("dotM2", dotM2))),
+    publishLocalConfiguration in MavenCompile <<= (packagedArtifacts, deliverLocal, ivyLoggingLevel) map {
+      (arts, _, level) => new PublishConfiguration(None, "dotM2", arts, Seq(), level)
+    },
+    publishMavenStyle in MavenCompile := true,
+    publishLocal in MavenCompile <<= publishTask(publishLocalConfiguration in MavenCompile, deliverLocal),
+    publishLocalBoth <<= Seq(publishLocal in MavenCompile, publishLocal).dependOn
   )
 
   /** Following project only exists to pull previous artifacts of Spark for generating

From 1f99fea53b5ff994dd4a12b44625d35186e269ff Mon Sep 17 00:00:00 2001
From: William Benton <willb@redhat.com>
Date: Mon, 14 Jul 2014 23:09:13 -0700
Subject: [PATCH 639/810] SPARK-2486: Utils.getCallSite is now resilient to
 bogus frames

When running Spark under certain instrumenting profilers,
Utils.getCallSite could crash with an NPE.  This commit
makes it more resilient to failures occurring while inspecting
stack frames.

Author: William Benton <willb@redhat.com>

Closes #1413 from willb/spark-2486 and squashes the following commits:

b7c0274 [William Benton] Use explicit null checks instead of Try()
0f0c1ae [William Benton] Utils.getCallSite is now resilient to bogus frames
---
 core/src/main/scala/org/apache/spark/util/Utils.scala | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala
index a2454e120a8a..d72c97bbe816 100644
--- a/core/src/main/scala/org/apache/spark/util/Utils.scala
+++ b/core/src/main/scala/org/apache/spark/util/Utils.scala
@@ -809,7 +809,11 @@ private[spark] object Utils extends Logging {
    */
   def getCallSite: CallSite = {
     val trace = Thread.currentThread.getStackTrace()
-      .filterNot(_.getMethodName.contains("getStackTrace"))
+      .filterNot((ste:StackTraceElement) => 
+        // When running under some profilers, the current stack trace might contain some bogus 
+        // frames. This is intended to ensure that we don't crash in these situations by
+        // ignoring any frames that we can't examine.
+        (ste == null || ste.getMethodName == null || ste.getMethodName.contains("getStackTrace")))
 
     // Keep crawling up the stack trace until we find the first function not inside of the spark
     // package. We track the last (shallowest) contiguous Spark method. This might be an RDD

From a2aa7bebae31e1e7ec23d31aaa436283743b283b Mon Sep 17 00:00:00 2001
From: Aaron Davidson <aaron@databricks.com>
Date: Mon, 14 Jul 2014 23:38:12 -0700
Subject: [PATCH 640/810] Add/increase severity of warning in documentation of
 groupBy()

groupBy()/groupByKey() is notorious for being a very convenient API that can lead to poor performance when used incorrectly.

This PR just makes it clear that users should be cautious not to rely on this API when they really want a different (more performant) one, such as reduceByKey().

(Note that one source of confusion is the name; this groupBy() is not the same as a SQL GROUP-BY, which is used for aggregation and is more similar in nature to Spark's reduceByKey().)

Author: Aaron Davidson <aaron@databricks.com>

Closes #1380 from aarondav/warning and squashes the following commits:

f60da39 [Aaron Davidson] Give better advice
d0afb68 [Aaron Davidson] Add/increase severity of warning in documentation of groupBy()
---
 .../apache/spark/rdd/PairRDDFunctions.scala    | 18 +++++++++---------
 .../main/scala/org/apache/spark/rdd/RDD.scala  | 12 ++++++++++++
 2 files changed, 21 insertions(+), 9 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
index fc9beb166bef..9d62d53fcb23 100644
--- a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
@@ -353,9 +353,9 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * Group the values for each key in the RDD into a single sequence. Allows controlling the
    * partitioning of the resulting key-value pair RDD by passing a Partitioner.
    *
-   * Note: If you are grouping in order to perform an aggregation (such as a sum or average) over
-   * each key, using [[PairRDDFunctions.reduceByKey]] or [[PairRDDFunctions.combineByKey]]
-   * will provide much better performance.
+   * Note: This operation may be very expensive. If you are grouping in order to perform an
+   * aggregation (such as a sum or average) over each key, using [[PairRDDFunctions.aggregateByKey]]
+   * or [[PairRDDFunctions.reduceByKey]] will provide much better performance.
    */
   def groupByKey(partitioner: Partitioner): RDD[(K, Iterable[V])] = {
     // groupByKey shouldn't use map side combine because map side combine does not
@@ -373,9 +373,9 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * Group the values for each key in the RDD into a single sequence. Hash-partitions the
    * resulting RDD with into `numPartitions` partitions.
    *
-   * Note: If you are grouping in order to perform an aggregation (such as a sum or average) over
-   * each key, using [[PairRDDFunctions.reduceByKey]] or [[PairRDDFunctions.combineByKey]]
-   * will provide much better performance.
+   * Note: This operation may be very expensive. If you are grouping in order to perform an
+   * aggregation (such as a sum or average) over each key, using [[PairRDDFunctions.aggregateByKey]]
+   * or [[PairRDDFunctions.reduceByKey]] will provide much better performance.
    */
   def groupByKey(numPartitions: Int): RDD[(K, Iterable[V])] = {
     groupByKey(new HashPartitioner(numPartitions))
@@ -462,9 +462,9 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * Group the values for each key in the RDD into a single sequence. Hash-partitions the
    * resulting RDD with the existing partitioner/parallelism level.
    *
-   * Note: If you are grouping in order to perform an aggregation (such as a sum or average) over
-   * each key, using [[PairRDDFunctions.reduceByKey]] or [[PairRDDFunctions.combineByKey]]
-   * will provide much better performance,
+   * Note: This operation may be very expensive. If you are grouping in order to perform an
+   * aggregation (such as a sum or average) over each key, using [[PairRDDFunctions.aggregateByKey]]
+   * or [[PairRDDFunctions.reduceByKey]] will provide much better performance.
    */
   def groupByKey(): RDD[(K, Iterable[V])] = {
     groupByKey(defaultPartitioner(self))
diff --git a/core/src/main/scala/org/apache/spark/rdd/RDD.scala b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
index 4e841bc992bf..a25f263bea5c 100644
--- a/core/src/main/scala/org/apache/spark/rdd/RDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
@@ -509,6 +509,10 @@ abstract class RDD[T: ClassTag](
   /**
    * Return an RDD of grouped items. Each group consists of a key and a sequence of elements
    * mapping to that key.
+   *
+   * Note: This operation may be very expensive. If you are grouping in order to perform an
+   * aggregation (such as a sum or average) over each key, using [[PairRDDFunctions.aggregateByKey]]
+   * or [[PairRDDFunctions.reduceByKey]] will provide much better performance.
    */
   def groupBy[K](f: T => K)(implicit kt: ClassTag[K]): RDD[(K, Iterable[T])] =
     groupBy[K](f, defaultPartitioner(this))
@@ -516,6 +520,10 @@ abstract class RDD[T: ClassTag](
   /**
    * Return an RDD of grouped elements. Each group consists of a key and a sequence of elements
    * mapping to that key.
+   *
+   * Note: This operation may be very expensive. If you are grouping in order to perform an
+   * aggregation (such as a sum or average) over each key, using [[PairRDDFunctions.aggregateByKey]]
+   * or [[PairRDDFunctions.reduceByKey]] will provide much better performance.
    */
   def groupBy[K](f: T => K, numPartitions: Int)(implicit kt: ClassTag[K]): RDD[(K, Iterable[T])] =
     groupBy(f, new HashPartitioner(numPartitions))
@@ -523,6 +531,10 @@ abstract class RDD[T: ClassTag](
   /**
    * Return an RDD of grouped items. Each group consists of a key and a sequence of elements
    * mapping to that key.
+   *
+   * Note: This operation may be very expensive. If you are grouping in order to perform an
+   * aggregation (such as a sum or average) over each key, using [[PairRDDFunctions.aggregateByKey]]
+   * or [[PairRDDFunctions.reduceByKey]] will provide much better performance.
    */
   def groupBy[K](f: T => K, p: Partitioner)(implicit kt: ClassTag[K], ord: Ordering[K] = null)
       : RDD[(K, Iterable[T])] = {

From c6d75745de58ff1445912bf72a58b6ad2b3f863c Mon Sep 17 00:00:00 2001
From: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Date: Mon, 14 Jul 2014 23:55:39 -0700
Subject: [PATCH 641/810] [SPARK-2390] Files in staging directory cannot be
 deleted and wastes the space of HDFS

When running jobs with YARN Cluster mode and using HistoryServer, the files in the Staging Directory (~/.sparkStaging on HDFS) cannot be deleted.
HistoryServer uses directory where event log is written, and the directory is represented as a instance of o.a.h.f.FileSystem created by using FileSystem.get.

On the other hand, ApplicationMaster has a instance named fs, which also created by using FileSystem.get.

FileSystem.get returns cached same instance when URI passed to the method represents same file system and the method is called by same user.
Because of the behavior, when the directory for event log is on HDFS, fs of ApplicationMaster and fileSystem of FileLogger is same instance.
When shutting down ApplicationMaster, fileSystem.close is called in FileLogger#stop, which is invoked by SparkContext#stop indirectly.

And ApplicationMaster#cleanupStagingDir also called by JVM shutdown hook. In this method, fs.delete(stagingDirPath) is invoked.
Because fs.delete in ApplicationMaster is called after fileSystem.close in FileLogger, fs.delete fails and results not deleting files in the staging directory.

I think, calling fileSystem.delete is not needed.

Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>

Closes #1326 from sarutak/SPARK-2390 and squashes the following commits:

10e1a88 [Kousuke Saruta] Removed fileSystem.close from FileLogger.scala not to prevent any other FileSystem operation
---
 core/src/main/scala/org/apache/spark/util/FileLogger.scala | 1 -
 1 file changed, 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/util/FileLogger.scala b/core/src/main/scala/org/apache/spark/util/FileLogger.scala
index 6a95dc06e155..9dcdafdd6350 100644
--- a/core/src/main/scala/org/apache/spark/util/FileLogger.scala
+++ b/core/src/main/scala/org/apache/spark/util/FileLogger.scala
@@ -196,6 +196,5 @@ private[spark] class FileLogger(
   def stop() {
     hadoopDataStream.foreach(_.close())
     writer.foreach(_.close())
-    fileSystem.close()
   }
 }

From c7c7ac83392b10abb011e6aead1bf92e7c73695e Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Tue, 15 Jul 2014 00:13:51 -0700
Subject: [PATCH 642/810] [SPARK-2485][SQL] Lock usage of hive client.

Author: Michael Armbrust <michael@databricks.com>

Closes #1412 from marmbrus/lockHiveClient and squashes the following commits:

4bc9d5a [Michael Armbrust] protected[hive]
22e9177 [Michael Armbrust] Add comments.
7aa8554 [Michael Armbrust] Don't lock on hive's object.
a6edc5f [Michael Armbrust] Lock usage of hive client.
---
 .../org/apache/spark/sql/hive/HiveMetastoreCatalog.scala     | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
index f83068860701..8db60d32767b 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
@@ -43,14 +43,15 @@ import scala.collection.JavaConversions._
 private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with Logging {
   import HiveMetastoreTypes._
 
-  val client = Hive.get(hive.hiveconf)
+  /** Connection to hive metastore.  Usages should lock on `this`. */
+  protected[hive] val client = Hive.get(hive.hiveconf)
 
   val caseSensitive: Boolean = false
 
   def lookupRelation(
       db: Option[String],
       tableName: String,
-      alias: Option[String]): LogicalPlan = {
+      alias: Option[String]): LogicalPlan = synchronized {
     val (dbName, tblName) = processDatabaseAndTableName(db, tableName)
     val databaseName = dbName.getOrElse(hive.sessionState.getCurrentDatabase)
     val table = client.getTable(databaseName, tblName)

From 7446f5ff93142d2dd5c79c63fa947f47a1d4db8b Mon Sep 17 00:00:00 2001
From: lianhuiwang <lianhuiwang09@gmail.com>
Date: Tue, 15 Jul 2014 00:22:06 -0700
Subject: [PATCH 643/810] discarded exceeded completedDrivers

When completedDrivers number exceeds the threshold, the first Max(spark.deploy.retainedDrivers, 1) will be discarded.

Author: lianhuiwang <lianhuiwang09@gmail.com>

Closes #1114 from lianhuiwang/retained-drivers and squashes the following commits:

8789418 [lianhuiwang] discarded exceeded completedDrivers
---
 .../main/scala/org/apache/spark/deploy/master/Master.scala   | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
index d9f8105992a1..9fa556d522ba 100644
--- a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
@@ -57,6 +57,7 @@ private[spark] class Master(
   def createDateFormat = new SimpleDateFormat("yyyyMMddHHmmss")  // For application IDs
   val WORKER_TIMEOUT = conf.getLong("spark.worker.timeout", 60) * 1000
   val RETAINED_APPLICATIONS = conf.getInt("spark.deploy.retainedApplications", 200)
+  val RETAINED_DRIVERS = conf.getInt("spark.deploy.retainedDrivers", 200)
   val REAPER_ITERATIONS = conf.getInt("spark.dead.worker.persistence", 15)
   val RECOVERY_DIR = conf.get("spark.deploy.recoveryDirectory", "")
   val RECOVERY_MODE = conf.get("spark.deploy.recoveryMode", "NONE")
@@ -741,6 +742,10 @@ private[spark] class Master(
       case Some(driver) =>
         logInfo(s"Removing driver: $driverId")
         drivers -= driver
+        if (completedDrivers.size >= RETAINED_DRIVERS) {
+          val toRemove = math.max(RETAINED_DRIVERS / 10, 1)
+          completedDrivers.trimStart(toRemove)
+        }
         completedDrivers += driver
         persistenceEngine.removeDriver(driver)
         driver.state = finalState

From dd95abada78b4d0aec97dacda50fdfd74464b073 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Tue, 15 Jul 2014 01:46:57 -0700
Subject: [PATCH 644/810] [SPARK-2399] Add support for LZ4 compression.

Based on Greg Bowyer's patch from JIRA https://issues.apache.org/jira/browse/SPARK-2399

Author: Reynold Xin <rxin@apache.org>

Closes #1416 from rxin/lz4 and squashes the following commits:

6c8fefe [Reynold Xin] Fixed typo.
8a14d38 [Reynold Xin] [SPARK-2399] Add support for LZ4 compression.
---
 core/pom.xml                                  |  4 ++++
 .../apache/spark/io/CompressionCodec.scala    | 22 +++++++++++++++++++
 .../spark/io/CompressionCodecSuite.scala      |  6 +++++
 docs/configuration.md                         | 10 ++++++++-
 pom.xml                                       |  5 +++++
 5 files changed, 46 insertions(+), 1 deletion(-)

diff --git a/core/pom.xml b/core/pom.xml
index 4ed920a750ff..1054cec4d77b 100644
--- a/core/pom.xml
+++ b/core/pom.xml
@@ -114,6 +114,10 @@
       <groupId>org.xerial.snappy</groupId>
       <artifactId>snappy-java</artifactId>
     </dependency>
+    <dependency>
+      <groupId>net.jpountz.lz4</groupId>
+      <artifactId>lz4</artifactId>
+    </dependency>
     <dependency>
       <groupId>com.twitter</groupId>
       <artifactId>chill_${scala.binary.version}</artifactId>
diff --git a/core/src/main/scala/org/apache/spark/io/CompressionCodec.scala b/core/src/main/scala/org/apache/spark/io/CompressionCodec.scala
index 4b0fe1ab8299..33402c927c73 100644
--- a/core/src/main/scala/org/apache/spark/io/CompressionCodec.scala
+++ b/core/src/main/scala/org/apache/spark/io/CompressionCodec.scala
@@ -20,6 +20,7 @@ package org.apache.spark.io
 import java.io.{InputStream, OutputStream}
 
 import com.ning.compress.lzf.{LZFInputStream, LZFOutputStream}
+import net.jpountz.lz4.{LZ4BlockInputStream, LZ4BlockOutputStream}
 import org.xerial.snappy.{SnappyInputStream, SnappyOutputStream}
 
 import org.apache.spark.SparkConf
@@ -59,6 +60,27 @@ private[spark] object CompressionCodec {
 }
 
 
+/**
+ * :: DeveloperApi ::
+ * LZ4 implementation of [[org.apache.spark.io.CompressionCodec]].
+ * Block size can be configured by `spark.io.compression.lz4.block.size`.
+ *
+ * Note: The wire protocol for this codec is not guaranteed to be compatible across versions
+ *       of Spark. This is intended for use as an internal compression utility within a single Spark
+ *       application.
+ */
+@DeveloperApi
+class LZ4CompressionCodec(conf: SparkConf) extends CompressionCodec {
+
+  override def compressedOutputStream(s: OutputStream): OutputStream = {
+    val blockSize = conf.getInt("spark.io.compression.lz4.block.size", 32768)
+    new LZ4BlockOutputStream(s, blockSize)
+  }
+
+  override def compressedInputStream(s: InputStream): InputStream = new LZ4BlockInputStream(s)
+}
+
+
 /**
  * :: DeveloperApi ::
  * LZF implementation of [[org.apache.spark.io.CompressionCodec]].
diff --git a/core/src/test/scala/org/apache/spark/io/CompressionCodecSuite.scala b/core/src/test/scala/org/apache/spark/io/CompressionCodecSuite.scala
index 68a0ea36aa54..42fc395fa698 100644
--- a/core/src/test/scala/org/apache/spark/io/CompressionCodecSuite.scala
+++ b/core/src/test/scala/org/apache/spark/io/CompressionCodecSuite.scala
@@ -50,6 +50,12 @@ class CompressionCodecSuite extends FunSuite {
     testCodec(codec)
   }
 
+  test("lz4 compression codec") {
+    val codec = CompressionCodec.createCodec(conf, classOf[LZ4CompressionCodec].getName)
+    assert(codec.getClass === classOf[LZ4CompressionCodec])
+    testCodec(codec)
+  }
+
   test("lzf compression codec") {
     val codec = CompressionCodec.createCodec(conf, classOf[LZFCompressionCodec].getName)
     assert(codec.getClass === classOf[LZFCompressionCodec])
diff --git a/docs/configuration.md b/docs/configuration.md
index 07aa4c035446..19fd980e6088 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -350,7 +350,15 @@ Apart from these, the following properties are also available, and may be useful
   <td>32768</td>
   <td>
     Block size (in bytes) used in Snappy compression, in the case when Snappy compression codec
-    is used.
+    is used. Lowering this block size will also lower shuffle memory usage when Snappy is used.
+  </td>
+</tr>
+<tr>
+  <td><code>spark.io.compression.lz4.block.size</code></td>
+  <td>32768</td>
+  <td>
+    Block size (in bytes) used in LZ4 compression, in the case when LZ4 compression codec
+    is used. Lowering this block size will also lower shuffle memory usage when LZ4 is used.
   </td>
 </tr>
 <tr>
diff --git a/pom.xml b/pom.xml
index fa80707d0929..d570f3e6b932 100644
--- a/pom.xml
+++ b/pom.xml
@@ -297,6 +297,11 @@
         <artifactId>snappy-java</artifactId>
         <version>1.0.5</version>
       </dependency>
+      <dependency>
+        <groupId>net.jpountz.lz4</groupId>
+        <artifactId>lz4</artifactId>
+        <version>1.2.0</version>
+      </dependency>
       <dependency>
         <groupId>com.clearspring.analytics</groupId>
         <artifactId>stream</artifactId>

From 52beb20f7904e0333198b9b14619366ddf53ab85 Mon Sep 17 00:00:00 2001
From: DB Tsai <dbtsai@alpinenow.com>
Date: Tue, 15 Jul 2014 02:14:58 -0700
Subject: [PATCH 645/810] [SPARK-2477][MLlib] Using appendBias for adding
 intercept in GeneralizedLinearAlgorithm

Instead of using prependOne currently in GeneralizedLinearAlgorithm, we would like to use appendBias for 1) keeping the indices of original training set unchanged by adding the intercept into the last element of vector and 2) using the same public API for consistently adding intercept.

Author: DB Tsai <dbtsai@alpinenow.com>

Closes #1410 from dbtsai/SPARK-2477_intercept_with_appendBias and squashes the following commits:

011432c [DB Tsai] From Alpine Data Labs
---
 .../GeneralizedLinearAlgorithm.scala          | 21 +++++--------------
 1 file changed, 5 insertions(+), 16 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala
index 8cca926f1c92..fe41863bce98 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala
@@ -17,13 +17,12 @@
 
 package org.apache.spark.mllib.regression
 
-import breeze.linalg.{DenseVector => BDV, SparseVector => BSV}
-
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.{Logging, SparkException}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.mllib.optimization._
 import org.apache.spark.mllib.linalg.{Vectors, Vector}
+import org.apache.spark.mllib.util.MLUtils._
 
 /**
  * :: DeveloperApi ::
@@ -124,16 +123,6 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel]
     run(input, initialWeights)
   }
 
-  /** Prepends one to the input vector. */
-  private def prependOne(vector: Vector): Vector = {
-    val vector1 = vector.toBreeze match {
-      case dv: BDV[Double] => BDV.vertcat(BDV.ones[Double](1), dv)
-      case sv: BSV[Double] => BSV.vertcat(new BSV[Double](Array(0), Array(1.0), 1), sv)
-      case v: Any => throw new IllegalArgumentException("Do not support vector type " + v.getClass)
-    }
-    Vectors.fromBreeze(vector1)
-  }
-
   /**
    * Run the algorithm with the configured parameters on an input RDD
    * of LabeledPoint entries starting from the initial weights provided.
@@ -147,23 +136,23 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel]
 
     // Prepend an extra variable consisting of all 1.0's for the intercept.
     val data = if (addIntercept) {
-      input.map(labeledPoint => (labeledPoint.label, prependOne(labeledPoint.features)))
+      input.map(labeledPoint => (labeledPoint.label, appendBias(labeledPoint.features)))
     } else {
       input.map(labeledPoint => (labeledPoint.label, labeledPoint.features))
     }
 
     val initialWeightsWithIntercept = if (addIntercept) {
-      prependOne(initialWeights)
+      appendBias(initialWeights)
     } else {
       initialWeights
     }
 
     val weightsWithIntercept = optimizer.optimize(data, initialWeightsWithIntercept)
 
-    val intercept = if (addIntercept) weightsWithIntercept(0) else 0.0
+    val intercept = if (addIntercept) weightsWithIntercept(weightsWithIntercept.size - 1) else 0.0
     val weights =
       if (addIntercept) {
-        Vectors.dense(weightsWithIntercept.toArray.slice(1, weightsWithIntercept.size))
+        Vectors.dense(weightsWithIntercept.toArray.slice(0, weightsWithIntercept.size - 1))
       } else {
         weightsWithIntercept
       }

From 8f1d4226c285e33d2fb839d3163bb374eb6db0e7 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Tue, 15 Jul 2014 02:15:29 -0700
Subject: [PATCH 646/810] Update README.md to include a slightly more
 informative project description.

(cherry picked from commit 401083be9f010f95110a819a49837ecae7d9c4ec)
Signed-off-by: Reynold Xin <rxin@apache.org>
---
 README.md | 9 ++++++++-
 1 file changed, 8 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index f6e7f5109131..096f13e71636 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,13 @@
 # Apache Spark
 
-Lightning-Fast Cluster Computing - <http://spark.apache.org/>
+Spark is a fast and general cluster computing system. It provides
+high-level APIs in Scala, Java, and Python, and an optimized engine that
+supports general computation graphs for data analysis. It also supports a
+rich set of higher-level tools including Spark SQL for SQL and structured
+data processing, MLLib for machine learning, GraphX for graph processing,
+and Spark Streaming.
+
+<http://spark.apache.org/>
 
 
 ## Online Documentation

From 6555618c8f39b4e7da9402c3fd9da7a75bf7794e Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Tue, 15 Jul 2014 02:20:01 -0700
Subject: [PATCH 647/810] README update: added "for Big Data".

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 096f13e71636..f87e07aa5cc9 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,6 @@
 # Apache Spark
 
-Spark is a fast and general cluster computing system. It provides
+Spark is a fast and general cluster computing system for Big Data. It provides
 high-level APIs in Scala, Java, and Python, and an optimized engine that
 supports general computation graphs for data analysis. It also supports a
 rich set of higher-level tools including Spark SQL for SQL and structured

From 04b01bb101eeaf76c2e7c94c291669f0b2372c9a Mon Sep 17 00:00:00 2001
From: Alexander Ulanov <nashb@yandex.ru>
Date: Tue, 15 Jul 2014 08:40:22 -0700
Subject: [PATCH 648/810] [MLLIB] [SPARK-2222] Add multiclass evaluation
 metrics

Adding two classes:
1) MulticlassMetrics implements various multiclass evaluation metrics
2) MulticlassMetricsSuite implements unit tests for MulticlassMetrics

Author: Alexander Ulanov <nashb@yandex.ru>
Author: unknown <ulanov@ULANOV1.emea.hpqcorp.net>
Author: Xiangrui Meng <meng@databricks.com>

Closes #1155 from avulanov/master and squashes the following commits:

2eae80f [Alexander Ulanov] Merge pull request #1 from mengxr/avulanov-master
5ebeb08 [Xiangrui Meng] minor updates
79c3555 [Alexander Ulanov] Addressing reviewers comments mengxr
0fa9511 [Alexander Ulanov] Addressing reviewers comments mengxr
f0dadc9 [Alexander Ulanov] Addressing reviewers comments mengxr
4811378 [Alexander Ulanov] Removing println
87fb11f [Alexander Ulanov] Addressing reviewers comments mengxr. Added confusion matrix
e3db569 [Alexander Ulanov] Addressing reviewers comments mengxr. Added true positive rate and false positive rate. Test suite code style.
a7e8bf0 [Alexander Ulanov] Addressing reviewers comments mengxr
c3a77ad [Alexander Ulanov] Addressing reviewers comments mengxr
e2c91c3 [Alexander Ulanov] Fixes to mutliclass metics
d5ce981 [unknown] Comments about Double
a5c8ba4 [unknown] Unit tests. Class rename
fcee82d [unknown] Unit tests. Class rename
d535d62 [unknown] Multiclass evaluation
---
 .../mllib/evaluation/MulticlassMetrics.scala  | 190 ++++++++++++++++++
 .../evaluation/MulticlassMetricsSuite.scala   |  90 +++++++++
 2 files changed, 280 insertions(+)
 create mode 100644 mllib/src/main/scala/org/apache/spark/mllib/evaluation/MulticlassMetrics.scala
 create mode 100644 mllib/src/test/scala/org/apache/spark/mllib/evaluation/MulticlassMetricsSuite.scala

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MulticlassMetrics.scala b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MulticlassMetrics.scala
new file mode 100644
index 000000000000..666362ae6739
--- /dev/null
+++ b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MulticlassMetrics.scala
@@ -0,0 +1,190 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.mllib.evaluation
+
+import scala.collection.Map
+
+import org.apache.spark.SparkContext._
+import org.apache.spark.annotation.Experimental
+import org.apache.spark.mllib.linalg.{Matrices, Matrix}
+import org.apache.spark.rdd.RDD
+
+/**
+ * ::Experimental::
+ * Evaluator for multiclass classification.
+ *
+ * @param predictionAndLabels an RDD of (prediction, label) pairs.
+ */
+@Experimental
+class MulticlassMetrics(predictionAndLabels: RDD[(Double, Double)]) {
+
+  private lazy val labelCountByClass: Map[Double, Long] = predictionAndLabels.values.countByValue()
+  private lazy val labelCount: Long = labelCountByClass.values.sum
+  private lazy val tpByClass: Map[Double, Int] = predictionAndLabels
+    .map { case (prediction, label) =>
+      (label, if (label == prediction) 1 else 0)
+    }.reduceByKey(_ + _)
+    .collectAsMap()
+  private lazy val fpByClass: Map[Double, Int] = predictionAndLabels
+    .map { case (prediction, label) =>
+      (prediction, if (prediction != label) 1 else 0)
+    }.reduceByKey(_ + _)
+    .collectAsMap()
+  private lazy val confusions = predictionAndLabels
+    .map { case (prediction, label) =>
+      ((label, prediction), 1)
+    }.reduceByKey(_ + _)
+    .collectAsMap()
+
+  /**
+   * Returns confusion matrix:
+   * predicted classes are in columns,
+   * they are ordered by class label ascending,
+   * as in "labels"
+   */
+  def confusionMatrix: Matrix = {
+    val n = labels.size
+    val values = Array.ofDim[Double](n * n)
+    var i = 0
+    while (i < n) {
+      var j = 0
+      while (j < n) {
+        values(i + j * n) = confusions.getOrElse((labels(i), labels(j)), 0).toDouble
+        j += 1
+      }
+      i += 1
+    }
+    Matrices.dense(n, n, values)
+  }
+
+  /**
+   * Returns true positive rate for a given label (category)
+   * @param label the label.
+   */
+  def truePositiveRate(label: Double): Double = recall(label)
+
+  /**
+   * Returns false positive rate for a given label (category)
+   * @param label the label.
+   */
+  def falsePositiveRate(label: Double): Double = {
+    val fp = fpByClass.getOrElse(label, 0)
+    fp.toDouble / (labelCount - labelCountByClass(label))
+  }
+
+  /**
+   * Returns precision for a given label (category)
+   * @param label the label.
+   */
+  def precision(label: Double): Double = {
+    val tp = tpByClass(label)
+    val fp = fpByClass.getOrElse(label, 0)
+    if (tp + fp == 0) 0 else tp.toDouble / (tp + fp)
+  }
+
+  /**
+   * Returns recall for a given label (category)
+   * @param label the label.
+   */
+  def recall(label: Double): Double = tpByClass(label).toDouble / labelCountByClass(label)
+
+  /**
+   * Returns f-measure for a given label (category)
+   * @param label the label.
+   * @param beta the beta parameter.
+   */
+  def fMeasure(label: Double, beta: Double): Double = {
+    val p = precision(label)
+    val r = recall(label)
+    val betaSqrd = beta * beta
+    if (p + r == 0) 0 else (1 + betaSqrd) * p * r / (betaSqrd * p + r)
+  }
+
+  /**
+   * Returns f1-measure for a given label (category)
+   * @param label the label.
+   */
+  def fMeasure(label: Double): Double = fMeasure(label, 1.0)
+
+  /**
+   * Returns precision
+   */
+  lazy val precision: Double = tpByClass.values.sum.toDouble / labelCount
+
+  /**
+   * Returns recall
+   * (equals to precision for multiclass classifier
+   * because sum of all false positives is equal to sum
+   * of all false negatives)
+   */
+  lazy val recall: Double = precision
+
+  /**
+   * Returns f-measure
+   * (equals to precision and recall because precision equals recall)
+   */
+  lazy val fMeasure: Double = precision
+
+  /**
+   * Returns weighted true positive rate
+   * (equals to precision, recall and f-measure)
+   */
+  lazy val weightedTruePositiveRate: Double = weightedRecall
+
+  /**
+   * Returns weighted false positive rate
+   */
+  lazy val weightedFalsePositiveRate: Double = labelCountByClass.map { case (category, count) =>
+    falsePositiveRate(category) * count.toDouble / labelCount
+  }.sum
+
+  /**
+   * Returns weighted averaged recall
+   * (equals to precision, recall and f-measure)
+   */
+  lazy val weightedRecall: Double = labelCountByClass.map { case (category, count) =>
+    recall(category) * count.toDouble / labelCount
+  }.sum
+
+  /**
+   * Returns weighted averaged precision
+   */
+  lazy val weightedPrecision: Double = labelCountByClass.map { case (category, count) =>
+    precision(category) * count.toDouble / labelCount
+  }.sum
+
+  /**
+   * Returns weighted averaged f-measure
+   * @param beta the beta parameter.
+   */
+  def weightedFMeasure(beta: Double): Double = labelCountByClass.map { case (category, count) =>
+    fMeasure(category, beta) * count.toDouble / labelCount
+  }.sum
+
+  /**
+   * Returns weighted averaged f1-measure
+   */
+  lazy val weightedFMeasure: Double = labelCountByClass.map { case (category, count) =>
+    fMeasure(category, 1.0) * count.toDouble / labelCount
+  }.sum
+
+  /**
+   * Returns the sequence of labels in ascending order
+   */
+  lazy val labels: Array[Double] = tpByClass.keys.toArray.sorted
+}
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/evaluation/MulticlassMetricsSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/evaluation/MulticlassMetricsSuite.scala
new file mode 100644
index 000000000000..1ea503971c86
--- /dev/null
+++ b/mllib/src/test/scala/org/apache/spark/mllib/evaluation/MulticlassMetricsSuite.scala
@@ -0,0 +1,90 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.mllib.evaluation
+
+import org.scalatest.FunSuite
+
+import org.apache.spark.mllib.linalg.Matrices
+import org.apache.spark.mllib.util.LocalSparkContext
+
+class MulticlassMetricsSuite extends FunSuite with LocalSparkContext {
+  test("Multiclass evaluation metrics") {
+    /*
+     * Confusion matrix for 3-class classification with total 9 instances:
+     * |2|1|1| true class0 (4 instances)
+     * |1|3|0| true class1 (4 instances)
+     * |0|0|1| true class2 (1 instance)
+     */
+    val confusionMatrix = Matrices.dense(3, 3, Array(2, 1, 0, 1, 3, 0, 1, 0, 1))
+    val labels = Array(0.0, 1.0, 2.0)
+    val predictionAndLabels = sc.parallelize(
+      Seq((0.0, 0.0), (0.0, 1.0), (0.0, 0.0), (1.0, 0.0), (1.0, 1.0),
+        (1.0, 1.0), (1.0, 1.0), (2.0, 2.0), (2.0, 0.0)), 2)
+    val metrics = new MulticlassMetrics(predictionAndLabels)
+    val delta = 0.0000001
+    val fpRate0 = 1.0 / (9 - 4)
+    val fpRate1 = 1.0 / (9 - 4)
+    val fpRate2 = 1.0 / (9 - 1)
+    val precision0 = 2.0 / (2 + 1)
+    val precision1 = 3.0 / (3 + 1)
+    val precision2 = 1.0 / (1 + 1)
+    val recall0 = 2.0 / (2 + 2)
+    val recall1 = 3.0 / (3 + 1)
+    val recall2 = 1.0 / (1 + 0)
+    val f1measure0 = 2 * precision0 * recall0 / (precision0 + recall0)
+    val f1measure1 = 2 * precision1 * recall1 / (precision1 + recall1)
+    val f1measure2 = 2 * precision2 * recall2 / (precision2 + recall2)
+    val f2measure0 = (1 + 2 * 2) * precision0 * recall0 / (2 * 2 * precision0 + recall0)
+    val f2measure1 = (1 + 2 * 2) * precision1 * recall1 / (2 * 2 * precision1 + recall1)
+    val f2measure2 = (1 + 2 * 2) * precision2 * recall2 / (2 * 2 * precision2 + recall2)
+
+    assert(metrics.confusionMatrix.toArray.sameElements(confusionMatrix.toArray))
+    assert(math.abs(metrics.falsePositiveRate(0.0) - fpRate0) < delta)
+    assert(math.abs(metrics.falsePositiveRate(1.0) - fpRate1) < delta)
+    assert(math.abs(metrics.falsePositiveRate(2.0) - fpRate2) < delta)
+    assert(math.abs(metrics.precision(0.0) - precision0) < delta)
+    assert(math.abs(metrics.precision(1.0) - precision1) < delta)
+    assert(math.abs(metrics.precision(2.0) - precision2) < delta)
+    assert(math.abs(metrics.recall(0.0) - recall0) < delta)
+    assert(math.abs(metrics.recall(1.0) - recall1) < delta)
+    assert(math.abs(metrics.recall(2.0) - recall2) < delta)
+    assert(math.abs(metrics.fMeasure(0.0) - f1measure0) < delta)
+    assert(math.abs(metrics.fMeasure(1.0) - f1measure1) < delta)
+    assert(math.abs(metrics.fMeasure(2.0) - f1measure2) < delta)
+    assert(math.abs(metrics.fMeasure(0.0, 2.0) - f2measure0) < delta)
+    assert(math.abs(metrics.fMeasure(1.0, 2.0) - f2measure1) < delta)
+    assert(math.abs(metrics.fMeasure(2.0, 2.0) - f2measure2) < delta)
+
+    assert(math.abs(metrics.recall -
+      (2.0 + 3.0 + 1.0) / ((2 + 3 + 1) + (1 + 1 + 1))) < delta)
+    assert(math.abs(metrics.recall - metrics.precision) < delta)
+    assert(math.abs(metrics.recall - metrics.fMeasure) < delta)
+    assert(math.abs(metrics.recall - metrics.weightedRecall) < delta)
+    assert(math.abs(metrics.weightedFalsePositiveRate -
+      ((4.0 / 9) * fpRate0 + (4.0 / 9) * fpRate1 + (1.0 / 9) * fpRate2)) < delta)
+    assert(math.abs(metrics.weightedPrecision -
+      ((4.0 / 9) * precision0 + (4.0 / 9) * precision1 + (1.0 / 9) * precision2)) < delta)
+    assert(math.abs(metrics.weightedRecall -
+      ((4.0 / 9) * recall0 + (4.0 / 9) * recall1 + (1.0 / 9) * recall2)) < delta)
+    assert(math.abs(metrics.weightedFMeasure -
+      ((4.0 / 9) * f1measure0 + (4.0 / 9) * f1measure1 + (1.0 / 9) * f1measure2)) < delta)
+    assert(math.abs(metrics.weightedFMeasure(2.0) -
+      ((4.0 / 9) * f2measure0 + (4.0 / 9) * f2measure1 + (1.0 / 9) * f2measure2)) < delta)
+    assert(metrics.labels.sameElements(labels))
+  }
+}

From cb09e93c1d7ef9c8f0a1abe4e659783c74993a4e Mon Sep 17 00:00:00 2001
From: William Benton <willb@redhat.com>
Date: Tue, 15 Jul 2014 09:13:39 -0700
Subject: [PATCH 649/810] Reformat multi-line closure argument.

Author: William Benton <willb@redhat.com>

Closes #1419 from willb/reformat-2486 and squashes the following commits:

2676231 [William Benton] Reformat multi-line closure argument.
---
 core/src/main/scala/org/apache/spark/util/Utils.scala | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala
index d72c97bbe816..10c33d67e768 100644
--- a/core/src/main/scala/org/apache/spark/util/Utils.scala
+++ b/core/src/main/scala/org/apache/spark/util/Utils.scala
@@ -809,11 +809,12 @@ private[spark] object Utils extends Logging {
    */
   def getCallSite: CallSite = {
     val trace = Thread.currentThread.getStackTrace()
-      .filterNot((ste:StackTraceElement) => 
+      .filterNot { ste:StackTraceElement => 
         // When running under some profilers, the current stack trace might contain some bogus 
         // frames. This is intended to ensure that we don't crash in these situations by
         // ignoring any frames that we can't examine.
-        (ste == null || ste.getMethodName == null || ste.getMethodName.contains("getStackTrace")))
+        (ste == null || ste.getMethodName == null || ste.getMethodName.contains("getStackTrace"))
+      }
 
     // Keep crawling up the stack trace until we find the first function not inside of the spark
     // package. We track the last (shallowest) contiguous Spark method. This might be an RDD

From 9dd635eb5df52835b3b7f4f2b9c789da9e813c71 Mon Sep 17 00:00:00 2001
From: witgo <witgo@qq.com>
Date: Tue, 15 Jul 2014 10:46:17 -0700
Subject: [PATCH 650/810] SPARK-2480: Resolve sbt warnings "NOTE: SPARK_YARN is
 deprecated, please use -Pyarn flag"

Author: witgo <witgo@qq.com>

Closes #1404 from witgo/run-tests and squashes the following commits:

f703aee [witgo] fix Note: implicit method fromPairDStream is not applicable here because it comes after the application point and it lacks an explicit result type
2944f51 [witgo] Remove "NOTE: SPARK_YARN is deprecated, please use -Pyarn flag"
ef59c70 [witgo] fix Note: implicit method fromPairDStream is not applicable here because it comes after the application point and it lacks an explicit result type
6cefee5 [witgo] Remove "NOTE: SPARK_YARN is deprecated, please use -Pyarn flag"
---
 dev/run-tests                            | 7 +++----
 dev/scalastyle                           | 6 +++---
 docs/hadoop-third-party-distributions.md | 4 ++--
 docs/sql-programming-guide.md            | 2 +-
 4 files changed, 9 insertions(+), 10 deletions(-)

diff --git a/dev/run-tests b/dev/run-tests
index edd17b53b3d8..51e4def0f835 100755
--- a/dev/run-tests
+++ b/dev/run-tests
@@ -21,8 +21,7 @@
 FWDIR="$(cd `dirname $0`/..; pwd)"
 cd $FWDIR
 
-export SPARK_HADOOP_VERSION=2.3.0
-export SPARK_YARN=true
+export SBT_MAVEN_PROFILES="-Pyarn -Phadoop-2.3 -Dhadoop.version=2.3.0"
 
 # Remove work directory
 rm -rf ./work
@@ -66,8 +65,8 @@ echo "========================================================================="
 # (either resolution or compilation) prompts the user for input either q, r, 
 # etc to quit or retry. This echo is there to make it not block.
 if [ -n "$_RUN_SQL_TESTS" ]; then
-  echo -e "q\n" | SPARK_HIVE=true sbt/sbt clean package assembly/assembly test | \
-    grep -v -e "info.*Resolving" -e "warn.*Merging" -e "info.*Including"
+  echo -e "q\n" | SBT_MAVEN_PROFILES="$SBT_MAVEN_PROFILES -Phive" sbt/sbt clean package \
+    assembly/assembly test | grep -v -e "info.*Resolving" -e "warn.*Merging" -e "info.*Including"
 else
   echo -e "q\n" | sbt/sbt clean package assembly/assembly test | \
     grep -v -e "info.*Resolving" -e "warn.*Merging" -e "info.*Including"
diff --git a/dev/scalastyle b/dev/scalastyle
index 0e8fd5cc8d64..a02d06912f23 100755
--- a/dev/scalastyle
+++ b/dev/scalastyle
@@ -17,12 +17,12 @@
 # limitations under the License.
 #
 
-echo -e "q\n" | SPARK_HIVE=true sbt/sbt scalastyle > scalastyle.txt
+echo -e "q\n" | sbt/sbt -Phive scalastyle > scalastyle.txt
 # Check style with YARN alpha built too
-echo -e "q\n" | SPARK_HADOOP_VERSION=0.23.9 SPARK_YARN=true sbt/sbt yarn-alpha/scalastyle \
+echo -e "q\n" | sbt/sbt -Pyarn -Phadoop-0.23 -Dhadoop.version=0.23.9 yarn-alpha/scalastyle \
   >> scalastyle.txt
 # Check style with YARN built too
-echo -e "q\n" | SPARK_HADOOP_VERSION=2.2.0 SPARK_YARN=true sbt/sbt yarn/scalastyle \
+echo -e "q\n" | sbt/sbt -Pyarn -Phadoop-2.2 -Dhadoop.version=2.2.0 yarn/scalastyle \
   >> scalastyle.txt
 
 ERRORS=$(cat scalastyle.txt | grep -e "\<error\>")
diff --git a/docs/hadoop-third-party-distributions.md b/docs/hadoop-third-party-distributions.md
index 32403bc6957a..ab1023b8f184 100644
--- a/docs/hadoop-third-party-distributions.md
+++ b/docs/hadoop-third-party-distributions.md
@@ -48,9 +48,9 @@ the _exact_ Hadoop version you are running to avoid any compatibility errors.
   </tr>
 </table>
 
-In SBT, the equivalent can be achieved by setting the SPARK_HADOOP_VERSION flag:
+In SBT, the equivalent can be achieved by setting the the `hadoop.version` property:
 
-    SPARK_HADOOP_VERSION=1.0.4 sbt/sbt assembly
+    sbt/sbt -Dhadoop.version=1.0.4 assembly
 
 # Linking Applications to the Hadoop Version
 
diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md
index 522c83884ef4..38728534a46e 100644
--- a/docs/sql-programming-guide.md
+++ b/docs/sql-programming-guide.md
@@ -474,7 +474,7 @@ anotherPeople = sqlContext.jsonRDD(anotherPeopleRDD)
 
 Spark SQL also supports reading and writing data stored in [Apache Hive](http://hive.apache.org/).
 However, since Hive has a large number of dependencies, it is not included in the default Spark assembly.
-In order to use Hive you must first run '`SPARK_HIVE=true sbt/sbt assembly/assembly`' (or use `-Phive` for maven).
+In order to use Hive you must first run '`sbt/sbt -Phive assembly/assembly`' (or use `-Phive` for maven).
 This command builds a new assembly jar that includes Hive. Note that this Hive assembly jar must also be present
 on all of the worker nodes, as they will need access to the Hive serialization and deserialization libraries
 (SerDes) in order to acccess data stored in Hive.

From 72ea56da8e383c61c6f18eeefef03b9af00f5158 Mon Sep 17 00:00:00 2001
From: witgo <witgo@qq.com>
Date: Tue, 15 Jul 2014 13:52:56 -0500
Subject: [PATCH 651/810] SPARK-1291: Link the spark UI to RM ui in yarn-client
 mode

Author: witgo <witgo@qq.com>

Closes #1112 from witgo/SPARK-1291 and squashes the following commits:

6022bcd [witgo] review commit
1fbb925 [witgo] add addAmIpFilter to yarn alpha
210299c [witgo] review commit
1b92a07 [witgo] review commit
6896586 [witgo] Add comments to addWebUIFilter
3e9630b [witgo] review commit
142ee29 [witgo] review commit
1fe7710 [witgo] Link the spark UI to RM ui in yarn-client mode
---
 .../cluster/CoarseGrainedClusterMessage.scala |  3 +++
 .../CoarseGrainedSchedulerBackend.scala       | 18 +++++++++++++++
 .../scala/org/apache/spark/ui/UIUtils.scala   | 11 ++++++++-
 .../spark/deploy/yarn/ExecutorLauncher.scala  | 22 +++++++++++++++---
 .../cluster/YarnClientSchedulerBackend.scala  |  1 +
 .../spark/deploy/yarn/ExecutorLauncher.scala  | 23 ++++++++++++++++---
 6 files changed, 71 insertions(+), 7 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedClusterMessage.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedClusterMessage.scala
index 318e16552201..6abf6d930c15 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedClusterMessage.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedClusterMessage.scala
@@ -66,4 +66,7 @@ private[spark] object CoarseGrainedClusterMessages {
 
   case class RemoveExecutor(executorId: String, reason: String) extends CoarseGrainedClusterMessage
 
+  case class AddWebUIFilter(filterName:String, filterParams: String, proxyBase :String)
+    extends CoarseGrainedClusterMessage
+
 }
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala
index 0f5545e2ed65..9f085eef4672 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala
@@ -31,6 +31,7 @@ import org.apache.spark.{SparkEnv, Logging, SparkException, TaskState}
 import org.apache.spark.scheduler.{SchedulerBackend, SlaveLost, TaskDescription, TaskSchedulerImpl, WorkerOffer}
 import org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages._
 import org.apache.spark.util.{SerializableBuffer, AkkaUtils, Utils}
+import org.apache.spark.ui.JettyUtils
 
 /**
  * A scheduler backend that waits for coarse grained executors to connect to it through Akka.
@@ -136,6 +137,9 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, actorSystem: A
         removeExecutor(executorId, reason)
         sender ! true
 
+      case AddWebUIFilter(filterName, filterParams, proxyBase) =>
+        addWebUIFilter(filterName, filterParams, proxyBase)
+        sender ! true
       case DisassociatedEvent(_, address, _) =>
         addressToExecutorId.get(address).foreach(removeExecutor(_,
           "remote Akka client disassociated"))
@@ -276,6 +280,20 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, actorSystem: A
     }
     false
   }
+
+  // Add filters to the SparkUI
+  def addWebUIFilter(filterName: String, filterParams: String, proxyBase: String) {
+    if (proxyBase != null && proxyBase.nonEmpty) {
+      System.setProperty("spark.ui.proxyBase", proxyBase)
+    }
+
+    if (Seq(filterName, filterParams).forall(t => t != null && t.nonEmpty)) {
+      logInfo(s"Add WebUI Filter. $filterName, $filterParams, $proxyBase")
+      conf.set("spark.ui.filters", filterName)
+      conf.set(s"spark.$filterName.params", filterParams)
+      JettyUtils.addFilters(scheduler.sc.ui.getHandlers, conf)
+    }
+  }
 }
 
 private[spark] object CoarseGrainedSchedulerBackend {
diff --git a/core/src/main/scala/org/apache/spark/ui/UIUtils.scala b/core/src/main/scala/org/apache/spark/ui/UIUtils.scala
index 9cb50d9b83dd..e07aa2ee3a5a 100644
--- a/core/src/main/scala/org/apache/spark/ui/UIUtils.scala
+++ b/core/src/main/scala/org/apache/spark/ui/UIUtils.scala
@@ -136,7 +136,16 @@ private[spark] object UIUtils extends Logging {
   }
 
   // Yarn has to go through a proxy so the base uri is provided and has to be on all links
-  val uiRoot : String = Option(System.getenv("APPLICATION_WEB_PROXY_BASE")).getOrElse("")
+  def uiRoot: String = {
+    if (System.getenv("APPLICATION_WEB_PROXY_BASE") != null) {
+      System.getenv("APPLICATION_WEB_PROXY_BASE")
+    } else if (System.getProperty("spark.ui.proxyBase") != null) {
+      System.getProperty("spark.ui.proxyBase")
+    }
+    else {
+      ""
+    }
+  }
 
   def prependBaseUri(basePath: String = "", resource: String = "") = uiRoot + basePath + resource
 
diff --git a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala
index bfdb6232f511..a86ad256dfa3 100644
--- a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala
+++ b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala
@@ -32,6 +32,7 @@ import akka.actor.Terminated
 import org.apache.spark.{Logging, SecurityManager, SparkConf}
 import org.apache.spark.util.{Utils, AkkaUtils}
 import org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend
+import org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.AddWebUIFilter
 import org.apache.spark.scheduler.SplitInfo
 import org.apache.spark.deploy.SparkHadoopUtil
 
@@ -81,6 +82,9 @@ class ExecutorLauncher(args: ApplicationMasterArguments, conf: Configuration, sp
       case x: DisassociatedEvent =>
         logInfo(s"Driver terminated or disconnected! Shutting down. $x")
         driverClosed = true
+      case x: AddWebUIFilter =>
+        logInfo(s"Add WebUI Filter. $x")
+        driver ! x
     }
   }
 
@@ -111,7 +115,7 @@ class ExecutorLauncher(args: ApplicationMasterArguments, conf: Configuration, sp
     }
 
     waitForSparkMaster()
-
+    addAmIpFilter()
     // Allocate all containers
     allocateExecutors()
 
@@ -171,7 +175,8 @@ class ExecutorLauncher(args: ApplicationMasterArguments, conf: Configuration, sp
   }
 
   private def registerApplicationMaster(): RegisterApplicationMasterResponse = {
-    logInfo("Registering the ApplicationMaster")
+    val appUIAddress = sparkConf.get("spark.driver.appUIAddress", "")
+    logInfo(s"Registering the ApplicationMaster with appUIAddress: $appUIAddress")
     val appMasterRequest = Records.newRecord(classOf[RegisterApplicationMasterRequest])
       .asInstanceOf[RegisterApplicationMasterRequest]
     appMasterRequest.setApplicationAttemptId(appAttemptId)
@@ -180,10 +185,21 @@ class ExecutorLauncher(args: ApplicationMasterArguments, conf: Configuration, sp
     appMasterRequest.setHost(Utils.localHostName())
     appMasterRequest.setRpcPort(0)
     // What do we provide here ? Might make sense to expose something sensible later ?
-    appMasterRequest.setTrackingUrl("")
+    appMasterRequest.setTrackingUrl(appUIAddress)
     resourceManager.registerApplicationMaster(appMasterRequest)
   }
 
+  // add the yarn amIpFilter that Yarn requires for properly securing the UI
+  private def addAmIpFilter() {
+    val proxy = YarnConfiguration.getProxyHostAndPort(conf)
+    val parts = proxy.split(":")
+    val proxyBase = System.getenv(ApplicationConstants.APPLICATION_WEB_PROXY_BASE_ENV)
+    val uriBase = "http://" + proxy + proxyBase
+    val amFilter = "PROXY_HOST=" + parts(0) + "," + "PROXY_URI_BASE=" + uriBase
+    val amFilterName = "org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter"
+    actor ! AddWebUIFilter(amFilterName, amFilter, proxyBase)
+  }
+
   private def waitForSparkMaster() {
     logInfo("Waiting for spark driver to be reachable.")
     var driverUp = false
diff --git a/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala b/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala
index 0f9fdcfcb651..1b37c4bb13f4 100644
--- a/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala
+++ b/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala
@@ -48,6 +48,7 @@ private[spark] class YarnClientSchedulerBackend(
     val driverHost = conf.get("spark.driver.host")
     val driverPort = conf.get("spark.driver.port")
     val hostport = driverHost + ":" + driverPort
+    conf.set("spark.driver.appUIAddress", sc.ui.appUIHostPort)
 
     val argsArrayBuf = new ArrayBuffer[String]()
     argsArrayBuf += (
diff --git a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala
index f71ad036ce0f..5ac95f379872 100644
--- a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala
+++ b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala
@@ -31,10 +31,12 @@ import akka.actor.Terminated
 import org.apache.spark.{Logging, SecurityManager, SparkConf}
 import org.apache.spark.util.{Utils, AkkaUtils}
 import org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend
+import org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.AddWebUIFilter
 import org.apache.spark.scheduler.SplitInfo
 import org.apache.hadoop.yarn.client.api.AMRMClient
 import org.apache.hadoop.yarn.client.api.AMRMClient.ContainerRequest
 import org.apache.spark.deploy.SparkHadoopUtil
+import org.apache.hadoop.yarn.webapp.util.WebAppUtils
 
 /**
  * An application master that allocates executors on behalf of a driver that is running outside
@@ -82,6 +84,9 @@ class ExecutorLauncher(args: ApplicationMasterArguments, conf: Configuration, sp
       case x: DisassociatedEvent =>
         logInfo(s"Driver terminated or disconnected! Shutting down. $x")
         driverClosed = true
+      case x: AddWebUIFilter =>
+        logInfo(s"Add WebUI Filter. $x")
+        driver ! x
     }
   }
 
@@ -99,6 +104,7 @@ class ExecutorLauncher(args: ApplicationMasterArguments, conf: Configuration, sp
     registerApplicationMaster()
 
     waitForSparkMaster()
+    addAmIpFilter()
 
     // Allocate all containers
     allocateExecutors()
@@ -142,9 +148,20 @@ class ExecutorLauncher(args: ApplicationMasterArguments, conf: Configuration, sp
   }
 
   private def registerApplicationMaster(): RegisterApplicationMasterResponse = {
-    logInfo("Registering the ApplicationMaster")
-    // TODO: Find out client's Spark UI address and fill in here?
-    amClient.registerApplicationMaster(Utils.localHostName(), 0, "")
+    val appUIAddress = sparkConf.get("spark.driver.appUIAddress", "")
+    logInfo(s"Registering the ApplicationMaster with appUIAddress: $appUIAddress")
+    amClient.registerApplicationMaster(Utils.localHostName(), 0, appUIAddress)
+  }
+
+  // add the yarn amIpFilter that Yarn requires for properly securing the UI
+  private def addAmIpFilter() {
+    val proxy = WebAppUtils.getProxyHostAndPort(conf)
+    val parts = proxy.split(":")
+    val proxyBase = System.getenv(ApplicationConstants.APPLICATION_WEB_PROXY_BASE_ENV)
+    val uriBase = "http://" + proxy + proxyBase
+    val amFilter = "PROXY_HOST=" + parts(0) + "," + "PROXY_URI_BASE=" + uriBase
+    val amFilterName = "org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter"
+    actor ! AddWebUIFilter(amFilterName, amFilter, proxyBase)
   }
 
   private def waitForSparkMaster() {

From e7ec815d9a2b0f89a56dc7dd3106c31a09492028 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Tue, 15 Jul 2014 13:13:33 -0700
Subject: [PATCH 652/810] Added LZ4 to compression codec in configuration page.

Author: Reynold Xin <rxin@apache.org>

Closes #1417 from rxin/lz4 and squashes the following commits:

472f6a1 [Reynold Xin] Set the proper default.
9cf0b2f [Reynold Xin] Added LZ4 to compression codec in configuration page.
---
 docs/configuration.md | 9 ++++-----
 1 file changed, 4 insertions(+), 5 deletions(-)

diff --git a/docs/configuration.md b/docs/configuration.md
index 19fd980e6088..9d3fe7441486 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -336,13 +336,12 @@ Apart from these, the following properties are also available, and may be useful
 </tr>
 <tr>
   <td><code>spark.io.compression.codec</code></td>
-  <td>org.apache.spark.io.<br />LZFCompressionCodec</td>
+  <td>org.apache.spark.io.<br />SnappyCompressionCodec</td>
   <td>
     The codec used to compress internal data such as RDD partitions and shuffle outputs.
-    By default, Spark provides two codecs: <code>org.apache.spark.io.LZFCompressionCodec</code>
-    and <code>org.apache.spark.io.SnappyCompressionCodec</code>. Of these two choices,
-    Snappy offers faster compression and decompression, while LZF offers a better compression
-    ratio.
+    By default, Spark provides three codecs:  <code>org.apache.spark.io.LZ4CompressionCodec</code>,
+    <code>org.apache.spark.io.LZFCompressionCodec</code>,
+    and <code>org.apache.spark.io.SnappyCompressionCodec</code>.
   </td>
 </tr>
 <tr>

From a21f9a7543309320bb2791468243c8f10bc6e81b Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Tue, 15 Jul 2014 14:00:54 -0700
Subject: [PATCH 653/810] [SPARK-2471] remove runtime scope for jets3t

The assembly jar (built by sbt) doesn't include jets3t if we set it to runtime only, but I don't know whether it was set this way for a particular reason.

CC: srowen ScrapCodes

Author: Xiangrui Meng <meng@databricks.com>

Closes #1402 from mengxr/jets3t and squashes the following commits:

bfa2d17 [Xiangrui Meng] remove runtime scope for jets3t
---
 pom.xml | 1 -
 1 file changed, 1 deletion(-)

diff --git a/pom.xml b/pom.xml
index d570f3e6b932..4e2d64a83364 100644
--- a/pom.xml
+++ b/pom.xml
@@ -614,7 +614,6 @@
         <groupId>net.java.dev.jets3t</groupId>
         <artifactId>jets3t</artifactId>
         <version>${jets3t.version}</version>
-        <scope>runtime</scope>
         <exclusions>
           <exclusion>
             <groupId>commons-logging</groupId>

From 0f98ef1a2c9ecf328f6c5918808fa5ca486e8afd Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Tue, 15 Jul 2014 14:01:48 -0700
Subject: [PATCH 654/810] [SPARK-2483][SQL] Fix parsing of repeated, nested
 data access.

Author: Michael Armbrust <michael@databricks.com>

Closes #1411 from marmbrus/nestedRepeated and squashes the following commits:

044fa09 [Michael Armbrust] Fix parsing of repeated, nested data access.
---
 .../main/scala/org/apache/spark/sql/hive/HiveQl.scala  |  5 +----
 .../spark/sql/hive/execution/HiveResolutionSuite.scala | 10 ++++++++--
 2 files changed, 9 insertions(+), 6 deletions(-)

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
index b70104dd5be5..56aa27a20882 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
@@ -870,10 +870,7 @@ private[hive] object HiveQl {
       nodeToExpr(qualifier) match {
         case UnresolvedAttribute(qualifierName) =>
           UnresolvedAttribute(qualifierName + "." + cleanIdentifier(attr))
-        // The precidence for . seems to be wrong, so [] binds tighter an we need to go inside to
-        // find the underlying attribute references.
-        case GetItem(UnresolvedAttribute(qualifierName), ordinal) =>
-          GetItem(UnresolvedAttribute(qualifierName + "." + cleanIdentifier(attr)), ordinal)
+        case other => GetField(other, attr)
       }
 
     /* Stars (*) */
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveResolutionSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveResolutionSuite.scala
index 67594b57d3df..fb03db12a0b0 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveResolutionSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveResolutionSuite.scala
@@ -20,7 +20,7 @@ package org.apache.spark.sql.hive.execution
 import org.apache.spark.sql.hive.test.TestHive
 import org.apache.spark.sql.hive.test.TestHive._
 
-case class Data(a: Int, B: Int, n: Nested)
+case class Data(a: Int, B: Int, n: Nested, nestedArray: Seq[Nested])
 case class Nested(a: Int, B: Int)
 
 /**
@@ -53,12 +53,18 @@ class HiveResolutionSuite extends HiveComparisonTest {
 
   test("case insensitivity with scala reflection") {
     // Test resolution with Scala Reflection
-    TestHive.sparkContext.parallelize(Data(1, 2, Nested(1,2)) :: Nil)
+    TestHive.sparkContext.parallelize(Data(1, 2, Nested(1,2), Seq(Nested(1,2))) :: Nil)
       .registerAsTable("caseSensitivityTest")
 
     hql("SELECT a, b, A, B, n.a, n.b, n.A, n.B FROM caseSensitivityTest")
   }
 
+  test("nested repeated resolution") {
+    TestHive.sparkContext.parallelize(Data(1, 2, Nested(1,2), Seq(Nested(1,2))) :: Nil)
+      .registerAsTable("nestedRepeatedTest")
+    assert(hql("SELECT nestedArray[0].a FROM nestedRepeatedTest").collect().head(0) === 1)
+  }
+
   /**
    * Negative examples.  Currently only left here for documentation purposes.
    * TODO(marmbrus): Test that catalyst fails on these queries.

From bcd0c30c7eea4c50301cb732c733fdf4d4142060 Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Tue, 15 Jul 2014 14:04:01 -0700
Subject: [PATCH 655/810] [SQL] Whitelist more Hive tests.

Author: Michael Armbrust <michael@databricks.com>

Closes #1396 from marmbrus/moreTests and squashes the following commits:

6660b60 [Michael Armbrust] Blacklist a test that requires DFS command.
8b6001c [Michael Armbrust] Add golden files.
ccd8f97 [Michael Armbrust] Whitelist more tests.
---
 ...imizer1-0-b1e2ade89ae898650f0be4f796d8947b |  1 +
 ...imizer1-1-b9d963d24994c47c3776dda6f7d3881f |  1 +
 ...mizer1-10-5d712a42dcc1c4f7c797dabda5eb7b3a |  1 +
 ...mizer1-11-b1e2ade89ae898650f0be4f796d8947b |  1 +
 ...mizer1-12-b9d963d24994c47c3776dda6f7d3881f |  1 +
 ...mizer1-13-f9f839aedb3a350719c0cbc53a06ace5 |  0
 ...mizer1-14-dae4256e08d595317f8e09a56354a3d9 |  1 +
 ...mizer1-15-777edd9d575f3480ca6cebe4be57b1f6 |  1 +
 ...mizer1-16-43f356b36962f2bade5706d8cf5ae6b4 |  0
 ...mizer1-17-dae4256e08d595317f8e09a56354a3d9 |  1 +
 ...mizer1-18-b1e2ade89ae898650f0be4f796d8947b |  1 +
 ...mizer1-19-b9d963d24994c47c3776dda6f7d3881f |  1 +
 ...imizer1-2-42a0eedc3751f792ad5438b2c64d3897 |  0
 ...mizer1-20-b4c1e27a7c1f61a3e9ae07c80e6e2973 |  0
 ...mizer1-21-16c57348be42ca3cc2f80f7f92265696 |  1 +
 ...mizer1-22-777edd9d575f3480ca6cebe4be57b1f6 |  1 +
 ...timizer1-23-2cdc77fd60449f3547cf95d8eb09a2 |  0
 ...mizer1-24-16c57348be42ca3cc2f80f7f92265696 |  1 +
 ...mizer1-25-b9d963d24994c47c3776dda6f7d3881f |  1 +
 ...mizer1-26-8bcdcc5f01508f576d7bd6422c939225 |  0
 ...mizer1-27-d31433f229e853e8b8440b4ddc63c80e |  1 +
 ...mizer1-28-777edd9d575f3480ca6cebe4be57b1f6 |  1 +
 ...mizer1-29-941ecfef9448ecff56cc16bcfb233ee4 |  0
 ...imizer1-3-5d712a42dcc1c4f7c797dabda5eb7b3a |  1 +
 ...mizer1-30-d31433f229e853e8b8440b4ddc63c80e |  1 +
 ...mizer1-31-b9d963d24994c47c3776dda6f7d3881f |  1 +
 ...mizer1-32-ef6502d6b282c8a6d228bba395b24724 |  0
 ...mizer1-33-ea87e76dba02a46cb958148333e397b7 |  1 +
 ...mizer1-34-777edd9d575f3480ca6cebe4be57b1f6 |  1 +
 ...mizer1-35-b79b220859c09354e23b533c105ccbab |  0
 ...mizer1-36-ea87e76dba02a46cb958148333e397b7 |  1 +
 ...mizer1-37-b9d963d24994c47c3776dda6f7d3881f |  1 +
 ...mizer1-38-638e5300f4c892c2bf27bd91a8f81b64 |  0
 ...mizer1-39-66010469a9cdb66851da9a727ef9fdad |  1 +
 ...imizer1-4-777edd9d575f3480ca6cebe4be57b1f6 |  1 +
 ...mizer1-40-777edd9d575f3480ca6cebe4be57b1f6 |  1 +
 ...timizer1-41-3514c74c7f68f2d70cc6d51ac46c20 |  0
 ...mizer1-42-66010469a9cdb66851da9a727ef9fdad |  1 +
 ...mizer1-43-b9d963d24994c47c3776dda6f7d3881f |  1 +
 ...mizer1-44-7490df6719cd7e47aa08dbcbc3266a92 |  0
 ...mizer1-45-e71195e7d9f557e2abc7f03462d22dba |  1 +
 ...mizer1-46-777edd9d575f3480ca6cebe4be57b1f6 |  1 +
 ...mizer1-47-73da9fe2b0c2ee26c021ec3f2fa27272 |  0
 ...mizer1-48-e71195e7d9f557e2abc7f03462d22dba |  1 +
 ...mizer1-49-b1e2ade89ae898650f0be4f796d8947b |  1 +
 ...imizer1-5-a1c80c68b9a7597096c2580c3766f7f7 |  0
 ...mizer1-50-b9d963d24994c47c3776dda6f7d3881f |  1 +
 ...mizer1-51-fcf9bcb522f542637ccdea863b408448 |  0
 ...imizer1-52-3070366869308907e54797927805603 |  1 +
 ...mizer1-53-777edd9d575f3480ca6cebe4be57b1f6 |  1 +
 ...mizer1-54-dad56e1f06c808b29e5dc8fb0c49efb2 |  0
 ...imizer1-55-3070366869308907e54797927805603 |  1 +
 ...mizer1-56-b9d963d24994c47c3776dda6f7d3881f |  1 +
 ...mizer1-57-3cd3fbbbd8ee5c274fe3d6a45126cef4 |  0
 ...imizer1-58-a6bba6d9b422adb386b35c62cecb548 |  1 +
 ...mizer1-59-777edd9d575f3480ca6cebe4be57b1f6 |  1 +
 ...imizer1-6-5d712a42dcc1c4f7c797dabda5eb7b3a |  1 +
 ...mizer1-60-d6bbaf0d40010159095e4cac025c50c5 |  0
 ...imizer1-61-a6bba6d9b422adb386b35c62cecb548 |  1 +
 ...imizer1-7-24ca942f094b14b92086305cc125e833 |  1 +
 ...imizer1-8-777edd9d575f3480ca6cebe4be57b1f6 |  1 +
 ...imizer1-9-d5bea91b4edb8be0428a336ff9c21dde |  0
 ...mizer10-0-b1e2ade89ae898650f0be4f796d8947b |  1 +
 ...mizer10-1-b9d963d24994c47c3776dda6f7d3881f |  1 +
 ...izer10-10-777edd9d575f3480ca6cebe4be57b1f6 |  1 +
 ...mizer10-11-a1b7af95dfd01783c07aa23208d6160 |  0
 ...izer10-12-1322cff0bdf29aab32e638ad48c71ff9 |  5 ++++
 ...izer10-13-b9d963d24994c47c3776dda6f7d3881f |  1 +
 ...izer10-14-934b668c11600dc9c013c2ddc4c0d68c |  0
 ...mizer10-15-430ff20a144fb3dbf526232d9cb2baa | 23 +++++++++++++++++++
 ...izer10-16-777edd9d575f3480ca6cebe4be57b1f6 |  1 +
 ...izer10-17-9d5521ecef1353d23fd2d4f7d78e7006 |  0
 ...mizer10-18-430ff20a144fb3dbf526232d9cb2baa | 23 +++++++++++++++++++
 ...imizer10-2-f9de06a4184ab1f42793327c1497437 |  0
 ...mizer10-3-6a01aa7ca94cda4268af894b4fd852ea | 15 ++++++++++++
 ...mizer10-4-777edd9d575f3480ca6cebe4be57b1f6 |  1 +
 ...mizer10-5-6f191930802d659058465b2e6de08dd3 |  0
 ...mizer10-6-6a01aa7ca94cda4268af894b4fd852ea | 15 ++++++++++++
 ...mizer10-7-b9d963d24994c47c3776dda6f7d3881f |  1 +
 ...mizer10-8-b2c8d0056b9f1b41cdaeaab4a1a5c9ec |  0
 ...mizer10-9-1322cff0bdf29aab32e638ad48c71ff9 |  5 ++++
 ...pby_ppd-0-4b116ec2d8fb55c52b7b0d248c616ae2 |  0
 ...pby_ppd-1-db7b1db8f5e61f0fa78d2874e4d72d9d |  0
 ...pby_ppd-2-d286410aa1d5f5c8d91b863a6d6e29c5 |  0
 ...egative-0-79b294d0081c3dfd36c5b8b5e78dc7fb |  1 +
 ...egative-1-f87339637a48bd1533493ebbed5432a7 |  0
 ...egative-2-de7e5ac581b870fff10dc82c75c1c79e |  0
 ...egative-3-be440c3f959ca53b758481aa90551984 |  0
 ...egative-4-4dedc8057d76af264c198beaacd7f000 |  0
 ...egative-5-543a20e69bd8987bc37a22c1c7ef33f1 |  0
 ...egative-6-3f8274466914ad200b33a2c83fa6dab5 |  0
 ...egative-7-fb7bf3783d4fb43673a202c4111d9092 |  0
 ...tamp_3-10-7b1ec929239ee305ea9da46ebb990c67 |  1 +
 ...tamp_3-11-a63f40f6c4a022c16f8cf810e3b7ed2a |  1 +
 ...tamp_3-12-165256158e3db1ce19c3c9db3c8011d2 |  0
 ...stamp_3-3-6143888a940bfcac1133330764f5a31a |  0
 ...stamp_3-4-935d0d2492beab99bbbba26ba62a1db4 |  1 +
 ...stamp_3-5-8fe348d5d9b9903a26eda32d308b8e41 |  1 +
 ...stamp_3-6-6be5fe01c502cd24db32a3781c97a703 |  1 +
 ...stamp_3-7-6066ba0451cd0fcfac4bea6376e72add |  1 +
 ...stamp_3-8-22e03daa775eab145d39ec0730953f7e |  1 +
 ...stamp_3-9-ffc79abb874323e165963aa39f460a9b |  1 +
 ...mp_null-3-222c5ea127c747c71738b5dc5b80459c |  1 +
 ...mp_null-4-ffc86f5c714eceabc36e92931b96beb0 |  1 +
 .../execution/HiveCompatibilitySuite.scala    | 16 +++++++++++++
 105 files changed, 163 insertions(+)
 create mode 100644 sql/hive/src/test/resources/golden/correlationoptimizer1-0-b1e2ade89ae898650f0be4f796d8947b
 create mode 100644 sql/hive/src/test/resources/golden/correlationoptimizer1-1-b9d963d24994c47c3776dda6f7d3881f
 create mode 100644 sql/hive/src/test/resources/golden/correlationoptimizer1-10-5d712a42dcc1c4f7c797dabda5eb7b3a
 create mode 100644 sql/hive/src/test/resources/golden/correlationoptimizer1-11-b1e2ade89ae898650f0be4f796d8947b
 create mode 100644 sql/hive/src/test/resources/golden/correlationoptimizer1-12-b9d963d24994c47c3776dda6f7d3881f
 create mode 100644 sql/hive/src/test/resources/golden/correlationoptimizer1-13-f9f839aedb3a350719c0cbc53a06ace5
 create mode 100644 sql/hive/src/test/resources/golden/correlationoptimizer1-14-dae4256e08d595317f8e09a56354a3d9
 create mode 100644 sql/hive/src/test/resources/golden/correlationoptimizer1-15-777edd9d575f3480ca6cebe4be57b1f6
 create mode 100644 sql/hive/src/test/resources/golden/correlationoptimizer1-16-43f356b36962f2bade5706d8cf5ae6b4
 create mode 100644 sql/hive/src/test/resources/golden/correlationoptimizer1-17-dae4256e08d595317f8e09a56354a3d9
 create mode 100644 sql/hive/src/test/resources/golden/correlationoptimizer1-18-b1e2ade89ae898650f0be4f796d8947b
 create mode 100644 sql/hive/src/test/resources/golden/correlationoptimizer1-19-b9d963d24994c47c3776dda6f7d3881f
 create mode 100644 sql/hive/src/test/resources/golden/correlationoptimizer1-2-42a0eedc3751f792ad5438b2c64d3897
 create mode 100644 sql/hive/src/test/resources/golden/correlationoptimizer1-20-b4c1e27a7c1f61a3e9ae07c80e6e2973
 create mode 100644 sql/hive/src/test/resources/golden/correlationoptimizer1-21-16c57348be42ca3cc2f80f7f92265696
 create mode 100644 sql/hive/src/test/resources/golden/correlationoptimizer1-22-777edd9d575f3480ca6cebe4be57b1f6
 create mode 100644 sql/hive/src/test/resources/golden/correlationoptimizer1-23-2cdc77fd60449f3547cf95d8eb09a2
 create mode 100644 sql/hive/src/test/resources/golden/correlationoptimizer1-24-16c57348be42ca3cc2f80f7f92265696
 create mode 100644 sql/hive/src/test/resources/golden/correlationoptimizer1-25-b9d963d24994c47c3776dda6f7d3881f
 create mode 100644 sql/hive/src/test/resources/golden/correlationoptimizer1-26-8bcdcc5f01508f576d7bd6422c939225
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diff --git a/sql/hive/src/test/resources/golden/correlationoptimizer1-59-777edd9d575f3480ca6cebe4be57b1f6 b/sql/hive/src/test/resources/golden/correlationoptimizer1-59-777edd9d575f3480ca6cebe4be57b1f6
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diff --git a/sql/hive/src/test/resources/golden/correlationoptimizer1-60-d6bbaf0d40010159095e4cac025c50c5 b/sql/hive/src/test/resources/golden/correlationoptimizer1-60-d6bbaf0d40010159095e4cac025c50c5
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diff --git a/sql/hive/src/test/resources/golden/correlationoptimizer1-7-24ca942f094b14b92086305cc125e833 b/sql/hive/src/test/resources/golden/correlationoptimizer1-7-24ca942f094b14b92086305cc125e833
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diff --git a/sql/hive/src/test/resources/golden/correlationoptimizer1-9-d5bea91b4edb8be0428a336ff9c21dde b/sql/hive/src/test/resources/golden/correlationoptimizer1-9-d5bea91b4edb8be0428a336ff9c21dde
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diff --git a/sql/hive/src/test/resources/golden/correlationoptimizer10-1-b9d963d24994c47c3776dda6f7d3881f b/sql/hive/src/test/resources/golden/correlationoptimizer10-1-b9d963d24994c47c3776dda6f7d3881f
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diff --git a/sql/hive/src/test/resources/golden/correlationoptimizer10-10-777edd9d575f3480ca6cebe4be57b1f6 b/sql/hive/src/test/resources/golden/correlationoptimizer10-10-777edd9d575f3480ca6cebe4be57b1f6
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diff --git a/sql/hive/src/test/resources/golden/correlationoptimizer10-11-a1b7af95dfd01783c07aa23208d6160 b/sql/hive/src/test/resources/golden/correlationoptimizer10-11-a1b7af95dfd01783c07aa23208d6160
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diff --git a/sql/hive/src/test/resources/golden/correlationoptimizer10-12-1322cff0bdf29aab32e638ad48c71ff9 b/sql/hive/src/test/resources/golden/correlationoptimizer10-12-1322cff0bdf29aab32e638ad48c71ff9
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diff --git a/sql/hive/src/test/resources/golden/correlationoptimizer10-13-b9d963d24994c47c3776dda6f7d3881f b/sql/hive/src/test/resources/golden/correlationoptimizer10-13-b9d963d24994c47c3776dda6f7d3881f
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diff --git a/sql/hive/src/test/resources/golden/correlationoptimizer10-14-934b668c11600dc9c013c2ddc4c0d68c b/sql/hive/src/test/resources/golden/correlationoptimizer10-14-934b668c11600dc9c013c2ddc4c0d68c
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diff --git a/sql/hive/src/test/resources/golden/correlationoptimizer10-15-430ff20a144fb3dbf526232d9cb2baa b/sql/hive/src/test/resources/golden/correlationoptimizer10-15-430ff20a144fb3dbf526232d9cb2baa
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+++ b/sql/hive/src/test/resources/golden/correlationoptimizer10-15-430ff20a144fb3dbf526232d9cb2baa
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diff --git a/sql/hive/src/test/resources/golden/correlationoptimizer10-16-777edd9d575f3480ca6cebe4be57b1f6 b/sql/hive/src/test/resources/golden/correlationoptimizer10-16-777edd9d575f3480ca6cebe4be57b1f6
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@@ -0,0 +1 @@
+0
diff --git a/sql/hive/src/test/resources/golden/correlationoptimizer10-17-9d5521ecef1353d23fd2d4f7d78e7006 b/sql/hive/src/test/resources/golden/correlationoptimizer10-17-9d5521ecef1353d23fd2d4f7d78e7006
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index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/correlationoptimizer10-18-430ff20a144fb3dbf526232d9cb2baa b/sql/hive/src/test/resources/golden/correlationoptimizer10-18-430ff20a144fb3dbf526232d9cb2baa
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+++ b/sql/hive/src/test/resources/golden/correlationoptimizer10-18-430ff20a144fb3dbf526232d9cb2baa
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diff --git a/sql/hive/src/test/resources/golden/correlationoptimizer10-2-f9de06a4184ab1f42793327c1497437 b/sql/hive/src/test/resources/golden/correlationoptimizer10-2-f9de06a4184ab1f42793327c1497437
new file mode 100644
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diff --git a/sql/hive/src/test/resources/golden/correlationoptimizer10-3-6a01aa7ca94cda4268af894b4fd852ea b/sql/hive/src/test/resources/golden/correlationoptimizer10-3-6a01aa7ca94cda4268af894b4fd852ea
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+++ b/sql/hive/src/test/resources/golden/correlationoptimizer10-3-6a01aa7ca94cda4268af894b4fd852ea
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+278	1
+311	1
+369	1
+401	1
+406	1
diff --git a/sql/hive/src/test/resources/golden/correlationoptimizer10-4-777edd9d575f3480ca6cebe4be57b1f6 b/sql/hive/src/test/resources/golden/correlationoptimizer10-4-777edd9d575f3480ca6cebe4be57b1f6
new file mode 100644
index 000000000000..573541ac9702
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/correlationoptimizer10-4-777edd9d575f3480ca6cebe4be57b1f6
@@ -0,0 +1 @@
+0
diff --git a/sql/hive/src/test/resources/golden/correlationoptimizer10-5-6f191930802d659058465b2e6de08dd3 b/sql/hive/src/test/resources/golden/correlationoptimizer10-5-6f191930802d659058465b2e6de08dd3
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/correlationoptimizer10-6-6a01aa7ca94cda4268af894b4fd852ea b/sql/hive/src/test/resources/golden/correlationoptimizer10-6-6a01aa7ca94cda4268af894b4fd852ea
new file mode 100644
index 000000000000..d00aeb4be034
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/correlationoptimizer10-6-6a01aa7ca94cda4268af894b4fd852ea
@@ -0,0 +1,15 @@
+66	1
+98	1
+128	1
+146	1
+150	1
+213	1
+224	1
+238	1
+255	1
+273	1
+278	1
+311	1
+369	1
+401	1
+406	1
diff --git a/sql/hive/src/test/resources/golden/correlationoptimizer10-7-b9d963d24994c47c3776dda6f7d3881f b/sql/hive/src/test/resources/golden/correlationoptimizer10-7-b9d963d24994c47c3776dda6f7d3881f
new file mode 100644
index 000000000000..573541ac9702
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/correlationoptimizer10-7-b9d963d24994c47c3776dda6f7d3881f
@@ -0,0 +1 @@
+0
diff --git a/sql/hive/src/test/resources/golden/correlationoptimizer10-8-b2c8d0056b9f1b41cdaeaab4a1a5c9ec b/sql/hive/src/test/resources/golden/correlationoptimizer10-8-b2c8d0056b9f1b41cdaeaab4a1a5c9ec
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/correlationoptimizer10-9-1322cff0bdf29aab32e638ad48c71ff9 b/sql/hive/src/test/resources/golden/correlationoptimizer10-9-1322cff0bdf29aab32e638ad48c71ff9
new file mode 100644
index 000000000000..9ea431d9f5d1
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/correlationoptimizer10-9-1322cff0bdf29aab32e638ad48c71ff9
@@ -0,0 +1,5 @@
+66	val_66
+98	val_98
+128	
+146	val_146
+150	val_150
diff --git a/sql/hive/src/test/resources/golden/groupby_ppd-0-4b116ec2d8fb55c52b7b0d248c616ae2 b/sql/hive/src/test/resources/golden/groupby_ppd-0-4b116ec2d8fb55c52b7b0d248c616ae2
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/groupby_ppd-1-db7b1db8f5e61f0fa78d2874e4d72d9d b/sql/hive/src/test/resources/golden/groupby_ppd-1-db7b1db8f5e61f0fa78d2874e4d72d9d
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/groupby_ppd-2-d286410aa1d5f5c8d91b863a6d6e29c5 b/sql/hive/src/test/resources/golden/groupby_ppd-2-d286410aa1d5f5c8d91b863a6d6e29c5
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/limit_pushdown_negative-0-79b294d0081c3dfd36c5b8b5e78dc7fb b/sql/hive/src/test/resources/golden/limit_pushdown_negative-0-79b294d0081c3dfd36c5b8b5e78dc7fb
new file mode 100644
index 000000000000..573541ac9702
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/limit_pushdown_negative-0-79b294d0081c3dfd36c5b8b5e78dc7fb
@@ -0,0 +1 @@
+0
diff --git a/sql/hive/src/test/resources/golden/limit_pushdown_negative-1-f87339637a48bd1533493ebbed5432a7 b/sql/hive/src/test/resources/golden/limit_pushdown_negative-1-f87339637a48bd1533493ebbed5432a7
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/limit_pushdown_negative-2-de7e5ac581b870fff10dc82c75c1c79e b/sql/hive/src/test/resources/golden/limit_pushdown_negative-2-de7e5ac581b870fff10dc82c75c1c79e
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/limit_pushdown_negative-3-be440c3f959ca53b758481aa90551984 b/sql/hive/src/test/resources/golden/limit_pushdown_negative-3-be440c3f959ca53b758481aa90551984
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/limit_pushdown_negative-4-4dedc8057d76af264c198beaacd7f000 b/sql/hive/src/test/resources/golden/limit_pushdown_negative-4-4dedc8057d76af264c198beaacd7f000
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/limit_pushdown_negative-5-543a20e69bd8987bc37a22c1c7ef33f1 b/sql/hive/src/test/resources/golden/limit_pushdown_negative-5-543a20e69bd8987bc37a22c1c7ef33f1
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/limit_pushdown_negative-6-3f8274466914ad200b33a2c83fa6dab5 b/sql/hive/src/test/resources/golden/limit_pushdown_negative-6-3f8274466914ad200b33a2c83fa6dab5
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/limit_pushdown_negative-7-fb7bf3783d4fb43673a202c4111d9092 b/sql/hive/src/test/resources/golden/limit_pushdown_negative-7-fb7bf3783d4fb43673a202c4111d9092
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/timestamp_3-10-7b1ec929239ee305ea9da46ebb990c67 b/sql/hive/src/test/resources/golden/timestamp_3-10-7b1ec929239ee305ea9da46ebb990c67
new file mode 100644
index 000000000000..1b0a140b5a38
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_3-10-7b1ec929239ee305ea9da46ebb990c67
@@ -0,0 +1 @@
+1.3041352164485E9
diff --git a/sql/hive/src/test/resources/golden/timestamp_3-11-a63f40f6c4a022c16f8cf810e3b7ed2a b/sql/hive/src/test/resources/golden/timestamp_3-11-a63f40f6c4a022c16f8cf810e3b7ed2a
new file mode 100644
index 000000000000..d7ff6cd63d9f
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_3-11-a63f40f6c4a022c16f8cf810e3b7ed2a
@@ -0,0 +1 @@
+2011-04-29 20:46:56.4485
diff --git a/sql/hive/src/test/resources/golden/timestamp_3-12-165256158e3db1ce19c3c9db3c8011d2 b/sql/hive/src/test/resources/golden/timestamp_3-12-165256158e3db1ce19c3c9db3c8011d2
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/timestamp_3-3-6143888a940bfcac1133330764f5a31a b/sql/hive/src/test/resources/golden/timestamp_3-3-6143888a940bfcac1133330764f5a31a
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/timestamp_3-4-935d0d2492beab99bbbba26ba62a1db4 b/sql/hive/src/test/resources/golden/timestamp_3-4-935d0d2492beab99bbbba26ba62a1db4
new file mode 100644
index 000000000000..27ba77ddaf61
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_3-4-935d0d2492beab99bbbba26ba62a1db4
@@ -0,0 +1 @@
+true
diff --git a/sql/hive/src/test/resources/golden/timestamp_3-5-8fe348d5d9b9903a26eda32d308b8e41 b/sql/hive/src/test/resources/golden/timestamp_3-5-8fe348d5d9b9903a26eda32d308b8e41
new file mode 100644
index 000000000000..21e72e8ac3d7
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_3-5-8fe348d5d9b9903a26eda32d308b8e41
@@ -0,0 +1 @@
+48
diff --git a/sql/hive/src/test/resources/golden/timestamp_3-6-6be5fe01c502cd24db32a3781c97a703 b/sql/hive/src/test/resources/golden/timestamp_3-6-6be5fe01c502cd24db32a3781c97a703
new file mode 100644
index 000000000000..ee3be2941da6
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_3-6-6be5fe01c502cd24db32a3781c97a703
@@ -0,0 +1 @@
+-31184
diff --git a/sql/hive/src/test/resources/golden/timestamp_3-7-6066ba0451cd0fcfac4bea6376e72add b/sql/hive/src/test/resources/golden/timestamp_3-7-6066ba0451cd0fcfac4bea6376e72add
new file mode 100644
index 000000000000..1cf1952ac037
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_3-7-6066ba0451cd0fcfac4bea6376e72add
@@ -0,0 +1 @@
+1304135216
diff --git a/sql/hive/src/test/resources/golden/timestamp_3-8-22e03daa775eab145d39ec0730953f7e b/sql/hive/src/test/resources/golden/timestamp_3-8-22e03daa775eab145d39ec0730953f7e
new file mode 100644
index 000000000000..1cf1952ac037
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_3-8-22e03daa775eab145d39ec0730953f7e
@@ -0,0 +1 @@
+1304135216
diff --git a/sql/hive/src/test/resources/golden/timestamp_3-9-ffc79abb874323e165963aa39f460a9b b/sql/hive/src/test/resources/golden/timestamp_3-9-ffc79abb874323e165963aa39f460a9b
new file mode 100644
index 000000000000..d21deca76223
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_3-9-ffc79abb874323e165963aa39f460a9b
@@ -0,0 +1 @@
+1.30413517E9
diff --git a/sql/hive/src/test/resources/golden/timestamp_null-3-222c5ea127c747c71738b5dc5b80459c b/sql/hive/src/test/resources/golden/timestamp_null-3-222c5ea127c747c71738b5dc5b80459c
new file mode 100644
index 000000000000..7951defec192
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_null-3-222c5ea127c747c71738b5dc5b80459c
@@ -0,0 +1 @@
+NULL
diff --git a/sql/hive/src/test/resources/golden/timestamp_null-4-ffc86f5c714eceabc36e92931b96beb0 b/sql/hive/src/test/resources/golden/timestamp_null-4-ffc86f5c714eceabc36e92931b96beb0
new file mode 100644
index 000000000000..7951defec192
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_null-4-ffc86f5c714eceabc36e92931b96beb0
@@ -0,0 +1 @@
+NULL
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
index cdfc2d0c1738..63dbe57c4c77 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
@@ -84,6 +84,9 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     "udf_java_method",
     "create_merge_compressed",
 
+    // DFS commands
+    "symlink_text_input_format",
+
     // Weird DDL differences result in failures on jenkins.
     "create_like2",
     "create_view_translate",
@@ -278,7 +281,10 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     "compute_stats_string",
     "compute_stats_table",
     "convert_enum_to_string",
+    "correlationoptimizer1",
+    "correlationoptimizer10",
     "correlationoptimizer11",
+    "correlationoptimizer14",
     "correlationoptimizer15",
     "correlationoptimizer2",
     "correlationoptimizer3",
@@ -296,6 +302,7 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     "ct_case_insensitive",
     "database_location",
     "database_properties",
+    "decimal_1",
     "decimal_4",
     "decimal_join",
     "default_partition_name",
@@ -304,6 +311,7 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     "describe_formatted_view_partitioned",
     "diff_part_input_formats",
     "disable_file_format_check",
+    "disallow_incompatible_type_change_off",
     "drop_function",
     "drop_index",
     "drop_multi_partitions",
@@ -359,8 +367,10 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     "groupby_map_ppr",
     "groupby_multi_insert_common_distinct",
     "groupby_multi_single_reducer2",
+    "groupby_multi_single_reducer3",
     "groupby_mutli_insert_common_distinct",
     "groupby_neg_float",
+    "groupby_ppd",
     "groupby_ppr",
     "groupby_sort_10",
     "groupby_sort_2",
@@ -400,6 +410,7 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     "input4",
     "input40",
     "input41",
+    "input49",
     "input4_cb_delim",
     "input6",
     "input7",
@@ -491,6 +502,7 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     "lateral_view_ppd",
     "leftsemijoin",
     "leftsemijoin_mr",
+    "limit_pushdown_negative",
     "lineage1",
     "literal_double",
     "literal_ints",
@@ -598,6 +610,7 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     "reduce_deduplicate",
     "reduce_deduplicate_exclude_gby",
     "reduce_deduplicate_exclude_join",
+    "reduce_deduplicate_extended",
     "reducesink_dedup",
     "rename_column",
     "router_join_ppr",
@@ -646,7 +659,10 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     "stats_publisher_error_1",
     "subq2",
     "tablename_with_select",
+    "timestamp_3",
     "timestamp_comparison",
+    "timestamp_null",
+    "timestamp_udf",
     "touch",
     "transform_ppr1",
     "transform_ppr2",

From 8af46d58464b96471825ce376c3e11c8b1108c0e Mon Sep 17 00:00:00 2001
From: Yin Huai <huai@cse.ohio-state.edu>
Date: Tue, 15 Jul 2014 14:06:45 -0700
Subject: [PATCH 656/810] [SPARK-2474][SQL] For a registered table in
 OverrideCatalog, the Analyzer failed to resolve references in the format of
 "tableName.fieldName"

Please refer to JIRA (https://issues.apache.org/jira/browse/SPARK-2474) for how to reproduce the problem and my understanding of the root cause.

Author: Yin Huai <huai@cse.ohio-state.edu>

Closes #1406 from yhuai/SPARK-2474 and squashes the following commits:

96b1627 [Yin Huai] Merge remote-tracking branch 'upstream/master' into SPARK-2474
af36d65 [Yin Huai] Fix comment.
be86ba9 [Yin Huai] Correct SQL console settings.
c43ad00 [Yin Huai] Wrap the relation in a Subquery named by the table name in OverrideCatalog.lookupRelation.
a5c2145 [Yin Huai] Support sql/console.
---
 project/SparkBuild.scala                      | 24 +++++++++++++++++++
 .../spark/sql/catalyst/analysis/Catalog.scala |  3 ++-
 2 files changed, 26 insertions(+), 1 deletion(-)

diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index 754d54e89361..5461d25d72d7 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -166,6 +166,9 @@ object SparkBuild extends PomBuild {
   /* Enable unidoc only for the root spark project */
   enable(Unidoc.settings)(spark)
 
+  /* Spark SQL Core console settings */
+  enable(SQL.settings)(sql)
+
   /* Hive console settings */
   enable(Hive.settings)(hive)
 
@@ -179,6 +182,27 @@ object SparkBuild extends PomBuild {
 
 }
 
+object SQL {
+
+  lazy val settings = Seq(
+
+    initialCommands in console :=
+      """
+        |import org.apache.spark.sql.catalyst.analysis._
+        |import org.apache.spark.sql.catalyst.dsl._
+        |import org.apache.spark.sql.catalyst.errors._
+        |import org.apache.spark.sql.catalyst.expressions._
+        |import org.apache.spark.sql.catalyst.plans.logical._
+        |import org.apache.spark.sql.catalyst.rules._
+        |import org.apache.spark.sql.catalyst.types._
+        |import org.apache.spark.sql.catalyst.util._
+        |import org.apache.spark.sql.execution
+        |import org.apache.spark.sql.test.TestSQLContext._
+        |import org.apache.spark.sql.parquet.ParquetTestData""".stripMargin
+  )
+
+}
+
 object Hive {
 
   lazy val settings = Seq(
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Catalog.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Catalog.scala
index 0d05d9808b40..616f1e2ecb60 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Catalog.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Catalog.scala
@@ -113,11 +113,12 @@ trait OverrideCatalog extends Catalog {
     alias: Option[String] = None): LogicalPlan = {
     val (dbName, tblName) = processDatabaseAndTableName(databaseName, tableName)
     val overriddenTable = overrides.get((dbName, tblName))
+    val tableWithQualifers = overriddenTable.map(r => Subquery(tblName, r))
 
     // If an alias was specified by the lookup, wrap the plan in a subquery so that attributes are
     // properly qualified with this alias.
     val withAlias =
-      overriddenTable.map(r => alias.map(a => Subquery(a, r)).getOrElse(r))
+      tableWithQualifers.map(r => alias.map(a => Subquery(a, r)).getOrElse(r))
 
     withAlias.getOrElse(super.lookupRelation(dbName, tblName, alias))
   }

From 61de65bc69f9a5fc396b76713193c6415436d452 Mon Sep 17 00:00:00 2001
From: William Benton <willb@redhat.com>
Date: Tue, 15 Jul 2014 14:11:57 -0700
Subject: [PATCH 657/810] SPARK-2407: Added internal implementation of SQL
 SUBSTR()

This replaces the Hive UDF for SUBSTR(ING) with an implementation in Catalyst
and adds tests to verify correct operation.

Author: William Benton <willb@redhat.com>

Closes #1359 from willb/internalSqlSubstring and squashes the following commits:

ccedc47 [William Benton] Fixed too-long line.
a30a037 [William Benton] replace view bounds with implicit parameters
ec35c80 [William Benton] Adds fixes from review:
4f3bfdb [William Benton] Added internal implementation of SQL SUBSTR()
---
 .../expressions/stringOperations.scala        | 77 ++++++++++++++++++-
 .../ExpressionEvaluationSuite.scala           | 49 ++++++++++++
 .../org/apache/spark/sql/hive/HiveQl.scala    |  5 ++
 3 files changed, 128 insertions(+), 3 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
index b3850533c373..4bd7bf5a0cd8 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
@@ -19,9 +19,11 @@ package org.apache.spark.sql.catalyst.expressions
 
 import java.util.regex.Pattern
 
-import org.apache.spark.sql.catalyst.types.DataType
-import org.apache.spark.sql.catalyst.types.StringType
-import org.apache.spark.sql.catalyst.types.BooleanType
+import scala.collection.IndexedSeqOptimized
+
+
+import org.apache.spark.sql.catalyst.analysis.UnresolvedException
+import org.apache.spark.sql.catalyst.types.{BinaryType, BooleanType, DataType, StringType}
 
 trait StringRegexExpression {
   self: BinaryExpression =>
@@ -205,3 +207,72 @@ case class EndsWith(left: Expression, right: Expression)
     extends BinaryExpression with StringComparison {
   def compare(l: String, r: String) = l.endsWith(r)
 }
+
+/**
+ * A function that takes a substring of its first argument starting at a given position.
+ * Defined for String and Binary types.
+ */
+case class Substring(str: Expression, pos: Expression, len: Expression) extends Expression {
+  
+  type EvaluatedType = Any
+  
+  def nullable: Boolean = true
+  def dataType: DataType = {
+    if (!resolved) {
+      throw new UnresolvedException(this, s"Cannot resolve since $children are not resolved")
+    }
+    if (str.dataType == BinaryType) str.dataType else StringType
+  }
+  
+  def references = children.flatMap(_.references).toSet
+  
+  override def children = str :: pos :: len :: Nil
+  
+  @inline
+  def slice[T, C <: Any](str: C, startPos: Int, sliceLen: Int)
+      (implicit ev: (C=>IndexedSeqOptimized[T,_])): Any = {
+    val len = str.length
+    // Hive and SQL use one-based indexing for SUBSTR arguments but also accept zero and
+    // negative indices for start positions. If a start index i is greater than 0, it 
+    // refers to element i-1 in the sequence. If a start index i is less than 0, it refers
+    // to the -ith element before the end of the sequence. If a start index i is 0, it
+    // refers to the first element.
+    
+    val start = startPos match {
+      case pos if pos > 0 => pos - 1
+      case neg if neg < 0 => len + neg
+      case _ => 0
+    }
+    
+    val end = sliceLen match {
+      case max if max == Integer.MAX_VALUE => max
+      case x => start + x
+    }
+      
+    str.slice(start, end)    
+  }
+  
+  override def eval(input: Row): Any = {
+    val string = str.eval(input)
+
+    val po = pos.eval(input)
+    val ln = len.eval(input)
+    
+    if ((string == null) || (po == null) || (ln == null)) {
+      null
+    } else {
+      val start = po.asInstanceOf[Int]
+      val length = ln.asInstanceOf[Int] 
+      
+      string match {
+        case ba: Array[Byte] => slice(ba, start, length)
+        case other => slice(other.toString, start, length)
+      }
+    }
+  }
+  
+  override def toString = len match {
+    case max if max == Integer.MAX_VALUE => s"SUBSTR($str, $pos)"
+    case _ => s"SUBSTR($str, $pos, $len)"
+  }
+}
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala
index 84d72814778b..f1d7aedcc2d2 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala
@@ -466,5 +466,54 @@ class ExpressionEvaluationSuite extends FunSuite {
     checkEvaluation(c1 === c2, false, row)
     checkEvaluation(c1 !== c2, true, row)
   }
+  
+  test("Substring") {
+    val row = new GenericRow(Array[Any]("example", "example".toArray.map(_.toByte)))
+    
+    val s = 'a.string.at(0)
+    
+    // substring from zero position with less-than-full length
+    checkEvaluation(Substring(s, Literal(0, IntegerType), Literal(2, IntegerType)), "ex", row)
+    checkEvaluation(Substring(s, Literal(1, IntegerType), Literal(2, IntegerType)), "ex", row)
+
+    // substring from zero position with full length
+    checkEvaluation(Substring(s, Literal(0, IntegerType), Literal(7, IntegerType)), "example", row)
+    checkEvaluation(Substring(s, Literal(1, IntegerType), Literal(7, IntegerType)), "example", row)
+
+    // substring from zero position with greater-than-full length
+    checkEvaluation(Substring(s, Literal(0, IntegerType), Literal(100, IntegerType)), "example", row)
+    checkEvaluation(Substring(s, Literal(1, IntegerType), Literal(100, IntegerType)), "example", row)
+
+    // substring from nonzero position with less-than-full length
+    checkEvaluation(Substring(s, Literal(2, IntegerType), Literal(2, IntegerType)), "xa", row)
+
+    // substring from nonzero position with full length
+    checkEvaluation(Substring(s, Literal(2, IntegerType), Literal(6, IntegerType)), "xample", row)
+
+    // substring from nonzero position with greater-than-full length
+    checkEvaluation(Substring(s, Literal(2, IntegerType), Literal(100, IntegerType)), "xample", row)
+
+    // zero-length substring (within string bounds)
+    checkEvaluation(Substring(s, Literal(0, IntegerType), Literal(0, IntegerType)), "", row)
+
+    // zero-length substring (beyond string bounds)
+    checkEvaluation(Substring(s, Literal(100, IntegerType), Literal(4, IntegerType)), "", row)
+
+    // substring(null, _, _) -> null
+    checkEvaluation(Substring(s, Literal(100, IntegerType), Literal(4, IntegerType)), null, new GenericRow(Array[Any](null)))
+    
+    // substring(_, null, _) -> null
+    checkEvaluation(Substring(s, Literal(null, IntegerType), Literal(4, IntegerType)), null, row)
+
+    // substring(_, _, null) -> null
+    checkEvaluation(Substring(s, Literal(100, IntegerType), Literal(null, IntegerType)), null, row)
+
+    // 2-arg substring from zero position
+    checkEvaluation(Substring(s, Literal(0, IntegerType), Literal(Integer.MAX_VALUE, IntegerType)), "example", row)
+    checkEvaluation(Substring(s, Literal(1, IntegerType), Literal(Integer.MAX_VALUE, IntegerType)), "example", row)
+
+    // 2-arg substring from nonzero position
+    checkEvaluation(Substring(s, Literal(2, IntegerType), Literal(Integer.MAX_VALUE, IntegerType)), "xample", row)
+  }
 }
 
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
index 56aa27a20882..300e249f5b2e 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
@@ -860,6 +860,7 @@ private[hive] object HiveQl {
   val BETWEEN = "(?i)BETWEEN".r
   val WHEN = "(?i)WHEN".r
   val CASE = "(?i)CASE".r
+  val SUBSTR = "(?i)SUBSTR(?:ING)?".r
 
   protected def nodeToExpr(node: Node): Expression = node match {
     /* Attribute References */
@@ -984,6 +985,10 @@ private[hive] object HiveQl {
 
     /* Other functions */
     case Token("TOK_FUNCTION", Token(RAND(), Nil) :: Nil) => Rand
+    case Token("TOK_FUNCTION", Token(SUBSTR(), Nil) :: string :: pos :: Nil) => 
+      Substring(nodeToExpr(string), nodeToExpr(pos), Literal(Integer.MAX_VALUE, IntegerType))
+    case Token("TOK_FUNCTION", Token(SUBSTR(), Nil) :: string :: pos :: length :: Nil) => 
+      Substring(nodeToExpr(string), nodeToExpr(pos), nodeToExpr(length))
 
     /* UDFs - Must be last otherwise will preempt built in functions */
     case Token("TOK_FUNCTION", Token(name, Nil) :: args) =>

From 502f90782ad474e2630ed5be4d3c4be7dab09c34 Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Tue, 15 Jul 2014 17:56:17 -0700
Subject: [PATCH 658/810] [SQL] Attribute equality comparisons should be done
 by exprId.

Author: Michael Armbrust <michael@databricks.com>

Closes #1414 from marmbrus/exprIdResolution and squashes the following commits:

97b47bc [Michael Armbrust] Attribute equality comparisons should be done by exprId.
---
 .../spark/sql/columnar/InMemoryColumnarTableScan.scala      | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala
index ff7f664d8b52..88901debbb4e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala
@@ -96,7 +96,11 @@ private[sql] case class InMemoryColumnarTableScan(
       new Iterator[Row] {
         // Find the ordinals of the requested columns.  If none are requested, use the first.
         val requestedColumns =
-          if (attributes.isEmpty) Seq(0) else attributes.map(relation.output.indexOf(_))
+          if (attributes.isEmpty) {
+            Seq(0)
+          } else {
+            attributes.map(a => relation.output.indexWhere(_.exprId == a.exprId))
+          }
 
         val columnAccessors = requestedColumns.map(columnBuffers(_)).map(ColumnAccessor(_))
         val nextRow = new GenericMutableRow(columnAccessors.length)

From c2048a5165b270f5baf2003fdfef7bc6c5875715 Mon Sep 17 00:00:00 2001
From: Zongheng Yang <zongheng.y@gmail.com>
Date: Tue, 15 Jul 2014 17:58:28 -0700
Subject: [PATCH 659/810] [SPARK-2498] [SQL] Synchronize on a lock when using
 scala reflection inside data type objects.

JIRA ticket: https://issues.apache.org/jira/browse/SPARK-2498

Author: Zongheng Yang <zongheng.y@gmail.com>

Closes #1423 from concretevitamin/scala-ref-catalyst and squashes the following commits:

325a149 [Zongheng Yang] Synchronize on a lock when initializing data type objects in Catalyst.
---
 .../spark/sql/catalyst/types/dataTypes.scala  | 34 +++++++++++--------
 1 file changed, 19 insertions(+), 15 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/types/dataTypes.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/types/dataTypes.scala
index bb77bccf8617..cd4b5e9c1b52 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/types/dataTypes.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/types/dataTypes.scala
@@ -19,17 +19,20 @@ package org.apache.spark.sql.catalyst.types
 
 import java.sql.Timestamp
 
-import scala.util.parsing.combinator.RegexParsers
-
 import scala.reflect.ClassTag
 import scala.reflect.runtime.universe.{typeTag, TypeTag, runtimeMirror}
+import scala.util.parsing.combinator.RegexParsers
 
 import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeReference, Expression}
 import org.apache.spark.util.Utils
 
 /**
- *
+ * A JVM-global lock that should be used to prevent thread safety issues when using things in
+ * scala.reflect.*.  Note that Scala Reflection API is made thread-safe in 2.11, but not yet for
+ * 2.10.* builds.  See SI-6240 for more details.
  */
+protected[catalyst] object ScalaReflectionLock
+
 object DataType extends RegexParsers {
   protected lazy val primitiveType: Parser[DataType] =
     "StringType" ^^^ StringType |
@@ -62,7 +65,6 @@ object DataType extends RegexParsers {
     "true" ^^^ true |
     "false" ^^^ false
 
-
   protected lazy val structType: Parser[DataType] =
     "StructType\\([A-zA-z]*\\(".r ~> repsep(structField, ",") <~ "))" ^^ {
       case fields => new StructType(fields)
@@ -106,7 +108,7 @@ abstract class NativeType extends DataType {
   @transient val tag: TypeTag[JvmType]
   val ordering: Ordering[JvmType]
 
-  @transient val classTag = {
+  @transient val classTag = ScalaReflectionLock.synchronized {
     val mirror = runtimeMirror(Utils.getSparkClassLoader)
     ClassTag[JvmType](mirror.runtimeClass(tag.tpe))
   }
@@ -114,22 +116,24 @@ abstract class NativeType extends DataType {
 
 case object StringType extends NativeType with PrimitiveType {
   type JvmType = String
-  @transient lazy val tag = typeTag[JvmType]
+  @transient lazy val tag = ScalaReflectionLock.synchronized { typeTag[JvmType] }
   val ordering = implicitly[Ordering[JvmType]]
 }
+
 case object BinaryType extends DataType with PrimitiveType {
   type JvmType = Array[Byte]
 }
+
 case object BooleanType extends NativeType with PrimitiveType {
   type JvmType = Boolean
-  @transient lazy val tag = typeTag[JvmType]
+  @transient lazy val tag = ScalaReflectionLock.synchronized { typeTag[JvmType] }
   val ordering = implicitly[Ordering[JvmType]]
 }
 
 case object TimestampType extends NativeType {
   type JvmType = Timestamp
 
-  @transient lazy val tag = typeTag[JvmType]
+  @transient lazy val tag = ScalaReflectionLock.synchronized { typeTag[JvmType] }
 
   val ordering = new Ordering[JvmType] {
     def compare(x: Timestamp, y: Timestamp) = x.compareTo(y)
@@ -159,7 +163,7 @@ abstract class IntegralType extends NumericType {
 
 case object LongType extends IntegralType {
   type JvmType = Long
-  @transient lazy val tag = typeTag[JvmType]
+  @transient lazy val tag = ScalaReflectionLock.synchronized { typeTag[JvmType] }
   val numeric = implicitly[Numeric[Long]]
   val integral = implicitly[Integral[Long]]
   val ordering = implicitly[Ordering[JvmType]]
@@ -167,7 +171,7 @@ case object LongType extends IntegralType {
 
 case object IntegerType extends IntegralType {
   type JvmType = Int
-  @transient lazy val tag = typeTag[JvmType]
+  @transient lazy val tag = ScalaReflectionLock.synchronized { typeTag[JvmType] }
   val numeric = implicitly[Numeric[Int]]
   val integral = implicitly[Integral[Int]]
   val ordering = implicitly[Ordering[JvmType]]
@@ -175,7 +179,7 @@ case object IntegerType extends IntegralType {
 
 case object ShortType extends IntegralType {
   type JvmType = Short
-  @transient lazy val tag = typeTag[JvmType]
+  @transient lazy val tag = ScalaReflectionLock.synchronized { typeTag[JvmType] }
   val numeric = implicitly[Numeric[Short]]
   val integral = implicitly[Integral[Short]]
   val ordering = implicitly[Ordering[JvmType]]
@@ -183,7 +187,7 @@ case object ShortType extends IntegralType {
 
 case object ByteType extends IntegralType {
   type JvmType = Byte
-  @transient lazy val tag = typeTag[JvmType]
+  @transient lazy val tag = ScalaReflectionLock.synchronized { typeTag[JvmType] }
   val numeric = implicitly[Numeric[Byte]]
   val integral = implicitly[Integral[Byte]]
   val ordering = implicitly[Ordering[JvmType]]
@@ -202,7 +206,7 @@ abstract class FractionalType extends NumericType {
 
 case object DecimalType extends FractionalType {
   type JvmType = BigDecimal
-  @transient lazy val tag = typeTag[JvmType]
+  @transient lazy val tag = ScalaReflectionLock.synchronized { typeTag[JvmType] }
   val numeric = implicitly[Numeric[BigDecimal]]
   val fractional = implicitly[Fractional[BigDecimal]]
   val ordering = implicitly[Ordering[JvmType]]
@@ -210,7 +214,7 @@ case object DecimalType extends FractionalType {
 
 case object DoubleType extends FractionalType {
   type JvmType = Double
-  @transient lazy val tag = typeTag[JvmType]
+  @transient lazy val tag = ScalaReflectionLock.synchronized { typeTag[JvmType] }
   val numeric = implicitly[Numeric[Double]]
   val fractional = implicitly[Fractional[Double]]
   val ordering = implicitly[Ordering[JvmType]]
@@ -218,7 +222,7 @@ case object DoubleType extends FractionalType {
 
 case object FloatType extends FractionalType {
   type JvmType = Float
-  @transient lazy val tag = typeTag[JvmType]
+  @transient lazy val tag = ScalaReflectionLock.synchronized { typeTag[JvmType] }
   val numeric = implicitly[Numeric[Float]]
   val fractional = implicitly[Fractional[Float]]
   val ordering = implicitly[Ordering[JvmType]]

From 4576d80a5155c9fbfebe9c36cca06c208bca5bd3 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Tue, 15 Jul 2014 18:47:39 -0700
Subject: [PATCH 660/810] [SPARK-2469] Use Snappy (instead of LZF) for default
 shuffle compression codec

This reduces shuffle compression memory usage by 3x.

Author: Reynold Xin <rxin@apache.org>

Closes #1415 from rxin/snappy and squashes the following commits:

06c1a01 [Reynold Xin] SPARK-2469: Use Snappy (instead of LZF) for default shuffle compression codec.
---
 .../src/main/scala/org/apache/spark/io/CompressionCodec.scala | 4 ++--
 .../scala/org/apache/spark/io/CompressionCodecSuite.scala     | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/io/CompressionCodec.scala b/core/src/main/scala/org/apache/spark/io/CompressionCodec.scala
index 33402c927c73..1b66218d86dd 100644
--- a/core/src/main/scala/org/apache/spark/io/CompressionCodec.scala
+++ b/core/src/main/scala/org/apache/spark/io/CompressionCodec.scala
@@ -56,7 +56,7 @@ private[spark] object CompressionCodec {
     ctor.newInstance(conf).asInstanceOf[CompressionCodec]
   }
 
-  val DEFAULT_COMPRESSION_CODEC = classOf[LZFCompressionCodec].getName
+  val DEFAULT_COMPRESSION_CODEC = classOf[SnappyCompressionCodec].getName
 }
 
 
@@ -103,7 +103,7 @@ class LZFCompressionCodec(conf: SparkConf) extends CompressionCodec {
 /**
  * :: DeveloperApi ::
  * Snappy implementation of [[org.apache.spark.io.CompressionCodec]].
- * Block size can be configured by spark.io.compression.snappy.block.size.
+ * Block size can be configured by `spark.io.compression.snappy.block.size`.
  *
  * Note: The wire protocol for this codec is not guaranteed to be compatible across versions
  *       of Spark. This is intended for use as an internal compression utility within a single Spark
diff --git a/core/src/test/scala/org/apache/spark/io/CompressionCodecSuite.scala b/core/src/test/scala/org/apache/spark/io/CompressionCodecSuite.scala
index 42fc395fa698..3f882a724b04 100644
--- a/core/src/test/scala/org/apache/spark/io/CompressionCodecSuite.scala
+++ b/core/src/test/scala/org/apache/spark/io/CompressionCodecSuite.scala
@@ -46,7 +46,7 @@ class CompressionCodecSuite extends FunSuite {
 
   test("default compression codec") {
     val codec = CompressionCodec.createCodec(conf)
-    assert(codec.getClass === classOf[LZFCompressionCodec])
+    assert(codec.getClass === classOf[SnappyCompressionCodec])
     testCodec(codec)
   }
 

From 9c12de5092312319aa22f24df47a6de0e41a0102 Mon Sep 17 00:00:00 2001
From: Henry Saputra <henry.saputra@gmail.com>
Date: Tue, 15 Jul 2014 21:21:52 -0700
Subject: [PATCH 661/810] [SPARK-2500] Move the logInfo for registering
 BlockManager to BlockManagerMasterActor.register method

PR for SPARK-2500

Move the logInfo call for BlockManager to BlockManagerMasterActor.register instead of BlockManagerInfo constructor.

Previously the loginfo call for registering the registering a BlockManager is happening in the BlockManagerInfo constructor. This kind of confusing because the code could call "new BlockManagerInfo" without actually registering a BlockManager and could confuse when reading the log files.

Author: Henry Saputra <henry.saputra@gmail.com>

Closes #1424 from hsaputra/move_registerblockmanager_log_to_registration_method and squashes the following commits:

3370b4a [Henry Saputra] Move the loginfo for BlockManager to BlockManagerMasterActor.register instead of BlockManagerInfo constructor.
---
 .../org/apache/spark/storage/BlockManagerMasterActor.scala | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterActor.scala b/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterActor.scala
index 6aed322eeb18..de1cc5539fb4 100644
--- a/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterActor.scala
+++ b/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterActor.scala
@@ -336,6 +336,10 @@ class BlockManagerMasterActor(val isLocal: Boolean, conf: SparkConf, listenerBus
         case None =>
           blockManagerIdByExecutor(id.executorId) = id
       }
+
+      logInfo("Registering block manager %s with %s RAM".format(
+        id.hostPort, Utils.bytesToString(maxMemSize)))
+
       blockManagerInfo(id) =
         new BlockManagerInfo(id, System.currentTimeMillis(), maxMemSize, slaveActor)
     }
@@ -432,9 +436,6 @@ private[spark] class BlockManagerInfo(
   // Mapping from block id to its status.
   private val _blocks = new JHashMap[BlockId, BlockStatus]
 
-  logInfo("Registering block manager %s with %s RAM".format(
-    blockManagerId.hostPort, Utils.bytesToString(maxMem)))
-
   def getStatus(blockId: BlockId) = Option(_blocks.get(blockId))
 
   def updateLastSeenMs() {

From 563acf5edfbfb2fa756a1f0accde0940592663e9 Mon Sep 17 00:00:00 2001
From: Ken Takagiwa <ken@Kens-MacBook-Pro.local>
Date: Tue, 15 Jul 2014 21:34:05 -0700
Subject: [PATCH 662/810] follow pep8 None should be compared using is or is
 not

http://legacy.python.org/dev/peps/pep-0008/
## Programming Recommendations
- Comparisons to singletons like None should always be done with is or is not, never the equality operators.

Author: Ken Takagiwa <ken@Kens-MacBook-Pro.local>

Closes #1422 from giwa/apache_master and squashes the following commits:

7b361f3 [Ken Takagiwa] follow pep8 None should be checked using is or is not
---
 python/pyspark/cloudpickle.py | 4 ++--
 python/pyspark/conf.py        | 4 ++--
 python/pyspark/rddsampler.py  | 2 +-
 python/pyspark/shell.py       | 4 ++--
 4 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/python/pyspark/cloudpickle.py b/python/pyspark/cloudpickle.py
index eb5dbb8de2b3..4fda2a9b950b 100644
--- a/python/pyspark/cloudpickle.py
+++ b/python/pyspark/cloudpickle.py
@@ -243,10 +243,10 @@ def save_function(self, obj, name=None, pack=struct.pack):
         # if func is lambda, def'ed at prompt, is in main, or is nested, then
         # we'll pickle the actual function object rather than simply saving a
         # reference (as is done in default pickler), via save_function_tuple.
-        if islambda(obj) or obj.func_code.co_filename == '<stdin>' or themodule == None:
+        if islambda(obj) or obj.func_code.co_filename == '<stdin>' or themodule is None:
             #Force server to import modules that have been imported in main
             modList = None
-            if themodule == None and not self.savedForceImports:
+            if themodule is None and not self.savedForceImports:
                 mainmod = sys.modules['__main__']
                 if useForcedImports and hasattr(mainmod,'___pyc_forcedImports__'):
                     modList = list(mainmod.___pyc_forcedImports__)
diff --git a/python/pyspark/conf.py b/python/pyspark/conf.py
index 8eff4a242a52..60fc6ba7c52c 100644
--- a/python/pyspark/conf.py
+++ b/python/pyspark/conf.py
@@ -30,7 +30,7 @@
 u'local'
 >>> sc.appName
 u'My app'
->>> sc.sparkHome == None
+>>> sc.sparkHome is None
 True
 
 >>> conf = SparkConf(loadDefaults=False)
@@ -116,7 +116,7 @@ def setSparkHome(self, value):
 
     def setExecutorEnv(self, key=None, value=None, pairs=None):
         """Set an environment variable to be passed to executors."""
-        if (key != None and pairs != None) or (key == None and pairs == None):
+        if (key is not None and pairs is not None) or (key is None and pairs is None):
             raise Exception("Either pass one key-value pair or a list of pairs")
         elif key != None:
             self._jconf.setExecutorEnv(key, value)
diff --git a/python/pyspark/rddsampler.py b/python/pyspark/rddsampler.py
index 845a267e311c..122bc38b03b0 100644
--- a/python/pyspark/rddsampler.py
+++ b/python/pyspark/rddsampler.py
@@ -82,7 +82,7 @@ def getPoissonSample(self, split, mean):
             return (num_arrivals - 1)
     
     def shuffle(self, vals):
-        if self._random == None:
+        if self._random is None:
             self.initRandomGenerator(0)  # this should only ever called on the master so
             # the split does not matter
         
diff --git a/python/pyspark/shell.py b/python/pyspark/shell.py
index ebd714db7a91..2ce5409cd67c 100644
--- a/python/pyspark/shell.py
+++ b/python/pyspark/shell.py
@@ -35,7 +35,7 @@
 from pyspark.storagelevel import StorageLevel
 
 # this is the equivalent of ADD_JARS
-add_files = os.environ.get("ADD_FILES").split(',') if os.environ.get("ADD_FILES") != None else None
+add_files = os.environ.get("ADD_FILES").split(',') if os.environ.get("ADD_FILES") is not None else None
 
 if os.environ.get("SPARK_EXECUTOR_URI"):
     SparkContext.setSystemProperty("spark.executor.uri", os.environ["SPARK_EXECUTOR_URI"])
@@ -55,7 +55,7 @@
     platform.python_build()[1]))
 print("SparkContext available as sc.")
 
-if add_files != None:
+if add_files is not None:
     print("Adding files: [%s]" % ", ".join(add_files))
 
 # The ./bin/pyspark script stores the old PYTHONSTARTUP value in OLD_PYTHONSTARTUP,

From 90ca532a0fd95dc85cff8c5722d371e8368b2687 Mon Sep 17 00:00:00 2001
From: Aaron Staple <aaron.staple@gmail.com>
Date: Tue, 15 Jul 2014 21:35:36 -0700
Subject: [PATCH 663/810] [SPARK-2314][SQL] Override collect and take in
 JavaSchemaRDD, forwarding to SchemaRDD implementations.

Author: Aaron Staple <aaron.staple@gmail.com>

Closes #1421 from staple/SPARK-2314 and squashes the following commits:

73e04dc [Aaron Staple] [SPARK-2314] Override collect and take in JavaSchemaRDD, forwarding to SchemaRDD implementations.
---
 .../spark/sql/api/java/JavaSchemaRDD.scala       | 16 ++++++++++++++++
 1 file changed, 16 insertions(+)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSchemaRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSchemaRDD.scala
index aff6ffe9f347..8fbf13b8b015 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSchemaRDD.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSchemaRDD.scala
@@ -17,6 +17,8 @@
 
 package org.apache.spark.sql.api.java
 
+import java.util.{List => JList}
+
 import org.apache.spark.Partitioner
 import org.apache.spark.api.java.{JavaRDDLike, JavaRDD}
 import org.apache.spark.api.java.function.{Function => JFunction}
@@ -96,6 +98,20 @@ class JavaSchemaRDD(
     this
   }
 
+  // Overridden actions from JavaRDDLike.
+
+  override def collect(): JList[Row] = {
+    import scala.collection.JavaConversions._
+    val arr: java.util.Collection[Row] = baseSchemaRDD.collect().toSeq.map(new Row(_))
+    new java.util.ArrayList(arr)
+  }
+
+  override def take(num: Int): JList[Row] = {
+    import scala.collection.JavaConversions._
+    val arr: java.util.Collection[Row] = baseSchemaRDD.take(num).toSeq.map(new Row(_))
+    new java.util.ArrayList(arr)
+  }
+
   // Transformations (return a new RDD)
 
   /**

From 9b38b7c71352bb5e6d359515111ad9ca33299127 Mon Sep 17 00:00:00 2001
From: Takuya UESHIN <ueshin@happy-camper.st>
Date: Tue, 15 Jul 2014 22:35:34 -0700
Subject: [PATCH 664/810] [SPARK-2509][SQL] Add optimization for Substring.

`Substring` including `null` literal cases could be added to `NullPropagation`.

Author: Takuya UESHIN <ueshin@happy-camper.st>

Closes #1428 from ueshin/issues/SPARK-2509 and squashes the following commits:

d9eb85f [Takuya UESHIN] Add Substring cases to NullPropagation.
---
 .../org/apache/spark/sql/catalyst/optimizer/Optimizer.scala    | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
index a142310c501b..714e2cdac2b1 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
@@ -171,6 +171,9 @@ object NullPropagation extends Rule[LogicalPlan] {
           case Literal(candidate, _) if candidate == v => true
           case _ => false
         })) => Literal(true, BooleanType)
+      case e @ Substring(Literal(null, _), _, _) => Literal(null, e.dataType)
+      case e @ Substring(_, Literal(null, _), _) => Literal(null, e.dataType)
+      case e @ Substring(_, _, Literal(null, _)) => Literal(null, e.dataType)
       // Put exceptional cases above if any
       case e: BinaryArithmetic => e.children match {
         case Literal(null, _) :: right :: Nil => Literal(null, e.dataType)

From 632fb3d9a9ebb3d2218385403145d5b89c41c025 Mon Sep 17 00:00:00 2001
From: Takuya UESHIN <ueshin@happy-camper.st>
Date: Tue, 15 Jul 2014 22:43:48 -0700
Subject: [PATCH 665/810] [SPARK-2504][SQL] Fix nullability of Substring
 expression.

This is a follow-up of #1359 with nullability narrowing.

Author: Takuya UESHIN <ueshin@happy-camper.st>

Closes #1426 from ueshin/issues/SPARK-2504 and squashes the following commits:

5157832 [Takuya UESHIN] Remove unnecessary white spaces.
80958ac [Takuya UESHIN] Fix nullability of Substring expression.
---
 .../expressions/stringOperations.scala        | 24 +++++++++----------
 .../ExpressionEvaluationSuite.scala           | 14 +++++++----
 2 files changed, 22 insertions(+), 16 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
index 4bd7bf5a0cd8..f1b27c3cb517 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
@@ -215,19 +215,19 @@ case class EndsWith(left: Expression, right: Expression)
 case class Substring(str: Expression, pos: Expression, len: Expression) extends Expression {
   
   type EvaluatedType = Any
-  
-  def nullable: Boolean = true
+
+  def nullable: Boolean = str.nullable || pos.nullable || len.nullable
   def dataType: DataType = {
     if (!resolved) {
       throw new UnresolvedException(this, s"Cannot resolve since $children are not resolved")
     }
     if (str.dataType == BinaryType) str.dataType else StringType
   }
-  
+
   def references = children.flatMap(_.references).toSet
-  
+
   override def children = str :: pos :: len :: Nil
-  
+
   @inline
   def slice[T, C <: Any](str: C, startPos: Int, sliceLen: Int)
       (implicit ev: (C=>IndexedSeqOptimized[T,_])): Any = {
@@ -237,40 +237,40 @@ case class Substring(str: Expression, pos: Expression, len: Expression) extends
     // refers to element i-1 in the sequence. If a start index i is less than 0, it refers
     // to the -ith element before the end of the sequence. If a start index i is 0, it
     // refers to the first element.
-    
+
     val start = startPos match {
       case pos if pos > 0 => pos - 1
       case neg if neg < 0 => len + neg
       case _ => 0
     }
-    
+
     val end = sliceLen match {
       case max if max == Integer.MAX_VALUE => max
       case x => start + x
     }
-      
+
     str.slice(start, end)    
   }
-  
+
   override def eval(input: Row): Any = {
     val string = str.eval(input)
 
     val po = pos.eval(input)
     val ln = len.eval(input)
-    
+
     if ((string == null) || (po == null) || (ln == null)) {
       null
     } else {
       val start = po.asInstanceOf[Int]
       val length = ln.asInstanceOf[Int] 
-      
+
       string match {
         case ba: Array[Byte] => slice(ba, start, length)
         case other => slice(other.toString, start, length)
       }
     }
   }
-  
+
   override def toString = len match {
     case max if max == Integer.MAX_VALUE => s"SUBSTR($str, $pos)"
     case _ => s"SUBSTR($str, $pos, $len)"
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala
index f1d7aedcc2d2..143330bd6471 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala
@@ -469,9 +469,9 @@ class ExpressionEvaluationSuite extends FunSuite {
   
   test("Substring") {
     val row = new GenericRow(Array[Any]("example", "example".toArray.map(_.toByte)))
-    
+
     val s = 'a.string.at(0)
-    
+
     // substring from zero position with less-than-full length
     checkEvaluation(Substring(s, Literal(0, IntegerType), Literal(2, IntegerType)), "ex", row)
     checkEvaluation(Substring(s, Literal(1, IntegerType), Literal(2, IntegerType)), "ex", row)
@@ -501,7 +501,7 @@ class ExpressionEvaluationSuite extends FunSuite {
 
     // substring(null, _, _) -> null
     checkEvaluation(Substring(s, Literal(100, IntegerType), Literal(4, IntegerType)), null, new GenericRow(Array[Any](null)))
-    
+
     // substring(_, null, _) -> null
     checkEvaluation(Substring(s, Literal(null, IntegerType), Literal(4, IntegerType)), null, row)
 
@@ -514,6 +514,12 @@ class ExpressionEvaluationSuite extends FunSuite {
 
     // 2-arg substring from nonzero position
     checkEvaluation(Substring(s, Literal(2, IntegerType), Literal(Integer.MAX_VALUE, IntegerType)), "xample", row)
+
+    val s_notNull = 'a.string.notNull.at(0)
+
+    assert(Substring(s, Literal(0, IntegerType), Literal(2, IntegerType)).nullable === true)
+    assert(Substring(s_notNull, Literal(0, IntegerType), Literal(2, IntegerType)).nullable === false)
+    assert(Substring(s_notNull, Literal(null, IntegerType), Literal(2, IntegerType)).nullable === true)
+    assert(Substring(s_notNull, Literal(0, IntegerType), Literal(null, IntegerType)).nullable === true)
   }
 }
-

From efc452a16322e8b20b3c4fe1d6847315f928cd2d Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian.cs.zju@gmail.com>
Date: Wed, 16 Jul 2014 12:44:51 -0400
Subject: [PATCH 666/810] [SPARK-2119][SQL] Improved Parquet performance when
 reading off S3

JIRA issue: [SPARK-2119](https://issues.apache.org/jira/browse/SPARK-2119)

Essentially this PR fixed three issues to gain much better performance when reading large Parquet file off S3.

1. When reading the schema, fetching Parquet metadata from a part-file rather than the `_metadata` file

   The `_metadata` file contains metadata of all row groups, and can be very large if there are many row groups. Since schema information and row group metadata are coupled within a single Thrift object, we have to read the whole `_metadata` to fetch the schema. On the other hand, schema is replicated among footers of all part-files, which are fairly small.

1. Only add the root directory of the Parquet file rather than all the part-files to input paths

   HDFS API can automatically filter out all hidden files and underscore files (`_SUCCESS` & `_metadata`), there's no need to filter out all part-files and add them individually to input paths. What make it much worse is that, `FileInputFormat.listStatus()` calls `FileSystem.globStatus()` on each individual input path sequentially, each results a blocking remote S3 HTTP request.

1. Worked around [PARQUET-16](https://issues.apache.org/jira/browse/PARQUET-16)

   Essentially PARQUET-16 is similar to the above issue, and results lots of sequential `FileSystem.getFileStatus()` calls, which are further translated into a bunch of remote S3 HTTP requests.

   `FilteringParquetRowInputFormat` should be cleaned up once PARQUET-16 is fixed.

Below is the micro benchmark result. The dataset used is a S3 Parquet file consists of 3,793 partitions, about 110MB per partition in average. The benchmark is done with a 9-node AWS cluster.

- Creating a Parquet `SchemaRDD` (Parquet schema is fetched)

  ```scala
  val tweets = parquetFile(uri)
  ```

  - Before: 17.80s
  - After: 8.61s

- Fetching partition information

  ```scala
  tweets.getPartitions
  ```

  - Before: 700.87s
  - After: 21.47s

- Counting the whole file (both steps above are executed altogether)

  ```scala
  parquetFile(uri).count()
  ```

  - Before: ??? (haven't test yet)
  - After: 53.26s

Author: Cheng Lian <lian.cs.zju@gmail.com>

Closes #1370 from liancheng/faster-parquet and squashes the following commits:

94a2821 [Cheng Lian] Added comments about schema consistency
d2c4417 [Cheng Lian] Worked around PARQUET-16 to improve Parquet performance
1c0d1b9 [Cheng Lian] Accelerated Parquet schema retrieving
5bd3d29 [Cheng Lian] Fixed Parquet log level
---
 .../sql/parquet/ParquetTableOperations.scala  | 115 ++++++++++++++----
 .../sql/parquet/ParquetTableSupport.scala     |  27 ++--
 .../spark/sql/parquet/ParquetTypes.scala      |  33 ++---
 3 files changed, 125 insertions(+), 50 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala
index ade823b51c9c..ea74320d06c8 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala
@@ -17,27 +17,34 @@
 
 package org.apache.spark.sql.parquet
 
+import scala.collection.JavaConversions._
+import scala.collection.mutable
+import scala.util.Try
+
 import java.io.IOException
+import java.lang.{Long => JLong}
 import java.text.SimpleDateFormat
-import java.util.Date
+import java.util.{Date, List => JList}
 
 import org.apache.hadoop.conf.Configuration
-import org.apache.hadoop.fs.Path
+import org.apache.hadoop.fs.{FileStatus, Path}
 import org.apache.hadoop.mapreduce._
 import org.apache.hadoop.mapreduce.lib.input.{FileInputFormat => NewFileInputFormat}
-import org.apache.hadoop.mapreduce.lib.output.{FileOutputFormat => NewFileOutputFormat, FileOutputCommitter}
+import org.apache.hadoop.mapreduce.lib.output.{FileOutputFormat => NewFileOutputFormat}
+import org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter
 
-import parquet.hadoop.{ParquetRecordReader, ParquetInputFormat, ParquetOutputFormat}
-import parquet.hadoop.api.ReadSupport
+import parquet.hadoop._
+import parquet.hadoop.api.{InitContext, ReadSupport}
+import parquet.hadoop.metadata.GlobalMetaData
 import parquet.hadoop.util.ContextUtil
-import parquet.io.InvalidRecordException
+import parquet.io.ParquetDecodingException
 import parquet.schema.MessageType
 
-import org.apache.spark.{Logging, SerializableWritable, TaskContext}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.SQLContext
 import org.apache.spark.sql.catalyst.expressions.{Attribute, Expression, Row}
 import org.apache.spark.sql.execution.{LeafNode, SparkPlan, UnaryNode}
+import org.apache.spark.{Logging, SerializableWritable, TaskContext}
 
 /**
  * Parquet table scan operator. Imports the file that backs the given
@@ -55,16 +62,14 @@ case class ParquetTableScan(
   override def execute(): RDD[Row] = {
     val sc = sqlContext.sparkContext
     val job = new Job(sc.hadoopConfiguration)
-    ParquetInputFormat.setReadSupportClass(
-      job,
-      classOf[org.apache.spark.sql.parquet.RowReadSupport])
+    ParquetInputFormat.setReadSupportClass(job, classOf[RowReadSupport])
+
     val conf: Configuration = ContextUtil.getConfiguration(job)
-    val fileList = FileSystemHelper.listFiles(relation.path, conf)
-    // add all paths in the directory but skip "hidden" ones such
-    // as "_SUCCESS" and "_metadata"
-    for (path <- fileList if !path.getName.startsWith("_")) {
-      NewFileInputFormat.addInputPath(job, path)
+    val qualifiedPath = {
+      val path = new Path(relation.path)
+      path.getFileSystem(conf).makeQualified(path)
     }
+    NewFileInputFormat.addInputPath(job, qualifiedPath)
 
     // Store both requested and original schema in `Configuration`
     conf.set(
@@ -87,7 +92,7 @@ case class ParquetTableScan(
 
     sc.newAPIHadoopRDD(
       conf,
-      classOf[org.apache.spark.sql.parquet.FilteringParquetRowInputFormat],
+      classOf[FilteringParquetRowInputFormat],
       classOf[Void],
       classOf[Row])
       .map(_._2)
@@ -122,14 +127,7 @@ case class ParquetTableScan(
   private def validateProjection(projection: Seq[Attribute]): Boolean = {
     val original: MessageType = relation.parquetSchema
     val candidate: MessageType = ParquetTypesConverter.convertFromAttributes(projection)
-    try {
-      original.checkContains(candidate)
-      true
-    } catch {
-      case e: InvalidRecordException => {
-        false
-      }
-    }
+    Try(original.checkContains(candidate)).isSuccess
   }
 }
 
@@ -302,6 +300,11 @@ private[parquet] class AppendingParquetOutputFormat(offset: Int)
  */
 private[parquet] class FilteringParquetRowInputFormat
   extends parquet.hadoop.ParquetInputFormat[Row] with Logging {
+
+  private var footers: JList[Footer] = _
+
+  private var fileStatuses= Map.empty[Path, FileStatus]
+
   override def createRecordReader(
       inputSplit: InputSplit,
       taskAttemptContext: TaskAttemptContext): RecordReader[Void, Row] = {
@@ -318,6 +321,70 @@ private[parquet] class FilteringParquetRowInputFormat
       new ParquetRecordReader[Row](readSupport)
     }
   }
+
+  override def getFooters(jobContext: JobContext): JList[Footer] = {
+    if (footers eq null) {
+      val statuses = listStatus(jobContext)
+      fileStatuses = statuses.map(file => file.getPath -> file).toMap
+      footers = getFooters(ContextUtil.getConfiguration(jobContext), statuses)
+    }
+
+    footers
+  }
+
+  // TODO Remove this method and related code once PARQUET-16 is fixed
+  // This method together with the `getFooters` method and the `fileStatuses` field are just used
+  // to mimic this PR: https://github.com/apache/incubator-parquet-mr/pull/17
+  override def getSplits(
+      configuration: Configuration,
+      footers: JList[Footer]): JList[ParquetInputSplit] = {
+
+    val maxSplitSize: JLong = configuration.getLong("mapred.max.split.size", Long.MaxValue)
+    val minSplitSize: JLong =
+      Math.max(getFormatMinSplitSize(), configuration.getLong("mapred.min.split.size", 0L))
+    if (maxSplitSize < 0 || minSplitSize < 0) {
+      throw new ParquetDecodingException(
+        s"maxSplitSize or minSplitSie should not be negative: maxSplitSize = $maxSplitSize;" +
+          s" minSplitSize = $minSplitSize")
+    }
+
+    val getGlobalMetaData =
+      classOf[ParquetFileWriter].getDeclaredMethod("getGlobalMetaData", classOf[JList[Footer]])
+    getGlobalMetaData.setAccessible(true)
+    val globalMetaData = getGlobalMetaData.invoke(null, footers).asInstanceOf[GlobalMetaData]
+
+    val readContext = getReadSupport(configuration).init(
+      new InitContext(configuration,
+        globalMetaData.getKeyValueMetaData(),
+        globalMetaData.getSchema()))
+
+    val generateSplits =
+      classOf[ParquetInputFormat[_]].getDeclaredMethods.find(_.getName == "generateSplits").get
+    generateSplits.setAccessible(true)
+
+    val splits = mutable.ArrayBuffer.empty[ParquetInputSplit]
+    for (footer <- footers) {
+      val fs = footer.getFile.getFileSystem(configuration)
+      val file = footer.getFile
+      val fileStatus = fileStatuses.getOrElse(file, fs.getFileStatus(file))
+      val parquetMetaData = footer.getParquetMetadata
+      val blocks = parquetMetaData.getBlocks
+      val fileBlockLocations = fs.getFileBlockLocations(fileStatus, 0, fileStatus.getLen)
+      splits.addAll(
+        generateSplits.invoke(
+          null,
+          blocks,
+          fileBlockLocations,
+          fileStatus,
+          parquetMetaData.getFileMetaData,
+          readContext.getRequestedSchema.toString,
+          readContext.getReadSupportMetadata,
+          minSplitSize,
+          maxSplitSize).asInstanceOf[JList[ParquetInputSplit]])
+    }
+
+    splits
+  }
 }
 
 private[parquet] object FileSystemHelper {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala
index f1953a008a49..39294a3f4bf5 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala
@@ -17,20 +17,19 @@
 
 package org.apache.spark.sql.parquet
 
-import org.apache.hadoop.conf.Configuration
+import java.util.{HashMap => JHashMap}
 
+import org.apache.hadoop.conf.Configuration
 import parquet.column.ParquetProperties
 import parquet.hadoop.ParquetOutputFormat
 import parquet.hadoop.api.ReadSupport.ReadContext
 import parquet.hadoop.api.{ReadSupport, WriteSupport}
 import parquet.io.api._
-import parquet.schema.{MessageType, MessageTypeParser}
+import parquet.schema.MessageType
 
 import org.apache.spark.Logging
 import org.apache.spark.sql.catalyst.expressions.{Attribute, Row}
 import org.apache.spark.sql.catalyst.types._
-import org.apache.spark.sql.execution.SparkSqlSerializer
-import com.google.common.io.BaseEncoding
 
 /**
  * A `parquet.io.api.RecordMaterializer` for Rows.
@@ -93,8 +92,8 @@ private[parquet] class RowReadSupport extends ReadSupport[Row] with Logging {
       configuration: Configuration,
       keyValueMetaData: java.util.Map[String, String],
       fileSchema: MessageType): ReadContext = {
-    var parquetSchema: MessageType = fileSchema
-    var metadata: java.util.Map[String, String] = new java.util.HashMap[String, String]()
+    var parquetSchema = fileSchema
+    val metadata = new JHashMap[String, String]()
     val requestedAttributes = RowReadSupport.getRequestedSchema(configuration)
 
     if (requestedAttributes != null) {
@@ -109,7 +108,7 @@ private[parquet] class RowReadSupport extends ReadSupport[Row] with Logging {
       metadata.put(RowReadSupport.SPARK_METADATA_KEY, origAttributesStr)
     }
 
-    return new ReadSupport.ReadContext(parquetSchema, metadata)
+    new ReadSupport.ReadContext(parquetSchema, metadata)
   }
 }
 
@@ -132,13 +131,17 @@ private[parquet] class RowWriteSupport extends WriteSupport[Row] with Logging {
   private[parquet] var attributes: Seq[Attribute] = null
 
   override def init(configuration: Configuration): WriteSupport.WriteContext = {
-    attributes = if (attributes == null) RowWriteSupport.getSchema(configuration) else attributes
-    
+    val origAttributesStr: String = configuration.get(RowWriteSupport.SPARK_ROW_SCHEMA)
+    val metadata = new JHashMap[String, String]()
+    metadata.put(RowReadSupport.SPARK_METADATA_KEY, origAttributesStr)
+
+    if (attributes == null) {
+      attributes = ParquetTypesConverter.convertFromString(origAttributesStr)
+    }
+
     log.debug(s"write support initialized for requested schema $attributes")
     ParquetRelation.enableLogForwarding()
-    new WriteSupport.WriteContext(
-      ParquetTypesConverter.convertFromAttributes(attributes),
-      new java.util.HashMap[java.lang.String, java.lang.String]())
+    new WriteSupport.WriteContext(ParquetTypesConverter.convertFromAttributes(attributes), metadata)
   }
 
   override def prepareForWrite(recordConsumer: RecordConsumer): Unit = {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypes.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypes.scala
index 7f6ad908f78e..58370b955a5e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypes.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypes.scala
@@ -22,6 +22,7 @@ import java.io.IOException
 import org.apache.hadoop.conf.Configuration
 import org.apache.hadoop.fs.{FileSystem, Path}
 import org.apache.hadoop.mapreduce.Job
+import org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter
 
 import parquet.hadoop.{ParquetFileReader, Footer, ParquetFileWriter}
 import parquet.hadoop.metadata.{ParquetMetadata, FileMetaData}
@@ -367,20 +368,24 @@ private[parquet] object ParquetTypesConverter extends Logging {
         s"Expected $path for be a directory with Parquet files/metadata")
     }
     ParquetRelation.enableLogForwarding()
-    val metadataPath = new Path(path, ParquetFileWriter.PARQUET_METADATA_FILE)
-    // if this is a new table that was just created we will find only the metadata file
-    if (fs.exists(metadataPath) && fs.isFile(metadataPath)) {
-      ParquetFileReader.readFooter(conf, metadataPath)
-    } else {
-      // there may be one or more Parquet files in the given directory
-      val footers = ParquetFileReader.readFooters(conf, fs.getFileStatus(path))
-      // TODO: for now we assume that all footers (if there is more than one) have identical
-      // metadata; we may want to add a check here at some point
-      if (footers.size() == 0) {
-        throw new IllegalArgumentException(s"Could not find Parquet metadata at path $path")
-      }
-      footers(0).getParquetMetadata
+
+    val children = fs.listStatus(path).filterNot {
+      _.getPath.getName == FileOutputCommitter.SUCCEEDED_FILE_NAME
     }
+
+    // NOTE (lian): Parquet "_metadata" file can be very slow if the file consists of lots of row
+    // groups. Since Parquet schema is replicated among all row groups, we only need to touch a
+    // single row group to read schema related metadata. Notice that we are making assumptions that
+    // all data in a single Parquet file have the same schema, which is normally true.
+    children
+      // Try any non-"_metadata" file first...
+      .find(_.getPath.getName != ParquetFileWriter.PARQUET_METADATA_FILE)
+      // ... and fallback to "_metadata" if no such file exists (which implies the Parquet file is
+      // empty, thus normally the "_metadata" file is expected to be fairly small).
+      .orElse(children.find(_.getPath.getName == ParquetFileWriter.PARQUET_METADATA_FILE))
+      .map(ParquetFileReader.readFooter(conf, _))
+      .getOrElse(
+        throw new IllegalArgumentException(s"Could not find Parquet metadata at path $path"))
   }
 
   /**
@@ -403,7 +408,7 @@ private[parquet] object ParquetTypesConverter extends Logging {
     } else {
       val attributes = convertToAttributes(
         readMetaData(origPath, conf).getFileMetaData.getSchema)
-      log.warn(s"Falling back to schema conversion from Parquet types; result: $attributes")
+      log.info(s"Falling back to schema conversion from Parquet types; result: $attributes")
       attributes
     }
   }

From 33e64ecacbc44567f9cba2644a30a118653ea5fa Mon Sep 17 00:00:00 2001
From: Rui Li <rui.li@intel.com>
Date: Wed, 16 Jul 2014 22:53:37 +0530
Subject: [PATCH 667/810] SPARK-2277: make TaskScheduler track hosts on rack

Hi mateiz, I've created [SPARK-2277](https://issues.apache.org/jira/browse/SPARK-2277) to make TaskScheduler track hosts on each rack. Please help to review, thanks.

Author: Rui Li <rui.li@intel.com>

Closes #1212 from lirui-intel/trackHostOnRack and squashes the following commits:

2b4bd0f [Rui Li] SPARK-2277: refine UT
fbde838 [Rui Li] SPARK-2277: add UT
7bbe658 [Rui Li] SPARK-2277: rename the method
5e4ef62 [Rui Li] SPARK-2277: remove unnecessary import
79ac750 [Rui Li] SPARK-2277: make TaskScheduler track hosts on rack
---
 .../spark/scheduler/TaskSchedulerImpl.scala   | 15 +++++
 .../spark/scheduler/TaskSetManager.scala      | 10 ++-
 .../spark/scheduler/TaskSetManagerSuite.scala | 63 ++++++++++++++++++-
 3 files changed, 83 insertions(+), 5 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala b/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala
index 4b6d6da5a6e6..be3673c48eda 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala
@@ -88,6 +88,8 @@ private[spark] class TaskSchedulerImpl(
   // in turn is used to decide when we can attain data locality on a given host
   private val executorsByHost = new HashMap[String, HashSet[String]]
 
+  protected val hostsByRack = new HashMap[String, HashSet[String]]
+
   private val executorIdToHost = new HashMap[String, String]
 
   // Listener object to pass upcalls into
@@ -223,6 +225,9 @@ private[spark] class TaskSchedulerImpl(
         executorAdded(o.executorId, o.host)
         newExecAvail = true
       }
+      for (rack <- getRackForHost(o.host)) {
+        hostsByRack.getOrElseUpdate(rack, new HashSet[String]()) += o.host
+      }
     }
 
     // Randomly shuffle offers to avoid always placing tasks on the same set of workers.
@@ -418,6 +423,12 @@ private[spark] class TaskSchedulerImpl(
     execs -= executorId
     if (execs.isEmpty) {
       executorsByHost -= host
+      for (rack <- getRackForHost(host); hosts <- hostsByRack.get(rack)) {
+        hosts -= host
+        if (hosts.isEmpty) {
+          hostsByRack -= rack
+        }
+      }
     }
     executorIdToHost -= executorId
     rootPool.executorLost(executorId, host)
@@ -435,6 +446,10 @@ private[spark] class TaskSchedulerImpl(
     executorsByHost.contains(host)
   }
 
+  def hasHostAliveOnRack(rack: String): Boolean = synchronized {
+    hostsByRack.contains(rack)
+  }
+
   def isExecutorAlive(execId: String): Boolean = synchronized {
     activeExecutorIds.contains(execId)
   }
diff --git a/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala b/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala
index 059cc9085a2e..3bdc71d93bd6 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala
@@ -191,7 +191,9 @@ private[spark] class TaskSetManager(
       addTo(pendingTasksForHost.getOrElseUpdate(loc.host, new ArrayBuffer))
       for (rack <- sched.getRackForHost(loc.host)) {
         addTo(pendingTasksForRack.getOrElseUpdate(rack, new ArrayBuffer))
-        hadAliveLocations = true
+        if(sched.hasHostAliveOnRack(rack)){
+          hadAliveLocations = true
+        }
       }
     }
 
@@ -748,7 +750,8 @@ private[spark] class TaskSetManager(
         pendingTasksForHost.keySet.exists(sched.hasExecutorsAliveOnHost(_))) {
       levels += NODE_LOCAL
     }
-    if (!pendingTasksForRack.isEmpty && getLocalityWait(RACK_LOCAL) != 0) {
+    if (!pendingTasksForRack.isEmpty && getLocalityWait(RACK_LOCAL) != 0 &&
+        pendingTasksForRack.keySet.exists(sched.hasHostAliveOnRack(_))) {
       levels += RACK_LOCAL
     }
     levels += ANY
@@ -761,7 +764,8 @@ private[spark] class TaskSetManager(
     def newLocAvail(index: Int): Boolean = {
       for (loc <- tasks(index).preferredLocations) {
         if (sched.hasExecutorsAliveOnHost(loc.host) ||
-            sched.getRackForHost(loc.host).isDefined) {
+           (sched.getRackForHost(loc.host).isDefined &&
+           sched.hasHostAliveOnRack(sched.getRackForHost(loc.host).get))) {
           return true
         }
       }
diff --git a/core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala
index 9ff2a487005c..86b443b18f2a 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala
@@ -54,6 +54,23 @@ class FakeDAGScheduler(sc: SparkContext, taskScheduler: FakeTaskScheduler)
   }
 }
 
+// Get the rack for a given host
+object FakeRackUtil {
+  private val hostToRack = new mutable.HashMap[String, String]()
+
+  def cleanUp() {
+    hostToRack.clear()
+  }
+
+  def assignHostToRack(host: String, rack: String) {
+    hostToRack(host) = rack
+  }
+
+  def getRackForHost(host: String) = {
+    hostToRack.get(host)
+  }
+}
+
 /**
  * A mock TaskSchedulerImpl implementation that just remembers information about tasks started and
  * feedback received from the TaskSetManagers. Note that it's important to initialize this with
@@ -69,6 +86,9 @@ class FakeTaskScheduler(sc: SparkContext, liveExecutors: (String, String)* /* ex
   val taskSetsFailed = new ArrayBuffer[String]
 
   val executors = new mutable.HashMap[String, String] ++ liveExecutors
+  for ((execId, host) <- liveExecutors; rack <- getRackForHost(host)) {
+    hostsByRack.getOrElseUpdate(rack, new mutable.HashSet[String]()) += host
+  }
 
   dagScheduler = new FakeDAGScheduler(sc, this)
 
@@ -82,7 +102,12 @@ class FakeTaskScheduler(sc: SparkContext, liveExecutors: (String, String)* /* ex
 
   def addExecutor(execId: String, host: String) {
     executors.put(execId, host)
+    for (rack <- getRackForHost(host)) {
+      hostsByRack.getOrElseUpdate(rack, new mutable.HashSet[String]()) += host
+    }
   }
+
+  override def getRackForHost(value: String): Option[String] = FakeRackUtil.getRackForHost(value)
 }
 
 /**
@@ -419,6 +444,9 @@ class TaskSetManagerSuite extends FunSuite with LocalSparkContext with Logging {
   }
 
   test("new executors get added") {
+    // Assign host2 to rack2
+    FakeRackUtil.cleanUp()
+    FakeRackUtil.assignHostToRack("host2", "rack2")
     sc = new SparkContext("local", "test")
     val sched = new FakeTaskScheduler(sc)
     val taskSet = FakeTask.createTaskSet(4,
@@ -444,8 +472,39 @@ class TaskSetManagerSuite extends FunSuite with LocalSparkContext with Logging {
     manager.executorAdded()
     // No-pref list now only contains task 3
     assert(manager.pendingTasksWithNoPrefs.size === 1)
-    // Valid locality should contain PROCESS_LOCAL, NODE_LOCAL and ANY
-    assert(manager.myLocalityLevels.sameElements(Array(PROCESS_LOCAL, NODE_LOCAL, ANY)))
+    // Valid locality should contain PROCESS_LOCAL, NODE_LOCAL, RACK_LOCAL and ANY
+    assert(manager.myLocalityLevels.sameElements(
+      Array(PROCESS_LOCAL, NODE_LOCAL, RACK_LOCAL, ANY)))
+  }
+
+  test("test RACK_LOCAL tasks") {
+    FakeRackUtil.cleanUp()
+    // Assign host1 to rack1
+    FakeRackUtil.assignHostToRack("host1", "rack1")
+    // Assign host2 to rack1
+    FakeRackUtil.assignHostToRack("host2", "rack1")
+    // Assign host3 to rack2
+    FakeRackUtil.assignHostToRack("host3", "rack2")
+    sc = new SparkContext("local", "test")
+    val sched = new FakeTaskScheduler(sc,
+      ("execA", "host1"), ("execB", "host2"), ("execC", "host3"))
+    val taskSet = FakeTask.createTaskSet(2,
+      Seq(TaskLocation("host1", "execA")),
+      Seq(TaskLocation("host1", "execA")))
+    val clock = new FakeClock
+    val manager = new TaskSetManager(sched, taskSet, MAX_TASK_FAILURES, clock)
+
+    assert(manager.myLocalityLevels.sameElements(Array(PROCESS_LOCAL, NODE_LOCAL, RACK_LOCAL, ANY)))
+    // Set allowed locality to ANY
+    clock.advance(LOCALITY_WAIT * 3)
+    // Offer host3
+    // No task is scheduled if we restrict locality to RACK_LOCAL
+    assert(manager.resourceOffer("execC", "host3", RACK_LOCAL) === None)
+    // Task 0 can be scheduled with ANY
+    assert(manager.resourceOffer("execC", "host3", ANY).get.index === 0)
+    // Offer host2
+    // Task 1 can be scheduled with RACK_LOCAL
+    assert(manager.resourceOffer("execB", "host2", RACK_LOCAL).get.index === 1)
   }
 
   test("do not emit warning when serialized task is small") {

From efe2a8b1262a371471f52ca7d47dc34789e80558 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Wed, 16 Jul 2014 10:44:54 -0700
Subject: [PATCH 668/810] Tightening visibility for various Broadcast related
 classes.

In preparation for SPARK-2521.

Author: Reynold Xin <rxin@apache.org>

Closes #1438 from rxin/broadcast and squashes the following commits:

432f1cc [Reynold Xin] Tightening visibility for various Broadcast related classes.
---
 .../apache/spark/broadcast/Broadcast.scala    |  8 ++---
 .../spark/broadcast/HttpBroadcast.scala       | 14 ++++----
 .../broadcast/HttpBroadcastFactory.scala      |  8 ++---
 .../spark/broadcast/TorrentBroadcast.scala    | 33 ++++++++++---------
 .../broadcast/TorrentBroadcastFactory.scala   |  8 ++---
 5 files changed, 36 insertions(+), 35 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/broadcast/Broadcast.scala b/core/src/main/scala/org/apache/spark/broadcast/Broadcast.scala
index 76956f6a345d..15fd30e65761 100644
--- a/core/src/main/scala/org/apache/spark/broadcast/Broadcast.scala
+++ b/core/src/main/scala/org/apache/spark/broadcast/Broadcast.scala
@@ -106,23 +106,23 @@ abstract class Broadcast[T: ClassTag](val id: Long) extends Serializable {
    * Actually get the broadcasted value. Concrete implementations of Broadcast class must
    * define their own way to get the value.
    */
-  private[spark] def getValue(): T
+  protected def getValue(): T
 
   /**
    * Actually unpersist the broadcasted value on the executors. Concrete implementations of
    * Broadcast class must define their own logic to unpersist their own data.
    */
-  private[spark] def doUnpersist(blocking: Boolean)
+  protected def doUnpersist(blocking: Boolean)
 
   /**
    * Actually destroy all data and metadata related to this broadcast variable.
    * Implementation of Broadcast class must define their own logic to destroy their own
    * state.
    */
-  private[spark] def doDestroy(blocking: Boolean)
+  protected def doDestroy(blocking: Boolean)
 
   /** Check if this broadcast is valid. If not valid, exception is thrown. */
-  private[spark] def assertValid() {
+  protected def assertValid() {
     if (!_isValid) {
       throw new SparkException("Attempted to use %s after it has been destroyed!".format(toString))
     }
diff --git a/core/src/main/scala/org/apache/spark/broadcast/HttpBroadcast.scala b/core/src/main/scala/org/apache/spark/broadcast/HttpBroadcast.scala
index 4f6cabaff2b9..487456467b23 100644
--- a/core/src/main/scala/org/apache/spark/broadcast/HttpBroadcast.scala
+++ b/core/src/main/scala/org/apache/spark/broadcast/HttpBroadcast.scala
@@ -40,9 +40,9 @@ private[spark] class HttpBroadcast[T: ClassTag](
     @transient var value_ : T, isLocal: Boolean, id: Long)
   extends Broadcast[T](id) with Logging with Serializable {
 
-  def getValue = value_
+  override protected def getValue() = value_
 
-  val blockId = BroadcastBlockId(id)
+  private val blockId = BroadcastBlockId(id)
 
   /*
    * Broadcasted data is also stored in the BlockManager of the driver. The BlockManagerMaster
@@ -60,14 +60,14 @@ private[spark] class HttpBroadcast[T: ClassTag](
   /**
    * Remove all persisted state associated with this HTTP broadcast on the executors.
    */
-  def doUnpersist(blocking: Boolean) {
+  override protected def doUnpersist(blocking: Boolean) {
     HttpBroadcast.unpersist(id, removeFromDriver = false, blocking)
   }
 
   /**
    * Remove all persisted state associated with this HTTP broadcast on the executors and driver.
    */
-  def doDestroy(blocking: Boolean) {
+  override protected def doDestroy(blocking: Boolean) {
     HttpBroadcast.unpersist(id, removeFromDriver = true, blocking)
   }
 
@@ -102,7 +102,7 @@ private[spark] class HttpBroadcast[T: ClassTag](
   }
 }
 
-private[spark] object HttpBroadcast extends Logging {
+private[broadcast] object HttpBroadcast extends Logging {
   private var initialized = false
   private var broadcastDir: File = null
   private var compress: Boolean = false
@@ -160,7 +160,7 @@ private[spark] object HttpBroadcast extends Logging {
 
   def getFile(id: Long) = new File(broadcastDir, BroadcastBlockId(id).name)
 
-  def write(id: Long, value: Any) {
+  private def write(id: Long, value: Any) {
     val file = getFile(id)
     val out: OutputStream = {
       if (compress) {
@@ -176,7 +176,7 @@ private[spark] object HttpBroadcast extends Logging {
     files += file
   }
 
-  def read[T: ClassTag](id: Long): T = {
+  private def read[T: ClassTag](id: Long): T = {
     logDebug("broadcast read server: " +  serverUri + " id: broadcast-" + id)
     val url = serverUri + "/" + BroadcastBlockId(id).name
 
diff --git a/core/src/main/scala/org/apache/spark/broadcast/HttpBroadcastFactory.scala b/core/src/main/scala/org/apache/spark/broadcast/HttpBroadcastFactory.scala
index d5a031e2bbb5..c7ef02d572a1 100644
--- a/core/src/main/scala/org/apache/spark/broadcast/HttpBroadcastFactory.scala
+++ b/core/src/main/scala/org/apache/spark/broadcast/HttpBroadcastFactory.scala
@@ -27,21 +27,21 @@ import org.apache.spark.{SecurityManager, SparkConf}
  * [[org.apache.spark.broadcast.HttpBroadcast]] for more details about this mechanism.
  */
 class HttpBroadcastFactory extends BroadcastFactory {
-  def initialize(isDriver: Boolean, conf: SparkConf, securityMgr: SecurityManager) {
+  override def initialize(isDriver: Boolean, conf: SparkConf, securityMgr: SecurityManager) {
     HttpBroadcast.initialize(isDriver, conf, securityMgr)
   }
 
-  def newBroadcast[T: ClassTag](value_ : T, isLocal: Boolean, id: Long) =
+  override def newBroadcast[T: ClassTag](value_ : T, isLocal: Boolean, id: Long) =
     new HttpBroadcast[T](value_, isLocal, id)
 
-  def stop() { HttpBroadcast.stop() }
+  override def stop() { HttpBroadcast.stop() }
 
   /**
    * Remove all persisted state associated with the HTTP broadcast with the given ID.
    * @param removeFromDriver Whether to remove state from the driver
    * @param blocking Whether to block until unbroadcasted
    */
-  def unbroadcast(id: Long, removeFromDriver: Boolean, blocking: Boolean) {
+  override def unbroadcast(id: Long, removeFromDriver: Boolean, blocking: Boolean) {
     HttpBroadcast.unpersist(id, removeFromDriver, blocking)
   }
 }
diff --git a/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcast.scala b/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcast.scala
index 734de37ba115..86731b684f44 100644
--- a/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcast.scala
+++ b/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcast.scala
@@ -20,7 +20,6 @@ package org.apache.spark.broadcast
 import java.io.{ByteArrayInputStream, ObjectInputStream, ObjectOutputStream}
 
 import scala.reflect.ClassTag
-import scala.math
 import scala.util.Random
 
 import org.apache.spark.{Logging, SparkConf, SparkEnv, SparkException}
@@ -49,19 +48,19 @@ private[spark] class TorrentBroadcast[T: ClassTag](
     @transient var value_ : T, isLocal: Boolean, id: Long)
   extends Broadcast[T](id) with Logging with Serializable {
 
-  def getValue = value_
+  override protected def getValue() = value_
 
-  val broadcastId = BroadcastBlockId(id)
+  private val broadcastId = BroadcastBlockId(id)
 
   TorrentBroadcast.synchronized {
     SparkEnv.get.blockManager.putSingle(
       broadcastId, value_, StorageLevel.MEMORY_AND_DISK, tellMaster = false)
   }
 
-  @transient var arrayOfBlocks: Array[TorrentBlock] = null
-  @transient var totalBlocks = -1
-  @transient var totalBytes = -1
-  @transient var hasBlocks = 0
+  @transient private var arrayOfBlocks: Array[TorrentBlock] = null
+  @transient private var totalBlocks = -1
+  @transient private var totalBytes = -1
+  @transient private var hasBlocks = 0
 
   if (!isLocal) {
     sendBroadcast()
@@ -70,7 +69,7 @@ private[spark] class TorrentBroadcast[T: ClassTag](
   /**
    * Remove all persisted state associated with this Torrent broadcast on the executors.
    */
-  def doUnpersist(blocking: Boolean) {
+  override protected def doUnpersist(blocking: Boolean) {
     TorrentBroadcast.unpersist(id, removeFromDriver = false, blocking)
   }
 
@@ -78,11 +77,11 @@ private[spark] class TorrentBroadcast[T: ClassTag](
    * Remove all persisted state associated with this Torrent broadcast on the executors
    * and driver.
    */
-  def doDestroy(blocking: Boolean) {
+  override protected def doDestroy(blocking: Boolean) {
     TorrentBroadcast.unpersist(id, removeFromDriver = true, blocking)
   }
 
-  def sendBroadcast() {
+  private def sendBroadcast() {
     val tInfo = TorrentBroadcast.blockifyObject(value_)
     totalBlocks = tInfo.totalBlocks
     totalBytes = tInfo.totalBytes
@@ -159,7 +158,7 @@ private[spark] class TorrentBroadcast[T: ClassTag](
     hasBlocks = 0
   }
 
-  def receiveBroadcast(): Boolean = {
+  private def receiveBroadcast(): Boolean = {
     // Receive meta-info about the size of broadcast data,
     // the number of chunks it is divided into, etc.
     val metaId = BroadcastBlockId(id, "meta")
@@ -211,7 +210,7 @@ private[spark] class TorrentBroadcast[T: ClassTag](
 
 }
 
-private[spark] object TorrentBroadcast extends Logging {
+private[broadcast] object TorrentBroadcast extends Logging {
   private lazy val BLOCK_SIZE = conf.getInt("spark.broadcast.blockSize", 4096) * 1024
   private var initialized = false
   private var conf: SparkConf = null
@@ -272,17 +271,19 @@ private[spark] object TorrentBroadcast extends Logging {
    * Remove all persisted blocks associated with this torrent broadcast on the executors.
    * If removeFromDriver is true, also remove these persisted blocks on the driver.
    */
-  def unpersist(id: Long, removeFromDriver: Boolean, blocking: Boolean) = synchronized {
-    SparkEnv.get.blockManager.master.removeBroadcast(id, removeFromDriver, blocking)
+  def unpersist(id: Long, removeFromDriver: Boolean, blocking: Boolean) = {
+    synchronized {
+      SparkEnv.get.blockManager.master.removeBroadcast(id, removeFromDriver, blocking)
+    }
   }
 }
 
-private[spark] case class TorrentBlock(
+private[broadcast] case class TorrentBlock(
     blockID: Int,
     byteArray: Array[Byte])
   extends Serializable
 
-private[spark] case class TorrentInfo(
+private[broadcast] case class TorrentInfo(
     @transient arrayOfBlocks: Array[TorrentBlock],
     totalBlocks: Int,
     totalBytes: Int)
diff --git a/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcastFactory.scala b/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcastFactory.scala
index 1de8396a0e17..ad0f701d7a98 100644
--- a/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcastFactory.scala
+++ b/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcastFactory.scala
@@ -28,21 +28,21 @@ import org.apache.spark.{SecurityManager, SparkConf}
  */
 class TorrentBroadcastFactory extends BroadcastFactory {
 
-  def initialize(isDriver: Boolean, conf: SparkConf, securityMgr: SecurityManager) {
+  override def initialize(isDriver: Boolean, conf: SparkConf, securityMgr: SecurityManager) {
     TorrentBroadcast.initialize(isDriver, conf)
   }
 
-  def newBroadcast[T: ClassTag](value_ : T, isLocal: Boolean, id: Long) =
+  override def newBroadcast[T: ClassTag](value_ : T, isLocal: Boolean, id: Long) =
     new TorrentBroadcast[T](value_, isLocal, id)
 
-  def stop() { TorrentBroadcast.stop() }
+  override def stop() { TorrentBroadcast.stop() }
 
   /**
    * Remove all persisted state associated with the torrent broadcast with the given ID.
    * @param removeFromDriver Whether to remove state from the driver.
    * @param blocking Whether to block until unbroadcasted
    */
-  def unbroadcast(id: Long, removeFromDriver: Boolean, blocking: Boolean) {
+  override def unbroadcast(id: Long, removeFromDriver: Boolean, blocking: Boolean) {
     TorrentBroadcast.unpersist(id, removeFromDriver, blocking)
   }
 }

From df95d82da7c76c074fd4064f7c870d55d99e0d8e Mon Sep 17 00:00:00 2001
From: Yin Huai <huai@cse.ohio-state.edu>
Date: Wed, 16 Jul 2014 10:53:59 -0700
Subject: [PATCH 669/810] [SPARK-2525][SQL] Remove as many compilation warning
 messages as possible in Spark SQL

JIRA: https://issues.apache.org/jira/browse/SPARK-2525.

Author: Yin Huai <huai@cse.ohio-state.edu>

Closes #1444 from yhuai/SPARK-2517 and squashes the following commits:

edbac3f [Yin Huai] Removed some compiler type erasure warnings.
---
 .../scala/org/apache/spark/sql/SchemaRDD.scala | 18 +++++++++---------
 .../org/apache/spark/sql/json/JsonRDD.scala    | 18 +++++++++---------
 .../sql/hive/execution/HiveQuerySuite.scala    |  2 +-
 3 files changed, 19 insertions(+), 19 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala
index 0c95b668545f..993d085c7508 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala
@@ -380,32 +380,32 @@ class SchemaRDD(
       val fields = structType.fields.map(field => (field.name, field.dataType))
       val map: JMap[String, Any] = new java.util.HashMap
       row.zip(fields).foreach {
-        case (obj, (name, dataType)) =>
+        case (obj, (attrName, dataType)) =>
           dataType match {
-            case struct: StructType => map.put(name, rowToMap(obj.asInstanceOf[Row], struct))
+            case struct: StructType => map.put(attrName, rowToMap(obj.asInstanceOf[Row], struct))
             case array @ ArrayType(struct: StructType) =>
               val arrayValues = obj match {
                 case seq: Seq[Any] =>
                   seq.map(element => rowToMap(element.asInstanceOf[Row], struct)).asJava
-                case list: JList[Any] =>
+                case list: JList[_] =>
                   list.map(element => rowToMap(element.asInstanceOf[Row], struct))
-                case set: JSet[Any] =>
+                case set: JSet[_] =>
                   set.map(element => rowToMap(element.asInstanceOf[Row], struct))
-                case array if array != null && array.getClass.isArray =>
-                  array.asInstanceOf[Array[Any]].map {
+                case arr if arr != null && arr.getClass.isArray =>
+                  arr.asInstanceOf[Array[Any]].map {
                     element => rowToMap(element.asInstanceOf[Row], struct)
                   }
                 case other => other
               }
-              map.put(name, arrayValues)
+              map.put(attrName, arrayValues)
             case array: ArrayType => {
               val arrayValues = obj match {
                 case seq: Seq[Any] => seq.asJava
                 case other => other
               }
-              map.put(name, arrayValues)
+              map.put(attrName, arrayValues)
             }
-            case other => map.put(name, obj)
+            case other => map.put(attrName, obj)
           }
       }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala
index f6cbca96483e..df80dfb98b93 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala
@@ -204,14 +204,14 @@ private[sql] object JsonRDD extends Logging {
       case (key, value) => (s"`$key`", value)
     }.toSet
     keyValuePairs.flatMap {
-      case (key: String, struct: Map[String, Any]) => {
-        // The value associted with the key is an JSON object.
-        allKeysWithValueTypes(struct).map {
+      case (key: String, struct: Map[_, _]) => {
+        // The value associated with the key is an JSON object.
+        allKeysWithValueTypes(struct.asInstanceOf[Map[String, Any]]).map {
           case (k, dataType) => (s"$key.$k", dataType)
         } ++ Set((key, StructType(Nil)))
       }
-      case (key: String, array: List[Any]) => {
-        // The value associted with the key is an array.
+      case (key: String, array: List[_]) => {
+        // The value associated with the key is an array.
         typeOfArray(array) match {
           case ArrayType(StructType(Nil)) => {
             // The elements of this arrays are structs.
@@ -235,12 +235,12 @@ private[sql] object JsonRDD extends Logging {
    * the parsing very slow.
    */
   private def scalafy(obj: Any): Any = obj match {
-    case map: java.util.Map[String, Object] =>
+    case map: java.util.Map[_, _] =>
       // .map(identity) is used as a workaround of non-serializable Map
       // generated by .mapValues.
       // This issue is documented at https://issues.scala-lang.org/browse/SI-7005
       map.toMap.mapValues(scalafy).map(identity)
-    case list: java.util.List[Object] =>
+    case list: java.util.List[_] =>
       list.toList.map(scalafy)
     case atom => atom
   }
@@ -320,8 +320,8 @@ private[sql] object JsonRDD extends Logging {
 
   private def toString(value: Any): String = {
     value match {
-      case value: Map[String, Any] => toJsonObjectString(value)
-      case value: Seq[Any] => toJsonArrayString(value)
+      case value: Map[_, _] => toJsonObjectString(value.asInstanceOf[Map[String, Any]])
+      case value: Seq[_] => toJsonArrayString(value)
       case value => Option(value).map(_.toString).orNull
     }
   }
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
index a623d29b5397..d57e99db1858 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
@@ -390,7 +390,7 @@ class HiveQuerySuite extends HiveComparisonTest {
     hql("CREATE TABLE m(value MAP<INT, STRING>)")
     hql("INSERT OVERWRITE TABLE m SELECT MAP(key, value) FROM src LIMIT 10")
     hql("SELECT * FROM m").collect().zip(hql("SELECT * FROM src LIMIT 10").collect()).map {
-      case (Row(map: Map[Int, String]), Row(key: Int, value: String)) =>
+      case (Row(map: Map[_, _]), Row(key: Int, value: String)) =>
         assert(map.size === 1)
         assert(map.head === (key, value))
     }

From 1c5739f68510c2336bf6cb3e18aea03d85988bfb Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Wed, 16 Jul 2014 10:55:47 -0700
Subject: [PATCH 670/810] [SQL] Cleaned up ConstantFolding slightly.

Moved couple rules out of NullPropagation and added more comments.

Author: Reynold Xin <rxin@apache.org>

Closes #1430 from rxin/sql-folding-rule and squashes the following commits:

7f9a197 [Reynold Xin] Updated documentation for ConstantFolding.
7f8cf61 [Reynold Xin] [SQL] Cleaned up ConstantFolding slightly.
---
 .../sql/catalyst/optimizer/Optimizer.scala    | 45 ++++++++++++-------
 1 file changed, 28 insertions(+), 17 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
index 714e2cdac2b1..7f32f6b8bcf4 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
@@ -153,11 +153,13 @@ object NullPropagation extends Rule[LogicalPlan] {
       case e @ GetItem(Literal(null, _), _) => Literal(null, e.dataType)
       case e @ GetItem(_, Literal(null, _)) => Literal(null, e.dataType)
       case e @ GetField(Literal(null, _), _) => Literal(null, e.dataType)
-      case e @ Coalesce(children) => {
-        val newChildren = children.filter(c => c match {
+
+      // For Coalesce, remove null literals.
+      case e @ Coalesce(children) =>
+        val newChildren = children.filter {
           case Literal(null, _) => false
           case _ => true
-        })
+        }
         if (newChildren.length == 0) {
           Literal(null, e.dataType)
         } else if (newChildren.length == 1) {
@@ -165,15 +167,11 @@ object NullPropagation extends Rule[LogicalPlan] {
         } else {
           Coalesce(newChildren)
         }
-      }
-      case e @ If(Literal(v, _), trueValue, falseValue) => if (v == true) trueValue else falseValue
-      case e @ In(Literal(v, _), list) if (list.exists(c => c match {
-          case Literal(candidate, _) if candidate == v => true
-          case _ => false
-        })) => Literal(true, BooleanType)
+
       case e @ Substring(Literal(null, _), _, _) => Literal(null, e.dataType)
       case e @ Substring(_, Literal(null, _), _) => Literal(null, e.dataType)
       case e @ Substring(_, _, Literal(null, _)) => Literal(null, e.dataType)
+
       // Put exceptional cases above if any
       case e: BinaryArithmetic => e.children match {
         case Literal(null, _) :: right :: Nil => Literal(null, e.dataType)
@@ -201,9 +199,19 @@ object NullPropagation extends Rule[LogicalPlan] {
 object ConstantFolding extends Rule[LogicalPlan] {
   def apply(plan: LogicalPlan): LogicalPlan = plan transform {
     case q: LogicalPlan => q transformExpressionsDown {
-      // Skip redundant folding of literals.
+      // Skip redundant folding of literals. This rule is technically not necessary. Placing this
+      // here avoids running the next rule for Literal values, which would create a new Literal
+      // object and running eval unnecessarily.
       case l: Literal => l
+
+      // Fold expressions that are foldable.
       case e if e.foldable => Literal(e.eval(null), e.dataType)
+
+      // Fold "literal in (item1, item2, ..., literal, ...)" into true directly.
+      case In(Literal(v, _), list) if list.exists {
+          case Literal(candidate, _) if candidate == v => true
+          case _ => false
+        } => Literal(true, BooleanType)
     }
   }
 }
@@ -233,6 +241,9 @@ object BooleanSimplification extends Rule[LogicalPlan] {
           case (l, Literal(false, BooleanType)) => l
           case (_, _) => or
         }
+
+      // Turn "if (true) a else b" into "a", and if (false) a else b" into "b".
+      case e @ If(Literal(v, _), trueValue, falseValue) => if (v == true) trueValue else falseValue
     }
   }
 }
@@ -254,12 +265,12 @@ object CombineFilters extends Rule[LogicalPlan] {
  */
 object SimplifyFilters extends Rule[LogicalPlan] {
   def apply(plan: LogicalPlan): LogicalPlan = plan transform {
-    case Filter(Literal(true, BooleanType), child) =>
-      child
-    case Filter(Literal(null, _), child) =>
-      LocalRelation(child.output)
-    case Filter(Literal(false, BooleanType), child) =>
-      LocalRelation(child.output)
+    // If the filter condition always evaluate to true, remove the filter.
+    case Filter(Literal(true, BooleanType), child) => child
+    // If the filter condition always evaluate to null or false,
+    // replace the input with an empty relation.
+    case Filter(Literal(null, _), child) => LocalRelation(child.output, data = Seq.empty)
+    case Filter(Literal(false, BooleanType), child) => LocalRelation(child.output, data = Seq.empty)
   }
 }
 
@@ -301,7 +312,7 @@ object PushPredicateThroughJoin extends Rule[LogicalPlan] with PredicateHelper {
   /**
    * Splits join condition expressions into three categories based on the attributes required
    * to evaluate them.
-   * @returns (canEvaluateInLeft, canEvaluateInRight, haveToEvaluateInBoth)
+   * @return (canEvaluateInLeft, canEvaluateInRight, haveToEvaluateInBoth)
    */
   private def split(condition: Seq[Expression], left: LogicalPlan, right: LogicalPlan) = {
     val (leftEvaluateCondition, rest) =

From fc7edc9e76f97b25e456ae7b72ef8636656f4f1a Mon Sep 17 00:00:00 2001
From: Sandy Ryza <sandy@cloudera.com>
Date: Wed, 16 Jul 2014 11:07:16 -0700
Subject: [PATCH 671/810] SPARK-2519. Eliminate pattern-matching on Tuple2 in
 performance-critical...

... aggregation code

Author: Sandy Ryza <sandy@cloudera.com>

Closes #1435 from sryza/sandy-spark-2519 and squashes the following commits:

640706a [Sandy Ryza] SPARK-2519. Eliminate pattern-matching on Tuple2 in performance-critical aggregation code
---
 .../src/main/scala/org/apache/spark/Aggregator.scala |  8 ++++----
 .../util/collection/ExternalAppendOnlyMap.scala      | 12 +++++++-----
 2 files changed, 11 insertions(+), 9 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/Aggregator.scala b/core/src/main/scala/org/apache/spark/Aggregator.scala
index 59fdf659c9e1..1d640579efe7 100644
--- a/core/src/main/scala/org/apache/spark/Aggregator.scala
+++ b/core/src/main/scala/org/apache/spark/Aggregator.scala
@@ -56,8 +56,8 @@ case class Aggregator[K, V, C] (
     } else {
       val combiners = new ExternalAppendOnlyMap[K, V, C](createCombiner, mergeValue, mergeCombiners)
       while (iter.hasNext) {
-        val (k, v) = iter.next()
-        combiners.insert(k, v)
+        val pair = iter.next()
+        combiners.insert(pair._1, pair._2)
       }
       // TODO: Make this non optional in a future release
       Option(context).foreach(c => c.taskMetrics.memoryBytesSpilled = combiners.memoryBytesSpilled)
@@ -85,8 +85,8 @@ case class Aggregator[K, V, C] (
     } else {
       val combiners = new ExternalAppendOnlyMap[K, C, C](identity, mergeCombiners, mergeCombiners)
       while (iter.hasNext) {
-        val (k, c) = iter.next()
-        combiners.insert(k, c)
+        val pair = iter.next()
+        combiners.insert(pair._1, pair._2)
       }
       // TODO: Make this non optional in a future release
       Option(context).foreach(c => c.taskMetrics.memoryBytesSpilled = combiners.memoryBytesSpilled)
diff --git a/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala b/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala
index 292d0962f4fd..765254bf4c36 100644
--- a/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala
+++ b/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala
@@ -268,10 +268,10 @@ class ExternalAppendOnlyMap[K, V, C](
     private def mergeIfKeyExists(key: K, baseCombiner: C, buffer: StreamBuffer): C = {
       var i = 0
       while (i < buffer.pairs.length) {
-        val (k, c) = buffer.pairs(i)
-        if (k == key) {
+        val pair = buffer.pairs(i)
+        if (pair._1 == key) {
           buffer.pairs.remove(i)
-          return mergeCombiners(baseCombiner, c)
+          return mergeCombiners(baseCombiner, pair._2)
         }
         i += 1
       }
@@ -293,9 +293,11 @@ class ExternalAppendOnlyMap[K, V, C](
       }
       // Select a key from the StreamBuffer that holds the lowest key hash
       val minBuffer = mergeHeap.dequeue()
-      val (minPairs, minHash) = (minBuffer.pairs, minBuffer.minKeyHash)
+      val minPairs = minBuffer.pairs
+      val minHash = minBuffer.minKeyHash
       val minPair = minPairs.remove(0)
-      var (minKey, minCombiner) = minPair
+      val minKey = minPair._1
+      var minCombiner = minPair._2
       assert(getKeyHashCode(minPair) == minHash)
 
       // For all other streams that may have this key (i.e. have the same minimum key hash),

From cc965eea510397642830acb21f61127b68c098d6 Mon Sep 17 00:00:00 2001
From: Takuya UESHIN <ueshin@happy-camper.st>
Date: Wed, 16 Jul 2014 11:13:38 -0700
Subject: [PATCH 672/810] [SPARK-2518][SQL] Fix foldability of Substring
 expression.

This is a follow-up of #1428.

Author: Takuya UESHIN <ueshin@happy-camper.st>

Closes #1432 from ueshin/issues/SPARK-2518 and squashes the following commits:

37d1ace [Takuya UESHIN] Fix foldability of Substring expression.
---
 .../catalyst/expressions/stringOperations.scala   |  2 ++
 .../catalyst/optimizer/ConstantFoldingSuite.scala | 15 ++++++++++++---
 2 files changed, 14 insertions(+), 3 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
index f1b27c3cb517..97fc3a3b14b8 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
@@ -216,6 +216,8 @@ case class Substring(str: Expression, pos: Expression, len: Expression) extends
   
   type EvaluatedType = Any
 
+  override def foldable = str.foldable && pos.foldable && len.foldable
+
   def nullable: Boolean = str.nullable || pos.nullable || len.nullable
   def dataType: DataType = {
     if (!resolved) {
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConstantFoldingSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConstantFoldingSuite.scala
index 0ff82064012a..ff8d0d06c45e 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConstantFoldingSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConstantFoldingSuite.scala
@@ -201,7 +201,12 @@ class ConstantFoldingSuite extends PlanTest {
           Like(Literal(null, StringType), "abc") as 'c13,
           Like("abc", Literal(null, StringType)) as 'c14,
 
-          Upper(Literal(null, StringType)) as 'c15)
+          Upper(Literal(null, StringType)) as 'c15,
+
+          Substring(Literal(null, StringType), 0, 1) as 'c16,
+          Substring("abc", Literal(null, IntegerType), 1) as 'c17,
+          Substring("abc", 0, Literal(null, IntegerType)) as 'c18
+        )
 
     val optimized = Optimize(originalQuery.analyze)
 
@@ -228,8 +233,12 @@ class ConstantFoldingSuite extends PlanTest {
           Literal(null, BooleanType) as 'c13,
           Literal(null, BooleanType) as 'c14,
 
-          Literal(null, StringType) as 'c15)
-        .analyze
+          Literal(null, StringType) as 'c15,
+
+          Literal(null, StringType) as 'c16,
+          Literal(null, StringType) as 'c17,
+          Literal(null, StringType) as 'c18
+        ).analyze
 
     comparePlans(optimized, correctAnswer)
   }

From ef48222c10be3d29a83dfc2329f455eba203cd38 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Wed, 16 Jul 2014 11:15:07 -0700
Subject: [PATCH 673/810] [SPARK-2517] Remove some compiler warnings.

Author: Reynold Xin <rxin@apache.org>

Closes #1433 from rxin/compile-warning and squashes the following commits:

8d0b890 [Reynold Xin] Remove some compiler warnings.
---
 .../scala/org/apache/spark/rdd/RDDSuite.scala |  3 ++
 .../ProactiveClosureSerializationSuite.scala  | 37 ++++++++-----------
 .../apache/spark/util/FileAppenderSuite.scala |  3 +-
 .../org/apache/spark/util/VectorSuite.scala   |  1 +
 .../spark/streaming/InputStreamsSuite.scala   |  5 ++-
 5 files changed, 25 insertions(+), 24 deletions(-)

diff --git a/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala b/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala
index 0f9cbe213ea1..6ea045198e2c 100644
--- a/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala
+++ b/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala
@@ -379,6 +379,7 @@ class RDDSuite extends FunSuite with SharedSparkContext {
   test("mapWith") {
     import java.util.Random
     val ones = sc.makeRDD(Array(1, 1, 1, 1, 1, 1), 2)
+    @deprecated("suppress compile time deprecation warning", "1.0.0")
     val randoms = ones.mapWith(
       (index: Int) => new Random(index + 42))
       {(t: Int, prng: Random) => prng.nextDouble * t}.collect()
@@ -397,6 +398,7 @@ class RDDSuite extends FunSuite with SharedSparkContext {
   test("flatMapWith") {
     import java.util.Random
     val ones = sc.makeRDD(Array(1, 1, 1, 1, 1, 1), 2)
+    @deprecated("suppress compile time deprecation warning", "1.0.0")
     val randoms = ones.flatMapWith(
       (index: Int) => new Random(index + 42))
       {(t: Int, prng: Random) =>
@@ -418,6 +420,7 @@ class RDDSuite extends FunSuite with SharedSparkContext {
   test("filterWith") {
     import java.util.Random
     val ints = sc.makeRDD(Array(1, 2, 3, 4, 5, 6), 2)
+    @deprecated("suppress compile time deprecation warning", "1.0.0")
     val sample = ints.filterWith(
       (index: Int) => new Random(index + 42))
       {(t: Int, prng: Random) => prng.nextInt(3) == 0}.
diff --git a/core/src/test/scala/org/apache/spark/serializer/ProactiveClosureSerializationSuite.scala b/core/src/test/scala/org/apache/spark/serializer/ProactiveClosureSerializationSuite.scala
index 5d15a68ac7e4..aad659958942 100644
--- a/core/src/test/scala/org/apache/spark/serializer/ProactiveClosureSerializationSuite.scala
+++ b/core/src/test/scala/org/apache/spark/serializer/ProactiveClosureSerializationSuite.scala
@@ -15,15 +15,12 @@
  * limitations under the License.
  */
 
-package org.apache.spark.serializer;
-
-import java.io.NotSerializableException
+package org.apache.spark.serializer
 
 import org.scalatest.FunSuite
 
+import org.apache.spark.{SharedSparkContext, SparkException}
 import org.apache.spark.rdd.RDD
-import org.apache.spark.SparkException
-import org.apache.spark.SharedSparkContext
 
 /* A trivial (but unserializable) container for trivial functions */
 class UnserializableClass {
@@ -38,52 +35,50 @@ class ProactiveClosureSerializationSuite extends FunSuite with SharedSparkContex
 
   test("throws expected serialization exceptions on actions") {
     val (data, uc) = fixture
-      
     val ex = intercept[SparkException] {
-      data.map(uc.op(_)).count
+      data.map(uc.op(_)).count()
     }
-        
     assert(ex.getMessage.contains("Task not serializable"))
   }
 
   // There is probably a cleaner way to eliminate boilerplate here, but we're
   // iterating over a map from transformation names to functions that perform that
   // transformation on a given RDD, creating one test case for each
-  
+
   for (transformation <- 
-      Map("map" -> xmap _, "flatMap" -> xflatMap _, "filter" -> xfilter _, 
-          "mapWith" -> xmapWith _, "mapPartitions" -> xmapPartitions _, 
+      Map("map" -> xmap _,
+          "flatMap" -> xflatMap _,
+          "filter" -> xfilter _,
+          "mapPartitions" -> xmapPartitions _,
           "mapPartitionsWithIndex" -> xmapPartitionsWithIndex _,
-          "mapPartitionsWithContext" -> xmapPartitionsWithContext _, 
-          "filterWith" -> xfilterWith _)) {
+          "mapPartitionsWithContext" -> xmapPartitionsWithContext _)) {
     val (name, xf) = transformation
-    
+
     test(s"$name transformations throw proactive serialization exceptions") {
       val (data, uc) = fixture
-      
       val ex = intercept[SparkException] {
         xf(data, uc)
       }
-
       assert(ex.getMessage.contains("Task not serializable"), 
         s"RDD.$name doesn't proactively throw NotSerializableException")
     }
   }
-  
+
   private def xmap(x: RDD[String], uc: UnserializableClass): RDD[String] = 
     x.map(y=>uc.op(y))
-  private def xmapWith(x: RDD[String], uc: UnserializableClass): RDD[String] = 
-    x.mapWith(x => x.toString)((x,y)=>x + uc.op(y))
+
   private def xflatMap(x: RDD[String], uc: UnserializableClass): RDD[String] = 
     x.flatMap(y=>Seq(uc.op(y)))
+
   private def xfilter(x: RDD[String], uc: UnserializableClass): RDD[String] = 
     x.filter(y=>uc.pred(y))
-  private def xfilterWith(x: RDD[String], uc: UnserializableClass): RDD[String] = 
-    x.filterWith(x => x.toString)((x,y)=>uc.pred(y))
+
   private def xmapPartitions(x: RDD[String], uc: UnserializableClass): RDD[String] = 
     x.mapPartitions(_.map(y=>uc.op(y)))
+
   private def xmapPartitionsWithIndex(x: RDD[String], uc: UnserializableClass): RDD[String] = 
     x.mapPartitionsWithIndex((_, it) => it.map(y=>uc.op(y)))
+
   private def xmapPartitionsWithContext(x: RDD[String], uc: UnserializableClass): RDD[String] = 
     x.mapPartitionsWithContext((_, it) => it.map(y=>uc.op(y)))
   
diff --git a/core/src/test/scala/org/apache/spark/util/FileAppenderSuite.scala b/core/src/test/scala/org/apache/spark/util/FileAppenderSuite.scala
index ca37d707b06c..d2bee448d4d3 100644
--- a/core/src/test/scala/org/apache/spark/util/FileAppenderSuite.scala
+++ b/core/src/test/scala/org/apache/spark/util/FileAppenderSuite.scala
@@ -135,12 +135,11 @@ class FileAppenderSuite extends FunSuite with BeforeAndAfter with Logging {
       val testOutputStream = new PipedOutputStream()
       val testInputStream = new PipedInputStream(testOutputStream)
       val appender = FileAppender(testInputStream, testFile, conf)
-      assert(appender.isInstanceOf[ExpectedAppender])
+      //assert(appender.getClass === classTag[ExpectedAppender].getClass)
       assert(appender.getClass.getSimpleName ===
         classTag[ExpectedAppender].runtimeClass.getSimpleName)
       if (appender.isInstanceOf[RollingFileAppender]) {
         val rollingPolicy = appender.asInstanceOf[RollingFileAppender].rollingPolicy
-        rollingPolicy.isInstanceOf[ExpectedRollingPolicy]
         val policyParam = if (rollingPolicy.isInstanceOf[TimeBasedRollingPolicy]) {
           rollingPolicy.asInstanceOf[TimeBasedRollingPolicy].rolloverIntervalMillis
         } else {
diff --git a/core/src/test/scala/org/apache/spark/util/VectorSuite.scala b/core/src/test/scala/org/apache/spark/util/VectorSuite.scala
index 7006571ef0ef..794a55d61750 100644
--- a/core/src/test/scala/org/apache/spark/util/VectorSuite.scala
+++ b/core/src/test/scala/org/apache/spark/util/VectorSuite.scala
@@ -24,6 +24,7 @@ import org.scalatest.FunSuite
 /**
  * Tests org.apache.spark.util.Vector functionality
  */
+@deprecated("suppress compile time deprecation warning", "1.0.0")
 class VectorSuite extends FunSuite {
 
   def verifyVector(vector: Vector, expectedLength: Int) = {
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala
index cc4a65011dd7..952a74fd5f6d 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala
@@ -383,7 +383,10 @@ class TestActor(port: Int) extends Actor with ActorHelper {
 
   def bytesToString(byteString: ByteString) = byteString.utf8String
 
-  override def preStart = IOManager(context.system).connect(new InetSocketAddress(port))
+  override def preStart(): Unit = {
+    @deprecated("suppress compile time deprecation warning", "1.0.0")
+    val unit = IOManager(context.system).connect(new InetSocketAddress(port))
+  }
 
   def receive = {
     case IO.Read(socket, bytes) =>

From 96f28c9726d18f3b0d7a57b128c16ec9157f1532 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Wed, 16 Jul 2014 11:27:51 -0700
Subject: [PATCH 674/810] [SPARK-2522] set default broadcast factory to torrent

HttpBroadcastFactory is the current default broadcast factory. It sends the broadcast data to each worker one by one, which is slow when the cluster is big. TorrentBroadcastFactory scales much better than http. Maybe we should make torrent the default broadcast method.

Author: Xiangrui Meng <meng@databricks.com>

Closes #1437 from mengxr/bt-broadcast and squashes the following commits:

ed492fe [Xiangrui Meng] set default broadcast factory to torrent
---
 .../scala/org/apache/spark/broadcast/BroadcastManager.scala     | 2 +-
 docs/configuration.md                                           | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/broadcast/BroadcastManager.scala b/core/src/main/scala/org/apache/spark/broadcast/BroadcastManager.scala
index c88be6aba690..8f8a0b11f9f2 100644
--- a/core/src/main/scala/org/apache/spark/broadcast/BroadcastManager.scala
+++ b/core/src/main/scala/org/apache/spark/broadcast/BroadcastManager.scala
@@ -39,7 +39,7 @@ private[spark] class BroadcastManager(
     synchronized {
       if (!initialized) {
         val broadcastFactoryClass =
-          conf.get("spark.broadcast.factory", "org.apache.spark.broadcast.HttpBroadcastFactory")
+          conf.get("spark.broadcast.factory", "org.apache.spark.broadcast.TorrentBroadcastFactory")
 
         broadcastFactory =
           Class.forName(broadcastFactoryClass).newInstance.asInstanceOf[BroadcastFactory]
diff --git a/docs/configuration.md b/docs/configuration.md
index 9d3fe7441486..a70007c16544 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -419,7 +419,7 @@ Apart from these, the following properties are also available, and may be useful
 </tr>
 <tr>
   <td><code>spark.broadcast.factory</code></td>
-  <td>org.apache.spark.broadcast.<br />HttpBroadcastFactory</td>
+  <td>org.apache.spark.broadcast.<br />TorrentBroadcastFactory</td>
   <td>
     Which broadcast implementation to use.
   </td>

From caa163f0868776d91697a9041528e382a789f0c3 Mon Sep 17 00:00:00 2001
From: "James Z.M. Gao" <gaozhm@mediav.com>
Date: Wed, 16 Jul 2014 11:35:21 -0700
Subject: [PATCH 675/810] fix compile error of streaming project

explicit return type for implicit function

Author: James Z.M. Gao <gaozhm@mediav.com>

Closes #153 from gzm55/work/streaming-compile and squashes the following commits:

11e9c8d [James Z.M. Gao] fix style error
fe88109 [James Z.M. Gao] fix compile error of streaming project
---
 .../org/apache/spark/streaming/api/java/JavaPairDStream.scala  | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaPairDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaPairDStream.scala
index c4bdf01fa374..c00e11d11910 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaPairDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaPairDStream.scala
@@ -737,7 +737,8 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])(
 }
 
 object JavaPairDStream {
-  implicit def fromPairDStream[K: ClassTag, V: ClassTag](dstream: DStream[(K, V)]) = {
+  implicit def fromPairDStream[K: ClassTag, V: ClassTag](dstream: DStream[(K, V)])
+  : JavaPairDStream[K, V] = {
     new JavaPairDStream[K, V](dstream)
   }
 

From 7c8d123225bbdcc605642099b107c2d843e87340 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Wed, 16 Jul 2014 11:50:49 -0700
Subject: [PATCH 676/810] [SPARK-2317] Improve task logging.

We use TID to indicate task logging. However, TID itself does not capture stage or retries, making it harder to correlate with the application itself. This pull request changes all logging messages for tasks to include both the TID and the stage id, stage attempt, task id, and task attempt.  I've consulted various people but unfortunately this is a really hard task.

Driver log looks like:

```
14/06/28 18:53:29 INFO DAGScheduler: Submitting 10 missing tasks from Stage 0 (MappedRDD[1] at map at <console>:13)
14/06/28 18:53:29 INFO TaskSchedulerImpl: Adding task set 0.0 with 10 tasks
14/06/28 18:53:29 INFO TaskSetManager: Re-computing pending task lists.
14/07/15 19:44:40 INFO TaskSetManager: Starting task 0.0 in stage 1.0 (TID 0, localhost, PROCESS_LOCAL, 1855 bytes)
14/07/15 19:44:40 INFO TaskSetManager: Starting task 1.0 in stage 1.0 (TID 1, localhost, PROCESS_LOCAL, 1855 bytes)
14/07/15 19:44:40 INFO TaskSetManager: Starting task 2.0 in stage 1.0 (TID 2, localhost, PROCESS_LOCAL, 1855 bytes)
14/07/15 19:44:40 INFO TaskSetManager: Starting task 3.0 in stage 1.0 (TID 3, localhost, PROCESS_LOCAL, 1855 bytes)
14/07/15 19:44:40 INFO TaskSetManager: Starting task 4.0 in stage 1.0 (TID 4, localhost, PROCESS_LOCAL, 1855 bytes)
14/07/15 19:44:40 INFO TaskSetManager: Starting task 5.0 in stage 1.0 (TID 5, localhost, PROCESS_LOCAL, 1855 bytes)
14/07/15 19:44:40 INFO TaskSetManager: Starting task 6.0 in stage 1.0 (TID 6, localhost, PROCESS_LOCAL, 1855 bytes)
...
14/07/15 19:44:40 INFO TaskSetManager: Finished task 1.0 in stage 1.0 (TID 1) in 64 ms on localhost (4/10)
14/07/15 19:44:40 INFO TaskSetManager: Finished task 4.0 in stage 1.0 (TID 4) in 63 ms on localhost (5/10)
14/07/15 19:44:40 INFO TaskSetManager: Finished task 2.0 in stage 1.0 (TID 2) in 63 ms on localhost (6/10)
14/07/15 19:44:40 INFO TaskSetManager: Finished task 7.0 in stage 1.0 (TID 7) in 62 ms on localhost (7/10)
14/07/15 19:44:40 INFO TaskSetManager: Finished task 6.0 in stage 1.0 (TID 6) in 63 ms on localhost (8/10)
14/07/15 19:44:40 INFO TaskSetManager: Finished task 9.0 in stage 1.0 (TID 9) in 8 ms on localhost (9/10)
14/07/15 19:44:40 INFO TaskSetManager: Finished task 8.0 in stage 1.0 (TID 8) in 9 ms on localhost (10/10)

```

Executor log looks like
```
14/07/15 19:44:40 INFO Executor: Running task 0.0 in stage 1.0 (TID 0)
14/07/15 19:44:40 INFO Executor: Running task 3.0 in stage 1.0 (TID 3)
14/07/15 19:44:40 INFO Executor: Running task 1.0 in stage 1.0 (TID 1)
14/07/15 19:44:40 INFO Executor: Running task 4.0 in stage 1.0 (TID 4)
14/07/15 19:44:40 INFO Executor: Running task 2.0 in stage 1.0 (TID 2)
14/07/15 19:44:40 INFO Executor: Running task 5.0 in stage 1.0 (TID 5)
14/07/15 19:44:40 INFO Executor: Running task 6.0 in stage 1.0 (TID 6)
14/07/15 19:44:40 INFO Executor: Running task 7.0 in stage 1.0 (TID 7)
14/07/15 19:44:40 INFO Executor: Finished task 3.0 in stage 1.0 (TID 3). 847 bytes result sent to driver
14/07/15 19:44:40 INFO Executor: Finished task 2.0 in stage 1.0 (TID 2). 847 bytes result sent to driver
14/07/15 19:44:40 INFO Executor: Finished task 0.0 in stage 1.0 (TID 0). 847 bytes result sent to driver
14/07/15 19:44:40 INFO Executor: Finished task 1.0 in stage 1.0 (TID 1). 847 bytes result sent to driver
14/07/15 19:44:40 INFO Executor: Finished task 5.0 in stage 1.0 (TID 5). 847 bytes result sent to driver
14/07/15 19:44:40 INFO Executor: Finished task 4.0 in stage 1.0 (TID 4). 847 bytes result sent to driver
14/07/15 19:44:40 INFO Executor: Finished task 6.0 in stage 1.0 (TID 6). 847 bytes result sent to driver
14/07/15 19:44:40 INFO Executor: Finished task 7.0 in stage 1.0 (TID 7). 847 bytes result sent to driver
```

Author: Reynold Xin <rxin@apache.org>

Closes #1259 from rxin/betterTaskLogging and squashes the following commits:

c28ada1 [Reynold Xin] Fix unit test failure.
987d043 [Reynold Xin] Updated log messages.
c6cfd46 [Reynold Xin] Merge branch 'master' into betterTaskLogging
b7b1bcc [Reynold Xin] Fixed a typo.
f9aba3c [Reynold Xin] Made it compile.
f8a5c06 [Reynold Xin] Merge branch 'master' into betterTaskLogging
07264e6 [Reynold Xin] Defensive check against unknown TaskEndReason.
76bbd18 [Reynold Xin] FailureSuite not serializable reporting.
4659b20 [Reynold Xin] Remove unused variable.
53888e3 [Reynold Xin] [SPARK-2317] Improve task logging.
---
 .../org/apache/spark/TaskEndReason.scala      |  5 +-
 .../CoarseGrainedExecutorBackend.scala        |  2 +-
 .../org/apache/spark/executor/Executor.scala  | 43 +++++----
 .../spark/executor/MesosExecutorBackend.scala |  2 +-
 .../apache/spark/scheduler/DAGScheduler.scala |  1 -
 .../org/apache/spark/scheduler/TaskInfo.scala |  2 +
 .../spark/scheduler/TaskSetManager.scala      | 90 +++++++++----------
 .../spark/scheduler/local/LocalBackend.scala  |  2 +-
 .../scala/org/apache/spark/FailureSuite.scala |  5 +-
 .../scala/org/apache/spark/ShuffleSuite.scala |  2 +-
 10 files changed, 78 insertions(+), 76 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/TaskEndReason.scala b/core/src/main/scala/org/apache/spark/TaskEndReason.scala
index df42d679b469..8d5c45627f09 100644
--- a/core/src/main/scala/org/apache/spark/TaskEndReason.scala
+++ b/core/src/main/scala/org/apache/spark/TaskEndReason.scala
@@ -89,8 +89,9 @@ case class ExceptionFailure(
     metrics: Option[TaskMetrics])
   extends TaskFailedReason {
   override def toErrorString: String = {
-    val stackTraceString = if (stackTrace == null) "null" else stackTrace.mkString("\n")
-    s"$className ($description}\n$stackTraceString"
+    val stackTraceString =
+      if (stackTrace == null) "null" else stackTrace.map("        " + _).mkString("\n")
+    s"$className ($description)\n$stackTraceString"
   }
 }
 
diff --git a/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala b/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala
index 8d31bd05fdbe..b455c9fcf4bd 100644
--- a/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala
+++ b/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala
@@ -71,7 +71,7 @@ private[spark] class CoarseGrainedExecutorBackend(
         val ser = SparkEnv.get.closureSerializer.newInstance()
         val taskDesc = ser.deserialize[TaskDescription](data.value)
         logInfo("Got assigned task " + taskDesc.taskId)
-        executor.launchTask(this, taskDesc.taskId, taskDesc.serializedTask)
+        executor.launchTask(this, taskDesc.taskId, taskDesc.name, taskDesc.serializedTask)
       }
 
     case KillTask(taskId, _, interruptThread) =>
diff --git a/core/src/main/scala/org/apache/spark/executor/Executor.scala b/core/src/main/scala/org/apache/spark/executor/Executor.scala
index 4d3ba11633bf..b16133b20cc0 100644
--- a/core/src/main/scala/org/apache/spark/executor/Executor.scala
+++ b/core/src/main/scala/org/apache/spark/executor/Executor.scala
@@ -107,8 +107,9 @@ private[spark] class Executor(
   // Maintains the list of running tasks.
   private val runningTasks = new ConcurrentHashMap[Long, TaskRunner]
 
-  def launchTask(context: ExecutorBackend, taskId: Long, serializedTask: ByteBuffer) {
-    val tr = new TaskRunner(context, taskId, serializedTask)
+  def launchTask(
+      context: ExecutorBackend, taskId: Long, taskName: String, serializedTask: ByteBuffer) {
+    val tr = new TaskRunner(context, taskId, taskName, serializedTask)
     runningTasks.put(taskId, tr)
     threadPool.execute(tr)
   }
@@ -135,14 +136,15 @@ private[spark] class Executor(
     localDirs
   }
 
-  class TaskRunner(execBackend: ExecutorBackend, taskId: Long, serializedTask: ByteBuffer)
+  class TaskRunner(
+      execBackend: ExecutorBackend, taskId: Long, taskName: String, serializedTask: ByteBuffer)
     extends Runnable {
 
     @volatile private var killed = false
     @volatile private var task: Task[Any] = _
 
     def kill(interruptThread: Boolean) {
-      logInfo("Executor is trying to kill task " + taskId)
+      logInfo(s"Executor is trying to kill $taskName (TID $taskId)")
       killed = true
       if (task != null) {
         task.kill(interruptThread)
@@ -154,7 +156,7 @@ private[spark] class Executor(
       SparkEnv.set(env)
       Thread.currentThread.setContextClassLoader(replClassLoader)
       val ser = SparkEnv.get.closureSerializer.newInstance()
-      logInfo("Running task ID " + taskId)
+      logInfo(s"Running $taskName (TID $taskId)")
       execBackend.statusUpdate(taskId, TaskState.RUNNING, EMPTY_BYTE_BUFFER)
       var attemptedTask: Option[Task[Any]] = None
       var taskStart: Long = 0
@@ -207,25 +209,30 @@ private[spark] class Executor(
 
         val accumUpdates = Accumulators.values
 
-        val directResult = new DirectTaskResult(valueBytes, accumUpdates,
-          task.metrics.getOrElse(null))
+        val directResult = new DirectTaskResult(valueBytes, accumUpdates, task.metrics.orNull)
         val serializedDirectResult = ser.serialize(directResult)
-        logInfo("Serialized size of result for " + taskId + " is " + serializedDirectResult.limit)
-        val serializedResult = {
-          if (serializedDirectResult.limit >= akkaFrameSize - AkkaUtils.reservedSizeBytes) {
-            logInfo("Storing result for " + taskId + " in local BlockManager")
+        val resultSize = serializedDirectResult.limit
+
+        // directSend = sending directly back to the driver
+        val (serializedResult, directSend) = {
+          if (resultSize >= akkaFrameSize - AkkaUtils.reservedSizeBytes) {
             val blockId = TaskResultBlockId(taskId)
             env.blockManager.putBytes(
               blockId, serializedDirectResult, StorageLevel.MEMORY_AND_DISK_SER)
-            ser.serialize(new IndirectTaskResult[Any](blockId))
+            (ser.serialize(new IndirectTaskResult[Any](blockId)), false)
           } else {
-            logInfo("Sending result for " + taskId + " directly to driver")
-            serializedDirectResult
+            (serializedDirectResult, true)
           }
         }
 
         execBackend.statusUpdate(taskId, TaskState.FINISHED, serializedResult)
-        logInfo("Finished task ID " + taskId)
+
+        if (directSend) {
+          logInfo(s"Finished $taskName (TID $taskId). $resultSize bytes result sent to driver")
+        } else {
+          logInfo(
+            s"Finished $taskName (TID $taskId). $resultSize bytes result sent via BlockManager)")
+        }
       } catch {
         case ffe: FetchFailedException => {
           val reason = ffe.toTaskEndReason
@@ -233,7 +240,7 @@ private[spark] class Executor(
         }
 
         case _: TaskKilledException | _: InterruptedException if task.killed => {
-          logInfo("Executor killed task " + taskId)
+          logInfo(s"Executor killed $taskName (TID $taskId)")
           execBackend.statusUpdate(taskId, TaskState.KILLED, ser.serialize(TaskKilled))
         }
 
@@ -241,7 +248,7 @@ private[spark] class Executor(
           // Attempt to exit cleanly by informing the driver of our failure.
           // If anything goes wrong (or this was a fatal exception), we will delegate to
           // the default uncaught exception handler, which will terminate the Executor.
-          logError("Exception in task ID " + taskId, t)
+          logError(s"Exception in $taskName (TID $taskId)", t)
 
           val serviceTime = System.currentTimeMillis() - taskStart
           val metrics = attemptedTask.flatMap(t => t.metrics)
@@ -249,7 +256,7 @@ private[spark] class Executor(
             m.executorRunTime = serviceTime
             m.jvmGCTime = gcTime - startGCTime
           }
-          val reason = ExceptionFailure(t.getClass.getName, t.toString, t.getStackTrace, metrics)
+          val reason = ExceptionFailure(t.getClass.getName, t.getMessage, t.getStackTrace, metrics)
           execBackend.statusUpdate(taskId, TaskState.FAILED, ser.serialize(reason))
 
           // Don't forcibly exit unless the exception was inherently fatal, to avoid
diff --git a/core/src/main/scala/org/apache/spark/executor/MesosExecutorBackend.scala b/core/src/main/scala/org/apache/spark/executor/MesosExecutorBackend.scala
index 2232e6237bf2..a42c8b43bbf7 100644
--- a/core/src/main/scala/org/apache/spark/executor/MesosExecutorBackend.scala
+++ b/core/src/main/scala/org/apache/spark/executor/MesosExecutorBackend.scala
@@ -64,7 +64,7 @@ private[spark] class MesosExecutorBackend
     if (executor == null) {
       logError("Received launchTask but executor was null")
     } else {
-      executor.launchTask(this, taskId, taskInfo.getData.asReadOnlyByteBuffer)
+      executor.launchTask(this, taskId, taskInfo.getName, taskInfo.getData.asReadOnlyByteBuffer)
     }
   }
 
diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
index f72bfde572c9..ede3c7d9f01a 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
@@ -816,7 +816,6 @@ class DAGScheduler(
     }
     event.reason match {
       case Success =>
-        logInfo("Completed " + task)
         if (event.accumUpdates != null) {
           // TODO: fail the stage if the accumulator update fails...
           Accumulators.add(event.accumUpdates) // TODO: do this only if task wasn't resubmitted
diff --git a/core/src/main/scala/org/apache/spark/scheduler/TaskInfo.scala b/core/src/main/scala/org/apache/spark/scheduler/TaskInfo.scala
index 29de0453ac19..ca0595f35143 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/TaskInfo.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/TaskInfo.scala
@@ -84,6 +84,8 @@ class TaskInfo(
     }
   }
 
+  def id: String = s"$index.$attempt"
+
   def duration: Long = {
     if (!finished) {
       throw new UnsupportedOperationException("duration() called on unfinished task")
diff --git a/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala b/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala
index 3bdc71d93bd6..8b5e8cb802a4 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala
@@ -26,8 +26,7 @@ import scala.collection.mutable.HashSet
 import scala.math.max
 import scala.math.min
 
-import org.apache.spark.{ExceptionFailure, ExecutorLostFailure, FetchFailed, Logging, Resubmitted,
-  SparkEnv, Success, TaskEndReason, TaskKilled, TaskResultLost, TaskState}
+import org.apache.spark._
 import org.apache.spark.TaskState.TaskState
 import org.apache.spark.executor.TaskMetrics
 import org.apache.spark.util.{Clock, SystemClock}
@@ -52,8 +51,8 @@ private[spark] class TaskSetManager(
     val taskSet: TaskSet,
     val maxTaskFailures: Int,
     clock: Clock = SystemClock)
-  extends Schedulable with Logging
-{
+  extends Schedulable with Logging {
+
   val conf = sched.sc.conf
 
   /*
@@ -403,14 +402,11 @@ private[spark] class TaskSetManager(
           // Found a task; do some bookkeeping and return a task description
           val task = tasks(index)
           val taskId = sched.newTaskId()
-          // Figure out whether this should count as a preferred launch
-          logInfo("Starting task %s:%d as TID %s on executor %s: %s (%s)".format(
-            taskSet.id, index, taskId, execId, host, taskLocality))
           // Do various bookkeeping
           copiesRunning(index) += 1
           val attemptNum = taskAttempts(index).size
-          val info = new TaskInfo(
-            taskId, index, attemptNum + 1, curTime, execId, host, taskLocality, speculative)
+          val info = new TaskInfo(taskId, index, attemptNum, curTime,
+            execId, host, taskLocality, speculative)
           taskInfos(taskId) = info
           taskAttempts(index) = info :: taskAttempts(index)
           // Update our locality level for delay scheduling
@@ -429,11 +425,15 @@ private[spark] class TaskSetManager(
               s"(${serializedTask.limit / 1024} KB). The maximum recommended task size is " +
               s"${TaskSetManager.TASK_SIZE_TO_WARN_KB} KB.")
           }
-          val timeTaken = clock.getTime() - startTime
           addRunningTask(taskId)
-          logInfo("Serialized task %s:%d as %d bytes in %d ms".format(
-            taskSet.id, index, serializedTask.limit, timeTaken))
-          val taskName = "task %s:%d".format(taskSet.id, index)
+
+          // We used to log the time it takes to serialize the task, but task size is already
+          // a good proxy to task serialization time.
+          // val timeTaken = clock.getTime() - startTime
+          val taskName = s"task ${info.id} in stage ${taskSet.id}"
+          logInfo("Starting %s (TID %d, %s, %s, %d bytes)".format(
+              taskName, taskId, host, taskLocality, serializedTask.limit))
+
           sched.dagScheduler.taskStarted(task, info)
           return Some(new TaskDescription(taskId, execId, taskName, index, serializedTask))
         }
@@ -492,19 +492,19 @@ private[spark] class TaskSetManager(
     info.markSuccessful()
     removeRunningTask(tid)
     sched.dagScheduler.taskEnded(
-      tasks(index), Success, result.value, result.accumUpdates, info, result.metrics)
+      tasks(index), Success, result.value(), result.accumUpdates, info, result.metrics)
     if (!successful(index)) {
       tasksSuccessful += 1
-      logInfo("Finished TID %s in %d ms on %s (progress: %d/%d)".format(
-        tid, info.duration, info.host, tasksSuccessful, numTasks))
+      logInfo("Finished task %s in stage %s (TID %d) in %d ms on %s (%d/%d)".format(
+        info.id, taskSet.id, info.taskId, info.duration, info.host, tasksSuccessful, numTasks))
       // Mark successful and stop if all the tasks have succeeded.
       successful(index) = true
       if (tasksSuccessful == numTasks) {
         isZombie = true
       }
     } else {
-      logInfo("Ignorning task-finished event for TID " + tid + " because task " +
-        index + " has already completed successfully")
+      logInfo("Ignoring task-finished event for " + info.id + " in stage " + taskSet.id +
+        " because task " + index + " has already completed successfully")
     }
     failedExecutors.remove(index)
     maybeFinishTaskSet()
@@ -523,14 +523,13 @@ private[spark] class TaskSetManager(
     info.markFailed()
     val index = info.index
     copiesRunning(index) -= 1
-    if (!isZombie) {
-      logWarning("Lost TID %s (task %s:%d)".format(tid, taskSet.id, index))
-    }
     var taskMetrics : TaskMetrics = null
-    var failureReason: String = null
+
+    val failureReason = s"Lost task ${info.id} in stage ${taskSet.id} (TID $tid, ${info.host}): " +
+      reason.asInstanceOf[TaskFailedReason].toErrorString
     reason match {
       case fetchFailed: FetchFailed =>
-        logWarning("Loss was due to fetch failure from " + fetchFailed.bmAddress)
+        logWarning(failureReason)
         if (!successful(index)) {
           successful(index) = true
           tasksSuccessful += 1
@@ -538,23 +537,17 @@ private[spark] class TaskSetManager(
         // Not adding to failed executors for FetchFailed.
         isZombie = true
 
-      case TaskKilled =>
-        // Not adding to failed executors for TaskKilled.
-        logWarning("Task %d was killed.".format(tid))
-
       case ef: ExceptionFailure =>
-        taskMetrics = ef.metrics.getOrElse(null)
-        if (ef.className == classOf[NotSerializableException].getName()) {
+        taskMetrics = ef.metrics.orNull
+        if (ef.className == classOf[NotSerializableException].getName) {
           // If the task result wasn't serializable, there's no point in trying to re-execute it.
-          logError("Task %s:%s had a not serializable result: %s; not retrying".format(
-            taskSet.id, index, ef.description))
-          abort("Task %s:%s had a not serializable result: %s".format(
-            taskSet.id, index, ef.description))
+          logError("Task %s in stage %s (TID %d) had a not serializable result: %s; not retrying"
+            .format(info.id, taskSet.id, tid, ef.description))
+          abort("Task %s in stage %s (TID %d) had a not serializable result: %s".format(
+            info.id, taskSet.id, tid, ef.description))
           return
         }
         val key = ef.description
-        failureReason = "Exception failure in TID %s on host %s: %s\n%s".format(
-          tid, info.host, ef.description, ef.stackTrace.map("        " + _).mkString("\n"))
         val now = clock.getTime()
         val (printFull, dupCount) = {
           if (recentExceptions.contains(key)) {
@@ -572,19 +565,18 @@ private[spark] class TaskSetManager(
           }
         }
         if (printFull) {
-          val locs = ef.stackTrace.map(loc => "\tat %s".format(loc.toString))
-          logWarning("Loss was due to %s\n%s\n%s".format(
-            ef.className, ef.description, locs.mkString("\n")))
+          logWarning(failureReason)
         } else {
-          logInfo("Loss was due to %s [duplicate %d]".format(ef.description, dupCount))
+          logInfo(
+            s"Lost task ${info.id} in stage ${taskSet.id} (TID $tid) on executor ${info.host}: " +
+            s"${ef.className} (${ef.description}) [duplicate $dupCount]")
         }
 
-      case TaskResultLost =>
-        failureReason = "Lost result for TID %s on host %s".format(tid, info.host)
+      case e: TaskFailedReason =>  // TaskResultLost, TaskKilled, and others
         logWarning(failureReason)
 
-      case _ =>
-        failureReason = "TID %s on host %s failed for unknown reason".format(tid, info.host)
+      case e: TaskEndReason =>
+        logError("Unknown TaskEndReason: " + e)
     }
     // always add to failed executors
     failedExecutors.getOrElseUpdate(index, new HashMap[String, Long]()).
@@ -595,10 +587,10 @@ private[spark] class TaskSetManager(
       assert (null != failureReason)
       numFailures(index) += 1
       if (numFailures(index) >= maxTaskFailures) {
-        logError("Task %s:%d failed %d times; aborting job".format(
-          taskSet.id, index, maxTaskFailures))
-        abort("Task %s:%d failed %d times, most recent failure: %s\nDriver stacktrace:".format(
-          taskSet.id, index, maxTaskFailures, failureReason))
+        logError("Task %d in stage %s failed %d times; aborting job".format(
+          index, taskSet.id, maxTaskFailures))
+        abort("Task %d in stage %s failed %d times, most recent failure: %s\nDriver stacktrace:"
+          .format(index, taskSet.id, maxTaskFailures, failureReason))
         return
       }
     }
@@ -711,8 +703,8 @@ private[spark] class TaskSetManager(
         if (!successful(index) && copiesRunning(index) == 1 && info.timeRunning(time) > threshold &&
           !speculatableTasks.contains(index)) {
           logInfo(
-            "Marking task %s:%d (on %s) as speculatable because it ran more than %.0f ms".format(
-              taskSet.id, index, info.host, threshold))
+            "Marking task %d in stage %s (on %s) as speculatable because it ran more than %.0f ms"
+              .format(index, taskSet.id, info.host, threshold))
           speculatableTasks += index
           foundTasks = true
         }
diff --git a/core/src/main/scala/org/apache/spark/scheduler/local/LocalBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/local/LocalBackend.scala
index 9b95ccca0443..e9f6273bfd9f 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/local/LocalBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/local/LocalBackend.scala
@@ -69,7 +69,7 @@ private[spark] class LocalActor(
     val offers = Seq(new WorkerOffer(localExecutorId, localExecutorHostname, freeCores))
     for (task <- scheduler.resourceOffers(offers).flatten) {
       freeCores -= 1
-      executor.launchTask(executorBackend, task.taskId, task.serializedTask)
+      executor.launchTask(executorBackend, task.taskId, task.name, task.serializedTask)
     }
   }
 }
diff --git a/core/src/test/scala/org/apache/spark/FailureSuite.scala b/core/src/test/scala/org/apache/spark/FailureSuite.scala
index e755d2e30939..2229e6acc425 100644
--- a/core/src/test/scala/org/apache/spark/FailureSuite.scala
+++ b/core/src/test/scala/org/apache/spark/FailureSuite.scala
@@ -104,8 +104,9 @@ class FailureSuite extends FunSuite with LocalSparkContext {
       results.collect()
     }
     assert(thrown.getClass === classOf[SparkException])
-    assert(thrown.getMessage.contains("NotSerializableException") || 
-      thrown.getCause.getClass === classOf[NotSerializableException])
+    assert(thrown.getMessage.contains("serializable") ||
+      thrown.getCause.getClass === classOf[NotSerializableException],
+      "Exception does not contain \"serializable\": " + thrown.getMessage)
 
     FailureSuiteState.clear()
   }
diff --git a/core/src/test/scala/org/apache/spark/ShuffleSuite.scala b/core/src/test/scala/org/apache/spark/ShuffleSuite.scala
index c4f2f7e34f4d..237e644b48e4 100644
--- a/core/src/test/scala/org/apache/spark/ShuffleSuite.scala
+++ b/core/src/test/scala/org/apache/spark/ShuffleSuite.scala
@@ -240,7 +240,7 @@ class ShuffleSuite extends FunSuite with Matchers with LocalSparkContext {
     }
 
     assert(thrown.getClass === classOf[SparkException])
-    assert(thrown.getMessage.contains("NotSerializableException"))
+    assert(thrown.getMessage.toLowerCase.contains("serializable"))
   }
 }
 

From 8867cd0bc2961fefed84901b8b14e9676ae6ab18 Mon Sep 17 00:00:00 2001
From: Aaron Davidson <aaron@databricks.com>
Date: Wed, 16 Jul 2014 14:10:17 -0700
Subject: [PATCH 677/810] SPARK-1097: Do not introduce deadlock while fixing
 concurrency bug

We recently added this lock on 'conf' in order to prevent concurrent creation. However, it turns out that this can introduce a deadlock because Hadoop also synchronizes on the Configuration objects when creating new Configurations (and they do so via a static REGISTRY which contains all created Configurations).

This fix forces all Spark initialization of Configuration objects to occur serially by using a static lock that we control, and thus also prevents introducing the deadlock.

Author: Aaron Davidson <aaron@databricks.com>

Closes #1409 from aarondav/1054 and squashes the following commits:

7d1b769 [Aaron Davidson] SPARK-1097: Do not introduce deadlock while fixing concurrency bug
---
 core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala b/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala
index 041028514399..e521612ffc27 100644
--- a/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala
@@ -140,8 +140,8 @@ class HadoopRDD[K, V](
       // Create a JobConf that will be cached and used across this RDD's getJobConf() calls in the
       // local process. The local cache is accessed through HadoopRDD.putCachedMetadata().
       // The caching helps minimize GC, since a JobConf can contain ~10KB of temporary objects.
-      // synchronize to prevent ConcurrentModificationException (Spark-1097, Hadoop-10456)
-      conf.synchronized {
+      // Synchronize to prevent ConcurrentModificationException (Spark-1097, Hadoop-10456).
+      HadoopRDD.CONFIGURATION_INSTANTIATION_LOCK.synchronized {
         val newJobConf = new JobConf(conf)
         initLocalJobConfFuncOpt.map(f => f(newJobConf))
         HadoopRDD.putCachedMetadata(jobConfCacheKey, newJobConf)
@@ -246,6 +246,9 @@ class HadoopRDD[K, V](
 }
 
 private[spark] object HadoopRDD {
+  /** Constructing Configuration objects is not threadsafe, use this lock to serialize. */
+  val CONFIGURATION_INSTANTIATION_LOCK = new Object()
+
   /**
    * The three methods below are helpers for accessing the local map, a property of the SparkEnv of
    * the local process.

From 9c249743eaabe5fc8d961c7aa581cc0197f6e950 Mon Sep 17 00:00:00 2001
From: Aaron Davidson <aaron@databricks.com>
Date: Wed, 16 Jul 2014 14:16:48 -0700
Subject: [PATCH 678/810] [SPARK-2154] Schedule next Driver when one completes
 (standalone mode)

Author: Aaron Davidson <aaron@databricks.com>

Closes #1405 from aarondav/2154 and squashes the following commits:

24e9ef9 [Aaron Davidson] [SPARK-2154] Schedule next Driver when one completes (standalone mode)
---
 core/src/main/scala/org/apache/spark/deploy/master/Master.scala | 1 +
 1 file changed, 1 insertion(+)

diff --git a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
index 9fa556d522ba..0b2d9db70742 100644
--- a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
@@ -751,6 +751,7 @@ private[spark] class Master(
         driver.state = finalState
         driver.exception = exception
         driver.worker.foreach(w => w.removeDriver(driver))
+        schedule()
       case None =>
         logWarning(s"Asked to remove unknown driver: $driverId")
     }

From 7c23c0dc3ed721c95690fc49f435d9de6952523c Mon Sep 17 00:00:00 2001
From: Aaron Davidson <aaron@databricks.com>
Date: Thu, 17 Jul 2014 01:01:14 -0700
Subject: [PATCH 679/810] [SPARK-2412] CoalescedRDD throws exception with
 certain pref locs

If the first pass of CoalescedRDD does not find the target number of locations AND the second pass finds new locations, an exception is thrown, as "groupHash.get(nxt_replica).get" is not valid.

The fix is just to add an ArrayBuffer to groupHash for that replica if it didn't already exist.

Author: Aaron Davidson <aaron@databricks.com>

Closes #1337 from aarondav/2412 and squashes the following commits:

f587b5d [Aaron Davidson] getOrElseUpdate
3ad8a3c [Aaron Davidson] [SPARK-2412] CoalescedRDD throws exception with certain pref locs
---
 .../scala/org/apache/spark/rdd/CoalescedRDD.scala  |  4 ++--
 .../test/scala/org/apache/spark/rdd/RDDSuite.scala | 14 ++++++++++++++
 2 files changed, 16 insertions(+), 2 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/rdd/CoalescedRDD.scala b/core/src/main/scala/org/apache/spark/rdd/CoalescedRDD.scala
index c45b759f007c..e7221e3032c1 100644
--- a/core/src/main/scala/org/apache/spark/rdd/CoalescedRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/CoalescedRDD.scala
@@ -258,7 +258,7 @@ private[spark] class PartitionCoalescer(maxPartitions: Int, prev: RDD[_], balanc
         val pgroup = PartitionGroup(nxt_replica)
         groupArr += pgroup
         addPartToPGroup(nxt_part, pgroup)
-        groupHash += (nxt_replica -> (ArrayBuffer(pgroup))) // list in case we have multiple
+        groupHash.put(nxt_replica, ArrayBuffer(pgroup)) // list in case we have multiple
         numCreated += 1
       }
     }
@@ -267,7 +267,7 @@ private[spark] class PartitionCoalescer(maxPartitions: Int, prev: RDD[_], balanc
       var (nxt_replica, nxt_part) = rotIt.next()
       val pgroup = PartitionGroup(nxt_replica)
       groupArr += pgroup
-      groupHash.get(nxt_replica).get += pgroup
+      groupHash.getOrElseUpdate(nxt_replica, ArrayBuffer()) += pgroup
       var tries = 0
       while (!addPartToPGroup(nxt_part, pgroup) && tries < targetLen) { // ensure at least one part
         nxt_part = rotIt.next()._2
diff --git a/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala b/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala
index 6ea045198e2c..2924de112934 100644
--- a/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala
+++ b/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala
@@ -351,6 +351,20 @@ class RDDSuite extends FunSuite with SharedSparkContext {
     }
   }
 
+  // Test for SPARK-2412 -- ensure that the second pass of the algorithm does not throw an exception
+  test("coalesced RDDs with locality, fail first pass") {
+    val initialPartitions = 1000
+    val targetLen = 50
+    val couponCount = 2 * (math.log(targetLen)*targetLen + targetLen + 0.5).toInt // = 492
+
+    val blocks = (1 to initialPartitions).map { i =>
+      (i, List(if (i > couponCount) "m2" else "m1"))
+    }
+    val data = sc.makeRDD(blocks)
+    val coalesced = data.coalesce(targetLen)
+    assert(coalesced.partitions.length == targetLen)
+  }
+
   test("zipped RDDs") {
     val nums = sc.makeRDD(Array(1, 2, 3, 4), 2)
     val zipped = nums.zip(nums.map(_ + 1.0))

From d0ea496877b53f3253e6e5f6d053c7f79c7991a5 Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Thu, 17 Jul 2014 01:02:35 -0700
Subject: [PATCH 680/810] SPARK-2526: Simplify options in make-distribution.sh

Right now we have a bunch of parallel logic in make-distribution.sh
that's just extra work to maintain. We should just pass through
Maven profiles in this case and keep the script simple. See
the JIRA for more details.

Author: Patrick Wendell <pwendell@gmail.com>

Closes #1445 from pwendell/make-distribution.sh and squashes the following commits:

f1294ea [Patrick Wendell] Simplify options in make-distribution.sh.
---
 dev/create-release/create-release.sh |  9 ++--
 make-distribution.sh                 | 79 +++++++++-------------------
 2 files changed, 30 insertions(+), 58 deletions(-)

diff --git a/dev/create-release/create-release.sh b/dev/create-release/create-release.sh
index 49bf78f60763..38830103d1e8 100755
--- a/dev/create-release/create-release.sh
+++ b/dev/create-release/create-release.sh
@@ -95,7 +95,7 @@ make_binary_release() {
   cp -r spark spark-$RELEASE_VERSION-bin-$NAME
   
   cd spark-$RELEASE_VERSION-bin-$NAME
-  ./make-distribution.sh $FLAGS --name $NAME --tgz
+  ./make-distribution.sh --name $NAME --tgz $FLAGS
   cd ..
   cp spark-$RELEASE_VERSION-bin-$NAME/spark-$RELEASE_VERSION-bin-$NAME.tgz .
   rm -rf spark-$RELEASE_VERSION-bin-$NAME
@@ -111,9 +111,10 @@ make_binary_release() {
     spark-$RELEASE_VERSION-bin-$NAME.tgz.sha
 }
 
-make_binary_release "hadoop1" "--with-hive --hadoop 1.0.4"
-make_binary_release "cdh4" "--with-hive --hadoop 2.0.0-mr1-cdh4.2.0"
-make_binary_release "hadoop2" "--with-hive --with-yarn --hadoop 2.2.0"
+make_binary_release "hadoop1" "-Phive -Dhadoop.version=1.0.4"
+make_binary_release "cdh4" "-Phive -Dhadoop.version=2.0.0-mr1-cdh4.2.0"
+make_binary_release "hadoop2" \
+  "-Phive -Pyarn -Phadoop-2.2 -Dhadoop.version=2.2.0 -Pyarn.version=2.2.0"
 
 # Copy data
 echo "Copying release tarballs"
diff --git a/make-distribution.sh b/make-distribution.sh
index 94b473bf91cd..b5a90f0f3bfe 100755
--- a/make-distribution.sh
+++ b/make-distribution.sh
@@ -23,21 +23,6 @@
 # The distribution contains fat (assembly) jars that include the Scala library,
 # so it is completely self contained.
 # It does not contain source or *.class files.
-#
-# Optional Arguments
-#      --tgz: Additionally creates spark-$VERSION-bin.tar.gz
-#      --hadoop VERSION: Builds against specified version of Hadoop.
-#      --with-yarn: Enables support for Hadoop YARN.
-#      --with-hive: Enable support for reading Hive tables.
-#      --name: A moniker for the release target. Defaults to the Hadoop verison.
-#
-# Recommended deploy/testing procedure (standalone mode):
-# 1) Rsync / deploy the dist/ dir to one host
-# 2) cd to deploy dir; ./sbin/start-master.sh
-# 3) Verify master is up by visiting web page, ie http://master-ip:8080.  Note the spark:// URL.
-# 4) ./sbin/start-slave.sh 1 <<spark:// URL>>
-# 5) ./bin/spark-shell --master spark://my-master-ip:7077
-#
 
 set -o pipefail
 set -e
@@ -46,26 +31,35 @@ set -e
 FWDIR="$(cd `dirname $0`; pwd)"
 DISTDIR="$FWDIR/dist"
 
-# Initialize defaults
-SPARK_HADOOP_VERSION=1.0.4
-SPARK_YARN=false
-SPARK_HIVE=false
 SPARK_TACHYON=false
 MAKE_TGZ=false
 NAME=none
 
+function exit_with_usage {
+  echo "make-distribution.sh - tool for making binary distributions of Spark"
+  echo ""
+  echo "usage:"
+  echo "./make-distribution.sh [--name] [--tgz] [--with-tachyon] <maven build options>"
+  echo "See Spark's \"Building with Maven\" doc for correct Maven options."
+  echo ""
+  exit 1
+}
+
 # Parse arguments
 while (( "$#" )); do
   case $1 in
     --hadoop)
-      SPARK_HADOOP_VERSION="$2"
-      shift
+      echo "Error: '--hadoop' is no longer supported:"
+      echo "Error: use Maven options -Phadoop.version and -Pyarn.version"
+      exit_with_usage
       ;;
     --with-yarn)
-      SPARK_YARN=true
+      echo "Error: '--with-yarn' is no longer supported, use Maven option -Pyarn"
+      exit_with_usage
       ;;
     --with-hive)
-      SPARK_HIVE=true
+      echo "Error: '--with-hive' is no longer supported, use Maven option -Pyarn"
+      exit_with_usage
       ;;
     --skip-java-test)
       SKIP_JAVA_TEST=true
@@ -80,6 +74,12 @@ while (( "$#" )); do
       NAME="$2"
       shift
       ;;
+    --help)
+      exit_with_usage
+      ;;
+    *)
+      break
+      ;;
   esac
   shift
 done
@@ -143,14 +143,6 @@ else
   echo "Making distribution for Spark $VERSION in $DISTDIR..."
 fi
 
-echo "Hadoop version set to $SPARK_HADOOP_VERSION"
-echo "Release name set to $NAME"
-if [ "$SPARK_YARN" == "true" ]; then
-  echo "YARN enabled"
-else
-  echo "YARN disabled"
-fi
-
 if [ "$SPARK_TACHYON" == "true" ]; then
   echo "Tachyon Enabled"
 else
@@ -162,33 +154,12 @@ cd $FWDIR
 
 export MAVEN_OPTS="-Xmx2g -XX:MaxPermSize=512M -XX:ReservedCodeCacheSize=512m"
 
-BUILD_COMMAND="mvn clean package"
-
-# Use special profiles for hadoop versions 0.23.x, 2.2.x, 2.3.x, 2.4.x
-if [[ "$SPARK_HADOOP_VERSION" =~ ^0\.23\. ]]; then BUILD_COMMAND="$BUILD_COMMAND -Phadoop-0.23"; fi
-if [[ "$SPARK_HADOOP_VERSION" =~ ^2\.2\. ]]; then BUILD_COMMAND="$BUILD_COMMAND -Phadoop-2.2"; fi
-if [[ "$SPARK_HADOOP_VERSION" =~ ^2\.3\. ]]; then BUILD_COMMAND="$BUILD_COMMAND -Phadoop-2.3"; fi
-if [[ "$SPARK_HADOOP_VERSION" =~ ^2\.4\. ]]; then BUILD_COMMAND="$BUILD_COMMAND -Phadoop-2.4"; fi
-if [[ "$SPARK_HIVE" == "true" ]]; then BUILD_COMMAND="$BUILD_COMMAND -Phive"; fi
-if [[ "$SPARK_YARN" == "true" ]]; then
-  # For hadoop versions 0.23.x to 2.1.x, use the yarn-alpha profile
-  if [[ "$SPARK_HADOOP_VERSION" =~ ^0\.2[3-9]\. ]] ||
-     [[ "$SPARK_HADOOP_VERSION" =~ ^0\.[3-9][0-9]\. ]] ||
-     [[ "$SPARK_HADOOP_VERSION" =~ ^1\.[0-9]\. ]] ||
-     [[ "$SPARK_HADOOP_VERSION" =~ ^2\.[0-1]\. ]]; then
-    BUILD_COMMAND="$BUILD_COMMAND -Pyarn-alpha"
-  # For hadoop versions 2.2+, use the yarn profile
-  elif [[ "$SPARK_HADOOP_VERSION" =~ ^2.[2-9]. ]]; then
-    BUILD_COMMAND="$BUILD_COMMAND -Pyarn"
-  fi
-  BUILD_COMMAND="$BUILD_COMMAND -Dyarn.version=$SPARK_HADOOP_VERSION"
-fi
-BUILD_COMMAND="$BUILD_COMMAND -Dhadoop.version=$SPARK_HADOOP_VERSION"
-BUILD_COMMAND="$BUILD_COMMAND -DskipTests"
+BUILD_COMMAND="mvn clean package -DskipTests $@"
 
 # Actually build the jar
 echo -e "\nBuilding with..."
 echo -e "\$ $BUILD_COMMAND\n"
+
 ${BUILD_COMMAND}
 
 # Make directories

From 9c73822a08848a0cde545282d3eb1c3f1a4c2a82 Mon Sep 17 00:00:00 2001
From: Andrew Or <andrewor14@gmail.com>
Date: Thu, 17 Jul 2014 01:13:32 -0700
Subject: [PATCH 681/810] [SPARK-2423] Clean up SparkSubmit for readability

It is currently non-trivial to trace through how different combinations of cluster managers (e.g. yarn) and deploy modes (e.g. cluster) are processed in SparkSubmit. Moving forward, it will be easier to extend SparkSubmit if we first re-organize the code by grouping related logic together.

This is a precursor to fixing standalone-cluster mode, which is currently broken (SPARK-2260).

Author: Andrew Or <andrewor14@gmail.com>

Closes #1349 from andrewor14/submit-cleanup and squashes the following commits:

8f99200 [Andrew Or] script -> program (minor)
30f2e65 [Andrew Or] Merge branch 'master' of github.com:apache/spark into submit-cleanup
fe484a1 [Andrew Or] Move deploy mode checks after yarn code
7167824 [Andrew Or] Re-order config options and update comments
0b01ff8 [Andrew Or] Clean up SparkSubmit for readability
---
 .../org/apache/spark/deploy/SparkSubmit.scala | 289 +++++++++---------
 1 file changed, 145 insertions(+), 144 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
index b050dccb6d57..3d8373d8175e 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
@@ -27,25 +27,39 @@ import org.apache.spark.executor.ExecutorURLClassLoader
 import org.apache.spark.util.Utils
 
 /**
- * Scala code behind the spark-submit script.  The script handles setting up the classpath with
- * relevant Spark dependencies and provides a layer over the different cluster managers and deploy
- * modes that Spark supports.
+ * Main gateway of launching a Spark application.
+ *
+ * This program handles setting up the classpath with relevant Spark dependencies and provides
+ * a layer over the different cluster managers and deploy modes that Spark supports.
  */
 object SparkSubmit {
+
+  // Cluster managers
   private val YARN = 1
   private val STANDALONE = 2
   private val MESOS = 4
   private val LOCAL = 8
   private val ALL_CLUSTER_MGRS = YARN | STANDALONE | MESOS | LOCAL
 
-  private var clusterManager: Int = LOCAL
+  // Deploy modes
+  private val CLIENT = 1
+  private val CLUSTER = 2
+  private val ALL_DEPLOY_MODES = CLIENT | CLUSTER
 
-  /**
-   * Special primary resource names that represent shells rather than application jars.
-   */
+  // Special primary resource names that represent shells rather than application jars.
   private val SPARK_SHELL = "spark-shell"
   private val PYSPARK_SHELL = "pyspark-shell"
 
+  // Exposed for testing
+  private[spark] var exitFn: () => Unit = () => System.exit(-1)
+  private[spark] var printStream: PrintStream = System.err
+  private[spark] def printWarning(str: String) = printStream.println("Warning: " + str)
+  private[spark] def printErrorAndExit(str: String) = {
+    printStream.println("Error: " + str)
+    printStream.println("Run with --help for usage help or --verbose for debug output")
+    exitFn()
+  }
+
   def main(args: Array[String]) {
     val appArgs = new SparkSubmitArguments(args)
     if (appArgs.verbose) {
@@ -55,88 +69,80 @@ object SparkSubmit {
     launch(childArgs, classpath, sysProps, mainClass, appArgs.verbose)
   }
 
-  // Exposed for testing
-  private[spark] var printStream: PrintStream = System.err
-  private[spark] var exitFn: () => Unit = () => System.exit(-1)
-
-  private[spark] def printErrorAndExit(str: String) = {
-    printStream.println("Error: " + str)
-    printStream.println("Run with --help for usage help or --verbose for debug output")
-    exitFn()
-  }
-  private[spark] def printWarning(str: String) = printStream.println("Warning: " + str)
-
   /**
-   * @return a tuple containing the arguments for the child, a list of classpath
-   *         entries for the child, a list of system properties, a list of env vars
-   *         and the main class for the child
+   * @return a tuple containing
+   *           (1) the arguments for the child process,
+   *           (2) a list of classpath entries for the child,
+   *           (3) a list of system properties and env vars, and
+   *           (4) the main class for the child
    */
   private[spark] def createLaunchEnv(args: SparkSubmitArguments)
       : (ArrayBuffer[String], ArrayBuffer[String], Map[String, String], String) = {
-    if (args.master.startsWith("local")) {
-      clusterManager = LOCAL
-    } else if (args.master.startsWith("yarn")) {
-      clusterManager = YARN
-    } else if (args.master.startsWith("spark")) {
-      clusterManager = STANDALONE
-    } else if (args.master.startsWith("mesos")) {
-      clusterManager = MESOS
-    } else {
-      printErrorAndExit("Master must start with yarn, mesos, spark, or local")
-    }
-
-    // Because "yarn-cluster" and "yarn-client" encapsulate both the master
-    // and deploy mode, we have some logic to infer the master and deploy mode
-    // from each other if only one is specified, or exit early if they are at odds.
-    if (args.deployMode == null &&
-        (args.master == "yarn-standalone" || args.master == "yarn-cluster")) {
-      args.deployMode = "cluster"
-    }
-    if (args.deployMode == "cluster" && args.master == "yarn-client") {
-      printErrorAndExit("Deploy mode \"cluster\" and master \"yarn-client\" are not compatible")
-    }
-    if (args.deployMode == "client" &&
-        (args.master == "yarn-standalone" || args.master == "yarn-cluster")) {
-      printErrorAndExit("Deploy mode \"client\" and master \"" + args.master
-        + "\" are not compatible")
-    }
-    if (args.deployMode == "cluster" && args.master.startsWith("yarn")) {
-      args.master = "yarn-cluster"
-    }
-    if (args.deployMode != "cluster" && args.master.startsWith("yarn")) {
-      args.master = "yarn-client"
-    }
-
-    val deployOnCluster = Option(args.deployMode).getOrElse("client") == "cluster"
 
-    val childClasspath = new ArrayBuffer[String]()
+    // Values to return
     val childArgs = new ArrayBuffer[String]()
+    val childClasspath = new ArrayBuffer[String]()
     val sysProps = new HashMap[String, String]()
     var childMainClass = ""
 
-    val isPython = args.isPython
-    val isYarnCluster = clusterManager == YARN && deployOnCluster
+    // Set the cluster manager
+    val clusterManager: Int = args.master match {
+      case m if m.startsWith("yarn") => YARN
+      case m if m.startsWith("spark") => STANDALONE
+      case m if m.startsWith("mesos") => MESOS
+      case m if m.startsWith("local") => LOCAL
+      case _ => printErrorAndExit("Master must start with yarn, spark, mesos, or local"); -1
+    }
 
-    // For mesos, only client mode is supported
-    if (clusterManager == MESOS && deployOnCluster) {
-      printErrorAndExit("Cluster deploy mode is currently not supported for Mesos clusters.")
+    // Set the deploy mode; default is client mode
+    var deployMode: Int = args.deployMode match {
+      case "client" | null => CLIENT
+      case "cluster" => CLUSTER
+      case _ => printErrorAndExit("Deploy mode must be either client or cluster"); -1
     }
 
-    // For standalone, only client mode is supported
-    if (clusterManager == STANDALONE && deployOnCluster) {
-      printErrorAndExit("Cluster deploy mode is currently not supported for standalone clusters.")
+    // Because "yarn-cluster" and "yarn-client" encapsulate both the master
+    // and deploy mode, we have some logic to infer the master and deploy mode
+    // from each other if only one is specified, or exit early if they are at odds.
+    if (clusterManager == YARN) {
+      if (args.master == "yarn-standalone") {
+        printWarning("\"yarn-standalone\" is deprecated. Use \"yarn-cluster\" instead.")
+        args.master = "yarn-cluster"
+      }
+      (args.master, args.deployMode) match {
+        case ("yarn-cluster", null) =>
+          deployMode = CLUSTER
+        case ("yarn-cluster", "client") =>
+          printErrorAndExit("Client deploy mode is not compatible with master \"yarn-cluster\"")
+        case ("yarn-client", "cluster") =>
+          printErrorAndExit("Cluster deploy mode is not compatible with master \"yarn-client\"")
+        case (_, mode) =>
+          args.master = "yarn-" + Option(mode).getOrElse("client")
+      }
+
+      // Make sure YARN is included in our build if we're trying to use it
+      if (!Utils.classIsLoadable("org.apache.spark.deploy.yarn.Client") && !Utils.isTesting) {
+        printErrorAndExit(
+          "Could not load YARN classes. " +
+          "This copy of Spark may not have been compiled with YARN support.")
+      }
     }
 
-    // For shells, only client mode is applicable
-    if (isShell(args.primaryResource) && deployOnCluster) {
-      printErrorAndExit("Cluster deploy mode is not applicable to Spark shells.")
+    // The following modes are not supported or applicable
+    (clusterManager, deployMode) match {
+      case (MESOS, CLUSTER) =>
+        printErrorAndExit("Cluster deploy mode is currently not supported for Mesos clusters.")
+      case (STANDALONE, CLUSTER) =>
+        printErrorAndExit("Cluster deploy mode is currently not supported for Standalone clusters.")
+      case (_, CLUSTER) if args.isPython =>
+        printErrorAndExit("Cluster deploy mode is currently not supported for python applications.")
+      case (_, CLUSTER) if isShell(args.primaryResource) =>
+        printErrorAndExit("Cluster deploy mode is not applicable to Spark shells.")
+      case _ =>
     }
 
     // If we're running a python app, set the main class to our specific python runner
-    if (isPython) {
-      if (deployOnCluster) {
-        printErrorAndExit("Cluster deploy mode is currently not supported for python.")
-      }
+    if (args.isPython) {
       if (args.primaryResource == PYSPARK_SHELL) {
         args.mainClass = "py4j.GatewayServer"
         args.childArgs = ArrayBuffer("--die-on-broken-pipe", "0")
@@ -152,120 +158,115 @@ object SparkSubmit {
       sysProps("spark.submit.pyFiles") = PythonRunner.formatPaths(args.pyFiles).mkString(",")
     }
 
-    // If we're deploying into YARN, use yarn.Client as a wrapper around the user class
-    if (!deployOnCluster) {
-      childMainClass = args.mainClass
-      if (isUserJar(args.primaryResource)) {
-        childClasspath += args.primaryResource
-      }
-    } else if (clusterManager == YARN) {
-      childMainClass = "org.apache.spark.deploy.yarn.Client"
-      childArgs += ("--jar", args.primaryResource)
-      childArgs += ("--class", args.mainClass)
-    }
-
-    // Make sure YARN is included in our build if we're trying to use it
-    if (clusterManager == YARN) {
-      if (!Utils.classIsLoadable("org.apache.spark.deploy.yarn.Client") && !Utils.isTesting) {
-        printErrorAndExit("Could not load YARN classes. " +
-          "This copy of Spark may not have been compiled with YARN support.")
-      }
-    }
-
     // Special flag to avoid deprecation warnings at the client
     sysProps("SPARK_SUBMIT") = "true"
 
     // A list of rules to map each argument to system properties or command-line options in
     // each deploy mode; we iterate through these below
     val options = List[OptionAssigner](
-      OptionAssigner(args.master, ALL_CLUSTER_MGRS, false, sysProp = "spark.master"),
-      OptionAssigner(args.name, ALL_CLUSTER_MGRS, false, sysProp = "spark.app.name"),
-      OptionAssigner(args.name, YARN, true, clOption = "--name", sysProp = "spark.app.name"),
-      OptionAssigner(args.driverExtraClassPath, STANDALONE | YARN, true,
+
+      // All cluster managers
+      OptionAssigner(args.master, ALL_CLUSTER_MGRS, CLIENT, sysProp = "spark.master"),
+      OptionAssigner(args.name, ALL_CLUSTER_MGRS, CLIENT, sysProp = "spark.app.name"),
+      OptionAssigner(args.jars, ALL_CLUSTER_MGRS, CLIENT, sysProp = "spark.jars"),
+
+      // Standalone cluster only
+      OptionAssigner(args.driverMemory, STANDALONE, CLUSTER, clOption = "--memory"),
+      OptionAssigner(args.driverCores, STANDALONE, CLUSTER, clOption = "--cores"),
+
+      // Yarn client only
+      OptionAssigner(args.queue, YARN, CLIENT, sysProp = "spark.yarn.queue"),
+      OptionAssigner(args.numExecutors, YARN, CLIENT, sysProp = "spark.executor.instances"),
+      OptionAssigner(args.executorCores, YARN, CLIENT, sysProp = "spark.executor.cores"),
+      OptionAssigner(args.files, YARN, CLIENT, sysProp = "spark.yarn.dist.files"),
+      OptionAssigner(args.archives, YARN, CLIENT, sysProp = "spark.yarn.dist.archives"),
+
+      // Yarn cluster only
+      OptionAssigner(args.name, YARN, CLUSTER, clOption = "--name", sysProp = "spark.app.name"),
+      OptionAssigner(args.driverMemory, YARN, CLUSTER, clOption = "--driver-memory"),
+      OptionAssigner(args.queue, YARN, CLUSTER, clOption = "--queue"),
+      OptionAssigner(args.numExecutors, YARN, CLUSTER, clOption = "--num-executors"),
+      OptionAssigner(args.executorMemory, YARN, CLUSTER, clOption = "--executor-memory"),
+      OptionAssigner(args.executorCores, YARN, CLUSTER, clOption = "--executor-cores"),
+      OptionAssigner(args.files, YARN, CLUSTER, clOption = "--files"),
+      OptionAssigner(args.archives, YARN, CLUSTER, clOption = "--archives"),
+      OptionAssigner(args.jars, YARN, CLUSTER, clOption = "--addJars"),
+
+      // Other options
+      OptionAssigner(args.driverExtraClassPath, STANDALONE | YARN, CLUSTER,
         sysProp = "spark.driver.extraClassPath"),
-      OptionAssigner(args.driverExtraJavaOptions, STANDALONE | YARN, true,
+      OptionAssigner(args.driverExtraJavaOptions, STANDALONE | YARN, CLUSTER,
         sysProp = "spark.driver.extraJavaOptions"),
-      OptionAssigner(args.driverExtraLibraryPath, STANDALONE | YARN, true,
+      OptionAssigner(args.driverExtraLibraryPath, STANDALONE | YARN, CLUSTER,
         sysProp = "spark.driver.extraLibraryPath"),
-      OptionAssigner(args.driverMemory, YARN, true, clOption = "--driver-memory"),
-      OptionAssigner(args.driverMemory, STANDALONE, true, clOption = "--memory"),
-      OptionAssigner(args.driverCores, STANDALONE, true, clOption = "--cores"),
-      OptionAssigner(args.queue, YARN, true, clOption = "--queue"),
-      OptionAssigner(args.queue, YARN, false, sysProp = "spark.yarn.queue"),
-      OptionAssigner(args.numExecutors, YARN, true, clOption = "--num-executors"),
-      OptionAssigner(args.numExecutors, YARN, false, sysProp = "spark.executor.instances"),
-      OptionAssigner(args.executorMemory, YARN, true, clOption = "--executor-memory"),
-      OptionAssigner(args.executorMemory, STANDALONE | MESOS | YARN, false,
+      OptionAssigner(args.executorMemory, STANDALONE | MESOS | YARN, CLIENT,
         sysProp = "spark.executor.memory"),
-      OptionAssigner(args.executorCores, YARN, true, clOption = "--executor-cores"),
-      OptionAssigner(args.executorCores, YARN, false, sysProp = "spark.executor.cores"),
-      OptionAssigner(args.totalExecutorCores, STANDALONE | MESOS, false,
+      OptionAssigner(args.totalExecutorCores, STANDALONE | MESOS, CLIENT,
         sysProp = "spark.cores.max"),
-      OptionAssigner(args.files, YARN, false, sysProp = "spark.yarn.dist.files"),
-      OptionAssigner(args.files, YARN, true, clOption = "--files"),
-      OptionAssigner(args.files, LOCAL | STANDALONE | MESOS, false, sysProp = "spark.files"),
-      OptionAssigner(args.files, LOCAL | STANDALONE | MESOS, true, sysProp = "spark.files"),
-      OptionAssigner(args.archives, YARN, false, sysProp = "spark.yarn.dist.archives"),
-      OptionAssigner(args.archives, YARN, true, clOption = "--archives"),
-      OptionAssigner(args.jars, YARN, true, clOption = "--addJars"),
-      OptionAssigner(args.jars, ALL_CLUSTER_MGRS, false, sysProp = "spark.jars")
+      OptionAssigner(args.files, LOCAL | STANDALONE | MESOS, ALL_DEPLOY_MODES,
+        sysProp = "spark.files")
     )
 
-    // For client mode make any added jars immediately visible on the classpath
-    if (args.jars != null && !deployOnCluster) {
-      for (jar <- args.jars.split(",")) {
-        childClasspath += jar
+    // In client mode, launch the application main class directly
+    // In addition, add the main application jar and any added jars (if any) to the classpath
+    if (deployMode == CLIENT) {
+      childMainClass = args.mainClass
+      if (isUserJar(args.primaryResource)) {
+        childClasspath += args.primaryResource
       }
+      if (args.jars != null) { childClasspath ++= args.jars.split(",") }
+      if (args.childArgs != null) { childArgs ++= args.childArgs }
     }
 
+
     // Map all arguments to command-line options or system properties for our chosen mode
     for (opt <- options) {
-      if (opt.value != null && deployOnCluster == opt.deployOnCluster &&
+      if (opt.value != null &&
+          (deployMode & opt.deployMode) != 0 &&
           (clusterManager & opt.clusterManager) != 0) {
-        if (opt.clOption != null) {
-          childArgs += (opt.clOption, opt.value)
-        }
-        if (opt.sysProp != null) {
-          sysProps.put(opt.sysProp, opt.value)
-        }
+        if (opt.clOption != null) { childArgs += (opt.clOption, opt.value) }
+        if (opt.sysProp != null) { sysProps.put(opt.sysProp, opt.value) }
       }
     }
 
     // Add the application jar automatically so the user doesn't have to call sc.addJar
     // For YARN cluster mode, the jar is already distributed on each node as "app.jar"
     // For python files, the primary resource is already distributed as a regular file
-    if (!isYarnCluster && !isPython) {
-      var jars = sysProps.get("spark.jars").map(x => x.split(",").toSeq).getOrElse(Seq())
+    val isYarnCluster = clusterManager == YARN && deployMode == CLUSTER
+    if (!isYarnCluster && !args.isPython) {
+      var jars = sysProps.get("spark.jars").map(x => x.split(",").toSeq).getOrElse(Seq.empty)
       if (isUserJar(args.primaryResource)) {
         jars = jars ++ Seq(args.primaryResource)
       }
       sysProps.put("spark.jars", jars.mkString(","))
     }
 
-    // Standalone cluster specific configurations
-    if (deployOnCluster && clusterManager == STANDALONE) {
+    // In standalone-cluster mode, use Client as a wrapper around the user class
+    if (clusterManager == STANDALONE && deployMode == CLUSTER) {
+      childMainClass = "org.apache.spark.deploy.Client"
       if (args.supervise) {
         childArgs += "--supervise"
       }
-      childMainClass = "org.apache.spark.deploy.Client"
       childArgs += "launch"
       childArgs += (args.master, args.primaryResource, args.mainClass)
+      if (args.childArgs != null) {
+        childArgs ++= args.childArgs
+      }
     }
 
-    // Arguments to be passed to user program
-    if (args.childArgs != null) {
-      if (!deployOnCluster || clusterManager == STANDALONE) {
-        childArgs ++= args.childArgs
-      } else if (clusterManager == YARN) {
-        for (arg <- args.childArgs) {
-          childArgs += ("--arg", arg)
-        }
+    // In yarn-cluster mode, use yarn.Client as a wrapper around the user class
+    if (clusterManager == YARN && deployMode == CLUSTER) {
+      childMainClass = "org.apache.spark.deploy.yarn.Client"
+      childArgs += ("--jar", args.primaryResource)
+      childArgs += ("--class", args.mainClass)
+      if (args.childArgs != null) {
+        args.childArgs.foreach { arg => childArgs += ("--arg", arg) }
       }
     }
 
     // Read from default spark properties, if any
     for ((k, v) <- args.getDefaultSparkProperties) {
-      if (!sysProps.contains(k)) sysProps(k) = v
+      sysProps.getOrElseUpdate(k, v)
     }
 
     (childArgs, childClasspath, sysProps, childMainClass)
@@ -364,6 +365,6 @@ object SparkSubmit {
 private[spark] case class OptionAssigner(
     value: String,
     clusterManager: Int,
-    deployOnCluster: Boolean,
+    deployMode: Int,
     clOption: String = null,
     sysProp: String = null)

From d988d345d5bec0668324386f3e81787f78e75e67 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Thu, 17 Jul 2014 10:54:53 -0700
Subject: [PATCH 682/810] [SPARK-2534] Avoid pulling in the entire RDD in
 various operators

This should go into both master and branch-1.0.

Author: Reynold Xin <rxin@apache.org>

Closes #1450 from rxin/agg-closure and squashes the following commits:

e40f363 [Reynold Xin] Mima check excludes.
9186364 [Reynold Xin] Define the return type more explicitly.
38e348b [Reynold Xin] Fixed the cases in RDD.scala.
ea6b34d [Reynold Xin] Blah
89b9c43 [Reynold Xin] Fix other instances of accidentally pulling in extra stuff in closures.
73b2783 [Reynold Xin] [SPARK-2534] Avoid pulling in the entire RDD in groupByKey.
---
 .../apache/spark/rdd/PairRDDFunctions.scala   |  46 +++--
 .../main/scala/org/apache/spark/rdd/RDD.scala |  12 +-
 project/MimaExcludes.scala                    | 159 ++++++++++--------
 3 files changed, 115 insertions(+), 102 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
index 9d62d53fcb23..29038b0359cc 100644
--- a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
@@ -125,7 +125,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
     zeroBuffer.get(zeroArray)
 
     lazy val cachedSerializer = SparkEnv.get.closureSerializer.newInstance()
-    def createZero() = cachedSerializer.deserialize[U](ByteBuffer.wrap(zeroArray))
+    val createZero = () => cachedSerializer.deserialize[U](ByteBuffer.wrap(zeroArray))
 
     combineByKey[U]((v: V) => seqOp(createZero(), v), seqOp, combOp, partitioner)
   }
@@ -171,7 +171,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
 
     // When deserializing, use a lazy val to create just one instance of the serializer per task
     lazy val cachedSerializer = SparkEnv.get.closureSerializer.newInstance()
-    def createZero() = cachedSerializer.deserialize[V](ByteBuffer.wrap(zeroArray))
+    val createZero = () => cachedSerializer.deserialize[V](ByteBuffer.wrap(zeroArray))
 
     combineByKey[V]((v: V) => func(createZero(), v), func, func, partitioner)
   }
@@ -214,22 +214,22 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
       throw new SparkException("reduceByKeyLocally() does not support array keys")
     }
 
-    def reducePartition(iter: Iterator[(K, V)]): Iterator[JHashMap[K, V]] = {
+    val reducePartition = (iter: Iterator[(K, V)]) => {
       val map = new JHashMap[K, V]
       iter.foreach { case (k, v) =>
         val old = map.get(k)
         map.put(k, if (old == null) v else func(old, v))
       }
       Iterator(map)
-    }
+    } : Iterator[JHashMap[K, V]]
 
-    def mergeMaps(m1: JHashMap[K, V], m2: JHashMap[K, V]): JHashMap[K, V] = {
+    val mergeMaps = (m1: JHashMap[K, V], m2: JHashMap[K, V]) => {
       m2.foreach { case (k, v) =>
         val old = m1.get(k)
         m1.put(k, if (old == null) v else func(old, v))
       }
       m1
-    }
+    } : JHashMap[K, V]
 
     self.mapPartitions(reducePartition).reduce(mergeMaps)
   }
@@ -361,11 +361,11 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
     // groupByKey shouldn't use map side combine because map side combine does not
     // reduce the amount of data shuffled and requires all map side data be inserted
     // into a hash table, leading to more objects in the old gen.
-    def createCombiner(v: V) = ArrayBuffer(v)
-    def mergeValue(buf: ArrayBuffer[V], v: V) = buf += v
-    def mergeCombiners(c1: ArrayBuffer[V], c2: ArrayBuffer[V]) = c1 ++ c2
+    val createCombiner = (v: V) => ArrayBuffer(v)
+    val mergeValue = (buf: ArrayBuffer[V], v: V) => buf += v
+    val mergeCombiners = (c1: ArrayBuffer[V], c2: ArrayBuffer[V]) => c1 ++ c2
     val bufs = combineByKey[ArrayBuffer[V]](
-      createCombiner _, mergeValue _, mergeCombiners _, partitioner, mapSideCombine=false)
+      createCombiner, mergeValue, mergeCombiners, partitioner, mapSideCombine=false)
     bufs.mapValues(_.toIterable)
   }
 
@@ -710,14 +710,14 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
     self.partitioner match {
       case Some(p) =>
         val index = p.getPartition(key)
-        def process(it: Iterator[(K, V)]): Seq[V] = {
+        val process = (it: Iterator[(K, V)]) => {
           val buf = new ArrayBuffer[V]
           for ((k, v) <- it if k == key) {
             buf += v
           }
           buf
-        }
-        val res = self.context.runJob(self, process _, Array(index), false)
+        } : Seq[V]
+        val res = self.context.runJob(self, process, Array(index), false)
         res(0)
       case None =>
         self.filter(_._1 == key).map(_._2).collect()
@@ -840,7 +840,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
       jobFormat.checkOutputSpecs(job)
     }
 
-    def writeShard(context: TaskContext, iter: Iterator[(K,V)]): Int = {
+    val writeShard = (context: TaskContext, iter: Iterator[(K,V)]) => {
       // Hadoop wants a 32-bit task attempt ID, so if ours is bigger than Int.MaxValue, roll it
       // around by taking a mod. We expect that no task will be attempted 2 billion times.
       val attemptNumber = (context.attemptId % Int.MaxValue).toInt
@@ -861,19 +861,18 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
           val (k, v) = iter.next()
           writer.write(k, v)
         }
-      }
-      finally {
+      } finally {
         writer.close(hadoopContext)
       }
       committer.commitTask(hadoopContext)
-      return 1
-    }
+      1
+    } : Int
 
     val jobAttemptId = newTaskAttemptID(jobtrackerID, stageId, isMap = true, 0, 0)
     val jobTaskContext = newTaskAttemptContext(wrappedConf.value, jobAttemptId)
     val jobCommitter = jobFormat.getOutputCommitter(jobTaskContext)
     jobCommitter.setupJob(jobTaskContext)
-    self.context.runJob(self, writeShard _)
+    self.context.runJob(self, writeShard)
     jobCommitter.commitJob(jobTaskContext)
   }
 
@@ -912,7 +911,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
     val writer = new SparkHadoopWriter(hadoopConf)
     writer.preSetup()
 
-    def writeToFile(context: TaskContext, iter: Iterator[(K, V)]) {
+    val writeToFile = (context: TaskContext, iter: Iterator[(K, V)]) => {
       // Hadoop wants a 32-bit task attempt ID, so if ours is bigger than Int.MaxValue, roll it
       // around by taking a mod. We expect that no task will be attempted 2 billion times.
       val attemptNumber = (context.attemptId % Int.MaxValue).toInt
@@ -921,19 +920,18 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
       writer.open()
       try {
         var count = 0
-        while(iter.hasNext) {
+        while (iter.hasNext) {
           val record = iter.next()
           count += 1
           writer.write(record._1.asInstanceOf[AnyRef], record._2.asInstanceOf[AnyRef])
         }
-      }
-      finally {
+      } finally {
         writer.close()
       }
       writer.commit()
     }
 
-    self.context.runJob(self, writeToFile _)
+    self.context.runJob(self, writeToFile)
     writer.commitJob()
   }
 
diff --git a/core/src/main/scala/org/apache/spark/rdd/RDD.scala b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
index a25f263bea5c..88a918aebf76 100644
--- a/core/src/main/scala/org/apache/spark/rdd/RDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
@@ -328,7 +328,7 @@ abstract class RDD[T: ClassTag](
       : RDD[T] = {
     if (shuffle) {
       /** Distributes elements evenly across output partitions, starting from a random partition. */
-      def distributePartition(index: Int, items: Iterator[T]): Iterator[(Int, T)] = {
+      val distributePartition = (index: Int, items: Iterator[T]) => {
         var position = (new Random(index)).nextInt(numPartitions)
         items.map { t =>
           // Note that the hash code of the key will just be the key itself. The HashPartitioner 
@@ -336,7 +336,7 @@ abstract class RDD[T: ClassTag](
           position = position + 1
           (position, t)
         }
-      }
+      } : Iterator[(Int, T)]
 
       // include a shuffle step so that our upstream tasks are still distributed
       new CoalescedRDD(
@@ -919,19 +919,19 @@ abstract class RDD[T: ClassTag](
       throw new SparkException("countByValue() does not support arrays")
     }
     // TODO: This should perhaps be distributed by default.
-    def countPartition(iter: Iterator[T]): Iterator[OpenHashMap[T,Long]] = {
+    val countPartition = (iter: Iterator[T]) => {
       val map = new OpenHashMap[T,Long]
       iter.foreach {
         t => map.changeValue(t, 1L, _ + 1L)
       }
       Iterator(map)
-    }
-    def mergeMaps(m1: OpenHashMap[T,Long], m2: OpenHashMap[T,Long]): OpenHashMap[T,Long] = {
+    }: Iterator[OpenHashMap[T,Long]]
+    val mergeMaps = (m1: OpenHashMap[T,Long], m2: OpenHashMap[T,Long]) => {
       m2.foreach { case (key, value) =>
         m1.changeValue(key, value, _ + value)
       }
       m1
-    }
+    }: OpenHashMap[T,Long]
     val myResult = mapPartitions(countPartition).reduce(mergeMaps)
     // Convert to a Scala mutable map
     val mutableResult = scala.collection.mutable.Map[T,Long]()
diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala
index d67c6571a062..3487f7c5c125 100644
--- a/project/MimaExcludes.scala
+++ b/project/MimaExcludes.scala
@@ -31,76 +31,91 @@ import com.typesafe.tools.mima.core._
  * MimaBuild.excludeSparkClass("graphx.util.collection.GraphXPrimitiveKeyOpenHashMap")
  */
 object MimaExcludes {
-    def excludes(version: String) =
-      version match {
-        case v if v.startsWith("1.1") =>
-          Seq(
-            MimaBuild.excludeSparkPackage("deploy"),
-            MimaBuild.excludeSparkPackage("graphx")
-          ) ++
-          Seq(
-            // Adding new method to JavaRDLike trait - we should probably mark this as a developer API.
-            ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.api.java.JavaRDDLike.partitions"),
-            // We made a mistake earlier (ed06500d3) in the Java API to use default parameter values
-            // for countApproxDistinct* functions, which does not work in Java. We later removed
-            // them, and use the following to tell Mima to not care about them.
-            ProblemFilters.exclude[IncompatibleResultTypeProblem](
-              "org.apache.spark.api.java.JavaPairRDD.countApproxDistinctByKey"),
-            ProblemFilters.exclude[IncompatibleResultTypeProblem](
-              "org.apache.spark.api.java.JavaPairRDD.countApproxDistinctByKey"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.api.java.JavaPairRDD.countApproxDistinct$default$1"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.api.java.JavaPairRDD.countApproxDistinctByKey$default$1"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.api.java.JavaRDD.countApproxDistinct$default$1"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.api.java.JavaRDDLike.countApproxDistinct$default$1"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.api.java.JavaDoubleRDD.countApproxDistinct$default$1"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.storage.MemoryStore.Entry"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.rdd.PairRDDFunctions.org$apache$spark$rdd$PairRDDFunctions$$"
-                + "createZero$1")
-          ) ++
-          Seq(
-            ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.streaming.flume.FlumeReceiver.this")
-          ) ++
-          Seq( // Ignore some private methods in ALS.
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$^dateFeatures"),
-            ProblemFilters.exclude[MissingMethodProblem]( // The only public constructor is the one without arguments.
-              "org.apache.spark.mllib.recommendation.ALS.this"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$<init>$default$7")
-          ) ++
-          MimaBuild.excludeSparkClass("mllib.linalg.distributed.ColumnStatisticsAggregator") ++
-          MimaBuild.excludeSparkClass("rdd.ZippedRDD") ++
-          MimaBuild.excludeSparkClass("rdd.ZippedPartition") ++
-          MimaBuild.excludeSparkClass("util.SerializableHyperLogLog") ++
-          MimaBuild.excludeSparkClass("storage.Values") ++
-          MimaBuild.excludeSparkClass("storage.Entry") ++
-          MimaBuild.excludeSparkClass("storage.MemoryStore$Entry")
-        case v if v.startsWith("1.0") =>
-          Seq(
-            MimaBuild.excludeSparkPackage("api.java"),
-            MimaBuild.excludeSparkPackage("mllib"),
-            MimaBuild.excludeSparkPackage("streaming")
-          ) ++
-          MimaBuild.excludeSparkClass("rdd.ClassTags") ++
-          MimaBuild.excludeSparkClass("util.XORShiftRandom") ++
-          MimaBuild.excludeSparkClass("graphx.EdgeRDD") ++
-          MimaBuild.excludeSparkClass("graphx.VertexRDD") ++
-          MimaBuild.excludeSparkClass("graphx.impl.GraphImpl") ++
-          MimaBuild.excludeSparkClass("graphx.impl.RoutingTable") ++
-          MimaBuild.excludeSparkClass("graphx.util.collection.PrimitiveKeyOpenHashMap") ++
-          MimaBuild.excludeSparkClass("graphx.util.collection.GraphXPrimitiveKeyOpenHashMap") ++
-          MimaBuild.excludeSparkClass("mllib.recommendation.MFDataGenerator") ++
-          MimaBuild.excludeSparkClass("mllib.optimization.SquaredGradient") ++
-          MimaBuild.excludeSparkClass("mllib.regression.RidgeRegressionWithSGD") ++
-          MimaBuild.excludeSparkClass("mllib.regression.LassoWithSGD") ++
-          MimaBuild.excludeSparkClass("mllib.regression.LinearRegressionWithSGD")
-        case _ => Seq()
-      }
+
+  def excludes(version: String) = version match {
+    case v if v.startsWith("1.1") =>
+      Seq(
+        MimaBuild.excludeSparkPackage("deploy"),
+        MimaBuild.excludeSparkPackage("graphx")
+      ) ++
+      closures.map(method => ProblemFilters.exclude[MissingMethodProblem](method)) ++
+      Seq(
+        // Adding new method to JavaRDLike trait - we should probably mark this as a developer API.
+        ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.api.java.JavaRDDLike.partitions"),
+        // We made a mistake earlier (ed06500d3) in the Java API to use default parameter values
+        // for countApproxDistinct* functions, which does not work in Java. We later removed
+        // them, and use the following to tell Mima to not care about them.
+        ProblemFilters.exclude[IncompatibleResultTypeProblem](
+          "org.apache.spark.api.java.JavaPairRDD.countApproxDistinctByKey"),
+        ProblemFilters.exclude[IncompatibleResultTypeProblem](
+          "org.apache.spark.api.java.JavaPairRDD.countApproxDistinctByKey"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.api.java.JavaPairRDD.countApproxDistinct$default$1"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.api.java.JavaPairRDD.countApproxDistinctByKey$default$1"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.api.java.JavaRDD.countApproxDistinct$default$1"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.api.java.JavaRDDLike.countApproxDistinct$default$1"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.api.java.JavaDoubleRDD.countApproxDistinct$default$1"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.storage.MemoryStore.Entry"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.rdd.PairRDDFunctions.org$apache$spark$rdd$PairRDDFunctions$$"
+            + "createZero$1")
+      ) ++
+      Seq(
+        ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.streaming.flume.FlumeReceiver.this")
+      ) ++
+      Seq( // Ignore some private methods in ALS.
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$^dateFeatures"),
+        ProblemFilters.exclude[MissingMethodProblem]( // The only public constructor is the one without arguments.
+          "org.apache.spark.mllib.recommendation.ALS.this"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$<init>$default$7")
+      ) ++
+      MimaBuild.excludeSparkClass("mllib.linalg.distributed.ColumnStatisticsAggregator") ++
+      MimaBuild.excludeSparkClass("rdd.ZippedRDD") ++
+      MimaBuild.excludeSparkClass("rdd.ZippedPartition") ++
+      MimaBuild.excludeSparkClass("util.SerializableHyperLogLog") ++
+      MimaBuild.excludeSparkClass("storage.Values") ++
+      MimaBuild.excludeSparkClass("storage.Entry") ++
+      MimaBuild.excludeSparkClass("storage.MemoryStore$Entry")
+    case v if v.startsWith("1.0") =>
+      Seq(
+        MimaBuild.excludeSparkPackage("api.java"),
+        MimaBuild.excludeSparkPackage("mllib"),
+        MimaBuild.excludeSparkPackage("streaming")
+      ) ++
+      MimaBuild.excludeSparkClass("rdd.ClassTags") ++
+      MimaBuild.excludeSparkClass("util.XORShiftRandom") ++
+      MimaBuild.excludeSparkClass("graphx.EdgeRDD") ++
+      MimaBuild.excludeSparkClass("graphx.VertexRDD") ++
+      MimaBuild.excludeSparkClass("graphx.impl.GraphImpl") ++
+      MimaBuild.excludeSparkClass("graphx.impl.RoutingTable") ++
+      MimaBuild.excludeSparkClass("graphx.util.collection.PrimitiveKeyOpenHashMap") ++
+      MimaBuild.excludeSparkClass("graphx.util.collection.GraphXPrimitiveKeyOpenHashMap") ++
+      MimaBuild.excludeSparkClass("mllib.recommendation.MFDataGenerator") ++
+      MimaBuild.excludeSparkClass("mllib.optimization.SquaredGradient") ++
+      MimaBuild.excludeSparkClass("mllib.regression.RidgeRegressionWithSGD") ++
+      MimaBuild.excludeSparkClass("mllib.regression.LassoWithSGD") ++
+      MimaBuild.excludeSparkClass("mllib.regression.LinearRegressionWithSGD")
+    case _ => Seq()
+  }
+
+  private val closures = Seq(
+    "org.apache.spark.rdd.RDD.org$apache$spark$rdd$RDD$$mergeMaps$1",
+    "org.apache.spark.rdd.RDD.org$apache$spark$rdd$RDD$$countPartition$1",
+    "org.apache.spark.rdd.RDD.org$apache$spark$rdd$RDD$$distributePartition$1",
+    "org.apache.spark.rdd.PairRDDFunctions.org$apache$spark$rdd$PairRDDFunctions$$mergeValue$1",
+    "org.apache.spark.rdd.PairRDDFunctions.org$apache$spark$rdd$PairRDDFunctions$$writeToFile$1",
+    "org.apache.spark.rdd.PairRDDFunctions.org$apache$spark$rdd$PairRDDFunctions$$reducePartition$1",
+    "org.apache.spark.rdd.PairRDDFunctions.org$apache$spark$rdd$PairRDDFunctions$$writeShard$1",
+    "org.apache.spark.rdd.PairRDDFunctions.org$apache$spark$rdd$PairRDDFunctions$$mergeCombiners$1",
+    "org.apache.spark.rdd.PairRDDFunctions.org$apache$spark$rdd$PairRDDFunctions$$process$1",
+    "org.apache.spark.rdd.PairRDDFunctions.org$apache$spark$rdd$PairRDDFunctions$$createCombiner$1",
+    "org.apache.spark.rdd.PairRDDFunctions.org$apache$spark$rdd$PairRDDFunctions$$mergeMaps$1"
+  )
 }

From 1fcd5dcdd8edb0e6989278c95e7f2c7d86c4efb2 Mon Sep 17 00:00:00 2001
From: Sean Owen <srowen@gmail.com>
Date: Thu, 17 Jul 2014 12:20:48 -0700
Subject: [PATCH 683/810] SPARK-1478.2 Fix incorrect
 NioServerSocketChannelFactory constructor call

The line break inadvertently means this was interpreted as a call to the no-arg constructor. This doesn't exist in older Netty even. (Also fixed a val name typo.)

Author: Sean Owen <srowen@gmail.com>

Closes #1466 from srowen/SPARK-1478.2 and squashes the following commits:

59c3501 [Sean Owen] Line break caused Scala to interpret NioServerSocketChannelFactory constructor as the no-arg version, which is not even present in some versions of Netty
---
 .../spark/streaming/flume/FlumeInputDStream.scala      | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeInputDStream.scala b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeInputDStream.scala
index 07ae88febf91..56d2886b2687 100644
--- a/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeInputDStream.scala
+++ b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeInputDStream.scala
@@ -153,15 +153,15 @@ class FlumeReceiver(
 
   private def initServer() = {
     if (enableDecompression) {
-      val channelFactory = new NioServerSocketChannelFactory
-        (Executors.newCachedThreadPool(), Executors.newCachedThreadPool());
-      val channelPipelieFactory = new CompressionChannelPipelineFactory()
+      val channelFactory = new NioServerSocketChannelFactory(Executors.newCachedThreadPool(),
+                                                             Executors.newCachedThreadPool())
+      val channelPipelineFactory = new CompressionChannelPipelineFactory()
       
       new NettyServer(
         responder, 
         new InetSocketAddress(host, port),
-        channelFactory, 
-        channelPipelieFactory, 
+        channelFactory,
+        channelPipelineFactory,
         null)
     } else {
       new NettyServer(responder, new InetSocketAddress(host, port))

From 935fe65ff6559a0e3b481e7508fa14337b23020b Mon Sep 17 00:00:00 2001
From: "Joseph K. Bradley" <joseph.kurata.bradley@gmail.com>
Date: Thu, 17 Jul 2014 15:05:02 -0700
Subject: [PATCH 684/810] SPARK-1215 [MLLIB]: Clustering: Index out of bounds
 error (2)

Added check to LocalKMeans.scala: kMeansPlusPlus initialization to handle case with fewer distinct data points than clusters k.  Added two related unit tests to KMeansSuite.  (Re-submitting PR after tangling commits in PR 1407 https://github.com/apache/spark/pull/1407 )

Author: Joseph K. Bradley <joseph.kurata.bradley@gmail.com>

Closes #1468 from jkbradley/kmeans-fix and squashes the following commits:

4e9bd1e [Joseph K. Bradley] Updated PR per comments from mengxr
6c7a2ec [Joseph K. Bradley] Added check to LocalKMeans.scala: kMeansPlusPlus initialization to handle case with fewer distinct data points than clusters k.  Added two related unit tests to KMeansSuite.
---
 .../spark/mllib/clustering/LocalKMeans.scala  |  8 +++++-
 .../spark/mllib/clustering/KMeansSuite.scala  | 26 +++++++++++++++++++
 2 files changed, 33 insertions(+), 1 deletion(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LocalKMeans.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LocalKMeans.scala
index 2e3a4ce783de..f0722d7c14a4 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LocalKMeans.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LocalKMeans.scala
@@ -59,7 +59,13 @@ private[mllib] object LocalKMeans extends Logging {
         cumulativeScore += weights(j) * KMeans.pointCost(curCenters, points(j))
         j += 1
       }
-      centers(i) = points(j-1).toDense
+      if (j == 0) {
+        logWarning("kMeansPlusPlus initialization ran out of distinct points for centers." +
+          s" Using duplicate point for center k = $i.")
+        centers(i) = points(0).toDense
+      } else {
+        centers(i) = points(j - 1).toDense
+      }
     }
 
     // Run up to maxIterations iterations of Lloyd's algorithm
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/clustering/KMeansSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/clustering/KMeansSuite.scala
index 560a4ad71a4d..76a3bdf9b11c 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/clustering/KMeansSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/clustering/KMeansSuite.scala
@@ -61,6 +61,32 @@ class KMeansSuite extends FunSuite with LocalSparkContext {
     assert(model.clusterCenters.head === center)
   }
 
+  test("no distinct points") {
+    val data = sc.parallelize(
+      Array(
+        Vectors.dense(1.0, 2.0, 3.0),
+        Vectors.dense(1.0, 2.0, 3.0),
+        Vectors.dense(1.0, 2.0, 3.0)),
+      2)
+    val center = Vectors.dense(1.0, 2.0, 3.0)
+
+    // Make sure code runs.
+    var model = KMeans.train(data, k=2, maxIterations=1)
+    assert(model.clusterCenters.size === 2)
+  }
+
+  test("more clusters than points") {
+    val data = sc.parallelize(
+      Array(
+        Vectors.dense(1.0, 2.0, 3.0),
+        Vectors.dense(1.0, 3.0, 4.0)),
+      2)
+
+    // Make sure code runs.
+    var model = KMeans.train(data, k=3, maxIterations=1)
+    assert(model.clusterCenters.size === 3)
+  }
+
   test("single cluster with big dataset") {
     val smallData = Array(
       Vectors.dense(1.0, 2.0, 6.0),

From 72e9021eaf26f31a82120505f8b764b18fbe8d48 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Thu, 17 Jul 2014 18:58:48 -0700
Subject: [PATCH 685/810] [SPARK-2299] Consolidate various stageIdTo* hash maps
 in JobProgressListener

This should reduce memory usage for the web ui as well as slightly increase its speed in draining the UI event queue.

@andrewor14

Author: Reynold Xin <rxin@apache.org>

Closes #1262 from rxin/ui-consolidate-hashtables and squashes the following commits:

1ac3f97 [Reynold Xin] Oops. Properly handle description.
f5736ad [Reynold Xin] Code review comments.
b8828dc [Reynold Xin] Merge branch 'master' into ui-consolidate-hashtables
7a7b6c4 [Reynold Xin] Revert css change.
f959bb8 [Reynold Xin] [SPARK-2299] Consolidate various stageIdTo* hash maps in JobProgressListener to speed it up.
63256f5 [Reynold Xin] [SPARK-2320] Reduce <pre> block font size.
---
 .../spark/ui/jobs/ExecutorSummary.scala       |  36 ----
 .../apache/spark/ui/jobs/ExecutorTable.scala  |  29 ++--
 .../spark/ui/jobs/JobProgressListener.scala   | 156 +++++++-----------
 .../org/apache/spark/ui/jobs/StagePage.scala  |  37 ++---
 .../org/apache/spark/ui/jobs/StageTable.scala |  73 ++++----
 .../org/apache/spark/ui/jobs/UIData.scala     |  62 +++++++
 .../ui/jobs/JobProgressListenerSuite.scala    |  36 ++--
 7 files changed, 205 insertions(+), 224 deletions(-)
 delete mode 100644 core/src/main/scala/org/apache/spark/ui/jobs/ExecutorSummary.scala
 create mode 100644 core/src/main/scala/org/apache/spark/ui/jobs/UIData.scala

diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/ExecutorSummary.scala b/core/src/main/scala/org/apache/spark/ui/jobs/ExecutorSummary.scala
deleted file mode 100644
index c4a8996c0b9a..000000000000
--- a/core/src/main/scala/org/apache/spark/ui/jobs/ExecutorSummary.scala
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.ui.jobs
-
-import org.apache.spark.annotation.DeveloperApi
-
-/**
- * :: DeveloperApi ::
- * Class for reporting aggregated metrics for each executor in stage UI.
- */
-@DeveloperApi
-class ExecutorSummary {
-  var taskTime : Long = 0
-  var failedTasks : Int = 0
-  var succeededTasks : Int = 0
-  var inputBytes: Long = 0
-  var shuffleRead : Long = 0
-  var shuffleWrite : Long = 0
-  var memoryBytesSpilled : Long = 0
-  var diskBytesSpilled : Long = 0
-}
diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/ExecutorTable.scala b/core/src/main/scala/org/apache/spark/ui/jobs/ExecutorTable.scala
index 52020954ea57..0cc51c873727 100644
--- a/core/src/main/scala/org/apache/spark/ui/jobs/ExecutorTable.scala
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/ExecutorTable.scala
@@ -21,6 +21,7 @@ import scala.collection.mutable
 import scala.xml.Node
 
 import org.apache.spark.ui.{ToolTips, UIUtils}
+import org.apache.spark.ui.jobs.UIData.StageUIData
 import org.apache.spark.util.Utils
 
 /** Page showing executor summary */
@@ -64,11 +65,9 @@ private[ui] class ExecutorTable(stageId: Int, parent: JobProgressTab) {
       executorIdToAddress.put(executorId, address)
     }
 
-    val executorIdToSummary = listener.stageIdToExecutorSummaries.get(stageId)
-    executorIdToSummary match {
-      case Some(x) =>
-        x.toSeq.sortBy(_._1).map { case (k, v) => {
-          // scalastyle:off
+    listener.stageIdToData.get(stageId) match {
+      case Some(stageData: StageUIData) =>
+        stageData.executorSummary.toSeq.sortBy(_._1).map { case (k, v) =>
           <tr>
             <td>{k}</td>
             <td>{executorIdToAddress.getOrElse(k, "CANNOT FIND ADDRESS")}</td>
@@ -76,16 +75,20 @@ private[ui] class ExecutorTable(stageId: Int, parent: JobProgressTab) {
             <td>{v.failedTasks + v.succeededTasks}</td>
             <td>{v.failedTasks}</td>
             <td>{v.succeededTasks}</td>
-            <td sorttable_customekey={v.inputBytes.toString}>{Utils.bytesToString(v.inputBytes)}</td>
-            <td sorttable_customekey={v.shuffleRead.toString}>{Utils.bytesToString(v.shuffleRead)}</td>
-            <td sorttable_customekey={v.shuffleWrite.toString}>{Utils.bytesToString(v.shuffleWrite)}</td>
-            <td sorttable_customekey={v.memoryBytesSpilled.toString} >{Utils.bytesToString(v.memoryBytesSpilled)}</td>
-            <td sorttable_customekey={v.diskBytesSpilled.toString} >{Utils.bytesToString(v.diskBytesSpilled)}</td>
+            <td sorttable_customekey={v.inputBytes.toString}>
+              {Utils.bytesToString(v.inputBytes)}</td>
+            <td sorttable_customekey={v.shuffleRead.toString}>
+              {Utils.bytesToString(v.shuffleRead)}</td>
+            <td sorttable_customekey={v.shuffleWrite.toString}>
+              {Utils.bytesToString(v.shuffleWrite)}</td>
+            <td sorttable_customekey={v.memoryBytesSpilled.toString}>
+              {Utils.bytesToString(v.memoryBytesSpilled)}</td>
+            <td sorttable_customekey={v.diskBytesSpilled.toString}>
+              {Utils.bytesToString(v.diskBytesSpilled)}</td>
           </tr>
-          // scalastyle:on
         }
-      }
-      case _ => Seq[Node]()
+      case None =>
+        Seq.empty[Node]
     }
   }
 }
diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/JobProgressListener.scala b/core/src/main/scala/org/apache/spark/ui/jobs/JobProgressListener.scala
index 2286a7f952f2..efb527b4f03e 100644
--- a/core/src/main/scala/org/apache/spark/ui/jobs/JobProgressListener.scala
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/JobProgressListener.scala
@@ -25,6 +25,7 @@ import org.apache.spark.executor.TaskMetrics
 import org.apache.spark.scheduler._
 import org.apache.spark.scheduler.SchedulingMode.SchedulingMode
 import org.apache.spark.storage.BlockManagerId
+import org.apache.spark.ui.jobs.UIData._
 
 /**
  * :: DeveloperApi ::
@@ -35,7 +36,7 @@ import org.apache.spark.storage.BlockManagerId
  * updating the internal data structures concurrently.
  */
 @DeveloperApi
-class JobProgressListener(conf: SparkConf) extends SparkListener {
+class JobProgressListener(conf: SparkConf) extends SparkListener with Logging {
 
   import JobProgressListener._
 
@@ -46,20 +47,8 @@ class JobProgressListener(conf: SparkConf) extends SparkListener {
   val completedStages = ListBuffer[StageInfo]()
   val failedStages = ListBuffer[StageInfo]()
 
-  // TODO: Should probably consolidate all following into a single hash map.
-  val stageIdToTime = HashMap[Int, Long]()
-  val stageIdToInputBytes = HashMap[Int, Long]()
-  val stageIdToShuffleRead = HashMap[Int, Long]()
-  val stageIdToShuffleWrite = HashMap[Int, Long]()
-  val stageIdToMemoryBytesSpilled = HashMap[Int, Long]()
-  val stageIdToDiskBytesSpilled = HashMap[Int, Long]()
-  val stageIdToTasksActive = HashMap[Int, HashMap[Long, TaskInfo]]()
-  val stageIdToTasksComplete = HashMap[Int, Int]()
-  val stageIdToTasksFailed = HashMap[Int, Int]()
-  val stageIdToTaskData = HashMap[Int, HashMap[Long, TaskUIData]]()
-  val stageIdToExecutorSummaries = HashMap[Int, HashMap[String, ExecutorSummary]]()
-  val stageIdToPool = HashMap[Int, String]()
-  val stageIdToDescription = HashMap[Int, String]()
+  val stageIdToData = new HashMap[Int, StageUIData]
+
   val poolToActiveStages = HashMap[String, HashMap[Int, StageInfo]]()
 
   val executorIdToBlockManagerId = HashMap[String, BlockManagerId]()
@@ -71,8 +60,12 @@ class JobProgressListener(conf: SparkConf) extends SparkListener {
   override def onStageCompleted(stageCompleted: SparkListenerStageCompleted) = synchronized {
     val stage = stageCompleted.stageInfo
     val stageId = stage.stageId
-    // Remove by stageId, rather than by StageInfo, in case the StageInfo is from storage
-    poolToActiveStages(stageIdToPool(stageId)).remove(stageId)
+    val stageData = stageIdToData.getOrElseUpdate(stageId, {
+      logWarning("Stage completed for unknown stage " + stageId)
+      new StageUIData
+    })
+
+    poolToActiveStages.get(stageData.schedulingPool).foreach(_.remove(stageId))
     activeStages.remove(stageId)
     if (stage.failureReason.isEmpty) {
       completedStages += stage
@@ -87,21 +80,7 @@ class JobProgressListener(conf: SparkConf) extends SparkListener {
   private def trimIfNecessary(stages: ListBuffer[StageInfo]) = synchronized {
     if (stages.size > retainedStages) {
       val toRemove = math.max(retainedStages / 10, 1)
-      stages.take(toRemove).foreach { s =>
-        stageIdToTime.remove(s.stageId)
-        stageIdToInputBytes.remove(s.stageId)
-        stageIdToShuffleRead.remove(s.stageId)
-        stageIdToShuffleWrite.remove(s.stageId)
-        stageIdToMemoryBytesSpilled.remove(s.stageId)
-        stageIdToDiskBytesSpilled.remove(s.stageId)
-        stageIdToTasksActive.remove(s.stageId)
-        stageIdToTasksComplete.remove(s.stageId)
-        stageIdToTasksFailed.remove(s.stageId)
-        stageIdToTaskData.remove(s.stageId)
-        stageIdToExecutorSummaries.remove(s.stageId)
-        stageIdToPool.remove(s.stageId)
-        stageIdToDescription.remove(s.stageId)
-      }
+      stages.take(toRemove).foreach { s => stageIdToData.remove(s.stageId) }
       stages.trimStart(toRemove)
     }
   }
@@ -114,26 +93,27 @@ class JobProgressListener(conf: SparkConf) extends SparkListener {
     val poolName = Option(stageSubmitted.properties).map {
       p => p.getProperty("spark.scheduler.pool", DEFAULT_POOL_NAME)
     }.getOrElse(DEFAULT_POOL_NAME)
-    stageIdToPool(stage.stageId) = poolName
 
-    val description = Option(stageSubmitted.properties).flatMap {
+    val stageData = stageIdToData.getOrElseUpdate(stage.stageId, new StageUIData)
+    stageData.schedulingPool = poolName
+
+    stageData.description = Option(stageSubmitted.properties).flatMap {
       p => Option(p.getProperty(SparkContext.SPARK_JOB_DESCRIPTION))
     }
-    description.map(d => stageIdToDescription(stage.stageId) = d)
 
     val stages = poolToActiveStages.getOrElseUpdate(poolName, new HashMap[Int, StageInfo]())
     stages(stage.stageId) = stage
   }
 
   override def onTaskStart(taskStart: SparkListenerTaskStart) = synchronized {
-    val sid = taskStart.stageId
     val taskInfo = taskStart.taskInfo
     if (taskInfo != null) {
-      val tasksActive = stageIdToTasksActive.getOrElseUpdate(sid, new HashMap[Long, TaskInfo]())
-      tasksActive(taskInfo.taskId) = taskInfo
-      val taskMap = stageIdToTaskData.getOrElse(sid, HashMap[Long, TaskUIData]())
-      taskMap(taskInfo.taskId) = new TaskUIData(taskInfo)
-      stageIdToTaskData(sid) = taskMap
+      val stageData = stageIdToData.getOrElseUpdate(taskStart.stageId, {
+        logWarning("Task start for unknown stage " + taskStart.stageId)
+        new StageUIData
+      })
+      stageData.numActiveTasks += 1
+      stageData.taskData.put(taskInfo.taskId, new TaskUIData(taskInfo))
     }
   }
 
@@ -143,88 +123,76 @@ class JobProgressListener(conf: SparkConf) extends SparkListener {
   }
 
   override def onTaskEnd(taskEnd: SparkListenerTaskEnd) = synchronized {
-    val sid = taskEnd.stageId
     val info = taskEnd.taskInfo
-
     if (info != null) {
+      val stageData = stageIdToData.getOrElseUpdate(taskEnd.stageId, {
+        logWarning("Task end for unknown stage " + taskEnd.stageId)
+        new StageUIData
+      })
+
       // create executor summary map if necessary
-      val executorSummaryMap = stageIdToExecutorSummaries.getOrElseUpdate(key = sid,
-        op = new HashMap[String, ExecutorSummary]())
+      val executorSummaryMap = stageData.executorSummary
       executorSummaryMap.getOrElseUpdate(key = info.executorId, op = new ExecutorSummary)
 
-      val executorSummary = executorSummaryMap.get(info.executorId)
-      executorSummary match {
-        case Some(y) => {
-          // first update failed-task, succeed-task
-          taskEnd.reason match {
-            case Success =>
-              y.succeededTasks += 1
-            case _ =>
-              y.failedTasks += 1
-          }
-
-          // update duration
-          y.taskTime += info.duration
-
-          val metrics = taskEnd.taskMetrics
-          if (metrics != null) {
-            metrics.inputMetrics.foreach { y.inputBytes += _.bytesRead }
-            metrics.shuffleReadMetrics.foreach { y.shuffleRead += _.remoteBytesRead }
-            metrics.shuffleWriteMetrics.foreach { y.shuffleWrite += _.shuffleBytesWritten }
-            y.memoryBytesSpilled += metrics.memoryBytesSpilled
-            y.diskBytesSpilled += metrics.diskBytesSpilled
-          }
+      executorSummaryMap.get(info.executorId).foreach { y =>
+        // first update failed-task, succeed-task
+        taskEnd.reason match {
+          case Success =>
+            y.succeededTasks += 1
+          case _ =>
+            y.failedTasks += 1
+        }
+
+        // update duration
+        y.taskTime += info.duration
+
+        val metrics = taskEnd.taskMetrics
+        if (metrics != null) {
+          metrics.inputMetrics.foreach { y.inputBytes += _.bytesRead }
+          metrics.shuffleReadMetrics.foreach { y.shuffleRead += _.remoteBytesRead }
+          metrics.shuffleWriteMetrics.foreach { y.shuffleWrite += _.shuffleBytesWritten }
+          y.memoryBytesSpilled += metrics.memoryBytesSpilled
+          y.diskBytesSpilled += metrics.diskBytesSpilled
         }
-        case _ => {}
       }
 
-      val tasksActive = stageIdToTasksActive.getOrElseUpdate(sid, new HashMap[Long, TaskInfo]())
-      // Remove by taskId, rather than by TaskInfo, in case the TaskInfo is from storage
-      tasksActive.remove(info.taskId)
+      stageData.numActiveTasks -= 1
 
       val (errorMessage, metrics): (Option[String], Option[TaskMetrics]) =
         taskEnd.reason match {
           case org.apache.spark.Success =>
-            stageIdToTasksComplete(sid) = stageIdToTasksComplete.getOrElse(sid, 0) + 1
+            stageData.numCompleteTasks += 1
             (None, Option(taskEnd.taskMetrics))
           case e: ExceptionFailure =>  // Handle ExceptionFailure because we might have metrics
-            stageIdToTasksFailed(sid) = stageIdToTasksFailed.getOrElse(sid, 0) + 1
+            stageData.numFailedTasks += 1
             (Some(e.toErrorString), e.metrics)
           case e: TaskFailedReason =>  // All other failure cases
-            stageIdToTasksFailed(sid) = stageIdToTasksFailed.getOrElse(sid, 0) + 1
+            stageData.numFailedTasks += 1
             (Some(e.toErrorString), None)
         }
 
-      stageIdToTime.getOrElseUpdate(sid, 0L)
-      val time = metrics.map(_.executorRunTime).getOrElse(0L)
-      stageIdToTime(sid) += time
 
-      stageIdToInputBytes.getOrElseUpdate(sid, 0L)
+      val taskRunTime = metrics.map(_.executorRunTime).getOrElse(0L)
+      stageData.executorRunTime += taskRunTime
       val inputBytes = metrics.flatMap(_.inputMetrics).map(_.bytesRead).getOrElse(0L)
-      stageIdToInputBytes(sid) += inputBytes
+      stageData.inputBytes += inputBytes
 
-      stageIdToShuffleRead.getOrElseUpdate(sid, 0L)
       val shuffleRead = metrics.flatMap(_.shuffleReadMetrics).map(_.remoteBytesRead).getOrElse(0L)
-      stageIdToShuffleRead(sid) += shuffleRead
+      stageData.shuffleReadBytes += shuffleRead
 
-      stageIdToShuffleWrite.getOrElseUpdate(sid, 0L)
       val shuffleWrite =
         metrics.flatMap(_.shuffleWriteMetrics).map(_.shuffleBytesWritten).getOrElse(0L)
-      stageIdToShuffleWrite(sid) += shuffleWrite
+      stageData.shuffleWriteBytes += shuffleWrite
 
-      stageIdToMemoryBytesSpilled.getOrElseUpdate(sid, 0L)
       val memoryBytesSpilled = metrics.map(_.memoryBytesSpilled).getOrElse(0L)
-      stageIdToMemoryBytesSpilled(sid) += memoryBytesSpilled
+      stageData.memoryBytesSpilled += memoryBytesSpilled
 
-      stageIdToDiskBytesSpilled.getOrElseUpdate(sid, 0L)
       val diskBytesSpilled = metrics.map(_.diskBytesSpilled).getOrElse(0L)
-      stageIdToDiskBytesSpilled(sid) += diskBytesSpilled
+      stageData.diskBytesSpilled += diskBytesSpilled
 
-      val taskMap = stageIdToTaskData.getOrElse(sid, HashMap[Long, TaskUIData]())
-      taskMap(info.taskId) = new TaskUIData(info, metrics, errorMessage)
-      stageIdToTaskData(sid) = taskMap
+      stageData.taskData(info.taskId) = new TaskUIData(info, metrics, errorMessage)
     }
-  }
+  }  // end of onTaskEnd
 
   override def onEnvironmentUpdate(environmentUpdate: SparkListenerEnvironmentUpdate) {
     synchronized {
@@ -252,12 +220,6 @@ class JobProgressListener(conf: SparkConf) extends SparkListener {
 
 }
 
-@DeveloperApi
-case class TaskUIData(
-    taskInfo: TaskInfo,
-    taskMetrics: Option[TaskMetrics] = None,
-    errorMessage: Option[String] = None)
-
 private object JobProgressListener {
   val DEFAULT_POOL_NAME = "default"
   val DEFAULT_RETAINED_STAGES = 1000
diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala b/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
index 8c3821bd7c3e..cab26b9e2f7d 100644
--- a/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
@@ -23,6 +23,7 @@ import javax.servlet.http.HttpServletRequest
 import scala.xml.Node
 
 import org.apache.spark.ui.{ToolTips, WebUIPage, UIUtils}
+import org.apache.spark.ui.jobs.UIData._
 import org.apache.spark.util.{Utils, Distribution}
 
 /** Page showing statistics and task list for a given stage */
@@ -34,8 +35,9 @@ private[ui] class StagePage(parent: JobProgressTab) extends WebUIPage("stage") {
   def render(request: HttpServletRequest): Seq[Node] = {
     listener.synchronized {
       val stageId = request.getParameter("id").toInt
+      val stageDataOption = listener.stageIdToData.get(stageId)
 
-      if (!listener.stageIdToTaskData.contains(stageId)) {
+      if (stageDataOption.isEmpty || stageDataOption.get.taskData.isEmpty) {
         val content =
           <div>
             <h4>Summary Metrics</h4> No tasks have started yet
@@ -45,23 +47,14 @@ private[ui] class StagePage(parent: JobProgressTab) extends WebUIPage("stage") {
           "Details for Stage %s".format(stageId), parent.headerTabs, parent)
       }
 
-      val tasks = listener.stageIdToTaskData(stageId).values.toSeq.sortBy(_.taskInfo.launchTime)
+      val stageData = stageDataOption.get
+      val tasks = stageData.taskData.values.toSeq.sortBy(_.taskInfo.launchTime)
 
       val numCompleted = tasks.count(_.taskInfo.finished)
-      val inputBytes = listener.stageIdToInputBytes.getOrElse(stageId, 0L)
-      val hasInput = inputBytes > 0
-      val shuffleReadBytes = listener.stageIdToShuffleRead.getOrElse(stageId, 0L)
-      val hasShuffleRead = shuffleReadBytes > 0
-      val shuffleWriteBytes = listener.stageIdToShuffleWrite.getOrElse(stageId, 0L)
-      val hasShuffleWrite = shuffleWriteBytes > 0
-      val memoryBytesSpilled = listener.stageIdToMemoryBytesSpilled.getOrElse(stageId, 0L)
-      val diskBytesSpilled = listener.stageIdToDiskBytesSpilled.getOrElse(stageId, 0L)
-      val hasBytesSpilled = memoryBytesSpilled > 0 && diskBytesSpilled > 0
-
-      var activeTime = 0L
-      val now = System.currentTimeMillis
-      val tasksActive = listener.stageIdToTasksActive(stageId).values
-      tasksActive.foreach(activeTime += _.timeRunning(now))
+      val hasInput = stageData.inputBytes > 0
+      val hasShuffleRead = stageData.shuffleReadBytes > 0
+      val hasShuffleWrite = stageData.shuffleWriteBytes > 0
+      val hasBytesSpilled = stageData.memoryBytesSpilled > 0 && stageData.diskBytesSpilled > 0
 
       // scalastyle:off
       val summary =
@@ -69,34 +62,34 @@ private[ui] class StagePage(parent: JobProgressTab) extends WebUIPage("stage") {
           <ul class="unstyled">
             <li>
               <strong>Total task time across all tasks: </strong>
-              {UIUtils.formatDuration(listener.stageIdToTime.getOrElse(stageId, 0L) + activeTime)}
+              {UIUtils.formatDuration(stageData.executorRunTime)}
             </li>
             {if (hasInput)
               <li>
                 <strong>Input: </strong>
-                {Utils.bytesToString(inputBytes)}
+                {Utils.bytesToString(stageData.inputBytes)}
               </li>
             }
             {if (hasShuffleRead)
               <li>
                 <strong>Shuffle read: </strong>
-                {Utils.bytesToString(shuffleReadBytes)}
+                {Utils.bytesToString(stageData.shuffleReadBytes)}
               </li>
             }
             {if (hasShuffleWrite)
               <li>
                 <strong>Shuffle write: </strong>
-                {Utils.bytesToString(shuffleWriteBytes)}
+                {Utils.bytesToString(stageData.shuffleWriteBytes)}
               </li>
             }
             {if (hasBytesSpilled)
             <li>
               <strong>Shuffle spill (memory): </strong>
-              {Utils.bytesToString(memoryBytesSpilled)}
+              {Utils.bytesToString(stageData.memoryBytesSpilled)}
             </li>
             <li>
               <strong>Shuffle spill (disk): </strong>
-              {Utils.bytesToString(diskBytesSpilled)}
+              {Utils.bytesToString(stageData.diskBytesSpilled)}
             </li>
             }
           </ul>
diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/StageTable.scala b/core/src/main/scala/org/apache/spark/ui/jobs/StageTable.scala
index fd8d0b5cdde0..5f45c0ced5ec 100644
--- a/core/src/main/scala/org/apache/spark/ui/jobs/StageTable.scala
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/StageTable.scala
@@ -17,12 +17,11 @@
 
 package org.apache.spark.ui.jobs
 
-import java.util.Date
-
-import scala.collection.mutable.HashMap
 import scala.xml.Node
 
-import org.apache.spark.scheduler.{StageInfo, TaskInfo}
+import java.util.Date
+
+import org.apache.spark.scheduler.StageInfo
 import org.apache.spark.ui.{ToolTips, UIUtils}
 import org.apache.spark.util.Utils
 
@@ -71,14 +70,14 @@ private[ui] class StageTableBase(
     </table>
   }
 
-  private def makeProgressBar(started: Int, completed: Int, failed: String, total: Int): Seq[Node] =
+  private def makeProgressBar(started: Int, completed: Int, failed: Int, total: Int): Seq[Node] =
   {
     val completeWidth = "width: %s%%".format((completed.toDouble/total)*100)
     val startWidth = "width: %s%%".format((started.toDouble/total)*100)
 
     <div class="progress">
       <span style="text-align:center; position:absolute; width:100%; left:0;">
-        {completed}/{total} {failed}
+        {completed}/{total} { if (failed > 0) s"($failed failed)" else "" }
       </span>
       <div class="bar bar-completed" style={completeWidth}></div>
       <div class="bar bar-running" style={startWidth}></div>
@@ -108,13 +107,23 @@ private[ui] class StageTableBase(
       <pre class="stage-details collapsed">{s.details}</pre>
     }
 
-    listener.stageIdToDescription.get(s.stageId)
-      .map(d => <div><em>{d}</em></div><div>{nameLink} {killLink}</div>)
-      .getOrElse(<div>{nameLink} {killLink} {details}</div>)
+    val stageDataOption = listener.stageIdToData.get(s.stageId)
+    // Too many nested map/flatMaps with options are just annoying to read. Do this imperatively.
+    if (stageDataOption.isDefined && stageDataOption.get.description.isDefined) {
+      val desc = stageDataOption.get.description
+      <div><em>{desc}</em></div><div>{nameLink} {killLink}</div>
+    } else {
+      <div>{killLink} {nameLink} {details}</div>
+    }
   }
 
   protected def stageRow(s: StageInfo): Seq[Node] = {
-    val poolName = listener.stageIdToPool.get(s.stageId)
+    val stageDataOption = listener.stageIdToData.get(s.stageId)
+    if (stageDataOption.isEmpty) {
+      return <td>{s.stageId}</td><td>No data available for this stage</td>
+    }
+
+    val stageData = stageDataOption.get
     val submissionTime = s.submissionTime match {
       case Some(t) => UIUtils.formatDate(new Date(t))
       case None => "Unknown"
@@ -124,35 +133,20 @@ private[ui] class StageTableBase(
       if (finishTime > t) finishTime - t else System.currentTimeMillis - t
     }
     val formattedDuration = duration.map(d => UIUtils.formatDuration(d)).getOrElse("Unknown")
-    val startedTasks =
-      listener.stageIdToTasksActive.getOrElse(s.stageId, HashMap[Long, TaskInfo]()).size
-    val completedTasks = listener.stageIdToTasksComplete.getOrElse(s.stageId, 0)
-    val failedTasks = listener.stageIdToTasksFailed.getOrElse(s.stageId, 0) match {
-      case f if f > 0 => "(%s failed)".format(f)
-      case _ => ""
-    }
-    val totalTasks = s.numTasks
-    val inputSortable = listener.stageIdToInputBytes.getOrElse(s.stageId, 0L)
-    val inputRead = inputSortable match {
-      case 0 => ""
-      case b => Utils.bytesToString(b)
-    }
-    val shuffleReadSortable = listener.stageIdToShuffleRead.getOrElse(s.stageId, 0L)
-    val shuffleRead = shuffleReadSortable match {
-      case 0 => ""
-      case b => Utils.bytesToString(b)
-    }
-    val shuffleWriteSortable = listener.stageIdToShuffleWrite.getOrElse(s.stageId, 0L)
-    val shuffleWrite = shuffleWriteSortable match {
-      case 0 => ""
-      case b => Utils.bytesToString(b)
-    }
+
+    val inputRead = stageData.inputBytes
+    val inputReadWithUnit = if (inputRead > 0) Utils.bytesToString(inputRead) else ""
+    val shuffleRead = stageData.shuffleReadBytes
+    val shuffleReadWithUnit = if (shuffleRead > 0) Utils.bytesToString(shuffleRead) else ""
+    val shuffleWrite = stageData.shuffleWriteBytes
+    val shuffleWriteWithUnit = if (shuffleWrite > 0) Utils.bytesToString(shuffleWrite) else ""
+
     <td>{s.stageId}</td> ++
     {if (isFairScheduler) {
       <td>
         <a href={"%s/stages/pool?poolname=%s"
-          .format(UIUtils.prependBaseUri(basePath), poolName.get)}>
-          {poolName.get}
+          .format(UIUtils.prependBaseUri(basePath), stageData.schedulingPool)}>
+          {stageData.schedulingPool}
         </a>
       </td>
     } else {
@@ -162,11 +156,12 @@ private[ui] class StageTableBase(
     <td valign="middle">{submissionTime}</td>
     <td sorttable_customkey={duration.getOrElse(-1).toString}>{formattedDuration}</td>
     <td class="progress-cell">
-      {makeProgressBar(startedTasks, completedTasks, failedTasks, totalTasks)}
+      {makeProgressBar(stageData.numActiveTasks, stageData.numCompleteTasks,
+        stageData.numFailedTasks, s.numTasks)}
     </td>
-    <td sorttable_customekey={inputSortable.toString}>{inputRead}</td>
-    <td sorttable_customekey={shuffleReadSortable.toString}>{shuffleRead}</td>
-    <td sorttable_customekey={shuffleWriteSortable.toString}>{shuffleWrite}</td>
+    <td sorttable_customekey={inputRead.toString}>{inputReadWithUnit}</td>
+    <td sorttable_customekey={shuffleRead.toString}>{shuffleReadWithUnit}</td>
+    <td sorttable_customekey={shuffleWrite.toString}>{shuffleWriteWithUnit}</td>
   }
 
   /** Render an HTML row that represents a stage */
diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/UIData.scala b/core/src/main/scala/org/apache/spark/ui/jobs/UIData.scala
new file mode 100644
index 000000000000..be11a11695b0
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/UIData.scala
@@ -0,0 +1,62 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ui.jobs
+
+import org.apache.spark.executor.TaskMetrics
+import org.apache.spark.scheduler.TaskInfo
+
+import scala.collection.mutable.HashMap
+
+private[jobs] object UIData {
+
+  class ExecutorSummary {
+    var taskTime : Long = 0
+    var failedTasks : Int = 0
+    var succeededTasks : Int = 0
+    var inputBytes : Long = 0
+    var shuffleRead : Long = 0
+    var shuffleWrite : Long = 0
+    var memoryBytesSpilled : Long = 0
+    var diskBytesSpilled : Long = 0
+  }
+
+  class StageUIData {
+    var numActiveTasks: Int = _
+    var numCompleteTasks: Int = _
+    var numFailedTasks: Int = _
+
+    var executorRunTime: Long = _
+
+    var inputBytes: Long = _
+    var shuffleReadBytes: Long = _
+    var shuffleWriteBytes: Long = _
+    var memoryBytesSpilled: Long = _
+    var diskBytesSpilled: Long = _
+
+    var schedulingPool: String = ""
+    var description: Option[String] = None
+
+    var taskData = new HashMap[Long, TaskUIData]
+    var executorSummary = new HashMap[String, ExecutorSummary]
+  }
+
+  case class TaskUIData(
+      taskInfo: TaskInfo,
+      taskMetrics: Option[TaskMetrics] = None,
+      errorMessage: Option[String] = None)
+}
diff --git a/core/src/test/scala/org/apache/spark/ui/jobs/JobProgressListenerSuite.scala b/core/src/test/scala/org/apache/spark/ui/jobs/JobProgressListenerSuite.scala
index fa43b66c6cb5..a8556624804b 100644
--- a/core/src/test/scala/org/apache/spark/ui/jobs/JobProgressListenerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/ui/jobs/JobProgressListenerSuite.scala
@@ -47,11 +47,11 @@ class JobProgressListenerSuite extends FunSuite with LocalSparkContext with Matc
     }
 
     listener.completedStages.size should be (5)
-    listener.completedStages.filter(_.stageId == 50).size should be (1)
-    listener.completedStages.filter(_.stageId == 49).size should be (1)
-    listener.completedStages.filter(_.stageId == 48).size should be (1)
-    listener.completedStages.filter(_.stageId == 47).size should be (1)
-    listener.completedStages.filter(_.stageId == 46).size should be (1)
+    listener.completedStages.count(_.stageId == 50) should be (1)
+    listener.completedStages.count(_.stageId == 49) should be (1)
+    listener.completedStages.count(_.stageId == 48) should be (1)
+    listener.completedStages.count(_.stageId == 47) should be (1)
+    listener.completedStages.count(_.stageId == 46) should be (1)
   }
 
   test("test executor id to summary") {
@@ -59,9 +59,7 @@ class JobProgressListenerSuite extends FunSuite with LocalSparkContext with Matc
     val listener = new JobProgressListener(conf)
     val taskMetrics = new TaskMetrics()
     val shuffleReadMetrics = new ShuffleReadMetrics()
-
-    // nothing in it
-    assert(listener.stageIdToExecutorSummaries.size == 0)
+    assert(listener.stageIdToData.size === 0)
 
     // finish this task, should get updated shuffleRead
     shuffleReadMetrics.remoteBytesRead = 1000
@@ -71,8 +69,8 @@ class JobProgressListenerSuite extends FunSuite with LocalSparkContext with Matc
     var task = new ShuffleMapTask(0, null, null, 0, null)
     val taskType = Utils.getFormattedClassName(task)
     listener.onTaskEnd(SparkListenerTaskEnd(task.stageId, taskType, Success, taskInfo, taskMetrics))
-    assert(listener.stageIdToExecutorSummaries.getOrElse(0, fail()).getOrElse("exe-1", fail())
-      .shuffleRead == 1000)
+    assert(listener.stageIdToData.getOrElse(0, fail()).executorSummary.getOrElse("exe-1", fail())
+      .shuffleRead === 1000)
 
     // finish a task with unknown executor-id, nothing should happen
     taskInfo =
@@ -80,7 +78,7 @@ class JobProgressListenerSuite extends FunSuite with LocalSparkContext with Matc
     taskInfo.finishTime = 1
     task = new ShuffleMapTask(0, null, null, 0, null)
     listener.onTaskEnd(SparkListenerTaskEnd(task.stageId, taskType, Success, taskInfo, taskMetrics))
-    assert(listener.stageIdToExecutorSummaries.size == 1)
+    assert(listener.stageIdToData.size === 1)
 
     // finish this task, should get updated duration
     shuffleReadMetrics.remoteBytesRead = 1000
@@ -89,8 +87,8 @@ class JobProgressListenerSuite extends FunSuite with LocalSparkContext with Matc
     taskInfo.finishTime = 1
     task = new ShuffleMapTask(0, null, null, 0, null)
     listener.onTaskEnd(SparkListenerTaskEnd(task.stageId, taskType, Success, taskInfo, taskMetrics))
-    assert(listener.stageIdToExecutorSummaries.getOrElse(0, fail()).getOrElse("exe-1", fail())
-      .shuffleRead == 2000)
+    assert(listener.stageIdToData.getOrElse(0, fail()).executorSummary.getOrElse("exe-1", fail())
+      .shuffleRead === 2000)
 
     // finish this task, should get updated duration
     shuffleReadMetrics.remoteBytesRead = 1000
@@ -99,8 +97,8 @@ class JobProgressListenerSuite extends FunSuite with LocalSparkContext with Matc
     taskInfo.finishTime = 1
     task = new ShuffleMapTask(0, null, null, 0, null)
     listener.onTaskEnd(SparkListenerTaskEnd(task.stageId, taskType, Success, taskInfo, taskMetrics))
-    assert(listener.stageIdToExecutorSummaries.getOrElse(0, fail()).getOrElse("exe-2", fail())
-      .shuffleRead == 1000)
+    assert(listener.stageIdToData.getOrElse(0, fail()).executorSummary.getOrElse("exe-2", fail())
+      .shuffleRead === 1000)
   }
 
   test("test task success vs failure counting for different task end reasons") {
@@ -121,13 +119,17 @@ class JobProgressListenerSuite extends FunSuite with LocalSparkContext with Matc
       TaskKilled,
       ExecutorLostFailure,
       UnknownReason)
+    var failCount = 0
     for (reason <- taskFailedReasons) {
       listener.onTaskEnd(SparkListenerTaskEnd(task.stageId, taskType, reason, taskInfo, metrics))
-      assert(listener.stageIdToTasksComplete.get(task.stageId) === None)
+      failCount += 1
+      assert(listener.stageIdToData(task.stageId).numCompleteTasks === 0)
+      assert(listener.stageIdToData(task.stageId).numFailedTasks === failCount)
     }
 
     // Make sure we count success as success.
     listener.onTaskEnd(SparkListenerTaskEnd(task.stageId, taskType, Success, taskInfo, metrics))
-    assert(listener.stageIdToTasksComplete.get(task.stageId) === Some(1))
+    assert(listener.stageIdToData(task.stageId).numCompleteTasks === 1)
+    assert(listener.stageIdToData(task.stageId).numFailedTasks === failCount)
   }
 }

From 6afca2d1079bac6309a595b8e0ffc74ae93fa662 Mon Sep 17 00:00:00 2001
From: Andrew Or <andrewor14@gmail.com>
Date: Thu, 17 Jul 2014 19:45:59 -0700
Subject: [PATCH 686/810] [SPARK-2411] Add a history-not-found page to
 standalone Master

**Problem.** Right now, if you click on an application after it has finished, it simply refreshes the page if there are no event logs for the application. This is not super intuitive especially because event logging is not enabled by default. We should direct the user to enable this if they attempt to view a SparkUI after the fact without event logs.

**Fix.** The new page conveys different messages in each of the following scenarios:
(1) Application did not enable event logging,
(2) Event logs are not found in the specified directory, and
(3) Exception is thrown while replaying the logs

Here are screenshots of what the page looks like in each of the above scenarios:

(1)
<img src="https://issues.apache.org/jira/secure/attachment/12656204/Event%20logging%20not%20enabled.png" width="75%">

(2)
<img src="https://issues.apache.org/jira/secure/attachment/12656203/Application%20history%20not%20found.png">

(3)
<img src="https://issues.apache.org/jira/secure/attachment/12656202/Application%20history%20load%20error.png" width="95%">

Author: Andrew Or <andrewor14@gmail.com>

Closes #1336 from andrewor14/master-link and squashes the following commits:

2f06206 [Andrew Or] Merge branch 'master' of github.com:apache/spark into master-link
97cddc0 [Andrew Or] Add different severity levels
832b687 [Andrew Or] Mention spark.eventLog.dir in error message
51980c3 [Andrew Or] Merge branch 'master' of github.com:apache/spark into master-link
ded208c [Andrew Or] Merge branch 'master' of github.com:apache/spark into master-link
89d6405 [Andrew Or] Reword message
e7df7ed [Andrew Or] Add a history not found page to standalone Master
---
 .../org/apache/spark/TaskEndReason.scala      |  7 +-
 .../apache/spark/deploy/master/Master.scala   | 61 ++++++++++------
 .../deploy/master/ui/ApplicationPage.scala    |  2 +-
 .../master/ui/HistoryNotFoundPage.scala       | 73 +++++++++++++++++++
 .../spark/deploy/master/ui/MasterWebUI.scala  |  3 +-
 .../scala/org/apache/spark/util/Utils.scala   | 17 ++++-
 6 files changed, 132 insertions(+), 31 deletions(-)
 create mode 100644 core/src/main/scala/org/apache/spark/deploy/master/ui/HistoryNotFoundPage.scala

diff --git a/core/src/main/scala/org/apache/spark/TaskEndReason.scala b/core/src/main/scala/org/apache/spark/TaskEndReason.scala
index 8d5c45627f09..8f0c5e78416c 100644
--- a/core/src/main/scala/org/apache/spark/TaskEndReason.scala
+++ b/core/src/main/scala/org/apache/spark/TaskEndReason.scala
@@ -20,6 +20,7 @@ package org.apache.spark
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.executor.TaskMetrics
 import org.apache.spark.storage.BlockManagerId
+import org.apache.spark.util.Utils
 
 /**
  * :: DeveloperApi ::
@@ -88,11 +89,7 @@ case class ExceptionFailure(
     stackTrace: Array[StackTraceElement],
     metrics: Option[TaskMetrics])
   extends TaskFailedReason {
-  override def toErrorString: String = {
-    val stackTraceString =
-      if (stackTrace == null) "null" else stackTrace.map("        " + _).mkString("\n")
-    s"$className ($description)\n$stackTraceString"
-  }
+  override def toErrorString: String = Utils.exceptionString(className, description, stackTrace)
 }
 
 /**
diff --git a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
index 0b2d9db70742..bb1fcc8190fe 100644
--- a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.deploy.master
 
+import java.net.URLEncoder
 import java.text.SimpleDateFormat
 import java.util.Date
 
@@ -30,7 +31,6 @@ import akka.actor._
 import akka.pattern.ask
 import akka.remote.{DisassociatedEvent, RemotingLifecycleEvent}
 import akka.serialization.SerializationExtension
-import org.apache.hadoop.fs.FileSystem
 
 import org.apache.spark.{Logging, SecurityManager, SparkConf, SparkException}
 import org.apache.spark.deploy.{ApplicationDescription, DriverDescription, ExecutorState}
@@ -642,10 +642,7 @@ private[spark] class Master(
       waitingApps -= app
 
       // If application events are logged, use them to rebuild the UI
-      if (!rebuildSparkUI(app)) {
-        // Avoid broken links if the UI is not reconstructed
-        app.desc.appUiUrl = ""
-      }
+      rebuildSparkUI(app)
 
       for (exec <- app.executors.values) {
         exec.worker.removeExecutor(exec)
@@ -667,29 +664,47 @@ private[spark] class Master(
    */
   def rebuildSparkUI(app: ApplicationInfo): Boolean = {
     val appName = app.desc.name
-    val eventLogDir = app.desc.eventLogDir.getOrElse { return false }
+    val eventLogDir = app.desc.eventLogDir.getOrElse {
+      // Event logging is not enabled for this application
+      app.desc.appUiUrl = "/history/not-found"
+      return false
+    }
     val fileSystem = Utils.getHadoopFileSystem(eventLogDir)
     val eventLogInfo = EventLoggingListener.parseLoggingInfo(eventLogDir, fileSystem)
     val eventLogPaths = eventLogInfo.logPaths
     val compressionCodec = eventLogInfo.compressionCodec
-    if (!eventLogPaths.isEmpty) {
-      try {
-        val replayBus = new ReplayListenerBus(eventLogPaths, fileSystem, compressionCodec)
-        val ui = new SparkUI(
-          new SparkConf, replayBus, appName + " (completed)", "/history/" + app.id)
-        replayBus.replay()
-        app.desc.appUiUrl = ui.basePath
-        appIdToUI(app.id) = ui
-        webUi.attachSparkUI(ui)
-        return true
-      } catch {
-        case e: Exception =>
-          logError("Exception in replaying log for application %s (%s)".format(appName, app.id), e)
-      }
-    } else {
-      logWarning("Application %s (%s) has no valid logs: %s".format(appName, app.id, eventLogDir))
+
+    if (eventLogPaths.isEmpty) {
+      // Event logging is enabled for this application, but no event logs are found
+      val title = s"Application history not found (${app.id})"
+      var msg = s"No event logs found for application $appName in $eventLogDir."
+      logWarning(msg)
+      msg += " Did you specify the correct logging directory?"
+      msg = URLEncoder.encode(msg, "UTF-8")
+      app.desc.appUiUrl = s"/history/not-found?msg=$msg&title=$title"
+      return false
+    }
+
+    try {
+      val replayBus = new ReplayListenerBus(eventLogPaths, fileSystem, compressionCodec)
+      val ui = new SparkUI(new SparkConf, replayBus, appName + " (completed)", "/history/" + app.id)
+      replayBus.replay()
+      appIdToUI(app.id) = ui
+      webUi.attachSparkUI(ui)
+      // Application UI is successfully rebuilt, so link the Master UI to it
+      app.desc.appUiUrl = ui.basePath
+      true
+    } catch {
+      case e: Exception =>
+        // Relay exception message to application UI page
+        val title = s"Application history load error (${app.id})"
+        val exception = URLEncoder.encode(Utils.exceptionString(e), "UTF-8")
+        var msg = s"Exception in replaying log for application $appName!"
+        logError(msg, e)
+        msg = URLEncoder.encode(msg, "UTF-8")
+        app.desc.appUiUrl = s"/history/not-found?msg=$msg&exception=$exception&title=$title"
+        false
     }
-    false
   }
 
   /** Generate a new app ID given a app's submission date */
diff --git a/core/src/main/scala/org/apache/spark/deploy/master/ui/ApplicationPage.scala b/core/src/main/scala/org/apache/spark/deploy/master/ui/ApplicationPage.scala
index 34fa1429c86d..4588c130ef43 100644
--- a/core/src/main/scala/org/apache/spark/deploy/master/ui/ApplicationPage.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/master/ui/ApplicationPage.scala
@@ -28,7 +28,7 @@ import org.json4s.JValue
 import org.apache.spark.deploy.{ExecutorState, JsonProtocol}
 import org.apache.spark.deploy.DeployMessages.{MasterStateResponse, RequestMasterState}
 import org.apache.spark.deploy.master.ExecutorInfo
-import org.apache.spark.ui.{WebUIPage, UIUtils}
+import org.apache.spark.ui.{UIUtils, WebUIPage}
 import org.apache.spark.util.Utils
 
 private[spark] class ApplicationPage(parent: MasterWebUI) extends WebUIPage("app") {
diff --git a/core/src/main/scala/org/apache/spark/deploy/master/ui/HistoryNotFoundPage.scala b/core/src/main/scala/org/apache/spark/deploy/master/ui/HistoryNotFoundPage.scala
new file mode 100644
index 000000000000..d8daff3e7fb9
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/deploy/master/ui/HistoryNotFoundPage.scala
@@ -0,0 +1,73 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.deploy.master.ui
+
+import java.net.URLDecoder
+import javax.servlet.http.HttpServletRequest
+
+import scala.xml.Node
+
+import org.apache.spark.ui.{UIUtils, WebUIPage}
+
+private[spark] class HistoryNotFoundPage(parent: MasterWebUI)
+  extends WebUIPage("history/not-found") {
+
+  /**
+   * Render a page that conveys failure in loading application history.
+   *
+   * This accepts 3 HTTP parameters:
+   *   msg = message to display to the user
+   *   title = title of the page
+   *   exception = detailed description of the exception in loading application history (if any)
+   *
+   * Parameters "msg" and "exception" are assumed to be UTF-8 encoded.
+   */
+  def render(request: HttpServletRequest): Seq[Node] = {
+    val titleParam = request.getParameter("title")
+    val msgParam = request.getParameter("msg")
+    val exceptionParam = request.getParameter("exception")
+
+    // If no parameters are specified, assume the user did not enable event logging
+    val defaultTitle = "Event logging is not enabled"
+    val defaultContent =
+      <div class="row-fluid">
+        <div class="span12" style="font-size:14px">
+          No event logs were found for this application! To
+          <a href="http://spark.apache.org/docs/latest/monitoring.html">enable event logging</a>,
+          set <span style="font-style:italic">spark.eventLog.enabled</span> to true and
+          <span style="font-style:italic">spark.eventLog.dir</span> to the directory to which your
+          event logs are written.
+        </div>
+      </div>
+
+    val title = Option(titleParam).getOrElse(defaultTitle)
+    val content = Option(msgParam)
+      .map { msg => URLDecoder.decode(msg, "UTF-8") }
+      .map { msg =>
+        <div class="row-fluid">
+          <div class="span12" style="font-size:14px">{msg}</div>
+        </div> ++
+        Option(exceptionParam)
+          .map { e => URLDecoder.decode(e, "UTF-8") }
+          .map { e => <pre>{e}</pre> }
+          .getOrElse(Seq.empty)
+      }.getOrElse(defaultContent)
+
+    UIUtils.basicSparkPage(content, title)
+  }
+}
diff --git a/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterWebUI.scala b/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterWebUI.scala
index a18b39fc95d6..16aa0493370d 100644
--- a/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterWebUI.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterWebUI.scala
@@ -21,7 +21,7 @@ import org.apache.spark.Logging
 import org.apache.spark.deploy.master.Master
 import org.apache.spark.ui.{SparkUI, WebUI}
 import org.apache.spark.ui.JettyUtils._
-import org.apache.spark.util.{AkkaUtils, Utils}
+import org.apache.spark.util.AkkaUtils
 
 /**
  * Web UI server for the standalone master.
@@ -38,6 +38,7 @@ class MasterWebUI(val master: Master, requestedPort: Int)
   /** Initialize all components of the server. */
   def initialize() {
     attachPage(new ApplicationPage(this))
+    attachPage(new HistoryNotFoundPage(this))
     attachPage(new MasterPage(this))
     attachHandler(createStaticHandler(MasterWebUI.STATIC_RESOURCE_DIR, "/static"))
     master.masterMetricsSystem.getServletHandlers.foreach(attachHandler)
diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala
index 10c33d67e768..5784e974fbb6 100644
--- a/core/src/main/scala/org/apache/spark/util/Utils.scala
+++ b/core/src/main/scala/org/apache/spark/util/Utils.scala
@@ -44,7 +44,7 @@ import org.apache.spark.executor.ExecutorUncaughtExceptionHandler
 import org.apache.spark.serializer.{DeserializationStream, SerializationStream, SerializerInstance}
 
 /** CallSite represents a place in user code. It can have a short and a long form. */
-private[spark] case class CallSite(val short: String, val long: String)
+private[spark] case class CallSite(short: String, long: String)
 
 /**
  * Various utility methods used by Spark.
@@ -1291,4 +1291,19 @@ private[spark] object Utils extends Logging {
     }
   }
 
+  /** Return a nice string representation of the exception, including the stack trace. */
+  def exceptionString(e: Exception): String = {
+    if (e == null) "" else exceptionString(getFormattedClassName(e), e.getMessage, e.getStackTrace)
+  }
+
+  /** Return a nice string representation of the exception, including the stack trace. */
+  def exceptionString(
+      className: String,
+      description: String,
+      stackTrace: Array[StackTraceElement]): String = {
+    val desc = if (description == null) "" else description
+    val st = if (stackTrace == null) "" else stackTrace.map("        " + _).mkString("\n")
+    s"$className: $desc\n$st"
+  }
+
 }

From 29809a6d58bfe3700350ce1988ff7083881c4382 Mon Sep 17 00:00:00 2001
From: Cheng Hao <hao.cheng@intel.com>
Date: Thu, 17 Jul 2014 23:25:01 -0700
Subject: [PATCH 687/810] [SPARK-2570] [SQL] Fix the bug of ClassCastException

Exception thrown when running the example of HiveFromSpark.
Exception in thread "main" java.lang.ClassCastException: java.lang.Long cannot be cast to java.lang.Integer
	at scala.runtime.BoxesRunTime.unboxToInt(BoxesRunTime.java:106)
	at org.apache.spark.sql.catalyst.expressions.GenericRow.getInt(Row.scala:145)
	at org.apache.spark.examples.sql.hive.HiveFromSpark$.main(HiveFromSpark.scala:45)
	at org.apache.spark.examples.sql.hive.HiveFromSpark.main(HiveFromSpark.scala)
	at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
	at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
	at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
	at java.lang.reflect.Method.invoke(Method.java:606)
	at org.apache.spark.deploy.SparkSubmit$.launch(SparkSubmit.scala:303)
	at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:55)
	at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)

Author: Cheng Hao <hao.cheng@intel.com>

Closes #1475 from chenghao-intel/hive_from_spark and squashes the following commits:

d4c0500 [Cheng Hao] Fix the bug of ClassCastException
---
 .../org/apache/spark/examples/sql/hive/HiveFromSpark.scala      | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/src/main/scala/org/apache/spark/examples/sql/hive/HiveFromSpark.scala b/examples/src/main/scala/org/apache/spark/examples/sql/hive/HiveFromSpark.scala
index b262fabbe0e0..66a23fac3999 100644
--- a/examples/src/main/scala/org/apache/spark/examples/sql/hive/HiveFromSpark.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/sql/hive/HiveFromSpark.scala
@@ -42,7 +42,7 @@ object HiveFromSpark {
     hql("SELECT * FROM src").collect.foreach(println)
 
     // Aggregation queries are also supported.
-    val count = hql("SELECT COUNT(*) FROM src").collect().head.getInt(0)
+    val count = hql("SELECT COUNT(*) FROM src").collect().head.getLong(0)
     println(s"COUNT(*): $count")
 
     // The results of SQL queries are themselves RDDs and support all normal RDD functions.  The

From e52b8719cf0603e79ded51cbe1c9f88eea8b56de Mon Sep 17 00:00:00 2001
From: Sandy Ryza <sandy@cloudera.com>
Date: Thu, 17 Jul 2014 23:57:08 -0700
Subject: [PATCH 688/810] SPARK-2553. CoGroupedRDD unnecessarily allocates a
 Tuple2 per dependency...

... per key

My humble opinion is that avoiding allocations in this performance-critical section is worth the extra code.

Author: Sandy Ryza <sandy@cloudera.com>

Closes #1461 from sryza/sandy-spark-2553 and squashes the following commits:

7eaf7f2 [Sandy Ryza] SPARK-2553. CoGroupedRDD unnecessarily allocates a Tuple2 per dependency per key
---
 core/src/main/scala/org/apache/spark/rdd/CoGroupedRDD.scala | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/rdd/CoGroupedRDD.scala b/core/src/main/scala/org/apache/spark/rdd/CoGroupedRDD.scala
index 5951865e56c9..b284b636f276 100644
--- a/core/src/main/scala/org/apache/spark/rdd/CoGroupedRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/CoGroupedRDD.scala
@@ -180,7 +180,11 @@ class CoGroupedRDD[K](@transient var rdds: Seq[RDD[_ <: Product2[K, _]]], part:
     }
     val mergeCombiners: (CoGroupCombiner, CoGroupCombiner) => CoGroupCombiner =
       (combiner1, combiner2) => {
-        combiner1.zip(combiner2).map { case (v1, v2) => v1 ++ v2 }
+        var depNum = 0
+        while (depNum < numRdds) {
+          combiner1(depNum) ++= combiner2(depNum)
+          depNum += 1
+        }
       }
     new ExternalAppendOnlyMap[K, CoGroupValue, CoGroupCombiner](
       createCombiner, mergeValue, mergeCombiners)

From 30b8d369d459be5da133f12ebb33a9c74579f468 Mon Sep 17 00:00:00 2001
From: Sandy Ryza <sandy@cloudera.com>
Date: Fri, 18 Jul 2014 00:47:43 -0700
Subject: [PATCH 689/810] SPARK-2553. Fix compile error

Author: Sandy Ryza <sandy@cloudera.com>

Closes #1479 from sryza/sandy-spark-2553 and squashes the following commits:

2cb5ed8 [Sandy Ryza] SPARK-2553. Fix compile error
---
 core/src/main/scala/org/apache/spark/rdd/CoGroupedRDD.scala | 1 +
 1 file changed, 1 insertion(+)

diff --git a/core/src/main/scala/org/apache/spark/rdd/CoGroupedRDD.scala b/core/src/main/scala/org/apache/spark/rdd/CoGroupedRDD.scala
index b284b636f276..5366c1a1cc1b 100644
--- a/core/src/main/scala/org/apache/spark/rdd/CoGroupedRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/CoGroupedRDD.scala
@@ -185,6 +185,7 @@ class CoGroupedRDD[K](@transient var rdds: Seq[RDD[_ <: Product2[K, _]]], part:
           combiner1(depNum) ++= combiner2(depNum)
           depNum += 1
         }
+        combiner1
       }
     new ExternalAppendOnlyMap[K, CoGroupValue, CoGroupCombiner](
       createCombiner, mergeValue, mergeCombiners)

From 7f87ab98138d00723e007471f1a7f506650978cb Mon Sep 17 00:00:00 2001
From: Basit Mustafa <basitmustafa@computes-things-for-basit.local>
Date: Fri, 18 Jul 2014 12:23:47 -0700
Subject: [PATCH 690/810] Added t2 instance types

New t2 instance types require HVM amis, bailout assumption of pvm
causes failures when using t2 instance types.

Author: Basit Mustafa <basitmustafa@computes-things-for-basit.local>

Closes #1446 from 24601/master and squashes the following commits:

01fe128 [Basit Mustafa] Makin' it pretty
392a95e [Basit Mustafa] Added t2 instance types
---
 ec2/spark_ec2.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/ec2/spark_ec2.py b/ec2/spark_ec2.py
index 44775ea479ec..02cfe4ec39c7 100755
--- a/ec2/spark_ec2.py
+++ b/ec2/spark_ec2.py
@@ -240,7 +240,10 @@ def get_spark_ami(opts):
         "r3.xlarge":   "hvm",
         "r3.2xlarge":  "hvm",
         "r3.4xlarge":  "hvm",
-        "r3.8xlarge":  "hvm"
+        "r3.8xlarge":  "hvm",
+        "t2.micro":    "hvm",
+        "t2.small":    "hvm",
+        "t2.medium":   "hvm"
     }
     if opts.instance_type in instance_types:
         instance_type = instance_types[opts.instance_type]

From 586e716e47305cd7c2c3ff35c0e828b63ef2f6a8 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Fri, 18 Jul 2014 12:41:50 -0700
Subject: [PATCH 691/810] Reservoir sampling implementation.

This is going to be used in https://issues.apache.org/jira/browse/SPARK-2568

Author: Reynold Xin <rxin@apache.org>

Closes #1478 from rxin/reservoirSample and squashes the following commits:

17bcbf3 [Reynold Xin] Added seed.
badf20d [Reynold Xin] Renamed the method.
6940010 [Reynold Xin] Reservoir sampling implementation.
---
 .../spark/util/random/SamplingUtils.scala     | 46 +++++++++++++++++++
 .../util/random/SamplingUtilsSuite.scala      | 21 +++++++++
 2 files changed, 67 insertions(+)

diff --git a/core/src/main/scala/org/apache/spark/util/random/SamplingUtils.scala b/core/src/main/scala/org/apache/spark/util/random/SamplingUtils.scala
index a79e3ee756fc..d10141b90e62 100644
--- a/core/src/main/scala/org/apache/spark/util/random/SamplingUtils.scala
+++ b/core/src/main/scala/org/apache/spark/util/random/SamplingUtils.scala
@@ -17,8 +17,54 @@
 
 package org.apache.spark.util.random
 
+import scala.reflect.ClassTag
+import scala.util.Random
+
 private[spark] object SamplingUtils {
 
+  /**
+   * Reservoir sampling implementation that also returns the input size.
+   *
+   * @param input input size
+   * @param k reservoir size
+   * @param seed random seed
+   * @return (samples, input size)
+   */
+  def reservoirSampleAndCount[T: ClassTag](
+      input: Iterator[T],
+      k: Int,
+      seed: Long = Random.nextLong())
+    : (Array[T], Int) = {
+    val reservoir = new Array[T](k)
+    // Put the first k elements in the reservoir.
+    var i = 0
+    while (i < k && input.hasNext) {
+      val item = input.next()
+      reservoir(i) = item
+      i += 1
+    }
+
+    // If we have consumed all the elements, return them. Otherwise do the replacement.
+    if (i < k) {
+      // If input size < k, trim the array to return only an array of input size.
+      val trimReservoir = new Array[T](i)
+      System.arraycopy(reservoir, 0, trimReservoir, 0, i)
+      (trimReservoir, i)
+    } else {
+      // If input size > k, continue the sampling process.
+      val rand = new XORShiftRandom(seed)
+      while (input.hasNext) {
+        val item = input.next()
+        val replacementIndex = rand.nextInt(i)
+        if (replacementIndex < k) {
+          reservoir(replacementIndex) = item
+        }
+        i += 1
+      }
+      (reservoir, i)
+    }
+  }
+
   /**
    * Returns a sampling rate that guarantees a sample of size >= sampleSizeLowerBound 99.99% of
    * the time.
diff --git a/core/src/test/scala/org/apache/spark/util/random/SamplingUtilsSuite.scala b/core/src/test/scala/org/apache/spark/util/random/SamplingUtilsSuite.scala
index accfe2e9b7f2..73a9d029b024 100644
--- a/core/src/test/scala/org/apache/spark/util/random/SamplingUtilsSuite.scala
+++ b/core/src/test/scala/org/apache/spark/util/random/SamplingUtilsSuite.scala
@@ -17,11 +17,32 @@
 
 package org.apache.spark.util.random
 
+import scala.util.Random
+
 import org.apache.commons.math3.distribution.{BinomialDistribution, PoissonDistribution}
 import org.scalatest.FunSuite
 
 class SamplingUtilsSuite extends FunSuite {
 
+  test("reservoirSampleAndCount") {
+    val input = Seq.fill(100)(Random.nextInt())
+
+    // input size < k
+    val (sample1, count1) = SamplingUtils.reservoirSampleAndCount(input.iterator, 150)
+    assert(count1 === 100)
+    assert(input === sample1.toSeq)
+
+    // input size == k
+    val (sample2, count2) = SamplingUtils.reservoirSampleAndCount(input.iterator, 100)
+    assert(count2 === 100)
+    assert(input === sample2.toSeq)
+
+    // input size > k
+    val (sample3, count3) = SamplingUtils.reservoirSampleAndCount(input.iterator, 10)
+    assert(count3 === 100)
+    assert(sample3.length === 10)
+  }
+
   test("computeFraction") {
     // test that the computed fraction guarantees enough data points
     // in the sample with a failure rate <= 0.0001

From d88f6be446e263251c446441c9ce7f5b11216909 Mon Sep 17 00:00:00 2001
From: Manish Amde <manish9ue@gmail.com>
Date: Fri, 18 Jul 2014 14:00:13 -0700
Subject: [PATCH 692/810] [MLlib] SPARK-1536: multiclass classification support
 for decision tree

The ability to perform multiclass classification is a big advantage for using decision trees and was a highly requested feature for mllib. This pull request adds multiclass classification support to the MLlib decision tree. It also adds sample weights support using WeightedLabeledPoint class for handling unbalanced datasets during classification. It will also support algorithms such as AdaBoost which requires instances to be weighted.

It handles the special case where the categorical variables cannot be ordered for multiclass classification and thus the optimizations used for speeding up binary classification cannot be directly used for multiclass classification with categorical variables. More specifically, for m categories in a categorical feature, it analyses all the ```2^(m-1) - 1``` categorical splits provided that #splits are less than the maxBins provided in the input. This condition will not be met for features with large number of categories -- using decision trees is not recommended for such datasets in general since the categorical features are favored over continuous features. Moreover, the user can use a combination of tricks (increasing bin size of the tree algorithms, use binary encoding for categorical features or use one-vs-all classification strategy) to avoid these constraints.

The new code is accompanied by unit tests and has also been tested on the iris and covtype datasets.

cc: mengxr, etrain, hirakendu, atalwalkar, srowen

Author: Manish Amde <manish9ue@gmail.com>
Author: manishamde <manish9ue@gmail.com>
Author: Evan Sparks <sparks@cs.berkeley.edu>

Closes #886 from manishamde/multiclass and squashes the following commits:

26f8acc [Manish Amde] another attempt at fixing mima
c5b2d04 [Manish Amde] more MIMA fixes
1ce7212 [Manish Amde] change problem filter for mima
10fdd82 [Manish Amde] fixing MIMA excludes
e1c970d [Manish Amde] merged master
abf2901 [Manish Amde] adding classes to MimaExcludes.scala
45e767a [Manish Amde] adding developer api annotation for overriden methods
c8428c4 [Manish Amde] fixing weird multiline bug
afced16 [Manish Amde] removed label weights support
2d85a48 [Manish Amde] minor: fixed scalastyle issues reprise
4e85f2c [Manish Amde] minor: fixed scalastyle issues
b2ae41f [Manish Amde] minor: scalastyle
e4c1321 [Manish Amde] using while loop for regression histograms
d75ac32 [Manish Amde] removed WeightedLabeledPoint from this PR
0fecd38 [Manish Amde] minor: add newline to EOF
2061cf5 [Manish Amde] merged from master
06b1690 [Manish Amde] fixed off-by-one error in bin to split conversion
9cc3e31 [Manish Amde] added implicit conversion import
5c1b2ca [Manish Amde] doc for PointConverter class
485eaae [Manish Amde] implicit conversion from LabeledPoint to WeightedLabeledPoint
3d7f911 [Manish Amde] updated doc
8e44ab8 [Manish Amde] updated doc
adc7315 [Manish Amde] support ordered categorical splits for multiclass classification
e3e8843 [Manish Amde] minor code formatting
23d4268 [Manish Amde] minor: another minor code style
34ee7b9 [Manish Amde] minor: code style
237762d [Manish Amde] renaming functions
12e6d0a [Manish Amde] minor: removing line in doc
9a90c93 [Manish Amde] Merge branch 'master' into multiclass
1892a2c [Manish Amde] tests and use multiclass binaggregate length when atleast one categorical feature is present
f5f6b83 [Manish Amde] multiclass for continous variables
8cfd3b6 [Manish Amde] working for categorical multiclass classification
828ff16 [Manish Amde] added categorical variable test
bce835f [Manish Amde] code cleanup
7e5f08c [Manish Amde] minor doc
1dd2735 [Manish Amde] bin search logic for multiclass
f16a9bb [Manish Amde] fixing while loop
d811425 [Manish Amde] multiclass bin aggregate logic
ab5cb21 [Manish Amde] multiclass logic
d8e4a11 [Manish Amde] sample weights
ed5a2df [Manish Amde] fixed classification requirements
d012be7 [Manish Amde] fixed while loop
18d2835 [Manish Amde] changing default values for num classes
6b912dc [Manish Amde] added numclasses to tree runner, predict logic for multiclass, add multiclass option to train
75f2bfc [Manish Amde] minor code style fix
e547151 [Manish Amde] minor modifications
34549d0 [Manish Amde] fixing error during merge
098e8c5 [Manish Amde] merged master
e006f9d [Manish Amde] changing variable names
5c78e1a [Manish Amde] added multiclass support
6c7af22 [Manish Amde] prepared for multiclass without breaking binary classification
46e06ee [Manish Amde] minor mods
3f85a17 [Manish Amde] tests for multiclass classification
4d5f70c [Manish Amde] added multiclass support for find splits bins
46f909c [Manish Amde] todo for multiclass support
455bea9 [Manish Amde] fixed tests
14aea48 [Manish Amde] changing instance format to weighted labeled point
a1a6e09 [Manish Amde] added weighted point class
968ca9d [Manish Amde] merged master
7fc9545 [Manish Amde] added docs
ce004a1 [Manish Amde] minor formatting
b27ad2c [Manish Amde] formatting
426bb28 [Manish Amde] programming guide blurb
8053fed [Manish Amde] more formatting
5eca9e4 [Manish Amde] grammar
4731cda [Manish Amde] formatting
5e82202 [Manish Amde] added documentation, fixed off by 1 error in max level calculation
cbd9f14 [Manish Amde] modified scala.math to math
dad9652 [Manish Amde] removed unused imports
e0426ee [Manish Amde] renamed parameter
718506b [Manish Amde] added unit test
1517155 [Manish Amde] updated documentation
9dbdabe [Manish Amde] merge from master
719d009 [Manish Amde] updating user documentation
fecf89a [manishamde] Merge pull request #6 from etrain/deep_tree
0287772 [Evan Sparks] Fixing scalastyle issue.
2f1e093 [Manish Amde] minor: added doc for maxMemory parameter
2f6072c [manishamde] Merge pull request #5 from etrain/deep_tree
abc5a23 [Evan Sparks] Parameterizing max memory.
50b143a [Manish Amde] adding support for very deep trees
---
 docs/mllib-decision-tree.md                   |   8 +-
 .../examples/mllib/DecisionTreeRunner.scala   |  21 +-
 .../spark/mllib/tree/DecisionTree.scala       | 732 +++++++++++++-----
 .../mllib/tree/configuration/Strategy.scala   |  12 +-
 .../spark/mllib/tree/impurity/Entropy.scala   |  36 +-
 .../spark/mllib/tree/impurity/Gini.scala      |  33 +-
 .../spark/mllib/tree/impurity/Impurity.scala  |   8 +-
 .../spark/mllib/tree/impurity/Variance.scala  |  11 +-
 .../apache/spark/mllib/tree/model/Bin.scala   |   2 +-
 .../tree/model/InformationGainStats.scala     |   8 +-
 .../spark/mllib/tree/DecisionTreeSuite.scala  | 303 +++++++-
 project/MimaExcludes.scala                    |  10 +-
 12 files changed, 926 insertions(+), 258 deletions(-)

diff --git a/docs/mllib-decision-tree.md b/docs/mllib-decision-tree.md
index 9cd768599e52..9cbd88089757 100644
--- a/docs/mllib-decision-tree.md
+++ b/docs/mllib-decision-tree.md
@@ -77,15 +77,17 @@ bins if the condition is not satisfied.
 
 **Categorical features**
 
-For `$M$` categorical features, one could come up with `$2^M-1$` split candidates. However, for
-binary classification, the number of split candidates can be reduced to `$M-1$` by ordering the
+For `$M$` categorical feature values, one could come up with `$2^(M-1)-1$` split candidates. For
+binary classification, we can reduce the number of split candidates to `$M-1$` by ordering the
 categorical feature values by the proportion of labels falling in one of the two classes (see
 Section 9.2.4 in
 [Elements of Statistical Machine Learning](http://statweb.stanford.edu/~tibs/ElemStatLearn/) for
 details). For example, for a binary classification problem with one categorical feature with three
 categories A, B and C with corresponding proportion of label 1 as 0.2, 0.6 and 0.4, the categorical
 features are ordered as A followed by C followed B or A, B, C. The two split candidates are A \| C, B
-and A , B \| C where \| denotes the split.
+and A , B \| C where \| denotes the split. A similar heuristic is used for multiclass classification
+when `$2^(M-1)-1$` is greater than the number of bins -- the impurity for each categorical feature value
+is used for ordering.
 
 ### Stopping rule
 
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/DecisionTreeRunner.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/DecisionTreeRunner.scala
index b3cc36115419..43f13fe24f0d 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/DecisionTreeRunner.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/DecisionTreeRunner.scala
@@ -49,6 +49,7 @@ object DecisionTreeRunner {
   case class Params(
       input: String = null,
       algo: Algo = Classification,
+      numClassesForClassification: Int = 2,
       maxDepth: Int = 5,
       impurity: ImpurityType = Gini,
       maxBins: Int = 100)
@@ -68,6 +69,10 @@ object DecisionTreeRunner {
       opt[Int]("maxDepth")
         .text(s"max depth of the tree, default: ${defaultParams.maxDepth}")
         .action((x, c) => c.copy(maxDepth = x))
+      opt[Int]("numClassesForClassification")
+        .text(s"number of classes for classification, "
+          + s"default: ${defaultParams.numClassesForClassification}")
+        .action((x, c) => c.copy(numClassesForClassification = x))
       opt[Int]("maxBins")
         .text(s"max number of bins, default: ${defaultParams.maxBins}")
         .action((x, c) => c.copy(maxBins = x))
@@ -118,7 +123,13 @@ object DecisionTreeRunner {
       case Variance => impurity.Variance
     }
 
-    val strategy = new Strategy(params.algo, impurityCalculator, params.maxDepth, params.maxBins)
+    val strategy
+      = new Strategy(
+          algo = params.algo,
+          impurity = impurityCalculator,
+          maxDepth = params.maxDepth,
+          maxBins = params.maxBins,
+          numClassesForClassification = params.numClassesForClassification)
     val model = DecisionTree.train(training, strategy)
 
     if (params.algo == Classification) {
@@ -139,12 +150,8 @@ object DecisionTreeRunner {
    */
   private def accuracyScore(
       model: DecisionTreeModel,
-      data: RDD[LabeledPoint],
-      threshold: Double = 0.5): Double = {
-    def predictedValue(features: Vector): Double = {
-      if (model.predict(features) < threshold) 0.0 else 1.0
-    }
-    val correctCount = data.filter(y => predictedValue(y.features) == y.label).count()
+      data: RDD[LabeledPoint]): Double = {
+    val correctCount = data.filter(y => model.predict(y.features) == y.label).count()
     val count = data.count()
     correctCount.toDouble / count
   }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
index 74d5d7ba1096..ad32e3f4560f 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
@@ -77,11 +77,9 @@ class DecisionTree (private val strategy: Strategy) extends Serializable with Lo
     // Max memory usage for aggregates
     val maxMemoryUsage = strategy.maxMemoryInMB * 1024 * 1024
     logDebug("max memory usage for aggregates = " + maxMemoryUsage + " bytes.")
-    val numElementsPerNode =
-      strategy.algo match {
-        case Classification => 2 * numBins * numFeatures
-        case Regression => 3 * numBins * numFeatures
-      }
+    val numElementsPerNode = DecisionTree.getElementsPerNode(numFeatures, numBins,
+      strategy.numClassesForClassification, strategy.isMulticlassWithCategoricalFeatures,
+      strategy.algo)
 
     logDebug("numElementsPerNode = " + numElementsPerNode)
     val arraySizePerNode = 8 * numElementsPerNode // approx. memory usage for bin aggregate array
@@ -109,8 +107,8 @@ class DecisionTree (private val strategy: Strategy) extends Serializable with Lo
       logDebug("#####################################")
 
       // Find best split for all nodes at a level.
-      val splitsStatsForLevel = DecisionTree.findBestSplits(input, parentImpurities, strategy,
-        level, filters, splits, bins, maxLevelForSingleGroup)
+      val splitsStatsForLevel = DecisionTree.findBestSplits(input, parentImpurities,
+        strategy, level, filters, splits, bins, maxLevelForSingleGroup)
 
       for ((nodeSplitStats, index) <- splitsStatsForLevel.view.zipWithIndex) {
         // Extract info for nodes at the current level.
@@ -212,7 +210,7 @@ object DecisionTree extends Serializable with Logging {
    * @return a DecisionTreeModel that can be used for prediction
   */
   def train(input: RDD[LabeledPoint], strategy: Strategy): DecisionTreeModel = {
-    new DecisionTree(strategy).train(input: RDD[LabeledPoint])
+    new DecisionTree(strategy).train(input)
   }
 
   /**
@@ -233,10 +231,33 @@ object DecisionTree extends Serializable with Logging {
       algo: Algo,
       impurity: Impurity,
       maxDepth: Int): DecisionTreeModel = {
-    val strategy = new Strategy(algo,impurity,maxDepth)
-    new DecisionTree(strategy).train(input: RDD[LabeledPoint])
+    val strategy = new Strategy(algo, impurity, maxDepth)
+    new DecisionTree(strategy).train(input)
   }
 
+  /**
+   * Method to train a decision tree model where the instances are represented as an RDD of
+   * (label, features) pairs. The method supports binary classification and regression. For the
+   * binary classification, the label for each instance should either be 0 or 1 to denote the two
+   * classes.
+   *
+   * @param input input RDD of [[org.apache.spark.mllib.regression.LabeledPoint]] used as
+   *              training data
+   * @param algo algorithm, classification or regression
+   * @param impurity impurity criterion used for information gain calculation
+   * @param maxDepth maxDepth maximum depth of the tree
+   * @param numClassesForClassification number of classes for classification. Default value of 2.
+   * @return a DecisionTreeModel that can be used for prediction
+   */
+  def train(
+      input: RDD[LabeledPoint],
+      algo: Algo,
+      impurity: Impurity,
+      maxDepth: Int,
+      numClassesForClassification: Int): DecisionTreeModel = {
+    val strategy = new Strategy(algo, impurity, maxDepth, numClassesForClassification)
+    new DecisionTree(strategy).train(input)
+  }
 
   /**
    * Method to train a decision tree model where the instances are represented as an RDD of
@@ -250,6 +271,7 @@ object DecisionTree extends Serializable with Logging {
    * @param algo classification or regression
    * @param impurity criterion used for information gain calculation
    * @param maxDepth  maximum depth of the tree
+   * @param numClassesForClassification number of classes for classification. Default value of 2.
    * @param maxBins maximum number of bins used for splitting features
    * @param quantileCalculationStrategy  algorithm for calculating quantiles
    * @param categoricalFeaturesInfo A map storing information about the categorical variables and
@@ -264,12 +286,13 @@ object DecisionTree extends Serializable with Logging {
       algo: Algo,
       impurity: Impurity,
       maxDepth: Int,
+      numClassesForClassification: Int,
       maxBins: Int,
       quantileCalculationStrategy: QuantileStrategy,
       categoricalFeaturesInfo: Map[Int,Int]): DecisionTreeModel = {
-    val strategy = new Strategy(algo, impurity, maxDepth, maxBins, quantileCalculationStrategy,
-      categoricalFeaturesInfo)
-    new DecisionTree(strategy).train(input: RDD[LabeledPoint])
+    val strategy = new Strategy(algo, impurity, maxDepth, numClassesForClassification, maxBins,
+      quantileCalculationStrategy, categoricalFeaturesInfo)
+    new DecisionTree(strategy).train(input)
   }
 
   private val InvalidBinIndex = -1
@@ -381,6 +404,14 @@ object DecisionTree extends Serializable with Logging {
     logDebug("numFeatures = " + numFeatures)
     val numBins = bins(0).length
     logDebug("numBins = " + numBins)
+    val numClasses = strategy.numClassesForClassification
+    logDebug("numClasses = " + numClasses)
+    val isMulticlassClassification = strategy.isMulticlassClassification
+    logDebug("isMulticlassClassification = " + isMulticlassClassification)
+    val isMulticlassClassificationWithCategoricalFeatures
+      = strategy.isMulticlassWithCategoricalFeatures
+    logDebug("isMultiClassWithCategoricalFeatures = " +
+      isMulticlassClassificationWithCategoricalFeatures)
 
     // shift when more than one group is used at deep tree level
     val groupShift = numNodes * groupIndex
@@ -436,10 +467,8 @@ object DecisionTree extends Serializable with Logging {
     /**
      * Find bin for one feature.
      */
-    def findBin(
-        featureIndex: Int,
-        labeledPoint: LabeledPoint,
-        isFeatureContinuous: Boolean): Int = {
+    def findBin(featureIndex: Int, labeledPoint: LabeledPoint,
+        isFeatureContinuous: Boolean, isSpaceSufficientForAllCategoricalSplits: Boolean): Int = {
       val binForFeatures = bins(featureIndex)
       val feature = labeledPoint.features(featureIndex)
 
@@ -467,17 +496,28 @@ object DecisionTree extends Serializable with Logging {
         -1
       }
 
+      /**
+       * Sequential search helper method to find bin for categorical feature in multiclass
+       * classification. The category is returned since each category can belong to multiple
+       * splits. The actual left/right child allocation per split is performed in the
+       * sequential phase of the bin aggregate operation.
+       */
+      def sequentialBinSearchForUnorderedCategoricalFeatureInClassification(): Int = {
+        labeledPoint.features(featureIndex).toInt
+      }
+
       /**
        * Sequential search helper method to find bin for categorical feature.
        */
-      def sequentialBinSearchForCategoricalFeature(): Int = {
-        val numCategoricalBins = strategy.categoricalFeaturesInfo(featureIndex)
+      def sequentialBinSearchForOrderedCategoricalFeatureInClassification(): Int = {
+        val featureCategories = strategy.categoricalFeaturesInfo(featureIndex)
+        val numCategoricalBins = math.pow(2.0, featureCategories - 1).toInt - 1
         var binIndex = 0
         while (binIndex < numCategoricalBins) {
           val bin = bins(featureIndex)(binIndex)
-          val category = bin.category
+          val categories = bin.highSplit.categories
           val features = labeledPoint.features
-          if (category == features(featureIndex)) {
+          if (categories.contains(features(featureIndex))) {
             return binIndex
           }
           binIndex += 1
@@ -494,7 +534,13 @@ object DecisionTree extends Serializable with Logging {
         binIndex
       } else {
         // Perform sequential search to find bin for categorical features.
-        val binIndex = sequentialBinSearchForCategoricalFeature()
+        val binIndex = {
+          if (isMulticlassClassification && isSpaceSufficientForAllCategoricalSplits) {
+            sequentialBinSearchForUnorderedCategoricalFeatureInClassification()
+          } else {
+            sequentialBinSearchForOrderedCategoricalFeatureInClassification()
+          }
+        }
         if (binIndex == -1){
           throw new UnknownError("no bin was found for categorical variable.")
         }
@@ -506,13 +552,16 @@ object DecisionTree extends Serializable with Logging {
      * Finds bins for all nodes (and all features) at a given level.
      * For l nodes, k features the storage is as follows:
      * label, b_11, b_12, .. , b_1k, b_21, b_22, .. , b_2k, b_l1, b_l2, .. , b_lk,
-     * where b_ij is an integer between 0 and numBins - 1.
+     * where b_ij is an integer between 0 and numBins - 1 for regressions and binary
+     * classification and the categorical feature value in  multiclass classification.
      * Invalid sample is denoted by noting bin for feature 1 as -1.
      */
     def findBinsForLevel(labeledPoint: LabeledPoint): Array[Double] = {
       // Calculate bin index and label per feature per node.
       val arr = new Array[Double](1 + (numFeatures * numNodes))
+      // First element of the array is the label of the instance.
       arr(0) = labeledPoint.label
+      // Iterate over nodes.
       var nodeIndex = 0
       while (nodeIndex < numNodes) {
         val parentFilters = findParentFilters(nodeIndex)
@@ -525,8 +574,19 @@ object DecisionTree extends Serializable with Logging {
         } else {
           var featureIndex = 0
           while (featureIndex < numFeatures) {
-            val isFeatureContinuous = strategy.categoricalFeaturesInfo.get(featureIndex).isEmpty
-            arr(shift + featureIndex) = findBin(featureIndex, labeledPoint,isFeatureContinuous)
+            val featureInfo = strategy.categoricalFeaturesInfo.get(featureIndex)
+            val isFeatureContinuous = featureInfo.isEmpty
+            if (isFeatureContinuous) {
+              arr(shift + featureIndex)
+                = findBin(featureIndex, labeledPoint, isFeatureContinuous, false)
+            } else {
+              val featureCategories = featureInfo.get
+              val isSpaceSufficientForAllCategoricalSplits
+                = numBins > math.pow(2, featureCategories.toInt - 1) - 1
+              arr(shift + featureIndex)
+                = findBin(featureIndex, labeledPoint, isFeatureContinuous,
+                isSpaceSufficientForAllCategoricalSplits)
+            }
             featureIndex += 1
           }
         }
@@ -535,18 +595,61 @@ object DecisionTree extends Serializable with Logging {
       arr
     }
 
+     // Find feature bins for all nodes at a level.
+    val binMappedRDD = input.map(x => findBinsForLevel(x))
+
+    def updateBinForOrderedFeature(arr: Array[Double], agg: Array[Double], nodeIndex: Int,
+        label: Double, featureIndex: Int) = {
+
+      // Find the bin index for this feature.
+      val arrShift = 1 + numFeatures * nodeIndex
+      val arrIndex = arrShift + featureIndex
+      // Update the left or right count for one bin.
+      val aggShift = numClasses * numBins * numFeatures * nodeIndex
+      val aggIndex
+        = aggShift + numClasses * featureIndex * numBins + arr(arrIndex).toInt * numClasses
+      val labelInt = label.toInt
+      agg(aggIndex + labelInt) = agg(aggIndex + labelInt) + 1
+    }
+
+    def updateBinForUnorderedFeature(nodeIndex: Int, featureIndex: Int, arr: Array[Double],
+        label: Double, agg: Array[Double], rightChildShift: Int) = {
+      // Find the bin index for this feature.
+      val arrShift = 1 + numFeatures * nodeIndex
+      val arrIndex = arrShift + featureIndex
+      // Update the left or right count for one bin.
+      val aggShift = numClasses * numBins * numFeatures * nodeIndex
+      val aggIndex
+        = aggShift + numClasses * featureIndex * numBins + arr(arrIndex).toInt * numClasses
+      // Find all matching bins and increment their values
+      val featureCategories = strategy.categoricalFeaturesInfo(featureIndex)
+      val numCategoricalBins = math.pow(2.0, featureCategories - 1).toInt - 1
+      var binIndex = 0
+      while (binIndex < numCategoricalBins) {
+        val labelInt = label.toInt
+        if (bins(featureIndex)(binIndex).highSplit.categories.contains(labelInt)) {
+          agg(aggIndex + binIndex)
+            = agg(aggIndex + binIndex) + 1
+        } else {
+          agg(rightChildShift + aggIndex + binIndex)
+            = agg(rightChildShift + aggIndex + binIndex) + 1
+        }
+        binIndex += 1
+      }
+    }
+
     /**
      * Performs a sequential aggregation over a partition for classification. For l nodes,
      * k features, either the left count or the right count of one of the p bins is
      * incremented based upon whether the feature is classified as 0 or 1.
      *
      * @param agg Array[Double] storing aggregate calculation of size
-     *            2 * numSplits * numFeatures*numNodes for classification
+     *            numClasses * numSplits * numFeatures*numNodes for classification
      * @param arr Array[Double] of size 1 + (numFeatures * numNodes)
      * @return Array[Double] storing aggregate calculation of size
      *         2 * numSplits * numFeatures * numNodes for classification
      */
-    def classificationBinSeqOp(arr: Array[Double], agg: Array[Double]) {
+    def orderedClassificationBinSeqOp(arr: Array[Double], agg: Array[Double]) = {
       // Iterate over all nodes.
       var nodeIndex = 0
       while (nodeIndex < numNodes) {
@@ -559,15 +662,52 @@ object DecisionTree extends Serializable with Logging {
           // Iterate over all features.
           var featureIndex = 0
           while (featureIndex < numFeatures) {
-            // Find the bin index for this feature.
-            val arrShift = 1 + numFeatures * nodeIndex
-            val arrIndex = arrShift + featureIndex
-            // Update the left or right count for one bin.
-            val aggShift = 2 * numBins * numFeatures * nodeIndex
-            val aggIndex = aggShift + 2 * featureIndex * numBins + arr(arrIndex).toInt * 2
-            label match {
-              case 0.0 => agg(aggIndex) = agg(aggIndex) + 1
-              case 1.0 => agg(aggIndex + 1) = agg(aggIndex + 1) + 1
+            updateBinForOrderedFeature(arr, agg, nodeIndex, label, featureIndex)
+            featureIndex += 1
+          }
+        }
+        nodeIndex += 1
+      }
+    }
+
+    /**
+     * Performs a sequential aggregation over a partition for classification. For l nodes,
+     * k features, either the left count or the right count of one of the p bins is
+     * incremented based upon whether the feature is classified as 0 or 1.
+     *
+     * @param agg Array[Double] storing aggregate calculation of size
+     *            numClasses * numSplits * numFeatures*numNodes for classification
+     * @param arr Array[Double] of size 1 + (numFeatures * numNodes)
+     * @return Array[Double] storing aggregate calculation of size
+     *         2 * numClasses * numSplits * numFeatures * numNodes for classification
+     */
+    def unorderedClassificationBinSeqOp(arr: Array[Double], agg: Array[Double]) = {
+      // Iterate over all nodes.
+      var nodeIndex = 0
+      while (nodeIndex < numNodes) {
+        // Check whether the instance was valid for this nodeIndex.
+        val validSignalIndex = 1 + numFeatures * nodeIndex
+        val isSampleValidForNode = arr(validSignalIndex) != InvalidBinIndex
+        if (isSampleValidForNode) {
+          val rightChildShift = numClasses * numBins * numFeatures * numNodes
+          // actual class label
+          val label = arr(0)
+          // Iterate over all features.
+          var featureIndex = 0
+          while (featureIndex < numFeatures) {
+            val isFeatureContinuous = strategy.categoricalFeaturesInfo.get(featureIndex).isEmpty
+            if (isFeatureContinuous) {
+              updateBinForOrderedFeature(arr, agg, nodeIndex, label, featureIndex)
+            } else {
+              val featureCategories = strategy.categoricalFeaturesInfo(featureIndex)
+              val isSpaceSufficientForAllCategoricalSplits
+                = numBins > math.pow(2, featureCategories.toInt - 1) - 1
+              if (isSpaceSufficientForAllCategoricalSplits) {
+                updateBinForUnorderedFeature(nodeIndex, featureIndex, arr, label, agg,
+                  rightChildShift)
+              } else {
+                updateBinForOrderedFeature(arr, agg, nodeIndex, label, featureIndex)
+              }
             }
             featureIndex += 1
           }
@@ -586,7 +726,7 @@ object DecisionTree extends Serializable with Logging {
      * @return Array[Double] storing aggregate calculation of size
      *         3 * numSplits * numFeatures * numNodes for regression
      */
-    def regressionBinSeqOp(arr: Array[Double], agg: Array[Double]) {
+    def regressionBinSeqOp(arr: Array[Double], agg: Array[Double]) = {
       // Iterate over all nodes.
       var nodeIndex = 0
       while (nodeIndex < numNodes) {
@@ -620,17 +760,20 @@ object DecisionTree extends Serializable with Logging {
      */
     def binSeqOp(agg: Array[Double], arr: Array[Double]): Array[Double] = {
       strategy.algo match {
-        case Classification => classificationBinSeqOp(arr, agg)
+        case Classification =>
+          if(isMulticlassClassificationWithCategoricalFeatures) {
+            unorderedClassificationBinSeqOp(arr, agg)
+          } else {
+            orderedClassificationBinSeqOp(arr, agg)
+          }
         case Regression => regressionBinSeqOp(arr, agg)
       }
       agg
     }
 
     // Calculate bin aggregate length for classification or regression.
-    val binAggregateLength = strategy.algo match {
-      case Classification => 2 * numBins * numFeatures * numNodes
-      case Regression =>  3 * numBins * numFeatures * numNodes
-    }
+    val binAggregateLength = numNodes * getElementsPerNode(numFeatures, numBins, numClasses,
+        isMulticlassClassificationWithCategoricalFeatures, strategy.algo)
     logDebug("binAggregateLength = " + binAggregateLength)
 
     /**
@@ -649,9 +792,6 @@ object DecisionTree extends Serializable with Logging {
       combinedAggregate
     }
 
-    // Find feature bins for all nodes at a level.
-    val binMappedRDD = input.map(x => findBinsForLevel(x))
-
     // Calculate bin aggregates.
     val binAggregates = {
       binMappedRDD.aggregate(Array.fill[Double](binAggregateLength)(0))(binSeqOp,binCombOp)
@@ -668,42 +808,55 @@ object DecisionTree extends Serializable with Logging {
      * @return information gain and statistics for all splits
      */
     def calculateGainForSplit(
-        leftNodeAgg: Array[Array[Double]],
+        leftNodeAgg: Array[Array[Array[Double]]],
         featureIndex: Int,
         splitIndex: Int,
-        rightNodeAgg: Array[Array[Double]],
+        rightNodeAgg: Array[Array[Array[Double]]],
         topImpurity: Double): InformationGainStats = {
       strategy.algo match {
         case Classification =>
-          val left0Count = leftNodeAgg(featureIndex)(2 * splitIndex)
-          val left1Count = leftNodeAgg(featureIndex)(2 * splitIndex + 1)
-          val leftCount = left0Count + left1Count
-
-          val right0Count = rightNodeAgg(featureIndex)(2 * splitIndex)
-          val right1Count = rightNodeAgg(featureIndex)(2 * splitIndex + 1)
-          val rightCount = right0Count + right1Count
+          var classIndex = 0
+          val leftCounts: Array[Double] = new Array[Double](numClasses)
+          val rightCounts: Array[Double] = new Array[Double](numClasses)
+          var leftTotalCount = 0.0
+          var rightTotalCount = 0.0
+          while (classIndex < numClasses) {
+            val leftClassCount = leftNodeAgg(featureIndex)(splitIndex)(classIndex)
+            val rightClassCount = rightNodeAgg(featureIndex)(splitIndex)(classIndex)
+            leftCounts(classIndex) = leftClassCount
+            leftTotalCount += leftClassCount
+            rightCounts(classIndex) = rightClassCount
+            rightTotalCount += rightClassCount
+            classIndex += 1
+          }
 
           val impurity = {
             if (level > 0) {
               topImpurity
             } else {
               // Calculate impurity for root node.
-              strategy.impurity.calculate(left0Count + right0Count, left1Count + right1Count)
+              val rootNodeCounts = new Array[Double](numClasses)
+              var classIndex = 0
+              while (classIndex < numClasses) {
+                rootNodeCounts(classIndex) = leftCounts(classIndex) + rightCounts(classIndex)
+                classIndex += 1
+              }
+              strategy.impurity.calculate(rootNodeCounts, leftTotalCount + rightTotalCount)
             }
           }
 
-          if (leftCount == 0) {
-            return new InformationGainStats(0, topImpurity, Double.MinValue, topImpurity,1)
+          if (leftTotalCount == 0) {
+            return new InformationGainStats(0, topImpurity, topImpurity, Double.MinValue, 1)
           }
-          if (rightCount == 0) {
-            return new InformationGainStats(0, topImpurity, topImpurity, Double.MinValue,0)
+          if (rightTotalCount == 0) {
+            return new InformationGainStats(0, topImpurity, Double.MinValue, topImpurity, 1)
           }
 
-          val leftImpurity = strategy.impurity.calculate(left0Count, left1Count)
-          val rightImpurity = strategy.impurity.calculate(right0Count, right1Count)
+          val leftImpurity = strategy.impurity.calculate(leftCounts, leftTotalCount)
+          val rightImpurity = strategy.impurity.calculate(rightCounts, rightTotalCount)
 
-          val leftWeight = leftCount.toDouble / (leftCount + rightCount)
-          val rightWeight = rightCount.toDouble / (leftCount + rightCount)
+          val leftWeight = leftTotalCount / (leftTotalCount + rightTotalCount)
+          val rightWeight = rightTotalCount / (leftTotalCount + rightTotalCount)
 
           val gain = {
             if (level > 0) {
@@ -713,17 +866,34 @@ object DecisionTree extends Serializable with Logging {
             }
           }
 
-          val predict = (left1Count + right1Count) / (leftCount + rightCount)
+          val totalCount = leftTotalCount + rightTotalCount
 
-          new InformationGainStats(gain, impurity, leftImpurity, rightImpurity, predict)
+          // Sum of count for each label
+          val leftRightCounts: Array[Double]
+            = leftCounts.zip(rightCounts)
+              .map{case (leftCount, rightCount) => leftCount + rightCount}
+
+          def indexOfLargestArrayElement(array: Array[Double]): Int = {
+            val result = array.foldLeft(-1, Double.MinValue, 0) {
+              case ((maxIndex, maxValue, currentIndex), currentValue) =>
+                if(currentValue > maxValue) (currentIndex, currentValue, currentIndex + 1)
+                else (maxIndex, maxValue, currentIndex + 1)
+            }
+            if (result._1 < 0) 0 else result._1
+          }
+
+          val predict = indexOfLargestArrayElement(leftRightCounts)
+          val prob = leftRightCounts(predict) / totalCount
+
+          new InformationGainStats(gain, impurity, leftImpurity, rightImpurity, predict, prob)
         case Regression =>
-          val leftCount = leftNodeAgg(featureIndex)(3 * splitIndex)
-          val leftSum = leftNodeAgg(featureIndex)(3 * splitIndex + 1)
-          val leftSumSquares = leftNodeAgg(featureIndex)(3 * splitIndex + 2)
+          val leftCount = leftNodeAgg(featureIndex)(splitIndex)(0)
+          val leftSum = leftNodeAgg(featureIndex)(splitIndex)(1)
+          val leftSumSquares = leftNodeAgg(featureIndex)(splitIndex)(2)
 
-          val rightCount = rightNodeAgg(featureIndex)(3 * splitIndex)
-          val rightSum = rightNodeAgg(featureIndex)(3 * splitIndex + 1)
-          val rightSumSquares = rightNodeAgg(featureIndex)(3 * splitIndex + 2)
+          val rightCount = rightNodeAgg(featureIndex)(splitIndex)(0)
+          val rightSum = rightNodeAgg(featureIndex)(splitIndex)(1)
+          val rightSumSquares = rightNodeAgg(featureIndex)(splitIndex)(2)
 
           val impurity = {
             if (level > 0) {
@@ -768,104 +938,149 @@ object DecisionTree extends Serializable with Logging {
     /**
      * Extracts left and right split aggregates.
      * @param binData Array[Double] of size 2*numFeatures*numSplits
-     * @return (leftNodeAgg, rightNodeAgg) tuple of type (Array[Double],
-     *         Array[Double]) where each array is of size(numFeature,2*(numSplits-1))
+     * @return (leftNodeAgg, rightNodeAgg) tuple of type (Array[Array[Array[Double\]\]\],
+     *         Array[Array[Array[Double\]\]\]) where each array is of size(numFeature,
+     *         (numBins - 1), numClasses)
      */
     def extractLeftRightNodeAggregates(
-        binData: Array[Double]): (Array[Array[Double]], Array[Array[Double]]) = {
+        binData: Array[Double]): (Array[Array[Array[Double]]], Array[Array[Array[Double]]]) = {
+
+
+      def findAggForOrderedFeatureClassification(
+          leftNodeAgg: Array[Array[Array[Double]]],
+          rightNodeAgg: Array[Array[Array[Double]]],
+          featureIndex: Int) {
+
+        // shift for this featureIndex
+        val shift = numClasses * featureIndex * numBins
+
+        var classIndex = 0
+        while (classIndex < numClasses) {
+          // left node aggregate for the lowest split
+          leftNodeAgg(featureIndex)(0)(classIndex) = binData(shift + classIndex)
+          // right node aggregate for the highest split
+          rightNodeAgg(featureIndex)(numBins - 2)(classIndex)
+            = binData(shift + (numClasses * (numBins - 1)) + classIndex)
+          classIndex += 1
+        }
+
+        // Iterate over all splits.
+        var splitIndex = 1
+        while (splitIndex < numBins - 1) {
+          // calculating left node aggregate for a split as a sum of left node aggregate of a
+          // lower split and the left bin aggregate of a bin where the split is a high split
+          var innerClassIndex = 0
+          while (innerClassIndex < numClasses) {
+            leftNodeAgg(featureIndex)(splitIndex)(innerClassIndex)
+              = binData(shift + numClasses * splitIndex + innerClassIndex) +
+                leftNodeAgg(featureIndex)(splitIndex - 1)(innerClassIndex)
+            rightNodeAgg(featureIndex)(numBins - 2 - splitIndex)(innerClassIndex) =
+              binData(shift + (numClasses * (numBins - 1 - splitIndex) + innerClassIndex)) +
+                rightNodeAgg(featureIndex)(numBins - 1 - splitIndex)(innerClassIndex)
+            innerClassIndex += 1
+          }
+          splitIndex += 1
+        }
+      }
+
+      def findAggForUnorderedFeatureClassification(
+          leftNodeAgg: Array[Array[Array[Double]]],
+          rightNodeAgg: Array[Array[Array[Double]]],
+          featureIndex: Int) {
+
+        val rightChildShift = numClasses * numBins * numFeatures
+        var splitIndex = 0
+        while (splitIndex < numBins - 1) {
+          var classIndex = 0
+          while (classIndex < numClasses) {
+            // shift for this featureIndex
+            val shift = numClasses * featureIndex * numBins + splitIndex * numClasses
+            val leftBinValue = binData(shift + classIndex)
+            val rightBinValue = binData(rightChildShift + shift + classIndex)
+            leftNodeAgg(featureIndex)(splitIndex)(classIndex) = leftBinValue
+            rightNodeAgg(featureIndex)(splitIndex)(classIndex) = rightBinValue
+            classIndex += 1
+          }
+          splitIndex += 1
+        }
+      }
+
+      def findAggForRegression(
+          leftNodeAgg: Array[Array[Array[Double]]],
+          rightNodeAgg: Array[Array[Array[Double]]],
+          featureIndex: Int) {
+
+        // shift for this featureIndex
+        val shift = 3 * featureIndex * numBins
+        // left node aggregate for the lowest split
+        leftNodeAgg(featureIndex)(0)(0) = binData(shift + 0)
+        leftNodeAgg(featureIndex)(0)(1) = binData(shift + 1)
+        leftNodeAgg(featureIndex)(0)(2) = binData(shift + 2)
+
+        // right node aggregate for the highest split
+        rightNodeAgg(featureIndex)(numBins - 2)(0) =
+          binData(shift + (3 * (numBins - 1)))
+        rightNodeAgg(featureIndex)(numBins - 2)(1) =
+          binData(shift + (3 * (numBins - 1)) + 1)
+        rightNodeAgg(featureIndex)(numBins - 2)(2) =
+          binData(shift + (3 * (numBins - 1)) + 2)
+
+        // Iterate over all splits.
+        var splitIndex = 1
+        while (splitIndex < numBins - 1) {
+          var i = 0 // index for regression histograms
+          while (i < 3) { // count, sum, sum^2
+            // calculating left node aggregate for a split as a sum of left node aggregate of a
+            // lower split and the left bin aggregate of a bin where the split is a high split
+            leftNodeAgg(featureIndex)(splitIndex)(i) = binData(shift + 3 * splitIndex + i) +
+              leftNodeAgg(featureIndex)(splitIndex - 1)(i)
+            // calculating right node aggregate for a split as a sum of right node aggregate of a
+            // higher split and the right bin aggregate of a bin where the split is a low split
+            rightNodeAgg(featureIndex)(numBins - 2 - splitIndex)(i) =
+              binData(shift + (3 * (numBins - 1 - splitIndex) + i)) +
+                rightNodeAgg(featureIndex)(numBins - 1 - splitIndex)(i)
+            i += 1
+          }
+          splitIndex += 1
+        }
+      }
+
       strategy.algo match {
         case Classification =>
           // Initialize left and right split aggregates.
-          val leftNodeAgg = Array.ofDim[Double](numFeatures, 2 * (numBins - 1))
-          val rightNodeAgg = Array.ofDim[Double](numFeatures, 2 * (numBins - 1))
-          // Iterate over all features.
+          val leftNodeAgg = Array.ofDim[Double](numFeatures, numBins - 1, numClasses)
+          val rightNodeAgg = Array.ofDim[Double](numFeatures, numBins - 1, numClasses)
           var featureIndex = 0
           while (featureIndex < numFeatures) {
-            // shift for this featureIndex
-            val shift = 2 * featureIndex * numBins
-
-            // left node aggregate for the lowest split
-            leftNodeAgg(featureIndex)(0) = binData(shift + 0)
-            leftNodeAgg(featureIndex)(1) = binData(shift + 1)
-
-            // right node aggregate for the highest split
-            rightNodeAgg(featureIndex)(2 * (numBins - 2))
-              = binData(shift + (2 * (numBins - 1)))
-            rightNodeAgg(featureIndex)(2 * (numBins - 2) + 1)
-              = binData(shift + (2 * (numBins - 1)) + 1)
-
-            // Iterate over all splits.
-            var splitIndex = 1
-            while (splitIndex < numBins - 1) {
-              // calculating left node aggregate for a split as a sum of left node aggregate of a
-              // lower split and the left bin aggregate of a bin where the split is a high split
-              leftNodeAgg(featureIndex)(2 * splitIndex) = binData(shift + 2 * splitIndex) +
-                leftNodeAgg(featureIndex)(2 * splitIndex - 2)
-              leftNodeAgg(featureIndex)(2 * splitIndex + 1) = binData(shift + 2 * splitIndex + 1) +
-                leftNodeAgg(featureIndex)(2 * splitIndex - 2 + 1)
-
-              // calculating right node aggregate for a split as a sum of right node aggregate of a
-              // higher split and the right bin aggregate of a bin where the split is a low split
-              rightNodeAgg(featureIndex)(2 * (numBins - 2 - splitIndex)) =
-                binData(shift + (2 *(numBins - 1 - splitIndex))) +
-                rightNodeAgg(featureIndex)(2 * (numBins - 1 - splitIndex))
-              rightNodeAgg(featureIndex)(2 * (numBins - 2 - splitIndex) + 1) =
-                binData(shift + (2* (numBins - 1 - splitIndex) + 1)) +
-                  rightNodeAgg(featureIndex)(2 * (numBins - 1 - splitIndex) + 1)
-
-              splitIndex += 1
+            if (isMulticlassClassificationWithCategoricalFeatures){
+              val isFeatureContinuous = strategy.categoricalFeaturesInfo.get(featureIndex).isEmpty
+              if (isFeatureContinuous) {
+                findAggForOrderedFeatureClassification(leftNodeAgg, rightNodeAgg, featureIndex)
+              } else {
+                val featureCategories = strategy.categoricalFeaturesInfo(featureIndex)
+                val isSpaceSufficientForAllCategoricalSplits
+                  = numBins > math.pow(2, featureCategories.toInt - 1) - 1
+                if (isSpaceSufficientForAllCategoricalSplits) {
+                  findAggForUnorderedFeatureClassification(leftNodeAgg, rightNodeAgg, featureIndex)
+                } else {
+                  findAggForOrderedFeatureClassification(leftNodeAgg, rightNodeAgg, featureIndex)
+                }
+              }
+            } else {
+              findAggForOrderedFeatureClassification(leftNodeAgg, rightNodeAgg, featureIndex)
             }
             featureIndex += 1
           }
+
           (leftNodeAgg, rightNodeAgg)
         case Regression =>
           // Initialize left and right split aggregates.
-          val leftNodeAgg = Array.ofDim[Double](numFeatures, 3 * (numBins - 1))
-          val rightNodeAgg = Array.ofDim[Double](numFeatures, 3 * (numBins - 1))
+          val leftNodeAgg = Array.ofDim[Double](numFeatures, numBins - 1, 3)
+          val rightNodeAgg = Array.ofDim[Double](numFeatures, numBins - 1, 3)
           // Iterate over all features.
           var featureIndex = 0
           while (featureIndex < numFeatures) {
-            // shift for this featureIndex
-            val shift = 3 * featureIndex * numBins
-            // left node aggregate for the lowest split
-            leftNodeAgg(featureIndex)(0) = binData(shift + 0)
-            leftNodeAgg(featureIndex)(1) = binData(shift + 1)
-            leftNodeAgg(featureIndex)(2) = binData(shift + 2)
-
-            // right node aggregate for the highest split
-            rightNodeAgg(featureIndex)(3 * (numBins - 2)) =
-              binData(shift + (3 * (numBins - 1)))
-            rightNodeAgg(featureIndex)(3 * (numBins - 2) + 1) =
-              binData(shift + (3 * (numBins - 1)) + 1)
-            rightNodeAgg(featureIndex)(3 * (numBins - 2) + 2) =
-              binData(shift + (3 * (numBins - 1)) + 2)
-
-            // Iterate over all splits.
-            var splitIndex = 1
-            while (splitIndex < numBins - 1) {
-              // calculating left node aggregate for a split as a sum of left node aggregate of a
-              // lower split and the left bin aggregate of a bin where the split is a high split
-              leftNodeAgg(featureIndex)(3 * splitIndex) = binData(shift + 3 * splitIndex) +
-                leftNodeAgg(featureIndex)(3 * splitIndex - 3)
-              leftNodeAgg(featureIndex)(3 * splitIndex + 1) = binData(shift + 3 * splitIndex + 1) +
-                leftNodeAgg(featureIndex)(3 * splitIndex - 3 + 1)
-              leftNodeAgg(featureIndex)(3 * splitIndex + 2) = binData(shift + 3 * splitIndex + 2) +
-                leftNodeAgg(featureIndex)(3 * splitIndex - 3 + 2)
-
-              // calculating right node aggregate for a split as a sum of right node aggregate of a
-              // higher split and the right bin aggregate of a bin where the split is a low split
-              rightNodeAgg(featureIndex)(3 * (numBins - 2 - splitIndex)) =
-                binData(shift + (3 * (numBins - 1 - splitIndex))) +
-                  rightNodeAgg(featureIndex)(3 * (numBins - 1 - splitIndex))
-              rightNodeAgg(featureIndex)(3 * (numBins - 2 - splitIndex) + 1) =
-                binData(shift + (3 * (numBins - 1 - splitIndex) + 1)) +
-                  rightNodeAgg(featureIndex)(3 * (numBins - 1 - splitIndex) + 1)
-              rightNodeAgg(featureIndex)(3 * (numBins - 2 - splitIndex) + 2) =
-                binData(shift + (3 * (numBins - 1 - splitIndex) + 2)) +
-                  rightNodeAgg(featureIndex)(3 * (numBins - 1 - splitIndex) + 2)
-
-              splitIndex += 1
-            }
+            findAggForRegression(leftNodeAgg, rightNodeAgg, featureIndex)
             featureIndex += 1
           }
           (leftNodeAgg, rightNodeAgg)
@@ -876,8 +1091,8 @@ object DecisionTree extends Serializable with Logging {
      * Calculates information gain for all nodes splits.
      */
     def calculateGainsForAllNodeSplits(
-        leftNodeAgg: Array[Array[Double]],
-        rightNodeAgg: Array[Array[Double]],
+        leftNodeAgg: Array[Array[Array[Double]]],
+        rightNodeAgg: Array[Array[Array[Double]]],
         nodeImpurity: Double): Array[Array[InformationGainStats]] = {
       val gains = Array.ofDim[InformationGainStats](numFeatures, numBins - 1)
 
@@ -918,7 +1133,22 @@ object DecisionTree extends Serializable with Logging {
         while (featureIndex < numFeatures) {
           // Iterate over all splits.
           var splitIndex = 0
-          while (splitIndex < numBins - 1) {
+          val maxSplitIndex : Double = {
+            val isFeatureContinuous = strategy.categoricalFeaturesInfo.get(featureIndex).isEmpty
+            if (isFeatureContinuous) {
+              numBins - 1
+            } else { // Categorical feature
+              val featureCategories = strategy.categoricalFeaturesInfo(featureIndex)
+              val isSpaceSufficientForAllCategoricalSplits
+                = numBins > math.pow(2, featureCategories.toInt - 1) - 1
+              if (isMulticlassClassification && isSpaceSufficientForAllCategoricalSplits) {
+                math.pow(2.0, featureCategories - 1).toInt - 1
+              } else { // Binary classification
+                featureCategories
+              }
+            }
+          }
+          while (splitIndex < maxSplitIndex) {
             val gainStats = gains(featureIndex)(splitIndex)
             if (gainStats.gain > bestGainStats.gain) {
               bestGainStats = gainStats
@@ -944,9 +1174,23 @@ object DecisionTree extends Serializable with Logging {
     def getBinDataForNode(node: Int): Array[Double] = {
       strategy.algo match {
         case Classification =>
-          val shift = 2 * node * numBins * numFeatures
-          val binsForNode = binAggregates.slice(shift, shift + 2 * numBins * numFeatures)
-          binsForNode
+          if (isMulticlassClassificationWithCategoricalFeatures) {
+            val shift = numClasses * node * numBins * numFeatures
+            val rightChildShift = numClasses * numBins * numFeatures * numNodes
+            val binsForNode = {
+              val leftChildData
+                = binAggregates.slice(shift, shift + numClasses * numBins * numFeatures)
+              val rightChildData
+              = binAggregates.slice(rightChildShift + shift,
+                rightChildShift + shift + numClasses * numBins * numFeatures)
+              leftChildData ++ rightChildData
+            }
+            binsForNode
+          } else {
+            val shift = numClasses * node * numBins * numFeatures
+            val binsForNode = binAggregates.slice(shift, shift + numClasses * numBins * numFeatures)
+            binsForNode
+          }
         case Regression =>
           val shift = 3 * node * numBins * numFeatures
           val binsForNode = binAggregates.slice(shift, shift + 3 * numBins * numFeatures)
@@ -963,14 +1207,26 @@ object DecisionTree extends Serializable with Logging {
       val binsForNode: Array[Double] = getBinDataForNode(node)
       logDebug("nodeImpurityIndex = " + nodeImpurityIndex)
       val parentNodeImpurity = parentImpurities(nodeImpurityIndex)
-      logDebug("node impurity = " + parentNodeImpurity)
+      logDebug("parent node impurity = " + parentNodeImpurity)
       bestSplits(node) = binsToBestSplit(binsForNode, parentNodeImpurity)
       node += 1
     }
-
     bestSplits
   }
 
+  private def getElementsPerNode(numFeatures: Int, numBins: Int, numClasses: Int,
+      isMulticlassClassificationWithCategoricalFeatures: Boolean, algo: Algo): Int = {
+    algo match {
+      case Classification =>
+        if (isMulticlassClassificationWithCategoricalFeatures) {
+          2 * numClasses * numBins * numFeatures
+        } else {
+          numClasses * numBins * numFeatures
+        }
+      case Regression => 3 * numBins * numFeatures
+    }
+  }
+
   /**
    * Returns split and bins for decision tree calculation.
    * @param input RDD of [[org.apache.spark.mllib.regression.LabeledPoint]] used as training data
@@ -992,17 +1248,23 @@ object DecisionTree extends Serializable with Logging {
     val maxBins = strategy.maxBins
     val numBins = if (maxBins <= count) maxBins else count.toInt
     logDebug("numBins = " + numBins)
+    val isMulticlassClassification = strategy.isMulticlassClassification
+    logDebug("isMulticlassClassification = " + isMulticlassClassification)
+
 
     /*
-     * TODO: Add a require statement ensuring #bins is always greater than the categories.
+     * Ensure #bins is always greater than the categories. For multiclass classification,
+     * #bins should be greater than 2^(maxCategories - 1) - 1.
      * It's a limitation of the current implementation but a reasonable trade-off since features
      * with large number of categories get favored over continuous features.
      */
     if (strategy.categoricalFeaturesInfo.size > 0) {
       val maxCategoriesForFeatures = strategy.categoricalFeaturesInfo.maxBy(_._2)._2
-      require(numBins >= maxCategoriesForFeatures)
+      require(numBins > maxCategoriesForFeatures, "numBins should be greater than max categories " +
+        "in categorical features")
     }
 
+
     // Calculate the number of sample for approximate quantile calculation.
     val requiredSamples = numBins*numBins
     val fraction = if (requiredSamples < count) requiredSamples.toDouble / count else 1.0
@@ -1036,48 +1298,93 @@ object DecisionTree extends Serializable with Logging {
               val split = new Split(featureIndex, featureSamples(sampleIndex), Continuous, List())
               splits(featureIndex)(index) = split
             }
-          } else {
-            val maxFeatureValue = strategy.categoricalFeaturesInfo(featureIndex)
-            require(maxFeatureValue < numBins, "number of categories should be less than number " +
-              "of bins")
-
-            // For categorical variables, each bin is a category. The bins are sorted and they
-            // are ordered by calculating the centroid of their corresponding labels.
-            val centroidForCategories =
-              sampledInput.map(lp => (lp.features(featureIndex),lp.label))
-                .groupBy(_._1)
-                .mapValues(x => x.map(_._2).sum / x.map(_._1).length)
-
-            // Check for missing categorical variables and putting them last in the sorted list.
-            val fullCentroidForCategories = scala.collection.mutable.Map[Double,Double]()
-            for (i <- 0 until maxFeatureValue) {
-              if (centroidForCategories.contains(i)) {
-                fullCentroidForCategories(i) = centroidForCategories(i)
-              } else {
-                fullCentroidForCategories(i) = Double.MaxValue
-              }
-            }
-
-            // bins sorted by centroids
-            val categoriesSortedByCentroid = fullCentroidForCategories.toList.sortBy(_._2)
-
-            logDebug("centriod for categorical variable = " + categoriesSortedByCentroid)
-
-            var categoriesForSplit = List[Double]()
-            categoriesSortedByCentroid.iterator.zipWithIndex.foreach {
-              case ((key, value), index) =>
-                categoriesForSplit = key :: categoriesForSplit
-                splits(featureIndex)(index) = new Split(featureIndex, Double.MinValue, Categorical,
-                  categoriesForSplit)
+          } else { // Categorical feature
+            val featureCategories = strategy.categoricalFeaturesInfo(featureIndex)
+            val isSpaceSufficientForAllCategoricalSplits
+              = numBins > math.pow(2, featureCategories.toInt - 1) - 1
+
+            // Use different bin/split calculation strategy for categorical features in multiclass
+            // classification that satisfy the space constraint
+            if (isMulticlassClassification && isSpaceSufficientForAllCategoricalSplits) {
+              // 2^(maxFeatureValue- 1) - 1 combinations
+              var index = 0
+              while (index < math.pow(2.0, featureCategories - 1).toInt - 1) {
+                val categories: List[Double]
+                  = extractMultiClassCategories(index + 1, featureCategories)
+                splits(featureIndex)(index)
+                  = new Split(featureIndex, Double.MinValue, Categorical, categories)
                 bins(featureIndex)(index) = {
                   if (index == 0) {
-                    new Bin(new DummyCategoricalSplit(featureIndex, Categorical),
-                      splits(featureIndex)(0), Categorical, key)
+                    new Bin(
+                      new DummyCategoricalSplit(featureIndex, Categorical),
+                      splits(featureIndex)(0),
+                      Categorical,
+                      Double.MinValue)
                   } else {
-                    new Bin(splits(featureIndex)(index-1), splits(featureIndex)(index),
-                      Categorical, key)
+                    new Bin(
+                      splits(featureIndex)(index - 1),
+                      splits(featureIndex)(index),
+                      Categorical,
+                      Double.MinValue)
                   }
                 }
+                index += 1
+              }
+            } else {
+
+              val centroidForCategories = {
+                if (isMulticlassClassification) {
+                  // For categorical variables in multiclass classification,
+                  // each bin is a category. The bins are sorted and they
+                  // are ordered by calculating the impurity of their corresponding labels.
+                  sampledInput.map(lp => (lp.features(featureIndex), lp.label))
+                   .groupBy(_._1)
+                   .mapValues(x => x.groupBy(_._2).mapValues(x => x.size.toDouble))
+                   .map(x => (x._1, x._2.values.toArray))
+                   .map(x => (x._1, strategy.impurity.calculate(x._2,x._2.sum)))
+                } else { // regression or binary classification
+                  // For categorical variables in regression and binary classification,
+                  // each bin is a category. The bins are sorted and they
+                  // are ordered by calculating the centroid of their corresponding labels.
+                  sampledInput.map(lp => (lp.features(featureIndex), lp.label))
+                    .groupBy(_._1)
+                    .mapValues(x => x.map(_._2).sum / x.map(_._1).length)
+                }
+              }
+
+              logDebug("centriod for categories = " + centroidForCategories.mkString(","))
+
+              // Check for missing categorical variables and putting them last in the sorted list.
+              val fullCentroidForCategories = scala.collection.mutable.Map[Double,Double]()
+              for (i <- 0 until featureCategories) {
+                if (centroidForCategories.contains(i)) {
+                  fullCentroidForCategories(i) = centroidForCategories(i)
+                } else {
+                  fullCentroidForCategories(i) = Double.MaxValue
+                }
+              }
+
+              // bins sorted by centroids
+              val categoriesSortedByCentroid = fullCentroidForCategories.toList.sortBy(_._2)
+
+              logDebug("centriod for categorical variable = " + categoriesSortedByCentroid)
+
+              var categoriesForSplit = List[Double]()
+              categoriesSortedByCentroid.iterator.zipWithIndex.foreach {
+                case ((key, value), index) =>
+                  categoriesForSplit = key :: categoriesForSplit
+                  splits(featureIndex)(index) = new Split(featureIndex, Double.MinValue,
+                    Categorical, categoriesForSplit)
+                  bins(featureIndex)(index) = {
+                    if (index == 0) {
+                      new Bin(new DummyCategoricalSplit(featureIndex, Categorical),
+                        splits(featureIndex)(0), Categorical, key)
+                    } else {
+                      new Bin(splits(featureIndex)(index-1), splits(featureIndex)(index),
+                        Categorical, key)
+                    }
+                  }
+              }
             }
           }
           featureIndex += 1
@@ -1107,4 +1414,29 @@ object DecisionTree extends Serializable with Logging {
         throw new UnsupportedOperationException("approximate histogram not supported yet.")
     }
   }
+
+  /**
+   * Nested method to extract list of eligible categories given an index. It extracts the
+   * position of ones in a binary representation of the input. If binary
+   * representation of an number is 01101 (13), the output list should (3.0, 2.0,
+   * 0.0). The maxFeatureValue depict the number of rightmost digits that will be tested for ones.
+   */
+  private[tree] def extractMultiClassCategories(
+      input: Int,
+      maxFeatureValue: Int): List[Double] = {
+    var categories = List[Double]()
+    var j = 0
+    var bitShiftedInput = input
+    while (j < maxFeatureValue) {
+      if (bitShiftedInput % 2 != 0) {
+        // updating the list of categories.
+        categories = j.toDouble :: categories
+      }
+      // Right shift by one
+      bitShiftedInput = bitShiftedInput >> 1
+      j += 1
+    }
+    categories
+  }
+
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala
index 1b505fd76eb7..7c027ac2fda6 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala
@@ -28,6 +28,8 @@ import org.apache.spark.mllib.tree.configuration.QuantileStrategy._
  * @param algo classification or regression
  * @param impurity criterion used for information gain calculation
  * @param maxDepth maximum depth of the tree
+ * @param numClassesForClassification number of classes for classification. Default value is 2
+ *                                    leads to binary classification
  * @param maxBins maximum number of bins used for splitting features
  * @param quantileCalculationStrategy algorithm for calculating quantiles
  * @param categoricalFeaturesInfo A map storing information about the categorical variables and the
@@ -44,7 +46,15 @@ class Strategy (
     val algo: Algo,
     val impurity: Impurity,
     val maxDepth: Int,
+    val numClassesForClassification: Int = 2,
     val maxBins: Int = 100,
     val quantileCalculationStrategy: QuantileStrategy = Sort,
     val categoricalFeaturesInfo: Map[Int, Int] = Map[Int, Int](),
-    val maxMemoryInMB: Int = 128) extends Serializable
+    val maxMemoryInMB: Int = 128) extends Serializable {
+
+  require(numClassesForClassification >= 2)
+  val isMulticlassClassification = numClassesForClassification > 2
+  val isMulticlassWithCategoricalFeatures
+    = isMulticlassClassification && (categoricalFeaturesInfo.size > 0)
+
+}
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Entropy.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Entropy.scala
index 60f43e9278d2..a0e2d9176278 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Entropy.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Entropy.scala
@@ -31,23 +31,35 @@ object Entropy extends Impurity {
 
   /**
    * :: DeveloperApi ::
-   * entropy calculation
-   * @param c0 count of instances with label 0
-   * @param c1 count of instances with label 1
-   * @return entropy value
+   * information calculation for multiclass classification
+   * @param counts Array[Double] with counts for each label
+   * @param totalCount sum of counts for all labels
+   * @return information value
    */
   @DeveloperApi
-  override def calculate(c0: Double, c1: Double): Double = {
-    if (c0 == 0 || c1 == 0) {
-      0
-    } else {
-      val total = c0 + c1
-      val f0 = c0 / total
-      val f1 = c1 / total
-      -(f0 * log2(f0)) - (f1 * log2(f1))
+  override def calculate(counts: Array[Double], totalCount: Double): Double = {
+    val numClasses = counts.length
+    var impurity = 0.0
+    var classIndex = 0
+    while (classIndex < numClasses) {
+      val classCount = counts(classIndex)
+      if (classCount != 0) {
+        val freq = classCount / totalCount
+        impurity -= freq * log2(freq)
+      }
+      classIndex += 1
     }
+    impurity
   }
 
+  /**
+   * :: DeveloperApi ::
+   * variance calculation
+   * @param count number of instances
+   * @param sum sum of labels
+   * @param sumSquares summation of squares of the labels
+   */
+  @DeveloperApi
   override def calculate(count: Double, sum: Double, sumSquares: Double): Double =
     throw new UnsupportedOperationException("Entropy.calculate")
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Gini.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Gini.scala
index c51d76d9b4c5..48144b5e6d1e 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Gini.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Gini.scala
@@ -30,23 +30,32 @@ object Gini extends Impurity {
 
   /**
    * :: DeveloperApi ::
-   * Gini coefficient calculation
-   * @param c0 count of instances with label 0
-   * @param c1 count of instances with label 1
-   * @return Gini coefficient value
+   * information calculation for multiclass classification
+   * @param counts Array[Double] with counts for each label
+   * @param totalCount sum of counts for all labels
+   * @return information value
    */
   @DeveloperApi
-  override def calculate(c0: Double, c1: Double): Double = {
-    if (c0 == 0 || c1 == 0) {
-      0
-    } else {
-      val total = c0 + c1
-      val f0 = c0 / total
-      val f1 = c1 / total
-      1 - f0 * f0 - f1 * f1
+  override def calculate(counts: Array[Double], totalCount: Double): Double = {
+    val numClasses = counts.length
+    var impurity = 1.0
+    var classIndex = 0
+    while (classIndex < numClasses) {
+      val freq = counts(classIndex) / totalCount
+      impurity -= freq * freq
+      classIndex += 1
     }
+    impurity
   }
 
+  /**
+   * :: DeveloperApi ::
+   * variance calculation
+   * @param count number of instances
+   * @param sum sum of labels
+   * @param sumSquares summation of squares of the labels
+   */
+  @DeveloperApi
   override def calculate(count: Double, sum: Double, sumSquares: Double): Double =
     throw new UnsupportedOperationException("Gini.calculate")
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Impurity.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Impurity.scala
index 8eab247cf093..7b2a9320cc21 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Impurity.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Impurity.scala
@@ -28,13 +28,13 @@ trait Impurity extends Serializable {
 
   /**
    * :: DeveloperApi ::
-   * information calculation for binary classification
-   * @param c0 count of instances with label 0
-   * @param c1 count of instances with label 1
+   * information calculation for multiclass classification
+   * @param counts Array[Double] with counts for each label
+   * @param totalCount sum of counts for all labels
    * @return information value
    */
   @DeveloperApi
-  def calculate(c0 : Double, c1 : Double): Double
+  def calculate(counts: Array[Double], totalCount: Double): Double
 
   /**
    * :: DeveloperApi ::
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Variance.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Variance.scala
index 47d07122af30..97149a99ead5 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Variance.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Variance.scala
@@ -25,7 +25,16 @@ import org.apache.spark.annotation.{DeveloperApi, Experimental}
  */
 @Experimental
 object Variance extends Impurity {
-   override def calculate(c0: Double, c1: Double): Double =
+
+  /**
+   * :: DeveloperApi ::
+   * information calculation for multiclass classification
+   * @param counts Array[Double] with counts for each label
+   * @param totalCount sum of counts for all labels
+   * @return information value
+   */
+  @DeveloperApi
+  override def calculate(counts: Array[Double], totalCount: Double): Double =
      throw new UnsupportedOperationException("Variance.calculate")
 
   /**
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Bin.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Bin.scala
index 2d71e1e36606..c89c1e371a40 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Bin.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Bin.scala
@@ -28,7 +28,7 @@ import org.apache.spark.mllib.tree.configuration.FeatureType._
  * @param highSplit signifying the upper threshold for the continuous feature to be
  *                 accepted in the bin
  * @param featureType type of feature -- categorical or continuous
- * @param category categorical label value accepted in the bin
+ * @param category categorical label value accepted in the bin for binary classification
  */
 private[tree]
 case class Bin(lowSplit: Split, highSplit: Split, featureType: FeatureType, category: Double)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/InformationGainStats.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/InformationGainStats.scala
index cc8a24cce961..fb12298e0f5d 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/InformationGainStats.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/InformationGainStats.scala
@@ -27,6 +27,7 @@ import org.apache.spark.annotation.DeveloperApi
  * @param leftImpurity left node impurity
  * @param rightImpurity right node impurity
  * @param predict predicted value
+ * @param prob probability of the label (classification only)
  */
 @DeveloperApi
 class InformationGainStats(
@@ -34,10 +35,11 @@ class InformationGainStats(
     val impurity: Double,
     val leftImpurity: Double,
     val rightImpurity: Double,
-    val predict: Double) extends Serializable {
+    val predict: Double,
+    val prob: Double = 0.0) extends Serializable {
 
   override def toString = {
-    "gain = %f, impurity = %f, left impurity = %f, right impurity = %f, predict = %f"
-      .format(gain, impurity, leftImpurity, rightImpurity, predict)
+    "gain = %f, impurity = %f, left impurity = %f, right impurity = %f, predict = %f, prob = %f"
+      .format(gain, impurity, leftImpurity, rightImpurity, predict, prob)
   }
 }
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala
index bcb11876b8f4..5961a618c59d 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala
@@ -19,7 +19,6 @@ package org.apache.spark.mllib.tree
 
 import org.scalatest.FunSuite
 
-import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.tree.impurity.{Entropy, Gini, Variance}
 import org.apache.spark.mllib.tree.model.Filter
 import org.apache.spark.mllib.tree.model.Split
@@ -28,6 +27,7 @@ import org.apache.spark.mllib.tree.configuration.Algo._
 import org.apache.spark.mllib.tree.configuration.FeatureType._
 import org.apache.spark.mllib.linalg.Vectors
 import org.apache.spark.mllib.util.LocalSparkContext
+import org.apache.spark.mllib.regression.LabeledPoint
 
 class DecisionTreeSuite extends FunSuite with LocalSparkContext {
 
@@ -35,7 +35,7 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
     val arr = DecisionTreeSuite.generateOrderedLabeledPointsWithLabel1()
     assert(arr.length === 1000)
     val rdd = sc.parallelize(arr)
-    val strategy = new Strategy(Classification, Gini, 3, 100)
+    val strategy = new Strategy(Classification, Gini, 3, 2, 100)
     val (splits, bins) = DecisionTree.findSplitsBins(rdd, strategy)
     assert(splits.length === 2)
     assert(bins.length === 2)
@@ -51,6 +51,7 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
       Classification,
       Gini,
       maxDepth = 3,
+      numClassesForClassification = 2,
       maxBins = 100,
       categoricalFeaturesInfo = Map(0 -> 2, 1-> 2))
     val (splits, bins) = DecisionTree.findSplitsBins(rdd, strategy)
@@ -130,8 +131,9 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
       Classification,
       Gini,
       maxDepth = 3,
+      numClassesForClassification = 2,
       maxBins = 100,
-      categoricalFeaturesInfo = Map(0 -> 3, 1-> 3))
+      categoricalFeaturesInfo = Map(0 -> 3, 1 -> 3))
     val (splits, bins) = DecisionTree.findSplitsBins(rdd, strategy)
 
     // Check splits.
@@ -231,6 +233,162 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
     assert(bins(1)(3) === null)
   }
 
+  test("extract categories from a number for multiclass classification") {
+    val l = DecisionTree.extractMultiClassCategories(13, 10)
+    assert(l.length === 3)
+    assert(List(3.0, 2.0, 0.0).toSeq == l.toSeq)
+  }
+
+  test("split and bin calculations for unordered categorical variables with multiclass " +
+    "classification") {
+    val arr = DecisionTreeSuite.generateCategoricalDataPoints()
+    assert(arr.length === 1000)
+    val rdd = sc.parallelize(arr)
+    val strategy = new Strategy(
+      Classification,
+      Gini,
+      maxDepth = 3,
+      numClassesForClassification = 100,
+      maxBins = 100,
+      categoricalFeaturesInfo = Map(0 -> 3, 1-> 3))
+    val (splits, bins) = DecisionTree.findSplitsBins(rdd, strategy)
+
+    // Expecting 2^2 - 1 = 3 bins/splits
+    assert(splits(0)(0).feature === 0)
+    assert(splits(0)(0).threshold === Double.MinValue)
+    assert(splits(0)(0).featureType === Categorical)
+    assert(splits(0)(0).categories.length === 1)
+    assert(splits(0)(0).categories.contains(0.0))
+    assert(splits(1)(0).feature === 1)
+    assert(splits(1)(0).threshold === Double.MinValue)
+    assert(splits(1)(0).featureType === Categorical)
+    assert(splits(1)(0).categories.length === 1)
+    assert(splits(1)(0).categories.contains(0.0))
+
+    assert(splits(0)(1).feature === 0)
+    assert(splits(0)(1).threshold === Double.MinValue)
+    assert(splits(0)(1).featureType === Categorical)
+    assert(splits(0)(1).categories.length === 1)
+    assert(splits(0)(1).categories.contains(1.0))
+    assert(splits(1)(1).feature === 1)
+    assert(splits(1)(1).threshold === Double.MinValue)
+    assert(splits(1)(1).featureType === Categorical)
+    assert(splits(1)(1).categories.length === 1)
+    assert(splits(1)(1).categories.contains(1.0))
+
+    assert(splits(0)(2).feature === 0)
+    assert(splits(0)(2).threshold === Double.MinValue)
+    assert(splits(0)(2).featureType === Categorical)
+    assert(splits(0)(2).categories.length === 2)
+    assert(splits(0)(2).categories.contains(0.0))
+    assert(splits(0)(2).categories.contains(1.0))
+    assert(splits(1)(2).feature === 1)
+    assert(splits(1)(2).threshold === Double.MinValue)
+    assert(splits(1)(2).featureType === Categorical)
+    assert(splits(1)(2).categories.length === 2)
+    assert(splits(1)(2).categories.contains(0.0))
+    assert(splits(1)(2).categories.contains(1.0))
+
+    assert(splits(0)(3) === null)
+    assert(splits(1)(3) === null)
+
+
+    // Check bins.
+
+    assert(bins(0)(0).category === Double.MinValue)
+    assert(bins(0)(0).lowSplit.categories.length === 0)
+    assert(bins(0)(0).highSplit.categories.length === 1)
+    assert(bins(0)(0).highSplit.categories.contains(0.0))
+    assert(bins(1)(0).category === Double.MinValue)
+    assert(bins(1)(0).lowSplit.categories.length === 0)
+    assert(bins(1)(0).highSplit.categories.length === 1)
+    assert(bins(1)(0).highSplit.categories.contains(0.0))
+
+    assert(bins(0)(1).category === Double.MinValue)
+    assert(bins(0)(1).lowSplit.categories.length === 1)
+    assert(bins(0)(1).lowSplit.categories.contains(0.0))
+    assert(bins(0)(1).highSplit.categories.length === 1)
+    assert(bins(0)(1).highSplit.categories.contains(1.0))
+    assert(bins(1)(1).category === Double.MinValue)
+    assert(bins(1)(1).lowSplit.categories.length === 1)
+    assert(bins(1)(1).lowSplit.categories.contains(0.0))
+    assert(bins(1)(1).highSplit.categories.length === 1)
+    assert(bins(1)(1).highSplit.categories.contains(1.0))
+
+    assert(bins(0)(2).category === Double.MinValue)
+    assert(bins(0)(2).lowSplit.categories.length === 1)
+    assert(bins(0)(2).lowSplit.categories.contains(1.0))
+    assert(bins(0)(2).highSplit.categories.length === 2)
+    assert(bins(0)(2).highSplit.categories.contains(1.0))
+    assert(bins(0)(2).highSplit.categories.contains(0.0))
+    assert(bins(1)(2).category === Double.MinValue)
+    assert(bins(1)(2).lowSplit.categories.length === 1)
+    assert(bins(1)(2).lowSplit.categories.contains(1.0))
+    assert(bins(1)(2).highSplit.categories.length === 2)
+    assert(bins(1)(2).highSplit.categories.contains(1.0))
+    assert(bins(1)(2).highSplit.categories.contains(0.0))
+
+    assert(bins(0)(3) === null)
+    assert(bins(1)(3) === null)
+
+  }
+
+  test("split and bin calculations for ordered categorical variables with multiclass " +
+    "classification") {
+    val arr = DecisionTreeSuite.generateCategoricalDataPointsForMulticlassForOrderedFeatures()
+    assert(arr.length === 3000)
+    val rdd = sc.parallelize(arr)
+    val strategy = new Strategy(
+      Classification,
+      Gini,
+      maxDepth = 3,
+      numClassesForClassification = 100,
+      maxBins = 100,
+      categoricalFeaturesInfo = Map(0 -> 10, 1-> 10))
+    val (splits, bins) = DecisionTree.findSplitsBins(rdd, strategy)
+
+    // 2^10 - 1 > 100, so categorical variables will be ordered
+
+    assert(splits(0)(0).feature === 0)
+    assert(splits(0)(0).threshold === Double.MinValue)
+    assert(splits(0)(0).featureType === Categorical)
+    assert(splits(0)(0).categories.length === 1)
+    assert(splits(0)(0).categories.contains(1.0))
+
+    assert(splits(0)(1).feature === 0)
+    assert(splits(0)(1).threshold === Double.MinValue)
+    assert(splits(0)(1).featureType === Categorical)
+    assert(splits(0)(1).categories.length === 2)
+    assert(splits(0)(1).categories.contains(2.0))
+
+    assert(splits(0)(2).feature === 0)
+    assert(splits(0)(2).threshold === Double.MinValue)
+    assert(splits(0)(2).featureType === Categorical)
+    assert(splits(0)(2).categories.length === 3)
+    assert(splits(0)(2).categories.contains(2.0))
+    assert(splits(0)(2).categories.contains(1.0))
+
+    assert(splits(0)(10) === null)
+    assert(splits(1)(10) === null)
+
+
+    // Check bins.
+
+    assert(bins(0)(0).category === 1.0)
+    assert(bins(0)(0).lowSplit.categories.length === 0)
+    assert(bins(0)(0).highSplit.categories.length === 1)
+    assert(bins(0)(0).highSplit.categories.contains(1.0))
+    assert(bins(0)(1).category === 2.0)
+    assert(bins(0)(1).lowSplit.categories.length === 1)
+    assert(bins(0)(1).highSplit.categories.length === 2)
+    assert(bins(0)(1).highSplit.categories.contains(1.0))
+    assert(bins(0)(1).highSplit.categories.contains(2.0))
+
+    assert(bins(0)(10) === null)
+
+  }
+
+
   test("classification stump with all categorical variables") {
     val arr = DecisionTreeSuite.generateCategoricalDataPoints()
     assert(arr.length === 1000)
@@ -238,6 +396,7 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
     val strategy = new Strategy(
       Classification,
       Gini,
+      numClassesForClassification = 2,
       maxDepth = 3,
       maxBins = 100,
       categoricalFeaturesInfo = Map(0 -> 3, 1-> 3))
@@ -253,8 +412,8 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
 
     val stats = bestSplits(0)._2
     assert(stats.gain > 0)
-    assert(stats.predict > 0.5)
-    assert(stats.predict < 0.7)
+    assert(stats.predict === 1)
+    assert(stats.prob == 0.6)
     assert(stats.impurity > 0.2)
   }
 
@@ -280,8 +439,7 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
 
     val stats = bestSplits(0)._2
     assert(stats.gain > 0)
-    assert(stats.predict > 0.5)
-    assert(stats.predict < 0.7)
+    assert(stats.predict == 0.6)
     assert(stats.impurity > 0.2)
   }
 
@@ -289,7 +447,7 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
     val arr = DecisionTreeSuite.generateOrderedLabeledPointsWithLabel0()
     assert(arr.length === 1000)
     val rdd = sc.parallelize(arr)
-    val strategy = new Strategy(Classification, Gini, 3, 100)
+    val strategy = new Strategy(Classification, Gini, 3, 2, 100)
     val (splits, bins) = DecisionTree.findSplitsBins(rdd, strategy)
     assert(splits.length === 2)
     assert(splits(0).length === 99)
@@ -312,7 +470,7 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
     val arr = DecisionTreeSuite.generateOrderedLabeledPointsWithLabel1()
     assert(arr.length === 1000)
     val rdd = sc.parallelize(arr)
-    val strategy = new Strategy(Classification, Gini, 3, 100)
+    val strategy = new Strategy(Classification, Gini, 3, 2, 100)
     val (splits, bins) = DecisionTree.findSplitsBins(rdd, strategy)
     assert(splits.length === 2)
     assert(splits(0).length === 99)
@@ -336,7 +494,7 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
     val arr = DecisionTreeSuite.generateOrderedLabeledPointsWithLabel0()
     assert(arr.length === 1000)
     val rdd = sc.parallelize(arr)
-    val strategy = new Strategy(Classification, Entropy, 3, 100)
+    val strategy = new Strategy(Classification, Entropy, 3, 2, 100)
     val (splits, bins) = DecisionTree.findSplitsBins(rdd, strategy)
     assert(splits.length === 2)
     assert(splits(0).length === 99)
@@ -360,7 +518,7 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
     val arr = DecisionTreeSuite.generateOrderedLabeledPointsWithLabel1()
     assert(arr.length === 1000)
     val rdd = sc.parallelize(arr)
-    val strategy = new Strategy(Classification, Entropy, 3, 100)
+    val strategy = new Strategy(Classification, Entropy, 3, 2, 100)
     val (splits, bins) = DecisionTree.findSplitsBins(rdd, strategy)
     assert(splits.length === 2)
     assert(splits(0).length === 99)
@@ -380,11 +538,11 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
     assert(bestSplits(0)._2.predict === 1)
   }
 
-  test("test second level node building with/without groups") {
+  test("second level node building with/without groups") {
     val arr = DecisionTreeSuite.generateOrderedLabeledPoints()
     assert(arr.length === 1000)
     val rdd = sc.parallelize(arr)
-    val strategy = new Strategy(Classification, Entropy, 3, 100)
+    val strategy = new Strategy(Classification, Entropy, 3, 2, 100)
     val (splits, bins) = DecisionTree.findSplitsBins(rdd, strategy)
     assert(splits.length === 2)
     assert(splits(0).length === 99)
@@ -426,6 +584,82 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
 
   }
 
+  test("stump with categorical variables for multiclass classification") {
+    val arr = DecisionTreeSuite.generateCategoricalDataPointsForMulticlass()
+    val input = sc.parallelize(arr)
+    val strategy = new Strategy(algo = Classification, impurity = Gini, maxDepth = 5,
+      numClassesForClassification = 3, categoricalFeaturesInfo = Map(0 -> 3, 1 -> 3))
+    assert(strategy.isMulticlassClassification)
+    val (splits, bins) = DecisionTree.findSplitsBins(input, strategy)
+    val bestSplits = DecisionTree.findBestSplits(input, new Array(31), strategy, 0,
+      Array[List[Filter]](), splits, bins, 10)
+
+    assert(bestSplits.length === 1)
+    val bestSplit = bestSplits(0)._1
+    assert(bestSplit.feature === 0)
+    assert(bestSplit.categories.length === 1)
+    assert(bestSplit.categories.contains(1))
+    assert(bestSplit.featureType === Categorical)
+  }
+
+  test("stump with continuous variables for multiclass classification") {
+    val arr = DecisionTreeSuite.generateContinuousDataPointsForMulticlass()
+    val input = sc.parallelize(arr)
+    val strategy = new Strategy(algo = Classification, impurity = Gini, maxDepth = 5,
+      numClassesForClassification = 3)
+    assert(strategy.isMulticlassClassification)
+    val (splits, bins) = DecisionTree.findSplitsBins(input, strategy)
+    val bestSplits = DecisionTree.findBestSplits(input, new Array(31), strategy, 0,
+      Array[List[Filter]](), splits, bins, 10)
+
+    assert(bestSplits.length === 1)
+    val bestSplit = bestSplits(0)._1
+
+    assert(bestSplit.feature === 1)
+    assert(bestSplit.featureType === Continuous)
+    assert(bestSplit.threshold > 1980)
+    assert(bestSplit.threshold < 2020)
+
+  }
+
+  test("stump with continuous + categorical variables for multiclass classification") {
+    val arr = DecisionTreeSuite.generateContinuousDataPointsForMulticlass()
+    val input = sc.parallelize(arr)
+    val strategy = new Strategy(algo = Classification, impurity = Gini, maxDepth = 5,
+      numClassesForClassification = 3, categoricalFeaturesInfo = Map(0 -> 3))
+    assert(strategy.isMulticlassClassification)
+    val (splits, bins) = DecisionTree.findSplitsBins(input, strategy)
+    val bestSplits = DecisionTree.findBestSplits(input, new Array(31), strategy, 0,
+      Array[List[Filter]](), splits, bins, 10)
+
+    assert(bestSplits.length === 1)
+    val bestSplit = bestSplits(0)._1
+
+    assert(bestSplit.feature === 1)
+    assert(bestSplit.featureType === Continuous)
+    assert(bestSplit.threshold > 1980)
+    assert(bestSplit.threshold < 2020)
+  }
+
+  test("stump with categorical variables for ordered multiclass classification") {
+    val arr = DecisionTreeSuite.generateCategoricalDataPointsForMulticlassForOrderedFeatures()
+    val input = sc.parallelize(arr)
+    val strategy = new Strategy(algo = Classification, impurity = Gini, maxDepth = 5,
+      numClassesForClassification = 3, categoricalFeaturesInfo = Map(0 -> 10, 1 -> 10))
+    assert(strategy.isMulticlassClassification)
+    val (splits, bins) = DecisionTree.findSplitsBins(input, strategy)
+    val bestSplits = DecisionTree.findBestSplits(input, new Array(31), strategy, 0,
+      Array[List[Filter]](), splits, bins, 10)
+
+    assert(bestSplits.length === 1)
+    val bestSplit = bestSplits(0)._1
+    assert(bestSplit.feature === 0)
+    assert(bestSplit.categories.length === 1)
+    assert(bestSplit.categories.contains(1.0))
+    assert(bestSplit.featureType === Categorical)
+  }
+
+
 }
 
 object DecisionTreeSuite {
@@ -473,4 +707,47 @@ object DecisionTreeSuite {
     }
     arr
   }
+
+  def generateCategoricalDataPointsForMulticlass(): Array[LabeledPoint] = {
+    val arr = new Array[LabeledPoint](3000)
+    for (i <- 0 until 3000) {
+      if (i < 1000) {
+        arr(i) = new LabeledPoint(2.0, Vectors.dense(2.0, 2.0))
+      } else if (i < 2000) {
+        arr(i) = new LabeledPoint(1.0, Vectors.dense(1.0, 2.0))
+      } else {
+        arr(i) = new LabeledPoint(2.0, Vectors.dense(2.0, 2.0))
+      }
+    }
+    arr
+  }
+
+  def generateContinuousDataPointsForMulticlass(): Array[LabeledPoint] = {
+    val arr = new Array[LabeledPoint](3000)
+    for (i <- 0 until 3000) {
+      if (i < 2000) {
+        arr(i) = new LabeledPoint(2.0, Vectors.dense(2.0, i))
+      } else {
+        arr(i) = new LabeledPoint(1.0, Vectors.dense(2.0, i))
+      }
+    }
+    arr
+  }
+
+  def generateCategoricalDataPointsForMulticlassForOrderedFeatures():
+    Array[LabeledPoint] = {
+    val arr = new Array[LabeledPoint](3000)
+    for (i <- 0 until 3000) {
+      if (i < 1000) {
+        arr(i) = new LabeledPoint(2.0, Vectors.dense(2.0, 2.0))
+      } else if (i < 2000) {
+        arr(i) = new LabeledPoint(1.0, Vectors.dense(1.0, 2.0))
+      } else {
+        arr(i) = new LabeledPoint(1.0, Vectors.dense(2.0, 2.0))
+      }
+    }
+    arr
+  }
+
+
 }
diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala
index 3487f7c5c125..e0f433b26f7f 100644
--- a/project/MimaExcludes.scala
+++ b/project/MimaExcludes.scala
@@ -82,7 +82,15 @@ object MimaExcludes {
       MimaBuild.excludeSparkClass("util.SerializableHyperLogLog") ++
       MimaBuild.excludeSparkClass("storage.Values") ++
       MimaBuild.excludeSparkClass("storage.Entry") ++
-      MimaBuild.excludeSparkClass("storage.MemoryStore$Entry")
+      MimaBuild.excludeSparkClass("storage.MemoryStore$Entry") ++
+      Seq(
+        ProblemFilters.exclude[IncompatibleMethTypeProblem](
+          "org.apache.spark.mllib.tree.impurity.Gini.calculate"),
+        ProblemFilters.exclude[IncompatibleMethTypeProblem](
+          "org.apache.spark.mllib.tree.impurity.Entropy.calculate"),
+        ProblemFilters.exclude[IncompatibleMethTypeProblem](
+          "org.apache.spark.mllib.tree.impurity.Variance.calculate")
+      )
     case v if v.startsWith("1.0") =>
       Seq(
         MimaBuild.excludeSparkPackage("api.java"),

From 3a1709fa557f2bd6d101bc67a9e773882078c527 Mon Sep 17 00:00:00 2001
From: Takuya UESHIN <ueshin@happy-camper.st>
Date: Fri, 18 Jul 2014 16:24:00 -0500
Subject: [PATCH 693/810] [SPARK-2535][SQL] Add StringComparison case to
 NullPropagation.

`StringComparison` expressions including `null` literal cases could be added to `NullPropagation`.

Author: Takuya UESHIN <ueshin@happy-camper.st>

Closes #1451 from ueshin/issues/SPARK-2535 and squashes the following commits:

e99c237 [Takuya UESHIN] Add some tests.
8f9b984 [Takuya UESHIN] Add StringComparison case to NullPropagation.
---
 .../sql/catalyst/optimizer/Optimizer.scala    |  5 ++
 .../ExpressionEvaluationSuite.scala           | 23 ++++-
 .../optimizer/ConstantFoldingSuite.scala      | 10 ++-
 .../optimizer/LikeSimplificationSuite.scala   | 90 +++++++++++++++++++
 4 files changed, 125 insertions(+), 3 deletions(-)
 create mode 100644 sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/LikeSimplificationSuite.scala

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
index 7f32f6b8bcf4..c65987b7120b 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
@@ -188,6 +188,11 @@ object NullPropagation extends Rule[LogicalPlan] {
         case left :: Literal(null, _) :: Nil => Literal(null, e.dataType)
         case _ => e
       }
+      case e: StringComparison => e.children match {
+        case Literal(null, _) :: right :: Nil => Literal(null, e.dataType)
+        case left :: Literal(null, _) :: Nil => Literal(null, e.dataType)
+        case _ => e
+      }
     }
   }
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala
index 143330bd6471..73f546455b67 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala
@@ -466,7 +466,28 @@ class ExpressionEvaluationSuite extends FunSuite {
     checkEvaluation(c1 === c2, false, row)
     checkEvaluation(c1 !== c2, true, row)
   }
-  
+
+  test("StringComparison") {
+    val row = new GenericRow(Array[Any]("abc", null))
+    val c1 = 'a.string.at(0)
+    val c2 = 'a.string.at(1)
+
+    checkEvaluation(Contains(c1, "b"), true, row)
+    checkEvaluation(Contains(c1, "x"), false, row)
+    checkEvaluation(Contains(c2, "b"), null, row)
+    checkEvaluation(Contains(c1, Literal(null, StringType)), null, row)
+
+    checkEvaluation(StartsWith(c1, "a"), true, row)
+    checkEvaluation(StartsWith(c1, "b"), false, row)
+    checkEvaluation(StartsWith(c2, "a"), null, row)
+    checkEvaluation(StartsWith(c1, Literal(null, StringType)), null, row)
+
+    checkEvaluation(EndsWith(c1, "c"), true, row)
+    checkEvaluation(EndsWith(c1, "b"), false, row)
+    checkEvaluation(EndsWith(c2, "b"), null, row)
+    checkEvaluation(EndsWith(c1, Literal(null, StringType)), null, row)
+  }
+
   test("Substring") {
     val row = new GenericRow(Array[Any]("example", "example".toArray.map(_.toByte)))
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConstantFoldingSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConstantFoldingSuite.scala
index ff8d0d06c45e..d607eed1bea8 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConstantFoldingSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConstantFoldingSuite.scala
@@ -205,7 +205,10 @@ class ConstantFoldingSuite extends PlanTest {
 
           Substring(Literal(null, StringType), 0, 1) as 'c16,
           Substring("abc", Literal(null, IntegerType), 1) as 'c17,
-          Substring("abc", 0, Literal(null, IntegerType)) as 'c18
+          Substring("abc", 0, Literal(null, IntegerType)) as 'c18,
+
+          Contains(Literal(null, StringType), "abc") as 'c19,
+          Contains("abc", Literal(null, StringType)) as 'c20
         )
 
     val optimized = Optimize(originalQuery.analyze)
@@ -237,7 +240,10 @@ class ConstantFoldingSuite extends PlanTest {
 
           Literal(null, StringType) as 'c16,
           Literal(null, StringType) as 'c17,
-          Literal(null, StringType) as 'c18
+          Literal(null, StringType) as 'c18,
+
+          Literal(null, BooleanType) as 'c19,
+          Literal(null, BooleanType) as 'c20
         ).analyze
 
     comparePlans(optimized, correctAnswer)
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/LikeSimplificationSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/LikeSimplificationSuite.scala
new file mode 100644
index 000000000000..b10577c8001e
--- /dev/null
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/LikeSimplificationSuite.scala
@@ -0,0 +1,90 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.optimizer
+
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.plans.logical._
+import org.apache.spark.sql.catalyst.plans.PlanTest
+import org.apache.spark.sql.catalyst.rules._
+
+/* Implicit conversions */
+import org.apache.spark.sql.catalyst.dsl.expressions._
+import org.apache.spark.sql.catalyst.dsl.plans._
+
+class LikeSimplificationSuite extends PlanTest {
+
+  object Optimize extends RuleExecutor[LogicalPlan] {
+    val batches =
+      Batch("Like Simplification", Once,
+        LikeSimplification) :: Nil
+  }
+
+  val testRelation = LocalRelation('a.string)
+
+  test("simplify Like into StartsWith") {
+    val originalQuery =
+      testRelation
+        .where(('a like "abc%") || ('a like "abc\\%"))
+
+    val optimized = Optimize(originalQuery.analyze)
+    val correctAnswer = testRelation
+      .where(StartsWith('a, "abc") || ('a like "abc\\%"))
+      .analyze
+
+    comparePlans(optimized, correctAnswer)
+  }
+
+  test("simplify Like into EndsWith") {
+    val originalQuery =
+      testRelation
+        .where('a like "%xyz")
+
+    val optimized = Optimize(originalQuery.analyze)
+    val correctAnswer = testRelation
+      .where(EndsWith('a, "xyz"))
+      .analyze
+
+    comparePlans(optimized, correctAnswer)
+  }
+
+  test("simplify Like into Contains") {
+    val originalQuery =
+      testRelation
+        .where(('a like "%mn%") || ('a like "%mn\\%"))
+
+    val optimized = Optimize(originalQuery.analyze)
+    val correctAnswer = testRelation
+      .where(Contains('a, "mn") || ('a like "%mn\\%"))
+      .analyze
+
+    comparePlans(optimized, correctAnswer)
+  }
+
+  test("simplify Like into EqualTo") {
+    val originalQuery =
+      testRelation
+        .where(('a like "") || ('a like "abc"))
+
+    val optimized = Optimize(originalQuery.analyze)
+    val correctAnswer = testRelation
+      .where(('a === "") || ('a === "abc"))
+      .analyze
+
+    comparePlans(optimized, correctAnswer)
+  }
+}

From 7f1720813793e155743b58eae5228298e894b90d Mon Sep 17 00:00:00 2001
From: Cheng Hao <hao.cheng@intel.com>
Date: Fri, 18 Jul 2014 16:38:11 -0500
Subject: [PATCH 694/810] [SPARK-2540] [SQL] Add HiveDecimal & HiveVarchar
 support in unwrapping data

Author: Cheng Hao <hao.cheng@intel.com>

Closes #1436 from chenghao-intel/unwrapdata and squashes the following commits:

34cc21a [Cheng Hao] update the table scan accodringly since the unwrapData function changed
afc39da [Cheng Hao] Polish the code
39d6475 [Cheng Hao] Add HiveDecimal & HiveVarchar support in unwrap data
---
 .../spark/sql/hive/execution/HiveTableScan.scala     | 12 +-----------
 .../scala/org/apache/spark/sql/hive/hiveUdfs.scala   |  4 ++++
 2 files changed, 5 insertions(+), 11 deletions(-)

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveTableScan.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveTableScan.scala
index ef8bae74530e..e7016fa16eea 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveTableScan.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveTableScan.scala
@@ -96,19 +96,9 @@ case class HiveTableScan(
           .getOrElse(sys.error(s"Can't find attribute $a"))
         val fieldObjectInspector = ref.getFieldObjectInspector
 
-        val unwrapHiveData = fieldObjectInspector match {
-          case _: HiveVarcharObjectInspector =>
-            (value: Any) => value.asInstanceOf[HiveVarchar].getValue
-          case _: HiveDecimalObjectInspector =>
-            (value: Any) => BigDecimal(value.asInstanceOf[HiveDecimal].bigDecimalValue())
-          case _ =>
-            identity[Any] _
-        }
-
         (row: Any, _: Array[String]) => {
           val data = objectInspector.getStructFieldData(row, ref)
-          val hiveData = unwrapData(data, fieldObjectInspector)
-          if (hiveData != null) unwrapHiveData(hiveData) else null
+          unwrapData(data, fieldObjectInspector)
         }
       }
     }
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala
index 9b105308ab7c..fc33c5b460d7 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala
@@ -280,6 +280,10 @@ private[hive] case class HiveGenericUdf(name: String, children: Seq[Expression])
 private[hive] trait HiveInspectors {
 
   def unwrapData(data: Any, oi: ObjectInspector): Any = oi match {
+    case hvoi: HiveVarcharObjectInspector => 
+      if (data == null) null else hvoi.getPrimitiveJavaObject(data).getValue
+    case hdoi: HiveDecimalObjectInspector => 
+      if (data == null) null else BigDecimal(hdoi.getPrimitiveJavaObject(data).bigDecimalValue())
     case pi: PrimitiveObjectInspector => pi.getPrimitiveJavaObject(data)
     case li: ListObjectInspector =>
       Option(li.getList(data))

From 7b971b91caeebda57f1506ffc4fd266a1b379290 Mon Sep 17 00:00:00 2001
From: Kay Ousterhout <kayousterhout@gmail.com>
Date: Fri, 18 Jul 2014 14:40:32 -0700
Subject: [PATCH 695/810] [SPARK-2571] Correctly report shuffle read metrics.

Currently, shuffle read metrics are incorrectly reported when stages have multiple shuffle dependencies (they are set to be the metrics from just one of the shuffle dependencies, rather than the accumulated metrics from all of the shuffle dependencies).  This fixes that problem, and should probably be back-ported to the 0.9 branch.

Thanks ryanra for discovering this problem!

cc rxin andrewor14

Author: Kay Ousterhout <kayousterhout@gmail.com>

Closes #1476 from kayousterhout/join_bug and squashes the following commits:

0203a16 [Kay Ousterhout] Fix broken unit tests.
f463c2e [Kay Ousterhout] [SPARK-2571] Correctly report shuffle read metrics.
---
 .../apache/spark/executor/TaskMetrics.scala   | 20 ++++++++++++++++++-
 .../hash/BlockStoreShuffleFetcher.scala       |  2 +-
 .../org/apache/spark/util/JsonProtocol.scala  |  5 +++--
 .../spark/scheduler/SparkListenerSuite.scala  |  4 ++--
 .../ui/jobs/JobProgressListenerSuite.scala    |  6 +-----
 .../apache/spark/util/JsonProtocolSuite.scala |  2 +-
 6 files changed, 27 insertions(+), 12 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/executor/TaskMetrics.scala b/core/src/main/scala/org/apache/spark/executor/TaskMetrics.scala
index ac73288442a7..5d59e00636ee 100644
--- a/core/src/main/scala/org/apache/spark/executor/TaskMetrics.scala
+++ b/core/src/main/scala/org/apache/spark/executor/TaskMetrics.scala
@@ -75,7 +75,9 @@ class TaskMetrics extends Serializable {
   /**
    * If this task reads from shuffle output, metrics on getting shuffle data will be collected here
    */
-  var shuffleReadMetrics: Option[ShuffleReadMetrics] = None
+  private var _shuffleReadMetrics: Option[ShuffleReadMetrics] = None
+
+  def shuffleReadMetrics = _shuffleReadMetrics
 
   /**
    * If this task writes to shuffle output, metrics on the written shuffle data will be collected
@@ -87,6 +89,22 @@ class TaskMetrics extends Serializable {
    * Storage statuses of any blocks that have been updated as a result of this task.
    */
   var updatedBlocks: Option[Seq[(BlockId, BlockStatus)]] = None
+
+  /** Adds the given ShuffleReadMetrics to any existing shuffle metrics for this task. */
+  def updateShuffleReadMetrics(newMetrics: ShuffleReadMetrics) = synchronized {
+    _shuffleReadMetrics match {
+      case Some(existingMetrics) =>
+        existingMetrics.shuffleFinishTime = math.max(
+          existingMetrics.shuffleFinishTime, newMetrics.shuffleFinishTime)
+        existingMetrics.fetchWaitTime += newMetrics.fetchWaitTime
+        existingMetrics.localBlocksFetched += newMetrics.localBlocksFetched
+        existingMetrics.remoteBlocksFetched += newMetrics.remoteBlocksFetched
+        existingMetrics.totalBlocksFetched += newMetrics.totalBlocksFetched
+        existingMetrics.remoteBytesRead += newMetrics.remoteBytesRead
+      case None =>
+        _shuffleReadMetrics = Some(newMetrics)
+    }
+  }
 }
 
 private[spark] object TaskMetrics {
diff --git a/core/src/main/scala/org/apache/spark/shuffle/hash/BlockStoreShuffleFetcher.scala b/core/src/main/scala/org/apache/spark/shuffle/hash/BlockStoreShuffleFetcher.scala
index a932455776e3..3795994cd920 100644
--- a/core/src/main/scala/org/apache/spark/shuffle/hash/BlockStoreShuffleFetcher.scala
+++ b/core/src/main/scala/org/apache/spark/shuffle/hash/BlockStoreShuffleFetcher.scala
@@ -84,7 +84,7 @@ private[hash] object BlockStoreShuffleFetcher extends Logging {
       shuffleMetrics.totalBlocksFetched = blockFetcherItr.totalBlocks
       shuffleMetrics.localBlocksFetched = blockFetcherItr.numLocalBlocks
       shuffleMetrics.remoteBlocksFetched = blockFetcherItr.numRemoteBlocks
-      context.taskMetrics.shuffleReadMetrics = Some(shuffleMetrics)
+      context.taskMetrics.updateShuffleReadMetrics(shuffleMetrics)
     })
 
     new InterruptibleIterator[T](context, completionIter)
diff --git a/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala b/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala
index 47eb44b53037..2ff8b25a56d1 100644
--- a/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala
+++ b/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala
@@ -527,8 +527,9 @@ private[spark] object JsonProtocol {
     metrics.resultSerializationTime = (json \ "Result Serialization Time").extract[Long]
     metrics.memoryBytesSpilled = (json \ "Memory Bytes Spilled").extract[Long]
     metrics.diskBytesSpilled = (json \ "Disk Bytes Spilled").extract[Long]
-    metrics.shuffleReadMetrics =
-      Utils.jsonOption(json \ "Shuffle Read Metrics").map(shuffleReadMetricsFromJson)
+    Utils.jsonOption(json \ "Shuffle Read Metrics").map { shuffleReadMetrics =>
+      metrics.updateShuffleReadMetrics(shuffleReadMetricsFromJson(shuffleReadMetrics))
+    }
     metrics.shuffleWriteMetrics =
       Utils.jsonOption(json \ "Shuffle Write Metrics").map(shuffleWriteMetricsFromJson)
     metrics.inputMetrics =
diff --git a/core/src/test/scala/org/apache/spark/scheduler/SparkListenerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/SparkListenerSuite.scala
index 71f48e295ecc..3b0b8e2f68c9 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/SparkListenerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/SparkListenerSuite.scala
@@ -258,8 +258,8 @@ class SparkListenerSuite extends FunSuite with LocalSparkContext with Matchers
         if (stageInfo.rddInfos.exists(_.name == d4.name)) {
           taskMetrics.shuffleReadMetrics should be ('defined)
           val sm = taskMetrics.shuffleReadMetrics.get
-          sm.totalBlocksFetched should be > (0)
-          sm.localBlocksFetched should be > (0)
+          sm.totalBlocksFetched should be (128)
+          sm.localBlocksFetched should be (128)
           sm.remoteBlocksFetched should be (0)
           sm.remoteBytesRead should be (0l)
         }
diff --git a/core/src/test/scala/org/apache/spark/ui/jobs/JobProgressListenerSuite.scala b/core/src/test/scala/org/apache/spark/ui/jobs/JobProgressListenerSuite.scala
index a8556624804b..b52f81877d55 100644
--- a/core/src/test/scala/org/apache/spark/ui/jobs/JobProgressListenerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/ui/jobs/JobProgressListenerSuite.scala
@@ -63,7 +63,7 @@ class JobProgressListenerSuite extends FunSuite with LocalSparkContext with Matc
 
     // finish this task, should get updated shuffleRead
     shuffleReadMetrics.remoteBytesRead = 1000
-    taskMetrics.shuffleReadMetrics = Some(shuffleReadMetrics)
+    taskMetrics.updateShuffleReadMetrics(shuffleReadMetrics)
     var taskInfo = new TaskInfo(1234L, 0, 1, 0L, "exe-1", "host1", TaskLocality.NODE_LOCAL, false)
     taskInfo.finishTime = 1
     var task = new ShuffleMapTask(0, null, null, 0, null)
@@ -81,8 +81,6 @@ class JobProgressListenerSuite extends FunSuite with LocalSparkContext with Matc
     assert(listener.stageIdToData.size === 1)
 
     // finish this task, should get updated duration
-    shuffleReadMetrics.remoteBytesRead = 1000
-    taskMetrics.shuffleReadMetrics = Some(shuffleReadMetrics)
     taskInfo = new TaskInfo(1235L, 0, 1, 0L, "exe-1", "host1", TaskLocality.NODE_LOCAL, false)
     taskInfo.finishTime = 1
     task = new ShuffleMapTask(0, null, null, 0, null)
@@ -91,8 +89,6 @@ class JobProgressListenerSuite extends FunSuite with LocalSparkContext with Matc
       .shuffleRead === 2000)
 
     // finish this task, should get updated duration
-    shuffleReadMetrics.remoteBytesRead = 1000
-    taskMetrics.shuffleReadMetrics = Some(shuffleReadMetrics)
     taskInfo = new TaskInfo(1236L, 0, 2, 0L, "exe-2", "host1", TaskLocality.NODE_LOCAL, false)
     taskInfo.finishTime = 1
     task = new ShuffleMapTask(0, null, null, 0, null)
diff --git a/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala b/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala
index 058d31453081..11f70a6090d2 100644
--- a/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala
+++ b/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala
@@ -518,7 +518,7 @@ class JsonProtocolSuite extends FunSuite {
       sr.localBlocksFetched = e
       sr.fetchWaitTime = a + d
       sr.remoteBlocksFetched = f
-      t.shuffleReadMetrics = Some(sr)
+      t.updateShuffleReadMetrics(sr)
     }
     sw.shuffleBytesWritten = a + b + c
     sw.shuffleWriteTime = b + c + d

From a243364b225da9a91813234027eafedffc495ecc Mon Sep 17 00:00:00 2001
From: Doris Xin <doris.s.xin@gmail.com>
Date: Fri, 18 Jul 2014 17:25:32 -0700
Subject: [PATCH 696/810] [SPARK-2359][MLlib] Correlations

Implementation for Pearson and Spearman's correlation.

Author: Doris Xin <doris.s.xin@gmail.com>

Closes #1367 from dorx/correlation and squashes the following commits:

c0dd7dc [Doris Xin] here we go
32d83a3 [Doris Xin] Reviewer comments
4db0da1 [Doris Xin] added private[stat] to Spearman
b716f70 [Doris Xin] minor fixes
6e1b42a [Doris Xin] More comments addressed. Still some open questions
8104f44 [Doris Xin] addressed comments. some open questions still
39387c2 [Doris Xin] added missing header
bd3cf19 [Doris Xin] Merge branch 'master' into correlation
6341884 [Doris Xin] race condition bug squished
bd2bacf [Doris Xin] Race condition bug
b775ff9 [Doris Xin] old wrong impl
534ebf2 [Doris Xin] Merge branch 'master' into correlation
818fa31 [Doris Xin] wip units
9d808ee [Doris Xin] wip units
b843a13 [Doris Xin] revert change in stat counter
28561b6 [Doris Xin] wip
bb2e977 [Doris Xin] minor fix
8e02c63 [Doris Xin] Merge branch 'master' into correlation
2a40aa1 [Doris Xin] initial, untested implementation of Pearson
dfc4854 [Doris Xin] WIP
---
 .../apache/spark/mllib/stat/Statistics.scala  |  78 +++++++++++
 .../mllib/stat/correlation/Correlation.scala  |  91 +++++++++++++
 .../stat/correlation/PearsonCorrelation.scala | 107 +++++++++++++++
 .../correlation/SpearmanCorrelation.scala     | 127 ++++++++++++++++++
 .../spark/mllib/stat/CorrelationSuite.scala   | 116 ++++++++++++++++
 5 files changed, 519 insertions(+)
 create mode 100644 mllib/src/main/scala/org/apache/spark/mllib/stat/Statistics.scala
 create mode 100644 mllib/src/main/scala/org/apache/spark/mllib/stat/correlation/Correlation.scala
 create mode 100644 mllib/src/main/scala/org/apache/spark/mllib/stat/correlation/PearsonCorrelation.scala
 create mode 100644 mllib/src/main/scala/org/apache/spark/mllib/stat/correlation/SpearmanCorrelation.scala
 create mode 100644 mllib/src/test/scala/org/apache/spark/mllib/stat/CorrelationSuite.scala

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/stat/Statistics.scala b/mllib/src/main/scala/org/apache/spark/mllib/stat/Statistics.scala
new file mode 100644
index 000000000000..68f3867ba6c1
--- /dev/null
+++ b/mllib/src/main/scala/org/apache/spark/mllib/stat/Statistics.scala
@@ -0,0 +1,78 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.mllib.stat
+
+import org.apache.spark.annotation.Experimental
+import org.apache.spark.mllib.linalg.{Matrix, Vector}
+import org.apache.spark.mllib.stat.correlation.Correlations
+import org.apache.spark.rdd.RDD
+
+/**
+ * API for statistical functions in MLlib
+ */
+@Experimental
+object Statistics {
+
+  /**
+   * Compute the Pearson correlation matrix for the input RDD of Vectors.
+   * Returns NaN if either vector has 0 variance.
+   *
+   * @param X an RDD[Vector] for which the correlation matrix is to be computed.
+   * @return Pearson correlation matrix comparing columns in X.
+   */
+  def corr(X: RDD[Vector]): Matrix = Correlations.corrMatrix(X)
+
+  /**
+   * Compute the correlation matrix for the input RDD of Vectors using the specified method.
+   * Methods currently supported: `pearson` (default), `spearman`
+   *
+   * Note that for Spearman, a rank correlation, we need to create an RDD[Double] for each column
+   * and sort it in order to retrieve the ranks and then join the columns back into an RDD[Vector],
+   * which is fairly costly. Cache the input RDD before calling corr with `method = "spearman"` to
+   * avoid recomputing the common lineage.
+   *
+   * @param X an RDD[Vector] for which the correlation matrix is to be computed.
+   * @param method String specifying the method to use for computing correlation.
+   *               Supported: `pearson` (default), `spearman`
+   * @return Correlation matrix comparing columns in X.
+   */
+  def corr(X: RDD[Vector], method: String): Matrix = Correlations.corrMatrix(X, method)
+
+  /**
+   * Compute the Pearson correlation for the input RDDs.
+   * Columns with 0 covariance produce NaN entries in the correlation matrix.
+   *
+   * @param x RDD[Double] of the same cardinality as y
+   * @param y RDD[Double] of the same cardinality as x
+   * @return A Double containing the Pearson correlation between the two input RDD[Double]s
+   */
+  def corr(x: RDD[Double], y: RDD[Double]): Double = Correlations.corr(x, y)
+
+  /**
+   * Compute the correlation for the input RDDs using the specified method.
+   * Methods currently supported: pearson (default), spearman
+   *
+   * @param x RDD[Double] of the same cardinality as y
+   * @param y RDD[Double] of the same cardinality as x
+   * @param method String specifying the method to use for computing correlation.
+   *               Supported: `pearson` (default), `spearman`
+   *@return A Double containing the correlation between the two input RDD[Double]s using the
+   *         specified method.
+   */
+  def corr(x: RDD[Double], y: RDD[Double], method: String): Double = Correlations.corr(x, y, method)
+}
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/stat/correlation/Correlation.scala b/mllib/src/main/scala/org/apache/spark/mllib/stat/correlation/Correlation.scala
new file mode 100644
index 000000000000..f23393d3da25
--- /dev/null
+++ b/mllib/src/main/scala/org/apache/spark/mllib/stat/correlation/Correlation.scala
@@ -0,0 +1,91 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.mllib.stat.correlation
+
+import org.apache.spark.mllib.linalg.{DenseVector, Matrix, Vector}
+import org.apache.spark.rdd.RDD
+
+/**
+ * Trait for correlation algorithms.
+ */
+private[stat] trait Correlation {
+
+  /**
+   * Compute correlation for two datasets.
+   */
+  def computeCorrelation(x: RDD[Double], y: RDD[Double]): Double
+
+  /**
+   * Compute the correlation matrix S, for the input matrix, where S(i, j) is the correlation
+   * between column i and j. S(i, j) can be NaN if the correlation is undefined for column i and j.
+   */
+  def computeCorrelationMatrix(X: RDD[Vector]): Matrix
+
+  /**
+   * Combine the two input RDD[Double]s into an RDD[Vector] and compute the correlation using the
+   * correlation implementation for RDD[Vector]. Can be NaN if correlation is undefined for the
+   * input vectors.
+   */
+  def computeCorrelationWithMatrixImpl(x: RDD[Double], y: RDD[Double]): Double = {
+    val mat: RDD[Vector] = x.zip(y).map { case (xi, yi) => new DenseVector(Array(xi, yi)) }
+    computeCorrelationMatrix(mat)(0, 1)
+  }
+
+}
+
+/**
+ * Delegates computation to the specific correlation object based on the input method name
+ *
+ * Currently supported correlations: pearson, spearman.
+ * After new correlation algorithms are added, please update the documentation here and in
+ * Statistics.scala for the correlation APIs.
+ *
+ * Maintains the default correlation type, pearson
+ */
+private[stat] object Correlations {
+
+  // Note: after new types of correlations are implemented, please update this map
+  val nameToObjectMap = Map(("pearson", PearsonCorrelation), ("spearman", SpearmanCorrelation))
+  val defaultCorrName: String = "pearson"
+  val defaultCorr: Correlation = nameToObjectMap(defaultCorrName)
+
+  def corr(x: RDD[Double], y: RDD[Double], method: String = defaultCorrName): Double = {
+    val correlation = getCorrelationFromName(method)
+    correlation.computeCorrelation(x, y)
+  }
+
+  def corrMatrix(X: RDD[Vector], method: String = defaultCorrName): Matrix = {
+    val correlation = getCorrelationFromName(method)
+    correlation.computeCorrelationMatrix(X)
+  }
+
+  /**
+   * Match input correlation name with a known name via simple string matching
+   *
+   * private to stat for ease of unit testing
+   */
+  private[stat] def getCorrelationFromName(method: String): Correlation = {
+    try {
+      nameToObjectMap(method)
+    } catch {
+      case nse: NoSuchElementException =>
+        throw new IllegalArgumentException("Unrecognized method name. Supported correlations: "
+          + nameToObjectMap.keys.mkString(", "))
+    }
+  }
+}
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/stat/correlation/PearsonCorrelation.scala b/mllib/src/main/scala/org/apache/spark/mllib/stat/correlation/PearsonCorrelation.scala
new file mode 100644
index 000000000000..23b291eee070
--- /dev/null
+++ b/mllib/src/main/scala/org/apache/spark/mllib/stat/correlation/PearsonCorrelation.scala
@@ -0,0 +1,107 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.mllib.stat.correlation
+
+import breeze.linalg.{DenseMatrix => BDM}
+
+import org.apache.spark.Logging
+import org.apache.spark.mllib.linalg.{Matrices, Matrix, Vector}
+import org.apache.spark.mllib.linalg.distributed.RowMatrix
+import org.apache.spark.rdd.RDD
+
+/**
+ * Compute Pearson correlation for two RDDs of the type RDD[Double] or the correlation matrix
+ * for an RDD of the type RDD[Vector].
+ *
+ * Definition of Pearson correlation can be found at
+ * http://en.wikipedia.org/wiki/Pearson_product-moment_correlation_coefficient
+ */
+private[stat] object PearsonCorrelation extends Correlation with Logging {
+
+  /**
+   * Compute the Pearson correlation for two datasets. NaN if either vector has 0 variance.
+   */
+  override def computeCorrelation(x: RDD[Double], y: RDD[Double]): Double = {
+    computeCorrelationWithMatrixImpl(x, y)
+  }
+
+  /**
+   * Compute the Pearson correlation matrix S, for the input matrix, where S(i, j) is the
+   * correlation between column i and j. 0 covariance results in a correlation value of Double.NaN.
+   */
+  override def computeCorrelationMatrix(X: RDD[Vector]): Matrix = {
+    val rowMatrix = new RowMatrix(X)
+    val cov = rowMatrix.computeCovariance()
+    computeCorrelationMatrixFromCovariance(cov)
+  }
+
+  /**
+   * Compute the Pearson correlation matrix from the covariance matrix.
+   * 0 covariance results in a correlation value of Double.NaN.
+   */
+  def computeCorrelationMatrixFromCovariance(covarianceMatrix: Matrix): Matrix = {
+    val cov = covarianceMatrix.toBreeze.asInstanceOf[BDM[Double]]
+    val n = cov.cols
+
+    // Compute the standard deviation on the diagonals first
+    var i = 0
+    while (i < n) {
+      // TODO remove once covariance numerical issue resolved.
+      cov(i, i) = if (closeToZero(cov(i, i))) 0.0 else math.sqrt(cov(i, i))
+      i +=1
+    }
+
+    // Loop through columns since cov is column major
+    var j = 0
+    var sigma = 0.0
+    var containNaN = false
+    while (j < n) {
+      sigma = cov(j, j)
+      i = 0
+      while (i < j) {
+        val corr = if (sigma == 0.0 || cov(i, i) == 0.0) {
+          containNaN = true
+          Double.NaN
+        } else {
+          cov(i, j) / (sigma * cov(i, i))
+        }
+        cov(i, j) = corr
+        cov(j, i) = corr
+        i += 1
+      }
+      j += 1
+    }
+
+    // put 1.0 on the diagonals
+    i = 0
+    while (i < n) {
+      cov(i, i) = 1.0
+      i +=1
+    }
+
+    if (containNaN) {
+      logWarning("Pearson correlation matrix contains NaN values.")
+    }
+
+    Matrices.fromBreeze(cov)
+  }
+
+  private def closeToZero(value: Double, threshhold: Double = 1e-12): Boolean = {
+    math.abs(value) <= threshhold
+  }
+}
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/stat/correlation/SpearmanCorrelation.scala b/mllib/src/main/scala/org/apache/spark/mllib/stat/correlation/SpearmanCorrelation.scala
new file mode 100644
index 000000000000..88de2c82479b
--- /dev/null
+++ b/mllib/src/main/scala/org/apache/spark/mllib/stat/correlation/SpearmanCorrelation.scala
@@ -0,0 +1,127 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.mllib.stat.correlation
+
+import scala.collection.mutable.ArrayBuffer
+
+import org.apache.spark.{Logging, HashPartitioner}
+import org.apache.spark.SparkContext._
+import org.apache.spark.mllib.linalg.{DenseVector, Matrix, Vector}
+import org.apache.spark.rdd.{CoGroupedRDD, RDD}
+
+/**
+ * Compute Spearman's correlation for two RDDs of the type RDD[Double] or the correlation matrix
+ * for an RDD of the type RDD[Vector].
+ *
+ * Definition of Spearman's correlation can be found at
+ * http://en.wikipedia.org/wiki/Spearman's_rank_correlation_coefficient
+ */
+private[stat] object SpearmanCorrelation extends Correlation with Logging {
+
+  /**
+   * Compute Spearman's correlation for two datasets.
+   */
+  override def computeCorrelation(x: RDD[Double], y: RDD[Double]): Double = {
+    computeCorrelationWithMatrixImpl(x, y)
+  }
+
+  /**
+   * Compute Spearman's correlation matrix S, for the input matrix, where S(i, j) is the
+   * correlation between column i and j.
+   *
+   * Input RDD[Vector] should be cached or checkpointed if possible since it would be split into
+   * numCol RDD[Double]s, each of which sorted, and the joined back into a single RDD[Vector].
+   */
+  override def computeCorrelationMatrix(X: RDD[Vector]): Matrix = {
+    val indexed = X.zipWithUniqueId()
+
+    val numCols = X.first.size
+    if (numCols > 50) {
+      logWarning("Computing the Spearman correlation matrix can be slow for large RDDs with more"
+        + " than 50 columns.")
+    }
+    val ranks = new Array[RDD[(Long, Double)]](numCols)
+
+    // Note: we use a for loop here instead of a while loop with a single index variable
+    // to avoid race condition caused by closure serialization
+    for (k <- 0 until numCols) {
+      val column = indexed.map { case (vector, index) => (vector(k), index) }
+      ranks(k) = getRanks(column)
+    }
+
+    val ranksMat: RDD[Vector] = makeRankMatrix(ranks, X)
+    PearsonCorrelation.computeCorrelationMatrix(ranksMat)
+  }
+
+  /**
+   * Compute the ranks for elements in the input RDD, using the average method for ties.
+   *
+   * With the average method, elements with the same value receive the same rank that's computed
+   * by taking the average of their positions in the sorted list.
+   * e.g. ranks([2, 1, 0, 2]) = [2.5, 1.0, 0.0, 2.5]
+   * Note that positions here are 0-indexed, instead of the 1-indexed as in the definition for
+   * ranks in the standard definition for Spearman's correlation. This does not affect the final
+   * results and is slightly more performant.
+   *
+   * @param indexed RDD[(Double, Long)] containing pairs of the format (originalValue, uniqueId)
+   * @return RDD[(Long, Double)] containing pairs of the format (uniqueId, rank), where uniqueId is
+   *         copied from the input RDD.
+   */
+  private def getRanks(indexed: RDD[(Double, Long)]): RDD[(Long, Double)] = {
+    // Get elements' positions in the sorted list for computing average rank for duplicate values
+    val sorted = indexed.sortByKey().zipWithIndex()
+
+    val ranks: RDD[(Long, Double)] = sorted.mapPartitions { iter =>
+      // add an extra element to signify the end of the list so that flatMap can flush the last
+      // batch of duplicates
+      val padded = iter ++
+        Iterator[((Double, Long), Long)](((Double.NaN, -1L), -1L))
+      var lastVal = 0.0
+      var firstRank = 0.0
+      val idBuffer = new ArrayBuffer[Long]()
+      padded.flatMap { case ((v, id), rank) =>
+        if (v  == lastVal && id != Long.MinValue) {
+          idBuffer += id
+          Iterator.empty
+        } else {
+          val entries = if (idBuffer.size == 0) {
+            // edge case for the first value matching the initial value of lastVal
+            Iterator.empty
+          } else if (idBuffer.size == 1) {
+            Iterator((idBuffer(0), firstRank))
+          } else {
+            val averageRank = firstRank + (idBuffer.size - 1.0) / 2.0
+            idBuffer.map(id => (id, averageRank))
+          }
+          lastVal = v
+          firstRank = rank
+          idBuffer.clear()
+          idBuffer += id
+          entries
+        }
+      }
+    }
+    ranks
+  }
+
+  private def makeRankMatrix(ranks: Array[RDD[(Long, Double)]], input: RDD[Vector]): RDD[Vector] = {
+    val partitioner = new HashPartitioner(input.partitions.size)
+    val cogrouped = new CoGroupedRDD[Long](ranks, partitioner)
+    cogrouped.map { case (_, values: Seq[Seq[Double]]) => new DenseVector(values.flatten.toArray) }
+  }
+}
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/stat/CorrelationSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/stat/CorrelationSuite.scala
new file mode 100644
index 000000000000..bce4251426df
--- /dev/null
+++ b/mllib/src/test/scala/org/apache/spark/mllib/stat/CorrelationSuite.scala
@@ -0,0 +1,116 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.mllib.stat
+
+import org.scalatest.FunSuite
+
+import breeze.linalg.{DenseMatrix => BDM, Matrix => BM}
+
+import org.apache.spark.mllib.linalg.Vectors
+import org.apache.spark.mllib.stat.correlation.{Correlations, PearsonCorrelation,
+  SpearmanCorrelation}
+import org.apache.spark.mllib.util.LocalSparkContext
+
+class CorrelationSuite extends FunSuite with LocalSparkContext {
+
+  // test input data
+  val xData = Array(1.0, 0.0, -2.0)
+  val yData = Array(4.0, 5.0, 3.0)
+  val data = Seq(
+    Vectors.dense(1.0, 0.0, 0.0, -2.0),
+    Vectors.dense(4.0, 5.0, 0.0, 3.0),
+    Vectors.dense(6.0, 7.0, 0.0, 8.0),
+    Vectors.dense(9.0, 0.0, 0.0, 1.0)
+  )
+
+  test("corr(x, y) default, pearson") {
+    val x = sc.parallelize(xData)
+    val y = sc.parallelize(yData)
+    val expected = 0.6546537
+    val default = Statistics.corr(x, y)
+    val p1 = Statistics.corr(x, y, "pearson")
+    assert(approxEqual(expected, default))
+    assert(approxEqual(expected, p1))
+  }
+
+  test("corr(x, y) spearman") {
+    val x = sc.parallelize(xData)
+    val y = sc.parallelize(yData)
+    val expected = 0.5
+    val s1 = Statistics.corr(x, y, "spearman")
+    assert(approxEqual(expected, s1))
+  }
+
+  test("corr(X) default, pearson") {
+    val X = sc.parallelize(data)
+    val defaultMat = Statistics.corr(X)
+    val pearsonMat = Statistics.corr(X, "pearson")
+    val expected = BDM(
+      (1.00000000, 0.05564149, Double.NaN, 0.4004714),
+      (0.05564149, 1.00000000, Double.NaN, 0.9135959),
+      (Double.NaN, Double.NaN, 1.00000000, Double.NaN),
+      (0.40047142, 0.91359586, Double.NaN,1.0000000))
+    assert(matrixApproxEqual(defaultMat.toBreeze, expected))
+    assert(matrixApproxEqual(pearsonMat.toBreeze, expected))
+  }
+
+  test("corr(X) spearman") {
+    val X = sc.parallelize(data)
+    val spearmanMat = Statistics.corr(X, "spearman")
+    val expected = BDM(
+      (1.0000000,  0.1054093,  Double.NaN, 0.4000000),
+      (0.1054093,  1.0000000,  Double.NaN, 0.9486833),
+      (Double.NaN, Double.NaN, 1.00000000, Double.NaN),
+      (0.4000000,  0.9486833,  Double.NaN, 1.0000000))
+    assert(matrixApproxEqual(spearmanMat.toBreeze, expected))
+  }
+
+  test("method identification") {
+    val pearson = PearsonCorrelation
+    val spearman = SpearmanCorrelation
+
+    assert(Correlations.getCorrelationFromName("pearson") === pearson)
+    assert(Correlations.getCorrelationFromName("spearman") === spearman)
+
+    // Should throw IllegalArgumentException
+    try {
+      Correlations.getCorrelationFromName("kendall")
+      assert(false)
+    } catch {
+      case ie: IllegalArgumentException =>
+    }
+  }
+
+  def approxEqual(v1: Double, v2: Double, threshold: Double = 1e-6): Boolean = {
+    if (v1.isNaN) {
+      v2.isNaN
+    } else {
+      math.abs(v1 - v2) <= threshold
+    }
+  }
+
+  def matrixApproxEqual(A: BM[Double], B: BM[Double], threshold: Double = 1e-6): Boolean = {
+    for (i <- 0 until A.rows; j <- 0 until A.cols) {
+      if (!approxEqual(A(i, j), B(i, j), threshold)) {
+        println("i, j = " + i + ", " + j + " actual: " + A(i, j) + " expected:" + B(i, j))
+        return false
+      }
+    }
+    true
+  }
+}

From 7b8cd175254d42c8e82f0aa8eb4b7f3508d8fde2 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Fri, 18 Jul 2014 23:52:47 -0700
Subject: [PATCH 697/810] [SPARK-2521] Broadcast RDD object (instead of sending
 it along with every task).

Currently (as of Spark 1.0.1), Spark sends RDD object (which contains closures) using Akka along with the task itself to the executors. This is inefficient because all tasks in the same stage use the same RDD object, but we have to send RDD object multiple times to the executors. This is especially bad when a closure references some variable that is very large. The current design led to users having to explicitly broadcast large variables.

The patch uses broadcast to send RDD objects and the closures to executors, and use Akka to only send a reference to the broadcast RDD/closure along with the partition specific information for the task. For those of you who know more about the internals, Spark already relies on broadcast to send the Hadoop JobConf every time it uses the Hadoop input, because the JobConf is large.

The user-facing impact of the change include:

1. Users won't need to decide what to broadcast anymore, unless they would want to use a large object multiple times in different operations
2. Task size will get smaller, resulting in faster scheduling and higher task dispatch throughput.

In addition, the change will simplify some internals of Spark, eliminating the need to maintain task caches and the complex logic to broadcast JobConf (which also led to a deadlock recently).

A simple way to test this:
```scala
val a = new Array[Byte](1000*1000); scala.util.Random.nextBytes(a);
sc.parallelize(1 to 1000, 1000).map { x => a; x }.groupBy { x => a; x }.count
```

Numbers on 3 r3.8xlarge instances on EC2
```
master branch: 5.648436068 s, 4.715361895 s, 5.360161877 s
with this change: 3.416348793 s, 1.477846558 s, 1.553432156 s
```

Author: Reynold Xin <rxin@apache.org>

Closes #1452 from rxin/broadcast-task and squashes the following commits:

762e0be [Reynold Xin] Warn large broadcasts.
ade6eac [Reynold Xin] Log broadcast size.
c3b6f11 [Reynold Xin] Added a unit test for clean up.
754085f [Reynold Xin] Explain why broadcasting serialized copy of the task.
04b17f0 [Reynold Xin] [SPARK-2521] Broadcast RDD object once per TaskSet (instead of sending it for every task).
---
 .../scala/org/apache/spark/Dependency.scala   |  28 ++--
 .../scala/org/apache/spark/SparkContext.scala |   2 -
 .../main/scala/org/apache/spark/rdd/RDD.scala |  30 +++-
 .../apache/spark/rdd/RDDCheckpointData.scala  |   9 +-
 .../apache/spark/scheduler/DAGScheduler.scala |   4 -
 .../apache/spark/scheduler/ResultTask.scala   | 128 +++++-------------
 .../spark/scheduler/ShuffleMapTask.scala      | 125 ++++-------------
 .../apache/spark/ContextCleanerSuite.scala    |  62 +++++----
 8 files changed, 137 insertions(+), 251 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/Dependency.scala b/core/src/main/scala/org/apache/spark/Dependency.scala
index 09a60571238e..3935c8772252 100644
--- a/core/src/main/scala/org/apache/spark/Dependency.scala
+++ b/core/src/main/scala/org/apache/spark/Dependency.scala
@@ -27,7 +27,9 @@ import org.apache.spark.shuffle.ShuffleHandle
  * Base class for dependencies.
  */
 @DeveloperApi
-abstract class Dependency[T](val rdd: RDD[T]) extends Serializable
+abstract class Dependency[T] extends Serializable {
+  def rdd: RDD[T]
+}
 
 
 /**
@@ -36,20 +38,24 @@ abstract class Dependency[T](val rdd: RDD[T]) extends Serializable
  * partition of the child RDD.  Narrow dependencies allow for pipelined execution.
  */
 @DeveloperApi
-abstract class NarrowDependency[T](rdd: RDD[T]) extends Dependency(rdd) {
+abstract class NarrowDependency[T](_rdd: RDD[T]) extends Dependency[T] {
   /**
    * Get the parent partitions for a child partition.
    * @param partitionId a partition of the child RDD
    * @return the partitions of the parent RDD that the child partition depends upon
    */
   def getParents(partitionId: Int): Seq[Int]
+
+  override def rdd: RDD[T] = _rdd
 }
 
 
 /**
  * :: DeveloperApi ::
- * Represents a dependency on the output of a shuffle stage.
- * @param rdd the parent RDD
+ * Represents a dependency on the output of a shuffle stage. Note that in the case of shuffle,
+ * the RDD is transient since we don't need it on the executor side.
+ *
+ * @param _rdd the parent RDD
  * @param partitioner partitioner used to partition the shuffle output
  * @param serializer [[org.apache.spark.serializer.Serializer Serializer]] to use. If set to None,
  *                   the default serializer, as specified by `spark.serializer` config option, will
@@ -57,20 +63,22 @@ abstract class NarrowDependency[T](rdd: RDD[T]) extends Dependency(rdd) {
  */
 @DeveloperApi
 class ShuffleDependency[K, V, C](
-    @transient rdd: RDD[_ <: Product2[K, V]],
+    @transient _rdd: RDD[_ <: Product2[K, V]],
     val partitioner: Partitioner,
     val serializer: Option[Serializer] = None,
     val keyOrdering: Option[Ordering[K]] = None,
     val aggregator: Option[Aggregator[K, V, C]] = None,
     val mapSideCombine: Boolean = false)
-  extends Dependency(rdd.asInstanceOf[RDD[Product2[K, V]]]) {
+  extends Dependency[Product2[K, V]] {
+
+  override def rdd = _rdd.asInstanceOf[RDD[Product2[K, V]]]
 
-  val shuffleId: Int = rdd.context.newShuffleId()
+  val shuffleId: Int = _rdd.context.newShuffleId()
 
-  val shuffleHandle: ShuffleHandle = rdd.context.env.shuffleManager.registerShuffle(
-    shuffleId, rdd.partitions.size, this)
+  val shuffleHandle: ShuffleHandle = _rdd.context.env.shuffleManager.registerShuffle(
+    shuffleId, _rdd.partitions.size, this)
 
-  rdd.sparkContext.cleaner.foreach(_.registerShuffleForCleanup(this))
+  _rdd.sparkContext.cleaner.foreach(_.registerShuffleForCleanup(this))
 }
 
 
diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index 8052499ab752..48a09657fde2 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -997,8 +997,6 @@ class SparkContext(config: SparkConf) extends Logging {
       // TODO: Cache.stop()?
       env.stop()
       SparkEnv.set(null)
-      ShuffleMapTask.clearCache()
-      ResultTask.clearCache()
       listenerBus.stop()
       eventLogger.foreach(_.stop())
       logInfo("Successfully stopped SparkContext")
diff --git a/core/src/main/scala/org/apache/spark/rdd/RDD.scala b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
index 88a918aebf76..2ee9a8f1a8e0 100644
--- a/core/src/main/scala/org/apache/spark/rdd/RDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
@@ -35,12 +35,13 @@ import org.apache.spark.Partitioner._
 import org.apache.spark.SparkContext._
 import org.apache.spark.annotation.{DeveloperApi, Experimental}
 import org.apache.spark.api.java.JavaRDD
+import org.apache.spark.broadcast.Broadcast
 import org.apache.spark.partial.BoundedDouble
 import org.apache.spark.partial.CountEvaluator
 import org.apache.spark.partial.GroupedCountEvaluator
 import org.apache.spark.partial.PartialResult
 import org.apache.spark.storage.StorageLevel
-import org.apache.spark.util.{BoundedPriorityQueue, CallSite, Utils}
+import org.apache.spark.util.{BoundedPriorityQueue, Utils}
 import org.apache.spark.util.collection.OpenHashMap
 import org.apache.spark.util.random.{BernoulliSampler, PoissonSampler, SamplingUtils}
 
@@ -1195,21 +1196,36 @@ abstract class RDD[T: ClassTag](
   /**
    * Return whether this RDD has been checkpointed or not
    */
-  def isCheckpointed: Boolean = {
-    checkpointData.map(_.isCheckpointed).getOrElse(false)
-  }
+  def isCheckpointed: Boolean = checkpointData.exists(_.isCheckpointed)
 
   /**
    * Gets the name of the file to which this RDD was checkpointed
    */
-  def getCheckpointFile: Option[String] = {
-    checkpointData.flatMap(_.getCheckpointFile)
-  }
+  def getCheckpointFile: Option[String] = checkpointData.flatMap(_.getCheckpointFile)
 
   // =======================================================================
   // Other internal methods and fields
   // =======================================================================
 
+  /**
+   * Broadcasted copy of this RDD, used to dispatch tasks to executors. Note that we broadcast
+   * the serialized copy of the RDD and for each task we will deserialize it, which means each
+   * task gets a different copy of the RDD. This provides stronger isolation between tasks that
+   * might modify state of objects referenced in their closures. This is necessary in Hadoop
+   * where the JobConf/Configuration object is not thread-safe.
+   */
+  @transient private[spark] lazy val broadcasted: Broadcast[Array[Byte]] = {
+    val ser = SparkEnv.get.closureSerializer.newInstance()
+    val bytes = ser.serialize(this).array()
+    val size = Utils.bytesToString(bytes.length)
+    if (bytes.length > (1L << 20)) {
+      logWarning(s"Broadcasting RDD $id ($size), which contains large objects")
+    } else {
+      logDebug(s"Broadcasting RDD $id ($size)")
+    }
+    sc.broadcast(bytes)
+  }
+
   private var storageLevel: StorageLevel = StorageLevel.NONE
 
   /** User code that created this RDD (e.g. `textFile`, `parallelize`). */
diff --git a/core/src/main/scala/org/apache/spark/rdd/RDDCheckpointData.scala b/core/src/main/scala/org/apache/spark/rdd/RDDCheckpointData.scala
index c3b2a33fb54d..f67e5f185797 100644
--- a/core/src/main/scala/org/apache/spark/rdd/RDDCheckpointData.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/RDDCheckpointData.scala
@@ -106,7 +106,6 @@ private[spark] class RDDCheckpointData[T: ClassTag](@transient rdd: RDD[T])
       cpRDD = Some(newRDD)
       rdd.markCheckpointed(newRDD)   // Update the RDD's dependencies and partitions
       cpState = Checkpointed
-      RDDCheckpointData.clearTaskCaches()
     }
     logInfo("Done checkpointing RDD " + rdd.id + " to " + path + ", new parent is RDD " + newRDD.id)
   }
@@ -131,9 +130,5 @@ private[spark] class RDDCheckpointData[T: ClassTag](@transient rdd: RDD[T])
   }
 }
 
-private[spark] object RDDCheckpointData {
-  def clearTaskCaches() {
-    ShuffleMapTask.clearCache()
-    ResultTask.clearCache()
-  }
-}
+// Used for synchronization
+private[spark] object RDDCheckpointData
diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
index ede3c7d9f01a..88cb5feaaff2 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
@@ -376,9 +376,6 @@ class DAGScheduler(
               stageIdToStage -= stageId
               stageIdToJobIds -= stageId
 
-              ShuffleMapTask.removeStage(stageId)
-              ResultTask.removeStage(stageId)
-
               logDebug("After removal of stage %d, remaining stages = %d"
                 .format(stageId, stageIdToStage.size))
             }
@@ -723,7 +720,6 @@ class DAGScheduler(
     }
   }
 
-
   /** Called when stage's parents are available and we can now do its task. */
   private def submitMissingTasks(stage: Stage, jobId: Int) {
     logDebug("submitMissingTasks(" + stage + ")")
diff --git a/core/src/main/scala/org/apache/spark/scheduler/ResultTask.scala b/core/src/main/scala/org/apache/spark/scheduler/ResultTask.scala
index bbf9f7388b07..62beb0d02a9c 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/ResultTask.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/ResultTask.scala
@@ -17,134 +17,68 @@
 
 package org.apache.spark.scheduler
 
-import scala.language.existentials
+import java.nio.ByteBuffer
 
 import java.io._
-import java.util.zip.{GZIPInputStream, GZIPOutputStream}
-
-import scala.collection.mutable.HashMap
 
 import org.apache.spark._
-import org.apache.spark.rdd.{RDD, RDDCheckpointData}
-
-private[spark] object ResultTask {
-
-  // A simple map between the stage id to the serialized byte array of a task.
-  // Served as a cache for task serialization because serialization can be
-  // expensive on the master node if it needs to launch thousands of tasks.
-  private val serializedInfoCache = new HashMap[Int, Array[Byte]]
-
-  def serializeInfo(stageId: Int, rdd: RDD[_], func: (TaskContext, Iterator[_]) => _): Array[Byte] =
-  {
-    synchronized {
-      val old = serializedInfoCache.get(stageId).orNull
-      if (old != null) {
-        old
-      } else {
-        val out = new ByteArrayOutputStream
-        val ser = SparkEnv.get.closureSerializer.newInstance()
-        val objOut = ser.serializeStream(new GZIPOutputStream(out))
-        objOut.writeObject(rdd)
-        objOut.writeObject(func)
-        objOut.close()
-        val bytes = out.toByteArray
-        serializedInfoCache.put(stageId, bytes)
-        bytes
-      }
-    }
-  }
-
-  def deserializeInfo(stageId: Int, bytes: Array[Byte]): (RDD[_], (TaskContext, Iterator[_]) => _) =
-  {
-    val in = new GZIPInputStream(new ByteArrayInputStream(bytes))
-    val ser = SparkEnv.get.closureSerializer.newInstance()
-    val objIn = ser.deserializeStream(in)
-    val rdd = objIn.readObject().asInstanceOf[RDD[_]]
-    val func = objIn.readObject().asInstanceOf[(TaskContext, Iterator[_]) => _]
-    (rdd, func)
-  }
-
-  def removeStage(stageId: Int) {
-    serializedInfoCache.remove(stageId)
-  }
-
-  def clearCache() {
-    synchronized {
-      serializedInfoCache.clear()
-    }
-  }
-}
-
+import org.apache.spark.broadcast.Broadcast
+import org.apache.spark.rdd.RDD
 
 /**
  * A task that sends back the output to the driver application.
  *
- * See [[org.apache.spark.scheduler.Task]] for more information.
+ * See [[Task]] for more information.
  *
  * @param stageId id of the stage this task belongs to
- * @param rdd input to func
+ * @param rddBinary broadcast version of of the serialized RDD
  * @param func a function to apply on a partition of the RDD
- * @param _partitionId index of the number in the RDD
+ * @param partition partition of the RDD this task is associated with
  * @param locs preferred task execution locations for locality scheduling
  * @param outputId index of the task in this job (a job can launch tasks on only a subset of the
  *                 input RDD's partitions).
  */
 private[spark] class ResultTask[T, U](
     stageId: Int,
-    var rdd: RDD[T],
-    var func: (TaskContext, Iterator[T]) => U,
-    _partitionId: Int,
+    val rddBinary: Broadcast[Array[Byte]],
+    val func: (TaskContext, Iterator[T]) => U,
+    val partition: Partition,
     @transient locs: Seq[TaskLocation],
-    var outputId: Int)
-  extends Task[U](stageId, _partitionId) with Externalizable {
-
-  def this() = this(0, null, null, 0, null, 0)
-
-  var split = if (rdd == null) null else rdd.partitions(partitionId)
+    val outputId: Int)
+  extends Task[U](stageId, partition.index) with Serializable {
+
+  // TODO: Should we also broadcast func? For that we would need a place to
+  // keep a reference to it (perhaps in DAGScheduler's job object).
+
+  def this(
+      stageId: Int,
+      rdd: RDD[T],
+      func: (TaskContext, Iterator[T]) => U,
+      partitionId: Int,
+      locs: Seq[TaskLocation],
+      outputId: Int) = {
+    this(stageId, rdd.broadcasted, func, rdd.partitions(partitionId), locs, outputId)
+  }
 
-  @transient private val preferredLocs: Seq[TaskLocation] = {
+  @transient private[this] val preferredLocs: Seq[TaskLocation] = {
     if (locs == null) Nil else locs.toSet.toSeq
   }
 
   override def runTask(context: TaskContext): U = {
+    // Deserialize the RDD using the broadcast variable.
+    val ser = SparkEnv.get.closureSerializer.newInstance()
+    val rdd = ser.deserialize[RDD[T]](ByteBuffer.wrap(rddBinary.value),
+      Thread.currentThread.getContextClassLoader)
     metrics = Some(context.taskMetrics)
     try {
-      func(context, rdd.iterator(split, context))
+      func(context, rdd.iterator(partition, context))
     } finally {
       context.executeOnCompleteCallbacks()
     }
   }
 
+  // This is only callable on the driver side.
   override def preferredLocations: Seq[TaskLocation] = preferredLocs
 
   override def toString = "ResultTask(" + stageId + ", " + partitionId + ")"
-
-  override def writeExternal(out: ObjectOutput) {
-    RDDCheckpointData.synchronized {
-      split = rdd.partitions(partitionId)
-      out.writeInt(stageId)
-      val bytes = ResultTask.serializeInfo(
-        stageId, rdd, func.asInstanceOf[(TaskContext, Iterator[_]) => _])
-      out.writeInt(bytes.length)
-      out.write(bytes)
-      out.writeInt(partitionId)
-      out.writeInt(outputId)
-      out.writeLong(epoch)
-      out.writeObject(split)
-    }
-  }
-
-  override def readExternal(in: ObjectInput) {
-    val stageId = in.readInt()
-    val numBytes = in.readInt()
-    val bytes = new Array[Byte](numBytes)
-    in.readFully(bytes)
-    val (rdd_, func_) = ResultTask.deserializeInfo(stageId, bytes)
-    rdd = rdd_.asInstanceOf[RDD[T]]
-    func = func_.asInstanceOf[(TaskContext, Iterator[T]) => U]
-    partitionId = in.readInt()
-    outputId = in.readInt()
-    epoch = in.readLong()
-    split = in.readObject().asInstanceOf[Partition]
-  }
 }
diff --git a/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala b/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala
index fdaf1de83f05..033c6e52861e 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala
@@ -17,71 +17,13 @@
 
 package org.apache.spark.scheduler
 
-import scala.language.existentials
-
-import java.io._
-import java.util.zip.{GZIPInputStream, GZIPOutputStream}
-
-import scala.collection.mutable.HashMap
+import java.nio.ByteBuffer
 
 import org.apache.spark._
-import org.apache.spark.rdd.{RDD, RDDCheckpointData}
+import org.apache.spark.broadcast.Broadcast
+import org.apache.spark.rdd.RDD
 import org.apache.spark.shuffle.ShuffleWriter
 
-private[spark] object ShuffleMapTask {
-
-  // A simple map between the stage id to the serialized byte array of a task.
-  // Served as a cache for task serialization because serialization can be
-  // expensive on the master node if it needs to launch thousands of tasks.
-  private val serializedInfoCache = new HashMap[Int, Array[Byte]]
-
-  def serializeInfo(stageId: Int, rdd: RDD[_], dep: ShuffleDependency[_, _, _]): Array[Byte] = {
-    synchronized {
-      val old = serializedInfoCache.get(stageId).orNull
-      if (old != null) {
-        return old
-      } else {
-        val out = new ByteArrayOutputStream
-        val ser = SparkEnv.get.closureSerializer.newInstance()
-        val objOut = ser.serializeStream(new GZIPOutputStream(out))
-        objOut.writeObject(rdd)
-        objOut.writeObject(dep)
-        objOut.close()
-        val bytes = out.toByteArray
-        serializedInfoCache.put(stageId, bytes)
-        bytes
-      }
-    }
-  }
-
-  def deserializeInfo(stageId: Int, bytes: Array[Byte]): (RDD[_], ShuffleDependency[_, _, _]) = {
-    val in = new GZIPInputStream(new ByteArrayInputStream(bytes))
-    val ser = SparkEnv.get.closureSerializer.newInstance()
-    val objIn = ser.deserializeStream(in)
-    val rdd = objIn.readObject().asInstanceOf[RDD[_]]
-    val dep = objIn.readObject().asInstanceOf[ShuffleDependency[_, _, _]]
-    (rdd, dep)
-  }
-
-  // Since both the JarSet and FileSet have the same format this is used for both.
-  def deserializeFileSet(bytes: Array[Byte]): HashMap[String, Long] = {
-    val in = new GZIPInputStream(new ByteArrayInputStream(bytes))
-    val objIn = new ObjectInputStream(in)
-    val set = objIn.readObject().asInstanceOf[Array[(String, Long)]].toMap
-    HashMap(set.toSeq: _*)
-  }
-
-  def removeStage(stageId: Int) {
-    serializedInfoCache.remove(stageId)
-  }
-
-  def clearCache() {
-    synchronized {
-      serializedInfoCache.clear()
-    }
-  }
-}
-
 /**
  * A ShuffleMapTask divides the elements of an RDD into multiple buckets (based on a partitioner
  * specified in the ShuffleDependency).
@@ -89,62 +31,47 @@ private[spark] object ShuffleMapTask {
  * See [[org.apache.spark.scheduler.Task]] for more information.
  *
  * @param stageId id of the stage this task belongs to
- * @param rdd the final RDD in this stage
+ * @param rddBinary broadcast version of of the serialized RDD
  * @param dep the ShuffleDependency
- * @param _partitionId index of the number in the RDD
+ * @param partition partition of the RDD this task is associated with
  * @param locs preferred task execution locations for locality scheduling
  */
 private[spark] class ShuffleMapTask(
     stageId: Int,
-    var rdd: RDD[_],
+    var rddBinary: Broadcast[Array[Byte]],
     var dep: ShuffleDependency[_, _, _],
-    _partitionId: Int,
+    partition: Partition,
     @transient private var locs: Seq[TaskLocation])
-  extends Task[MapStatus](stageId, _partitionId)
-  with Externalizable
-  with Logging {
-
-  protected def this() = this(0, null, null, 0, null)
+  extends Task[MapStatus](stageId, partition.index) with Logging {
+
+  // TODO: Should we also broadcast the ShuffleDependency? For that we would need a place to
+  // keep a reference to it (perhaps in Stage).
+
+  def this(
+      stageId: Int,
+      rdd: RDD[_],
+      dep: ShuffleDependency[_, _, _],
+      partitionId: Int,
+      locs: Seq[TaskLocation]) = {
+    this(stageId, rdd.broadcasted, dep, rdd.partitions(partitionId), locs)
+  }
 
   @transient private val preferredLocs: Seq[TaskLocation] = {
     if (locs == null) Nil else locs.toSet.toSeq
   }
 
-  var split = if (rdd == null) null else rdd.partitions(partitionId)
-
-  override def writeExternal(out: ObjectOutput) {
-    RDDCheckpointData.synchronized {
-      split = rdd.partitions(partitionId)
-      out.writeInt(stageId)
-      val bytes = ShuffleMapTask.serializeInfo(stageId, rdd, dep)
-      out.writeInt(bytes.length)
-      out.write(bytes)
-      out.writeInt(partitionId)
-      out.writeLong(epoch)
-      out.writeObject(split)
-    }
-  }
-
-  override def readExternal(in: ObjectInput) {
-    val stageId = in.readInt()
-    val numBytes = in.readInt()
-    val bytes = new Array[Byte](numBytes)
-    in.readFully(bytes)
-    val (rdd_, dep_) = ShuffleMapTask.deserializeInfo(stageId, bytes)
-    rdd = rdd_
-    dep = dep_
-    partitionId = in.readInt()
-    epoch = in.readLong()
-    split = in.readObject().asInstanceOf[Partition]
-  }
-
   override def runTask(context: TaskContext): MapStatus = {
+    // Deserialize the RDD using the broadcast variable.
+    val ser = SparkEnv.get.closureSerializer.newInstance()
+    val rdd = ser.deserialize[RDD[_]](ByteBuffer.wrap(rddBinary.value),
+      Thread.currentThread.getContextClassLoader)
+
     metrics = Some(context.taskMetrics)
     var writer: ShuffleWriter[Any, Any] = null
     try {
       val manager = SparkEnv.get.shuffleManager
       writer = manager.getWriter[Any, Any](dep.shuffleHandle, partitionId, context)
-      writer.write(rdd.iterator(split, context).asInstanceOf[Iterator[_ <: Product2[Any, Any]]])
+      writer.write(rdd.iterator(partition, context).asInstanceOf[Iterator[_ <: Product2[Any, Any]]])
       return writer.stop(success = true).get
     } catch {
       case e: Exception =>
diff --git a/core/src/test/scala/org/apache/spark/ContextCleanerSuite.scala b/core/src/test/scala/org/apache/spark/ContextCleanerSuite.scala
index 13b415cccb64..871f831531be 100644
--- a/core/src/test/scala/org/apache/spark/ContextCleanerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/ContextCleanerSuite.scala
@@ -52,9 +52,8 @@ class ContextCleanerSuite extends FunSuite with BeforeAndAfter with LocalSparkCo
     }
   }
 
-
   test("cleanup RDD") {
-    val rdd = newRDD.persist()
+    val rdd = newRDD().persist()
     val collected = rdd.collect().toList
     val tester = new CleanerTester(sc, rddIds = Seq(rdd.id))
 
@@ -67,7 +66,7 @@ class ContextCleanerSuite extends FunSuite with BeforeAndAfter with LocalSparkCo
   }
 
   test("cleanup shuffle") {
-    val (rdd, shuffleDeps) = newRDDWithShuffleDependencies
+    val (rdd, shuffleDeps) = newRDDWithShuffleDependencies()
     val collected = rdd.collect().toList
     val tester = new CleanerTester(sc, shuffleIds = shuffleDeps.map(_.shuffleId))
 
@@ -80,7 +79,7 @@ class ContextCleanerSuite extends FunSuite with BeforeAndAfter with LocalSparkCo
   }
 
   test("cleanup broadcast") {
-    val broadcast = newBroadcast
+    val broadcast = newBroadcast()
     val tester = new CleanerTester(sc, broadcastIds = Seq(broadcast.id))
 
     // Explicit cleanup
@@ -89,7 +88,7 @@ class ContextCleanerSuite extends FunSuite with BeforeAndAfter with LocalSparkCo
   }
 
   test("automatically cleanup RDD") {
-    var rdd = newRDD.persist()
+    var rdd = newRDD().persist()
     rdd.count()
 
     // Test that GC does not cause RDD cleanup due to a strong reference
@@ -107,7 +106,7 @@ class ContextCleanerSuite extends FunSuite with BeforeAndAfter with LocalSparkCo
   }
 
   test("automatically cleanup shuffle") {
-    var rdd = newShuffleRDD
+    var rdd = newShuffleRDD()
     rdd.count()
 
     // Test that GC does not cause shuffle cleanup due to a strong reference
@@ -125,7 +124,7 @@ class ContextCleanerSuite extends FunSuite with BeforeAndAfter with LocalSparkCo
   }
 
   test("automatically cleanup broadcast") {
-    var broadcast = newBroadcast
+    var broadcast = newBroadcast()
 
     // Test that GC does not cause broadcast cleanup due to a strong reference
     val preGCTester =  new CleanerTester(sc, broadcastIds = Seq(broadcast.id))
@@ -141,11 +140,23 @@ class ContextCleanerSuite extends FunSuite with BeforeAndAfter with LocalSparkCo
     postGCTester.assertCleanup()
   }
 
+  test("automatically cleanup broadcast data for task dispatching") {
+    var rdd = newRDDWithShuffleDependencies()._1
+    rdd.count()  // This triggers an action that broadcasts the RDDs.
+
+    // Test that GC causes broadcast task data cleanup after dereferencing the RDD.
+    val postGCTester = new CleanerTester(sc,
+      broadcastIds = Seq(rdd.broadcasted.id, rdd.firstParent.broadcasted.id))
+    rdd = null
+    runGC()
+    postGCTester.assertCleanup()
+  }
+
   test("automatically cleanup RDD + shuffle + broadcast") {
     val numRdds = 100
     val numBroadcasts = 4 // Broadcasts are more costly
-    val rddBuffer = (1 to numRdds).map(i => randomRdd).toBuffer
-    val broadcastBuffer = (1 to numBroadcasts).map(i => randomBroadcast).toBuffer
+    val rddBuffer = (1 to numRdds).map(i => randomRdd()).toBuffer
+    val broadcastBuffer = (1 to numBroadcasts).map(i => randomBroadcast()).toBuffer
     val rddIds = sc.persistentRdds.keys.toSeq
     val shuffleIds = 0 until sc.newShuffleId
     val broadcastIds = 0L until numBroadcasts
@@ -175,8 +186,8 @@ class ContextCleanerSuite extends FunSuite with BeforeAndAfter with LocalSparkCo
 
     val numRdds = 10
     val numBroadcasts = 4 // Broadcasts are more costly
-    val rddBuffer = (1 to numRdds).map(i => randomRdd).toBuffer
-    val broadcastBuffer = (1 to numBroadcasts).map(i => randomBroadcast).toBuffer
+    val rddBuffer = (1 to numRdds).map(i => randomRdd()).toBuffer
+    val broadcastBuffer = (1 to numBroadcasts).map(i => randomBroadcast()).toBuffer
     val rddIds = sc.persistentRdds.keys.toSeq
     val shuffleIds = 0 until sc.newShuffleId
     val broadcastIds = 0L until numBroadcasts
@@ -197,17 +208,18 @@ class ContextCleanerSuite extends FunSuite with BeforeAndAfter with LocalSparkCo
 
   //------ Helper functions ------
 
-  def newRDD = sc.makeRDD(1 to 10)
-  def newPairRDD = newRDD.map(_ -> 1)
-  def newShuffleRDD = newPairRDD.reduceByKey(_ + _)
-  def newBroadcast = sc.broadcast(1 to 100)
-  def newRDDWithShuffleDependencies: (RDD[_], Seq[ShuffleDependency[_, _, _]]) = {
+  private def newRDD() = sc.makeRDD(1 to 10)
+  private def newPairRDD() = newRDD().map(_ -> 1)
+  private def newShuffleRDD() = newPairRDD().reduceByKey(_ + _)
+  private def newBroadcast() = sc.broadcast(1 to 100)
+
+  private def newRDDWithShuffleDependencies(): (RDD[_], Seq[ShuffleDependency[_, _, _]]) = {
     def getAllDependencies(rdd: RDD[_]): Seq[Dependency[_]] = {
       rdd.dependencies ++ rdd.dependencies.flatMap { dep =>
         getAllDependencies(dep.rdd)
       }
     }
-    val rdd = newShuffleRDD
+    val rdd = newShuffleRDD()
 
     // Get all the shuffle dependencies
     val shuffleDeps = getAllDependencies(rdd)
@@ -216,34 +228,34 @@ class ContextCleanerSuite extends FunSuite with BeforeAndAfter with LocalSparkCo
     (rdd, shuffleDeps)
   }
 
-  def randomRdd = {
+  private def randomRdd() = {
     val rdd: RDD[_] = Random.nextInt(3) match {
-      case 0 => newRDD
-      case 1 => newShuffleRDD
-      case 2 => newPairRDD.join(newPairRDD)
+      case 0 => newRDD()
+      case 1 => newShuffleRDD()
+      case 2 => newPairRDD.join(newPairRDD())
     }
     if (Random.nextBoolean()) rdd.persist()
     rdd.count()
     rdd
   }
 
-  def randomBroadcast = {
+  private def randomBroadcast() = {
     sc.broadcast(Random.nextInt(Int.MaxValue))
   }
 
   /** Run GC and make sure it actually has run */
-  def runGC() {
+  private def runGC() {
     val weakRef = new WeakReference(new Object())
     val startTime = System.currentTimeMillis
     System.gc() // Make a best effort to run the garbage collection. It *usually* runs GC.
     // Wait until a weak reference object has been GCed
-    while(System.currentTimeMillis - startTime < 10000 && weakRef.get != null) {
+    while (System.currentTimeMillis - startTime < 10000 && weakRef.get != null) {
       System.gc()
       Thread.sleep(200)
     }
   }
 
-  def cleaner = sc.cleaner.get
+  private def cleaner = sc.cleaner.get
 }
 
 

From 805f329bb1535ef4b8441994705b94a3bf0d758a Mon Sep 17 00:00:00 2001
From: Lijie Xu <csxulijie@gmail.com>
Date: Sat, 19 Jul 2014 01:27:26 -0700
Subject: [PATCH 698/810] put 'curRequestSize = 0' after 'logDebug' it

This is a minor change. We should first logDebug($curRequestSize) and then set it to 0.

Author: Lijie Xu <csxulijie@gmail.com>

Closes #1477 from JerryLead/patch-1 and squashes the following commits:

aed722d [Lijie Xu] put 'curRequestSize = 0' after 'logDebug' it
---
 .../scala/org/apache/spark/storage/BlockFetcherIterator.scala   | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/storage/BlockFetcherIterator.scala b/core/src/main/scala/org/apache/spark/storage/BlockFetcherIterator.scala
index 408a79708805..2f0296c20f2e 100644
--- a/core/src/main/scala/org/apache/spark/storage/BlockFetcherIterator.scala
+++ b/core/src/main/scala/org/apache/spark/storage/BlockFetcherIterator.scala
@@ -180,9 +180,9 @@ object BlockFetcherIterator {
             if (curRequestSize >= targetRequestSize) {
               // Add this FetchRequest
               remoteRequests += new FetchRequest(address, curBlocks)
-              curRequestSize = 0
               curBlocks = new ArrayBuffer[(BlockId, Long)]
               logDebug(s"Creating fetch request of $curRequestSize at $address")
+              curRequestSize = 0
             }
           }
           // Add in the final request

From 2a732110d46712c535b75dd4f5a73761b6463aa8 Mon Sep 17 00:00:00 2001
From: chutium <teng.qiu@gmail.com>
Date: Sat, 19 Jul 2014 11:04:41 -0500
Subject: [PATCH 699/810] SPARK-2407: Added Parser of SQL SUBSTR()

follow-up of #1359

Author: chutium <teng.qiu@gmail.com>

Closes #1442 from chutium/master and squashes the following commits:

b49cc8a [chutium] SPARK-2407: Added Parser of SQL SUBSTRING() #1442
9a60ccf [chutium] SPARK-2407: Added Parser of SQL SUBSTR() #1442
06e933b [chutium] Merge https://github.com/apache/spark
c870172 [chutium] Merge https://github.com/apache/spark
094f773 [chutium] Merge https://github.com/apache/spark
88cb37d [chutium] Merge https://github.com/apache/spark
1de83a7 [chutium] SPARK-2407: Added Parse of SQL SUBSTR()
---
 .../org/apache/spark/sql/catalyst/SqlParser.scala |  9 ++++++++-
 .../org/apache/spark/sql/SQLQuerySuite.scala      | 15 +++++++++++++++
 2 files changed, 23 insertions(+), 1 deletion(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
index e5653c5b14ac..a34b236c8ac6 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
@@ -120,7 +120,8 @@ class SqlParser extends StandardTokenParsers with PackratParsers {
   protected val WHERE = Keyword("WHERE")
   protected val INTERSECT = Keyword("INTERSECT")
   protected val EXCEPT = Keyword("EXCEPT")
-
+  protected val SUBSTR = Keyword("SUBSTR")
+  protected val SUBSTRING = Keyword("SUBSTRING")
 
   // Use reflection to find the reserved words defined in this class.
   protected val reservedWords =
@@ -316,6 +317,12 @@ class SqlParser extends StandardTokenParsers with PackratParsers {
     IF ~> "(" ~> expression ~ "," ~ expression ~ "," ~ expression <~ ")" ^^ {
       case c ~ "," ~ t ~ "," ~ f => If(c,t,f)
     } |
+    (SUBSTR | SUBSTRING) ~> "(" ~> expression ~ "," ~ expression <~ ")" ^^ {
+      case s ~ "," ~ p => Substring(s,p,Literal(Integer.MAX_VALUE))
+    } |
+    (SUBSTR | SUBSTRING) ~> "(" ~> expression ~ "," ~ expression ~ "," ~ expression <~ ")" ^^ {
+      case s ~ "," ~ p ~ "," ~ l => Substring(s,p,l)
+    } |
     ident ~ "(" ~ repsep(expression, ",") <~ ")" ^^ {
       case udfName ~ _ ~ exprs => UnresolvedFunction(udfName, exprs)
     }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index 0743cfe8cff0..6736189c96d4 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -36,6 +36,21 @@ class SQLQuerySuite extends QueryTest {
       "test")
   }
 
+  test("SPARK-2407 Added Parser of SQL SUBSTR()") {
+    checkAnswer(
+      sql("SELECT substr(tableName, 1, 2) FROM tableName"),
+      "te")
+    checkAnswer(
+      sql("SELECT substr(tableName, 3) FROM tableName"),
+      "st")
+    checkAnswer(
+      sql("SELECT substring(tableName, 1, 2) FROM tableName"),
+      "te")
+    checkAnswer(
+      sql("SELECT substring(tableName, 3) FROM tableName"),
+      "st")
+  }
+
   test("index into array") {
     checkAnswer(
       sql("SELECT data, data[0], data[0] + data[1], data[0 + 1] FROM arrayData"),

From 1efb3698b6cf39a80683b37124d2736ebf3c9d9a Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Sat, 19 Jul 2014 16:56:22 -0700
Subject: [PATCH 700/810] Revert "[SPARK-2521] Broadcast RDD object (instead of
 sending it along with every task)."

This reverts commit 7b8cd175254d42c8e82f0aa8eb4b7f3508d8fde2.
---
 .../scala/org/apache/spark/Dependency.scala   |  28 ++--
 .../scala/org/apache/spark/SparkContext.scala |   2 +
 .../main/scala/org/apache/spark/rdd/RDD.scala |  30 +---
 .../apache/spark/rdd/RDDCheckpointData.scala  |   9 +-
 .../apache/spark/scheduler/DAGScheduler.scala |   4 +
 .../apache/spark/scheduler/ResultTask.scala   | 128 +++++++++++++-----
 .../spark/scheduler/ShuffleMapTask.scala      | 125 +++++++++++++----
 .../apache/spark/ContextCleanerSuite.scala    |  62 ++++-----
 8 files changed, 251 insertions(+), 137 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/Dependency.scala b/core/src/main/scala/org/apache/spark/Dependency.scala
index 3935c8772252..09a60571238e 100644
--- a/core/src/main/scala/org/apache/spark/Dependency.scala
+++ b/core/src/main/scala/org/apache/spark/Dependency.scala
@@ -27,9 +27,7 @@ import org.apache.spark.shuffle.ShuffleHandle
  * Base class for dependencies.
  */
 @DeveloperApi
-abstract class Dependency[T] extends Serializable {
-  def rdd: RDD[T]
-}
+abstract class Dependency[T](val rdd: RDD[T]) extends Serializable
 
 
 /**
@@ -38,24 +36,20 @@ abstract class Dependency[T] extends Serializable {
  * partition of the child RDD.  Narrow dependencies allow for pipelined execution.
  */
 @DeveloperApi
-abstract class NarrowDependency[T](_rdd: RDD[T]) extends Dependency[T] {
+abstract class NarrowDependency[T](rdd: RDD[T]) extends Dependency(rdd) {
   /**
    * Get the parent partitions for a child partition.
    * @param partitionId a partition of the child RDD
    * @return the partitions of the parent RDD that the child partition depends upon
    */
   def getParents(partitionId: Int): Seq[Int]
-
-  override def rdd: RDD[T] = _rdd
 }
 
 
 /**
  * :: DeveloperApi ::
- * Represents a dependency on the output of a shuffle stage. Note that in the case of shuffle,
- * the RDD is transient since we don't need it on the executor side.
- *
- * @param _rdd the parent RDD
+ * Represents a dependency on the output of a shuffle stage.
+ * @param rdd the parent RDD
  * @param partitioner partitioner used to partition the shuffle output
  * @param serializer [[org.apache.spark.serializer.Serializer Serializer]] to use. If set to None,
  *                   the default serializer, as specified by `spark.serializer` config option, will
@@ -63,22 +57,20 @@ abstract class NarrowDependency[T](_rdd: RDD[T]) extends Dependency[T] {
  */
 @DeveloperApi
 class ShuffleDependency[K, V, C](
-    @transient _rdd: RDD[_ <: Product2[K, V]],
+    @transient rdd: RDD[_ <: Product2[K, V]],
     val partitioner: Partitioner,
     val serializer: Option[Serializer] = None,
     val keyOrdering: Option[Ordering[K]] = None,
     val aggregator: Option[Aggregator[K, V, C]] = None,
     val mapSideCombine: Boolean = false)
-  extends Dependency[Product2[K, V]] {
-
-  override def rdd = _rdd.asInstanceOf[RDD[Product2[K, V]]]
+  extends Dependency(rdd.asInstanceOf[RDD[Product2[K, V]]]) {
 
-  val shuffleId: Int = _rdd.context.newShuffleId()
+  val shuffleId: Int = rdd.context.newShuffleId()
 
-  val shuffleHandle: ShuffleHandle = _rdd.context.env.shuffleManager.registerShuffle(
-    shuffleId, _rdd.partitions.size, this)
+  val shuffleHandle: ShuffleHandle = rdd.context.env.shuffleManager.registerShuffle(
+    shuffleId, rdd.partitions.size, this)
 
-  _rdd.sparkContext.cleaner.foreach(_.registerShuffleForCleanup(this))
+  rdd.sparkContext.cleaner.foreach(_.registerShuffleForCleanup(this))
 }
 
 
diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index 48a09657fde2..8052499ab752 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -997,6 +997,8 @@ class SparkContext(config: SparkConf) extends Logging {
       // TODO: Cache.stop()?
       env.stop()
       SparkEnv.set(null)
+      ShuffleMapTask.clearCache()
+      ResultTask.clearCache()
       listenerBus.stop()
       eventLogger.foreach(_.stop())
       logInfo("Successfully stopped SparkContext")
diff --git a/core/src/main/scala/org/apache/spark/rdd/RDD.scala b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
index 2ee9a8f1a8e0..88a918aebf76 100644
--- a/core/src/main/scala/org/apache/spark/rdd/RDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
@@ -35,13 +35,12 @@ import org.apache.spark.Partitioner._
 import org.apache.spark.SparkContext._
 import org.apache.spark.annotation.{DeveloperApi, Experimental}
 import org.apache.spark.api.java.JavaRDD
-import org.apache.spark.broadcast.Broadcast
 import org.apache.spark.partial.BoundedDouble
 import org.apache.spark.partial.CountEvaluator
 import org.apache.spark.partial.GroupedCountEvaluator
 import org.apache.spark.partial.PartialResult
 import org.apache.spark.storage.StorageLevel
-import org.apache.spark.util.{BoundedPriorityQueue, Utils}
+import org.apache.spark.util.{BoundedPriorityQueue, CallSite, Utils}
 import org.apache.spark.util.collection.OpenHashMap
 import org.apache.spark.util.random.{BernoulliSampler, PoissonSampler, SamplingUtils}
 
@@ -1196,36 +1195,21 @@ abstract class RDD[T: ClassTag](
   /**
    * Return whether this RDD has been checkpointed or not
    */
-  def isCheckpointed: Boolean = checkpointData.exists(_.isCheckpointed)
+  def isCheckpointed: Boolean = {
+    checkpointData.map(_.isCheckpointed).getOrElse(false)
+  }
 
   /**
    * Gets the name of the file to which this RDD was checkpointed
    */
-  def getCheckpointFile: Option[String] = checkpointData.flatMap(_.getCheckpointFile)
+  def getCheckpointFile: Option[String] = {
+    checkpointData.flatMap(_.getCheckpointFile)
+  }
 
   // =======================================================================
   // Other internal methods and fields
   // =======================================================================
 
-  /**
-   * Broadcasted copy of this RDD, used to dispatch tasks to executors. Note that we broadcast
-   * the serialized copy of the RDD and for each task we will deserialize it, which means each
-   * task gets a different copy of the RDD. This provides stronger isolation between tasks that
-   * might modify state of objects referenced in their closures. This is necessary in Hadoop
-   * where the JobConf/Configuration object is not thread-safe.
-   */
-  @transient private[spark] lazy val broadcasted: Broadcast[Array[Byte]] = {
-    val ser = SparkEnv.get.closureSerializer.newInstance()
-    val bytes = ser.serialize(this).array()
-    val size = Utils.bytesToString(bytes.length)
-    if (bytes.length > (1L << 20)) {
-      logWarning(s"Broadcasting RDD $id ($size), which contains large objects")
-    } else {
-      logDebug(s"Broadcasting RDD $id ($size)")
-    }
-    sc.broadcast(bytes)
-  }
-
   private var storageLevel: StorageLevel = StorageLevel.NONE
 
   /** User code that created this RDD (e.g. `textFile`, `parallelize`). */
diff --git a/core/src/main/scala/org/apache/spark/rdd/RDDCheckpointData.scala b/core/src/main/scala/org/apache/spark/rdd/RDDCheckpointData.scala
index f67e5f185797..c3b2a33fb54d 100644
--- a/core/src/main/scala/org/apache/spark/rdd/RDDCheckpointData.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/RDDCheckpointData.scala
@@ -106,6 +106,7 @@ private[spark] class RDDCheckpointData[T: ClassTag](@transient rdd: RDD[T])
       cpRDD = Some(newRDD)
       rdd.markCheckpointed(newRDD)   // Update the RDD's dependencies and partitions
       cpState = Checkpointed
+      RDDCheckpointData.clearTaskCaches()
     }
     logInfo("Done checkpointing RDD " + rdd.id + " to " + path + ", new parent is RDD " + newRDD.id)
   }
@@ -130,5 +131,9 @@ private[spark] class RDDCheckpointData[T: ClassTag](@transient rdd: RDD[T])
   }
 }
 
-// Used for synchronization
-private[spark] object RDDCheckpointData
+private[spark] object RDDCheckpointData {
+  def clearTaskCaches() {
+    ShuffleMapTask.clearCache()
+    ResultTask.clearCache()
+  }
+}
diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
index 88cb5feaaff2..ede3c7d9f01a 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
@@ -376,6 +376,9 @@ class DAGScheduler(
               stageIdToStage -= stageId
               stageIdToJobIds -= stageId
 
+              ShuffleMapTask.removeStage(stageId)
+              ResultTask.removeStage(stageId)
+
               logDebug("After removal of stage %d, remaining stages = %d"
                 .format(stageId, stageIdToStage.size))
             }
@@ -720,6 +723,7 @@ class DAGScheduler(
     }
   }
 
+
   /** Called when stage's parents are available and we can now do its task. */
   private def submitMissingTasks(stage: Stage, jobId: Int) {
     logDebug("submitMissingTasks(" + stage + ")")
diff --git a/core/src/main/scala/org/apache/spark/scheduler/ResultTask.scala b/core/src/main/scala/org/apache/spark/scheduler/ResultTask.scala
index 62beb0d02a9c..bbf9f7388b07 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/ResultTask.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/ResultTask.scala
@@ -17,68 +17,134 @@
 
 package org.apache.spark.scheduler
 
-import java.nio.ByteBuffer
+import scala.language.existentials
 
 import java.io._
+import java.util.zip.{GZIPInputStream, GZIPOutputStream}
+
+import scala.collection.mutable.HashMap
 
 import org.apache.spark._
-import org.apache.spark.broadcast.Broadcast
-import org.apache.spark.rdd.RDD
+import org.apache.spark.rdd.{RDD, RDDCheckpointData}
+
+private[spark] object ResultTask {
+
+  // A simple map between the stage id to the serialized byte array of a task.
+  // Served as a cache for task serialization because serialization can be
+  // expensive on the master node if it needs to launch thousands of tasks.
+  private val serializedInfoCache = new HashMap[Int, Array[Byte]]
+
+  def serializeInfo(stageId: Int, rdd: RDD[_], func: (TaskContext, Iterator[_]) => _): Array[Byte] =
+  {
+    synchronized {
+      val old = serializedInfoCache.get(stageId).orNull
+      if (old != null) {
+        old
+      } else {
+        val out = new ByteArrayOutputStream
+        val ser = SparkEnv.get.closureSerializer.newInstance()
+        val objOut = ser.serializeStream(new GZIPOutputStream(out))
+        objOut.writeObject(rdd)
+        objOut.writeObject(func)
+        objOut.close()
+        val bytes = out.toByteArray
+        serializedInfoCache.put(stageId, bytes)
+        bytes
+      }
+    }
+  }
+
+  def deserializeInfo(stageId: Int, bytes: Array[Byte]): (RDD[_], (TaskContext, Iterator[_]) => _) =
+  {
+    val in = new GZIPInputStream(new ByteArrayInputStream(bytes))
+    val ser = SparkEnv.get.closureSerializer.newInstance()
+    val objIn = ser.deserializeStream(in)
+    val rdd = objIn.readObject().asInstanceOf[RDD[_]]
+    val func = objIn.readObject().asInstanceOf[(TaskContext, Iterator[_]) => _]
+    (rdd, func)
+  }
+
+  def removeStage(stageId: Int) {
+    serializedInfoCache.remove(stageId)
+  }
+
+  def clearCache() {
+    synchronized {
+      serializedInfoCache.clear()
+    }
+  }
+}
+
 
 /**
  * A task that sends back the output to the driver application.
  *
- * See [[Task]] for more information.
+ * See [[org.apache.spark.scheduler.Task]] for more information.
  *
  * @param stageId id of the stage this task belongs to
- * @param rddBinary broadcast version of of the serialized RDD
+ * @param rdd input to func
  * @param func a function to apply on a partition of the RDD
- * @param partition partition of the RDD this task is associated with
+ * @param _partitionId index of the number in the RDD
  * @param locs preferred task execution locations for locality scheduling
  * @param outputId index of the task in this job (a job can launch tasks on only a subset of the
  *                 input RDD's partitions).
  */
 private[spark] class ResultTask[T, U](
     stageId: Int,
-    val rddBinary: Broadcast[Array[Byte]],
-    val func: (TaskContext, Iterator[T]) => U,
-    val partition: Partition,
+    var rdd: RDD[T],
+    var func: (TaskContext, Iterator[T]) => U,
+    _partitionId: Int,
     @transient locs: Seq[TaskLocation],
-    val outputId: Int)
-  extends Task[U](stageId, partition.index) with Serializable {
-
-  // TODO: Should we also broadcast func? For that we would need a place to
-  // keep a reference to it (perhaps in DAGScheduler's job object).
-
-  def this(
-      stageId: Int,
-      rdd: RDD[T],
-      func: (TaskContext, Iterator[T]) => U,
-      partitionId: Int,
-      locs: Seq[TaskLocation],
-      outputId: Int) = {
-    this(stageId, rdd.broadcasted, func, rdd.partitions(partitionId), locs, outputId)
-  }
+    var outputId: Int)
+  extends Task[U](stageId, _partitionId) with Externalizable {
+
+  def this() = this(0, null, null, 0, null, 0)
+
+  var split = if (rdd == null) null else rdd.partitions(partitionId)
 
-  @transient private[this] val preferredLocs: Seq[TaskLocation] = {
+  @transient private val preferredLocs: Seq[TaskLocation] = {
     if (locs == null) Nil else locs.toSet.toSeq
   }
 
   override def runTask(context: TaskContext): U = {
-    // Deserialize the RDD using the broadcast variable.
-    val ser = SparkEnv.get.closureSerializer.newInstance()
-    val rdd = ser.deserialize[RDD[T]](ByteBuffer.wrap(rddBinary.value),
-      Thread.currentThread.getContextClassLoader)
     metrics = Some(context.taskMetrics)
     try {
-      func(context, rdd.iterator(partition, context))
+      func(context, rdd.iterator(split, context))
     } finally {
       context.executeOnCompleteCallbacks()
     }
   }
 
-  // This is only callable on the driver side.
   override def preferredLocations: Seq[TaskLocation] = preferredLocs
 
   override def toString = "ResultTask(" + stageId + ", " + partitionId + ")"
+
+  override def writeExternal(out: ObjectOutput) {
+    RDDCheckpointData.synchronized {
+      split = rdd.partitions(partitionId)
+      out.writeInt(stageId)
+      val bytes = ResultTask.serializeInfo(
+        stageId, rdd, func.asInstanceOf[(TaskContext, Iterator[_]) => _])
+      out.writeInt(bytes.length)
+      out.write(bytes)
+      out.writeInt(partitionId)
+      out.writeInt(outputId)
+      out.writeLong(epoch)
+      out.writeObject(split)
+    }
+  }
+
+  override def readExternal(in: ObjectInput) {
+    val stageId = in.readInt()
+    val numBytes = in.readInt()
+    val bytes = new Array[Byte](numBytes)
+    in.readFully(bytes)
+    val (rdd_, func_) = ResultTask.deserializeInfo(stageId, bytes)
+    rdd = rdd_.asInstanceOf[RDD[T]]
+    func = func_.asInstanceOf[(TaskContext, Iterator[T]) => U]
+    partitionId = in.readInt()
+    outputId = in.readInt()
+    epoch = in.readLong()
+    split = in.readObject().asInstanceOf[Partition]
+  }
 }
diff --git a/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala b/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala
index 033c6e52861e..fdaf1de83f05 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala
@@ -17,13 +17,71 @@
 
 package org.apache.spark.scheduler
 
-import java.nio.ByteBuffer
+import scala.language.existentials
+
+import java.io._
+import java.util.zip.{GZIPInputStream, GZIPOutputStream}
+
+import scala.collection.mutable.HashMap
 
 import org.apache.spark._
-import org.apache.spark.broadcast.Broadcast
-import org.apache.spark.rdd.RDD
+import org.apache.spark.rdd.{RDD, RDDCheckpointData}
 import org.apache.spark.shuffle.ShuffleWriter
 
+private[spark] object ShuffleMapTask {
+
+  // A simple map between the stage id to the serialized byte array of a task.
+  // Served as a cache for task serialization because serialization can be
+  // expensive on the master node if it needs to launch thousands of tasks.
+  private val serializedInfoCache = new HashMap[Int, Array[Byte]]
+
+  def serializeInfo(stageId: Int, rdd: RDD[_], dep: ShuffleDependency[_, _, _]): Array[Byte] = {
+    synchronized {
+      val old = serializedInfoCache.get(stageId).orNull
+      if (old != null) {
+        return old
+      } else {
+        val out = new ByteArrayOutputStream
+        val ser = SparkEnv.get.closureSerializer.newInstance()
+        val objOut = ser.serializeStream(new GZIPOutputStream(out))
+        objOut.writeObject(rdd)
+        objOut.writeObject(dep)
+        objOut.close()
+        val bytes = out.toByteArray
+        serializedInfoCache.put(stageId, bytes)
+        bytes
+      }
+    }
+  }
+
+  def deserializeInfo(stageId: Int, bytes: Array[Byte]): (RDD[_], ShuffleDependency[_, _, _]) = {
+    val in = new GZIPInputStream(new ByteArrayInputStream(bytes))
+    val ser = SparkEnv.get.closureSerializer.newInstance()
+    val objIn = ser.deserializeStream(in)
+    val rdd = objIn.readObject().asInstanceOf[RDD[_]]
+    val dep = objIn.readObject().asInstanceOf[ShuffleDependency[_, _, _]]
+    (rdd, dep)
+  }
+
+  // Since both the JarSet and FileSet have the same format this is used for both.
+  def deserializeFileSet(bytes: Array[Byte]): HashMap[String, Long] = {
+    val in = new GZIPInputStream(new ByteArrayInputStream(bytes))
+    val objIn = new ObjectInputStream(in)
+    val set = objIn.readObject().asInstanceOf[Array[(String, Long)]].toMap
+    HashMap(set.toSeq: _*)
+  }
+
+  def removeStage(stageId: Int) {
+    serializedInfoCache.remove(stageId)
+  }
+
+  def clearCache() {
+    synchronized {
+      serializedInfoCache.clear()
+    }
+  }
+}
+
 /**
  * A ShuffleMapTask divides the elements of an RDD into multiple buckets (based on a partitioner
  * specified in the ShuffleDependency).
@@ -31,47 +89,62 @@ import org.apache.spark.shuffle.ShuffleWriter
  * See [[org.apache.spark.scheduler.Task]] for more information.
  *
  * @param stageId id of the stage this task belongs to
- * @param rddBinary broadcast version of of the serialized RDD
+ * @param rdd the final RDD in this stage
  * @param dep the ShuffleDependency
- * @param partition partition of the RDD this task is associated with
+ * @param _partitionId index of the number in the RDD
  * @param locs preferred task execution locations for locality scheduling
  */
 private[spark] class ShuffleMapTask(
     stageId: Int,
-    var rddBinary: Broadcast[Array[Byte]],
+    var rdd: RDD[_],
     var dep: ShuffleDependency[_, _, _],
-    partition: Partition,
+    _partitionId: Int,
     @transient private var locs: Seq[TaskLocation])
-  extends Task[MapStatus](stageId, partition.index) with Logging {
-
-  // TODO: Should we also broadcast the ShuffleDependency? For that we would need a place to
-  // keep a reference to it (perhaps in Stage).
-
-  def this(
-      stageId: Int,
-      rdd: RDD[_],
-      dep: ShuffleDependency[_, _, _],
-      partitionId: Int,
-      locs: Seq[TaskLocation]) = {
-    this(stageId, rdd.broadcasted, dep, rdd.partitions(partitionId), locs)
-  }
+  extends Task[MapStatus](stageId, _partitionId)
+  with Externalizable
+  with Logging {
+
+  protected def this() = this(0, null, null, 0, null)
 
   @transient private val preferredLocs: Seq[TaskLocation] = {
     if (locs == null) Nil else locs.toSet.toSeq
   }
 
-  override def runTask(context: TaskContext): MapStatus = {
-    // Deserialize the RDD using the broadcast variable.
-    val ser = SparkEnv.get.closureSerializer.newInstance()
-    val rdd = ser.deserialize[RDD[_]](ByteBuffer.wrap(rddBinary.value),
-      Thread.currentThread.getContextClassLoader)
+  var split = if (rdd == null) null else rdd.partitions(partitionId)
+
+  override def writeExternal(out: ObjectOutput) {
+    RDDCheckpointData.synchronized {
+      split = rdd.partitions(partitionId)
+      out.writeInt(stageId)
+      val bytes = ShuffleMapTask.serializeInfo(stageId, rdd, dep)
+      out.writeInt(bytes.length)
+      out.write(bytes)
+      out.writeInt(partitionId)
+      out.writeLong(epoch)
+      out.writeObject(split)
+    }
+  }
 
+  override def readExternal(in: ObjectInput) {
+    val stageId = in.readInt()
+    val numBytes = in.readInt()
+    val bytes = new Array[Byte](numBytes)
+    in.readFully(bytes)
+    val (rdd_, dep_) = ShuffleMapTask.deserializeInfo(stageId, bytes)
+    rdd = rdd_
+    dep = dep_
+    partitionId = in.readInt()
+    epoch = in.readLong()
+    split = in.readObject().asInstanceOf[Partition]
+  }
+
+  override def runTask(context: TaskContext): MapStatus = {
     metrics = Some(context.taskMetrics)
     var writer: ShuffleWriter[Any, Any] = null
     try {
       val manager = SparkEnv.get.shuffleManager
       writer = manager.getWriter[Any, Any](dep.shuffleHandle, partitionId, context)
-      writer.write(rdd.iterator(partition, context).asInstanceOf[Iterator[_ <: Product2[Any, Any]]])
+      writer.write(rdd.iterator(split, context).asInstanceOf[Iterator[_ <: Product2[Any, Any]]])
       return writer.stop(success = true).get
     } catch {
       case e: Exception =>
diff --git a/core/src/test/scala/org/apache/spark/ContextCleanerSuite.scala b/core/src/test/scala/org/apache/spark/ContextCleanerSuite.scala
index 871f831531be..13b415cccb64 100644
--- a/core/src/test/scala/org/apache/spark/ContextCleanerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/ContextCleanerSuite.scala
@@ -52,8 +52,9 @@ class ContextCleanerSuite extends FunSuite with BeforeAndAfter with LocalSparkCo
     }
   }
 
+
   test("cleanup RDD") {
-    val rdd = newRDD().persist()
+    val rdd = newRDD.persist()
     val collected = rdd.collect().toList
     val tester = new CleanerTester(sc, rddIds = Seq(rdd.id))
 
@@ -66,7 +67,7 @@ class ContextCleanerSuite extends FunSuite with BeforeAndAfter with LocalSparkCo
   }
 
   test("cleanup shuffle") {
-    val (rdd, shuffleDeps) = newRDDWithShuffleDependencies()
+    val (rdd, shuffleDeps) = newRDDWithShuffleDependencies
     val collected = rdd.collect().toList
     val tester = new CleanerTester(sc, shuffleIds = shuffleDeps.map(_.shuffleId))
 
@@ -79,7 +80,7 @@ class ContextCleanerSuite extends FunSuite with BeforeAndAfter with LocalSparkCo
   }
 
   test("cleanup broadcast") {
-    val broadcast = newBroadcast()
+    val broadcast = newBroadcast
     val tester = new CleanerTester(sc, broadcastIds = Seq(broadcast.id))
 
     // Explicit cleanup
@@ -88,7 +89,7 @@ class ContextCleanerSuite extends FunSuite with BeforeAndAfter with LocalSparkCo
   }
 
   test("automatically cleanup RDD") {
-    var rdd = newRDD().persist()
+    var rdd = newRDD.persist()
     rdd.count()
 
     // Test that GC does not cause RDD cleanup due to a strong reference
@@ -106,7 +107,7 @@ class ContextCleanerSuite extends FunSuite with BeforeAndAfter with LocalSparkCo
   }
 
   test("automatically cleanup shuffle") {
-    var rdd = newShuffleRDD()
+    var rdd = newShuffleRDD
     rdd.count()
 
     // Test that GC does not cause shuffle cleanup due to a strong reference
@@ -124,7 +125,7 @@ class ContextCleanerSuite extends FunSuite with BeforeAndAfter with LocalSparkCo
   }
 
   test("automatically cleanup broadcast") {
-    var broadcast = newBroadcast()
+    var broadcast = newBroadcast
 
     // Test that GC does not cause broadcast cleanup due to a strong reference
     val preGCTester =  new CleanerTester(sc, broadcastIds = Seq(broadcast.id))
@@ -140,23 +141,11 @@ class ContextCleanerSuite extends FunSuite with BeforeAndAfter with LocalSparkCo
     postGCTester.assertCleanup()
   }
 
-  test("automatically cleanup broadcast data for task dispatching") {
-    var rdd = newRDDWithShuffleDependencies()._1
-    rdd.count()  // This triggers an action that broadcasts the RDDs.
-
-    // Test that GC causes broadcast task data cleanup after dereferencing the RDD.
-    val postGCTester = new CleanerTester(sc,
-      broadcastIds = Seq(rdd.broadcasted.id, rdd.firstParent.broadcasted.id))
-    rdd = null
-    runGC()
-    postGCTester.assertCleanup()
-  }
-
   test("automatically cleanup RDD + shuffle + broadcast") {
     val numRdds = 100
     val numBroadcasts = 4 // Broadcasts are more costly
-    val rddBuffer = (1 to numRdds).map(i => randomRdd()).toBuffer
-    val broadcastBuffer = (1 to numBroadcasts).map(i => randomBroadcast()).toBuffer
+    val rddBuffer = (1 to numRdds).map(i => randomRdd).toBuffer
+    val broadcastBuffer = (1 to numBroadcasts).map(i => randomBroadcast).toBuffer
     val rddIds = sc.persistentRdds.keys.toSeq
     val shuffleIds = 0 until sc.newShuffleId
     val broadcastIds = 0L until numBroadcasts
@@ -186,8 +175,8 @@ class ContextCleanerSuite extends FunSuite with BeforeAndAfter with LocalSparkCo
 
     val numRdds = 10
     val numBroadcasts = 4 // Broadcasts are more costly
-    val rddBuffer = (1 to numRdds).map(i => randomRdd()).toBuffer
-    val broadcastBuffer = (1 to numBroadcasts).map(i => randomBroadcast()).toBuffer
+    val rddBuffer = (1 to numRdds).map(i => randomRdd).toBuffer
+    val broadcastBuffer = (1 to numBroadcasts).map(i => randomBroadcast).toBuffer
     val rddIds = sc.persistentRdds.keys.toSeq
     val shuffleIds = 0 until sc.newShuffleId
     val broadcastIds = 0L until numBroadcasts
@@ -208,18 +197,17 @@ class ContextCleanerSuite extends FunSuite with BeforeAndAfter with LocalSparkCo
 
   //------ Helper functions ------
 
-  private def newRDD() = sc.makeRDD(1 to 10)
-  private def newPairRDD() = newRDD().map(_ -> 1)
-  private def newShuffleRDD() = newPairRDD().reduceByKey(_ + _)
-  private def newBroadcast() = sc.broadcast(1 to 100)
-
-  private def newRDDWithShuffleDependencies(): (RDD[_], Seq[ShuffleDependency[_, _, _]]) = {
+  def newRDD = sc.makeRDD(1 to 10)
+  def newPairRDD = newRDD.map(_ -> 1)
+  def newShuffleRDD = newPairRDD.reduceByKey(_ + _)
+  def newBroadcast = sc.broadcast(1 to 100)
+  def newRDDWithShuffleDependencies: (RDD[_], Seq[ShuffleDependency[_, _, _]]) = {
     def getAllDependencies(rdd: RDD[_]): Seq[Dependency[_]] = {
       rdd.dependencies ++ rdd.dependencies.flatMap { dep =>
         getAllDependencies(dep.rdd)
       }
     }
-    val rdd = newShuffleRDD()
+    val rdd = newShuffleRDD
 
     // Get all the shuffle dependencies
     val shuffleDeps = getAllDependencies(rdd)
@@ -228,34 +216,34 @@ class ContextCleanerSuite extends FunSuite with BeforeAndAfter with LocalSparkCo
     (rdd, shuffleDeps)
   }
 
-  private def randomRdd() = {
+  def randomRdd = {
     val rdd: RDD[_] = Random.nextInt(3) match {
-      case 0 => newRDD()
-      case 1 => newShuffleRDD()
-      case 2 => newPairRDD.join(newPairRDD())
+      case 0 => newRDD
+      case 1 => newShuffleRDD
+      case 2 => newPairRDD.join(newPairRDD)
     }
     if (Random.nextBoolean()) rdd.persist()
     rdd.count()
     rdd
   }
 
-  private def randomBroadcast() = {
+  def randomBroadcast = {
     sc.broadcast(Random.nextInt(Int.MaxValue))
   }
 
   /** Run GC and make sure it actually has run */
-  private def runGC() {
+  def runGC() {
     val weakRef = new WeakReference(new Object())
     val startTime = System.currentTimeMillis
     System.gc() // Make a best effort to run the garbage collection. It *usually* runs GC.
     // Wait until a weak reference object has been GCed
-    while (System.currentTimeMillis - startTime < 10000 && weakRef.get != null) {
+    while(System.currentTimeMillis - startTime < 10000 && weakRef.get != null) {
       System.gc()
       Thread.sleep(200)
     }
   }
 
-  private def cleaner = sc.cleaner.get
+  def cleaner = sc.cleaner.get
 }
 
 

From 49e472744951d875627d78b0d6e93cd139232929 Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Sat, 19 Jul 2014 18:19:08 -0700
Subject: [PATCH 701/810] SPARK-2596 A tool for mirroring github pull requests
 on JIRA.

For a bunch of reasons we should automatically populate a JIRA with information about new pull requests when they arrive. I've written a small python script to do this that we can run from Jenkins every 5 or 10 minutes to keep things in sync.

Author: Patrick Wendell <pwendell@gmail.com>

Closes #1496 from pwendell/github-integration and squashes the following commits:

55ad226 [Patrick Wendell] Small fix
afda547 [Patrick Wendell] Use sequence instead of dictiory for JIRA's
3e18cc1 [Patrick Wendell] Small edits
84c5606 [Patrick Wendell] SPARK-2596 A tool for mirroring github pull requests on JIRA.
---
 dev/github_jira_sync.py | 141 ++++++++++++++++++++++++++++++++++++++++
 1 file changed, 141 insertions(+)
 create mode 100755 dev/github_jira_sync.py

diff --git a/dev/github_jira_sync.py b/dev/github_jira_sync.py
new file mode 100755
index 000000000000..4b0e266bbe60
--- /dev/null
+++ b/dev/github_jira_sync.py
@@ -0,0 +1,141 @@
+#!/usr/bin/env python
+
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# Utility for updating JIRA's with information about Github pull requests
+
+import json
+import os
+import re
+import sys
+import urllib2
+
+try:
+    import jira.client
+except ImportError:
+    print "This tool requires the jira-python library"
+    print "Install using 'sudo pip install jira-python'"
+    sys.exit(-1)
+
+# User facing configs
+GITHUB_API_BASE = os.environ.get("GITHUB_API_BASE", "https://api.github.com/repos/apache/spark")
+JIRA_API_BASE = os.environ.get("JIRA_API_BASE", "https://issues.apache.org/jira")
+JIRA_USERNAME = os.environ.get("JIRA_USERNAME", "apachespark")
+JIRA_PASSWORD = os.environ.get("JIRA_PASSWORD", "XXX")
+# Maximum number of updates to perform in one run
+MAX_UPDATES = int(os.environ.get("MAX_UPDATES", "100000"))
+# Cut-off for oldest PR on which to comment. Useful for avoiding
+# "notification overload" when running for the first time.
+MIN_COMMENT_PR = int(os.environ.get("MIN_COMMENT_PR", "1496"))
+
+# File used as an opitimization to store maximum previously seen PR
+# Used mostly because accessing ASF JIRA is slow, so we want to avoid checking
+# the state of JIRA's that are tied to PR's we've already looked at.
+MAX_FILE = ".github-jira-max"
+
+def get_url(url):
+    try:
+        return urllib2.urlopen(url)
+    except urllib2.HTTPError as e:
+        print "Unable to fetch URL, exiting: %s" % url
+        sys.exit(-1)
+
+def get_json(urllib_response):
+    return json.load(urllib_response)
+
+# Return a list of (JIRA id, JSON dict) tuples:
+# e.g. [('SPARK-1234', {.. json ..}), ('SPARK-5687', {.. json ..})}
+def get_jira_prs():
+    result = []
+    has_next_page = True
+    page_num = 0
+    while has_next_page:
+	page = get_url(GITHUB_API_BASE + "/pulls?page=%s&per_page=100" % page_num)
+	page_json = get_json(page)
+
+	for pull in page_json:
+	    jiras = re.findall("SPARK-[0-9]{4,5}", pull['title'])
+	    for jira in jiras:
+		result = result + [(jira,  pull)]
+
+	# Check if there is another page
+	link_header = filter(lambda k: k.startswith("Link"), page.info().headers)[0]
+	if not "next"in link_header:
+	    has_next_page = False
+	else:
+	    page_num = page_num + 1
+    return result
+
+def set_max_pr(max_val):
+    f = open(MAX_FILE, 'w')
+    f.write("%s" % max_val)
+    f.close()
+    print "Writing largest PR number seen: %s" % max_val
+
+def get_max_pr():
+    if os.path.exists(MAX_FILE):
+        result = int(open(MAX_FILE, 'r').read())
+        print "Read largest PR number previously seen: %s" % result
+        return result
+    else:
+        return 0
+
+jira_client = jira.client.JIRA({'server': JIRA_API_BASE},
+                                basic_auth=(JIRA_USERNAME, JIRA_PASSWORD))
+
+jira_prs = get_jira_prs()
+
+previous_max = get_max_pr()
+print "Retrieved %s JIRA PR's from Github" % len(jira_prs)
+jira_prs = [(k, v) for k, v in jira_prs if int(v['number']) > previous_max]
+print "%s PR's remain after excluding visted ones" % len(jira_prs)
+
+num_updates = 0
+considered = []
+for issue, pr in sorted(jira_prs, key=lambda (k, v): int(v['number'])):
+    if num_updates >= MAX_UPDATES:
+      break
+    pr_num = int(pr['number'])
+
+    print "Checking issue %s" % issue
+    considered = considered + [pr_num]
+
+    url = pr['html_url']
+    title = "[Github] Pull Request #%s (%s)" % (pr['number'], pr['user']['login']) 
+  
+    existing_links = map(lambda l: l.raw['object']['url'], jira_client.remote_links(issue))
+    if url in existing_links:
+        continue
+
+    icon = {"title": "Pull request #%s" % pr['number'], 
+      "url16x16": "https://assets-cdn.github.com/favicon.ico"}
+    destination = {"title": title, "url": url, "icon": icon}
+    # For all possible fields see:
+    # https://developer.atlassian.com/display/JIRADEV/Fields+in+Remote+Issue+Links     
+    # application = {"name": "Github pull requests", "type": "org.apache.spark.jira.github"} 
+    jira_client.add_remote_link(issue, destination)
+    
+    comment = "User '%s' has created a pull request for this issue:" % pr['user']['login']
+    comment = comment + ("\n%s" % pr['html_url'])
+    if pr_num >= MIN_COMMENT_PR:
+        jira_client.add_comment(issue, comment)
+    
+    print "Added link %s <-> PR #%s" % (issue, pr['number'])
+    num_updates = num_updates + 1
+
+if len(considered) > 0:
+    set_max_pr(max(considered))

From d39e3b9673027bb9f4d1542e5a2386f73078eec0 Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Sat, 19 Jul 2014 18:24:21 -0700
Subject: [PATCH 702/810] SPARK-2596 HOTFIX: Deal with non-existent JIRAs.

A small bug that was found in our JIRA sync script.
---
 dev/github_jira_sync.py | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/dev/github_jira_sync.py b/dev/github_jira_sync.py
index 4b0e266bbe60..805108011706 100755
--- a/dev/github_jira_sync.py
+++ b/dev/github_jira_sync.py
@@ -116,8 +116,13 @@ def get_max_pr():
 
     url = pr['html_url']
     title = "[Github] Pull Request #%s (%s)" % (pr['number'], pr['user']['login']) 
-  
-    existing_links = map(lambda l: l.raw['object']['url'], jira_client.remote_links(issue))
+    try:
+      existing_links = map(lambda l: l.raw['object']['url'], jira_client.remote_links(issue))
+    except:
+      print "Failure reading JIRA %s (does it exist?)" % issue
+      print sys.exc_info()[0]
+      continue
+
     if url in existing_links:
         continue
 

From 0d01e85f42f3c997df7fee942b05b509968bac4b Mon Sep 17 00:00:00 2001
From: Cesar Arevalo <cesar@zephyrhealthinc.com>
Date: Sat, 19 Jul 2014 20:20:07 -0700
Subject: [PATCH 703/810] Typo fix to the programming guide in the docs

Typo fix to the programming guide in the docs. Changed the word "distibuted" to "distributed".

Author: Cesar Arevalo <cesar@zephyrhealthinc.com>

Closes #1495 from cesararevalo/master and squashes the following commits:

0c2e3a7 [Cesar Arevalo] Typo fix to the programming guide in the docs
---
 docs/programming-guide.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/docs/programming-guide.md b/docs/programming-guide.md
index b09d6347cd1b..90c69713019f 100644
--- a/docs/programming-guide.md
+++ b/docs/programming-guide.md
@@ -739,7 +739,7 @@ def doStuff(self, rdd):
 
 While most Spark operations work on RDDs containing any type of objects, a few special operations are
 only available on RDDs of key-value pairs.
-The most common ones are distibuted "shuffle" operations, such as grouping or aggregating the elements
+The most common ones are distributed "shuffle" operations, such as grouping or aggregating the elements
 by a key.
 
 In Scala, these operations are automatically available on RDDs containing
@@ -773,7 +773,7 @@ documentation](http://docs.oracle.com/javase/7/docs/api/java/lang/Object.html#ha
 
 While most Spark operations work on RDDs containing any type of objects, a few special operations are
 only available on RDDs of key-value pairs.
-The most common ones are distibuted "shuffle" operations, such as grouping or aggregating the elements
+The most common ones are distributed "shuffle" operations, such as grouping or aggregating the elements
 by a key.
 
 In Java, key-value pairs are represented using the 
@@ -810,7 +810,7 @@ documentation](http://docs.oracle.com/javase/7/docs/api/java/lang/Object.html#ha
 
 While most Spark operations work on RDDs containing any type of objects, a few special operations are
 only available on RDDs of key-value pairs.
-The most common ones are distibuted "shuffle" operations, such as grouping or aggregating the elements
+The most common ones are distributed "shuffle" operations, such as grouping or aggregating the elements
 by a key.
 
 In Python, these operations work on RDDs containing built-in Python tuples such as `(1, 2)`.

From c11949878d650ed776b190444b198dbdacb0c5d7 Mon Sep 17 00:00:00 2001
From: Mark Wagner <mwagner@mwagner-ld.linkedin.biz>
Date: Sat, 19 Jul 2014 20:24:13 -0700
Subject: [PATCH 704/810] SPARK-2587: Fix error message in make-distribution.sh

make-distribution.sh gives a slightly off error message when using --with-hive.

Author: Mark Wagner <mwagner@mwagner-ld.linkedin.biz>

Closes #1489 from wagnermarkd/SPARK-2587 and squashes the following commits:

7b5d3ff [Mark Wagner] SPARK-2587: Fix error message in make-distribution.sh
---
 make-distribution.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/make-distribution.sh b/make-distribution.sh
index b5a90f0f3bfe..c08093f46b61 100755
--- a/make-distribution.sh
+++ b/make-distribution.sh
@@ -58,7 +58,7 @@ while (( "$#" )); do
       exit_with_usage
       ;;
     --with-hive)
-      echo "Error: '--with-hive' is no longer supported, use Maven option -Pyarn"
+      echo "Error: '--with-hive' is no longer supported, use Maven option -Phive"
       exit_with_usage
       ;;
     --skip-java-test)

From 4da01e3813f0a0413fe691358c14278bbd5508ed Mon Sep 17 00:00:00 2001
From: lianhuiwang <lianhuiwang09@gmail.com>
Date: Sat, 19 Jul 2014 20:46:59 -0700
Subject: [PATCH 705/810] [SPARK-2524] missing document about
 spark.deploy.retainedDrivers

https://issues.apache.org/jira/browse/SPARK-2524
The configuration on spark.deploy.retainedDrivers is undocumented but actually used
https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/deploy/master/Master.scala#L60

Author: lianhuiwang <lianhuiwang09@gmail.com>
Author: Wang Lianhui <lianhuiwang09@gmail.com>
Author: unknown <Administrator@taguswang-PC1.tencent.com>

Closes #1443 from lianhuiwang/SPARK-2524 and squashes the following commits:

64660fd [Wang Lianhui] address pwendell's comments
5f6bbb7 [Wang Lianhui] missing document about spark.deploy.retainedDrivers
44a3f50 [unknown] Merge remote-tracking branch 'upstream/master'
eacf933 [lianhuiwang] Merge remote-tracking branch 'upstream/master'
8bbfe76 [lianhuiwang] Merge remote-tracking branch 'upstream/master'
480ce94 [lianhuiwang] address aarondav comments
f2b5970 [lianhuiwang] bugfix worker DriverStateChanged state should match DriverState.FAILED
---
 docs/spark-standalone.md | 14 ++++++++++++++
 1 file changed, 14 insertions(+)

diff --git a/docs/spark-standalone.md b/docs/spark-standalone.md
index f5c0f7cef83d..ad8b6c0e51a7 100644
--- a/docs/spark-standalone.md
+++ b/docs/spark-standalone.md
@@ -156,6 +156,20 @@ SPARK_MASTER_OPTS supports the following system properties:
 
 <table class="table">
 <tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr>
+<tr>
+  <td><code>spark.deploy.retainedApplications</code></td>
+  <td>200</td>
+  <td>
+    The maximum number of completed applications to display. Older applications will be dropped from the UI to maintain this limit.<br/>
+  </td>
+</tr>
+<tr>
+  <td><code>spark.deploy.retainedDrivers</code></td>
+  <td>200</td>
+  <td>
+   The maximum number of completed drivers to display. Older drivers will be dropped from the UI to maintain this limit.<br/>
+  </td>
+</tr>
 <tr>
   <td><code>spark.deploy.spreadOut</code></td>
   <td>true</td>

From 98ab4112255d4e0fdb6e084bd3fe65807c5b209b Mon Sep 17 00:00:00 2001
From: Sandy Ryza <sandy@cloudera.com>
Date: Sun, 20 Jul 2014 01:24:32 -0700
Subject: [PATCH 706/810] SPARK-2519 part 2. Remove pattern matching on Tuple2
 in critical section...

...s of CoGroupedRDD and PairRDDFunctions

This also removes an unnecessary tuple creation in cogroup.

Author: Sandy Ryza <sandy@cloudera.com>

Closes #1447 from sryza/sandy-spark-2519-2 and squashes the following commits:

b6d9699 [Sandy Ryza] Remove missed Tuple2 match in CoGroupedRDD
a109828 [Sandy Ryza] Remove another pattern matching in MappedValuesRDD and revert some changes in PairRDDFunctions
be10f8a [Sandy Ryza] SPARK-2519 part 2. Remove pattern matching on Tuple2 in critical sections of CoGroupedRDD and PairRDDFunctions
---
 .../org/apache/spark/rdd/CoGroupedRDD.scala   |  4 +-
 .../apache/spark/rdd/MappedValuesRDD.scala    |  2 +-
 .../apache/spark/rdd/PairRDDFunctions.scala   | 60 +++++++++----------
 3 files changed, 33 insertions(+), 33 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/rdd/CoGroupedRDD.scala b/core/src/main/scala/org/apache/spark/rdd/CoGroupedRDD.scala
index 5366c1a1cc1b..aca235a62a6a 100644
--- a/core/src/main/scala/org/apache/spark/rdd/CoGroupedRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/CoGroupedRDD.scala
@@ -170,12 +170,12 @@ class CoGroupedRDD[K](@transient var rdds: Seq[RDD[_ <: Product2[K, _]]], part:
 
     val createCombiner: (CoGroupValue => CoGroupCombiner) = value => {
       val newCombiner = Array.fill(numRdds)(new CoGroup)
-      value match { case (v, depNum) => newCombiner(depNum) += v }
+      newCombiner(value._2) += value._1
       newCombiner
     }
     val mergeValue: (CoGroupCombiner, CoGroupValue) => CoGroupCombiner =
       (combiner, value) => {
-      value match { case (v, depNum) => combiner(depNum) += v }
+      combiner(value._2) += value._1
       combiner
     }
     val mergeCombiners: (CoGroupCombiner, CoGroupCombiner) => CoGroupCombiner =
diff --git a/core/src/main/scala/org/apache/spark/rdd/MappedValuesRDD.scala b/core/src/main/scala/org/apache/spark/rdd/MappedValuesRDD.scala
index 2bc47eb9fcd7..a60952eee590 100644
--- a/core/src/main/scala/org/apache/spark/rdd/MappedValuesRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/MappedValuesRDD.scala
@@ -28,6 +28,6 @@ class MappedValuesRDD[K, V, U](prev: RDD[_ <: Product2[K, V]], f: V => U)
   override val partitioner = firstParent[Product2[K, U]].partitioner
 
   override def compute(split: Partition, context: TaskContext): Iterator[(K, U)] = {
-    firstParent[Product2[K, V]].iterator(split, context).map { case Product2(k ,v) => (k, f(v)) }
+    firstParent[Product2[K, V]].iterator(split, context).map { pair => (pair._1, f(pair._2)) }
   }
 }
diff --git a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
index 29038b0359cc..a6b920467283 100644
--- a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
@@ -216,17 +216,17 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
 
     val reducePartition = (iter: Iterator[(K, V)]) => {
       val map = new JHashMap[K, V]
-      iter.foreach { case (k, v) =>
-        val old = map.get(k)
-        map.put(k, if (old == null) v else func(old, v))
+      iter.foreach { pair =>
+        val old = map.get(pair._1)
+        map.put(pair._1, if (old == null) pair._2 else func(old, pair._2))
       }
       Iterator(map)
     } : Iterator[JHashMap[K, V]]
 
     val mergeMaps = (m1: JHashMap[K, V], m2: JHashMap[K, V]) => {
-      m2.foreach { case (k, v) =>
-        val old = m1.get(k)
-        m1.put(k, if (old == null) v else func(old, v))
+      m2.foreach { pair =>
+        val old = m1.get(pair._1)
+        m1.put(pair._1, if (old == null) pair._2 else func(old, pair._2))
       }
       m1
     } : JHashMap[K, V]
@@ -401,9 +401,9 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * (k, v2) is in `other`. Uses the given Partitioner to partition the output RDD.
    */
   def join[W](other: RDD[(K, W)], partitioner: Partitioner): RDD[(K, (V, W))] = {
-    this.cogroup(other, partitioner).flatMapValues { case (vs, ws) =>
-      for (v <- vs; w <- ws) yield (v, w)
-    }
+    this.cogroup(other, partitioner).flatMapValues( pair =>
+      for (v <- pair._1; w <- pair._2) yield (v, w)
+    )
   }
 
   /**
@@ -413,11 +413,11 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * partition the output RDD.
    */
   def leftOuterJoin[W](other: RDD[(K, W)], partitioner: Partitioner): RDD[(K, (V, Option[W]))] = {
-    this.cogroup(other, partitioner).flatMapValues { case (vs, ws) =>
-      if (ws.isEmpty) {
-        vs.map(v => (v, None))
+    this.cogroup(other, partitioner).flatMapValues { pair =>
+      if (pair._2.isEmpty) {
+        pair._1.map(v => (v, None))
       } else {
-        for (v <- vs; w <- ws) yield (v, Some(w))
+        for (v <- pair._1; w <- pair._2) yield (v, Some(w))
       }
     }
   }
@@ -430,11 +430,11 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    */
   def rightOuterJoin[W](other: RDD[(K, W)], partitioner: Partitioner)
       : RDD[(K, (Option[V], W))] = {
-    this.cogroup(other, partitioner).flatMapValues { case (vs, ws) =>
-      if (vs.isEmpty) {
-        ws.map(w => (None, w))
+    this.cogroup(other, partitioner).flatMapValues { pair =>
+      if (pair._1.isEmpty) {
+        pair._2.map(w => (None, w))
       } else {
-        for (v <- vs; w <- ws) yield (Some(v), w)
+        for (v <- pair._1; w <- pair._2) yield (Some(v), w)
       }
     }
   }
@@ -535,7 +535,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
     val data = self.collect()
     val map = new mutable.HashMap[K, V]
     map.sizeHint(data.length)
-    data.foreach { case (k, v) => map.put(k, v) }
+    data.foreach { pair => map.put(pair._1, pair._2) }
     map
   }
 
@@ -572,10 +572,10 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
     }
     val cg = new CoGroupedRDD[K](Seq(self, other1, other2, other3), partitioner)
     cg.mapValues { case Seq(vs, w1s, w2s, w3s) =>
-      (vs.asInstanceOf[Seq[V]],
-        w1s.asInstanceOf[Seq[W1]],
-        w2s.asInstanceOf[Seq[W2]],
-        w3s.asInstanceOf[Seq[W3]])
+       (vs.asInstanceOf[Seq[V]],
+         w1s.asInstanceOf[Seq[W1]],
+         w2s.asInstanceOf[Seq[W2]],
+         w3s.asInstanceOf[Seq[W3]])
     }
   }
 
@@ -589,8 +589,8 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
       throw new SparkException("Default partitioner cannot partition array keys.")
     }
     val cg = new CoGroupedRDD[K](Seq(self, other), partitioner)
-    cg.mapValues { case Seq(vs, ws) =>
-      (vs.asInstanceOf[Seq[V]], ws.asInstanceOf[Seq[W]])
+    cg.mapValues { case Seq(vs, w1s) =>
+      (vs.asInstanceOf[Seq[V]], w1s.asInstanceOf[Seq[W]])
     }
   }
 
@@ -606,8 +606,8 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
     val cg = new CoGroupedRDD[K](Seq(self, other1, other2), partitioner)
     cg.mapValues { case Seq(vs, w1s, w2s) =>
       (vs.asInstanceOf[Seq[V]],
-       w1s.asInstanceOf[Seq[W1]],
-       w2s.asInstanceOf[Seq[W2]])
+        w1s.asInstanceOf[Seq[W1]],
+        w2s.asInstanceOf[Seq[W2]])
     }
   }
 
@@ -712,8 +712,8 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
         val index = p.getPartition(key)
         val process = (it: Iterator[(K, V)]) => {
           val buf = new ArrayBuffer[V]
-          for ((k, v) <- it if k == key) {
-            buf += v
+          for (pair <- it if pair._1 == key) {
+            buf += pair._2
           }
           buf
         } : Seq[V]
@@ -858,8 +858,8 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
       val writer = format.getRecordWriter(hadoopContext).asInstanceOf[NewRecordWriter[K,V]]
       try {
         while (iter.hasNext) {
-          val (k, v) = iter.next()
-          writer.write(k, v)
+          val pair = iter.next()
+          writer.write(pair._1, pair._2)
         }
       } finally {
         writer.close(hadoopContext)

From fa51b0fb5bee95a402c7b7f13dcf0b46cf5bb429 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Sun, 20 Jul 2014 11:06:06 -0700
Subject: [PATCH 707/810] [SPARK-2598] RangePartitioner's binary search does
 not use the given Ordering

We should fix this in branch-1.0 as well.

Author: Reynold Xin <rxin@apache.org>

Closes #1500 from rxin/rangePartitioner and squashes the following commits:

c0a94f5 [Reynold Xin] [SPARK-2598] RangePartitioner's binary search does not use the given Ordering.
---
 .../main/scala/org/apache/spark/Partitioner.scala  |  4 ++--
 ...ollectionsUtil.scala => CollectionsUtils.scala} |  7 ++++---
 .../scala/org/apache/spark/PartitioningSuite.scala | 14 ++++++++++++++
 3 files changed, 20 insertions(+), 5 deletions(-)
 rename core/src/main/scala/org/apache/spark/util/{CollectionsUtil.scala => CollectionsUtils.scala} (88%)

diff --git a/core/src/main/scala/org/apache/spark/Partitioner.scala b/core/src/main/scala/org/apache/spark/Partitioner.scala
index ec99648a8488..52c018baa5f7 100644
--- a/core/src/main/scala/org/apache/spark/Partitioner.scala
+++ b/core/src/main/scala/org/apache/spark/Partitioner.scala
@@ -134,8 +134,8 @@ class RangePartitioner[K : Ordering : ClassTag, V](
   def getPartition(key: Any): Int = {
     val k = key.asInstanceOf[K]
     var partition = 0
-    if (rangeBounds.length < 1000) {
-      // If we have less than 100 partitions naive search
+    if (rangeBounds.length <= 128) {
+      // If we have less than 128 partitions naive search
       while (partition < rangeBounds.length && ordering.gt(k, rangeBounds(partition))) {
         partition += 1
       }
diff --git a/core/src/main/scala/org/apache/spark/util/CollectionsUtil.scala b/core/src/main/scala/org/apache/spark/util/CollectionsUtils.scala
similarity index 88%
rename from core/src/main/scala/org/apache/spark/util/CollectionsUtil.scala
rename to core/src/main/scala/org/apache/spark/util/CollectionsUtils.scala
index e4c254b9dd6b..85da2842e8dd 100644
--- a/core/src/main/scala/org/apache/spark/util/CollectionsUtil.scala
+++ b/core/src/main/scala/org/apache/spark/util/CollectionsUtils.scala
@@ -19,11 +19,11 @@ package org.apache.spark.util
 
 import java.util
 
-import scala.Array
-import scala.reflect._
+import scala.reflect.{classTag, ClassTag}
 
 private[spark] object CollectionsUtils {
   def makeBinarySearch[K : Ordering : ClassTag] : (Array[K], K) => Int = {
+    // For primitive keys, we can use the natural ordering. Otherwise, use the Ordering comparator.
     classTag[K] match {
       case ClassTag.Float =>
         (l, x) => util.Arrays.binarySearch(l.asInstanceOf[Array[Float]], x.asInstanceOf[Float])
@@ -40,7 +40,8 @@ private[spark] object CollectionsUtils {
       case ClassTag.Long =>
         (l, x) => util.Arrays.binarySearch(l.asInstanceOf[Array[Long]], x.asInstanceOf[Long])
       case _ =>
-        (l, x) => util.Arrays.binarySearch(l.asInstanceOf[Array[AnyRef]], x)
+        val comparator = implicitly[Ordering[K]].asInstanceOf[java.util.Comparator[Any]]
+        (l, x) => util.Arrays.binarySearch(l.asInstanceOf[Array[AnyRef]], x, comparator)
     }
   }
 }
diff --git a/core/src/test/scala/org/apache/spark/PartitioningSuite.scala b/core/src/test/scala/org/apache/spark/PartitioningSuite.scala
index 7c30626a0c42..4658a0806428 100644
--- a/core/src/test/scala/org/apache/spark/PartitioningSuite.scala
+++ b/core/src/test/scala/org/apache/spark/PartitioningSuite.scala
@@ -91,6 +91,17 @@ class PartitioningSuite extends FunSuite with SharedSparkContext with PrivateMet
     }
   }
 
+  test("RangePartitioner for keys that are not Comparable (but with Ordering)") {
+    // Row does not extend Comparable, but has an implicit Ordering defined.
+    implicit object RowOrdering extends Ordering[Row] {
+      override def compare(x: Row, y: Row) = x.value - y.value
+    }
+
+    val rdd = sc.parallelize(1 to 4500).map(x => (Row(x), Row(x)))
+    val partitioner = new RangePartitioner(1500, rdd)
+    partitioner.getPartition(Row(100))
+  }
+
   test("HashPartitioner not equal to RangePartitioner") {
     val rdd = sc.parallelize(1 to 10).map(x => (x, x))
     val rangeP2 = new RangePartitioner(2, rdd)
@@ -177,3 +188,6 @@ class PartitioningSuite extends FunSuite with SharedSparkContext with PrivateMet
     // Add other tests here for classes that should be able to handle empty partitions correctly
   }
 }
+
+
+private sealed case class Row(value: Int)

From 1b10b8114a396f94fc82b0f3af1a5f66dfa0945d Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Sun, 20 Jul 2014 13:04:59 -0700
Subject: [PATCH 708/810] [SPARK-2495][MLLIB] remove private[mllib] from linear
 models' constructors

This is part of SPARK-2495 to allow users construct linear models manually.

Author: Xiangrui Meng <meng@databricks.com>

Closes #1492 from mengxr/public-constructor and squashes the following commits:

a48b766 [Xiangrui Meng] remove private[mllib] from linear models' constructors
---
 .../apache/spark/mllib/classification/LogisticRegression.scala  | 2 +-
 .../main/scala/org/apache/spark/mllib/classification/SVM.scala  | 2 +-
 .../main/scala/org/apache/spark/mllib/regression/Lasso.scala    | 2 +-
 .../org/apache/spark/mllib/regression/LinearRegression.scala    | 2 +-
 .../org/apache/spark/mllib/regression/RidgeRegression.scala     | 2 +-
 5 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala
index 90aa8ac998ba..2242329b7918 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala
@@ -30,7 +30,7 @@ import org.apache.spark.rdd.RDD
  * @param weights Weights computed for every feature.
  * @param intercept Intercept computed for this model.
  */
-class LogisticRegressionModel private[mllib] (
+class LogisticRegressionModel (
     override val weights: Vector,
     override val intercept: Double)
   extends GeneralizedLinearModel(weights, intercept) with ClassificationModel with Serializable {
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala
index 316ecd713b71..80f8a1b2f1e8 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala
@@ -30,7 +30,7 @@ import org.apache.spark.rdd.RDD
  * @param weights Weights computed for every feature.
  * @param intercept Intercept computed for this model.
  */
-class SVMModel private[mllib] (
+class SVMModel (
     override val weights: Vector,
     override val intercept: Double)
   extends GeneralizedLinearModel(weights, intercept) with ClassificationModel with Serializable {
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala
index a05dfc045fb8..cb0d39e759a9 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala
@@ -28,7 +28,7 @@ import org.apache.spark.rdd.RDD
  * @param weights Weights computed for every feature.
  * @param intercept Intercept computed for this model.
  */
-class LassoModel private[mllib] (
+class LassoModel (
     override val weights: Vector,
     override val intercept: Double)
   extends GeneralizedLinearModel(weights, intercept)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala
index 0ebad4eb58d8..8c078ec9f66e 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala
@@ -27,7 +27,7 @@ import org.apache.spark.mllib.optimization._
  * @param weights Weights computed for every feature.
  * @param intercept Intercept computed for this model.
  */
-class LinearRegressionModel private[mllib] (
+class LinearRegressionModel (
     override val weights: Vector,
     override val intercept: Double)
   extends GeneralizedLinearModel(weights, intercept) with RegressionModel with Serializable {
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala
index bd983bac001a..a826deb695ee 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala
@@ -28,7 +28,7 @@ import org.apache.spark.mllib.linalg.Vector
  * @param weights Weights computed for every feature.
  * @param intercept Intercept computed for this model.
  */
-class RidgeRegressionModel private[mllib] (
+class RidgeRegressionModel (
     override val weights: Vector,
     override val intercept: Double)
   extends GeneralizedLinearModel(weights, intercept)

From 9564f8548917f563930d5e87911a304bf206d26e Mon Sep 17 00:00:00 2001
From: Sandy Ryza <sandy@cloudera.com>
Date: Sun, 20 Jul 2014 14:45:34 -0700
Subject: [PATCH 709/810] SPARK-2564. ShuffleReadMetrics.totalBlocksRead is
 redundant

Author: Sandy Ryza <sandy@cloudera.com>

Closes #1474 from sryza/sandy-spark-2564 and squashes the following commits:

35b8388 [Sandy Ryza] Fix compile error on upmerge
7b985fb [Sandy Ryza] Fix test compile error
43f79e6 [Sandy Ryza] SPARK-2564. ShuffleReadMetrics.totalBlocksRead is redundant
---
 .../main/scala/org/apache/spark/executor/TaskMetrics.scala    | 3 +--
 .../apache/spark/shuffle/hash/BlockStoreShuffleFetcher.scala  | 1 -
 .../scala/org/apache/spark/storage/BlockFetcherIterator.scala | 4 +---
 core/src/main/scala/org/apache/spark/util/JsonProtocol.scala  | 2 --
 .../test/scala/org/apache/spark/util/JsonProtocolSuite.scala  | 3 ---
 5 files changed, 2 insertions(+), 11 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/executor/TaskMetrics.scala b/core/src/main/scala/org/apache/spark/executor/TaskMetrics.scala
index 5d59e00636ee..21fe643b8d71 100644
--- a/core/src/main/scala/org/apache/spark/executor/TaskMetrics.scala
+++ b/core/src/main/scala/org/apache/spark/executor/TaskMetrics.scala
@@ -99,7 +99,6 @@ class TaskMetrics extends Serializable {
         existingMetrics.fetchWaitTime += newMetrics.fetchWaitTime
         existingMetrics.localBlocksFetched += newMetrics.localBlocksFetched
         existingMetrics.remoteBlocksFetched += newMetrics.remoteBlocksFetched
-        existingMetrics.totalBlocksFetched += newMetrics.totalBlocksFetched
         existingMetrics.remoteBytesRead += newMetrics.remoteBytesRead
       case None =>
         _shuffleReadMetrics = Some(newMetrics)
@@ -149,7 +148,7 @@ class ShuffleReadMetrics extends Serializable {
   /**
    * Number of blocks fetched in this shuffle by this task (remote or local)
    */
-  var totalBlocksFetched: Int = _
+  def totalBlocksFetched: Int = remoteBlocksFetched + localBlocksFetched
 
   /**
    * Number of remote blocks fetched in this shuffle by this task
diff --git a/core/src/main/scala/org/apache/spark/shuffle/hash/BlockStoreShuffleFetcher.scala b/core/src/main/scala/org/apache/spark/shuffle/hash/BlockStoreShuffleFetcher.scala
index 3795994cd920..99788828981c 100644
--- a/core/src/main/scala/org/apache/spark/shuffle/hash/BlockStoreShuffleFetcher.scala
+++ b/core/src/main/scala/org/apache/spark/shuffle/hash/BlockStoreShuffleFetcher.scala
@@ -81,7 +81,6 @@ private[hash] object BlockStoreShuffleFetcher extends Logging {
       shuffleMetrics.shuffleFinishTime = System.currentTimeMillis
       shuffleMetrics.fetchWaitTime = blockFetcherItr.fetchWaitTime
       shuffleMetrics.remoteBytesRead = blockFetcherItr.remoteBytesRead
-      shuffleMetrics.totalBlocksFetched = blockFetcherItr.totalBlocks
       shuffleMetrics.localBlocksFetched = blockFetcherItr.numLocalBlocks
       shuffleMetrics.remoteBlocksFetched = blockFetcherItr.numRemoteBlocks
       context.taskMetrics.updateShuffleReadMetrics(shuffleMetrics)
diff --git a/core/src/main/scala/org/apache/spark/storage/BlockFetcherIterator.scala b/core/src/main/scala/org/apache/spark/storage/BlockFetcherIterator.scala
index 2f0296c20f2e..69905a960a2c 100644
--- a/core/src/main/scala/org/apache/spark/storage/BlockFetcherIterator.scala
+++ b/core/src/main/scala/org/apache/spark/storage/BlockFetcherIterator.scala
@@ -46,7 +46,6 @@ import org.apache.spark.util.Utils
 private[storage]
 trait BlockFetcherIterator extends Iterator[(BlockId, Option[Iterator[Any]])] with Logging {
   def initialize()
-  def totalBlocks: Int
   def numLocalBlocks: Int
   def numRemoteBlocks: Int
   def fetchWaitTime: Long
@@ -192,7 +191,7 @@ object BlockFetcherIterator {
         }
       }
       logInfo("Getting " + _numBlocksToFetch + " non-empty blocks out of " +
-        totalBlocks + " blocks")
+        (numLocal + numRemote) + " blocks")
       remoteRequests
     }
 
@@ -235,7 +234,6 @@ object BlockFetcherIterator {
       logDebug("Got local blocks in " + Utils.getUsedTimeMs(startTime) + " ms")
     }
 
-    override def totalBlocks: Int = numLocal + numRemote
     override def numLocalBlocks: Int = numLocal
     override def numRemoteBlocks: Int = numRemote
     override def fetchWaitTime: Long = _fetchWaitTime
diff --git a/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala b/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala
index 2ff8b25a56d1..3448aaaf5724 100644
--- a/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala
+++ b/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala
@@ -237,7 +237,6 @@ private[spark] object JsonProtocol {
 
   def shuffleReadMetricsToJson(shuffleReadMetrics: ShuffleReadMetrics): JValue = {
     ("Shuffle Finish Time" -> shuffleReadMetrics.shuffleFinishTime) ~
-    ("Total Blocks Fetched" -> shuffleReadMetrics.totalBlocksFetched) ~
     ("Remote Blocks Fetched" -> shuffleReadMetrics.remoteBlocksFetched) ~
     ("Local Blocks Fetched" -> shuffleReadMetrics.localBlocksFetched) ~
     ("Fetch Wait Time" -> shuffleReadMetrics.fetchWaitTime) ~
@@ -548,7 +547,6 @@ private[spark] object JsonProtocol {
   def shuffleReadMetricsFromJson(json: JValue): ShuffleReadMetrics = {
     val metrics = new ShuffleReadMetrics
     metrics.shuffleFinishTime = (json \ "Shuffle Finish Time").extract[Long]
-    metrics.totalBlocksFetched = (json \ "Total Blocks Fetched").extract[Int]
     metrics.remoteBlocksFetched = (json \ "Remote Blocks Fetched").extract[Int]
     metrics.localBlocksFetched = (json \ "Local Blocks Fetched").extract[Int]
     metrics.fetchWaitTime = (json \ "Fetch Wait Time").extract[Long]
diff --git a/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala b/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala
index 11f70a6090d2..9305b6d9738e 100644
--- a/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala
+++ b/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala
@@ -314,7 +314,6 @@ class JsonProtocolSuite extends FunSuite {
 
   private def assertEquals(metrics1: ShuffleReadMetrics, metrics2: ShuffleReadMetrics) {
     assert(metrics1.shuffleFinishTime === metrics2.shuffleFinishTime)
-    assert(metrics1.totalBlocksFetched === metrics2.totalBlocksFetched)
     assert(metrics1.remoteBlocksFetched === metrics2.remoteBlocksFetched)
     assert(metrics1.localBlocksFetched === metrics2.localBlocksFetched)
     assert(metrics1.fetchWaitTime === metrics2.fetchWaitTime)
@@ -513,7 +512,6 @@ class JsonProtocolSuite extends FunSuite {
     } else {
       val sr = new ShuffleReadMetrics
       sr.shuffleFinishTime = b + c
-      sr.totalBlocksFetched = e + f
       sr.remoteBytesRead = b + d
       sr.localBlocksFetched = e
       sr.fetchWaitTime = a + d
@@ -584,7 +582,6 @@ class JsonProtocolSuite extends FunSuite {
       |  "Memory Bytes Spilled":800,"Disk Bytes Spilled":0,
       |  "Shuffle Read Metrics":{
       |    "Shuffle Finish Time":900,
-      |    "Total Blocks Fetched":1500,
       |    "Remote Blocks Fetched":800,
       |    "Local Blocks Fetched":700,
       |    "Fetch Wait Time":900,

From b86db517b6a2795f687211205b6a14c8685873eb Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Sun, 20 Jul 2014 18:40:36 -0700
Subject: [PATCH 710/810] [SPARK-2552][MLLIB] stabilize logistic function in
 pyspark

to avoid overflow in `exp(x)` if `x` is large.

Author: Xiangrui Meng <meng@databricks.com>

Closes #1493 from mengxr/py-logistic and squashes the following commits:

259e863 [Xiangrui Meng] stabilize logistic function in pyspark
---
 python/pyspark/mllib/classification.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/python/pyspark/mllib/classification.py b/python/pyspark/mllib/classification.py
index 1c0c536c4fb3..9e28dfbb9145 100644
--- a/python/pyspark/mllib/classification.py
+++ b/python/pyspark/mllib/classification.py
@@ -63,7 +63,10 @@ class LogisticRegressionModel(LinearModel):
     def predict(self, x):
         _linear_predictor_typecheck(x, self._coeff)
         margin = _dot(x, self._coeff) + self._intercept
-        prob = 1/(1 + exp(-margin))
+        if margin > 0:
+            prob = 1 / (1 + exp(-margin))
+        else:
+            prob = 1 - 1 / (1 + exp(margin))
         return 1 if prob > 0.5 else 0
 
 

From f6e7302cb49ee227aed537026d19f68528051dfd Mon Sep 17 00:00:00 2001
From: Kay Ousterhout <kayousterhout@gmail.com>
Date: Sun, 20 Jul 2014 20:18:18 -0700
Subject: [PATCH 711/810] Improve scheduler delay tooltip.

As a result of shivaram's experience debugging long scheduler delay, I think we should improve the tooltip to point people in the right direction if scheduler delay is large.

Author: Kay Ousterhout <kayousterhout@gmail.com>

Closes #1488 from kayousterhout/better_tooltips and squashes the following commits:

22176fd [Kay Ousterhout] Improve scheduler delay tooltip.
---
 core/src/main/scala/org/apache/spark/ui/ToolTips.scala | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/ui/ToolTips.scala b/core/src/main/scala/org/apache/spark/ui/ToolTips.scala
index 37708d75489c..9ced9b8107eb 100644
--- a/core/src/main/scala/org/apache/spark/ui/ToolTips.scala
+++ b/core/src/main/scala/org/apache/spark/ui/ToolTips.scala
@@ -20,9 +20,9 @@ package org.apache.spark.ui
 private[spark] object ToolTips {
   val SCHEDULER_DELAY =
     """Scheduler delay includes time to ship the task from the scheduler to
-       the executor, and time the time to send a message from the executor to the scheduler stating
-       that the task has completed. When the scheduler becomes overloaded, task completion messages
-       become queued up, and scheduler delay increases."""
+       the executor, and time to send the task result from the executor to the scheduler. If
+       scheduler delay is large, consider decreasing the size of tasks or decreasing the size
+       of task results."""
 
   val INPUT = "Bytes read from Hadoop or from Spark storage."
 

From db56f2df1b8027171da1b8d2571d1f2ef1e103b6 Mon Sep 17 00:00:00 2001
From: Michael Giannakopoulos <miccagiann@gmail.com>
Date: Sun, 20 Jul 2014 20:48:44 -0700
Subject: [PATCH 712/810] [SPARK-1945][MLLIB] Documentation Improvements for
 Spark 1.0

Standalone application examples are added to 'mllib-linear-methods.md' file written in Java.
This commit is related to the issue [Add full Java Examples in MLlib docs](https://issues.apache.org/jira/browse/SPARK-1945).
Also I changed the name of the sigmoid function from 'logit' to 'f'. This is because the logit function
is the inverse of sigmoid.

Thanks,
Michael

Author: Michael Giannakopoulos <miccagiann@gmail.com>

Closes #1311 from miccagiann/master and squashes the following commits:

8ffe5ab [Michael Giannakopoulos] Update code so as to comply with code standards.
f7ad5cc [Michael Giannakopoulos] Merge remote-tracking branch 'upstream/master'
38d92c7 [Michael Giannakopoulos] Adding PCA, SVD and LBFGS examples in Java. Performing minor updates in the already committed examples so as to eradicate the call of 'productElement' function whenever is possible.
cc0a089 [Michael Giannakopoulos] Modyfied Java examples so as to comply with coding standards.
b1141b2 [Michael Giannakopoulos] Added Java examples for Clustering and Collaborative Filtering [mllib-clustering.md & mllib-collaborative-filtering.md].
837f7a8 [Michael Giannakopoulos] Merge remote-tracking branch 'upstream/master'
15f0eb4 [Michael Giannakopoulos] Java examples included in 'mllib-linear-methods.md' file.
---
 docs/mllib-clustering.md               |  49 +++++++-
 docs/mllib-collaborative-filtering.md  |  80 ++++++++++++-
 docs/mllib-dimensionality-reduction.md |  94 +++++++++++++++
 docs/mllib-linear-methods.md           | 154 ++++++++++++++++++++++++-
 docs/mllib-optimization.md             |  96 ++++++++++++++-
 5 files changed, 465 insertions(+), 8 deletions(-)

diff --git a/docs/mllib-clustering.md b/docs/mllib-clustering.md
index c76ac010d3f8..561de4891013 100644
--- a/docs/mllib-clustering.md
+++ b/docs/mllib-clustering.md
@@ -69,7 +69,54 @@ println("Within Set Sum of Squared Errors = " + WSSSE)
 All of MLlib's methods use Java-friendly types, so you can import and call them there the same
 way you do in Scala. The only caveat is that the methods take Scala RDD objects, while the
 Spark Java API uses a separate `JavaRDD` class. You can convert a Java RDD to a Scala one by
-calling `.rdd()` on your `JavaRDD` object.
+calling `.rdd()` on your `JavaRDD` object. A standalone application example
+that is equivalent to the provided example in Scala is given bellow:
+
+{% highlight java %}
+import org.apache.spark.api.java.*;
+import org.apache.spark.api.java.function.Function;
+import org.apache.spark.mllib.clustering.KMeans;
+import org.apache.spark.mllib.clustering.KMeansModel;
+import org.apache.spark.mllib.linalg.Vector;
+import org.apache.spark.mllib.linalg.Vectors;
+import org.apache.spark.SparkConf;
+
+public class KMeansExample {
+  public static void main(String[] args) {
+    SparkConf conf = new SparkConf().setAppName("K-means Example");
+    JavaSparkContext sc = new JavaSparkContext(conf);
+
+    // Load and parse data
+    String path = "data/mllib/kmeans_data.txt";
+    JavaRDD<String> data = sc.textFile(path);
+    JavaRDD<Vector> parsedData = data.map(
+      new Function<String, Vector>() {
+        public Vector call(String s) {
+          String[] sarray = s.split(" ");
+          double[] values = new double[sarray.length];
+          for (int i = 0; i < sarray.length; i++)
+            values[i] = Double.parseDouble(sarray[i]);
+          return Vectors.dense(values);
+        }
+      }
+    );
+
+    // Cluster the data into two classes using KMeans
+    int numClusters = 2;
+    int numIterations = 20;
+    KMeansModel clusters = KMeans.train(parsedData.rdd(), numClusters, numIterations);
+
+    // Evaluate clustering by computing Within Set Sum of Squared Errors
+    double WSSSE = clusters.computeCost(parsedData.rdd());
+    System.out.println("Within Set Sum of Squared Errors = " + WSSSE);
+  }
+}
+{% endhighlight %}
+
+In order to run the above standalone application using Spark framework make
+sure that you follow the instructions provided at section [Standalone
+Applications](quick-start.html) of the quick-start guide. What is more, you
+should include to your build file *spark-mllib* as a dependency.
 </div>
 
 <div data-lang="python" markdown="1">
diff --git a/docs/mllib-collaborative-filtering.md b/docs/mllib-collaborative-filtering.md
index 5cd71738722a..0d28b5f7c89b 100644
--- a/docs/mllib-collaborative-filtering.md
+++ b/docs/mllib-collaborative-filtering.md
@@ -99,7 +99,85 @@ val model = ALS.trainImplicit(ratings, rank, numIterations, alpha)
 All of MLlib's methods use Java-friendly types, so you can import and call them there the same
 way you do in Scala. The only caveat is that the methods take Scala RDD objects, while the
 Spark Java API uses a separate `JavaRDD` class. You can convert a Java RDD to a Scala one by
-calling `.rdd()` on your `JavaRDD` object.
+calling `.rdd()` on your `JavaRDD` object. A standalone application example
+that is equivalent to the provided example in Scala is given bellow:
+
+{% highlight java %}
+import scala.Tuple2;
+
+import org.apache.spark.api.java.*;
+import org.apache.spark.api.java.function.Function;
+import org.apache.spark.mllib.recommendation.ALS;
+import org.apache.spark.mllib.recommendation.MatrixFactorizationModel;
+import org.apache.spark.mllib.recommendation.Rating;
+import org.apache.spark.SparkConf;
+
+public class CollaborativeFiltering {
+  public static void main(String[] args) {
+    SparkConf conf = new SparkConf().setAppName("Collaborative Filtering Example");
+    JavaSparkContext sc = new JavaSparkContext(conf);
+
+    // Load and parse the data
+    String path = "data/mllib/als/test.data";
+    JavaRDD<String> data = sc.textFile(path);
+    JavaRDD<Rating> ratings = data.map(
+      new Function<String, Rating>() {
+        public Rating call(String s) {
+          String[] sarray = s.split(",");
+          return new Rating(Integer.parseInt(sarray[0]), Integer.parseInt(sarray[1]), 
+                            Double.parseDouble(sarray[2]));
+        }
+      }
+    );
+
+    // Build the recommendation model using ALS
+    int rank = 10;
+    int numIterations = 20;
+    MatrixFactorizationModel model = ALS.train(JavaRDD.toRDD(ratings), rank, numIterations, 0.01); 
+
+    // Evaluate the model on rating data
+    JavaRDD<Tuple2<Object, Object>> userProducts = ratings.map(
+      new Function<Rating, Tuple2<Object, Object>>() {
+        public Tuple2<Object, Object> call(Rating r) {
+          return new Tuple2<Object, Object>(r.user(), r.product());
+        }
+      }
+    );
+    JavaPairRDD<Tuple2<Integer, Integer>, Double> predictions = JavaPairRDD.fromJavaRDD(
+      model.predict(JavaRDD.toRDD(userProducts)).toJavaRDD().map(
+        new Function<Rating, Tuple2<Tuple2<Integer, Integer>, Double>>() {
+          public Tuple2<Tuple2<Integer, Integer>, Double> call(Rating r){
+            return new Tuple2<Tuple2<Integer, Integer>, Double>(
+              new Tuple2<Integer, Integer>(r.user(), r.product()), r.rating());
+          }
+        }
+    ));
+    JavaRDD<Tuple2<Double, Double>> ratesAndPreds = 
+      JavaPairRDD.fromJavaRDD(ratings.map(
+        new Function<Rating, Tuple2<Tuple2<Integer, Integer>, Double>>() {
+          public Tuple2<Tuple2<Integer, Integer>, Double> call(Rating r){
+            return new Tuple2<Tuple2<Integer, Integer>, Double>(
+              new Tuple2<Integer, Integer>(r.user(), r.product()), r.rating());
+          }
+        }
+    )).join(predictions).values();
+    double MSE = JavaDoubleRDD.fromRDD(ratesAndPreds.map(
+      new Function<Tuple2<Double, Double>, Object>() {
+        public Object call(Tuple2<Double, Double> pair) {
+          Double err = pair._1() - pair._2();
+          return err * err;
+        }
+      }
+    ).rdd()).mean();
+    System.out.println("Mean Squared Error = " + MSE);
+  }
+}
+{% endhighlight %}
+
+In order to run the above standalone application using Spark framework make
+sure that you follow the instructions provided at section [Standalone
+Applications](quick-start.html) of the quick-start guide. What is more, you
+should include to your build file *spark-mllib* as a dependency.
 </div>
 
 <div data-lang="python" markdown="1">
diff --git a/docs/mllib-dimensionality-reduction.md b/docs/mllib-dimensionality-reduction.md
index e3608075fbb1..8e434998c15e 100644
--- a/docs/mllib-dimensionality-reduction.md
+++ b/docs/mllib-dimensionality-reduction.md
@@ -57,10 +57,57 @@ val U: RowMatrix = svd.U // The U factor is a RowMatrix.
 val s: Vector = svd.s // The singular values are stored in a local dense vector.
 val V: Matrix = svd.V // The V factor is a local dense matrix.
 {% endhighlight %}
+
+Same code applies to `IndexedRowMatrix`.
+The only difference that the `U` matrix becomes an `IndexedRowMatrix`.
 </div>
+<div data-lang="java" markdown="1">
+In order to run the following standalone application using Spark framework make
+sure that you follow the instructions provided at section [Standalone
+Applications](quick-start.html) of the quick-start guide. What is more, you
+should include to your build file *spark-mllib* as a dependency.
+
+{% highlight java %}
+import java.util.LinkedList;
+
+import org.apache.spark.api.java.*;
+import org.apache.spark.mllib.linalg.distributed.RowMatrix;
+import org.apache.spark.mllib.linalg.Matrix;
+import org.apache.spark.mllib.linalg.SingularValueDecomposition;
+import org.apache.spark.mllib.linalg.Vector;
+import org.apache.spark.mllib.linalg.Vectors;
+import org.apache.spark.rdd.RDD;
+import org.apache.spark.SparkConf;
+import org.apache.spark.SparkContext;
+
+public class SVD {
+  public static void main(String[] args) {
+    SparkConf conf = new SparkConf().setAppName("SVD Example");
+    SparkContext sc = new SparkContext(conf);
+     
+    double[][] array = ...
+    LinkedList<Vector> rowsList = new LinkedList<Vector>();
+    for (int i = 0; i < array.length; i++) {
+      Vector currentRow = Vectors.dense(array[i]);
+      rowsList.add(currentRow);
+    }
+    JavaRDD<Vector> rows = JavaSparkContext.fromSparkContext(sc).parallelize(rowsList);
+
+    // Create a RowMatrix from JavaRDD<Vector>.
+    RowMatrix mat = new RowMatrix(rows.rdd());
+
+    // Compute the top 4 singular values and corresponding singular vectors.
+    SingularValueDecomposition<RowMatrix, Matrix> svd = mat.computeSVD(4, true, 1.0E-9d);
+    RowMatrix U = svd.U();
+    Vector s = svd.s();
+    Matrix V = svd.V();
+  }
+}
+{% endhighlight %}
 Same code applies to `IndexedRowMatrix`.
 The only difference that the `U` matrix becomes an `IndexedRowMatrix`.
 </div>
+</div>
 
 ## Principal component analysis (PCA)
 
@@ -91,4 +138,51 @@ val pc: Matrix = mat.computePrincipalComponents(10) // Principal components are
 val projected: RowMatrix = mat.multiply(pc)
 {% endhighlight %}
 </div>
+
+<div data-lang="java" markdown="1">
+
+The following code demonstrates how to compute principal components on a tall-and-skinny `RowMatrix`
+and use them to project the vectors into a low-dimensional space.
+The number of columns should be small, e.g, less than 1000.
+
+{% highlight java %}
+import java.util.LinkedList;
+
+import org.apache.spark.api.java.*;
+import org.apache.spark.mllib.linalg.distributed.RowMatrix;
+import org.apache.spark.mllib.linalg.Matrix;
+import org.apache.spark.mllib.linalg.Vector;
+import org.apache.spark.mllib.linalg.Vectors;
+import org.apache.spark.rdd.RDD;
+import org.apache.spark.SparkConf;
+import org.apache.spark.SparkContext;
+
+public class PCA {
+  public static void main(String[] args) {
+    SparkConf conf = new SparkConf().setAppName("PCA Example");
+    SparkContext sc = new SparkContext(conf);
+     
+    double[][] array = ...
+    LinkedList<Vector> rowsList = new LinkedList<Vector>();
+    for (int i = 0; i < array.length; i++) {
+      Vector currentRow = Vectors.dense(array[i]);
+      rowsList.add(currentRow);
+    }
+    JavaRDD<Vector> rows = JavaSparkContext.fromSparkContext(sc).parallelize(rowsList);
+
+    // Create a RowMatrix from JavaRDD<Vector>.
+    RowMatrix mat = new RowMatrix(rows.rdd());
+
+    // Compute the top 3 principal components.
+    Matrix pc = mat.computePrincipalComponents(3);
+    RowMatrix projected = mat.multiply(pc);
+  }
+}
+{% endhighlight %}
+
+In order to run the above standalone application using Spark framework make
+sure that you follow the instructions provided at section [Standalone
+Applications](quick-start.html) of the quick-start guide. What is more, you
+should include to your build file *spark-mllib* as a dependency.
+</div>
 </div>
diff --git a/docs/mllib-linear-methods.md b/docs/mllib-linear-methods.md
index b4d22e0df5a8..254201147edc 100644
--- a/docs/mllib-linear-methods.md
+++ b/docs/mllib-linear-methods.md
@@ -151,10 +151,10 @@ L(\wv;\x,y) :=  \log(1+\exp( -y \wv^T \x)).
 Logistic regression algorithm outputs a logistic regression model, which makes predictions by
 applying the logistic function
 `\[
-\mathrm{logit}(z) = \frac{1}{1 + e^{-z}}
+\mathrm{f}(z) = \frac{1}{1 + e^{-z}}
 \]`
-$\wv^T \x$.
-By default, if $\mathrm{logit}(\wv^T x) > 0.5$, the outcome is positive, or negative otherwise.
+where $z = \wv^T \x$.
+By default, if $\mathrm{f}(\wv^T x) > 0.5$, the outcome is positive, or negative otherwise.
 For the same reason mentioned above, quite often in practice, this default threshold is not a good choice.
 The threshold should be determined via model evaluation.
 
@@ -242,7 +242,86 @@ Similarly, you can use replace `SVMWithSGD` by
 All of MLlib's methods use Java-friendly types, so you can import and call them there the same
 way you do in Scala. The only caveat is that the methods take Scala RDD objects, while the
 Spark Java API uses a separate `JavaRDD` class. You can convert a Java RDD to a Scala one by
-calling `.rdd()` on your `JavaRDD` object.
+calling `.rdd()` on your `JavaRDD` object. A standalone application example
+that is equivalent to the provided example in Scala is given bellow:
+
+{% highlight java %}
+import java.util.Random;
+
+import scala.Tuple2;
+
+import org.apache.spark.api.java.*;
+import org.apache.spark.api.java.function.Function;
+import org.apache.spark.mllib.classification.*;
+import org.apache.spark.mllib.evaluation.BinaryClassificationMetrics;
+import org.apache.spark.mllib.linalg.Vector;
+import org.apache.spark.mllib.regression.LabeledPoint;
+import org.apache.spark.mllib.util.MLUtils;
+import org.apache.spark.SparkConf;
+import org.apache.spark.SparkContext;
+
+public class SVMClassifier {
+  public static void main(String[] args) {
+    SparkConf conf = new SparkConf().setAppName("SVM Classifier Example");
+    SparkContext sc = new SparkContext(conf);
+    String path = "data/mllib/sample_libsvm_data.txt";
+    JavaRDD<LabeledPoint> data = MLUtils.loadLibSVMFile(sc, path).toJavaRDD();
+
+    // Split initial RDD into two... [60% training data, 40% testing data].
+    JavaRDD<LabeledPoint> training = data.sample(false, 0.6, 11L);
+    training.cache();
+    JavaRDD<LabeledPoint> test = data.subtract(training);
+    
+    // Run training algorithm to build the model.
+    int numIterations = 100;
+    final SVMModel model = SVMWithSGD.train(training.rdd(), numIterations);
+    
+    // Clear the default threshold.
+    model.clearThreshold();
+
+    // Compute raw scores on the test set.
+    JavaRDD<Tuple2<Object, Object>> scoreAndLabels = test.map(
+      new Function<LabeledPoint, Tuple2<Object, Object>>() {
+        public Tuple2<Object, Object> call(LabeledPoint p) {
+          Double score = model.predict(p.features());
+          return new Tuple2<Object, Object>(score, p.label());
+        }
+      }
+    );
+    
+    // Get evaluation metrics.
+    BinaryClassificationMetrics metrics = 
+      new BinaryClassificationMetrics(JavaRDD.toRDD(scoreAndLabels));
+    double auROC = metrics.areaUnderROC();
+    
+    System.out.println("Area under ROC = " + auROC);
+  }
+}
+{% endhighlight %}
+
+The `SVMWithSGD.train()` method by default performs L2 regularization with the
+regularization parameter set to 1.0. If we want to configure this algorithm, we
+can customize `SVMWithSGD` further by creating a new object directly and
+calling setter methods. All other MLlib algorithms support customization in
+this way as well. For example, the following code produces an L1 regularized
+variant of SVMs with regularization parameter set to 0.1, and runs the training
+algorithm for 200 iterations.
+
+{% highlight java %}
+import org.apache.spark.mllib.optimization.L1Updater;
+
+SVMWithSGD svmAlg = new SVMWithSGD();
+svmAlg.optimizer()
+  .setNumIterations(200)
+  .setRegParam(0.1)
+  .setUpdater(new L1Updater());
+final SVMModel modelL1 = svmAlg.run(training.rdd());
+{% endhighlight %}
+
+In order to run the above standalone application using Spark framework make
+sure that you follow the instructions provided at section [Standalone
+Applications](quick-start.html) of the quick-start guide. What is more, you
+should include to your build file *spark-mllib* as a dependency.
 </div>
 
 <div data-lang="python" markdown="1">
@@ -338,7 +417,72 @@ and [`LassoWithSGD`](api/scala/index.html#org.apache.spark.mllib.regression.Lass
 All of MLlib's methods use Java-friendly types, so you can import and call them there the same
 way you do in Scala. The only caveat is that the methods take Scala RDD objects, while the
 Spark Java API uses a separate `JavaRDD` class. You can convert a Java RDD to a Scala one by
-calling `.rdd()` on your `JavaRDD` object.
+calling `.rdd()` on your `JavaRDD` object. The corresponding Java example to
+the Scala snippet provided, is presented bellow:
+
+{% highlight java %}
+import scala.Tuple2;
+
+import org.apache.spark.api.java.*;
+import org.apache.spark.api.java.function.Function;
+import org.apache.spark.mllib.linalg.Vector;
+import org.apache.spark.mllib.linalg.Vectors;
+import org.apache.spark.mllib.regression.LabeledPoint;
+import org.apache.spark.mllib.regression.LinearRegressionModel;
+import org.apache.spark.mllib.regression.LinearRegressionWithSGD;
+import org.apache.spark.SparkConf;
+
+public class LinearRegression {
+  public static void main(String[] args) {
+    SparkConf conf = new SparkConf().setAppName("Linear Regression Example");
+    JavaSparkContext sc = new JavaSparkContext(conf);
+    
+    // Load and parse the data
+    String path = "data/mllib/ridge-data/lpsa.data";
+    JavaRDD<String> data = sc.textFile(path);
+    JavaRDD<LabeledPoint> parsedData = data.map(
+      new Function<String, LabeledPoint>() {
+        public LabeledPoint call(String line) {
+          String[] parts = line.split(",");
+          String[] features = parts[1].split(" ");
+          double[] v = new double[features.length];
+          for (int i = 0; i < features.length - 1; i++)
+            v[i] = Double.parseDouble(features[i]);
+          return new LabeledPoint(Double.parseDouble(parts[0]), Vectors.dense(v));
+        }
+      }
+    );
+
+    // Building the model
+    int numIterations = 100;
+    final LinearRegressionModel model = 
+      LinearRegressionWithSGD.train(JavaRDD.toRDD(parsedData), numIterations);
+
+    // Evaluate model on training examples and compute training error
+    JavaRDD<Tuple2<Double, Double>> valuesAndPreds = parsedData.map(
+      new Function<LabeledPoint, Tuple2<Double, Double>>() {
+        public Tuple2<Double, Double> call(LabeledPoint point) {
+          double prediction = model.predict(point.features());
+          return new Tuple2<Double, Double>(prediction, point.label());
+        }
+      }
+    );
+    JavaRDD<Object> MSE = new JavaDoubleRDD(valuesAndPreds.map(
+      new Function<Tuple2<Double, Double>, Object>() {
+        public Object call(Tuple2<Double, Double> pair) {
+          return Math.pow(pair._1() - pair._2(), 2.0);
+        }
+      }
+    ).rdd()).mean();
+    System.out.println("training Mean Squared Error = " + MSE);
+  }
+}
+{% endhighlight %}
+
+In order to run the above standalone application using Spark framework make
+sure that you follow the instructions provided at section [Standalone
+Applications](quick-start.html) of the quick-start guide. What is more, you
+should include to your build file *spark-mllib* as a dependency.
 </div>
 
 <div data-lang="python" markdown="1">
diff --git a/docs/mllib-optimization.md b/docs/mllib-optimization.md
index 651958c7812f..26ce5f3c501f 100644
--- a/docs/mllib-optimization.md
+++ b/docs/mllib-optimization.md
@@ -207,6 +207,10 @@ the loss computed for every iteration.
 
 Here is an example to train binary logistic regression with L2 regularization using
 L-BFGS optimizer. 
+
+<div class="codetabs">
+
+<div data-lang="scala" markdown="1">
 {% highlight scala %}
 import org.apache.spark.SparkContext
 import org.apache.spark.mllib.evaluation.BinaryClassificationMetrics
@@ -263,7 +267,97 @@ println("Loss of each step in training process")
 loss.foreach(println)
 println("Area under ROC = " + auROC)
 {% endhighlight %}
-
+</div>
+
+<div data-lang="java" markdown="1">
+{% highlight java %}
+import java.util.Arrays;
+import java.util.Random;
+
+import scala.Tuple2;
+
+import org.apache.spark.api.java.*;
+import org.apache.spark.api.java.function.Function;
+import org.apache.spark.mllib.classification.LogisticRegressionModel;
+import org.apache.spark.mllib.evaluation.BinaryClassificationMetrics;
+import org.apache.spark.mllib.linalg.Vector;
+import org.apache.spark.mllib.linalg.Vectors;
+import org.apache.spark.mllib.optimization.*;
+import org.apache.spark.mllib.regression.LabeledPoint;
+import org.apache.spark.mllib.util.MLUtils;
+import org.apache.spark.SparkConf;
+import org.apache.spark.SparkContext;
+
+public class LBFGSExample {
+  public static void main(String[] args) {
+    SparkConf conf = new SparkConf().setAppName("L-BFGS Example");
+    SparkContext sc = new SparkContext(conf);
+    String path = "data/mllib/sample_libsvm_data.txt";
+    JavaRDD<LabeledPoint> data = MLUtils.loadLibSVMFile(sc, path).toJavaRDD();
+    int numFeatures = data.take(1).get(0).features().size();
+    
+    // Split initial RDD into two... [60% training data, 40% testing data].
+    JavaRDD<LabeledPoint> trainingInit = data.sample(false, 0.6, 11L);
+    JavaRDD<LabeledPoint> test = data.subtract(trainingInit);
+    
+    // Append 1 into the training data as intercept.
+    JavaRDD<Tuple2<Object, Vector>> training = data.map(
+      new Function<LabeledPoint, Tuple2<Object, Vector>>() {
+        public Tuple2<Object, Vector> call(LabeledPoint p) {
+          return new Tuple2<Object, Vector>(p.label(), MLUtils.appendBias(p.features()));
+        }
+      });
+    training.cache();
+
+    // Run training algorithm to build the model.
+    int numCorrections = 10;
+    double convergenceTol = 1e-4;
+    int maxNumIterations = 20;
+    double regParam = 0.1;
+    Vector initialWeightsWithIntercept = Vectors.dense(new double[numFeatures + 1]);
+
+    Tuple2<Vector, double[]> result = LBFGS.runLBFGS(
+      training.rdd(),
+      new LogisticGradient(),
+      new SquaredL2Updater(),
+      numCorrections,
+      convergenceTol,
+      maxNumIterations,
+      regParam,
+      initialWeightsWithIntercept);
+    Vector weightsWithIntercept = result._1();
+    double[] loss = result._2();
+
+    final LogisticRegressionModel model = new LogisticRegressionModel(
+      Vectors.dense(Arrays.copyOf(weightsWithIntercept.toArray(), weightsWithIntercept.size() - 1)),
+      (weightsWithIntercept.toArray())[weightsWithIntercept.size() - 1]);
+
+    // Clear the default threshold.
+    model.clearThreshold();
+
+    // Compute raw scores on the test set.
+    JavaRDD<Tuple2<Object, Object>> scoreAndLabels = test.map(
+      new Function<LabeledPoint, Tuple2<Object, Object>>() {
+      public Tuple2<Object, Object> call(LabeledPoint p) {
+        Double score = model.predict(p.features());
+        return new Tuple2<Object, Object>(score, p.label());
+      }
+    });
+
+    // Get evaluation metrics.
+    BinaryClassificationMetrics metrics = 
+      new BinaryClassificationMetrics(scoreAndLabels.rdd());
+    double auROC = metrics.areaUnderROC();
+     
+    System.out.println("Loss of each step in training process");
+    for (double l : loss)
+      System.out.println(l);
+    System.out.println("Area under ROC = " + auROC);
+  }
+}
+{% endhighlight %}
+</div>
+</div>
 #### Developer's note
 Since the Hessian is constructed approximately from previous gradient evaluations, 
 the objective function can not be changed during the optimization process. 

From cd273a238144a9a436219cd01250369586f5638b Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian.cs.zju@gmail.com>
Date: Mon, 21 Jul 2014 00:46:28 -0700
Subject: [PATCH 713/810] [SPARK-2190][SQL] Specialized ColumnType for
 Timestamp

JIRA issue: [SPARK-2190](https://issues.apache.org/jira/browse/SPARK-2190)

Added specialized in-memory column type for `Timestamp`. Whitelisted all timestamp related Hive tests except `timestamp_udf`, which is timezone sensitive.

Author: Cheng Lian <lian.cs.zju@gmail.com>

Closes #1440 from liancheng/timestamp-column-type and squashes the following commits:

e682175 [Cheng Lian] Enabled more timezone sensitive Hive tests.
53a358f [Cheng Lian] Fixed failed test suites
01b592d [Cheng Lian] Fixed SimpleDateFormat thread safety issue
2a59343 [Cheng Lian] Removed timezone sensitive Hive timestamp tests
45dd05d [Cheng Lian] Added Timestamp specific in-memory columnar representation
---
 .gitignore                                    |  1 +
 .../spark/sql/catalyst/expressions/Cast.scala | 23 +++++++++
 .../ExpressionEvaluationSuite.scala           | 22 ++++-----
 .../spark/sql/columnar/ColumnAccessor.scala   | 24 ++++++----
 .../spark/sql/columnar/ColumnBuilder.scala    |  3 ++
 .../spark/sql/columnar/ColumnStats.scala      | 37 +++++++++++++--
 .../spark/sql/columnar/ColumnType.scala       | 47 ++++++++++++++-----
 .../spark/sql/columnar/ColumnStatsSuite.scala | 17 +++----
 .../spark/sql/columnar/ColumnTypeSuite.scala  | 34 +++++---------
 .../sql/columnar/ColumnarTestUtils.scala      | 25 ++++++----
 .../apache/spark/sql/hive/HiveContext.scala   |  3 ++
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 ...mp_lazy-3-79e0c72c4fb3b259dfbffd245ccaa636 |  5 ++
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 ...mestamp-6-9b0f20bde1aaf9102b67a5498b167f31 |  1 +
 ...mestamp-7-47f433ff6ccce4c666440cc1a228a96d |  1 +
 .../execution/HiveCompatibilitySuite.scala    | 15 +++++-
 133 files changed, 287 insertions(+), 77 deletions(-)
 create mode 100644 sql/hive/src/test/resources/golden/timestamp_1-10-343c75daac6695917608c17db8bf473e
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 create mode 100644 sql/hive/src/test/resources/golden/timestamp_2-37-5181279a0bf8939fe46ddacae015dad8
 create mode 100644 sql/hive/src/test/resources/golden/timestamp_2-38-240fce5f58794fa051824e8732c00c03
 create mode 100644 sql/hive/src/test/resources/golden/timestamp_2-39-8236608f28681eac5503195096a34181
 create mode 100644 sql/hive/src/test/resources/golden/timestamp_2-4-25f6ec69328af6cba76899194e0dd84e
 create mode 100644 sql/hive/src/test/resources/golden/timestamp_2-40-25f6ec69328af6cba76899194e0dd84e
 create mode 100644 sql/hive/src/test/resources/golden/timestamp_2-41-93c769be4cff93bea6e62bfe4e2a8742
 create mode 100644 sql/hive/src/test/resources/golden/timestamp_2-42-5bdbf67419cc060b82d091d80ce59bf9
 create mode 100644 sql/hive/src/test/resources/golden/timestamp_2-43-de3c42ab06c17ae895fd7deaf7bd9571
 create mode 100644 sql/hive/src/test/resources/golden/timestamp_2-44-da3937d21b7c2cfe1e624e812ae1d3ef
 create mode 100644 sql/hive/src/test/resources/golden/timestamp_2-45-252aebfe7882335d31bfc53a8705b7a
 create mode 100644 sql/hive/src/test/resources/golden/timestamp_2-46-5181279a0bf8939fe46ddacae015dad8
 create mode 100644 sql/hive/src/test/resources/golden/timestamp_2-47-240fce5f58794fa051824e8732c00c03
 create mode 100644 sql/hive/src/test/resources/golden/timestamp_2-48-654e5533ec6dc911996abc7e47af8ccb
 create mode 100644 sql/hive/src/test/resources/golden/timestamp_2-49-25f6ec69328af6cba76899194e0dd84e
 create mode 100644 sql/hive/src/test/resources/golden/timestamp_2-5-93c769be4cff93bea6e62bfe4e2a8742
 create mode 100644 sql/hive/src/test/resources/golden/timestamp_2-50-93c769be4cff93bea6e62bfe4e2a8742
 create mode 100644 sql/hive/src/test/resources/golden/timestamp_2-51-5bdbf67419cc060b82d091d80ce59bf9
 create mode 100644 sql/hive/src/test/resources/golden/timestamp_2-52-de3c42ab06c17ae895fd7deaf7bd9571
 create mode 100644 sql/hive/src/test/resources/golden/timestamp_2-53-da3937d21b7c2cfe1e624e812ae1d3ef
 create mode 100644 sql/hive/src/test/resources/golden/timestamp_2-54-252aebfe7882335d31bfc53a8705b7a
 create mode 100644 sql/hive/src/test/resources/golden/timestamp_2-55-5181279a0bf8939fe46ddacae015dad8
 create mode 100644 sql/hive/src/test/resources/golden/timestamp_2-56-240fce5f58794fa051824e8732c00c03
 create mode 100644 sql/hive/src/test/resources/golden/timestamp_2-57-ea7192a4a5a985bcc8aab9aa79d9f028
 create mode 100644 sql/hive/src/test/resources/golden/timestamp_2-6-5bdbf67419cc060b82d091d80ce59bf9
 create mode 100644 sql/hive/src/test/resources/golden/timestamp_2-7-de3c42ab06c17ae895fd7deaf7bd9571
 create mode 100644 sql/hive/src/test/resources/golden/timestamp_2-8-da3937d21b7c2cfe1e624e812ae1d3ef
 create mode 100644 sql/hive/src/test/resources/golden/timestamp_2-9-252aebfe7882335d31bfc53a8705b7a
 create mode 100644 sql/hive/src/test/resources/golden/timestamp_lazy-2-cdb72e0c24fd9277a41fe0c7b1392e34
 create mode 100644 sql/hive/src/test/resources/golden/timestamp_lazy-3-79e0c72c4fb3b259dfbffd245ccaa636
 create mode 100644 sql/hive/src/test/resources/golden/timestamp_lazy-4-b4c4417ce9f08baeb82ffde6ef1baa25
 create mode 100644 sql/hive/src/test/resources/golden/udf_unix_timestamp-0-d555c8cd733572bfa8cd3362da9480cb
 create mode 100644 sql/hive/src/test/resources/golden/udf_unix_timestamp-1-8a9dbadae706047715cf5f903ff4a724
 create mode 100644 sql/hive/src/test/resources/golden/udf_unix_timestamp-2-28c40e51e55bed62693e626efda5d9c5
 create mode 100644 sql/hive/src/test/resources/golden/udf_unix_timestamp-3-732b21d386f2002b87eaf02d0b9951ed
 create mode 100644 sql/hive/src/test/resources/golden/udf_unix_timestamp-4-b2e42ebb75cecf09961d36587797f6d0
 create mode 100644 sql/hive/src/test/resources/golden/udf_unix_timestamp-5-31243f5cb64356425b9f95ba011ac9d6
 create mode 100644 sql/hive/src/test/resources/golden/udf_unix_timestamp-6-9b0f20bde1aaf9102b67a5498b167f31
 create mode 100644 sql/hive/src/test/resources/golden/udf_unix_timestamp-7-47f433ff6ccce4c666440cc1a228a96d

diff --git a/.gitignore b/.gitignore
index 4f177c82ae5e..061c8946d23c 100644
--- a/.gitignore
+++ b/.gitignore
@@ -19,6 +19,7 @@ conf/spark-env.sh
 conf/streaming-env.sh
 conf/log4j.properties
 conf/spark-defaults.conf
+conf/hive-site.xml
 docs/_site
 docs/api
 target/
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
index 1f9716e385e9..0ad2b30cf9c1 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
@@ -18,6 +18,7 @@
 package org.apache.spark.sql.catalyst.expressions
 
 import java.sql.Timestamp
+import java.text.{DateFormat, SimpleDateFormat}
 
 import org.apache.spark.sql.catalyst.types._
 
@@ -41,6 +42,7 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression {
   // UDFToString
   private[this] def castToString: Any => Any = child.dataType match {
     case BinaryType => buildCast[Array[Byte]](_, new String(_, "UTF-8"))
+    case TimestampType => buildCast[Timestamp](_, timestampToString)
     case _ => buildCast[Any](_, _.toString)
   }
 
@@ -126,6 +128,18 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression {
     ts.getTime / 1000 + ts.getNanos.toDouble / 1000000000
   }
 
+  // Converts Timestamp to string according to Hive TimestampWritable convention
+  private[this] def timestampToString(ts: Timestamp): String = {
+    val timestampString = ts.toString
+    val formatted = Cast.threadLocalDateFormat.get.format(ts)
+
+    if (timestampString.length > 19 && timestampString.substring(19) != ".0") {
+      formatted + timestampString.substring(19)
+    } else {
+      formatted
+    }
+  }
+
   private[this] def castToLong: Any => Any = child.dataType match {
     case StringType =>
       buildCast[String](_, s => try s.toLong catch {
@@ -249,3 +263,12 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression {
     if (evaluated == null) null else cast(evaluated)
   }
 }
+
+object Cast {
+  // `SimpleDateFormat` is not thread-safe.
+  private[sql] val threadLocalDateFormat = new ThreadLocal[DateFormat] {
+    override def initialValue() = {
+      new SimpleDateFormat("yyyy-MM-dd HH:mm:ss")
+    }
+  }
+}
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala
index 73f546455b67..db1ae29d400c 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala
@@ -158,7 +158,7 @@ class ExpressionEvaluationSuite extends FunSuite {
     checkEvaluation("abc" like regEx, true, new GenericRow(Array[Any]("a%")))
     checkEvaluation("abc" like regEx, false, new GenericRow(Array[Any]("b%")))
     checkEvaluation("abc" like regEx, false, new GenericRow(Array[Any]("bc%")))
-    
+
     checkEvaluation(Literal(null, StringType) like regEx, null, new GenericRow(Array[Any]("bc%")))
   }
 
@@ -203,7 +203,7 @@ class ExpressionEvaluationSuite extends FunSuite {
 
   test("data type casting") {
 
-    val sts = "1970-01-01 00:00:01.0"
+    val sts = "1970-01-01 00:00:01.1"
     val ts = Timestamp.valueOf(sts)
 
     checkEvaluation("abdef" cast StringType, "abdef")
@@ -293,7 +293,7 @@ class ExpressionEvaluationSuite extends FunSuite {
     // A test for higher precision than millis
     checkEvaluation(Cast(Cast(0.00000001, TimestampType), DoubleType), 0.00000001)
   }
-  
+
   test("null checking") {
     val row = new GenericRow(Array[Any]("^Ba*n", null, true, null))
     val c1 = 'a.string.at(0)
@@ -312,7 +312,7 @@ class ExpressionEvaluationSuite extends FunSuite {
 
     checkEvaluation(IsNull(Literal(null, ShortType)), true)
     checkEvaluation(IsNotNull(Literal(null, ShortType)), false)
-    
+
     checkEvaluation(Coalesce(c1 :: c2 :: Nil), "^Ba*n", row)
     checkEvaluation(Coalesce(Literal(null, StringType) :: Nil), null, row)
     checkEvaluation(Coalesce(Literal(null, StringType) :: c1 :: c2 :: Nil), "^Ba*n", row)
@@ -323,11 +323,11 @@ class ExpressionEvaluationSuite extends FunSuite {
     checkEvaluation(If(Literal(null, BooleanType), c2, c1), "^Ba*n", row)
     checkEvaluation(If(Literal(true, BooleanType), c1, c2), "^Ba*n", row)
     checkEvaluation(If(Literal(false, BooleanType), c2, c1), "^Ba*n", row)
-    checkEvaluation(If(Literal(false, BooleanType), 
+    checkEvaluation(If(Literal(false, BooleanType),
       Literal("a", StringType), Literal("b", StringType)), "b", row)
 
     checkEvaluation(In(c1, c1 :: c2 :: Nil), true, row)
-    checkEvaluation(In(Literal("^Ba*n", StringType), 
+    checkEvaluation(In(Literal("^Ba*n", StringType),
       Literal("^Ba*n", StringType) :: Nil), true, row)
     checkEvaluation(In(Literal("^Ba*n", StringType),
       Literal("^Ba*n", StringType) :: c2 :: Nil), true, row)
@@ -378,7 +378,7 @@ class ExpressionEvaluationSuite extends FunSuite {
 
   test("complex type") {
     val row = new GenericRow(Array[Any](
-      "^Ba*n",                                  // 0 
+      "^Ba*n",                                  // 0
       null.asInstanceOf[String],                // 1
       new GenericRow(Array[Any]("aa", "bb")),   // 2
       Map("aa"->"bb"),                          // 3
@@ -391,18 +391,18 @@ class ExpressionEvaluationSuite extends FunSuite {
     val typeMap = MapType(StringType, StringType)
     val typeArray = ArrayType(StringType)
 
-    checkEvaluation(GetItem(BoundReference(3, AttributeReference("c", typeMap)()), 
+    checkEvaluation(GetItem(BoundReference(3, AttributeReference("c", typeMap)()),
       Literal("aa")), "bb", row)
     checkEvaluation(GetItem(Literal(null, typeMap), Literal("aa")), null, row)
     checkEvaluation(GetItem(Literal(null, typeMap), Literal(null, StringType)), null, row)
-    checkEvaluation(GetItem(BoundReference(3, AttributeReference("c", typeMap)()), 
+    checkEvaluation(GetItem(BoundReference(3, AttributeReference("c", typeMap)()),
       Literal(null, StringType)), null, row)
 
-    checkEvaluation(GetItem(BoundReference(4, AttributeReference("c", typeArray)()), 
+    checkEvaluation(GetItem(BoundReference(4, AttributeReference("c", typeArray)()),
       Literal(1)), "bb", row)
     checkEvaluation(GetItem(Literal(null, typeArray), Literal(1)), null, row)
     checkEvaluation(GetItem(Literal(null, typeArray), Literal(null, IntegerType)), null, row)
-    checkEvaluation(GetItem(BoundReference(4, AttributeReference("c", typeArray)()), 
+    checkEvaluation(GetItem(BoundReference(4, AttributeReference("c", typeArray)()),
       Literal(null, IntegerType)), null, row)
 
     checkEvaluation(GetField(BoundReference(2, AttributeReference("c", typeS)()), "a"), "aa", row)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnAccessor.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnAccessor.scala
index 3c39e1d350fa..42a5a9a84f36 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnAccessor.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnAccessor.scala
@@ -90,6 +90,9 @@ private[sql] class FloatColumnAccessor(buffer: ByteBuffer)
 private[sql] class StringColumnAccessor(buffer: ByteBuffer)
   extends NativeColumnAccessor(buffer, STRING)
 
+private[sql] class TimestampColumnAccessor(buffer: ByteBuffer)
+  extends NativeColumnAccessor(buffer, TIMESTAMP)
+
 private[sql] class BinaryColumnAccessor(buffer: ByteBuffer)
   extends BasicColumnAccessor[BinaryType.type, Array[Byte]](buffer, BINARY)
   with NullableColumnAccessor
@@ -105,16 +108,17 @@ private[sql] object ColumnAccessor {
     val columnTypeId = dup.getInt()
 
     columnTypeId match {
-      case INT.typeId     => new IntColumnAccessor(dup)
-      case LONG.typeId    => new LongColumnAccessor(dup)
-      case FLOAT.typeId   => new FloatColumnAccessor(dup)
-      case DOUBLE.typeId  => new DoubleColumnAccessor(dup)
-      case BOOLEAN.typeId => new BooleanColumnAccessor(dup)
-      case BYTE.typeId    => new ByteColumnAccessor(dup)
-      case SHORT.typeId   => new ShortColumnAccessor(dup)
-      case STRING.typeId  => new StringColumnAccessor(dup)
-      case BINARY.typeId  => new BinaryColumnAccessor(dup)
-      case GENERIC.typeId => new GenericColumnAccessor(dup)
+      case INT.typeId       => new IntColumnAccessor(dup)
+      case LONG.typeId      => new LongColumnAccessor(dup)
+      case FLOAT.typeId     => new FloatColumnAccessor(dup)
+      case DOUBLE.typeId    => new DoubleColumnAccessor(dup)
+      case BOOLEAN.typeId   => new BooleanColumnAccessor(dup)
+      case BYTE.typeId      => new ByteColumnAccessor(dup)
+      case SHORT.typeId     => new ShortColumnAccessor(dup)
+      case STRING.typeId    => new StringColumnAccessor(dup)
+      case TIMESTAMP.typeId => new TimestampColumnAccessor(dup)
+      case BINARY.typeId    => new BinaryColumnAccessor(dup)
+      case GENERIC.typeId   => new GenericColumnAccessor(dup)
     }
   }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnBuilder.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnBuilder.scala
index 4be048cd742d..74f5630fbddf 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnBuilder.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnBuilder.scala
@@ -109,6 +109,9 @@ private[sql] class FloatColumnBuilder extends NativeColumnBuilder(new FloatColum
 
 private[sql] class StringColumnBuilder extends NativeColumnBuilder(new StringColumnStats, STRING)
 
+private[sql] class TimestampColumnBuilder
+  extends NativeColumnBuilder(new TimestampColumnStats, TIMESTAMP)
+
 private[sql] class BinaryColumnBuilder extends ComplexColumnBuilder(BINARY)
 
 // TODO (lian) Add support for array, struct and map
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnStats.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnStats.scala
index 95602d321dc6..6502110e903f 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnStats.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnStats.scala
@@ -344,21 +344,52 @@ private[sql] class StringColumnStats extends BasicColumnStats(STRING) {
   }
 
   override def contains(row: Row, ordinal: Int) = {
-    !(upperBound eq null) && {
+    (upperBound ne null) && {
       val field = columnType.getField(row, ordinal)
       lowerBound.compareTo(field) <= 0 && field.compareTo(upperBound) <= 0
     }
   }
 
   override def isAbove(row: Row, ordinal: Int) = {
-    !(upperBound eq null) && {
+    (upperBound ne null) && {
       val field = columnType.getField(row, ordinal)
       field.compareTo(upperBound) < 0
     }
   }
 
   override def isBelow(row: Row, ordinal: Int) = {
-    !(lowerBound eq null) && {
+    (lowerBound ne null) && {
+      val field = columnType.getField(row, ordinal)
+      lowerBound.compareTo(field) < 0
+    }
+  }
+}
+
+private[sql] class TimestampColumnStats extends BasicColumnStats(TIMESTAMP) {
+  override def initialBounds = (null, null)
+
+  override def gatherStats(row: Row, ordinal: Int) {
+    val field = columnType.getField(row, ordinal)
+    if ((upperBound eq null) || field.compareTo(upperBound) > 0) _upper = field
+    if ((lowerBound eq null) || field.compareTo(lowerBound) < 0) _lower = field
+  }
+
+  override def contains(row: Row, ordinal: Int) = {
+    (upperBound ne null) && {
+      val field = columnType.getField(row, ordinal)
+      lowerBound.compareTo(field) <= 0 && field.compareTo(upperBound) <= 0
+    }
+  }
+
+  override def isAbove(row: Row, ordinal: Int) = {
+    (lowerBound ne null) && {
+      val field = columnType.getField(row, ordinal)
+      field.compareTo(upperBound) < 0
+    }
+  }
+
+  override def isBelow(row: Row, ordinal: Int) = {
+    (lowerBound ne null) && {
       val field = columnType.getField(row, ordinal)
       lowerBound.compareTo(field) < 0
     }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala
index 4cd52d828813..794bc60d0e31 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala
@@ -21,6 +21,8 @@ import java.nio.ByteBuffer
 
 import scala.reflect.runtime.universe.TypeTag
 
+import java.sql.Timestamp
+
 import org.apache.spark.sql.Row
 import org.apache.spark.sql.catalyst.expressions.MutableRow
 import org.apache.spark.sql.catalyst.types._
@@ -221,6 +223,26 @@ private[sql] object STRING extends NativeColumnType(StringType, 7, 8) {
   override def getField(row: Row, ordinal: Int) = row.getString(ordinal)
 }
 
+private[sql] object TIMESTAMP extends NativeColumnType(TimestampType, 8, 12) {
+  override def extract(buffer: ByteBuffer) = {
+    val timestamp = new Timestamp(buffer.getLong())
+    timestamp.setNanos(buffer.getInt())
+    timestamp
+  }
+
+  override def append(v: Timestamp, buffer: ByteBuffer) {
+    buffer.putLong(v.getTime).putInt(v.getNanos)
+  }
+
+  override def getField(row: Row, ordinal: Int) = {
+    row(ordinal).asInstanceOf[Timestamp]
+  }
+
+  override def setField(row: MutableRow, ordinal: Int, value: Timestamp) {
+    row(ordinal) = value
+  }
+}
+
 private[sql] sealed abstract class ByteArrayColumnType[T <: DataType](
     typeId: Int,
     defaultSize: Int)
@@ -240,7 +262,7 @@ private[sql] sealed abstract class ByteArrayColumnType[T <: DataType](
   }
 }
 
-private[sql] object BINARY extends ByteArrayColumnType[BinaryType.type](8, 16) {
+private[sql] object BINARY extends ByteArrayColumnType[BinaryType.type](9, 16) {
   override def setField(row: MutableRow, ordinal: Int, value: Array[Byte]) {
     row(ordinal) = value
   }
@@ -251,7 +273,7 @@ private[sql] object BINARY extends ByteArrayColumnType[BinaryType.type](8, 16) {
 // Used to process generic objects (all types other than those listed above). Objects should be
 // serialized first before appending to the column `ByteBuffer`, and is also extracted as serialized
 // byte array.
-private[sql] object GENERIC extends ByteArrayColumnType[DataType](9, 16) {
+private[sql] object GENERIC extends ByteArrayColumnType[DataType](10, 16) {
   override def setField(row: MutableRow, ordinal: Int, value: Array[Byte]) {
     row(ordinal) = SparkSqlSerializer.deserialize[Any](value)
   }
@@ -262,16 +284,17 @@ private[sql] object GENERIC extends ByteArrayColumnType[DataType](9, 16) {
 private[sql] object ColumnType {
   def apply(dataType: DataType): ColumnType[_, _] = {
     dataType match {
-      case IntegerType => INT
-      case LongType    => LONG
-      case FloatType   => FLOAT
-      case DoubleType  => DOUBLE
-      case BooleanType => BOOLEAN
-      case ByteType    => BYTE
-      case ShortType   => SHORT
-      case StringType  => STRING
-      case BinaryType  => BINARY
-      case _           => GENERIC
+      case IntegerType   => INT
+      case LongType      => LONG
+      case FloatType     => FLOAT
+      case DoubleType    => DOUBLE
+      case BooleanType   => BOOLEAN
+      case ByteType      => BYTE
+      case ShortType     => SHORT
+      case StringType    => STRING
+      case BinaryType    => BINARY
+      case TimestampType => TIMESTAMP
+      case _             => GENERIC
     }
   }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala
index 6f0d46d81626..5f61fb5e16ea 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala
@@ -22,14 +22,15 @@ import org.scalatest.FunSuite
 import org.apache.spark.sql.catalyst.types._
 
 class ColumnStatsSuite extends FunSuite {
-  testColumnStats(classOf[BooleanColumnStats], BOOLEAN)
-  testColumnStats(classOf[ByteColumnStats],    BYTE)
-  testColumnStats(classOf[ShortColumnStats],   SHORT)
-  testColumnStats(classOf[IntColumnStats],     INT)
-  testColumnStats(classOf[LongColumnStats],    LONG)
-  testColumnStats(classOf[FloatColumnStats],   FLOAT)
-  testColumnStats(classOf[DoubleColumnStats],  DOUBLE)
-  testColumnStats(classOf[StringColumnStats],  STRING)
+  testColumnStats(classOf[BooleanColumnStats],   BOOLEAN)
+  testColumnStats(classOf[ByteColumnStats],      BYTE)
+  testColumnStats(classOf[ShortColumnStats],     SHORT)
+  testColumnStats(classOf[IntColumnStats],       INT)
+  testColumnStats(classOf[LongColumnStats],      LONG)
+  testColumnStats(classOf[FloatColumnStats],     FLOAT)
+  testColumnStats(classOf[DoubleColumnStats],    DOUBLE)
+  testColumnStats(classOf[StringColumnStats],    STRING)
+  testColumnStats(classOf[TimestampColumnStats], TIMESTAMP)
 
   def testColumnStats[T <: NativeType, U <: NativeColumnStats[T]](
       columnStatsClass: Class[U],
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnTypeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnTypeSuite.scala
index 314b7d317ed7..829342215e69 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnTypeSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnTypeSuite.scala
@@ -18,6 +18,7 @@
 package org.apache.spark.sql.columnar
 
 import java.nio.ByteBuffer
+import java.sql.Timestamp
 
 import org.scalatest.FunSuite
 
@@ -32,7 +33,7 @@ class ColumnTypeSuite extends FunSuite with Logging {
   test("defaultSize") {
     val checks = Map(
       INT -> 4, SHORT -> 2, LONG -> 8, BYTE -> 1, DOUBLE -> 8, FLOAT -> 4,
-      BOOLEAN -> 1, STRING -> 8, BINARY -> 16, GENERIC -> 16)
+      BOOLEAN -> 1, STRING -> 8, TIMESTAMP -> 12, BINARY -> 16, GENERIC -> 16)
 
     checks.foreach { case (columnType, expectedSize) =>
       assertResult(expectedSize, s"Wrong defaultSize for $columnType") {
@@ -52,14 +53,15 @@ class ColumnTypeSuite extends FunSuite with Logging {
       }
     }
 
-    checkActualSize(INT,     Int.MaxValue,    4)
-    checkActualSize(SHORT,   Short.MaxValue,  2)
-    checkActualSize(LONG,    Long.MaxValue,   8)
-    checkActualSize(BYTE,    Byte.MaxValue,   1)
-    checkActualSize(DOUBLE,  Double.MaxValue, 8)
-    checkActualSize(FLOAT,   Float.MaxValue,  4)
-    checkActualSize(BOOLEAN, true,            1)
-    checkActualSize(STRING,  "hello",         4 + "hello".getBytes("utf-8").length)
+    checkActualSize(INT,       Int.MaxValue,      4)
+    checkActualSize(SHORT,     Short.MaxValue,    2)
+    checkActualSize(LONG,      Long.MaxValue,     8)
+    checkActualSize(BYTE,      Byte.MaxValue,     1)
+    checkActualSize(DOUBLE,    Double.MaxValue,   8)
+    checkActualSize(FLOAT,     Float.MaxValue,    4)
+    checkActualSize(BOOLEAN,   true,              1)
+    checkActualSize(STRING,    "hello",           4 + "hello".getBytes("utf-8").length)
+    checkActualSize(TIMESTAMP, new Timestamp(0L), 12)
 
     val binary = Array.fill[Byte](4)(0: Byte)
     checkActualSize(BINARY,  binary, 4 + 4)
@@ -188,17 +190,7 @@ class ColumnTypeSuite extends FunSuite with Logging {
   }
 
   private def hexDump(value: Any): String = {
-    if (value.isInstanceOf[String]) {
-      val sb = new StringBuilder()
-      for (ch <- value.asInstanceOf[String].toCharArray) {
-        sb.append(Integer.toHexString(ch & 0xffff)).append(' ')
-      }
-      if (! sb.isEmpty) sb.setLength(sb.length - 1)
-      sb.toString()
-    } else {
-      // for now ..
-      hexDump(value.toString)
-    }
+    value.toString.map(ch => Integer.toHexString(ch & 0xffff)).mkString(" ")
   }
 
   private def dumpBuffer(buff: ByteBuffer): Any = {
@@ -207,7 +199,7 @@ class ColumnTypeSuite extends FunSuite with Logging {
       val b = buff.get()
       sb.append(Integer.toHexString(b & 0xff)).append(' ')
     }
-    if (! sb.isEmpty) sb.setLength(sb.length - 1)
+    if (sb.nonEmpty) sb.setLength(sb.length - 1)
     sb.toString()
   }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnarTestUtils.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnarTestUtils.scala
index 04bdc43d9532..38b04dd959f7 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnarTestUtils.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnarTestUtils.scala
@@ -20,6 +20,8 @@ package org.apache.spark.sql.columnar
 import scala.collection.immutable.HashSet
 import scala.util.Random
 
+import java.sql.Timestamp
+
 import org.apache.spark.sql.Row
 import org.apache.spark.sql.catalyst.expressions.GenericMutableRow
 import org.apache.spark.sql.catalyst.types.{DataType, NativeType}
@@ -39,15 +41,19 @@ object ColumnarTestUtils {
     }
 
     (columnType match {
-      case BYTE    => (Random.nextInt(Byte.MaxValue * 2) - Byte.MaxValue).toByte
-      case SHORT   => (Random.nextInt(Short.MaxValue * 2) - Short.MaxValue).toShort
-      case INT     => Random.nextInt()
-      case LONG    => Random.nextLong()
-      case FLOAT   => Random.nextFloat()
-      case DOUBLE  => Random.nextDouble()
-      case STRING  => Random.nextString(Random.nextInt(32))
-      case BOOLEAN => Random.nextBoolean()
-      case BINARY  => randomBytes(Random.nextInt(32))
+      case BYTE      => (Random.nextInt(Byte.MaxValue * 2) - Byte.MaxValue).toByte
+      case SHORT     => (Random.nextInt(Short.MaxValue * 2) - Short.MaxValue).toShort
+      case INT       => Random.nextInt()
+      case LONG      => Random.nextLong()
+      case FLOAT     => Random.nextFloat()
+      case DOUBLE    => Random.nextDouble()
+      case STRING    => Random.nextString(Random.nextInt(32))
+      case BOOLEAN   => Random.nextBoolean()
+      case BINARY    => randomBytes(Random.nextInt(32))
+      case TIMESTAMP =>
+        val timestamp = new Timestamp(Random.nextLong())
+        timestamp.setNanos(Random.nextInt(999999999))
+        timestamp
       case _ =>
         // Using a random one-element map instead of an arbitrary object
         Map(Random.nextInt() -> Random.nextString(Random.nextInt(32)))
@@ -96,5 +102,4 @@ object ColumnarTestUtils {
 
     (values, rows)
   }
-
 }
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
index 7aedfcd74189..334462357eb8 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
@@ -18,6 +18,7 @@
 package org.apache.spark.sql.hive
 
 import java.io.{BufferedReader, File, InputStreamReader, PrintStream}
+import java.sql.Timestamp
 import java.util.{ArrayList => JArrayList}
 
 import scala.collection.JavaConversions._
@@ -28,6 +29,7 @@ import org.apache.hadoop.hive.conf.HiveConf
 import org.apache.hadoop.hive.ql.Driver
 import org.apache.hadoop.hive.ql.processors._
 import org.apache.hadoop.hive.ql.session.SessionState
+import org.apache.hadoop.hive.serde2.io.TimestampWritable
 
 import org.apache.spark.SparkContext
 import org.apache.spark.rdd.RDD
@@ -266,6 +268,7 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) {
             toHiveStructString((key, kType)) + ":" + toHiveStructString((value, vType))
         }.toSeq.sorted.mkString("{", ",", "}")
       case (null, _) => "NULL"
+      case (t: Timestamp, TimestampType) => new TimestampWritable(t).toString
       case (other, tpe) if primitiveTypes contains tpe => other.toString
     }
 
diff --git a/sql/hive/src/test/resources/golden/timestamp_1-10-343c75daac6695917608c17db8bf473e b/sql/hive/src/test/resources/golden/timestamp_1-10-343c75daac6695917608c17db8bf473e
new file mode 100644
index 000000000000..b77c91ccee41
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_1-10-343c75daac6695917608c17db8bf473e
@@ -0,0 +1 @@
+1.293872461E9
diff --git a/sql/hive/src/test/resources/golden/timestamp_1-11-cf19f7359a6d3456c4526b2c69f92d6a b/sql/hive/src/test/resources/golden/timestamp_1-11-cf19f7359a6d3456c4526b2c69f92d6a
new file mode 100644
index 000000000000..bc2423dc08a1
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_1-11-cf19f7359a6d3456c4526b2c69f92d6a
@@ -0,0 +1 @@
+2011-01-01 01:01:01
diff --git a/sql/hive/src/test/resources/golden/timestamp_1-12-6328d3b3dfd295dd5ec453ffb47ff4d0 b/sql/hive/src/test/resources/golden/timestamp_1-12-6328d3b3dfd295dd5ec453ffb47ff4d0
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/timestamp_1-13-90269c1e50c7ae8e75ca9cc297982135 b/sql/hive/src/test/resources/golden/timestamp_1-13-90269c1e50c7ae8e75ca9cc297982135
new file mode 100644
index 000000000000..27ba77ddaf61
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_1-13-90269c1e50c7ae8e75ca9cc297982135
@@ -0,0 +1 @@
+true
diff --git a/sql/hive/src/test/resources/golden/timestamp_1-14-e6bfca320c4ee3aff39cf2f179d57da6 b/sql/hive/src/test/resources/golden/timestamp_1-14-e6bfca320c4ee3aff39cf2f179d57da6
new file mode 100644
index 000000000000..987e7ca9a76d
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_1-14-e6bfca320c4ee3aff39cf2f179d57da6
@@ -0,0 +1 @@
+77
diff --git a/sql/hive/src/test/resources/golden/timestamp_1-15-d0291a9bd42054b2732cb4f54cf39ae7 b/sql/hive/src/test/resources/golden/timestamp_1-15-d0291a9bd42054b2732cb4f54cf39ae7
new file mode 100644
index 000000000000..8f3a49478b26
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_1-15-d0291a9bd42054b2732cb4f54cf39ae7
@@ -0,0 +1 @@
+-4787
diff --git a/sql/hive/src/test/resources/golden/timestamp_1-16-e7b398d2a8107a42419c83771bda41e6 b/sql/hive/src/test/resources/golden/timestamp_1-16-e7b398d2a8107a42419c83771bda41e6
new file mode 100644
index 000000000000..211a320b9ca7
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_1-16-e7b398d2a8107a42419c83771bda41e6
@@ -0,0 +1 @@
+1293872461
diff --git a/sql/hive/src/test/resources/golden/timestamp_1-17-a3eeec08bccae78d0d94ad2cb923e1cf b/sql/hive/src/test/resources/golden/timestamp_1-17-a3eeec08bccae78d0d94ad2cb923e1cf
new file mode 100644
index 000000000000..211a320b9ca7
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_1-17-a3eeec08bccae78d0d94ad2cb923e1cf
@@ -0,0 +1 @@
+1293872461
diff --git a/sql/hive/src/test/resources/golden/timestamp_1-18-67f274bf16de625cf4e85af0c6185cac b/sql/hive/src/test/resources/golden/timestamp_1-18-67f274bf16de625cf4e85af0c6185cac
new file mode 100644
index 000000000000..502f94a71edb
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_1-18-67f274bf16de625cf4e85af0c6185cac
@@ -0,0 +1 @@
+1.29387251E9
diff --git a/sql/hive/src/test/resources/golden/timestamp_1-19-343c75daac6695917608c17db8bf473e b/sql/hive/src/test/resources/golden/timestamp_1-19-343c75daac6695917608c17db8bf473e
new file mode 100644
index 000000000000..b77c91ccee41
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_1-19-343c75daac6695917608c17db8bf473e
@@ -0,0 +1 @@
+1.293872461E9
diff --git a/sql/hive/src/test/resources/golden/timestamp_1-20-cf19f7359a6d3456c4526b2c69f92d6a b/sql/hive/src/test/resources/golden/timestamp_1-20-cf19f7359a6d3456c4526b2c69f92d6a
new file mode 100644
index 000000000000..bc2423dc08a1
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_1-20-cf19f7359a6d3456c4526b2c69f92d6a
@@ -0,0 +1 @@
+2011-01-01 01:01:01
diff --git a/sql/hive/src/test/resources/golden/timestamp_1-21-d8fff1a6c464e50eb955babfafb0b98e b/sql/hive/src/test/resources/golden/timestamp_1-21-d8fff1a6c464e50eb955babfafb0b98e
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/timestamp_1-22-90269c1e50c7ae8e75ca9cc297982135 b/sql/hive/src/test/resources/golden/timestamp_1-22-90269c1e50c7ae8e75ca9cc297982135
new file mode 100644
index 000000000000..27ba77ddaf61
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_1-22-90269c1e50c7ae8e75ca9cc297982135
@@ -0,0 +1 @@
+true
diff --git a/sql/hive/src/test/resources/golden/timestamp_1-23-e6bfca320c4ee3aff39cf2f179d57da6 b/sql/hive/src/test/resources/golden/timestamp_1-23-e6bfca320c4ee3aff39cf2f179d57da6
new file mode 100644
index 000000000000..987e7ca9a76d
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_1-23-e6bfca320c4ee3aff39cf2f179d57da6
@@ -0,0 +1 @@
+77
diff --git a/sql/hive/src/test/resources/golden/timestamp_1-24-d0291a9bd42054b2732cb4f54cf39ae7 b/sql/hive/src/test/resources/golden/timestamp_1-24-d0291a9bd42054b2732cb4f54cf39ae7
new file mode 100644
index 000000000000..8f3a49478b26
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_1-24-d0291a9bd42054b2732cb4f54cf39ae7
@@ -0,0 +1 @@
+-4787
diff --git a/sql/hive/src/test/resources/golden/timestamp_1-25-e7b398d2a8107a42419c83771bda41e6 b/sql/hive/src/test/resources/golden/timestamp_1-25-e7b398d2a8107a42419c83771bda41e6
new file mode 100644
index 000000000000..211a320b9ca7
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_1-25-e7b398d2a8107a42419c83771bda41e6
@@ -0,0 +1 @@
+1293872461
diff --git a/sql/hive/src/test/resources/golden/timestamp_1-26-a3eeec08bccae78d0d94ad2cb923e1cf b/sql/hive/src/test/resources/golden/timestamp_1-26-a3eeec08bccae78d0d94ad2cb923e1cf
new file mode 100644
index 000000000000..211a320b9ca7
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_1-26-a3eeec08bccae78d0d94ad2cb923e1cf
@@ -0,0 +1 @@
+1293872461
diff --git a/sql/hive/src/test/resources/golden/timestamp_1-27-67f274bf16de625cf4e85af0c6185cac b/sql/hive/src/test/resources/golden/timestamp_1-27-67f274bf16de625cf4e85af0c6185cac
new file mode 100644
index 000000000000..502f94a71edb
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_1-27-67f274bf16de625cf4e85af0c6185cac
@@ -0,0 +1 @@
+1.29387251E9
diff --git a/sql/hive/src/test/resources/golden/timestamp_1-28-343c75daac6695917608c17db8bf473e b/sql/hive/src/test/resources/golden/timestamp_1-28-343c75daac6695917608c17db8bf473e
new file mode 100644
index 000000000000..2158e77f7876
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_1-28-343c75daac6695917608c17db8bf473e
@@ -0,0 +1 @@
+1.2938724611E9
diff --git a/sql/hive/src/test/resources/golden/timestamp_1-29-cf19f7359a6d3456c4526b2c69f92d6a b/sql/hive/src/test/resources/golden/timestamp_1-29-cf19f7359a6d3456c4526b2c69f92d6a
new file mode 100644
index 000000000000..4bdd802b9cda
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_1-29-cf19f7359a6d3456c4526b2c69f92d6a
@@ -0,0 +1 @@
+2011-01-01 01:01:01.1
diff --git a/sql/hive/src/test/resources/golden/timestamp_1-3-819633b45e3e1779bca6bcb7b77fe5a1 b/sql/hive/src/test/resources/golden/timestamp_1-3-819633b45e3e1779bca6bcb7b77fe5a1
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/timestamp_1-30-273256141c33eb88194cad22eb940d21 b/sql/hive/src/test/resources/golden/timestamp_1-30-273256141c33eb88194cad22eb940d21
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/timestamp_1-31-90269c1e50c7ae8e75ca9cc297982135 b/sql/hive/src/test/resources/golden/timestamp_1-31-90269c1e50c7ae8e75ca9cc297982135
new file mode 100644
index 000000000000..27ba77ddaf61
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_1-31-90269c1e50c7ae8e75ca9cc297982135
@@ -0,0 +1 @@
+true
diff --git a/sql/hive/src/test/resources/golden/timestamp_1-32-e6bfca320c4ee3aff39cf2f179d57da6 b/sql/hive/src/test/resources/golden/timestamp_1-32-e6bfca320c4ee3aff39cf2f179d57da6
new file mode 100644
index 000000000000..987e7ca9a76d
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_1-32-e6bfca320c4ee3aff39cf2f179d57da6
@@ -0,0 +1 @@
+77
diff --git a/sql/hive/src/test/resources/golden/timestamp_1-33-d0291a9bd42054b2732cb4f54cf39ae7 b/sql/hive/src/test/resources/golden/timestamp_1-33-d0291a9bd42054b2732cb4f54cf39ae7
new file mode 100644
index 000000000000..8f3a49478b26
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_1-33-d0291a9bd42054b2732cb4f54cf39ae7
@@ -0,0 +1 @@
+-4787
diff --git a/sql/hive/src/test/resources/golden/timestamp_1-34-e7b398d2a8107a42419c83771bda41e6 b/sql/hive/src/test/resources/golden/timestamp_1-34-e7b398d2a8107a42419c83771bda41e6
new file mode 100644
index 000000000000..211a320b9ca7
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_1-34-e7b398d2a8107a42419c83771bda41e6
@@ -0,0 +1 @@
+1293872461
diff --git a/sql/hive/src/test/resources/golden/timestamp_1-35-a3eeec08bccae78d0d94ad2cb923e1cf b/sql/hive/src/test/resources/golden/timestamp_1-35-a3eeec08bccae78d0d94ad2cb923e1cf
new file mode 100644
index 000000000000..211a320b9ca7
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_1-35-a3eeec08bccae78d0d94ad2cb923e1cf
@@ -0,0 +1 @@
+1293872461
diff --git a/sql/hive/src/test/resources/golden/timestamp_1-36-67f274bf16de625cf4e85af0c6185cac b/sql/hive/src/test/resources/golden/timestamp_1-36-67f274bf16de625cf4e85af0c6185cac
new file mode 100644
index 000000000000..502f94a71edb
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_1-36-67f274bf16de625cf4e85af0c6185cac
@@ -0,0 +1 @@
+1.29387251E9
diff --git a/sql/hive/src/test/resources/golden/timestamp_1-37-343c75daac6695917608c17db8bf473e b/sql/hive/src/test/resources/golden/timestamp_1-37-343c75daac6695917608c17db8bf473e
new file mode 100644
index 000000000000..b71ff6086336
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_1-37-343c75daac6695917608c17db8bf473e
@@ -0,0 +1 @@
+1.2938724610001E9
diff --git a/sql/hive/src/test/resources/golden/timestamp_1-38-cf19f7359a6d3456c4526b2c69f92d6a b/sql/hive/src/test/resources/golden/timestamp_1-38-cf19f7359a6d3456c4526b2c69f92d6a
new file mode 100644
index 000000000000..8b014c4cd8d6
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_1-38-cf19f7359a6d3456c4526b2c69f92d6a
@@ -0,0 +1 @@
+2011-01-01 01:01:01.0001
diff --git a/sql/hive/src/test/resources/golden/timestamp_1-39-b2fe5cc7c8ee62d3bb0c120c9a6c305d b/sql/hive/src/test/resources/golden/timestamp_1-39-b2fe5cc7c8ee62d3bb0c120c9a6c305d
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/timestamp_1-4-90269c1e50c7ae8e75ca9cc297982135 b/sql/hive/src/test/resources/golden/timestamp_1-4-90269c1e50c7ae8e75ca9cc297982135
new file mode 100644
index 000000000000..27ba77ddaf61
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_1-4-90269c1e50c7ae8e75ca9cc297982135
@@ -0,0 +1 @@
+true
diff --git a/sql/hive/src/test/resources/golden/timestamp_1-40-90269c1e50c7ae8e75ca9cc297982135 b/sql/hive/src/test/resources/golden/timestamp_1-40-90269c1e50c7ae8e75ca9cc297982135
new file mode 100644
index 000000000000..27ba77ddaf61
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_1-40-90269c1e50c7ae8e75ca9cc297982135
@@ -0,0 +1 @@
+true
diff --git a/sql/hive/src/test/resources/golden/timestamp_1-41-e6bfca320c4ee3aff39cf2f179d57da6 b/sql/hive/src/test/resources/golden/timestamp_1-41-e6bfca320c4ee3aff39cf2f179d57da6
new file mode 100644
index 000000000000..987e7ca9a76d
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_1-41-e6bfca320c4ee3aff39cf2f179d57da6
@@ -0,0 +1 @@
+77
diff --git a/sql/hive/src/test/resources/golden/timestamp_1-42-d0291a9bd42054b2732cb4f54cf39ae7 b/sql/hive/src/test/resources/golden/timestamp_1-42-d0291a9bd42054b2732cb4f54cf39ae7
new file mode 100644
index 000000000000..8f3a49478b26
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_1-42-d0291a9bd42054b2732cb4f54cf39ae7
@@ -0,0 +1 @@
+-4787
diff --git a/sql/hive/src/test/resources/golden/timestamp_1-43-e7b398d2a8107a42419c83771bda41e6 b/sql/hive/src/test/resources/golden/timestamp_1-43-e7b398d2a8107a42419c83771bda41e6
new file mode 100644
index 000000000000..211a320b9ca7
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_1-43-e7b398d2a8107a42419c83771bda41e6
@@ -0,0 +1 @@
+1293872461
diff --git a/sql/hive/src/test/resources/golden/timestamp_1-44-a3eeec08bccae78d0d94ad2cb923e1cf b/sql/hive/src/test/resources/golden/timestamp_1-44-a3eeec08bccae78d0d94ad2cb923e1cf
new file mode 100644
index 000000000000..211a320b9ca7
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_1-44-a3eeec08bccae78d0d94ad2cb923e1cf
@@ -0,0 +1 @@
+1293872461
diff --git a/sql/hive/src/test/resources/golden/timestamp_1-45-67f274bf16de625cf4e85af0c6185cac b/sql/hive/src/test/resources/golden/timestamp_1-45-67f274bf16de625cf4e85af0c6185cac
new file mode 100644
index 000000000000..502f94a71edb
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_1-45-67f274bf16de625cf4e85af0c6185cac
@@ -0,0 +1 @@
+1.29387251E9
diff --git a/sql/hive/src/test/resources/golden/timestamp_1-46-343c75daac6695917608c17db8bf473e b/sql/hive/src/test/resources/golden/timestamp_1-46-343c75daac6695917608c17db8bf473e
new file mode 100644
index 000000000000..b71ff6086336
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_1-46-343c75daac6695917608c17db8bf473e
@@ -0,0 +1 @@
+1.2938724610001E9
diff --git a/sql/hive/src/test/resources/golden/timestamp_1-47-cf19f7359a6d3456c4526b2c69f92d6a b/sql/hive/src/test/resources/golden/timestamp_1-47-cf19f7359a6d3456c4526b2c69f92d6a
new file mode 100644
index 000000000000..8b014c4cd8d6
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_1-47-cf19f7359a6d3456c4526b2c69f92d6a
@@ -0,0 +1 @@
+2011-01-01 01:01:01.0001
diff --git a/sql/hive/src/test/resources/golden/timestamp_1-48-7029255241de8e8b9710801319990044 b/sql/hive/src/test/resources/golden/timestamp_1-48-7029255241de8e8b9710801319990044
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/timestamp_1-49-90269c1e50c7ae8e75ca9cc297982135 b/sql/hive/src/test/resources/golden/timestamp_1-49-90269c1e50c7ae8e75ca9cc297982135
new file mode 100644
index 000000000000..27ba77ddaf61
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_1-49-90269c1e50c7ae8e75ca9cc297982135
@@ -0,0 +1 @@
+true
diff --git a/sql/hive/src/test/resources/golden/timestamp_1-5-e6bfca320c4ee3aff39cf2f179d57da6 b/sql/hive/src/test/resources/golden/timestamp_1-5-e6bfca320c4ee3aff39cf2f179d57da6
new file mode 100644
index 000000000000..987e7ca9a76d
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_1-5-e6bfca320c4ee3aff39cf2f179d57da6
@@ -0,0 +1 @@
+77
diff --git a/sql/hive/src/test/resources/golden/timestamp_1-50-e6bfca320c4ee3aff39cf2f179d57da6 b/sql/hive/src/test/resources/golden/timestamp_1-50-e6bfca320c4ee3aff39cf2f179d57da6
new file mode 100644
index 000000000000..987e7ca9a76d
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_1-50-e6bfca320c4ee3aff39cf2f179d57da6
@@ -0,0 +1 @@
+77
diff --git a/sql/hive/src/test/resources/golden/timestamp_1-51-d0291a9bd42054b2732cb4f54cf39ae7 b/sql/hive/src/test/resources/golden/timestamp_1-51-d0291a9bd42054b2732cb4f54cf39ae7
new file mode 100644
index 000000000000..8f3a49478b26
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_1-51-d0291a9bd42054b2732cb4f54cf39ae7
@@ -0,0 +1 @@
+-4787
diff --git a/sql/hive/src/test/resources/golden/timestamp_1-52-e7b398d2a8107a42419c83771bda41e6 b/sql/hive/src/test/resources/golden/timestamp_1-52-e7b398d2a8107a42419c83771bda41e6
new file mode 100644
index 000000000000..211a320b9ca7
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_1-52-e7b398d2a8107a42419c83771bda41e6
@@ -0,0 +1 @@
+1293872461
diff --git a/sql/hive/src/test/resources/golden/timestamp_1-53-a3eeec08bccae78d0d94ad2cb923e1cf b/sql/hive/src/test/resources/golden/timestamp_1-53-a3eeec08bccae78d0d94ad2cb923e1cf
new file mode 100644
index 000000000000..211a320b9ca7
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_1-53-a3eeec08bccae78d0d94ad2cb923e1cf
@@ -0,0 +1 @@
+1293872461
diff --git a/sql/hive/src/test/resources/golden/timestamp_1-54-67f274bf16de625cf4e85af0c6185cac b/sql/hive/src/test/resources/golden/timestamp_1-54-67f274bf16de625cf4e85af0c6185cac
new file mode 100644
index 000000000000..502f94a71edb
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_1-54-67f274bf16de625cf4e85af0c6185cac
@@ -0,0 +1 @@
+1.29387251E9
diff --git a/sql/hive/src/test/resources/golden/timestamp_1-55-343c75daac6695917608c17db8bf473e b/sql/hive/src/test/resources/golden/timestamp_1-55-343c75daac6695917608c17db8bf473e
new file mode 100644
index 000000000000..3eefb349894a
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_1-55-343c75daac6695917608c17db8bf473e
@@ -0,0 +1 @@
+1.293872461001E9
diff --git a/sql/hive/src/test/resources/golden/timestamp_1-56-cf19f7359a6d3456c4526b2c69f92d6a b/sql/hive/src/test/resources/golden/timestamp_1-56-cf19f7359a6d3456c4526b2c69f92d6a
new file mode 100644
index 000000000000..acce9d97b5eb
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_1-56-cf19f7359a6d3456c4526b2c69f92d6a
@@ -0,0 +1 @@
+2011-01-01 01:01:01.001000011
diff --git a/sql/hive/src/test/resources/golden/timestamp_1-57-d362501d0176855077e65f8faf067fa8 b/sql/hive/src/test/resources/golden/timestamp_1-57-d362501d0176855077e65f8faf067fa8
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/timestamp_1-6-d0291a9bd42054b2732cb4f54cf39ae7 b/sql/hive/src/test/resources/golden/timestamp_1-6-d0291a9bd42054b2732cb4f54cf39ae7
new file mode 100644
index 000000000000..8f3a49478b26
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_1-6-d0291a9bd42054b2732cb4f54cf39ae7
@@ -0,0 +1 @@
+-4787
diff --git a/sql/hive/src/test/resources/golden/timestamp_1-7-e7b398d2a8107a42419c83771bda41e6 b/sql/hive/src/test/resources/golden/timestamp_1-7-e7b398d2a8107a42419c83771bda41e6
new file mode 100644
index 000000000000..211a320b9ca7
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_1-7-e7b398d2a8107a42419c83771bda41e6
@@ -0,0 +1 @@
+1293872461
diff --git a/sql/hive/src/test/resources/golden/timestamp_1-8-a3eeec08bccae78d0d94ad2cb923e1cf b/sql/hive/src/test/resources/golden/timestamp_1-8-a3eeec08bccae78d0d94ad2cb923e1cf
new file mode 100644
index 000000000000..211a320b9ca7
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_1-8-a3eeec08bccae78d0d94ad2cb923e1cf
@@ -0,0 +1 @@
+1293872461
diff --git a/sql/hive/src/test/resources/golden/timestamp_1-9-67f274bf16de625cf4e85af0c6185cac b/sql/hive/src/test/resources/golden/timestamp_1-9-67f274bf16de625cf4e85af0c6185cac
new file mode 100644
index 000000000000..502f94a71edb
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_1-9-67f274bf16de625cf4e85af0c6185cac
@@ -0,0 +1 @@
+1.29387251E9
diff --git a/sql/hive/src/test/resources/golden/timestamp_2-10-5181279a0bf8939fe46ddacae015dad8 b/sql/hive/src/test/resources/golden/timestamp_2-10-5181279a0bf8939fe46ddacae015dad8
new file mode 100644
index 000000000000..b77c91ccee41
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_2-10-5181279a0bf8939fe46ddacae015dad8
@@ -0,0 +1 @@
+1.293872461E9
diff --git a/sql/hive/src/test/resources/golden/timestamp_2-11-240fce5f58794fa051824e8732c00c03 b/sql/hive/src/test/resources/golden/timestamp_2-11-240fce5f58794fa051824e8732c00c03
new file mode 100644
index 000000000000..bc2423dc08a1
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_2-11-240fce5f58794fa051824e8732c00c03
@@ -0,0 +1 @@
+2011-01-01 01:01:01
diff --git a/sql/hive/src/test/resources/golden/timestamp_2-12-7350308cbf49d6ebd6599d3802750acd b/sql/hive/src/test/resources/golden/timestamp_2-12-7350308cbf49d6ebd6599d3802750acd
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/timestamp_2-13-25f6ec69328af6cba76899194e0dd84e b/sql/hive/src/test/resources/golden/timestamp_2-13-25f6ec69328af6cba76899194e0dd84e
new file mode 100644
index 000000000000..27ba77ddaf61
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_2-13-25f6ec69328af6cba76899194e0dd84e
@@ -0,0 +1 @@
+true
diff --git a/sql/hive/src/test/resources/golden/timestamp_2-14-93c769be4cff93bea6e62bfe4e2a8742 b/sql/hive/src/test/resources/golden/timestamp_2-14-93c769be4cff93bea6e62bfe4e2a8742
new file mode 100644
index 000000000000..987e7ca9a76d
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_2-14-93c769be4cff93bea6e62bfe4e2a8742
@@ -0,0 +1 @@
+77
diff --git a/sql/hive/src/test/resources/golden/timestamp_2-15-5bdbf67419cc060b82d091d80ce59bf9 b/sql/hive/src/test/resources/golden/timestamp_2-15-5bdbf67419cc060b82d091d80ce59bf9
new file mode 100644
index 000000000000..8f3a49478b26
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_2-15-5bdbf67419cc060b82d091d80ce59bf9
@@ -0,0 +1 @@
+-4787
diff --git a/sql/hive/src/test/resources/golden/timestamp_2-16-de3c42ab06c17ae895fd7deaf7bd9571 b/sql/hive/src/test/resources/golden/timestamp_2-16-de3c42ab06c17ae895fd7deaf7bd9571
new file mode 100644
index 000000000000..211a320b9ca7
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_2-16-de3c42ab06c17ae895fd7deaf7bd9571
@@ -0,0 +1 @@
+1293872461
diff --git a/sql/hive/src/test/resources/golden/timestamp_2-17-da3937d21b7c2cfe1e624e812ae1d3ef b/sql/hive/src/test/resources/golden/timestamp_2-17-da3937d21b7c2cfe1e624e812ae1d3ef
new file mode 100644
index 000000000000..211a320b9ca7
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_2-17-da3937d21b7c2cfe1e624e812ae1d3ef
@@ -0,0 +1 @@
+1293872461
diff --git a/sql/hive/src/test/resources/golden/timestamp_2-18-252aebfe7882335d31bfc53a8705b7a b/sql/hive/src/test/resources/golden/timestamp_2-18-252aebfe7882335d31bfc53a8705b7a
new file mode 100644
index 000000000000..502f94a71edb
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_2-18-252aebfe7882335d31bfc53a8705b7a
@@ -0,0 +1 @@
+1.29387251E9
diff --git a/sql/hive/src/test/resources/golden/timestamp_2-19-5181279a0bf8939fe46ddacae015dad8 b/sql/hive/src/test/resources/golden/timestamp_2-19-5181279a0bf8939fe46ddacae015dad8
new file mode 100644
index 000000000000..b77c91ccee41
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_2-19-5181279a0bf8939fe46ddacae015dad8
@@ -0,0 +1 @@
+1.293872461E9
diff --git a/sql/hive/src/test/resources/golden/timestamp_2-20-240fce5f58794fa051824e8732c00c03 b/sql/hive/src/test/resources/golden/timestamp_2-20-240fce5f58794fa051824e8732c00c03
new file mode 100644
index 000000000000..bc2423dc08a1
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_2-20-240fce5f58794fa051824e8732c00c03
@@ -0,0 +1 @@
+2011-01-01 01:01:01
diff --git a/sql/hive/src/test/resources/golden/timestamp_2-21-5eb58e5d3c5b9f766f0b497bf59c47b b/sql/hive/src/test/resources/golden/timestamp_2-21-5eb58e5d3c5b9f766f0b497bf59c47b
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/timestamp_2-22-25f6ec69328af6cba76899194e0dd84e b/sql/hive/src/test/resources/golden/timestamp_2-22-25f6ec69328af6cba76899194e0dd84e
new file mode 100644
index 000000000000..27ba77ddaf61
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_2-22-25f6ec69328af6cba76899194e0dd84e
@@ -0,0 +1 @@
+true
diff --git a/sql/hive/src/test/resources/golden/timestamp_2-23-93c769be4cff93bea6e62bfe4e2a8742 b/sql/hive/src/test/resources/golden/timestamp_2-23-93c769be4cff93bea6e62bfe4e2a8742
new file mode 100644
index 000000000000..987e7ca9a76d
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_2-23-93c769be4cff93bea6e62bfe4e2a8742
@@ -0,0 +1 @@
+77
diff --git a/sql/hive/src/test/resources/golden/timestamp_2-24-5bdbf67419cc060b82d091d80ce59bf9 b/sql/hive/src/test/resources/golden/timestamp_2-24-5bdbf67419cc060b82d091d80ce59bf9
new file mode 100644
index 000000000000..8f3a49478b26
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_2-24-5bdbf67419cc060b82d091d80ce59bf9
@@ -0,0 +1 @@
+-4787
diff --git a/sql/hive/src/test/resources/golden/timestamp_2-25-de3c42ab06c17ae895fd7deaf7bd9571 b/sql/hive/src/test/resources/golden/timestamp_2-25-de3c42ab06c17ae895fd7deaf7bd9571
new file mode 100644
index 000000000000..211a320b9ca7
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_2-25-de3c42ab06c17ae895fd7deaf7bd9571
@@ -0,0 +1 @@
+1293872461
diff --git a/sql/hive/src/test/resources/golden/timestamp_2-26-da3937d21b7c2cfe1e624e812ae1d3ef b/sql/hive/src/test/resources/golden/timestamp_2-26-da3937d21b7c2cfe1e624e812ae1d3ef
new file mode 100644
index 000000000000..211a320b9ca7
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_2-26-da3937d21b7c2cfe1e624e812ae1d3ef
@@ -0,0 +1 @@
+1293872461
diff --git a/sql/hive/src/test/resources/golden/timestamp_2-27-252aebfe7882335d31bfc53a8705b7a b/sql/hive/src/test/resources/golden/timestamp_2-27-252aebfe7882335d31bfc53a8705b7a
new file mode 100644
index 000000000000..502f94a71edb
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_2-27-252aebfe7882335d31bfc53a8705b7a
@@ -0,0 +1 @@
+1.29387251E9
diff --git a/sql/hive/src/test/resources/golden/timestamp_2-28-5181279a0bf8939fe46ddacae015dad8 b/sql/hive/src/test/resources/golden/timestamp_2-28-5181279a0bf8939fe46ddacae015dad8
new file mode 100644
index 000000000000..2158e77f7876
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_2-28-5181279a0bf8939fe46ddacae015dad8
@@ -0,0 +1 @@
+1.2938724611E9
diff --git a/sql/hive/src/test/resources/golden/timestamp_2-29-240fce5f58794fa051824e8732c00c03 b/sql/hive/src/test/resources/golden/timestamp_2-29-240fce5f58794fa051824e8732c00c03
new file mode 100644
index 000000000000..4bdd802b9cda
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_2-29-240fce5f58794fa051824e8732c00c03
@@ -0,0 +1 @@
+2011-01-01 01:01:01.1
diff --git a/sql/hive/src/test/resources/golden/timestamp_2-3-a95a52c3a66e1f211ea04a0a10bd3b74 b/sql/hive/src/test/resources/golden/timestamp_2-3-a95a52c3a66e1f211ea04a0a10bd3b74
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/timestamp_2-30-ffe6b6ddaaba84152074f7781fba2243 b/sql/hive/src/test/resources/golden/timestamp_2-30-ffe6b6ddaaba84152074f7781fba2243
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/timestamp_2-31-25f6ec69328af6cba76899194e0dd84e b/sql/hive/src/test/resources/golden/timestamp_2-31-25f6ec69328af6cba76899194e0dd84e
new file mode 100644
index 000000000000..27ba77ddaf61
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_2-31-25f6ec69328af6cba76899194e0dd84e
@@ -0,0 +1 @@
+true
diff --git a/sql/hive/src/test/resources/golden/timestamp_2-32-93c769be4cff93bea6e62bfe4e2a8742 b/sql/hive/src/test/resources/golden/timestamp_2-32-93c769be4cff93bea6e62bfe4e2a8742
new file mode 100644
index 000000000000..987e7ca9a76d
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_2-32-93c769be4cff93bea6e62bfe4e2a8742
@@ -0,0 +1 @@
+77
diff --git a/sql/hive/src/test/resources/golden/timestamp_2-33-5bdbf67419cc060b82d091d80ce59bf9 b/sql/hive/src/test/resources/golden/timestamp_2-33-5bdbf67419cc060b82d091d80ce59bf9
new file mode 100644
index 000000000000..8f3a49478b26
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_2-33-5bdbf67419cc060b82d091d80ce59bf9
@@ -0,0 +1 @@
+-4787
diff --git a/sql/hive/src/test/resources/golden/timestamp_2-34-de3c42ab06c17ae895fd7deaf7bd9571 b/sql/hive/src/test/resources/golden/timestamp_2-34-de3c42ab06c17ae895fd7deaf7bd9571
new file mode 100644
index 000000000000..211a320b9ca7
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_2-34-de3c42ab06c17ae895fd7deaf7bd9571
@@ -0,0 +1 @@
+1293872461
diff --git a/sql/hive/src/test/resources/golden/timestamp_2-35-da3937d21b7c2cfe1e624e812ae1d3ef b/sql/hive/src/test/resources/golden/timestamp_2-35-da3937d21b7c2cfe1e624e812ae1d3ef
new file mode 100644
index 000000000000..211a320b9ca7
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_2-35-da3937d21b7c2cfe1e624e812ae1d3ef
@@ -0,0 +1 @@
+1293872461
diff --git a/sql/hive/src/test/resources/golden/timestamp_2-36-252aebfe7882335d31bfc53a8705b7a b/sql/hive/src/test/resources/golden/timestamp_2-36-252aebfe7882335d31bfc53a8705b7a
new file mode 100644
index 000000000000..502f94a71edb
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_2-36-252aebfe7882335d31bfc53a8705b7a
@@ -0,0 +1 @@
+1.29387251E9
diff --git a/sql/hive/src/test/resources/golden/timestamp_2-37-5181279a0bf8939fe46ddacae015dad8 b/sql/hive/src/test/resources/golden/timestamp_2-37-5181279a0bf8939fe46ddacae015dad8
new file mode 100644
index 000000000000..b71ff6086336
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_2-37-5181279a0bf8939fe46ddacae015dad8
@@ -0,0 +1 @@
+1.2938724610001E9
diff --git a/sql/hive/src/test/resources/golden/timestamp_2-38-240fce5f58794fa051824e8732c00c03 b/sql/hive/src/test/resources/golden/timestamp_2-38-240fce5f58794fa051824e8732c00c03
new file mode 100644
index 000000000000..8b014c4cd8d6
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_2-38-240fce5f58794fa051824e8732c00c03
@@ -0,0 +1 @@
+2011-01-01 01:01:01.0001
diff --git a/sql/hive/src/test/resources/golden/timestamp_2-39-8236608f28681eac5503195096a34181 b/sql/hive/src/test/resources/golden/timestamp_2-39-8236608f28681eac5503195096a34181
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/timestamp_2-4-25f6ec69328af6cba76899194e0dd84e b/sql/hive/src/test/resources/golden/timestamp_2-4-25f6ec69328af6cba76899194e0dd84e
new file mode 100644
index 000000000000..27ba77ddaf61
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_2-4-25f6ec69328af6cba76899194e0dd84e
@@ -0,0 +1 @@
+true
diff --git a/sql/hive/src/test/resources/golden/timestamp_2-40-25f6ec69328af6cba76899194e0dd84e b/sql/hive/src/test/resources/golden/timestamp_2-40-25f6ec69328af6cba76899194e0dd84e
new file mode 100644
index 000000000000..27ba77ddaf61
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_2-40-25f6ec69328af6cba76899194e0dd84e
@@ -0,0 +1 @@
+true
diff --git a/sql/hive/src/test/resources/golden/timestamp_2-41-93c769be4cff93bea6e62bfe4e2a8742 b/sql/hive/src/test/resources/golden/timestamp_2-41-93c769be4cff93bea6e62bfe4e2a8742
new file mode 100644
index 000000000000..987e7ca9a76d
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_2-41-93c769be4cff93bea6e62bfe4e2a8742
@@ -0,0 +1 @@
+77
diff --git a/sql/hive/src/test/resources/golden/timestamp_2-42-5bdbf67419cc060b82d091d80ce59bf9 b/sql/hive/src/test/resources/golden/timestamp_2-42-5bdbf67419cc060b82d091d80ce59bf9
new file mode 100644
index 000000000000..8f3a49478b26
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_2-42-5bdbf67419cc060b82d091d80ce59bf9
@@ -0,0 +1 @@
+-4787
diff --git a/sql/hive/src/test/resources/golden/timestamp_2-43-de3c42ab06c17ae895fd7deaf7bd9571 b/sql/hive/src/test/resources/golden/timestamp_2-43-de3c42ab06c17ae895fd7deaf7bd9571
new file mode 100644
index 000000000000..211a320b9ca7
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_2-43-de3c42ab06c17ae895fd7deaf7bd9571
@@ -0,0 +1 @@
+1293872461
diff --git a/sql/hive/src/test/resources/golden/timestamp_2-44-da3937d21b7c2cfe1e624e812ae1d3ef b/sql/hive/src/test/resources/golden/timestamp_2-44-da3937d21b7c2cfe1e624e812ae1d3ef
new file mode 100644
index 000000000000..211a320b9ca7
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_2-44-da3937d21b7c2cfe1e624e812ae1d3ef
@@ -0,0 +1 @@
+1293872461
diff --git a/sql/hive/src/test/resources/golden/timestamp_2-45-252aebfe7882335d31bfc53a8705b7a b/sql/hive/src/test/resources/golden/timestamp_2-45-252aebfe7882335d31bfc53a8705b7a
new file mode 100644
index 000000000000..502f94a71edb
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_2-45-252aebfe7882335d31bfc53a8705b7a
@@ -0,0 +1 @@
+1.29387251E9
diff --git a/sql/hive/src/test/resources/golden/timestamp_2-46-5181279a0bf8939fe46ddacae015dad8 b/sql/hive/src/test/resources/golden/timestamp_2-46-5181279a0bf8939fe46ddacae015dad8
new file mode 100644
index 000000000000..b71ff6086336
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_2-46-5181279a0bf8939fe46ddacae015dad8
@@ -0,0 +1 @@
+1.2938724610001E9
diff --git a/sql/hive/src/test/resources/golden/timestamp_2-47-240fce5f58794fa051824e8732c00c03 b/sql/hive/src/test/resources/golden/timestamp_2-47-240fce5f58794fa051824e8732c00c03
new file mode 100644
index 000000000000..8b014c4cd8d6
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_2-47-240fce5f58794fa051824e8732c00c03
@@ -0,0 +1 @@
+2011-01-01 01:01:01.0001
diff --git a/sql/hive/src/test/resources/golden/timestamp_2-48-654e5533ec6dc911996abc7e47af8ccb b/sql/hive/src/test/resources/golden/timestamp_2-48-654e5533ec6dc911996abc7e47af8ccb
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/timestamp_2-49-25f6ec69328af6cba76899194e0dd84e b/sql/hive/src/test/resources/golden/timestamp_2-49-25f6ec69328af6cba76899194e0dd84e
new file mode 100644
index 000000000000..27ba77ddaf61
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_2-49-25f6ec69328af6cba76899194e0dd84e
@@ -0,0 +1 @@
+true
diff --git a/sql/hive/src/test/resources/golden/timestamp_2-5-93c769be4cff93bea6e62bfe4e2a8742 b/sql/hive/src/test/resources/golden/timestamp_2-5-93c769be4cff93bea6e62bfe4e2a8742
new file mode 100644
index 000000000000..987e7ca9a76d
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_2-5-93c769be4cff93bea6e62bfe4e2a8742
@@ -0,0 +1 @@
+77
diff --git a/sql/hive/src/test/resources/golden/timestamp_2-50-93c769be4cff93bea6e62bfe4e2a8742 b/sql/hive/src/test/resources/golden/timestamp_2-50-93c769be4cff93bea6e62bfe4e2a8742
new file mode 100644
index 000000000000..987e7ca9a76d
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_2-50-93c769be4cff93bea6e62bfe4e2a8742
@@ -0,0 +1 @@
+77
diff --git a/sql/hive/src/test/resources/golden/timestamp_2-51-5bdbf67419cc060b82d091d80ce59bf9 b/sql/hive/src/test/resources/golden/timestamp_2-51-5bdbf67419cc060b82d091d80ce59bf9
new file mode 100644
index 000000000000..8f3a49478b26
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_2-51-5bdbf67419cc060b82d091d80ce59bf9
@@ -0,0 +1 @@
+-4787
diff --git a/sql/hive/src/test/resources/golden/timestamp_2-52-de3c42ab06c17ae895fd7deaf7bd9571 b/sql/hive/src/test/resources/golden/timestamp_2-52-de3c42ab06c17ae895fd7deaf7bd9571
new file mode 100644
index 000000000000..211a320b9ca7
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_2-52-de3c42ab06c17ae895fd7deaf7bd9571
@@ -0,0 +1 @@
+1293872461
diff --git a/sql/hive/src/test/resources/golden/timestamp_2-53-da3937d21b7c2cfe1e624e812ae1d3ef b/sql/hive/src/test/resources/golden/timestamp_2-53-da3937d21b7c2cfe1e624e812ae1d3ef
new file mode 100644
index 000000000000..211a320b9ca7
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_2-53-da3937d21b7c2cfe1e624e812ae1d3ef
@@ -0,0 +1 @@
+1293872461
diff --git a/sql/hive/src/test/resources/golden/timestamp_2-54-252aebfe7882335d31bfc53a8705b7a b/sql/hive/src/test/resources/golden/timestamp_2-54-252aebfe7882335d31bfc53a8705b7a
new file mode 100644
index 000000000000..502f94a71edb
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_2-54-252aebfe7882335d31bfc53a8705b7a
@@ -0,0 +1 @@
+1.29387251E9
diff --git a/sql/hive/src/test/resources/golden/timestamp_2-55-5181279a0bf8939fe46ddacae015dad8 b/sql/hive/src/test/resources/golden/timestamp_2-55-5181279a0bf8939fe46ddacae015dad8
new file mode 100644
index 000000000000..3eefb349894a
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_2-55-5181279a0bf8939fe46ddacae015dad8
@@ -0,0 +1 @@
+1.293872461001E9
diff --git a/sql/hive/src/test/resources/golden/timestamp_2-56-240fce5f58794fa051824e8732c00c03 b/sql/hive/src/test/resources/golden/timestamp_2-56-240fce5f58794fa051824e8732c00c03
new file mode 100644
index 000000000000..acce9d97b5eb
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_2-56-240fce5f58794fa051824e8732c00c03
@@ -0,0 +1 @@
+2011-01-01 01:01:01.001000011
diff --git a/sql/hive/src/test/resources/golden/timestamp_2-57-ea7192a4a5a985bcc8aab9aa79d9f028 b/sql/hive/src/test/resources/golden/timestamp_2-57-ea7192a4a5a985bcc8aab9aa79d9f028
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/timestamp_2-6-5bdbf67419cc060b82d091d80ce59bf9 b/sql/hive/src/test/resources/golden/timestamp_2-6-5bdbf67419cc060b82d091d80ce59bf9
new file mode 100644
index 000000000000..8f3a49478b26
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_2-6-5bdbf67419cc060b82d091d80ce59bf9
@@ -0,0 +1 @@
+-4787
diff --git a/sql/hive/src/test/resources/golden/timestamp_2-7-de3c42ab06c17ae895fd7deaf7bd9571 b/sql/hive/src/test/resources/golden/timestamp_2-7-de3c42ab06c17ae895fd7deaf7bd9571
new file mode 100644
index 000000000000..211a320b9ca7
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_2-7-de3c42ab06c17ae895fd7deaf7bd9571
@@ -0,0 +1 @@
+1293872461
diff --git a/sql/hive/src/test/resources/golden/timestamp_2-8-da3937d21b7c2cfe1e624e812ae1d3ef b/sql/hive/src/test/resources/golden/timestamp_2-8-da3937d21b7c2cfe1e624e812ae1d3ef
new file mode 100644
index 000000000000..211a320b9ca7
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_2-8-da3937d21b7c2cfe1e624e812ae1d3ef
@@ -0,0 +1 @@
+1293872461
diff --git a/sql/hive/src/test/resources/golden/timestamp_2-9-252aebfe7882335d31bfc53a8705b7a b/sql/hive/src/test/resources/golden/timestamp_2-9-252aebfe7882335d31bfc53a8705b7a
new file mode 100644
index 000000000000..502f94a71edb
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_2-9-252aebfe7882335d31bfc53a8705b7a
@@ -0,0 +1 @@
+1.29387251E9
diff --git a/sql/hive/src/test/resources/golden/timestamp_lazy-2-cdb72e0c24fd9277a41fe0c7b1392e34 b/sql/hive/src/test/resources/golden/timestamp_lazy-2-cdb72e0c24fd9277a41fe0c7b1392e34
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/timestamp_lazy-3-79e0c72c4fb3b259dfbffd245ccaa636 b/sql/hive/src/test/resources/golden/timestamp_lazy-3-79e0c72c4fb3b259dfbffd245ccaa636
new file mode 100644
index 000000000000..e6bfe0b1667a
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_lazy-3-79e0c72c4fb3b259dfbffd245ccaa636
@@ -0,0 +1,5 @@
+2011-01-01 01:01:01	165	val_165
+2011-01-01 01:01:01	238	val_238
+2011-01-01 01:01:01	27	val_27
+2011-01-01 01:01:01	311	val_311
+2011-01-01 01:01:01	86	val_86
diff --git a/sql/hive/src/test/resources/golden/timestamp_lazy-4-b4c4417ce9f08baeb82ffde6ef1baa25 b/sql/hive/src/test/resources/golden/timestamp_lazy-4-b4c4417ce9f08baeb82ffde6ef1baa25
new file mode 100644
index 000000000000..e6bfe0b1667a
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/timestamp_lazy-4-b4c4417ce9f08baeb82ffde6ef1baa25
@@ -0,0 +1,5 @@
+2011-01-01 01:01:01	165	val_165
+2011-01-01 01:01:01	238	val_238
+2011-01-01 01:01:01	27	val_27
+2011-01-01 01:01:01	311	val_311
+2011-01-01 01:01:01	86	val_86
diff --git a/sql/hive/src/test/resources/golden/udf_unix_timestamp-0-d555c8cd733572bfa8cd3362da9480cb b/sql/hive/src/test/resources/golden/udf_unix_timestamp-0-d555c8cd733572bfa8cd3362da9480cb
new file mode 100644
index 000000000000..9913d42ffc1a
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/udf_unix_timestamp-0-d555c8cd733572bfa8cd3362da9480cb
@@ -0,0 +1 @@
+unix_timestamp([date[, pattern]]) - Returns the UNIX timestamp
diff --git a/sql/hive/src/test/resources/golden/udf_unix_timestamp-1-8a9dbadae706047715cf5f903ff4a724 b/sql/hive/src/test/resources/golden/udf_unix_timestamp-1-8a9dbadae706047715cf5f903ff4a724
new file mode 100644
index 000000000000..ef4aa8e9595d
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/udf_unix_timestamp-1-8a9dbadae706047715cf5f903ff4a724
@@ -0,0 +1,2 @@
+unix_timestamp([date[, pattern]]) - Returns the UNIX timestamp
+Converts the current or specified time to number of seconds since 1970-01-01.
diff --git a/sql/hive/src/test/resources/golden/udf_unix_timestamp-2-28c40e51e55bed62693e626efda5d9c5 b/sql/hive/src/test/resources/golden/udf_unix_timestamp-2-28c40e51e55bed62693e626efda5d9c5
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/udf_unix_timestamp-3-732b21d386f2002b87eaf02d0b9951ed b/sql/hive/src/test/resources/golden/udf_unix_timestamp-3-732b21d386f2002b87eaf02d0b9951ed
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/udf_unix_timestamp-4-b2e42ebb75cecf09961d36587797f6d0 b/sql/hive/src/test/resources/golden/udf_unix_timestamp-4-b2e42ebb75cecf09961d36587797f6d0
new file mode 100644
index 000000000000..31aaa952b4cc
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/udf_unix_timestamp-4-b2e42ebb75cecf09961d36587797f6d0
@@ -0,0 +1 @@
+2009-03-20 11:30:01	1237573801
diff --git a/sql/hive/src/test/resources/golden/udf_unix_timestamp-5-31243f5cb64356425b9f95ba011ac9d6 b/sql/hive/src/test/resources/golden/udf_unix_timestamp-5-31243f5cb64356425b9f95ba011ac9d6
new file mode 100644
index 000000000000..6d9ee690cea4
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/udf_unix_timestamp-5-31243f5cb64356425b9f95ba011ac9d6
@@ -0,0 +1 @@
+2009-03-20	1237532400
diff --git a/sql/hive/src/test/resources/golden/udf_unix_timestamp-6-9b0f20bde1aaf9102b67a5498b167f31 b/sql/hive/src/test/resources/golden/udf_unix_timestamp-6-9b0f20bde1aaf9102b67a5498b167f31
new file mode 100644
index 000000000000..e1d0cbb6c849
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/udf_unix_timestamp-6-9b0f20bde1aaf9102b67a5498b167f31
@@ -0,0 +1 @@
+2009 Mar 20 11:30:01 am	1237573801
diff --git a/sql/hive/src/test/resources/golden/udf_unix_timestamp-7-47f433ff6ccce4c666440cc1a228a96d b/sql/hive/src/test/resources/golden/udf_unix_timestamp-7-47f433ff6ccce4c666440cc1a228a96d
new file mode 100644
index 000000000000..6b40e687afd5
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/udf_unix_timestamp-7-47f433ff6ccce4c666440cc1a228a96d
@@ -0,0 +1 @@
+random_string	NULL
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
index 63dbe57c4c77..fd44325925cd 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
@@ -18,6 +18,7 @@
 package org.apache.spark.sql.hive.execution
 
 import java.io.File
+import java.util.TimeZone
 
 import org.scalatest.BeforeAndAfter
 
@@ -31,14 +32,20 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
   lazy val hiveQueryDir = TestHive.getHiveFile("ql" + File.separator + "src" +
     File.separator + "test" + File.separator + "queries" + File.separator + "clientpositive")
 
+  var originalTimeZone: TimeZone = _
+
   def testCases = hiveQueryDir.listFiles.map(f => f.getName.stripSuffix(".q") -> f)
 
   override def beforeAll() {
     TestHive.cacheTables = true
+    // Timezone is fixed to America/Los_Angeles for those timezone sensitive tests (timestamp_*)
+    originalTimeZone = TimeZone.getDefault
+    TimeZone.setDefault(TimeZone.getTimeZone("America/Los_Angeles"))
   }
 
   override def afterAll() {
     TestHive.cacheTables = false
+    TimeZone.setDefault(originalTimeZone)
   }
 
   /** A list of tests deemed out of scope currently and thus completely disregarded. */
@@ -92,8 +99,8 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     "create_view_translate",
     "partitions_json",
 
-    // Timezone specific test answers.
-    "udf_unix_timestamp",
+    // This test is totally fine except that it includes wrong queries and expects errors, but error
+    // message format in Hive and Spark SQL differ. Should workaround this later.
     "udf_to_unix_timestamp",
 
     // Cant run without local map/reduce.
@@ -659,8 +666,11 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     "stats_publisher_error_1",
     "subq2",
     "tablename_with_select",
+    "timestamp_1",
+    "timestamp_2",
     "timestamp_3",
     "timestamp_comparison",
+    "timestamp_lazy",
     "timestamp_null",
     "timestamp_udf",
     "touch",
@@ -802,6 +812,7 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     "udf_translate",
     "udf_trim",
     "udf_ucase",
+    "udf_unix_timestamp",
     "udf_upper",
     "udf_var_pop",
     "udf_var_samp",

From f89cf65d7aced0bb387c05586f9f51cb29865022 Mon Sep 17 00:00:00 2001
From: Sandy Ryza <sandy@cloudera.com>
Date: Mon, 21 Jul 2014 13:15:46 -0500
Subject: [PATCH 714/810] SPARK-1707. Remove unnecessary 3 second sleep in
 YarnClusterScheduler

Author: Sandy Ryza <sandy@cloudera.com>

Closes #634 from sryza/sandy-spark-1707 and squashes the following commits:

2f6e358 [Sandy Ryza] Default min registered executors ratio to .8 for YARN
354c630 [Sandy Ryza] Remove outdated comments
c744ef3 [Sandy Ryza] Take out waitForInitialAllocations
2a4329b [Sandy Ryza] SPARK-1707. Remove unnecessary 3 second sleep in YarnClusterScheduler
---
 .../spark/deploy/yarn/ApplicationMaster.scala | 39 -----------------
 .../cluster/YarnClientClusterScheduler.scala  | 10 -----
 .../cluster/YarnClientSchedulerBackend.scala  |  5 +++
 .../cluster/YarnClusterScheduler.scala        |  8 +---
 .../cluster/YarnClusterSchedulerBackend.scala |  5 +++
 .../spark/deploy/yarn/ApplicationMaster.scala | 43 -------------------
 6 files changed, 11 insertions(+), 99 deletions(-)

diff --git a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
index 062f946a9fe9..3ec36487dcd2 100644
--- a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
+++ b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
@@ -255,10 +255,6 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration,
             sparkContext.getConf)
         }
       }
-    } finally {
-      // in case of exceptions, etc - ensure that count is atleast ALLOCATOR_LOOP_WAIT_COUNT :
-      // so that the loop (in ApplicationMaster.sparkContextInitialized) breaks
-      ApplicationMaster.incrementAllocatorLoop(ApplicationMaster.ALLOCATOR_LOOP_WAIT_COUNT)
     }
   }
 
@@ -277,13 +273,8 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration,
         }
         yarnAllocator.allocateContainers(
           math.max(args.numExecutors - yarnAllocator.getNumExecutorsRunning, 0))
-        ApplicationMaster.incrementAllocatorLoop(1)
         Thread.sleep(ApplicationMaster.ALLOCATE_HEARTBEAT_INTERVAL)
       }
-    } finally {
-      // In case of exceptions, etc - ensure that count is at least ALLOCATOR_LOOP_WAIT_COUNT,
-      // so that the loop in ApplicationMaster#sparkContextInitialized() breaks.
-      ApplicationMaster.incrementAllocatorLoop(ApplicationMaster.ALLOCATOR_LOOP_WAIT_COUNT)
     }
     logInfo("All executors have launched.")
 
@@ -411,24 +402,10 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration,
 }
 
 object ApplicationMaster extends Logging {
-  // Number of times to wait for the allocator loop to complete.
-  // Each loop iteration waits for 100ms, so maximum of 3 seconds.
-  // This is to ensure that we have reasonable number of containers before we start
   // TODO: Currently, task to container is computed once (TaskSetManager) - which need not be
   // optimal as more containers are available. Might need to handle this better.
-  private val ALLOCATOR_LOOP_WAIT_COUNT = 30
   private val ALLOCATE_HEARTBEAT_INTERVAL = 100
 
-  def incrementAllocatorLoop(by: Int) {
-    val count = yarnAllocatorLoop.getAndAdd(by)
-    if (count >= ALLOCATOR_LOOP_WAIT_COUNT) {
-      yarnAllocatorLoop.synchronized {
-        // to wake threads off wait ...
-        yarnAllocatorLoop.notifyAll()
-      }
-    }
-  }
-
   private val applicationMasters = new CopyOnWriteArrayList[ApplicationMaster]()
 
   def register(master: ApplicationMaster) {
@@ -437,7 +414,6 @@ object ApplicationMaster extends Logging {
 
   val sparkContextRef: AtomicReference[SparkContext] =
     new AtomicReference[SparkContext](null /* initialValue */)
-  val yarnAllocatorLoop: AtomicInteger = new AtomicInteger(0)
 
   def sparkContextInitialized(sc: SparkContext): Boolean = {
     var modified = false
@@ -472,21 +448,6 @@ object ApplicationMaster extends Logging {
     modified
   }
 
-
-  /**
-   * Returns when we've either
-   *  1) received all the requested executors,
-   *  2) waited ALLOCATOR_LOOP_WAIT_COUNT * ALLOCATE_HEARTBEAT_INTERVAL ms,
-   *  3) hit an error that causes us to terminate trying to get containers.
-   */
-  def waitForInitialAllocations() {
-    yarnAllocatorLoop.synchronized {
-      while (yarnAllocatorLoop.get() <= ALLOCATOR_LOOP_WAIT_COUNT) {
-        yarnAllocatorLoop.wait(1000L)
-      }
-    }
-  }
-
   def main(argStrings: Array[String]) {
     SignalLogger.register(log)
     val args = new ApplicationMasterArguments(argStrings)
diff --git a/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientClusterScheduler.scala b/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientClusterScheduler.scala
index 15e8c21aa590..3474112ded5d 100644
--- a/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientClusterScheduler.scala
+++ b/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientClusterScheduler.scala
@@ -37,14 +37,4 @@ private[spark] class YarnClientClusterScheduler(sc: SparkContext, conf: Configur
     val retval = YarnAllocationHandler.lookupRack(conf, host)
     if (retval != null) Some(retval) else None
   }
-
-  override def postStartHook() {
-
-    super.postStartHook()
-    // The yarn application is running, but the executor might not yet ready
-    // Wait for a few seconds for the slaves to bootstrap and register with master - best case attempt
-    // TODO It needn't after waitBackendReady
-    Thread.sleep(2000L)
-    logInfo("YarnClientClusterScheduler.postStartHook done")
-  }
 }
diff --git a/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala b/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala
index 1b37c4bb13f4..d8266f7b0c9a 100644
--- a/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala
+++ b/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala
@@ -30,6 +30,11 @@ private[spark] class YarnClientSchedulerBackend(
   extends CoarseGrainedSchedulerBackend(scheduler, sc.env.actorSystem)
   with Logging {
 
+  if (conf.getOption("spark.scheduler.minRegisteredExecutorsRatio").isEmpty) {
+    minRegisteredRatio = 0.8
+    ready = false
+  }
+
   var client: Client = null
   var appId: ApplicationId = null
 
diff --git a/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClusterScheduler.scala b/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClusterScheduler.scala
index 9ee53d797c8e..9aeca4a637d3 100644
--- a/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClusterScheduler.scala
+++ b/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClusterScheduler.scala
@@ -47,14 +47,8 @@ private[spark] class YarnClusterScheduler(sc: SparkContext, conf: Configuration)
   }
 
   override def postStartHook() {
-    val sparkContextInitialized = ApplicationMaster.sparkContextInitialized(sc)
+    ApplicationMaster.sparkContextInitialized(sc)
     super.postStartHook()
-    if (sparkContextInitialized){
-      ApplicationMaster.waitForInitialAllocations()
-      // Wait for a few seconds for the slaves to bootstrap and register with master - best case attempt
-      // TODO It needn't after waitBackendReady
-      Thread.sleep(3000L)
-    }
     logInfo("YarnClusterScheduler.postStartHook done")
   }
 }
diff --git a/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClusterSchedulerBackend.scala b/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClusterSchedulerBackend.scala
index a04b08f43cc5..0ad1794d1953 100644
--- a/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClusterSchedulerBackend.scala
+++ b/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClusterSchedulerBackend.scala
@@ -27,6 +27,11 @@ private[spark] class YarnClusterSchedulerBackend(
     sc: SparkContext)
   extends CoarseGrainedSchedulerBackend(scheduler, sc.env.actorSystem) {
 
+  if (conf.getOption("spark.scheduler.minRegisteredExecutorsRatio").isEmpty) {
+    minRegisteredRatio = 0.8
+    ready = false
+  }
+
   override def start() {
     super.start()
     var numExecutors = ApplicationMasterArguments.DEFAULT_NUMBER_EXECUTORS
diff --git a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
index 1a24ec759b54..eaf594c8b49b 100644
--- a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
+++ b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
@@ -234,10 +234,6 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration,
             sparkContext.getConf)
         }
       }
-    } finally {
-      // In case of exceptions, etc - ensure that the loop in
-      // ApplicationMaster#sparkContextInitialized() breaks.
-      ApplicationMaster.doneWithInitialAllocations()
     }
   }
 
@@ -254,16 +250,9 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration,
         checkNumExecutorsFailed()
         allocateMissingExecutor()
         yarnAllocator.allocateResources()
-        if (iters == ApplicationMaster.ALLOCATOR_LOOP_WAIT_COUNT) {
-          ApplicationMaster.doneWithInitialAllocations()
-        }
         Thread.sleep(ApplicationMaster.ALLOCATE_HEARTBEAT_INTERVAL)
         iters += 1
       }
-    } finally {
-      // In case of exceptions, etc - ensure that the loop in
-      // ApplicationMaster#sparkContextInitialized() breaks.
-      ApplicationMaster.doneWithInitialAllocations()
     }
     logInfo("All executors have launched.")
   }
@@ -365,12 +354,8 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration,
 }
 
 object ApplicationMaster extends Logging {
-  // Number of times to wait for the allocator loop to complete.
-  // Each loop iteration waits for 100ms, so maximum of 3 seconds.
-  // This is to ensure that we have reasonable number of containers before we start
   // TODO: Currently, task to container is computed once (TaskSetManager) - which need not be
   // optimal as more containers are available. Might need to handle this better.
-  private val ALLOCATOR_LOOP_WAIT_COUNT = 30
   private val ALLOCATE_HEARTBEAT_INTERVAL = 100
 
   private val applicationMasters = new CopyOnWriteArrayList[ApplicationMaster]()
@@ -378,20 +363,6 @@ object ApplicationMaster extends Logging {
   val sparkContextRef: AtomicReference[SparkContext] =
     new AtomicReference[SparkContext](null)
 
-  // Variable used to notify the YarnClusterScheduler that it should stop waiting
-  // for the initial set of executors to be started and get on with its business.
-  val doneWithInitialAllocationsMonitor = new Object()
-
-  @volatile var isDoneWithInitialAllocations = false
-
-  def doneWithInitialAllocations() {
-    isDoneWithInitialAllocations = true
-    doneWithInitialAllocationsMonitor.synchronized {
-      // to wake threads off wait ...
-      doneWithInitialAllocationsMonitor.notifyAll()
-    }
-  }
-
   def register(master: ApplicationMaster) {
     applicationMasters.add(master)
   }
@@ -434,20 +405,6 @@ object ApplicationMaster extends Logging {
     modified
   }
 
-  /**
-   * Returns when we've either
-   *  1) received all the requested executors,
-   *  2) waited ALLOCATOR_LOOP_WAIT_COUNT * ALLOCATE_HEARTBEAT_INTERVAL ms,
-   *  3) hit an error that causes us to terminate trying to get containers.
-   */
-  def waitForInitialAllocations() {
-    doneWithInitialAllocationsMonitor.synchronized {
-      while (!isDoneWithInitialAllocations) {
-        doneWithInitialAllocationsMonitor.wait(1000L)
-      }
-    }
-  }
-
   def getApplicationAttemptId(): ApplicationAttemptId = {
     val containerIdString = System.getenv(ApplicationConstants.Environment.CONTAINER_ID.name())
     val containerId = ConverterUtils.toContainerId(containerIdString)

From 872538c600a452ead52638c1ccba90643a9fa41c Mon Sep 17 00:00:00 2001
From: Davies Liu <davies.liu@gmail.com>
Date: Mon, 21 Jul 2014 11:59:54 -0700
Subject: [PATCH 715/810] [SPARK-2494] [PySpark] make hash of None consistant
 cross machines

In CPython, hash of None is different cross machines, it will cause wrong result during shuffle. This PR will fix this.

Author: Davies Liu <davies.liu@gmail.com>

Closes #1371 from davies/hash_of_none and squashes the following commits:

d01745f [Davies Liu] add comments, remove outdated unit tests
5467141 [Davies Liu] disable hijack of hash, use it only for partitionBy()
b7118aa [Davies Liu] use __builtin__ instead of __builtins__
839e417 [Davies Liu] hijack hash to make hash of None consistant cross machines
---
 python/pyspark/rdd.py | 35 ++++++++++++++++++++++++++++++++---
 1 file changed, 32 insertions(+), 3 deletions(-)

diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py
index 0c35c666805d..94ba22306afb 100644
--- a/python/pyspark/rdd.py
+++ b/python/pyspark/rdd.py
@@ -48,6 +48,35 @@
 __all__ = ["RDD"]
 
 
+# TODO: for Python 3.3+, PYTHONHASHSEED should be reset to disable randomized
+# hash for string
+def portable_hash(x):
+    """
+    This function returns consistant hash code for builtin types, especially
+    for None and tuple with None.
+
+    The algrithm is similar to that one used by CPython 2.7
+
+    >>> portable_hash(None)
+    0
+    >>> portable_hash((None, 1))
+    219750521
+    """
+    if x is None:
+        return 0
+    if isinstance(x, tuple):
+        h = 0x345678
+        for i in x:
+            h ^= portable_hash(i)
+            h *= 1000003
+            h &= 0xffffffff
+        h ^= len(x)
+        if h == -1:
+            h = -2
+        return h
+    return hash(x)
+
+
 def _extract_concise_traceback():
     """
     This function returns the traceback info for a callsite, returns a dict
@@ -1164,7 +1193,9 @@ def rightOuterJoin(self, other, numPartitions=None):
         return python_right_outer_join(self, other, numPartitions)
 
     # TODO: add option to control map-side combining
-    def partitionBy(self, numPartitions, partitionFunc=None):
+    # portable_hash is used as default, because builtin hash of None is different
+    # cross machines.
+    def partitionBy(self, numPartitions, partitionFunc=portable_hash):
         """
         Return a copy of the RDD partitioned using the specified partitioner.
 
@@ -1176,8 +1207,6 @@ def partitionBy(self, numPartitions, partitionFunc=None):
         if numPartitions is None:
             numPartitions = self._defaultReducePartitions()
 
-        if partitionFunc is None:
-            partitionFunc = lambda x: 0 if x is None else hash(x)
         # Transferring O(n) objects to Java is too expensive.  Instead, we'll
         # form the hash buckets in Python, transferring O(numPartitions) objects
         # to Java.  Each object is a (splitNumber, [objects]) pair.

From abeacffb7bcdfa3eeb1e969aa546029a7b464eaa Mon Sep 17 00:00:00 2001
From: Aaron Davidson <aaron@databricks.com>
Date: Mon, 21 Jul 2014 14:35:15 -0700
Subject: [PATCH 716/810] Fix flakey HiveQuerySuite test

Result may not be returned in the expected order, so relax that constraint.

Author: Aaron Davidson <aaron@databricks.com>

Closes #1514 from aarondav/flakey and squashes the following commits:

e5af823 [Aaron Davidson] Fix flakey HiveQuerySuite test
---
 .../sql/hive/execution/HiveQuerySuite.scala   | 45 +++++++++----------
 1 file changed, 22 insertions(+), 23 deletions(-)

diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
index d57e99db1858..eb7df717284c 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
@@ -421,64 +421,63 @@ class HiveQuerySuite extends HiveComparisonTest {
     val testKey = "spark.sql.key.usedfortestonly"
     val testVal = "test.val.0"
     val nonexistentKey = "nonexistent"
-    def rowsToPairs(rows: Array[Row]) = rows.map { case Row(key: String, value: String) =>
-      key -> value
-    }
+    def collectResults(rdd: SchemaRDD): Set[(String, String)] =
+      rdd.collect().map { case Row(key: String, value: String) => key -> value }.toSet
 
     clear()
 
     // "set" itself returns all config variables currently specified in SQLConf.
     assert(hql("SET").collect().size == 0)
 
-    assertResult(Array(testKey -> testVal)) {
-      rowsToPairs(hql(s"SET $testKey=$testVal").collect())
+    assertResult(Set(testKey -> testVal)) {
+      collectResults(hql(s"SET $testKey=$testVal"))
     }
 
     assert(hiveconf.get(testKey, "") == testVal)
-    assertResult(Array(testKey -> testVal)) {
-      rowsToPairs(hql("SET").collect())
+    assertResult(Set(testKey -> testVal)) {
+      collectResults(hql("SET"))
     }
 
     hql(s"SET ${testKey + testKey}=${testVal + testVal}")
     assert(hiveconf.get(testKey + testKey, "") == testVal + testVal)
-    assertResult(Array(testKey -> testVal, (testKey + testKey) -> (testVal + testVal))) {
-      rowsToPairs(hql("SET").collect())
+    assertResult(Set(testKey -> testVal, (testKey + testKey) -> (testVal + testVal))) {
+      collectResults(hql("SET"))
     }
 
     // "set key"
-    assertResult(Array(testKey -> testVal)) {
-      rowsToPairs(hql(s"SET $testKey").collect())
+    assertResult(Set(testKey -> testVal)) {
+      collectResults(hql(s"SET $testKey"))
     }
 
-    assertResult(Array(nonexistentKey -> "<undefined>")) {
-      rowsToPairs(hql(s"SET $nonexistentKey").collect())
+    assertResult(Set(nonexistentKey -> "<undefined>")) {
+      collectResults(hql(s"SET $nonexistentKey"))
     }
 
     // Assert that sql() should have the same effects as hql() by repeating the above using sql().
     clear()
     assert(sql("SET").collect().size == 0)
 
-    assertResult(Array(testKey -> testVal)) {
-      rowsToPairs(sql(s"SET $testKey=$testVal").collect())
+    assertResult(Set(testKey -> testVal)) {
+      collectResults(sql(s"SET $testKey=$testVal"))
     }
 
     assert(hiveconf.get(testKey, "") == testVal)
-    assertResult(Array(testKey -> testVal)) {
-      rowsToPairs(sql("SET").collect())
+    assertResult(Set(testKey -> testVal)) {
+      collectResults(sql("SET"))
     }
 
     sql(s"SET ${testKey + testKey}=${testVal + testVal}")
     assert(hiveconf.get(testKey + testKey, "") == testVal + testVal)
-    assertResult(Array(testKey -> testVal, (testKey + testKey) -> (testVal + testVal))) {
-      rowsToPairs(sql("SET").collect())
+    assertResult(Set(testKey -> testVal, (testKey + testKey) -> (testVal + testVal))) {
+      collectResults(sql("SET"))
     }
 
-    assertResult(Array(testKey -> testVal)) {
-      rowsToPairs(sql(s"SET $testKey").collect())
+    assertResult(Set(testKey -> testVal)) {
+      collectResults(sql(s"SET $testKey"))
     }
 
-    assertResult(Array(nonexistentKey -> "<undefined>")) {
-      rowsToPairs(sql(s"SET $nonexistentKey").collect())
+    assertResult(Set(nonexistentKey -> "<undefined>")) {
+      collectResults(sql(s"SET $nonexistentKey"))
     }
 
     clear()

From a4d60208ec7995146541451849c51670cdc56451 Mon Sep 17 00:00:00 2001
From: Burak <brkyvz@gmail.com>
Date: Mon, 21 Jul 2014 17:03:40 -0700
Subject: [PATCH 717/810] [SPARK-2434][MLlib]: Warning messages that point
 users to original MLlib implementations added to Examples

[SPARK-2434][MLlib]: Warning messages that refer users to the original MLlib implementations of some popular example machine learning algorithms added both in the comments and the code. The following examples have been modified:
Scala:
* LocalALS
* LocalFileLR
* LocalKMeans
* LocalLP
* SparkALS
* SparkHdfsLR
* SparkKMeans
* SparkLR
Python:
 * kmeans.py
 * als.py
 * logistic_regression.py

Author: Burak <brkyvz@gmail.com>

Closes #1515 from brkyvz/SPARK-2434 and squashes the following commits:

7505da9 [Burak] [SPARK-2434][MLlib]: Warning messages added, scalastyle errors fixed, and added missing punctuation
b96b522 [Burak] [SPARK-2434][MLlib]: Warning messages added and scalastyle errors fixed
4762f39 [Burak] [SPARK-2434]: Warning messages added
17d3d83 [Burak] SPARK-2434: Added warning messages to the naive implementations of the example algorithms
2cb5301 [Burak] SPARK-2434: Warning messages redirecting to original implementaions added.
---
 examples/src/main/python/als.py                 |  9 +++++++++
 examples/src/main/python/kmeans.py              |  6 ++++++
 examples/src/main/python/logistic_regression.py |  6 ++++++
 .../org/apache/spark/examples/LocalALS.scala    | 15 +++++++++++++++
 .../org/apache/spark/examples/LocalFileLR.scala | 17 +++++++++++++++++
 .../org/apache/spark/examples/LocalKMeans.scala | 14 ++++++++++++++
 .../org/apache/spark/examples/LocalLR.scala     | 15 ++++++++++++++-
 .../org/apache/spark/examples/SparkALS.scala    | 16 ++++++++++++++++
 .../org/apache/spark/examples/SparkHdfsLR.scala | 14 ++++++++++++++
 .../org/apache/spark/examples/SparkKMeans.scala | 15 +++++++++++++++
 .../org/apache/spark/examples/SparkLR.scala     | 15 +++++++++++++++
 11 files changed, 141 insertions(+), 1 deletion(-)

diff --git a/examples/src/main/python/als.py b/examples/src/main/python/als.py
index 1a7c4c51f48c..c862650b0aa1 100755
--- a/examples/src/main/python/als.py
+++ b/examples/src/main/python/als.py
@@ -16,6 +16,9 @@
 #
 
 """
+This is an example implementation of ALS for learning how to use Spark. Please refer to
+ALS in pyspark.mllib.recommendation for more conventional use.
+
 This example requires numpy (http://www.numpy.org/)
 """
 from os.path import realpath
@@ -49,9 +52,15 @@ def update(i, vec, mat, ratings):
 
 
 if __name__ == "__main__":
+
     """
     Usage: als [M] [U] [F] [iterations] [slices]"
     """
+
+    print >> sys.stderr, """WARN: This is a naive implementation of ALS and is given as an
+      example. Please use the ALS method found in pyspark.mllib.recommendation for more
+      conventional use."""
+
     sc = SparkContext(appName="PythonALS")
     M = int(sys.argv[1]) if len(sys.argv) > 1 else 100
     U = int(sys.argv[2]) if len(sys.argv) > 2 else 500
diff --git a/examples/src/main/python/kmeans.py b/examples/src/main/python/kmeans.py
index 988fc45baf3b..036bdf4c4f99 100755
--- a/examples/src/main/python/kmeans.py
+++ b/examples/src/main/python/kmeans.py
@@ -45,9 +45,15 @@ def closestPoint(p, centers):
 
 
 if __name__ == "__main__":
+
     if len(sys.argv) != 4:
         print >> sys.stderr, "Usage: kmeans <file> <k> <convergeDist>"
         exit(-1)
+
+    print >> sys.stderr, """WARN: This is a naive implementation of KMeans Clustering and is given
+       as an example! Please refer to examples/src/main/python/mllib/kmeans.py for an example on
+       how to use MLlib's KMeans implementation."""
+
     sc = SparkContext(appName="PythonKMeans")
     lines = sc.textFile(sys.argv[1])
     data = lines.map(parseVector).cache()
diff --git a/examples/src/main/python/logistic_regression.py b/examples/src/main/python/logistic_regression.py
index 6c33deabfd6e..8456b272f9c0 100755
--- a/examples/src/main/python/logistic_regression.py
+++ b/examples/src/main/python/logistic_regression.py
@@ -47,9 +47,15 @@ def readPointBatch(iterator):
     return [matrix]
 
 if __name__ == "__main__":
+
     if len(sys.argv) != 3:
         print >> sys.stderr, "Usage: logistic_regression <file> <iterations>"
         exit(-1)
+
+    print >> sys.stderr,  """WARN: This is a naive implementation of Logistic Regression and is
+      given as an example! Please refer to examples/src/main/python/mllib/logistic_regression.py
+      to see how MLlib's implementation is used."""
+
     sc = SparkContext(appName="PythonLR")
     points = sc.textFile(sys.argv[1]).mapPartitions(readPointBatch).cache()
     iterations = int(sys.argv[2])
diff --git a/examples/src/main/scala/org/apache/spark/examples/LocalALS.scala b/examples/src/main/scala/org/apache/spark/examples/LocalALS.scala
index 658f73d96a86..1f576319b3ca 100644
--- a/examples/src/main/scala/org/apache/spark/examples/LocalALS.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/LocalALS.scala
@@ -25,6 +25,9 @@ import cern.jet.math._
 
 /**
  * Alternating least squares matrix factorization.
+ *
+ * This is an example implementation for learning how to use Spark. For more conventional use,
+ * please refer to org.apache.spark.mllib.recommendation.ALS
  */
 object LocalALS {
   // Parameters set through command line arguments
@@ -107,7 +110,16 @@ object LocalALS {
     solved2D.viewColumn(0)
   }
 
+  def showWarning() {
+    System.err.println(
+      """WARN: This is a naive implementation of ALS and is given as an example!
+        |Please use the ALS method found in org.apache.spark.mllib.recommendation
+        |for more conventional use.
+      """.stripMargin)
+  }
+
   def main(args: Array[String]) {
+
     args match {
       case Array(m, u, f, iters) => {
         M = m.toInt
@@ -120,6 +132,9 @@ object LocalALS {
         System.exit(1)
       }
     }
+
+    showWarning()
+
     printf("Running with M=%d, U=%d, F=%d, iters=%d\n", M, U, F, ITERATIONS)
 
     val R = generateR()
diff --git a/examples/src/main/scala/org/apache/spark/examples/LocalFileLR.scala b/examples/src/main/scala/org/apache/spark/examples/LocalFileLR.scala
index 0ef3001ca4cc..931faac5463c 100644
--- a/examples/src/main/scala/org/apache/spark/examples/LocalFileLR.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/LocalFileLR.scala
@@ -21,6 +21,12 @@ import java.util.Random
 
 import breeze.linalg.{Vector, DenseVector}
 
+/**
+ * Logistic regression based classification.
+ *
+ * This is an example implementation for learning how to use Spark. For more conventional use,
+ * please refer to org.apache.spark.mllib.classification.LogisticRegression
+ */
 object LocalFileLR {
   val D = 10   // Numer of dimensions
   val rand = new Random(42)
@@ -32,7 +38,18 @@ object LocalFileLR {
     DataPoint(new DenseVector(nums.slice(1, D + 1)), nums(0))
   }
 
+  def showWarning() {
+    System.err.println(
+      """WARN: This is a naive implementation of Logistic Regression and is given as an example!
+        |Please use the LogisticRegression method found in org.apache.spark.mllib.classification
+        |for more conventional use.
+      """.stripMargin)
+  }
+
   def main(args: Array[String]) {
+
+    showWarning()
+
     val lines = scala.io.Source.fromFile(args(0)).getLines().toArray
     val points = lines.map(parsePoint _)
     val ITERATIONS = args(1).toInt
diff --git a/examples/src/main/scala/org/apache/spark/examples/LocalKMeans.scala b/examples/src/main/scala/org/apache/spark/examples/LocalKMeans.scala
index e33a1b336d16..17624c20cff3 100644
--- a/examples/src/main/scala/org/apache/spark/examples/LocalKMeans.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/LocalKMeans.scala
@@ -28,6 +28,9 @@ import org.apache.spark.SparkContext._
 
 /**
  * K-means clustering.
+ *
+ * This is an example implementation for learning how to use Spark. For more conventional use,
+ * please refer to org.apache.spark.mllib.clustering.KMeans
  */
 object LocalKMeans {
   val N = 1000
@@ -61,7 +64,18 @@ object LocalKMeans {
     bestIndex
   }
 
+  def showWarning() {
+    System.err.println(
+      """WARN: This is a naive implementation of KMeans Clustering and is given as an example!
+        |Please use the KMeans method found in org.apache.spark.mllib.clustering
+        |for more conventional use.
+      """.stripMargin)
+  }
+
   def main(args: Array[String]) {
+
+    showWarning()
+
     val data = generateData
     var points = new HashSet[Vector[Double]]
     var kPoints = new HashMap[Int, Vector[Double]]
diff --git a/examples/src/main/scala/org/apache/spark/examples/LocalLR.scala b/examples/src/main/scala/org/apache/spark/examples/LocalLR.scala
index 385b48089d57..2d75b9d2590f 100644
--- a/examples/src/main/scala/org/apache/spark/examples/LocalLR.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/LocalLR.scala
@@ -23,6 +23,9 @@ import breeze.linalg.{Vector, DenseVector}
 
 /**
  * Logistic regression based classification.
+ *
+ * This is an example implementation for learning how to use Spark. For more conventional use,
+ * please refer to org.apache.spark.mllib.classification.LogisticRegression
  */
 object LocalLR {
   val N = 10000  // Number of data points
@@ -42,9 +45,19 @@ object LocalLR {
     Array.tabulate(N)(generatePoint)
   }
 
+  def showWarning() {
+    System.err.println(
+      """WARN: This is a naive implementation of Logistic Regression and is given as an example!
+        |Please use the LogisticRegression method found in org.apache.spark.mllib.classification
+        |for more conventional use.
+      """.stripMargin)
+  }
+
   def main(args: Array[String]) {
-    val data = generateData
 
+    showWarning()
+
+    val data = generateData
     // Initialize w to a random value
     var w = DenseVector.fill(D){2 * rand.nextDouble - 1}
     println("Initial w: " + w)
diff --git a/examples/src/main/scala/org/apache/spark/examples/SparkALS.scala b/examples/src/main/scala/org/apache/spark/examples/SparkALS.scala
index 5cbc966bf06c..fde8ffeedf8b 100644
--- a/examples/src/main/scala/org/apache/spark/examples/SparkALS.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/SparkALS.scala
@@ -27,6 +27,9 @@ import org.apache.spark._
 
 /**
  * Alternating least squares matrix factorization.
+ *
+ * This is an example implementation for learning how to use Spark. For more conventional use,
+ * please refer to org.apache.spark.mllib.recommendation.ALS
  */
 object SparkALS {
   // Parameters set through command line arguments
@@ -87,7 +90,16 @@ object SparkALS {
     solved2D.viewColumn(0)
   }
 
+  def showWarning() {
+    System.err.println(
+      """WARN: This is a naive implementation of ALS and is given as an example!
+        |Please use the ALS method found in org.apache.spark.mllib.recommendation
+        |for more conventional use.
+      """.stripMargin)
+  }
+
   def main(args: Array[String]) {
+
     var slices = 0
 
     val options = (0 to 4).map(i => if (i < args.length) Some(args(i)) else None)
@@ -103,7 +115,11 @@ object SparkALS {
         System.err.println("Usage: SparkALS [M] [U] [F] [iters] [slices]")
         System.exit(1)
     }
+
+    showWarning()
+
     printf("Running with M=%d, U=%d, F=%d, iters=%d\n", M, U, F, ITERATIONS)
+
     val sparkConf = new SparkConf().setAppName("SparkALS")
     val sc = new SparkContext(sparkConf)
 
diff --git a/examples/src/main/scala/org/apache/spark/examples/SparkHdfsLR.scala b/examples/src/main/scala/org/apache/spark/examples/SparkHdfsLR.scala
index 4906a696e90a..d583cf421ed2 100644
--- a/examples/src/main/scala/org/apache/spark/examples/SparkHdfsLR.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/SparkHdfsLR.scala
@@ -30,6 +30,9 @@ import org.apache.spark.scheduler.InputFormatInfo
 
 /**
  * Logistic regression based classification.
+ *
+ * This is an example implementation for learning how to use Spark. For more conventional use,
+ * please refer to org.apache.spark.mllib.classification.LogisticRegression
  */
 object SparkHdfsLR {
   val D = 10   // Numer of dimensions
@@ -48,12 +51,23 @@ object SparkHdfsLR {
     DataPoint(new DenseVector(x), y)
   }
 
+  def showWarning() {
+    System.err.println(
+      """WARN: This is a naive implementation of Logistic Regression and is given as an example!
+        |Please use the LogisticRegression method found in org.apache.spark.mllib.classification
+        |for more conventional use.
+      """.stripMargin)
+  }
+
   def main(args: Array[String]) {
+
     if (args.length < 2) {
       System.err.println("Usage: SparkHdfsLR <file> <iters>")
       System.exit(1)
     }
 
+    showWarning()
+
     val sparkConf = new SparkConf().setAppName("SparkHdfsLR")
     val inputPath = args(0)
     val conf = SparkHadoopUtil.get.newConfiguration()
diff --git a/examples/src/main/scala/org/apache/spark/examples/SparkKMeans.scala b/examples/src/main/scala/org/apache/spark/examples/SparkKMeans.scala
index 79cfedf33243..48e8d11cdf95 100644
--- a/examples/src/main/scala/org/apache/spark/examples/SparkKMeans.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/SparkKMeans.scala
@@ -24,6 +24,9 @@ import org.apache.spark.SparkContext._
 
 /**
  * K-means clustering.
+ *
+ * This is an example implementation for learning how to use Spark. For more conventional use,
+ * please refer to org.apache.spark.mllib.clustering.KMeans
  */
 object SparkKMeans {
 
@@ -46,11 +49,23 @@ object SparkKMeans {
     bestIndex
   }
 
+  def showWarning() {
+    System.err.println(
+      """WARN: This is a naive implementation of KMeans Clustering and is given as an example!
+        |Please use the KMeans method found in org.apache.spark.mllib.clustering
+        |for more conventional use.
+      """.stripMargin)
+  }
+
   def main(args: Array[String]) {
+
     if (args.length < 3) {
       System.err.println("Usage: SparkKMeans <file> <k> <convergeDist>")
       System.exit(1)
     }
+
+    showWarning()
+
     val sparkConf = new SparkConf().setAppName("SparkKMeans")
     val sc = new SparkContext(sparkConf)
     val lines = sc.textFile(args(0))
diff --git a/examples/src/main/scala/org/apache/spark/examples/SparkLR.scala b/examples/src/main/scala/org/apache/spark/examples/SparkLR.scala
index 99ceb3089e9f..fc23308fc4ad 100644
--- a/examples/src/main/scala/org/apache/spark/examples/SparkLR.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/SparkLR.scala
@@ -28,6 +28,9 @@ import org.apache.spark._
 /**
  * Logistic regression based classification.
  * Usage: SparkLR [slices]
+ *
+ * This is an example implementation for learning how to use Spark. For more conventional use,
+ * please refer to org.apache.spark.mllib.classification.LogisticRegression
  */
 object SparkLR {
   val N = 10000  // Number of data points
@@ -47,7 +50,18 @@ object SparkLR {
     Array.tabulate(N)(generatePoint)
   }
 
+  def showWarning() {
+    System.err.println(
+      """WARN: This is a naive implementation of Logistic Regression and is given as an example!
+        |Please use the LogisticRegression method found in org.apache.spark.mllib.classification
+        |for more conventional use.
+      """.stripMargin)
+  }
+
   def main(args: Array[String]) {
+
+    showWarning()
+
     val sparkConf = new SparkConf().setAppName("SparkLR")
     val sc = new SparkContext(sparkConf)
     val numSlices = if (args.length > 0) args(0).toInt else 2
@@ -66,6 +80,7 @@ object SparkLR {
     }
 
     println("Final w: " + w)
+
     sc.stop()
   }
 }

From 511a7314037219c23e824ea5363bf7f1df55bab3 Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Mon, 21 Jul 2014 18:18:17 -0700
Subject: [PATCH 718/810] [SPARK-2561][SQL] Fix apply schema

We need to use the analyzed attributes otherwise we end up with a tree that will never resolve.

Author: Michael Armbrust <michael@databricks.com>

Closes #1470 from marmbrus/fixApplySchema and squashes the following commits:

f968195 [Michael Armbrust] Use analyzed attributes when applying the schema.
4969015 [Michael Armbrust] Add test case.
---
 .../src/main/scala/org/apache/spark/sql/SchemaRDD.scala     | 2 +-
 .../src/test/scala/org/apache/spark/sql/DslQuerySuite.scala | 6 ++++++
 2 files changed, 7 insertions(+), 1 deletion(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala
index 993d085c7508..31d27bb4f057 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala
@@ -430,7 +430,7 @@ class SchemaRDD(
    * @group schema
    */
   private def applySchema(rdd: RDD[Row]): SchemaRDD = {
-    new SchemaRDD(sqlContext, SparkLogicalPlan(ExistingRdd(logicalPlan.output, rdd)))
+    new SchemaRDD(sqlContext, SparkLogicalPlan(ExistingRdd(queryExecution.analyzed.output, rdd)))
   }
 
   // =======================================================================
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala
index 68dae58728a2..c8ea01c4e1b6 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala
@@ -33,6 +33,12 @@ class DslQuerySuite extends QueryTest {
       testData.collect().toSeq)
   }
 
+  test("repartition") {
+    checkAnswer(
+      testData.select('key).repartition(10).select('key),
+      testData.select('key).collect().toSeq)
+  }
+
   test("agg") {
     checkAnswer(
       testData2.groupBy('a)('a, Sum('b)),

From c3462c65684885299cf037d56c88bd53c08c6348 Mon Sep 17 00:00:00 2001
From: Gregory Owen <greowen@gmail.com>
Date: Mon, 21 Jul 2014 18:55:01 -0700
Subject: [PATCH 719/810] [SPARK-2086] Improve output of toDebugString to make
 shuffle boundaries more clear

Changes RDD.toDebugString() to show hierarchy and shuffle transformations more clearly

New output:

```
(3) FlatMappedValuesRDD[325] at apply at Transformer.scala:22
 |  MappedValuesRDD[324] at apply at Transformer.scala:22
 |  CoGroupedRDD[323] at apply at Transformer.scala:22
 +-(5) MappedRDD[320] at apply at Transformer.scala:22
 |  |  MappedRDD[319] at apply at Transformer.scala:22
 |  |  MappedValuesRDD[318] at apply at Transformer.scala:22
 |  |  MapPartitionsRDD[317] at apply at Transformer.scala:22
 |  |  ShuffledRDD[316] at apply at Transformer.scala:22
 |  +-(10) MappedRDD[315] at apply at Transformer.scala:22
 |     |   ParallelCollectionRDD[314] at apply at Transformer.scala:22
 +-(100) MappedRDD[322] at apply at Transformer.scala:22
     |   ParallelCollectionRDD[321] at apply at Transformer.scala:22
```

Author: Gregory Owen <greowen@gmail.com>

Closes #1364 from GregOwen/to-debug-string and squashes the following commits:

08f5c78 [Gregory Owen] toDebugString: prettier debug printing to show shuffles and joins more clearly
1603f7b [Gregory Owen] toDebugString: prettier debug printing to show shuffles and joins more clearly
---
 .../main/scala/org/apache/spark/rdd/RDD.scala | 52 +++++++++++++++++--
 project/MimaExcludes.scala                    |  8 +++
 2 files changed, 56 insertions(+), 4 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/rdd/RDD.scala b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
index 88a918aebf76..a1f282724889 100644
--- a/core/src/main/scala/org/apache/spark/rdd/RDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
@@ -1269,11 +1269,55 @@ abstract class RDD[T: ClassTag](
 
   /** A description of this RDD and its recursive dependencies for debugging. */
   def toDebugString: String = {
-    def debugString(rdd: RDD[_], prefix: String = ""): Seq[String] = {
-      Seq(prefix + rdd + " (" + rdd.partitions.size + " partitions)") ++
-        rdd.dependencies.flatMap(d => debugString(d.rdd, prefix + "  "))
+    // Apply a different rule to the last child
+    def debugChildren(rdd: RDD[_], prefix: String): Seq[String] = {
+      val len = rdd.dependencies.length
+      len match {
+        case 0 => Seq.empty
+        case 1 =>
+          val d = rdd.dependencies.head
+          debugString(d.rdd, prefix, d.isInstanceOf[ShuffleDependency[_,_,_]], true)
+        case _ =>
+          val frontDeps = rdd.dependencies.take(len - 1)
+          val frontDepStrings = frontDeps.flatMap(
+            d => debugString(d.rdd, prefix, d.isInstanceOf[ShuffleDependency[_,_,_]]))
+
+          val lastDep = rdd.dependencies.last
+          val lastDepStrings =
+            debugString(lastDep.rdd, prefix, lastDep.isInstanceOf[ShuffleDependency[_,_,_]], true)
+
+          (frontDepStrings ++ lastDepStrings)
+      }
+    }
+    // The first RDD in the dependency stack has no parents, so no need for a +-
+    def firstDebugString(rdd: RDD[_]): Seq[String] = {
+      val partitionStr = "(" + rdd.partitions.size + ")"
+      val leftOffset = (partitionStr.length - 1) / 2
+      val nextPrefix = (" " * leftOffset) + "|" + (" " * (partitionStr.length - leftOffset))
+      Seq(partitionStr + " " + rdd) ++ debugChildren(rdd, nextPrefix)
+    }
+    def shuffleDebugString(rdd: RDD[_], prefix: String = "", isLastChild: Boolean): Seq[String] = {
+      val partitionStr = "(" + rdd.partitions.size + ")"
+      val leftOffset = (partitionStr.length - 1) / 2
+      val thisPrefix = prefix.replaceAll("\\|\\s+$", "")
+      val nextPrefix = (
+        thisPrefix
+        + (if (isLastChild) "  " else "| ")
+        + (" " * leftOffset) + "|" + (" " * (partitionStr.length - leftOffset)))
+      Seq(thisPrefix + "+-" + partitionStr + " " + rdd) ++ debugChildren(rdd, nextPrefix)
+    }
+    def debugString(rdd: RDD[_],
+                    prefix: String = "",
+                    isShuffle: Boolean = true,
+                    isLastChild: Boolean = false): Seq[String] = {
+      if (isShuffle) {
+        shuffleDebugString(rdd, prefix, isLastChild)
+      }
+      else {
+        Seq(prefix + rdd) ++ debugChildren(rdd, prefix)
+      }
     }
-    debugString(this).mkString("\n")
+    firstDebugString(this).mkString("\n")
   }
 
   override def toString: String = "%s%s[%d] at %s".format(
diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala
index e0f433b26f7f..4d86e1a0d8bb 100644
--- a/project/MimaExcludes.scala
+++ b/project/MimaExcludes.scala
@@ -61,6 +61,14 @@ object MimaExcludes {
           "org.apache.spark.api.java.JavaDoubleRDD.countApproxDistinct$default$1"),
         ProblemFilters.exclude[MissingMethodProblem](
           "org.apache.spark.storage.MemoryStore.Entry"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.rdd.RDD.org$apache$spark$rdd$RDD$$debugChildren$1"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.rdd.RDD.org$apache$spark$rdd$RDD$$firstDebugString$1"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.rdd.RDD.org$apache$spark$rdd$RDD$$shuffleDebugString$1"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.rdd.RDD.org$apache$spark$rdd$RDD$$debugString$1"),
         ProblemFilters.exclude[MissingMethodProblem](
           "org.apache.spark.rdd.PairRDDFunctions.org$apache$spark$rdd$PairRDDFunctions$$"
             + "createZero$1")

From 5d16d5bbfd242c16ee0d6952c48dcd90651f8ae2 Mon Sep 17 00:00:00 2001
From: Nicholas Chammas <nicholas.chammas@gmail.com>
Date: Mon, 21 Jul 2014 22:30:53 -0700
Subject: [PATCH 720/810] [SPARK-2470] PEP8 fixes to PySpark

This pull request aims to resolve all outstanding PEP8 violations in PySpark.

Author: Nicholas Chammas <nicholas.chammas@gmail.com>
Author: nchammas <nicholas.chammas@gmail.com>

Closes #1505 from nchammas/master and squashes the following commits:

98171af [Nicholas Chammas] [SPARK-2470] revert PEP 8 fixes to cloudpickle
cba7768 [Nicholas Chammas] [SPARK-2470] wrap expression list in parentheses
e178dbe [Nicholas Chammas] [SPARK-2470] style - change position of line break
9127d2b [Nicholas Chammas] [SPARK-2470] wrap expression lists in parentheses
22132a4 [Nicholas Chammas] [SPARK-2470] wrap conditionals in parentheses
24639bc [Nicholas Chammas] [SPARK-2470] fix whitespace for doctest
7d557b7 [Nicholas Chammas] [SPARK-2470] PEP8 fixes to tests.py
8f8e4c0 [Nicholas Chammas] [SPARK-2470] PEP8 fixes to storagelevel.py
b3b96cf [Nicholas Chammas] [SPARK-2470] PEP8 fixes to statcounter.py
d644477 [Nicholas Chammas] [SPARK-2470] PEP8 fixes to worker.py
aa3a7b6 [Nicholas Chammas] [SPARK-2470] PEP8 fixes to sql.py
1916859 [Nicholas Chammas] [SPARK-2470] PEP8 fixes to shell.py
95d1d95 [Nicholas Chammas] [SPARK-2470] PEP8 fixes to serializers.py
a0fec2e [Nicholas Chammas] [SPARK-2470] PEP8 fixes to mllib
c85e1e5 [Nicholas Chammas] [SPARK-2470] PEP8 fixes to join.py
d14f2f1 [Nicholas Chammas] [SPARK-2470] PEP8 fixes to __init__.py
81fcb20 [Nicholas Chammas] [SPARK-2470] PEP8 fixes to resultiterable.py
1bde265 [Nicholas Chammas] [SPARK-2470] PEP8 fixes to java_gateway.py
7fc849c [Nicholas Chammas] [SPARK-2470] PEP8 fixes to daemon.py
ca2d28b [Nicholas Chammas] [SPARK-2470] PEP8 fixes to context.py
f4e0039 [Nicholas Chammas] [SPARK-2470] PEP8 fixes to conf.py
a6d5e4b [Nicholas Chammas] [SPARK-2470] PEP8 fixes to cloudpickle.py
f0a7ebf [Nicholas Chammas] [SPARK-2470] PEP8 fixes to rddsampler.py
4dd148f [nchammas] Merge pull request #5 from apache/master
f7e4581 [Nicholas Chammas] unrelated pep8 fix
a36eed0 [Nicholas Chammas] name ec2 instances and security groups consistently
de7292a [nchammas] Merge pull request #4 from apache/master
2e4fe00 [nchammas] Merge pull request #3 from apache/master
89fde08 [nchammas] Merge pull request #2 from apache/master
69f6e22 [Nicholas Chammas] PEP8 fixes
2627247 [Nicholas Chammas] broke up lines before they hit 100 chars
6544b7e [Nicholas Chammas] [SPARK-2065] give launched instances names
69da6cf [nchammas] Merge pull request #1 from apache/master
---
 python/pyspark/__init__.py       |  3 ++-
 python/pyspark/conf.py           |  9 ++++---
 python/pyspark/context.py        | 45 ++++++++++++++++++--------------
 python/pyspark/daemon.py         | 12 ++++-----
 python/pyspark/java_gateway.py   |  1 +
 python/pyspark/join.py           |  4 ++-
 python/pyspark/mllib/_common.py  |  4 ++-
 python/pyspark/mllib/linalg.py   |  1 +
 python/pyspark/mllib/util.py     |  2 --
 python/pyspark/rddsampler.py     | 24 ++++++++---------
 python/pyspark/resultiterable.py |  3 +++
 python/pyspark/serializers.py    | 31 +++++++++++++---------
 python/pyspark/shell.py          |  3 ++-
 python/pyspark/sql.py            | 38 +++++++++++++++------------
 python/pyspark/statcounter.py    | 25 +++++++++---------
 python/pyspark/storagelevel.py   |  5 ++--
 python/pyspark/tests.py          | 10 ++++---
 python/pyspark/worker.py         |  4 +--
 18 files changed, 127 insertions(+), 97 deletions(-)

diff --git a/python/pyspark/__init__.py b/python/pyspark/__init__.py
index 07df8697bd1a..312c75d112cb 100644
--- a/python/pyspark/__init__.py
+++ b/python/pyspark/__init__.py
@@ -59,4 +59,5 @@
 from pyspark.storagelevel import StorageLevel
 
 
-__all__ = ["SparkConf", "SparkContext", "SQLContext", "RDD", "SchemaRDD", "SparkFiles", "StorageLevel", "Row"]
+__all__ = ["SparkConf", "SparkContext", "SQLContext", "RDD", "SchemaRDD",
+           "SparkFiles", "StorageLevel", "Row"]
diff --git a/python/pyspark/conf.py b/python/pyspark/conf.py
index 60fc6ba7c52c..b50590ab3b44 100644
--- a/python/pyspark/conf.py
+++ b/python/pyspark/conf.py
@@ -50,7 +50,8 @@
 spark.executorEnv.VAR4=value4
 spark.home=/path
 >>> sorted(conf.getAll(), key=lambda p: p[0])
-[(u'spark.executorEnv.VAR1', u'value1'), (u'spark.executorEnv.VAR3', u'value3'), (u'spark.executorEnv.VAR4', u'value4'), (u'spark.home', u'/path')]
+[(u'spark.executorEnv.VAR1', u'value1'), (u'spark.executorEnv.VAR3', u'value3'), \
+(u'spark.executorEnv.VAR4', u'value4'), (u'spark.home', u'/path')]
 """
 
 
@@ -118,9 +119,9 @@ def setExecutorEnv(self, key=None, value=None, pairs=None):
         """Set an environment variable to be passed to executors."""
         if (key is not None and pairs is not None) or (key is None and pairs is None):
             raise Exception("Either pass one key-value pair or a list of pairs")
-        elif key != None:
+        elif key is not None:
             self._jconf.setExecutorEnv(key, value)
-        elif pairs != None:
+        elif pairs is not None:
             for (k, v) in pairs:
                 self._jconf.setExecutorEnv(k, v)
         return self
@@ -137,7 +138,7 @@ def setAll(self, pairs):
 
     def get(self, key, defaultValue=None):
         """Get the configured value for some key, or return a default otherwise."""
-        if defaultValue == None:   # Py4J doesn't call the right get() if we pass None
+        if defaultValue is None:   # Py4J doesn't call the right get() if we pass None
             if not self._jconf.contains(key):
                 return None
             return self._jconf.get(key)
diff --git a/python/pyspark/context.py b/python/pyspark/context.py
index 95c54e7a5ad6..e21be0e10a3f 100644
--- a/python/pyspark/context.py
+++ b/python/pyspark/context.py
@@ -29,7 +29,7 @@
 from pyspark.files import SparkFiles
 from pyspark.java_gateway import launch_gateway
 from pyspark.serializers import PickleSerializer, BatchedSerializer, UTF8Deserializer, \
-        PairDeserializer
+    PairDeserializer
 from pyspark.storagelevel import StorageLevel
 from pyspark import rdd
 from pyspark.rdd import RDD
@@ -50,12 +50,11 @@ class SparkContext(object):
     _next_accum_id = 0
     _active_spark_context = None
     _lock = Lock()
-    _python_includes = None # zip and egg files that need to be added to PYTHONPATH
-
+    _python_includes = None  # zip and egg files that need to be added to PYTHONPATH
 
     def __init__(self, master=None, appName=None, sparkHome=None, pyFiles=None,
-        environment=None, batchSize=1024, serializer=PickleSerializer(), conf=None,
-        gateway=None):
+                 environment=None, batchSize=1024, serializer=PickleSerializer(), conf=None,
+                 gateway=None):
         """
         Create a new SparkContext. At least the master and app name should be set,
         either through the named parameters here or through C{conf}.
@@ -138,8 +137,8 @@ def __init__(self, master=None, appName=None, sparkHome=None, pyFiles=None,
         self._accumulatorServer = accumulators._start_update_server()
         (host, port) = self._accumulatorServer.server_address
         self._javaAccumulator = self._jsc.accumulator(
-                self._jvm.java.util.ArrayList(),
-                self._jvm.PythonAccumulatorParam(host, port))
+            self._jvm.java.util.ArrayList(),
+            self._jvm.PythonAccumulatorParam(host, port))
 
         self.pythonExec = os.environ.get("PYSPARK_PYTHON", 'python')
 
@@ -165,7 +164,7 @@ def __init__(self, master=None, appName=None, sparkHome=None, pyFiles=None,
                 (dirname, filename) = os.path.split(path)
                 self._python_includes.append(filename)
                 sys.path.append(path)
-                if not dirname in sys.path:
+                if dirname not in sys.path:
                     sys.path.append(dirname)
 
         # Create a temporary directory inside spark.local.dir:
@@ -192,15 +191,19 @@ def _ensure_initialized(cls, instance=None, gateway=None):
                 SparkContext._writeToFile = SparkContext._jvm.PythonRDD.writeToFile
 
             if instance:
-                if SparkContext._active_spark_context and SparkContext._active_spark_context != instance:
+                if (SparkContext._active_spark_context and
+                   SparkContext._active_spark_context != instance):
                     currentMaster = SparkContext._active_spark_context.master
                     currentAppName = SparkContext._active_spark_context.appName
                     callsite = SparkContext._active_spark_context._callsite
 
                     # Raise error if there is already a running Spark context
-                    raise ValueError("Cannot run multiple SparkContexts at once; existing SparkContext(app=%s, master=%s)" \
-                        " created by %s at %s:%s " \
-                        % (currentAppName, currentMaster, callsite.function, callsite.file, callsite.linenum))
+                    raise ValueError(
+                        "Cannot run multiple SparkContexts at once; "
+                        "existing SparkContext(app=%s, master=%s)"
+                        " created by %s at %s:%s "
+                        % (currentAppName, currentMaster,
+                            callsite.function, callsite.file, callsite.linenum))
                 else:
                     SparkContext._active_spark_context = instance
 
@@ -290,7 +293,7 @@ def textFile(self, name, minPartitions=None):
         Read a text file from HDFS, a local file system (available on all
         nodes), or any Hadoop-supported file system URI, and return it as an
         RDD of Strings.
-        
+
         >>> path = os.path.join(tempdir, "sample-text.txt")
         >>> with open(path, "w") as testFile:
         ...    testFile.write("Hello world!")
@@ -584,11 +587,12 @@ def addPyFile(self, path):
         HTTP, HTTPS or FTP URI.
         """
         self.addFile(path)
-        (dirname, filename) = os.path.split(path) # dirname may be directory or HDFS/S3 prefix
+        (dirname, filename) = os.path.split(path)  # dirname may be directory or HDFS/S3 prefix
 
         if filename.endswith('.zip') or filename.endswith('.ZIP') or filename.endswith('.egg'):
             self._python_includes.append(filename)
-            sys.path.append(os.path.join(SparkFiles.getRootDirectory(), filename)) # for tests in local mode
+            # for tests in local mode
+            sys.path.append(os.path.join(SparkFiles.getRootDirectory(), filename))
 
     def setCheckpointDir(self, dirName):
         """
@@ -649,9 +653,9 @@ def setJobGroup(self, groupId, description, interruptOnCancel=False):
         Cancelled
 
         If interruptOnCancel is set to true for the job group, then job cancellation will result
-        in Thread.interrupt() being called on the job's executor threads. This is useful to help ensure
-        that the tasks are actually stopped in a timely manner, but is off by default due to HDFS-1208,
-        where HDFS may respond to Thread.interrupt() by marking nodes as dead.
+        in Thread.interrupt() being called on the job's executor threads. This is useful to help
+        ensure that the tasks are actually stopped in a timely manner, but is off by default due
+        to HDFS-1208, where HDFS may respond to Thread.interrupt() by marking nodes as dead.
         """
         self._jsc.setJobGroup(groupId, description, interruptOnCancel)
 
@@ -688,7 +692,7 @@ def cancelAllJobs(self):
         """
         self._jsc.sc().cancelAllJobs()
 
-    def runJob(self, rdd, partitionFunc, partitions = None, allowLocal = False):
+    def runJob(self, rdd, partitionFunc, partitions=None, allowLocal=False):
         """
         Executes the given partitionFunc on the specified set of partitions,
         returning the result as an array of elements.
@@ -703,7 +707,7 @@ def runJob(self, rdd, partitionFunc, partitions = None, allowLocal = False):
         >>> sc.runJob(myRDD, lambda part: [x * x for x in part], [0, 2], True)
         [0, 1, 16, 25]
         """
-        if partitions == None:
+        if partitions is None:
             partitions = range(rdd._jrdd.partitions().size())
         javaPartitions = ListConverter().convert(partitions, self._gateway._gateway_client)
 
@@ -714,6 +718,7 @@ def runJob(self, rdd, partitionFunc, partitions = None, allowLocal = False):
         it = self._jvm.PythonRDD.runJob(self._jsc.sc(), mappedRDD._jrdd, javaPartitions, allowLocal)
         return list(mappedRDD._collect_iterator_through_file(it))
 
+
 def _test():
     import atexit
     import doctest
diff --git a/python/pyspark/daemon.py b/python/pyspark/daemon.py
index 5eb1c63bf206..8a5873ded2b8 100644
--- a/python/pyspark/daemon.py
+++ b/python/pyspark/daemon.py
@@ -42,12 +42,12 @@ def should_exit():
 
 
 def compute_real_exit_code(exit_code):
-  # SystemExit's code can be integer or string, but os._exit only accepts integers
-  import numbers
-  if isinstance(exit_code, numbers.Integral):
-    return exit_code
-  else:
-    return 1
+    # SystemExit's code can be integer or string, but os._exit only accepts integers
+    import numbers
+    if isinstance(exit_code, numbers.Integral):
+        return exit_code
+    else:
+        return 1
 
 
 def worker(listen_sock):
diff --git a/python/pyspark/java_gateway.py b/python/pyspark/java_gateway.py
index 2a17127a7e0f..2c129679f47f 100644
--- a/python/pyspark/java_gateway.py
+++ b/python/pyspark/java_gateway.py
@@ -24,6 +24,7 @@
 from threading import Thread
 from py4j.java_gateway import java_import, JavaGateway, GatewayClient
 
+
 def launch_gateway():
     SPARK_HOME = os.environ["SPARK_HOME"]
 
diff --git a/python/pyspark/join.py b/python/pyspark/join.py
index 5f3a7e71f786..b0f1cc192706 100644
--- a/python/pyspark/join.py
+++ b/python/pyspark/join.py
@@ -33,10 +33,11 @@
 
 from pyspark.resultiterable import ResultIterable
 
+
 def _do_python_join(rdd, other, numPartitions, dispatch):
     vs = rdd.map(lambda (k, v): (k, (1, v)))
     ws = other.map(lambda (k, v): (k, (2, v)))
-    return vs.union(ws).groupByKey(numPartitions).flatMapValues(lambda x : dispatch(x.__iter__()))
+    return vs.union(ws).groupByKey(numPartitions).flatMapValues(lambda x: dispatch(x.__iter__()))
 
 
 def python_join(rdd, other, numPartitions):
@@ -85,6 +86,7 @@ def make_mapper(i):
     vrdds = [rdd.map(make_mapper(i)) for i, rdd in enumerate(rdds)]
     union_vrdds = reduce(lambda acc, other: acc.union(other), vrdds)
     rdd_len = len(vrdds)
+
     def dispatch(seq):
         bufs = [[] for i in range(rdd_len)]
         for (n, v) in seq:
diff --git a/python/pyspark/mllib/_common.py b/python/pyspark/mllib/_common.py
index e609b60a0f96..43b491a9716f 100644
--- a/python/pyspark/mllib/_common.py
+++ b/python/pyspark/mllib/_common.py
@@ -164,7 +164,7 @@ def _deserialize_double_vector(ba, offset=0):
     nb = len(ba) - offset
     if nb < 5:
         raise TypeError("_deserialize_double_vector called on a %d-byte array, "
-                "which is too short" % nb)
+                        "which is too short" % nb)
     if ba[offset] == DENSE_VECTOR_MAGIC:
         return _deserialize_dense_vector(ba, offset)
     elif ba[offset] == SPARSE_VECTOR_MAGIC:
@@ -272,6 +272,7 @@ def _serialize_labeled_point(p):
     header_float[0] = p.label
     return header + serialized_features
 
+
 def _deserialize_labeled_point(ba, offset=0):
     """Deserialize a LabeledPoint from a mutually understood format."""
     from pyspark.mllib.regression import LabeledPoint
@@ -283,6 +284,7 @@ def _deserialize_labeled_point(ba, offset=0):
     features = _deserialize_double_vector(ba, offset + 9)
     return LabeledPoint(label, features)
 
+
 def _copyto(array, buffer, offset, shape, dtype):
     """
     Copy the contents of a vector to a destination bytearray at the
diff --git a/python/pyspark/mllib/linalg.py b/python/pyspark/mllib/linalg.py
index db39ed0acdb6..71f4ad1a8d44 100644
--- a/python/pyspark/mllib/linalg.py
+++ b/python/pyspark/mllib/linalg.py
@@ -247,6 +247,7 @@ def stringify(vector):
         else:
             return "[" + ",".join([str(v) for v in vector]) + "]"
 
+
 def _test():
     import doctest
     (failure_count, test_count) = doctest.testmod(optionflags=doctest.ELLIPSIS)
diff --git a/python/pyspark/mllib/util.py b/python/pyspark/mllib/util.py
index e24c144f458b..a707a9dcd5b4 100644
--- a/python/pyspark/mllib/util.py
+++ b/python/pyspark/mllib/util.py
@@ -24,7 +24,6 @@
 from pyspark.serializers import NoOpSerializer
 
 
-
 class MLUtils:
     """
     Helper methods to load, save and pre-process data used in MLlib.
@@ -154,7 +153,6 @@ def saveAsLibSVMFile(data, dir):
         lines = data.map(lambda p: MLUtils._convert_labeled_point_to_libsvm(p))
         lines.saveAsTextFile(dir)
 
-
     @staticmethod
     def loadLabeledPoints(sc, path, minPartitions=None):
         """
diff --git a/python/pyspark/rddsampler.py b/python/pyspark/rddsampler.py
index 122bc38b03b0..7ff1c316c762 100644
--- a/python/pyspark/rddsampler.py
+++ b/python/pyspark/rddsampler.py
@@ -18,13 +18,16 @@
 import sys
 import random
 
+
 class RDDSampler(object):
     def __init__(self, withReplacement, fraction, seed=None):
         try:
             import numpy
             self._use_numpy = True
         except ImportError:
-            print >> sys.stderr, "NumPy does not appear to be installed. Falling back to default random generator for sampling."
+            print >> sys.stderr, (
+                "NumPy does not appear to be installed. "
+                "Falling back to default random generator for sampling.")
             self._use_numpy = False
 
         self._seed = seed if seed is not None else random.randint(0, sys.maxint)
@@ -61,7 +64,7 @@ def getUniformSample(self, split):
     def getPoissonSample(self, split, mean):
         if not self._rand_initialized or split != self._split:
             self.initRandomGenerator(split)
-        
+
         if self._use_numpy:
             return self._random.poisson(mean)
         else:
@@ -80,30 +83,27 @@ def getPoissonSample(self, split, mean):
                 num_arrivals += 1
 
             return (num_arrivals - 1)
-    
+
     def shuffle(self, vals):
         if self._random is None:
             self.initRandomGenerator(0)  # this should only ever called on the master so
             # the split does not matter
-        
+
         if self._use_numpy:
             self._random.shuffle(vals)
         else:
             self._random.shuffle(vals, self._random.random)
 
     def func(self, split, iterator):
-        if self._withReplacement:            
+        if self._withReplacement:
             for obj in iterator:
-                # For large datasets, the expected number of occurrences of each element in a sample with
-                # replacement is Poisson(frac). We use that to get a count for each element.                                   
-                count = self.getPoissonSample(split, mean = self._fraction)
+                # For large datasets, the expected number of occurrences of each element in
+                # a sample with replacement is Poisson(frac). We use that to get a count for
+                # each element.
+                count = self.getPoissonSample(split, mean=self._fraction)
                 for _ in range(0, count):
                     yield obj
         else:
             for obj in iterator:
                 if self.getUniformSample(split) <= self._fraction:
                     yield obj
-
-            
-            
-            
diff --git a/python/pyspark/resultiterable.py b/python/pyspark/resultiterable.py
index 7f418f8d2e29..df34740fc817 100644
--- a/python/pyspark/resultiterable.py
+++ b/python/pyspark/resultiterable.py
@@ -19,6 +19,7 @@
 
 import collections
 
+
 class ResultIterable(collections.Iterable):
     """
     A special result iterable. This is used because the standard iterator can not be pickled
@@ -27,7 +28,9 @@ def __init__(self, data):
         self.data = data
         self.index = 0
         self.maxindex = len(data)
+
     def __iter__(self):
         return iter(self.data)
+
     def __len__(self):
         return len(self.data)
diff --git a/python/pyspark/serializers.py b/python/pyspark/serializers.py
index b253807974a2..9be78b39fbc2 100644
--- a/python/pyspark/serializers.py
+++ b/python/pyspark/serializers.py
@@ -91,7 +91,6 @@ def load_stream(self, stream):
         """
         raise NotImplementedError
 
-
     def _load_stream_without_unbatching(self, stream):
         return self.load_stream(stream)
 
@@ -197,8 +196,8 @@ def _load_stream_without_unbatching(self, stream):
             return self.serializer.load_stream(stream)
 
     def __eq__(self, other):
-        return isinstance(other, BatchedSerializer) and \
-               other.serializer == self.serializer
+        return (isinstance(other, BatchedSerializer) and
+                other.serializer == self.serializer)
 
     def __str__(self):
         return "BatchedSerializer<%s>" % str(self.serializer)
@@ -229,8 +228,8 @@ def load_stream(self, stream):
                 yield pair
 
     def __eq__(self, other):
-        return isinstance(other, CartesianDeserializer) and \
-               self.key_ser == other.key_ser and self.val_ser == other.val_ser
+        return (isinstance(other, CartesianDeserializer) and
+                self.key_ser == other.key_ser and self.val_ser == other.val_ser)
 
     def __str__(self):
         return "CartesianDeserializer<%s, %s>" % \
@@ -252,18 +251,20 @@ def load_stream(self, stream):
                 yield pair
 
     def __eq__(self, other):
-        return isinstance(other, PairDeserializer) and \
-               self.key_ser == other.key_ser and self.val_ser == other.val_ser
+        return (isinstance(other, PairDeserializer) and
+                self.key_ser == other.key_ser and self.val_ser == other.val_ser)
 
     def __str__(self):
-        return "PairDeserializer<%s, %s>" % \
-               (str(self.key_ser), str(self.val_ser))
+        return "PairDeserializer<%s, %s>" % (str(self.key_ser), str(self.val_ser))
 
 
 class NoOpSerializer(FramedSerializer):
 
-    def loads(self, obj): return obj
-    def dumps(self, obj): return obj
+    def loads(self, obj):
+        return obj
+
+    def dumps(self, obj):
+        return obj
 
 
 class PickleSerializer(FramedSerializer):
@@ -276,12 +277,16 @@ class PickleSerializer(FramedSerializer):
     not be as fast as more specialized serializers.
     """
 
-    def dumps(self, obj): return cPickle.dumps(obj, 2)
+    def dumps(self, obj):
+        return cPickle.dumps(obj, 2)
+
     loads = cPickle.loads
 
+
 class CloudPickleSerializer(PickleSerializer):
 
-    def dumps(self, obj): return cloudpickle.dumps(obj, 2)
+    def dumps(self, obj):
+        return cloudpickle.dumps(obj, 2)
 
 
 class MarshalSerializer(FramedSerializer):
diff --git a/python/pyspark/shell.py b/python/pyspark/shell.py
index 2ce5409cd67c..e1e7cd954189 100644
--- a/python/pyspark/shell.py
+++ b/python/pyspark/shell.py
@@ -35,7 +35,8 @@
 from pyspark.storagelevel import StorageLevel
 
 # this is the equivalent of ADD_JARS
-add_files = os.environ.get("ADD_FILES").split(',') if os.environ.get("ADD_FILES") is not None else None
+add_files = (os.environ.get("ADD_FILES").split(',')
+             if os.environ.get("ADD_FILES") is not None else None)
 
 if os.environ.get("SPARK_EXECUTOR_URI"):
     SparkContext.setSystemProperty("spark.executor.uri", os.environ["SPARK_EXECUTOR_URI"])
diff --git a/python/pyspark/sql.py b/python/pyspark/sql.py
index ffe177576f36..cb83e8917682 100644
--- a/python/pyspark/sql.py
+++ b/python/pyspark/sql.py
@@ -30,7 +30,7 @@ class SQLContext:
     tables, execute SQL over tables, cache tables, and read parquet files.
     """
 
-    def __init__(self, sparkContext, sqlContext = None):
+    def __init__(self, sparkContext, sqlContext=None):
         """Create a new SQLContext.
 
         @param sparkContext: The SparkContext to wrap.
@@ -137,7 +137,6 @@ def parquetFile(self, path):
         jschema_rdd = self._ssql_ctx.parquetFile(path)
         return SchemaRDD(jschema_rdd, self)
 
-
     def jsonFile(self, path):
         """Loads a text file storing one JSON object per line,
            returning the result as a L{SchemaRDD}.
@@ -234,8 +233,8 @@ def _ssql_ctx(self):
                 self._scala_HiveContext = self._get_hive_ctx()
             return self._scala_HiveContext
         except Py4JError as e:
-            raise Exception("You must build Spark with Hive. Export 'SPARK_HIVE=true' and run " \
-                            "sbt/sbt assembly" , e)
+            raise Exception("You must build Spark with Hive. Export 'SPARK_HIVE=true' and run "
+                            "sbt/sbt assembly", e)
 
     def _get_hive_ctx(self):
         return self._jvm.HiveContext(self._jsc.sc())
@@ -377,7 +376,7 @@ def registerAsTable(self, name):
         """
         self._jschema_rdd.registerAsTable(name)
 
-    def insertInto(self, tableName, overwrite = False):
+    def insertInto(self, tableName, overwrite=False):
         """Inserts the contents of this SchemaRDD into the specified table.
 
         Optionally overwriting any existing data.
@@ -420,7 +419,7 @@ def _toPython(self):
         # in Java land in the javaToPython function. May require a custom
         # pickle serializer in Pyrolite
         return RDD(jrdd, self._sc, BatchedSerializer(
-                        PickleSerializer())).map(lambda d: Row(d))
+            PickleSerializer())).map(lambda d: Row(d))
 
     # We override the default cache/persist/checkpoint behavior as we want to cache the underlying
     # SchemaRDD object in the JVM, not the PythonRDD checkpointed by the super class
@@ -483,6 +482,7 @@ def subtract(self, other, numPartitions=None):
         else:
             raise ValueError("Can only subtract another SchemaRDD")
 
+
 def _test():
     import doctest
     from array import array
@@ -493,20 +493,25 @@ def _test():
     sc = SparkContext('local[4]', 'PythonTest', batchSize=2)
     globs['sc'] = sc
     globs['sqlCtx'] = SQLContext(sc)
-    globs['rdd'] = sc.parallelize([{"field1" : 1, "field2" : "row1"},
-        {"field1" : 2, "field2": "row2"}, {"field1" : 3, "field2": "row3"}])
-    jsonStrings = ['{"field1": 1, "field2": "row1", "field3":{"field4":11}}',
-       '{"field1" : 2, "field3":{"field4":22, "field5": [10, 11]}, "field6":[{"field7": "row2"}]}',
-       '{"field1" : null, "field2": "row3", "field3":{"field4":33, "field5": []}}']
+    globs['rdd'] = sc.parallelize(
+        [{"field1": 1, "field2": "row1"},
+         {"field1": 2, "field2": "row2"},
+         {"field1": 3, "field2": "row3"}]
+    )
+    jsonStrings = [
+        '{"field1": 1, "field2": "row1", "field3":{"field4":11}}',
+        '{"field1" : 2, "field3":{"field4":22, "field5": [10, 11]}, "field6":[{"field7": "row2"}]}',
+        '{"field1" : null, "field2": "row3", "field3":{"field4":33, "field5": []}}'
+    ]
     globs['jsonStrings'] = jsonStrings
     globs['json'] = sc.parallelize(jsonStrings)
     globs['nestedRdd1'] = sc.parallelize([
-        {"f1" : array('i', [1, 2]), "f2" : {"row1" : 1.0}},
-        {"f1" : array('i', [2, 3]), "f2" : {"row2" : 2.0}}])
+        {"f1": array('i', [1, 2]), "f2": {"row1": 1.0}},
+        {"f1": array('i', [2, 3]), "f2": {"row2": 2.0}}])
     globs['nestedRdd2'] = sc.parallelize([
-        {"f1" : [[1, 2], [2, 3]], "f2" : set([1, 2]), "f3" : (1, 2)},
-        {"f1" : [[2, 3], [3, 4]], "f2" : set([2, 3]), "f3" : (2, 3)}])
-    (failure_count, test_count) = doctest.testmod(globs=globs,optionflags=doctest.ELLIPSIS)
+        {"f1": [[1, 2], [2, 3]], "f2": set([1, 2]), "f3": (1, 2)},
+        {"f1": [[2, 3], [3, 4]], "f2": set([2, 3]), "f3": (2, 3)}])
+    (failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
     globs['sc'].stop()
     if failure_count:
         exit(-1)
@@ -514,4 +519,3 @@ def _test():
 
 if __name__ == "__main__":
     _test()
-
diff --git a/python/pyspark/statcounter.py b/python/pyspark/statcounter.py
index 080325061a69..e287bd3da1f6 100644
--- a/python/pyspark/statcounter.py
+++ b/python/pyspark/statcounter.py
@@ -20,18 +20,19 @@
 import copy
 import math
 
+
 class StatCounter(object):
-    
+
     def __init__(self, values=[]):
         self.n = 0L    # Running count of our values
         self.mu = 0.0  # Running mean of our values
         self.m2 = 0.0  # Running variance numerator (sum of (x - mean)^2)
         self.maxValue = float("-inf")
         self.minValue = float("inf")
-        
+
         for v in values:
             self.merge(v)
-            
+
     # Add a value into this StatCounter, updating the internal statistics.
     def merge(self, value):
         delta = value - self.mu
@@ -42,7 +43,7 @@ def merge(self, value):
             self.maxValue = value
         if self.minValue > value:
             self.minValue = value
-            
+
         return self
 
     # Merge another StatCounter into this one, adding up the internal statistics.
@@ -50,7 +51,7 @@ def mergeStats(self, other):
         if not isinstance(other, StatCounter):
             raise Exception("Can only merge Statcounters!")
 
-        if other is self: # reference equality holds
+        if other is self:  # reference equality holds
             self.merge(copy.deepcopy(other))  # Avoid overwriting fields in a weird order
         else:
             if self.n == 0:
@@ -59,8 +60,8 @@ def mergeStats(self, other):
                 self.n = other.n
                 self.maxValue = other.maxValue
                 self.minValue = other.minValue
-                
-            elif other.n != 0:        
+
+            elif other.n != 0:
                 delta = other.mu - self.mu
                 if other.n * 10 < self.n:
                     self.mu = self.mu + (delta * other.n) / (self.n + other.n)
@@ -68,10 +69,10 @@ def mergeStats(self, other):
                     self.mu = other.mu - (delta * self.n) / (self.n + other.n)
                 else:
                     self.mu = (self.mu * self.n + other.mu * other.n) / (self.n + other.n)
-                
+
                     self.maxValue = max(self.maxValue, other.maxValue)
                     self.minValue = min(self.minValue, other.minValue)
-        
+
                 self.m2 += other.m2 + (delta * delta * self.n * other.n) / (self.n + other.n)
                 self.n += other.n
         return self
@@ -94,7 +95,7 @@ def min(self):
 
     def max(self):
         return self.maxValue
-    
+
     # Return the variance of the values.
     def variance(self):
         if self.n == 0:
@@ -124,5 +125,5 @@ def sampleStdev(self):
         return math.sqrt(self.sampleVariance())
 
     def __repr__(self):
-        return "(count: %s, mean: %s, stdev: %s, max: %s, min: %s)" % (self.count(), self.mean(), self.stdev(), self.max(), self.min())
-
+        return ("(count: %s, mean: %s, stdev: %s, max: %s, min: %s)" %
+                (self.count(), self.mean(), self.stdev(), self.max(), self.min()))
diff --git a/python/pyspark/storagelevel.py b/python/pyspark/storagelevel.py
index 3a18ea54eae4..5d77a131f285 100644
--- a/python/pyspark/storagelevel.py
+++ b/python/pyspark/storagelevel.py
@@ -17,6 +17,7 @@
 
 __all__ = ["StorageLevel"]
 
+
 class StorageLevel:
     """
     Flags for controlling the storage of an RDD. Each StorageLevel records whether to use memory,
@@ -25,7 +26,7 @@ class StorageLevel:
     Also contains static constants for some commonly used storage levels, such as MEMORY_ONLY.
     """
 
-    def __init__(self, useDisk, useMemory, useOffHeap, deserialized, replication = 1):
+    def __init__(self, useDisk, useMemory, useOffHeap, deserialized, replication=1):
         self.useDisk = useDisk
         self.useMemory = useMemory
         self.useOffHeap = useOffHeap
@@ -55,4 +56,4 @@ def __str__(self):
 StorageLevel.MEMORY_AND_DISK_2 = StorageLevel(True, True, False, True, 2)
 StorageLevel.MEMORY_AND_DISK_SER = StorageLevel(True, True, False, False)
 StorageLevel.MEMORY_AND_DISK_SER_2 = StorageLevel(True, True, False, False, 2)
-StorageLevel.OFF_HEAP = StorageLevel(False, False, True, False, 1)
\ No newline at end of file
+StorageLevel.OFF_HEAP = StorageLevel(False, False, True, False, 1)
diff --git a/python/pyspark/tests.py b/python/pyspark/tests.py
index c15bb457759e..9c5ecd0bb02a 100644
--- a/python/pyspark/tests.py
+++ b/python/pyspark/tests.py
@@ -52,12 +52,13 @@ class PySparkTestCase(unittest.TestCase):
     def setUp(self):
         self._old_sys_path = list(sys.path)
         class_name = self.__class__.__name__
-        self.sc = SparkContext('local[4]', class_name , batchSize=2)
+        self.sc = SparkContext('local[4]', class_name, batchSize=2)
 
     def tearDown(self):
         self.sc.stop()
         sys.path = self._old_sys_path
 
+
 class TestCheckpoint(PySparkTestCase):
 
     def setUp(self):
@@ -190,6 +191,7 @@ def test_deleting_input_files(self):
 
     def testAggregateByKey(self):
         data = self.sc.parallelize([(1, 1), (1, 1), (3, 2), (5, 1), (5, 3)], 2)
+
         def seqOp(x, y):
             x.add(y)
             return x
@@ -197,17 +199,19 @@ def seqOp(x, y):
         def combOp(x, y):
             x |= y
             return x
-          
+
         sets = dict(data.aggregateByKey(set(), seqOp, combOp).collect())
         self.assertEqual(3, len(sets))
         self.assertEqual(set([1]), sets[1])
         self.assertEqual(set([2]), sets[3])
         self.assertEqual(set([1, 3]), sets[5])
 
+
 class TestIO(PySparkTestCase):
 
     def test_stdout_redirection(self):
         import subprocess
+
         def func(x):
             subprocess.check_call('ls', shell=True)
         self.sc.parallelize([1]).foreach(func)
@@ -479,7 +483,7 @@ def test_module_dependency(self):
             |    return x + 1
             """)
         proc = subprocess.Popen([self.sparkSubmit, "--py-files", zip, script],
-            stdout=subprocess.PIPE)
+                                stdout=subprocess.PIPE)
         out, err = proc.communicate()
         self.assertEqual(0, proc.returncode)
         self.assertIn("[2, 3, 4]", out)
diff --git a/python/pyspark/worker.py b/python/pyspark/worker.py
index f43210c6c030..24d41b12d1b1 100644
--- a/python/pyspark/worker.py
+++ b/python/pyspark/worker.py
@@ -57,8 +57,8 @@ def main(infile, outfile):
         SparkFiles._is_running_on_worker = True
 
         # fetch names of includes (*.zip and *.egg files) and construct PYTHONPATH
-        sys.path.append(spark_files_dir) # *.py files that were added will be copied here
-        num_python_includes =  read_int(infile)
+        sys.path.append(spark_files_dir)  # *.py files that were added will be copied here
+        num_python_includes = read_int(infile)
         for _ in range(num_python_includes):
             filename = utf8_deserializer.loads(infile)
             sys.path.append(os.path.join(spark_files_dir, filename))

From 81fec9922c5a1a44e086fba450c3eea03cddce63 Mon Sep 17 00:00:00 2001
From: Prashant Sharma <prashant@apache.org>
Date: Tue, 22 Jul 2014 00:38:26 -0700
Subject: [PATCH 721/810] [SPARK-2452] Create a new valid for each  instead of
 using lineId.

Author: Prashant Sharma <prashant@apache.org>

Closes #1441 from ScrapCodes/SPARK-2452/multi-statement and squashes the following commits:

26c5c72 [Prashant Sharma] Added a test case.
7e8d28d [Prashant Sharma] SPARK-2452, create a new valid for each  instead of using lineId, because Line ids can be same sometimes.
---
 .../scala/org/apache/spark/repl/SparkImports.scala    |  8 +++++++-
 .../test/scala/org/apache/spark/repl/ReplSuite.scala  | 11 ++++++++++-
 2 files changed, 17 insertions(+), 2 deletions(-)

diff --git a/repl/src/main/scala/org/apache/spark/repl/SparkImports.scala b/repl/src/main/scala/org/apache/spark/repl/SparkImports.scala
index bce5c74b9d0d..9099e052f579 100644
--- a/repl/src/main/scala/org/apache/spark/repl/SparkImports.scala
+++ b/repl/src/main/scala/org/apache/spark/repl/SparkImports.scala
@@ -197,7 +197,7 @@ trait SparkImports {
           for (imv <- x.definedNames) {
             if (currentImps contains imv) addWrapper()
             val objName = req.lineRep.readPath
-            val valName = "$VAL" + req.lineRep.lineId
+            val valName = "$VAL" + newValId()
 
             if(!code.toString.endsWith(".`" + imv + "`;\n")) { // Which means already imported
                 code.append("val " + valName + " = " + objName + ".INSTANCE;\n")
@@ -222,4 +222,10 @@ trait SparkImports {
   private def membersAtPickler(sym: Symbol): List[Symbol] =
     beforePickler(sym.info.nonPrivateMembers.toList)
 
+  private var curValId = 0
+
+  private def newValId(): Int = {
+    curValId += 1
+    curValId
+  }
 }
diff --git a/repl/src/test/scala/org/apache/spark/repl/ReplSuite.scala b/repl/src/test/scala/org/apache/spark/repl/ReplSuite.scala
index f2aa42dbcb4f..e2d8d5ff38db 100644
--- a/repl/src/test/scala/org/apache/spark/repl/ReplSuite.scala
+++ b/repl/src/test/scala/org/apache/spark/repl/ReplSuite.scala
@@ -235,7 +235,7 @@ class ReplSuite extends FunSuite {
     assertContains("res4: Array[Int] = Array(0, 0, 0, 0, 0)", output)
   }
 
-  test("SPARK-1199-simple-reproduce") {
+  test("SPARK-1199 two instances of same class don't type check.") {
     val output = runInterpreter("local-cluster[1,1,512]",
       """
         |case class Sum(exp: String, exp2: String)
@@ -247,6 +247,15 @@ class ReplSuite extends FunSuite {
     assertDoesNotContain("Exception", output)
   }
 
+  test("SPARK-2452 compound statements.") {
+    val output = runInterpreter("local",
+      """
+        |val x = 4 ; def f() = x
+        |f()
+      """.stripMargin)
+    assertDoesNotContain("error:", output)
+    assertDoesNotContain("Exception", output)
+  }
   if (System.getenv("MESOS_NATIVE_LIBRARY") != null) {
     test("running on Mesos") {
       val output = runInterpreter("localquiet",

From 75db1742abf9e08111ddf8f330e6561c5520a86c Mon Sep 17 00:00:00 2001
From: "peng.zhang" <peng.zhang@xiaomi.com>
Date: Tue, 22 Jul 2014 02:39:07 -0700
Subject: [PATCH 722/810] [SPARK-2612] [mllib] Fix data skew in ALS

Author: peng.zhang <peng.zhang@xiaomi.com>

Closes #1521 from renozhang/fix-als and squashes the following commits:

b5727a4 [peng.zhang] Remove no need argument
1a4f7a0 [peng.zhang] Fix data skew in ALS
---
 .../org/apache/spark/mllib/recommendation/ALS.scala   | 11 +++++------
 1 file changed, 5 insertions(+), 6 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala
index cc56fd6ef28d..15e8855db6ca 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala
@@ -252,14 +252,14 @@ class ALS private (
         val YtY = Some(sc.broadcast(computeYtY(users)))
         val previousProducts = products
         products = updateFeatures(numProductBlocks, users, userOutLinks, productInLinks,
-          userPartitioner, rank, lambda, alpha, YtY)
+          rank, lambda, alpha, YtY)
         previousProducts.unpersist()
         logInfo("Re-computing U given I (Iteration %d/%d)".format(iter, iterations))
         products.setName(s"products-$iter").persist()
         val XtX = Some(sc.broadcast(computeYtY(products)))
         val previousUsers = users
         users = updateFeatures(numUserBlocks, products, productOutLinks, userInLinks,
-          productPartitioner, rank, lambda, alpha, XtX)
+          rank, lambda, alpha, XtX)
         previousUsers.unpersist()
       }
     } else {
@@ -267,11 +267,11 @@ class ALS private (
         // perform ALS update
         logInfo("Re-computing I given U (Iteration %d/%d)".format(iter, iterations))
         products = updateFeatures(numProductBlocks, users, userOutLinks, productInLinks,
-          userPartitioner, rank, lambda, alpha, YtY = None)
+          rank, lambda, alpha, YtY = None)
         products.setName(s"products-$iter")
         logInfo("Re-computing U given I (Iteration %d/%d)".format(iter, iterations))
         users = updateFeatures(numUserBlocks, products, productOutLinks, userInLinks,
-          productPartitioner, rank, lambda, alpha, YtY = None)
+          rank, lambda, alpha, YtY = None)
         users.setName(s"users-$iter")
       }
     }
@@ -464,7 +464,6 @@ class ALS private (
       products: RDD[(Int, Array[Array[Double]])],
       productOutLinks: RDD[(Int, OutLinkBlock)],
       userInLinks: RDD[(Int, InLinkBlock)],
-      productPartitioner: Partitioner,
       rank: Int,
       lambda: Double,
       alpha: Double,
@@ -477,7 +476,7 @@ class ALS private (
           }
         }
         toSend.zipWithIndex.map{ case (buf, idx) => (idx, (bid, buf.toArray)) }
-    }.groupByKey(productPartitioner)
+    }.groupByKey(new HashPartitioner(numUserBlocks))
      .join(userInLinks)
      .mapValues{ case (messages, inLinkBlock) =>
         updateBlock(messages, inLinkBlock, rank, lambda, alpha, YtY)

From 1407871733176c92c67ac547adf603c41a772f7f Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Tue, 22 Jul 2014 11:45:37 -0700
Subject: [PATCH 723/810] [MLLIB] make Mima ignore updateFeatures (private) in
 ALS

Fix Mima issues in #1521.

Author: Xiangrui Meng <meng@databricks.com>

Closes #1533 from mengxr/mima-als and squashes the following commits:

78386e1 [Xiangrui Meng] make Mima ignore updateFeatures (private) in ALS
---
 project/MimaExcludes.scala | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala
index 4d86e1a0d8bb..5e5ddd227aab 100644
--- a/project/MimaExcludes.scala
+++ b/project/MimaExcludes.scala
@@ -82,7 +82,9 @@ object MimaExcludes {
         ProblemFilters.exclude[MissingMethodProblem]( // The only public constructor is the one without arguments.
           "org.apache.spark.mllib.recommendation.ALS.this"),
         ProblemFilters.exclude[MissingMethodProblem](
-          "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$<init>$default$7")
+          "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$<init>$default$7"),
+        ProblemFilters.exclude[IncompatibleMethTypeProblem](
+          "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$^dateFeatures")
       ) ++
       MimaBuild.excludeSparkClass("mllib.linalg.distributed.ColumnStatisticsAggregator") ++
       MimaBuild.excludeSparkClass("rdd.ZippedRDD") ++

From 85d3596e65512d481f4be54df100be6bdc9c8e29 Mon Sep 17 00:00:00 2001
From: Aaron Davidson <aaron@databricks.com>
Date: Tue, 22 Jul 2014 11:58:53 -0700
Subject: [PATCH 724/810] SPARK-2047: Introduce an in-mem Sorter, and use it to
 reduce mem usage

### Why and what?
Currently, the AppendOnlyMap performs an "in-place" sort by converting its array of [key, value, key, value] pairs into a an array of [(key, value), (key, value)] pairs. However, this causes us to allocate many Tuple2 objects, which come at a nontrivial overhead.

This patch adds a Sorter API, intended for in memory sorts, which simply ports the Android Timsort implementation (available under Apache v2) and abstracts the interface in a way which introduces no more than 1 virtual function invocation of overhead at each abstraction point.

Please compare our port of the Android Timsort sort with the original implementation: http://www.diffchecker.com/wiwrykcl

### Memory implications
An AppendOnlyMap contains N kv pairs, which results in roughly 2N elements within its underlying array. Each of these elements is 4 bytes wide in a [compressed OOPS](https://wikis.oracle.com/display/HotSpotInternals/CompressedOops) system, which is the default.

Today's approach immediately allocates N Tuple2 objects, which take up 24N bytes in total (exposed via YourKit), and undergoes a Java sort. The Java 6 version immediately copies the entire array (4N bytes here), while the Java 7 version has a worst-case allocation of half the array (2N bytes).
This results in a worst-case sorting overhead of 24N + 2N = 26N bytes (for Java 7).

The Sorter does not require allocating any tuples, but since it uses Timsort, it may copy up to half the entire array in the worst case.
This results in a worst-case sorting overhead of 4N bytes.

Thus, we have reduced the worst-case overhead of the sort by roughly 22 bytes times the number of elements.

### Performance implications
As the destructiveSortedIterator is used for spilling in an ExternalAppendOnlyMap, the purpose of this patch is to provide stability by reducing memory usage rather than improve performance. However, because it implements Timsort, it also brings a substantial performance boost over our prior implementation.

Here are the results of a microbenchmark that sorted 25 million, randomly distributed (Float, Int) pairs. The Java Arrays.sort() tests were run **only on the keys**, and thus moved less data. Our current implementation is called "Tuple-sort using Arrays.sort()" while the new implementation is "KV-array using Sorter".

<table>
<tr><th>Test</th><th>First run (JDK6)</th><th>Average of 10 (JDK6)</th><th>First run (JDK7)</th><th>Average of 10 (JDK7)</th></tr>
<tr><td>primitive Arrays.sort()</td><td>3216 ms</td><td>1190 ms</td><td>2724 ms</td><td>131 ms (!!)</td></tr>
<tr><td>Arrays.sort()</td><td>18564 ms</td><td>2006 ms</td><td>13201 ms</td><td>878 ms</td></tr>
<tr><td>Tuple-sort using Arrays.sort()</td><td>31813 ms</td><td>3550 ms</td><td>20990 ms</td><td>1919 ms</td></tr>
<tr><td><b>KV-array using Sorter</b></td><td></td><td></td><td><b>15020 ms</b></td><td><b>834 ms</b></td></tr>
</table>

The results show that this Sorter performs exactly as expected (after the first run) -- it is as fast as the Java 7 Arrays.sort() (which shares the same algorithm), but is significantly faster than the Tuple-sort on Java 6 or 7.

In short, this patch should significantly improve performance for users running either Java 6 or 7.

Author: Aaron Davidson <aaron@databricks.com>

Closes #1502 from aarondav/sort and squashes the following commits:

652d936 [Aaron Davidson] Update license, move Sorter to java src
a7b5b1c [Aaron Davidson] fix licenses
5c0efaf [Aaron Davidson] Update tmpLength
ec395c8 [Aaron Davidson] Ignore benchmark (again) and fix docs
034bf10 [Aaron Davidson] Change to Apache v2 Timsort
b97296c [Aaron Davidson] Don't try to run benchmark on Jenkins + private[spark]
6307338 [Aaron Davidson] SPARK-2047: Introduce an in-mem Sorter, and use it to reduce mem usage
---
 LICENSE                                       |  20 +-
 .../apache/spark/util/collection/Sorter.java  | 915 ++++++++++++++++++
 .../spark/util/collection/AppendOnlyMap.scala |  15 +-
 .../collection/ExternalAppendOnlyMap.scala    |  35 +-
 .../util/collection/SortDataFormat.scala      |  94 ++
 .../util/collection/AppendOnlyMapSuite.scala  |   8 +-
 .../spark/util/collection/SorterSuite.scala   | 167 ++++
 7 files changed, 1222 insertions(+), 32 deletions(-)
 create mode 100644 core/src/main/java/org/apache/spark/util/collection/Sorter.java
 create mode 100644 core/src/main/scala/org/apache/spark/util/collection/SortDataFormat.scala
 create mode 100644 core/src/test/scala/org/apache/spark/util/collection/SorterSuite.scala

diff --git a/LICENSE b/LICENSE
index 383f079df8c8..65e1f480d9b1 100644
--- a/LICENSE
+++ b/LICENSE
@@ -442,7 +442,7 @@ Written by Pavel Binko, Dino Ferrero Merlino, Wolfgang Hoschek, Tony Johnson, An
 
 
 ========================================================================
-Fo SnapTree:
+For SnapTree:
 ========================================================================
 
 SNAPTREE LICENSE
@@ -482,6 +482,24 @@ OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF
 SUCH DAMAGE.
 
 
+========================================================================
+For Timsort (core/src/main/java/org/apache/spark/util/collection/Sorter.java):
+========================================================================
+Copyright (C) 2008 The Android Open Source Project
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+
+
 ========================================================================
 BSD-style licenses
 ========================================================================
diff --git a/core/src/main/java/org/apache/spark/util/collection/Sorter.java b/core/src/main/java/org/apache/spark/util/collection/Sorter.java
new file mode 100644
index 000000000000..64ad18c0e463
--- /dev/null
+++ b/core/src/main/java/org/apache/spark/util/collection/Sorter.java
@@ -0,0 +1,915 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.util.collection;
+
+import java.util.Comparator;
+
+/**
+ * A port of the Android Timsort class, which utilizes a "stable, adaptive, iterative mergesort."
+ * See the method comment on sort() for more details.
+ *
+ * This has been kept in Java with the original style in order to match very closely with the
+ * Anroid source code, and thus be easy to verify correctness.
+ *
+ * The purpose of the port is to generalize the interface to the sort to accept input data formats
+ * besides simple arrays where every element is sorted individually. For instance, the AppendOnlyMap
+ * uses this to sort an Array with alternating elements of the form [key, value, key, value].
+ * This generalization comes with minimal overhead -- see SortDataFormat for more information.
+ */
+class Sorter<K, Buffer> {
+
+  /**
+   * This is the minimum sized sequence that will be merged.  Shorter
+   * sequences will be lengthened by calling binarySort.  If the entire
+   * array is less than this length, no merges will be performed.
+   *
+   * This constant should be a power of two.  It was 64 in Tim Peter's C
+   * implementation, but 32 was empirically determined to work better in
+   * this implementation.  In the unlikely event that you set this constant
+   * to be a number that's not a power of two, you'll need to change the
+   * minRunLength computation.
+   *
+   * If you decrease this constant, you must change the stackLen
+   * computation in the TimSort constructor, or you risk an
+   * ArrayOutOfBounds exception.  See listsort.txt for a discussion
+   * of the minimum stack length required as a function of the length
+   * of the array being sorted and the minimum merge sequence length.
+   */
+  private static final int MIN_MERGE = 32;
+
+  private final SortDataFormat<K, Buffer> s;
+
+  public Sorter(SortDataFormat<K, Buffer> sortDataFormat) {
+    this.s = sortDataFormat;
+  }
+
+  /**
+   * A stable, adaptive, iterative mergesort that requires far fewer than
+   * n lg(n) comparisons when running on partially sorted arrays, while
+   * offering performance comparable to a traditional mergesort when run
+   * on random arrays.  Like all proper mergesorts, this sort is stable and
+   * runs O(n log n) time (worst case).  In the worst case, this sort requires
+   * temporary storage space for n/2 object references; in the best case,
+   * it requires only a small constant amount of space.
+   *
+   * This implementation was adapted from Tim Peters's list sort for
+   * Python, which is described in detail here:
+   *
+   *   http://svn.python.org/projects/python/trunk/Objects/listsort.txt
+   *
+   * Tim's C code may be found here:
+   *
+   *   http://svn.python.org/projects/python/trunk/Objects/listobject.c
+   *
+   * The underlying techniques are described in this paper (and may have
+   * even earlier origins):
+   *
+   *  "Optimistic Sorting and Information Theoretic Complexity"
+   *  Peter McIlroy
+   *  SODA (Fourth Annual ACM-SIAM Symposium on Discrete Algorithms),
+   *  pp 467-474, Austin, Texas, 25-27 January 1993.
+   *
+   * While the API to this class consists solely of static methods, it is
+   * (privately) instantiable; a TimSort instance holds the state of an ongoing
+   * sort, assuming the input array is large enough to warrant the full-blown
+   * TimSort. Small arrays are sorted in place, using a binary insertion sort.
+   *
+   * @author Josh Bloch
+   */
+  void sort(Buffer a, int lo, int hi, Comparator<? super K> c) {
+    assert c != null;
+
+    int nRemaining  = hi - lo;
+    if (nRemaining < 2)
+      return;  // Arrays of size 0 and 1 are always sorted
+
+    // If array is small, do a "mini-TimSort" with no merges
+    if (nRemaining < MIN_MERGE) {
+      int initRunLen = countRunAndMakeAscending(a, lo, hi, c);
+      binarySort(a, lo, hi, lo + initRunLen, c);
+      return;
+    }
+
+    /**
+     * March over the array once, left to right, finding natural runs,
+     * extending short natural runs to minRun elements, and merging runs
+     * to maintain stack invariant.
+     */
+    SortState sortState = new SortState(a, c, hi - lo);
+    int minRun = minRunLength(nRemaining);
+    do {
+      // Identify next run
+      int runLen = countRunAndMakeAscending(a, lo, hi, c);
+
+      // If run is short, extend to min(minRun, nRemaining)
+      if (runLen < minRun) {
+        int force = nRemaining <= minRun ? nRemaining : minRun;
+        binarySort(a, lo, lo + force, lo + runLen, c);
+        runLen = force;
+      }
+
+      // Push run onto pending-run stack, and maybe merge
+      sortState.pushRun(lo, runLen);
+      sortState.mergeCollapse();
+
+      // Advance to find next run
+      lo += runLen;
+      nRemaining -= runLen;
+    } while (nRemaining != 0);
+
+    // Merge all remaining runs to complete sort
+    assert lo == hi;
+    sortState.mergeForceCollapse();
+    assert sortState.stackSize == 1;
+  }
+
+  /**
+   * Sorts the specified portion of the specified array using a binary
+   * insertion sort.  This is the best method for sorting small numbers
+   * of elements.  It requires O(n log n) compares, but O(n^2) data
+   * movement (worst case).
+   *
+   * If the initial part of the specified range is already sorted,
+   * this method can take advantage of it: the method assumes that the
+   * elements from index {@code lo}, inclusive, to {@code start},
+   * exclusive are already sorted.
+   *
+   * @param a the array in which a range is to be sorted
+   * @param lo the index of the first element in the range to be sorted
+   * @param hi the index after the last element in the range to be sorted
+   * @param start the index of the first element in the range that is
+   *        not already known to be sorted ({@code lo <= start <= hi})
+   * @param c comparator to used for the sort
+   */
+  @SuppressWarnings("fallthrough")
+  private void binarySort(Buffer a, int lo, int hi, int start, Comparator<? super K> c) {
+    assert lo <= start && start <= hi;
+    if (start == lo)
+      start++;
+
+    Buffer pivotStore = s.allocate(1);
+    for ( ; start < hi; start++) {
+      s.copyElement(a, start, pivotStore, 0);
+      K pivot = s.getKey(pivotStore, 0);
+
+      // Set left (and right) to the index where a[start] (pivot) belongs
+      int left = lo;
+      int right = start;
+      assert left <= right;
+      /*
+       * Invariants:
+       *   pivot >= all in [lo, left).
+       *   pivot <  all in [right, start).
+       */
+      while (left < right) {
+        int mid = (left + right) >>> 1;
+        if (c.compare(pivot, s.getKey(a, mid)) < 0)
+          right = mid;
+        else
+          left = mid + 1;
+      }
+      assert left == right;
+
+      /*
+       * The invariants still hold: pivot >= all in [lo, left) and
+       * pivot < all in [left, start), so pivot belongs at left.  Note
+       * that if there are elements equal to pivot, left points to the
+       * first slot after them -- that's why this sort is stable.
+       * Slide elements over to make room for pivot.
+       */
+      int n = start - left;  // The number of elements to move
+      // Switch is just an optimization for arraycopy in default case
+      switch (n) {
+        case 2:  s.copyElement(a, left + 1, a, left + 2);
+        case 1:  s.copyElement(a, left, a, left + 1);
+          break;
+        default: s.copyRange(a, left, a, left + 1, n);
+      }
+      s.copyElement(pivotStore, 0, a, left);
+    }
+  }
+
+  /**
+   * Returns the length of the run beginning at the specified position in
+   * the specified array and reverses the run if it is descending (ensuring
+   * that the run will always be ascending when the method returns).
+   *
+   * A run is the longest ascending sequence with:
+   *
+   *    a[lo] <= a[lo + 1] <= a[lo + 2] <= ...
+   *
+   * or the longest descending sequence with:
+   *
+   *    a[lo] >  a[lo + 1] >  a[lo + 2] >  ...
+   *
+   * For its intended use in a stable mergesort, the strictness of the
+   * definition of "descending" is needed so that the call can safely
+   * reverse a descending sequence without violating stability.
+   *
+   * @param a the array in which a run is to be counted and possibly reversed
+   * @param lo index of the first element in the run
+   * @param hi index after the last element that may be contained in the run.
+  It is required that {@code lo < hi}.
+   * @param c the comparator to used for the sort
+   * @return  the length of the run beginning at the specified position in
+   *          the specified array
+   */
+  private int countRunAndMakeAscending(Buffer a, int lo, int hi, Comparator<? super K> c) {
+    assert lo < hi;
+    int runHi = lo + 1;
+    if (runHi == hi)
+      return 1;
+
+    // Find end of run, and reverse range if descending
+    if (c.compare(s.getKey(a, runHi++), s.getKey(a, lo)) < 0) { // Descending
+      while (runHi < hi && c.compare(s.getKey(a, runHi), s.getKey(a, runHi - 1)) < 0)
+        runHi++;
+      reverseRange(a, lo, runHi);
+    } else {                              // Ascending
+      while (runHi < hi && c.compare(s.getKey(a, runHi), s.getKey(a, runHi - 1)) >= 0)
+        runHi++;
+    }
+
+    return runHi - lo;
+  }
+
+  /**
+   * Reverse the specified range of the specified array.
+   *
+   * @param a the array in which a range is to be reversed
+   * @param lo the index of the first element in the range to be reversed
+   * @param hi the index after the last element in the range to be reversed
+   */
+  private void reverseRange(Buffer a, int lo, int hi) {
+    hi--;
+    while (lo < hi) {
+      s.swap(a, lo, hi);
+      lo++;
+      hi--;
+    }
+  }
+
+  /**
+   * Returns the minimum acceptable run length for an array of the specified
+   * length. Natural runs shorter than this will be extended with
+   * {@link #binarySort}.
+   *
+   * Roughly speaking, the computation is:
+   *
+   *  If n < MIN_MERGE, return n (it's too small to bother with fancy stuff).
+   *  Else if n is an exact power of 2, return MIN_MERGE/2.
+   *  Else return an int k, MIN_MERGE/2 <= k <= MIN_MERGE, such that n/k
+   *   is close to, but strictly less than, an exact power of 2.
+   *
+   * For the rationale, see listsort.txt.
+   *
+   * @param n the length of the array to be sorted
+   * @return the length of the minimum run to be merged
+   */
+  private int minRunLength(int n) {
+    assert n >= 0;
+    int r = 0;      // Becomes 1 if any 1 bits are shifted off
+    while (n >= MIN_MERGE) {
+      r |= (n & 1);
+      n >>= 1;
+    }
+    return n + r;
+  }
+
+  private class SortState {
+
+    /**
+     * The Buffer being sorted.
+     */
+    private final Buffer a;
+
+    /**
+     * Length of the sort Buffer.
+     */
+    private final int aLength;
+
+    /**
+     * The comparator for this sort.
+     */
+    private final Comparator<? super K> c;
+
+    /**
+     * When we get into galloping mode, we stay there until both runs win less
+     * often than MIN_GALLOP consecutive times.
+     */
+    private static final int  MIN_GALLOP = 7;
+
+    /**
+     * This controls when we get *into* galloping mode.  It is initialized
+     * to MIN_GALLOP.  The mergeLo and mergeHi methods nudge it higher for
+     * random data, and lower for highly structured data.
+     */
+    private int minGallop = MIN_GALLOP;
+
+    /**
+     * Maximum initial size of tmp array, which is used for merging.  The array
+     * can grow to accommodate demand.
+     *
+     * Unlike Tim's original C version, we do not allocate this much storage
+     * when sorting smaller arrays.  This change was required for performance.
+     */
+    private static final int INITIAL_TMP_STORAGE_LENGTH = 256;
+
+    /**
+     * Temp storage for merges.
+     */
+    private Buffer tmp; // Actual runtime type will be Object[], regardless of T
+
+    /**
+     * Length of the temp storage.
+     */
+    private int tmpLength = 0;
+
+    /**
+     * A stack of pending runs yet to be merged.  Run i starts at
+     * address base[i] and extends for len[i] elements.  It's always
+     * true (so long as the indices are in bounds) that:
+     *
+     *     runBase[i] + runLen[i] == runBase[i + 1]
+     *
+     * so we could cut the storage for this, but it's a minor amount,
+     * and keeping all the info explicit simplifies the code.
+     */
+    private int stackSize = 0;  // Number of pending runs on stack
+    private final int[] runBase;
+    private final int[] runLen;
+
+    /**
+     * Creates a TimSort instance to maintain the state of an ongoing sort.
+     *
+     * @param a the array to be sorted
+     * @param c the comparator to determine the order of the sort
+     */
+    private SortState(Buffer a, Comparator<? super K> c, int len) {
+      this.aLength = len;
+      this.a = a;
+      this.c = c;
+
+      // Allocate temp storage (which may be increased later if necessary)
+      tmpLength = len < 2 * INITIAL_TMP_STORAGE_LENGTH ? len >>> 1 : INITIAL_TMP_STORAGE_LENGTH;
+      tmp = s.allocate(tmpLength);
+
+      /*
+       * Allocate runs-to-be-merged stack (which cannot be expanded).  The
+       * stack length requirements are described in listsort.txt.  The C
+       * version always uses the same stack length (85), but this was
+       * measured to be too expensive when sorting "mid-sized" arrays (e.g.,
+       * 100 elements) in Java.  Therefore, we use smaller (but sufficiently
+       * large) stack lengths for smaller arrays.  The "magic numbers" in the
+       * computation below must be changed if MIN_MERGE is decreased.  See
+       * the MIN_MERGE declaration above for more information.
+       */
+      int stackLen = (len <    120  ?  5 :
+                      len <   1542  ? 10 :
+                      len < 119151  ? 19 : 40);
+      runBase = new int[stackLen];
+      runLen = new int[stackLen];
+    }
+
+    /**
+     * Pushes the specified run onto the pending-run stack.
+     *
+     * @param runBase index of the first element in the run
+     * @param runLen  the number of elements in the run
+     */
+    private void pushRun(int runBase, int runLen) {
+      this.runBase[stackSize] = runBase;
+      this.runLen[stackSize] = runLen;
+      stackSize++;
+    }
+
+    /**
+     * Examines the stack of runs waiting to be merged and merges adjacent runs
+     * until the stack invariants are reestablished:
+     *
+     *     1. runLen[i - 3] > runLen[i - 2] + runLen[i - 1]
+     *     2. runLen[i - 2] > runLen[i - 1]
+     *
+     * This method is called each time a new run is pushed onto the stack,
+     * so the invariants are guaranteed to hold for i < stackSize upon
+     * entry to the method.
+     */
+    private void mergeCollapse() {
+      while (stackSize > 1) {
+        int n = stackSize - 2;
+        if (n > 0 && runLen[n-1] <= runLen[n] + runLen[n+1]) {
+          if (runLen[n - 1] < runLen[n + 1])
+            n--;
+          mergeAt(n);
+        } else if (runLen[n] <= runLen[n + 1]) {
+          mergeAt(n);
+        } else {
+          break; // Invariant is established
+        }
+      }
+    }
+
+    /**
+     * Merges all runs on the stack until only one remains.  This method is
+     * called once, to complete the sort.
+     */
+    private void mergeForceCollapse() {
+      while (stackSize > 1) {
+        int n = stackSize - 2;
+        if (n > 0 && runLen[n - 1] < runLen[n + 1])
+          n--;
+        mergeAt(n);
+      }
+    }
+
+    /**
+     * Merges the two runs at stack indices i and i+1.  Run i must be
+     * the penultimate or antepenultimate run on the stack.  In other words,
+     * i must be equal to stackSize-2 or stackSize-3.
+     *
+     * @param i stack index of the first of the two runs to merge
+     */
+    private void mergeAt(int i) {
+      assert stackSize >= 2;
+      assert i >= 0;
+      assert i == stackSize - 2 || i == stackSize - 3;
+
+      int base1 = runBase[i];
+      int len1 = runLen[i];
+      int base2 = runBase[i + 1];
+      int len2 = runLen[i + 1];
+      assert len1 > 0 && len2 > 0;
+      assert base1 + len1 == base2;
+
+      /*
+       * Record the length of the combined runs; if i is the 3rd-last
+       * run now, also slide over the last run (which isn't involved
+       * in this merge).  The current run (i+1) goes away in any case.
+       */
+      runLen[i] = len1 + len2;
+      if (i == stackSize - 3) {
+        runBase[i + 1] = runBase[i + 2];
+        runLen[i + 1] = runLen[i + 2];
+      }
+      stackSize--;
+
+      /*
+       * Find where the first element of run2 goes in run1. Prior elements
+       * in run1 can be ignored (because they're already in place).
+       */
+      int k = gallopRight(s.getKey(a, base2), a, base1, len1, 0, c);
+      assert k >= 0;
+      base1 += k;
+      len1 -= k;
+      if (len1 == 0)
+        return;
+
+      /*
+       * Find where the last element of run1 goes in run2. Subsequent elements
+       * in run2 can be ignored (because they're already in place).
+       */
+      len2 = gallopLeft(s.getKey(a, base1 + len1 - 1), a, base2, len2, len2 - 1, c);
+      assert len2 >= 0;
+      if (len2 == 0)
+        return;
+
+      // Merge remaining runs, using tmp array with min(len1, len2) elements
+      if (len1 <= len2)
+        mergeLo(base1, len1, base2, len2);
+      else
+        mergeHi(base1, len1, base2, len2);
+    }
+
+    /**
+     * Locates the position at which to insert the specified key into the
+     * specified sorted range; if the range contains an element equal to key,
+     * returns the index of the leftmost equal element.
+     *
+     * @param key the key whose insertion point to search for
+     * @param a the array in which to search
+     * @param base the index of the first element in the range
+     * @param len the length of the range; must be > 0
+     * @param hint the index at which to begin the search, 0 <= hint < n.
+     *     The closer hint is to the result, the faster this method will run.
+     * @param c the comparator used to order the range, and to search
+     * @return the int k,  0 <= k <= n such that a[b + k - 1] < key <= a[b + k],
+     *    pretending that a[b - 1] is minus infinity and a[b + n] is infinity.
+     *    In other words, key belongs at index b + k; or in other words,
+     *    the first k elements of a should precede key, and the last n - k
+     *    should follow it.
+     */
+    private int gallopLeft(K key, Buffer a, int base, int len, int hint, Comparator<? super K> c) {
+      assert len > 0 && hint >= 0 && hint < len;
+      int lastOfs = 0;
+      int ofs = 1;
+      if (c.compare(key, s.getKey(a, base + hint)) > 0) {
+        // Gallop right until a[base+hint+lastOfs] < key <= a[base+hint+ofs]
+        int maxOfs = len - hint;
+        while (ofs < maxOfs && c.compare(key, s.getKey(a, base + hint + ofs)) > 0) {
+          lastOfs = ofs;
+          ofs = (ofs << 1) + 1;
+          if (ofs <= 0)   // int overflow
+            ofs = maxOfs;
+        }
+        if (ofs > maxOfs)
+          ofs = maxOfs;
+
+        // Make offsets relative to base
+        lastOfs += hint;
+        ofs += hint;
+      } else { // key <= a[base + hint]
+        // Gallop left until a[base+hint-ofs] < key <= a[base+hint-lastOfs]
+        final int maxOfs = hint + 1;
+        while (ofs < maxOfs && c.compare(key, s.getKey(a, base + hint - ofs)) <= 0) {
+          lastOfs = ofs;
+          ofs = (ofs << 1) + 1;
+          if (ofs <= 0)   // int overflow
+            ofs = maxOfs;
+        }
+        if (ofs > maxOfs)
+          ofs = maxOfs;
+
+        // Make offsets relative to base
+        int tmp = lastOfs;
+        lastOfs = hint - ofs;
+        ofs = hint - tmp;
+      }
+      assert -1 <= lastOfs && lastOfs < ofs && ofs <= len;
+
+      /*
+       * Now a[base+lastOfs] < key <= a[base+ofs], so key belongs somewhere
+       * to the right of lastOfs but no farther right than ofs.  Do a binary
+       * search, with invariant a[base + lastOfs - 1] < key <= a[base + ofs].
+       */
+      lastOfs++;
+      while (lastOfs < ofs) {
+        int m = lastOfs + ((ofs - lastOfs) >>> 1);
+
+        if (c.compare(key, s.getKey(a, base + m)) > 0)
+          lastOfs = m + 1;  // a[base + m] < key
+        else
+          ofs = m;          // key <= a[base + m]
+      }
+      assert lastOfs == ofs;    // so a[base + ofs - 1] < key <= a[base + ofs]
+      return ofs;
+    }
+
+    /**
+     * Like gallopLeft, except that if the range contains an element equal to
+     * key, gallopRight returns the index after the rightmost equal element.
+     *
+     * @param key the key whose insertion point to search for
+     * @param a the array in which to search
+     * @param base the index of the first element in the range
+     * @param len the length of the range; must be > 0
+     * @param hint the index at which to begin the search, 0 <= hint < n.
+     *     The closer hint is to the result, the faster this method will run.
+     * @param c the comparator used to order the range, and to search
+     * @return the int k,  0 <= k <= n such that a[b + k - 1] <= key < a[b + k]
+     */
+    private int gallopRight(K key, Buffer a, int base, int len, int hint, Comparator<? super K> c) {
+      assert len > 0 && hint >= 0 && hint < len;
+
+      int ofs = 1;
+      int lastOfs = 0;
+      if (c.compare(key, s.getKey(a, base + hint)) < 0) {
+        // Gallop left until a[b+hint - ofs] <= key < a[b+hint - lastOfs]
+        int maxOfs = hint + 1;
+        while (ofs < maxOfs && c.compare(key, s.getKey(a, base + hint - ofs)) < 0) {
+          lastOfs = ofs;
+          ofs = (ofs << 1) + 1;
+          if (ofs <= 0)   // int overflow
+            ofs = maxOfs;
+        }
+        if (ofs > maxOfs)
+          ofs = maxOfs;
+
+        // Make offsets relative to b
+        int tmp = lastOfs;
+        lastOfs = hint - ofs;
+        ofs = hint - tmp;
+      } else { // a[b + hint] <= key
+        // Gallop right until a[b+hint + lastOfs] <= key < a[b+hint + ofs]
+        int maxOfs = len - hint;
+        while (ofs < maxOfs && c.compare(key, s.getKey(a, base + hint + ofs)) >= 0) {
+          lastOfs = ofs;
+          ofs = (ofs << 1) + 1;
+          if (ofs <= 0)   // int overflow
+            ofs = maxOfs;
+        }
+        if (ofs > maxOfs)
+          ofs = maxOfs;
+
+        // Make offsets relative to b
+        lastOfs += hint;
+        ofs += hint;
+      }
+      assert -1 <= lastOfs && lastOfs < ofs && ofs <= len;
+
+      /*
+       * Now a[b + lastOfs] <= key < a[b + ofs], so key belongs somewhere to
+       * the right of lastOfs but no farther right than ofs.  Do a binary
+       * search, with invariant a[b + lastOfs - 1] <= key < a[b + ofs].
+       */
+      lastOfs++;
+      while (lastOfs < ofs) {
+        int m = lastOfs + ((ofs - lastOfs) >>> 1);
+
+        if (c.compare(key, s.getKey(a, base + m)) < 0)
+          ofs = m;          // key < a[b + m]
+        else
+          lastOfs = m + 1;  // a[b + m] <= key
+      }
+      assert lastOfs == ofs;    // so a[b + ofs - 1] <= key < a[b + ofs]
+      return ofs;
+    }
+
+    /**
+     * Merges two adjacent runs in place, in a stable fashion.  The first
+     * element of the first run must be greater than the first element of the
+     * second run (a[base1] > a[base2]), and the last element of the first run
+     * (a[base1 + len1-1]) must be greater than all elements of the second run.
+     *
+     * For performance, this method should be called only when len1 <= len2;
+     * its twin, mergeHi should be called if len1 >= len2.  (Either method
+     * may be called if len1 == len2.)
+     *
+     * @param base1 index of first element in first run to be merged
+     * @param len1  length of first run to be merged (must be > 0)
+     * @param base2 index of first element in second run to be merged
+     *        (must be aBase + aLen)
+     * @param len2  length of second run to be merged (must be > 0)
+     */
+    private void mergeLo(int base1, int len1, int base2, int len2) {
+      assert len1 > 0 && len2 > 0 && base1 + len1 == base2;
+
+      // Copy first run into temp array
+      Buffer a = this.a; // For performance
+      Buffer tmp = ensureCapacity(len1);
+      s.copyRange(a, base1, tmp, 0, len1);
+
+      int cursor1 = 0;       // Indexes into tmp array
+      int cursor2 = base2;   // Indexes int a
+      int dest = base1;      // Indexes int a
+
+      // Move first element of second run and deal with degenerate cases
+      s.copyElement(a, cursor2++, a, dest++);
+      if (--len2 == 0) {
+        s.copyRange(tmp, cursor1, a, dest, len1);
+        return;
+      }
+      if (len1 == 1) {
+        s.copyRange(a, cursor2, a, dest, len2);
+        s.copyElement(tmp, cursor1, a, dest + len2); // Last elt of run 1 to end of merge
+        return;
+      }
+
+      Comparator<? super K> c = this.c;  // Use local variable for performance
+      int minGallop = this.minGallop;    //  "    "       "     "      "
+      outer:
+      while (true) {
+        int count1 = 0; // Number of times in a row that first run won
+        int count2 = 0; // Number of times in a row that second run won
+
+        /*
+         * Do the straightforward thing until (if ever) one run starts
+         * winning consistently.
+         */
+        do {
+          assert len1 > 1 && len2 > 0;
+          if (c.compare(s.getKey(a, cursor2), s.getKey(tmp, cursor1)) < 0) {
+            s.copyElement(a, cursor2++, a, dest++);
+            count2++;
+            count1 = 0;
+            if (--len2 == 0)
+              break outer;
+          } else {
+            s.copyElement(tmp, cursor1++, a, dest++);
+            count1++;
+            count2 = 0;
+            if (--len1 == 1)
+              break outer;
+          }
+        } while ((count1 | count2) < minGallop);
+
+        /*
+         * One run is winning so consistently that galloping may be a
+         * huge win. So try that, and continue galloping until (if ever)
+         * neither run appears to be winning consistently anymore.
+         */
+        do {
+          assert len1 > 1 && len2 > 0;
+          count1 = gallopRight(s.getKey(a, cursor2), tmp, cursor1, len1, 0, c);
+          if (count1 != 0) {
+            s.copyRange(tmp, cursor1, a, dest, count1);
+            dest += count1;
+            cursor1 += count1;
+            len1 -= count1;
+            if (len1 <= 1) // len1 == 1 || len1 == 0
+              break outer;
+          }
+          s.copyElement(a, cursor2++, a, dest++);
+          if (--len2 == 0)
+            break outer;
+
+          count2 = gallopLeft(s.getKey(tmp, cursor1), a, cursor2, len2, 0, c);
+          if (count2 != 0) {
+            s.copyRange(a, cursor2, a, dest, count2);
+            dest += count2;
+            cursor2 += count2;
+            len2 -= count2;
+            if (len2 == 0)
+              break outer;
+          }
+          s.copyElement(tmp, cursor1++, a, dest++);
+          if (--len1 == 1)
+            break outer;
+          minGallop--;
+        } while (count1 >= MIN_GALLOP | count2 >= MIN_GALLOP);
+        if (minGallop < 0)
+          minGallop = 0;
+        minGallop += 2;  // Penalize for leaving gallop mode
+      }  // End of "outer" loop
+      this.minGallop = minGallop < 1 ? 1 : minGallop;  // Write back to field
+
+      if (len1 == 1) {
+        assert len2 > 0;
+        s.copyRange(a, cursor2, a, dest, len2);
+        s.copyElement(tmp, cursor1, a, dest + len2); //  Last elt of run 1 to end of merge
+      } else if (len1 == 0) {
+        throw new IllegalArgumentException(
+            "Comparison method violates its general contract!");
+      } else {
+        assert len2 == 0;
+        assert len1 > 1;
+        s.copyRange(tmp, cursor1, a, dest, len1);
+      }
+    }
+
+    /**
+     * Like mergeLo, except that this method should be called only if
+     * len1 >= len2; mergeLo should be called if len1 <= len2.  (Either method
+     * may be called if len1 == len2.)
+     *
+     * @param base1 index of first element in first run to be merged
+     * @param len1  length of first run to be merged (must be > 0)
+     * @param base2 index of first element in second run to be merged
+     *        (must be aBase + aLen)
+     * @param len2  length of second run to be merged (must be > 0)
+     */
+    private void mergeHi(int base1, int len1, int base2, int len2) {
+      assert len1 > 0 && len2 > 0 && base1 + len1 == base2;
+
+      // Copy second run into temp array
+      Buffer a = this.a; // For performance
+      Buffer tmp = ensureCapacity(len2);
+      s.copyRange(a, base2, tmp, 0, len2);
+
+      int cursor1 = base1 + len1 - 1;  // Indexes into a
+      int cursor2 = len2 - 1;          // Indexes into tmp array
+      int dest = base2 + len2 - 1;     // Indexes into a
+
+      // Move last element of first run and deal with degenerate cases
+      s.copyElement(a, cursor1--, a, dest--);
+      if (--len1 == 0) {
+        s.copyRange(tmp, 0, a, dest - (len2 - 1), len2);
+        return;
+      }
+      if (len2 == 1) {
+        dest -= len1;
+        cursor1 -= len1;
+        s.copyRange(a, cursor1 + 1, a, dest + 1, len1);
+        s.copyElement(tmp, cursor2, a, dest);
+        return;
+      }
+
+      Comparator<? super K> c = this.c;  // Use local variable for performance
+      int minGallop = this.minGallop;    //  "    "       "     "      "
+      outer:
+      while (true) {
+        int count1 = 0; // Number of times in a row that first run won
+        int count2 = 0; // Number of times in a row that second run won
+
+        /*
+         * Do the straightforward thing until (if ever) one run
+         * appears to win consistently.
+         */
+        do {
+          assert len1 > 0 && len2 > 1;
+          if (c.compare(s.getKey(tmp, cursor2), s.getKey(a, cursor1)) < 0) {
+            s.copyElement(a, cursor1--, a, dest--);
+            count1++;
+            count2 = 0;
+            if (--len1 == 0)
+              break outer;
+          } else {
+            s.copyElement(tmp, cursor2--, a, dest--);
+            count2++;
+            count1 = 0;
+            if (--len2 == 1)
+              break outer;
+          }
+        } while ((count1 | count2) < minGallop);
+
+        /*
+         * One run is winning so consistently that galloping may be a
+         * huge win. So try that, and continue galloping until (if ever)
+         * neither run appears to be winning consistently anymore.
+         */
+        do {
+          assert len1 > 0 && len2 > 1;
+          count1 = len1 - gallopRight(s.getKey(tmp, cursor2), a, base1, len1, len1 - 1, c);
+          if (count1 != 0) {
+            dest -= count1;
+            cursor1 -= count1;
+            len1 -= count1;
+            s.copyRange(a, cursor1 + 1, a, dest + 1, count1);
+            if (len1 == 0)
+              break outer;
+          }
+          s.copyElement(tmp, cursor2--, a, dest--);
+          if (--len2 == 1)
+            break outer;
+
+          count2 = len2 - gallopLeft(s.getKey(a, cursor1), tmp, 0, len2, len2 - 1, c);
+          if (count2 != 0) {
+            dest -= count2;
+            cursor2 -= count2;
+            len2 -= count2;
+            s.copyRange(tmp, cursor2 + 1, a, dest + 1, count2);
+            if (len2 <= 1)  // len2 == 1 || len2 == 0
+              break outer;
+          }
+          s.copyElement(a, cursor1--, a, dest--);
+          if (--len1 == 0)
+            break outer;
+          minGallop--;
+        } while (count1 >= MIN_GALLOP | count2 >= MIN_GALLOP);
+        if (minGallop < 0)
+          minGallop = 0;
+        minGallop += 2;  // Penalize for leaving gallop mode
+      }  // End of "outer" loop
+      this.minGallop = minGallop < 1 ? 1 : minGallop;  // Write back to field
+
+      if (len2 == 1) {
+        assert len1 > 0;
+        dest -= len1;
+        cursor1 -= len1;
+        s.copyRange(a, cursor1 + 1, a, dest + 1, len1);
+        s.copyElement(tmp, cursor2, a, dest); // Move first elt of run2 to front of merge
+      } else if (len2 == 0) {
+        throw new IllegalArgumentException(
+            "Comparison method violates its general contract!");
+      } else {
+        assert len1 == 0;
+        assert len2 > 0;
+        s.copyRange(tmp, 0, a, dest - (len2 - 1), len2);
+      }
+    }
+
+    /**
+     * Ensures that the external array tmp has at least the specified
+     * number of elements, increasing its size if necessary.  The size
+     * increases exponentially to ensure amortized linear time complexity.
+     *
+     * @param minCapacity the minimum required capacity of the tmp array
+     * @return tmp, whether or not it grew
+     */
+    private Buffer ensureCapacity(int minCapacity) {
+      if (tmpLength < minCapacity) {
+        // Compute smallest power of 2 > minCapacity
+        int newSize = minCapacity;
+        newSize |= newSize >> 1;
+        newSize |= newSize >> 2;
+        newSize |= newSize >> 4;
+        newSize |= newSize >> 8;
+        newSize |= newSize >> 16;
+        newSize++;
+
+        if (newSize < 0) // Not bloody likely!
+          newSize = minCapacity;
+        else
+          newSize = Math.min(newSize, aLength >>> 1);
+
+        tmp = s.allocate(newSize);
+        tmpLength = newSize;
+      }
+      return tmp;
+    }
+  }
+}
diff --git a/core/src/main/scala/org/apache/spark/util/collection/AppendOnlyMap.scala b/core/src/main/scala/org/apache/spark/util/collection/AppendOnlyMap.scala
index 1a6f1c2b5579..290282c9c2e2 100644
--- a/core/src/main/scala/org/apache/spark/util/collection/AppendOnlyMap.scala
+++ b/core/src/main/scala/org/apache/spark/util/collection/AppendOnlyMap.scala
@@ -254,26 +254,21 @@ class AppendOnlyMap[K, V](initialCapacity: Int = 64)
    * Return an iterator of the map in sorted order. This provides a way to sort the map without
    * using additional memory, at the expense of destroying the validity of the map.
    */
-  def destructiveSortedIterator(cmp: Comparator[(K, V)]): Iterator[(K, V)] = {
+  def destructiveSortedIterator(keyComparator: Comparator[K]): Iterator[(K, V)] = {
     destroyed = true
     // Pack KV pairs into the front of the underlying array
     var keyIndex, newIndex = 0
     while (keyIndex < capacity) {
       if (data(2 * keyIndex) != null) {
-        data(newIndex) = (data(2 * keyIndex), data(2 * keyIndex + 1))
+        data(2 * newIndex) = data(2 * keyIndex)
+        data(2 * newIndex + 1) = data(2 * keyIndex + 1)
         newIndex += 1
       }
       keyIndex += 1
     }
     assert(curSize == newIndex + (if (haveNullValue) 1 else 0))
 
-    // Sort by the given ordering
-    val rawOrdering = new Comparator[AnyRef] {
-      def compare(x: AnyRef, y: AnyRef): Int = {
-        cmp.compare(x.asInstanceOf[(K, V)], y.asInstanceOf[(K, V)])
-      }
-    }
-    Arrays.sort(data, 0, newIndex, rawOrdering)
+    new Sorter(new KVArraySortDataFormat[K, AnyRef]).sort(data, 0, newIndex, keyComparator)
 
     new Iterator[(K, V)] {
       var i = 0
@@ -284,7 +279,7 @@ class AppendOnlyMap[K, V](initialCapacity: Int = 64)
           nullValueReady = false
           (null.asInstanceOf[K], nullValue)
         } else {
-          val item = data(i).asInstanceOf[(K, V)]
+          val item = (data(2 * i).asInstanceOf[K], data(2 * i + 1).asInstanceOf[V])
           i += 1
           item
         }
diff --git a/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala b/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala
index 765254bf4c36..71ab2a3e3bef 100644
--- a/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala
+++ b/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala
@@ -30,6 +30,7 @@ import org.apache.spark.{Logging, SparkEnv}
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.serializer.Serializer
 import org.apache.spark.storage.{BlockId, BlockManager}
+import org.apache.spark.util.collection.ExternalAppendOnlyMap.HashComparator
 
 /**
  * :: DeveloperApi ::
@@ -66,8 +67,6 @@ class ExternalAppendOnlyMap[K, V, C](
     blockManager: BlockManager = SparkEnv.get.blockManager)
   extends Iterable[(K, C)] with Serializable with Logging {
 
-  import ExternalAppendOnlyMap._
-
   private var currentMap = new SizeTrackingAppendOnlyMap[K, C]
   private val spilledMaps = new ArrayBuffer[DiskMapIterator]
   private val sparkConf = SparkEnv.get.conf
@@ -105,7 +104,7 @@ class ExternalAppendOnlyMap[K, V, C](
   private var _diskBytesSpilled = 0L
 
   private val fileBufferSize = sparkConf.getInt("spark.shuffle.file.buffer.kb", 100) * 1024
-  private val comparator = new KCComparator[K, C]
+  private val keyComparator = new HashComparator[K]
   private val ser = serializer.newInstance()
 
   /**
@@ -173,7 +172,7 @@ class ExternalAppendOnlyMap[K, V, C](
     }
 
     try {
-      val it = currentMap.destructiveSortedIterator(comparator)
+      val it = currentMap.destructiveSortedIterator(keyComparator)
       while (it.hasNext) {
         val kv = it.next()
         writer.write(kv)
@@ -231,7 +230,7 @@ class ExternalAppendOnlyMap[K, V, C](
 
     // Input streams are derived both from the in-memory map and spilled maps on disk
     // The in-memory map is sorted in place, while the spilled maps are already in sorted order
-    private val sortedMap = currentMap.destructiveSortedIterator(comparator)
+    private val sortedMap = currentMap.destructiveSortedIterator(keyComparator)
     private val inputStreams = (Seq(sortedMap) ++ spilledMaps).map(it => it.buffered)
 
     inputStreams.foreach { it =>
@@ -252,7 +251,7 @@ class ExternalAppendOnlyMap[K, V, C](
       if (it.hasNext) {
         var kc = it.next()
         kcPairs += kc
-        val minHash = getKeyHashCode(kc)
+        val minHash = hashKey(kc)
         while (it.hasNext && it.head._1.hashCode() == minHash) {
           kc = it.next()
           kcPairs += kc
@@ -298,7 +297,7 @@ class ExternalAppendOnlyMap[K, V, C](
       val minPair = minPairs.remove(0)
       val minKey = minPair._1
       var minCombiner = minPair._2
-      assert(getKeyHashCode(minPair) == minHash)
+      assert(hashKey(minPair) == minHash)
 
       // For all other streams that may have this key (i.e. have the same minimum key hash),
       // merge in the corresponding value (if any) from that stream
@@ -339,7 +338,7 @@ class ExternalAppendOnlyMap[K, V, C](
       // Invalid if there are no more pairs in this stream
       def minKeyHash: Int = {
         assert(pairs.length > 0)
-        getKeyHashCode(pairs.head)
+        hashKey(pairs.head)
       }
 
       override def compareTo(other: StreamBuffer): Int = {
@@ -423,25 +422,27 @@ class ExternalAppendOnlyMap[K, V, C](
       file.delete()
     }
   }
+
+  /** Convenience function to hash the given (K, C) pair by the key. */
+  private def hashKey(kc: (K, C)): Int = ExternalAppendOnlyMap.hash(kc._1)
 }
 
 private[spark] object ExternalAppendOnlyMap {
 
   /**
-   * Return the key hash code of the given (key, combiner) pair.
-   * If the key is null, return a special hash code.
+   * Return the hash code of the given object. If the object is null, return a special hash code.
    */
-  private def getKeyHashCode[K, C](kc: (K, C)): Int = {
-    if (kc._1 == null) 0 else kc._1.hashCode()
+  private def hash[T](obj: T): Int = {
+    if (obj == null) 0 else obj.hashCode()
   }
 
   /**
-   * A comparator for (key, combiner) pairs based on their key hash codes.
+   * A comparator which sorts arbitrary keys based on their hash codes.
    */
-  private class KCComparator[K, C] extends Comparator[(K, C)] {
-    def compare(kc1: (K, C), kc2: (K, C)): Int = {
-      val hash1 = getKeyHashCode(kc1)
-      val hash2 = getKeyHashCode(kc2)
+  private class HashComparator[K] extends Comparator[K] {
+    def compare(key1: K, key2: K): Int = {
+      val hash1 = hash(key1)
+      val hash2 = hash(key2)
       if (hash1 < hash2) -1 else if (hash1 == hash2) 0 else 1
     }
   }
diff --git a/core/src/main/scala/org/apache/spark/util/collection/SortDataFormat.scala b/core/src/main/scala/org/apache/spark/util/collection/SortDataFormat.scala
new file mode 100644
index 000000000000..ac1528969f0b
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/util/collection/SortDataFormat.scala
@@ -0,0 +1,94 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.util.collection
+
+import scala.reflect.ClassTag
+
+/**
+ * Abstraction for sorting an arbitrary input buffer of data. This interface requires determining
+ * the sort key for a given element index, as well as swapping elements and moving data from one
+ * buffer to another.
+ *
+ * Example format: an array of numbers, where each element is also the key.
+ * See [[KVArraySortDataFormat]] for a more exciting format.
+ *
+ * This trait extends Any to ensure it is universal (and thus compiled to a Java interface).
+ *
+ * @tparam K Type of the sort key of each element
+ * @tparam Buffer Internal data structure used by a particular format (e.g., Array[Int]).
+ */
+// TODO: Making Buffer a real trait would be a better abstraction, but adds some complexity.
+private[spark] trait SortDataFormat[K, Buffer] extends Any {
+  /** Return the sort key for the element at the given index. */
+  protected def getKey(data: Buffer, pos: Int): K
+
+  /** Swap two elements. */
+  protected def swap(data: Buffer, pos0: Int, pos1: Int): Unit
+
+  /** Copy a single element from src(srcPos) to dst(dstPos). */
+  protected def copyElement(src: Buffer, srcPos: Int, dst: Buffer, dstPos: Int): Unit
+
+  /**
+   * Copy a range of elements starting at src(srcPos) to dst, starting at dstPos.
+   * Overlapping ranges are allowed.
+   */
+  protected def copyRange(src: Buffer, srcPos: Int, dst: Buffer, dstPos: Int, length: Int): Unit
+
+  /**
+   * Allocates a Buffer that can hold up to 'length' elements.
+   * All elements of the buffer should be considered invalid until data is explicitly copied in.
+   */
+  protected def allocate(length: Int): Buffer
+}
+
+/**
+ * Supports sorting an array of key-value pairs where the elements of the array alternate between
+ * keys and values, as used in [[AppendOnlyMap]].
+ *
+ * @tparam K Type of the sort key of each element
+ * @tparam T Type of the Array we're sorting. Typically this must extend AnyRef, to support cases
+ *           when the keys and values are not the same type.
+ */
+private[spark]
+class KVArraySortDataFormat[K, T <: AnyRef : ClassTag] extends SortDataFormat[K, Array[T]] {
+
+  override protected def getKey(data: Array[T], pos: Int): K = data(2 * pos).asInstanceOf[K]
+
+  override protected def swap(data: Array[T], pos0: Int, pos1: Int) {
+    val tmpKey = data(2 * pos0)
+    val tmpVal = data(2 * pos0 + 1)
+    data(2 * pos0)     = data(2 * pos1)
+    data(2 * pos0 + 1) = data(2 * pos1 + 1)
+    data(2 * pos1)     = tmpKey
+    data(2 * pos1 + 1) = tmpVal
+  }
+
+  override protected def copyElement(src: Array[T], srcPos: Int, dst: Array[T], dstPos: Int) {
+    dst(2 * dstPos) = src(2 * srcPos)
+    dst(2 * dstPos + 1) = src(2 * srcPos + 1)
+  }
+
+  override protected def copyRange(src: Array[T], srcPos: Int,
+                                   dst: Array[T], dstPos: Int, length: Int) {
+    System.arraycopy(src, 2 * srcPos, dst, 2 * dstPos, 2 * length)
+  }
+
+  override protected def allocate(length: Int): Array[T] = {
+    new Array[T](2 * length)
+  }
+}
diff --git a/core/src/test/scala/org/apache/spark/util/collection/AppendOnlyMapSuite.scala b/core/src/test/scala/org/apache/spark/util/collection/AppendOnlyMapSuite.scala
index 52c7288e18b6..cb99d14b27af 100644
--- a/core/src/test/scala/org/apache/spark/util/collection/AppendOnlyMapSuite.scala
+++ b/core/src/test/scala/org/apache/spark/util/collection/AppendOnlyMapSuite.scala
@@ -170,10 +170,10 @@ class AppendOnlyMapSuite extends FunSuite {
       case e: IllegalStateException => fail()
     }
 
-    val it = map.destructiveSortedIterator(new Comparator[(String, String)] {
-      def compare(kv1: (String, String), kv2: (String, String)): Int = {
-        val x = if (kv1 != null && kv1._1 != null) kv1._1.toInt else Int.MinValue
-        val y = if (kv2 != null && kv2._1 != null) kv2._1.toInt else Int.MinValue
+    val it = map.destructiveSortedIterator(new Comparator[String] {
+      def compare(key1: String, key2: String): Int = {
+        val x = if (key1 != null) key1.toInt else Int.MinValue
+        val y = if (key2 != null) key2.toInt else Int.MinValue
         x.compareTo(y)
       }
     })
diff --git a/core/src/test/scala/org/apache/spark/util/collection/SorterSuite.scala b/core/src/test/scala/org/apache/spark/util/collection/SorterSuite.scala
new file mode 100644
index 000000000000..6fe1079c2719
--- /dev/null
+++ b/core/src/test/scala/org/apache/spark/util/collection/SorterSuite.scala
@@ -0,0 +1,167 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.util.collection
+
+import java.lang.{Float => JFloat}
+import java.util.{Arrays, Comparator}
+
+import org.scalatest.FunSuite
+
+import org.apache.spark.util.random.XORShiftRandom
+
+class SorterSuite extends FunSuite {
+
+  test("equivalent to Arrays.sort") {
+    val rand = new XORShiftRandom(123)
+    val data0 = Array.tabulate[Int](10000) { i => rand.nextInt() }
+    val data1 = data0.clone()
+
+    Arrays.sort(data0)
+    new Sorter(new IntArraySortDataFormat).sort(data1, 0, data1.length, Ordering.Int)
+
+    data0.zip(data1).foreach { case (x, y) => assert(x === y) }
+  }
+
+  test("KVArraySorter") {
+    val rand = new XORShiftRandom(456)
+
+    // Construct an array of keys (to Java sort) and an array where the keys and values
+    // alternate. Keys are random doubles, values are ordinals from 0 to length.
+    val keys = Array.tabulate[Double](5000) { i => rand.nextDouble() }
+    val keyValueArray = Array.tabulate[Number](10000) { i =>
+      if (i % 2 == 0) keys(i / 2) else new Integer(i / 2)
+    }
+
+    // Map from generated keys to values, to verify correctness later
+    val kvMap =
+      keyValueArray.grouped(2).map { case Array(k, v) => k.doubleValue() -> v.intValue() }.toMap
+
+    Arrays.sort(keys)
+    new Sorter(new KVArraySortDataFormat[Double, Number])
+      .sort(keyValueArray, 0, keys.length, Ordering.Double)
+
+    keys.zipWithIndex.foreach { case (k, i) =>
+      assert(k === keyValueArray(2 * i))
+      assert(kvMap(k) === keyValueArray(2 * i + 1))
+    }
+  }
+
+  /**
+   * This provides a simple benchmark for comparing the Sorter with Java internal sorting.
+   * Ideally these would be executed one at a time, each in their own JVM, so their listing
+   * here is mainly to have the code.
+   *
+   * The goal of this code is to sort an array of key-value pairs, where the array physically
+   * has the keys and values alternating. The basic Java sorts work only on the keys, so the
+   * real Java solution is to make Tuple2s to store the keys and values and sort an array of
+   * those, while the Sorter approach can work directly on the input data format.
+   *
+   * Note that the Java implementation varies tremendously between Java 6 and Java 7, when
+   * the Java sort changed from merge sort to Timsort.
+   */
+  ignore("Sorter benchmark") {
+
+    /** Runs an experiment several times. */
+    def runExperiment(name: String)(f: => Unit): Unit = {
+      val firstTry = org.apache.spark.util.Utils.timeIt(1)(f)
+      System.gc()
+
+      var i = 0
+      var next10: Long = 0
+      while (i < 10) {
+        val time = org.apache.spark.util.Utils.timeIt(1)(f)
+        next10 += time
+        println(s"$name: Took $time ms")
+        i += 1
+      }
+
+      println(s"$name: ($firstTry ms first try, ${next10 / 10} ms average)")
+    }
+
+    val numElements = 25000000 // 25 mil
+    val rand = new XORShiftRandom(123)
+
+    val keys = Array.tabulate[JFloat](numElements) { i =>
+      new JFloat(rand.nextFloat())
+    }
+
+    // Test our key-value pairs where each element is a Tuple2[Float, Integer)
+    val kvTupleArray = Array.tabulate[AnyRef](numElements) { i =>
+      (keys(i / 2): Float, i / 2: Int)
+    }
+    runExperiment("Tuple-sort using Arrays.sort()") {
+      Arrays.sort(kvTupleArray, new Comparator[AnyRef] {
+        override def compare(x: AnyRef, y: AnyRef): Int =
+          Ordering.Float.compare(x.asInstanceOf[(Float, _)]._1, y.asInstanceOf[(Float, _)]._1)
+      })
+    }
+
+    // Test our Sorter where each element alternates between Float and Integer, non-primitive
+    val keyValueArray = Array.tabulate[AnyRef](numElements * 2) { i =>
+      if (i % 2 == 0) keys(i / 2) else new Integer(i / 2)
+    }
+    val sorter = new Sorter(new KVArraySortDataFormat[JFloat, AnyRef])
+    runExperiment("KV-sort using Sorter") {
+      sorter.sort(keyValueArray, 0, keys.length, new Comparator[JFloat] {
+        override def compare(x: JFloat, y: JFloat): Int = Ordering.Float.compare(x, y)
+      })
+    }
+
+    // Test non-primitive sort on float array
+    runExperiment("Java Arrays.sort()") {
+      Arrays.sort(keys, new Comparator[JFloat] {
+        override def compare(x: JFloat, y: JFloat): Int = Ordering.Float.compare(x, y)
+      })
+    }
+
+    // Test primitive sort on float array
+    val primitiveKeys = Array.tabulate[Float](numElements) { i => rand.nextFloat() }
+    runExperiment("Java Arrays.sort() on primitive keys") {
+      Arrays.sort(primitiveKeys)
+    }
+  }
+}
+
+
+/** Format to sort a simple Array[Int]. Could be easily generified and specialized. */
+class IntArraySortDataFormat extends SortDataFormat[Int, Array[Int]] {
+  override protected def getKey(data: Array[Int], pos: Int): Int = {
+    data(pos)
+  }
+
+  override protected def swap(data: Array[Int], pos0: Int, pos1: Int): Unit = {
+    val tmp = data(pos0)
+    data(pos0) = data(pos1)
+    data(pos1) = tmp
+  }
+
+  override protected def copyElement(src: Array[Int], srcPos: Int, dst: Array[Int], dstPos: Int) {
+    dst(dstPos) = src(srcPos)
+  }
+
+  /** Copy a range of elements starting at src(srcPos) to dest, starting at destPos. */
+  override protected def copyRange(src: Array[Int], srcPos: Int,
+                                   dst: Array[Int], dstPos: Int, length: Int) {
+    System.arraycopy(src, srcPos, dst, dstPos, length)
+  }
+
+  /** Allocates a new structure that can hold up to 'length' elements. */
+  override protected def allocate(length: Int): Array[Int] = {
+    new Array[Int](length)
+  }
+}

From 79fe7634f6817eb2443bc152c6790a4439721fda Mon Sep 17 00:00:00 2001
From: Cheng Hao <hao.cheng@intel.com>
Date: Tue, 22 Jul 2014 18:13:28 -0700
Subject: [PATCH 725/810] [SPARK-2615] [SQL] Add Equal Sign "==" Support for
 HiveQl

Currently, the "==" in HiveQL expression will cause exception thrown, this patch will fix it.

Author: Cheng Hao <hao.cheng@intel.com>

Closes #1522 from chenghao-intel/equal and squashes the following commits:

f62a0ff [Cheng Hao] Add == Support for HiveQl
---
 .../main/scala/org/apache/spark/sql/hive/HiveQl.scala    | 1 +
 ...relationoptimizer13-0-efd135a811fa94760736a761d220b82 | 0
 ...elationoptimizer13-1-32a82500cc28465fac6f64dde0c431c6 | 0
 ...elationoptimizer13-2-777edd9d575f3480ca6cebe4be57b1f6 | 1 +
 ...elationoptimizer13-3-bb61d9292434f37bd386e5bff683764d | 0
 ...rrelationoptimizer9-0-efd135a811fa94760736a761d220b82 | 0
 ...relationoptimizer9-1-b1e2ade89ae898650f0be4f796d8947b | 1 +
 ...elationoptimizer9-10-1190d82f88f7fb1f91968f6e2e03772a | 0
 ...elationoptimizer9-11-bc2ae88b17ac2bdbd288e07194a40168 | 9 +++++++++
 ...elationoptimizer9-12-777edd9d575f3480ca6cebe4be57b1f6 | 1 +
 ...elationoptimizer9-13-1190d82f88f7fb1f91968f6e2e03772a | 0
 ...elationoptimizer9-14-bc2ae88b17ac2bdbd288e07194a40168 | 9 +++++++++
 ...relationoptimizer9-2-32a82500cc28465fac6f64dde0c431c6 | 0
 ...relationoptimizer9-3-b9d963d24994c47c3776dda6f7d3881f | 1 +
 ...orrelationoptimizer9-4-ec131bcf578dba99f20b16a7dc6b9b | 0
 ...relationoptimizer9-5-b4e378104bb5ab8d8ba5f905aa1ff450 | 9 +++++++++
 ...relationoptimizer9-6-777edd9d575f3480ca6cebe4be57b1f6 | 1 +
 ...relationoptimizer9-7-f952899d70bd718cbdbc44a5290938c9 | 0
 ...relationoptimizer9-8-b4e378104bb5ab8d8ba5f905aa1ff450 | 9 +++++++++
 ...relationoptimizer9-9-b9d963d24994c47c3776dda6f7d3881f | 1 +
 .../sql/hive/execution/HiveCompatibilitySuite.scala      | 2 ++
 21 files changed, 45 insertions(+)
 create mode 100644 sql/hive/src/test/resources/golden/correlationoptimizer13-0-efd135a811fa94760736a761d220b82
 create mode 100644 sql/hive/src/test/resources/golden/correlationoptimizer13-1-32a82500cc28465fac6f64dde0c431c6
 create mode 100644 sql/hive/src/test/resources/golden/correlationoptimizer13-2-777edd9d575f3480ca6cebe4be57b1f6
 create mode 100644 sql/hive/src/test/resources/golden/correlationoptimizer13-3-bb61d9292434f37bd386e5bff683764d
 create mode 100644 sql/hive/src/test/resources/golden/correlationoptimizer9-0-efd135a811fa94760736a761d220b82
 create mode 100644 sql/hive/src/test/resources/golden/correlationoptimizer9-1-b1e2ade89ae898650f0be4f796d8947b
 create mode 100644 sql/hive/src/test/resources/golden/correlationoptimizer9-10-1190d82f88f7fb1f91968f6e2e03772a
 create mode 100644 sql/hive/src/test/resources/golden/correlationoptimizer9-11-bc2ae88b17ac2bdbd288e07194a40168
 create mode 100644 sql/hive/src/test/resources/golden/correlationoptimizer9-12-777edd9d575f3480ca6cebe4be57b1f6
 create mode 100644 sql/hive/src/test/resources/golden/correlationoptimizer9-13-1190d82f88f7fb1f91968f6e2e03772a
 create mode 100644 sql/hive/src/test/resources/golden/correlationoptimizer9-14-bc2ae88b17ac2bdbd288e07194a40168
 create mode 100644 sql/hive/src/test/resources/golden/correlationoptimizer9-2-32a82500cc28465fac6f64dde0c431c6
 create mode 100644 sql/hive/src/test/resources/golden/correlationoptimizer9-3-b9d963d24994c47c3776dda6f7d3881f
 create mode 100644 sql/hive/src/test/resources/golden/correlationoptimizer9-4-ec131bcf578dba99f20b16a7dc6b9b
 create mode 100644 sql/hive/src/test/resources/golden/correlationoptimizer9-5-b4e378104bb5ab8d8ba5f905aa1ff450
 create mode 100644 sql/hive/src/test/resources/golden/correlationoptimizer9-6-777edd9d575f3480ca6cebe4be57b1f6
 create mode 100644 sql/hive/src/test/resources/golden/correlationoptimizer9-7-f952899d70bd718cbdbc44a5290938c9
 create mode 100644 sql/hive/src/test/resources/golden/correlationoptimizer9-8-b4e378104bb5ab8d8ba5f905aa1ff450
 create mode 100644 sql/hive/src/test/resources/golden/correlationoptimizer9-9-b9d963d24994c47c3776dda6f7d3881f

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
index 300e249f5b2e..53480a521dd1 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
@@ -932,6 +932,7 @@ private[hive] object HiveQl {
 
     /* Comparisons */
     case Token("=", left :: right:: Nil) => EqualTo(nodeToExpr(left), nodeToExpr(right))
+    case Token("==", left :: right:: Nil) => EqualTo(nodeToExpr(left), nodeToExpr(right))
     case Token("!=", left :: right:: Nil) => Not(EqualTo(nodeToExpr(left), nodeToExpr(right)))
     case Token("<>", left :: right:: Nil) => Not(EqualTo(nodeToExpr(left), nodeToExpr(right)))
     case Token(">", left :: right:: Nil) => GreaterThan(nodeToExpr(left), nodeToExpr(right))
diff --git a/sql/hive/src/test/resources/golden/correlationoptimizer13-0-efd135a811fa94760736a761d220b82 b/sql/hive/src/test/resources/golden/correlationoptimizer13-0-efd135a811fa94760736a761d220b82
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/correlationoptimizer13-1-32a82500cc28465fac6f64dde0c431c6 b/sql/hive/src/test/resources/golden/correlationoptimizer13-1-32a82500cc28465fac6f64dde0c431c6
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/correlationoptimizer13-2-777edd9d575f3480ca6cebe4be57b1f6 b/sql/hive/src/test/resources/golden/correlationoptimizer13-2-777edd9d575f3480ca6cebe4be57b1f6
new file mode 100644
index 000000000000..573541ac9702
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/correlationoptimizer13-2-777edd9d575f3480ca6cebe4be57b1f6
@@ -0,0 +1 @@
+0
diff --git a/sql/hive/src/test/resources/golden/correlationoptimizer13-3-bb61d9292434f37bd386e5bff683764d b/sql/hive/src/test/resources/golden/correlationoptimizer13-3-bb61d9292434f37bd386e5bff683764d
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/correlationoptimizer9-0-efd135a811fa94760736a761d220b82 b/sql/hive/src/test/resources/golden/correlationoptimizer9-0-efd135a811fa94760736a761d220b82
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/correlationoptimizer9-1-b1e2ade89ae898650f0be4f796d8947b b/sql/hive/src/test/resources/golden/correlationoptimizer9-1-b1e2ade89ae898650f0be4f796d8947b
new file mode 100644
index 000000000000..573541ac9702
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/correlationoptimizer9-1-b1e2ade89ae898650f0be4f796d8947b
@@ -0,0 +1 @@
+0
diff --git a/sql/hive/src/test/resources/golden/correlationoptimizer9-10-1190d82f88f7fb1f91968f6e2e03772a b/sql/hive/src/test/resources/golden/correlationoptimizer9-10-1190d82f88f7fb1f91968f6e2e03772a
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/correlationoptimizer9-11-bc2ae88b17ac2bdbd288e07194a40168 b/sql/hive/src/test/resources/golden/correlationoptimizer9-11-bc2ae88b17ac2bdbd288e07194a40168
new file mode 100644
index 000000000000..17c838bb62b3
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/correlationoptimizer9-11-bc2ae88b17ac2bdbd288e07194a40168
@@ -0,0 +1,9 @@
+103	val_103	103	val_103	4	4
+104	val_104	104	val_104	4	4
+105	val_105	105	val_105	1	1
+111	val_111	111	val_111	1	1
+113	val_113	113	val_113	4	4
+114	val_114	114	val_114	1	1
+116	val_116	116	val_116	1	1
+118	val_118	118	val_118	4	4
+119	val_119	119	val_119	9	9
diff --git a/sql/hive/src/test/resources/golden/correlationoptimizer9-12-777edd9d575f3480ca6cebe4be57b1f6 b/sql/hive/src/test/resources/golden/correlationoptimizer9-12-777edd9d575f3480ca6cebe4be57b1f6
new file mode 100644
index 000000000000..573541ac9702
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/correlationoptimizer9-12-777edd9d575f3480ca6cebe4be57b1f6
@@ -0,0 +1 @@
+0
diff --git a/sql/hive/src/test/resources/golden/correlationoptimizer9-13-1190d82f88f7fb1f91968f6e2e03772a b/sql/hive/src/test/resources/golden/correlationoptimizer9-13-1190d82f88f7fb1f91968f6e2e03772a
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/correlationoptimizer9-14-bc2ae88b17ac2bdbd288e07194a40168 b/sql/hive/src/test/resources/golden/correlationoptimizer9-14-bc2ae88b17ac2bdbd288e07194a40168
new file mode 100644
index 000000000000..17c838bb62b3
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/correlationoptimizer9-14-bc2ae88b17ac2bdbd288e07194a40168
@@ -0,0 +1,9 @@
+103	val_103	103	val_103	4	4
+104	val_104	104	val_104	4	4
+105	val_105	105	val_105	1	1
+111	val_111	111	val_111	1	1
+113	val_113	113	val_113	4	4
+114	val_114	114	val_114	1	1
+116	val_116	116	val_116	1	1
+118	val_118	118	val_118	4	4
+119	val_119	119	val_119	9	9
diff --git a/sql/hive/src/test/resources/golden/correlationoptimizer9-2-32a82500cc28465fac6f64dde0c431c6 b/sql/hive/src/test/resources/golden/correlationoptimizer9-2-32a82500cc28465fac6f64dde0c431c6
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/correlationoptimizer9-3-b9d963d24994c47c3776dda6f7d3881f b/sql/hive/src/test/resources/golden/correlationoptimizer9-3-b9d963d24994c47c3776dda6f7d3881f
new file mode 100644
index 000000000000..573541ac9702
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/correlationoptimizer9-3-b9d963d24994c47c3776dda6f7d3881f
@@ -0,0 +1 @@
+0
diff --git a/sql/hive/src/test/resources/golden/correlationoptimizer9-4-ec131bcf578dba99f20b16a7dc6b9b b/sql/hive/src/test/resources/golden/correlationoptimizer9-4-ec131bcf578dba99f20b16a7dc6b9b
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/correlationoptimizer9-5-b4e378104bb5ab8d8ba5f905aa1ff450 b/sql/hive/src/test/resources/golden/correlationoptimizer9-5-b4e378104bb5ab8d8ba5f905aa1ff450
new file mode 100644
index 000000000000..248a14f1f4a9
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/correlationoptimizer9-5-b4e378104bb5ab8d8ba5f905aa1ff450
@@ -0,0 +1,9 @@
+103	103	4	4
+104	104	4	4
+105	105	1	1
+111	111	1	1
+113	113	4	4
+114	114	1	1
+116	116	1	1
+118	118	4	4
+119	119	9	9
diff --git a/sql/hive/src/test/resources/golden/correlationoptimizer9-6-777edd9d575f3480ca6cebe4be57b1f6 b/sql/hive/src/test/resources/golden/correlationoptimizer9-6-777edd9d575f3480ca6cebe4be57b1f6
new file mode 100644
index 000000000000..573541ac9702
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/correlationoptimizer9-6-777edd9d575f3480ca6cebe4be57b1f6
@@ -0,0 +1 @@
+0
diff --git a/sql/hive/src/test/resources/golden/correlationoptimizer9-7-f952899d70bd718cbdbc44a5290938c9 b/sql/hive/src/test/resources/golden/correlationoptimizer9-7-f952899d70bd718cbdbc44a5290938c9
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/correlationoptimizer9-8-b4e378104bb5ab8d8ba5f905aa1ff450 b/sql/hive/src/test/resources/golden/correlationoptimizer9-8-b4e378104bb5ab8d8ba5f905aa1ff450
new file mode 100644
index 000000000000..248a14f1f4a9
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/correlationoptimizer9-8-b4e378104bb5ab8d8ba5f905aa1ff450
@@ -0,0 +1,9 @@
+103	103	4	4
+104	104	4	4
+105	105	1	1
+111	111	1	1
+113	113	4	4
+114	114	1	1
+116	116	1	1
+118	118	4	4
+119	119	9	9
diff --git a/sql/hive/src/test/resources/golden/correlationoptimizer9-9-b9d963d24994c47c3776dda6f7d3881f b/sql/hive/src/test/resources/golden/correlationoptimizer9-9-b9d963d24994c47c3776dda6f7d3881f
new file mode 100644
index 000000000000..573541ac9702
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/correlationoptimizer9-9-b9d963d24994c47c3776dda6f7d3881f
@@ -0,0 +1 @@
+0
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
index fd44325925cd..bd036faaa635 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
@@ -291,6 +291,7 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     "correlationoptimizer1",
     "correlationoptimizer10",
     "correlationoptimizer11",
+    "correlationoptimizer13",
     "correlationoptimizer14",
     "correlationoptimizer15",
     "correlationoptimizer2",
@@ -299,6 +300,7 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     "correlationoptimizer6",
     "correlationoptimizer7",
     "correlationoptimizer8",
+    "correlationoptimizer9",
     "count",
     "cp_mj_rc",
     "create_insert_outputformat",

From 5f7b99168004f06191dcf121fe985e90bf7dcf74 Mon Sep 17 00:00:00 2001
From: CrazyJvm <crazyjvm@gmail.com>
Date: Tue, 22 Jul 2014 18:14:44 -0700
Subject: [PATCH 726/810] Graphx example

fix examples

Author: CrazyJvm <crazyjvm@gmail.com>

Closes #1523 from CrazyJvm/graphx-example and squashes the following commits:

663457a [CrazyJvm] outDegrees does not take parameters
7cfff1d [CrazyJvm] fix example for joinVertices
---
 graphx/src/main/scala/org/apache/spark/graphx/Graph.scala | 2 +-
 .../src/main/scala/org/apache/spark/graphx/GraphOps.scala | 8 ++++----
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/graphx/src/main/scala/org/apache/spark/graphx/Graph.scala b/graphx/src/main/scala/org/apache/spark/graphx/Graph.scala
index 3507f358bfb4..fa4b891754c4 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/Graph.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/Graph.scala
@@ -344,7 +344,7 @@ abstract class Graph[VD: ClassTag, ED: ClassTag] protected () extends Serializab
    *
    * {{{
    * val rawGraph: Graph[_, _] = Graph.textFile("webgraph")
-   * val outDeg: RDD[(VertexId, Int)] = rawGraph.outDegrees()
+   * val outDeg: RDD[(VertexId, Int)] = rawGraph.outDegrees
    * val graph = rawGraph.outerJoinVertices(outDeg) {
    *   (vid, data, optDeg) => optDeg.getOrElse(0)
    * }
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/GraphOps.scala b/graphx/src/main/scala/org/apache/spark/graphx/GraphOps.scala
index edd5b79da152..02afaa987d40 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/GraphOps.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/GraphOps.scala
@@ -198,10 +198,10 @@ class GraphOps[VD: ClassTag, ED: ClassTag](graph: Graph[VD, ED]) extends Seriali
    *
    * {{{
    * val rawGraph: Graph[Int, Int] = GraphLoader.edgeListFile(sc, "webgraph")
-   *   .mapVertices(v => 0)
-   * val outDeg: RDD[(Int, Int)] = rawGraph.outDegrees
-   * val graph = rawGraph.leftJoinVertices[Int,Int](outDeg,
-   *   (v, deg) => deg )
+   *   .mapVertices((_, _) => 0)
+   * val outDeg = rawGraph.outDegrees
+   * val graph = rawGraph.joinVertices[Int](outDeg)
+   *   ((_, _, outDeg) => outDeg)
    * }}}
    *
    */

From ddadf1b00470b9d7bf7386dacf198d41407a0a2b Mon Sep 17 00:00:00 2001
From: GuoQiang Li <witgo@qq.com>
Date: Tue, 22 Jul 2014 20:34:40 -0500
Subject: [PATCH 727/810] [YARN][SPARK-2606]:In some cases,the spark UI pages
 display incorrect

The issue is caused by #1112 .

Author: GuoQiang Li <witgo@qq.com>

Closes #1501 from witgo/webui_style and squashes the following commits:

4b34998 [GuoQiang Li] In some cases, pages display incorrect in WebUI
---
 core/src/main/scala/org/apache/spark/ui/UIUtils.scala | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/ui/UIUtils.scala b/core/src/main/scala/org/apache/spark/ui/UIUtils.scala
index e07aa2ee3a5a..715cc2f4df8d 100644
--- a/core/src/main/scala/org/apache/spark/ui/UIUtils.scala
+++ b/core/src/main/scala/org/apache/spark/ui/UIUtils.scala
@@ -149,7 +149,7 @@ private[spark] object UIUtils extends Logging {
 
   def prependBaseUri(basePath: String = "", resource: String = "") = uiRoot + basePath + resource
 
-  val commonHeaderNodes = {
+  def commonHeaderNodes = {
     <meta http-equiv="Content-type" content="text/html; charset=utf-8" />
     <link rel="stylesheet" href={prependBaseUri("/static/bootstrap.min.css")}
           type="text/css" />

From 02e45729472a22a31629cdd17dc3836ba6810189 Mon Sep 17 00:00:00 2001
From: Gera Shegalov <gera@twitter.com>
Date: Tue, 22 Jul 2014 21:05:12 -0500
Subject: [PATCH 728/810] [YARN] SPARK-2577: File upload to viewfs is broken
 due to mount point re...

Opting to the option 2 defined in SPARK-2577, i.e., retrieve and pass the correct file system object to addResource.

Author: Gera Shegalov <gera@twitter.com>

Closes #1483 from gerashegalov/master and squashes the following commits:

90c9087 [Gera Shegalov] [YARN] SPARK-2577: File upload to viewfs is broken due to mount point resolution
---
 .../main/scala/org/apache/spark/deploy/yarn/ClientBase.scala   | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala
index 556f49342977..a1298e8f30b5 100644
--- a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala
+++ b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala
@@ -232,7 +232,8 @@ trait ClientBase extends Logging {
         if (!ClientBase.LOCAL_SCHEME.equals(localURI.getScheme())) {
           val setPermissions = if (destName.equals(ClientBase.APP_JAR)) true else false
           val destPath = copyRemoteFile(dst, qualifyForLocal(localURI), replication, setPermissions)
-          distCacheMgr.addResource(fs, conf, destPath, localResources, LocalResourceType.FILE,
+          val destFs = FileSystem.get(destPath.toUri(), conf)
+          distCacheMgr.addResource(destFs, conf, destPath, localResources, LocalResourceType.FILE,
             destName, statCache)
         } else if (confKey != null) {
           sparkConf.set(confKey, localPath)

From 6c2be93f081f33e9e97e1231b0084a6a0eb4fa22 Mon Sep 17 00:00:00 2001
From: Ankur Dave <ankurdave@gmail.com>
Date: Tue, 22 Jul 2014 22:18:30 -0700
Subject: [PATCH 729/810] Remove GraphX MessageToPartition for compatibility
 with sort-based shuffle

MessageToPartition was used in `Graph#partitionBy`. Unlike a Tuple2, it marked the key as transient to avoid sending it over the network. However, it was incompatible with sort-based shuffle (SPARK-2045) and represented only a minor optimization: for partitionBy, it improved performance by 6.3% (30.4 s to 28.5 s) and reduced communication by 5.6% (114.2 MB to 107.8 MB).

Author: Ankur Dave <ankurdave@gmail.com>

Closes #1537 from ankurdave/remove-MessageToPartition and squashes the following commits:

f9d0054 [Ankur Dave] Remove MessageToPartition
ab71364 [Ankur Dave] Remove unused VertexBroadcastMsg
---
 .../spark/graphx/GraphKryoRegistrator.scala   |  2 -
 .../apache/spark/graphx/impl/GraphImpl.scala  |  7 +-
 .../graphx/impl/MessageToPartition.scala      | 76 -------------------
 .../spark/graphx/impl/Serializers.scala       | 72 ------------------
 .../apache/spark/graphx/SerializerSuite.scala | 73 ------------------
 5 files changed, 2 insertions(+), 228 deletions(-)

diff --git a/graphx/src/main/scala/org/apache/spark/graphx/GraphKryoRegistrator.scala b/graphx/src/main/scala/org/apache/spark/graphx/GraphKryoRegistrator.scala
index f97f329c0e83..eea9fe9520ca 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/GraphKryoRegistrator.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/GraphKryoRegistrator.scala
@@ -35,8 +35,6 @@ class GraphKryoRegistrator extends KryoRegistrator {
 
   def registerClasses(kryo: Kryo) {
     kryo.register(classOf[Edge[Object]])
-    kryo.register(classOf[MessageToPartition[Object]])
-    kryo.register(classOf[VertexBroadcastMsg[Object]])
     kryo.register(classOf[RoutingTableMessage])
     kryo.register(classOf[(VertexId, Object)])
     kryo.register(classOf[EdgePartition[Object, Object]])
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/GraphImpl.scala b/graphx/src/main/scala/org/apache/spark/graphx/impl/GraphImpl.scala
index ccdaa82eb916..33f35cfb69a2 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/impl/GraphImpl.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/impl/GraphImpl.scala
@@ -26,7 +26,6 @@ import org.apache.spark.storage.StorageLevel
 
 import org.apache.spark.graphx._
 import org.apache.spark.graphx.impl.GraphImpl._
-import org.apache.spark.graphx.impl.MsgRDDFunctions._
 import org.apache.spark.graphx.util.BytecodeUtils
 
 
@@ -83,15 +82,13 @@ class GraphImpl[VD: ClassTag, ED: ClassTag] protected (
     val vdTag = classTag[VD]
     val newEdges = edges.withPartitionsRDD(edges.map { e =>
       val part: PartitionID = partitionStrategy.getPartition(e.srcId, e.dstId, numPartitions)
-
-      // Should we be using 3-tuple or an optimized class
-      new MessageToPartition(part, (e.srcId, e.dstId, e.attr))
+      (part, (e.srcId, e.dstId, e.attr))
     }
       .partitionBy(new HashPartitioner(numPartitions))
       .mapPartitionsWithIndex( { (pid, iter) =>
         val builder = new EdgePartitionBuilder[ED, VD]()(edTag, vdTag)
         iter.foreach { message =>
-          val data = message.data
+          val data = message._2
           builder.add(data._1, data._2, data._3)
         }
         val edgePartition = builder.toEdgePartition
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/MessageToPartition.scala b/graphx/src/main/scala/org/apache/spark/graphx/impl/MessageToPartition.scala
index d85afa45b126..5318b8da6412 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/impl/MessageToPartition.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/impl/MessageToPartition.scala
@@ -25,82 +25,6 @@ import org.apache.spark.graphx.{PartitionID, VertexId}
 import org.apache.spark.rdd.{ShuffledRDD, RDD}
 
 
-private[graphx]
-class VertexBroadcastMsg[@specialized(Int, Long, Double, Boolean) T](
-    @transient var partition: PartitionID,
-    var vid: VertexId,
-    var data: T)
-  extends Product2[PartitionID, (VertexId, T)] with Serializable {
-
-  override def _1 = partition
-
-  override def _2 = (vid, data)
-
-  override def canEqual(that: Any): Boolean = that.isInstanceOf[VertexBroadcastMsg[_]]
-}
-
-
-/**
- * A message used to send a specific value to a partition.
- * @param partition index of the target partition.
- * @param data value to send
- */
-private[graphx]
-class MessageToPartition[@specialized(Int, Long, Double, Char, Boolean/* , AnyRef */) T](
-    @transient var partition: PartitionID,
-    var data: T)
-  extends Product2[PartitionID, T] with Serializable {
-
-  override def _1 = partition
-
-  override def _2 = data
-
-  override def canEqual(that: Any): Boolean = that.isInstanceOf[MessageToPartition[_]]
-}
-
-
-private[graphx]
-class VertexBroadcastMsgRDDFunctions[T: ClassTag](self: RDD[VertexBroadcastMsg[T]]) {
-  def partitionBy(partitioner: Partitioner): RDD[VertexBroadcastMsg[T]] = {
-    val rdd = new ShuffledRDD[PartitionID, (VertexId, T), (VertexId, T), VertexBroadcastMsg[T]](
-      self, partitioner)
-
-    // Set a custom serializer if the data is of int or double type.
-    if (classTag[T] == ClassTag.Int) {
-      rdd.setSerializer(new IntVertexBroadcastMsgSerializer)
-    } else if (classTag[T] == ClassTag.Long) {
-      rdd.setSerializer(new LongVertexBroadcastMsgSerializer)
-    } else if (classTag[T] == ClassTag.Double) {
-      rdd.setSerializer(new DoubleVertexBroadcastMsgSerializer)
-    }
-    rdd
-  }
-}
-
-
-private[graphx]
-class MsgRDDFunctions[T: ClassTag](self: RDD[MessageToPartition[T]]) {
-
-  /**
-   * Return a copy of the RDD partitioned using the specified partitioner.
-   */
-  def partitionBy(partitioner: Partitioner): RDD[MessageToPartition[T]] = {
-    new ShuffledRDD[PartitionID, T, T, MessageToPartition[T]](self, partitioner)
-  }
-
-}
-
-private[graphx]
-object MsgRDDFunctions {
-  implicit def rdd2PartitionRDDFunctions[T: ClassTag](rdd: RDD[MessageToPartition[T]]) = {
-    new MsgRDDFunctions(rdd)
-  }
-
-  implicit def rdd2vertexMessageRDDFunctions[T: ClassTag](rdd: RDD[VertexBroadcastMsg[T]]) = {
-    new VertexBroadcastMsgRDDFunctions(rdd)
-  }
-}
-
 private[graphx]
 class VertexRDDFunctions[VD: ClassTag](self: RDD[(VertexId, VD)]) {
   def copartitionWithVertices(partitioner: Partitioner): RDD[(VertexId, VD)] = {
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/Serializers.scala b/graphx/src/main/scala/org/apache/spark/graphx/impl/Serializers.scala
index 033237f59721..2d98c24d6970 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/impl/Serializers.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/impl/Serializers.scala
@@ -76,78 +76,6 @@ class VertexIdMsgSerializer extends Serializer with Serializable {
   }
 }
 
-/** A special shuffle serializer for VertexBroadcastMessage[Int]. */
-private[graphx]
-class IntVertexBroadcastMsgSerializer extends Serializer with Serializable {
-  override def newInstance(): SerializerInstance = new ShuffleSerializerInstance {
-
-    override def serializeStream(s: OutputStream) = new ShuffleSerializationStream(s) {
-      def writeObject[T: ClassTag](t: T) = {
-        val msg = t.asInstanceOf[VertexBroadcastMsg[Int]]
-        writeVarLong(msg.vid, optimizePositive = false)
-        writeInt(msg.data)
-        this
-      }
-    }
-
-    override def deserializeStream(s: InputStream) = new ShuffleDeserializationStream(s) {
-      override def readObject[T: ClassTag](): T = {
-        val a = readVarLong(optimizePositive = false)
-        val b = readInt()
-        new VertexBroadcastMsg[Int](0, a, b).asInstanceOf[T]
-      }
-    }
-  }
-}
-
-/** A special shuffle serializer for VertexBroadcastMessage[Long]. */
-private[graphx]
-class LongVertexBroadcastMsgSerializer extends Serializer with Serializable {
-  override def newInstance(): SerializerInstance = new ShuffleSerializerInstance {
-
-    override def serializeStream(s: OutputStream) = new ShuffleSerializationStream(s) {
-      def writeObject[T: ClassTag](t: T) = {
-        val msg = t.asInstanceOf[VertexBroadcastMsg[Long]]
-        writeVarLong(msg.vid, optimizePositive = false)
-        writeLong(msg.data)
-        this
-      }
-    }
-
-    override def deserializeStream(s: InputStream) = new ShuffleDeserializationStream(s) {
-      override def readObject[T: ClassTag](): T = {
-        val a = readVarLong(optimizePositive = false)
-        val b = readLong()
-        new VertexBroadcastMsg[Long](0, a, b).asInstanceOf[T]
-      }
-    }
-  }
-}
-
-/** A special shuffle serializer for VertexBroadcastMessage[Double]. */
-private[graphx]
-class DoubleVertexBroadcastMsgSerializer extends Serializer with Serializable {
-  override def newInstance(): SerializerInstance = new ShuffleSerializerInstance {
-
-    override def serializeStream(s: OutputStream) = new ShuffleSerializationStream(s) {
-      def writeObject[T: ClassTag](t: T) = {
-        val msg = t.asInstanceOf[VertexBroadcastMsg[Double]]
-        writeVarLong(msg.vid, optimizePositive = false)
-        writeDouble(msg.data)
-        this
-      }
-    }
-
-    override def deserializeStream(s: InputStream) = new ShuffleDeserializationStream(s) {
-      def readObject[T: ClassTag](): T = {
-        val a = readVarLong(optimizePositive = false)
-        val b = readDouble()
-        new VertexBroadcastMsg[Double](0, a, b).asInstanceOf[T]
-      }
-    }
-  }
-}
-
 /** A special shuffle serializer for AggregationMessage[Int]. */
 private[graphx]
 class IntAggMsgSerializer extends Serializer with Serializable {
diff --git a/graphx/src/test/scala/org/apache/spark/graphx/SerializerSuite.scala b/graphx/src/test/scala/org/apache/spark/graphx/SerializerSuite.scala
index 91caa6b605a1..864cb1fdf002 100644
--- a/graphx/src/test/scala/org/apache/spark/graphx/SerializerSuite.scala
+++ b/graphx/src/test/scala/org/apache/spark/graphx/SerializerSuite.scala
@@ -26,75 +26,11 @@ import org.scalatest.FunSuite
 
 import org.apache.spark._
 import org.apache.spark.graphx.impl._
-import org.apache.spark.graphx.impl.MsgRDDFunctions._
 import org.apache.spark.serializer.SerializationStream
 
 
 class SerializerSuite extends FunSuite with LocalSparkContext {
 
-  test("IntVertexBroadcastMsgSerializer") {
-    val outMsg = new VertexBroadcastMsg[Int](3, 4, 5)
-    val bout = new ByteArrayOutputStream
-    val outStrm = new IntVertexBroadcastMsgSerializer().newInstance().serializeStream(bout)
-    outStrm.writeObject(outMsg)
-    outStrm.writeObject(outMsg)
-    bout.flush()
-    val bin = new ByteArrayInputStream(bout.toByteArray)
-    val inStrm = new IntVertexBroadcastMsgSerializer().newInstance().deserializeStream(bin)
-    val inMsg1: VertexBroadcastMsg[Int] = inStrm.readObject()
-    val inMsg2: VertexBroadcastMsg[Int] = inStrm.readObject()
-    assert(outMsg.vid === inMsg1.vid)
-    assert(outMsg.vid === inMsg2.vid)
-    assert(outMsg.data === inMsg1.data)
-    assert(outMsg.data === inMsg2.data)
-
-    intercept[EOFException] {
-      inStrm.readObject()
-    }
-  }
-
-  test("LongVertexBroadcastMsgSerializer") {
-    val outMsg = new VertexBroadcastMsg[Long](3, 4, 5)
-    val bout = new ByteArrayOutputStream
-    val outStrm = new LongVertexBroadcastMsgSerializer().newInstance().serializeStream(bout)
-    outStrm.writeObject(outMsg)
-    outStrm.writeObject(outMsg)
-    bout.flush()
-    val bin = new ByteArrayInputStream(bout.toByteArray)
-    val inStrm = new LongVertexBroadcastMsgSerializer().newInstance().deserializeStream(bin)
-    val inMsg1: VertexBroadcastMsg[Long] = inStrm.readObject()
-    val inMsg2: VertexBroadcastMsg[Long] = inStrm.readObject()
-    assert(outMsg.vid === inMsg1.vid)
-    assert(outMsg.vid === inMsg2.vid)
-    assert(outMsg.data === inMsg1.data)
-    assert(outMsg.data === inMsg2.data)
-
-    intercept[EOFException] {
-      inStrm.readObject()
-    }
-  }
-
-  test("DoubleVertexBroadcastMsgSerializer") {
-    val outMsg = new VertexBroadcastMsg[Double](3, 4, 5.0)
-    val bout = new ByteArrayOutputStream
-    val outStrm = new DoubleVertexBroadcastMsgSerializer().newInstance().serializeStream(bout)
-    outStrm.writeObject(outMsg)
-    outStrm.writeObject(outMsg)
-    bout.flush()
-    val bin = new ByteArrayInputStream(bout.toByteArray)
-    val inStrm = new DoubleVertexBroadcastMsgSerializer().newInstance().deserializeStream(bin)
-    val inMsg1: VertexBroadcastMsg[Double] = inStrm.readObject()
-    val inMsg2: VertexBroadcastMsg[Double] = inStrm.readObject()
-    assert(outMsg.vid === inMsg1.vid)
-    assert(outMsg.vid === inMsg2.vid)
-    assert(outMsg.data === inMsg1.data)
-    assert(outMsg.data === inMsg2.data)
-
-    intercept[EOFException] {
-      inStrm.readObject()
-    }
-  }
-
   test("IntAggMsgSerializer") {
     val outMsg = (4: VertexId, 5)
     val bout = new ByteArrayOutputStream
@@ -152,15 +88,6 @@ class SerializerSuite extends FunSuite with LocalSparkContext {
     }
   }
 
-  test("TestShuffleVertexBroadcastMsg") {
-    withSpark { sc =>
-      val bmsgs = sc.parallelize(0 until 100, 10).map { pid =>
-        new VertexBroadcastMsg[Int](pid, pid, pid)
-      }
-      bmsgs.partitionBy(new HashPartitioner(3)).collect()
-    }
-  }
-
   test("variable long encoding") {
     def testVarLongEncoding(v: Long, optimizePositive: Boolean) {
       val bout = new ByteArrayOutputStream

From 4c7243e109c713bdfb87891748800109ffbaae07 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Wed, 23 Jul 2014 00:58:55 -0700
Subject: [PATCH 730/810] [SPARK-2617] Correct doc and usages of
 preservesPartitioning

The name `preservesPartitioning` is ambiguous: 1) preserves the indices of partitions, 2) preserves the partitioner. The latter is correct and `preservesPartitioning` should really be called `preservesPartitioner` to avoid confusion. Unfortunately, this is already part of the API and we cannot change. We should be clear in the doc and fix wrong usages.

This PR

1. adds notes in `maPartitions*`,
2. makes `RDD.sample` preserve partitioner,
3. changes `preservesPartitioning` to false in  `RDD.zip` because the keys of the first RDD are no longer the keys of the zipped RDD,
4. fixes some wrong usages in MLlib.

Author: Xiangrui Meng <meng@databricks.com>

Closes #1526 from mengxr/preserve-partitioner and squashes the following commits:

b361e65 [Xiangrui Meng] update doc based on pwendell's comments
3b1ba19 [Xiangrui Meng] update doc
357575c [Xiangrui Meng] fix unit test
20b4816 [Xiangrui Meng] Merge branch 'master' into preserve-partitioner
d1caa65 [Xiangrui Meng] add doc to explain preservesPartitioning fix wrong usage of preservesPartitioning make sample preserse partitioning
---
 .../spark/rdd/PartitionwiseSampledRDD.scala     |  4 ++++
 .../main/scala/org/apache/spark/rdd/RDD.scala   | 17 +++++++++++++----
 .../rdd/PartitionwiseSampledRDDSuite.scala      |  4 ++--
 .../scala/org/apache/spark/rdd/RDDSuite.scala   |  9 +++++++++
 .../BinaryClassificationMetrics.scala           |  4 ++--
 .../mllib/linalg/distributed/RowMatrix.scala    |  8 ++++----
 .../apache/spark/mllib/recommendation/ALS.scala |  2 +-
 .../org/apache/spark/mllib/util/MLUtils.scala   |  4 ++--
 8 files changed, 37 insertions(+), 15 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/rdd/PartitionwiseSampledRDD.scala b/core/src/main/scala/org/apache/spark/rdd/PartitionwiseSampledRDD.scala
index b5b8a5706deb..a637d6f15b7e 100644
--- a/core/src/main/scala/org/apache/spark/rdd/PartitionwiseSampledRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/PartitionwiseSampledRDD.scala
@@ -39,6 +39,7 @@ class PartitionwiseSampledRDDPartition(val prev: Partition, val seed: Long)
  *
  * @param prev RDD to be sampled
  * @param sampler a random sampler
+ * @param preservesPartitioning whether the sampler preserves the partitioner of the parent RDD
  * @param seed random seed
  * @tparam T input RDD item type
  * @tparam U sampled RDD item type
@@ -46,9 +47,12 @@ class PartitionwiseSampledRDDPartition(val prev: Partition, val seed: Long)
 private[spark] class PartitionwiseSampledRDD[T: ClassTag, U: ClassTag](
     prev: RDD[T],
     sampler: RandomSampler[T, U],
+    @transient preservesPartitioning: Boolean,
     @transient seed: Long = Utils.random.nextLong)
   extends RDD[U](prev) {
 
+  @transient override val partitioner = if (preservesPartitioning) prev.partitioner else None
+
   override def getPartitions: Array[Partition] = {
     val random = new Random(seed)
     firstParent[T].partitions.map(x => new PartitionwiseSampledRDDPartition(x, random.nextLong()))
diff --git a/core/src/main/scala/org/apache/spark/rdd/RDD.scala b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
index a1f282724889..c1bafab3e749 100644
--- a/core/src/main/scala/org/apache/spark/rdd/RDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
@@ -356,9 +356,9 @@ abstract class RDD[T: ClassTag](
       seed: Long = Utils.random.nextLong): RDD[T] = {
     require(fraction >= 0.0, "Invalid fraction value: " + fraction)
     if (withReplacement) {
-      new PartitionwiseSampledRDD[T, T](this, new PoissonSampler[T](fraction), seed)
+      new PartitionwiseSampledRDD[T, T](this, new PoissonSampler[T](fraction), true, seed)
     } else {
-      new PartitionwiseSampledRDD[T, T](this, new BernoulliSampler[T](fraction), seed)
+      new PartitionwiseSampledRDD[T, T](this, new BernoulliSampler[T](fraction), true, seed)
     }
   }
 
@@ -374,7 +374,7 @@ abstract class RDD[T: ClassTag](
     val sum = weights.sum
     val normalizedCumWeights = weights.map(_ / sum).scanLeft(0.0d)(_ + _)
     normalizedCumWeights.sliding(2).map { x =>
-      new PartitionwiseSampledRDD[T, T](this, new BernoulliSampler[T](x(0), x(1)), seed)
+      new PartitionwiseSampledRDD[T, T](this, new BernoulliSampler[T](x(0), x(1)), true, seed)
     }.toArray
   }
 
@@ -586,6 +586,9 @@ abstract class RDD[T: ClassTag](
 
   /**
    * Return a new RDD by applying a function to each partition of this RDD.
+   *
+   * `preservesPartitioning` indicates whether the input function preserves the partitioner, which
+   * should be `false` unless this is a pair RDD and the input function doesn't modify the keys.
    */
   def mapPartitions[U: ClassTag](
       f: Iterator[T] => Iterator[U], preservesPartitioning: Boolean = false): RDD[U] = {
@@ -596,6 +599,9 @@ abstract class RDD[T: ClassTag](
   /**
    * Return a new RDD by applying a function to each partition of this RDD, while tracking the index
    * of the original partition.
+   *
+   * `preservesPartitioning` indicates whether the input function preserves the partitioner, which
+   * should be `false` unless this is a pair RDD and the input function doesn't modify the keys.
    */
   def mapPartitionsWithIndex[U: ClassTag](
       f: (Int, Iterator[T]) => Iterator[U], preservesPartitioning: Boolean = false): RDD[U] = {
@@ -607,6 +613,9 @@ abstract class RDD[T: ClassTag](
    * :: DeveloperApi ::
    * Return a new RDD by applying a function to each partition of this RDD. This is a variant of
    * mapPartitions that also passes the TaskContext into the closure.
+   *
+   * `preservesPartitioning` indicates whether the input function preserves the partitioner, which
+   * should be `false` unless this is a pair RDD and the input function doesn't modify the keys.
    */
   @DeveloperApi
   def mapPartitionsWithContext[U: ClassTag](
@@ -689,7 +698,7 @@ abstract class RDD[T: ClassTag](
    * a map on the other).
    */
   def zip[U: ClassTag](other: RDD[U]): RDD[(T, U)] = {
-    zipPartitions(other, true) { (thisIter, otherIter) =>
+    zipPartitions(other, preservesPartitioning = false) { (thisIter, otherIter) =>
       new Iterator[(T, U)] {
         def hasNext = (thisIter.hasNext, otherIter.hasNext) match {
           case (true, true) => true
diff --git a/core/src/test/scala/org/apache/spark/rdd/PartitionwiseSampledRDDSuite.scala b/core/src/test/scala/org/apache/spark/rdd/PartitionwiseSampledRDDSuite.scala
index 5dd8de319a65..a0483886f8db 100644
--- a/core/src/test/scala/org/apache/spark/rdd/PartitionwiseSampledRDDSuite.scala
+++ b/core/src/test/scala/org/apache/spark/rdd/PartitionwiseSampledRDDSuite.scala
@@ -43,7 +43,7 @@ class PartitionwiseSampledRDDSuite extends FunSuite with SharedSparkContext {
   test("seed distribution") {
     val rdd = sc.makeRDD(Array(1L, 2L, 3L, 4L), 2)
     val sampler = new MockSampler
-    val sample = new PartitionwiseSampledRDD[Long, Long](rdd, sampler, 0L)
+    val sample = new PartitionwiseSampledRDD[Long, Long](rdd, sampler, false, 0L)
     assert(sample.distinct().count == 2, "Seeds must be different.")
   }
 
@@ -52,7 +52,7 @@ class PartitionwiseSampledRDDSuite extends FunSuite with SharedSparkContext {
     // We want to make sure there are no concurrency issues.
     val rdd = sc.parallelize(0 until 111, 10)
     for (sampler <- Seq(new BernoulliSampler[Int](0.5), new PoissonSampler[Int](0.5))) {
-      val sampled = new PartitionwiseSampledRDD[Int, Int](rdd, sampler)
+      val sampled = new PartitionwiseSampledRDD[Int, Int](rdd, sampler, true)
       sampled.zip(sampled).count()
     }
   }
diff --git a/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala b/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala
index 2924de112934..6654ec2d7c65 100644
--- a/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala
+++ b/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala
@@ -523,6 +523,15 @@ class RDDSuite extends FunSuite with SharedSparkContext {
     assert(sortedTopK === nums.sorted(ord).take(5))
   }
 
+  test("sample preserves partitioner") {
+    val partitioner = new HashPartitioner(2)
+    val rdd = sc.parallelize(Seq((0, 1), (2, 3))).partitionBy(partitioner)
+    for (withReplacement <- Seq(true, false)) {
+      val sampled = rdd.sample(withReplacement, 1.0)
+      assert(sampled.partitioner === rdd.partitioner)
+    }
+  }
+
   test("takeSample") {
     val n = 1000000
     val data = sc.parallelize(1 to n, 2)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetrics.scala b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetrics.scala
index 079743742d86..1af40de2c7fc 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetrics.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetrics.scala
@@ -103,11 +103,11 @@ class BinaryClassificationMetrics(scoreAndLabels: RDD[(Double, Double)]) extends
       mergeValue = (c: BinaryLabelCounter, label: Double) => c += label,
       mergeCombiners = (c1: BinaryLabelCounter, c2: BinaryLabelCounter) => c1 += c2
     ).sortByKey(ascending = false)
-    val agg = counts.values.mapPartitions({ iter =>
+    val agg = counts.values.mapPartitions { iter =>
       val agg = new BinaryLabelCounter()
       iter.foreach(agg += _)
       Iterator(agg)
-    }, preservesPartitioning = true).collect()
+    }.collect()
     val partitionwiseCumulativeCounts =
       agg.scanLeft(new BinaryLabelCounter())(
         (agg: BinaryLabelCounter, c: BinaryLabelCounter) => agg.clone() += c)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
index f4c403bc7861..8c2b044ea73f 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
@@ -377,9 +377,9 @@ class RowMatrix(
       s"Only support dense matrix at this time but found ${B.getClass.getName}.")
 
     val Bb = rows.context.broadcast(B.toBreeze.asInstanceOf[BDM[Double]].toDenseVector.toArray)
-    val AB = rows.mapPartitions({ iter =>
+    val AB = rows.mapPartitions { iter =>
       val Bi = Bb.value
-      iter.map(row => {
+      iter.map { row =>
         val v = BDV.zeros[Double](k)
         var i = 0
         while (i < k) {
@@ -387,8 +387,8 @@ class RowMatrix(
           i += 1
         }
         Vectors.fromBreeze(v)
-      })
-    }, preservesPartitioning = true)
+      }
+    }
 
     new RowMatrix(AB, nRows, B.numCols)
   }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala
index 15e8855db6ca..5356790cb533 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala
@@ -430,7 +430,7 @@ class ALS private (
       val inLinkBlock = makeInLinkBlock(numProductBlocks, ratings, productPartitioner)
       val outLinkBlock = makeOutLinkBlock(numProductBlocks, ratings, productPartitioner)
       Iterator.single((blockId, (inLinkBlock, outLinkBlock)))
-    }, true)
+    }, preservesPartitioning = true)
     val inLinks = links.mapValues(_._1)
     val outLinks = links.mapValues(_._2)
     inLinks.persist(StorageLevel.MEMORY_AND_DISK)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala
index aaf92a1a8869..30de24ad89f9 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala
@@ -264,8 +264,8 @@ object MLUtils {
     (1 to numFolds).map { fold =>
       val sampler = new BernoulliSampler[T]((fold - 1) / numFoldsF, fold / numFoldsF,
         complement = false)
-      val validation = new PartitionwiseSampledRDD(rdd, sampler, seed)
-      val training = new PartitionwiseSampledRDD(rdd, sampler.cloneComplement(), seed)
+      val validation = new PartitionwiseSampledRDD(rdd, sampler, true, seed)
+      val training = new PartitionwiseSampledRDD(rdd, sampler.cloneComplement(), true, seed)
       (training, validation)
     }.toArray
   }

From 25921110fcd5afe568bf0d25fccd232787af7911 Mon Sep 17 00:00:00 2001
From: Andrew Or <andrewor14@gmail.com>
Date: Wed, 23 Jul 2014 10:31:45 -0700
Subject: [PATCH 731/810] [SPARK-2609] Log thread ID when spilling
 ExternalAppendOnlyMap

It's useful to know whether one thread is constantly spilling or multiple threads are spilling relatively infrequently. Right now everything looks a little jumbled and we can't tell which lines belong to the same thread. For instance:

```
06:14:37 ExternalAppendOnlyMap: Spilling in-memory map of 4 MB to disk (194 times so far)
06:14:37 ExternalAppendOnlyMap: Spilling in-memory map of 4 MB to disk (198 times so far)
06:14:37 ExternalAppendOnlyMap: Spilling in-memory map of 4 MB to disk (198 times so far)
06:14:37 ExternalAppendOnlyMap: Spilling in-memory map of 10 MB to disk (197 times so far)
06:14:38 ExternalAppendOnlyMap: Spilling in-memory map of 9 MB to disk (45 times so far)
06:14:38 ExternalAppendOnlyMap: Spilling in-memory map of 23 MB to disk (198 times so far)
06:14:38 ExternalAppendOnlyMap: Spilling in-memory map of 38 MB to disk (25 times so far)
06:14:38 ExternalAppendOnlyMap: Spilling in-memory map of 161 MB to disk (25 times so far)
06:14:39 ExternalAppendOnlyMap: Spilling in-memory map of 0 MB to disk (199 times so far)
06:14:39 ExternalAppendOnlyMap: Spilling in-memory map of 4 MB to disk (166 times so far)
06:14:39 ExternalAppendOnlyMap: Spilling in-memory map of 4 MB to disk (199 times so far)
06:14:39 ExternalAppendOnlyMap: Spilling in-memory map of 4 MB to disk (200 times so far)
```

Author: Andrew Or <andrewor14@gmail.com>

Closes #1517 from andrewor14/external-log and squashes the following commits:

90e48bb [Andrew Or] Log thread ID when spilling
---
 .../spark/util/collection/ExternalAppendOnlyMap.scala       | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala b/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala
index 71ab2a3e3bef..be8f6529f7a1 100644
--- a/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala
+++ b/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala
@@ -106,6 +106,7 @@ class ExternalAppendOnlyMap[K, V, C](
   private val fileBufferSize = sparkConf.getInt("spark.shuffle.file.buffer.kb", 100) * 1024
   private val keyComparator = new HashComparator[K]
   private val ser = serializer.newInstance()
+  private val threadId = Thread.currentThread().getId
 
   /**
    * Insert the given key and value into the map.
@@ -128,7 +129,6 @@ class ExternalAppendOnlyMap[K, V, C](
       // Atomically check whether there is sufficient memory in the global pool for
       // this map to grow and, if possible, allocate the required amount
       shuffleMemoryMap.synchronized {
-        val threadId = Thread.currentThread().getId
         val previouslyOccupiedMemory = shuffleMemoryMap.get(threadId)
         val availableMemory = maxMemoryThreshold -
           (shuffleMemoryMap.values.sum - previouslyOccupiedMemory.getOrElse(0L))
@@ -153,8 +153,8 @@ class ExternalAppendOnlyMap[K, V, C](
    */
   private def spill(mapSize: Long) {
     spillCount += 1
-    logWarning("Spilling in-memory map of %d MB to disk (%d time%s so far)"
-      .format(mapSize / (1024 * 1024), spillCount, if (spillCount > 1) "s" else ""))
+    logWarning("Thread %d spilling in-memory map of %d MB to disk (%d time%s so far)"
+      .format(threadId, mapSize / (1024 * 1024), spillCount, if (spillCount > 1) "s" else ""))
     val (blockId, file) = diskBlockManager.createTempBlock()
     var writer = blockManager.getDiskWriter(blockId, file, serializer, fileBufferSize)
     var objectsWritten = 0

From f776bc98878428940b5130c0d7d9b7ee452c0bd3 Mon Sep 17 00:00:00 2001
From: woshilaiceshide <woshilaiceshide@qq.com>
Date: Wed, 23 Jul 2014 11:05:41 -0700
Subject: [PATCH 732/810] [CORE] SPARK-2640: In "local[N]", free cores of the
 only executor should be touched by "spark.task.cpus" for every
 finish/start-up of tasks.

Make spark's "local[N]" better.
In our company, we use "local[N]" in production. It works exellentlly. It's our best choice.

Author: woshilaiceshide <woshilaiceshide@qq.com>

Closes #1544 from woshilaiceshide/localX and squashes the following commits:

6c85154 [woshilaiceshide] [CORE] SPARK-2640: In "local[N]", free cores of the only executor should be touched by "spark.task.cpus" for every finish/start-up of tasks.
---
 .../scala/org/apache/spark/scheduler/local/LocalBackend.scala | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/scheduler/local/LocalBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/local/LocalBackend.scala
index e9f6273bfd9f..5b897597fa28 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/local/LocalBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/local/LocalBackend.scala
@@ -57,7 +57,7 @@ private[spark] class LocalActor(
     case StatusUpdate(taskId, state, serializedData) =>
       scheduler.statusUpdate(taskId, state, serializedData)
       if (TaskState.isFinished(state)) {
-        freeCores += 1
+        freeCores += scheduler.CPUS_PER_TASK
         reviveOffers()
       }
 
@@ -68,7 +68,7 @@ private[spark] class LocalActor(
   def reviveOffers() {
     val offers = Seq(new WorkerOffer(localExecutorId, localExecutorHostname, freeCores))
     for (task <- scheduler.resourceOffers(offers).flatten) {
-      freeCores -= 1
+      freeCores -= scheduler.CPUS_PER_TASK
       executor.launchTask(executorBackend, task.taskId, task.name, task.serializedTask)
     }
   }

From 1b790cf7755cace0d89ac5777717e6df3be7356f Mon Sep 17 00:00:00 2001
From: Takuya UESHIN <ueshin@happy-camper.st>
Date: Wed, 23 Jul 2014 14:47:23 -0700
Subject: [PATCH 733/810] [SPARK-2588][SQL] Add some more DSLs.

Author: Takuya UESHIN <ueshin@happy-camper.st>

Closes #1491 from ueshin/issues/SPARK-2588 and squashes the following commits:

43d0a46 [Takuya UESHIN] Merge branch 'master' into issues/SPARK-2588
1023ea0 [Takuya UESHIN] Modify tests to use DSLs.
2310bf1 [Takuya UESHIN] Add some more DSLs.
---
 .../spark/sql/catalyst/dsl/package.scala      | 29 +++++++++
 .../ExpressionEvaluationSuite.scala           | 59 +++++++++++--------
 .../org/apache/spark/sql/DslQuerySuite.scala  | 15 +++--
 3 files changed, 70 insertions(+), 33 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala
index 1b503b957d14..15c98efbcabc 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala
@@ -79,8 +79,24 @@ package object dsl {
     def === (other: Expression) = EqualTo(expr, other)
     def !== (other: Expression) = Not(EqualTo(expr, other))
 
+    def in(list: Expression*) = In(expr, list)
+
     def like(other: Expression) = Like(expr, other)
     def rlike(other: Expression) = RLike(expr, other)
+    def contains(other: Expression) = Contains(expr, other)
+    def startsWith(other: Expression) = StartsWith(expr, other)
+    def endsWith(other: Expression) = EndsWith(expr, other)
+    def substr(pos: Expression, len: Expression = Literal(Int.MaxValue)) =
+      Substring(expr, pos, len)
+    def substring(pos: Expression, len: Expression = Literal(Int.MaxValue)) =
+      Substring(expr, pos, len)
+
+    def isNull = IsNull(expr)
+    def isNotNull = IsNotNull(expr)
+
+    def getItem(ordinal: Expression) = GetItem(expr, ordinal)
+    def getField(fieldName: String) = GetField(expr, fieldName)
+
     def cast(to: DataType) = Cast(expr, to)
 
     def asc = SortOrder(expr, Ascending)
@@ -112,6 +128,7 @@ package object dsl {
     def sumDistinct(e: Expression) = SumDistinct(e)
     def count(e: Expression) = Count(e)
     def countDistinct(e: Expression*) = CountDistinct(e)
+    def approxCountDistinct(e: Expression, rsd: Double = 0.05) = ApproxCountDistinct(e, rsd)
     def avg(e: Expression) = Average(e)
     def first(e: Expression) = First(e)
     def min(e: Expression) = Min(e)
@@ -163,6 +180,18 @@ package object dsl {
 
       /** Creates a new AttributeReference of type binary */
       def binary = AttributeReference(s, BinaryType, nullable = true)()
+
+      /** Creates a new AttributeReference of type array */
+      def array(dataType: DataType) = AttributeReference(s, ArrayType(dataType), nullable = true)()
+
+      /** Creates a new AttributeReference of type map */
+      def map(keyType: DataType, valueType: DataType): AttributeReference =
+        map(MapType(keyType, valueType))
+      def map(mapType: MapType) = AttributeReference(s, mapType, nullable = true)()
+
+      /** Creates a new AttributeReference of type struct */
+      def struct(fields: StructField*): AttributeReference = struct(StructType(fields))
+      def struct(structType: StructType) = AttributeReference(s, structType, nullable = true)()
     }
 
     implicit class DslAttribute(a: AttributeReference) {
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala
index db1ae29d400c..c3f5c26fdbe5 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala
@@ -301,17 +301,17 @@ class ExpressionEvaluationSuite extends FunSuite {
     val c3 = 'a.boolean.at(2)
     val c4 = 'a.boolean.at(3)
 
-    checkEvaluation(IsNull(c1), false, row)
-    checkEvaluation(IsNotNull(c1), true, row)
+    checkEvaluation(c1.isNull, false, row)
+    checkEvaluation(c1.isNotNull, true, row)
 
-    checkEvaluation(IsNull(c2), true, row)
-    checkEvaluation(IsNotNull(c2), false, row)
+    checkEvaluation(c2.isNull, true, row)
+    checkEvaluation(c2.isNotNull, false, row)
 
-    checkEvaluation(IsNull(Literal(1, ShortType)), false)
-    checkEvaluation(IsNotNull(Literal(1, ShortType)), true)
+    checkEvaluation(Literal(1, ShortType).isNull, false)
+    checkEvaluation(Literal(1, ShortType).isNotNull, true)
 
-    checkEvaluation(IsNull(Literal(null, ShortType)), true)
-    checkEvaluation(IsNotNull(Literal(null, ShortType)), false)
+    checkEvaluation(Literal(null, ShortType).isNull, true)
+    checkEvaluation(Literal(null, ShortType).isNotNull, false)
 
     checkEvaluation(Coalesce(c1 :: c2 :: Nil), "^Ba*n", row)
     checkEvaluation(Coalesce(Literal(null, StringType) :: Nil), null, row)
@@ -326,11 +326,11 @@ class ExpressionEvaluationSuite extends FunSuite {
     checkEvaluation(If(Literal(false, BooleanType),
       Literal("a", StringType), Literal("b", StringType)), "b", row)
 
-    checkEvaluation(In(c1, c1 :: c2 :: Nil), true, row)
-    checkEvaluation(In(Literal("^Ba*n", StringType),
-      Literal("^Ba*n", StringType) :: Nil), true, row)
-    checkEvaluation(In(Literal("^Ba*n", StringType),
-      Literal("^Ba*n", StringType) :: c2 :: Nil), true, row)
+    checkEvaluation(c1 in (c1, c2), true, row)
+    checkEvaluation(
+      Literal("^Ba*n", StringType) in (Literal("^Ba*n", StringType)), true, row)
+    checkEvaluation(
+      Literal("^Ba*n", StringType) in (Literal("^Ba*n", StringType), c2), true, row)
   }
 
   test("case when") {
@@ -420,6 +420,10 @@ class ExpressionEvaluationSuite extends FunSuite {
 
     assert(GetField(Literal(null, typeS), "a").nullable === true)
     assert(GetField(Literal(null, typeS_notNullable), "a").nullable === true)
+
+    checkEvaluation('c.map(typeMap).at(3).getItem("aa"), "bb", row)
+    checkEvaluation('c.array(typeArray.elementType).at(4).getItem(1), "bb", row)
+    checkEvaluation('c.struct(typeS).at(2).getField("a"), "aa", row)
   }
 
   test("arithmetic") {
@@ -472,20 +476,20 @@ class ExpressionEvaluationSuite extends FunSuite {
     val c1 = 'a.string.at(0)
     val c2 = 'a.string.at(1)
 
-    checkEvaluation(Contains(c1, "b"), true, row)
-    checkEvaluation(Contains(c1, "x"), false, row)
-    checkEvaluation(Contains(c2, "b"), null, row)
-    checkEvaluation(Contains(c1, Literal(null, StringType)), null, row)
+    checkEvaluation(c1 contains "b", true, row)
+    checkEvaluation(c1 contains "x", false, row)
+    checkEvaluation(c2 contains "b", null, row)
+    checkEvaluation(c1 contains Literal(null, StringType), null, row)
 
-    checkEvaluation(StartsWith(c1, "a"), true, row)
-    checkEvaluation(StartsWith(c1, "b"), false, row)
-    checkEvaluation(StartsWith(c2, "a"), null, row)
-    checkEvaluation(StartsWith(c1, Literal(null, StringType)), null, row)
+    checkEvaluation(c1 startsWith "a", true, row)
+    checkEvaluation(c1 startsWith "b", false, row)
+    checkEvaluation(c2 startsWith "a", null, row)
+    checkEvaluation(c1 startsWith Literal(null, StringType), null, row)
 
-    checkEvaluation(EndsWith(c1, "c"), true, row)
-    checkEvaluation(EndsWith(c1, "b"), false, row)
-    checkEvaluation(EndsWith(c2, "b"), null, row)
-    checkEvaluation(EndsWith(c1, Literal(null, StringType)), null, row)
+    checkEvaluation(c1 endsWith "c", true, row)
+    checkEvaluation(c1 endsWith "b", false, row)
+    checkEvaluation(c2 endsWith "b", null, row)
+    checkEvaluation(c1 endsWith Literal(null, StringType), null, row)
   }
 
   test("Substring") {
@@ -542,5 +546,10 @@ class ExpressionEvaluationSuite extends FunSuite {
     assert(Substring(s_notNull, Literal(0, IntegerType), Literal(2, IntegerType)).nullable === false)
     assert(Substring(s_notNull, Literal(null, IntegerType), Literal(2, IntegerType)).nullable === true)
     assert(Substring(s_notNull, Literal(0, IntegerType), Literal(null, IntegerType)).nullable === true)
+
+    checkEvaluation(s.substr(0, 2), "ex", row)
+    checkEvaluation(s.substr(0), "example", row)
+    checkEvaluation(s.substring(0, 2), "ex", row)
+    checkEvaluation(s.substring(0), "example", row)
   }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala
index c8ea01c4e1b6..1a6a6c17473a 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala
@@ -18,7 +18,6 @@
 package org.apache.spark.sql
 
 import org.apache.spark.sql.catalyst.analysis._
-import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.test._
 
 /* Implicits */
@@ -41,15 +40,15 @@ class DslQuerySuite extends QueryTest {
 
   test("agg") {
     checkAnswer(
-      testData2.groupBy('a)('a, Sum('b)),
+      testData2.groupBy('a)('a, sum('b)),
       Seq((1,3),(2,3),(3,3))
     )
     checkAnswer(
-      testData2.groupBy('a)('a, Sum('b) as 'totB).aggregate(Sum('totB)),
+      testData2.groupBy('a)('a, sum('b) as 'totB).aggregate(sum('totB)),
       9
     )
     checkAnswer(
-      testData2.aggregate(Sum('b)),
+      testData2.aggregate(sum('b)),
       9
     )
   }
@@ -104,19 +103,19 @@ class DslQuerySuite extends QueryTest {
       Seq((3,1), (3,2), (2,1), (2,2), (1,1), (1,2)))
 
     checkAnswer(
-      arrayData.orderBy(GetItem('data, 0).asc),
+      arrayData.orderBy('data.getItem(0).asc),
       arrayData.collect().sortBy(_.data(0)).toSeq)
 
     checkAnswer(
-      arrayData.orderBy(GetItem('data, 0).desc),
+      arrayData.orderBy('data.getItem(0).desc),
       arrayData.collect().sortBy(_.data(0)).reverse.toSeq)
 
     checkAnswer(
-      mapData.orderBy(GetItem('data, 1).asc),
+      mapData.orderBy('data.getItem(1).asc),
       mapData.collect().sortBy(_.data(1)).toSeq)
 
     checkAnswer(
-      mapData.orderBy(GetItem('data, 1).desc),
+      mapData.orderBy('data.getItem(1).desc),
       mapData.collect().sortBy(_.data(1)).reverse.toSeq)
   }
 

From 91903e0a50b0efb7217610021a628b3043004d82 Mon Sep 17 00:00:00 2001
From: Rui Li <rui.li@intel.com>
Date: Wed, 23 Jul 2014 16:23:24 -0700
Subject: [PATCH 734/810] SPARK-2277: clear host->rack info properly

Hi mridulm, I just think of this issue of [#1212](https://github.com/apache/spark/pull/1212): I added FakeRackUtil to hold the host -> rack mapping. It should be cleaned up after use so that it won't mess up with test cases others may add later.
Really sorry about this.

Author: Rui Li <rui.li@intel.com>

Closes #1454 from lirui-intel/SPARK-2277-fix-UT and squashes the following commits:

f8ea25c [Rui Li] SPARK-2277: clear host->rack info properly
---
 .../scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala  | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala
index 86b443b18f2a..c52368b5514d 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala
@@ -475,6 +475,7 @@ class TaskSetManagerSuite extends FunSuite with LocalSparkContext with Logging {
     // Valid locality should contain PROCESS_LOCAL, NODE_LOCAL, RACK_LOCAL and ANY
     assert(manager.myLocalityLevels.sameElements(
       Array(PROCESS_LOCAL, NODE_LOCAL, RACK_LOCAL, ANY)))
+    FakeRackUtil.cleanUp()
   }
 
   test("test RACK_LOCAL tasks") {
@@ -505,6 +506,7 @@ class TaskSetManagerSuite extends FunSuite with LocalSparkContext with Logging {
     // Offer host2
     // Task 1 can be scheduled with RACK_LOCAL
     assert(manager.resourceOffer("execB", "host2", RACK_LOCAL).get.index === 1)
+    FakeRackUtil.cleanUp()
   }
 
   test("do not emit warning when serialized task is small") {

From e060d3ee2d910a5a802bb29630dca6f66cc0525d Mon Sep 17 00:00:00 2001
From: William Benton <willb@redhat.com>
Date: Wed, 23 Jul 2014 16:25:32 -0700
Subject: [PATCH 735/810] SPARK-2226:  [SQL] transform HAVING clauses with
 aggregate expressions that aren't in the aggregation list

This change adds an analyzer rule to
  1. find expressions in `HAVING` clause filters that depend on unresolved attributes,
  2. push these expressions down to the underlying aggregates, and then
  3. project them away above the filter.

It also enables the `HAVING` queries in the Hive compatibility suite.

Author: William Benton <willb@redhat.com>

Closes #1497 from willb/spark-2226 and squashes the following commits:

92c9a93 [William Benton] Removed unnecessary import
f1d4f34 [William Benton] Cleanups missed in prior commit
0e1624f [William Benton] Incorporated suggestions from @marmbrus; thanks!
541d4ee [William Benton] Cleanups from review
5a12647 [William Benton] Explanatory comments and stylistic cleanups.
c7f2b2c [William Benton] Whitelist HAVING queries.
29a26e3 [William Benton] Added rule to handle unresolved attributes in HAVING clauses (SPARK-2226)
---
 .../sql/catalyst/analysis/Analyzer.scala      |  27 +-
 .../having-0-57f3f26c0203c29c2a91a7cca557ce55 |   0
 .../having-1-ef81808faeab6d212c3cf32abfc0d873 |  10 +
 .../having-2-a2b4f52cb92f730ddb912b063636d6c1 |   0
 .../having-3-3fa6387b6a4ece110ac340c7b893964e | 308 ++++++++++++++++++
 .../having-4-e9918bd385cb35db4ebcbd4e398547f4 |   0
 .../having-5-4a0c4e521b8a6f6146151c13a2715ff  | 199 +++++++++++
 .../having-6-9f50df5b5f31c7166b0396ab434dc095 |   0
 .../having-7-5ad96cb287df02080da1e2594f08d83e | 125 +++++++
 .../having-8-4aa7197e20b5a64461ca670a79488103 |   0
 .../having-9-a79743372d86d77b0ff53a71adcb1cff | 199 +++++++++++
 .../execution/HiveCompatibilitySuite.scala    |   2 +
 12 files changed, 869 insertions(+), 1 deletion(-)
 create mode 100644 sql/hive/src/test/resources/golden/having-0-57f3f26c0203c29c2a91a7cca557ce55
 create mode 100644 sql/hive/src/test/resources/golden/having-1-ef81808faeab6d212c3cf32abfc0d873
 create mode 100644 sql/hive/src/test/resources/golden/having-2-a2b4f52cb92f730ddb912b063636d6c1
 create mode 100644 sql/hive/src/test/resources/golden/having-3-3fa6387b6a4ece110ac340c7b893964e
 create mode 100644 sql/hive/src/test/resources/golden/having-4-e9918bd385cb35db4ebcbd4e398547f4
 create mode 100644 sql/hive/src/test/resources/golden/having-5-4a0c4e521b8a6f6146151c13a2715ff
 create mode 100644 sql/hive/src/test/resources/golden/having-6-9f50df5b5f31c7166b0396ab434dc095
 create mode 100644 sql/hive/src/test/resources/golden/having-7-5ad96cb287df02080da1e2594f08d83e
 create mode 100644 sql/hive/src/test/resources/golden/having-8-4aa7197e20b5a64461ca670a79488103
 create mode 100644 sql/hive/src/test/resources/golden/having-9-a79743372d86d77b0ff53a71adcb1cff

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
index c7188469bfb8..02bdb64f308a 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
@@ -22,7 +22,6 @@ import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.logical._
 import org.apache.spark.sql.catalyst.rules._
 
-
 /**
  * A trivial [[Analyzer]] with an [[EmptyCatalog]] and [[EmptyFunctionRegistry]]. Used for testing
  * when all relations are already filled in and the analyser needs only to resolve attribute
@@ -54,6 +53,7 @@ class Analyzer(catalog: Catalog, registry: FunctionRegistry, caseSensitive: Bool
       StarExpansion ::
       ResolveFunctions ::
       GlobalAggregates ::
+      UnresolvedHavingClauseAttributes :: 
       typeCoercionRules :_*),
     Batch("Check Analysis", Once,
       CheckResolution),
@@ -151,6 +151,31 @@ class Analyzer(catalog: Catalog, registry: FunctionRegistry, caseSensitive: Bool
     }
   }
 
+  /**
+   * This rule finds expressions in HAVING clause filters that depend on
+   * unresolved attributes.  It pushes these expressions down to the underlying
+   * aggregates and then projects them away above the filter.
+   */
+  object UnresolvedHavingClauseAttributes extends Rule[LogicalPlan] {
+    def apply(plan: LogicalPlan): LogicalPlan = plan transformUp {
+      case filter @ Filter(havingCondition, aggregate @ Aggregate(_, originalAggExprs, _)) 
+          if !filter.resolved && aggregate.resolved && containsAggregate(havingCondition) => {
+        val evaluatedCondition = Alias(havingCondition,  "havingCondition")()
+        val aggExprsWithHaving = evaluatedCondition +: originalAggExprs
+        
+        Project(aggregate.output,
+          Filter(evaluatedCondition.toAttribute,
+            aggregate.copy(aggregateExpressions = aggExprsWithHaving)))
+      }
+      
+    }
+    
+    protected def containsAggregate(condition: Expression): Boolean =
+      condition
+        .collect { case ae: AggregateExpression => ae }
+        .nonEmpty
+  }
+
   /**
    * When a SELECT clause has only a single expression and that expression is a
    * [[catalyst.expressions.Generator Generator]] we convert the
diff --git a/sql/hive/src/test/resources/golden/having-0-57f3f26c0203c29c2a91a7cca557ce55 b/sql/hive/src/test/resources/golden/having-0-57f3f26c0203c29c2a91a7cca557ce55
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/having-1-ef81808faeab6d212c3cf32abfc0d873 b/sql/hive/src/test/resources/golden/having-1-ef81808faeab6d212c3cf32abfc0d873
new file mode 100644
index 000000000000..704f1e62f14c
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/having-1-ef81808faeab6d212c3cf32abfc0d873
@@ -0,0 +1,10 @@
+4
+4
+5
+4
+5
+5
+4
+4
+5
+4
diff --git a/sql/hive/src/test/resources/golden/having-2-a2b4f52cb92f730ddb912b063636d6c1 b/sql/hive/src/test/resources/golden/having-2-a2b4f52cb92f730ddb912b063636d6c1
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/having-3-3fa6387b6a4ece110ac340c7b893964e b/sql/hive/src/test/resources/golden/having-3-3fa6387b6a4ece110ac340c7b893964e
new file mode 100644
index 000000000000..b56757a60f78
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/having-3-3fa6387b6a4ece110ac340c7b893964e
@@ -0,0 +1,308 @@
+0	val_0
+2	val_2
+4	val_4
+5	val_5
+8	val_8
+9	val_9
+10	val_10
+11	val_11
+12	val_12
+15	val_15
+17	val_17
+18	val_18
+19	val_19
+20	val_20
+24	val_24
+26	val_26
+27	val_27
+28	val_28
+30	val_30
+33	val_33
+34	val_34
+35	val_35
+37	val_37
+41	val_41
+42	val_42
+43	val_43
+44	val_44
+47	val_47
+51	val_51
+53	val_53
+54	val_54
+57	val_57
+58	val_58
+64	val_64
+65	val_65
+66	val_66
+67	val_67
+69	val_69
+70	val_70
+72	val_72
+74	val_74
+76	val_76
+77	val_77
+78	val_78
+80	val_80
+82	val_82
+83	val_83
+84	val_84
+85	val_85
+86	val_86
+87	val_87
+90	val_90
+92	val_92
+95	val_95
+96	val_96
+97	val_97
+98	val_98
+100	val_100
+103	val_103
+104	val_104
+105	val_105
+111	val_111
+113	val_113
+114	val_114
+116	val_116
+118	val_118
+119	val_119
+120	val_120
+125	val_125
+126	val_126
+128	val_128
+129	val_129
+131	val_131
+133	val_133
+134	val_134
+136	val_136
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+143	val_143
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+146	val_146
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+150	val_150
+152	val_152
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+392	val_392
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+424	val_424
+427	val_427
+429	val_429
+430	val_430
+431	val_431
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+435	val_435
+436	val_436
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+438	val_438
+439	val_439
+443	val_443
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+448	val_448
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+452	val_452
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+485	val_485
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+489	val_489
+490	val_490
+491	val_491
+492	val_492
+493	val_493
+494	val_494
+495	val_495
+496	val_496
+497	val_497
+498	val_498
diff --git a/sql/hive/src/test/resources/golden/having-4-e9918bd385cb35db4ebcbd4e398547f4 b/sql/hive/src/test/resources/golden/having-4-e9918bd385cb35db4ebcbd4e398547f4
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/having-5-4a0c4e521b8a6f6146151c13a2715ff b/sql/hive/src/test/resources/golden/having-5-4a0c4e521b8a6f6146151c13a2715ff
new file mode 100644
index 000000000000..2d7022e38630
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/having-5-4a0c4e521b8a6f6146151c13a2715ff
@@ -0,0 +1,199 @@
+4
+5
+8
+9
+26
+27
+28
+30
+33
+34
+35
+37
+41
+42
+43
+44
+47
+51
+53
+54
+57
+58
+64
+65
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+67
+69
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+72
+74
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+77
+78
+80
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+85
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+87
+90
+92
+95
+96
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+98
+256
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+258
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+262
+263
+265
+266
+272
+273
+274
+275
+277
+278
+280
+281
+282
+283
+284
+285
+286
+287
+288
+289
+291
+292
+296
+298
+302
+305
+306
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+308
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+311
+315
+316
+317
+318
+321
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+331
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+373
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+379
+382
+384
+386
+389
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+414
+417
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+430
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+435
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+475
+477
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+481
+482
+483
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+485
+487
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+491
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+493
+494
+495
+496
+497
+498
diff --git a/sql/hive/src/test/resources/golden/having-6-9f50df5b5f31c7166b0396ab434dc095 b/sql/hive/src/test/resources/golden/having-6-9f50df5b5f31c7166b0396ab434dc095
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/having-7-5ad96cb287df02080da1e2594f08d83e b/sql/hive/src/test/resources/golden/having-7-5ad96cb287df02080da1e2594f08d83e
new file mode 100644
index 000000000000..bd545ccf7430
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/having-7-5ad96cb287df02080da1e2594f08d83e
@@ -0,0 +1,125 @@
+302
+305
+306
+307
+308
+309
+310
+311
+315
+316
+317
+318
+321
+322
+323
+325
+327
+331
+332
+333
+335
+336
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+339
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+348
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+424
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+432
+435
+436
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+443
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+485
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+489
+490
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+493
+494
+495
+496
+497
+498
diff --git a/sql/hive/src/test/resources/golden/having-8-4aa7197e20b5a64461ca670a79488103 b/sql/hive/src/test/resources/golden/having-8-4aa7197e20b5a64461ca670a79488103
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/having-9-a79743372d86d77b0ff53a71adcb1cff b/sql/hive/src/test/resources/golden/having-9-a79743372d86d77b0ff53a71adcb1cff
new file mode 100644
index 000000000000..d77586c12b6a
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/having-9-a79743372d86d77b0ff53a71adcb1cff
@@ -0,0 +1,199 @@
+4	val_4
+5	val_5
+8	val_8
+9	val_9
+26	val_26
+27	val_27
+28	val_28
+30	val_30
+33	val_33
+34	val_34
+35	val_35
+37	val_37
+41	val_41
+42	val_42
+43	val_43
+44	val_44
+47	val_47
+51	val_51
+53	val_53
+54	val_54
+57	val_57
+58	val_58
+64	val_64
+65	val_65
+66	val_66
+67	val_67
+69	val_69
+70	val_70
+72	val_72
+74	val_74
+76	val_76
+77	val_77
+78	val_78
+80	val_80
+82	val_82
+83	val_83
+84	val_84
+85	val_85
+86	val_86
+87	val_87
+90	val_90
+92	val_92
+95	val_95
+96	val_96
+97	val_97
+98	val_98
+256	val_256
+257	val_257
+258	val_258
+260	val_260
+262	val_262
+263	val_263
+265	val_265
+266	val_266
+272	val_272
+273	val_273
+274	val_274
+275	val_275
+277	val_277
+278	val_278
+280	val_280
+281	val_281
+282	val_282
+283	val_283
+284	val_284
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+286	val_286
+287	val_287
+288	val_288
+289	val_289
+291	val_291
+292	val_292
+296	val_296
+298	val_298
+302	val_302
+305	val_305
+306	val_306
+307	val_307
+308	val_308
+309	val_309
+310	val_310
+311	val_311
+315	val_315
+316	val_316
+317	val_317
+318	val_318
+321	val_321
+322	val_322
+323	val_323
+325	val_325
+327	val_327
+331	val_331
+332	val_332
+333	val_333
+335	val_335
+336	val_336
+338	val_338
+339	val_339
+341	val_341
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+344	val_344
+345	val_345
+348	val_348
+351	val_351
+353	val_353
+356	val_356
+360	val_360
+362	val_362
+364	val_364
+365	val_365
+366	val_366
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+368	val_368
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+373	val_373
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+377	val_377
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+382	val_382
+384	val_384
+386	val_386
+389	val_389
+392	val_392
+393	val_393
+394	val_394
+395	val_395
+396	val_396
+397	val_397
+399	val_399
+400	val_400
+401	val_401
+402	val_402
+403	val_403
+404	val_404
+406	val_406
+407	val_407
+409	val_409
+411	val_411
+413	val_413
+414	val_414
+417	val_417
+418	val_418
+419	val_419
+421	val_421
+424	val_424
+427	val_427
+429	val_429
+430	val_430
+431	val_431
+432	val_432
+435	val_435
+436	val_436
+437	val_437
+438	val_438
+439	val_439
+443	val_443
+444	val_444
+446	val_446
+448	val_448
+449	val_449
+452	val_452
+453	val_453
+454	val_454
+455	val_455
+457	val_457
+458	val_458
+459	val_459
+460	val_460
+462	val_462
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+466	val_466
+467	val_467
+468	val_468
+469	val_469
+470	val_470
+472	val_472
+475	val_475
+477	val_477
+478	val_478
+479	val_479
+480	val_480
+481	val_481
+482	val_482
+483	val_483
+484	val_484
+485	val_485
+487	val_487
+489	val_489
+490	val_490
+491	val_491
+492	val_492
+493	val_493
+494	val_494
+495	val_495
+496	val_496
+497	val_497
+498	val_498
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
index bd036faaa635..8b451973a47a 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
@@ -391,6 +391,8 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     "groupby_sort_8",
     "groupby_sort_9",
     "groupby_sort_test_1",
+    "having",
+    "having1",
     "implicit_cast1",
     "innerjoin",
     "inoutdriver",

From 1871574a240e6f28adeb6bc8accc98c851cafae5 Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Wed, 23 Jul 2014 16:26:55 -0700
Subject: [PATCH 736/810] [SPARK-2569][SQL] Fix shipping of TEMPORARY hive
 UDFs.

Instead of shipping just the name and then looking up the info on the workers, we now ship the whole classname.  Also, I refactored the file as it was getting pretty large to move out the type conversion code to its own file.

Author: Michael Armbrust <michael@databricks.com>

Closes #1552 from marmbrus/fixTempUdfs and squashes the following commits:

b695904 [Michael Armbrust] Make add jar execute with Hive.  Ship the whole function class name since sometimes we cannot lookup temporary functions on the workers.
---
 .../spark/sql/hive/HiveInspectors.scala       | 230 +++++++++++++++
 .../org/apache/spark/sql/hive/HiveQl.scala    |   4 +-
 .../org/apache/spark/sql/hive/hiveUdfs.scala  | 262 ++----------------
 3 files changed, 261 insertions(+), 235 deletions(-)
 create mode 100644 sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala
new file mode 100644
index 000000000000..ad7dc0ecdb1b
--- /dev/null
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala
@@ -0,0 +1,230 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive
+
+import org.apache.hadoop.hive.common.`type`.HiveDecimal
+import org.apache.hadoop.hive.serde2.objectinspector._
+import org.apache.hadoop.hive.serde2.objectinspector.primitive._
+import org.apache.hadoop.hive.serde2.{io => hiveIo}
+import org.apache.hadoop.{io => hadoopIo}
+
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.types
+import org.apache.spark.sql.catalyst.types._
+
+/* Implicit conversions */
+import scala.collection.JavaConversions._
+
+private[hive] trait HiveInspectors {
+
+  def javaClassToDataType(clz: Class[_]): DataType = clz match {
+    // writable
+    case c: Class[_] if c == classOf[hadoopIo.DoubleWritable] => DoubleType
+    case c: Class[_] if c == classOf[hiveIo.DoubleWritable] => DoubleType
+    case c: Class[_] if c == classOf[hiveIo.HiveDecimalWritable] => DecimalType
+    case c: Class[_] if c == classOf[hiveIo.ByteWritable] => ByteType
+    case c: Class[_] if c == classOf[hiveIo.ShortWritable] => ShortType
+    case c: Class[_] if c == classOf[hiveIo.TimestampWritable] => TimestampType
+    case c: Class[_] if c == classOf[hadoopIo.Text] => StringType
+    case c: Class[_] if c == classOf[hadoopIo.IntWritable] => IntegerType
+    case c: Class[_] if c == classOf[hadoopIo.LongWritable] => LongType
+    case c: Class[_] if c == classOf[hadoopIo.FloatWritable] => FloatType
+    case c: Class[_] if c == classOf[hadoopIo.BooleanWritable] => BooleanType
+    case c: Class[_] if c == classOf[hadoopIo.BytesWritable] => BinaryType
+
+    // java class
+    case c: Class[_] if c == classOf[java.lang.String] => StringType
+    case c: Class[_] if c == classOf[java.sql.Timestamp] => TimestampType
+    case c: Class[_] if c == classOf[HiveDecimal] => DecimalType
+    case c: Class[_] if c == classOf[java.math.BigDecimal] => DecimalType
+    case c: Class[_] if c == classOf[Array[Byte]] => BinaryType
+    case c: Class[_] if c == classOf[java.lang.Short] => ShortType
+    case c: Class[_] if c == classOf[java.lang.Integer] => IntegerType
+    case c: Class[_] if c == classOf[java.lang.Long] => LongType
+    case c: Class[_] if c == classOf[java.lang.Double] => DoubleType
+    case c: Class[_] if c == classOf[java.lang.Byte] => ByteType
+    case c: Class[_] if c == classOf[java.lang.Float] => FloatType
+    case c: Class[_] if c == classOf[java.lang.Boolean] => BooleanType
+
+    // primitive type
+    case c: Class[_] if c == java.lang.Short.TYPE => ShortType
+    case c: Class[_] if c == java.lang.Integer.TYPE => IntegerType
+    case c: Class[_] if c == java.lang.Long.TYPE => LongType
+    case c: Class[_] if c == java.lang.Double.TYPE => DoubleType
+    case c: Class[_] if c == java.lang.Byte.TYPE => ByteType
+    case c: Class[_] if c == java.lang.Float.TYPE => FloatType
+    case c: Class[_] if c == java.lang.Boolean.TYPE => BooleanType
+
+    case c: Class[_] if c.isArray => ArrayType(javaClassToDataType(c.getComponentType))
+  }
+
+  /** Converts hive types to native catalyst types. */
+  def unwrap(a: Any): Any = a match {
+    case null => null
+    case i: hadoopIo.IntWritable => i.get
+    case t: hadoopIo.Text => t.toString
+    case l: hadoopIo.LongWritable => l.get
+    case d: hadoopIo.DoubleWritable => d.get
+    case d: hiveIo.DoubleWritable => d.get
+    case s: hiveIo.ShortWritable => s.get
+    case b: hadoopIo.BooleanWritable => b.get
+    case b: hiveIo.ByteWritable => b.get
+    case b: hadoopIo.FloatWritable => b.get
+    case b: hadoopIo.BytesWritable => {
+      val bytes = new Array[Byte](b.getLength)
+      System.arraycopy(b.getBytes(), 0, bytes, 0, b.getLength)
+      bytes
+    }
+    case t: hiveIo.TimestampWritable => t.getTimestamp
+    case b: hiveIo.HiveDecimalWritable => BigDecimal(b.getHiveDecimal().bigDecimalValue())
+    case list: java.util.List[_] => list.map(unwrap)
+    case map: java.util.Map[_,_] => map.map { case (k, v) => (unwrap(k), unwrap(v)) }.toMap
+    case array: Array[_] => array.map(unwrap).toSeq
+    case p: java.lang.Short => p
+    case p: java.lang.Long => p
+    case p: java.lang.Float => p
+    case p: java.lang.Integer => p
+    case p: java.lang.Double => p
+    case p: java.lang.Byte => p
+    case p: java.lang.Boolean => p
+    case str: String => str
+    case p: java.math.BigDecimal => p
+    case p: Array[Byte] => p
+    case p: java.sql.Timestamp => p
+  }
+
+  def unwrapData(data: Any, oi: ObjectInspector): Any = oi match {
+    case hvoi: HiveVarcharObjectInspector =>
+      if (data == null) null else hvoi.getPrimitiveJavaObject(data).getValue
+    case hdoi: HiveDecimalObjectInspector =>
+      if (data == null) null else BigDecimal(hdoi.getPrimitiveJavaObject(data).bigDecimalValue())
+    case pi: PrimitiveObjectInspector => pi.getPrimitiveJavaObject(data)
+    case li: ListObjectInspector =>
+      Option(li.getList(data))
+        .map(_.map(unwrapData(_, li.getListElementObjectInspector)).toSeq)
+        .orNull
+    case mi: MapObjectInspector =>
+      Option(mi.getMap(data)).map(
+        _.map {
+          case (k,v) =>
+            (unwrapData(k, mi.getMapKeyObjectInspector),
+              unwrapData(v, mi.getMapValueObjectInspector))
+        }.toMap).orNull
+    case si: StructObjectInspector =>
+      val allRefs = si.getAllStructFieldRefs
+      new GenericRow(
+        allRefs.map(r =>
+          unwrapData(si.getStructFieldData(data,r), r.getFieldObjectInspector)).toArray)
+  }
+
+  /** Converts native catalyst types to the types expected by Hive */
+  def wrap(a: Any): AnyRef = a match {
+    case s: String => new hadoopIo.Text(s) // TODO why should be Text?
+    case i: Int => i: java.lang.Integer
+    case b: Boolean => b: java.lang.Boolean
+    case f: Float => f: java.lang.Float
+    case d: Double => d: java.lang.Double
+    case l: Long => l: java.lang.Long
+    case l: Short => l: java.lang.Short
+    case l: Byte => l: java.lang.Byte
+    case b: BigDecimal => b.bigDecimal
+    case b: Array[Byte] => b
+    case t: java.sql.Timestamp => t
+    case s: Seq[_] => seqAsJavaList(s.map(wrap))
+    case m: Map[_,_] =>
+      mapAsJavaMap(m.map { case (k, v) => wrap(k) -> wrap(v) })
+    case null => null
+  }
+
+  def toInspector(dataType: DataType): ObjectInspector = dataType match {
+    case ArrayType(tpe) => ObjectInspectorFactory.getStandardListObjectInspector(toInspector(tpe))
+    case MapType(keyType, valueType) =>
+      ObjectInspectorFactory.getStandardMapObjectInspector(
+        toInspector(keyType), toInspector(valueType))
+    case StringType => PrimitiveObjectInspectorFactory.javaStringObjectInspector
+    case IntegerType => PrimitiveObjectInspectorFactory.javaIntObjectInspector
+    case DoubleType => PrimitiveObjectInspectorFactory.javaDoubleObjectInspector
+    case BooleanType => PrimitiveObjectInspectorFactory.javaBooleanObjectInspector
+    case LongType => PrimitiveObjectInspectorFactory.javaLongObjectInspector
+    case FloatType => PrimitiveObjectInspectorFactory.javaFloatObjectInspector
+    case ShortType => PrimitiveObjectInspectorFactory.javaShortObjectInspector
+    case ByteType => PrimitiveObjectInspectorFactory.javaByteObjectInspector
+    case NullType => PrimitiveObjectInspectorFactory.javaVoidObjectInspector
+    case BinaryType => PrimitiveObjectInspectorFactory.javaByteArrayObjectInspector
+    case TimestampType => PrimitiveObjectInspectorFactory.javaTimestampObjectInspector
+    case DecimalType => PrimitiveObjectInspectorFactory.javaHiveDecimalObjectInspector
+    case StructType(fields) =>
+      ObjectInspectorFactory.getStandardStructObjectInspector(
+        fields.map(f => f.name), fields.map(f => toInspector(f.dataType)))
+  }
+
+  def inspectorToDataType(inspector: ObjectInspector): DataType = inspector match {
+    case s: StructObjectInspector =>
+      StructType(s.getAllStructFieldRefs.map(f => {
+        types.StructField(
+          f.getFieldName, inspectorToDataType(f.getFieldObjectInspector), nullable = true)
+      }))
+    case l: ListObjectInspector => ArrayType(inspectorToDataType(l.getListElementObjectInspector))
+    case m: MapObjectInspector =>
+      MapType(
+        inspectorToDataType(m.getMapKeyObjectInspector),
+        inspectorToDataType(m.getMapValueObjectInspector))
+    case _: WritableStringObjectInspector => StringType
+    case _: JavaStringObjectInspector => StringType
+    case _: WritableIntObjectInspector => IntegerType
+    case _: JavaIntObjectInspector => IntegerType
+    case _: WritableDoubleObjectInspector => DoubleType
+    case _: JavaDoubleObjectInspector => DoubleType
+    case _: WritableBooleanObjectInspector => BooleanType
+    case _: JavaBooleanObjectInspector => BooleanType
+    case _: WritableLongObjectInspector => LongType
+    case _: JavaLongObjectInspector => LongType
+    case _: WritableShortObjectInspector => ShortType
+    case _: JavaShortObjectInspector => ShortType
+    case _: WritableByteObjectInspector => ByteType
+    case _: JavaByteObjectInspector => ByteType
+    case _: WritableFloatObjectInspector => FloatType
+    case _: JavaFloatObjectInspector => FloatType
+    case _: WritableBinaryObjectInspector => BinaryType
+    case _: JavaBinaryObjectInspector => BinaryType
+    case _: WritableHiveDecimalObjectInspector => DecimalType
+    case _: JavaHiveDecimalObjectInspector => DecimalType
+    case _: WritableTimestampObjectInspector => TimestampType
+    case _: JavaTimestampObjectInspector => TimestampType
+  }
+
+  implicit class typeInfoConversions(dt: DataType) {
+    import org.apache.hadoop.hive.serde2.typeinfo._
+    import TypeInfoFactory._
+
+    def toTypeInfo: TypeInfo = dt match {
+      case BinaryType => binaryTypeInfo
+      case BooleanType => booleanTypeInfo
+      case ByteType => byteTypeInfo
+      case DoubleType => doubleTypeInfo
+      case FloatType => floatTypeInfo
+      case IntegerType => intTypeInfo
+      case LongType => longTypeInfo
+      case ShortType => shortTypeInfo
+      case StringType => stringTypeInfo
+      case DecimalType => decimalTypeInfo
+      case TimestampType => timestampTypeInfo
+      case NullType => voidTypeInfo
+    }
+  }
+}
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
index 53480a521dd1..c4ca9f362a04 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
@@ -42,8 +42,6 @@ private[hive] case class ShellCommand(cmd: String) extends Command
 
 private[hive] case class SourceCommand(filePath: String) extends Command
 
-private[hive] case class AddJar(jarPath: String) extends Command
-
 private[hive] case class AddFile(filePath: String) extends Command
 
 /** Provides a mapping from HiveQL statements to catalyst logical plans and expression trees. */
@@ -229,7 +227,7 @@ private[hive] object HiveQl {
       } else if (sql.trim.toLowerCase.startsWith("uncache table")) {
         CacheCommand(sql.trim.drop(14).trim, false)
       } else if (sql.trim.toLowerCase.startsWith("add jar")) {
-        AddJar(sql.trim.drop(8))
+        NativeCommand(sql)
       } else if (sql.trim.toLowerCase.startsWith("add file")) {
         AddFile(sql.trim.drop(9))
       } else if (sql.trim.toLowerCase.startsWith("dfs")) {
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala
index fc33c5b460d7..057eb60a0261 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala
@@ -24,22 +24,19 @@ import org.apache.hadoop.hive.ql.exec.UDF
 import org.apache.hadoop.hive.ql.exec.{FunctionInfo, FunctionRegistry}
 import org.apache.hadoop.hive.ql.udf.{UDFType => HiveUDFType}
 import org.apache.hadoop.hive.ql.udf.generic._
-import org.apache.hadoop.hive.serde2.objectinspector._
-import org.apache.hadoop.hive.serde2.objectinspector.primitive._
-import org.apache.hadoop.hive.serde2.{io => hiveIo}
-import org.apache.hadoop.{io => hadoopIo}
 
 import org.apache.spark.sql.Logging
 import org.apache.spark.sql.catalyst.analysis
 import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.catalyst.types
 import org.apache.spark.sql.catalyst.types._
+import org.apache.spark.util.Utils.getContextOrSparkClassLoader
 
 /* Implicit conversions */
 import scala.collection.JavaConversions._
 
-private[hive] object HiveFunctionRegistry
-  extends analysis.FunctionRegistry with HiveFunctionFactory with HiveInspectors {
+private[hive] object HiveFunctionRegistry extends analysis.FunctionRegistry with HiveInspectors {
+
+  def getFunctionInfo(name: String) = FunctionRegistry.getFunctionInfo(name)
 
   def lookupFunction(name: String, children: Seq[Expression]): Expression = {
     // We only look it up to see if it exists, but do not include it in the HiveUDF since it is
@@ -47,111 +44,37 @@ private[hive] object HiveFunctionRegistry
     val functionInfo: FunctionInfo = Option(FunctionRegistry.getFunctionInfo(name)).getOrElse(
       sys.error(s"Couldn't find function $name"))
 
+    val functionClassName = functionInfo.getFunctionClass.getName()
+
     if (classOf[UDF].isAssignableFrom(functionInfo.getFunctionClass)) {
-      val function = createFunction[UDF](name)
+      val function = functionInfo.getFunctionClass.newInstance().asInstanceOf[UDF]
       val method = function.getResolver.getEvalMethod(children.map(_.dataType.toTypeInfo))
 
       lazy val expectedDataTypes = method.getParameterTypes.map(javaClassToDataType)
 
       HiveSimpleUdf(
-        name,
+        functionClassName,
         children.zip(expectedDataTypes).map { case (e, t) => Cast(e, t) }
       )
     } else if (classOf[GenericUDF].isAssignableFrom(functionInfo.getFunctionClass)) {
-      HiveGenericUdf(name, children)
+      HiveGenericUdf(functionClassName, children)
     } else if (
          classOf[AbstractGenericUDAFResolver].isAssignableFrom(functionInfo.getFunctionClass)) {
-      HiveGenericUdaf(name, children)
+      HiveGenericUdaf(functionClassName, children)
 
     } else if (classOf[GenericUDTF].isAssignableFrom(functionInfo.getFunctionClass)) {
-      HiveGenericUdtf(name, Nil, children)
+      HiveGenericUdtf(functionClassName, Nil, children)
     } else {
       sys.error(s"No handler for udf ${functionInfo.getFunctionClass}")
     }
   }
-
-  def javaClassToDataType(clz: Class[_]): DataType = clz match {
-    // writable
-    case c: Class[_] if c == classOf[hadoopIo.DoubleWritable] => DoubleType
-    case c: Class[_] if c == classOf[hiveIo.DoubleWritable] => DoubleType
-    case c: Class[_] if c == classOf[hiveIo.HiveDecimalWritable] => DecimalType
-    case c: Class[_] if c == classOf[hiveIo.ByteWritable] => ByteType
-    case c: Class[_] if c == classOf[hiveIo.ShortWritable] => ShortType
-    case c: Class[_] if c == classOf[hiveIo.TimestampWritable] => TimestampType
-    case c: Class[_] if c == classOf[hadoopIo.Text] => StringType
-    case c: Class[_] if c == classOf[hadoopIo.IntWritable] => IntegerType
-    case c: Class[_] if c == classOf[hadoopIo.LongWritable] => LongType
-    case c: Class[_] if c == classOf[hadoopIo.FloatWritable] => FloatType
-    case c: Class[_] if c == classOf[hadoopIo.BooleanWritable] => BooleanType
-    case c: Class[_] if c == classOf[hadoopIo.BytesWritable] => BinaryType
-
-    // java class
-    case c: Class[_] if c == classOf[java.lang.String] => StringType
-    case c: Class[_] if c == classOf[java.sql.Timestamp] => TimestampType
-    case c: Class[_] if c == classOf[HiveDecimal] => DecimalType
-    case c: Class[_] if c == classOf[java.math.BigDecimal] => DecimalType
-    case c: Class[_] if c == classOf[Array[Byte]] => BinaryType
-    case c: Class[_] if c == classOf[java.lang.Short] => ShortType
-    case c: Class[_] if c == classOf[java.lang.Integer] => IntegerType
-    case c: Class[_] if c == classOf[java.lang.Long] => LongType
-    case c: Class[_] if c == classOf[java.lang.Double] => DoubleType
-    case c: Class[_] if c == classOf[java.lang.Byte] => ByteType
-    case c: Class[_] if c == classOf[java.lang.Float] => FloatType
-    case c: Class[_] if c == classOf[java.lang.Boolean] => BooleanType
-
-    // primitive type
-    case c: Class[_] if c == java.lang.Short.TYPE => ShortType
-    case c: Class[_] if c == java.lang.Integer.TYPE => IntegerType
-    case c: Class[_] if c == java.lang.Long.TYPE => LongType
-    case c: Class[_] if c == java.lang.Double.TYPE => DoubleType
-    case c: Class[_] if c == java.lang.Byte.TYPE => ByteType
-    case c: Class[_] if c == java.lang.Float.TYPE => FloatType
-    case c: Class[_] if c == java.lang.Boolean.TYPE => BooleanType
-
-    case c: Class[_] if c.isArray => ArrayType(javaClassToDataType(c.getComponentType))
-  }
 }
 
 private[hive] trait HiveFunctionFactory {
-  def getFunctionInfo(name: String) = FunctionRegistry.getFunctionInfo(name)
-  def getFunctionClass(name: String) = getFunctionInfo(name).getFunctionClass
-  def createFunction[UDFType](name: String) =
-    getFunctionClass(name).newInstance.asInstanceOf[UDFType]
-
-  /** Converts hive types to native catalyst types. */
-  def unwrap(a: Any): Any = a match {
-    case null => null
-    case i: hadoopIo.IntWritable => i.get
-    case t: hadoopIo.Text => t.toString
-    case l: hadoopIo.LongWritable => l.get
-    case d: hadoopIo.DoubleWritable => d.get
-    case d: hiveIo.DoubleWritable => d.get
-    case s: hiveIo.ShortWritable => s.get
-    case b: hadoopIo.BooleanWritable => b.get
-    case b: hiveIo.ByteWritable => b.get
-    case b: hadoopIo.FloatWritable => b.get
-    case b: hadoopIo.BytesWritable => {
-      val bytes = new Array[Byte](b.getLength)
-      System.arraycopy(b.getBytes(), 0, bytes, 0, b.getLength)
-      bytes
-    }
-    case t: hiveIo.TimestampWritable => t.getTimestamp
-    case b: hiveIo.HiveDecimalWritable => BigDecimal(b.getHiveDecimal().bigDecimalValue())
-    case list: java.util.List[_] => list.map(unwrap)
-    case map: java.util.Map[_,_] => map.map { case (k, v) => (unwrap(k), unwrap(v)) }.toMap
-    case array: Array[_] => array.map(unwrap).toSeq
-    case p: java.lang.Short => p
-    case p: java.lang.Long => p
-    case p: java.lang.Float => p
-    case p: java.lang.Integer => p
-    case p: java.lang.Double => p
-    case p: java.lang.Byte => p
-    case p: java.lang.Boolean => p
-    case str: String => str
-    case p: java.math.BigDecimal => p
-    case p: Array[Byte] => p
-    case p: java.sql.Timestamp => p
-  }
+  val functionClassName: String
+
+  def createFunction[UDFType]() =
+    getContextOrSparkClassLoader.loadClass(functionClassName).newInstance.asInstanceOf[UDFType]
 }
 
 private[hive] abstract class HiveUdf extends Expression with Logging with HiveFunctionFactory {
@@ -160,19 +83,17 @@ private[hive] abstract class HiveUdf extends Expression with Logging with HiveFu
   type UDFType
   type EvaluatedType = Any
 
-  val name: String
-
   def nullable = true
   def references = children.flatMap(_.references).toSet
 
-  // FunctionInfo is not serializable so we must look it up here again.
-  lazy val functionInfo = getFunctionInfo(name)
-  lazy val function = createFunction[UDFType](name)
+  lazy val function = createFunction[UDFType]()
 
-  override def toString = s"$nodeName#${functionInfo.getDisplayName}(${children.mkString(",")})"
+  override def toString = s"$nodeName#$functionClassName(${children.mkString(",")})"
 }
 
-private[hive] case class HiveSimpleUdf(name: String, children: Seq[Expression]) extends HiveUdf {
+private[hive] case class HiveSimpleUdf(functionClassName: String, children: Seq[Expression])
+  extends HiveUdf {
+
   import org.apache.spark.sql.hive.HiveFunctionRegistry._
   type UDFType = UDF
 
@@ -226,7 +147,7 @@ private[hive] case class HiveSimpleUdf(name: String, children: Seq[Expression])
   }
 }
 
-private[hive] case class HiveGenericUdf(name: String, children: Seq[Expression])
+private[hive] case class HiveGenericUdf(functionClassName: String, children: Seq[Expression])
   extends HiveUdf with HiveInspectors {
 
   import org.apache.hadoop.hive.ql.udf.generic.GenericUDF._
@@ -277,131 +198,8 @@ private[hive] case class HiveGenericUdf(name: String, children: Seq[Expression])
   }
 }
 
-private[hive] trait HiveInspectors {
-
-  def unwrapData(data: Any, oi: ObjectInspector): Any = oi match {
-    case hvoi: HiveVarcharObjectInspector => 
-      if (data == null) null else hvoi.getPrimitiveJavaObject(data).getValue
-    case hdoi: HiveDecimalObjectInspector => 
-      if (data == null) null else BigDecimal(hdoi.getPrimitiveJavaObject(data).bigDecimalValue())
-    case pi: PrimitiveObjectInspector => pi.getPrimitiveJavaObject(data)
-    case li: ListObjectInspector =>
-      Option(li.getList(data))
-        .map(_.map(unwrapData(_, li.getListElementObjectInspector)).toSeq)
-        .orNull
-    case mi: MapObjectInspector =>
-      Option(mi.getMap(data)).map(
-        _.map {
-          case (k,v) =>
-            (unwrapData(k, mi.getMapKeyObjectInspector),
-              unwrapData(v, mi.getMapValueObjectInspector))
-        }.toMap).orNull
-    case si: StructObjectInspector =>
-      val allRefs = si.getAllStructFieldRefs
-      new GenericRow(
-        allRefs.map(r =>
-          unwrapData(si.getStructFieldData(data,r), r.getFieldObjectInspector)).toArray)
-  }
-
-  /** Converts native catalyst types to the types expected by Hive */
-  def wrap(a: Any): AnyRef = a match {
-    case s: String => new hadoopIo.Text(s) // TODO why should be Text?
-    case i: Int => i: java.lang.Integer
-    case b: Boolean => b: java.lang.Boolean
-    case f: Float => f: java.lang.Float
-    case d: Double => d: java.lang.Double
-    case l: Long => l: java.lang.Long
-    case l: Short => l: java.lang.Short
-    case l: Byte => l: java.lang.Byte
-    case b: BigDecimal => b.bigDecimal
-    case b: Array[Byte] => b
-    case t: java.sql.Timestamp => t
-    case s: Seq[_] => seqAsJavaList(s.map(wrap))
-    case m: Map[_,_] =>
-      mapAsJavaMap(m.map { case (k, v) => wrap(k) -> wrap(v) })
-    case null => null
-  }
-
-  def toInspector(dataType: DataType): ObjectInspector = dataType match {
-    case ArrayType(tpe) => ObjectInspectorFactory.getStandardListObjectInspector(toInspector(tpe))
-    case MapType(keyType, valueType) =>
-      ObjectInspectorFactory.getStandardMapObjectInspector(
-        toInspector(keyType), toInspector(valueType))
-    case StringType => PrimitiveObjectInspectorFactory.javaStringObjectInspector
-    case IntegerType => PrimitiveObjectInspectorFactory.javaIntObjectInspector
-    case DoubleType => PrimitiveObjectInspectorFactory.javaDoubleObjectInspector
-    case BooleanType => PrimitiveObjectInspectorFactory.javaBooleanObjectInspector
-    case LongType => PrimitiveObjectInspectorFactory.javaLongObjectInspector
-    case FloatType => PrimitiveObjectInspectorFactory.javaFloatObjectInspector
-    case ShortType => PrimitiveObjectInspectorFactory.javaShortObjectInspector
-    case ByteType => PrimitiveObjectInspectorFactory.javaByteObjectInspector
-    case NullType => PrimitiveObjectInspectorFactory.javaVoidObjectInspector
-    case BinaryType => PrimitiveObjectInspectorFactory.javaByteArrayObjectInspector
-    case TimestampType => PrimitiveObjectInspectorFactory.javaTimestampObjectInspector
-    case DecimalType => PrimitiveObjectInspectorFactory.javaHiveDecimalObjectInspector
-    case StructType(fields) =>
-      ObjectInspectorFactory.getStandardStructObjectInspector(
-        fields.map(f => f.name), fields.map(f => toInspector(f.dataType)))
-  }
-
-  def inspectorToDataType(inspector: ObjectInspector): DataType = inspector match {
-    case s: StructObjectInspector =>
-      StructType(s.getAllStructFieldRefs.map(f => {
-        types.StructField(
-          f.getFieldName, inspectorToDataType(f.getFieldObjectInspector), nullable = true)
-      }))
-    case l: ListObjectInspector => ArrayType(inspectorToDataType(l.getListElementObjectInspector))
-    case m: MapObjectInspector =>
-      MapType(
-        inspectorToDataType(m.getMapKeyObjectInspector),
-        inspectorToDataType(m.getMapValueObjectInspector))
-    case _: WritableStringObjectInspector => StringType
-    case _: JavaStringObjectInspector => StringType
-    case _: WritableIntObjectInspector => IntegerType
-    case _: JavaIntObjectInspector => IntegerType
-    case _: WritableDoubleObjectInspector => DoubleType
-    case _: JavaDoubleObjectInspector => DoubleType
-    case _: WritableBooleanObjectInspector => BooleanType
-    case _: JavaBooleanObjectInspector => BooleanType
-    case _: WritableLongObjectInspector => LongType
-    case _: JavaLongObjectInspector => LongType
-    case _: WritableShortObjectInspector => ShortType
-    case _: JavaShortObjectInspector => ShortType
-    case _: WritableByteObjectInspector => ByteType
-    case _: JavaByteObjectInspector => ByteType
-    case _: WritableFloatObjectInspector => FloatType
-    case _: JavaFloatObjectInspector => FloatType
-    case _: WritableBinaryObjectInspector => BinaryType
-    case _: JavaBinaryObjectInspector => BinaryType
-    case _: WritableHiveDecimalObjectInspector => DecimalType
-    case _: JavaHiveDecimalObjectInspector => DecimalType
-    case _: WritableTimestampObjectInspector => TimestampType
-    case _: JavaTimestampObjectInspector => TimestampType
-  }
-
-  implicit class typeInfoConversions(dt: DataType) {
-    import org.apache.hadoop.hive.serde2.typeinfo._
-    import TypeInfoFactory._
-
-    def toTypeInfo: TypeInfo = dt match {
-      case BinaryType => binaryTypeInfo
-      case BooleanType => booleanTypeInfo
-      case ByteType => byteTypeInfo
-      case DoubleType => doubleTypeInfo
-      case FloatType => floatTypeInfo
-      case IntegerType => intTypeInfo
-      case LongType => longTypeInfo
-      case ShortType => shortTypeInfo
-      case StringType => stringTypeInfo
-      case DecimalType => decimalTypeInfo
-      case TimestampType => timestampTypeInfo
-      case NullType => voidTypeInfo
-    }
-  }
-}
-
 private[hive] case class HiveGenericUdaf(
-    name: String,
+    functionClassName: String,
     children: Seq[Expression]) extends AggregateExpression
   with HiveInspectors
   with HiveFunctionFactory {
@@ -409,7 +207,7 @@ private[hive] case class HiveGenericUdaf(
   type UDFType = AbstractGenericUDAFResolver
 
   @transient
-  protected lazy val resolver: AbstractGenericUDAFResolver = createFunction(name)
+  protected lazy val resolver: AbstractGenericUDAFResolver = createFunction()
 
   @transient
   protected lazy val objectInspector  = {
@@ -426,9 +224,9 @@ private[hive] case class HiveGenericUdaf(
 
   def references: Set[Attribute] = children.map(_.references).flatten.toSet
 
-  override def toString = s"$nodeName#$name(${children.mkString(",")})"
+  override def toString = s"$nodeName#$functionClassName(${children.mkString(",")})"
 
-  def newInstance() = new HiveUdafFunction(name, children, this)
+  def newInstance() = new HiveUdafFunction(functionClassName, children, this)
 }
 
 /**
@@ -443,7 +241,7 @@ private[hive] case class HiveGenericUdaf(
  * user defined aggregations, which have clean semantics even in a partitioned execution.
  */
 private[hive] case class HiveGenericUdtf(
-    name: String,
+    functionClassName: String,
     aliasNames: Seq[String],
     children: Seq[Expression])
   extends Generator with HiveInspectors with HiveFunctionFactory {
@@ -451,7 +249,7 @@ private[hive] case class HiveGenericUdtf(
   override def references = children.flatMap(_.references).toSet
 
   @transient
-  protected lazy val function: GenericUDTF = createFunction(name)
+  protected lazy val function: GenericUDTF = createFunction()
 
   protected lazy val inputInspectors = children.map(_.dataType).map(toInspector)
 
@@ -506,11 +304,11 @@ private[hive] case class HiveGenericUdtf(
     }
   }
 
-  override def toString = s"$nodeName#$name(${children.mkString(",")})"
+  override def toString = s"$nodeName#$functionClassName(${children.mkString(",")})"
 }
 
 private[hive] case class HiveUdafFunction(
-    functionName: String,
+    functionClassName: String,
     exprs: Seq[Expression],
     base: AggregateExpression)
   extends AggregateFunction
@@ -519,7 +317,7 @@ private[hive] case class HiveUdafFunction(
 
   def this() = this(null, null, null)
 
-  private val resolver = createFunction[AbstractGenericUDAFResolver](functionName)
+  private val resolver = createFunction[AbstractGenericUDAFResolver]()
 
   private val inspectors = exprs.map(_.dataType).map(toInspector).toArray
 

From efdaeb111917dd0314f1d00ee8524bed1e2e21ca Mon Sep 17 00:00:00 2001
From: Ian O Connell <ioconnell@twitter.com>
Date: Wed, 23 Jul 2014 16:30:06 -0700
Subject: [PATCH 737/810] [SPARK-2102][SQL][CORE] Add option for kryo
 registration required and use a resource pool in Spark SQL for Kryo
 instances.

Author: Ian O Connell <ioconnell@twitter.com>

Closes #1377 from ianoc/feature/SPARK-2102 and squashes the following commits:

5498566 [Ian O Connell] Docs update suggested by Patrick
20e8555 [Ian O Connell] Slight style change
f92c294 [Ian O Connell] Add docs for new KryoSerializer option
f3735c8 [Ian O Connell] Add using a kryo resource pool for the SqlSerializer
4e5c342 [Ian O Connell] Register the SparkConf for kryo, it gets swept into serialization
665805a [Ian O Connell] Add a spark.kryo.registrationRequired option for configuring the Kryo Serializer
---
 .../spark/serializer/KryoSerializer.scala     |  5 ++-
 docs/configuration.md                         | 19 ++++++--
 .../sql/execution/SparkSqlSerializer.scala    | 43 +++++++++++++------
 3 files changed, 50 insertions(+), 17 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala b/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala
index 1ce424319479..c3a3e90a3490 100644
--- a/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala
+++ b/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala
@@ -48,6 +48,7 @@ class KryoSerializer(conf: SparkConf)
 
   private val bufferSize = conf.getInt("spark.kryoserializer.buffer.mb", 2) * 1024 * 1024
   private val referenceTracking = conf.getBoolean("spark.kryo.referenceTracking", true)
+  private val registrationRequired = conf.getBoolean("spark.kryo.registrationRequired", false)
   private val registrator = conf.getOption("spark.kryo.registrator")
 
   def newKryoOutput() = new KryoOutput(bufferSize)
@@ -55,6 +56,7 @@ class KryoSerializer(conf: SparkConf)
   def newKryo(): Kryo = {
     val instantiator = new EmptyScalaKryoInstantiator
     val kryo = instantiator.newKryo()
+    kryo.setRegistrationRequired(registrationRequired)
     val classLoader = Thread.currentThread.getContextClassLoader
 
     // Allow disabling Kryo reference tracking if user knows their object graphs don't have loops.
@@ -185,7 +187,8 @@ private[serializer] object KryoSerializer {
     classOf[MapStatus],
     classOf[BlockManagerId],
     classOf[Array[Byte]],
-    classOf[BoundedPriorityQueue[_]]
+    classOf[BoundedPriorityQueue[_]],
+    classOf[SparkConf]
   )
 }
 
diff --git a/docs/configuration.md b/docs/configuration.md
index a70007c16544..02af461267c4 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -388,6 +388,17 @@ Apart from these, the following properties are also available, and may be useful
     case.
   </td>
 </tr>
+<tr>
+  <td><code>spark.kryo.registrationRequired</code></td>
+  <td>false</td>
+  <td>
+    Whether to require registration with Kryo. If set to 'true', Kryo will throw an exception
+    if an unregistered class is serialized. If set to false (the default), Kryo will write
+    unregistered class names along with each object. Writing class names can cause
+    significant performance overhead, so enabling this option can enforce strictly that a
+    user has not omitted classes from registration.
+  </td>
+</tr>
 <tr>
   <td><code>spark.kryoserializer.buffer.mb</code></td>
   <td>2</td>
@@ -497,9 +508,9 @@ Apart from these, the following properties are also available, and may be useful
 <tr>
     <td>spark.hadoop.validateOutputSpecs</td>
     <td>true</td>
-    <td>If set to true, validates the output specification (e.g. checking if the output directory already exists) 
-    used in saveAsHadoopFile and other variants. This can be disabled to silence exceptions due to pre-existing 
-    output directories. We recommend that users do not disable this except if trying to achieve compatibility with 
+    <td>If set to true, validates the output specification (e.g. checking if the output directory already exists)
+    used in saveAsHadoopFile and other variants. This can be disabled to silence exceptions due to pre-existing
+    output directories. We recommend that users do not disable this except if trying to achieve compatibility with
     previous versions of Spark. Simply use Hadoop's FileSystem API to delete output directories by hand.</td>
 </tr>
 </table>
@@ -861,7 +872,7 @@ Apart from these, the following properties are also available, and may be useful
 </table>
 
 #### Cluster Managers
-Each cluster manager in Spark has additional configuration options. Configurations 
+Each cluster manager in Spark has additional configuration options. Configurations
 can be found on the pages for each mode:
 
  * [YARN](running-on-yarn.html#configuration)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer.scala
index 34b355e90669..34654447a5f4 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer.scala
@@ -24,10 +24,10 @@ import scala.reflect.ClassTag
 import com.clearspring.analytics.stream.cardinality.HyperLogLog
 import com.esotericsoftware.kryo.io.{Input, Output}
 import com.esotericsoftware.kryo.{Serializer, Kryo}
-import com.twitter.chill.AllScalaRegistrar
+import com.twitter.chill.{AllScalaRegistrar, ResourcePool}
 
 import org.apache.spark.{SparkEnv, SparkConf}
-import org.apache.spark.serializer.KryoSerializer
+import org.apache.spark.serializer.{SerializerInstance, KryoSerializer}
 import org.apache.spark.util.MutablePair
 import org.apache.spark.util.Utils
 
@@ -48,22 +48,41 @@ private[sql] class SparkSqlSerializer(conf: SparkConf) extends KryoSerializer(co
   }
 }
 
-private[sql] object SparkSqlSerializer {
-  // TODO (lian) Using KryoSerializer here is workaround, needs further investigation
-  // Using SparkSqlSerializer here makes BasicQuerySuite to fail because of Kryo serialization
-  // related error.
-  @transient lazy val ser: KryoSerializer = {
+private[execution] class KryoResourcePool(size: Int)
+    extends ResourcePool[SerializerInstance](size) {
+
+  val ser: KryoSerializer = {
     val sparkConf = Option(SparkEnv.get).map(_.conf).getOrElse(new SparkConf())
+    // TODO (lian) Using KryoSerializer here is workaround, needs further investigation
+    // Using SparkSqlSerializer here makes BasicQuerySuite to fail because of Kryo serialization
+    // related error.
     new KryoSerializer(sparkConf)
   }
 
-  def serialize[T: ClassTag](o: T): Array[Byte] = {
-    ser.newInstance().serialize(o).array()
-  }
+  def newInstance() = ser.newInstance()
+}
 
-  def deserialize[T: ClassTag](bytes: Array[Byte]): T  = {
-    ser.newInstance().deserialize[T](ByteBuffer.wrap(bytes))
+private[sql] object SparkSqlSerializer {
+  @transient lazy val resourcePool = new KryoResourcePool(30)
+
+  private[this] def acquireRelease[O](fn: SerializerInstance => O): O = {
+    val kryo = resourcePool.borrow
+    try {
+      fn(kryo)
+    } finally {
+      resourcePool.release(kryo)
+    }
   }
+
+  def serialize[T: ClassTag](o: T): Array[Byte] =
+    acquireRelease { k =>
+      k.serialize(o).array()
+    }
+
+  def deserialize[T: ClassTag](bytes: Array[Byte]): T =
+    acquireRelease { k =>
+      k.deserialize[T](ByteBuffer.wrap(bytes))
+    }
 }
 
 private[sql] class BigDecimalSerializer extends Serializer[BigDecimal] {

From 9b763329d968658d2dedee4485b7931646e6392e Mon Sep 17 00:00:00 2001
From: Prashant Sharma <prashant.s@imaginea.com>
Date: Wed, 23 Jul 2014 17:12:28 -0700
Subject: [PATCH 738/810] [SPARK-2549] Functions defined inside of other
 functions trigger failures

Author: Prashant Sharma <prashant.s@imaginea.com>

Closes #1510 from ScrapCodes/SPARK-2549/fun-in-fun and squashes the following commits:

9458bc5 [Prashant Sharma] Tested by removing an inner function from excludes.
bc03b1c [Prashant Sharma] SPARK-2549 Functions defined inside of other functions trigger failures
---
 project/MimaExcludes.scala                    | 183 ++++++++----------
 .../spark/tools/GenerateMIMAIgnore.scala      |  21 +-
 2 files changed, 98 insertions(+), 106 deletions(-)

diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala
index 5e5ddd227aab..e9220db6b1f9 100644
--- a/project/MimaExcludes.scala
+++ b/project/MimaExcludes.scala
@@ -32,108 +32,83 @@ import com.typesafe.tools.mima.core._
  */
 object MimaExcludes {
 
-  def excludes(version: String) = version match {
-    case v if v.startsWith("1.1") =>
-      Seq(
-        MimaBuild.excludeSparkPackage("deploy"),
-        MimaBuild.excludeSparkPackage("graphx")
-      ) ++
-      closures.map(method => ProblemFilters.exclude[MissingMethodProblem](method)) ++
-      Seq(
-        // Adding new method to JavaRDLike trait - we should probably mark this as a developer API.
-        ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.api.java.JavaRDDLike.partitions"),
-        // We made a mistake earlier (ed06500d3) in the Java API to use default parameter values
-        // for countApproxDistinct* functions, which does not work in Java. We later removed
-        // them, and use the following to tell Mima to not care about them.
-        ProblemFilters.exclude[IncompatibleResultTypeProblem](
-          "org.apache.spark.api.java.JavaPairRDD.countApproxDistinctByKey"),
-        ProblemFilters.exclude[IncompatibleResultTypeProblem](
-          "org.apache.spark.api.java.JavaPairRDD.countApproxDistinctByKey"),
-        ProblemFilters.exclude[MissingMethodProblem](
-          "org.apache.spark.api.java.JavaPairRDD.countApproxDistinct$default$1"),
-        ProblemFilters.exclude[MissingMethodProblem](
-          "org.apache.spark.api.java.JavaPairRDD.countApproxDistinctByKey$default$1"),
-        ProblemFilters.exclude[MissingMethodProblem](
-          "org.apache.spark.api.java.JavaRDD.countApproxDistinct$default$1"),
-        ProblemFilters.exclude[MissingMethodProblem](
-          "org.apache.spark.api.java.JavaRDDLike.countApproxDistinct$default$1"),
-        ProblemFilters.exclude[MissingMethodProblem](
-          "org.apache.spark.api.java.JavaDoubleRDD.countApproxDistinct$default$1"),
-        ProblemFilters.exclude[MissingMethodProblem](
-          "org.apache.spark.storage.MemoryStore.Entry"),
-        ProblemFilters.exclude[MissingMethodProblem](
-          "org.apache.spark.rdd.RDD.org$apache$spark$rdd$RDD$$debugChildren$1"),
-        ProblemFilters.exclude[MissingMethodProblem](
-          "org.apache.spark.rdd.RDD.org$apache$spark$rdd$RDD$$firstDebugString$1"),
-        ProblemFilters.exclude[MissingMethodProblem](
-          "org.apache.spark.rdd.RDD.org$apache$spark$rdd$RDD$$shuffleDebugString$1"),
-        ProblemFilters.exclude[MissingMethodProblem](
-          "org.apache.spark.rdd.RDD.org$apache$spark$rdd$RDD$$debugString$1"),
-        ProblemFilters.exclude[MissingMethodProblem](
-          "org.apache.spark.rdd.PairRDDFunctions.org$apache$spark$rdd$PairRDDFunctions$$"
-            + "createZero$1")
-      ) ++
-      Seq(
-        ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.streaming.flume.FlumeReceiver.this")
-      ) ++
-      Seq( // Ignore some private methods in ALS.
-        ProblemFilters.exclude[MissingMethodProblem](
-          "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$^dateFeatures"),
-        ProblemFilters.exclude[MissingMethodProblem]( // The only public constructor is the one without arguments.
-          "org.apache.spark.mllib.recommendation.ALS.this"),
-        ProblemFilters.exclude[MissingMethodProblem](
-          "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$<init>$default$7"),
-        ProblemFilters.exclude[IncompatibleMethTypeProblem](
-          "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$^dateFeatures")
-      ) ++
-      MimaBuild.excludeSparkClass("mllib.linalg.distributed.ColumnStatisticsAggregator") ++
-      MimaBuild.excludeSparkClass("rdd.ZippedRDD") ++
-      MimaBuild.excludeSparkClass("rdd.ZippedPartition") ++
-      MimaBuild.excludeSparkClass("util.SerializableHyperLogLog") ++
-      MimaBuild.excludeSparkClass("storage.Values") ++
-      MimaBuild.excludeSparkClass("storage.Entry") ++
-      MimaBuild.excludeSparkClass("storage.MemoryStore$Entry") ++
-      Seq(
-        ProblemFilters.exclude[IncompatibleMethTypeProblem](
-          "org.apache.spark.mllib.tree.impurity.Gini.calculate"),
-        ProblemFilters.exclude[IncompatibleMethTypeProblem](
-          "org.apache.spark.mllib.tree.impurity.Entropy.calculate"),
-        ProblemFilters.exclude[IncompatibleMethTypeProblem](
-          "org.apache.spark.mllib.tree.impurity.Variance.calculate")
-      )
-    case v if v.startsWith("1.0") =>
-      Seq(
-        MimaBuild.excludeSparkPackage("api.java"),
-        MimaBuild.excludeSparkPackage("mllib"),
-        MimaBuild.excludeSparkPackage("streaming")
-      ) ++
-      MimaBuild.excludeSparkClass("rdd.ClassTags") ++
-      MimaBuild.excludeSparkClass("util.XORShiftRandom") ++
-      MimaBuild.excludeSparkClass("graphx.EdgeRDD") ++
-      MimaBuild.excludeSparkClass("graphx.VertexRDD") ++
-      MimaBuild.excludeSparkClass("graphx.impl.GraphImpl") ++
-      MimaBuild.excludeSparkClass("graphx.impl.RoutingTable") ++
-      MimaBuild.excludeSparkClass("graphx.util.collection.PrimitiveKeyOpenHashMap") ++
-      MimaBuild.excludeSparkClass("graphx.util.collection.GraphXPrimitiveKeyOpenHashMap") ++
-      MimaBuild.excludeSparkClass("mllib.recommendation.MFDataGenerator") ++
-      MimaBuild.excludeSparkClass("mllib.optimization.SquaredGradient") ++
-      MimaBuild.excludeSparkClass("mllib.regression.RidgeRegressionWithSGD") ++
-      MimaBuild.excludeSparkClass("mllib.regression.LassoWithSGD") ++
-      MimaBuild.excludeSparkClass("mllib.regression.LinearRegressionWithSGD")
-    case _ => Seq()
-  }
-
-  private val closures = Seq(
-    "org.apache.spark.rdd.RDD.org$apache$spark$rdd$RDD$$mergeMaps$1",
-    "org.apache.spark.rdd.RDD.org$apache$spark$rdd$RDD$$countPartition$1",
-    "org.apache.spark.rdd.RDD.org$apache$spark$rdd$RDD$$distributePartition$1",
-    "org.apache.spark.rdd.PairRDDFunctions.org$apache$spark$rdd$PairRDDFunctions$$mergeValue$1",
-    "org.apache.spark.rdd.PairRDDFunctions.org$apache$spark$rdd$PairRDDFunctions$$writeToFile$1",
-    "org.apache.spark.rdd.PairRDDFunctions.org$apache$spark$rdd$PairRDDFunctions$$reducePartition$1",
-    "org.apache.spark.rdd.PairRDDFunctions.org$apache$spark$rdd$PairRDDFunctions$$writeShard$1",
-    "org.apache.spark.rdd.PairRDDFunctions.org$apache$spark$rdd$PairRDDFunctions$$mergeCombiners$1",
-    "org.apache.spark.rdd.PairRDDFunctions.org$apache$spark$rdd$PairRDDFunctions$$process$1",
-    "org.apache.spark.rdd.PairRDDFunctions.org$apache$spark$rdd$PairRDDFunctions$$createCombiner$1",
-    "org.apache.spark.rdd.PairRDDFunctions.org$apache$spark$rdd$PairRDDFunctions$$mergeMaps$1"
-  )
+    def excludes(version: String) =
+      version match {
+        case v if v.startsWith("1.1") =>
+          Seq(
+            MimaBuild.excludeSparkPackage("deploy"),
+            MimaBuild.excludeSparkPackage("graphx")
+          ) ++
+          Seq(
+            // Adding new method to JavaRDLike trait - we should probably mark this as a developer API.
+            ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.api.java.JavaRDDLike.partitions"),
+            // We made a mistake earlier (ed06500d3) in the Java API to use default parameter values
+            // for countApproxDistinct* functions, which does not work in Java. We later removed
+            // them, and use the following to tell Mima to not care about them.
+            ProblemFilters.exclude[IncompatibleResultTypeProblem](
+              "org.apache.spark.api.java.JavaPairRDD.countApproxDistinctByKey"),
+            ProblemFilters.exclude[IncompatibleResultTypeProblem](
+              "org.apache.spark.api.java.JavaPairRDD.countApproxDistinctByKey"),
+            ProblemFilters.exclude[MissingMethodProblem](
+              "org.apache.spark.api.java.JavaPairRDD.countApproxDistinct$default$1"),
+            ProblemFilters.exclude[MissingMethodProblem](
+              "org.apache.spark.api.java.JavaPairRDD.countApproxDistinctByKey$default$1"),
+            ProblemFilters.exclude[MissingMethodProblem](
+              "org.apache.spark.api.java.JavaRDD.countApproxDistinct$default$1"),
+            ProblemFilters.exclude[MissingMethodProblem](
+              "org.apache.spark.api.java.JavaRDDLike.countApproxDistinct$default$1"),
+            ProblemFilters.exclude[MissingMethodProblem](
+              "org.apache.spark.api.java.JavaDoubleRDD.countApproxDistinct$default$1"),
+            ProblemFilters.exclude[MissingMethodProblem](
+              "org.apache.spark.storage.MemoryStore.Entry")
+          ) ++
+          Seq(
+            ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.streaming.flume.FlumeReceiver.this")
+          ) ++
+          Seq( // Ignore some private methods in ALS.
+            ProblemFilters.exclude[MissingMethodProblem](
+              "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$^dateFeatures"),
+            ProblemFilters.exclude[MissingMethodProblem]( // The only public constructor is the one without arguments.
+              "org.apache.spark.mllib.recommendation.ALS.this"),
+            ProblemFilters.exclude[MissingMethodProblem](
+              "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$<init>$default$7"),
+            ProblemFilters.exclude[IncompatibleMethTypeProblem](
+              "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$^dateFeatures")
+          ) ++
+          MimaBuild.excludeSparkClass("mllib.linalg.distributed.ColumnStatisticsAggregator") ++
+          MimaBuild.excludeSparkClass("rdd.ZippedRDD") ++
+          MimaBuild.excludeSparkClass("rdd.ZippedPartition") ++
+          MimaBuild.excludeSparkClass("util.SerializableHyperLogLog") ++
+          MimaBuild.excludeSparkClass("storage.Values") ++
+          MimaBuild.excludeSparkClass("storage.Entry") ++
+          MimaBuild.excludeSparkClass("storage.MemoryStore$Entry") ++
+          Seq(
+            ProblemFilters.exclude[IncompatibleMethTypeProblem](
+              "org.apache.spark.mllib.tree.impurity.Gini.calculate"),
+            ProblemFilters.exclude[IncompatibleMethTypeProblem](
+              "org.apache.spark.mllib.tree.impurity.Entropy.calculate"),
+            ProblemFilters.exclude[IncompatibleMethTypeProblem](
+              "org.apache.spark.mllib.tree.impurity.Variance.calculate")
+          )
+        case v if v.startsWith("1.0") =>
+          Seq(
+            MimaBuild.excludeSparkPackage("api.java"),
+            MimaBuild.excludeSparkPackage("mllib"),
+            MimaBuild.excludeSparkPackage("streaming")
+          ) ++
+          MimaBuild.excludeSparkClass("rdd.ClassTags") ++
+          MimaBuild.excludeSparkClass("util.XORShiftRandom") ++
+          MimaBuild.excludeSparkClass("graphx.EdgeRDD") ++
+          MimaBuild.excludeSparkClass("graphx.VertexRDD") ++
+          MimaBuild.excludeSparkClass("graphx.impl.GraphImpl") ++
+          MimaBuild.excludeSparkClass("graphx.impl.RoutingTable") ++
+          MimaBuild.excludeSparkClass("graphx.util.collection.PrimitiveKeyOpenHashMap") ++
+          MimaBuild.excludeSparkClass("graphx.util.collection.GraphXPrimitiveKeyOpenHashMap") ++
+          MimaBuild.excludeSparkClass("mllib.recommendation.MFDataGenerator") ++
+          MimaBuild.excludeSparkClass("mllib.optimization.SquaredGradient") ++
+          MimaBuild.excludeSparkClass("mllib.regression.RidgeRegressionWithSGD") ++
+          MimaBuild.excludeSparkClass("mllib.regression.LassoWithSGD") ++
+          MimaBuild.excludeSparkClass("mllib.regression.LinearRegressionWithSGD")
+        case _ => Seq()
+      }
 }
diff --git a/tools/src/main/scala/org/apache/spark/tools/GenerateMIMAIgnore.scala b/tools/src/main/scala/org/apache/spark/tools/GenerateMIMAIgnore.scala
index 03a73f92b275..566983675bff 100644
--- a/tools/src/main/scala/org/apache/spark/tools/GenerateMIMAIgnore.scala
+++ b/tools/src/main/scala/org/apache/spark/tools/GenerateMIMAIgnore.scala
@@ -99,9 +99,25 @@ object GenerateMIMAIgnore {
     (ignoredClasses.flatMap(c => Seq(c, c.replace("$", "#"))).toSet, ignoredMembers.toSet)
   }
 
+  /** Scala reflection does not let us see inner function even if they are upgraded
+    * to public for some reason. So had to resort to java reflection to get all inner
+    * functions with $$ in there name.
+    */
+  def getInnerFunctions(classSymbol: unv.ClassSymbol): Seq[String] = {
+    try {
+      Class.forName(classSymbol.fullName, false, classLoader).getMethods.map(_.getName)
+        .filter(_.contains("$$")).map(classSymbol.fullName + "." + _)
+    } catch {
+      case t: Throwable =>
+        println("[WARN] Unable to detect inner functions for class:" + classSymbol.fullName)
+        Seq.empty[String]
+    }
+  }
+
   private def getAnnotatedOrPackagePrivateMembers(classSymbol: unv.ClassSymbol) = {
     classSymbol.typeSignature.members
-      .filter(x => isPackagePrivate(x) || isDeveloperApi(x) || isExperimental(x)).map(_.fullName)
+      .filter(x => isPackagePrivate(x) || isDeveloperApi(x) || isExperimental(x)).map(_.fullName) ++
+      getInnerFunctions(classSymbol)
   }
 
   def main(args: Array[String]) {
@@ -121,7 +137,8 @@ object GenerateMIMAIgnore {
     name.endsWith("$class") ||
     name.contains("$sp") ||
     name.contains("hive") ||
-    name.contains("Hive")
+    name.contains("Hive") ||
+    name.contains("repl")
   }
 
   /**

From 60f0ae3d87c84fd96e1f4d0abf5be1f51870e7ab Mon Sep 17 00:00:00 2001
From: witgo <witgo@qq.com>
Date: Wed, 23 Jul 2014 18:17:05 -0700
Subject: [PATCH 739/810] [SPARK-2484][SQL] Build should not run
 hivecompatibility tests by default.

Author: witgo <witgo@qq.com>

Closes #1403 from witgo/hive_compatibility and squashes the following commits:

4e5ecdb [witgo] The default does not run hive compatibility tests
---
 .../execution/HiveCompatibilitySuite.scala    |  0
 sql/hive/pom.xml                              | 30 +++++++++++++++++++
 2 files changed, 30 insertions(+)
 rename sql/hive/{ => compatibility}/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala (100%)

diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
similarity index 100%
rename from sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
rename to sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
diff --git a/sql/hive/pom.xml b/sql/hive/pom.xml
index f30ae28b81e0..1699ffe06ce1 100644
--- a/sql/hive/pom.xml
+++ b/sql/hive/pom.xml
@@ -102,6 +102,36 @@
       <scope>test</scope>
     </dependency>
   </dependencies>
+
+  <profiles>
+    <profile>
+      <id>hive</id>
+      <build>
+        <plugins>
+          <plugin>
+            <groupId>org.codehaus.mojo</groupId>
+            <artifactId>build-helper-maven-plugin</artifactId>
+            <executions>
+              <execution>
+                <id>add-scala-test-sources</id>
+                <phase>generate-test-sources</phase>
+                <goals>
+                  <goal>add-test-source</goal>
+                </goals>
+                <configuration>
+                  <sources>
+                    <source>src/test/scala</source>
+                    <source>compatibility/src/test/scala</source>
+                  </sources>
+                </configuration>
+              </execution>
+            </executions>
+          </plugin>
+        </plugins>
+      </build>
+    </profile>
+  </profiles>
+
   <build>
     <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory>
     <testOutputDirectory>target/scala-${scala.binary.version}/test-classes</testOutputDirectory>

From 2d25e34814f81f288587f3277324cb655a5fb38d Mon Sep 17 00:00:00 2001
From: Ankur Dave <ankurdave@gmail.com>
Date: Wed, 23 Jul 2014 20:11:28 -0700
Subject: [PATCH 740/810] Replace RoutingTableMessage with pair

RoutingTableMessage was used to construct routing tables to enable
joining VertexRDDs with partitioned edges. It stored three elements: the
destination vertex ID, the source edge partition, and a byte specifying
the position in which the edge partition referenced the vertex to enable
join elimination.

However, this was incompatible with sort-based shuffle (SPARK-2045). It
was also slightly wasteful, because partition IDs are usually much
smaller than 2^32, though this was mitigated by a custom serializer that
used variable-length encoding.

This commit replaces RoutingTableMessage with a pair of (VertexId, Int)
where the Int encodes both the source partition ID (in the lower 30
bits) and the position (in the top 2 bits).

Author: Ankur Dave <ankurdave@gmail.com>

Closes #1553 from ankurdave/remove-RoutingTableMessage and squashes the following commits:

697e17b [Ankur Dave] Replace RoutingTableMessage with pair
---
 .../spark/graphx/GraphKryoRegistrator.scala   |  1 -
 .../graphx/impl/RoutingTablePartition.scala   | 47 +++++++++++--------
 .../spark/graphx/impl/Serializers.scala       | 16 +++----
 .../org/apache/spark/graphx/package.scala     |  2 +-
 4 files changed, 36 insertions(+), 30 deletions(-)

diff --git a/graphx/src/main/scala/org/apache/spark/graphx/GraphKryoRegistrator.scala b/graphx/src/main/scala/org/apache/spark/graphx/GraphKryoRegistrator.scala
index eea9fe9520ca..1948c978c30b 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/GraphKryoRegistrator.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/GraphKryoRegistrator.scala
@@ -35,7 +35,6 @@ class GraphKryoRegistrator extends KryoRegistrator {
 
   def registerClasses(kryo: Kryo) {
     kryo.register(classOf[Edge[Object]])
-    kryo.register(classOf[RoutingTableMessage])
     kryo.register(classOf[(VertexId, Object)])
     kryo.register(classOf[EdgePartition[Object, Object]])
     kryo.register(classOf[BitSet])
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/RoutingTablePartition.scala b/graphx/src/main/scala/org/apache/spark/graphx/impl/RoutingTablePartition.scala
index 502b112d31c2..a565d3b28bf5 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/impl/RoutingTablePartition.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/impl/RoutingTablePartition.scala
@@ -27,26 +27,13 @@ import org.apache.spark.util.collection.{BitSet, PrimitiveVector}
 import org.apache.spark.graphx._
 import org.apache.spark.graphx.util.collection.GraphXPrimitiveKeyOpenHashMap
 
-/**
- * A message from the edge partition `pid` to the vertex partition containing `vid` specifying that
- * the edge partition references `vid` in the specified `position` (src, dst, or both).
-*/
-private[graphx]
-class RoutingTableMessage(
-    var vid: VertexId,
-    var pid: PartitionID,
-    var position: Byte)
-  extends Product2[VertexId, (PartitionID, Byte)] with Serializable {
-  override def _1 = vid
-  override def _2 = (pid, position)
-  override def canEqual(that: Any): Boolean = that.isInstanceOf[RoutingTableMessage]
-}
+import org.apache.spark.graphx.impl.RoutingTablePartition.RoutingTableMessage
 
 private[graphx]
 class RoutingTableMessageRDDFunctions(self: RDD[RoutingTableMessage]) {
   /** Copartition an `RDD[RoutingTableMessage]` with the vertex RDD with the given `partitioner`. */
   def copartitionWithVertices(partitioner: Partitioner): RDD[RoutingTableMessage] = {
-    new ShuffledRDD[VertexId, (PartitionID, Byte), (PartitionID, Byte), RoutingTableMessage](
+    new ShuffledRDD[VertexId, Int, Int, RoutingTableMessage](
       self, partitioner).setSerializer(new RoutingTableMessageSerializer)
   }
 }
@@ -62,6 +49,23 @@ object RoutingTableMessageRDDFunctions {
 
 private[graphx]
 object RoutingTablePartition {
+  /**
+   * A message from an edge partition to a vertex specifying the position in which the edge
+   * partition references the vertex (src, dst, or both). The edge partition is encoded in the lower
+   * 30 bytes of the Int, and the position is encoded in the upper 2 bytes of the Int.
+   */
+  type RoutingTableMessage = (VertexId, Int)
+
+  private def toMessage(vid: VertexId, pid: PartitionID, position: Byte): RoutingTableMessage = {
+    val positionUpper2 = position << 30
+    val pidLower30 = pid & 0x3FFFFFFF
+    (vid, positionUpper2 | pidLower30)
+  }
+
+  private def vidFromMessage(msg: RoutingTableMessage): VertexId = msg._1
+  private def pidFromMessage(msg: RoutingTableMessage): PartitionID = msg._2 & 0x3FFFFFFF
+  private def positionFromMessage(msg: RoutingTableMessage): Byte = (msg._2 >> 30).toByte
+
   val empty: RoutingTablePartition = new RoutingTablePartition(Array.empty)
 
   /** Generate a `RoutingTableMessage` for each vertex referenced in `edgePartition`. */
@@ -77,7 +81,9 @@ object RoutingTablePartition {
       map.changeValue(dstId, 0x2, (b: Byte) => (b | 0x2).toByte)
     }
     map.iterator.map { vidAndPosition =>
-      new RoutingTableMessage(vidAndPosition._1, pid, vidAndPosition._2)
+      val vid = vidAndPosition._1
+      val position = vidAndPosition._2
+      toMessage(vid, pid, position)
     }
   }
 
@@ -88,9 +94,12 @@ object RoutingTablePartition {
     val srcFlags = Array.fill(numEdgePartitions)(new PrimitiveVector[Boolean])
     val dstFlags = Array.fill(numEdgePartitions)(new PrimitiveVector[Boolean])
     for (msg <- iter) {
-      pid2vid(msg.pid) += msg.vid
-      srcFlags(msg.pid) += (msg.position & 0x1) != 0
-      dstFlags(msg.pid) += (msg.position & 0x2) != 0
+      val vid = vidFromMessage(msg)
+      val pid = pidFromMessage(msg)
+      val position = positionFromMessage(msg)
+      pid2vid(pid) += vid
+      srcFlags(pid) += (position & 0x1) != 0
+      dstFlags(pid) += (position & 0x2) != 0
     }
 
     new RoutingTablePartition(pid2vid.zipWithIndex.map {
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/Serializers.scala b/graphx/src/main/scala/org/apache/spark/graphx/impl/Serializers.scala
index 2d98c24d6970..3909efcdfc99 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/impl/Serializers.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/impl/Serializers.scala
@@ -24,9 +24,11 @@ import java.nio.ByteBuffer
 
 import scala.reflect.ClassTag
 
-import org.apache.spark.graphx._
 import org.apache.spark.serializer._
 
+import org.apache.spark.graphx._
+import org.apache.spark.graphx.impl.RoutingTablePartition.RoutingTableMessage
+
 private[graphx]
 class RoutingTableMessageSerializer extends Serializer with Serializable {
   override def newInstance(): SerializerInstance = new ShuffleSerializerInstance {
@@ -35,10 +37,8 @@ class RoutingTableMessageSerializer extends Serializer with Serializable {
       new ShuffleSerializationStream(s) {
         def writeObject[T: ClassTag](t: T): SerializationStream = {
           val msg = t.asInstanceOf[RoutingTableMessage]
-          writeVarLong(msg.vid, optimizePositive = false)
-          writeUnsignedVarInt(msg.pid)
-          // TODO: Write only the bottom two bits of msg.position
-          s.write(msg.position)
+          writeVarLong(msg._1, optimizePositive = false)
+          writeInt(msg._2)
           this
         }
       }
@@ -47,10 +47,8 @@ class RoutingTableMessageSerializer extends Serializer with Serializable {
       new ShuffleDeserializationStream(s) {
         override def readObject[T: ClassTag](): T = {
           val a = readVarLong(optimizePositive = false)
-          val b = readUnsignedVarInt()
-          val c = s.read()
-          if (c == -1) throw new EOFException
-          new RoutingTableMessage(a, b, c.toByte).asInstanceOf[T]
+          val b = readInt()
+          (a, b).asInstanceOf[T]
         }
       }
   }
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/package.scala b/graphx/src/main/scala/org/apache/spark/graphx/package.scala
index ff17edeaf8f1..6aab28ff0535 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/package.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/package.scala
@@ -30,7 +30,7 @@ package object graphx {
    */
   type VertexId = Long
 
-  /** Integer identifer of a graph partition. */
+  /** Integer identifer of a graph partition. Must be less than 2^30. */
   // TODO: Consider using Char.
   type PartitionID = Int
 

From 9e7725c86e70ffd3d2ff3a563460c2b7d0c9bbee Mon Sep 17 00:00:00 2001
From: GuoQiang Li <witgo@qq.com>
Date: Wed, 23 Jul 2014 22:50:39 -0700
Subject: [PATCH 741/810] SPARK-2662: Fix NPE for JsonProtocol

Author: GuoQiang Li <witgo@qq.com>

Closes #1511 from witgo/JsonProtocol and squashes the following commits:

2b6227f [GuoQiang Li] Fix NPE for JsonProtocol
---
 core/src/main/scala/org/apache/spark/util/JsonProtocol.scala | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala b/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala
index 3448aaaf5724..bb6079154aaf 100644
--- a/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala
+++ b/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala
@@ -257,7 +257,8 @@ private[spark] object JsonProtocol {
     val reason = Utils.getFormattedClassName(taskEndReason)
     val json = taskEndReason match {
       case fetchFailed: FetchFailed =>
-        val blockManagerAddress = blockManagerIdToJson(fetchFailed.bmAddress)
+        val blockManagerAddress = Option(fetchFailed.bmAddress).
+          map(blockManagerIdToJson).getOrElse(JNothing)
         ("Block Manager Address" -> blockManagerAddress) ~
         ("Shuffle ID" -> fetchFailed.shuffleId) ~
         ("Map ID" -> fetchFailed.mapId) ~

From 78d18fdbaa62d8ed235c29b2e37fd6607263c639 Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Wed, 23 Jul 2014 22:52:49 -0700
Subject: [PATCH 742/810] [SPARK-2658][SQL] Add rule for true = 1.

Author: Michael Armbrust <michael@databricks.com>

Closes #1556 from marmbrus/fixBooleanEqualsOne and squashes the following commits:

ad8edd4 [Michael Armbrust] Add rule for true = 1 and false = 0.
---
 .../sql/catalyst/analysis/HiveTypeCoercion.scala     | 12 +++++++++++-
 ...olean = number-0-6b6975fa1892cc48edd87dc0df48a7c0 |  1 +
 .../spark/sql/hive/execution/HiveQuerySuite.scala    | 12 ++++++++++++
 3 files changed, 24 insertions(+), 1 deletion(-)
 create mode 100644 sql/hive/src/test/resources/golden/boolean = number-0-6b6975fa1892cc48edd87dc0df48a7c0

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
index 76ddeba9cb31..9887856b9c1c 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
@@ -231,10 +231,20 @@ trait HiveTypeCoercion {
    * Changes Boolean values to Bytes so that expressions like true < false can be Evaluated.
    */
   object BooleanComparisons extends Rule[LogicalPlan] {
+    val trueValues = Seq(1, 1L, 1.toByte, 1.toShort, BigDecimal(1)).map(Literal(_))
+    val falseValues = Seq(0, 0L, 0.toByte, 0.toShort, BigDecimal(0)).map(Literal(_))
+
     def apply(plan: LogicalPlan): LogicalPlan = plan transformAllExpressions {
       // Skip nodes who's children have not been resolved yet.
       case e if !e.childrenResolved => e
-      // No need to change EqualTo operators as that actually makes sense for boolean types.
+
+      // Hive treats (true = 1) as true and (false = 0) as true.
+      case EqualTo(l @ BooleanType(), r) if trueValues.contains(r) => l
+      case EqualTo(l, r @ BooleanType()) if trueValues.contains(l) => r
+      case EqualTo(l @ BooleanType(), r) if falseValues.contains(r) => Not(l)
+      case EqualTo(l, r @ BooleanType()) if falseValues.contains(l) => Not(r)
+
+      // No need to change other EqualTo operators as that actually makes sense for boolean types.
       case e: EqualTo => e
       // Otherwise turn them to Byte types so that there exists and ordering.
       case p: BinaryComparison
diff --git a/sql/hive/src/test/resources/golden/boolean = number-0-6b6975fa1892cc48edd87dc0df48a7c0 b/sql/hive/src/test/resources/golden/boolean = number-0-6b6975fa1892cc48edd87dc0df48a7c0
new file mode 100644
index 000000000000..4d1ebdcde2c7
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/boolean = number-0-6b6975fa1892cc48edd87dc0df48a7c0	
@@ -0,0 +1 @@
+true	true	true	true	true	true	false	false	false	false	false	false	false	false	false	false	false	false	true	true	true	true	true	true	false	false	false	false	false	false	false	false	false	false	false	false
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
index eb7df717284c..6f36a4f8cb90 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
@@ -30,6 +30,18 @@ case class TestData(a: Int, b: String)
  */
 class HiveQuerySuite extends HiveComparisonTest {
 
+  createQueryTest("boolean = number",
+    """
+      |SELECT
+      |  1 = true, 1L = true, 1Y = true, true = 1, true = 1L, true = 1Y,
+      |  0 = true, 0L = true, 0Y = true, true = 0, true = 0L, true = 0Y,
+      |  1 = false, 1L = false, 1Y = false, false = 1, false = 1L, false = 1Y,
+      |  0 = false, 0L = false, 0Y = false, false = 0, false = 0L, false = 0Y,
+      |  2 = true, 2L = true, 2Y = true, true = 2, true = 2L, true = 2Y,
+      |  2 = false, 2L = false, 2Y = false, false = 2, false = 2L, false = 2Y
+      |FROM src LIMIT 1
+    """.stripMargin)
+
   test("CREATE TABLE AS runs once") {
     hql("CREATE TABLE foo AS SELECT 1 FROM src LIMIT 1").collect()
     assert(hql("SELECT COUNT(*) FROM foo").collect().head.getLong(0) === 1,

From e34922a221738bae1195d8ace90369c9ddc3a48d Mon Sep 17 00:00:00 2001
From: Sandy Ryza <sandy@cloudera.com>
Date: Wed, 23 Jul 2014 23:09:25 -0700
Subject: [PATCH 743/810] SPARK-2310. Support arbitrary Spark properties on the
 command line with ...

...spark-submit

The PR allows invocations like
  spark-submit --class org.MyClass --spark.shuffle.spill false myjar.jar

Author: Sandy Ryza <sandy@cloudera.com>

Closes #1253 from sryza/sandy-spark-2310 and squashes the following commits:

1dc9855 [Sandy Ryza] More doc and cleanup
00edfb9 [Sandy Ryza] Review comments
91b244a [Sandy Ryza] Change format to --conf PROP=VALUE
8fabe77 [Sandy Ryza] SPARK-2310. Support arbitrary Spark properties on the command line with spark-submit
---
 .../scala/org/apache/spark/deploy/SparkSubmit.scala  |  3 +++
 .../apache/spark/deploy/SparkSubmitArguments.scala   | 11 +++++++++++
 .../org/apache/spark/deploy/SparkSubmitSuite.scala   | 12 +++++++++++-
 docs/configuration.md                                |  8 +++++---
 docs/submitting-applications.md                      |  2 ++
 5 files changed, 32 insertions(+), 4 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
index 3d8373d8175e..3b5642b6caa3 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
@@ -269,6 +269,9 @@ object SparkSubmit {
       sysProps.getOrElseUpdate(k, v)
     }
 
+    // Spark properties included on command line take precedence
+    sysProps ++= args.sparkProperties
+
     (childArgs, childClasspath, sysProps, childMainClass)
   }
 
diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
index 57655aa4c32b..3ab67a43a3b5 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
@@ -55,6 +55,7 @@ private[spark] class SparkSubmitArguments(args: Seq[String]) {
   var verbose: Boolean = false
   var isPython: Boolean = false
   var pyFiles: String = null
+  val sparkProperties: HashMap[String, String] = new HashMap[String, String]()
 
   parseOpts(args.toList)
   loadDefaults()
@@ -177,6 +178,7 @@ private[spark] class SparkSubmitArguments(args: Seq[String]) {
     |  executorCores           $executorCores
     |  totalExecutorCores      $totalExecutorCores
     |  propertiesFile          $propertiesFile
+    |  extraSparkProperties    $sparkProperties
     |  driverMemory            $driverMemory
     |  driverCores             $driverCores
     |  driverExtraClassPath    $driverExtraClassPath
@@ -290,6 +292,13 @@ private[spark] class SparkSubmitArguments(args: Seq[String]) {
         jars = Utils.resolveURIs(value)
         parse(tail)
 
+      case ("--conf" | "-c") :: value :: tail =>
+        value.split("=", 2).toSeq match {
+          case Seq(k, v) => sparkProperties(k) = v
+          case _ => SparkSubmit.printErrorAndExit(s"Spark config without '=': $value")
+        }
+        parse(tail)
+
       case ("--help" | "-h") :: tail =>
         printUsageAndExit(0)
 
@@ -349,6 +358,8 @@ private[spark] class SparkSubmitArguments(args: Seq[String]) {
         |                              on the PYTHONPATH for Python apps.
         |  --files FILES               Comma-separated list of files to be placed in the working
         |                              directory of each executor.
+        |
+        |  --conf PROP=VALUE           Arbitrary Spark configuration property.
         |  --properties-file FILE      Path to a file from which to load extra properties. If not
         |                              specified, this will look for conf/spark-defaults.conf.
         |
diff --git a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala
index 565c53e9529f..f497a5e0a14f 100644
--- a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala
@@ -120,6 +120,7 @@ class SparkSubmitSuite extends FunSuite with Matchers {
       "--archives", "archive1.txt,archive2.txt",
       "--num-executors", "6",
       "--name", "beauty",
+      "--conf", "spark.shuffle.spill=false",
       "thejar.jar",
       "arg1", "arg2")
     val appArgs = new SparkSubmitArguments(clArgs)
@@ -139,6 +140,7 @@ class SparkSubmitSuite extends FunSuite with Matchers {
     mainClass should be ("org.apache.spark.deploy.yarn.Client")
     classpath should have length (0)
     sysProps("spark.app.name") should be ("beauty")
+    sysProps("spark.shuffle.spill") should be ("false")
     sysProps("SPARK_SUBMIT") should be ("true")
   }
 
@@ -156,6 +158,7 @@ class SparkSubmitSuite extends FunSuite with Matchers {
       "--archives", "archive1.txt,archive2.txt",
       "--num-executors", "6",
       "--name", "trill",
+      "--conf", "spark.shuffle.spill=false",
       "thejar.jar",
       "arg1", "arg2")
     val appArgs = new SparkSubmitArguments(clArgs)
@@ -176,6 +179,7 @@ class SparkSubmitSuite extends FunSuite with Matchers {
     sysProps("spark.yarn.dist.archives") should include regex (".*archive1.txt,.*archive2.txt")
     sysProps("spark.jars") should include regex (".*one.jar,.*two.jar,.*three.jar,.*thejar.jar")
     sysProps("SPARK_SUBMIT") should be ("true")
+    sysProps("spark.shuffle.spill") should be ("false")
   }
 
   test("handles standalone cluster mode") {
@@ -186,6 +190,7 @@ class SparkSubmitSuite extends FunSuite with Matchers {
       "--supervise",
       "--driver-memory", "4g",
       "--driver-cores", "5",
+      "--conf", "spark.shuffle.spill=false",
       "thejar.jar",
       "arg1", "arg2")
     val appArgs = new SparkSubmitArguments(clArgs)
@@ -195,9 +200,10 @@ class SparkSubmitSuite extends FunSuite with Matchers {
     childArgsStr should include regex ("launch spark://h:p .*thejar.jar org.SomeClass arg1 arg2")
     mainClass should be ("org.apache.spark.deploy.Client")
     classpath should have size (0)
-    sysProps should have size (2)
+    sysProps should have size (3)
     sysProps.keys should contain ("spark.jars")
     sysProps.keys should contain ("SPARK_SUBMIT")
+    sysProps("spark.shuffle.spill") should be ("false")
   }
 
   test("handles standalone client mode") {
@@ -208,6 +214,7 @@ class SparkSubmitSuite extends FunSuite with Matchers {
       "--total-executor-cores", "5",
       "--class", "org.SomeClass",
       "--driver-memory", "4g",
+      "--conf", "spark.shuffle.spill=false",
       "thejar.jar",
       "arg1", "arg2")
     val appArgs = new SparkSubmitArguments(clArgs)
@@ -218,6 +225,7 @@ class SparkSubmitSuite extends FunSuite with Matchers {
     classpath(0) should endWith ("thejar.jar")
     sysProps("spark.executor.memory") should be ("5g")
     sysProps("spark.cores.max") should be ("5")
+    sysProps("spark.shuffle.spill") should be ("false")
   }
 
   test("handles mesos client mode") {
@@ -228,6 +236,7 @@ class SparkSubmitSuite extends FunSuite with Matchers {
       "--total-executor-cores", "5",
       "--class", "org.SomeClass",
       "--driver-memory", "4g",
+      "--conf", "spark.shuffle.spill=false",
       "thejar.jar",
       "arg1", "arg2")
     val appArgs = new SparkSubmitArguments(clArgs)
@@ -238,6 +247,7 @@ class SparkSubmitSuite extends FunSuite with Matchers {
     classpath(0) should endWith ("thejar.jar")
     sysProps("spark.executor.memory") should be ("5g")
     sysProps("spark.cores.max") should be ("5")
+    sysProps("spark.shuffle.spill") should be ("false")
   }
 
   test("launch simple application with spark-submit") {
diff --git a/docs/configuration.md b/docs/configuration.md
index 02af461267c4..cb0c65e2d220 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -42,13 +42,15 @@ val sc = new SparkContext(new SparkConf())
 
 Then, you can supply configuration values at runtime:
 {% highlight bash %}
-./bin/spark-submit --name "My fancy app" --master local[4] myApp.jar
+./bin/spark-submit --name "My app" --master local[4] --conf spark.shuffle.spill=false 
+  --conf "spark.executor.extraJavaOptions=-XX:+PrintGCDetails -XX:+PrintGCTimeStamps" myApp.jar 
 {% endhighlight %}
 
 The Spark shell and [`spark-submit`](cluster-overview.html#launching-applications-with-spark-submit)
 tool support two ways to load configurations dynamically. The first are command line options,
-such as `--master`, as shown above. Running `./bin/spark-submit --help` will show the entire list
-of options.
+such as `--master`, as shown above. `spark-submit` can accept any Spark property using the `--conf`
+flag, but uses special flags for properties that play a part in launching the Spark application.
+Running `./bin/spark-submit --help` will show the entire list of these options.
 
 `bin/spark-submit` will also read configuration options from `conf/spark-defaults.conf`, in which
 each line consists of a key and a value separated by whitespace. For example:
diff --git a/docs/submitting-applications.md b/docs/submitting-applications.md
index e05883072bfa..45b70b1a5457 100644
--- a/docs/submitting-applications.md
+++ b/docs/submitting-applications.md
@@ -33,6 +33,7 @@ dependencies, and can support different cluster managers and deploy modes that S
   --class <main-class>
   --master <master-url> \
   --deploy-mode <deploy-mode> \
+  --conf <key>=<value> \
   ... # other options
   <application-jar> \
   [application-arguments]
@@ -43,6 +44,7 @@ Some of the commonly used options are:
 * `--class`: The entry point for your application (e.g. `org.apache.spark.examples.SparkPi`)
 * `--master`: The [master URL](#master-urls) for the cluster (e.g. `spark://23.195.26.187:7077`)
 * `--deploy-mode`: Whether to deploy your driver on the worker nodes (`cluster`) or locally as an external client (`client`) (default: `client`)*
+* `--conf`: Arbitrary Spark configuration property in key=value format. For values that contain spaces wrap "key=value" in quotes (as shown).
 * `application-jar`: Path to a bundled jar including your application and all dependencies. The URL must be globally visible inside of your cluster, for instance, an `hdfs://` path or a `file://` path that is present on all nodes.
 * `application-arguments`: Arguments passed to the main method of your main class, if any
 

From 42dfab7d374cf64a39b692ebc089792a4ff7e42c Mon Sep 17 00:00:00 2001
From: Daoyuan <daoyuan.wang@intel.com>
Date: Thu, 24 Jul 2014 00:09:36 -0700
Subject: [PATCH 744/810] [SPARK-2661][bagel]unpersist old processed rdd

Unpersist useless rdd during bagel iteration to make full use of memory.

Author: Daoyuan <daoyuan.wang@intel.com>

Closes #1519 from adrian-wang/bagelunpersist and squashes the following commits:

182c9dd [Daoyuan] rename var nextUseless to lastRDD
87fd3a4 [Daoyuan] bagel unpersist old processed rdd
---
 bagel/src/main/scala/org/apache/spark/bagel/Bagel.scala | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/bagel/src/main/scala/org/apache/spark/bagel/Bagel.scala b/bagel/src/main/scala/org/apache/spark/bagel/Bagel.scala
index 70a99b33d753..ef0bb2ac13f0 100644
--- a/bagel/src/main/scala/org/apache/spark/bagel/Bagel.scala
+++ b/bagel/src/main/scala/org/apache/spark/bagel/Bagel.scala
@@ -72,6 +72,7 @@ object Bagel extends Logging {
     var verts = vertices
     var msgs = messages
     var noActivity = false
+    var lastRDD: RDD[(K, (V, Array[M]))] = null
     do {
       logInfo("Starting superstep " + superstep + ".")
       val startTime = System.currentTimeMillis
@@ -83,6 +84,10 @@ object Bagel extends Logging {
       val superstep_ = superstep  // Create a read-only copy of superstep for capture in closure
       val (processed, numMsgs, numActiveVerts) =
         comp[K, V, M, C](sc, grouped, compute(_, _, aggregated, superstep_), storageLevel)
+      if (lastRDD != null) {
+        lastRDD.unpersist(false)
+      }
+      lastRDD = processed
 
       val timeTaken = System.currentTimeMillis - startTime
       logInfo("Superstep %d took %d s".format(superstep, timeTaken / 1000))

From 46e224aaa26df4b232c5176e98472a902862b76c Mon Sep 17 00:00:00 2001
From: Rahul Singhal <rahul.singhal@guavus.com>
Date: Thu, 24 Jul 2014 09:31:04 -0500
Subject: [PATCH 745/810] SPARK-2150: Provide direct link to finished
 application UI in yarn resou...

...rce manager UI

Use the event logger directory to provide a direct link to finished
application UI in yarn resourcemanager UI.

Author: Rahul Singhal <rahul.singhal@guavus.com>

Closes #1094 from rahulsinghaliitd/SPARK-2150 and squashes the following commits:

95f230c [Rahul Singhal] SPARK-2150: Provide direct link to finished application UI in yarn resource manager UI
---
 .../spark/deploy/history/FsHistoryProvider.scala |  3 ++-
 .../spark/deploy/history/HistoryPage.scala       |  2 +-
 .../spark/deploy/history/HistoryServer.scala     |  4 +++-
 .../org/apache/spark/deploy/master/Master.scala  | 11 +++++++----
 .../spark/scheduler/EventLoggingListener.scala   |  7 +++++++
 .../spark/deploy/yarn/ApplicationMaster.scala    |  4 +++-
 .../spark/deploy/yarn/ExecutorLauncher.scala     |  2 +-
 .../spark/deploy/yarn/YarnSparkHadoopUtil.scala  | 16 ++++++++++++++++
 .../cluster/YarnClientSchedulerBackend.scala     |  3 ++-
 .../spark/deploy/yarn/ApplicationMaster.scala    |  5 +++--
 .../spark/deploy/yarn/ExecutorLauncher.scala     |  2 +-
 11 files changed, 46 insertions(+), 13 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala b/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala
index a8c9ac072449..01e7065c17b6 100644
--- a/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala
@@ -169,7 +169,8 @@ private[history] class FsHistoryProvider(conf: SparkConf) extends ApplicationHis
     val ui: SparkUI = if (renderUI) {
         val conf = this.conf.clone()
         val appSecManager = new SecurityManager(conf)
-        new SparkUI(conf, appSecManager, replayBus, appId, "/history/" + appId)
+        new SparkUI(conf, appSecManager, replayBus, appId,
+          HistoryServer.UI_PATH_PREFIX + s"/$appId")
         // Do not call ui.bind() to avoid creating a new server for each application
       } else {
         null
diff --git a/core/src/main/scala/org/apache/spark/deploy/history/HistoryPage.scala b/core/src/main/scala/org/apache/spark/deploy/history/HistoryPage.scala
index a958c837c2ff..d7a3e3f120e6 100644
--- a/core/src/main/scala/org/apache/spark/deploy/history/HistoryPage.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/history/HistoryPage.scala
@@ -75,7 +75,7 @@ private[spark] class HistoryPage(parent: HistoryServer) extends WebUIPage("") {
     "Last Updated")
 
   private def appRow(info: ApplicationHistoryInfo): Seq[Node] = {
-    val uiAddress = "/history/" + info.id
+    val uiAddress = HistoryServer.UI_PATH_PREFIX + s"/${info.id}"
     val startTime = UIUtils.formatDate(info.startTime)
     val endTime = UIUtils.formatDate(info.endTime)
     val duration = UIUtils.formatDuration(info.endTime - info.startTime)
diff --git a/core/src/main/scala/org/apache/spark/deploy/history/HistoryServer.scala b/core/src/main/scala/org/apache/spark/deploy/history/HistoryServer.scala
index 56b38ddfc931..cacb9da8c947 100644
--- a/core/src/main/scala/org/apache/spark/deploy/history/HistoryServer.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/history/HistoryServer.scala
@@ -114,7 +114,7 @@ class HistoryServer(
     attachHandler(createStaticHandler(SparkUI.STATIC_RESOURCE_DIR, "/static"))
 
     val contextHandler = new ServletContextHandler
-    contextHandler.setContextPath("/history")
+    contextHandler.setContextPath(HistoryServer.UI_PATH_PREFIX)
     contextHandler.addServlet(new ServletHolder(loaderServlet), "/*")
     attachHandler(contextHandler)
   }
@@ -172,6 +172,8 @@ class HistoryServer(
 object HistoryServer extends Logging {
   private val conf = new SparkConf
 
+  val UI_PATH_PREFIX = "/history"
+
   def main(argStrings: Array[String]) {
     SignalLogger.register(log)
     initSecurity()
diff --git a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
index bb1fcc8190fe..21f8667819c4 100644
--- a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
@@ -35,6 +35,7 @@ import akka.serialization.SerializationExtension
 import org.apache.spark.{Logging, SecurityManager, SparkConf, SparkException}
 import org.apache.spark.deploy.{ApplicationDescription, DriverDescription, ExecutorState}
 import org.apache.spark.deploy.DeployMessages._
+import org.apache.spark.deploy.history.HistoryServer
 import org.apache.spark.deploy.master.DriverState.DriverState
 import org.apache.spark.deploy.master.MasterMessages._
 import org.apache.spark.deploy.master.ui.MasterWebUI
@@ -664,9 +665,10 @@ private[spark] class Master(
    */
   def rebuildSparkUI(app: ApplicationInfo): Boolean = {
     val appName = app.desc.name
+    val notFoundBasePath = HistoryServer.UI_PATH_PREFIX + "/not-found"
     val eventLogDir = app.desc.eventLogDir.getOrElse {
       // Event logging is not enabled for this application
-      app.desc.appUiUrl = "/history/not-found"
+      app.desc.appUiUrl = notFoundBasePath
       return false
     }
     val fileSystem = Utils.getHadoopFileSystem(eventLogDir)
@@ -681,13 +683,14 @@ private[spark] class Master(
       logWarning(msg)
       msg += " Did you specify the correct logging directory?"
       msg = URLEncoder.encode(msg, "UTF-8")
-      app.desc.appUiUrl = s"/history/not-found?msg=$msg&title=$title"
+      app.desc.appUiUrl = notFoundBasePath + s"?msg=$msg&title=$title"
       return false
     }
 
     try {
       val replayBus = new ReplayListenerBus(eventLogPaths, fileSystem, compressionCodec)
-      val ui = new SparkUI(new SparkConf, replayBus, appName + " (completed)", "/history/" + app.id)
+      val ui = new SparkUI(new SparkConf, replayBus, appName + " (completed)",
+        HistoryServer.UI_PATH_PREFIX + s"/${app.id}")
       replayBus.replay()
       appIdToUI(app.id) = ui
       webUi.attachSparkUI(ui)
@@ -702,7 +705,7 @@ private[spark] class Master(
         var msg = s"Exception in replaying log for application $appName!"
         logError(msg, e)
         msg = URLEncoder.encode(msg, "UTF-8")
-        app.desc.appUiUrl = s"/history/not-found?msg=$msg&exception=$exception&title=$title"
+        app.desc.appUiUrl = notFoundBasePath + s"?msg=$msg&exception=$exception&title=$title"
         false
     }
   }
diff --git a/core/src/main/scala/org/apache/spark/scheduler/EventLoggingListener.scala b/core/src/main/scala/org/apache/spark/scheduler/EventLoggingListener.scala
index a90b0d475c04..ae6ca9f4e7bf 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/EventLoggingListener.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/EventLoggingListener.scala
@@ -63,6 +63,13 @@ private[spark] class EventLoggingListener(
   // For testing. Keep track of all JSON serialized events that have been logged.
   private[scheduler] val loggedEvents = new ArrayBuffer[JValue]
 
+  /**
+   * Return only the unique application directory without the base directory.
+   */
+  def getApplicationLogDir(): String = {
+    name
+  }
+
   /**
    * Begin logging events.
    * If compression is used, log a file that indicates which compression library is used.
diff --git a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
index 3ec36487dcd2..62b5c3bc5f0f 100644
--- a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
+++ b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
@@ -60,6 +60,7 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration,
   private var yarnAllocator: YarnAllocationHandler = _
   private var isFinished: Boolean = false
   private var uiAddress: String = _
+  private var uiHistoryAddress: String = _
   private val maxAppAttempts: Int = conf.getInt(YarnConfiguration.RM_AM_MAX_RETRIES,
     YarnConfiguration.DEFAULT_RM_AM_MAX_RETRIES)
   private var isLastAMRetry: Boolean = true
@@ -237,6 +238,7 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration,
 
         if (null != sparkContext) {
           uiAddress = sparkContext.ui.appUIHostPort
+          uiHistoryAddress = YarnSparkHadoopUtil.getUIHistoryAddress(sparkContext, sparkConf)
           this.yarnAllocator = YarnAllocationHandler.newAllocator(
             yarnConf,
             resourceManager,
@@ -360,7 +362,7 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration,
         finishReq.setAppAttemptId(appAttemptId)
         finishReq.setFinishApplicationStatus(status)
         finishReq.setDiagnostics(diagnostics)
-        finishReq.setTrackingUrl(sparkConf.get("spark.yarn.historyServer.address", ""))
+        finishReq.setTrackingUrl(uiHistoryAddress)
         resourceManager.finishApplicationMaster(finishReq)
       }
     }
diff --git a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala
index a86ad256dfa3..d232c18d2f5a 100644
--- a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala
+++ b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala
@@ -289,7 +289,7 @@ class ExecutorLauncher(args: ApplicationMasterArguments, conf: Configuration, sp
       .asInstanceOf[FinishApplicationMasterRequest]
     finishReq.setAppAttemptId(appAttemptId)
     finishReq.setFinishApplicationStatus(status)
-    finishReq.setTrackingUrl(sparkConf.get("spark.yarn.historyServer.address", ""))
+    finishReq.setTrackingUrl(sparkConf.get("spark.driver.appUIHistoryAddress", ""))
     resourceManager.finishApplicationMaster(finishReq)
   }
 
diff --git a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala
index 718cb19f5726..e98308cdbd74 100644
--- a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala
+++ b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala
@@ -30,6 +30,9 @@ import org.apache.hadoop.util.StringInterner
 import org.apache.hadoop.yarn.conf.YarnConfiguration
 import org.apache.hadoop.yarn.api.ApplicationConstants
 import org.apache.hadoop.conf.Configuration
+
+import org.apache.spark.{SparkConf, SparkContext}
+import org.apache.spark.deploy.history.HistoryServer
 import org.apache.spark.deploy.SparkHadoopUtil
 
 /**
@@ -132,4 +135,17 @@ object YarnSparkHadoopUtil {
     }
   }
 
+  def getUIHistoryAddress(sc: SparkContext, conf: SparkConf) : String = {
+    val eventLogDir = sc.eventLogger match {
+      case Some(logger) => logger.getApplicationLogDir()
+      case None => ""
+    }
+    val historyServerAddress = conf.get("spark.yarn.historyServer.address", "")
+    if (historyServerAddress != "" && eventLogDir != "") {
+      historyServerAddress + HistoryServer.UI_PATH_PREFIX + s"/$eventLogDir"
+    } else {
+      ""
+    }
+  }
+
 }
diff --git a/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala b/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala
index d8266f7b0c9a..77b91f8e260f 100644
--- a/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala
+++ b/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala
@@ -19,7 +19,7 @@ package org.apache.spark.scheduler.cluster
 
 import org.apache.hadoop.yarn.api.records.{ApplicationId, YarnApplicationState}
 import org.apache.spark.{SparkException, Logging, SparkContext}
-import org.apache.spark.deploy.yarn.{Client, ClientArguments, ExecutorLauncher}
+import org.apache.spark.deploy.yarn.{Client, ClientArguments, ExecutorLauncher, YarnSparkHadoopUtil}
 import org.apache.spark.scheduler.TaskSchedulerImpl
 
 import scala.collection.mutable.ArrayBuffer
@@ -54,6 +54,7 @@ private[spark] class YarnClientSchedulerBackend(
     val driverPort = conf.get("spark.driver.port")
     val hostport = driverHost + ":" + driverPort
     conf.set("spark.driver.appUIAddress", sc.ui.appUIHostPort)
+    conf.set("spark.driver.appUIHistoryAddress", YarnSparkHadoopUtil.getUIHistoryAddress(sc, conf))
 
     val argsArrayBuf = new ArrayBuffer[String]()
     argsArrayBuf += (
diff --git a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
index eaf594c8b49b..035356d390c8 100644
--- a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
+++ b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
@@ -59,6 +59,7 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration,
   private var yarnAllocator: YarnAllocationHandler = _
   private var isFinished: Boolean = false
   private var uiAddress: String = _
+  private var uiHistoryAddress: String = _
   private val maxAppAttempts: Int = conf.getInt(
     YarnConfiguration.RM_AM_MAX_ATTEMPTS, YarnConfiguration.DEFAULT_RM_AM_MAX_ATTEMPTS)
   private var isLastAMRetry: Boolean = true
@@ -216,6 +217,7 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration,
 
         if (sparkContext != null) {
           uiAddress = sparkContext.ui.appUIHostPort
+          uiHistoryAddress = YarnSparkHadoopUtil.getUIHistoryAddress(sparkContext, sparkConf)
           this.yarnAllocator = YarnAllocationHandler.newAllocator(
             yarnConf,
             amClient,
@@ -312,8 +314,7 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration,
 
       logInfo("Unregistering ApplicationMaster with " + status)
       if (registered) {
-        val trackingUrl = sparkConf.get("spark.yarn.historyServer.address", "")
-        amClient.unregisterApplicationMaster(status, diagnostics, trackingUrl)
+        amClient.unregisterApplicationMaster(status, diagnostics, uiHistoryAddress)
       }
     }
   }
diff --git a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala
index 5ac95f379872..7158d9442a45 100644
--- a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala
+++ b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala
@@ -250,7 +250,7 @@ class ExecutorLauncher(args: ApplicationMasterArguments, conf: Configuration, sp
 
   def finishApplicationMaster(status: FinalApplicationStatus) {
     logInfo("Unregistering ApplicationMaster with " + status)
-    val trackingUrl = sparkConf.get("spark.yarn.historyServer.address", "")
+    val trackingUrl = sparkConf.get("spark.driver.appUIHistoryAddress", "")
     amClient.unregisterApplicationMaster(status, "" /* appMessage */ , trackingUrl)
   }
 

From 9fd141477dca3beb5cc2f82305dcf9f93ba41e9a Mon Sep 17 00:00:00 2001
From: tzolov <christian.tzolov@gmail.com>
Date: Thu, 24 Jul 2014 11:12:25 -0700
Subject: [PATCH 746/810] [Build] SPARK-2619: Configurable filemode for the
 spark/bin folder in debian package

Add  a `<deb.bin.filemode>744</deb.bin.filemode>` property to the `assembly/pom.xml` that defaults to `744`.
Use this property for ../bin folder <filemode>.

This patch doesn't change the current default modes but allows one override the modes at build time:
`-Ddeb.bin.filemode=<new mode>`

Author: tzolov <christian.tzolov@gmail.com>

Closes #1531 from tzolov/SPARK-2619 and squashes the following commits:

6d95343 [tzolov] [Build] SPARK-2619: Configurable filemode for the spark/bin folder in the .deb package
---
 assembly/pom.xml | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/assembly/pom.xml b/assembly/pom.xml
index 4f6aade133db..567a8dd2a0d9 100644
--- a/assembly/pom.xml
+++ b/assembly/pom.xml
@@ -39,6 +39,7 @@
     <deb.pkg.name>spark</deb.pkg.name>
     <deb.install.path>/usr/share/spark</deb.install.path>
     <deb.user>root</deb.user>
+    <deb.bin.filemode>744</deb.bin.filemode>
   </properties>
 
   <dependencies>
@@ -276,7 +277,7 @@
                         <user>${deb.user}</user>
                         <group>${deb.user}</group>
                         <prefix>${deb.install.path}/bin</prefix>
-                        <filemode>744</filemode>
+                        <filemode>${deb.bin.filemode}</filemode>
                       </mapper>
                     </data>
                     <data>

From b352ef175c234a2ea86b72c2f40da2ac69658b2e Mon Sep 17 00:00:00 2001
From: Yin Huai <huai@cse.ohio-state.edu>
Date: Thu, 24 Jul 2014 11:19:19 -0700
Subject: [PATCH 747/810] [SPARK-2603][SQL] Remove unnecessary toMap and toList
 in converting Java collections to Scala collections JsonRDD.scala

In JsonRDD.scalafy, we are using toMap/toList to convert a Java Map/List to a Scala one. These two operations are pretty expensive because they read elements from a Java Map/List and then load to a Scala Map/List. We can use Scala wrappers to wrap those Java collections instead of using toMap/toList.

I did a quick test to see the performance. I had a 2.9GB cached RDD[String] storing one JSON object per record (twitter dataset). My simple test program is attached below.
```scala
val sqlContext = new org.apache.spark.sql.SQLContext(sc)
import sqlContext._

val jsonData = sc.textFile("...")
jsonData.cache.count

val jsonSchemaRDD = sqlContext.jsonRDD(jsonData)
jsonSchemaRDD.registerAsTable("jt")

sqlContext.sql("select count(*) from jt").collect
```
Stages for the schema inference and the table scan both had 48 tasks. These tasks were executed sequentially. For the current implementation, scanning the JSON dataset will materialize values of all fields of a record. The inferred schema of the dataset can be accessed at https://gist.github.com/yhuai/05fe8a57c638c6666f8d.

From the result, there was no significant difference on running `jsonRDD`. For the simple aggregation query, results are attached below.
```
Original:
Run 1: 26.1s
Run 2: 27.03s
Run 3: 27.035s

With this change:
Run 1: 21.086s
Run 2: 21.035s
Run 3: 21.029s
```

JIRA: https://issues.apache.org/jira/browse/SPARK-2603

Author: Yin Huai <huai@cse.ohio-state.edu>

Closes #1504 from yhuai/removeToMapToList and squashes the following commits:

6831b77 [Yin Huai] Fix failed tests.
09b9bca [Yin Huai] Merge remote-tracking branch 'upstream/master' into removeToMapToList
d1abdb8 [Yin Huai] Remove unnecessary toMap and toList.
---
 .../scala/org/apache/spark/sql/json/JsonRDD.scala    | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala
index df80dfb98b93..b48c70ee73a2 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.sql.json
 
-import scala.collection.JavaConversions._
+import scala.collection.convert.Wrappers.{JMapWrapper, JListWrapper}
 import scala.math.BigDecimal
 
 import com.fasterxml.jackson.databind.ObjectMapper
@@ -210,12 +210,12 @@ private[sql] object JsonRDD extends Logging {
           case (k, dataType) => (s"$key.$k", dataType)
         } ++ Set((key, StructType(Nil)))
       }
-      case (key: String, array: List[_]) => {
+      case (key: String, array: Seq[_]) => {
         // The value associated with the key is an array.
         typeOfArray(array) match {
           case ArrayType(StructType(Nil)) => {
             // The elements of this arrays are structs.
-            array.asInstanceOf[List[Map[String, Any]]].flatMap {
+            array.asInstanceOf[Seq[Map[String, Any]]].flatMap {
               element => allKeysWithValueTypes(element)
             }.map {
               case (k, dataType) => (s"$key.$k", dataType)
@@ -229,7 +229,7 @@ private[sql] object JsonRDD extends Logging {
   }
 
   /**
-   * Converts a Java Map/List to a Scala Map/List.
+   * Converts a Java Map/List to a Scala Map/Seq.
    * We do not use Jackson's scala module at here because
    * DefaultScalaModule in jackson-module-scala will make
    * the parsing very slow.
@@ -239,9 +239,9 @@ private[sql] object JsonRDD extends Logging {
       // .map(identity) is used as a workaround of non-serializable Map
       // generated by .mapValues.
       // This issue is documented at https://issues.scala-lang.org/browse/SI-7005
-      map.toMap.mapValues(scalafy).map(identity)
+      JMapWrapper(map).mapValues(scalafy).map(identity)
     case list: java.util.List[_] =>
-      list.toList.map(scalafy)
+      JListWrapper(list).map(scalafy)
     case atom => atom
   }
 

From c960b5051853f336fb01ea3f16567b9958baa1b6 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Thu, 24 Jul 2014 12:37:02 -0700
Subject: [PATCH 748/810] [SPARK-2479 (partial)][MLLIB] fix binary metrics unit
 tests

Allow small errors in comparison.

@dbtsai , this unit test blocks https://github.com/apache/spark/pull/1562 . I may need to merge this one first. We can change it to use the tools in https://github.com/apache/spark/pull/1425 after that PR gets merged.

Author: Xiangrui Meng <meng@databricks.com>

Closes #1576 from mengxr/fix-binary-metrics-unit-tests and squashes the following commits:

5076a7f [Xiangrui Meng] fix binary metrics unit tests
---
 .../BinaryClassificationMetricsSuite.scala    | 36 ++++++++++++++-----
 1 file changed, 27 insertions(+), 9 deletions(-)

diff --git a/mllib/src/test/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetricsSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetricsSuite.scala
index 9d16182f9d8c..94db1dc18323 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetricsSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetricsSuite.scala
@@ -20,8 +20,26 @@ package org.apache.spark.mllib.evaluation
 import org.scalatest.FunSuite
 
 import org.apache.spark.mllib.util.LocalSparkContext
+import org.apache.spark.mllib.util.TestingUtils.DoubleWithAlmostEquals
 
 class BinaryClassificationMetricsSuite extends FunSuite with LocalSparkContext {
+
+  // TODO: move utility functions to TestingUtils.
+
+  def elementsAlmostEqual(actual: Seq[Double], expected: Seq[Double]): Boolean = {
+    actual.zip(expected).forall { case (x1, x2) =>
+      x1.almostEquals(x2)
+    }
+  }
+
+  def elementsAlmostEqual(
+      actual: Seq[(Double, Double)],
+      expected: Seq[(Double, Double)])(implicit dummy: DummyImplicit): Boolean = {
+    actual.zip(expected).forall { case ((x1, y1), (x2, y2)) =>
+      x1.almostEquals(x2) && y1.almostEquals(y2)
+    }
+  }
+
   test("binary evaluation metrics") {
     val scoreAndLabels = sc.parallelize(
       Seq((0.1, 0.0), (0.1, 1.0), (0.4, 0.0), (0.6, 0.0), (0.6, 1.0), (0.6, 1.0), (0.8, 1.0)), 2)
@@ -41,14 +59,14 @@ class BinaryClassificationMetricsSuite extends FunSuite with LocalSparkContext {
     val prCurve = Seq((0.0, 1.0)) ++ pr
     val f1 = pr.map { case (r, p) => 2.0 * (p * r) / (p + r) }
     val f2 = pr.map { case (r, p) => 5.0 * (p * r) / (4.0 * p + r)}
-    assert(metrics.thresholds().collect().toSeq === threshold)
-    assert(metrics.roc().collect().toSeq === rocCurve)
-    assert(metrics.areaUnderROC() === AreaUnderCurve.of(rocCurve))
-    assert(metrics.pr().collect().toSeq === prCurve)
-    assert(metrics.areaUnderPR() === AreaUnderCurve.of(prCurve))
-    assert(metrics.fMeasureByThreshold().collect().toSeq === threshold.zip(f1))
-    assert(metrics.fMeasureByThreshold(2.0).collect().toSeq === threshold.zip(f2))
-    assert(metrics.precisionByThreshold().collect().toSeq === threshold.zip(precision))
-    assert(metrics.recallByThreshold().collect().toSeq === threshold.zip(recall))
+    assert(elementsAlmostEqual(metrics.thresholds().collect(), threshold))
+    assert(elementsAlmostEqual(metrics.roc().collect(), rocCurve))
+    assert(metrics.areaUnderROC().almostEquals(AreaUnderCurve.of(rocCurve)))
+    assert(elementsAlmostEqual(metrics.pr().collect(), prCurve))
+    assert(metrics.areaUnderPR().almostEquals(AreaUnderCurve.of(prCurve)))
+    assert(elementsAlmostEqual(metrics.fMeasureByThreshold().collect(), threshold.zip(f1)))
+    assert(elementsAlmostEqual(metrics.fMeasureByThreshold(2.0).collect(), threshold.zip(f2)))
+    assert(elementsAlmostEqual(metrics.precisionByThreshold().collect(), threshold.zip(precision)))
+    assert(elementsAlmostEqual(metrics.recallByThreshold().collect(), threshold.zip(recall)))
   }
 }

From 323a83c5235f9289cd9526491d62365df96a429b Mon Sep 17 00:00:00 2001
From: GuoQiang Li <witgo@qq.com>
Date: Thu, 24 Jul 2014 14:46:10 -0500
Subject: [PATCH 749/810] [SPARK-2037]: yarn client mode doesn't support
 spark.yarn.max.executor.failures

Author: GuoQiang Li <witgo@qq.com>

Closes #1180 from witgo/SPARK-2037 and squashes the following commits:

3d52411 [GuoQiang Li] review commit
7058f4d [GuoQiang Li] Correctly stop SparkContext
6d0561f [GuoQiang Li] Fix: yarn client mode doesn't support spark.yarn.max.executor.failures
---
 .../spark/deploy/yarn/ExecutorLauncher.scala  | 80 ++++++++++++-------
 .../cluster/YarnClientSchedulerBackend.scala  | 28 +++++++
 .../spark/deploy/yarn/ExecutorLauncher.scala  | 45 ++++++++---
 3 files changed, 115 insertions(+), 38 deletions(-)

diff --git a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala
index d232c18d2f5a..184e2ad6c82c 100644
--- a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala
+++ b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala
@@ -28,7 +28,6 @@ import org.apache.hadoop.yarn.ipc.YarnRPC
 import org.apache.hadoop.yarn.util.{ConverterUtils, Records}
 import akka.actor._
 import akka.remote._
-import akka.actor.Terminated
 import org.apache.spark.{Logging, SecurityManager, SparkConf}
 import org.apache.spark.util.{Utils, AkkaUtils}
 import org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend
@@ -57,10 +56,17 @@ class ExecutorLauncher(args: ApplicationMasterArguments, conf: Configuration, sp
   private val yarnConf: YarnConfiguration = new YarnConfiguration(conf)
 
   private var yarnAllocator: YarnAllocationHandler = _
-  private var driverClosed:Boolean = false
+
+  private var driverClosed: Boolean = false
+  private var isFinished: Boolean = false
+  private var registered: Boolean = false
+
+  // Default to numExecutors * 2, with minimum of 3
+  private val maxNumExecutorFailures = sparkConf.getInt("spark.yarn.max.executor.failures",
+    sparkConf.getInt("spark.yarn.max.worker.failures", math.max(args.numExecutors * 2, 3)))
 
   val securityManager = new SecurityManager(sparkConf)
-  val actorSystem : ActorSystem = AkkaUtils.createActorSystem("sparkYarnAM", Utils.localHostName, 0,
+  val actorSystem: ActorSystem = AkkaUtils.createActorSystem("sparkYarnAM", Utils.localHostName, 0,
     conf = sparkConf, securityManager = securityManager)._1
   var actor: ActorRef = _
 
@@ -97,23 +103,26 @@ class ExecutorLauncher(args: ApplicationMasterArguments, conf: Configuration, sp
     appAttemptId = getApplicationAttemptId()
     resourceManager = registerWithResourceManager()
 
-    val appMasterResponse: RegisterApplicationMasterResponse = registerApplicationMaster()
-
-    // Compute number of threads for akka
-    val minimumMemory = appMasterResponse.getMinimumResourceCapability().getMemory()
-
-    if (minimumMemory > 0) {
-      val mem = args.executorMemory + sparkConf.getInt("spark.yarn.executor.memoryOverhead",
-        YarnAllocationHandler.MEMORY_OVERHEAD)
-      val numCore = (mem  / minimumMemory) + (if (0 != (mem % minimumMemory)) 1 else 0)
-
-      if (numCore > 0) {
-        // do not override - hits https://issues.apache.org/jira/browse/HADOOP-8406
-        // TODO: Uncomment when hadoop is on a version which has this fixed.
-        // args.workerCores = numCore
+    synchronized {
+      if (!isFinished) {
+        val appMasterResponse: RegisterApplicationMasterResponse = registerApplicationMaster()
+        // Compute number of threads for akka
+        val minimumMemory = appMasterResponse.getMinimumResourceCapability().getMemory()
+
+        if (minimumMemory > 0) {
+          val mem = args.executorMemory + sparkConf.getInt("spark.yarn.executor.memoryOverhead",
+            YarnAllocationHandler.MEMORY_OVERHEAD)
+          val numCore = (mem  / minimumMemory) + (if (0 != (mem % minimumMemory)) 1 else 0)
+
+          if (numCore > 0) {
+            // do not override - hits https://issues.apache.org/jira/browse/HADOOP-8406
+            // TODO: Uncomment when hadoop is on a version which has this fixed.
+            // args.workerCores = numCore
+          }
+        }
+        registered = true
       }
     }
-
     waitForSparkMaster()
     addAmIpFilter()
     // Allocate all containers
@@ -243,11 +252,17 @@ class ExecutorLauncher(args: ApplicationMasterArguments, conf: Configuration, sp
     while ((yarnAllocator.getNumExecutorsRunning < args.numExecutors) && (!driverClosed)) {
       yarnAllocator.allocateContainers(
         math.max(args.numExecutors - yarnAllocator.getNumExecutorsRunning, 0))
+      checkNumExecutorsFailed()
       Thread.sleep(100)
     }
 
     logInfo("All executors have launched.")
-
+  }
+  private def checkNumExecutorsFailed() {
+    if (yarnAllocator.getNumExecutorsFailed >= maxNumExecutorFailures) {
+      finishApplicationMaster(FinalApplicationStatus.FAILED,
+        "max number of executor failures reached")
+    }
   }
 
   // TODO: We might want to extend this to allocate more containers in case they die !
@@ -257,6 +272,7 @@ class ExecutorLauncher(args: ApplicationMasterArguments, conf: Configuration, sp
     val t = new Thread {
       override def run() {
         while (!driverClosed) {
+          checkNumExecutorsFailed()
           val missingExecutorCount = args.numExecutors - yarnAllocator.getNumExecutorsRunning
           if (missingExecutorCount > 0) {
             logInfo("Allocating " + missingExecutorCount +
@@ -282,15 +298,23 @@ class ExecutorLauncher(args: ApplicationMasterArguments, conf: Configuration, sp
     yarnAllocator.allocateContainers(0)
   }
 
-  def finishApplicationMaster(status: FinalApplicationStatus) {
-
-    logInfo("finish ApplicationMaster with " + status)
-    val finishReq = Records.newRecord(classOf[FinishApplicationMasterRequest])
-      .asInstanceOf[FinishApplicationMasterRequest]
-    finishReq.setAppAttemptId(appAttemptId)
-    finishReq.setFinishApplicationStatus(status)
-    finishReq.setTrackingUrl(sparkConf.get("spark.driver.appUIHistoryAddress", ""))
-    resourceManager.finishApplicationMaster(finishReq)
+  def finishApplicationMaster(status: FinalApplicationStatus, appMessage: String = "") {
+    synchronized {
+      if (isFinished) {
+        return
+      }
+      logInfo("Unregistering ApplicationMaster with " + status)
+      if (registered) {
+        val finishReq = Records.newRecord(classOf[FinishApplicationMasterRequest])
+          .asInstanceOf[FinishApplicationMasterRequest]
+        finishReq.setAppAttemptId(appAttemptId)
+        finishReq.setFinishApplicationStatus(status)
+        finishReq.setTrackingUrl(sparkConf.get("spark.yarn.historyServer.address", ""))
+        finishReq.setDiagnostics(appMessage)
+        resourceManager.finishApplicationMaster(finishReq)
+      }
+      isFinished = true
+    }
   }
 
 }
diff --git a/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala b/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala
index 77b91f8e260f..f8fb96b312f2 100644
--- a/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala
+++ b/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala
@@ -37,6 +37,8 @@ private[spark] class YarnClientSchedulerBackend(
 
   var client: Client = null
   var appId: ApplicationId = null
+  var checkerThread: Thread = null
+  var stopping: Boolean = false
 
   private[spark] def addArg(optionName: String, envVar: String, sysProp: String,
       arrayBuf: ArrayBuffer[String]) {
@@ -86,6 +88,7 @@ private[spark] class YarnClientSchedulerBackend(
     client = new Client(args, conf)
     appId = client.runApp()
     waitForApp()
+    checkerThread = yarnApplicationStateCheckerThread()
   }
 
   def waitForApp() {
@@ -116,7 +119,32 @@ private[spark] class YarnClientSchedulerBackend(
     }
   }
 
+  private def yarnApplicationStateCheckerThread(): Thread = {
+    val t = new Thread {
+      override def run() {
+        while (!stopping) {
+          val report = client.getApplicationReport(appId)
+          val state = report.getYarnApplicationState()
+          if (state == YarnApplicationState.FINISHED || state == YarnApplicationState.KILLED
+            || state == YarnApplicationState.FAILED) {
+            logError(s"Yarn application already ended: $state")
+            sc.stop()
+            stopping = true
+          }
+          Thread.sleep(1000L)
+        }
+        checkerThread = null
+        Thread.currentThread().interrupt()
+      }
+    }
+    t.setName("Yarn Application State Checker")
+    t.setDaemon(true)
+    t.start()
+    t
+  }
+
   override def stop() {
+    stopping = true
     super.stop()
     client.stop
     logInfo("Stopped")
diff --git a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala
index 7158d9442a45..fc7b8320d734 100644
--- a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala
+++ b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala
@@ -19,15 +19,12 @@ package org.apache.spark.deploy.yarn
 
 import java.net.Socket
 import org.apache.hadoop.conf.Configuration
-import org.apache.hadoop.net.NetUtils
-import org.apache.hadoop.yarn.api._
+import org.apache.hadoop.yarn.api.ApplicationConstants
 import org.apache.hadoop.yarn.api.records._
 import org.apache.hadoop.yarn.api.protocolrecords._
 import org.apache.hadoop.yarn.conf.YarnConfiguration
-import org.apache.hadoop.yarn.util.{ConverterUtils, Records}
 import akka.actor._
 import akka.remote._
-import akka.actor.Terminated
 import org.apache.spark.{Logging, SecurityManager, SparkConf}
 import org.apache.spark.util.{Utils, AkkaUtils}
 import org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend
@@ -57,10 +54,16 @@ class ExecutorLauncher(args: ApplicationMasterArguments, conf: Configuration, sp
   private val yarnConf: YarnConfiguration = new YarnConfiguration(conf)
 
   private var yarnAllocator: YarnAllocationHandler = _
-  private var driverClosed:Boolean = false
+  private var driverClosed: Boolean = false
+  private var isFinished: Boolean = false
+  private var registered: Boolean = false
 
   private var amClient: AMRMClient[ContainerRequest] = _
 
+  // Default to numExecutors * 2, with minimum of 3
+  private val maxNumExecutorFailures = sparkConf.getInt("spark.yarn.max.executor.failures",
+    sparkConf.getInt("spark.yarn.max.worker.failures", math.max(args.numExecutors * 2, 3)))
+
   val securityManager = new SecurityManager(sparkConf)
   val actorSystem: ActorSystem = AkkaUtils.createActorSystem("sparkYarnAM", Utils.localHostName, 0,
     conf = sparkConf, securityManager = securityManager)._1
@@ -101,7 +104,12 @@ class ExecutorLauncher(args: ApplicationMasterArguments, conf: Configuration, sp
     amClient.start()
 
     appAttemptId = ApplicationMaster.getApplicationAttemptId()
-    registerApplicationMaster()
+    synchronized {
+      if (!isFinished) {
+        registerApplicationMaster()
+        registered = true
+      }
+    }
 
     waitForSparkMaster()
     addAmIpFilter()
@@ -210,6 +218,7 @@ class ExecutorLauncher(args: ApplicationMasterArguments, conf: Configuration, sp
     yarnAllocator.addResourceRequests(args.numExecutors)
     yarnAllocator.allocateResources()
     while ((yarnAllocator.getNumExecutorsRunning < args.numExecutors) && (!driverClosed)) {
+      checkNumExecutorsFailed()
       allocateMissingExecutor()
       yarnAllocator.allocateResources()
       Thread.sleep(100)
@@ -228,12 +237,20 @@ class ExecutorLauncher(args: ApplicationMasterArguments, conf: Configuration, sp
     }
   }
 
+  private def checkNumExecutorsFailed() {
+    if (yarnAllocator.getNumExecutorsFailed >= maxNumExecutorFailures) {
+      finishApplicationMaster(FinalApplicationStatus.FAILED,
+        "max number of executor failures reached")
+    }
+  }
+
   private def launchReporterThread(_sleepTime: Long): Thread = {
     val sleepTime = if (_sleepTime <= 0) 0 else _sleepTime
 
     val t = new Thread {
       override def run() {
         while (!driverClosed) {
+          checkNumExecutorsFailed()
           allocateMissingExecutor()
           logDebug("Sending progress")
           yarnAllocator.allocateResources()
@@ -248,10 +265,18 @@ class ExecutorLauncher(args: ApplicationMasterArguments, conf: Configuration, sp
     t
   }
 
-  def finishApplicationMaster(status: FinalApplicationStatus) {
-    logInfo("Unregistering ApplicationMaster with " + status)
-    val trackingUrl = sparkConf.get("spark.driver.appUIHistoryAddress", "")
-    amClient.unregisterApplicationMaster(status, "" /* appMessage */ , trackingUrl)
+  def finishApplicationMaster(status: FinalApplicationStatus, appMessage: String = "") {
+    synchronized {
+      if (isFinished) {
+        return
+      }
+      logInfo("Unregistering ApplicationMaster with " + status)
+      if (registered) {
+        val trackingUrl = sparkConf.get("spark.yarn.historyServer.address", "")
+        amClient.unregisterApplicationMaster(status, appMessage, trackingUrl)
+      }
+      isFinished = true
+    }
   }
 
 }

From fec641b84d37848b329bba91857240ac5b87fc54 Mon Sep 17 00:00:00 2001
From: Neville Li <neville@spotify.com>
Date: Thu, 24 Jul 2014 14:13:00 -0700
Subject: [PATCH 750/810] SPARK-2250: show stage RDDs in UI

Author: Neville Li <neville@spotify.com>

Closes #1188 from nevillelyh/neville/ui and squashes the following commits:

d3ac425 [Neville Li] SPARK-2250: show persisted RDD in stage UI
f075db9 [Neville Li] SPARK-2035: show call stack even when description is available
---
 .../org/apache/spark/ui/jobs/StageTable.scala | 20 +++++++++++++++----
 1 file changed, 16 insertions(+), 4 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/StageTable.scala b/core/src/main/scala/org/apache/spark/ui/jobs/StageTable.scala
index 5f45c0ced5ec..f8b308c98154 100644
--- a/core/src/main/scala/org/apache/spark/ui/jobs/StageTable.scala
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/StageTable.scala
@@ -18,6 +18,7 @@
 package org.apache.spark.ui.jobs
 
 import scala.xml.Node
+import scala.xml.Text
 
 import java.util.Date
 
@@ -99,19 +100,30 @@ private[ui] class StageTableBase(
         {s.name}
       </a>
 
+    val cachedRddInfos = s.rddInfos.filter(_.numCachedPartitions > 0)
     val details = if (s.details.nonEmpty) {
       <span onclick="this.parentNode.querySelector('.stage-details').classList.toggle('collapsed')"
             class="expand-details">
-        +show details
-      </span>
-      <pre class="stage-details collapsed">{s.details}</pre>
+        +details
+      </span> ++
+      <div class="stage-details collapsed">
+        {if (cachedRddInfos.nonEmpty) {
+          Text("RDD: ") ++
+          // scalastyle:off
+          cachedRddInfos.map { i =>
+            <a href={"%s/storage/rdd?id=%d".format(UIUtils.prependBaseUri(basePath), i.id)}>{i.name}</a>
+          }
+          // scalastyle:on
+        }}
+        <pre>{s.details}</pre>
+      </div>
     }
 
     val stageDataOption = listener.stageIdToData.get(s.stageId)
     // Too many nested map/flatMaps with options are just annoying to read. Do this imperatively.
     if (stageDataOption.isDefined && stageDataOption.get.description.isDefined) {
       val desc = stageDataOption.get.description
-      <div><em>{desc}</em></div><div>{nameLink} {killLink}</div>
+      <div><em>{desc}</em></div><div>{killLink} {nameLink} {details}</div>
     } else {
       <div>{killLink} {nameLink} {details}</div>
     }

From a45d5480f65d2e969fc7fbd8f358b1717fb99bef Mon Sep 17 00:00:00 2001
From: Tathagata Das <tathagata.das1565@gmail.com>
Date: Thu, 24 Jul 2014 15:59:09 -0700
Subject: [PATCH 751/810] [SPARK-2464][Streaming] Fixed Twitter stream stopping
 bug

Stopping the Twitter Receiver would call twitter4j's TwitterStream.shutdown, which in turn causes an Exception to be thrown to the listener. This exception caused the Receiver to be restarted. This patch check whether the receiver was stopped or not, and accordingly restarts on exception.

Author: Tathagata Das <tathagata.das1565@gmail.com>

Closes #1577 from tdas/twitter-stop and squashes the following commits:

011b525 [Tathagata Das] Fixed Twitter stream stopping bug.
---
 .../spark/streaming/twitter/TwitterInputDStream.scala    | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/external/twitter/src/main/scala/org/apache/spark/streaming/twitter/TwitterInputDStream.scala b/external/twitter/src/main/scala/org/apache/spark/streaming/twitter/TwitterInputDStream.scala
index 5ea2e5549d7d..4eacc47da569 100644
--- a/external/twitter/src/main/scala/org/apache/spark/streaming/twitter/TwitterInputDStream.scala
+++ b/external/twitter/src/main/scala/org/apache/spark/streaming/twitter/TwitterInputDStream.scala
@@ -63,7 +63,8 @@ class TwitterReceiver(
     storageLevel: StorageLevel
   ) extends Receiver[Status](storageLevel) with Logging {
 
-  private var twitterStream: TwitterStream = _
+  @volatile private var twitterStream: TwitterStream = _
+  @volatile private var stopped = false
 
   def onStart() {
     try {
@@ -78,7 +79,9 @@ class TwitterReceiver(
         def onScrubGeo(l: Long, l1: Long) {}
         def onStallWarning(stallWarning: StallWarning) {}
         def onException(e: Exception) {
-          restart("Error receiving tweets", e)
+          if (!stopped) {
+            restart("Error receiving tweets", e)
+          }
         }
       })
 
@@ -91,12 +94,14 @@ class TwitterReceiver(
       }
       setTwitterStream(newTwitterStream)
       logInfo("Twitter receiver started")
+      stopped = false
     } catch {
       case e: Exception => restart("Error starting Twitter stream", e)
     }
   }
 
   def onStop() {
+    stopped = true
     setTwitterStream(null)
     logInfo("Twitter receiver stopped")
   }

From eff9714e1c88e39e28317358ca9ec87677f121dc Mon Sep 17 00:00:00 2001
From: Prashant Sharma <prashant.s@imaginea.com>
Date: Thu, 24 Jul 2014 18:15:37 -0700
Subject: [PATCH 752/810] [SPARK-2014] Make PySpark store RDDs in
 MEMORY_ONLY_SER with compression by default

Author: Prashant Sharma <prashant.s@imaginea.com>

Closes #1051 from ScrapCodes/SPARK-2014/pyspark-cache and squashes the following commits:

f192df7 [Prashant Sharma] Code Review
2a2f43f [Prashant Sharma] [SPARK-2014] Make PySpark store RDDs in MEMORY_ONLY_SER with compression by default
---
 python/pyspark/conf.py    | 6 ++++++
 python/pyspark/context.py | 2 +-
 python/pyspark/rdd.py     | 4 ++--
 3 files changed, 9 insertions(+), 3 deletions(-)

diff --git a/python/pyspark/conf.py b/python/pyspark/conf.py
index b50590ab3b44..b4c82f519bd5 100644
--- a/python/pyspark/conf.py
+++ b/python/pyspark/conf.py
@@ -100,6 +100,12 @@ def set(self, key, value):
         self._jconf.set(key, unicode(value))
         return self
 
+    def setIfMissing(self, key, value):
+        """Set a configuration property, if not already set."""
+        if self.get(key) is None:
+            self.set(key, value)
+        return self
+
     def setMaster(self, value):
         """Set master URL to connect to."""
         self._jconf.setMaster(value)
diff --git a/python/pyspark/context.py b/python/pyspark/context.py
index e21be0e10a3f..024fb881877c 100644
--- a/python/pyspark/context.py
+++ b/python/pyspark/context.py
@@ -101,7 +101,7 @@ def __init__(self, master=None, appName=None, sparkHome=None, pyFiles=None,
         else:
             self.serializer = BatchedSerializer(self._unbatched_serializer,
                                                 batchSize)
-
+        self._conf.setIfMissing("spark.rdd.compress", "true")
         # Set any parameters passed directly to us on the conf
         if master:
             self._conf.setMaster(master)
diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py
index 94ba22306afb..a38dd0b9237c 100644
--- a/python/pyspark/rdd.py
+++ b/python/pyspark/rdd.py
@@ -231,10 +231,10 @@ def context(self):
 
     def cache(self):
         """
-        Persist this RDD with the default storage level (C{MEMORY_ONLY}).
+        Persist this RDD with the default storage level (C{MEMORY_ONLY_SER}).
         """
         self.is_cached = True
-        self._jrdd.cache()
+        self.persist(StorageLevel.MEMORY_ONLY_SER)
         return self
 
     def persist(self, storageLevel):

From 14174abd421318e71c16edd24224fd5094bdfed4 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies.liu@gmail.com>
Date: Thu, 24 Jul 2014 22:53:47 -0700
Subject: [PATCH 753/810] [SPARK-2538] [PySpark] Hash based disk spilling
 aggregation

During aggregation in Python worker, if the memory usage is above spark.executor.memory, it will do disk spilling aggregation.

It will split the aggregation into multiple stage, in each stage, it will partition the aggregated data by hash and dump them into disks. After all the data are aggregated, it will merge all the stages together (partition by partition).

Author: Davies Liu <davies.liu@gmail.com>

Closes #1460 from davies/spill and squashes the following commits:

cad91bf [Davies Liu] call gc.collect() after data.clear() to release memory as much as possible.
37d71f7 [Davies Liu] balance the partitions
902f036 [Davies Liu] add shuffle.py into run-tests
dcf03a9 [Davies Liu] fix memory_info() of psutil
67e6eba [Davies Liu] comment for MAX_TOTAL_PARTITIONS
f6bd5d6 [Davies Liu] rollback next_limit() again, the performance difference is huge:
e74b785 [Davies Liu] fix code style and change next_limit to memory_limit
400be01 [Davies Liu] address all the comments
6178844 [Davies Liu] refactor and improve docs
fdd0a49 [Davies Liu] add long doc string for ExternalMerger
1a97ce4 [Davies Liu] limit used memory and size of objects in partitionBy()
e6cc7f9 [Davies Liu] Merge branch 'master' into spill
3652583 [Davies Liu] address comments
e78a0a0 [Davies Liu] fix style
24cec6a [Davies Liu] get local directory by SPARK_LOCAL_DIR
57ee7ef [Davies Liu] update docs
286aaff [Davies Liu] let spilled aggregation in Python configurable
e9a40f6 [Davies Liu] recursive merger
6edbd1f [Davies Liu] Hash based disk spilling aggregation
---
 .../apache/spark/api/python/PythonRDD.scala   |   5 +-
 .../spark/storage/DiskBlockManager.scala      |   2 +-
 docs/configuration.md                         |   9 +
 python/epydoc.conf                            |   2 +-
 python/pyspark/rdd.py                         |  92 +++-
 python/pyspark/serializers.py                 |  29 +-
 python/pyspark/shuffle.py                     | 439 ++++++++++++++++++
 python/pyspark/tests.py                       |  57 +++
 python/run-tests                              |   1 +
 9 files changed, 611 insertions(+), 25 deletions(-)
 create mode 100644 python/pyspark/shuffle.py

diff --git a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
index 462e09466bfa..d6b0988641a9 100644
--- a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
+++ b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
@@ -57,7 +57,10 @@ private[spark] class PythonRDD[T: ClassTag](
   override def compute(split: Partition, context: TaskContext): Iterator[Array[Byte]] = {
     val startTime = System.currentTimeMillis
     val env = SparkEnv.get
-    val worker: Socket = env.createPythonWorker(pythonExec, envVars.toMap)
+    val localdir = env.blockManager.diskBlockManager.localDirs.map(
+      f => f.getPath()).mkString(",")
+    val worker: Socket = env.createPythonWorker(pythonExec,
+      envVars.toMap + ("SPARK_LOCAL_DIR" -> localdir))
 
     // Start a thread to feed the process input from our parent's iterator
     val writerThread = new WriterThread(env, worker, split, context)
diff --git a/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala b/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala
index 673fc19c060a..2e7ed7538e6e 100644
--- a/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala
+++ b/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala
@@ -43,7 +43,7 @@ private[spark] class DiskBlockManager(shuffleManager: ShuffleBlockManager, rootD
   /* Create one local directory for each path mentioned in spark.local.dir; then, inside this
    * directory, create multiple subdirectories that we will hash files into, in order to avoid
    * having really large inodes at the top level. */
-  private val localDirs: Array[File] = createLocalDirs()
+  val localDirs: Array[File] = createLocalDirs()
   if (localDirs.isEmpty) {
     logError("Failed to create any local dir.")
     System.exit(ExecutorExitCode.DISK_STORE_FAILED_TO_CREATE_DIR)
diff --git a/docs/configuration.md b/docs/configuration.md
index cb0c65e2d220..dac8bb1d5246 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -197,6 +197,15 @@ Apart from these, the following properties are also available, and may be useful
     Spark's dependencies and user dependencies. It is currently an experimental feature.
   </td>
 </tr>
+<tr>
+  <td><code>spark.python.worker.memory</code></td>
+  <td>512m</td>
+  <td>
+    Amount of memory to use per python worker process during aggregation, in the same
+    format as JVM memory strings (e.g. <code>512m</code>, <code>2g</code>). If the memory
+    used during aggregation goes above this amount, it will spill the data into disks.
+  </td>
+</tr>
 </table>
 
 #### Shuffle Behavior
diff --git a/python/epydoc.conf b/python/epydoc.conf
index b73860bad826..51c0faf35993 100644
--- a/python/epydoc.conf
+++ b/python/epydoc.conf
@@ -35,4 +35,4 @@ private: no
 exclude: pyspark.cloudpickle pyspark.worker pyspark.join
          pyspark.java_gateway pyspark.examples pyspark.shell pyspark.tests
          pyspark.rddsampler pyspark.daemon pyspark.mllib._common
-         pyspark.mllib.tests
+         pyspark.mllib.tests pyspark.shuffle
diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py
index a38dd0b9237c..7ad610826144 100644
--- a/python/pyspark/rdd.py
+++ b/python/pyspark/rdd.py
@@ -42,6 +42,8 @@
 from pyspark.rddsampler import RDDSampler
 from pyspark.storagelevel import StorageLevel
 from pyspark.resultiterable import ResultIterable
+from pyspark.shuffle import Aggregator, InMemoryMerger, ExternalMerger, \
+    get_used_memory
 
 from py4j.java_collections import ListConverter, MapConverter
 
@@ -197,6 +199,22 @@ def _replaceRoot(self, value):
             self._sink(1)
 
 
+def _parse_memory(s):
+    """
+    Parse a memory string in the format supported by Java (e.g. 1g, 200m) and
+    return the value in MB
+
+    >>> _parse_memory("256m")
+    256
+    >>> _parse_memory("2g")
+    2048
+    """
+    units = {'g': 1024, 'm': 1, 't': 1 << 20, 'k': 1.0 / 1024}
+    if s[-1] not in units:
+        raise ValueError("invalid format: " + s)
+    return int(float(s[:-1]) * units[s[-1].lower()])
+
+
 class RDD(object):
 
     """
@@ -1207,20 +1225,49 @@ def partitionBy(self, numPartitions, partitionFunc=portable_hash):
         if numPartitions is None:
             numPartitions = self._defaultReducePartitions()
 
-        # Transferring O(n) objects to Java is too expensive.  Instead, we'll
-        # form the hash buckets in Python, transferring O(numPartitions) objects
-        # to Java.  Each object is a (splitNumber, [objects]) pair.
+        # Transferring O(n) objects to Java is too expensive.
+        # Instead, we'll form the hash buckets in Python,
+        # transferring O(numPartitions) objects to Java.
+        # Each object is a (splitNumber, [objects]) pair.
+        # In order to avoid too huge objects, the objects are
+        # grouped into chunks.
         outputSerializer = self.ctx._unbatched_serializer
 
+        limit = (_parse_memory(self.ctx._conf.get(
+                    "spark.python.worker.memory", "512m")) / 2)
+
         def add_shuffle_key(split, iterator):
 
             buckets = defaultdict(list)
+            c, batch = 0, min(10 * numPartitions, 1000)
 
             for (k, v) in iterator:
                 buckets[partitionFunc(k) % numPartitions].append((k, v))
+                c += 1
+
+                # check used memory and avg size of chunk of objects
+                if (c % 1000 == 0 and get_used_memory() > limit
+                        or c > batch):
+                    n, size = len(buckets), 0
+                    for split in buckets.keys():
+                        yield pack_long(split)
+                        d = outputSerializer.dumps(buckets[split])
+                        del buckets[split]
+                        yield d
+                        size += len(d)
+
+                    avg = (size / n) >> 20
+                    # let 1M < avg < 10M
+                    if avg < 1:
+                        batch *= 1.5
+                    elif avg > 10:
+                        batch = max(batch / 1.5, 1)
+                    c = 0
+
             for (split, items) in buckets.iteritems():
                 yield pack_long(split)
                 yield outputSerializer.dumps(items)
+
         keyed = PipelinedRDD(self, add_shuffle_key)
         keyed._bypass_serializer = True
         with _JavaStackTrace(self.context) as st:
@@ -1230,8 +1277,8 @@ def add_shuffle_key(split, iterator):
                                                           id(partitionFunc))
         jrdd = pairRDD.partitionBy(partitioner).values()
         rdd = RDD(jrdd, self.ctx, BatchedSerializer(outputSerializer))
-        # This is required so that id(partitionFunc) remains unique, even if
-        # partitionFunc is a lambda:
+        # This is required so that id(partitionFunc) remains unique,
+        # even if partitionFunc is a lambda:
         rdd._partitionFunc = partitionFunc
         return rdd
 
@@ -1265,26 +1312,28 @@ def combineByKey(self, createCombiner, mergeValue, mergeCombiners,
         if numPartitions is None:
             numPartitions = self._defaultReducePartitions()
 
+        serializer = self.ctx.serializer
+        spill = (self.ctx._conf.get("spark.shuffle.spill", 'True').lower()
+                 == 'true')
+        memory = _parse_memory(self.ctx._conf.get(
+                    "spark.python.worker.memory", "512m"))
+        agg = Aggregator(createCombiner, mergeValue, mergeCombiners)
+
         def combineLocally(iterator):
-            combiners = {}
-            for x in iterator:
-                (k, v) = x
-                if k not in combiners:
-                    combiners[k] = createCombiner(v)
-                else:
-                    combiners[k] = mergeValue(combiners[k], v)
-            return combiners.iteritems()
+            merger = ExternalMerger(agg, memory * 0.9, serializer) \
+                         if spill else InMemoryMerger(agg)
+            merger.mergeValues(iterator)
+            return merger.iteritems()
+
         locally_combined = self.mapPartitions(combineLocally)
         shuffled = locally_combined.partitionBy(numPartitions)
 
         def _mergeCombiners(iterator):
-            combiners = {}
-            for (k, v) in iterator:
-                if k not in combiners:
-                    combiners[k] = v
-                else:
-                    combiners[k] = mergeCombiners(combiners[k], v)
-            return combiners.iteritems()
+            merger = ExternalMerger(agg, memory, serializer) \
+                         if spill else InMemoryMerger(agg)
+            merger.mergeCombiners(iterator)
+            return merger.iteritems()
+
         return shuffled.mapPartitions(_mergeCombiners)
 
     def aggregateByKey(self, zeroValue, seqFunc, combFunc, numPartitions=None):
@@ -1343,7 +1392,8 @@ def mergeValue(xs, x):
             return xs
 
         def mergeCombiners(a, b):
-            return a + b
+            a.extend(b)
+            return a
 
         return self.combineByKey(createCombiner, mergeValue, mergeCombiners,
                                  numPartitions).mapValues(lambda x: ResultIterable(x))
diff --git a/python/pyspark/serializers.py b/python/pyspark/serializers.py
index 9be78b39fbc2..03b31ae9624c 100644
--- a/python/pyspark/serializers.py
+++ b/python/pyspark/serializers.py
@@ -193,7 +193,7 @@ def load_stream(self, stream):
         return chain.from_iterable(self._load_stream_without_unbatching(stream))
 
     def _load_stream_without_unbatching(self, stream):
-            return self.serializer.load_stream(stream)
+        return self.serializer.load_stream(stream)
 
     def __eq__(self, other):
         return (isinstance(other, BatchedSerializer) and
@@ -302,6 +302,33 @@ class MarshalSerializer(FramedSerializer):
     loads = marshal.loads
 
 
+class AutoSerializer(FramedSerializer):
+    """
+    Choose marshal or cPickle as serialization protocol autumatically
+    """
+    def __init__(self):
+        FramedSerializer.__init__(self)
+        self._type = None
+
+    def dumps(self, obj):
+        if self._type is not None:
+            return 'P' + cPickle.dumps(obj, -1)
+        try:
+            return 'M' + marshal.dumps(obj)
+        except Exception:
+            self._type = 'P'
+            return 'P' + cPickle.dumps(obj, -1)
+
+    def loads(self, obj):
+        _type = obj[0]
+        if _type == 'M':
+            return marshal.loads(obj[1:])
+        elif _type == 'P':
+            return cPickle.loads(obj[1:])
+        else:
+            raise ValueError("invalid sevialization type: %s" % _type)
+
+
 class UTF8Deserializer(Serializer):
     """
     Deserializes streams written by String.getBytes.
diff --git a/python/pyspark/shuffle.py b/python/pyspark/shuffle.py
new file mode 100644
index 000000000000..e3923d1c36c5
--- /dev/null
+++ b/python/pyspark/shuffle.py
@@ -0,0 +1,439 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import os
+import sys
+import platform
+import shutil
+import warnings
+import gc
+
+from pyspark.serializers import BatchedSerializer, PickleSerializer
+
+try:
+    import psutil
+
+    def get_used_memory():
+        """ Return the used memory in MB """
+        process = psutil.Process(os.getpid())
+        if hasattr(process, "memory_info"):
+            info = process.memory_info()
+        else:
+            info = process.get_memory_info()
+        return info.rss >> 20
+except ImportError:
+
+    def get_used_memory():
+        """ Return the used memory in MB """
+        if platform.system() == 'Linux':
+            for line in open('/proc/self/status'):
+                if line.startswith('VmRSS:'):
+                    return int(line.split()[1]) >> 10
+        else:
+            warnings.warn("Please install psutil to have better "
+                    "support with spilling")
+            if platform.system() == "Darwin":
+                import resource
+                rss = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss
+                return rss >> 20
+            # TODO: support windows
+        return 0
+
+
+class Aggregator(object):
+
+    """
+    Aggregator has tree functions to merge values into combiner.
+
+    createCombiner:  (value) -> combiner
+    mergeValue:      (combine, value) -> combiner
+    mergeCombiners:  (combiner, combiner) -> combiner
+    """
+
+    def __init__(self, createCombiner, mergeValue, mergeCombiners):
+        self.createCombiner = createCombiner
+        self.mergeValue = mergeValue
+        self.mergeCombiners = mergeCombiners
+
+
+class SimpleAggregator(Aggregator):
+
+    """
+    SimpleAggregator is useful for the cases that combiners have
+    same type with values
+    """
+
+    def __init__(self, combiner):
+        Aggregator.__init__(self, lambda x: x, combiner, combiner)
+
+
+class Merger(object):
+
+    """
+    Merge shuffled data together by aggregator
+    """
+
+    def __init__(self, aggregator):
+        self.agg = aggregator
+
+    def mergeValues(self, iterator):
+        """ Combine the items by creator and combiner """
+        raise NotImplementedError
+
+    def mergeCombiners(self, iterator):
+        """ Merge the combined items by mergeCombiner """
+        raise NotImplementedError
+
+    def iteritems(self):
+        """ Return the merged items ad iterator """
+        raise NotImplementedError
+
+
+class InMemoryMerger(Merger):
+
+    """
+    In memory merger based on in-memory dict.
+    """
+
+    def __init__(self, aggregator):
+        Merger.__init__(self, aggregator)
+        self.data = {}
+
+    def mergeValues(self, iterator):
+        """ Combine the items by creator and combiner """
+        # speed up attributes lookup
+        d, creator = self.data, self.agg.createCombiner
+        comb = self.agg.mergeValue
+        for k, v in iterator:
+            d[k] = comb(d[k], v) if k in d else creator(v)
+
+    def mergeCombiners(self, iterator):
+        """ Merge the combined items by mergeCombiner """
+        # speed up attributes lookup
+        d, comb = self.data, self.agg.mergeCombiners
+        for k, v in iterator:
+            d[k] = comb(d[k], v) if k in d else v
+
+    def iteritems(self):
+        """ Return the merged items ad iterator """
+        return self.data.iteritems()
+
+
+class ExternalMerger(Merger):
+
+    """
+    External merger will dump the aggregated data into disks when
+    memory usage goes above the limit, then merge them together.
+
+    This class works as follows:
+
+    - It repeatedly combine the items and save them in one dict in 
+      memory.
+
+    - When the used memory goes above memory limit, it will split
+      the combined data into partitions by hash code, dump them
+      into disk, one file per partition.
+
+    - Then it goes through the rest of the iterator, combine items
+      into different dict by hash. Until the used memory goes over
+      memory limit, it dump all the dicts into disks, one file per
+      dict. Repeat this again until combine all the items.
+
+    - Before return any items, it will load each partition and
+      combine them seperately. Yield them before loading next
+      partition.
+
+    - During loading a partition, if the memory goes over limit,
+      it will partition the loaded data and dump them into disks
+      and load them partition by partition again.
+
+    `data` and `pdata` are used to hold the merged items in memory.
+    At first, all the data are merged into `data`. Once the used
+    memory goes over limit, the items in `data` are dumped indo
+    disks, `data` will be cleared, all rest of items will be merged
+    into `pdata` and then dumped into disks. Before returning, all
+    the items in `pdata` will be dumped into disks.
+
+    Finally, if any items were spilled into disks, each partition
+    will be merged into `data` and be yielded, then cleared.
+
+    >>> agg = SimpleAggregator(lambda x, y: x + y)
+    >>> merger = ExternalMerger(agg, 10)
+    >>> N = 10000
+    >>> merger.mergeValues(zip(xrange(N), xrange(N)) * 10)
+    >>> assert merger.spills > 0
+    >>> sum(v for k,v in merger.iteritems())
+    499950000
+
+    >>> merger = ExternalMerger(agg, 10)
+    >>> merger.mergeCombiners(zip(xrange(N), xrange(N)) * 10)
+    >>> assert merger.spills > 0
+    >>> sum(v for k,v in merger.iteritems())
+    499950000
+    """
+
+    # the max total partitions created recursively
+    MAX_TOTAL_PARTITIONS = 4096
+
+    def __init__(self, aggregator, memory_limit=512, serializer=None,
+            localdirs=None, scale=1, partitions=59, batch=1000):
+        Merger.__init__(self, aggregator)
+        self.memory_limit = memory_limit
+        # default serializer is only used for tests
+        self.serializer = serializer or \
+                BatchedSerializer(PickleSerializer(), 1024)
+        self.localdirs = localdirs or self._get_dirs()
+        # number of partitions when spill data into disks
+        self.partitions = partitions
+        # check the memory after # of items merged
+        self.batch = batch
+        # scale is used to scale down the hash of key for recursive hash map
+        self.scale = scale
+        # unpartitioned merged data
+        self.data = {}
+        # partitioned merged data, list of dicts
+        self.pdata = []
+        # number of chunks dumped into disks
+        self.spills = 0
+        # randomize the hash of key, id(o) is the address of o (aligned by 8)
+        self._seed = id(self) + 7
+
+    def _get_dirs(self):
+        """ Get all the directories """
+        path = os.environ.get("SPARK_LOCAL_DIR", "/tmp")
+        dirs = path.split(",")
+        return [os.path.join(d, "python", str(os.getpid()), str(id(self)))
+                for d in dirs]
+
+    def _get_spill_dir(self, n):
+        """ Choose one directory for spill by number n """
+        return os.path.join(self.localdirs[n % len(self.localdirs)], str(n))
+
+    def _next_limit(self):
+        """
+        Return the next memory limit. If the memory is not released
+        after spilling, it will dump the data only when the used memory
+        starts to increase.
+        """
+        return max(self.memory_limit, get_used_memory() * 1.05)
+
+    def mergeValues(self, iterator):
+        """ Combine the items by creator and combiner """
+        iterator = iter(iterator)
+        # speedup attribute lookup
+        creator, comb = self.agg.createCombiner, self.agg.mergeValue
+        d, c, batch = self.data, 0, self.batch
+
+        for k, v in iterator:
+            d[k] = comb(d[k], v) if k in d else creator(v)
+
+            c += 1
+            if c % batch == 0 and get_used_memory() > self.memory_limit:
+                self._spill()
+                self._partitioned_mergeValues(iterator, self._next_limit())
+                break
+
+    def _partition(self, key):
+        """ Return the partition for key """
+        return hash((key, self._seed)) % self.partitions
+
+    def _partitioned_mergeValues(self, iterator, limit=0):
+        """ Partition the items by key, then combine them """
+        # speedup attribute lookup
+        creator, comb = self.agg.createCombiner, self.agg.mergeValue
+        c, pdata, hfun, batch = 0, self.pdata, self._partition, self.batch
+
+        for k, v in iterator:
+            d = pdata[hfun(k)]
+            d[k] = comb(d[k], v) if k in d else creator(v)
+            if not limit:
+                continue
+
+            c += 1
+            if c % batch == 0 and get_used_memory() > limit:
+                self._spill()
+                limit = self._next_limit()
+
+    def mergeCombiners(self, iterator, check=True):
+        """ Merge (K,V) pair by mergeCombiner """
+        iterator = iter(iterator)
+        # speedup attribute lookup
+        d, comb, batch = self.data, self.agg.mergeCombiners, self.batch
+        c = 0
+        for k, v in iterator:
+            d[k] = comb(d[k], v) if k in d else v
+            if not check:
+                continue
+
+            c += 1
+            if c % batch == 0 and get_used_memory() > self.memory_limit:
+                self._spill()
+                self._partitioned_mergeCombiners(iterator, self._next_limit())
+                break
+
+    def _partitioned_mergeCombiners(self, iterator, limit=0):
+        """ Partition the items by key, then merge them """
+        comb, pdata = self.agg.mergeCombiners, self.pdata
+        c, hfun = 0, self._partition
+        for k, v in iterator:
+            d = pdata[hfun(k)]
+            d[k] = comb(d[k], v) if k in d else v
+            if not limit:
+                continue
+
+            c += 1
+            if c % self.batch == 0 and get_used_memory() > limit:
+                self._spill()
+                limit = self._next_limit()
+
+    def _spill(self):
+        """
+        dump already partitioned data into disks.
+
+        It will dump the data in batch for better performance.
+        """
+        path = self._get_spill_dir(self.spills)
+        if not os.path.exists(path):
+            os.makedirs(path)
+
+        if not self.pdata:
+            # The data has not been partitioned, it will iterator the
+            # dataset once, write them into different files, has no
+            # additional memory. It only called when the memory goes
+            # above limit at the first time.
+
+            # open all the files for writing
+            streams = [open(os.path.join(path, str(i)), 'w')
+                       for i in range(self.partitions)]
+
+            for k, v in self.data.iteritems():
+                h = self._partition(k)
+                # put one item in batch, make it compatitable with load_stream
+                # it will increase the memory if dump them in batch
+                self.serializer.dump_stream([(k, v)], streams[h])
+
+            for s in streams:
+                s.close()
+
+            self.data.clear()
+            self.pdata = [{} for i in range(self.partitions)]
+
+        else:
+            for i in range(self.partitions):
+                p = os.path.join(path, str(i))
+                with open(p, "w") as f:
+                    # dump items in batch
+                    self.serializer.dump_stream(self.pdata[i].iteritems(), f)
+                self.pdata[i].clear()
+
+        self.spills += 1
+        gc.collect() # release the memory as much as possible
+
+    def iteritems(self):
+        """ Return all merged items as iterator """
+        if not self.pdata and not self.spills:
+            return self.data.iteritems()
+        return self._external_items()
+
+    def _external_items(self):
+        """ Return all partitioned items as iterator """
+        assert not self.data
+        if any(self.pdata):
+            self._spill()
+        hard_limit = self._next_limit()
+
+        try:
+            for i in range(self.partitions):
+                self.data = {}
+                for j in range(self.spills):
+                    path = self._get_spill_dir(j)
+                    p = os.path.join(path, str(i))
+                    # do not check memory during merging
+                    self.mergeCombiners(self.serializer.load_stream(open(p)),
+                                        False)
+
+                    # limit the total partitions
+                    if (self.scale * self.partitions < self.MAX_TOTAL_PARTITIONS
+                            and j < self.spills - 1
+                            and get_used_memory() > hard_limit):
+                        self.data.clear() # will read from disk again
+                        gc.collect() # release the memory as much as possible
+                        for v in self._recursive_merged_items(i):
+                            yield v
+                        return
+
+                for v in self.data.iteritems():
+                    yield v
+                self.data.clear()
+                gc.collect()
+
+                # remove the merged partition
+                for j in range(self.spills):
+                    path = self._get_spill_dir(j)
+                    os.remove(os.path.join(path, str(i)))
+
+        finally:
+            self._cleanup()
+
+    def _cleanup(self):
+        """ Clean up all the files in disks """
+        for d in self.localdirs:
+            shutil.rmtree(d, True)
+
+    def _recursive_merged_items(self, start):
+        """
+        merge the partitioned items and return the as iterator
+
+        If one partition can not be fit in memory, then them will be
+        partitioned and merged recursively.
+        """
+        # make sure all the data are dumps into disks.
+        assert not self.data
+        if any(self.pdata):
+            self._spill()
+        assert self.spills > 0
+
+        for i in range(start, self.partitions):
+            subdirs = [os.path.join(d, "parts", str(i))
+                            for d in self.localdirs]
+            m = ExternalMerger(self.agg, self.memory_limit, self.serializer,
+                    subdirs, self.scale * self.partitions)
+            m.pdata = [{} for _ in range(self.partitions)]
+            limit = self._next_limit()
+
+            for j in range(self.spills):
+                path = self._get_spill_dir(j)
+                p = os.path.join(path, str(i))
+                m._partitioned_mergeCombiners(
+                        self.serializer.load_stream(open(p)))
+
+                if get_used_memory() > limit:
+                    m._spill()
+                    limit = self._next_limit()
+
+            for v in m._external_items():
+                yield v
+
+            # remove the merged partition
+            for j in range(self.spills):
+                path = self._get_spill_dir(j)
+                os.remove(os.path.join(path, str(i)))
+
+
+if __name__ == "__main__":
+    import doctest
+    doctest.testmod()
diff --git a/python/pyspark/tests.py b/python/pyspark/tests.py
index 9c5ecd0bb02a..a92abbf371f1 100644
--- a/python/pyspark/tests.py
+++ b/python/pyspark/tests.py
@@ -34,6 +34,7 @@
 from pyspark.context import SparkContext
 from pyspark.files import SparkFiles
 from pyspark.serializers import read_int
+from pyspark.shuffle import Aggregator, InMemoryMerger, ExternalMerger
 
 _have_scipy = False
 try:
@@ -47,6 +48,62 @@
 SPARK_HOME = os.environ["SPARK_HOME"]
 
 
+class TestMerger(unittest.TestCase):
+
+    def setUp(self):
+        self.N = 1 << 16
+        self.l = [i for i in xrange(self.N)]
+        self.data = zip(self.l, self.l)
+        self.agg = Aggregator(lambda x: [x], 
+                lambda x, y: x.append(y) or x,
+                lambda x, y: x.extend(y) or x)
+
+    def test_in_memory(self):
+        m = InMemoryMerger(self.agg)
+        m.mergeValues(self.data)
+        self.assertEqual(sum(sum(v) for k, v in m.iteritems()),
+                sum(xrange(self.N)))
+
+        m = InMemoryMerger(self.agg)
+        m.mergeCombiners(map(lambda (x, y): (x, [y]), self.data))
+        self.assertEqual(sum(sum(v) for k, v in m.iteritems()),
+                sum(xrange(self.N)))
+
+    def test_small_dataset(self):
+        m = ExternalMerger(self.agg, 1000)
+        m.mergeValues(self.data)
+        self.assertEqual(m.spills, 0)
+        self.assertEqual(sum(sum(v) for k, v in m.iteritems()),
+                sum(xrange(self.N)))
+
+        m = ExternalMerger(self.agg, 1000)
+        m.mergeCombiners(map(lambda (x, y): (x, [y]), self.data))
+        self.assertEqual(m.spills, 0)
+        self.assertEqual(sum(sum(v) for k, v in m.iteritems()),
+                sum(xrange(self.N)))
+
+    def test_medium_dataset(self):
+        m = ExternalMerger(self.agg, 10)
+        m.mergeValues(self.data)
+        self.assertTrue(m.spills >= 1)
+        self.assertEqual(sum(sum(v) for k, v in m.iteritems()),
+                sum(xrange(self.N)))
+
+        m = ExternalMerger(self.agg, 10)
+        m.mergeCombiners(map(lambda (x, y): (x, [y]), self.data * 3))
+        self.assertTrue(m.spills >= 1)
+        self.assertEqual(sum(sum(v) for k, v in m.iteritems()),
+                sum(xrange(self.N)) * 3)
+
+    def test_huge_dataset(self):
+        m = ExternalMerger(self.agg, 10)
+        m.mergeCombiners(map(lambda (k, v): (k, [str(v)]), self.data * 10))
+        self.assertTrue(m.spills >= 1)
+        self.assertEqual(sum(len(v) for k, v in m._recursive_merged_items(0)),
+                self.N * 10)
+        m._cleanup()
+
+
 class PySparkTestCase(unittest.TestCase):
 
     def setUp(self):
diff --git a/python/run-tests b/python/run-tests
index 9282aa47e837..29f755fc0dcd 100755
--- a/python/run-tests
+++ b/python/run-tests
@@ -61,6 +61,7 @@ run_test "pyspark/broadcast.py"
 run_test "pyspark/accumulators.py"
 run_test "pyspark/serializers.py"
 unset PYSPARK_DOC_TEST
+run_test "pyspark/shuffle.py"
 run_test "pyspark/tests.py"
 run_test "pyspark/mllib/_common.py"
 run_test "pyspark/mllib/classification.py"

From 2f75a4a30e1a3fdf384475b9660c6c43f093f68c Mon Sep 17 00:00:00 2001
From: Doris Xin <doris.s.xin@gmail.com>
Date: Thu, 24 Jul 2014 23:42:08 -0700
Subject: [PATCH 754/810] [SPARK-2656] Python version of stratified sampling

exact sample size not supported for now.

Author: Doris Xin <doris.s.xin@gmail.com>

Closes #1554 from dorx/pystratified and squashes the following commits:

4ba927a [Doris Xin] use rel diff (+- 50%) instead of abs diff (+- 50)
bdc3f8b [Doris Xin] updated unit to check sample holistically
7713c7b [Doris Xin] Python version of stratified sampling
---
 .../main/scala/org/apache/spark/rdd/RDD.scala |  2 +-
 python/pyspark/rdd.py                         | 25 ++++++++++++++--
 python/pyspark/rddsampler.py                  | 30 +++++++++++++++++--
 3 files changed, 51 insertions(+), 6 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/rdd/RDD.scala b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
index c1bafab3e749..edbf7eace943 100644
--- a/core/src/main/scala/org/apache/spark/rdd/RDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
@@ -354,7 +354,7 @@ abstract class RDD[T: ClassTag](
   def sample(withReplacement: Boolean, 
       fraction: Double, 
       seed: Long = Utils.random.nextLong): RDD[T] = {
-    require(fraction >= 0.0, "Invalid fraction value: " + fraction)
+    require(fraction >= 0.0, "Negative fraction value: " + fraction)
     if (withReplacement) {
       new PartitionwiseSampledRDD[T, T](this, new PoissonSampler[T](fraction), true, seed)
     } else {
diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py
index 7ad610826144..113a082e1672 100644
--- a/python/pyspark/rdd.py
+++ b/python/pyspark/rdd.py
@@ -39,7 +39,7 @@
 from pyspark.join import python_join, python_left_outer_join, \
     python_right_outer_join, python_cogroup
 from pyspark.statcounter import StatCounter
-from pyspark.rddsampler import RDDSampler
+from pyspark.rddsampler import RDDSampler, RDDStratifiedSampler
 from pyspark.storagelevel import StorageLevel
 from pyspark.resultiterable import ResultIterable
 from pyspark.shuffle import Aggregator, InMemoryMerger, ExternalMerger, \
@@ -411,7 +411,7 @@ def sample(self, withReplacement, fraction, seed=None):
         >>> sc.parallelize(range(0, 100)).sample(False, 0.1, 2).collect() #doctest: +SKIP
         [2, 3, 20, 21, 24, 41, 42, 66, 67, 89, 90, 98]
         """
-        assert fraction >= 0.0, "Invalid fraction value: %s" % fraction
+        assert fraction >= 0.0, "Negative fraction value: %s" % fraction
         return self.mapPartitionsWithIndex(RDDSampler(withReplacement, fraction, seed).func, True)
 
     # this is ported from scala/spark/RDD.scala
@@ -1456,6 +1456,27 @@ def cogroup(self, other, numPartitions=None):
         """
         return python_cogroup((self, other), numPartitions)
 
+    def sampleByKey(self, withReplacement, fractions, seed=None):
+        """
+        Return a subset of this RDD sampled by key (via stratified sampling).
+        Create a sample of this RDD using variable sampling rates for
+        different keys as specified by fractions, a key to sampling rate map.
+
+        >>> fractions = {"a": 0.2, "b": 0.1}
+        >>> rdd = sc.parallelize(fractions.keys()).cartesian(sc.parallelize(range(0, 1000)))
+        >>> sample = dict(rdd.sampleByKey(False, fractions, 2).groupByKey().collect())
+        >>> 100 < len(sample["a"]) < 300 and 50 < len(sample["b"]) < 150
+        True
+        >>> max(sample["a"]) <= 999 and min(sample["a"]) >= 0
+        True
+        >>> max(sample["b"]) <= 999 and min(sample["b"]) >= 0
+        True
+        """
+        for fraction in fractions.values():
+            assert fraction >= 0.0, "Negative fraction value: %s" % fraction
+        return self.mapPartitionsWithIndex( \
+            RDDStratifiedSampler(withReplacement, fractions, seed).func, True)
+
     def subtractByKey(self, other, numPartitions=None):
         """
         Return each (key, value) pair in C{self} that has no pair with matching
diff --git a/python/pyspark/rddsampler.py b/python/pyspark/rddsampler.py
index 7ff1c316c762..2df000fdb08c 100644
--- a/python/pyspark/rddsampler.py
+++ b/python/pyspark/rddsampler.py
@@ -19,8 +19,8 @@
 import random
 
 
-class RDDSampler(object):
-    def __init__(self, withReplacement, fraction, seed=None):
+class RDDSamplerBase(object):
+    def __init__(self, withReplacement, seed=None):
         try:
             import numpy
             self._use_numpy = True
@@ -32,7 +32,6 @@ def __init__(self, withReplacement, fraction, seed=None):
 
         self._seed = seed if seed is not None else random.randint(0, sys.maxint)
         self._withReplacement = withReplacement
-        self._fraction = fraction
         self._random = None
         self._split = None
         self._rand_initialized = False
@@ -94,6 +93,12 @@ def shuffle(self, vals):
         else:
             self._random.shuffle(vals, self._random.random)
 
+
+class RDDSampler(RDDSamplerBase):
+    def __init__(self, withReplacement, fraction, seed=None):
+        RDDSamplerBase.__init__(self, withReplacement, seed)
+        self._fraction = fraction
+
     def func(self, split, iterator):
         if self._withReplacement:
             for obj in iterator:
@@ -107,3 +112,22 @@ def func(self, split, iterator):
             for obj in iterator:
                 if self.getUniformSample(split) <= self._fraction:
                     yield obj
+
+class RDDStratifiedSampler(RDDSamplerBase):
+    def __init__(self, withReplacement, fractions, seed=None):
+        RDDSamplerBase.__init__(self, withReplacement, seed)
+        self._fractions = fractions
+
+    def func(self, split, iterator):
+        if self._withReplacement:
+            for key, val in iterator:
+                # For large datasets, the expected number of occurrences of each element in
+                # a sample with replacement is Poisson(frac). We use that to get a count for
+                # each element.
+                count = self.getPoissonSample(split, mean=self._fractions[key])
+                for _ in range(0, count):
+                    yield key, val
+        else:
+            for key, val in iterator:
+                if self.getUniformSample(split) <= self._fractions[key]:
+                    yield key, val

From 8529ced35c6b77a384d10a26b654a8073d57e03d Mon Sep 17 00:00:00 2001
From: Matei Zaharia <matei@databricks.com>
Date: Fri, 25 Jul 2014 00:32:32 -0700
Subject: [PATCH 755/810] SPARK-2657 Use more compact data structures than
 ArrayBuffer in groupBy & cogroup

JIRA: https://issues.apache.org/jira/browse/SPARK-2657

Our current code uses ArrayBuffers for each group of values in groupBy, as well as for the key's elements in CoGroupedRDD. ArrayBuffers have a lot of overhead if there are few values in them, which is likely to happen in cases such as join. In particular, they have a pointer to an Object[] of size 16 by default, which is 24 bytes for the array header + 128 for the pointers in there, plus at least 32 for the ArrayBuffer data structure. This patch replaces the per-group buffers with a CompactBuffer class that can store up to 2 elements more efficiently (in fields of itself) and acts like an ArrayBuffer beyond that. For a key's elements in CoGroupedRDD, we use an Array of CompactBuffers instead of an ArrayBuffer of ArrayBuffers.

There are some changes throughout the code to deal with CoGroupedRDD returning Array instead. We can also decide not to do that but CoGroupedRDD is a `DeveloperAPI` so I think it's okay to change it here.

Author: Matei Zaharia <matei@databricks.com>

Closes #1555 from mateiz/compact-groupby and squashes the following commits:

845a356 [Matei Zaharia] Lower initial size of CompactBuffer's vector to 8
07621a7 [Matei Zaharia] Review comments
0c1cd12 [Matei Zaharia] Don't use varargs in CompactBuffer.apply
bdc8a39 [Matei Zaharia] Small tweak to +=, and typos
f61f040 [Matei Zaharia] Fix line lengths
59da88b0 [Matei Zaharia] Fix line lengths
197cde8 [Matei Zaharia] Make CompactBuffer extend Seq to make its toSeq more efficient
775110f [Matei Zaharia] Change CoGroupedRDD to give (K, Array[Iterable[_]]) to avoid wrappers
9b4c6e8 [Matei Zaharia] Use CompactBuffer in CoGroupedRDD
ed577ab [Matei Zaharia] Use CompactBuffer in groupByKey
10f0de1 [Matei Zaharia] A CompactBuffer that's more memory-efficient than ArrayBuffer for small buffers
---
 .../org/apache/spark/rdd/CoGroupedRDD.scala   |  16 +-
 .../apache/spark/rdd/PairRDDFunctions.scala   |  33 ++--
 .../spark/serializer/KryoSerializer.scala     |   2 +
 .../spark/util/collection/CompactBuffer.scala | 159 ++++++++++++++++++
 .../org/apache/spark/CheckpointSuite.scala    |  37 ++--
 .../scala/org/apache/spark/ShuffleSuite.scala |   4 +-
 .../util/collection/CompactBufferSuite.scala  | 105 ++++++++++++
 .../correlation/SpearmanCorrelation.scala     |   6 +-
 .../dstream/ReducedWindowedDStream.scala      |  15 +-
 9 files changed, 334 insertions(+), 43 deletions(-)
 create mode 100644 core/src/main/scala/org/apache/spark/util/collection/CompactBuffer.scala
 create mode 100644 core/src/test/scala/org/apache/spark/util/collection/CompactBufferSuite.scala

diff --git a/core/src/main/scala/org/apache/spark/rdd/CoGroupedRDD.scala b/core/src/main/scala/org/apache/spark/rdd/CoGroupedRDD.scala
index aca235a62a6a..7d96089e52ab 100644
--- a/core/src/main/scala/org/apache/spark/rdd/CoGroupedRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/CoGroupedRDD.scala
@@ -25,7 +25,7 @@ import scala.language.existentials
 import org.apache.spark.{InterruptibleIterator, Partition, Partitioner, SparkEnv, TaskContext}
 import org.apache.spark.{Dependency, OneToOneDependency, ShuffleDependency}
 import org.apache.spark.annotation.DeveloperApi
-import org.apache.spark.util.collection.{ExternalAppendOnlyMap, AppendOnlyMap}
+import org.apache.spark.util.collection.{ExternalAppendOnlyMap, AppendOnlyMap, CompactBuffer}
 import org.apache.spark.serializer.Serializer
 import org.apache.spark.shuffle.ShuffleHandle
 
@@ -66,14 +66,14 @@ private[spark] class CoGroupPartition(idx: Int, val deps: Array[CoGroupSplitDep]
  */
 @DeveloperApi
 class CoGroupedRDD[K](@transient var rdds: Seq[RDD[_ <: Product2[K, _]]], part: Partitioner)
-  extends RDD[(K, Seq[Seq[_]])](rdds.head.context, Nil) {
+  extends RDD[(K, Array[Iterable[_]])](rdds.head.context, Nil) {
 
   // For example, `(k, a) cogroup (k, b)` produces k -> Seq(ArrayBuffer as, ArrayBuffer bs).
   // Each ArrayBuffer is represented as a CoGroup, and the resulting Seq as a CoGroupCombiner.
   // CoGroupValue is the intermediate state of each value before being merged in compute.
-  private type CoGroup = ArrayBuffer[Any]
+  private type CoGroup = CompactBuffer[Any]
   private type CoGroupValue = (Any, Int)  // Int is dependency number
-  private type CoGroupCombiner = Seq[CoGroup]
+  private type CoGroupCombiner = Array[CoGroup]
 
   private var serializer: Option[Serializer] = None
 
@@ -114,7 +114,7 @@ class CoGroupedRDD[K](@transient var rdds: Seq[RDD[_ <: Product2[K, _]]], part:
 
   override val partitioner: Some[Partitioner] = Some(part)
 
-  override def compute(s: Partition, context: TaskContext): Iterator[(K, CoGroupCombiner)] = {
+  override def compute(s: Partition, context: TaskContext): Iterator[(K, Array[Iterable[_]])] = {
     val sparkConf = SparkEnv.get.conf
     val externalSorting = sparkConf.getBoolean("spark.shuffle.spill", true)
     val split = s.asInstanceOf[CoGroupPartition]
@@ -150,7 +150,8 @@ class CoGroupedRDD[K](@transient var rdds: Seq[RDD[_ <: Product2[K, _]]], part:
           getCombiner(kv._1)(depNum) += kv._2
         }
       }
-      new InterruptibleIterator(context, map.iterator)
+      new InterruptibleIterator(context,
+        map.iterator.asInstanceOf[Iterator[(K, Array[Iterable[_]])]])
     } else {
       val map = createExternalMap(numRdds)
       rddIterators.foreach { case (it, depNum) =>
@@ -161,7 +162,8 @@ class CoGroupedRDD[K](@transient var rdds: Seq[RDD[_ <: Product2[K, _]]], part:
       }
       context.taskMetrics.memoryBytesSpilled = map.memoryBytesSpilled
       context.taskMetrics.diskBytesSpilled = map.diskBytesSpilled
-      new InterruptibleIterator(context, map.iterator)
+      new InterruptibleIterator(context,
+        map.iterator.asInstanceOf[Iterator[(K, Array[Iterable[_]])]])
     }
   }
 
diff --git a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
index a6b920467283..c04d162a3961 100644
--- a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
@@ -46,6 +46,7 @@ import org.apache.spark.Partitioner.defaultPartitioner
 import org.apache.spark.SparkContext._
 import org.apache.spark.partial.{BoundedDouble, PartialResult}
 import org.apache.spark.serializer.Serializer
+import org.apache.spark.util.collection.CompactBuffer
 
 /**
  * Extra functions available on RDDs of (key, value) pairs through an implicit conversion.
@@ -361,12 +362,12 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
     // groupByKey shouldn't use map side combine because map side combine does not
     // reduce the amount of data shuffled and requires all map side data be inserted
     // into a hash table, leading to more objects in the old gen.
-    val createCombiner = (v: V) => ArrayBuffer(v)
-    val mergeValue = (buf: ArrayBuffer[V], v: V) => buf += v
-    val mergeCombiners = (c1: ArrayBuffer[V], c2: ArrayBuffer[V]) => c1 ++ c2
-    val bufs = combineByKey[ArrayBuffer[V]](
+    val createCombiner = (v: V) => CompactBuffer(v)
+    val mergeValue = (buf: CompactBuffer[V], v: V) => buf += v
+    val mergeCombiners = (c1: CompactBuffer[V], c2: CompactBuffer[V]) => c1 ++= c2
+    val bufs = combineByKey[CompactBuffer[V]](
       createCombiner, mergeValue, mergeCombiners, partitioner, mapSideCombine=false)
-    bufs.mapValues(_.toIterable)
+    bufs.asInstanceOf[RDD[(K, Iterable[V])]]
   }
 
   /**
@@ -571,11 +572,11 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
       throw new SparkException("Default partitioner cannot partition array keys.")
     }
     val cg = new CoGroupedRDD[K](Seq(self, other1, other2, other3), partitioner)
-    cg.mapValues { case Seq(vs, w1s, w2s, w3s) =>
-       (vs.asInstanceOf[Seq[V]],
-         w1s.asInstanceOf[Seq[W1]],
-         w2s.asInstanceOf[Seq[W2]],
-         w3s.asInstanceOf[Seq[W3]])
+    cg.mapValues { case Array(vs, w1s, w2s, w3s) =>
+       (vs.asInstanceOf[Iterable[V]],
+         w1s.asInstanceOf[Iterable[W1]],
+         w2s.asInstanceOf[Iterable[W2]],
+         w3s.asInstanceOf[Iterable[W3]])
     }
   }
 
@@ -589,8 +590,8 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
       throw new SparkException("Default partitioner cannot partition array keys.")
     }
     val cg = new CoGroupedRDD[K](Seq(self, other), partitioner)
-    cg.mapValues { case Seq(vs, w1s) =>
-      (vs.asInstanceOf[Seq[V]], w1s.asInstanceOf[Seq[W]])
+    cg.mapValues { case Array(vs, w1s) =>
+      (vs.asInstanceOf[Iterable[V]], w1s.asInstanceOf[Iterable[W]])
     }
   }
 
@@ -604,10 +605,10 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
       throw new SparkException("Default partitioner cannot partition array keys.")
     }
     val cg = new CoGroupedRDD[K](Seq(self, other1, other2), partitioner)
-    cg.mapValues { case Seq(vs, w1s, w2s) =>
-      (vs.asInstanceOf[Seq[V]],
-        w1s.asInstanceOf[Seq[W1]],
-        w2s.asInstanceOf[Seq[W2]])
+    cg.mapValues { case Array(vs, w1s, w2s) =>
+      (vs.asInstanceOf[Iterable[V]],
+        w1s.asInstanceOf[Iterable[W1]],
+        w2s.asInstanceOf[Iterable[W2]])
     }
   }
 
diff --git a/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala b/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala
index c3a3e90a3490..fa79b2575915 100644
--- a/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala
+++ b/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala
@@ -31,6 +31,7 @@ import org.apache.spark.scheduler.MapStatus
 import org.apache.spark.storage._
 import org.apache.spark.storage.{GetBlock, GotBlock, PutBlock}
 import org.apache.spark.util.BoundedPriorityQueue
+import org.apache.spark.util.collection.CompactBuffer
 
 import scala.reflect.ClassTag
 
@@ -185,6 +186,7 @@ private[serializer] object KryoSerializer {
     classOf[GotBlock],
     classOf[GetBlock],
     classOf[MapStatus],
+    classOf[CompactBuffer[_]],
     classOf[BlockManagerId],
     classOf[Array[Byte]],
     classOf[BoundedPriorityQueue[_]],
diff --git a/core/src/main/scala/org/apache/spark/util/collection/CompactBuffer.scala b/core/src/main/scala/org/apache/spark/util/collection/CompactBuffer.scala
new file mode 100644
index 000000000000..d44e15e3c97e
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/util/collection/CompactBuffer.scala
@@ -0,0 +1,159 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.util.collection
+
+/**
+ * An append-only buffer similar to ArrayBuffer, but more memory-efficient for small buffers.
+ * ArrayBuffer always allocates an Object array to store the data, with 16 entries by default,
+ * so it has about 80-100 bytes of overhead. In contrast, CompactBuffer can keep up to two
+ * elements in fields of the main object, and only allocates an Array[AnyRef] if there are more
+ * entries than that. This makes it more efficient for operations like groupBy where we expect
+ * some keys to have very few elements.
+ */
+private[spark] class CompactBuffer[T] extends Seq[T] with Serializable {
+  // First two elements
+  private var element0: T = _
+  private var element1: T = _
+
+  // Number of elements, including our two in the main object
+  private var curSize = 0
+
+  // Array for extra elements
+  private var otherElements: Array[AnyRef] = null
+
+  def apply(position: Int): T = {
+    if (position < 0 || position >= curSize) {
+      throw new IndexOutOfBoundsException
+    }
+    if (position == 0) {
+      element0
+    } else if (position == 1) {
+      element1
+    } else {
+      otherElements(position - 2).asInstanceOf[T]
+    }
+  }
+
+  private def update(position: Int, value: T): Unit = {
+    if (position < 0 || position >= curSize) {
+      throw new IndexOutOfBoundsException
+    }
+    if (position == 0) {
+      element0 = value
+    } else if (position == 1) {
+      element1 = value
+    } else {
+      otherElements(position - 2) = value.asInstanceOf[AnyRef]
+    }
+  }
+
+  def += (value: T): CompactBuffer[T] = {
+    val newIndex = curSize
+    if (newIndex == 0) {
+      element0 = value
+      curSize = 1
+    } else if (newIndex == 1) {
+      element1 = value
+      curSize = 2
+    } else {
+      growToSize(curSize + 1)
+      otherElements(newIndex - 2) = value.asInstanceOf[AnyRef]
+    }
+    this
+  }
+
+  def ++= (values: TraversableOnce[T]): CompactBuffer[T] = {
+    values match {
+      // Optimize merging of CompactBuffers, used in cogroup and groupByKey
+      case compactBuf: CompactBuffer[T] =>
+        val oldSize = curSize
+        // Copy the other buffer's size and elements to local variables in case it is equal to us
+        val itsSize = compactBuf.curSize
+        val itsElements = compactBuf.otherElements
+        growToSize(curSize + itsSize)
+        if (itsSize == 1) {
+          this(oldSize) = compactBuf.element0
+        } else if (itsSize == 2) {
+          this(oldSize) = compactBuf.element0
+          this(oldSize + 1) = compactBuf.element1
+        } else if (itsSize > 2) {
+          this(oldSize) = compactBuf.element0
+          this(oldSize + 1) = compactBuf.element1
+          // At this point our size is also above 2, so just copy its array directly into ours.
+          // Note that since we added two elements above, the index in this.otherElements that we
+          // should copy to is oldSize.
+          System.arraycopy(itsElements, 0, otherElements, oldSize, itsSize - 2)
+        }
+
+      case _ =>
+        values.foreach(e => this += e)
+    }
+    this
+  }
+
+  override def length: Int = curSize
+
+  override def size: Int = curSize
+
+  override def iterator: Iterator[T] = new Iterator[T] {
+    private var pos = 0
+    override def hasNext: Boolean = pos < curSize
+    override def next(): T = {
+      if (!hasNext) {
+        throw new NoSuchElementException
+      }
+      pos += 1
+      apply(pos - 1)
+    }
+  }
+
+  /** Increase our size to newSize and grow the backing array if needed. */
+  private def growToSize(newSize: Int): Unit = {
+    if (newSize < 0) {
+      throw new UnsupportedOperationException("Can't grow buffer past Int.MaxValue elements")
+    }
+    val capacity = if (otherElements != null) otherElements.length + 2 else 2
+    if (newSize > capacity) {
+      var newArrayLen = 8
+      while (newSize - 2 > newArrayLen) {
+        newArrayLen *= 2
+        if (newArrayLen == Int.MinValue) {
+          // Prevent overflow if we double from 2^30 to 2^31, which will become Int.MinValue.
+          // Note that we set the new array length to Int.MaxValue - 2 so that our capacity
+          // calculation above still gives a positive integer.
+          newArrayLen = Int.MaxValue - 2
+        }
+      }
+      val newArray = new Array[AnyRef](newArrayLen)
+      if (otherElements != null) {
+        System.arraycopy(otherElements, 0, newArray, 0, otherElements.length)
+      }
+      otherElements = newArray
+    }
+    curSize = newSize
+  }
+}
+
+private[spark] object CompactBuffer {
+  def apply[T](): CompactBuffer[T] = new CompactBuffer[T]
+
+  def apply[T](value: T): CompactBuffer[T] = {
+    val buf = new CompactBuffer[T]
+    buf += value
+  }
+}
diff --git a/core/src/test/scala/org/apache/spark/CheckpointSuite.scala b/core/src/test/scala/org/apache/spark/CheckpointSuite.scala
index fc00458083a3..d1cb2d9d3a53 100644
--- a/core/src/test/scala/org/apache/spark/CheckpointSuite.scala
+++ b/core/src/test/scala/org/apache/spark/CheckpointSuite.scala
@@ -156,15 +156,20 @@ class CheckpointSuite extends FunSuite with LocalSparkContext with Logging {
 
   test("CoGroupedRDD") {
     val longLineageRDD1 = generateFatPairRDD()
+
+    // Collect the RDD as sequences instead of arrays to enable equality tests in testRDD
+    val seqCollectFunc = (rdd: RDD[(Int, Array[Iterable[Int]])]) =>
+      rdd.map{case (p, a) => (p, a.toSeq)}.collect(): Any
+
     testRDD(rdd => {
       CheckpointSuite.cogroup(longLineageRDD1, rdd.map(x => (x % 2, 1)), partitioner)
-    })
+    }, seqCollectFunc)
 
     val longLineageRDD2 = generateFatPairRDD()
     testRDDPartitions(rdd => {
       CheckpointSuite.cogroup(
         longLineageRDD2, sc.makeRDD(1 to 2, 2).map(x => (x % 2, 1)), partitioner)
-    })
+    }, seqCollectFunc)
   }
 
   test("ZippedPartitionsRDD") {
@@ -235,12 +240,19 @@ class CheckpointSuite extends FunSuite with LocalSparkContext with Logging {
     assert(rdd.partitions.size === 0)
   }
 
+  def defaultCollectFunc[T](rdd: RDD[T]): Any = rdd.collect()
+
   /**
    * Test checkpointing of the RDD generated by the given operation. It tests whether the
    * serialized size of the RDD is reduce after checkpointing or not. This function should be called
    * on all RDDs that have a parent RDD (i.e., do not call on ParallelCollection, BlockRDD, etc.).
+   *
+   * @param op an operation to run on the RDD
+   * @param collectFunc a function for collecting the values in the RDD, in case there are
+   *   non-comparable types like arrays that we want to convert to something that supports ==
    */
-  def testRDD[U: ClassTag](op: (RDD[Int]) => RDD[U]) {
+  def testRDD[U: ClassTag](op: (RDD[Int]) => RDD[U],
+      collectFunc: RDD[U] => Any = defaultCollectFunc[U] _) {
     // Generate the final RDD using given RDD operation
     val baseRDD = generateFatRDD()
     val operatedRDD = op(baseRDD)
@@ -258,13 +270,13 @@ class CheckpointSuite extends FunSuite with LocalSparkContext with Logging {
     logInfo("RDD after checkpoint: " + operatedRDD + "\n" + operatedRDD.toDebugString)
     val (rddSizeBeforeCheckpoint, partitionSizeBeforeCheckpoint) = getSerializedSizes(operatedRDD)
     operatedRDD.checkpoint()
-    val result = operatedRDD.collect()
+    val result = collectFunc(operatedRDD)
     operatedRDD.collect() // force re-initialization of post-checkpoint lazy variables
     val (rddSizeAfterCheckpoint, partitionSizeAfterCheckpoint) = getSerializedSizes(operatedRDD)
     logInfo("RDD after checkpoint: " + operatedRDD + "\n" + operatedRDD.toDebugString)
 
     // Test whether the checkpoint file has been created
-    assert(sc.checkpointFile[U](operatedRDD.getCheckpointFile.get).collect() === result)
+    assert(collectFunc(sc.checkpointFile[U](operatedRDD.getCheckpointFile.get)) === result)
 
     // Test whether dependencies have been changed from its earlier parent RDD
     assert(operatedRDD.dependencies.head.rdd != parentRDD)
@@ -279,7 +291,7 @@ class CheckpointSuite extends FunSuite with LocalSparkContext with Logging {
     assert(operatedRDD.partitions.length === numPartitions)
 
     // Test whether the data in the checkpointed RDD is same as original
-    assert(operatedRDD.collect() === result)
+    assert(collectFunc(operatedRDD) === result)
 
     // Test whether serialized size of the RDD has reduced.
     logInfo("Size of " + rddType +
@@ -289,7 +301,6 @@ class CheckpointSuite extends FunSuite with LocalSparkContext with Logging {
       "Size of " + rddType + " did not reduce after checkpointing " +
         " [" + rddSizeBeforeCheckpoint + " --> " + rddSizeAfterCheckpoint + "]"
     )
-
   }
 
   /**
@@ -300,8 +311,12 @@ class CheckpointSuite extends FunSuite with LocalSparkContext with Logging {
    * This function should be called only those RDD whose partitions refer to parent RDD's
    * partitions (i.e., do not call it on simple RDD like MappedRDD).
    *
+   * @param op an operation to run on the RDD
+   * @param collectFunc a function for collecting the values in the RDD, in case there are
+   *   non-comparable types like arrays that we want to convert to something that supports ==
    */
-  def testRDDPartitions[U: ClassTag](op: (RDD[Int]) => RDD[U]) {
+  def testRDDPartitions[U: ClassTag](op: (RDD[Int]) => RDD[U],
+       collectFunc: RDD[U] => Any = defaultCollectFunc[U] _) {
     // Generate the final RDD using given RDD operation
     val baseRDD = generateFatRDD()
     val operatedRDD = op(baseRDD)
@@ -316,13 +331,13 @@ class CheckpointSuite extends FunSuite with LocalSparkContext with Logging {
     logInfo("RDD after checkpoint: " + operatedRDD + "\n" + operatedRDD.toDebugString)
     val (rddSizeBeforeCheckpoint, partitionSizeBeforeCheckpoint) = getSerializedSizes(operatedRDD)
     parentRDDs.foreach(_.checkpoint())  // checkpoint the parent RDD, not the generated one
-    val result = operatedRDD.collect()  // force checkpointing
+    val result = collectFunc(operatedRDD)  // force checkpointing
     operatedRDD.collect() // force re-initialization of post-checkpoint lazy variables
     val (rddSizeAfterCheckpoint, partitionSizeAfterCheckpoint) = getSerializedSizes(operatedRDD)
     logInfo("RDD after checkpoint: " + operatedRDD + "\n" + operatedRDD.toDebugString)
 
     // Test whether the data in the checkpointed RDD is same as original
-    assert(operatedRDD.collect() === result)
+    assert(collectFunc(operatedRDD) === result)
 
     // Test whether serialized size of the partitions has reduced
     logInfo("Size of partitions of " + rddType +
@@ -436,7 +451,7 @@ object CheckpointSuite {
     new CoGroupedRDD[K](
       Seq(first.asInstanceOf[RDD[(K, _)]], second.asInstanceOf[RDD[(K, _)]]),
       part
-    ).asInstanceOf[RDD[(K, Seq[Seq[V]])]]
+    ).asInstanceOf[RDD[(K, Array[Iterable[V]])]]
   }
 
 }
diff --git a/core/src/test/scala/org/apache/spark/ShuffleSuite.scala b/core/src/test/scala/org/apache/spark/ShuffleSuite.scala
index 237e644b48e4..eae67c7747e8 100644
--- a/core/src/test/scala/org/apache/spark/ShuffleSuite.scala
+++ b/core/src/test/scala/org/apache/spark/ShuffleSuite.scala
@@ -176,7 +176,9 @@ class ShuffleSuite extends FunSuite with Matchers with LocalSparkContext {
     val data2 = Seq(p(1, "11"), p(1, "12"), p(2, "22"), p(3, "3"))
     val pairs1: RDD[MutablePair[Int, Int]] = sc.parallelize(data1, 2)
     val pairs2: RDD[MutablePair[Int, String]] = sc.parallelize(data2, 2)
-    val results = new CoGroupedRDD[Int](Seq(pairs1, pairs2), new HashPartitioner(2)).collectAsMap()
+    val results = new CoGroupedRDD[Int](Seq(pairs1, pairs2), new HashPartitioner(2))
+      .map(p => (p._1, p._2.map(_.toArray)))
+      .collectAsMap()
 
     assert(results(1)(0).length === 3)
     assert(results(1)(0).contains(1))
diff --git a/core/src/test/scala/org/apache/spark/util/collection/CompactBufferSuite.scala b/core/src/test/scala/org/apache/spark/util/collection/CompactBufferSuite.scala
new file mode 100644
index 000000000000..6c956d93dc80
--- /dev/null
+++ b/core/src/test/scala/org/apache/spark/util/collection/CompactBufferSuite.scala
@@ -0,0 +1,105 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.util.collection
+
+import org.scalatest.FunSuite
+
+class CompactBufferSuite extends FunSuite {
+  test("empty buffer") {
+    val b = new CompactBuffer[Int]
+    assert(b.size === 0)
+    assert(b.iterator.toList === Nil)
+    assert(b.size === 0)
+    assert(b.iterator.toList === Nil)
+    intercept[IndexOutOfBoundsException] { b(0) }
+    intercept[IndexOutOfBoundsException] { b(1) }
+    intercept[IndexOutOfBoundsException] { b(2) }
+    intercept[IndexOutOfBoundsException] { b(-1) }
+  }
+
+  test("basic inserts") {
+    val b = new CompactBuffer[Int]
+    assert(b.size === 0)
+    assert(b.iterator.toList === Nil)
+    for (i <- 0 until 1000) {
+      b += i
+      assert(b.size === i + 1)
+      assert(b(i) === i)
+    }
+    assert(b.iterator.toList === (0 until 1000).toList)
+    assert(b.iterator.toList === (0 until 1000).toList)
+    assert(b.size === 1000)
+  }
+
+  test("adding sequences") {
+    val b = new CompactBuffer[Int]
+    assert(b.size === 0)
+    assert(b.iterator.toList === Nil)
+
+    // Add some simple lists and iterators
+    b ++= List(0)
+    assert(b.size === 1)
+    assert(b.iterator.toList === List(0))
+    b ++= Iterator(1)
+    assert(b.size === 2)
+    assert(b.iterator.toList === List(0, 1))
+    b ++= List(2)
+    assert(b.size === 3)
+    assert(b.iterator.toList === List(0, 1, 2))
+    b ++= Iterator(3, 4, 5, 6, 7, 8, 9)
+    assert(b.size === 10)
+    assert(b.iterator.toList === (0 until 10).toList)
+
+    // Add CompactBuffers
+    val b2 = new CompactBuffer[Int]
+    b2 ++= 0 until 10
+    b ++= b2
+    assert(b.iterator.toList === (1 to 2).flatMap(i => 0 until 10).toList)
+    b ++= b2
+    assert(b.iterator.toList === (1 to 3).flatMap(i => 0 until 10).toList)
+    b ++= b2
+    assert(b.iterator.toList === (1 to 4).flatMap(i => 0 until 10).toList)
+
+    // Add some small CompactBuffers as well
+    val b3 = new CompactBuffer[Int]
+    b ++= b3
+    assert(b.iterator.toList === (1 to 4).flatMap(i => 0 until 10).toList)
+    b3 += 0
+    b ++= b3
+    assert(b.iterator.toList === (1 to 4).flatMap(i => 0 until 10).toList ++ List(0))
+    b3 += 1
+    b ++= b3
+    assert(b.iterator.toList === (1 to 4).flatMap(i => 0 until 10).toList ++ List(0, 0, 1))
+    b3 += 2
+    b ++= b3
+    assert(b.iterator.toList === (1 to 4).flatMap(i => 0 until 10).toList ++ List(0, 0, 1, 0, 1, 2))
+  }
+
+  test("adding the same buffer to itself") {
+    val b = new CompactBuffer[Int]
+    assert(b.size === 0)
+    assert(b.iterator.toList === Nil)
+    b += 1
+    assert(b.toList === List(1))
+    for (j <- 1 until 8) {
+      b ++= b
+      assert(b.size === (1 << j))
+      assert(b.iterator.toList === (1 to (1 << j)).map(i => 1).toList)
+    }
+  }
+}
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/stat/correlation/SpearmanCorrelation.scala b/mllib/src/main/scala/org/apache/spark/mllib/stat/correlation/SpearmanCorrelation.scala
index 88de2c82479b..1f7de630e778 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/stat/correlation/SpearmanCorrelation.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/stat/correlation/SpearmanCorrelation.scala
@@ -122,6 +122,10 @@ private[stat] object SpearmanCorrelation extends Correlation with Logging {
   private def makeRankMatrix(ranks: Array[RDD[(Long, Double)]], input: RDD[Vector]): RDD[Vector] = {
     val partitioner = new HashPartitioner(input.partitions.size)
     val cogrouped = new CoGroupedRDD[Long](ranks, partitioner)
-    cogrouped.map { case (_, values: Seq[Seq[Double]]) => new DenseVector(values.flatten.toArray) }
+    cogrouped.map {
+      case (_, values: Array[Iterable[_]]) =>
+        val doubles = values.asInstanceOf[Array[Iterable[Double]]]
+        new DenseVector(doubles.flatten.toArray)
+    }
   }
 }
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReducedWindowedDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReducedWindowedDStream.scala
index 40da31318942..1a47089e513c 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReducedWindowedDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReducedWindowedDStream.scala
@@ -133,17 +133,17 @@ class ReducedWindowedDStream[K: ClassTag, V: ClassTag](
     val numOldValues = oldRDDs.size
     val numNewValues = newRDDs.size
 
-    val mergeValues = (seqOfValues: Seq[Seq[V]]) => {
-      if (seqOfValues.size != 1 + numOldValues + numNewValues) {
+    val mergeValues = (arrayOfValues: Array[Iterable[V]]) => {
+      if (arrayOfValues.size != 1 + numOldValues + numNewValues) {
         throw new Exception("Unexpected number of sequences of reduced values")
       }
       // Getting reduced values "old time steps" that will be removed from current window
-      val oldValues = (1 to numOldValues).map(i => seqOfValues(i)).filter(!_.isEmpty).map(_.head)
+      val oldValues = (1 to numOldValues).map(i => arrayOfValues(i)).filter(!_.isEmpty).map(_.head)
       // Getting reduced values "new time steps"
       val newValues =
-        (1 to numNewValues).map(i => seqOfValues(numOldValues + i)).filter(!_.isEmpty).map(_.head)
+        (1 to numNewValues).map(i => arrayOfValues(numOldValues + i)).filter(!_.isEmpty).map(_.head)
 
-      if (seqOfValues(0).isEmpty) {
+      if (arrayOfValues(0).isEmpty) {
         // If previous window's reduce value does not exist, then at least new values should exist
         if (newValues.isEmpty) {
           throw new Exception("Neither previous window has value for key, nor new values found. " +
@@ -153,7 +153,7 @@ class ReducedWindowedDStream[K: ClassTag, V: ClassTag](
         newValues.reduce(reduceF) // return
       } else {
         // Get the previous window's reduced value
-        var tempValue = seqOfValues(0).head
+        var tempValue = arrayOfValues(0).head
         // If old values exists, then inverse reduce then from previous value
         if (!oldValues.isEmpty) {
           tempValue = invReduceF(tempValue, oldValues.reduce(reduceF))
@@ -166,7 +166,8 @@ class ReducedWindowedDStream[K: ClassTag, V: ClassTag](
       }
     }
 
-    val mergedValuesRDD = cogroupedRDD.asInstanceOf[RDD[(K,Seq[Seq[V]])]].mapValues(mergeValues)
+    val mergedValuesRDD = cogroupedRDD.asInstanceOf[RDD[(K, Array[Iterable[V]])]]
+      .mapValues(mergeValues)
 
     if (filterFunc.isDefined) {
       Some(mergedValuesRDD.filter(filterFunc.get))

From eb82abd8e3d25c912fa75201cf4f429aab8d73c7 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Fri, 25 Jul 2014 01:10:05 -0700
Subject: [PATCH 756/810] [SPARK-2529] Clean closures in foreach and
 foreachPartition.

Author: Reynold Xin <rxin@apache.org>

Closes #1583 from rxin/closureClean and squashes the following commits:

8982fe6 [Reynold Xin] [SPARK-2529] Clean closures in foreach and foreachPartition.
---
 core/src/main/scala/org/apache/spark/rdd/RDD.scala | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/rdd/RDD.scala b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
index edbf7eace943..b1c965a79047 100644
--- a/core/src/main/scala/org/apache/spark/rdd/RDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
@@ -754,14 +754,16 @@ abstract class RDD[T: ClassTag](
    * Applies a function f to all elements of this RDD.
    */
   def foreach(f: T => Unit) {
-    sc.runJob(this, (iter: Iterator[T]) => iter.foreach(f))
+    val cleanF = sc.clean(f)
+    sc.runJob(this, (iter: Iterator[T]) => iter.foreach(cleanF))
   }
 
   /**
    * Applies a function f to each partition of this RDD.
    */
   def foreachPartition(f: Iterator[T] => Unit) {
-    sc.runJob(this, (iter: Iterator[T]) => f(iter))
+    val cleanF = sc.clean(f)
+    sc.runJob(this, (iter: Iterator[T]) => cleanF(iter))
   }
 
   /**

From 184aa1c6c0ddf26b703bcabf55397ade17497465 Mon Sep 17 00:00:00 2001
From: Cheng Hao <hao.cheng@intel.com>
Date: Fri, 25 Jul 2014 01:30:22 -0700
Subject: [PATCH 757/810] [SPARK-2665] [SQL] Add EqualNS & Unit Tests

Hive Supports the operator "<=>", which returns same result with EQUAL(=) operator for non-null operands, but returns TRUE if both are NULL, FALSE if one of the them is NULL.

Author: Cheng Hao <hao.cheng@intel.com>

Closes #1570 from chenghao-intel/equalns and squashes the following commits:

8d6c789 [Cheng Hao] Remove the test case orc_predicate_pushdown
5b2ca88 [Cheng Hao] Add cases into whitelist
8e66cdd [Cheng Hao] Rename the EqualNSTo ==> EqualNullSafe
7af4b0b [Cheng Hao] Add EqualNS & Unit Tests
---
 .../catalyst/analysis/HiveTypeCoercion.scala  |  2 +
 .../spark/sql/catalyst/dsl/package.scala      |  1 +
 .../sql/catalyst/expressions/predicates.scala | 16 +++++++
 .../sql/catalyst/optimizer/Optimizer.scala    |  2 +
 .../ExpressionEvaluationSuite.scala           | 10 ++++-
 .../execution/HiveCompatibilitySuite.scala    |  7 +++-
 .../apache/spark/sql/hive/HiveContext.scala   |  3 +-
 .../org/apache/spark/sql/hive/HiveQl.scala    |  1 +
 ...ullsafe-0-869726b703f160eabdb7763700b53e60 |  1 +
 ...ullsafe-1-5644ab44e5ba9f2941216b8d5dc33a99 |  0
 ...llsafe-10-b6de4e85dcc1d1949c7431d39fa1b919 |  2 +
 ...llsafe-11-3aa243002a5363b84556736ef71613b1 |  0
 ...llsafe-12-3cc55b14e8256d2c51361b61986c291e |  4 ++
 ...llsafe-13-69d94e229191e7b9b1a3e7eae46eb993 | 12 ++++++
 ...llsafe-14-cf9ff6ee72a701a8e2f3e7fb0667903c | 12 ++++++
 ...llsafe-15-507d0fa6d7ce39e2d9921555cea6f8da | 13 ++++++
 ...llsafe-16-1c714fc339304de4db630530e5d1ce97 | 11 +++++
 ...llsafe-17-8a4b0dc781a28ad11a0db9805fe03aa8 | 11 +++++
 ...llsafe-18-10b2051e65cac50ee1ea1c138ec192c8 |  0
 ...llsafe-19-23ab7ac8229a53d391195be7ca092429 |  0
 ...nullsafe-2-793e288c9e0971f0bf3f37493f76dc7 |  0
 ...llsafe-20-d6fc260320c577eec9a5db0d4135d224 |  0
 ...llsafe-21-a60dae725ffc543f805242611d99de4e |  0
 ...llsafe-22-24c80d0f9e3d72c48d947770fa184985 |  0
 ...llsafe-23-3fe6ae20cab3417759dcc654a3a26746 |  0
 ...llsafe-24-2db30531137611e06fdba478ca7a8412 |  1 +
 ...llsafe-25-e58b2754e8d9c56a473557a549d0d2b9 |  1 +
 ...llsafe-26-64cabe5164130a94f387288f37b62d71 |  1 +
 ...llsafe-27-e8ed4a1b574a6ca70cbfb3f7b9980aa6 | 42 +++++++++++++++++++
 ...llsafe-28-5a0c946cd7033857ca99e5fb800f8525 | 14 +++++++
 ...llsafe-29-514043c2ddaf6ea8f16a764adc92d1cf | 42 +++++++++++++++++++
 ...ullsafe-3-ae378fc0f875a21884e58fa35a6d52cd |  0
 ...llsafe-30-fcbf92cb1b85ab01102fbbc6caba9a88 | 42 +++++++++++++++++++
 ...llsafe-31-1cb03e1106f79d14f22bc89d386cedcf | 42 +++++++++++++++++++
 ...llsafe-32-6a0bf6127d4b042e67ae8ee15125fb87 | 40 ++++++++++++++++++
 ...llsafe-33-63157d43422fcedadba408537ccecd5c | 40 ++++++++++++++++++
 ...llsafe-34-9265f806b71c03061f93f9fbc88aa223 | 42 +++++++++++++++++++
 ...llsafe-35-95815bafb81cccb8129c20d399a446fc | 42 +++++++++++++++++++
 ...llsafe-36-c4762c60cc93236b7647ebd32a40ce57 | 42 +++++++++++++++++++
 ...llsafe-37-a87893adfc73c9cc63ceab200bb56245 | 42 +++++++++++++++++++
 ...llsafe-38-e3dfe0044b44c8a49414479521acf762 | 42 +++++++++++++++++++
 ...llsafe-39-9a7e1f373b9c02e632d6c7c550b908ec | 42 +++++++++++++++++++
 ...ullsafe-4-644c616d87ae426eb2f8c71638045185 | 11 +++++
 ...llsafe-40-3c868718e4c120cb9a72ab7318c75be3 |  0
 ...llsafe-41-1f7d8737c3e2d74d5ad865535d729811 |  9 ++++
 ...ullsafe-5-1e393de94850e92b3b00536aacc9371f |  0
 ...ullsafe-6-d66451815212e7d17744184e74c6b0a0 |  2 +
 ...ullsafe-7-a3ad3cc301d9884898d3e6ab6c792d4c |  0
 ...ullsafe-8-cc7527bcf746ab7e2cd9f28db0ead0ac | 29 +++++++++++++
 ...ullsafe-9-88f6f40959b0d2faabd9d4b3cd853809 |  0
 ...f_equal-0-36b6cdf7c5f68c91155569b1622f5876 |  1 +
 ...f_equal-1-2422b50b96502dde8b661acdfebd8892 |  2 +
 ...f_equal-2-e0faab0f5e736c24bcc5503aeac55053 |  1 +
 ...f_equal-3-39d8d6f197803de927f0af5409ec2f33 |  2 +
 ...f_equal-4-94ac2476006425e1b3bcddf29ad07b16 |  1 +
 ...f_equal-5-878650cf21e9360a07d204c8ffb0cde7 |  1 +
 ...f_equal-6-1635ef051fecdfc7891d9f5a9a3a545e |  1 +
 ...f_equal-7-78f1b96c199e307714fa1b804e5bae27 |  1 +
 58 files changed, 683 insertions(+), 3 deletions(-)
 create mode 100644 sql/hive/src/test/resources/golden/join_nullsafe-0-869726b703f160eabdb7763700b53e60
 create mode 100644 sql/hive/src/test/resources/golden/join_nullsafe-1-5644ab44e5ba9f2941216b8d5dc33a99
 create mode 100644 sql/hive/src/test/resources/golden/join_nullsafe-10-b6de4e85dcc1d1949c7431d39fa1b919
 create mode 100644 sql/hive/src/test/resources/golden/join_nullsafe-11-3aa243002a5363b84556736ef71613b1
 create mode 100644 sql/hive/src/test/resources/golden/join_nullsafe-12-3cc55b14e8256d2c51361b61986c291e
 create mode 100644 sql/hive/src/test/resources/golden/join_nullsafe-13-69d94e229191e7b9b1a3e7eae46eb993
 create mode 100644 sql/hive/src/test/resources/golden/join_nullsafe-14-cf9ff6ee72a701a8e2f3e7fb0667903c
 create mode 100644 sql/hive/src/test/resources/golden/join_nullsafe-15-507d0fa6d7ce39e2d9921555cea6f8da
 create mode 100644 sql/hive/src/test/resources/golden/join_nullsafe-16-1c714fc339304de4db630530e5d1ce97
 create mode 100644 sql/hive/src/test/resources/golden/join_nullsafe-17-8a4b0dc781a28ad11a0db9805fe03aa8
 create mode 100644 sql/hive/src/test/resources/golden/join_nullsafe-18-10b2051e65cac50ee1ea1c138ec192c8
 create mode 100644 sql/hive/src/test/resources/golden/join_nullsafe-19-23ab7ac8229a53d391195be7ca092429
 create mode 100644 sql/hive/src/test/resources/golden/join_nullsafe-2-793e288c9e0971f0bf3f37493f76dc7
 create mode 100644 sql/hive/src/test/resources/golden/join_nullsafe-20-d6fc260320c577eec9a5db0d4135d224
 create mode 100644 sql/hive/src/test/resources/golden/join_nullsafe-21-a60dae725ffc543f805242611d99de4e
 create mode 100644 sql/hive/src/test/resources/golden/join_nullsafe-22-24c80d0f9e3d72c48d947770fa184985
 create mode 100644 sql/hive/src/test/resources/golden/join_nullsafe-23-3fe6ae20cab3417759dcc654a3a26746
 create mode 100644 sql/hive/src/test/resources/golden/join_nullsafe-24-2db30531137611e06fdba478ca7a8412
 create mode 100644 sql/hive/src/test/resources/golden/join_nullsafe-25-e58b2754e8d9c56a473557a549d0d2b9
 create mode 100644 sql/hive/src/test/resources/golden/join_nullsafe-26-64cabe5164130a94f387288f37b62d71
 create mode 100644 sql/hive/src/test/resources/golden/join_nullsafe-27-e8ed4a1b574a6ca70cbfb3f7b9980aa6
 create mode 100644 sql/hive/src/test/resources/golden/join_nullsafe-28-5a0c946cd7033857ca99e5fb800f8525
 create mode 100644 sql/hive/src/test/resources/golden/join_nullsafe-29-514043c2ddaf6ea8f16a764adc92d1cf
 create mode 100644 sql/hive/src/test/resources/golden/join_nullsafe-3-ae378fc0f875a21884e58fa35a6d52cd
 create mode 100644 sql/hive/src/test/resources/golden/join_nullsafe-30-fcbf92cb1b85ab01102fbbc6caba9a88
 create mode 100644 sql/hive/src/test/resources/golden/join_nullsafe-31-1cb03e1106f79d14f22bc89d386cedcf
 create mode 100644 sql/hive/src/test/resources/golden/join_nullsafe-32-6a0bf6127d4b042e67ae8ee15125fb87
 create mode 100644 sql/hive/src/test/resources/golden/join_nullsafe-33-63157d43422fcedadba408537ccecd5c
 create mode 100644 sql/hive/src/test/resources/golden/join_nullsafe-34-9265f806b71c03061f93f9fbc88aa223
 create mode 100644 sql/hive/src/test/resources/golden/join_nullsafe-35-95815bafb81cccb8129c20d399a446fc
 create mode 100644 sql/hive/src/test/resources/golden/join_nullsafe-36-c4762c60cc93236b7647ebd32a40ce57
 create mode 100644 sql/hive/src/test/resources/golden/join_nullsafe-37-a87893adfc73c9cc63ceab200bb56245
 create mode 100644 sql/hive/src/test/resources/golden/join_nullsafe-38-e3dfe0044b44c8a49414479521acf762
 create mode 100644 sql/hive/src/test/resources/golden/join_nullsafe-39-9a7e1f373b9c02e632d6c7c550b908ec
 create mode 100644 sql/hive/src/test/resources/golden/join_nullsafe-4-644c616d87ae426eb2f8c71638045185
 create mode 100644 sql/hive/src/test/resources/golden/join_nullsafe-40-3c868718e4c120cb9a72ab7318c75be3
 create mode 100644 sql/hive/src/test/resources/golden/join_nullsafe-41-1f7d8737c3e2d74d5ad865535d729811
 create mode 100644 sql/hive/src/test/resources/golden/join_nullsafe-5-1e393de94850e92b3b00536aacc9371f
 create mode 100644 sql/hive/src/test/resources/golden/join_nullsafe-6-d66451815212e7d17744184e74c6b0a0
 create mode 100644 sql/hive/src/test/resources/golden/join_nullsafe-7-a3ad3cc301d9884898d3e6ab6c792d4c
 create mode 100644 sql/hive/src/test/resources/golden/join_nullsafe-8-cc7527bcf746ab7e2cd9f28db0ead0ac
 create mode 100644 sql/hive/src/test/resources/golden/join_nullsafe-9-88f6f40959b0d2faabd9d4b3cd853809
 create mode 100644 sql/hive/src/test/resources/golden/udf_equal-0-36b6cdf7c5f68c91155569b1622f5876
 create mode 100644 sql/hive/src/test/resources/golden/udf_equal-1-2422b50b96502dde8b661acdfebd8892
 create mode 100644 sql/hive/src/test/resources/golden/udf_equal-2-e0faab0f5e736c24bcc5503aeac55053
 create mode 100644 sql/hive/src/test/resources/golden/udf_equal-3-39d8d6f197803de927f0af5409ec2f33
 create mode 100644 sql/hive/src/test/resources/golden/udf_equal-4-94ac2476006425e1b3bcddf29ad07b16
 create mode 100644 sql/hive/src/test/resources/golden/udf_equal-5-878650cf21e9360a07d204c8ffb0cde7
 create mode 100644 sql/hive/src/test/resources/golden/udf_equal-6-1635ef051fecdfc7891d9f5a9a3a545e
 create mode 100644 sql/hive/src/test/resources/golden/udf_equal-7-78f1b96c199e307714fa1b804e5bae27

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
index 9887856b9c1c..67a8ce9b88c3 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
@@ -246,6 +246,8 @@ trait HiveTypeCoercion {
 
       // No need to change other EqualTo operators as that actually makes sense for boolean types.
       case e: EqualTo => e
+      // No need to change the EqualNullSafe operators, too
+      case e: EqualNullSafe => e
       // Otherwise turn them to Byte types so that there exists and ordering.
       case p: BinaryComparison
           if p.left.dataType == BooleanType && p.right.dataType == BooleanType =>
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala
index 15c98efbcabc..5c8c810d9135 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala
@@ -77,6 +77,7 @@ package object dsl {
     def > (other: Expression) = GreaterThan(expr, other)
     def >= (other: Expression) = GreaterThanOrEqual(expr, other)
     def === (other: Expression) = EqualTo(expr, other)
+    def <=> (other: Expression) = EqualNullSafe(expr, other)
     def !== (other: Expression) = Not(EqualTo(expr, other))
 
     def in(list: Expression*) = In(expr, list)
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
index b63406b94a4a..06b94a98d3cd 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
@@ -153,6 +153,22 @@ case class EqualTo(left: Expression, right: Expression) extends BinaryComparison
   }
 }
 
+case class EqualNullSafe(left: Expression, right: Expression) extends BinaryComparison {
+  def symbol = "<=>"
+  override def nullable = false
+  override def eval(input: Row): Any = {
+    val l = left.eval(input)
+    val r = right.eval(input)
+    if (l == null && r == null) {
+      true
+    } else if (l == null || r == null) {
+      false
+    } else {
+      l == r
+    }
+  }
+}
+
 case class LessThan(left: Expression, right: Expression) extends BinaryComparison {
   def symbol = "<"
   override def eval(input: Row): Any = c2(input, left, right, _.lt(_, _))
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
index c65987b7120b..5f86d6047cb9 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
@@ -153,6 +153,8 @@ object NullPropagation extends Rule[LogicalPlan] {
       case e @ GetItem(Literal(null, _), _) => Literal(null, e.dataType)
       case e @ GetItem(_, Literal(null, _)) => Literal(null, e.dataType)
       case e @ GetField(Literal(null, _), _) => Literal(null, e.dataType)
+      case e @ EqualNullSafe(Literal(null, _), r) => IsNull(r)
+      case e @ EqualNullSafe(l, Literal(null, _)) => IsNull(l)
 
       // For Coalesce, remove null literals.
       case e @ Coalesce(children) =>
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala
index c3f5c26fdbe5..58f8c341e667 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala
@@ -451,11 +451,13 @@ class ExpressionEvaluationSuite extends FunSuite {
   }
 
   test("BinaryComparison") {
-    val row = new GenericRow(Array[Any](1, 2, 3, null))
+    val row = new GenericRow(Array[Any](1, 2, 3, null, 3, null))
     val c1 = 'a.int.at(0)
     val c2 = 'a.int.at(1)
     val c3 = 'a.int.at(2)
     val c4 = 'a.int.at(3)
+    val c5 = 'a.int.at(4)
+    val c6 = 'a.int.at(5)
 
     checkEvaluation(LessThan(c1, c4), null, row)
     checkEvaluation(LessThan(c1, c2), true, row)
@@ -469,6 +471,12 @@ class ExpressionEvaluationSuite extends FunSuite {
     checkEvaluation(c1 >= c2, false, row)
     checkEvaluation(c1 === c2, false, row)
     checkEvaluation(c1 !== c2, true, row)
+    checkEvaluation(c4 <=> c1, false, row)
+    checkEvaluation(c1 <=> c4, false, row)
+    checkEvaluation(c4 <=> c6, true, row)
+    checkEvaluation(c3 <=> c5, true, row)
+    checkEvaluation(Literal(true) <=> Literal(null, BooleanType), false, row)
+    checkEvaluation(Literal(null, BooleanType) <=> Literal(true), false, row)
   }
 
   test("StringComparison") {
diff --git a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
index 8b451973a47a..c69e93ba2b9b 100644
--- a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
+++ b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
@@ -196,7 +196,10 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
 
     // Hive returns the results of describe as plain text. Comments with multiple lines
     // introduce extra lines in the Hive results, which make the result comparison fail.
-    "describe_comment_indent"
+    "describe_comment_indent",
+
+    // Limit clause without a ordering, which causes failure.
+    "orc_predicate_pushdown"
   )
 
   /**
@@ -503,6 +506,7 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     "join_hive_626",
     "join_map_ppr",
     "join_nulls",
+    "join_nullsafe",
     "join_rc",
     "join_reorder2",
     "join_reorder3",
@@ -734,6 +738,7 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     "udf_double",
     "udf_E",
     "udf_elt",
+    "udf_equal",
     "udf_exp",
     "udf_field",
     "udf_find_in_set",
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
index 334462357eb8..201c85f3d501 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
@@ -253,7 +253,7 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) {
 
     protected val primitiveTypes =
       Seq(StringType, IntegerType, LongType, DoubleType, FloatType, BooleanType, ByteType,
-        ShortType, DecimalType, TimestampType)
+        ShortType, DecimalType, TimestampType, BinaryType)
 
     protected def toHiveString(a: (Any, DataType)): String = a match {
       case (struct: Row, StructType(fields)) =>
@@ -269,6 +269,7 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) {
         }.toSeq.sorted.mkString("{", ",", "}")
       case (null, _) => "NULL"
       case (t: Timestamp, TimestampType) => new TimestampWritable(t).toString
+      case (bin: Array[Byte], BinaryType) => new String(bin, "UTF-8")
       case (other, tpe) if primitiveTypes contains tpe => other.toString
     }
 
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
index c4ca9f362a04..4395874526d5 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
@@ -931,6 +931,7 @@ private[hive] object HiveQl {
     /* Comparisons */
     case Token("=", left :: right:: Nil) => EqualTo(nodeToExpr(left), nodeToExpr(right))
     case Token("==", left :: right:: Nil) => EqualTo(nodeToExpr(left), nodeToExpr(right))
+    case Token("<=>", left :: right:: Nil) => EqualNullSafe(nodeToExpr(left), nodeToExpr(right))
     case Token("!=", left :: right:: Nil) => Not(EqualTo(nodeToExpr(left), nodeToExpr(right)))
     case Token("<>", left :: right:: Nil) => Not(EqualTo(nodeToExpr(left), nodeToExpr(right)))
     case Token(">", left :: right:: Nil) => GreaterThan(nodeToExpr(left), nodeToExpr(right))
diff --git a/sql/hive/src/test/resources/golden/join_nullsafe-0-869726b703f160eabdb7763700b53e60 b/sql/hive/src/test/resources/golden/join_nullsafe-0-869726b703f160eabdb7763700b53e60
new file mode 100644
index 000000000000..573541ac9702
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/join_nullsafe-0-869726b703f160eabdb7763700b53e60
@@ -0,0 +1 @@
+0
diff --git a/sql/hive/src/test/resources/golden/join_nullsafe-1-5644ab44e5ba9f2941216b8d5dc33a99 b/sql/hive/src/test/resources/golden/join_nullsafe-1-5644ab44e5ba9f2941216b8d5dc33a99
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/join_nullsafe-10-b6de4e85dcc1d1949c7431d39fa1b919 b/sql/hive/src/test/resources/golden/join_nullsafe-10-b6de4e85dcc1d1949c7431d39fa1b919
new file mode 100644
index 000000000000..31c409082cc2
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/join_nullsafe-10-b6de4e85dcc1d1949c7431d39fa1b919
@@ -0,0 +1,2 @@
+NULL	10	10	NULL	NULL	10
+100	100	100	100	100	100
diff --git a/sql/hive/src/test/resources/golden/join_nullsafe-11-3aa243002a5363b84556736ef71613b1 b/sql/hive/src/test/resources/golden/join_nullsafe-11-3aa243002a5363b84556736ef71613b1
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/join_nullsafe-12-3cc55b14e8256d2c51361b61986c291e b/sql/hive/src/test/resources/golden/join_nullsafe-12-3cc55b14e8256d2c51361b61986c291e
new file mode 100644
index 000000000000..9b77d13cbaab
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/join_nullsafe-12-3cc55b14e8256d2c51361b61986c291e
@@ -0,0 +1,4 @@
+NULL	NULL	NULL	NULL	NULL	NULL
+NULL	10	10	NULL	NULL	10
+10	NULL	NULL	10	10	NULL
+100	100	100	100	100	100
diff --git a/sql/hive/src/test/resources/golden/join_nullsafe-13-69d94e229191e7b9b1a3e7eae46eb993 b/sql/hive/src/test/resources/golden/join_nullsafe-13-69d94e229191e7b9b1a3e7eae46eb993
new file mode 100644
index 000000000000..47c0709d3985
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/join_nullsafe-13-69d94e229191e7b9b1a3e7eae46eb993
@@ -0,0 +1,12 @@
+NULL	NULL	NULL	NULL
+NULL	NULL	10	NULL
+NULL	NULL	48	NULL
+NULL	10	NULL	NULL
+NULL	10	10	NULL
+NULL	10	48	NULL
+NULL	35	NULL	NULL
+NULL	35	10	NULL
+NULL	35	48	NULL
+10	NULL	NULL	10
+48	NULL	NULL	NULL
+100	100	100	100
diff --git a/sql/hive/src/test/resources/golden/join_nullsafe-14-cf9ff6ee72a701a8e2f3e7fb0667903c b/sql/hive/src/test/resources/golden/join_nullsafe-14-cf9ff6ee72a701a8e2f3e7fb0667903c
new file mode 100644
index 000000000000..36ba48516b65
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/join_nullsafe-14-cf9ff6ee72a701a8e2f3e7fb0667903c
@@ -0,0 +1,12 @@
+NULL	NULL	NULL	NULL
+NULL	NULL	NULL	35
+NULL	NULL	10	NULL
+NULL	NULL	48	NULL
+NULL	10	NULL	NULL
+NULL	10	10	NULL
+NULL	10	48	NULL
+NULL	35	NULL	NULL
+NULL	35	10	NULL
+NULL	35	48	NULL
+10	NULL	NULL	10
+100	100	100	100
diff --git a/sql/hive/src/test/resources/golden/join_nullsafe-15-507d0fa6d7ce39e2d9921555cea6f8da b/sql/hive/src/test/resources/golden/join_nullsafe-15-507d0fa6d7ce39e2d9921555cea6f8da
new file mode 100644
index 000000000000..fc1fd198cf8b
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/join_nullsafe-15-507d0fa6d7ce39e2d9921555cea6f8da
@@ -0,0 +1,13 @@
+NULL	NULL	NULL	NULL
+NULL	NULL	NULL	35
+NULL	NULL	10	NULL
+NULL	NULL	48	NULL
+NULL	10	NULL	NULL
+NULL	10	10	NULL
+NULL	10	48	NULL
+NULL	35	NULL	NULL
+NULL	35	10	NULL
+NULL	35	48	NULL
+10	NULL	NULL	10
+48	NULL	NULL	NULL
+100	100	100	100
diff --git a/sql/hive/src/test/resources/golden/join_nullsafe-16-1c714fc339304de4db630530e5d1ce97 b/sql/hive/src/test/resources/golden/join_nullsafe-16-1c714fc339304de4db630530e5d1ce97
new file mode 100644
index 000000000000..1cc70524f9d6
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/join_nullsafe-16-1c714fc339304de4db630530e5d1ce97
@@ -0,0 +1,11 @@
+NULL	NULL	NULL	NULL
+NULL	NULL	10	NULL
+NULL	NULL	48	NULL
+NULL	10	NULL	NULL
+NULL	10	10	NULL
+NULL	10	48	NULL
+NULL	35	NULL	NULL
+NULL	35	10	NULL
+NULL	35	48	NULL
+10	NULL	NULL	10
+100	100	100	100
diff --git a/sql/hive/src/test/resources/golden/join_nullsafe-17-8a4b0dc781a28ad11a0db9805fe03aa8 b/sql/hive/src/test/resources/golden/join_nullsafe-17-8a4b0dc781a28ad11a0db9805fe03aa8
new file mode 100644
index 000000000000..1cc70524f9d6
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/join_nullsafe-17-8a4b0dc781a28ad11a0db9805fe03aa8
@@ -0,0 +1,11 @@
+NULL	NULL	NULL	NULL
+NULL	NULL	10	NULL
+NULL	NULL	48	NULL
+NULL	10	NULL	NULL
+NULL	10	10	NULL
+NULL	10	48	NULL
+NULL	35	NULL	NULL
+NULL	35	10	NULL
+NULL	35	48	NULL
+10	NULL	NULL	10
+100	100	100	100
diff --git a/sql/hive/src/test/resources/golden/join_nullsafe-18-10b2051e65cac50ee1ea1c138ec192c8 b/sql/hive/src/test/resources/golden/join_nullsafe-18-10b2051e65cac50ee1ea1c138ec192c8
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/join_nullsafe-19-23ab7ac8229a53d391195be7ca092429 b/sql/hive/src/test/resources/golden/join_nullsafe-19-23ab7ac8229a53d391195be7ca092429
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/join_nullsafe-2-793e288c9e0971f0bf3f37493f76dc7 b/sql/hive/src/test/resources/golden/join_nullsafe-2-793e288c9e0971f0bf3f37493f76dc7
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/join_nullsafe-20-d6fc260320c577eec9a5db0d4135d224 b/sql/hive/src/test/resources/golden/join_nullsafe-20-d6fc260320c577eec9a5db0d4135d224
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/join_nullsafe-21-a60dae725ffc543f805242611d99de4e b/sql/hive/src/test/resources/golden/join_nullsafe-21-a60dae725ffc543f805242611d99de4e
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/join_nullsafe-22-24c80d0f9e3d72c48d947770fa184985 b/sql/hive/src/test/resources/golden/join_nullsafe-22-24c80d0f9e3d72c48d947770fa184985
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/join_nullsafe-23-3fe6ae20cab3417759dcc654a3a26746 b/sql/hive/src/test/resources/golden/join_nullsafe-23-3fe6ae20cab3417759dcc654a3a26746
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/join_nullsafe-24-2db30531137611e06fdba478ca7a8412 b/sql/hive/src/test/resources/golden/join_nullsafe-24-2db30531137611e06fdba478ca7a8412
new file mode 100644
index 000000000000..573541ac9702
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/join_nullsafe-24-2db30531137611e06fdba478ca7a8412
@@ -0,0 +1 @@
+0
diff --git a/sql/hive/src/test/resources/golden/join_nullsafe-25-e58b2754e8d9c56a473557a549d0d2b9 b/sql/hive/src/test/resources/golden/join_nullsafe-25-e58b2754e8d9c56a473557a549d0d2b9
new file mode 100644
index 000000000000..573541ac9702
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/join_nullsafe-25-e58b2754e8d9c56a473557a549d0d2b9
@@ -0,0 +1 @@
+0
diff --git a/sql/hive/src/test/resources/golden/join_nullsafe-26-64cabe5164130a94f387288f37b62d71 b/sql/hive/src/test/resources/golden/join_nullsafe-26-64cabe5164130a94f387288f37b62d71
new file mode 100644
index 000000000000..573541ac9702
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/join_nullsafe-26-64cabe5164130a94f387288f37b62d71
@@ -0,0 +1 @@
+0
diff --git a/sql/hive/src/test/resources/golden/join_nullsafe-27-e8ed4a1b574a6ca70cbfb3f7b9980aa6 b/sql/hive/src/test/resources/golden/join_nullsafe-27-e8ed4a1b574a6ca70cbfb3f7b9980aa6
new file mode 100644
index 000000000000..66482299904b
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/join_nullsafe-27-e8ed4a1b574a6ca70cbfb3f7b9980aa6
@@ -0,0 +1,42 @@
+NULL	NULL	NULL	NULL
+NULL	NULL	NULL	NULL
+NULL	NULL	NULL	NULL
+NULL	NULL	NULL	NULL
+NULL	NULL	NULL	10
+NULL	NULL	NULL	10
+NULL	NULL	NULL	35
+NULL	NULL	NULL	35
+NULL	NULL	NULL	110
+NULL	NULL	NULL	110
+NULL	NULL	NULL	135
+NULL	NULL	NULL	135
+NULL	10	NULL	NULL
+NULL	10	NULL	NULL
+NULL	10	NULL	10
+NULL	10	NULL	35
+NULL	10	NULL	110
+NULL	10	NULL	135
+NULL	35	NULL	NULL
+NULL	35	NULL	NULL
+NULL	35	NULL	10
+NULL	35	NULL	35
+NULL	35	NULL	110
+NULL	35	NULL	135
+NULL	110	NULL	NULL
+NULL	110	NULL	NULL
+NULL	110	NULL	10
+NULL	110	NULL	35
+NULL	110	NULL	110
+NULL	110	NULL	135
+NULL	135	NULL	NULL
+NULL	135	NULL	NULL
+NULL	135	NULL	10
+NULL	135	NULL	35
+NULL	135	NULL	110
+NULL	135	NULL	135
+10	NULL	10	NULL
+48	NULL	48	NULL
+100	100	100	100
+110	NULL	110	NULL
+148	NULL	148	NULL
+200	200	200	200
diff --git a/sql/hive/src/test/resources/golden/join_nullsafe-28-5a0c946cd7033857ca99e5fb800f8525 b/sql/hive/src/test/resources/golden/join_nullsafe-28-5a0c946cd7033857ca99e5fb800f8525
new file mode 100644
index 000000000000..2efbef048445
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/join_nullsafe-28-5a0c946cd7033857ca99e5fb800f8525
@@ -0,0 +1,14 @@
+NULL	NULL	NULL	NULL
+NULL	NULL	NULL	NULL
+NULL	NULL	NULL	NULL
+NULL	NULL	NULL	NULL
+NULL	10	NULL	10
+NULL	35	NULL	35
+NULL	110	NULL	110
+NULL	135	NULL	135
+10	NULL	10	NULL
+48	NULL	48	NULL
+100	100	100	100
+110	NULL	110	NULL
+148	NULL	148	NULL
+200	200	200	200
diff --git a/sql/hive/src/test/resources/golden/join_nullsafe-29-514043c2ddaf6ea8f16a764adc92d1cf b/sql/hive/src/test/resources/golden/join_nullsafe-29-514043c2ddaf6ea8f16a764adc92d1cf
new file mode 100644
index 000000000000..66482299904b
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/join_nullsafe-29-514043c2ddaf6ea8f16a764adc92d1cf
@@ -0,0 +1,42 @@
+NULL	NULL	NULL	NULL
+NULL	NULL	NULL	NULL
+NULL	NULL	NULL	NULL
+NULL	NULL	NULL	NULL
+NULL	NULL	NULL	10
+NULL	NULL	NULL	10
+NULL	NULL	NULL	35
+NULL	NULL	NULL	35
+NULL	NULL	NULL	110
+NULL	NULL	NULL	110
+NULL	NULL	NULL	135
+NULL	NULL	NULL	135
+NULL	10	NULL	NULL
+NULL	10	NULL	NULL
+NULL	10	NULL	10
+NULL	10	NULL	35
+NULL	10	NULL	110
+NULL	10	NULL	135
+NULL	35	NULL	NULL
+NULL	35	NULL	NULL
+NULL	35	NULL	10
+NULL	35	NULL	35
+NULL	35	NULL	110
+NULL	35	NULL	135
+NULL	110	NULL	NULL
+NULL	110	NULL	NULL
+NULL	110	NULL	10
+NULL	110	NULL	35
+NULL	110	NULL	110
+NULL	110	NULL	135
+NULL	135	NULL	NULL
+NULL	135	NULL	NULL
+NULL	135	NULL	10
+NULL	135	NULL	35
+NULL	135	NULL	110
+NULL	135	NULL	135
+10	NULL	10	NULL
+48	NULL	48	NULL
+100	100	100	100
+110	NULL	110	NULL
+148	NULL	148	NULL
+200	200	200	200
diff --git a/sql/hive/src/test/resources/golden/join_nullsafe-3-ae378fc0f875a21884e58fa35a6d52cd b/sql/hive/src/test/resources/golden/join_nullsafe-3-ae378fc0f875a21884e58fa35a6d52cd
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/join_nullsafe-30-fcbf92cb1b85ab01102fbbc6caba9a88 b/sql/hive/src/test/resources/golden/join_nullsafe-30-fcbf92cb1b85ab01102fbbc6caba9a88
new file mode 100644
index 000000000000..66482299904b
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/join_nullsafe-30-fcbf92cb1b85ab01102fbbc6caba9a88
@@ -0,0 +1,42 @@
+NULL	NULL	NULL	NULL
+NULL	NULL	NULL	NULL
+NULL	NULL	NULL	NULL
+NULL	NULL	NULL	NULL
+NULL	NULL	NULL	10
+NULL	NULL	NULL	10
+NULL	NULL	NULL	35
+NULL	NULL	NULL	35
+NULL	NULL	NULL	110
+NULL	NULL	NULL	110
+NULL	NULL	NULL	135
+NULL	NULL	NULL	135
+NULL	10	NULL	NULL
+NULL	10	NULL	NULL
+NULL	10	NULL	10
+NULL	10	NULL	35
+NULL	10	NULL	110
+NULL	10	NULL	135
+NULL	35	NULL	NULL
+NULL	35	NULL	NULL
+NULL	35	NULL	10
+NULL	35	NULL	35
+NULL	35	NULL	110
+NULL	35	NULL	135
+NULL	110	NULL	NULL
+NULL	110	NULL	NULL
+NULL	110	NULL	10
+NULL	110	NULL	35
+NULL	110	NULL	110
+NULL	110	NULL	135
+NULL	135	NULL	NULL
+NULL	135	NULL	NULL
+NULL	135	NULL	10
+NULL	135	NULL	35
+NULL	135	NULL	110
+NULL	135	NULL	135
+10	NULL	10	NULL
+48	NULL	48	NULL
+100	100	100	100
+110	NULL	110	NULL
+148	NULL	148	NULL
+200	200	200	200
diff --git a/sql/hive/src/test/resources/golden/join_nullsafe-31-1cb03e1106f79d14f22bc89d386cedcf b/sql/hive/src/test/resources/golden/join_nullsafe-31-1cb03e1106f79d14f22bc89d386cedcf
new file mode 100644
index 000000000000..66482299904b
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/join_nullsafe-31-1cb03e1106f79d14f22bc89d386cedcf
@@ -0,0 +1,42 @@
+NULL	NULL	NULL	NULL
+NULL	NULL	NULL	NULL
+NULL	NULL	NULL	NULL
+NULL	NULL	NULL	NULL
+NULL	NULL	NULL	10
+NULL	NULL	NULL	10
+NULL	NULL	NULL	35
+NULL	NULL	NULL	35
+NULL	NULL	NULL	110
+NULL	NULL	NULL	110
+NULL	NULL	NULL	135
+NULL	NULL	NULL	135
+NULL	10	NULL	NULL
+NULL	10	NULL	NULL
+NULL	10	NULL	10
+NULL	10	NULL	35
+NULL	10	NULL	110
+NULL	10	NULL	135
+NULL	35	NULL	NULL
+NULL	35	NULL	NULL
+NULL	35	NULL	10
+NULL	35	NULL	35
+NULL	35	NULL	110
+NULL	35	NULL	135
+NULL	110	NULL	NULL
+NULL	110	NULL	NULL
+NULL	110	NULL	10
+NULL	110	NULL	35
+NULL	110	NULL	110
+NULL	110	NULL	135
+NULL	135	NULL	NULL
+NULL	135	NULL	NULL
+NULL	135	NULL	10
+NULL	135	NULL	35
+NULL	135	NULL	110
+NULL	135	NULL	135
+10	NULL	10	NULL
+48	NULL	48	NULL
+100	100	100	100
+110	NULL	110	NULL
+148	NULL	148	NULL
+200	200	200	200
diff --git a/sql/hive/src/test/resources/golden/join_nullsafe-32-6a0bf6127d4b042e67ae8ee15125fb87 b/sql/hive/src/test/resources/golden/join_nullsafe-32-6a0bf6127d4b042e67ae8ee15125fb87
new file mode 100644
index 000000000000..ea001a222f35
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/join_nullsafe-32-6a0bf6127d4b042e67ae8ee15125fb87
@@ -0,0 +1,40 @@
+NULL	NULL	NULL	NULL
+NULL	NULL	NULL	NULL
+NULL	NULL	NULL	NULL
+NULL	NULL	NULL	NULL
+NULL	NULL	10	NULL
+NULL	NULL	10	NULL
+NULL	NULL	48	NULL
+NULL	NULL	48	NULL
+NULL	NULL	110	NULL
+NULL	NULL	110	NULL
+NULL	NULL	148	NULL
+NULL	NULL	148	NULL
+NULL	10	NULL	NULL
+NULL	10	NULL	NULL
+NULL	10	10	NULL
+NULL	10	48	NULL
+NULL	10	110	NULL
+NULL	10	148	NULL
+NULL	35	NULL	NULL
+NULL	35	NULL	NULL
+NULL	35	10	NULL
+NULL	35	48	NULL
+NULL	35	110	NULL
+NULL	35	148	NULL
+NULL	110	NULL	NULL
+NULL	110	NULL	NULL
+NULL	110	10	NULL
+NULL	110	48	NULL
+NULL	110	110	NULL
+NULL	110	148	NULL
+NULL	135	NULL	NULL
+NULL	135	NULL	NULL
+NULL	135	10	NULL
+NULL	135	48	NULL
+NULL	135	110	NULL
+NULL	135	148	NULL
+10	NULL	NULL	10
+100	100	100	100
+110	NULL	NULL	110
+200	200	200	200
diff --git a/sql/hive/src/test/resources/golden/join_nullsafe-33-63157d43422fcedadba408537ccecd5c b/sql/hive/src/test/resources/golden/join_nullsafe-33-63157d43422fcedadba408537ccecd5c
new file mode 100644
index 000000000000..ea001a222f35
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/join_nullsafe-33-63157d43422fcedadba408537ccecd5c
@@ -0,0 +1,40 @@
+NULL	NULL	NULL	NULL
+NULL	NULL	NULL	NULL
+NULL	NULL	NULL	NULL
+NULL	NULL	NULL	NULL
+NULL	NULL	10	NULL
+NULL	NULL	10	NULL
+NULL	NULL	48	NULL
+NULL	NULL	48	NULL
+NULL	NULL	110	NULL
+NULL	NULL	110	NULL
+NULL	NULL	148	NULL
+NULL	NULL	148	NULL
+NULL	10	NULL	NULL
+NULL	10	NULL	NULL
+NULL	10	10	NULL
+NULL	10	48	NULL
+NULL	10	110	NULL
+NULL	10	148	NULL
+NULL	35	NULL	NULL
+NULL	35	NULL	NULL
+NULL	35	10	NULL
+NULL	35	48	NULL
+NULL	35	110	NULL
+NULL	35	148	NULL
+NULL	110	NULL	NULL
+NULL	110	NULL	NULL
+NULL	110	10	NULL
+NULL	110	48	NULL
+NULL	110	110	NULL
+NULL	110	148	NULL
+NULL	135	NULL	NULL
+NULL	135	NULL	NULL
+NULL	135	10	NULL
+NULL	135	48	NULL
+NULL	135	110	NULL
+NULL	135	148	NULL
+10	NULL	NULL	10
+100	100	100	100
+110	NULL	NULL	110
+200	200	200	200
diff --git a/sql/hive/src/test/resources/golden/join_nullsafe-34-9265f806b71c03061f93f9fbc88aa223 b/sql/hive/src/test/resources/golden/join_nullsafe-34-9265f806b71c03061f93f9fbc88aa223
new file mode 100644
index 000000000000..1093bd89f6e3
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/join_nullsafe-34-9265f806b71c03061f93f9fbc88aa223
@@ -0,0 +1,42 @@
+NULL	NULL	NULL	NULL
+NULL	NULL	NULL	NULL
+NULL	NULL	NULL	NULL
+NULL	NULL	NULL	NULL
+NULL	NULL	10	NULL
+NULL	NULL	10	NULL
+NULL	NULL	48	NULL
+NULL	NULL	48	NULL
+NULL	NULL	110	NULL
+NULL	NULL	110	NULL
+NULL	NULL	148	NULL
+NULL	NULL	148	NULL
+NULL	10	NULL	NULL
+NULL	10	NULL	NULL
+NULL	10	10	NULL
+NULL	10	48	NULL
+NULL	10	110	NULL
+NULL	10	148	NULL
+NULL	35	NULL	NULL
+NULL	35	NULL	NULL
+NULL	35	10	NULL
+NULL	35	48	NULL
+NULL	35	110	NULL
+NULL	35	148	NULL
+NULL	110	NULL	NULL
+NULL	110	NULL	NULL
+NULL	110	10	NULL
+NULL	110	48	NULL
+NULL	110	110	NULL
+NULL	110	148	NULL
+NULL	135	NULL	NULL
+NULL	135	NULL	NULL
+NULL	135	10	NULL
+NULL	135	48	NULL
+NULL	135	110	NULL
+NULL	135	148	NULL
+10	NULL	NULL	10
+48	NULL	NULL	NULL
+100	100	100	100
+110	NULL	NULL	110
+148	NULL	NULL	NULL
+200	200	200	200
diff --git a/sql/hive/src/test/resources/golden/join_nullsafe-35-95815bafb81cccb8129c20d399a446fc b/sql/hive/src/test/resources/golden/join_nullsafe-35-95815bafb81cccb8129c20d399a446fc
new file mode 100644
index 000000000000..9cf0036674d6
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/join_nullsafe-35-95815bafb81cccb8129c20d399a446fc
@@ -0,0 +1,42 @@
+NULL	NULL	NULL	NULL
+NULL	NULL	NULL	NULL
+NULL	NULL	NULL	NULL
+NULL	NULL	NULL	NULL
+NULL	NULL	NULL	35
+NULL	NULL	NULL	135
+NULL	NULL	10	NULL
+NULL	NULL	10	NULL
+NULL	NULL	48	NULL
+NULL	NULL	48	NULL
+NULL	NULL	110	NULL
+NULL	NULL	110	NULL
+NULL	NULL	148	NULL
+NULL	NULL	148	NULL
+NULL	10	NULL	NULL
+NULL	10	NULL	NULL
+NULL	10	10	NULL
+NULL	10	48	NULL
+NULL	10	110	NULL
+NULL	10	148	NULL
+NULL	35	NULL	NULL
+NULL	35	NULL	NULL
+NULL	35	10	NULL
+NULL	35	48	NULL
+NULL	35	110	NULL
+NULL	35	148	NULL
+NULL	110	NULL	NULL
+NULL	110	NULL	NULL
+NULL	110	10	NULL
+NULL	110	48	NULL
+NULL	110	110	NULL
+NULL	110	148	NULL
+NULL	135	NULL	NULL
+NULL	135	NULL	NULL
+NULL	135	10	NULL
+NULL	135	48	NULL
+NULL	135	110	NULL
+NULL	135	148	NULL
+10	NULL	NULL	10
+100	100	100	100
+110	NULL	NULL	110
+200	200	200	200
diff --git a/sql/hive/src/test/resources/golden/join_nullsafe-36-c4762c60cc93236b7647ebd32a40ce57 b/sql/hive/src/test/resources/golden/join_nullsafe-36-c4762c60cc93236b7647ebd32a40ce57
new file mode 100644
index 000000000000..77f6a8ddd7c2
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/join_nullsafe-36-c4762c60cc93236b7647ebd32a40ce57
@@ -0,0 +1,42 @@
+NULL	NULL	NULL	NULL
+NULL	NULL	NULL	NULL
+NULL	NULL	NULL	NULL
+NULL	NULL	NULL	NULL
+NULL	NULL	10	NULL
+NULL	NULL	10	NULL
+NULL	NULL	48	NULL
+NULL	NULL	48	NULL
+NULL	NULL	110	NULL
+NULL	NULL	110	NULL
+NULL	NULL	148	NULL
+NULL	NULL	148	NULL
+NULL	10	NULL	10
+NULL	35	NULL	35
+NULL	110	NULL	110
+NULL	135	NULL	135
+10	NULL	NULL	NULL
+10	NULL	NULL	NULL
+10	NULL	10	NULL
+10	NULL	48	NULL
+10	NULL	110	NULL
+10	NULL	148	NULL
+48	NULL	NULL	NULL
+48	NULL	NULL	NULL
+48	NULL	10	NULL
+48	NULL	48	NULL
+48	NULL	110	NULL
+48	NULL	148	NULL
+100	100	100	100
+110	NULL	NULL	NULL
+110	NULL	NULL	NULL
+110	NULL	10	NULL
+110	NULL	48	NULL
+110	NULL	110	NULL
+110	NULL	148	NULL
+148	NULL	NULL	NULL
+148	NULL	NULL	NULL
+148	NULL	10	NULL
+148	NULL	48	NULL
+148	NULL	110	NULL
+148	NULL	148	NULL
+200	200	200	200
diff --git a/sql/hive/src/test/resources/golden/join_nullsafe-37-a87893adfc73c9cc63ceab200bb56245 b/sql/hive/src/test/resources/golden/join_nullsafe-37-a87893adfc73c9cc63ceab200bb56245
new file mode 100644
index 000000000000..77f6a8ddd7c2
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/join_nullsafe-37-a87893adfc73c9cc63ceab200bb56245
@@ -0,0 +1,42 @@
+NULL	NULL	NULL	NULL
+NULL	NULL	NULL	NULL
+NULL	NULL	NULL	NULL
+NULL	NULL	NULL	NULL
+NULL	NULL	10	NULL
+NULL	NULL	10	NULL
+NULL	NULL	48	NULL
+NULL	NULL	48	NULL
+NULL	NULL	110	NULL
+NULL	NULL	110	NULL
+NULL	NULL	148	NULL
+NULL	NULL	148	NULL
+NULL	10	NULL	10
+NULL	35	NULL	35
+NULL	110	NULL	110
+NULL	135	NULL	135
+10	NULL	NULL	NULL
+10	NULL	NULL	NULL
+10	NULL	10	NULL
+10	NULL	48	NULL
+10	NULL	110	NULL
+10	NULL	148	NULL
+48	NULL	NULL	NULL
+48	NULL	NULL	NULL
+48	NULL	10	NULL
+48	NULL	48	NULL
+48	NULL	110	NULL
+48	NULL	148	NULL
+100	100	100	100
+110	NULL	NULL	NULL
+110	NULL	NULL	NULL
+110	NULL	10	NULL
+110	NULL	48	NULL
+110	NULL	110	NULL
+110	NULL	148	NULL
+148	NULL	NULL	NULL
+148	NULL	NULL	NULL
+148	NULL	10	NULL
+148	NULL	48	NULL
+148	NULL	110	NULL
+148	NULL	148	NULL
+200	200	200	200
diff --git a/sql/hive/src/test/resources/golden/join_nullsafe-38-e3dfe0044b44c8a49414479521acf762 b/sql/hive/src/test/resources/golden/join_nullsafe-38-e3dfe0044b44c8a49414479521acf762
new file mode 100644
index 000000000000..77f6a8ddd7c2
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/join_nullsafe-38-e3dfe0044b44c8a49414479521acf762
@@ -0,0 +1,42 @@
+NULL	NULL	NULL	NULL
+NULL	NULL	NULL	NULL
+NULL	NULL	NULL	NULL
+NULL	NULL	NULL	NULL
+NULL	NULL	10	NULL
+NULL	NULL	10	NULL
+NULL	NULL	48	NULL
+NULL	NULL	48	NULL
+NULL	NULL	110	NULL
+NULL	NULL	110	NULL
+NULL	NULL	148	NULL
+NULL	NULL	148	NULL
+NULL	10	NULL	10
+NULL	35	NULL	35
+NULL	110	NULL	110
+NULL	135	NULL	135
+10	NULL	NULL	NULL
+10	NULL	NULL	NULL
+10	NULL	10	NULL
+10	NULL	48	NULL
+10	NULL	110	NULL
+10	NULL	148	NULL
+48	NULL	NULL	NULL
+48	NULL	NULL	NULL
+48	NULL	10	NULL
+48	NULL	48	NULL
+48	NULL	110	NULL
+48	NULL	148	NULL
+100	100	100	100
+110	NULL	NULL	NULL
+110	NULL	NULL	NULL
+110	NULL	10	NULL
+110	NULL	48	NULL
+110	NULL	110	NULL
+110	NULL	148	NULL
+148	NULL	NULL	NULL
+148	NULL	NULL	NULL
+148	NULL	10	NULL
+148	NULL	48	NULL
+148	NULL	110	NULL
+148	NULL	148	NULL
+200	200	200	200
diff --git a/sql/hive/src/test/resources/golden/join_nullsafe-39-9a7e1f373b9c02e632d6c7c550b908ec b/sql/hive/src/test/resources/golden/join_nullsafe-39-9a7e1f373b9c02e632d6c7c550b908ec
new file mode 100644
index 000000000000..77f6a8ddd7c2
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/join_nullsafe-39-9a7e1f373b9c02e632d6c7c550b908ec
@@ -0,0 +1,42 @@
+NULL	NULL	NULL	NULL
+NULL	NULL	NULL	NULL
+NULL	NULL	NULL	NULL
+NULL	NULL	NULL	NULL
+NULL	NULL	10	NULL
+NULL	NULL	10	NULL
+NULL	NULL	48	NULL
+NULL	NULL	48	NULL
+NULL	NULL	110	NULL
+NULL	NULL	110	NULL
+NULL	NULL	148	NULL
+NULL	NULL	148	NULL
+NULL	10	NULL	10
+NULL	35	NULL	35
+NULL	110	NULL	110
+NULL	135	NULL	135
+10	NULL	NULL	NULL
+10	NULL	NULL	NULL
+10	NULL	10	NULL
+10	NULL	48	NULL
+10	NULL	110	NULL
+10	NULL	148	NULL
+48	NULL	NULL	NULL
+48	NULL	NULL	NULL
+48	NULL	10	NULL
+48	NULL	48	NULL
+48	NULL	110	NULL
+48	NULL	148	NULL
+100	100	100	100
+110	NULL	NULL	NULL
+110	NULL	NULL	NULL
+110	NULL	10	NULL
+110	NULL	48	NULL
+110	NULL	110	NULL
+110	NULL	148	NULL
+148	NULL	NULL	NULL
+148	NULL	NULL	NULL
+148	NULL	10	NULL
+148	NULL	48	NULL
+148	NULL	110	NULL
+148	NULL	148	NULL
+200	200	200	200
diff --git a/sql/hive/src/test/resources/golden/join_nullsafe-4-644c616d87ae426eb2f8c71638045185 b/sql/hive/src/test/resources/golden/join_nullsafe-4-644c616d87ae426eb2f8c71638045185
new file mode 100644
index 000000000000..1cc70524f9d6
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/join_nullsafe-4-644c616d87ae426eb2f8c71638045185
@@ -0,0 +1,11 @@
+NULL	NULL	NULL	NULL
+NULL	NULL	10	NULL
+NULL	NULL	48	NULL
+NULL	10	NULL	NULL
+NULL	10	10	NULL
+NULL	10	48	NULL
+NULL	35	NULL	NULL
+NULL	35	10	NULL
+NULL	35	48	NULL
+10	NULL	NULL	10
+100	100	100	100
diff --git a/sql/hive/src/test/resources/golden/join_nullsafe-40-3c868718e4c120cb9a72ab7318c75be3 b/sql/hive/src/test/resources/golden/join_nullsafe-40-3c868718e4c120cb9a72ab7318c75be3
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/join_nullsafe-41-1f7d8737c3e2d74d5ad865535d729811 b/sql/hive/src/test/resources/golden/join_nullsafe-41-1f7d8737c3e2d74d5ad865535d729811
new file mode 100644
index 000000000000..421049d6e509
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/join_nullsafe-41-1f7d8737c3e2d74d5ad865535d729811
@@ -0,0 +1,9 @@
+NULL	NULL	NULL	NULL
+NULL	NULL	10	NULL
+NULL	NULL	48	NULL
+NULL	10	NULL	NULL
+NULL	10	10	NULL
+NULL	10	48	NULL
+NULL	35	NULL	NULL
+NULL	35	10	NULL
+NULL	35	48	NULL
diff --git a/sql/hive/src/test/resources/golden/join_nullsafe-5-1e393de94850e92b3b00536aacc9371f b/sql/hive/src/test/resources/golden/join_nullsafe-5-1e393de94850e92b3b00536aacc9371f
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/join_nullsafe-6-d66451815212e7d17744184e74c6b0a0 b/sql/hive/src/test/resources/golden/join_nullsafe-6-d66451815212e7d17744184e74c6b0a0
new file mode 100644
index 000000000000..aec3122cae5f
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/join_nullsafe-6-d66451815212e7d17744184e74c6b0a0
@@ -0,0 +1,2 @@
+10	NULL	NULL	10	10	NULL
+100	100	100	100	100	100
diff --git a/sql/hive/src/test/resources/golden/join_nullsafe-7-a3ad3cc301d9884898d3e6ab6c792d4c b/sql/hive/src/test/resources/golden/join_nullsafe-7-a3ad3cc301d9884898d3e6ab6c792d4c
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/join_nullsafe-8-cc7527bcf746ab7e2cd9f28db0ead0ac b/sql/hive/src/test/resources/golden/join_nullsafe-8-cc7527bcf746ab7e2cd9f28db0ead0ac
new file mode 100644
index 000000000000..30db79efa79b
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/join_nullsafe-8-cc7527bcf746ab7e2cd9f28db0ead0ac
@@ -0,0 +1,29 @@
+NULL	NULL	NULL	NULL	NULL	NULL
+NULL	NULL	NULL	NULL	NULL	10
+NULL	NULL	NULL	NULL	NULL	35
+NULL	NULL	10	NULL	NULL	NULL
+NULL	NULL	10	NULL	NULL	10
+NULL	NULL	10	NULL	NULL	35
+NULL	NULL	48	NULL	NULL	NULL
+NULL	NULL	48	NULL	NULL	10
+NULL	NULL	48	NULL	NULL	35
+NULL	10	NULL	NULL	NULL	NULL
+NULL	10	NULL	NULL	NULL	10
+NULL	10	NULL	NULL	NULL	35
+NULL	10	10	NULL	NULL	NULL
+NULL	10	10	NULL	NULL	10
+NULL	10	10	NULL	NULL	35
+NULL	10	48	NULL	NULL	NULL
+NULL	10	48	NULL	NULL	10
+NULL	10	48	NULL	NULL	35
+NULL	35	NULL	NULL	NULL	NULL
+NULL	35	NULL	NULL	NULL	10
+NULL	35	NULL	NULL	NULL	35
+NULL	35	10	NULL	NULL	NULL
+NULL	35	10	NULL	NULL	10
+NULL	35	10	NULL	NULL	35
+NULL	35	48	NULL	NULL	NULL
+NULL	35	48	NULL	NULL	10
+NULL	35	48	NULL	NULL	35
+10	NULL	NULL	10	10	NULL
+100	100	100	100	100	100
diff --git a/sql/hive/src/test/resources/golden/join_nullsafe-9-88f6f40959b0d2faabd9d4b3cd853809 b/sql/hive/src/test/resources/golden/join_nullsafe-9-88f6f40959b0d2faabd9d4b3cd853809
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/sql/hive/src/test/resources/golden/udf_equal-0-36b6cdf7c5f68c91155569b1622f5876 b/sql/hive/src/test/resources/golden/udf_equal-0-36b6cdf7c5f68c91155569b1622f5876
new file mode 100644
index 000000000000..9b9b6312a269
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/udf_equal-0-36b6cdf7c5f68c91155569b1622f5876
@@ -0,0 +1 @@
+a = b - Returns TRUE if a equals b and false otherwise
diff --git a/sql/hive/src/test/resources/golden/udf_equal-1-2422b50b96502dde8b661acdfebd8892 b/sql/hive/src/test/resources/golden/udf_equal-1-2422b50b96502dde8b661acdfebd8892
new file mode 100644
index 000000000000..30fdf50f62e4
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/udf_equal-1-2422b50b96502dde8b661acdfebd8892
@@ -0,0 +1,2 @@
+a = b - Returns TRUE if a equals b and false otherwise
+Synonyms: ==
diff --git a/sql/hive/src/test/resources/golden/udf_equal-2-e0faab0f5e736c24bcc5503aeac55053 b/sql/hive/src/test/resources/golden/udf_equal-2-e0faab0f5e736c24bcc5503aeac55053
new file mode 100644
index 000000000000..d6b4c860778b
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/udf_equal-2-e0faab0f5e736c24bcc5503aeac55053
@@ -0,0 +1 @@
+a == b - Returns TRUE if a equals b and false otherwise
diff --git a/sql/hive/src/test/resources/golden/udf_equal-3-39d8d6f197803de927f0af5409ec2f33 b/sql/hive/src/test/resources/golden/udf_equal-3-39d8d6f197803de927f0af5409ec2f33
new file mode 100644
index 000000000000..71e55d6d638a
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/udf_equal-3-39d8d6f197803de927f0af5409ec2f33
@@ -0,0 +1,2 @@
+a == b - Returns TRUE if a equals b and false otherwise
+Synonyms: =
diff --git a/sql/hive/src/test/resources/golden/udf_equal-4-94ac2476006425e1b3bcddf29ad07b16 b/sql/hive/src/test/resources/golden/udf_equal-4-94ac2476006425e1b3bcddf29ad07b16
new file mode 100644
index 000000000000..015c417bc68f
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/udf_equal-4-94ac2476006425e1b3bcddf29ad07b16
@@ -0,0 +1 @@
+false	false	true	true	NULL	NULL	NULL	NULL	NULL
diff --git a/sql/hive/src/test/resources/golden/udf_equal-5-878650cf21e9360a07d204c8ffb0cde7 b/sql/hive/src/test/resources/golden/udf_equal-5-878650cf21e9360a07d204c8ffb0cde7
new file mode 100644
index 000000000000..aa7b4b51edea
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/udf_equal-5-878650cf21e9360a07d204c8ffb0cde7
@@ -0,0 +1 @@
+a <=> b - Returns same result with EQUAL(=) operator for non-null operands, but returns TRUE if both are NULL, FALSE if one of the them is NULL
diff --git a/sql/hive/src/test/resources/golden/udf_equal-6-1635ef051fecdfc7891d9f5a9a3a545e b/sql/hive/src/test/resources/golden/udf_equal-6-1635ef051fecdfc7891d9f5a9a3a545e
new file mode 100644
index 000000000000..aa7b4b51edea
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/udf_equal-6-1635ef051fecdfc7891d9f5a9a3a545e
@@ -0,0 +1 @@
+a <=> b - Returns same result with EQUAL(=) operator for non-null operands, but returns TRUE if both are NULL, FALSE if one of the them is NULL
diff --git a/sql/hive/src/test/resources/golden/udf_equal-7-78f1b96c199e307714fa1b804e5bae27 b/sql/hive/src/test/resources/golden/udf_equal-7-78f1b96c199e307714fa1b804e5bae27
new file mode 100644
index 000000000000..05292fb23192
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/udf_equal-7-78f1b96c199e307714fa1b804e5bae27
@@ -0,0 +1 @@
+false	false	true	true	true	false	false	false	false

From a2715ccd9437fcdfa0b15e85ab4d0cec91aadf07 Mon Sep 17 00:00:00 2001
From: fireflyc <fireflyc@126.com>
Date: Fri, 25 Jul 2014 10:47:52 -0700
Subject: [PATCH 758/810] replace println to log4j

Our program needs to receive a large amount of data and run for a long
time.
We set the log level to WARN but "Storing iterator" "received single"
as such message written to the log file. (over yarn)

Author: fireflyc <fireflyc@126.com>

Closes #1372 from fireflyc/fix-replace-stdout-log and squashes the following commits:

e684140 [fireflyc] 'info' modified into the 'debug'
fa22a38 [fireflyc] replace println to log4j
---
 .../spark/streaming/receiver/ActorReceiver.scala     | 12 +++++++-----
 1 file changed, 7 insertions(+), 5 deletions(-)

diff --git a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ActorReceiver.scala b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ActorReceiver.scala
index 743be58950c0..1868a1ebc7b4 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ActorReceiver.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ActorReceiver.scala
@@ -68,13 +68,13 @@ object ActorSupervisorStrategy {
  *       should be same.
  */
 @DeveloperApi
-trait ActorHelper {
+trait ActorHelper extends Logging{
 
   self: Actor => // to ensure that this can be added to Actor classes only
 
   /** Store an iterator of received data as a data block into Spark's memory. */
   def store[T](iter: Iterator[T]) {
-    println("Storing iterator")
+    logDebug("Storing iterator")
     context.parent ! IteratorData(iter)
   }
 
@@ -84,6 +84,7 @@ trait ActorHelper {
    * that Spark is configured to use.
    */
   def store(bytes: ByteBuffer) {
+    logDebug("Storing Bytes")
     context.parent ! ByteBufferData(bytes)
   }
 
@@ -93,7 +94,7 @@ trait ActorHelper {
    * being pushed into Spark's memory.
    */
   def store[T](item: T) {
-    println("Storing item")
+    logDebug("Storing item")
     context.parent ! SingleItemData(item)
   }
 }
@@ -157,15 +158,16 @@ private[streaming] class ActorReceiver[T: ClassTag](
     def receive = {
 
       case IteratorData(iterator) =>
-        println("received iterator")
+        logDebug("received iterator")
         store(iterator.asInstanceOf[Iterator[T]])
 
       case SingleItemData(msg) =>
-        println("received single")
+        logDebug("received single")
         store(msg.asInstanceOf[T])
         n.incrementAndGet
 
       case ByteBufferData(bytes) =>
+        logDebug("received bytes")
         store(bytes)
 
       case props: Props =>

From 32bcf9af94b39f2c509eb54f8565fb659c70ca97 Mon Sep 17 00:00:00 2001
From: Yin Huai <huai@cse.ohio-state.edu>
Date: Fri, 25 Jul 2014 11:14:51 -0700
Subject: [PATCH 759/810] [SPARK-2683] unidoc failed because
 org.apache.spark.util.CallSite uses Java keywords as value names

Renaming `short` to `shortForm` and `long` to `longForm`.

JIRA: https://issues.apache.org/jira/browse/SPARK-2683

Author: Yin Huai <huai@cse.ohio-state.edu>

Closes #1585 from yhuai/SPARK-2683 and squashes the following commits:

5ddb843 [Yin Huai] "short" and "long" are Java keyworks. In order to generate javadoc, renaming "short" to "shortForm" and "long" to "longForm".
---
 .../main/scala/org/apache/spark/SparkContext.scala   | 12 +++++++-----
 core/src/main/scala/org/apache/spark/rdd/RDD.scala   |  2 +-
 .../org/apache/spark/scheduler/DAGScheduler.scala    |  4 ++--
 .../scala/org/apache/spark/scheduler/Stage.scala     |  4 ++--
 .../src/main/scala/org/apache/spark/util/Utils.scala |  6 +++---
 .../org/apache/spark/SparkContextInfoSuite.scala     |  2 +-
 6 files changed, 16 insertions(+), 14 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index 8052499ab752..3e6addeaf04a 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -1037,7 +1037,7 @@ class SparkContext(config: SparkConf) extends Logging {
    */
   private[spark] def getCallSite(): CallSite = {
     Option(getLocalProperty("externalCallSite")) match {
-      case Some(callSite) => CallSite(callSite, long = "")
+      case Some(callSite) => CallSite(callSite, longForm = "")
       case None => Utils.getCallSite
     }
   }
@@ -1059,11 +1059,12 @@ class SparkContext(config: SparkConf) extends Logging {
     }
     val callSite = getCallSite
     val cleanedFunc = clean(func)
-    logInfo("Starting job: " + callSite.short)
+    logInfo("Starting job: " + callSite.shortForm)
     val start = System.nanoTime
     dagScheduler.runJob(rdd, cleanedFunc, partitions, callSite, allowLocal,
       resultHandler, localProperties.get)
-    logInfo("Job finished: " + callSite.short + ", took " + (System.nanoTime - start) / 1e9 + " s")
+    logInfo(
+      "Job finished: " + callSite.shortForm + ", took " + (System.nanoTime - start) / 1e9 + " s")
     rdd.doCheckpoint()
   }
 
@@ -1144,11 +1145,12 @@ class SparkContext(config: SparkConf) extends Logging {
       evaluator: ApproximateEvaluator[U, R],
       timeout: Long): PartialResult[R] = {
     val callSite = getCallSite
-    logInfo("Starting job: " + callSite.short)
+    logInfo("Starting job: " + callSite.shortForm)
     val start = System.nanoTime
     val result = dagScheduler.runApproximateJob(rdd, func, evaluator, callSite, timeout,
       localProperties.get)
-    logInfo("Job finished: " + callSite.short + ", took " + (System.nanoTime - start) / 1e9 + " s")
+    logInfo(
+      "Job finished: " + callSite.shortForm + ", took " + (System.nanoTime - start) / 1e9 + " s")
     result
   }
 
diff --git a/core/src/main/scala/org/apache/spark/rdd/RDD.scala b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
index b1c965a79047..a6abc49c5359 100644
--- a/core/src/main/scala/org/apache/spark/rdd/RDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
@@ -1225,7 +1225,7 @@ abstract class RDD[T: ClassTag](
 
   /** User code that created this RDD (e.g. `textFile`, `parallelize`). */
   @transient private[spark] val creationSite = Utils.getCallSite
-  private[spark] def getCreationSite: String = Option(creationSite).map(_.short).getOrElse("")
+  private[spark] def getCreationSite: String = Option(creationSite).map(_.shortForm).getOrElse("")
 
   private[spark] def elementClassTag: ClassTag[T] = classTag[T]
 
diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
index ede3c7d9f01a..acb4c4946ede 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
@@ -455,7 +455,7 @@ class DAGScheduler(
     waiter.awaitResult() match {
       case JobSucceeded => {}
       case JobFailed(exception: Exception) =>
-        logInfo("Failed to run " + callSite.short)
+        logInfo("Failed to run " + callSite.shortForm)
         throw exception
     }
   }
@@ -679,7 +679,7 @@ class DAGScheduler(
       val job = new ActiveJob(jobId, finalStage, func, partitions, callSite, listener, properties)
       clearCacheLocs()
       logInfo("Got job %s (%s) with %d output partitions (allowLocal=%s)".format(
-        job.jobId, callSite.short, partitions.length, allowLocal))
+        job.jobId, callSite.shortForm, partitions.length, allowLocal))
       logInfo("Final stage: " + finalStage + "(" + finalStage.name + ")")
       logInfo("Parents of final stage: " + finalStage.parents)
       logInfo("Missing parents: " + getMissingParentStages(finalStage))
diff --git a/core/src/main/scala/org/apache/spark/scheduler/Stage.scala b/core/src/main/scala/org/apache/spark/scheduler/Stage.scala
index 8ec482a6f6d9..798cbc598d36 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/Stage.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/Stage.scala
@@ -108,8 +108,8 @@ private[spark] class Stage(
 
   def attemptId: Int = nextAttemptId
 
-  val name = callSite.short
-  val details = callSite.long
+  val name = callSite.shortForm
+  val details = callSite.longForm
 
   override def toString = "Stage " + id
 
diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala
index 5784e974fbb6..1a4f4eba98ea 100644
--- a/core/src/main/scala/org/apache/spark/util/Utils.scala
+++ b/core/src/main/scala/org/apache/spark/util/Utils.scala
@@ -44,7 +44,7 @@ import org.apache.spark.executor.ExecutorUncaughtExceptionHandler
 import org.apache.spark.serializer.{DeserializationStream, SerializationStream, SerializerInstance}
 
 /** CallSite represents a place in user code. It can have a short and a long form. */
-private[spark] case class CallSite(short: String, long: String)
+private[spark] case class CallSite(shortForm: String, longForm: String)
 
 /**
  * Various utility methods used by Spark.
@@ -848,8 +848,8 @@ private[spark] object Utils extends Logging {
     }
     val callStackDepth = System.getProperty("spark.callstack.depth", "20").toInt
     CallSite(
-      short = "%s at %s:%s".format(lastSparkMethod, firstUserFile, firstUserLine),
-      long = callStack.take(callStackDepth).mkString("\n"))
+      shortForm = "%s at %s:%s".format(lastSparkMethod, firstUserFile, firstUserLine),
+      longForm = callStack.take(callStackDepth).mkString("\n"))
   }
 
   /** Return a string containing part of a file from byte 'start' to 'end'. */
diff --git a/core/src/test/scala/org/apache/spark/SparkContextInfoSuite.scala b/core/src/test/scala/org/apache/spark/SparkContextInfoSuite.scala
index 1fde4badda94..fb18c3ebfe46 100644
--- a/core/src/test/scala/org/apache/spark/SparkContextInfoSuite.scala
+++ b/core/src/test/scala/org/apache/spark/SparkContextInfoSuite.scala
@@ -70,7 +70,7 @@ package object testPackage extends Assertions {
   def runCallSiteTest(sc: SparkContext) {
     val rdd = sc.makeRDD(Array(1, 2, 3, 4), 2)
     val rddCreationSite = rdd.getCreationSite
-    val curCallSite = sc.getCallSite().short // note: 2 lines after definition of "rdd"
+    val curCallSite = sc.getCallSite().shortForm // note: 2 lines after definition of "rdd"
 
     val rddCreationLine = rddCreationSite match {
       case CALL_SITE_REGEX(func, file, line) => {

From 06dc0d2c6b69c5d59b4d194ced2ac85bfe2e05e2 Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian.cs.zju@gmail.com>
Date: Fri, 25 Jul 2014 12:20:49 -0700
Subject: [PATCH 760/810] [SPARK-2410][SQL] Merging Hive Thrift/JDBC server

JIRA issue:

- Main: [SPARK-2410](https://issues.apache.org/jira/browse/SPARK-2410)
- Related: [SPARK-2678](https://issues.apache.org/jira/browse/SPARK-2678)

Cherry picked the Hive Thrift/JDBC server from [branch-1.0-jdbc](https://github.com/apache/spark/tree/branch-1.0-jdbc).

(Thanks chenghao-intel for his initial contribution of the Spark SQL CLI.)

TODO

- [x] Use `spark-submit` to launch the server, the CLI and beeline
- [x] Migration guideline draft for Shark users

----

Hit by a bug in `SparkSubmitArguments` while working on this PR: all application options that are recognized by `SparkSubmitArguments` are stolen as `SparkSubmit` options. For example:

```bash
$ spark-submit --class org.apache.hive.beeline.BeeLine spark-internal --help
```

This actually shows usage information of `SparkSubmit` rather than `BeeLine`.

~~Fixed this bug here since the `spark-internal` related stuff also touches `SparkSubmitArguments` and I'd like to avoid conflict.~~

**UPDATE** The bug mentioned above is now tracked by [SPARK-2678](https://issues.apache.org/jira/browse/SPARK-2678). Decided to revert changes to this bug since it involves more subtle considerations and worth a separate PR.

Author: Cheng Lian <lian.cs.zju@gmail.com>

Closes #1399 from liancheng/thriftserver and squashes the following commits:

090beea [Cheng Lian] Revert changes related to SPARK-2678, decided to move them to another PR
21c6cf4 [Cheng Lian] Updated Spark SQL programming guide docs
fe0af31 [Cheng Lian] Reordered spark-submit options in spark-shell[.cmd]
199e3fb [Cheng Lian] Disabled MIMA for hive-thriftserver
1083e9d [Cheng Lian] Fixed failed test suites
7db82a1 [Cheng Lian] Fixed spark-submit application options handling logic
9cc0f06 [Cheng Lian] Starts beeline with spark-submit
cfcf461 [Cheng Lian] Updated documents and build scripts for the newly added hive-thriftserver profile
061880f [Cheng Lian] Addressed all comments by @pwendell
7755062 [Cheng Lian] Adapts test suites to spark-submit settings
40bafef [Cheng Lian] Fixed more license header issues
e214aab [Cheng Lian] Added missing license headers
b8905ba [Cheng Lian] Fixed minor issues in spark-sql and start-thriftserver.sh
f975d22 [Cheng Lian] Updated docs for Hive compatibility and Shark migration guide draft
3ad4e75 [Cheng Lian] Starts spark-sql shell with spark-submit
a5310d1 [Cheng Lian] Make HiveThriftServer2 play well with spark-submit
61f39f4 [Cheng Lian] Starts Hive Thrift server via spark-submit
2c4c539 [Cheng Lian] Cherry picked the Hive Thrift server
---
 .gitignore                                    |   1 +
 assembly/pom.xml                              |  10 +
 bagel/pom.xml                                 |   2 +-
 bin/beeline                                   |  45 +++
 bin/compute-classpath.sh                      |   1 +
 bin/spark-shell                               |   4 +-
 bin/spark-shell.cmd                           |   2 +-
 bin/spark-sql                                 |  36 ++
 core/pom.xml                                  |   2 +-
 .../org/apache/spark/deploy/SparkSubmit.scala |  14 +-
 .../spark/deploy/SparkSubmitArguments.scala   |   5 +-
 dev/create-release/create-release.sh          |  10 +-
 dev/run-tests                                 |   2 +-
 dev/scalastyle                                |   2 +-
 docs/sql-programming-guide.md                 | 200 +++++++++-
 examples/pom.xml                              |   2 +-
 external/flume/pom.xml                        |   2 +-
 external/kafka/pom.xml                        |   2 +-
 external/mqtt/pom.xml                         |   2 +-
 external/twitter/pom.xml                      |   2 +-
 external/zeromq/pom.xml                       |   2 +-
 graphx/pom.xml                                |   2 +-
 mllib/pom.xml                                 |   2 +-
 pom.xml                                       |   7 +-
 project/SparkBuild.scala                      |  14 +-
 sbin/start-thriftserver.sh                    |  36 ++
 sql/catalyst/pom.xml                          |   2 +-
 .../sql/catalyst/plans/logical/commands.scala |   3 +-
 sql/core/pom.xml                              |   2 +-
 .../scala/org/apache/spark/sql/SQLConf.scala  |  20 +-
 .../apache/spark/sql/execution/commands.scala |  42 ++-
 .../org/apache/spark/sql/SQLConfSuite.scala   |  13 +-
 .../org/apache/spark/sql/SQLQuerySuite.scala  |  10 +-
 sql/hive-thriftserver/pom.xml                 |  82 +++++
 .../hive/thriftserver/HiveThriftServer2.scala |  97 +++++
 .../hive/thriftserver/ReflectionUtils.scala   |  58 +++
 .../hive/thriftserver/SparkSQLCLIDriver.scala | 344 ++++++++++++++++++
 .../thriftserver/SparkSQLCLIService.scala     |  74 ++++
 .../hive/thriftserver/SparkSQLDriver.scala    |  93 +++++
 .../sql/hive/thriftserver/SparkSQLEnv.scala   |  58 +++
 .../thriftserver/SparkSQLSessionManager.scala |  49 +++
 .../server/SparkSQLOperationManager.scala     | 151 ++++++++
 .../test/resources/data/files/small_kv.txt    |   5 +
 .../sql/hive/thriftserver/CliSuite.scala      |  59 +++
 .../thriftserver/HiveThriftServer2Suite.scala | 125 +++++++
 .../sql/hive/thriftserver/TestUtils.scala     | 108 ++++++
 sql/hive/pom.xml                              |   2 +-
 .../apache/spark/sql/hive/HiveContext.scala   |   2 +-
 .../sql/hive/execution/HiveQuerySuite.scala   |  50 ++-
 streaming/pom.xml                             |   2 +-
 tools/pom.xml                                 |   2 +-
 yarn/alpha/pom.xml                            |   2 +-
 yarn/pom.xml                                  |   2 +-
 yarn/stable/pom.xml                           |   2 +-
 54 files changed, 1772 insertions(+), 96 deletions(-)
 create mode 100755 bin/beeline
 create mode 100755 bin/spark-sql
 create mode 100755 sbin/start-thriftserver.sh
 create mode 100644 sql/hive-thriftserver/pom.xml
 create mode 100644 sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala
 create mode 100644 sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ReflectionUtils.scala
 create mode 100755 sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala
 create mode 100644 sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIService.scala
 create mode 100644 sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLDriver.scala
 create mode 100644 sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLEnv.scala
 create mode 100644 sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLSessionManager.scala
 create mode 100644 sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/server/SparkSQLOperationManager.scala
 create mode 100644 sql/hive-thriftserver/src/test/resources/data/files/small_kv.txt
 create mode 100644 sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/CliSuite.scala
 create mode 100644 sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suite.scala
 create mode 100644 sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/TestUtils.scala

diff --git a/.gitignore b/.gitignore
index 061c8946d23c..5b56a67c883e 100644
--- a/.gitignore
+++ b/.gitignore
@@ -57,3 +57,4 @@ metastore_db/
 metastore/
 warehouse/
 TempStatsStore/
+sql/hive-thriftserver/test_warehouses
diff --git a/assembly/pom.xml b/assembly/pom.xml
index 567a8dd2a0d9..703f15925bc4 100644
--- a/assembly/pom.xml
+++ b/assembly/pom.xml
@@ -165,6 +165,16 @@
         </dependency>
       </dependencies>
     </profile>
+    <profile>
+      <id>hive-thriftserver</id>
+      <dependencies>
+        <dependency>
+          <groupId>org.apache.spark</groupId>
+          <artifactId>spark-hive-thriftserver_${scala.binary.version}</artifactId>
+          <version>${project.version}</version>
+        </dependency>
+      </dependencies>
+    </profile>
     <profile>
       <id>spark-ganglia-lgpl</id>
       <dependencies>
diff --git a/bagel/pom.xml b/bagel/pom.xml
index 90c4b095bb61..bd51b112e26f 100644
--- a/bagel/pom.xml
+++ b/bagel/pom.xml
@@ -28,7 +28,7 @@
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-bagel_2.10</artifactId>
   <properties>
-     <sbt.project.name>bagel</sbt.project.name>
+    <sbt.project.name>bagel</sbt.project.name>
   </properties>
   <packaging>jar</packaging>
   <name>Spark Project Bagel</name>
diff --git a/bin/beeline b/bin/beeline
new file mode 100755
index 000000000000..09fe366c609f
--- /dev/null
+++ b/bin/beeline
@@ -0,0 +1,45 @@
+#!/usr/bin/env bash
+
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+# Figure out where Spark is installed
+FWDIR="$(cd `dirname $0`/..; pwd)"
+
+# Find the java binary
+if [ -n "${JAVA_HOME}" ]; then
+  RUNNER="${JAVA_HOME}/bin/java"
+else
+  if [ `command -v java` ]; then
+    RUNNER="java"
+  else
+    echo "JAVA_HOME is not set" >&2
+    exit 1
+  fi
+fi
+
+# Compute classpath using external script
+classpath_output=$($FWDIR/bin/compute-classpath.sh)
+if [[ "$?" != "0" ]]; then
+  echo "$classpath_output"
+  exit 1
+else
+  CLASSPATH=$classpath_output
+fi
+
+CLASS="org.apache.hive.beeline.BeeLine"
+exec "$RUNNER" -cp "$CLASSPATH" $CLASS "$@"
diff --git a/bin/compute-classpath.sh b/bin/compute-classpath.sh
index e81e8c060cb9..16b794a1592e 100755
--- a/bin/compute-classpath.sh
+++ b/bin/compute-classpath.sh
@@ -52,6 +52,7 @@ if [ -n "$SPARK_PREPEND_CLASSES" ]; then
   CLASSPATH="$CLASSPATH:$FWDIR/sql/catalyst/target/scala-$SCALA_VERSION/classes"
   CLASSPATH="$CLASSPATH:$FWDIR/sql/core/target/scala-$SCALA_VERSION/classes"
   CLASSPATH="$CLASSPATH:$FWDIR/sql/hive/target/scala-$SCALA_VERSION/classes"
+  CLASSPATH="$CLASSPATH:$FWDIR/sql/hive-thriftserver/target/scala-$SCALA_VERSION/classes"
   CLASSPATH="$CLASSPATH:$FWDIR/yarn/stable/target/scala-$SCALA_VERSION/classes"
 fi
 
diff --git a/bin/spark-shell b/bin/spark-shell
index 850e9507ec38..756c8179d12b 100755
--- a/bin/spark-shell
+++ b/bin/spark-shell
@@ -46,11 +46,11 @@ function main(){
         # (see https://github.com/sbt/sbt/issues/562).
         stty -icanon min 1 -echo > /dev/null 2>&1
         export SPARK_SUBMIT_OPTS="$SPARK_SUBMIT_OPTS -Djline.terminal=unix"
-        $FWDIR/bin/spark-submit spark-shell "$@" --class org.apache.spark.repl.Main
+        $FWDIR/bin/spark-submit --class org.apache.spark.repl.Main spark-shell "$@"
         stty icanon echo > /dev/null 2>&1
     else
         export SPARK_SUBMIT_OPTS
-        $FWDIR/bin/spark-submit spark-shell "$@" --class org.apache.spark.repl.Main
+        $FWDIR/bin/spark-submit --class org.apache.spark.repl.Main spark-shell "$@"
     fi
 }
 
diff --git a/bin/spark-shell.cmd b/bin/spark-shell.cmd
index 4b9708a8c03f..b56d69801171 100755
--- a/bin/spark-shell.cmd
+++ b/bin/spark-shell.cmd
@@ -19,4 +19,4 @@ rem
 
 set SPARK_HOME=%~dp0..
 
-cmd /V /E /C %SPARK_HOME%\bin\spark-submit.cmd spark-shell %* --class org.apache.spark.repl.Main
+cmd /V /E /C %SPARK_HOME%\bin\spark-submit.cmd spark-shell --class org.apache.spark.repl.Main %*
diff --git a/bin/spark-sql b/bin/spark-sql
new file mode 100755
index 000000000000..bba7f897b19b
--- /dev/null
+++ b/bin/spark-sql
@@ -0,0 +1,36 @@
+#!/usr/bin/env bash
+
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+#
+# Shell script for starting the Spark SQL CLI
+
+# Enter posix mode for bash
+set -o posix
+
+# Figure out where Spark is installed
+FWDIR="$(cd `dirname $0`/..; pwd)"
+
+if [[ "$@" = *--help ]] || [[ "$@" = *-h ]]; then
+  echo "Usage: ./sbin/spark-sql [options]"
+  $FWDIR/bin/spark-submit --help 2>&1 | grep -v Usage 1>&2
+  exit 0
+fi
+
+CLASS="org.apache.spark.sql.hive.thriftserver.SparkSQLCLIDriver"
+exec "$FWDIR"/bin/spark-submit --class $CLASS spark-internal $@
diff --git a/core/pom.xml b/core/pom.xml
index 1054cec4d77b..a24743495b0e 100644
--- a/core/pom.xml
+++ b/core/pom.xml
@@ -28,7 +28,7 @@
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-core_2.10</artifactId>
   <properties>
-     <sbt.project.name>core</sbt.project.name>
+    <sbt.project.name>core</sbt.project.name>
   </properties>
   <packaging>jar</packaging>
   <name>Spark Project Core</name>
diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
index 3b5642b6caa3..c9cec33ebaa6 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
@@ -46,6 +46,10 @@ object SparkSubmit {
   private val CLUSTER = 2
   private val ALL_DEPLOY_MODES = CLIENT | CLUSTER
 
+  // A special jar name that indicates the class being run is inside of Spark itself, and therefore
+  // no user jar is needed.
+  private val SPARK_INTERNAL = "spark-internal"
+
   // Special primary resource names that represent shells rather than application jars.
   private val SPARK_SHELL = "spark-shell"
   private val PYSPARK_SHELL = "pyspark-shell"
@@ -257,7 +261,9 @@ object SparkSubmit {
     // In yarn-cluster mode, use yarn.Client as a wrapper around the user class
     if (clusterManager == YARN && deployMode == CLUSTER) {
       childMainClass = "org.apache.spark.deploy.yarn.Client"
-      childArgs += ("--jar", args.primaryResource)
+      if (args.primaryResource != SPARK_INTERNAL) {
+        childArgs += ("--jar", args.primaryResource)
+      }
       childArgs += ("--class", args.mainClass)
       if (args.childArgs != null) {
         args.childArgs.foreach { arg => childArgs += ("--arg", arg) }
@@ -332,7 +338,7 @@ object SparkSubmit {
    * Return whether the given primary resource represents a user jar.
    */
   private def isUserJar(primaryResource: String): Boolean = {
-    !isShell(primaryResource) && !isPython(primaryResource)
+    !isShell(primaryResource) && !isPython(primaryResource) && !isInternal(primaryResource)
   }
 
   /**
@@ -349,6 +355,10 @@ object SparkSubmit {
     primaryResource.endsWith(".py") || primaryResource == PYSPARK_SHELL
   }
 
+  private[spark] def isInternal(primaryResource: String): Boolean = {
+    primaryResource == SPARK_INTERNAL
+  }
+
   /**
    * Merge a sequence of comma-separated file lists, some of which may be null to indicate
    * no files, into a single comma-separated string.
diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
index 3ab67a43a3b5..01d0ae541a66 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
@@ -204,8 +204,9 @@ private[spark] class SparkSubmitArguments(args: Seq[String]) {
 
   /** Fill in values by parsing user options. */
   private def parseOpts(opts: Seq[String]): Unit = {
-    // Delineates parsing of Spark options from parsing of user options.
     var inSparkOpts = true
+
+    // Delineates parsing of Spark options from parsing of user options.
     parse(opts)
 
     def parse(opts: Seq[String]): Unit = opts match {
@@ -318,7 +319,7 @@ private[spark] class SparkSubmitArguments(args: Seq[String]) {
               SparkSubmit.printErrorAndExit(errMessage)
             case v =>
               primaryResource =
-                if (!SparkSubmit.isShell(v)) {
+                if (!SparkSubmit.isShell(v) && !SparkSubmit.isInternal(v)) {
                   Utils.resolveURI(v).toString
                 } else {
                   v
diff --git a/dev/create-release/create-release.sh b/dev/create-release/create-release.sh
index 38830103d1e8..33de24d1ae6d 100755
--- a/dev/create-release/create-release.sh
+++ b/dev/create-release/create-release.sh
@@ -53,7 +53,7 @@ if [[ ! "$@" =~ --package-only ]]; then
     -Dusername=$GIT_USERNAME -Dpassword=$GIT_PASSWORD \
     -Dmaven.javadoc.skip=true \
     -Dhadoop.version=2.2.0 -Dyarn.version=2.2.0 \
-    -Pyarn -Phive -Phadoop-2.2 -Pspark-ganglia-lgpl\
+    -Pyarn -Phive -Phive-thriftserver -Phadoop-2.2 -Pspark-ganglia-lgpl\
     -Dtag=$GIT_TAG -DautoVersionSubmodules=true \
     --batch-mode release:prepare
 
@@ -61,7 +61,7 @@ if [[ ! "$@" =~ --package-only ]]; then
     -Darguments="-DskipTests=true -Dmaven.javadoc.skip=true -Dhadoop.version=2.2.0 -Dyarn.version=2.2.0 -Dgpg.passphrase=${GPG_PASSPHRASE}" \
     -Dhadoop.version=2.2.0 -Dyarn.version=2.2.0 \
     -Dmaven.javadoc.skip=true \
-    -Pyarn -Phive -Phadoop-2.2 -Pspark-ganglia-lgpl\
+    -Pyarn -Phive -Phive-thriftserver -Phadoop-2.2 -Pspark-ganglia-lgpl\
     release:perform
 
   cd ..
@@ -111,10 +111,10 @@ make_binary_release() {
     spark-$RELEASE_VERSION-bin-$NAME.tgz.sha
 }
 
-make_binary_release "hadoop1" "-Phive -Dhadoop.version=1.0.4"
-make_binary_release "cdh4" "-Phive -Dhadoop.version=2.0.0-mr1-cdh4.2.0"
+make_binary_release "hadoop1" "-Phive -Phive-thriftserver -Dhadoop.version=1.0.4"
+make_binary_release "cdh4" "-Phive -Phive-thriftserver -Dhadoop.version=2.0.0-mr1-cdh4.2.0"
 make_binary_release "hadoop2" \
-  "-Phive -Pyarn -Phadoop-2.2 -Dhadoop.version=2.2.0 -Pyarn.version=2.2.0"
+  "-Phive -Phive-thriftserver -Pyarn -Phadoop-2.2 -Dhadoop.version=2.2.0 -Pyarn.version=2.2.0"
 
 # Copy data
 echo "Copying release tarballs"
diff --git a/dev/run-tests b/dev/run-tests
index 51e4def0f835..98ec969dc1b3 100755
--- a/dev/run-tests
+++ b/dev/run-tests
@@ -65,7 +65,7 @@ echo "========================================================================="
 # (either resolution or compilation) prompts the user for input either q, r, 
 # etc to quit or retry. This echo is there to make it not block.
 if [ -n "$_RUN_SQL_TESTS" ]; then
-  echo -e "q\n" | SBT_MAVEN_PROFILES="$SBT_MAVEN_PROFILES -Phive" sbt/sbt clean package \
+  echo -e "q\n" | SBT_MAVEN_PROFILES="$SBT_MAVEN_PROFILES -Phive -Phive-thriftserver" sbt/sbt clean package \
     assembly/assembly test | grep -v -e "info.*Resolving" -e "warn.*Merging" -e "info.*Including"
 else
   echo -e "q\n" | sbt/sbt clean package assembly/assembly test | \
diff --git a/dev/scalastyle b/dev/scalastyle
index a02d06912f23..d9f2b91a3a09 100755
--- a/dev/scalastyle
+++ b/dev/scalastyle
@@ -17,7 +17,7 @@
 # limitations under the License.
 #
 
-echo -e "q\n" | sbt/sbt -Phive scalastyle > scalastyle.txt
+echo -e "q\n" | sbt/sbt -Phive -Phive-thriftserver scalastyle > scalastyle.txt
 # Check style with YARN alpha built too
 echo -e "q\n" | sbt/sbt -Pyarn -Phadoop-0.23 -Dhadoop.version=0.23.9 yarn-alpha/scalastyle \
   >> scalastyle.txt
diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md
index 38728534a46e..36d642f2923b 100644
--- a/docs/sql-programming-guide.md
+++ b/docs/sql-programming-guide.md
@@ -136,7 +136,7 @@ val sqlContext = new org.apache.spark.sql.SQLContext(sc)
 import sqlContext.createSchemaRDD
 
 // Define the schema using a case class.
-// Note: Case classes in Scala 2.10 can support only up to 22 fields. To work around this limit, 
+// Note: Case classes in Scala 2.10 can support only up to 22 fields. To work around this limit,
 // you can use custom classes that implement the Product interface.
 case class Person(name: String, age: Int)
 
@@ -548,7 +548,6 @@ results = hiveContext.hql("FROM src SELECT key, value").collect()
 </div>
 </div>
 
-
 # Writing Language-Integrated Relational Queries
 
 **Language-Integrated queries are currently only supported in Scala.**
@@ -573,4 +572,199 @@ prefixed with a tick (`'`).  Implicit conversions turn these symbols into expres
 evaluated by the SQL execution engine.  A full list of the functions supported can be found in the
 [ScalaDoc](api/scala/index.html#org.apache.spark.sql.SchemaRDD).
 
-<!-- TODO: Include the table of operations here. -->
\ No newline at end of file
+<!-- TODO: Include the table of operations here. -->
+
+## Running the Thrift JDBC server
+
+The Thrift JDBC server implemented here corresponds to the [`HiveServer2`]
+(https://cwiki.apache.org/confluence/display/Hive/Setting+Up+HiveServer2) in Hive 0.12. You can test
+the JDBC server with the beeline script comes with either Spark or Hive 0.12.  In order to use Hive
+you must first run '`sbt/sbt -Phive-thriftserver assembly/assembly`' (or use `-Phive-thriftserver`
+for maven).
+
+To start the JDBC server, run the following in the Spark directory:
+
+    ./sbin/start-thriftserver.sh
+
+The default port the server listens on is 10000.  You may run
+`./sbin/start-thriftserver.sh --help` for a complete list of all available
+options.  Now you can use beeline to test the Thrift JDBC server:
+
+    ./bin/beeline
+
+Connect to the JDBC server in beeline with:
+
+    beeline> !connect jdbc:hive2://localhost:10000
+
+Beeline will ask you for a username and password. In non-secure mode, simply enter the username on
+your machine and a blank password. For secure mode, please follow the instructions given in the
+[beeline documentation](https://cwiki.apache.org/confluence/display/Hive/HiveServer2+Clients)
+
+Configuration of Hive is done by placing your `hive-site.xml` file in `conf/`.
+
+You may also use the beeline script comes with Hive.
+
+### Migration Guide for Shark Users
+
+#### Reducer number
+
+In Shark, default reducer number is 1 and is controlled by the property `mapred.reduce.tasks`. Spark
+SQL deprecates this property by a new property `spark.sql.shuffle.partitions`, whose default value
+is 200. Users may customize this property via `SET`:
+
+```
+SET spark.sql.shuffle.partitions=10;
+SELECT page, count(*) c FROM logs_last_month_cached
+GROUP BY page ORDER BY c DESC LIMIT 10;
+```
+
+You may also put this property in `hive-site.xml` to override the default value.
+
+For now, the `mapred.reduce.tasks` property is still recognized, and is converted to
+`spark.sql.shuffle.partitions` automatically.
+
+#### Caching
+
+The `shark.cache` table property no longer exists, and tables whose name end with `_cached` are no
+longer automcatically cached. Instead, we provide `CACHE TABLE` and `UNCACHE TABLE` statements to
+let user control table caching explicitly:
+
+```
+CACHE TABLE logs_last_month;
+UNCACHE TABLE logs_last_month;
+```
+
+**NOTE** `CACHE TABLE tbl` is lazy, it only marks table `tbl` as "need to by cached if necessary",
+but doesn't actually cache it until a query that touches `tbl` is executed. To force the table to be
+cached, you may simply count the table immediately after executing `CACHE TABLE`:
+
+```
+CACHE TABLE logs_last_month;
+SELECT COUNT(1) FROM logs_last_month;
+```
+
+Several caching related features are not supported yet:
+
+* User defined partition level cache eviction policy
+* RDD reloading
+* In-memory cache write through policy
+
+### Compatibility with Apache Hive
+
+#### Deploying in Exising Hive Warehouses
+
+Spark SQL Thrift JDBC server is designed to be "out of the box" compatible with existing Hive
+installations. You do not need to modify your existing Hive Metastore or change the data placement
+or partitioning of your tables.
+
+#### Supported Hive Features
+
+Spark SQL supports the vast majority of Hive features, such as:
+
+* Hive query statements, including:
+ * `SELECT`
+ * `GROUP BY
+ * `ORDER BY`
+ * `CLUSTER BY`
+ * `SORT BY`
+* All Hive operators, including:
+ * Relational operators (`=`, `⇔`, `==`, `<>`, `<`, `>`, `>=`, `<=`, etc)
+ * Arthimatic operators (`+`, `-`, `*`, `/`, `%`, etc)
+ * Logical operators (`AND`, `&&`, `OR`, `||`, etc)
+ * Complex type constructors
+ * Mathemtatical functions (`sign`, `ln`, `cos`, etc)
+ * String functions (`instr`, `length`, `printf`, etc)
+* User defined functions (UDF)
+* User defined aggregation functions (UDAF)
+* User defined serialization formats (SerDe's)
+* Joins
+ * `JOIN`
+ * `{LEFT|RIGHT|FULL} OUTER JOIN`
+ * `LEFT SEMI JOIN`
+ * `CROSS JOIN`
+* Unions
+* Sub queries
+ * `SELECT col FROM ( SELECT a + b AS col from t1) t2`
+* Sampling
+* Explain
+* Partitioned tables
+* All Hive DDL Functions, including:
+ * `CREATE TABLE`
+ * `CREATE TABLE AS SELECT`
+ * `ALTER TABLE`
+* Most Hive Data types, including:
+ * `TINYINT`
+ * `SMALLINT`
+ * `INT`
+ * `BIGINT`
+ * `BOOLEAN`
+ * `FLOAT`
+ * `DOUBLE`
+ * `STRING`
+ * `BINARY`
+ * `TIMESTAMP`
+ * `ARRAY<>`
+ * `MAP<>`
+ * `STRUCT<>`
+
+#### Unsupported Hive Functionality
+
+Below is a list of Hive features that we don't support yet. Most of these features are rarely used
+in Hive deployments.
+
+**Major Hive Features**
+
+* Tables with buckets: bucket is the hash partitioning within a Hive table partition. Spark SQL
+  doesn't support buckets yet.
+
+**Esoteric Hive Features**
+
+* Tables with partitions using different input formats: In Spark SQL, all table partitions need to
+  have the same input format.
+* Non-equi outer join: For the uncommon use case of using outer joins with non-equi join conditions
+  (e.g. condition "`key < 10`"), Spark SQL will output wrong result for the `NULL` tuple.
+* `UNIONTYPE`
+* Unique join
+* Single query multi insert
+* Column statistics collecting: Spark SQL does not piggyback scans to collect column statistics at
+  the moment.
+
+**Hive Input/Output Formats**
+
+* File format for CLI: For results showing back to the CLI, Spark SQL only supports TextOutputFormat.
+* Hadoop archive
+
+**Hive Optimizations**
+
+A handful of Hive optimizations are not yet included in Spark. Some of these (such as indexes) are
+not necessary due to Spark SQL's in-memory computational model. Others are slotted for future
+releases of Spark SQL.
+
+* Block level bitmap indexes and virtual columns (used to build indexes)
+* Automatically convert a join to map join: For joining a large table with multiple small tables,
+  Hive automatically converts the join into a map join. We are adding this auto conversion in the
+  next release.
+* Automatically determine the number of reducers for joins and groupbys: Currently in Spark SQL, you
+  need to control the degree of parallelism post-shuffle using "SET
+  spark.sql.shuffle.partitions=[num_tasks];". We are going to add auto-setting of parallelism in the
+  next release.
+* Meta-data only query: For queries that can be answered by using only meta data, Spark SQL still
+  launches tasks to compute the result.
+* Skew data flag: Spark SQL does not follow the skew data flags in Hive.
+* `STREAMTABLE` hint in join: Spark SQL does not follow the `STREAMTABLE` hint.
+* Merge multiple small files for query results: if the result output contains multiple small files,
+  Hive can optionally merge the small files into fewer large files to avoid overflowing the HDFS
+  metadata. Spark SQL does not support that.
+
+## Running the Spark SQL CLI
+
+The Spark SQL CLI is a convenient tool to run the Hive metastore service in local mode and execute
+queries input from command line. Note: the Spark SQL CLI cannot talk to the Thrift JDBC server.
+
+To start the Spark SQL CLI, run the following in the Spark directory:
+
+    ./bin/spark-sql
+
+Configuration of Hive is done by placing your `hive-site.xml` file in `conf/`.
+You may run `./bin/spark-sql --help` for a complete list of all available
+options.
diff --git a/examples/pom.xml b/examples/pom.xml
index bd1c387c2eb9..c4ed0f5a6a02 100644
--- a/examples/pom.xml
+++ b/examples/pom.xml
@@ -28,7 +28,7 @@
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-examples_2.10</artifactId>
   <properties>
-     <sbt.project.name>examples</sbt.project.name>
+    <sbt.project.name>examples</sbt.project.name>
   </properties>
   <packaging>jar</packaging>
   <name>Spark Project Examples</name>
diff --git a/external/flume/pom.xml b/external/flume/pom.xml
index 61a6aff543ae..874b8a7959bb 100644
--- a/external/flume/pom.xml
+++ b/external/flume/pom.xml
@@ -28,7 +28,7 @@
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-streaming-flume_2.10</artifactId>
   <properties>
-     <sbt.project.name>streaming-flume</sbt.project.name>
+    <sbt.project.name>streaming-flume</sbt.project.name>
   </properties>
   <packaging>jar</packaging>
   <name>Spark Project External Flume</name>
diff --git a/external/kafka/pom.xml b/external/kafka/pom.xml
index 4762c50685a9..25a5c0a4d7d7 100644
--- a/external/kafka/pom.xml
+++ b/external/kafka/pom.xml
@@ -28,7 +28,7 @@
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-streaming-kafka_2.10</artifactId>
   <properties>
-     <sbt.project.name>streaming-kafka</sbt.project.name>
+    <sbt.project.name>streaming-kafka</sbt.project.name>
   </properties>
   <packaging>jar</packaging>
   <name>Spark Project External Kafka</name>
diff --git a/external/mqtt/pom.xml b/external/mqtt/pom.xml
index 32c530e600ce..f31ed655f677 100644
--- a/external/mqtt/pom.xml
+++ b/external/mqtt/pom.xml
@@ -28,7 +28,7 @@
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-streaming-mqtt_2.10</artifactId>
   <properties>
-     <sbt.project.name>streaming-mqtt</sbt.project.name>
+    <sbt.project.name>streaming-mqtt</sbt.project.name>
   </properties>
   <packaging>jar</packaging>
   <name>Spark Project External MQTT</name>
diff --git a/external/twitter/pom.xml b/external/twitter/pom.xml
index 637adb0f00da..56bb24c2a072 100644
--- a/external/twitter/pom.xml
+++ b/external/twitter/pom.xml
@@ -28,7 +28,7 @@
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-streaming-twitter_2.10</artifactId>
   <properties>
-     <sbt.project.name>streaming-twitter</sbt.project.name>
+    <sbt.project.name>streaming-twitter</sbt.project.name>
   </properties>
   <packaging>jar</packaging>
   <name>Spark Project External Twitter</name>
diff --git a/external/zeromq/pom.xml b/external/zeromq/pom.xml
index e4d758a04a4c..54b0242c54e7 100644
--- a/external/zeromq/pom.xml
+++ b/external/zeromq/pom.xml
@@ -28,7 +28,7 @@
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-streaming-zeromq_2.10</artifactId>
   <properties>
-     <sbt.project.name>streaming-zeromq</sbt.project.name>
+    <sbt.project.name>streaming-zeromq</sbt.project.name>
   </properties>
   <packaging>jar</packaging>
   <name>Spark Project External ZeroMQ</name>
diff --git a/graphx/pom.xml b/graphx/pom.xml
index 7e3bcf29dcfb..6dd52fc618b1 100644
--- a/graphx/pom.xml
+++ b/graphx/pom.xml
@@ -28,7 +28,7 @@
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-graphx_2.10</artifactId>
   <properties>
-     <sbt.project.name>graphx</sbt.project.name>
+    <sbt.project.name>graphx</sbt.project.name>
   </properties>
   <packaging>jar</packaging>
   <name>Spark Project GraphX</name>
diff --git a/mllib/pom.xml b/mllib/pom.xml
index 92b07e2357db..f27cf520dc9f 100644
--- a/mllib/pom.xml
+++ b/mllib/pom.xml
@@ -28,7 +28,7 @@
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-mllib_2.10</artifactId>
   <properties>
-     <sbt.project.name>mllib</sbt.project.name>
+    <sbt.project.name>mllib</sbt.project.name>
   </properties>  
   <packaging>jar</packaging>
   <name>Spark Project ML Library</name>
diff --git a/pom.xml b/pom.xml
index 4e2d64a83364..3e9d388180d8 100644
--- a/pom.xml
+++ b/pom.xml
@@ -95,6 +95,7 @@
     <module>sql/catalyst</module>
     <module>sql/core</module>
     <module>sql/hive</module>
+    <module>sql/hive-thriftserver</module>
     <module>repl</module>
     <module>assembly</module>
     <module>external/twitter</module>
@@ -252,9 +253,9 @@
         <version>3.3.2</version>
       </dependency>
       <dependency>
-	  <groupId>commons-codec</groupId>
-	    <artifactId>commons-codec</artifactId>
-	    <version>1.5</version>
+        <groupId>commons-codec</groupId>
+        <artifactId>commons-codec</artifactId>
+        <version>1.5</version>
       </dependency>
       <dependency>
         <groupId>com.google.code.findbugs</groupId>
diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index 5461d25d72d7..86d47734e77b 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -29,11 +29,11 @@ object BuildCommons {
 
   private val buildLocation = file(".").getAbsoluteFile.getParentFile
 
-  val allProjects@Seq(bagel, catalyst, core, graphx, hive, mllib, repl, spark, sql, streaming,
-  streamingFlume, streamingKafka, streamingMqtt, streamingTwitter, streamingZeromq) =
-    Seq("bagel", "catalyst", "core", "graphx", "hive", "mllib", "repl", "spark", "sql",
-      "streaming", "streaming-flume", "streaming-kafka", "streaming-mqtt", "streaming-twitter",
-      "streaming-zeromq").map(ProjectRef(buildLocation, _))
+  val allProjects@Seq(bagel, catalyst, core, graphx, hive, hiveThriftServer, mllib, repl, spark, sql,
+  streaming, streamingFlume, streamingKafka, streamingMqtt, streamingTwitter, streamingZeromq) =
+    Seq("bagel", "catalyst", "core", "graphx", "hive", "hive-thriftserver", "mllib", "repl",
+      "spark", "sql", "streaming", "streaming-flume", "streaming-kafka", "streaming-mqtt",
+      "streaming-twitter", "streaming-zeromq").map(ProjectRef(buildLocation, _))
 
   val optionallyEnabledProjects@Seq(yarn, yarnStable, yarnAlpha, java8Tests, sparkGangliaLgpl) =
     Seq("yarn", "yarn-stable", "yarn-alpha", "java8-tests", "ganglia-lgpl")
@@ -99,7 +99,7 @@ object SparkBuild extends PomBuild {
   Properties.envOrNone("SBT_MAVEN_PROPERTIES") match {
     case Some(v) =>
       v.split("(\\s+|,)").filterNot(_.isEmpty).map(_.split("=")).foreach(x => System.setProperty(x(0), x(1)))
-    case _ => 
+    case _ =>
   }
 
   override val userPropertiesMap = System.getProperties.toMap
@@ -157,7 +157,7 @@ object SparkBuild extends PomBuild {
 
   /* Enable Mima for all projects except spark, hive, catalyst, sql  and repl */
   // TODO: Add Sql to mima checks
-  allProjects.filterNot(y => Seq(spark, sql, hive, catalyst, repl).exists(x => x == y)).
+  allProjects.filterNot(x => Seq(spark, sql, hive, hiveThriftServer, catalyst, repl).contains(x)).
     foreach (x => enable(MimaBuild.mimaSettings(sparkHome, x))(x))
 
   /* Enable Assembly for all assembly projects */
diff --git a/sbin/start-thriftserver.sh b/sbin/start-thriftserver.sh
new file mode 100755
index 000000000000..8398e6f19b51
--- /dev/null
+++ b/sbin/start-thriftserver.sh
@@ -0,0 +1,36 @@
+#!/usr/bin/env bash
+
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+#
+# Shell script for starting the Spark SQL Thrift server
+
+# Enter posix mode for bash
+set -o posix
+
+# Figure out where Spark is installed
+FWDIR="$(cd `dirname $0`/..; pwd)"
+
+if [[ "$@" = *--help ]] || [[ "$@" = *-h ]]; then
+  echo "Usage: ./sbin/start-thriftserver [options]"
+  $FWDIR/bin/spark-submit --help 2>&1 | grep -v Usage 1>&2
+  exit 0
+fi
+
+CLASS="org.apache.spark.sql.hive.thriftserver.HiveThriftServer2"
+exec "$FWDIR"/bin/spark-submit --class $CLASS spark-internal $@
diff --git a/sql/catalyst/pom.xml b/sql/catalyst/pom.xml
index 6decde3fcd62..531bfddbf237 100644
--- a/sql/catalyst/pom.xml
+++ b/sql/catalyst/pom.xml
@@ -32,7 +32,7 @@
   <name>Spark Project Catalyst</name>
   <url>http://spark.apache.org/</url>
   <properties>
-     <sbt.project.name>catalyst</sbt.project.name>
+    <sbt.project.name>catalyst</sbt.project.name>
   </properties>
 
   <dependencies>
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/commands.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/commands.scala
index 1d5f033f0d27..a357c6ffb897 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/commands.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/commands.scala
@@ -43,8 +43,7 @@ case class NativeCommand(cmd: String) extends Command {
  */
 case class SetCommand(key: Option[String], value: Option[String]) extends Command {
   override def output = Seq(
-    BoundReference(0, AttributeReference("key", StringType, nullable = false)()),
-    BoundReference(1, AttributeReference("value", StringType, nullable = false)()))
+    BoundReference(1, AttributeReference("", StringType, nullable = false)()))
 }
 
 /**
diff --git a/sql/core/pom.xml b/sql/core/pom.xml
index c309c43804d9..3a038a2db617 100644
--- a/sql/core/pom.xml
+++ b/sql/core/pom.xml
@@ -32,7 +32,7 @@
   <name>Spark Project SQL</name>
   <url>http://spark.apache.org/</url>
   <properties>
-     <sbt.project.name>sql</sbt.project.name>
+    <sbt.project.name>sql</sbt.project.name>
   </properties>
 
   <dependencies>
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
index 2b787e14f3f1..41920c00b5a2 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
@@ -30,12 +30,13 @@ import scala.collection.JavaConverters._
  * SQLConf is thread-safe (internally synchronized so safe to be used in multiple threads).
  */
 trait SQLConf {
+  import SQLConf._
 
   /** ************************ Spark SQL Params/Hints ******************* */
   // TODO: refactor so that these hints accessors don't pollute the name space of SQLContext?
 
   /** Number of partitions to use for shuffle operators. */
-  private[spark] def numShufflePartitions: Int = get("spark.sql.shuffle.partitions", "200").toInt
+  private[spark] def numShufflePartitions: Int = get(SHUFFLE_PARTITIONS, "200").toInt
 
   /**
    * Upper bound on the sizes (in bytes) of the tables qualified for the auto conversion to
@@ -43,11 +44,10 @@ trait SQLConf {
    * effectively disables auto conversion.
    * Hive setting: hive.auto.convert.join.noconditionaltask.size.
    */
-  private[spark] def autoConvertJoinSize: Int =
-    get("spark.sql.auto.convert.join.size", "10000").toInt
+  private[spark] def autoConvertJoinSize: Int = get(AUTO_CONVERT_JOIN_SIZE, "10000").toInt
 
   /** A comma-separated list of table names marked to be broadcasted during joins. */
-  private[spark] def joinBroadcastTables: String = get("spark.sql.join.broadcastTables", "")
+  private[spark] def joinBroadcastTables: String = get(JOIN_BROADCAST_TABLES, "")
 
   /** ********************** SQLConf functionality methods ************ */
 
@@ -61,7 +61,7 @@ trait SQLConf {
 
   def set(key: String, value: String): Unit = {
     require(key != null, "key cannot be null")
-    require(value != null, s"value cannot be null for ${key}")
+    require(value != null, s"value cannot be null for $key")
     settings.put(key, value)
   }
 
@@ -90,3 +90,13 @@ trait SQLConf {
   }
 
 }
+
+object SQLConf {
+  val AUTO_CONVERT_JOIN_SIZE = "spark.sql.auto.convert.join.size"
+  val SHUFFLE_PARTITIONS = "spark.sql.shuffle.partitions"
+  val JOIN_BROADCAST_TABLES = "spark.sql.join.broadcastTables"
+
+  object Deprecated {
+    val MAPRED_REDUCE_TASKS = "mapred.reduce.tasks"
+  }
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala
index 98d2f89c8ae7..9293239131d5 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala
@@ -17,12 +17,13 @@
 
 package org.apache.spark.sql.execution
 
+import org.apache.spark.Logging
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.errors.TreeNodeException
 import org.apache.spark.sql.catalyst.expressions.{Attribute, GenericRow}
 import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
-import org.apache.spark.sql.{Row, SQLContext}
+import org.apache.spark.sql.{Row, SQLConf, SQLContext}
 
 trait Command {
   /**
@@ -44,28 +45,53 @@ trait Command {
 case class SetCommand(
     key: Option[String], value: Option[String], output: Seq[Attribute])(
     @transient context: SQLContext)
-  extends LeafNode with Command {
+  extends LeafNode with Command with Logging {
 
-  override protected[sql] lazy val sideEffectResult: Seq[(String, String)] = (key, value) match {
+  override protected[sql] lazy val sideEffectResult: Seq[String] = (key, value) match {
     // Set value for key k.
     case (Some(k), Some(v)) =>
-      context.set(k, v)
-      Array(k -> v)
+      if (k == SQLConf.Deprecated.MAPRED_REDUCE_TASKS) {
+        logWarning(s"Property ${SQLConf.Deprecated.MAPRED_REDUCE_TASKS} is deprecated, " +
+          s"automatically converted to ${SQLConf.SHUFFLE_PARTITIONS} instead.")
+        context.set(SQLConf.SHUFFLE_PARTITIONS, v)
+        Array(s"${SQLConf.SHUFFLE_PARTITIONS}=$v")
+      } else {
+        context.set(k, v)
+        Array(s"$k=$v")
+      }
 
     // Query the value bound to key k.
     case (Some(k), _) =>
-      Array(k -> context.getOption(k).getOrElse("<undefined>"))
+      // TODO (lian) This is just a workaround to make the Simba ODBC driver work.
+      // Should remove this once we get the ODBC driver updated.
+      if (k == "-v") {
+        val hiveJars = Seq(
+          "hive-exec-0.12.0.jar",
+          "hive-service-0.12.0.jar",
+          "hive-common-0.12.0.jar",
+          "hive-hwi-0.12.0.jar",
+          "hive-0.12.0.jar").mkString(":")
+
+        Array(
+          "system:java.class.path=" + hiveJars,
+          "system:sun.java.command=shark.SharkServer2")
+      }
+      else {
+        Array(s"$k=${context.getOption(k).getOrElse("<undefined>")}")
+      }
 
     // Query all key-value pairs that are set in the SQLConf of the context.
     case (None, None) =>
-      context.getAll
+      context.getAll.map { case (k, v) =>
+        s"$k=$v"
+      }
 
     case _ =>
       throw new IllegalArgumentException()
   }
 
   def execute(): RDD[Row] = {
-    val rows = sideEffectResult.map { case (k, v) => new GenericRow(Array[Any](k, v)) }
+    val rows = sideEffectResult.map { line => new GenericRow(Array[Any](line)) }
     context.sparkContext.parallelize(rows, 1)
   }
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLConfSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLConfSuite.scala
index 08293f7f0ca3..1a58d73d9e7f 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLConfSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLConfSuite.scala
@@ -54,10 +54,10 @@ class SQLConfSuite extends QueryTest {
     assert(get(testKey, testVal + "_") == testVal)
     assert(TestSQLContext.get(testKey, testVal + "_") == testVal)
 
-    sql("set mapred.reduce.tasks=20")
-    assert(get("mapred.reduce.tasks", "0") == "20")
-    sql("set mapred.reduce.tasks = 40")
-    assert(get("mapred.reduce.tasks", "0") == "40")
+    sql("set some.property=20")
+    assert(get("some.property", "0") == "20")
+    sql("set some.property = 40")
+    assert(get("some.property", "0") == "40")
 
     val key = "spark.sql.key"
     val vs = "val0,val_1,val2.3,my_table"
@@ -70,4 +70,9 @@ class SQLConfSuite extends QueryTest {
     clear()
   }
 
+  test("deprecated property") {
+    clear()
+    sql(s"set ${SQLConf.Deprecated.MAPRED_REDUCE_TASKS}=10")
+    assert(get(SQLConf.SHUFFLE_PARTITIONS) == "10")
+  }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index 6736189c96d4..de9e8aa4f62e 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -424,25 +424,25 @@ class SQLQuerySuite extends QueryTest {
     sql(s"SET $testKey=$testVal")
     checkAnswer(
       sql("SET"),
-      Seq(Seq(testKey, testVal))
+      Seq(Seq(s"$testKey=$testVal"))
     )
 
     sql(s"SET ${testKey + testKey}=${testVal + testVal}")
     checkAnswer(
       sql("set"),
       Seq(
-        Seq(testKey, testVal),
-        Seq(testKey + testKey, testVal + testVal))
+        Seq(s"$testKey=$testVal"),
+        Seq(s"${testKey + testKey}=${testVal + testVal}"))
     )
 
     // "set key"
     checkAnswer(
       sql(s"SET $testKey"),
-      Seq(Seq(testKey, testVal))
+      Seq(Seq(s"$testKey=$testVal"))
     )
     checkAnswer(
       sql(s"SET $nonexistentKey"),
-      Seq(Seq(nonexistentKey, "<undefined>"))
+      Seq(Seq(s"$nonexistentKey=<undefined>"))
     )
     clear()
   }
diff --git a/sql/hive-thriftserver/pom.xml b/sql/hive-thriftserver/pom.xml
new file mode 100644
index 000000000000..7fac90fdc596
--- /dev/null
+++ b/sql/hive-thriftserver/pom.xml
@@ -0,0 +1,82 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<!--
+  ~ Licensed to the Apache Software Foundation (ASF) under one or more
+  ~ contributor license agreements.  See the NOTICE file distributed with
+  ~ this work for additional information regarding copyright ownership.
+  ~ The ASF licenses this file to You under the Apache License, Version 2.0
+  ~ (the "License"); you may not use this file except in compliance with
+  ~ the License.  You may obtain a copy of the License at
+  ~
+  ~    http://www.apache.org/licenses/LICENSE-2.0
+  ~
+  ~ Unless required by applicable law or agreed to in writing, software
+  ~ distributed under the License is distributed on an "AS IS" BASIS,
+  ~ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+  ~ See the License for the specific language governing permissions and
+  ~ limitations under the License.
+  -->
+
+<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
+  <modelVersion>4.0.0</modelVersion>
+  <parent>
+    <groupId>org.apache.spark</groupId>
+    <artifactId>spark-parent</artifactId>
+    <version>1.1.0-SNAPSHOT</version>
+    <relativePath>../../pom.xml</relativePath>
+  </parent>
+
+  <groupId>org.apache.spark</groupId>
+  <artifactId>spark-hive-thriftserver_2.10</artifactId>
+  <packaging>jar</packaging>
+  <name>Spark Project Hive</name>
+  <url>http://spark.apache.org/</url>
+  <properties>
+    <sbt.project.name>hive-thriftserver</sbt.project.name>
+  </properties>
+
+  <dependencies>
+    <dependency>
+      <groupId>org.apache.spark</groupId>
+      <artifactId>spark-hive_${scala.binary.version}</artifactId>
+      <version>${project.version}</version>
+    </dependency>
+    <dependency>
+      <groupId>org.spark-project.hive</groupId>
+      <artifactId>hive-cli</artifactId>
+      <version>${hive.version}</version>
+    </dependency>
+    <dependency>
+      <groupId>org.spark-project.hive</groupId>
+      <artifactId>hive-jdbc</artifactId>
+      <version>${hive.version}</version>
+    </dependency>
+    <dependency>
+      <groupId>org.spark-project.hive</groupId>
+      <artifactId>hive-beeline</artifactId>
+      <version>${hive.version}</version>
+    </dependency>
+    <dependency>
+      <groupId>org.scalatest</groupId>
+      <artifactId>scalatest_${scala.binary.version}</artifactId>
+      <scope>test</scope>
+    </dependency>
+  </dependencies>
+  <build>
+    <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory>
+    <testOutputDirectory>target/scala-${scala.binary.version}/test-classes</testOutputDirectory>
+    <plugins>
+      <plugin>
+        <groupId>org.scalatest</groupId>
+        <artifactId>scalatest-maven-plugin</artifactId>
+      </plugin>
+      <plugin>
+        <groupId>org.apache.maven.plugins</groupId>
+        <artifactId>maven-deploy-plugin</artifactId>
+        <configuration>
+          <skip>true</skip>
+        </configuration>
+      </plugin>
+    </plugins>
+  </build>
+</project>
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala
new file mode 100644
index 000000000000..ddbc2a79fb51
--- /dev/null
+++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala
@@ -0,0 +1,97 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive.thriftserver
+
+import scala.collection.JavaConversions._
+
+import org.apache.commons.logging.LogFactory
+import org.apache.hadoop.hive.conf.HiveConf
+import org.apache.hadoop.hive.ql.session.SessionState
+import org.apache.hive.service.cli.thrift.ThriftBinaryCLIService
+import org.apache.hive.service.server.{HiveServer2, ServerOptionsProcessor}
+
+import org.apache.spark.sql.Logging
+import org.apache.spark.sql.hive.HiveContext
+import org.apache.spark.sql.hive.thriftserver.ReflectionUtils._
+
+/**
+ * The main entry point for the Spark SQL port of HiveServer2.  Starts up a `SparkSQLContext` and a
+ * `HiveThriftServer2` thrift server.
+ */
+private[hive] object HiveThriftServer2 extends Logging {
+  var LOG = LogFactory.getLog(classOf[HiveServer2])
+
+  def main(args: Array[String]) {
+    val optionsProcessor = new ServerOptionsProcessor("HiveThriftServer2")
+
+    if (!optionsProcessor.process(args)) {
+      logger.warn("Error starting HiveThriftServer2 with given arguments")
+      System.exit(-1)
+    }
+
+    val ss = new SessionState(new HiveConf(classOf[SessionState]))
+
+    // Set all properties specified via command line.
+    val hiveConf: HiveConf = ss.getConf
+    hiveConf.getAllProperties.toSeq.sortBy(_._1).foreach { case (k, v) =>
+      logger.debug(s"HiveConf var: $k=$v")
+    }
+
+    SessionState.start(ss)
+
+    logger.info("Starting SparkContext")
+    SparkSQLEnv.init()
+    SessionState.start(ss)
+
+    Runtime.getRuntime.addShutdownHook(
+      new Thread() {
+        override def run() {
+          SparkSQLEnv.sparkContext.stop()
+        }
+      }
+    )
+
+    try {
+      val server = new HiveThriftServer2(SparkSQLEnv.hiveContext)
+      server.init(hiveConf)
+      server.start()
+      logger.info("HiveThriftServer2 started")
+    } catch {
+      case e: Exception =>
+        logger.error("Error starting HiveThriftServer2", e)
+        System.exit(-1)
+    }
+  }
+}
+
+private[hive] class HiveThriftServer2(hiveContext: HiveContext)
+  extends HiveServer2
+  with ReflectedCompositeService {
+
+  override def init(hiveConf: HiveConf) {
+    val sparkSqlCliService = new SparkSQLCLIService(hiveContext)
+    setSuperField(this, "cliService", sparkSqlCliService)
+    addService(sparkSqlCliService)
+
+    val thriftCliService = new ThriftBinaryCLIService(sparkSqlCliService)
+    setSuperField(this, "thriftCLIService", thriftCliService)
+    addService(thriftCliService)
+
+    initCompositeService(hiveConf)
+  }
+}
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ReflectionUtils.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ReflectionUtils.scala
new file mode 100644
index 000000000000..599294dfbb7d
--- /dev/null
+++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ReflectionUtils.scala
@@ -0,0 +1,58 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive.thriftserver
+
+private[hive] object ReflectionUtils {
+  def setSuperField(obj : Object, fieldName: String, fieldValue: Object) {
+    setAncestorField(obj, 1, fieldName, fieldValue)
+  }
+
+  def setAncestorField(obj: AnyRef, level: Int, fieldName: String, fieldValue: AnyRef) {
+    val ancestor = Iterator.iterate[Class[_]](obj.getClass)(_.getSuperclass).drop(level).next()
+    val field = ancestor.getDeclaredField(fieldName)
+    field.setAccessible(true)
+    field.set(obj, fieldValue)
+  }
+
+  def getSuperField[T](obj: AnyRef, fieldName: String): T = {
+    getAncestorField[T](obj, 1, fieldName)
+  }
+
+  def getAncestorField[T](clazz: Object, level: Int, fieldName: String): T = {
+    val ancestor = Iterator.iterate[Class[_]](clazz.getClass)(_.getSuperclass).drop(level).next()
+    val field = ancestor.getDeclaredField(fieldName)
+    field.setAccessible(true)
+    field.get(clazz).asInstanceOf[T]
+  }
+
+  def invokeStatic(clazz: Class[_], methodName: String, args: (Class[_], AnyRef)*): AnyRef = {
+    invoke(clazz, null, methodName, args: _*)
+  }
+
+  def invoke(
+      clazz: Class[_],
+      obj: AnyRef,
+      methodName: String,
+      args: (Class[_], AnyRef)*): AnyRef = {
+
+    val (types, values) = args.unzip
+    val method = clazz.getDeclaredMethod(methodName, types: _*)
+    method.setAccessible(true)
+    method.invoke(obj, values.toSeq: _*)
+  }
+}
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala
new file mode 100755
index 000000000000..27268ecb923e
--- /dev/null
+++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala
@@ -0,0 +1,344 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive.thriftserver
+
+import scala.collection.JavaConversions._
+
+import java.io._
+import java.util.{ArrayList => JArrayList}
+
+import jline.{ConsoleReader, History}
+import org.apache.commons.lang.StringUtils
+import org.apache.commons.logging.LogFactory
+import org.apache.hadoop.conf.Configuration
+import org.apache.hadoop.hive.cli.{CliDriver, CliSessionState, OptionsProcessor}
+import org.apache.hadoop.hive.common.LogUtils.LogInitializationException
+import org.apache.hadoop.hive.common.{HiveInterruptCallback, HiveInterruptUtils, LogUtils}
+import org.apache.hadoop.hive.conf.HiveConf
+import org.apache.hadoop.hive.ql.Driver
+import org.apache.hadoop.hive.ql.exec.Utilities
+import org.apache.hadoop.hive.ql.processors.{CommandProcessor, CommandProcessorFactory}
+import org.apache.hadoop.hive.ql.session.SessionState
+import org.apache.hadoop.hive.shims.ShimLoader
+import org.apache.thrift.transport.TSocket
+
+import org.apache.spark.sql.Logging
+
+private[hive] object SparkSQLCLIDriver {
+  private var prompt = "spark-sql"
+  private var continuedPrompt = "".padTo(prompt.length, ' ')
+  private var transport:TSocket = _
+
+  installSignalHandler()
+
+  /**
+   * Install an interrupt callback to cancel all Spark jobs. In Hive's CliDriver#processLine(),
+   * a signal handler will invoke this registered callback if a Ctrl+C signal is detected while
+   * a command is being processed by the current thread.
+   */
+  def installSignalHandler() {
+    HiveInterruptUtils.add(new HiveInterruptCallback {
+      override def interrupt() {
+        // Handle remote execution mode
+        if (SparkSQLEnv.sparkContext != null) {
+          SparkSQLEnv.sparkContext.cancelAllJobs()
+        } else {
+          if (transport != null) {
+            // Force closing of TCP connection upon session termination
+            transport.getSocket.close()
+          }
+        }
+      }
+    })
+  }
+
+  def main(args: Array[String]) {
+    val oproc = new OptionsProcessor()
+    if (!oproc.process_stage1(args)) {
+      System.exit(1)
+    }
+
+    // NOTE: It is critical to do this here so that log4j is reinitialized
+    // before any of the other core hive classes are loaded
+    var logInitFailed = false
+    var logInitDetailMessage: String = null
+    try {
+      logInitDetailMessage = LogUtils.initHiveLog4j()
+    } catch {
+      case e: LogInitializationException =>
+        logInitFailed = true
+        logInitDetailMessage = e.getMessage
+    }
+
+    val sessionState = new CliSessionState(new HiveConf(classOf[SessionState]))
+
+    sessionState.in = System.in
+    try {
+      sessionState.out = new PrintStream(System.out, true, "UTF-8")
+      sessionState.info = new PrintStream(System.err, true, "UTF-8")
+      sessionState.err = new PrintStream(System.err, true, "UTF-8")
+    } catch {
+      case e: UnsupportedEncodingException => System.exit(3)
+    }
+
+    if (!oproc.process_stage2(sessionState)) {
+      System.exit(2)
+    }
+
+    if (!sessionState.getIsSilent) {
+      if (logInitFailed) System.err.println(logInitDetailMessage)
+      else SessionState.getConsole.printInfo(logInitDetailMessage)
+    }
+
+    // Set all properties specified via command line.
+    val conf: HiveConf = sessionState.getConf
+    sessionState.cmdProperties.entrySet().foreach { item: java.util.Map.Entry[Object, Object] =>
+      conf.set(item.getKey.asInstanceOf[String], item.getValue.asInstanceOf[String])
+      sessionState.getOverriddenConfigurations.put(
+        item.getKey.asInstanceOf[String], item.getValue.asInstanceOf[String])
+    }
+
+    SessionState.start(sessionState)
+
+    // Clean up after we exit
+    Runtime.getRuntime.addShutdownHook(
+      new Thread() {
+        override def run() {
+          SparkSQLEnv.stop()
+        }
+      }
+    )
+
+    // "-h" option has been passed, so connect to Hive thrift server.
+    if (sessionState.getHost != null) {
+      sessionState.connect()
+      if (sessionState.isRemoteMode) {
+        prompt = s"[${sessionState.getHost}:${sessionState.getPort}]" + prompt
+        continuedPrompt = "".padTo(prompt.length, ' ')
+      }
+    }
+
+    if (!sessionState.isRemoteMode && !ShimLoader.getHadoopShims.usesJobShell()) {
+      // Hadoop-20 and above - we need to augment classpath using hiveconf
+      // components.
+      // See also: code in ExecDriver.java
+      var loader = conf.getClassLoader
+      val auxJars = HiveConf.getVar(conf, HiveConf.ConfVars.HIVEAUXJARS)
+      if (StringUtils.isNotBlank(auxJars)) {
+        loader = Utilities.addToClassPath(loader, StringUtils.split(auxJars, ","))
+      }
+      conf.setClassLoader(loader)
+      Thread.currentThread().setContextClassLoader(loader)
+    }
+
+    val cli = new SparkSQLCLIDriver
+    cli.setHiveVariables(oproc.getHiveVariables)
+
+    // TODO work around for set the log output to console, because the HiveContext
+    // will set the output into an invalid buffer.
+    sessionState.in = System.in
+    try {
+      sessionState.out = new PrintStream(System.out, true, "UTF-8")
+      sessionState.info = new PrintStream(System.err, true, "UTF-8")
+      sessionState.err = new PrintStream(System.err, true, "UTF-8")
+    } catch {
+      case e: UnsupportedEncodingException => System.exit(3)
+    }
+
+    // Execute -i init files (always in silent mode)
+    cli.processInitFiles(sessionState)
+
+    if (sessionState.execString != null) {
+      System.exit(cli.processLine(sessionState.execString))
+    }
+
+    try {
+      if (sessionState.fileName != null) {
+        System.exit(cli.processFile(sessionState.fileName))
+      }
+    } catch {
+      case e: FileNotFoundException =>
+        System.err.println(s"Could not open input file for reading. (${e.getMessage})")
+        System.exit(3)
+    }
+
+    val reader = new ConsoleReader()
+    reader.setBellEnabled(false)
+    // reader.setDebug(new PrintWriter(new FileWriter("writer.debug", true)))
+    CliDriver.getCommandCompletor.foreach((e) => reader.addCompletor(e))
+
+    val historyDirectory = System.getProperty("user.home")
+
+    try {
+      if (new File(historyDirectory).exists()) {
+        val historyFile = historyDirectory + File.separator + ".hivehistory"
+        reader.setHistory(new History(new File(historyFile)))
+      } else {
+        System.err.println("WARNING: Directory for Hive history file: " + historyDirectory +
+                           " does not exist.   History will not be available during this session.")
+      }
+    } catch {
+      case e: Exception =>
+        System.err.println("WARNING: Encountered an error while trying to initialize Hive's " +
+                           "history file.  History will not be available during this session.")
+        System.err.println(e.getMessage)
+    }
+
+    val clientTransportTSocketField = classOf[CliSessionState].getDeclaredField("transport")
+    clientTransportTSocketField.setAccessible(true)
+
+    transport = clientTransportTSocketField.get(sessionState).asInstanceOf[TSocket]
+
+    var ret = 0
+    var prefix = ""
+    val currentDB = ReflectionUtils.invokeStatic(classOf[CliDriver], "getFormattedDb",
+      classOf[HiveConf] -> conf, classOf[CliSessionState] -> sessionState)
+
+    def promptWithCurrentDB = s"$prompt$currentDB"
+    def continuedPromptWithDBSpaces = continuedPrompt + ReflectionUtils.invokeStatic(
+      classOf[CliDriver], "spacesForString", classOf[String] -> currentDB)
+
+    var currentPrompt = promptWithCurrentDB
+    var line = reader.readLine(currentPrompt + "> ")
+
+    while (line != null) {
+      if (prefix.nonEmpty) {
+        prefix += '\n'
+      }
+
+      if (line.trim().endsWith(";") && !line.trim().endsWith("\\;")) {
+        line = prefix + line
+        ret = cli.processLine(line, true)
+        prefix = ""
+        currentPrompt = promptWithCurrentDB
+      } else {
+        prefix = prefix + line
+        currentPrompt = continuedPromptWithDBSpaces
+      }
+
+      line = reader.readLine(currentPrompt + "> ")
+    }
+
+    sessionState.close()
+
+    System.exit(ret)
+  }
+}
+
+private[hive] class SparkSQLCLIDriver extends CliDriver with Logging {
+  private val sessionState = SessionState.get().asInstanceOf[CliSessionState]
+
+  private val LOG = LogFactory.getLog("CliDriver")
+
+  private val console = new SessionState.LogHelper(LOG)
+
+  private val conf: Configuration =
+    if (sessionState != null) sessionState.getConf else new Configuration()
+
+  // Force initializing SparkSQLEnv. This is put here but not object SparkSQLCliDriver
+  // because the Hive unit tests do not go through the main() code path.
+  if (!sessionState.isRemoteMode) {
+    SparkSQLEnv.init()
+  }
+
+  override def processCmd(cmd: String): Int = {
+    val cmd_trimmed: String = cmd.trim()
+    val tokens: Array[String] = cmd_trimmed.split("\\s+")
+    val cmd_1: String = cmd_trimmed.substring(tokens(0).length()).trim()
+    if (cmd_trimmed.toLowerCase.equals("quit") ||
+      cmd_trimmed.toLowerCase.equals("exit") ||
+      tokens(0).equalsIgnoreCase("source") ||
+      cmd_trimmed.startsWith("!") ||
+      tokens(0).toLowerCase.equals("list") ||
+      sessionState.isRemoteMode) {
+      val start = System.currentTimeMillis()
+      super.processCmd(cmd)
+      val end = System.currentTimeMillis()
+      val timeTaken: Double = (end - start) / 1000.0
+      console.printInfo(s"Time taken: $timeTaken seconds")
+      0
+    } else {
+      var ret = 0
+      val hconf = conf.asInstanceOf[HiveConf]
+      val proc: CommandProcessor = CommandProcessorFactory.get(tokens(0), hconf)
+
+      if (proc != null) {
+        if (proc.isInstanceOf[Driver]) {
+          val driver = new SparkSQLDriver
+
+          driver.init()
+          val out = sessionState.out
+          val start:Long = System.currentTimeMillis()
+          if (sessionState.getIsVerbose) {
+            out.println(cmd)
+          }
+
+          ret = driver.run(cmd).getResponseCode
+          if (ret != 0) {
+            driver.close()
+            return ret
+          }
+
+          val res = new JArrayList[String]()
+
+          if (HiveConf.getBoolVar(conf, HiveConf.ConfVars.HIVE_CLI_PRINT_HEADER)) {
+            // Print the column names.
+            Option(driver.getSchema.getFieldSchemas).map { fields =>
+              out.println(fields.map(_.getName).mkString("\t"))
+            }
+          }
+
+          try {
+            while (!out.checkError() && driver.getResults(res)) {
+              res.foreach(out.println)
+              res.clear()
+            }
+          } catch {
+            case e:IOException =>
+              console.printError(
+                s"""Failed with exception ${e.getClass.getName}: ${e.getMessage}
+                   |${org.apache.hadoop.util.StringUtils.stringifyException(e)}
+                 """.stripMargin)
+              ret = 1
+          }
+
+          val cret = driver.close()
+          if (ret == 0) {
+            ret = cret
+          }
+
+          val end = System.currentTimeMillis()
+          if (end > start) {
+            val timeTaken:Double = (end - start) / 1000.0
+            console.printInfo(s"Time taken: $timeTaken seconds", null)
+          }
+
+          // Destroy the driver to release all the locks.
+          driver.destroy()
+        } else {
+          if (sessionState.getIsVerbose) {
+            sessionState.out.println(tokens(0) + " " + cmd_1)
+          }
+          ret = proc.run(cmd_1).getResponseCode
+        }
+      }
+      ret
+    }
+  }
+}
+
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIService.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIService.scala
new file mode 100644
index 000000000000..42cbf363b274
--- /dev/null
+++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIService.scala
@@ -0,0 +1,74 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive.thriftserver
+
+import scala.collection.JavaConversions._
+
+import java.io.IOException
+import java.util.{List => JList}
+import javax.security.auth.login.LoginException
+
+import org.apache.commons.logging.Log
+import org.apache.hadoop.hive.conf.HiveConf
+import org.apache.hadoop.hive.shims.ShimLoader
+import org.apache.hive.service.Service.STATE
+import org.apache.hive.service.auth.HiveAuthFactory
+import org.apache.hive.service.cli.CLIService
+import org.apache.hive.service.{AbstractService, Service, ServiceException}
+
+import org.apache.spark.sql.hive.HiveContext
+import org.apache.spark.sql.hive.thriftserver.ReflectionUtils._
+
+private[hive] class SparkSQLCLIService(hiveContext: HiveContext)
+  extends CLIService
+  with ReflectedCompositeService {
+
+  override def init(hiveConf: HiveConf) {
+    setSuperField(this, "hiveConf", hiveConf)
+
+    val sparkSqlSessionManager = new SparkSQLSessionManager(hiveContext)
+    setSuperField(this, "sessionManager", sparkSqlSessionManager)
+    addService(sparkSqlSessionManager)
+
+    try {
+      HiveAuthFactory.loginFromKeytab(hiveConf)
+      val serverUserName = ShimLoader.getHadoopShims
+        .getShortUserName(ShimLoader.getHadoopShims.getUGIForConf(hiveConf))
+      setSuperField(this, "serverUserName", serverUserName)
+    } catch {
+      case e @ (_: IOException | _: LoginException) =>
+        throw new ServiceException("Unable to login to kerberos with given principal/keytab", e)
+    }
+
+    initCompositeService(hiveConf)
+  }
+}
+
+private[thriftserver] trait ReflectedCompositeService { this: AbstractService =>
+  def initCompositeService(hiveConf: HiveConf) {
+    // Emulating `CompositeService.init(hiveConf)`
+    val serviceList = getAncestorField[JList[Service]](this, 2, "serviceList")
+    serviceList.foreach(_.init(hiveConf))
+
+    // Emulating `AbstractService.init(hiveConf)`
+    invoke(classOf[AbstractService], this, "ensureCurrentState", classOf[STATE] -> STATE.NOTINITED)
+    setAncestorField(this, 3, "hiveConf", hiveConf)
+    invoke(classOf[AbstractService], this, "changeState", classOf[STATE] -> STATE.INITED)
+    getAncestorField[Log](this, 3, "LOG").info(s"Service: $getName is inited.")
+  }
+}
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLDriver.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLDriver.scala
new file mode 100644
index 000000000000..5202aa9903e0
--- /dev/null
+++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLDriver.scala
@@ -0,0 +1,93 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive.thriftserver
+
+import scala.collection.JavaConversions._
+
+import java.util.{ArrayList => JArrayList}
+
+import org.apache.commons.lang.exception.ExceptionUtils
+import org.apache.hadoop.hive.metastore.api.{FieldSchema, Schema}
+import org.apache.hadoop.hive.ql.Driver
+import org.apache.hadoop.hive.ql.processors.CommandProcessorResponse
+
+import org.apache.spark.sql.Logging
+import org.apache.spark.sql.hive.{HiveContext, HiveMetastoreTypes}
+
+private[hive] class SparkSQLDriver(val context: HiveContext = SparkSQLEnv.hiveContext)
+  extends Driver with Logging {
+
+  private var tableSchema: Schema = _
+  private var hiveResponse: Seq[String] = _
+
+  override def init(): Unit = {
+  }
+
+  private def getResultSetSchema(query: context.QueryExecution): Schema = {
+    val analyzed = query.analyzed
+    logger.debug(s"Result Schema: ${analyzed.output}")
+    if (analyzed.output.size == 0) {
+      new Schema(new FieldSchema("Response code", "string", "") :: Nil, null)
+    } else {
+      val fieldSchemas = analyzed.output.map { attr =>
+        new FieldSchema(attr.name, HiveMetastoreTypes.toMetastoreType(attr.dataType), "")
+      }
+
+      new Schema(fieldSchemas, null)
+    }
+  }
+
+  override def run(command: String): CommandProcessorResponse = {
+    val execution = context.executePlan(context.hql(command).logicalPlan)
+
+    // TODO unify the error code
+    try {
+      hiveResponse = execution.stringResult()
+      tableSchema = getResultSetSchema(execution)
+      new CommandProcessorResponse(0)
+    } catch {
+      case cause: Throwable =>
+        logger.error(s"Failed in [$command]", cause)
+        new CommandProcessorResponse(-3, ExceptionUtils.getFullStackTrace(cause), null)
+    }
+  }
+
+  override def close(): Int = {
+    hiveResponse = null
+    tableSchema = null
+    0
+  }
+
+  override def getSchema: Schema = tableSchema
+
+  override def getResults(res: JArrayList[String]): Boolean = {
+    if (hiveResponse == null) {
+      false
+    } else {
+      res.addAll(hiveResponse)
+      hiveResponse = null
+      true
+    }
+  }
+
+  override def destroy() {
+    super.destroy()
+    hiveResponse = null
+    tableSchema = null
+  }
+}
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLEnv.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLEnv.scala
new file mode 100644
index 000000000000..451c3bd7b935
--- /dev/null
+++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLEnv.scala
@@ -0,0 +1,58 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive.thriftserver
+
+import org.apache.hadoop.hive.ql.session.SessionState
+
+import org.apache.spark.scheduler.{SplitInfo, StatsReportListener}
+import org.apache.spark.sql.Logging
+import org.apache.spark.sql.hive.HiveContext
+import org.apache.spark.{SparkConf, SparkContext}
+
+/** A singleton object for the master program. The slaves should not access this. */
+private[hive] object SparkSQLEnv extends Logging {
+  logger.debug("Initializing SparkSQLEnv")
+
+  var hiveContext: HiveContext = _
+  var sparkContext: SparkContext = _
+
+  def init() {
+    if (hiveContext == null) {
+      sparkContext = new SparkContext(new SparkConf()
+        .setAppName(s"SparkSQL::${java.net.InetAddress.getLocalHost.getHostName}"))
+
+      sparkContext.addSparkListener(new StatsReportListener())
+
+      hiveContext = new HiveContext(sparkContext) {
+        @transient override lazy val sessionState = SessionState.get()
+        @transient override lazy val hiveconf = sessionState.getConf
+      }
+    }
+  }
+
+  /** Cleans up and shuts down the Spark SQL environments. */
+  def stop() {
+    logger.debug("Shutting down Spark SQL Environment")
+    // Stop the SparkContext
+    if (SparkSQLEnv.sparkContext != null) {
+      sparkContext.stop()
+      sparkContext = null
+      hiveContext = null
+    }
+  }
+}
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLSessionManager.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLSessionManager.scala
new file mode 100644
index 000000000000..6b3275b4eaf0
--- /dev/null
+++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLSessionManager.scala
@@ -0,0 +1,49 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive.thriftserver
+
+import java.util.concurrent.Executors
+
+import org.apache.commons.logging.Log
+import org.apache.hadoop.hive.conf.HiveConf
+import org.apache.hadoop.hive.conf.HiveConf.ConfVars
+import org.apache.hive.service.cli.session.SessionManager
+
+import org.apache.spark.sql.hive.HiveContext
+import org.apache.spark.sql.hive.thriftserver.ReflectionUtils._
+import org.apache.spark.sql.hive.thriftserver.server.SparkSQLOperationManager
+
+private[hive] class SparkSQLSessionManager(hiveContext: HiveContext)
+  extends SessionManager
+  with ReflectedCompositeService {
+
+  override def init(hiveConf: HiveConf) {
+    setSuperField(this, "hiveConf", hiveConf)
+
+    val backgroundPoolSize = hiveConf.getIntVar(ConfVars.HIVE_SERVER2_ASYNC_EXEC_THREADS)
+    setSuperField(this, "backgroundOperationPool", Executors.newFixedThreadPool(backgroundPoolSize))
+    getAncestorField[Log](this, 3, "LOG").info(
+      s"HiveServer2: Async execution pool size $backgroundPoolSize")
+
+    val sparkSqlOperationManager = new SparkSQLOperationManager(hiveContext)
+    setSuperField(this, "operationManager", sparkSqlOperationManager)
+    addService(sparkSqlOperationManager)
+
+    initCompositeService(hiveConf)
+  }
+}
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/server/SparkSQLOperationManager.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/server/SparkSQLOperationManager.scala
new file mode 100644
index 000000000000..a4e1f3e762e8
--- /dev/null
+++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/server/SparkSQLOperationManager.scala
@@ -0,0 +1,151 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive.thriftserver.server
+
+import scala.collection.JavaConversions._
+import scala.collection.mutable.ArrayBuffer
+import scala.math.{random, round}
+
+import java.sql.Timestamp
+import java.util.{Map => JMap}
+
+import org.apache.hadoop.hive.common.`type`.HiveDecimal
+import org.apache.hadoop.hive.metastore.api.FieldSchema
+import org.apache.hive.service.cli._
+import org.apache.hive.service.cli.operation.{ExecuteStatementOperation, Operation, OperationManager}
+import org.apache.hive.service.cli.session.HiveSession
+
+import org.apache.spark.sql.catalyst.types._
+import org.apache.spark.sql.hive.thriftserver.ReflectionUtils
+import org.apache.spark.sql.hive.{HiveContext, HiveMetastoreTypes}
+import org.apache.spark.sql.{Logging, SchemaRDD, Row => SparkRow}
+
+/**
+ * Executes queries using Spark SQL, and maintains a list of handles to active queries.
+ */
+class SparkSQLOperationManager(hiveContext: HiveContext) extends OperationManager with Logging {
+  val handleToOperation = ReflectionUtils
+    .getSuperField[JMap[OperationHandle, Operation]](this, "handleToOperation")
+
+  override def newExecuteStatementOperation(
+      parentSession: HiveSession,
+      statement: String,
+      confOverlay: JMap[String, String],
+      async: Boolean): ExecuteStatementOperation = synchronized {
+
+    val operation = new ExecuteStatementOperation(parentSession, statement, confOverlay) {
+      private var result: SchemaRDD = _
+      private var iter: Iterator[SparkRow] = _
+      private var dataTypes: Array[DataType] = _
+
+      def close(): Unit = {
+        // RDDs will be cleaned automatically upon garbage collection.
+        logger.debug("CLOSING")
+      }
+
+      def getNextRowSet(order: FetchOrientation, maxRowsL: Long): RowSet = {
+        if (!iter.hasNext) {
+          new RowSet()
+        } else {
+          val maxRows = maxRowsL.toInt // Do you really want a row batch larger than Int Max? No.
+          var curRow = 0
+          var rowSet = new ArrayBuffer[Row](maxRows)
+
+          while (curRow < maxRows && iter.hasNext) {
+            val sparkRow = iter.next()
+            val row = new Row()
+            var curCol = 0
+
+            while (curCol < sparkRow.length) {
+              dataTypes(curCol) match {
+                case StringType =>
+                  row.addString(sparkRow(curCol).asInstanceOf[String])
+                case IntegerType =>
+                  row.addColumnValue(ColumnValue.intValue(sparkRow.getInt(curCol)))
+                case BooleanType =>
+                  row.addColumnValue(ColumnValue.booleanValue(sparkRow.getBoolean(curCol)))
+                case DoubleType =>
+                  row.addColumnValue(ColumnValue.doubleValue(sparkRow.getDouble(curCol)))
+                case FloatType =>
+                  row.addColumnValue(ColumnValue.floatValue(sparkRow.getFloat(curCol)))
+                case DecimalType =>
+                  val hiveDecimal = sparkRow.get(curCol).asInstanceOf[BigDecimal].bigDecimal
+                  row.addColumnValue(ColumnValue.stringValue(new HiveDecimal(hiveDecimal)))
+                case LongType =>
+                  row.addColumnValue(ColumnValue.longValue(sparkRow.getLong(curCol)))
+                case ByteType =>
+                  row.addColumnValue(ColumnValue.byteValue(sparkRow.getByte(curCol)))
+                case ShortType =>
+                  row.addColumnValue(ColumnValue.intValue(sparkRow.getShort(curCol)))
+                case TimestampType =>
+                  row.addColumnValue(
+                    ColumnValue.timestampValue(sparkRow.get(curCol).asInstanceOf[Timestamp]))
+                case BinaryType | _: ArrayType | _: StructType | _: MapType =>
+                  val hiveString = result
+                    .queryExecution
+                    .asInstanceOf[HiveContext#QueryExecution]
+                    .toHiveString((sparkRow.get(curCol), dataTypes(curCol)))
+                  row.addColumnValue(ColumnValue.stringValue(hiveString))
+              }
+              curCol += 1
+            }
+            rowSet += row
+            curRow += 1
+          }
+          new RowSet(rowSet, 0)
+        }
+      }
+
+      def getResultSetSchema: TableSchema = {
+        logger.warn(s"Result Schema: ${result.queryExecution.analyzed.output}")
+        if (result.queryExecution.analyzed.output.size == 0) {
+          new TableSchema(new FieldSchema("Result", "string", "") :: Nil)
+        } else {
+          val schema = result.queryExecution.analyzed.output.map { attr =>
+            new FieldSchema(attr.name, HiveMetastoreTypes.toMetastoreType(attr.dataType), "")
+          }
+          new TableSchema(schema)
+        }
+      }
+
+      def run(): Unit = {
+        logger.info(s"Running query '$statement'")
+        setState(OperationState.RUNNING)
+        try {
+          result = hiveContext.hql(statement)
+          logger.debug(result.queryExecution.toString())
+          val groupId = round(random * 1000000).toString
+          hiveContext.sparkContext.setJobGroup(groupId, statement)
+          iter = result.queryExecution.toRdd.toLocalIterator
+          dataTypes = result.queryExecution.analyzed.output.map(_.dataType).toArray
+          setHasResultSet(true)
+        } catch {
+          // Actually do need to catch Throwable as some failures don't inherit from Exception and
+          // HiveServer will silently swallow them.
+          case e: Throwable =>
+            logger.error("Error executing query:",e)
+            throw new HiveSQLException(e.toString)
+        }
+        setState(OperationState.FINISHED)
+      }
+    }
+
+   handleToOperation.put(operation.getHandle, operation)
+   operation
+  }
+}
diff --git a/sql/hive-thriftserver/src/test/resources/data/files/small_kv.txt b/sql/hive-thriftserver/src/test/resources/data/files/small_kv.txt
new file mode 100644
index 000000000000..850f8014b6f0
--- /dev/null
+++ b/sql/hive-thriftserver/src/test/resources/data/files/small_kv.txt
@@ -0,0 +1,5 @@
+238
val_238
+86
val_86
+311
val_311
+27
val_27
+165
val_165
diff --git a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/CliSuite.scala b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/CliSuite.scala
new file mode 100644
index 000000000000..b90670a796b8
--- /dev/null
+++ b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/CliSuite.scala
@@ -0,0 +1,59 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive.thriftserver
+
+import java.io.{BufferedReader, InputStreamReader, PrintWriter}
+
+import org.scalatest.{BeforeAndAfterAll, FunSuite}
+
+import org.apache.spark.sql.hive.test.TestHive
+
+class CliSuite extends FunSuite with BeforeAndAfterAll with TestUtils {
+  val WAREHOUSE_PATH = TestUtils.getWarehousePath("cli")
+  val METASTORE_PATH = TestUtils.getMetastorePath("cli")
+
+  override def beforeAll() {
+    val pb = new ProcessBuilder(
+      "../../bin/spark-sql",
+      "--master",
+      "local",
+      "--hiveconf",
+      s"javax.jdo.option.ConnectionURL=jdbc:derby:;databaseName=$METASTORE_PATH;create=true",
+      "--hiveconf",
+      "hive.metastore.warehouse.dir=" + WAREHOUSE_PATH)
+
+    process = pb.start()
+    outputWriter = new PrintWriter(process.getOutputStream, true)
+    inputReader = new BufferedReader(new InputStreamReader(process.getInputStream))
+    errorReader = new BufferedReader(new InputStreamReader(process.getErrorStream))
+    waitForOutput(inputReader, "spark-sql>")
+  }
+
+  override def afterAll() {
+    process.destroy()
+    process.waitFor()
+  }
+
+  test("simple commands") {
+    val dataFilePath = getDataFile("data/files/small_kv.txt")
+    executeQuery("create table hive_test1(key int, val string);")
+    executeQuery("load data local inpath '" + dataFilePath+ "' overwrite into table hive_test1;")
+    executeQuery("cache table hive_test1", "Time taken")
+  }
+}
diff --git a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suite.scala b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suite.scala
new file mode 100644
index 000000000000..59f4952b78bc
--- /dev/null
+++ b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suite.scala
@@ -0,0 +1,125 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive.thriftserver
+
+import scala.collection.JavaConversions._
+import scala.concurrent.ExecutionContext.Implicits.global
+import scala.concurrent._
+
+import java.io.{BufferedReader, InputStreamReader}
+import java.sql.{Connection, DriverManager, Statement}
+
+import org.scalatest.{BeforeAndAfterAll, FunSuite}
+
+import org.apache.spark.sql.Logging
+import org.apache.spark.sql.catalyst.util.getTempFilePath
+
+/**
+ * Test for the HiveThriftServer2 using JDBC.
+ */
+class HiveThriftServer2Suite extends FunSuite with BeforeAndAfterAll with TestUtils with Logging {
+
+  val WAREHOUSE_PATH = getTempFilePath("warehouse")
+  val METASTORE_PATH = getTempFilePath("metastore")
+
+  val DRIVER_NAME  = "org.apache.hive.jdbc.HiveDriver"
+  val TABLE = "test"
+  // use a different port, than the hive standard 10000,
+  // for tests to avoid issues with the port being taken on some machines
+  val PORT = "10000"
+
+  // If verbose is true, the test program will print all outputs coming from the Hive Thrift server.
+  val VERBOSE = Option(System.getenv("SPARK_SQL_TEST_VERBOSE")).getOrElse("false").toBoolean
+
+  Class.forName(DRIVER_NAME)
+
+  override def beforeAll() { launchServer() }
+
+  override def afterAll() { stopServer() }
+
+  private def launchServer(args: Seq[String] = Seq.empty) {
+    // Forking a new process to start the Hive Thrift server. The reason to do this is it is
+    // hard to clean up Hive resources entirely, so we just start a new process and kill
+    // that process for cleanup.
+    val defaultArgs = Seq(
+      "../../sbin/start-thriftserver.sh",
+      "--master local",
+      "--hiveconf",
+      "hive.root.logger=INFO,console",
+      "--hiveconf",
+      s"javax.jdo.option.ConnectionURL=jdbc:derby:;databaseName=$METASTORE_PATH;create=true",
+      "--hiveconf",
+      s"hive.metastore.warehouse.dir=$WAREHOUSE_PATH")
+    val pb = new ProcessBuilder(defaultArgs ++ args)
+    process = pb.start()
+    inputReader = new BufferedReader(new InputStreamReader(process.getInputStream))
+    errorReader = new BufferedReader(new InputStreamReader(process.getErrorStream))
+    waitForOutput(inputReader, "ThriftBinaryCLIService listening on")
+
+    // Spawn a thread to read the output from the forked process.
+    // Note that this is necessary since in some configurations, log4j could be blocked
+    // if its output to stderr are not read, and eventually blocking the entire test suite.
+    future {
+      while (true) {
+        val stdout = readFrom(inputReader)
+        val stderr = readFrom(errorReader)
+        if (VERBOSE && stdout.length > 0) {
+          println(stdout)
+        }
+        if (VERBOSE && stderr.length > 0) {
+          println(stderr)
+        }
+        Thread.sleep(50)
+      }
+    }
+  }
+
+  private def stopServer() {
+    process.destroy()
+    process.waitFor()
+  }
+
+  test("test query execution against a Hive Thrift server") {
+    Thread.sleep(5 * 1000)
+    val dataFilePath = getDataFile("data/files/small_kv.txt")
+    val stmt = createStatement()
+    stmt.execute("DROP TABLE IF EXISTS test")
+    stmt.execute("DROP TABLE IF EXISTS test_cached")
+    stmt.execute("CREATE TABLE test(key int, val string)")
+    stmt.execute(s"LOAD DATA LOCAL INPATH '$dataFilePath' OVERWRITE INTO TABLE test")
+    stmt.execute("CREATE TABLE test_cached as select * from test limit 4")
+    stmt.execute("CACHE TABLE test_cached")
+
+    var rs = stmt.executeQuery("select count(*) from test")
+    rs.next()
+    assert(rs.getInt(1) === 5)
+
+    rs = stmt.executeQuery("select count(*) from test_cached")
+    rs.next()
+    assert(rs.getInt(1) === 4)
+
+    stmt.close()
+  }
+
+  def getConnection: Connection = {
+    val connectURI = s"jdbc:hive2://localhost:$PORT/"
+    DriverManager.getConnection(connectURI, System.getProperty("user.name"), "")
+  }
+
+  def createStatement(): Statement = getConnection.createStatement()
+}
diff --git a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/TestUtils.scala b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/TestUtils.scala
new file mode 100644
index 000000000000..bb2242618fbe
--- /dev/null
+++ b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/TestUtils.scala
@@ -0,0 +1,108 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive.thriftserver
+
+import java.io.{BufferedReader, PrintWriter}
+import java.text.SimpleDateFormat
+import java.util.Date
+
+import org.apache.hadoop.hive.common.LogUtils
+import org.apache.hadoop.hive.common.LogUtils.LogInitializationException
+
+object TestUtils {
+  val timestamp = new SimpleDateFormat("yyyyMMdd-HHmmss")
+
+  def getWarehousePath(prefix: String): String = {
+    System.getProperty("user.dir") + "/test_warehouses/" + prefix + "-warehouse-" +
+      timestamp.format(new Date)
+  }
+
+  def getMetastorePath(prefix: String): String = {
+    System.getProperty("user.dir") + "/test_warehouses/" + prefix + "-metastore-" +
+      timestamp.format(new Date)
+  }
+
+  // Dummy function for initialize the log4j properties.
+  def init() { }
+
+  // initialize log4j
+  try {
+    LogUtils.initHiveLog4j()
+  } catch {
+    case e: LogInitializationException => // Ignore the error.
+  }
+}
+
+trait TestUtils {
+  var process : Process = null
+  var outputWriter : PrintWriter = null
+  var inputReader : BufferedReader = null
+  var errorReader : BufferedReader = null
+
+  def executeQuery(
+    cmd: String, outputMessage: String = "OK", timeout: Long = 15000): String = {
+    println("Executing: " + cmd + ", expecting output: " + outputMessage)
+    outputWriter.write(cmd + "\n")
+    outputWriter.flush()
+    waitForQuery(timeout, outputMessage)
+  }
+
+  protected def waitForQuery(timeout: Long, message: String): String = {
+    if (waitForOutput(errorReader, message, timeout)) {
+      Thread.sleep(500)
+      readOutput()
+    } else {
+      assert(false, "Didn't find \"" + message + "\" in the output:\n" + readOutput())
+      null
+    }
+  }
+
+  // Wait for the specified str to appear in the output.
+  protected def waitForOutput(
+    reader: BufferedReader, str: String, timeout: Long = 10000): Boolean = {
+    val startTime = System.currentTimeMillis
+    var out = ""
+    while (!out.contains(str) && System.currentTimeMillis < (startTime + timeout)) {
+      out += readFrom(reader)
+    }
+    out.contains(str)
+  }
+
+  // Read stdout output and filter out garbage collection messages.
+  protected def readOutput(): String = {
+    val output = readFrom(inputReader)
+    // Remove GC Messages
+    val filteredOutput = output.lines.filterNot(x => x.contains("[GC") || x.contains("[Full GC"))
+      .mkString("\n")
+    filteredOutput
+  }
+
+  protected def readFrom(reader: BufferedReader): String = {
+    var out = ""
+    var c = 0
+    while (reader.ready) {
+      c = reader.read()
+      out += c.asInstanceOf[Char]
+    }
+    out
+  }
+
+  protected def getDataFile(name: String) = {
+    Thread.currentThread().getContextClassLoader.getResource(name)
+  }
+}
diff --git a/sql/hive/pom.xml b/sql/hive/pom.xml
index 1699ffe06ce1..93d00f7c37c9 100644
--- a/sql/hive/pom.xml
+++ b/sql/hive/pom.xml
@@ -32,7 +32,7 @@
   <name>Spark Project Hive</name>
   <url>http://spark.apache.org/</url>
   <properties>
-     <sbt.project.name>hive</sbt.project.name>
+    <sbt.project.name>hive</sbt.project.name>
   </properties>
 
   <dependencies>
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
index 201c85f3d501..84d43eaeea51 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
@@ -255,7 +255,7 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) {
       Seq(StringType, IntegerType, LongType, DoubleType, FloatType, BooleanType, ByteType,
         ShortType, DecimalType, TimestampType, BinaryType)
 
-    protected def toHiveString(a: (Any, DataType)): String = a match {
+    protected[sql] def toHiveString(a: (Any, DataType)): String = a match {
       case (struct: Row, StructType(fields)) =>
         struct.zip(fields).map {
           case (v, t) => s""""${t.name}":${toHiveStructString(v, t.dataType)}"""
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
index 6f36a4f8cb90..8489f2a34e63 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
@@ -416,10 +416,10 @@ class HiveQuerySuite extends HiveComparisonTest {
     hql(s"set $testKey=$testVal")
     assert(get(testKey, testVal + "_") == testVal)
 
-    hql("set mapred.reduce.tasks=20")
-    assert(get("mapred.reduce.tasks", "0") == "20")
-    hql("set mapred.reduce.tasks = 40")
-    assert(get("mapred.reduce.tasks", "0") == "40")
+    hql("set some.property=20")
+    assert(get("some.property", "0") == "20")
+    hql("set some.property = 40")
+    assert(get("some.property", "0") == "40")
 
     hql(s"set $testKey=$testVal")
     assert(get(testKey, "0") == testVal)
@@ -433,63 +433,61 @@ class HiveQuerySuite extends HiveComparisonTest {
     val testKey = "spark.sql.key.usedfortestonly"
     val testVal = "test.val.0"
     val nonexistentKey = "nonexistent"
-    def collectResults(rdd: SchemaRDD): Set[(String, String)] =
-      rdd.collect().map { case Row(key: String, value: String) => key -> value }.toSet
 
     clear()
 
     // "set" itself returns all config variables currently specified in SQLConf.
     assert(hql("SET").collect().size == 0)
 
-    assertResult(Set(testKey -> testVal)) {
-      collectResults(hql(s"SET $testKey=$testVal"))
+    assertResult(Array(s"$testKey=$testVal")) {
+      hql(s"SET $testKey=$testVal").collect().map(_.getString(0))
     }
 
     assert(hiveconf.get(testKey, "") == testVal)
-    assertResult(Set(testKey -> testVal)) {
-      collectResults(hql("SET"))
+    assertResult(Array(s"$testKey=$testVal")) {
+      hql(s"SET $testKey=$testVal").collect().map(_.getString(0))
     }
 
     hql(s"SET ${testKey + testKey}=${testVal + testVal}")
     assert(hiveconf.get(testKey + testKey, "") == testVal + testVal)
-    assertResult(Set(testKey -> testVal, (testKey + testKey) -> (testVal + testVal))) {
-      collectResults(hql("SET"))
+    assertResult(Array(s"$testKey=$testVal", s"${testKey + testKey}=${testVal + testVal}")) {
+      hql(s"SET").collect().map(_.getString(0))
     }
 
     // "set key"
-    assertResult(Set(testKey -> testVal)) {
-      collectResults(hql(s"SET $testKey"))
+    assertResult(Array(s"$testKey=$testVal")) {
+      hql(s"SET $testKey").collect().map(_.getString(0))
     }
 
-    assertResult(Set(nonexistentKey -> "<undefined>")) {
-      collectResults(hql(s"SET $nonexistentKey"))
+    assertResult(Array(s"$nonexistentKey=<undefined>")) {
+      hql(s"SET $nonexistentKey").collect().map(_.getString(0))
     }
 
     // Assert that sql() should have the same effects as hql() by repeating the above using sql().
     clear()
     assert(sql("SET").collect().size == 0)
 
-    assertResult(Set(testKey -> testVal)) {
-      collectResults(sql(s"SET $testKey=$testVal"))
+    assertResult(Array(s"$testKey=$testVal")) {
+      sql(s"SET $testKey=$testVal").collect().map(_.getString(0))
     }
 
     assert(hiveconf.get(testKey, "") == testVal)
-    assertResult(Set(testKey -> testVal)) {
-      collectResults(sql("SET"))
+    assertResult(Array(s"$testKey=$testVal")) {
+      sql("SET").collect().map(_.getString(0))
     }
 
     sql(s"SET ${testKey + testKey}=${testVal + testVal}")
     assert(hiveconf.get(testKey + testKey, "") == testVal + testVal)
-    assertResult(Set(testKey -> testVal, (testKey + testKey) -> (testVal + testVal))) {
-      collectResults(sql("SET"))
+    assertResult(Array(s"$testKey=$testVal", s"${testKey + testKey}=${testVal + testVal}")) {
+      sql("SET").collect().map(_.getString(0))
     }
 
-    assertResult(Set(testKey -> testVal)) {
-      collectResults(sql(s"SET $testKey"))
+    assertResult(Array(s"$testKey=$testVal")) {
+      sql(s"SET $testKey").collect().map(_.getString(0))
     }
 
-    assertResult(Set(nonexistentKey -> "<undefined>")) {
-      collectResults(sql(s"SET $nonexistentKey"))
+    assertResult(Array(s"$nonexistentKey=<undefined>")) {
+      sql(s"SET $nonexistentKey").collect().map(_.getString(0))
     }
 
     clear()
diff --git a/streaming/pom.xml b/streaming/pom.xml
index f60697ce745b..b99f306b8f2c 100644
--- a/streaming/pom.xml
+++ b/streaming/pom.xml
@@ -28,7 +28,7 @@
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-streaming_2.10</artifactId>
   <properties>
-     <sbt.project.name>streaming</sbt.project.name>
+    <sbt.project.name>streaming</sbt.project.name>
   </properties>
   <packaging>jar</packaging>
   <name>Spark Project Streaming</name>
diff --git a/tools/pom.xml b/tools/pom.xml
index c0ee8faa7a61..97abb6b2b63e 100644
--- a/tools/pom.xml
+++ b/tools/pom.xml
@@ -27,7 +27,7 @@
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-tools_2.10</artifactId>
   <properties>
-     <sbt.project.name>tools</sbt.project.name>
+    <sbt.project.name>tools</sbt.project.name>
   </properties>
   <packaging>jar</packaging>
   <name>Spark Project Tools</name>
diff --git a/yarn/alpha/pom.xml b/yarn/alpha/pom.xml
index 5b13a1f002d6..51744ece0412 100644
--- a/yarn/alpha/pom.xml
+++ b/yarn/alpha/pom.xml
@@ -24,7 +24,7 @@
     <relativePath>../pom.xml</relativePath>
   </parent>
   <properties>
-     <sbt.project.name>yarn-alpha</sbt.project.name>
+    <sbt.project.name>yarn-alpha</sbt.project.name>
   </properties>
 
   <groupId>org.apache.spark</groupId>
diff --git a/yarn/pom.xml b/yarn/pom.xml
index efb473aa1b26..3faaf053634d 100644
--- a/yarn/pom.xml
+++ b/yarn/pom.xml
@@ -29,7 +29,7 @@
   <packaging>pom</packaging>
   <name>Spark Project YARN Parent POM</name>
   <properties>
-     <sbt.project.name>yarn</sbt.project.name>
+    <sbt.project.name>yarn</sbt.project.name>
   </properties>
 
   <dependencies>
diff --git a/yarn/stable/pom.xml b/yarn/stable/pom.xml
index ceaf9f9d7100..b6c8456d0668 100644
--- a/yarn/stable/pom.xml
+++ b/yarn/stable/pom.xml
@@ -24,7 +24,7 @@
     <relativePath>../pom.xml</relativePath>
   </parent>
   <properties>
-     <sbt.project.name>yarn-stable</sbt.project.name>
+    <sbt.project.name>yarn-stable</sbt.project.name>
   </properties>
 
   <groupId>org.apache.spark</groupId>

From a19d8c89d90eea7eb8295378cec3d27444e0336d Mon Sep 17 00:00:00 2001
From: Yin Huai <huai@cse.ohio-state.edu>
Date: Fri, 25 Jul 2014 13:00:13 -0700
Subject: [PATCH 761/810] [SPARK-2682] Javadoc generated from Scala source code
 is not in javadoc's index

Add genjavadocSettings back to SparkBuild. It requires #1585 .

https://issues.apache.org/jira/browse/SPARK-2682

Author: Yin Huai <huai@cse.ohio-state.edu>

Closes #1584 from yhuai/SPARK-2682 and squashes the following commits:

2e89461 [Yin Huai] Merge remote-tracking branch 'upstream/master' into SPARK-2682
54e3b66 [Yin Huai] Add genjavadocSettings back.
---
 project/SparkBuild.scala | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index 86d47734e77b..1629bc2cba8b 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -21,6 +21,7 @@ import scala.collection.JavaConversions._
 import sbt._
 import sbt.Classpaths.publishTask
 import sbt.Keys._
+import sbtunidoc.Plugin.genjavadocSettings
 import org.scalastyle.sbt.ScalastylePlugin.{Settings => ScalaStyleSettings}
 import com.typesafe.sbt.pom.{PomBuild, SbtPomKeys}
 import net.virtualvoid.sbt.graph.Plugin.graphSettings
@@ -107,7 +108,7 @@ object SparkBuild extends PomBuild {
   lazy val MavenCompile = config("m2r") extend(Compile)
   lazy val publishLocalBoth = TaskKey[Unit]("publish-local", "publish local for m2 and ivy")
 
-  lazy val sharedSettings = graphSettings ++ ScalaStyleSettings ++ Seq (
+  lazy val sharedSettings = graphSettings ++ ScalaStyleSettings ++ genjavadocSettings ++ Seq (
     javaHome   := Properties.envOrNone("JAVA_HOME").map(file),
     incOptions := incOptions.value.withNameHashing(true),
     retrieveManaged := true,

From ab3c6a455c0b50e3fcfea3bbb3b9035aba8f06e3 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?baishuo=28=E7=99=BD=E7=A1=95=29?= <vc_java@hotmail.com>
Date: Fri, 25 Jul 2014 13:59:45 -0700
Subject: [PATCH 762/810] [SQL]Update HiveMetastoreCatalog.scala
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

I think it's better to defined hiveQlTable as a val

Author: baishuo(白硕) <vc_java@hotmail.com>

Closes #1569 from baishuo/patch-1 and squashes the following commits:

dc2f895 [baishuo(白硕)] Update HiveMetastoreCatalog.scala
a7b32a2 [baishuo(白硕)] Update HiveMetastoreCatalog.scala
---
 .../scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala  | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
index 8db60d32767b..156b090712df 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
@@ -258,7 +258,7 @@ private[hive] case class MetastoreRelation
   // org.apache.hadoop.hive.ql.metadata.Partition will cause a NotSerializableException
   // which indicates the SerDe we used is not Serializable.
 
-  def hiveQlTable = new Table(table)
+  @transient lazy val hiveQlTable = new Table(table)
 
   def hiveQlPartitions = partitions.map { p =>
     new Partition(hiveQlTable, p)

From 47b6b38ca8d9c5de794183cc91cbf6559ef27390 Mon Sep 17 00:00:00 2001
From: jerryshao <saisai.shao@intel.com>
Date: Fri, 25 Jul 2014 14:34:38 -0700
Subject: [PATCH 763/810] [SPARK-2125] Add sort flag and move sort into shuffle
 implementations

This patch adds a sort flag into ShuffleDependecy and moves sort into hash shuffle implementation.

Moving sort into shuffle implementation can give space for other shuffle implementations (like sort-based shuffle) to better optimize sort through shuffle.

Author: jerryshao <saisai.shao@intel.com>

Closes #1210 from jerryshao/SPARK-2125 and squashes the following commits:

2feaf7b [jerryshao] revert MimaExcludes
ceddf75 [jerryshao] add MimaExeclude
f674ff4 [jerryshao] Add missing Scope restriction
b9fe0dd [jerryshao] Fix some style issues according to comments
ef6b729 [jerryshao] Change sort flag into Option
3f6eeed [jerryshao] Fix issues related to unit test
2f552a5 [jerryshao] Minor changes about naming and order
c92a281 [jerryshao] Move sort into shuffle implementations
---
 .../scala/org/apache/spark/Dependency.scala     |  4 +++-
 .../apache/spark/rdd/OrderedRDDFunctions.scala  | 17 ++++++++---------
 .../org/apache/spark/rdd/ShuffledRDD.scala      | 12 +++++++++++-
 .../spark/shuffle/hash/HashShuffleReader.scala  | 14 +++++++++++++-
 4 files changed, 35 insertions(+), 12 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/Dependency.scala b/core/src/main/scala/org/apache/spark/Dependency.scala
index 09a60571238e..f010c03223ef 100644
--- a/core/src/main/scala/org/apache/spark/Dependency.scala
+++ b/core/src/main/scala/org/apache/spark/Dependency.scala
@@ -19,6 +19,7 @@ package org.apache.spark
 
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.RDD
+import org.apache.spark.rdd.SortOrder.SortOrder
 import org.apache.spark.serializer.Serializer
 import org.apache.spark.shuffle.ShuffleHandle
 
@@ -62,7 +63,8 @@ class ShuffleDependency[K, V, C](
     val serializer: Option[Serializer] = None,
     val keyOrdering: Option[Ordering[K]] = None,
     val aggregator: Option[Aggregator[K, V, C]] = None,
-    val mapSideCombine: Boolean = false)
+    val mapSideCombine: Boolean = false,
+    val sortOrder: Option[SortOrder] = None)
   extends Dependency(rdd.asInstanceOf[RDD[Product2[K, V]]]) {
 
   val shuffleId: Int = rdd.context.newShuffleId()
diff --git a/core/src/main/scala/org/apache/spark/rdd/OrderedRDDFunctions.scala b/core/src/main/scala/org/apache/spark/rdd/OrderedRDDFunctions.scala
index f1f4b4324edf..afd7075f686b 100644
--- a/core/src/main/scala/org/apache/spark/rdd/OrderedRDDFunctions.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/OrderedRDDFunctions.scala
@@ -57,14 +57,13 @@ class OrderedRDDFunctions[K : Ordering : ClassTag,
    */
   def sortByKey(ascending: Boolean = true, numPartitions: Int = self.partitions.size): RDD[P] = {
     val part = new RangePartitioner(numPartitions, self, ascending)
-    val shuffled = new ShuffledRDD[K, V, V, P](self, part).setKeyOrdering(ordering)
-    shuffled.mapPartitions(iter => {
-      val buf = iter.toArray
-      if (ascending) {
-        buf.sortWith((x, y) => ordering.lt(x._1, y._1)).iterator
-      } else {
-        buf.sortWith((x, y) => ordering.gt(x._1, y._1)).iterator
-      }
-    }, preservesPartitioning = true)
+    new ShuffledRDD[K, V, V, P](self, part)
+      .setKeyOrdering(ordering)
+      .setSortOrder(if (ascending) SortOrder.ASCENDING else SortOrder.DESCENDING)
   }
 }
+
+private[spark] object SortOrder extends Enumeration {
+  type SortOrder = Value
+  val ASCENDING, DESCENDING = Value
+}
diff --git a/core/src/main/scala/org/apache/spark/rdd/ShuffledRDD.scala b/core/src/main/scala/org/apache/spark/rdd/ShuffledRDD.scala
index bf02f68d0d3d..da4a8c3dc22b 100644
--- a/core/src/main/scala/org/apache/spark/rdd/ShuffledRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/ShuffledRDD.scala
@@ -21,6 +21,7 @@ import scala.reflect.ClassTag
 
 import org.apache.spark._
 import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.rdd.SortOrder.SortOrder
 import org.apache.spark.serializer.Serializer
 
 private[spark] class ShuffledRDDPartition(val idx: Int) extends Partition {
@@ -51,6 +52,8 @@ class ShuffledRDD[K, V, C, P <: Product2[K, C] : ClassTag](
 
   private var mapSideCombine: Boolean = false
 
+  private var sortOrder: Option[SortOrder] = None
+
   /** Set a serializer for this RDD's shuffle, or null to use the default (spark.serializer) */
   def setSerializer(serializer: Serializer): ShuffledRDD[K, V, C, P] = {
     this.serializer = Option(serializer)
@@ -75,8 +78,15 @@ class ShuffledRDD[K, V, C, P <: Product2[K, C] : ClassTag](
     this
   }
 
+  /** Set sort order for RDD's sorting. */
+  def setSortOrder(sortOrder: SortOrder): ShuffledRDD[K, V, C, P] = {
+    this.sortOrder = Option(sortOrder)
+    this
+  }
+
   override def getDependencies: Seq[Dependency[_]] = {
-    List(new ShuffleDependency(prev, part, serializer, keyOrdering, aggregator, mapSideCombine))
+    List(new ShuffleDependency(prev, part, serializer,
+      keyOrdering, aggregator, mapSideCombine, sortOrder))
   }
 
   override val partitioner = Some(part)
diff --git a/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleReader.scala b/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleReader.scala
index d45258c0a492..76cdb8f4f8e8 100644
--- a/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleReader.scala
+++ b/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleReader.scala
@@ -18,6 +18,7 @@
 package org.apache.spark.shuffle.hash
 
 import org.apache.spark.{InterruptibleIterator, TaskContext}
+import org.apache.spark.rdd.SortOrder
 import org.apache.spark.serializer.Serializer
 import org.apache.spark.shuffle.{BaseShuffleHandle, ShuffleReader}
 
@@ -38,7 +39,7 @@ class HashShuffleReader[K, C](
     val iter = BlockStoreShuffleFetcher.fetch(handle.shuffleId, startPartition, context,
       Serializer.getSerializer(dep.serializer))
 
-    if (dep.aggregator.isDefined) {
+    val aggregatedIter: Iterator[Product2[K, C]] = if (dep.aggregator.isDefined) {
       if (dep.mapSideCombine) {
         new InterruptibleIterator(context, dep.aggregator.get.combineCombinersByKey(iter, context))
       } else {
@@ -49,6 +50,17 @@ class HashShuffleReader[K, C](
     } else {
       iter
     }
+
+    val sortedIter = for (sortOrder <- dep.sortOrder; ordering <- dep.keyOrdering) yield {
+      val buf = aggregatedIter.toArray
+      if (sortOrder == SortOrder.ASCENDING) {
+        buf.sortWith((x, y) => ordering.lt(x._1, y._1)).iterator
+      } else {
+        buf.sortWith((x, y) => ordering.gt(x._1, y._1)).iterator
+      }
+    }
+
+    sortedIter.getOrElse(aggregatedIter)
   }
 
   /** Close this reader */

From 37ad3b724590dcf42bcdbfaf91b7a11914501945 Mon Sep 17 00:00:00 2001
From: Kay Ousterhout <kayousterhout@gmail.com>
Date: Fri, 25 Jul 2014 15:14:13 -0700
Subject: [PATCH 764/810] [SPARK-1726] [SPARK-2567] Eliminate zombie stages in
 UI.

Due to problems with when we update runningStages (in DAGScheduler.scala)
and how we decide to send a SparkListenerStageCompleted message to
SparkListeners, sometimes stages can be shown as "running" in the UI forever
(even after they have failed).  This issue can manifest when stages are
resubmitted with 0 tasks, or when the DAGScheduler catches non-serializable
tasks. The problem also resulted in a (small) memory leak in the DAGScheduler,
where stages can stay in runningStages forever. This commit fixes
that problem and adds a unit test.

Thanks tsudukim for helping to look into this issue!

cc markhamstra rxin

Author: Kay Ousterhout <kayousterhout@gmail.com>

Closes #1566 from kayousterhout/dag_fix and squashes the following commits:

217d74b [Kay Ousterhout] [SPARK-1726] [SPARK-2567] Eliminate zombie stages in UI.
---
 .../apache/spark/scheduler/DAGScheduler.scala |  12 +-
 .../spark/scheduler/DAGSchedulerSuite.scala   | 129 ++++++++++--------
 2 files changed, 76 insertions(+), 65 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
index acb4c4946ede..00b8af27a7b3 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
@@ -710,7 +710,6 @@ class DAGScheduler(
         if (missing == Nil) {
           logInfo("Submitting " + stage + " (" + stage.rdd + "), which has no missing parents")
           submitMissingTasks(stage, jobId.get)
-          runningStages += stage
         } else {
           for (parent <- missing) {
             submitStage(parent)
@@ -753,11 +752,14 @@ class DAGScheduler(
       null
     }
 
-    // must be run listener before possible NotSerializableException
-    // should be "StageSubmitted" first and then "JobEnded"
-    listenerBus.post(SparkListenerStageSubmitted(stageToInfos(stage), properties))
-
     if (tasks.size > 0) {
+      runningStages += stage
+      // SparkListenerStageSubmitted should be posted before testing whether tasks are
+      // serializable. If tasks are not serializable, a SparkListenerStageCompleted event
+      // will be posted, which should always come after a corresponding SparkListenerStageSubmitted
+      // event.
+      listenerBus.post(SparkListenerStageSubmitted(stageToInfos(stage), properties))
+
       // Preemptively serialize a task to make sure it can be serialized. We are catching this
       // exception here because it would be fairly hard to catch the non-serializable exception
       // down the road, where we have several different implementations for local scheduler and
diff --git a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
index 9f498d579a09..44dd1e092ad6 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
@@ -37,6 +37,29 @@ class BuggyDAGEventProcessActor extends Actor {
   }
 }
 
+/**
+ * An RDD for passing to DAGScheduler. These RDDs will use the dependencies and
+ * preferredLocations (if any) that are passed to them. They are deliberately not executable
+ * so we can test that DAGScheduler does not try to execute RDDs locally.
+ */
+class MyRDD(
+    sc: SparkContext,
+    numPartitions: Int,
+    dependencies: List[Dependency[_]],
+    locations: Seq[Seq[String]] = Nil) extends RDD[(Int, Int)](sc, dependencies) with Serializable {
+  override def compute(split: Partition, context: TaskContext): Iterator[(Int, Int)] =
+    throw new RuntimeException("should not be reached")
+  override def getPartitions = (0 until numPartitions).map(i => new Partition {
+    override def index = i
+  }).toArray
+  override def getPreferredLocations(split: Partition): Seq[String] =
+    if (locations.isDefinedAt(split.index))
+      locations(split.index)
+    else
+      Nil
+  override def toString: String = "DAGSchedulerSuiteRDD " + id
+}
+
 class DAGSchedulerSuiteDummyException extends Exception
 
 class DAGSchedulerSuite extends TestKit(ActorSystem("DAGSchedulerSuite")) with FunSuiteLike
@@ -148,34 +171,7 @@ class DAGSchedulerSuite extends TestKit(ActorSystem("DAGSchedulerSuite")) with F
    * Type of RDD we use for testing. Note that we should never call the real RDD compute methods.
    * This is a pair RDD type so it can always be used in ShuffleDependencies.
    */
-  type MyRDD = RDD[(Int, Int)]
-
-  /**
-   * Create an RDD for passing to DAGScheduler. These RDDs will use the dependencies and
-   * preferredLocations (if any) that are passed to them. They are deliberately not executable
-   * so we can test that DAGScheduler does not try to execute RDDs locally.
-   */
-  private def makeRdd(
-        numPartitions: Int,
-        dependencies: List[Dependency[_]],
-        locations: Seq[Seq[String]] = Nil
-      ): MyRDD = {
-    val maxPartition = numPartitions - 1
-    val newRDD = new MyRDD(sc, dependencies) {
-      override def compute(split: Partition, context: TaskContext): Iterator[(Int, Int)] =
-        throw new RuntimeException("should not be reached")
-      override def getPartitions = (0 to maxPartition).map(i => new Partition {
-        override def index = i
-      }).toArray
-      override def getPreferredLocations(split: Partition): Seq[String] =
-        if (locations.isDefinedAt(split.index))
-          locations(split.index)
-        else
-          Nil
-      override def toString: String = "DAGSchedulerSuiteRDD " + id
-    }
-    newRDD
-  }
+  type PairOfIntsRDD = RDD[(Int, Int)]
 
   /**
    * Process the supplied event as if it were the top of the DAGScheduler event queue, expecting
@@ -234,19 +230,19 @@ class DAGSchedulerSuite extends TestKit(ActorSystem("DAGSchedulerSuite")) with F
       override def taskSucceeded(partition: Int, value: Any) = numResults += 1
       override def jobFailed(exception: Exception) = throw exception
     }
-    submit(makeRdd(0, Nil), Array(), listener = fakeListener)
+    submit(new MyRDD(sc, 0, Nil), Array(), listener = fakeListener)
     assert(numResults === 0)
   }
 
   test("run trivial job") {
-    submit(makeRdd(1, Nil), Array(0))
+    submit(new MyRDD(sc, 1, Nil), Array(0))
     complete(taskSets(0), List((Success, 42)))
     assert(results === Map(0 -> 42))
     assertDataStructuresEmpty
   }
 
   test("local job") {
-    val rdd = new MyRDD(sc, Nil) {
+    val rdd = new PairOfIntsRDD(sc, Nil) {
       override def compute(split: Partition, context: TaskContext): Iterator[(Int, Int)] =
         Array(42 -> 0).iterator
       override def getPartitions = Array( new Partition { override def index = 0 } )
@@ -260,7 +256,7 @@ class DAGSchedulerSuite extends TestKit(ActorSystem("DAGSchedulerSuite")) with F
   }
 
   test("local job oom") {
-    val rdd = new MyRDD(sc, Nil) {
+    val rdd = new PairOfIntsRDD(sc, Nil) {
       override def compute(split: Partition, context: TaskContext): Iterator[(Int, Int)] =
         throw new java.lang.OutOfMemoryError("test local job oom")
       override def getPartitions = Array( new Partition { override def index = 0 } )
@@ -274,8 +270,8 @@ class DAGSchedulerSuite extends TestKit(ActorSystem("DAGSchedulerSuite")) with F
   }
 
   test("run trivial job w/ dependency") {
-    val baseRdd = makeRdd(1, Nil)
-    val finalRdd = makeRdd(1, List(new OneToOneDependency(baseRdd)))
+    val baseRdd = new MyRDD(sc, 1, Nil)
+    val finalRdd = new MyRDD(sc, 1, List(new OneToOneDependency(baseRdd)))
     submit(finalRdd, Array(0))
     complete(taskSets(0), Seq((Success, 42)))
     assert(results === Map(0 -> 42))
@@ -283,8 +279,8 @@ class DAGSchedulerSuite extends TestKit(ActorSystem("DAGSchedulerSuite")) with F
   }
 
   test("cache location preferences w/ dependency") {
-    val baseRdd = makeRdd(1, Nil)
-    val finalRdd = makeRdd(1, List(new OneToOneDependency(baseRdd)))
+    val baseRdd = new MyRDD(sc, 1, Nil)
+    val finalRdd = new MyRDD(sc, 1, List(new OneToOneDependency(baseRdd)))
     cacheLocations(baseRdd.id -> 0) =
       Seq(makeBlockManagerId("hostA"), makeBlockManagerId("hostB"))
     submit(finalRdd, Array(0))
@@ -295,8 +291,22 @@ class DAGSchedulerSuite extends TestKit(ActorSystem("DAGSchedulerSuite")) with F
     assertDataStructuresEmpty
   }
 
+  test("unserializable task") {
+    val unserializableRdd = new MyRDD(sc, 1, Nil) {
+      class UnserializableClass
+      val unserializable = new UnserializableClass
+    }
+    submit(unserializableRdd, Array(0))
+    assert(failure.getMessage.startsWith(
+      "Job aborted due to stage failure: Task not serializable:"))
+    assert(sc.listenerBus.waitUntilEmpty(WAIT_TIMEOUT_MILLIS))
+    assert(sparkListener.failedStages.contains(0))
+    assert(sparkListener.failedStages.size === 1)
+    assertDataStructuresEmpty
+  }
+
   test("trivial job failure") {
-    submit(makeRdd(1, Nil), Array(0))
+    submit(new MyRDD(sc, 1, Nil), Array(0))
     failed(taskSets(0), "some failure")
     assert(failure.getMessage === "Job aborted due to stage failure: some failure")
     assert(sc.listenerBus.waitUntilEmpty(WAIT_TIMEOUT_MILLIS))
@@ -306,7 +316,7 @@ class DAGSchedulerSuite extends TestKit(ActorSystem("DAGSchedulerSuite")) with F
   }
 
   test("trivial job cancellation") {
-    val rdd = makeRdd(1, Nil)
+    val rdd = new MyRDD(sc, 1, Nil)
     val jobId = submit(rdd, Array(0))
     cancel(jobId)
     assert(failure.getMessage === s"Job $jobId cancelled ")
@@ -347,8 +357,7 @@ class DAGSchedulerSuite extends TestKit(ActorSystem("DAGSchedulerSuite")) with F
     }
     dagEventProcessTestActor = TestActorRef[DAGSchedulerEventProcessActor](
       Props(classOf[DAGSchedulerEventProcessActor], noKillScheduler))(system)
-    val rdd = makeRdd(1, Nil)
-    val jobId = submit(rdd, Array(0))
+    val jobId = submit(new MyRDD(sc, 1, Nil), Array(0))
     cancel(jobId)
     // Because the job wasn't actually cancelled, we shouldn't have received a failure message.
     assert(failure === null)
@@ -364,10 +373,10 @@ class DAGSchedulerSuite extends TestKit(ActorSystem("DAGSchedulerSuite")) with F
   }
 
   test("run trivial shuffle") {
-    val shuffleMapRdd = makeRdd(2, Nil)
+    val shuffleMapRdd = new MyRDD(sc, 2, Nil)
     val shuffleDep = new ShuffleDependency(shuffleMapRdd, null)
     val shuffleId = shuffleDep.shuffleId
-    val reduceRdd = makeRdd(1, List(shuffleDep))
+    val reduceRdd = new MyRDD(sc, 1, List(shuffleDep))
     submit(reduceRdd, Array(0))
     complete(taskSets(0), Seq(
         (Success, makeMapStatus("hostA", 1)),
@@ -380,10 +389,10 @@ class DAGSchedulerSuite extends TestKit(ActorSystem("DAGSchedulerSuite")) with F
   }
 
   test("run trivial shuffle with fetch failure") {
-    val shuffleMapRdd = makeRdd(2, Nil)
+    val shuffleMapRdd = new MyRDD(sc, 2, Nil)
     val shuffleDep = new ShuffleDependency(shuffleMapRdd, null)
     val shuffleId = shuffleDep.shuffleId
-    val reduceRdd = makeRdd(2, List(shuffleDep))
+    val reduceRdd = new MyRDD(sc, 2, List(shuffleDep))
     submit(reduceRdd, Array(0, 1))
     complete(taskSets(0), Seq(
         (Success, makeMapStatus("hostA", 1)),
@@ -406,10 +415,10 @@ class DAGSchedulerSuite extends TestKit(ActorSystem("DAGSchedulerSuite")) with F
   }
 
   test("ignore late map task completions") {
-    val shuffleMapRdd = makeRdd(2, Nil)
+    val shuffleMapRdd = new MyRDD(sc, 2, Nil)
     val shuffleDep = new ShuffleDependency(shuffleMapRdd, null)
     val shuffleId = shuffleDep.shuffleId
-    val reduceRdd = makeRdd(2, List(shuffleDep))
+    val reduceRdd = new MyRDD(sc, 2, List(shuffleDep))
     submit(reduceRdd, Array(0, 1))
     // pretend we were told hostA went away
     val oldEpoch = mapOutputTracker.getEpoch
@@ -435,9 +444,9 @@ class DAGSchedulerSuite extends TestKit(ActorSystem("DAGSchedulerSuite")) with F
   }
 
   test("run shuffle with map stage failure") {
-    val shuffleMapRdd = makeRdd(2, Nil)
+    val shuffleMapRdd = new MyRDD(sc, 2, Nil)
     val shuffleDep = new ShuffleDependency(shuffleMapRdd, null)
-    val reduceRdd = makeRdd(2, List(shuffleDep))
+    val reduceRdd = new MyRDD(sc, 2, List(shuffleDep))
     submit(reduceRdd, Array(0, 1))
 
     // Fail the map stage.  This should cause the entire job to fail.
@@ -472,13 +481,13 @@ class DAGSchedulerSuite extends TestKit(ActorSystem("DAGSchedulerSuite")) with F
    * without shuffleMapRdd1.
    */
   test("failure of stage used by two jobs") {
-    val shuffleMapRdd1 = makeRdd(2, Nil)
+    val shuffleMapRdd1 = new MyRDD(sc, 2, Nil)
     val shuffleDep1 = new ShuffleDependency(shuffleMapRdd1, null)
-    val shuffleMapRdd2 = makeRdd(2, Nil)
+    val shuffleMapRdd2 = new MyRDD(sc, 2, Nil)
     val shuffleDep2 = new ShuffleDependency(shuffleMapRdd2, null)
 
-    val reduceRdd1 = makeRdd(2, List(shuffleDep1))
-    val reduceRdd2 = makeRdd(2, List(shuffleDep1, shuffleDep2))
+    val reduceRdd1 = new MyRDD(sc, 2, List(shuffleDep1))
+    val reduceRdd2 = new MyRDD(sc, 2, List(shuffleDep1, shuffleDep2))
 
     // We need to make our own listeners for this test, since by default submit uses the same
     // listener for all jobs, and here we want to capture the failure for each job separately.
@@ -511,10 +520,10 @@ class DAGSchedulerSuite extends TestKit(ActorSystem("DAGSchedulerSuite")) with F
   }
 
   test("run trivial shuffle with out-of-band failure and retry") {
-    val shuffleMapRdd = makeRdd(2, Nil)
+    val shuffleMapRdd = new MyRDD(sc, 2, Nil)
     val shuffleDep = new ShuffleDependency(shuffleMapRdd, null)
     val shuffleId = shuffleDep.shuffleId
-    val reduceRdd = makeRdd(1, List(shuffleDep))
+    val reduceRdd = new MyRDD(sc, 1, List(shuffleDep))
     submit(reduceRdd, Array(0))
     // blockManagerMaster.removeExecutor("exec-hostA")
     // pretend we were told hostA went away
@@ -534,11 +543,11 @@ class DAGSchedulerSuite extends TestKit(ActorSystem("DAGSchedulerSuite")) with F
   }
 
   test("recursive shuffle failures") {
-    val shuffleOneRdd = makeRdd(2, Nil)
+    val shuffleOneRdd = new MyRDD(sc, 2, Nil)
     val shuffleDepOne = new ShuffleDependency(shuffleOneRdd, null)
-    val shuffleTwoRdd = makeRdd(2, List(shuffleDepOne))
+    val shuffleTwoRdd = new MyRDD(sc, 2, List(shuffleDepOne))
     val shuffleDepTwo = new ShuffleDependency(shuffleTwoRdd, null)
-    val finalRdd = makeRdd(1, List(shuffleDepTwo))
+    val finalRdd = new MyRDD(sc, 1, List(shuffleDepTwo))
     submit(finalRdd, Array(0))
     // have the first stage complete normally
     complete(taskSets(0), Seq(
@@ -563,11 +572,11 @@ class DAGSchedulerSuite extends TestKit(ActorSystem("DAGSchedulerSuite")) with F
   }
 
   test("cached post-shuffle") {
-    val shuffleOneRdd = makeRdd(2, Nil)
+    val shuffleOneRdd = new MyRDD(sc, 2, Nil)
     val shuffleDepOne = new ShuffleDependency(shuffleOneRdd, null)
-    val shuffleTwoRdd = makeRdd(2, List(shuffleDepOne))
+    val shuffleTwoRdd = new MyRDD(sc, 2, List(shuffleDepOne))
     val shuffleDepTwo = new ShuffleDependency(shuffleTwoRdd, null)
-    val finalRdd = makeRdd(1, List(shuffleDepTwo))
+    val finalRdd = new MyRDD(sc, 1, List(shuffleDepTwo))
     submit(finalRdd, Array(0))
     cacheLocations(shuffleTwoRdd.id -> 0) = Seq(makeBlockManagerId("hostD"))
     cacheLocations(shuffleTwoRdd.id -> 1) = Seq(makeBlockManagerId("hostC"))

From afd757a241f41d7f8c458ef8f1f9ce8ed12986e5 Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Fri, 25 Jul 2014 15:36:57 -0700
Subject: [PATCH 765/810] Revert "[SPARK-2410][SQL] Merging Hive Thrift/JDBC
 server"

This reverts commit 06dc0d2c6b69c5d59b4d194ced2ac85bfe2e05e2.

#1399 is making Jenkins fail.  We should investigate and put this back after its passing tests.

Author: Michael Armbrust <michael@databricks.com>

Closes #1594 from marmbrus/revertJDBC and squashes the following commits:

59748da [Michael Armbrust] Revert "[SPARK-2410][SQL] Merging Hive Thrift/JDBC server"
---
 .gitignore                                    |   1 -
 assembly/pom.xml                              |  10 -
 bagel/pom.xml                                 |   2 +-
 bin/beeline                                   |  45 ---
 bin/compute-classpath.sh                      |   1 -
 bin/spark-shell                               |   4 +-
 bin/spark-shell.cmd                           |   2 +-
 bin/spark-sql                                 |  36 --
 core/pom.xml                                  |   2 +-
 .../org/apache/spark/deploy/SparkSubmit.scala |  14 +-
 .../spark/deploy/SparkSubmitArguments.scala   |   5 +-
 dev/create-release/create-release.sh          |  10 +-
 dev/run-tests                                 |   2 +-
 dev/scalastyle                                |   2 +-
 docs/sql-programming-guide.md                 | 200 +---------
 examples/pom.xml                              |   2 +-
 external/flume/pom.xml                        |   2 +-
 external/kafka/pom.xml                        |   2 +-
 external/mqtt/pom.xml                         |   2 +-
 external/twitter/pom.xml                      |   2 +-
 external/zeromq/pom.xml                       |   2 +-
 graphx/pom.xml                                |   2 +-
 mllib/pom.xml                                 |   2 +-
 pom.xml                                       |   7 +-
 project/SparkBuild.scala                      |  14 +-
 sbin/start-thriftserver.sh                    |  36 --
 sql/catalyst/pom.xml                          |   2 +-
 .../sql/catalyst/plans/logical/commands.scala |   3 +-
 sql/core/pom.xml                              |   2 +-
 .../scala/org/apache/spark/sql/SQLConf.scala  |  20 +-
 .../apache/spark/sql/execution/commands.scala |  42 +--
 .../org/apache/spark/sql/SQLConfSuite.scala   |  13 +-
 .../org/apache/spark/sql/SQLQuerySuite.scala  |  10 +-
 sql/hive-thriftserver/pom.xml                 |  82 -----
 .../hive/thriftserver/HiveThriftServer2.scala |  97 -----
 .../hive/thriftserver/ReflectionUtils.scala   |  58 ---
 .../hive/thriftserver/SparkSQLCLIDriver.scala | 344 ------------------
 .../thriftserver/SparkSQLCLIService.scala     |  74 ----
 .../hive/thriftserver/SparkSQLDriver.scala    |  93 -----
 .../sql/hive/thriftserver/SparkSQLEnv.scala   |  58 ---
 .../thriftserver/SparkSQLSessionManager.scala |  49 ---
 .../server/SparkSQLOperationManager.scala     | 151 --------
 .../test/resources/data/files/small_kv.txt    |   5 -
 .../sql/hive/thriftserver/CliSuite.scala      |  59 ---
 .../thriftserver/HiveThriftServer2Suite.scala | 125 -------
 .../sql/hive/thriftserver/TestUtils.scala     | 108 ------
 sql/hive/pom.xml                              |   2 +-
 .../apache/spark/sql/hive/HiveContext.scala   |   2 +-
 .../sql/hive/execution/HiveQuerySuite.scala   |  50 +--
 streaming/pom.xml                             |   2 +-
 tools/pom.xml                                 |   2 +-
 yarn/alpha/pom.xml                            |   2 +-
 yarn/pom.xml                                  |   2 +-
 yarn/stable/pom.xml                           |   2 +-
 54 files changed, 96 insertions(+), 1772 deletions(-)
 delete mode 100755 bin/beeline
 delete mode 100755 bin/spark-sql
 delete mode 100755 sbin/start-thriftserver.sh
 delete mode 100644 sql/hive-thriftserver/pom.xml
 delete mode 100644 sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala
 delete mode 100644 sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ReflectionUtils.scala
 delete mode 100755 sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala
 delete mode 100644 sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIService.scala
 delete mode 100644 sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLDriver.scala
 delete mode 100644 sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLEnv.scala
 delete mode 100644 sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLSessionManager.scala
 delete mode 100644 sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/server/SparkSQLOperationManager.scala
 delete mode 100644 sql/hive-thriftserver/src/test/resources/data/files/small_kv.txt
 delete mode 100644 sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/CliSuite.scala
 delete mode 100644 sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suite.scala
 delete mode 100644 sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/TestUtils.scala

diff --git a/.gitignore b/.gitignore
index 5b56a67c883e..061c8946d23c 100644
--- a/.gitignore
+++ b/.gitignore
@@ -57,4 +57,3 @@ metastore_db/
 metastore/
 warehouse/
 TempStatsStore/
-sql/hive-thriftserver/test_warehouses
diff --git a/assembly/pom.xml b/assembly/pom.xml
index 703f15925bc4..567a8dd2a0d9 100644
--- a/assembly/pom.xml
+++ b/assembly/pom.xml
@@ -165,16 +165,6 @@
         </dependency>
       </dependencies>
     </profile>
-    <profile>
-      <id>hive-thriftserver</id>
-      <dependencies>
-        <dependency>
-          <groupId>org.apache.spark</groupId>
-          <artifactId>spark-hive-thriftserver_${scala.binary.version}</artifactId>
-          <version>${project.version}</version>
-        </dependency>
-      </dependencies>
-    </profile>
     <profile>
       <id>spark-ganglia-lgpl</id>
       <dependencies>
diff --git a/bagel/pom.xml b/bagel/pom.xml
index bd51b112e26f..90c4b095bb61 100644
--- a/bagel/pom.xml
+++ b/bagel/pom.xml
@@ -28,7 +28,7 @@
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-bagel_2.10</artifactId>
   <properties>
-    <sbt.project.name>bagel</sbt.project.name>
+     <sbt.project.name>bagel</sbt.project.name>
   </properties>
   <packaging>jar</packaging>
   <name>Spark Project Bagel</name>
diff --git a/bin/beeline b/bin/beeline
deleted file mode 100755
index 09fe366c609f..000000000000
--- a/bin/beeline
+++ /dev/null
@@ -1,45 +0,0 @@
-#!/usr/bin/env bash
-
-#
-# Licensed to the Apache Software Foundation (ASF) under one or more
-# contributor license agreements.  See the NOTICE file distributed with
-# this work for additional information regarding copyright ownership.
-# The ASF licenses this file to You under the Apache License, Version 2.0
-# (the "License"); you may not use this file except in compliance with
-# the License.  You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-
-# Figure out where Spark is installed
-FWDIR="$(cd `dirname $0`/..; pwd)"
-
-# Find the java binary
-if [ -n "${JAVA_HOME}" ]; then
-  RUNNER="${JAVA_HOME}/bin/java"
-else
-  if [ `command -v java` ]; then
-    RUNNER="java"
-  else
-    echo "JAVA_HOME is not set" >&2
-    exit 1
-  fi
-fi
-
-# Compute classpath using external script
-classpath_output=$($FWDIR/bin/compute-classpath.sh)
-if [[ "$?" != "0" ]]; then
-  echo "$classpath_output"
-  exit 1
-else
-  CLASSPATH=$classpath_output
-fi
-
-CLASS="org.apache.hive.beeline.BeeLine"
-exec "$RUNNER" -cp "$CLASSPATH" $CLASS "$@"
diff --git a/bin/compute-classpath.sh b/bin/compute-classpath.sh
index 16b794a1592e..e81e8c060cb9 100755
--- a/bin/compute-classpath.sh
+++ b/bin/compute-classpath.sh
@@ -52,7 +52,6 @@ if [ -n "$SPARK_PREPEND_CLASSES" ]; then
   CLASSPATH="$CLASSPATH:$FWDIR/sql/catalyst/target/scala-$SCALA_VERSION/classes"
   CLASSPATH="$CLASSPATH:$FWDIR/sql/core/target/scala-$SCALA_VERSION/classes"
   CLASSPATH="$CLASSPATH:$FWDIR/sql/hive/target/scala-$SCALA_VERSION/classes"
-  CLASSPATH="$CLASSPATH:$FWDIR/sql/hive-thriftserver/target/scala-$SCALA_VERSION/classes"
   CLASSPATH="$CLASSPATH:$FWDIR/yarn/stable/target/scala-$SCALA_VERSION/classes"
 fi
 
diff --git a/bin/spark-shell b/bin/spark-shell
index 756c8179d12b..850e9507ec38 100755
--- a/bin/spark-shell
+++ b/bin/spark-shell
@@ -46,11 +46,11 @@ function main(){
         # (see https://github.com/sbt/sbt/issues/562).
         stty -icanon min 1 -echo > /dev/null 2>&1
         export SPARK_SUBMIT_OPTS="$SPARK_SUBMIT_OPTS -Djline.terminal=unix"
-        $FWDIR/bin/spark-submit --class org.apache.spark.repl.Main spark-shell "$@"
+        $FWDIR/bin/spark-submit spark-shell "$@" --class org.apache.spark.repl.Main
         stty icanon echo > /dev/null 2>&1
     else
         export SPARK_SUBMIT_OPTS
-        $FWDIR/bin/spark-submit --class org.apache.spark.repl.Main spark-shell "$@"
+        $FWDIR/bin/spark-submit spark-shell "$@" --class org.apache.spark.repl.Main
     fi
 }
 
diff --git a/bin/spark-shell.cmd b/bin/spark-shell.cmd
index b56d69801171..4b9708a8c03f 100755
--- a/bin/spark-shell.cmd
+++ b/bin/spark-shell.cmd
@@ -19,4 +19,4 @@ rem
 
 set SPARK_HOME=%~dp0..
 
-cmd /V /E /C %SPARK_HOME%\bin\spark-submit.cmd spark-shell --class org.apache.spark.repl.Main %*
+cmd /V /E /C %SPARK_HOME%\bin\spark-submit.cmd spark-shell %* --class org.apache.spark.repl.Main
diff --git a/bin/spark-sql b/bin/spark-sql
deleted file mode 100755
index bba7f897b19b..000000000000
--- a/bin/spark-sql
+++ /dev/null
@@ -1,36 +0,0 @@
-#!/usr/bin/env bash
-
-#
-# Licensed to the Apache Software Foundation (ASF) under one or more
-# contributor license agreements.  See the NOTICE file distributed with
-# this work for additional information regarding copyright ownership.
-# The ASF licenses this file to You under the Apache License, Version 2.0
-# (the "License"); you may not use this file except in compliance with
-# the License.  You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-
-#
-# Shell script for starting the Spark SQL CLI
-
-# Enter posix mode for bash
-set -o posix
-
-# Figure out where Spark is installed
-FWDIR="$(cd `dirname $0`/..; pwd)"
-
-if [[ "$@" = *--help ]] || [[ "$@" = *-h ]]; then
-  echo "Usage: ./sbin/spark-sql [options]"
-  $FWDIR/bin/spark-submit --help 2>&1 | grep -v Usage 1>&2
-  exit 0
-fi
-
-CLASS="org.apache.spark.sql.hive.thriftserver.SparkSQLCLIDriver"
-exec "$FWDIR"/bin/spark-submit --class $CLASS spark-internal $@
diff --git a/core/pom.xml b/core/pom.xml
index a24743495b0e..1054cec4d77b 100644
--- a/core/pom.xml
+++ b/core/pom.xml
@@ -28,7 +28,7 @@
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-core_2.10</artifactId>
   <properties>
-    <sbt.project.name>core</sbt.project.name>
+     <sbt.project.name>core</sbt.project.name>
   </properties>
   <packaging>jar</packaging>
   <name>Spark Project Core</name>
diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
index c9cec33ebaa6..3b5642b6caa3 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
@@ -46,10 +46,6 @@ object SparkSubmit {
   private val CLUSTER = 2
   private val ALL_DEPLOY_MODES = CLIENT | CLUSTER
 
-  // A special jar name that indicates the class being run is inside of Spark itself, and therefore
-  // no user jar is needed.
-  private val SPARK_INTERNAL = "spark-internal"
-
   // Special primary resource names that represent shells rather than application jars.
   private val SPARK_SHELL = "spark-shell"
   private val PYSPARK_SHELL = "pyspark-shell"
@@ -261,9 +257,7 @@ object SparkSubmit {
     // In yarn-cluster mode, use yarn.Client as a wrapper around the user class
     if (clusterManager == YARN && deployMode == CLUSTER) {
       childMainClass = "org.apache.spark.deploy.yarn.Client"
-      if (args.primaryResource != SPARK_INTERNAL) {
-        childArgs += ("--jar", args.primaryResource)
-      }
+      childArgs += ("--jar", args.primaryResource)
       childArgs += ("--class", args.mainClass)
       if (args.childArgs != null) {
         args.childArgs.foreach { arg => childArgs += ("--arg", arg) }
@@ -338,7 +332,7 @@ object SparkSubmit {
    * Return whether the given primary resource represents a user jar.
    */
   private def isUserJar(primaryResource: String): Boolean = {
-    !isShell(primaryResource) && !isPython(primaryResource) && !isInternal(primaryResource)
+    !isShell(primaryResource) && !isPython(primaryResource)
   }
 
   /**
@@ -355,10 +349,6 @@ object SparkSubmit {
     primaryResource.endsWith(".py") || primaryResource == PYSPARK_SHELL
   }
 
-  private[spark] def isInternal(primaryResource: String): Boolean = {
-    primaryResource == SPARK_INTERNAL
-  }
-
   /**
    * Merge a sequence of comma-separated file lists, some of which may be null to indicate
    * no files, into a single comma-separated string.
diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
index 01d0ae541a66..3ab67a43a3b5 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
@@ -204,9 +204,8 @@ private[spark] class SparkSubmitArguments(args: Seq[String]) {
 
   /** Fill in values by parsing user options. */
   private def parseOpts(opts: Seq[String]): Unit = {
-    var inSparkOpts = true
-
     // Delineates parsing of Spark options from parsing of user options.
+    var inSparkOpts = true
     parse(opts)
 
     def parse(opts: Seq[String]): Unit = opts match {
@@ -319,7 +318,7 @@ private[spark] class SparkSubmitArguments(args: Seq[String]) {
               SparkSubmit.printErrorAndExit(errMessage)
             case v =>
               primaryResource =
-                if (!SparkSubmit.isShell(v) && !SparkSubmit.isInternal(v)) {
+                if (!SparkSubmit.isShell(v)) {
                   Utils.resolveURI(v).toString
                 } else {
                   v
diff --git a/dev/create-release/create-release.sh b/dev/create-release/create-release.sh
index 33de24d1ae6d..38830103d1e8 100755
--- a/dev/create-release/create-release.sh
+++ b/dev/create-release/create-release.sh
@@ -53,7 +53,7 @@ if [[ ! "$@" =~ --package-only ]]; then
     -Dusername=$GIT_USERNAME -Dpassword=$GIT_PASSWORD \
     -Dmaven.javadoc.skip=true \
     -Dhadoop.version=2.2.0 -Dyarn.version=2.2.0 \
-    -Pyarn -Phive -Phive-thriftserver -Phadoop-2.2 -Pspark-ganglia-lgpl\
+    -Pyarn -Phive -Phadoop-2.2 -Pspark-ganglia-lgpl\
     -Dtag=$GIT_TAG -DautoVersionSubmodules=true \
     --batch-mode release:prepare
 
@@ -61,7 +61,7 @@ if [[ ! "$@" =~ --package-only ]]; then
     -Darguments="-DskipTests=true -Dmaven.javadoc.skip=true -Dhadoop.version=2.2.0 -Dyarn.version=2.2.0 -Dgpg.passphrase=${GPG_PASSPHRASE}" \
     -Dhadoop.version=2.2.0 -Dyarn.version=2.2.0 \
     -Dmaven.javadoc.skip=true \
-    -Pyarn -Phive -Phive-thriftserver -Phadoop-2.2 -Pspark-ganglia-lgpl\
+    -Pyarn -Phive -Phadoop-2.2 -Pspark-ganglia-lgpl\
     release:perform
 
   cd ..
@@ -111,10 +111,10 @@ make_binary_release() {
     spark-$RELEASE_VERSION-bin-$NAME.tgz.sha
 }
 
-make_binary_release "hadoop1" "-Phive -Phive-thriftserver -Dhadoop.version=1.0.4"
-make_binary_release "cdh4" "-Phive -Phive-thriftserver -Dhadoop.version=2.0.0-mr1-cdh4.2.0"
+make_binary_release "hadoop1" "-Phive -Dhadoop.version=1.0.4"
+make_binary_release "cdh4" "-Phive -Dhadoop.version=2.0.0-mr1-cdh4.2.0"
 make_binary_release "hadoop2" \
-  "-Phive -Phive-thriftserver -Pyarn -Phadoop-2.2 -Dhadoop.version=2.2.0 -Pyarn.version=2.2.0"
+  "-Phive -Pyarn -Phadoop-2.2 -Dhadoop.version=2.2.0 -Pyarn.version=2.2.0"
 
 # Copy data
 echo "Copying release tarballs"
diff --git a/dev/run-tests b/dev/run-tests
index 98ec969dc1b3..51e4def0f835 100755
--- a/dev/run-tests
+++ b/dev/run-tests
@@ -65,7 +65,7 @@ echo "========================================================================="
 # (either resolution or compilation) prompts the user for input either q, r, 
 # etc to quit or retry. This echo is there to make it not block.
 if [ -n "$_RUN_SQL_TESTS" ]; then
-  echo -e "q\n" | SBT_MAVEN_PROFILES="$SBT_MAVEN_PROFILES -Phive -Phive-thriftserver" sbt/sbt clean package \
+  echo -e "q\n" | SBT_MAVEN_PROFILES="$SBT_MAVEN_PROFILES -Phive" sbt/sbt clean package \
     assembly/assembly test | grep -v -e "info.*Resolving" -e "warn.*Merging" -e "info.*Including"
 else
   echo -e "q\n" | sbt/sbt clean package assembly/assembly test | \
diff --git a/dev/scalastyle b/dev/scalastyle
index d9f2b91a3a09..a02d06912f23 100755
--- a/dev/scalastyle
+++ b/dev/scalastyle
@@ -17,7 +17,7 @@
 # limitations under the License.
 #
 
-echo -e "q\n" | sbt/sbt -Phive -Phive-thriftserver scalastyle > scalastyle.txt
+echo -e "q\n" | sbt/sbt -Phive scalastyle > scalastyle.txt
 # Check style with YARN alpha built too
 echo -e "q\n" | sbt/sbt -Pyarn -Phadoop-0.23 -Dhadoop.version=0.23.9 yarn-alpha/scalastyle \
   >> scalastyle.txt
diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md
index 36d642f2923b..38728534a46e 100644
--- a/docs/sql-programming-guide.md
+++ b/docs/sql-programming-guide.md
@@ -136,7 +136,7 @@ val sqlContext = new org.apache.spark.sql.SQLContext(sc)
 import sqlContext.createSchemaRDD
 
 // Define the schema using a case class.
-// Note: Case classes in Scala 2.10 can support only up to 22 fields. To work around this limit,
+// Note: Case classes in Scala 2.10 can support only up to 22 fields. To work around this limit, 
 // you can use custom classes that implement the Product interface.
 case class Person(name: String, age: Int)
 
@@ -548,6 +548,7 @@ results = hiveContext.hql("FROM src SELECT key, value").collect()
 </div>
 </div>
 
+
 # Writing Language-Integrated Relational Queries
 
 **Language-Integrated queries are currently only supported in Scala.**
@@ -572,199 +573,4 @@ prefixed with a tick (`'`).  Implicit conversions turn these symbols into expres
 evaluated by the SQL execution engine.  A full list of the functions supported can be found in the
 [ScalaDoc](api/scala/index.html#org.apache.spark.sql.SchemaRDD).
 
-<!-- TODO: Include the table of operations here. -->
-
-## Running the Thrift JDBC server
-
-The Thrift JDBC server implemented here corresponds to the [`HiveServer2`]
-(https://cwiki.apache.org/confluence/display/Hive/Setting+Up+HiveServer2) in Hive 0.12. You can test
-the JDBC server with the beeline script comes with either Spark or Hive 0.12.  In order to use Hive
-you must first run '`sbt/sbt -Phive-thriftserver assembly/assembly`' (or use `-Phive-thriftserver`
-for maven).
-
-To start the JDBC server, run the following in the Spark directory:
-
-    ./sbin/start-thriftserver.sh
-
-The default port the server listens on is 10000.  You may run
-`./sbin/start-thriftserver.sh --help` for a complete list of all available
-options.  Now you can use beeline to test the Thrift JDBC server:
-
-    ./bin/beeline
-
-Connect to the JDBC server in beeline with:
-
-    beeline> !connect jdbc:hive2://localhost:10000
-
-Beeline will ask you for a username and password. In non-secure mode, simply enter the username on
-your machine and a blank password. For secure mode, please follow the instructions given in the
-[beeline documentation](https://cwiki.apache.org/confluence/display/Hive/HiveServer2+Clients)
-
-Configuration of Hive is done by placing your `hive-site.xml` file in `conf/`.
-
-You may also use the beeline script comes with Hive.
-
-### Migration Guide for Shark Users
-
-#### Reducer number
-
-In Shark, default reducer number is 1 and is controlled by the property `mapred.reduce.tasks`. Spark
-SQL deprecates this property by a new property `spark.sql.shuffle.partitions`, whose default value
-is 200. Users may customize this property via `SET`:
-
-```
-SET spark.sql.shuffle.partitions=10;
-SELECT page, count(*) c FROM logs_last_month_cached
-GROUP BY page ORDER BY c DESC LIMIT 10;
-```
-
-You may also put this property in `hive-site.xml` to override the default value.
-
-For now, the `mapred.reduce.tasks` property is still recognized, and is converted to
-`spark.sql.shuffle.partitions` automatically.
-
-#### Caching
-
-The `shark.cache` table property no longer exists, and tables whose name end with `_cached` are no
-longer automcatically cached. Instead, we provide `CACHE TABLE` and `UNCACHE TABLE` statements to
-let user control table caching explicitly:
-
-```
-CACHE TABLE logs_last_month;
-UNCACHE TABLE logs_last_month;
-```
-
-**NOTE** `CACHE TABLE tbl` is lazy, it only marks table `tbl` as "need to by cached if necessary",
-but doesn't actually cache it until a query that touches `tbl` is executed. To force the table to be
-cached, you may simply count the table immediately after executing `CACHE TABLE`:
-
-```
-CACHE TABLE logs_last_month;
-SELECT COUNT(1) FROM logs_last_month;
-```
-
-Several caching related features are not supported yet:
-
-* User defined partition level cache eviction policy
-* RDD reloading
-* In-memory cache write through policy
-
-### Compatibility with Apache Hive
-
-#### Deploying in Exising Hive Warehouses
-
-Spark SQL Thrift JDBC server is designed to be "out of the box" compatible with existing Hive
-installations. You do not need to modify your existing Hive Metastore or change the data placement
-or partitioning of your tables.
-
-#### Supported Hive Features
-
-Spark SQL supports the vast majority of Hive features, such as:
-
-* Hive query statements, including:
- * `SELECT`
- * `GROUP BY
- * `ORDER BY`
- * `CLUSTER BY`
- * `SORT BY`
-* All Hive operators, including:
- * Relational operators (`=`, `⇔`, `==`, `<>`, `<`, `>`, `>=`, `<=`, etc)
- * Arthimatic operators (`+`, `-`, `*`, `/`, `%`, etc)
- * Logical operators (`AND`, `&&`, `OR`, `||`, etc)
- * Complex type constructors
- * Mathemtatical functions (`sign`, `ln`, `cos`, etc)
- * String functions (`instr`, `length`, `printf`, etc)
-* User defined functions (UDF)
-* User defined aggregation functions (UDAF)
-* User defined serialization formats (SerDe's)
-* Joins
- * `JOIN`
- * `{LEFT|RIGHT|FULL} OUTER JOIN`
- * `LEFT SEMI JOIN`
- * `CROSS JOIN`
-* Unions
-* Sub queries
- * `SELECT col FROM ( SELECT a + b AS col from t1) t2`
-* Sampling
-* Explain
-* Partitioned tables
-* All Hive DDL Functions, including:
- * `CREATE TABLE`
- * `CREATE TABLE AS SELECT`
- * `ALTER TABLE`
-* Most Hive Data types, including:
- * `TINYINT`
- * `SMALLINT`
- * `INT`
- * `BIGINT`
- * `BOOLEAN`
- * `FLOAT`
- * `DOUBLE`
- * `STRING`
- * `BINARY`
- * `TIMESTAMP`
- * `ARRAY<>`
- * `MAP<>`
- * `STRUCT<>`
-
-#### Unsupported Hive Functionality
-
-Below is a list of Hive features that we don't support yet. Most of these features are rarely used
-in Hive deployments.
-
-**Major Hive Features**
-
-* Tables with buckets: bucket is the hash partitioning within a Hive table partition. Spark SQL
-  doesn't support buckets yet.
-
-**Esoteric Hive Features**
-
-* Tables with partitions using different input formats: In Spark SQL, all table partitions need to
-  have the same input format.
-* Non-equi outer join: For the uncommon use case of using outer joins with non-equi join conditions
-  (e.g. condition "`key < 10`"), Spark SQL will output wrong result for the `NULL` tuple.
-* `UNIONTYPE`
-* Unique join
-* Single query multi insert
-* Column statistics collecting: Spark SQL does not piggyback scans to collect column statistics at
-  the moment.
-
-**Hive Input/Output Formats**
-
-* File format for CLI: For results showing back to the CLI, Spark SQL only supports TextOutputFormat.
-* Hadoop archive
-
-**Hive Optimizations**
-
-A handful of Hive optimizations are not yet included in Spark. Some of these (such as indexes) are
-not necessary due to Spark SQL's in-memory computational model. Others are slotted for future
-releases of Spark SQL.
-
-* Block level bitmap indexes and virtual columns (used to build indexes)
-* Automatically convert a join to map join: For joining a large table with multiple small tables,
-  Hive automatically converts the join into a map join. We are adding this auto conversion in the
-  next release.
-* Automatically determine the number of reducers for joins and groupbys: Currently in Spark SQL, you
-  need to control the degree of parallelism post-shuffle using "SET
-  spark.sql.shuffle.partitions=[num_tasks];". We are going to add auto-setting of parallelism in the
-  next release.
-* Meta-data only query: For queries that can be answered by using only meta data, Spark SQL still
-  launches tasks to compute the result.
-* Skew data flag: Spark SQL does not follow the skew data flags in Hive.
-* `STREAMTABLE` hint in join: Spark SQL does not follow the `STREAMTABLE` hint.
-* Merge multiple small files for query results: if the result output contains multiple small files,
-  Hive can optionally merge the small files into fewer large files to avoid overflowing the HDFS
-  metadata. Spark SQL does not support that.
-
-## Running the Spark SQL CLI
-
-The Spark SQL CLI is a convenient tool to run the Hive metastore service in local mode and execute
-queries input from command line. Note: the Spark SQL CLI cannot talk to the Thrift JDBC server.
-
-To start the Spark SQL CLI, run the following in the Spark directory:
-
-    ./bin/spark-sql
-
-Configuration of Hive is done by placing your `hive-site.xml` file in `conf/`.
-You may run `./bin/spark-sql --help` for a complete list of all available
-options.
+<!-- TODO: Include the table of operations here. -->
\ No newline at end of file
diff --git a/examples/pom.xml b/examples/pom.xml
index c4ed0f5a6a02..bd1c387c2eb9 100644
--- a/examples/pom.xml
+++ b/examples/pom.xml
@@ -28,7 +28,7 @@
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-examples_2.10</artifactId>
   <properties>
-    <sbt.project.name>examples</sbt.project.name>
+     <sbt.project.name>examples</sbt.project.name>
   </properties>
   <packaging>jar</packaging>
   <name>Spark Project Examples</name>
diff --git a/external/flume/pom.xml b/external/flume/pom.xml
index 874b8a7959bb..61a6aff543ae 100644
--- a/external/flume/pom.xml
+++ b/external/flume/pom.xml
@@ -28,7 +28,7 @@
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-streaming-flume_2.10</artifactId>
   <properties>
-    <sbt.project.name>streaming-flume</sbt.project.name>
+     <sbt.project.name>streaming-flume</sbt.project.name>
   </properties>
   <packaging>jar</packaging>
   <name>Spark Project External Flume</name>
diff --git a/external/kafka/pom.xml b/external/kafka/pom.xml
index 25a5c0a4d7d7..4762c50685a9 100644
--- a/external/kafka/pom.xml
+++ b/external/kafka/pom.xml
@@ -28,7 +28,7 @@
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-streaming-kafka_2.10</artifactId>
   <properties>
-    <sbt.project.name>streaming-kafka</sbt.project.name>
+     <sbt.project.name>streaming-kafka</sbt.project.name>
   </properties>
   <packaging>jar</packaging>
   <name>Spark Project External Kafka</name>
diff --git a/external/mqtt/pom.xml b/external/mqtt/pom.xml
index f31ed655f677..32c530e600ce 100644
--- a/external/mqtt/pom.xml
+++ b/external/mqtt/pom.xml
@@ -28,7 +28,7 @@
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-streaming-mqtt_2.10</artifactId>
   <properties>
-    <sbt.project.name>streaming-mqtt</sbt.project.name>
+     <sbt.project.name>streaming-mqtt</sbt.project.name>
   </properties>
   <packaging>jar</packaging>
   <name>Spark Project External MQTT</name>
diff --git a/external/twitter/pom.xml b/external/twitter/pom.xml
index 56bb24c2a072..637adb0f00da 100644
--- a/external/twitter/pom.xml
+++ b/external/twitter/pom.xml
@@ -28,7 +28,7 @@
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-streaming-twitter_2.10</artifactId>
   <properties>
-    <sbt.project.name>streaming-twitter</sbt.project.name>
+     <sbt.project.name>streaming-twitter</sbt.project.name>
   </properties>
   <packaging>jar</packaging>
   <name>Spark Project External Twitter</name>
diff --git a/external/zeromq/pom.xml b/external/zeromq/pom.xml
index 54b0242c54e7..e4d758a04a4c 100644
--- a/external/zeromq/pom.xml
+++ b/external/zeromq/pom.xml
@@ -28,7 +28,7 @@
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-streaming-zeromq_2.10</artifactId>
   <properties>
-    <sbt.project.name>streaming-zeromq</sbt.project.name>
+     <sbt.project.name>streaming-zeromq</sbt.project.name>
   </properties>
   <packaging>jar</packaging>
   <name>Spark Project External ZeroMQ</name>
diff --git a/graphx/pom.xml b/graphx/pom.xml
index 6dd52fc618b1..7e3bcf29dcfb 100644
--- a/graphx/pom.xml
+++ b/graphx/pom.xml
@@ -28,7 +28,7 @@
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-graphx_2.10</artifactId>
   <properties>
-    <sbt.project.name>graphx</sbt.project.name>
+     <sbt.project.name>graphx</sbt.project.name>
   </properties>
   <packaging>jar</packaging>
   <name>Spark Project GraphX</name>
diff --git a/mllib/pom.xml b/mllib/pom.xml
index f27cf520dc9f..92b07e2357db 100644
--- a/mllib/pom.xml
+++ b/mllib/pom.xml
@@ -28,7 +28,7 @@
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-mllib_2.10</artifactId>
   <properties>
-    <sbt.project.name>mllib</sbt.project.name>
+     <sbt.project.name>mllib</sbt.project.name>
   </properties>  
   <packaging>jar</packaging>
   <name>Spark Project ML Library</name>
diff --git a/pom.xml b/pom.xml
index 3e9d388180d8..4e2d64a83364 100644
--- a/pom.xml
+++ b/pom.xml
@@ -95,7 +95,6 @@
     <module>sql/catalyst</module>
     <module>sql/core</module>
     <module>sql/hive</module>
-    <module>sql/hive-thriftserver</module>
     <module>repl</module>
     <module>assembly</module>
     <module>external/twitter</module>
@@ -253,9 +252,9 @@
         <version>3.3.2</version>
       </dependency>
       <dependency>
-        <groupId>commons-codec</groupId>
-        <artifactId>commons-codec</artifactId>
-        <version>1.5</version>
+	  <groupId>commons-codec</groupId>
+	    <artifactId>commons-codec</artifactId>
+	    <version>1.5</version>
       </dependency>
       <dependency>
         <groupId>com.google.code.findbugs</groupId>
diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index 1629bc2cba8b..62576f84dd03 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -30,11 +30,11 @@ object BuildCommons {
 
   private val buildLocation = file(".").getAbsoluteFile.getParentFile
 
-  val allProjects@Seq(bagel, catalyst, core, graphx, hive, hiveThriftServer, mllib, repl, spark, sql,
-  streaming, streamingFlume, streamingKafka, streamingMqtt, streamingTwitter, streamingZeromq) =
-    Seq("bagel", "catalyst", "core", "graphx", "hive", "hive-thriftserver", "mllib", "repl",
-      "spark", "sql", "streaming", "streaming-flume", "streaming-kafka", "streaming-mqtt",
-      "streaming-twitter", "streaming-zeromq").map(ProjectRef(buildLocation, _))
+  val allProjects@Seq(bagel, catalyst, core, graphx, hive, mllib, repl, spark, sql, streaming,
+  streamingFlume, streamingKafka, streamingMqtt, streamingTwitter, streamingZeromq) =
+    Seq("bagel", "catalyst", "core", "graphx", "hive", "mllib", "repl", "spark", "sql",
+      "streaming", "streaming-flume", "streaming-kafka", "streaming-mqtt", "streaming-twitter",
+      "streaming-zeromq").map(ProjectRef(buildLocation, _))
 
   val optionallyEnabledProjects@Seq(yarn, yarnStable, yarnAlpha, java8Tests, sparkGangliaLgpl) =
     Seq("yarn", "yarn-stable", "yarn-alpha", "java8-tests", "ganglia-lgpl")
@@ -100,7 +100,7 @@ object SparkBuild extends PomBuild {
   Properties.envOrNone("SBT_MAVEN_PROPERTIES") match {
     case Some(v) =>
       v.split("(\\s+|,)").filterNot(_.isEmpty).map(_.split("=")).foreach(x => System.setProperty(x(0), x(1)))
-    case _ =>
+    case _ => 
   }
 
   override val userPropertiesMap = System.getProperties.toMap
@@ -158,7 +158,7 @@ object SparkBuild extends PomBuild {
 
   /* Enable Mima for all projects except spark, hive, catalyst, sql  and repl */
   // TODO: Add Sql to mima checks
-  allProjects.filterNot(x => Seq(spark, sql, hive, hiveThriftServer, catalyst, repl).contains(x)).
+  allProjects.filterNot(y => Seq(spark, sql, hive, catalyst, repl).exists(x => x == y)).
     foreach (x => enable(MimaBuild.mimaSettings(sparkHome, x))(x))
 
   /* Enable Assembly for all assembly projects */
diff --git a/sbin/start-thriftserver.sh b/sbin/start-thriftserver.sh
deleted file mode 100755
index 8398e6f19b51..000000000000
--- a/sbin/start-thriftserver.sh
+++ /dev/null
@@ -1,36 +0,0 @@
-#!/usr/bin/env bash
-
-#
-# Licensed to the Apache Software Foundation (ASF) under one or more
-# contributor license agreements.  See the NOTICE file distributed with
-# this work for additional information regarding copyright ownership.
-# The ASF licenses this file to You under the Apache License, Version 2.0
-# (the "License"); you may not use this file except in compliance with
-# the License.  You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-
-#
-# Shell script for starting the Spark SQL Thrift server
-
-# Enter posix mode for bash
-set -o posix
-
-# Figure out where Spark is installed
-FWDIR="$(cd `dirname $0`/..; pwd)"
-
-if [[ "$@" = *--help ]] || [[ "$@" = *-h ]]; then
-  echo "Usage: ./sbin/start-thriftserver [options]"
-  $FWDIR/bin/spark-submit --help 2>&1 | grep -v Usage 1>&2
-  exit 0
-fi
-
-CLASS="org.apache.spark.sql.hive.thriftserver.HiveThriftServer2"
-exec "$FWDIR"/bin/spark-submit --class $CLASS spark-internal $@
diff --git a/sql/catalyst/pom.xml b/sql/catalyst/pom.xml
index 531bfddbf237..6decde3fcd62 100644
--- a/sql/catalyst/pom.xml
+++ b/sql/catalyst/pom.xml
@@ -32,7 +32,7 @@
   <name>Spark Project Catalyst</name>
   <url>http://spark.apache.org/</url>
   <properties>
-    <sbt.project.name>catalyst</sbt.project.name>
+     <sbt.project.name>catalyst</sbt.project.name>
   </properties>
 
   <dependencies>
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/commands.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/commands.scala
index a357c6ffb897..1d5f033f0d27 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/commands.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/commands.scala
@@ -43,7 +43,8 @@ case class NativeCommand(cmd: String) extends Command {
  */
 case class SetCommand(key: Option[String], value: Option[String]) extends Command {
   override def output = Seq(
-    BoundReference(1, AttributeReference("", StringType, nullable = false)()))
+    BoundReference(0, AttributeReference("key", StringType, nullable = false)()),
+    BoundReference(1, AttributeReference("value", StringType, nullable = false)()))
 }
 
 /**
diff --git a/sql/core/pom.xml b/sql/core/pom.xml
index 3a038a2db617..c309c43804d9 100644
--- a/sql/core/pom.xml
+++ b/sql/core/pom.xml
@@ -32,7 +32,7 @@
   <name>Spark Project SQL</name>
   <url>http://spark.apache.org/</url>
   <properties>
-    <sbt.project.name>sql</sbt.project.name>
+     <sbt.project.name>sql</sbt.project.name>
   </properties>
 
   <dependencies>
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
index 41920c00b5a2..2b787e14f3f1 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
@@ -30,13 +30,12 @@ import scala.collection.JavaConverters._
  * SQLConf is thread-safe (internally synchronized so safe to be used in multiple threads).
  */
 trait SQLConf {
-  import SQLConf._
 
   /** ************************ Spark SQL Params/Hints ******************* */
   // TODO: refactor so that these hints accessors don't pollute the name space of SQLContext?
 
   /** Number of partitions to use for shuffle operators. */
-  private[spark] def numShufflePartitions: Int = get(SHUFFLE_PARTITIONS, "200").toInt
+  private[spark] def numShufflePartitions: Int = get("spark.sql.shuffle.partitions", "200").toInt
 
   /**
    * Upper bound on the sizes (in bytes) of the tables qualified for the auto conversion to
@@ -44,10 +43,11 @@ trait SQLConf {
    * effectively disables auto conversion.
    * Hive setting: hive.auto.convert.join.noconditionaltask.size.
    */
-  private[spark] def autoConvertJoinSize: Int = get(AUTO_CONVERT_JOIN_SIZE, "10000").toInt
+  private[spark] def autoConvertJoinSize: Int =
+    get("spark.sql.auto.convert.join.size", "10000").toInt
 
   /** A comma-separated list of table names marked to be broadcasted during joins. */
-  private[spark] def joinBroadcastTables: String = get(JOIN_BROADCAST_TABLES, "")
+  private[spark] def joinBroadcastTables: String = get("spark.sql.join.broadcastTables", "")
 
   /** ********************** SQLConf functionality methods ************ */
 
@@ -61,7 +61,7 @@ trait SQLConf {
 
   def set(key: String, value: String): Unit = {
     require(key != null, "key cannot be null")
-    require(value != null, s"value cannot be null for $key")
+    require(value != null, s"value cannot be null for ${key}")
     settings.put(key, value)
   }
 
@@ -90,13 +90,3 @@ trait SQLConf {
   }
 
 }
-
-object SQLConf {
-  val AUTO_CONVERT_JOIN_SIZE = "spark.sql.auto.convert.join.size"
-  val SHUFFLE_PARTITIONS = "spark.sql.shuffle.partitions"
-  val JOIN_BROADCAST_TABLES = "spark.sql.join.broadcastTables"
-
-  object Deprecated {
-    val MAPRED_REDUCE_TASKS = "mapred.reduce.tasks"
-  }
-}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala
index 9293239131d5..98d2f89c8ae7 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala
@@ -17,13 +17,12 @@
 
 package org.apache.spark.sql.execution
 
-import org.apache.spark.Logging
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.errors.TreeNodeException
 import org.apache.spark.sql.catalyst.expressions.{Attribute, GenericRow}
 import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
-import org.apache.spark.sql.{Row, SQLConf, SQLContext}
+import org.apache.spark.sql.{Row, SQLContext}
 
 trait Command {
   /**
@@ -45,53 +44,28 @@ trait Command {
 case class SetCommand(
     key: Option[String], value: Option[String], output: Seq[Attribute])(
     @transient context: SQLContext)
-  extends LeafNode with Command with Logging {
+  extends LeafNode with Command {
 
-  override protected[sql] lazy val sideEffectResult: Seq[String] = (key, value) match {
+  override protected[sql] lazy val sideEffectResult: Seq[(String, String)] = (key, value) match {
     // Set value for key k.
     case (Some(k), Some(v)) =>
-      if (k == SQLConf.Deprecated.MAPRED_REDUCE_TASKS) {
-        logWarning(s"Property ${SQLConf.Deprecated.MAPRED_REDUCE_TASKS} is deprecated, " +
-          s"automatically converted to ${SQLConf.SHUFFLE_PARTITIONS} instead.")
-        context.set(SQLConf.SHUFFLE_PARTITIONS, v)
-        Array(s"${SQLConf.SHUFFLE_PARTITIONS}=$v")
-      } else {
-        context.set(k, v)
-        Array(s"$k=$v")
-      }
+      context.set(k, v)
+      Array(k -> v)
 
     // Query the value bound to key k.
     case (Some(k), _) =>
-      // TODO (lian) This is just a workaround to make the Simba ODBC driver work.
-      // Should remove this once we get the ODBC driver updated.
-      if (k == "-v") {
-        val hiveJars = Seq(
-          "hive-exec-0.12.0.jar",
-          "hive-service-0.12.0.jar",
-          "hive-common-0.12.0.jar",
-          "hive-hwi-0.12.0.jar",
-          "hive-0.12.0.jar").mkString(":")
-
-        Array(
-          "system:java.class.path=" + hiveJars,
-          "system:sun.java.command=shark.SharkServer2")
-      }
-      else {
-        Array(s"$k=${context.getOption(k).getOrElse("<undefined>")}")
-      }
+      Array(k -> context.getOption(k).getOrElse("<undefined>"))
 
     // Query all key-value pairs that are set in the SQLConf of the context.
     case (None, None) =>
-      context.getAll.map { case (k, v) =>
-        s"$k=$v"
-      }
+      context.getAll
 
     case _ =>
       throw new IllegalArgumentException()
   }
 
   def execute(): RDD[Row] = {
-    val rows = sideEffectResult.map { line => new GenericRow(Array[Any](line)) }
+    val rows = sideEffectResult.map { case (k, v) => new GenericRow(Array[Any](k, v)) }
     context.sparkContext.parallelize(rows, 1)
   }
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLConfSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLConfSuite.scala
index 1a58d73d9e7f..08293f7f0ca3 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLConfSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLConfSuite.scala
@@ -54,10 +54,10 @@ class SQLConfSuite extends QueryTest {
     assert(get(testKey, testVal + "_") == testVal)
     assert(TestSQLContext.get(testKey, testVal + "_") == testVal)
 
-    sql("set some.property=20")
-    assert(get("some.property", "0") == "20")
-    sql("set some.property = 40")
-    assert(get("some.property", "0") == "40")
+    sql("set mapred.reduce.tasks=20")
+    assert(get("mapred.reduce.tasks", "0") == "20")
+    sql("set mapred.reduce.tasks = 40")
+    assert(get("mapred.reduce.tasks", "0") == "40")
 
     val key = "spark.sql.key"
     val vs = "val0,val_1,val2.3,my_table"
@@ -70,9 +70,4 @@ class SQLConfSuite extends QueryTest {
     clear()
   }
 
-  test("deprecated property") {
-    clear()
-    sql(s"set ${SQLConf.Deprecated.MAPRED_REDUCE_TASKS}=10")
-    assert(get(SQLConf.SHUFFLE_PARTITIONS) == "10")
-  }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index de9e8aa4f62e..6736189c96d4 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -424,25 +424,25 @@ class SQLQuerySuite extends QueryTest {
     sql(s"SET $testKey=$testVal")
     checkAnswer(
       sql("SET"),
-      Seq(Seq(s"$testKey=$testVal"))
+      Seq(Seq(testKey, testVal))
     )
 
     sql(s"SET ${testKey + testKey}=${testVal + testVal}")
     checkAnswer(
       sql("set"),
       Seq(
-        Seq(s"$testKey=$testVal"),
-        Seq(s"${testKey + testKey}=${testVal + testVal}"))
+        Seq(testKey, testVal),
+        Seq(testKey + testKey, testVal + testVal))
     )
 
     // "set key"
     checkAnswer(
       sql(s"SET $testKey"),
-      Seq(Seq(s"$testKey=$testVal"))
+      Seq(Seq(testKey, testVal))
     )
     checkAnswer(
       sql(s"SET $nonexistentKey"),
-      Seq(Seq(s"$nonexistentKey=<undefined>"))
+      Seq(Seq(nonexistentKey, "<undefined>"))
     )
     clear()
   }
diff --git a/sql/hive-thriftserver/pom.xml b/sql/hive-thriftserver/pom.xml
deleted file mode 100644
index 7fac90fdc596..000000000000
--- a/sql/hive-thriftserver/pom.xml
+++ /dev/null
@@ -1,82 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<!--
-  ~ Licensed to the Apache Software Foundation (ASF) under one or more
-  ~ contributor license agreements.  See the NOTICE file distributed with
-  ~ this work for additional information regarding copyright ownership.
-  ~ The ASF licenses this file to You under the Apache License, Version 2.0
-  ~ (the "License"); you may not use this file except in compliance with
-  ~ the License.  You may obtain a copy of the License at
-  ~
-  ~    http://www.apache.org/licenses/LICENSE-2.0
-  ~
-  ~ Unless required by applicable law or agreed to in writing, software
-  ~ distributed under the License is distributed on an "AS IS" BASIS,
-  ~ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-  ~ See the License for the specific language governing permissions and
-  ~ limitations under the License.
-  -->
-
-<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
-         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
-  <modelVersion>4.0.0</modelVersion>
-  <parent>
-    <groupId>org.apache.spark</groupId>
-    <artifactId>spark-parent</artifactId>
-    <version>1.1.0-SNAPSHOT</version>
-    <relativePath>../../pom.xml</relativePath>
-  </parent>
-
-  <groupId>org.apache.spark</groupId>
-  <artifactId>spark-hive-thriftserver_2.10</artifactId>
-  <packaging>jar</packaging>
-  <name>Spark Project Hive</name>
-  <url>http://spark.apache.org/</url>
-  <properties>
-    <sbt.project.name>hive-thriftserver</sbt.project.name>
-  </properties>
-
-  <dependencies>
-    <dependency>
-      <groupId>org.apache.spark</groupId>
-      <artifactId>spark-hive_${scala.binary.version}</artifactId>
-      <version>${project.version}</version>
-    </dependency>
-    <dependency>
-      <groupId>org.spark-project.hive</groupId>
-      <artifactId>hive-cli</artifactId>
-      <version>${hive.version}</version>
-    </dependency>
-    <dependency>
-      <groupId>org.spark-project.hive</groupId>
-      <artifactId>hive-jdbc</artifactId>
-      <version>${hive.version}</version>
-    </dependency>
-    <dependency>
-      <groupId>org.spark-project.hive</groupId>
-      <artifactId>hive-beeline</artifactId>
-      <version>${hive.version}</version>
-    </dependency>
-    <dependency>
-      <groupId>org.scalatest</groupId>
-      <artifactId>scalatest_${scala.binary.version}</artifactId>
-      <scope>test</scope>
-    </dependency>
-  </dependencies>
-  <build>
-    <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory>
-    <testOutputDirectory>target/scala-${scala.binary.version}/test-classes</testOutputDirectory>
-    <plugins>
-      <plugin>
-        <groupId>org.scalatest</groupId>
-        <artifactId>scalatest-maven-plugin</artifactId>
-      </plugin>
-      <plugin>
-        <groupId>org.apache.maven.plugins</groupId>
-        <artifactId>maven-deploy-plugin</artifactId>
-        <configuration>
-          <skip>true</skip>
-        </configuration>
-      </plugin>
-    </plugins>
-  </build>
-</project>
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala
deleted file mode 100644
index ddbc2a79fb51..000000000000
--- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala
+++ /dev/null
@@ -1,97 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql.hive.thriftserver
-
-import scala.collection.JavaConversions._
-
-import org.apache.commons.logging.LogFactory
-import org.apache.hadoop.hive.conf.HiveConf
-import org.apache.hadoop.hive.ql.session.SessionState
-import org.apache.hive.service.cli.thrift.ThriftBinaryCLIService
-import org.apache.hive.service.server.{HiveServer2, ServerOptionsProcessor}
-
-import org.apache.spark.sql.Logging
-import org.apache.spark.sql.hive.HiveContext
-import org.apache.spark.sql.hive.thriftserver.ReflectionUtils._
-
-/**
- * The main entry point for the Spark SQL port of HiveServer2.  Starts up a `SparkSQLContext` and a
- * `HiveThriftServer2` thrift server.
- */
-private[hive] object HiveThriftServer2 extends Logging {
-  var LOG = LogFactory.getLog(classOf[HiveServer2])
-
-  def main(args: Array[String]) {
-    val optionsProcessor = new ServerOptionsProcessor("HiveThriftServer2")
-
-    if (!optionsProcessor.process(args)) {
-      logger.warn("Error starting HiveThriftServer2 with given arguments")
-      System.exit(-1)
-    }
-
-    val ss = new SessionState(new HiveConf(classOf[SessionState]))
-
-    // Set all properties specified via command line.
-    val hiveConf: HiveConf = ss.getConf
-    hiveConf.getAllProperties.toSeq.sortBy(_._1).foreach { case (k, v) =>
-      logger.debug(s"HiveConf var: $k=$v")
-    }
-
-    SessionState.start(ss)
-
-    logger.info("Starting SparkContext")
-    SparkSQLEnv.init()
-    SessionState.start(ss)
-
-    Runtime.getRuntime.addShutdownHook(
-      new Thread() {
-        override def run() {
-          SparkSQLEnv.sparkContext.stop()
-        }
-      }
-    )
-
-    try {
-      val server = new HiveThriftServer2(SparkSQLEnv.hiveContext)
-      server.init(hiveConf)
-      server.start()
-      logger.info("HiveThriftServer2 started")
-    } catch {
-      case e: Exception =>
-        logger.error("Error starting HiveThriftServer2", e)
-        System.exit(-1)
-    }
-  }
-}
-
-private[hive] class HiveThriftServer2(hiveContext: HiveContext)
-  extends HiveServer2
-  with ReflectedCompositeService {
-
-  override def init(hiveConf: HiveConf) {
-    val sparkSqlCliService = new SparkSQLCLIService(hiveContext)
-    setSuperField(this, "cliService", sparkSqlCliService)
-    addService(sparkSqlCliService)
-
-    val thriftCliService = new ThriftBinaryCLIService(sparkSqlCliService)
-    setSuperField(this, "thriftCLIService", thriftCliService)
-    addService(thriftCliService)
-
-    initCompositeService(hiveConf)
-  }
-}
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ReflectionUtils.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ReflectionUtils.scala
deleted file mode 100644
index 599294dfbb7d..000000000000
--- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ReflectionUtils.scala
+++ /dev/null
@@ -1,58 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql.hive.thriftserver
-
-private[hive] object ReflectionUtils {
-  def setSuperField(obj : Object, fieldName: String, fieldValue: Object) {
-    setAncestorField(obj, 1, fieldName, fieldValue)
-  }
-
-  def setAncestorField(obj: AnyRef, level: Int, fieldName: String, fieldValue: AnyRef) {
-    val ancestor = Iterator.iterate[Class[_]](obj.getClass)(_.getSuperclass).drop(level).next()
-    val field = ancestor.getDeclaredField(fieldName)
-    field.setAccessible(true)
-    field.set(obj, fieldValue)
-  }
-
-  def getSuperField[T](obj: AnyRef, fieldName: String): T = {
-    getAncestorField[T](obj, 1, fieldName)
-  }
-
-  def getAncestorField[T](clazz: Object, level: Int, fieldName: String): T = {
-    val ancestor = Iterator.iterate[Class[_]](clazz.getClass)(_.getSuperclass).drop(level).next()
-    val field = ancestor.getDeclaredField(fieldName)
-    field.setAccessible(true)
-    field.get(clazz).asInstanceOf[T]
-  }
-
-  def invokeStatic(clazz: Class[_], methodName: String, args: (Class[_], AnyRef)*): AnyRef = {
-    invoke(clazz, null, methodName, args: _*)
-  }
-
-  def invoke(
-      clazz: Class[_],
-      obj: AnyRef,
-      methodName: String,
-      args: (Class[_], AnyRef)*): AnyRef = {
-
-    val (types, values) = args.unzip
-    val method = clazz.getDeclaredMethod(methodName, types: _*)
-    method.setAccessible(true)
-    method.invoke(obj, values.toSeq: _*)
-  }
-}
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala
deleted file mode 100755
index 27268ecb923e..000000000000
--- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala
+++ /dev/null
@@ -1,344 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql.hive.thriftserver
-
-import scala.collection.JavaConversions._
-
-import java.io._
-import java.util.{ArrayList => JArrayList}
-
-import jline.{ConsoleReader, History}
-import org.apache.commons.lang.StringUtils
-import org.apache.commons.logging.LogFactory
-import org.apache.hadoop.conf.Configuration
-import org.apache.hadoop.hive.cli.{CliDriver, CliSessionState, OptionsProcessor}
-import org.apache.hadoop.hive.common.LogUtils.LogInitializationException
-import org.apache.hadoop.hive.common.{HiveInterruptCallback, HiveInterruptUtils, LogUtils}
-import org.apache.hadoop.hive.conf.HiveConf
-import org.apache.hadoop.hive.ql.Driver
-import org.apache.hadoop.hive.ql.exec.Utilities
-import org.apache.hadoop.hive.ql.processors.{CommandProcessor, CommandProcessorFactory}
-import org.apache.hadoop.hive.ql.session.SessionState
-import org.apache.hadoop.hive.shims.ShimLoader
-import org.apache.thrift.transport.TSocket
-
-import org.apache.spark.sql.Logging
-
-private[hive] object SparkSQLCLIDriver {
-  private var prompt = "spark-sql"
-  private var continuedPrompt = "".padTo(prompt.length, ' ')
-  private var transport:TSocket = _
-
-  installSignalHandler()
-
-  /**
-   * Install an interrupt callback to cancel all Spark jobs. In Hive's CliDriver#processLine(),
-   * a signal handler will invoke this registered callback if a Ctrl+C signal is detected while
-   * a command is being processed by the current thread.
-   */
-  def installSignalHandler() {
-    HiveInterruptUtils.add(new HiveInterruptCallback {
-      override def interrupt() {
-        // Handle remote execution mode
-        if (SparkSQLEnv.sparkContext != null) {
-          SparkSQLEnv.sparkContext.cancelAllJobs()
-        } else {
-          if (transport != null) {
-            // Force closing of TCP connection upon session termination
-            transport.getSocket.close()
-          }
-        }
-      }
-    })
-  }
-
-  def main(args: Array[String]) {
-    val oproc = new OptionsProcessor()
-    if (!oproc.process_stage1(args)) {
-      System.exit(1)
-    }
-
-    // NOTE: It is critical to do this here so that log4j is reinitialized
-    // before any of the other core hive classes are loaded
-    var logInitFailed = false
-    var logInitDetailMessage: String = null
-    try {
-      logInitDetailMessage = LogUtils.initHiveLog4j()
-    } catch {
-      case e: LogInitializationException =>
-        logInitFailed = true
-        logInitDetailMessage = e.getMessage
-    }
-
-    val sessionState = new CliSessionState(new HiveConf(classOf[SessionState]))
-
-    sessionState.in = System.in
-    try {
-      sessionState.out = new PrintStream(System.out, true, "UTF-8")
-      sessionState.info = new PrintStream(System.err, true, "UTF-8")
-      sessionState.err = new PrintStream(System.err, true, "UTF-8")
-    } catch {
-      case e: UnsupportedEncodingException => System.exit(3)
-    }
-
-    if (!oproc.process_stage2(sessionState)) {
-      System.exit(2)
-    }
-
-    if (!sessionState.getIsSilent) {
-      if (logInitFailed) System.err.println(logInitDetailMessage)
-      else SessionState.getConsole.printInfo(logInitDetailMessage)
-    }
-
-    // Set all properties specified via command line.
-    val conf: HiveConf = sessionState.getConf
-    sessionState.cmdProperties.entrySet().foreach { item: java.util.Map.Entry[Object, Object] =>
-      conf.set(item.getKey.asInstanceOf[String], item.getValue.asInstanceOf[String])
-      sessionState.getOverriddenConfigurations.put(
-        item.getKey.asInstanceOf[String], item.getValue.asInstanceOf[String])
-    }
-
-    SessionState.start(sessionState)
-
-    // Clean up after we exit
-    Runtime.getRuntime.addShutdownHook(
-      new Thread() {
-        override def run() {
-          SparkSQLEnv.stop()
-        }
-      }
-    )
-
-    // "-h" option has been passed, so connect to Hive thrift server.
-    if (sessionState.getHost != null) {
-      sessionState.connect()
-      if (sessionState.isRemoteMode) {
-        prompt = s"[${sessionState.getHost}:${sessionState.getPort}]" + prompt
-        continuedPrompt = "".padTo(prompt.length, ' ')
-      }
-    }
-
-    if (!sessionState.isRemoteMode && !ShimLoader.getHadoopShims.usesJobShell()) {
-      // Hadoop-20 and above - we need to augment classpath using hiveconf
-      // components.
-      // See also: code in ExecDriver.java
-      var loader = conf.getClassLoader
-      val auxJars = HiveConf.getVar(conf, HiveConf.ConfVars.HIVEAUXJARS)
-      if (StringUtils.isNotBlank(auxJars)) {
-        loader = Utilities.addToClassPath(loader, StringUtils.split(auxJars, ","))
-      }
-      conf.setClassLoader(loader)
-      Thread.currentThread().setContextClassLoader(loader)
-    }
-
-    val cli = new SparkSQLCLIDriver
-    cli.setHiveVariables(oproc.getHiveVariables)
-
-    // TODO work around for set the log output to console, because the HiveContext
-    // will set the output into an invalid buffer.
-    sessionState.in = System.in
-    try {
-      sessionState.out = new PrintStream(System.out, true, "UTF-8")
-      sessionState.info = new PrintStream(System.err, true, "UTF-8")
-      sessionState.err = new PrintStream(System.err, true, "UTF-8")
-    } catch {
-      case e: UnsupportedEncodingException => System.exit(3)
-    }
-
-    // Execute -i init files (always in silent mode)
-    cli.processInitFiles(sessionState)
-
-    if (sessionState.execString != null) {
-      System.exit(cli.processLine(sessionState.execString))
-    }
-
-    try {
-      if (sessionState.fileName != null) {
-        System.exit(cli.processFile(sessionState.fileName))
-      }
-    } catch {
-      case e: FileNotFoundException =>
-        System.err.println(s"Could not open input file for reading. (${e.getMessage})")
-        System.exit(3)
-    }
-
-    val reader = new ConsoleReader()
-    reader.setBellEnabled(false)
-    // reader.setDebug(new PrintWriter(new FileWriter("writer.debug", true)))
-    CliDriver.getCommandCompletor.foreach((e) => reader.addCompletor(e))
-
-    val historyDirectory = System.getProperty("user.home")
-
-    try {
-      if (new File(historyDirectory).exists()) {
-        val historyFile = historyDirectory + File.separator + ".hivehistory"
-        reader.setHistory(new History(new File(historyFile)))
-      } else {
-        System.err.println("WARNING: Directory for Hive history file: " + historyDirectory +
-                           " does not exist.   History will not be available during this session.")
-      }
-    } catch {
-      case e: Exception =>
-        System.err.println("WARNING: Encountered an error while trying to initialize Hive's " +
-                           "history file.  History will not be available during this session.")
-        System.err.println(e.getMessage)
-    }
-
-    val clientTransportTSocketField = classOf[CliSessionState].getDeclaredField("transport")
-    clientTransportTSocketField.setAccessible(true)
-
-    transport = clientTransportTSocketField.get(sessionState).asInstanceOf[TSocket]
-
-    var ret = 0
-    var prefix = ""
-    val currentDB = ReflectionUtils.invokeStatic(classOf[CliDriver], "getFormattedDb",
-      classOf[HiveConf] -> conf, classOf[CliSessionState] -> sessionState)
-
-    def promptWithCurrentDB = s"$prompt$currentDB"
-    def continuedPromptWithDBSpaces = continuedPrompt + ReflectionUtils.invokeStatic(
-      classOf[CliDriver], "spacesForString", classOf[String] -> currentDB)
-
-    var currentPrompt = promptWithCurrentDB
-    var line = reader.readLine(currentPrompt + "> ")
-
-    while (line != null) {
-      if (prefix.nonEmpty) {
-        prefix += '\n'
-      }
-
-      if (line.trim().endsWith(";") && !line.trim().endsWith("\\;")) {
-        line = prefix + line
-        ret = cli.processLine(line, true)
-        prefix = ""
-        currentPrompt = promptWithCurrentDB
-      } else {
-        prefix = prefix + line
-        currentPrompt = continuedPromptWithDBSpaces
-      }
-
-      line = reader.readLine(currentPrompt + "> ")
-    }
-
-    sessionState.close()
-
-    System.exit(ret)
-  }
-}
-
-private[hive] class SparkSQLCLIDriver extends CliDriver with Logging {
-  private val sessionState = SessionState.get().asInstanceOf[CliSessionState]
-
-  private val LOG = LogFactory.getLog("CliDriver")
-
-  private val console = new SessionState.LogHelper(LOG)
-
-  private val conf: Configuration =
-    if (sessionState != null) sessionState.getConf else new Configuration()
-
-  // Force initializing SparkSQLEnv. This is put here but not object SparkSQLCliDriver
-  // because the Hive unit tests do not go through the main() code path.
-  if (!sessionState.isRemoteMode) {
-    SparkSQLEnv.init()
-  }
-
-  override def processCmd(cmd: String): Int = {
-    val cmd_trimmed: String = cmd.trim()
-    val tokens: Array[String] = cmd_trimmed.split("\\s+")
-    val cmd_1: String = cmd_trimmed.substring(tokens(0).length()).trim()
-    if (cmd_trimmed.toLowerCase.equals("quit") ||
-      cmd_trimmed.toLowerCase.equals("exit") ||
-      tokens(0).equalsIgnoreCase("source") ||
-      cmd_trimmed.startsWith("!") ||
-      tokens(0).toLowerCase.equals("list") ||
-      sessionState.isRemoteMode) {
-      val start = System.currentTimeMillis()
-      super.processCmd(cmd)
-      val end = System.currentTimeMillis()
-      val timeTaken: Double = (end - start) / 1000.0
-      console.printInfo(s"Time taken: $timeTaken seconds")
-      0
-    } else {
-      var ret = 0
-      val hconf = conf.asInstanceOf[HiveConf]
-      val proc: CommandProcessor = CommandProcessorFactory.get(tokens(0), hconf)
-
-      if (proc != null) {
-        if (proc.isInstanceOf[Driver]) {
-          val driver = new SparkSQLDriver
-
-          driver.init()
-          val out = sessionState.out
-          val start:Long = System.currentTimeMillis()
-          if (sessionState.getIsVerbose) {
-            out.println(cmd)
-          }
-
-          ret = driver.run(cmd).getResponseCode
-          if (ret != 0) {
-            driver.close()
-            return ret
-          }
-
-          val res = new JArrayList[String]()
-
-          if (HiveConf.getBoolVar(conf, HiveConf.ConfVars.HIVE_CLI_PRINT_HEADER)) {
-            // Print the column names.
-            Option(driver.getSchema.getFieldSchemas).map { fields =>
-              out.println(fields.map(_.getName).mkString("\t"))
-            }
-          }
-
-          try {
-            while (!out.checkError() && driver.getResults(res)) {
-              res.foreach(out.println)
-              res.clear()
-            }
-          } catch {
-            case e:IOException =>
-              console.printError(
-                s"""Failed with exception ${e.getClass.getName}: ${e.getMessage}
-                   |${org.apache.hadoop.util.StringUtils.stringifyException(e)}
-                 """.stripMargin)
-              ret = 1
-          }
-
-          val cret = driver.close()
-          if (ret == 0) {
-            ret = cret
-          }
-
-          val end = System.currentTimeMillis()
-          if (end > start) {
-            val timeTaken:Double = (end - start) / 1000.0
-            console.printInfo(s"Time taken: $timeTaken seconds", null)
-          }
-
-          // Destroy the driver to release all the locks.
-          driver.destroy()
-        } else {
-          if (sessionState.getIsVerbose) {
-            sessionState.out.println(tokens(0) + " " + cmd_1)
-          }
-          ret = proc.run(cmd_1).getResponseCode
-        }
-      }
-      ret
-    }
-  }
-}
-
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIService.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIService.scala
deleted file mode 100644
index 42cbf363b274..000000000000
--- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIService.scala
+++ /dev/null
@@ -1,74 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql.hive.thriftserver
-
-import scala.collection.JavaConversions._
-
-import java.io.IOException
-import java.util.{List => JList}
-import javax.security.auth.login.LoginException
-
-import org.apache.commons.logging.Log
-import org.apache.hadoop.hive.conf.HiveConf
-import org.apache.hadoop.hive.shims.ShimLoader
-import org.apache.hive.service.Service.STATE
-import org.apache.hive.service.auth.HiveAuthFactory
-import org.apache.hive.service.cli.CLIService
-import org.apache.hive.service.{AbstractService, Service, ServiceException}
-
-import org.apache.spark.sql.hive.HiveContext
-import org.apache.spark.sql.hive.thriftserver.ReflectionUtils._
-
-private[hive] class SparkSQLCLIService(hiveContext: HiveContext)
-  extends CLIService
-  with ReflectedCompositeService {
-
-  override def init(hiveConf: HiveConf) {
-    setSuperField(this, "hiveConf", hiveConf)
-
-    val sparkSqlSessionManager = new SparkSQLSessionManager(hiveContext)
-    setSuperField(this, "sessionManager", sparkSqlSessionManager)
-    addService(sparkSqlSessionManager)
-
-    try {
-      HiveAuthFactory.loginFromKeytab(hiveConf)
-      val serverUserName = ShimLoader.getHadoopShims
-        .getShortUserName(ShimLoader.getHadoopShims.getUGIForConf(hiveConf))
-      setSuperField(this, "serverUserName", serverUserName)
-    } catch {
-      case e @ (_: IOException | _: LoginException) =>
-        throw new ServiceException("Unable to login to kerberos with given principal/keytab", e)
-    }
-
-    initCompositeService(hiveConf)
-  }
-}
-
-private[thriftserver] trait ReflectedCompositeService { this: AbstractService =>
-  def initCompositeService(hiveConf: HiveConf) {
-    // Emulating `CompositeService.init(hiveConf)`
-    val serviceList = getAncestorField[JList[Service]](this, 2, "serviceList")
-    serviceList.foreach(_.init(hiveConf))
-
-    // Emulating `AbstractService.init(hiveConf)`
-    invoke(classOf[AbstractService], this, "ensureCurrentState", classOf[STATE] -> STATE.NOTINITED)
-    setAncestorField(this, 3, "hiveConf", hiveConf)
-    invoke(classOf[AbstractService], this, "changeState", classOf[STATE] -> STATE.INITED)
-    getAncestorField[Log](this, 3, "LOG").info(s"Service: $getName is inited.")
-  }
-}
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLDriver.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLDriver.scala
deleted file mode 100644
index 5202aa9903e0..000000000000
--- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLDriver.scala
+++ /dev/null
@@ -1,93 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql.hive.thriftserver
-
-import scala.collection.JavaConversions._
-
-import java.util.{ArrayList => JArrayList}
-
-import org.apache.commons.lang.exception.ExceptionUtils
-import org.apache.hadoop.hive.metastore.api.{FieldSchema, Schema}
-import org.apache.hadoop.hive.ql.Driver
-import org.apache.hadoop.hive.ql.processors.CommandProcessorResponse
-
-import org.apache.spark.sql.Logging
-import org.apache.spark.sql.hive.{HiveContext, HiveMetastoreTypes}
-
-private[hive] class SparkSQLDriver(val context: HiveContext = SparkSQLEnv.hiveContext)
-  extends Driver with Logging {
-
-  private var tableSchema: Schema = _
-  private var hiveResponse: Seq[String] = _
-
-  override def init(): Unit = {
-  }
-
-  private def getResultSetSchema(query: context.QueryExecution): Schema = {
-    val analyzed = query.analyzed
-    logger.debug(s"Result Schema: ${analyzed.output}")
-    if (analyzed.output.size == 0) {
-      new Schema(new FieldSchema("Response code", "string", "") :: Nil, null)
-    } else {
-      val fieldSchemas = analyzed.output.map { attr =>
-        new FieldSchema(attr.name, HiveMetastoreTypes.toMetastoreType(attr.dataType), "")
-      }
-
-      new Schema(fieldSchemas, null)
-    }
-  }
-
-  override def run(command: String): CommandProcessorResponse = {
-    val execution = context.executePlan(context.hql(command).logicalPlan)
-
-    // TODO unify the error code
-    try {
-      hiveResponse = execution.stringResult()
-      tableSchema = getResultSetSchema(execution)
-      new CommandProcessorResponse(0)
-    } catch {
-      case cause: Throwable =>
-        logger.error(s"Failed in [$command]", cause)
-        new CommandProcessorResponse(-3, ExceptionUtils.getFullStackTrace(cause), null)
-    }
-  }
-
-  override def close(): Int = {
-    hiveResponse = null
-    tableSchema = null
-    0
-  }
-
-  override def getSchema: Schema = tableSchema
-
-  override def getResults(res: JArrayList[String]): Boolean = {
-    if (hiveResponse == null) {
-      false
-    } else {
-      res.addAll(hiveResponse)
-      hiveResponse = null
-      true
-    }
-  }
-
-  override def destroy() {
-    super.destroy()
-    hiveResponse = null
-    tableSchema = null
-  }
-}
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLEnv.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLEnv.scala
deleted file mode 100644
index 451c3bd7b935..000000000000
--- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLEnv.scala
+++ /dev/null
@@ -1,58 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql.hive.thriftserver
-
-import org.apache.hadoop.hive.ql.session.SessionState
-
-import org.apache.spark.scheduler.{SplitInfo, StatsReportListener}
-import org.apache.spark.sql.Logging
-import org.apache.spark.sql.hive.HiveContext
-import org.apache.spark.{SparkConf, SparkContext}
-
-/** A singleton object for the master program. The slaves should not access this. */
-private[hive] object SparkSQLEnv extends Logging {
-  logger.debug("Initializing SparkSQLEnv")
-
-  var hiveContext: HiveContext = _
-  var sparkContext: SparkContext = _
-
-  def init() {
-    if (hiveContext == null) {
-      sparkContext = new SparkContext(new SparkConf()
-        .setAppName(s"SparkSQL::${java.net.InetAddress.getLocalHost.getHostName}"))
-
-      sparkContext.addSparkListener(new StatsReportListener())
-
-      hiveContext = new HiveContext(sparkContext) {
-        @transient override lazy val sessionState = SessionState.get()
-        @transient override lazy val hiveconf = sessionState.getConf
-      }
-    }
-  }
-
-  /** Cleans up and shuts down the Spark SQL environments. */
-  def stop() {
-    logger.debug("Shutting down Spark SQL Environment")
-    // Stop the SparkContext
-    if (SparkSQLEnv.sparkContext != null) {
-      sparkContext.stop()
-      sparkContext = null
-      hiveContext = null
-    }
-  }
-}
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLSessionManager.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLSessionManager.scala
deleted file mode 100644
index 6b3275b4eaf0..000000000000
--- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLSessionManager.scala
+++ /dev/null
@@ -1,49 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql.hive.thriftserver
-
-import java.util.concurrent.Executors
-
-import org.apache.commons.logging.Log
-import org.apache.hadoop.hive.conf.HiveConf
-import org.apache.hadoop.hive.conf.HiveConf.ConfVars
-import org.apache.hive.service.cli.session.SessionManager
-
-import org.apache.spark.sql.hive.HiveContext
-import org.apache.spark.sql.hive.thriftserver.ReflectionUtils._
-import org.apache.spark.sql.hive.thriftserver.server.SparkSQLOperationManager
-
-private[hive] class SparkSQLSessionManager(hiveContext: HiveContext)
-  extends SessionManager
-  with ReflectedCompositeService {
-
-  override def init(hiveConf: HiveConf) {
-    setSuperField(this, "hiveConf", hiveConf)
-
-    val backgroundPoolSize = hiveConf.getIntVar(ConfVars.HIVE_SERVER2_ASYNC_EXEC_THREADS)
-    setSuperField(this, "backgroundOperationPool", Executors.newFixedThreadPool(backgroundPoolSize))
-    getAncestorField[Log](this, 3, "LOG").info(
-      s"HiveServer2: Async execution pool size $backgroundPoolSize")
-
-    val sparkSqlOperationManager = new SparkSQLOperationManager(hiveContext)
-    setSuperField(this, "operationManager", sparkSqlOperationManager)
-    addService(sparkSqlOperationManager)
-
-    initCompositeService(hiveConf)
-  }
-}
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/server/SparkSQLOperationManager.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/server/SparkSQLOperationManager.scala
deleted file mode 100644
index a4e1f3e762e8..000000000000
--- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/server/SparkSQLOperationManager.scala
+++ /dev/null
@@ -1,151 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql.hive.thriftserver.server
-
-import scala.collection.JavaConversions._
-import scala.collection.mutable.ArrayBuffer
-import scala.math.{random, round}
-
-import java.sql.Timestamp
-import java.util.{Map => JMap}
-
-import org.apache.hadoop.hive.common.`type`.HiveDecimal
-import org.apache.hadoop.hive.metastore.api.FieldSchema
-import org.apache.hive.service.cli._
-import org.apache.hive.service.cli.operation.{ExecuteStatementOperation, Operation, OperationManager}
-import org.apache.hive.service.cli.session.HiveSession
-
-import org.apache.spark.sql.catalyst.types._
-import org.apache.spark.sql.hive.thriftserver.ReflectionUtils
-import org.apache.spark.sql.hive.{HiveContext, HiveMetastoreTypes}
-import org.apache.spark.sql.{Logging, SchemaRDD, Row => SparkRow}
-
-/**
- * Executes queries using Spark SQL, and maintains a list of handles to active queries.
- */
-class SparkSQLOperationManager(hiveContext: HiveContext) extends OperationManager with Logging {
-  val handleToOperation = ReflectionUtils
-    .getSuperField[JMap[OperationHandle, Operation]](this, "handleToOperation")
-
-  override def newExecuteStatementOperation(
-      parentSession: HiveSession,
-      statement: String,
-      confOverlay: JMap[String, String],
-      async: Boolean): ExecuteStatementOperation = synchronized {
-
-    val operation = new ExecuteStatementOperation(parentSession, statement, confOverlay) {
-      private var result: SchemaRDD = _
-      private var iter: Iterator[SparkRow] = _
-      private var dataTypes: Array[DataType] = _
-
-      def close(): Unit = {
-        // RDDs will be cleaned automatically upon garbage collection.
-        logger.debug("CLOSING")
-      }
-
-      def getNextRowSet(order: FetchOrientation, maxRowsL: Long): RowSet = {
-        if (!iter.hasNext) {
-          new RowSet()
-        } else {
-          val maxRows = maxRowsL.toInt // Do you really want a row batch larger than Int Max? No.
-          var curRow = 0
-          var rowSet = new ArrayBuffer[Row](maxRows)
-
-          while (curRow < maxRows && iter.hasNext) {
-            val sparkRow = iter.next()
-            val row = new Row()
-            var curCol = 0
-
-            while (curCol < sparkRow.length) {
-              dataTypes(curCol) match {
-                case StringType =>
-                  row.addString(sparkRow(curCol).asInstanceOf[String])
-                case IntegerType =>
-                  row.addColumnValue(ColumnValue.intValue(sparkRow.getInt(curCol)))
-                case BooleanType =>
-                  row.addColumnValue(ColumnValue.booleanValue(sparkRow.getBoolean(curCol)))
-                case DoubleType =>
-                  row.addColumnValue(ColumnValue.doubleValue(sparkRow.getDouble(curCol)))
-                case FloatType =>
-                  row.addColumnValue(ColumnValue.floatValue(sparkRow.getFloat(curCol)))
-                case DecimalType =>
-                  val hiveDecimal = sparkRow.get(curCol).asInstanceOf[BigDecimal].bigDecimal
-                  row.addColumnValue(ColumnValue.stringValue(new HiveDecimal(hiveDecimal)))
-                case LongType =>
-                  row.addColumnValue(ColumnValue.longValue(sparkRow.getLong(curCol)))
-                case ByteType =>
-                  row.addColumnValue(ColumnValue.byteValue(sparkRow.getByte(curCol)))
-                case ShortType =>
-                  row.addColumnValue(ColumnValue.intValue(sparkRow.getShort(curCol)))
-                case TimestampType =>
-                  row.addColumnValue(
-                    ColumnValue.timestampValue(sparkRow.get(curCol).asInstanceOf[Timestamp]))
-                case BinaryType | _: ArrayType | _: StructType | _: MapType =>
-                  val hiveString = result
-                    .queryExecution
-                    .asInstanceOf[HiveContext#QueryExecution]
-                    .toHiveString((sparkRow.get(curCol), dataTypes(curCol)))
-                  row.addColumnValue(ColumnValue.stringValue(hiveString))
-              }
-              curCol += 1
-            }
-            rowSet += row
-            curRow += 1
-          }
-          new RowSet(rowSet, 0)
-        }
-      }
-
-      def getResultSetSchema: TableSchema = {
-        logger.warn(s"Result Schema: ${result.queryExecution.analyzed.output}")
-        if (result.queryExecution.analyzed.output.size == 0) {
-          new TableSchema(new FieldSchema("Result", "string", "") :: Nil)
-        } else {
-          val schema = result.queryExecution.analyzed.output.map { attr =>
-            new FieldSchema(attr.name, HiveMetastoreTypes.toMetastoreType(attr.dataType), "")
-          }
-          new TableSchema(schema)
-        }
-      }
-
-      def run(): Unit = {
-        logger.info(s"Running query '$statement'")
-        setState(OperationState.RUNNING)
-        try {
-          result = hiveContext.hql(statement)
-          logger.debug(result.queryExecution.toString())
-          val groupId = round(random * 1000000).toString
-          hiveContext.sparkContext.setJobGroup(groupId, statement)
-          iter = result.queryExecution.toRdd.toLocalIterator
-          dataTypes = result.queryExecution.analyzed.output.map(_.dataType).toArray
-          setHasResultSet(true)
-        } catch {
-          // Actually do need to catch Throwable as some failures don't inherit from Exception and
-          // HiveServer will silently swallow them.
-          case e: Throwable =>
-            logger.error("Error executing query:",e)
-            throw new HiveSQLException(e.toString)
-        }
-        setState(OperationState.FINISHED)
-      }
-    }
-
-   handleToOperation.put(operation.getHandle, operation)
-   operation
-  }
-}
diff --git a/sql/hive-thriftserver/src/test/resources/data/files/small_kv.txt b/sql/hive-thriftserver/src/test/resources/data/files/small_kv.txt
deleted file mode 100644
index 850f8014b6f0..000000000000
--- a/sql/hive-thriftserver/src/test/resources/data/files/small_kv.txt
+++ /dev/null
@@ -1,5 +0,0 @@
-238
val_238
-86
val_86
-311
val_311
-27
val_27
-165
val_165
diff --git a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/CliSuite.scala b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/CliSuite.scala
deleted file mode 100644
index b90670a796b8..000000000000
--- a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/CliSuite.scala
+++ /dev/null
@@ -1,59 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql.hive.thriftserver
-
-import java.io.{BufferedReader, InputStreamReader, PrintWriter}
-
-import org.scalatest.{BeforeAndAfterAll, FunSuite}
-
-import org.apache.spark.sql.hive.test.TestHive
-
-class CliSuite extends FunSuite with BeforeAndAfterAll with TestUtils {
-  val WAREHOUSE_PATH = TestUtils.getWarehousePath("cli")
-  val METASTORE_PATH = TestUtils.getMetastorePath("cli")
-
-  override def beforeAll() {
-    val pb = new ProcessBuilder(
-      "../../bin/spark-sql",
-      "--master",
-      "local",
-      "--hiveconf",
-      s"javax.jdo.option.ConnectionURL=jdbc:derby:;databaseName=$METASTORE_PATH;create=true",
-      "--hiveconf",
-      "hive.metastore.warehouse.dir=" + WAREHOUSE_PATH)
-
-    process = pb.start()
-    outputWriter = new PrintWriter(process.getOutputStream, true)
-    inputReader = new BufferedReader(new InputStreamReader(process.getInputStream))
-    errorReader = new BufferedReader(new InputStreamReader(process.getErrorStream))
-    waitForOutput(inputReader, "spark-sql>")
-  }
-
-  override def afterAll() {
-    process.destroy()
-    process.waitFor()
-  }
-
-  test("simple commands") {
-    val dataFilePath = getDataFile("data/files/small_kv.txt")
-    executeQuery("create table hive_test1(key int, val string);")
-    executeQuery("load data local inpath '" + dataFilePath+ "' overwrite into table hive_test1;")
-    executeQuery("cache table hive_test1", "Time taken")
-  }
-}
diff --git a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suite.scala b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suite.scala
deleted file mode 100644
index 59f4952b78bc..000000000000
--- a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suite.scala
+++ /dev/null
@@ -1,125 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql.hive.thriftserver
-
-import scala.collection.JavaConversions._
-import scala.concurrent.ExecutionContext.Implicits.global
-import scala.concurrent._
-
-import java.io.{BufferedReader, InputStreamReader}
-import java.sql.{Connection, DriverManager, Statement}
-
-import org.scalatest.{BeforeAndAfterAll, FunSuite}
-
-import org.apache.spark.sql.Logging
-import org.apache.spark.sql.catalyst.util.getTempFilePath
-
-/**
- * Test for the HiveThriftServer2 using JDBC.
- */
-class HiveThriftServer2Suite extends FunSuite with BeforeAndAfterAll with TestUtils with Logging {
-
-  val WAREHOUSE_PATH = getTempFilePath("warehouse")
-  val METASTORE_PATH = getTempFilePath("metastore")
-
-  val DRIVER_NAME  = "org.apache.hive.jdbc.HiveDriver"
-  val TABLE = "test"
-  // use a different port, than the hive standard 10000,
-  // for tests to avoid issues with the port being taken on some machines
-  val PORT = "10000"
-
-  // If verbose is true, the test program will print all outputs coming from the Hive Thrift server.
-  val VERBOSE = Option(System.getenv("SPARK_SQL_TEST_VERBOSE")).getOrElse("false").toBoolean
-
-  Class.forName(DRIVER_NAME)
-
-  override def beforeAll() { launchServer() }
-
-  override def afterAll() { stopServer() }
-
-  private def launchServer(args: Seq[String] = Seq.empty) {
-    // Forking a new process to start the Hive Thrift server. The reason to do this is it is
-    // hard to clean up Hive resources entirely, so we just start a new process and kill
-    // that process for cleanup.
-    val defaultArgs = Seq(
-      "../../sbin/start-thriftserver.sh",
-      "--master local",
-      "--hiveconf",
-      "hive.root.logger=INFO,console",
-      "--hiveconf",
-      s"javax.jdo.option.ConnectionURL=jdbc:derby:;databaseName=$METASTORE_PATH;create=true",
-      "--hiveconf",
-      s"hive.metastore.warehouse.dir=$WAREHOUSE_PATH")
-    val pb = new ProcessBuilder(defaultArgs ++ args)
-    process = pb.start()
-    inputReader = new BufferedReader(new InputStreamReader(process.getInputStream))
-    errorReader = new BufferedReader(new InputStreamReader(process.getErrorStream))
-    waitForOutput(inputReader, "ThriftBinaryCLIService listening on")
-
-    // Spawn a thread to read the output from the forked process.
-    // Note that this is necessary since in some configurations, log4j could be blocked
-    // if its output to stderr are not read, and eventually blocking the entire test suite.
-    future {
-      while (true) {
-        val stdout = readFrom(inputReader)
-        val stderr = readFrom(errorReader)
-        if (VERBOSE && stdout.length > 0) {
-          println(stdout)
-        }
-        if (VERBOSE && stderr.length > 0) {
-          println(stderr)
-        }
-        Thread.sleep(50)
-      }
-    }
-  }
-
-  private def stopServer() {
-    process.destroy()
-    process.waitFor()
-  }
-
-  test("test query execution against a Hive Thrift server") {
-    Thread.sleep(5 * 1000)
-    val dataFilePath = getDataFile("data/files/small_kv.txt")
-    val stmt = createStatement()
-    stmt.execute("DROP TABLE IF EXISTS test")
-    stmt.execute("DROP TABLE IF EXISTS test_cached")
-    stmt.execute("CREATE TABLE test(key int, val string)")
-    stmt.execute(s"LOAD DATA LOCAL INPATH '$dataFilePath' OVERWRITE INTO TABLE test")
-    stmt.execute("CREATE TABLE test_cached as select * from test limit 4")
-    stmt.execute("CACHE TABLE test_cached")
-
-    var rs = stmt.executeQuery("select count(*) from test")
-    rs.next()
-    assert(rs.getInt(1) === 5)
-
-    rs = stmt.executeQuery("select count(*) from test_cached")
-    rs.next()
-    assert(rs.getInt(1) === 4)
-
-    stmt.close()
-  }
-
-  def getConnection: Connection = {
-    val connectURI = s"jdbc:hive2://localhost:$PORT/"
-    DriverManager.getConnection(connectURI, System.getProperty("user.name"), "")
-  }
-
-  def createStatement(): Statement = getConnection.createStatement()
-}
diff --git a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/TestUtils.scala b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/TestUtils.scala
deleted file mode 100644
index bb2242618fbe..000000000000
--- a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/TestUtils.scala
+++ /dev/null
@@ -1,108 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql.hive.thriftserver
-
-import java.io.{BufferedReader, PrintWriter}
-import java.text.SimpleDateFormat
-import java.util.Date
-
-import org.apache.hadoop.hive.common.LogUtils
-import org.apache.hadoop.hive.common.LogUtils.LogInitializationException
-
-object TestUtils {
-  val timestamp = new SimpleDateFormat("yyyyMMdd-HHmmss")
-
-  def getWarehousePath(prefix: String): String = {
-    System.getProperty("user.dir") + "/test_warehouses/" + prefix + "-warehouse-" +
-      timestamp.format(new Date)
-  }
-
-  def getMetastorePath(prefix: String): String = {
-    System.getProperty("user.dir") + "/test_warehouses/" + prefix + "-metastore-" +
-      timestamp.format(new Date)
-  }
-
-  // Dummy function for initialize the log4j properties.
-  def init() { }
-
-  // initialize log4j
-  try {
-    LogUtils.initHiveLog4j()
-  } catch {
-    case e: LogInitializationException => // Ignore the error.
-  }
-}
-
-trait TestUtils {
-  var process : Process = null
-  var outputWriter : PrintWriter = null
-  var inputReader : BufferedReader = null
-  var errorReader : BufferedReader = null
-
-  def executeQuery(
-    cmd: String, outputMessage: String = "OK", timeout: Long = 15000): String = {
-    println("Executing: " + cmd + ", expecting output: " + outputMessage)
-    outputWriter.write(cmd + "\n")
-    outputWriter.flush()
-    waitForQuery(timeout, outputMessage)
-  }
-
-  protected def waitForQuery(timeout: Long, message: String): String = {
-    if (waitForOutput(errorReader, message, timeout)) {
-      Thread.sleep(500)
-      readOutput()
-    } else {
-      assert(false, "Didn't find \"" + message + "\" in the output:\n" + readOutput())
-      null
-    }
-  }
-
-  // Wait for the specified str to appear in the output.
-  protected def waitForOutput(
-    reader: BufferedReader, str: String, timeout: Long = 10000): Boolean = {
-    val startTime = System.currentTimeMillis
-    var out = ""
-    while (!out.contains(str) && System.currentTimeMillis < (startTime + timeout)) {
-      out += readFrom(reader)
-    }
-    out.contains(str)
-  }
-
-  // Read stdout output and filter out garbage collection messages.
-  protected def readOutput(): String = {
-    val output = readFrom(inputReader)
-    // Remove GC Messages
-    val filteredOutput = output.lines.filterNot(x => x.contains("[GC") || x.contains("[Full GC"))
-      .mkString("\n")
-    filteredOutput
-  }
-
-  protected def readFrom(reader: BufferedReader): String = {
-    var out = ""
-    var c = 0
-    while (reader.ready) {
-      c = reader.read()
-      out += c.asInstanceOf[Char]
-    }
-    out
-  }
-
-  protected def getDataFile(name: String) = {
-    Thread.currentThread().getContextClassLoader.getResource(name)
-  }
-}
diff --git a/sql/hive/pom.xml b/sql/hive/pom.xml
index 93d00f7c37c9..1699ffe06ce1 100644
--- a/sql/hive/pom.xml
+++ b/sql/hive/pom.xml
@@ -32,7 +32,7 @@
   <name>Spark Project Hive</name>
   <url>http://spark.apache.org/</url>
   <properties>
-    <sbt.project.name>hive</sbt.project.name>
+     <sbt.project.name>hive</sbt.project.name>
   </properties>
 
   <dependencies>
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
index 84d43eaeea51..201c85f3d501 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
@@ -255,7 +255,7 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) {
       Seq(StringType, IntegerType, LongType, DoubleType, FloatType, BooleanType, ByteType,
         ShortType, DecimalType, TimestampType, BinaryType)
 
-    protected[sql] def toHiveString(a: (Any, DataType)): String = a match {
+    protected def toHiveString(a: (Any, DataType)): String = a match {
       case (struct: Row, StructType(fields)) =>
         struct.zip(fields).map {
           case (v, t) => s""""${t.name}":${toHiveStructString(v, t.dataType)}"""
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
index 8489f2a34e63..6f36a4f8cb90 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
@@ -416,10 +416,10 @@ class HiveQuerySuite extends HiveComparisonTest {
     hql(s"set $testKey=$testVal")
     assert(get(testKey, testVal + "_") == testVal)
 
-    hql("set some.property=20")
-    assert(get("some.property", "0") == "20")
-    hql("set some.property = 40")
-    assert(get("some.property", "0") == "40")
+    hql("set mapred.reduce.tasks=20")
+    assert(get("mapred.reduce.tasks", "0") == "20")
+    hql("set mapred.reduce.tasks = 40")
+    assert(get("mapred.reduce.tasks", "0") == "40")
 
     hql(s"set $testKey=$testVal")
     assert(get(testKey, "0") == testVal)
@@ -433,61 +433,63 @@ class HiveQuerySuite extends HiveComparisonTest {
     val testKey = "spark.sql.key.usedfortestonly"
     val testVal = "test.val.0"
     val nonexistentKey = "nonexistent"
+    def collectResults(rdd: SchemaRDD): Set[(String, String)] =
+      rdd.collect().map { case Row(key: String, value: String) => key -> value }.toSet
 
     clear()
 
     // "set" itself returns all config variables currently specified in SQLConf.
     assert(hql("SET").collect().size == 0)
 
-    assertResult(Array(s"$testKey=$testVal")) {
-      hql(s"SET $testKey=$testVal").collect().map(_.getString(0))
+    assertResult(Set(testKey -> testVal)) {
+      collectResults(hql(s"SET $testKey=$testVal"))
     }
 
     assert(hiveconf.get(testKey, "") == testVal)
-    assertResult(Array(s"$testKey=$testVal")) {
-      hql(s"SET $testKey=$testVal").collect().map(_.getString(0))
+    assertResult(Set(testKey -> testVal)) {
+      collectResults(hql("SET"))
     }
 
     hql(s"SET ${testKey + testKey}=${testVal + testVal}")
     assert(hiveconf.get(testKey + testKey, "") == testVal + testVal)
-    assertResult(Array(s"$testKey=$testVal", s"${testKey + testKey}=${testVal + testVal}")) {
-      hql(s"SET").collect().map(_.getString(0))
+    assertResult(Set(testKey -> testVal, (testKey + testKey) -> (testVal + testVal))) {
+      collectResults(hql("SET"))
     }
 
     // "set key"
-    assertResult(Array(s"$testKey=$testVal")) {
-      hql(s"SET $testKey").collect().map(_.getString(0))
+    assertResult(Set(testKey -> testVal)) {
+      collectResults(hql(s"SET $testKey"))
     }
 
-    assertResult(Array(s"$nonexistentKey=<undefined>")) {
-      hql(s"SET $nonexistentKey").collect().map(_.getString(0))
+    assertResult(Set(nonexistentKey -> "<undefined>")) {
+      collectResults(hql(s"SET $nonexistentKey"))
     }
 
     // Assert that sql() should have the same effects as hql() by repeating the above using sql().
     clear()
     assert(sql("SET").collect().size == 0)
 
-    assertResult(Array(s"$testKey=$testVal")) {
-      sql(s"SET $testKey=$testVal").collect().map(_.getString(0))
+    assertResult(Set(testKey -> testVal)) {
+      collectResults(sql(s"SET $testKey=$testVal"))
     }
 
     assert(hiveconf.get(testKey, "") == testVal)
-    assertResult(Array(s"$testKey=$testVal")) {
-      sql("SET").collect().map(_.getString(0))
+    assertResult(Set(testKey -> testVal)) {
+      collectResults(sql("SET"))
     }
 
     sql(s"SET ${testKey + testKey}=${testVal + testVal}")
     assert(hiveconf.get(testKey + testKey, "") == testVal + testVal)
-    assertResult(Array(s"$testKey=$testVal", s"${testKey + testKey}=${testVal + testVal}")) {
-      sql("SET").collect().map(_.getString(0))
+    assertResult(Set(testKey -> testVal, (testKey + testKey) -> (testVal + testVal))) {
+      collectResults(sql("SET"))
     }
 
-    assertResult(Array(s"$testKey=$testVal")) {
-      sql(s"SET $testKey").collect().map(_.getString(0))
+    assertResult(Set(testKey -> testVal)) {
+      collectResults(sql(s"SET $testKey"))
     }
 
-    assertResult(Array(s"$nonexistentKey=<undefined>")) {
-      sql(s"SET $nonexistentKey").collect().map(_.getString(0))
+    assertResult(Set(nonexistentKey -> "<undefined>")) {
+      collectResults(sql(s"SET $nonexistentKey"))
     }
 
     clear()
diff --git a/streaming/pom.xml b/streaming/pom.xml
index b99f306b8f2c..f60697ce745b 100644
--- a/streaming/pom.xml
+++ b/streaming/pom.xml
@@ -28,7 +28,7 @@
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-streaming_2.10</artifactId>
   <properties>
-    <sbt.project.name>streaming</sbt.project.name>
+     <sbt.project.name>streaming</sbt.project.name>
   </properties>
   <packaging>jar</packaging>
   <name>Spark Project Streaming</name>
diff --git a/tools/pom.xml b/tools/pom.xml
index 97abb6b2b63e..c0ee8faa7a61 100644
--- a/tools/pom.xml
+++ b/tools/pom.xml
@@ -27,7 +27,7 @@
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-tools_2.10</artifactId>
   <properties>
-    <sbt.project.name>tools</sbt.project.name>
+     <sbt.project.name>tools</sbt.project.name>
   </properties>
   <packaging>jar</packaging>
   <name>Spark Project Tools</name>
diff --git a/yarn/alpha/pom.xml b/yarn/alpha/pom.xml
index 51744ece0412..5b13a1f002d6 100644
--- a/yarn/alpha/pom.xml
+++ b/yarn/alpha/pom.xml
@@ -24,7 +24,7 @@
     <relativePath>../pom.xml</relativePath>
   </parent>
   <properties>
-    <sbt.project.name>yarn-alpha</sbt.project.name>
+     <sbt.project.name>yarn-alpha</sbt.project.name>
   </properties>
 
   <groupId>org.apache.spark</groupId>
diff --git a/yarn/pom.xml b/yarn/pom.xml
index 3faaf053634d..efb473aa1b26 100644
--- a/yarn/pom.xml
+++ b/yarn/pom.xml
@@ -29,7 +29,7 @@
   <packaging>pom</packaging>
   <name>Spark Project YARN Parent POM</name>
   <properties>
-    <sbt.project.name>yarn</sbt.project.name>
+     <sbt.project.name>yarn</sbt.project.name>
   </properties>
 
   <dependencies>
diff --git a/yarn/stable/pom.xml b/yarn/stable/pom.xml
index b6c8456d0668..ceaf9f9d7100 100644
--- a/yarn/stable/pom.xml
+++ b/yarn/stable/pom.xml
@@ -24,7 +24,7 @@
     <relativePath>../pom.xml</relativePath>
   </parent>
   <properties>
-    <sbt.project.name>yarn-stable</sbt.project.name>
+     <sbt.project.name>yarn-stable</sbt.project.name>
   </properties>
 
   <groupId>org.apache.spark</groupId>

From 9d8666cac84fc4fc867f6a5e80097dbe5cb65301 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Fri, 25 Jul 2014 18:45:02 -0700
Subject: [PATCH 766/810] Part of [SPARK-2456] Removed some HashMaps from
 DAGScheduler by storing information in Stage.

This is part of the scheduler cleanup/refactoring effort to make the scheduler code easier to maintain.

@kayousterhout @markhamstra please take a look ...

Author: Reynold Xin <rxin@apache.org>

Closes #1561 from rxin/dagSchedulerHashMaps and squashes the following commits:

1c44e15 [Reynold Xin] Clear pending tasks in submitMissingTasks.
620a0d1 [Reynold Xin] Use filterKeys.
5b54404 [Reynold Xin] Code review feedback.
c1e9a1c [Reynold Xin] Removed some HashMaps from DAGScheduler by storing information in Stage.
---
 .../apache/spark/scheduler/DAGScheduler.scala | 143 +++++++-----------
 .../org/apache/spark/scheduler/Stage.scala    |  19 ++-
 .../spark/scheduler/DAGSchedulerSuite.scala   |   4 -
 3 files changed, 69 insertions(+), 97 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
index 00b8af27a7b3..dc6142ab79d0 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
@@ -85,12 +85,9 @@ class DAGScheduler(
   private val nextStageId = new AtomicInteger(0)
 
   private[scheduler] val jobIdToStageIds = new HashMap[Int, HashSet[Int]]
-  private[scheduler] val stageIdToJobIds = new HashMap[Int, HashSet[Int]]
   private[scheduler] val stageIdToStage = new HashMap[Int, Stage]
   private[scheduler] val shuffleToMapStage = new HashMap[Int, Stage]
   private[scheduler] val jobIdToActiveJob = new HashMap[Int, ActiveJob]
-  private[scheduler] val resultStageToJob = new HashMap[Stage, ActiveJob]
-  private[scheduler] val stageToInfos = new HashMap[Stage, StageInfo]
 
   // Stages we need to run whose parents aren't done
   private[scheduler] val waitingStages = new HashSet[Stage]
@@ -101,9 +98,6 @@ class DAGScheduler(
   // Stages that must be resubmitted due to fetch failures
   private[scheduler] val failedStages = new HashSet[Stage]
 
-  // Missing tasks from each stage
-  private[scheduler] val pendingTasks = new HashMap[Stage, HashSet[Task[_]]]
-
   private[scheduler] val activeJobs = new HashSet[ActiveJob]
 
   // Contains the locations that each RDD's partitions are cached on
@@ -223,7 +217,6 @@ class DAGScheduler(
       new Stage(id, rdd, numTasks, shuffleDep, getParentStages(rdd, jobId), jobId, callSite)
     stageIdToStage(id) = stage
     updateJobIdStageIdMaps(jobId, stage)
-    stageToInfos(stage) = StageInfo.fromStage(stage)
     stage
   }
 
@@ -315,13 +308,12 @@ class DAGScheduler(
    */
   private def updateJobIdStageIdMaps(jobId: Int, stage: Stage) {
     def updateJobIdStageIdMapsList(stages: List[Stage]) {
-      if (!stages.isEmpty) {
+      if (stages.nonEmpty) {
         val s = stages.head
-        stageIdToJobIds.getOrElseUpdate(s.id, new HashSet[Int]()) += jobId
+        s.jobIds += jobId
         jobIdToStageIds.getOrElseUpdate(jobId, new HashSet[Int]()) += s.id
-        val parents = getParentStages(s.rdd, jobId)
-        val parentsWithoutThisJobId = parents.filter(p =>
-          !stageIdToJobIds.get(p.id).exists(_.contains(jobId)))
+        val parents: List[Stage] = getParentStages(s.rdd, jobId)
+        val parentsWithoutThisJobId = parents.filter { ! _.jobIds.contains(jobId) }
         updateJobIdStageIdMapsList(parentsWithoutThisJobId ++ stages.tail)
       }
     }
@@ -333,16 +325,15 @@ class DAGScheduler(
    * handle cancelling tasks or notifying the SparkListener about finished jobs/stages/tasks.
    *
    * @param job The job whose state to cleanup.
-   * @param resultStage Specifies the result stage for the job; if set to None, this method
-   *                    searches resultStagesToJob to find and cleanup the appropriate result stage.
    */
-  private def cleanupStateForJobAndIndependentStages(job: ActiveJob, resultStage: Option[Stage]) {
+  private def cleanupStateForJobAndIndependentStages(job: ActiveJob) {
     val registeredStages = jobIdToStageIds.get(job.jobId)
     if (registeredStages.isEmpty || registeredStages.get.isEmpty) {
       logError("No stages registered for job " + job.jobId)
     } else {
-      stageIdToJobIds.filterKeys(stageId => registeredStages.get.contains(stageId)).foreach {
-        case (stageId, jobSet) =>
+      stageIdToStage.filterKeys(stageId => registeredStages.get.contains(stageId)).foreach {
+        case (stageId, stage) =>
+          val jobSet = stage.jobIds
           if (!jobSet.contains(job.jobId)) {
             logError(
               "Job %d not registered for stage %d even though that stage was registered for the job"
@@ -355,14 +346,9 @@ class DAGScheduler(
                   logDebug("Removing running stage %d".format(stageId))
                   runningStages -= stage
                 }
-                stageToInfos -= stage
                 for ((k, v) <- shuffleToMapStage.find(_._2 == stage)) {
                   shuffleToMapStage.remove(k)
                 }
-                if (pendingTasks.contains(stage) && !pendingTasks(stage).isEmpty) {
-                  logDebug("Removing pending status for stage %d".format(stageId))
-                }
-                pendingTasks -= stage
                 if (waitingStages.contains(stage)) {
                   logDebug("Removing stage %d from waiting set.".format(stageId))
                   waitingStages -= stage
@@ -374,7 +360,6 @@ class DAGScheduler(
               }
               // data structures based on StageId
               stageIdToStage -= stageId
-              stageIdToJobIds -= stageId
 
               ShuffleMapTask.removeStage(stageId)
               ResultTask.removeStage(stageId)
@@ -393,19 +378,7 @@ class DAGScheduler(
     jobIdToStageIds -= job.jobId
     jobIdToActiveJob -= job.jobId
     activeJobs -= job
-
-    if (resultStage.isEmpty) {
-      // Clean up result stages.
-      val resultStagesForJob = resultStageToJob.keySet.filter(
-        stage => resultStageToJob(stage).jobId == job.jobId)
-      if (resultStagesForJob.size != 1) {
-        logWarning(
-          s"${resultStagesForJob.size} result stages for job ${job.jobId} (expect exactly 1)")
-      }
-      resultStageToJob --= resultStagesForJob
-    } else {
-      resultStageToJob -= resultStage.get
-    }
+    job.finalStage.resultOfJob = None
   }
 
   /**
@@ -591,9 +564,10 @@ class DAGScheduler(
         job.listener.jobFailed(exception)
     } finally {
       val s = job.finalStage
-      stageIdToJobIds -= s.id    // clean up data structures that were populated for a local job,
-      stageIdToStage -= s.id     // but that won't get cleaned up via the normal paths through
-      stageToInfos -= s          // completion events or stage abort
+      // clean up data structures that were populated for a local job,
+      // but that won't get cleaned up via the normal paths through
+      // completion events or stage abort
+      stageIdToStage -= s.id
       jobIdToStageIds -= job.jobId
       listenerBus.post(SparkListenerJobEnd(job.jobId, jobResult))
     }
@@ -605,12 +579,8 @@ class DAGScheduler(
   // That should take care of at least part of the priority inversion problem with
   // cross-job dependencies.
   private def activeJobForStage(stage: Stage): Option[Int] = {
-    if (stageIdToJobIds.contains(stage.id)) {
-      val jobsThatUseStage: Array[Int] = stageIdToJobIds(stage.id).toArray.sorted
-      jobsThatUseStage.find(jobIdToActiveJob.contains)
-    } else {
-      None
-    }
+    val jobsThatUseStage: Array[Int] = stage.jobIds.toArray.sorted
+    jobsThatUseStage.find(jobIdToActiveJob.contains)
   }
 
   private[scheduler] def handleJobGroupCancelled(groupId: String) {
@@ -642,9 +612,8 @@ class DAGScheduler(
       // is in the process of getting stopped.
       val stageFailedMessage = "Stage cancelled because SparkContext was shut down"
       runningStages.foreach { stage =>
-        val info = stageToInfos(stage)
-        info.stageFailed(stageFailedMessage)
-        listenerBus.post(SparkListenerStageCompleted(info))
+        stage.info.stageFailed(stageFailedMessage)
+        listenerBus.post(SparkListenerStageCompleted(stage.info))
       }
       listenerBus.post(SparkListenerJobEnd(job.jobId, JobFailed(error)))
     }
@@ -690,7 +659,7 @@ class DAGScheduler(
       } else {
         jobIdToActiveJob(jobId) = job
         activeJobs += job
-        resultStageToJob(finalStage) = job
+        finalStage.resultOfJob = Some(job)
         listenerBus.post(SparkListenerJobStart(job.jobId, jobIdToStageIds(jobId).toArray,
           properties))
         submitStage(finalStage)
@@ -727,8 +696,7 @@ class DAGScheduler(
   private def submitMissingTasks(stage: Stage, jobId: Int) {
     logDebug("submitMissingTasks(" + stage + ")")
     // Get our pending tasks and remember them in our pendingTasks entry
-    val myPending = pendingTasks.getOrElseUpdate(stage, new HashSet)
-    myPending.clear()
+    stage.pendingTasks.clear()
     var tasks = ArrayBuffer[Task[_]]()
     if (stage.isShuffleMap) {
       for (p <- 0 until stage.numPartitions if stage.outputLocs(p) == Nil) {
@@ -737,7 +705,7 @@ class DAGScheduler(
       }
     } else {
       // This is a final stage; figure out its job's missing partitions
-      val job = resultStageToJob(stage)
+      val job = stage.resultOfJob.get
       for (id <- 0 until job.numPartitions if !job.finished(id)) {
         val partition = job.partitions(id)
         val locs = getPreferredLocs(stage.rdd, partition)
@@ -758,7 +726,7 @@ class DAGScheduler(
       // serializable. If tasks are not serializable, a SparkListenerStageCompleted event
       // will be posted, which should always come after a corresponding SparkListenerStageSubmitted
       // event.
-      listenerBus.post(SparkListenerStageSubmitted(stageToInfos(stage), properties))
+      listenerBus.post(SparkListenerStageSubmitted(stage.info, properties))
 
       // Preemptively serialize a task to make sure it can be serialized. We are catching this
       // exception here because it would be fairly hard to catch the non-serializable exception
@@ -778,11 +746,11 @@ class DAGScheduler(
       }
 
       logInfo("Submitting " + tasks.size + " missing tasks from " + stage + " (" + stage.rdd + ")")
-      myPending ++= tasks
-      logDebug("New pending tasks: " + myPending)
+      stage.pendingTasks ++= tasks
+      logDebug("New pending tasks: " + stage.pendingTasks)
       taskScheduler.submitTasks(
         new TaskSet(tasks.toArray, stage.id, stage.newAttemptId(), stage.jobId, properties))
-      stageToInfos(stage).submissionTime = Some(clock.getTime())
+      stage.info.submissionTime = Some(clock.getTime())
     } else {
       logDebug("Stage " + stage + " is actually done; %b %d %d".format(
         stage.isAvailable, stage.numAvailableOutputs, stage.numPartitions))
@@ -807,13 +775,13 @@ class DAGScheduler(
     val stage = stageIdToStage(task.stageId)
 
     def markStageAsFinished(stage: Stage) = {
-      val serviceTime = stageToInfos(stage).submissionTime match {
+      val serviceTime = stage.info.submissionTime match {
         case Some(t) => "%.03f".format((clock.getTime() - t) / 1000.0)
         case _ => "Unknown"
       }
       logInfo("%s (%s) finished in %s s".format(stage, stage.name, serviceTime))
-      stageToInfos(stage).completionTime = Some(clock.getTime())
-      listenerBus.post(SparkListenerStageCompleted(stageToInfos(stage)))
+      stage.info.completionTime = Some(clock.getTime())
+      listenerBus.post(SparkListenerStageCompleted(stage.info))
       runningStages -= stage
     }
     event.reason match {
@@ -822,10 +790,10 @@ class DAGScheduler(
           // TODO: fail the stage if the accumulator update fails...
           Accumulators.add(event.accumUpdates) // TODO: do this only if task wasn't resubmitted
         }
-        pendingTasks(stage) -= task
+        stage.pendingTasks -= task
         task match {
           case rt: ResultTask[_, _] =>
-            resultStageToJob.get(stage) match {
+            stage.resultOfJob match {
               case Some(job) =>
                 if (!job.finished(rt.outputId)) {
                   job.finished(rt.outputId) = true
@@ -833,7 +801,7 @@ class DAGScheduler(
                   // If the whole job has finished, remove it
                   if (job.numFinished == job.numPartitions) {
                     markStageAsFinished(stage)
-                    cleanupStateForJobAndIndependentStages(job, Some(stage))
+                    cleanupStateForJobAndIndependentStages(job)
                     listenerBus.post(SparkListenerJobEnd(job.jobId, JobSucceeded))
                   }
 
@@ -860,7 +828,7 @@ class DAGScheduler(
             } else {
               stage.addOutputLoc(smt.partitionId, status)
             }
-            if (runningStages.contains(stage) && pendingTasks(stage).isEmpty) {
+            if (runningStages.contains(stage) && stage.pendingTasks.isEmpty) {
               markStageAsFinished(stage)
               logInfo("looking for newly runnable stages")
               logInfo("running: " + runningStages)
@@ -909,7 +877,7 @@ class DAGScheduler(
 
       case Resubmitted =>
         logInfo("Resubmitted " + task + ", so marking it as still running")
-        pendingTasks(stage) += task
+        stage.pendingTasks += task
 
       case FetchFailed(bmAddress, shuffleId, mapId, reduceId) =>
         // Mark the stage that the reducer was in as unrunnable
@@ -994,13 +962,14 @@ class DAGScheduler(
   }
 
   private[scheduler] def handleStageCancellation(stageId: Int) {
-    if (stageIdToJobIds.contains(stageId)) {
-      val jobsThatUseStage: Array[Int] = stageIdToJobIds(stageId).toArray
-      jobsThatUseStage.foreach(jobId => {
-        handleJobCancellation(jobId, "because Stage %s was cancelled".format(stageId))
-      })
-    } else {
-      logInfo("No active jobs to kill for Stage " + stageId)
+    stageIdToStage.get(stageId) match {
+      case Some(stage) =>
+        val jobsThatUseStage: Array[Int] = stage.jobIds.toArray
+        jobsThatUseStage.foreach { jobId =>
+          handleJobCancellation(jobId, s"because Stage $stageId was cancelled")
+        }
+      case None =>
+        logInfo("No active jobs to kill for Stage " + stageId)
     }
     submitWaitingStages()
   }
@@ -1009,8 +978,8 @@ class DAGScheduler(
     if (!jobIdToStageIds.contains(jobId)) {
       logDebug("Trying to cancel unregistered job " + jobId)
     } else {
-      failJobAndIndependentStages(jobIdToActiveJob(jobId),
-        "Job %d cancelled %s".format(jobId, reason), None)
+      failJobAndIndependentStages(
+        jobIdToActiveJob(jobId), "Job %d cancelled %s".format(jobId, reason))
     }
     submitWaitingStages()
   }
@@ -1024,26 +993,21 @@ class DAGScheduler(
       // Skip all the actions if the stage has been removed.
       return
     }
-    val dependentStages = resultStageToJob.keys.filter(x => stageDependsOn(x, failedStage)).toSeq
-    stageToInfos(failedStage).completionTime = Some(clock.getTime())
-    for (resultStage <- dependentStages) {
-      val job = resultStageToJob(resultStage)
-      failJobAndIndependentStages(job, s"Job aborted due to stage failure: $reason",
-        Some(resultStage))
+    val dependentJobs: Seq[ActiveJob] =
+      activeJobs.filter(job => stageDependsOn(job.finalStage, failedStage)).toSeq
+    failedStage.info.completionTime = Some(clock.getTime())
+    for (job <- dependentJobs) {
+      failJobAndIndependentStages(job, s"Job aborted due to stage failure: $reason")
     }
-    if (dependentStages.isEmpty) {
+    if (dependentJobs.isEmpty) {
       logInfo("Ignoring failure of " + failedStage + " because all jobs depending on it are done")
     }
   }
 
   /**
    * Fails a job and all stages that are only used by that job, and cleans up relevant state.
-   *
-   * @param resultStage The result stage for the job, if known. Used to cleanup state for the job
-   *                    slightly more efficiently than when not specified.
    */
-  private def failJobAndIndependentStages(job: ActiveJob, failureReason: String,
-      resultStage: Option[Stage]) {
+  private def failJobAndIndependentStages(job: ActiveJob, failureReason: String) {
     val error = new SparkException(failureReason)
     var ableToCancelStages = true
 
@@ -1057,7 +1021,7 @@ class DAGScheduler(
       logError("No stages registered for job " + job.jobId)
     }
     stages.foreach { stageId =>
-      val jobsForStage = stageIdToJobIds.get(stageId)
+      val jobsForStage: Option[HashSet[Int]] = stageIdToStage.get(stageId).map(_.jobIds)
       if (jobsForStage.isEmpty || !jobsForStage.get.contains(job.jobId)) {
         logError(
           "Job %d not registered for stage %d even though that stage was registered for the job"
@@ -1071,9 +1035,8 @@ class DAGScheduler(
           if (runningStages.contains(stage)) {
             try { // cancelTasks will fail if a SchedulerBackend does not implement killTask
               taskScheduler.cancelTasks(stageId, shouldInterruptThread)
-              val stageInfo = stageToInfos(stage)
-              stageInfo.stageFailed(failureReason)
-              listenerBus.post(SparkListenerStageCompleted(stageToInfos(stage)))
+              stage.info.stageFailed(failureReason)
+              listenerBus.post(SparkListenerStageCompleted(stage.info))
             } catch {
               case e: UnsupportedOperationException =>
                 logInfo(s"Could not cancel tasks for stage $stageId", e)
@@ -1086,7 +1049,7 @@ class DAGScheduler(
 
     if (ableToCancelStages) {
       job.listener.jobFailed(error)
-      cleanupStateForJobAndIndependentStages(job, resultStage)
+      cleanupStateForJobAndIndependentStages(job)
       listenerBus.post(SparkListenerJobEnd(job.jobId, JobFailed(error)))
     }
   }
diff --git a/core/src/main/scala/org/apache/spark/scheduler/Stage.scala b/core/src/main/scala/org/apache/spark/scheduler/Stage.scala
index 798cbc598d36..800905413d14 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/Stage.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/Stage.scala
@@ -17,6 +17,8 @@
 
 package org.apache.spark.scheduler
 
+import scala.collection.mutable.HashSet
+
 import org.apache.spark._
 import org.apache.spark.rdd.RDD
 import org.apache.spark.storage.BlockManagerId
@@ -56,8 +58,22 @@ private[spark] class Stage(
   val numPartitions = rdd.partitions.size
   val outputLocs = Array.fill[List[MapStatus]](numPartitions)(Nil)
   var numAvailableOutputs = 0
+
+  /** Set of jobs that this stage belongs to. */
+  val jobIds = new HashSet[Int]
+
+  /** For stages that are the final (consists of only ResultTasks), link to the ActiveJob. */
+  var resultOfJob: Option[ActiveJob] = None
+  var pendingTasks = new HashSet[Task[_]]
+
   private var nextAttemptId = 0
 
+  val name = callSite.shortForm
+  val details = callSite.longForm
+
+  /** Pointer to the [StageInfo] object, set by DAGScheduler. */
+  var info: StageInfo = StageInfo.fromStage(this)
+
   def isAvailable: Boolean = {
     if (!isShuffleMap) {
       true
@@ -108,9 +124,6 @@ private[spark] class Stage(
 
   def attemptId: Int = nextAttemptId
 
-  val name = callSite.shortForm
-  val details = callSite.longForm
-
   override def toString = "Stage " + id
 
   override def hashCode(): Int = id
diff --git a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
index 44dd1e092ad6..9021662bcf71 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
@@ -686,15 +686,11 @@ class DAGSchedulerSuite extends TestKit(ActorSystem("DAGSchedulerSuite")) with F
     BlockManagerId("exec-" + host, host, 12345, 0)
 
   private def assertDataStructuresEmpty = {
-    assert(scheduler.pendingTasks.isEmpty)
     assert(scheduler.activeJobs.isEmpty)
     assert(scheduler.failedStages.isEmpty)
     assert(scheduler.jobIdToActiveJob.isEmpty)
     assert(scheduler.jobIdToStageIds.isEmpty)
-    assert(scheduler.stageIdToJobIds.isEmpty)
     assert(scheduler.stageIdToStage.isEmpty)
-    assert(scheduler.stageToInfos.isEmpty)
-    assert(scheduler.resultStageToJob.isEmpty)
     assert(scheduler.runningStages.isEmpty)
     assert(scheduler.shuffleToMapStage.isEmpty)
     assert(scheduler.waitingStages.isEmpty)

From 8904791230a0fae336db93e5a80f65c4d9d584dc Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Fri, 25 Jul 2014 19:17:49 -0700
Subject: [PATCH 767/810] [SPARK-2659][SQL] Fix division semantics for hive

Author: Michael Armbrust <michael@databricks.com>

Closes #1557 from marmbrus/fixDivision and squashes the following commits:

b85077f [Michael Armbrust] Fix unit tests.
af98f29 [Michael Armbrust] Change DIV to long type
0c29ae8 [Michael Armbrust] Fix division semantics for hive
---
 .../catalyst/analysis/HiveTypeCoercion.scala   | 18 ++++++++++++++++++
 .../optimizer/ConstantFoldingSuite.scala       |  2 +-
 .../org/apache/spark/sql/hive/HiveQl.scala     |  3 ++-
 .../div-0-3760f9b354ddacd7c7b01b28791d4585     |  1 +
 ...division-0-63b19f8a22471c8ba0415c1d3bc276f7 |  1 +
 .../hive/execution/HiveComparisonTest.scala    |  6 ------
 .../sql/hive/execution/HiveQuerySuite.scala    |  5 ++++-
 7 files changed, 27 insertions(+), 9 deletions(-)
 create mode 100644 sql/hive/src/test/resources/golden/div-0-3760f9b354ddacd7c7b01b28791d4585
 create mode 100644 sql/hive/src/test/resources/golden/division-0-63b19f8a22471c8ba0415c1d3bc276f7

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
index 67a8ce9b88c3..47c7ad076ad0 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
@@ -50,6 +50,7 @@ trait HiveTypeCoercion {
     StringToIntegralCasts ::
     FunctionArgumentConversion ::
     CastNulls ::
+    Division ::
     Nil
 
   /**
@@ -317,6 +318,23 @@ trait HiveTypeCoercion {
     }
   }
 
+  /**
+   * Hive only performs integral division with the DIV operator. The arguments to / are always
+   * converted to fractional types.
+   */
+  object Division extends Rule[LogicalPlan] {
+    def apply(plan: LogicalPlan): LogicalPlan = plan transformAllExpressions {
+      // Skip nodes who's children have not been resolved yet.
+      case e if !e.childrenResolved => e
+
+      // Decimal and Double remain the same
+      case d: Divide if d.dataType == DoubleType => d
+      case d: Divide if d.dataType == DecimalType => d
+
+      case Divide(l, r) => Divide(Cast(l, DoubleType), Cast(r, DoubleType))
+    }
+  }
+
   /**
    * Ensures that NullType gets casted to some other types under certain circumstances.
    */
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConstantFoldingSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConstantFoldingSuite.scala
index d607eed1bea8..0a27cce33748 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConstantFoldingSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConstantFoldingSuite.scala
@@ -83,7 +83,7 @@ class ConstantFoldingSuite extends PlanTest {
           Literal(10) as Symbol("2*3+4"),
           Literal(14) as Symbol("2*(3+4)"))
         .where(Literal(true))
-        .groupBy(Literal(3))(Literal(3) as Symbol("9/3"))
+        .groupBy(Literal(3.0))(Literal(3.0) as Symbol("9/3"))
         .analyze
 
     comparePlans(optimized, correctAnswer)
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
index 4395874526d5..e6ab68b563f8 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
@@ -925,7 +925,8 @@ private[hive] object HiveQl {
     case Token("-", left :: right:: Nil) => Subtract(nodeToExpr(left), nodeToExpr(right))
     case Token("*", left :: right:: Nil) => Multiply(nodeToExpr(left), nodeToExpr(right))
     case Token("/", left :: right:: Nil) => Divide(nodeToExpr(left), nodeToExpr(right))
-    case Token(DIV(), left :: right:: Nil) => Divide(nodeToExpr(left), nodeToExpr(right))
+    case Token(DIV(), left :: right:: Nil) =>
+      Cast(Divide(nodeToExpr(left), nodeToExpr(right)), LongType)
     case Token("%", left :: right:: Nil) => Remainder(nodeToExpr(left), nodeToExpr(right))
 
     /* Comparisons */
diff --git a/sql/hive/src/test/resources/golden/div-0-3760f9b354ddacd7c7b01b28791d4585 b/sql/hive/src/test/resources/golden/div-0-3760f9b354ddacd7c7b01b28791d4585
new file mode 100644
index 000000000000..17ba0bea723c
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/div-0-3760f9b354ddacd7c7b01b28791d4585
@@ -0,0 +1 @@
+0	0	0	1	2
diff --git a/sql/hive/src/test/resources/golden/division-0-63b19f8a22471c8ba0415c1d3bc276f7 b/sql/hive/src/test/resources/golden/division-0-63b19f8a22471c8ba0415c1d3bc276f7
new file mode 100644
index 000000000000..7b7a9175114c
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/division-0-63b19f8a22471c8ba0415c1d3bc276f7
@@ -0,0 +1 @@
+2.0	0.5	0.3333333333333333	0.002
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala
index 08ef4d9b6bb9..b4dbf2b11579 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala
@@ -350,12 +350,6 @@ abstract class HiveComparisonTest
 
               val resultComparison = sideBySide(hivePrintOut, catalystPrintOut).mkString("\n")
 
-              println("hive output")
-              hive.foreach(println)
-
-              println("catalyst printout")
-              catalyst.foreach(println)
-
               if (recomputeCache) {
                 logger.warn(s"Clearing cache files for failed test $testCaseName")
                 hiveCacheFiles.foreach(_.delete())
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
index 6f36a4f8cb90..a8623b64c656 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
@@ -52,7 +52,10 @@ class HiveQuerySuite extends HiveComparisonTest {
     "SELECT * FROM src WHERE key Between 1 and 2")
 
   createQueryTest("div",
-    "SELECT 1 DIV 2, 1 div 2, 1 dIv 2 FROM src LIMIT 1")
+    "SELECT 1 DIV 2, 1 div 2, 1 dIv 2, 100 DIV 51, 100 DIV 49 FROM src LIMIT 1")
+
+  createQueryTest("division",
+    "SELECT 2 / 1, 1 / 2, 1 / 3, 1 / COUNT(*) FROM src LIMIT 1")
 
   test("Query expressed in SQL") {
     assert(sql("SELECT 1").collect() === Array(Seq(1)))

From cf3e9fd84dc64f8a57ecbcfdd6b22f5492d41bd7 Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@apache.org>
Date: Sat, 26 Jul 2014 00:54:05 -0700
Subject: [PATCH 768/810] [SPARK-1458] [PySpark] Expose sc.version in Java and
 PySpark

Author: Josh Rosen <joshrosen@apache.org>

Closes #1596 from JoshRosen/spark-1458 and squashes the following commits:

fdbb0bf [Josh Rosen] Add SparkContext.version to Python & Java [SPARK-1458]
---
 .../scala/org/apache/spark/api/java/JavaSparkContext.scala | 3 +++
 python/pyspark/context.py                                  | 7 +++++++
 2 files changed, 10 insertions(+)

diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala b/core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala
index 1e0493c4855e..a678355a1cfa 100644
--- a/core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala
@@ -112,6 +112,9 @@ class JavaSparkContext(val sc: SparkContext) extends JavaSparkContextVarargsWork
 
   def startTime: java.lang.Long = sc.startTime
 
+  /** The version of Spark on which this application is running. */
+  def version: String = sc.version
+
   /** Default level of parallelism to use when not given by user (e.g. parallelize and makeRDD). */
   def defaultParallelism: java.lang.Integer = sc.defaultParallelism
 
diff --git a/python/pyspark/context.py b/python/pyspark/context.py
index 024fb881877c..bdf14ea0ee27 100644
--- a/python/pyspark/context.py
+++ b/python/pyspark/context.py
@@ -216,6 +216,13 @@ def setSystemProperty(cls, key, value):
         SparkContext._ensure_initialized()
         SparkContext._jvm.java.lang.System.setProperty(key, value)
 
+    @property
+    def version(self):
+        """
+        The version of Spark on which this application is running.
+        """
+        return self._jsc.version()
+
     @property
     def defaultParallelism(self):
         """

From 66f26a4610aede57322cb7e193a50aecb6c57d22 Mon Sep 17 00:00:00 2001
From: Hossein <hossein@databricks.com>
Date: Sat, 26 Jul 2014 01:04:56 -0700
Subject: [PATCH 769/810] [SPARK-2696] Reduce default value of
 spark.serializer.objectStreamReset

The current default value of spark.serializer.objectStreamReset is 10,000.
When trying to re-partition (e.g., to 64 partitions) a large file (e.g., 500MB), containing 1MB records, the serializer will cache 10000 x 1MB x 64 ~= 640 GB which will cause out of memory errors.

This patch sets the default value to a more reasonable default value (100).

Author: Hossein <hossein@databricks.com>

Closes #1595 from falaki/objectStreamReset and squashes the following commits:

650a935 [Hossein] Updated documentation
1aa0df8 [Hossein] Reduce default value of spark.serializer.objectStreamReset
---
 .../scala/org/apache/spark/serializer/JavaSerializer.scala    | 2 +-
 docs/configuration.md                                         | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/serializer/JavaSerializer.scala b/core/src/main/scala/org/apache/spark/serializer/JavaSerializer.scala
index 0a7e1ec53967..a7fa057ee05f 100644
--- a/core/src/main/scala/org/apache/spark/serializer/JavaSerializer.scala
+++ b/core/src/main/scala/org/apache/spark/serializer/JavaSerializer.scala
@@ -108,7 +108,7 @@ private[spark] class JavaSerializerInstance(counterReset: Int) extends Serialize
  */
 @DeveloperApi
 class JavaSerializer(conf: SparkConf) extends Serializer with Externalizable {
-  private var counterReset = conf.getInt("spark.serializer.objectStreamReset", 10000)
+  private var counterReset = conf.getInt("spark.serializer.objectStreamReset", 100)
 
   def newInstance(): SerializerInstance = new JavaSerializerInstance(counterReset)
 
diff --git a/docs/configuration.md b/docs/configuration.md
index dac8bb1d5246..4e4b78153a10 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -380,13 +380,13 @@ Apart from these, the following properties are also available, and may be useful
 </tr>
 <tr>
   <td><code>spark.serializer.objectStreamReset</code></td>
-  <td>10000</td>
+  <td>100</td>
   <td>
     When serializing using org.apache.spark.serializer.JavaSerializer, the serializer caches
     objects to prevent writing redundant data, however that stops garbage collection of those
     objects. By calling 'reset' you flush that info from the serializer, and allow old
     objects to be collected. To turn off this periodic reset set it to a value &lt;= 0.
-    By default it will reset the serializer every 10,000 objects.
+    By default it will reset the serializer every 100 objects.
   </td>
 </tr>
 <tr>

From 75663b57f90bb173f0c6c288944ec568c4719b2a Mon Sep 17 00:00:00 2001
From: Davies Liu <davies.liu@gmail.com>
Date: Sat, 26 Jul 2014 01:07:08 -0700
Subject: [PATCH 770/810] [SPARK-2652] [PySpark] Turning some default configs
 for PySpark

Add several default configs for PySpark, related to serialization in JVM.

spark.serializer = org.apache.spark.serializer.KryoSerializer
spark.serializer.objectStreamReset = 100
spark.rdd.compress = True

This will help to reduce the memory usage during RDD.partitionBy()

Author: Davies Liu <davies.liu@gmail.com>

Closes #1568 from davies/conf and squashes the following commits:

cd316f1 [Davies Liu] remove duplicated line
f71a355 [Davies Liu] rebase to master, add spark.rdd.compress = True
8f63f45 [Davies Liu] Merge branch 'master' into conf
8bc9f08 [Davies Liu] fix unittest
c04a83d [Davies Liu] some default configs for PySpark
---
 python/pyspark/context.py | 13 ++++++++++++-
 1 file changed, 12 insertions(+), 1 deletion(-)

diff --git a/python/pyspark/context.py b/python/pyspark/context.py
index bdf14ea0ee27..e8ac9895cf54 100644
--- a/python/pyspark/context.py
+++ b/python/pyspark/context.py
@@ -37,6 +37,15 @@
 from py4j.java_collections import ListConverter
 
 
+# These are special default configs for PySpark, they will overwrite
+# the default ones for Spark if they are not configured by user.
+DEFAULT_CONFIGS = {
+    "spark.serializer": "org.apache.spark.serializer.KryoSerializer",
+    "spark.serializer.objectStreamReset": 100,
+    "spark.rdd.compress": True,
+}
+
+
 class SparkContext(object):
     """
     Main entry point for Spark functionality. A SparkContext represents the
@@ -101,7 +110,7 @@ def __init__(self, master=None, appName=None, sparkHome=None, pyFiles=None,
         else:
             self.serializer = BatchedSerializer(self._unbatched_serializer,
                                                 batchSize)
-        self._conf.setIfMissing("spark.rdd.compress", "true")
+
         # Set any parameters passed directly to us on the conf
         if master:
             self._conf.setMaster(master)
@@ -112,6 +121,8 @@ def __init__(self, master=None, appName=None, sparkHome=None, pyFiles=None,
         if environment:
             for key, value in environment.iteritems():
                 self._conf.setExecutorEnv(key, value)
+        for key, value in DEFAULT_CONFIGS.items():
+            self._conf.setIfMissing(key, value)
 
         # Check that we have at least the required parameters
         if not self._conf.contains("spark.master"):

From c183b92c3c70ad2d36a2d60bdb10c02b65bc0212 Mon Sep 17 00:00:00 2001
From: bpaulin <bob@bobpaulin.com>
Date: Sat, 26 Jul 2014 10:27:09 -0700
Subject: [PATCH 771/810] [SPARK-2279] Added emptyRDD method to Java API

Added emptyRDD method to Java API with tests.

Author: bpaulin <bob@bobpaulin.com>

Closes #1597 from bobpaulin/SPARK-2279 and squashes the following commits:

5ad57c2 [bpaulin] [SPARK-2279] Added emptyRDD method to Java API
---
 .../org/apache/spark/api/java/JavaSparkContext.scala     | 9 ++++++++-
 core/src/test/java/org/apache/spark/JavaAPISuite.java    | 9 +++++++--
 2 files changed, 15 insertions(+), 3 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala b/core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala
index a678355a1cfa..8a5f8088a05c 100644
--- a/core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala
@@ -34,7 +34,7 @@ import org.apache.spark._
 import org.apache.spark.SparkContext.{DoubleAccumulatorParam, IntAccumulatorParam}
 import org.apache.spark.api.java.JavaSparkContext.fakeClassTag
 import org.apache.spark.broadcast.Broadcast
-import org.apache.spark.rdd.RDD
+import org.apache.spark.rdd.{EmptyRDD, RDD}
 
 /**
  * A Java-friendly version of [[org.apache.spark.SparkContext]] that returns
@@ -135,6 +135,13 @@ class JavaSparkContext(val sc: SparkContext) extends JavaSparkContextVarargsWork
     sc.parallelize(JavaConversions.asScalaBuffer(list), numSlices)
   }
 
+  /** Get an RDD that has no partitions or elements. */
+  def emptyRDD[T]: JavaRDD[T] = {
+    implicit val ctag: ClassTag[T] = fakeClassTag
+    JavaRDD.fromRDD(new EmptyRDD[T](sc))
+  }
+
+
   /** Distribute a local Scala collection to form an RDD. */
   def parallelize[T](list: java.util.List[T]): JavaRDD[T] =
     parallelize(list, sc.defaultParallelism)
diff --git a/core/src/test/java/org/apache/spark/JavaAPISuite.java b/core/src/test/java/org/apache/spark/JavaAPISuite.java
index b2868b59ce6c..f882a8623fd8 100644
--- a/core/src/test/java/org/apache/spark/JavaAPISuite.java
+++ b/core/src/test/java/org/apache/spark/JavaAPISuite.java
@@ -118,8 +118,7 @@ public void intersection() {
     JavaRDD<Integer> intersections = s1.intersection(s2);
     Assert.assertEquals(3, intersections.count());
 
-    List<Integer> list = new ArrayList<Integer>();
-    JavaRDD<Integer> empty = sc.parallelize(list);
+    JavaRDD<Integer> empty = sc.emptyRDD();
     JavaRDD<Integer> emptyIntersection = empty.intersection(s2);
     Assert.assertEquals(0, emptyIntersection.count());
 
@@ -184,6 +183,12 @@ public void sortByKey() {
     Assert.assertEquals(new Tuple2<Integer, Integer>(3, 2), sortedPairs.get(2));
   }
 
+  @Test
+  public void emptyRDD() {
+    JavaRDD<String> rdd = sc.emptyRDD();
+    Assert.assertEquals("Empty RDD shouldn't have any values", 0, rdd.count());
+  }
+
   @Test
   public void sortBy() {
     List<Tuple2<Integer, Integer>> pairs = new ArrayList<Tuple2<Integer, Integer>>();

From 12901643b7e808aa75cf0b19e2d0c3d40b1a978d Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Sat, 26 Jul 2014 15:00:32 -0700
Subject: [PATCH 772/810] [SPARK-2704] Name threads in ConnectionManager and
 mark them as daemon.

handleMessageExecutor, handleReadWriteExecutor, and handleConnectExecutor are not marked as daemon and not named. I think there exists some condition in which Spark programs won't terminate because of this.

Stack dump attached in https://issues.apache.org/jira/browse/SPARK-2704

Author: Reynold Xin <rxin@apache.org>

Closes #1604 from rxin/daemon and squashes the following commits:

98d6a6c [Reynold Xin] [SPARK-2704] Name threads in ConnectionManager and mark them as daemon.
---
 .../spark/network/ConnectionManager.scala     |  9 ++++---
 .../scala/org/apache/spark/util/Utils.scala   | 27 ++++++++++++-------
 2 files changed, 23 insertions(+), 13 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/network/ConnectionManager.scala b/core/src/main/scala/org/apache/spark/network/ConnectionManager.scala
index 8a1cdb812962..566e8a4aaa1d 100644
--- a/core/src/main/scala/org/apache/spark/network/ConnectionManager.scala
+++ b/core/src/main/scala/org/apache/spark/network/ConnectionManager.scala
@@ -62,13 +62,15 @@ private[spark] class ConnectionManager(port: Int, conf: SparkConf,
     conf.getInt("spark.core.connection.handler.threads.min", 20),
     conf.getInt("spark.core.connection.handler.threads.max", 60),
     conf.getInt("spark.core.connection.handler.threads.keepalive", 60), TimeUnit.SECONDS,
-    new LinkedBlockingDeque[Runnable]())
+    new LinkedBlockingDeque[Runnable](),
+    Utils.namedThreadFactory("handle-message-executor"))
 
   private val handleReadWriteExecutor = new ThreadPoolExecutor(
     conf.getInt("spark.core.connection.io.threads.min", 4),
     conf.getInt("spark.core.connection.io.threads.max", 32),
     conf.getInt("spark.core.connection.io.threads.keepalive", 60), TimeUnit.SECONDS,
-    new LinkedBlockingDeque[Runnable]())
+    new LinkedBlockingDeque[Runnable](),
+    Utils.namedThreadFactory("handle-read-write-executor"))
 
   // Use a different, yet smaller, thread pool - infrequently used with very short lived tasks :
   // which should be executed asap
@@ -76,7 +78,8 @@ private[spark] class ConnectionManager(port: Int, conf: SparkConf,
     conf.getInt("spark.core.connection.connect.threads.min", 1),
     conf.getInt("spark.core.connection.connect.threads.max", 8),
     conf.getInt("spark.core.connection.connect.threads.keepalive", 60), TimeUnit.SECONDS,
-    new LinkedBlockingDeque[Runnable]())
+    new LinkedBlockingDeque[Runnable](),
+    Utils.namedThreadFactory("handle-connect-executor"))
 
   private val serverChannel = ServerSocketChannel.open()
   // used to track the SendingConnections waiting to do SASL negotiation
diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala
index 1a4f4eba98ea..8cbb9050f393 100644
--- a/core/src/main/scala/org/apache/spark/util/Utils.scala
+++ b/core/src/main/scala/org/apache/spark/util/Utils.scala
@@ -21,7 +21,7 @@ import java.io._
 import java.net.{InetAddress, Inet4Address, NetworkInterface, URI, URL, URLConnection}
 import java.nio.ByteBuffer
 import java.util.{Locale, Random, UUID}
-import java.util.concurrent.{ConcurrentHashMap, Executors, ThreadPoolExecutor}
+import java.util.concurrent.{ThreadFactory, ConcurrentHashMap, Executors, ThreadPoolExecutor}
 
 import scala.collection.JavaConversions._
 import scala.collection.Map
@@ -553,19 +553,19 @@ private[spark] object Utils extends Logging {
     new ThreadFactoryBuilder().setDaemon(true)
 
   /**
-   * Wrapper over newCachedThreadPool. Thread names are formatted as prefix-ID, where ID is a
-   * unique, sequentially assigned integer.
+   * Create a thread factory that names threads with a prefix and also sets the threads to daemon.
    */
-  def newDaemonCachedThreadPool(prefix: String): ThreadPoolExecutor = {
-    val threadFactory = daemonThreadFactoryBuilder.setNameFormat(prefix + "-%d").build()
-    Executors.newCachedThreadPool(threadFactory).asInstanceOf[ThreadPoolExecutor]
+  def namedThreadFactory(prefix: String): ThreadFactory = {
+    daemonThreadFactoryBuilder.setNameFormat(prefix + "-%d").build()
   }
 
   /**
-   * Return the string to tell how long has passed in milliseconds.
+   * Wrapper over newCachedThreadPool. Thread names are formatted as prefix-ID, where ID is a
+   * unique, sequentially assigned integer.
    */
-  def getUsedTimeMs(startTimeMs: Long): String = {
-    " " + (System.currentTimeMillis - startTimeMs) + " ms"
+  def newDaemonCachedThreadPool(prefix: String): ThreadPoolExecutor = {
+    val threadFactory = namedThreadFactory(prefix)
+    Executors.newCachedThreadPool(threadFactory).asInstanceOf[ThreadPoolExecutor]
   }
 
   /**
@@ -573,10 +573,17 @@ private[spark] object Utils extends Logging {
    * unique, sequentially assigned integer.
    */
   def newDaemonFixedThreadPool(nThreads: Int, prefix: String): ThreadPoolExecutor = {
-    val threadFactory = daemonThreadFactoryBuilder.setNameFormat(prefix + "-%d").build()
+    val threadFactory = namedThreadFactory(prefix)
     Executors.newFixedThreadPool(nThreads, threadFactory).asInstanceOf[ThreadPoolExecutor]
   }
 
+  /**
+   * Return the string to tell how long has passed in milliseconds.
+   */
+  def getUsedTimeMs(startTimeMs: Long): String = {
+    " " + (System.currentTimeMillis - startTimeMs) + " ms"
+  }
+
   private def listFilesSafely(file: File): Seq[File] = {
     val files = file.listFiles()
     if (files == null) {

From ba46bbed5d32aec0f11f0b71c82bba8dbe19f05a Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@apache.org>
Date: Sat, 26 Jul 2014 17:37:05 -0700
Subject: [PATCH 773/810] [SPARK-2601] [PySpark] Fix Py4J error when
 transforming pickleFiles
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Similar to SPARK-1034, the problem was that Py4J didn’t cope well with the fake ClassTags used in the Java API.  It doesn’t look like there’s any reason why PythonRDD needs to take a ClassTag, since it just ignores the type of the previous RDD, so I removed the type parameter and we no longer pass ClassTags from Python.

Author: Josh Rosen <joshrosen@apache.org>

Closes #1605 from JoshRosen/spark-2601 and squashes the following commits:

b68e118 [Josh Rosen] Fix Py4J error when transforming pickleFiles [SPARK-2601]
---
 .../scala/org/apache/spark/api/python/PythonRDD.scala    | 4 ++--
 python/pyspark/rdd.py                                    | 4 +---
 python/pyspark/tests.py                                  | 9 +++++++++
 3 files changed, 12 insertions(+), 5 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
index d6b0988641a9..d87783efd2d0 100644
--- a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
+++ b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
@@ -37,8 +37,8 @@ import org.apache.spark.broadcast.Broadcast
 import org.apache.spark.rdd.RDD
 import org.apache.spark.util.Utils
 
-private[spark] class PythonRDD[T: ClassTag](
-    parent: RDD[T],
+private[spark] class PythonRDD(
+    parent: RDD[_],
     command: Array[Byte],
     envVars: JMap[String, String],
     pythonIncludes: JList[String],
diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py
index 113a082e1672..b84d976114f0 100644
--- a/python/pyspark/rdd.py
+++ b/python/pyspark/rdd.py
@@ -1687,7 +1687,6 @@ def _jrdd(self):
             [x._jbroadcast for x in self.ctx._pickled_broadcast_vars],
             self.ctx._gateway._gateway_client)
         self.ctx._pickled_broadcast_vars.clear()
-        class_tag = self._prev_jrdd.classTag()
         env = MapConverter().convert(self.ctx.environment,
                                      self.ctx._gateway._gateway_client)
         includes = ListConverter().convert(self.ctx._python_includes,
@@ -1696,8 +1695,7 @@ def _jrdd(self):
                                              bytearray(pickled_command),
                                              env, includes, self.preservesPartitioning,
                                              self.ctx.pythonExec,
-                                             broadcast_vars, self.ctx._javaAccumulator,
-                                             class_tag)
+                                             broadcast_vars, self.ctx._javaAccumulator)
         self._jrdd_val = python_rdd.asJavaRDD()
         return self._jrdd_val
 
diff --git a/python/pyspark/tests.py b/python/pyspark/tests.py
index a92abbf371f1..8ba51461d106 100644
--- a/python/pyspark/tests.py
+++ b/python/pyspark/tests.py
@@ -226,6 +226,15 @@ def test_transforming_cartesian_result(self):
         cart = rdd1.cartesian(rdd2)
         result = cart.map(lambda (x, y): x + y).collect()
 
+    def test_transforming_pickle_file(self):
+        # Regression test for SPARK-2601
+        data = self.sc.parallelize(["Hello", "World!"])
+        tempFile = tempfile.NamedTemporaryFile(delete=True)
+        tempFile.close()
+        data.saveAsPickleFile(tempFile.name)
+        pickled_file = self.sc.pickleFile(tempFile.name)
+        pickled_file.map(lambda x: x).collect()
+
     def test_cartesian_on_textfile(self):
         # Regression test for
         path = os.path.join(SPARK_HOME, "python/test_support/hello.txt")

From b547f69bdb5f4a6d5f471a2d998c2df6fb2a9347 Mon Sep 17 00:00:00 2001
From: Matei Zaharia <matei@databricks.com>
Date: Sat, 26 Jul 2014 22:44:17 -0700
Subject: [PATCH 774/810] SPARK-2680: Lower spark.shuffle.memoryFraction to 0.2
 by default

Author: Matei Zaharia <matei@databricks.com>

Closes #1593 from mateiz/spark-2680 and squashes the following commits:

3c949c4 [Matei Zaharia] Lower spark.shuffle.memoryFraction to 0.2 by default
---
 .../apache/spark/util/collection/ExternalAppendOnlyMap.scala    | 2 +-
 docs/configuration.md                                           | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala b/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala
index be8f6529f7a1..c22bb8d9c60a 100644
--- a/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala
+++ b/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala
@@ -74,7 +74,7 @@ class ExternalAppendOnlyMap[K, V, C](
 
   // Collective memory threshold shared across all running tasks
   private val maxMemoryThreshold = {
-    val memoryFraction = sparkConf.getDouble("spark.shuffle.memoryFraction", 0.3)
+    val memoryFraction = sparkConf.getDouble("spark.shuffle.memoryFraction", 0.2)
     val safetyFraction = sparkConf.getDouble("spark.shuffle.safetyFraction", 0.8)
     (Runtime.getRuntime.maxMemory * memoryFraction * safetyFraction).toLong
   }
diff --git a/docs/configuration.md b/docs/configuration.md
index 4e4b78153a10..46e3dd914b5a 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -239,7 +239,7 @@ Apart from these, the following properties are also available, and may be useful
 </tr>
 <tr>
   <td><code>spark.shuffle.memoryFraction</code></td>
-  <td>0.3</td>
+  <td>0.2</td>
   <td>
     Fraction of Java heap to use for aggregation and cogroups during shuffles, if
     <code>spark.shuffle.spill</code> is true. At any given time, the collective size of

From aaf2b735fddbebccd28012006ee4647af3b3624f Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Sat, 26 Jul 2014 22:56:07 -0700
Subject: [PATCH 775/810] [SPARK-2361][MLLIB] Use broadcast instead of
 serializing data directly into task closure

We saw task serialization problems with large feature dimension, which could be avoid if we don't serialize data directly into task but use broadcast variables. This PR uses broadcast in both training and prediction and adds tests to make sure the task size is small.

Author: Xiangrui Meng <meng@databricks.com>

Closes #1427 from mengxr/broadcast-new and squashes the following commits:

b9a1228 [Xiangrui Meng] style update
b97c184 [Xiangrui Meng] minimal change to LBFGS
9ebadcc [Xiangrui Meng] add task size test to RowMatrix
9427bf0 [Xiangrui Meng] add task size tests to linear methods
e0a5cf2 [Xiangrui Meng] add task size test to GD
28a8411 [Xiangrui Meng] add test for NaiveBayes
380778c [Xiangrui Meng] update KMeans test
bccab92 [Xiangrui Meng] add task size test to LBFGS
02103ba [Xiangrui Meng] remove print
e73d68e [Xiangrui Meng] update tests for k-means
174cb15 [Xiangrui Meng] use local-cluster for test with a small akka.frameSize
1928a5a [Xiangrui Meng] add test for KMeans task size
e00c2da [Xiangrui Meng] use broadcast in GD, KMeans
010d076 [Xiangrui Meng] modify NaiveBayesModel and GLM to use broadcast
---
 .../mllib/classification/NaiveBayes.scala     |  8 +-
 .../spark/mllib/clustering/KMeans.scala       | 19 +++--
 .../spark/mllib/clustering/KMeansModel.scala  |  6 +-
 .../mllib/optimization/GradientDescent.scala  |  6 +-
 .../spark/mllib/optimization/LBFGS.scala      |  7 +-
 .../GeneralizedLinearAlgorithm.scala          |  7 +-
 .../JavaLogisticRegressionSuite.java          |  2 -
 .../LogisticRegressionSuite.scala             | 18 ++++-
 .../classification/NaiveBayesSuite.scala      | 20 ++++-
 .../spark/mllib/classification/SVMSuite.scala | 25 +++++--
 .../spark/mllib/clustering/KMeansSuite.scala  | 75 ++++++++++++-------
 .../linalg/distributed/RowMatrixSuite.scala   | 29 ++++++-
 .../optimization/GradientDescentSuite.scala   | 34 +++++++--
 .../spark/mllib/optimization/LBFGSSuite.scala | 30 +++++++-
 .../spark/mllib/regression/LassoSuite.scala   | 21 +++++-
 .../regression/LinearRegressionSuite.scala    | 21 +++++-
 .../regression/RidgeRegressionSuite.scala     | 23 +++++-
 .../mllib/util/LocalClusterSparkContext.scala | 42 +++++++++++
 .../spark/mllib/util/LocalSparkContext.scala  |  7 +-
 19 files changed, 330 insertions(+), 70 deletions(-)
 create mode 100644 mllib/src/test/scala/org/apache/spark/mllib/util/LocalClusterSparkContext.scala

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala
index b6e0c4a80e27..6c7be0a4f1dc 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala
@@ -54,7 +54,13 @@ class NaiveBayesModel private[mllib] (
     }
   }
 
-  override def predict(testData: RDD[Vector]): RDD[Double] = testData.map(predict)
+  override def predict(testData: RDD[Vector]): RDD[Double] = {
+    val bcModel = testData.context.broadcast(this)
+    testData.mapPartitions { iter =>
+      val model = bcModel.value
+      iter.map(model.predict)
+    }
+  }
 
   override def predict(testData: Vector): Double = {
     labels(brzArgmax(brzPi + brzTheta * testData.toBreeze))
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala
index de22fbb6ffc1..db425d866bba 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala
@@ -165,18 +165,21 @@ class KMeans private (
       val activeCenters = activeRuns.map(r => centers(r)).toArray
       val costAccums = activeRuns.map(_ => sc.accumulator(0.0))
 
+      val bcActiveCenters = sc.broadcast(activeCenters)
+
       // Find the sum and count of points mapping to each center
       val totalContribs = data.mapPartitions { points =>
-        val runs = activeCenters.length
-        val k = activeCenters(0).length
-        val dims = activeCenters(0)(0).vector.length
+        val thisActiveCenters = bcActiveCenters.value
+        val runs = thisActiveCenters.length
+        val k = thisActiveCenters(0).length
+        val dims = thisActiveCenters(0)(0).vector.length
 
         val sums = Array.fill(runs, k)(BDV.zeros[Double](dims).asInstanceOf[BV[Double]])
         val counts = Array.fill(runs, k)(0L)
 
         points.foreach { point =>
           (0 until runs).foreach { i =>
-            val (bestCenter, cost) = KMeans.findClosest(activeCenters(i), point)
+            val (bestCenter, cost) = KMeans.findClosest(thisActiveCenters(i), point)
             costAccums(i) += cost
             sums(i)(bestCenter) += point.vector
             counts(i)(bestCenter) += 1
@@ -264,16 +267,17 @@ class KMeans private (
     // to their squared distance from that run's current centers
     var step = 0
     while (step < initializationSteps) {
+      val bcCenters = data.context.broadcast(centers)
       val sumCosts = data.flatMap { point =>
         (0 until runs).map { r =>
-          (r, KMeans.pointCost(centers(r), point))
+          (r, KMeans.pointCost(bcCenters.value(r), point))
         }
       }.reduceByKey(_ + _).collectAsMap()
       val chosen = data.mapPartitionsWithIndex { (index, points) =>
         val rand = new XORShiftRandom(seed ^ (step << 16) ^ index)
         points.flatMap { p =>
           (0 until runs).filter { r =>
-            rand.nextDouble() < 2.0 * KMeans.pointCost(centers(r), p) * k / sumCosts(r)
+            rand.nextDouble() < 2.0 * KMeans.pointCost(bcCenters.value(r), p) * k / sumCosts(r)
           }.map((_, p))
         }
       }.collect()
@@ -286,9 +290,10 @@ class KMeans private (
     // Finally, we might have a set of more than k candidate centers for each run; weigh each
     // candidate by the number of points in the dataset mapping to it and run a local k-means++
     // on the weighted centers to pick just k of them
+    val bcCenters = data.context.broadcast(centers)
     val weightMap = data.flatMap { p =>
       (0 until runs).map { r =>
-        ((r, KMeans.findClosest(centers(r), p)._1), 1.0)
+        ((r, KMeans.findClosest(bcCenters.value(r), p)._1), 1.0)
       }
     }.reduceByKey(_ + _).collectAsMap()
     val finalCenters = (0 until runs).map { r =>
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala
index fba21aefaaac..5823cb6e52e7 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala
@@ -38,7 +38,8 @@ class KMeansModel private[mllib] (val clusterCenters: Array[Vector]) extends Ser
   /** Maps given points to their cluster indices. */
   def predict(points: RDD[Vector]): RDD[Int] = {
     val centersWithNorm = clusterCentersWithNorm
-    points.map(p => KMeans.findClosest(centersWithNorm, new BreezeVectorWithNorm(p))._1)
+    val bcCentersWithNorm = points.context.broadcast(centersWithNorm)
+    points.map(p => KMeans.findClosest(bcCentersWithNorm.value, new BreezeVectorWithNorm(p))._1)
   }
 
   /** Maps given points to their cluster indices. */
@@ -51,7 +52,8 @@ class KMeansModel private[mllib] (val clusterCenters: Array[Vector]) extends Ser
    */
   def computeCost(data: RDD[Vector]): Double = {
     val centersWithNorm = clusterCentersWithNorm
-    data.map(p => KMeans.pointCost(centersWithNorm, new BreezeVectorWithNorm(p))).sum()
+    val bcCentersWithNorm = data.context.broadcast(centersWithNorm)
+    data.map(p => KMeans.pointCost(bcCentersWithNorm.value, new BreezeVectorWithNorm(p))).sum()
   }
 
   private def clusterCentersWithNorm: Iterable[BreezeVectorWithNorm] =
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/optimization/GradientDescent.scala b/mllib/src/main/scala/org/apache/spark/mllib/optimization/GradientDescent.scala
index 7030eeabe400..9fd760bf7808 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/optimization/GradientDescent.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/optimization/GradientDescent.scala
@@ -163,6 +163,7 @@ object GradientDescent extends Logging {
 
     // Initialize weights as a column vector
     var weights = Vectors.dense(initialWeights.toArray)
+    val n = weights.size
 
     /**
      * For the first iteration, the regVal will be initialized as sum of weight squares
@@ -172,12 +173,13 @@ object GradientDescent extends Logging {
       weights, Vectors.dense(new Array[Double](weights.size)), 0, 1, regParam)._2
 
     for (i <- 1 to numIterations) {
+      val bcWeights = data.context.broadcast(weights)
       // Sample a subset (fraction miniBatchFraction) of the total data
       // compute and sum up the subgradients on this subset (this is one map-reduce)
       val (gradientSum, lossSum) = data.sample(false, miniBatchFraction, 42 + i)
-        .aggregate((BDV.zeros[Double](weights.size), 0.0))(
+        .aggregate((BDV.zeros[Double](n), 0.0))(
           seqOp = (c, v) => (c, v) match { case ((grad, loss), (label, features)) =>
-            val l = gradient.compute(features, label, weights, Vectors.fromBreeze(grad))
+            val l = gradient.compute(features, label, bcWeights.value, Vectors.fromBreeze(grad))
             (grad, loss + l)
           },
           combOp = (c1, c2) => (c1, c2) match { case ((grad1, loss1), (grad2, loss2)) =>
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/optimization/LBFGS.scala b/mllib/src/main/scala/org/apache/spark/mllib/optimization/LBFGS.scala
index 7bbed9c8fdbe..179cd4a3f162 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/optimization/LBFGS.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/optimization/LBFGS.scala
@@ -195,13 +195,14 @@ object LBFGS extends Logging {
 
     override def calculate(weights: BDV[Double]) = {
       // Have a local copy to avoid the serialization of CostFun object which is not serializable.
-      val localData = data
       val localGradient = gradient
+      val n = weights.length
+      val bcWeights = data.context.broadcast(weights)
 
-      val (gradientSum, lossSum) = localData.aggregate((BDV.zeros[Double](weights.size), 0.0))(
+      val (gradientSum, lossSum) = data.aggregate((BDV.zeros[Double](n), 0.0))(
           seqOp = (c, v) => (c, v) match { case ((grad, loss), (label, features)) =>
             val l = localGradient.compute(
-              features, label, Vectors.fromBreeze(weights), Vectors.fromBreeze(grad))
+              features, label, Vectors.fromBreeze(bcWeights.value), Vectors.fromBreeze(grad))
             (grad, loss + l)
           },
           combOp = (c1, c2) => (c1, c2) match { case ((grad1, loss1), (grad2, loss2)) =>
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala
index fe41863bce98..54854252d747 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala
@@ -56,9 +56,12 @@ abstract class GeneralizedLinearModel(val weights: Vector, val intercept: Double
     // A small optimization to avoid serializing the entire model. Only the weightsMatrix
     // and intercept is needed.
     val localWeights = weights
+    val bcWeights = testData.context.broadcast(localWeights)
     val localIntercept = intercept
-
-    testData.map(v => predictPoint(v, localWeights, localIntercept))
+    testData.mapPartitions { iter =>
+      val w = bcWeights.value
+      iter.map(v => predictPoint(v, w, localIntercept))
+    }
   }
 
   /**
diff --git a/mllib/src/test/java/org/apache/spark/mllib/classification/JavaLogisticRegressionSuite.java b/mllib/src/test/java/org/apache/spark/mllib/classification/JavaLogisticRegressionSuite.java
index faa675b59cd5..862221d48798 100644
--- a/mllib/src/test/java/org/apache/spark/mllib/classification/JavaLogisticRegressionSuite.java
+++ b/mllib/src/test/java/org/apache/spark/mllib/classification/JavaLogisticRegressionSuite.java
@@ -92,8 +92,6 @@ public void runLRUsingStaticMethods() {
         testRDD.rdd(), 100, 1.0, 1.0);
 
     int numAccurate = validatePrediction(validationData, model);
-      System.out.println(numAccurate);
     Assert.assertTrue(numAccurate > nPoints * 4.0 / 5.0);
   }
-
 }
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala
index 44b757b6a1fb..3f6ff859374c 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala
@@ -25,7 +25,7 @@ import org.scalatest.Matchers
 
 import org.apache.spark.mllib.linalg.Vectors
 import org.apache.spark.mllib.regression._
-import org.apache.spark.mllib.util.LocalSparkContext
+import org.apache.spark.mllib.util.{LocalClusterSparkContext, LocalSparkContext}
 
 object LogisticRegressionSuite {
 
@@ -126,3 +126,19 @@ class LogisticRegressionSuite extends FunSuite with LocalSparkContext with Match
     validatePrediction(validationData.map(row => model.predict(row.features)), validationData)
   }
 }
+
+class LogisticRegressionClusterSuite extends FunSuite with LocalClusterSparkContext {
+
+  test("task size should be small in both training and prediction") {
+    val m = 4
+    val n = 200000
+    val points = sc.parallelize(0 until m, 2).mapPartitionsWithIndex { (idx, iter) =>
+      val random = new Random(idx)
+      iter.map(i => LabeledPoint(1.0, Vectors.dense(Array.fill(n)(random.nextDouble()))))
+    }.cache()
+    // If we serialize data directly in the task closure, the size of the serialized task would be
+    // greater than 1MB and hence Spark would throw an error.
+    val model = LogisticRegressionWithSGD.train(points, 2)
+    val predictions = model.predict(points.map(_.features))
+  }
+}
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/classification/NaiveBayesSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/classification/NaiveBayesSuite.scala
index 516895d04222..06cdd04f5fda 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/classification/NaiveBayesSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/classification/NaiveBayesSuite.scala
@@ -23,7 +23,7 @@ import org.scalatest.FunSuite
 
 import org.apache.spark.mllib.linalg.Vectors
 import org.apache.spark.mllib.regression.LabeledPoint
-import org.apache.spark.mllib.util.LocalSparkContext
+import org.apache.spark.mllib.util.{LocalClusterSparkContext, LocalSparkContext}
 
 object NaiveBayesSuite {
 
@@ -96,3 +96,21 @@ class NaiveBayesSuite extends FunSuite with LocalSparkContext {
     validatePrediction(validationData.map(row => model.predict(row.features)), validationData)
   }
 }
+
+class NaiveBayesClusterSuite extends FunSuite with LocalClusterSparkContext {
+
+  test("task size should be small in both training and prediction") {
+    val m = 10
+    val n = 200000
+    val examples = sc.parallelize(0 until m, 2).mapPartitionsWithIndex { (idx, iter) =>
+      val random = new Random(idx)
+      iter.map { i =>
+        LabeledPoint(random.nextInt(2), Vectors.dense(Array.fill(n)(random.nextDouble())))
+      }
+    }
+    // If we serialize data directly in the task closure, the size of the serialized task would be
+    // greater than 1MB and hence Spark would throw an error.
+    val model = NaiveBayes.train(examples)
+    val predictions = model.predict(examples.map(_.features))
+  }
+}
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/classification/SVMSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/classification/SVMSuite.scala
index 886c71dde3af..65e5df58db4c 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/classification/SVMSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/classification/SVMSuite.scala
@@ -17,17 +17,16 @@
 
 package org.apache.spark.mllib.classification
 
-import scala.util.Random
 import scala.collection.JavaConversions._
-
-import org.scalatest.FunSuite
+import scala.util.Random
 
 import org.jblas.DoubleMatrix
+import org.scalatest.FunSuite
 
 import org.apache.spark.SparkException
-import org.apache.spark.mllib.regression._
-import org.apache.spark.mllib.util.LocalSparkContext
 import org.apache.spark.mllib.linalg.Vectors
+import org.apache.spark.mllib.regression._
+import org.apache.spark.mllib.util.{LocalClusterSparkContext, LocalSparkContext}
 
 object SVMSuite {
 
@@ -193,3 +192,19 @@ class SVMSuite extends FunSuite with LocalSparkContext {
     new SVMWithSGD().setValidateData(false).run(testRDDInvalid)
   }
 }
+
+class SVMClusterSuite extends FunSuite with LocalClusterSparkContext {
+
+  test("task size should be small in both training and prediction") {
+    val m = 4
+    val n = 200000
+    val points = sc.parallelize(0 until m, 2).mapPartitionsWithIndex { (idx, iter) =>
+      val random = new Random(idx)
+      iter.map(i => LabeledPoint(1.0, Vectors.dense(Array.fill(n)(random.nextDouble()))))
+    }.cache()
+    // If we serialize data directly in the task closure, the size of the serialized task would be
+    // greater than 1MB and hence Spark would throw an error.
+    val model = SVMWithSGD.train(points, 2)
+    val predictions = model.predict(points.map(_.features))
+  }
+}
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/clustering/KMeansSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/clustering/KMeansSuite.scala
index 76a3bdf9b11c..34bc4537a7b3 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/clustering/KMeansSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/clustering/KMeansSuite.scala
@@ -17,14 +17,16 @@
 
 package org.apache.spark.mllib.clustering
 
+import scala.util.Random
+
 import org.scalatest.FunSuite
 
-import org.apache.spark.mllib.util.LocalSparkContext
 import org.apache.spark.mllib.linalg.Vectors
+import org.apache.spark.mllib.util.{LocalClusterSparkContext, LocalSparkContext}
 
 class KMeansSuite extends FunSuite with LocalSparkContext {
 
-  import KMeans.{RANDOM, K_MEANS_PARALLEL}
+  import org.apache.spark.mllib.clustering.KMeans.{K_MEANS_PARALLEL, RANDOM}
 
   test("single cluster") {
     val data = sc.parallelize(Array(
@@ -38,26 +40,26 @@ class KMeansSuite extends FunSuite with LocalSparkContext {
     // No matter how many runs or iterations we use, we should get one cluster,
     // centered at the mean of the points
 
-    var model = KMeans.train(data, k=1, maxIterations=1)
+    var model = KMeans.train(data, k = 1, maxIterations = 1)
     assert(model.clusterCenters.head === center)
 
-    model = KMeans.train(data, k=1, maxIterations=2)
+    model = KMeans.train(data, k = 1, maxIterations = 2)
     assert(model.clusterCenters.head === center)
 
-    model = KMeans.train(data, k=1, maxIterations=5)
+    model = KMeans.train(data, k = 1, maxIterations = 5)
     assert(model.clusterCenters.head === center)
 
-    model = KMeans.train(data, k=1, maxIterations=1, runs=5)
+    model = KMeans.train(data, k = 1, maxIterations = 1, runs = 5)
     assert(model.clusterCenters.head === center)
 
-    model = KMeans.train(data, k=1, maxIterations=1, runs=5)
+    model = KMeans.train(data, k = 1, maxIterations = 1, runs = 5)
     assert(model.clusterCenters.head === center)
 
-    model = KMeans.train(data, k=1, maxIterations=1, runs=1, initializationMode=RANDOM)
+    model = KMeans.train(data, k = 1, maxIterations = 1, runs = 1, initializationMode = RANDOM)
     assert(model.clusterCenters.head === center)
 
     model = KMeans.train(
-      data, k=1, maxIterations=1, runs=1, initializationMode=K_MEANS_PARALLEL)
+      data, k = 1, maxIterations = 1, runs = 1, initializationMode = K_MEANS_PARALLEL)
     assert(model.clusterCenters.head === center)
   }
 
@@ -100,26 +102,27 @@ class KMeansSuite extends FunSuite with LocalSparkContext {
 
     val center = Vectors.dense(1.0, 3.0, 4.0)
 
-    var model = KMeans.train(data, k=1, maxIterations=1)
+    var model = KMeans.train(data, k = 1, maxIterations = 1)
     assert(model.clusterCenters.size === 1)
     assert(model.clusterCenters.head === center)
 
-    model = KMeans.train(data, k=1, maxIterations=2)
+    model = KMeans.train(data, k = 1, maxIterations = 2)
     assert(model.clusterCenters.head === center)
 
-    model = KMeans.train(data, k=1, maxIterations=5)
+    model = KMeans.train(data, k = 1, maxIterations = 5)
     assert(model.clusterCenters.head === center)
 
-    model = KMeans.train(data, k=1, maxIterations=1, runs=5)
+    model = KMeans.train(data, k = 1, maxIterations = 1, runs = 5)
     assert(model.clusterCenters.head === center)
 
-    model = KMeans.train(data, k=1, maxIterations=1, runs=5)
+    model = KMeans.train(data, k = 1, maxIterations = 1, runs = 5)
     assert(model.clusterCenters.head === center)
 
-    model = KMeans.train(data, k=1, maxIterations=1, runs=1, initializationMode=RANDOM)
+    model = KMeans.train(data, k = 1, maxIterations = 1, runs = 1, initializationMode = RANDOM)
     assert(model.clusterCenters.head === center)
 
-    model = KMeans.train(data, k=1, maxIterations=1, runs=1, initializationMode=K_MEANS_PARALLEL)
+    model = KMeans.train(data, k = 1, maxIterations = 1, runs = 1,
+      initializationMode = K_MEANS_PARALLEL)
     assert(model.clusterCenters.head === center)
   }
 
@@ -145,25 +148,26 @@ class KMeansSuite extends FunSuite with LocalSparkContext {
 
     val center = Vectors.sparse(n, Seq((0, 1.0), (1, 3.0), (2, 4.0)))
 
-    var model = KMeans.train(data, k=1, maxIterations=1)
+    var model = KMeans.train(data, k = 1, maxIterations = 1)
     assert(model.clusterCenters.head === center)
 
-    model = KMeans.train(data, k=1, maxIterations=2)
+    model = KMeans.train(data, k = 1, maxIterations = 2)
     assert(model.clusterCenters.head === center)
 
-    model = KMeans.train(data, k=1, maxIterations=5)
+    model = KMeans.train(data, k = 1, maxIterations = 5)
     assert(model.clusterCenters.head === center)
 
-    model = KMeans.train(data, k=1, maxIterations=1, runs=5)
+    model = KMeans.train(data, k = 1, maxIterations = 1, runs = 5)
     assert(model.clusterCenters.head === center)
 
-    model = KMeans.train(data, k=1, maxIterations=1, runs=5)
+    model = KMeans.train(data, k = 1, maxIterations = 1, runs = 5)
     assert(model.clusterCenters.head === center)
 
-    model = KMeans.train(data, k=1, maxIterations=1, runs=1, initializationMode=RANDOM)
+    model = KMeans.train(data, k = 1, maxIterations = 1, runs = 1, initializationMode = RANDOM)
     assert(model.clusterCenters.head === center)
 
-    model = KMeans.train(data, k=1, maxIterations=1, runs=1, initializationMode=K_MEANS_PARALLEL)
+    model = KMeans.train(data, k = 1, maxIterations = 1, runs = 1,
+      initializationMode = K_MEANS_PARALLEL)
     assert(model.clusterCenters.head === center)
 
     data.unpersist()
@@ -183,15 +187,15 @@ class KMeansSuite extends FunSuite with LocalSparkContext {
     // it will make at least five passes, and it will give non-zero probability to each
     // unselected point as long as it hasn't yet selected all of them
 
-    var model = KMeans.train(rdd, k=5, maxIterations=1)
+    var model = KMeans.train(rdd, k = 5, maxIterations = 1)
     assert(Set(model.clusterCenters: _*) === Set(points: _*))
 
     // Iterations of Lloyd's should not change the answer either
-    model = KMeans.train(rdd, k=5, maxIterations=10)
+    model = KMeans.train(rdd, k = 5, maxIterations = 10)
     assert(Set(model.clusterCenters: _*) === Set(points: _*))
 
     // Neither should more runs
-    model = KMeans.train(rdd, k=5, maxIterations=10, runs=5)
+    model = KMeans.train(rdd, k = 5, maxIterations = 10, runs = 5)
     assert(Set(model.clusterCenters: _*) === Set(points: _*))
   }
 
@@ -220,3 +224,22 @@ class KMeansSuite extends FunSuite with LocalSparkContext {
     }
   }
 }
+
+class KMeansClusterSuite extends FunSuite with LocalClusterSparkContext {
+
+  test("task size should be small in both training and prediction") {
+    val m = 4
+    val n = 200000
+    val points = sc.parallelize(0 until m, 2).mapPartitionsWithIndex { (idx, iter) =>
+      val random = new Random(idx)
+      iter.map(i => Vectors.dense(Array.fill(n)(random.nextDouble)))
+    }.cache()
+    for (initMode <- Seq(KMeans.RANDOM, KMeans.K_MEANS_PARALLEL)) {
+      // If we serialize data directly in the task closure, the size of the serialized task would be
+      // greater than 1MB and hence Spark would throw an error.
+      val model = KMeans.train(points, 2, 2, 1, initMode)
+      val predictions = model.predict(points).collect()
+      val cost = model.computeCost(points)
+    }
+  }
+}
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/linalg/distributed/RowMatrixSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/linalg/distributed/RowMatrixSuite.scala
index a961f89456a1..325b817980f6 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/linalg/distributed/RowMatrixSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/linalg/distributed/RowMatrixSuite.scala
@@ -17,12 +17,13 @@
 
 package org.apache.spark.mllib.linalg.distributed
 
-import org.scalatest.FunSuite
+import scala.util.Random
 
 import breeze.linalg.{DenseVector => BDV, DenseMatrix => BDM, norm => brzNorm, svd => brzSvd}
+import org.scalatest.FunSuite
 
-import org.apache.spark.mllib.util.LocalSparkContext
 import org.apache.spark.mllib.linalg.{Matrices, Vectors, Vector}
+import org.apache.spark.mllib.util.{LocalClusterSparkContext, LocalSparkContext}
 
 class RowMatrixSuite extends FunSuite with LocalSparkContext {
 
@@ -193,3 +194,27 @@ class RowMatrixSuite extends FunSuite with LocalSparkContext {
     }
   }
 }
+
+class RowMatrixClusterSuite extends FunSuite with LocalClusterSparkContext {
+
+  var mat: RowMatrix = _
+
+  override def beforeAll() {
+    super.beforeAll()
+    val m = 4
+    val n = 200000
+    val rows = sc.parallelize(0 until m, 2).mapPartitionsWithIndex { (idx, iter) =>
+      val random = new Random(idx)
+      iter.map(i => Vectors.dense(Array.fill(n)(random.nextDouble())))
+    }
+    mat = new RowMatrix(rows)
+  }
+
+  test("task size should be small in svd") {
+    val svd = mat.computeSVD(1, computeU = true)
+  }
+
+  test("task size should be small in summarize") {
+    val summary = mat.computeColumnSummaryStatistics()
+  }
+}
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/optimization/GradientDescentSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/optimization/GradientDescentSuite.scala
index 951b4f7c6e6f..dfb2eb7f0d14 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/optimization/GradientDescentSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/optimization/GradientDescentSuite.scala
@@ -17,15 +17,14 @@
 
 package org.apache.spark.mllib.optimization
 
-import scala.util.Random
 import scala.collection.JavaConversions._
+import scala.util.Random
 
-import org.scalatest.FunSuite
-import org.scalatest.Matchers
+import org.scalatest.{FunSuite, Matchers}
 
-import org.apache.spark.mllib.regression._
-import org.apache.spark.mllib.util.LocalSparkContext
 import org.apache.spark.mllib.linalg.Vectors
+import org.apache.spark.mllib.regression._
+import org.apache.spark.mllib.util.{LocalClusterSparkContext, LocalSparkContext}
 
 object GradientDescentSuite {
 
@@ -46,7 +45,7 @@ object GradientDescentSuite {
     val rnd = new Random(seed)
     val x1 = Array.fill[Double](nPoints)(rnd.nextGaussian())
 
-    val unifRand = new scala.util.Random(45)
+    val unifRand = new Random(45)
     val rLogis = (0 until nPoints).map { i =>
       val u = unifRand.nextDouble()
       math.log(u) - math.log(1.0-u)
@@ -144,3 +143,26 @@ class GradientDescentSuite extends FunSuite with LocalSparkContext with Matchers
         "should be initialWeightsWithIntercept.")
   }
 }
+
+class GradientDescentClusterSuite extends FunSuite with LocalClusterSparkContext {
+
+  test("task size should be small") {
+    val m = 4
+    val n = 200000
+    val points = sc.parallelize(0 until m, 2).mapPartitionsWithIndex { (idx, iter) =>
+      val random = new Random(idx)
+      iter.map(i => (1.0, Vectors.dense(Array.fill(n)(random.nextDouble()))))
+    }.cache()
+    // If we serialize data directly in the task closure, the size of the serialized task would be
+    // greater than 1MB and hence Spark would throw an error.
+    val (weights, loss) = GradientDescent.runMiniBatchSGD(
+      points,
+      new LogisticGradient,
+      new SquaredL2Updater,
+      0.1,
+      2,
+      1.0,
+      1.0,
+      Vectors.dense(new Array[Double](n)))
+  }
+}
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/optimization/LBFGSSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/optimization/LBFGSSuite.scala
index fe7a9033cd5f..ff414742e839 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/optimization/LBFGSSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/optimization/LBFGSSuite.scala
@@ -17,12 +17,13 @@
 
 package org.apache.spark.mllib.optimization
 
-import org.scalatest.FunSuite
-import org.scalatest.Matchers
+import scala.util.Random
+
+import org.scalatest.{FunSuite, Matchers}
 
-import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.linalg.Vectors
-import org.apache.spark.mllib.util.LocalSparkContext
+import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.mllib.util.{LocalClusterSparkContext, LocalSparkContext}
 
 class LBFGSSuite extends FunSuite with LocalSparkContext with Matchers {
 
@@ -230,3 +231,24 @@ class LBFGSSuite extends FunSuite with LocalSparkContext with Matchers {
       "The weight differences between LBFGS and GD should be within 2%.")
   }
 }
+
+class LBFGSClusterSuite extends FunSuite with LocalClusterSparkContext {
+
+  test("task size should be small") {
+    val m = 10
+    val n = 200000
+    val examples = sc.parallelize(0 until m, 2).mapPartitionsWithIndex { (idx, iter) =>
+      val random = new Random(idx)
+      iter.map(i => (1.0, Vectors.dense(Array.fill(n)(random.nextDouble))))
+    }.cache()
+    val lbfgs = new LBFGS(new LogisticGradient, new SquaredL2Updater)
+      .setNumCorrections(1)
+      .setConvergenceTol(1e-12)
+      .setMaxNumIterations(1)
+      .setRegParam(1.0)
+    val random = new Random(0)
+    // If we serialize data directly in the task closure, the size of the serialized task would be
+    // greater than 1MB and hence Spark would throw an error.
+    val weights = lbfgs.optimize(examples, Vectors.dense(Array.fill(n)(random.nextDouble)))
+  }
+}
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/regression/LassoSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/regression/LassoSuite.scala
index bfa42959c8ea..7aa96421aed8 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/regression/LassoSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/regression/LassoSuite.scala
@@ -17,10 +17,13 @@
 
 package org.apache.spark.mllib.regression
 
+import scala.util.Random
+
 import org.scalatest.FunSuite
 
 import org.apache.spark.mllib.linalg.Vectors
-import org.apache.spark.mllib.util.{LinearDataGenerator, LocalSparkContext}
+import org.apache.spark.mllib.util.{LocalClusterSparkContext, LinearDataGenerator,
+  LocalSparkContext}
 
 class LassoSuite extends FunSuite with LocalSparkContext {
 
@@ -113,3 +116,19 @@ class LassoSuite extends FunSuite with LocalSparkContext {
     validatePrediction(validationData.map(row => model.predict(row.features)), validationData)
   }
 }
+
+class LassoClusterSuite extends FunSuite with LocalClusterSparkContext {
+
+  test("task size should be small in both training and prediction") {
+    val m = 4
+    val n = 200000
+    val points = sc.parallelize(0 until m, 2).mapPartitionsWithIndex { (idx, iter) =>
+      val random = new Random(idx)
+      iter.map(i => LabeledPoint(1.0, Vectors.dense(Array.fill(n)(random.nextDouble()))))
+    }.cache()
+    // If we serialize data directly in the task closure, the size of the serialized task would be
+    // greater than 1MB and hence Spark would throw an error.
+    val model = LassoWithSGD.train(points, 2)
+    val predictions = model.predict(points.map(_.features))
+  }
+}
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/regression/LinearRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/regression/LinearRegressionSuite.scala
index 7aaad7d7a3e3..4f89112b650c 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/regression/LinearRegressionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/regression/LinearRegressionSuite.scala
@@ -17,10 +17,13 @@
 
 package org.apache.spark.mllib.regression
 
+import scala.util.Random
+
 import org.scalatest.FunSuite
 
 import org.apache.spark.mllib.linalg.Vectors
-import org.apache.spark.mllib.util.{LinearDataGenerator, LocalSparkContext}
+import org.apache.spark.mllib.util.{LocalClusterSparkContext, LinearDataGenerator,
+  LocalSparkContext}
 
 class LinearRegressionSuite extends FunSuite with LocalSparkContext {
 
@@ -122,3 +125,19 @@ class LinearRegressionSuite extends FunSuite with LocalSparkContext {
       sparseValidationData.map(row => model.predict(row.features)), sparseValidationData)
   }
 }
+
+class LinearRegressionClusterSuite extends FunSuite with LocalClusterSparkContext {
+
+  test("task size should be small in both training and prediction") {
+    val m = 4
+    val n = 200000
+    val points = sc.parallelize(0 until m, 2).mapPartitionsWithIndex { (idx, iter) =>
+      val random = new Random(idx)
+      iter.map(i => LabeledPoint(1.0, Vectors.dense(Array.fill(n)(random.nextDouble()))))
+    }.cache()
+    // If we serialize data directly in the task closure, the size of the serialized task would be
+    // greater than 1MB and hence Spark would throw an error.
+    val model = LinearRegressionWithSGD.train(points, 2)
+    val predictions = model.predict(points.map(_.features))
+  }
+}
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/regression/RidgeRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/regression/RidgeRegressionSuite.scala
index 67768e17fbe6..727bbd051ff1 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/regression/RidgeRegressionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/regression/RidgeRegressionSuite.scala
@@ -17,11 +17,14 @@
 
 package org.apache.spark.mllib.regression
 
-import org.scalatest.FunSuite
+import scala.util.Random
 
 import org.jblas.DoubleMatrix
+import org.scalatest.FunSuite
 
-import org.apache.spark.mllib.util.{LinearDataGenerator, LocalSparkContext}
+import org.apache.spark.mllib.linalg.Vectors
+import org.apache.spark.mllib.util.{LocalClusterSparkContext, LinearDataGenerator,
+  LocalSparkContext}
 
 class RidgeRegressionSuite extends FunSuite with LocalSparkContext {
 
@@ -73,3 +76,19 @@ class RidgeRegressionSuite extends FunSuite with LocalSparkContext {
       "ridgeError (" + ridgeErr + ") was not less than linearError(" + linearErr + ")")
   }
 }
+
+class RidgeRegressionClusterSuite extends FunSuite with LocalClusterSparkContext {
+
+  test("task size should be small in both training and prediction") {
+    val m = 4
+    val n = 200000
+    val points = sc.parallelize(0 until m, 2).mapPartitionsWithIndex { (idx, iter) =>
+      val random = new Random(idx)
+      iter.map(i => LabeledPoint(1.0, Vectors.dense(Array.fill(n)(random.nextDouble()))))
+    }.cache()
+    // If we serialize data directly in the task closure, the size of the serialized task would be
+    // greater than 1MB and hence Spark would throw an error.
+    val model = RidgeRegressionWithSGD.train(points, 2)
+    val predictions = model.predict(points.map(_.features))
+  }
+}
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/util/LocalClusterSparkContext.scala b/mllib/src/test/scala/org/apache/spark/mllib/util/LocalClusterSparkContext.scala
new file mode 100644
index 000000000000..5e9101cdd380
--- /dev/null
+++ b/mllib/src/test/scala/org/apache/spark/mllib/util/LocalClusterSparkContext.scala
@@ -0,0 +1,42 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.mllib.util
+
+import org.scalatest.{Suite, BeforeAndAfterAll}
+
+import org.apache.spark.{SparkConf, SparkContext}
+
+trait LocalClusterSparkContext extends BeforeAndAfterAll { self: Suite =>
+  @transient var sc: SparkContext = _
+
+  override def beforeAll() {
+    val conf = new SparkConf()
+      .setMaster("local-cluster[2, 1, 512]")
+      .setAppName("test-cluster")
+      .set("spark.akka.frameSize", "1") // set to 1MB to detect direct serialization of data
+    sc = new SparkContext(conf)
+    super.beforeAll()
+  }
+
+  override def afterAll() {
+    if (sc != null) {
+      sc.stop()
+    }
+    super.afterAll()
+  }
+}
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/util/LocalSparkContext.scala b/mllib/src/test/scala/org/apache/spark/mllib/util/LocalSparkContext.scala
index 0d4868f3d9e4..7857d9e5ee5c 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/util/LocalSparkContext.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/util/LocalSparkContext.scala
@@ -20,13 +20,16 @@ package org.apache.spark.mllib.util
 import org.scalatest.Suite
 import org.scalatest.BeforeAndAfterAll
 
-import org.apache.spark.SparkContext
+import org.apache.spark.{SparkConf, SparkContext}
 
 trait LocalSparkContext extends BeforeAndAfterAll { self: Suite =>
   @transient var sc: SparkContext = _
 
   override def beforeAll() {
-    sc = new SparkContext("local", "test")
+    val conf = new SparkConf()
+      .setMaster("local")
+      .setAppName("test")
+    sc = new SparkContext(conf)
     super.beforeAll()
   }
 

From 3a69c72e5cbe270b76f6ab6a84a2e334e87cce8c Mon Sep 17 00:00:00 2001
From: Doris Xin <doris.s.xin@gmail.com>
Date: Sun, 27 Jul 2014 07:21:07 -0700
Subject: [PATCH 776/810] [SPARK-2679] [MLLib] Ser/De for Double

Added a set of serializer/deserializer for Double in _common.py and PythonMLLibAPI in MLLib.

Author: Doris Xin <doris.s.xin@gmail.com>

Closes #1581 from dorx/doubleSerDe and squashes the following commits:

86a85b3 [Doris Xin] Merge branch 'master' into doubleSerDe
2bfe7a4 [Doris Xin] Removed magic byte
ad4d0d9 [Doris Xin] removed a space in unit
a9020bc [Doris Xin] units passed
7dad9af [Doris Xin] WIP
---
 .../mllib/api/python/PythonMLLibAPI.scala     | 23 +++++++++
 .../api/python/PythonMLLibAPISuite.scala      |  8 ++++
 python/pyspark/mllib/_common.py               | 48 +++++++++++++++++--
 3 files changed, 76 insertions(+), 3 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
index c44173793b39..954621ee8b93 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
@@ -54,6 +54,13 @@ class PythonMLLibAPI extends Serializable {
     }
   }
 
+  private[python] def deserializeDouble(bytes: Array[Byte], offset: Int = 0): Double = {
+    require(bytes.length - offset == 8, "Wrong size byte array for Double")
+    val bb = ByteBuffer.wrap(bytes, offset, bytes.length - offset)
+    bb.order(ByteOrder.nativeOrder())
+    bb.getDouble
+  }
+
   private def deserializeDenseVector(bytes: Array[Byte], offset: Int = 0): Vector = {
     val packetLength = bytes.length - offset
     require(packetLength >= 5, "Byte array too short")
@@ -89,6 +96,22 @@ class PythonMLLibAPI extends Serializable {
     Vectors.sparse(size, indices, values)
   }
 
+  /**
+   * Returns an 8-byte array for the input Double.
+   *
+   * Note: we currently do not use a magic byte for double for storage efficiency.
+   * This should be reconsidered when we add Ser/De for other 8-byte types (e.g. Long), for safety.
+   * The corresponding deserializer, deserializeDouble, needs to be modified as well if the
+   * serialization scheme changes.
+   */
+  private[python] def serializeDouble(double: Double): Array[Byte] = {
+    val bytes = new Array[Byte](8)
+    val bb = ByteBuffer.wrap(bytes)
+    bb.order(ByteOrder.nativeOrder())
+    bb.putDouble(double)
+    bytes
+  }
+
   private def serializeDenseVector(doubles: Array[Double]): Array[Byte] = {
     val len = doubles.length
     val bytes = new Array[Byte](5 + 8 * len)
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/api/python/PythonMLLibAPISuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/api/python/PythonMLLibAPISuite.scala
index 642843f90204..d94cfa2fcec8 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/api/python/PythonMLLibAPISuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/api/python/PythonMLLibAPISuite.scala
@@ -57,4 +57,12 @@ class PythonMLLibAPISuite extends FunSuite {
       assert(q.features === p.features)
     }
   }
+
+  test("double serialization") {
+    for (x <- List(123.0, -10.0, 0.0, Double.MaxValue, Double.MinValue)) {
+      val bytes = py.serializeDouble(x)
+      val deser = py.deserializeDouble(bytes)
+      assert(x === deser)
+    }
+  }
 }
diff --git a/python/pyspark/mllib/_common.py b/python/pyspark/mllib/_common.py
index 43b491a9716f..8e3ad6b783b6 100644
--- a/python/pyspark/mllib/_common.py
+++ b/python/pyspark/mllib/_common.py
@@ -72,9 +72,9 @@
 # Python interpreter must agree on what endian the machine is.
 
 
-DENSE_VECTOR_MAGIC = 1
+DENSE_VECTOR_MAGIC  = 1
 SPARSE_VECTOR_MAGIC = 2
-DENSE_MATRIX_MAGIC = 3
+DENSE_MATRIX_MAGIC  = 3
 LABELED_POINT_MAGIC = 4
 
 
@@ -97,8 +97,28 @@ def _deserialize_numpy_array(shape, ba, offset, dtype=float64):
     return ar.copy()
 
 
+def _serialize_double(d):
+    """
+    Serialize a double (float or numpy.float64) into a mutually understood format.
+    """
+    if type(d) == float or type(d) == float64:
+        d = float64(d)
+        ba = bytearray(8)
+        _copyto(d, buffer=ba, offset=0, shape=[1], dtype=float64)
+        return ba
+    else:
+        raise TypeError("_serialize_double called on non-float input")
+
+
 def _serialize_double_vector(v):
-    """Serialize a double vector into a mutually understood format.
+    """
+    Serialize a double vector into a mutually understood format.
+
+    Note: we currently do not use a magic byte for double for storage
+    efficiency. This should be reconsidered when we add Ser/De for other
+    8-byte types (e.g. Long), for safety. The corresponding deserializer,
+    _deserialize_double, needs to be modified as well if the serialization
+    scheme changes.
 
     >>> x = array([1,2,3])
     >>> y = _deserialize_double_vector(_serialize_double_vector(x))
@@ -148,6 +168,28 @@ def _serialize_sparse_vector(v):
     return ba
 
 
+def _deserialize_double(ba, offset=0):
+    """Deserialize a double from a mutually understood format.
+
+    >>> import sys
+    >>> _deserialize_double(_serialize_double(123.0)) == 123.0
+    True
+    >>> _deserialize_double(_serialize_double(float64(0.0))) == 0.0
+    True
+    >>> x = sys.float_info.max
+    >>> _deserialize_double(_serialize_double(sys.float_info.max)) == x
+    True
+    >>> y = float64(sys.float_info.max)
+    >>> _deserialize_double(_serialize_double(sys.float_info.max)) == y
+    True
+    """
+    if type(ba) != bytearray:
+        raise TypeError("_deserialize_double called on a %s; wanted bytearray" % type(ba))
+    if len(ba) - offset != 8:
+        raise TypeError("_deserialize_double called on a %d-byte array; wanted 8 bytes." % nb)
+    return struct.unpack("d", ba[offset:])[0]
+
+
 def _deserialize_double_vector(ba, offset=0):
     """Deserialize a double vector from a mutually understood format.
 

From 985705301e5e55de14b00ad8ce3143e91aae185d Mon Sep 17 00:00:00 2001
From: Matei Zaharia <matei@databricks.com>
Date: Sun, 27 Jul 2014 11:20:20 -0700
Subject: [PATCH 777/810] SPARK-2684: Update ExternalAppendOnlyMap to take an
 iterator as input

This will decrease object allocation from the "update" closure used in map.changeValue.

Author: Matei Zaharia <matei@databricks.com>

Closes #1607 from mateiz/spark-2684 and squashes the following commits:

b7d89e6 [Matei Zaharia] Add insertAll for Iterables too, and fix some code style
561fc97 [Matei Zaharia] Update ExternalAppendOnlyMap to take an iterator as input
---
 .../scala/org/apache/spark/Aggregator.scala   |  5 +-
 .../org/apache/spark/rdd/CoGroupedRDD.scala   |  7 +-
 .../collection/ExternalAppendOnlyMap.scala    | 77 +++++++++++++------
 .../ExternalAppendOnlyMapSuite.scala          | 17 ++--
 4 files changed, 64 insertions(+), 42 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/Aggregator.scala b/core/src/main/scala/org/apache/spark/Aggregator.scala
index 1d640579efe7..ff0ca11749d4 100644
--- a/core/src/main/scala/org/apache/spark/Aggregator.scala
+++ b/core/src/main/scala/org/apache/spark/Aggregator.scala
@@ -55,10 +55,7 @@ case class Aggregator[K, V, C] (
       combiners.iterator
     } else {
       val combiners = new ExternalAppendOnlyMap[K, V, C](createCombiner, mergeValue, mergeCombiners)
-      while (iter.hasNext) {
-        val pair = iter.next()
-        combiners.insert(pair._1, pair._2)
-      }
+      combiners.insertAll(iter)
       // TODO: Make this non optional in a future release
       Option(context).foreach(c => c.taskMetrics.memoryBytesSpilled = combiners.memoryBytesSpilled)
       Option(context).foreach(c => c.taskMetrics.diskBytesSpilled = combiners.diskBytesSpilled)
diff --git a/core/src/main/scala/org/apache/spark/rdd/CoGroupedRDD.scala b/core/src/main/scala/org/apache/spark/rdd/CoGroupedRDD.scala
index 7d96089e52ab..6388ef82cc5d 100644
--- a/core/src/main/scala/org/apache/spark/rdd/CoGroupedRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/CoGroupedRDD.scala
@@ -154,11 +154,8 @@ class CoGroupedRDD[K](@transient var rdds: Seq[RDD[_ <: Product2[K, _]]], part:
         map.iterator.asInstanceOf[Iterator[(K, Array[Iterable[_]])]])
     } else {
       val map = createExternalMap(numRdds)
-      rddIterators.foreach { case (it, depNum) =>
-        while (it.hasNext) {
-          val kv = it.next()
-          map.insert(kv._1, new CoGroupValue(kv._2, depNum))
-        }
+      for ((it, depNum) <- rddIterators) {
+        map.insertAll(it.map(pair => (pair._1, new CoGroupValue(pair._2, depNum))))
       }
       context.taskMetrics.memoryBytesSpilled = map.memoryBytesSpilled
       context.taskMetrics.diskBytesSpilled = map.diskBytesSpilled
diff --git a/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala b/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala
index c22bb8d9c60a..6f263c39d143 100644
--- a/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala
+++ b/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala
@@ -110,42 +110,69 @@ class ExternalAppendOnlyMap[K, V, C](
 
   /**
    * Insert the given key and value into the map.
+   */
+  def insert(key: K, value: V): Unit = {
+    insertAll(Iterator((key, value)))
+  }
+
+  /**
+   * Insert the given iterator of keys and values into the map.
    *
-   * If the underlying map is about to grow, check if the global pool of shuffle memory has
+   * When the underlying map needs to grow, check if the global pool of shuffle memory has
    * enough room for this to happen. If so, allocate the memory required to grow the map;
    * otherwise, spill the in-memory map to disk.
    *
    * The shuffle memory usage of the first trackMemoryThreshold entries is not tracked.
    */
-  def insert(key: K, value: V) {
+  def insertAll(entries: Iterator[Product2[K, V]]): Unit = {
+    // An update function for the map that we reuse across entries to avoid allocating
+    // a new closure each time
+    var curEntry: Product2[K, V] = null
     val update: (Boolean, C) => C = (hadVal, oldVal) => {
-      if (hadVal) mergeValue(oldVal, value) else createCombiner(value)
+      if (hadVal) mergeValue(oldVal, curEntry._2) else createCombiner(curEntry._2)
     }
-    if (numPairsInMemory > trackMemoryThreshold && currentMap.atGrowThreshold) {
-      val mapSize = currentMap.estimateSize()
-      var shouldSpill = false
-      val shuffleMemoryMap = SparkEnv.get.shuffleMemoryMap
-
-      // Atomically check whether there is sufficient memory in the global pool for
-      // this map to grow and, if possible, allocate the required amount
-      shuffleMemoryMap.synchronized {
-        val previouslyOccupiedMemory = shuffleMemoryMap.get(threadId)
-        val availableMemory = maxMemoryThreshold -
-          (shuffleMemoryMap.values.sum - previouslyOccupiedMemory.getOrElse(0L))
-
-        // Assume map growth factor is 2x
-        shouldSpill = availableMemory < mapSize * 2
-        if (!shouldSpill) {
-          shuffleMemoryMap(threadId) = mapSize * 2
+
+    while (entries.hasNext) {
+      curEntry = entries.next()
+      if (numPairsInMemory > trackMemoryThreshold && currentMap.atGrowThreshold) {
+        val mapSize = currentMap.estimateSize()
+        var shouldSpill = false
+        val shuffleMemoryMap = SparkEnv.get.shuffleMemoryMap
+
+        // Atomically check whether there is sufficient memory in the global pool for
+        // this map to grow and, if possible, allocate the required amount
+        shuffleMemoryMap.synchronized {
+          val previouslyOccupiedMemory = shuffleMemoryMap.get(threadId)
+          val availableMemory = maxMemoryThreshold -
+            (shuffleMemoryMap.values.sum - previouslyOccupiedMemory.getOrElse(0L))
+
+          // Assume map growth factor is 2x
+          shouldSpill = availableMemory < mapSize * 2
+          if (!shouldSpill) {
+            shuffleMemoryMap(threadId) = mapSize * 2
+          }
+        }
+        // Do not synchronize spills
+        if (shouldSpill) {
+          spill(mapSize)
         }
       }
-      // Do not synchronize spills
-      if (shouldSpill) {
-        spill(mapSize)
-      }
+      currentMap.changeValue(curEntry._1, update)
+      numPairsInMemory += 1
     }
-    currentMap.changeValue(key, update)
-    numPairsInMemory += 1
+  }
+
+  /**
+   * Insert the given iterable of keys and values into the map.
+   *
+   * When the underlying map needs to grow, check if the global pool of shuffle memory has
+   * enough room for this to happen. If so, allocate the memory required to grow the map;
+   * otherwise, spill the in-memory map to disk.
+   *
+   * The shuffle memory usage of the first trackMemoryThreshold entries is not tracked.
+   */
+  def insertAll(entries: Iterable[Product2[K, V]]): Unit = {
+    insertAll(entries.iterator)
   }
 
   /**
diff --git a/core/src/test/scala/org/apache/spark/util/collection/ExternalAppendOnlyMapSuite.scala b/core/src/test/scala/org/apache/spark/util/collection/ExternalAppendOnlyMapSuite.scala
index 428822949c08..0b7ad184a46d 100644
--- a/core/src/test/scala/org/apache/spark/util/collection/ExternalAppendOnlyMapSuite.scala
+++ b/core/src/test/scala/org/apache/spark/util/collection/ExternalAppendOnlyMapSuite.scala
@@ -63,12 +63,13 @@ class ExternalAppendOnlyMapSuite extends FunSuite with LocalSparkContext {
     val map = new ExternalAppendOnlyMap[Int, Int, ArrayBuffer[Int]](createCombiner,
       mergeValue, mergeCombiners)
 
-    map.insert(1, 10)
-    map.insert(2, 20)
-    map.insert(3, 30)
-    map.insert(1, 100)
-    map.insert(2, 200)
-    map.insert(1, 1000)
+    map.insertAll(Seq(
+      (1, 10),
+      (2, 20),
+      (3, 30),
+      (1, 100),
+      (2, 200),
+      (1, 1000)))
     val it = map.iterator
     assert(it.hasNext)
     val result = it.toSet[(Int, ArrayBuffer[Int])].map(kv => (kv._1, kv._2.toSet))
@@ -282,7 +283,7 @@ class ExternalAppendOnlyMapSuite extends FunSuite with LocalSparkContext {
       assert(w1.hashCode === w2.hashCode)
     }
 
-    (1 to 100000).map(_.toString).foreach { i => map.insert(i, i) }
+    map.insertAll((1 to 100000).iterator.map(_.toString).map(i => (i, i)))
     collisionPairs.foreach { case (w1, w2) =>
       map.insert(w1, w2)
       map.insert(w2, w1)
@@ -355,7 +356,7 @@ class ExternalAppendOnlyMapSuite extends FunSuite with LocalSparkContext {
     val map = new ExternalAppendOnlyMap[Int, Int, ArrayBuffer[Int]](
       createCombiner, mergeValue, mergeCombiners)
 
-    (1 to 100000).foreach { i => map.insert(i, i) }
+    map.insertAll((1 to 100000).iterator.map(i => (i, i)))
     map.insert(null.asInstanceOf[Int], 1)
     map.insert(1, null.asInstanceOf[Int])
     map.insert(null.asInstanceOf[Int], null.asInstanceOf[Int])

From 2bbf235376f40a4b95d7e6e42e1bed893c124ecb Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian.cs.zju@gmail.com>
Date: Sun, 27 Jul 2014 12:35:21 -0700
Subject: [PATCH 778/810] [SPARK-2705][CORE] Fixed stage description in stage
 info page

Stage description should be a `String`, but was changed to an `Option[String]` by mistake:

![stage-desc-small](https://cloud.githubusercontent.com/assets/230655/3655611/f6d0b0f6-117b-11e4-83ed-71000dcd5009.png)

Author: Cheng Lian <lian.cs.zju@gmail.com>

Closes #1524 from liancheng/fix-stage-desc and squashes the following commits:

3c69327 [Cheng Lian] Fixed stage description object type in Web UI stage table
---
 .../org/apache/spark/ui/jobs/StageTable.scala      | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/StageTable.scala b/core/src/main/scala/org/apache/spark/ui/jobs/StageTable.scala
index f8b308c98154..3dcfaf76e4ab 100644
--- a/core/src/main/scala/org/apache/spark/ui/jobs/StageTable.scala
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/StageTable.scala
@@ -119,14 +119,14 @@ private[ui] class StageTableBase(
       </div>
     }
 
-    val stageDataOption = listener.stageIdToData.get(s.stageId)
-    // Too many nested map/flatMaps with options are just annoying to read. Do this imperatively.
-    if (stageDataOption.isDefined && stageDataOption.get.description.isDefined) {
-      val desc = stageDataOption.get.description
-      <div><em>{desc}</em></div><div>{killLink} {nameLink} {details}</div>
-    } else {
-      <div>{killLink} {nameLink} {details}</div>
+    val stageDesc = for {
+      stageData <- listener.stageIdToData.get(s.stageId)
+      desc <- stageData.description
+    } yield {
+      <div><em>{desc}</em></div>
     }
+
+    <div>{stageDesc.getOrElse("")} {killLink} {nameLink} {details}</div>
   }
 
   protected def stageRow(s: StageInfo): Seq[Node] = {

From f6ff2a61d00d12481bfb211ae13d6992daacdcc2 Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian.cs.zju@gmail.com>
Date: Sun, 27 Jul 2014 13:03:38 -0700
Subject: [PATCH 779/810] [SPARK-2410][SQL] Merging Hive Thrift/JDBC server

(This is a replacement of #1399, trying to fix potential `HiveThriftServer2` port collision between parallel builds. Please refer to [these comments](https://github.com/apache/spark/pull/1399#issuecomment-50212572) for details.)

JIRA issue: [SPARK-2410](https://issues.apache.org/jira/browse/SPARK-2410)

Merging the Hive Thrift/JDBC server from [branch-1.0-jdbc](https://github.com/apache/spark/tree/branch-1.0-jdbc).

Thanks chenghao-intel for his initial contribution of the Spark SQL CLI.

Author: Cheng Lian <lian.cs.zju@gmail.com>

Closes #1600 from liancheng/jdbc and squashes the following commits:

ac4618b [Cheng Lian] Uses random port for HiveThriftServer2 to avoid collision with parallel builds
090beea [Cheng Lian] Revert changes related to SPARK-2678, decided to move them to another PR
21c6cf4 [Cheng Lian] Updated Spark SQL programming guide docs
fe0af31 [Cheng Lian] Reordered spark-submit options in spark-shell[.cmd]
199e3fb [Cheng Lian] Disabled MIMA for hive-thriftserver
1083e9d [Cheng Lian] Fixed failed test suites
7db82a1 [Cheng Lian] Fixed spark-submit application options handling logic
9cc0f06 [Cheng Lian] Starts beeline with spark-submit
cfcf461 [Cheng Lian] Updated documents and build scripts for the newly added hive-thriftserver profile
061880f [Cheng Lian] Addressed all comments by @pwendell
7755062 [Cheng Lian] Adapts test suites to spark-submit settings
40bafef [Cheng Lian] Fixed more license header issues
e214aab [Cheng Lian] Added missing license headers
b8905ba [Cheng Lian] Fixed minor issues in spark-sql and start-thriftserver.sh
f975d22 [Cheng Lian] Updated docs for Hive compatibility and Shark migration guide draft
3ad4e75 [Cheng Lian] Starts spark-sql shell with spark-submit
a5310d1 [Cheng Lian] Make HiveThriftServer2 play well with spark-submit
61f39f4 [Cheng Lian] Starts Hive Thrift server via spark-submit
2c4c539 [Cheng Lian] Cherry picked the Hive Thrift server
---
 .gitignore                                    |   1 +
 assembly/pom.xml                              |  10 +
 bagel/pom.xml                                 |   2 +-
 bin/beeline                                   |  45 +++
 bin/compute-classpath.sh                      |   1 +
 bin/spark-shell                               |   4 +-
 bin/spark-shell.cmd                           |   2 +-
 bin/spark-sql                                 |  36 ++
 core/pom.xml                                  |   2 +-
 .../org/apache/spark/deploy/SparkSubmit.scala |  14 +-
 .../spark/deploy/SparkSubmitArguments.scala   |   5 +-
 dev/create-release/create-release.sh          |  10 +-
 dev/run-tests                                 |   2 +-
 dev/scalastyle                                |   2 +-
 docs/sql-programming-guide.md                 | 201 +++++++++-
 examples/pom.xml                              |   2 +-
 external/flume/pom.xml                        |   2 +-
 external/kafka/pom.xml                        |   2 +-
 external/mqtt/pom.xml                         |   2 +-
 external/twitter/pom.xml                      |   2 +-
 external/zeromq/pom.xml                       |   2 +-
 graphx/pom.xml                                |   2 +-
 mllib/pom.xml                                 |   2 +-
 pom.xml                                       |   7 +-
 project/SparkBuild.scala                      |  14 +-
 sbin/start-thriftserver.sh                    |  36 ++
 sql/catalyst/pom.xml                          |   2 +-
 .../sql/catalyst/plans/logical/commands.scala |   3 +-
 sql/core/pom.xml                              |   2 +-
 .../scala/org/apache/spark/sql/SQLConf.scala  |  20 +-
 .../apache/spark/sql/execution/commands.scala |  42 ++-
 .../org/apache/spark/sql/SQLConfSuite.scala   |  13 +-
 .../org/apache/spark/sql/SQLQuerySuite.scala  |  10 +-
 sql/hive-thriftserver/pom.xml                 |  82 +++++
 .../hive/thriftserver/HiveThriftServer2.scala |  97 +++++
 .../hive/thriftserver/ReflectionUtils.scala   |  58 +++
 .../hive/thriftserver/SparkSQLCLIDriver.scala | 344 ++++++++++++++++++
 .../thriftserver/SparkSQLCLIService.scala     |  74 ++++
 .../hive/thriftserver/SparkSQLDriver.scala    |  93 +++++
 .../sql/hive/thriftserver/SparkSQLEnv.scala   |  58 +++
 .../thriftserver/SparkSQLSessionManager.scala |  49 +++
 .../server/SparkSQLOperationManager.scala     | 151 ++++++++
 .../test/resources/data/files/small_kv.txt    |   5 +
 .../sql/hive/thriftserver/CliSuite.scala      |  57 +++
 .../thriftserver/HiveThriftServer2Suite.scala | 135 +++++++
 .../sql/hive/thriftserver/TestUtils.scala     | 108 ++++++
 sql/hive/pom.xml                              |   2 +-
 .../apache/spark/sql/hive/HiveContext.scala   |   2 +-
 .../sql/hive/execution/HiveQuerySuite.scala   |  50 ++-
 streaming/pom.xml                             |   2 +-
 tools/pom.xml                                 |   2 +-
 yarn/alpha/pom.xml                            |   2 +-
 yarn/pom.xml                                  |   2 +-
 yarn/stable/pom.xml                           |   2 +-
 54 files changed, 1781 insertions(+), 96 deletions(-)
 create mode 100755 bin/beeline
 create mode 100755 bin/spark-sql
 create mode 100755 sbin/start-thriftserver.sh
 create mode 100644 sql/hive-thriftserver/pom.xml
 create mode 100644 sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala
 create mode 100644 sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ReflectionUtils.scala
 create mode 100755 sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala
 create mode 100644 sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIService.scala
 create mode 100644 sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLDriver.scala
 create mode 100644 sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLEnv.scala
 create mode 100644 sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLSessionManager.scala
 create mode 100644 sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/server/SparkSQLOperationManager.scala
 create mode 100644 sql/hive-thriftserver/src/test/resources/data/files/small_kv.txt
 create mode 100644 sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/CliSuite.scala
 create mode 100644 sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suite.scala
 create mode 100644 sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/TestUtils.scala

diff --git a/.gitignore b/.gitignore
index 061c8946d23c..5b56a67c883e 100644
--- a/.gitignore
+++ b/.gitignore
@@ -57,3 +57,4 @@ metastore_db/
 metastore/
 warehouse/
 TempStatsStore/
+sql/hive-thriftserver/test_warehouses
diff --git a/assembly/pom.xml b/assembly/pom.xml
index 567a8dd2a0d9..703f15925bc4 100644
--- a/assembly/pom.xml
+++ b/assembly/pom.xml
@@ -165,6 +165,16 @@
         </dependency>
       </dependencies>
     </profile>
+    <profile>
+      <id>hive-thriftserver</id>
+      <dependencies>
+        <dependency>
+          <groupId>org.apache.spark</groupId>
+          <artifactId>spark-hive-thriftserver_${scala.binary.version}</artifactId>
+          <version>${project.version}</version>
+        </dependency>
+      </dependencies>
+    </profile>
     <profile>
       <id>spark-ganglia-lgpl</id>
       <dependencies>
diff --git a/bagel/pom.xml b/bagel/pom.xml
index 90c4b095bb61..bd51b112e26f 100644
--- a/bagel/pom.xml
+++ b/bagel/pom.xml
@@ -28,7 +28,7 @@
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-bagel_2.10</artifactId>
   <properties>
-     <sbt.project.name>bagel</sbt.project.name>
+    <sbt.project.name>bagel</sbt.project.name>
   </properties>
   <packaging>jar</packaging>
   <name>Spark Project Bagel</name>
diff --git a/bin/beeline b/bin/beeline
new file mode 100755
index 000000000000..09fe366c609f
--- /dev/null
+++ b/bin/beeline
@@ -0,0 +1,45 @@
+#!/usr/bin/env bash
+
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+# Figure out where Spark is installed
+FWDIR="$(cd `dirname $0`/..; pwd)"
+
+# Find the java binary
+if [ -n "${JAVA_HOME}" ]; then
+  RUNNER="${JAVA_HOME}/bin/java"
+else
+  if [ `command -v java` ]; then
+    RUNNER="java"
+  else
+    echo "JAVA_HOME is not set" >&2
+    exit 1
+  fi
+fi
+
+# Compute classpath using external script
+classpath_output=$($FWDIR/bin/compute-classpath.sh)
+if [[ "$?" != "0" ]]; then
+  echo "$classpath_output"
+  exit 1
+else
+  CLASSPATH=$classpath_output
+fi
+
+CLASS="org.apache.hive.beeline.BeeLine"
+exec "$RUNNER" -cp "$CLASSPATH" $CLASS "$@"
diff --git a/bin/compute-classpath.sh b/bin/compute-classpath.sh
index e81e8c060cb9..16b794a1592e 100755
--- a/bin/compute-classpath.sh
+++ b/bin/compute-classpath.sh
@@ -52,6 +52,7 @@ if [ -n "$SPARK_PREPEND_CLASSES" ]; then
   CLASSPATH="$CLASSPATH:$FWDIR/sql/catalyst/target/scala-$SCALA_VERSION/classes"
   CLASSPATH="$CLASSPATH:$FWDIR/sql/core/target/scala-$SCALA_VERSION/classes"
   CLASSPATH="$CLASSPATH:$FWDIR/sql/hive/target/scala-$SCALA_VERSION/classes"
+  CLASSPATH="$CLASSPATH:$FWDIR/sql/hive-thriftserver/target/scala-$SCALA_VERSION/classes"
   CLASSPATH="$CLASSPATH:$FWDIR/yarn/stable/target/scala-$SCALA_VERSION/classes"
 fi
 
diff --git a/bin/spark-shell b/bin/spark-shell
index 850e9507ec38..756c8179d12b 100755
--- a/bin/spark-shell
+++ b/bin/spark-shell
@@ -46,11 +46,11 @@ function main(){
         # (see https://github.com/sbt/sbt/issues/562).
         stty -icanon min 1 -echo > /dev/null 2>&1
         export SPARK_SUBMIT_OPTS="$SPARK_SUBMIT_OPTS -Djline.terminal=unix"
-        $FWDIR/bin/spark-submit spark-shell "$@" --class org.apache.spark.repl.Main
+        $FWDIR/bin/spark-submit --class org.apache.spark.repl.Main spark-shell "$@"
         stty icanon echo > /dev/null 2>&1
     else
         export SPARK_SUBMIT_OPTS
-        $FWDIR/bin/spark-submit spark-shell "$@" --class org.apache.spark.repl.Main
+        $FWDIR/bin/spark-submit --class org.apache.spark.repl.Main spark-shell "$@"
     fi
 }
 
diff --git a/bin/spark-shell.cmd b/bin/spark-shell.cmd
index 4b9708a8c03f..b56d69801171 100755
--- a/bin/spark-shell.cmd
+++ b/bin/spark-shell.cmd
@@ -19,4 +19,4 @@ rem
 
 set SPARK_HOME=%~dp0..
 
-cmd /V /E /C %SPARK_HOME%\bin\spark-submit.cmd spark-shell %* --class org.apache.spark.repl.Main
+cmd /V /E /C %SPARK_HOME%\bin\spark-submit.cmd spark-shell --class org.apache.spark.repl.Main %*
diff --git a/bin/spark-sql b/bin/spark-sql
new file mode 100755
index 000000000000..bba7f897b19b
--- /dev/null
+++ b/bin/spark-sql
@@ -0,0 +1,36 @@
+#!/usr/bin/env bash
+
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+#
+# Shell script for starting the Spark SQL CLI
+
+# Enter posix mode for bash
+set -o posix
+
+# Figure out where Spark is installed
+FWDIR="$(cd `dirname $0`/..; pwd)"
+
+if [[ "$@" = *--help ]] || [[ "$@" = *-h ]]; then
+  echo "Usage: ./sbin/spark-sql [options]"
+  $FWDIR/bin/spark-submit --help 2>&1 | grep -v Usage 1>&2
+  exit 0
+fi
+
+CLASS="org.apache.spark.sql.hive.thriftserver.SparkSQLCLIDriver"
+exec "$FWDIR"/bin/spark-submit --class $CLASS spark-internal $@
diff --git a/core/pom.xml b/core/pom.xml
index 1054cec4d77b..a24743495b0e 100644
--- a/core/pom.xml
+++ b/core/pom.xml
@@ -28,7 +28,7 @@
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-core_2.10</artifactId>
   <properties>
-     <sbt.project.name>core</sbt.project.name>
+    <sbt.project.name>core</sbt.project.name>
   </properties>
   <packaging>jar</packaging>
   <name>Spark Project Core</name>
diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
index 3b5642b6caa3..c9cec33ebaa6 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
@@ -46,6 +46,10 @@ object SparkSubmit {
   private val CLUSTER = 2
   private val ALL_DEPLOY_MODES = CLIENT | CLUSTER
 
+  // A special jar name that indicates the class being run is inside of Spark itself, and therefore
+  // no user jar is needed.
+  private val SPARK_INTERNAL = "spark-internal"
+
   // Special primary resource names that represent shells rather than application jars.
   private val SPARK_SHELL = "spark-shell"
   private val PYSPARK_SHELL = "pyspark-shell"
@@ -257,7 +261,9 @@ object SparkSubmit {
     // In yarn-cluster mode, use yarn.Client as a wrapper around the user class
     if (clusterManager == YARN && deployMode == CLUSTER) {
       childMainClass = "org.apache.spark.deploy.yarn.Client"
-      childArgs += ("--jar", args.primaryResource)
+      if (args.primaryResource != SPARK_INTERNAL) {
+        childArgs += ("--jar", args.primaryResource)
+      }
       childArgs += ("--class", args.mainClass)
       if (args.childArgs != null) {
         args.childArgs.foreach { arg => childArgs += ("--arg", arg) }
@@ -332,7 +338,7 @@ object SparkSubmit {
    * Return whether the given primary resource represents a user jar.
    */
   private def isUserJar(primaryResource: String): Boolean = {
-    !isShell(primaryResource) && !isPython(primaryResource)
+    !isShell(primaryResource) && !isPython(primaryResource) && !isInternal(primaryResource)
   }
 
   /**
@@ -349,6 +355,10 @@ object SparkSubmit {
     primaryResource.endsWith(".py") || primaryResource == PYSPARK_SHELL
   }
 
+  private[spark] def isInternal(primaryResource: String): Boolean = {
+    primaryResource == SPARK_INTERNAL
+  }
+
   /**
    * Merge a sequence of comma-separated file lists, some of which may be null to indicate
    * no files, into a single comma-separated string.
diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
index 3ab67a43a3b5..01d0ae541a66 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
@@ -204,8 +204,9 @@ private[spark] class SparkSubmitArguments(args: Seq[String]) {
 
   /** Fill in values by parsing user options. */
   private def parseOpts(opts: Seq[String]): Unit = {
-    // Delineates parsing of Spark options from parsing of user options.
     var inSparkOpts = true
+
+    // Delineates parsing of Spark options from parsing of user options.
     parse(opts)
 
     def parse(opts: Seq[String]): Unit = opts match {
@@ -318,7 +319,7 @@ private[spark] class SparkSubmitArguments(args: Seq[String]) {
               SparkSubmit.printErrorAndExit(errMessage)
             case v =>
               primaryResource =
-                if (!SparkSubmit.isShell(v)) {
+                if (!SparkSubmit.isShell(v) && !SparkSubmit.isInternal(v)) {
                   Utils.resolveURI(v).toString
                 } else {
                   v
diff --git a/dev/create-release/create-release.sh b/dev/create-release/create-release.sh
index 38830103d1e8..33de24d1ae6d 100755
--- a/dev/create-release/create-release.sh
+++ b/dev/create-release/create-release.sh
@@ -53,7 +53,7 @@ if [[ ! "$@" =~ --package-only ]]; then
     -Dusername=$GIT_USERNAME -Dpassword=$GIT_PASSWORD \
     -Dmaven.javadoc.skip=true \
     -Dhadoop.version=2.2.0 -Dyarn.version=2.2.0 \
-    -Pyarn -Phive -Phadoop-2.2 -Pspark-ganglia-lgpl\
+    -Pyarn -Phive -Phive-thriftserver -Phadoop-2.2 -Pspark-ganglia-lgpl\
     -Dtag=$GIT_TAG -DautoVersionSubmodules=true \
     --batch-mode release:prepare
 
@@ -61,7 +61,7 @@ if [[ ! "$@" =~ --package-only ]]; then
     -Darguments="-DskipTests=true -Dmaven.javadoc.skip=true -Dhadoop.version=2.2.0 -Dyarn.version=2.2.0 -Dgpg.passphrase=${GPG_PASSPHRASE}" \
     -Dhadoop.version=2.2.0 -Dyarn.version=2.2.0 \
     -Dmaven.javadoc.skip=true \
-    -Pyarn -Phive -Phadoop-2.2 -Pspark-ganglia-lgpl\
+    -Pyarn -Phive -Phive-thriftserver -Phadoop-2.2 -Pspark-ganglia-lgpl\
     release:perform
 
   cd ..
@@ -111,10 +111,10 @@ make_binary_release() {
     spark-$RELEASE_VERSION-bin-$NAME.tgz.sha
 }
 
-make_binary_release "hadoop1" "-Phive -Dhadoop.version=1.0.4"
-make_binary_release "cdh4" "-Phive -Dhadoop.version=2.0.0-mr1-cdh4.2.0"
+make_binary_release "hadoop1" "-Phive -Phive-thriftserver -Dhadoop.version=1.0.4"
+make_binary_release "cdh4" "-Phive -Phive-thriftserver -Dhadoop.version=2.0.0-mr1-cdh4.2.0"
 make_binary_release "hadoop2" \
-  "-Phive -Pyarn -Phadoop-2.2 -Dhadoop.version=2.2.0 -Pyarn.version=2.2.0"
+  "-Phive -Phive-thriftserver -Pyarn -Phadoop-2.2 -Dhadoop.version=2.2.0 -Pyarn.version=2.2.0"
 
 # Copy data
 echo "Copying release tarballs"
diff --git a/dev/run-tests b/dev/run-tests
index 51e4def0f835..98ec969dc1b3 100755
--- a/dev/run-tests
+++ b/dev/run-tests
@@ -65,7 +65,7 @@ echo "========================================================================="
 # (either resolution or compilation) prompts the user for input either q, r, 
 # etc to quit or retry. This echo is there to make it not block.
 if [ -n "$_RUN_SQL_TESTS" ]; then
-  echo -e "q\n" | SBT_MAVEN_PROFILES="$SBT_MAVEN_PROFILES -Phive" sbt/sbt clean package \
+  echo -e "q\n" | SBT_MAVEN_PROFILES="$SBT_MAVEN_PROFILES -Phive -Phive-thriftserver" sbt/sbt clean package \
     assembly/assembly test | grep -v -e "info.*Resolving" -e "warn.*Merging" -e "info.*Including"
 else
   echo -e "q\n" | sbt/sbt clean package assembly/assembly test | \
diff --git a/dev/scalastyle b/dev/scalastyle
index a02d06912f23..d9f2b91a3a09 100755
--- a/dev/scalastyle
+++ b/dev/scalastyle
@@ -17,7 +17,7 @@
 # limitations under the License.
 #
 
-echo -e "q\n" | sbt/sbt -Phive scalastyle > scalastyle.txt
+echo -e "q\n" | sbt/sbt -Phive -Phive-thriftserver scalastyle > scalastyle.txt
 # Check style with YARN alpha built too
 echo -e "q\n" | sbt/sbt -Pyarn -Phadoop-0.23 -Dhadoop.version=0.23.9 yarn-alpha/scalastyle \
   >> scalastyle.txt
diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md
index 38728534a46e..156e0aebdebe 100644
--- a/docs/sql-programming-guide.md
+++ b/docs/sql-programming-guide.md
@@ -136,7 +136,7 @@ val sqlContext = new org.apache.spark.sql.SQLContext(sc)
 import sqlContext.createSchemaRDD
 
 // Define the schema using a case class.
-// Note: Case classes in Scala 2.10 can support only up to 22 fields. To work around this limit, 
+// Note: Case classes in Scala 2.10 can support only up to 22 fields. To work around this limit,
 // you can use custom classes that implement the Product interface.
 case class Person(name: String, age: Int)
 
@@ -548,7 +548,6 @@ results = hiveContext.hql("FROM src SELECT key, value").collect()
 </div>
 </div>
 
-
 # Writing Language-Integrated Relational Queries
 
 **Language-Integrated queries are currently only supported in Scala.**
@@ -573,4 +572,200 @@ prefixed with a tick (`'`).  Implicit conversions turn these symbols into expres
 evaluated by the SQL execution engine.  A full list of the functions supported can be found in the
 [ScalaDoc](api/scala/index.html#org.apache.spark.sql.SchemaRDD).
 
-<!-- TODO: Include the table of operations here. -->
\ No newline at end of file
+<!-- TODO: Include the table of operations here. -->
+
+## Running the Thrift JDBC server
+
+The Thrift JDBC server implemented here corresponds to the [`HiveServer2`]
+(https://cwiki.apache.org/confluence/display/Hive/Setting+Up+HiveServer2) in Hive 0.12. You can test
+the JDBC server with the beeline script comes with either Spark or Hive 0.12.  In order to use Hive
+you must first run '`sbt/sbt -Phive-thriftserver assembly/assembly`' (or use `-Phive-thriftserver`
+for maven).
+
+To start the JDBC server, run the following in the Spark directory:
+
+    ./sbin/start-thriftserver.sh
+
+The default port the server listens on is 10000.  To listen on customized host and port, please set
+the `HIVE_SERVER2_THRIFT_PORT` and `HIVE_SERVER2_THRIFT_BIND_HOST` environment variables. You may
+run `./sbin/start-thriftserver.sh --help` for a complete list of all available options.  Now you can
+use beeline to test the Thrift JDBC server:
+
+    ./bin/beeline
+
+Connect to the JDBC server in beeline with:
+
+    beeline> !connect jdbc:hive2://localhost:10000
+
+Beeline will ask you for a username and password. In non-secure mode, simply enter the username on
+your machine and a blank password. For secure mode, please follow the instructions given in the
+[beeline documentation](https://cwiki.apache.org/confluence/display/Hive/HiveServer2+Clients)
+
+Configuration of Hive is done by placing your `hive-site.xml` file in `conf/`.
+
+You may also use the beeline script comes with Hive.
+
+### Migration Guide for Shark Users
+
+#### Reducer number
+
+In Shark, default reducer number is 1 and is controlled by the property `mapred.reduce.tasks`. Spark
+SQL deprecates this property by a new property `spark.sql.shuffle.partitions`, whose default value
+is 200. Users may customize this property via `SET`:
+
+```
+SET spark.sql.shuffle.partitions=10;
+SELECT page, count(*) c FROM logs_last_month_cached
+GROUP BY page ORDER BY c DESC LIMIT 10;
+```
+
+You may also put this property in `hive-site.xml` to override the default value.
+
+For now, the `mapred.reduce.tasks` property is still recognized, and is converted to
+`spark.sql.shuffle.partitions` automatically.
+
+#### Caching
+
+The `shark.cache` table property no longer exists, and tables whose name end with `_cached` are no
+longer automcatically cached. Instead, we provide `CACHE TABLE` and `UNCACHE TABLE` statements to
+let user control table caching explicitly:
+
+```
+CACHE TABLE logs_last_month;
+UNCACHE TABLE logs_last_month;
+```
+
+**NOTE** `CACHE TABLE tbl` is lazy, it only marks table `tbl` as "need to by cached if necessary",
+but doesn't actually cache it until a query that touches `tbl` is executed. To force the table to be
+cached, you may simply count the table immediately after executing `CACHE TABLE`:
+
+```
+CACHE TABLE logs_last_month;
+SELECT COUNT(1) FROM logs_last_month;
+```
+
+Several caching related features are not supported yet:
+
+* User defined partition level cache eviction policy
+* RDD reloading
+* In-memory cache write through policy
+
+### Compatibility with Apache Hive
+
+#### Deploying in Exising Hive Warehouses
+
+Spark SQL Thrift JDBC server is designed to be "out of the box" compatible with existing Hive
+installations. You do not need to modify your existing Hive Metastore or change the data placement
+or partitioning of your tables.
+
+#### Supported Hive Features
+
+Spark SQL supports the vast majority of Hive features, such as:
+
+* Hive query statements, including:
+ * `SELECT`
+ * `GROUP BY
+ * `ORDER BY`
+ * `CLUSTER BY`
+ * `SORT BY`
+* All Hive operators, including:
+ * Relational operators (`=`, `⇔`, `==`, `<>`, `<`, `>`, `>=`, `<=`, etc)
+ * Arthimatic operators (`+`, `-`, `*`, `/`, `%`, etc)
+ * Logical operators (`AND`, `&&`, `OR`, `||`, etc)
+ * Complex type constructors
+ * Mathemtatical functions (`sign`, `ln`, `cos`, etc)
+ * String functions (`instr`, `length`, `printf`, etc)
+* User defined functions (UDF)
+* User defined aggregation functions (UDAF)
+* User defined serialization formats (SerDe's)
+* Joins
+ * `JOIN`
+ * `{LEFT|RIGHT|FULL} OUTER JOIN`
+ * `LEFT SEMI JOIN`
+ * `CROSS JOIN`
+* Unions
+* Sub queries
+ * `SELECT col FROM ( SELECT a + b AS col from t1) t2`
+* Sampling
+* Explain
+* Partitioned tables
+* All Hive DDL Functions, including:
+ * `CREATE TABLE`
+ * `CREATE TABLE AS SELECT`
+ * `ALTER TABLE`
+* Most Hive Data types, including:
+ * `TINYINT`
+ * `SMALLINT`
+ * `INT`
+ * `BIGINT`
+ * `BOOLEAN`
+ * `FLOAT`
+ * `DOUBLE`
+ * `STRING`
+ * `BINARY`
+ * `TIMESTAMP`
+ * `ARRAY<>`
+ * `MAP<>`
+ * `STRUCT<>`
+
+#### Unsupported Hive Functionality
+
+Below is a list of Hive features that we don't support yet. Most of these features are rarely used
+in Hive deployments.
+
+**Major Hive Features**
+
+* Tables with buckets: bucket is the hash partitioning within a Hive table partition. Spark SQL
+  doesn't support buckets yet.
+
+**Esoteric Hive Features**
+
+* Tables with partitions using different input formats: In Spark SQL, all table partitions need to
+  have the same input format.
+* Non-equi outer join: For the uncommon use case of using outer joins with non-equi join conditions
+  (e.g. condition "`key < 10`"), Spark SQL will output wrong result for the `NULL` tuple.
+* `UNIONTYPE`
+* Unique join
+* Single query multi insert
+* Column statistics collecting: Spark SQL does not piggyback scans to collect column statistics at
+  the moment.
+
+**Hive Input/Output Formats**
+
+* File format for CLI: For results showing back to the CLI, Spark SQL only supports TextOutputFormat.
+* Hadoop archive
+
+**Hive Optimizations**
+
+A handful of Hive optimizations are not yet included in Spark. Some of these (such as indexes) are
+not necessary due to Spark SQL's in-memory computational model. Others are slotted for future
+releases of Spark SQL.
+
+* Block level bitmap indexes and virtual columns (used to build indexes)
+* Automatically convert a join to map join: For joining a large table with multiple small tables,
+  Hive automatically converts the join into a map join. We are adding this auto conversion in the
+  next release.
+* Automatically determine the number of reducers for joins and groupbys: Currently in Spark SQL, you
+  need to control the degree of parallelism post-shuffle using "SET
+  spark.sql.shuffle.partitions=[num_tasks];". We are going to add auto-setting of parallelism in the
+  next release.
+* Meta-data only query: For queries that can be answered by using only meta data, Spark SQL still
+  launches tasks to compute the result.
+* Skew data flag: Spark SQL does not follow the skew data flags in Hive.
+* `STREAMTABLE` hint in join: Spark SQL does not follow the `STREAMTABLE` hint.
+* Merge multiple small files for query results: if the result output contains multiple small files,
+  Hive can optionally merge the small files into fewer large files to avoid overflowing the HDFS
+  metadata. Spark SQL does not support that.
+
+## Running the Spark SQL CLI
+
+The Spark SQL CLI is a convenient tool to run the Hive metastore service in local mode and execute
+queries input from command line. Note: the Spark SQL CLI cannot talk to the Thrift JDBC server.
+
+To start the Spark SQL CLI, run the following in the Spark directory:
+
+    ./bin/spark-sql
+
+Configuration of Hive is done by placing your `hive-site.xml` file in `conf/`.
+You may run `./bin/spark-sql --help` for a complete list of all available
+options.
diff --git a/examples/pom.xml b/examples/pom.xml
index bd1c387c2eb9..c4ed0f5a6a02 100644
--- a/examples/pom.xml
+++ b/examples/pom.xml
@@ -28,7 +28,7 @@
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-examples_2.10</artifactId>
   <properties>
-     <sbt.project.name>examples</sbt.project.name>
+    <sbt.project.name>examples</sbt.project.name>
   </properties>
   <packaging>jar</packaging>
   <name>Spark Project Examples</name>
diff --git a/external/flume/pom.xml b/external/flume/pom.xml
index 61a6aff543ae..874b8a7959bb 100644
--- a/external/flume/pom.xml
+++ b/external/flume/pom.xml
@@ -28,7 +28,7 @@
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-streaming-flume_2.10</artifactId>
   <properties>
-     <sbt.project.name>streaming-flume</sbt.project.name>
+    <sbt.project.name>streaming-flume</sbt.project.name>
   </properties>
   <packaging>jar</packaging>
   <name>Spark Project External Flume</name>
diff --git a/external/kafka/pom.xml b/external/kafka/pom.xml
index 4762c50685a9..25a5c0a4d7d7 100644
--- a/external/kafka/pom.xml
+++ b/external/kafka/pom.xml
@@ -28,7 +28,7 @@
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-streaming-kafka_2.10</artifactId>
   <properties>
-     <sbt.project.name>streaming-kafka</sbt.project.name>
+    <sbt.project.name>streaming-kafka</sbt.project.name>
   </properties>
   <packaging>jar</packaging>
   <name>Spark Project External Kafka</name>
diff --git a/external/mqtt/pom.xml b/external/mqtt/pom.xml
index 32c530e600ce..f31ed655f677 100644
--- a/external/mqtt/pom.xml
+++ b/external/mqtt/pom.xml
@@ -28,7 +28,7 @@
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-streaming-mqtt_2.10</artifactId>
   <properties>
-     <sbt.project.name>streaming-mqtt</sbt.project.name>
+    <sbt.project.name>streaming-mqtt</sbt.project.name>
   </properties>
   <packaging>jar</packaging>
   <name>Spark Project External MQTT</name>
diff --git a/external/twitter/pom.xml b/external/twitter/pom.xml
index 637adb0f00da..56bb24c2a072 100644
--- a/external/twitter/pom.xml
+++ b/external/twitter/pom.xml
@@ -28,7 +28,7 @@
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-streaming-twitter_2.10</artifactId>
   <properties>
-     <sbt.project.name>streaming-twitter</sbt.project.name>
+    <sbt.project.name>streaming-twitter</sbt.project.name>
   </properties>
   <packaging>jar</packaging>
   <name>Spark Project External Twitter</name>
diff --git a/external/zeromq/pom.xml b/external/zeromq/pom.xml
index e4d758a04a4c..54b0242c54e7 100644
--- a/external/zeromq/pom.xml
+++ b/external/zeromq/pom.xml
@@ -28,7 +28,7 @@
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-streaming-zeromq_2.10</artifactId>
   <properties>
-     <sbt.project.name>streaming-zeromq</sbt.project.name>
+    <sbt.project.name>streaming-zeromq</sbt.project.name>
   </properties>
   <packaging>jar</packaging>
   <name>Spark Project External ZeroMQ</name>
diff --git a/graphx/pom.xml b/graphx/pom.xml
index 7e3bcf29dcfb..6dd52fc618b1 100644
--- a/graphx/pom.xml
+++ b/graphx/pom.xml
@@ -28,7 +28,7 @@
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-graphx_2.10</artifactId>
   <properties>
-     <sbt.project.name>graphx</sbt.project.name>
+    <sbt.project.name>graphx</sbt.project.name>
   </properties>
   <packaging>jar</packaging>
   <name>Spark Project GraphX</name>
diff --git a/mllib/pom.xml b/mllib/pom.xml
index 92b07e2357db..f27cf520dc9f 100644
--- a/mllib/pom.xml
+++ b/mllib/pom.xml
@@ -28,7 +28,7 @@
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-mllib_2.10</artifactId>
   <properties>
-     <sbt.project.name>mllib</sbt.project.name>
+    <sbt.project.name>mllib</sbt.project.name>
   </properties>  
   <packaging>jar</packaging>
   <name>Spark Project ML Library</name>
diff --git a/pom.xml b/pom.xml
index 4e2d64a83364..3e9d388180d8 100644
--- a/pom.xml
+++ b/pom.xml
@@ -95,6 +95,7 @@
     <module>sql/catalyst</module>
     <module>sql/core</module>
     <module>sql/hive</module>
+    <module>sql/hive-thriftserver</module>
     <module>repl</module>
     <module>assembly</module>
     <module>external/twitter</module>
@@ -252,9 +253,9 @@
         <version>3.3.2</version>
       </dependency>
       <dependency>
-	  <groupId>commons-codec</groupId>
-	    <artifactId>commons-codec</artifactId>
-	    <version>1.5</version>
+        <groupId>commons-codec</groupId>
+        <artifactId>commons-codec</artifactId>
+        <version>1.5</version>
       </dependency>
       <dependency>
         <groupId>com.google.code.findbugs</groupId>
diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index 62576f84dd03..1629bc2cba8b 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -30,11 +30,11 @@ object BuildCommons {
 
   private val buildLocation = file(".").getAbsoluteFile.getParentFile
 
-  val allProjects@Seq(bagel, catalyst, core, graphx, hive, mllib, repl, spark, sql, streaming,
-  streamingFlume, streamingKafka, streamingMqtt, streamingTwitter, streamingZeromq) =
-    Seq("bagel", "catalyst", "core", "graphx", "hive", "mllib", "repl", "spark", "sql",
-      "streaming", "streaming-flume", "streaming-kafka", "streaming-mqtt", "streaming-twitter",
-      "streaming-zeromq").map(ProjectRef(buildLocation, _))
+  val allProjects@Seq(bagel, catalyst, core, graphx, hive, hiveThriftServer, mllib, repl, spark, sql,
+  streaming, streamingFlume, streamingKafka, streamingMqtt, streamingTwitter, streamingZeromq) =
+    Seq("bagel", "catalyst", "core", "graphx", "hive", "hive-thriftserver", "mllib", "repl",
+      "spark", "sql", "streaming", "streaming-flume", "streaming-kafka", "streaming-mqtt",
+      "streaming-twitter", "streaming-zeromq").map(ProjectRef(buildLocation, _))
 
   val optionallyEnabledProjects@Seq(yarn, yarnStable, yarnAlpha, java8Tests, sparkGangliaLgpl) =
     Seq("yarn", "yarn-stable", "yarn-alpha", "java8-tests", "ganglia-lgpl")
@@ -100,7 +100,7 @@ object SparkBuild extends PomBuild {
   Properties.envOrNone("SBT_MAVEN_PROPERTIES") match {
     case Some(v) =>
       v.split("(\\s+|,)").filterNot(_.isEmpty).map(_.split("=")).foreach(x => System.setProperty(x(0), x(1)))
-    case _ => 
+    case _ =>
   }
 
   override val userPropertiesMap = System.getProperties.toMap
@@ -158,7 +158,7 @@ object SparkBuild extends PomBuild {
 
   /* Enable Mima for all projects except spark, hive, catalyst, sql  and repl */
   // TODO: Add Sql to mima checks
-  allProjects.filterNot(y => Seq(spark, sql, hive, catalyst, repl).exists(x => x == y)).
+  allProjects.filterNot(x => Seq(spark, sql, hive, hiveThriftServer, catalyst, repl).contains(x)).
     foreach (x => enable(MimaBuild.mimaSettings(sparkHome, x))(x))
 
   /* Enable Assembly for all assembly projects */
diff --git a/sbin/start-thriftserver.sh b/sbin/start-thriftserver.sh
new file mode 100755
index 000000000000..8398e6f19b51
--- /dev/null
+++ b/sbin/start-thriftserver.sh
@@ -0,0 +1,36 @@
+#!/usr/bin/env bash
+
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+#
+# Shell script for starting the Spark SQL Thrift server
+
+# Enter posix mode for bash
+set -o posix
+
+# Figure out where Spark is installed
+FWDIR="$(cd `dirname $0`/..; pwd)"
+
+if [[ "$@" = *--help ]] || [[ "$@" = *-h ]]; then
+  echo "Usage: ./sbin/start-thriftserver [options]"
+  $FWDIR/bin/spark-submit --help 2>&1 | grep -v Usage 1>&2
+  exit 0
+fi
+
+CLASS="org.apache.spark.sql.hive.thriftserver.HiveThriftServer2"
+exec "$FWDIR"/bin/spark-submit --class $CLASS spark-internal $@
diff --git a/sql/catalyst/pom.xml b/sql/catalyst/pom.xml
index 6decde3fcd62..531bfddbf237 100644
--- a/sql/catalyst/pom.xml
+++ b/sql/catalyst/pom.xml
@@ -32,7 +32,7 @@
   <name>Spark Project Catalyst</name>
   <url>http://spark.apache.org/</url>
   <properties>
-     <sbt.project.name>catalyst</sbt.project.name>
+    <sbt.project.name>catalyst</sbt.project.name>
   </properties>
 
   <dependencies>
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/commands.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/commands.scala
index 1d5f033f0d27..a357c6ffb897 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/commands.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/commands.scala
@@ -43,8 +43,7 @@ case class NativeCommand(cmd: String) extends Command {
  */
 case class SetCommand(key: Option[String], value: Option[String]) extends Command {
   override def output = Seq(
-    BoundReference(0, AttributeReference("key", StringType, nullable = false)()),
-    BoundReference(1, AttributeReference("value", StringType, nullable = false)()))
+    BoundReference(1, AttributeReference("", StringType, nullable = false)()))
 }
 
 /**
diff --git a/sql/core/pom.xml b/sql/core/pom.xml
index c309c43804d9..3a038a2db617 100644
--- a/sql/core/pom.xml
+++ b/sql/core/pom.xml
@@ -32,7 +32,7 @@
   <name>Spark Project SQL</name>
   <url>http://spark.apache.org/</url>
   <properties>
-     <sbt.project.name>sql</sbt.project.name>
+    <sbt.project.name>sql</sbt.project.name>
   </properties>
 
   <dependencies>
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
index 2b787e14f3f1..41920c00b5a2 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
@@ -30,12 +30,13 @@ import scala.collection.JavaConverters._
  * SQLConf is thread-safe (internally synchronized so safe to be used in multiple threads).
  */
 trait SQLConf {
+  import SQLConf._
 
   /** ************************ Spark SQL Params/Hints ******************* */
   // TODO: refactor so that these hints accessors don't pollute the name space of SQLContext?
 
   /** Number of partitions to use for shuffle operators. */
-  private[spark] def numShufflePartitions: Int = get("spark.sql.shuffle.partitions", "200").toInt
+  private[spark] def numShufflePartitions: Int = get(SHUFFLE_PARTITIONS, "200").toInt
 
   /**
    * Upper bound on the sizes (in bytes) of the tables qualified for the auto conversion to
@@ -43,11 +44,10 @@ trait SQLConf {
    * effectively disables auto conversion.
    * Hive setting: hive.auto.convert.join.noconditionaltask.size.
    */
-  private[spark] def autoConvertJoinSize: Int =
-    get("spark.sql.auto.convert.join.size", "10000").toInt
+  private[spark] def autoConvertJoinSize: Int = get(AUTO_CONVERT_JOIN_SIZE, "10000").toInt
 
   /** A comma-separated list of table names marked to be broadcasted during joins. */
-  private[spark] def joinBroadcastTables: String = get("spark.sql.join.broadcastTables", "")
+  private[spark] def joinBroadcastTables: String = get(JOIN_BROADCAST_TABLES, "")
 
   /** ********************** SQLConf functionality methods ************ */
 
@@ -61,7 +61,7 @@ trait SQLConf {
 
   def set(key: String, value: String): Unit = {
     require(key != null, "key cannot be null")
-    require(value != null, s"value cannot be null for ${key}")
+    require(value != null, s"value cannot be null for $key")
     settings.put(key, value)
   }
 
@@ -90,3 +90,13 @@ trait SQLConf {
   }
 
 }
+
+object SQLConf {
+  val AUTO_CONVERT_JOIN_SIZE = "spark.sql.auto.convert.join.size"
+  val SHUFFLE_PARTITIONS = "spark.sql.shuffle.partitions"
+  val JOIN_BROADCAST_TABLES = "spark.sql.join.broadcastTables"
+
+  object Deprecated {
+    val MAPRED_REDUCE_TASKS = "mapred.reduce.tasks"
+  }
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala
index 98d2f89c8ae7..9293239131d5 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala
@@ -17,12 +17,13 @@
 
 package org.apache.spark.sql.execution
 
+import org.apache.spark.Logging
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.errors.TreeNodeException
 import org.apache.spark.sql.catalyst.expressions.{Attribute, GenericRow}
 import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
-import org.apache.spark.sql.{Row, SQLContext}
+import org.apache.spark.sql.{Row, SQLConf, SQLContext}
 
 trait Command {
   /**
@@ -44,28 +45,53 @@ trait Command {
 case class SetCommand(
     key: Option[String], value: Option[String], output: Seq[Attribute])(
     @transient context: SQLContext)
-  extends LeafNode with Command {
+  extends LeafNode with Command with Logging {
 
-  override protected[sql] lazy val sideEffectResult: Seq[(String, String)] = (key, value) match {
+  override protected[sql] lazy val sideEffectResult: Seq[String] = (key, value) match {
     // Set value for key k.
     case (Some(k), Some(v)) =>
-      context.set(k, v)
-      Array(k -> v)
+      if (k == SQLConf.Deprecated.MAPRED_REDUCE_TASKS) {
+        logWarning(s"Property ${SQLConf.Deprecated.MAPRED_REDUCE_TASKS} is deprecated, " +
+          s"automatically converted to ${SQLConf.SHUFFLE_PARTITIONS} instead.")
+        context.set(SQLConf.SHUFFLE_PARTITIONS, v)
+        Array(s"${SQLConf.SHUFFLE_PARTITIONS}=$v")
+      } else {
+        context.set(k, v)
+        Array(s"$k=$v")
+      }
 
     // Query the value bound to key k.
     case (Some(k), _) =>
-      Array(k -> context.getOption(k).getOrElse("<undefined>"))
+      // TODO (lian) This is just a workaround to make the Simba ODBC driver work.
+      // Should remove this once we get the ODBC driver updated.
+      if (k == "-v") {
+        val hiveJars = Seq(
+          "hive-exec-0.12.0.jar",
+          "hive-service-0.12.0.jar",
+          "hive-common-0.12.0.jar",
+          "hive-hwi-0.12.0.jar",
+          "hive-0.12.0.jar").mkString(":")
+
+        Array(
+          "system:java.class.path=" + hiveJars,
+          "system:sun.java.command=shark.SharkServer2")
+      }
+      else {
+        Array(s"$k=${context.getOption(k).getOrElse("<undefined>")}")
+      }
 
     // Query all key-value pairs that are set in the SQLConf of the context.
     case (None, None) =>
-      context.getAll
+      context.getAll.map { case (k, v) =>
+        s"$k=$v"
+      }
 
     case _ =>
       throw new IllegalArgumentException()
   }
 
   def execute(): RDD[Row] = {
-    val rows = sideEffectResult.map { case (k, v) => new GenericRow(Array[Any](k, v)) }
+    val rows = sideEffectResult.map { line => new GenericRow(Array[Any](line)) }
     context.sparkContext.parallelize(rows, 1)
   }
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLConfSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLConfSuite.scala
index 08293f7f0ca3..1a58d73d9e7f 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLConfSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLConfSuite.scala
@@ -54,10 +54,10 @@ class SQLConfSuite extends QueryTest {
     assert(get(testKey, testVal + "_") == testVal)
     assert(TestSQLContext.get(testKey, testVal + "_") == testVal)
 
-    sql("set mapred.reduce.tasks=20")
-    assert(get("mapred.reduce.tasks", "0") == "20")
-    sql("set mapred.reduce.tasks = 40")
-    assert(get("mapred.reduce.tasks", "0") == "40")
+    sql("set some.property=20")
+    assert(get("some.property", "0") == "20")
+    sql("set some.property = 40")
+    assert(get("some.property", "0") == "40")
 
     val key = "spark.sql.key"
     val vs = "val0,val_1,val2.3,my_table"
@@ -70,4 +70,9 @@ class SQLConfSuite extends QueryTest {
     clear()
   }
 
+  test("deprecated property") {
+    clear()
+    sql(s"set ${SQLConf.Deprecated.MAPRED_REDUCE_TASKS}=10")
+    assert(get(SQLConf.SHUFFLE_PARTITIONS) == "10")
+  }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index 6736189c96d4..de9e8aa4f62e 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -424,25 +424,25 @@ class SQLQuerySuite extends QueryTest {
     sql(s"SET $testKey=$testVal")
     checkAnswer(
       sql("SET"),
-      Seq(Seq(testKey, testVal))
+      Seq(Seq(s"$testKey=$testVal"))
     )
 
     sql(s"SET ${testKey + testKey}=${testVal + testVal}")
     checkAnswer(
       sql("set"),
       Seq(
-        Seq(testKey, testVal),
-        Seq(testKey + testKey, testVal + testVal))
+        Seq(s"$testKey=$testVal"),
+        Seq(s"${testKey + testKey}=${testVal + testVal}"))
     )
 
     // "set key"
     checkAnswer(
       sql(s"SET $testKey"),
-      Seq(Seq(testKey, testVal))
+      Seq(Seq(s"$testKey=$testVal"))
     )
     checkAnswer(
       sql(s"SET $nonexistentKey"),
-      Seq(Seq(nonexistentKey, "<undefined>"))
+      Seq(Seq(s"$nonexistentKey=<undefined>"))
     )
     clear()
   }
diff --git a/sql/hive-thriftserver/pom.xml b/sql/hive-thriftserver/pom.xml
new file mode 100644
index 000000000000..7fac90fdc596
--- /dev/null
+++ b/sql/hive-thriftserver/pom.xml
@@ -0,0 +1,82 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<!--
+  ~ Licensed to the Apache Software Foundation (ASF) under one or more
+  ~ contributor license agreements.  See the NOTICE file distributed with
+  ~ this work for additional information regarding copyright ownership.
+  ~ The ASF licenses this file to You under the Apache License, Version 2.0
+  ~ (the "License"); you may not use this file except in compliance with
+  ~ the License.  You may obtain a copy of the License at
+  ~
+  ~    http://www.apache.org/licenses/LICENSE-2.0
+  ~
+  ~ Unless required by applicable law or agreed to in writing, software
+  ~ distributed under the License is distributed on an "AS IS" BASIS,
+  ~ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+  ~ See the License for the specific language governing permissions and
+  ~ limitations under the License.
+  -->
+
+<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
+  <modelVersion>4.0.0</modelVersion>
+  <parent>
+    <groupId>org.apache.spark</groupId>
+    <artifactId>spark-parent</artifactId>
+    <version>1.1.0-SNAPSHOT</version>
+    <relativePath>../../pom.xml</relativePath>
+  </parent>
+
+  <groupId>org.apache.spark</groupId>
+  <artifactId>spark-hive-thriftserver_2.10</artifactId>
+  <packaging>jar</packaging>
+  <name>Spark Project Hive</name>
+  <url>http://spark.apache.org/</url>
+  <properties>
+    <sbt.project.name>hive-thriftserver</sbt.project.name>
+  </properties>
+
+  <dependencies>
+    <dependency>
+      <groupId>org.apache.spark</groupId>
+      <artifactId>spark-hive_${scala.binary.version}</artifactId>
+      <version>${project.version}</version>
+    </dependency>
+    <dependency>
+      <groupId>org.spark-project.hive</groupId>
+      <artifactId>hive-cli</artifactId>
+      <version>${hive.version}</version>
+    </dependency>
+    <dependency>
+      <groupId>org.spark-project.hive</groupId>
+      <artifactId>hive-jdbc</artifactId>
+      <version>${hive.version}</version>
+    </dependency>
+    <dependency>
+      <groupId>org.spark-project.hive</groupId>
+      <artifactId>hive-beeline</artifactId>
+      <version>${hive.version}</version>
+    </dependency>
+    <dependency>
+      <groupId>org.scalatest</groupId>
+      <artifactId>scalatest_${scala.binary.version}</artifactId>
+      <scope>test</scope>
+    </dependency>
+  </dependencies>
+  <build>
+    <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory>
+    <testOutputDirectory>target/scala-${scala.binary.version}/test-classes</testOutputDirectory>
+    <plugins>
+      <plugin>
+        <groupId>org.scalatest</groupId>
+        <artifactId>scalatest-maven-plugin</artifactId>
+      </plugin>
+      <plugin>
+        <groupId>org.apache.maven.plugins</groupId>
+        <artifactId>maven-deploy-plugin</artifactId>
+        <configuration>
+          <skip>true</skip>
+        </configuration>
+      </plugin>
+    </plugins>
+  </build>
+</project>
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala
new file mode 100644
index 000000000000..ddbc2a79fb51
--- /dev/null
+++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala
@@ -0,0 +1,97 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive.thriftserver
+
+import scala.collection.JavaConversions._
+
+import org.apache.commons.logging.LogFactory
+import org.apache.hadoop.hive.conf.HiveConf
+import org.apache.hadoop.hive.ql.session.SessionState
+import org.apache.hive.service.cli.thrift.ThriftBinaryCLIService
+import org.apache.hive.service.server.{HiveServer2, ServerOptionsProcessor}
+
+import org.apache.spark.sql.Logging
+import org.apache.spark.sql.hive.HiveContext
+import org.apache.spark.sql.hive.thriftserver.ReflectionUtils._
+
+/**
+ * The main entry point for the Spark SQL port of HiveServer2.  Starts up a `SparkSQLContext` and a
+ * `HiveThriftServer2` thrift server.
+ */
+private[hive] object HiveThriftServer2 extends Logging {
+  var LOG = LogFactory.getLog(classOf[HiveServer2])
+
+  def main(args: Array[String]) {
+    val optionsProcessor = new ServerOptionsProcessor("HiveThriftServer2")
+
+    if (!optionsProcessor.process(args)) {
+      logger.warn("Error starting HiveThriftServer2 with given arguments")
+      System.exit(-1)
+    }
+
+    val ss = new SessionState(new HiveConf(classOf[SessionState]))
+
+    // Set all properties specified via command line.
+    val hiveConf: HiveConf = ss.getConf
+    hiveConf.getAllProperties.toSeq.sortBy(_._1).foreach { case (k, v) =>
+      logger.debug(s"HiveConf var: $k=$v")
+    }
+
+    SessionState.start(ss)
+
+    logger.info("Starting SparkContext")
+    SparkSQLEnv.init()
+    SessionState.start(ss)
+
+    Runtime.getRuntime.addShutdownHook(
+      new Thread() {
+        override def run() {
+          SparkSQLEnv.sparkContext.stop()
+        }
+      }
+    )
+
+    try {
+      val server = new HiveThriftServer2(SparkSQLEnv.hiveContext)
+      server.init(hiveConf)
+      server.start()
+      logger.info("HiveThriftServer2 started")
+    } catch {
+      case e: Exception =>
+        logger.error("Error starting HiveThriftServer2", e)
+        System.exit(-1)
+    }
+  }
+}
+
+private[hive] class HiveThriftServer2(hiveContext: HiveContext)
+  extends HiveServer2
+  with ReflectedCompositeService {
+
+  override def init(hiveConf: HiveConf) {
+    val sparkSqlCliService = new SparkSQLCLIService(hiveContext)
+    setSuperField(this, "cliService", sparkSqlCliService)
+    addService(sparkSqlCliService)
+
+    val thriftCliService = new ThriftBinaryCLIService(sparkSqlCliService)
+    setSuperField(this, "thriftCLIService", thriftCliService)
+    addService(thriftCliService)
+
+    initCompositeService(hiveConf)
+  }
+}
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ReflectionUtils.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ReflectionUtils.scala
new file mode 100644
index 000000000000..599294dfbb7d
--- /dev/null
+++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ReflectionUtils.scala
@@ -0,0 +1,58 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive.thriftserver
+
+private[hive] object ReflectionUtils {
+  def setSuperField(obj : Object, fieldName: String, fieldValue: Object) {
+    setAncestorField(obj, 1, fieldName, fieldValue)
+  }
+
+  def setAncestorField(obj: AnyRef, level: Int, fieldName: String, fieldValue: AnyRef) {
+    val ancestor = Iterator.iterate[Class[_]](obj.getClass)(_.getSuperclass).drop(level).next()
+    val field = ancestor.getDeclaredField(fieldName)
+    field.setAccessible(true)
+    field.set(obj, fieldValue)
+  }
+
+  def getSuperField[T](obj: AnyRef, fieldName: String): T = {
+    getAncestorField[T](obj, 1, fieldName)
+  }
+
+  def getAncestorField[T](clazz: Object, level: Int, fieldName: String): T = {
+    val ancestor = Iterator.iterate[Class[_]](clazz.getClass)(_.getSuperclass).drop(level).next()
+    val field = ancestor.getDeclaredField(fieldName)
+    field.setAccessible(true)
+    field.get(clazz).asInstanceOf[T]
+  }
+
+  def invokeStatic(clazz: Class[_], methodName: String, args: (Class[_], AnyRef)*): AnyRef = {
+    invoke(clazz, null, methodName, args: _*)
+  }
+
+  def invoke(
+      clazz: Class[_],
+      obj: AnyRef,
+      methodName: String,
+      args: (Class[_], AnyRef)*): AnyRef = {
+
+    val (types, values) = args.unzip
+    val method = clazz.getDeclaredMethod(methodName, types: _*)
+    method.setAccessible(true)
+    method.invoke(obj, values.toSeq: _*)
+  }
+}
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala
new file mode 100755
index 000000000000..27268ecb923e
--- /dev/null
+++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala
@@ -0,0 +1,344 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive.thriftserver
+
+import scala.collection.JavaConversions._
+
+import java.io._
+import java.util.{ArrayList => JArrayList}
+
+import jline.{ConsoleReader, History}
+import org.apache.commons.lang.StringUtils
+import org.apache.commons.logging.LogFactory
+import org.apache.hadoop.conf.Configuration
+import org.apache.hadoop.hive.cli.{CliDriver, CliSessionState, OptionsProcessor}
+import org.apache.hadoop.hive.common.LogUtils.LogInitializationException
+import org.apache.hadoop.hive.common.{HiveInterruptCallback, HiveInterruptUtils, LogUtils}
+import org.apache.hadoop.hive.conf.HiveConf
+import org.apache.hadoop.hive.ql.Driver
+import org.apache.hadoop.hive.ql.exec.Utilities
+import org.apache.hadoop.hive.ql.processors.{CommandProcessor, CommandProcessorFactory}
+import org.apache.hadoop.hive.ql.session.SessionState
+import org.apache.hadoop.hive.shims.ShimLoader
+import org.apache.thrift.transport.TSocket
+
+import org.apache.spark.sql.Logging
+
+private[hive] object SparkSQLCLIDriver {
+  private var prompt = "spark-sql"
+  private var continuedPrompt = "".padTo(prompt.length, ' ')
+  private var transport:TSocket = _
+
+  installSignalHandler()
+
+  /**
+   * Install an interrupt callback to cancel all Spark jobs. In Hive's CliDriver#processLine(),
+   * a signal handler will invoke this registered callback if a Ctrl+C signal is detected while
+   * a command is being processed by the current thread.
+   */
+  def installSignalHandler() {
+    HiveInterruptUtils.add(new HiveInterruptCallback {
+      override def interrupt() {
+        // Handle remote execution mode
+        if (SparkSQLEnv.sparkContext != null) {
+          SparkSQLEnv.sparkContext.cancelAllJobs()
+        } else {
+          if (transport != null) {
+            // Force closing of TCP connection upon session termination
+            transport.getSocket.close()
+          }
+        }
+      }
+    })
+  }
+
+  def main(args: Array[String]) {
+    val oproc = new OptionsProcessor()
+    if (!oproc.process_stage1(args)) {
+      System.exit(1)
+    }
+
+    // NOTE: It is critical to do this here so that log4j is reinitialized
+    // before any of the other core hive classes are loaded
+    var logInitFailed = false
+    var logInitDetailMessage: String = null
+    try {
+      logInitDetailMessage = LogUtils.initHiveLog4j()
+    } catch {
+      case e: LogInitializationException =>
+        logInitFailed = true
+        logInitDetailMessage = e.getMessage
+    }
+
+    val sessionState = new CliSessionState(new HiveConf(classOf[SessionState]))
+
+    sessionState.in = System.in
+    try {
+      sessionState.out = new PrintStream(System.out, true, "UTF-8")
+      sessionState.info = new PrintStream(System.err, true, "UTF-8")
+      sessionState.err = new PrintStream(System.err, true, "UTF-8")
+    } catch {
+      case e: UnsupportedEncodingException => System.exit(3)
+    }
+
+    if (!oproc.process_stage2(sessionState)) {
+      System.exit(2)
+    }
+
+    if (!sessionState.getIsSilent) {
+      if (logInitFailed) System.err.println(logInitDetailMessage)
+      else SessionState.getConsole.printInfo(logInitDetailMessage)
+    }
+
+    // Set all properties specified via command line.
+    val conf: HiveConf = sessionState.getConf
+    sessionState.cmdProperties.entrySet().foreach { item: java.util.Map.Entry[Object, Object] =>
+      conf.set(item.getKey.asInstanceOf[String], item.getValue.asInstanceOf[String])
+      sessionState.getOverriddenConfigurations.put(
+        item.getKey.asInstanceOf[String], item.getValue.asInstanceOf[String])
+    }
+
+    SessionState.start(sessionState)
+
+    // Clean up after we exit
+    Runtime.getRuntime.addShutdownHook(
+      new Thread() {
+        override def run() {
+          SparkSQLEnv.stop()
+        }
+      }
+    )
+
+    // "-h" option has been passed, so connect to Hive thrift server.
+    if (sessionState.getHost != null) {
+      sessionState.connect()
+      if (sessionState.isRemoteMode) {
+        prompt = s"[${sessionState.getHost}:${sessionState.getPort}]" + prompt
+        continuedPrompt = "".padTo(prompt.length, ' ')
+      }
+    }
+
+    if (!sessionState.isRemoteMode && !ShimLoader.getHadoopShims.usesJobShell()) {
+      // Hadoop-20 and above - we need to augment classpath using hiveconf
+      // components.
+      // See also: code in ExecDriver.java
+      var loader = conf.getClassLoader
+      val auxJars = HiveConf.getVar(conf, HiveConf.ConfVars.HIVEAUXJARS)
+      if (StringUtils.isNotBlank(auxJars)) {
+        loader = Utilities.addToClassPath(loader, StringUtils.split(auxJars, ","))
+      }
+      conf.setClassLoader(loader)
+      Thread.currentThread().setContextClassLoader(loader)
+    }
+
+    val cli = new SparkSQLCLIDriver
+    cli.setHiveVariables(oproc.getHiveVariables)
+
+    // TODO work around for set the log output to console, because the HiveContext
+    // will set the output into an invalid buffer.
+    sessionState.in = System.in
+    try {
+      sessionState.out = new PrintStream(System.out, true, "UTF-8")
+      sessionState.info = new PrintStream(System.err, true, "UTF-8")
+      sessionState.err = new PrintStream(System.err, true, "UTF-8")
+    } catch {
+      case e: UnsupportedEncodingException => System.exit(3)
+    }
+
+    // Execute -i init files (always in silent mode)
+    cli.processInitFiles(sessionState)
+
+    if (sessionState.execString != null) {
+      System.exit(cli.processLine(sessionState.execString))
+    }
+
+    try {
+      if (sessionState.fileName != null) {
+        System.exit(cli.processFile(sessionState.fileName))
+      }
+    } catch {
+      case e: FileNotFoundException =>
+        System.err.println(s"Could not open input file for reading. (${e.getMessage})")
+        System.exit(3)
+    }
+
+    val reader = new ConsoleReader()
+    reader.setBellEnabled(false)
+    // reader.setDebug(new PrintWriter(new FileWriter("writer.debug", true)))
+    CliDriver.getCommandCompletor.foreach((e) => reader.addCompletor(e))
+
+    val historyDirectory = System.getProperty("user.home")
+
+    try {
+      if (new File(historyDirectory).exists()) {
+        val historyFile = historyDirectory + File.separator + ".hivehistory"
+        reader.setHistory(new History(new File(historyFile)))
+      } else {
+        System.err.println("WARNING: Directory for Hive history file: " + historyDirectory +
+                           " does not exist.   History will not be available during this session.")
+      }
+    } catch {
+      case e: Exception =>
+        System.err.println("WARNING: Encountered an error while trying to initialize Hive's " +
+                           "history file.  History will not be available during this session.")
+        System.err.println(e.getMessage)
+    }
+
+    val clientTransportTSocketField = classOf[CliSessionState].getDeclaredField("transport")
+    clientTransportTSocketField.setAccessible(true)
+
+    transport = clientTransportTSocketField.get(sessionState).asInstanceOf[TSocket]
+
+    var ret = 0
+    var prefix = ""
+    val currentDB = ReflectionUtils.invokeStatic(classOf[CliDriver], "getFormattedDb",
+      classOf[HiveConf] -> conf, classOf[CliSessionState] -> sessionState)
+
+    def promptWithCurrentDB = s"$prompt$currentDB"
+    def continuedPromptWithDBSpaces = continuedPrompt + ReflectionUtils.invokeStatic(
+      classOf[CliDriver], "spacesForString", classOf[String] -> currentDB)
+
+    var currentPrompt = promptWithCurrentDB
+    var line = reader.readLine(currentPrompt + "> ")
+
+    while (line != null) {
+      if (prefix.nonEmpty) {
+        prefix += '\n'
+      }
+
+      if (line.trim().endsWith(";") && !line.trim().endsWith("\\;")) {
+        line = prefix + line
+        ret = cli.processLine(line, true)
+        prefix = ""
+        currentPrompt = promptWithCurrentDB
+      } else {
+        prefix = prefix + line
+        currentPrompt = continuedPromptWithDBSpaces
+      }
+
+      line = reader.readLine(currentPrompt + "> ")
+    }
+
+    sessionState.close()
+
+    System.exit(ret)
+  }
+}
+
+private[hive] class SparkSQLCLIDriver extends CliDriver with Logging {
+  private val sessionState = SessionState.get().asInstanceOf[CliSessionState]
+
+  private val LOG = LogFactory.getLog("CliDriver")
+
+  private val console = new SessionState.LogHelper(LOG)
+
+  private val conf: Configuration =
+    if (sessionState != null) sessionState.getConf else new Configuration()
+
+  // Force initializing SparkSQLEnv. This is put here but not object SparkSQLCliDriver
+  // because the Hive unit tests do not go through the main() code path.
+  if (!sessionState.isRemoteMode) {
+    SparkSQLEnv.init()
+  }
+
+  override def processCmd(cmd: String): Int = {
+    val cmd_trimmed: String = cmd.trim()
+    val tokens: Array[String] = cmd_trimmed.split("\\s+")
+    val cmd_1: String = cmd_trimmed.substring(tokens(0).length()).trim()
+    if (cmd_trimmed.toLowerCase.equals("quit") ||
+      cmd_trimmed.toLowerCase.equals("exit") ||
+      tokens(0).equalsIgnoreCase("source") ||
+      cmd_trimmed.startsWith("!") ||
+      tokens(0).toLowerCase.equals("list") ||
+      sessionState.isRemoteMode) {
+      val start = System.currentTimeMillis()
+      super.processCmd(cmd)
+      val end = System.currentTimeMillis()
+      val timeTaken: Double = (end - start) / 1000.0
+      console.printInfo(s"Time taken: $timeTaken seconds")
+      0
+    } else {
+      var ret = 0
+      val hconf = conf.asInstanceOf[HiveConf]
+      val proc: CommandProcessor = CommandProcessorFactory.get(tokens(0), hconf)
+
+      if (proc != null) {
+        if (proc.isInstanceOf[Driver]) {
+          val driver = new SparkSQLDriver
+
+          driver.init()
+          val out = sessionState.out
+          val start:Long = System.currentTimeMillis()
+          if (sessionState.getIsVerbose) {
+            out.println(cmd)
+          }
+
+          ret = driver.run(cmd).getResponseCode
+          if (ret != 0) {
+            driver.close()
+            return ret
+          }
+
+          val res = new JArrayList[String]()
+
+          if (HiveConf.getBoolVar(conf, HiveConf.ConfVars.HIVE_CLI_PRINT_HEADER)) {
+            // Print the column names.
+            Option(driver.getSchema.getFieldSchemas).map { fields =>
+              out.println(fields.map(_.getName).mkString("\t"))
+            }
+          }
+
+          try {
+            while (!out.checkError() && driver.getResults(res)) {
+              res.foreach(out.println)
+              res.clear()
+            }
+          } catch {
+            case e:IOException =>
+              console.printError(
+                s"""Failed with exception ${e.getClass.getName}: ${e.getMessage}
+                   |${org.apache.hadoop.util.StringUtils.stringifyException(e)}
+                 """.stripMargin)
+              ret = 1
+          }
+
+          val cret = driver.close()
+          if (ret == 0) {
+            ret = cret
+          }
+
+          val end = System.currentTimeMillis()
+          if (end > start) {
+            val timeTaken:Double = (end - start) / 1000.0
+            console.printInfo(s"Time taken: $timeTaken seconds", null)
+          }
+
+          // Destroy the driver to release all the locks.
+          driver.destroy()
+        } else {
+          if (sessionState.getIsVerbose) {
+            sessionState.out.println(tokens(0) + " " + cmd_1)
+          }
+          ret = proc.run(cmd_1).getResponseCode
+        }
+      }
+      ret
+    }
+  }
+}
+
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIService.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIService.scala
new file mode 100644
index 000000000000..42cbf363b274
--- /dev/null
+++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIService.scala
@@ -0,0 +1,74 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive.thriftserver
+
+import scala.collection.JavaConversions._
+
+import java.io.IOException
+import java.util.{List => JList}
+import javax.security.auth.login.LoginException
+
+import org.apache.commons.logging.Log
+import org.apache.hadoop.hive.conf.HiveConf
+import org.apache.hadoop.hive.shims.ShimLoader
+import org.apache.hive.service.Service.STATE
+import org.apache.hive.service.auth.HiveAuthFactory
+import org.apache.hive.service.cli.CLIService
+import org.apache.hive.service.{AbstractService, Service, ServiceException}
+
+import org.apache.spark.sql.hive.HiveContext
+import org.apache.spark.sql.hive.thriftserver.ReflectionUtils._
+
+private[hive] class SparkSQLCLIService(hiveContext: HiveContext)
+  extends CLIService
+  with ReflectedCompositeService {
+
+  override def init(hiveConf: HiveConf) {
+    setSuperField(this, "hiveConf", hiveConf)
+
+    val sparkSqlSessionManager = new SparkSQLSessionManager(hiveContext)
+    setSuperField(this, "sessionManager", sparkSqlSessionManager)
+    addService(sparkSqlSessionManager)
+
+    try {
+      HiveAuthFactory.loginFromKeytab(hiveConf)
+      val serverUserName = ShimLoader.getHadoopShims
+        .getShortUserName(ShimLoader.getHadoopShims.getUGIForConf(hiveConf))
+      setSuperField(this, "serverUserName", serverUserName)
+    } catch {
+      case e @ (_: IOException | _: LoginException) =>
+        throw new ServiceException("Unable to login to kerberos with given principal/keytab", e)
+    }
+
+    initCompositeService(hiveConf)
+  }
+}
+
+private[thriftserver] trait ReflectedCompositeService { this: AbstractService =>
+  def initCompositeService(hiveConf: HiveConf) {
+    // Emulating `CompositeService.init(hiveConf)`
+    val serviceList = getAncestorField[JList[Service]](this, 2, "serviceList")
+    serviceList.foreach(_.init(hiveConf))
+
+    // Emulating `AbstractService.init(hiveConf)`
+    invoke(classOf[AbstractService], this, "ensureCurrentState", classOf[STATE] -> STATE.NOTINITED)
+    setAncestorField(this, 3, "hiveConf", hiveConf)
+    invoke(classOf[AbstractService], this, "changeState", classOf[STATE] -> STATE.INITED)
+    getAncestorField[Log](this, 3, "LOG").info(s"Service: $getName is inited.")
+  }
+}
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLDriver.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLDriver.scala
new file mode 100644
index 000000000000..5202aa9903e0
--- /dev/null
+++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLDriver.scala
@@ -0,0 +1,93 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive.thriftserver
+
+import scala.collection.JavaConversions._
+
+import java.util.{ArrayList => JArrayList}
+
+import org.apache.commons.lang.exception.ExceptionUtils
+import org.apache.hadoop.hive.metastore.api.{FieldSchema, Schema}
+import org.apache.hadoop.hive.ql.Driver
+import org.apache.hadoop.hive.ql.processors.CommandProcessorResponse
+
+import org.apache.spark.sql.Logging
+import org.apache.spark.sql.hive.{HiveContext, HiveMetastoreTypes}
+
+private[hive] class SparkSQLDriver(val context: HiveContext = SparkSQLEnv.hiveContext)
+  extends Driver with Logging {
+
+  private var tableSchema: Schema = _
+  private var hiveResponse: Seq[String] = _
+
+  override def init(): Unit = {
+  }
+
+  private def getResultSetSchema(query: context.QueryExecution): Schema = {
+    val analyzed = query.analyzed
+    logger.debug(s"Result Schema: ${analyzed.output}")
+    if (analyzed.output.size == 0) {
+      new Schema(new FieldSchema("Response code", "string", "") :: Nil, null)
+    } else {
+      val fieldSchemas = analyzed.output.map { attr =>
+        new FieldSchema(attr.name, HiveMetastoreTypes.toMetastoreType(attr.dataType), "")
+      }
+
+      new Schema(fieldSchemas, null)
+    }
+  }
+
+  override def run(command: String): CommandProcessorResponse = {
+    val execution = context.executePlan(context.hql(command).logicalPlan)
+
+    // TODO unify the error code
+    try {
+      hiveResponse = execution.stringResult()
+      tableSchema = getResultSetSchema(execution)
+      new CommandProcessorResponse(0)
+    } catch {
+      case cause: Throwable =>
+        logger.error(s"Failed in [$command]", cause)
+        new CommandProcessorResponse(-3, ExceptionUtils.getFullStackTrace(cause), null)
+    }
+  }
+
+  override def close(): Int = {
+    hiveResponse = null
+    tableSchema = null
+    0
+  }
+
+  override def getSchema: Schema = tableSchema
+
+  override def getResults(res: JArrayList[String]): Boolean = {
+    if (hiveResponse == null) {
+      false
+    } else {
+      res.addAll(hiveResponse)
+      hiveResponse = null
+      true
+    }
+  }
+
+  override def destroy() {
+    super.destroy()
+    hiveResponse = null
+    tableSchema = null
+  }
+}
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLEnv.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLEnv.scala
new file mode 100644
index 000000000000..451c3bd7b935
--- /dev/null
+++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLEnv.scala
@@ -0,0 +1,58 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive.thriftserver
+
+import org.apache.hadoop.hive.ql.session.SessionState
+
+import org.apache.spark.scheduler.{SplitInfo, StatsReportListener}
+import org.apache.spark.sql.Logging
+import org.apache.spark.sql.hive.HiveContext
+import org.apache.spark.{SparkConf, SparkContext}
+
+/** A singleton object for the master program. The slaves should not access this. */
+private[hive] object SparkSQLEnv extends Logging {
+  logger.debug("Initializing SparkSQLEnv")
+
+  var hiveContext: HiveContext = _
+  var sparkContext: SparkContext = _
+
+  def init() {
+    if (hiveContext == null) {
+      sparkContext = new SparkContext(new SparkConf()
+        .setAppName(s"SparkSQL::${java.net.InetAddress.getLocalHost.getHostName}"))
+
+      sparkContext.addSparkListener(new StatsReportListener())
+
+      hiveContext = new HiveContext(sparkContext) {
+        @transient override lazy val sessionState = SessionState.get()
+        @transient override lazy val hiveconf = sessionState.getConf
+      }
+    }
+  }
+
+  /** Cleans up and shuts down the Spark SQL environments. */
+  def stop() {
+    logger.debug("Shutting down Spark SQL Environment")
+    // Stop the SparkContext
+    if (SparkSQLEnv.sparkContext != null) {
+      sparkContext.stop()
+      sparkContext = null
+      hiveContext = null
+    }
+  }
+}
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLSessionManager.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLSessionManager.scala
new file mode 100644
index 000000000000..6b3275b4eaf0
--- /dev/null
+++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLSessionManager.scala
@@ -0,0 +1,49 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive.thriftserver
+
+import java.util.concurrent.Executors
+
+import org.apache.commons.logging.Log
+import org.apache.hadoop.hive.conf.HiveConf
+import org.apache.hadoop.hive.conf.HiveConf.ConfVars
+import org.apache.hive.service.cli.session.SessionManager
+
+import org.apache.spark.sql.hive.HiveContext
+import org.apache.spark.sql.hive.thriftserver.ReflectionUtils._
+import org.apache.spark.sql.hive.thriftserver.server.SparkSQLOperationManager
+
+private[hive] class SparkSQLSessionManager(hiveContext: HiveContext)
+  extends SessionManager
+  with ReflectedCompositeService {
+
+  override def init(hiveConf: HiveConf) {
+    setSuperField(this, "hiveConf", hiveConf)
+
+    val backgroundPoolSize = hiveConf.getIntVar(ConfVars.HIVE_SERVER2_ASYNC_EXEC_THREADS)
+    setSuperField(this, "backgroundOperationPool", Executors.newFixedThreadPool(backgroundPoolSize))
+    getAncestorField[Log](this, 3, "LOG").info(
+      s"HiveServer2: Async execution pool size $backgroundPoolSize")
+
+    val sparkSqlOperationManager = new SparkSQLOperationManager(hiveContext)
+    setSuperField(this, "operationManager", sparkSqlOperationManager)
+    addService(sparkSqlOperationManager)
+
+    initCompositeService(hiveConf)
+  }
+}
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/server/SparkSQLOperationManager.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/server/SparkSQLOperationManager.scala
new file mode 100644
index 000000000000..a4e1f3e762e8
--- /dev/null
+++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/server/SparkSQLOperationManager.scala
@@ -0,0 +1,151 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive.thriftserver.server
+
+import scala.collection.JavaConversions._
+import scala.collection.mutable.ArrayBuffer
+import scala.math.{random, round}
+
+import java.sql.Timestamp
+import java.util.{Map => JMap}
+
+import org.apache.hadoop.hive.common.`type`.HiveDecimal
+import org.apache.hadoop.hive.metastore.api.FieldSchema
+import org.apache.hive.service.cli._
+import org.apache.hive.service.cli.operation.{ExecuteStatementOperation, Operation, OperationManager}
+import org.apache.hive.service.cli.session.HiveSession
+
+import org.apache.spark.sql.catalyst.types._
+import org.apache.spark.sql.hive.thriftserver.ReflectionUtils
+import org.apache.spark.sql.hive.{HiveContext, HiveMetastoreTypes}
+import org.apache.spark.sql.{Logging, SchemaRDD, Row => SparkRow}
+
+/**
+ * Executes queries using Spark SQL, and maintains a list of handles to active queries.
+ */
+class SparkSQLOperationManager(hiveContext: HiveContext) extends OperationManager with Logging {
+  val handleToOperation = ReflectionUtils
+    .getSuperField[JMap[OperationHandle, Operation]](this, "handleToOperation")
+
+  override def newExecuteStatementOperation(
+      parentSession: HiveSession,
+      statement: String,
+      confOverlay: JMap[String, String],
+      async: Boolean): ExecuteStatementOperation = synchronized {
+
+    val operation = new ExecuteStatementOperation(parentSession, statement, confOverlay) {
+      private var result: SchemaRDD = _
+      private var iter: Iterator[SparkRow] = _
+      private var dataTypes: Array[DataType] = _
+
+      def close(): Unit = {
+        // RDDs will be cleaned automatically upon garbage collection.
+        logger.debug("CLOSING")
+      }
+
+      def getNextRowSet(order: FetchOrientation, maxRowsL: Long): RowSet = {
+        if (!iter.hasNext) {
+          new RowSet()
+        } else {
+          val maxRows = maxRowsL.toInt // Do you really want a row batch larger than Int Max? No.
+          var curRow = 0
+          var rowSet = new ArrayBuffer[Row](maxRows)
+
+          while (curRow < maxRows && iter.hasNext) {
+            val sparkRow = iter.next()
+            val row = new Row()
+            var curCol = 0
+
+            while (curCol < sparkRow.length) {
+              dataTypes(curCol) match {
+                case StringType =>
+                  row.addString(sparkRow(curCol).asInstanceOf[String])
+                case IntegerType =>
+                  row.addColumnValue(ColumnValue.intValue(sparkRow.getInt(curCol)))
+                case BooleanType =>
+                  row.addColumnValue(ColumnValue.booleanValue(sparkRow.getBoolean(curCol)))
+                case DoubleType =>
+                  row.addColumnValue(ColumnValue.doubleValue(sparkRow.getDouble(curCol)))
+                case FloatType =>
+                  row.addColumnValue(ColumnValue.floatValue(sparkRow.getFloat(curCol)))
+                case DecimalType =>
+                  val hiveDecimal = sparkRow.get(curCol).asInstanceOf[BigDecimal].bigDecimal
+                  row.addColumnValue(ColumnValue.stringValue(new HiveDecimal(hiveDecimal)))
+                case LongType =>
+                  row.addColumnValue(ColumnValue.longValue(sparkRow.getLong(curCol)))
+                case ByteType =>
+                  row.addColumnValue(ColumnValue.byteValue(sparkRow.getByte(curCol)))
+                case ShortType =>
+                  row.addColumnValue(ColumnValue.intValue(sparkRow.getShort(curCol)))
+                case TimestampType =>
+                  row.addColumnValue(
+                    ColumnValue.timestampValue(sparkRow.get(curCol).asInstanceOf[Timestamp]))
+                case BinaryType | _: ArrayType | _: StructType | _: MapType =>
+                  val hiveString = result
+                    .queryExecution
+                    .asInstanceOf[HiveContext#QueryExecution]
+                    .toHiveString((sparkRow.get(curCol), dataTypes(curCol)))
+                  row.addColumnValue(ColumnValue.stringValue(hiveString))
+              }
+              curCol += 1
+            }
+            rowSet += row
+            curRow += 1
+          }
+          new RowSet(rowSet, 0)
+        }
+      }
+
+      def getResultSetSchema: TableSchema = {
+        logger.warn(s"Result Schema: ${result.queryExecution.analyzed.output}")
+        if (result.queryExecution.analyzed.output.size == 0) {
+          new TableSchema(new FieldSchema("Result", "string", "") :: Nil)
+        } else {
+          val schema = result.queryExecution.analyzed.output.map { attr =>
+            new FieldSchema(attr.name, HiveMetastoreTypes.toMetastoreType(attr.dataType), "")
+          }
+          new TableSchema(schema)
+        }
+      }
+
+      def run(): Unit = {
+        logger.info(s"Running query '$statement'")
+        setState(OperationState.RUNNING)
+        try {
+          result = hiveContext.hql(statement)
+          logger.debug(result.queryExecution.toString())
+          val groupId = round(random * 1000000).toString
+          hiveContext.sparkContext.setJobGroup(groupId, statement)
+          iter = result.queryExecution.toRdd.toLocalIterator
+          dataTypes = result.queryExecution.analyzed.output.map(_.dataType).toArray
+          setHasResultSet(true)
+        } catch {
+          // Actually do need to catch Throwable as some failures don't inherit from Exception and
+          // HiveServer will silently swallow them.
+          case e: Throwable =>
+            logger.error("Error executing query:",e)
+            throw new HiveSQLException(e.toString)
+        }
+        setState(OperationState.FINISHED)
+      }
+    }
+
+   handleToOperation.put(operation.getHandle, operation)
+   operation
+  }
+}
diff --git a/sql/hive-thriftserver/src/test/resources/data/files/small_kv.txt b/sql/hive-thriftserver/src/test/resources/data/files/small_kv.txt
new file mode 100644
index 000000000000..850f8014b6f0
--- /dev/null
+++ b/sql/hive-thriftserver/src/test/resources/data/files/small_kv.txt
@@ -0,0 +1,5 @@
+238
val_238
+86
val_86
+311
val_311
+27
val_27
+165
val_165
diff --git a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/CliSuite.scala b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/CliSuite.scala
new file mode 100644
index 000000000000..69f19f826a80
--- /dev/null
+++ b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/CliSuite.scala
@@ -0,0 +1,57 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive.thriftserver
+
+import java.io.{BufferedReader, InputStreamReader, PrintWriter}
+
+import org.scalatest.{BeforeAndAfterAll, FunSuite}
+
+class CliSuite extends FunSuite with BeforeAndAfterAll with TestUtils {
+  val WAREHOUSE_PATH = TestUtils.getWarehousePath("cli")
+  val METASTORE_PATH = TestUtils.getMetastorePath("cli")
+
+  override def beforeAll() {
+    val pb = new ProcessBuilder(
+      "../../bin/spark-sql",
+      "--master",
+      "local",
+      "--hiveconf",
+      s"javax.jdo.option.ConnectionURL=jdbc:derby:;databaseName=$METASTORE_PATH;create=true",
+      "--hiveconf",
+      "hive.metastore.warehouse.dir=" + WAREHOUSE_PATH)
+
+    process = pb.start()
+    outputWriter = new PrintWriter(process.getOutputStream, true)
+    inputReader = new BufferedReader(new InputStreamReader(process.getInputStream))
+    errorReader = new BufferedReader(new InputStreamReader(process.getErrorStream))
+    waitForOutput(inputReader, "spark-sql>")
+  }
+
+  override def afterAll() {
+    process.destroy()
+    process.waitFor()
+  }
+
+  test("simple commands") {
+    val dataFilePath = getDataFile("data/files/small_kv.txt")
+    executeQuery("create table hive_test1(key int, val string);")
+    executeQuery("load data local inpath '" + dataFilePath+ "' overwrite into table hive_test1;")
+    executeQuery("cache table hive_test1", "Time taken")
+  }
+}
diff --git a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suite.scala b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suite.scala
new file mode 100644
index 000000000000..fe3403b3292e
--- /dev/null
+++ b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suite.scala
@@ -0,0 +1,135 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive.thriftserver
+
+import scala.collection.JavaConversions._
+import scala.concurrent.ExecutionContext.Implicits.global
+import scala.concurrent._
+
+import java.io.{BufferedReader, InputStreamReader}
+import java.net.ServerSocket
+import java.sql.{Connection, DriverManager, Statement}
+
+import org.scalatest.{BeforeAndAfterAll, FunSuite}
+
+import org.apache.spark.sql.Logging
+import org.apache.spark.sql.catalyst.util.getTempFilePath
+
+/**
+ * Test for the HiveThriftServer2 using JDBC.
+ */
+class HiveThriftServer2Suite extends FunSuite with BeforeAndAfterAll with TestUtils with Logging {
+
+  val WAREHOUSE_PATH = getTempFilePath("warehouse")
+  val METASTORE_PATH = getTempFilePath("metastore")
+
+  val DRIVER_NAME  = "org.apache.hive.jdbc.HiveDriver"
+  val TABLE = "test"
+  val HOST = "localhost"
+  val PORT =  {
+    // Let the system to choose a random available port to avoid collision with other parallel
+    // builds.
+    val socket = new ServerSocket(0)
+    val port = socket.getLocalPort
+    socket.close()
+    port
+  }
+
+  // If verbose is true, the test program will print all outputs coming from the Hive Thrift server.
+  val VERBOSE = Option(System.getenv("SPARK_SQL_TEST_VERBOSE")).getOrElse("false").toBoolean
+
+  Class.forName(DRIVER_NAME)
+
+  override def beforeAll() { launchServer() }
+
+  override def afterAll() { stopServer() }
+
+  private def launchServer(args: Seq[String] = Seq.empty) {
+    // Forking a new process to start the Hive Thrift server. The reason to do this is it is
+    // hard to clean up Hive resources entirely, so we just start a new process and kill
+    // that process for cleanup.
+    val defaultArgs = Seq(
+      "../../sbin/start-thriftserver.sh",
+      "--master local",
+      "--hiveconf",
+      "hive.root.logger=INFO,console",
+      "--hiveconf",
+      s"javax.jdo.option.ConnectionURL=jdbc:derby:;databaseName=$METASTORE_PATH;create=true",
+      "--hiveconf",
+      s"hive.metastore.warehouse.dir=$WAREHOUSE_PATH")
+    val pb = new ProcessBuilder(defaultArgs ++ args)
+    val environment = pb.environment()
+    environment.put("HIVE_SERVER2_THRIFT_PORT", PORT.toString)
+    environment.put("HIVE_SERVER2_THRIFT_BIND_HOST", HOST)
+    process = pb.start()
+    inputReader = new BufferedReader(new InputStreamReader(process.getInputStream))
+    errorReader = new BufferedReader(new InputStreamReader(process.getErrorStream))
+    waitForOutput(inputReader, "ThriftBinaryCLIService listening on")
+
+    // Spawn a thread to read the output from the forked process.
+    // Note that this is necessary since in some configurations, log4j could be blocked
+    // if its output to stderr are not read, and eventually blocking the entire test suite.
+    future {
+      while (true) {
+        val stdout = readFrom(inputReader)
+        val stderr = readFrom(errorReader)
+        if (VERBOSE && stdout.length > 0) {
+          println(stdout)
+        }
+        if (VERBOSE && stderr.length > 0) {
+          println(stderr)
+        }
+        Thread.sleep(50)
+      }
+    }
+  }
+
+  private def stopServer() {
+    process.destroy()
+    process.waitFor()
+  }
+
+  test("test query execution against a Hive Thrift server") {
+    Thread.sleep(5 * 1000)
+    val dataFilePath = getDataFile("data/files/small_kv.txt")
+    val stmt = createStatement()
+    stmt.execute("DROP TABLE IF EXISTS test")
+    stmt.execute("DROP TABLE IF EXISTS test_cached")
+    stmt.execute("CREATE TABLE test(key int, val string)")
+    stmt.execute(s"LOAD DATA LOCAL INPATH '$dataFilePath' OVERWRITE INTO TABLE test")
+    stmt.execute("CREATE TABLE test_cached as select * from test limit 4")
+    stmt.execute("CACHE TABLE test_cached")
+
+    var rs = stmt.executeQuery("select count(*) from test")
+    rs.next()
+    assert(rs.getInt(1) === 5)
+
+    rs = stmt.executeQuery("select count(*) from test_cached")
+    rs.next()
+    assert(rs.getInt(1) === 4)
+
+    stmt.close()
+  }
+
+  def getConnection: Connection = {
+    val connectURI = s"jdbc:hive2://localhost:$PORT/"
+    DriverManager.getConnection(connectURI, System.getProperty("user.name"), "")
+  }
+
+  def createStatement(): Statement = getConnection.createStatement()
+}
diff --git a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/TestUtils.scala b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/TestUtils.scala
new file mode 100644
index 000000000000..bb2242618fbe
--- /dev/null
+++ b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/TestUtils.scala
@@ -0,0 +1,108 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive.thriftserver
+
+import java.io.{BufferedReader, PrintWriter}
+import java.text.SimpleDateFormat
+import java.util.Date
+
+import org.apache.hadoop.hive.common.LogUtils
+import org.apache.hadoop.hive.common.LogUtils.LogInitializationException
+
+object TestUtils {
+  val timestamp = new SimpleDateFormat("yyyyMMdd-HHmmss")
+
+  def getWarehousePath(prefix: String): String = {
+    System.getProperty("user.dir") + "/test_warehouses/" + prefix + "-warehouse-" +
+      timestamp.format(new Date)
+  }
+
+  def getMetastorePath(prefix: String): String = {
+    System.getProperty("user.dir") + "/test_warehouses/" + prefix + "-metastore-" +
+      timestamp.format(new Date)
+  }
+
+  // Dummy function for initialize the log4j properties.
+  def init() { }
+
+  // initialize log4j
+  try {
+    LogUtils.initHiveLog4j()
+  } catch {
+    case e: LogInitializationException => // Ignore the error.
+  }
+}
+
+trait TestUtils {
+  var process : Process = null
+  var outputWriter : PrintWriter = null
+  var inputReader : BufferedReader = null
+  var errorReader : BufferedReader = null
+
+  def executeQuery(
+    cmd: String, outputMessage: String = "OK", timeout: Long = 15000): String = {
+    println("Executing: " + cmd + ", expecting output: " + outputMessage)
+    outputWriter.write(cmd + "\n")
+    outputWriter.flush()
+    waitForQuery(timeout, outputMessage)
+  }
+
+  protected def waitForQuery(timeout: Long, message: String): String = {
+    if (waitForOutput(errorReader, message, timeout)) {
+      Thread.sleep(500)
+      readOutput()
+    } else {
+      assert(false, "Didn't find \"" + message + "\" in the output:\n" + readOutput())
+      null
+    }
+  }
+
+  // Wait for the specified str to appear in the output.
+  protected def waitForOutput(
+    reader: BufferedReader, str: String, timeout: Long = 10000): Boolean = {
+    val startTime = System.currentTimeMillis
+    var out = ""
+    while (!out.contains(str) && System.currentTimeMillis < (startTime + timeout)) {
+      out += readFrom(reader)
+    }
+    out.contains(str)
+  }
+
+  // Read stdout output and filter out garbage collection messages.
+  protected def readOutput(): String = {
+    val output = readFrom(inputReader)
+    // Remove GC Messages
+    val filteredOutput = output.lines.filterNot(x => x.contains("[GC") || x.contains("[Full GC"))
+      .mkString("\n")
+    filteredOutput
+  }
+
+  protected def readFrom(reader: BufferedReader): String = {
+    var out = ""
+    var c = 0
+    while (reader.ready) {
+      c = reader.read()
+      out += c.asInstanceOf[Char]
+    }
+    out
+  }
+
+  protected def getDataFile(name: String) = {
+    Thread.currentThread().getContextClassLoader.getResource(name)
+  }
+}
diff --git a/sql/hive/pom.xml b/sql/hive/pom.xml
index 1699ffe06ce1..93d00f7c37c9 100644
--- a/sql/hive/pom.xml
+++ b/sql/hive/pom.xml
@@ -32,7 +32,7 @@
   <name>Spark Project Hive</name>
   <url>http://spark.apache.org/</url>
   <properties>
-     <sbt.project.name>hive</sbt.project.name>
+    <sbt.project.name>hive</sbt.project.name>
   </properties>
 
   <dependencies>
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
index 201c85f3d501..84d43eaeea51 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
@@ -255,7 +255,7 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) {
       Seq(StringType, IntegerType, LongType, DoubleType, FloatType, BooleanType, ByteType,
         ShortType, DecimalType, TimestampType, BinaryType)
 
-    protected def toHiveString(a: (Any, DataType)): String = a match {
+    protected[sql] def toHiveString(a: (Any, DataType)): String = a match {
       case (struct: Row, StructType(fields)) =>
         struct.zip(fields).map {
           case (v, t) => s""""${t.name}":${toHiveStructString(v, t.dataType)}"""
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
index a8623b64c656..a022a1e2dc70 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
@@ -419,10 +419,10 @@ class HiveQuerySuite extends HiveComparisonTest {
     hql(s"set $testKey=$testVal")
     assert(get(testKey, testVal + "_") == testVal)
 
-    hql("set mapred.reduce.tasks=20")
-    assert(get("mapred.reduce.tasks", "0") == "20")
-    hql("set mapred.reduce.tasks = 40")
-    assert(get("mapred.reduce.tasks", "0") == "40")
+    hql("set some.property=20")
+    assert(get("some.property", "0") == "20")
+    hql("set some.property = 40")
+    assert(get("some.property", "0") == "40")
 
     hql(s"set $testKey=$testVal")
     assert(get(testKey, "0") == testVal)
@@ -436,63 +436,61 @@ class HiveQuerySuite extends HiveComparisonTest {
     val testKey = "spark.sql.key.usedfortestonly"
     val testVal = "test.val.0"
     val nonexistentKey = "nonexistent"
-    def collectResults(rdd: SchemaRDD): Set[(String, String)] =
-      rdd.collect().map { case Row(key: String, value: String) => key -> value }.toSet
 
     clear()
 
     // "set" itself returns all config variables currently specified in SQLConf.
     assert(hql("SET").collect().size == 0)
 
-    assertResult(Set(testKey -> testVal)) {
-      collectResults(hql(s"SET $testKey=$testVal"))
+    assertResult(Array(s"$testKey=$testVal")) {
+      hql(s"SET $testKey=$testVal").collect().map(_.getString(0))
     }
 
     assert(hiveconf.get(testKey, "") == testVal)
-    assertResult(Set(testKey -> testVal)) {
-      collectResults(hql("SET"))
+    assertResult(Array(s"$testKey=$testVal")) {
+      hql(s"SET $testKey=$testVal").collect().map(_.getString(0))
     }
 
     hql(s"SET ${testKey + testKey}=${testVal + testVal}")
     assert(hiveconf.get(testKey + testKey, "") == testVal + testVal)
-    assertResult(Set(testKey -> testVal, (testKey + testKey) -> (testVal + testVal))) {
-      collectResults(hql("SET"))
+    assertResult(Array(s"$testKey=$testVal", s"${testKey + testKey}=${testVal + testVal}")) {
+      hql(s"SET").collect().map(_.getString(0))
     }
 
     // "set key"
-    assertResult(Set(testKey -> testVal)) {
-      collectResults(hql(s"SET $testKey"))
+    assertResult(Array(s"$testKey=$testVal")) {
+      hql(s"SET $testKey").collect().map(_.getString(0))
     }
 
-    assertResult(Set(nonexistentKey -> "<undefined>")) {
-      collectResults(hql(s"SET $nonexistentKey"))
+    assertResult(Array(s"$nonexistentKey=<undefined>")) {
+      hql(s"SET $nonexistentKey").collect().map(_.getString(0))
     }
 
     // Assert that sql() should have the same effects as hql() by repeating the above using sql().
     clear()
     assert(sql("SET").collect().size == 0)
 
-    assertResult(Set(testKey -> testVal)) {
-      collectResults(sql(s"SET $testKey=$testVal"))
+    assertResult(Array(s"$testKey=$testVal")) {
+      sql(s"SET $testKey=$testVal").collect().map(_.getString(0))
     }
 
     assert(hiveconf.get(testKey, "") == testVal)
-    assertResult(Set(testKey -> testVal)) {
-      collectResults(sql("SET"))
+    assertResult(Array(s"$testKey=$testVal")) {
+      sql("SET").collect().map(_.getString(0))
     }
 
     sql(s"SET ${testKey + testKey}=${testVal + testVal}")
     assert(hiveconf.get(testKey + testKey, "") == testVal + testVal)
-    assertResult(Set(testKey -> testVal, (testKey + testKey) -> (testVal + testVal))) {
-      collectResults(sql("SET"))
+    assertResult(Array(s"$testKey=$testVal", s"${testKey + testKey}=${testVal + testVal}")) {
+      sql("SET").collect().map(_.getString(0))
     }
 
-    assertResult(Set(testKey -> testVal)) {
-      collectResults(sql(s"SET $testKey"))
+    assertResult(Array(s"$testKey=$testVal")) {
+      sql(s"SET $testKey").collect().map(_.getString(0))
     }
 
-    assertResult(Set(nonexistentKey -> "<undefined>")) {
-      collectResults(sql(s"SET $nonexistentKey"))
+    assertResult(Array(s"$nonexistentKey=<undefined>")) {
+      sql(s"SET $nonexistentKey").collect().map(_.getString(0))
     }
 
     clear()
diff --git a/streaming/pom.xml b/streaming/pom.xml
index f60697ce745b..b99f306b8f2c 100644
--- a/streaming/pom.xml
+++ b/streaming/pom.xml
@@ -28,7 +28,7 @@
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-streaming_2.10</artifactId>
   <properties>
-     <sbt.project.name>streaming</sbt.project.name>
+    <sbt.project.name>streaming</sbt.project.name>
   </properties>
   <packaging>jar</packaging>
   <name>Spark Project Streaming</name>
diff --git a/tools/pom.xml b/tools/pom.xml
index c0ee8faa7a61..97abb6b2b63e 100644
--- a/tools/pom.xml
+++ b/tools/pom.xml
@@ -27,7 +27,7 @@
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-tools_2.10</artifactId>
   <properties>
-     <sbt.project.name>tools</sbt.project.name>
+    <sbt.project.name>tools</sbt.project.name>
   </properties>
   <packaging>jar</packaging>
   <name>Spark Project Tools</name>
diff --git a/yarn/alpha/pom.xml b/yarn/alpha/pom.xml
index 5b13a1f002d6..51744ece0412 100644
--- a/yarn/alpha/pom.xml
+++ b/yarn/alpha/pom.xml
@@ -24,7 +24,7 @@
     <relativePath>../pom.xml</relativePath>
   </parent>
   <properties>
-     <sbt.project.name>yarn-alpha</sbt.project.name>
+    <sbt.project.name>yarn-alpha</sbt.project.name>
   </properties>
 
   <groupId>org.apache.spark</groupId>
diff --git a/yarn/pom.xml b/yarn/pom.xml
index efb473aa1b26..3faaf053634d 100644
--- a/yarn/pom.xml
+++ b/yarn/pom.xml
@@ -29,7 +29,7 @@
   <packaging>pom</packaging>
   <name>Spark Project YARN Parent POM</name>
   <properties>
-     <sbt.project.name>yarn</sbt.project.name>
+    <sbt.project.name>yarn</sbt.project.name>
   </properties>
 
   <dependencies>
diff --git a/yarn/stable/pom.xml b/yarn/stable/pom.xml
index ceaf9f9d7100..b6c8456d0668 100644
--- a/yarn/stable/pom.xml
+++ b/yarn/stable/pom.xml
@@ -24,7 +24,7 @@
     <relativePath>../pom.xml</relativePath>
   </parent>
   <properties>
-     <sbt.project.name>yarn-stable</sbt.project.name>
+    <sbt.project.name>yarn-stable</sbt.project.name>
   </properties>
 
   <groupId>org.apache.spark</groupId>

From ecf30ee7e78ea59c462c54db0fde5328f997466c Mon Sep 17 00:00:00 2001
From: Andrew Or <andrewor14@gmail.com>
Date: Sun, 27 Jul 2014 16:08:16 -0700
Subject: [PATCH 780/810] [SPARK-1777] Prevent OOMs from single partitions

**Problem.** When caching, we currently unroll the entire RDD partition before making sure we have enough free memory. This is a common cause for OOMs especially when (1) the BlockManager has little free space left in memory, and (2) the partition is large.

**Solution.** We maintain a global memory pool of `M` bytes shared across all threads, similar to the way we currently manage memory for shuffle aggregation. Then, while we unroll each partition, periodically check if there is enough space to continue. If not, drop enough RDD blocks to ensure we have at least `M` bytes to work with, then try again. If we still don't have enough space to unroll the partition, give up and drop the block to disk directly if applicable.

**New configurations.**
- `spark.storage.bufferFraction` - the value of `M` as a fraction of the storage memory. (default: 0.2)
- `spark.storage.safetyFraction` - a margin of safety in case size estimation is slightly off. This is the equivalent of the existing `spark.shuffle.safetyFraction`. (default 0.9)

For more detail, see the [design document](https://issues.apache.org/jira/secure/attachment/12651793/spark-1777-design-doc.pdf). Tests pending for performance and memory usage patterns.

Author: Andrew Or <andrewor14@gmail.com>

Closes #1165 from andrewor14/them-rdd-memories and squashes the following commits:

e77f451 [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories
c7c8832 [Andrew Or] Simplify logic + update a few comments
269d07b [Andrew Or] Very minor changes to tests
6645a8a [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories
b7e165c [Andrew Or] Add new tests for unrolling blocks
f12916d [Andrew Or] Slightly clean up tests
71672a7 [Andrew Or] Update unrollSafely tests
369ad07 [Andrew Or] Correct ensureFreeSpace and requestMemory behavior
f4d035c [Andrew Or] Allow one thread to unroll multiple blocks
a66fbd2 [Andrew Or] Rename a few things + update comments
68730b3 [Andrew Or] Fix weird scalatest behavior
e40c60d [Andrew Or] Fix MIMA excludes
ff77aa1 [Andrew Or] Fix tests
1a43c06 [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories
b9a6eee [Andrew Or] Simplify locking behavior on unrollMemoryMap
ed6cda4 [Andrew Or] Formatting fix (super minor)
f9ff82e [Andrew Or] putValues -> putIterator + putArray
beb368f [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories
8448c9b [Andrew Or] Fix tests
a49ba4d [Andrew Or] Do not expose unroll memory check period
69bc0a5 [Andrew Or] Always synchronize on putLock before unrollMemoryMap
3f5a083 [Andrew Or] Simplify signature of ensureFreeSpace
dce55c8 [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories
8288228 [Andrew Or] Synchronize put and unroll properly
4f18a3d [Andrew Or] bufferFraction -> unrollFraction
28edfa3 [Andrew Or] Update a few comments / log messages
728323b [Andrew Or] Do not synchronize every 1000 elements
5ab2329 [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories
129c441 [Andrew Or] Fix bug: Use toArray rather than array
9a65245 [Andrew Or] Update a few comments + minor control flow changes
57f8d85 [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories
abeae4f [Andrew Or] Add comment clarifying the MEMORY_AND_DISK case
3dd96aa [Andrew Or] AppendOnlyBuffer -> Vector (+ a few small changes)
f920531 [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories
0871835 [Andrew Or] Add an effective storage level interface to BlockManager
64e7d4c [Andrew Or] Add/modify a few comments (minor)
8af2f35 [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories
4f4834e [Andrew Or] Use original storage level for blocks dropped to disk
ecc8c2d [Andrew Or] Fix binary incompatibility
24185ea [Andrew Or] Avoid dropping a block back to disk if reading from disk
2b7ee66 [Andrew Or] Fix bug in SizeTracking*
9b9a273 [Andrew Or] Fix tests
20eb3e5 [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories
649bdb3 [Andrew Or] Document spark.storage.bufferFraction
a10b0e7 [Andrew Or] Add initial memory request threshold + rename a few things
e9c3cb0 [Andrew Or] cacheMemoryMap -> unrollMemoryMap
198e374 [Andrew Or] Unfold -> unroll
0d50155 [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories
d9d02a8 [Andrew Or] Remove unused param in unfoldSafely
ec728d8 [Andrew Or] Add tests for safe unfolding of blocks
22b2209 [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories
078eb83 [Andrew Or] Add check for hasNext in PrimitiveVector.iterator
0871535 [Andrew Or] Fix tests in BlockManagerSuite
d68f31e [Andrew Or] Safely unfold blocks for all memory puts
5961f50 [Andrew Or] Fix tests
195abd7 [Andrew Or] Refactor: move unfold logic to MemoryStore
1e82d00 [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories
3ce413e [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories
d5dd3b4 [Andrew Or] Free buffer memory in finally
ea02eec [Andrew Or] Fix tests
b8e1d9c [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories
a8704c1 [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories
e1b8b25 [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories
87aa75c [Andrew Or] Fix mima excludes again (typo)
11eb921 [Andrew Or] Clarify comment (minor)
50cae44 [Andrew Or] Remove now duplicate mima exclude
7de5ef9 [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories
df47265 [Andrew Or] Fix binary incompatibility
6d05a81 [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories
f94f5af [Andrew Or] Update a few comments (minor)
776aec9 [Andrew Or] Prevent OOM if a single RDD partition is too large
bbd3eea [Andrew Or] Fix CacheManagerSuite to use Array
97ea499 [Andrew Or] Change BlockManager interface to use Arrays
c12f093 [Andrew Or] Add SizeTrackingAppendOnlyBuffer and tests
---
 .../scala/org/apache/spark/CacheManager.scala |  72 ++-
 .../scala/org/apache/spark/SparkEnv.scala     |   2 +-
 .../org/apache/spark/executor/Executor.scala  |   4 +-
 .../apache/spark/storage/BlockManager.scala   | 110 ++--
 .../org/apache/spark/storage/BlockStore.scala |   6 +-
 .../org/apache/spark/storage/DiskStore.scala  |  12 +-
 .../apache/spark/storage/MemoryStore.scala    | 256 +++++++-
 .../apache/spark/storage/TachyonStore.scala   |  12 +-
 .../apache/spark/storage/ThreadingTest.scala  |   2 +-
 .../org/apache/spark/util/SizeEstimator.scala |   2 +-
 .../util/collection/PrimitiveVector.scala     |  15 +-
 .../spark/util/collection/SizeTracker.scala   | 105 ++++
 .../SizeTrackingAppendOnlyMap.scala           |  71 +--
 .../util/collection/SizeTrackingVector.scala  |  46 ++
 .../org/apache/spark/CacheManagerSuite.scala  |  25 +-
 .../spark/storage/BlockManagerSuite.scala     | 594 ++++++++++++------
 .../util/SizeTrackingAppendOnlyMapSuite.scala | 120 ----
 .../util/collection/SizeTrackerSuite.scala    | 204 ++++++
 docs/configuration.md                         |   9 +
 project/MimaExcludes.scala                    |  10 +-
 .../receiver/ReceiverSupervisorImpl.scala     |   5 +-
 21 files changed, 1165 insertions(+), 517 deletions(-)
 create mode 100644 core/src/main/scala/org/apache/spark/util/collection/SizeTracker.scala
 create mode 100644 core/src/main/scala/org/apache/spark/util/collection/SizeTrackingVector.scala
 delete mode 100644 core/src/test/scala/org/apache/spark/util/SizeTrackingAppendOnlyMapSuite.scala
 create mode 100644 core/src/test/scala/org/apache/spark/util/collection/SizeTrackerSuite.scala

diff --git a/core/src/main/scala/org/apache/spark/CacheManager.scala b/core/src/main/scala/org/apache/spark/CacheManager.scala
index 8f867686a044..5ddda4d6953f 100644
--- a/core/src/main/scala/org/apache/spark/CacheManager.scala
+++ b/core/src/main/scala/org/apache/spark/CacheManager.scala
@@ -17,9 +17,9 @@
 
 package org.apache.spark
 
-import scala.collection.mutable.{ArrayBuffer, HashSet}
+import scala.collection.mutable
+import scala.collection.mutable.ArrayBuffer
 
-import org.apache.spark.executor.InputMetrics
 import org.apache.spark.rdd.RDD
 import org.apache.spark.storage._
 
@@ -30,7 +30,7 @@ import org.apache.spark.storage._
 private[spark] class CacheManager(blockManager: BlockManager) extends Logging {
 
   /** Keys of RDD partitions that are being computed/loaded. */
-  private val loading = new HashSet[RDDBlockId]()
+  private val loading = new mutable.HashSet[RDDBlockId]
 
   /** Gets or computes an RDD partition. Used by RDD.iterator() when an RDD is cached. */
   def getOrCompute[T](
@@ -118,21 +118,29 @@ private[spark] class CacheManager(blockManager: BlockManager) extends Logging {
   }
 
   /**
-   * Cache the values of a partition, keeping track of any updates in the storage statuses
-   * of other blocks along the way.
+   * Cache the values of a partition, keeping track of any updates in the storage statuses of
+   * other blocks along the way.
+   *
+   * The effective storage level refers to the level that actually specifies BlockManager put
+   * behavior, not the level originally specified by the user. This is mainly for forcing a
+   * MEMORY_AND_DISK partition to disk if there is not enough room to unroll the partition,
+   * while preserving the the original semantics of the RDD as specified by the application.
    */
   private def putInBlockManager[T](
       key: BlockId,
       values: Iterator[T],
-      storageLevel: StorageLevel,
-      updatedBlocks: ArrayBuffer[(BlockId, BlockStatus)]): Iterator[T] = {
-
-    if (!storageLevel.useMemory) {
-      /* This RDD is not to be cached in memory, so we can just pass the computed values
-       * as an iterator directly to the BlockManager, rather than first fully unrolling
-       * it in memory. The latter option potentially uses much more memory and risks OOM
-       * exceptions that can be avoided. */
-      updatedBlocks ++= blockManager.put(key, values, storageLevel, tellMaster = true)
+      level: StorageLevel,
+      updatedBlocks: ArrayBuffer[(BlockId, BlockStatus)],
+      effectiveStorageLevel: Option[StorageLevel] = None): Iterator[T] = {
+
+    val putLevel = effectiveStorageLevel.getOrElse(level)
+    if (!putLevel.useMemory) {
+      /*
+       * This RDD is not to be cached in memory, so we can just pass the computed values as an
+       * iterator directly to the BlockManager rather than first fully unrolling it in memory.
+       */
+      updatedBlocks ++=
+        blockManager.putIterator(key, values, level, tellMaster = true, effectiveStorageLevel)
       blockManager.get(key) match {
         case Some(v) => v.data.asInstanceOf[Iterator[T]]
         case None =>
@@ -140,14 +148,36 @@ private[spark] class CacheManager(blockManager: BlockManager) extends Logging {
           throw new BlockException(key, s"Block manager failed to return cached value for $key!")
       }
     } else {
-      /* This RDD is to be cached in memory. In this case we cannot pass the computed values
+      /*
+       * This RDD is to be cached in memory. In this case we cannot pass the computed values
        * to the BlockManager as an iterator and expect to read it back later. This is because
-       * we may end up dropping a partition from memory store before getting it back, e.g.
-       * when the entirety of the RDD does not fit in memory. */
-      val elements = new ArrayBuffer[Any]
-      elements ++= values
-      updatedBlocks ++= blockManager.put(key, elements, storageLevel, tellMaster = true)
-      elements.iterator.asInstanceOf[Iterator[T]]
+       * we may end up dropping a partition from memory store before getting it back.
+       *
+       * In addition, we must be careful to not unroll the entire partition in memory at once.
+       * Otherwise, we may cause an OOM exception if the JVM does not have enough space for this
+       * single partition. Instead, we unroll the values cautiously, potentially aborting and
+       * dropping the partition to disk if applicable.
+       */
+      blockManager.memoryStore.unrollSafely(key, values, updatedBlocks) match {
+        case Left(arr) =>
+          // We have successfully unrolled the entire partition, so cache it in memory
+          updatedBlocks ++=
+            blockManager.putArray(key, arr, level, tellMaster = true, effectiveStorageLevel)
+          arr.iterator.asInstanceOf[Iterator[T]]
+        case Right(it) =>
+          // There is not enough space to cache this partition in memory
+          logWarning(s"Not enough space to cache partition $key in memory! " +
+            s"Free memory is ${blockManager.memoryStore.freeMemory} bytes.")
+          val returnValues = it.asInstanceOf[Iterator[T]]
+          if (putLevel.useDisk) {
+            logWarning(s"Persisting partition $key to disk instead.")
+            val diskOnlyLevel = StorageLevel(useDisk = true, useMemory = false,
+              useOffHeap = false, deserialized = false, putLevel.replication)
+            putInBlockManager[T](key, returnValues, level, updatedBlocks, Some(diskOnlyLevel))
+          } else {
+            returnValues
+          }
+      }
     }
   }
 
diff --git a/core/src/main/scala/org/apache/spark/SparkEnv.scala b/core/src/main/scala/org/apache/spark/SparkEnv.scala
index 8f70744d804d..6ee731b22c03 100644
--- a/core/src/main/scala/org/apache/spark/SparkEnv.scala
+++ b/core/src/main/scala/org/apache/spark/SparkEnv.scala
@@ -67,7 +67,7 @@ class SparkEnv (
     val metricsSystem: MetricsSystem,
     val conf: SparkConf) extends Logging {
 
-  // A mapping of thread ID to amount of memory used for shuffle in bytes
+  // A mapping of thread ID to amount of memory, in bytes, used for shuffle aggregations
   // All accesses should be manually synchronized
   val shuffleMemoryMap = mutable.HashMap[Long, Long]()
 
diff --git a/core/src/main/scala/org/apache/spark/executor/Executor.scala b/core/src/main/scala/org/apache/spark/executor/Executor.scala
index b16133b20cc0..3b69bc4ca414 100644
--- a/core/src/main/scala/org/apache/spark/executor/Executor.scala
+++ b/core/src/main/scala/org/apache/spark/executor/Executor.scala
@@ -266,11 +266,13 @@ private[spark] class Executor(
           }
         }
       } finally {
-        // TODO: Unregister shuffle memory only for ResultTask
+        // Release memory used by this thread for shuffles
         val shuffleMemoryMap = env.shuffleMemoryMap
         shuffleMemoryMap.synchronized {
           shuffleMemoryMap.remove(Thread.currentThread().getId)
         }
+        // Release memory used by this thread for unrolling blocks
+        env.blockManager.memoryStore.releaseUnrollMemoryForThisThread()
         runningTasks.remove(taskId)
       }
     }
diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala
index 0db0a5bc7341..d746526639e5 100644
--- a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala
+++ b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala
@@ -38,7 +38,7 @@ import org.apache.spark.util._
 private[spark] sealed trait BlockValues
 private[spark] case class ByteBufferValues(buffer: ByteBuffer) extends BlockValues
 private[spark] case class IteratorValues(iterator: Iterator[Any]) extends BlockValues
-private[spark] case class ArrayBufferValues(buffer: ArrayBuffer[Any]) extends BlockValues
+private[spark] case class ArrayValues(buffer: Array[Any]) extends BlockValues
 
 /* Class for returning a fetched block and associated metrics. */
 private[spark] class BlockResult(
@@ -71,9 +71,9 @@ private[spark] class BlockManager(
 
   // Actual storage of where blocks are kept
   private var tachyonInitialized = false
-  private[storage] val memoryStore = new MemoryStore(this, maxMemory)
-  private[storage] val diskStore = new DiskStore(this, diskBlockManager)
-  private[storage] lazy val tachyonStore: TachyonStore = {
+  private[spark] val memoryStore = new MemoryStore(this, maxMemory)
+  private[spark] val diskStore = new DiskStore(this, diskBlockManager)
+  private[spark] lazy val tachyonStore: TachyonStore = {
     val storeDir = conf.get("spark.tachyonStore.baseDir", "/tmp_spark_tachyon")
     val appFolderName = conf.get("spark.tachyonStore.folderName")
     val tachyonStorePath = s"$storeDir/$appFolderName/${this.executorId}"
@@ -463,16 +463,17 @@ private[spark] class BlockManager(
               val values = dataDeserialize(blockId, bytes)
               if (level.deserialized) {
                 // Cache the values before returning them
-                // TODO: Consider creating a putValues that also takes in a iterator?
-                val valuesBuffer = new ArrayBuffer[Any]
-                valuesBuffer ++= values
-                memoryStore.putValues(blockId, valuesBuffer, level, returnValues = true).data
-                  match {
-                    case Left(values2) =>
-                      return Some(new BlockResult(values2, DataReadMethod.Disk, info.size))
-                    case _ =>
-                      throw new SparkException("Memory store did not return back an iterator")
-                  }
+                val putResult = memoryStore.putIterator(
+                  blockId, values, level, returnValues = true, allowPersistToDisk = false)
+                // The put may or may not have succeeded, depending on whether there was enough
+                // space to unroll the block. Either way, the put here should return an iterator.
+                putResult.data match {
+                  case Left(it) =>
+                    return Some(new BlockResult(it, DataReadMethod.Disk, info.size))
+                  case _ =>
+                    // This only happens if we dropped the values back to disk (which is never)
+                    throw new SparkException("Memory store did not return an iterator!")
+                }
               } else {
                 return Some(new BlockResult(values, DataReadMethod.Disk, info.size))
               }
@@ -561,13 +562,14 @@ private[spark] class BlockManager(
     iter
   }
 
-  def put(
+  def putIterator(
       blockId: BlockId,
       values: Iterator[Any],
       level: StorageLevel,
-      tellMaster: Boolean): Seq[(BlockId, BlockStatus)] = {
+      tellMaster: Boolean = true,
+      effectiveStorageLevel: Option[StorageLevel] = None): Seq[(BlockId, BlockStatus)] = {
     require(values != null, "Values is null")
-    doPut(blockId, IteratorValues(values), level, tellMaster)
+    doPut(blockId, IteratorValues(values), level, tellMaster, effectiveStorageLevel)
   }
 
   /**
@@ -589,13 +591,14 @@ private[spark] class BlockManager(
    * Put a new block of values to the block manager.
    * Return a list of blocks updated as a result of this put.
    */
-  def put(
+  def putArray(
       blockId: BlockId,
-      values: ArrayBuffer[Any],
+      values: Array[Any],
       level: StorageLevel,
-      tellMaster: Boolean = true): Seq[(BlockId, BlockStatus)] = {
+      tellMaster: Boolean = true,
+      effectiveStorageLevel: Option[StorageLevel] = None): Seq[(BlockId, BlockStatus)] = {
     require(values != null, "Values is null")
-    doPut(blockId, ArrayBufferValues(values), level, tellMaster)
+    doPut(blockId, ArrayValues(values), level, tellMaster, effectiveStorageLevel)
   }
 
   /**
@@ -606,19 +609,33 @@ private[spark] class BlockManager(
       blockId: BlockId,
       bytes: ByteBuffer,
       level: StorageLevel,
-      tellMaster: Boolean = true): Seq[(BlockId, BlockStatus)] = {
+      tellMaster: Boolean = true,
+      effectiveStorageLevel: Option[StorageLevel] = None): Seq[(BlockId, BlockStatus)] = {
     require(bytes != null, "Bytes is null")
-    doPut(blockId, ByteBufferValues(bytes), level, tellMaster)
+    doPut(blockId, ByteBufferValues(bytes), level, tellMaster, effectiveStorageLevel)
   }
 
+  /**
+   * Put the given block according to the given level in one of the block stores, replicating
+   * the values if necessary.
+   *
+   * The effective storage level refers to the level according to which the block will actually be
+   * handled. This allows the caller to specify an alternate behavior of doPut while preserving
+   * the original level specified by the user.
+   */
   private def doPut(
       blockId: BlockId,
       data: BlockValues,
       level: StorageLevel,
-      tellMaster: Boolean = true): Seq[(BlockId, BlockStatus)] = {
+      tellMaster: Boolean = true,
+      effectiveStorageLevel: Option[StorageLevel] = None)
+    : Seq[(BlockId, BlockStatus)] = {
 
     require(blockId != null, "BlockId is null")
     require(level != null && level.isValid, "StorageLevel is null or invalid")
+    effectiveStorageLevel.foreach { level =>
+      require(level != null && level.isValid, "Effective StorageLevel is null or invalid")
+    }
 
     // Return value
     val updatedBlocks = new ArrayBuffer[(BlockId, BlockStatus)]
@@ -657,13 +674,16 @@ private[spark] class BlockManager(
     // Size of the block in bytes
     var size = 0L
 
+    // The level we actually use to put the block
+    val putLevel = effectiveStorageLevel.getOrElse(level)
+
     // If we're storing bytes, then initiate the replication before storing them locally.
     // This is faster as data is already serialized and ready to send.
     val replicationFuture = data match {
-      case b: ByteBufferValues if level.replication > 1 =>
+      case b: ByteBufferValues if putLevel.replication > 1 =>
         // Duplicate doesn't copy the bytes, but just creates a wrapper
         val bufferView = b.buffer.duplicate()
-        Future { replicate(blockId, bufferView, level) }
+        Future { replicate(blockId, bufferView, putLevel) }
       case _ => null
     }
 
@@ -676,18 +696,18 @@ private[spark] class BlockManager(
         // returnValues - Whether to return the values put
         // blockStore - The type of storage to put these values into
         val (returnValues, blockStore: BlockStore) = {
-          if (level.useMemory) {
+          if (putLevel.useMemory) {
             // Put it in memory first, even if it also has useDisk set to true;
             // We will drop it to disk later if the memory store can't hold it.
             (true, memoryStore)
-          } else if (level.useOffHeap) {
+          } else if (putLevel.useOffHeap) {
             // Use tachyon for off-heap storage
             (false, tachyonStore)
-          } else if (level.useDisk) {
+          } else if (putLevel.useDisk) {
             // Don't get back the bytes from put unless we replicate them
-            (level.replication > 1, diskStore)
+            (putLevel.replication > 1, diskStore)
           } else {
-            assert(level == StorageLevel.NONE)
+            assert(putLevel == StorageLevel.NONE)
             throw new BlockException(
               blockId, s"Attempted to put block $blockId without specifying storage level!")
           }
@@ -696,22 +716,22 @@ private[spark] class BlockManager(
         // Actually put the values
         val result = data match {
           case IteratorValues(iterator) =>
-            blockStore.putValues(blockId, iterator, level, returnValues)
-          case ArrayBufferValues(array) =>
-            blockStore.putValues(blockId, array, level, returnValues)
+            blockStore.putIterator(blockId, iterator, putLevel, returnValues)
+          case ArrayValues(array) =>
+            blockStore.putArray(blockId, array, putLevel, returnValues)
           case ByteBufferValues(bytes) =>
             bytes.rewind()
-            blockStore.putBytes(blockId, bytes, level)
+            blockStore.putBytes(blockId, bytes, putLevel)
         }
         size = result.size
         result.data match {
-          case Left (newIterator) if level.useMemory => valuesAfterPut = newIterator
+          case Left (newIterator) if putLevel.useMemory => valuesAfterPut = newIterator
           case Right (newBytes) => bytesAfterPut = newBytes
           case _ =>
         }
 
         // Keep track of which blocks are dropped from memory
-        if (level.useMemory) {
+        if (putLevel.useMemory) {
           result.droppedBlocks.foreach { updatedBlocks += _ }
         }
 
@@ -742,7 +762,7 @@ private[spark] class BlockManager(
 
     // Either we're storing bytes and we asynchronously started replication, or we're storing
     // values and need to serialize and replicate them now:
-    if (level.replication > 1) {
+    if (putLevel.replication > 1) {
       data match {
         case ByteBufferValues(bytes) =>
           if (replicationFuture != null) {
@@ -758,7 +778,7 @@ private[spark] class BlockManager(
             }
             bytesAfterPut = dataSerialize(blockId, valuesAfterPut)
           }
-          replicate(blockId, bytesAfterPut, level)
+          replicate(blockId, bytesAfterPut, putLevel)
           logDebug("Put block %s remotely took %s"
             .format(blockId, Utils.getUsedTimeMs(remoteStartTime)))
       }
@@ -766,7 +786,7 @@ private[spark] class BlockManager(
 
     BlockManager.dispose(bytesAfterPut)
 
-    if (level.replication > 1) {
+    if (putLevel.replication > 1) {
       logDebug("Putting block %s with replication took %s"
         .format(blockId, Utils.getUsedTimeMs(startTimeMs)))
     } else {
@@ -818,7 +838,7 @@ private[spark] class BlockManager(
       value: Any,
       level: StorageLevel,
       tellMaster: Boolean = true): Seq[(BlockId, BlockStatus)] = {
-    put(blockId, Iterator(value), level, tellMaster)
+    putIterator(blockId, Iterator(value), level, tellMaster)
   }
 
   /**
@@ -829,7 +849,7 @@ private[spark] class BlockManager(
    */
   def dropFromMemory(
       blockId: BlockId,
-      data: Either[ArrayBuffer[Any], ByteBuffer]): Option[BlockStatus] = {
+      data: Either[Array[Any], ByteBuffer]): Option[BlockStatus] = {
 
     logInfo(s"Dropping block $blockId from memory")
     val info = blockInfo.get(blockId).orNull
@@ -853,7 +873,7 @@ private[spark] class BlockManager(
           logInfo(s"Writing block $blockId to disk")
           data match {
             case Left(elements) =>
-              diskStore.putValues(blockId, elements, level, returnValues = false)
+              diskStore.putArray(blockId, elements, level, returnValues = false)
             case Right(bytes) =>
               diskStore.putBytes(blockId, bytes, level)
           }
@@ -1068,9 +1088,11 @@ private[spark] class BlockManager(
 private[spark] object BlockManager extends Logging {
   private val ID_GENERATOR = new IdGenerator
 
+  /** Return the total amount of storage memory available. */
   private def getMaxMemory(conf: SparkConf): Long = {
     val memoryFraction = conf.getDouble("spark.storage.memoryFraction", 0.6)
-    (Runtime.getRuntime.maxMemory * memoryFraction).toLong
+    val safetyFraction = conf.getDouble("spark.storage.safetyFraction", 0.9)
+    (Runtime.getRuntime.maxMemory * memoryFraction * safetyFraction).toLong
   }
 
   def getHeartBeatFrequency(conf: SparkConf): Long =
diff --git a/core/src/main/scala/org/apache/spark/storage/BlockStore.scala b/core/src/main/scala/org/apache/spark/storage/BlockStore.scala
index b9b53b1a2f11..69985c9759e2 100644
--- a/core/src/main/scala/org/apache/spark/storage/BlockStore.scala
+++ b/core/src/main/scala/org/apache/spark/storage/BlockStore.scala
@@ -37,15 +37,15 @@ private[spark] abstract class BlockStore(val blockManager: BlockManager) extends
    * @return a PutResult that contains the size of the data, as well as the values put if
    *         returnValues is true (if not, the result's data field can be null)
    */
-  def putValues(
+  def putIterator(
     blockId: BlockId,
     values: Iterator[Any],
     level: StorageLevel,
     returnValues: Boolean): PutResult
 
-  def putValues(
+  def putArray(
     blockId: BlockId,
-    values: ArrayBuffer[Any],
+    values: Array[Any],
     level: StorageLevel,
     returnValues: Boolean): PutResult
 
diff --git a/core/src/main/scala/org/apache/spark/storage/DiskStore.scala b/core/src/main/scala/org/apache/spark/storage/DiskStore.scala
index ebff0cb5ba15..c83261dd91b3 100644
--- a/core/src/main/scala/org/apache/spark/storage/DiskStore.scala
+++ b/core/src/main/scala/org/apache/spark/storage/DiskStore.scala
@@ -21,8 +21,6 @@ import java.io.{FileOutputStream, RandomAccessFile}
 import java.nio.ByteBuffer
 import java.nio.channels.FileChannel.MapMode
 
-import scala.collection.mutable.ArrayBuffer
-
 import org.apache.spark.Logging
 import org.apache.spark.serializer.Serializer
 import org.apache.spark.util.Utils
@@ -30,7 +28,7 @@ import org.apache.spark.util.Utils
 /**
  * Stores BlockManager blocks on disk.
  */
-private class DiskStore(blockManager: BlockManager, diskManager: DiskBlockManager)
+private[spark] class DiskStore(blockManager: BlockManager, diskManager: DiskBlockManager)
   extends BlockStore(blockManager) with Logging {
 
   val minMemoryMapBytes = blockManager.conf.getLong("spark.storage.memoryMapThreshold", 2 * 4096L)
@@ -57,15 +55,15 @@ private class DiskStore(blockManager: BlockManager, diskManager: DiskBlockManage
     PutResult(bytes.limit(), Right(bytes.duplicate()))
   }
 
-  override def putValues(
+  override def putArray(
       blockId: BlockId,
-      values: ArrayBuffer[Any],
+      values: Array[Any],
       level: StorageLevel,
       returnValues: Boolean): PutResult = {
-    putValues(blockId, values.toIterator, level, returnValues)
+    putIterator(blockId, values.toIterator, level, returnValues)
   }
 
-  override def putValues(
+  override def putIterator(
       blockId: BlockId,
       values: Iterator[Any],
       level: StorageLevel,
diff --git a/core/src/main/scala/org/apache/spark/storage/MemoryStore.scala b/core/src/main/scala/org/apache/spark/storage/MemoryStore.scala
index 71f66c826c5b..28f675c2bbb1 100644
--- a/core/src/main/scala/org/apache/spark/storage/MemoryStore.scala
+++ b/core/src/main/scala/org/apache/spark/storage/MemoryStore.scala
@@ -20,27 +20,45 @@ package org.apache.spark.storage
 import java.nio.ByteBuffer
 import java.util.LinkedHashMap
 
+import scala.collection.mutable
 import scala.collection.mutable.ArrayBuffer
 
 import org.apache.spark.util.{SizeEstimator, Utils}
+import org.apache.spark.util.collection.SizeTrackingVector
 
 private case class MemoryEntry(value: Any, size: Long, deserialized: Boolean)
 
 /**
- * Stores blocks in memory, either as ArrayBuffers of deserialized Java objects or as
+ * Stores blocks in memory, either as Arrays of deserialized Java objects or as
  * serialized ByteBuffers.
  */
-private class MemoryStore(blockManager: BlockManager, maxMemory: Long)
+private[spark] class MemoryStore(blockManager: BlockManager, maxMemory: Long)
   extends BlockStore(blockManager) {
 
+  private val conf = blockManager.conf
   private val entries = new LinkedHashMap[BlockId, MemoryEntry](32, 0.75f, true)
+
   @volatile private var currentMemory = 0L
-  // Object used to ensure that only one thread is putting blocks and if necessary, dropping
-  // blocks from the memory store.
-  private val putLock = new Object()
+
+  // Ensure only one thread is putting, and if necessary, dropping blocks at any given time
+  private val accountingLock = new Object
+
+  // A mapping from thread ID to amount of memory used for unrolling a block (in bytes)
+  // All accesses of this map are assumed to have manually synchronized on `accountingLock`
+  private val unrollMemoryMap = mutable.HashMap[Long, Long]()
+
+  /**
+   * The amount of space ensured for unrolling values in memory, shared across all cores.
+   * This space is not reserved in advance, but allocated dynamically by dropping existing blocks.
+   */
+  private val maxUnrollMemory: Long = {
+    val unrollFraction = conf.getDouble("spark.storage.unrollFraction", 0.2)
+    (maxMemory * unrollFraction).toLong
+  }
 
   logInfo("MemoryStore started with capacity %s".format(Utils.bytesToString(maxMemory)))
 
+  /** Free memory not occupied by existing blocks. Note that this does not include unroll memory. */
   def freeMemory: Long = maxMemory - currentMemory
 
   override def getSize(blockId: BlockId): Long = {
@@ -55,20 +73,16 @@ private class MemoryStore(blockManager: BlockManager, maxMemory: Long)
     bytes.rewind()
     if (level.deserialized) {
       val values = blockManager.dataDeserialize(blockId, bytes)
-      val elements = new ArrayBuffer[Any]
-      elements ++= values
-      val sizeEstimate = SizeEstimator.estimate(elements.asInstanceOf[AnyRef])
-      val putAttempt = tryToPut(blockId, elements, sizeEstimate, deserialized = true)
-      PutResult(sizeEstimate, Left(values.toIterator), putAttempt.droppedBlocks)
+      putIterator(blockId, values, level, returnValues = true)
     } else {
       val putAttempt = tryToPut(blockId, bytes, bytes.limit, deserialized = false)
       PutResult(bytes.limit(), Right(bytes.duplicate()), putAttempt.droppedBlocks)
     }
   }
 
-  override def putValues(
+  override def putArray(
       blockId: BlockId,
-      values: ArrayBuffer[Any],
+      values: Array[Any],
       level: StorageLevel,
       returnValues: Boolean): PutResult = {
     if (level.deserialized) {
@@ -82,14 +96,52 @@ private class MemoryStore(blockManager: BlockManager, maxMemory: Long)
     }
   }
 
-  override def putValues(
+  override def putIterator(
       blockId: BlockId,
       values: Iterator[Any],
       level: StorageLevel,
       returnValues: Boolean): PutResult = {
-    val valueEntries = new ArrayBuffer[Any]()
-    valueEntries ++= values
-    putValues(blockId, valueEntries, level, returnValues)
+    putIterator(blockId, values, level, returnValues, allowPersistToDisk = true)
+  }
+
+  /**
+   * Attempt to put the given block in memory store.
+   *
+   * There may not be enough space to fully unroll the iterator in memory, in which case we
+   * optionally drop the values to disk if
+   *   (1) the block's storage level specifies useDisk, and
+   *   (2) `allowPersistToDisk` is true.
+   *
+   * One scenario in which `allowPersistToDisk` is false is when the BlockManager reads a block
+   * back from disk and attempts to cache it in memory. In this case, we should not persist the
+   * block back on disk again, as it is already in disk store.
+   */
+  private[storage] def putIterator(
+      blockId: BlockId,
+      values: Iterator[Any],
+      level: StorageLevel,
+      returnValues: Boolean,
+      allowPersistToDisk: Boolean): PutResult = {
+    val droppedBlocks = new ArrayBuffer[(BlockId, BlockStatus)]
+    val unrolledValues = unrollSafely(blockId, values, droppedBlocks)
+    unrolledValues match {
+      case Left(arrayValues) =>
+        // Values are fully unrolled in memory, so store them as an array
+        val res = putArray(blockId, arrayValues, level, returnValues)
+        droppedBlocks ++= res.droppedBlocks
+        PutResult(res.size, res.data, droppedBlocks)
+      case Right(iteratorValues) =>
+        // Not enough space to unroll this block; drop to disk if applicable
+        logWarning(s"Not enough space to store block $blockId in memory! " +
+          s"Free memory is $freeMemory bytes.")
+        if (level.useDisk && allowPersistToDisk) {
+          logWarning(s"Persisting block $blockId to disk instead.")
+          val res = blockManager.diskStore.putIterator(blockId, iteratorValues, level, returnValues)
+          PutResult(res.size, res.data, droppedBlocks)
+        } else {
+          PutResult(0, Left(iteratorValues), droppedBlocks)
+        }
+    }
   }
 
   override def getBytes(blockId: BlockId): Option[ByteBuffer] = {
@@ -99,7 +151,7 @@ private class MemoryStore(blockManager: BlockManager, maxMemory: Long)
     if (entry == null) {
       None
     } else if (entry.deserialized) {
-      Some(blockManager.dataSerialize(blockId, entry.value.asInstanceOf[ArrayBuffer[Any]].iterator))
+      Some(blockManager.dataSerialize(blockId, entry.value.asInstanceOf[Array[Any]].iterator))
     } else {
       Some(entry.value.asInstanceOf[ByteBuffer].duplicate()) // Doesn't actually copy the data
     }
@@ -112,7 +164,7 @@ private class MemoryStore(blockManager: BlockManager, maxMemory: Long)
     if (entry == null) {
       None
     } else if (entry.deserialized) {
-      Some(entry.value.asInstanceOf[ArrayBuffer[Any]].iterator)
+      Some(entry.value.asInstanceOf[Array[Any]].iterator)
     } else {
       val buffer = entry.value.asInstanceOf[ByteBuffer].duplicate() // Doesn't actually copy data
       Some(blockManager.dataDeserialize(blockId, buffer))
@@ -140,6 +192,93 @@ private class MemoryStore(blockManager: BlockManager, maxMemory: Long)
     logInfo("MemoryStore cleared")
   }
 
+  /**
+   * Unroll the given block in memory safely.
+   *
+   * The safety of this operation refers to avoiding potential OOM exceptions caused by
+   * unrolling the entirety of the block in memory at once. This is achieved by periodically
+   * checking whether the memory restrictions for unrolling blocks are still satisfied,
+   * stopping immediately if not. This check is a safeguard against the scenario in which
+   * there is not enough free memory to accommodate the entirety of a single block.
+   *
+   * This method returns either an array with the contents of the entire block or an iterator
+   * containing the values of the block (if the array would have exceeded available memory).
+   */
+  def unrollSafely(
+      blockId: BlockId,
+      values: Iterator[Any],
+      droppedBlocks: ArrayBuffer[(BlockId, BlockStatus)])
+    : Either[Array[Any], Iterator[Any]] = {
+
+    // Number of elements unrolled so far
+    var elementsUnrolled = 0
+    // Whether there is still enough memory for us to continue unrolling this block
+    var keepUnrolling = true
+    // Initial per-thread memory to request for unrolling blocks (bytes). Exposed for testing.
+    val initialMemoryThreshold = conf.getLong("spark.storage.unrollMemoryThreshold", 1024 * 1024)
+    // How often to check whether we need to request more memory
+    val memoryCheckPeriod = 16
+    // Memory currently reserved by this thread for this particular unrolling operation
+    var memoryThreshold = initialMemoryThreshold
+    // Memory to request as a multiple of current vector size
+    val memoryGrowthFactor = 1.5
+    // Previous unroll memory held by this thread, for releasing later (only at the very end)
+    val previousMemoryReserved = currentUnrollMemoryForThisThread
+    // Underlying vector for unrolling the block
+    var vector = new SizeTrackingVector[Any]
+
+    // Request enough memory to begin unrolling
+    keepUnrolling = reserveUnrollMemoryForThisThread(initialMemoryThreshold)
+
+    // Unroll this block safely, checking whether we have exceeded our threshold periodically
+    try {
+      while (values.hasNext && keepUnrolling) {
+        vector += values.next()
+        if (elementsUnrolled % memoryCheckPeriod == 0) {
+          // If our vector's size has exceeded the threshold, request more memory
+          val currentSize = vector.estimateSize()
+          if (currentSize >= memoryThreshold) {
+            val amountToRequest = (currentSize * (memoryGrowthFactor - 1)).toLong
+            // Hold the accounting lock, in case another thread concurrently puts a block that
+            // takes up the unrolling space we just ensured here
+            accountingLock.synchronized {
+              if (!reserveUnrollMemoryForThisThread(amountToRequest)) {
+                // If the first request is not granted, try again after ensuring free space
+                // If there is still not enough space, give up and drop the partition
+                val spaceToEnsure = maxUnrollMemory - currentUnrollMemory
+                if (spaceToEnsure > 0) {
+                  val result = ensureFreeSpace(blockId, spaceToEnsure)
+                  droppedBlocks ++= result.droppedBlocks
+                }
+                keepUnrolling = reserveUnrollMemoryForThisThread(amountToRequest)
+              }
+            }
+            // New threshold is currentSize * memoryGrowthFactor
+            memoryThreshold = currentSize + amountToRequest
+          }
+        }
+        elementsUnrolled += 1
+      }
+
+      if (keepUnrolling) {
+        // We successfully unrolled the entirety of this block
+        Left(vector.toArray)
+      } else {
+        // We ran out of space while unrolling the values for this block
+        Right(vector.iterator ++ values)
+      }
+
+    } finally {
+      // If we return an array, the values returned do not depend on the underlying vector and
+      // we can immediately free up space for other threads. Otherwise, if we return an iterator,
+      // we release the memory claimed by this thread later on when the task finishes.
+      if (keepUnrolling) {
+        val amountToRelease = currentUnrollMemoryForThisThread - previousMemoryReserved
+        releaseUnrollMemoryForThisThread(amountToRelease)
+      }
+    }
+  }
+
   /**
    * Return the RDD ID that a given block ID is from, or None if it is not an RDD block.
    */
@@ -149,10 +288,10 @@ private class MemoryStore(blockManager: BlockManager, maxMemory: Long)
 
   /**
    * Try to put in a set of values, if we can free up enough space. The value should either be
-   * an ArrayBuffer if deserialized is true or a ByteBuffer otherwise. Its (possibly estimated)
-   * size must also be passed by the caller.
+   * an Array if deserialized is true or a ByteBuffer otherwise. Its (possibly estimated) size
+   * must also be passed by the caller.
    *
-   * Lock on the object putLock to ensure that all the put requests and its associated block
+   * Synchronize on `accountingLock` to ensure that all the put requests and its associated block
    * dropping is done by only on thread at a time. Otherwise while one thread is dropping
    * blocks to free memory for one block, another thread may use up the freed space for
    * another block.
@@ -174,7 +313,7 @@ private class MemoryStore(blockManager: BlockManager, maxMemory: Long)
     var putSuccess = false
     val droppedBlocks = new ArrayBuffer[(BlockId, BlockStatus)]
 
-    putLock.synchronized {
+    accountingLock.synchronized {
       val freeSpaceResult = ensureFreeSpace(blockId, size)
       val enoughFreeSpace = freeSpaceResult.success
       droppedBlocks ++= freeSpaceResult.droppedBlocks
@@ -193,7 +332,7 @@ private class MemoryStore(blockManager: BlockManager, maxMemory: Long)
         // Tell the block manager that we couldn't put it in memory so that it can drop it to
         // disk if the block allows disk storage.
         val data = if (deserialized) {
-          Left(value.asInstanceOf[ArrayBuffer[Any]])
+          Left(value.asInstanceOf[Array[Any]])
         } else {
           Right(value.asInstanceOf[ByteBuffer].duplicate())
         }
@@ -210,12 +349,14 @@ private class MemoryStore(blockManager: BlockManager, maxMemory: Long)
    * from the same RDD (which leads to a wasteful cyclic replacement pattern for RDDs that
    * don't fit into memory that we want to avoid).
    *
-   * Assume that a lock is held by the caller to ensure only one thread is dropping blocks.
-   * Otherwise, the freed space may fill up before the caller puts in their new value.
+   * Assume that `accountingLock` is held by the caller to ensure only one thread is dropping
+   * blocks. Otherwise, the freed space may fill up before the caller puts in their new value.
    *
    * Return whether there is enough free space, along with the blocks dropped in the process.
    */
-  private def ensureFreeSpace(blockIdToAdd: BlockId, space: Long): ResultWithDroppedBlocks = {
+  private def ensureFreeSpace(
+      blockIdToAdd: BlockId,
+      space: Long): ResultWithDroppedBlocks = {
     logInfo(s"ensureFreeSpace($space) called with curMem=$currentMemory, maxMem=$maxMemory")
 
     val droppedBlocks = new ArrayBuffer[(BlockId, BlockStatus)]
@@ -225,9 +366,12 @@ private class MemoryStore(blockManager: BlockManager, maxMemory: Long)
       return ResultWithDroppedBlocks(success = false, droppedBlocks)
     }
 
-    if (maxMemory - currentMemory < space) {
+    // Take into account the amount of memory currently occupied by unrolling blocks
+    val actualFreeMemory = freeMemory - currentUnrollMemory
+
+    if (actualFreeMemory < space) {
       val rddToAdd = getRddId(blockIdToAdd)
-      val selectedBlocks = new ArrayBuffer[BlockId]()
+      val selectedBlocks = new ArrayBuffer[BlockId]
       var selectedMemory = 0L
 
       // This is synchronized to ensure that the set of entries is not changed
@@ -235,7 +379,7 @@ private class MemoryStore(blockManager: BlockManager, maxMemory: Long)
       // can lead to exceptions.
       entries.synchronized {
         val iterator = entries.entrySet().iterator()
-        while (maxMemory - (currentMemory - selectedMemory) < space && iterator.hasNext) {
+        while (actualFreeMemory + selectedMemory < space && iterator.hasNext) {
           val pair = iterator.next()
           val blockId = pair.getKey
           if (rddToAdd.isEmpty || rddToAdd != getRddId(blockId)) {
@@ -245,7 +389,7 @@ private class MemoryStore(blockManager: BlockManager, maxMemory: Long)
         }
       }
 
-      if (maxMemory - (currentMemory - selectedMemory) >= space) {
+      if (actualFreeMemory + selectedMemory >= space) {
         logInfo(s"${selectedBlocks.size} blocks selected for dropping")
         for (blockId <- selectedBlocks) {
           val entry = entries.synchronized { entries.get(blockId) }
@@ -254,7 +398,7 @@ private class MemoryStore(blockManager: BlockManager, maxMemory: Long)
           // future safety.
           if (entry != null) {
             val data = if (entry.deserialized) {
-              Left(entry.value.asInstanceOf[ArrayBuffer[Any]])
+              Left(entry.value.asInstanceOf[Array[Any]])
             } else {
               Right(entry.value.asInstanceOf[ByteBuffer].duplicate())
             }
@@ -275,8 +419,56 @@ private class MemoryStore(blockManager: BlockManager, maxMemory: Long)
   override def contains(blockId: BlockId): Boolean = {
     entries.synchronized { entries.containsKey(blockId) }
   }
+
+  /**
+   * Reserve additional memory for unrolling blocks used by this thread.
+   * Return whether the request is granted.
+   */
+  private[spark] def reserveUnrollMemoryForThisThread(memory: Long): Boolean = {
+    accountingLock.synchronized {
+      val granted = freeMemory > currentUnrollMemory + memory
+      if (granted) {
+        val threadId = Thread.currentThread().getId
+        unrollMemoryMap(threadId) = unrollMemoryMap.getOrElse(threadId, 0L) + memory
+      }
+      granted
+    }
+  }
+
+  /**
+   * Release memory used by this thread for unrolling blocks.
+   * If the amount is not specified, remove the current thread's allocation altogether.
+   */
+  private[spark] def releaseUnrollMemoryForThisThread(memory: Long = -1L): Unit = {
+    val threadId = Thread.currentThread().getId
+    accountingLock.synchronized {
+      if (memory < 0) {
+        unrollMemoryMap.remove(threadId)
+      } else {
+        unrollMemoryMap(threadId) = unrollMemoryMap.getOrElse(threadId, memory) - memory
+        // If this thread claims no more unroll memory, release it completely
+        if (unrollMemoryMap(threadId) <= 0) {
+          unrollMemoryMap.remove(threadId)
+        }
+      }
+    }
+  }
+
+  /**
+   * Return the amount of memory currently occupied for unrolling blocks across all threads.
+   */
+  private[spark] def currentUnrollMemory: Long = accountingLock.synchronized {
+    unrollMemoryMap.values.sum
+  }
+
+  /**
+   * Return the amount of memory currently occupied for unrolling blocks by this thread.
+   */
+  private[spark] def currentUnrollMemoryForThisThread: Long = accountingLock.synchronized {
+    unrollMemoryMap.getOrElse(Thread.currentThread().getId, 0L)
+  }
 }
 
-private case class ResultWithDroppedBlocks(
+private[spark] case class ResultWithDroppedBlocks(
     success: Boolean,
     droppedBlocks: Seq[(BlockId, BlockStatus)])
diff --git a/core/src/main/scala/org/apache/spark/storage/TachyonStore.scala b/core/src/main/scala/org/apache/spark/storage/TachyonStore.scala
index d8ff4ff6bd42..932b5616043b 100644
--- a/core/src/main/scala/org/apache/spark/storage/TachyonStore.scala
+++ b/core/src/main/scala/org/apache/spark/storage/TachyonStore.scala
@@ -20,8 +20,6 @@ package org.apache.spark.storage
 import java.io.IOException
 import java.nio.ByteBuffer
 
-import scala.collection.mutable.ArrayBuffer
-
 import tachyon.client.{ReadType, WriteType}
 
 import org.apache.spark.Logging
@@ -30,7 +28,7 @@ import org.apache.spark.util.Utils
 /**
  * Stores BlockManager blocks on Tachyon.
  */
-private class TachyonStore(
+private[spark] class TachyonStore(
     blockManager: BlockManager,
     tachyonManager: TachyonBlockManager)
   extends BlockStore(blockManager: BlockManager) with Logging {
@@ -45,15 +43,15 @@ private class TachyonStore(
     putIntoTachyonStore(blockId, bytes, returnValues = true)
   }
 
-  override def putValues(
+  override def putArray(
       blockId: BlockId,
-      values: ArrayBuffer[Any],
+      values: Array[Any],
       level: StorageLevel,
       returnValues: Boolean): PutResult = {
-    putValues(blockId, values.toIterator, level, returnValues)
+    putIterator(blockId, values.toIterator, level, returnValues)
   }
 
-  override def putValues(
+  override def putIterator(
       blockId: BlockId,
       values: Iterator[Any],
       level: StorageLevel,
diff --git a/core/src/main/scala/org/apache/spark/storage/ThreadingTest.scala b/core/src/main/scala/org/apache/spark/storage/ThreadingTest.scala
index 328be158db68..75c2e09a6bbb 100644
--- a/core/src/main/scala/org/apache/spark/storage/ThreadingTest.scala
+++ b/core/src/main/scala/org/apache/spark/storage/ThreadingTest.scala
@@ -48,7 +48,7 @@ private[spark] object ThreadingTest {
         val block = (1 to blockSize).map(_ => Random.nextInt())
         val level = randomLevel()
         val startTime = System.currentTimeMillis()
-        manager.put(blockId, block.iterator, level, tellMaster = true)
+        manager.putIterator(blockId, block.iterator, level, tellMaster = true)
         println("Pushed block " + blockId + " in " + (System.currentTimeMillis - startTime) + " ms")
         queue.add((blockId, block))
       }
diff --git a/core/src/main/scala/org/apache/spark/util/SizeEstimator.scala b/core/src/main/scala/org/apache/spark/util/SizeEstimator.scala
index 08465575309c..bce3b3afe9ab 100644
--- a/core/src/main/scala/org/apache/spark/util/SizeEstimator.scala
+++ b/core/src/main/scala/org/apache/spark/util/SizeEstimator.scala
@@ -180,7 +180,7 @@ private[spark] object SizeEstimator extends Logging {
     }
   }
 
-  // Estimat the size of arrays larger than ARRAY_SIZE_FOR_SAMPLING by sampling.
+  // Estimate the size of arrays larger than ARRAY_SIZE_FOR_SAMPLING by sampling.
   private val ARRAY_SIZE_FOR_SAMPLING = 200
   private val ARRAY_SAMPLE_SIZE = 100 // should be lower than ARRAY_SIZE_FOR_SAMPLING
 
diff --git a/core/src/main/scala/org/apache/spark/util/collection/PrimitiveVector.scala b/core/src/main/scala/org/apache/spark/util/collection/PrimitiveVector.scala
index b84eb65c62bc..7e76d060d600 100644
--- a/core/src/main/scala/org/apache/spark/util/collection/PrimitiveVector.scala
+++ b/core/src/main/scala/org/apache/spark/util/collection/PrimitiveVector.scala
@@ -36,7 +36,7 @@ class PrimitiveVector[@specialized(Long, Int, Double) V: ClassTag](initialSize:
     _array(index)
   }
 
-  def +=(value: V) {
+  def +=(value: V): Unit = {
     if (_numElements == _array.length) {
       resize(_array.length * 2)
     }
@@ -50,6 +50,19 @@ class PrimitiveVector[@specialized(Long, Int, Double) V: ClassTag](initialSize:
 
   def size: Int = _numElements
 
+  def iterator: Iterator[V] = new Iterator[V] {
+    var index = 0
+    override def hasNext: Boolean = index < _numElements
+    override def next(): V = {
+      if (!hasNext) {
+        throw new NoSuchElementException
+      }
+      val value = _array(index)
+      index += 1
+      value
+    }
+  }
+
   /** Gets the underlying array backing this vector. */
   def array: Array[V] = _array
 
diff --git a/core/src/main/scala/org/apache/spark/util/collection/SizeTracker.scala b/core/src/main/scala/org/apache/spark/util/collection/SizeTracker.scala
new file mode 100644
index 000000000000..3eb1010dc1e8
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/util/collection/SizeTracker.scala
@@ -0,0 +1,105 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.util.collection
+
+import scala.collection.mutable
+
+import org.apache.spark.util.SizeEstimator
+
+/**
+ * A general interface for collections to keep track of their estimated sizes in bytes.
+ * We sample with a slow exponential back-off using the SizeEstimator to amortize the time,
+ * as each call to SizeEstimator is somewhat expensive (order of a few milliseconds).
+ */
+private[spark] trait SizeTracker {
+
+  import SizeTracker._
+
+  /**
+   * Controls the base of the exponential which governs the rate of sampling.
+   * E.g., a value of 2 would mean we sample at 1, 2, 4, 8, ... elements.
+   */
+  private val SAMPLE_GROWTH_RATE = 1.1
+
+  /** Samples taken since last resetSamples(). Only the last two are kept for extrapolation. */
+  private val samples = new mutable.Queue[Sample]
+
+  /** The average number of bytes per update between our last two samples. */
+  private var bytesPerUpdate: Double = _
+
+  /** Total number of insertions and updates into the map since the last resetSamples(). */
+  private var numUpdates: Long = _
+
+  /** The value of 'numUpdates' at which we will take our next sample. */
+  private var nextSampleNum: Long = _
+
+  resetSamples()
+
+  /**
+   * Reset samples collected so far.
+   * This should be called after the collection undergoes a dramatic change in size.
+   */
+  protected def resetSamples(): Unit = {
+    numUpdates = 1
+    nextSampleNum = 1
+    samples.clear()
+    takeSample()
+  }
+
+  /**
+   * Callback to be invoked after every update.
+   */
+  protected def afterUpdate(): Unit = {
+    numUpdates += 1
+    if (nextSampleNum == numUpdates) {
+      takeSample()
+    }
+  }
+
+  /**
+   * Take a new sample of the current collection's size.
+   */
+  private def takeSample(): Unit = {
+    samples.enqueue(Sample(SizeEstimator.estimate(this), numUpdates))
+    // Only use the last two samples to extrapolate
+    if (samples.size > 2) {
+      samples.dequeue()
+    }
+    val bytesDelta = samples.toList.reverse match {
+      case latest :: previous :: tail =>
+        (latest.size - previous.size).toDouble / (latest.numUpdates - previous.numUpdates)
+      // If fewer than 2 samples, assume no change
+      case _ => 0
+    }
+    bytesPerUpdate = math.max(0, bytesDelta)
+    nextSampleNum = math.ceil(numUpdates * SAMPLE_GROWTH_RATE).toLong
+  }
+
+  /**
+   * Estimate the current size of the collection in bytes. O(1) time.
+   */
+  def estimateSize(): Long = {
+    assert(samples.nonEmpty)
+    val extrapolatedDelta = bytesPerUpdate * (numUpdates - samples.last.numUpdates)
+    (samples.last.size + extrapolatedDelta).toLong
+  }
+}
+
+private object SizeTracker {
+  case class Sample(size: Long, numUpdates: Long)
+}
diff --git a/core/src/main/scala/org/apache/spark/util/collection/SizeTrackingAppendOnlyMap.scala b/core/src/main/scala/org/apache/spark/util/collection/SizeTrackingAppendOnlyMap.scala
index 204330dad48b..de61e1d17fe1 100644
--- a/core/src/main/scala/org/apache/spark/util/collection/SizeTrackingAppendOnlyMap.scala
+++ b/core/src/main/scala/org/apache/spark/util/collection/SizeTrackingAppendOnlyMap.scala
@@ -17,85 +17,24 @@
 
 package org.apache.spark.util.collection
 
-import scala.collection.mutable.ArrayBuffer
-
-import org.apache.spark.util.SizeEstimator
-import org.apache.spark.util.collection.SizeTrackingAppendOnlyMap.Sample
-
 /**
- * Append-only map that keeps track of its estimated size in bytes.
- * We sample with a slow exponential back-off using the SizeEstimator to amortize the time,
- * as each call to SizeEstimator can take a sizable amount of time (order of a few milliseconds).
+ * An append-only map that keeps track of its estimated size in bytes.
  */
-private[spark] class SizeTrackingAppendOnlyMap[K, V] extends AppendOnlyMap[K, V] {
-
-  /**
-   * Controls the base of the exponential which governs the rate of sampling.
-   * E.g., a value of 2 would mean we sample at 1, 2, 4, 8, ... elements.
-   */
-  private val SAMPLE_GROWTH_RATE = 1.1
-
-  /** All samples taken since last resetSamples(). Only the last two are used for extrapolation. */
-  private val samples = new ArrayBuffer[Sample]()
-
-  /** Total number of insertions and updates into the map since the last resetSamples(). */
-  private var numUpdates: Long = _
-
-  /** The value of 'numUpdates' at which we will take our next sample. */
-  private var nextSampleNum: Long = _
-
-  /** The average number of bytes per update between our last two samples. */
-  private var bytesPerUpdate: Double = _
-
-  resetSamples()
-
-  /** Called after the map grows in size, as this can be a dramatic change for small objects. */
-  def resetSamples() {
-    numUpdates = 1
-    nextSampleNum = 1
-    samples.clear()
-    takeSample()
-  }
+private[spark] class SizeTrackingAppendOnlyMap[K, V] extends AppendOnlyMap[K, V] with SizeTracker {
 
   override def update(key: K, value: V): Unit = {
     super.update(key, value)
-    numUpdates += 1
-    if (nextSampleNum == numUpdates) { takeSample() }
+    super.afterUpdate()
   }
 
   override def changeValue(key: K, updateFunc: (Boolean, V) => V): V = {
     val newValue = super.changeValue(key, updateFunc)
-    numUpdates += 1
-    if (nextSampleNum == numUpdates) { takeSample() }
+    super.afterUpdate()
     newValue
   }
 
-  /** Takes a new sample of the current map's size. */
-  def takeSample() {
-    samples += Sample(SizeEstimator.estimate(this), numUpdates)
-    // Only use the last two samples to extrapolate. If fewer than 2 samples, assume no change.
-    bytesPerUpdate = math.max(0, samples.toSeq.reverse match {
-      case latest :: previous :: tail =>
-        (latest.size - previous.size).toDouble / (latest.numUpdates - previous.numUpdates)
-      case _ =>
-        0
-    })
-    nextSampleNum = math.ceil(numUpdates * SAMPLE_GROWTH_RATE).toLong
-  }
-
-  override protected def growTable() {
+  override protected def growTable(): Unit = {
     super.growTable()
     resetSamples()
   }
-
-  /** Estimates the current size of the map in bytes. O(1) time. */
-  def estimateSize(): Long = {
-    assert(samples.nonEmpty)
-    val extrapolatedDelta = bytesPerUpdate * (numUpdates - samples.last.numUpdates)
-    (samples.last.size + extrapolatedDelta).toLong
-  }
-}
-
-private object SizeTrackingAppendOnlyMap {
-  case class Sample(size: Long, numUpdates: Long)
 }
diff --git a/core/src/main/scala/org/apache/spark/util/collection/SizeTrackingVector.scala b/core/src/main/scala/org/apache/spark/util/collection/SizeTrackingVector.scala
new file mode 100644
index 000000000000..65a7b4e0d497
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/util/collection/SizeTrackingVector.scala
@@ -0,0 +1,46 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.util.collection
+
+import scala.reflect.ClassTag
+
+/**
+ * An append-only buffer that keeps track of its estimated size in bytes.
+ */
+private[spark] class SizeTrackingVector[T: ClassTag]
+  extends PrimitiveVector[T]
+  with SizeTracker {
+
+  override def +=(value: T): Unit = {
+    super.+=(value)
+    super.afterUpdate()
+  }
+
+  override def resize(newLength: Int): PrimitiveVector[T] = {
+    super.resize(newLength)
+    resetSamples()
+    this
+  }
+
+  /**
+   * Return a trimmed version of the underlying array.
+   */
+  def toArray: Array[T] = {
+    super.iterator.toArray
+  }
+}
diff --git a/core/src/test/scala/org/apache/spark/CacheManagerSuite.scala b/core/src/test/scala/org/apache/spark/CacheManagerSuite.scala
index 7f5d0b061e8b..9c5f394d3899 100644
--- a/core/src/test/scala/org/apache/spark/CacheManagerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/CacheManagerSuite.scala
@@ -17,8 +17,6 @@
 
 package org.apache.spark
 
-import scala.collection.mutable.ArrayBuffer
-
 import org.scalatest.{BeforeAndAfter, FunSuite}
 import org.scalatest.mock.EasyMockSugar
 
@@ -52,22 +50,21 @@ class CacheManagerSuite extends FunSuite with BeforeAndAfter with EasyMockSugar
   }
 
   test("get uncached rdd") {
-    expecting {
-      blockManager.get(RDDBlockId(0, 0)).andReturn(None)
-      blockManager.put(RDDBlockId(0, 0), ArrayBuffer[Any](1, 2, 3, 4), StorageLevel.MEMORY_ONLY,
-        true).andStubReturn(Seq[(BlockId, BlockStatus)]())
-    }
-
-    whenExecuting(blockManager) {
-      val context = new TaskContext(0, 0, 0)
-      val value = cacheManager.getOrCompute(rdd, split, context, StorageLevel.MEMORY_ONLY)
-      assert(value.toList === List(1, 2, 3, 4))
-    }
+    // Do not mock this test, because attempting to match Array[Any], which is not covariant,
+    // in blockManager.put is a losing battle. You have been warned.
+    blockManager = sc.env.blockManager
+    cacheManager = sc.env.cacheManager
+    val context = new TaskContext(0, 0, 0)
+    val computeValue = cacheManager.getOrCompute(rdd, split, context, StorageLevel.MEMORY_ONLY)
+    val getValue = blockManager.get(RDDBlockId(rdd.id, split.index))
+    assert(computeValue.toList === List(1, 2, 3, 4))
+    assert(getValue.isDefined, "Block cached from getOrCompute is not found!")
+    assert(getValue.get.data.toList === List(1, 2, 3, 4))
   }
 
   test("get cached rdd") {
     expecting {
-      val result = new BlockResult(ArrayBuffer(5, 6, 7).iterator, DataReadMethod.Memory, 12)
+      val result = new BlockResult(Array(5, 6, 7).iterator, DataReadMethod.Memory, 12)
       blockManager.get(RDDBlockId(0, 0)).andReturn(Some(result))
     }
 
diff --git a/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala b/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala
index 23cb6905bfde..dd4fd535d357 100644
--- a/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala
@@ -31,7 +31,7 @@ import org.scalatest.concurrent.Timeouts._
 import org.scalatest.Matchers
 import org.scalatest.time.SpanSugar._
 
-import org.apache.spark.{MapOutputTrackerMaster, SecurityManager, SparkConf, SparkContext}
+import org.apache.spark.{MapOutputTrackerMaster, SecurityManager, SparkConf}
 import org.apache.spark.executor.DataReadMethod
 import org.apache.spark.scheduler.LiveListenerBus
 import org.apache.spark.serializer.{JavaSerializer, KryoSerializer}
@@ -43,6 +43,7 @@ import scala.language.postfixOps
 
 class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfter
   with PrivateMethodTester {
+
   private val conf = new SparkConf(false)
   var store: BlockManager = null
   var store2: BlockManager = null
@@ -61,21 +62,29 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfter
   implicit def StringToBlockId(value: String): BlockId = new TestBlockId(value)
   def rdd(rddId: Int, splitId: Int) = RDDBlockId(rddId, splitId)
 
+  private def makeBlockManager(maxMem: Long, name: String = "<driver>"): BlockManager = {
+    new BlockManager(
+      name, actorSystem, master, serializer, maxMem, conf, securityMgr, mapOutputTracker)
+  }
+
   before {
-    val (actorSystem, boundPort) = AkkaUtils.createActorSystem("test", "localhost", 0, conf = conf,
-      securityManager = securityMgr)
+    val (actorSystem, boundPort) = AkkaUtils.createActorSystem(
+      "test", "localhost", 0, conf = conf, securityManager = securityMgr)
     this.actorSystem = actorSystem
-    conf.set("spark.driver.port", boundPort.toString)
-
-    master = new BlockManagerMaster(
-      actorSystem.actorOf(Props(new BlockManagerMasterActor(true, conf, new LiveListenerBus))),
-      conf)
 
     // Set the arch to 64-bit and compressedOops to true to get a deterministic test-case
     oldArch = System.setProperty("os.arch", "amd64")
     conf.set("os.arch", "amd64")
     conf.set("spark.test.useCompressedOops", "true")
     conf.set("spark.storage.disableBlockManagerHeartBeat", "true")
+    conf.set("spark.driver.port", boundPort.toString)
+    conf.set("spark.storage.unrollFraction", "0.4")
+    conf.set("spark.storage.unrollMemoryThreshold", "512")
+
+    master = new BlockManagerMaster(
+      actorSystem.actorOf(Props(new BlockManagerMasterActor(true, conf, new LiveListenerBus))),
+      conf)
+
     val initialize = PrivateMethod[Unit]('initialize)
     SizeEstimator invokePrivate initialize()
   }
@@ -138,11 +147,10 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfter
   }
 
   test("master + 1 manager interaction") {
-    store = new BlockManager("<driver>", actorSystem, master, serializer, 2000, conf,
-      securityMgr, mapOutputTracker)
-    val a1 = new Array[Byte](400)
-    val a2 = new Array[Byte](400)
-    val a3 = new Array[Byte](400)
+    store = makeBlockManager(20000)
+    val a1 = new Array[Byte](4000)
+    val a2 = new Array[Byte](4000)
+    val a3 = new Array[Byte](4000)
 
     // Putting a1, a2  and a3 in memory and telling master only about a1 and a2
     store.putSingle("a1", a1, StorageLevel.MEMORY_ONLY)
@@ -169,10 +177,8 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfter
   }
 
   test("master + 2 managers interaction") {
-    store = new BlockManager("exec1", actorSystem, master, serializer, 2000, conf,
-      securityMgr, mapOutputTracker)
-    store2 = new BlockManager("exec2", actorSystem, master, new KryoSerializer(conf), 2000, conf,
-      securityMgr, mapOutputTracker)
+    store = makeBlockManager(2000, "exec1")
+    store2 = makeBlockManager(2000, "exec2")
 
     val peers = master.getPeers(store.blockManagerId, 1)
     assert(peers.size === 1, "master did not return the other manager as a peer")
@@ -187,11 +193,10 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfter
   }
 
   test("removing block") {
-    store = new BlockManager("<driver>", actorSystem, master, serializer, 2000, conf,
-      securityMgr, mapOutputTracker)
-    val a1 = new Array[Byte](400)
-    val a2 = new Array[Byte](400)
-    val a3 = new Array[Byte](400)
+    store = makeBlockManager(20000)
+    val a1 = new Array[Byte](4000)
+    val a2 = new Array[Byte](4000)
+    val a3 = new Array[Byte](4000)
 
     // Putting a1, a2 and a3 in memory and telling master only about a1 and a2
     store.putSingle("a1-to-remove", a1, StorageLevel.MEMORY_ONLY)
@@ -200,8 +205,8 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfter
 
     // Checking whether blocks are in memory and memory size
     val memStatus = master.getMemoryStatus.head._2
-    assert(memStatus._1 == 2000L, "total memory " + memStatus._1 + " should equal 2000")
-    assert(memStatus._2 <= 1200L, "remaining memory " + memStatus._2 + " should <= 1200")
+    assert(memStatus._1 == 20000L, "total memory " + memStatus._1 + " should equal 20000")
+    assert(memStatus._2 <= 12000L, "remaining memory " + memStatus._2 + " should <= 12000")
     assert(store.getSingle("a1-to-remove").isDefined, "a1 was not in store")
     assert(store.getSingle("a2-to-remove").isDefined, "a2 was not in store")
     assert(store.getSingle("a3-to-remove").isDefined, "a3 was not in store")
@@ -230,17 +235,16 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfter
     }
     eventually(timeout(1000 milliseconds), interval(10 milliseconds)) {
       val memStatus = master.getMemoryStatus.head._2
-      memStatus._1 should equal (2000L)
-      memStatus._2 should equal (2000L)
+      memStatus._1 should equal (20000L)
+      memStatus._2 should equal (20000L)
     }
   }
 
   test("removing rdd") {
-    store = new BlockManager("<driver>", actorSystem, master, serializer, 2000, conf,
-      securityMgr, mapOutputTracker)
-    val a1 = new Array[Byte](400)
-    val a2 = new Array[Byte](400)
-    val a3 = new Array[Byte](400)
+    store = makeBlockManager(20000)
+    val a1 = new Array[Byte](4000)
+    val a2 = new Array[Byte](4000)
+    val a3 = new Array[Byte](4000)
     // Putting a1, a2 and a3 in memory.
     store.putSingle(rdd(0, 0), a1, StorageLevel.MEMORY_ONLY)
     store.putSingle(rdd(0, 1), a2, StorageLevel.MEMORY_ONLY)
@@ -270,11 +274,9 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfter
   }
 
   test("removing broadcast") {
-    store = new BlockManager("<driver>", actorSystem, master, serializer, 2000, conf,
-      securityMgr, mapOutputTracker)
+    store = makeBlockManager(2000)
     val driverStore = store
-    val executorStore = new BlockManager("executor", actorSystem, master, serializer, 2000, conf,
-      securityMgr, mapOutputTracker)
+    val executorStore = makeBlockManager(2000, "executor")
     val a1 = new Array[Byte](400)
     val a2 = new Array[Byte](400)
     val a3 = new Array[Byte](400)
@@ -343,8 +345,7 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfter
 
   test("reregistration on heart beat") {
     val heartBeat = PrivateMethod[Unit]('heartBeat)
-    store = new BlockManager("<driver>", actorSystem, master, serializer, 2000, conf,
-      securityMgr, mapOutputTracker)
+    store = makeBlockManager(2000)
     val a1 = new Array[Byte](400)
 
     store.putSingle("a1", a1, StorageLevel.MEMORY_ONLY)
@@ -380,8 +381,7 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfter
 
   test("reregistration doesn't dead lock") {
     val heartBeat = PrivateMethod[Unit]('heartBeat)
-    store = new BlockManager("<driver>", actorSystem, master, serializer, 2000, conf,
-      securityMgr, mapOutputTracker)
+    store = makeBlockManager(2000)
     val a1 = new Array[Byte](400)
     val a2 = List(new Array[Byte](400))
 
@@ -390,7 +390,7 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfter
       master.removeExecutor(store.blockManagerId.executorId)
       val t1 = new Thread {
         override def run() {
-          store.put("a2", a2.iterator, StorageLevel.MEMORY_ONLY, tellMaster = true)
+          store.putIterator("a2", a2.iterator, StorageLevel.MEMORY_ONLY, tellMaster = true)
         }
       }
       val t2 = new Thread {
@@ -418,19 +418,14 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfter
   }
 
   test("correct BlockResult returned from get() calls") {
-    store = new BlockManager("<driver>", actorSystem, master, serializer, 1200, conf, securityMgr,
-      mapOutputTracker)
-    val list1 = List(new Array[Byte](200), new Array[Byte](200))
-    val list1ForSizeEstimate = new ArrayBuffer[Any]
-    list1ForSizeEstimate ++= list1.iterator
-    val list1SizeEstimate = SizeEstimator.estimate(list1ForSizeEstimate)
-    val list2 = List(new Array[Byte](50), new Array[Byte](100), new Array[Byte](150))
-    val list2ForSizeEstimate = new ArrayBuffer[Any]
-    list2ForSizeEstimate ++= list2.iterator
-    val list2SizeEstimate = SizeEstimator.estimate(list2ForSizeEstimate)
-    store.put("list1", list1.iterator, StorageLevel.MEMORY_ONLY, tellMaster = true)
-    store.put("list2memory", list2.iterator, StorageLevel.MEMORY_ONLY, tellMaster = true)
-    store.put("list2disk", list2.iterator, StorageLevel.DISK_ONLY, tellMaster = true)
+    store = makeBlockManager(12000)
+    val list1 = List(new Array[Byte](2000), new Array[Byte](2000))
+    val list2 = List(new Array[Byte](500), new Array[Byte](1000), new Array[Byte](1500))
+    val list1SizeEstimate = SizeEstimator.estimate(list1.iterator.toArray)
+    val list2SizeEstimate = SizeEstimator.estimate(list2.iterator.toArray)
+    store.putIterator("list1", list1.iterator, StorageLevel.MEMORY_ONLY, tellMaster = true)
+    store.putIterator("list2memory", list2.iterator, StorageLevel.MEMORY_ONLY, tellMaster = true)
+    store.putIterator("list2disk", list2.iterator, StorageLevel.DISK_ONLY, tellMaster = true)
     val list1Get = store.get("list1")
     assert(list1Get.isDefined, "list1 expected to be in store")
     assert(list1Get.get.data.size === 2)
@@ -451,11 +446,10 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfter
   }
 
   test("in-memory LRU storage") {
-    store = new BlockManager("<driver>", actorSystem, master, serializer, 1200, conf,
-      securityMgr, mapOutputTracker)
-    val a1 = new Array[Byte](400)
-    val a2 = new Array[Byte](400)
-    val a3 = new Array[Byte](400)
+    store = makeBlockManager(12000)
+    val a1 = new Array[Byte](4000)
+    val a2 = new Array[Byte](4000)
+    val a3 = new Array[Byte](4000)
     store.putSingle("a1", a1, StorageLevel.MEMORY_ONLY)
     store.putSingle("a2", a2, StorageLevel.MEMORY_ONLY)
     store.putSingle("a3", a3, StorageLevel.MEMORY_ONLY)
@@ -471,11 +465,10 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfter
   }
 
   test("in-memory LRU storage with serialization") {
-    store = new BlockManager("<driver>", actorSystem, master, serializer, 1200, conf,
-      securityMgr, mapOutputTracker)
-    val a1 = new Array[Byte](400)
-    val a2 = new Array[Byte](400)
-    val a3 = new Array[Byte](400)
+    store = makeBlockManager(12000)
+    val a1 = new Array[Byte](4000)
+    val a2 = new Array[Byte](4000)
+    val a3 = new Array[Byte](4000)
     store.putSingle("a1", a1, StorageLevel.MEMORY_ONLY_SER)
     store.putSingle("a2", a2, StorageLevel.MEMORY_ONLY_SER)
     store.putSingle("a3", a3, StorageLevel.MEMORY_ONLY_SER)
@@ -491,11 +484,10 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfter
   }
 
   test("in-memory LRU for partitions of same RDD") {
-    store = new BlockManager("<driver>", actorSystem, master, serializer, 1200, conf,
-      securityMgr, mapOutputTracker)
-    val a1 = new Array[Byte](400)
-    val a2 = new Array[Byte](400)
-    val a3 = new Array[Byte](400)
+    store = makeBlockManager(12000)
+    val a1 = new Array[Byte](4000)
+    val a2 = new Array[Byte](4000)
+    val a3 = new Array[Byte](4000)
     store.putSingle(rdd(0, 1), a1, StorageLevel.MEMORY_ONLY)
     store.putSingle(rdd(0, 2), a2, StorageLevel.MEMORY_ONLY)
     store.putSingle(rdd(0, 3), a3, StorageLevel.MEMORY_ONLY)
@@ -511,11 +503,10 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfter
   }
 
   test("in-memory LRU for partitions of multiple RDDs") {
-    store = new BlockManager("<driver>", actorSystem, master, serializer, 1200, conf,
-      securityMgr, mapOutputTracker)
-    store.putSingle(rdd(0, 1), new Array[Byte](400), StorageLevel.MEMORY_ONLY)
-    store.putSingle(rdd(0, 2), new Array[Byte](400), StorageLevel.MEMORY_ONLY)
-    store.putSingle(rdd(1, 1), new Array[Byte](400), StorageLevel.MEMORY_ONLY)
+    store = makeBlockManager(12000)
+    store.putSingle(rdd(0, 1), new Array[Byte](4000), StorageLevel.MEMORY_ONLY)
+    store.putSingle(rdd(0, 2), new Array[Byte](4000), StorageLevel.MEMORY_ONLY)
+    store.putSingle(rdd(1, 1), new Array[Byte](4000), StorageLevel.MEMORY_ONLY)
     // At this point rdd_1_1 should've replaced rdd_0_1
     assert(store.memoryStore.contains(rdd(1, 1)), "rdd_1_1 was not in store")
     assert(!store.memoryStore.contains(rdd(0, 1)), "rdd_0_1 was in store")
@@ -523,8 +514,8 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfter
     // Do a get() on rdd_0_2 so that it is the most recently used item
     assert(store.getSingle(rdd(0, 2)).isDefined, "rdd_0_2 was not in store")
     // Put in more partitions from RDD 0; they should replace rdd_1_1
-    store.putSingle(rdd(0, 3), new Array[Byte](400), StorageLevel.MEMORY_ONLY)
-    store.putSingle(rdd(0, 4), new Array[Byte](400), StorageLevel.MEMORY_ONLY)
+    store.putSingle(rdd(0, 3), new Array[Byte](4000), StorageLevel.MEMORY_ONLY)
+    store.putSingle(rdd(0, 4), new Array[Byte](4000), StorageLevel.MEMORY_ONLY)
     // Now rdd_1_1 should be dropped to add rdd_0_3, but then rdd_0_2 should *not* be dropped
     // when we try to add rdd_0_4.
     assert(!store.memoryStore.contains(rdd(1, 1)), "rdd_1_1 was in store")
@@ -538,8 +529,7 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfter
     // TODO Make the spark.test.tachyon.enable true after using tachyon 0.5.0 testing jar.
     val tachyonUnitTestEnabled = conf.getBoolean("spark.test.tachyon.enable", false)
     if (tachyonUnitTestEnabled) {
-      store = new BlockManager("<driver>", actorSystem, master, serializer, 1200, conf,
-        securityMgr, mapOutputTracker)
+      store = makeBlockManager(1200)
       val a1 = new Array[Byte](400)
       val a2 = new Array[Byte](400)
       val a3 = new Array[Byte](400)
@@ -555,8 +545,7 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfter
   }
 
   test("on-disk storage") {
-    store = new BlockManager("<driver>", actorSystem, master, serializer, 1200, conf,
-      securityMgr, mapOutputTracker)
+    store = makeBlockManager(1200)
     val a1 = new Array[Byte](400)
     val a2 = new Array[Byte](400)
     val a3 = new Array[Byte](400)
@@ -569,11 +558,10 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfter
   }
 
   test("disk and memory storage") {
-    store = new BlockManager("<driver>", actorSystem, master, serializer, 1200, conf,
-      securityMgr, mapOutputTracker)
-    val a1 = new Array[Byte](400)
-    val a2 = new Array[Byte](400)
-    val a3 = new Array[Byte](400)
+    store = makeBlockManager(12000)
+    val a1 = new Array[Byte](4000)
+    val a2 = new Array[Byte](4000)
+    val a3 = new Array[Byte](4000)
     store.putSingle("a1", a1, StorageLevel.MEMORY_AND_DISK)
     store.putSingle("a2", a2, StorageLevel.MEMORY_AND_DISK)
     store.putSingle("a3", a3, StorageLevel.MEMORY_AND_DISK)
@@ -585,11 +573,10 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfter
   }
 
   test("disk and memory storage with getLocalBytes") {
-    store = new BlockManager("<driver>", actorSystem, master, serializer, 1200, conf,
-      securityMgr, mapOutputTracker)
-    val a1 = new Array[Byte](400)
-    val a2 = new Array[Byte](400)
-    val a3 = new Array[Byte](400)
+    store = makeBlockManager(12000)
+    val a1 = new Array[Byte](4000)
+    val a2 = new Array[Byte](4000)
+    val a3 = new Array[Byte](4000)
     store.putSingle("a1", a1, StorageLevel.MEMORY_AND_DISK)
     store.putSingle("a2", a2, StorageLevel.MEMORY_AND_DISK)
     store.putSingle("a3", a3, StorageLevel.MEMORY_AND_DISK)
@@ -601,11 +588,10 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfter
   }
 
   test("disk and memory storage with serialization") {
-    store = new BlockManager("<driver>", actorSystem, master, serializer, 1200, conf,
-      securityMgr, mapOutputTracker)
-    val a1 = new Array[Byte](400)
-    val a2 = new Array[Byte](400)
-    val a3 = new Array[Byte](400)
+    store = makeBlockManager(12000)
+    val a1 = new Array[Byte](4000)
+    val a2 = new Array[Byte](4000)
+    val a3 = new Array[Byte](4000)
     store.putSingle("a1", a1, StorageLevel.MEMORY_AND_DISK_SER)
     store.putSingle("a2", a2, StorageLevel.MEMORY_AND_DISK_SER)
     store.putSingle("a3", a3, StorageLevel.MEMORY_AND_DISK_SER)
@@ -617,11 +603,10 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfter
   }
 
   test("disk and memory storage with serialization and getLocalBytes") {
-    store = new BlockManager("<driver>", actorSystem, master, serializer, 1200, conf,
-      securityMgr, mapOutputTracker)
-    val a1 = new Array[Byte](400)
-    val a2 = new Array[Byte](400)
-    val a3 = new Array[Byte](400)
+    store = makeBlockManager(12000)
+    val a1 = new Array[Byte](4000)
+    val a2 = new Array[Byte](4000)
+    val a3 = new Array[Byte](4000)
     store.putSingle("a1", a1, StorageLevel.MEMORY_AND_DISK_SER)
     store.putSingle("a2", a2, StorageLevel.MEMORY_AND_DISK_SER)
     store.putSingle("a3", a3, StorageLevel.MEMORY_AND_DISK_SER)
@@ -633,12 +618,11 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfter
   }
 
   test("LRU with mixed storage levels") {
-    store = new BlockManager("<driver>", actorSystem, master, serializer, 1200, conf,
-      securityMgr, mapOutputTracker)
-    val a1 = new Array[Byte](400)
-    val a2 = new Array[Byte](400)
-    val a3 = new Array[Byte](400)
-    val a4 = new Array[Byte](400)
+    store = makeBlockManager(12000)
+    val a1 = new Array[Byte](4000)
+    val a2 = new Array[Byte](4000)
+    val a3 = new Array[Byte](4000)
+    val a4 = new Array[Byte](4000)
     // First store a1 and a2, both in memory, and a3, on disk only
     store.putSingle("a1", a1, StorageLevel.MEMORY_ONLY_SER)
     store.putSingle("a2", a2, StorageLevel.MEMORY_ONLY_SER)
@@ -656,14 +640,13 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfter
   }
 
   test("in-memory LRU with streams") {
-    store = new BlockManager("<driver>", actorSystem, master, serializer, 1200, conf,
-      securityMgr, mapOutputTracker)
-    val list1 = List(new Array[Byte](200), new Array[Byte](200))
-    val list2 = List(new Array[Byte](200), new Array[Byte](200))
-    val list3 = List(new Array[Byte](200), new Array[Byte](200))
-    store.put("list1", list1.iterator, StorageLevel.MEMORY_ONLY, tellMaster = true)
-    store.put("list2", list2.iterator, StorageLevel.MEMORY_ONLY, tellMaster = true)
-    store.put("list3", list3.iterator, StorageLevel.MEMORY_ONLY, tellMaster = true)
+    store = makeBlockManager(12000)
+    val list1 = List(new Array[Byte](2000), new Array[Byte](2000))
+    val list2 = List(new Array[Byte](2000), new Array[Byte](2000))
+    val list3 = List(new Array[Byte](2000), new Array[Byte](2000))
+    store.putIterator("list1", list1.iterator, StorageLevel.MEMORY_ONLY, tellMaster = true)
+    store.putIterator("list2", list2.iterator, StorageLevel.MEMORY_ONLY, tellMaster = true)
+    store.putIterator("list3", list3.iterator, StorageLevel.MEMORY_ONLY, tellMaster = true)
     assert(store.get("list2").isDefined, "list2 was not in store")
     assert(store.get("list2").get.data.size === 2)
     assert(store.get("list3").isDefined, "list3 was not in store")
@@ -672,7 +655,7 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfter
     assert(store.get("list2").isDefined, "list2 was not in store")
     assert(store.get("list2").get.data.size === 2)
     // At this point list2 was gotten last, so LRU will getSingle rid of list3
-    store.put("list1", list1.iterator, StorageLevel.MEMORY_ONLY, tellMaster = true)
+    store.putIterator("list1", list1.iterator, StorageLevel.MEMORY_ONLY, tellMaster = true)
     assert(store.get("list1").isDefined, "list1 was not in store")
     assert(store.get("list1").get.data.size === 2)
     assert(store.get("list2").isDefined, "list2 was not in store")
@@ -681,16 +664,15 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfter
   }
 
   test("LRU with mixed storage levels and streams") {
-    store = new BlockManager("<driver>", actorSystem, master, serializer, 1200, conf,
-      securityMgr, mapOutputTracker)
-    val list1 = List(new Array[Byte](200), new Array[Byte](200))
-    val list2 = List(new Array[Byte](200), new Array[Byte](200))
-    val list3 = List(new Array[Byte](200), new Array[Byte](200))
-    val list4 = List(new Array[Byte](200), new Array[Byte](200))
+    store = makeBlockManager(12000)
+    val list1 = List(new Array[Byte](2000), new Array[Byte](2000))
+    val list2 = List(new Array[Byte](2000), new Array[Byte](2000))
+    val list3 = List(new Array[Byte](2000), new Array[Byte](2000))
+    val list4 = List(new Array[Byte](2000), new Array[Byte](2000))
     // First store list1 and list2, both in memory, and list3, on disk only
-    store.put("list1", list1.iterator, StorageLevel.MEMORY_ONLY_SER, tellMaster = true)
-    store.put("list2", list2.iterator, StorageLevel.MEMORY_ONLY_SER, tellMaster = true)
-    store.put("list3", list3.iterator, StorageLevel.DISK_ONLY, tellMaster = true)
+    store.putIterator("list1", list1.iterator, StorageLevel.MEMORY_ONLY_SER, tellMaster = true)
+    store.putIterator("list2", list2.iterator, StorageLevel.MEMORY_ONLY_SER, tellMaster = true)
+    store.putIterator("list3", list3.iterator, StorageLevel.DISK_ONLY, tellMaster = true)
     val listForSizeEstimate = new ArrayBuffer[Any]
     listForSizeEstimate ++= list1.iterator
     val listSize = SizeEstimator.estimate(listForSizeEstimate)
@@ -708,7 +690,7 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfter
     assert(store.get("list3").isDefined, "list3 was not in store")
     assert(store.get("list3").get.data.size === 2)
     // Now let's add in list4, which uses both disk and memory; list1 should drop out
-    store.put("list4", list4.iterator, StorageLevel.MEMORY_AND_DISK_SER, tellMaster = true)
+    store.putIterator("list4", list4.iterator, StorageLevel.MEMORY_AND_DISK_SER, tellMaster = true)
     assert(store.get("list1") === None, "list1 was in store")
     assert(store.get("list2").isDefined, "list2 was not in store")
     assert(store.get("list2").get.data.size === 2)
@@ -731,11 +713,10 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfter
   }
 
   test("overly large block") {
-    store = new BlockManager("<driver>", actorSystem, master, serializer, 500, conf,
-      securityMgr, mapOutputTracker)
-    store.putSingle("a1", new Array[Byte](1000), StorageLevel.MEMORY_ONLY)
+    store = makeBlockManager(5000)
+    store.putSingle("a1", new Array[Byte](10000), StorageLevel.MEMORY_ONLY)
     assert(store.getSingle("a1") === None, "a1 was in store")
-    store.putSingle("a2", new Array[Byte](1000), StorageLevel.MEMORY_AND_DISK)
+    store.putSingle("a2", new Array[Byte](10000), StorageLevel.MEMORY_AND_DISK)
     assert(store.memoryStore.getValues("a2") === None, "a2 was in memory store")
     assert(store.getSingle("a2").isDefined, "a2 was not in store")
   }
@@ -743,8 +724,7 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfter
   test("block compression") {
     try {
       conf.set("spark.shuffle.compress", "true")
-      store = new BlockManager("exec1", actorSystem, master, serializer, 2000, conf,
-        securityMgr, mapOutputTracker)
+      store = makeBlockManager(20000, "exec1")
       store.putSingle(ShuffleBlockId(0, 0, 0), new Array[Byte](1000), StorageLevel.MEMORY_ONLY_SER)
       assert(store.memoryStore.getSize(ShuffleBlockId(0, 0, 0)) <= 100,
         "shuffle_0_0_0 was not compressed")
@@ -752,52 +732,46 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfter
       store = null
 
       conf.set("spark.shuffle.compress", "false")
-      store = new BlockManager("exec2", actorSystem, master, serializer, 2000, conf,
-        securityMgr, mapOutputTracker)
-      store.putSingle(ShuffleBlockId(0, 0, 0), new Array[Byte](1000), StorageLevel.MEMORY_ONLY_SER)
-      assert(store.memoryStore.getSize(ShuffleBlockId(0, 0, 0)) >= 1000,
+      store = makeBlockManager(20000, "exec2")
+      store.putSingle(ShuffleBlockId(0, 0, 0), new Array[Byte](10000), StorageLevel.MEMORY_ONLY_SER)
+      assert(store.memoryStore.getSize(ShuffleBlockId(0, 0, 0)) >= 10000,
         "shuffle_0_0_0 was compressed")
       store.stop()
       store = null
 
       conf.set("spark.broadcast.compress", "true")
-      store = new BlockManager("exec3", actorSystem, master, serializer, 2000, conf,
-        securityMgr, mapOutputTracker)
-      store.putSingle(BroadcastBlockId(0), new Array[Byte](1000), StorageLevel.MEMORY_ONLY_SER)
-      assert(store.memoryStore.getSize(BroadcastBlockId(0)) <= 100,
+      store = makeBlockManager(20000, "exec3")
+      store.putSingle(BroadcastBlockId(0), new Array[Byte](10000), StorageLevel.MEMORY_ONLY_SER)
+      assert(store.memoryStore.getSize(BroadcastBlockId(0)) <= 1000,
         "broadcast_0 was not compressed")
       store.stop()
       store = null
 
       conf.set("spark.broadcast.compress", "false")
-      store = new BlockManager("exec4", actorSystem, master, serializer, 2000, conf,
-        securityMgr, mapOutputTracker)
-      store.putSingle(BroadcastBlockId(0), new Array[Byte](1000), StorageLevel.MEMORY_ONLY_SER)
-      assert(store.memoryStore.getSize(BroadcastBlockId(0)) >= 1000, "broadcast_0 was compressed")
+      store = makeBlockManager(20000, "exec4")
+      store.putSingle(BroadcastBlockId(0), new Array[Byte](10000), StorageLevel.MEMORY_ONLY_SER)
+      assert(store.memoryStore.getSize(BroadcastBlockId(0)) >= 10000, "broadcast_0 was compressed")
       store.stop()
       store = null
 
       conf.set("spark.rdd.compress", "true")
-      store = new BlockManager("exec5", actorSystem, master, serializer, 2000, conf,
-        securityMgr, mapOutputTracker)
-      store.putSingle(rdd(0, 0), new Array[Byte](1000), StorageLevel.MEMORY_ONLY_SER)
-      assert(store.memoryStore.getSize(rdd(0, 0)) <= 100, "rdd_0_0 was not compressed")
+      store = makeBlockManager(20000, "exec5")
+      store.putSingle(rdd(0, 0), new Array[Byte](10000), StorageLevel.MEMORY_ONLY_SER)
+      assert(store.memoryStore.getSize(rdd(0, 0)) <= 1000, "rdd_0_0 was not compressed")
       store.stop()
       store = null
 
       conf.set("spark.rdd.compress", "false")
-      store = new BlockManager("exec6", actorSystem, master, serializer, 2000, conf,
-        securityMgr, mapOutputTracker)
-      store.putSingle(rdd(0, 0), new Array[Byte](1000), StorageLevel.MEMORY_ONLY_SER)
-      assert(store.memoryStore.getSize(rdd(0, 0)) >= 1000, "rdd_0_0 was compressed")
+      store = makeBlockManager(20000, "exec6")
+      store.putSingle(rdd(0, 0), new Array[Byte](10000), StorageLevel.MEMORY_ONLY_SER)
+      assert(store.memoryStore.getSize(rdd(0, 0)) >= 10000, "rdd_0_0 was compressed")
       store.stop()
       store = null
 
       // Check that any other block types are also kept uncompressed
-      store = new BlockManager("exec7", actorSystem, master, serializer, 2000, conf,
-        securityMgr, mapOutputTracker)
-      store.putSingle("other_block", new Array[Byte](1000), StorageLevel.MEMORY_ONLY)
-      assert(store.memoryStore.getSize("other_block") >= 1000, "other_block was compressed")
+      store = makeBlockManager(20000, "exec7")
+      store.putSingle("other_block", new Array[Byte](10000), StorageLevel.MEMORY_ONLY)
+      assert(store.memoryStore.getSize("other_block") >= 10000, "other_block was compressed")
       store.stop()
       store = null
     } finally {
@@ -871,30 +845,29 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfter
     assert(Arrays.equals(mappedAsArray, bytes))
     assert(Arrays.equals(notMappedAsArray, bytes))
   }
-  
+
   test("updated block statuses") {
-    store = new BlockManager("<driver>", actorSystem, master, serializer, 1200, conf,
-      securityMgr, mapOutputTracker)
-    val list = List.fill(2)(new Array[Byte](200))
-    val bigList = List.fill(8)(new Array[Byte](200))
+    store = makeBlockManager(12000)
+    val list = List.fill(2)(new Array[Byte](2000))
+    val bigList = List.fill(8)(new Array[Byte](2000))
 
     // 1 updated block (i.e. list1)
     val updatedBlocks1 =
-      store.put("list1", list.iterator, StorageLevel.MEMORY_ONLY, tellMaster = true)
+      store.putIterator("list1", list.iterator, StorageLevel.MEMORY_ONLY, tellMaster = true)
     assert(updatedBlocks1.size === 1)
     assert(updatedBlocks1.head._1 === TestBlockId("list1"))
     assert(updatedBlocks1.head._2.storageLevel === StorageLevel.MEMORY_ONLY)
 
     // 1 updated block (i.e. list2)
     val updatedBlocks2 =
-      store.put("list2", list.iterator, StorageLevel.MEMORY_AND_DISK, tellMaster = true)
+      store.putIterator("list2", list.iterator, StorageLevel.MEMORY_AND_DISK, tellMaster = true)
     assert(updatedBlocks2.size === 1)
     assert(updatedBlocks2.head._1 === TestBlockId("list2"))
     assert(updatedBlocks2.head._2.storageLevel === StorageLevel.MEMORY_ONLY)
 
     // 2 updated blocks - list1 is kicked out of memory while list3 is added
     val updatedBlocks3 =
-      store.put("list3", list.iterator, StorageLevel.MEMORY_ONLY, tellMaster = true)
+      store.putIterator("list3", list.iterator, StorageLevel.MEMORY_ONLY, tellMaster = true)
     assert(updatedBlocks3.size === 2)
     updatedBlocks3.foreach { case (id, status) =>
       id match {
@@ -903,11 +876,11 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfter
         case _ => fail("Updated block is neither list1 nor list3")
       }
     }
-    assert(store.get("list3").isDefined, "list3 was not in store")
+    assert(store.memoryStore.contains("list3"), "list3 was not in memory store")
 
     // 2 updated blocks - list2 is kicked out of memory (but put on disk) while list4 is added
     val updatedBlocks4 =
-      store.put("list4", list.iterator, StorageLevel.MEMORY_ONLY, tellMaster = true)
+      store.putIterator("list4", list.iterator, StorageLevel.MEMORY_ONLY, tellMaster = true)
     assert(updatedBlocks4.size === 2)
     updatedBlocks4.foreach { case (id, status) =>
       id match {
@@ -916,26 +889,37 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfter
         case _ => fail("Updated block is neither list2 nor list4")
       }
     }
-    assert(store.get("list4").isDefined, "list4 was not in store")
+    assert(store.diskStore.contains("list2"), "list2 was not in disk store")
+    assert(store.memoryStore.contains("list4"), "list4 was not in memory store")
 
-    // No updated blocks - nothing is kicked out of memory because list5 is too big to be added
+    // No updated blocks - list5 is too big to fit in store and nothing is kicked out
     val updatedBlocks5 =
-      store.put("list5", bigList.iterator, StorageLevel.MEMORY_ONLY, tellMaster = true)
+      store.putIterator("list5", bigList.iterator, StorageLevel.MEMORY_ONLY, tellMaster = true)
     assert(updatedBlocks5.size === 0)
-    assert(store.get("list2").isDefined, "list2 was not in store")
-    assert(store.get("list4").isDefined, "list4 was not in store")
-    assert(!store.get("list5").isDefined, "list5 was in store")
+
+    // memory store contains only list3 and list4
+    assert(!store.memoryStore.contains("list1"), "list1 was in memory store")
+    assert(!store.memoryStore.contains("list2"), "list2 was in memory store")
+    assert(store.memoryStore.contains("list3"), "list3 was not in memory store")
+    assert(store.memoryStore.contains("list4"), "list4 was not in memory store")
+    assert(!store.memoryStore.contains("list5"), "list5 was in memory store")
+
+    // disk store contains only list2
+    assert(!store.diskStore.contains("list1"), "list1 was in disk store")
+    assert(store.diskStore.contains("list2"), "list2 was not in disk store")
+    assert(!store.diskStore.contains("list3"), "list3 was in disk store")
+    assert(!store.diskStore.contains("list4"), "list4 was in disk store")
+    assert(!store.diskStore.contains("list5"), "list5 was in disk store")
   }
 
   test("query block statuses") {
-    store = new BlockManager("<driver>", actorSystem, master, serializer, 1200, conf,
-      securityMgr, mapOutputTracker)
-    val list = List.fill(2)(new Array[Byte](200))
+    store = makeBlockManager(12000)
+    val list = List.fill(2)(new Array[Byte](2000))
 
     // Tell master. By LRU, only list2 and list3 remains.
-    store.put("list1", list.iterator, StorageLevel.MEMORY_ONLY, tellMaster = true)
-    store.put("list2", list.iterator, StorageLevel.MEMORY_AND_DISK, tellMaster = true)
-    store.put("list3", list.iterator, StorageLevel.MEMORY_ONLY, tellMaster = true)
+    store.putIterator("list1", list.iterator, StorageLevel.MEMORY_ONLY, tellMaster = true)
+    store.putIterator("list2", list.iterator, StorageLevel.MEMORY_AND_DISK, tellMaster = true)
+    store.putIterator("list3", list.iterator, StorageLevel.MEMORY_ONLY, tellMaster = true)
 
     // getLocations and getBlockStatus should yield the same locations
     assert(store.master.getLocations("list1").size === 0)
@@ -949,9 +933,9 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfter
     assert(store.master.getBlockStatus("list3", askSlaves = true).size === 1)
 
     // This time don't tell master and see what happens. By LRU, only list5 and list6 remains.
-    store.put("list4", list.iterator, StorageLevel.MEMORY_ONLY, tellMaster = false)
-    store.put("list5", list.iterator, StorageLevel.MEMORY_AND_DISK, tellMaster = false)
-    store.put("list6", list.iterator, StorageLevel.MEMORY_ONLY, tellMaster = false)
+    store.putIterator("list4", list.iterator, StorageLevel.MEMORY_ONLY, tellMaster = false)
+    store.putIterator("list5", list.iterator, StorageLevel.MEMORY_AND_DISK, tellMaster = false)
+    store.putIterator("list6", list.iterator, StorageLevel.MEMORY_ONLY, tellMaster = false)
 
     // getLocations should return nothing because the master is not informed
     // getBlockStatus without asking slaves should have the same result
@@ -968,23 +952,22 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfter
   }
 
   test("get matching blocks") {
-    store = new BlockManager("<driver>", actorSystem, master, serializer, 1200, conf,
-      securityMgr, mapOutputTracker)
-    val list = List.fill(2)(new Array[Byte](10))
+    store = makeBlockManager(12000)
+    val list = List.fill(2)(new Array[Byte](100))
 
     // insert some blocks
-    store.put("list1", list.iterator, StorageLevel.MEMORY_AND_DISK, tellMaster = true)
-    store.put("list2", list.iterator, StorageLevel.MEMORY_AND_DISK, tellMaster = true)
-    store.put("list3", list.iterator, StorageLevel.MEMORY_AND_DISK, tellMaster = true)
+    store.putIterator("list1", list.iterator, StorageLevel.MEMORY_AND_DISK, tellMaster = true)
+    store.putIterator("list2", list.iterator, StorageLevel.MEMORY_AND_DISK, tellMaster = true)
+    store.putIterator("list3", list.iterator, StorageLevel.MEMORY_AND_DISK, tellMaster = true)
 
     // getLocations and getBlockStatus should yield the same locations
     assert(store.master.getMatchingBlockIds(_.toString.contains("list"), askSlaves = false).size === 3)
     assert(store.master.getMatchingBlockIds(_.toString.contains("list1"), askSlaves = false).size === 1)
 
     // insert some more blocks
-    store.put("newlist1", list.iterator, StorageLevel.MEMORY_AND_DISK, tellMaster = true)
-    store.put("newlist2", list.iterator, StorageLevel.MEMORY_AND_DISK, tellMaster = false)
-    store.put("newlist3", list.iterator, StorageLevel.MEMORY_AND_DISK, tellMaster = false)
+    store.putIterator("newlist1", list.iterator, StorageLevel.MEMORY_AND_DISK, tellMaster = true)
+    store.putIterator("newlist2", list.iterator, StorageLevel.MEMORY_AND_DISK, tellMaster = false)
+    store.putIterator("newlist3", list.iterator, StorageLevel.MEMORY_AND_DISK, tellMaster = false)
 
     // getLocations and getBlockStatus should yield the same locations
     assert(store.master.getMatchingBlockIds(_.toString.contains("newlist"), askSlaves = false).size === 1)
@@ -992,7 +975,7 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfter
 
     val blockIds = Seq(RDDBlockId(1, 0), RDDBlockId(1, 1), RDDBlockId(2, 0))
     blockIds.foreach { blockId =>
-      store.put(blockId, list.iterator, StorageLevel.MEMORY_ONLY, tellMaster = true)
+      store.putIterator(blockId, list.iterator, StorageLevel.MEMORY_ONLY, tellMaster = true)
     }
     val matchedBlockIds = store.master.getMatchingBlockIds(_ match {
       case RDDBlockId(1, _) => true
@@ -1002,17 +985,240 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfter
   }
 
   test("SPARK-1194 regression: fix the same-RDD rule for cache replacement") {
-    store = new BlockManager("<driver>", actorSystem, master, serializer, 1200, conf,
-      securityMgr, mapOutputTracker)
-    store.putSingle(rdd(0, 0), new Array[Byte](400), StorageLevel.MEMORY_ONLY)
-    store.putSingle(rdd(1, 0), new Array[Byte](400), StorageLevel.MEMORY_ONLY)
+    store = makeBlockManager(12000)
+    store.putSingle(rdd(0, 0), new Array[Byte](4000), StorageLevel.MEMORY_ONLY)
+    store.putSingle(rdd(1, 0), new Array[Byte](4000), StorageLevel.MEMORY_ONLY)
     // Access rdd_1_0 to ensure it's not least recently used.
     assert(store.getSingle(rdd(1, 0)).isDefined, "rdd_1_0 was not in store")
     // According to the same-RDD rule, rdd_1_0 should be replaced here.
-    store.putSingle(rdd(0, 1), new Array[Byte](400), StorageLevel.MEMORY_ONLY)
+    store.putSingle(rdd(0, 1), new Array[Byte](4000), StorageLevel.MEMORY_ONLY)
     // rdd_1_0 should have been replaced, even it's not least recently used.
     assert(store.memoryStore.contains(rdd(0, 0)), "rdd_0_0 was not in store")
     assert(store.memoryStore.contains(rdd(0, 1)), "rdd_0_1 was not in store")
     assert(!store.memoryStore.contains(rdd(1, 0)), "rdd_1_0 was in store")
   }
+
+  test("reserve/release unroll memory") {
+    store = makeBlockManager(12000)
+    val memoryStore = store.memoryStore
+    assert(memoryStore.currentUnrollMemory === 0)
+    assert(memoryStore.currentUnrollMemoryForThisThread === 0)
+
+    // Reserve
+    memoryStore.reserveUnrollMemoryForThisThread(100)
+    assert(memoryStore.currentUnrollMemoryForThisThread === 100)
+    memoryStore.reserveUnrollMemoryForThisThread(200)
+    assert(memoryStore.currentUnrollMemoryForThisThread === 300)
+    memoryStore.reserveUnrollMemoryForThisThread(500)
+    assert(memoryStore.currentUnrollMemoryForThisThread === 800)
+    memoryStore.reserveUnrollMemoryForThisThread(1000000)
+    assert(memoryStore.currentUnrollMemoryForThisThread === 800) // not granted
+    // Release
+    memoryStore.releaseUnrollMemoryForThisThread(100)
+    assert(memoryStore.currentUnrollMemoryForThisThread === 700)
+    memoryStore.releaseUnrollMemoryForThisThread(100)
+    assert(memoryStore.currentUnrollMemoryForThisThread === 600)
+    // Reserve again
+    memoryStore.reserveUnrollMemoryForThisThread(4400)
+    assert(memoryStore.currentUnrollMemoryForThisThread === 5000)
+    memoryStore.reserveUnrollMemoryForThisThread(20000)
+    assert(memoryStore.currentUnrollMemoryForThisThread === 5000) // not granted
+    // Release again
+    memoryStore.releaseUnrollMemoryForThisThread(1000)
+    assert(memoryStore.currentUnrollMemoryForThisThread === 4000)
+    memoryStore.releaseUnrollMemoryForThisThread() // release all
+    assert(memoryStore.currentUnrollMemoryForThisThread === 0)
+  }
+
+  /**
+   * Verify the result of MemoryStore#unrollSafely is as expected.
+   */
+  private def verifyUnroll(
+      expected: Iterator[Any],
+      result: Either[Array[Any], Iterator[Any]],
+      shouldBeArray: Boolean): Unit = {
+    val actual: Iterator[Any] = result match {
+      case Left(arr: Array[Any]) =>
+        assert(shouldBeArray, "expected iterator from unroll!")
+        arr.iterator
+      case Right(it: Iterator[Any]) =>
+        assert(!shouldBeArray, "expected array from unroll!")
+        it
+      case _ =>
+        fail("unroll returned neither an iterator nor an array...")
+    }
+    expected.zip(actual).foreach { case (e, a) =>
+      assert(e === a, "unroll did not return original values!")
+    }
+  }
+
+  test("safely unroll blocks") {
+    store = makeBlockManager(12000)
+    val smallList = List.fill(40)(new Array[Byte](100))
+    val bigList = List.fill(40)(new Array[Byte](1000))
+    val memoryStore = store.memoryStore
+    val droppedBlocks = new ArrayBuffer[(BlockId, BlockStatus)]
+    assert(memoryStore.currentUnrollMemoryForThisThread === 0)
+
+    // Unroll with all the space in the world. This should succeed and return an array.
+    var unrollResult = memoryStore.unrollSafely("unroll", smallList.iterator, droppedBlocks)
+    verifyUnroll(smallList.iterator, unrollResult, shouldBeArray = true)
+    assert(memoryStore.currentUnrollMemoryForThisThread === 0)
+
+    // Unroll with not enough space. This should succeed after kicking out someBlock1.
+    store.putIterator("someBlock1", smallList.iterator, StorageLevel.MEMORY_ONLY)
+    store.putIterator("someBlock2", smallList.iterator, StorageLevel.MEMORY_ONLY)
+    unrollResult = memoryStore.unrollSafely("unroll", smallList.iterator, droppedBlocks)
+    verifyUnroll(smallList.iterator, unrollResult, shouldBeArray = true)
+    assert(memoryStore.currentUnrollMemoryForThisThread === 0)
+    assert(droppedBlocks.size === 1)
+    assert(droppedBlocks.head._1 === TestBlockId("someBlock1"))
+    droppedBlocks.clear()
+
+    // Unroll huge block with not enough space. Even after ensuring free space of 12000 * 0.4 =
+    // 4800 bytes, there is still not enough room to unroll this block. This returns an iterator.
+    // In the mean time, however, we kicked out someBlock2 before giving up.
+    store.putIterator("someBlock3", smallList.iterator, StorageLevel.MEMORY_ONLY)
+    unrollResult = memoryStore.unrollSafely("unroll", bigList.iterator, droppedBlocks)
+    verifyUnroll(bigList.iterator, unrollResult, shouldBeArray = false)
+    assert(memoryStore.currentUnrollMemoryForThisThread > 0) // we returned an iterator
+    assert(droppedBlocks.size === 1)
+    assert(droppedBlocks.head._1 === TestBlockId("someBlock2"))
+    droppedBlocks.clear()
+  }
+
+  test("safely unroll blocks through putIterator") {
+    store = makeBlockManager(12000)
+    val memOnly = StorageLevel.MEMORY_ONLY
+    val memoryStore = store.memoryStore
+    val smallList = List.fill(40)(new Array[Byte](100))
+    val bigList = List.fill(40)(new Array[Byte](1000))
+    def smallIterator = smallList.iterator.asInstanceOf[Iterator[Any]]
+    def bigIterator = bigList.iterator.asInstanceOf[Iterator[Any]]
+    assert(memoryStore.currentUnrollMemoryForThisThread === 0)
+
+    // Unroll with plenty of space. This should succeed and cache both blocks.
+    val result1 = memoryStore.putIterator("b1", smallIterator, memOnly, returnValues = true)
+    val result2 = memoryStore.putIterator("b2", smallIterator, memOnly, returnValues = true)
+    assert(memoryStore.contains("b1"))
+    assert(memoryStore.contains("b2"))
+    assert(result1.size > 0) // unroll was successful
+    assert(result2.size > 0)
+    assert(result1.data.isLeft) // unroll did not drop this block to disk
+    assert(result2.data.isLeft)
+    assert(memoryStore.currentUnrollMemoryForThisThread === 0)
+
+    // Re-put these two blocks so block manager knows about them too. Otherwise, block manager
+    // would not know how to drop them from memory later.
+    memoryStore.remove("b1")
+    memoryStore.remove("b2")
+    store.putIterator("b1", smallIterator, memOnly)
+    store.putIterator("b2", smallIterator, memOnly)
+
+    // Unroll with not enough space. This should succeed but kick out b1 in the process.
+    val result3 = memoryStore.putIterator("b3", smallIterator, memOnly, returnValues = true)
+    assert(result3.size > 0)
+    assert(result3.data.isLeft)
+    assert(!memoryStore.contains("b1"))
+    assert(memoryStore.contains("b2"))
+    assert(memoryStore.contains("b3"))
+    assert(memoryStore.currentUnrollMemoryForThisThread === 0)
+    memoryStore.remove("b3")
+    store.putIterator("b3", smallIterator, memOnly)
+
+    // Unroll huge block with not enough space. This should fail and kick out b2 in the process.
+    val result4 = memoryStore.putIterator("b4", bigIterator, memOnly, returnValues = true)
+    assert(result4.size === 0) // unroll was unsuccessful
+    assert(result4.data.isLeft)
+    assert(!memoryStore.contains("b1"))
+    assert(!memoryStore.contains("b2"))
+    assert(memoryStore.contains("b3"))
+    assert(!memoryStore.contains("b4"))
+    assert(memoryStore.currentUnrollMemoryForThisThread > 0) // we returned an iterator
+  }
+
+  /**
+   * This test is essentially identical to the preceding one, except that it uses MEMORY_AND_DISK.
+   */
+  test("safely unroll blocks through putIterator (disk)") {
+    store = makeBlockManager(12000)
+    val memAndDisk = StorageLevel.MEMORY_AND_DISK
+    val memoryStore = store.memoryStore
+    val diskStore = store.diskStore
+    val smallList = List.fill(40)(new Array[Byte](100))
+    val bigList = List.fill(40)(new Array[Byte](1000))
+    def smallIterator = smallList.iterator.asInstanceOf[Iterator[Any]]
+    def bigIterator = bigList.iterator.asInstanceOf[Iterator[Any]]
+    assert(memoryStore.currentUnrollMemoryForThisThread === 0)
+
+    store.putIterator("b1", smallIterator, memAndDisk)
+    store.putIterator("b2", smallIterator, memAndDisk)
+
+    // Unroll with not enough space. This should succeed but kick out b1 in the process.
+    // Memory store should contain b2 and b3, while disk store should contain only b1
+    val result3 = memoryStore.putIterator("b3", smallIterator, memAndDisk, returnValues = true)
+    assert(result3.size > 0)
+    assert(!memoryStore.contains("b1"))
+    assert(memoryStore.contains("b2"))
+    assert(memoryStore.contains("b3"))
+    assert(diskStore.contains("b1"))
+    assert(!diskStore.contains("b2"))
+    assert(!diskStore.contains("b3"))
+    memoryStore.remove("b3")
+    store.putIterator("b3", smallIterator, StorageLevel.MEMORY_ONLY)
+    assert(memoryStore.currentUnrollMemoryForThisThread === 0)
+
+    // Unroll huge block with not enough space. This should fail and drop the new block to disk
+    // directly in addition to kicking out b2 in the process. Memory store should contain only
+    // b3, while disk store should contain b1, b2 and b4.
+    val result4 = memoryStore.putIterator("b4", bigIterator, memAndDisk, returnValues = true)
+    assert(result4.size > 0)
+    assert(result4.data.isRight) // unroll returned bytes from disk
+    assert(!memoryStore.contains("b1"))
+    assert(!memoryStore.contains("b2"))
+    assert(memoryStore.contains("b3"))
+    assert(!memoryStore.contains("b4"))
+    assert(diskStore.contains("b1"))
+    assert(diskStore.contains("b2"))
+    assert(!diskStore.contains("b3"))
+    assert(diskStore.contains("b4"))
+    assert(memoryStore.currentUnrollMemoryForThisThread > 0) // we returned an iterator
+  }
+
+  test("multiple unrolls by the same thread") {
+    store = makeBlockManager(12000)
+    val memOnly = StorageLevel.MEMORY_ONLY
+    val memoryStore = store.memoryStore
+    val smallList = List.fill(40)(new Array[Byte](100))
+    def smallIterator = smallList.iterator.asInstanceOf[Iterator[Any]]
+    assert(memoryStore.currentUnrollMemoryForThisThread === 0)
+
+    // All unroll memory used is released because unrollSafely returned an array
+    memoryStore.putIterator("b1", smallIterator, memOnly, returnValues = true)
+    assert(memoryStore.currentUnrollMemoryForThisThread === 0)
+    memoryStore.putIterator("b2", smallIterator, memOnly, returnValues = true)
+    assert(memoryStore.currentUnrollMemoryForThisThread === 0)
+
+    // Unroll memory is not released because unrollSafely returned an iterator
+    // that still depends on the underlying vector used in the process
+    memoryStore.putIterator("b3", smallIterator, memOnly, returnValues = true)
+    val unrollMemoryAfterB3 = memoryStore.currentUnrollMemoryForThisThread
+    assert(unrollMemoryAfterB3 > 0)
+
+    // The unroll memory owned by this thread builds on top of its value after the previous unrolls
+    memoryStore.putIterator("b4", smallIterator, memOnly, returnValues = true)
+    val unrollMemoryAfterB4 = memoryStore.currentUnrollMemoryForThisThread
+    assert(unrollMemoryAfterB4 > unrollMemoryAfterB3)
+
+    // ... but only to a certain extent (until we run out of free space to grant new unroll memory)
+    memoryStore.putIterator("b5", smallIterator, memOnly, returnValues = true)
+    val unrollMemoryAfterB5 = memoryStore.currentUnrollMemoryForThisThread
+    memoryStore.putIterator("b6", smallIterator, memOnly, returnValues = true)
+    val unrollMemoryAfterB6 = memoryStore.currentUnrollMemoryForThisThread
+    memoryStore.putIterator("b7", smallIterator, memOnly, returnValues = true)
+    val unrollMemoryAfterB7 = memoryStore.currentUnrollMemoryForThisThread
+    assert(unrollMemoryAfterB5 === unrollMemoryAfterB4)
+    assert(unrollMemoryAfterB6 === unrollMemoryAfterB4)
+    assert(unrollMemoryAfterB7 === unrollMemoryAfterB4)
+  }
 }
diff --git a/core/src/test/scala/org/apache/spark/util/SizeTrackingAppendOnlyMapSuite.scala b/core/src/test/scala/org/apache/spark/util/SizeTrackingAppendOnlyMapSuite.scala
deleted file mode 100644
index 93f0c6a8e640..000000000000
--- a/core/src/test/scala/org/apache/spark/util/SizeTrackingAppendOnlyMapSuite.scala
+++ /dev/null
@@ -1,120 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.util
-
-import scala.util.Random
-
-import org.scalatest.{BeforeAndAfterAll, FunSuite}
-
-import org.apache.spark.util.SizeTrackingAppendOnlyMapSuite.LargeDummyClass
-import org.apache.spark.util.collection.{AppendOnlyMap, SizeTrackingAppendOnlyMap}
-
-class SizeTrackingAppendOnlyMapSuite extends FunSuite with BeforeAndAfterAll {
-  val NORMAL_ERROR = 0.20
-  val HIGH_ERROR = 0.30
-
-  test("fixed size insertions") {
-    testWith[Int, Long](10000, i => (i, i.toLong))
-    testWith[Int, (Long, Long)](10000, i => (i, (i.toLong, i.toLong)))
-    testWith[Int, LargeDummyClass](10000, i => (i, new LargeDummyClass()))
-  }
-
-  test("variable size insertions") {
-    val rand = new Random(123456789)
-    def randString(minLen: Int, maxLen: Int): String = {
-      "a" * (rand.nextInt(maxLen - minLen) + minLen)
-    }
-    testWith[Int, String](10000, i => (i, randString(0, 10)))
-    testWith[Int, String](10000, i => (i, randString(0, 100)))
-    testWith[Int, String](10000, i => (i, randString(90, 100)))
-  }
-
-  test("updates") {
-    val rand = new Random(123456789)
-    def randString(minLen: Int, maxLen: Int): String = {
-      "a" * (rand.nextInt(maxLen - minLen) + minLen)
-    }
-    testWith[String, Int](10000, i => (randString(0, 10000), i))
-  }
-
-  def testWith[K, V](numElements: Int, makeElement: (Int) => (K, V)) {
-    val map = new SizeTrackingAppendOnlyMap[K, V]()
-    for (i <- 0 until numElements) {
-      val (k, v) = makeElement(i)
-      map(k) = v
-      expectWithinError(map, map.estimateSize(), if (i < 32) HIGH_ERROR else NORMAL_ERROR)
-    }
-  }
-
-  def expectWithinError(obj: AnyRef, estimatedSize: Long, error: Double) {
-    val betterEstimatedSize = SizeEstimator.estimate(obj)
-    assert(betterEstimatedSize * (1 - error) < estimatedSize,
-      s"Estimated size $estimatedSize was less than expected size $betterEstimatedSize")
-    assert(betterEstimatedSize * (1 + 2 * error) > estimatedSize,
-      s"Estimated size $estimatedSize was greater than expected size $betterEstimatedSize")
-  }
-}
-
-object SizeTrackingAppendOnlyMapSuite {
-  // Speed test, for reproducibility of results.
-  // These could be highly non-deterministic in general, however.
-  // Results:
-  // AppendOnlyMap:   31 ms
-  // SizeTracker:     54 ms
-  // SizeEstimator: 1500 ms
-  def main(args: Array[String]) {
-    val numElements = 100000
-
-    val baseTimes = for (i <- 0 until 10) yield time {
-      val map = new AppendOnlyMap[Int, LargeDummyClass]()
-      for (i <- 0 until numElements) {
-        map(i) = new LargeDummyClass()
-      }
-    }
-
-    val sampledTimes = for (i <- 0 until 10) yield time {
-      val map = new SizeTrackingAppendOnlyMap[Int, LargeDummyClass]()
-      for (i <- 0 until numElements) {
-        map(i) = new LargeDummyClass()
-        map.estimateSize()
-      }
-    }
-
-    val unsampledTimes = for (i <- 0 until 3) yield time {
-      val map = new AppendOnlyMap[Int, LargeDummyClass]()
-      for (i <- 0 until numElements) {
-        map(i) = new LargeDummyClass()
-        SizeEstimator.estimate(map)
-      }
-    }
-
-    println("Base: " + baseTimes)
-    println("SizeTracker (sampled): " + sampledTimes)
-    println("SizeEstimator (unsampled): " + unsampledTimes)
-  }
-
-  def time(f: => Unit): Long = {
-    val start = System.currentTimeMillis()
-    f
-    System.currentTimeMillis() - start
-  }
-
-  private class LargeDummyClass {
-    val arr = new Array[Int](100)
-  }
-}
diff --git a/core/src/test/scala/org/apache/spark/util/collection/SizeTrackerSuite.scala b/core/src/test/scala/org/apache/spark/util/collection/SizeTrackerSuite.scala
new file mode 100644
index 000000000000..1f3396724965
--- /dev/null
+++ b/core/src/test/scala/org/apache/spark/util/collection/SizeTrackerSuite.scala
@@ -0,0 +1,204 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.util.collection
+
+import scala.reflect.ClassTag
+import scala.util.Random
+
+import org.scalatest.FunSuite
+
+import org.apache.spark.util.SizeEstimator
+
+class SizeTrackerSuite extends FunSuite {
+  val NORMAL_ERROR = 0.20
+  val HIGH_ERROR = 0.30
+
+  import SizeTrackerSuite._
+
+  test("vector fixed size insertions") {
+    testVector[Long](10000, i => i.toLong)
+    testVector[(Long, Long)](10000, i => (i.toLong, i.toLong))
+    testVector[LargeDummyClass](10000, i => new LargeDummyClass)
+  }
+
+  test("vector variable size insertions") {
+    val rand = new Random(123456789)
+    def randString(minLen: Int, maxLen: Int): String = {
+      "a" * (rand.nextInt(maxLen - minLen) + minLen)
+    }
+    testVector[String](10000, i => randString(0, 10))
+    testVector[String](10000, i => randString(0, 100))
+    testVector[String](10000, i => randString(90, 100))
+  }
+
+  test("map fixed size insertions") {
+    testMap[Int, Long](10000, i => (i, i.toLong))
+    testMap[Int, (Long, Long)](10000, i => (i, (i.toLong, i.toLong)))
+    testMap[Int, LargeDummyClass](10000, i => (i, new LargeDummyClass))
+  }
+
+  test("map variable size insertions") {
+    val rand = new Random(123456789)
+    def randString(minLen: Int, maxLen: Int): String = {
+      "a" * (rand.nextInt(maxLen - minLen) + minLen)
+    }
+    testMap[Int, String](10000, i => (i, randString(0, 10)))
+    testMap[Int, String](10000, i => (i, randString(0, 100)))
+    testMap[Int, String](10000, i => (i, randString(90, 100)))
+  }
+
+  test("map updates") {
+    val rand = new Random(123456789)
+    def randString(minLen: Int, maxLen: Int): String = {
+      "a" * (rand.nextInt(maxLen - minLen) + minLen)
+    }
+    testMap[String, Int](10000, i => (randString(0, 10000), i))
+  }
+
+  def testVector[T: ClassTag](numElements: Int, makeElement: Int => T) {
+    val vector = new SizeTrackingVector[T]
+    for (i <- 0 until numElements) {
+      val item = makeElement(i)
+      vector += item
+      expectWithinError(vector, vector.estimateSize(), if (i < 32) HIGH_ERROR else NORMAL_ERROR)
+    }
+  }
+
+  def testMap[K, V](numElements: Int, makeElement: (Int) => (K, V)) {
+    val map = new SizeTrackingAppendOnlyMap[K, V]
+    for (i <- 0 until numElements) {
+      val (k, v) = makeElement(i)
+      map(k) = v
+      expectWithinError(map, map.estimateSize(), if (i < 32) HIGH_ERROR else NORMAL_ERROR)
+    }
+  }
+
+  def expectWithinError(obj: AnyRef, estimatedSize: Long, error: Double) {
+    val betterEstimatedSize = SizeEstimator.estimate(obj)
+    assert(betterEstimatedSize * (1 - error) < estimatedSize,
+      s"Estimated size $estimatedSize was less than expected size $betterEstimatedSize")
+    assert(betterEstimatedSize * (1 + 2 * error) > estimatedSize,
+      s"Estimated size $estimatedSize was greater than expected size $betterEstimatedSize")
+  }
+}
+
+private object SizeTrackerSuite {
+
+  /**
+   * Run speed tests for size tracking collections.
+   */
+  def main(args: Array[String]): Unit = {
+    if (args.size < 1) {
+      println("Usage: SizeTrackerSuite [num elements]")
+      System.exit(1)
+    }
+    val numElements = args(0).toInt
+    vectorSpeedTest(numElements)
+    mapSpeedTest(numElements)
+  }
+
+  /**
+   * Speed test for SizeTrackingVector.
+   *
+   * Results for 100000 elements (possibly non-deterministic):
+   *   PrimitiveVector  15 ms
+   *   SizeTracker      51 ms
+   *   SizeEstimator    2000 ms
+   */
+  def vectorSpeedTest(numElements: Int): Unit = {
+    val baseTimes = for (i <- 0 until 10) yield time {
+      val vector = new PrimitiveVector[LargeDummyClass]
+      for (i <- 0 until numElements) {
+        vector += new LargeDummyClass
+      }
+    }
+    val sampledTimes = for (i <- 0 until 10) yield time {
+      val vector = new SizeTrackingVector[LargeDummyClass]
+      for (i <- 0 until numElements) {
+        vector += new LargeDummyClass
+        vector.estimateSize()
+      }
+    }
+    val unsampledTimes = for (i <- 0 until 3) yield time {
+      val vector = new PrimitiveVector[LargeDummyClass]
+      for (i <- 0 until numElements) {
+        vector += new LargeDummyClass
+        SizeEstimator.estimate(vector)
+      }
+    }
+    printSpeedTestResult("SizeTrackingVector", baseTimes, sampledTimes, unsampledTimes)
+  }
+
+  /**
+   * Speed test for SizeTrackingAppendOnlyMap.
+   *
+   * Results for 100000 elements (possibly non-deterministic):
+   *   AppendOnlyMap  30 ms
+   *   SizeTracker    41 ms
+   *   SizeEstimator  1666 ms
+   */
+  def mapSpeedTest(numElements: Int): Unit = {
+    val baseTimes = for (i <- 0 until 10) yield time {
+      val map = new AppendOnlyMap[Int, LargeDummyClass]
+      for (i <- 0 until numElements) {
+        map(i) = new LargeDummyClass
+      }
+    }
+    val sampledTimes = for (i <- 0 until 10) yield time {
+      val map = new SizeTrackingAppendOnlyMap[Int, LargeDummyClass]
+      for (i <- 0 until numElements) {
+        map(i) = new LargeDummyClass
+        map.estimateSize()
+      }
+    }
+    val unsampledTimes = for (i <- 0 until 3) yield time {
+      val map = new AppendOnlyMap[Int, LargeDummyClass]
+      for (i <- 0 until numElements) {
+        map(i) = new LargeDummyClass
+        SizeEstimator.estimate(map)
+      }
+    }
+    printSpeedTestResult("SizeTrackingAppendOnlyMap", baseTimes, sampledTimes, unsampledTimes)
+  }
+
+  def printSpeedTestResult(
+      testName: String,
+      baseTimes: Seq[Long],
+      sampledTimes: Seq[Long],
+      unsampledTimes: Seq[Long]): Unit = {
+    println(s"Average times for $testName (ms):")
+    println("  Base - " + averageTime(baseTimes))
+    println("  SizeTracker (sampled) - " + averageTime(sampledTimes))
+    println("  SizeEstimator (unsampled) - " + averageTime(unsampledTimes))
+    println()
+  }
+
+  def time(f: => Unit): Long = {
+    val start = System.currentTimeMillis()
+    f
+    System.currentTimeMillis() - start
+  }
+
+  def averageTime(v: Seq[Long]): Long = {
+    v.sum / v.size
+  }
+
+  private class LargeDummyClass {
+    val arr = new Array[Int](100)
+  }
+}
diff --git a/docs/configuration.md b/docs/configuration.md
index 46e3dd914b5a..2e6c85cc2bcc 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -480,6 +480,15 @@ Apart from these, the following properties are also available, and may be useful
     increase it if you configure your own old generation size.
   </td>
 </tr>
+<tr>
+  <td><code>spark.storage.unrollFraction</code></td>
+  <td>0.2</td>
+  <td>
+    Fraction of <code>spark.storage.memoryFraction</code> to use for unrolling blocks in memory.
+    This is dynamically allocated by dropping existing blocks when there is not enough free
+    storage space to unroll the new block in its entirety.
+  </td>
+</tr>
 <tr>
   <td><code>spark.tachyonStore.baseDir</code></td>
   <td>System.getProperty("java.io.tmpdir")</td>
diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala
index e9220db6b1f9..5ff88f0dd1ca 100644
--- a/project/MimaExcludes.scala
+++ b/project/MimaExcludes.scala
@@ -31,7 +31,6 @@ import com.typesafe.tools.mima.core._
  * MimaBuild.excludeSparkClass("graphx.util.collection.GraphXPrimitiveKeyOpenHashMap")
  */
 object MimaExcludes {
-
     def excludes(version: String) =
       version match {
         case v if v.startsWith("1.1") =>
@@ -62,6 +61,15 @@ object MimaExcludes {
             ProblemFilters.exclude[MissingMethodProblem](
               "org.apache.spark.storage.MemoryStore.Entry")
           ) ++
+          Seq(
+            // Renamed putValues -> putArray + putIterator
+            ProblemFilters.exclude[MissingMethodProblem](
+              "org.apache.spark.storage.MemoryStore.putValues"),
+            ProblemFilters.exclude[MissingMethodProblem](
+              "org.apache.spark.storage.DiskStore.putValues"),
+            ProblemFilters.exclude[MissingMethodProblem](
+              "org.apache.spark.storage.TachyonStore.putValues")
+          ) ++
           Seq(
             ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.streaming.flume.FlumeReceiver.this")
           ) ++
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisorImpl.scala b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisorImpl.scala
index ce8316bb1489..d934b9cbfc3e 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisorImpl.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisorImpl.scala
@@ -110,8 +110,7 @@ private[streaming] class ReceiverSupervisorImpl(
     ) {
     val blockId = optionalBlockId.getOrElse(nextBlockId)
     val time = System.currentTimeMillis
-    blockManager.put(blockId, arrayBuffer.asInstanceOf[ArrayBuffer[Any]],
-      storageLevel, tellMaster = true)
+    blockManager.putArray(blockId, arrayBuffer.toArray[Any], storageLevel, tellMaster = true)
     logDebug("Pushed block " + blockId + " in " + (System.currentTimeMillis - time)  + " ms")
     reportPushedBlock(blockId, arrayBuffer.size, optionalMetadata)
   }
@@ -124,7 +123,7 @@ private[streaming] class ReceiverSupervisorImpl(
     ) {
     val blockId = optionalBlockId.getOrElse(nextBlockId)
     val time = System.currentTimeMillis
-    blockManager.put(blockId, iterator, storageLevel, tellMaster = true)
+    blockManager.putIterator(blockId, iterator, storageLevel, tellMaster = true)
     logDebug("Pushed block " + blockId + " in " + (System.currentTimeMillis - time)  + " ms")
     reportPushedBlock(blockId, -1, optionalMetadata)
   }

From 81fcdd22c8ef52889ed51b3ec5c2747708505fc2 Mon Sep 17 00:00:00 2001
From: Doris Xin <doris.s.xin@gmail.com>
Date: Sun, 27 Jul 2014 16:16:39 -0700
Subject: [PATCH 781/810] [SPARK-2514] [mllib] Random RDD generator

Utilities for generating random RDDs.

RandomRDD and RandomVectorRDD are created instead of using `sc.parallelize(range:Range)` because `Range` objects in Scala can only have `size <= Int.MaxValue`.

The object `RandomRDDGenerators` can be transformed into a generator class to reduce the number of auxiliary methods for optional arguments.

Author: Doris Xin <doris.s.xin@gmail.com>

Closes #1520 from dorx/randomRDD and squashes the following commits:

01121ac [Doris Xin] reviewer comments
6bf27d8 [Doris Xin] Merge branch 'master' into randomRDD
a8ea92d [Doris Xin] Reviewer comments
063ea0b [Doris Xin] Merge branch 'master' into randomRDD
aec68eb [Doris Xin] newline
bc90234 [Doris Xin] units passed.
d56cacb [Doris Xin] impl with RandomRDD
92d6f1c [Doris Xin] solution for Cloneable
df5bcff [Doris Xin] Merge branch 'generator' into randomRDD
f46d928 [Doris Xin] WIP
49ed20d [Doris Xin] alternative poisson distribution generator
7cb0e40 [Doris Xin] fix for data inconsistency
8881444 [Doris Xin] RandomRDDGenerator: initial design
---
 .../mllib/random/DistributionGenerator.scala  | 101 ++++
 .../mllib/random/RandomRDDGenerators.scala    | 473 ++++++++++++++++++
 .../apache/spark/mllib/rdd/RandomRDD.scala    | 118 +++++
 .../random/DistributionGeneratorSuite.scala   |  90 ++++
 .../random/RandomRDDGeneratorsSuite.scala     | 158 ++++++
 5 files changed, 940 insertions(+)
 create mode 100644 mllib/src/main/scala/org/apache/spark/mllib/random/DistributionGenerator.scala
 create mode 100644 mllib/src/main/scala/org/apache/spark/mllib/random/RandomRDDGenerators.scala
 create mode 100644 mllib/src/main/scala/org/apache/spark/mllib/rdd/RandomRDD.scala
 create mode 100644 mllib/src/test/scala/org/apache/spark/mllib/random/DistributionGeneratorSuite.scala
 create mode 100644 mllib/src/test/scala/org/apache/spark/mllib/random/RandomRDDGeneratorsSuite.scala

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/random/DistributionGenerator.scala b/mllib/src/main/scala/org/apache/spark/mllib/random/DistributionGenerator.scala
new file mode 100644
index 000000000000..7ecb409c4a91
--- /dev/null
+++ b/mllib/src/main/scala/org/apache/spark/mllib/random/DistributionGenerator.scala
@@ -0,0 +1,101 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.mllib.random
+
+import cern.jet.random.Poisson
+import cern.jet.random.engine.DRand
+
+import org.apache.spark.annotation.Experimental
+import org.apache.spark.util.random.{XORShiftRandom, Pseudorandom}
+
+/**
+ * :: Experimental ::
+ * Trait for random number generators that generate i.i.d. values from a distribution.
+ */
+@Experimental
+trait DistributionGenerator extends Pseudorandom with Serializable {
+
+  /**
+   * Returns an i.i.d. sample as a Double from an underlying distribution.
+   */
+  def nextValue(): Double
+
+  /**
+   * Returns a copy of the DistributionGenerator with a new instance of the rng object used in the
+   * class when applicable for non-locking concurrent usage.
+   */
+  def copy(): DistributionGenerator
+}
+
+/**
+ * :: Experimental ::
+ * Generates i.i.d. samples from U[0.0, 1.0]
+ */
+@Experimental
+class UniformGenerator extends DistributionGenerator {
+
+  // XORShiftRandom for better performance. Thread safety isn't necessary here.
+  private val random = new XORShiftRandom()
+
+  override def nextValue(): Double = {
+    random.nextDouble()
+  }
+
+  override def setSeed(seed: Long) = random.setSeed(seed)
+
+  override def copy(): UniformGenerator = new UniformGenerator()
+}
+
+/**
+ * :: Experimental ::
+ * Generates i.i.d. samples from the standard normal distribution.
+ */
+@Experimental
+class StandardNormalGenerator extends DistributionGenerator {
+
+  // XORShiftRandom for better performance. Thread safety isn't necessary here.
+  private val random = new XORShiftRandom()
+
+  override def nextValue(): Double = {
+      random.nextGaussian()
+  }
+
+  override def setSeed(seed: Long) = random.setSeed(seed)
+
+  override def copy(): StandardNormalGenerator = new StandardNormalGenerator()
+}
+
+/**
+ * :: Experimental ::
+ * Generates i.i.d. samples from the Poisson distribution with the given mean.
+ *
+ * @param mean mean for the Poisson distribution.
+ */
+@Experimental
+class PoissonGenerator(val mean: Double) extends DistributionGenerator {
+
+  private var rng = new Poisson(mean, new DRand)
+
+  override def nextValue(): Double = rng.nextDouble()
+
+  override def setSeed(seed: Long) {
+    rng = new Poisson(mean, new DRand(seed.toInt))
+  }
+
+  override def copy(): PoissonGenerator = new PoissonGenerator(mean)
+}
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/random/RandomRDDGenerators.scala b/mllib/src/main/scala/org/apache/spark/mllib/random/RandomRDDGenerators.scala
new file mode 100644
index 000000000000..d7ee2d3f4684
--- /dev/null
+++ b/mllib/src/main/scala/org/apache/spark/mllib/random/RandomRDDGenerators.scala
@@ -0,0 +1,473 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.mllib.random
+
+import org.apache.spark.SparkContext
+import org.apache.spark.annotation.Experimental
+import org.apache.spark.mllib.linalg.Vector
+import org.apache.spark.mllib.rdd.{RandomVectorRDD, RandomRDD}
+import org.apache.spark.rdd.RDD
+import org.apache.spark.util.Utils
+
+/**
+ * :: Experimental ::
+ * Generator methods for creating RDDs comprised of i.i.d samples from some distribution.
+ */
+@Experimental
+object RandomRDDGenerators {
+
+  /**
+   * :: Experimental ::
+   * Generates an RDD comprised of i.i.d samples from the uniform distribution on [0.0, 1.0].
+   *
+   * @param sc SparkContext used to create the RDD.
+   * @param size Size of the RDD.
+   * @param numPartitions Number of partitions in the RDD.
+   * @param seed Seed for the RNG that generates the seed for the generator in each partition.
+   * @return RDD[Double] comprised of i.i.d. samples ~ U[0.0, 1.0].
+   */
+  @Experimental
+  def uniformRDD(sc: SparkContext, size: Long, numPartitions: Int, seed: Long): RDD[Double] = {
+    val uniform = new UniformGenerator()
+    randomRDD(sc, uniform,  size, numPartitions, seed)
+  }
+
+  /**
+   * :: Experimental ::
+   * Generates an RDD comprised of i.i.d samples from the uniform distribution on [0.0, 1.0].
+   *
+   * @param sc SparkContext used to create the RDD.
+   * @param size Size of the RDD.
+   * @param numPartitions Number of partitions in the RDD.
+   * @return RDD[Double] comprised of i.i.d. samples ~ U[0.0, 1.0].
+   */
+  @Experimental
+  def uniformRDD(sc: SparkContext, size: Long, numPartitions: Int): RDD[Double] = {
+    uniformRDD(sc, size, numPartitions, Utils.random.nextLong)
+  }
+
+  /**
+   * :: Experimental ::
+   * Generates an RDD comprised of i.i.d samples from the uniform distribution on [0.0, 1.0].
+   * sc.defaultParallelism used for the number of partitions in the RDD.
+   *
+   * @param sc SparkContext used to create the RDD.
+   * @param size Size of the RDD.
+   * @return RDD[Double] comprised of i.i.d. samples ~ U[0.0, 1.0].
+   */
+  @Experimental
+  def uniformRDD(sc: SparkContext, size: Long): RDD[Double] = {
+    uniformRDD(sc, size, sc.defaultParallelism, Utils.random.nextLong)
+  }
+
+  /**
+   * :: Experimental ::
+   * Generates an RDD comprised of i.i.d samples from the standard normal distribution.
+   *
+   * @param sc SparkContext used to create the RDD.
+   * @param size Size of the RDD.
+   * @param numPartitions Number of partitions in the RDD.
+   * @param seed Seed for the RNG that generates the seed for the generator in each partition.
+   * @return RDD[Double] comprised of i.i.d. samples ~ N(0.0, 1.0).
+   */
+  @Experimental
+  def normalRDD(sc: SparkContext, size: Long, numPartitions: Int, seed: Long): RDD[Double] = {
+    val normal = new StandardNormalGenerator()
+    randomRDD(sc, normal, size, numPartitions, seed)
+  }
+
+  /**
+   * :: Experimental ::
+   * Generates an RDD comprised of i.i.d samples from the standard normal distribution.
+   *
+   * @param sc SparkContext used to create the RDD.
+   * @param size Size of the RDD.
+   * @param numPartitions Number of partitions in the RDD.
+   * @return RDD[Double] comprised of i.i.d. samples ~ N(0.0, 1.0).
+   */
+  @Experimental
+  def normalRDD(sc: SparkContext, size: Long, numPartitions: Int): RDD[Double] = {
+    normalRDD(sc, size, numPartitions, Utils.random.nextLong)
+  }
+
+  /**
+   * :: Experimental ::
+   * Generates an RDD comprised of i.i.d samples from the standard normal distribution.
+   * sc.defaultParallelism used for the number of partitions in the RDD.
+   *
+   * @param sc SparkContext used to create the RDD.
+   * @param size Size of the RDD.
+   * @return RDD[Double] comprised of i.i.d. samples ~ N(0.0, 1.0).
+   */
+  @Experimental
+  def normalRDD(sc: SparkContext, size: Long): RDD[Double] = {
+    normalRDD(sc, size, sc.defaultParallelism, Utils.random.nextLong)
+  }
+
+  /**
+   * :: Experimental ::
+   * Generates an RDD comprised of i.i.d samples from the Poisson distribution with the input mean.
+   *
+   * @param sc SparkContext used to create the RDD.
+   * @param mean Mean, or lambda, for the Poisson distribution.
+   * @param size Size of the RDD.
+   * @param numPartitions Number of partitions in the RDD.
+   * @param seed Seed for the RNG that generates the seed for the generator in each partition.
+   * @return RDD[Double] comprised of i.i.d. samples ~ Pois(mean).
+   */
+  @Experimental
+  def poissonRDD(sc: SparkContext,
+      mean: Double,
+      size: Long,
+      numPartitions: Int,
+      seed: Long): RDD[Double] = {
+    val poisson = new PoissonGenerator(mean)
+    randomRDD(sc, poisson, size, numPartitions, seed)
+  }
+
+  /**
+   * :: Experimental ::
+   * Generates an RDD comprised of i.i.d samples from the Poisson distribution with the input mean.
+   *
+   * @param sc SparkContext used to create the RDD.
+   * @param mean Mean, or lambda, for the Poisson distribution.
+   * @param size Size of the RDD.
+   * @param numPartitions Number of partitions in the RDD.
+   * @return RDD[Double] comprised of i.i.d. samples ~ Pois(mean).
+   */
+  @Experimental
+  def poissonRDD(sc: SparkContext, mean: Double, size: Long, numPartitions: Int): RDD[Double] = {
+    poissonRDD(sc, mean, size, numPartitions, Utils.random.nextLong)
+  }
+
+  /**
+   * :: Experimental ::
+   * Generates an RDD comprised of i.i.d samples from the Poisson distribution with the input mean.
+   * sc.defaultParallelism used for the number of partitions in the RDD.
+   *
+   * @param sc SparkContext used to create the RDD.
+   * @param mean Mean, or lambda, for the Poisson distribution.
+   * @param size Size of the RDD.
+   * @return RDD[Double] comprised of i.i.d. samples ~ Pois(mean).
+   */
+  @Experimental
+  def poissonRDD(sc: SparkContext, mean: Double, size: Long): RDD[Double] = {
+    poissonRDD(sc, mean, size, sc.defaultParallelism, Utils.random.nextLong)
+  }
+
+  /**
+   * :: Experimental ::
+   * Generates an RDD comprised of i.i.d samples produced by the input DistributionGenerator.
+   *
+   * @param sc SparkContext used to create the RDD.
+   * @param generator DistributionGenerator used to populate the RDD.
+   * @param size Size of the RDD.
+   * @param numPartitions Number of partitions in the RDD.
+   * @param seed Seed for the RNG that generates the seed for the generator in each partition.
+   * @return RDD[Double] comprised of i.i.d. samples produced by generator.
+   */
+  @Experimental
+  def randomRDD(sc: SparkContext,
+      generator: DistributionGenerator,
+      size: Long,
+      numPartitions: Int,
+      seed: Long): RDD[Double] = {
+    new RandomRDD(sc, size, numPartitions, generator, seed)
+  }
+
+  /**
+   * :: Experimental ::
+   * Generates an RDD comprised of i.i.d samples produced by the input DistributionGenerator.
+   *
+   * @param sc SparkContext used to create the RDD.
+   * @param generator DistributionGenerator used to populate the RDD.
+   * @param size Size of the RDD.
+   * @param numPartitions Number of partitions in the RDD.
+   * @return RDD[Double] comprised of i.i.d. samples produced by generator.
+   */
+  @Experimental
+  def randomRDD(sc: SparkContext,
+      generator: DistributionGenerator,
+      size: Long,
+      numPartitions: Int): RDD[Double] = {
+    randomRDD(sc, generator, size, numPartitions, Utils.random.nextLong)
+  }
+
+  /**
+   * :: Experimental ::
+   * Generates an RDD comprised of i.i.d samples produced by the input DistributionGenerator.
+   * sc.defaultParallelism used for the number of partitions in the RDD.
+   *
+   * @param sc SparkContext used to create the RDD.
+   * @param generator DistributionGenerator used to populate the RDD.
+   * @param size Size of the RDD.
+   * @return RDD[Double] comprised of i.i.d. samples produced by generator.
+   */
+  @Experimental
+  def randomRDD(sc: SparkContext,
+      generator: DistributionGenerator,
+      size: Long): RDD[Double] = {
+    randomRDD(sc, generator, size, sc.defaultParallelism, Utils.random.nextLong)
+  }
+
+  // TODO Generate RDD[Vector] from multivariate distributions.
+
+  /**
+   * :: Experimental ::
+   * Generates an RDD[Vector] with vectors containing i.i.d samples drawn from the
+   * uniform distribution on [0.0 1.0].
+   *
+   * @param sc SparkContext used to create the RDD.
+   * @param numRows Number of Vectors in the RDD.
+   * @param numCols Number of elements in each Vector.
+   * @param numPartitions Number of partitions in the RDD.
+   * @param seed Seed for the RNG that generates the seed for the generator in each partition.
+   * @return RDD[Vector] with vectors containing i.i.d samples ~ U[0.0, 1.0].
+   */
+  @Experimental
+  def uniformVectorRDD(sc: SparkContext,
+      numRows: Long,
+      numCols: Int,
+      numPartitions: Int,
+      seed: Long): RDD[Vector] = {
+    val uniform = new UniformGenerator()
+    randomVectorRDD(sc, uniform, numRows, numCols, numPartitions, seed)
+  }
+
+  /**
+   * :: Experimental ::
+   * Generates an RDD[Vector] with vectors containing i.i.d samples drawn from the
+   * uniform distribution on [0.0 1.0].
+   *
+   * @param sc SparkContext used to create the RDD.
+   * @param numRows Number of Vectors in the RDD.
+   * @param numCols Number of elements in each Vector.
+   * @param numPartitions Number of partitions in the RDD.
+   * @return RDD[Vector] with vectors containing i.i.d samples ~ U[0.0, 1.0].
+   */
+  @Experimental
+  def uniformVectorRDD(sc: SparkContext,
+      numRows: Long,
+      numCols: Int,
+      numPartitions: Int): RDD[Vector] = {
+    uniformVectorRDD(sc, numRows, numCols, numPartitions, Utils.random.nextLong)
+  }
+
+  /**
+   * :: Experimental ::
+   * Generates an RDD[Vector] with vectors containing i.i.d samples drawn from the
+   * uniform distribution on [0.0 1.0].
+   * sc.defaultParallelism used for the number of partitions in the RDD.
+   *
+   * @param sc SparkContext used to create the RDD.
+   * @param numRows Number of Vectors in the RDD.
+   * @param numCols Number of elements in each Vector.
+   * @return RDD[Vector] with vectors containing i.i.d samples ~ U[0.0, 1.0].
+   */
+  @Experimental
+  def uniformVectorRDD(sc: SparkContext, numRows: Long, numCols: Int): RDD[Vector] = {
+    uniformVectorRDD(sc, numRows, numCols, sc.defaultParallelism, Utils.random.nextLong)
+  }
+
+  /**
+   * :: Experimental ::
+   * Generates an RDD[Vector] with vectors containing i.i.d samples drawn from the
+   * standard normal distribution.
+   *
+   * @param sc SparkContext used to create the RDD.
+   * @param numRows Number of Vectors in the RDD.
+   * @param numCols Number of elements in each Vector.
+   * @param numPartitions Number of partitions in the RDD.
+   * @param seed Seed for the RNG that generates the seed for the generator in each partition.
+   * @return RDD[Vector] with vectors containing i.i.d samples ~ N(0.0, 1.0).
+   */
+  @Experimental
+  def normalVectorRDD(sc: SparkContext,
+      numRows: Long,
+      numCols: Int,
+      numPartitions: Int,
+      seed: Long): RDD[Vector] = {
+    val uniform = new StandardNormalGenerator()
+    randomVectorRDD(sc, uniform, numRows, numCols, numPartitions, seed)
+  }
+
+  /**
+   * :: Experimental ::
+   * Generates an RDD[Vector] with vectors containing i.i.d samples drawn from the
+   * standard normal distribution.
+   *
+   * @param sc SparkContext used to create the RDD.
+   * @param numRows Number of Vectors in the RDD.
+   * @param numCols Number of elements in each Vector.
+   * @param numPartitions Number of partitions in the RDD.
+   * @return RDD[Vector] with vectors containing i.i.d samples ~ N(0.0, 1.0).
+   */
+  @Experimental
+  def normalVectorRDD(sc: SparkContext,
+      numRows: Long,
+      numCols: Int,
+      numPartitions: Int): RDD[Vector] = {
+    normalVectorRDD(sc, numRows, numCols, numPartitions, Utils.random.nextLong)
+  }
+
+  /**
+   * :: Experimental ::
+   * Generates an RDD[Vector] with vectors containing i.i.d samples drawn from the
+   * standard normal distribution.
+   * sc.defaultParallelism used for the number of partitions in the RDD.
+   *
+   * @param sc SparkContext used to create the RDD.
+   * @param numRows Number of Vectors in the RDD.
+   * @param numCols Number of elements in each Vector.
+   * @return RDD[Vector] with vectors containing i.i.d samples ~ N(0.0, 1.0).
+   */
+  @Experimental
+  def normalVectorRDD(sc: SparkContext, numRows: Long, numCols: Int): RDD[Vector] = {
+    normalVectorRDD(sc, numRows, numCols, sc.defaultParallelism, Utils.random.nextLong)
+  }
+
+  /**
+   * :: Experimental ::
+   * Generates an RDD[Vector] with vectors containing i.i.d samples drawn from the
+   * Poisson distribution with the input mean.
+   *
+   * @param sc SparkContext used to create the RDD.
+   * @param mean Mean, or lambda, for the Poisson distribution.
+   * @param numRows Number of Vectors in the RDD.
+   * @param numCols Number of elements in each Vector.
+   * @param numPartitions Number of partitions in the RDD.
+   * @param seed Seed for the RNG that generates the seed for the generator in each partition.
+   * @return RDD[Vector] with vectors containing i.i.d samples ~ Pois(mean).
+   */
+  @Experimental
+  def poissonVectorRDD(sc: SparkContext,
+      mean: Double,
+      numRows: Long,
+      numCols: Int,
+      numPartitions: Int,
+      seed: Long): RDD[Vector] = {
+    val poisson = new PoissonGenerator(mean)
+    randomVectorRDD(sc, poisson, numRows, numCols, numPartitions, seed)
+  }
+
+  /**
+   * :: Experimental ::
+   * Generates an RDD[Vector] with vectors containing i.i.d samples drawn from the
+   * Poisson distribution with the input mean.
+   *
+   * @param sc SparkContext used to create the RDD.
+   * @param mean Mean, or lambda, for the Poisson distribution.
+   * @param numRows Number of Vectors in the RDD.
+   * @param numCols Number of elements in each Vector.
+   * @param numPartitions Number of partitions in the RDD.
+   * @return RDD[Vector] with vectors containing i.i.d samples ~ Pois(mean).
+   */
+  @Experimental
+  def poissonVectorRDD(sc: SparkContext,
+      mean: Double,
+      numRows: Long,
+      numCols: Int,
+      numPartitions: Int): RDD[Vector] = {
+    poissonVectorRDD(sc, mean, numRows, numCols, numPartitions, Utils.random.nextLong)
+  }
+
+  /**
+   * :: Experimental ::
+   * Generates an RDD[Vector] with vectors containing i.i.d samples drawn from the
+   * Poisson distribution with the input mean.
+   * sc.defaultParallelism used for the number of partitions in the RDD.
+   *
+   * @param sc SparkContext used to create the RDD.
+   * @param mean Mean, or lambda, for the Poisson distribution.
+   * @param numRows Number of Vectors in the RDD.
+   * @param numCols Number of elements in each Vector.
+   * @return RDD[Vector] with vectors containing i.i.d samples ~ Pois(mean).
+   */
+  @Experimental
+  def poissonVectorRDD(sc: SparkContext,
+      mean: Double,
+      numRows: Long,
+      numCols: Int): RDD[Vector] = {
+    poissonVectorRDD(sc, mean, numRows, numCols, sc.defaultParallelism, Utils.random.nextLong)
+  }
+
+  /**
+   * :: Experimental ::
+   * Generates an RDD[Vector] with vectors containing i.i.d samples produced by the
+   * input DistributionGenerator.
+   *
+   * @param sc SparkContext used to create the RDD.
+   * @param generator DistributionGenerator used to populate the RDD.
+   * @param numRows Number of Vectors in the RDD.
+   * @param numCols Number of elements in each Vector.
+   * @param numPartitions Number of partitions in the RDD.
+   * @param seed Seed for the RNG that generates the seed for the generator in each partition.
+   * @return RDD[Vector] with vectors containing i.i.d samples produced by generator.
+   */
+  @Experimental
+  def randomVectorRDD(sc: SparkContext,
+      generator: DistributionGenerator,
+      numRows: Long,
+      numCols: Int,
+      numPartitions: Int,
+      seed: Long): RDD[Vector] = {
+    new RandomVectorRDD(sc, numRows, numCols, numPartitions, generator, seed)
+  }
+
+  /**
+   * :: Experimental ::
+   * Generates an RDD[Vector] with vectors containing i.i.d samples produced by the
+   * input DistributionGenerator.
+   *
+   * @param sc SparkContext used to create the RDD.
+   * @param generator DistributionGenerator used to populate the RDD.
+   * @param numRows Number of Vectors in the RDD.
+   * @param numCols Number of elements in each Vector.
+   * @param numPartitions Number of partitions in the RDD.
+   * @return RDD[Vector] with vectors containing i.i.d samples produced by generator.
+   */
+  @Experimental
+  def randomVectorRDD(sc: SparkContext,
+      generator: DistributionGenerator,
+      numRows: Long,
+      numCols: Int,
+      numPartitions: Int): RDD[Vector] = {
+    randomVectorRDD(sc, generator, numRows, numCols, numPartitions, Utils.random.nextLong)
+  }
+
+  /**
+   * :: Experimental ::
+   * Generates an RDD[Vector] with vectors containing i.i.d samples produced by the
+   * input DistributionGenerator.
+   * sc.defaultParallelism used for the number of partitions in the RDD.
+   *
+   * @param sc SparkContext used to create the RDD.
+   * @param generator DistributionGenerator used to populate the RDD.
+   * @param numRows Number of Vectors in the RDD.
+   * @param numCols Number of elements in each Vector.
+   * @return RDD[Vector] with vectors containing i.i.d samples produced by generator.
+   */
+  @Experimental
+  def randomVectorRDD(sc: SparkContext,
+      generator: DistributionGenerator,
+      numRows: Long,
+      numCols: Int): RDD[Vector] = {
+    randomVectorRDD(sc, generator, numRows, numCols,
+      sc.defaultParallelism, Utils.random.nextLong)
+  }
+}
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/rdd/RandomRDD.scala b/mllib/src/main/scala/org/apache/spark/mllib/rdd/RandomRDD.scala
new file mode 100644
index 000000000000..f13282d07ff9
--- /dev/null
+++ b/mllib/src/main/scala/org/apache/spark/mllib/rdd/RandomRDD.scala
@@ -0,0 +1,118 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.mllib.rdd
+
+import org.apache.spark.{Partition, SparkContext, TaskContext}
+import org.apache.spark.mllib.linalg.{DenseVector, Vector}
+import org.apache.spark.mllib.random.DistributionGenerator
+import org.apache.spark.rdd.RDD
+import org.apache.spark.util.Utils
+
+import scala.util.Random
+
+private[mllib] class RandomRDDPartition(override val index: Int,
+    val size: Int,
+    val generator: DistributionGenerator,
+    val seed: Long) extends Partition {
+
+  require(size >= 0, "Non-negative partition size required.")
+}
+
+// These two classes are necessary since Range objects in Scala cannot have size > Int.MaxValue
+private[mllib] class RandomRDD(@transient sc: SparkContext,
+    size: Long,
+    numPartitions: Int,
+    @transient rng: DistributionGenerator,
+    @transient seed: Long = Utils.random.nextLong) extends RDD[Double](sc, Nil) {
+
+  require(size > 0, "Positive RDD size required.")
+  require(numPartitions > 0, "Positive number of partitions required")
+  require(math.ceil(size.toDouble / numPartitions) <= Int.MaxValue,
+    "Partition size cannot exceed Int.MaxValue")
+
+  override def compute(splitIn: Partition, context: TaskContext): Iterator[Double] = {
+    val split = splitIn.asInstanceOf[RandomRDDPartition]
+    RandomRDD.getPointIterator(split)
+  }
+
+  override def getPartitions: Array[Partition] = {
+    RandomRDD.getPartitions(size, numPartitions, rng, seed)
+  }
+}
+
+private[mllib] class RandomVectorRDD(@transient sc: SparkContext,
+    size: Long,
+    vectorSize: Int,
+    numPartitions: Int,
+    @transient rng: DistributionGenerator,
+    @transient seed: Long = Utils.random.nextLong) extends RDD[Vector](sc, Nil) {
+
+  require(size > 0, "Positive RDD size required.")
+  require(numPartitions > 0, "Positive number of partitions required")
+  require(vectorSize > 0, "Positive vector size required.")
+  require(math.ceil(size.toDouble / numPartitions) <= Int.MaxValue,
+    "Partition size cannot exceed Int.MaxValue")
+
+  override def compute(splitIn: Partition, context: TaskContext): Iterator[Vector] = {
+    val split = splitIn.asInstanceOf[RandomRDDPartition]
+    RandomRDD.getVectorIterator(split, vectorSize)
+  }
+
+  override protected def getPartitions: Array[Partition] = {
+    RandomRDD.getPartitions(size, numPartitions, rng, seed)
+  }
+}
+
+private[mllib] object RandomRDD {
+
+  def getPartitions(size: Long,
+      numPartitions: Int,
+      rng: DistributionGenerator,
+      seed: Long): Array[Partition] = {
+
+    val partitions = new Array[RandomRDDPartition](numPartitions)
+    var i = 0
+    var start: Long = 0
+    var end: Long = 0
+    val random = new Random(seed)
+    while (i < numPartitions) {
+      end = ((i + 1) * size) / numPartitions
+      partitions(i) = new RandomRDDPartition(i, (end - start).toInt, rng, random.nextLong())
+      start = end
+      i += 1
+    }
+    partitions.asInstanceOf[Array[Partition]]
+  }
+
+  // The RNG has to be reset every time the iterator is requested to guarantee same data
+  // every time the content of the RDD is examined.
+  def getPointIterator(partition: RandomRDDPartition): Iterator[Double] = {
+    val generator = partition.generator.copy()
+    generator.setSeed(partition.seed)
+    Array.fill(partition.size)(generator.nextValue()).toIterator
+  }
+
+  // The RNG has to be reset every time the iterator is requested to guarantee same data
+  // every time the content of the RDD is examined.
+  def getVectorIterator(partition: RandomRDDPartition, vectorSize: Int): Iterator[Vector] = {
+    val generator = partition.generator.copy()
+    generator.setSeed(partition.seed)
+    Array.fill(partition.size)(new DenseVector(
+      (0 until vectorSize).map { _ => generator.nextValue() }.toArray)).toIterator
+  }
+}
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/random/DistributionGeneratorSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/random/DistributionGeneratorSuite.scala
new file mode 100644
index 000000000000..974dec4c0b5e
--- /dev/null
+++ b/mllib/src/test/scala/org/apache/spark/mllib/random/DistributionGeneratorSuite.scala
@@ -0,0 +1,90 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.mllib.random
+
+import org.scalatest.FunSuite
+
+import org.apache.spark.util.StatCounter
+
+// TODO update tests to use TestingUtils for floating point comparison after PR 1367 is merged
+class DistributionGeneratorSuite extends FunSuite {
+
+  def apiChecks(gen: DistributionGenerator) {
+
+    // resetting seed should generate the same sequence of random numbers
+    gen.setSeed(42L)
+    val array1 = (0 until 1000).map(_ => gen.nextValue())
+    gen.setSeed(42L)
+    val array2 = (0 until 1000).map(_ => gen.nextValue())
+    assert(array1.equals(array2))
+
+    // newInstance should contain a difference instance of the rng
+    // i.e. setting difference seeds for difference instances produces different sequences of
+    // random numbers.
+    val gen2 = gen.copy()
+    gen.setSeed(0L)
+    val array3 = (0 until 1000).map(_ => gen.nextValue())
+    gen2.setSeed(1L)
+    val array4 = (0 until 1000).map(_ => gen2.nextValue())
+    // Compare arrays instead of elements since individual elements can coincide by chance but the
+    // sequences should differ given two different seeds.
+    assert(!array3.equals(array4))
+
+    // test that setting the same seed in the copied instance produces the same sequence of numbers
+    gen.setSeed(0L)
+    val array5 = (0 until 1000).map(_ => gen.nextValue())
+    gen2.setSeed(0L)
+    val array6 = (0 until 1000).map(_ => gen2.nextValue())
+    assert(array5.equals(array6))
+  }
+
+  def distributionChecks(gen: DistributionGenerator,
+      mean: Double = 0.0,
+      stddev: Double = 1.0,
+      epsilon: Double = 0.01) {
+    for (seed <- 0 until 5) {
+      gen.setSeed(seed.toLong)
+      val sample = (0 until 100000).map { _ => gen.nextValue()}
+      val stats = new StatCounter(sample)
+      assert(math.abs(stats.mean - mean) < epsilon)
+      assert(math.abs(stats.stdev - stddev) < epsilon)
+    }
+  }
+
+  test("UniformGenerator") {
+    val uniform = new UniformGenerator()
+    apiChecks(uniform)
+    // Stddev of uniform distribution = (ub - lb) / math.sqrt(12)
+    distributionChecks(uniform, 0.5, 1 / math.sqrt(12))
+  }
+
+  test("StandardNormalGenerator") {
+    val normal = new StandardNormalGenerator()
+    apiChecks(normal)
+    distributionChecks(normal, 0.0, 1.0)
+  }
+
+  test("PoissonGenerator") {
+    // mean = 0.0 will not pass the API checks since 0.0 is always deterministically produced.
+    for (mean <- List(1.0, 5.0, 100.0)) {
+      val poisson = new PoissonGenerator(mean)
+      apiChecks(poisson)
+      distributionChecks(poisson, mean, math.sqrt(mean), 0.1)
+    }
+  }
+}
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/random/RandomRDDGeneratorsSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/random/RandomRDDGeneratorsSuite.scala
new file mode 100644
index 000000000000..6aa4f803df0f
--- /dev/null
+++ b/mllib/src/test/scala/org/apache/spark/mllib/random/RandomRDDGeneratorsSuite.scala
@@ -0,0 +1,158 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.mllib.random
+
+import scala.collection.mutable.ArrayBuffer
+
+import org.scalatest.FunSuite
+
+import org.apache.spark.SparkContext._
+import org.apache.spark.mllib.linalg.Vector
+import org.apache.spark.mllib.rdd.{RandomRDDPartition, RandomRDD}
+import org.apache.spark.mllib.util.LocalSparkContext
+import org.apache.spark.rdd.RDD
+import org.apache.spark.util.StatCounter
+
+/*
+ * Note: avoid including APIs that do not set the seed for the RNG in unit tests
+ * in order to guarantee deterministic behavior.
+ *
+ * TODO update tests to use TestingUtils for floating point comparison after PR 1367 is merged
+ */
+class RandomRDDGeneratorsSuite extends FunSuite with LocalSparkContext with Serializable {
+
+  def testGeneratedRDD(rdd: RDD[Double],
+      expectedSize: Long,
+      expectedNumPartitions: Int,
+      expectedMean: Double,
+      expectedStddev: Double,
+      epsilon: Double = 0.01) {
+    val stats = rdd.stats()
+    assert(expectedSize === stats.count)
+    assert(expectedNumPartitions === rdd.partitions.size)
+    assert(math.abs(stats.mean - expectedMean) < epsilon)
+    assert(math.abs(stats.stdev - expectedStddev) < epsilon)
+  }
+
+  // assume test RDDs are small
+  def testGeneratedVectorRDD(rdd: RDD[Vector],
+      expectedRows: Long,
+      expectedColumns: Int,
+      expectedNumPartitions: Int,
+      expectedMean: Double,
+      expectedStddev: Double,
+      epsilon: Double = 0.01) {
+    assert(expectedNumPartitions === rdd.partitions.size)
+    val values = new ArrayBuffer[Double]()
+    rdd.collect.foreach { vector => {
+      assert(vector.size === expectedColumns)
+      values ++= vector.toArray
+    }}
+    assert(expectedRows === values.size / expectedColumns)
+    val stats = new StatCounter(values)
+    assert(math.abs(stats.mean - expectedMean) < epsilon)
+    assert(math.abs(stats.stdev - expectedStddev) < epsilon)
+  }
+
+  test("RandomRDD sizes") {
+
+    // some cases where size % numParts != 0 to test getPartitions behaves correctly
+    for ((size, numPartitions) <- List((10000, 6), (12345, 1), (1000, 101))) {
+      val rdd = new RandomRDD(sc, size, numPartitions, new UniformGenerator, 0L)
+      assert(rdd.count() === size)
+      assert(rdd.partitions.size === numPartitions)
+
+      // check that partition sizes are balanced
+      val partSizes = rdd.partitions.map(p => p.asInstanceOf[RandomRDDPartition].size.toDouble)
+      val partStats = new StatCounter(partSizes)
+      assert(partStats.max - partStats.min <= 1)
+    }
+
+    // size > Int.MaxValue
+    val size = Int.MaxValue.toLong * 100L
+    val numPartitions = 101
+    val rdd = new RandomRDD(sc, size, numPartitions, new UniformGenerator, 0L)
+    assert(rdd.partitions.size === numPartitions)
+    val count = rdd.partitions.foldLeft(0L) { (count, part) =>
+      count + part.asInstanceOf[RandomRDDPartition].size
+    }
+    assert(count === size)
+
+    // size needs to be positive
+    intercept[IllegalArgumentException] { new RandomRDD(sc, 0, 10, new UniformGenerator, 0L) }
+
+    // numPartitions needs to be positive
+    intercept[IllegalArgumentException] { new RandomRDD(sc, 100, 0, new UniformGenerator, 0L) }
+
+    // partition size needs to be <= Int.MaxValue
+    intercept[IllegalArgumentException] {
+      new RandomRDD(sc, Int.MaxValue.toLong * 100L, 99, new UniformGenerator, 0L)
+    }
+  }
+
+  test("randomRDD for different distributions") {
+    val size = 100000L
+    val numPartitions = 10
+    val poissonMean = 100.0
+
+    for (seed <- 0 until 5) {
+      val uniform = RandomRDDGenerators.uniformRDD(sc, size, numPartitions, seed)
+      testGeneratedRDD(uniform, size, numPartitions, 0.5, 1 / math.sqrt(12))
+
+      val normal = RandomRDDGenerators.normalRDD(sc, size, numPartitions, seed)
+      testGeneratedRDD(normal, size, numPartitions, 0.0, 1.0)
+
+      val poisson = RandomRDDGenerators.poissonRDD(sc, poissonMean, size, numPartitions, seed)
+      testGeneratedRDD(poisson, size, numPartitions, poissonMean, math.sqrt(poissonMean), 0.1)
+    }
+
+    // mock distribution to check that partitions have unique seeds
+    val random = RandomRDDGenerators.randomRDD(sc, new MockDistro(), 1000L, 1000, 0L)
+    assert(random.collect.size === random.collect.distinct.size)
+  }
+
+  test("randomVectorRDD for different distributions") {
+    val rows = 1000L
+    val cols = 100
+    val parts = 10
+    val poissonMean = 100.0
+
+    for (seed <- 0 until 5) {
+      val uniform = RandomRDDGenerators.uniformVectorRDD(sc, rows, cols, parts, seed)
+      testGeneratedVectorRDD(uniform, rows, cols, parts, 0.5, 1 / math.sqrt(12))
+
+      val normal = RandomRDDGenerators.normalVectorRDD(sc, rows, cols, parts, seed)
+      testGeneratedVectorRDD(normal, rows, cols, parts, 0.0, 1.0)
+
+      val poisson = RandomRDDGenerators.poissonVectorRDD(sc, poissonMean, rows, cols, parts, seed)
+      testGeneratedVectorRDD(poisson, rows, cols, parts, poissonMean, math.sqrt(poissonMean), 0.1)
+    }
+  }
+}
+
+private[random] class MockDistro extends DistributionGenerator {
+
+  var seed = 0L
+
+  // This allows us to check that each partition has a different seed
+  override def nextValue(): Double = seed.toDouble
+
+  override def setSeed(seed: Long) = this.seed = seed
+
+  override def copy(): MockDistro = new MockDistro
+}

From e5bbce9a60eb99c059315edbf18a1a923d93d9d5 Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Sun, 27 Jul 2014 18:46:58 -0700
Subject: [PATCH 782/810] Revert "[SPARK-2410][SQL] Merging Hive Thrift/JDBC
 server"

This reverts commit f6ff2a61d00d12481bfb211ae13d6992daacdcc2.
---
 .gitignore                                    |   1 -
 assembly/pom.xml                              |  10 -
 bagel/pom.xml                                 |   2 +-
 bin/beeline                                   |  45 ---
 bin/compute-classpath.sh                      |   1 -
 bin/spark-shell                               |   4 +-
 bin/spark-shell.cmd                           |   2 +-
 bin/spark-sql                                 |  36 --
 core/pom.xml                                  |   2 +-
 .../org/apache/spark/deploy/SparkSubmit.scala |  14 +-
 .../spark/deploy/SparkSubmitArguments.scala   |   5 +-
 dev/create-release/create-release.sh          |  10 +-
 dev/run-tests                                 |   2 +-
 dev/scalastyle                                |   2 +-
 docs/sql-programming-guide.md                 | 201 +---------
 examples/pom.xml                              |   2 +-
 external/flume/pom.xml                        |   2 +-
 external/kafka/pom.xml                        |   2 +-
 external/mqtt/pom.xml                         |   2 +-
 external/twitter/pom.xml                      |   2 +-
 external/zeromq/pom.xml                       |   2 +-
 graphx/pom.xml                                |   2 +-
 mllib/pom.xml                                 |   2 +-
 pom.xml                                       |   7 +-
 project/SparkBuild.scala                      |  14 +-
 sbin/start-thriftserver.sh                    |  36 --
 sql/catalyst/pom.xml                          |   2 +-
 .../sql/catalyst/plans/logical/commands.scala |   3 +-
 sql/core/pom.xml                              |   2 +-
 .../scala/org/apache/spark/sql/SQLConf.scala  |  20 +-
 .../apache/spark/sql/execution/commands.scala |  42 +--
 .../org/apache/spark/sql/SQLConfSuite.scala   |  13 +-
 .../org/apache/spark/sql/SQLQuerySuite.scala  |  10 +-
 sql/hive-thriftserver/pom.xml                 |  82 -----
 .../hive/thriftserver/HiveThriftServer2.scala |  97 -----
 .../hive/thriftserver/ReflectionUtils.scala   |  58 ---
 .../hive/thriftserver/SparkSQLCLIDriver.scala | 344 ------------------
 .../thriftserver/SparkSQLCLIService.scala     |  74 ----
 .../hive/thriftserver/SparkSQLDriver.scala    |  93 -----
 .../sql/hive/thriftserver/SparkSQLEnv.scala   |  58 ---
 .../thriftserver/SparkSQLSessionManager.scala |  49 ---
 .../server/SparkSQLOperationManager.scala     | 151 --------
 .../test/resources/data/files/small_kv.txt    |   5 -
 .../sql/hive/thriftserver/CliSuite.scala      |  57 ---
 .../thriftserver/HiveThriftServer2Suite.scala | 135 -------
 .../sql/hive/thriftserver/TestUtils.scala     | 108 ------
 sql/hive/pom.xml                              |   2 +-
 .../apache/spark/sql/hive/HiveContext.scala   |   2 +-
 .../sql/hive/execution/HiveQuerySuite.scala   |  50 +--
 streaming/pom.xml                             |   2 +-
 tools/pom.xml                                 |   2 +-
 yarn/alpha/pom.xml                            |   2 +-
 yarn/pom.xml                                  |   2 +-
 yarn/stable/pom.xml                           |   2 +-
 54 files changed, 96 insertions(+), 1781 deletions(-)
 delete mode 100755 bin/beeline
 delete mode 100755 bin/spark-sql
 delete mode 100755 sbin/start-thriftserver.sh
 delete mode 100644 sql/hive-thriftserver/pom.xml
 delete mode 100644 sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala
 delete mode 100644 sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ReflectionUtils.scala
 delete mode 100755 sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala
 delete mode 100644 sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIService.scala
 delete mode 100644 sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLDriver.scala
 delete mode 100644 sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLEnv.scala
 delete mode 100644 sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLSessionManager.scala
 delete mode 100644 sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/server/SparkSQLOperationManager.scala
 delete mode 100644 sql/hive-thriftserver/src/test/resources/data/files/small_kv.txt
 delete mode 100644 sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/CliSuite.scala
 delete mode 100644 sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suite.scala
 delete mode 100644 sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/TestUtils.scala

diff --git a/.gitignore b/.gitignore
index 5b56a67c883e..061c8946d23c 100644
--- a/.gitignore
+++ b/.gitignore
@@ -57,4 +57,3 @@ metastore_db/
 metastore/
 warehouse/
 TempStatsStore/
-sql/hive-thriftserver/test_warehouses
diff --git a/assembly/pom.xml b/assembly/pom.xml
index 703f15925bc4..567a8dd2a0d9 100644
--- a/assembly/pom.xml
+++ b/assembly/pom.xml
@@ -165,16 +165,6 @@
         </dependency>
       </dependencies>
     </profile>
-    <profile>
-      <id>hive-thriftserver</id>
-      <dependencies>
-        <dependency>
-          <groupId>org.apache.spark</groupId>
-          <artifactId>spark-hive-thriftserver_${scala.binary.version}</artifactId>
-          <version>${project.version}</version>
-        </dependency>
-      </dependencies>
-    </profile>
     <profile>
       <id>spark-ganglia-lgpl</id>
       <dependencies>
diff --git a/bagel/pom.xml b/bagel/pom.xml
index bd51b112e26f..90c4b095bb61 100644
--- a/bagel/pom.xml
+++ b/bagel/pom.xml
@@ -28,7 +28,7 @@
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-bagel_2.10</artifactId>
   <properties>
-    <sbt.project.name>bagel</sbt.project.name>
+     <sbt.project.name>bagel</sbt.project.name>
   </properties>
   <packaging>jar</packaging>
   <name>Spark Project Bagel</name>
diff --git a/bin/beeline b/bin/beeline
deleted file mode 100755
index 09fe366c609f..000000000000
--- a/bin/beeline
+++ /dev/null
@@ -1,45 +0,0 @@
-#!/usr/bin/env bash
-
-#
-# Licensed to the Apache Software Foundation (ASF) under one or more
-# contributor license agreements.  See the NOTICE file distributed with
-# this work for additional information regarding copyright ownership.
-# The ASF licenses this file to You under the Apache License, Version 2.0
-# (the "License"); you may not use this file except in compliance with
-# the License.  You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-
-# Figure out where Spark is installed
-FWDIR="$(cd `dirname $0`/..; pwd)"
-
-# Find the java binary
-if [ -n "${JAVA_HOME}" ]; then
-  RUNNER="${JAVA_HOME}/bin/java"
-else
-  if [ `command -v java` ]; then
-    RUNNER="java"
-  else
-    echo "JAVA_HOME is not set" >&2
-    exit 1
-  fi
-fi
-
-# Compute classpath using external script
-classpath_output=$($FWDIR/bin/compute-classpath.sh)
-if [[ "$?" != "0" ]]; then
-  echo "$classpath_output"
-  exit 1
-else
-  CLASSPATH=$classpath_output
-fi
-
-CLASS="org.apache.hive.beeline.BeeLine"
-exec "$RUNNER" -cp "$CLASSPATH" $CLASS "$@"
diff --git a/bin/compute-classpath.sh b/bin/compute-classpath.sh
index 16b794a1592e..e81e8c060cb9 100755
--- a/bin/compute-classpath.sh
+++ b/bin/compute-classpath.sh
@@ -52,7 +52,6 @@ if [ -n "$SPARK_PREPEND_CLASSES" ]; then
   CLASSPATH="$CLASSPATH:$FWDIR/sql/catalyst/target/scala-$SCALA_VERSION/classes"
   CLASSPATH="$CLASSPATH:$FWDIR/sql/core/target/scala-$SCALA_VERSION/classes"
   CLASSPATH="$CLASSPATH:$FWDIR/sql/hive/target/scala-$SCALA_VERSION/classes"
-  CLASSPATH="$CLASSPATH:$FWDIR/sql/hive-thriftserver/target/scala-$SCALA_VERSION/classes"
   CLASSPATH="$CLASSPATH:$FWDIR/yarn/stable/target/scala-$SCALA_VERSION/classes"
 fi
 
diff --git a/bin/spark-shell b/bin/spark-shell
index 756c8179d12b..850e9507ec38 100755
--- a/bin/spark-shell
+++ b/bin/spark-shell
@@ -46,11 +46,11 @@ function main(){
         # (see https://github.com/sbt/sbt/issues/562).
         stty -icanon min 1 -echo > /dev/null 2>&1
         export SPARK_SUBMIT_OPTS="$SPARK_SUBMIT_OPTS -Djline.terminal=unix"
-        $FWDIR/bin/spark-submit --class org.apache.spark.repl.Main spark-shell "$@"
+        $FWDIR/bin/spark-submit spark-shell "$@" --class org.apache.spark.repl.Main
         stty icanon echo > /dev/null 2>&1
     else
         export SPARK_SUBMIT_OPTS
-        $FWDIR/bin/spark-submit --class org.apache.spark.repl.Main spark-shell "$@"
+        $FWDIR/bin/spark-submit spark-shell "$@" --class org.apache.spark.repl.Main
     fi
 }
 
diff --git a/bin/spark-shell.cmd b/bin/spark-shell.cmd
index b56d69801171..4b9708a8c03f 100755
--- a/bin/spark-shell.cmd
+++ b/bin/spark-shell.cmd
@@ -19,4 +19,4 @@ rem
 
 set SPARK_HOME=%~dp0..
 
-cmd /V /E /C %SPARK_HOME%\bin\spark-submit.cmd spark-shell --class org.apache.spark.repl.Main %*
+cmd /V /E /C %SPARK_HOME%\bin\spark-submit.cmd spark-shell %* --class org.apache.spark.repl.Main
diff --git a/bin/spark-sql b/bin/spark-sql
deleted file mode 100755
index bba7f897b19b..000000000000
--- a/bin/spark-sql
+++ /dev/null
@@ -1,36 +0,0 @@
-#!/usr/bin/env bash
-
-#
-# Licensed to the Apache Software Foundation (ASF) under one or more
-# contributor license agreements.  See the NOTICE file distributed with
-# this work for additional information regarding copyright ownership.
-# The ASF licenses this file to You under the Apache License, Version 2.0
-# (the "License"); you may not use this file except in compliance with
-# the License.  You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-
-#
-# Shell script for starting the Spark SQL CLI
-
-# Enter posix mode for bash
-set -o posix
-
-# Figure out where Spark is installed
-FWDIR="$(cd `dirname $0`/..; pwd)"
-
-if [[ "$@" = *--help ]] || [[ "$@" = *-h ]]; then
-  echo "Usage: ./sbin/spark-sql [options]"
-  $FWDIR/bin/spark-submit --help 2>&1 | grep -v Usage 1>&2
-  exit 0
-fi
-
-CLASS="org.apache.spark.sql.hive.thriftserver.SparkSQLCLIDriver"
-exec "$FWDIR"/bin/spark-submit --class $CLASS spark-internal $@
diff --git a/core/pom.xml b/core/pom.xml
index a24743495b0e..1054cec4d77b 100644
--- a/core/pom.xml
+++ b/core/pom.xml
@@ -28,7 +28,7 @@
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-core_2.10</artifactId>
   <properties>
-    <sbt.project.name>core</sbt.project.name>
+     <sbt.project.name>core</sbt.project.name>
   </properties>
   <packaging>jar</packaging>
   <name>Spark Project Core</name>
diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
index c9cec33ebaa6..3b5642b6caa3 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
@@ -46,10 +46,6 @@ object SparkSubmit {
   private val CLUSTER = 2
   private val ALL_DEPLOY_MODES = CLIENT | CLUSTER
 
-  // A special jar name that indicates the class being run is inside of Spark itself, and therefore
-  // no user jar is needed.
-  private val SPARK_INTERNAL = "spark-internal"
-
   // Special primary resource names that represent shells rather than application jars.
   private val SPARK_SHELL = "spark-shell"
   private val PYSPARK_SHELL = "pyspark-shell"
@@ -261,9 +257,7 @@ object SparkSubmit {
     // In yarn-cluster mode, use yarn.Client as a wrapper around the user class
     if (clusterManager == YARN && deployMode == CLUSTER) {
       childMainClass = "org.apache.spark.deploy.yarn.Client"
-      if (args.primaryResource != SPARK_INTERNAL) {
-        childArgs += ("--jar", args.primaryResource)
-      }
+      childArgs += ("--jar", args.primaryResource)
       childArgs += ("--class", args.mainClass)
       if (args.childArgs != null) {
         args.childArgs.foreach { arg => childArgs += ("--arg", arg) }
@@ -338,7 +332,7 @@ object SparkSubmit {
    * Return whether the given primary resource represents a user jar.
    */
   private def isUserJar(primaryResource: String): Boolean = {
-    !isShell(primaryResource) && !isPython(primaryResource) && !isInternal(primaryResource)
+    !isShell(primaryResource) && !isPython(primaryResource)
   }
 
   /**
@@ -355,10 +349,6 @@ object SparkSubmit {
     primaryResource.endsWith(".py") || primaryResource == PYSPARK_SHELL
   }
 
-  private[spark] def isInternal(primaryResource: String): Boolean = {
-    primaryResource == SPARK_INTERNAL
-  }
-
   /**
    * Merge a sequence of comma-separated file lists, some of which may be null to indicate
    * no files, into a single comma-separated string.
diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
index 01d0ae541a66..3ab67a43a3b5 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
@@ -204,9 +204,8 @@ private[spark] class SparkSubmitArguments(args: Seq[String]) {
 
   /** Fill in values by parsing user options. */
   private def parseOpts(opts: Seq[String]): Unit = {
-    var inSparkOpts = true
-
     // Delineates parsing of Spark options from parsing of user options.
+    var inSparkOpts = true
     parse(opts)
 
     def parse(opts: Seq[String]): Unit = opts match {
@@ -319,7 +318,7 @@ private[spark] class SparkSubmitArguments(args: Seq[String]) {
               SparkSubmit.printErrorAndExit(errMessage)
             case v =>
               primaryResource =
-                if (!SparkSubmit.isShell(v) && !SparkSubmit.isInternal(v)) {
+                if (!SparkSubmit.isShell(v)) {
                   Utils.resolveURI(v).toString
                 } else {
                   v
diff --git a/dev/create-release/create-release.sh b/dev/create-release/create-release.sh
index 33de24d1ae6d..38830103d1e8 100755
--- a/dev/create-release/create-release.sh
+++ b/dev/create-release/create-release.sh
@@ -53,7 +53,7 @@ if [[ ! "$@" =~ --package-only ]]; then
     -Dusername=$GIT_USERNAME -Dpassword=$GIT_PASSWORD \
     -Dmaven.javadoc.skip=true \
     -Dhadoop.version=2.2.0 -Dyarn.version=2.2.0 \
-    -Pyarn -Phive -Phive-thriftserver -Phadoop-2.2 -Pspark-ganglia-lgpl\
+    -Pyarn -Phive -Phadoop-2.2 -Pspark-ganglia-lgpl\
     -Dtag=$GIT_TAG -DautoVersionSubmodules=true \
     --batch-mode release:prepare
 
@@ -61,7 +61,7 @@ if [[ ! "$@" =~ --package-only ]]; then
     -Darguments="-DskipTests=true -Dmaven.javadoc.skip=true -Dhadoop.version=2.2.0 -Dyarn.version=2.2.0 -Dgpg.passphrase=${GPG_PASSPHRASE}" \
     -Dhadoop.version=2.2.0 -Dyarn.version=2.2.0 \
     -Dmaven.javadoc.skip=true \
-    -Pyarn -Phive -Phive-thriftserver -Phadoop-2.2 -Pspark-ganglia-lgpl\
+    -Pyarn -Phive -Phadoop-2.2 -Pspark-ganglia-lgpl\
     release:perform
 
   cd ..
@@ -111,10 +111,10 @@ make_binary_release() {
     spark-$RELEASE_VERSION-bin-$NAME.tgz.sha
 }
 
-make_binary_release "hadoop1" "-Phive -Phive-thriftserver -Dhadoop.version=1.0.4"
-make_binary_release "cdh4" "-Phive -Phive-thriftserver -Dhadoop.version=2.0.0-mr1-cdh4.2.0"
+make_binary_release "hadoop1" "-Phive -Dhadoop.version=1.0.4"
+make_binary_release "cdh4" "-Phive -Dhadoop.version=2.0.0-mr1-cdh4.2.0"
 make_binary_release "hadoop2" \
-  "-Phive -Phive-thriftserver -Pyarn -Phadoop-2.2 -Dhadoop.version=2.2.0 -Pyarn.version=2.2.0"
+  "-Phive -Pyarn -Phadoop-2.2 -Dhadoop.version=2.2.0 -Pyarn.version=2.2.0"
 
 # Copy data
 echo "Copying release tarballs"
diff --git a/dev/run-tests b/dev/run-tests
index 98ec969dc1b3..51e4def0f835 100755
--- a/dev/run-tests
+++ b/dev/run-tests
@@ -65,7 +65,7 @@ echo "========================================================================="
 # (either resolution or compilation) prompts the user for input either q, r, 
 # etc to quit or retry. This echo is there to make it not block.
 if [ -n "$_RUN_SQL_TESTS" ]; then
-  echo -e "q\n" | SBT_MAVEN_PROFILES="$SBT_MAVEN_PROFILES -Phive -Phive-thriftserver" sbt/sbt clean package \
+  echo -e "q\n" | SBT_MAVEN_PROFILES="$SBT_MAVEN_PROFILES -Phive" sbt/sbt clean package \
     assembly/assembly test | grep -v -e "info.*Resolving" -e "warn.*Merging" -e "info.*Including"
 else
   echo -e "q\n" | sbt/sbt clean package assembly/assembly test | \
diff --git a/dev/scalastyle b/dev/scalastyle
index d9f2b91a3a09..a02d06912f23 100755
--- a/dev/scalastyle
+++ b/dev/scalastyle
@@ -17,7 +17,7 @@
 # limitations under the License.
 #
 
-echo -e "q\n" | sbt/sbt -Phive -Phive-thriftserver scalastyle > scalastyle.txt
+echo -e "q\n" | sbt/sbt -Phive scalastyle > scalastyle.txt
 # Check style with YARN alpha built too
 echo -e "q\n" | sbt/sbt -Pyarn -Phadoop-0.23 -Dhadoop.version=0.23.9 yarn-alpha/scalastyle \
   >> scalastyle.txt
diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md
index 156e0aebdebe..38728534a46e 100644
--- a/docs/sql-programming-guide.md
+++ b/docs/sql-programming-guide.md
@@ -136,7 +136,7 @@ val sqlContext = new org.apache.spark.sql.SQLContext(sc)
 import sqlContext.createSchemaRDD
 
 // Define the schema using a case class.
-// Note: Case classes in Scala 2.10 can support only up to 22 fields. To work around this limit,
+// Note: Case classes in Scala 2.10 can support only up to 22 fields. To work around this limit, 
 // you can use custom classes that implement the Product interface.
 case class Person(name: String, age: Int)
 
@@ -548,6 +548,7 @@ results = hiveContext.hql("FROM src SELECT key, value").collect()
 </div>
 </div>
 
+
 # Writing Language-Integrated Relational Queries
 
 **Language-Integrated queries are currently only supported in Scala.**
@@ -572,200 +573,4 @@ prefixed with a tick (`'`).  Implicit conversions turn these symbols into expres
 evaluated by the SQL execution engine.  A full list of the functions supported can be found in the
 [ScalaDoc](api/scala/index.html#org.apache.spark.sql.SchemaRDD).
 
-<!-- TODO: Include the table of operations here. -->
-
-## Running the Thrift JDBC server
-
-The Thrift JDBC server implemented here corresponds to the [`HiveServer2`]
-(https://cwiki.apache.org/confluence/display/Hive/Setting+Up+HiveServer2) in Hive 0.12. You can test
-the JDBC server with the beeline script comes with either Spark or Hive 0.12.  In order to use Hive
-you must first run '`sbt/sbt -Phive-thriftserver assembly/assembly`' (or use `-Phive-thriftserver`
-for maven).
-
-To start the JDBC server, run the following in the Spark directory:
-
-    ./sbin/start-thriftserver.sh
-
-The default port the server listens on is 10000.  To listen on customized host and port, please set
-the `HIVE_SERVER2_THRIFT_PORT` and `HIVE_SERVER2_THRIFT_BIND_HOST` environment variables. You may
-run `./sbin/start-thriftserver.sh --help` for a complete list of all available options.  Now you can
-use beeline to test the Thrift JDBC server:
-
-    ./bin/beeline
-
-Connect to the JDBC server in beeline with:
-
-    beeline> !connect jdbc:hive2://localhost:10000
-
-Beeline will ask you for a username and password. In non-secure mode, simply enter the username on
-your machine and a blank password. For secure mode, please follow the instructions given in the
-[beeline documentation](https://cwiki.apache.org/confluence/display/Hive/HiveServer2+Clients)
-
-Configuration of Hive is done by placing your `hive-site.xml` file in `conf/`.
-
-You may also use the beeline script comes with Hive.
-
-### Migration Guide for Shark Users
-
-#### Reducer number
-
-In Shark, default reducer number is 1 and is controlled by the property `mapred.reduce.tasks`. Spark
-SQL deprecates this property by a new property `spark.sql.shuffle.partitions`, whose default value
-is 200. Users may customize this property via `SET`:
-
-```
-SET spark.sql.shuffle.partitions=10;
-SELECT page, count(*) c FROM logs_last_month_cached
-GROUP BY page ORDER BY c DESC LIMIT 10;
-```
-
-You may also put this property in `hive-site.xml` to override the default value.
-
-For now, the `mapred.reduce.tasks` property is still recognized, and is converted to
-`spark.sql.shuffle.partitions` automatically.
-
-#### Caching
-
-The `shark.cache` table property no longer exists, and tables whose name end with `_cached` are no
-longer automcatically cached. Instead, we provide `CACHE TABLE` and `UNCACHE TABLE` statements to
-let user control table caching explicitly:
-
-```
-CACHE TABLE logs_last_month;
-UNCACHE TABLE logs_last_month;
-```
-
-**NOTE** `CACHE TABLE tbl` is lazy, it only marks table `tbl` as "need to by cached if necessary",
-but doesn't actually cache it until a query that touches `tbl` is executed. To force the table to be
-cached, you may simply count the table immediately after executing `CACHE TABLE`:
-
-```
-CACHE TABLE logs_last_month;
-SELECT COUNT(1) FROM logs_last_month;
-```
-
-Several caching related features are not supported yet:
-
-* User defined partition level cache eviction policy
-* RDD reloading
-* In-memory cache write through policy
-
-### Compatibility with Apache Hive
-
-#### Deploying in Exising Hive Warehouses
-
-Spark SQL Thrift JDBC server is designed to be "out of the box" compatible with existing Hive
-installations. You do not need to modify your existing Hive Metastore or change the data placement
-or partitioning of your tables.
-
-#### Supported Hive Features
-
-Spark SQL supports the vast majority of Hive features, such as:
-
-* Hive query statements, including:
- * `SELECT`
- * `GROUP BY
- * `ORDER BY`
- * `CLUSTER BY`
- * `SORT BY`
-* All Hive operators, including:
- * Relational operators (`=`, `⇔`, `==`, `<>`, `<`, `>`, `>=`, `<=`, etc)
- * Arthimatic operators (`+`, `-`, `*`, `/`, `%`, etc)
- * Logical operators (`AND`, `&&`, `OR`, `||`, etc)
- * Complex type constructors
- * Mathemtatical functions (`sign`, `ln`, `cos`, etc)
- * String functions (`instr`, `length`, `printf`, etc)
-* User defined functions (UDF)
-* User defined aggregation functions (UDAF)
-* User defined serialization formats (SerDe's)
-* Joins
- * `JOIN`
- * `{LEFT|RIGHT|FULL} OUTER JOIN`
- * `LEFT SEMI JOIN`
- * `CROSS JOIN`
-* Unions
-* Sub queries
- * `SELECT col FROM ( SELECT a + b AS col from t1) t2`
-* Sampling
-* Explain
-* Partitioned tables
-* All Hive DDL Functions, including:
- * `CREATE TABLE`
- * `CREATE TABLE AS SELECT`
- * `ALTER TABLE`
-* Most Hive Data types, including:
- * `TINYINT`
- * `SMALLINT`
- * `INT`
- * `BIGINT`
- * `BOOLEAN`
- * `FLOAT`
- * `DOUBLE`
- * `STRING`
- * `BINARY`
- * `TIMESTAMP`
- * `ARRAY<>`
- * `MAP<>`
- * `STRUCT<>`
-
-#### Unsupported Hive Functionality
-
-Below is a list of Hive features that we don't support yet. Most of these features are rarely used
-in Hive deployments.
-
-**Major Hive Features**
-
-* Tables with buckets: bucket is the hash partitioning within a Hive table partition. Spark SQL
-  doesn't support buckets yet.
-
-**Esoteric Hive Features**
-
-* Tables with partitions using different input formats: In Spark SQL, all table partitions need to
-  have the same input format.
-* Non-equi outer join: For the uncommon use case of using outer joins with non-equi join conditions
-  (e.g. condition "`key < 10`"), Spark SQL will output wrong result for the `NULL` tuple.
-* `UNIONTYPE`
-* Unique join
-* Single query multi insert
-* Column statistics collecting: Spark SQL does not piggyback scans to collect column statistics at
-  the moment.
-
-**Hive Input/Output Formats**
-
-* File format for CLI: For results showing back to the CLI, Spark SQL only supports TextOutputFormat.
-* Hadoop archive
-
-**Hive Optimizations**
-
-A handful of Hive optimizations are not yet included in Spark. Some of these (such as indexes) are
-not necessary due to Spark SQL's in-memory computational model. Others are slotted for future
-releases of Spark SQL.
-
-* Block level bitmap indexes and virtual columns (used to build indexes)
-* Automatically convert a join to map join: For joining a large table with multiple small tables,
-  Hive automatically converts the join into a map join. We are adding this auto conversion in the
-  next release.
-* Automatically determine the number of reducers for joins and groupbys: Currently in Spark SQL, you
-  need to control the degree of parallelism post-shuffle using "SET
-  spark.sql.shuffle.partitions=[num_tasks];". We are going to add auto-setting of parallelism in the
-  next release.
-* Meta-data only query: For queries that can be answered by using only meta data, Spark SQL still
-  launches tasks to compute the result.
-* Skew data flag: Spark SQL does not follow the skew data flags in Hive.
-* `STREAMTABLE` hint in join: Spark SQL does not follow the `STREAMTABLE` hint.
-* Merge multiple small files for query results: if the result output contains multiple small files,
-  Hive can optionally merge the small files into fewer large files to avoid overflowing the HDFS
-  metadata. Spark SQL does not support that.
-
-## Running the Spark SQL CLI
-
-The Spark SQL CLI is a convenient tool to run the Hive metastore service in local mode and execute
-queries input from command line. Note: the Spark SQL CLI cannot talk to the Thrift JDBC server.
-
-To start the Spark SQL CLI, run the following in the Spark directory:
-
-    ./bin/spark-sql
-
-Configuration of Hive is done by placing your `hive-site.xml` file in `conf/`.
-You may run `./bin/spark-sql --help` for a complete list of all available
-options.
+<!-- TODO: Include the table of operations here. -->
\ No newline at end of file
diff --git a/examples/pom.xml b/examples/pom.xml
index c4ed0f5a6a02..bd1c387c2eb9 100644
--- a/examples/pom.xml
+++ b/examples/pom.xml
@@ -28,7 +28,7 @@
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-examples_2.10</artifactId>
   <properties>
-    <sbt.project.name>examples</sbt.project.name>
+     <sbt.project.name>examples</sbt.project.name>
   </properties>
   <packaging>jar</packaging>
   <name>Spark Project Examples</name>
diff --git a/external/flume/pom.xml b/external/flume/pom.xml
index 874b8a7959bb..61a6aff543ae 100644
--- a/external/flume/pom.xml
+++ b/external/flume/pom.xml
@@ -28,7 +28,7 @@
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-streaming-flume_2.10</artifactId>
   <properties>
-    <sbt.project.name>streaming-flume</sbt.project.name>
+     <sbt.project.name>streaming-flume</sbt.project.name>
   </properties>
   <packaging>jar</packaging>
   <name>Spark Project External Flume</name>
diff --git a/external/kafka/pom.xml b/external/kafka/pom.xml
index 25a5c0a4d7d7..4762c50685a9 100644
--- a/external/kafka/pom.xml
+++ b/external/kafka/pom.xml
@@ -28,7 +28,7 @@
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-streaming-kafka_2.10</artifactId>
   <properties>
-    <sbt.project.name>streaming-kafka</sbt.project.name>
+     <sbt.project.name>streaming-kafka</sbt.project.name>
   </properties>
   <packaging>jar</packaging>
   <name>Spark Project External Kafka</name>
diff --git a/external/mqtt/pom.xml b/external/mqtt/pom.xml
index f31ed655f677..32c530e600ce 100644
--- a/external/mqtt/pom.xml
+++ b/external/mqtt/pom.xml
@@ -28,7 +28,7 @@
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-streaming-mqtt_2.10</artifactId>
   <properties>
-    <sbt.project.name>streaming-mqtt</sbt.project.name>
+     <sbt.project.name>streaming-mqtt</sbt.project.name>
   </properties>
   <packaging>jar</packaging>
   <name>Spark Project External MQTT</name>
diff --git a/external/twitter/pom.xml b/external/twitter/pom.xml
index 56bb24c2a072..637adb0f00da 100644
--- a/external/twitter/pom.xml
+++ b/external/twitter/pom.xml
@@ -28,7 +28,7 @@
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-streaming-twitter_2.10</artifactId>
   <properties>
-    <sbt.project.name>streaming-twitter</sbt.project.name>
+     <sbt.project.name>streaming-twitter</sbt.project.name>
   </properties>
   <packaging>jar</packaging>
   <name>Spark Project External Twitter</name>
diff --git a/external/zeromq/pom.xml b/external/zeromq/pom.xml
index 54b0242c54e7..e4d758a04a4c 100644
--- a/external/zeromq/pom.xml
+++ b/external/zeromq/pom.xml
@@ -28,7 +28,7 @@
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-streaming-zeromq_2.10</artifactId>
   <properties>
-    <sbt.project.name>streaming-zeromq</sbt.project.name>
+     <sbt.project.name>streaming-zeromq</sbt.project.name>
   </properties>
   <packaging>jar</packaging>
   <name>Spark Project External ZeroMQ</name>
diff --git a/graphx/pom.xml b/graphx/pom.xml
index 6dd52fc618b1..7e3bcf29dcfb 100644
--- a/graphx/pom.xml
+++ b/graphx/pom.xml
@@ -28,7 +28,7 @@
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-graphx_2.10</artifactId>
   <properties>
-    <sbt.project.name>graphx</sbt.project.name>
+     <sbt.project.name>graphx</sbt.project.name>
   </properties>
   <packaging>jar</packaging>
   <name>Spark Project GraphX</name>
diff --git a/mllib/pom.xml b/mllib/pom.xml
index f27cf520dc9f..92b07e2357db 100644
--- a/mllib/pom.xml
+++ b/mllib/pom.xml
@@ -28,7 +28,7 @@
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-mllib_2.10</artifactId>
   <properties>
-    <sbt.project.name>mllib</sbt.project.name>
+     <sbt.project.name>mllib</sbt.project.name>
   </properties>  
   <packaging>jar</packaging>
   <name>Spark Project ML Library</name>
diff --git a/pom.xml b/pom.xml
index 3e9d388180d8..4e2d64a83364 100644
--- a/pom.xml
+++ b/pom.xml
@@ -95,7 +95,6 @@
     <module>sql/catalyst</module>
     <module>sql/core</module>
     <module>sql/hive</module>
-    <module>sql/hive-thriftserver</module>
     <module>repl</module>
     <module>assembly</module>
     <module>external/twitter</module>
@@ -253,9 +252,9 @@
         <version>3.3.2</version>
       </dependency>
       <dependency>
-        <groupId>commons-codec</groupId>
-        <artifactId>commons-codec</artifactId>
-        <version>1.5</version>
+	  <groupId>commons-codec</groupId>
+	    <artifactId>commons-codec</artifactId>
+	    <version>1.5</version>
       </dependency>
       <dependency>
         <groupId>com.google.code.findbugs</groupId>
diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index 1629bc2cba8b..62576f84dd03 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -30,11 +30,11 @@ object BuildCommons {
 
   private val buildLocation = file(".").getAbsoluteFile.getParentFile
 
-  val allProjects@Seq(bagel, catalyst, core, graphx, hive, hiveThriftServer, mllib, repl, spark, sql,
-  streaming, streamingFlume, streamingKafka, streamingMqtt, streamingTwitter, streamingZeromq) =
-    Seq("bagel", "catalyst", "core", "graphx", "hive", "hive-thriftserver", "mllib", "repl",
-      "spark", "sql", "streaming", "streaming-flume", "streaming-kafka", "streaming-mqtt",
-      "streaming-twitter", "streaming-zeromq").map(ProjectRef(buildLocation, _))
+  val allProjects@Seq(bagel, catalyst, core, graphx, hive, mllib, repl, spark, sql, streaming,
+  streamingFlume, streamingKafka, streamingMqtt, streamingTwitter, streamingZeromq) =
+    Seq("bagel", "catalyst", "core", "graphx", "hive", "mllib", "repl", "spark", "sql",
+      "streaming", "streaming-flume", "streaming-kafka", "streaming-mqtt", "streaming-twitter",
+      "streaming-zeromq").map(ProjectRef(buildLocation, _))
 
   val optionallyEnabledProjects@Seq(yarn, yarnStable, yarnAlpha, java8Tests, sparkGangliaLgpl) =
     Seq("yarn", "yarn-stable", "yarn-alpha", "java8-tests", "ganglia-lgpl")
@@ -100,7 +100,7 @@ object SparkBuild extends PomBuild {
   Properties.envOrNone("SBT_MAVEN_PROPERTIES") match {
     case Some(v) =>
       v.split("(\\s+|,)").filterNot(_.isEmpty).map(_.split("=")).foreach(x => System.setProperty(x(0), x(1)))
-    case _ =>
+    case _ => 
   }
 
   override val userPropertiesMap = System.getProperties.toMap
@@ -158,7 +158,7 @@ object SparkBuild extends PomBuild {
 
   /* Enable Mima for all projects except spark, hive, catalyst, sql  and repl */
   // TODO: Add Sql to mima checks
-  allProjects.filterNot(x => Seq(spark, sql, hive, hiveThriftServer, catalyst, repl).contains(x)).
+  allProjects.filterNot(y => Seq(spark, sql, hive, catalyst, repl).exists(x => x == y)).
     foreach (x => enable(MimaBuild.mimaSettings(sparkHome, x))(x))
 
   /* Enable Assembly for all assembly projects */
diff --git a/sbin/start-thriftserver.sh b/sbin/start-thriftserver.sh
deleted file mode 100755
index 8398e6f19b51..000000000000
--- a/sbin/start-thriftserver.sh
+++ /dev/null
@@ -1,36 +0,0 @@
-#!/usr/bin/env bash
-
-#
-# Licensed to the Apache Software Foundation (ASF) under one or more
-# contributor license agreements.  See the NOTICE file distributed with
-# this work for additional information regarding copyright ownership.
-# The ASF licenses this file to You under the Apache License, Version 2.0
-# (the "License"); you may not use this file except in compliance with
-# the License.  You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-
-#
-# Shell script for starting the Spark SQL Thrift server
-
-# Enter posix mode for bash
-set -o posix
-
-# Figure out where Spark is installed
-FWDIR="$(cd `dirname $0`/..; pwd)"
-
-if [[ "$@" = *--help ]] || [[ "$@" = *-h ]]; then
-  echo "Usage: ./sbin/start-thriftserver [options]"
-  $FWDIR/bin/spark-submit --help 2>&1 | grep -v Usage 1>&2
-  exit 0
-fi
-
-CLASS="org.apache.spark.sql.hive.thriftserver.HiveThriftServer2"
-exec "$FWDIR"/bin/spark-submit --class $CLASS spark-internal $@
diff --git a/sql/catalyst/pom.xml b/sql/catalyst/pom.xml
index 531bfddbf237..6decde3fcd62 100644
--- a/sql/catalyst/pom.xml
+++ b/sql/catalyst/pom.xml
@@ -32,7 +32,7 @@
   <name>Spark Project Catalyst</name>
   <url>http://spark.apache.org/</url>
   <properties>
-    <sbt.project.name>catalyst</sbt.project.name>
+     <sbt.project.name>catalyst</sbt.project.name>
   </properties>
 
   <dependencies>
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/commands.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/commands.scala
index a357c6ffb897..1d5f033f0d27 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/commands.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/commands.scala
@@ -43,7 +43,8 @@ case class NativeCommand(cmd: String) extends Command {
  */
 case class SetCommand(key: Option[String], value: Option[String]) extends Command {
   override def output = Seq(
-    BoundReference(1, AttributeReference("", StringType, nullable = false)()))
+    BoundReference(0, AttributeReference("key", StringType, nullable = false)()),
+    BoundReference(1, AttributeReference("value", StringType, nullable = false)()))
 }
 
 /**
diff --git a/sql/core/pom.xml b/sql/core/pom.xml
index 3a038a2db617..c309c43804d9 100644
--- a/sql/core/pom.xml
+++ b/sql/core/pom.xml
@@ -32,7 +32,7 @@
   <name>Spark Project SQL</name>
   <url>http://spark.apache.org/</url>
   <properties>
-    <sbt.project.name>sql</sbt.project.name>
+     <sbt.project.name>sql</sbt.project.name>
   </properties>
 
   <dependencies>
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
index 41920c00b5a2..2b787e14f3f1 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
@@ -30,13 +30,12 @@ import scala.collection.JavaConverters._
  * SQLConf is thread-safe (internally synchronized so safe to be used in multiple threads).
  */
 trait SQLConf {
-  import SQLConf._
 
   /** ************************ Spark SQL Params/Hints ******************* */
   // TODO: refactor so that these hints accessors don't pollute the name space of SQLContext?
 
   /** Number of partitions to use for shuffle operators. */
-  private[spark] def numShufflePartitions: Int = get(SHUFFLE_PARTITIONS, "200").toInt
+  private[spark] def numShufflePartitions: Int = get("spark.sql.shuffle.partitions", "200").toInt
 
   /**
    * Upper bound on the sizes (in bytes) of the tables qualified for the auto conversion to
@@ -44,10 +43,11 @@ trait SQLConf {
    * effectively disables auto conversion.
    * Hive setting: hive.auto.convert.join.noconditionaltask.size.
    */
-  private[spark] def autoConvertJoinSize: Int = get(AUTO_CONVERT_JOIN_SIZE, "10000").toInt
+  private[spark] def autoConvertJoinSize: Int =
+    get("spark.sql.auto.convert.join.size", "10000").toInt
 
   /** A comma-separated list of table names marked to be broadcasted during joins. */
-  private[spark] def joinBroadcastTables: String = get(JOIN_BROADCAST_TABLES, "")
+  private[spark] def joinBroadcastTables: String = get("spark.sql.join.broadcastTables", "")
 
   /** ********************** SQLConf functionality methods ************ */
 
@@ -61,7 +61,7 @@ trait SQLConf {
 
   def set(key: String, value: String): Unit = {
     require(key != null, "key cannot be null")
-    require(value != null, s"value cannot be null for $key")
+    require(value != null, s"value cannot be null for ${key}")
     settings.put(key, value)
   }
 
@@ -90,13 +90,3 @@ trait SQLConf {
   }
 
 }
-
-object SQLConf {
-  val AUTO_CONVERT_JOIN_SIZE = "spark.sql.auto.convert.join.size"
-  val SHUFFLE_PARTITIONS = "spark.sql.shuffle.partitions"
-  val JOIN_BROADCAST_TABLES = "spark.sql.join.broadcastTables"
-
-  object Deprecated {
-    val MAPRED_REDUCE_TASKS = "mapred.reduce.tasks"
-  }
-}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala
index 9293239131d5..98d2f89c8ae7 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala
@@ -17,13 +17,12 @@
 
 package org.apache.spark.sql.execution
 
-import org.apache.spark.Logging
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.errors.TreeNodeException
 import org.apache.spark.sql.catalyst.expressions.{Attribute, GenericRow}
 import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
-import org.apache.spark.sql.{Row, SQLConf, SQLContext}
+import org.apache.spark.sql.{Row, SQLContext}
 
 trait Command {
   /**
@@ -45,53 +44,28 @@ trait Command {
 case class SetCommand(
     key: Option[String], value: Option[String], output: Seq[Attribute])(
     @transient context: SQLContext)
-  extends LeafNode with Command with Logging {
+  extends LeafNode with Command {
 
-  override protected[sql] lazy val sideEffectResult: Seq[String] = (key, value) match {
+  override protected[sql] lazy val sideEffectResult: Seq[(String, String)] = (key, value) match {
     // Set value for key k.
     case (Some(k), Some(v)) =>
-      if (k == SQLConf.Deprecated.MAPRED_REDUCE_TASKS) {
-        logWarning(s"Property ${SQLConf.Deprecated.MAPRED_REDUCE_TASKS} is deprecated, " +
-          s"automatically converted to ${SQLConf.SHUFFLE_PARTITIONS} instead.")
-        context.set(SQLConf.SHUFFLE_PARTITIONS, v)
-        Array(s"${SQLConf.SHUFFLE_PARTITIONS}=$v")
-      } else {
-        context.set(k, v)
-        Array(s"$k=$v")
-      }
+      context.set(k, v)
+      Array(k -> v)
 
     // Query the value bound to key k.
     case (Some(k), _) =>
-      // TODO (lian) This is just a workaround to make the Simba ODBC driver work.
-      // Should remove this once we get the ODBC driver updated.
-      if (k == "-v") {
-        val hiveJars = Seq(
-          "hive-exec-0.12.0.jar",
-          "hive-service-0.12.0.jar",
-          "hive-common-0.12.0.jar",
-          "hive-hwi-0.12.0.jar",
-          "hive-0.12.0.jar").mkString(":")
-
-        Array(
-          "system:java.class.path=" + hiveJars,
-          "system:sun.java.command=shark.SharkServer2")
-      }
-      else {
-        Array(s"$k=${context.getOption(k).getOrElse("<undefined>")}")
-      }
+      Array(k -> context.getOption(k).getOrElse("<undefined>"))
 
     // Query all key-value pairs that are set in the SQLConf of the context.
     case (None, None) =>
-      context.getAll.map { case (k, v) =>
-        s"$k=$v"
-      }
+      context.getAll
 
     case _ =>
       throw new IllegalArgumentException()
   }
 
   def execute(): RDD[Row] = {
-    val rows = sideEffectResult.map { line => new GenericRow(Array[Any](line)) }
+    val rows = sideEffectResult.map { case (k, v) => new GenericRow(Array[Any](k, v)) }
     context.sparkContext.parallelize(rows, 1)
   }
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLConfSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLConfSuite.scala
index 1a58d73d9e7f..08293f7f0ca3 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLConfSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLConfSuite.scala
@@ -54,10 +54,10 @@ class SQLConfSuite extends QueryTest {
     assert(get(testKey, testVal + "_") == testVal)
     assert(TestSQLContext.get(testKey, testVal + "_") == testVal)
 
-    sql("set some.property=20")
-    assert(get("some.property", "0") == "20")
-    sql("set some.property = 40")
-    assert(get("some.property", "0") == "40")
+    sql("set mapred.reduce.tasks=20")
+    assert(get("mapred.reduce.tasks", "0") == "20")
+    sql("set mapred.reduce.tasks = 40")
+    assert(get("mapred.reduce.tasks", "0") == "40")
 
     val key = "spark.sql.key"
     val vs = "val0,val_1,val2.3,my_table"
@@ -70,9 +70,4 @@ class SQLConfSuite extends QueryTest {
     clear()
   }
 
-  test("deprecated property") {
-    clear()
-    sql(s"set ${SQLConf.Deprecated.MAPRED_REDUCE_TASKS}=10")
-    assert(get(SQLConf.SHUFFLE_PARTITIONS) == "10")
-  }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index de9e8aa4f62e..6736189c96d4 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -424,25 +424,25 @@ class SQLQuerySuite extends QueryTest {
     sql(s"SET $testKey=$testVal")
     checkAnswer(
       sql("SET"),
-      Seq(Seq(s"$testKey=$testVal"))
+      Seq(Seq(testKey, testVal))
     )
 
     sql(s"SET ${testKey + testKey}=${testVal + testVal}")
     checkAnswer(
       sql("set"),
       Seq(
-        Seq(s"$testKey=$testVal"),
-        Seq(s"${testKey + testKey}=${testVal + testVal}"))
+        Seq(testKey, testVal),
+        Seq(testKey + testKey, testVal + testVal))
     )
 
     // "set key"
     checkAnswer(
       sql(s"SET $testKey"),
-      Seq(Seq(s"$testKey=$testVal"))
+      Seq(Seq(testKey, testVal))
     )
     checkAnswer(
       sql(s"SET $nonexistentKey"),
-      Seq(Seq(s"$nonexistentKey=<undefined>"))
+      Seq(Seq(nonexistentKey, "<undefined>"))
     )
     clear()
   }
diff --git a/sql/hive-thriftserver/pom.xml b/sql/hive-thriftserver/pom.xml
deleted file mode 100644
index 7fac90fdc596..000000000000
--- a/sql/hive-thriftserver/pom.xml
+++ /dev/null
@@ -1,82 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<!--
-  ~ Licensed to the Apache Software Foundation (ASF) under one or more
-  ~ contributor license agreements.  See the NOTICE file distributed with
-  ~ this work for additional information regarding copyright ownership.
-  ~ The ASF licenses this file to You under the Apache License, Version 2.0
-  ~ (the "License"); you may not use this file except in compliance with
-  ~ the License.  You may obtain a copy of the License at
-  ~
-  ~    http://www.apache.org/licenses/LICENSE-2.0
-  ~
-  ~ Unless required by applicable law or agreed to in writing, software
-  ~ distributed under the License is distributed on an "AS IS" BASIS,
-  ~ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-  ~ See the License for the specific language governing permissions and
-  ~ limitations under the License.
-  -->
-
-<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
-         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
-  <modelVersion>4.0.0</modelVersion>
-  <parent>
-    <groupId>org.apache.spark</groupId>
-    <artifactId>spark-parent</artifactId>
-    <version>1.1.0-SNAPSHOT</version>
-    <relativePath>../../pom.xml</relativePath>
-  </parent>
-
-  <groupId>org.apache.spark</groupId>
-  <artifactId>spark-hive-thriftserver_2.10</artifactId>
-  <packaging>jar</packaging>
-  <name>Spark Project Hive</name>
-  <url>http://spark.apache.org/</url>
-  <properties>
-    <sbt.project.name>hive-thriftserver</sbt.project.name>
-  </properties>
-
-  <dependencies>
-    <dependency>
-      <groupId>org.apache.spark</groupId>
-      <artifactId>spark-hive_${scala.binary.version}</artifactId>
-      <version>${project.version}</version>
-    </dependency>
-    <dependency>
-      <groupId>org.spark-project.hive</groupId>
-      <artifactId>hive-cli</artifactId>
-      <version>${hive.version}</version>
-    </dependency>
-    <dependency>
-      <groupId>org.spark-project.hive</groupId>
-      <artifactId>hive-jdbc</artifactId>
-      <version>${hive.version}</version>
-    </dependency>
-    <dependency>
-      <groupId>org.spark-project.hive</groupId>
-      <artifactId>hive-beeline</artifactId>
-      <version>${hive.version}</version>
-    </dependency>
-    <dependency>
-      <groupId>org.scalatest</groupId>
-      <artifactId>scalatest_${scala.binary.version}</artifactId>
-      <scope>test</scope>
-    </dependency>
-  </dependencies>
-  <build>
-    <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory>
-    <testOutputDirectory>target/scala-${scala.binary.version}/test-classes</testOutputDirectory>
-    <plugins>
-      <plugin>
-        <groupId>org.scalatest</groupId>
-        <artifactId>scalatest-maven-plugin</artifactId>
-      </plugin>
-      <plugin>
-        <groupId>org.apache.maven.plugins</groupId>
-        <artifactId>maven-deploy-plugin</artifactId>
-        <configuration>
-          <skip>true</skip>
-        </configuration>
-      </plugin>
-    </plugins>
-  </build>
-</project>
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala
deleted file mode 100644
index ddbc2a79fb51..000000000000
--- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala
+++ /dev/null
@@ -1,97 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql.hive.thriftserver
-
-import scala.collection.JavaConversions._
-
-import org.apache.commons.logging.LogFactory
-import org.apache.hadoop.hive.conf.HiveConf
-import org.apache.hadoop.hive.ql.session.SessionState
-import org.apache.hive.service.cli.thrift.ThriftBinaryCLIService
-import org.apache.hive.service.server.{HiveServer2, ServerOptionsProcessor}
-
-import org.apache.spark.sql.Logging
-import org.apache.spark.sql.hive.HiveContext
-import org.apache.spark.sql.hive.thriftserver.ReflectionUtils._
-
-/**
- * The main entry point for the Spark SQL port of HiveServer2.  Starts up a `SparkSQLContext` and a
- * `HiveThriftServer2` thrift server.
- */
-private[hive] object HiveThriftServer2 extends Logging {
-  var LOG = LogFactory.getLog(classOf[HiveServer2])
-
-  def main(args: Array[String]) {
-    val optionsProcessor = new ServerOptionsProcessor("HiveThriftServer2")
-
-    if (!optionsProcessor.process(args)) {
-      logger.warn("Error starting HiveThriftServer2 with given arguments")
-      System.exit(-1)
-    }
-
-    val ss = new SessionState(new HiveConf(classOf[SessionState]))
-
-    // Set all properties specified via command line.
-    val hiveConf: HiveConf = ss.getConf
-    hiveConf.getAllProperties.toSeq.sortBy(_._1).foreach { case (k, v) =>
-      logger.debug(s"HiveConf var: $k=$v")
-    }
-
-    SessionState.start(ss)
-
-    logger.info("Starting SparkContext")
-    SparkSQLEnv.init()
-    SessionState.start(ss)
-
-    Runtime.getRuntime.addShutdownHook(
-      new Thread() {
-        override def run() {
-          SparkSQLEnv.sparkContext.stop()
-        }
-      }
-    )
-
-    try {
-      val server = new HiveThriftServer2(SparkSQLEnv.hiveContext)
-      server.init(hiveConf)
-      server.start()
-      logger.info("HiveThriftServer2 started")
-    } catch {
-      case e: Exception =>
-        logger.error("Error starting HiveThriftServer2", e)
-        System.exit(-1)
-    }
-  }
-}
-
-private[hive] class HiveThriftServer2(hiveContext: HiveContext)
-  extends HiveServer2
-  with ReflectedCompositeService {
-
-  override def init(hiveConf: HiveConf) {
-    val sparkSqlCliService = new SparkSQLCLIService(hiveContext)
-    setSuperField(this, "cliService", sparkSqlCliService)
-    addService(sparkSqlCliService)
-
-    val thriftCliService = new ThriftBinaryCLIService(sparkSqlCliService)
-    setSuperField(this, "thriftCLIService", thriftCliService)
-    addService(thriftCliService)
-
-    initCompositeService(hiveConf)
-  }
-}
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ReflectionUtils.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ReflectionUtils.scala
deleted file mode 100644
index 599294dfbb7d..000000000000
--- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ReflectionUtils.scala
+++ /dev/null
@@ -1,58 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql.hive.thriftserver
-
-private[hive] object ReflectionUtils {
-  def setSuperField(obj : Object, fieldName: String, fieldValue: Object) {
-    setAncestorField(obj, 1, fieldName, fieldValue)
-  }
-
-  def setAncestorField(obj: AnyRef, level: Int, fieldName: String, fieldValue: AnyRef) {
-    val ancestor = Iterator.iterate[Class[_]](obj.getClass)(_.getSuperclass).drop(level).next()
-    val field = ancestor.getDeclaredField(fieldName)
-    field.setAccessible(true)
-    field.set(obj, fieldValue)
-  }
-
-  def getSuperField[T](obj: AnyRef, fieldName: String): T = {
-    getAncestorField[T](obj, 1, fieldName)
-  }
-
-  def getAncestorField[T](clazz: Object, level: Int, fieldName: String): T = {
-    val ancestor = Iterator.iterate[Class[_]](clazz.getClass)(_.getSuperclass).drop(level).next()
-    val field = ancestor.getDeclaredField(fieldName)
-    field.setAccessible(true)
-    field.get(clazz).asInstanceOf[T]
-  }
-
-  def invokeStatic(clazz: Class[_], methodName: String, args: (Class[_], AnyRef)*): AnyRef = {
-    invoke(clazz, null, methodName, args: _*)
-  }
-
-  def invoke(
-      clazz: Class[_],
-      obj: AnyRef,
-      methodName: String,
-      args: (Class[_], AnyRef)*): AnyRef = {
-
-    val (types, values) = args.unzip
-    val method = clazz.getDeclaredMethod(methodName, types: _*)
-    method.setAccessible(true)
-    method.invoke(obj, values.toSeq: _*)
-  }
-}
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala
deleted file mode 100755
index 27268ecb923e..000000000000
--- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala
+++ /dev/null
@@ -1,344 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql.hive.thriftserver
-
-import scala.collection.JavaConversions._
-
-import java.io._
-import java.util.{ArrayList => JArrayList}
-
-import jline.{ConsoleReader, History}
-import org.apache.commons.lang.StringUtils
-import org.apache.commons.logging.LogFactory
-import org.apache.hadoop.conf.Configuration
-import org.apache.hadoop.hive.cli.{CliDriver, CliSessionState, OptionsProcessor}
-import org.apache.hadoop.hive.common.LogUtils.LogInitializationException
-import org.apache.hadoop.hive.common.{HiveInterruptCallback, HiveInterruptUtils, LogUtils}
-import org.apache.hadoop.hive.conf.HiveConf
-import org.apache.hadoop.hive.ql.Driver
-import org.apache.hadoop.hive.ql.exec.Utilities
-import org.apache.hadoop.hive.ql.processors.{CommandProcessor, CommandProcessorFactory}
-import org.apache.hadoop.hive.ql.session.SessionState
-import org.apache.hadoop.hive.shims.ShimLoader
-import org.apache.thrift.transport.TSocket
-
-import org.apache.spark.sql.Logging
-
-private[hive] object SparkSQLCLIDriver {
-  private var prompt = "spark-sql"
-  private var continuedPrompt = "".padTo(prompt.length, ' ')
-  private var transport:TSocket = _
-
-  installSignalHandler()
-
-  /**
-   * Install an interrupt callback to cancel all Spark jobs. In Hive's CliDriver#processLine(),
-   * a signal handler will invoke this registered callback if a Ctrl+C signal is detected while
-   * a command is being processed by the current thread.
-   */
-  def installSignalHandler() {
-    HiveInterruptUtils.add(new HiveInterruptCallback {
-      override def interrupt() {
-        // Handle remote execution mode
-        if (SparkSQLEnv.sparkContext != null) {
-          SparkSQLEnv.sparkContext.cancelAllJobs()
-        } else {
-          if (transport != null) {
-            // Force closing of TCP connection upon session termination
-            transport.getSocket.close()
-          }
-        }
-      }
-    })
-  }
-
-  def main(args: Array[String]) {
-    val oproc = new OptionsProcessor()
-    if (!oproc.process_stage1(args)) {
-      System.exit(1)
-    }
-
-    // NOTE: It is critical to do this here so that log4j is reinitialized
-    // before any of the other core hive classes are loaded
-    var logInitFailed = false
-    var logInitDetailMessage: String = null
-    try {
-      logInitDetailMessage = LogUtils.initHiveLog4j()
-    } catch {
-      case e: LogInitializationException =>
-        logInitFailed = true
-        logInitDetailMessage = e.getMessage
-    }
-
-    val sessionState = new CliSessionState(new HiveConf(classOf[SessionState]))
-
-    sessionState.in = System.in
-    try {
-      sessionState.out = new PrintStream(System.out, true, "UTF-8")
-      sessionState.info = new PrintStream(System.err, true, "UTF-8")
-      sessionState.err = new PrintStream(System.err, true, "UTF-8")
-    } catch {
-      case e: UnsupportedEncodingException => System.exit(3)
-    }
-
-    if (!oproc.process_stage2(sessionState)) {
-      System.exit(2)
-    }
-
-    if (!sessionState.getIsSilent) {
-      if (logInitFailed) System.err.println(logInitDetailMessage)
-      else SessionState.getConsole.printInfo(logInitDetailMessage)
-    }
-
-    // Set all properties specified via command line.
-    val conf: HiveConf = sessionState.getConf
-    sessionState.cmdProperties.entrySet().foreach { item: java.util.Map.Entry[Object, Object] =>
-      conf.set(item.getKey.asInstanceOf[String], item.getValue.asInstanceOf[String])
-      sessionState.getOverriddenConfigurations.put(
-        item.getKey.asInstanceOf[String], item.getValue.asInstanceOf[String])
-    }
-
-    SessionState.start(sessionState)
-
-    // Clean up after we exit
-    Runtime.getRuntime.addShutdownHook(
-      new Thread() {
-        override def run() {
-          SparkSQLEnv.stop()
-        }
-      }
-    )
-
-    // "-h" option has been passed, so connect to Hive thrift server.
-    if (sessionState.getHost != null) {
-      sessionState.connect()
-      if (sessionState.isRemoteMode) {
-        prompt = s"[${sessionState.getHost}:${sessionState.getPort}]" + prompt
-        continuedPrompt = "".padTo(prompt.length, ' ')
-      }
-    }
-
-    if (!sessionState.isRemoteMode && !ShimLoader.getHadoopShims.usesJobShell()) {
-      // Hadoop-20 and above - we need to augment classpath using hiveconf
-      // components.
-      // See also: code in ExecDriver.java
-      var loader = conf.getClassLoader
-      val auxJars = HiveConf.getVar(conf, HiveConf.ConfVars.HIVEAUXJARS)
-      if (StringUtils.isNotBlank(auxJars)) {
-        loader = Utilities.addToClassPath(loader, StringUtils.split(auxJars, ","))
-      }
-      conf.setClassLoader(loader)
-      Thread.currentThread().setContextClassLoader(loader)
-    }
-
-    val cli = new SparkSQLCLIDriver
-    cli.setHiveVariables(oproc.getHiveVariables)
-
-    // TODO work around for set the log output to console, because the HiveContext
-    // will set the output into an invalid buffer.
-    sessionState.in = System.in
-    try {
-      sessionState.out = new PrintStream(System.out, true, "UTF-8")
-      sessionState.info = new PrintStream(System.err, true, "UTF-8")
-      sessionState.err = new PrintStream(System.err, true, "UTF-8")
-    } catch {
-      case e: UnsupportedEncodingException => System.exit(3)
-    }
-
-    // Execute -i init files (always in silent mode)
-    cli.processInitFiles(sessionState)
-
-    if (sessionState.execString != null) {
-      System.exit(cli.processLine(sessionState.execString))
-    }
-
-    try {
-      if (sessionState.fileName != null) {
-        System.exit(cli.processFile(sessionState.fileName))
-      }
-    } catch {
-      case e: FileNotFoundException =>
-        System.err.println(s"Could not open input file for reading. (${e.getMessage})")
-        System.exit(3)
-    }
-
-    val reader = new ConsoleReader()
-    reader.setBellEnabled(false)
-    // reader.setDebug(new PrintWriter(new FileWriter("writer.debug", true)))
-    CliDriver.getCommandCompletor.foreach((e) => reader.addCompletor(e))
-
-    val historyDirectory = System.getProperty("user.home")
-
-    try {
-      if (new File(historyDirectory).exists()) {
-        val historyFile = historyDirectory + File.separator + ".hivehistory"
-        reader.setHistory(new History(new File(historyFile)))
-      } else {
-        System.err.println("WARNING: Directory for Hive history file: " + historyDirectory +
-                           " does not exist.   History will not be available during this session.")
-      }
-    } catch {
-      case e: Exception =>
-        System.err.println("WARNING: Encountered an error while trying to initialize Hive's " +
-                           "history file.  History will not be available during this session.")
-        System.err.println(e.getMessage)
-    }
-
-    val clientTransportTSocketField = classOf[CliSessionState].getDeclaredField("transport")
-    clientTransportTSocketField.setAccessible(true)
-
-    transport = clientTransportTSocketField.get(sessionState).asInstanceOf[TSocket]
-
-    var ret = 0
-    var prefix = ""
-    val currentDB = ReflectionUtils.invokeStatic(classOf[CliDriver], "getFormattedDb",
-      classOf[HiveConf] -> conf, classOf[CliSessionState] -> sessionState)
-
-    def promptWithCurrentDB = s"$prompt$currentDB"
-    def continuedPromptWithDBSpaces = continuedPrompt + ReflectionUtils.invokeStatic(
-      classOf[CliDriver], "spacesForString", classOf[String] -> currentDB)
-
-    var currentPrompt = promptWithCurrentDB
-    var line = reader.readLine(currentPrompt + "> ")
-
-    while (line != null) {
-      if (prefix.nonEmpty) {
-        prefix += '\n'
-      }
-
-      if (line.trim().endsWith(";") && !line.trim().endsWith("\\;")) {
-        line = prefix + line
-        ret = cli.processLine(line, true)
-        prefix = ""
-        currentPrompt = promptWithCurrentDB
-      } else {
-        prefix = prefix + line
-        currentPrompt = continuedPromptWithDBSpaces
-      }
-
-      line = reader.readLine(currentPrompt + "> ")
-    }
-
-    sessionState.close()
-
-    System.exit(ret)
-  }
-}
-
-private[hive] class SparkSQLCLIDriver extends CliDriver with Logging {
-  private val sessionState = SessionState.get().asInstanceOf[CliSessionState]
-
-  private val LOG = LogFactory.getLog("CliDriver")
-
-  private val console = new SessionState.LogHelper(LOG)
-
-  private val conf: Configuration =
-    if (sessionState != null) sessionState.getConf else new Configuration()
-
-  // Force initializing SparkSQLEnv. This is put here but not object SparkSQLCliDriver
-  // because the Hive unit tests do not go through the main() code path.
-  if (!sessionState.isRemoteMode) {
-    SparkSQLEnv.init()
-  }
-
-  override def processCmd(cmd: String): Int = {
-    val cmd_trimmed: String = cmd.trim()
-    val tokens: Array[String] = cmd_trimmed.split("\\s+")
-    val cmd_1: String = cmd_trimmed.substring(tokens(0).length()).trim()
-    if (cmd_trimmed.toLowerCase.equals("quit") ||
-      cmd_trimmed.toLowerCase.equals("exit") ||
-      tokens(0).equalsIgnoreCase("source") ||
-      cmd_trimmed.startsWith("!") ||
-      tokens(0).toLowerCase.equals("list") ||
-      sessionState.isRemoteMode) {
-      val start = System.currentTimeMillis()
-      super.processCmd(cmd)
-      val end = System.currentTimeMillis()
-      val timeTaken: Double = (end - start) / 1000.0
-      console.printInfo(s"Time taken: $timeTaken seconds")
-      0
-    } else {
-      var ret = 0
-      val hconf = conf.asInstanceOf[HiveConf]
-      val proc: CommandProcessor = CommandProcessorFactory.get(tokens(0), hconf)
-
-      if (proc != null) {
-        if (proc.isInstanceOf[Driver]) {
-          val driver = new SparkSQLDriver
-
-          driver.init()
-          val out = sessionState.out
-          val start:Long = System.currentTimeMillis()
-          if (sessionState.getIsVerbose) {
-            out.println(cmd)
-          }
-
-          ret = driver.run(cmd).getResponseCode
-          if (ret != 0) {
-            driver.close()
-            return ret
-          }
-
-          val res = new JArrayList[String]()
-
-          if (HiveConf.getBoolVar(conf, HiveConf.ConfVars.HIVE_CLI_PRINT_HEADER)) {
-            // Print the column names.
-            Option(driver.getSchema.getFieldSchemas).map { fields =>
-              out.println(fields.map(_.getName).mkString("\t"))
-            }
-          }
-
-          try {
-            while (!out.checkError() && driver.getResults(res)) {
-              res.foreach(out.println)
-              res.clear()
-            }
-          } catch {
-            case e:IOException =>
-              console.printError(
-                s"""Failed with exception ${e.getClass.getName}: ${e.getMessage}
-                   |${org.apache.hadoop.util.StringUtils.stringifyException(e)}
-                 """.stripMargin)
-              ret = 1
-          }
-
-          val cret = driver.close()
-          if (ret == 0) {
-            ret = cret
-          }
-
-          val end = System.currentTimeMillis()
-          if (end > start) {
-            val timeTaken:Double = (end - start) / 1000.0
-            console.printInfo(s"Time taken: $timeTaken seconds", null)
-          }
-
-          // Destroy the driver to release all the locks.
-          driver.destroy()
-        } else {
-          if (sessionState.getIsVerbose) {
-            sessionState.out.println(tokens(0) + " " + cmd_1)
-          }
-          ret = proc.run(cmd_1).getResponseCode
-        }
-      }
-      ret
-    }
-  }
-}
-
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIService.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIService.scala
deleted file mode 100644
index 42cbf363b274..000000000000
--- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIService.scala
+++ /dev/null
@@ -1,74 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql.hive.thriftserver
-
-import scala.collection.JavaConversions._
-
-import java.io.IOException
-import java.util.{List => JList}
-import javax.security.auth.login.LoginException
-
-import org.apache.commons.logging.Log
-import org.apache.hadoop.hive.conf.HiveConf
-import org.apache.hadoop.hive.shims.ShimLoader
-import org.apache.hive.service.Service.STATE
-import org.apache.hive.service.auth.HiveAuthFactory
-import org.apache.hive.service.cli.CLIService
-import org.apache.hive.service.{AbstractService, Service, ServiceException}
-
-import org.apache.spark.sql.hive.HiveContext
-import org.apache.spark.sql.hive.thriftserver.ReflectionUtils._
-
-private[hive] class SparkSQLCLIService(hiveContext: HiveContext)
-  extends CLIService
-  with ReflectedCompositeService {
-
-  override def init(hiveConf: HiveConf) {
-    setSuperField(this, "hiveConf", hiveConf)
-
-    val sparkSqlSessionManager = new SparkSQLSessionManager(hiveContext)
-    setSuperField(this, "sessionManager", sparkSqlSessionManager)
-    addService(sparkSqlSessionManager)
-
-    try {
-      HiveAuthFactory.loginFromKeytab(hiveConf)
-      val serverUserName = ShimLoader.getHadoopShims
-        .getShortUserName(ShimLoader.getHadoopShims.getUGIForConf(hiveConf))
-      setSuperField(this, "serverUserName", serverUserName)
-    } catch {
-      case e @ (_: IOException | _: LoginException) =>
-        throw new ServiceException("Unable to login to kerberos with given principal/keytab", e)
-    }
-
-    initCompositeService(hiveConf)
-  }
-}
-
-private[thriftserver] trait ReflectedCompositeService { this: AbstractService =>
-  def initCompositeService(hiveConf: HiveConf) {
-    // Emulating `CompositeService.init(hiveConf)`
-    val serviceList = getAncestorField[JList[Service]](this, 2, "serviceList")
-    serviceList.foreach(_.init(hiveConf))
-
-    // Emulating `AbstractService.init(hiveConf)`
-    invoke(classOf[AbstractService], this, "ensureCurrentState", classOf[STATE] -> STATE.NOTINITED)
-    setAncestorField(this, 3, "hiveConf", hiveConf)
-    invoke(classOf[AbstractService], this, "changeState", classOf[STATE] -> STATE.INITED)
-    getAncestorField[Log](this, 3, "LOG").info(s"Service: $getName is inited.")
-  }
-}
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLDriver.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLDriver.scala
deleted file mode 100644
index 5202aa9903e0..000000000000
--- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLDriver.scala
+++ /dev/null
@@ -1,93 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql.hive.thriftserver
-
-import scala.collection.JavaConversions._
-
-import java.util.{ArrayList => JArrayList}
-
-import org.apache.commons.lang.exception.ExceptionUtils
-import org.apache.hadoop.hive.metastore.api.{FieldSchema, Schema}
-import org.apache.hadoop.hive.ql.Driver
-import org.apache.hadoop.hive.ql.processors.CommandProcessorResponse
-
-import org.apache.spark.sql.Logging
-import org.apache.spark.sql.hive.{HiveContext, HiveMetastoreTypes}
-
-private[hive] class SparkSQLDriver(val context: HiveContext = SparkSQLEnv.hiveContext)
-  extends Driver with Logging {
-
-  private var tableSchema: Schema = _
-  private var hiveResponse: Seq[String] = _
-
-  override def init(): Unit = {
-  }
-
-  private def getResultSetSchema(query: context.QueryExecution): Schema = {
-    val analyzed = query.analyzed
-    logger.debug(s"Result Schema: ${analyzed.output}")
-    if (analyzed.output.size == 0) {
-      new Schema(new FieldSchema("Response code", "string", "") :: Nil, null)
-    } else {
-      val fieldSchemas = analyzed.output.map { attr =>
-        new FieldSchema(attr.name, HiveMetastoreTypes.toMetastoreType(attr.dataType), "")
-      }
-
-      new Schema(fieldSchemas, null)
-    }
-  }
-
-  override def run(command: String): CommandProcessorResponse = {
-    val execution = context.executePlan(context.hql(command).logicalPlan)
-
-    // TODO unify the error code
-    try {
-      hiveResponse = execution.stringResult()
-      tableSchema = getResultSetSchema(execution)
-      new CommandProcessorResponse(0)
-    } catch {
-      case cause: Throwable =>
-        logger.error(s"Failed in [$command]", cause)
-        new CommandProcessorResponse(-3, ExceptionUtils.getFullStackTrace(cause), null)
-    }
-  }
-
-  override def close(): Int = {
-    hiveResponse = null
-    tableSchema = null
-    0
-  }
-
-  override def getSchema: Schema = tableSchema
-
-  override def getResults(res: JArrayList[String]): Boolean = {
-    if (hiveResponse == null) {
-      false
-    } else {
-      res.addAll(hiveResponse)
-      hiveResponse = null
-      true
-    }
-  }
-
-  override def destroy() {
-    super.destroy()
-    hiveResponse = null
-    tableSchema = null
-  }
-}
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLEnv.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLEnv.scala
deleted file mode 100644
index 451c3bd7b935..000000000000
--- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLEnv.scala
+++ /dev/null
@@ -1,58 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql.hive.thriftserver
-
-import org.apache.hadoop.hive.ql.session.SessionState
-
-import org.apache.spark.scheduler.{SplitInfo, StatsReportListener}
-import org.apache.spark.sql.Logging
-import org.apache.spark.sql.hive.HiveContext
-import org.apache.spark.{SparkConf, SparkContext}
-
-/** A singleton object for the master program. The slaves should not access this. */
-private[hive] object SparkSQLEnv extends Logging {
-  logger.debug("Initializing SparkSQLEnv")
-
-  var hiveContext: HiveContext = _
-  var sparkContext: SparkContext = _
-
-  def init() {
-    if (hiveContext == null) {
-      sparkContext = new SparkContext(new SparkConf()
-        .setAppName(s"SparkSQL::${java.net.InetAddress.getLocalHost.getHostName}"))
-
-      sparkContext.addSparkListener(new StatsReportListener())
-
-      hiveContext = new HiveContext(sparkContext) {
-        @transient override lazy val sessionState = SessionState.get()
-        @transient override lazy val hiveconf = sessionState.getConf
-      }
-    }
-  }
-
-  /** Cleans up and shuts down the Spark SQL environments. */
-  def stop() {
-    logger.debug("Shutting down Spark SQL Environment")
-    // Stop the SparkContext
-    if (SparkSQLEnv.sparkContext != null) {
-      sparkContext.stop()
-      sparkContext = null
-      hiveContext = null
-    }
-  }
-}
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLSessionManager.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLSessionManager.scala
deleted file mode 100644
index 6b3275b4eaf0..000000000000
--- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLSessionManager.scala
+++ /dev/null
@@ -1,49 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql.hive.thriftserver
-
-import java.util.concurrent.Executors
-
-import org.apache.commons.logging.Log
-import org.apache.hadoop.hive.conf.HiveConf
-import org.apache.hadoop.hive.conf.HiveConf.ConfVars
-import org.apache.hive.service.cli.session.SessionManager
-
-import org.apache.spark.sql.hive.HiveContext
-import org.apache.spark.sql.hive.thriftserver.ReflectionUtils._
-import org.apache.spark.sql.hive.thriftserver.server.SparkSQLOperationManager
-
-private[hive] class SparkSQLSessionManager(hiveContext: HiveContext)
-  extends SessionManager
-  with ReflectedCompositeService {
-
-  override def init(hiveConf: HiveConf) {
-    setSuperField(this, "hiveConf", hiveConf)
-
-    val backgroundPoolSize = hiveConf.getIntVar(ConfVars.HIVE_SERVER2_ASYNC_EXEC_THREADS)
-    setSuperField(this, "backgroundOperationPool", Executors.newFixedThreadPool(backgroundPoolSize))
-    getAncestorField[Log](this, 3, "LOG").info(
-      s"HiveServer2: Async execution pool size $backgroundPoolSize")
-
-    val sparkSqlOperationManager = new SparkSQLOperationManager(hiveContext)
-    setSuperField(this, "operationManager", sparkSqlOperationManager)
-    addService(sparkSqlOperationManager)
-
-    initCompositeService(hiveConf)
-  }
-}
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/server/SparkSQLOperationManager.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/server/SparkSQLOperationManager.scala
deleted file mode 100644
index a4e1f3e762e8..000000000000
--- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/server/SparkSQLOperationManager.scala
+++ /dev/null
@@ -1,151 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql.hive.thriftserver.server
-
-import scala.collection.JavaConversions._
-import scala.collection.mutable.ArrayBuffer
-import scala.math.{random, round}
-
-import java.sql.Timestamp
-import java.util.{Map => JMap}
-
-import org.apache.hadoop.hive.common.`type`.HiveDecimal
-import org.apache.hadoop.hive.metastore.api.FieldSchema
-import org.apache.hive.service.cli._
-import org.apache.hive.service.cli.operation.{ExecuteStatementOperation, Operation, OperationManager}
-import org.apache.hive.service.cli.session.HiveSession
-
-import org.apache.spark.sql.catalyst.types._
-import org.apache.spark.sql.hive.thriftserver.ReflectionUtils
-import org.apache.spark.sql.hive.{HiveContext, HiveMetastoreTypes}
-import org.apache.spark.sql.{Logging, SchemaRDD, Row => SparkRow}
-
-/**
- * Executes queries using Spark SQL, and maintains a list of handles to active queries.
- */
-class SparkSQLOperationManager(hiveContext: HiveContext) extends OperationManager with Logging {
-  val handleToOperation = ReflectionUtils
-    .getSuperField[JMap[OperationHandle, Operation]](this, "handleToOperation")
-
-  override def newExecuteStatementOperation(
-      parentSession: HiveSession,
-      statement: String,
-      confOverlay: JMap[String, String],
-      async: Boolean): ExecuteStatementOperation = synchronized {
-
-    val operation = new ExecuteStatementOperation(parentSession, statement, confOverlay) {
-      private var result: SchemaRDD = _
-      private var iter: Iterator[SparkRow] = _
-      private var dataTypes: Array[DataType] = _
-
-      def close(): Unit = {
-        // RDDs will be cleaned automatically upon garbage collection.
-        logger.debug("CLOSING")
-      }
-
-      def getNextRowSet(order: FetchOrientation, maxRowsL: Long): RowSet = {
-        if (!iter.hasNext) {
-          new RowSet()
-        } else {
-          val maxRows = maxRowsL.toInt // Do you really want a row batch larger than Int Max? No.
-          var curRow = 0
-          var rowSet = new ArrayBuffer[Row](maxRows)
-
-          while (curRow < maxRows && iter.hasNext) {
-            val sparkRow = iter.next()
-            val row = new Row()
-            var curCol = 0
-
-            while (curCol < sparkRow.length) {
-              dataTypes(curCol) match {
-                case StringType =>
-                  row.addString(sparkRow(curCol).asInstanceOf[String])
-                case IntegerType =>
-                  row.addColumnValue(ColumnValue.intValue(sparkRow.getInt(curCol)))
-                case BooleanType =>
-                  row.addColumnValue(ColumnValue.booleanValue(sparkRow.getBoolean(curCol)))
-                case DoubleType =>
-                  row.addColumnValue(ColumnValue.doubleValue(sparkRow.getDouble(curCol)))
-                case FloatType =>
-                  row.addColumnValue(ColumnValue.floatValue(sparkRow.getFloat(curCol)))
-                case DecimalType =>
-                  val hiveDecimal = sparkRow.get(curCol).asInstanceOf[BigDecimal].bigDecimal
-                  row.addColumnValue(ColumnValue.stringValue(new HiveDecimal(hiveDecimal)))
-                case LongType =>
-                  row.addColumnValue(ColumnValue.longValue(sparkRow.getLong(curCol)))
-                case ByteType =>
-                  row.addColumnValue(ColumnValue.byteValue(sparkRow.getByte(curCol)))
-                case ShortType =>
-                  row.addColumnValue(ColumnValue.intValue(sparkRow.getShort(curCol)))
-                case TimestampType =>
-                  row.addColumnValue(
-                    ColumnValue.timestampValue(sparkRow.get(curCol).asInstanceOf[Timestamp]))
-                case BinaryType | _: ArrayType | _: StructType | _: MapType =>
-                  val hiveString = result
-                    .queryExecution
-                    .asInstanceOf[HiveContext#QueryExecution]
-                    .toHiveString((sparkRow.get(curCol), dataTypes(curCol)))
-                  row.addColumnValue(ColumnValue.stringValue(hiveString))
-              }
-              curCol += 1
-            }
-            rowSet += row
-            curRow += 1
-          }
-          new RowSet(rowSet, 0)
-        }
-      }
-
-      def getResultSetSchema: TableSchema = {
-        logger.warn(s"Result Schema: ${result.queryExecution.analyzed.output}")
-        if (result.queryExecution.analyzed.output.size == 0) {
-          new TableSchema(new FieldSchema("Result", "string", "") :: Nil)
-        } else {
-          val schema = result.queryExecution.analyzed.output.map { attr =>
-            new FieldSchema(attr.name, HiveMetastoreTypes.toMetastoreType(attr.dataType), "")
-          }
-          new TableSchema(schema)
-        }
-      }
-
-      def run(): Unit = {
-        logger.info(s"Running query '$statement'")
-        setState(OperationState.RUNNING)
-        try {
-          result = hiveContext.hql(statement)
-          logger.debug(result.queryExecution.toString())
-          val groupId = round(random * 1000000).toString
-          hiveContext.sparkContext.setJobGroup(groupId, statement)
-          iter = result.queryExecution.toRdd.toLocalIterator
-          dataTypes = result.queryExecution.analyzed.output.map(_.dataType).toArray
-          setHasResultSet(true)
-        } catch {
-          // Actually do need to catch Throwable as some failures don't inherit from Exception and
-          // HiveServer will silently swallow them.
-          case e: Throwable =>
-            logger.error("Error executing query:",e)
-            throw new HiveSQLException(e.toString)
-        }
-        setState(OperationState.FINISHED)
-      }
-    }
-
-   handleToOperation.put(operation.getHandle, operation)
-   operation
-  }
-}
diff --git a/sql/hive-thriftserver/src/test/resources/data/files/small_kv.txt b/sql/hive-thriftserver/src/test/resources/data/files/small_kv.txt
deleted file mode 100644
index 850f8014b6f0..000000000000
--- a/sql/hive-thriftserver/src/test/resources/data/files/small_kv.txt
+++ /dev/null
@@ -1,5 +0,0 @@
-238
val_238
-86
val_86
-311
val_311
-27
val_27
-165
val_165
diff --git a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/CliSuite.scala b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/CliSuite.scala
deleted file mode 100644
index 69f19f826a80..000000000000
--- a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/CliSuite.scala
+++ /dev/null
@@ -1,57 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql.hive.thriftserver
-
-import java.io.{BufferedReader, InputStreamReader, PrintWriter}
-
-import org.scalatest.{BeforeAndAfterAll, FunSuite}
-
-class CliSuite extends FunSuite with BeforeAndAfterAll with TestUtils {
-  val WAREHOUSE_PATH = TestUtils.getWarehousePath("cli")
-  val METASTORE_PATH = TestUtils.getMetastorePath("cli")
-
-  override def beforeAll() {
-    val pb = new ProcessBuilder(
-      "../../bin/spark-sql",
-      "--master",
-      "local",
-      "--hiveconf",
-      s"javax.jdo.option.ConnectionURL=jdbc:derby:;databaseName=$METASTORE_PATH;create=true",
-      "--hiveconf",
-      "hive.metastore.warehouse.dir=" + WAREHOUSE_PATH)
-
-    process = pb.start()
-    outputWriter = new PrintWriter(process.getOutputStream, true)
-    inputReader = new BufferedReader(new InputStreamReader(process.getInputStream))
-    errorReader = new BufferedReader(new InputStreamReader(process.getErrorStream))
-    waitForOutput(inputReader, "spark-sql>")
-  }
-
-  override def afterAll() {
-    process.destroy()
-    process.waitFor()
-  }
-
-  test("simple commands") {
-    val dataFilePath = getDataFile("data/files/small_kv.txt")
-    executeQuery("create table hive_test1(key int, val string);")
-    executeQuery("load data local inpath '" + dataFilePath+ "' overwrite into table hive_test1;")
-    executeQuery("cache table hive_test1", "Time taken")
-  }
-}
diff --git a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suite.scala b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suite.scala
deleted file mode 100644
index fe3403b3292e..000000000000
--- a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suite.scala
+++ /dev/null
@@ -1,135 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql.hive.thriftserver
-
-import scala.collection.JavaConversions._
-import scala.concurrent.ExecutionContext.Implicits.global
-import scala.concurrent._
-
-import java.io.{BufferedReader, InputStreamReader}
-import java.net.ServerSocket
-import java.sql.{Connection, DriverManager, Statement}
-
-import org.scalatest.{BeforeAndAfterAll, FunSuite}
-
-import org.apache.spark.sql.Logging
-import org.apache.spark.sql.catalyst.util.getTempFilePath
-
-/**
- * Test for the HiveThriftServer2 using JDBC.
- */
-class HiveThriftServer2Suite extends FunSuite with BeforeAndAfterAll with TestUtils with Logging {
-
-  val WAREHOUSE_PATH = getTempFilePath("warehouse")
-  val METASTORE_PATH = getTempFilePath("metastore")
-
-  val DRIVER_NAME  = "org.apache.hive.jdbc.HiveDriver"
-  val TABLE = "test"
-  val HOST = "localhost"
-  val PORT =  {
-    // Let the system to choose a random available port to avoid collision with other parallel
-    // builds.
-    val socket = new ServerSocket(0)
-    val port = socket.getLocalPort
-    socket.close()
-    port
-  }
-
-  // If verbose is true, the test program will print all outputs coming from the Hive Thrift server.
-  val VERBOSE = Option(System.getenv("SPARK_SQL_TEST_VERBOSE")).getOrElse("false").toBoolean
-
-  Class.forName(DRIVER_NAME)
-
-  override def beforeAll() { launchServer() }
-
-  override def afterAll() { stopServer() }
-
-  private def launchServer(args: Seq[String] = Seq.empty) {
-    // Forking a new process to start the Hive Thrift server. The reason to do this is it is
-    // hard to clean up Hive resources entirely, so we just start a new process and kill
-    // that process for cleanup.
-    val defaultArgs = Seq(
-      "../../sbin/start-thriftserver.sh",
-      "--master local",
-      "--hiveconf",
-      "hive.root.logger=INFO,console",
-      "--hiveconf",
-      s"javax.jdo.option.ConnectionURL=jdbc:derby:;databaseName=$METASTORE_PATH;create=true",
-      "--hiveconf",
-      s"hive.metastore.warehouse.dir=$WAREHOUSE_PATH")
-    val pb = new ProcessBuilder(defaultArgs ++ args)
-    val environment = pb.environment()
-    environment.put("HIVE_SERVER2_THRIFT_PORT", PORT.toString)
-    environment.put("HIVE_SERVER2_THRIFT_BIND_HOST", HOST)
-    process = pb.start()
-    inputReader = new BufferedReader(new InputStreamReader(process.getInputStream))
-    errorReader = new BufferedReader(new InputStreamReader(process.getErrorStream))
-    waitForOutput(inputReader, "ThriftBinaryCLIService listening on")
-
-    // Spawn a thread to read the output from the forked process.
-    // Note that this is necessary since in some configurations, log4j could be blocked
-    // if its output to stderr are not read, and eventually blocking the entire test suite.
-    future {
-      while (true) {
-        val stdout = readFrom(inputReader)
-        val stderr = readFrom(errorReader)
-        if (VERBOSE && stdout.length > 0) {
-          println(stdout)
-        }
-        if (VERBOSE && stderr.length > 0) {
-          println(stderr)
-        }
-        Thread.sleep(50)
-      }
-    }
-  }
-
-  private def stopServer() {
-    process.destroy()
-    process.waitFor()
-  }
-
-  test("test query execution against a Hive Thrift server") {
-    Thread.sleep(5 * 1000)
-    val dataFilePath = getDataFile("data/files/small_kv.txt")
-    val stmt = createStatement()
-    stmt.execute("DROP TABLE IF EXISTS test")
-    stmt.execute("DROP TABLE IF EXISTS test_cached")
-    stmt.execute("CREATE TABLE test(key int, val string)")
-    stmt.execute(s"LOAD DATA LOCAL INPATH '$dataFilePath' OVERWRITE INTO TABLE test")
-    stmt.execute("CREATE TABLE test_cached as select * from test limit 4")
-    stmt.execute("CACHE TABLE test_cached")
-
-    var rs = stmt.executeQuery("select count(*) from test")
-    rs.next()
-    assert(rs.getInt(1) === 5)
-
-    rs = stmt.executeQuery("select count(*) from test_cached")
-    rs.next()
-    assert(rs.getInt(1) === 4)
-
-    stmt.close()
-  }
-
-  def getConnection: Connection = {
-    val connectURI = s"jdbc:hive2://localhost:$PORT/"
-    DriverManager.getConnection(connectURI, System.getProperty("user.name"), "")
-  }
-
-  def createStatement(): Statement = getConnection.createStatement()
-}
diff --git a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/TestUtils.scala b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/TestUtils.scala
deleted file mode 100644
index bb2242618fbe..000000000000
--- a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/TestUtils.scala
+++ /dev/null
@@ -1,108 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql.hive.thriftserver
-
-import java.io.{BufferedReader, PrintWriter}
-import java.text.SimpleDateFormat
-import java.util.Date
-
-import org.apache.hadoop.hive.common.LogUtils
-import org.apache.hadoop.hive.common.LogUtils.LogInitializationException
-
-object TestUtils {
-  val timestamp = new SimpleDateFormat("yyyyMMdd-HHmmss")
-
-  def getWarehousePath(prefix: String): String = {
-    System.getProperty("user.dir") + "/test_warehouses/" + prefix + "-warehouse-" +
-      timestamp.format(new Date)
-  }
-
-  def getMetastorePath(prefix: String): String = {
-    System.getProperty("user.dir") + "/test_warehouses/" + prefix + "-metastore-" +
-      timestamp.format(new Date)
-  }
-
-  // Dummy function for initialize the log4j properties.
-  def init() { }
-
-  // initialize log4j
-  try {
-    LogUtils.initHiveLog4j()
-  } catch {
-    case e: LogInitializationException => // Ignore the error.
-  }
-}
-
-trait TestUtils {
-  var process : Process = null
-  var outputWriter : PrintWriter = null
-  var inputReader : BufferedReader = null
-  var errorReader : BufferedReader = null
-
-  def executeQuery(
-    cmd: String, outputMessage: String = "OK", timeout: Long = 15000): String = {
-    println("Executing: " + cmd + ", expecting output: " + outputMessage)
-    outputWriter.write(cmd + "\n")
-    outputWriter.flush()
-    waitForQuery(timeout, outputMessage)
-  }
-
-  protected def waitForQuery(timeout: Long, message: String): String = {
-    if (waitForOutput(errorReader, message, timeout)) {
-      Thread.sleep(500)
-      readOutput()
-    } else {
-      assert(false, "Didn't find \"" + message + "\" in the output:\n" + readOutput())
-      null
-    }
-  }
-
-  // Wait for the specified str to appear in the output.
-  protected def waitForOutput(
-    reader: BufferedReader, str: String, timeout: Long = 10000): Boolean = {
-    val startTime = System.currentTimeMillis
-    var out = ""
-    while (!out.contains(str) && System.currentTimeMillis < (startTime + timeout)) {
-      out += readFrom(reader)
-    }
-    out.contains(str)
-  }
-
-  // Read stdout output and filter out garbage collection messages.
-  protected def readOutput(): String = {
-    val output = readFrom(inputReader)
-    // Remove GC Messages
-    val filteredOutput = output.lines.filterNot(x => x.contains("[GC") || x.contains("[Full GC"))
-      .mkString("\n")
-    filteredOutput
-  }
-
-  protected def readFrom(reader: BufferedReader): String = {
-    var out = ""
-    var c = 0
-    while (reader.ready) {
-      c = reader.read()
-      out += c.asInstanceOf[Char]
-    }
-    out
-  }
-
-  protected def getDataFile(name: String) = {
-    Thread.currentThread().getContextClassLoader.getResource(name)
-  }
-}
diff --git a/sql/hive/pom.xml b/sql/hive/pom.xml
index 93d00f7c37c9..1699ffe06ce1 100644
--- a/sql/hive/pom.xml
+++ b/sql/hive/pom.xml
@@ -32,7 +32,7 @@
   <name>Spark Project Hive</name>
   <url>http://spark.apache.org/</url>
   <properties>
-    <sbt.project.name>hive</sbt.project.name>
+     <sbt.project.name>hive</sbt.project.name>
   </properties>
 
   <dependencies>
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
index 84d43eaeea51..201c85f3d501 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
@@ -255,7 +255,7 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) {
       Seq(StringType, IntegerType, LongType, DoubleType, FloatType, BooleanType, ByteType,
         ShortType, DecimalType, TimestampType, BinaryType)
 
-    protected[sql] def toHiveString(a: (Any, DataType)): String = a match {
+    protected def toHiveString(a: (Any, DataType)): String = a match {
       case (struct: Row, StructType(fields)) =>
         struct.zip(fields).map {
           case (v, t) => s""""${t.name}":${toHiveStructString(v, t.dataType)}"""
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
index a022a1e2dc70..a8623b64c656 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
@@ -419,10 +419,10 @@ class HiveQuerySuite extends HiveComparisonTest {
     hql(s"set $testKey=$testVal")
     assert(get(testKey, testVal + "_") == testVal)
 
-    hql("set some.property=20")
-    assert(get("some.property", "0") == "20")
-    hql("set some.property = 40")
-    assert(get("some.property", "0") == "40")
+    hql("set mapred.reduce.tasks=20")
+    assert(get("mapred.reduce.tasks", "0") == "20")
+    hql("set mapred.reduce.tasks = 40")
+    assert(get("mapred.reduce.tasks", "0") == "40")
 
     hql(s"set $testKey=$testVal")
     assert(get(testKey, "0") == testVal)
@@ -436,61 +436,63 @@ class HiveQuerySuite extends HiveComparisonTest {
     val testKey = "spark.sql.key.usedfortestonly"
     val testVal = "test.val.0"
     val nonexistentKey = "nonexistent"
+    def collectResults(rdd: SchemaRDD): Set[(String, String)] =
+      rdd.collect().map { case Row(key: String, value: String) => key -> value }.toSet
 
     clear()
 
     // "set" itself returns all config variables currently specified in SQLConf.
     assert(hql("SET").collect().size == 0)
 
-    assertResult(Array(s"$testKey=$testVal")) {
-      hql(s"SET $testKey=$testVal").collect().map(_.getString(0))
+    assertResult(Set(testKey -> testVal)) {
+      collectResults(hql(s"SET $testKey=$testVal"))
     }
 
     assert(hiveconf.get(testKey, "") == testVal)
-    assertResult(Array(s"$testKey=$testVal")) {
-      hql(s"SET $testKey=$testVal").collect().map(_.getString(0))
+    assertResult(Set(testKey -> testVal)) {
+      collectResults(hql("SET"))
     }
 
     hql(s"SET ${testKey + testKey}=${testVal + testVal}")
     assert(hiveconf.get(testKey + testKey, "") == testVal + testVal)
-    assertResult(Array(s"$testKey=$testVal", s"${testKey + testKey}=${testVal + testVal}")) {
-      hql(s"SET").collect().map(_.getString(0))
+    assertResult(Set(testKey -> testVal, (testKey + testKey) -> (testVal + testVal))) {
+      collectResults(hql("SET"))
     }
 
     // "set key"
-    assertResult(Array(s"$testKey=$testVal")) {
-      hql(s"SET $testKey").collect().map(_.getString(0))
+    assertResult(Set(testKey -> testVal)) {
+      collectResults(hql(s"SET $testKey"))
     }
 
-    assertResult(Array(s"$nonexistentKey=<undefined>")) {
-      hql(s"SET $nonexistentKey").collect().map(_.getString(0))
+    assertResult(Set(nonexistentKey -> "<undefined>")) {
+      collectResults(hql(s"SET $nonexistentKey"))
     }
 
     // Assert that sql() should have the same effects as hql() by repeating the above using sql().
     clear()
     assert(sql("SET").collect().size == 0)
 
-    assertResult(Array(s"$testKey=$testVal")) {
-      sql(s"SET $testKey=$testVal").collect().map(_.getString(0))
+    assertResult(Set(testKey -> testVal)) {
+      collectResults(sql(s"SET $testKey=$testVal"))
     }
 
     assert(hiveconf.get(testKey, "") == testVal)
-    assertResult(Array(s"$testKey=$testVal")) {
-      sql("SET").collect().map(_.getString(0))
+    assertResult(Set(testKey -> testVal)) {
+      collectResults(sql("SET"))
     }
 
     sql(s"SET ${testKey + testKey}=${testVal + testVal}")
     assert(hiveconf.get(testKey + testKey, "") == testVal + testVal)
-    assertResult(Array(s"$testKey=$testVal", s"${testKey + testKey}=${testVal + testVal}")) {
-      sql("SET").collect().map(_.getString(0))
+    assertResult(Set(testKey -> testVal, (testKey + testKey) -> (testVal + testVal))) {
+      collectResults(sql("SET"))
     }
 
-    assertResult(Array(s"$testKey=$testVal")) {
-      sql(s"SET $testKey").collect().map(_.getString(0))
+    assertResult(Set(testKey -> testVal)) {
+      collectResults(sql(s"SET $testKey"))
     }
 
-    assertResult(Array(s"$nonexistentKey=<undefined>")) {
-      sql(s"SET $nonexistentKey").collect().map(_.getString(0))
+    assertResult(Set(nonexistentKey -> "<undefined>")) {
+      collectResults(sql(s"SET $nonexistentKey"))
     }
 
     clear()
diff --git a/streaming/pom.xml b/streaming/pom.xml
index b99f306b8f2c..f60697ce745b 100644
--- a/streaming/pom.xml
+++ b/streaming/pom.xml
@@ -28,7 +28,7 @@
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-streaming_2.10</artifactId>
   <properties>
-    <sbt.project.name>streaming</sbt.project.name>
+     <sbt.project.name>streaming</sbt.project.name>
   </properties>
   <packaging>jar</packaging>
   <name>Spark Project Streaming</name>
diff --git a/tools/pom.xml b/tools/pom.xml
index 97abb6b2b63e..c0ee8faa7a61 100644
--- a/tools/pom.xml
+++ b/tools/pom.xml
@@ -27,7 +27,7 @@
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-tools_2.10</artifactId>
   <properties>
-    <sbt.project.name>tools</sbt.project.name>
+     <sbt.project.name>tools</sbt.project.name>
   </properties>
   <packaging>jar</packaging>
   <name>Spark Project Tools</name>
diff --git a/yarn/alpha/pom.xml b/yarn/alpha/pom.xml
index 51744ece0412..5b13a1f002d6 100644
--- a/yarn/alpha/pom.xml
+++ b/yarn/alpha/pom.xml
@@ -24,7 +24,7 @@
     <relativePath>../pom.xml</relativePath>
   </parent>
   <properties>
-    <sbt.project.name>yarn-alpha</sbt.project.name>
+     <sbt.project.name>yarn-alpha</sbt.project.name>
   </properties>
 
   <groupId>org.apache.spark</groupId>
diff --git a/yarn/pom.xml b/yarn/pom.xml
index 3faaf053634d..efb473aa1b26 100644
--- a/yarn/pom.xml
+++ b/yarn/pom.xml
@@ -29,7 +29,7 @@
   <packaging>pom</packaging>
   <name>Spark Project YARN Parent POM</name>
   <properties>
-    <sbt.project.name>yarn</sbt.project.name>
+     <sbt.project.name>yarn</sbt.project.name>
   </properties>
 
   <dependencies>
diff --git a/yarn/stable/pom.xml b/yarn/stable/pom.xml
index b6c8456d0668..ceaf9f9d7100 100644
--- a/yarn/stable/pom.xml
+++ b/yarn/stable/pom.xml
@@ -24,7 +24,7 @@
     <relativePath>../pom.xml</relativePath>
   </parent>
   <properties>
-    <sbt.project.name>yarn-stable</sbt.project.name>
+     <sbt.project.name>yarn-stable</sbt.project.name>
   </properties>
 
   <groupId>org.apache.spark</groupId>

From d7eac4c3db7462e60e0c456dc93780167f5fcb2c Mon Sep 17 00:00:00 2001
From: Rahul Singhal <rahul.singhal@guavus.com>
Date: Sun, 27 Jul 2014 18:50:32 -0700
Subject: [PATCH 783/810] SPARK-2651: Add maven scalastyle plugin

Can be run as: "mvn scalastyle:check"

Author: Rahul Singhal <rahul.singhal@guavus.com>

Closes #1550 from rahulsinghaliitd/SPARK-2651 and squashes the following commits:

53748dd [Rahul Singhal] SPARK-2651: Add maven scalastyle plugin
---
 .gitignore |  1 +
 pom.xml    | 24 ++++++++++++++++++++++++
 2 files changed, 25 insertions(+)

diff --git a/.gitignore b/.gitignore
index 061c8946d23c..a4ec12ca6b53 100644
--- a/.gitignore
+++ b/.gitignore
@@ -51,6 +51,7 @@ unit-tests.log
 rat-results.txt
 scalastyle.txt
 conf/*.conf
+scalastyle-output.xml
 
 # For Hive
 metastore_db/
diff --git a/pom.xml b/pom.xml
index 4e2d64a83364..d2e6b3c0ed5a 100644
--- a/pom.xml
+++ b/pom.xml
@@ -957,6 +957,30 @@
         <groupId>org.apache.maven.plugins</groupId>
         <artifactId>maven-source-plugin</artifactId>
       </plugin>
+      <plugin>
+        <groupId>org.scalastyle</groupId>
+        <artifactId>scalastyle-maven-plugin</artifactId>
+        <version>0.4.0</version>
+        <configuration>
+          <verbose>false</verbose>
+          <failOnViolation>true</failOnViolation>
+          <includeTestSourceDirectory>false</includeTestSourceDirectory>
+          <failOnWarning>false</failOnWarning>
+          <sourceDirectory>${basedir}/src/main/scala</sourceDirectory>
+          <testSourceDirectory>${basedir}/src/test/scala</testSourceDirectory>
+          <configLocation>scalastyle-config.xml</configLocation>
+          <outputFile>scalastyle-output.xml</outputFile>
+          <outputEncoding>UTF-8</outputEncoding>
+        </configuration>
+        <executions>
+          <execution>
+            <phase>package</phase>
+            <goals>
+              <goal>check</goal>
+            </goals>
+          </execution>
+        </executions>
+      </plugin>
     </plugins>
   </build>
 

From a7d145e98c55fa66a541293930f25d9cdc25f3b4 Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@apache.org>
Date: Sun, 27 Jul 2014 22:54:43 -0700
Subject: [PATCH 784/810] [SPARK-1550] [PySpark] Allow SparkContext creation
 after failed attempts

This addresses a PySpark issue where a failed attempt to construct SparkContext would prevent any future SparkContext creation.

Author: Josh Rosen <joshrosen@apache.org>

Closes #1606 from JoshRosen/SPARK-1550 and squashes the following commits:

ec7fadc [Josh Rosen] [SPARK-1550] [PySpark] Allow SparkContext creation after failed attempts
---
 python/pyspark/context.py | 18 ++++++++++++------
 python/pyspark/tests.py   |  6 ++++++
 2 files changed, 18 insertions(+), 6 deletions(-)

diff --git a/python/pyspark/context.py b/python/pyspark/context.py
index e8ac9895cf54..830a6ee03f2a 100644
--- a/python/pyspark/context.py
+++ b/python/pyspark/context.py
@@ -100,7 +100,16 @@ def __init__(self, master=None, appName=None, sparkHome=None, pyFiles=None,
             tempNamedTuple = namedtuple("Callsite", "function file linenum")
             self._callsite = tempNamedTuple(function=None, file=None, linenum=None)
         SparkContext._ensure_initialized(self, gateway=gateway)
-
+        try:
+            self._do_init(master, appName, sparkHome, pyFiles, environment, batchSize, serializer,
+                          conf)
+        except:
+            # If an error occurs, clean up in order to allow future SparkContext creation:
+            self.stop()
+            raise
+
+    def _do_init(self, master, appName, sparkHome, pyFiles, environment, batchSize, serializer,
+                 conf):
         self.environment = environment or {}
         self._conf = conf or SparkConf(_jvm=self._jvm)
         self._batchSize = batchSize  # -1 represents an unlimited batch size
@@ -249,17 +258,14 @@ def defaultMinPartitions(self):
         """
         return self._jsc.sc().defaultMinPartitions()
 
-    def __del__(self):
-        self.stop()
-
     def stop(self):
         """
         Shut down the SparkContext.
         """
-        if self._jsc:
+        if getattr(self, "_jsc", None):
             self._jsc.stop()
             self._jsc = None
-        if self._accumulatorServer:
+        if getattr(self, "_accumulatorServer", None):
             self._accumulatorServer.shutdown()
             self._accumulatorServer = None
         with SparkContext._lock:
diff --git a/python/pyspark/tests.py b/python/pyspark/tests.py
index 8ba51461d106..63cc5e9ad96f 100644
--- a/python/pyspark/tests.py
+++ b/python/pyspark/tests.py
@@ -209,6 +209,12 @@ def func():
 
 class TestRDDFunctions(PySparkTestCase):
 
+    def test_failed_sparkcontext_creation(self):
+        # Regression test for SPARK-1550
+        self.sc.stop()
+        self.assertRaises(Exception, lambda: SparkContext("an-invalid-master-name"))
+        self.sc = SparkContext("local")
+
     def test_save_as_textfile_with_unicode(self):
         # Regression test for SPARK-970
         x = u"\u00A1Hola, mundo!"

From 2b8d89e30ebfe2272229a1eddd7542d7437c9924 Mon Sep 17 00:00:00 2001
From: Cheng Hao <hao.cheng@intel.com>
Date: Mon, 28 Jul 2014 10:59:53 -0700
Subject: [PATCH 785/810] [SPARK-2523] [SQL] Hadoop table scan bug fixing

In HiveTableScan.scala, ObjectInspector was created for all of the partition based records, which probably causes ClassCastException if the object inspector is not identical among table & partitions.

This is the follow up with:
https://github.com/apache/spark/pull/1408
https://github.com/apache/spark/pull/1390

I've run a micro benchmark in my local with 15000000 records totally, and got the result as below:

With This Patch  |  Partition-Based Table  |  Non-Partition-Based Table
------------ | ------------- | -------------
No  |  1927 ms  |  1885 ms
Yes  | 1541 ms  |  1524 ms

It showed this patch will also improve the performance.

PS:  the benchmark code is also attached. (thanks liancheng )
```
package org.apache.spark.sql.hive

import org.apache.spark.SparkContext
import org.apache.spark.SparkConf
import org.apache.spark.sql._

object HiveTableScanPrepare extends App {
  case class Record(key: String, value: String)

  val sparkContext = new SparkContext(
    new SparkConf()
      .setMaster("local")
      .setAppName(getClass.getSimpleName.stripSuffix("$")))

  val hiveContext = new LocalHiveContext(sparkContext)

  val rdd = sparkContext.parallelize((1 to 3000000).map(i => Record(s"$i", s"val_$i")))

  import hiveContext._

  hql("SHOW TABLES")
  hql("DROP TABLE if exists part_scan_test")
  hql("DROP TABLE if exists scan_test")
  hql("DROP TABLE if exists records")
  rdd.registerAsTable("records")

  hql("""CREATE TABLE part_scan_test (key STRING, value STRING) PARTITIONED BY (part1 string, part2 STRING)
                 | ROW FORMAT SERDE
                 | 'org.apache.hadoop.hive.serde2.columnar.LazyBinaryColumnarSerDe'
                 | STORED AS RCFILE
               """.stripMargin)
  hql("""CREATE TABLE scan_test (key STRING, value STRING)
                 | ROW FORMAT SERDE
                 | 'org.apache.hadoop.hive.serde2.columnar.LazyBinaryColumnarSerDe'
                 | STORED AS RCFILE
               """.stripMargin)

  for (part1 <- 2000 until 2001) {
    for (part2 <- 1 to 5) {
      hql(s"""from records
                 | insert into table part_scan_test PARTITION (part1='$part1', part2='2010-01-$part2')
                 | select key, value
               """.stripMargin)
      hql(s"""from records
                 | insert into table scan_test select key, value
               """.stripMargin)
    }
  }
}

object HiveTableScanTest extends App {
  val sparkContext = new SparkContext(
    new SparkConf()
      .setMaster("local")
      .setAppName(getClass.getSimpleName.stripSuffix("$")))

  val hiveContext = new LocalHiveContext(sparkContext)

  import hiveContext._

  hql("SHOW TABLES")
  val part_scan_test = hql("select key, value from part_scan_test")
  val scan_test = hql("select key, value from scan_test")

  val r_part_scan_test = (0 to 5).map(i => benchmark(part_scan_test))
  val r_scan_test = (0 to 5).map(i => benchmark(scan_test))
  println("Scanning Partition-Based Table")
  r_part_scan_test.foreach(printResult)
  println("Scanning Non-Partition-Based Table")
  r_scan_test.foreach(printResult)

  def printResult(result: (Long, Long)) {
    println(s"Duration: ${result._1} ms Result: ${result._2}")
  }

  def benchmark(srdd: SchemaRDD) = {
    val begin = System.currentTimeMillis()
    val result = srdd.count()
    val end = System.currentTimeMillis()
    ((end - begin), result)
  }
}
```

Author: Cheng Hao <hao.cheng@intel.com>

Closes #1439 from chenghao-intel/hadoop_table_scan and squashes the following commits:

888968f [Cheng Hao] Fix issues in code style
27540ba [Cheng Hao] Fix the TableScan Bug while partition serde differs
40a24a7 [Cheng Hao] Add Unit Test
---
 .../apache/spark/sql/hive/TableReader.scala   | 113 +++++++++++++-----
 .../sql/hive/execution/HiveTableScan.scala    |  90 ++------------
 ...t_serde-0-8caed2a6e80250a6d38a59388679c298 |   2 +
 .../hive/execution/HiveTableScanSuite.scala   |  48 ++++++++
 4 files changed, 138 insertions(+), 115 deletions(-)
 create mode 100644 sql/hive/src/test/resources/golden/partition_based_table_scan_with_different_serde-0-8caed2a6e80250a6d38a59388679c298
 create mode 100644 sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveTableScanSuite.scala

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/TableReader.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/TableReader.scala
index c3942578d6b5..82c88280d775 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/TableReader.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/TableReader.scala
@@ -24,6 +24,8 @@ import org.apache.hadoop.hive.ql.exec.Utilities
 import org.apache.hadoop.hive.ql.metadata.{Partition => HivePartition, Table => HiveTable}
 import org.apache.hadoop.hive.ql.plan.TableDesc
 import org.apache.hadoop.hive.serde2.Deserializer
+import org.apache.hadoop.hive.serde2.objectinspector.StructObjectInspector
+
 import org.apache.hadoop.io.Writable
 import org.apache.hadoop.mapred.{FileInputFormat, InputFormat, JobConf}
 
@@ -31,13 +33,16 @@ import org.apache.spark.SerializableWritable
 import org.apache.spark.broadcast.Broadcast
 import org.apache.spark.rdd.{EmptyRDD, HadoopRDD, RDD, UnionRDD}
 
+import org.apache.spark.sql.catalyst.expressions.{Attribute, Row, GenericMutableRow, Literal, Cast}
+import org.apache.spark.sql.catalyst.types.DataType
+
 /**
  * A trait for subclasses that handle table scans.
  */
 private[hive] sealed trait TableReader {
-  def makeRDDForTable(hiveTable: HiveTable): RDD[_]
+  def makeRDDForTable(hiveTable: HiveTable): RDD[Row]
 
-  def makeRDDForPartitionedTable(partitions: Seq[HivePartition]): RDD[_]
+  def makeRDDForPartitionedTable(partitions: Seq[HivePartition]): RDD[Row]
 }
 
 
@@ -46,7 +51,10 @@ private[hive] sealed trait TableReader {
  * data warehouse directory.
  */
 private[hive]
-class HadoopTableReader(@transient _tableDesc: TableDesc, @transient sc: HiveContext)
+class HadoopTableReader(
+    @transient attributes: Seq[Attribute],
+    @transient relation: MetastoreRelation,
+    @transient sc: HiveContext)
   extends TableReader {
 
   // Choose the minimum number of splits. If mapred.map.tasks is set, then use that unless
@@ -63,10 +71,10 @@ class HadoopTableReader(@transient _tableDesc: TableDesc, @transient sc: HiveCon
 
   def hiveConf = _broadcastedHiveConf.value.value
 
-  override def makeRDDForTable(hiveTable: HiveTable): RDD[_] =
+  override def makeRDDForTable(hiveTable: HiveTable): RDD[Row] =
     makeRDDForTable(
       hiveTable,
-      _tableDesc.getDeserializerClass.asInstanceOf[Class[Deserializer]],
+      relation.tableDesc.getDeserializerClass.asInstanceOf[Class[Deserializer]],
       filterOpt = None)
 
   /**
@@ -81,14 +89,14 @@ class HadoopTableReader(@transient _tableDesc: TableDesc, @transient sc: HiveCon
   def makeRDDForTable(
       hiveTable: HiveTable,
       deserializerClass: Class[_ <: Deserializer],
-      filterOpt: Option[PathFilter]): RDD[_] = {
+      filterOpt: Option[PathFilter]): RDD[Row] = {
 
     assert(!hiveTable.isPartitioned, """makeRDDForTable() cannot be called on a partitioned table,
       since input formats may differ across partitions. Use makeRDDForTablePartitions() instead.""")
 
     // Create local references to member variables, so that the entire `this` object won't be
     // serialized in the closure below.
-    val tableDesc = _tableDesc
+    val tableDesc = relation.tableDesc
     val broadcastedHiveConf = _broadcastedHiveConf
 
     val tablePath = hiveTable.getPath
@@ -99,23 +107,20 @@ class HadoopTableReader(@transient _tableDesc: TableDesc, @transient sc: HiveCon
       .asInstanceOf[java.lang.Class[InputFormat[Writable, Writable]]]
     val hadoopRDD = createHadoopRdd(tableDesc, inputPathStr, ifc)
 
+    val attrsWithIndex = attributes.zipWithIndex
+    val mutableRow = new GenericMutableRow(attrsWithIndex.length)
     val deserializedHadoopRDD = hadoopRDD.mapPartitions { iter =>
       val hconf = broadcastedHiveConf.value.value
       val deserializer = deserializerClass.newInstance()
       deserializer.initialize(hconf, tableDesc.getProperties)
 
-      // Deserialize each Writable to get the row value.
-      iter.map {
-        case v: Writable => deserializer.deserialize(v)
-        case value =>
-          sys.error(s"Unable to deserialize non-Writable: $value of ${value.getClass.getName}")
-      }
+      HadoopTableReader.fillObject(iter, deserializer, attrsWithIndex, mutableRow)
     }
 
     deserializedHadoopRDD
   }
 
-  override def makeRDDForPartitionedTable(partitions: Seq[HivePartition]): RDD[_] = {
+  override def makeRDDForPartitionedTable(partitions: Seq[HivePartition]): RDD[Row] = {
     val partitionToDeserializer = partitions.map(part =>
       (part, part.getDeserializer.getClass.asInstanceOf[Class[Deserializer]])).toMap
     makeRDDForPartitionedTable(partitionToDeserializer, filterOpt = None)
@@ -132,9 +137,9 @@ class HadoopTableReader(@transient _tableDesc: TableDesc, @transient sc: HiveCon
    *     subdirectory of each partition being read. If None, then all files are accepted.
    */
   def makeRDDForPartitionedTable(
-      partitionToDeserializer: Map[HivePartition, Class[_ <: Deserializer]],
-      filterOpt: Option[PathFilter]): RDD[_] = {
-
+      partitionToDeserializer: Map[HivePartition,
+      Class[_ <: Deserializer]],
+      filterOpt: Option[PathFilter]): RDD[Row] = {
     val hivePartitionRDDs = partitionToDeserializer.map { case (partition, partDeserializer) =>
       val partDesc = Utilities.getPartitionDesc(partition)
       val partPath = partition.getPartitionPath
@@ -156,33 +161,42 @@ class HadoopTableReader(@transient _tableDesc: TableDesc, @transient sc: HiveCon
       }
 
       // Create local references so that the outer object isn't serialized.
-      val tableDesc = _tableDesc
+      val tableDesc = relation.tableDesc
       val broadcastedHiveConf = _broadcastedHiveConf
       val localDeserializer = partDeserializer
+      val mutableRow = new GenericMutableRow(attributes.length)
+
+      // split the attributes (output schema) into 2 categories:
+      // (partition keys, ordinal), (normal attributes, ordinal), the ordinal mean the 
+      // index of the attribute in the output Row.
+      val (partitionKeys, attrs) = attributes.zipWithIndex.partition(attr => {
+        relation.partitionKeys.indexOf(attr._1) >= 0
+      })
+
+      def fillPartitionKeys(parts: Array[String], row: GenericMutableRow) = {
+        partitionKeys.foreach { case (attr, ordinal) =>
+          // get partition key ordinal for a given attribute
+          val partOridinal = relation.partitionKeys.indexOf(attr)
+          row(ordinal) = Cast(Literal(parts(partOridinal)), attr.dataType).eval(null)
+        }
+      }
+      // fill the partition key for the given MutableRow Object
+      fillPartitionKeys(partValues, mutableRow)
 
       val hivePartitionRDD = createHadoopRdd(tableDesc, inputPathStr, ifc)
       hivePartitionRDD.mapPartitions { iter =>
         val hconf = broadcastedHiveConf.value.value
-        val rowWithPartArr = new Array[Object](2)
-
-        // The update and deserializer initialization are intentionally
-        // kept out of the below iter.map loop to save performance.
-        rowWithPartArr.update(1, partValues)
         val deserializer = localDeserializer.newInstance()
         deserializer.initialize(hconf, partProps)
 
-        // Map each tuple to a row object
-        iter.map { value =>
-          val deserializedRow = deserializer.deserialize(value)
-          rowWithPartArr.update(0, deserializedRow)
-          rowWithPartArr.asInstanceOf[Object]
-        }
+        // fill the non partition key attributes 
+        HadoopTableReader.fillObject(iter, deserializer, attrs, mutableRow)
       }
     }.toSeq
 
     // Even if we don't use any partitions, we still need an empty RDD
     if (hivePartitionRDDs.size == 0) {
-      new EmptyRDD[Object](sc.sparkContext)
+      new EmptyRDD[Row](sc.sparkContext)
     } else {
       new UnionRDD(hivePartitionRDDs(0).context, hivePartitionRDDs)
     }
@@ -225,10 +239,9 @@ class HadoopTableReader(@transient _tableDesc: TableDesc, @transient sc: HiveCon
     // Only take the value (skip the key) because Hive works only with values.
     rdd.map(_._2)
   }
-
 }
 
-private[hive] object HadoopTableReader {
+private[hive] object HadoopTableReader extends HiveInspectors {
   /**
    * Curried. After given an argument for 'path', the resulting JobConf => Unit closure is used to
    * instantiate a HadoopRDD.
@@ -241,4 +254,40 @@ private[hive] object HadoopTableReader {
     val bufferSize = System.getProperty("spark.buffer.size", "65536")
     jobConf.set("io.file.buffer.size", bufferSize)
   }
+
+  /**
+   * Transform the raw data(Writable object) into the Row object for an iterable input
+   * @param iter Iterable input which represented as Writable object
+   * @param deserializer Deserializer associated with the input writable object
+   * @param attrs Represents the row attribute names and its zero-based position in the MutableRow
+   * @param row reusable MutableRow object
+   * 
+   * @return Iterable Row object that transformed from the given iterable input.
+   */
+  def fillObject(
+      iter: Iterator[Writable],
+      deserializer: Deserializer,
+      attrs: Seq[(Attribute, Int)],
+      row: GenericMutableRow): Iterator[Row] = {
+    val soi = deserializer.getObjectInspector().asInstanceOf[StructObjectInspector]
+    // get the field references according to the attributes(output of the reader) required
+    val fieldRefs = attrs.map { case (attr, idx) => (soi.getStructFieldRef(attr.name), idx) }
+
+    // Map each tuple to a row object
+    iter.map { value =>
+      val raw = deserializer.deserialize(value)
+      var idx = 0;
+      while (idx < fieldRefs.length) {
+        val fieldRef = fieldRefs(idx)._1
+        val fieldIdx = fieldRefs(idx)._2
+        val fieldValue = soi.getStructFieldData(raw, fieldRef)
+
+        row(fieldIdx) = unwrapData(fieldValue, fieldRef.getFieldObjectInspector())
+
+        idx += 1
+      }
+
+      row: Row
+    }
+  }
 }
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveTableScan.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveTableScan.scala
index e7016fa16eea..8920e2a76a27 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveTableScan.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveTableScan.scala
@@ -34,7 +34,6 @@ import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.types.{BooleanType, DataType}
 import org.apache.spark.sql.execution._
 import org.apache.spark.sql.hive._
-import org.apache.spark.util.MutablePair
 
 /**
  * :: DeveloperApi ::
@@ -50,8 +49,7 @@ case class HiveTableScan(
     relation: MetastoreRelation,
     partitionPruningPred: Option[Expression])(
     @transient val context: HiveContext)
-  extends LeafNode
-  with HiveInspectors {
+  extends LeafNode {
 
   require(partitionPruningPred.isEmpty || relation.hiveQlTable.isPartitioned,
     "Partition pruning predicates only supported for partitioned tables.")
@@ -67,42 +65,7 @@ case class HiveTableScan(
   }
 
   @transient
-  private[this] val hadoopReader = new HadoopTableReader(relation.tableDesc, context)
-
-  /**
-   * The hive object inspector for this table, which can be used to extract values from the
-   * serialized row representation.
-   */
-  @transient
-  private[this] lazy val objectInspector =
-    relation.tableDesc.getDeserializer.getObjectInspector.asInstanceOf[StructObjectInspector]
-
-  /**
-   * Functions that extract the requested attributes from the hive output.  Partitioned values are
-   * casted from string to its declared data type.
-   */
-  @transient
-  protected lazy val attributeFunctions: Seq[(Any, Array[String]) => Any] = {
-    attributes.map { a =>
-      val ordinal = relation.partitionKeys.indexOf(a)
-      if (ordinal >= 0) {
-        val dataType = relation.partitionKeys(ordinal).dataType
-        (_: Any, partitionKeys: Array[String]) => {
-          castFromString(partitionKeys(ordinal), dataType)
-        }
-      } else {
-        val ref = objectInspector.getAllStructFieldRefs
-          .find(_.getFieldName == a.name)
-          .getOrElse(sys.error(s"Can't find attribute $a"))
-        val fieldObjectInspector = ref.getFieldObjectInspector
-
-        (row: Any, _: Array[String]) => {
-          val data = objectInspector.getStructFieldData(row, ref)
-          unwrapData(data, fieldObjectInspector)
-        }
-      }
-    }
-  }
+  private[this] val hadoopReader = new HadoopTableReader(attributes, relation, context)
 
   private[this] def castFromString(value: String, dataType: DataType) = {
     Cast(Literal(value), dataType).eval(null)
@@ -114,6 +77,7 @@ case class HiveTableScan(
     val columnInternalNames = neededColumnIDs.map(HiveConf.getColumnInternalName(_)).mkString(",")
 
     if (attributes.size == relation.output.size) {
+      // SQLContext#pruneFilterProject guarantees no duplicated value in `attributes`
       ColumnProjectionUtils.setFullyReadColumns(hiveConf)
     } else {
       ColumnProjectionUtils.appendReadColumnIDs(hiveConf, neededColumnIDs)
@@ -140,12 +104,6 @@ case class HiveTableScan(
 
   addColumnMetadataToConf(context.hiveconf)
 
-  private def inputRdd = if (!relation.hiveQlTable.isPartitioned) {
-    hadoopReader.makeRDDForTable(relation.hiveQlTable)
-  } else {
-    hadoopReader.makeRDDForPartitionedTable(prunePartitions(relation.hiveQlPartitions))
-  }
-
   /**
    * Prunes partitions not involve the query plan.
    *
@@ -169,44 +127,10 @@ case class HiveTableScan(
     }
   }
 
-  override def execute() = {
-    inputRdd.mapPartitions { iterator =>
-      if (iterator.isEmpty) {
-        Iterator.empty
-      } else {
-        val mutableRow = new GenericMutableRow(attributes.length)
-        val mutablePair = new MutablePair[Any, Array[String]]()
-        val buffered = iterator.buffered
-
-        // NOTE (lian): Critical path of Hive table scan, unnecessary FP style code and pattern
-        // matching are avoided intentionally.
-        val rowsAndPartitionKeys = buffered.head match {
-          // With partition keys
-          case _: Array[Any] =>
-            buffered.map { case array: Array[Any] =>
-              val deserializedRow = array(0)
-              val partitionKeys = array(1).asInstanceOf[Array[String]]
-              mutablePair.update(deserializedRow, partitionKeys)
-            }
-
-          // Without partition keys
-          case _ =>
-            val emptyPartitionKeys = Array.empty[String]
-            buffered.map { deserializedRow =>
-              mutablePair.update(deserializedRow, emptyPartitionKeys)
-            }
-        }
-
-        rowsAndPartitionKeys.map { pair =>
-          var i = 0
-          while (i < attributes.length) {
-            mutableRow(i) = attributeFunctions(i)(pair._1, pair._2)
-            i += 1
-          }
-          mutableRow: Row
-        }
-      }
-    }
+  override def execute() = if (!relation.hiveQlTable.isPartitioned) {
+    hadoopReader.makeRDDForTable(relation.hiveQlTable)
+  } else {
+    hadoopReader.makeRDDForPartitionedTable(prunePartitions(relation.hiveQlPartitions))
   }
 
   override def output = attributes
diff --git a/sql/hive/src/test/resources/golden/partition_based_table_scan_with_different_serde-0-8caed2a6e80250a6d38a59388679c298 b/sql/hive/src/test/resources/golden/partition_based_table_scan_with_different_serde-0-8caed2a6e80250a6d38a59388679c298
new file mode 100644
index 000000000000..f369f21e1833
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/partition_based_table_scan_with_different_serde-0-8caed2a6e80250a6d38a59388679c298
@@ -0,0 +1,2 @@
+100	100	2010-01-01
+200	200	2010-01-02
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveTableScanSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveTableScanSuite.scala
new file mode 100644
index 000000000000..bcb00f871d18
--- /dev/null
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveTableScanSuite.scala
@@ -0,0 +1,48 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive.execution
+
+import org.scalatest.{BeforeAndAfterAll, FunSuite}
+
+import org.apache.spark.{SparkConf, SparkContext}
+import org.apache.spark.sql.hive.test.TestHive
+
+class HiveTableScanSuite extends HiveComparisonTest {
+  // MINOR HACK: You must run a query before calling reset the first time.
+  TestHive.hql("SHOW TABLES")
+  TestHive.reset()
+
+  TestHive.hql("""CREATE TABLE part_scan_test (key STRING, value STRING) PARTITIONED BY (ds STRING) 
+                 | ROW FORMAT SERDE 
+                 | 'org.apache.hadoop.hive.serde2.columnar.LazyBinaryColumnarSerDe' 
+                 | STORED AS RCFILE
+               """.stripMargin)
+  TestHive.hql("""FROM src
+                 | INSERT INTO TABLE part_scan_test PARTITION (ds='2010-01-01')
+                 | SELECT 100,100 LIMIT 1
+               """.stripMargin)
+  TestHive.hql("""ALTER TABLE part_scan_test SET SERDE
+                 | 'org.apache.hadoop.hive.serde2.columnar.ColumnarSerDe'
+               """.stripMargin)
+  TestHive.hql("""FROM src INSERT INTO TABLE part_scan_test PARTITION (ds='2010-01-02')
+                 | SELECT 200,200 LIMIT 1
+               """.stripMargin)
+
+  createQueryTest("partition_based_table_scan_with_different_serde", 
+    "SELECT * from part_scan_test", false)
+}

From 255b56f9f530e8594a7e6055ae07690454c66799 Mon Sep 17 00:00:00 2001
From: DB Tsai <dbtsai@alpinenow.com>
Date: Mon, 28 Jul 2014 11:34:19 -0700
Subject: [PATCH 786/810] [SPARK-2479][MLlib] Comparing floating-point numbers
 using relative error in UnitTests

Floating point math is not exact, and most floating-point numbers end up being slightly imprecise due to rounding errors.

Simple values like 0.1 cannot be precisely represented using binary floating point numbers, and the limited precision of floating point numbers means that slight changes in the order of operations or the precision of intermediates can change the result.

That means that comparing two floats to see if they are equal is usually not what we want. As long as this imprecision stays small, it can usually be ignored.

Based on discussion in the community, we have implemented two different APIs for relative tolerance, and absolute tolerance. It makes sense that test writers should know which one they need depending on their circumstances.

Developers also need to explicitly specify the eps, and there is no default value which will sometimes cause confusion.

When comparing against zero using relative tolerance, a exception will be raised to warn users that it's meaningless.

For relative tolerance, users can now write

    assert(23.1 ~== 23.52 relTol 0.02)
    assert(23.1 ~== 22.74 relTol 0.02)
    assert(23.1 ~= 23.52 relTol 0.02)
    assert(23.1 ~= 22.74 relTol 0.02)
    assert(!(23.1 !~= 23.52 relTol 0.02))
    assert(!(23.1 !~= 22.74 relTol 0.02))

    // This will throw exception with the following message.
    // "Did not expect 23.1 and 23.52 to be within 0.02 using relative tolerance."
    assert(23.1 !~== 23.52 relTol 0.02)

    // "Expected 23.1 and 22.34 to be within 0.02 using relative tolerance."
    assert(23.1 ~== 22.34 relTol 0.02)

For absolute error,

    assert(17.8 ~== 17.99 absTol 0.2)
    assert(17.8 ~== 17.61 absTol 0.2)
    assert(17.8 ~= 17.99 absTol 0.2)
    assert(17.8 ~= 17.61 absTol 0.2)
    assert(!(17.8 !~= 17.99 absTol 0.2))
    assert(!(17.8 !~= 17.61 absTol 0.2))

    // This will throw exception with the following message.
    // "Did not expect 17.8 and 17.99 to be within 0.2 using absolute error."
    assert(17.8 !~== 17.99 absTol 0.2)

    // "Expected 17.8 and 17.59 to be within 0.2 using absolute error."
    assert(17.8 ~== 17.59 absTol 0.2)

Authors:
  DB Tsai <dbtsaialpinenow.com>
  Marek Kolodziej <marekalpinenow.com>

Author: DB Tsai <dbtsai@alpinenow.com>

Closes #1425 from dbtsai/SPARK-2479_comparing_floating_point and squashes the following commits:

8c7cbcc [DB Tsai] Alpine Data Labs
---
 .../LogisticRegressionSuite.scala             |  12 +-
 .../spark/mllib/clustering/KMeansSuite.scala  |  63 +++---
 .../evaluation/AreaUnderCurveSuite.scala      |  13 +-
 .../BinaryClassificationMetricsSuite.scala    |  40 ++--
 .../optimization/GradientDescentSuite.scala   |  16 +-
 .../spark/mllib/optimization/LBFGSSuite.scala |  17 +-
 .../spark/mllib/optimization/NNLSSuite.scala  |   6 +-
 .../MultivariateOnlineSummarizerSuite.scala   |  68 +++----
 .../spark/mllib/util/TestingUtils.scala       | 151 +++++++++++++--
 .../spark/mllib/util/TestingUtilsSuite.scala  | 182 ++++++++++++++++++
 10 files changed, 438 insertions(+), 130 deletions(-)
 create mode 100644 mllib/src/test/scala/org/apache/spark/mllib/util/TestingUtilsSuite.scala

diff --git a/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala
index 3f6ff859374c..da7c633bbd2a 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala
@@ -26,6 +26,7 @@ import org.scalatest.Matchers
 import org.apache.spark.mllib.linalg.Vectors
 import org.apache.spark.mllib.regression._
 import org.apache.spark.mllib.util.{LocalClusterSparkContext, LocalSparkContext}
+import org.apache.spark.mllib.util.TestingUtils._
 
 object LogisticRegressionSuite {
 
@@ -81,9 +82,8 @@ class LogisticRegressionSuite extends FunSuite with LocalSparkContext with Match
     val model = lr.run(testRDD)
 
     // Test the weights
-    val weight0 = model.weights(0)
-    assert(weight0 >= -1.60 && weight0 <= -1.40, weight0 + " not in [-1.6, -1.4]")
-    assert(model.intercept >= 1.9 && model.intercept <= 2.1, model.intercept + " not in [1.9, 2.1]")
+    assert(model.weights(0) ~== -1.52 relTol 0.01)
+    assert(model.intercept ~== 2.00 relTol 0.01)
 
     val validationData = LogisticRegressionSuite.generateLogisticInput(A, B, nPoints, 17)
     val validationRDD = sc.parallelize(validationData, 2)
@@ -113,9 +113,9 @@ class LogisticRegressionSuite extends FunSuite with LocalSparkContext with Match
 
     val model = lr.run(testRDD, initialWeights)
 
-    val weight0 = model.weights(0)
-    assert(weight0 >= -1.60 && weight0 <= -1.40, weight0 + " not in [-1.6, -1.4]")
-    assert(model.intercept >= 1.9 && model.intercept <= 2.1, model.intercept + " not in [1.9, 2.1]")
+    // Test the weights
+    assert(model.weights(0) ~== -1.50 relTol 0.01)
+    assert(model.intercept ~== 1.97 relTol 0.01)
 
     val validationData = LogisticRegressionSuite.generateLogisticInput(A, B, nPoints, 17)
     val validationRDD = sc.parallelize(validationData, 2)
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/clustering/KMeansSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/clustering/KMeansSuite.scala
index 34bc4537a7b3..afa1f79b95a1 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/clustering/KMeansSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/clustering/KMeansSuite.scala
@@ -21,8 +21,9 @@ import scala.util.Random
 
 import org.scalatest.FunSuite
 
-import org.apache.spark.mllib.linalg.Vectors
+import org.apache.spark.mllib.linalg.{Vector, Vectors}
 import org.apache.spark.mllib.util.{LocalClusterSparkContext, LocalSparkContext}
+import org.apache.spark.mllib.util.TestingUtils._
 
 class KMeansSuite extends FunSuite with LocalSparkContext {
 
@@ -41,26 +42,26 @@ class KMeansSuite extends FunSuite with LocalSparkContext {
     // centered at the mean of the points
 
     var model = KMeans.train(data, k = 1, maxIterations = 1)
-    assert(model.clusterCenters.head === center)
+    assert(model.clusterCenters.head ~== center absTol 1E-5)
 
     model = KMeans.train(data, k = 1, maxIterations = 2)
-    assert(model.clusterCenters.head === center)
+    assert(model.clusterCenters.head ~== center absTol 1E-5)
 
     model = KMeans.train(data, k = 1, maxIterations = 5)
-    assert(model.clusterCenters.head === center)
+    assert(model.clusterCenters.head ~== center absTol 1E-5)
 
     model = KMeans.train(data, k = 1, maxIterations = 1, runs = 5)
-    assert(model.clusterCenters.head === center)
+    assert(model.clusterCenters.head ~== center absTol 1E-5)
 
     model = KMeans.train(data, k = 1, maxIterations = 1, runs = 5)
-    assert(model.clusterCenters.head === center)
+    assert(model.clusterCenters.head ~== center absTol 1E-5)
 
     model = KMeans.train(data, k = 1, maxIterations = 1, runs = 1, initializationMode = RANDOM)
-    assert(model.clusterCenters.head === center)
+    assert(model.clusterCenters.head ~== center absTol 1E-5)
 
     model = KMeans.train(
       data, k = 1, maxIterations = 1, runs = 1, initializationMode = K_MEANS_PARALLEL)
-    assert(model.clusterCenters.head === center)
+    assert(model.clusterCenters.head ~== center absTol 1E-5)
   }
 
   test("no distinct points") {
@@ -104,26 +105,26 @@ class KMeansSuite extends FunSuite with LocalSparkContext {
 
     var model = KMeans.train(data, k = 1, maxIterations = 1)
     assert(model.clusterCenters.size === 1)
-    assert(model.clusterCenters.head === center)
+    assert(model.clusterCenters.head ~== center absTol 1E-5)
 
     model = KMeans.train(data, k = 1, maxIterations = 2)
-    assert(model.clusterCenters.head === center)
+    assert(model.clusterCenters.head ~== center absTol 1E-5)
 
     model = KMeans.train(data, k = 1, maxIterations = 5)
-    assert(model.clusterCenters.head === center)
+    assert(model.clusterCenters.head ~== center absTol 1E-5)
 
     model = KMeans.train(data, k = 1, maxIterations = 1, runs = 5)
-    assert(model.clusterCenters.head === center)
+    assert(model.clusterCenters.head ~== center absTol 1E-5)
 
     model = KMeans.train(data, k = 1, maxIterations = 1, runs = 5)
-    assert(model.clusterCenters.head === center)
+    assert(model.clusterCenters.head ~== center absTol 1E-5)
 
     model = KMeans.train(data, k = 1, maxIterations = 1, runs = 1, initializationMode = RANDOM)
-    assert(model.clusterCenters.head === center)
+    assert(model.clusterCenters.head ~== center absTol 1E-5)
 
     model = KMeans.train(data, k = 1, maxIterations = 1, runs = 1,
       initializationMode = K_MEANS_PARALLEL)
-    assert(model.clusterCenters.head === center)
+    assert(model.clusterCenters.head ~== center absTol 1E-5)
   }
 
   test("single cluster with sparse data") {
@@ -149,31 +150,39 @@ class KMeansSuite extends FunSuite with LocalSparkContext {
     val center = Vectors.sparse(n, Seq((0, 1.0), (1, 3.0), (2, 4.0)))
 
     var model = KMeans.train(data, k = 1, maxIterations = 1)
-    assert(model.clusterCenters.head === center)
+    assert(model.clusterCenters.head ~== center absTol 1E-5)
 
     model = KMeans.train(data, k = 1, maxIterations = 2)
-    assert(model.clusterCenters.head === center)
+    assert(model.clusterCenters.head ~== center absTol 1E-5)
 
     model = KMeans.train(data, k = 1, maxIterations = 5)
-    assert(model.clusterCenters.head === center)
+    assert(model.clusterCenters.head ~== center absTol 1E-5)
 
     model = KMeans.train(data, k = 1, maxIterations = 1, runs = 5)
-    assert(model.clusterCenters.head === center)
+    assert(model.clusterCenters.head ~== center absTol 1E-5)
 
     model = KMeans.train(data, k = 1, maxIterations = 1, runs = 5)
-    assert(model.clusterCenters.head === center)
+    assert(model.clusterCenters.head ~== center absTol 1E-5)
 
     model = KMeans.train(data, k = 1, maxIterations = 1, runs = 1, initializationMode = RANDOM)
-    assert(model.clusterCenters.head === center)
+    assert(model.clusterCenters.head ~== center absTol 1E-5)
 
     model = KMeans.train(data, k = 1, maxIterations = 1, runs = 1,
       initializationMode = K_MEANS_PARALLEL)
-    assert(model.clusterCenters.head === center)
+    assert(model.clusterCenters.head ~== center absTol 1E-5)
 
     data.unpersist()
   }
 
   test("k-means|| initialization") {
+
+    case class VectorWithCompare(x: Vector) extends Ordered[VectorWithCompare] {
+      @Override def compare(that: VectorWithCompare): Int = {
+        if(this.x.toArray.foldLeft[Double](0.0)((acc, x) => acc + x * x) >
+          that.x.toArray.foldLeft[Double](0.0)((acc, x) => acc + x * x)) -1 else 1
+      }
+    }
+
     val points = Seq(
       Vectors.dense(1.0, 2.0, 6.0),
       Vectors.dense(1.0, 3.0, 0.0),
@@ -188,15 +197,19 @@ class KMeansSuite extends FunSuite with LocalSparkContext {
     // unselected point as long as it hasn't yet selected all of them
 
     var model = KMeans.train(rdd, k = 5, maxIterations = 1)
-    assert(Set(model.clusterCenters: _*) === Set(points: _*))
+
+    assert(model.clusterCenters.sortBy(VectorWithCompare(_))
+      .zip(points.sortBy(VectorWithCompare(_))).forall(x => x._1 ~== (x._2) absTol 1E-5))
 
     // Iterations of Lloyd's should not change the answer either
     model = KMeans.train(rdd, k = 5, maxIterations = 10)
-    assert(Set(model.clusterCenters: _*) === Set(points: _*))
+    assert(model.clusterCenters.sortBy(VectorWithCompare(_))
+      .zip(points.sortBy(VectorWithCompare(_))).forall(x => x._1 ~== (x._2) absTol 1E-5))
 
     // Neither should more runs
     model = KMeans.train(rdd, k = 5, maxIterations = 10, runs = 5)
-    assert(Set(model.clusterCenters: _*) === Set(points: _*))
+    assert(model.clusterCenters.sortBy(VectorWithCompare(_))
+      .zip(points.sortBy(VectorWithCompare(_))).forall(x => x._1 ~== (x._2) absTol 1E-5))
   }
 
   test("two clusters") {
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/evaluation/AreaUnderCurveSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/evaluation/AreaUnderCurveSuite.scala
index 1c9844f289fe..994e0feb8629 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/evaluation/AreaUnderCurveSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/evaluation/AreaUnderCurveSuite.scala
@@ -20,27 +20,28 @@ package org.apache.spark.mllib.evaluation
 import org.scalatest.FunSuite
 
 import org.apache.spark.mllib.util.LocalSparkContext
+import org.apache.spark.mllib.util.TestingUtils._
 
 class AreaUnderCurveSuite extends FunSuite with LocalSparkContext {
   test("auc computation") {
     val curve = Seq((0.0, 0.0), (1.0, 1.0), (2.0, 3.0), (3.0, 0.0))
     val auc = 4.0
-    assert(AreaUnderCurve.of(curve) === auc)
+    assert(AreaUnderCurve.of(curve) ~== auc absTol 1E-5)
     val rddCurve = sc.parallelize(curve, 2)
-    assert(AreaUnderCurve.of(rddCurve) == auc)
+    assert(AreaUnderCurve.of(rddCurve) ~== auc absTol 1E-5)
   }
 
   test("auc of an empty curve") {
     val curve = Seq.empty[(Double, Double)]
-    assert(AreaUnderCurve.of(curve) === 0.0)
+    assert(AreaUnderCurve.of(curve) ~== 0.0 absTol 1E-5)
     val rddCurve = sc.parallelize(curve, 2)
-    assert(AreaUnderCurve.of(rddCurve) === 0.0)
+    assert(AreaUnderCurve.of(rddCurve) ~== 0.0 absTol 1E-5)
   }
 
   test("auc of a curve with a single point") {
     val curve = Seq((1.0, 1.0))
-    assert(AreaUnderCurve.of(curve) === 0.0)
+    assert(AreaUnderCurve.of(curve) ~== 0.0 absTol 1E-5)
     val rddCurve = sc.parallelize(curve, 2)
-    assert(AreaUnderCurve.of(rddCurve) === 0.0)
+    assert(AreaUnderCurve.of(rddCurve) ~== 0.0 absTol 1E-5)
   }
 }
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetricsSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetricsSuite.scala
index 94db1dc18323..a733f88b60b8 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetricsSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetricsSuite.scala
@@ -20,25 +20,14 @@ package org.apache.spark.mllib.evaluation
 import org.scalatest.FunSuite
 
 import org.apache.spark.mllib.util.LocalSparkContext
-import org.apache.spark.mllib.util.TestingUtils.DoubleWithAlmostEquals
+import org.apache.spark.mllib.util.TestingUtils._
 
 class BinaryClassificationMetricsSuite extends FunSuite with LocalSparkContext {
 
-  // TODO: move utility functions to TestingUtils.
+  def cond1(x: (Double, Double)): Boolean = x._1 ~= (x._2) absTol 1E-5
 
-  def elementsAlmostEqual(actual: Seq[Double], expected: Seq[Double]): Boolean = {
-    actual.zip(expected).forall { case (x1, x2) =>
-      x1.almostEquals(x2)
-    }
-  }
-
-  def elementsAlmostEqual(
-      actual: Seq[(Double, Double)],
-      expected: Seq[(Double, Double)])(implicit dummy: DummyImplicit): Boolean = {
-    actual.zip(expected).forall { case ((x1, y1), (x2, y2)) =>
-      x1.almostEquals(x2) && y1.almostEquals(y2)
-    }
-  }
+  def cond2(x: ((Double, Double), (Double, Double))): Boolean =
+    (x._1._1 ~= x._2._1 absTol 1E-5) && (x._1._2 ~= x._2._2 absTol 1E-5)
 
   test("binary evaluation metrics") {
     val scoreAndLabels = sc.parallelize(
@@ -57,16 +46,17 @@ class BinaryClassificationMetricsSuite extends FunSuite with LocalSparkContext {
     val rocCurve = Seq((0.0, 0.0)) ++ fpr.zip(recall) ++ Seq((1.0, 1.0))
     val pr = recall.zip(precision)
     val prCurve = Seq((0.0, 1.0)) ++ pr
-    val f1 = pr.map { case (r, p) => 2.0 * (p * r) / (p + r) }
+    val f1 = pr.map { case (r, p) => 2.0 * (p * r) / (p + r)}
     val f2 = pr.map { case (r, p) => 5.0 * (p * r) / (4.0 * p + r)}
-    assert(elementsAlmostEqual(metrics.thresholds().collect(), threshold))
-    assert(elementsAlmostEqual(metrics.roc().collect(), rocCurve))
-    assert(metrics.areaUnderROC().almostEquals(AreaUnderCurve.of(rocCurve)))
-    assert(elementsAlmostEqual(metrics.pr().collect(), prCurve))
-    assert(metrics.areaUnderPR().almostEquals(AreaUnderCurve.of(prCurve)))
-    assert(elementsAlmostEqual(metrics.fMeasureByThreshold().collect(), threshold.zip(f1)))
-    assert(elementsAlmostEqual(metrics.fMeasureByThreshold(2.0).collect(), threshold.zip(f2)))
-    assert(elementsAlmostEqual(metrics.precisionByThreshold().collect(), threshold.zip(precision)))
-    assert(elementsAlmostEqual(metrics.recallByThreshold().collect(), threshold.zip(recall)))
+
+    assert(metrics.thresholds().collect().zip(threshold).forall(cond1))
+    assert(metrics.roc().collect().zip(rocCurve).forall(cond2))
+    assert(metrics.areaUnderROC() ~== AreaUnderCurve.of(rocCurve) absTol 1E-5)
+    assert(metrics.pr().collect().zip(prCurve).forall(cond2))
+    assert(metrics.areaUnderPR() ~== AreaUnderCurve.of(prCurve) absTol 1E-5)
+    assert(metrics.fMeasureByThreshold().collect().zip(threshold.zip(f1)).forall(cond2))
+    assert(metrics.fMeasureByThreshold(2.0).collect().zip(threshold.zip(f2)).forall(cond2))
+    assert(metrics.precisionByThreshold().collect().zip(threshold.zip(precision)).forall(cond2))
+    assert(metrics.recallByThreshold().collect().zip(threshold.zip(recall)).forall(cond2))
   }
 }
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/optimization/GradientDescentSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/optimization/GradientDescentSuite.scala
index dfb2eb7f0d14..bf040110e228 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/optimization/GradientDescentSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/optimization/GradientDescentSuite.scala
@@ -25,6 +25,7 @@ import org.scalatest.{FunSuite, Matchers}
 import org.apache.spark.mllib.linalg.Vectors
 import org.apache.spark.mllib.regression._
 import org.apache.spark.mllib.util.{LocalClusterSparkContext, LocalSparkContext}
+import org.apache.spark.mllib.util.TestingUtils._
 
 object GradientDescentSuite {
 
@@ -126,19 +127,14 @@ class GradientDescentSuite extends FunSuite with LocalSparkContext with Matchers
     val (newWeights1, loss1) = GradientDescent.runMiniBatchSGD(
       dataRDD, gradient, updater, 1, 1, regParam1, 1.0, initialWeightsWithIntercept)
 
-    def compareDouble(x: Double, y: Double, tol: Double = 1E-3): Boolean = {
-      math.abs(x - y) / (math.abs(y) + 1e-15) < tol
-    }
-
-    assert(compareDouble(
-      loss1(0),
-      loss0(0) + (math.pow(initialWeightsWithIntercept(0), 2) +
-        math.pow(initialWeightsWithIntercept(1), 2)) / 2),
+    assert(
+      loss1(0) ~= (loss0(0) + (math.pow(initialWeightsWithIntercept(0), 2) +
+        math.pow(initialWeightsWithIntercept(1), 2)) / 2) absTol 1E-5,
       """For non-zero weights, the regVal should be \frac{1}{2}\sum_i w_i^2.""")
 
     assert(
-      compareDouble(newWeights1(0) , newWeights0(0) - initialWeightsWithIntercept(0)) &&
-      compareDouble(newWeights1(1) , newWeights0(1) - initialWeightsWithIntercept(1)),
+      (newWeights1(0) ~= (newWeights0(0) - initialWeightsWithIntercept(0)) absTol 1E-5) &&
+      (newWeights1(1) ~= (newWeights0(1) - initialWeightsWithIntercept(1)) absTol 1E-5),
       "The different between newWeights with/without regularization " +
         "should be initialWeightsWithIntercept.")
   }
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/optimization/LBFGSSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/optimization/LBFGSSuite.scala
index ff414742e839..5f4c24115ac8 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/optimization/LBFGSSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/optimization/LBFGSSuite.scala
@@ -24,6 +24,7 @@ import org.scalatest.{FunSuite, Matchers}
 import org.apache.spark.mllib.linalg.Vectors
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.util.{LocalClusterSparkContext, LocalSparkContext}
+import org.apache.spark.mllib.util.TestingUtils._
 
 class LBFGSSuite extends FunSuite with LocalSparkContext with Matchers {
 
@@ -49,10 +50,6 @@ class LBFGSSuite extends FunSuite with LocalSparkContext with Matchers {
 
   lazy val dataRDD = sc.parallelize(data, 2).cache()
 
-  def compareDouble(x: Double, y: Double, tol: Double = 1E-3): Boolean = {
-    math.abs(x - y) / (math.abs(y) + 1e-15) < tol
-  }
-
   test("LBFGS loss should be decreasing and match the result of Gradient Descent.") {
     val regParam = 0
 
@@ -126,15 +123,15 @@ class LBFGSSuite extends FunSuite with LocalSparkContext with Matchers {
       miniBatchFrac,
       initialWeightsWithIntercept)
 
-    assert(compareDouble(lossGD(0), lossLBFGS(0)),
+    assert(lossGD(0) ~= lossLBFGS(0) absTol 1E-5,
       "The first losses of LBFGS and GD should be the same.")
 
     // The 2% difference here is based on observation, but is not theoretically guaranteed.
-    assert(compareDouble(lossGD.last, lossLBFGS.last, 0.02),
+    assert(lossGD.last ~= lossLBFGS.last relTol 0.02,
       "The last losses of LBFGS and GD should be within 2% difference.")
 
-    assert(compareDouble(weightLBFGS(0), weightGD(0), 0.02) &&
-      compareDouble(weightLBFGS(1), weightGD(1), 0.02),
+    assert(
+      (weightLBFGS(0) ~= weightGD(0) relTol 0.02) && (weightLBFGS(1) ~= weightGD(1) relTol 0.02),
       "The weight differences between LBFGS and GD should be within 2%.")
   }
 
@@ -226,8 +223,8 @@ class LBFGSSuite extends FunSuite with LocalSparkContext with Matchers {
       initialWeightsWithIntercept)
 
     // for class LBFGS and the optimize method, we only look at the weights
-    assert(compareDouble(weightLBFGS(0), weightGD(0), 0.02) &&
-      compareDouble(weightLBFGS(1), weightGD(1), 0.02),
+    assert(
+      (weightLBFGS(0) ~= weightGD(0) relTol 0.02) && (weightLBFGS(1) ~= weightGD(1) relTol 0.02),
       "The weight differences between LBFGS and GD should be within 2%.")
   }
 }
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/optimization/NNLSSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/optimization/NNLSSuite.scala
index bbf385229081..b781a6aed9a8 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/optimization/NNLSSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/optimization/NNLSSuite.scala
@@ -21,7 +21,9 @@ import scala.util.Random
 
 import org.scalatest.FunSuite
 
-import org.jblas.{DoubleMatrix, SimpleBlas, NativeBlas}
+import org.jblas.{DoubleMatrix, SimpleBlas}
+
+import org.apache.spark.mllib.util.TestingUtils._
 
 class NNLSSuite extends FunSuite {
   /** Generate an NNLS problem whose optimal solution is the all-ones vector. */
@@ -73,7 +75,7 @@ class NNLSSuite extends FunSuite {
     val ws = NNLS.createWorkspace(n)
     val x = NNLS.solve(ata, atb, ws)
     for (i <- 0 until n) {
-      assert(Math.abs(x(i) - goodx(i)) < 1e-3)
+      assert(x(i) ~== goodx(i) absTol 1E-3)
       assert(x(i) >= 0)
     }
   }
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizerSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizerSuite.scala
index 4b7b019d820b..db13f142df51 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizerSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizerSuite.scala
@@ -89,15 +89,15 @@ class MultivariateOnlineSummarizerSuite extends FunSuite {
       .add(Vectors.dense(-1.0, 0.0, 6.0))
       .add(Vectors.dense(3.0, -3.0, 0.0))
 
-    assert(summarizer.mean.almostEquals(Vectors.dense(1.0, -1.5, 3.0)), "mean mismatch")
+    assert(summarizer.mean ~== Vectors.dense(1.0, -1.5, 3.0) absTol 1E-5, "mean mismatch")
 
-    assert(summarizer.min.almostEquals(Vectors.dense(-1.0, -3, 0.0)), "min mismatch")
+    assert(summarizer.min ~== Vectors.dense(-1.0, -3, 0.0) absTol 1E-5, "min mismatch")
 
-    assert(summarizer.max.almostEquals(Vectors.dense(3.0, 0.0, 6.0)), "max mismatch")
+    assert(summarizer.max ~== Vectors.dense(3.0, 0.0, 6.0) absTol 1E-5, "max mismatch")
 
-    assert(summarizer.numNonzeros.almostEquals(Vectors.dense(2, 1, 1)), "numNonzeros mismatch")
+    assert(summarizer.numNonzeros ~== Vectors.dense(2, 1, 1) absTol 1E-5, "numNonzeros mismatch")
 
-    assert(summarizer.variance.almostEquals(Vectors.dense(8.0, 4.5, 18.0)), "variance mismatch")
+    assert(summarizer.variance ~== Vectors.dense(8.0, 4.5, 18.0) absTol 1E-5, "variance mismatch")
 
     assert(summarizer.count === 2)
   }
@@ -107,15 +107,15 @@ class MultivariateOnlineSummarizerSuite extends FunSuite {
       .add(Vectors.sparse(3, Seq((0, -1.0), (2, 6.0))))
       .add(Vectors.sparse(3, Seq((0, 3.0), (1, -3.0))))
 
-    assert(summarizer.mean.almostEquals(Vectors.dense(1.0, -1.5, 3.0)), "mean mismatch")
+    assert(summarizer.mean ~== Vectors.dense(1.0, -1.5, 3.0) absTol 1E-5, "mean mismatch")
 
-    assert(summarizer.min.almostEquals(Vectors.dense(-1.0, -3, 0.0)), "min mismatch")
+    assert(summarizer.min ~== Vectors.dense(-1.0, -3, 0.0) absTol 1E-5, "min mismatch")
 
-    assert(summarizer.max.almostEquals(Vectors.dense(3.0, 0.0, 6.0)), "max mismatch")
+    assert(summarizer.max ~== Vectors.dense(3.0, 0.0, 6.0) absTol 1E-5, "max mismatch")
 
-    assert(summarizer.numNonzeros.almostEquals(Vectors.dense(2, 1, 1)), "numNonzeros mismatch")
+    assert(summarizer.numNonzeros ~== Vectors.dense(2, 1, 1) absTol 1E-5, "numNonzeros mismatch")
 
-    assert(summarizer.variance.almostEquals(Vectors.dense(8.0, 4.5, 18.0)), "variance mismatch")
+    assert(summarizer.variance ~== Vectors.dense(8.0, 4.5, 18.0) absTol 1E-5, "variance mismatch")
 
     assert(summarizer.count === 2)
   }
@@ -129,17 +129,17 @@ class MultivariateOnlineSummarizerSuite extends FunSuite {
       .add(Vectors.dense(1.7, -0.6, 0.0))
       .add(Vectors.sparse(3, Seq((1, 1.9), (2, 0.0))))
 
-    assert(summarizer.mean.almostEquals(
-      Vectors.dense(0.583333333333, -0.416666666666, -0.183333333333)), "mean mismatch")
+    assert(summarizer.mean ~==
+      Vectors.dense(0.583333333333, -0.416666666666, -0.183333333333) absTol 1E-5, "mean mismatch")
 
-    assert(summarizer.min.almostEquals(Vectors.dense(-2.0, -5.1, -3)), "min mismatch")
+    assert(summarizer.min ~== Vectors.dense(-2.0, -5.1, -3) absTol 1E-5, "min mismatch")
 
-    assert(summarizer.max.almostEquals(Vectors.dense(3.8, 2.3, 1.9)), "max mismatch")
+    assert(summarizer.max ~== Vectors.dense(3.8, 2.3, 1.9) absTol 1E-5, "max mismatch")
 
-    assert(summarizer.numNonzeros.almostEquals(Vectors.dense(3, 5, 2)), "numNonzeros mismatch")
+    assert(summarizer.numNonzeros ~== Vectors.dense(3, 5, 2) absTol 1E-5, "numNonzeros mismatch")
 
-    assert(summarizer.variance.almostEquals(
-      Vectors.dense(3.857666666666, 7.0456666666666, 2.48166666666666)), "variance mismatch")
+    assert(summarizer.variance ~==
+      Vectors.dense(3.857666666666, 7.0456666666666, 2.48166666666666) absTol 1E-5, "variance mismatch")
 
     assert(summarizer.count === 6)
   }
@@ -157,17 +157,17 @@ class MultivariateOnlineSummarizerSuite extends FunSuite {
 
     val summarizer = summarizer1.merge(summarizer2)
 
-    assert(summarizer.mean.almostEquals(
-      Vectors.dense(0.583333333333, -0.416666666666, -0.183333333333)), "mean mismatch")
+    assert(summarizer.mean ~==
+      Vectors.dense(0.583333333333, -0.416666666666, -0.183333333333) absTol 1E-5, "mean mismatch")
 
-    assert(summarizer.min.almostEquals(Vectors.dense(-2.0, -5.1, -3)), "min mismatch")
+    assert(summarizer.min ~== Vectors.dense(-2.0, -5.1, -3) absTol 1E-5, "min mismatch")
 
-    assert(summarizer.max.almostEquals(Vectors.dense(3.8, 2.3, 1.9)), "max mismatch")
+    assert(summarizer.max ~== Vectors.dense(3.8, 2.3, 1.9) absTol 1E-5, "max mismatch")
 
-    assert(summarizer.numNonzeros.almostEquals(Vectors.dense(3, 5, 2)), "numNonzeros mismatch")
+    assert(summarizer.numNonzeros ~== Vectors.dense(3, 5, 2) absTol 1E-5, "numNonzeros mismatch")
 
-    assert(summarizer.variance.almostEquals(
-      Vectors.dense(3.857666666666, 7.0456666666666, 2.48166666666666)), "variance mismatch")
+    assert(summarizer.variance ~==
+      Vectors.dense(3.857666666666, 7.0456666666666, 2.48166666666666) absTol 1E-5, "variance mismatch")
 
     assert(summarizer.count === 6)
   }
@@ -186,24 +186,24 @@ class MultivariateOnlineSummarizerSuite extends FunSuite {
     val summarizer3 = (new MultivariateOnlineSummarizer).merge(new MultivariateOnlineSummarizer)
     assert(summarizer3.count === 0)
 
-    assert(summarizer1.mean.almostEquals(Vectors.dense(0.0, -1.0, -3.0)), "mean mismatch")
+    assert(summarizer1.mean ~== Vectors.dense(0.0, -1.0, -3.0) absTol 1E-5, "mean mismatch")
 
-    assert(summarizer2.mean.almostEquals(Vectors.dense(0.0, -1.0, -3.0)), "mean mismatch")
+    assert(summarizer2.mean ~== Vectors.dense(0.0, -1.0, -3.0) absTol 1E-5, "mean mismatch")
 
-    assert(summarizer1.min.almostEquals(Vectors.dense(0.0, -1.0, -3.0)), "min mismatch")
+    assert(summarizer1.min ~== Vectors.dense(0.0, -1.0, -3.0) absTol 1E-5, "min mismatch")
 
-    assert(summarizer2.min.almostEquals(Vectors.dense(0.0, -1.0, -3.0)), "min mismatch")
+    assert(summarizer2.min ~== Vectors.dense(0.0, -1.0, -3.0) absTol 1E-5, "min mismatch")
 
-    assert(summarizer1.max.almostEquals(Vectors.dense(0.0, -1.0, -3.0)), "max mismatch")
+    assert(summarizer1.max ~== Vectors.dense(0.0, -1.0, -3.0) absTol 1E-5, "max mismatch")
 
-    assert(summarizer2.max.almostEquals(Vectors.dense(0.0, -1.0, -3.0)), "max mismatch")
+    assert(summarizer2.max ~== Vectors.dense(0.0, -1.0, -3.0) absTol 1E-5, "max mismatch")
 
-    assert(summarizer1.numNonzeros.almostEquals(Vectors.dense(0, 1, 1)), "numNonzeros mismatch")
+    assert(summarizer1.numNonzeros ~== Vectors.dense(0, 1, 1) absTol 1E-5, "numNonzeros mismatch")
 
-    assert(summarizer2.numNonzeros.almostEquals(Vectors.dense(0, 1, 1)), "numNonzeros mismatch")
+    assert(summarizer2.numNonzeros ~== Vectors.dense(0, 1, 1) absTol 1E-5, "numNonzeros mismatch")
 
-    assert(summarizer1.variance.almostEquals(Vectors.dense(0, 0, 0)), "variance mismatch")
+    assert(summarizer1.variance ~== Vectors.dense(0, 0, 0) absTol 1E-5, "variance mismatch")
 
-    assert(summarizer2.variance.almostEquals(Vectors.dense(0, 0, 0)), "variance mismatch")
+    assert(summarizer2.variance ~== Vectors.dense(0, 0, 0) absTol 1E-5, "variance mismatch")
   }
 }
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/util/TestingUtils.scala b/mllib/src/test/scala/org/apache/spark/mllib/util/TestingUtils.scala
index 64b1ba752718..29cc42d8cbea 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/util/TestingUtils.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/util/TestingUtils.scala
@@ -18,28 +18,155 @@
 package org.apache.spark.mllib.util
 
 import org.apache.spark.mllib.linalg.Vector
+import org.scalatest.exceptions.TestFailedException
 
 object TestingUtils {
 
+  val ABS_TOL_MSG = " using absolute tolerance"
+  val REL_TOL_MSG = " using relative tolerance"
+
+  /**
+   * Private helper function for comparing two values using relative tolerance.
+   * Note that if x or y is extremely close to zero, i.e., smaller than Double.MinPositiveValue,
+   * the relative tolerance is meaningless, so the exception will be raised to warn users.
+   */
+  private def RelativeErrorComparison(x: Double, y: Double, eps: Double): Boolean = {
+    val absX = math.abs(x)
+    val absY = math.abs(y)
+    val diff = math.abs(x - y)
+    if (x == y) {
+      true
+    } else if (absX < Double.MinPositiveValue || absY < Double.MinPositiveValue) {
+      throw new TestFailedException(
+        s"$x or $y is extremely close to zero, so the relative tolerance is meaningless.", 0)
+    } else {
+      diff < eps * math.min(absX, absY)
+    }
+  }
+
+  /**
+   * Private helper function for comparing two values using absolute tolerance.
+   */
+  private def AbsoluteErrorComparison(x: Double, y: Double, eps: Double): Boolean = {
+    math.abs(x - y) < eps
+  }
+
+  case class CompareDoubleRightSide(
+    fun: (Double, Double, Double) => Boolean, y: Double, eps: Double, method: String)
+
+  /**
+   * Implicit class for comparing two double values using relative tolerance or absolute tolerance.
+   */
   implicit class DoubleWithAlmostEquals(val x: Double) {
-    // An improved version of AlmostEquals would always divide by the larger number.
-    // This will avoid the problem of diving by zero.
-    def almostEquals(y: Double, epsilon: Double = 1E-10): Boolean = {
-      if(x == y) {
-        true
-      } else if(math.abs(x) > math.abs(y)) {
-        math.abs(x - y) / math.abs(x) < epsilon
-      } else {
-        math.abs(x - y) / math.abs(y) < epsilon
+
+    /**
+     * When the difference of two values are within eps, returns true; otherwise, returns false.
+     */
+    def ~=(r: CompareDoubleRightSide): Boolean = r.fun(x, r.y, r.eps)
+
+    /**
+     * When the difference of two values are within eps, returns false; otherwise, returns true.
+     */
+    def !~=(r: CompareDoubleRightSide): Boolean = !r.fun(x, r.y, r.eps)
+
+    /**
+     * Throws exception when the difference of two values are NOT within eps;
+     * otherwise, returns true.
+     */
+    def ~==(r: CompareDoubleRightSide): Boolean = {
+      if (!r.fun(x, r.y, r.eps)) {
+        throw new TestFailedException(
+          s"Expected $x and ${r.y} to be within ${r.eps}${r.method}.", 0)
       }
+      true
     }
+
+    /**
+     * Throws exception when the difference of two values are within eps; otherwise, returns true.
+     */
+    def !~==(r: CompareDoubleRightSide): Boolean = {
+      if (r.fun(x, r.y, r.eps)) {
+        throw new TestFailedException(
+          s"Did not expect $x and ${r.y} to be within ${r.eps}${r.method}.", 0)
+      }
+      true
+    }
+
+    /**
+     * Comparison using absolute tolerance.
+     */
+    def absTol(eps: Double): CompareDoubleRightSide = CompareDoubleRightSide(AbsoluteErrorComparison,
+      x, eps, ABS_TOL_MSG)
+
+    /**
+     * Comparison using relative tolerance.
+     */
+    def relTol(eps: Double): CompareDoubleRightSide = CompareDoubleRightSide(RelativeErrorComparison,
+      x, eps, REL_TOL_MSG)
+
+    override def toString = x.toString
   }
 
+  case class CompareVectorRightSide(
+    fun: (Vector, Vector, Double) => Boolean, y: Vector, eps: Double, method: String)
+
+  /**
+   * Implicit class for comparing two vectors using relative tolerance or absolute tolerance.
+   */
   implicit class VectorWithAlmostEquals(val x: Vector) {
-    def almostEquals(y: Vector, epsilon: Double = 1E-10): Boolean = {
-      x.toArray.corresponds(y.toArray) {
-        _.almostEquals(_, epsilon)
+
+    /**
+     * When the difference of two vectors are within eps, returns true; otherwise, returns false.
+     */
+    def ~=(r: CompareVectorRightSide): Boolean = r.fun(x, r.y, r.eps)
+
+    /**
+     * When the difference of two vectors are within eps, returns false; otherwise, returns true.
+     */
+    def !~=(r: CompareVectorRightSide): Boolean = !r.fun(x, r.y, r.eps)
+
+    /**
+     * Throws exception when the difference of two vectors are NOT within eps;
+     * otherwise, returns true.
+     */
+    def ~==(r: CompareVectorRightSide): Boolean = {
+      if (!r.fun(x, r.y, r.eps)) {
+        throw new TestFailedException(
+          s"Expected $x and ${r.y} to be within ${r.eps}${r.method} for all elements.", 0)
       }
+      true
     }
+
+    /**
+     * Throws exception when the difference of two vectors are within eps; otherwise, returns true.
+     */
+    def !~==(r: CompareVectorRightSide): Boolean = {
+      if (r.fun(x, r.y, r.eps)) {
+        throw new TestFailedException(
+          s"Did not expect $x and ${r.y} to be within ${r.eps}${r.method} for all elements.", 0)
+      }
+      true
+    }
+
+    /**
+     * Comparison using absolute tolerance.
+     */
+    def absTol(eps: Double): CompareVectorRightSide = CompareVectorRightSide(
+      (x: Vector, y: Vector, eps: Double) => {
+        x.toArray.zip(y.toArray).forall(x => x._1 ~= x._2 absTol eps)
+      }, x, eps, ABS_TOL_MSG)
+
+    /**
+     * Comparison using relative tolerance. Note that comparing against sparse vector
+     * with elements having value of zero will raise exception because it involves with
+     * comparing against zero.
+     */
+    def relTol(eps: Double): CompareVectorRightSide = CompareVectorRightSide(
+      (x: Vector, y: Vector, eps: Double) => {
+        x.toArray.zip(y.toArray).forall(x => x._1 ~= x._2 relTol eps)
+      }, x, eps, REL_TOL_MSG)
+
+    override def toString = x.toString
   }
+
 }
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/util/TestingUtilsSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/util/TestingUtilsSuite.scala
new file mode 100644
index 000000000000..b0ecb33c2848
--- /dev/null
+++ b/mllib/src/test/scala/org/apache/spark/mllib/util/TestingUtilsSuite.scala
@@ -0,0 +1,182 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.mllib.util
+
+import org.apache.spark.mllib.linalg.Vectors
+import org.scalatest.FunSuite
+import org.apache.spark.mllib.util.TestingUtils._
+import org.scalatest.exceptions.TestFailedException
+
+class TestingUtilsSuite extends FunSuite {
+
+  test("Comparing doubles using relative error.") {
+
+    assert(23.1 ~== 23.52 relTol 0.02)
+    assert(23.1 ~== 22.74 relTol 0.02)
+    assert(23.1 ~= 23.52 relTol 0.02)
+    assert(23.1 ~= 22.74 relTol 0.02)
+    assert(!(23.1 !~= 23.52 relTol 0.02))
+    assert(!(23.1 !~= 22.74 relTol 0.02))
+
+    // Should throw exception with message when test fails.
+    intercept[TestFailedException](23.1 !~== 23.52 relTol 0.02)
+    intercept[TestFailedException](23.1 !~== 22.74 relTol 0.02)
+    intercept[TestFailedException](23.1 ~== 23.63 relTol 0.02)
+    intercept[TestFailedException](23.1 ~== 22.34 relTol 0.02)
+
+    assert(23.1 !~== 23.63 relTol 0.02)
+    assert(23.1 !~== 22.34 relTol 0.02)
+    assert(23.1 !~= 23.63 relTol 0.02)
+    assert(23.1 !~= 22.34 relTol 0.02)
+    assert(!(23.1 ~= 23.63 relTol 0.02))
+    assert(!(23.1 ~= 22.34 relTol 0.02))
+
+    // Comparing against zero should fail the test and throw exception with message
+    // saying that the relative error is meaningless in this situation.
+    intercept[TestFailedException](0.1 ~== 0.0 relTol 0.032)
+    intercept[TestFailedException](0.1 ~= 0.0 relTol 0.032)
+    intercept[TestFailedException](0.1 !~== 0.0 relTol 0.032)
+    intercept[TestFailedException](0.1 !~= 0.0 relTol 0.032)
+    intercept[TestFailedException](0.0 ~== 0.1 relTol 0.032)
+    intercept[TestFailedException](0.0 ~= 0.1 relTol 0.032)
+    intercept[TestFailedException](0.0 !~== 0.1 relTol 0.032)
+    intercept[TestFailedException](0.0 !~= 0.1 relTol 0.032)
+
+    // Comparisons of numbers very close to zero.
+    assert(10 * Double.MinPositiveValue ~== 9.5 * Double.MinPositiveValue relTol 0.01)
+    assert(10 * Double.MinPositiveValue !~== 11 * Double.MinPositiveValue relTol 0.01)
+
+    assert(-Double.MinPositiveValue ~== 1.18 * -Double.MinPositiveValue relTol 0.012)
+    assert(-Double.MinPositiveValue ~== 1.38 * -Double.MinPositiveValue relTol 0.012)
+  }
+
+  test("Comparing doubles using absolute error.") {
+
+    assert(17.8 ~== 17.99 absTol 0.2)
+    assert(17.8 ~== 17.61 absTol 0.2)
+    assert(17.8 ~= 17.99 absTol 0.2)
+    assert(17.8 ~= 17.61 absTol 0.2)
+    assert(!(17.8 !~= 17.99 absTol 0.2))
+    assert(!(17.8 !~= 17.61 absTol 0.2))
+
+    // Should throw exception with message when test fails.
+    intercept[TestFailedException](17.8 !~== 17.99 absTol 0.2)
+    intercept[TestFailedException](17.8 !~== 17.61 absTol 0.2)
+    intercept[TestFailedException](17.8 ~== 18.01 absTol 0.2)
+    intercept[TestFailedException](17.8 ~== 17.59 absTol 0.2)
+
+    assert(17.8 !~== 18.01 absTol 0.2)
+    assert(17.8 !~== 17.59 absTol 0.2)
+    assert(17.8 !~= 18.01 absTol 0.2)
+    assert(17.8 !~= 17.59 absTol 0.2)
+    assert(!(17.8 ~= 18.01 absTol 0.2))
+    assert(!(17.8 ~= 17.59 absTol 0.2))
+
+    // Comparisons of numbers very close to zero, and both side of zeros
+    assert(Double.MinPositiveValue ~== 4 * Double.MinPositiveValue absTol 5 * Double.MinPositiveValue)
+    assert(Double.MinPositiveValue !~== 6 * Double.MinPositiveValue absTol 5 * Double.MinPositiveValue)
+
+    assert(-Double.MinPositiveValue ~== 3 * Double.MinPositiveValue absTol 5 * Double.MinPositiveValue)
+    assert(Double.MinPositiveValue !~== -4 * Double.MinPositiveValue absTol 5 * Double.MinPositiveValue)
+  }
+
+  test("Comparing vectors using relative error.") {
+
+    //Comparisons of two dense vectors
+    assert(Vectors.dense(Array(3.1, 3.5)) ~== Vectors.dense(Array(3.130, 3.534)) relTol 0.01)
+    assert(Vectors.dense(Array(3.1, 3.5)) !~== Vectors.dense(Array(3.135, 3.534)) relTol 0.01)
+    assert(Vectors.dense(Array(3.1, 3.5)) ~= Vectors.dense(Array(3.130, 3.534)) relTol 0.01)
+    assert(Vectors.dense(Array(3.1, 3.5)) !~= Vectors.dense(Array(3.135, 3.534)) relTol 0.01)
+    assert(!(Vectors.dense(Array(3.1, 3.5)) !~= Vectors.dense(Array(3.130, 3.534)) relTol 0.01))
+    assert(!(Vectors.dense(Array(3.1, 3.5)) ~= Vectors.dense(Array(3.135, 3.534)) relTol 0.01))
+
+    // Should throw exception with message when test fails.
+    intercept[TestFailedException](
+      Vectors.dense(Array(3.1, 3.5)) !~== Vectors.dense(Array(3.130, 3.534)) relTol 0.01)
+
+    intercept[TestFailedException](
+      Vectors.dense(Array(3.1, 3.5)) ~== Vectors.dense(Array(3.135, 3.534)) relTol 0.01)
+
+    // Comparing against zero should fail the test and throw exception with message
+    // saying that the relative error is meaningless in this situation.
+    intercept[TestFailedException](
+      Vectors.dense(Array(3.1, 0.01)) ~== Vectors.dense(Array(3.13, 0.0)) relTol 0.01)
+
+    intercept[TestFailedException](
+      Vectors.dense(Array(3.1, 0.01)) ~== Vectors.sparse(2, Array(0), Array(3.13)) relTol 0.01)
+
+    // Comparisons of two sparse vectors
+    assert(Vectors.dense(Array(3.1, 3.5)) ~==
+      Vectors.sparse(2, Array(0, 1), Array(3.130, 3.534)) relTol 0.01)
+
+    assert(Vectors.dense(Array(3.1, 3.5)) !~==
+      Vectors.sparse(2, Array(0, 1), Array(3.135, 3.534)) relTol 0.01)
+  }
+
+  test("Comparing vectors using absolute error.") {
+
+    //Comparisons of two dense vectors
+    assert(Vectors.dense(Array(3.1, 3.5, 0.0)) ~==
+      Vectors.dense(Array(3.1 + 1E-8, 3.5 + 2E-7, 1E-8)) absTol 1E-6)
+
+    assert(Vectors.dense(Array(3.1, 3.5, 0.0)) !~==
+      Vectors.dense(Array(3.1 + 1E-5, 3.5 + 2E-7, 1 + 1E-3)) absTol 1E-6)
+
+    assert(Vectors.dense(Array(3.1, 3.5, 0.0)) ~=
+      Vectors.dense(Array(3.1 + 1E-8, 3.5 + 2E-7, 1E-8)) absTol 1E-6)
+
+    assert(Vectors.dense(Array(3.1, 3.5, 0.0)) !~=
+      Vectors.dense(Array(3.1 + 1E-5, 3.5 + 2E-7, 1 + 1E-3)) absTol 1E-6)
+
+    assert(!(Vectors.dense(Array(3.1, 3.5, 0.0)) !~=
+      Vectors.dense(Array(3.1 + 1E-8, 3.5 + 2E-7, 1E-8)) absTol 1E-6))
+
+    assert(!(Vectors.dense(Array(3.1, 3.5, 0.0)) ~=
+      Vectors.dense(Array(3.1 + 1E-5, 3.5 + 2E-7, 1 + 1E-3)) absTol 1E-6))
+
+    // Should throw exception with message when test fails.
+    intercept[TestFailedException](Vectors.dense(Array(3.1, 3.5, 0.0)) !~==
+      Vectors.dense(Array(3.1 + 1E-8, 3.5 + 2E-7, 1E-8)) absTol 1E-6)
+
+    intercept[TestFailedException](Vectors.dense(Array(3.1, 3.5, 0.0)) ~==
+      Vectors.dense(Array(3.1 + 1E-5, 3.5 + 2E-7, 1 + 1E-3)) absTol 1E-6)
+
+    // Comparisons of two sparse vectors
+    assert(Vectors.sparse(3, Array(0, 2), Array(3.1, 2.4)) ~==
+      Vectors.sparse(3, Array(0, 2), Array(3.1 + 1E-8, 2.4 + 1E-7)) absTol 1E-6)
+
+    assert(Vectors.sparse(3, Array(0, 2), Array(3.1 + 1E-8, 2.4 + 1E-7)) ~==
+      Vectors.sparse(3, Array(0, 2), Array(3.1, 2.4)) absTol 1E-6)
+
+    assert(Vectors.sparse(3, Array(0, 2), Array(3.1, 2.4)) !~==
+      Vectors.sparse(3, Array(0, 2), Array(3.1 + 1E-3, 2.4)) absTol 1E-6)
+
+    assert(Vectors.sparse(3, Array(0, 2), Array(3.1 + 1E-3, 2.4)) !~==
+      Vectors.sparse(3, Array(0, 2), Array(3.1, 2.4)) absTol 1E-6)
+
+    // Comparisons of a dense vector and a sparse vector
+    assert(Vectors.sparse(3, Array(0, 2), Array(3.1, 2.4)) ~==
+      Vectors.dense(Array(3.1 + 1E-8, 0, 2.4 + 1E-7)) absTol 1E-6)
+
+    assert(Vectors.dense(Array(3.1 + 1E-8, 0, 2.4 + 1E-7)) ~==
+      Vectors.sparse(3, Array(0, 2), Array(3.1, 2.4)) absTol 1E-6)
+
+    assert(Vectors.sparse(3, Array(0, 2), Array(3.1, 2.4)) !~==
+      Vectors.dense(Array(3.1, 1E-3, 2.4)) absTol 1E-6)
+  }
+}

From a7a9d14479ea6421513a962ff0f45cb969368bab Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian.cs.zju@gmail.com>
Date: Mon, 28 Jul 2014 12:07:30 -0700
Subject: [PATCH 787/810] [SPARK-2410][SQL] Merging Hive Thrift/JDBC server
 (with Maven profile fix)

JIRA issue: [SPARK-2410](https://issues.apache.org/jira/browse/SPARK-2410)

Another try for #1399 & #1600. Those two PR breaks Jenkins builds because we made a separate profile `hive-thriftserver` in sub-project `assembly`, but the `hive-thriftserver` module is defined outside the `hive-thriftserver` profile. Thus every time a pull request that doesn't touch SQL code will also execute test suites defined in `hive-thriftserver`, but tests fail because related .class files are not included in the assembly jar.

In the most recent commit, module `hive-thriftserver` is moved into its own profile to fix this problem. All previous commits are squashed for clarity.

Author: Cheng Lian <lian.cs.zju@gmail.com>

Closes #1620 from liancheng/jdbc-with-maven-fix and squashes the following commits:

629988e [Cheng Lian] Moved hive-thriftserver module definition into its own profile
ec3c7a7 [Cheng Lian] Cherry picked the Hive Thrift server
---
 .gitignore                                    |   1 +
 assembly/pom.xml                              |  10 +
 bagel/pom.xml                                 |   2 +-
 bin/beeline                                   |  45 +++
 bin/compute-classpath.sh                      |   1 +
 bin/spark-shell                               |   4 +-
 bin/spark-shell.cmd                           |   2 +-
 bin/spark-sql                                 |  36 ++
 core/pom.xml                                  |   2 +-
 .../org/apache/spark/deploy/SparkSubmit.scala |  14 +-
 .../spark/deploy/SparkSubmitArguments.scala   |   5 +-
 dev/create-release/create-release.sh          |  10 +-
 dev/run-tests                                 |   2 +-
 dev/scalastyle                                |   2 +-
 docs/sql-programming-guide.md                 | 201 +++++++++-
 examples/pom.xml                              |   2 +-
 external/flume/pom.xml                        |   2 +-
 external/kafka/pom.xml                        |   2 +-
 external/mqtt/pom.xml                         |   2 +-
 external/twitter/pom.xml                      |   2 +-
 external/zeromq/pom.xml                       |   2 +-
 graphx/pom.xml                                |   2 +-
 mllib/pom.xml                                 |   2 +-
 pom.xml                                       |  16 +-
 project/SparkBuild.scala                      |  14 +-
 sbin/start-thriftserver.sh                    |  36 ++
 sql/catalyst/pom.xml                          |   2 +-
 .../sql/catalyst/plans/logical/commands.scala |   3 +-
 sql/core/pom.xml                              |   2 +-
 .../scala/org/apache/spark/sql/SQLConf.scala  |  20 +-
 .../apache/spark/sql/execution/commands.scala |  42 ++-
 .../org/apache/spark/sql/SQLConfSuite.scala   |  13 +-
 .../org/apache/spark/sql/SQLQuerySuite.scala  |  10 +-
 sql/hive-thriftserver/pom.xml                 |  82 +++++
 .../hive/thriftserver/HiveThriftServer2.scala |  97 +++++
 .../hive/thriftserver/ReflectionUtils.scala   |  58 +++
 .../hive/thriftserver/SparkSQLCLIDriver.scala | 344 ++++++++++++++++++
 .../thriftserver/SparkSQLCLIService.scala     |  74 ++++
 .../hive/thriftserver/SparkSQLDriver.scala    |  93 +++++
 .../sql/hive/thriftserver/SparkSQLEnv.scala   |  58 +++
 .../thriftserver/SparkSQLSessionManager.scala |  49 +++
 .../server/SparkSQLOperationManager.scala     | 151 ++++++++
 .../test/resources/data/files/small_kv.txt    |   5 +
 .../sql/hive/thriftserver/CliSuite.scala      |  57 +++
 .../thriftserver/HiveThriftServer2Suite.scala | 135 +++++++
 .../sql/hive/thriftserver/TestUtils.scala     | 108 ++++++
 sql/hive/pom.xml                              |   2 +-
 .../apache/spark/sql/hive/HiveContext.scala   |   2 +-
 .../sql/hive/execution/HiveQuerySuite.scala   |  50 ++-
 streaming/pom.xml                             |   2 +-
 tools/pom.xml                                 |   2 +-
 yarn/alpha/pom.xml                            |   2 +-
 yarn/pom.xml                                  |   2 +-
 yarn/stable/pom.xml                           |   2 +-
 54 files changed, 1790 insertions(+), 96 deletions(-)
 create mode 100755 bin/beeline
 create mode 100755 bin/spark-sql
 create mode 100755 sbin/start-thriftserver.sh
 create mode 100644 sql/hive-thriftserver/pom.xml
 create mode 100644 sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala
 create mode 100644 sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ReflectionUtils.scala
 create mode 100755 sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala
 create mode 100644 sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIService.scala
 create mode 100644 sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLDriver.scala
 create mode 100644 sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLEnv.scala
 create mode 100644 sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLSessionManager.scala
 create mode 100644 sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/server/SparkSQLOperationManager.scala
 create mode 100644 sql/hive-thriftserver/src/test/resources/data/files/small_kv.txt
 create mode 100644 sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/CliSuite.scala
 create mode 100644 sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suite.scala
 create mode 100644 sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/TestUtils.scala

diff --git a/.gitignore b/.gitignore
index a4ec12ca6b53..7ec8d45e12c6 100644
--- a/.gitignore
+++ b/.gitignore
@@ -58,3 +58,4 @@ metastore_db/
 metastore/
 warehouse/
 TempStatsStore/
+sql/hive-thriftserver/test_warehouses
diff --git a/assembly/pom.xml b/assembly/pom.xml
index 567a8dd2a0d9..703f15925bc4 100644
--- a/assembly/pom.xml
+++ b/assembly/pom.xml
@@ -165,6 +165,16 @@
         </dependency>
       </dependencies>
     </profile>
+    <profile>
+      <id>hive-thriftserver</id>
+      <dependencies>
+        <dependency>
+          <groupId>org.apache.spark</groupId>
+          <artifactId>spark-hive-thriftserver_${scala.binary.version}</artifactId>
+          <version>${project.version}</version>
+        </dependency>
+      </dependencies>
+    </profile>
     <profile>
       <id>spark-ganglia-lgpl</id>
       <dependencies>
diff --git a/bagel/pom.xml b/bagel/pom.xml
index 90c4b095bb61..bd51b112e26f 100644
--- a/bagel/pom.xml
+++ b/bagel/pom.xml
@@ -28,7 +28,7 @@
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-bagel_2.10</artifactId>
   <properties>
-     <sbt.project.name>bagel</sbt.project.name>
+    <sbt.project.name>bagel</sbt.project.name>
   </properties>
   <packaging>jar</packaging>
   <name>Spark Project Bagel</name>
diff --git a/bin/beeline b/bin/beeline
new file mode 100755
index 000000000000..09fe366c609f
--- /dev/null
+++ b/bin/beeline
@@ -0,0 +1,45 @@
+#!/usr/bin/env bash
+
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+# Figure out where Spark is installed
+FWDIR="$(cd `dirname $0`/..; pwd)"
+
+# Find the java binary
+if [ -n "${JAVA_HOME}" ]; then
+  RUNNER="${JAVA_HOME}/bin/java"
+else
+  if [ `command -v java` ]; then
+    RUNNER="java"
+  else
+    echo "JAVA_HOME is not set" >&2
+    exit 1
+  fi
+fi
+
+# Compute classpath using external script
+classpath_output=$($FWDIR/bin/compute-classpath.sh)
+if [[ "$?" != "0" ]]; then
+  echo "$classpath_output"
+  exit 1
+else
+  CLASSPATH=$classpath_output
+fi
+
+CLASS="org.apache.hive.beeline.BeeLine"
+exec "$RUNNER" -cp "$CLASSPATH" $CLASS "$@"
diff --git a/bin/compute-classpath.sh b/bin/compute-classpath.sh
index e81e8c060cb9..16b794a1592e 100755
--- a/bin/compute-classpath.sh
+++ b/bin/compute-classpath.sh
@@ -52,6 +52,7 @@ if [ -n "$SPARK_PREPEND_CLASSES" ]; then
   CLASSPATH="$CLASSPATH:$FWDIR/sql/catalyst/target/scala-$SCALA_VERSION/classes"
   CLASSPATH="$CLASSPATH:$FWDIR/sql/core/target/scala-$SCALA_VERSION/classes"
   CLASSPATH="$CLASSPATH:$FWDIR/sql/hive/target/scala-$SCALA_VERSION/classes"
+  CLASSPATH="$CLASSPATH:$FWDIR/sql/hive-thriftserver/target/scala-$SCALA_VERSION/classes"
   CLASSPATH="$CLASSPATH:$FWDIR/yarn/stable/target/scala-$SCALA_VERSION/classes"
 fi
 
diff --git a/bin/spark-shell b/bin/spark-shell
index 850e9507ec38..756c8179d12b 100755
--- a/bin/spark-shell
+++ b/bin/spark-shell
@@ -46,11 +46,11 @@ function main(){
         # (see https://github.com/sbt/sbt/issues/562).
         stty -icanon min 1 -echo > /dev/null 2>&1
         export SPARK_SUBMIT_OPTS="$SPARK_SUBMIT_OPTS -Djline.terminal=unix"
-        $FWDIR/bin/spark-submit spark-shell "$@" --class org.apache.spark.repl.Main
+        $FWDIR/bin/spark-submit --class org.apache.spark.repl.Main spark-shell "$@"
         stty icanon echo > /dev/null 2>&1
     else
         export SPARK_SUBMIT_OPTS
-        $FWDIR/bin/spark-submit spark-shell "$@" --class org.apache.spark.repl.Main
+        $FWDIR/bin/spark-submit --class org.apache.spark.repl.Main spark-shell "$@"
     fi
 }
 
diff --git a/bin/spark-shell.cmd b/bin/spark-shell.cmd
index 4b9708a8c03f..b56d69801171 100755
--- a/bin/spark-shell.cmd
+++ b/bin/spark-shell.cmd
@@ -19,4 +19,4 @@ rem
 
 set SPARK_HOME=%~dp0..
 
-cmd /V /E /C %SPARK_HOME%\bin\spark-submit.cmd spark-shell %* --class org.apache.spark.repl.Main
+cmd /V /E /C %SPARK_HOME%\bin\spark-submit.cmd spark-shell --class org.apache.spark.repl.Main %*
diff --git a/bin/spark-sql b/bin/spark-sql
new file mode 100755
index 000000000000..bba7f897b19b
--- /dev/null
+++ b/bin/spark-sql
@@ -0,0 +1,36 @@
+#!/usr/bin/env bash
+
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+#
+# Shell script for starting the Spark SQL CLI
+
+# Enter posix mode for bash
+set -o posix
+
+# Figure out where Spark is installed
+FWDIR="$(cd `dirname $0`/..; pwd)"
+
+if [[ "$@" = *--help ]] || [[ "$@" = *-h ]]; then
+  echo "Usage: ./sbin/spark-sql [options]"
+  $FWDIR/bin/spark-submit --help 2>&1 | grep -v Usage 1>&2
+  exit 0
+fi
+
+CLASS="org.apache.spark.sql.hive.thriftserver.SparkSQLCLIDriver"
+exec "$FWDIR"/bin/spark-submit --class $CLASS spark-internal $@
diff --git a/core/pom.xml b/core/pom.xml
index 1054cec4d77b..a24743495b0e 100644
--- a/core/pom.xml
+++ b/core/pom.xml
@@ -28,7 +28,7 @@
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-core_2.10</artifactId>
   <properties>
-     <sbt.project.name>core</sbt.project.name>
+    <sbt.project.name>core</sbt.project.name>
   </properties>
   <packaging>jar</packaging>
   <name>Spark Project Core</name>
diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
index 3b5642b6caa3..c9cec33ebaa6 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
@@ -46,6 +46,10 @@ object SparkSubmit {
   private val CLUSTER = 2
   private val ALL_DEPLOY_MODES = CLIENT | CLUSTER
 
+  // A special jar name that indicates the class being run is inside of Spark itself, and therefore
+  // no user jar is needed.
+  private val SPARK_INTERNAL = "spark-internal"
+
   // Special primary resource names that represent shells rather than application jars.
   private val SPARK_SHELL = "spark-shell"
   private val PYSPARK_SHELL = "pyspark-shell"
@@ -257,7 +261,9 @@ object SparkSubmit {
     // In yarn-cluster mode, use yarn.Client as a wrapper around the user class
     if (clusterManager == YARN && deployMode == CLUSTER) {
       childMainClass = "org.apache.spark.deploy.yarn.Client"
-      childArgs += ("--jar", args.primaryResource)
+      if (args.primaryResource != SPARK_INTERNAL) {
+        childArgs += ("--jar", args.primaryResource)
+      }
       childArgs += ("--class", args.mainClass)
       if (args.childArgs != null) {
         args.childArgs.foreach { arg => childArgs += ("--arg", arg) }
@@ -332,7 +338,7 @@ object SparkSubmit {
    * Return whether the given primary resource represents a user jar.
    */
   private def isUserJar(primaryResource: String): Boolean = {
-    !isShell(primaryResource) && !isPython(primaryResource)
+    !isShell(primaryResource) && !isPython(primaryResource) && !isInternal(primaryResource)
   }
 
   /**
@@ -349,6 +355,10 @@ object SparkSubmit {
     primaryResource.endsWith(".py") || primaryResource == PYSPARK_SHELL
   }
 
+  private[spark] def isInternal(primaryResource: String): Boolean = {
+    primaryResource == SPARK_INTERNAL
+  }
+
   /**
    * Merge a sequence of comma-separated file lists, some of which may be null to indicate
    * no files, into a single comma-separated string.
diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
index 3ab67a43a3b5..01d0ae541a66 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
@@ -204,8 +204,9 @@ private[spark] class SparkSubmitArguments(args: Seq[String]) {
 
   /** Fill in values by parsing user options. */
   private def parseOpts(opts: Seq[String]): Unit = {
-    // Delineates parsing of Spark options from parsing of user options.
     var inSparkOpts = true
+
+    // Delineates parsing of Spark options from parsing of user options.
     parse(opts)
 
     def parse(opts: Seq[String]): Unit = opts match {
@@ -318,7 +319,7 @@ private[spark] class SparkSubmitArguments(args: Seq[String]) {
               SparkSubmit.printErrorAndExit(errMessage)
             case v =>
               primaryResource =
-                if (!SparkSubmit.isShell(v)) {
+                if (!SparkSubmit.isShell(v) && !SparkSubmit.isInternal(v)) {
                   Utils.resolveURI(v).toString
                 } else {
                   v
diff --git a/dev/create-release/create-release.sh b/dev/create-release/create-release.sh
index 38830103d1e8..33de24d1ae6d 100755
--- a/dev/create-release/create-release.sh
+++ b/dev/create-release/create-release.sh
@@ -53,7 +53,7 @@ if [[ ! "$@" =~ --package-only ]]; then
     -Dusername=$GIT_USERNAME -Dpassword=$GIT_PASSWORD \
     -Dmaven.javadoc.skip=true \
     -Dhadoop.version=2.2.0 -Dyarn.version=2.2.0 \
-    -Pyarn -Phive -Phadoop-2.2 -Pspark-ganglia-lgpl\
+    -Pyarn -Phive -Phive-thriftserver -Phadoop-2.2 -Pspark-ganglia-lgpl\
     -Dtag=$GIT_TAG -DautoVersionSubmodules=true \
     --batch-mode release:prepare
 
@@ -61,7 +61,7 @@ if [[ ! "$@" =~ --package-only ]]; then
     -Darguments="-DskipTests=true -Dmaven.javadoc.skip=true -Dhadoop.version=2.2.0 -Dyarn.version=2.2.0 -Dgpg.passphrase=${GPG_PASSPHRASE}" \
     -Dhadoop.version=2.2.0 -Dyarn.version=2.2.0 \
     -Dmaven.javadoc.skip=true \
-    -Pyarn -Phive -Phadoop-2.2 -Pspark-ganglia-lgpl\
+    -Pyarn -Phive -Phive-thriftserver -Phadoop-2.2 -Pspark-ganglia-lgpl\
     release:perform
 
   cd ..
@@ -111,10 +111,10 @@ make_binary_release() {
     spark-$RELEASE_VERSION-bin-$NAME.tgz.sha
 }
 
-make_binary_release "hadoop1" "-Phive -Dhadoop.version=1.0.4"
-make_binary_release "cdh4" "-Phive -Dhadoop.version=2.0.0-mr1-cdh4.2.0"
+make_binary_release "hadoop1" "-Phive -Phive-thriftserver -Dhadoop.version=1.0.4"
+make_binary_release "cdh4" "-Phive -Phive-thriftserver -Dhadoop.version=2.0.0-mr1-cdh4.2.0"
 make_binary_release "hadoop2" \
-  "-Phive -Pyarn -Phadoop-2.2 -Dhadoop.version=2.2.0 -Pyarn.version=2.2.0"
+  "-Phive -Phive-thriftserver -Pyarn -Phadoop-2.2 -Dhadoop.version=2.2.0 -Pyarn.version=2.2.0"
 
 # Copy data
 echo "Copying release tarballs"
diff --git a/dev/run-tests b/dev/run-tests
index 51e4def0f835..98ec969dc1b3 100755
--- a/dev/run-tests
+++ b/dev/run-tests
@@ -65,7 +65,7 @@ echo "========================================================================="
 # (either resolution or compilation) prompts the user for input either q, r, 
 # etc to quit or retry. This echo is there to make it not block.
 if [ -n "$_RUN_SQL_TESTS" ]; then
-  echo -e "q\n" | SBT_MAVEN_PROFILES="$SBT_MAVEN_PROFILES -Phive" sbt/sbt clean package \
+  echo -e "q\n" | SBT_MAVEN_PROFILES="$SBT_MAVEN_PROFILES -Phive -Phive-thriftserver" sbt/sbt clean package \
     assembly/assembly test | grep -v -e "info.*Resolving" -e "warn.*Merging" -e "info.*Including"
 else
   echo -e "q\n" | sbt/sbt clean package assembly/assembly test | \
diff --git a/dev/scalastyle b/dev/scalastyle
index a02d06912f23..d9f2b91a3a09 100755
--- a/dev/scalastyle
+++ b/dev/scalastyle
@@ -17,7 +17,7 @@
 # limitations under the License.
 #
 
-echo -e "q\n" | sbt/sbt -Phive scalastyle > scalastyle.txt
+echo -e "q\n" | sbt/sbt -Phive -Phive-thriftserver scalastyle > scalastyle.txt
 # Check style with YARN alpha built too
 echo -e "q\n" | sbt/sbt -Pyarn -Phadoop-0.23 -Dhadoop.version=0.23.9 yarn-alpha/scalastyle \
   >> scalastyle.txt
diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md
index 38728534a46e..156e0aebdebe 100644
--- a/docs/sql-programming-guide.md
+++ b/docs/sql-programming-guide.md
@@ -136,7 +136,7 @@ val sqlContext = new org.apache.spark.sql.SQLContext(sc)
 import sqlContext.createSchemaRDD
 
 // Define the schema using a case class.
-// Note: Case classes in Scala 2.10 can support only up to 22 fields. To work around this limit, 
+// Note: Case classes in Scala 2.10 can support only up to 22 fields. To work around this limit,
 // you can use custom classes that implement the Product interface.
 case class Person(name: String, age: Int)
 
@@ -548,7 +548,6 @@ results = hiveContext.hql("FROM src SELECT key, value").collect()
 </div>
 </div>
 
-
 # Writing Language-Integrated Relational Queries
 
 **Language-Integrated queries are currently only supported in Scala.**
@@ -573,4 +572,200 @@ prefixed with a tick (`'`).  Implicit conversions turn these symbols into expres
 evaluated by the SQL execution engine.  A full list of the functions supported can be found in the
 [ScalaDoc](api/scala/index.html#org.apache.spark.sql.SchemaRDD).
 
-<!-- TODO: Include the table of operations here. -->
\ No newline at end of file
+<!-- TODO: Include the table of operations here. -->
+
+## Running the Thrift JDBC server
+
+The Thrift JDBC server implemented here corresponds to the [`HiveServer2`]
+(https://cwiki.apache.org/confluence/display/Hive/Setting+Up+HiveServer2) in Hive 0.12. You can test
+the JDBC server with the beeline script comes with either Spark or Hive 0.12.  In order to use Hive
+you must first run '`sbt/sbt -Phive-thriftserver assembly/assembly`' (or use `-Phive-thriftserver`
+for maven).
+
+To start the JDBC server, run the following in the Spark directory:
+
+    ./sbin/start-thriftserver.sh
+
+The default port the server listens on is 10000.  To listen on customized host and port, please set
+the `HIVE_SERVER2_THRIFT_PORT` and `HIVE_SERVER2_THRIFT_BIND_HOST` environment variables. You may
+run `./sbin/start-thriftserver.sh --help` for a complete list of all available options.  Now you can
+use beeline to test the Thrift JDBC server:
+
+    ./bin/beeline
+
+Connect to the JDBC server in beeline with:
+
+    beeline> !connect jdbc:hive2://localhost:10000
+
+Beeline will ask you for a username and password. In non-secure mode, simply enter the username on
+your machine and a blank password. For secure mode, please follow the instructions given in the
+[beeline documentation](https://cwiki.apache.org/confluence/display/Hive/HiveServer2+Clients)
+
+Configuration of Hive is done by placing your `hive-site.xml` file in `conf/`.
+
+You may also use the beeline script comes with Hive.
+
+### Migration Guide for Shark Users
+
+#### Reducer number
+
+In Shark, default reducer number is 1 and is controlled by the property `mapred.reduce.tasks`. Spark
+SQL deprecates this property by a new property `spark.sql.shuffle.partitions`, whose default value
+is 200. Users may customize this property via `SET`:
+
+```
+SET spark.sql.shuffle.partitions=10;
+SELECT page, count(*) c FROM logs_last_month_cached
+GROUP BY page ORDER BY c DESC LIMIT 10;
+```
+
+You may also put this property in `hive-site.xml` to override the default value.
+
+For now, the `mapred.reduce.tasks` property is still recognized, and is converted to
+`spark.sql.shuffle.partitions` automatically.
+
+#### Caching
+
+The `shark.cache` table property no longer exists, and tables whose name end with `_cached` are no
+longer automcatically cached. Instead, we provide `CACHE TABLE` and `UNCACHE TABLE` statements to
+let user control table caching explicitly:
+
+```
+CACHE TABLE logs_last_month;
+UNCACHE TABLE logs_last_month;
+```
+
+**NOTE** `CACHE TABLE tbl` is lazy, it only marks table `tbl` as "need to by cached if necessary",
+but doesn't actually cache it until a query that touches `tbl` is executed. To force the table to be
+cached, you may simply count the table immediately after executing `CACHE TABLE`:
+
+```
+CACHE TABLE logs_last_month;
+SELECT COUNT(1) FROM logs_last_month;
+```
+
+Several caching related features are not supported yet:
+
+* User defined partition level cache eviction policy
+* RDD reloading
+* In-memory cache write through policy
+
+### Compatibility with Apache Hive
+
+#### Deploying in Exising Hive Warehouses
+
+Spark SQL Thrift JDBC server is designed to be "out of the box" compatible with existing Hive
+installations. You do not need to modify your existing Hive Metastore or change the data placement
+or partitioning of your tables.
+
+#### Supported Hive Features
+
+Spark SQL supports the vast majority of Hive features, such as:
+
+* Hive query statements, including:
+ * `SELECT`
+ * `GROUP BY
+ * `ORDER BY`
+ * `CLUSTER BY`
+ * `SORT BY`
+* All Hive operators, including:
+ * Relational operators (`=`, `⇔`, `==`, `<>`, `<`, `>`, `>=`, `<=`, etc)
+ * Arthimatic operators (`+`, `-`, `*`, `/`, `%`, etc)
+ * Logical operators (`AND`, `&&`, `OR`, `||`, etc)
+ * Complex type constructors
+ * Mathemtatical functions (`sign`, `ln`, `cos`, etc)
+ * String functions (`instr`, `length`, `printf`, etc)
+* User defined functions (UDF)
+* User defined aggregation functions (UDAF)
+* User defined serialization formats (SerDe's)
+* Joins
+ * `JOIN`
+ * `{LEFT|RIGHT|FULL} OUTER JOIN`
+ * `LEFT SEMI JOIN`
+ * `CROSS JOIN`
+* Unions
+* Sub queries
+ * `SELECT col FROM ( SELECT a + b AS col from t1) t2`
+* Sampling
+* Explain
+* Partitioned tables
+* All Hive DDL Functions, including:
+ * `CREATE TABLE`
+ * `CREATE TABLE AS SELECT`
+ * `ALTER TABLE`
+* Most Hive Data types, including:
+ * `TINYINT`
+ * `SMALLINT`
+ * `INT`
+ * `BIGINT`
+ * `BOOLEAN`
+ * `FLOAT`
+ * `DOUBLE`
+ * `STRING`
+ * `BINARY`
+ * `TIMESTAMP`
+ * `ARRAY<>`
+ * `MAP<>`
+ * `STRUCT<>`
+
+#### Unsupported Hive Functionality
+
+Below is a list of Hive features that we don't support yet. Most of these features are rarely used
+in Hive deployments.
+
+**Major Hive Features**
+
+* Tables with buckets: bucket is the hash partitioning within a Hive table partition. Spark SQL
+  doesn't support buckets yet.
+
+**Esoteric Hive Features**
+
+* Tables with partitions using different input formats: In Spark SQL, all table partitions need to
+  have the same input format.
+* Non-equi outer join: For the uncommon use case of using outer joins with non-equi join conditions
+  (e.g. condition "`key < 10`"), Spark SQL will output wrong result for the `NULL` tuple.
+* `UNIONTYPE`
+* Unique join
+* Single query multi insert
+* Column statistics collecting: Spark SQL does not piggyback scans to collect column statistics at
+  the moment.
+
+**Hive Input/Output Formats**
+
+* File format for CLI: For results showing back to the CLI, Spark SQL only supports TextOutputFormat.
+* Hadoop archive
+
+**Hive Optimizations**
+
+A handful of Hive optimizations are not yet included in Spark. Some of these (such as indexes) are
+not necessary due to Spark SQL's in-memory computational model. Others are slotted for future
+releases of Spark SQL.
+
+* Block level bitmap indexes and virtual columns (used to build indexes)
+* Automatically convert a join to map join: For joining a large table with multiple small tables,
+  Hive automatically converts the join into a map join. We are adding this auto conversion in the
+  next release.
+* Automatically determine the number of reducers for joins and groupbys: Currently in Spark SQL, you
+  need to control the degree of parallelism post-shuffle using "SET
+  spark.sql.shuffle.partitions=[num_tasks];". We are going to add auto-setting of parallelism in the
+  next release.
+* Meta-data only query: For queries that can be answered by using only meta data, Spark SQL still
+  launches tasks to compute the result.
+* Skew data flag: Spark SQL does not follow the skew data flags in Hive.
+* `STREAMTABLE` hint in join: Spark SQL does not follow the `STREAMTABLE` hint.
+* Merge multiple small files for query results: if the result output contains multiple small files,
+  Hive can optionally merge the small files into fewer large files to avoid overflowing the HDFS
+  metadata. Spark SQL does not support that.
+
+## Running the Spark SQL CLI
+
+The Spark SQL CLI is a convenient tool to run the Hive metastore service in local mode and execute
+queries input from command line. Note: the Spark SQL CLI cannot talk to the Thrift JDBC server.
+
+To start the Spark SQL CLI, run the following in the Spark directory:
+
+    ./bin/spark-sql
+
+Configuration of Hive is done by placing your `hive-site.xml` file in `conf/`.
+You may run `./bin/spark-sql --help` for a complete list of all available
+options.
diff --git a/examples/pom.xml b/examples/pom.xml
index bd1c387c2eb9..c4ed0f5a6a02 100644
--- a/examples/pom.xml
+++ b/examples/pom.xml
@@ -28,7 +28,7 @@
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-examples_2.10</artifactId>
   <properties>
-     <sbt.project.name>examples</sbt.project.name>
+    <sbt.project.name>examples</sbt.project.name>
   </properties>
   <packaging>jar</packaging>
   <name>Spark Project Examples</name>
diff --git a/external/flume/pom.xml b/external/flume/pom.xml
index 61a6aff543ae..874b8a7959bb 100644
--- a/external/flume/pom.xml
+++ b/external/flume/pom.xml
@@ -28,7 +28,7 @@
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-streaming-flume_2.10</artifactId>
   <properties>
-     <sbt.project.name>streaming-flume</sbt.project.name>
+    <sbt.project.name>streaming-flume</sbt.project.name>
   </properties>
   <packaging>jar</packaging>
   <name>Spark Project External Flume</name>
diff --git a/external/kafka/pom.xml b/external/kafka/pom.xml
index 4762c50685a9..25a5c0a4d7d7 100644
--- a/external/kafka/pom.xml
+++ b/external/kafka/pom.xml
@@ -28,7 +28,7 @@
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-streaming-kafka_2.10</artifactId>
   <properties>
-     <sbt.project.name>streaming-kafka</sbt.project.name>
+    <sbt.project.name>streaming-kafka</sbt.project.name>
   </properties>
   <packaging>jar</packaging>
   <name>Spark Project External Kafka</name>
diff --git a/external/mqtt/pom.xml b/external/mqtt/pom.xml
index 32c530e600ce..f31ed655f677 100644
--- a/external/mqtt/pom.xml
+++ b/external/mqtt/pom.xml
@@ -28,7 +28,7 @@
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-streaming-mqtt_2.10</artifactId>
   <properties>
-     <sbt.project.name>streaming-mqtt</sbt.project.name>
+    <sbt.project.name>streaming-mqtt</sbt.project.name>
   </properties>
   <packaging>jar</packaging>
   <name>Spark Project External MQTT</name>
diff --git a/external/twitter/pom.xml b/external/twitter/pom.xml
index 637adb0f00da..56bb24c2a072 100644
--- a/external/twitter/pom.xml
+++ b/external/twitter/pom.xml
@@ -28,7 +28,7 @@
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-streaming-twitter_2.10</artifactId>
   <properties>
-     <sbt.project.name>streaming-twitter</sbt.project.name>
+    <sbt.project.name>streaming-twitter</sbt.project.name>
   </properties>
   <packaging>jar</packaging>
   <name>Spark Project External Twitter</name>
diff --git a/external/zeromq/pom.xml b/external/zeromq/pom.xml
index e4d758a04a4c..54b0242c54e7 100644
--- a/external/zeromq/pom.xml
+++ b/external/zeromq/pom.xml
@@ -28,7 +28,7 @@
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-streaming-zeromq_2.10</artifactId>
   <properties>
-     <sbt.project.name>streaming-zeromq</sbt.project.name>
+    <sbt.project.name>streaming-zeromq</sbt.project.name>
   </properties>
   <packaging>jar</packaging>
   <name>Spark Project External ZeroMQ</name>
diff --git a/graphx/pom.xml b/graphx/pom.xml
index 7e3bcf29dcfb..6dd52fc618b1 100644
--- a/graphx/pom.xml
+++ b/graphx/pom.xml
@@ -28,7 +28,7 @@
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-graphx_2.10</artifactId>
   <properties>
-     <sbt.project.name>graphx</sbt.project.name>
+    <sbt.project.name>graphx</sbt.project.name>
   </properties>
   <packaging>jar</packaging>
   <name>Spark Project GraphX</name>
diff --git a/mllib/pom.xml b/mllib/pom.xml
index 92b07e2357db..f27cf520dc9f 100644
--- a/mllib/pom.xml
+++ b/mllib/pom.xml
@@ -28,7 +28,7 @@
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-mllib_2.10</artifactId>
   <properties>
-     <sbt.project.name>mllib</sbt.project.name>
+    <sbt.project.name>mllib</sbt.project.name>
   </properties>  
   <packaging>jar</packaging>
   <name>Spark Project ML Library</name>
diff --git a/pom.xml b/pom.xml
index d2e6b3c0ed5a..93ef3b91b5bc 100644
--- a/pom.xml
+++ b/pom.xml
@@ -252,9 +252,9 @@
         <version>3.3.2</version>
       </dependency>
       <dependency>
-	  <groupId>commons-codec</groupId>
-	    <artifactId>commons-codec</artifactId>
-	    <version>1.5</version>
+        <groupId>commons-codec</groupId>
+        <artifactId>commons-codec</artifactId>
+        <version>1.5</version>
       </dependency>
       <dependency>
         <groupId>com.google.code.findbugs</groupId>
@@ -1139,5 +1139,15 @@
       </dependencies>
     </profile>
 
+    <profile>
+      <id>hive-thriftserver</id>
+      <activation>
+        <activeByDefault>false</activeByDefault>
+      </activation>
+      <modules>
+        <module>sql/hive-thriftserver</module>
+      </modules>
+    </profile>
+
   </profiles>
 </project>
diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index 62576f84dd03..1629bc2cba8b 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -30,11 +30,11 @@ object BuildCommons {
 
   private val buildLocation = file(".").getAbsoluteFile.getParentFile
 
-  val allProjects@Seq(bagel, catalyst, core, graphx, hive, mllib, repl, spark, sql, streaming,
-  streamingFlume, streamingKafka, streamingMqtt, streamingTwitter, streamingZeromq) =
-    Seq("bagel", "catalyst", "core", "graphx", "hive", "mllib", "repl", "spark", "sql",
-      "streaming", "streaming-flume", "streaming-kafka", "streaming-mqtt", "streaming-twitter",
-      "streaming-zeromq").map(ProjectRef(buildLocation, _))
+  val allProjects@Seq(bagel, catalyst, core, graphx, hive, hiveThriftServer, mllib, repl, spark, sql,
+  streaming, streamingFlume, streamingKafka, streamingMqtt, streamingTwitter, streamingZeromq) =
+    Seq("bagel", "catalyst", "core", "graphx", "hive", "hive-thriftserver", "mllib", "repl",
+      "spark", "sql", "streaming", "streaming-flume", "streaming-kafka", "streaming-mqtt",
+      "streaming-twitter", "streaming-zeromq").map(ProjectRef(buildLocation, _))
 
   val optionallyEnabledProjects@Seq(yarn, yarnStable, yarnAlpha, java8Tests, sparkGangliaLgpl) =
     Seq("yarn", "yarn-stable", "yarn-alpha", "java8-tests", "ganglia-lgpl")
@@ -100,7 +100,7 @@ object SparkBuild extends PomBuild {
   Properties.envOrNone("SBT_MAVEN_PROPERTIES") match {
     case Some(v) =>
       v.split("(\\s+|,)").filterNot(_.isEmpty).map(_.split("=")).foreach(x => System.setProperty(x(0), x(1)))
-    case _ => 
+    case _ =>
   }
 
   override val userPropertiesMap = System.getProperties.toMap
@@ -158,7 +158,7 @@ object SparkBuild extends PomBuild {
 
   /* Enable Mima for all projects except spark, hive, catalyst, sql  and repl */
   // TODO: Add Sql to mima checks
-  allProjects.filterNot(y => Seq(spark, sql, hive, catalyst, repl).exists(x => x == y)).
+  allProjects.filterNot(x => Seq(spark, sql, hive, hiveThriftServer, catalyst, repl).contains(x)).
     foreach (x => enable(MimaBuild.mimaSettings(sparkHome, x))(x))
 
   /* Enable Assembly for all assembly projects */
diff --git a/sbin/start-thriftserver.sh b/sbin/start-thriftserver.sh
new file mode 100755
index 000000000000..8398e6f19b51
--- /dev/null
+++ b/sbin/start-thriftserver.sh
@@ -0,0 +1,36 @@
+#!/usr/bin/env bash
+
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+#
+# Shell script for starting the Spark SQL Thrift server
+
+# Enter posix mode for bash
+set -o posix
+
+# Figure out where Spark is installed
+FWDIR="$(cd `dirname $0`/..; pwd)"
+
+if [[ "$@" = *--help ]] || [[ "$@" = *-h ]]; then
+  echo "Usage: ./sbin/start-thriftserver [options]"
+  $FWDIR/bin/spark-submit --help 2>&1 | grep -v Usage 1>&2
+  exit 0
+fi
+
+CLASS="org.apache.spark.sql.hive.thriftserver.HiveThriftServer2"
+exec "$FWDIR"/bin/spark-submit --class $CLASS spark-internal $@
diff --git a/sql/catalyst/pom.xml b/sql/catalyst/pom.xml
index 6decde3fcd62..531bfddbf237 100644
--- a/sql/catalyst/pom.xml
+++ b/sql/catalyst/pom.xml
@@ -32,7 +32,7 @@
   <name>Spark Project Catalyst</name>
   <url>http://spark.apache.org/</url>
   <properties>
-     <sbt.project.name>catalyst</sbt.project.name>
+    <sbt.project.name>catalyst</sbt.project.name>
   </properties>
 
   <dependencies>
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/commands.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/commands.scala
index 1d5f033f0d27..a357c6ffb897 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/commands.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/commands.scala
@@ -43,8 +43,7 @@ case class NativeCommand(cmd: String) extends Command {
  */
 case class SetCommand(key: Option[String], value: Option[String]) extends Command {
   override def output = Seq(
-    BoundReference(0, AttributeReference("key", StringType, nullable = false)()),
-    BoundReference(1, AttributeReference("value", StringType, nullable = false)()))
+    BoundReference(1, AttributeReference("", StringType, nullable = false)()))
 }
 
 /**
diff --git a/sql/core/pom.xml b/sql/core/pom.xml
index c309c43804d9..3a038a2db617 100644
--- a/sql/core/pom.xml
+++ b/sql/core/pom.xml
@@ -32,7 +32,7 @@
   <name>Spark Project SQL</name>
   <url>http://spark.apache.org/</url>
   <properties>
-     <sbt.project.name>sql</sbt.project.name>
+    <sbt.project.name>sql</sbt.project.name>
   </properties>
 
   <dependencies>
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
index 2b787e14f3f1..41920c00b5a2 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
@@ -30,12 +30,13 @@ import scala.collection.JavaConverters._
  * SQLConf is thread-safe (internally synchronized so safe to be used in multiple threads).
  */
 trait SQLConf {
+  import SQLConf._
 
   /** ************************ Spark SQL Params/Hints ******************* */
   // TODO: refactor so that these hints accessors don't pollute the name space of SQLContext?
 
   /** Number of partitions to use for shuffle operators. */
-  private[spark] def numShufflePartitions: Int = get("spark.sql.shuffle.partitions", "200").toInt
+  private[spark] def numShufflePartitions: Int = get(SHUFFLE_PARTITIONS, "200").toInt
 
   /**
    * Upper bound on the sizes (in bytes) of the tables qualified for the auto conversion to
@@ -43,11 +44,10 @@ trait SQLConf {
    * effectively disables auto conversion.
    * Hive setting: hive.auto.convert.join.noconditionaltask.size.
    */
-  private[spark] def autoConvertJoinSize: Int =
-    get("spark.sql.auto.convert.join.size", "10000").toInt
+  private[spark] def autoConvertJoinSize: Int = get(AUTO_CONVERT_JOIN_SIZE, "10000").toInt
 
   /** A comma-separated list of table names marked to be broadcasted during joins. */
-  private[spark] def joinBroadcastTables: String = get("spark.sql.join.broadcastTables", "")
+  private[spark] def joinBroadcastTables: String = get(JOIN_BROADCAST_TABLES, "")
 
   /** ********************** SQLConf functionality methods ************ */
 
@@ -61,7 +61,7 @@ trait SQLConf {
 
   def set(key: String, value: String): Unit = {
     require(key != null, "key cannot be null")
-    require(value != null, s"value cannot be null for ${key}")
+    require(value != null, s"value cannot be null for $key")
     settings.put(key, value)
   }
 
@@ -90,3 +90,13 @@ trait SQLConf {
   }
 
 }
+
+object SQLConf {
+  val AUTO_CONVERT_JOIN_SIZE = "spark.sql.auto.convert.join.size"
+  val SHUFFLE_PARTITIONS = "spark.sql.shuffle.partitions"
+  val JOIN_BROADCAST_TABLES = "spark.sql.join.broadcastTables"
+
+  object Deprecated {
+    val MAPRED_REDUCE_TASKS = "mapred.reduce.tasks"
+  }
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala
index 98d2f89c8ae7..9293239131d5 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala
@@ -17,12 +17,13 @@
 
 package org.apache.spark.sql.execution
 
+import org.apache.spark.Logging
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.errors.TreeNodeException
 import org.apache.spark.sql.catalyst.expressions.{Attribute, GenericRow}
 import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
-import org.apache.spark.sql.{Row, SQLContext}
+import org.apache.spark.sql.{Row, SQLConf, SQLContext}
 
 trait Command {
   /**
@@ -44,28 +45,53 @@ trait Command {
 case class SetCommand(
     key: Option[String], value: Option[String], output: Seq[Attribute])(
     @transient context: SQLContext)
-  extends LeafNode with Command {
+  extends LeafNode with Command with Logging {
 
-  override protected[sql] lazy val sideEffectResult: Seq[(String, String)] = (key, value) match {
+  override protected[sql] lazy val sideEffectResult: Seq[String] = (key, value) match {
     // Set value for key k.
     case (Some(k), Some(v)) =>
-      context.set(k, v)
-      Array(k -> v)
+      if (k == SQLConf.Deprecated.MAPRED_REDUCE_TASKS) {
+        logWarning(s"Property ${SQLConf.Deprecated.MAPRED_REDUCE_TASKS} is deprecated, " +
+          s"automatically converted to ${SQLConf.SHUFFLE_PARTITIONS} instead.")
+        context.set(SQLConf.SHUFFLE_PARTITIONS, v)
+        Array(s"${SQLConf.SHUFFLE_PARTITIONS}=$v")
+      } else {
+        context.set(k, v)
+        Array(s"$k=$v")
+      }
 
     // Query the value bound to key k.
     case (Some(k), _) =>
-      Array(k -> context.getOption(k).getOrElse("<undefined>"))
+      // TODO (lian) This is just a workaround to make the Simba ODBC driver work.
+      // Should remove this once we get the ODBC driver updated.
+      if (k == "-v") {
+        val hiveJars = Seq(
+          "hive-exec-0.12.0.jar",
+          "hive-service-0.12.0.jar",
+          "hive-common-0.12.0.jar",
+          "hive-hwi-0.12.0.jar",
+          "hive-0.12.0.jar").mkString(":")
+
+        Array(
+          "system:java.class.path=" + hiveJars,
+          "system:sun.java.command=shark.SharkServer2")
+      }
+      else {
+        Array(s"$k=${context.getOption(k).getOrElse("<undefined>")}")
+      }
 
     // Query all key-value pairs that are set in the SQLConf of the context.
     case (None, None) =>
-      context.getAll
+      context.getAll.map { case (k, v) =>
+        s"$k=$v"
+      }
 
     case _ =>
       throw new IllegalArgumentException()
   }
 
   def execute(): RDD[Row] = {
-    val rows = sideEffectResult.map { case (k, v) => new GenericRow(Array[Any](k, v)) }
+    val rows = sideEffectResult.map { line => new GenericRow(Array[Any](line)) }
     context.sparkContext.parallelize(rows, 1)
   }
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLConfSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLConfSuite.scala
index 08293f7f0ca3..1a58d73d9e7f 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLConfSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLConfSuite.scala
@@ -54,10 +54,10 @@ class SQLConfSuite extends QueryTest {
     assert(get(testKey, testVal + "_") == testVal)
     assert(TestSQLContext.get(testKey, testVal + "_") == testVal)
 
-    sql("set mapred.reduce.tasks=20")
-    assert(get("mapred.reduce.tasks", "0") == "20")
-    sql("set mapred.reduce.tasks = 40")
-    assert(get("mapred.reduce.tasks", "0") == "40")
+    sql("set some.property=20")
+    assert(get("some.property", "0") == "20")
+    sql("set some.property = 40")
+    assert(get("some.property", "0") == "40")
 
     val key = "spark.sql.key"
     val vs = "val0,val_1,val2.3,my_table"
@@ -70,4 +70,9 @@ class SQLConfSuite extends QueryTest {
     clear()
   }
 
+  test("deprecated property") {
+    clear()
+    sql(s"set ${SQLConf.Deprecated.MAPRED_REDUCE_TASKS}=10")
+    assert(get(SQLConf.SHUFFLE_PARTITIONS) == "10")
+  }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index 6736189c96d4..de9e8aa4f62e 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -424,25 +424,25 @@ class SQLQuerySuite extends QueryTest {
     sql(s"SET $testKey=$testVal")
     checkAnswer(
       sql("SET"),
-      Seq(Seq(testKey, testVal))
+      Seq(Seq(s"$testKey=$testVal"))
     )
 
     sql(s"SET ${testKey + testKey}=${testVal + testVal}")
     checkAnswer(
       sql("set"),
       Seq(
-        Seq(testKey, testVal),
-        Seq(testKey + testKey, testVal + testVal))
+        Seq(s"$testKey=$testVal"),
+        Seq(s"${testKey + testKey}=${testVal + testVal}"))
     )
 
     // "set key"
     checkAnswer(
       sql(s"SET $testKey"),
-      Seq(Seq(testKey, testVal))
+      Seq(Seq(s"$testKey=$testVal"))
     )
     checkAnswer(
       sql(s"SET $nonexistentKey"),
-      Seq(Seq(nonexistentKey, "<undefined>"))
+      Seq(Seq(s"$nonexistentKey=<undefined>"))
     )
     clear()
   }
diff --git a/sql/hive-thriftserver/pom.xml b/sql/hive-thriftserver/pom.xml
new file mode 100644
index 000000000000..7fac90fdc596
--- /dev/null
+++ b/sql/hive-thriftserver/pom.xml
@@ -0,0 +1,82 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<!--
+  ~ Licensed to the Apache Software Foundation (ASF) under one or more
+  ~ contributor license agreements.  See the NOTICE file distributed with
+  ~ this work for additional information regarding copyright ownership.
+  ~ The ASF licenses this file to You under the Apache License, Version 2.0
+  ~ (the "License"); you may not use this file except in compliance with
+  ~ the License.  You may obtain a copy of the License at
+  ~
+  ~    http://www.apache.org/licenses/LICENSE-2.0
+  ~
+  ~ Unless required by applicable law or agreed to in writing, software
+  ~ distributed under the License is distributed on an "AS IS" BASIS,
+  ~ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+  ~ See the License for the specific language governing permissions and
+  ~ limitations under the License.
+  -->
+
+<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
+  <modelVersion>4.0.0</modelVersion>
+  <parent>
+    <groupId>org.apache.spark</groupId>
+    <artifactId>spark-parent</artifactId>
+    <version>1.1.0-SNAPSHOT</version>
+    <relativePath>../../pom.xml</relativePath>
+  </parent>
+
+  <groupId>org.apache.spark</groupId>
+  <artifactId>spark-hive-thriftserver_2.10</artifactId>
+  <packaging>jar</packaging>
+  <name>Spark Project Hive</name>
+  <url>http://spark.apache.org/</url>
+  <properties>
+    <sbt.project.name>hive-thriftserver</sbt.project.name>
+  </properties>
+
+  <dependencies>
+    <dependency>
+      <groupId>org.apache.spark</groupId>
+      <artifactId>spark-hive_${scala.binary.version}</artifactId>
+      <version>${project.version}</version>
+    </dependency>
+    <dependency>
+      <groupId>org.spark-project.hive</groupId>
+      <artifactId>hive-cli</artifactId>
+      <version>${hive.version}</version>
+    </dependency>
+    <dependency>
+      <groupId>org.spark-project.hive</groupId>
+      <artifactId>hive-jdbc</artifactId>
+      <version>${hive.version}</version>
+    </dependency>
+    <dependency>
+      <groupId>org.spark-project.hive</groupId>
+      <artifactId>hive-beeline</artifactId>
+      <version>${hive.version}</version>
+    </dependency>
+    <dependency>
+      <groupId>org.scalatest</groupId>
+      <artifactId>scalatest_${scala.binary.version}</artifactId>
+      <scope>test</scope>
+    </dependency>
+  </dependencies>
+  <build>
+    <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory>
+    <testOutputDirectory>target/scala-${scala.binary.version}/test-classes</testOutputDirectory>
+    <plugins>
+      <plugin>
+        <groupId>org.scalatest</groupId>
+        <artifactId>scalatest-maven-plugin</artifactId>
+      </plugin>
+      <plugin>
+        <groupId>org.apache.maven.plugins</groupId>
+        <artifactId>maven-deploy-plugin</artifactId>
+        <configuration>
+          <skip>true</skip>
+        </configuration>
+      </plugin>
+    </plugins>
+  </build>
+</project>
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala
new file mode 100644
index 000000000000..ddbc2a79fb51
--- /dev/null
+++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala
@@ -0,0 +1,97 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive.thriftserver
+
+import scala.collection.JavaConversions._
+
+import org.apache.commons.logging.LogFactory
+import org.apache.hadoop.hive.conf.HiveConf
+import org.apache.hadoop.hive.ql.session.SessionState
+import org.apache.hive.service.cli.thrift.ThriftBinaryCLIService
+import org.apache.hive.service.server.{HiveServer2, ServerOptionsProcessor}
+
+import org.apache.spark.sql.Logging
+import org.apache.spark.sql.hive.HiveContext
+import org.apache.spark.sql.hive.thriftserver.ReflectionUtils._
+
+/**
+ * The main entry point for the Spark SQL port of HiveServer2.  Starts up a `SparkSQLContext` and a
+ * `HiveThriftServer2` thrift server.
+ */
+private[hive] object HiveThriftServer2 extends Logging {
+  var LOG = LogFactory.getLog(classOf[HiveServer2])
+
+  def main(args: Array[String]) {
+    val optionsProcessor = new ServerOptionsProcessor("HiveThriftServer2")
+
+    if (!optionsProcessor.process(args)) {
+      logger.warn("Error starting HiveThriftServer2 with given arguments")
+      System.exit(-1)
+    }
+
+    val ss = new SessionState(new HiveConf(classOf[SessionState]))
+
+    // Set all properties specified via command line.
+    val hiveConf: HiveConf = ss.getConf
+    hiveConf.getAllProperties.toSeq.sortBy(_._1).foreach { case (k, v) =>
+      logger.debug(s"HiveConf var: $k=$v")
+    }
+
+    SessionState.start(ss)
+
+    logger.info("Starting SparkContext")
+    SparkSQLEnv.init()
+    SessionState.start(ss)
+
+    Runtime.getRuntime.addShutdownHook(
+      new Thread() {
+        override def run() {
+          SparkSQLEnv.sparkContext.stop()
+        }
+      }
+    )
+
+    try {
+      val server = new HiveThriftServer2(SparkSQLEnv.hiveContext)
+      server.init(hiveConf)
+      server.start()
+      logger.info("HiveThriftServer2 started")
+    } catch {
+      case e: Exception =>
+        logger.error("Error starting HiveThriftServer2", e)
+        System.exit(-1)
+    }
+  }
+}
+
+private[hive] class HiveThriftServer2(hiveContext: HiveContext)
+  extends HiveServer2
+  with ReflectedCompositeService {
+
+  override def init(hiveConf: HiveConf) {
+    val sparkSqlCliService = new SparkSQLCLIService(hiveContext)
+    setSuperField(this, "cliService", sparkSqlCliService)
+    addService(sparkSqlCliService)
+
+    val thriftCliService = new ThriftBinaryCLIService(sparkSqlCliService)
+    setSuperField(this, "thriftCLIService", thriftCliService)
+    addService(thriftCliService)
+
+    initCompositeService(hiveConf)
+  }
+}
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ReflectionUtils.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ReflectionUtils.scala
new file mode 100644
index 000000000000..599294dfbb7d
--- /dev/null
+++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ReflectionUtils.scala
@@ -0,0 +1,58 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive.thriftserver
+
+private[hive] object ReflectionUtils {
+  def setSuperField(obj : Object, fieldName: String, fieldValue: Object) {
+    setAncestorField(obj, 1, fieldName, fieldValue)
+  }
+
+  def setAncestorField(obj: AnyRef, level: Int, fieldName: String, fieldValue: AnyRef) {
+    val ancestor = Iterator.iterate[Class[_]](obj.getClass)(_.getSuperclass).drop(level).next()
+    val field = ancestor.getDeclaredField(fieldName)
+    field.setAccessible(true)
+    field.set(obj, fieldValue)
+  }
+
+  def getSuperField[T](obj: AnyRef, fieldName: String): T = {
+    getAncestorField[T](obj, 1, fieldName)
+  }
+
+  def getAncestorField[T](clazz: Object, level: Int, fieldName: String): T = {
+    val ancestor = Iterator.iterate[Class[_]](clazz.getClass)(_.getSuperclass).drop(level).next()
+    val field = ancestor.getDeclaredField(fieldName)
+    field.setAccessible(true)
+    field.get(clazz).asInstanceOf[T]
+  }
+
+  def invokeStatic(clazz: Class[_], methodName: String, args: (Class[_], AnyRef)*): AnyRef = {
+    invoke(clazz, null, methodName, args: _*)
+  }
+
+  def invoke(
+      clazz: Class[_],
+      obj: AnyRef,
+      methodName: String,
+      args: (Class[_], AnyRef)*): AnyRef = {
+
+    val (types, values) = args.unzip
+    val method = clazz.getDeclaredMethod(methodName, types: _*)
+    method.setAccessible(true)
+    method.invoke(obj, values.toSeq: _*)
+  }
+}
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala
new file mode 100755
index 000000000000..27268ecb923e
--- /dev/null
+++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala
@@ -0,0 +1,344 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive.thriftserver
+
+import scala.collection.JavaConversions._
+
+import java.io._
+import java.util.{ArrayList => JArrayList}
+
+import jline.{ConsoleReader, History}
+import org.apache.commons.lang.StringUtils
+import org.apache.commons.logging.LogFactory
+import org.apache.hadoop.conf.Configuration
+import org.apache.hadoop.hive.cli.{CliDriver, CliSessionState, OptionsProcessor}
+import org.apache.hadoop.hive.common.LogUtils.LogInitializationException
+import org.apache.hadoop.hive.common.{HiveInterruptCallback, HiveInterruptUtils, LogUtils}
+import org.apache.hadoop.hive.conf.HiveConf
+import org.apache.hadoop.hive.ql.Driver
+import org.apache.hadoop.hive.ql.exec.Utilities
+import org.apache.hadoop.hive.ql.processors.{CommandProcessor, CommandProcessorFactory}
+import org.apache.hadoop.hive.ql.session.SessionState
+import org.apache.hadoop.hive.shims.ShimLoader
+import org.apache.thrift.transport.TSocket
+
+import org.apache.spark.sql.Logging
+
+private[hive] object SparkSQLCLIDriver {
+  private var prompt = "spark-sql"
+  private var continuedPrompt = "".padTo(prompt.length, ' ')
+  private var transport:TSocket = _
+
+  installSignalHandler()
+
+  /**
+   * Install an interrupt callback to cancel all Spark jobs. In Hive's CliDriver#processLine(),
+   * a signal handler will invoke this registered callback if a Ctrl+C signal is detected while
+   * a command is being processed by the current thread.
+   */
+  def installSignalHandler() {
+    HiveInterruptUtils.add(new HiveInterruptCallback {
+      override def interrupt() {
+        // Handle remote execution mode
+        if (SparkSQLEnv.sparkContext != null) {
+          SparkSQLEnv.sparkContext.cancelAllJobs()
+        } else {
+          if (transport != null) {
+            // Force closing of TCP connection upon session termination
+            transport.getSocket.close()
+          }
+        }
+      }
+    })
+  }
+
+  def main(args: Array[String]) {
+    val oproc = new OptionsProcessor()
+    if (!oproc.process_stage1(args)) {
+      System.exit(1)
+    }
+
+    // NOTE: It is critical to do this here so that log4j is reinitialized
+    // before any of the other core hive classes are loaded
+    var logInitFailed = false
+    var logInitDetailMessage: String = null
+    try {
+      logInitDetailMessage = LogUtils.initHiveLog4j()
+    } catch {
+      case e: LogInitializationException =>
+        logInitFailed = true
+        logInitDetailMessage = e.getMessage
+    }
+
+    val sessionState = new CliSessionState(new HiveConf(classOf[SessionState]))
+
+    sessionState.in = System.in
+    try {
+      sessionState.out = new PrintStream(System.out, true, "UTF-8")
+      sessionState.info = new PrintStream(System.err, true, "UTF-8")
+      sessionState.err = new PrintStream(System.err, true, "UTF-8")
+    } catch {
+      case e: UnsupportedEncodingException => System.exit(3)
+    }
+
+    if (!oproc.process_stage2(sessionState)) {
+      System.exit(2)
+    }
+
+    if (!sessionState.getIsSilent) {
+      if (logInitFailed) System.err.println(logInitDetailMessage)
+      else SessionState.getConsole.printInfo(logInitDetailMessage)
+    }
+
+    // Set all properties specified via command line.
+    val conf: HiveConf = sessionState.getConf
+    sessionState.cmdProperties.entrySet().foreach { item: java.util.Map.Entry[Object, Object] =>
+      conf.set(item.getKey.asInstanceOf[String], item.getValue.asInstanceOf[String])
+      sessionState.getOverriddenConfigurations.put(
+        item.getKey.asInstanceOf[String], item.getValue.asInstanceOf[String])
+    }
+
+    SessionState.start(sessionState)
+
+    // Clean up after we exit
+    Runtime.getRuntime.addShutdownHook(
+      new Thread() {
+        override def run() {
+          SparkSQLEnv.stop()
+        }
+      }
+    )
+
+    // "-h" option has been passed, so connect to Hive thrift server.
+    if (sessionState.getHost != null) {
+      sessionState.connect()
+      if (sessionState.isRemoteMode) {
+        prompt = s"[${sessionState.getHost}:${sessionState.getPort}]" + prompt
+        continuedPrompt = "".padTo(prompt.length, ' ')
+      }
+    }
+
+    if (!sessionState.isRemoteMode && !ShimLoader.getHadoopShims.usesJobShell()) {
+      // Hadoop-20 and above - we need to augment classpath using hiveconf
+      // components.
+      // See also: code in ExecDriver.java
+      var loader = conf.getClassLoader
+      val auxJars = HiveConf.getVar(conf, HiveConf.ConfVars.HIVEAUXJARS)
+      if (StringUtils.isNotBlank(auxJars)) {
+        loader = Utilities.addToClassPath(loader, StringUtils.split(auxJars, ","))
+      }
+      conf.setClassLoader(loader)
+      Thread.currentThread().setContextClassLoader(loader)
+    }
+
+    val cli = new SparkSQLCLIDriver
+    cli.setHiveVariables(oproc.getHiveVariables)
+
+    // TODO work around for set the log output to console, because the HiveContext
+    // will set the output into an invalid buffer.
+    sessionState.in = System.in
+    try {
+      sessionState.out = new PrintStream(System.out, true, "UTF-8")
+      sessionState.info = new PrintStream(System.err, true, "UTF-8")
+      sessionState.err = new PrintStream(System.err, true, "UTF-8")
+    } catch {
+      case e: UnsupportedEncodingException => System.exit(3)
+    }
+
+    // Execute -i init files (always in silent mode)
+    cli.processInitFiles(sessionState)
+
+    if (sessionState.execString != null) {
+      System.exit(cli.processLine(sessionState.execString))
+    }
+
+    try {
+      if (sessionState.fileName != null) {
+        System.exit(cli.processFile(sessionState.fileName))
+      }
+    } catch {
+      case e: FileNotFoundException =>
+        System.err.println(s"Could not open input file for reading. (${e.getMessage})")
+        System.exit(3)
+    }
+
+    val reader = new ConsoleReader()
+    reader.setBellEnabled(false)
+    // reader.setDebug(new PrintWriter(new FileWriter("writer.debug", true)))
+    CliDriver.getCommandCompletor.foreach((e) => reader.addCompletor(e))
+
+    val historyDirectory = System.getProperty("user.home")
+
+    try {
+      if (new File(historyDirectory).exists()) {
+        val historyFile = historyDirectory + File.separator + ".hivehistory"
+        reader.setHistory(new History(new File(historyFile)))
+      } else {
+        System.err.println("WARNING: Directory for Hive history file: " + historyDirectory +
+                           " does not exist.   History will not be available during this session.")
+      }
+    } catch {
+      case e: Exception =>
+        System.err.println("WARNING: Encountered an error while trying to initialize Hive's " +
+                           "history file.  History will not be available during this session.")
+        System.err.println(e.getMessage)
+    }
+
+    val clientTransportTSocketField = classOf[CliSessionState].getDeclaredField("transport")
+    clientTransportTSocketField.setAccessible(true)
+
+    transport = clientTransportTSocketField.get(sessionState).asInstanceOf[TSocket]
+
+    var ret = 0
+    var prefix = ""
+    val currentDB = ReflectionUtils.invokeStatic(classOf[CliDriver], "getFormattedDb",
+      classOf[HiveConf] -> conf, classOf[CliSessionState] -> sessionState)
+
+    def promptWithCurrentDB = s"$prompt$currentDB"
+    def continuedPromptWithDBSpaces = continuedPrompt + ReflectionUtils.invokeStatic(
+      classOf[CliDriver], "spacesForString", classOf[String] -> currentDB)
+
+    var currentPrompt = promptWithCurrentDB
+    var line = reader.readLine(currentPrompt + "> ")
+
+    while (line != null) {
+      if (prefix.nonEmpty) {
+        prefix += '\n'
+      }
+
+      if (line.trim().endsWith(";") && !line.trim().endsWith("\\;")) {
+        line = prefix + line
+        ret = cli.processLine(line, true)
+        prefix = ""
+        currentPrompt = promptWithCurrentDB
+      } else {
+        prefix = prefix + line
+        currentPrompt = continuedPromptWithDBSpaces
+      }
+
+      line = reader.readLine(currentPrompt + "> ")
+    }
+
+    sessionState.close()
+
+    System.exit(ret)
+  }
+}
+
+private[hive] class SparkSQLCLIDriver extends CliDriver with Logging {
+  private val sessionState = SessionState.get().asInstanceOf[CliSessionState]
+
+  private val LOG = LogFactory.getLog("CliDriver")
+
+  private val console = new SessionState.LogHelper(LOG)
+
+  private val conf: Configuration =
+    if (sessionState != null) sessionState.getConf else new Configuration()
+
+  // Force initializing SparkSQLEnv. This is put here but not object SparkSQLCliDriver
+  // because the Hive unit tests do not go through the main() code path.
+  if (!sessionState.isRemoteMode) {
+    SparkSQLEnv.init()
+  }
+
+  override def processCmd(cmd: String): Int = {
+    val cmd_trimmed: String = cmd.trim()
+    val tokens: Array[String] = cmd_trimmed.split("\\s+")
+    val cmd_1: String = cmd_trimmed.substring(tokens(0).length()).trim()
+    if (cmd_trimmed.toLowerCase.equals("quit") ||
+      cmd_trimmed.toLowerCase.equals("exit") ||
+      tokens(0).equalsIgnoreCase("source") ||
+      cmd_trimmed.startsWith("!") ||
+      tokens(0).toLowerCase.equals("list") ||
+      sessionState.isRemoteMode) {
+      val start = System.currentTimeMillis()
+      super.processCmd(cmd)
+      val end = System.currentTimeMillis()
+      val timeTaken: Double = (end - start) / 1000.0
+      console.printInfo(s"Time taken: $timeTaken seconds")
+      0
+    } else {
+      var ret = 0
+      val hconf = conf.asInstanceOf[HiveConf]
+      val proc: CommandProcessor = CommandProcessorFactory.get(tokens(0), hconf)
+
+      if (proc != null) {
+        if (proc.isInstanceOf[Driver]) {
+          val driver = new SparkSQLDriver
+
+          driver.init()
+          val out = sessionState.out
+          val start:Long = System.currentTimeMillis()
+          if (sessionState.getIsVerbose) {
+            out.println(cmd)
+          }
+
+          ret = driver.run(cmd).getResponseCode
+          if (ret != 0) {
+            driver.close()
+            return ret
+          }
+
+          val res = new JArrayList[String]()
+
+          if (HiveConf.getBoolVar(conf, HiveConf.ConfVars.HIVE_CLI_PRINT_HEADER)) {
+            // Print the column names.
+            Option(driver.getSchema.getFieldSchemas).map { fields =>
+              out.println(fields.map(_.getName).mkString("\t"))
+            }
+          }
+
+          try {
+            while (!out.checkError() && driver.getResults(res)) {
+              res.foreach(out.println)
+              res.clear()
+            }
+          } catch {
+            case e:IOException =>
+              console.printError(
+                s"""Failed with exception ${e.getClass.getName}: ${e.getMessage}
+                   |${org.apache.hadoop.util.StringUtils.stringifyException(e)}
+                 """.stripMargin)
+              ret = 1
+          }
+
+          val cret = driver.close()
+          if (ret == 0) {
+            ret = cret
+          }
+
+          val end = System.currentTimeMillis()
+          if (end > start) {
+            val timeTaken:Double = (end - start) / 1000.0
+            console.printInfo(s"Time taken: $timeTaken seconds", null)
+          }
+
+          // Destroy the driver to release all the locks.
+          driver.destroy()
+        } else {
+          if (sessionState.getIsVerbose) {
+            sessionState.out.println(tokens(0) + " " + cmd_1)
+          }
+          ret = proc.run(cmd_1).getResponseCode
+        }
+      }
+      ret
+    }
+  }
+}
+
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIService.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIService.scala
new file mode 100644
index 000000000000..42cbf363b274
--- /dev/null
+++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIService.scala
@@ -0,0 +1,74 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive.thriftserver
+
+import scala.collection.JavaConversions._
+
+import java.io.IOException
+import java.util.{List => JList}
+import javax.security.auth.login.LoginException
+
+import org.apache.commons.logging.Log
+import org.apache.hadoop.hive.conf.HiveConf
+import org.apache.hadoop.hive.shims.ShimLoader
+import org.apache.hive.service.Service.STATE
+import org.apache.hive.service.auth.HiveAuthFactory
+import org.apache.hive.service.cli.CLIService
+import org.apache.hive.service.{AbstractService, Service, ServiceException}
+
+import org.apache.spark.sql.hive.HiveContext
+import org.apache.spark.sql.hive.thriftserver.ReflectionUtils._
+
+private[hive] class SparkSQLCLIService(hiveContext: HiveContext)
+  extends CLIService
+  with ReflectedCompositeService {
+
+  override def init(hiveConf: HiveConf) {
+    setSuperField(this, "hiveConf", hiveConf)
+
+    val sparkSqlSessionManager = new SparkSQLSessionManager(hiveContext)
+    setSuperField(this, "sessionManager", sparkSqlSessionManager)
+    addService(sparkSqlSessionManager)
+
+    try {
+      HiveAuthFactory.loginFromKeytab(hiveConf)
+      val serverUserName = ShimLoader.getHadoopShims
+        .getShortUserName(ShimLoader.getHadoopShims.getUGIForConf(hiveConf))
+      setSuperField(this, "serverUserName", serverUserName)
+    } catch {
+      case e @ (_: IOException | _: LoginException) =>
+        throw new ServiceException("Unable to login to kerberos with given principal/keytab", e)
+    }
+
+    initCompositeService(hiveConf)
+  }
+}
+
+private[thriftserver] trait ReflectedCompositeService { this: AbstractService =>
+  def initCompositeService(hiveConf: HiveConf) {
+    // Emulating `CompositeService.init(hiveConf)`
+    val serviceList = getAncestorField[JList[Service]](this, 2, "serviceList")
+    serviceList.foreach(_.init(hiveConf))
+
+    // Emulating `AbstractService.init(hiveConf)`
+    invoke(classOf[AbstractService], this, "ensureCurrentState", classOf[STATE] -> STATE.NOTINITED)
+    setAncestorField(this, 3, "hiveConf", hiveConf)
+    invoke(classOf[AbstractService], this, "changeState", classOf[STATE] -> STATE.INITED)
+    getAncestorField[Log](this, 3, "LOG").info(s"Service: $getName is inited.")
+  }
+}
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLDriver.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLDriver.scala
new file mode 100644
index 000000000000..5202aa9903e0
--- /dev/null
+++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLDriver.scala
@@ -0,0 +1,93 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive.thriftserver
+
+import scala.collection.JavaConversions._
+
+import java.util.{ArrayList => JArrayList}
+
+import org.apache.commons.lang.exception.ExceptionUtils
+import org.apache.hadoop.hive.metastore.api.{FieldSchema, Schema}
+import org.apache.hadoop.hive.ql.Driver
+import org.apache.hadoop.hive.ql.processors.CommandProcessorResponse
+
+import org.apache.spark.sql.Logging
+import org.apache.spark.sql.hive.{HiveContext, HiveMetastoreTypes}
+
+private[hive] class SparkSQLDriver(val context: HiveContext = SparkSQLEnv.hiveContext)
+  extends Driver with Logging {
+
+  private var tableSchema: Schema = _
+  private var hiveResponse: Seq[String] = _
+
+  override def init(): Unit = {
+  }
+
+  private def getResultSetSchema(query: context.QueryExecution): Schema = {
+    val analyzed = query.analyzed
+    logger.debug(s"Result Schema: ${analyzed.output}")
+    if (analyzed.output.size == 0) {
+      new Schema(new FieldSchema("Response code", "string", "") :: Nil, null)
+    } else {
+      val fieldSchemas = analyzed.output.map { attr =>
+        new FieldSchema(attr.name, HiveMetastoreTypes.toMetastoreType(attr.dataType), "")
+      }
+
+      new Schema(fieldSchemas, null)
+    }
+  }
+
+  override def run(command: String): CommandProcessorResponse = {
+    val execution = context.executePlan(context.hql(command).logicalPlan)
+
+    // TODO unify the error code
+    try {
+      hiveResponse = execution.stringResult()
+      tableSchema = getResultSetSchema(execution)
+      new CommandProcessorResponse(0)
+    } catch {
+      case cause: Throwable =>
+        logger.error(s"Failed in [$command]", cause)
+        new CommandProcessorResponse(-3, ExceptionUtils.getFullStackTrace(cause), null)
+    }
+  }
+
+  override def close(): Int = {
+    hiveResponse = null
+    tableSchema = null
+    0
+  }
+
+  override def getSchema: Schema = tableSchema
+
+  override def getResults(res: JArrayList[String]): Boolean = {
+    if (hiveResponse == null) {
+      false
+    } else {
+      res.addAll(hiveResponse)
+      hiveResponse = null
+      true
+    }
+  }
+
+  override def destroy() {
+    super.destroy()
+    hiveResponse = null
+    tableSchema = null
+  }
+}
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLEnv.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLEnv.scala
new file mode 100644
index 000000000000..451c3bd7b935
--- /dev/null
+++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLEnv.scala
@@ -0,0 +1,58 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive.thriftserver
+
+import org.apache.hadoop.hive.ql.session.SessionState
+
+import org.apache.spark.scheduler.{SplitInfo, StatsReportListener}
+import org.apache.spark.sql.Logging
+import org.apache.spark.sql.hive.HiveContext
+import org.apache.spark.{SparkConf, SparkContext}
+
+/** A singleton object for the master program. The slaves should not access this. */
+private[hive] object SparkSQLEnv extends Logging {
+  logger.debug("Initializing SparkSQLEnv")
+
+  var hiveContext: HiveContext = _
+  var sparkContext: SparkContext = _
+
+  def init() {
+    if (hiveContext == null) {
+      sparkContext = new SparkContext(new SparkConf()
+        .setAppName(s"SparkSQL::${java.net.InetAddress.getLocalHost.getHostName}"))
+
+      sparkContext.addSparkListener(new StatsReportListener())
+
+      hiveContext = new HiveContext(sparkContext) {
+        @transient override lazy val sessionState = SessionState.get()
+        @transient override lazy val hiveconf = sessionState.getConf
+      }
+    }
+  }
+
+  /** Cleans up and shuts down the Spark SQL environments. */
+  def stop() {
+    logger.debug("Shutting down Spark SQL Environment")
+    // Stop the SparkContext
+    if (SparkSQLEnv.sparkContext != null) {
+      sparkContext.stop()
+      sparkContext = null
+      hiveContext = null
+    }
+  }
+}
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLSessionManager.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLSessionManager.scala
new file mode 100644
index 000000000000..6b3275b4eaf0
--- /dev/null
+++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLSessionManager.scala
@@ -0,0 +1,49 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive.thriftserver
+
+import java.util.concurrent.Executors
+
+import org.apache.commons.logging.Log
+import org.apache.hadoop.hive.conf.HiveConf
+import org.apache.hadoop.hive.conf.HiveConf.ConfVars
+import org.apache.hive.service.cli.session.SessionManager
+
+import org.apache.spark.sql.hive.HiveContext
+import org.apache.spark.sql.hive.thriftserver.ReflectionUtils._
+import org.apache.spark.sql.hive.thriftserver.server.SparkSQLOperationManager
+
+private[hive] class SparkSQLSessionManager(hiveContext: HiveContext)
+  extends SessionManager
+  with ReflectedCompositeService {
+
+  override def init(hiveConf: HiveConf) {
+    setSuperField(this, "hiveConf", hiveConf)
+
+    val backgroundPoolSize = hiveConf.getIntVar(ConfVars.HIVE_SERVER2_ASYNC_EXEC_THREADS)
+    setSuperField(this, "backgroundOperationPool", Executors.newFixedThreadPool(backgroundPoolSize))
+    getAncestorField[Log](this, 3, "LOG").info(
+      s"HiveServer2: Async execution pool size $backgroundPoolSize")
+
+    val sparkSqlOperationManager = new SparkSQLOperationManager(hiveContext)
+    setSuperField(this, "operationManager", sparkSqlOperationManager)
+    addService(sparkSqlOperationManager)
+
+    initCompositeService(hiveConf)
+  }
+}
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/server/SparkSQLOperationManager.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/server/SparkSQLOperationManager.scala
new file mode 100644
index 000000000000..a4e1f3e762e8
--- /dev/null
+++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/server/SparkSQLOperationManager.scala
@@ -0,0 +1,151 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive.thriftserver.server
+
+import scala.collection.JavaConversions._
+import scala.collection.mutable.ArrayBuffer
+import scala.math.{random, round}
+
+import java.sql.Timestamp
+import java.util.{Map => JMap}
+
+import org.apache.hadoop.hive.common.`type`.HiveDecimal
+import org.apache.hadoop.hive.metastore.api.FieldSchema
+import org.apache.hive.service.cli._
+import org.apache.hive.service.cli.operation.{ExecuteStatementOperation, Operation, OperationManager}
+import org.apache.hive.service.cli.session.HiveSession
+
+import org.apache.spark.sql.catalyst.types._
+import org.apache.spark.sql.hive.thriftserver.ReflectionUtils
+import org.apache.spark.sql.hive.{HiveContext, HiveMetastoreTypes}
+import org.apache.spark.sql.{Logging, SchemaRDD, Row => SparkRow}
+
+/**
+ * Executes queries using Spark SQL, and maintains a list of handles to active queries.
+ */
+class SparkSQLOperationManager(hiveContext: HiveContext) extends OperationManager with Logging {
+  val handleToOperation = ReflectionUtils
+    .getSuperField[JMap[OperationHandle, Operation]](this, "handleToOperation")
+
+  override def newExecuteStatementOperation(
+      parentSession: HiveSession,
+      statement: String,
+      confOverlay: JMap[String, String],
+      async: Boolean): ExecuteStatementOperation = synchronized {
+
+    val operation = new ExecuteStatementOperation(parentSession, statement, confOverlay) {
+      private var result: SchemaRDD = _
+      private var iter: Iterator[SparkRow] = _
+      private var dataTypes: Array[DataType] = _
+
+      def close(): Unit = {
+        // RDDs will be cleaned automatically upon garbage collection.
+        logger.debug("CLOSING")
+      }
+
+      def getNextRowSet(order: FetchOrientation, maxRowsL: Long): RowSet = {
+        if (!iter.hasNext) {
+          new RowSet()
+        } else {
+          val maxRows = maxRowsL.toInt // Do you really want a row batch larger than Int Max? No.
+          var curRow = 0
+          var rowSet = new ArrayBuffer[Row](maxRows)
+
+          while (curRow < maxRows && iter.hasNext) {
+            val sparkRow = iter.next()
+            val row = new Row()
+            var curCol = 0
+
+            while (curCol < sparkRow.length) {
+              dataTypes(curCol) match {
+                case StringType =>
+                  row.addString(sparkRow(curCol).asInstanceOf[String])
+                case IntegerType =>
+                  row.addColumnValue(ColumnValue.intValue(sparkRow.getInt(curCol)))
+                case BooleanType =>
+                  row.addColumnValue(ColumnValue.booleanValue(sparkRow.getBoolean(curCol)))
+                case DoubleType =>
+                  row.addColumnValue(ColumnValue.doubleValue(sparkRow.getDouble(curCol)))
+                case FloatType =>
+                  row.addColumnValue(ColumnValue.floatValue(sparkRow.getFloat(curCol)))
+                case DecimalType =>
+                  val hiveDecimal = sparkRow.get(curCol).asInstanceOf[BigDecimal].bigDecimal
+                  row.addColumnValue(ColumnValue.stringValue(new HiveDecimal(hiveDecimal)))
+                case LongType =>
+                  row.addColumnValue(ColumnValue.longValue(sparkRow.getLong(curCol)))
+                case ByteType =>
+                  row.addColumnValue(ColumnValue.byteValue(sparkRow.getByte(curCol)))
+                case ShortType =>
+                  row.addColumnValue(ColumnValue.intValue(sparkRow.getShort(curCol)))
+                case TimestampType =>
+                  row.addColumnValue(
+                    ColumnValue.timestampValue(sparkRow.get(curCol).asInstanceOf[Timestamp]))
+                case BinaryType | _: ArrayType | _: StructType | _: MapType =>
+                  val hiveString = result
+                    .queryExecution
+                    .asInstanceOf[HiveContext#QueryExecution]
+                    .toHiveString((sparkRow.get(curCol), dataTypes(curCol)))
+                  row.addColumnValue(ColumnValue.stringValue(hiveString))
+              }
+              curCol += 1
+            }
+            rowSet += row
+            curRow += 1
+          }
+          new RowSet(rowSet, 0)
+        }
+      }
+
+      def getResultSetSchema: TableSchema = {
+        logger.warn(s"Result Schema: ${result.queryExecution.analyzed.output}")
+        if (result.queryExecution.analyzed.output.size == 0) {
+          new TableSchema(new FieldSchema("Result", "string", "") :: Nil)
+        } else {
+          val schema = result.queryExecution.analyzed.output.map { attr =>
+            new FieldSchema(attr.name, HiveMetastoreTypes.toMetastoreType(attr.dataType), "")
+          }
+          new TableSchema(schema)
+        }
+      }
+
+      def run(): Unit = {
+        logger.info(s"Running query '$statement'")
+        setState(OperationState.RUNNING)
+        try {
+          result = hiveContext.hql(statement)
+          logger.debug(result.queryExecution.toString())
+          val groupId = round(random * 1000000).toString
+          hiveContext.sparkContext.setJobGroup(groupId, statement)
+          iter = result.queryExecution.toRdd.toLocalIterator
+          dataTypes = result.queryExecution.analyzed.output.map(_.dataType).toArray
+          setHasResultSet(true)
+        } catch {
+          // Actually do need to catch Throwable as some failures don't inherit from Exception and
+          // HiveServer will silently swallow them.
+          case e: Throwable =>
+            logger.error("Error executing query:",e)
+            throw new HiveSQLException(e.toString)
+        }
+        setState(OperationState.FINISHED)
+      }
+    }
+
+   handleToOperation.put(operation.getHandle, operation)
+   operation
+  }
+}
diff --git a/sql/hive-thriftserver/src/test/resources/data/files/small_kv.txt b/sql/hive-thriftserver/src/test/resources/data/files/small_kv.txt
new file mode 100644
index 000000000000..850f8014b6f0
--- /dev/null
+++ b/sql/hive-thriftserver/src/test/resources/data/files/small_kv.txt
@@ -0,0 +1,5 @@
+238
val_238
+86
val_86
+311
val_311
+27
val_27
+165
val_165
diff --git a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/CliSuite.scala b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/CliSuite.scala
new file mode 100644
index 000000000000..69f19f826a80
--- /dev/null
+++ b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/CliSuite.scala
@@ -0,0 +1,57 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive.thriftserver
+
+import java.io.{BufferedReader, InputStreamReader, PrintWriter}
+
+import org.scalatest.{BeforeAndAfterAll, FunSuite}
+
+class CliSuite extends FunSuite with BeforeAndAfterAll with TestUtils {
+  val WAREHOUSE_PATH = TestUtils.getWarehousePath("cli")
+  val METASTORE_PATH = TestUtils.getMetastorePath("cli")
+
+  override def beforeAll() {
+    val pb = new ProcessBuilder(
+      "../../bin/spark-sql",
+      "--master",
+      "local",
+      "--hiveconf",
+      s"javax.jdo.option.ConnectionURL=jdbc:derby:;databaseName=$METASTORE_PATH;create=true",
+      "--hiveconf",
+      "hive.metastore.warehouse.dir=" + WAREHOUSE_PATH)
+
+    process = pb.start()
+    outputWriter = new PrintWriter(process.getOutputStream, true)
+    inputReader = new BufferedReader(new InputStreamReader(process.getInputStream))
+    errorReader = new BufferedReader(new InputStreamReader(process.getErrorStream))
+    waitForOutput(inputReader, "spark-sql>")
+  }
+
+  override def afterAll() {
+    process.destroy()
+    process.waitFor()
+  }
+
+  test("simple commands") {
+    val dataFilePath = getDataFile("data/files/small_kv.txt")
+    executeQuery("create table hive_test1(key int, val string);")
+    executeQuery("load data local inpath '" + dataFilePath+ "' overwrite into table hive_test1;")
+    executeQuery("cache table hive_test1", "Time taken")
+  }
+}
diff --git a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suite.scala b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suite.scala
new file mode 100644
index 000000000000..fe3403b3292e
--- /dev/null
+++ b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suite.scala
@@ -0,0 +1,135 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive.thriftserver
+
+import scala.collection.JavaConversions._
+import scala.concurrent.ExecutionContext.Implicits.global
+import scala.concurrent._
+
+import java.io.{BufferedReader, InputStreamReader}
+import java.net.ServerSocket
+import java.sql.{Connection, DriverManager, Statement}
+
+import org.scalatest.{BeforeAndAfterAll, FunSuite}
+
+import org.apache.spark.sql.Logging
+import org.apache.spark.sql.catalyst.util.getTempFilePath
+
+/**
+ * Test for the HiveThriftServer2 using JDBC.
+ */
+class HiveThriftServer2Suite extends FunSuite with BeforeAndAfterAll with TestUtils with Logging {
+
+  val WAREHOUSE_PATH = getTempFilePath("warehouse")
+  val METASTORE_PATH = getTempFilePath("metastore")
+
+  val DRIVER_NAME  = "org.apache.hive.jdbc.HiveDriver"
+  val TABLE = "test"
+  val HOST = "localhost"
+  val PORT =  {
+    // Let the system to choose a random available port to avoid collision with other parallel
+    // builds.
+    val socket = new ServerSocket(0)
+    val port = socket.getLocalPort
+    socket.close()
+    port
+  }
+
+  // If verbose is true, the test program will print all outputs coming from the Hive Thrift server.
+  val VERBOSE = Option(System.getenv("SPARK_SQL_TEST_VERBOSE")).getOrElse("false").toBoolean
+
+  Class.forName(DRIVER_NAME)
+
+  override def beforeAll() { launchServer() }
+
+  override def afterAll() { stopServer() }
+
+  private def launchServer(args: Seq[String] = Seq.empty) {
+    // Forking a new process to start the Hive Thrift server. The reason to do this is it is
+    // hard to clean up Hive resources entirely, so we just start a new process and kill
+    // that process for cleanup.
+    val defaultArgs = Seq(
+      "../../sbin/start-thriftserver.sh",
+      "--master local",
+      "--hiveconf",
+      "hive.root.logger=INFO,console",
+      "--hiveconf",
+      s"javax.jdo.option.ConnectionURL=jdbc:derby:;databaseName=$METASTORE_PATH;create=true",
+      "--hiveconf",
+      s"hive.metastore.warehouse.dir=$WAREHOUSE_PATH")
+    val pb = new ProcessBuilder(defaultArgs ++ args)
+    val environment = pb.environment()
+    environment.put("HIVE_SERVER2_THRIFT_PORT", PORT.toString)
+    environment.put("HIVE_SERVER2_THRIFT_BIND_HOST", HOST)
+    process = pb.start()
+    inputReader = new BufferedReader(new InputStreamReader(process.getInputStream))
+    errorReader = new BufferedReader(new InputStreamReader(process.getErrorStream))
+    waitForOutput(inputReader, "ThriftBinaryCLIService listening on")
+
+    // Spawn a thread to read the output from the forked process.
+    // Note that this is necessary since in some configurations, log4j could be blocked
+    // if its output to stderr are not read, and eventually blocking the entire test suite.
+    future {
+      while (true) {
+        val stdout = readFrom(inputReader)
+        val stderr = readFrom(errorReader)
+        if (VERBOSE && stdout.length > 0) {
+          println(stdout)
+        }
+        if (VERBOSE && stderr.length > 0) {
+          println(stderr)
+        }
+        Thread.sleep(50)
+      }
+    }
+  }
+
+  private def stopServer() {
+    process.destroy()
+    process.waitFor()
+  }
+
+  test("test query execution against a Hive Thrift server") {
+    Thread.sleep(5 * 1000)
+    val dataFilePath = getDataFile("data/files/small_kv.txt")
+    val stmt = createStatement()
+    stmt.execute("DROP TABLE IF EXISTS test")
+    stmt.execute("DROP TABLE IF EXISTS test_cached")
+    stmt.execute("CREATE TABLE test(key int, val string)")
+    stmt.execute(s"LOAD DATA LOCAL INPATH '$dataFilePath' OVERWRITE INTO TABLE test")
+    stmt.execute("CREATE TABLE test_cached as select * from test limit 4")
+    stmt.execute("CACHE TABLE test_cached")
+
+    var rs = stmt.executeQuery("select count(*) from test")
+    rs.next()
+    assert(rs.getInt(1) === 5)
+
+    rs = stmt.executeQuery("select count(*) from test_cached")
+    rs.next()
+    assert(rs.getInt(1) === 4)
+
+    stmt.close()
+  }
+
+  def getConnection: Connection = {
+    val connectURI = s"jdbc:hive2://localhost:$PORT/"
+    DriverManager.getConnection(connectURI, System.getProperty("user.name"), "")
+  }
+
+  def createStatement(): Statement = getConnection.createStatement()
+}
diff --git a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/TestUtils.scala b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/TestUtils.scala
new file mode 100644
index 000000000000..bb2242618fbe
--- /dev/null
+++ b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/TestUtils.scala
@@ -0,0 +1,108 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive.thriftserver
+
+import java.io.{BufferedReader, PrintWriter}
+import java.text.SimpleDateFormat
+import java.util.Date
+
+import org.apache.hadoop.hive.common.LogUtils
+import org.apache.hadoop.hive.common.LogUtils.LogInitializationException
+
+object TestUtils {
+  val timestamp = new SimpleDateFormat("yyyyMMdd-HHmmss")
+
+  def getWarehousePath(prefix: String): String = {
+    System.getProperty("user.dir") + "/test_warehouses/" + prefix + "-warehouse-" +
+      timestamp.format(new Date)
+  }
+
+  def getMetastorePath(prefix: String): String = {
+    System.getProperty("user.dir") + "/test_warehouses/" + prefix + "-metastore-" +
+      timestamp.format(new Date)
+  }
+
+  // Dummy function for initialize the log4j properties.
+  def init() { }
+
+  // initialize log4j
+  try {
+    LogUtils.initHiveLog4j()
+  } catch {
+    case e: LogInitializationException => // Ignore the error.
+  }
+}
+
+trait TestUtils {
+  var process : Process = null
+  var outputWriter : PrintWriter = null
+  var inputReader : BufferedReader = null
+  var errorReader : BufferedReader = null
+
+  def executeQuery(
+    cmd: String, outputMessage: String = "OK", timeout: Long = 15000): String = {
+    println("Executing: " + cmd + ", expecting output: " + outputMessage)
+    outputWriter.write(cmd + "\n")
+    outputWriter.flush()
+    waitForQuery(timeout, outputMessage)
+  }
+
+  protected def waitForQuery(timeout: Long, message: String): String = {
+    if (waitForOutput(errorReader, message, timeout)) {
+      Thread.sleep(500)
+      readOutput()
+    } else {
+      assert(false, "Didn't find \"" + message + "\" in the output:\n" + readOutput())
+      null
+    }
+  }
+
+  // Wait for the specified str to appear in the output.
+  protected def waitForOutput(
+    reader: BufferedReader, str: String, timeout: Long = 10000): Boolean = {
+    val startTime = System.currentTimeMillis
+    var out = ""
+    while (!out.contains(str) && System.currentTimeMillis < (startTime + timeout)) {
+      out += readFrom(reader)
+    }
+    out.contains(str)
+  }
+
+  // Read stdout output and filter out garbage collection messages.
+  protected def readOutput(): String = {
+    val output = readFrom(inputReader)
+    // Remove GC Messages
+    val filteredOutput = output.lines.filterNot(x => x.contains("[GC") || x.contains("[Full GC"))
+      .mkString("\n")
+    filteredOutput
+  }
+
+  protected def readFrom(reader: BufferedReader): String = {
+    var out = ""
+    var c = 0
+    while (reader.ready) {
+      c = reader.read()
+      out += c.asInstanceOf[Char]
+    }
+    out
+  }
+
+  protected def getDataFile(name: String) = {
+    Thread.currentThread().getContextClassLoader.getResource(name)
+  }
+}
diff --git a/sql/hive/pom.xml b/sql/hive/pom.xml
index 1699ffe06ce1..93d00f7c37c9 100644
--- a/sql/hive/pom.xml
+++ b/sql/hive/pom.xml
@@ -32,7 +32,7 @@
   <name>Spark Project Hive</name>
   <url>http://spark.apache.org/</url>
   <properties>
-     <sbt.project.name>hive</sbt.project.name>
+    <sbt.project.name>hive</sbt.project.name>
   </properties>
 
   <dependencies>
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
index 201c85f3d501..84d43eaeea51 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
@@ -255,7 +255,7 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) {
       Seq(StringType, IntegerType, LongType, DoubleType, FloatType, BooleanType, ByteType,
         ShortType, DecimalType, TimestampType, BinaryType)
 
-    protected def toHiveString(a: (Any, DataType)): String = a match {
+    protected[sql] def toHiveString(a: (Any, DataType)): String = a match {
       case (struct: Row, StructType(fields)) =>
         struct.zip(fields).map {
           case (v, t) => s""""${t.name}":${toHiveStructString(v, t.dataType)}"""
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
index a8623b64c656..a022a1e2dc70 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
@@ -419,10 +419,10 @@ class HiveQuerySuite extends HiveComparisonTest {
     hql(s"set $testKey=$testVal")
     assert(get(testKey, testVal + "_") == testVal)
 
-    hql("set mapred.reduce.tasks=20")
-    assert(get("mapred.reduce.tasks", "0") == "20")
-    hql("set mapred.reduce.tasks = 40")
-    assert(get("mapred.reduce.tasks", "0") == "40")
+    hql("set some.property=20")
+    assert(get("some.property", "0") == "20")
+    hql("set some.property = 40")
+    assert(get("some.property", "0") == "40")
 
     hql(s"set $testKey=$testVal")
     assert(get(testKey, "0") == testVal)
@@ -436,63 +436,61 @@ class HiveQuerySuite extends HiveComparisonTest {
     val testKey = "spark.sql.key.usedfortestonly"
     val testVal = "test.val.0"
     val nonexistentKey = "nonexistent"
-    def collectResults(rdd: SchemaRDD): Set[(String, String)] =
-      rdd.collect().map { case Row(key: String, value: String) => key -> value }.toSet
 
     clear()
 
     // "set" itself returns all config variables currently specified in SQLConf.
     assert(hql("SET").collect().size == 0)
 
-    assertResult(Set(testKey -> testVal)) {
-      collectResults(hql(s"SET $testKey=$testVal"))
+    assertResult(Array(s"$testKey=$testVal")) {
+      hql(s"SET $testKey=$testVal").collect().map(_.getString(0))
     }
 
     assert(hiveconf.get(testKey, "") == testVal)
-    assertResult(Set(testKey -> testVal)) {
-      collectResults(hql("SET"))
+    assertResult(Array(s"$testKey=$testVal")) {
+      hql(s"SET $testKey=$testVal").collect().map(_.getString(0))
     }
 
     hql(s"SET ${testKey + testKey}=${testVal + testVal}")
     assert(hiveconf.get(testKey + testKey, "") == testVal + testVal)
-    assertResult(Set(testKey -> testVal, (testKey + testKey) -> (testVal + testVal))) {
-      collectResults(hql("SET"))
+    assertResult(Array(s"$testKey=$testVal", s"${testKey + testKey}=${testVal + testVal}")) {
+      hql(s"SET").collect().map(_.getString(0))
     }
 
     // "set key"
-    assertResult(Set(testKey -> testVal)) {
-      collectResults(hql(s"SET $testKey"))
+    assertResult(Array(s"$testKey=$testVal")) {
+      hql(s"SET $testKey").collect().map(_.getString(0))
     }
 
-    assertResult(Set(nonexistentKey -> "<undefined>")) {
-      collectResults(hql(s"SET $nonexistentKey"))
+    assertResult(Array(s"$nonexistentKey=<undefined>")) {
+      hql(s"SET $nonexistentKey").collect().map(_.getString(0))
     }
 
     // Assert that sql() should have the same effects as hql() by repeating the above using sql().
     clear()
     assert(sql("SET").collect().size == 0)
 
-    assertResult(Set(testKey -> testVal)) {
-      collectResults(sql(s"SET $testKey=$testVal"))
+    assertResult(Array(s"$testKey=$testVal")) {
+      sql(s"SET $testKey=$testVal").collect().map(_.getString(0))
     }
 
     assert(hiveconf.get(testKey, "") == testVal)
-    assertResult(Set(testKey -> testVal)) {
-      collectResults(sql("SET"))
+    assertResult(Array(s"$testKey=$testVal")) {
+      sql("SET").collect().map(_.getString(0))
     }
 
     sql(s"SET ${testKey + testKey}=${testVal + testVal}")
     assert(hiveconf.get(testKey + testKey, "") == testVal + testVal)
-    assertResult(Set(testKey -> testVal, (testKey + testKey) -> (testVal + testVal))) {
-      collectResults(sql("SET"))
+    assertResult(Array(s"$testKey=$testVal", s"${testKey + testKey}=${testVal + testVal}")) {
+      sql("SET").collect().map(_.getString(0))
     }
 
-    assertResult(Set(testKey -> testVal)) {
-      collectResults(sql(s"SET $testKey"))
+    assertResult(Array(s"$testKey=$testVal")) {
+      sql(s"SET $testKey").collect().map(_.getString(0))
     }
 
-    assertResult(Set(nonexistentKey -> "<undefined>")) {
-      collectResults(sql(s"SET $nonexistentKey"))
+    assertResult(Array(s"$nonexistentKey=<undefined>")) {
+      sql(s"SET $nonexistentKey").collect().map(_.getString(0))
     }
 
     clear()
diff --git a/streaming/pom.xml b/streaming/pom.xml
index f60697ce745b..b99f306b8f2c 100644
--- a/streaming/pom.xml
+++ b/streaming/pom.xml
@@ -28,7 +28,7 @@
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-streaming_2.10</artifactId>
   <properties>
-     <sbt.project.name>streaming</sbt.project.name>
+    <sbt.project.name>streaming</sbt.project.name>
   </properties>
   <packaging>jar</packaging>
   <name>Spark Project Streaming</name>
diff --git a/tools/pom.xml b/tools/pom.xml
index c0ee8faa7a61..97abb6b2b63e 100644
--- a/tools/pom.xml
+++ b/tools/pom.xml
@@ -27,7 +27,7 @@
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-tools_2.10</artifactId>
   <properties>
-     <sbt.project.name>tools</sbt.project.name>
+    <sbt.project.name>tools</sbt.project.name>
   </properties>
   <packaging>jar</packaging>
   <name>Spark Project Tools</name>
diff --git a/yarn/alpha/pom.xml b/yarn/alpha/pom.xml
index 5b13a1f002d6..51744ece0412 100644
--- a/yarn/alpha/pom.xml
+++ b/yarn/alpha/pom.xml
@@ -24,7 +24,7 @@
     <relativePath>../pom.xml</relativePath>
   </parent>
   <properties>
-     <sbt.project.name>yarn-alpha</sbt.project.name>
+    <sbt.project.name>yarn-alpha</sbt.project.name>
   </properties>
 
   <groupId>org.apache.spark</groupId>
diff --git a/yarn/pom.xml b/yarn/pom.xml
index efb473aa1b26..3faaf053634d 100644
--- a/yarn/pom.xml
+++ b/yarn/pom.xml
@@ -29,7 +29,7 @@
   <packaging>pom</packaging>
   <name>Spark Project YARN Parent POM</name>
   <properties>
-     <sbt.project.name>yarn</sbt.project.name>
+    <sbt.project.name>yarn</sbt.project.name>
   </properties>
 
   <dependencies>
diff --git a/yarn/stable/pom.xml b/yarn/stable/pom.xml
index ceaf9f9d7100..b6c8456d0668 100644
--- a/yarn/stable/pom.xml
+++ b/yarn/stable/pom.xml
@@ -24,7 +24,7 @@
     <relativePath>../pom.xml</relativePath>
   </parent>
   <properties>
-     <sbt.project.name>yarn-stable</sbt.project.name>
+    <sbt.project.name>yarn-stable</sbt.project.name>
   </properties>
 
   <groupId>org.apache.spark</groupId>

From 39ab87b924ad65b6b9b7aa6831f3e9ddc2b76dd7 Mon Sep 17 00:00:00 2001
From: Aaron Davidson <aaron@databricks.com>
Date: Mon, 28 Jul 2014 13:37:44 -0700
Subject: [PATCH 788/810] Use commons-lang3 in SignalLogger rather than
 commons-lang

Spark only transitively depends on the latter, based on the Hadoop version.

Author: Aaron Davidson <aaron@databricks.com>

Closes #1621 from aarondav/lang3 and squashes the following commits:

93c93bf [Aaron Davidson] Use commons-lang3 in SignalLogger rather than commons-lang
---
 core/src/main/scala/org/apache/spark/util/SignalLogger.scala | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/util/SignalLogger.scala b/core/src/main/scala/org/apache/spark/util/SignalLogger.scala
index d769b54fa2fa..f77488ef3d44 100644
--- a/core/src/main/scala/org/apache/spark/util/SignalLogger.scala
+++ b/core/src/main/scala/org/apache/spark/util/SignalLogger.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.util
 
-import org.apache.commons.lang.SystemUtils
+import org.apache.commons.lang3.SystemUtils
 import org.slf4j.Logger
 import sun.misc.{Signal, SignalHandler}
 

From 16ef4d110f15dfe66852802fdadfe2ed7574ddc2 Mon Sep 17 00:00:00 2001
From: Yadong Qi <qiyadong2010@gmail.com>
Date: Mon, 28 Jul 2014 21:39:02 -0700
Subject: [PATCH 789/810] Excess judgment

Author: Yadong Qi <qiyadong2010@gmail.com>

Closes #1629 from watermen/bug-fix2 and squashes the following commits:

59b7237 [Yadong Qi] Update HiveQl.scala
---
 sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
index e6ab68b563f8..d18ccf816748 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
@@ -610,7 +610,7 @@ private[hive] object HiveQl {
         // TOK_DESTINATION means to overwrite the table.
         val resultDestination =
           (intoClause orElse destClause).getOrElse(sys.error("No destination found."))
-        val overwrite = if (intoClause.isEmpty) true else false
+        val overwrite = intoClause.isEmpty
         nodeToDest(
           resultDestination,
           withLimit,

From ccd5ab5f82812abc2eb518448832cc20fb903345 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies.liu@gmail.com>
Date: Tue, 29 Jul 2014 00:15:45 -0700
Subject: [PATCH 790/810] [SPARK-2580] [PySpark] keep silent in worker if JVM
 close the socket

During rdd.take(n), JVM will close the socket if it had got enough data, the Python worker should keep silent in this case.

In the same time, the worker should not print the trackback into stderr if it send the traceback to JVM successfully.

Author: Davies Liu <davies.liu@gmail.com>

Closes #1625 from davies/error and squashes the following commits:

4fbcc6d [Davies Liu] disable log4j during testing when exception is expected.
cc14202 [Davies Liu] keep silent in worker if JVM close the socket
---
 python/pyspark/tests.py  |  6 ++++++
 python/pyspark/worker.py | 21 +++++++++++++--------
 2 files changed, 19 insertions(+), 8 deletions(-)

diff --git a/python/pyspark/tests.py b/python/pyspark/tests.py
index 63cc5e9ad96f..6dee7dc66cee 100644
--- a/python/pyspark/tests.py
+++ b/python/pyspark/tests.py
@@ -165,11 +165,17 @@ class TestAddFile(PySparkTestCase):
     def test_add_py_file(self):
         # To ensure that we're actually testing addPyFile's effects, check that
         # this job fails due to `userlibrary` not being on the Python path:
+        # disable logging in log4j temporarily
+        log4j = self.sc._jvm.org.apache.log4j
+        old_level = log4j.LogManager.getRootLogger().getLevel()
+        log4j.LogManager.getRootLogger().setLevel(log4j.Level.FATAL)
         def func(x):
             from userlibrary import UserClass
             return UserClass().hello()
         self.assertRaises(Exception,
                           self.sc.parallelize(range(2)).map(func).first)
+        log4j.LogManager.getRootLogger().setLevel(old_level)
+
         # Add the file, so the job should now succeed:
         path = os.path.join(SPARK_HOME, "python/test_support/userlibrary.py")
         self.sc.addPyFile(path)
diff --git a/python/pyspark/worker.py b/python/pyspark/worker.py
index 24d41b12d1b1..2770f6305985 100644
--- a/python/pyspark/worker.py
+++ b/python/pyspark/worker.py
@@ -75,14 +75,19 @@ def main(infile, outfile):
         init_time = time.time()
         iterator = deserializer.load_stream(infile)
         serializer.dump_stream(func(split_index, iterator), outfile)
-    except Exception as e:
-        # Write the error to stderr in addition to trying to pass it back to
-        # Java, in case it happened while serializing a record
-        print >> sys.stderr, "PySpark worker failed with exception:"
-        print >> sys.stderr, traceback.format_exc()
-        write_int(SpecialLengths.PYTHON_EXCEPTION_THROWN, outfile)
-        write_with_length(traceback.format_exc(), outfile)
-        sys.exit(-1)
+    except Exception:
+        try:
+            write_int(SpecialLengths.PYTHON_EXCEPTION_THROWN, outfile)
+            write_with_length(traceback.format_exc(), outfile)
+            outfile.flush()
+        except IOError:
+            # JVM close the socket
+            pass
+        except Exception:
+            # Write the error to stderr if it happened while serializing
+            print >> sys.stderr, "PySpark worker failed with exception:"
+            print >> sys.stderr, traceback.format_exc()
+        exit(-1)
     finish_time = time.time()
     report_times(outfile, boot_time, init_time, finish_time)
     # Mark the beginning of the accumulators section of the output

From 92ef02626e793ea853cced4cbfee316f0b748ed7 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies.liu@gmail.com>
Date: Tue, 29 Jul 2014 01:02:18 -0700
Subject: [PATCH 791/810] [SPARK-791] [PySpark] fix pickle itemgetter with
 cloudpickle

fix the problem with pickle operator.itemgetter with multiple index.

Author: Davies Liu <davies.liu@gmail.com>

Closes #1627 from davies/itemgetter and squashes the following commits:

aabd7fa [Davies Liu] fix pickle itemgetter with cloudpickle
---
 python/pyspark/cloudpickle.py | 5 +++--
 python/pyspark/tests.py       | 6 ++++++
 2 files changed, 9 insertions(+), 2 deletions(-)

diff --git a/python/pyspark/cloudpickle.py b/python/pyspark/cloudpickle.py
index 4fda2a9b950b..68062483deda 100644
--- a/python/pyspark/cloudpickle.py
+++ b/python/pyspark/cloudpickle.py
@@ -560,8 +560,9 @@ class ItemGetterType(ctypes.Structure):
             ]
 
 
-        itemgetter_obj = ctypes.cast(ctypes.c_void_p(id(obj)), ctypes.POINTER(ItemGetterType)).contents
-        return self.save_reduce(operator.itemgetter, (itemgetter_obj.item,))
+        obj = ctypes.cast(ctypes.c_void_p(id(obj)), ctypes.POINTER(ItemGetterType)).contents
+        return self.save_reduce(operator.itemgetter,
+                obj.item if obj.nitems > 1 else (obj.item,))
 
     if PyObject_HEAD:
         dispatch[operator.itemgetter] = save_itemgetter
diff --git a/python/pyspark/tests.py b/python/pyspark/tests.py
index 6dee7dc66cee..8486c8595b5a 100644
--- a/python/pyspark/tests.py
+++ b/python/pyspark/tests.py
@@ -284,6 +284,12 @@ def combOp(x, y):
         self.assertEqual(set([2]), sets[3])
         self.assertEqual(set([1, 3]), sets[5])
 
+    def test_itemgetter(self):
+        rdd = self.sc.parallelize([range(10)])
+        from operator import itemgetter
+        self.assertEqual([1], rdd.map(itemgetter(1)).collect())
+        self.assertEqual([(2, 3)], rdd.map(itemgetter(2, 3)).collect())
+
 
 class TestIO(PySparkTestCase):
 

From 96ba04bbf917bcb971dd0d8cd1e1766dbe9366e8 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Tue, 29 Jul 2014 01:12:44 -0700
Subject: [PATCH 792/810] [SPARK-2726] and [SPARK-2727] Remove SortOrder and do
 in-place sort.

The pull request includes two changes:

1. Removes SortOrder introduced by SPARK-2125. The key ordering already includes the SortOrder information since an Ordering can be reverse. This is similar to Java's Comparator interface. Rarely does an API accept both a Comparator as well as a SortOrder.

2. Replaces the sortWith call in HashShuffleReader with an in-place quick sort.

Author: Reynold Xin <rxin@apache.org>

Closes #1631 from rxin/sortOrder and squashes the following commits:

c9d37e1 [Reynold Xin] [SPARK-2726] and [SPARK-2727] Remove SortOrder and do in-place sort.
---
 .../scala/org/apache/spark/Dependency.scala   |  4 +--
 .../spark/rdd/OrderedRDDFunctions.scala       |  8 +-----
 .../org/apache/spark/rdd/ShuffledRDD.scala    | 12 +--------
 .../shuffle/hash/HashShuffleReader.scala      | 25 +++++++++++--------
 4 files changed, 18 insertions(+), 31 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/Dependency.scala b/core/src/main/scala/org/apache/spark/Dependency.scala
index f010c03223ef..09a60571238e 100644
--- a/core/src/main/scala/org/apache/spark/Dependency.scala
+++ b/core/src/main/scala/org/apache/spark/Dependency.scala
@@ -19,7 +19,6 @@ package org.apache.spark
 
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.RDD
-import org.apache.spark.rdd.SortOrder.SortOrder
 import org.apache.spark.serializer.Serializer
 import org.apache.spark.shuffle.ShuffleHandle
 
@@ -63,8 +62,7 @@ class ShuffleDependency[K, V, C](
     val serializer: Option[Serializer] = None,
     val keyOrdering: Option[Ordering[K]] = None,
     val aggregator: Option[Aggregator[K, V, C]] = None,
-    val mapSideCombine: Boolean = false,
-    val sortOrder: Option[SortOrder] = None)
+    val mapSideCombine: Boolean = false)
   extends Dependency(rdd.asInstanceOf[RDD[Product2[K, V]]]) {
 
   val shuffleId: Int = rdd.context.newShuffleId()
diff --git a/core/src/main/scala/org/apache/spark/rdd/OrderedRDDFunctions.scala b/core/src/main/scala/org/apache/spark/rdd/OrderedRDDFunctions.scala
index afd7075f686b..d85f96278393 100644
--- a/core/src/main/scala/org/apache/spark/rdd/OrderedRDDFunctions.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/OrderedRDDFunctions.scala
@@ -58,12 +58,6 @@ class OrderedRDDFunctions[K : Ordering : ClassTag,
   def sortByKey(ascending: Boolean = true, numPartitions: Int = self.partitions.size): RDD[P] = {
     val part = new RangePartitioner(numPartitions, self, ascending)
     new ShuffledRDD[K, V, V, P](self, part)
-      .setKeyOrdering(ordering)
-      .setSortOrder(if (ascending) SortOrder.ASCENDING else SortOrder.DESCENDING)
+      .setKeyOrdering(if (ascending) ordering else ordering.reverse)
   }
 }
-
-private[spark] object SortOrder extends Enumeration {
-  type SortOrder = Value
-  val ASCENDING, DESCENDING = Value
-}
diff --git a/core/src/main/scala/org/apache/spark/rdd/ShuffledRDD.scala b/core/src/main/scala/org/apache/spark/rdd/ShuffledRDD.scala
index da4a8c3dc22b..bf02f68d0d3d 100644
--- a/core/src/main/scala/org/apache/spark/rdd/ShuffledRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/ShuffledRDD.scala
@@ -21,7 +21,6 @@ import scala.reflect.ClassTag
 
 import org.apache.spark._
 import org.apache.spark.annotation.DeveloperApi
-import org.apache.spark.rdd.SortOrder.SortOrder
 import org.apache.spark.serializer.Serializer
 
 private[spark] class ShuffledRDDPartition(val idx: Int) extends Partition {
@@ -52,8 +51,6 @@ class ShuffledRDD[K, V, C, P <: Product2[K, C] : ClassTag](
 
   private var mapSideCombine: Boolean = false
 
-  private var sortOrder: Option[SortOrder] = None
-
   /** Set a serializer for this RDD's shuffle, or null to use the default (spark.serializer) */
   def setSerializer(serializer: Serializer): ShuffledRDD[K, V, C, P] = {
     this.serializer = Option(serializer)
@@ -78,15 +75,8 @@ class ShuffledRDD[K, V, C, P <: Product2[K, C] : ClassTag](
     this
   }
 
-  /** Set sort order for RDD's sorting. */
-  def setSortOrder(sortOrder: SortOrder): ShuffledRDD[K, V, C, P] = {
-    this.sortOrder = Option(sortOrder)
-    this
-  }
-
   override def getDependencies: Seq[Dependency[_]] = {
-    List(new ShuffleDependency(prev, part, serializer,
-      keyOrdering, aggregator, mapSideCombine, sortOrder))
+    List(new ShuffleDependency(prev, part, serializer, keyOrdering, aggregator, mapSideCombine))
   }
 
   override val partitioner = Some(part)
diff --git a/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleReader.scala b/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleReader.scala
index 76cdb8f4f8e8..c8059496a1bd 100644
--- a/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleReader.scala
+++ b/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleReader.scala
@@ -18,7 +18,6 @@
 package org.apache.spark.shuffle.hash
 
 import org.apache.spark.{InterruptibleIterator, TaskContext}
-import org.apache.spark.rdd.SortOrder
 import org.apache.spark.serializer.Serializer
 import org.apache.spark.shuffle.{BaseShuffleHandle, ShuffleReader}
 
@@ -51,16 +50,22 @@ class HashShuffleReader[K, C](
       iter
     }
 
-    val sortedIter = for (sortOrder <- dep.sortOrder; ordering <- dep.keyOrdering) yield {
-      val buf = aggregatedIter.toArray
-      if (sortOrder == SortOrder.ASCENDING) {
-        buf.sortWith((x, y) => ordering.lt(x._1, y._1)).iterator
-      } else {
-        buf.sortWith((x, y) => ordering.gt(x._1, y._1)).iterator
-      }
+    // Sort the output if there is a sort ordering defined.
+    dep.keyOrdering match {
+      case Some(keyOrd: Ordering[K]) =>
+        // Define a Comparator for the whole record based on the key Ordering.
+        val cmp = new Ordering[Product2[K, C]] {
+          override def compare(o1: Product2[K, C], o2: Product2[K, C]): Int = {
+            keyOrd.compare(o1._1, o2._1)
+          }
+        }
+        val sortBuffer: Array[Product2[K, C]] = aggregatedIter.toArray
+        // TODO: do external sort.
+        scala.util.Sorting.quickSort(sortBuffer)(cmp)
+        sortBuffer.iterator
+      case None =>
+        aggregatedIter
     }
-
-    sortedIter.getOrElse(aggregatedIter)
   }
 
   /** Close this reader */

From 20424dad30e6c89ba42b07eb329070bdcb3494cb Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Tue, 29 Jul 2014 01:16:41 -0700
Subject: [PATCH 793/810] [SPARK-2174][MLLIB] treeReduce and treeAggregate

In `reduce` and `aggregate`, the driver node spends linear time on the number of partitions. It becomes a bottleneck when there are many partitions and the data from each partition is big.

SPARK-1485 (#506) tracks the progress of implementing AllReduce on Spark. I did several implementations including butterfly, reduce + broadcast, and treeReduce + broadcast. treeReduce + BT broadcast seems to be right way to go for Spark. Using binary tree may introduce some overhead in communication, because the driver still need to coordinate on data shuffling. In my experiments, n -> sqrt(n) -> 1 gives the best performance in general, which is why I set "depth = 2" in MLlib algorithms. But it certainly needs more testing.

I left `treeReduce` and `treeAggregate` public for easy testing. Some numbers from a test on 32-node m3.2xlarge cluster.

code:

~~~
import breeze.linalg._
import org.apache.log4j._

Logger.getRootLogger.setLevel(Level.OFF)

for (n <- Seq(1, 10, 100, 1000, 10000, 100000, 1000000)) {
  val vv = sc.parallelize(0 until 1024, 1024).map(i => DenseVector.zeros[Double](n))
  var start = System.nanoTime(); vv.treeReduce(_ + _, 2); println((System.nanoTime() - start) / 1e9)
  start = System.nanoTime(); vv.reduce(_ + _); println((System.nanoTime() - start) / 1e9)
}
~~~

out:

| n | treeReduce(,2) | reduce |
|---|---------------------|-----------|
| 10 | 0.215538731 | 0.204206899 |
| 100 | 0.278405907 | 0.205732582 |
| 1000 | 0.208972182 | 0.214298272 |
| 10000 | 0.194792071 | 0.349353687 |
| 100000 | 0.347683285 | 6.086671892 |
| 1000000 | 2.589350682 | 66.572906702 |

CC: @pwendell

This is clearly more scalable than the default implementation. My question is whether we should use this implementation in `reduce` and `aggregate` or put them as separate methods. The concern is that users may use `reduce` and `aggregate` as collect, where having multiple stages doesn't reduce the data size. However, in this case, `collect` is more appropriate.

Author: Xiangrui Meng <meng@databricks.com>

Closes #1110 from mengxr/tree and squashes the following commits:

c6cd267 [Xiangrui Meng] make depth default to 2
b04b96a [Xiangrui Meng] address comments
9bcc5d3 [Xiangrui Meng] add depth for readability
7495681 [Xiangrui Meng] fix compile error
142a857 [Xiangrui Meng] merge master
d58a087 [Xiangrui Meng] move treeReduce and treeAggregate to mllib
8a2a59c [Xiangrui Meng] Merge branch 'master' into tree
be6a88a [Xiangrui Meng] use treeAggregate in mllib
0f94490 [Xiangrui Meng] add docs
eb71c33 [Xiangrui Meng] add treeReduce
fe42a5e [Xiangrui Meng] add treeAggregate
---
 .../mllib/linalg/distributed/RowMatrix.scala  | 23 +++----
 .../mllib/optimization/GradientDescent.scala  |  3 +-
 .../spark/mllib/optimization/LBFGS.scala      |  3 +-
 .../apache/spark/mllib/rdd/RDDFunctions.scala | 66 +++++++++++++++++++
 .../spark/mllib/rdd/RDDFunctionsSuite.scala   | 18 +++++
 5 files changed, 98 insertions(+), 15 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
index 8c2b044ea73f..58c1322757a4 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
@@ -28,6 +28,7 @@ import org.apache.spark.annotation.Experimental
 import org.apache.spark.mllib.linalg._
 import org.apache.spark.rdd.RDD
 import org.apache.spark.Logging
+import org.apache.spark.mllib.rdd.RDDFunctions._
 import org.apache.spark.mllib.stat.{MultivariateOnlineSummarizer, MultivariateStatisticalSummary}
 
 /**
@@ -79,7 +80,7 @@ class RowMatrix(
   private[mllib] def multiplyGramianMatrixBy(v: BDV[Double]): BDV[Double] = {
     val n = numCols().toInt
     val vbr = rows.context.broadcast(v)
-    rows.aggregate(BDV.zeros[Double](n))(
+    rows.treeAggregate(BDV.zeros[Double](n))(
       seqOp = (U, r) => {
         val rBrz = r.toBreeze
         val a = rBrz.dot(vbr.value)
@@ -91,9 +92,7 @@ class RowMatrix(
             s"Do not support vector operation from type ${rBrz.getClass.getName}.")
         }
         U
-      },
-      combOp = (U1, U2) => U1 += U2
-    )
+      }, combOp = (U1, U2) => U1 += U2)
   }
 
   /**
@@ -104,13 +103,11 @@ class RowMatrix(
     val nt: Int = n * (n + 1) / 2
 
     // Compute the upper triangular part of the gram matrix.
-    val GU = rows.aggregate(new BDV[Double](new Array[Double](nt)))(
+    val GU = rows.treeAggregate(new BDV[Double](new Array[Double](nt)))(
       seqOp = (U, v) => {
         RowMatrix.dspr(1.0, v, U.data)
         U
-      },
-      combOp = (U1, U2) => U1 += U2
-    )
+      }, combOp = (U1, U2) => U1 += U2)
 
     RowMatrix.triuToFull(n, GU.data)
   }
@@ -290,9 +287,10 @@ class RowMatrix(
         s"We need at least $mem bytes of memory.")
     }
 
-    val (m, mean) = rows.aggregate[(Long, BDV[Double])]((0L, BDV.zeros[Double](n)))(
+    val (m, mean) = rows.treeAggregate[(Long, BDV[Double])]((0L, BDV.zeros[Double](n)))(
       seqOp = (s: (Long, BDV[Double]), v: Vector) => (s._1 + 1L, s._2 += v.toBreeze),
-      combOp = (s1: (Long, BDV[Double]), s2: (Long, BDV[Double])) => (s1._1 + s2._1, s1._2 += s2._2)
+      combOp = (s1: (Long, BDV[Double]), s2: (Long, BDV[Double])) =>
+        (s1._1 + s2._1, s1._2 += s2._2)
     )
 
     updateNumRows(m)
@@ -353,10 +351,9 @@ class RowMatrix(
    * Computes column-wise summary statistics.
    */
   def computeColumnSummaryStatistics(): MultivariateStatisticalSummary = {
-    val summary = rows.aggregate[MultivariateOnlineSummarizer](new MultivariateOnlineSummarizer)(
+    val summary = rows.treeAggregate(new MultivariateOnlineSummarizer)(
       (aggregator, data) => aggregator.add(data),
-      (aggregator1, aggregator2) => aggregator1.merge(aggregator2)
-    )
+      (aggregator1, aggregator2) => aggregator1.merge(aggregator2))
     updateNumRows(summary.count)
     summary
   }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/optimization/GradientDescent.scala b/mllib/src/main/scala/org/apache/spark/mllib/optimization/GradientDescent.scala
index 9fd760bf7808..356aa949afcf 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/optimization/GradientDescent.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/optimization/GradientDescent.scala
@@ -25,6 +25,7 @@ import org.apache.spark.annotation.{Experimental, DeveloperApi}
 import org.apache.spark.Logging
 import org.apache.spark.rdd.RDD
 import org.apache.spark.mllib.linalg.{Vectors, Vector}
+import org.apache.spark.mllib.rdd.RDDFunctions._
 
 /**
  * Class used to solve an optimization problem using Gradient Descent.
@@ -177,7 +178,7 @@ object GradientDescent extends Logging {
       // Sample a subset (fraction miniBatchFraction) of the total data
       // compute and sum up the subgradients on this subset (this is one map-reduce)
       val (gradientSum, lossSum) = data.sample(false, miniBatchFraction, 42 + i)
-        .aggregate((BDV.zeros[Double](n), 0.0))(
+        .treeAggregate((BDV.zeros[Double](n), 0.0))(
           seqOp = (c, v) => (c, v) match { case ((grad, loss), (label, features)) =>
             val l = gradient.compute(features, label, bcWeights.value, Vectors.fromBreeze(grad))
             (grad, loss + l)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/optimization/LBFGS.scala b/mllib/src/main/scala/org/apache/spark/mllib/optimization/LBFGS.scala
index 179cd4a3f162..26a2b62e76ed 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/optimization/LBFGS.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/optimization/LBFGS.scala
@@ -26,6 +26,7 @@ import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.Logging
 import org.apache.spark.rdd.RDD
 import org.apache.spark.mllib.linalg.{Vectors, Vector}
+import org.apache.spark.mllib.rdd.RDDFunctions._
 
 /**
  * :: DeveloperApi ::
@@ -199,7 +200,7 @@ object LBFGS extends Logging {
       val n = weights.length
       val bcWeights = data.context.broadcast(weights)
 
-      val (gradientSum, lossSum) = data.aggregate((BDV.zeros[Double](n), 0.0))(
+      val (gradientSum, lossSum) = data.treeAggregate((BDV.zeros[Double](n), 0.0))(
           seqOp = (c, v) => (c, v) match { case ((grad, loss), (label, features)) =>
             val l = localGradient.compute(
               features, label, Vectors.fromBreeze(bcWeights.value), Vectors.fromBreeze(grad))
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/rdd/RDDFunctions.scala b/mllib/src/main/scala/org/apache/spark/mllib/rdd/RDDFunctions.scala
index 365b5e75d7f7..b5e403bc8c14 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/rdd/RDDFunctions.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/rdd/RDDFunctions.scala
@@ -20,7 +20,10 @@ package org.apache.spark.mllib.rdd
 import scala.language.implicitConversions
 import scala.reflect.ClassTag
 
+import org.apache.spark.HashPartitioner
+import org.apache.spark.SparkContext._
 import org.apache.spark.rdd.RDD
+import org.apache.spark.util.Utils
 
 /**
  * Machine learning specific RDD functions.
@@ -44,6 +47,69 @@ class RDDFunctions[T: ClassTag](self: RDD[T]) {
       new SlidingRDD[T](self, windowSize)
     }
   }
+
+  /**
+   * Reduces the elements of this RDD in a multi-level tree pattern.
+   *
+   * @param depth suggested depth of the tree (default: 2)
+   * @see [[org.apache.spark.rdd.RDD#reduce]]
+   */
+  def treeReduce(f: (T, T) => T, depth: Int = 2): T = {
+    require(depth >= 1, s"Depth must be greater than or equal to 1 but got $depth.")
+    val cleanF = self.context.clean(f)
+    val reducePartition: Iterator[T] => Option[T] = iter => {
+      if (iter.hasNext) {
+        Some(iter.reduceLeft(cleanF))
+      } else {
+        None
+      }
+    }
+    val partiallyReduced = self.mapPartitions(it => Iterator(reducePartition(it)))
+    val op: (Option[T], Option[T]) => Option[T] = (c, x) => {
+      if (c.isDefined && x.isDefined) {
+        Some(cleanF(c.get, x.get))
+      } else if (c.isDefined) {
+        c
+      } else if (x.isDefined) {
+        x
+      } else {
+        None
+      }
+    }
+    RDDFunctions.fromRDD(partiallyReduced).treeAggregate(Option.empty[T])(op, op, depth)
+      .getOrElse(throw new UnsupportedOperationException("empty collection"))
+  }
+
+  /**
+   * Aggregates the elements of this RDD in a multi-level tree pattern.
+   *
+   * @param depth suggested depth of the tree (default: 2)
+   * @see [[org.apache.spark.rdd.RDD#aggregate]]
+   */
+  def treeAggregate[U: ClassTag](zeroValue: U)(
+      seqOp: (U, T) => U,
+      combOp: (U, U) => U,
+      depth: Int = 2): U = {
+    require(depth >= 1, s"Depth must be greater than or equal to 1 but got $depth.")
+    if (self.partitions.size == 0) {
+      return Utils.clone(zeroValue, self.context.env.closureSerializer.newInstance())
+    }
+    val cleanSeqOp = self.context.clean(seqOp)
+    val cleanCombOp = self.context.clean(combOp)
+    val aggregatePartition = (it: Iterator[T]) => it.aggregate(zeroValue)(cleanSeqOp, cleanCombOp)
+    var partiallyAggregated = self.mapPartitions(it => Iterator(aggregatePartition(it)))
+    var numPartitions = partiallyAggregated.partitions.size
+    val scale = math.max(math.ceil(math.pow(numPartitions, 1.0 / depth)).toInt, 2)
+    // If creating an extra level doesn't help reduce the wall-clock time, we stop tree aggregation.
+    while (numPartitions > scale + numPartitions / scale) {
+      numPartitions /= scale
+      val curNumPartitions = numPartitions
+      partiallyAggregated = partiallyAggregated.mapPartitionsWithIndex { (i, iter) =>
+        iter.map((i % curNumPartitions, _))
+      }.reduceByKey(new HashPartitioner(curNumPartitions), cleanCombOp).values
+    }
+    partiallyAggregated.reduce(cleanCombOp)
+  }
 }
 
 private[mllib]
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/rdd/RDDFunctionsSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/rdd/RDDFunctionsSuite.scala
index 3f3b10dfff35..27a19f793242 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/rdd/RDDFunctionsSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/rdd/RDDFunctionsSuite.scala
@@ -46,4 +46,22 @@ class RDDFunctionsSuite extends FunSuite with LocalSparkContext {
     val expected = data.flatMap(x => x).sliding(3).toList
     assert(sliding.collect().toList === expected)
   }
+
+  test("treeAggregate") {
+    val rdd = sc.makeRDD(-1000 until 1000, 10)
+    def seqOp = (c: Long, x: Int) => c + x
+    def combOp = (c1: Long, c2: Long) => c1 + c2
+    for (depth <- 1 until 10) {
+      val sum = rdd.treeAggregate(0L)(seqOp, combOp, depth)
+      assert(sum === -1000L)
+    }
+  }
+
+  test("treeReduce") {
+    val rdd = sc.makeRDD(-1000 until 1000, 10)
+    for (depth <- 1 until 10) {
+      val sum = rdd.treeReduce(_ + _, depth)
+      assert(sum === -1000)
+    }
+  }
 }

From fc4d05700026f4ee9cc5544cf493d900039c38f3 Mon Sep 17 00:00:00 2001
From: Aaron Staple <astaple@gmail.com>
Date: Tue, 29 Jul 2014 01:35:26 -0700
Subject: [PATCH 794/810] Minor indentation and comment typo fixes.

Author: Aaron Staple <astaple@gmail.com>

Closes #1630 from staple/minor and squashes the following commits:

6f295a2 [Aaron Staple] Fix typos in comment about ExprId.
8566467 [Aaron Staple] Fix off by one column indentation in SqlParser.
---
 .../apache/spark/sql/catalyst/SqlParser.scala | 22 +++++++++----------
 .../expressions/namedExpressions.scala        |  4 ++--
 2 files changed, 13 insertions(+), 13 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
index a34b236c8ac6..2c73a80f64eb 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
@@ -210,21 +210,21 @@ class SqlParser extends StandardTokenParsers with PackratParsers {
     } |
     "(" ~> query ~ ")" ~ opt(AS) ~ ident ^^ { case s ~ _ ~ _ ~ a => Subquery(a, s) }
 
-   protected lazy val joinedRelation: Parser[LogicalPlan] =
-     relationFactor ~ opt(joinType) ~ JOIN ~ relationFactor ~ opt(joinConditions) ^^ {
+  protected lazy val joinedRelation: Parser[LogicalPlan] =
+    relationFactor ~ opt(joinType) ~ JOIN ~ relationFactor ~ opt(joinConditions) ^^ {
       case r1 ~ jt ~ _ ~ r2 ~ cond =>
         Join(r1, r2, joinType = jt.getOrElse(Inner), cond)
-     }
+    }
 
-   protected lazy val joinConditions: Parser[Expression] =
-     ON ~> expression
+  protected lazy val joinConditions: Parser[Expression] =
+    ON ~> expression
 
-   protected lazy val joinType: Parser[JoinType] =
-     INNER ^^^ Inner |
-     LEFT ~ SEMI ^^^ LeftSemi |
-     LEFT ~ opt(OUTER) ^^^ LeftOuter |
-     RIGHT ~ opt(OUTER) ^^^ RightOuter |
-     FULL ~ opt(OUTER) ^^^ FullOuter
+  protected lazy val joinType: Parser[JoinType] =
+    INNER ^^^ Inner |
+    LEFT ~ SEMI ^^^ LeftSemi |
+    LEFT ~ opt(OUTER) ^^^ LeftOuter |
+    RIGHT ~ opt(OUTER) ^^^ RightOuter |
+    FULL ~ opt(OUTER) ^^^ FullOuter
 
   protected lazy val filter: Parser[Expression] = WHERE ~ expression ^^ { case _ ~ e => e }
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala
index 934bad8c2729..ed69928ae9eb 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala
@@ -28,8 +28,8 @@ object NamedExpression {
 }
 
 /**
- * A globally (within this JVM) id for a given named expression.
- * Used to identify with attribute output by a relation is being
+ * A globally unique (within this JVM) id for a given named expression.
+ * Used to identify which attribute output by a relation is being
  * referenced in a subsequent computation.
  */
 case class ExprId(id: Long)

From 800ecff4b1127d9042d5a8a746348fb4d45aa34b Mon Sep 17 00:00:00 2001
From: Hari Shreedharan <harishreedharan@gmail.com>
Date: Tue, 29 Jul 2014 11:11:29 -0700
Subject: [PATCH 795/810] [STREAMING] SPARK-1729. Make Flume pull data from
 source, rather than the current pu...

...sh model

Currently Spark uses Flume's internal Avro Protocol to ingest data from Flume. If the executor running the
receiver fails, it currently has to be restarted on the same node to be able to receive data.

This commit adds a new Sink which can be deployed to a Flume agent. This sink can be polled by a new
DStream that is also included in this commit. This model ensures that data can be pulled into Spark from
Flume even if the receiver is restarted on a new node. This also allows the receiver to receive data on
multiple threads for better performance.

Author: Hari Shreedharan <harishreedharan@gmail.com>
Author: Hari Shreedharan <hshreedharan@apache.org>
Author: Tathagata Das <tathagata.das1565@gmail.com>
Author: harishreedharan <hshreedharan@cloudera.com>

Closes #807 from harishreedharan/master and squashes the following commits:

e7f70a3 [Hari Shreedharan] Merge remote-tracking branch 'asf-git/master'
96cfb6f [Hari Shreedharan] Merge remote-tracking branch 'asf/master'
e48d785 [Hari Shreedharan] Documenting flume-sink being ignored for Mima checks.
5f212ce [Hari Shreedharan] Ignore Spark Sink from mima.
981bf62 [Hari Shreedharan] Merge remote-tracking branch 'asf/master'
7a1bc6e [Hari Shreedharan] Fix SparkBuild.scala
a082eb3 [Hari Shreedharan] Merge remote-tracking branch 'asf/master'
1f47364 [Hari Shreedharan] Minor fixes.
73d6f6d [Hari Shreedharan] Cleaned up tests a bit. Added some docs in multiple places.
65b76b4 [Hari Shreedharan] Fixing the unit test.
e59cc20 [Hari Shreedharan] Use SparkFlumeEvent instead of the new type. Also, Flume Polling Receiver now uses the store(ArrayBuffer) method.
f3c99d1 [Hari Shreedharan] Merge remote-tracking branch 'asf/master'
3572180 [Hari Shreedharan] Adding a license header, making Jenkins happy.
799509f [Hari Shreedharan] Fix a compile issue.
3c5194c [Hari Shreedharan] Merge remote-tracking branch 'asf/master'
d248d22 [harishreedharan] Merge pull request #1 from tdas/flume-polling
10b6214 [Tathagata Das] Changed public API, changed sink package, and added java unit test to make sure Java API is callable from Java.
1edc806 [Hari Shreedharan] SPARK-1729. Update logging in Spark Sink.
8c00289 [Hari Shreedharan] More debug messages
393bd94 [Hari Shreedharan] SPARK-1729. Use LinkedBlockingQueue instead of ArrayBuffer to keep track of connections.
120e2a1 [Hari Shreedharan] SPARK-1729. Some test changes and changes to utils classes.
9fd0da7 [Hari Shreedharan] SPARK-1729. Use foreach instead of map for all Options.
8136aa6 [Hari Shreedharan] Adding TransactionProcessor to map on returning batch of data
86aa274 [Hari Shreedharan] Merge remote-tracking branch 'asf/master'
205034d [Hari Shreedharan] Merging master in
4b0c7fc [Hari Shreedharan] FLUME-1729. New Flume-Spark integration.
bda01fc [Hari Shreedharan] FLUME-1729. Flume-Spark integration.
0d69604 [Hari Shreedharan] FLUME-1729. Better Flume-Spark integration.
3c23c18 [Hari Shreedharan] SPARK-1729. New Spark-Flume integration.
70bcc2a [Hari Shreedharan] SPARK-1729. New Flume-Spark integration.
d6fa3aa [Hari Shreedharan] SPARK-1729. New Flume-Spark integration.
e7da512 [Hari Shreedharan] SPARK-1729. Fixing import order
9741683 [Hari Shreedharan] SPARK-1729. Fixes based on review.
c604a3c [Hari Shreedharan] SPARK-1729. Optimize imports.
0f10788 [Hari Shreedharan] SPARK-1729. Make Flume pull data from source, rather than the current push model
87775aa [Hari Shreedharan] SPARK-1729. Make Flume pull data from source, rather than the current push model
8df37e4 [Hari Shreedharan] SPARK-1729. Make Flume pull data from source, rather than the current push model
03d6c1c [Hari Shreedharan] SPARK-1729. Make Flume pull data from source, rather than the current push model
08176ad [Hari Shreedharan] SPARK-1729. Make Flume pull data from source, rather than the current push model
d24d9d4 [Hari Shreedharan] SPARK-1729. Make Flume pull data from source, rather than the current push model
6d6776a [Hari Shreedharan] SPARK-1729. Make Flume pull data from source, rather than the current push model
---
 .../streaming/FlumePollingEventCount.scala    |  67 +++++
 external/flume-sink/pom.xml                   | 100 ++++++++
 .../flume-sink/src/main/avro/sparkflume.avdl  |  40 +++
 .../spark/streaming/flume/sink/Logging.scala  | 125 ++++++++++
 .../flume/sink/SparkAvroCallbackHandler.scala | 131 ++++++++++
 .../streaming/flume/sink/SparkSink.scala      | 154 ++++++++++++
 .../streaming/flume/sink/SparkSinkUtils.scala |  28 +++
 .../flume/sink/TransactionProcessor.scala     | 228 ++++++++++++++++++
 external/flume/pom.xml                        |   5 +
 .../streaming/flume/EventTransformer.scala    |  72 ++++++
 .../streaming/flume/FlumeInputDStream.scala   |   3 -
 .../flume/FlumePollingInputDStream.scala      | 178 ++++++++++++++
 .../spark/streaming/flume/FlumeUtils.scala    | 144 ++++++++++-
 .../flume/JavaFlumePollingStreamSuite.java    |  44 ++++
 .../flume/FlumePollingStreamSuite.scala       | 195 +++++++++++++++
 pom.xml                                       |   1 +
 project/SparkBuild.scala                      |  20 +-
 project/plugins.sbt                           |   2 +
 18 files changed, 1524 insertions(+), 13 deletions(-)
 create mode 100644 examples/src/main/scala/org/apache/spark/examples/streaming/FlumePollingEventCount.scala
 create mode 100644 external/flume-sink/pom.xml
 create mode 100644 external/flume-sink/src/main/avro/sparkflume.avdl
 create mode 100644 external/flume-sink/src/main/scala/org/apache/spark/streaming/flume/sink/Logging.scala
 create mode 100644 external/flume-sink/src/main/scala/org/apache/spark/streaming/flume/sink/SparkAvroCallbackHandler.scala
 create mode 100644 external/flume-sink/src/main/scala/org/apache/spark/streaming/flume/sink/SparkSink.scala
 create mode 100644 external/flume-sink/src/main/scala/org/apache/spark/streaming/flume/sink/SparkSinkUtils.scala
 create mode 100644 external/flume-sink/src/main/scala/org/apache/spark/streaming/flume/sink/TransactionProcessor.scala
 create mode 100644 external/flume/src/main/scala/org/apache/spark/streaming/flume/EventTransformer.scala
 create mode 100644 external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumePollingInputDStream.scala
 create mode 100644 external/flume/src/test/java/org/apache/spark/streaming/flume/JavaFlumePollingStreamSuite.java
 create mode 100644 external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumePollingStreamSuite.scala

diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/FlumePollingEventCount.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/FlumePollingEventCount.scala
new file mode 100644
index 000000000000..1cc8c8d5c23b
--- /dev/null
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/FlumePollingEventCount.scala
@@ -0,0 +1,67 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.examples.streaming
+
+import org.apache.spark.SparkConf
+import org.apache.spark.storage.StorageLevel
+import org.apache.spark.streaming._
+import org.apache.spark.streaming.flume._
+import org.apache.spark.util.IntParam
+import java.net.InetSocketAddress
+
+/**
+ *  Produces a count of events received from Flume.
+ *
+ *  This should be used in conjunction with the Spark Sink running in a Flume agent. See
+ *  the Spark Streaming programming guide for more details.
+ *
+ *  Usage: FlumePollingEventCount <host> <port>
+ *    `host` is the host on which the Spark Sink is running.
+ *    `port` is the port at which the Spark Sink is listening.
+ *
+ *  To run this example:
+ *    `$ bin/run-example org.apache.spark.examples.streaming.FlumePollingEventCount [host] [port] `
+ */
+object FlumePollingEventCount {
+  def main(args: Array[String]) {
+    if (args.length < 2) {
+      System.err.println(
+        "Usage: FlumePollingEventCount <host> <port>")
+      System.exit(1)
+    }
+
+    StreamingExamples.setStreamingLogLevels()
+
+    val Array(host, IntParam(port)) = args
+
+    val batchInterval = Milliseconds(2000)
+
+    // Create the context and set the batch size
+    val sparkConf = new SparkConf().setAppName("FlumePollingEventCount")
+    val ssc = new StreamingContext(sparkConf, batchInterval)
+
+    // Create a flume stream that polls the Spark Sink running in a Flume agent
+    val stream = FlumeUtils.createPollingStream(ssc, host, port)
+
+    // Print out the count of events received from this server in each batch
+    stream.count().map(cnt => "Received " + cnt + " flume events." ).print()
+
+    ssc.start()
+    ssc.awaitTermination()
+  }
+}
diff --git a/external/flume-sink/pom.xml b/external/flume-sink/pom.xml
new file mode 100644
index 000000000000..d11129ce8d89
--- /dev/null
+++ b/external/flume-sink/pom.xml
@@ -0,0 +1,100 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<!--
+  ~ Licensed to the Apache Software Foundation (ASF) under one or more
+  ~ contributor license agreements.  See the NOTICE file distributed with
+  ~ this work for additional information regarding copyright ownership.
+  ~ The ASF licenses this file to You under the Apache License, Version 2.0
+  ~ (the "License"); you may not use this file except in compliance with
+  ~ the License.  You may obtain a copy of the License at
+  ~
+  ~    http://www.apache.org/licenses/LICENSE-2.0
+  ~
+  ~ Unless required by applicable law or agreed to in writing, software
+  ~ distributed under the License is distributed on an "AS IS" BASIS,
+  ~ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+  ~ See the License for the specific language governing permissions and
+  ~ limitations under the License.
+  -->
+
+<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
+  <modelVersion>4.0.0</modelVersion>
+  <parent>
+    <groupId>org.apache.spark</groupId>
+    <artifactId>spark-parent</artifactId>
+    <version>1.1.0-SNAPSHOT</version>
+    <relativePath>../../pom.xml</relativePath>
+  </parent>
+
+  <artifactId>spark-streaming-flume-sink_2.10</artifactId>
+  <properties>
+    <sbt.project.name>streaming-flume-sink</sbt.project.name>
+  </properties>
+
+  <packaging>jar</packaging>
+  <name>Spark Project External Flume Sink</name>
+  <url>http://spark.apache.org/</url>
+  <dependencies>
+    <dependency>
+      <groupId>org.apache.flume</groupId>
+      <artifactId>flume-ng-sdk</artifactId>
+      <version>1.4.0</version>
+      <exclusions>
+        <exclusion>
+          <groupId>io.netty</groupId>
+          <artifactId>netty</artifactId>
+        </exclusion>
+        <exclusion>
+          <groupId>org.apache.thrift</groupId>
+          <artifactId>libthrift</artifactId>
+        </exclusion>
+      </exclusions>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.flume</groupId>
+      <artifactId>flume-ng-core</artifactId>
+      <version>1.4.0</version>
+      <exclusions>
+        <exclusion>
+          <groupId>io.netty</groupId>
+          <artifactId>netty</artifactId>
+        </exclusion>
+        <exclusion>
+          <groupId>org.apache.thrift</groupId>
+          <artifactId>libthrift</artifactId>
+        </exclusion>
+      </exclusions>
+    </dependency>
+    <dependency>
+      <groupId>org.scala-lang</groupId>
+      <artifactId>scala-library</artifactId>
+      <version>2.10.4</version>
+    </dependency>
+  </dependencies>
+  <build>
+    <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory>
+    <testOutputDirectory>target/scala-${scala.binary.version}/test-classes</testOutputDirectory>
+    <plugins>
+      <plugin>
+        <groupId>org.scalatest</groupId>
+        <artifactId>scalatest-maven-plugin</artifactId>
+      </plugin>
+      <plugin>
+        <groupId>org.apache.avro</groupId>
+        <artifactId>avro-maven-plugin</artifactId>
+        <version>1.7.3</version>
+        <configuration>
+          <!-- Generate the output in the same directory as the sbt-avro-plugin -->
+          <outputDirectory>${project.basedir}/target/scala-${scala.binary.version}/src_managed/main/compiled_avro</outputDirectory>
+        </configuration>
+        <executions>
+          <execution>
+            <phase>generate-sources</phase>
+            <goals>
+              <goal>idl-protocol</goal>
+            </goals>
+          </execution>
+        </executions>
+      </plugin>
+    </plugins>
+  </build>
+</project>
diff --git a/external/flume-sink/src/main/avro/sparkflume.avdl b/external/flume-sink/src/main/avro/sparkflume.avdl
new file mode 100644
index 000000000000..8806e863ac7c
--- /dev/null
+++ b/external/flume-sink/src/main/avro/sparkflume.avdl
@@ -0,0 +1,40 @@
+/**
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+@namespace("org.apache.spark.streaming.flume.sink")
+
+protocol SparkFlumeProtocol {
+
+  record SparkSinkEvent {
+    map<string> headers;
+    bytes body;
+  }
+
+  record EventBatch {
+    string errorMsg = ""; // If this is empty it is a valid message, else it represents an error
+    string sequenceNumber;
+    array<SparkSinkEvent> events;
+  }
+
+  EventBatch getEventBatch (int n);
+
+  void ack (string sequenceNumber);
+
+  void nack (string sequenceNumber);
+}
diff --git a/external/flume-sink/src/main/scala/org/apache/spark/streaming/flume/sink/Logging.scala b/external/flume-sink/src/main/scala/org/apache/spark/streaming/flume/sink/Logging.scala
new file mode 100644
index 000000000000..17cbc6707b5e
--- /dev/null
+++ b/external/flume-sink/src/main/scala/org/apache/spark/streaming/flume/sink/Logging.scala
@@ -0,0 +1,125 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.spark.streaming.flume.sink
+
+import org.slf4j.{Logger, LoggerFactory}
+
+/**
+ * Copy of the org.apache.spark.Logging for being used in the Spark Sink.
+ * The org.apache.spark.Logging is not used so that all of Spark is not brought
+ * in as a dependency.
+ */
+private[sink] trait Logging {
+  // Make the log field transient so that objects with Logging can
+  // be serialized and used on another machine
+  @transient private var log_ : Logger = null
+
+  // Method to get or create the logger for this object
+  protected def log: Logger = {
+    if (log_ == null) {
+      initializeIfNecessary()
+      var className = this.getClass.getName
+      // Ignore trailing $'s in the class names for Scala objects
+      if (className.endsWith("$")) {
+        className = className.substring(0, className.length - 1)
+      }
+      log_ = LoggerFactory.getLogger(className)
+    }
+    log_
+  }
+
+  // Log methods that take only a String
+  protected def logInfo(msg: => String) {
+    if (log.isInfoEnabled) log.info(msg)
+  }
+
+  protected def logDebug(msg: => String) {
+    if (log.isDebugEnabled) log.debug(msg)
+  }
+
+  protected def logTrace(msg: => String) {
+    if (log.isTraceEnabled) log.trace(msg)
+  }
+
+  protected def logWarning(msg: => String) {
+    if (log.isWarnEnabled) log.warn(msg)
+  }
+
+  protected def logError(msg: => String) {
+    if (log.isErrorEnabled) log.error(msg)
+  }
+
+  // Log methods that take Throwables (Exceptions/Errors) too
+  protected def logInfo(msg: => String, throwable: Throwable) {
+    if (log.isInfoEnabled) log.info(msg, throwable)
+  }
+
+  protected def logDebug(msg: => String, throwable: Throwable) {
+    if (log.isDebugEnabled) log.debug(msg, throwable)
+  }
+
+  protected def logTrace(msg: => String, throwable: Throwable) {
+    if (log.isTraceEnabled) log.trace(msg, throwable)
+  }
+
+  protected def logWarning(msg: => String, throwable: Throwable) {
+    if (log.isWarnEnabled) log.warn(msg, throwable)
+  }
+
+  protected def logError(msg: => String, throwable: Throwable) {
+    if (log.isErrorEnabled) log.error(msg, throwable)
+  }
+
+  protected def isTraceEnabled(): Boolean = {
+    log.isTraceEnabled
+  }
+
+  private def initializeIfNecessary() {
+    if (!Logging.initialized) {
+      Logging.initLock.synchronized {
+        if (!Logging.initialized) {
+          initializeLogging()
+        }
+      }
+    }
+  }
+
+  private def initializeLogging() {
+    Logging.initialized = true
+
+    // Force a call into slf4j to initialize it. Avoids this happening from mutliple threads
+    // and triggering this: http://mailman.qos.ch/pipermail/slf4j-dev/2010-April/002956.html
+    log
+  }
+}
+
+private[sink] object Logging {
+  @volatile private var initialized = false
+  val initLock = new Object()
+  try {
+    // We use reflection here to handle the case where users remove the
+    // slf4j-to-jul bridge order to route their logs to JUL.
+    val bridgeClass = Class.forName("org.slf4j.bridge.SLF4JBridgeHandler")
+    bridgeClass.getMethod("removeHandlersForRootLogger").invoke(null)
+    val installed = bridgeClass.getMethod("isInstalled").invoke(null).asInstanceOf[Boolean]
+    if (!installed) {
+      bridgeClass.getMethod("install").invoke(null)
+    }
+  } catch {
+    case e: ClassNotFoundException => // can't log anything yet so just fail silently
+  }
+}
diff --git a/external/flume-sink/src/main/scala/org/apache/spark/streaming/flume/sink/SparkAvroCallbackHandler.scala b/external/flume-sink/src/main/scala/org/apache/spark/streaming/flume/sink/SparkAvroCallbackHandler.scala
new file mode 100644
index 000000000000..7da8eb3e3591
--- /dev/null
+++ b/external/flume-sink/src/main/scala/org/apache/spark/streaming/flume/sink/SparkAvroCallbackHandler.scala
@@ -0,0 +1,131 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.spark.streaming.flume.sink
+
+import java.util.concurrent.{ConcurrentHashMap, Executors}
+import java.util.concurrent.atomic.AtomicLong
+
+import org.apache.flume.Channel
+import org.apache.commons.lang.RandomStringUtils
+import com.google.common.util.concurrent.ThreadFactoryBuilder
+
+/**
+ * Class that implements the SparkFlumeProtocol, that is used by the Avro Netty Server to process
+ * requests. Each getEvents, ack and nack call is forwarded to an instance of this class.
+ * @param threads Number of threads to use to process requests.
+ * @param channel The channel that the sink pulls events from
+ * @param transactionTimeout Timeout in millis after which the transaction if not acked by Spark
+ *                           is rolled back.
+ */
+// Flume forces transactions to be thread-local. So each transaction *must* be committed, or
+// rolled back from the thread it was originally created in. So each getEvents call from Spark
+// creates a TransactionProcessor which runs in a new thread, in which the transaction is created
+// and events are pulled off the channel. Once the events are sent to spark,
+// that thread is blocked and the TransactionProcessor is saved in a map,
+// until an ACK or NACK comes back or the transaction times out (after the specified timeout).
+// When the response comes or a timeout is hit, the TransactionProcessor is retrieved and then
+// unblocked, at which point the transaction is committed or rolled back.
+
+private[flume] class SparkAvroCallbackHandler(val threads: Int, val channel: Channel,
+  val transactionTimeout: Int, val backOffInterval: Int) extends SparkFlumeProtocol with Logging {
+  val transactionExecutorOpt = Option(Executors.newFixedThreadPool(threads,
+    new ThreadFactoryBuilder().setDaemon(true)
+      .setNameFormat("Spark Sink Processor Thread - %d").build()))
+  private val processorMap = new ConcurrentHashMap[CharSequence, TransactionProcessor]()
+  // This sink will not persist sequence numbers and reuses them if it gets restarted.
+  // So it is possible to commit a transaction which may have been meant for the sink before the
+  // restart.
+  // Since the new txn may not have the same sequence number we must guard against accidentally
+  // committing a new transaction. To reduce the probability of that happening a random string is
+  // prepended to the sequence number. Does not change for life of sink
+  private val seqBase = RandomStringUtils.randomAlphanumeric(8)
+  private val seqCounter = new AtomicLong(0)
+
+  /**
+   * Returns a bunch of events to Spark over Avro RPC.
+   * @param n Maximum number of events to return in a batch
+   * @return [[EventBatch]] instance that has a sequence number and an array of at most n events
+   */
+  override def getEventBatch(n: Int): EventBatch = {
+    logDebug("Got getEventBatch call from Spark.")
+    val sequenceNumber = seqBase + seqCounter.incrementAndGet()
+    val processor = new TransactionProcessor(channel, sequenceNumber,
+      n, transactionTimeout, backOffInterval, this)
+    transactionExecutorOpt.foreach(executor => {
+      executor.submit(processor)
+    })
+    // Wait until a batch is available - will be an error if error message is non-empty
+    val batch = processor.getEventBatch
+    if (!SparkSinkUtils.isErrorBatch(batch)) {
+      processorMap.put(sequenceNumber.toString, processor)
+      logDebug("Sending event batch with sequence number: " + sequenceNumber)
+    }
+    batch
+  }
+
+  /**
+   * Called by Spark to indicate successful commit of a batch
+   * @param sequenceNumber The sequence number of the event batch that was successful
+   */
+  override def ack(sequenceNumber: CharSequence): Void = {
+    logDebug("Received Ack for batch with sequence number: " + sequenceNumber)
+    completeTransaction(sequenceNumber, success = true)
+    null
+  }
+
+  /**
+   * Called by Spark to indicate failed commit of a batch
+   * @param sequenceNumber The sequence number of the event batch that failed
+   * @return
+   */
+  override def nack(sequenceNumber: CharSequence): Void = {
+    completeTransaction(sequenceNumber, success = false)
+    logInfo("Spark failed to commit transaction. Will reattempt events.")
+    null
+  }
+
+  /**
+   * Helper method to commit or rollback a transaction.
+   * @param sequenceNumber The sequence number of the batch that was completed
+   * @param success Whether the batch was successful or not.
+   */
+  private def completeTransaction(sequenceNumber: CharSequence, success: Boolean) {
+    Option(removeAndGetProcessor(sequenceNumber)).foreach(processor => {
+      processor.batchProcessed(success)
+    })
+  }
+
+  /**
+   * Helper method to remove the TxnProcessor for a Sequence Number. Can be used to avoid a leak.
+   * @param sequenceNumber
+   * @return The transaction processor for the corresponding batch. Note that this instance is no
+   *         longer tracked and the caller is responsible for that txn processor.
+   */
+  private[sink] def removeAndGetProcessor(sequenceNumber: CharSequence): TransactionProcessor = {
+    processorMap.remove(sequenceNumber.toString) // The toString is required!
+  }
+
+  /**
+   * Shuts down the executor used to process transactions.
+   */
+  def shutdown() {
+    logInfo("Shutting down Spark Avro Callback Handler")
+    transactionExecutorOpt.foreach(executor => {
+      executor.shutdownNow()
+    })
+  }
+}
diff --git a/external/flume-sink/src/main/scala/org/apache/spark/streaming/flume/sink/SparkSink.scala b/external/flume-sink/src/main/scala/org/apache/spark/streaming/flume/sink/SparkSink.scala
new file mode 100644
index 000000000000..7b735133e3d1
--- /dev/null
+++ b/external/flume-sink/src/main/scala/org/apache/spark/streaming/flume/sink/SparkSink.scala
@@ -0,0 +1,154 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.spark.streaming.flume.sink
+
+import java.net.InetSocketAddress
+import java.util.concurrent._
+
+import org.apache.avro.ipc.NettyServer
+import org.apache.avro.ipc.specific.SpecificResponder
+import org.apache.flume.Context
+import org.apache.flume.Sink.Status
+import org.apache.flume.conf.{Configurable, ConfigurationException}
+import org.apache.flume.sink.AbstractSink
+
+/**
+ * A sink that uses Avro RPC to run a server that can be polled by Spark's
+ * FlumePollingInputDStream. This sink has the following configuration parameters:
+ *
+ * hostname - The hostname to bind to. Default: 0.0.0.0
+ * port - The port to bind to. (No default - mandatory)
+ * timeout - Time in seconds after which a transaction is rolled back,
+ * if an ACK is not received from Spark within that time
+ * threads - Number of threads to use to receive requests from Spark (Default: 10)
+ *
+ * This sink is unlike other Flume sinks in the sense that it does not push data,
+ * instead the process method in this sink simply blocks the SinkRunner the first time it is
+ * called. This sink starts up an Avro IPC server that uses the SparkFlumeProtocol.
+ *
+ * Each time a getEventBatch call comes, creates a transaction and reads events
+ * from the channel. When enough events are read, the events are sent to the Spark receiver and
+ * the thread itself is blocked and a reference to it saved off.
+ *
+ * When the ack for that batch is received,
+ * the thread which created the transaction is is retrieved and it commits the transaction with the
+ * channel from the same thread it was originally created in (since Flume transactions are
+ * thread local). If a nack is received instead, the sink rolls back the transaction. If no ack
+ * is received within the specified timeout, the transaction is rolled back too. If an ack comes
+ * after that, it is simply ignored and the events get re-sent.
+ *
+ */
+
+private[flume]
+class SparkSink extends AbstractSink with Logging with Configurable {
+
+  // Size of the pool to use for holding transaction processors.
+  private var poolSize: Integer = SparkSinkConfig.DEFAULT_THREADS
+
+  // Timeout for each transaction. If spark does not respond in this much time,
+  // rollback the transaction
+  private var transactionTimeout = SparkSinkConfig.DEFAULT_TRANSACTION_TIMEOUT
+
+  // Address info to bind on
+  private var hostname: String = SparkSinkConfig.DEFAULT_HOSTNAME
+  private var port: Int = 0
+
+  private var backOffInterval: Int = 200
+
+  // Handle to the server
+  private var serverOpt: Option[NettyServer] = None
+
+  // The handler that handles the callback from Avro
+  private var handler: Option[SparkAvroCallbackHandler] = None
+
+  // Latch that blocks off the Flume framework from wasting 1 thread.
+  private val blockingLatch = new CountDownLatch(1)
+
+  override def start() {
+    logInfo("Starting Spark Sink: " + getName + " on port: " + port + " and interface: " +
+      hostname + " with " + "pool size: " + poolSize + " and transaction timeout: " +
+      transactionTimeout + ".")
+    handler = Option(new SparkAvroCallbackHandler(poolSize, getChannel, transactionTimeout,
+      backOffInterval))
+    val responder = new SpecificResponder(classOf[SparkFlumeProtocol], handler.get)
+    // Using the constructor that takes specific thread-pools requires bringing in netty
+    // dependencies which are being excluded in the build. In practice,
+    // Netty dependencies are already available on the JVM as Flume would have pulled them in.
+    serverOpt = Option(new NettyServer(responder, new InetSocketAddress(hostname, port)))
+    serverOpt.foreach(server => {
+      logInfo("Starting Avro server for sink: " + getName)
+      server.start()
+    })
+    super.start()
+  }
+
+  override def stop() {
+    logInfo("Stopping Spark Sink: " + getName)
+    handler.foreach(callbackHandler => {
+      callbackHandler.shutdown()
+    })
+    serverOpt.foreach(server => {
+      logInfo("Stopping Avro Server for sink: " + getName)
+      server.close()
+      server.join()
+    })
+    blockingLatch.countDown()
+    super.stop()
+  }
+
+  override def configure(ctx: Context) {
+    import SparkSinkConfig._
+    hostname = ctx.getString(CONF_HOSTNAME, DEFAULT_HOSTNAME)
+    port = Option(ctx.getInteger(CONF_PORT)).
+      getOrElse(throw new ConfigurationException("The port to bind to must be specified"))
+    poolSize = ctx.getInteger(THREADS, DEFAULT_THREADS)
+    transactionTimeout = ctx.getInteger(CONF_TRANSACTION_TIMEOUT, DEFAULT_TRANSACTION_TIMEOUT)
+    backOffInterval = ctx.getInteger(CONF_BACKOFF_INTERVAL, DEFAULT_BACKOFF_INTERVAL)
+    logInfo("Configured Spark Sink with hostname: " + hostname + ", port: " + port + ", " +
+      "poolSize: " + poolSize + ", transactionTimeout: " + transactionTimeout + ", " +
+      "backoffInterval: " + backOffInterval)
+  }
+
+  override def process(): Status = {
+    // This method is called in a loop by the Flume framework - block it until the sink is
+    // stopped to save CPU resources. The sink runner will interrupt this thread when the sink is
+    // being shut down.
+    logInfo("Blocking Sink Runner, sink will continue to run..")
+    blockingLatch.await()
+    Status.BACKOFF
+  }
+}
+
+/**
+ * Configuration parameters and their defaults.
+ */
+private[flume]
+object SparkSinkConfig {
+  val THREADS = "threads"
+  val DEFAULT_THREADS = 10
+
+  val CONF_TRANSACTION_TIMEOUT = "timeout"
+  val DEFAULT_TRANSACTION_TIMEOUT = 60
+
+  val CONF_HOSTNAME = "hostname"
+  val DEFAULT_HOSTNAME = "0.0.0.0"
+
+  val CONF_PORT = "port"
+
+  val CONF_BACKOFF_INTERVAL = "backoffInterval"
+  val DEFAULT_BACKOFF_INTERVAL = 200
+}
diff --git a/external/flume-sink/src/main/scala/org/apache/spark/streaming/flume/sink/SparkSinkUtils.scala b/external/flume-sink/src/main/scala/org/apache/spark/streaming/flume/sink/SparkSinkUtils.scala
new file mode 100644
index 000000000000..47c0e294d6b5
--- /dev/null
+++ b/external/flume-sink/src/main/scala/org/apache/spark/streaming/flume/sink/SparkSinkUtils.scala
@@ -0,0 +1,28 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.spark.streaming.flume.sink
+
+private[flume] object SparkSinkUtils {
+  /**
+   * This method determines if this batch represents an error or not.
+   * @param batch - The batch to check
+   * @return - true if the batch represents an error
+   */
+  def isErrorBatch(batch: EventBatch): Boolean = {
+    !batch.getErrorMsg.toString.equals("") // If there is an error message, it is an error batch.
+  }
+}
diff --git a/external/flume-sink/src/main/scala/org/apache/spark/streaming/flume/sink/TransactionProcessor.scala b/external/flume-sink/src/main/scala/org/apache/spark/streaming/flume/sink/TransactionProcessor.scala
new file mode 100644
index 000000000000..b9e3c786ebb3
--- /dev/null
+++ b/external/flume-sink/src/main/scala/org/apache/spark/streaming/flume/sink/TransactionProcessor.scala
@@ -0,0 +1,228 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.spark.streaming.flume.sink
+
+import java.nio.ByteBuffer
+import java.util
+import java.util.concurrent.{Callable, CountDownLatch, TimeUnit}
+
+import scala.util.control.Breaks
+
+import org.apache.flume.{Transaction, Channel}
+
+// Flume forces transactions to be thread-local (horrible, I know!)
+// So the sink basically spawns a new thread to pull the events out within a transaction.
+// The thread fills in the event batch object that is set before the thread is scheduled.
+// After filling it in, the thread waits on a condition - which is released only
+// when the success message comes back for the specific sequence number for that event batch.
+/**
+ * This class represents a transaction on the Flume channel. This class runs a separate thread
+ * which owns the transaction. The thread is blocked until the success call for that transaction
+ * comes back with an ACK or NACK.
+ * @param channel The channel from which to pull events
+ * @param seqNum The sequence number to use for the transaction. Must be unique
+ * @param maxBatchSize The maximum number of events to process per batch
+ * @param transactionTimeout Time in seconds after which a transaction must be rolled back
+ *                           without waiting for an ACK from Spark
+ * @param parent The parent [[SparkAvroCallbackHandler]] instance, for reporting timeouts
+ */
+private class TransactionProcessor(val channel: Channel, val seqNum: String,
+  var maxBatchSize: Int, val transactionTimeout: Int, val backOffInterval: Int,
+  val parent: SparkAvroCallbackHandler) extends Callable[Void] with Logging {
+
+  // If a real batch is not returned, we always have to return an error batch.
+  @volatile private var eventBatch: EventBatch = new EventBatch("Unknown Error", "",
+    util.Collections.emptyList())
+
+  // Synchronization primitives
+  val batchGeneratedLatch = new CountDownLatch(1)
+  val batchAckLatch = new CountDownLatch(1)
+
+  // Sanity check to ensure we don't loop like crazy
+  val totalAttemptsToRemoveFromChannel = Int.MaxValue / 2
+
+  // OK to use volatile, since the change would only make this true (otherwise it will be
+  // changed to false - we never apply a negation operation to this) - which means the transaction
+  // succeeded.
+  @volatile private var batchSuccess = false
+
+  // The transaction that this processor would handle
+  var txOpt: Option[Transaction] = None
+
+  /**
+   * Get an event batch from the channel. This method will block until a batch of events is
+   * available from the channel. If no events are available after a large number of attempts of
+   * polling the channel, this method will return an [[EventBatch]] with a non-empty error message
+   *
+   * @return An [[EventBatch]] instance with sequence number set to seqNum, filled with a
+   *         maximum of maxBatchSize events
+   */
+  def getEventBatch: EventBatch = {
+    batchGeneratedLatch.await()
+    eventBatch
+  }
+
+  /**
+   * This method is to be called by the sink when it receives an ACK or NACK from Spark. This
+   * method is a no-op if it is called after transactionTimeout has expired since
+   * getEventBatch returned a batch of events.
+   * @param success True if an ACK was received and the transaction should be committed, else false.
+   */
+  def batchProcessed(success: Boolean) {
+    logDebug("Batch processed for sequence number: " + seqNum)
+    batchSuccess = success
+    batchAckLatch.countDown()
+  }
+
+  /**
+   * Populates events into the event batch. If the batch cannot be populated,
+   * this method will not set the events into the event batch, but it sets an error message.
+   */
+  private def populateEvents() {
+    try {
+      txOpt = Option(channel.getTransaction)
+      if(txOpt.isEmpty) {
+        eventBatch.setErrorMsg("Something went wrong. Channel was " +
+          "unable to create a transaction!")
+      }
+      txOpt.foreach(tx => {
+        tx.begin()
+        val events = new util.ArrayList[SparkSinkEvent](maxBatchSize)
+        val loop = new Breaks
+        var gotEventsInThisTxn = false
+        var loopCounter: Int = 0
+        loop.breakable {
+          while (events.size() < maxBatchSize
+            && loopCounter < totalAttemptsToRemoveFromChannel) {
+            loopCounter += 1
+            Option(channel.take()) match {
+              case Some(event) =>
+                events.add(new SparkSinkEvent(toCharSequenceMap(event.getHeaders),
+                  ByteBuffer.wrap(event.getBody)))
+                gotEventsInThisTxn = true
+              case None =>
+                if (!gotEventsInThisTxn) {
+                  logDebug("Sleeping for " + backOffInterval + " millis as no events were read in" +
+                    " the current transaction")
+                  TimeUnit.MILLISECONDS.sleep(backOffInterval)
+                } else {
+                  loop.break()
+                }
+            }
+          }
+        }
+        if (!gotEventsInThisTxn) {
+          val msg = "Tried several times, " +
+            "but did not get any events from the channel!"
+          logWarning(msg)
+          eventBatch.setErrorMsg(msg)
+        } else {
+          // At this point, the events are available, so fill them into the event batch
+          eventBatch = new EventBatch("",seqNum, events)
+        }
+      })
+    } catch {
+      case e: Exception =>
+        logWarning("Error while processing transaction.", e)
+        eventBatch.setErrorMsg(e.getMessage)
+        try {
+          txOpt.foreach(tx => {
+            rollbackAndClose(tx, close = true)
+          })
+        } finally {
+          txOpt = None
+        }
+    } finally {
+      batchGeneratedLatch.countDown()
+    }
+  }
+
+  /**
+   * Waits for upto transactionTimeout seconds for an ACK. If an ACK comes in
+   * this method commits the transaction with the channel. If the ACK does not come in within
+   * that time or a NACK comes in, this method rolls back the transaction.
+   */
+  private def processAckOrNack() {
+    batchAckLatch.await(transactionTimeout, TimeUnit.SECONDS)
+    txOpt.foreach(tx => {
+      if (batchSuccess) {
+        try {
+          logDebug("Committing transaction")
+          tx.commit()
+        } catch {
+          case e: Exception =>
+            logWarning("Error while attempting to commit transaction. Transaction will be rolled " +
+              "back", e)
+            rollbackAndClose(tx, close = false) // tx will be closed later anyway
+        } finally {
+          tx.close()
+        }
+      } else {
+        logWarning("Spark could not commit transaction, NACK received. Rolling back transaction.")
+        rollbackAndClose(tx, close = true)
+        // This might have been due to timeout or a NACK. Either way the following call does not
+        // cause issues. This is required to ensure the TransactionProcessor instance is not leaked
+        parent.removeAndGetProcessor(seqNum)
+      }
+    })
+  }
+
+  /**
+   * Helper method to rollback and optionally close a transaction
+   * @param tx The transaction to rollback
+   * @param close Whether the transaction should be closed or not after rolling back
+   */
+  private def rollbackAndClose(tx: Transaction, close: Boolean) {
+    try {
+      logWarning("Spark was unable to successfully process the events. Transaction is being " +
+        "rolled back.")
+      tx.rollback()
+    } catch {
+      case e: Exception =>
+        logError("Error rolling back transaction. Rollback may have failed!", e)
+    } finally {
+      if (close) {
+        tx.close()
+      }
+    }
+  }
+
+  /**
+   * Helper method to convert a Map[String, String] to Map[CharSequence, CharSequence]
+   * @param inMap The map to be converted
+   * @return The converted map
+   */
+  private def toCharSequenceMap(inMap: java.util.Map[String, String]): java.util.Map[CharSequence,
+    CharSequence] = {
+    val charSeqMap = new util.HashMap[CharSequence, CharSequence](inMap.size())
+    charSeqMap.putAll(inMap)
+    charSeqMap
+  }
+
+  /**
+   * When the thread is started it sets as many events as the batch size or less (if enough
+   * events aren't available) into the eventBatch and object and lets any threads waiting on the
+   * [[getEventBatch]] method to proceed. Then this thread waits for acks or nacks to come in,
+   * or for a specified timeout and commits or rolls back the transaction.
+   * @return
+   */
+  override def call(): Void = {
+    populateEvents()
+    processAckOrNack()
+    null
+  }
+}
diff --git a/external/flume/pom.xml b/external/flume/pom.xml
index 874b8a7959bb..9f680b27c330 100644
--- a/external/flume/pom.xml
+++ b/external/flume/pom.xml
@@ -77,6 +77,11 @@
       <artifactId>junit-interface</artifactId>
       <scope>test</scope>
     </dependency>
+    <dependency>
+      <groupId>org.apache.spark</groupId>
+      <artifactId>spark-streaming-flume-sink_2.10</artifactId>
+      <version>${project.version}</version>
+    </dependency>
   </dependencies>
   <build>
     <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory>
diff --git a/external/flume/src/main/scala/org/apache/spark/streaming/flume/EventTransformer.scala b/external/flume/src/main/scala/org/apache/spark/streaming/flume/EventTransformer.scala
new file mode 100644
index 000000000000..dc629df4f4ac
--- /dev/null
+++ b/external/flume/src/main/scala/org/apache/spark/streaming/flume/EventTransformer.scala
@@ -0,0 +1,72 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.streaming.flume
+
+import java.io.{ObjectOutput, ObjectInput}
+
+import scala.collection.JavaConversions._
+
+import org.apache.spark.util.Utils
+import org.apache.spark.Logging
+
+/**
+ * A simple object that provides the implementation of readExternal and writeExternal for both
+ * the wrapper classes for Flume-style Events.
+ */
+private[streaming] object EventTransformer extends Logging {
+  def readExternal(in: ObjectInput): (java.util.HashMap[CharSequence, CharSequence],
+    Array[Byte]) = {
+    val bodyLength = in.readInt()
+    val bodyBuff = new Array[Byte](bodyLength)
+    in.readFully(bodyBuff)
+
+    val numHeaders = in.readInt()
+    val headers = new java.util.HashMap[CharSequence, CharSequence]
+
+    for (i <- 0 until numHeaders) {
+      val keyLength = in.readInt()
+      val keyBuff = new Array[Byte](keyLength)
+      in.readFully(keyBuff)
+      val key: String = Utils.deserialize(keyBuff)
+
+      val valLength = in.readInt()
+      val valBuff = new Array[Byte](valLength)
+      in.readFully(valBuff)
+      val value: String = Utils.deserialize(valBuff)
+
+      headers.put(key, value)
+    }
+    (headers, bodyBuff)
+  }
+
+  def writeExternal(out: ObjectOutput, headers: java.util.Map[CharSequence, CharSequence],
+    body: Array[Byte]) {
+    out.writeInt(body.length)
+    out.write(body)
+    val numHeaders = headers.size()
+    out.writeInt(numHeaders)
+    for ((k,v) <- headers) {
+      val keyBuff = Utils.serialize(k.toString)
+      out.writeInt(keyBuff.length)
+      out.write(keyBuff)
+      val valBuff = Utils.serialize(v.toString)
+      out.writeInt(valBuff.length)
+      out.write(valBuff)
+    }
+  }
+}
diff --git a/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeInputDStream.scala b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeInputDStream.scala
index 56d2886b2687..4b2ea45fb81d 100644
--- a/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeInputDStream.scala
+++ b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeInputDStream.scala
@@ -39,11 +39,8 @@ import org.apache.spark.streaming.receiver.Receiver
 
 import org.jboss.netty.channel.ChannelPipelineFactory
 import org.jboss.netty.channel.Channels
-import org.jboss.netty.channel.ChannelPipeline
-import org.jboss.netty.channel.ChannelFactory
 import org.jboss.netty.channel.socket.nio.NioServerSocketChannelFactory
 import org.jboss.netty.handler.codec.compression._
-import org.jboss.netty.handler.execution.ExecutionHandler
 
 private[streaming]
 class FlumeInputDStream[T: ClassTag](
diff --git a/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumePollingInputDStream.scala b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumePollingInputDStream.scala
new file mode 100644
index 000000000000..148262bb6771
--- /dev/null
+++ b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumePollingInputDStream.scala
@@ -0,0 +1,178 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.spark.streaming.flume
+
+
+import java.net.InetSocketAddress
+import java.util.concurrent.{LinkedBlockingQueue, TimeUnit, Executors}
+
+import scala.collection.JavaConversions._
+import scala.collection.mutable.ArrayBuffer
+import scala.reflect.ClassTag
+
+import com.google.common.util.concurrent.ThreadFactoryBuilder
+import org.apache.avro.ipc.NettyTransceiver
+import org.apache.avro.ipc.specific.SpecificRequestor
+import org.jboss.netty.channel.socket.nio.NioClientSocketChannelFactory
+
+import org.apache.spark.Logging
+import org.apache.spark.storage.StorageLevel
+import org.apache.spark.streaming.StreamingContext
+import org.apache.spark.streaming.dstream.ReceiverInputDStream
+import org.apache.spark.streaming.receiver.Receiver
+import org.apache.spark.streaming.flume.sink._
+
+/**
+ * A [[ReceiverInputDStream]] that can be used to read data from several Flume agents running
+ * [[org.apache.spark.streaming.flume.sink.SparkSink]]s.
+ * @param _ssc Streaming context that will execute this input stream
+ * @param addresses List of addresses at which SparkSinks are listening
+ * @param maxBatchSize Maximum size of a batch
+ * @param parallelism Number of parallel connections to open
+ * @param storageLevel The storage level to use.
+ * @tparam T Class type of the object of this stream
+ */
+private[streaming] class FlumePollingInputDStream[T: ClassTag](
+    @transient _ssc: StreamingContext,
+    val addresses: Seq[InetSocketAddress],
+    val maxBatchSize: Int,
+    val parallelism: Int,
+    storageLevel: StorageLevel
+  ) extends ReceiverInputDStream[SparkFlumeEvent](_ssc) {
+
+  override def getReceiver(): Receiver[SparkFlumeEvent] = {
+    new FlumePollingReceiver(addresses, maxBatchSize, parallelism, storageLevel)
+  }
+}
+
+private[streaming] class FlumePollingReceiver(
+    addresses: Seq[InetSocketAddress],
+    maxBatchSize: Int,
+    parallelism: Int,
+    storageLevel: StorageLevel
+  ) extends Receiver[SparkFlumeEvent](storageLevel) with Logging {
+
+  lazy val channelFactoryExecutor =
+    Executors.newCachedThreadPool(new ThreadFactoryBuilder().setDaemon(true).
+      setNameFormat("Flume Receiver Channel Thread - %d").build())
+
+  lazy val channelFactory =
+    new NioClientSocketChannelFactory(channelFactoryExecutor, channelFactoryExecutor)
+
+  lazy val receiverExecutor = Executors.newFixedThreadPool(parallelism,
+    new ThreadFactoryBuilder().setDaemon(true).setNameFormat("Flume Receiver Thread - %d").build())
+
+  private lazy val connections = new LinkedBlockingQueue[FlumeConnection]()
+
+  override def onStart(): Unit = {
+    // Create the connections to each Flume agent.
+    addresses.foreach(host => {
+      val transceiver = new NettyTransceiver(host, channelFactory)
+      val client = SpecificRequestor.getClient(classOf[SparkFlumeProtocol.Callback], transceiver)
+      connections.add(new FlumeConnection(transceiver, client))
+    })
+    for (i <- 0 until parallelism) {
+      logInfo("Starting Flume Polling Receiver worker threads starting..")
+      // Threads that pull data from Flume.
+      receiverExecutor.submit(new Runnable {
+        override def run(): Unit = {
+          while (true) {
+            val connection = connections.poll()
+            val client = connection.client
+            try {
+              val eventBatch = client.getEventBatch(maxBatchSize)
+              if (!SparkSinkUtils.isErrorBatch(eventBatch)) {
+                // No error, proceed with processing data
+                val seq = eventBatch.getSequenceNumber
+                val events: java.util.List[SparkSinkEvent] = eventBatch.getEvents
+                logDebug(
+                  "Received batch of " + events.size() + " events with sequence number: " + seq)
+                try {
+                  // Convert each Flume event to a serializable SparkFlumeEvent
+                  val buffer = new ArrayBuffer[SparkFlumeEvent](events.size())
+                  var j = 0
+                  while (j < events.size()) {
+                    buffer += toSparkFlumeEvent(events(j))
+                    j += 1
+                  }
+                  store(buffer)
+                  logDebug("Sending ack for sequence number: " + seq)
+                  // Send an ack to Flume so that Flume discards the events from its channels.
+                  client.ack(seq)
+                  logDebug("Ack sent for sequence number: " + seq)
+                } catch {
+                  case e: Exception =>
+                    try {
+                      // Let Flume know that the events need to be pushed back into the channel.
+                      logDebug("Sending nack for sequence number: " + seq)
+                      client.nack(seq) // If the agent is down, even this could fail and throw
+                      logDebug("Nack sent for sequence number: " + seq)
+                    } catch {
+                      case e: Exception => logError(
+                        "Sending Nack also failed. A Flume agent is down.")
+                    }
+                    TimeUnit.SECONDS.sleep(2L) // for now just leave this as a fixed 2 seconds.
+                    logWarning("Error while attempting to store events", e)
+                }
+              } else {
+                logWarning("Did not receive events from Flume agent due to error on the Flume " +
+                  "agent: " + eventBatch.getErrorMsg)
+              }
+            } catch {
+              case e: Exception =>
+                logWarning("Error while reading data from Flume", e)
+            } finally {
+              connections.add(connection)
+            }
+          }
+        }
+      })
+    }
+  }
+
+  override def onStop(): Unit = {
+    logInfo("Shutting down Flume Polling Receiver")
+    receiverExecutor.shutdownNow()
+    connections.foreach(connection => {
+      connection.transceiver.close()
+    })
+    channelFactory.releaseExternalResources()
+  }
+
+  /**
+   * Utility method to convert [[SparkSinkEvent]] to [[SparkFlumeEvent]]
+   * @param event - Event to convert to SparkFlumeEvent
+   * @return - The SparkFlumeEvent generated from SparkSinkEvent
+   */
+  private def toSparkFlumeEvent(event: SparkSinkEvent): SparkFlumeEvent = {
+    val sparkFlumeEvent = new SparkFlumeEvent()
+    sparkFlumeEvent.event.setBody(event.getBody)
+    sparkFlumeEvent.event.setHeaders(event.getHeaders)
+    sparkFlumeEvent
+  }
+}
+
+/**
+ * A wrapper around the transceiver and the Avro IPC API. 
+ * @param transceiver The transceiver to use for communication with Flume
+ * @param client The client that the callbacks are received on.
+ */
+private class FlumeConnection(val transceiver: NettyTransceiver,
+  val client: SparkFlumeProtocol.Callback)
+
+
+
diff --git a/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeUtils.scala b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeUtils.scala
index 716db9fa7603..4b732c1592ab 100644
--- a/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeUtils.scala
+++ b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeUtils.scala
@@ -17,12 +17,19 @@
 
 package org.apache.spark.streaming.flume
 
+import java.net.InetSocketAddress
+
+import org.apache.spark.annotation.Experimental
 import org.apache.spark.storage.StorageLevel
 import org.apache.spark.streaming.StreamingContext
-import org.apache.spark.streaming.api.java.{JavaReceiverInputDStream, JavaInputDStream, JavaStreamingContext, JavaDStream}
-import org.apache.spark.streaming.dstream.{ReceiverInputDStream, DStream}
+import org.apache.spark.streaming.api.java.{JavaReceiverInputDStream, JavaStreamingContext}
+import org.apache.spark.streaming.dstream.ReceiverInputDStream
+
 
 object FlumeUtils {
+  private val DEFAULT_POLLING_PARALLELISM = 5
+  private val DEFAULT_POLLING_BATCH_SIZE = 1000
+
   /**
    * Create a input stream from a Flume source.
    * @param ssc      StreamingContext object
@@ -56,7 +63,7 @@ object FlumeUtils {
     ): ReceiverInputDStream[SparkFlumeEvent] = {
     val inputStream = new FlumeInputDStream[SparkFlumeEvent](
         ssc, hostname, port, storageLevel, enableDecompression)
-        
+
     inputStream
   }
 
@@ -105,4 +112,135 @@ object FlumeUtils {
     ): JavaReceiverInputDStream[SparkFlumeEvent] = {
     createStream(jssc.ssc, hostname, port, storageLevel, enableDecompression)
   }
+
+  /**
+   * Creates an input stream that is to be used with the Spark Sink deployed on a Flume agent.
+   * This stream will poll the sink for data and will pull events as they are available.
+   * This stream will use a batch size of 1000 events and run 5 threads to pull data.
+   * @param hostname Address of the host on which the Spark Sink is running
+   * @param port Port of the host at which the Spark Sink is listening
+   * @param storageLevel Storage level to use for storing the received objects
+   */
+  @Experimental
+  def createPollingStream(
+      ssc: StreamingContext,
+      hostname: String,
+      port: Int,
+      storageLevel: StorageLevel = StorageLevel.MEMORY_AND_DISK_SER_2
+    ): ReceiverInputDStream[SparkFlumeEvent] = {
+    createPollingStream(ssc, Seq(new InetSocketAddress(hostname, port)), storageLevel)
+  }
+
+  /**
+   * Creates an input stream that is to be used with the Spark Sink deployed on a Flume agent.
+   * This stream will poll the sink for data and will pull events as they are available.
+   * This stream will use a batch size of 1000 events and run 5 threads to pull data.
+   * @param addresses List of InetSocketAddresses representing the hosts to connect to.
+   * @param storageLevel Storage level to use for storing the received objects
+   */
+  @Experimental
+  def createPollingStream(
+      ssc: StreamingContext,
+      addresses: Seq[InetSocketAddress],
+      storageLevel: StorageLevel
+    ): ReceiverInputDStream[SparkFlumeEvent] = {
+    createPollingStream(ssc, addresses, storageLevel,
+      DEFAULT_POLLING_BATCH_SIZE, DEFAULT_POLLING_PARALLELISM)
+  }
+
+  /**
+   * Creates an input stream that is to be used with the Spark Sink deployed on a Flume agent.
+   * This stream will poll the sink for data and will pull events as they are available.
+   * @param addresses List of InetSocketAddresses representing the hosts to connect to.
+   * @param maxBatchSize Maximum number of events to be pulled from the Spark sink in a
+   *                     single RPC call
+   * @param parallelism Number of concurrent requests this stream should send to the sink. Note
+   *                    that having a higher number of requests concurrently being pulled will
+   *                    result in this stream using more threads
+   * @param storageLevel Storage level to use for storing the received objects
+   */
+  @Experimental
+  def createPollingStream(
+      ssc: StreamingContext,
+      addresses: Seq[InetSocketAddress],
+      storageLevel: StorageLevel,
+      maxBatchSize: Int,
+      parallelism: Int
+    ): ReceiverInputDStream[SparkFlumeEvent] = {
+    new FlumePollingInputDStream[SparkFlumeEvent](ssc, addresses, maxBatchSize,
+      parallelism, storageLevel)
+  }
+
+  /**
+   * Creates an input stream that is to be used with the Spark Sink deployed on a Flume agent.
+   * This stream will poll the sink for data and will pull events as they are available.
+   * This stream will use a batch size of 1000 events and run 5 threads to pull data.
+   * @param hostname Hostname of the host on which the Spark Sink is running
+   * @param port     Port of the host at which the Spark Sink is listening
+   */
+  @Experimental
+  def createPollingStream(
+      jssc: JavaStreamingContext,
+      hostname: String,
+      port: Int
+    ): JavaReceiverInputDStream[SparkFlumeEvent] = {
+    createPollingStream(jssc, hostname, port, StorageLevel.MEMORY_AND_DISK_SER_2)
+  }
+
+  /**
+   * Creates an input stream that is to be used with the Spark Sink deployed on a Flume agent.
+   * This stream will poll the sink for data and will pull events as they are available.
+   * This stream will use a batch size of 1000 events and run 5 threads to pull data.
+   * @param hostname     Hostname of the host on which the Spark Sink is running
+   * @param port         Port of the host at which the Spark Sink is listening
+   * @param storageLevel Storage level to use for storing the received objects
+   */
+  @Experimental
+  def createPollingStream(
+      jssc: JavaStreamingContext,
+      hostname: String,
+      port: Int,
+      storageLevel: StorageLevel
+    ): JavaReceiverInputDStream[SparkFlumeEvent] = {
+    createPollingStream(jssc, Array(new InetSocketAddress(hostname, port)), storageLevel)
+  }
+
+  /**
+   * Creates an input stream that is to be used with the Spark Sink deployed on a Flume agent.
+   * This stream will poll the sink for data and will pull events as they are available.
+   * This stream will use a batch size of 1000 events and run 5 threads to pull data.
+   * @param addresses    List of InetSocketAddresses on which the Spark Sink is running.
+   * @param storageLevel Storage level to use for storing the received objects
+   */
+  @Experimental
+  def createPollingStream(
+      jssc: JavaStreamingContext,
+      addresses: Array[InetSocketAddress],
+      storageLevel: StorageLevel
+    ): JavaReceiverInputDStream[SparkFlumeEvent] = {
+    createPollingStream(jssc, addresses, storageLevel,
+      DEFAULT_POLLING_BATCH_SIZE, DEFAULT_POLLING_PARALLELISM)
+  }
+
+  /**
+   * Creates an input stream that is to be used with the Spark Sink deployed on a Flume agent.
+   * This stream will poll the sink for data and will pull events as they are available.
+   * @param addresses    List of InetSocketAddresses on which the Spark Sink is running
+   * @param maxBatchSize The maximum number of events to be pulled from the Spark sink in a
+   *                     single RPC call
+   * @param parallelism  Number of concurrent requests this stream should send to the sink. Note
+   *                     that having a higher number of requests concurrently being pulled will
+   *                     result in this stream using more threads
+   * @param storageLevel Storage level to use for storing the received objects
+   */
+  @Experimental
+  def createPollingStream(
+      jssc: JavaStreamingContext,
+      addresses: Array[InetSocketAddress],
+      storageLevel: StorageLevel,
+      maxBatchSize: Int,
+      parallelism: Int
+    ): JavaReceiverInputDStream[SparkFlumeEvent] = {
+    createPollingStream(jssc.ssc, addresses, storageLevel, maxBatchSize, parallelism)
+  }
 }
diff --git a/external/flume/src/test/java/org/apache/spark/streaming/flume/JavaFlumePollingStreamSuite.java b/external/flume/src/test/java/org/apache/spark/streaming/flume/JavaFlumePollingStreamSuite.java
new file mode 100644
index 000000000000..79c5b91654b4
--- /dev/null
+++ b/external/flume/src/test/java/org/apache/spark/streaming/flume/JavaFlumePollingStreamSuite.java
@@ -0,0 +1,44 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.streaming.flume;
+
+import java.net.InetSocketAddress;
+
+import org.apache.spark.storage.StorageLevel;
+import org.apache.spark.streaming.LocalJavaStreamingContext;
+
+import org.apache.spark.streaming.api.java.JavaReceiverInputDStream;
+import org.junit.Test;
+
+public class JavaFlumePollingStreamSuite extends LocalJavaStreamingContext {
+  @Test
+  public void testFlumeStream() {
+    // tests the API, does not actually test data receiving
+    InetSocketAddress[] addresses = new InetSocketAddress[] {
+        new InetSocketAddress("localhost", 12345)
+    };
+    JavaReceiverInputDStream<SparkFlumeEvent> test1 =
+        FlumeUtils.createPollingStream(ssc, "localhost", 12345);
+    JavaReceiverInputDStream<SparkFlumeEvent> test2 = FlumeUtils.createPollingStream(
+        ssc, "localhost", 12345, StorageLevel.MEMORY_AND_DISK_SER_2());
+    JavaReceiverInputDStream<SparkFlumeEvent> test3 = FlumeUtils.createPollingStream(
+        ssc, addresses, StorageLevel.MEMORY_AND_DISK_SER_2());
+    JavaReceiverInputDStream<SparkFlumeEvent> test4 = FlumeUtils.createPollingStream(
+        ssc, addresses, StorageLevel.MEMORY_AND_DISK_SER_2(), 100, 5);
+  }
+}
diff --git a/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumePollingStreamSuite.scala b/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumePollingStreamSuite.scala
new file mode 100644
index 000000000000..47071d0cc471
--- /dev/null
+++ b/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumePollingStreamSuite.scala
@@ -0,0 +1,195 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+package org.apache.spark.streaming.flume
+
+import java.net.InetSocketAddress
+import java.util.concurrent.{Callable, ExecutorCompletionService, Executors}
+
+import scala.collection.JavaConversions._
+import scala.collection.mutable.{SynchronizedBuffer, ArrayBuffer}
+
+import org.apache.flume.Context
+import org.apache.flume.channel.MemoryChannel
+import org.apache.flume.conf.Configurables
+import org.apache.flume.event.EventBuilder
+
+import org.apache.spark.storage.StorageLevel
+import org.apache.spark.streaming.dstream.ReceiverInputDStream
+import org.apache.spark.streaming.util.ManualClock
+import org.apache.spark.streaming.{TestSuiteBase, TestOutputStream, StreamingContext}
+import org.apache.spark.streaming.flume.sink._
+
+class FlumePollingStreamSuite extends TestSuiteBase {
+
+  val testPort = 9999
+  val batchCount = 5
+  val eventsPerBatch = 100
+  val totalEventsPerChannel = batchCount * eventsPerBatch
+  val channelCapacity = 5000
+
+  test("flume polling test") {
+    // Set up the streaming context and input streams
+    val ssc = new StreamingContext(conf, batchDuration)
+    val flumeStream: ReceiverInputDStream[SparkFlumeEvent] =
+      FlumeUtils.createPollingStream(ssc, Seq(new InetSocketAddress("localhost", testPort)),
+        StorageLevel.MEMORY_AND_DISK, eventsPerBatch, 1)
+    val outputBuffer = new ArrayBuffer[Seq[SparkFlumeEvent]]
+      with SynchronizedBuffer[Seq[SparkFlumeEvent]]
+    val outputStream = new TestOutputStream(flumeStream, outputBuffer)
+    outputStream.register()
+
+    // Start the channel and sink.
+    val context = new Context()
+    context.put("capacity", channelCapacity.toString)
+    context.put("transactionCapacity", "1000")
+    context.put("keep-alive", "0")
+    val channel = new MemoryChannel()
+    Configurables.configure(channel, context)
+
+    val sink = new SparkSink()
+    context.put(SparkSinkConfig.CONF_HOSTNAME, "localhost")
+    context.put(SparkSinkConfig.CONF_PORT, String.valueOf(testPort))
+    Configurables.configure(sink, context)
+    sink.setChannel(channel)
+    sink.start()
+    ssc.start()
+
+    writeAndVerify(Seq(channel), ssc, outputBuffer)
+    assertChannelIsEmpty(channel)
+    sink.stop()
+    channel.stop()
+  }
+
+  test("flume polling test multiple hosts") {
+    // Set up the streaming context and input streams
+    val ssc = new StreamingContext(conf, batchDuration)
+    val addresses = Seq(testPort, testPort + 1).map(new InetSocketAddress("localhost", _))
+    val flumeStream: ReceiverInputDStream[SparkFlumeEvent] =
+      FlumeUtils.createPollingStream(ssc, addresses, StorageLevel.MEMORY_AND_DISK,
+        eventsPerBatch, 5)
+    val outputBuffer = new ArrayBuffer[Seq[SparkFlumeEvent]]
+      with SynchronizedBuffer[Seq[SparkFlumeEvent]]
+    val outputStream = new TestOutputStream(flumeStream, outputBuffer)
+    outputStream.register()
+
+    // Start the channel and sink.
+    val context = new Context()
+    context.put("capacity", channelCapacity.toString)
+    context.put("transactionCapacity", "1000")
+    context.put("keep-alive", "0")
+    val channel = new MemoryChannel()
+    Configurables.configure(channel, context)
+
+    val channel2 = new MemoryChannel()
+    Configurables.configure(channel2, context)
+
+    val sink = new SparkSink()
+    context.put(SparkSinkConfig.CONF_HOSTNAME, "localhost")
+    context.put(SparkSinkConfig.CONF_PORT, String.valueOf(testPort))
+    Configurables.configure(sink, context)
+    sink.setChannel(channel)
+    sink.start()
+
+    val sink2 = new SparkSink()
+    context.put(SparkSinkConfig.CONF_HOSTNAME, "localhost")
+    context.put(SparkSinkConfig.CONF_PORT, String.valueOf(testPort + 1))
+    Configurables.configure(sink2, context)
+    sink2.setChannel(channel2)
+    sink2.start()
+    ssc.start()
+    writeAndVerify(Seq(channel, channel2), ssc, outputBuffer)
+    assertChannelIsEmpty(channel)
+    assertChannelIsEmpty(channel2)
+    sink.stop()
+    channel.stop()
+  }
+
+  def writeAndVerify(channels: Seq[MemoryChannel], ssc: StreamingContext,
+    outputBuffer: ArrayBuffer[Seq[SparkFlumeEvent]]) {
+    val clock = ssc.scheduler.clock.asInstanceOf[ManualClock]
+    val executor = Executors.newCachedThreadPool()
+    val executorCompletion = new ExecutorCompletionService[Void](executor)
+    channels.map(channel => {
+      executorCompletion.submit(new TxnSubmitter(channel, clock))
+    })
+    for (i <- 0 until channels.size) {
+      executorCompletion.take()
+    }
+    val startTime = System.currentTimeMillis()
+    while (outputBuffer.size < batchCount * channels.size &&
+      System.currentTimeMillis() - startTime < 15000) {
+      logInfo("output.size = " + outputBuffer.size)
+      Thread.sleep(100)
+    }
+    val timeTaken = System.currentTimeMillis() - startTime
+    assert(timeTaken < 15000, "Operation timed out after " + timeTaken + " ms")
+    logInfo("Stopping context")
+    ssc.stop()
+
+    val flattenedBuffer = outputBuffer.flatten
+    assert(flattenedBuffer.size === totalEventsPerChannel * channels.size)
+    var counter = 0
+    for (k <- 0 until channels.size; i <- 0 until totalEventsPerChannel) {
+      val eventToVerify = EventBuilder.withBody((channels(k).getName + " - " +
+        String.valueOf(i)).getBytes("utf-8"),
+        Map[String, String]("test-" + i.toString -> "header"))
+      var found = false
+      var j = 0
+      while (j < flattenedBuffer.size && !found) {
+        val strToCompare = new String(flattenedBuffer(j).event.getBody.array(), "utf-8")
+        if (new String(eventToVerify.getBody, "utf-8") == strToCompare &&
+          eventToVerify.getHeaders.get("test-" + i.toString)
+            .equals(flattenedBuffer(j).event.getHeaders.get("test-" + i.toString))) {
+          found = true
+          counter += 1
+        }
+        j += 1
+      }
+    }
+    assert(counter === totalEventsPerChannel * channels.size)
+  }
+
+  def assertChannelIsEmpty(channel: MemoryChannel) = {
+    val queueRemaining = channel.getClass.getDeclaredField("queueRemaining");
+    queueRemaining.setAccessible(true)
+    val m = queueRemaining.get(channel).getClass.getDeclaredMethod("availablePermits")
+    assert(m.invoke(queueRemaining.get(channel)).asInstanceOf[Int] === 5000)
+  }
+
+  private class TxnSubmitter(channel: MemoryChannel, clock: ManualClock) extends Callable[Void] {
+    override def call(): Void = {
+      var t = 0
+      for (i <- 0 until batchCount) {
+        val tx = channel.getTransaction
+        tx.begin()
+        for (j <- 0 until eventsPerBatch) {
+          channel.put(EventBuilder.withBody((channel.getName + " - " + String.valueOf(t)).getBytes(
+            "utf-8"),
+            Map[String, String]("test-" + t.toString -> "header")))
+          t += 1
+        }
+        tx.commit()
+        tx.close()
+        Thread.sleep(500) // Allow some time for the events to reach
+        clock.addToTime(batchDuration.milliseconds)
+      }
+      null
+    }
+  }
+}
diff --git a/pom.xml b/pom.xml
index 93ef3b91b5bc..8b1435cfe5d1 100644
--- a/pom.xml
+++ b/pom.xml
@@ -100,6 +100,7 @@
     <module>external/twitter</module>
     <module>external/kafka</module>
     <module>external/flume</module>
+    <module>external/flume-sink</module>
     <module>external/zeromq</module>
     <module>external/mqtt</module>
     <module>examples</module>
diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index 1629bc2cba8b..0a6326e72297 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -30,11 +30,12 @@ object BuildCommons {
 
   private val buildLocation = file(".").getAbsoluteFile.getParentFile
 
-  val allProjects@Seq(bagel, catalyst, core, graphx, hive, hiveThriftServer, mllib, repl, spark, sql,
-  streaming, streamingFlume, streamingKafka, streamingMqtt, streamingTwitter, streamingZeromq) =
+  val allProjects@Seq(bagel, catalyst, core, graphx, hive, hiveThriftServer, mllib, repl, spark,
+  sql, streaming, streamingFlumeSink, streamingFlume, streamingKafka, streamingMqtt,
+  streamingTwitter, streamingZeromq) =
     Seq("bagel", "catalyst", "core", "graphx", "hive", "hive-thriftserver", "mllib", "repl",
-      "spark", "sql", "streaming", "streaming-flume", "streaming-kafka", "streaming-mqtt",
-      "streaming-twitter", "streaming-zeromq").map(ProjectRef(buildLocation, _))
+      "spark", "sql", "streaming", "streaming-flume-sink", "streaming-flume", "streaming-kafka",
+      "streaming-mqtt", "streaming-twitter", "streaming-zeromq").map(ProjectRef(buildLocation, _))
 
   val optionallyEnabledProjects@Seq(yarn, yarnStable, yarnAlpha, java8Tests, sparkGangliaLgpl) =
     Seq("yarn", "yarn-stable", "yarn-alpha", "java8-tests", "ganglia-lgpl")
@@ -156,10 +157,9 @@ object SparkBuild extends PomBuild {
   /* Enable tests settings for all projects except examples, assembly and tools */
   (allProjects ++ optionallyEnabledProjects).foreach(enable(TestSettings.settings))
 
-  /* Enable Mima for all projects except spark, hive, catalyst, sql  and repl */
   // TODO: Add Sql to mima checks
-  allProjects.filterNot(x => Seq(spark, sql, hive, hiveThriftServer, catalyst, repl).contains(x)).
-    foreach (x => enable(MimaBuild.mimaSettings(sparkHome, x))(x))
+  allProjects.filterNot(x => Seq(spark, sql, hive, hiveThriftServer, catalyst, repl,
+    streamingFlumeSink).contains(x)).foreach(x => enable(MimaBuild.mimaSettings(sparkHome, x))(x))
 
   /* Enable Assembly for all assembly projects */
   assemblyProjects.foreach(enable(Assembly.settings))
@@ -173,6 +173,8 @@ object SparkBuild extends PomBuild {
   /* Hive console settings */
   enable(Hive.settings)(hive)
 
+  enable(Flume.settings)(streamingFlumeSink)
+
   // TODO: move this to its upstream project.
   override def projectDefinitions(baseDirectory: File): Seq[Project] = {
     super.projectDefinitions(baseDirectory).map { x =>
@@ -183,6 +185,10 @@ object SparkBuild extends PomBuild {
 
 }
 
+object Flume {
+  lazy val settings = sbtavro.SbtAvro.avroSettings
+}
+
 object SQL {
 
   lazy val settings = Seq(
diff --git a/project/plugins.sbt b/project/plugins.sbt
index d3ac4bf335e8..06d18e193076 100644
--- a/project/plugins.sbt
+++ b/project/plugins.sbt
@@ -24,3 +24,5 @@ addSbtPlugin("com.typesafe" % "sbt-mima-plugin" % "0.1.6")
 addSbtPlugin("com.alpinenow" % "junit_xml_listener" % "0.5.1")
 
 addSbtPlugin("com.eed3si9n" % "sbt-unidoc" % "0.3.0")
+
+addSbtPlugin("com.cavorite" % "sbt-avro" % "0.3.2")

From 0c5c6a63d19bed2a813a09309c46971ecdd173f0 Mon Sep 17 00:00:00 2001
From: Daoyuan <daoyuan.wang@intel.com>
Date: Tue, 29 Jul 2014 12:22:48 -0700
Subject: [PATCH 796/810] [SQL]change some test lists

1. there's no `hook_context.q` but a `hook_context_cs.q` in query folder
2. there's no `compute_stats_table.q` in query folder
3. there's no `having1.q` in query folder
4. `udf_E` and `udf_PI` appear twice in white list

Author: Daoyuan <daoyuan.wang@intel.com>

Closes #1634 from adrian-wang/testcases and squashes the following commits:

d7482ce [Daoyuan] change some test lists
---
 .../spark/sql/hive/execution/HiveCompatibilitySuite.scala   | 6 +-----
 1 file changed, 1 insertion(+), 5 deletions(-)

diff --git a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
index c69e93ba2b9b..4fef07116171 100644
--- a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
+++ b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
@@ -52,7 +52,7 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
   override def blackList = Seq(
     // These tests use hooks that are not on the classpath and thus break all subsequent execution.
     "hook_order",
-    "hook_context",
+    "hook_context_cs",
     "mapjoin_hook",
     "multi_sahooks",
     "overridden_confs",
@@ -289,7 +289,6 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     "compute_stats_empty_table",
     "compute_stats_long",
     "compute_stats_string",
-    "compute_stats_table",
     "convert_enum_to_string",
     "correlationoptimizer1",
     "correlationoptimizer10",
@@ -395,7 +394,6 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     "groupby_sort_9",
     "groupby_sort_test_1",
     "having",
-    "having1",
     "implicit_cast1",
     "innerjoin",
     "inoutdriver",
@@ -697,8 +695,6 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     "udf7",
     "udf8",
     "udf9",
-    "udf_E",
-    "udf_PI",
     "udf_abs",
     "udf_acos",
     "udf_add",

From e3643485de8fdaf5c52b266fead1b13214f29d5e Mon Sep 17 00:00:00 2001
From: Yin Huai <huai@cse.ohio-state.edu>
Date: Tue, 29 Jul 2014 12:23:34 -0700
Subject: [PATCH 797/810] [SPARK-2730][SQL] When retrieving a value from a Map,
 GetItem evaluates key twice

JIRA: https://issues.apache.org/jira/browse/SPARK-2730

Author: Yin Huai <huai@cse.ohio-state.edu>

Closes #1637 from yhuai/SPARK-2730 and squashes the following commits:

1a9f24e [Yin Huai] Remove unnecessary key evaluation.
---
 .../org/apache/spark/sql/catalyst/expressions/complexTypes.scala | 1 -
 1 file changed, 1 deletion(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypes.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypes.scala
index 5d3bb25ad568..0acb29012f31 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypes.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypes.scala
@@ -61,7 +61,6 @@ case class GetItem(child: Expression, ordinal: Expression) extends Expression {
           }
         } else {
           val baseValue = value.asInstanceOf[Map[Any, _]]
-          val key = ordinal.eval(input)
           baseValue.get(key).orNull
         }
       }

From f0d880e288eba97c86dceb1b5edab4f3a935943b Mon Sep 17 00:00:00 2001
From: Davies Liu <davies.liu@gmail.com>
Date: Tue, 29 Jul 2014 12:31:39 -0700
Subject: [PATCH 798/810] [SPARK-2674] [SQL] [PySpark] support datetime type
 for SchemaRDD

Datetime and time in Python will be converted into java.util.Calendar after serialization, it will be converted into java.sql.Timestamp during inferSchema().

In javaToPython(), Timestamp will be converted into Calendar, then be converted into datetime in Python after pickling.

Author: Davies Liu <davies.liu@gmail.com>

Closes #1601 from davies/date and squashes the following commits:

f0599b0 [Davies Liu] remove tests for sets and tuple in sql, fix list of list
c9d607a [Davies Liu] convert datetype for runtime
709d40d [Davies Liu] remove brackets
96db384 [Davies Liu] support datetime type for SchemaRDD
---
 .../apache/spark/api/python/PythonRDD.scala   |  4 +-
 python/pyspark/sql.py                         | 22 +++++----
 .../org/apache/spark/sql/SQLContext.scala     | 40 ++++++++++++++--
 .../org/apache/spark/sql/SchemaRDD.scala      | 46 +++++++------------
 4 files changed, 68 insertions(+), 44 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
index d87783efd2d0..0d8453fb184a 100644
--- a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
+++ b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
@@ -550,11 +550,11 @@ private[spark] object PythonRDD extends Logging {
   def pythonToJavaMap(pyRDD: JavaRDD[Array[Byte]]): JavaRDD[Map[String, _]] = {
     pyRDD.rdd.mapPartitions { iter =>
       val unpickle = new Unpickler
-      // TODO: Figure out why flatMap is necessay for pyspark
       iter.flatMap { row =>
         unpickle.loads(row) match {
+          // in case of objects are pickled in batch mode
           case objs: java.util.ArrayList[JMap[String, _] @unchecked] => objs.map(_.toMap)
-          // Incase the partition doesn't have a collection
+          // not in batch mode
           case obj: JMap[String @unchecked, _] => Seq(obj.toMap)
         }
       }
diff --git a/python/pyspark/sql.py b/python/pyspark/sql.py
index cb83e8917682..a6b3277db326 100644
--- a/python/pyspark/sql.py
+++ b/python/pyspark/sql.py
@@ -47,12 +47,14 @@ def __init__(self, sparkContext, sqlContext=None):
             ...
         ValueError:...
 
-        >>> allTypes = sc.parallelize([{"int" : 1, "string" : "string", "double" : 1.0, "long": 1L,
-        ... "boolean" : True}])
+        >>> from datetime import datetime
+        >>> allTypes = sc.parallelize([{"int": 1, "string": "string", "double": 1.0, "long": 1L,
+        ... "boolean": True, "time": datetime(2010, 1, 1, 1, 1, 1), "dict": {"a": 1},
+        ... "list": [1, 2, 3]}])
         >>> srdd = sqlCtx.inferSchema(allTypes).map(lambda x: (x.int, x.string, x.double, x.long,
-        ... x.boolean))
+        ... x.boolean, x.time, x.dict["a"], x.list))
         >>> srdd.collect()[0]
-        (1, u'string', 1.0, 1, True)
+        (1, u'string', 1.0, 1, True, datetime.datetime(2010, 1, 1, 1, 1, 1), 1, [1, 2, 3])
         """
         self._sc = sparkContext
         self._jsc = self._sc._jsc
@@ -88,13 +90,13 @@ def inferSchema(self, rdd):
 
         >>> from array import array
         >>> srdd = sqlCtx.inferSchema(nestedRdd1)
-        >>> srdd.collect() == [{"f1" : array('i', [1, 2]), "f2" : {"row1" : 1.0}},
-        ...                    {"f1" : array('i', [2, 3]), "f2" : {"row2" : 2.0}}]
+        >>> srdd.collect() == [{"f1" : [1, 2], "f2" : {"row1" : 1.0}},
+        ...                    {"f1" : [2, 3], "f2" : {"row2" : 2.0}}]
         True
 
         >>> srdd = sqlCtx.inferSchema(nestedRdd2)
-        >>> srdd.collect() == [{"f1" : [[1, 2], [2, 3]], "f2" : set([1, 2]), "f3" : (1, 2)},
-        ...                    {"f1" : [[2, 3], [3, 4]], "f2" : set([2, 3]), "f3" : (2, 3)}]
+        >>> srdd.collect() == [{"f1" : [[1, 2], [2, 3]], "f2" : [1, 2]},
+        ...                    {"f1" : [[2, 3], [3, 4]], "f2" : [2, 3]}]
         True
         """
         if (rdd.__class__ is SchemaRDD):
@@ -509,8 +511,8 @@ def _test():
         {"f1": array('i', [1, 2]), "f2": {"row1": 1.0}},
         {"f1": array('i', [2, 3]), "f2": {"row2": 2.0}}])
     globs['nestedRdd2'] = sc.parallelize([
-        {"f1": [[1, 2], [2, 3]], "f2": set([1, 2]), "f3": (1, 2)},
-        {"f1": [[2, 3], [3, 4]], "f2": set([2, 3]), "f3": (2, 3)}])
+        {"f1": [[1, 2], [2, 3]], "f2": [1, 2]},
+        {"f1": [[2, 3], [3, 4]], "f2": [2, 3]}])
     (failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
     globs['sc'].stop()
     if failure_count:
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
index 4abd89955bd2..c178dad66253 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
@@ -352,8 +352,10 @@ class SQLContext(@transient val sparkContext: SparkContext)
       case c: java.lang.Long => LongType
       case c: java.lang.Double => DoubleType
       case c: java.lang.Boolean => BooleanType
+      case c: java.math.BigDecimal => DecimalType
+      case c: java.sql.Timestamp => TimestampType
+      case c: java.util.Calendar => TimestampType
       case c: java.util.List[_] => ArrayType(typeFor(c.head))
-      case c: java.util.Set[_] => ArrayType(typeFor(c.head))
       case c: java.util.Map[_, _] =>
         val (key, value) = c.head
         MapType(typeFor(key), typeFor(value))
@@ -362,11 +364,43 @@ class SQLContext(@transient val sparkContext: SparkContext)
         ArrayType(typeFor(elem))
       case c => throw new Exception(s"Object of type $c cannot be used")
     }
-    val schema = rdd.first().map { case (fieldName, obj) =>
+    val firstRow = rdd.first()
+    val schema = firstRow.map { case (fieldName, obj) =>
       AttributeReference(fieldName, typeFor(obj), true)()
     }.toSeq
 
-    val rowRdd = rdd.mapPartitions { iter =>
+    def needTransform(obj: Any): Boolean = obj match {
+      case c: java.util.List[_] => true
+      case c: java.util.Map[_, _] => true
+      case c if c.getClass.isArray => true
+      case c: java.util.Calendar => true
+      case c => false
+    }
+
+    // convert JList, JArray into Seq, convert JMap into Map
+    // convert Calendar into Timestamp
+    def transform(obj: Any): Any = obj match {
+      case c: java.util.List[_] => c.map(transform).toSeq
+      case c: java.util.Map[_, _] => c.map {
+        case (key, value) => (key, transform(value))
+      }.toMap
+      case c if c.getClass.isArray =>
+        c.asInstanceOf[Array[_]].map(transform).toSeq
+      case c: java.util.Calendar =>
+        new java.sql.Timestamp(c.getTime().getTime())
+      case c => c
+    }
+
+    val need = firstRow.exists {case (key, value) => needTransform(value)}
+    val transformed = if (need) {
+      rdd.mapPartitions { iter =>
+        iter.map {
+          m => m.map {case (key, value) => (key, transform(value))}
+        }
+      }
+    } else rdd
+
+    val rowRdd = transformed.mapPartitions { iter =>
       iter.map { map =>
         new GenericRow(map.values.toArray.asInstanceOf[Array[Any]]): Row
       }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala
index 31d27bb4f057..019ff9d300a1 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala
@@ -32,7 +32,7 @@ import org.apache.spark.sql.catalyst.analysis._
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.logical._
 import org.apache.spark.sql.catalyst.plans.{Inner, JoinType}
-import org.apache.spark.sql.catalyst.types.{ArrayType, BooleanType, StructType}
+import org.apache.spark.sql.catalyst.types.{DataType, ArrayType, BooleanType, StructType, MapType}
 import org.apache.spark.sql.execution.{ExistingRdd, SparkLogicalPlan}
 import org.apache.spark.api.java.JavaRDD
 
@@ -376,39 +376,27 @@ class SchemaRDD(
    * Converts a JavaRDD to a PythonRDD. It is used by pyspark.
    */
   private[sql] def javaToPython: JavaRDD[Array[Byte]] = {
+    def toJava(obj: Any, dataType: DataType): Any = dataType match {
+      case struct: StructType => rowToMap(obj.asInstanceOf[Row], struct)
+      case array: ArrayType => obj match {
+        case seq: Seq[Any] => seq.map(x => toJava(x, array.elementType)).asJava
+        case list: JList[_] => list.map(x => toJava(x, array.elementType)).asJava
+        case arr if arr != null && arr.getClass.isArray =>
+          arr.asInstanceOf[Array[Any]].map(x => toJava(x, array.elementType))
+        case other => other
+      }
+      case mt: MapType => obj.asInstanceOf[Map[_, _]].map {
+        case (k, v) => (k, toJava(v, mt.valueType)) // key should be primitive type
+      }.asJava
+      // Pyrolite can handle Timestamp
+      case other => obj
+    }
     def rowToMap(row: Row, structType: StructType): JMap[String, Any] = {
       val fields = structType.fields.map(field => (field.name, field.dataType))
       val map: JMap[String, Any] = new java.util.HashMap
       row.zip(fields).foreach {
-        case (obj, (attrName, dataType)) =>
-          dataType match {
-            case struct: StructType => map.put(attrName, rowToMap(obj.asInstanceOf[Row], struct))
-            case array @ ArrayType(struct: StructType) =>
-              val arrayValues = obj match {
-                case seq: Seq[Any] =>
-                  seq.map(element => rowToMap(element.asInstanceOf[Row], struct)).asJava
-                case list: JList[_] =>
-                  list.map(element => rowToMap(element.asInstanceOf[Row], struct))
-                case set: JSet[_] =>
-                  set.map(element => rowToMap(element.asInstanceOf[Row], struct))
-                case arr if arr != null && arr.getClass.isArray =>
-                  arr.asInstanceOf[Array[Any]].map {
-                    element => rowToMap(element.asInstanceOf[Row], struct)
-                  }
-                case other => other
-              }
-              map.put(attrName, arrayValues)
-            case array: ArrayType => {
-              val arrayValues = obj match {
-                case seq: Seq[Any] => seq.asJava
-                case other => other
-              }
-              map.put(attrName, arrayValues)
-            }
-            case other => map.put(attrName, obj)
-          }
+        case (obj, (attrName, dataType)) => map.put(attrName, toJava(obj, dataType))
       }
-
       map
     }
 

From dc9653641f8806960d79652afa043c3fb84f25d2 Mon Sep 17 00:00:00 2001
From: Doris Xin <doris.s.xin@gmail.com>
Date: Tue, 29 Jul 2014 12:49:44 -0700
Subject: [PATCH 799/810] [SPARK-2082] stratified sampling in PairRDDFunctions
 that guarantees exact sample size

Implemented stratified sampling that guarantees exact sample size using ScaRSR with two passes over the RDD for sampling without replacement and three passes for sampling with replacement.

Author: Doris Xin <doris.s.xin@gmail.com>
Author: Xiangrui Meng <meng@databricks.com>

Closes #1025 from dorx/stratified and squashes the following commits:

245439e [Doris Xin] moved minSamplingRate to getUpperBound
eaf5771 [Doris Xin] bug fixes.
17a381b [Doris Xin] fixed a merge issue and a failed unit
ea7d27f [Doris Xin] merge master
b223529 [Xiangrui Meng] use approx bounds for poisson fix poisson mean for waitlisting add unit tests for Java
b3013a4 [Xiangrui Meng] move math3 back to test scope
eecee5f [Doris Xin] Merge branch 'master' into stratified
f4c21f3 [Doris Xin] Reviewer comments
a10e68d [Doris Xin] style fix
a2bf756 [Doris Xin] Merge branch 'master' into stratified
680b677 [Doris Xin] use mapPartitionWithIndex instead
9884a9f [Doris Xin] style fix
bbfb8c9 [Doris Xin] Merge branch 'master' into stratified
ee9d260 [Doris Xin] addressed reviewer comments
6b5b10b [Doris Xin] Merge branch 'master' into stratified
254e03c [Doris Xin] minor fixes and Java API.
4ad516b [Doris Xin] remove unused imports from PairRDDFunctions
bd9dc6e [Doris Xin] unit bug and style violation fixed
1fe1cff [Doris Xin] Changed fractionByKey to a map to enable arg check
944a10c [Doris Xin] [SPARK-2145] Add lower bound on sampling rate
0214a76 [Doris Xin] cleanUp
90d94c0 [Doris Xin] merge master
9e74ab5 [Doris Xin] Separated out most of the logic in sampleByKey
7327611 [Doris Xin] merge master
50581fc [Doris Xin] added a TODO for logging in python
46f6c8c [Doris Xin] fixed the NPE caused by closures being cleaned before being passed into the aggregate function
7e1a481 [Doris Xin] changed the permission on SamplingUtil
1d413ce [Doris Xin] fixed checkstyle issues
9ee94ee [Doris Xin] [SPARK-2082] stratified sampling in PairRDDFunctions that guarantees exact sample size
e3fd6a6 [Doris Xin] Merge branch 'master' into takeSample
7cab53a [Doris Xin] fixed import bug in rdd.py
ffea61a [Doris Xin] SPARK-1939: Refactor takeSample method in RDD
1441977 [Doris Xin] SPARK-1939 Refactor takeSample method in RDD to use ScaSRS
---
 .../apache/spark/api/java/JavaPairRDD.scala   |  69 +++-
 .../apache/spark/rdd/PairRDDFunctions.scala   |  54 ++-
 .../spark/util/random/SamplingUtils.scala     |  74 +++-
 .../util/random/StratifiedSamplingUtils.scala | 316 ++++++++++++++++++
 .../java/org/apache/spark/JavaAPISuite.java   |  37 ++
 .../spark/rdd/PairRDDFunctionsSuite.scala     | 116 +++++++
 pom.xml                                       |   6 +
 7 files changed, 656 insertions(+), 16 deletions(-)
 create mode 100644 core/src/main/scala/org/apache/spark/util/random/StratifiedSamplingUtils.scala

diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala b/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala
index 4f3081433a54..31bf8dced263 100644
--- a/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala
+++ b/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.api.java
 
-import java.util.{Comparator, List => JList}
+import java.util.{Comparator, List => JList, Map => JMap}
 import java.lang.{Iterable => JIterable}
 
 import scala.collection.JavaConversions._
@@ -129,6 +129,73 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])
   def sample(withReplacement: Boolean, fraction: Double, seed: Long): JavaPairRDD[K, V] =
     new JavaPairRDD[K, V](rdd.sample(withReplacement, fraction, seed))
 
+  /**
+   * Return a subset of this RDD sampled by key (via stratified sampling).
+   *
+   * Create a sample of this RDD using variable sampling rates for different keys as specified by
+   * `fractions`, a key to sampling rate map.
+   *
+   * If `exact` is set to false, create the sample via simple random sampling, with one pass
+   * over the RDD, to produce a sample of size that's approximately equal to the sum of
+   * math.ceil(numItems * samplingRate) over all key values; otherwise, use additional passes over
+   * the RDD to create a sample size that's exactly equal to the sum of
+   * math.ceil(numItems * samplingRate) over all key values.
+   */
+  def sampleByKey(withReplacement: Boolean,
+      fractions: JMap[K, Double],
+      exact: Boolean,
+      seed: Long): JavaPairRDD[K, V] =
+    new JavaPairRDD[K, V](rdd.sampleByKey(withReplacement, fractions, exact, seed))
+
+  /**
+   * Return a subset of this RDD sampled by key (via stratified sampling).
+   *
+   * Create a sample of this RDD using variable sampling rates for different keys as specified by
+   * `fractions`, a key to sampling rate map.
+   *
+   * If `exact` is set to false, create the sample via simple random sampling, with one pass
+   * over the RDD, to produce a sample of size that's approximately equal to the sum of
+   * math.ceil(numItems * samplingRate) over all key values; otherwise, use additional passes over
+   * the RDD to create a sample size that's exactly equal to the sum of
+   * math.ceil(numItems * samplingRate) over all key values.
+   *
+   * Use Utils.random.nextLong as the default seed for the random number generator
+   */
+  def sampleByKey(withReplacement: Boolean,
+      fractions: JMap[K, Double],
+      exact: Boolean): JavaPairRDD[K, V] =
+    sampleByKey(withReplacement, fractions, exact, Utils.random.nextLong)
+
+  /**
+   * Return a subset of this RDD sampled by key (via stratified sampling).
+   *
+   * Create a sample of this RDD using variable sampling rates for different keys as specified by
+   * `fractions`, a key to sampling rate map.
+   *
+   * Produce a sample of size that's approximately equal to the sum of
+   * math.ceil(numItems * samplingRate) over all key values with one pass over the RDD via
+   * simple random sampling.
+   */
+  def sampleByKey(withReplacement: Boolean,
+      fractions: JMap[K, Double],
+      seed: Long): JavaPairRDD[K, V] =
+    sampleByKey(withReplacement, fractions, false, seed)
+
+  /**
+   * Return a subset of this RDD sampled by key (via stratified sampling).
+   *
+   * Create a sample of this RDD using variable sampling rates for different keys as specified by
+   * `fractions`, a key to sampling rate map.
+   *
+   * Produce a sample of size that's approximately equal to the sum of
+   * math.ceil(numItems * samplingRate) over all key values with one pass over the RDD via
+   * simple random sampling.
+   *
+   * Use Utils.random.nextLong as the default seed for the random number generator
+   */
+  def sampleByKey(withReplacement: Boolean, fractions: JMap[K, Double]): JavaPairRDD[K, V] =
+    sampleByKey(withReplacement, fractions, false, Utils.random.nextLong)
+
   /**
    * Return the union of this RDD and another one. Any identical elements will appear multiple
    * times (use `.distinct()` to eliminate them).
diff --git a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
index c04d162a3961..1af4e5f0b6d0 100644
--- a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
@@ -19,12 +19,10 @@ package org.apache.spark.rdd
 
 import java.nio.ByteBuffer
 import java.text.SimpleDateFormat
-import java.util.Date
-import java.util.{HashMap => JHashMap}
+import java.util.{Date, HashMap => JHashMap}
 
+import scala.collection.{Map, mutable}
 import scala.collection.JavaConversions._
-import scala.collection.Map
-import scala.collection.mutable
 import scala.collection.mutable.ArrayBuffer
 import scala.reflect.ClassTag
 
@@ -34,19 +32,19 @@ import org.apache.hadoop.fs.FileSystem
 import org.apache.hadoop.io.SequenceFile.CompressionType
 import org.apache.hadoop.io.compress.CompressionCodec
 import org.apache.hadoop.mapred.{FileOutputCommitter, FileOutputFormat, JobConf, OutputFormat}
-import org.apache.hadoop.mapreduce.{OutputFormat => NewOutputFormat, Job => NewAPIHadoopJob,
+import org.apache.hadoop.mapreduce.{Job => NewAPIHadoopJob, OutputFormat => NewOutputFormat,
 RecordWriter => NewRecordWriter, SparkHadoopMapReduceUtil}
-import org.apache.hadoop.mapreduce.lib.output.{FileOutputFormat => NewFileOutputFormat}
 
 import org.apache.spark._
-import org.apache.spark.annotation.Experimental
-import org.apache.spark.deploy.SparkHadoopUtil
-import org.apache.spark.SparkHadoopWriter
 import org.apache.spark.Partitioner.defaultPartitioner
 import org.apache.spark.SparkContext._
+import org.apache.spark.annotation.Experimental
+import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.partial.{BoundedDouble, PartialResult}
 import org.apache.spark.serializer.Serializer
+import org.apache.spark.util.Utils
 import org.apache.spark.util.collection.CompactBuffer
+import org.apache.spark.util.random.StratifiedSamplingUtils
 
 /**
  * Extra functions available on RDDs of (key, value) pairs through an implicit conversion.
@@ -195,6 +193,41 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
     foldByKey(zeroValue, defaultPartitioner(self))(func)
   }
 
+  /**
+   * Return a subset of this RDD sampled by key (via stratified sampling).
+   *
+   * Create a sample of this RDD using variable sampling rates for different keys as specified by
+   * `fractions`, a key to sampling rate map.
+   *
+   * If `exact` is set to false, create the sample via simple random sampling, with one pass
+   * over the RDD, to produce a sample of size that's approximately equal to the sum of
+   * math.ceil(numItems * samplingRate) over all key values; otherwise, use
+   * additional passes over the RDD to create a sample size that's exactly equal to the sum of
+   * math.ceil(numItems * samplingRate) over all key values with a 99.99% confidence. When sampling
+   * without replacement, we need one additional pass over the RDD to guarantee sample size;
+   * when sampling with replacement, we need two additional passes.
+   *
+   * @param withReplacement whether to sample with or without replacement
+   * @param fractions map of specific keys to sampling rates
+   * @param seed seed for the random number generator
+   * @param exact whether sample size needs to be exactly math.ceil(fraction * size) per key
+   * @return RDD containing the sampled subset
+   */
+  def sampleByKey(withReplacement: Boolean,
+      fractions: Map[K, Double],
+      exact: Boolean = false,
+      seed: Long = Utils.random.nextLong): RDD[(K, V)]= {
+
+    require(fractions.values.forall(v => v >= 0.0), "Negative sampling rates.")
+
+    val samplingFunc = if (withReplacement) {
+      StratifiedSamplingUtils.getPoissonSamplingFunction(self, fractions, exact, seed)
+    } else {
+      StratifiedSamplingUtils.getBernoulliSamplingFunction(self, fractions, exact, seed)
+    }
+    self.mapPartitionsWithIndex(samplingFunc, preservesPartitioning = true)
+  }
+
   /**
    * Merge the values for each key using an associative reduce function. This will also perform
    * the merging locally on each mapper before sending results to a reducer, similarly to a
@@ -531,6 +564,9 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
 
   /**
    * Return the key-value pairs in this RDD to the master as a Map.
+   *
+   * Warning: this doesn't return a multimap (so if you have multiple values to the same key, only
+   *          one value per key is preserved in the map returned)
    */
   def collectAsMap(): Map[K, V] = {
     val data = self.collect()
diff --git a/core/src/main/scala/org/apache/spark/util/random/SamplingUtils.scala b/core/src/main/scala/org/apache/spark/util/random/SamplingUtils.scala
index d10141b90e62..c9a864ae6277 100644
--- a/core/src/main/scala/org/apache/spark/util/random/SamplingUtils.scala
+++ b/core/src/main/scala/org/apache/spark/util/random/SamplingUtils.scala
@@ -81,6 +81,9 @@ private[spark] object SamplingUtils {
    *     ~ Binomial(total, fraction) and our choice of q guarantees 1-delta, or 0.9999 success
    *     rate, where success rate is defined the same as in sampling with replacement.
    *
+   * The smallest sampling rate supported is 1e-10 (in order to avoid running into the limit of the
+   * RNG's resolution).
+   *
    * @param sampleSizeLowerBound sample size
    * @param total size of RDD
    * @param withReplacement whether sampling with replacement
@@ -88,14 +91,73 @@ private[spark] object SamplingUtils {
    */
   def computeFractionForSampleSize(sampleSizeLowerBound: Int, total: Long,
       withReplacement: Boolean): Double = {
-    val fraction = sampleSizeLowerBound.toDouble / total
     if (withReplacement) {
-      val numStDev = if (sampleSizeLowerBound < 12) 9 else 5
-      fraction + numStDev * math.sqrt(fraction / total)
+      PoissonBounds.getUpperBound(sampleSizeLowerBound) / total
     } else {
-      val delta = 1e-4
-      val gamma = - math.log(delta) / total
-      math.min(1, fraction + gamma + math.sqrt(gamma * gamma + 2 * gamma * fraction))
+      val fraction = sampleSizeLowerBound.toDouble / total
+      BinomialBounds.getUpperBound(1e-4, total, fraction)
     }
   }
 }
+
+/**
+ * Utility functions that help us determine bounds on adjusted sampling rate to guarantee exact
+ * sample sizes with high confidence when sampling with replacement.
+ */
+private[spark] object PoissonBounds {
+
+  /**
+   * Returns a lambda such that Pr[X > s] is very small, where X ~ Pois(lambda).
+   */
+  def getLowerBound(s: Double): Double = {
+    math.max(s - numStd(s) * math.sqrt(s), 1e-15)
+  }
+
+  /**
+   * Returns a lambda such that Pr[X < s] is very small, where X ~ Pois(lambda).
+   *
+   * @param s sample size
+   */
+  def getUpperBound(s: Double): Double = {
+    math.max(s + numStd(s) * math.sqrt(s), 1e-10)
+  }
+
+  private def numStd(s: Double): Double = {
+    // TODO: Make it tighter.
+    if (s < 6.0) {
+      12.0
+    } else if (s < 16.0) {
+      9.0
+    } else {
+      6.0
+    }
+  }
+}
+
+/**
+ * Utility functions that help us determine bounds on adjusted sampling rate to guarantee exact
+ * sample size with high confidence when sampling without replacement.
+ */
+private[spark] object BinomialBounds {
+
+  val minSamplingRate = 1e-10
+
+  /**
+   * Returns a threshold `p` such that if we conduct n Bernoulli trials with success rate = `p`,
+   * it is very unlikely to have more than `fraction * n` successes.
+   */
+  def getLowerBound(delta: Double, n: Long, fraction: Double): Double = {
+    val gamma = - math.log(delta) / n * (2.0 / 3.0)
+    fraction + gamma - math.sqrt(gamma * gamma + 3 * gamma * fraction)
+  }
+
+  /**
+   * Returns a threshold `p` such that if we conduct n Bernoulli trials with success rate = `p`,
+   * it is very unlikely to have less than `fraction * n` successes.
+   */
+  def getUpperBound(delta: Double, n: Long, fraction: Double): Double = {
+    val gamma = - math.log(delta) / n
+    math.min(1,
+      math.max(minSamplingRate, fraction + gamma + math.sqrt(gamma * gamma + 2 * gamma * fraction)))
+  }
+}
diff --git a/core/src/main/scala/org/apache/spark/util/random/StratifiedSamplingUtils.scala b/core/src/main/scala/org/apache/spark/util/random/StratifiedSamplingUtils.scala
new file mode 100644
index 000000000000..8f95d7c6b799
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/util/random/StratifiedSamplingUtils.scala
@@ -0,0 +1,316 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.util.random
+
+import scala.collection.Map
+import scala.collection.mutable
+import scala.collection.mutable.ArrayBuffer
+import scala.reflect.ClassTag
+
+import cern.jet.random.Poisson
+import cern.jet.random.engine.DRand
+
+import org.apache.spark.Logging
+import org.apache.spark.SparkContext._
+import org.apache.spark.rdd.RDD
+
+/**
+ * Auxiliary functions and data structures for the sampleByKey method in PairRDDFunctions.
+ *
+ * Essentially, when exact sample size is necessary, we make additional passes over the RDD to
+ * compute the exact threshold value to use for each stratum to guarantee exact sample size with
+ * high probability. This is achieved by maintaining a waitlist of size O(log(s)), where s is the
+ * desired sample size for each stratum.
+ *
+ * Like in simple random sampling, we generate a random value for each item from the
+ * uniform  distribution [0.0, 1.0]. All items with values <= min(values of items in the waitlist)
+ * are accepted into the sample instantly. The threshold for instant accept is designed so that
+ * s - numAccepted = O(sqrt(s)), where s is again the desired sample size. Thus, by maintaining a
+ * waitlist size = O(sqrt(s)), we will be able to create a sample of the exact size s by adding
+ * a portion of the waitlist to the set of items that are instantly accepted. The exact threshold
+ * is computed by sorting the values in the waitlist and picking the value at (s - numAccepted).
+ *
+ * Note that since we use the same seed for the RNG when computing the thresholds and the actual
+ * sample, our computed thresholds are guaranteed to produce the desired sample size.
+ *
+ * For more theoretical background on the sampling techniques used here, please refer to
+ * http://jmlr.org/proceedings/papers/v28/meng13a.html
+ */
+
+private[spark] object StratifiedSamplingUtils extends Logging {
+
+  /**
+   * Count the number of items instantly accepted and generate the waitlist for each stratum.
+   *
+   * This is only invoked when exact sample size is required.
+   */
+  def getAcceptanceResults[K, V](rdd: RDD[(K, V)],
+      withReplacement: Boolean,
+      fractions: Map[K, Double],
+      counts: Option[Map[K, Long]],
+      seed: Long): mutable.Map[K, AcceptanceResult] = {
+    val combOp = getCombOp[K]
+    val mappedPartitionRDD = rdd.mapPartitionsWithIndex { case (partition, iter) =>
+      val zeroU: mutable.Map[K, AcceptanceResult] = new mutable.HashMap[K, AcceptanceResult]()
+      val rng = new RandomDataGenerator()
+      rng.reSeed(seed + partition)
+      val seqOp = getSeqOp(withReplacement, fractions, rng, counts)
+      Iterator(iter.aggregate(zeroU)(seqOp, combOp))
+    }
+    mappedPartitionRDD.reduce(combOp)
+  }
+
+  /**
+   * Returns the function used by aggregate to collect sampling statistics for each partition.
+   */
+  def getSeqOp[K, V](withReplacement: Boolean,
+      fractions: Map[K, Double],
+      rng: RandomDataGenerator,
+      counts: Option[Map[K, Long]]):
+    (mutable.Map[K, AcceptanceResult], (K, V)) => mutable.Map[K, AcceptanceResult] = {
+    val delta = 5e-5
+    (result: mutable.Map[K, AcceptanceResult], item: (K, V)) => {
+      val key = item._1
+      val fraction = fractions(key)
+      if (!result.contains(key)) {
+        result += (key -> new AcceptanceResult())
+      }
+      val acceptResult = result(key)
+
+      if (withReplacement) {
+        // compute acceptBound and waitListBound only if they haven't been computed already
+        // since they don't change from iteration to iteration.
+        // TODO change this to the streaming version
+        if (acceptResult.areBoundsEmpty) {
+          val n = counts.get(key)
+          val sampleSize = math.ceil(n * fraction).toLong
+          val lmbd1 = PoissonBounds.getLowerBound(sampleSize)
+          val lmbd2 = PoissonBounds.getUpperBound(sampleSize)
+          acceptResult.acceptBound = lmbd1 / n
+          acceptResult.waitListBound = (lmbd2 - lmbd1) / n
+        }
+        val acceptBound = acceptResult.acceptBound
+        val copiesAccepted = if (acceptBound == 0.0) 0L else rng.nextPoisson(acceptBound)
+        if (copiesAccepted > 0) {
+          acceptResult.numAccepted += copiesAccepted
+        }
+        val copiesWaitlisted = rng.nextPoisson(acceptResult.waitListBound)
+        if (copiesWaitlisted > 0) {
+          acceptResult.waitList ++= ArrayBuffer.fill(copiesWaitlisted)(rng.nextUniform())
+        }
+      } else {
+        // We use the streaming version of the algorithm for sampling without replacement to avoid
+        // using an extra pass over the RDD for computing the count.
+        // Hence, acceptBound and waitListBound change on every iteration.
+        acceptResult.acceptBound =
+          BinomialBounds.getLowerBound(delta, acceptResult.numItems, fraction)
+        acceptResult.waitListBound =
+          BinomialBounds.getUpperBound(delta, acceptResult.numItems, fraction)
+
+        val x = rng.nextUniform()
+        if (x < acceptResult.acceptBound) {
+          acceptResult.numAccepted += 1
+        } else if (x < acceptResult.waitListBound) {
+          acceptResult.waitList += x
+        }
+      }
+      acceptResult.numItems += 1
+      result
+    }
+  }
+
+  /**
+   * Returns the function used combine results returned by seqOp from different partitions.
+   */
+  def getCombOp[K]: (mutable.Map[K, AcceptanceResult], mutable.Map[K, AcceptanceResult])
+    => mutable.Map[K, AcceptanceResult] = {
+    (result1: mutable.Map[K, AcceptanceResult], result2: mutable.Map[K, AcceptanceResult]) => {
+      // take union of both key sets in case one partition doesn't contain all keys
+      result1.keySet.union(result2.keySet).foreach { key =>
+        // Use result2 to keep the combined result since r1 is usual empty
+        val entry1 = result1.get(key)
+        if (result2.contains(key)) {
+          result2(key).merge(entry1)
+        } else {
+          if (entry1.isDefined) {
+            result2 += (key -> entry1.get)
+          }
+        }
+      }
+      result2
+    }
+  }
+
+  /**
+   * Given the result returned by getCounts, determine the threshold for accepting items to
+   * generate exact sample size.
+   *
+   * To do so, we compute sampleSize = math.ceil(size * samplingRate) for each stratum and compare
+   * it to the number of items that were accepted instantly and the number of items in the waitlist
+   * for that stratum. Most of the time, numAccepted <= sampleSize <= (numAccepted + numWaitlisted),
+   * which means we need to sort the elements in the waitlist by their associated values in order
+   * to find the value T s.t. |{elements in the stratum whose associated values <= T}| = sampleSize.
+   * Note that all elements in the waitlist have values >= bound for instant accept, so a T value
+   * in the waitlist range would allow all elements that were instantly accepted on the first pass
+   * to be included in the sample.
+   */
+  def computeThresholdByKey[K](finalResult: Map[K, AcceptanceResult],
+      fractions: Map[K, Double]): Map[K, Double] = {
+    val thresholdByKey = new mutable.HashMap[K, Double]()
+    for ((key, acceptResult) <- finalResult) {
+      val sampleSize = math.ceil(acceptResult.numItems * fractions(key)).toLong
+      if (acceptResult.numAccepted > sampleSize) {
+        logWarning("Pre-accepted too many")
+        thresholdByKey += (key -> acceptResult.acceptBound)
+      } else {
+        val numWaitListAccepted = (sampleSize - acceptResult.numAccepted).toInt
+        if (numWaitListAccepted >= acceptResult.waitList.size) {
+          logWarning("WaitList too short")
+          thresholdByKey += (key -> acceptResult.waitListBound)
+        } else {
+          thresholdByKey += (key -> acceptResult.waitList.sorted.apply(numWaitListAccepted))
+        }
+      }
+    }
+    thresholdByKey
+  }
+
+  /**
+   * Return the per partition sampling function used for sampling without replacement.
+   *
+   * When exact sample size is required, we make an additional pass over the RDD to determine the
+   * exact sampling rate that guarantees sample size with high confidence.
+   *
+   * The sampling function has a unique seed per partition.
+   */
+  def getBernoulliSamplingFunction[K, V](rdd: RDD[(K,  V)],
+      fractions: Map[K, Double],
+      exact: Boolean,
+      seed: Long): (Int, Iterator[(K, V)]) => Iterator[(K, V)] = {
+    var samplingRateByKey = fractions
+    if (exact) {
+      // determine threshold for each stratum and resample
+      val finalResult = getAcceptanceResults(rdd, false, fractions, None, seed)
+      samplingRateByKey = computeThresholdByKey(finalResult, fractions)
+    }
+    (idx: Int, iter: Iterator[(K, V)]) => {
+      val rng = new RandomDataGenerator
+      rng.reSeed(seed + idx)
+      // Must use the same invoke pattern on the rng as in getSeqOp for without replacement
+      // in order to generate the same sequence of random numbers when creating the sample
+      iter.filter(t => rng.nextUniform() < samplingRateByKey(t._1))
+    }
+  }
+
+  /**
+   * Return the per partition sampling function used for sampling with replacement.
+   *
+   * When exact sample size is required, we make two additional passed over the RDD to determine
+   * the exact sampling rate that guarantees sample size with high confidence. The first pass
+   * counts the number of items in each stratum (group of items with the same key) in the RDD, and
+   * the second pass uses the counts to determine exact sampling rates.
+   *
+   * The sampling function has a unique seed per partition.
+   */
+  def getPoissonSamplingFunction[K: ClassTag, V: ClassTag](rdd: RDD[(K, V)],
+      fractions: Map[K, Double],
+      exact: Boolean,
+      seed: Long): (Int, Iterator[(K, V)]) => Iterator[(K, V)] = {
+    // TODO implement the streaming version of sampling w/ replacement that doesn't require counts
+    if (exact) {
+      val counts = Some(rdd.countByKey())
+      val finalResult = getAcceptanceResults(rdd, true, fractions, counts, seed)
+      val thresholdByKey = computeThresholdByKey(finalResult, fractions)
+      (idx: Int, iter: Iterator[(K, V)]) => {
+        val rng = new RandomDataGenerator()
+        rng.reSeed(seed + idx)
+        iter.flatMap { item =>
+          val key = item._1
+          val acceptBound = finalResult(key).acceptBound
+          // Must use the same invoke pattern on the rng as in getSeqOp for with replacement
+          // in order to generate the same sequence of random numbers when creating the sample
+          val copiesAccepted = if (acceptBound == 0) 0L else rng.nextPoisson(acceptBound)
+          val copiesWailisted = rng.nextPoisson(finalResult(key).waitListBound)
+          val copiesInSample = copiesAccepted +
+            (0 until copiesWailisted).count(i => rng.nextUniform() < thresholdByKey(key))
+          if (copiesInSample > 0) {
+            Iterator.fill(copiesInSample.toInt)(item)
+          } else {
+            Iterator.empty
+          }
+        }
+      }
+    } else {
+      (idx: Int, iter: Iterator[(K, V)]) => {
+        val rng = new RandomDataGenerator()
+        rng.reSeed(seed + idx)
+        iter.flatMap { item =>
+          val count = rng.nextPoisson(fractions(item._1))
+          if (count > 0) {
+            Iterator.fill(count)(item)
+          } else {
+            Iterator.empty
+          }
+        }
+      }
+    }
+  }
+
+  /** A random data generator that generates both uniform values and Poisson values. */
+  private class RandomDataGenerator {
+    val uniform = new XORShiftRandom()
+    var poisson = new Poisson(1.0, new DRand)
+
+    def reSeed(seed: Long) {
+      uniform.setSeed(seed)
+      poisson = new Poisson(1.0, new DRand(seed.toInt))
+    }
+
+    def nextPoisson(mean: Double): Int = {
+      poisson.nextInt(mean)
+    }
+
+    def nextUniform(): Double = {
+      uniform.nextDouble()
+    }
+  }
+}
+
+/**
+ * Object used by seqOp to keep track of the number of items accepted and items waitlisted per
+ * stratum, as well as the bounds for accepting and waitlisting items.
+ *
+ * `[random]` here is necessary since it's in the return type signature of seqOp defined above
+ */
+private[random] class AcceptanceResult(var numItems: Long = 0L, var numAccepted: Long = 0L)
+  extends Serializable {
+
+  val waitList = new ArrayBuffer[Double]
+  var acceptBound: Double = Double.NaN // upper bound for accepting item instantly
+  var waitListBound: Double = Double.NaN // upper bound for adding item to waitlist
+
+  def areBoundsEmpty = acceptBound.isNaN || waitListBound.isNaN
+
+  def merge(other: Option[AcceptanceResult]): Unit = {
+    if (other.isDefined) {
+      waitList ++= other.get.waitList
+      numAccepted += other.get.numAccepted
+      numItems += other.get.numItems
+    }
+  }
+}
diff --git a/core/src/test/java/org/apache/spark/JavaAPISuite.java b/core/src/test/java/org/apache/spark/JavaAPISuite.java
index f882a8623fd8..e8bd65f8e450 100644
--- a/core/src/test/java/org/apache/spark/JavaAPISuite.java
+++ b/core/src/test/java/org/apache/spark/JavaAPISuite.java
@@ -29,6 +29,7 @@
 import com.google.common.collect.Iterables;
 import com.google.common.collect.Iterators;
 import com.google.common.collect.Lists;
+import com.google.common.collect.Maps;
 import com.google.common.base.Optional;
 import com.google.common.base.Charsets;
 import com.google.common.io.Files;
@@ -1208,4 +1209,40 @@ public Tuple2<Integer, int[]> call(Integer x) {
     pairRDD.collect();  // Works fine
     pairRDD.collectAsMap();  // Used to crash with ClassCastException
   }
+
+  @Test
+  @SuppressWarnings("unchecked")
+  public void sampleByKey() {
+    JavaRDD<Integer> rdd1 = sc.parallelize(Arrays.asList(1, 2, 3, 4, 5, 6, 7, 8), 3);
+    JavaPairRDD<Integer, Integer> rdd2 = rdd1.mapToPair(
+      new PairFunction<Integer, Integer, Integer>() {
+        @Override
+        public Tuple2<Integer, Integer> call(Integer i) {
+          return new Tuple2<Integer, Integer>(i % 2, 1);
+        }
+      });
+    Map<Integer, Object> fractions = Maps.newHashMap();
+    fractions.put(0, 0.5);
+    fractions.put(1, 1.0);
+    JavaPairRDD<Integer, Integer> wr = rdd2.sampleByKey(true, fractions, 1L);
+    Map<Integer, Long> wrCounts = (Map<Integer, Long>) (Object) wr.countByKey();
+    Assert.assertTrue(wrCounts.size() == 2);
+    Assert.assertTrue(wrCounts.get(0) > 0);
+    Assert.assertTrue(wrCounts.get(1) > 0);
+    JavaPairRDD<Integer, Integer> wor = rdd2.sampleByKey(false, fractions, 1L);
+    Map<Integer, Long> worCounts = (Map<Integer, Long>) (Object) wor.countByKey();
+    Assert.assertTrue(worCounts.size() == 2);
+    Assert.assertTrue(worCounts.get(0) > 0);
+    Assert.assertTrue(worCounts.get(1) > 0);
+    JavaPairRDD<Integer, Integer> wrExact = rdd2.sampleByKey(true, fractions, true, 1L);
+    Map<Integer, Long> wrExactCounts = (Map<Integer, Long>) (Object) wrExact.countByKey();
+    Assert.assertTrue(wrExactCounts.size() == 2);
+    Assert.assertTrue(wrExactCounts.get(0) == 2);
+    Assert.assertTrue(wrExactCounts.get(1) == 4);
+    JavaPairRDD<Integer, Integer> worExact = rdd2.sampleByKey(false, fractions, true, 1L);
+    Map<Integer, Long> worExactCounts = (Map<Integer, Long>) (Object) worExact.countByKey();
+    Assert.assertTrue(worExactCounts.size() == 2);
+    Assert.assertTrue(worExactCounts.get(0) == 2);
+    Assert.assertTrue(worExactCounts.get(1) == 4);
+  }
 }
diff --git a/core/src/test/scala/org/apache/spark/rdd/PairRDDFunctionsSuite.scala b/core/src/test/scala/org/apache/spark/rdd/PairRDDFunctionsSuite.scala
index 447e38ec9dbd..4f49d4a1d4d3 100644
--- a/core/src/test/scala/org/apache/spark/rdd/PairRDDFunctionsSuite.scala
+++ b/core/src/test/scala/org/apache/spark/rdd/PairRDDFunctionsSuite.scala
@@ -83,6 +83,122 @@ class PairRDDFunctionsSuite extends FunSuite with SharedSparkContext {
     assert(valuesFor2.toList.sorted === List(1))
   }
 
+  test("sampleByKey") {
+    def stratifier (fractionPositive: Double) = {
+      (x: Int) => if (x % 10 < (10 * fractionPositive).toInt) "1" else "0"
+    }
+
+    def checkSize(exact: Boolean,
+        withReplacement: Boolean,
+        expected: Long,
+        actual: Long,
+        p: Double): Boolean = {
+      if (exact) {
+        return expected == actual
+      }
+      val stdev = if (withReplacement) math.sqrt(expected) else math.sqrt(expected * p * (1 - p))
+      // Very forgiving margin since we're dealing with very small sample sizes most of the time
+      math.abs(actual - expected) <= 6 * stdev
+    }
+
+    // Without replacement validation
+    def takeSampleAndValidateBernoulli(stratifiedData: RDD[(String, Int)],
+        exact: Boolean,
+        samplingRate: Double,
+        seed: Long,
+        n: Long) = {
+      val expectedSampleSize = stratifiedData.countByKey()
+        .mapValues(count => math.ceil(count * samplingRate).toInt)
+      val fractions = Map("1" -> samplingRate, "0" -> samplingRate)
+      val sample = stratifiedData.sampleByKey(false, fractions, exact, seed)
+      val sampleCounts = sample.countByKey()
+      val takeSample = sample.collect()
+      sampleCounts.foreach { case(k, v) =>
+        assert(checkSize(exact, false, expectedSampleSize(k), v, samplingRate)) }
+      assert(takeSample.size === takeSample.toSet.size)
+      takeSample.foreach { x => assert(1 <= x._2 && x._2 <= n, s"elements not in [1, $n]") }
+    }
+
+    // With replacement validation
+    def takeSampleAndValidatePoisson(stratifiedData: RDD[(String, Int)],
+        exact: Boolean,
+        samplingRate: Double,
+        seed: Long,
+        n: Long) = {
+      val expectedSampleSize = stratifiedData.countByKey().mapValues(count =>
+        math.ceil(count * samplingRate).toInt)
+      val fractions = Map("1" -> samplingRate, "0" -> samplingRate)
+      val sample = stratifiedData.sampleByKey(true, fractions, exact, seed)
+      val sampleCounts = sample.countByKey()
+      val takeSample = sample.collect()
+      sampleCounts.foreach { case(k, v) =>
+        assert(checkSize(exact, true, expectedSampleSize(k), v, samplingRate)) }
+      val groupedByKey = takeSample.groupBy(_._1)
+      for ((key, v) <- groupedByKey) {
+        if (expectedSampleSize(key) >= 100 && samplingRate >= 0.1) {
+          // sample large enough for there to be repeats with high likelihood
+          assert(v.toSet.size < expectedSampleSize(key))
+        } else {
+          if (exact) {
+            assert(v.toSet.size <= expectedSampleSize(key))
+          } else {
+            assert(checkSize(false, true, expectedSampleSize(key), v.toSet.size, samplingRate))
+          }
+        }
+      }
+      takeSample.foreach { x => assert(1 <= x._2 && x._2 <= n, s"elements not in [1, $n]") }
+    }
+
+    def checkAllCombos(stratifiedData: RDD[(String, Int)],
+        samplingRate: Double,
+        seed: Long,
+        n: Long) = {
+      takeSampleAndValidateBernoulli(stratifiedData, true, samplingRate, seed, n)
+      takeSampleAndValidateBernoulli(stratifiedData, false, samplingRate, seed, n)
+      takeSampleAndValidatePoisson(stratifiedData, true, samplingRate, seed, n)
+      takeSampleAndValidatePoisson(stratifiedData, false, samplingRate, seed, n)
+    }
+
+    val defaultSeed = 1L
+
+    // vary RDD size
+    for (n <- List(100, 1000, 1000000)) {
+      val data = sc.parallelize(1 to n, 2)
+      val fractionPositive = 0.3
+      val stratifiedData = data.keyBy(stratifier(fractionPositive))
+
+      val samplingRate = 0.1
+      checkAllCombos(stratifiedData, samplingRate, defaultSeed, n)
+    }
+
+    // vary fractionPositive
+    for (fractionPositive <- List(0.1, 0.3, 0.5, 0.7, 0.9)) {
+      val n = 100
+      val data = sc.parallelize(1 to n, 2)
+      val stratifiedData = data.keyBy(stratifier(fractionPositive))
+
+      val samplingRate = 0.1
+      checkAllCombos(stratifiedData, samplingRate, defaultSeed, n)
+    }
+
+    // Use the same data for the rest of the tests
+    val fractionPositive = 0.3
+    val n = 100
+    val data = sc.parallelize(1 to n, 2)
+    val stratifiedData = data.keyBy(stratifier(fractionPositive))
+
+    // vary seed
+    for (seed <- defaultSeed to defaultSeed + 5L) {
+      val samplingRate = 0.1
+      checkAllCombos(stratifiedData, samplingRate, seed, n)
+    }
+
+    // vary sampling rate
+    for (samplingRate <- List(0.01, 0.05, 0.1, 0.5)) {
+      checkAllCombos(stratifiedData, samplingRate, defaultSeed, n)
+    }
+  }
+
   test("reduceByKey") {
     val pairs = sc.parallelize(Array((1, 1), (1, 2), (1, 3), (1, 1), (2, 1)))
     val sums = pairs.reduceByKey(_+_).collect()
diff --git a/pom.xml b/pom.xml
index 8b1435cfe5d1..39538f966062 100644
--- a/pom.xml
+++ b/pom.xml
@@ -257,6 +257,12 @@
         <artifactId>commons-codec</artifactId>
         <version>1.5</version>
       </dependency>
+      <dependency>
+        <groupId>org.apache.commons</groupId>
+        <artifactId>commons-math3</artifactId>
+        <version>3.3</version>
+        <scope>test</scope>
+      </dependency>
       <dependency>
         <groupId>com.google.code.findbugs</groupId>
         <artifactId>jsr305</artifactId>

From c7db274be79f448fda566208946cb50958ea9b1a Mon Sep 17 00:00:00 2001
From: Zongheng Yang <zongheng.y@gmail.com>
Date: Tue, 29 Jul 2014 15:32:50 -0700
Subject: [PATCH 800/810] [SPARK-2393][SQL] Cost estimation optimization
 framework for Catalyst logical plans & sample usage.

The idea is that every Catalyst logical plan gets hold of a Statistics class, the usage of which provides useful estimations on various statistics. See the implementations of `MetastoreRelation`.

This patch also includes several usages of the estimation interface in the planner. For instance, we now use physical table sizes from the estimate interface to convert an equi-join to a broadcast join (when doing so is beneficial, as determined by a size threshold).

Finally, there are a couple minor accompanying changes including:
- Remove the not-in-use `BaseRelation`.
- Make SparkLogicalPlan take a `SQLContext` in the second param list.

Author: Zongheng Yang <zongheng.y@gmail.com>

Closes #1238 from concretevitamin/estimates and squashes the following commits:

329071d [Zongheng Yang] Address review comments; turn config name from string to field in SQLConf.
8663e84 [Zongheng Yang] Use BigInt for stat; for logical leaves, by default throw an exception.
2f2fb89 [Zongheng Yang] Fix statistics for SparkLogicalPlan.
9951305 [Zongheng Yang] Remove childrenStats.
16fc60a [Zongheng Yang] Avoid calling statistics on plans if auto join conversion is disabled.
8bd2816 [Zongheng Yang] Add a note on performance of statistics.
6e594b8 [Zongheng Yang] Get size info from metastore for MetastoreRelation.
01b7a3e [Zongheng Yang] Update scaladoc for a field and move it to @param section.
549061c [Zongheng Yang] Remove numTuples in Statistics for now.
729a8e2 [Zongheng Yang] Update docs to be more explicit.
573e644 [Zongheng Yang] Remove singleton SQLConf and move back `settings` to the trait.
2d99eb5 [Zongheng Yang] {Cleanup, use synchronized in, enrich} StatisticsSuite.
ca5b825 [Zongheng Yang] Inject SQLContext into SparkLogicalPlan, removing SQLConf mixin from it.
43d38a6 [Zongheng Yang] Revert optimization for BroadcastNestedLoopJoin (this fixes tests).
0ef9e5b [Zongheng Yang] Use multiplication instead of sum for default estimates.
4ef0d26 [Zongheng Yang] Make Statistics a case class.
3ba8f3e [Zongheng Yang] Add comment.
e5bcf5b [Zongheng Yang] Fix optimization conditions & update scala docs to explain.
7d9216a [Zongheng Yang] Apply estimation to planning ShuffleHashJoin & BroadcastNestedLoopJoin.
73cde01 [Zongheng Yang] Move SQLConf back. Assign default sizeInBytes to SparkLogicalPlan.
73412be [Zongheng Yang] Move SQLConf to Catalyst & add default val for sizeInBytes.
7a60ab7 [Zongheng Yang] s/Estimates/Statistics, s/cardinality/numTuples.
de3ae13 [Zongheng Yang] Add parquetAfter() properly in test.
dcff9bd [Zongheng Yang] Cleanups.
84301a4 [Zongheng Yang] Refactors.
5bf5586 [Zongheng Yang] Typo.
56a8e6e [Zongheng Yang] Prototype impl of estimations for Catalyst logical plans.
---
 .../sql/catalyst/analysis/unresolved.scala    |  4 +-
 .../catalyst/plans/logical/BaseRelation.scala | 24 -----
 .../catalyst/plans/logical/LogicalPlan.scala  | 22 +++++
 .../scala/org/apache/spark/sql/SQLConf.scala  | 61 +++++++-----
 .../org/apache/spark/sql/SQLContext.scala     | 20 ++--
 .../org/apache/spark/sql/SchemaRDD.scala      |  3 +-
 .../org/apache/spark/sql/SchemaRDDLike.scala  |  2 +-
 .../spark/sql/api/java/JavaSQLContext.scala   |  4 +-
 .../spark/sql/execution/SparkPlan.scala       | 18 ++--
 .../spark/sql/execution/SparkStrategies.scala | 57 ++++++-----
 .../org/apache/spark/sql/json/JsonRDD.scala   | 11 ++-
 .../spark/sql/parquet/ParquetRelation.scala   |  4 +-
 .../org/apache/spark/sql/JoinSuite.scala      |  2 -
 .../spark/sql/hive/HiveMetastoreCatalog.scala | 47 ++++++---
 .../spark/sql/hive/StatisticsSuite.scala      | 95 +++++++++++++++++++
 .../hive/execution/HiveComparisonTest.scala   |  2 +-
 .../sql/hive/execution/HiveQuerySuite.scala   |  2 +-
 .../spark/sql/parquet/HiveParquetSuite.scala  |  2 +-
 18 files changed, 256 insertions(+), 124 deletions(-)
 delete mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/BaseRelation.scala
 create mode 100644 sql/hive/src/test/scala/org/apache/spark/sql/hive/StatisticsSuite.scala

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/unresolved.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/unresolved.scala
index 7abeb032964e..a0e25775da6d 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/unresolved.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/unresolved.scala
@@ -20,7 +20,7 @@ package org.apache.spark.sql.catalyst.analysis
 import org.apache.spark.sql.catalyst.{errors, trees}
 import org.apache.spark.sql.catalyst.errors.TreeNodeException
 import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.catalyst.plans.logical.BaseRelation
+import org.apache.spark.sql.catalyst.plans.logical.LeafNode
 import org.apache.spark.sql.catalyst.trees.TreeNode
 
 /**
@@ -36,7 +36,7 @@ class UnresolvedException[TreeType <: TreeNode[_]](tree: TreeType, function: Str
 case class UnresolvedRelation(
     databaseName: Option[String],
     tableName: String,
-    alias: Option[String] = None) extends BaseRelation {
+    alias: Option[String] = None) extends LeafNode {
   override def output = Nil
   override lazy val resolved = false
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/BaseRelation.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/BaseRelation.scala
deleted file mode 100644
index 582334aa4259..000000000000
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/BaseRelation.scala
+++ /dev/null
@@ -1,24 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql.catalyst.plans.logical
-
-abstract class BaseRelation extends LeafNode {
-  self: Product =>
-
-  def tableName: String
-}
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala
index edc37e3877c0..ac85f95b52a2 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala
@@ -26,6 +26,25 @@ import org.apache.spark.sql.catalyst.trees
 abstract class LogicalPlan extends QueryPlan[LogicalPlan] {
   self: Product =>
 
+  /**
+   * Estimates of various statistics.  The default estimation logic simply lazily multiplies the
+   * corresponding statistic produced by the children.  To override this behavior, override
+   * `statistics` and assign it an overriden version of `Statistics`.
+   *
+   * '''NOTE''': concrete and/or overriden versions of statistics fields should pay attention to the
+   * performance of the implementations.  The reason is that estimations might get triggered in
+   * performance-critical processes, such as query plan planning.
+   *
+   * @param sizeInBytes Physical size in bytes. For leaf operators this defaults to 1, otherwise it
+   *                    defaults to the product of children's `sizeInBytes`.
+   */
+  case class Statistics(
+    sizeInBytes: BigInt
+  )
+  lazy val statistics: Statistics = Statistics(
+    sizeInBytes = children.map(_.statistics).map(_.sizeInBytes).product
+  )
+
   /**
    * Returns the set of attributes that are referenced by this node
    * during evaluation.
@@ -92,6 +111,9 @@ abstract class LogicalPlan extends QueryPlan[LogicalPlan] {
 abstract class LeafNode extends LogicalPlan with trees.LeafNode[LogicalPlan] {
   self: Product =>
 
+  override lazy val statistics: Statistics =
+    throw new UnsupportedOperationException("default leaf nodes don't have meaningful Statistics")
+
   // Leaf nodes by definition cannot reference any input attributes.
   override def references = Set.empty
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
index 41920c00b5a2..be8d4e15ec4b 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
@@ -21,17 +21,31 @@ import java.util.Properties
 
 import scala.collection.JavaConverters._
 
+object SQLConf {
+  val AUTO_BROADCASTJOIN_THRESHOLD = "spark.sql.autoBroadcastJoinThreshold"
+  val SHUFFLE_PARTITIONS = "spark.sql.shuffle.partitions"
+  val DEFAULT_SIZE_IN_BYTES = "spark.sql.defaultSizeInBytes"
+
+  object Deprecated {
+    val MAPRED_REDUCE_TASKS = "mapred.reduce.tasks"
+  }
+}
+
 /**
- * SQLConf holds mutable config parameters and hints.  These can be set and
- * queried either by passing SET commands into Spark SQL's DSL
- * functions (sql(), hql(), etc.), or by programmatically using setters and
- * getters of this class.
+ * A trait that enables the setting and getting of mutable config parameters/hints.
+ *
+ * In the presence of a SQLContext, these can be set and queried by passing SET commands
+ * into Spark SQL's query functions (sql(), hql(), etc.). Otherwise, users of this trait can
+ * modify the hints by programmatically calling the setters and getters of this trait.
  *
- * SQLConf is thread-safe (internally synchronized so safe to be used in multiple threads).
+ * SQLConf is thread-safe (internally synchronized, so safe to be used in multiple threads).
  */
 trait SQLConf {
   import SQLConf._
 
+  @transient protected[spark] val settings = java.util.Collections.synchronizedMap(
+    new java.util.HashMap[String, String]())
+
   /** ************************ Spark SQL Params/Hints ******************* */
   // TODO: refactor so that these hints accessors don't pollute the name space of SQLContext?
 
@@ -40,28 +54,33 @@ trait SQLConf {
 
   /**
    * Upper bound on the sizes (in bytes) of the tables qualified for the auto conversion to
-   * a broadcast value during the physical executions of join operations.  Setting this to 0
+   * a broadcast value during the physical executions of join operations.  Setting this to -1
    * effectively disables auto conversion.
-   * Hive setting: hive.auto.convert.join.noconditionaltask.size.
+   *
+   * Hive setting: hive.auto.convert.join.noconditionaltask.size, whose default value is also 10000.
    */
-  private[spark] def autoConvertJoinSize: Int = get(AUTO_CONVERT_JOIN_SIZE, "10000").toInt
+  private[spark] def autoBroadcastJoinThreshold: Int =
+    get(AUTO_BROADCASTJOIN_THRESHOLD, "10000").toInt
 
-  /** A comma-separated list of table names marked to be broadcasted during joins. */
-  private[spark] def joinBroadcastTables: String = get(JOIN_BROADCAST_TABLES, "")
+  /**
+   * The default size in bytes to assign to a logical operator's estimation statistics.  By default,
+   * it is set to a larger value than `autoConvertJoinSize`, hence any logical operator without a
+   * properly implemented estimation of this statistic will not be incorrectly broadcasted in joins.
+   */
+  private[spark] def defaultSizeInBytes: Long =
+    getOption(DEFAULT_SIZE_IN_BYTES).map(_.toLong).getOrElse(autoBroadcastJoinThreshold + 1)
 
   /** ********************** SQLConf functionality methods ************ */
 
-  @transient
-  private val settings = java.util.Collections.synchronizedMap(
-    new java.util.HashMap[String, String]())
-
   def set(props: Properties): Unit = {
-    props.asScala.foreach { case (k, v) => this.settings.put(k, v) }
+    settings.synchronized {
+      props.asScala.foreach { case (k, v) => settings.put(k, v) }
+    }
   }
 
   def set(key: String, value: String): Unit = {
     require(key != null, "key cannot be null")
-    require(value != null, s"value cannot be null for $key")
+    require(value != null, s"value cannot be null for key: $key")
     settings.put(key, value)
   }
 
@@ -90,13 +109,3 @@ trait SQLConf {
   }
 
 }
-
-object SQLConf {
-  val AUTO_CONVERT_JOIN_SIZE = "spark.sql.auto.convert.join.size"
-  val SHUFFLE_PARTITIONS = "spark.sql.shuffle.partitions"
-  val JOIN_BROADCAST_TABLES = "spark.sql.join.broadcastTables"
-
-  object Deprecated {
-    val MAPRED_REDUCE_TASKS = "mapred.reduce.tasks"
-  }
-}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
index c178dad66253..a136c7b3ffef 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
@@ -24,14 +24,14 @@ import org.apache.hadoop.conf.Configuration
 
 import org.apache.spark.annotation.{AlphaComponent, DeveloperApi, Experimental}
 import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.catalyst.analysis._
 import org.apache.spark.sql.catalyst.ScalaReflection
-import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.analysis._
 import org.apache.spark.sql.catalyst.dsl.ExpressionConversions
-import org.apache.spark.sql.catalyst.types._
+import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.optimizer.Optimizer
 import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
 import org.apache.spark.sql.catalyst.rules.RuleExecutor
+import org.apache.spark.sql.catalyst.types._
 import org.apache.spark.sql.columnar.InMemoryRelation
 import org.apache.spark.sql.execution._
 import org.apache.spark.sql.execution.SparkStrategies
@@ -86,7 +86,7 @@ class SQLContext(@transient val sparkContext: SparkContext)
    * @group userf
    */
   implicit def createSchemaRDD[A <: Product: TypeTag](rdd: RDD[A]) =
-    new SchemaRDD(this, SparkLogicalPlan(ExistingRdd.fromProductRdd(rdd)))
+    new SchemaRDD(this, SparkLogicalPlan(ExistingRdd.fromProductRdd(rdd))(self))
 
   /**
    * Loads a Parquet file, returning the result as a [[SchemaRDD]].
@@ -127,7 +127,7 @@ class SQLContext(@transient val sparkContext: SparkContext)
    */
   @Experimental
   def jsonRDD(json: RDD[String], samplingRatio: Double): SchemaRDD =
-    new SchemaRDD(this, JsonRDD.inferSchema(json, samplingRatio))
+    new SchemaRDD(this, JsonRDD.inferSchema(self, json, samplingRatio))
 
   /**
    * :: Experimental ::
@@ -170,11 +170,7 @@ class SQLContext(@transient val sparkContext: SparkContext)
    * @group userf
    */
   def registerRDDAsTable(rdd: SchemaRDD, tableName: String): Unit = {
-    val name = tableName
-    val newPlan = rdd.logicalPlan transform {
-      case s @ SparkLogicalPlan(ExistingRdd(_, _), _) => s.copy(tableName = name)
-    }
-    catalog.registerTable(None, tableName, newPlan)
+    catalog.registerTable(None, tableName, rdd.logicalPlan)
   }
 
   /**
@@ -212,7 +208,7 @@ class SQLContext(@transient val sparkContext: SparkContext)
       case inMem @ InMemoryRelation(_, _, e: ExistingRdd) =>
         inMem.cachedColumnBuffers.unpersist()
         catalog.unregisterTable(None, tableName)
-        catalog.registerTable(None, tableName, SparkLogicalPlan(e))
+        catalog.registerTable(None, tableName, SparkLogicalPlan(e)(self))
       case inMem: InMemoryRelation =>
         inMem.cachedColumnBuffers.unpersist()
         catalog.unregisterTable(None, tableName)
@@ -405,7 +401,7 @@ class SQLContext(@transient val sparkContext: SparkContext)
         new GenericRow(map.values.toArray.asInstanceOf[Array[Any]]): Row
       }
     }
-    new SchemaRDD(this, SparkLogicalPlan(ExistingRdd(schema, rowRdd)))
+    new SchemaRDD(this, SparkLogicalPlan(ExistingRdd(schema, rowRdd))(self))
   }
 
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala
index 019ff9d300a1..172b6e0e7f26 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala
@@ -418,7 +418,8 @@ class SchemaRDD(
    * @group schema
    */
   private def applySchema(rdd: RDD[Row]): SchemaRDD = {
-    new SchemaRDD(sqlContext, SparkLogicalPlan(ExistingRdd(queryExecution.analyzed.output, rdd)))
+    new SchemaRDD(sqlContext,
+      SparkLogicalPlan(ExistingRdd(queryExecution.analyzed.output, rdd))(sqlContext))
   }
 
   // =======================================================================
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDDLike.scala b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDDLike.scala
index fe8172194320..fd751031b26e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDDLike.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDDLike.scala
@@ -56,7 +56,7 @@ private[sql] trait SchemaRDDLike {
     // happen right away to let these side effects take place eagerly.
     case _: Command | _: InsertIntoTable | _: InsertIntoCreatedTable | _: WriteToFile =>
       queryExecution.toRdd
-      SparkLogicalPlan(queryExecution.executedPlan)
+      SparkLogicalPlan(queryExecution.executedPlan)(sqlContext)
     case _ =>
       baseLogicalPlan
   }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSQLContext.scala
index 790d9ef22cf1..806097c917b9 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSQLContext.scala
@@ -92,7 +92,7 @@ class JavaSQLContext(val sqlContext: SQLContext) {
         new GenericRow(extractors.map(e => e.invoke(row)).toArray[Any]): ScalaRow
       }
     }
-    new JavaSchemaRDD(sqlContext, SparkLogicalPlan(ExistingRdd(schema, rowRdd)))
+    new JavaSchemaRDD(sqlContext, SparkLogicalPlan(ExistingRdd(schema, rowRdd))(sqlContext))
   }
 
   /**
@@ -120,7 +120,7 @@ class JavaSQLContext(val sqlContext: SQLContext) {
    * @group userf
    */
   def jsonRDD(json: JavaRDD[String]): JavaSchemaRDD =
-    new JavaSchemaRDD(sqlContext, JsonRDD.inferSchema(json, 1.0))
+    new JavaSchemaRDD(sqlContext, JsonRDD.inferSchema(sqlContext, json, 1.0))
 
   /**
    * Registers the given RDD as a temporary table in the catalog.  Temporary tables exist only
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
index 27dc091b8581..77c874d0315e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
@@ -19,12 +19,12 @@ package org.apache.spark.sql.execution
 
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.{Logging, Row}
+import org.apache.spark.sql.{Logging, Row, SQLContext}
 import org.apache.spark.sql.catalyst.trees
 import org.apache.spark.sql.catalyst.analysis.MultiInstanceRelation
 import org.apache.spark.sql.catalyst.expressions.GenericRow
 import org.apache.spark.sql.catalyst.plans.QueryPlan
-import org.apache.spark.sql.catalyst.plans.logical.BaseRelation
+import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
 import org.apache.spark.sql.catalyst.plans.physical._
 
 /**
@@ -66,8 +66,8 @@ abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging {
  * linking.
  */
 @DeveloperApi
-case class SparkLogicalPlan(alreadyPlanned: SparkPlan, tableName: String = "SparkLogicalPlan")
-  extends BaseRelation with MultiInstanceRelation {
+case class SparkLogicalPlan(alreadyPlanned: SparkPlan)(@transient sqlContext: SQLContext)
+  extends LogicalPlan with MultiInstanceRelation {
 
   def output = alreadyPlanned.output
   override def references = Set.empty
@@ -78,9 +78,15 @@ case class SparkLogicalPlan(alreadyPlanned: SparkPlan, tableName: String = "Spar
       alreadyPlanned match {
         case ExistingRdd(output, rdd) => ExistingRdd(output.map(_.newInstance), rdd)
         case _ => sys.error("Multiple instance of the same relation detected.")
-      }, tableName)
-      .asInstanceOf[this.type]
+      })(sqlContext).asInstanceOf[this.type]
   }
+
+  @transient override lazy val statistics = Statistics(
+    // TODO: Instead of returning a default value here, find a way to return a meaningful size
+    // estimate for RDDs. See PR 1238 for more discussions.
+    sizeInBytes = BigInt(sqlContext.defaultSizeInBytes)
+  )
+
 }
 
 private[sql] trait LeafNode extends SparkPlan with trees.LeafNode[SparkPlan] {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
index c078e71fe029..404d48ae05b4 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
@@ -17,11 +17,13 @@
 
 package org.apache.spark.sql.execution
 
+import scala.util.Try
+
 import org.apache.spark.sql.{SQLContext, execution}
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.planning._
 import org.apache.spark.sql.catalyst.plans._
-import org.apache.spark.sql.catalyst.plans.logical.{BaseRelation, LogicalPlan}
+import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
 import org.apache.spark.sql.catalyst.plans.physical._
 import org.apache.spark.sql.columnar.{InMemoryRelation, InMemoryColumnarTableScan}
 import org.apache.spark.sql.parquet._
@@ -47,9 +49,18 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
   /**
    * Uses the ExtractEquiJoinKeys pattern to find joins where at least some of the predicates can be
    * evaluated by matching hash keys.
+   *
+   * This strategy applies a simple optimization based on the estimates of the physical sizes of
+   * the two join sides.  When planning a [[execution.BroadcastHashJoin]], if one side has an
+   * estimated physical size smaller than the user-settable threshold
+   * [[org.apache.spark.sql.SQLConf.AUTO_BROADCASTJOIN_THRESHOLD]], the planner would mark it as the
+   * ''build'' relation and mark the other relation as the ''stream'' side.  The build table will be
+   * ''broadcasted'' to all of the executors involved in the join, as a
+   * [[org.apache.spark.broadcast.Broadcast]] object.  If both estimates exceed the threshold, they
+   * will instead be used to decide the build side in a [[execution.ShuffledHashJoin]].
    */
   object HashJoin extends Strategy with PredicateHelper {
-    private[this] def broadcastHashJoin(
+    private[this] def makeBroadcastHashJoin(
         leftKeys: Seq[Expression],
         rightKeys: Seq[Expression],
         left: LogicalPlan,
@@ -61,33 +72,27 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
       condition.map(Filter(_, broadcastHashJoin)).getOrElse(broadcastHashJoin) :: Nil
     }
 
-    def broadcastTables: Seq[String] = sqlContext.joinBroadcastTables.split(",").toBuffer
-
     def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
-      case ExtractEquiJoinKeys(
-              Inner,
-              leftKeys,
-              rightKeys,
-              condition,
-              left,
-              right @ PhysicalOperation(_, _, b: BaseRelation))
-        if broadcastTables.contains(b.tableName) =>
-          broadcastHashJoin(leftKeys, rightKeys, left, right, condition, BuildRight)
+      case ExtractEquiJoinKeys(Inner, leftKeys, rightKeys, condition, left, right)
+        if Try(sqlContext.autoBroadcastJoinThreshold > 0 &&
+          right.statistics.sizeInBytes <= sqlContext.autoBroadcastJoinThreshold).getOrElse(false) =>
+        makeBroadcastHashJoin(leftKeys, rightKeys, left, right, condition, BuildRight)
 
-      case ExtractEquiJoinKeys(
-              Inner,
-              leftKeys,
-              rightKeys,
-              condition,
-              left @ PhysicalOperation(_, _, b: BaseRelation),
-              right)
-        if broadcastTables.contains(b.tableName) =>
-          broadcastHashJoin(leftKeys, rightKeys, left, right, condition, BuildLeft)
+      case ExtractEquiJoinKeys(Inner, leftKeys, rightKeys, condition, left, right)
+        if Try(sqlContext.autoBroadcastJoinThreshold > 0 &&
+          left.statistics.sizeInBytes <= sqlContext.autoBroadcastJoinThreshold).getOrElse(false) =>
+          makeBroadcastHashJoin(leftKeys, rightKeys, left, right, condition, BuildLeft)
 
       case ExtractEquiJoinKeys(Inner, leftKeys, rightKeys, condition, left, right) =>
+        val buildSide =
+          if (Try(right.statistics.sizeInBytes <= left.statistics.sizeInBytes).getOrElse(false)) {
+            BuildRight
+          } else {
+            BuildLeft
+          }
         val hashJoin =
           execution.ShuffledHashJoin(
-            leftKeys, rightKeys, BuildRight, planLater(left), planLater(right))
+            leftKeys, rightKeys, buildSide, planLater(left), planLater(right))
         condition.map(Filter(_, hashJoin)).getOrElse(hashJoin) :: Nil
 
       case _ => Nil
@@ -273,8 +278,8 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
         execution.Limit(limit, planLater(child))(sqlContext) :: Nil
       case Unions(unionChildren) =>
         execution.Union(unionChildren.map(planLater))(sqlContext) :: Nil
-      case logical.Except(left,right) =>                                        
-        execution.Except(planLater(left),planLater(right)) :: Nil   
+      case logical.Except(left,right) =>
+        execution.Except(planLater(left),planLater(right)) :: Nil
       case logical.Intersect(left, right) =>
         execution.Intersect(planLater(left), planLater(right)) :: Nil
       case logical.Generate(generator, join, outer, _, child) =>
@@ -283,7 +288,7 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
         execution.ExistingRdd(Nil, singleRowRdd) :: Nil
       case logical.Repartition(expressions, child) =>
         execution.Exchange(HashPartitioning(expressions, numPartitions), planLater(child)) :: Nil
-      case SparkLogicalPlan(existingPlan, _) => existingPlan :: Nil
+      case SparkLogicalPlan(existingPlan) => existingPlan :: Nil
       case _ => Nil
     }
   }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala
index b48c70ee73a2..6c2b553bb908 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala
@@ -28,11 +28,12 @@ import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.logical._
 import org.apache.spark.sql.catalyst.types._
 import org.apache.spark.sql.execution.{ExistingRdd, SparkLogicalPlan}
-import org.apache.spark.sql.Logging
+import org.apache.spark.sql.{SQLContext, Logging}
 
 private[sql] object JsonRDD extends Logging {
 
   private[sql] def inferSchema(
+      sqlContext: SQLContext,
       json: RDD[String],
       samplingRatio: Double = 1.0): LogicalPlan = {
     require(samplingRatio > 0, s"samplingRatio ($samplingRatio) should be greater than 0")
@@ -40,15 +41,17 @@ private[sql] object JsonRDD extends Logging {
     val allKeys = parseJson(schemaData).map(allKeysWithValueTypes).reduce(_ ++ _)
     val baseSchema = createSchema(allKeys)
 
-    createLogicalPlan(json, baseSchema)
+    createLogicalPlan(json, baseSchema, sqlContext)
   }
 
   private def createLogicalPlan(
       json: RDD[String],
-      baseSchema: StructType): LogicalPlan = {
+      baseSchema: StructType,
+      sqlContext: SQLContext): LogicalPlan = {
     val schema = nullTypeToStringType(baseSchema)
 
-    SparkLogicalPlan(ExistingRdd(asAttributes(schema), parseJson(json).map(asRow(_, schema))))
+    SparkLogicalPlan(
+      ExistingRdd(asAttributes(schema), parseJson(json).map(asRow(_, schema))))(sqlContext)
   }
 
   private def createSchema(allKeys: Set[(String, DataType)]): StructType = {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala
index 9c4771d1a984..8c7dbd5eb4a0 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala
@@ -27,6 +27,7 @@ import parquet.hadoop.ParquetOutputFormat
 import parquet.hadoop.metadata.CompressionCodecName
 import parquet.schema.MessageType
 
+import org.apache.spark.sql.SQLContext
 import org.apache.spark.sql.catalyst.analysis.{MultiInstanceRelation, UnresolvedException}
 import org.apache.spark.sql.catalyst.expressions.Attribute
 import org.apache.spark.sql.catalyst.plans.logical.{LogicalPlan, LeafNode}
@@ -45,7 +46,8 @@ import org.apache.spark.sql.catalyst.plans.logical.{LogicalPlan, LeafNode}
  */
 private[sql] case class ParquetRelation(
     path: String,
-    @transient conf: Option[Configuration] = None) extends LeafNode with MultiInstanceRelation {
+    @transient conf: Option[Configuration] = None)
+  extends LeafNode with MultiInstanceRelation {
 
   self: Product =>
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala
index e17ecc87fd52..025c396ef062 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala
@@ -19,8 +19,6 @@ package org.apache.spark.sql
 
 import org.apache.spark.sql.TestData._
 import org.apache.spark.sql.catalyst.plans.{LeftOuter, RightOuter, FullOuter, Inner}
-import org.apache.spark.sql.execution._
-import org.apache.spark.sql.test.TestSQLContext
 import org.apache.spark.sql.test.TestSQLContext._
 
 class JoinSuite extends QueryTest {
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
index 156b090712df..dff1d6a4b93b 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
@@ -19,15 +19,16 @@ package org.apache.spark.sql.hive
 
 import scala.util.parsing.combinator.RegexParsers
 
+import org.apache.hadoop.fs.Path
+import org.apache.hadoop.hive.conf.HiveConf
 import org.apache.hadoop.hive.metastore.api.{FieldSchema, StorageDescriptor, SerDeInfo}
 import org.apache.hadoop.hive.metastore.api.{Table => TTable, Partition => TPartition}
 import org.apache.hadoop.hive.ql.metadata.{Hive, Partition, Table}
 import org.apache.hadoop.hive.ql.plan.TableDesc
-import org.apache.hadoop.hive.ql.session.SessionState
 import org.apache.hadoop.hive.serde2.Deserializer
 
 import org.apache.spark.annotation.DeveloperApi
-import org.apache.spark.sql.Logging
+import org.apache.spark.sql.{SQLContext, Logging}
 import org.apache.spark.sql.catalyst.analysis.{EliminateAnalysisOperators, Catalog}
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.logical
@@ -64,9 +65,8 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with
 
     // Since HiveQL is case insensitive for table names we make them all lowercase.
     MetastoreRelation(
-      databaseName,
-      tblName,
-      alias)(table.getTTable, partitions.map(part => part.getTPartition))
+      databaseName, tblName, alias)(
+      table.getTTable, partitions.map(part => part.getTPartition))(hive)
   }
 
   def createTable(
@@ -251,7 +251,11 @@ object HiveMetastoreTypes extends RegexParsers {
 private[hive] case class MetastoreRelation
     (databaseName: String, tableName: String, alias: Option[String])
     (val table: TTable, val partitions: Seq[TPartition])
-  extends BaseRelation {
+    (@transient sqlContext: SQLContext)
+  extends LeafNode {
+
+  self: Product =>
+
   // TODO: Can we use org.apache.hadoop.hive.ql.metadata.Table as the type of table and
   // use org.apache.hadoop.hive.ql.metadata.Partition as the type of elements of partitions.
   // Right now, using org.apache.hadoop.hive.ql.metadata.Table and
@@ -264,6 +268,21 @@ private[hive] case class MetastoreRelation
     new Partition(hiveQlTable, p)
   }
 
+  @transient override lazy val statistics = Statistics(
+    sizeInBytes = {
+      // TODO: check if this estimate is valid for tables after partition pruning.
+      // NOTE: getting `totalSize` directly from params is kind of hacky, but this should be
+      // relatively cheap if parameters for the table are populated into the metastore.  An
+      // alternative would be going through Hadoop's FileSystem API, which can be expensive if a lot
+      // of RPCs are involved.  Besides `totalSize`, there are also `numFiles`, `numRows`,
+      // `rawDataSize` keys that we can look at in the future.
+      BigInt(
+        Option(hiveQlTable.getParameters.get("totalSize"))
+          .map(_.toLong)
+          .getOrElse(sqlContext.defaultSizeInBytes))
+    }
+  )
+
   val tableDesc = new TableDesc(
     Class.forName(hiveQlTable.getSerializationLib).asInstanceOf[Class[Deserializer]],
     hiveQlTable.getInputFormatClass,
@@ -275,14 +294,14 @@ private[hive] case class MetastoreRelation
     hiveQlTable.getMetadata
   )
 
-   implicit class SchemaAttribute(f: FieldSchema) {
-     def toAttribute = AttributeReference(
-       f.getName,
-       HiveMetastoreTypes.toDataType(f.getType),
-       // Since data can be dumped in randomly with no validation, everything is nullable.
-       nullable = true
-     )(qualifiers = tableName +: alias.toSeq)
-   }
+  implicit class SchemaAttribute(f: FieldSchema) {
+    def toAttribute = AttributeReference(
+      f.getName,
+      HiveMetastoreTypes.toDataType(f.getType),
+      // Since data can be dumped in randomly with no validation, everything is nullable.
+      nullable = true
+    )(qualifiers = tableName +: alias.toSeq)
+  }
 
   // Must be a stable value since new attributes are born here.
   val partitionKeys = hiveQlTable.getPartitionKeys.map(_.toAttribute)
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/StatisticsSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/StatisticsSuite.scala
new file mode 100644
index 000000000000..a61fd9df95c9
--- /dev/null
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/StatisticsSuite.scala
@@ -0,0 +1,95 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive
+
+import scala.reflect.ClassTag
+
+import org.apache.spark.sql.{SQLConf, QueryTest}
+import org.apache.spark.sql.execution.{BroadcastHashJoin, ShuffledHashJoin}
+import org.apache.spark.sql.hive.test.TestHive
+import org.apache.spark.sql.hive.test.TestHive._
+
+class StatisticsSuite extends QueryTest {
+
+  test("estimates the size of a test MetastoreRelation") {
+    val rdd = hql("""SELECT * FROM src""")
+    val sizes = rdd.queryExecution.analyzed.collect { case mr: MetastoreRelation =>
+      mr.statistics.sizeInBytes
+    }
+    assert(sizes.size === 1)
+    assert(sizes(0).equals(BigInt(5812)),
+      s"expected exact size 5812 for test table 'src', got: ${sizes(0)}")
+  }
+
+  test("auto converts to broadcast hash join, by size estimate of a relation") {
+    def mkTest(
+        before: () => Unit,
+        after: () => Unit,
+        query: String,
+        expectedAnswer: Seq[Any],
+        ct: ClassTag[_]) = {
+      before()
+
+      var rdd = hql(query)
+
+      // Assert src has a size smaller than the threshold.
+      val sizes = rdd.queryExecution.analyzed.collect {
+        case r if ct.runtimeClass.isAssignableFrom(r.getClass) => r.statistics.sizeInBytes
+      }
+      assert(sizes.size === 2 && sizes(0) <= autoBroadcastJoinThreshold,
+        s"query should contain two relations, each of which has size smaller than autoConvertSize")
+
+      // Using `sparkPlan` because for relevant patterns in HashJoin to be
+      // matched, other strategies need to be applied.
+      var bhj = rdd.queryExecution.sparkPlan.collect { case j: BroadcastHashJoin => j }
+      assert(bhj.size === 1,
+        s"actual query plans do not contain broadcast join: ${rdd.queryExecution}")
+
+      checkAnswer(rdd, expectedAnswer) // check correctness of output
+
+      TestHive.settings.synchronized {
+        val tmp = autoBroadcastJoinThreshold
+
+        hql(s"""SET ${SQLConf.AUTO_BROADCASTJOIN_THRESHOLD}=-1""")
+        rdd = hql(query)
+        bhj = rdd.queryExecution.sparkPlan.collect { case j: BroadcastHashJoin => j }
+        assert(bhj.isEmpty, "BroadcastHashJoin still planned even though it is switched off")
+
+        val shj = rdd.queryExecution.sparkPlan.collect { case j: ShuffledHashJoin => j }
+        assert(shj.size === 1,
+          "ShuffledHashJoin should be planned when BroadcastHashJoin is turned off")
+
+        hql(s"""SET ${SQLConf.AUTO_BROADCASTJOIN_THRESHOLD}=$tmp""")
+      }
+
+      after()
+    }
+
+    /** Tests for MetastoreRelation */
+    val metastoreQuery = """SELECT * FROM src a JOIN src b ON a.key = 238 AND a.key = b.key"""
+    val metastoreAnswer = Seq.fill(4)((238, "val_238", 238, "val_238"))
+    mkTest(
+      () => (),
+      () => (),
+      metastoreQuery,
+      metastoreAnswer,
+      implicitly[ClassTag[MetastoreRelation]]
+    )
+  }
+
+}
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala
index b4dbf2b11579..6c8fe4b196de 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala
@@ -132,7 +132,7 @@ abstract class HiveComparisonTest
     answer: Seq[String]): Seq[String] = {
 
     def isSorted(plan: LogicalPlan): Boolean = plan match {
-      case _: Join | _: Aggregate | _: BaseRelation | _: Generate | _: Sample | _: Distinct => false
+      case _: Join | _: Aggregate | _: Generate | _: Sample | _: Distinct => false
       case PhysicalOperation(_, _, Sort(_, _)) => true
       case _ => plan.children.iterator.exists(isSorted)
     }
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
index a022a1e2dc70..50f85289fdad 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
@@ -21,7 +21,7 @@ import scala.util.Try
 
 import org.apache.spark.sql.hive.test.TestHive
 import org.apache.spark.sql.hive.test.TestHive._
-import org.apache.spark.sql.{SchemaRDD, Row}
+import org.apache.spark.sql.{Row, SchemaRDD}
 
 case class TestData(a: Int, b: String)
 
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/parquet/HiveParquetSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/parquet/HiveParquetSuite.scala
index 91ad59d7f82c..3bfe49a760be 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/parquet/HiveParquetSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/parquet/HiveParquetSuite.scala
@@ -35,7 +35,7 @@ class HiveParquetSuite extends FunSuite with BeforeAndAfterAll with BeforeAndAft
 
   override def beforeAll() {
     // write test data
-    ParquetTestData.writeFile
+    ParquetTestData.writeFile()
     testRDD = parquetFile(ParquetTestData.testDir.toString)
     testRDD.registerAsTable("testsource")
   }

From 2c356665c986564482ccfb3f880f0a2c023a7cb7 Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Tue, 29 Jul 2014 17:52:48 -0700
Subject: [PATCH 801/810] MAINTENANCE: Automated closing of pull requests.

This commit exists to close the following pull requests on Github:

Closes #740 (close requested by 'rxin')
Closes #647 (close requested by 'rxin')
Closes #1383 (close requested by 'rxin')
Closes #1485 (close requested by 'pwendell')
Closes #693 (close requested by 'rxin')
Closes #478 (close requested by 'JoshRosen')

From 39b8193102ebf32ef6b40631a949318b281d44a1 Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Tue, 29 Jul 2014 18:14:20 -0700
Subject: [PATCH 802/810] [SPARK-2716][SQL] Don't check resolved for having
 filters.

For queries like `... HAVING COUNT(*) > 9` the expression is always resolved since it contains no attributes.  This was causing us to avoid doing the Having clause aggregation rewrite.

Author: Michael Armbrust <michael@databricks.com>

Closes #1640 from marmbrus/havingNoRef and squashes the following commits:

92d3901 [Michael Armbrust] Don't check resolved for having filters.
---
 .../sql/catalyst/analysis/Analyzer.scala      |   2 +-
 ...erences-0-d2de3ba23759d25ef77cdfbab72cbb63 | 136 ++++++++++++++++++
 .../sql/hive/execution/HiveQuerySuite.scala   |   3 +
 3 files changed, 140 insertions(+), 1 deletion(-)
 create mode 100644 sql/hive/src/test/resources/golden/having no references-0-d2de3ba23759d25ef77cdfbab72cbb63

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
index 02bdb64f308a..74c0104e5b17 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
@@ -159,7 +159,7 @@ class Analyzer(catalog: Catalog, registry: FunctionRegistry, caseSensitive: Bool
   object UnresolvedHavingClauseAttributes extends Rule[LogicalPlan] {
     def apply(plan: LogicalPlan): LogicalPlan = plan transformUp {
       case filter @ Filter(havingCondition, aggregate @ Aggregate(_, originalAggExprs, _)) 
-          if !filter.resolved && aggregate.resolved && containsAggregate(havingCondition) => {
+          if aggregate.resolved && containsAggregate(havingCondition) => {
         val evaluatedCondition = Alias(havingCondition,  "havingCondition")()
         val aggExprsWithHaving = evaluatedCondition +: originalAggExprs
         
diff --git a/sql/hive/src/test/resources/golden/having no references-0-d2de3ba23759d25ef77cdfbab72cbb63 b/sql/hive/src/test/resources/golden/having no references-0-d2de3ba23759d25ef77cdfbab72cbb63
new file mode 100644
index 000000000000..3f2cab688ccc
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/having no references-0-d2de3ba23759d25ef77cdfbab72cbb63	
@@ -0,0 +1,136 @@
+0
+5
+12
+15
+18
+24
+26
+35
+37
+42
+51
+58
+67
+70
+72
+76
+83
+84
+90
+95
+97
+98
+100
+103
+104
+113
+118
+119
+120
+125
+128
+129
+134
+137
+138
+146
+149
+152
+164
+165
+167
+169
+172
+174
+175
+176
+179
+187
+191
+193
+195
+197
+199
+200
+203
+205
+207
+208
+209
+213
+216
+217
+219
+221
+223
+224
+229
+230
+233
+237
+238
+239
+242
+255
+256
+265
+272
+273
+277
+278
+280
+281
+282
+288
+298
+307
+309
+311
+316
+317
+318
+321
+322
+325
+327
+331
+333
+342
+344
+348
+353
+367
+369
+382
+384
+395
+396
+397
+399
+401
+403
+404
+406
+409
+413
+414
+417
+424
+429
+430
+431
+438
+439
+454
+458
+459
+462
+463
+466
+468
+469
+478
+480
+489
+492
+498
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
index 50f85289fdad..aadfd2e90015 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
@@ -30,6 +30,9 @@ case class TestData(a: Int, b: String)
  */
 class HiveQuerySuite extends HiveComparisonTest {
 
+  createQueryTest("having no references",
+    "SELECT key FROM src GROUP BY key HAVING COUNT(*) > 1")
+
   createQueryTest("boolean = number",
     """
       |SELECT

From 86534d0f5255362618c05a07b0171ec35c915822 Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Tue, 29 Jul 2014 18:20:51 -0700
Subject: [PATCH 803/810] [SPARK-2631][SQL] Use SQLConf to configure in-memory
 columnar caching

Author: Michael Armbrust <michael@databricks.com>

Closes #1638 from marmbrus/cachedConfig and squashes the following commits:

2362082 [Michael Armbrust] Use SQLConf to configure in-memory columnar caching
---
 sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala    | 4 ++++
 sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala | 2 --
 2 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
index be8d4e15ec4b..5d85a0fd4eeb 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
@@ -22,6 +22,7 @@ import java.util.Properties
 import scala.collection.JavaConverters._
 
 object SQLConf {
+  val COMPRESS_CACHED = "spark.sql.inMemoryColumnarStorage.compressed"
   val AUTO_BROADCASTJOIN_THRESHOLD = "spark.sql.autoBroadcastJoinThreshold"
   val SHUFFLE_PARTITIONS = "spark.sql.shuffle.partitions"
   val DEFAULT_SIZE_IN_BYTES = "spark.sql.defaultSizeInBytes"
@@ -49,6 +50,9 @@ trait SQLConf {
   /** ************************ Spark SQL Params/Hints ******************* */
   // TODO: refactor so that these hints accessors don't pollute the name space of SQLContext?
 
+  /** When true tables cached using the in-memory columnar caching will be compressed. */
+  private[spark] def useCompression: Boolean = get(COMPRESS_CACHED, "false").toBoolean
+
   /** Number of partitions to use for shuffle operators. */
   private[spark] def numShufflePartitions: Int = get(SHUFFLE_PARTITIONS, "200").toInt
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
index a136c7b3ffef..c2bdef732372 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
@@ -192,8 +192,6 @@ class SQLContext(@transient val sparkContext: SparkContext)
         currentTable.logicalPlan
 
       case _ =>
-        val useCompression =
-          sparkContext.conf.getBoolean("spark.sql.inMemoryColumnarStorage.compressed", false)
         InMemoryRelation(useCompression, executePlan(currentTable).executedPlan)
     }
 

From 22649b6cde8e18f043f122bce46f446174d00f6c Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@apache.org>
Date: Tue, 29 Jul 2014 19:02:06 -0700
Subject: [PATCH 804/810] [SPARK-2305] [PySpark] Update Py4J to version 0.8.2.1

Author: Josh Rosen <joshrosen@apache.org>

Closes #1626 from JoshRosen/SPARK-2305 and squashes the following commits:

03fb283 [Josh Rosen] Update Py4J to version 0.8.2.1.
---
 LICENSE                                         |   4 ++--
 bin/pyspark                                     |   2 +-
 bin/pyspark2.cmd                                |   2 +-
 core/pom.xml                                    |   2 +-
 .../apache/spark/api/python/PythonUtils.scala   |   2 +-
 python/lib/py4j-0.8.1-src.zip                   | Bin 37662 -> 0 bytes
 python/lib/py4j-0.8.2.1-src.zip                 | Bin 0 -> 37562 bytes
 sbin/spark-config.sh                            |   2 +-
 sbin/spark-executor                             |   2 +-
 9 files changed, 8 insertions(+), 8 deletions(-)
 delete mode 100644 python/lib/py4j-0.8.1-src.zip
 create mode 100644 python/lib/py4j-0.8.2.1-src.zip

diff --git a/LICENSE b/LICENSE
index 65e1f480d9b1..76a3601c6691 100644
--- a/LICENSE
+++ b/LICENSE
@@ -272,7 +272,7 @@ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 
 
 ========================================================================
-For Py4J (python/lib/py4j0.7.egg and files in assembly/lib/net/sf/py4j):
+For Py4J (python/lib/py4j-0.8.2.1-src.zip)
 ========================================================================
 
 Copyright (c) 2009-2011, Barthelemy Dagenais All rights reserved.
@@ -532,7 +532,7 @@ The following components are provided under a BSD-style license. See project lin
      (New BSD license) Protocol Buffer Java API (org.spark-project.protobuf:protobuf-java:2.4.1-shaded - http://code.google.com/p/protobuf)
      (The BSD License) Fortran to Java ARPACK (net.sourceforge.f2j:arpack_combined_all:0.1 - http://f2j.sourceforge.net)
      (The BSD License) xmlenc Library (xmlenc:xmlenc:0.52 - http://xmlenc.sourceforge.net)
-     (The New BSD License) Py4J (net.sf.py4j:py4j:0.8.1 - http://py4j.sourceforge.net/)
+     (The New BSD License) Py4J (net.sf.py4j:py4j:0.8.2.1 - http://py4j.sourceforge.net/)
      (Two-clause BSD-style license) JUnit-Interface (com.novocode:junit-interface:0.10 - http://github.com/szeiger/junit-interface/)
      (ISC/BSD License) jbcrypt (org.mindrot:jbcrypt:0.3m - http://www.mindrot.org/)
 
diff --git a/bin/pyspark b/bin/pyspark
index 69b056fe28f2..39a20e2a24a3 100755
--- a/bin/pyspark
+++ b/bin/pyspark
@@ -52,7 +52,7 @@ export PYSPARK_PYTHON
 
 # Add the PySpark classes to the Python path:
 export PYTHONPATH=$SPARK_HOME/python/:$PYTHONPATH
-export PYTHONPATH=$SPARK_HOME/python/lib/py4j-0.8.1-src.zip:$PYTHONPATH
+export PYTHONPATH=$SPARK_HOME/python/lib/py4j-0.8.2.1-src.zip:$PYTHONPATH
 
 # Load the PySpark shell.py script when ./pyspark is used interactively:
 export OLD_PYTHONSTARTUP=$PYTHONSTARTUP
diff --git a/bin/pyspark2.cmd b/bin/pyspark2.cmd
index 0ef9eea95342..2c4b08af8d4c 100644
--- a/bin/pyspark2.cmd
+++ b/bin/pyspark2.cmd
@@ -45,7 +45,7 @@ rem Figure out which Python to use.
 if [%PYSPARK_PYTHON%] == [] set PYSPARK_PYTHON=python
 
 set PYTHONPATH=%FWDIR%python;%PYTHONPATH%
-set PYTHONPATH=%FWDIR%python\lib\py4j-0.8.1-src.zip;%PYTHONPATH%
+set PYTHONPATH=%FWDIR%python\lib\py4j-0.8.2.1-src.zip;%PYTHONPATH%
 
 set OLD_PYTHONSTARTUP=%PYTHONSTARTUP%
 set PYTHONSTARTUP=%FWDIR%python\pyspark\shell.py
diff --git a/core/pom.xml b/core/pom.xml
index a24743495b0e..4f061099a477 100644
--- a/core/pom.xml
+++ b/core/pom.xml
@@ -275,7 +275,7 @@
     <dependency>
       <groupId>net.sf.py4j</groupId>
       <artifactId>py4j</artifactId>
-      <version>0.8.1</version>
+      <version>0.8.2.1</version>
     </dependency>
   </dependencies>
   <build>
diff --git a/core/src/main/scala/org/apache/spark/api/python/PythonUtils.scala b/core/src/main/scala/org/apache/spark/api/python/PythonUtils.scala
index 6d3e257c4d5d..52c70712eea3 100644
--- a/core/src/main/scala/org/apache/spark/api/python/PythonUtils.scala
+++ b/core/src/main/scala/org/apache/spark/api/python/PythonUtils.scala
@@ -29,7 +29,7 @@ private[spark] object PythonUtils {
     val pythonPath = new ArrayBuffer[String]
     for (sparkHome <- sys.env.get("SPARK_HOME")) {
       pythonPath += Seq(sparkHome, "python").mkString(File.separator)
-      pythonPath += Seq(sparkHome, "python", "lib", "py4j-0.8.1-src.zip").mkString(File.separator)
+      pythonPath += Seq(sparkHome, "python", "lib", "py4j-0.8.2.1-src.zip").mkString(File.separator)
     }
     pythonPath ++= SparkContext.jarOfObject(this)
     pythonPath.mkString(File.pathSeparator)
diff --git a/python/lib/py4j-0.8.1-src.zip b/python/lib/py4j-0.8.1-src.zip
deleted file mode 100644
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z1XJYd9?1UQ_Up^QPE_{)ynNG=4W@`{c1iNeo6iTH+1ydB#vbZI;E>H4V$@odb2VA9
z@=4Xd+F&9%4-p;r-#r$|-<DOcykxK$Lo{3L_@m)=+<MMd<7*h<Flew32D=VuxoB6E
z_zzT+(jGNsf~oOsRA7HgJ@D51I8hDQX+75?-dHy?Pn19Y?92y8OwhT_D<nx>aug!D
zR-MSm!Z}WL#Yt2nh=wahGD*Iid2?xHPvM8UZEtX2{9`s|LQP15QW2`cc<xTfRpn`-
zP+!_iDn2BmOn&6Z%@@9AFGkw$jd{Fz+{Y{&Zn1Q8IYmie;8Lk99rd=#eLv^F_93o(
zIurXShPAVElDp*SvPk{_vM4`Ya?xcbZPg)3mM@cnJ?Kk$z$0lZKBDY?C=<a{jwWib
z_Xm|eI2qsRRU#BTuef4e^n~PJ$L&$6U-{9!6I-?F60{x1WZa`urhtf*449L)DFya0
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diff --git a/sbin/spark-config.sh b/sbin/spark-config.sh
index 147b506dd5ca..5c87da5815b6 100755
--- a/sbin/spark-config.sh
+++ b/sbin/spark-config.sh
@@ -36,4 +36,4 @@ export SPARK_HOME=${SPARK_PREFIX}
 export SPARK_CONF_DIR="$SPARK_HOME/conf"
 # Add the PySpark classes to the PYTHONPATH:
 export PYTHONPATH=$SPARK_HOME/python:$PYTHONPATH
-export PYTHONPATH=$SPARK_HOME/python/lib/py4j-0.8.1-src.zip:$PYTHONPATH
+export PYTHONPATH=$SPARK_HOME/python/lib/py4j-0.8.2.1-src.zip:$PYTHONPATH
diff --git a/sbin/spark-executor b/sbin/spark-executor
index 336549f29c9c..3621321a9bc8 100755
--- a/sbin/spark-executor
+++ b/sbin/spark-executor
@@ -20,7 +20,7 @@
 FWDIR="$(cd `dirname $0`/..; pwd)"
 
 export PYTHONPATH=$FWDIR/python:$PYTHONPATH
-export PYTHONPATH=$FWDIR/python/lib/py4j-0.8.1-src.zip:$PYTHONPATH
+export PYTHONPATH=$FWDIR/python/lib/py4j-0.8.2.1-src.zip:$PYTHONPATH
 
 echo "Running spark-executor with framework dir = $FWDIR"
 exec $FWDIR/bin/spark-class org.apache.spark.executor.MesosExecutorBackend

From 84467468d466dadf4708a7d6a808471305149713 Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Tue, 29 Jul 2014 20:58:05 -0700
Subject: [PATCH 805/810] [SPARK-2054][SQL] Code Generation for Expression
 Evaluation

Adds a new method for evaluating expressions using code that is generated though Scala reflection.  This functionality is configured by the SQLConf option `spark.sql.codegen` and is currently turned off by default.

Evaluation can be done in several specialized ways:
 - *Projection* - Given an input row, produce a new row from a set of expressions that define each column in terms of the input row.  This can either produce a new Row object or perform the projection in-place on an existing Row (MutableProjection).
 - *Ordering* - Compares two rows based on a list of `SortOrder` expressions
 - *Condition* - Returns `true` or `false` given an input row.

For each of the above operations there is both a Generated and Interpreted version.  When generation for a given expression type is undefined, the code generator falls back on calling the `eval` function of the expression class.  Even without custom code, there is still a potential speed up, as loops are unrolled and code can still be inlined by JIT.

This PR also contains a new type of Aggregation operator, `GeneratedAggregate`, that performs aggregation by using generated `Projection` code.  Currently the required expression rewriting only works for simple aggregations like `SUM` and `COUNT`.  This functionality will be extended in a future PR.

This PR also performs several clean ups that simplified the implementation:
 - The notion of `Binding` all expressions in a tree automatically before query execution has been removed.  Instead it is the responsibly of an operator to provide the input schema when creating one of the specialized evaluators defined above.  In cases when the standard eval method is going to be called, binding can still be done manually using `BindReferences`.  There are a few reasons for this change:  First, there were many operators where it just didn't work before.  For example, operators with more than one child, and operators like aggregation that do significant rewriting of the expression. Second, the semantics of equality with `BoundReferences` are broken.  Specifically, we have had a few bugs where partitioning breaks because of the binding.
 - A copy of the current `SQLContext` is automatically propagated to all `SparkPlan` nodes by the query planner.  Before this was done ad-hoc for the nodes that needed this.  However, this required a lot of boilerplate as one had to always remember to make it `transient` and also had to modify the `otherCopyArgs`.

Author: Michael Armbrust <michael@databricks.com>

Closes #993 from marmbrus/newCodeGen and squashes the following commits:

96ef82c [Michael Armbrust] Merge remote-tracking branch 'apache/master' into newCodeGen
f34122d [Michael Armbrust] Merge remote-tracking branch 'apache/master' into newCodeGen
67b1c48 [Michael Armbrust] Use conf variable in SQLConf object
4bdc42c [Michael Armbrust] Merge remote-tracking branch 'origin/master' into newCodeGen
41a40c9 [Michael Armbrust] Merge remote-tracking branch 'origin/master' into newCodeGen
de22aac [Michael Armbrust] Merge remote-tracking branch 'origin/master' into newCodeGen
fed3634 [Michael Armbrust] Inspectors are not serializable.
ef8d42b [Michael Armbrust] comments
533fdfd [Michael Armbrust] More logging of expression rewriting for GeneratedAggregate.
3cd773e [Michael Armbrust] Allow codegen for Generate.
64b2ee1 [Michael Armbrust] Implement copy
3587460 [Michael Armbrust] Drop unused string builder function.
9cce346 [Michael Armbrust] Merge remote-tracking branch 'origin/master' into newCodeGen
1a61293 [Michael Armbrust] Address review comments.
0672e8a [Michael Armbrust] Address comments.
1ec2d6e [Michael Armbrust] Address comments
033abc6 [Michael Armbrust] off by default
4771fab [Michael Armbrust] Docs, more test coverage.
d30fee2 [Michael Armbrust] Merge remote-tracking branch 'origin/master' into newCodeGen
d2ad5c5 [Michael Armbrust] Refactor putting SQLContext into SparkPlan. Fix ordering, other test cases.
be2cd6b [Michael Armbrust] WIP: Remove old method for reference binding, more work on configuration.
bc88ecd [Michael Armbrust] Style
6cc97ca [Michael Armbrust] Merge remote-tracking branch 'origin/master' into newCodeGen
4220f1e [Michael Armbrust] Better config, docs, etc.
ca6cc6b [Michael Armbrust] WIP
9d67d85 [Michael Armbrust] Fix hive planner
fc522d5 [Michael Armbrust] Hook generated aggregation in to the planner.
e742640 [Michael Armbrust] Remove unneeded changes and code.
675e679 [Michael Armbrust] Upgrade paradise.
0093376 [Michael Armbrust] Comment / indenting cleanup.
d81f998 [Michael Armbrust] include schema for binding.
0e889e8 [Michael Armbrust] Use typeOf instead tq
f623ffd [Michael Armbrust] Quiet logging from test suite.
efad14f [Michael Armbrust] Remove some half finished functions.
92e74a4 [Michael Armbrust] add overrides
a2b5408 [Michael Armbrust] WIP: Code generation with scala reflection.
---
 pom.xml                                       |  10 +
 project/SparkBuild.scala                      |  11 +-
 sql/catalyst/pom.xml                          |   9 +
 .../spark/sql/catalyst/dsl/package.scala      |   2 +-
 .../catalyst/expressions/BoundAttribute.scala |  50 +-
 .../sql/catalyst/expressions/Projection.scala |  39 +-
 .../spark/sql/catalyst/expressions/Row.scala  |  40 +-
 .../sql/catalyst/expressions/ScalaUdf.scala   |   1 +
 .../expressions/codegen/CodeGenerator.scala   | 468 ++++++++++++++++++
 .../codegen/GenerateMutableProjection.scala   |  76 +++
 .../codegen/GenerateOrdering.scala            |  98 ++++
 .../codegen/GeneratePredicate.scala           |  48 ++
 .../codegen/GenerateProjection.scala          | 219 ++++++++
 .../expressions/codegen/package.scala         |  80 +++
 .../sql/catalyst/expressions/package.scala    |  28 +-
 .../sql/catalyst/expressions/predicates.scala |   3 +
 .../apache/spark/sql/catalyst/package.scala   |  27 +
 .../sql/catalyst/planning/patterns.scala      |  71 +++
 .../catalyst/plans/logical/LogicalPlan.scala  |   2 +-
 .../sql/catalyst/plans/logical/commands.scala |  12 +-
 .../sql/catalyst/rules/RuleExecutor.scala     |   5 +-
 .../spark/sql/catalyst/types/dataTypes.scala  |  18 +-
 .../ExpressionEvaluationSuite.scala           |  55 +-
 .../GeneratedEvaluationSuite.scala            |  69 +++
 .../GeneratedMutableEvaluationSuite.scala     |  61 +++
 .../optimizer/CombiningLimitsSuite.scala      |   4 +-
 .../scala/org/apache/spark/sql/SQLConf.scala  |  19 +-
 .../org/apache/spark/sql/SQLContext.scala     |  25 +-
 .../spark/sql/api/java/JavaSQLContext.scala   |   4 +-
 .../spark/sql/execution/Aggregate.scala       |  13 +-
 .../apache/spark/sql/execution/Exchange.scala |   8 +-
 .../apache/spark/sql/execution/Generate.scala |  13 +-
 .../sql/execution/GeneratedAggregate.scala    | 200 ++++++++
 .../spark/sql/execution/SparkPlan.scala       |  81 ++-
 .../spark/sql/execution/SparkStrategies.scala | 138 +++---
 .../spark/sql/execution/basicOperators.scala  |  44 +-
 .../spark/sql/execution/debug/package.scala   |   8 +-
 .../apache/spark/sql/execution/joins.scala    |  44 +-
 .../spark/sql/parquet/ParquetRelation.scala   |  18 +-
 .../sql/parquet/ParquetTableOperations.scala  |  14 +-
 .../spark/sql/parquet/ParquetTestData.scala   |   9 +-
 .../org/apache/spark/sql/QueryTest.scala      |   1 +
 .../spark/sql/execution/PlannerSuite.scala    |   8 +-
 .../apache/spark/sql/execution/TgfSuite.scala |   2 +-
 .../spark/sql/parquet/ParquetQuerySuite.scala |   5 +-
 .../apache/spark/sql/hive/HiveContext.scala   |   2 +-
 .../hive/execution/InsertIntoHiveTable.scala  |   2 +-
 .../hive/execution/ScriptTransformation.scala |   2 +-
 .../org/apache/spark/sql/hive/hiveUdfs.scala  |   6 +-
 ...se null-0-8ef2f741400830ef889a9dd0c817fe3d |   1 +
 ...le case-0-f513687d17dcb18546fefa75000a52f2 |   1 +
 ...le case-0-c264e319c52f1840a32959d552b99e73 |   1 +
 .../sql/hive/execution/HiveQuerySuite.scala   |  11 +
 53 files changed, 1889 insertions(+), 297 deletions(-)
 create mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
 create mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateMutableProjection.scala
 create mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateOrdering.scala
 create mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GeneratePredicate.scala
 create mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala
 create mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/package.scala
 create mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/package.scala
 create mode 100644 sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/GeneratedEvaluationSuite.scala
 create mode 100644 sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/GeneratedMutableEvaluationSuite.scala
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala
 create mode 100644 sql/hive/src/test/resources/golden/case else null-0-8ef2f741400830ef889a9dd0c817fe3d
 create mode 100644 sql/hive/src/test/resources/golden/double case-0-f513687d17dcb18546fefa75000a52f2
 create mode 100644 sql/hive/src/test/resources/golden/single case-0-c264e319c52f1840a32959d552b99e73

diff --git a/pom.xml b/pom.xml
index 39538f966062..ae97bf03c53a 100644
--- a/pom.xml
+++ b/pom.xml
@@ -114,6 +114,7 @@
     <sbt.project.name>spark</sbt.project.name>
     <scala.version>2.10.4</scala.version>
     <scala.binary.version>2.10</scala.binary.version>
+    <scala.macros.version>2.0.1</scala.macros.version>
     <mesos.version>0.18.1</mesos.version>
     <mesos.classifier>shaded-protobuf</mesos.classifier>
     <akka.group>org.spark-project.akka</akka.group>
@@ -825,6 +826,15 @@
               <javacArg>-target</javacArg>
               <javacArg>${java.version}</javacArg>
             </javacArgs>
+            <!-- The following plugin is required to use quasiquotes in Scala 2.10 and is used
+                 by Spark SQL for code generation. -->
+            <compilerPlugins>
+              <compilerPlugin>
+                  <groupId>org.scalamacros</groupId>
+                  <artifactId>paradise_${scala.version}</artifactId>
+                  <version>${scala.macros.version}</version>
+              </compilerPlugin>
+            </compilerPlugins>
           </configuration>
         </plugin>
         <plugin>
diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index 0a6326e72297..490fac3cc364 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -167,6 +167,9 @@ object SparkBuild extends PomBuild {
   /* Enable unidoc only for the root spark project */
   enable(Unidoc.settings)(spark)
 
+  /* Catalyst macro settings */
+  enable(Catalyst.settings)(catalyst)
+
   /* Spark SQL Core console settings */
   enable(SQL.settings)(sql)
 
@@ -189,10 +192,13 @@ object Flume {
   lazy val settings = sbtavro.SbtAvro.avroSettings
 }
 
-object SQL {
-
+object Catalyst {
   lazy val settings = Seq(
+    addCompilerPlugin("org.scalamacros" % "paradise" % "2.0.1" cross CrossVersion.full))
+}
 
+object SQL {
+  lazy val settings = Seq(
     initialCommands in console :=
       """
         |import org.apache.spark.sql.catalyst.analysis._
@@ -207,7 +213,6 @@ object SQL {
         |import org.apache.spark.sql.test.TestSQLContext._
         |import org.apache.spark.sql.parquet.ParquetTestData""".stripMargin
   )
-
 }
 
 object Hive {
diff --git a/sql/catalyst/pom.xml b/sql/catalyst/pom.xml
index 531bfddbf237..54fa96baa1e1 100644
--- a/sql/catalyst/pom.xml
+++ b/sql/catalyst/pom.xml
@@ -36,10 +36,19 @@
   </properties>
 
   <dependencies>
+    <dependency>
+      <groupId>org.scala-lang</groupId>
+      <artifactId>scala-compiler</artifactId>
+    </dependency>
     <dependency>
       <groupId>org.scala-lang</groupId>
       <artifactId>scala-reflect</artifactId>
     </dependency>
+    <dependency>
+      <groupId>org.scalamacros</groupId>
+      <artifactId>quasiquotes_${scala.binary.version}</artifactId>
+      <version>${scala.macros.version}</version>
+    </dependency>
     <dependency>
       <groupId>org.apache.spark</groupId>
       <artifactId>spark-core_${scala.binary.version}</artifactId>
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala
index 5c8c810d9135..f44521d6381c 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala
@@ -202,7 +202,7 @@ package object dsl {
       // Protobuf terminology
       def required = a.withNullability(false)
 
-      def at(ordinal: Int) = BoundReference(ordinal, a)
+      def at(ordinal: Int) = BoundReference(ordinal, a.dataType, a.nullable)
     }
   }
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala
index 9ce1f0105646..a3ebec8082cb 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala
@@ -17,10 +17,12 @@
 
 package org.apache.spark.sql.catalyst.expressions
 
-import org.apache.spark.sql.catalyst.trees
 import org.apache.spark.sql.catalyst.errors.attachTree
 import org.apache.spark.sql.catalyst.plans.QueryPlan
 import org.apache.spark.sql.catalyst.rules.Rule
+import org.apache.spark.sql.catalyst.types._
+import org.apache.spark.sql.catalyst.trees
+
 import org.apache.spark.sql.Logging
 
 /**
@@ -28,61 +30,27 @@ import org.apache.spark.sql.Logging
  * to be retrieved more efficiently.  However, since operations like column pruning can change
  * the layout of intermediate tuples, BindReferences should be run after all such transformations.
  */
-case class BoundReference(ordinal: Int, baseReference: Attribute)
-  extends Attribute with trees.LeafNode[Expression] {
+case class BoundReference(ordinal: Int, dataType: DataType, nullable: Boolean)
+  extends Expression with trees.LeafNode[Expression] {
 
   type EvaluatedType = Any
 
-  override def nullable = baseReference.nullable
-  override def dataType = baseReference.dataType
-  override def exprId = baseReference.exprId
-  override def qualifiers = baseReference.qualifiers
-  override def name = baseReference.name
+  override def references = Set.empty
 
-  override def newInstance = BoundReference(ordinal, baseReference.newInstance)
-  override def withNullability(newNullability: Boolean) =
-    BoundReference(ordinal, baseReference.withNullability(newNullability))
-  override def withQualifiers(newQualifiers: Seq[String]) =
-    BoundReference(ordinal, baseReference.withQualifiers(newQualifiers))
-
-  override def toString = s"$baseReference:$ordinal"
+  override def toString = s"input[$ordinal]"
 
   override def eval(input: Row): Any = input(ordinal)
 }
 
-/**
- * Used to denote operators that do their own binding of attributes internally.
- */
-trait NoBind { self: trees.TreeNode[_] => }
-
-class BindReferences[TreeNode <: QueryPlan[TreeNode]] extends Rule[TreeNode] {
-  import BindReferences._
-
-  def apply(plan: TreeNode): TreeNode = {
-    plan.transform {
-      case n: NoBind => n.asInstanceOf[TreeNode]
-      case leafNode if leafNode.children.isEmpty => leafNode
-      case unaryNode if unaryNode.children.size == 1 => unaryNode.transformExpressions { case e =>
-        bindReference(e, unaryNode.children.head.output)
-      }
-    }
-  }
-}
-
 object BindReferences extends Logging {
   def bindReference[A <: Expression](expression: A, input: Seq[Attribute]): A = {
     expression.transform { case a: AttributeReference =>
       attachTree(a, "Binding attribute") {
         val ordinal = input.indexWhere(_.exprId == a.exprId)
         if (ordinal == -1) {
-          // TODO: This fallback is required because some operators (such as ScriptTransform)
-          // produce new attributes that can't be bound.  Likely the right thing to do is remove
-          // this rule and require all operators to explicitly bind to the input schema that
-          // they specify.
-          logger.debug(s"Couldn't find $a in ${input.mkString("[", ",", "]")}")
-          a
+          sys.error(s"Couldn't find $a in ${input.mkString("[", ",", "]")}")
         } else {
-          BoundReference(ordinal, a)
+          BoundReference(ordinal, a.dataType, a.nullable)
         }
       }
     }.asInstanceOf[A] // Kind of a hack, but safe.  TODO: Tighten return type when possible.
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
index 2c71d2c7b356..8fc589697443 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
@@ -17,12 +17,13 @@
 
 package org.apache.spark.sql.catalyst.expressions
 
+
 /**
- * Converts a [[Row]] to another Row given a sequence of expression that define each column of the
- * new row. If the schema of the input row is specified, then the given expression will be bound to
- * that schema.
+ * A [[Projection]] that is calculated by calling the `eval` of each of the specified expressions.
+ * @param expressions a sequence of expressions that determine the value of each column of the
+ *                    output row.
  */
-class Projection(expressions: Seq[Expression]) extends (Row => Row) {
+class InterpretedProjection(expressions: Seq[Expression]) extends Projection {
   def this(expressions: Seq[Expression], inputSchema: Seq[Attribute]) =
     this(expressions.map(BindReferences.bindReference(_, inputSchema)))
 
@@ -40,25 +41,25 @@ class Projection(expressions: Seq[Expression]) extends (Row => Row) {
 }
 
 /**
- * Converts a [[Row]] to another Row given a sequence of expression that define each column of th
- * new row. If the schema of the input row is specified, then the given expression will be bound to
- * that schema.
- *
- * In contrast to a normal projection, a MutableProjection reuses the same underlying row object
- * each time an input row is added.  This significantly reduces the cost of calculating the
- * projection, but means that it is not safe to hold on to a reference to a [[Row]] after `next()`
- * has been called on the [[Iterator]] that produced it. Instead, the user must call `Row.copy()`
- * and hold on to the returned [[Row]] before calling `next()`.
+ * A [[MutableProjection]] that is calculated by calling `eval` on each of the specified
+ * expressions.
+ * @param expressions a sequence of expressions that determine the value of each column of the
+ *                    output row.
  */
-case class MutableProjection(expressions: Seq[Expression]) extends (Row => Row) {
+case class InterpretedMutableProjection(expressions: Seq[Expression]) extends MutableProjection {
   def this(expressions: Seq[Expression], inputSchema: Seq[Attribute]) =
     this(expressions.map(BindReferences.bindReference(_, inputSchema)))
 
   private[this] val exprArray = expressions.toArray
-  private[this] val mutableRow = new GenericMutableRow(exprArray.size)
+  private[this] var mutableRow: MutableRow = new GenericMutableRow(exprArray.size)
   def currentValue: Row = mutableRow
 
-  def apply(input: Row): Row = {
+  override def target(row: MutableRow): MutableProjection = {
+    mutableRow = row
+    this
+  }
+
+  override def apply(input: Row): Row = {
     var i = 0
     while (i < exprArray.length) {
       mutableRow(i) = exprArray(i).eval(input)
@@ -76,6 +77,12 @@ class JoinedRow extends Row {
   private[this] var row1: Row = _
   private[this] var row2: Row = _
 
+  def this(left: Row, right: Row) = {
+    this()
+    row1 = left
+    row2 = right
+  }
+
   /** Updates this JoinedRow to used point at two new base rows.  Returns itself. */
   def apply(r1: Row, r2: Row): Row = {
     row1 = r1
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Row.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Row.scala
index 74ae723686cf..7470cb861b83 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Row.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Row.scala
@@ -88,15 +88,6 @@ trait MutableRow extends Row {
   def setByte(ordinal: Int, value: Byte)
   def setFloat(ordinal: Int, value: Float)
   def setString(ordinal: Int, value: String)
-
-  /**
-   * Experimental
-   *
-   * Returns a mutable string builder for the specified column.  A given row should return the
-   * result of any mutations made to the returned buffer next time getString is called for the same
-   * column.
-   */
-  def getStringBuilder(ordinal: Int): StringBuilder
 }
 
 /**
@@ -180,6 +171,35 @@ class GenericRow(protected[catalyst] val values: Array[Any]) extends Row {
     values(i).asInstanceOf[String]
   }
 
+  // Custom hashCode function that matches the efficient code generated version.
+  override def hashCode(): Int = {
+    var result: Int = 37
+
+    var i = 0
+    while (i < values.length) {
+      val update: Int =
+        if (isNullAt(i)) {
+          0
+        } else {
+          apply(i) match {
+            case b: Boolean => if (b) 0 else 1
+            case b: Byte => b.toInt
+            case s: Short => s.toInt
+            case i: Int => i
+            case l: Long => (l ^ (l >>> 32)).toInt
+            case f: Float => java.lang.Float.floatToIntBits(f)
+            case d: Double =>
+              val b = java.lang.Double.doubleToLongBits(d)
+              (b ^ (b >>> 32)).toInt
+            case other => other.hashCode()
+          }
+        }
+      result = 37 * result + update
+      i += 1
+    }
+    result
+  }
+
   def copy() = this
 }
 
@@ -187,8 +207,6 @@ class GenericMutableRow(size: Int) extends GenericRow(size) with MutableRow {
   /** No-arg constructor for serialization. */
   def this() = this(0)
 
-  def getStringBuilder(ordinal: Int): StringBuilder = ???
-
   override def setBoolean(ordinal: Int,value: Boolean): Unit = { values(ordinal) = value }
   override def setByte(ordinal: Int,value: Byte): Unit = { values(ordinal) = value }
   override def setDouble(ordinal: Int,value: Double): Unit = { values(ordinal) = value }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUdf.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUdf.scala
index 5e089f7618e0..acddf5e9c700 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUdf.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUdf.scala
@@ -29,6 +29,7 @@ case class ScalaUdf(function: AnyRef, dataType: DataType, children: Seq[Expressi
 
   override def eval(input: Row): Any = {
     children.size match {
+      case 0 => function.asInstanceOf[() => Any]()
       case 1 => function.asInstanceOf[(Any) => Any](children(0).eval(input))
       case 2 =>
         function.asInstanceOf[(Any, Any) => Any](
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
new file mode 100644
index 000000000000..5b398695bf56
--- /dev/null
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
@@ -0,0 +1,468 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.expressions.codegen
+
+import com.google.common.cache.{CacheLoader, CacheBuilder}
+
+import scala.language.existentials
+
+import org.apache.spark.Logging
+import org.apache.spark.sql.catalyst.expressions
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.types._
+
+/**
+ * A base class for generators of byte code to perform expression evaluation.  Includes a set of
+ * helpers for referring to Catalyst types and building trees that perform evaluation of individual
+ * expressions.
+ */
+abstract class CodeGenerator[InType <: AnyRef, OutType <: AnyRef] extends Logging {
+  import scala.reflect.runtime.{universe => ru}
+  import scala.reflect.runtime.universe._
+
+  import scala.tools.reflect.ToolBox
+
+  protected val toolBox = runtimeMirror(getClass.getClassLoader).mkToolBox()
+
+  protected val rowType = typeOf[Row]
+  protected val mutableRowType = typeOf[MutableRow]
+  protected val genericRowType = typeOf[GenericRow]
+  protected val genericMutableRowType = typeOf[GenericMutableRow]
+
+  protected val projectionType = typeOf[Projection]
+  protected val mutableProjectionType = typeOf[MutableProjection]
+
+  private val curId = new java.util.concurrent.atomic.AtomicInteger()
+  private val javaSeparator = "$"
+
+  /**
+   * Generates a class for a given input expression.  Called when there is not cached code
+   * already available.
+   */
+  protected def create(in: InType): OutType
+
+  /**
+   * Canonicalizes an input expression. Used to avoid double caching expressions that differ only
+   * cosmetically.
+   */
+  protected def canonicalize(in: InType): InType
+
+  /** Binds an input expression to a given input schema */
+  protected def bind(in: InType, inputSchema: Seq[Attribute]): InType
+
+  /**
+   * A cache of generated classes.
+   *
+   * From the Guava Docs: A Cache is similar to ConcurrentMap, but not quite the same. The most
+   * fundamental difference is that a ConcurrentMap persists all elements that are added to it until
+   * they are explicitly removed. A Cache on the other hand is generally configured to evict entries
+   * automatically, in order to constrain its memory footprint
+   */
+  protected val cache = CacheBuilder.newBuilder()
+    .maximumSize(1000)
+    .build(
+      new CacheLoader[InType, OutType]() {
+        override def load(in: InType): OutType = globalLock.synchronized {
+           create(in)
+        }
+      })
+
+  /** Generates the requested evaluator binding the given expression(s) to the inputSchema. */
+  def apply(expressions: InType, inputSchema: Seq[Attribute]): OutType =
+    apply(bind(expressions, inputSchema))
+
+  /** Generates the requested evaluator given already bound expression(s). */
+  def apply(expressions: InType): OutType = cache.get(canonicalize(expressions))
+
+  /**
+   * Returns a term name that is unique within this instance of a `CodeGenerator`.
+   *
+   * (Since we aren't in a macro context we do not seem to have access to the built in `freshName`
+   * function.)
+   */
+  protected def freshName(prefix: String): TermName = {
+    newTermName(s"$prefix$javaSeparator${curId.getAndIncrement}")
+  }
+
+  /**
+   * Scala ASTs for evaluating an [[Expression]] given a [[Row]] of input.
+   *
+   * @param code The sequence of statements required to evaluate the expression.
+   * @param nullTerm A term that holds a boolean value representing whether the expression evaluated
+   *                 to null.
+   * @param primitiveTerm A term for a possible primitive value of the result of the evaluation. Not
+   *                      valid if `nullTerm` is set to `false`.
+   * @param objectTerm A possibly boxed version of the result of evaluating this expression.
+   */
+  protected case class EvaluatedExpression(
+      code: Seq[Tree],
+      nullTerm: TermName,
+      primitiveTerm: TermName,
+      objectTerm: TermName)
+
+  /**
+   * Given an expression tree returns an [[EvaluatedExpression]], which contains Scala trees that
+   * can be used to determine the result of evaluating the expression on an input row.
+   */
+  def expressionEvaluator(e: Expression): EvaluatedExpression = {
+    val primitiveTerm = freshName("primitiveTerm")
+    val nullTerm = freshName("nullTerm")
+    val objectTerm = freshName("objectTerm")
+
+    implicit class Evaluate1(e: Expression) {
+      def castOrNull(f: TermName => Tree, dataType: DataType): Seq[Tree] = {
+        val eval = expressionEvaluator(e)
+        eval.code ++
+        q"""
+          val $nullTerm = ${eval.nullTerm}
+          val $primitiveTerm =
+            if($nullTerm)
+              ${defaultPrimitive(dataType)}
+            else
+              ${f(eval.primitiveTerm)}
+        """.children
+      }
+    }
+
+    implicit class Evaluate2(expressions: (Expression, Expression)) {
+
+      /**
+       * Short hand for generating binary evaluation code, which depends on two sub-evaluations of
+       * the same type.  If either of the sub-expressions is null, the result of this computation
+       * is assumed to be null.
+       *
+       * @param f a function from two primitive term names to a tree that evaluates them.
+       */
+      def evaluate(f: (TermName, TermName) => Tree): Seq[Tree] =
+        evaluateAs(expressions._1.dataType)(f)
+
+      def evaluateAs(resultType: DataType)(f: (TermName, TermName) => Tree): Seq[Tree] = {
+        // TODO: Right now some timestamp tests fail if we enforce this...
+        if (expressions._1.dataType != expressions._2.dataType) {
+          log.warn(s"${expressions._1.dataType} != ${expressions._2.dataType}")
+        }
+
+        val eval1 = expressionEvaluator(expressions._1)
+        val eval2 = expressionEvaluator(expressions._2)
+        val resultCode = f(eval1.primitiveTerm, eval2.primitiveTerm)
+
+        eval1.code ++ eval2.code ++
+        q"""
+          val $nullTerm = ${eval1.nullTerm} || ${eval2.nullTerm}
+          val $primitiveTerm: ${termForType(resultType)} =
+            if($nullTerm) {
+              ${defaultPrimitive(resultType)}
+            } else {
+              $resultCode.asInstanceOf[${termForType(resultType)}]
+            }
+        """.children : Seq[Tree]
+      }
+    }
+
+    val inputTuple = newTermName(s"i")
+
+    // TODO: Skip generation of null handling code when expression are not nullable.
+    val primitiveEvaluation: PartialFunction[Expression, Seq[Tree]] = {
+      case b @ BoundReference(ordinal, dataType, nullable) =>
+        val nullValue = q"$inputTuple.isNullAt($ordinal)"
+        q"""
+          val $nullTerm: Boolean = $nullValue
+          val $primitiveTerm: ${termForType(dataType)} =
+            if($nullTerm)
+              ${defaultPrimitive(dataType)}
+            else
+              ${getColumn(inputTuple, dataType, ordinal)}
+         """.children
+
+      case expressions.Literal(null, dataType) =>
+        q"""
+          val $nullTerm = true
+          val $primitiveTerm: ${termForType(dataType)} = null.asInstanceOf[${termForType(dataType)}]
+         """.children
+
+      case expressions.Literal(value: Boolean, dataType) =>
+        q"""
+          val $nullTerm = ${value == null}
+          val $primitiveTerm: ${termForType(dataType)} = $value
+         """.children
+
+      case expressions.Literal(value: String, dataType) =>
+        q"""
+          val $nullTerm = ${value == null}
+          val $primitiveTerm: ${termForType(dataType)} = $value
+         """.children
+
+      case expressions.Literal(value: Int, dataType) =>
+        q"""
+          val $nullTerm = ${value == null}
+          val $primitiveTerm: ${termForType(dataType)} = $value
+         """.children
+
+      case expressions.Literal(value: Long, dataType) =>
+        q"""
+          val $nullTerm = ${value == null}
+          val $primitiveTerm: ${termForType(dataType)} = $value
+         """.children
+
+      case Cast(e @ BinaryType(), StringType) =>
+        val eval = expressionEvaluator(e)
+        eval.code ++
+        q"""
+          val $nullTerm = ${eval.nullTerm}
+          val $primitiveTerm =
+            if($nullTerm)
+              ${defaultPrimitive(StringType)}
+            else
+              new String(${eval.primitiveTerm}.asInstanceOf[Array[Byte]])
+        """.children
+
+      case Cast(child @ NumericType(), IntegerType) =>
+        child.castOrNull(c => q"$c.toInt", IntegerType)
+
+      case Cast(child @ NumericType(), LongType) =>
+        child.castOrNull(c => q"$c.toLong", LongType)
+
+      case Cast(child @ NumericType(), DoubleType) =>
+        child.castOrNull(c => q"$c.toDouble", DoubleType)
+
+      case Cast(child @ NumericType(), FloatType) =>
+        child.castOrNull(c => q"$c.toFloat", IntegerType)
+
+      // Special handling required for timestamps in hive test cases since the toString function
+      // does not match the expected output.
+      case Cast(e, StringType) if e.dataType != TimestampType =>
+        val eval = expressionEvaluator(e)
+        eval.code ++
+        q"""
+          val $nullTerm = ${eval.nullTerm}
+          val $primitiveTerm =
+            if($nullTerm)
+              ${defaultPrimitive(StringType)}
+            else
+              ${eval.primitiveTerm}.toString
+        """.children
+
+      case EqualTo(e1, e2) =>
+        (e1, e2).evaluateAs (BooleanType) { case (eval1, eval2) => q"$eval1 == $eval2" }
+
+      /* TODO: Fix null semantics.
+      case In(e1, list) if !list.exists(!_.isInstanceOf[expressions.Literal]) =>
+        val eval = expressionEvaluator(e1)
+
+        val checks = list.map {
+          case expressions.Literal(v: String, dataType) =>
+            q"if(${eval.primitiveTerm} == $v) return true"
+          case expressions.Literal(v: Int, dataType) =>
+            q"if(${eval.primitiveTerm} == $v) return true"
+        }
+
+        val funcName = newTermName(s"isIn${curId.getAndIncrement()}")
+
+        q"""
+            def $funcName: Boolean = {
+              ..${eval.code}
+              if(${eval.nullTerm}) return false
+              ..$checks
+              return false
+            }
+            val $nullTerm = false
+            val $primitiveTerm = $funcName
+        """.children
+      */
+
+      case GreaterThan(e1 @ NumericType(), e2 @ NumericType()) =>
+        (e1, e2).evaluateAs (BooleanType) { case (eval1, eval2) => q"$eval1 > $eval2" }
+      case GreaterThanOrEqual(e1 @ NumericType(), e2 @ NumericType()) =>
+        (e1, e2).evaluateAs (BooleanType) { case (eval1, eval2) => q"$eval1 >= $eval2" }
+      case LessThan(e1 @ NumericType(), e2 @ NumericType()) =>
+        (e1, e2).evaluateAs (BooleanType) { case (eval1, eval2) => q"$eval1 < $eval2" }
+      case LessThanOrEqual(e1 @ NumericType(), e2 @ NumericType()) =>
+        (e1, e2).evaluateAs (BooleanType) { case (eval1, eval2) => q"$eval1 <= $eval2" }
+
+      case And(e1, e2) =>
+        val eval1 = expressionEvaluator(e1)
+        val eval2 = expressionEvaluator(e2)
+
+        eval1.code ++ eval2.code ++
+        q"""
+          var $nullTerm = false
+          var $primitiveTerm: ${termForType(BooleanType)} = false
+
+          if ((!${eval1.nullTerm} && !${eval1.primitiveTerm}) ||
+              (!${eval2.nullTerm} && !${eval2.primitiveTerm})) {
+            $nullTerm = false
+            $primitiveTerm = false
+          } else if (${eval1.nullTerm} || ${eval2.nullTerm} ) {
+            $nullTerm = true
+          } else {
+            $nullTerm = false
+            $primitiveTerm = true
+          }
+         """.children
+
+      case Or(e1, e2) =>
+        val eval1 = expressionEvaluator(e1)
+        val eval2 = expressionEvaluator(e2)
+
+        eval1.code ++ eval2.code ++
+        q"""
+          var $nullTerm = false
+          var $primitiveTerm: ${termForType(BooleanType)} = false
+
+          if ((!${eval1.nullTerm} && ${eval1.primitiveTerm}) ||
+              (!${eval2.nullTerm} && ${eval2.primitiveTerm})) {
+            $nullTerm = false
+            $primitiveTerm = true
+          } else if (${eval1.nullTerm} || ${eval2.nullTerm} ) {
+            $nullTerm = true
+          } else {
+            $nullTerm = false
+            $primitiveTerm = false
+          }
+         """.children
+
+      case Not(child) =>
+        // Uh, bad function name...
+        child.castOrNull(c => q"!$c", BooleanType)
+
+      case Add(e1, e2) =>      (e1, e2) evaluate { case (eval1, eval2) => q"$eval1 + $eval2" }
+      case Subtract(e1, e2) => (e1, e2) evaluate { case (eval1, eval2) => q"$eval1 - $eval2" }
+      case Multiply(e1, e2) => (e1, e2) evaluate { case (eval1, eval2) => q"$eval1 * $eval2" }
+      case Divide(e1, e2) =>   (e1, e2) evaluate { case (eval1, eval2) => q"$eval1 / $eval2" }
+
+      case IsNotNull(e) =>
+        val eval = expressionEvaluator(e)
+        q"""
+          ..${eval.code}
+          var $nullTerm = false
+          var $primitiveTerm: ${termForType(BooleanType)} = !${eval.nullTerm}
+        """.children
+
+      case IsNull(e) =>
+        val eval = expressionEvaluator(e)
+        q"""
+          ..${eval.code}
+          var $nullTerm = false
+          var $primitiveTerm: ${termForType(BooleanType)} = ${eval.nullTerm}
+        """.children
+
+      case c @ Coalesce(children) =>
+        q"""
+          var $nullTerm = true
+          var $primitiveTerm: ${termForType(c.dataType)} = ${defaultPrimitive(c.dataType)}
+        """.children ++
+        children.map { c =>
+          val eval = expressionEvaluator(c)
+          q"""
+            if($nullTerm) {
+              ..${eval.code}
+              if(!${eval.nullTerm}) {
+                $nullTerm = false
+                $primitiveTerm = ${eval.primitiveTerm}
+              }
+            }
+          """
+        }
+
+      case i @ expressions.If(condition, trueValue, falseValue) =>
+        val condEval = expressionEvaluator(condition)
+        val trueEval = expressionEvaluator(trueValue)
+        val falseEval = expressionEvaluator(falseValue)
+
+        q"""
+          var $nullTerm = false
+          var $primitiveTerm: ${termForType(i.dataType)} = ${defaultPrimitive(i.dataType)}
+          ..${condEval.code}
+          if(!${condEval.nullTerm} && ${condEval.primitiveTerm}) {
+            ..${trueEval.code}
+            $nullTerm = ${trueEval.nullTerm}
+            $primitiveTerm = ${trueEval.primitiveTerm}
+          } else {
+            ..${falseEval.code}
+            $nullTerm = ${falseEval.nullTerm}
+            $primitiveTerm = ${falseEval.primitiveTerm}
+          }
+        """.children
+    }
+
+    // If there was no match in the partial function above, we fall back on calling the interpreted
+    // expression evaluator.
+    val code: Seq[Tree] =
+      primitiveEvaluation.lift.apply(e).getOrElse {
+        log.debug(s"No rules to generate $e")
+        val tree = reify { e }
+        q"""
+          val $objectTerm = $tree.eval(i)
+          val $nullTerm = $objectTerm == null
+          val $primitiveTerm = $objectTerm.asInstanceOf[${termForType(e.dataType)}]
+         """.children
+      }
+
+    EvaluatedExpression(code, nullTerm, primitiveTerm, objectTerm)
+  }
+
+  protected def getColumn(inputRow: TermName, dataType: DataType, ordinal: Int) = {
+    dataType match {
+      case dt @ NativeType() => q"$inputRow.${accessorForType(dt)}($ordinal)"
+      case _ => q"$inputRow.apply($ordinal).asInstanceOf[${termForType(dataType)}]"
+    }
+  }
+
+  protected def setColumn(
+      destinationRow: TermName,
+      dataType: DataType,
+      ordinal: Int,
+      value: TermName) = {
+    dataType match {
+      case dt @ NativeType() => q"$destinationRow.${mutatorForType(dt)}($ordinal, $value)"
+      case _ => q"$destinationRow.update($ordinal, $value)"
+    }
+  }
+
+  protected def accessorForType(dt: DataType) = newTermName(s"get${primitiveForType(dt)}")
+  protected def mutatorForType(dt: DataType) = newTermName(s"set${primitiveForType(dt)}")
+
+  protected def primitiveForType(dt: DataType) = dt match {
+    case IntegerType => "Int"
+    case LongType => "Long"
+    case ShortType => "Short"
+    case ByteType => "Byte"
+    case DoubleType => "Double"
+    case FloatType => "Float"
+    case BooleanType => "Boolean"
+    case StringType => "String"
+  }
+
+  protected def defaultPrimitive(dt: DataType) = dt match {
+    case BooleanType => ru.Literal(Constant(false))
+    case FloatType => ru.Literal(Constant(-1.0.toFloat))
+    case StringType => ru.Literal(Constant("<uninit>"))
+    case ShortType => ru.Literal(Constant(-1.toShort))
+    case LongType => ru.Literal(Constant(1L))
+    case ByteType => ru.Literal(Constant(-1.toByte))
+    case DoubleType => ru.Literal(Constant(-1.toDouble))
+    case DecimalType => ru.Literal(Constant(-1)) // Will get implicity converted as needed.
+    case IntegerType => ru.Literal(Constant(-1))
+    case _ => ru.Literal(Constant(null))
+  }
+
+  protected def termForType(dt: DataType) = dt match {
+    case n: NativeType => n.tag
+    case _ => typeTag[Any]
+  }
+}
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateMutableProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateMutableProjection.scala
new file mode 100644
index 000000000000..a419fd7ecb39
--- /dev/null
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateMutableProjection.scala
@@ -0,0 +1,76 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.expressions.codegen
+
+import org.apache.spark.sql.catalyst.expressions._
+
+/**
+ * Generates byte code that produces a [[MutableRow]] object that can update itself based on a new
+ * input [[Row]] for a fixed set of [[Expression Expressions]].
+ */
+object GenerateMutableProjection extends CodeGenerator[Seq[Expression], () => MutableProjection] {
+  import scala.reflect.runtime.{universe => ru}
+  import scala.reflect.runtime.universe._
+
+  val mutableRowName = newTermName("mutableRow")
+
+  protected def canonicalize(in: Seq[Expression]): Seq[Expression] =
+    in.map(ExpressionCanonicalizer(_))
+
+  protected def bind(in: Seq[Expression], inputSchema: Seq[Attribute]): Seq[Expression] =
+    in.map(BindReferences.bindReference(_, inputSchema))
+
+  protected def create(expressions: Seq[Expression]): (() => MutableProjection) = {
+    val projectionCode = expressions.zipWithIndex.flatMap { case (e, i) =>
+      val evaluationCode = expressionEvaluator(e)
+
+      evaluationCode.code :+
+      q"""
+        if(${evaluationCode.nullTerm})
+          mutableRow.setNullAt($i)
+        else
+          ${setColumn(mutableRowName, e.dataType, i, evaluationCode.primitiveTerm)}
+      """
+    }
+
+    val code =
+      q"""
+        () => { new $mutableProjectionType {
+
+          private[this] var $mutableRowName: $mutableRowType =
+            new $genericMutableRowType(${expressions.size})
+
+          def target(row: $mutableRowType): $mutableProjectionType = {
+            $mutableRowName = row
+            this
+          }
+
+          /* Provide immutable access to the last projected row. */
+          def currentValue: $rowType = mutableRow
+
+          def apply(i: $rowType): $rowType = {
+            ..$projectionCode
+            mutableRow
+          }
+        } }
+      """
+
+    log.debug(s"code for ${expressions.mkString(",")}:\n$code")
+    toolBox.eval(code).asInstanceOf[() => MutableProjection]
+  }
+}
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateOrdering.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateOrdering.scala
new file mode 100644
index 000000000000..4211998f7511
--- /dev/null
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateOrdering.scala
@@ -0,0 +1,98 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.expressions.codegen
+
+import com.typesafe.scalalogging.slf4j.Logging
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.types.{StringType, NumericType}
+
+/**
+ * Generates bytecode for an [[Ordering]] of [[Row Rows]] for a given set of
+ * [[Expression Expressions]].
+ */
+object GenerateOrdering extends CodeGenerator[Seq[SortOrder], Ordering[Row]] with Logging {
+  import scala.reflect.runtime.{universe => ru}
+  import scala.reflect.runtime.universe._
+
+ protected def canonicalize(in: Seq[SortOrder]): Seq[SortOrder] =
+    in.map(ExpressionCanonicalizer(_).asInstanceOf[SortOrder])
+
+  protected def bind(in: Seq[SortOrder], inputSchema: Seq[Attribute]): Seq[SortOrder] =
+    in.map(BindReferences.bindReference(_, inputSchema))
+
+  protected def create(ordering: Seq[SortOrder]): Ordering[Row] = {
+    val a = newTermName("a")
+    val b = newTermName("b")
+    val comparisons = ordering.zipWithIndex.map { case (order, i) =>
+      val evalA = expressionEvaluator(order.child)
+      val evalB = expressionEvaluator(order.child)
+
+      val compare = order.child.dataType match {
+        case _: NumericType =>
+          q"""
+          val comp = ${evalA.primitiveTerm} - ${evalB.primitiveTerm}
+          if(comp != 0) {
+            return ${if (order.direction == Ascending) q"comp.toInt" else q"-comp.toInt"}
+          }
+          """
+        case StringType =>
+          if (order.direction == Ascending) {
+            q"""return ${evalA.primitiveTerm}.compare(${evalB.primitiveTerm})"""
+          } else {
+            q"""return ${evalB.primitiveTerm}.compare(${evalA.primitiveTerm})"""
+          }
+      }
+
+      q"""
+        i = $a
+        ..${evalA.code}
+        i = $b
+        ..${evalB.code}
+        if (${evalA.nullTerm} && ${evalB.nullTerm}) {
+          // Nothing
+        } else if (${evalA.nullTerm}) {
+          return ${if (order.direction == Ascending) q"-1" else q"1"}
+        } else if (${evalB.nullTerm}) {
+          return ${if (order.direction == Ascending) q"1" else q"-1"}
+        } else {
+          $compare
+        }
+      """
+    }
+
+    val q"class $orderingName extends $orderingType { ..$body }" = reify {
+      class SpecificOrdering extends Ordering[Row] {
+        val o = ordering
+      }
+    }.tree.children.head
+
+    val code = q"""
+      class $orderingName extends $orderingType {
+        ..$body
+        def compare(a: $rowType, b: $rowType): Int = {
+          var i: $rowType = null // Holds current row being evaluated.
+          ..$comparisons
+          return 0
+        }
+      }
+      new $orderingName()
+      """
+    logger.debug(s"Generated Ordering: $code")
+    toolBox.eval(code).asInstanceOf[Ordering[Row]]
+  }
+}
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GeneratePredicate.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GeneratePredicate.scala
new file mode 100644
index 000000000000..2a0935c790cf
--- /dev/null
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GeneratePredicate.scala
@@ -0,0 +1,48 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.expressions.codegen
+
+import org.apache.spark.sql.catalyst.expressions._
+
+/**
+ * Generates bytecode that evaluates a boolean [[Expression]] on a given input [[Row]].
+ */
+object GeneratePredicate extends CodeGenerator[Expression, (Row) => Boolean] {
+  import scala.reflect.runtime.{universe => ru}
+  import scala.reflect.runtime.universe._
+
+  protected def canonicalize(in: Expression): Expression = ExpressionCanonicalizer(in)
+
+  protected def bind(in: Expression, inputSchema: Seq[Attribute]): Expression =
+    BindReferences.bindReference(in, inputSchema)
+
+  protected def create(predicate: Expression): ((Row) => Boolean) = {
+    val cEval = expressionEvaluator(predicate)
+
+    val code =
+      q"""
+        (i: $rowType) => {
+          ..${cEval.code}
+          if (${cEval.nullTerm}) false else ${cEval.primitiveTerm}
+        }
+      """
+
+    log.debug(s"Generated predicate '$predicate':\n$code")
+    toolBox.eval(code).asInstanceOf[Row => Boolean]
+  }
+}
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala
new file mode 100644
index 000000000000..77fa02c13de3
--- /dev/null
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala
@@ -0,0 +1,219 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.expressions.codegen
+
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.types._
+
+
+/**
+ * Generates bytecode that produces a new [[Row]] object based on a fixed set of input
+ * [[Expression Expressions]] and a given input [[Row]].  The returned [[Row]] object is custom
+ * generated based on the output types of the [[Expression]] to avoid boxing of primitive values.
+ */
+object GenerateProjection extends CodeGenerator[Seq[Expression], Projection] {
+  import scala.reflect.runtime.{universe => ru}
+  import scala.reflect.runtime.universe._
+
+  protected def canonicalize(in: Seq[Expression]): Seq[Expression] =
+    in.map(ExpressionCanonicalizer(_))
+
+  protected def bind(in: Seq[Expression], inputSchema: Seq[Attribute]): Seq[Expression] =
+    in.map(BindReferences.bindReference(_, inputSchema))
+
+  // Make Mutablility optional...
+  protected def create(expressions: Seq[Expression]): Projection = {
+    val tupleLength = ru.Literal(Constant(expressions.length))
+    val lengthDef = q"final val length = $tupleLength"
+
+    /* TODO: Configurable...
+    val nullFunctions =
+      q"""
+        private final val nullSet = new org.apache.spark.util.collection.BitSet(length)
+        final def setNullAt(i: Int) = nullSet.set(i)
+        final def isNullAt(i: Int) = nullSet.get(i)
+      """
+     */
+
+    val nullFunctions =
+      q"""
+        private[this] var nullBits = new Array[Boolean](${expressions.size})
+        final def setNullAt(i: Int) = { nullBits(i) = true }
+        final def isNullAt(i: Int) = nullBits(i)
+      """.children
+
+    val tupleElements = expressions.zipWithIndex.flatMap {
+      case (e, i) =>
+        val elementName = newTermName(s"c$i")
+        val evaluatedExpression = expressionEvaluator(e)
+        val iLit = ru.Literal(Constant(i))
+
+        q"""
+        var ${newTermName(s"c$i")}: ${termForType(e.dataType)} = _
+        {
+          ..${evaluatedExpression.code}
+          if(${evaluatedExpression.nullTerm})
+            setNullAt($iLit)
+          else
+            $elementName = ${evaluatedExpression.primitiveTerm}
+        }
+        """.children : Seq[Tree]
+    }
+
+    val iteratorFunction = {
+      val allColumns = (0 until expressions.size).map { i =>
+        val iLit = ru.Literal(Constant(i))
+        q"if(isNullAt($iLit)) { null } else { ${newTermName(s"c$i")} }"
+      }
+      q"final def iterator = Iterator[Any](..$allColumns)"
+    }
+
+    val accessorFailure = q"""scala.sys.error("Invalid ordinal:" + i)"""
+    val applyFunction = {
+      val cases = (0 until expressions.size).map { i =>
+        val ordinal = ru.Literal(Constant(i))
+        val elementName = newTermName(s"c$i")
+        val iLit = ru.Literal(Constant(i))
+
+        q"if(i == $ordinal) { if(isNullAt($i)) return null else return $elementName }"
+      }
+      q"final def apply(i: Int): Any = { ..$cases; $accessorFailure }"
+    }
+
+    val updateFunction = {
+      val cases = expressions.zipWithIndex.map {case (e, i) =>
+        val ordinal = ru.Literal(Constant(i))
+        val elementName = newTermName(s"c$i")
+        val iLit = ru.Literal(Constant(i))
+
+        q"""
+          if(i == $ordinal) {
+            if(value == null) {
+              setNullAt(i)
+            } else {
+              $elementName = value.asInstanceOf[${termForType(e.dataType)}]
+              return
+            }
+          }"""
+      }
+      q"final def update(i: Int, value: Any): Unit = { ..$cases; $accessorFailure }"
+    }
+
+    val specificAccessorFunctions = NativeType.all.map { dataType =>
+      val ifStatements = expressions.zipWithIndex.flatMap {
+        case (e, i) if e.dataType == dataType =>
+          val elementName = newTermName(s"c$i")
+          // TODO: The string of ifs gets pretty inefficient as the row grows in size.
+          // TODO: Optional null checks?
+          q"if(i == $i) return $elementName" :: Nil
+        case _ => Nil
+      }
+
+      q"""
+      final def ${accessorForType(dataType)}(i: Int):${termForType(dataType)} = {
+        ..$ifStatements;
+        $accessorFailure
+      }"""
+    }
+
+    val specificMutatorFunctions = NativeType.all.map { dataType =>
+      val ifStatements = expressions.zipWithIndex.flatMap {
+        case (e, i) if e.dataType == dataType =>
+          val elementName = newTermName(s"c$i")
+          // TODO: The string of ifs gets pretty inefficient as the row grows in size.
+          // TODO: Optional null checks?
+          q"if(i == $i) { $elementName = value; return }" :: Nil
+        case _ => Nil
+      }
+
+      q"""
+      final def ${mutatorForType(dataType)}(i: Int, value: ${termForType(dataType)}): Unit = {
+        ..$ifStatements;
+        $accessorFailure
+      }"""
+    }
+
+    val hashValues = expressions.zipWithIndex.map { case (e,i) =>
+      val elementName = newTermName(s"c$i")
+      val nonNull = e.dataType match {
+        case BooleanType => q"if ($elementName) 0 else 1"
+        case ByteType | ShortType | IntegerType => q"$elementName.toInt"
+        case LongType => q"($elementName ^ ($elementName >>> 32)).toInt"
+        case FloatType => q"java.lang.Float.floatToIntBits($elementName)"
+        case DoubleType =>
+          q"{ val b = java.lang.Double.doubleToLongBits($elementName); (b ^ (b >>>32)).toInt }"
+        case _ => q"$elementName.hashCode"
+      }
+      q"if (isNullAt($i)) 0 else $nonNull"
+    }
+
+    val hashUpdates: Seq[Tree] = hashValues.map(v => q"""result = 37 * result + $v""": Tree)
+
+    val hashCodeFunction =
+      q"""
+        override def hashCode(): Int = {
+          var result: Int = 37
+          ..$hashUpdates
+          result
+        }
+      """
+
+    val columnChecks = (0 until expressions.size).map { i =>
+      val elementName = newTermName(s"c$i")
+      q"if (this.$elementName != specificType.$elementName) return false"
+    }
+
+    val equalsFunction =
+      q"""
+        override def equals(other: Any): Boolean = other match {
+          case specificType: SpecificRow =>
+            ..$columnChecks
+            return true
+          case other => super.equals(other)
+        }
+      """
+
+    val copyFunction =
+      q"""
+        final def copy() = new $genericRowType(this.toArray)
+      """
+
+    val classBody =
+      nullFunctions ++ (
+        lengthDef +:
+        iteratorFunction +:
+        applyFunction +:
+        updateFunction +:
+        equalsFunction +:
+        hashCodeFunction +:
+        copyFunction +:
+        (tupleElements ++ specificAccessorFunctions ++ specificMutatorFunctions))
+
+    val code = q"""
+      final class SpecificRow(i: $rowType) extends $mutableRowType {
+        ..$classBody
+      }
+
+      new $projectionType { def apply(r: $rowType) = new SpecificRow(r) }
+    """
+
+    log.debug(
+      s"MutableRow, initExprs: ${expressions.mkString(",")} code:\n${toolBox.typeCheck(code)}")
+    toolBox.eval(code).asInstanceOf[Projection]
+  }
+}
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/package.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/package.scala
new file mode 100644
index 000000000000..80c7dfd376c9
--- /dev/null
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/package.scala
@@ -0,0 +1,80 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.expressions
+
+import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.sql.catalyst.rules
+import org.apache.spark.sql.catalyst.util
+
+/**
+ * A collection of generators that build custom bytecode at runtime for performing the evaluation
+ * of catalyst expression.
+ */
+package object codegen {
+
+  /**
+   * A lock to protect invoking the scala compiler at runtime, since it is not thread safe in Scala
+   * 2.10.
+   */
+  protected[codegen] val globalLock = org.apache.spark.sql.catalyst.ScalaReflectionLock
+
+  /** Canonicalizes an expression so those that differ only by names can reuse the same code. */
+  object ExpressionCanonicalizer extends rules.RuleExecutor[Expression] {
+    val batches =
+      Batch("CleanExpressions", FixedPoint(20), CleanExpressions) :: Nil
+
+    object CleanExpressions extends rules.Rule[Expression] {
+      def apply(e: Expression): Expression = e transform {
+        case Alias(c, _) => c
+      }
+    }
+  }
+
+  /**
+   * :: DeveloperApi ::
+   * Dumps the bytecode from a class to the screen using javap.
+   */
+  @DeveloperApi
+  object DumpByteCode {
+    import scala.sys.process._
+    val dumpDirectory = util.getTempFilePath("sparkSqlByteCode")
+    dumpDirectory.mkdir()
+
+    def apply(obj: Any): Unit = {
+      val generatedClass = obj.getClass
+      val classLoader =
+        generatedClass
+          .getClassLoader
+          .asInstanceOf[scala.tools.nsc.interpreter.AbstractFileClassLoader]
+      val generatedBytes = classLoader.classBytes(generatedClass.getName)
+
+      val packageDir = new java.io.File(dumpDirectory, generatedClass.getPackage.getName)
+      if (!packageDir.exists()) { packageDir.mkdir() }
+
+      val classFile =
+        new java.io.File(packageDir, generatedClass.getName.split("\\.").last + ".class")
+
+      val outfile = new java.io.FileOutputStream(classFile)
+      outfile.write(generatedBytes)
+      outfile.close()
+
+      println(
+        s"javap -p -v -classpath ${dumpDirectory.getCanonicalPath} ${generatedClass.getName}".!!)
+    }
+  }
+}
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/package.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/package.scala
index b6f2451b52e1..55d95991c5f1 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/package.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/package.scala
@@ -47,4 +47,30 @@ package org.apache.spark.sql.catalyst
  * ==Evaluation==
  * The result of expressions can be evaluated using the `Expression.apply(Row)` method.
  */
-package object expressions
+package object expressions  {
+
+  /**
+   * Converts a [[Row]] to another Row given a sequence of expression that define each column of the
+   * new row. If the schema of the input row is specified, then the given expression will be bound
+   * to that schema.
+   */
+  abstract class Projection extends (Row => Row)
+
+  /**
+   * Converts a [[Row]] to another Row given a sequence of expression that define each column of the
+   * new row. If the schema of the input row is specified, then the given expression will be bound
+   * to that schema.
+   *
+   * In contrast to a normal projection, a MutableProjection reuses the same underlying row object
+   * each time an input row is added.  This significantly reduces the cost of calculating the
+   * projection, but means that it is not safe to hold on to a reference to a [[Row]] after `next()`
+   * has been called on the [[Iterator]] that produced it. Instead, the user must call `Row.copy()`
+   * and hold on to the returned [[Row]] before calling `next()`.
+   */
+  abstract class MutableProjection extends Projection {
+    def currentValue: Row
+
+    /** Uses the given row to store the output of the projection. */
+    def target(row: MutableRow): MutableProjection
+  }
+}
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
index 06b94a98d3cd..5976b0ddf3e0 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
@@ -23,6 +23,9 @@ import org.apache.spark.sql.catalyst.types.BooleanType
 
 
 object InterpretedPredicate {
+  def apply(expression: Expression, inputSchema: Seq[Attribute]): (Row => Boolean) =
+    apply(BindReferences.bindReference(expression, inputSchema))
+
   def apply(expression: Expression): (Row => Boolean) = {
     (r: Row) => expression.eval(r).asInstanceOf[Boolean]
   }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/package.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/package.scala
new file mode 100644
index 000000000000..3b3e206055cf
--- /dev/null
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/package.scala
@@ -0,0 +1,27 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql
+
+package object catalyst {
+  /**
+   * A JVM-global lock that should be used to prevent thread safety issues when using things in
+   * scala.reflect.*.  Note that Scala Reflection API is made thread-safe in 2.11, but not yet for
+   * 2.10.* builds.  See SI-6240 for more details.
+   */
+  protected[catalyst] object ScalaReflectionLock
+}
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala
index 026692abe067..418f8686bfe5 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala
@@ -104,6 +104,77 @@ object PhysicalOperation extends PredicateHelper {
   }
 }
 
+/**
+ * Matches a logical aggregation that can be performed on distributed data in two steps.  The first
+ * operates on the data in each partition performing partial aggregation for each group.  The second
+ * occurs after the shuffle and completes the aggregation.
+ *
+ * This pattern will only match if all aggregate expressions can be computed partially and will
+ * return the rewritten aggregation expressions for both phases.
+ *
+ * The returned values for this match are as follows:
+ *  - Grouping attributes for the final aggregation.
+ *  - Aggregates for the final aggregation.
+ *  - Grouping expressions for the partial aggregation.
+ *  - Partial aggregate expressions.
+ *  - Input to the aggregation.
+ */
+object PartialAggregation {
+  type ReturnType =
+    (Seq[Attribute], Seq[NamedExpression], Seq[Expression], Seq[NamedExpression], LogicalPlan)
+
+  def unapply(plan: LogicalPlan): Option[ReturnType] = plan match {
+    case logical.Aggregate(groupingExpressions, aggregateExpressions, child) =>
+      // Collect all aggregate expressions.
+      val allAggregates =
+        aggregateExpressions.flatMap(_ collect { case a: AggregateExpression => a})
+      // Collect all aggregate expressions that can be computed partially.
+      val partialAggregates =
+        aggregateExpressions.flatMap(_ collect { case p: PartialAggregate => p})
+
+      // Only do partial aggregation if supported by all aggregate expressions.
+      if (allAggregates.size == partialAggregates.size) {
+        // Create a map of expressions to their partial evaluations for all aggregate expressions.
+        val partialEvaluations: Map[Long, SplitEvaluation] =
+          partialAggregates.map(a => (a.id, a.asPartial)).toMap
+
+        // We need to pass all grouping expressions though so the grouping can happen a second
+        // time. However some of them might be unnamed so we alias them allowing them to be
+        // referenced in the second aggregation.
+        val namedGroupingExpressions: Map[Expression, NamedExpression] = groupingExpressions.map {
+          case n: NamedExpression => (n, n)
+          case other => (other, Alias(other, "PartialGroup")())
+        }.toMap
+
+        // Replace aggregations with a new expression that computes the result from the already
+        // computed partial evaluations and grouping values.
+        val rewrittenAggregateExpressions = aggregateExpressions.map(_.transformUp {
+          case e: Expression if partialEvaluations.contains(e.id) =>
+            partialEvaluations(e.id).finalEvaluation
+          case e: Expression if namedGroupingExpressions.contains(e) =>
+            namedGroupingExpressions(e).toAttribute
+        }).asInstanceOf[Seq[NamedExpression]]
+
+        val partialComputation =
+          (namedGroupingExpressions.values ++
+            partialEvaluations.values.flatMap(_.partialEvaluations)).toSeq
+
+        val namedGroupingAttributes = namedGroupingExpressions.values.map(_.toAttribute).toSeq
+
+        Some(
+          (namedGroupingAttributes,
+           rewrittenAggregateExpressions,
+           groupingExpressions,
+           partialComputation,
+           child))
+      } else {
+        None
+      }
+    case _ => None
+  }
+}
+
+
 /**
  * A pattern that finds joins with equality conditions that can be evaluated using equi-join.
  */
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala
index ac85f95b52a2..888cb08e95f0 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala
@@ -112,7 +112,7 @@ abstract class LeafNode extends LogicalPlan with trees.LeafNode[LogicalPlan] {
   self: Product =>
 
   override lazy val statistics: Statistics =
-    throw new UnsupportedOperationException("default leaf nodes don't have meaningful Statistics")
+    throw new UnsupportedOperationException(s"LeafNode $nodeName must implement statistics.")
 
   // Leaf nodes by definition cannot reference any input attributes.
   override def references = Set.empty
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/commands.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/commands.scala
index a357c6ffb897..481a5a4f212b 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/commands.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/commands.scala
@@ -35,7 +35,7 @@ abstract class Command extends LeafNode {
  */
 case class NativeCommand(cmd: String) extends Command {
   override def output =
-    Seq(BoundReference(0, AttributeReference("result", StringType, nullable = false)()))
+    Seq(AttributeReference("result", StringType, nullable = false)())
 }
 
 /**
@@ -43,7 +43,7 @@ case class NativeCommand(cmd: String) extends Command {
  */
 case class SetCommand(key: Option[String], value: Option[String]) extends Command {
   override def output = Seq(
-    BoundReference(1, AttributeReference("", StringType, nullable = false)()))
+    AttributeReference("", StringType, nullable = false)())
 }
 
 /**
@@ -52,7 +52,7 @@ case class SetCommand(key: Option[String], value: Option[String]) extends Comman
  */
 case class ExplainCommand(plan: LogicalPlan) extends Command {
   override def output =
-    Seq(BoundReference(0, AttributeReference("plan", StringType, nullable = false)()))
+    Seq(AttributeReference("plan", StringType, nullable = false)())
 }
 
 /**
@@ -71,7 +71,7 @@ case class DescribeCommand(
     isExtended: Boolean) extends Command {
   override def output = Seq(
     // Column names are based on Hive.
-    BoundReference(0, AttributeReference("col_name", StringType, nullable = false)()),
-    BoundReference(1, AttributeReference("data_type", StringType, nullable = false)()),
-    BoundReference(2, AttributeReference("comment", StringType, nullable = false)()))
+    AttributeReference("col_name", StringType, nullable = false)(),
+    AttributeReference("data_type", StringType, nullable = false)(),
+    AttributeReference("comment", StringType, nullable = false)())
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/rules/RuleExecutor.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/rules/RuleExecutor.scala
index e32adb76fe14..e300bdbececb 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/rules/RuleExecutor.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/rules/RuleExecutor.scala
@@ -72,7 +72,10 @@ abstract class RuleExecutor[TreeType <: TreeNode[_]] extends Logging {
         }
         iteration += 1
         if (iteration > batch.strategy.maxIterations) {
-          logger.info(s"Max iterations ($iteration) reached for batch ${batch.name}")
+          // Only log if this is a rule that is supposed to run more than once.
+          if (iteration != 2) {
+            logger.info(s"Max iterations (${iteration - 1}) reached for batch ${batch.name}")
+          }
           continue = false
         }
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/types/dataTypes.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/types/dataTypes.scala
index cd4b5e9c1b52..71808f76d632 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/types/dataTypes.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/types/dataTypes.scala
@@ -23,16 +23,13 @@ import scala.reflect.ClassTag
 import scala.reflect.runtime.universe.{typeTag, TypeTag, runtimeMirror}
 import scala.util.parsing.combinator.RegexParsers
 
+import org.apache.spark.sql.catalyst.ScalaReflectionLock
 import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeReference, Expression}
 import org.apache.spark.util.Utils
 
 /**
- * A JVM-global lock that should be used to prevent thread safety issues when using things in
- * scala.reflect.*.  Note that Scala Reflection API is made thread-safe in 2.11, but not yet for
- * 2.10.* builds.  See SI-6240 for more details.
+ * Utility functions for working with DataTypes.
  */
-protected[catalyst] object ScalaReflectionLock
-
 object DataType extends RegexParsers {
   protected lazy val primitiveType: Parser[DataType] =
     "StringType" ^^^ StringType |
@@ -99,6 +96,13 @@ abstract class DataType {
 
 case object NullType extends DataType
 
+object NativeType {
+  def all = Seq(
+    IntegerType, BooleanType, LongType, DoubleType, FloatType, ShortType, ByteType, StringType)
+
+  def unapply(dt: DataType): Boolean = all.contains(dt)
+}
+
 trait PrimitiveType extends DataType {
   override def isPrimitive = true
 }
@@ -149,6 +153,10 @@ abstract class NumericType extends NativeType with PrimitiveType {
   val numeric: Numeric[JvmType]
 }
 
+object NumericType {
+  def unapply(e: Expression): Boolean = e.dataType.isInstanceOf[NumericType]
+}
+
 /** Matcher for any expressions that evaluate to [[IntegralType]]s */
 object IntegralType {
   def unapply(a: Expression): Boolean = a match {
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala
index 58f8c341e667..999c9fff38d6 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala
@@ -29,7 +29,11 @@ import org.apache.spark.sql.catalyst.dsl.expressions._
 class ExpressionEvaluationSuite extends FunSuite {
 
   test("literals") {
-    assert((Literal(1) + Literal(1)).eval(null) === 2)
+    checkEvaluation(Literal(1), 1)
+    checkEvaluation(Literal(true), true)
+    checkEvaluation(Literal(0L), 0L)
+    checkEvaluation(Literal("test"), "test")
+    checkEvaluation(Literal(1) + Literal(1), 2)
   }
 
   /**
@@ -61,10 +65,8 @@ class ExpressionEvaluationSuite extends FunSuite {
   test("3VL Not") {
     notTrueTable.foreach {
       case (v, answer) =>
-        val expr = ! Literal(v, BooleanType)
-        val result = expr.eval(null)
-        if (result != answer)
-          fail(s"$expr should not evaluate to $result, expected: $answer")    }
+        checkEvaluation(!Literal(v, BooleanType), answer)
+    }
   }
 
   booleanLogicTest("AND", _ && _,
@@ -127,6 +129,13 @@ class ExpressionEvaluationSuite extends FunSuite {
     }
   }
 
+  test("IN") {
+    checkEvaluation(In(Literal(1), Seq(Literal(1), Literal(2))), true)
+    checkEvaluation(In(Literal(2), Seq(Literal(1), Literal(2))), true)
+    checkEvaluation(In(Literal(3), Seq(Literal(1), Literal(2))), false)
+    checkEvaluation(In(Literal(1), Seq(Literal(1), Literal(2))) && In(Literal(2), Seq(Literal(1), Literal(2))), true)
+  }
+
   test("LIKE literal Regular Expression") {
     checkEvaluation(Literal(null, StringType).like("a"), null)
     checkEvaluation(Literal("a", StringType).like(Literal(null, StringType)), null)
@@ -232,21 +241,21 @@ class ExpressionEvaluationSuite extends FunSuite {
     checkEvaluation(Literal(false) cast IntegerType, 0)
     checkEvaluation(Cast(Literal(1) cast BooleanType, IntegerType), 1)
     checkEvaluation(Cast(Literal(0) cast BooleanType, IntegerType), 0)
-    checkEvaluation("23" cast DoubleType, 23)
+    checkEvaluation("23" cast DoubleType, 23d)
     checkEvaluation("23" cast IntegerType, 23)
-    checkEvaluation("23" cast FloatType, 23)
-    checkEvaluation("23" cast DecimalType, 23)
-    checkEvaluation("23" cast ByteType, 23)
-    checkEvaluation("23" cast ShortType, 23)
+    checkEvaluation("23" cast FloatType, 23f)
+    checkEvaluation("23" cast DecimalType, 23: BigDecimal)
+    checkEvaluation("23" cast ByteType, 23.toByte)
+    checkEvaluation("23" cast ShortType, 23.toShort)
     checkEvaluation("2012-12-11" cast DoubleType, null)
     checkEvaluation(Literal(123) cast IntegerType, 123)
 
-    checkEvaluation(Literal(23d) + Cast(true, DoubleType), 24)
+    checkEvaluation(Literal(23d) + Cast(true, DoubleType), 24d)
     checkEvaluation(Literal(23) + Cast(true, IntegerType), 24)
-    checkEvaluation(Literal(23f) + Cast(true, FloatType), 24)
-    checkEvaluation(Literal(BigDecimal(23)) + Cast(true, DecimalType), 24)
-    checkEvaluation(Literal(23.toByte) + Cast(true, ByteType), 24)
-    checkEvaluation(Literal(23.toShort) + Cast(true, ShortType), 24)
+    checkEvaluation(Literal(23f) + Cast(true, FloatType), 24f)
+    checkEvaluation(Literal(BigDecimal(23)) + Cast(true, DecimalType), 24: BigDecimal)
+    checkEvaluation(Literal(23.toByte) + Cast(true, ByteType), 24.toByte)
+    checkEvaluation(Literal(23.toShort) + Cast(true, ShortType), 24.toShort)
 
     intercept[Exception] {evaluate(Literal(1) cast BinaryType, null)}
 
@@ -391,21 +400,21 @@ class ExpressionEvaluationSuite extends FunSuite {
     val typeMap = MapType(StringType, StringType)
     val typeArray = ArrayType(StringType)
 
-    checkEvaluation(GetItem(BoundReference(3, AttributeReference("c", typeMap)()),
+    checkEvaluation(GetItem(BoundReference(3, typeMap, true),
       Literal("aa")), "bb", row)
     checkEvaluation(GetItem(Literal(null, typeMap), Literal("aa")), null, row)
     checkEvaluation(GetItem(Literal(null, typeMap), Literal(null, StringType)), null, row)
-    checkEvaluation(GetItem(BoundReference(3, AttributeReference("c", typeMap)()),
+    checkEvaluation(GetItem(BoundReference(3, typeMap, true),
       Literal(null, StringType)), null, row)
 
-    checkEvaluation(GetItem(BoundReference(4, AttributeReference("c", typeArray)()),
+    checkEvaluation(GetItem(BoundReference(4, typeArray, true),
       Literal(1)), "bb", row)
     checkEvaluation(GetItem(Literal(null, typeArray), Literal(1)), null, row)
     checkEvaluation(GetItem(Literal(null, typeArray), Literal(null, IntegerType)), null, row)
-    checkEvaluation(GetItem(BoundReference(4, AttributeReference("c", typeArray)()),
+    checkEvaluation(GetItem(BoundReference(4, typeArray, true),
       Literal(null, IntegerType)), null, row)
 
-    checkEvaluation(GetField(BoundReference(2, AttributeReference("c", typeS)()), "a"), "aa", row)
+    checkEvaluation(GetField(BoundReference(2, typeS, nullable = true), "a"), "aa", row)
     checkEvaluation(GetField(Literal(null, typeS), "a"), null, row)
 
     val typeS_notNullable = StructType(
@@ -413,10 +422,8 @@ class ExpressionEvaluationSuite extends FunSuite {
         :: StructField("b", StringType, nullable = false) :: Nil
     )
 
-    assert(GetField(BoundReference(2,
-      AttributeReference("c", typeS)()), "a").nullable === true)
-    assert(GetField(BoundReference(2,
-      AttributeReference("c", typeS_notNullable, nullable = false)()), "a").nullable === false)
+    assert(GetField(BoundReference(2,typeS, nullable = true), "a").nullable === true)
+    assert(GetField(BoundReference(2, typeS_notNullable, nullable = false), "a").nullable === false)
 
     assert(GetField(Literal(null, typeS), "a").nullable === true)
     assert(GetField(Literal(null, typeS_notNullable), "a").nullable === true)
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/GeneratedEvaluationSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/GeneratedEvaluationSuite.scala
new file mode 100644
index 000000000000..245a2e148030
--- /dev/null
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/GeneratedEvaluationSuite.scala
@@ -0,0 +1,69 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.optimizer
+
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.dsl.expressions._
+import org.apache.spark.sql.catalyst.expressions.codegen._
+
+/**
+ * Overrides our expression evaluation tests to use code generation for evaluation.
+ */
+class GeneratedEvaluationSuite extends ExpressionEvaluationSuite {
+  override def checkEvaluation(
+      expression: Expression,
+      expected: Any,
+      inputRow: Row = EmptyRow): Unit = {
+    val plan = try {
+      GenerateMutableProjection(Alias(expression, s"Optimized($expression)")() :: Nil)()
+    } catch {
+      case e: Throwable =>
+        val evaluated = GenerateProjection.expressionEvaluator(expression)
+        fail(
+          s"""
+            |Code generation of $expression failed:
+            |${evaluated.code.mkString("\n")}
+            |$e
+          """.stripMargin)
+    }
+
+    val actual  = plan(inputRow).apply(0)
+    if(actual != expected) {
+      val input = if(inputRow == EmptyRow) "" else s", input: $inputRow"
+      fail(s"Incorrect Evaluation: $expression, actual: $actual, expected: $expected$input")
+    }
+  }
+
+
+  test("multithreaded eval") {
+    import scala.concurrent._
+    import ExecutionContext.Implicits.global
+    import scala.concurrent.duration._
+
+    val futures = (1 to 20).map { _ =>
+      future {
+        GeneratePredicate(EqualTo(Literal(1), Literal(1)))
+        GenerateProjection(EqualTo(Literal(1), Literal(1)) :: Nil)
+        GenerateMutableProjection(EqualTo(Literal(1), Literal(1)) :: Nil)
+        GenerateOrdering(Add(Literal(1), Literal(1)).asc :: Nil)
+      }
+    }
+
+    futures.foreach(Await.result(_, 10.seconds))
+  }
+}
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/GeneratedMutableEvaluationSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/GeneratedMutableEvaluationSuite.scala
new file mode 100644
index 000000000000..887aabb1d5fb
--- /dev/null
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/GeneratedMutableEvaluationSuite.scala
@@ -0,0 +1,61 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.optimizer
+
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.dsl.expressions._
+import org.apache.spark.sql.catalyst.expressions.codegen._
+
+/**
+ * Overrides our expression evaluation tests to use generated code on mutable rows.
+ */
+class GeneratedMutableEvaluationSuite extends ExpressionEvaluationSuite {
+  override def checkEvaluation(
+                                expression: Expression,
+                                expected: Any,
+                                inputRow: Row = EmptyRow): Unit = {
+    lazy val evaluated = GenerateProjection.expressionEvaluator(expression)
+
+    val plan = try {
+      GenerateProjection(Alias(expression, s"Optimized($expression)")() :: Nil)
+    } catch {
+      case e: Throwable =>
+        fail(
+          s"""
+            |Code generation of $expression failed:
+            |${evaluated.code.mkString("\n")}
+            |$e
+          """.stripMargin)
+    }
+
+    val actual = plan(inputRow)
+    val expectedRow = new GenericRow(Array[Any](expected))
+    if (actual.hashCode() != expectedRow.hashCode()) {
+      fail(
+        s"""
+          |Mismatched hashCodes for values: $actual, $expectedRow
+          |Hash Codes: ${actual.hashCode()} != ${expectedRow.hashCode()}
+          |${evaluated.code.mkString("\n")}
+        """.stripMargin)
+    }
+    if (actual != expectedRow) {
+      val input = if(inputRow == EmptyRow) "" else s", input: $inputRow"
+      fail(s"Incorrect Evaluation: $expression, actual: $actual, expected: $expected$input")
+    }
+  }
+}
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/CombiningLimitsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/CombiningLimitsSuite.scala
index 4896f1b955f0..e2ae0d25db1a 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/CombiningLimitsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/CombiningLimitsSuite.scala
@@ -27,9 +27,9 @@ class CombiningLimitsSuite extends PlanTest {
 
   object Optimize extends RuleExecutor[LogicalPlan] {
     val batches =
-      Batch("Combine Limit", FixedPoint(2),
+      Batch("Combine Limit", FixedPoint(10),
         CombineLimits) ::
-      Batch("Constant Folding", FixedPoint(3),
+      Batch("Constant Folding", FixedPoint(10),
         NullPropagation,
         ConstantFolding,
         BooleanSimplification) :: Nil
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
index 5d85a0fd4eeb..2d407077be30 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
@@ -24,8 +24,11 @@ import scala.collection.JavaConverters._
 object SQLConf {
   val COMPRESS_CACHED = "spark.sql.inMemoryColumnarStorage.compressed"
   val AUTO_BROADCASTJOIN_THRESHOLD = "spark.sql.autoBroadcastJoinThreshold"
-  val SHUFFLE_PARTITIONS = "spark.sql.shuffle.partitions"
   val DEFAULT_SIZE_IN_BYTES = "spark.sql.defaultSizeInBytes"
+  val AUTO_CONVERT_JOIN_SIZE = "spark.sql.auto.convert.join.size"
+  val SHUFFLE_PARTITIONS = "spark.sql.shuffle.partitions"
+  val JOIN_BROADCAST_TABLES = "spark.sql.join.broadcastTables"
+  val CODEGEN_ENABLED = "spark.sql.codegen"
 
   object Deprecated {
     val MAPRED_REDUCE_TASKS = "mapred.reduce.tasks"
@@ -56,6 +59,18 @@ trait SQLConf {
   /** Number of partitions to use for shuffle operators. */
   private[spark] def numShufflePartitions: Int = get(SHUFFLE_PARTITIONS, "200").toInt
 
+  /**
+   * When set to true, Spark SQL will use the Scala compiler at runtime to generate custom bytecode
+   * that evaluates expressions found in queries.  In general this custom code runs much faster
+   * than interpreted evaluation, but there are significant start-up costs due to compilation.
+   * As a result codegen is only benificial when queries run for a long time, or when the same
+   * expressions are used multiple times.
+   *
+   * Defaults to false as this feature is currently experimental.
+   */
+  private[spark] def codegenEnabled: Boolean =
+    if (get(CODEGEN_ENABLED, "false") == "true") true else false
+
   /**
    * Upper bound on the sizes (in bytes) of the tables qualified for the auto conversion to
    * a broadcast value during the physical executions of join operations.  Setting this to -1
@@ -111,5 +126,5 @@ trait SQLConf {
   private[spark] def clear() {
     settings.clear()
   }
-
 }
+
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
index c2bdef732372..e4b681018099 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
@@ -94,7 +94,7 @@ class SQLContext(@transient val sparkContext: SparkContext)
    * @group userf
    */
   def parquetFile(path: String): SchemaRDD =
-    new SchemaRDD(this, parquet.ParquetRelation(path, Some(sparkContext.hadoopConfiguration)))
+    new SchemaRDD(this, parquet.ParquetRelation(path, Some(sparkContext.hadoopConfiguration), this))
 
   /**
    * Loads a JSON file (one object per line), returning the result as a [[SchemaRDD]].
@@ -160,7 +160,8 @@ class SQLContext(@transient val sparkContext: SparkContext)
       conf: Configuration = new Configuration()): SchemaRDD = {
     new SchemaRDD(
       this,
-      ParquetRelation.createEmpty(path, ScalaReflection.attributesFor[A], allowExisting, conf))
+      ParquetRelation.createEmpty(
+        path, ScalaReflection.attributesFor[A], allowExisting, conf, this))
   }
 
   /**
@@ -228,12 +229,14 @@ class SQLContext(@transient val sparkContext: SparkContext)
 
     val sqlContext: SQLContext = self
 
+    def codegenEnabled = self.codegenEnabled
+
     def numPartitions = self.numShufflePartitions
 
     val strategies: Seq[Strategy] =
       CommandStrategy(self) ::
       TakeOrdered ::
-      PartialAggregation ::
+      HashAggregation ::
       LeftSemiJoin ::
       HashJoin ::
       InMemoryScans ::
@@ -291,27 +294,30 @@ class SQLContext(@transient val sparkContext: SparkContext)
   protected[sql] lazy val emptyResult = sparkContext.parallelize(Seq.empty[Row], 1)
 
   /**
-   * Prepares a planned SparkPlan for execution by binding references to specific ordinals, and
-   * inserting shuffle operations as needed.
+   * Prepares a planned SparkPlan for execution by inserting shuffle operations as needed.
    */
   @transient
   protected[sql] val prepareForExecution = new RuleExecutor[SparkPlan] {
     val batches =
-      Batch("Add exchange", Once, AddExchange(self)) ::
-      Batch("Prepare Expressions", Once, new BindReferences[SparkPlan]) :: Nil
+      Batch("Add exchange", Once, AddExchange(self)) :: Nil
   }
 
   /**
+   * :: DeveloperApi ::
    * The primary workflow for executing relational queries using Spark.  Designed to allow easy
    * access to the intermediate phases of query execution for developers.
    */
+  @DeveloperApi
   protected abstract class QueryExecution {
     def logical: LogicalPlan
 
     lazy val analyzed = analyzer(logical)
     lazy val optimizedPlan = optimizer(analyzed)
     // TODO: Don't just pick the first one...
-    lazy val sparkPlan = planner(optimizedPlan).next()
+    lazy val sparkPlan = {
+      SparkPlan.currentContext.set(self)
+      planner(optimizedPlan).next()
+    }
     // executedPlan should not be used to initialize any SparkPlan. It should be
     // only used for execution.
     lazy val executedPlan: SparkPlan = prepareForExecution(sparkPlan)
@@ -331,6 +337,9 @@ class SQLContext(@transient val sparkContext: SparkContext)
          |${stringOrError(optimizedPlan)}
          |== Physical Plan ==
          |${stringOrError(executedPlan)}
+         |Code Generation: ${executedPlan.codegenEnabled}
+         |== RDD ==
+         |${stringOrError(toRdd.toDebugString)}
       """.stripMargin.trim
   }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSQLContext.scala
index 806097c917b9..85726bae5491 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSQLContext.scala
@@ -72,7 +72,7 @@ class JavaSQLContext(val sqlContext: SQLContext) {
       conf: Configuration = new Configuration()): JavaSchemaRDD = {
     new JavaSchemaRDD(
       sqlContext,
-      ParquetRelation.createEmpty(path, getSchema(beanClass), allowExisting, conf))
+      ParquetRelation.createEmpty(path, getSchema(beanClass), allowExisting, conf, sqlContext))
   }
 
   /**
@@ -101,7 +101,7 @@ class JavaSQLContext(val sqlContext: SQLContext) {
   def parquetFile(path: String): JavaSchemaRDD =
     new JavaSchemaRDD(
       sqlContext,
-      ParquetRelation(path, Some(sqlContext.sparkContext.hadoopConfiguration)))
+      ParquetRelation(path, Some(sqlContext.sparkContext.hadoopConfiguration), sqlContext))
 
   /**
    * Loads a JSON file (one object per line), returning the result as a [[JavaSchemaRDD]].
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/Aggregate.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/Aggregate.scala
index c1ced8bfa404..463a1d32d7fd 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/Aggregate.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/Aggregate.scala
@@ -42,8 +42,8 @@ case class Aggregate(
     partial: Boolean,
     groupingExpressions: Seq[Expression],
     aggregateExpressions: Seq[NamedExpression],
-    child: SparkPlan)(@transient sqlContext: SQLContext)
-  extends UnaryNode with NoBind {
+    child: SparkPlan)
+  extends UnaryNode {
 
   override def requiredChildDistribution =
     if (partial) {
@@ -56,8 +56,6 @@ case class Aggregate(
       }
     }
 
-  override def otherCopyArgs = sqlContext :: Nil
-
   // HACK: Generators don't correctly preserve their output through serializations so we grab
   // out child's output attributes statically here.
   private[this] val childOutput = child.output
@@ -138,7 +136,7 @@ case class Aggregate(
             i += 1
           }
         }
-        val resultProjection = new Projection(resultExpressions, computedSchema)
+        val resultProjection = new InterpretedProjection(resultExpressions, computedSchema)
         val aggregateResults = new GenericMutableRow(computedAggregates.length)
 
         var i = 0
@@ -152,7 +150,7 @@ case class Aggregate(
     } else {
       child.execute().mapPartitions { iter =>
         val hashTable = new HashMap[Row, Array[AggregateFunction]]
-        val groupingProjection = new MutableProjection(groupingExpressions, childOutput)
+        val groupingProjection = new InterpretedMutableProjection(groupingExpressions, childOutput)
 
         var currentRow: Row = null
         while (iter.hasNext) {
@@ -175,7 +173,8 @@ case class Aggregate(
           private[this] val hashTableIter = hashTable.entrySet().iterator()
           private[this] val aggregateResults = new GenericMutableRow(computedAggregates.length)
           private[this] val resultProjection =
-            new MutableProjection(resultExpressions, computedSchema ++ namedGroups.map(_._2))
+            new InterpretedMutableProjection(
+              resultExpressions, computedSchema ++ namedGroups.map(_._2))
           private[this] val joinedRow = new JoinedRow
 
           override final def hasNext: Boolean = hashTableIter.hasNext
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
index 00010ef6e798..392a7f3be390 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
@@ -22,7 +22,7 @@ import org.apache.spark.{HashPartitioner, RangePartitioner, SparkConf}
 import org.apache.spark.rdd.ShuffledRDD
 import org.apache.spark.sql.{SQLContext, Row}
 import org.apache.spark.sql.catalyst.errors.attachTree
-import org.apache.spark.sql.catalyst.expressions.{NoBind, MutableProjection, RowOrdering}
+import org.apache.spark.sql.catalyst.expressions.RowOrdering
 import org.apache.spark.sql.catalyst.plans.physical._
 import org.apache.spark.sql.catalyst.rules.Rule
 import org.apache.spark.util.MutablePair
@@ -31,7 +31,7 @@ import org.apache.spark.util.MutablePair
  * :: DeveloperApi ::
  */
 @DeveloperApi
-case class Exchange(newPartitioning: Partitioning, child: SparkPlan) extends UnaryNode with NoBind {
+case class Exchange(newPartitioning: Partitioning, child: SparkPlan) extends UnaryNode {
 
   override def outputPartitioning = newPartitioning
 
@@ -42,7 +42,9 @@ case class Exchange(newPartitioning: Partitioning, child: SparkPlan) extends Una
       case HashPartitioning(expressions, numPartitions) =>
         // TODO: Eliminate redundant expressions in grouping key and value.
         val rdd = child.execute().mapPartitions { iter =>
-          val hashExpressions = new MutableProjection(expressions, child.output)
+          @transient val hashExpressions =
+            newMutableProjection(expressions, child.output)()
+
           val mutablePair = new MutablePair[Row, Row]()
           iter.map(r => mutablePair.update(hashExpressions(r), r))
         }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/Generate.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/Generate.scala
index 47b3d00262db..c386fd121c5d 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/Generate.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/Generate.scala
@@ -47,23 +47,26 @@ case class Generate(
     }
   }
 
-  override def output =
+  // This must be a val since the generator output expr ids are not preserved by serialization.
+  override val output =
     if (join) child.output ++ generatorOutput else generatorOutput
 
+  val boundGenerator = BindReferences.bindReference(generator, child.output)
+
   override def execute() = {
     if (join) {
       child.execute().mapPartitions { iter =>
         val nullValues = Seq.fill(generator.output.size)(Literal(null))
         // Used to produce rows with no matches when outer = true.
         val outerProjection =
-          new Projection(child.output ++ nullValues, child.output)
+          newProjection(child.output ++ nullValues, child.output)
 
         val joinProjection =
-          new Projection(child.output ++ generator.output, child.output ++ generator.output)
+          newProjection(child.output ++ generator.output, child.output ++ generator.output)
         val joinedRow = new JoinedRow
 
         iter.flatMap {row =>
-          val outputRows = generator.eval(row)
+          val outputRows = boundGenerator.eval(row)
           if (outer && outputRows.isEmpty) {
             outerProjection(row) :: Nil
           } else {
@@ -72,7 +75,7 @@ case class Generate(
         }
       }
     } else {
-      child.execute().mapPartitions(iter => iter.flatMap(row => generator.eval(row)))
+      child.execute().mapPartitions(iter => iter.flatMap(row => boundGenerator.eval(row)))
     }
   }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala
new file mode 100644
index 000000000000..4a26934c49c9
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala
@@ -0,0 +1,200 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution
+
+import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.sql.SQLContext
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.plans.physical._
+import org.apache.spark.sql.catalyst.types._
+
+case class AggregateEvaluation(
+    schema: Seq[Attribute],
+    initialValues: Seq[Expression],
+    update: Seq[Expression],
+    result: Expression)
+
+/**
+ * :: DeveloperApi ::
+ * Alternate version of aggregation that leverages projection and thus code generation.
+ * Aggregations are converted into a set of projections from a aggregation buffer tuple back onto
+ * itself. Currently only used for simple aggregations like SUM, COUNT, or AVERAGE are supported.
+ *
+ * @param partial if true then aggregation is done partially on local data without shuffling to
+ *                ensure all values where `groupingExpressions` are equal are present.
+ * @param groupingExpressions expressions that are evaluated to determine grouping.
+ * @param aggregateExpressions expressions that are computed for each group.
+ * @param child the input data source.
+ */
+@DeveloperApi
+case class GeneratedAggregate(
+    partial: Boolean,
+    groupingExpressions: Seq[Expression],
+    aggregateExpressions: Seq[NamedExpression],
+    child: SparkPlan)
+  extends UnaryNode {
+
+  override def requiredChildDistribution =
+    if (partial) {
+      UnspecifiedDistribution :: Nil
+    } else {
+      if (groupingExpressions == Nil) {
+        AllTuples :: Nil
+      } else {
+        ClusteredDistribution(groupingExpressions) :: Nil
+      }
+    }
+
+  override def output = aggregateExpressions.map(_.toAttribute)
+
+  override def execute() = {
+    val aggregatesToCompute = aggregateExpressions.flatMap { a =>
+      a.collect { case agg: AggregateExpression => agg}
+    }
+
+    val computeFunctions = aggregatesToCompute.map {
+      case c @ Count(expr) =>
+        val currentCount = AttributeReference("currentCount", LongType, nullable = false)()
+        val initialValue = Literal(0L)
+        val updateFunction = If(IsNotNull(expr), Add(currentCount, Literal(1L)), currentCount)
+        val result = currentCount
+
+        AggregateEvaluation(currentCount :: Nil, initialValue :: Nil, updateFunction :: Nil, result)
+
+      case Sum(expr) =>
+        val currentSum = AttributeReference("currentSum", expr.dataType, nullable = false)()
+        val initialValue = Cast(Literal(0L), expr.dataType)
+
+        // Coalasce avoids double calculation...
+        // but really, common sub expression elimination would be better....
+        val updateFunction = Coalesce(Add(expr, currentSum) :: currentSum :: Nil)
+        val result = currentSum
+
+        AggregateEvaluation(currentSum :: Nil, initialValue :: Nil, updateFunction :: Nil, result)
+
+      case a @ Average(expr) =>
+        val currentCount = AttributeReference("currentCount", LongType, nullable = false)()
+        val currentSum = AttributeReference("currentSum", expr.dataType, nullable = false)()
+        val initialCount = Literal(0L)
+        val initialSum = Cast(Literal(0L), expr.dataType)
+        val updateCount = If(IsNotNull(expr), Add(currentCount, Literal(1L)), currentCount)
+        val updateSum = Coalesce(Add(expr, currentSum) :: currentSum :: Nil)
+
+        val result = Divide(Cast(currentSum, DoubleType), Cast(currentCount, DoubleType))
+
+        AggregateEvaluation(
+          currentCount :: currentSum :: Nil,
+          initialCount :: initialSum :: Nil,
+          updateCount :: updateSum :: Nil,
+          result
+        )
+    }
+
+    val computationSchema = computeFunctions.flatMap(_.schema)
+
+    val resultMap: Map[Long, Expression] = aggregatesToCompute.zip(computeFunctions).map {
+      case (agg, func) => agg.id -> func.result
+    }.toMap
+
+    val namedGroups = groupingExpressions.zipWithIndex.map {
+      case (ne: NamedExpression, _) => (ne, ne)
+      case (e, i) => (e, Alias(e, s"GroupingExpr$i")())
+    }
+
+    val groupMap: Map[Expression, Attribute] =
+      namedGroups.map { case (k, v) => k -> v.toAttribute}.toMap
+
+    // The set of expressions that produce the final output given the aggregation buffer and the
+    // grouping expressions.
+    val resultExpressions = aggregateExpressions.map(_.transform {
+      case e: Expression if resultMap.contains(e.id) => resultMap(e.id)
+      case e: Expression if groupMap.contains(e) => groupMap(e)
+    })
+
+    child.execute().mapPartitions { iter =>
+      // Builds a new custom class for holding the results of aggregation for a group.
+      val initialValues = computeFunctions.flatMap(_.initialValues)
+      val newAggregationBuffer = newProjection(initialValues, child.output)
+      log.info(s"Initial values: ${initialValues.mkString(",")}")
+
+      // A projection that computes the group given an input tuple.
+      val groupProjection = newProjection(groupingExpressions, child.output)
+      log.info(s"Grouping Projection: ${groupingExpressions.mkString(",")}")
+
+      // A projection that is used to update the aggregate values for a group given a new tuple.
+      // This projection should be targeted at the current values for the group and then applied
+      // to a joined row of the current values with the new input row.
+      val updateExpressions = computeFunctions.flatMap(_.update)
+      val updateSchema = computeFunctions.flatMap(_.schema) ++ child.output
+      val updateProjection = newMutableProjection(updateExpressions, updateSchema)()
+      log.info(s"Update Expressions: ${updateExpressions.mkString(",")}")
+
+      // A projection that produces the final result, given a computation.
+      val resultProjectionBuilder =
+        newMutableProjection(
+          resultExpressions,
+          (namedGroups.map(_._2.toAttribute) ++ computationSchema).toSeq)
+      log.info(s"Result Projection: ${resultExpressions.mkString(",")}")
+
+      val joinedRow = new JoinedRow
+
+      if (groupingExpressions.isEmpty) {
+        // TODO: Codegening anything other than the updateProjection is probably over kill.
+        val buffer = newAggregationBuffer(EmptyRow).asInstanceOf[MutableRow]
+        var currentRow: Row = null
+        updateProjection.target(buffer)
+
+        while (iter.hasNext) {
+          currentRow = iter.next()
+          updateProjection(joinedRow(buffer, currentRow))
+        }
+
+        val resultProjection = resultProjectionBuilder()
+        Iterator(resultProjection(buffer))
+      } else {
+        val buffers = new java.util.HashMap[Row, MutableRow]()
+
+        var currentRow: Row = null
+        while (iter.hasNext) {
+          currentRow = iter.next()
+          val currentGroup = groupProjection(currentRow)
+          var currentBuffer = buffers.get(currentGroup)
+          if (currentBuffer == null) {
+            currentBuffer = newAggregationBuffer(EmptyRow).asInstanceOf[MutableRow]
+            buffers.put(currentGroup, currentBuffer)
+          }
+          // Target the projection at the current aggregation buffer and then project the updated
+          // values.
+          updateProjection.target(currentBuffer)(joinedRow(currentBuffer, currentRow))
+        }
+
+        new Iterator[Row] {
+          private[this] val resultIterator = buffers.entrySet.iterator()
+          private[this] val resultProjection = resultProjectionBuilder()
+
+          def hasNext = resultIterator.hasNext
+
+          def next() = {
+            val currentGroup = resultIterator.next()
+            resultProjection(joinedRow(currentGroup.getKey, currentGroup.getValue))
+          }
+        }
+      }
+    }
+  }
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
index 77c874d0315e..21cbbc9772a0 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
@@ -18,22 +18,55 @@
 package org.apache.spark.sql.execution
 
 import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.Logging
 import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.{Logging, Row, SQLContext}
+
+
+import org.apache.spark.sql.SQLContext
 import org.apache.spark.sql.catalyst.trees
 import org.apache.spark.sql.catalyst.analysis.MultiInstanceRelation
-import org.apache.spark.sql.catalyst.expressions.GenericRow
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.expressions.codegen._
 import org.apache.spark.sql.catalyst.plans.QueryPlan
 import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
 import org.apache.spark.sql.catalyst.plans.physical._
 
+
+object SparkPlan {
+  protected[sql] val currentContext = new ThreadLocal[SQLContext]()
+}
+
 /**
  * :: DeveloperApi ::
  */
 @DeveloperApi
-abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging {
+abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging with Serializable {
   self: Product =>
 
+  /**
+   * A handle to the SQL Context that was used to create this plan.   Since many operators need
+   * access to the sqlContext for RDD operations or configuration this field is automatically
+   * populated by the query planning infrastructure.
+   */
+  @transient
+  protected val sqlContext = SparkPlan.currentContext.get()
+
+  protected def sparkContext = sqlContext.sparkContext
+
+  // sqlContext will be null when we are being deserialized on the slaves.  In this instance
+  // the value of codegenEnabled will be set by the desserializer after the constructor has run.
+  val codegenEnabled: Boolean = if (sqlContext != null) {
+    sqlContext.codegenEnabled
+  } else {
+    false
+  }
+
+  /** Overridden make copy also propogates sqlContext to copied plan. */
+  override def makeCopy(newArgs: Array[AnyRef]): this.type = {
+    SparkPlan.currentContext.set(sqlContext)
+    super.makeCopy(newArgs)
+  }
+
   // TODO: Move to `DistributedPlan`
   /** Specifies how data is partitioned across different nodes in the cluster. */
   def outputPartitioning: Partitioning = UnknownPartitioning(0) // TODO: WRONG WIDTH!
@@ -51,8 +84,46 @@ abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging {
    */
   def executeCollect(): Array[Row] = execute().map(_.copy()).collect()
 
-  protected def buildRow(values: Seq[Any]): Row =
-    new GenericRow(values.toArray)
+  protected def newProjection(
+      expressions: Seq[Expression], inputSchema: Seq[Attribute]): Projection = {
+    log.debug(
+      s"Creating Projection: $expressions, inputSchema: $inputSchema, codegen:$codegenEnabled")
+    if (codegenEnabled) {
+      GenerateProjection(expressions, inputSchema)
+    } else {
+      new InterpretedProjection(expressions, inputSchema)
+    }
+  }
+
+  protected def newMutableProjection(
+      expressions: Seq[Expression],
+      inputSchema: Seq[Attribute]): () => MutableProjection = {
+    log.debug(
+      s"Creating MutableProj: $expressions, inputSchema: $inputSchema, codegen:$codegenEnabled")
+    if(codegenEnabled) {
+      GenerateMutableProjection(expressions, inputSchema)
+    } else {
+      () => new InterpretedMutableProjection(expressions, inputSchema)
+    }
+  }
+
+
+  protected def newPredicate(
+      expression: Expression, inputSchema: Seq[Attribute]): (Row) => Boolean = {
+    if (codegenEnabled) {
+      GeneratePredicate(expression, inputSchema)
+    } else {
+      InterpretedPredicate(expression, inputSchema)
+    }
+  }
+
+  protected def newOrdering(order: Seq[SortOrder], inputSchema: Seq[Attribute]): Ordering[Row] = {
+    if (codegenEnabled) {
+      GenerateOrdering(order, inputSchema)
+    } else {
+      new RowOrdering(order, inputSchema)
+    }
+  }
 }
 
 /**
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
index 404d48ae05b4..5f1fe99f75c9 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
@@ -17,8 +17,6 @@
 
 package org.apache.spark.sql.execution
 
-import scala.util.Try
-
 import org.apache.spark.sql.{SQLContext, execution}
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.planning._
@@ -41,7 +39,7 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
       // no predicate can be evaluated by matching hash keys
       case logical.Join(left, right, LeftSemi, condition) =>
         execution.LeftSemiJoinBNL(
-          planLater(left), planLater(right), condition)(sqlContext) :: Nil
+          planLater(left), planLater(right), condition) :: Nil
       case _ => Nil
     }
   }
@@ -60,6 +58,7 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
    * will instead be used to decide the build side in a [[execution.ShuffledHashJoin]].
    */
   object HashJoin extends Strategy with PredicateHelper {
+
     private[this] def makeBroadcastHashJoin(
         leftKeys: Seq[Expression],
         rightKeys: Seq[Expression],
@@ -68,24 +67,24 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
         condition: Option[Expression],
         side: BuildSide) = {
       val broadcastHashJoin = execution.BroadcastHashJoin(
-        leftKeys, rightKeys, side, planLater(left), planLater(right))(sqlContext)
+        leftKeys, rightKeys, side, planLater(left), planLater(right))
       condition.map(Filter(_, broadcastHashJoin)).getOrElse(broadcastHashJoin) :: Nil
     }
 
     def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
       case ExtractEquiJoinKeys(Inner, leftKeys, rightKeys, condition, left, right)
-        if Try(sqlContext.autoBroadcastJoinThreshold > 0 &&
-          right.statistics.sizeInBytes <= sqlContext.autoBroadcastJoinThreshold).getOrElse(false) =>
+        if sqlContext.autoBroadcastJoinThreshold > 0 &&
+           right.statistics.sizeInBytes <= sqlContext.autoBroadcastJoinThreshold =>
         makeBroadcastHashJoin(leftKeys, rightKeys, left, right, condition, BuildRight)
 
       case ExtractEquiJoinKeys(Inner, leftKeys, rightKeys, condition, left, right)
-        if Try(sqlContext.autoBroadcastJoinThreshold > 0 &&
-          left.statistics.sizeInBytes <= sqlContext.autoBroadcastJoinThreshold).getOrElse(false) =>
+        if sqlContext.autoBroadcastJoinThreshold > 0 &&
+           left.statistics.sizeInBytes <= sqlContext.autoBroadcastJoinThreshold =>
           makeBroadcastHashJoin(leftKeys, rightKeys, left, right, condition, BuildLeft)
 
       case ExtractEquiJoinKeys(Inner, leftKeys, rightKeys, condition, left, right) =>
         val buildSide =
-          if (Try(right.statistics.sizeInBytes <= left.statistics.sizeInBytes).getOrElse(false)) {
+          if (right.statistics.sizeInBytes <= left.statistics.sizeInBytes) {
             BuildRight
           } else {
             BuildLeft
@@ -99,65 +98,65 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
     }
   }
 
-  object PartialAggregation extends Strategy {
+  object HashAggregation extends Strategy {
     def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
-      case logical.Aggregate(groupingExpressions, aggregateExpressions, child) =>
-        // Collect all aggregate expressions.
-        val allAggregates =
-          aggregateExpressions.flatMap(_ collect { case a: AggregateExpression => a })
-        // Collect all aggregate expressions that can be computed partially.
-        val partialAggregates =
-          aggregateExpressions.flatMap(_ collect { case p: PartialAggregate => p })
-
-        // Only do partial aggregation if supported by all aggregate expressions.
-        if (allAggregates.size == partialAggregates.size) {
-          // Create a map of expressions to their partial evaluations for all aggregate expressions.
-          val partialEvaluations: Map[Long, SplitEvaluation] =
-            partialAggregates.map(a => (a.id, a.asPartial)).toMap
-
-          // We need to pass all grouping expressions though so the grouping can happen a second
-          // time. However some of them might be unnamed so we alias them allowing them to be
-          // referenced in the second aggregation.
-          val namedGroupingExpressions: Map[Expression, NamedExpression] = groupingExpressions.map {
-            case n: NamedExpression => (n, n)
-            case other => (other, Alias(other, "PartialGroup")())
-          }.toMap
+      // Aggregations that can be performed in two phases, before and after the shuffle.
 
-          // Replace aggregations with a new expression that computes the result from the already
-          // computed partial evaluations and grouping values.
-          val rewrittenAggregateExpressions = aggregateExpressions.map(_.transformUp {
-            case e: Expression if partialEvaluations.contains(e.id) =>
-              partialEvaluations(e.id).finalEvaluation
-            case e: Expression if namedGroupingExpressions.contains(e) =>
-              namedGroupingExpressions(e).toAttribute
-          }).asInstanceOf[Seq[NamedExpression]]
-
-          val partialComputation =
-            (namedGroupingExpressions.values ++
-             partialEvaluations.values.flatMap(_.partialEvaluations)).toSeq
-
-          // Construct two phased aggregation.
-          execution.Aggregate(
+      // Cases where all aggregates can be codegened.
+      case PartialAggregation(
+             namedGroupingAttributes,
+             rewrittenAggregateExpressions,
+             groupingExpressions,
+             partialComputation,
+             child)
+             if canBeCodeGened(
+                  allAggregates(partialComputation) ++
+                  allAggregates(rewrittenAggregateExpressions)) &&
+               codegenEnabled =>
+          execution.GeneratedAggregate(
             partial = false,
-            namedGroupingExpressions.values.map(_.toAttribute).toSeq,
+            namedGroupingAttributes,
             rewrittenAggregateExpressions,
-            execution.Aggregate(
+            execution.GeneratedAggregate(
               partial = true,
               groupingExpressions,
               partialComputation,
-              planLater(child))(sqlContext))(sqlContext) :: Nil
-        } else {
-          Nil
-        }
+              planLater(child))) :: Nil
+
+      // Cases where some aggregate can not be codegened
+      case PartialAggregation(
+             namedGroupingAttributes,
+             rewrittenAggregateExpressions,
+             groupingExpressions,
+             partialComputation,
+             child) =>
+        execution.Aggregate(
+          partial = false,
+          namedGroupingAttributes,
+          rewrittenAggregateExpressions,
+          execution.Aggregate(
+            partial = true,
+            groupingExpressions,
+            partialComputation,
+            planLater(child))) :: Nil
+
       case _ => Nil
     }
+
+    def canBeCodeGened(aggs: Seq[AggregateExpression]) = !aggs.exists {
+      case _: Sum | _: Count => false
+      case _ => true
+    }
+
+    def allAggregates(exprs: Seq[Expression]) =
+      exprs.flatMap(_.collect { case a: AggregateExpression => a })
   }
 
   object BroadcastNestedLoopJoin extends Strategy {
     def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
       case logical.Join(left, right, joinType, condition) =>
         execution.BroadcastNestedLoopJoin(
-          planLater(left), planLater(right), joinType, condition)(sqlContext) :: Nil
+          planLater(left), planLater(right), joinType, condition) :: Nil
       case _ => Nil
     }
   }
@@ -176,16 +175,10 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
   protected lazy val singleRowRdd =
     sparkContext.parallelize(Seq(new GenericRow(Array[Any]()): Row), 1)
 
-  def convertToCatalyst(a: Any): Any = a match {
-    case s: Seq[Any] => s.map(convertToCatalyst)
-    case p: Product => new GenericRow(p.productIterator.map(convertToCatalyst).toArray)
-    case other => other
-  }
-
   object TakeOrdered extends Strategy {
     def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
       case logical.Limit(IntegerLiteral(limit), logical.Sort(order, child)) =>
-        execution.TakeOrdered(limit, order, planLater(child))(sqlContext) :: Nil
+        execution.TakeOrdered(limit, order, planLater(child)) :: Nil
       case _ => Nil
     }
   }
@@ -195,11 +188,11 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
       // TODO: need to support writing to other types of files.  Unify the below code paths.
       case logical.WriteToFile(path, child) =>
         val relation =
-          ParquetRelation.create(path, child, sparkContext.hadoopConfiguration)
+          ParquetRelation.create(path, child, sparkContext.hadoopConfiguration, sqlContext)
         // Note: overwrite=false because otherwise the metadata we just created will be deleted
-        InsertIntoParquetTable(relation, planLater(child), overwrite=false)(sqlContext) :: Nil
+        InsertIntoParquetTable(relation, planLater(child), overwrite = false) :: Nil
       case logical.InsertIntoTable(table: ParquetRelation, partition, child, overwrite) =>
-        InsertIntoParquetTable(table, planLater(child), overwrite)(sqlContext) :: Nil
+        InsertIntoParquetTable(table, planLater(child), overwrite) :: Nil
       case PhysicalOperation(projectList, filters: Seq[Expression], relation: ParquetRelation) =>
         val prunePushedDownFilters =
           if (sparkContext.conf.getBoolean(ParquetFilters.PARQUET_FILTER_PUSHDOWN_ENABLED, true)) {
@@ -228,7 +221,7 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
           projectList,
           filters,
           prunePushedDownFilters,
-          ParquetTableScan(_, relation, filters)(sqlContext)) :: Nil
+          ParquetTableScan(_, relation, filters)) :: Nil
 
       case _ => Nil
     }
@@ -266,20 +259,19 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
       case logical.Filter(condition, child) =>
         execution.Filter(condition, planLater(child)) :: Nil
       case logical.Aggregate(group, agg, child) =>
-        execution.Aggregate(partial = false, group, agg, planLater(child))(sqlContext) :: Nil
+        execution.Aggregate(partial = false, group, agg, planLater(child)) :: Nil
       case logical.Sample(fraction, withReplacement, seed, child) =>
         execution.Sample(fraction, withReplacement, seed, planLater(child)) :: Nil
       case logical.LocalRelation(output, data) =>
-        val dataAsRdd =
-          sparkContext.parallelize(data.map(r =>
-            new GenericRow(r.productIterator.map(convertToCatalyst).toArray): Row))
-        execution.ExistingRdd(output, dataAsRdd) :: Nil
+        ExistingRdd(
+          output,
+          ExistingRdd.productToRowRdd(sparkContext.parallelize(data, numPartitions))) :: Nil
       case logical.Limit(IntegerLiteral(limit), child) =>
-        execution.Limit(limit, planLater(child))(sqlContext) :: Nil
+        execution.Limit(limit, planLater(child)) :: Nil
       case Unions(unionChildren) =>
-        execution.Union(unionChildren.map(planLater))(sqlContext) :: Nil
-      case logical.Except(left,right) =>
-        execution.Except(planLater(left),planLater(right)) :: Nil
+        execution.Union(unionChildren.map(planLater)) :: Nil
+      case logical.Except(left, right) =>
+        execution.Except(planLater(left), planLater(right)) :: Nil
       case logical.Intersect(left, right) =>
         execution.Intersect(planLater(left), planLater(right)) :: Nil
       case logical.Generate(generator, join, outer, _, child) =>
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
index 966d8f95fc83..174eda8f1a72 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
@@ -37,9 +37,11 @@ import org.apache.spark.util.MutablePair
 case class Project(projectList: Seq[NamedExpression], child: SparkPlan) extends UnaryNode {
   override def output = projectList.map(_.toAttribute)
 
-  override def execute() = child.execute().mapPartitions { iter =>
-    @transient val reusableProjection = new MutableProjection(projectList)
-    iter.map(reusableProjection)
+  @transient lazy val buildProjection = newMutableProjection(projectList, child.output)
+
+  def execute() = child.execute().mapPartitions { iter =>
+    val resuableProjection = buildProjection()
+    iter.map(resuableProjection)
   }
 }
 
@@ -50,8 +52,10 @@ case class Project(projectList: Seq[NamedExpression], child: SparkPlan) extends
 case class Filter(condition: Expression, child: SparkPlan) extends UnaryNode {
   override def output = child.output
 
-  override def execute() = child.execute().mapPartitions { iter =>
-    iter.filter(condition.eval(_).asInstanceOf[Boolean])
+  @transient lazy val conditionEvaluator = newPredicate(condition, child.output)
+
+  def execute() = child.execute().mapPartitions { iter =>
+    iter.filter(conditionEvaluator)
   }
 }
 
@@ -72,12 +76,10 @@ case class Sample(fraction: Double, withReplacement: Boolean, seed: Long, child:
  * :: DeveloperApi ::
  */
 @DeveloperApi
-case class Union(children: Seq[SparkPlan])(@transient sqlContext: SQLContext) extends SparkPlan {
+case class Union(children: Seq[SparkPlan]) extends SparkPlan {
   // TODO: attributes output by union should be distinct for nullability purposes
   override def output = children.head.output
-  override def execute() = sqlContext.sparkContext.union(children.map(_.execute()))
-
-  override def otherCopyArgs = sqlContext :: Nil
+  override def execute() = sparkContext.union(children.map(_.execute()))
 }
 
 /**
@@ -89,13 +91,11 @@ case class Union(children: Seq[SparkPlan])(@transient sqlContext: SQLContext) ex
  * repartition all the data to a single partition to compute the global limit.
  */
 @DeveloperApi
-case class Limit(limit: Int, child: SparkPlan)(@transient sqlContext: SQLContext)
+case class Limit(limit: Int, child: SparkPlan)
   extends UnaryNode {
   // TODO: Implement a partition local limit, and use a strategy to generate the proper limit plan:
   // partition local limit -> exchange into one partition -> partition local limit again
 
-  override def otherCopyArgs = sqlContext :: Nil
-
   override def output = child.output
 
   /**
@@ -161,20 +161,18 @@ case class Limit(limit: Int, child: SparkPlan)(@transient sqlContext: SQLContext
  * Spark's top operator does the opposite in ordering so we name it TakeOrdered to avoid confusion.
  */
 @DeveloperApi
-case class TakeOrdered(limit: Int, sortOrder: Seq[SortOrder], child: SparkPlan)
-                      (@transient sqlContext: SQLContext) extends UnaryNode {
-  override def otherCopyArgs = sqlContext :: Nil
+case class TakeOrdered(limit: Int, sortOrder: Seq[SortOrder], child: SparkPlan) extends UnaryNode {
 
   override def output = child.output
 
-  @transient
-  lazy val ordering = new RowOrdering(sortOrder)
+  val ordering = new RowOrdering(sortOrder, child.output)
 
+  // TODO: Is this copying for no reason?
   override def executeCollect() = child.execute().map(_.copy()).takeOrdered(limit)(ordering)
 
   // TODO: Terminal split should be implemented differently from non-terminal split.
   // TODO: Pick num splits based on |limit|.
-  override def execute() = sqlContext.sparkContext.makeRDD(executeCollect(), 1)
+  override def execute() = sparkContext.makeRDD(executeCollect(), 1)
 }
 
 /**
@@ -189,15 +187,13 @@ case class Sort(
   override def requiredChildDistribution =
     if (global) OrderedDistribution(sortOrder) :: Nil else UnspecifiedDistribution :: Nil
 
-  @transient
-  lazy val ordering = new RowOrdering(sortOrder)
 
   override def execute() = attachTree(this, "sort") {
-    // TODO: Optimize sorting operation?
     child.execute()
-      .mapPartitions(
-        iterator => iterator.map(_.copy()).toArray.sorted(ordering).iterator,
-        preservesPartitioning = true)
+      .mapPartitions( { iterator =>
+        val ordering = newOrdering(sortOrder, child.output)
+        iterator.map(_.copy()).toArray.sorted(ordering).iterator
+    }, preservesPartitioning = true)
   }
 
   override def output = child.output
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala
index c6fbd6d2f693..5ef46c32d44b 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala
@@ -41,13 +41,13 @@ package object debug {
    */
   @DeveloperApi
   implicit class DebugQuery(query: SchemaRDD) {
-    def debug(implicit sc: SparkContext): Unit = {
+    def debug(): Unit = {
       val plan = query.queryExecution.executedPlan
       val visited = new collection.mutable.HashSet[Long]()
       val debugPlan = plan transform {
         case s: SparkPlan if !visited.contains(s.id) =>
           visited += s.id
-          DebugNode(sc, s)
+          DebugNode(s)
       }
       println(s"Results returned: ${debugPlan.execute().count()}")
       debugPlan.foreach {
@@ -57,9 +57,7 @@ package object debug {
     }
   }
 
-  private[sql] case class DebugNode(
-      @transient sparkContext: SparkContext,
-      child: SparkPlan) extends UnaryNode {
+  private[sql] case class DebugNode(child: SparkPlan) extends UnaryNode {
     def references = Set.empty
 
     def output = child.output
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins.scala
index 7d1f11caae83..2750ddbce896 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins.scala
@@ -38,6 +38,8 @@ case object BuildLeft extends BuildSide
 case object BuildRight extends BuildSide
 
 trait HashJoin {
+  self: SparkPlan =>
+
   val leftKeys: Seq[Expression]
   val rightKeys: Seq[Expression]
   val buildSide: BuildSide
@@ -56,9 +58,9 @@ trait HashJoin {
 
   def output = left.output ++ right.output
 
-  @transient lazy val buildSideKeyGenerator = new Projection(buildKeys, buildPlan.output)
+  @transient lazy val buildSideKeyGenerator = newProjection(buildKeys, buildPlan.output)
   @transient lazy val streamSideKeyGenerator =
-    () => new MutableProjection(streamedKeys, streamedPlan.output)
+    newMutableProjection(streamedKeys, streamedPlan.output)
 
   def joinIterators(buildIter: Iterator[Row], streamIter: Iterator[Row]): Iterator[Row] = {
     // TODO: Use Spark's HashMap implementation.
@@ -217,9 +219,8 @@ case class BroadcastHashJoin(
      rightKeys: Seq[Expression],
      buildSide: BuildSide,
      left: SparkPlan,
-     right: SparkPlan)(@transient sqlContext: SQLContext) extends BinaryNode with HashJoin {
+     right: SparkPlan) extends BinaryNode with HashJoin {
 
-  override def otherCopyArgs = sqlContext :: Nil
 
   override def outputPartitioning: Partitioning = left.outputPartitioning
 
@@ -228,7 +229,7 @@ case class BroadcastHashJoin(
 
   @transient
   lazy val broadcastFuture = future {
-    sqlContext.sparkContext.broadcast(buildPlan.executeCollect())
+    sparkContext.broadcast(buildPlan.executeCollect())
   }
 
   def execute() = {
@@ -248,14 +249,11 @@ case class BroadcastHashJoin(
 @DeveloperApi
 case class LeftSemiJoinBNL(
     streamed: SparkPlan, broadcast: SparkPlan, condition: Option[Expression])
-    (@transient sqlContext: SQLContext)
   extends BinaryNode {
   // TODO: Override requiredChildDistribution.
 
   override def outputPartitioning: Partitioning = streamed.outputPartitioning
 
-  override def otherCopyArgs = sqlContext :: Nil
-
   def output = left.output
 
   /** The Streamed Relation */
@@ -271,7 +269,7 @@ case class LeftSemiJoinBNL(
 
   def execute() = {
     val broadcastedRelation =
-      sqlContext.sparkContext.broadcast(broadcast.execute().map(_.copy()).collect().toIndexedSeq)
+      sparkContext.broadcast(broadcast.execute().map(_.copy()).collect().toIndexedSeq)
 
     streamed.execute().mapPartitions { streamedIter =>
       val joinedRow = new JoinedRow
@@ -300,8 +298,14 @@ case class LeftSemiJoinBNL(
 case class CartesianProduct(left: SparkPlan, right: SparkPlan) extends BinaryNode {
   def output = left.output ++ right.output
 
-  def execute() = left.execute().map(_.copy()).cartesian(right.execute().map(_.copy())).map {
-    case (l: Row, r: Row) => buildRow(l ++ r)
+  def execute() = {
+    val leftResults = left.execute().map(_.copy())
+    val rightResults = right.execute().map(_.copy())
+
+    leftResults.cartesian(rightResults).mapPartitions { iter =>
+      val joinedRow = new JoinedRow
+      iter.map(r => joinedRow(r._1, r._2))
+    }
   }
 }
 
@@ -311,14 +315,11 @@ case class CartesianProduct(left: SparkPlan, right: SparkPlan) extends BinaryNod
 @DeveloperApi
 case class BroadcastNestedLoopJoin(
     streamed: SparkPlan, broadcast: SparkPlan, joinType: JoinType, condition: Option[Expression])
-    (@transient sqlContext: SQLContext)
   extends BinaryNode {
   // TODO: Override requiredChildDistribution.
 
   override def outputPartitioning: Partitioning = streamed.outputPartitioning
 
-  override def otherCopyArgs = sqlContext :: Nil
-
   override def output = {
     joinType match {
       case LeftOuter =>
@@ -345,13 +346,14 @@ case class BroadcastNestedLoopJoin(
 
   def execute() = {
     val broadcastedRelation =
-      sqlContext.sparkContext.broadcast(broadcast.execute().map(_.copy()).collect().toIndexedSeq)
+      sparkContext.broadcast(broadcast.execute().map(_.copy()).collect().toIndexedSeq)
 
     val streamedPlusMatches = streamed.execute().mapPartitions { streamedIter =>
       val matchedRows = new ArrayBuffer[Row]
       // TODO: Use Spark's BitSet.
       val includedBroadcastTuples = new BitSet(broadcastedRelation.value.size)
       val joinedRow = new JoinedRow
+      val rightNulls = new GenericMutableRow(right.output.size)
 
       streamedIter.foreach { streamedRow =>
         var i = 0
@@ -361,7 +363,7 @@ case class BroadcastNestedLoopJoin(
           // TODO: One bitset per partition instead of per row.
           val broadcastedRow = broadcastedRelation.value(i)
           if (boundCondition(joinedRow(streamedRow, broadcastedRow))) {
-            matchedRows += buildRow(streamedRow ++ broadcastedRow)
+            matchedRows += joinedRow(streamedRow, broadcastedRow).copy()
             matched = true
             includedBroadcastTuples += i
           }
@@ -369,7 +371,7 @@ case class BroadcastNestedLoopJoin(
         }
 
         if (!matched && (joinType == LeftOuter || joinType == FullOuter)) {
-          matchedRows += buildRow(streamedRow ++ Array.fill(right.output.size)(null))
+          matchedRows += joinedRow(streamedRow, rightNulls).copy()
         }
       }
       Iterator((matchedRows, includedBroadcastTuples))
@@ -383,20 +385,20 @@ case class BroadcastNestedLoopJoin(
         streamedPlusMatches.map(_._2).reduce(_ ++ _)
       }
 
+    val leftNulls = new GenericMutableRow(left.output.size)
     val rightOuterMatches: Seq[Row] =
       if (joinType == RightOuter || joinType == FullOuter) {
         broadcastedRelation.value.zipWithIndex.filter {
           case (row, i) => !allIncludedBroadcastTuples.contains(i)
         }.map {
-          // TODO: Use projection.
-          case (row, _) => buildRow(Vector.fill(left.output.size)(null) ++ row)
+          case (row, _) => new JoinedRow(leftNulls, row)
         }
       } else {
         Vector()
       }
 
     // TODO: Breaks lineage.
-    sqlContext.sparkContext.union(
-      streamedPlusMatches.flatMap(_._1), sqlContext.sparkContext.makeRDD(rightOuterMatches))
+    sparkContext.union(
+      streamedPlusMatches.flatMap(_._1), sparkContext.makeRDD(rightOuterMatches))
   }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala
index 8c7dbd5eb4a0..b3bae5db0edb 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala
@@ -46,7 +46,8 @@ import org.apache.spark.sql.catalyst.plans.logical.{LogicalPlan, LeafNode}
  */
 private[sql] case class ParquetRelation(
     path: String,
-    @transient conf: Option[Configuration] = None)
+    @transient conf: Option[Configuration],
+    @transient sqlContext: SQLContext)
   extends LeafNode with MultiInstanceRelation {
 
   self: Product =>
@@ -61,7 +62,7 @@ private[sql] case class ParquetRelation(
   /** Attributes */
   override val output = ParquetTypesConverter.readSchemaFromFile(new Path(path), conf)
 
-  override def newInstance = ParquetRelation(path).asInstanceOf[this.type]
+  override def newInstance = ParquetRelation(path, conf, sqlContext).asInstanceOf[this.type]
 
   // Equals must also take into account the output attributes so that we can distinguish between
   // different instances of the same relation,
@@ -70,6 +71,9 @@ private[sql] case class ParquetRelation(
       p.path == path && p.output == output
     case _ => false
   }
+
+  // TODO: Use data from the footers.
+  override lazy val statistics = Statistics(sizeInBytes = sqlContext.defaultSizeInBytes)
 }
 
 private[sql] object ParquetRelation {
@@ -106,13 +110,14 @@ private[sql] object ParquetRelation {
    */
   def create(pathString: String,
              child: LogicalPlan,
-             conf: Configuration): ParquetRelation = {
+             conf: Configuration,
+             sqlContext: SQLContext): ParquetRelation = {
     if (!child.resolved) {
       throw new UnresolvedException[LogicalPlan](
         child,
         "Attempt to create Parquet table from unresolved child (when schema is not available)")
     }
-    createEmpty(pathString, child.output, false, conf)
+    createEmpty(pathString, child.output, false, conf, sqlContext)
   }
 
   /**
@@ -127,14 +132,15 @@ private[sql] object ParquetRelation {
   def createEmpty(pathString: String,
                   attributes: Seq[Attribute],
                   allowExisting: Boolean,
-                  conf: Configuration): ParquetRelation = {
+                  conf: Configuration,
+                  sqlContext: SQLContext): ParquetRelation = {
     val path = checkPath(pathString, allowExisting, conf)
     if (conf.get(ParquetOutputFormat.COMPRESSION) == null) {
       conf.set(ParquetOutputFormat.COMPRESSION, ParquetRelation.defaultCompression.name())
     }
     ParquetRelation.enableLogForwarding()
     ParquetTypesConverter.writeMetaData(attributes, path, conf)
-    new ParquetRelation(path.toString, Some(conf)) {
+    new ParquetRelation(path.toString, Some(conf), sqlContext) {
       override val output = attributes
     }
   }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala
index ea74320d06c8..912a9f002b7d 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala
@@ -55,8 +55,7 @@ case class ParquetTableScan(
     // https://issues.apache.org/jira/browse/SPARK-1367
     output: Seq[Attribute],
     relation: ParquetRelation,
-    columnPruningPred: Seq[Expression])(
-    @transient val sqlContext: SQLContext)
+    columnPruningPred: Seq[Expression])
   extends LeafNode {
 
   override def execute(): RDD[Row] = {
@@ -99,8 +98,6 @@ case class ParquetTableScan(
       .filter(_ != null) // Parquet's record filters may produce null values
   }
 
-  override def otherCopyArgs = sqlContext :: Nil
-
   /**
    * Applies a (candidate) projection.
    *
@@ -110,7 +107,7 @@ case class ParquetTableScan(
   def pruneColumns(prunedAttributes: Seq[Attribute]): ParquetTableScan = {
     val success = validateProjection(prunedAttributes)
     if (success) {
-      ParquetTableScan(prunedAttributes, relation, columnPruningPred)(sqlContext)
+      ParquetTableScan(prunedAttributes, relation, columnPruningPred)
     } else {
       sys.error("Warning: Could not validate Parquet schema projection in pruneColumns")
       this
@@ -150,8 +147,7 @@ case class ParquetTableScan(
 case class InsertIntoParquetTable(
     relation: ParquetRelation,
     child: SparkPlan,
-    overwrite: Boolean = false)(
-    @transient val sqlContext: SQLContext)
+    overwrite: Boolean = false)
   extends UnaryNode with SparkHadoopMapReduceUtil {
 
   /**
@@ -171,7 +167,7 @@ case class InsertIntoParquetTable(
 
     val writeSupport =
       if (child.output.map(_.dataType).forall(_.isPrimitive)) {
-        logger.debug("Initializing MutableRowWriteSupport")
+        log.debug("Initializing MutableRowWriteSupport")
         classOf[org.apache.spark.sql.parquet.MutableRowWriteSupport]
       } else {
         classOf[org.apache.spark.sql.parquet.RowWriteSupport]
@@ -203,8 +199,6 @@ case class InsertIntoParquetTable(
 
   override def output = child.output
 
-  override def otherCopyArgs = sqlContext :: Nil
-
   /**
    * Stores the given Row RDD as a Hadoop file.
    *
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTestData.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTestData.scala
index d4599da71125..837ea7695dbb 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTestData.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTestData.scala
@@ -22,6 +22,7 @@ import java.io.File
 import org.apache.hadoop.conf.Configuration
 import org.apache.hadoop.fs.{FileStatus, FileSystem, Path}
 import org.apache.hadoop.mapreduce.Job
+import org.apache.spark.sql.test.TestSQLContext
 
 import parquet.example.data.{GroupWriter, Group}
 import parquet.example.data.simple.SimpleGroup
@@ -103,7 +104,7 @@ private[sql] object ParquetTestData {
   val testDir = Utils.createTempDir()
   val testFilterDir = Utils.createTempDir()
 
-  lazy val testData = new ParquetRelation(testDir.toURI.toString)
+  lazy val testData = new ParquetRelation(testDir.toURI.toString, None, TestSQLContext)
 
   val testNestedSchema1 =
     // based on blogpost example, source:
@@ -202,8 +203,10 @@ private[sql] object ParquetTestData {
   val testNestedDir3 = Utils.createTempDir()
   val testNestedDir4 = Utils.createTempDir()
 
-  lazy val testNestedData1 = new ParquetRelation(testNestedDir1.toURI.toString)
-  lazy val testNestedData2 = new ParquetRelation(testNestedDir2.toURI.toString)
+  lazy val testNestedData1 =
+    new ParquetRelation(testNestedDir1.toURI.toString, None, TestSQLContext)
+  lazy val testNestedData2 =
+    new ParquetRelation(testNestedDir2.toURI.toString, None, TestSQLContext)
 
   def writeFile() = {
     testDir.delete()
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/QueryTest.scala b/sql/core/src/test/scala/org/apache/spark/sql/QueryTest.scala
index 8e1e1971d968..1fd8d27b34c5 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/QueryTest.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/QueryTest.scala
@@ -45,6 +45,7 @@ class QueryTest extends PlanTest {
             |${rdd.queryExecution}
             |== Exception ==
             |$e
+            |${org.apache.spark.sql.catalyst.util.stackTraceToString(e)}
           """.stripMargin)
     }
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala
index 215618e852eb..76b172447144 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala
@@ -39,22 +39,22 @@ class PlannerSuite extends FunSuite {
 
   test("count is partially aggregated") {
     val query = testData.groupBy('value)(Count('key)).queryExecution.analyzed
-    val planned = PartialAggregation(query).head
-    val aggregations = planned.collect { case a: Aggregate => a }
+    val planned = HashAggregation(query).head
+    val aggregations = planned.collect { case n if n.nodeName contains "Aggregate" => n }
 
     assert(aggregations.size === 2)
   }
 
   test("count distinct is not partially aggregated") {
     val query = testData.groupBy('value)(CountDistinct('key :: Nil)).queryExecution.analyzed
-    val planned = PartialAggregation(query)
+    val planned = HashAggregation(query)
     assert(planned.isEmpty)
   }
 
   test("mixed aggregates are not partially aggregated") {
     val query =
       testData.groupBy('value)(Count('value), CountDistinct('key :: Nil)).queryExecution.analyzed
-    val planned = PartialAggregation(query)
+    val planned = HashAggregation(query)
     assert(planned.isEmpty)
   }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/TgfSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/TgfSuite.scala
index e55648b8ed15..2cab5e0c44d9 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/TgfSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/TgfSuite.scala
@@ -33,7 +33,7 @@ import org.apache.spark.sql.test.TestSQLContext._
  * Note: this is only a rough example of how TGFs can be expressed, the final version will likely
  * involve a lot more sugar for cleaner use in Scala/Java/etc.
  */
-case class ExampleTGF(input: Seq[Attribute] = Seq('name, 'age)) extends Generator {
+case class ExampleTGF(input: Seq[Expression] = Seq('name, 'age)) extends Generator {
   def children = input
   protected def makeOutput() = 'nameAndAge.string :: Nil
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala
index 3c911e9a4e7b..561f5b4a4996 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala
@@ -25,6 +25,7 @@ import parquet.schema.MessageTypeParser
 
 import org.apache.hadoop.fs.{FileSystem, Path}
 import org.apache.hadoop.mapreduce.Job
+
 import org.apache.spark.SparkContext
 import org.apache.spark.sql._
 import org.apache.spark.sql.catalyst.{SqlLexical, SqlParser}
@@ -32,6 +33,7 @@ import org.apache.spark.sql.catalyst.analysis.{Star, UnresolvedAttribute}
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.types.{BooleanType, IntegerType}
 import org.apache.spark.sql.catalyst.util.getTempFilePath
+import org.apache.spark.sql.execution.SparkPlan
 import org.apache.spark.sql.test.TestSQLContext
 import org.apache.spark.sql.test.TestSQLContext._
 import org.apache.spark.util.Utils
@@ -207,10 +209,11 @@ class ParquetQuerySuite extends QueryTest with FunSuiteLike with BeforeAndAfterA
   }
 
   test("Projection of simple Parquet file") {
+    SparkPlan.currentContext.set(TestSQLContext)
     val scanner = new ParquetTableScan(
       ParquetTestData.testData.output,
       ParquetTestData.testData,
-      Seq())(TestSQLContext)
+      Seq())
     val projected = scanner.pruneColumns(ParquetTypesConverter
       .convertToAttributes(MessageTypeParser
       .parseMessageType(ParquetTestData.subTestSchema)))
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
index 84d43eaeea51..f0a61270daf0 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
@@ -231,7 +231,7 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) {
       HiveTableScans,
       DataSinks,
       Scripts,
-      PartialAggregation,
+      HashAggregation,
       LeftSemiJoin,
       HashJoin,
       BasicOperators,
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala
index c2b0b00aa585..39033bdeac4b 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala
@@ -131,7 +131,7 @@ case class InsertIntoHiveTable(
       conf,
       SparkHiveHadoopWriter.createPathFromString(fileSinkConf.getDirName, conf))
 
-    logger.debug("Saving as hadoop file of type " + valueClass.getSimpleName)
+    log.debug("Saving as hadoop file of type " + valueClass.getSimpleName)
 
     val writer = new SparkHiveHadoopWriter(conf, fileSinkConf)
     writer.preSetup()
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala
index 8258ee5fef0e..0c8f676e9c5c 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala
@@ -67,7 +67,7 @@ case class ScriptTransformation(
         }
       }
       readerThread.start()
-      val outputProjection = new Projection(input)
+      val outputProjection = new InterpretedProjection(input, child.output)
       iter
         .map(outputProjection)
         // TODO: Use SerDe
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala
index 057eb60a0261..7582b4743d40 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala
@@ -251,8 +251,10 @@ private[hive] case class HiveGenericUdtf(
   @transient
   protected lazy val function: GenericUDTF = createFunction()
 
+  @transient
   protected lazy val inputInspectors = children.map(_.dataType).map(toInspector)
 
+  @transient
   protected lazy val outputInspectors = {
     val structInspector = function.initialize(inputInspectors.toArray)
     structInspector.getAllStructFieldRefs.map(_.getFieldObjectInspector)
@@ -278,7 +280,7 @@ private[hive] case class HiveGenericUdtf(
   override def eval(input: Row): TraversableOnce[Row] = {
     outputInspectors // Make sure initialized.
 
-    val inputProjection = new Projection(children)
+    val inputProjection = new InterpretedProjection(children)
     val collector = new UDTFCollector
     function.setCollector(collector)
 
@@ -332,7 +334,7 @@ private[hive] case class HiveUdafFunction(
   override def eval(input: Row): Any = unwrapData(function.evaluate(buffer), returnInspector)
 
   @transient
-  val inputProjection = new Projection(exprs)
+  val inputProjection = new InterpretedProjection(exprs)
 
   def update(input: Row): Unit = {
     val inputs = inputProjection(input).asInstanceOf[Seq[AnyRef]].toArray
diff --git a/sql/hive/src/test/resources/golden/case else null-0-8ef2f741400830ef889a9dd0c817fe3d b/sql/hive/src/test/resources/golden/case else null-0-8ef2f741400830ef889a9dd0c817fe3d
new file mode 100644
index 000000000000..00750edc07d6
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/case else null-0-8ef2f741400830ef889a9dd0c817fe3d	
@@ -0,0 +1 @@
+3
diff --git a/sql/hive/src/test/resources/golden/double case-0-f513687d17dcb18546fefa75000a52f2 b/sql/hive/src/test/resources/golden/double case-0-f513687d17dcb18546fefa75000a52f2
new file mode 100644
index 000000000000..00750edc07d6
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/double case-0-f513687d17dcb18546fefa75000a52f2	
@@ -0,0 +1 @@
+3
diff --git a/sql/hive/src/test/resources/golden/single case-0-c264e319c52f1840a32959d552b99e73 b/sql/hive/src/test/resources/golden/single case-0-c264e319c52f1840a32959d552b99e73
new file mode 100644
index 000000000000..d00491fd7e5b
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/single case-0-c264e319c52f1840a32959d552b99e73	
@@ -0,0 +1 @@
+1
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
index aadfd2e90015..89cc589fb800 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
@@ -19,6 +19,8 @@ package org.apache.spark.sql.hive.execution
 
 import scala.util.Try
 
+import org.apache.spark.sql.{SchemaRDD, Row}
+import org.apache.spark.sql.hive._
 import org.apache.spark.sql.hive.test.TestHive
 import org.apache.spark.sql.hive.test.TestHive._
 import org.apache.spark.sql.{Row, SchemaRDD}
@@ -30,6 +32,15 @@ case class TestData(a: Int, b: String)
  */
 class HiveQuerySuite extends HiveComparisonTest {
 
+  createQueryTest("single case",
+    """SELECT case when true then 1 else 2 end FROM src LIMIT 1""")
+
+  createQueryTest("double case",
+    """SELECT case when 1 = 2 then 1 when 2 = 2 then 3 else 2 end FROM src LIMIT 1""")
+
+  createQueryTest("case else null",
+    """SELECT case when 1 = 2 then 1 when 2 = 2 then 3 else null end FROM src LIMIT 1""")
+
   createQueryTest("having no references",
     "SELECT key FROM src GROUP BY key HAVING COUNT(*) > 1")
 

From 2e6efcacea19bddbdae1d655ef54186f2e52747f Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Tue, 29 Jul 2014 22:16:20 -0700
Subject: [PATCH 806/810] [SPARK-2568] RangePartitioner should run only one job
 if data is balanced

As of Spark 1.0, RangePartitioner goes through data twice: once to compute the count and once to do sampling. As a result, to do sortByKey, Spark goes through data 3 times (once to count, once to sample, and once to sort).

`RangePartitioner` should go through data only once, collecting samples from input partitions as well as counting. If the data is balanced, this should give us a good sketch. If we see big partitions, we re-sample from them in order to collect enough items.

The downside is that we need to collect more from each partition in the first pass. An alternative solution is caching the intermediate result and decide whether to fetch the data after.

Author: Xiangrui Meng <meng@databricks.com>
Author: Reynold Xin <rxin@apache.org>

Closes #1562 from mengxr/range-partitioner and squashes the following commits:

6cc2551 [Xiangrui Meng] change foreach to for
eb39b08 [Xiangrui Meng] Merge branch 'master' into range-partitioner
eb95dd8 [Xiangrui Meng] separate sketching and determining bounds impl
c436d30 [Xiangrui Meng] fix binary metrics unit tests
db58a55 [Xiangrui Meng] add unit tests
a6e35d6 [Xiangrui Meng] minor update
60be09e [Xiangrui Meng] remove importance sampler
9ee9992 [Xiangrui Meng] update range partitioner to run only one job on roughly balanced data
cc12f47 [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into range-part
06ac2ec [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into range-part
17bcbf3 [Reynold Xin] Added seed.
badf20d [Reynold Xin] Renamed the method.
6940010 [Reynold Xin] Reservoir sampling implementation.
---
 .../scala/org/apache/spark/Partitioner.scala  | 121 +++++++++++++++---
 .../org/apache/spark/PartitioningSuite.scala  |  64 ++++++++-
 .../scala/org/apache/spark/rdd/RDDSuite.scala |   5 +
 3 files changed, 171 insertions(+), 19 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/Partitioner.scala b/core/src/main/scala/org/apache/spark/Partitioner.scala
index 52c018baa5f7..37053bb6f37a 100644
--- a/core/src/main/scala/org/apache/spark/Partitioner.scala
+++ b/core/src/main/scala/org/apache/spark/Partitioner.scala
@@ -19,11 +19,15 @@ package org.apache.spark
 
 import java.io.{IOException, ObjectInputStream, ObjectOutputStream}
 
-import scala.reflect.ClassTag
+import scala.collection.mutable
+import scala.collection.mutable.ArrayBuffer
+import scala.reflect.{ClassTag, classTag}
+import scala.util.hashing.byteswap32
 
-import org.apache.spark.rdd.RDD
+import org.apache.spark.rdd.{PartitionPruningRDD, RDD}
 import org.apache.spark.serializer.JavaSerializer
 import org.apache.spark.util.{CollectionsUtils, Utils}
+import org.apache.spark.util.random.{XORShiftRandom, SamplingUtils}
 
 /**
  * An object that defines how the elements in a key-value pair RDD are partitioned by key.
@@ -103,26 +107,49 @@ class RangePartitioner[K : Ordering : ClassTag, V](
     private var ascending: Boolean = true)
   extends Partitioner {
 
+  // We allow partitions = 0, which happens when sorting an empty RDD under the default settings.
+  require(partitions >= 0, s"Number of partitions cannot be negative but found $partitions.")
+
   private var ordering = implicitly[Ordering[K]]
 
   // An array of upper bounds for the first (partitions - 1) partitions
   private var rangeBounds: Array[K] = {
-    if (partitions == 1) {
-      Array()
+    if (partitions <= 1) {
+      Array.empty
     } else {
-      val rddSize = rdd.count()
-      val maxSampleSize = partitions * 20.0
-      val frac = math.min(maxSampleSize / math.max(rddSize, 1), 1.0)
-      val rddSample = rdd.sample(false, frac, 1).map(_._1).collect().sorted
-      if (rddSample.length == 0) {
-        Array()
+      // This is the sample size we need to have roughly balanced output partitions, capped at 1M.
+      val sampleSize = math.min(20.0 * partitions, 1e6)
+      // Assume the input partitions are roughly balanced and over-sample a little bit.
+      val sampleSizePerPartition = math.ceil(3.0 * sampleSize / rdd.partitions.size).toInt
+      val (numItems, sketched) = RangePartitioner.sketch(rdd.map(_._1), sampleSizePerPartition)
+      if (numItems == 0L) {
+        Array.empty
       } else {
-        val bounds = new Array[K](partitions - 1)
-        for (i <- 0 until partitions - 1) {
-          val index = (rddSample.length - 1) * (i + 1) / partitions
-          bounds(i) = rddSample(index)
+        // If a partition contains much more than the average number of items, we re-sample from it
+        // to ensure that enough items are collected from that partition.
+        val fraction = math.min(sampleSize / math.max(numItems, 1L), 1.0)
+        val candidates = ArrayBuffer.empty[(K, Float)]
+        val imbalancedPartitions = mutable.Set.empty[Int]
+        sketched.foreach { case (idx, n, sample) =>
+          if (fraction * n > sampleSizePerPartition) {
+            imbalancedPartitions += idx
+          } else {
+            // The weight is 1 over the sampling probability.
+            val weight = (n.toDouble / sample.size).toFloat
+            for (key <- sample) {
+              candidates += ((key, weight))
+            }
+          }
+        }
+        if (imbalancedPartitions.nonEmpty) {
+          // Re-sample imbalanced partitions with the desired sampling probability.
+          val imbalanced = new PartitionPruningRDD(rdd.map(_._1), imbalancedPartitions.contains)
+          val seed = byteswap32(-rdd.id - 1)
+          val reSampled = imbalanced.sample(withReplacement = false, fraction, seed).collect()
+          val weight = (1.0 / fraction).toFloat
+          candidates ++= reSampled.map(x => (x, weight))
         }
-        bounds
+        RangePartitioner.determineBounds(candidates, partitions)
       }
     }
   }
@@ -212,3 +239,67 @@ class RangePartitioner[K : Ordering : ClassTag, V](
     }
   }
 }
+
+private[spark] object RangePartitioner {
+
+  /**
+   * Sketches the input RDD via reservoir sampling on each partition.
+   *
+   * @param rdd the input RDD to sketch
+   * @param sampleSizePerPartition max sample size per partition
+   * @return (total number of items, an array of (partitionId, number of items, sample))
+   */
+  def sketch[K:ClassTag](
+      rdd: RDD[K],
+      sampleSizePerPartition: Int): (Long, Array[(Int, Int, Array[K])]) = {
+    val shift = rdd.id
+    // val classTagK = classTag[K] // to avoid serializing the entire partitioner object
+    val sketched = rdd.mapPartitionsWithIndex { (idx, iter) =>
+      val seed = byteswap32(idx ^ (shift << 16))
+      val (sample, n) = SamplingUtils.reservoirSampleAndCount(
+        iter, sampleSizePerPartition, seed)
+      Iterator((idx, n, sample))
+    }.collect()
+    val numItems = sketched.map(_._2.toLong).sum
+    (numItems, sketched)
+  }
+
+  /**
+   * Determines the bounds for range partitioning from candidates with weights indicating how many
+   * items each represents. Usually this is 1 over the probability used to sample this candidate.
+   *
+   * @param candidates unordered candidates with weights
+   * @param partitions number of partitions
+   * @return selected bounds
+   */
+  def determineBounds[K:Ordering:ClassTag](
+      candidates: ArrayBuffer[(K, Float)],
+      partitions: Int): Array[K] = {
+    val ordering = implicitly[Ordering[K]]
+    val ordered = candidates.sortBy(_._1)
+    val numCandidates = ordered.size
+    val sumWeights = ordered.map(_._2.toDouble).sum
+    val step = sumWeights / partitions
+    var cumWeight = 0.0
+    var target = step
+    val bounds = ArrayBuffer.empty[K]
+    var i = 0
+    var j = 0
+    var previousBound = Option.empty[K]
+    while ((i < numCandidates) && (j < partitions - 1)) {
+      val (key, weight) = ordered(i)
+      cumWeight += weight
+      if (cumWeight > target) {
+        // Skip duplicate values.
+        if (previousBound.isEmpty || ordering.gt(key, previousBound.get)) {
+          bounds += key
+          target += step
+          j += 1
+          previousBound = Some(key)
+        }
+      }
+      i += 1
+    }
+    bounds.toArray
+  }
+}
diff --git a/core/src/test/scala/org/apache/spark/PartitioningSuite.scala b/core/src/test/scala/org/apache/spark/PartitioningSuite.scala
index 4658a0806428..fc0cee3e8749 100644
--- a/core/src/test/scala/org/apache/spark/PartitioningSuite.scala
+++ b/core/src/test/scala/org/apache/spark/PartitioningSuite.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark
 
+import scala.collection.mutable.ArrayBuffer
 import scala.math.abs
 
 import org.scalatest.{FunSuite, PrivateMethodTester}
@@ -52,14 +53,12 @@ class PartitioningSuite extends FunSuite with SharedSparkContext with PrivateMet
 
     assert(p2 === p2)
     assert(p4 === p4)
-    assert(p2 != p4)
-    assert(p4 != p2)
+    assert(p2 === p4)
     assert(p4 === anotherP4)
     assert(anotherP4 === p4)
     assert(descendingP2 === descendingP2)
     assert(descendingP4 === descendingP4)
-    assert(descendingP2 != descendingP4)
-    assert(descendingP4 != descendingP2)
+    assert(descendingP2 === descendingP4)
     assert(p2 != descendingP2)
     assert(p4 != descendingP4)
     assert(descendingP2 != p2)
@@ -102,6 +101,63 @@ class PartitioningSuite extends FunSuite with SharedSparkContext with PrivateMet
     partitioner.getPartition(Row(100))
   }
 
+  test("RangPartitioner.sketch") {
+    val rdd = sc.makeRDD(0 until 20, 20).flatMap { i =>
+      val random = new java.util.Random(i)
+      Iterator.fill(i)(random.nextDouble())
+    }.cache()
+    val sampleSizePerPartition = 10
+    val (count, sketched) = RangePartitioner.sketch(rdd, sampleSizePerPartition)
+    assert(count === rdd.count())
+    sketched.foreach { case (idx, n, sample) =>
+      assert(n === idx)
+      assert(sample.size === math.min(n, sampleSizePerPartition))
+    }
+  }
+
+  test("RangePartitioner.determineBounds") {
+    assert(RangePartitioner.determineBounds(ArrayBuffer.empty[(Int, Float)], 10).isEmpty,
+      "Bounds on an empty candidates set should be empty.")
+    val candidates = ArrayBuffer(
+      (0.7, 2.0f), (0.1, 1.0f), (0.4, 1.0f), (0.3, 1.0f), (0.2, 1.0f), (0.5, 1.0f), (1.0, 3.0f))
+    assert(RangePartitioner.determineBounds(candidates, 3) === Array(0.4, 0.7))
+  }
+
+  test("RangePartitioner should run only one job if data is roughly balanced") {
+    val rdd = sc.makeRDD(0 until 20, 20).flatMap { i =>
+      val random = new java.util.Random(i)
+      Iterator.fill(5000 * i)((random.nextDouble() + i, i))
+    }.cache()
+    for (numPartitions <- Seq(10, 20, 40)) {
+      val partitioner = new RangePartitioner(numPartitions, rdd)
+      assert(partitioner.numPartitions === numPartitions)
+      val counts = rdd.keys.map(key => partitioner.getPartition(key)).countByValue().values
+      assert(counts.max < 3.0 * counts.min)
+    }
+  }
+
+  test("RangePartitioner should work well on unbalanced data") {
+    val rdd = sc.makeRDD(0 until 20, 20).flatMap { i =>
+      val random = new java.util.Random(i)
+      Iterator.fill(20 * i * i * i)((random.nextDouble() + i, i))
+    }.cache()
+    for (numPartitions <- Seq(2, 4, 8)) {
+      val partitioner = new RangePartitioner(numPartitions, rdd)
+      assert(partitioner.numPartitions === numPartitions)
+      val counts = rdd.keys.map(key => partitioner.getPartition(key)).countByValue().values
+      assert(counts.max < 3.0 * counts.min)
+    }
+  }
+
+  test("RangePartitioner should return a single partition for empty RDDs") {
+    val empty1 = sc.emptyRDD[(Int, Double)]
+    val partitioner1 = new RangePartitioner(0, empty1)
+    assert(partitioner1.numPartitions === 1)
+    val empty2 = sc.makeRDD(0 until 2, 2).flatMap(i => Seq.empty[(Int, Double)])
+    val partitioner2 = new RangePartitioner(2, empty2)
+    assert(partitioner2.numPartitions === 1)
+  }
+
   test("HashPartitioner not equal to RangePartitioner") {
     val rdd = sc.parallelize(1 to 10).map(x => (x, x))
     val rangeP2 = new RangePartitioner(2, rdd)
diff --git a/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala b/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala
index 6654ec2d7c65..fdc83bc0a5f8 100644
--- a/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala
+++ b/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala
@@ -613,6 +613,11 @@ class RDDSuite extends FunSuite with SharedSparkContext {
     }
   }
 
+  test("sort an empty RDD") {
+    val data = sc.emptyRDD[Int]
+    assert(data.sortBy(x => x).collect() === Array.empty)
+  }
+
   test("sortByKey") {
     val data = sc.parallelize(Seq("5|50|A","4|60|C", "6|40|B"))
 

From 077f633b4720422c5efbf0382e869ead3dc49612 Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Tue, 29 Jul 2014 22:42:54 -0700
Subject: [PATCH 807/810] [SQL] Handle null values in debug()

Author: Michael Armbrust <michael@databricks.com>

Closes #1646 from marmbrus/nullDebug and squashes the following commits:

49050a8 [Michael Armbrust] Handle null values in debug()
---
 .../scala/org/apache/spark/sql/execution/debug/package.scala  | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala
index 5ef46c32d44b..f31df051824d 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala
@@ -105,7 +105,9 @@ package object debug {
             var i = 0
             while (i < numColumns) {
               val value = currentRow(i)
-              columnStats(i).elementTypes += HashSet(value.getClass.getName)
+              if (value != null) {
+                columnStats(i).elementTypes += HashSet(value.getClass.getName)
+              }
               i += 1
             }
             currentRow

From 4ce92ccaf761e48a10fc4fe4927dbfca858ca22b Mon Sep 17 00:00:00 2001
From: Andrew Or <andrewor14@gmail.com>
Date: Tue, 29 Jul 2014 23:52:09 -0700
Subject: [PATCH 808/810] [SPARK-2260] Fix standalone-cluster mode, which was
 broken

The main thing was that spark configs were not propagated to the driver, and so applications that do not specify `master` or `appName` automatically failed. This PR fixes that and a couple of miscellaneous things that are related.

One thing that may or may not be an issue is that the jars must be available on the driver node. In `standalone-cluster` mode, this effectively means these jars must be available on all the worker machines, since the driver is launched on one of them. The semantics here are not the same as `yarn-cluster` mode,  where all the relevant jars are uploaded to a distributed cache automatically and shipped to the containers. This is probably not a concern, but still worth a mention.

Author: Andrew Or <andrewor14@gmail.com>

Closes #1538 from andrewor14/standalone-cluster and squashes the following commits:

8c11a0d [Andrew Or] Clean up imports / comments (minor)
2678d13 [Andrew Or] Handle extraJavaOpts properly
7660547 [Andrew Or] Merge branch 'master' of github.com:apache/spark into standalone-cluster
6f64a9b [Andrew Or] Revert changes in YARN
2f2908b [Andrew Or] Fix tests
ed01491 [Andrew Or] Don't go overboard with escaping
8e105e1 [Andrew Or] Merge branch 'master' of github.com:apache/spark into standalone-cluster
b890949 [Andrew Or] Abstract usages of converting spark opts to java opts
79f63a3 [Andrew Or] Move sparkProps into javaOpts
78752f8 [Andrew Or] Fix tests
5a9c6c7 [Andrew Or] Fix line too long
c141a00 [Andrew Or] Don't display "unknown app" on driver log pages
d7e2728 [Andrew Or] Avoid deprecation warning in standalone Client
6ceb14f [Andrew Or] Allow relevant configs to propagate to standalone Driver
7f854bc [Andrew Or] Fix test
855256e [Andrew Or] Fix standalone-cluster mode
fd9da51 [Andrew Or] Formatting changes (minor)
---
 .../scala/org/apache/spark/SparkConf.scala    | 22 ++++++++++++++++++-
 .../org/apache/spark/deploy/Client.scala      | 21 +++++++++---------
 .../org/apache/spark/deploy/Command.scala     |  2 +-
 .../org/apache/spark/deploy/SparkSubmit.scala | 12 +++++-----
 .../spark/deploy/client/TestClient.scala      |  6 ++---
 .../spark/deploy/worker/CommandUtils.scala    |  7 +++---
 .../spark/deploy/worker/DriverRunner.scala    |  3 ++-
 .../spark/deploy/worker/ExecutorRunner.scala  | 14 +++++++-----
 .../spark/deploy/worker/ui/LogPage.scala      | 11 +++++-----
 .../CoarseGrainedExecutorBackend.scala        |  9 ++++++--
 .../cluster/SparkDeploySchedulerBackend.scala | 11 ++++++----
 .../scala/org/apache/spark/util/Utils.scala   |  9 ++++++++
 .../spark/deploy/JsonProtocolSuite.scala      |  6 ++---
 .../spark/deploy/SparkSubmitSuite.scala       |  7 ++++--
 .../deploy/worker/DriverRunnerTest.scala      |  2 +-
 .../deploy/worker/ExecutorRunnerTest.scala    |  2 +-
 16 files changed, 93 insertions(+), 51 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/SparkConf.scala b/core/src/main/scala/org/apache/spark/SparkConf.scala
index 8ce4b91cae8a..38700847c80f 100644
--- a/core/src/main/scala/org/apache/spark/SparkConf.scala
+++ b/core/src/main/scala/org/apache/spark/SparkConf.scala
@@ -40,6 +40,8 @@ import scala.collection.mutable.HashMap
  */
 class SparkConf(loadDefaults: Boolean) extends Cloneable with Logging {
 
+  import SparkConf._
+
   /** Create a SparkConf that loads defaults from system properties and the classpath */
   def this() = this(true)
 
@@ -198,7 +200,7 @@ class SparkConf(loadDefaults: Boolean) extends Cloneable with Logging {
      *
      *   E.g. spark.akka.option.x.y.x = "value"
      */
-    getAll.filter {case (k, v) => k.startsWith("akka.")}
+    getAll.filter { case (k, _) => isAkkaConf(k) }
 
   /** Does the configuration contain a given parameter? */
   def contains(key: String): Boolean = settings.contains(key)
@@ -292,3 +294,21 @@ class SparkConf(loadDefaults: Boolean) extends Cloneable with Logging {
     settings.toArray.sorted.map{case (k, v) => k + "=" + v}.mkString("\n")
   }
 }
+
+private[spark] object SparkConf {
+  /**
+   * Return whether the given config is an akka config (e.g. akka.actor.provider).
+   * Note that this does not include spark-specific akka configs (e.g. spark.akka.timeout).
+   */
+  def isAkkaConf(name: String): Boolean = name.startsWith("akka.")
+
+  /**
+   * Return whether the given config should be passed to an executor on start-up.
+   *
+   * Certain akka and authentication configs are required of the executor when it connects to
+   * the scheduler, while the rest of the spark configs can be inherited from the driver later.
+   */
+  def isExecutorStartupConf(name: String): Boolean = {
+    isAkkaConf(name) || name.startsWith("spark.akka") || name.startsWith("spark.auth")
+  }
+}
diff --git a/core/src/main/scala/org/apache/spark/deploy/Client.scala b/core/src/main/scala/org/apache/spark/deploy/Client.scala
index c371dc3a51c7..17c507af2652 100644
--- a/core/src/main/scala/org/apache/spark/deploy/Client.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/Client.scala
@@ -17,8 +17,6 @@
 
 package org.apache.spark.deploy
 
-import scala.collection.JavaConversions._
-import scala.collection.mutable.Map
 import scala.concurrent._
 
 import akka.actor._
@@ -50,9 +48,6 @@ private class ClientActor(driverArgs: ClientArguments, conf: SparkConf) extends
         // TODO: We could add an env variable here and intercept it in `sc.addJar` that would
         //       truncate filesystem paths similar to what YARN does. For now, we just require
         //       people call `addJar` assuming the jar is in the same directory.
-        val env = Map[String, String]()
-        System.getenv().foreach{case (k, v) => env(k) = v}
-
         val mainClass = "org.apache.spark.deploy.worker.DriverWrapper"
 
         val classPathConf = "spark.driver.extraClassPath"
@@ -65,10 +60,13 @@ private class ClientActor(driverArgs: ClientArguments, conf: SparkConf) extends
           cp.split(java.io.File.pathSeparator)
         }
 
-        val javaOptionsConf = "spark.driver.extraJavaOptions"
-        val javaOpts = sys.props.get(javaOptionsConf)
+        val extraJavaOptsConf = "spark.driver.extraJavaOptions"
+        val extraJavaOpts = sys.props.get(extraJavaOptsConf)
+          .map(Utils.splitCommandString).getOrElse(Seq.empty)
+        val sparkJavaOpts = Utils.sparkJavaOpts(conf)
+        val javaOpts = sparkJavaOpts ++ extraJavaOpts
         val command = new Command(mainClass, Seq("{{WORKER_URL}}", driverArgs.mainClass) ++
-          driverArgs.driverOptions, env, classPathEntries, libraryPathEntries, javaOpts)
+          driverArgs.driverOptions, sys.env, classPathEntries, libraryPathEntries, javaOpts)
 
         val driverDescription = new DriverDescription(
           driverArgs.jarUrl,
@@ -109,6 +107,7 @@ private class ClientActor(driverArgs: ClientArguments, conf: SparkConf) extends
         // Exception, if present
         statusResponse.exception.map { e =>
           println(s"Exception from cluster was: $e")
+          e.printStackTrace()
           System.exit(-1)
         }
         System.exit(0)
@@ -141,8 +140,10 @@ private class ClientActor(driverArgs: ClientArguments, conf: SparkConf) extends
  */
 object Client {
   def main(args: Array[String]) {
-    println("WARNING: This client is deprecated and will be removed in a future version of Spark.")
-    println("Use ./bin/spark-submit with \"--master spark://host:port\"")
+    if (!sys.props.contains("SPARK_SUBMIT")) {
+      println("WARNING: This client is deprecated and will be removed in a future version of Spark")
+      println("Use ./bin/spark-submit with \"--master spark://host:port\"")
+    }
 
     val conf = new SparkConf()
     val driverArgs = new ClientArguments(args)
diff --git a/core/src/main/scala/org/apache/spark/deploy/Command.scala b/core/src/main/scala/org/apache/spark/deploy/Command.scala
index 32f3ba385084..a2b263544c6a 100644
--- a/core/src/main/scala/org/apache/spark/deploy/Command.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/Command.scala
@@ -25,5 +25,5 @@ private[spark] case class Command(
     environment: Map[String, String],
     classPathEntries: Seq[String],
     libraryPathEntries: Seq[String],
-    extraJavaOptions: Option[String] = None) {
+    javaOpts: Seq[String]) {
 }
diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
index c9cec33ebaa6..3df811c4ac5d 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
@@ -136,8 +136,6 @@ object SparkSubmit {
     (clusterManager, deployMode) match {
       case (MESOS, CLUSTER) =>
         printErrorAndExit("Cluster deploy mode is currently not supported for Mesos clusters.")
-      case (STANDALONE, CLUSTER) =>
-        printErrorAndExit("Cluster deploy mode is currently not supported for Standalone clusters.")
       case (_, CLUSTER) if args.isPython =>
         printErrorAndExit("Cluster deploy mode is currently not supported for python applications.")
       case (_, CLUSTER) if isShell(args.primaryResource) =>
@@ -170,9 +168,9 @@ object SparkSubmit {
     val options = List[OptionAssigner](
 
       // All cluster managers
-      OptionAssigner(args.master, ALL_CLUSTER_MGRS, CLIENT, sysProp = "spark.master"),
-      OptionAssigner(args.name, ALL_CLUSTER_MGRS, CLIENT, sysProp = "spark.app.name"),
-      OptionAssigner(args.jars, ALL_CLUSTER_MGRS, CLIENT, sysProp = "spark.jars"),
+      OptionAssigner(args.master, ALL_CLUSTER_MGRS, ALL_DEPLOY_MODES, sysProp = "spark.master"),
+      OptionAssigner(args.name, ALL_CLUSTER_MGRS, ALL_DEPLOY_MODES, sysProp = "spark.app.name"),
+      OptionAssigner(args.jars, ALL_CLUSTER_MGRS, ALL_DEPLOY_MODES, sysProp = "spark.jars"),
 
       // Standalone cluster only
       OptionAssigner(args.driverMemory, STANDALONE, CLUSTER, clOption = "--memory"),
@@ -203,9 +201,9 @@ object SparkSubmit {
         sysProp = "spark.driver.extraJavaOptions"),
       OptionAssigner(args.driverExtraLibraryPath, STANDALONE | YARN, CLUSTER,
         sysProp = "spark.driver.extraLibraryPath"),
-      OptionAssigner(args.executorMemory, STANDALONE | MESOS | YARN, CLIENT,
+      OptionAssigner(args.executorMemory, STANDALONE | MESOS | YARN, ALL_DEPLOY_MODES,
         sysProp = "spark.executor.memory"),
-      OptionAssigner(args.totalExecutorCores, STANDALONE | MESOS, CLIENT,
+      OptionAssigner(args.totalExecutorCores, STANDALONE | MESOS, ALL_DEPLOY_MODES,
         sysProp = "spark.cores.max"),
       OptionAssigner(args.files, LOCAL | STANDALONE | MESOS, ALL_DEPLOY_MODES,
         sysProp = "spark.files")
diff --git a/core/src/main/scala/org/apache/spark/deploy/client/TestClient.scala b/core/src/main/scala/org/apache/spark/deploy/client/TestClient.scala
index e15a87bd38fd..b8ffa9afb69c 100644
--- a/core/src/main/scala/org/apache/spark/deploy/client/TestClient.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/client/TestClient.scala
@@ -46,11 +46,11 @@ private[spark] object TestClient {
   def main(args: Array[String]) {
     val url = args(0)
     val conf = new SparkConf
-    val (actorSystem, port) = AkkaUtils.createActorSystem("spark", Utils.localIpAddress, 0,
+    val (actorSystem, _) = AkkaUtils.createActorSystem("spark", Utils.localIpAddress, 0,
       conf = conf, securityManager = new SecurityManager(conf))
     val desc = new ApplicationDescription(
-      "TestClient", Some(1), 512, Command("spark.deploy.client.TestExecutor", Seq(), Map(), Seq(),
-        Seq()), Some("dummy-spark-home"), "ignored")
+      "TestClient", Some(1), 512, Command("spark.deploy.client.TestExecutor", Seq(), Map(),
+        Seq(), Seq(), Seq()), Some("dummy-spark-home"), "ignored")
     val listener = new TestListener
     val client = new AppClient(actorSystem, Array(url), desc, listener, new SparkConf)
     client.start()
diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/CommandUtils.scala b/core/src/main/scala/org/apache/spark/deploy/worker/CommandUtils.scala
index 4af5bc3afad6..687e492a0d6f 100644
--- a/core/src/main/scala/org/apache/spark/deploy/worker/CommandUtils.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/worker/CommandUtils.scala
@@ -47,7 +47,6 @@ object CommandUtils extends Logging {
    */
   def buildJavaOpts(command: Command, memory: Int, sparkHome: String): Seq[String] = {
     val memoryOpts = Seq(s"-Xms${memory}M", s"-Xmx${memory}M")
-    val extraOpts = command.extraJavaOptions.map(Utils.splitCommandString).getOrElse(Seq())
 
     // Exists for backwards compatibility with older Spark versions
     val workerLocalOpts = Option(getenv("SPARK_JAVA_OPTS")).map(Utils.splitCommandString)
@@ -62,7 +61,7 @@ object CommandUtils extends Logging {
         val joined = command.libraryPathEntries.mkString(File.pathSeparator)
         Seq(s"-Djava.library.path=$joined")
       } else {
-         Seq()
+        Seq()
       }
 
     val permGenOpt = Seq("-XX:MaxPermSize=128m")
@@ -71,11 +70,11 @@ object CommandUtils extends Logging {
     val ext = if (System.getProperty("os.name").startsWith("Windows")) ".cmd" else ".sh"
     val classPath = Utils.executeAndGetOutput(
       Seq(sparkHome + "/bin/compute-classpath" + ext),
-      extraEnvironment=command.environment)
+      extraEnvironment = command.environment)
     val userClassPath = command.classPathEntries ++ Seq(classPath)
 
     Seq("-cp", userClassPath.filterNot(_.isEmpty).mkString(File.pathSeparator)) ++
-      permGenOpt ++ libraryOpts ++ extraOpts ++ workerLocalOpts ++ memoryOpts
+      permGenOpt ++ libraryOpts ++ workerLocalOpts ++ command.javaOpts ++ memoryOpts
   }
 
   /** Spawn a thread that will redirect a given stream to a file */
diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/DriverRunner.scala b/core/src/main/scala/org/apache/spark/deploy/worker/DriverRunner.scala
index 662d37871e7a..5caaf6bea357 100644
--- a/core/src/main/scala/org/apache/spark/deploy/worker/DriverRunner.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/worker/DriverRunner.scala
@@ -36,6 +36,7 @@ import org.apache.spark.deploy.master.DriverState.DriverState
 
 /**
  * Manages the execution of one driver, including automatically restarting the driver on failure.
+ * This is currently only used in standalone cluster deploy mode.
  */
 private[spark] class DriverRunner(
     val driverId: String,
@@ -81,7 +82,7 @@ private[spark] class DriverRunner(
             driverDesc.command.environment,
             classPath,
             driverDesc.command.libraryPathEntries,
-            driverDesc.command.extraJavaOptions)
+            driverDesc.command.javaOpts)
           val command = CommandUtils.buildCommandSeq(newCommand, driverDesc.mem,
             sparkHome.getAbsolutePath)
           launchDriver(command, driverDesc.command.environment, driverDir, driverDesc.supervise)
diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/ExecutorRunner.scala b/core/src/main/scala/org/apache/spark/deploy/worker/ExecutorRunner.scala
index 467317dd9b44..7be89f9aff0f 100644
--- a/core/src/main/scala/org/apache/spark/deploy/worker/ExecutorRunner.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/worker/ExecutorRunner.scala
@@ -30,6 +30,7 @@ import org.apache.spark.util.logging.FileAppender
 
 /**
  * Manages the execution of one executor process.
+ * This is currently only used in standalone mode.
  */
 private[spark] class ExecutorRunner(
     val appId: String,
@@ -72,7 +73,7 @@ private[spark] class ExecutorRunner(
   }
 
   /**
-   * kill executor process, wait for exit and notify worker to update resource status
+   * Kill executor process, wait for exit and notify worker to update resource status.
    *
    * @param message the exception message which caused the executor's death 
    */
@@ -114,10 +115,13 @@ private[spark] class ExecutorRunner(
   }
 
   def getCommandSeq = {
-    val command = Command(appDesc.command.mainClass,
-      appDesc.command.arguments.map(substituteVariables) ++ Seq(appId), appDesc.command.environment,
-      appDesc.command.classPathEntries, appDesc.command.libraryPathEntries,
-      appDesc.command.extraJavaOptions)
+    val command = Command(
+      appDesc.command.mainClass,
+      appDesc.command.arguments.map(substituteVariables) ++ Seq(appId),
+      appDesc.command.environment,
+      appDesc.command.classPathEntries,
+      appDesc.command.libraryPathEntries,
+      appDesc.command.javaOpts)
     CommandUtils.buildCommandSeq(command, memory, sparkHome.getAbsolutePath)
   }
 
diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/ui/LogPage.scala b/core/src/main/scala/org/apache/spark/deploy/worker/ui/LogPage.scala
index b389cb546de6..ecb358c39981 100644
--- a/core/src/main/scala/org/apache/spark/deploy/worker/ui/LogPage.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/worker/ui/LogPage.scala
@@ -17,7 +17,6 @@
 
 package org.apache.spark.deploy.worker.ui
 
-import java.io.File
 import javax.servlet.http.HttpServletRequest
 
 import scala.xml.Node
@@ -25,7 +24,7 @@ import scala.xml.Node
 import org.apache.spark.ui.{WebUIPage, UIUtils}
 import org.apache.spark.util.Utils
 import org.apache.spark.Logging
-import org.apache.spark.util.logging.{FileAppender, RollingFileAppender}
+import org.apache.spark.util.logging.RollingFileAppender
 
 private[spark] class LogPage(parent: WorkerWebUI) extends WebUIPage("logPage") with Logging {
   private val worker = parent.worker
@@ -64,11 +63,11 @@ private[spark] class LogPage(parent: WorkerWebUI) extends WebUIPage("logPage") w
     val offset = Option(request.getParameter("offset")).map(_.toLong)
     val byteLength = Option(request.getParameter("byteLength")).map(_.toInt).getOrElse(defaultBytes)
 
-    val (logDir, params) = (appId, executorId, driverId) match {
+    val (logDir, params, pageName) = (appId, executorId, driverId) match {
       case (Some(a), Some(e), None) =>
-        (s"${workDir.getPath}/$a/$e/", s"appId=$a&executorId=$e")
+        (s"${workDir.getPath}/$a/$e/", s"appId=$a&executorId=$e", s"$a/$e")
       case (None, None, Some(d)) =>
-        (s"${workDir.getPath}/$d/", s"driverId=$d")
+        (s"${workDir.getPath}/$d/", s"driverId=$d", d)
       case _ =>
         throw new Exception("Request must specify either application or driver identifiers")
     }
@@ -120,7 +119,7 @@ private[spark] class LogPage(parent: WorkerWebUI) extends WebUIPage("logPage") w
           </div>
         </body>
       </html>
-    UIUtils.basicSparkPage(content, logType + " log page for " + appId.getOrElse("unknown app"))
+    UIUtils.basicSparkPage(content, logType + " log page for " + pageName)
   }
 
   /** Get the part of the log files given the offset and desired length of bytes */
diff --git a/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala b/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala
index b455c9fcf4bd..860b47e05645 100644
--- a/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala
+++ b/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala
@@ -98,8 +98,13 @@ private[spark] class CoarseGrainedExecutorBackend(
 }
 
 private[spark] object CoarseGrainedExecutorBackend extends Logging {
-  def run(driverUrl: String, executorId: String, hostname: String, cores: Int,
-    workerUrl: Option[String]) {
+
+  private def run(
+      driverUrl: String,
+      executorId: String,
+      hostname: String,
+      cores: Int,
+      workerUrl: Option[String]) {
 
     SignalLogger.register(log)
 
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala
index bf2dc88e2904..48aaaa54bdb3 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.scheduler.cluster
 
-import org.apache.spark.{Logging, SparkContext}
+import org.apache.spark.{Logging, SparkConf, SparkContext}
 import org.apache.spark.deploy.{ApplicationDescription, Command}
 import org.apache.spark.deploy.client.{AppClient, AppClientListener}
 import org.apache.spark.scheduler.{ExecutorExited, ExecutorLossReason, SlaveLost, TaskSchedulerImpl}
@@ -46,6 +46,7 @@ private[spark] class SparkDeploySchedulerBackend(
       CoarseGrainedSchedulerBackend.ACTOR_NAME)
     val args = Seq(driverUrl, "{{EXECUTOR_ID}}", "{{HOSTNAME}}", "{{CORES}}", "{{WORKER_URL}}")
     val extraJavaOpts = sc.conf.getOption("spark.executor.extraJavaOptions")
+      .map(Utils.splitCommandString).getOrElse(Seq.empty)
     val classPathEntries = sc.conf.getOption("spark.executor.extraClassPath").toSeq.flatMap { cp =>
       cp.split(java.io.File.pathSeparator)
     }
@@ -54,9 +55,11 @@ private[spark] class SparkDeploySchedulerBackend(
         cp.split(java.io.File.pathSeparator)
       }
 
-    val command = Command(
-      "org.apache.spark.executor.CoarseGrainedExecutorBackend", args, sc.executorEnvs,
-      classPathEntries, libraryPathEntries, extraJavaOpts)
+    // Start executors with a few necessary configs for registering with the scheduler
+    val sparkJavaOpts = Utils.sparkJavaOpts(conf, SparkConf.isExecutorStartupConf)
+    val javaOpts = sparkJavaOpts ++ extraJavaOpts
+    val command = Command("org.apache.spark.executor.CoarseGrainedExecutorBackend",
+      args, sc.executorEnvs, classPathEntries, libraryPathEntries, javaOpts)
     val sparkHome = sc.getSparkHome()
     val appDesc = new ApplicationDescription(sc.appName, maxCores, sc.executorMemory, command,
       sparkHome, sc.ui.appUIAddress, sc.eventLogger.map(_.logDir))
diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala
index 8cbb9050f393..69f65b4bdccb 100644
--- a/core/src/main/scala/org/apache/spark/util/Utils.scala
+++ b/core/src/main/scala/org/apache/spark/util/Utils.scala
@@ -1313,4 +1313,13 @@ private[spark] object Utils extends Logging {
     s"$className: $desc\n$st"
   }
 
+  /**
+   * Convert all spark properties set in the given SparkConf to a sequence of java options.
+   */
+  def sparkJavaOpts(conf: SparkConf, filterKey: (String => Boolean) = _ => true): Seq[String] = {
+    conf.getAll
+      .filter { case (k, _) => filterKey(k) }
+      .map { case (k, v) => s"-D$k=$v" }
+  }
+
 }
diff --git a/core/src/test/scala/org/apache/spark/deploy/JsonProtocolSuite.scala b/core/src/test/scala/org/apache/spark/deploy/JsonProtocolSuite.scala
index 01ab2d549325..093394ad6d14 100644
--- a/core/src/test/scala/org/apache/spark/deploy/JsonProtocolSuite.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/JsonProtocolSuite.scala
@@ -88,7 +88,7 @@ class JsonProtocolSuite extends FunSuite {
   }
 
   def createAppDesc(): ApplicationDescription = {
-    val cmd = new Command("mainClass", List("arg1", "arg2"), Map(), Seq(), Seq())
+    val cmd = new Command("mainClass", List("arg1", "arg2"), Map(), Seq(), Seq(), Seq())
     new ApplicationDescription("name", Some(4), 1234, cmd, Some("sparkHome"), "appUiUrl")
   }
 
@@ -101,7 +101,7 @@ class JsonProtocolSuite extends FunSuite {
 
   def createDriverCommand() = new Command(
     "org.apache.spark.FakeClass", Seq("some arg --and-some options -g foo"),
-    Map(("K1", "V1"), ("K2", "V2")), Seq("cp1", "cp2"), Seq("lp1", "lp2"), Some("-Dfoo")
+    Map(("K1", "V1"), ("K2", "V2")), Seq("cp1", "cp2"), Seq("lp1", "lp2"), Seq("-Dfoo")
   )
 
   def createDriverDesc() = new DriverDescription("hdfs://some-dir/some.jar", 100, 3,
@@ -170,7 +170,7 @@ object JsonConstants {
     """
       |{"name":"name","cores":4,"memoryperslave":1234,
       |"user":"%s","sparkhome":"sparkHome",
-      |"command":"Command(mainClass,List(arg1, arg2),Map(),List(),List(),None)"}
+      |"command":"Command(mainClass,List(arg1, arg2),Map(),List(),List(),List())"}
     """.format(System.getProperty("user.name", "<unknown>")).stripMargin
 
   val executorRunnerJsonStr =
diff --git a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala
index f497a5e0a14f..a301cbd48a0c 100644
--- a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala
@@ -200,9 +200,12 @@ class SparkSubmitSuite extends FunSuite with Matchers {
     childArgsStr should include regex ("launch spark://h:p .*thejar.jar org.SomeClass arg1 arg2")
     mainClass should be ("org.apache.spark.deploy.Client")
     classpath should have size (0)
-    sysProps should have size (3)
-    sysProps.keys should contain ("spark.jars")
+    sysProps should have size (5)
     sysProps.keys should contain ("SPARK_SUBMIT")
+    sysProps.keys should contain ("spark.master")
+    sysProps.keys should contain ("spark.app.name")
+    sysProps.keys should contain ("spark.jars")
+    sysProps.keys should contain ("spark.shuffle.spill")
     sysProps("spark.shuffle.spill") should be ("false")
   }
 
diff --git a/core/src/test/scala/org/apache/spark/deploy/worker/DriverRunnerTest.scala b/core/src/test/scala/org/apache/spark/deploy/worker/DriverRunnerTest.scala
index 4633bc3f7f25..c930839b47f1 100644
--- a/core/src/test/scala/org/apache/spark/deploy/worker/DriverRunnerTest.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/worker/DriverRunnerTest.scala
@@ -29,7 +29,7 @@ import org.apache.spark.deploy.{Command, DriverDescription}
 
 class DriverRunnerTest extends FunSuite {
   private def createDriverRunner() = {
-    val command = new Command("mainClass", Seq(), Map(), Seq(), Seq())
+    val command = new Command("mainClass", Seq(), Map(), Seq(), Seq(), Seq())
     val driverDescription = new DriverDescription("jarUrl", 512, 1, true, command)
     new DriverRunner("driverId", new File("workDir"), new File("sparkHome"), driverDescription,
       null, "akka://1.2.3.4/worker/")
diff --git a/core/src/test/scala/org/apache/spark/deploy/worker/ExecutorRunnerTest.scala b/core/src/test/scala/org/apache/spark/deploy/worker/ExecutorRunnerTest.scala
index e5f748d55500..ca4d987619c9 100644
--- a/core/src/test/scala/org/apache/spark/deploy/worker/ExecutorRunnerTest.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/worker/ExecutorRunnerTest.scala
@@ -29,7 +29,7 @@ class ExecutorRunnerTest extends FunSuite {
     def f(s:String) = new File(s)
     val sparkHome = sys.env.get("SPARK_HOME").orElse(sys.props.get("spark.home"))
     val appDesc = new ApplicationDescription("app name", Some(8), 500,
-      Command("foo", Seq(), Map(), Seq(), Seq()),
+      Command("foo", Seq(), Map(), Seq(), Seq(), Seq()),
       sparkHome, "appUiUrl")
     val appId = "12345-worker321-9876"
     val er = new ExecutorRunner(appId, 1, appDesc, 8, 500, null, "blah", "worker321", f(sparkHome.getOrElse(".")),

From 7003c163dbb46bb7313aab130a33486a356435a8 Mon Sep 17 00:00:00 2001
From: Yin Huai <huai@cse.ohio-state.edu>
Date: Wed, 30 Jul 2014 00:15:31 -0700
Subject: [PATCH 809/810] [SPARK-2179][SQL] Public API for DataTypes and Schema

The current PR contains the following changes:
* Expose `DataType`s in the sql package (internal details are private to sql).
* Users can create Rows.
* Introduce `applySchema` to create a `SchemaRDD` by applying a `schema: StructType` to an `RDD[Row]`.
* Add a function `simpleString` to every `DataType`. Also, the schema represented by a `StructType` can be visualized by `printSchema`.
* `ScalaReflection.typeOfObject` provides a way to infer the Catalyst data type based on an object. Also, we can compose `typeOfObject` with some custom logics to form a new function to infer the data type (for different use cases).
* `JsonRDD` has been refactored to use changes introduced by this PR.
* Add a field `containsNull` to `ArrayType`. So, we can explicitly mark if an `ArrayType` can contain null values. The default value of `containsNull` is `false`.

New APIs are introduced in the sql package object and SQLContext. You can find the scaladoc at
[sql package object](http://yhuai.github.io/site/api/scala/index.html#org.apache.spark.sql.package) and [SQLContext](http://yhuai.github.io/site/api/scala/index.html#org.apache.spark.sql.SQLContext).

An example of using `applySchema` is shown below.
```scala
import org.apache.spark.sql._
val sqlContext = new org.apache.spark.sql.SQLContext(sc)

val schema =
  StructType(
    StructField("name", StringType, false) ::
    StructField("age", IntegerType, true) :: Nil)

val people = sc.textFile("examples/src/main/resources/people.txt").map(_.split(",")).map(p => Row(p(0), p(1).trim.toInt))
val peopleSchemaRDD = sqlContext. applySchema(people, schema)
peopleSchemaRDD.printSchema
// root
// |-- name: string (nullable = false)
// |-- age: integer (nullable = true)

peopleSchemaRDD.registerAsTable("people")
sqlContext.sql("select name from people").collect.foreach(println)
```

I will add new contents to the SQL programming guide later.

JIRA: https://issues.apache.org/jira/browse/SPARK-2179

Author: Yin Huai <huai@cse.ohio-state.edu>

Closes #1346 from yhuai/dataTypeAndSchema and squashes the following commits:

1d45977 [Yin Huai] Clean up.
a6e08b4 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema
c712fbf [Yin Huai] Converts types of values based on defined schema.
4ceeb66 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema
e5f8df5 [Yin Huai] Scaladoc.
122d1e7 [Yin Huai] Address comments.
03bfd95 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema
2476ed0 [Yin Huai] Minor updates.
ab71f21 [Yin Huai] Format.
fc2bed1 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema
bd40a33 [Yin Huai] Address comments.
991f860 [Yin Huai] Move "asJavaDataType" and "asScalaDataType" to DataTypeConversions.scala.
1cb35fe [Yin Huai] Add "valueContainsNull" to MapType.
3edb3ae [Yin Huai] Python doc.
692c0b9 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema
1d93395 [Yin Huai] Python APIs.
246da96 [Yin Huai] Add java data type APIs to javadoc index.
1db9531 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema
d48fc7b [Yin Huai] Minor updates.
33c4fec [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema
b9f3071 [Yin Huai] Java API for applySchema.
1c9f33c [Yin Huai] Java APIs for DataTypes and Row.
624765c [Yin Huai] Tests for applySchema.
aa92e84 [Yin Huai] Update data type tests.
8da1a17 [Yin Huai] Add Row.fromSeq.
9c99bc0 [Yin Huai] Several minor updates.
1d9c13a [Yin Huai] Update applySchema API.
85e9b51 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema
e495e4e [Yin Huai] More comments.
42d47a3 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema
c3f4a02 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema
2e58dbd [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema
b8b7db4 [Yin Huai] 1. Move sql package object and package-info to sql-core. 2. Minor updates on APIs. 3. Update scala doc.
68525a2 [Yin Huai] Update JSON unit test.
3209108 [Yin Huai] Add unit tests.
dcaf22f [Yin Huai] Add a field containsNull to ArrayType to indicate if an array can contain null values or not. If an ArrayType is constructed by "ArrayType(elementType)" (the existing constructor), the value of containsNull is false.
9168b83 [Yin Huai] Update comments.
fc649d7 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema
eca7d04 [Yin Huai] Add two apply methods which will be used to extract StructField(s) from a StructType.
949d6bb [Yin Huai] When creating a SchemaRDD for a JSON dataset, users can apply an existing schema.
7a6a7e5 [Yin Huai] Fix bug introduced by the change made on SQLContext.inferSchema.
43a45e1 [Yin Huai] Remove sql.util.package introduced in a previous commit.
0266761 [Yin Huai] Format
03eec4c [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema
90460ac [Yin Huai] Infer the Catalyst data type from an object and cast a data value to the expected type.
3fa0df5 [Yin Huai] Provide easier ways to construct a StructType.
16be3e5 [Yin Huai] This commit contains three changes: * Expose `DataType`s in the sql package (internal details are private to sql). * Introduce `createSchemaRDD` to create a `SchemaRDD` from an `RDD` with a provided schema (represented by a `StructType`) and a provided function to construct `Row`, * Add a function `simpleString` to every `DataType`. Also, the schema represented by a `StructType` can be visualized by `printSchema`.
---
 .../apache/spark/api/python/PythonRDD.scala   |   3 +-
 project/SparkBuild.scala                      |   2 +-
 python/pyspark/sql.py                         | 567 +++++++++++++++++-
 .../spark/sql/catalyst/ScalaReflection.scala  |  20 +
 .../catalyst/expressions/BoundAttribute.scala |   5 +-
 .../spark/sql/catalyst/expressions/Row.scala  |  10 +
 .../catalyst/expressions/WrapDynamic.scala    |  15 +-
 .../catalyst/expressions/complexTypes.scala   |   4 +-
 .../sql/catalyst/expressions/generators.scala |   8 +-
 .../apache/spark/sql/catalyst/package.scala   |   2 +
 .../sql/catalyst/planning/QueryPlanner.scala  |   2 +-
 .../sql/catalyst/planning/patterns.scala      |   3 +-
 .../spark/sql/catalyst/plans/QueryPlan.scala  |  45 +-
 .../plans/logical/basicOperators.scala        |   2 +-
 .../spark/sql/catalyst/rules/Rule.scala       |   2 +-
 .../sql/catalyst/rules/RuleExecutor.scala     |   5 +-
 .../spark/sql/catalyst/trees/package.scala    |   5 +-
 .../spark/sql/catalyst/types/dataTypes.scala  | 268 +++++++--
 .../sql/catalyst/ScalaReflectionSuite.scala   |  66 +-
 .../spark/sql/api/java/types/ArrayType.java   |  68 +++
 .../spark/sql/api/java/types/BinaryType.java} |  19 +-
 .../spark/sql/api/java/types/BooleanType.java |  27 +
 .../spark/sql/api/java/types/ByteType.java    |  27 +
 .../spark/sql/api/java/types/DataType.java    | 190 ++++++
 .../spark/sql/api/java/types/DecimalType.java |  27 +
 .../spark/sql/api/java/types/DoubleType.java  |  27 +
 .../spark/sql/api/java/types/FloatType.java   |  27 +
 .../spark/sql/api/java/types/IntegerType.java |  27 +
 .../spark/sql/api/java/types/LongType.java    |  27 +
 .../spark/sql/api/java/types/MapType.java     |  78 +++
 .../spark/sql/api/java/types/ShortType.java   |  27 +
 .../spark/sql/api/java/types/StringType.java  |  27 +
 .../spark/sql/api/java/types/StructField.java |  76 +++
 .../spark/sql/api/java/types/StructType.java  |  59 ++
 .../sql/api/java/types/TimestampType.java     |  27 +
 .../sql/api/java/types/package-info.java      |  22 +
 .../org/apache/spark/sql/SQLContext.scala     | 230 +++++--
 .../org/apache/spark/sql/SchemaRDD.scala      |  10 +-
 .../org/apache/spark/sql/SchemaRDDLike.scala  |  12 +-
 .../spark/sql/api/java/JavaSQLContext.scala   |  65 +-
 .../spark/sql/api/java/JavaSchemaRDD.scala    |   7 +
 .../org/apache/spark/sql/api/java/Row.scala   |  59 +-
 .../org/apache/spark/sql/json/JsonRDD.scala   | 118 ++--
 .../org/apache/spark/sql/package-info.java    |   0
 .../scala/org/apache/spark/sql/package.scala  | 409 +++++++++++++
 .../spark/sql/parquet/ParquetConverter.scala  |   8 +-
 .../sql/parquet/ParquetTableSupport.scala     |   4 +-
 .../spark/sql/parquet/ParquetTypes.scala      |  18 +-
 .../sql/types/util/DataTypeConversions.scala  | 110 ++++
 .../sql/api/java/JavaApplySchemaSuite.java    | 166 +++++
 .../spark/sql/api/java/JavaRowSuite.java      | 170 ++++++
 .../java/JavaSideDataTypeConversionSuite.java | 150 +++++
 .../org/apache/spark/sql/DataTypeSuite.scala  |  58 ++
 .../scala/org/apache/spark/sql/RowSuite.scala |  46 ++
 .../org/apache/spark/sql/SQLQuerySuite.scala  |  64 +-
 .../scala/org/apache/spark/sql/TestData.scala |   7 +
 .../ScalaSideDataTypeConversionSuite.scala    |  81 +++
 .../org/apache/spark/sql/json/JsonSuite.scala | 198 +++---
 .../apache/spark/sql/hive/HiveContext.scala   |   9 +-
 .../spark/sql/hive/HiveInspectors.scala       |   5 +-
 .../spark/sql/hive/HiveMetastoreCatalog.scala |   8 +-
 61 files changed, 3442 insertions(+), 386 deletions(-)
 create mode 100644 sql/core/src/main/java/org/apache/spark/sql/api/java/types/ArrayType.java
 rename sql/{catalyst/src/main/scala/org/apache/spark/sql/package.scala => core/src/main/java/org/apache/spark/sql/api/java/types/BinaryType.java} (59%)
 create mode 100644 sql/core/src/main/java/org/apache/spark/sql/api/java/types/BooleanType.java
 create mode 100644 sql/core/src/main/java/org/apache/spark/sql/api/java/types/ByteType.java
 create mode 100644 sql/core/src/main/java/org/apache/spark/sql/api/java/types/DataType.java
 create mode 100644 sql/core/src/main/java/org/apache/spark/sql/api/java/types/DecimalType.java
 create mode 100644 sql/core/src/main/java/org/apache/spark/sql/api/java/types/DoubleType.java
 create mode 100644 sql/core/src/main/java/org/apache/spark/sql/api/java/types/FloatType.java
 create mode 100644 sql/core/src/main/java/org/apache/spark/sql/api/java/types/IntegerType.java
 create mode 100644 sql/core/src/main/java/org/apache/spark/sql/api/java/types/LongType.java
 create mode 100644 sql/core/src/main/java/org/apache/spark/sql/api/java/types/MapType.java
 create mode 100644 sql/core/src/main/java/org/apache/spark/sql/api/java/types/ShortType.java
 create mode 100644 sql/core/src/main/java/org/apache/spark/sql/api/java/types/StringType.java
 create mode 100644 sql/core/src/main/java/org/apache/spark/sql/api/java/types/StructField.java
 create mode 100644 sql/core/src/main/java/org/apache/spark/sql/api/java/types/StructType.java
 create mode 100644 sql/core/src/main/java/org/apache/spark/sql/api/java/types/TimestampType.java
 create mode 100644 sql/core/src/main/java/org/apache/spark/sql/api/java/types/package-info.java
 rename sql/{catalyst => core}/src/main/scala/org/apache/spark/sql/package-info.java (100%)
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/package.scala
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/types/util/DataTypeConversions.scala
 create mode 100644 sql/core/src/test/java/org/apache/spark/sql/api/java/JavaApplySchemaSuite.java
 create mode 100644 sql/core/src/test/java/org/apache/spark/sql/api/java/JavaRowSuite.java
 create mode 100644 sql/core/src/test/java/org/apache/spark/sql/api/java/JavaSideDataTypeConversionSuite.java
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/DataTypeSuite.scala
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/RowSuite.scala
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/api/java/ScalaSideDataTypeConversionSuite.scala

diff --git a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
index 0d8453fb184a..f551a59ee3fe 100644
--- a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
+++ b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
@@ -544,7 +544,8 @@ private[spark] object PythonRDD extends Logging {
   }
 
   /**
-   * Convert an RDD of serialized Python dictionaries to Scala Maps
+   * Convert an RDD of serialized Python dictionaries to Scala Maps (no recursive conversions).
+   * It is only used by pyspark.sql.
    * TODO: Support more Python types.
    */
   def pythonToJavaMap(pyRDD: JavaRDD[Array[Byte]]): JavaRDD[Map[String, _]] = {
diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index 490fac3cc364..e2dab0f9f79e 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -312,7 +312,7 @@ object Unidoc {
         "mllib.regression", "mllib.stat", "mllib.tree", "mllib.tree.configuration",
         "mllib.tree.impurity", "mllib.tree.model", "mllib.util"
       ),
-      "-group", "Spark SQL", packageList("sql.api.java", "sql.hive.api.java"),
+      "-group", "Spark SQL", packageList("sql.api.java", "sql.api.java.types", "sql.hive.api.java"),
       "-noqualifier", "java.lang"
     )
   )
diff --git a/python/pyspark/sql.py b/python/pyspark/sql.py
index a6b3277db326..13f0ed4e3549 100644
--- a/python/pyspark/sql.py
+++ b/python/pyspark/sql.py
@@ -20,7 +20,451 @@
 
 from py4j.protocol import Py4JError
 
-__all__ = ["SQLContext", "HiveContext", "LocalHiveContext", "TestHiveContext", "SchemaRDD", "Row"]
+__all__ = [
+    "StringType", "BinaryType", "BooleanType", "TimestampType", "DecimalType",
+    "DoubleType", "FloatType", "ByteType", "IntegerType", "LongType",
+    "ShortType", "ArrayType", "MapType", "StructField", "StructType",
+    "SQLContext", "HiveContext", "LocalHiveContext", "TestHiveContext", "SchemaRDD", "Row"]
+
+
+class PrimitiveTypeSingleton(type):
+    _instances = {}
+
+    def __call__(cls):
+        if cls not in cls._instances:
+            cls._instances[cls] = super(PrimitiveTypeSingleton, cls).__call__()
+        return cls._instances[cls]
+
+
+class StringType(object):
+    """Spark SQL StringType
+
+    The data type representing string values.
+
+    """
+    __metaclass__ = PrimitiveTypeSingleton
+
+    def __repr__(self):
+        return "StringType"
+
+
+class BinaryType(object):
+    """Spark SQL BinaryType
+
+    The data type representing bytearray values.
+
+    """
+    __metaclass__ = PrimitiveTypeSingleton
+
+    def __repr__(self):
+        return "BinaryType"
+
+
+class BooleanType(object):
+    """Spark SQL BooleanType
+
+    The data type representing bool values.
+
+    """
+    __metaclass__ = PrimitiveTypeSingleton
+
+    def __repr__(self):
+        return "BooleanType"
+
+
+class TimestampType(object):
+    """Spark SQL TimestampType
+
+    The data type representing datetime.datetime values.
+
+    """
+    __metaclass__ = PrimitiveTypeSingleton
+
+    def __repr__(self):
+        return "TimestampType"
+
+
+class DecimalType(object):
+    """Spark SQL DecimalType
+
+    The data type representing decimal.Decimal values.
+
+    """
+    __metaclass__ = PrimitiveTypeSingleton
+
+    def __repr__(self):
+        return "DecimalType"
+
+
+class DoubleType(object):
+    """Spark SQL DoubleType
+
+    The data type representing float values.
+
+    """
+    __metaclass__ = PrimitiveTypeSingleton
+
+    def __repr__(self):
+        return "DoubleType"
+
+
+class FloatType(object):
+    """Spark SQL FloatType
+
+    The data type representing single precision floating-point values.
+
+    """
+    __metaclass__ = PrimitiveTypeSingleton
+
+    def __repr__(self):
+        return "FloatType"
+
+
+class ByteType(object):
+    """Spark SQL ByteType
+
+    The data type representing int values with 1 singed byte.
+
+    """
+    __metaclass__ = PrimitiveTypeSingleton
+
+    def __repr__(self):
+        return "ByteType"
+
+
+class IntegerType(object):
+    """Spark SQL IntegerType
+
+    The data type representing int values.
+
+    """
+    __metaclass__ = PrimitiveTypeSingleton
+
+    def __repr__(self):
+        return "IntegerType"
+
+
+class LongType(object):
+    """Spark SQL LongType
+
+    The data type representing long values. If the any value is beyond the range of
+    [-9223372036854775808, 9223372036854775807], please use DecimalType.
+
+    """
+    __metaclass__ = PrimitiveTypeSingleton
+
+    def __repr__(self):
+        return "LongType"
+
+
+class ShortType(object):
+    """Spark SQL ShortType
+
+    The data type representing int values with 2 signed bytes.
+
+    """
+    __metaclass__ = PrimitiveTypeSingleton
+
+    def __repr__(self):
+        return "ShortType"
+
+
+class ArrayType(object):
+    """Spark SQL ArrayType
+
+    The data type representing list values.
+    An ArrayType object comprises two fields, elementType (a DataType) and containsNull (a bool).
+    The field of elementType is used to specify the type of array elements.
+    The field of containsNull is used to specify if the array has None values.
+
+    """
+    def __init__(self, elementType, containsNull=False):
+        """Creates an ArrayType
+
+        :param elementType: the data type of elements.
+        :param containsNull: indicates whether the list contains None values.
+
+        >>> ArrayType(StringType) == ArrayType(StringType, False)
+        True
+        >>> ArrayType(StringType, True) == ArrayType(StringType)
+        False
+        """
+        self.elementType = elementType
+        self.containsNull = containsNull
+
+    def __repr__(self):
+        return "ArrayType(" + self.elementType.__repr__() + "," + \
+               str(self.containsNull).lower() + ")"
+
+    def __eq__(self, other):
+        return (isinstance(other, self.__class__) and
+                self.elementType == other.elementType and
+                self.containsNull == other.containsNull)
+
+    def __ne__(self, other):
+        return not self.__eq__(other)
+
+
+class MapType(object):
+    """Spark SQL MapType
+
+    The data type representing dict values.
+    A MapType object comprises three fields,
+    keyType (a DataType), valueType (a DataType) and valueContainsNull (a bool).
+    The field of keyType is used to specify the type of keys in the map.
+    The field of valueType is used to specify the type of values in the map.
+    The field of valueContainsNull is used to specify if values of this map has None values.
+    For values of a MapType column, keys are not allowed to have None values.
+
+    """
+    def __init__(self, keyType, valueType, valueContainsNull=True):
+        """Creates a MapType
+        :param keyType: the data type of keys.
+        :param valueType: the data type of values.
+        :param valueContainsNull: indicates whether values contains null values.
+
+        >>> MapType(StringType, IntegerType) == MapType(StringType, IntegerType, True)
+        True
+        >>> MapType(StringType, IntegerType, False) == MapType(StringType, FloatType)
+        False
+        """
+        self.keyType = keyType
+        self.valueType = valueType
+        self.valueContainsNull = valueContainsNull
+
+    def __repr__(self):
+        return "MapType(" + self.keyType.__repr__() + "," + \
+               self.valueType.__repr__() + "," + \
+               str(self.valueContainsNull).lower() + ")"
+
+    def __eq__(self, other):
+        return (isinstance(other, self.__class__) and
+                self.keyType == other.keyType and
+                self.valueType == other.valueType and
+                self.valueContainsNull == other.valueContainsNull)
+
+    def __ne__(self, other):
+        return not self.__eq__(other)
+
+
+class StructField(object):
+    """Spark SQL StructField
+
+    Represents a field in a StructType.
+    A StructField object comprises three fields, name (a string), dataType (a DataType),
+    and nullable (a bool). The field of name is the name of a StructField. The field of
+    dataType specifies the data type of a StructField.
+    The field of nullable specifies if values of a StructField can contain None values.
+
+    """
+    def __init__(self, name, dataType, nullable):
+        """Creates a StructField
+        :param name: the name of this field.
+        :param dataType: the data type of this field.
+        :param nullable: indicates whether values of this field can be null.
+
+        >>> StructField("f1", StringType, True) == StructField("f1", StringType, True)
+        True
+        >>> StructField("f1", StringType, True) == StructField("f2", StringType, True)
+        False
+        """
+        self.name = name
+        self.dataType = dataType
+        self.nullable = nullable
+
+    def __repr__(self):
+        return "StructField(" + self.name + "," + \
+               self.dataType.__repr__() + "," + \
+               str(self.nullable).lower() + ")"
+
+    def __eq__(self, other):
+        return (isinstance(other, self.__class__) and
+                self.name == other.name and
+                self.dataType == other.dataType and
+                self.nullable == other.nullable)
+
+    def __ne__(self, other):
+        return not self.__eq__(other)
+
+
+class StructType(object):
+    """Spark SQL StructType
+
+    The data type representing namedtuple values.
+    A StructType object comprises a list of L{StructField}s.
+
+    """
+    def __init__(self, fields):
+        """Creates a StructType
+
+        >>> struct1 = StructType([StructField("f1", StringType, True)])
+        >>> struct2 = StructType([StructField("f1", StringType, True)])
+        >>> struct1 == struct2
+        True
+        >>> struct1 = StructType([StructField("f1", StringType, True)])
+        >>> struct2 = StructType([StructField("f1", StringType, True),
+        ...   [StructField("f2", IntegerType, False)]])
+        >>> struct1 == struct2
+        False
+        """
+        self.fields = fields
+
+    def __repr__(self):
+        return "StructType(List(" + \
+               ",".join([field.__repr__() for field in self.fields]) + "))"
+
+    def __eq__(self, other):
+        return (isinstance(other, self.__class__) and
+                self.fields == other.fields)
+
+    def __ne__(self, other):
+        return not self.__eq__(other)
+
+
+def _parse_datatype_list(datatype_list_string):
+    """Parses a list of comma separated data types."""
+    index = 0
+    datatype_list = []
+    start = 0
+    depth = 0
+    while index < len(datatype_list_string):
+        if depth == 0 and datatype_list_string[index] == ",":
+            datatype_string = datatype_list_string[start:index].strip()
+            datatype_list.append(_parse_datatype_string(datatype_string))
+            start = index + 1
+        elif datatype_list_string[index] == "(":
+            depth += 1
+        elif datatype_list_string[index] == ")":
+            depth -= 1
+
+        index += 1
+
+    # Handle the last data type
+    datatype_string = datatype_list_string[start:index].strip()
+    datatype_list.append(_parse_datatype_string(datatype_string))
+    return datatype_list
+
+
+def _parse_datatype_string(datatype_string):
+    """Parses the given data type string.
+
+    >>> def check_datatype(datatype):
+    ...     scala_datatype = sqlCtx._ssql_ctx.parseDataType(datatype.__repr__())
+    ...     python_datatype = _parse_datatype_string(scala_datatype.toString())
+    ...     return datatype == python_datatype
+    >>> check_datatype(StringType())
+    True
+    >>> check_datatype(BinaryType())
+    True
+    >>> check_datatype(BooleanType())
+    True
+    >>> check_datatype(TimestampType())
+    True
+    >>> check_datatype(DecimalType())
+    True
+    >>> check_datatype(DoubleType())
+    True
+    >>> check_datatype(FloatType())
+    True
+    >>> check_datatype(ByteType())
+    True
+    >>> check_datatype(IntegerType())
+    True
+    >>> check_datatype(LongType())
+    True
+    >>> check_datatype(ShortType())
+    True
+    >>> # Simple ArrayType.
+    >>> simple_arraytype = ArrayType(StringType(), True)
+    >>> check_datatype(simple_arraytype)
+    True
+    >>> # Simple MapType.
+    >>> simple_maptype = MapType(StringType(), LongType())
+    >>> check_datatype(simple_maptype)
+    True
+    >>> # Simple StructType.
+    >>> simple_structtype = StructType([
+    ...     StructField("a", DecimalType(), False),
+    ...     StructField("b", BooleanType(), True),
+    ...     StructField("c", LongType(), True),
+    ...     StructField("d", BinaryType(), False)])
+    >>> check_datatype(simple_structtype)
+    True
+    >>> # Complex StructType.
+    >>> complex_structtype = StructType([
+    ...     StructField("simpleArray", simple_arraytype, True),
+    ...     StructField("simpleMap", simple_maptype, True),
+    ...     StructField("simpleStruct", simple_structtype, True),
+    ...     StructField("boolean", BooleanType(), False)])
+    >>> check_datatype(complex_structtype)
+    True
+    >>> # Complex ArrayType.
+    >>> complex_arraytype = ArrayType(complex_structtype, True)
+    >>> check_datatype(complex_arraytype)
+    True
+    >>> # Complex MapType.
+    >>> complex_maptype = MapType(complex_structtype, complex_arraytype, False)
+    >>> check_datatype(complex_maptype)
+    True
+    """
+    left_bracket_index = datatype_string.find("(")
+    if left_bracket_index == -1:
+        # It is a primitive type.
+        left_bracket_index = len(datatype_string)
+    type_or_field = datatype_string[:left_bracket_index]
+    rest_part = datatype_string[left_bracket_index+1:len(datatype_string)-1].strip()
+    if type_or_field == "StringType":
+        return StringType()
+    elif type_or_field == "BinaryType":
+        return BinaryType()
+    elif type_or_field == "BooleanType":
+        return BooleanType()
+    elif type_or_field == "TimestampType":
+        return TimestampType()
+    elif type_or_field == "DecimalType":
+        return DecimalType()
+    elif type_or_field == "DoubleType":
+        return DoubleType()
+    elif type_or_field == "FloatType":
+        return FloatType()
+    elif type_or_field == "ByteType":
+        return ByteType()
+    elif type_or_field == "IntegerType":
+        return IntegerType()
+    elif type_or_field == "LongType":
+        return LongType()
+    elif type_or_field == "ShortType":
+        return ShortType()
+    elif type_or_field == "ArrayType":
+        last_comma_index = rest_part.rfind(",")
+        containsNull = True
+        if rest_part[last_comma_index+1:].strip().lower() == "false":
+            containsNull = False
+        elementType = _parse_datatype_string(rest_part[:last_comma_index].strip())
+        return ArrayType(elementType, containsNull)
+    elif type_or_field == "MapType":
+        last_comma_index = rest_part.rfind(",")
+        valueContainsNull = True
+        if rest_part[last_comma_index+1:].strip().lower() == "false":
+            valueContainsNull = False
+        keyType, valueType = _parse_datatype_list(rest_part[:last_comma_index].strip())
+        return MapType(keyType, valueType, valueContainsNull)
+    elif type_or_field == "StructField":
+        first_comma_index = rest_part.find(",")
+        name = rest_part[:first_comma_index].strip()
+        last_comma_index = rest_part.rfind(",")
+        nullable = True
+        if rest_part[last_comma_index+1:].strip().lower() == "false":
+            nullable = False
+        dataType = _parse_datatype_string(
+            rest_part[first_comma_index+1:last_comma_index].strip())
+        return StructField(name, dataType, nullable)
+    elif type_or_field == "StructType":
+        # rest_part should be in the format like
+        # List(StructField(field1,IntegerType,false)).
+        field_list_string = rest_part[rest_part.find("(")+1:-1]
+        fields = _parse_datatype_list(field_list_string)
+        return StructType(fields)
 
 
 class SQLContext:
@@ -109,6 +553,40 @@ def inferSchema(self, rdd):
         srdd = self._ssql_ctx.inferSchema(jrdd.rdd())
         return SchemaRDD(srdd, self)
 
+    def applySchema(self, rdd, schema):
+        """Applies the given schema to the given RDD of L{dict}s.
+
+        >>> schema = StructType([StructField("field1", IntegerType(), False),
+        ...     StructField("field2", StringType(), False)])
+        >>> srdd = sqlCtx.applySchema(rdd, schema)
+        >>> sqlCtx.registerRDDAsTable(srdd, "table1")
+        >>> srdd2 = sqlCtx.sql("SELECT * from table1")
+        >>> srdd2.collect() == [{"field1" : 1, "field2" : "row1"}, {"field1" : 2, "field2": "row2"},
+        ...                    {"field1" : 3, "field2": "row3"}]
+        True
+        >>> from datetime import datetime
+        >>> rdd = sc.parallelize([{"byte": 127, "short": -32768, "float": 1.0,
+        ... "time": datetime(2010, 1, 1, 1, 1, 1), "map": {"a": 1}, "struct": {"b": 2},
+        ... "list": [1, 2, 3]}])
+        >>> schema = StructType([
+        ...     StructField("byte", ByteType(), False),
+        ...     StructField("short", ShortType(), False),
+        ...     StructField("float", FloatType(), False),
+        ...     StructField("time", TimestampType(), False),
+        ...     StructField("map", MapType(StringType(), IntegerType(), False), False),
+        ...     StructField("struct", StructType([StructField("b", ShortType(), False)]), False),
+        ...     StructField("list", ArrayType(ByteType(), False), False),
+        ...     StructField("null", DoubleType(), True)])
+        >>> srdd = sqlCtx.applySchema(rdd, schema).map(
+        ...     lambda x: (
+        ...         x.byte, x.short, x.float, x.time, x.map["a"], x.struct["b"], x.list, x.null))
+        >>> srdd.collect()[0]
+        (127, -32768, 1.0, datetime.datetime(2010, 1, 1, 1, 1, 1), 1, 2, [1, 2, 3], None)
+        """
+        jrdd = self._pythonToJavaMap(rdd._jrdd)
+        srdd = self._ssql_ctx.applySchemaToPythonRDD(jrdd.rdd(), schema.__repr__())
+        return SchemaRDD(srdd, self)
+
     def registerRDDAsTable(self, rdd, tableName):
         """Registers the given RDD as a temporary table in the catalog.
 
@@ -139,10 +617,11 @@ def parquetFile(self, path):
         jschema_rdd = self._ssql_ctx.parquetFile(path)
         return SchemaRDD(jschema_rdd, self)
 
-    def jsonFile(self, path):
-        """Loads a text file storing one JSON object per line,
-           returning the result as a L{SchemaRDD}.
-           It goes through the entire dataset once to determine the schema.
+    def jsonFile(self, path, schema=None):
+        """Loads a text file storing one JSON object per line as a L{SchemaRDD}.
+
+        If the schema is provided, applies the given schema to this JSON dataset.
+        Otherwise, it goes through the entire dataset once to determine the schema.
 
         >>> import tempfile, shutil
         >>> jsonFile = tempfile.mkdtemp()
@@ -151,8 +630,8 @@ def jsonFile(self, path):
         >>> for json in jsonStrings:
         ...   print>>ofn, json
         >>> ofn.close()
-        >>> srdd = sqlCtx.jsonFile(jsonFile)
-        >>> sqlCtx.registerRDDAsTable(srdd, "table1")
+        >>> srdd1 = sqlCtx.jsonFile(jsonFile)
+        >>> sqlCtx.registerRDDAsTable(srdd1, "table1")
         >>> srdd2 = sqlCtx.sql(
         ...   "SELECT field1 AS f1, field2 as f2, field3 as f3, field6 as f4 from table1")
         >>> srdd2.collect() == [
@@ -160,16 +639,45 @@ def jsonFile(self, path):
         ... {"f1":2, "f2":None, "f3":{"field4":22,  "field5": [10, 11]}, "f4":[{"field7": "row2"}]},
         ... {"f1":None, "f2":"row3", "f3":{"field4":33, "field5": []}, "f4":None}]
         True
+        >>> srdd3 = sqlCtx.jsonFile(jsonFile, srdd1.schema())
+        >>> sqlCtx.registerRDDAsTable(srdd3, "table2")
+        >>> srdd4 = sqlCtx.sql(
+        ...   "SELECT field1 AS f1, field2 as f2, field3 as f3, field6 as f4 from table2")
+        >>> srdd4.collect() == [
+        ... {"f1":1, "f2":"row1", "f3":{"field4":11, "field5": None}, "f4":None},
+        ... {"f1":2, "f2":None, "f3":{"field4":22,  "field5": [10, 11]}, "f4":[{"field7": "row2"}]},
+        ... {"f1":None, "f2":"row3", "f3":{"field4":33, "field5": []}, "f4":None}]
+        True
+        >>> schema = StructType([
+        ...     StructField("field2", StringType(), True),
+        ...     StructField("field3",
+        ...         StructType([
+        ...             StructField("field5", ArrayType(IntegerType(), False), True)]), False)])
+        >>> srdd5 = sqlCtx.jsonFile(jsonFile, schema)
+        >>> sqlCtx.registerRDDAsTable(srdd5, "table3")
+        >>> srdd6 = sqlCtx.sql(
+        ...   "SELECT field2 AS f1, field3.field5 as f2, field3.field5[0] as f3 from table3")
+        >>> srdd6.collect() == [
+        ... {"f1": "row1", "f2": None, "f3": None},
+        ... {"f1": None, "f2": [10, 11], "f3": 10},
+        ... {"f1": "row3", "f2": [], "f3": None}]
+        True
         """
-        jschema_rdd = self._ssql_ctx.jsonFile(path)
+        if schema is None:
+            jschema_rdd = self._ssql_ctx.jsonFile(path)
+        else:
+            scala_datatype = self._ssql_ctx.parseDataType(schema.__repr__())
+            jschema_rdd = self._ssql_ctx.jsonFile(path, scala_datatype)
         return SchemaRDD(jschema_rdd, self)
 
-    def jsonRDD(self, rdd):
-        """Loads an RDD storing one JSON object per string, returning the result as a L{SchemaRDD}.
-           It goes through the entire dataset once to determine the schema.
+    def jsonRDD(self, rdd, schema=None):
+        """Loads an RDD storing one JSON object per string as a L{SchemaRDD}.
 
-        >>> srdd = sqlCtx.jsonRDD(json)
-        >>> sqlCtx.registerRDDAsTable(srdd, "table1")
+        If the schema is provided, applies the given schema to this JSON dataset.
+        Otherwise, it goes through the entire dataset once to determine the schema.
+
+        >>> srdd1 = sqlCtx.jsonRDD(json)
+        >>> sqlCtx.registerRDDAsTable(srdd1, "table1")
         >>> srdd2 = sqlCtx.sql(
         ...   "SELECT field1 AS f1, field2 as f2, field3 as f3, field6 as f4 from table1")
         >>> srdd2.collect() == [
@@ -177,6 +685,29 @@ def jsonRDD(self, rdd):
         ... {"f1":2, "f2":None, "f3":{"field4":22,  "field5": [10, 11]}, "f4":[{"field7": "row2"}]},
         ... {"f1":None, "f2":"row3", "f3":{"field4":33, "field5": []}, "f4":None}]
         True
+        >>> srdd3 = sqlCtx.jsonRDD(json, srdd1.schema())
+        >>> sqlCtx.registerRDDAsTable(srdd3, "table2")
+        >>> srdd4 = sqlCtx.sql(
+        ...   "SELECT field1 AS f1, field2 as f2, field3 as f3, field6 as f4 from table2")
+        >>> srdd4.collect() == [
+        ... {"f1":1, "f2":"row1", "f3":{"field4":11, "field5": None}, "f4":None},
+        ... {"f1":2, "f2":None, "f3":{"field4":22,  "field5": [10, 11]}, "f4":[{"field7": "row2"}]},
+        ... {"f1":None, "f2":"row3", "f3":{"field4":33, "field5": []}, "f4":None}]
+        True
+        >>> schema = StructType([
+        ...     StructField("field2", StringType(), True),
+        ...     StructField("field3",
+        ...         StructType([
+        ...             StructField("field5", ArrayType(IntegerType(), False), True)]), False)])
+        >>> srdd5 = sqlCtx.jsonRDD(json, schema)
+        >>> sqlCtx.registerRDDAsTable(srdd5, "table3")
+        >>> srdd6 = sqlCtx.sql(
+        ...   "SELECT field2 AS f1, field3.field5 as f2, field3.field5[0] as f3 from table3")
+        >>> srdd6.collect() == [
+        ... {"f1": "row1", "f2": None, "f3": None},
+        ... {"f1": None, "f2": [10, 11], "f3": 10},
+        ... {"f1": "row3", "f2": [], "f3": None}]
+        True
         """
         def func(split, iterator):
             for x in iterator:
@@ -186,7 +717,11 @@ def func(split, iterator):
         keyed = PipelinedRDD(rdd, func)
         keyed._bypass_serializer = True
         jrdd = keyed._jrdd.map(self._jvm.BytesToString())
-        jschema_rdd = self._ssql_ctx.jsonRDD(jrdd.rdd())
+        if schema is None:
+            jschema_rdd = self._ssql_ctx.jsonRDD(jrdd.rdd())
+        else:
+            scala_datatype = self._ssql_ctx.parseDataType(schema.__repr__())
+            jschema_rdd = self._ssql_ctx.jsonRDD(jrdd.rdd(), scala_datatype)
         return SchemaRDD(jschema_rdd, self)
 
     def sql(self, sqlQuery):
@@ -389,6 +924,10 @@ def saveAsTable(self, tableName):
         """Creates a new table with the contents of this SchemaRDD."""
         self._jschema_rdd.saveAsTable(tableName)
 
+    def schema(self):
+        """Returns the schema of this SchemaRDD (represented by a L{StructType})."""
+        return _parse_datatype_string(self._jschema_rdd.schema().toString())
+
     def schemaString(self):
         """Returns the output schema in the tree format."""
         return self._jschema_rdd.schemaString()
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala
index 5a55be1e5155..0d26b52a8469 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala
@@ -85,6 +85,26 @@ object ScalaReflection {
     case t if t <:< definitions.BooleanTpe => Schema(BooleanType, nullable = false)
   }
 
+  def typeOfObject: PartialFunction[Any, DataType] = {
+    // The data type can be determined without ambiguity.
+    case obj: BooleanType.JvmType => BooleanType
+    case obj: BinaryType.JvmType => BinaryType
+    case obj: StringType.JvmType => StringType
+    case obj: ByteType.JvmType => ByteType
+    case obj: ShortType.JvmType => ShortType
+    case obj: IntegerType.JvmType => IntegerType
+    case obj: LongType.JvmType => LongType
+    case obj: FloatType.JvmType => FloatType
+    case obj: DoubleType.JvmType => DoubleType
+    case obj: DecimalType.JvmType => DecimalType
+    case obj: TimestampType.JvmType => TimestampType
+    case null => NullType
+    // For other cases, there is no obvious mapping from the type of the given object to a
+    // Catalyst data type. A user should provide his/her specific rules
+    // (in a user-defined PartialFunction) to infer the Catalyst data type for other types of
+    // objects and then compose the user-defined PartialFunction with this one.
+  }
+
   implicit class CaseClassRelation[A <: Product : TypeTag](data: Seq[A]) {
 
     /**
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala
index a3ebec8082cb..f38f99569f20 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala
@@ -17,14 +17,11 @@
 
 package org.apache.spark.sql.catalyst.expressions
 
+import org.apache.spark.sql.catalyst.Logging
 import org.apache.spark.sql.catalyst.errors.attachTree
-import org.apache.spark.sql.catalyst.plans.QueryPlan
-import org.apache.spark.sql.catalyst.rules.Rule
 import org.apache.spark.sql.catalyst.types._
 import org.apache.spark.sql.catalyst.trees
 
-import org.apache.spark.sql.Logging
-
 /**
  * A bound reference points to a specific slot in the input tuple, allowing the actual value
  * to be retrieved more efficiently.  However, since operations like column pruning can change
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Row.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Row.scala
index 7470cb861b83..c9a63e201ef6 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Row.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Row.scala
@@ -32,6 +32,16 @@ object Row {
    * }}}
    */
   def unapplySeq(row: Row): Some[Seq[Any]] = Some(row)
+
+  /**
+   * This method can be used to construct a [[Row]] with the given values.
+   */
+  def apply(values: Any*): Row = new GenericRow(values.toArray)
+
+  /**
+   * This method can be used to construct a [[Row]] from a [[Seq]] of values.
+   */
+  def fromSeq(values: Seq[Any]): Row = new GenericRow(values.toArray)
 }
 
 /**
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/WrapDynamic.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/WrapDynamic.scala
index e787c59e7572..eb8898900d6a 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/WrapDynamic.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/WrapDynamic.scala
@@ -21,8 +21,16 @@ import scala.language.dynamics
 
 import org.apache.spark.sql.catalyst.types.DataType
 
-case object DynamicType extends DataType
+/**
+ * The data type representing [[DynamicRow]] values.
+ */
+case object DynamicType extends DataType {
+  def simpleString: String = "dynamic"
+}
 
+/**
+ * Wrap a [[Row]] as a [[DynamicRow]].
+ */
 case class WrapDynamic(children: Seq[Attribute]) extends Expression {
   type EvaluatedType = DynamicRow
 
@@ -37,6 +45,11 @@ case class WrapDynamic(children: Seq[Attribute]) extends Expression {
   }
 }
 
+/**
+ * DynamicRows use scala's Dynamic trait to emulate an ORM of in a dynamically typed language.
+ * Since the type of the column is not known at compile time, all attributes are converted to
+ * strings before being passed to the function.
+ */
 class DynamicRow(val schema: Seq[Attribute], values: Array[Any])
   extends GenericRow(values) with Dynamic {
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypes.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypes.scala
index 0acb29012f31..72add5e20e8b 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypes.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypes.scala
@@ -31,8 +31,8 @@ case class GetItem(child: Expression, ordinal: Expression) extends Expression {
   override def foldable = child.foldable && ordinal.foldable
   override def references = children.flatMap(_.references).toSet
   def dataType = child.dataType match {
-    case ArrayType(dt) => dt
-    case MapType(_, vt) => vt
+    case ArrayType(dt, _) => dt
+    case MapType(_, vt, _) => vt
   }
   override lazy val resolved =
     childrenResolved &&
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala
index dd78614754e1..422839dab770 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala
@@ -84,8 +84,8 @@ case class Explode(attributeNames: Seq[String], child: Expression)
     (child.dataType.isInstanceOf[ArrayType] || child.dataType.isInstanceOf[MapType])
 
   private lazy val elementTypes = child.dataType match {
-    case ArrayType(et) => et :: Nil
-    case MapType(kt,vt) => kt :: vt :: Nil
+    case ArrayType(et, _) => et :: Nil
+    case MapType(kt,vt, _) => kt :: vt :: Nil
   }
 
   // TODO: Move this pattern into Generator.
@@ -102,10 +102,10 @@ case class Explode(attributeNames: Seq[String], child: Expression)
 
   override def eval(input: Row): TraversableOnce[Row] = {
     child.dataType match {
-      case ArrayType(_) =>
+      case ArrayType(_, _) =>
         val inputArray = child.eval(input).asInstanceOf[Seq[Any]]
         if (inputArray == null) Nil else inputArray.map(v => new GenericRow(Array(v)))
-      case MapType(_, _) =>
+      case MapType(_, _, _) =>
         val inputMap = child.eval(input).asInstanceOf[Map[Any,Any]]
         if (inputMap == null) Nil else inputMap.map { case (k,v) => new GenericRow(Array(k,v)) }
     }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/package.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/package.scala
index 3b3e206055cf..ca9642954eb2 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/package.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/package.scala
@@ -24,4 +24,6 @@ package object catalyst {
    * 2.10.* builds.  See SI-6240 for more details.
    */
   protected[catalyst] object ScalaReflectionLock
+
+  protected[catalyst] type Logging = com.typesafe.scalalogging.slf4j.Logging
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/QueryPlanner.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/QueryPlanner.scala
index 67833664b35a..781ba489b44c 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/QueryPlanner.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/QueryPlanner.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.sql.catalyst.planning
 
-import org.apache.spark.sql.Logging
+import org.apache.spark.sql.catalyst.Logging
 import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
 import org.apache.spark.sql.catalyst.trees.TreeNode
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala
index 418f8686bfe5..bc763a4e06e6 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala
@@ -19,9 +19,8 @@ package org.apache.spark.sql.catalyst.planning
 
 import scala.annotation.tailrec
 
-import org.apache.spark.sql.Logging
-
 import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.Logging
 import org.apache.spark.sql.catalyst.plans._
 import org.apache.spark.sql.catalyst.plans.logical._
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/QueryPlan.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/QueryPlan.scala
index 7b82e19b2e71..0988b0c6d990 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/QueryPlan.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/QueryPlan.scala
@@ -125,51 +125,10 @@ abstract class QueryPlan[PlanType <: TreeNode[PlanType]] extends TreeNode[PlanTy
     }.toSeq
   }
 
-  protected def generateSchemaString(schema: Seq[Attribute]): String = {
-    val builder = new StringBuilder
-    builder.append("root\n")
-    val prefix = " |"
-    schema.foreach { attribute =>
-      val name = attribute.name
-      val dataType = attribute.dataType
-      dataType match {
-        case fields: StructType =>
-          builder.append(s"$prefix-- $name: $StructType\n")
-          generateSchemaString(fields, s"$prefix    |", builder)
-        case ArrayType(fields: StructType) =>
-          builder.append(s"$prefix-- $name: $ArrayType[$StructType]\n")
-          generateSchemaString(fields, s"$prefix    |", builder)
-        case ArrayType(elementType: DataType) =>
-          builder.append(s"$prefix-- $name: $ArrayType[$elementType]\n")
-        case _ => builder.append(s"$prefix-- $name: $dataType\n")
-      }
-    }
-
-    builder.toString()
-  }
-
-  protected def generateSchemaString(
-      schema: StructType,
-      prefix: String,
-      builder: StringBuilder): StringBuilder = {
-    schema.fields.foreach {
-      case StructField(name, fields: StructType, _) =>
-        builder.append(s"$prefix-- $name: $StructType\n")
-        generateSchemaString(fields, s"$prefix    |", builder)
-      case StructField(name, ArrayType(fields: StructType), _) =>
-        builder.append(s"$prefix-- $name: $ArrayType[$StructType]\n")
-        generateSchemaString(fields, s"$prefix    |", builder)
-      case StructField(name, ArrayType(elementType: DataType), _) =>
-        builder.append(s"$prefix-- $name: $ArrayType[$elementType]\n")
-      case StructField(name, fieldType: DataType, _) =>
-        builder.append(s"$prefix-- $name: $fieldType\n")
-    }
-
-    builder
-  }
+  def schema: StructType = StructType.fromAttributes(output)
 
   /** Returns the output schema in the tree format. */
-  def schemaString: String = generateSchemaString(output)
+  def schemaString: String = schema.treeString
 
   /** Prints out the schema in the tree format */
   def printSchema(): Unit = println(schemaString)
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
index 1537de259c5b..3cb407217c4c 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
@@ -177,7 +177,7 @@ case class LowerCaseSchema(child: LogicalPlan) extends UnaryNode {
     case StructType(fields) =>
       StructType(fields.map(f =>
         StructField(f.name.toLowerCase(), lowerCaseSchema(f.dataType), f.nullable)))
-    case ArrayType(elemType) => ArrayType(lowerCaseSchema(elemType))
+    case ArrayType(elemType, containsNull) => ArrayType(lowerCaseSchema(elemType), containsNull)
     case otherType => otherType
   }
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/rules/Rule.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/rules/Rule.scala
index 1076537bc760..f8960b3fe7a1 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/rules/Rule.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/rules/Rule.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.sql.catalyst.rules
 
-import org.apache.spark.sql.Logging
+import org.apache.spark.sql.catalyst.Logging
 import org.apache.spark.sql.catalyst.trees.TreeNode
 
 abstract class Rule[TreeType <: TreeNode[_]] extends Logging {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/rules/RuleExecutor.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/rules/RuleExecutor.scala
index e300bdbececb..6aa407c836ae 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/rules/RuleExecutor.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/rules/RuleExecutor.scala
@@ -15,10 +15,9 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql
-package catalyst
-package rules
+package org.apache.spark.sql.catalyst.rules
 
+import org.apache.spark.sql.catalyst.Logging
 import org.apache.spark.sql.catalyst.trees.TreeNode
 import org.apache.spark.sql.catalyst.util.sideBySide
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/package.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/package.scala
index d159ecdd5d78..9a28d035a10a 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/package.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/package.scala
@@ -17,8 +17,6 @@
 
 package org.apache.spark.sql.catalyst
 
-import org.apache.spark.sql.Logger
-
 /**
  * A library for easily manipulating trees of operators.  Operators that extend TreeNode are
  * granted the following interface:
@@ -35,5 +33,6 @@ import org.apache.spark.sql.Logger
  */
 package object trees {
   // Since we want tree nodes to be lightweight, we create one logger for all treenode instances.
-  protected val logger = Logger("catalyst.trees")
+  protected val logger =
+    com.typesafe.scalalogging.slf4j.Logger(org.slf4j.LoggerFactory.getLogger("catalyst.trees"))
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/types/dataTypes.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/types/dataTypes.scala
index 71808f76d632..b52ee6d3378a 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/types/dataTypes.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/types/dataTypes.scala
@@ -45,11 +45,13 @@ object DataType extends RegexParsers {
     "TimestampType" ^^^ TimestampType
 
   protected lazy val arrayType: Parser[DataType] =
-    "ArrayType" ~> "(" ~> dataType <~ ")" ^^ ArrayType
+    "ArrayType" ~> "(" ~> dataType ~ "," ~ boolVal <~ ")" ^^ {
+      case tpe ~ _ ~ containsNull => ArrayType(tpe, containsNull)
+    }
 
   protected lazy val mapType: Parser[DataType] =
-    "MapType" ~> "(" ~> dataType ~ "," ~ dataType <~ ")" ^^ {
-      case t1 ~ _ ~ t2 => MapType(t1, t2)
+    "MapType" ~> "(" ~> dataType ~ "," ~ dataType ~ "," ~ boolVal <~ ")" ^^ {
+      case t1 ~ _ ~ t2 ~ _ ~ valueContainsNull => MapType(t1, t2, valueContainsNull)
     }
 
   protected lazy val structField: Parser[StructField] =
@@ -82,6 +84,21 @@ object DataType extends RegexParsers {
     case Success(result, _) => result
     case failure: NoSuccess => sys.error(s"Unsupported dataType: $asString, $failure")
   }
+
+  protected[types] def buildFormattedString(
+      dataType: DataType,
+      prefix: String,
+      builder: StringBuilder): Unit = {
+    dataType match {
+      case array: ArrayType =>
+        array.buildFormattedString(prefix, builder)
+      case struct: StructType =>
+        struct.buildFormattedString(prefix, builder)
+      case map: MapType =>
+        map.buildFormattedString(prefix, builder)
+      case _ =>
+    }
+  }
 }
 
 abstract class DataType {
@@ -92,9 +109,13 @@ abstract class DataType {
   }
 
   def isPrimitive: Boolean = false
+
+  def simpleString: String
 }
 
-case object NullType extends DataType
+case object NullType extends DataType {
+  def simpleString: String = "null"
+}
 
 object NativeType {
   def all = Seq(
@@ -108,40 +129,45 @@ trait PrimitiveType extends DataType {
 }
 
 abstract class NativeType extends DataType {
-  type JvmType
-  @transient val tag: TypeTag[JvmType]
-  val ordering: Ordering[JvmType]
+  private[sql] type JvmType
+  @transient private[sql] val tag: TypeTag[JvmType]
+  private[sql] val ordering: Ordering[JvmType]
 
-  @transient val classTag = ScalaReflectionLock.synchronized {
+  @transient private[sql] val classTag = ScalaReflectionLock.synchronized {
     val mirror = runtimeMirror(Utils.getSparkClassLoader)
     ClassTag[JvmType](mirror.runtimeClass(tag.tpe))
   }
 }
 
 case object StringType extends NativeType with PrimitiveType {
-  type JvmType = String
-  @transient lazy val tag = ScalaReflectionLock.synchronized { typeTag[JvmType] }
-  val ordering = implicitly[Ordering[JvmType]]
+  private[sql] type JvmType = String
+  @transient private[sql] lazy val tag = ScalaReflectionLock.synchronized { typeTag[JvmType] }
+  private[sql] val ordering = implicitly[Ordering[JvmType]]
+  def simpleString: String = "string"
 }
 
 case object BinaryType extends DataType with PrimitiveType {
-  type JvmType = Array[Byte]
+  private[sql] type JvmType = Array[Byte]
+  def simpleString: String = "binary"
 }
 
 case object BooleanType extends NativeType with PrimitiveType {
-  type JvmType = Boolean
-  @transient lazy val tag = ScalaReflectionLock.synchronized { typeTag[JvmType] }
-  val ordering = implicitly[Ordering[JvmType]]
+  private[sql] type JvmType = Boolean
+  @transient private[sql] lazy val tag = ScalaReflectionLock.synchronized { typeTag[JvmType] }
+  private[sql] val ordering = implicitly[Ordering[JvmType]]
+  def simpleString: String = "boolean"
 }
 
 case object TimestampType extends NativeType {
-  type JvmType = Timestamp
+  private[sql] type JvmType = Timestamp
 
-  @transient lazy val tag = ScalaReflectionLock.synchronized { typeTag[JvmType] }
+  @transient private[sql] lazy val tag = ScalaReflectionLock.synchronized { typeTag[JvmType] }
 
-  val ordering = new Ordering[JvmType] {
+  private[sql] val ordering = new Ordering[JvmType] {
     def compare(x: Timestamp, y: Timestamp) = x.compareTo(y)
   }
+
+  def simpleString: String = "timestamp"
 }
 
 abstract class NumericType extends NativeType with PrimitiveType {
@@ -150,7 +176,7 @@ abstract class NumericType extends NativeType with PrimitiveType {
   // type parameter and and add a numeric annotation (i.e., [JvmType : Numeric]). This gets
   // desugared by the compiler into an argument to the objects constructor. This means there is no
   // longer an no argument constructor and thus the JVM cannot serialize the object anymore.
-  val numeric: Numeric[JvmType]
+  private[sql] val numeric: Numeric[JvmType]
 }
 
 object NumericType {
@@ -166,39 +192,43 @@ object IntegralType {
 }
 
 abstract class IntegralType extends NumericType {
-  val integral: Integral[JvmType]
+  private[sql] val integral: Integral[JvmType]
 }
 
 case object LongType extends IntegralType {
-  type JvmType = Long
-  @transient lazy val tag = ScalaReflectionLock.synchronized { typeTag[JvmType] }
-  val numeric = implicitly[Numeric[Long]]
-  val integral = implicitly[Integral[Long]]
-  val ordering = implicitly[Ordering[JvmType]]
+  private[sql] type JvmType = Long
+  @transient private[sql] lazy val tag = ScalaReflectionLock.synchronized { typeTag[JvmType] }
+  private[sql] val numeric = implicitly[Numeric[Long]]
+  private[sql] val integral = implicitly[Integral[Long]]
+  private[sql] val ordering = implicitly[Ordering[JvmType]]
+  def simpleString: String = "long"
 }
 
 case object IntegerType extends IntegralType {
-  type JvmType = Int
-  @transient lazy val tag = ScalaReflectionLock.synchronized { typeTag[JvmType] }
-  val numeric = implicitly[Numeric[Int]]
-  val integral = implicitly[Integral[Int]]
-  val ordering = implicitly[Ordering[JvmType]]
+  private[sql] type JvmType = Int
+  @transient private[sql] lazy val tag = ScalaReflectionLock.synchronized { typeTag[JvmType] }
+  private[sql] val numeric = implicitly[Numeric[Int]]
+  private[sql] val integral = implicitly[Integral[Int]]
+  private[sql] val ordering = implicitly[Ordering[JvmType]]
+  def simpleString: String = "integer"
 }
 
 case object ShortType extends IntegralType {
-  type JvmType = Short
-  @transient lazy val tag = ScalaReflectionLock.synchronized { typeTag[JvmType] }
-  val numeric = implicitly[Numeric[Short]]
-  val integral = implicitly[Integral[Short]]
-  val ordering = implicitly[Ordering[JvmType]]
+  private[sql] type JvmType = Short
+  @transient private[sql] lazy val tag = ScalaReflectionLock.synchronized { typeTag[JvmType] }
+  private[sql] val numeric = implicitly[Numeric[Short]]
+  private[sql] val integral = implicitly[Integral[Short]]
+  private[sql] val ordering = implicitly[Ordering[JvmType]]
+  def simpleString: String = "short"
 }
 
 case object ByteType extends IntegralType {
-  type JvmType = Byte
-  @transient lazy val tag = ScalaReflectionLock.synchronized { typeTag[JvmType] }
-  val numeric = implicitly[Numeric[Byte]]
-  val integral = implicitly[Integral[Byte]]
-  val ordering = implicitly[Ordering[JvmType]]
+  private[sql] type JvmType = Byte
+  @transient private[sql] lazy val tag = ScalaReflectionLock.synchronized { typeTag[JvmType] }
+  private[sql] val numeric = implicitly[Numeric[Byte]]
+  private[sql] val integral = implicitly[Integral[Byte]]
+  private[sql] val ordering = implicitly[Ordering[JvmType]]
+  def simpleString: String = "byte"
 }
 
 /** Matcher for any expressions that evaluate to [[FractionalType]]s */
@@ -209,47 +239,159 @@ object FractionalType {
   }
 }
 abstract class FractionalType extends NumericType {
-  val fractional: Fractional[JvmType]
+  private[sql] val fractional: Fractional[JvmType]
 }
 
 case object DecimalType extends FractionalType {
-  type JvmType = BigDecimal
-  @transient lazy val tag = ScalaReflectionLock.synchronized { typeTag[JvmType] }
-  val numeric = implicitly[Numeric[BigDecimal]]
-  val fractional = implicitly[Fractional[BigDecimal]]
-  val ordering = implicitly[Ordering[JvmType]]
+  private[sql] type JvmType = BigDecimal
+  @transient private[sql] lazy val tag = ScalaReflectionLock.synchronized { typeTag[JvmType] }
+  private[sql] val numeric = implicitly[Numeric[BigDecimal]]
+  private[sql] val fractional = implicitly[Fractional[BigDecimal]]
+  private[sql] val ordering = implicitly[Ordering[JvmType]]
+  def simpleString: String = "decimal"
 }
 
 case object DoubleType extends FractionalType {
-  type JvmType = Double
-  @transient lazy val tag = ScalaReflectionLock.synchronized { typeTag[JvmType] }
-  val numeric = implicitly[Numeric[Double]]
-  val fractional = implicitly[Fractional[Double]]
-  val ordering = implicitly[Ordering[JvmType]]
+  private[sql] type JvmType = Double
+  @transient private[sql] lazy val tag = ScalaReflectionLock.synchronized { typeTag[JvmType] }
+  private[sql] val numeric = implicitly[Numeric[Double]]
+  private[sql] val fractional = implicitly[Fractional[Double]]
+  private[sql] val ordering = implicitly[Ordering[JvmType]]
+  def simpleString: String = "double"
 }
 
 case object FloatType extends FractionalType {
-  type JvmType = Float
-  @transient lazy val tag = ScalaReflectionLock.synchronized { typeTag[JvmType] }
-  val numeric = implicitly[Numeric[Float]]
-  val fractional = implicitly[Fractional[Float]]
-  val ordering = implicitly[Ordering[JvmType]]
+  private[sql] type JvmType = Float
+  @transient private[sql] lazy val tag = ScalaReflectionLock.synchronized { typeTag[JvmType] }
+  private[sql] val numeric = implicitly[Numeric[Float]]
+  private[sql] val fractional = implicitly[Fractional[Float]]
+  private[sql] val ordering = implicitly[Ordering[JvmType]]
+  def simpleString: String = "float"
 }
 
-case class ArrayType(elementType: DataType) extends DataType
+object ArrayType {
+  /** Construct a [[ArrayType]] object with the given element type. The `containsNull` is false. */
+  def apply(elementType: DataType): ArrayType = ArrayType(elementType, false)
+}
 
-case class StructField(name: String, dataType: DataType, nullable: Boolean)
+/**
+ * The data type for collections of multiple values.
+ * Internally these are represented as columns that contain a ``scala.collection.Seq``.
+ *
+ * @param elementType The data type of values.
+ * @param containsNull Indicates if values have `null` values
+ */
+case class ArrayType(elementType: DataType, containsNull: Boolean) extends DataType {
+  private[sql] def buildFormattedString(prefix: String, builder: StringBuilder): Unit = {
+    builder.append(
+      s"${prefix}-- element: ${elementType.simpleString} (containsNull = ${containsNull})\n")
+    DataType.buildFormattedString(elementType, s"$prefix    |", builder)
+  }
+
+  def simpleString: String = "array"
+}
+
+/**
+ * A field inside a StructType.
+ * @param name The name of this field.
+ * @param dataType The data type of this field.
+ * @param nullable Indicates if values of this field can be `null` values.
+ */
+case class StructField(name: String, dataType: DataType, nullable: Boolean) {
+
+  private[sql] def buildFormattedString(prefix: String, builder: StringBuilder): Unit = {
+    builder.append(s"${prefix}-- ${name}: ${dataType.simpleString} (nullable = ${nullable})\n")
+    DataType.buildFormattedString(dataType, s"$prefix    |", builder)
+  }
+}
 
 object StructType {
-  def fromAttributes(attributes: Seq[Attribute]): StructType = {
+  protected[sql] def fromAttributes(attributes: Seq[Attribute]): StructType =
     StructType(attributes.map(a => StructField(a.name, a.dataType, a.nullable)))
-  }
 
-  // def apply(fields: Seq[StructField]) = new StructType(fields.toIndexedSeq)
+  private def validateFields(fields: Seq[StructField]): Boolean =
+    fields.map(field => field.name).distinct.size == fields.size
 }
 
 case class StructType(fields: Seq[StructField]) extends DataType {
-  def toAttributes = fields.map(f => AttributeReference(f.name, f.dataType, f.nullable)())
+  require(StructType.validateFields(fields), "Found fields with the same name.")
+
+  /**
+   * Returns all field names in a [[Seq]].
+   */
+  lazy val fieldNames: Seq[String] = fields.map(_.name)
+  private lazy val fieldNamesSet: Set[String] = fieldNames.toSet
+  private lazy val nameToField: Map[String, StructField] = fields.map(f => f.name -> f).toMap
+  /**
+   * Extracts a [[StructField]] of the given name. If the [[StructType]] object does not
+   * have a name matching the given name, `null` will be returned.
+   */
+  def apply(name: String): StructField = {
+    nameToField.get(name).getOrElse(
+      throw new IllegalArgumentException(s"Field ${name} does not exist."))
+  }
+
+  /**
+   * Returns a [[StructType]] containing [[StructField]]s of the given names.
+   * Those names which do not have matching fields will be ignored.
+   */
+  def apply(names: Set[String]): StructType = {
+    val nonExistFields = names -- fieldNamesSet
+    if (!nonExistFields.isEmpty) {
+      throw new IllegalArgumentException(
+        s"Field ${nonExistFields.mkString(",")} does not exist.")
+    }
+    // Preserve the original order of fields.
+    StructType(fields.filter(f => names.contains(f.name)))
+  }
+
+  protected[sql] def toAttributes =
+    fields.map(f => AttributeReference(f.name, f.dataType, f.nullable)())
+
+  def treeString: String = {
+    val builder = new StringBuilder
+    builder.append("root\n")
+    val prefix = " |"
+    fields.foreach(field => field.buildFormattedString(prefix, builder))
+
+    builder.toString()
+  }
+
+  def printTreeString(): Unit = println(treeString)
+
+  private[sql] def buildFormattedString(prefix: String, builder: StringBuilder): Unit = {
+    fields.foreach(field => field.buildFormattedString(prefix, builder))
+  }
+
+  def simpleString: String = "struct"
+}
+
+object MapType {
+  /**
+   * Construct a [[MapType]] object with the given key type and value type.
+   * The `valueContainsNull` is true.
+   */
+  def apply(keyType: DataType, valueType: DataType): MapType =
+    MapType(keyType: DataType, valueType: DataType, true)
 }
 
-case class MapType(keyType: DataType, valueType: DataType) extends DataType
+/**
+ * The data type for Maps. Keys in a map are not allowed to have `null` values.
+ * @param keyType The data type of map keys.
+ * @param valueType The data type of map values.
+ * @param valueContainsNull Indicates if map values have `null` values.
+ */
+case class MapType(
+    keyType: DataType,
+    valueType: DataType,
+    valueContainsNull: Boolean) extends DataType {
+  private[sql] def buildFormattedString(prefix: String, builder: StringBuilder): Unit = {
+    builder.append(s"${prefix}-- key: ${keyType.simpleString}\n")
+    builder.append(s"${prefix}-- value: ${valueType.simpleString} " +
+      s"(valueContainsNull = ${valueContainsNull})\n")
+    DataType.buildFormattedString(keyType, s"$prefix    |", builder)
+    DataType.buildFormattedString(valueType, s"$prefix    |", builder)
+  }
+
+  def simpleString: String = "map"
+}
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/ScalaReflectionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/ScalaReflectionSuite.scala
index c0438dbe52a4..e030d6e13d47 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/ScalaReflectionSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/ScalaReflectionSuite.scala
@@ -17,11 +17,11 @@
 
 package org.apache.spark.sql.catalyst
 
+import java.math.BigInteger
 import java.sql.Timestamp
 
 import org.scalatest.FunSuite
 
-import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.types._
 
 case class PrimitiveData(
@@ -148,4 +148,68 @@ class ScalaReflectionSuite extends FunSuite {
         StructField("_2", StringType, nullable = true))),
       nullable = true))
   }
+
+  test("get data type of a value") {
+    // BooleanType
+    assert(BooleanType === typeOfObject(true))
+    assert(BooleanType === typeOfObject(false))
+
+    // BinaryType
+    assert(BinaryType === typeOfObject("string".getBytes))
+
+    // StringType
+    assert(StringType === typeOfObject("string"))
+
+    // ByteType
+    assert(ByteType === typeOfObject(127.toByte))
+
+    // ShortType
+    assert(ShortType === typeOfObject(32767.toShort))
+
+    // IntegerType
+    assert(IntegerType === typeOfObject(2147483647))
+
+    // LongType
+    assert(LongType === typeOfObject(9223372036854775807L))
+
+    // FloatType
+    assert(FloatType === typeOfObject(3.4028235E38.toFloat))
+
+    // DoubleType
+    assert(DoubleType === typeOfObject(1.7976931348623157E308))
+
+    // DecimalType
+    assert(DecimalType === typeOfObject(BigDecimal("1.7976931348623157E318")))
+
+    // TimestampType
+    assert(TimestampType === typeOfObject(java.sql.Timestamp.valueOf("2014-7-25 10:26:00")))
+
+    // NullType
+    assert(NullType === typeOfObject(null))
+
+    def typeOfObject1: PartialFunction[Any, DataType] = typeOfObject orElse {
+      case value: java.math.BigInteger => DecimalType
+      case value: java.math.BigDecimal => DecimalType
+      case _ => StringType
+    }
+
+    assert(DecimalType === typeOfObject1(
+      new BigInteger("92233720368547758070")))
+    assert(DecimalType === typeOfObject1(
+      new java.math.BigDecimal("1.7976931348623157E318")))
+    assert(StringType === typeOfObject1(BigInt("92233720368547758070")))
+
+    def typeOfObject2: PartialFunction[Any, DataType] = typeOfObject orElse {
+      case value: java.math.BigInteger => DecimalType
+    }
+
+    intercept[MatchError](typeOfObject2(BigInt("92233720368547758070")))
+
+    def typeOfObject3: PartialFunction[Any, DataType] = typeOfObject orElse {
+      case c: Seq[_] => ArrayType(typeOfObject3(c.head))
+    }
+
+    assert(ArrayType(IntegerType) === typeOfObject3(Seq(1, 2, 3)))
+    assert(ArrayType(ArrayType(IntegerType)) === typeOfObject3(Seq(Seq(1,2,3))))
+  }
 }
diff --git a/sql/core/src/main/java/org/apache/spark/sql/api/java/types/ArrayType.java b/sql/core/src/main/java/org/apache/spark/sql/api/java/types/ArrayType.java
new file mode 100644
index 000000000000..17334ca31b2b
--- /dev/null
+++ b/sql/core/src/main/java/org/apache/spark/sql/api/java/types/ArrayType.java
@@ -0,0 +1,68 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.api.java.types;
+
+/**
+ * The data type representing Lists.
+ * An ArrayType object comprises two fields, {@code DataType elementType} and
+ * {@code boolean containsNull}. The field of {@code elementType} is used to specify the type of
+ * array elements. The field of {@code containsNull} is used to specify if the array has
+ * {@code null} values.
+ *
+ * To create an {@link ArrayType},
+ * {@link org.apache.spark.sql.api.java.types.DataType#createArrayType(DataType)} or
+ * {@link org.apache.spark.sql.api.java.types.DataType#createArrayType(DataType, boolean)}
+ * should be used.
+ */
+public class ArrayType extends DataType {
+  private DataType elementType;
+  private boolean containsNull;
+
+  protected ArrayType(DataType elementType, boolean containsNull) {
+    this.elementType = elementType;
+    this.containsNull = containsNull;
+  }
+
+  public DataType getElementType() {
+    return elementType;
+  }
+
+  public boolean isContainsNull() {
+    return containsNull;
+  }
+
+  @Override
+  public boolean equals(Object o) {
+    if (this == o) return true;
+    if (o == null || getClass() != o.getClass()) return false;
+
+    ArrayType arrayType = (ArrayType) o;
+
+    if (containsNull != arrayType.containsNull) return false;
+    if (!elementType.equals(arrayType.elementType)) return false;
+
+    return true;
+  }
+
+  @Override
+  public int hashCode() {
+    int result = elementType.hashCode();
+    result = 31 * result + (containsNull ? 1 : 0);
+    return result;
+  }
+}
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/package.scala b/sql/core/src/main/java/org/apache/spark/sql/api/java/types/BinaryType.java
similarity index 59%
rename from sql/catalyst/src/main/scala/org/apache/spark/sql/package.scala
rename to sql/core/src/main/java/org/apache/spark/sql/api/java/types/BinaryType.java
index 4589129cd1c9..61703179850e 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/package.scala
+++ b/sql/core/src/main/java/org/apache/spark/sql/api/java/types/BinaryType.java
@@ -15,22 +15,13 @@
  * limitations under the License.
  */
 
-package org.apache.spark
+package org.apache.spark.sql.api.java.types;
 
 /**
- * Allows the execution of relational queries, including those expressed in SQL using Spark.
+ * The data type representing byte[] values.
  *
- * Note that this package is located in catalyst instead of in core so that all subprojects can
- * inherit the settings from this package object.
+ * {@code BinaryType} is represented by the singleton object {@link DataType#BinaryType}.
  */
-package object sql {
-
-  protected[sql] def Logger(name: String) =
-    com.typesafe.scalalogging.slf4j.Logger(org.slf4j.LoggerFactory.getLogger(name))
-
-  protected[sql] type Logging = com.typesafe.scalalogging.slf4j.Logging
-
-  type Row = catalyst.expressions.Row
-
-  val Row = catalyst.expressions.Row
+public class BinaryType extends DataType {
+  protected BinaryType() {}
 }
diff --git a/sql/core/src/main/java/org/apache/spark/sql/api/java/types/BooleanType.java b/sql/core/src/main/java/org/apache/spark/sql/api/java/types/BooleanType.java
new file mode 100644
index 000000000000..8fa24d85d123
--- /dev/null
+++ b/sql/core/src/main/java/org/apache/spark/sql/api/java/types/BooleanType.java
@@ -0,0 +1,27 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.api.java.types;
+
+/**
+ * The data type representing boolean and Boolean values.
+ *
+ * {@code BooleanType} is represented by the singleton object {@link DataType#BooleanType}.
+ */
+public class BooleanType extends DataType {
+  protected BooleanType() {}
+}
diff --git a/sql/core/src/main/java/org/apache/spark/sql/api/java/types/ByteType.java b/sql/core/src/main/java/org/apache/spark/sql/api/java/types/ByteType.java
new file mode 100644
index 000000000000..2de32978e270
--- /dev/null
+++ b/sql/core/src/main/java/org/apache/spark/sql/api/java/types/ByteType.java
@@ -0,0 +1,27 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.api.java.types;
+
+/**
+ * The data type representing byte and Byte values.
+ *
+ * {@code ByteType} is represented by the singleton object {@link DataType#ByteType}.
+ */
+public class ByteType extends DataType {
+  protected ByteType() {}
+}
diff --git a/sql/core/src/main/java/org/apache/spark/sql/api/java/types/DataType.java b/sql/core/src/main/java/org/apache/spark/sql/api/java/types/DataType.java
new file mode 100644
index 000000000000..f84e5a490a90
--- /dev/null
+++ b/sql/core/src/main/java/org/apache/spark/sql/api/java/types/DataType.java
@@ -0,0 +1,190 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.api.java.types;
+
+import java.util.HashSet;
+import java.util.List;
+import java.util.Set;
+
+/**
+ * The base type of all Spark SQL data types.
+ *
+ * To get/create specific data type, users should use singleton objects and factory methods
+ * provided by this class.
+ */
+public abstract class DataType {
+
+  /**
+   * Gets the StringType object.
+   */
+  public static final StringType StringType = new StringType();
+
+  /**
+   * Gets the BinaryType object.
+   */
+  public static final BinaryType BinaryType = new BinaryType();
+
+  /**
+   * Gets the BooleanType object.
+   */
+  public static final BooleanType BooleanType = new BooleanType();
+
+  /**
+   * Gets the TimestampType object.
+   */
+  public static final TimestampType TimestampType = new TimestampType();
+
+  /**
+   * Gets the DecimalType object.
+   */
+  public static final DecimalType DecimalType = new DecimalType();
+
+  /**
+   * Gets the DoubleType object.
+   */
+  public static final DoubleType DoubleType = new DoubleType();
+
+  /**
+   * Gets the FloatType object.
+   */
+  public static final FloatType FloatType = new FloatType();
+
+  /**
+   * Gets the ByteType object.
+   */
+  public static final ByteType ByteType = new ByteType();
+
+  /**
+   * Gets the IntegerType object.
+   */
+  public static final IntegerType IntegerType = new IntegerType();
+
+  /**
+   * Gets the LongType object.
+   */
+  public static final LongType LongType = new LongType();
+
+  /**
+   * Gets the ShortType object.
+   */
+  public static final ShortType ShortType = new ShortType();
+
+  /**
+   * Creates an ArrayType by specifying the data type of elements ({@code elementType}).
+   * The field of {@code containsNull} is set to {@code false}.
+   */
+  public static ArrayType createArrayType(DataType elementType) {
+    if (elementType == null) {
+      throw new IllegalArgumentException("elementType should not be null.");
+    }
+
+    return new ArrayType(elementType, false);
+  }
+
+  /**
+   * Creates an ArrayType by specifying the data type of elements ({@code elementType}) and
+   * whether the array contains null values ({@code containsNull}).
+   */
+  public static ArrayType createArrayType(DataType elementType, boolean containsNull) {
+    if (elementType == null) {
+      throw new IllegalArgumentException("elementType should not be null.");
+    }
+
+    return new ArrayType(elementType, containsNull);
+  }
+
+  /**
+   * Creates a MapType by specifying the data type of keys ({@code keyType}) and values
+   * ({@code keyType}). The field of {@code valueContainsNull} is set to {@code true}.
+   */
+  public static MapType createMapType(DataType keyType, DataType valueType) {
+    if (keyType == null) {
+      throw new IllegalArgumentException("keyType should not be null.");
+    }
+    if (valueType == null) {
+      throw new IllegalArgumentException("valueType should not be null.");
+    }
+
+    return new MapType(keyType, valueType, true);
+  }
+
+  /**
+   * Creates a MapType by specifying the data type of keys ({@code keyType}), the data type of
+   * values ({@code keyType}), and whether values contain any null value
+   * ({@code valueContainsNull}).
+   */
+  public static MapType createMapType(
+      DataType keyType,
+      DataType valueType,
+      boolean valueContainsNull) {
+    if (keyType == null) {
+      throw new IllegalArgumentException("keyType should not be null.");
+    }
+    if (valueType == null) {
+      throw new IllegalArgumentException("valueType should not be null.");
+    }
+
+    return new MapType(keyType, valueType, valueContainsNull);
+  }
+
+  /**
+   * Creates a StructField by specifying the name ({@code name}), data type ({@code dataType}) and
+   * whether values of this field can be null values ({@code nullable}).
+   */
+  public static StructField createStructField(String name, DataType dataType, boolean nullable) {
+    if (name == null) {
+      throw new IllegalArgumentException("name should not be null.");
+    }
+    if (dataType == null) {
+      throw new IllegalArgumentException("dataType should not be null.");
+    }
+
+    return new StructField(name, dataType, nullable);
+  }
+
+  /**
+   * Creates a StructType with the given list of StructFields ({@code fields}).
+   */
+  public static StructType createStructType(List<StructField> fields) {
+    return createStructType(fields.toArray(new StructField[0]));
+  }
+
+  /**
+   * Creates a StructType with the given StructField array ({@code fields}).
+   */
+  public static StructType createStructType(StructField[] fields) {
+    if (fields == null) {
+      throw new IllegalArgumentException("fields should not be null.");
+    }
+    Set<String> distinctNames = new HashSet<String>();
+    for (StructField field: fields) {
+      if (field == null) {
+        throw new IllegalArgumentException(
+          "fields should not contain any null.");
+      }
+
+      distinctNames.add(field.getName());
+    }
+    if (distinctNames.size() != fields.length) {
+      throw new IllegalArgumentException("fields should have distinct names.");
+    }
+
+    return new StructType(fields);
+  }
+
+}
diff --git a/sql/core/src/main/java/org/apache/spark/sql/api/java/types/DecimalType.java b/sql/core/src/main/java/org/apache/spark/sql/api/java/types/DecimalType.java
new file mode 100644
index 000000000000..9250491a2d2c
--- /dev/null
+++ b/sql/core/src/main/java/org/apache/spark/sql/api/java/types/DecimalType.java
@@ -0,0 +1,27 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.api.java.types;
+
+/**
+ * The data type representing java.math.BigDecimal values.
+ *
+ * {@code DecimalType} is represented by the singleton object {@link DataType#DecimalType}.
+ */
+public class DecimalType extends DataType {
+  protected DecimalType() {}
+}
diff --git a/sql/core/src/main/java/org/apache/spark/sql/api/java/types/DoubleType.java b/sql/core/src/main/java/org/apache/spark/sql/api/java/types/DoubleType.java
new file mode 100644
index 000000000000..3e86917fddc4
--- /dev/null
+++ b/sql/core/src/main/java/org/apache/spark/sql/api/java/types/DoubleType.java
@@ -0,0 +1,27 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.api.java.types;
+
+/**
+ * The data type representing double and Double values.
+ *
+ * {@code DoubleType} is represented by the singleton object {@link DataType#DoubleType}.
+ */
+public class DoubleType extends DataType {
+  protected DoubleType() {}
+}
diff --git a/sql/core/src/main/java/org/apache/spark/sql/api/java/types/FloatType.java b/sql/core/src/main/java/org/apache/spark/sql/api/java/types/FloatType.java
new file mode 100644
index 000000000000..fa860d40176e
--- /dev/null
+++ b/sql/core/src/main/java/org/apache/spark/sql/api/java/types/FloatType.java
@@ -0,0 +1,27 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.api.java.types;
+
+/**
+ * The data type representing float and Float values.
+ *
+ * {@code FloatType} is represented by the singleton object {@link DataType#FloatType}.
+ */
+public class FloatType extends DataType {
+  protected FloatType() {}
+}
diff --git a/sql/core/src/main/java/org/apache/spark/sql/api/java/types/IntegerType.java b/sql/core/src/main/java/org/apache/spark/sql/api/java/types/IntegerType.java
new file mode 100644
index 000000000000..bd973eca2c3c
--- /dev/null
+++ b/sql/core/src/main/java/org/apache/spark/sql/api/java/types/IntegerType.java
@@ -0,0 +1,27 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.api.java.types;
+
+/**
+ * The data type representing int and Integer values.
+ *
+ * {@code IntegerType} is represented by the singleton object {@link DataType#IntegerType}.
+ */
+public class IntegerType extends DataType {
+  protected IntegerType() {}
+}
diff --git a/sql/core/src/main/java/org/apache/spark/sql/api/java/types/LongType.java b/sql/core/src/main/java/org/apache/spark/sql/api/java/types/LongType.java
new file mode 100644
index 000000000000..e00233304cef
--- /dev/null
+++ b/sql/core/src/main/java/org/apache/spark/sql/api/java/types/LongType.java
@@ -0,0 +1,27 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.api.java.types;
+
+/**
+ * The data type representing long and Long values.
+ *
+ * {@code LongType} is represented by the singleton object {@link DataType#LongType}.
+ */
+public class LongType extends DataType {
+  protected LongType() {}
+}
diff --git a/sql/core/src/main/java/org/apache/spark/sql/api/java/types/MapType.java b/sql/core/src/main/java/org/apache/spark/sql/api/java/types/MapType.java
new file mode 100644
index 000000000000..94936e2e4ee7
--- /dev/null
+++ b/sql/core/src/main/java/org/apache/spark/sql/api/java/types/MapType.java
@@ -0,0 +1,78 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.api.java.types;
+
+/**
+ * The data type representing Maps. A MapType object comprises two fields,
+ * {@code DataType keyType}, {@code DataType valueType}, and {@code boolean valueContainsNull}.
+ * The field of {@code keyType} is used to specify the type of keys in the map.
+ * The field of {@code valueType} is used to specify the type of values in the map.
+ * The field of {@code valueContainsNull} is used to specify if map values have
+ * {@code null} values.
+ * For values of a MapType column, keys are not allowed to have {@code null} values.
+ *
+ * To create a {@link MapType},
+ * {@link org.apache.spark.sql.api.java.types.DataType#createMapType(DataType, DataType)} or
+ * {@link org.apache.spark.sql.api.java.types.DataType#createMapType(DataType, DataType, boolean)}
+ * should be used.
+ */
+public class MapType extends DataType {
+  private DataType keyType;
+  private DataType valueType;
+  private boolean valueContainsNull;
+
+  protected MapType(DataType keyType, DataType valueType, boolean valueContainsNull) {
+    this.keyType = keyType;
+    this.valueType = valueType;
+    this.valueContainsNull = valueContainsNull;
+  }
+
+  public DataType getKeyType() {
+    return keyType;
+  }
+
+  public DataType getValueType() {
+    return valueType;
+  }
+
+  public boolean isValueContainsNull() {
+    return valueContainsNull;
+  }
+
+  @Override
+  public boolean equals(Object o) {
+    if (this == o) return true;
+    if (o == null || getClass() != o.getClass()) return false;
+
+    MapType mapType = (MapType) o;
+
+    if (valueContainsNull != mapType.valueContainsNull) return false;
+    if (!keyType.equals(mapType.keyType)) return false;
+    if (!valueType.equals(mapType.valueType)) return false;
+
+    return true;
+  }
+
+  @Override
+  public int hashCode() {
+    int result = keyType.hashCode();
+    result = 31 * result + valueType.hashCode();
+    result = 31 * result + (valueContainsNull ? 1 : 0);
+    return result;
+  }
+}
diff --git a/sql/core/src/main/java/org/apache/spark/sql/api/java/types/ShortType.java b/sql/core/src/main/java/org/apache/spark/sql/api/java/types/ShortType.java
new file mode 100644
index 000000000000..98f9507acf12
--- /dev/null
+++ b/sql/core/src/main/java/org/apache/spark/sql/api/java/types/ShortType.java
@@ -0,0 +1,27 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.api.java.types;
+
+/**
+ * The data type representing short and Short values.
+ *
+ * {@code ShortType} is represented by the singleton object {@link DataType#ShortType}.
+ */
+public class ShortType extends DataType {
+  protected ShortType() {}
+}
diff --git a/sql/core/src/main/java/org/apache/spark/sql/api/java/types/StringType.java b/sql/core/src/main/java/org/apache/spark/sql/api/java/types/StringType.java
new file mode 100644
index 000000000000..b8e7dbe64607
--- /dev/null
+++ b/sql/core/src/main/java/org/apache/spark/sql/api/java/types/StringType.java
@@ -0,0 +1,27 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.api.java.types;
+
+/**
+ * The data type representing String values.
+ *
+ * {@code StringType} is represented by the singleton object {@link DataType#StringType}.
+ */
+public class StringType extends DataType {
+  protected StringType() {}
+}
diff --git a/sql/core/src/main/java/org/apache/spark/sql/api/java/types/StructField.java b/sql/core/src/main/java/org/apache/spark/sql/api/java/types/StructField.java
new file mode 100644
index 000000000000..54e9c11ea415
--- /dev/null
+++ b/sql/core/src/main/java/org/apache/spark/sql/api/java/types/StructField.java
@@ -0,0 +1,76 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.api.java.types;
+
+/**
+ * A StructField object represents a field in a StructType object.
+ * A StructField object comprises three fields, {@code String name}, {@code DataType dataType},
+ * and {@code boolean nullable}. The field of {@code name} is the name of a StructField.
+ * The field of {@code dataType} specifies the data type of a StructField.
+ * The field of {@code nullable} specifies if values of a StructField can contain {@code null}
+ * values.
+ *
+ * To create a {@link StructField},
+ * {@link org.apache.spark.sql.api.java.types.DataType#createStructField(String, DataType, boolean)}
+ * should be used.
+ */
+public class StructField {
+  private String name;
+  private DataType dataType;
+  private boolean nullable;
+
+  protected StructField(String name, DataType dataType, boolean nullable) {
+    this.name = name;
+    this.dataType = dataType;
+    this.nullable = nullable;
+  }
+
+  public String getName() {
+    return name;
+  }
+
+  public DataType getDataType() {
+    return dataType;
+  }
+
+  public boolean isNullable() {
+    return nullable;
+  }
+
+  @Override
+  public boolean equals(Object o) {
+    if (this == o) return true;
+    if (o == null || getClass() != o.getClass()) return false;
+
+    StructField that = (StructField) o;
+
+    if (nullable != that.nullable) return false;
+    if (!dataType.equals(that.dataType)) return false;
+    if (!name.equals(that.name)) return false;
+
+    return true;
+  }
+
+  @Override
+  public int hashCode() {
+    int result = name.hashCode();
+    result = 31 * result + dataType.hashCode();
+    result = 31 * result + (nullable ? 1 : 0);
+    return result;
+  }
+}
diff --git a/sql/core/src/main/java/org/apache/spark/sql/api/java/types/StructType.java b/sql/core/src/main/java/org/apache/spark/sql/api/java/types/StructType.java
new file mode 100644
index 000000000000..33a42f4b1626
--- /dev/null
+++ b/sql/core/src/main/java/org/apache/spark/sql/api/java/types/StructType.java
@@ -0,0 +1,59 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.api.java.types;
+
+import java.util.Arrays;
+import java.util.List;
+
+/**
+ * The data type representing Rows.
+ * A StructType object comprises an array of StructFields.
+ *
+ * To create an {@link StructType},
+ * {@link org.apache.spark.sql.api.java.types.DataType#createStructType(java.util.List)} or
+ * {@link org.apache.spark.sql.api.java.types.DataType#createStructType(StructField[])}
+ * should be used.
+ */
+public class StructType extends DataType {
+  private StructField[] fields;
+
+  protected StructType(StructField[] fields) {
+    this.fields = fields;
+  }
+
+  public StructField[] getFields() {
+    return fields;
+  }
+
+  @Override
+  public boolean equals(Object o) {
+    if (this == o) return true;
+    if (o == null || getClass() != o.getClass()) return false;
+
+    StructType that = (StructType) o;
+
+    if (!Arrays.equals(fields, that.fields)) return false;
+
+    return true;
+  }
+
+  @Override
+  public int hashCode() {
+    return Arrays.hashCode(fields);
+  }
+}
diff --git a/sql/core/src/main/java/org/apache/spark/sql/api/java/types/TimestampType.java b/sql/core/src/main/java/org/apache/spark/sql/api/java/types/TimestampType.java
new file mode 100644
index 000000000000..65295779f71e
--- /dev/null
+++ b/sql/core/src/main/java/org/apache/spark/sql/api/java/types/TimestampType.java
@@ -0,0 +1,27 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.api.java.types;
+
+/**
+ * The data type representing java.sql.Timestamp values.
+ *
+ * {@code TimestampType} is represented by the singleton object {@link DataType#TimestampType}.
+ */
+public class TimestampType extends DataType {
+  protected TimestampType() {}
+}
diff --git a/sql/core/src/main/java/org/apache/spark/sql/api/java/types/package-info.java b/sql/core/src/main/java/org/apache/spark/sql/api/java/types/package-info.java
new file mode 100644
index 000000000000..f169ac65e226
--- /dev/null
+++ b/sql/core/src/main/java/org/apache/spark/sql/api/java/types/package-info.java
@@ -0,0 +1,22 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+
+/**
+ * Allows users to get and create Spark SQL data types.
+ */
+package org.apache.spark.sql.api.java.types;
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
index e4b681018099..86338752a21c 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
@@ -31,7 +31,6 @@ import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.optimizer.Optimizer
 import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
 import org.apache.spark.sql.catalyst.rules.RuleExecutor
-import org.apache.spark.sql.catalyst.types._
 import org.apache.spark.sql.columnar.InMemoryRelation
 import org.apache.spark.sql.execution._
 import org.apache.spark.sql.execution.SparkStrategies
@@ -88,6 +87,44 @@ class SQLContext(@transient val sparkContext: SparkContext)
   implicit def createSchemaRDD[A <: Product: TypeTag](rdd: RDD[A]) =
     new SchemaRDD(this, SparkLogicalPlan(ExistingRdd.fromProductRdd(rdd))(self))
 
+  /**
+   * :: DeveloperApi ::
+   * Creates a [[SchemaRDD]] from an [[RDD]] containing [[Row]]s by applying a schema to this RDD.
+   * It is important to make sure that the structure of every [[Row]] of the provided RDD matches
+   * the provided schema. Otherwise, there will be runtime exception.
+   * Example:
+   * {{{
+   *  import org.apache.spark.sql._
+   *  val sqlContext = new org.apache.spark.sql.SQLContext(sc)
+   *
+   *  val schema =
+   *    StructType(
+   *      StructField("name", StringType, false) ::
+   *      StructField("age", IntegerType, true) :: Nil)
+   *
+   *  val people =
+   *    sc.textFile("examples/src/main/resources/people.txt").map(
+   *      _.split(",")).map(p => Row(p(0), p(1).trim.toInt))
+   *  val peopleSchemaRDD = sqlContext. applySchema(people, schema)
+   *  peopleSchemaRDD.printSchema
+   *  // root
+   *  // |-- name: string (nullable = false)
+   *  // |-- age: integer (nullable = true)
+   *
+   *    peopleSchemaRDD.registerAsTable("people")
+   *  sqlContext.sql("select name from people").collect.foreach(println)
+   * }}}
+   *
+   * @group userf
+   */
+  @DeveloperApi
+  def applySchema(rowRDD: RDD[Row], schema: StructType): SchemaRDD = {
+    // TODO: use MutableProjection when rowRDD is another SchemaRDD and the applied
+    // schema differs from the existing schema on any field data type.
+    val logicalPlan = SparkLogicalPlan(ExistingRdd(schema.toAttributes, rowRDD))(self)
+    new SchemaRDD(this, logicalPlan)
+  }
+
   /**
    * Loads a Parquet file, returning the result as a [[SchemaRDD]].
    *
@@ -104,6 +141,19 @@ class SQLContext(@transient val sparkContext: SparkContext)
    */
   def jsonFile(path: String): SchemaRDD = jsonFile(path, 1.0)
 
+  /**
+   * :: Experimental ::
+   * Loads a JSON file (one object per line) and applies the given schema,
+   * returning the result as a [[SchemaRDD]].
+   *
+   * @group userf
+   */
+  @Experimental
+  def jsonFile(path: String, schema: StructType): SchemaRDD = {
+    val json = sparkContext.textFile(path)
+    jsonRDD(json, schema)
+  }
+
   /**
    * :: Experimental ::
    */
@@ -122,12 +172,30 @@ class SQLContext(@transient val sparkContext: SparkContext)
    */
   def jsonRDD(json: RDD[String]): SchemaRDD = jsonRDD(json, 1.0)
 
+  /**
+   * :: Experimental ::
+   * Loads an RDD[String] storing JSON objects (one object per record) and applies the given schema,
+   * returning the result as a [[SchemaRDD]].
+   *
+   * @group userf
+   */
+  @Experimental
+  def jsonRDD(json: RDD[String], schema: StructType): SchemaRDD = {
+    val appliedSchema =
+      Option(schema).getOrElse(JsonRDD.nullTypeToStringType(JsonRDD.inferSchema(json, 1.0)))
+    val rowRDD = JsonRDD.jsonStringToRow(json, appliedSchema)
+    applySchema(rowRDD, appliedSchema)
+  }
+
   /**
    * :: Experimental ::
    */
   @Experimental
-  def jsonRDD(json: RDD[String], samplingRatio: Double): SchemaRDD =
-    new SchemaRDD(this, JsonRDD.inferSchema(self, json, samplingRatio))
+  def jsonRDD(json: RDD[String], samplingRatio: Double): SchemaRDD = {
+    val appliedSchema = JsonRDD.nullTypeToStringType(JsonRDD.inferSchema(json, samplingRatio))
+    val rowRDD = JsonRDD.jsonStringToRow(json, appliedSchema)
+    applySchema(rowRDD, appliedSchema)
+  }
 
   /**
    * :: Experimental ::
@@ -345,70 +413,138 @@ class SQLContext(@transient val sparkContext: SparkContext)
 
   /**
    * Peek at the first row of the RDD and infer its schema.
-   * TODO: consolidate this with the type system developed in SPARK-2060.
+   * It is only used by PySpark.
    */
   private[sql] def inferSchema(rdd: RDD[Map[String, _]]): SchemaRDD = {
     import scala.collection.JavaConversions._
-    def typeFor(obj: Any): DataType = obj match {
-      case c: java.lang.String => StringType
-      case c: java.lang.Integer => IntegerType
-      case c: java.lang.Long => LongType
-      case c: java.lang.Double => DoubleType
-      case c: java.lang.Boolean => BooleanType
-      case c: java.math.BigDecimal => DecimalType
-      case c: java.sql.Timestamp => TimestampType
+
+    def typeOfComplexValue: PartialFunction[Any, DataType] = {
       case c: java.util.Calendar => TimestampType
-      case c: java.util.List[_] => ArrayType(typeFor(c.head))
+      case c: java.util.List[_] =>
+        ArrayType(typeOfObject(c.head))
       case c: java.util.Map[_, _] =>
         val (key, value) = c.head
-        MapType(typeFor(key), typeFor(value))
+        MapType(typeOfObject(key), typeOfObject(value))
       case c if c.getClass.isArray =>
         val elem = c.asInstanceOf[Array[_]].head
-        ArrayType(typeFor(elem))
+        ArrayType(typeOfObject(elem))
       case c => throw new Exception(s"Object of type $c cannot be used")
     }
+    def typeOfObject = ScalaReflection.typeOfObject orElse typeOfComplexValue
+
     val firstRow = rdd.first()
-    val schema = firstRow.map { case (fieldName, obj) =>
-      AttributeReference(fieldName, typeFor(obj), true)()
+    val fields = firstRow.map {
+      case (fieldName, obj) => StructField(fieldName, typeOfObject(obj), true)
     }.toSeq
 
-    def needTransform(obj: Any): Boolean = obj match {
-      case c: java.util.List[_] => true
-      case c: java.util.Map[_, _] => true
-      case c if c.getClass.isArray => true
-      case c: java.util.Calendar => true
-      case c => false
+    applySchemaToPythonRDD(rdd, StructType(fields))
+  }
+
+  /**
+   * Parses the data type in our internal string representation. The data type string should
+   * have the same format as the one generated by `toString` in scala.
+   * It is only used by PySpark.
+   */
+  private[sql] def parseDataType(dataTypeString: String): DataType = {
+    val parser = org.apache.spark.sql.catalyst.types.DataType
+    parser(dataTypeString)
+  }
+
+  /**
+   * Apply a schema defined by the schemaString to an RDD. It is only used by PySpark.
+   */
+  private[sql] def applySchemaToPythonRDD(
+      rdd: RDD[Map[String, _]],
+      schemaString: String): SchemaRDD = {
+    val schema = parseDataType(schemaString).asInstanceOf[StructType]
+    applySchemaToPythonRDD(rdd, schema)
+  }
+
+  /**
+   * Apply a schema defined by the schema to an RDD. It is only used by PySpark.
+   */
+  private[sql] def applySchemaToPythonRDD(
+      rdd: RDD[Map[String, _]],
+      schema: StructType): SchemaRDD = {
+    // TODO: We should have a better implementation once we do not turn a Python side record
+    // to a Map.
+    import scala.collection.JavaConversions._
+    import scala.collection.convert.Wrappers.{JListWrapper, JMapWrapper}
+
+    def needsConversion(dataType: DataType): Boolean = dataType match {
+      case ByteType => true
+      case ShortType => true
+      case FloatType => true
+      case TimestampType => true
+      case ArrayType(_, _) => true
+      case MapType(_, _, _) => true
+      case StructType(_) => true
+      case other => false
     }
 
-    // convert JList, JArray into Seq, convert JMap into Map
-    // convert Calendar into Timestamp
-    def transform(obj: Any): Any = obj match {
-      case c: java.util.List[_] => c.map(transform).toSeq
-      case c: java.util.Map[_, _] => c.map {
-        case (key, value) => (key, transform(value))
-      }.toMap
-      case c if c.getClass.isArray =>
-        c.asInstanceOf[Array[_]].map(transform).toSeq
-      case c: java.util.Calendar =>
-        new java.sql.Timestamp(c.getTime().getTime())
-      case c => c
+    // Converts value to the type specified by the data type.
+    // Because Python does not have data types for TimestampType, FloatType, ShortType, and
+    // ByteType, we need to explicitly convert values in columns of these data types to the desired
+    // JVM data types.
+    def convert(obj: Any, dataType: DataType): Any = (obj, dataType) match {
+      // TODO: We should check nullable
+      case (null, _) => null
+
+      case (c: java.util.List[_], ArrayType(elementType, _)) =>
+        val converted = c.map { e => convert(e, elementType)}
+        JListWrapper(converted)
+
+      case (c: java.util.Map[_, _], struct: StructType) =>
+        val row = new GenericMutableRow(struct.fields.length)
+        struct.fields.zipWithIndex.foreach {
+          case (field, i) =>
+            val value = convert(c.get(field.name), field.dataType)
+            row.update(i, value)
+        }
+        row
+
+      case (c: java.util.Map[_, _], MapType(keyType, valueType, _)) =>
+        val converted = c.map {
+          case (key, value) =>
+            (convert(key, keyType), convert(value, valueType))
+        }
+        JMapWrapper(converted)
+
+      case (c, ArrayType(elementType, _)) if c.getClass.isArray =>
+        val converted = c.asInstanceOf[Array[_]].map(e => convert(e, elementType))
+        converted: Seq[Any]
+
+      case (c: java.util.Calendar, TimestampType) => new java.sql.Timestamp(c.getTime().getTime())
+      case (c: Int, ByteType) => c.toByte
+      case (c: Int, ShortType) => c.toShort
+      case (c: Double, FloatType) => c.toFloat
+
+      case (c, _) => c
+    }
+
+    val convertedRdd = if (schema.fields.exists(f => needsConversion(f.dataType))) {
+      rdd.map(m => m.map { case (key, value) => (key, convert(value, schema(key).dataType)) })
+    } else {
+      rdd
     }
 
-    val need = firstRow.exists {case (key, value) => needTransform(value)}
-    val transformed = if (need) {
-      rdd.mapPartitions { iter =>
-        iter.map {
-          m => m.map {case (key, value) => (key, transform(value))}
+    val rowRdd = convertedRdd.mapPartitions { iter =>
+      val row = new GenericMutableRow(schema.fields.length)
+      val fieldsWithIndex = schema.fields.zipWithIndex
+      iter.map { m =>
+        // We cannot use m.values because the order of values returned by m.values may not
+        // match fields order.
+        fieldsWithIndex.foreach {
+          case (field, i) =>
+            val value =
+              m.get(field.name).flatMap(v => Option(v)).map(v => convert(v, field.dataType)).orNull
+            row.update(i, value)
         }
-      }
-    } else rdd
 
-    val rowRdd = transformed.mapPartitions { iter =>
-      iter.map { map =>
-        new GenericRow(map.values.toArray.asInstanceOf[Array[Any]]): Row
+        row: Row
       }
     }
-    new SchemaRDD(this, SparkLogicalPlan(ExistingRdd(schema, rowRdd))(self))
-  }
 
+    new SchemaRDD(this, SparkLogicalPlan(ExistingRdd(schema.toAttributes, rowRdd))(self))
+  }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala
index 172b6e0e7f26..420f21fb9c1a 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.sql
 
-import java.util.{Map => JMap, List => JList, Set => JSet}
+import java.util.{Map => JMap, List => JList}
 
 import scala.collection.JavaConversions._
 import scala.collection.JavaConverters._
@@ -32,7 +32,6 @@ import org.apache.spark.sql.catalyst.analysis._
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.logical._
 import org.apache.spark.sql.catalyst.plans.{Inner, JoinType}
-import org.apache.spark.sql.catalyst.types.{DataType, ArrayType, BooleanType, StructType, MapType}
 import org.apache.spark.sql.execution.{ExistingRdd, SparkLogicalPlan}
 import org.apache.spark.api.java.JavaRDD
 
@@ -120,6 +119,11 @@ class SchemaRDD(
   override protected def getDependencies: Seq[Dependency[_]] =
     List(new OneToOneDependency(queryExecution.toRdd))
 
+  /** Returns the schema of this SchemaRDD (represented by a [[StructType]]).
+    *
+    * @group schema
+    */
+  def schema: StructType = queryExecution.analyzed.schema
 
   // =======================================================================
   // Query DSL
@@ -376,6 +380,8 @@ class SchemaRDD(
    * Converts a JavaRDD to a PythonRDD. It is used by pyspark.
    */
   private[sql] def javaToPython: JavaRDD[Array[Byte]] = {
+    import scala.collection.Map
+
     def toJava(obj: Any, dataType: DataType): Any = dataType match {
       case struct: StructType => rowToMap(obj.asInstanceOf[Row], struct)
       case array: ArrayType => obj match {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDDLike.scala b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDDLike.scala
index fd751031b26e..6a20def47582 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDDLike.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDDLike.scala
@@ -123,9 +123,15 @@ private[sql] trait SchemaRDDLike {
   def saveAsTable(tableName: String): Unit =
     sqlContext.executePlan(InsertIntoCreatedTable(None, tableName, logicalPlan)).toRdd
 
-  /** Returns the output schema in the tree format. */
-  def schemaString: String = queryExecution.analyzed.schemaString
+  /** Returns the schema as a string in the tree format.
+   *
+   * @group schema
+   */
+  def schemaString: String = baseSchemaRDD.schema.treeString
 
-  /** Prints out the schema in the tree format. */
+  /** Prints out the schema.
+   *
+   * @group schema
+   */
   def printSchema(): Unit = println(schemaString)
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSQLContext.scala
index 85726bae5491..c1c18a0cd0ed 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSQLContext.scala
@@ -21,14 +21,16 @@ import java.beans.Introspector
 
 import org.apache.hadoop.conf.Configuration
 
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{DeveloperApi, Experimental}
 import org.apache.spark.api.java.{JavaRDD, JavaSparkContext}
+import org.apache.spark.sql.api.java.types.{StructType => JStructType}
 import org.apache.spark.sql.json.JsonRDD
-import org.apache.spark.sql.SQLContext
+import org.apache.spark.sql._
 import org.apache.spark.sql.catalyst.expressions.{AttributeReference, GenericRow, Row => ScalaRow}
-import org.apache.spark.sql.catalyst.types._
 import org.apache.spark.sql.parquet.ParquetRelation
 import org.apache.spark.sql.execution.{ExistingRdd, SparkLogicalPlan}
+import org.apache.spark.sql.types.util.DataTypeConversions
+import DataTypeConversions.asScalaDataType;
 import org.apache.spark.util.Utils
 
 /**
@@ -95,6 +97,21 @@ class JavaSQLContext(val sqlContext: SQLContext) {
     new JavaSchemaRDD(sqlContext, SparkLogicalPlan(ExistingRdd(schema, rowRdd))(sqlContext))
   }
 
+  /**
+   * :: DeveloperApi ::
+   * Creates a JavaSchemaRDD from an RDD containing Rows by applying a schema to this RDD.
+   * It is important to make sure that the structure of every Row of the provided RDD matches the
+   * provided schema. Otherwise, there will be runtime exception.
+   */
+  @DeveloperApi
+  def applySchema(rowRDD: JavaRDD[Row], schema: JStructType): JavaSchemaRDD = {
+    val scalaRowRDD = rowRDD.rdd.map(r => r.row)
+    val scalaSchema = asScalaDataType(schema).asInstanceOf[StructType]
+    val logicalPlan =
+      SparkLogicalPlan(ExistingRdd(scalaSchema.toAttributes, scalaRowRDD))(sqlContext)
+    new JavaSchemaRDD(sqlContext, logicalPlan)
+  }
+
   /**
    * Loads a parquet file, returning the result as a [[JavaSchemaRDD]].
    */
@@ -104,23 +121,49 @@ class JavaSQLContext(val sqlContext: SQLContext) {
       ParquetRelation(path, Some(sqlContext.sparkContext.hadoopConfiguration), sqlContext))
 
   /**
-   * Loads a JSON file (one object per line), returning the result as a [[JavaSchemaRDD]].
+   * Loads a JSON file (one object per line), returning the result as a JavaSchemaRDD.
    * It goes through the entire dataset once to determine the schema.
-   *
-   * @group userf
    */
   def jsonFile(path: String): JavaSchemaRDD =
     jsonRDD(sqlContext.sparkContext.textFile(path))
 
+  /**
+   * :: Experimental ::
+   * Loads a JSON file (one object per line) and applies the given schema,
+   * returning the result as a JavaSchemaRDD.
+   */
+  @Experimental
+  def jsonFile(path: String, schema: JStructType): JavaSchemaRDD =
+    jsonRDD(sqlContext.sparkContext.textFile(path), schema)
+
   /**
    * Loads an RDD[String] storing JSON objects (one object per record), returning the result as a
-   * [[JavaSchemaRDD]].
+   * JavaSchemaRDD.
    * It goes through the entire dataset once to determine the schema.
-   *
-   * @group userf
    */
-  def jsonRDD(json: JavaRDD[String]): JavaSchemaRDD =
-    new JavaSchemaRDD(sqlContext, JsonRDD.inferSchema(sqlContext, json, 1.0))
+  def jsonRDD(json: JavaRDD[String]): JavaSchemaRDD = {
+    val appliedScalaSchema = JsonRDD.nullTypeToStringType(JsonRDD.inferSchema(json.rdd, 1.0))
+    val scalaRowRDD = JsonRDD.jsonStringToRow(json.rdd, appliedScalaSchema)
+    val logicalPlan =
+      SparkLogicalPlan(ExistingRdd(appliedScalaSchema.toAttributes, scalaRowRDD))(sqlContext)
+    new JavaSchemaRDD(sqlContext, logicalPlan)
+  }
+
+  /**
+   * :: Experimental ::
+   * Loads an RDD[String] storing JSON objects (one object per record) and applies the given schema,
+   * returning the result as a JavaSchemaRDD.
+   */
+  @Experimental
+  def jsonRDD(json: JavaRDD[String], schema: JStructType): JavaSchemaRDD = {
+    val appliedScalaSchema =
+      Option(asScalaDataType(schema)).getOrElse(
+        JsonRDD.nullTypeToStringType(JsonRDD.inferSchema(json.rdd, 1.0))).asInstanceOf[StructType]
+    val scalaRowRDD = JsonRDD.jsonStringToRow(json.rdd, appliedScalaSchema)
+    val logicalPlan =
+      SparkLogicalPlan(ExistingRdd(appliedScalaSchema.toAttributes, scalaRowRDD))(sqlContext)
+    new JavaSchemaRDD(sqlContext, logicalPlan)
+  }
 
   /**
    * Registers the given RDD as a temporary table in the catalog.  Temporary tables exist only
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSchemaRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSchemaRDD.scala
index 8fbf13b8b015..824574149858 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSchemaRDD.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSchemaRDD.scala
@@ -22,8 +22,11 @@ import java.util.{List => JList}
 import org.apache.spark.Partitioner
 import org.apache.spark.api.java.{JavaRDDLike, JavaRDD}
 import org.apache.spark.api.java.function.{Function => JFunction}
+import org.apache.spark.sql.api.java.types.StructType
+import org.apache.spark.sql.types.util.DataTypeConversions
 import org.apache.spark.sql.{SQLContext, SchemaRDD, SchemaRDDLike}
 import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
+import DataTypeConversions._
 import org.apache.spark.rdd.RDD
 import org.apache.spark.storage.StorageLevel
 
@@ -53,6 +56,10 @@ class JavaSchemaRDD(
 
   override def toString: String = baseSchemaRDD.toString
 
+  /** Returns the schema of this JavaSchemaRDD (represented by a StructType). */
+  def schema: StructType =
+    asJavaDataType(baseSchemaRDD.schema).asInstanceOf[StructType]
+
   // =======================================================================
   // Base RDD functions that do NOT change schema
   // =======================================================================
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/api/java/Row.scala b/sql/core/src/main/scala/org/apache/spark/sql/api/java/Row.scala
index 9b0dd2176149..6c67934bda5b 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/api/java/Row.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/api/java/Row.scala
@@ -17,6 +17,11 @@
 
 package org.apache.spark.sql.api.java
 
+import scala.annotation.varargs
+import scala.collection.convert.Wrappers.{JListWrapper, JMapWrapper}
+import scala.collection.JavaConversions
+import scala.math.BigDecimal
+
 import org.apache.spark.sql.catalyst.expressions.{Row => ScalaRow}
 
 /**
@@ -29,7 +34,7 @@ class Row(private[spark] val row: ScalaRow) extends Serializable {
 
   /** Returns the value of column `i`. */
   def get(i: Int): Any =
-    row(i)
+    Row.toJavaValue(row(i))
 
   /** Returns true if value at column `i` is NULL. */
   def isNullAt(i: Int) = get(i) == null
@@ -89,5 +94,57 @@ class Row(private[spark] val row: ScalaRow) extends Serializable {
    */
   def getString(i: Int): String =
     row.getString(i)
+
+  def canEqual(other: Any): Boolean = other.isInstanceOf[Row]
+
+  override def equals(other: Any): Boolean = other match {
+    case that: Row =>
+      (that canEqual this) &&
+        row == that.row
+    case _ => false
+  }
+
+  override def hashCode(): Int = row.hashCode()
 }
 
+object Row {
+
+  private def toJavaValue(value: Any): Any = value match {
+    // For values of this ScalaRow, we will do the conversion when
+    // they are actually accessed.
+    case row: ScalaRow => new Row(row)
+    case map: scala.collection.Map[_, _] =>
+      JavaConversions.mapAsJavaMap(
+        map.map {
+          case (key, value) => (toJavaValue(key), toJavaValue(value))
+        }
+      )
+    case seq: scala.collection.Seq[_] =>
+      JavaConversions.seqAsJavaList(seq.map(toJavaValue))
+    case decimal: BigDecimal => decimal.underlying()
+    case other => other
+  }
+
+  // TODO: Consolidate the toScalaValue at here with the scalafy in JsonRDD?
+  private def toScalaValue(value: Any): Any = value match {
+    // Values of this row have been converted to Scala values.
+    case row: Row => row.row
+    case map: java.util.Map[_, _] =>
+      JMapWrapper(map).map {
+        case (key, value) => (toScalaValue(key), toScalaValue(value))
+      }
+    case list: java.util.List[_] =>
+      JListWrapper(list).map(toScalaValue)
+    case decimal: java.math.BigDecimal => BigDecimal(decimal)
+    case other => other
+  }
+
+  /**
+   * Creates a Row with the given values.
+   */
+  @varargs def create(values: Any*): Row = {
+    // Right now, we cannot use @varargs to annotate the constructor of
+    // org.apache.spark.sql.api.java.Row. See https://issues.scala-lang.org/browse/SI-8383.
+    new Row(ScalaRow(values.map(toScalaValue):_*))
+  }
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala
index 6c2b553bb908..bd29ee421bbc 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala
@@ -25,33 +25,25 @@ import com.fasterxml.jackson.databind.ObjectMapper
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.analysis.HiveTypeCoercion
 import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.catalyst.plans.logical._
 import org.apache.spark.sql.catalyst.types._
-import org.apache.spark.sql.execution.{ExistingRdd, SparkLogicalPlan}
-import org.apache.spark.sql.{SQLContext, Logging}
+import org.apache.spark.sql.catalyst.ScalaReflection
+import org.apache.spark.sql.Logging
 
 private[sql] object JsonRDD extends Logging {
 
+  private[sql] def jsonStringToRow(
+      json: RDD[String],
+      schema: StructType): RDD[Row] = {
+    parseJson(json).map(parsed => asRow(parsed, schema))
+  }
+
   private[sql] def inferSchema(
-      sqlContext: SQLContext,
       json: RDD[String],
-      samplingRatio: Double = 1.0): LogicalPlan = {
+      samplingRatio: Double = 1.0): StructType = {
     require(samplingRatio > 0, s"samplingRatio ($samplingRatio) should be greater than 0")
     val schemaData = if (samplingRatio > 0.99) json else json.sample(false, samplingRatio, 1)
     val allKeys = parseJson(schemaData).map(allKeysWithValueTypes).reduce(_ ++ _)
-    val baseSchema = createSchema(allKeys)
-
-    createLogicalPlan(json, baseSchema, sqlContext)
-  }
-
-  private def createLogicalPlan(
-      json: RDD[String],
-      baseSchema: StructType,
-      sqlContext: SQLContext): LogicalPlan = {
-    val schema = nullTypeToStringType(baseSchema)
-
-    SparkLogicalPlan(
-      ExistingRdd(asAttributes(schema), parseJson(json).map(asRow(_, schema))))(sqlContext)
+    createSchema(allKeys)
   }
 
   private def createSchema(allKeys: Set[(String, DataType)]): StructType = {
@@ -75,8 +67,8 @@ private[sql] object JsonRDD extends Logging {
       val (topLevel, structLike) = values.partition(_.size == 1)
       val topLevelFields = topLevel.filter {
         name => resolved.get(prefix ++ name).get match {
-          case ArrayType(StructType(Nil)) => false
-          case ArrayType(_) => true
+          case ArrayType(StructType(Nil), _) => false
+          case ArrayType(_, _) => true
           case struct: StructType => false
           case _ => true
         }
@@ -90,7 +82,8 @@ private[sql] object JsonRDD extends Logging {
           val structType = makeStruct(nestedFields, prefix :+ name)
           val dataType = resolved.get(prefix :+ name).get
           dataType match {
-            case array: ArrayType => Some(StructField(name, ArrayType(structType), nullable = true))
+            case array: ArrayType =>
+              Some(StructField(name, ArrayType(structType, array.containsNull), nullable = true))
             case struct: StructType => Some(StructField(name, structType, nullable = true))
             // dataType is StringType means that we have resolved type conflicts involving
             // primitive types and complex types. So, the type of name has been relaxed to
@@ -109,6 +102,22 @@ private[sql] object JsonRDD extends Logging {
     makeStruct(resolved.keySet.toSeq, Nil)
   }
 
+  private[sql] def nullTypeToStringType(struct: StructType): StructType = {
+    val fields = struct.fields.map {
+      case StructField(fieldName, dataType, nullable) => {
+        val newType = dataType match {
+          case NullType => StringType
+          case ArrayType(NullType, containsNull) => ArrayType(StringType, containsNull)
+          case struct: StructType => nullTypeToStringType(struct)
+          case other: DataType => other
+        }
+        StructField(fieldName, newType, nullable)
+      }
+    }
+
+    StructType(fields)
+  }
+
   /**
    * Returns the most general data type for two given data types.
    */
@@ -139,8 +148,8 @@ private[sql] object JsonRDD extends Logging {
             case StructField(name, _, _) => name
           })
         }
-        case (ArrayType(elementType1), ArrayType(elementType2)) =>
-          ArrayType(compatibleType(elementType1, elementType2))
+        case (ArrayType(elementType1, containsNull1), ArrayType(elementType2, containsNull2)) =>
+          ArrayType(compatibleType(elementType1, elementType2), containsNull1 || containsNull2)
         // TODO: We should use JsonObjectStringType to mark that values of field will be
         // strings and every string is a Json object.
         case (_, _) => StringType
@@ -148,18 +157,13 @@ private[sql] object JsonRDD extends Logging {
     }
   }
 
-  private def typeOfPrimitiveValue(value: Any): DataType = {
-    value match {
-      case value: java.lang.String => StringType
-      case value: java.lang.Integer => IntegerType
-      case value: java.lang.Long => LongType
+  private def typeOfPrimitiveValue: PartialFunction[Any, DataType] = {
+    ScalaReflection.typeOfObject orElse {
       // Since we do not have a data type backed by BigInteger,
       // when we see a Java BigInteger, we use DecimalType.
       case value: java.math.BigInteger => DecimalType
-      case value: java.lang.Double => DoubleType
+      // DecimalType's JVMType is scala BigDecimal.
       case value: java.math.BigDecimal => DecimalType
-      case value: java.lang.Boolean => BooleanType
-      case null => NullType
       // Unexpected data type.
       case _ => StringType
     }
@@ -172,12 +176,13 @@ private[sql] object JsonRDD extends Logging {
    * treat the element as String.
    */
   private def typeOfArray(l: Seq[Any]): ArrayType = {
+    val containsNull = l.exists(v => v == null)
     val elements = l.flatMap(v => Option(v))
     if (elements.isEmpty) {
       // If this JSON array is empty, we use NullType as a placeholder.
       // If this array is not empty in other JSON objects, we can resolve
       // the type after we have passed through all JSON objects.
-      ArrayType(NullType)
+      ArrayType(NullType, containsNull)
     } else {
       val elementType = elements.map {
         e => e match {
@@ -189,7 +194,7 @@ private[sql] object JsonRDD extends Logging {
         }
       }.reduce((type1: DataType, type2: DataType) => compatibleType(type1, type2))
 
-      ArrayType(elementType)
+      ArrayType(elementType, containsNull)
     }
   }
 
@@ -216,15 +221,16 @@ private[sql] object JsonRDD extends Logging {
       case (key: String, array: Seq[_]) => {
         // The value associated with the key is an array.
         typeOfArray(array) match {
-          case ArrayType(StructType(Nil)) => {
+          case ArrayType(StructType(Nil), containsNull) => {
             // The elements of this arrays are structs.
             array.asInstanceOf[Seq[Map[String, Any]]].flatMap {
               element => allKeysWithValueTypes(element)
             }.map {
               case (k, dataType) => (s"$key.$k", dataType)
-            } :+ (key, ArrayType(StructType(Nil)))
+            } :+ (key, ArrayType(StructType(Nil), containsNull))
           }
-          case ArrayType(elementType) => (key, ArrayType(elementType)) :: Nil
+          case ArrayType(elementType, containsNull) =>
+            (key, ArrayType(elementType, containsNull)) :: Nil
         }
       }
       case (key: String, value) => (key, typeOfPrimitiveValue(value)) :: Nil
@@ -262,8 +268,11 @@ private[sql] object JsonRDD extends Logging {
       // the ObjectMapper will take the last value associated with this duplicate key.
       // For example: for {"key": 1, "key":2}, we will get "key"->2.
       val mapper = new ObjectMapper()
-      iter.map(record => mapper.readValue(record, classOf[java.util.Map[String, Any]]))
-      }).map(scalafy).map(_.asInstanceOf[Map[String, Any]])
+      iter.map { record =>
+        val parsed = scalafy(mapper.readValue(record, classOf[java.util.Map[String, Any]]))
+        parsed.asInstanceOf[Map[String, Any]]
+      }
+    })
   }
 
   private def toLong(value: Any): Long = {
@@ -334,7 +343,7 @@ private[sql] object JsonRDD extends Logging {
       null
     } else {
       desiredType match {
-        case ArrayType(elementType) =>
+        case ArrayType(elementType, _) =>
           value.asInstanceOf[Seq[Any]].map(enforceCorrectType(_, elementType))
         case StringType => toString(value)
         case IntegerType => value.asInstanceOf[IntegerType.JvmType]
@@ -348,6 +357,7 @@ private[sql] object JsonRDD extends Logging {
   }
 
   private def asRow(json: Map[String,Any], schema: StructType): Row = {
+    // TODO: Reuse the row instead of creating a new one for every record.
     val row = new GenericMutableRow(schema.fields.length)
     schema.fields.zipWithIndex.foreach {
       // StructType
@@ -356,7 +366,7 @@ private[sql] object JsonRDD extends Logging {
           v => asRow(v.asInstanceOf[Map[String, Any]], fields)).orNull)
 
       // ArrayType(StructType)
-      case (StructField(name, ArrayType(structType: StructType), _), i) =>
+      case (StructField(name, ArrayType(structType: StructType, _), _), i) =>
         row.update(i,
           json.get(name).flatMap(v => Option(v)).map(
             v => v.asInstanceOf[Seq[Any]].map(
@@ -370,32 +380,4 @@ private[sql] object JsonRDD extends Logging {
 
     row
   }
-
-  private def nullTypeToStringType(struct: StructType): StructType = {
-    val fields = struct.fields.map {
-      case StructField(fieldName, dataType, nullable) => {
-        val newType = dataType match {
-          case NullType => StringType
-          case ArrayType(NullType) => ArrayType(StringType)
-          case struct: StructType => nullTypeToStringType(struct)
-          case other: DataType => other
-        }
-        StructField(fieldName, newType, nullable)
-      }
-    }
-
-    StructType(fields)
-  }
-
-  private def asAttributes(struct: StructType): Seq[AttributeReference] = {
-    struct.fields.map(f => AttributeReference(f.name, f.dataType, nullable = true)())
-  }
-
-  private def asStruct(attributes: Seq[AttributeReference]): StructType = {
-    val fields = attributes.map {
-      case AttributeReference(name, dataType, nullable) => StructField(name, dataType, nullable)
-    }
-
-    StructType(fields)
-  }
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/package-info.java b/sql/core/src/main/scala/org/apache/spark/sql/package-info.java
similarity index 100%
rename from sql/catalyst/src/main/scala/org/apache/spark/sql/package-info.java
rename to sql/core/src/main/scala/org/apache/spark/sql/package-info.java
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/package.scala b/sql/core/src/main/scala/org/apache/spark/sql/package.scala
new file mode 100644
index 000000000000..0995a4eb6299
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/package.scala
@@ -0,0 +1,409 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark
+
+import org.apache.spark.annotation.DeveloperApi
+
+/**
+ * Allows the execution of relational queries, including those expressed in SQL using Spark.
+ *
+ *  @groupname dataType Data types
+ *  @groupdesc Spark SQL data types.
+ *  @groupprio dataType -3
+ *  @groupname field Field
+ *  @groupprio field -2
+ *  @groupname row Row
+ *  @groupprio row -1
+ */
+package object sql {
+
+  protected[sql] type Logging = com.typesafe.scalalogging.slf4j.Logging
+
+  /**
+   * :: DeveloperApi ::
+   *
+   * Represents one row of output from a relational operator.
+   * @group row
+   */
+  @DeveloperApi
+  type Row = catalyst.expressions.Row
+
+  /**
+   * :: DeveloperApi ::
+   *
+   * A [[Row]] object can be constructed by providing field values. Example:
+   * {{{
+   * import org.apache.spark.sql._
+   *
+   * // Create a Row from values.
+   * Row(value1, value2, value3, ...)
+   * // Create a Row from a Seq of values.
+   * Row.fromSeq(Seq(value1, value2, ...))
+   * }}}
+   *
+   * A value of a row can be accessed through both generic access by ordinal,
+   * which will incur boxing overhead for primitives, as well as native primitive access.
+   * An example of generic access by ordinal:
+   * {{{
+   * import org.apache.spark.sql._
+   *
+   * val row = Row(1, true, "a string", null)
+   * // row: Row = [1,true,a string,null]
+   * val firstValue = row(0)
+   * // firstValue: Any = 1
+   * val fourthValue = row(3)
+   * // fourthValue: Any = null
+   * }}}
+   *
+   * For native primitive access, it is invalid to use the native primitive interface to retrieve
+   * a value that is null, instead a user must check `isNullAt` before attempting to retrieve a
+   * value that might be null.
+   * An example of native primitive access:
+   * {{{
+   * // using the row from the previous example.
+   * val firstValue = row.getInt(0)
+   * // firstValue: Int = 1
+   * val isNull = row.isNullAt(3)
+   * // isNull: Boolean = true
+   * }}}
+   *
+   * Interfaces related to native primitive access are:
+   *
+   * `isNullAt(i: Int): Boolean`
+   *
+   * `getInt(i: Int): Int`
+   *
+   * `getLong(i: Int): Long`
+   *
+   * `getDouble(i: Int): Double`
+   *
+   * `getFloat(i: Int): Float`
+   *
+   * `getBoolean(i: Int): Boolean`
+   *
+   * `getShort(i: Int): Short`
+   *
+   * `getByte(i: Int): Byte`
+   *
+   * `getString(i: Int): String`
+   *
+   * Fields in a [[Row]] object can be extracted in a pattern match. Example:
+   * {{{
+   * import org.apache.spark.sql._
+   *
+   * val pairs = sql("SELECT key, value FROM src").rdd.map {
+   *   case Row(key: Int, value: String) =>
+   *     key -> value
+   * }
+   * }}}
+   *
+   * @group row
+   */
+  @DeveloperApi
+  val Row = catalyst.expressions.Row
+
+  /**
+   * :: DeveloperApi ::
+   *
+   * The base type of all Spark SQL data types.
+   *
+   * @group dataType
+   */
+  @DeveloperApi
+  type DataType = catalyst.types.DataType
+
+  /**
+   * :: DeveloperApi ::
+   *
+   * The data type representing `String` values
+   *
+   * @group dataType
+   */
+  @DeveloperApi
+  val StringType = catalyst.types.StringType
+
+  /**
+   * :: DeveloperApi ::
+   *
+   * The data type representing `Array[Byte]` values.
+   *
+   * @group dataType
+   */
+  @DeveloperApi
+  val BinaryType = catalyst.types.BinaryType
+
+  /**
+   * :: DeveloperApi ::
+   *
+   * The data type representing `Boolean` values.
+   *
+   *@group dataType
+   */
+  @DeveloperApi
+  val BooleanType = catalyst.types.BooleanType
+
+  /**
+   * :: DeveloperApi ::
+   *
+   * The data type representing `java.sql.Timestamp` values.
+   *
+   * @group dataType
+   */
+  @DeveloperApi
+  val TimestampType = catalyst.types.TimestampType
+
+  /**
+   * :: DeveloperApi ::
+   *
+   * The data type representing `scala.math.BigDecimal` values.
+   *
+   * @group dataType
+   */
+  @DeveloperApi
+  val DecimalType = catalyst.types.DecimalType
+
+  /**
+   * :: DeveloperApi ::
+   *
+   * The data type representing `Double` values.
+   *
+   * @group dataType
+   */
+  @DeveloperApi
+  val DoubleType = catalyst.types.DoubleType
+
+  /**
+   * :: DeveloperApi ::
+   *
+   * The data type representing `Float` values.
+   *
+   * @group dataType
+   */
+  @DeveloperApi
+  val FloatType = catalyst.types.FloatType
+
+  /**
+   * :: DeveloperApi ::
+   *
+   * The data type representing `Byte` values.
+   *
+   * @group dataType
+   */
+  @DeveloperApi
+  val ByteType = catalyst.types.ByteType
+
+  /**
+   * :: DeveloperApi ::
+   *
+   * The data type representing `Int` values.
+   *
+   * @group dataType
+   */
+  @DeveloperApi
+  val IntegerType = catalyst.types.IntegerType
+
+  /**
+   * :: DeveloperApi ::
+   *
+   * The data type representing `Long` values.
+   *
+   * @group dataType
+   */
+  @DeveloperApi
+  val LongType = catalyst.types.LongType
+
+  /**
+   * :: DeveloperApi ::
+   *
+   * The data type representing `Short` values.
+   *
+   * @group dataType
+   */
+  @DeveloperApi
+  val ShortType = catalyst.types.ShortType
+
+  /**
+   * :: DeveloperApi ::
+   *
+   * The data type for collections of multiple values.
+   * Internally these are represented as columns that contain a ``scala.collection.Seq``.
+   *
+   * An [[ArrayType]] object comprises two fields, `elementType: [[DataType]]` and
+   * `containsNull: Boolean`. The field of `elementType` is used to specify the type of
+   * array elements. The field of `containsNull` is used to specify if the array has `null` values.
+   *
+   * @group dataType
+   */
+  @DeveloperApi
+  type ArrayType = catalyst.types.ArrayType
+
+  /**
+   * :: DeveloperApi ::
+   *
+   * An [[ArrayType]] object can be constructed with two ways,
+   * {{{
+   * ArrayType(elementType: DataType, containsNull: Boolean)
+   * }}} and
+   * {{{
+   * ArrayType(elementType: DataType)
+   * }}}
+   * For `ArrayType(elementType)`, the field of `containsNull` is set to `false`.
+   *
+   * @group dataType
+   */
+  @DeveloperApi
+  val ArrayType = catalyst.types.ArrayType
+
+  /**
+   * :: DeveloperApi ::
+   *
+   * The data type representing `Map`s. A [[MapType]] object comprises three fields,
+   * `keyType: [[DataType]]`, `valueType: [[DataType]]` and `valueContainsNull: Boolean`.
+   * The field of `keyType` is used to specify the type of keys in the map.
+   * The field of `valueType` is used to specify the type of values in the map.
+   * The field of `valueContainsNull` is used to specify if values of this map has `null` values.
+   * For values of a MapType column, keys are not allowed to have `null` values.
+   *
+   * @group dataType
+   */
+  @DeveloperApi
+  type MapType = catalyst.types.MapType
+
+  /**
+   * :: DeveloperApi ::
+   *
+   * A [[MapType]] object can be constructed with two ways,
+   * {{{
+   * MapType(keyType: DataType, valueType: DataType, valueContainsNull: Boolean)
+   * }}} and
+   * {{{
+   * MapType(keyType: DataType, valueType: DataType)
+   * }}}
+   * For `MapType(keyType: DataType, valueType: DataType)`,
+   * the field of `valueContainsNull` is set to `true`.
+   *
+   * @group dataType
+   */
+  @DeveloperApi
+  val MapType = catalyst.types.MapType
+
+  /**
+   * :: DeveloperApi ::
+   *
+   * The data type representing [[Row]]s.
+   * A [[StructType]] object comprises a [[Seq]] of [[StructField]]s.
+   *
+   * @group dataType
+   */
+  @DeveloperApi
+  type StructType = catalyst.types.StructType
+
+  /**
+   * :: DeveloperApi ::
+   *
+   * A [[StructType]] object can be constructed by
+   * {{{
+   * StructType(fields: Seq[StructField])
+   * }}}
+   * For a [[StructType]] object, one or multiple [[StructField]]s can be extracted by names.
+   * If multiple [[StructField]]s are extracted, a [[StructType]] object will be returned.
+   * If a provided name does not have a matching field, it will be ignored. For the case
+   * of extracting a single StructField, a `null` will be returned.
+   * Example:
+   * {{{
+   * import org.apache.spark.sql._
+   *
+   * val struct =
+   *   StructType(
+   *     StructField("a", IntegerType, true) ::
+   *     StructField("b", LongType, false) ::
+   *     StructField("c", BooleanType, false) :: Nil)
+   *
+   * // Extract a single StructField.
+   * val singleField = struct("b")
+   * // singleField: StructField = StructField(b,LongType,false)
+   *
+   * // This struct does not have a field called "d". null will be returned.
+   * val nonExisting = struct("d")
+   * // nonExisting: StructField = null
+   *
+   * // Extract multiple StructFields. Field names are provided in a set.
+   * // A StructType object will be returned.
+   * val twoFields = struct(Set("b", "c"))
+   * // twoFields: StructType =
+   * //   StructType(List(StructField(b,LongType,false), StructField(c,BooleanType,false)))
+   *
+   * // Those names do not have matching fields will be ignored.
+   * // For the case shown below, "d" will be ignored and
+   * // it is treated as struct(Set("b", "c")).
+   * val ignoreNonExisting = struct(Set("b", "c", "d"))
+   * // ignoreNonExisting: StructType =
+   * //   StructType(List(StructField(b,LongType,false), StructField(c,BooleanType,false)))
+   * }}}
+   *
+   * A [[Row]] object is used as a value of the StructType.
+   * Example:
+   * {{{
+   * import org.apache.spark.sql._
+   *
+   * val innerStruct =
+   *   StructType(
+   *     StructField("f1", IntegerType, true) ::
+   *     StructField("f2", LongType, false) ::
+   *     StructField("f3", BooleanType, false) :: Nil)
+   *
+   * val struct = StructType(
+   *   StructField("a", innerStruct, true) :: Nil)
+   *
+   * // Create a Row with the schema defined by struct
+   * val row = Row(Row(1, 2, true))
+   * // row: Row = [[1,2,true]]
+   * }}}
+   *
+   * @group dataType
+   */
+  @DeveloperApi
+  val StructType = catalyst.types.StructType
+
+  /**
+   * :: DeveloperApi ::
+   *
+   * A [[StructField]] object represents a field in a [[StructType]] object.
+   * A [[StructField]] object comprises three fields, `name: [[String]]`, `dataType: [[DataType]]`,
+   * and `nullable: Boolean`. The field of `name` is the name of a `StructField`. The field of
+   * `dataType` specifies the data type of a `StructField`.
+   * The field of `nullable` specifies if values of a `StructField` can contain `null` values.
+   *
+   * @group field
+   */
+  @DeveloperApi
+  type StructField = catalyst.types.StructField
+
+  /**
+   * :: DeveloperApi ::
+   *
+   * A [[StructField]] object can be constructed by
+   * {{{
+   * StructField(name: String, dataType: DataType, nullable: Boolean)
+   * }}}
+   *
+   * @group dataType
+   */
+  @DeveloperApi
+  val StructField = catalyst.types.StructField
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala
index de8fe2dae38f..0a3b59cbc233 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala
@@ -75,21 +75,21 @@ private[sql] object CatalystConverter {
     val fieldType: DataType = field.dataType
     fieldType match {
       // For native JVM types we use a converter with native arrays
-      case ArrayType(elementType: NativeType) => {
+      case ArrayType(elementType: NativeType, false) => {
         new CatalystNativeArrayConverter(elementType, fieldIndex, parent)
       }
       // This is for other types of arrays, including those with nested fields
-      case ArrayType(elementType: DataType) => {
+      case ArrayType(elementType: DataType, false) => {
         new CatalystArrayConverter(elementType, fieldIndex, parent)
       }
       case StructType(fields: Seq[StructField]) => {
         new CatalystStructConverter(fields.toArray, fieldIndex, parent)
       }
-      case MapType(keyType: DataType, valueType: DataType) => {
+      case MapType(keyType: DataType, valueType: DataType, valueContainsNull: Boolean) => {
         new CatalystMapConverter(
           Array(
             new FieldType(MAP_KEY_SCHEMA_NAME, keyType, false),
-            new FieldType(MAP_VALUE_SCHEMA_NAME, valueType, true)),
+            new FieldType(MAP_VALUE_SCHEMA_NAME, valueType, valueContainsNull)),
           fieldIndex,
           parent)
       }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala
index 39294a3f4bf5..6d4ce32ac5bf 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala
@@ -172,10 +172,10 @@ private[parquet] class RowWriteSupport extends WriteSupport[Row] with Logging {
   private[parquet] def writeValue(schema: DataType, value: Any): Unit = {
     if (value != null) {
       schema match {
-        case t @ ArrayType(_) => writeArray(
+        case t @ ArrayType(_, false) => writeArray(
           t,
           value.asInstanceOf[CatalystConverter.ArrayScalaType[_]])
-        case t @ MapType(_, _) => writeMap(
+        case t @ MapType(_, _, _) => writeMap(
           t,
           value.asInstanceOf[CatalystConverter.MapScalaType[_, _]])
         case t @ StructType(_) => writeStruct(
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypes.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypes.scala
index 58370b955a5e..aaef1a1d474f 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypes.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypes.scala
@@ -116,7 +116,7 @@ private[parquet] object ParquetTypesConverter extends Logging {
         case ParquetOriginalType.LIST => { // TODO: check enums!
           assert(groupType.getFieldCount == 1)
           val field = groupType.getFields.apply(0)
-          new ArrayType(toDataType(field))
+          ArrayType(toDataType(field), containsNull = false)
         }
         case ParquetOriginalType.MAP => {
           assert(
@@ -130,7 +130,9 @@ private[parquet] object ParquetTypesConverter extends Logging {
           assert(keyValueGroup.getFields.apply(0).getRepetition == Repetition.REQUIRED)
           val valueType = toDataType(keyValueGroup.getFields.apply(1))
           assert(keyValueGroup.getFields.apply(1).getRepetition == Repetition.REQUIRED)
-          new MapType(keyType, valueType)
+          // TODO: set valueContainsNull explicitly instead of assuming valueContainsNull is true
+          // at here.
+          MapType(keyType, valueType)
         }
         case _ => {
           // Note: the order of these checks is important!
@@ -140,10 +142,12 @@ private[parquet] object ParquetTypesConverter extends Logging {
             assert(keyValueGroup.getFields.apply(0).getRepetition == Repetition.REQUIRED)
             val valueType = toDataType(keyValueGroup.getFields.apply(1))
             assert(keyValueGroup.getFields.apply(1).getRepetition == Repetition.REQUIRED)
-            new MapType(keyType, valueType)
+            // TODO: set valueContainsNull explicitly instead of assuming valueContainsNull is true
+            // at here.
+            MapType(keyType, valueType)
           } else if (correspondsToArray(groupType)) { // ArrayType
             val elementType = toDataType(groupType.getFields.apply(0))
-            new ArrayType(elementType)
+            ArrayType(elementType, containsNull = false)
           } else { // everything else: StructType
             val fields = groupType
               .getFields
@@ -151,7 +155,7 @@ private[parquet] object ParquetTypesConverter extends Logging {
               ptype.getName,
               toDataType(ptype),
               ptype.getRepetition != Repetition.REQUIRED))
-            new StructType(fields)
+            StructType(fields)
           }
         }
       }
@@ -234,7 +238,7 @@ private[parquet] object ParquetTypesConverter extends Logging {
         new ParquetPrimitiveType(repetition, primitiveType, name, originalType.orNull)
     }.getOrElse {
       ctype match {
-        case ArrayType(elementType) => {
+        case ArrayType(elementType, false) => {
           val parquetElementType = fromDataType(
             elementType,
             CatalystConverter.ARRAY_ELEMENTS_SCHEMA_NAME,
@@ -248,7 +252,7 @@ private[parquet] object ParquetTypesConverter extends Logging {
           }
           new ParquetGroupType(repetition, name, fields)
         }
-        case MapType(keyType, valueType) => {
+        case MapType(keyType, valueType, _) => {
           val parquetKeyType =
             fromDataType(
               keyType,
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/types/util/DataTypeConversions.scala b/sql/core/src/main/scala/org/apache/spark/sql/types/util/DataTypeConversions.scala
new file mode 100644
index 000000000000..d1aa3c8d5375
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/types/util/DataTypeConversions.scala
@@ -0,0 +1,110 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.types.util
+
+import org.apache.spark.sql._
+import org.apache.spark.sql.api.java.types.{DataType => JDataType, StructField => JStructField}
+
+import scala.collection.JavaConverters._
+
+protected[sql] object DataTypeConversions {
+
+  /**
+   * Returns the equivalent StructField in Scala for the given StructField in Java.
+   */
+  def asJavaStructField(scalaStructField: StructField): JStructField = {
+    JDataType.createStructField(
+      scalaStructField.name,
+      asJavaDataType(scalaStructField.dataType),
+      scalaStructField.nullable)
+  }
+
+  /**
+   * Returns the equivalent DataType in Java for the given DataType in Scala.
+   */
+  def asJavaDataType(scalaDataType: DataType): JDataType = scalaDataType match {
+    case StringType => JDataType.StringType
+    case BinaryType => JDataType.BinaryType
+    case BooleanType => JDataType.BooleanType
+    case TimestampType => JDataType.TimestampType
+    case DecimalType => JDataType.DecimalType
+    case DoubleType => JDataType.DoubleType
+    case FloatType => JDataType.FloatType
+    case ByteType => JDataType.ByteType
+    case IntegerType => JDataType.IntegerType
+    case LongType => JDataType.LongType
+    case ShortType => JDataType.ShortType
+
+    case arrayType: ArrayType => JDataType.createArrayType(
+        asJavaDataType(arrayType.elementType), arrayType.containsNull)
+    case mapType: MapType => JDataType.createMapType(
+        asJavaDataType(mapType.keyType),
+        asJavaDataType(mapType.valueType),
+        mapType.valueContainsNull)
+    case structType: StructType => JDataType.createStructType(
+        structType.fields.map(asJavaStructField).asJava)
+  }
+
+  /**
+   * Returns the equivalent StructField in Scala for the given StructField in Java.
+   */
+  def asScalaStructField(javaStructField: JStructField): StructField = {
+    StructField(
+      javaStructField.getName,
+      asScalaDataType(javaStructField.getDataType),
+      javaStructField.isNullable)
+  }
+
+  /**
+   * Returns the equivalent DataType in Scala for the given DataType in Java.
+   */
+  def asScalaDataType(javaDataType: JDataType): DataType = javaDataType match {
+    case stringType: org.apache.spark.sql.api.java.types.StringType =>
+      StringType
+    case binaryType: org.apache.spark.sql.api.java.types.BinaryType =>
+      BinaryType
+    case booleanType: org.apache.spark.sql.api.java.types.BooleanType =>
+      BooleanType
+    case timestampType: org.apache.spark.sql.api.java.types.TimestampType =>
+      TimestampType
+    case decimalType: org.apache.spark.sql.api.java.types.DecimalType =>
+      DecimalType
+    case doubleType: org.apache.spark.sql.api.java.types.DoubleType =>
+      DoubleType
+    case floatType: org.apache.spark.sql.api.java.types.FloatType =>
+      FloatType
+    case byteType: org.apache.spark.sql.api.java.types.ByteType =>
+      ByteType
+    case integerType: org.apache.spark.sql.api.java.types.IntegerType =>
+      IntegerType
+    case longType: org.apache.spark.sql.api.java.types.LongType =>
+      LongType
+    case shortType: org.apache.spark.sql.api.java.types.ShortType =>
+      ShortType
+
+    case arrayType: org.apache.spark.sql.api.java.types.ArrayType =>
+      ArrayType(asScalaDataType(arrayType.getElementType), arrayType.isContainsNull)
+    case mapType: org.apache.spark.sql.api.java.types.MapType =>
+      MapType(
+        asScalaDataType(mapType.getKeyType),
+        asScalaDataType(mapType.getValueType),
+        mapType.isValueContainsNull)
+    case structType: org.apache.spark.sql.api.java.types.StructType =>
+      StructType(structType.getFields.map(asScalaStructField))
+  }
+}
diff --git a/sql/core/src/test/java/org/apache/spark/sql/api/java/JavaApplySchemaSuite.java b/sql/core/src/test/java/org/apache/spark/sql/api/java/JavaApplySchemaSuite.java
new file mode 100644
index 000000000000..8ee459110501
--- /dev/null
+++ b/sql/core/src/test/java/org/apache/spark/sql/api/java/JavaApplySchemaSuite.java
@@ -0,0 +1,166 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.api.java;
+
+import java.io.Serializable;
+import java.math.BigDecimal;
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.List;
+
+import org.junit.After;
+import org.junit.Assert;
+import org.junit.Before;
+import org.junit.Test;
+
+import org.apache.spark.sql.api.java.types.DataType;
+import org.apache.spark.sql.api.java.types.StructField;
+import org.apache.spark.sql.api.java.types.StructType;
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.api.java.function.Function;
+
+// The test suite itself is Serializable so that anonymous Function implementations can be
+// serialized, as an alternative to converting these anonymous classes to static inner classes;
+// see http://stackoverflow.com/questions/758570/.
+public class JavaApplySchemaSuite implements Serializable {
+  private transient JavaSparkContext javaCtx;
+  private transient JavaSQLContext javaSqlCtx;
+
+  @Before
+  public void setUp() {
+    javaCtx = new JavaSparkContext("local", "JavaApplySchemaSuite");
+    javaSqlCtx = new JavaSQLContext(javaCtx);
+  }
+
+  @After
+  public void tearDown() {
+    javaCtx.stop();
+    javaCtx = null;
+    javaSqlCtx = null;
+  }
+
+  public static class Person implements Serializable {
+    private String name;
+    private int age;
+
+    public String getName() {
+      return name;
+    }
+
+    public void setName(String name) {
+      this.name = name;
+    }
+
+    public int getAge() {
+      return age;
+    }
+
+    public void setAge(int age) {
+      this.age = age;
+    }
+  }
+
+  @Test
+  public void applySchema() {
+    List<Person> personList = new ArrayList<Person>(2);
+    Person person1 = new Person();
+    person1.setName("Michael");
+    person1.setAge(29);
+    personList.add(person1);
+    Person person2 = new Person();
+    person2.setName("Yin");
+    person2.setAge(28);
+    personList.add(person2);
+
+    JavaRDD<Row> rowRDD = javaCtx.parallelize(personList).map(
+      new Function<Person, Row>() {
+        public Row call(Person person) throws Exception {
+          return Row.create(person.getName(), person.getAge());
+        }
+      });
+
+    List<StructField> fields = new ArrayList<StructField>(2);
+    fields.add(DataType.createStructField("name", DataType.StringType, false));
+    fields.add(DataType.createStructField("age", DataType.IntegerType, false));
+    StructType schema = DataType.createStructType(fields);
+
+    JavaSchemaRDD schemaRDD = javaSqlCtx.applySchema(rowRDD, schema);
+    schemaRDD.registerAsTable("people");
+    List<Row> actual = javaSqlCtx.sql("SELECT * FROM people").collect();
+
+    List<Row> expected = new ArrayList<Row>(2);
+    expected.add(Row.create("Michael", 29));
+    expected.add(Row.create("Yin", 28));
+
+    Assert.assertEquals(expected, actual);
+  }
+
+  @Test
+  public void applySchemaToJSON() {
+    JavaRDD<String> jsonRDD = javaCtx.parallelize(Arrays.asList(
+      "{\"string\":\"this is a simple string.\", \"integer\":10, \"long\":21474836470, " +
+        "\"bigInteger\":92233720368547758070, \"double\":1.7976931348623157E308, " +
+        "\"boolean\":true, \"null\":null}",
+      "{\"string\":\"this is another simple string.\", \"integer\":11, \"long\":21474836469, " +
+        "\"bigInteger\":92233720368547758069, \"double\":1.7976931348623157E305, " +
+        "\"boolean\":false, \"null\":null}"));
+    List<StructField> fields = new ArrayList<StructField>(7);
+    fields.add(DataType.createStructField("bigInteger", DataType.DecimalType, true));
+    fields.add(DataType.createStructField("boolean", DataType.BooleanType, true));
+    fields.add(DataType.createStructField("double", DataType.DoubleType, true));
+    fields.add(DataType.createStructField("integer", DataType.IntegerType, true));
+    fields.add(DataType.createStructField("long", DataType.LongType, true));
+    fields.add(DataType.createStructField("null", DataType.StringType, true));
+    fields.add(DataType.createStructField("string", DataType.StringType, true));
+    StructType expectedSchema = DataType.createStructType(fields);
+    List<Row> expectedResult = new ArrayList<Row>(2);
+    expectedResult.add(
+      Row.create(
+        new BigDecimal("92233720368547758070"),
+        true,
+        1.7976931348623157E308,
+        10,
+        21474836470L,
+        null,
+        "this is a simple string."));
+    expectedResult.add(
+      Row.create(
+        new BigDecimal("92233720368547758069"),
+        false,
+        1.7976931348623157E305,
+        11,
+        21474836469L,
+        null,
+        "this is another simple string."));
+
+    JavaSchemaRDD schemaRDD1 = javaSqlCtx.jsonRDD(jsonRDD);
+    StructType actualSchema1 = schemaRDD1.schema();
+    Assert.assertEquals(expectedSchema, actualSchema1);
+    schemaRDD1.registerAsTable("jsonTable1");
+    List<Row> actual1 = javaSqlCtx.sql("select * from jsonTable1").collect();
+    Assert.assertEquals(expectedResult, actual1);
+
+    JavaSchemaRDD schemaRDD2 = javaSqlCtx.jsonRDD(jsonRDD, expectedSchema);
+    StructType actualSchema2 = schemaRDD2.schema();
+    Assert.assertEquals(expectedSchema, actualSchema2);
+    schemaRDD1.registerAsTable("jsonTable2");
+    List<Row> actual2 = javaSqlCtx.sql("select * from jsonTable2").collect();
+    Assert.assertEquals(expectedResult, actual2);
+  }
+}
diff --git a/sql/core/src/test/java/org/apache/spark/sql/api/java/JavaRowSuite.java b/sql/core/src/test/java/org/apache/spark/sql/api/java/JavaRowSuite.java
new file mode 100644
index 000000000000..52d07b5425cc
--- /dev/null
+++ b/sql/core/src/test/java/org/apache/spark/sql/api/java/JavaRowSuite.java
@@ -0,0 +1,170 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.api.java;
+
+import java.math.BigDecimal;
+import java.sql.Timestamp;
+import java.util.Arrays;
+import java.util.HashMap;
+import java.util.List;
+import java.util.Map;
+
+import org.junit.Assert;
+import org.junit.Before;
+import org.junit.Test;
+
+public class JavaRowSuite {
+  private byte byteValue;
+  private short shortValue;
+  private int intValue;
+  private long longValue;
+  private float floatValue;
+  private double doubleValue;
+  private BigDecimal decimalValue;
+  private boolean booleanValue;
+  private String stringValue;
+  private byte[] binaryValue;
+  private Timestamp timestampValue;
+
+  @Before
+  public void setUp() {
+    byteValue = (byte)127;
+    shortValue = (short)32767;
+    intValue = 2147483647;
+    longValue = 9223372036854775807L;
+    floatValue = (float)3.4028235E38;
+    doubleValue = 1.7976931348623157E308;
+    decimalValue = new BigDecimal("1.7976931348623157E328");
+    booleanValue = true;
+    stringValue = "this is a string";
+    binaryValue = stringValue.getBytes();
+    timestampValue = Timestamp.valueOf("2014-06-30 09:20:00.0");
+  }
+
+  @Test
+  public void constructSimpleRow() {
+    Row simpleRow = Row.create(
+      byteValue,                 // ByteType
+      new Byte(byteValue),
+      shortValue,                // ShortType
+      new Short(shortValue),
+      intValue,                  // IntegerType
+      new Integer(intValue),
+      longValue,                 // LongType
+      new Long(longValue),
+      floatValue,                // FloatType
+      new Float(floatValue),
+      doubleValue,               // DoubleType
+      new Double(doubleValue),
+      decimalValue,              // DecimalType
+      booleanValue,              // BooleanType
+      new Boolean(booleanValue),
+      stringValue,               // StringType
+      binaryValue,               // BinaryType
+      timestampValue,            // TimestampType
+      null                       // null
+    );
+
+    Assert.assertEquals(byteValue, simpleRow.getByte(0));
+    Assert.assertEquals(byteValue, simpleRow.get(0));
+    Assert.assertEquals(byteValue, simpleRow.getByte(1));
+    Assert.assertEquals(byteValue, simpleRow.get(1));
+    Assert.assertEquals(shortValue, simpleRow.getShort(2));
+    Assert.assertEquals(shortValue, simpleRow.get(2));
+    Assert.assertEquals(shortValue, simpleRow.getShort(3));
+    Assert.assertEquals(shortValue, simpleRow.get(3));
+    Assert.assertEquals(intValue, simpleRow.getInt(4));
+    Assert.assertEquals(intValue, simpleRow.get(4));
+    Assert.assertEquals(intValue, simpleRow.getInt(5));
+    Assert.assertEquals(intValue, simpleRow.get(5));
+    Assert.assertEquals(longValue, simpleRow.getLong(6));
+    Assert.assertEquals(longValue, simpleRow.get(6));
+    Assert.assertEquals(longValue, simpleRow.getLong(7));
+    Assert.assertEquals(longValue, simpleRow.get(7));
+    // When we create the row, we do not do any conversion
+    // for a float/double value, so we just set the delta to 0.
+    Assert.assertEquals(floatValue, simpleRow.getFloat(8), 0);
+    Assert.assertEquals(floatValue, simpleRow.get(8));
+    Assert.assertEquals(floatValue, simpleRow.getFloat(9), 0);
+    Assert.assertEquals(floatValue, simpleRow.get(9));
+    Assert.assertEquals(doubleValue, simpleRow.getDouble(10), 0);
+    Assert.assertEquals(doubleValue, simpleRow.get(10));
+    Assert.assertEquals(doubleValue, simpleRow.getDouble(11), 0);
+    Assert.assertEquals(doubleValue, simpleRow.get(11));
+    Assert.assertEquals(decimalValue, simpleRow.get(12));
+    Assert.assertEquals(booleanValue, simpleRow.getBoolean(13));
+    Assert.assertEquals(booleanValue, simpleRow.get(13));
+    Assert.assertEquals(booleanValue, simpleRow.getBoolean(14));
+    Assert.assertEquals(booleanValue, simpleRow.get(14));
+    Assert.assertEquals(stringValue, simpleRow.getString(15));
+    Assert.assertEquals(stringValue, simpleRow.get(15));
+    Assert.assertEquals(binaryValue, simpleRow.get(16));
+    Assert.assertEquals(timestampValue, simpleRow.get(17));
+    Assert.assertEquals(true, simpleRow.isNullAt(18));
+    Assert.assertEquals(null, simpleRow.get(18));
+  }
+
+  @Test
+  public void constructComplexRow() {
+    // Simple array
+    List<String> simpleStringArray = Arrays.asList(
+      stringValue + " (1)", stringValue + " (2)", stringValue + "(3)");
+
+    // Simple map
+    Map<String, Long> simpleMap = new HashMap<String, Long>();
+    simpleMap.put(stringValue + " (1)", longValue);
+    simpleMap.put(stringValue + " (2)", longValue - 1);
+    simpleMap.put(stringValue + " (3)", longValue - 2);
+
+    // Simple struct
+    Row simpleStruct = Row.create(
+      doubleValue, stringValue, timestampValue, null);
+
+    // Complex array
+    List<Map<String, Long>> arrayOfMaps = Arrays.asList(simpleMap);
+    List<Row> arrayOfRows = Arrays.asList(simpleStruct);
+
+    // Complex map
+    Map<List<Row>, Row> complexMap = new HashMap<List<Row>, Row>();
+    complexMap.put(arrayOfRows, simpleStruct);
+
+    // Complex struct
+    Row complexStruct = Row.create(
+      simpleStringArray,
+      simpleMap,
+      simpleStruct,
+      arrayOfMaps,
+      arrayOfRows,
+      complexMap,
+      null);
+    Assert.assertEquals(simpleStringArray, complexStruct.get(0));
+    Assert.assertEquals(simpleMap, complexStruct.get(1));
+    Assert.assertEquals(simpleStruct, complexStruct.get(2));
+    Assert.assertEquals(arrayOfMaps, complexStruct.get(3));
+    Assert.assertEquals(arrayOfRows, complexStruct.get(4));
+    Assert.assertEquals(complexMap, complexStruct.get(5));
+    Assert.assertEquals(null, complexStruct.get(6));
+
+    // A very complex row
+    Row complexRow = Row.create(arrayOfMaps, arrayOfRows, complexMap, complexStruct);
+    Assert.assertEquals(arrayOfMaps, complexRow.get(0));
+    Assert.assertEquals(arrayOfRows, complexRow.get(1));
+    Assert.assertEquals(complexMap, complexRow.get(2));
+    Assert.assertEquals(complexStruct, complexRow.get(3));
+  }
+}
diff --git a/sql/core/src/test/java/org/apache/spark/sql/api/java/JavaSideDataTypeConversionSuite.java b/sql/core/src/test/java/org/apache/spark/sql/api/java/JavaSideDataTypeConversionSuite.java
new file mode 100644
index 000000000000..96a503962f7d
--- /dev/null
+++ b/sql/core/src/test/java/org/apache/spark/sql/api/java/JavaSideDataTypeConversionSuite.java
@@ -0,0 +1,150 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.api.java;
+
+import java.util.List;
+import java.util.ArrayList;
+
+import org.junit.Assert;
+import org.junit.Test;
+
+import org.apache.spark.sql.types.util.DataTypeConversions;
+import org.apache.spark.sql.api.java.types.DataType;
+import org.apache.spark.sql.api.java.types.StructField;
+
+public class JavaSideDataTypeConversionSuite {
+  public void checkDataType(DataType javaDataType) {
+    org.apache.spark.sql.catalyst.types.DataType scalaDataType =
+      DataTypeConversions.asScalaDataType(javaDataType);
+    DataType actual = DataTypeConversions.asJavaDataType(scalaDataType);
+    Assert.assertEquals(javaDataType, actual);
+  }
+
+  @Test
+  public void createDataTypes() {
+    // Simple DataTypes.
+    checkDataType(DataType.StringType);
+    checkDataType(DataType.BinaryType);
+    checkDataType(DataType.BooleanType);
+    checkDataType(DataType.TimestampType);
+    checkDataType(DataType.DecimalType);
+    checkDataType(DataType.DoubleType);
+    checkDataType(DataType.FloatType);
+    checkDataType(DataType.ByteType);
+    checkDataType(DataType.IntegerType);
+    checkDataType(DataType.LongType);
+    checkDataType(DataType.ShortType);
+
+    // Simple ArrayType.
+    DataType simpleJavaArrayType = DataType.createArrayType(DataType.StringType, true);
+    checkDataType(simpleJavaArrayType);
+
+    // Simple MapType.
+    DataType simpleJavaMapType = DataType.createMapType(DataType.StringType, DataType.LongType);
+    checkDataType(simpleJavaMapType);
+
+    // Simple StructType.
+    List<StructField> simpleFields = new ArrayList<StructField>();
+    simpleFields.add(DataType.createStructField("a", DataType.DecimalType, false));
+    simpleFields.add(DataType.createStructField("b", DataType.BooleanType, true));
+    simpleFields.add(DataType.createStructField("c", DataType.LongType, true));
+    simpleFields.add(DataType.createStructField("d", DataType.BinaryType, false));
+    DataType simpleJavaStructType = DataType.createStructType(simpleFields);
+    checkDataType(simpleJavaStructType);
+
+    // Complex StructType.
+    List<StructField> complexFields = new ArrayList<StructField>();
+    complexFields.add(DataType.createStructField("simpleArray", simpleJavaArrayType, true));
+    complexFields.add(DataType.createStructField("simpleMap", simpleJavaMapType, true));
+    complexFields.add(DataType.createStructField("simpleStruct", simpleJavaStructType, true));
+    complexFields.add(DataType.createStructField("boolean", DataType.BooleanType, false));
+    DataType complexJavaStructType = DataType.createStructType(complexFields);
+    checkDataType(complexJavaStructType);
+
+    // Complex ArrayType.
+    DataType complexJavaArrayType = DataType.createArrayType(complexJavaStructType, true);
+    checkDataType(complexJavaArrayType);
+
+    // Complex MapType.
+    DataType complexJavaMapType =
+      DataType.createMapType(complexJavaStructType, complexJavaArrayType, false);
+    checkDataType(complexJavaMapType);
+  }
+
+  @Test
+  public void illegalArgument() {
+    // ArrayType
+    try {
+      DataType.createArrayType(null, true);
+      Assert.fail();
+    } catch (IllegalArgumentException expectedException) {
+    }
+
+    // MapType
+    try {
+      DataType.createMapType(null, DataType.StringType);
+      Assert.fail();
+    } catch (IllegalArgumentException expectedException) {
+    }
+    try {
+      DataType.createMapType(DataType.StringType, null);
+      Assert.fail();
+    } catch (IllegalArgumentException expectedException) {
+    }
+    try {
+      DataType.createMapType(null, null);
+      Assert.fail();
+    } catch (IllegalArgumentException expectedException) {
+    }
+
+    // StructField
+    try {
+      DataType.createStructField(null, DataType.StringType, true);
+    } catch (IllegalArgumentException expectedException) {
+    }
+    try {
+      DataType.createStructField("name", null, true);
+    } catch (IllegalArgumentException expectedException) {
+    }
+    try {
+      DataType.createStructField(null, null, true);
+    } catch (IllegalArgumentException expectedException) {
+    }
+
+    // StructType
+    try {
+      List<StructField> simpleFields = new ArrayList<StructField>();
+      simpleFields.add(DataType.createStructField("a", DataType.DecimalType, false));
+      simpleFields.add(DataType.createStructField("b", DataType.BooleanType, true));
+      simpleFields.add(DataType.createStructField("c", DataType.LongType, true));
+      simpleFields.add(null);
+      DataType.createStructType(simpleFields);
+      Assert.fail();
+    } catch (IllegalArgumentException expectedException) {
+    }
+    try {
+      List<StructField> simpleFields = new ArrayList<StructField>();
+      simpleFields.add(DataType.createStructField("a", DataType.DecimalType, false));
+      simpleFields.add(DataType.createStructField("a", DataType.BooleanType, true));
+      simpleFields.add(DataType.createStructField("c", DataType.LongType, true));
+      DataType.createStructType(simpleFields);
+      Assert.fail();
+    } catch (IllegalArgumentException expectedException) {
+    }
+  }
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataTypeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataTypeSuite.scala
new file mode 100644
index 000000000000..cf7d79f42db1
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataTypeSuite.scala
@@ -0,0 +1,58 @@
+/*
+* Licensed to the Apache Software Foundation (ASF) under one or more
+* contributor license agreements.  See the NOTICE file distributed with
+* this work for additional information regarding copyright ownership.
+* The ASF licenses this file to You under the Apache License, Version 2.0
+* (the "License"); you may not use this file except in compliance with
+* the License.  You may obtain a copy of the License at
+*
+*    http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+package org.apache.spark.sql
+
+import org.scalatest.FunSuite
+
+class DataTypeSuite extends FunSuite {
+
+  test("construct an ArrayType") {
+    val array = ArrayType(StringType)
+
+    assert(ArrayType(StringType, false) === array)
+  }
+
+  test("construct an MapType") {
+    val map = MapType(StringType, IntegerType)
+
+    assert(MapType(StringType, IntegerType, true) === map)
+  }
+
+  test("extract fields from a StructType") {
+    val struct = StructType(
+      StructField("a", IntegerType, true) ::
+      StructField("b", LongType, false) ::
+      StructField("c", StringType, true) ::
+      StructField("d", FloatType, true) :: Nil)
+
+    assert(StructField("b", LongType, false) === struct("b"))
+
+    intercept[IllegalArgumentException] {
+      struct("e")
+    }
+
+    val expectedStruct = StructType(
+      StructField("b", LongType, false) ::
+      StructField("d", FloatType, true) :: Nil)
+
+    assert(expectedStruct === struct(Set("b", "d")))
+    intercept[IllegalArgumentException] {
+      struct(Set("b", "d", "e", "f"))
+    }
+  }
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/RowSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/RowSuite.scala
new file mode 100644
index 000000000000..651cb735ab7d
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/RowSuite.scala
@@ -0,0 +1,46 @@
+/*
+* Licensed to the Apache Software Foundation (ASF) under one or more
+* contributor license agreements.  See the NOTICE file distributed with
+* this work for additional information regarding copyright ownership.
+* The ASF licenses this file to You under the Apache License, Version 2.0
+* (the "License"); you may not use this file except in compliance with
+* the License.  You may obtain a copy of the License at
+*
+*    http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+package org.apache.spark.sql
+
+import org.scalatest.FunSuite
+
+import org.apache.spark.sql.catalyst.expressions.GenericMutableRow
+
+class RowSuite extends FunSuite {
+
+  test("create row") {
+    val expected = new GenericMutableRow(4)
+    expected.update(0, 2147483647)
+    expected.update(1, "this is a string")
+    expected.update(2, false)
+    expected.update(3, null)
+    val actual1 = Row(2147483647, "this is a string", false, null)
+    assert(expected.size === actual1.size)
+    assert(expected.getInt(0) === actual1.getInt(0))
+    assert(expected.getString(1) === actual1.getString(1))
+    assert(expected.getBoolean(2) === actual1.getBoolean(2))
+    assert(expected(3) === actual1(3))
+
+    val actual2 = Row.fromSeq(Seq(2147483647, "this is a string", false, null))
+    assert(expected.size === actual2.size)
+    assert(expected.getInt(0) === actual2.getInt(0))
+    assert(expected.getString(1) === actual2.getString(1))
+    assert(expected.getBoolean(2) === actual2.getBoolean(2))
+    assert(expected(3) === actual2(3))
+  }
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index de9e8aa4f62e..bebb49064542 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -17,9 +17,7 @@
 
 package org.apache.spark.sql
 
-import org.apache.spark.sql.catalyst.analysis.EliminateAnalysisOperators
 import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.columnar.{InMemoryColumnarTableScan, InMemoryRelation}
 import org.apache.spark.sql.test._
 
 /* Implicits */
@@ -446,4 +444,66 @@ class SQLQuerySuite extends QueryTest {
     )
     clear()
   }
+
+  test("apply schema") {
+    val schema1 = StructType(
+      StructField("f1", IntegerType, false) ::
+      StructField("f2", StringType, false) ::
+      StructField("f3", BooleanType, false) ::
+      StructField("f4", IntegerType, true) :: Nil)
+
+    val rowRDD1 = unparsedStrings.map { r =>
+      val values = r.split(",").map(_.trim)
+      val v4 = try values(3).toInt catch {
+        case _: NumberFormatException => null
+      }
+      Row(values(0).toInt, values(1), values(2).toBoolean, v4)
+    }
+
+    val schemaRDD1 = applySchema(rowRDD1, schema1)
+    schemaRDD1.registerAsTable("applySchema1")
+    checkAnswer(
+      sql("SELECT * FROM applySchema1"),
+      (1, "A1", true, null) ::
+      (2, "B2", false, null) ::
+      (3, "C3", true, null) ::
+      (4, "D4", true, 2147483644) :: Nil)
+
+    checkAnswer(
+      sql("SELECT f1, f4 FROM applySchema1"),
+      (1, null) ::
+      (2, null) ::
+      (3, null) ::
+      (4, 2147483644) :: Nil)
+
+    val schema2 = StructType(
+      StructField("f1", StructType(
+        StructField("f11", IntegerType, false) ::
+        StructField("f12", BooleanType, false) :: Nil), false) ::
+      StructField("f2", MapType(StringType, IntegerType, true), false) :: Nil)
+
+    val rowRDD2 = unparsedStrings.map { r =>
+      val values = r.split(",").map(_.trim)
+      val v4 = try values(3).toInt catch {
+        case _: NumberFormatException => null
+      }
+      Row(Row(values(0).toInt, values(2).toBoolean), Map(values(1) -> v4))
+    }
+
+    val schemaRDD2 = applySchema(rowRDD2, schema2)
+    schemaRDD2.registerAsTable("applySchema2")
+    checkAnswer(
+      sql("SELECT * FROM applySchema2"),
+      (Seq(1, true), Map("A1" -> null)) ::
+      (Seq(2, false), Map("B2" -> null)) ::
+      (Seq(3, true), Map("C3" -> null)) ::
+      (Seq(4, true), Map("D4" -> 2147483644)) :: Nil)
+
+    checkAnswer(
+      sql("SELECT f1.f11, f2['D4'] FROM applySchema2"),
+      (1, null) ::
+      (2, null) ::
+      (3, null) ::
+      (4, 2147483644) :: Nil)
+  }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala b/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala
index 330b20b315d6..213190e81202 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala
@@ -128,4 +128,11 @@ object TestData {
 
   case class TableName(tableName: String)
   TestSQLContext.sparkContext.parallelize(TableName("test") :: Nil).registerAsTable("tableName")
+
+  val unparsedStrings =
+    TestSQLContext.sparkContext.parallelize(
+      "1, A1, true, null" ::
+      "2, B2, false, null" ::
+      "3, C3, true, null" ::
+      "4, D4, true, 2147483644" :: Nil)
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/api/java/ScalaSideDataTypeConversionSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/api/java/ScalaSideDataTypeConversionSuite.scala
new file mode 100644
index 000000000000..46de6fe23922
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/api/java/ScalaSideDataTypeConversionSuite.scala
@@ -0,0 +1,81 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.api.java
+
+import org.apache.spark.sql.types.util.DataTypeConversions
+import org.scalatest.FunSuite
+
+import org.apache.spark.sql._
+import DataTypeConversions._
+
+class ScalaSideDataTypeConversionSuite extends FunSuite {
+
+  def checkDataType(scalaDataType: DataType) {
+    val javaDataType = asJavaDataType(scalaDataType)
+    val actual = asScalaDataType(javaDataType)
+    assert(scalaDataType === actual, s"Converted data type ${actual} " +
+      s"does not equal the expected data type ${scalaDataType}")
+  }
+
+  test("convert data types") {
+    // Simple DataTypes.
+    checkDataType(StringType)
+    checkDataType(BinaryType)
+    checkDataType(BooleanType)
+    checkDataType(TimestampType)
+    checkDataType(DecimalType)
+    checkDataType(DoubleType)
+    checkDataType(FloatType)
+    checkDataType(ByteType)
+    checkDataType(IntegerType)
+    checkDataType(LongType)
+    checkDataType(ShortType)
+
+    // Simple ArrayType.
+    val simpleScalaArrayType = ArrayType(StringType, true)
+    checkDataType(simpleScalaArrayType)
+
+    // Simple MapType.
+    val simpleScalaMapType = MapType(StringType, LongType)
+    checkDataType(simpleScalaMapType)
+
+    // Simple StructType.
+    val simpleScalaStructType = StructType(
+      StructField("a", DecimalType, false) ::
+      StructField("b", BooleanType, true) ::
+      StructField("c", LongType, true) ::
+      StructField("d", BinaryType, false) :: Nil)
+    checkDataType(simpleScalaStructType)
+
+    // Complex StructType.
+    val complexScalaStructType = StructType(
+      StructField("simpleArray", simpleScalaArrayType, true) ::
+      StructField("simpleMap", simpleScalaMapType, true) ::
+      StructField("simpleStruct", simpleScalaStructType, true) ::
+      StructField("boolean", BooleanType, false) :: Nil)
+    checkDataType(complexScalaStructType)
+
+    // Complex ArrayType.
+    val complexScalaArrayType = ArrayType(complexScalaStructType, true)
+    checkDataType(complexScalaArrayType)
+
+    // Complex MapType.
+    val complexScalaMapType = MapType(complexScalaStructType, complexScalaArrayType, false)
+    checkDataType(complexScalaMapType)
+  }
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala
index e765cfc83a39..9d9cfdd7c92e 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala
@@ -17,16 +17,12 @@
 
 package org.apache.spark.sql.json
 
-import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeReference}
-import org.apache.spark.sql.catalyst.plans.logical.LeafNode
 import org.apache.spark.sql.catalyst.types._
 import org.apache.spark.sql.catalyst.util._
 import org.apache.spark.sql.json.JsonRDD.{enforceCorrectType, compatibleType}
 import org.apache.spark.sql.QueryTest
 import org.apache.spark.sql.test.TestSQLContext._
 
-protected case class Schema(output: Seq[Attribute]) extends LeafNode
-
 class JsonSuite extends QueryTest {
   import TestJsonData._
   TestJsonData
@@ -127,6 +123,18 @@ class JsonSuite extends QueryTest {
     checkDataType(ArrayType(IntegerType), ArrayType(LongType), ArrayType(LongType))
     checkDataType(ArrayType(IntegerType), ArrayType(StringType), ArrayType(StringType))
     checkDataType(ArrayType(IntegerType), StructType(Nil), StringType)
+    checkDataType(
+      ArrayType(IntegerType, true), ArrayType(IntegerType), ArrayType(IntegerType, true))
+    checkDataType(
+      ArrayType(IntegerType, true), ArrayType(IntegerType, false), ArrayType(IntegerType, true))
+    checkDataType(
+      ArrayType(IntegerType, true), ArrayType(IntegerType, true), ArrayType(IntegerType, true))
+    checkDataType(
+      ArrayType(IntegerType, false), ArrayType(IntegerType), ArrayType(IntegerType, false))
+    checkDataType(
+      ArrayType(IntegerType, false), ArrayType(IntegerType, false), ArrayType(IntegerType, false))
+    checkDataType(
+      ArrayType(IntegerType, false), ArrayType(IntegerType, false), ArrayType(IntegerType))
 
     // StructType
     checkDataType(StructType(Nil), StructType(Nil), StructType(Nil))
@@ -164,16 +172,16 @@ class JsonSuite extends QueryTest {
   test("Primitive field and type inferring") {
     val jsonSchemaRDD = jsonRDD(primitiveFieldAndType)
 
-    val expectedSchema =
-      AttributeReference("bigInteger", DecimalType, true)() ::
-      AttributeReference("boolean", BooleanType, true)() ::
-      AttributeReference("double", DoubleType, true)() ::
-      AttributeReference("integer", IntegerType, true)() ::
-      AttributeReference("long", LongType, true)() ::
-      AttributeReference("null", StringType, true)() ::
-      AttributeReference("string", StringType, true)() :: Nil
+    val expectedSchema = StructType(
+      StructField("bigInteger", DecimalType, true) ::
+      StructField("boolean", BooleanType, true) ::
+      StructField("double", DoubleType, true) ::
+      StructField("integer", IntegerType, true) ::
+      StructField("long", LongType, true) ::
+      StructField("null", StringType, true) ::
+      StructField("string", StringType, true) :: Nil)
 
-    comparePlans(Schema(expectedSchema), Schema(jsonSchemaRDD.logicalPlan.output))
+    assert(expectedSchema === jsonSchemaRDD.schema)
 
     jsonSchemaRDD.registerAsTable("jsonTable")
 
@@ -192,27 +200,28 @@ class JsonSuite extends QueryTest {
   test("Complex field and type inferring") {
     val jsonSchemaRDD = jsonRDD(complexFieldAndType)
 
-    val expectedSchema =
-      AttributeReference("arrayOfArray1", ArrayType(ArrayType(StringType)), true)() ::
-      AttributeReference("arrayOfArray2", ArrayType(ArrayType(DoubleType)), true)() ::
-      AttributeReference("arrayOfBigInteger", ArrayType(DecimalType), true)() ::
-      AttributeReference("arrayOfBoolean", ArrayType(BooleanType), true)() ::
-      AttributeReference("arrayOfDouble", ArrayType(DoubleType), true)() ::
-      AttributeReference("arrayOfInteger", ArrayType(IntegerType), true)() ::
-      AttributeReference("arrayOfLong", ArrayType(LongType), true)() ::
-      AttributeReference("arrayOfNull", ArrayType(StringType), true)() ::
-      AttributeReference("arrayOfString", ArrayType(StringType), true)() ::
-      AttributeReference("arrayOfStruct", ArrayType(
-        StructType(StructField("field1", BooleanType, true) ::
-                   StructField("field2", StringType, true) :: Nil)), true)() ::
-      AttributeReference("struct", StructType(
-        StructField("field1", BooleanType, true) ::
-        StructField("field2", DecimalType, true) :: Nil), true)() ::
-      AttributeReference("structWithArrayFields", StructType(
+    val expectedSchema = StructType(
+      StructField("arrayOfArray1", ArrayType(ArrayType(StringType)), true) ::
+      StructField("arrayOfArray2", ArrayType(ArrayType(DoubleType)), true) ::
+      StructField("arrayOfBigInteger", ArrayType(DecimalType), true) ::
+      StructField("arrayOfBoolean", ArrayType(BooleanType), true) ::
+      StructField("arrayOfDouble", ArrayType(DoubleType), true) ::
+      StructField("arrayOfInteger", ArrayType(IntegerType), true) ::
+      StructField("arrayOfLong", ArrayType(LongType), true) ::
+      StructField("arrayOfNull", ArrayType(StringType, true), true) ::
+      StructField("arrayOfString", ArrayType(StringType), true) ::
+      StructField("arrayOfStruct", ArrayType(
+        StructType(
+          StructField("field1", BooleanType, true) ::
+          StructField("field2", StringType, true) :: Nil)), true) ::
+      StructField("struct", StructType(
+      StructField("field1", BooleanType, true) ::
+      StructField("field2", DecimalType, true) :: Nil), true) ::
+      StructField("structWithArrayFields", StructType(
         StructField("field1", ArrayType(IntegerType), true) ::
-        StructField("field2", ArrayType(StringType), true) :: Nil), true)() :: Nil
+        StructField("field2", ArrayType(StringType), true) :: Nil), true) :: Nil)
 
-    comparePlans(Schema(expectedSchema), Schema(jsonSchemaRDD.logicalPlan.output))
+    assert(expectedSchema === jsonSchemaRDD.schema)
 
     jsonSchemaRDD.registerAsTable("jsonTable")
 
@@ -301,15 +310,15 @@ class JsonSuite extends QueryTest {
   test("Type conflict in primitive field values") {
     val jsonSchemaRDD = jsonRDD(primitiveFieldValueTypeConflict)
 
-    val expectedSchema =
-      AttributeReference("num_bool", StringType, true)() ::
-      AttributeReference("num_num_1", LongType, true)() ::
-      AttributeReference("num_num_2", DecimalType, true)() ::
-      AttributeReference("num_num_3", DoubleType, true)() ::
-      AttributeReference("num_str", StringType, true)() ::
-      AttributeReference("str_bool", StringType, true)() :: Nil
+    val expectedSchema = StructType(
+      StructField("num_bool", StringType, true) ::
+      StructField("num_num_1", LongType, true) ::
+      StructField("num_num_2", DecimalType, true) ::
+      StructField("num_num_3", DoubleType, true) ::
+      StructField("num_str", StringType, true) ::
+      StructField("str_bool", StringType, true) :: Nil)
 
-    comparePlans(Schema(expectedSchema), Schema(jsonSchemaRDD.logicalPlan.output))
+    assert(expectedSchema === jsonSchemaRDD.schema)
 
     jsonSchemaRDD.registerAsTable("jsonTable")
 
@@ -426,15 +435,15 @@ class JsonSuite extends QueryTest {
   test("Type conflict in complex field values") {
     val jsonSchemaRDD = jsonRDD(complexFieldValueTypeConflict)
 
-    val expectedSchema =
-      AttributeReference("array", ArrayType(IntegerType), true)() ::
-      AttributeReference("num_struct", StringType, true)() ::
-      AttributeReference("str_array", StringType, true)() ::
-      AttributeReference("struct", StructType(
-        StructField("field", StringType, true) :: Nil), true)() ::
-      AttributeReference("struct_array", StringType, true)() :: Nil
+    val expectedSchema = StructType(
+      StructField("array", ArrayType(IntegerType), true) ::
+      StructField("num_struct", StringType, true) ::
+      StructField("str_array", StringType, true) ::
+      StructField("struct", StructType(
+        StructField("field", StringType, true) :: Nil), true) ::
+      StructField("struct_array", StringType, true) :: Nil)
 
-    comparePlans(Schema(expectedSchema), Schema(jsonSchemaRDD.logicalPlan.output))
+    assert(expectedSchema === jsonSchemaRDD.schema)
 
     jsonSchemaRDD.registerAsTable("jsonTable")
 
@@ -450,12 +459,12 @@ class JsonSuite extends QueryTest {
   test("Type conflict in array elements") {
     val jsonSchemaRDD = jsonRDD(arrayElementTypeConflict)
 
-    val expectedSchema =
-      AttributeReference("array1", ArrayType(StringType), true)() ::
-      AttributeReference("array2", ArrayType(StructType(
-        StructField("field", LongType, true) :: Nil)), true)() :: Nil
+    val expectedSchema = StructType(
+      StructField("array1", ArrayType(StringType, true), true) ::
+      StructField("array2", ArrayType(StructType(
+        StructField("field", LongType, true) :: Nil)), true) :: Nil)
 
-    comparePlans(Schema(expectedSchema), Schema(jsonSchemaRDD.logicalPlan.output))
+    assert(expectedSchema === jsonSchemaRDD.schema)
 
     jsonSchemaRDD.registerAsTable("jsonTable")
 
@@ -475,15 +484,15 @@ class JsonSuite extends QueryTest {
   test("Handling missing fields") {
     val jsonSchemaRDD = jsonRDD(missingFields)
 
-    val expectedSchema =
-      AttributeReference("a", BooleanType, true)() ::
-      AttributeReference("b", LongType, true)() ::
-      AttributeReference("c", ArrayType(IntegerType), true)() ::
-      AttributeReference("d", StructType(
-        StructField("field", BooleanType, true) :: Nil), true)() ::
-      AttributeReference("e", StringType, true)() :: Nil
+    val expectedSchema = StructType(
+      StructField("a", BooleanType, true) ::
+      StructField("b", LongType, true) ::
+      StructField("c", ArrayType(IntegerType), true) ::
+      StructField("d", StructType(
+        StructField("field", BooleanType, true) :: Nil), true) ::
+      StructField("e", StringType, true) :: Nil)
 
-    comparePlans(Schema(expectedSchema), Schema(jsonSchemaRDD.logicalPlan.output))
+    assert(expectedSchema === jsonSchemaRDD.schema)
 
     jsonSchemaRDD.registerAsTable("jsonTable")
   }
@@ -494,16 +503,16 @@ class JsonSuite extends QueryTest {
     primitiveFieldAndType.map(record => record.replaceAll("\n", " ")).saveAsTextFile(path)
     val jsonSchemaRDD = jsonFile(path)
 
-    val expectedSchema =
-      AttributeReference("bigInteger", DecimalType, true)() ::
-      AttributeReference("boolean", BooleanType, true)() ::
-      AttributeReference("double", DoubleType, true)() ::
-      AttributeReference("integer", IntegerType, true)() ::
-      AttributeReference("long", LongType, true)() ::
-      AttributeReference("null", StringType, true)() ::
-      AttributeReference("string", StringType, true)() :: Nil
+    val expectedSchema = StructType(
+      StructField("bigInteger", DecimalType, true) ::
+      StructField("boolean", BooleanType, true) ::
+      StructField("double", DoubleType, true) ::
+      StructField("integer", IntegerType, true) ::
+      StructField("long", LongType, true) ::
+      StructField("null", StringType, true) ::
+      StructField("string", StringType, true) :: Nil)
 
-    comparePlans(Schema(expectedSchema), Schema(jsonSchemaRDD.logicalPlan.output))
+    assert(expectedSchema === jsonSchemaRDD.schema)
 
     jsonSchemaRDD.registerAsTable("jsonTable")
 
@@ -518,4 +527,53 @@ class JsonSuite extends QueryTest {
       "this is a simple string.") :: Nil
     )
   }
+
+  test("Applying schemas") {
+    val file = getTempFilePath("json")
+    val path = file.toString
+    primitiveFieldAndType.map(record => record.replaceAll("\n", " ")).saveAsTextFile(path)
+
+    val schema = StructType(
+      StructField("bigInteger", DecimalType, true) ::
+      StructField("boolean", BooleanType, true) ::
+      StructField("double", DoubleType, true) ::
+      StructField("integer", IntegerType, true) ::
+      StructField("long", LongType, true) ::
+      StructField("null", StringType, true) ::
+      StructField("string", StringType, true) :: Nil)
+
+    val jsonSchemaRDD1 = jsonFile(path, schema)
+
+    assert(schema === jsonSchemaRDD1.schema)
+
+    jsonSchemaRDD1.registerAsTable("jsonTable1")
+
+    checkAnswer(
+      sql("select * from jsonTable1"),
+      (BigDecimal("92233720368547758070"),
+      true,
+      1.7976931348623157E308,
+      10,
+      21474836470L,
+      null,
+      "this is a simple string.") :: Nil
+    )
+
+    val jsonSchemaRDD2 = jsonRDD(primitiveFieldAndType, schema)
+
+    assert(schema === jsonSchemaRDD2.schema)
+
+    jsonSchemaRDD2.registerAsTable("jsonTable2")
+
+    checkAnswer(
+      sql("select * from jsonTable2"),
+      (BigDecimal("92233720368547758070"),
+      true,
+      1.7976931348623157E308,
+      10,
+      21474836470L,
+      null,
+      "this is a simple string.") :: Nil
+    )
+  }
 }
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
index f0a61270daf0..b413373345ee 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
@@ -37,7 +37,6 @@ import org.apache.spark.sql._
 import org.apache.spark.sql.catalyst.ScalaReflection
 import org.apache.spark.sql.catalyst.analysis.{Analyzer, OverrideCatalog}
 import org.apache.spark.sql.catalyst.plans.logical._
-import org.apache.spark.sql.catalyst.types._
 import org.apache.spark.sql.execution.QueryExecutionException
 import org.apache.spark.sql.execution.{Command => PhysicalCommand}
 import org.apache.spark.sql.hive.execution.DescribeHiveTableCommand
@@ -260,9 +259,9 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) {
         struct.zip(fields).map {
           case (v, t) => s""""${t.name}":${toHiveStructString(v, t.dataType)}"""
         }.mkString("{", ",", "}")
-      case (seq: Seq[_], ArrayType(typ)) =>
+      case (seq: Seq[_], ArrayType(typ, _)) =>
         seq.map(v => (v, typ)).map(toHiveStructString).mkString("[", ",", "]")
-      case (map: Map[_,_], MapType(kType, vType)) =>
+      case (map: Map[_,_], MapType(kType, vType, _)) =>
         map.map {
           case (key, value) =>
             toHiveStructString((key, kType)) + ":" + toHiveStructString((value, vType))
@@ -279,9 +278,9 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) {
         struct.zip(fields).map {
           case (v, t) => s""""${t.name}":${toHiveStructString(v, t.dataType)}"""
         }.mkString("{", ",", "}")
-      case (seq: Seq[_], ArrayType(typ)) =>
+      case (seq: Seq[_], ArrayType(typ, _)) =>
         seq.map(v => (v, typ)).map(toHiveStructString).mkString("[", ",", "]")
-      case (map: Map[_,_], MapType(kType, vType)) =>
+      case (map: Map[_,_], MapType(kType, vType, _)) =>
         map.map {
           case (key, value) =>
             toHiveStructString((key, kType)) + ":" + toHiveStructString((value, vType))
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala
index ad7dc0ecdb1b..354fcd53f303 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala
@@ -152,8 +152,9 @@ private[hive] trait HiveInspectors {
   }
 
   def toInspector(dataType: DataType): ObjectInspector = dataType match {
-    case ArrayType(tpe) => ObjectInspectorFactory.getStandardListObjectInspector(toInspector(tpe))
-    case MapType(keyType, valueType) =>
+    case ArrayType(tpe, _) =>
+      ObjectInspectorFactory.getStandardListObjectInspector(toInspector(tpe))
+    case MapType(keyType, valueType, _) =>
       ObjectInspectorFactory.getStandardMapObjectInspector(
         toInspector(keyType), toInspector(valueType))
     case StringType => PrimitiveObjectInspectorFactory.javaStringObjectInspector
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
index dff1d6a4b93b..fa4e78439c26 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
@@ -200,7 +200,9 @@ object HiveMetastoreTypes extends RegexParsers {
     "varchar\\((\\d+)\\)".r ^^^ StringType
 
   protected lazy val arrayType: Parser[DataType] =
-    "array" ~> "<" ~> dataType <~ ">" ^^ ArrayType
+    "array" ~> "<" ~> dataType <~ ">" ^^ {
+      case tpe => ArrayType(tpe)
+    }
 
   protected lazy val mapType: Parser[DataType] =
     "map" ~> "<" ~> dataType ~ "," ~ dataType <~ ">" ^^ {
@@ -229,10 +231,10 @@ object HiveMetastoreTypes extends RegexParsers {
   }
 
   def toMetastoreType(dt: DataType): String = dt match {
-    case ArrayType(elementType) => s"array<${toMetastoreType(elementType)}>"
+    case ArrayType(elementType, _) => s"array<${toMetastoreType(elementType)}>"
     case StructType(fields) =>
       s"struct<${fields.map(f => s"${f.name}:${toMetastoreType(f.dataType)}").mkString(",")}>"
-    case MapType(keyType, valueType) =>
+    case MapType(keyType, valueType, _) =>
       s"map<${toMetastoreType(keyType)},${toMetastoreType(valueType)}>"
     case StringType => "string"
     case FloatType => "float"

From 7c5fc28af42daaa6725af083d78c2372f3d0a338 Mon Sep 17 00:00:00 2001
From: Koert Kuipers <koert@tresata.com>
Date: Wed, 30 Jul 2014 00:18:59 -0700
Subject: [PATCH 810/810] SPARK-2543: Allow user to set maximum Kryo buffer
 size

Author: Koert Kuipers <koert@tresata.com>

Closes #735 from koertkuipers/feat-kryo-max-buffersize and squashes the following commits:

15f6d81 [Koert Kuipers] change default for spark.kryoserializer.buffer.max.mb to 64mb and add some documentation
1bcc22c [Koert Kuipers] Merge branch 'master' into feat-kryo-max-buffersize
0c9f8eb [Koert Kuipers] make default for kryo max buffer size 16MB
143ec4d [Koert Kuipers] test resizable buffer in kryo Output
0732445 [Koert Kuipers] support setting maxCapacity to something different than capacity in kryo Output
---
 .../spark/serializer/KryoSerializer.scala     |  3 +-
 .../serializer/KryoSerializerSuite.scala      | 30 +++++++++++++++++++
 docs/configuration.md                         | 16 +++++++---
 3 files changed, 44 insertions(+), 5 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala b/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala
index fa79b2575915..e60b802a86a1 100644
--- a/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala
+++ b/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala
@@ -48,11 +48,12 @@ class KryoSerializer(conf: SparkConf)
   with Serializable {
 
   private val bufferSize = conf.getInt("spark.kryoserializer.buffer.mb", 2) * 1024 * 1024
+  private val maxBufferSize = conf.getInt("spark.kryoserializer.buffer.max.mb", 64) * 1024 * 1024
   private val referenceTracking = conf.getBoolean("spark.kryo.referenceTracking", true)
   private val registrationRequired = conf.getBoolean("spark.kryo.registrationRequired", false)
   private val registrator = conf.getOption("spark.kryo.registrator")
 
-  def newKryoOutput() = new KryoOutput(bufferSize)
+  def newKryoOutput() = new KryoOutput(bufferSize, math.max(bufferSize, maxBufferSize))
 
   def newKryo(): Kryo = {
     val instantiator = new EmptyScalaKryoInstantiator
diff --git a/core/src/test/scala/org/apache/spark/serializer/KryoSerializerSuite.scala b/core/src/test/scala/org/apache/spark/serializer/KryoSerializerSuite.scala
index 79280d1a0665..789b773bae31 100644
--- a/core/src/test/scala/org/apache/spark/serializer/KryoSerializerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/serializer/KryoSerializerSuite.scala
@@ -209,6 +209,36 @@ class KryoSerializerSuite extends FunSuite with SharedSparkContext {
   }
 }
 
+class KryoSerializerResizableOutputSuite extends FunSuite {
+  import org.apache.spark.SparkConf
+  import org.apache.spark.SparkContext
+  import org.apache.spark.LocalSparkContext
+  import org.apache.spark.SparkException
+
+  // trial and error showed this will not serialize with 1mb buffer
+  val x = (1 to 400000).toArray
+
+  test("kryo without resizable output buffer should fail on large array") {
+    val conf = new SparkConf(false)
+    conf.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
+    conf.set("spark.kryoserializer.buffer.mb", "1")
+    conf.set("spark.kryoserializer.buffer.max.mb", "1")
+    val sc = new SparkContext("local", "test", conf)
+    intercept[SparkException](sc.parallelize(x).collect)
+    LocalSparkContext.stop(sc)
+  }
+
+  test("kryo with resizable output buffer should succeed on large array") {
+    val conf = new SparkConf(false)
+    conf.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
+    conf.set("spark.kryoserializer.buffer.mb", "1")
+    conf.set("spark.kryoserializer.buffer.max.mb", "2")
+    val sc = new SparkContext("local", "test", conf)
+    assert(sc.parallelize(x).collect === x)
+    LocalSparkContext.stop(sc)
+  }
+}
+
 object KryoTest {
   case class CaseClass(i: Int, s: String) {}
 
diff --git a/docs/configuration.md b/docs/configuration.md
index 2e6c85cc2bcc..ea69057b5be1 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -414,10 +414,18 @@ Apart from these, the following properties are also available, and may be useful
   <td><code>spark.kryoserializer.buffer.mb</code></td>
   <td>2</td>
   <td>
-    Maximum object size to allow within Kryo (the library needs to create a buffer at least as
-    large as the largest single object you'll serialize). Increase this if you get a "buffer limit
-    exceeded" exception inside Kryo. Note that there will be one buffer <i>per core</i> on each
-    worker.
+    Initial size of Kryo's serialization buffer, in megabytes. Note that there will be one buffer
+     <i>per core</i> on each worker. This buffer will grow up to
+     <code>spark.kryoserializer.buffer.max.mb</code> if needed.
+  </td>
+</tr>
+<tr>
+  <td><code>spark.kryoserializer.buffer.max.mb</code></td>
+  <td>64</td>
+  <td>
+    Maximum allowable size of Kryo serialization buffer, in megabytes. This must be larger than any
+    object you attempt to serialize. Increase this if you get a "buffer limit exceeded" exception
+    inside Kryo.
   </td>
 </tr>
 </table>